ASSESSMENT OF CLIMATE CHANGE EFFECTS ON MUSTARD YIELD USING InfoCrop MODEL FOR WESTERN HARYANA By
DIVESH CHOUDHARY 2011A02D Thesis submitted to Chaudhary Charan Singh Haryana Agricultural University in the partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY IN AGRICULTURAL METEOROLOGY
COLLEGE OF AGRICULTURE CCS HARYANA AGRICULTURAL UNIVERSITY HISAR – 125 004 (HARYANA)
2016
CERTIFICATE – I This is to certify that this dissertation entitled, “Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana” submitted for the degree of Doctor of Philosophy in the subject of Agricultural Meteorology of the Chaudhary Charan Singh Haryana Agricultural University, Hisar is a bonafide research work carried out by Mr. Divesh Choudhary, Adm # 2011A02D under my supervision and that no part of this dissertation has been submitted for any other degree. The assistance and help received during the course of these investigations have been fully acknowledged.
[Dr. Raj Singh] MAJOR ADVISOR Professor and Head Department of Agricultural Meteorology CCS Haryana Agricultural University Hisar, 125004
CERTIFICATE – II This is to certify that this thesis entitled “Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana” submitted by Mr. Divesh Choudhary, Adm # 2011A02D to the Chaudhary Charan Singh Haryana Agricultural University, Hisar in partial fulfillment of the requirement for the degree of Doctor of Philosophy in the subject of Agricultural Meteorology has been approved by the student's advisory committee after an oral examination on the same, in collaboration with an External Examination.
MAJOR ADVISOR
HEAD OF THE DEPARTMENT
DEAN, POST-GRADUATE STUDIES
EXTERNAL EXAMINER
Acknowledgements It is by the profound love of my parents and benediction of the almighty that I have been able to complete my studies successfully hitherto and present this piece of work uninterruptedly for which I am eternally indebted to them. In all my humility I place my profound etiquette to my Major Advisor and Chairman of my Advisory Committee Dr. Raj Singh, Professor & head, Department of Agricultural Meteorology, Chaudhary Charan Singh Haryana Agricultural University, Hisar for his valuable suggestions, indebted help, guidance and caring attitude during the course of my work. I wish to proffer my genuine thanks to the members of my Advisory Committee, Dr. Ram Niwas, Professor (Department of Agricultural Meteorology), Dr. R. K. Pannu, Dean (College of Agriculture), Dr. (Mrs) Kiran Kapoor, Scientist (Department of Agril. Economics), Dr. Dinesh Tomar, Assistant Scientist (Department of Soil Science) for their continued help and contributions to the overall success of my studies. I express my deep sense of reverence and gratitude to Dr. Diwan Singh, Dr. Surender Singh, Dr. M.L. Khichar, Dr. C. S. Dagar, Dr. Anil Kumar and Dr. Anurag Department of Agricultural Meteorology for providing me, valuable guidance, and fruitful suggestions. I am extremely grateful and obliged to Dr. R. K. Rattan, Principal Scientist, Dr. S. Naresh Kumar, Principal Scientist, CESCRA, IARI, New Delhi for their technical help, guidance and providing PRECIS data during investigation. I am also thankful to INSPIRE programme, DST, New Delhi for financial assistance in the form of Fellowship during my Ph. D. programme. I am sincerely grateful to all faculty members, technical, office and field staff in the Department of Agricultural Meteorology for their humanitarian concern and everlasting guidance during the course of this investigation. The words can hardly express my feeling of indebtness to appreciate my seniors, friends and Juniors Dr. Naresh, Dr. Mukesh, Rajiv, Vijender, Manmohan, Vister, Akshey, Vipin, Abhinav, Ehatsam, Mukesh, Premdeep, Rati, Yogesh who have given me their valuable time and energy at various stages of this work. Above all there are moments where I always felt the need of person for whom I proud to have them in my life i.e. my grandfather and mother Late Sh. Mangal Singh and Smt. Basanti Devi; my mother Smt. Nirmala Devi and father Sh. Hari Singh Choudhary, who taught me the value of life, humanity and today I realize the pain staking efforts taken up by them to stand tough to conquer this world. I shall be failing in my duties if I do not express my cordial thanks to my beloved younger brother Mr. Shekhar, my wife Babita (Choti), my sisters Neetu, and my niece Gungun in terms of affection, constant encouragement and moral boosting throughout my Ph. D. programme. Finally I thank one and all that who helped me directly and indirectly during my endeavour of education at CCS HAU.
Hisar March, 2016
(Divesh Choudhary)
CONTENTS Chapter
Description
Page(s)
I
INTRODUCTION
1-4
II
REVIEW OF LITERATURE
5-15
III
MATERIAL AND METHODS
16-38
IV
RESULTS
39-127
V
DISCUSSION
128-142
VI
SUMMARY AND CONCLUSION
143-149
BIBLIOGRAPHY
i-ix
LIST OF TABLES Table No.
Description
Page
1.1
Summary characteristics of the four SERES storylines
4
3.1
Mechanical compositions of soil (per cent Fraction)
17
3.2
Chemical properties of soil
17
3.3
Cropping history of the experimental field
18
3.4
List of inputs required for InfoCrop model v.2.0
27
3.5
Categorization of genetic coefficient of mustard for InfoCrop v.2.0 model
29
3.6
List of inputs required for WOFOST v.7.1.7
30
3.7
Categorization of genetic coefficient of mustard for WOFOST model
34
4.1
Influence of sowing dates on phenophase development in mustard varieties during 2012-13
45
4.2
Influence of sowing dates on phenophase development in mustard varieties during 2013-14
45
4.3
Plant height (cm) of mustard varieties at various growth intervals under different sowing environments during 2012-13 and 2013-14
46
4.4
Interaction between dates of sowing and varieties on plant height at physiological maturity of mustard during both years
46
4.5
Effect of sowing dates and varieties on accumulated growing degree days (°C day) at various growth intervals in mustard during 2012-13 and 2013-14
49
4.6
Effect of sowing dates and varieties on accumulated growing degree days (°C day) for various phenophases of mustard during 2012-13
49
4.7
Effect of sowing dates and varieties on accumulated growing degree days (°C day) for various phenophases of mustard during 2013-14
50
4.8
Effect of sowing dates and varieties on accumulated photo thermal unit (°C day hours) for various phenophases of mustard during 2012-13
50
4.9
Effect of sowing dates and varieties on accumulated Photo Thermal Unit (°C day hours) for various phenophases of mustard during 2013-14
51
4.10
Effect of sowing dates and varieties on accumulated Helio Thermal Unit (°C day hours) for various phenophases of mustard during 2012-13
51
4.11
Effect of sowing dates and varieties on accumulated Helio Thermal Unit (°C day hours) for various phenophases of mustard during 2013-14
52
4.12
Effect of sowing dates and varieties on Radiation Use Efficiency (g/MJ) at various growth intervals in mustard during 2012-13 and 2013-14
52
4.13
Effect of sowing dates and varieties on Thermal Use Efficiency (g/m2 °C day) at various growth intervals in mustard during 2012-13 and 2013-14
53
Table No.
Description
Page
4.14
Effect of sowing dates and varieties on the total biomass and its allocation (g/m2) in different plant parts of mustard during 2012-13
54
4.15
Effect of sowing dates and varieties on the total biomass and its allocation (g/m2) in different plant parts of mustard during 2013-14
57
4.16
Effect of different growing environments and varieties energy balance component at three growth stages (mW m-2) over mustard crop and bare field
63
4.17
Effect of different growing environment and varieties on extinction coefficients (k) over mustard
63
4.18
Effect of different growing environment and varieties on optical characteristics in mustard
64
4.19
Effect of growing environments and varieties on yield and yield attributes in mustard during 2012-13
65
4.20
Effect of growing environment and varieties on yield and yield attributes in mustard during 2013-14
67
4.21
Interaction effect of growing environment and varieties on mustard seed yield (q/ha) of mustard during 2012-13 and 2013-14
69
4.22
Correlation coefficients of growth parameters with weather parameters during vegetative and reproductive phases of mustard Correlation coefficients of yield and its attributes with weather parameters during vegetative and reproductive phases in mustard (2012-13)
70
4.24
Correlation coefficients of yield and its attributes with weather parameters during vegetative and reproductive phases of mustard (2013-14)
72
4.25
Correlation coefficients of yield and its attributes with weather parameters during vegetative and reproductive phases of mustard (pooled data)
73
4.26
Genotypic characteristics of mustard cv. RH 30, Laxmi and RH 0749 used in InfoCrop model
74
4.27
Genotypic characteristics of mustard cv. RH 30, Laxmi and RH 0749 used in WOFOST model
75
4.28
Validation of InfoCrop and WOFOST model for days taken to 50 % flowering of mustard under different growing environment and varieties (2012-13 and 2013-14)
80
4.29
Validation of InfoCrop and WOFOST model for days taken to maturity of mustard under different growing environment and varieties (2012-13 and 2013-14)
81
4.30
Validation of InfoCrop and WOFOST model for Leaf Area Index (LAI) of mustard under different growing environment and varieties (2012-13 and 2013-14)
82
4.31
Validation of InfoCrop and WOFOST model for 1000-seed weight (g) of mustard under different growing environment and varieties (2012-13 and 2013-14)
83
4.23
71
Table No.
Description
Page
4.32
Validation of InfoCrop and WOFOST model for seed yield (kg ha-1) of mustard under different growing environment and varieties (2012-13 and 2013-14)
84
4.33
Validation of InfoCrop and WOFOST model for biological yield (kg ha-1) of mustard under different growing environment and varieties (2012-13 and 2013-14)
85
4.34
Validation of InfoCrop and WOFOST model for Harvest Index (%) of mustard under different growing environment and varieties (2012-13 and 2013-14)
86
4.35
Variability and trend analysis of maximum temperature at Hisar (1970-2014)
94
4.36
Variability and trend analysis of minimum temperature at Hisar (1970-2014)
94
4.37
Variability and trend analysis of rainfall at Hisar (1970-2014)
95
4.38
Percent change in weather parameters of different PRECISE projected periods as compared to baseline period
99
4.39
Projected gains in seed yield of mustard varieties with different adaptation options during 2nd fortnight of Oct. sowing under A1b 2080 projected climate change scenario
122
4.40
Projected gains in seed yield of mustard varieties with different adaptation options during 2nd fortnight of Oct. sowing under A2 2080 projected climate change scenario
124
4.41
Net vulnerability assessment of mustard crop against different adaptation measures during A1b 2080 projected climate change scenario
127
4.42
Net vulnerability assessment of mustard crop against different adaptation measures during A2 2080 projected climate change scenario
127
LIST OF FIGURES Figure No.
Description
Page
3.1
Lay-out plan of experiment in both the years
20
3.2
Context diagram of InfoCrop depicting the input requirement on the left hand side and its possible application on the right
26
3.3
Key inputs for InfoCrop model
26
3.4
Crop growth precesses simulated by WOFOST. T a and T p are actual and potential transpiration rate (Koning et. al., 1993).
30
4.1
Mean weekly maximum temperature along with normal during 2012-13 and 2013-14
40
4.2
Mean weekly minimum temperature along with normal during 2012-13 and 2013-14
41
4.3
Mean weekly relative humidity along with normal during 2012-13 and 2013-14
42
4.4
Mean weekly rainfall along with normal during 2012-13 and 2013-14
42
4.5
Mean weekly bright sunshine along with normal during 2012-13 and 2013-14
43
4.6
Mean weekly pan evaporation along with normal during 2012-13 and 2013-14
43
4.7
Weekly wind speed along with normal during 2012-13 and 2013-14
44
4.8
Effect of sowing time and varieties on LAI at various growth intervals for mustard during 2012-13 and 2013-14
61
4.9
Interaction effect of change in mean ambient temperature (°C) and CO2 conc. (ppm) on simulated mustard seed yield (% change) using InfoCrop model
89
4.10
Interaction effect of change in mean ambient temperature (°C) and CO2 conc. (ppm) on simulated mustard seed yield (% change) using WOFOST model
90
4.11
Interaction effect of change in mean ambient temperature (°C) and ranfall change (%) on simulated mustard seed yield (% change) using InfoCrop model
91
4.12
Interaction effect of change in mean ambient temperature (°C) and ranfall change (%) on simulated mustard seed yield (% change) using WOFOST model
92
4.13
PRECIS projected maximum and minimum temperature; and rainfall in A1b 2030 scenario (2020-2049) and observed values of baseline periods
97
Figure No.
Description
Page
4.14
PRECIS projected maximum and minimum temperature; and rainfall in A1b 2080 scenario (2070-2099) and observed values of baseline periods
98
4.15
PRECIS projected maximum and minimum temperature; and rainfall in A2 2080 scenario (2070-2099) and observed values of baseline periods
99
4.16
Mean days taken to 50 % flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
103
4.17
Mean days taken to 50 % flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
104
4.18
Mean per cent change in days taken to 50% flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
105
4.19
Mean per cent change in days taken to 50% flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
105
4.20
Mean days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
106
4.21
Mean days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
107
4.22
Mean per cent change in days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
108
4.23
Mean per cent change in days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
108
4.24
Mean Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
109
4.25
Mean Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
110
4.26
Mean per cent change to Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
111
4.27
Mean per cent change to Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard
111
Figure No.
Description
Page
varieties using WOFOST model 4.28
Mean test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
112
4.29
Mean test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
113
4.30
Mean per cent change to test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
114
4.31
Mean per cent change to test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
114
4.32
Mean seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
115
4.33
Mean seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
116
4.34
Mean per cent change to seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
117
4.35
Mean per cent change to seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
117
4.36
Mean biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
118
4.37
Mean biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
119
4.38
Mean per cent change to biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
120
4.39
Mean per cent change to biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
120
CHAPTER-I
INTRODUCTION Climate has a vital role on biosphere, and impact of climate change on agriculture will be one of the major deciding factors influencing the future food security of humankind on the earth. Rapeseed mustard includes eight different species viz., Indian mustard, toria, yellow sarson, brown sarson, gobhi sarson, karan rai, black mustard and taramira, which are cultivated in 53 countries across the world (Hedge, 2005). India is the third largest rapeseedmustard producer in the world after China and Canada with 12 per cent of the world’s total production. However in international market, the Indian cultivars due to their high content of erucic acid and glucosinolates has limited preference (Kumar et al., 2009). In India, Rapeseed-mustard contributes about 20-22 per cent in the total oilseeds production of the country. The state of Haryana alone contributes 10.23 per cent of the total rapeseed-mustard production of the country. Total area, production and productivity under rapeseed-mustard was 71.30 lakh ha, 73.00 lakh tonnes and 1128 kg ha-1, respectively for the year 2013-14. The area, production and productivity have increased by 5.6, 7 and 1.5 per cent, respectively, as compared to previous season (Anonymous, 2015). Indian mustard is mainly used for the extraction of mustard oil, black mustard as a spice. Area under mustard in Haryana has increased from 1.98 lakh ha in 1966-67 to 5.70 lakh ha in 2013-14. There has been considerable increase in the productivity of mustard from 405 kg ha-1 in 1966-67 to 1639 kg ha-1 in 2013-14 (Indiastat, 2014). However, India is still a net importer of vegetable oils and almost 40 per cent of its annual edible oil needs are met through import. In future, the demand for oilseeds production is likely to go up significantly due to increase in population and income. The domestic production of edible oil has remained almost stagnant during the last five years. It was 8.3 tonne in 2005-06 and 8.2 tonne in 2011-12. However, the consumption has increased from 12.6 tonne in 2005-06 to 17.0 tonne in 2011-12. The gap in demand and supply is being bridged by import of edible oil, which has increased from 4.3 mt in 2005-06 to 8.8 tonne in 2011-12 (Anonymous, 2012). Mustard is very sensitive to climatic variables, and hence, climate change could have significant effect on its production. The decline and/or stagnation in mustard yield causing negative growth rate from 1997 was possibly due to unfavorable monsoon, which created moisture stress (drought and excess rainfall) and temperature increase (Kumar, 2005). High temperature during early stages of mustard crop establishment (mid September to early November), cold spell, fog and intermittent rains during flowering to pod formation stage
1
affect the crop adversely and cause considerable yield losses by physiological disorders along with proliferation of aphid pest, white rust, downy mildew and stem rot diseases. Climate change is expected to affect agriculture very differently in different parts of the world. The resulting effects among various continents depend on current climatic and soil conditions, availability of resources and infrastructure use to cope with climate change. These differences are also expected to greatly influence the responsiveness to climatic change. The IPCC has projected a temperature increase of 0.5-1.2°C by 2020, 0.88-3.16°C by 2050 and 1.56-5.44°C by 2080 for the Indian region, depending on the scenario of future development (IPCC, 2007). It is very likely that hot extremes, heat waves and heavy precipitation events will become more frequent. Overall, the temperature increase is likely to be higher in winter season, and precipitation is likely to decrease. Rising of 1°C temperature to the normal reduce mustard yield by 450 kg/ha and shortened the maturity period (Anonymous, 2010). Kalra et al. (2008) studied temperature and mustard yield for Haryana region and found that for every 1°C rise in temperature reduce yield to the tune of 2.01q/ha. In Australia, where the average annual rainfall is less than 13 inches (330.2 mm), Indian mustard is preferred over canola as a crop. Likewise, in drier regions of Russia, India, China and Canada, where rainfall tends to fluctuate, brown mustard is given precedence. Indian mustard has been a preferred crop in areas where water supply is inadequate or unreliable (McCaffery et al., 2009; Oram et al., 2005). The crops like potato and rapeseed-mustard shown positive response to increased maximum temperature might be due to its strong positive correlations with diurnal temperature range (Chaudhari et al., 2009). InfoCrop and WOFOST are dynamic crop yield simulation models developed to deal with the interaction among weather, crop/variety, soils and management practices besides major pest. These models have the capability of analysis of experimental data, estimate the potential yield, yield gaps and also assess the impacts of climate variability and climate change. InfoCrop model has capacity to evaluate the production of major annual crops viz., rice, wheat, sorghum, millet, sugarcane, chickpea, pigeon pea, cotton, maize, groundnut, potato and of course mustard and equipped with inbuilt data base of Indian soils. The InfoCrop and WOFOST models have the capability of analysis of experimental data, estimate the potential yield, yield gaps and also assess the impacts of climate variability and climate change. These models also efficiently work for management optimization and assess environmental impact study. Thus, these models are most versatile and have many agricultural applications used for decision support system for Agro-technology transfer. PRECIS is an atmospheric and land surface model of high resolution and limited area, which is locatable over any part of the globe. It has a horizontal resolution of 0.44° (~50 km) or 0.22° (~25 km) and 19 levels in the vertical. Dynamic flow, the atmospheric sulphur cycle, clouds and precipitation, radioactive process, the land surface and the deep soil are the
2
processes formulated in PRECIS and it is forced at its lateral boundaries by the simulations of a global climate models viz., HadCM3, HadRM3 and ECHAM. The output of PRECIS is post-processed and used for various impact studies. The weather variables derived can be used to find out the expressions of each of them by working out deviations, depicting as charts or graphs and can also be used to drive other models to understand the impact (Ramaraj et al., 2009). SRES refers to the scenarios described in the IPCC Special Report on Emissions Scenarios (SRES, 2000). The SRES scenarios are grouped into four scenario families (A1, A2, B1 and B2) that explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting GHG emissions. The SRES scenarios do not include additional climate policies above current ones. The emission projections are widely used in the assessment of future climate change, and their underlying assumptions with respect to socio-economic, demographic and technological change serve as inputs to many recent climate change vulnerability and impact assessments. The A1 storyline assumes a world of very rapid economic growth, a global population that peaks in mid-century and rapid introduction of new and more efficient technologies. A1 is divided into three groups that describe alternative directions of technological change, i.e., fossil intensive (A1F1), non-fossil energy resources (A1T) and a balance across all sources (A1b). The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing global population. Economic development is primarily regionally oriented and per capita economic growth and technological changes are more fragmented and slower than in other storylines (Jones et al., 2004). To sustain agricultural production under threat of projected climate change various adaptation measures such as changes in land use and management, development of resources conserving technologies, natural resource management policies, improved risk management through early system and crop insurance, shifting season/sowing windows, alteration in irrigation/fertilizer management are to be addressed. An integrated approach for quantification of climate change impacts, adaptation and vulnerability assessment is required for bridging the knowledge gaps, which can accelerate the development of adverse-climate tolerant varieties or climate resilient varieties, adaptation technologies and regional assessments for adaptation strategies for minimizing the adverse impacts and maximizing the benefits, if any, due to climate change (Kumar, 2014). Vulnerability assessment of crop or system is important for deriving the policy framework for making the crop/system resilient to climate change. This type of analysis of a system will provide information/guidelines for developing adaptation strategies and also provide technology development need assessment.
3
More economic focus A1 storyline A2 storyline World: market oriented. World: differential. Economy: fast per capita growth. Economy: regional oriented, Population: 2050 peak, then decline. lowest per capita growth. Governance: strong regional interactions, Population: continuously income convergence. increasing. Technology: three scenario groupsGovernance: self-reliance with preservation of local identities. A1F1: fossil intensive. Technology: slowest and most A1T: non-fossil energy sources. fragmented development. A1b: balanced across all sources 1.1 -2.9°C 1.4 -6.4°C B1 storyline B2 story line World: convergent. World: local solutions Economy: service and information based Economy: intermediate growth. lower growth than A1. Population: continuously Population: same as A1 increasing at lower rate than A2. Governance: global solutions to economic, Governance: local and regional social and environmental sustainability. solutions to environmental Technology: clean and resource efficient. protection and social equity. Technology: more rapid than 2.0 - 5.4°C A2, less rapid, more diverse than A1/B1. 1.4 - 3.8°C More environmental focus
Regionalization
Globalization
Table 1.1: Summary characteristics of the four SERES storylines
Source: IPCC (2007) Adapted from climate change 2007: Working Group II
The scientific information on impact of projected climate change on mustard crop in Haryana state is limited. Hence, keeping in view ecological and economic importance of these areas, diversity of agriculture and its importance for sustainable livelihood of the local population, the projected climate change and its likely impact on sustainable production of mustard crop, the present investigation has been carried out with the following objectives: 1. To find out the relationship between growth and yield parameters with weather variables for mustard crop 2. To validate and carryout sensitivity analysis of InfoCrop model for mustard crop 3. To assess the impact of A2 scenario on mustard crop
4
CHAPTER-II
REVIEW OF LITERATURE This chapter contains the available literature published on the research work related to the problem chosen as reviewed by the author. Though the review of pertinent literature has been mainly confined to mustard crop, relevant research work on some other crops and models have also been reviewed and relevant information included wherever necessary. The literature reviewed is arranged under the following broad headings, for convenience: 2.1
Effect of dates of sowing on growth and development of mustard
2.2
Effect of weather parameters on phenology, growth and development of mustard
2.3
Crop growth simulation modeling: InfoCrop model, WOFOST model and Sensitivity analysis
2.4
Impact assessment of climate change on mustard 2.4.1
Downscaling of climate using regional climate models (RCMs) for climate change study: PRECIS model
2.4.2 2.5
Climate change and mustard
Adaptations measures and vulnerability
2.1 Effect of dates of sowing on growth and development of mustard Change in sowing dates lead to change in thermal environment of the cultivars with respect to different growth and development stages, leading to variation with respect to completion of life cycle (Adak et al., 2011a, b). Reduction in the number of days taken by the crop to complete life cycle between seedling emergence and maturity with delay in sowing date was due to higher temperature at later part of growth resulted in forced maturity. Similarly the late sown crop accumulated 9-20% lower GDD during total crop duration period compared to early and normal sowing (Adak et al., 2011b). Thermal requirement for attaining any phenological event in mustard crop varied from cultivar to cultivar and its accumulation decreased gradually as sowing was delayed (Chand et al., 1995). Das et al. (2009) observed longer vegetative stage and shorter reproductive stages in mustard with delay in sowing. The vegetative stage of late sown crop was exposed to low temperature and lower amount of solar radiation and hence required more number of days for accumulation of given amount of growing degree days. The total crop duration also decreased with delay in sowing. Increase in temperature during second fortnight of February adversely affected the productivity of late sown mustard crop (Ghosh and Chatterjee, 1988). Every crop needs a specific amount of growing degree days to enter into
5
reproductive phase from vegetative phase. Early sowing (1st October) resulted in absorbing sufficient growing degree days in relatively less time due to prevalence of higher temperature and longer sunshine hour during post-sowing period (Kanth et al., 2000). Weerakoon and Somaratne (2011) conducted a study to establish the relationship between growth, yield with two growing seasons Maha (October to March-receives North eastern showers) and Yala (April to September- receives South western showers) using ten mustard accessions (AC 501, 515, 580, 790, 1099, 1814, 2122, 5088, 7788 and 8831) at Nagollagma, Sri Lanka. They obtained significantly the highest yield from three mustard accessions (AC 580, 5088 and 7788) during the Maha season and AC 7788 produced the highest yield in Yala season as well, shown adaptability to seasonal variations. Maturity phase was enhanced with delayed sowing in B. rapa (Liyong et al., 2007; Jun et al., 2007). The crop growth rate for all the species was found higher for normal sowing and it decreased with delay in sowing. The rates of crop growth and development are a function of the energy receipt and thermal regime in any given crop growth season (Neogi et al., 2005). Higher crop growth rate associated with timely sowing was mainly due to high leaf area index, which accumulated dry matter at a faster rate per unit leaf area per unit time and subsequently dry matter decrease with delay in sowing (Thurling, 1974). Duration of pod filling was longer for early sown crops in B. napus (Robertson et al., 2002; Poureisa and Nabipour (2007), and in B. juncea (Nanda et al., 1996). Singh et al. (2002) reported that the yielding ability of a crop is dependent on investment of a greater proportion of biomass and yield to variation in their edaphic and environmental conditions, which was achieved through change in sowing dates. 2.2 Effect of weather parameters on phenology, growth and development of mustard Three Rabi crops, i.e., wheat, potato and rapeseed-mustard showed negative response of yield to increased minimum temperature, while potato and rapeseed-mustard showed positive response to increased maximum temperature during vegetative growth due to its strong positive correlations with diurnal temperature range (Chaudhari et al., 2009). The impacts of diurnal temperature range changes on yield were generally less for wheat while more for potato and rapeseed-mustard crops with the unit increase in diurnal temperature range. Kumar et al. (2010a) reported that increasing temperature reduced the days to flowering and days to maturity thus shortening the seed formation period. A higher temperature leads to higher respiration rates, reduces biomass production resulted in smaller and lighter grain therefore lower crop yield. While plotting relationship of days to maturity against mean temperature gave nearly a linear relationship, it indicates that each degree rise in temperature the crop mature 8 days earlier (Mendham et al., 1981). The dependence of yield on seasonal temperature in northern India environment for wheat, barley, gram, winter maize and mustard showed a linear decreasing trend of 4.26, 2.77,
6
0.32, 1.52 and 1.32 q/ha, respectively (Kalra et al., 2003). Kumar et al. (2010b) reported that annual maximum temperature is likely to rise by 5.25°C with maximum of 7.55°C for November and minimum 3.21°C for May. Similarly, minimum temperature is also likely to rise by 4.83°C with highest increase of 6.34°C during February. Crop duration is also likely to reduce by 25 to 30 days, mainly due to fast accumulation of thermal unit (GDD) required for crop maturity. The increase in temperature lowered the days to flowering and days to maturity, which in turn shorten the total crop duration. Warmer temperature accelerates growth and development leading to less time for carbon fixation and biomass accumulation before seed set resulting in poor yield (Rawson, 1992; Morrison, 1996). Nanda et al. (1994) reported that reduction in seed yield of Brassica species under late sown condition might be attributed to increase in temperature at the time of pod growth and seed filling stages, which reduced the dry matter accumulation into the seed and shortened the seed-filling period. The October sown crop recorded higher seed yield due to exposure of higher growing degree-days. It can be concluded that accumulated growing degree-days has a positive relationship with seed yield (Roy et al., 2005). The abnormal rise in temperature (terminal heat stress) during February adversely affected the productivity of Brassica under late sown conditions in Haryana (Singh and Singh, 2005). The days to maturity was affected as seasonal (winter) temperature range fluctuated below 17°C and above 22°C and the forced maturity (rate of hastening of maturity is 1.72 days per degree rise in temperature) might reduce the yield significantly in North India (Kalra et al., 2008). High temperature at flowering stage causes reduction in seed yield as it may lead to pollen sterility. Although mustard is a long day plant requiring 16 h of light period in 24 h cycle, it can made to flowering if it is provided with a cycle of 8 h of light period with 4 h of dark period (short night). Mustard can be made to flower in about 50 days under 16/8 h light/dark period (Singh et al., 2014). The yield of most of the mustard varieties throughout the world has decreased with increasing temperature but B. juncea showed tolerance to high temperature and water deficit in low rainfall areas in Western Australia, however the yield potential decrease with high temperature (Si and Walton, 2004). Adak et al. (2009) observed that weekly minimum temperature in the second season was higher at the initial crop growth stages (from 1st week to 12th weeks after sowing) by the about 0.3 to 7°C as compared to 1st season facilitating rapid crop growth in mustard crop at IARI, New Delhi. Srivastava et al. (2011) observed that 75% variation in biomass of mustard could be explained through growing degree-days (GDD). GDD and photo thermal unit (PTU) may be used for quantification of Brassica biomass and oil content, respectively through simulation model. The yield and oil content of Brassica cultivars were highly influenced by the
7
differential thermal environment and delay of sowing decreased seed yield and oil content significantly. Neogi et al. (2005) quantified the oil content of Brassica through thermal indices and found that GDD and PTU significantly explained the variability of oil content of different cultivars of mustard. Pidgeon et al. (2001) reported that changes in climate affect crop radiation use efficiency (RUE). Spatial variation in temperature as well as rainfall and its distribution led to spatial variation in yield reduction. Superior performance of mustard in low rainfall environment of Western Australia under late sowing was conformed its adaptation ability and greater tolerance to water and heat stress than canola (Gunasekera et al., 2003; Kumar, 1994). The normal or early sowing of Pusa Jaikisan or Pusa Bold cultivar have significant interaction, and practiced for achieving higher seed yield, radiation and water use efficiency in semi arid environment of north and north-western part of India (Pradhan, et al., 2014). The variation in accumulated heat units and number of days taken for attaining various growth stages in cultivars did not indicate any specific trend. Among cultivars, ‘RH 30’ mustard (Brassica juncea L. Czern.) and HNS 8 rape (B. napus L.) behaved in similar manner as they reached harvest in short duration and accumulated least heat units. Cultivar BC 2 Ethiopian mustard (B. carinata A. Braun) took maximum days for harvesting (Singh et al., 1993). The value of GDD increased from 1270 to 1684°C day in Pusa Jaikisan and Varuna, the seed yield also increased and with the further increase in GDD accumulation, there was a decline in seed yield, whereas at the value of 1606°C day, the yield was founds to be highest (Neogi et al., 2005). Jain et al. (1986) reported that delay in sowing significantly declined the seed yield of mustard with the delayed the date of sowing, i.e., 25 October, 5 November and 15 November. The higher seed yield with early date of sowing was due to significantly higher number of total branches/plant and number of siliquae per plant. The reduction in seed yield in delayed sowing was probably due to shortening of the growing period particularly reproductive phase of the crop. Tripathi (2005) found that the leaf area index was significantly and positive correlated with maximum, minimum temperature and bright sunshine hours during vegetative and flowering phenophases but negatively correlated with these weather parameters during seed development phase. With the delay in sowing, the higher mean temperature was experienced during flowering, which led to accelerate the decrease of LAI and reduction of the flowering period (Poureisa and Nabipour, 2007). A linear and positive relation between leaf area development and photo synthetically active radiation (PAR) interception, which leads to higher dry matter production (Gill and Bains, 2008). Leaf area index (LAI) plays an important role for crop growth based on its interception and utilization of PAR (Photosynthetically Active Radiation) for producing dry
8
matter (Kumar et al., 2007). Kumari and Rao (2005); Mendham et al. (1981); Rameshwar et al. (2000) and Tripathi (2005) found that delayed sowing reduced leaf area. Rameshwar et al. (2000) reported that RGR (Relative Growth Rate) increased with delay in sowing in B. napus. Kumari and Rao (2005) observed that LAI, CGR (Crop growth rate), RGR and NAR (Net assimilation rate) in B. juncea were lowest in crops sown earlier than 1st October due to higher temperature prevailed during initial stages of the crop growth. The higher temperature during the vegetative period reduces LAI and physiological activities causing lower seed yield in the earliest sown crop (15th September). Nanda et al. (1995) and Tripathi (2005) revealed that planting date significantly affected the duration of first leaf appearance and no significant effect on the rate of appearance of subsequent leaves. 2.3 Crop growth simulation modeling: InfoCrop model, WOFOST model and sensitivity analysis The crop growth simulation model has been defined as a simplified representation of the physical, chemical and physiological mechanisms underlying plant and crop growth processes. A crop simulation model is a computer model used to simulate reality. Crop simulation models are state of the art technology that allows a user to estimate crop growth and yield as a function of weather conditions and management scenarios. Various crop simulation models are used now a day viz., DSSAT (Decision Support System for Agrotechnology Transfer), WOFOST (WOrld FOod STudy), WTGROWS (WheaT GROWth Simulator), InfoCrop, BACROS (Simple and Universal CRop growth simulator), SWAP (Soil Water Atmosphere Plant) etc. Crop simulation modeling has the capability of forecast yield levels based on the prevailing weather conditions and past production records. However, before adopting yield forecasting models, such models need to be calibrated and validated for a given set of environmental condition. Crop growth simulation models are quantitative tool based on scientific knowledge that can evaluate the effect of climatic, edaphic, hydrological and agronomic factors on crop growth and yield. Crop models are covering wide range of crops and are applicable to study the crop production under limited water availability and irrigation condition both (Kundu et al., 1982). Various crop growth models are extensively used for assessment of crop growth and yield. The simulation models could be used for identifying crop production constraints under field conditions in different agro climatic zones. These models could be a substitute to multi location field trials for introducing a variety in different agro climatic zones and thus saving time and money. Hence crop simulation modeling is felt necessary for maximizing the agricultural production through better crop management practices. Stewart (1970) presented a model that simulated net photosynthesis of field corn by making use of computer simulation and numerical analysis technique as well as useful
9
characteristics of earlier developed simulation system of assimilation, transpiration and respiration. The InfoCrop model overestimated LAI and biomass at the initial crop growth stages but underestimated at the peak/maximum level, whereas overestimated the seed yield for mustard cv. Pusa Jaikisan, sown on 15th and 30th October of the year 2005-06 and 2006-07. Actual seed yield was of the order of 23 to 37 q ha-1 while the simulated yield was of the order of 37 to 42 q ha-1 with RMSE value of 3.14 indicating significant differences between observed and simulated yield (Adak et al., 2009). The InfoCrop model v.1.1 was calibrated and validated with experimental data of three cultivars of rice (NDR 97, NDR 359 and Swarna Sub-1) conducted during 2002 to 2012 at Kumarganj, Faizabad (U.P.). The model performance were evaluated using MAE, MBE, RMSE and observed that InfoCrop model was able to predict the growth parameters like days taken to anthesis, maximum leaf area index, biomass and yield with reasonably good accuracy of error % less than 10 (Kumar et. al., 2015). The InfoCrop model was validated under middle Gujarat region for Rabi (Choudhary et al., 2014a) and Kharif (Choudhary et al., 2014b) maize (cv. GS-2 and GM-3) for phenological stages, leaf area index (LAI), biomass, yield and yield attributes and compared with observed data. The results revealed that the model underestimated LAI and overestimated rest of the parameters for both the seasons. The average error % estimated for maize cv. GS-2 and GM-3 were 11.43 and 13.70 during Rabi and 9.69 and 10.95 during Kharif, respectively, within acceptable range of less than 15% with significant accuracy. Akula (2003) validated WTGROWS and InfoCrop model using field experimentation data collected for two years at Anand (Gujarat) India for wheat cv. GW-496. The results showed that mean measured grain yield were 4608±620 kg ha-1, whereas simulated by InfoCrop and WTGROWS were 4296±918 and 4537±874 kg ha-1, respectively. The correlation coefficient between measured and simulated yield by each of the two models was nearly indicated in WTGROWS (r = 0.95) and InfoCrop (r = 0.96). Akula et al. (2005b) used the InfoCrop model to estimate the variability in wheat production levels in Anand and Panchmahal districts of Gujarat and found that the wheat production was plateau in both the districts while, the potential yield was estimated by the model was 2.4 times higher than actual yield given the indication about the possibility of harnessing higher wheat yield in these districts. Predictive performance of WOFOST (Diepen et al., 1989) model for wheat crop has been investigated at Hisar (Shekhar et al., 2008). Wheat grain and straw yield simulated by model had deviation around 11 and 33%, respectively. On average, the model over estimated grain yield by 57 kg ha-1 and under estimated straw yield by 771 kg ha-1. Mukherjee et al. (2011) evaluated two simulation models (WOFOST and ORYZA 2000) to predict growth and productivity of rice under central plain zone of Punjab. The simulated values of dry weight of
10
leaves, stem, above ground biomass, leaf area index and grain yield did not differ significantly with observed values. Based on statistical evaluation of performance of crop simulation models, ORYZA 2000 showed an advantage over WOFOST model in simulating crop growth parameters and grain yield of rice. Mishra et al. (2013) simulated the growth and yield of four wheat cultivars (GW 322, GW 496, GW 366 and GW 1139) at different dates of sowing (1st Nov., 15th Nov., 30th Nov. and 15th Dec.) using WOFOST model under middle Gujarat region. Grain yield, anthesis date, days to maturity, harvest index was simulated satisfactorily by the model for all the selected cultivars of wheat. Mishra et al. (2015) analyzed the sensitivity of WOFOST model for four cultivars of wheat (GW 322, GW 496, GW 366 and GW 1139) to change in sunshine hours (BSS), maximum and minimum temperature (Tmax and Tmin) for growth and yield at Anand, Gujarat conditions. They created the artificial climatic conditions by increasing and decreasing the BSS from 0.5 to 2.5 hours and temperature (maximum and minimum) from 1.0 and 5.0°C at different phenological stages (viz. tillering to booting, booting to flowering, flowering to milking, milking to dough and sowing to physiological maturity) of the crop. They revealed that the increase in BSS was found to increase the yield in all cultivars while rise in maximum and minimum temperature had adverse effect on wheat yield. They also found that increase in the maximum temperature by 5°C may cause reduction in yield by 24 to 29% and the effect of the minimum temperature was in the similar order. Among the cultivars, GW 496 was found to be most sensitive to maximum temperature and less to BSS hours. The sensitivity analysis of the InfoCrop model revealed that change in weather parameters i.e. with unit increase in mean temperature, simulated yield decreased and vice versa. The model was also found more sensitive to initial nitrogen content present in the soil at the time of sowing rather than water content (Akula, 2005a). Confalonieri et al. (2009) studied the calibration and validation of WARM (Water Accounting Rice Model), CropSyst (Crop System Simulator) and WOFOST models for rice under flooded and non-limiting conditions of water at Povally, Itally during year 1989 to 2004. They computed the multiplemetrix indicator (MQI) range between 0 (best) ≤ MQI ≤ 1 (worst), and compared with actual data for WARM (0.037), CropSyst (0.167) and WOFOST (0.173). The individual validation metrics was also found similar such as modeling efficiency (EF >0.90) and correlation coefficient (R >0.98) for all the three models. The sensitivity analysis of the models revealed that WARM, CropSyst and WOFOST were sensitive 30, 10 and 20% of the different model parameters. Catalin et al. (2009) validated the WOFOST model from crop growth monitoring system (CGMS) for Romania and checked the adaptability for the climate change study for winter crops mainly for cereals. They validated the WOFOST and CGMS and found that these were interesting tools for study the plant-soil-atmosphere interactions.
11
2.4 Impact assessment of climate change on mustard 2.4.1 Downscaling of climate using regional climate models (RCMs) for climate change study: PRECIS model The Global Climate models (GCMs) are computer driven models, which use differential equations based on basic laws of physics, fluid motion and chemistry. It works with a horizontal resolution between 150 and 600 km with 10-20 vertical layers in atmosphere overland and 30 layers over ocean. The GCMs normally used are atmospheric GCMs (AGCM), oceanic GCMs (OGCM) and atmosphere-ocean coupled GCMs (AOGCM). There are about 23 GCMs available from various countries with varying reliabilities. These GCMs lack accuracy due to their insufficient spatial and temporal resolution (Wilby and Wingley, 1997) and hence they lack in simulation of atmospheric features influencing regional climate. Thus, they were emphasizing on need for downscaling to get finer scale features. Marengo and Ambrizzi (2006) had run PRECISE under A2 scenario for Brazil to study the impact of climate change. From the study, it was stated that large warming will occur during the period from 2071 to 2100 for western-central region of Brazil up to 6˚C and rainfall tends to reduce for South-Eastern and Southern Brazil. The possible future scenarios would have a negative impact in most of the agriculture production. Rupakumar et al. (2006) used PRECIS to analyse the climate change impacts over India for the 21st century under A2 and B2 high-resolution scenario. The model simulation revealed that surface air temperature as well as rainfall showed similar patterns of projected changes under A2 and B2 scenarios, but B2 scenario shows slightly lower magnitude of the projected change. The warming is monotonously widespread over the country, but there were substantial spatial differences in projected rainfall changes. West central India showed maximum expected increase in rainfall. Extremes in maximum temperature were found to increase faster than the day temperature. Karmalkar et al. (2008) run PRECIS regional climate model under SRES A2 scenario for the Coasta Rican montane forests to study the impact of climate change. The analysis suggested that effect would be more pronounced at high elevations than in the lowlands. The region will experience increase in temperature and precipitation will be less up to 30 % in high elevation pacific slopes and in Caribbean lowlands. Along with this frequency of high precipitation amounts decrease and low precipitation amounts increase. Xu et al. (2006) had used PRECIS to analyse the surface air temperature and precipitation under A2 and B2 scenarios over China for various time slices of 21st century. Based on the analysis, they concluded that at the end of 21st century average temperature would increase by 3°C and precipitation by 10 %. They reported that the drought with high temperature events in the northern part of China, flooding in summer and drought in winter in southern part of China would be enhanced. The PRECISE Caribbean project used PRECIS to study the effect of climate change over Caribbean. Temperature and rainfall are taken into
12
account. The results inferred that the annual warming rate varies from 0.1 to 0.4°C for medium and high emission scenario. The upward trend in the mean temperature seems to be largely driven by changes in the minimum temperature. Regarding rainfall the changes in annual average precipitation suggests drying across Caribbean basin (Taylor et al., 2007). 2.4.2 Climate change and mustard In recent years there has been a growing concern that changes in climate will lead to significant damage to both market and non-market sectors. The climate change will have a negative effect in many countries. Crop simulation models are used to estimate the impacts of climate change on agricultural production. The application of crop models to study the potential impact of climate change and climate variability provides a direct link between model, agro meteorology and the concerns of the society. The results of crop simulation model studies related to climate change are reviewed here. Kumar et al. (2010) examined the impact of climate change using PRECIS downscaled weather data of baseline period (1961-1990) and A2a scenario for projected period (2071-2100) for mustard crop at IARI, New Delhi. They found that the average annual maximum temperature for the projected period was likely to be higher than the base period by 5.2°C with maximum of 7.5°C for November and minimum 3.2°C for May. Similarly, the average minimum temperature is likely to rise by 4.8°C with maximum increase of 6.34°C in February. The crop simulation study suggested no or little change in mustard production under unlimited soil moisture and nitrogen conditions, but higher coefficients of variations (33%) showed unstable crop performance. A case study revealed the impact of climate change on Indian mustard (Northern, Central, and Eastern Indo-Gangetic plains (IGP); Central and Western India) during 2020, 2050 and 2080 for A1, A2 and B2 projected climate change scenarios. Under irrigated conditions, the yield reduction in 2020, 2050, and 2080 would be highest in Eastern-IGP region followed by Central-IGP and Northern-IGP. This may be due to projected rise in maximum temperature in Eastern-IGP where maximum and minimum temperature would rise by 5.1 and 5.6°C in 2080 whereas, in Western India, yield reduction gradually increased from 2020 to 2080 as compared to Central India. The rainfall pattern projected to increase in 2050 at all locations but 2020 and 2080 reduced in the three IGPs. Due to rainfall fluctuations higher yield reduction in rainfed mustard in these three locations (Boomiraj et al., 2013). Abdul Haris et al. (2015) studied the impact of projected climate change on potato cv. Kufri Ashoka (Rabi sown) using InfoCrop model under different Bihar locations (Pusa, Madhepura, Patna and Sabour). They revealed that the crop planted on 2nd December, showed decline in yield ranging from 3.3 to 5.9% for 2020, 12.5 to 15% for 2050 and 19.3 to 24.8% for 2080 time period across the locations studied. They also observed that delaying by 20 days successively from 2nd December through 22nd December showed a progressively
13
lower decline in the projected yield at all locations. Chaudhari et al. (2009) examined results based on A2 scenario of temperature and precipitation change as derived from PRECIS model. They found that during the period 2071-2100 the rice yield in irrigated regions would reduce up to 32 per cent in Haryana followed by 18 per cent in Punjab while it may increase in rainfed regions up to 28 per cent in Orissa followed by 18 per cent in Madhya Pradesh. The reduction in wheat yield will be 21 per cent in East Rajasthan followed by 18 per cent in West Rajasthan and 14 per cent in East Madhya Pradesh. Mandal (1998), Chatterjee (1998) and Sahoo (1999) calibrated and validated the CERES-maize, CERES-sorghum and WOFOST models under the Indian environment and subsequently used them to study the impact of climate change (CO2 levels: 350 and 700 ppm and temperature rise from 1 to 4°C with 1°C increment) on phenology, growth and yield of different cultivars. Mandal (1998) observed that an increase in temperature up to 2°C did not influence the potential yield and above ground biomass of chickpea under irrigated condition significantly. Pre-anthesis and total crop duration got shorten with the temperature rise. Nitrogen uptake and total water use (as evapo-transpiration) were not significantly different up to 2°C rise. The elevated CO2 increased potential grain yield under irrigated and rainfed conditions. There was a linear increase in grain yield, as the CO2 concentration increased from 350 to 700 ppm. Potential grain yield of pigeonpea decreased over the control when the temperature was increased by 1°C (using WOFOST). Wheat grain production in the main arable areas of the European conditions was calculated using WOFOST, with historical weather data and average soil characteristics (Wolf, 1993) and sensitivity of the model to individual weather variables was determined. Subsequent analyses were made using climate change scenarios with and without the direct effects of increased atmospheric CO2. The impact of crop management (irrigation and cultivar type) in a changed climate was also assessed. He observed the direct effect of increasing CO2 was also taken into account, the average water-limited grain production in the Europe increased by about 1000 kg ha-1or more. 2.5 Adaptations measures and vulnerability Singh et al. (2010) at IARI, New Delhi resulted that four medium duration varieties of wheat (PBW 343, HD 2329 HD 2285 and Kundan), one long duration (PBW 343) and one short duration variety (HD 2285) sown on 1st,15th and 31st December, delay in sowing, cause deferential reduction in yield among the varieties of different duration. Under late sown conditions, short duration varieties seed yield lowest then medium and long duration varieties. A medium duration variety (HD2329) had produced higher yield among the 1st Dec. sown varieties. On the other hand, long duration variety (PBW 343) produced more yield among the variety sown on 15th December. They also assessed climate change impact on maize yield and yield attributes by using the results of different scenarios (A1b 2030, A1b 2080, A2 2080 and B2 2080). They found that adaptation by improved variety with additional amount of
14
nitrogen had positive gain by about 24 % in A1b 2030 and 14.8 % in B2 2080 scenarios. Pushpalatha et al. (2008) in their study at IARI, New Delhi revealed that wheat cv. HD 2285 found tolerant to terminal high temperature, while become susceptible when high temperature spells come during pre-anthesis period. Therefore, there is a need, to develop wheat varieties which will be tolerant not only to terminal high temperature stress, but to a warmer climatic condition. Kumar et al. (2010b) studied the sensitivity of wheat crop to projected period (20712100) climate under irrigated condition at Central Gujarat. Low yield area at each degree rise in average temperature over crop growing period will take toll of 3.02 q ha-1 of wheat and similar result was also observed under restricted management conditions. One degree rise in temperature will reduce yield by about 2.0 q ha-1 under restricted irrigation management conditions. They observed that short duration crop (100-105 days) was likely to further shorten by 15 to 20 days under projected climatic condition for A2a scenario and in all management practices like shifting in sowing date, number and amount of irrigation coupled with amount of nitrogen tried for adaptation options was found beneficial and in all cases there was substantial yield loss. The adaptation strategies viz., change in sowing date and suitable crop variety as simulated by InfoCrop model helped the maize crop to recover from the yield loss due to projected climate change. The crop was positively responded and yield was increased to the tune of 6, 1, 2 and 5 per cent in Ranga reddy, Medak, Jagitial and Warangal, respectively in 2020. The yield loss was reduced by 5, 15, 6 and 3 per cent in these Districts respectively during 2050. The adaptation strategies improved the grain yield by 10, 12, 9 and 8 per cent in above mention district during 2080 under projected climatic impacts impacts (Rao et al., 2010). Reduction in wheat production by 10 per cent under anticipated enhancement 0.5ºC in mean temperature in the high yield States of Punjab, Haryana and Uttar Pradesh has been estimated by Sinha and Swaminathan (1991). In the areas of less productivity, the reduction was still higher. They suggested various adaptation measures like agronomic practices including fertilizer application, tillage, grain drying and other cultural operations should be adjusted to overcome the problem. Khan et al. (2009) studied various organic and inorganic nitrogen treatments significantly affected all parameters except grains per cob in maize at Peshawar, Pakistan. They observed that farmyard manure application @ 20 tons ha-1 combined with 60 kg N ha-1, through chemical fertilizer performed better than all other treatments and resulted in greater emergence, taller plants, higher thousand-grain weight, more leaf area index and greater grain and biological yield. They concluded that FYM application in combination with minimum N is an alternative and sustainable practice of soil management for crop production.
15
CHAPTER-III
MATERIAL AND METHODS In order to accomplish the objectives of the study entitled ‘Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana’; the field experiment was conducted during Rabi 2012-13 and 2013-14. The details of materials used and techniques adopted during the course of investigation are described in this chapter under following heads and sub-heads of each objective: 3.1 Experimental site and location The field experiment was conducted at Research Farm of the Department of Agricultural Meteorology, Chaudhary Charan Singh Haryana Agricultural University, Hisar located at 29 ° 10'N, 75 °46'E and altitude of 215.2 m. 3.2 Climate and weather conditions of experiment location in brief The climate of Hisar is mainly characterized by its continental location on outer margins of the monsoon region. It is situated in the tract of semi-arid and sub-tropical monsoonal climate. The south-westerly wind (monsoon) in the summer season brings rain from first week of July to mid of September. Thereafter, from October to the end of June, weather remains dry except for few light showers received from the westerly disturbance. Summers are extremely hot, while winters are fairly cool. The maximum temperature above 45°C is common during summer, on the other hand, the minimum temperature as low as below freezing point and grass minimum temperature values of below -5°C are recorded on many occasions in winter. Moreover, the extreme temperature fluctuations may occur within a very short-time interval. The occurrence of frost on certain days is also not an unusual feature here. The coefficient of variation for annual rainfall ranges between 45 and 50 per cent, whereas, the seasonal variation during monsoon season can goes up to 80 per cent and during winter season around 65 per cent. The average annual rainfall in the tract is around 450 mm, most of which is received during south-west monsoon season. 3.3 Soil analysis of the experimental field The soils of Hisar are derived from Indo-Gangetic alluvium, which are very deep and sandy loam in texture and have some amount of calcium carbonate in the soil profile. Physico-chemical analysis of the soil was done by taking random soil samples from 0 to 30, 30 to 60 and 60 to 90 cm soil profile depths before sowing of the experimental field. The composite soil samples collected from various places were mixed, air dried, well crush to pass through two mm sieve.
16
3.3.1 Mechanical analysis of soil For mechanical analysis of soil, the composite soil samples were analyzed by International Pipette Method (Piper, 1966). On t he basis of mechanical composition, the soil is categorized as sandy loam in texture (Table 3.1). 3.3.2 Chemical analysis of soil The chemical analysis of the soil were carried out with standard methods based on composite soil sample of 0-30 cm depth, which was thoroughly mixed, dried under shade and grind into fine powdery mass. The values of N, P, K, organic matter, pH and electrical conductivity were determined with standard techniques and given in Table 3.2. Table 3.1: Mechanical composition of soil (per cent Fraction) Depth of soil profile (cm)
Components
0-30 cm
30-60 cm
60-90 cm
Sand (%)
57.38
56.56
56.42
Silt (%)
26.35
26.82
26.68
Clay (%)
16.27
16.62
16.90
Sandy loam
Sandy loam
Sandy loam
Textural class
Table 3.2: Chemical properties of soil Value recoded
Soil parameters
Method of estimation
Soil pH (1:2 soil water suspension)
7.9
Glass electrode pH meter (Jackson, 1973)
EC (dSm -1 at 25°C) (1:2 soil water suspension)
0.93
Conductivity bridge method (Richards, 1954)
Organic carbon (%)
0.39
Walkley and Black’s wet oxidation method (Jackson, 1973)
Available N (kg ha -1)
193
Alkaline permanganate method (Subhiah and Asija, 1956)
17
Olsen’s method (Olsen et al., 1954)
356
Flame photometer method (Richards, 1954)
Available P2 O5 (kg ha -1) -1
Available K 2O (kg ha )
The above analyses indicate that the soil of experiment site was low in organic carbon and nitrogen, medium in phosphorus and rich in potassium and slightly alkaline in reaction. 3.4 Cropping history of the experimental field The cropping history of the experimental field is presented in Table 3.3.
17
Table 3.3: Cropping history of the experimental field Year
Kharif
Rabi
2008-09
Dhaincha
Mustard
2009-10
Dhaincha
Mustard
2010-11
Dhaincha
Mustard Experiment
2011-12
Dhaincha
Mustard Experiment
2012-13
Dhaincha
Mustard Experiment
2013-14
Dhaincha
Mustard Experiment
3.5 Experimental Details The experiment was laid out in split-plot design with four replications. The layout plan of experiment is given in Fig 3.1. The details of experiment given below: Crop
:
Mustard
Experimental design
:
Split-Plot Design
Replications
:
4
Main plot treatments
:
3
Date of sowing
:
D1 : Oct. 10, 2012 and Oct. 21, 2013 D2 : Oct. 25, 2012 and Oct. 30, 2013 D3 : Nov. 8, 2012 and Nov. 10, 2013
Sub plot treatments
:
3
:
V1 : RH 30 V2 : Laxmi V3 : RH 0749
Treatment combinations
:
9
Gross plot size
:
6.0 × 4.5 m2
Net plot size
:
4.8 × 3.5 m2
Spacing
:
30 × 15 cm2
Total number of plots
:
36
Varieties
3.6 Cultural operations 3.6.1 Land preparation The Dhaincha in the field was ploughed and stubbles were mixed well. After irrigation, when the field came to batter condition the field was ploughed up thrice with tractor drawn disc-harrow followed by planking to attain a good tilth for proper germination. 3.6.2 Seed and sowing The crop was sown by hand drawn plough by pora method as per experimental details.
18
3.6.3 Fertilizer application The recommended doses of nitrogen (80 kg N ha -1) and phosphrous (40 kg P 2 O5 ha-1) along with gypsum were applied. Full dose of phosphorus and half dose of nitrogen were applied at sowing time and remaining half dose of nitrogen was applied after first irrigation. The sources of nitrogen and phosphorus were urea (46% N) and single super phosphate (16 % P 2 O5), respectively. 3.6.4 Irrigation The crop was sown with pre-sowing irrigation and two post-sowing irrigations were applied, one at 50 per cent flowering and second at siliqua formation stage to all the treatments as per recommendations of the Package of Practices of CCS HAU, Hisar during both the crop seasons. 3.6.5 Thinning and weeding Thinning of extra plants was done 20 days after sowing by hand-pulling to maintain spacing of 15 cm between plants in a row as per treatment. To eliminate weeds in field, two hoeings were done. The first hoeing was done after 30-35 days of sowing another after first irrigation when field was in batter condition. 3.6.6 Plant protection against pest/diseases The early sowing crop was attacked by white rust disease and recommended control measures were adopted. Aphid attack was not noticed in all dates of sowing. The crop was sprayed with Imidachlorprid (16.8 per cent) @ 40 ml per acre at 10 DAS for protection against white fly. The same pesticide was sprayed again after 20 days to all plots. 3.6.7 Harvesting and threshing The crop was harvested manually with sickles. Before harvesting, five tagged plants were pulled out from each plot to record post-harvest observations. The net plots harvested separately and left in the respective plots for sun drying. The crop was threshed with the help of thresher after one week. The seed and stover yields were recorded in each plot. 3.7 Observations recorded 3.7.1 Agro-Physiological observations recorded 3.7.1.1 Phenological observations The crop was closely observed at an interval of 2-3 days for the commencement of different phenological stages viz., emergence (P 1), four leaf stage (P 2), early vegetative phase (P 3), 50% flowering (P 4), 50% pod development (P 5), start of seed filling (P 6), end of seed filling (P 7) and physiological maturity (P 8). The crop phenology was identified in each treatment when more than 50 % plants attains and used for further analysis.
19
W
N
S
R4
R3
R2
D2V1
D3V2
D1V3
D2V2
D3V1
D1V2
D3V2
D2V3
D3V3
D1V1
D3V1
2.0 m
R1 4.8m 0.75m
E
D3V3
3.5m
D3V3
D1V2
D2V3 D2V2
Irrigation Channel
D3V1
D1V3
PATH
D3V2
Irrigation Channel
0.5m
D1V2 D1V1
D1V1
D2V1
D1V3
D1V3
D2V1
D3V2
D2V3
D1V2
D2V3
D3V3
D2V2
D1V1
D2V2
D3V1
D2V1
Main Irrigation Channel Fig 3.1: Lay-out plan of experiment in both the years D 1 : Oct. 10, 2012 and Oct. 21, 2013 D 2 : Oct. 25, 2012 and Oct. 30, 2013 D 3 : Nov. 8, 2012 and Nov. 10, 2013
V 1 : RH 30 V 2 : Laxmi V 3 : RH 0749
20
3.7.1.2 Plant height Plant height was measured at 30 days interval after sowing till physiological maturity on five tagged plants in each plot. The height was measured from base of the plant to the tip of the main stem of randomly tagged plants and expressed in centimeters (cm) and mean values were calculated. 3.7.1.3 Leaf Area Index The plant leaves separated from samples taken for dry matter were used for determining leaf area from each plot at 30 days interval after sowing. The green leaf area (cm 2) was recorded using leaf area meter (LI-3000 Area meter, LI-COR Biosciences, Nebroska, USA). The leaf area measured with the help of leaf area meter was used to compute the leaf area index by the following formula. Leaf area (cm 2) LAI = –––––––––––––––––––––––––––– Land area covered by plant (cm 2) 3.7.1.4 Dry matter production and distribution The five randomly selected plants from destructive sampling were used to record the dry matter production at 30 days interval after sowing. The sampled plants were separated in to roots, stems, leaves and reproductive parts (flowers, buds and siliquae) and sun dried. Further, the samples were oven dried at 65°C to 70°C till the constant weight. The biomass/dry matter accumulation in different plant parts was converted to weight per square meter. 3.7.2 Meteorological observations Daily
weather
data
for
both
crop
seasons
were
collected
from
agrometeorological observatory situated about 10 m away from the experiment field. Weekly values of weather variables were computed to describe prevailed weather situation during crop growth. 3.7.3 Yield and yield attributes 3.7.3.1 Number of branches The total number of primary and secondary branches produced per plant counted at harvest and mean value of five plants uprooted for biomass observation in all the treatments. 3.7.3.2 Siliqua length (cm) The mean of five siliquae per plant of five tagged plants was recorded as siliqua length per plant.
21
3.7.3.3 Number of siliquae per plant and per square meter Total number of siliquae per plant was recorded from five tagged plants at harvest. Harvest of five plants was recorded as the number of siliquae produced per plant then converted into number of siliquae per square meter. 3.7.3.4 Number of seed per siliqua Hundred siliquae were taken from five plants and were threshed and cleaned. The number of seeds was counted from these siliquae. The average number of seed per siliqua was workout. 3.7.3.5 Seed yield per plant (g) and per square meter The randomly five tagged plants were harvested and threshed for seed per plant. Mean yield of five tagged plants were weighed as gram per plant. The seed yield per plant was then converted to seed yield per square meter. 3.7.3.6 1000 seeds weight (Test weight) 1000 seeds were counted manually from random sample and then weighed to record test weight in grams. 3.7.3.7 Seed yield (q ha-1) The crop harvested from net plot was threshed after sun drying. Seeds were separated, air dried, cleaned and weighed, and seed yield per ha was worked out and expressed in quintal per hectare. 3.7.3.8 Biological yield (q ha-1) Biological yield from net plot was weighed and expressed as quintal per hectare. It is nothing but the seed yield and stover yield together. 3.7.3.9 Harvest Index (HI, %) The harvest index for each plot was calculated by dividing the total seed yield by the total biological yield (seed + stover yield) of the same net plot and multiplied by 100 as given below: seed yield HI = ––––––––––––––– x 100 Biological yield 3.7.3.10 Oil content (%) Oil content of dried seeds was determined by Nuclear Magnetic Resonance (MK IIIA new port analyzer). 3.7.3.11 Oil yield (q ha-1) Oil yield was calculated by using following formula. Oil content (%) Oil yield (q ha-1) = ––––––––––––––– x seed yield (q ha-1) 100
22
3.7.4 Agro meteorological observations 3.7.4.1 Diurnal micro meteorological observations The following hourly micro-meteorological observations were recorded on a clear days from 0800 to 1700 hours at three growth stages (50% flowering; start of seed filling and end of seed filling) and bare field. 3.7.4.1.1 Solar radiation The amount of solar radiation received by crop was measured with the help of pyranometer (Medoes and Co., Australia) connected to a digital multivoltmeter. The measurements were made at one meter height above crop canopy. While making measurement, the pyranometer was kept horizontally so as to follow the cosine law. 3.7.4.1.2 Net radiation It was measured at one meter height above crop canopy with net radiometer (Medoes and Co., Australia) connected to a digital multivoltmeter. 3.7.4.1.3 Air temperature and vapour pressure The dry and wet bulb temperatures were measured with Assmann Psychrometer in the crop at three heights (near ground surface, mid height of crop and above the crop). The vapour pressure values were obtained using Psychrometric tables of India Meteorological Department. The dry bulb temperature values at these heights were considered as air temperature values for those heights. 3.7.4.1.4 Soil heat flux It was measured with the help of soil heat flux plates (Medoes and Co., Australia), which were kept at 5 cm soil depth in bare and cropped field and connected to a digital multivoltmeter. 3.7.4.1.5 Calculations of micrometeorological Parameters Energy balance of a crop volume is given by the equation: Rn = G + A+ LE + Mi …. (i) where, Rn is net radiation (mW/cm 2 ), G is ground heat flux (mW/cm 2 ), A is sensible heat flux (mW/cm 2 ), LE is latent heat of vapour flux (mW/cm 2) and Mi is miscellaneous energy used in physiological processes of plant, mW/cm 2 (this parameter is generally neglected because of its low value of less than 2 %) The computation of latent heat flux over the crop canopy with following formula: LE = (Rn-G)/(1-β) …. (ii)
23
Where, β = Bowen ratio and its inferred from the measurement of dry bulb and wet bulb temperatures using Assmann Psychrometer at three heights (top, middle and bottom) and its represented as below (Denmead and Mclory, 1970): β = 0.66 x dt/de …… (iii) Where, dt is temperature gradient between two heights (at height were observation was recorded over the crop canopy during clear days observation) and de is vapour pressure gradient between two heights. The sensible heat flux (A) was calculated from the energy balance equation using measured R n and G and, calculated LE value and given as. A = R n-(G + LE) ……. (iv) The PAR was measured with line quantum sensor (LI-191B, LICOR Inc., USA) by keeping the sensor above, middle crop surface and ground surface inside the crop canopy along with bare field. The reflected PAR was also measured keeping the sensor inverted above the crop. The daily intercepted photosynthetically active radiation (iPAR) was calculated as per the procedure adop ted by Rosenthal and Gerik (1991). PAR = Rs x 0.49 ……… (v) where, Rs is solar radiation received at the surface of earth. iPAR = (I – e -kf) PAR ……. (vi) where, k is extinction coefficient and f is leaf area index, k was calculated by expression which mention below. k = ln (I/I 0 )/f ….. (vii) where, I0 is incident radiation at the top of crop of the canopy, I is radiation energy at the bottom of the crop canopy. 3.7.4.2 Agro-meteorological indices 3.7.4.2.1 Growing degree days (GDD) Cumulative growing degree days were determined by summing the daily mean temperature above base temperature, expressed in °C day. For Brassica species, T base is considered as 5°C following Morrison (1996). This was calculated by using the following formula: ....…. (i) where, T max = Daily maximum temperature (°C),
24
T min = Daily minimum temperature (°C), T b = Minimum threshold/base temperature (°C). 3.7.4.2.2 Photo thermal unit (PTU) Cumulative photo thermal unit were determined by multiplying the GDD to the maximum possible sunshine hours, expressed in °C day hours.
……. (ii) 3.7.4.2.3 Helio thermal unit (HTU) Cumulative helio thermal unit were determined by multiplying the GDD to the actual bright sunshine hours, expressed in ºC day hours.
……. (iii) 3.7.4.2.4 Radiation use efficiency (RUE) Radiation use efficiency (RUE) is defined as the amount of dry matter produced for the unit intercepted photosynthetically active radiation (PAR). RUE of mustard at 30 days interval was computed by expression.
….….. (iv)
3.7.4.2.5 Thermal use efficiency (TUE)
The thermal use efficiency was computed to compare the relative performance of different cultivars and treatments accumulated at 30 days interval with respect with to utilization of heat, using following formula:
…... (v) 3.8 InfoCrop v.2.0 model InfoCrop is a dynamic crop-yield simulation model. This model was developed at the Center for Application of Systems Simulation, IARI, New Delhi. Basically InfoCrop is a modular decision support system for crop modeling applications. The basic code is in FST-DOS version. But windows version is also available with visual basic as front end and MS-Access as back end. The former version has been more useful to the model developers while the latter is useful for model applications. It is more users friendly with applications focused front end. Other important features of the model are that it is open, has a modular (plug-in-plug out) structure and lends itself easy to adopt. To initialize the parameters and to generate the output, either daily or at maturity, the following file management is necessary: INFOCROP.FST (+varients) FST.LIS
25
MODEL.DAT (for input data) TIMER.DAT (for timer control data) RERUNS.DAT RES.DAT(detailed daily output) SUM.OUT (summery output at maturity) The
crop
growth
processes
that
can
be
simulated
are:
phenology,
photosynthesis, respiration, leaf area growth, assimilates partitioning, source-sink balance, nutrient uptake partitioning and transpiration. These processes are arranged in sub models. The key component and modules of InfoCrop are illustrated in fig. 3.2. Weather: Rainfall, Temperature, Solar radiation
Potential yield estimation
Yield gap estimation
Pests: type, population
Crop/ variety: physiology, phenology, morphology
Yield forecasting
InfoCrop model
Agronomic inputs: seeds, FYM, irrigation, fertilizer, biopesticides
Optimizing management practices Impact assessment of climatic variability and climate change
Soil: texture, salinity, sodicity, fertility
Plant type design and evaluation
Fig. 3.2: Context diagram of InfoCrop depicting the input requirement on the left hand side and its possible application on the right Pests
Damage mechanism
Soil water
Weather
Crop growth and yield
N stress in crops
Soil N balance
Fig. 3.3: Key inputs for InfoCrop model
26
Water stress in crops
3.8.1 Input for InfoCrop v.2.0 model The inputs required for InfoCrop v. 2.0 model is listed separately in Tables 3.4. The model requires cultivar specific eight genotypic characteristics listed in Table 3.5. Table 3.4: List of inputs required for InfoCrop model v.2.0 Input variables
Acronyms
Unit
Site data Latitude
LAT
Degree
Longitude
Long
Degree
Altitude
Alt
Meter
Daily weather data YYYY or dd-mm-yy or dd-mm-yyyy or dd-mon-yy
Date/year Station number Julian days
JD
Days
Solar radiation
RDD
KJ m-2
Maximum temperature
TMAX
°C
Minimum temperature
TMIN
°C
Vapour pressure
VP
K Pa
Wind Speed
WDST
M sec -1
Rainfall
TRAIN
Mm
Relative humidity morning
RHMIN
%
Soil texture/district master parameters pH of soil
PHFAC
Electrical conductivity
EC
ds/m (0 to 1)
Slope
SLOPE
%
Thickness of layer
TKL
Mm
Sand content
SAND
%
Silt content
SILT
%
Clay content
CLAY
%
Saturation fraction
WCST
0 to 1
Field capacity fraction
WCFC
0 to 1
Wilting point fraction
WCWP
0 to 1
Saturation hydraulic conductivity
KSAT
mm/day
Bulk density
BDL
Mg/m3
Organic carbon
SOC
%
27
Soil moisture fraction at sowing
WCL
0.1 to 0.4
Initial soil Ammonium
NHAPL
(1 to 40 kg/ha)
Initial soil nitrate
NOAPL
(1 to 50 kg/ha)
Crop data Crop name Input sowing depth
SOWDEP
cm
Input seed rate
SEEDRT
Kg ha -1
DATEB
Days of the year
Maximum possible crop duration Default sowing date
Crop/variety management data Thermal time for Germination
TTGERM
degree day
Thermal time for seedling emergence to anthesis
TTVG
degree day
Thermal time for anthesis to maturity
TTGF
degree day
Base temperature
TGBD
°C
Optimum temperature
TOPT
°C
Maximum temperature
Tmax
°C
Relative growth rate of leaf area
LAII
°C/d
Specific leaf area
SLAVAR
m2 /mg
Index of greenness of leaves
Scale 0.8 to 1.2
Extinction coefficient of leaves at flowering
ha soil/ha leaf fraction
Radiation use efficiency
RUE
g/MJ/day
Root growth rate
RWRT
mm/d
Sensitivity of crop to flooding
FLDLCRP
Scale 1 to 1.2
Index of nitrogen
NI
Scale 0.7 to 1.0
Slope of storage organ number/m2 to dry matter during storage organ formation
SOPOT
Storage organ/kg/day
Potential storage organ weight
POTGWT
mm/grain
Nitrogen content of storage organ
NUPTK
fraction
Sensitivity of storage organ setting to low temperature
TPHIGH
Scale 0 to 1.5
Sensitivity of storage organ setting to high temperature
TPLOW
Scale 0 to 1.5
28
3.8.2 Calibration of the model Calibration of model involves adjusting certain model parameters or relationships to make the model work for any desired location. When using a crop model, one has to estimate the cultivar characteristics if they have not been previously determined. The model requires seven cultivar specific genetic coefficients. The details of these coefficients are given in Table 3.5. Table 3.5 Categorization of genetic coefficient of mustard for InfoCrop v.2.0 model Genetic constant description
Acronyms
Unit
Thermal time for germination to emergence
TTGERM
degree day
Thermal time for seedling emergence to anthesis
TTVG
degree day
Thermal time for anthesis to maturity
TTGF
degree day
Specific leaf area of variety
SLAVAR
fraction
Potential rate of growth
ZRTPOT
mm/d
Maximum number of grains per hectare
GNOMAX
grains per hectare
Potential weight of a grain
POTGWT
mg/grain
3.9 WOFOST v.7.1.7 model In principle, WOFOST can simulate the growth of any annual crop growing at any location. It is capable to simulate crop growth in three production situations: Potential, water-limited and nutrient-limited (Van Ittersum and Rabbinge, 1997). It is a tool for quantitative analysis of growth and production of annual field crops. WOFOST (WOrld FOod STudy) is a member of the family of crop growth models developed in Wageningen by the school of C.T. de Wit. Related models are SUCROS (Simple and Universal CRop growth Simulator), MACROS (Modules of an Annual CROp Simulator) and ORYZA1. All these models follow the hierarchical distinction between potential and limited production and share similar crop growth submodels, with light interception and CO 2 assimilation as growth driving processes and crop phenological development as growth controlling process. However, the submodels describing the soil water balance and the crop nutrient uptake may vary much in approach and level of detail. Development of WOFOST has been driven by its applications in several studies. WOFOST was originally developed to assess yield potential of various annual crops in tropical countries. WOFOST has been used to assess the effect of climate change on crop growth. The model is particularly suited to quantify the combined effect of changes in CO 2, temperature, rainfall and solar radiation, on crop development, crop growth and crop water use, as all the relevant processes are simulated separately while taking due account of their interactions.
29
Figure 3.4: Crop growth precesses simulated by WOFOST. T a and T p are actual and potential transpiration rate (Koning et. al., 1993). 3.9.1 Input for WOFOST v.7.1.7 model The inputs required for InfoCrop models is listed separately in Tables 3.6. Table 3.6: List of inputs required for WOFOST v.7.1.7 Input variables
Acronyms
Unit
Site data Station name for identification
STATION_NAME
Latitude
LATITUDE
Degree
Longitude
LONGITUDE
Degree
Altitude
ALTITUDE
m
Weather data Calculated Angstrom A
ANGSTROM_A
Calculated Angstrom B
ANGSTROM_B
The reference number of the station
WMO_O
Day of the year
DAY
d
Minimum temperature
MINIMUM_TEMPERATURE
°C
Maximum temperature
MAXIMUM_TEMPERATURE
°C
Daily irradiation
CALCULATED_RADIATION
KJ/m2/day
Daily wind speed at 10 m
WINDSPEED
m/s
Daily rainfall
RAINFALL
mm
30
Daily morning vapour pressure
VAPOUR PRESSURE
Description of the observatory
WCCDESCRIPTION
Year of data collection
WCCYEARNR
K Pa
Soil data Name of soil series or type’
SOLNAM
Physical soil characteristic Soil water retention Volumetric soil moisture content as function of pF
SMTAB
log (cm); cm 3 cm-3
Soil moisture content at wilting point
SMW
cm3 cm-3
Soil moisture content at field capacity
SMFCF
cm3 cm-3
Soil moisture content at saturation
SM0
cm3 cm-3
Critical soil air content for aeration
CRAIRC
cm3 cm-3
10-log hydraulic conductivity as function of pF
CONTAB
log (cm); log (cm day -1)
Hydraulic conductivity of saturated soil
K0
cm day-1
Maximum percolation rate root zone
SOPE
cm day-1
Maximum percolation rate sub soil
KSUB
cm day-1
Hydraulic conductivity
Soil workability parameters 1 st topsoil seepage parameter deep seedbed
SPADS
2 nd topsoil seepage parameter deep seedbed
SPODS
1 st topsoil seepage parameter shallow seedbed
SPASS
2 nd topsoil seepage parameter shallow seedbed
SPOSS
Required moisture deficit deep seedbed
DEFLIM Crop data
Name of the crop
CRPNAM
31
Emergence Lower threshold temperature for emergence
TBASEM
°C
Maximum efficiency temperature for emergence
TEFFMX
°C
Temperature sum from sowing to emergence
TSUMEM
°C d
Phenology Indicates weather pre anthesis development depends
IDSL
Optimum day length for development
DLO
hr
Critical daylength (lower threshold)
DLC
hr
Temperature sum from emergence to anthesis
TSUM1
°C d
Temperature sum from anthesis to maturity
TSUM2
°C d
Daily increase in temperature sum
DTSMTB
Initial Developmental Stage
DVSI
Development stage at harvest (= 2.0 at maturity)
DVSEND
Initial parameters Initial total crop dry weight
TDWI
kg ha -1
Leaf area index at emergence
LAIEM
ha ha -1
Maximum relative increase in LAI
RGRLAI
ha ha -1 d -1
Green area Specific leaf area
SLATB
Specific pod area
SPA
ha kg-1
Specific stem area
SSATB
ha kg-1
Life span of leaves growing at 35°C
SPAN
Lower threshold temperature for ageing of leaves
TBASE
°C
Extinction coefficient for diffuse visible light
KDIFTB
-
Light-use efficiency single leaf
EFFTB
kg ha -1 hr -1 J -1 m2 s
Function of leaf angle
AMAXTB
Assimilation
32
Reduction factor of AMAX
TMPFTB
Reduction factor of gross assimilation rate
TMNFTB
Conversion of assimilates into biomass Efficiency of conversion into leaves
CVL
kg kg-1
Efficiency of conversion into storage organ
CVO
kg kg-1
Efficiency of conversion into roots
CVR
kg kg-1
Efficiency of conversion into stems
CVS
kg kg-1
Maintenance respiration Relative increase in respiration rate per 10°C temperature increase
Q10
Relative maintenance respiration rate leaves
RML
kg CH 2 O kg-1 d -1
Relative maintenance respiration rate storage organ
RMO
kg CH 2 O kg-1 d -1
Relative maintenance respiration rate roots
RMR
kg CH 2 O kg-1 d -1
Relative maintenance respiration rate stems
RMS
kg CH 2 O kg-1 d -1
Reduction factor for senescence
RFSETB
Partitioning Fraction of total dry matter to roots as a function of DVS
FRTB
kg kg-1
Fraction of total dry matter to leaves as a function of DVS
FLTB
kg kg-1
Fraction of total dry matter to stems as a function of DVS
FSTB
kg kg-1
Fraction of total dry matter to storage organs as a function of DVS
FOTB
kg kg-1
3.9.2 Calibration of WOFOST v.7.1.7 model The crop genetic coefficients were calibrated with experimental results conducted at this station and/or collected from the literature. There were 16 most critical genetic character identified for calibration. The genetic coefficients are given in Table 3.7.
33
Table 3.7: Categorization of genetic coefficient of mustard for WOFOST model Genetic constant description
Acronyms
Unit
Lower threshold temp. for emergence
TBASEM
°C
Temperature sum from sowing to emergence
TSUMEM
°C
Temperature sum from emergence to anthesis
TSUM1
°C
Temperature sum from anthesis to maturity
TSUM2
°C
Optimum day length for development
DLO
hr
Initial total crop dry weight
TDWI
Kg ha -1
Leaf area index at emergence
LAIEM
Life span of leaves growing at 35 °C
SPAN
day
Efficiency of conversion into leaves
CVL
Kg ha -1
Efficiency of conversion into storage organs
CVO
Kg ha -1
Efficiency of conversion into roots
CVR
Kg ha -1
Efficiency of conversion into stems
CVS
Kg ha -1
Relative maintenance respiration rate of leaves
RML
KgCH2 O kg-1 d -1
Relative maintenance respiration rate of storage organs
RMO
KgCH2 O kg-1 d -1
Relative maintenance respiration rate of roots
RMR
KgCH2 O kg-1 d -1
Relative maintenance respiration rate stems
RMS
KgCH2 O kg-1 d -1
3.10 Validation and simulation of models The models were run and validated by comparing the predicted output with observed parameters. Deviation of predicted from observed were calculated and accuracy of the model to predict different crop parameters was quantified, then the simulated for the further study. 3.11 Statistical analysis Data collected during the study were statistically analyzed by using the technique of analysis of variance (ANOVA). Standard deviation of maximum, minimum temperature, rainfall and solar radiation has been calculated on monthly, seasonal and annual basis by using the expression: S.D. (σ) = √∑(Xi – X )2/n Where, Xi is the ith observation. X is the mean of observations. n is the total number of observations. Coefficient of variation (C.V.) of maximum, minimum temperature, rainfall and solar radiation has been calculated on monthly, seasonal and annual basis by using the expression: C.V = (Standard Deviation / Mean) × 100 To judge the significant difference between means of two treatments, the critical difference (C.D.) was work ed out using following formula:
34
CD = √2 X EMS/n
X t value at 5%
where, CD = critical difference EMS = error mean sum of square n = number of observations t = value of t-distribution at 5 % level of error degree of freedom 3.12 Models comparison Generally, correlation coefficient (r) and regression coefficient (R) are determined to evaluate the association between the observed and predicted values despite the fact that their magnitudes are consistently not related to accuracy of prediction. Hence, to achieve accuracy, the test criteria suggested by Wiltlmott (1982) were followed while evaluating the performance of the models. Here, accuracy means the degree to which model predictions approach the magnitude of their observed counterparts. They are listed as: Summary measures, Difference measures and Descriptive measures. (Willmott (1982). Summary measures, include the mean of observed (O) and simulated (P) values, standard deviation of observations (So) and that of predictions (Sp), slope (b) and intercept (a) of the least square regressi on (Pi= a + b* Oi). These measures describe only the quality of the simulation. Difference measures locate and quantify the errors. They include Mean Absolute Error (MAE), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The MAE and RMSE elucidate the magnitude of the average error but do not provide information about the relative size of the average difference between the observed and predicted. But, MBE indicates the direction of the error magnitude. A negative MBE means, the predictions are smaller than the observation and positive MBE means over prediction. MAE =
n
1P i 1
i
O i 1 n
……… (i)
n
MBE = Pi O i n
……… (ii)
i 1
RMSE
n = P i O i 1
i
2
n
1
2
……… (iii)
Graf et al., 1991 used the R and standardized mean square error, V to test the goodness of fit and compared simulated data with observed data for rice growth and development. Where, R quantified the model’s ability to produce the observed growth and development pattern and, V is a measure that revealed the model’s tendency to generally over- or underestimate the field observations.
35
n
R=
(P i 1
- Oi )
i
O i 1
n
V=
…………… (iv)
n
(P i 1
i
i
-Oi )2
n
O i 1
…………… (v)
2 i
Willmott (1982) calculated an index of agreement (D) as follows.
D 1
2 2 Pi O i IPi' I IO i' I n
n
i 1
i 1
……… (vi)
O (D) 1 where,
P i' = Pi – P and O i' = Oi – O. The summary measures describe the quality of simulation while the difference
measures try to locate and quantify errors. Besides the above test criteria, error per cent was also calculated in different treatment under study to express the deviation more scientifically. This is as follow:
PE =
RMSE O
100
3.13 Climatic variability analysis Daily weather data recorded at Agrimet Observetory of Department of Agricultural Meteorology, CCS HAU, Hisar was collected for a 45 year period (19702014). The weather variables included in the study were maximum temperature, minimum temperature and rainfall. Analysis of these weather parameters was carried out to study the climatic variability on seasonal and annual basis. To detect the trend for Hisar over time series, long term data were analysed. The magnitude of variability in a time series was determined using statistical method i.e. mean, standerd deviation, coefficient of varience, regression equation, significance of the trend and Theil-Sen’s analysis (Slope/year and SE). 3.14 Climate change study For detail climate change impact study, weather data for Aa1b , A2 scenario, derived using PRECIS (Providing REgional Climates for Impact Studies) downscaled model prepared by Indian Institute of Tropical Meteorology, Pune in a grid size of 0.44°. Four periods of weather data each, one for baseline i.e. 1970-1990 (Anon. 2014; URL: http://ds.data.jma.go.jp/gmd/wdcgg/; NOAA 2014; Scott, 2013) (here after referred as
36
baseline period) and another for projected climate change scenarios: A1b 2030 (20202049), A1b 2080 (2070 -2099) and A2 2080 (2070-2099) with the CO 2 concentration with their respective scenarios are A1b 2030- 447 ppm, A1b 2080- 639 ppm and A2 2080- 682 ppm (Soora, 2013) (here after referred as projected scenario period) were considered for climate change quantification and impact assessment study of mustard crop. 3.14.1 Weather data preparation for climate change study Differences were observed between PRECIS baseline daily weather data and actual weather data for the same period (Tripathy et. al., 2009). With assumption and common consensus in the network project, about the differences between PRECIS baseline (1970-1990) and projected (A1b 2030 (2020-2049), A1b 2080 (2070 -2099) and A2 2080 (2070-2099) are to be applied for climate change, thirty year monthly average of daily weather data for baseline period were subtracted from corresponding projected A1b and A2 scenario data for various parameters and the differences obtained were used for computing weather data for projected period using actual observed data. In case of rainfall, though no satisfactory method could be evolved but percentage difference of monthly sum of thirty year average data, between projected output and baseline output were used as correction factor. For computing weather data (except daily rainfall) for projected period from actual observed data of baseline period following method was used. Xpni ∆i Xpni
= = =
Xoni
=
Āpi
=
Abi
=
n Ni
= =
Xoni + ∆i + (∆i +1-∆i)*n/Ni Āpi-Ābi Weather parameter of nth day starting from middle (15 th) of i th month for projected periods i.e. A1b 2030, A1b 2080 and A 2 2080. Observed weather parameter of nth day starting from middle (15 th) of i th month for baseline period (1970-1990). Average of 30 years (A1b 2030, A1b 2080 and A 2 2080) monthly average of daily weather parameter for projected period. Average of 30 years (1970-1990) monthly average of daily weather parameter for base line period. ranges from 0 to N i number of days between middle of i th and (i + l) th months
For computation of rainfall data of projected scenario period, the percentage increment (change) of monthly rainfall in projected scenario over model baseline were used as multiple to the observed rainfall data of baseline period. Rpni Rpni
= =
Roni*(1+(R pi avg – Roi avg)/R oi avg) n th day (from beginning of month) computed rainfall of i th month for projected period.
37
Roni
=
Rpi avg = Roi avg =
n th day (from beginning of month) observed rainfall of i th month under base line period. 30 years average of monthly sum of rainfall of i th month under projected condition. 30 year average of monthly sum of actual observed rainfall of i th month under baseline period.
3.14.2 Impact assessment, adaptation options and vulnerability for mustard cultivars Thirty years weather data for different projected period obtained by the above method along with twenty one years actual observed data suitably adjusted to the baseline period (1970-1990) were observed for climate change impact study on mustard cv. RH 30, Laxmi and RH 0749. For the purpose of evaluating different adaptations options and identify best adaptations mechanism, different realistic hypothetical set of crop management data were used for climate change impact study using InfoCrop v.2.0 and WOFOST v. 7.1.7 simulation models. The net vulnerability in the respective scenarios was derived from the following formulae: Vulnerability = Impact (yield loss due to climate change) - Adaptation gain
38
CHAPTER-IV
EXPERIMENTAL RESULTS The present field experiments were carried out on mustard during Rabi seasons of the years 2012-13 and 2013-14 to study “Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana”. The results obtained from the field experiments on different aspects are presented in this chapter in light of objectives put forth for the study with the help of appropriate Tables and Figures. 4.1 Weather conditions prevailed during crop seasons Weekly values of different weather parameters during Rabi (2012-13 and 201314) were calculated on the basis of daily data recorded at Agrometeorology Observatory, CCS HAU, Hisar situated at a distance of 10 m from the experimental site. These weekly values along with the normal values for various weather parameters are presented in Figures 4.1 to 4.7. A detailed enumeration of the prevailed weather has been given in the following sections: 4.1.1 Maximum temperature Weekly mean maximum temperature during Rabi 2012-13 fluctuated from normal throughout the entire crop season which is depicted in Fig. 4.1. The mean weekly maximum temperature was 34.3, 29.2 and 29.2°C at time of sowing for D 1 (10 th Oct.), D2 (25 th Oct.) and D 3 (8 th Nov.) sown crop, respectively. The first date of sowing (10 th Oct.) experienced higher temperature (34.3°C) during emergence as compared to second (25 th Oct.) and third date of sowing (8 th Nov.) which experienced (29.2°C). This showed that the direct effect on days taken to emergence which was highest in D 3. Lowest value of mean maximum temperature was recorded on 1 st SMW (Standard Meteorological Week) i.e. 11.6°C which coincided with active start of seed filling (P 6) stage of D 1 and D 2, and 50% pod development (P 5) stage of D 3 during 2012-13. Maximum temperature gradually decreased from 40 th SMW to 1st SMW and then continued to increase afterwards till end of seed filling (P 7) during crop growth seasons of 2012-13. Although it fluctuating from end of seed filling to physiological maturity but remain below normal which helped in better seed filling sating. The weekly mean maximum temperatures remained below to its normal values except 47 th to 50th SMW throughout Rabi 2013-14 as compare to previous crop season 2012-13. During this crop season its value ranged between 16.4 to 33.2°C, while it was observed 11.6 to 35.4°C in the previous year.
39
During Rabi 2013-14, maximum temperature was observed lower till early vegetative phase (P 3), four leaf stage (P 2) and emergence (P 1) among D1, D2 and D3, respectively. Thereafter, it increases till 50% pod filling (P 5), 50% flowering (P 4) and early vegetative stage (P 3) of D 1, D2 and D 3, respectively. During this period, temperature goes higher as compared to normal values. Then decline till 1 st SMW, and reached to lowest value of this season, which coincide with active start of seed filling (P 6) stage of D 1, 50% pod development (P 5) stage of D 2 and 50% flowering of D 3, respectively. But in the year 2012-13, reproductive phases in all the sowing dates experienced higher temperature at initial growth stages. But mean maximum temperature remained below normal during entire growth period 2012-13, as compared to 2013-14. A comparison among two years showed that mean maximum temperature prevailed from emergence to early vegetative phase were higher (1.9°C) in 2013-14 as compared to 2012-13. But it was lower (0.5°C) for the other developmental stages in 2013-14 in comparison to 2012-13.
Fig 4.1: Mean weekly maximum temperature along with normal during 2012-13 and 2013-14 4.1.2 Minimum temperature The mean weekly minimum temperature was lower than normal during sowing to emergence for D 1 (10th Oct.) during Rabi 2012-13 (Fig. 4.2). The mean weekly minimum temperature varied from 16.8 (emergence-P 1 ) to 1.6°C (start of seed fillingP 6). Then it fluctuates through subsequent stages i.e. end of seed filling-P 7 (4.0°C) to physiological maturity-P 8 (14.2°C) in case of D1 sowing date. In Case of D2 (25th Oct.) and D 3 (8 th Oct.), mean weekly minimum temperature persisted almost parallel, which, helped in profuse better growth during early vegetative phases during both the crop seasons. During Rabi 2013-14, the minimum temperature ranged from 16.3°C at emergence to 15.3°C at maturity. Lowest mean value of minimum temperature was
40
attained (2.4°C) at start of seed filling stage (P 6) of D 1 . It remained above normal in most of the phenophases. The mean weekly minimum temperature was 1.2°C higher during 2013-14 as compared to 2012-13. Most importantly, from end of seed filling (P 7) to physiological maturity (P 8), the mean minimum temperature continuously increases and remained above normal i.e. 1.3 and 1.1°C during 2012-13 and 2013-14, respectively.
Fig 4.2: Mean weekly minimum temperature along with normal during 2012-13 and 2013-14 4.1.3 Relative Humidity Mean weekly morning and evening relative humidity values fluctuated along with their normal values among different standard meteorological weeks (Fig. 4.3). Weekly morning relative humidity was 80.6 (D 1), 92.1 (D 2) and 91.1 per cent (D 3), and 94.9 (D 1), 89.1 (D 2) and 95.6 per cent (D 3) during emergence (P 1) during 2012-13 and 2013-14, respectively. These values fluctuated for whole season but lowest value (75.6 and 83.3 per cent) was recorded during physiological maturity (P 8) during 2012-13 and 2013-14 crop seasons, respectively. The mean morning relative humidity during Rabi 2012-13 was recorded 14.3 per cent higher than 2013-14 crop season at emergence (P 1), whereas, in case of D 1 and D2 date of sowings experienced not much fluctuation during 50% pod development (P 5 ) except physiological maturity (P 8) of both the crop seasons. The highest mean weekly morning relative value of 99 per cent was attained during 1st and 3 rd SMW during 2012-13 and 2013-14, respectively. The mean weekly evening relative humidity during Rabi 2012-13 was ranged from 28.6 to 81.0 per cent for the whole crop season, whereas, for Rabi 2013-14 this range was 36.9 to 83.6 per cent. This parameter continued fluctuate and was above normal during most of the crop growth period in both the years.
41
4.1.4 Rainfall Amount of rainfall as well as its distribution during the crop growth season is very important for growth and development of the crop. During Rabi season of 2012-13, total rainfall received was 117.7mm in 12 rainy days. The vegetative phases of all the sowing dates received 5.4mm rainfall (Fig. 4.4). The highest rainfall was received during 3 rd SMW (43.0mm), which, coincide with end of seed filling stage (P 7) of D 1 and D2 , and start of seed filling stage of D 3. Rest of the rainfall was received during emergence (P 1), 50% flowering (P 4) and physiological maturity (P 8) under different sowing dates of both crop seasons.
Fig 4.3: Mean weekly relative humidity along with normal during 2012-13 and 2013-14 During year 2013-14, the highest rainfall of 20.3mm was recorded during physiological maturity (P 8 ) in 10 th SMW. During Rabi 2012-13, the highest total rainfall was 117.7mm as compared to Rabi 2013-14 (85.9) w.r.t normal’s rainfall (62.6mm).
Fig 4.4: Mean weekly rainfall along with normal during 2012-13 and 2013-14 4.1.5 Bright Sunshine Hours The crop growing season of Rabi 2012-13 was characterized by lower sunshine duration than normal and depicted in Fig. 4.5. The weekly mean sunshine hours values
42
were fluctuated throughout the crop season as compared to normal during 2012-13. The weekly mean sunshine hour values were higher during 51 st, 4th, 9th and 10th SMWs as compared to normal. The highest sunshine hours (9.5h) were recorded at physiological maturity (P 8) and lowest (2.0h) at start of seed filling (P 6) during 2012-13. However, the weekly mean sunshine hours recorded, below normal except 46 th, 47 th, 48th and 52 nd SMWs of crop season 2013-14. The highest sunshine hours (8.3h) were recorded at physiological maturity (P 8) and lowest (2.0h) at 50% pod development (P 5), 50% flowering (P 4) and early vegetative phase (P 3 ) of D 1 (21st Oct.) , D2 (30 th Oct.) and D 3 (10 th Nov.) sowing dates of 2013-14 crop season, respectively.
Fig 4.5: Mean weekly bright sunshine along with normal during 2012-13 and 2013-14 4.1.6 Pan evaporation During the crop season of 2012-13, the mean weekly pan evaporation values were below normal for entire period (Fig. 4.6).
Fig 4.6: Mean weekly pan evaporation along with normal during 2012-13 and 2013-14
43
It was close to normal during 50% flowering (P 4) and end of seed filling (P 7) in D 1 (10 th Oct.) and D 2 (25 th Oct.), whereas, early vegetative phase (P 3) and start of seed filling (P 6) in D 3 (8 th Nov.) which experienced low pan evaporation. However, it was lower for entire crop season during 2012-13 and 2013-14 as compared to normal values. The seasonal mean weekly cumulative pan evaporation values were 63.5mm in 2012-13 and 45.1mm in 2013-14 as against the normal of 85.8mm, this indicated non-stress conditions for most part of the growing season. 4.1.7 Wind speed The mean weekly wind speed values during both crop seasons were mostly lower from normal (Fig 4.7). The mean weekly wind speed values remained in the range of 1.3 to 5.5 km hr -1 during 2012-13. However, the range of mean weekly wind speed was low (1.6 to 5.4 km hr -1) in 2013-14. Therefore, the days were calm during 2013-14 as compared to 2012-13 crop seasons.
Fig 4.7: Weekly wind speed along with normal during 2012-13 and 2013-14 4.2 Crop phenology The observations on the phenological events of the mustard crop reflected the influence of weather elements. In the present study the occurrence of different phenological events viz. emergence (P1 ), four leaf stage (P 2), early vegetative phase (P3), 50 % flowering (P 4), 50 % pod development (P5), start of seed filling (P6), end of seed filling (P 7), physiological maturity (P8) were recorded. The phenophases for different varieties under various dates of sowing for two crop season i.e. 2012-13 and 2013-14 are presented in Tables 4.1 and 4.2.
44
Table 4.1: Influence of sowing dates on phenophase development in mustard varieties during 2012-13 Treatment P1 P2 P3 P4 P5 P6 P7 P8 4.4 14.8 43.6 55.5 69.2 75.4 95.3 139.9 D1 5.1 12.7 43.1 54.3 63.9 73.6 92.9 128.9 D2 th 5.4 10.4 42.5 52.7 61.8 71.5 92.3 125.0 D3-8 Nov. 4.9 12.6 43.1 54.2 64.9 73.5 93.5 131.3 Mean ± SD ±0.2 ±2.2 ±0.6 ±1.4 ±3.8 ±1.9 ±1.6 ±7.7 CV % 0.03 0.17 0.01 0.03 0.06 0.03 0.02 0.06 5.1 11.3 42.4 53.0 61.8 72.2 91.2 124.8 V1 5.1 12.5 42.6 54.1 64.0 73.4 94.1 133.3 V2 5.4 14.1 44.2 55.4 65.0 74.9 95.3 135.8 V3 5.2 12.6 43.1 54.2 63.6 73.5 93.5 131.3 Mean ± SD ±0.2 ±1.4 ±1.0 ±1.2 ±1.6 ±1.4 ±2.1 ±5.8 CV % 0.03 0.11 0.02 0.02 0.03 0.02 0.02 0.04
Where, P1 = Emergence, P2= four leaf stage, P3 = Early vegetative phase, P4 = 50 % flowering, P5 = 50 % pod development, P6 = Start of seed filling, P7 = End of seed filling, P8 = Physiological maturity
Table 4.2: Influence of sowing dates during 2013-14 Treatment P1 P2 4.8 13.2 D1 5.1 11.8 D2 5.6 10.5 D3 5.2 11.8 Mean ± SD ±0.6 ±1.4 CV 0.12 0.11 4.8 11.3 V1 5.0 11.9 V2 5.5 12.2 V3 5.1 11.8 Mean ± SD ±0.4 ±0.5 CV % 0.07 0.04
on phenophase development in mustard varieties P3 41.2 41.7 42.0 41.6 ±0.4 0.01 40.0 41.4 43.1 41.5 ±1.6 0.04
P4 51.4 51.1 54.9 52.5 ±2.1 0.04 51.0 52.2 54.2 52.5 ±1.6 0.03
P5 63.7 61.8 59.2 61.6 ±2.3 0.04 59.9 61.7 63.1 61.6 ±1.6 0.03
P6 73.2 72.1 69.7 71.7 ±1.8 0.02 70.2 71.7 73.2 71.7 ±1.5 0.02
P7 97.1 93.7 91.0 93.9 ±3.1 0.03 92.0 93.2 96.7 94.0 ±2.4 0.03
P8 132.8 122.3 119.0 124.7 ±7.2 0.06 120.4 125.5 128.2 124.7 ±4.0 0.03
Where, P1 = Emergence, P2= four leaf stage, P3 = Early vegetative phase, P4 = 50 % flowering, P5 = 50 % pod development, P6 = Start of seed filling, P7 = End of seed filling, P8 = Physiological maturity
It is evident from the above mentioned tables that the dates of sowing make difference significantly in all important phenophases in both the crop seasons. Among the dates of sowing D1 and D2 took more days for emergence followed by D3 during both the crop seasons. Duration of reproductive phase became shorter as the date of sowing delayed in the season. Duration of most of the phenophases was longer during 2012-13 as compared to 2013-14, except emergence (P1) and end of seed filling (P7), which was higher in 2013-14. During 2012-13, mustard crop took 139.9 days to attain physiological maturity (P8) in first sowing, whereas, it took 132.8 days to mature under same growing environment in the year 2013-14. Among the three varieties viz. RH 30 (V1), Laxmi (V2) and RH 0749 (V3) in respect of sowing dates, there were no much significant difference among cultivars. The cv. RH 0749 took more days (135.8 in 2012-13 and 128.7 in 2013-14) to attain physiological maturity (P8) followed by Laxmi (133.3 in 2012-13 and 125.5 in 2013-14) than RH 30 (124.8 in 2012-13 and 120.7 in 2013-14).
45
4.3 Plant height Three different growing environments had significant influence on plant height attained at different days after sowing during crop growing season of both the years (Table 4.3). Maximum crop height was recorded at physiological maturity under all sowing dates. Among different growing environments, D1 recorded taller plants at all growth intervals followed by D2 and D3. However, height of crop plant was more in all the growth intervals during 2012-13 as compared to 2013-14. Table 4.3: Plant height (cm) of mustard varieties at various growth intervals different sowing environments during 2012-13 and 2013-14 2012-13 2013-14 30 60 90 120 30 60 90 120 Treatment PM DAS DAS DAS DAS DAS DAS DAS DAS 41.9 93.1 158.4 185.8 195.1 39.8 103.4 149.3 177.6 D1 36.7 81.4 147.9 177.7 186.4 34.8 90.8 130.8 153.1 D2 30.1 65.5 133.7 165.9 174.7 28.6 74.2 107.3 125.4 D3 CD at 5% 2.0 2.1 8.7 6.2 8.0 2.7 3.7 6.3 8.7 33.2 71.5 135.6 167.8 175.6 31.0 79.9 118.0 136.5 V1 34.1 80.8 147.1 175.0 183.5 32.7 83.7 123.6 141.3 V2 41.5 87.8 157.3 186.6 195.1 39.4 104.7 145.9 178.3 V3 CD at 5% 2.1 1.9 8.2 6.0 9.1 1.9 4.9 4.7 8.0 CD at 5% 1.56 1.75 6.42 5.48 7.23 2.0 3.83 5.21 6.14 (D x V) CD at 5% 1.63 1.20 4.23 5.21 6.10 1.63 3.10 4.31 5.31 (V x D)
under
PM 186.0 172.5 134.4 5.3 147.9 159.4 185.7 7.6 5.83 4.78
Where, DAS- days after sowing
Table 4.4: Interaction between dates of sowing and varieties on plant height at physiological maturity of mustard during both years 2012-13 Treatments V1 V2 V3 Mean 185.9 191.9 203.5 D1 195.1 177.2 185.1 196.8 D2 186.4 165.5 173.4 185.1 D3 174.7 Mean 175.6 183.5 195.1 CD at 5% (D x V) 7.2 CD at 5% (V x D) 6.1 2013-14 Treatments V1 V2 V3 Mean 174.4 173.6 206.0 D1 186.0 156.3 161.2 204.0 D2 172.5 112.4 143.4 147.1 D3 134.4 mean 147.9 159.4 185.7 CD at 5% (D x V) 5.8 CD at 5% (V x D) 4.8
46
In first date of sowing, maximum plant height attained was 195.1 cm in Rabi 2012-13, whereas, during Rabi 2013-14 it was 186.0 cm. But under late sown condition, the plant height was recorded 174.7 and 134.4 cm at physiological maturity during 2012-13 and 2013-14, respectively. In case of varieties, maximum plant height attained by RH 0749 (V 3) in both the crop seasons followed by Laxmi (V 2) and RH 30 (V 1). During 2012-13, RH 0749 reached to 195.1 cm height, whereas, in 2013-14 it was 185.7 cm. The interaction between different sowing environments and varieties on plant height in mustard is given in Table 4.4. The delay in sowing resulted into decreased plant height in varieties significantly during both the crop seasons. The variety RH 0749 attained the maximum height of 203.5 and 206.0 cm in first date of sowing, whereas, minimum plant height was observed in 10 th Nov. sown crop i.e. 165.5 and 112.4 cm in variety RH 30 during the years 2012-13 and 2013-14, respectively. 4.4 Agrometeorological indices The agrometeorological indices were derived from the direct recorded meteorological observations. Agrometeorological indices are very useful in establishing weather and crop yield relationship and hence can be used as a helpful tool for crop yield prediction. 4.4.1 Growing Degree Days (GDD) Accumulated GDD during different growth intervals in all the treatments are presented in Table 4.5. Maximum GDD values were recorded at physiological maturity under all sowing dates. Among growing environment treatments, D1 utilized maximum GDD in all the growth intervals followed by D 2 and D 3. However, accumulated GDD was more in all the growth intervals during 2012-13 as compared to 2013-14. In first sowing date of mustard, GDD value attained a maximum of 1563.0°C day in Rabi 2012-13, whereas, it was 1512.0 °C day during Rabi 2013-14. But under late sown condition, the maximum GDD value of mustard crop was 1179.1°C day and 1137.1 °C day in 2012-13 and 2013-14, respectively. In case of varieties, maximum GDD value obtained in RH 0749 (V 3) under both the years followed by Laxmi (V 2) and RH 30 (V 1). During 2012-13, RH 0749 GDD value reached to 1590.8 °C day, whereas, it was 1493.1 °C day in 2013-14. Accumulated GDD during different phenological stages in all the treatments are presented in Tables 4.6 and 4.7 for 2012-13 and 2013-14, respectively. The data presented in tables showed that growing environments have showed the impact on accumulated GDD at all the phenophases. Accumulation of GDD was higher under 10 th Oct. sown crop at all the phenophases in comparison to the other sowing dates. The accumulated GDD from emergence (P 1) to physiological maturity were 1582.2, 1354.1 and 1290.5 °C day in 10 th Oct., 25 th Oct. and 10 th Nov. sown crop during 2012-13, respectively, while during 2013-14 these were 1476.5, 1278.2 and 1148.7°C day in 22 nd Oct., 30th Oct. and 10 th Nov. sown mustard crop respectively. Among the varieties, RH
47
0749 (V 3) accumulated the highest GDD from emergence (P 1) to physiological maturity (P 8) in both the years followed by Laxmi (V 2) and RH 30 (V 1). During 2012-13, the GDD value for RH 0749 reached to 1557.8°C day, whereas, this value was 1449.5°C day in 2013-14. 4.4.2 Photothermal units (PTU) Photothermal unit (PTU) (a product of GDD and maximum possible sunshine) requirement of mustard crop at different phenophases in all the treatments and both the crop seasons are presented in Tables 4.8 and 4.9. As the crop progressed towards maturity, consumption of PTU has been increased and maximum was recorded at physiological maturity. Among the growing environments, mustard crop exposed to first date of sowing acquired maximum PTU at all the phenophases followed by second and third date of sowing in both the crop seasons. Among the varieties, a similar trend in photothermal units was observed as incase of GDD values at all the phenophases in both the crop seasons. 4.4.3 Heliothermal Units (HTU) Accumulated heliothermal units (HTU) a t different phenophases in mustard during Rabi 2012-13 and 2013- 14 is presented in the Tables 4.10 and 4.11. The mustard crop sown on D 1 date of sowing consumed higher heliothermal units over D2 and D3 during both the crop seasons. The consumption of PTU was recorded maximum at physiological maturity in both the crop seasons. Among the varieties, a similar trend in heliothermal units was observed as that of photothermal unit at all the phenophases in both the crop seasons. 4.4.4 Radiation Use Efficiency (RUE) Radiation use efficiency (RUE) of mustard under various treatments at different phenophases during two consecutive crop seasons (Rabi 2012-13 and 2013-14) is presented in Table 4.12. The RUE gradually increased with crop growth and attained maximum value at seed filling stage (90 DAS). Thereafter, it declined till physiological maturity (P 8) in both the crop seasons. First date of sowing was more efficient in utilizing radiation incident upon the crop canopy, followed by D 2 and D 3. The differences were statistically significantly differed at all the growth intervals both the crop seasons. However, RUE values were more in 2012-13 then 2013-14. Maximum RUE during Rabi 2012-13 at 90 DAS were recorded as 3.27, 2.99 and 2.75 g MJ -1 in D 1, D2 and D3 , respectively. When considering varieties, the mean RUE was significantly higher in RH 0749 (V 3) under both the crop seasons followed by Laxmi (V 2) and RH 30(V 1). During 2012-13, maximum RUE utilized by RH 0749 at 90 DAS was 3.39 g MJ -1, whereas, it was maximum of 3.17 g MJ -1 during 2013-14. 4.4.5 Thermal Use Efficiency (TUE) The quantification of thermal use efficiency (the amount of dry matter produced per unit of growing degree day) is important for determination of yield potential in different environment.
48
Table 4.5: Effect of sowing dates and varieties on accumulated growing degree days (°C day) at various growth intervals in mustard during 2012-13 and 2013-14 2012-13 2013-14 Treatment 30 DAS 60 DAS 90 DAS 120 DAS PM 30 DAS 60 DAS 90 DAS 120 DAS PM 537.0 788.5 1147.1 1416.2 1563.0 505.4 842.5 1142.0 1387.0 1512.0 D1 469.3 678.7 1002.5 1247.2 1399.2 441.4 666.1 997.0 1220.4 1353.7 D2 384.0 510.4 781.3 1022.4 1179.1 361.2 574.6 781.1 1031.3 1137.1 D3 463.4 659.2 977.0 1228.6 1380.4 436.0 694.4 973.4 1212.9 1334.3 Mean±SD ±76.7 ±140.1 ±184.2 ±197.6 ±192.6 ±72.3 ±136.2 ±181.6 ±178.0 ±188.2 CV % 16.5 21.2 18.9 16.1 14.0 16.6 19.6 18.7 14.7 14.1 410.6 550.7 848.7 1161.3 1256.7 383.7 576.4 858.0 1121.8 1209.3 V1 466.3 636.7 965.2 1202.9 1300.4 430.1 653.3 905.6 1164.7 1293.8 V2 513.5 790.1 1117.1 1399.0 1590.8 494.1 853.4 1156.4 1274.7 1493.1 V3 463.4 659.2 977.0 1228.6 1380.4 436.0 694.4 973.4 1212.9 1334.3 Mean±SD ±51.5 ±121.3 ±134.6 ±126.9 ±181.6 ±55.4 ±143.0 ±160.3 ±78.9 ±145.7 CV % 11.1 18.4 13.8 10.3 13.2 12.7 20.6 16.5 6.5 10.9 Where, DAS- days after sowing
Table 4.6: Effect of sowing dates and varieties on accumulated growing degree days (°C day) for various phenophases of mustard during 2012-13 Treatment D1 D2 D3 Mean ± SD CV % V1 V2 V3 Mean ± SD CV %
P1 110.3 85.6 79.4 91.8 ± 16.3 17.8 88.8 91.4 95.0 91.8 ± 3.1 3.4
P2 294.7 199.2 150.9 214.9 ± 33.2 15.4 194.4 211.9 238.6 214.9 ± 22.3 10.4
P3 716.8 588.7 501.0 602.2 ± 108.5 18.0 591.0 596.0 619.5 602.2 ± 15.2 2.5
P4 852.0 699.9 569.6 707.2 ± 141.3 20.0 698.8 710.0 712.7 707.2 ± 7.4 1.0
P5 957.3 796.7 626.1 793.4 ± 165.6 20.9 786.5 792.0 801.5 793.4 ± 7.6 1.0
P6 1039.3 825.9 681.7 849.0 ± 179.9 21.2 846.7 849.6 850.6 849.0 ± 2.0 0.2
P7 1166.3 960.7 844.0 990.3 ± 163.2 16.5 957.5 1005.3 1008.1 990.3 ± 28.4 2.9
P8 1582.2 1354.1 1290.5 1408.9 ± 153.4 10.9 1347.8 1435.9 1557.8 1408.9 ± 105.5 7.5
Where, P1 = Emergence, P2= four leaf stage, P3 = Early vegetative phase, P4 = 50 % flowering, P5 = 50 % pod development, P6 = Start of seed filling, P7 = End of seed filling, P8 = Physiological maturity
49
Table 4.7: Effect of sowing dates and varieties on accumulated growing degree days (°C day) for various phenophases of mustard during 2013-14 Treatment D1 D2 D3 Mean ± SD CV % V1 V2 V3 Mean ± SD CV %
P1 112.4 91.8 85.1 96.4 ± 14.2 14.8 81.6 86.3 91.4 96.4 ± 4.9 5.1
P2 251.7 185.0 130.2 189.0 ± 20.8 11.0 171.5 182.1 193.3 189.0 ± 10.9 5.8
P3 630.1 566.4 504.2 566.9 ± 63.0 11.1 549.9 565.3 585.5 566.9 ± 17.9 3.1
P4 757.0 663.3 521.4 647.2 ± 118.6 18.3 637.3 642.4 662.0 647.2 ± 13.0 2.0
P5 878.3 741.3 620.4 746.7 ±129.0 17.3 738.8 747.3 753.9 746.7 ± 7.6 1.0
P6 942.4 801.7 685.6 809.9 ± 128.6 15.9 797.6 809.8 822.3 809.9 ± 12.4 1.5
P7 1113.9 980.7 892.5 995.7 ± 111.5 11.2 979.4 986.4 1021.3 995.7 ± 22.4 2.3
P8 1476.5 1278.2 1148.7 1301.1 ± 165.1 12.7 1229.5 1324.4 1449.5 1301.1 ± 110.3 8.5
Where, P1 = Emergence, P2= four leaf stage, P3 = Early vegetative phase, P4 = 50 % flowering, P5 = 50 % pod development, P6 = Start of seed filling, P7 = End of seed filling, P8 = Physiological maturity
Table 4.8: Effect of sowing dates and varieties on accumulated photo thermal unit (°C day hours) for various phenophases of mustard during 201213 Treatment P1 P2 P3 P4 P5 P6 P7 P8 1247.8 3362.9 8011.2 9514.2 10586.9 11459.8 12793.5 17651.6 D1 958.4 2232.9 6435.3 7618.9 8381.5 8902.3 10426.0 14891.5 D2 869.5 1628.2 5318.8 6008.1 6425.1 7243.6 9346.7 14344.6 D3 Mean ± SD 1025.2±197.8 2408.0±480.5 6588.4±1352.7 7713.7±1755.0 8464.5±2082.1 9201.9±2124.0 10855.4±1763.1 15629.2±1772.6 CV % 19.3 20.0 20.5 22.8 24.6 23.1 16.2 11.3 976.3 2191.2 5472.2 6620.2 7534.7 8139.9 10944.2 14897.4 V1 1031.0 2369.0 6517.9 7731.4 8419.6 9231.1 11696.1 15875.3 V2 1068.3 2663.8 7775.2 8789.7 9439.3 10234.5 12925.8 16115.0 V3 Mean ± SD 1025.2±46.3 2408.0±238.7 6588.4±1153.1 7713.8±1084.9 8464.5±953.1 9201.8±1047.6 11855.4±1000.4 15629.2±645.0 CV % 4.5 9.9 17.5 14.1 11.3 11.4 8.4 4.1
50
Table 4.9: Effect of sowing dates and varieties on accumulated Photo Thermal Unit (°C day hours) for various phenophases of mustard during 201314 Treatment P1 P2 P3 P4 P5 P6 P7 P8 1269.4 2825.1 6944.7 8301.3 9591.9 10253.2 12133.7 16233.5 D1 1020.2 2046.2 6155.1 7186.2 8016.3 8667.9 10613.4 14998.6 D2 902.6 1417.2 5418.5 6343.9 6659.7 7359.3 9652.5 13357.8 D3 Mean ± SD 1064.1 ± 187.3 2096.2 ± 305.3 6172.8 ± 763.3 7277.1 ± 981.9 8089.3±1467.5 8760.1±1449.2 10799.9±1251.1 14863.3±1442.6 CV % 17.6 14.6 12.4 13.5 18.1 16.5 11.6 9.7 910.2 1330.9 5988.6 6153.2 7094.8 8633.4 10687.7 13547.1 V1 962.3 2014.5 6157.4 7267.3 8007.8 9746.5 11637.1 14264.9 V2 1199.7 2793.1 6972.3 8410.8 9165.3 10900.7 12074.9 16778.9 V3 Mean ± SD 1064.1±154.3 2096.2±331.6 6172.8±526.0 7277.1±1128.8 8089.3±1037.7 8760.1±1133.7 10799.9±709.1 14863.3±1697.1 CV % 14.5 15.8 8.5 15.5 12.8 12.9 6.6 11.4 Where, P1 = Emergence, P2= four leaf stage, P3 = Early vegetative phase, P4 = 50 % flowering, P5 = 50 % pod development, P6 = Start of seed filling, P7 = End of seed filling, P8 = Physiological maturity
Table 4.10: Effect of sowing dates and varieties on accumulated Helio Thermal Unit (°C day hours) for various phenophases of mustard during 2012-13 Treatment P1 P2 P3 P4 P5 P6 P7 P8 989.2 2420.4 4235.4 5980.8 6640.2 7069.7 8776.5 11563.0 D1 776.7 1197.1 3720.4 4365.9 4756.0 6999.3 7795.5 9563.0 D2 675.8 871.5 2946.1 3616.1 3821.4 5203.1 7341.4 8763.6 D3 Mean ± SD 813.9±160.0 1496.3±216.7 3634.0±649.0 4654.3±1208.4 5072.5±1435.8 6424.0±1057.9 7971.1±733.5 9963.2±1442.0 CV % 19.7 14.5 17.9 26.0 28.3 16.5 9.2 14.5 664.7 835.1 2925.9 3674.2 4130.5 5376.4 6336.3 8406.9 V1 834.3 1265.7 3616.9 4595.8 5055.6 6464.1 7753.8 9927.5 V2 942.7 2388.3 4193.3 5692.8 6031.5 7431.6 8923.3 11555.1 V3 Mean ± SD 813.9±140.1 1496.3±301.9 3634.0±634.6 4654.3±1010.6 5072.5±950.6 6424.0±1028.2 7971.1±1295.5 9963.2±1574.4 CV % 17.2 20.2 17.5 21.7 18.7 16.0 16.3 15.8
51
Table 4.11: Effect of sowing dates and varieties on accumulated Helio Thermal Unit (°C day hours) for various phenophases of mustard during 2013-14 Treatment P1 P2 P3 P4 P5 P6 P7 P8 886.6 1846.3 3429.6 4706.4 5625.4 6921.8 7669.4 9832.4 D1 721.1 1032.4 2400.4 4053.4 4516.8 5841.5 6544.2 8438.7 D2 632.8 747.2 2287.1 3717.5 3860.4 5140.9 6119.9 8067.1 D3 Mean ± SD 746.8 ± 128.8 1208.6 ± 170.3 2705.7 ± 629.5 4159.1 ± 502.9 4667.5 ± 892.1 5968.1 ± 897.2 6777.8 ± 800.7 8779.4 ± 930.7 CV % 17.3 14.1 23.3 12.1 19.1 15.0 11.8 10.6 551.8 1027.7 2202.4 3101.7 3669.8 4941.0 5904.8 7371.1 V1 680.8 1550.5 2399.5 3869.6 4806.3 5976.2 6755.2 8776.4 V2 877.7 1747.8 3515.2 4805.9 5726.5 6987.0 7873.5 9990.7 V3 Mean ± SD 746.8 ± 164.1 1208.6 ± 172.1 2705.7 ± 707.9 4159.1 ± 853.5 4667.5 ± 1030.2 5968.1 ± 1023.0 6777.8 ± 987.4 8779.4 ± 1311.0 CV % 22.0 14.2 26.2 20.5 22.1 17.1 14.6 14.9 Where, P1 = Emergence, P2= four leaf stage, P3 = Early vegetative phase, P4 = 50 % flowering, P5 = 50 % pod development, P6 = Start of seed filling, P7 = End of seed filling, P8 = Physiological maturity
Table 4.12: Effect of sowing dates and varieties on Radiation Use Efficiency (g/MJ) at various growth intervals in mustard during 2012-13 and 2013-14 2012-13 2013-14 Treatment
30 DAS
60 DAS
90 DAS
120 DAS
PM
30 DAS
60 DAS
90 DAS
120 DAS
PM
D1
1.16
2.06
3.27
2.63
1.90
1.09
1.88
3.00
2.48
1.71
D2
1.09
1.97
2.99
2.48
1.60
1.00
1.46
2.76
2.11
1.21
D3
1.05
1.87
2.75
2.24
1.44
0.89
1.10
2.39
1.76
0.98
1.1 ± 0.03
2.0 ± 0.12
3.0 ± 0.10
2.5 ± 0.06
1.6 ± 0.30
1.0 ± 0.04
1.5 ± 0.05
2.7 ± 0.03
2.1 ± 0.08
1.3 ± 0.18
V1
0.86
1.80
2.80
2.16
1.50
0.84
1.11
2.49
1.32
1.00
V2
1.11
2.00
2.85
2.58
1.63
1.11
1.59
2.52
2.39
1.2
V3
1.41
2.2
3.39
2.69
1.72
1.16
1.79
3.17
2.63
1.6
1.1 ± 0.19
2.0 ± 0.22
3.0 ± 0.08
2.5 ± 0.03
1.6 ± 0.06
1.0 ± 0.09
1.5 ± 0.05
2.7 ± 0.08
2.1 ± 0.08
1.3 ± 0.04
Mean ± SE
Mean ± SE
52
Table 4.13: Effect of sowing dates and varieties on Thermal Use Efficiency (g/m2 °C day) at various growth intervals in mustard during 2012-13 and 2013-14 2012-13 2013-14 Treatment
30 DAS
60 DAS
90 DAS
120 DAS
PM
30 DAS
60 DAS
90 DAS
120 DAS
PM
D1
0.33
0.89
1.59
0.98
0.66
0.28
0.79
1.42
0.88
0.61
D2
0.23
0.73
1.44
0.83
0.52
0.19
0.70
1.31
0.76
0.43
D3
0.19
0.60
1.29
0.79
0.46
0.16
0.55
1.19
0.63
0.35
Mean ± SE
0.25 0.74 1.44 0.87 0.55 0.21 ±0.01 ±0.01 ±0.03 ±0.02 ±0.01 ±0.01
0.68 ±0.01
1.31 ±0.01
0.76 ±0.01
0.46 ±0.02
V1
0.18
0.62
1.30
0.77
0.47
0.16
0.52
1.18
0.64
0.35
V2
0.25
0.74
1.42
0.85
0.58
0.22
0.70
1.35
0.77
0.45
V3
0.33
0.87
1.60
0.97
0.66
0.29
0.8
1.43
0.89
0.61
Mean ± SE
0.25 0.74 1.44 0.87 0.55 0.21 ±0.01 ±0.01 ±0.03 ±0.01 ±0.02 ±0.02
0.68 ±0.01
1.31 ±0.03
0.76 ±0.04
0.46 ±0.01
where DAS- days after sowing
TUE during 2013-14 was reduced as compared to 2012-13 (Table 4.13). The delay in sowing of the crop due to abnormal weather conditions during 2013-14 resulted in lower TUE. The TUE was maximum at 90 days after sowing (end of seed filling-P7) during both the crop seasons and lowest at 30 days after sowing (early vegetative stage-P3). The delay in sowing reduced the TUE significantly. This was due to less biomass production and less number of accumulated GDD consumed in late sown crop. Among the varieties, the TUE was higher in RH 0749(V3) followed by Laxmi (V2) and minimum in RH 30 (V1) during both the crop seasons. 4.5 Biomass and its partitioning The division of assimilates (photosynthate) among different parts of the plant termed partitioning, affects both productivity and survival of plant. A continuous gain in biomass in the plants till crop reached at physiological maturity (P8). The partitioning of biomass into different components of mustard as influenced by experimental treatments in two crop seasons are presented in Tables 4.14 and 4.15. The values in parenthesis are the per cent allocation of total biomass into various plant parts. In 2013-14, the accumulation of dry matter and its allocation to different plant parts was reduced due to delay in sowing because of abnormal weather conditions. The pattern of biomass allocation to plant parts changed with occurrence of different phenological events. Further, it was observed that the sowing dates and varieties influenced this pattern significantly. Evidently, dry matter accumulation was more in first date of sowing (D1) followed by D2 and D3 in 2012-13 and 2013-14. The highest
53
Table 4.14: Effect of sowing dates and varieties on the total biomass and its allocation (g/m2) in different plant parts of mustard during 2012-13 30 DAS
Treatments
60 DAS
Leaf
Stem
Apparent Root
Siliquae
Total
Leaf
Stem
Apparent Root
Siliquae
Total
D1
79.0 (57.8)
44.4 (32.5)
13.2 (9.7)
-
136.6
125.7 (55.6)
63.6 (28.1)
28.6 (12.6)
8.4 (3.7)
226.2
D2
73.4 (59.3)
40.1 (32.4)
10.3 (8.3)
-
123.8
112.6 (57.7)
54.5 (27.9)
21.4 (11.0)
6.6 (3.4)
195.1
D3
60.2 (63.2)
25.7 (27.0)
9.5 (10.0)
-
95.3
97.3 (59.4)
41.1 (25.1)
19.5 (11.9)
6.1 (3.7)
163.9
2.7
2.1
1.6
-
6.4
9.6
6.3
2.1
0.7
10.7
V1
65.7 (61.0)
32.0 (30.2)
10.4 (8.8)
-
108.1
99.1 (60.4)
46.5 (27.5)
17.0 (8.8)
6.4 (3.3)
169.0
V2
72.2 (60.3)
35.8 (29.4)
11.2 (10.3)
-
119.2
116.8 (55.9)
53.2 (26.2)
24.7 (13.9)
7.0 (4.0)
201.7
V3
74.6 (58.1)
42.3 (32.9)
11.5 (9.0)
-
128.5
119.8 (55.8)
59.5 (27.7)
27.8 (12.9)
7.6 (3.5)
214.8
CD at 5%
2.5
2.0
1.4
-
6.5
8.6
5.8
3.2
2.6
11.5
CD at 5% (DxV)
2.1
1.8
1.0
-
4.6
7.7
6.8
3.4
2.1
9.5
CD at 5% (VxD)
2.4
1.9
0.7
-
3.5
6.4
5.0
3.2
1.1
7.2
CD at 5%
*Parenthesis figures are in percent Where DAS- days after sowing
Contd…
54
Treatments
90 DAS Leaf
Stem
120 DAS
Apparent Root
Sillique
Total
Leaf
Stem
Apparent Root
Sillique
Total
D1
199.9 (40.4) 203.9 (41.2)
46.9 (9.5)
44.6 (9.0)
495.2 28.6 (2.0) 644.9 (43.9)
113.0 (7.7)
681.2 (46.4) 1467.6
D2
181.1 (41.9) 178.4 (41.3)
38.1 (8.8)
34.2 (7.9)
431.8 39.9 (3.2) 564.5 (45.8)
91.1 (7.4)
537.3 (43.6) 1232.7
D3
153.7 (42.8) 138.6 (38.6)
34.6 (9.6)
32.3 (9.0)
359.2 40.8 (3.8) 462.9 (42.8)
83.3 (7.7)
494.3 (45.7) 1081.3
4.8
6.9
CD at 5%
9.3
12.3
27.1
4.8
14.1
12.0
27.9
23.8
V1
159.6 (43.9) 154.4 (41.8)
28.4 (6.7)
32.3 (7.6)
374.7 20.1 (1.8) 526.7 (47.7)
72.1 (6.4)
500.1 (44.7) 1119.0
V2
186.0 (40.3) 177.0 (39.0)
44.7 (11.3)
37.7 (9.5)
445.4 30.2 (2.4) 558.8 (44.1)
96.5 (7.6)
582.2 (45.9) 1267.7
V3
189.3 (40.6) 189.4 (40.6)
46.5 (10.0)
41.2 (8.8)
466.3 58.9 (4.2) 586.7 (42.1)
118.9 (8.5)
630.5 (45.2) 1395.0
CD at 5%
9.0
10.6
4.3
5.5
25.9
2.2
13.2
13.5
19.2
21.0
CD at 5 % (DxV)
7.3
11.3
3.7
5.9
22.2
3.2
11.5
12.7
22.5
18.9
CD at 5 % (VxD)
5.2
9.0
2.1
6.2
17.9
2.5
10.8
10.9
19.9
16.4
*Parenthesis figures are in percent Where DAS- days after sowing
Contd…
55
Treatments
PM Leaf
Stem
Apparent Root
Sillique
Total
D1
5.3 (0.4)
631.6 (41.7)
72.9 (4.8)
806.5 (53.2)
1516.3
D2
5.9 (0.5)
548.8 (43.9)
59.7 (4.8)
636.1 (50.9)
1250.6
D3
6.2 (0.6)
440.0 (40.5)
54.4 (5.0)
585.8 (53.9)
1086.3
0.6
11.9
4.3
20.6
23.4
V1
4.4 (0.4)
482.0 (45.7)
41.4 (3.5)
592.5 (50.5)
1120.3
V2
5.8 (0.5)
536.2 (39.4)
64.0 (5.2)
672.9 (54.9)
1278.9
V3
7.1 (0.5)
602.3 (41.4)
81.6 (5.6)
763.0 (52.5)
1454.1
CD at 5%
0.3
8.4
6.2
17.1
28.4
CD at 5% (D x V)
0.5
9.2
4.8
21.4
26.7
CD at 5% (D x V)
0.4
7.7
4.0
18.4
28.1
CD at 5%
*Parenthesis figures are in percent
56
Table 4.15: Effect of sowing dates and varieties on the total biomass and its allocation (g/m2) in different plant parts of mustard during 2013-14 Treatments
30 DAS
60 DAS
Leaf
Stem
Apparent Root
Sillique
Total
Leaf
Stem
Apparent Root
Sillique
Total
D1
68.6 (58.6)
36.8 (31.4)
11.7 (10.0)
-
117.1
102.1 (53.3)
58.2 (30.4)
24.2 (12.6)
7.3 (3.8)
191.7
D2
67.5 (61.4)
32.6 (29.7)
9.8 (8.9)
-
109.9
98.7 (55.9)
51.8 (29.3)
20.1 (11.4)
6.1 (3.5)
176.7
D3
53.3 (62.9)
23.6 (27.8)
7.9 (9.3)
-
84.8
79.0 (56.5)
39.1 (28.0)
16.2 (11.6)
5.6 (4.0)
139.8
7.0
6.9
0.9
-
4.6
16.5
6.6
2.0
1.0
15.8
V1
58.6 (59.5)
27.8 (31.2)
8.7 (9.2)
-
95.1
91.0 (54.9)
45.6 (29.4)
17.3 (12.5)
5.3 (3.2)
159.2
V2
60.1 (62.2)
30.7 (28.8)
9.1 (9.0)
-
99.9
92.3 (57.2)
48.7 (28.3)
20.7 (10.7)
6.2 (3.8)
167.9
V3
70.6 (60.6)
34.4 (29.5)
11.5 (9.9)
-
116.6
96.5 (53.3)
54.6 (30.2)
22.4 (12.4)
7.4 (4.1)
181.0
CD at 5%
6.1
9.0
1.3
-
5.1
7.4
6.6
2.7
1.0
12.5
CD at 5% (D x V)
5.8
9.4
0.7
-
5.6
6.2
5.6
2.3
0.5
14.2
CD at 5% (D x V)
5.2
7.4
0.6
-
4.7
5.3
5.1
1.2
0.4
13.8
CD at 5%
*Parenthesis figures are in percent Where DAS- days after sowing
Contd…
57
Treatments
90 DAS Leaf
Stem
120 DAS
Apparent Root
Sillique
Total
Leaf
Stem
Apparent Root
Sillique
Total
D1
177.9 (38.9) 196.9 (43.0)
41.4 (9.1)
41.5 (9.1)
457.7 21.8 (1.6) 625.2 (45.4)
102.4 (7.4)
607.6 (44.1) 1377.2
D2
169.5 (41.4) 170.8 (41.7)
34.4 (8.4)
34.6 (8.5)
409.3 27.1 (2.3) 544.0 (46.7)
87.3 (7.5)
506.1 (43.5) 1164.5
D3
134.3 (41.6) 128.6 (39.9)
29.7 (9.2)
30.0 (9.3)
322.6 33.7 (3.4) 407.5 (41.5)
75.3 (7.7)
465.8 (47.4)
982.4
3.5
7.0
5.7
18.5
22.7
CD at 5%
12.9
12.0
24.1
3.7
15.7
V1
149.8 (40.8) 152.7 (42.0)
30.6 (9.2)
30.5 (8.1)
363.6 20.1 (1.8) 452.3 (45.3)
78.2 (7.5)
495.6 (45.3) 1046.2
V2
154.5 (40.7) 158.8 (41.5)
34.8 (8.3)
35.1 (9.5)
383.2 29.3 (2.7) 495.6 (42.2)
82.1 (7.3)
511.9 (47.8) 1118.9
V3
177.6 (40.1) 184.8 (41.7)
40.1 (9.1)
40.5 (9.2)
442.8 33.2 (2.5) 628.8 (47.0)
104.6 (7.8)
572.1 (42.7) 1338.7
CD at 5%
12.2
12.9
4.6
4.9
25.2
6.0
17.3
12.0
16.3
28.5
CD at 5% (D x V)
9.7
11.3
4.1
5.8
22.8
3.3
13.6
8.4
16.8
24.5
CD at 5% (D x V)
7.9
9.1
3.7
4.3
18.7
2.1
11.3
6.3
18.3
22.0
*Parenthesis figures are in percent Where DAS- days after sowing
Contd…
58
Treatments
PM Leaf
Stem
Apparent Root
Sillique
Total
D1
3.6 (0.3)
587.4 (45.1)
66.8 (5.1)
645.5 (49.5)
1303.3
D2
4.4 (0.4)
510.4 (46.9)
55.2 (5.1)
519.0 (47.7)
1089.0
D3
5.5 (0.6)
383.1 (42.1)
47.3 (5.2)
475.3 (52.2)
911.1
0.8
16.2
5.7
19.4
33.0
V1
3.5 (0.4)
431.3 (49.2)
53.1 (6.5)
420.4 (43.9)
908.3
V2
4.9 (0.5)
470.8 (41.7)
54.0 (5.2)
545.2 (52.7)
1074.9
V3
5.2 (0.4)
578.6 (44.1)
62.1 (4.1)
674.2 (51.4)
1311.1
CD at 5%
1.0
19.3
7.3
18.4
30.7
CD at 5% (D x V)
0.7
13.5
4.7
19.5
29.6
CD at 5% (D x V)
0.5
11.2
3.8
17.3
28.2
CD at 5%
*Parenthesis figures are in percent
59
dry matter (g m-2) was recorded at physiological maturity during both the crop seasons. However, Rabi season of 2012-13 accumulated more dry matter as compared to 2013-14 season at all the growth intervals. Among the growth intervals, at 30, 60 and 90 DAS highest biomass allocation was observed in leaf followed by stem and minimum in roots. At 60 and 90 DAS, due to appearance of many flowers and few siliquae, the partitioning of reproductive parts was recorded. The overall pattern of biomass allocation to plant parts followed the same trend as observed at 30 DAS with minimum dry matter gain in siliquae till 60 DAS. However, with the advancement of crop age at 90 DAS, the highest biomass was recorded in stem followed by leaf, roots and minimum in siliquae. Later on, at 120 DAS and physiological maturity the highest biomass was observed in stem followed by siliquae, roots and minimum in leaves. The delayed sowing reduced the biomass accumulation in different plant parts in 2012-13 and 2013-14 at all the growth intervals. The delayed sowing reduced the stem biomass significantly in both the crop seasons. Among the varieties, the biomass accumulation in different plant parts of RH 0749 (V 3) was significantly higher followed by Laxmi (V 2) and RH 30(V 1) in irrespective of the growth intervals in both the crop seasons. The varieties perform same trend of biomass allocation as the sowing dates for the different growth intervals. In all plant parts, biomass accumulation was higher in RH 0749 (V 3) as compared to other two cultivars during year 2012-13 and 2013-14. 4.6 Leaf Area Index A comparative account of leaf area index (LAI) during 30 days intervals is presented in Fig. 4.8. From the figures, it is evident that the LAI was less during 2013-14 than 2012-13. The LAI increased progressively from 30 DAS (early vegetative stage-P3) till 90 DAS (start of seed filling-P6) and then declined drastically upto 120 DAS (end of seed filling-P7) during both crop seasons. Among dates of sowing, D1 crop recorded maximum LAI production at all the growth intervals, whereas, lowest under D3 crop. Varieties also differ significantly at all the growth intervals in both the crop seasons. RH 0749 (V3) was superior in LAI production followed by Laxmi (V2) and RH 30 (V1) in both the crop seasons. At all the growth intervals, RH 0749 attained higher LAI followed by Laxmi and RH 30. 4.7 Radiation studies The radiation from the sun is the prime source of energy which controls the levels of the photosynthetic activity in plants. Therefore, this natural resource plays an important role in growth and development of plants. The radiation is not only a source of energy for photosynthesis, but it also regulates the temperature of the plants. The radiation provides the energy for growth and dry matter accumulation and thus, established an upper limit of productivity in crops besides, subjected to the restriction by other environmental constraints.
60
2012-13
2013-14
Fig. 4.8 Effect of sowing time and varieties on LAI at various growth intervals for mustard during 2012-13 and 2013-14 Where, D 1 : Oct. 10, 2012 and Oct. 21, 2013; D 2 : Oct. 25, 2012 and Oct. 30, 2013; D 3 : Nov. 8, 2012 and Nov. 10, 2013
V 1 : RH 30; V 2 : Laxmi; V 3 : RH 0749
61
4.7.1 Energy balance components over mustard crop canopy The study of solar radiation absorption in crop canopies and its use to determine the crop response is essential in assessment of crop productivity. Crop responds to instantaneous values of the incident solar radiation which influences plant temperatures directly and affect the biochemical processes in plants, indirectly. The diurnal energy balance components observations were recorded on clear days over plots sown as per treatments. Net radiation is the main parameter in several methods of evapotranspiration estimation. The energy balance study over crop surface can be helpful in understanding of various physical and physiological processes taking place in the crop environment. The incident radiation is dependent on several factors like height, arrangement of plants, plant structure, leaf angle and geometry, leaf size, anatomy and age. The diurnal pattern of energy balance components namely, net radiation (Rn), latent heat vaporization (LE), sensible heat flux (A) and soil heat flux (G) were studied at three growth stages viz. 50 % flowering (P4), start of seed flowering (P6) and end of seed filling (P7). The results showed that around 61.8 to 74.4 per cent of the net radiation was utilized as LE, 20.8 to 27.8 per cent as sensible heat flux and 3.6 to 10.9 percent as soil heat flux (Table 4.16). The moderate values of sensible heat component favored the growth and development of crop by providing essential energy to generate requisite air temperatures. In the bare field, soil heat flux component values were higher (16.2 to 19.5 percent) than that observed over the crop field. The LE values were slightly higher at 50 % flowering (P4) other than start of seed filling (P6) and end of seed filling stage (P7) in both the crop seasons. 4.7.2 Light extinction coefficient The light extinction coefficient (k) is a measure of the attenuation of the radiation when passing through the canopy per unit LAI. The values of k calculated for different treatments are presented in Table 4.17. Among the different sowing dates, the highest values (0.97 and 0.68) of k were observed in D1 at 50 % flowering stage (P4) in 2012-13 and 201314, respectively. The lowest values (0.64 and 0.47) of k were recorded in D3 at the end of seed filling (P7) in both the crop seasons, respectively. In general, the k values were less in 201314 as compared to 2012-13. The extinction coefficient continued to increase till the crop attained maximum LAI but decreased thereafter successively till the maturity of the crop which is shown in Table 4.17. Among the cultivars, RH 0749 (V 3) recorded maximum k values of 0.92 and 0.67 at 50 % flowering (P 4) during 2012-13 and 2013-14. The minimum k values were observed for RH 30 (V 1) as compared to other cultivars for both crop seasons. The values of k varied from 0.67 to 0.92 in 2012-13, and 0.47 to 0.67 in 2013-14 among various varieties.
62
Table 4.16: Effect of different growing environments and varieties energy balance component at three growth stages (mW m -2) over mustard crop and bare field 2012-13 2013-14 Treatments Rn LE A G Rn LE A G 50% flowering (P 4) 276.8 80.8 14.3 249.6 97.1 18.6 371.9 365.3 D1 (74.4) (21.7) (3.8) (68.3) (26.6) (5.1) 273.2 82.3 13.2 243.9 88.3 31.2 368.7 363.5 D2 (74.1) (22.3) (3.6) (67.1) (24.3) (8.6) 264.6 76.1 24.9 229.4 84.1 36.8 356.7 350.3 D3 (72.4) (20.8) (6.8) (65.5) (24.0) (10.5) 231.7 70.2 66.1 233.3 63.8 67.9 368.0 365.0 Bare (63.0) (19.1) (17.9) (63.9) (17.5) (18.6) Start of seed filling (P 6) 242.4 86.6 16.3 213.4 82.0 31.6 345.3 327.1 D1 (70.2) (25.1) (4.7) (65.3) (25.1) (9.7) 230.5 81.5 26.0 202.7 76.4 34.0 338.0 313.1 D2 (68.2) (24.1) (7.7) (64.7) (24.4) (10.9) 213.3 76.4 32.5 193.8 73.1 38.5 322.2 305.3 D3 (66.3) (23.7) (10.1) (63.5) (23.9) (12.6) 206.8 72.3 53.9 197.8 73.5 53.6 333.0 324.9 Bare (62.1) (21.7) (16.2) (60.9) (22.6) (16.5) End of seed filling (P 7) 218.5 79.5 30.9 249.9 87.3 17.9 328.9 355.2 D1 (66.4) (24.2) (9.4) (70.4) (24.6) (5.0) 206.7 83.9 31.8 240.1 79.8 23.0 322.4 343.0 D2 (64.1) (26.0) (9.9) (70.0) (23.3) (6.7) 195.8 88.1 32.8 230.7 78.2 29.7 316.6 338.6 D3 (61.8) (27.8) (10.3) (68.1) (23.1) (8.8) 196.6 65.4 58.2 205.2 74.0 67.8 320.2 347.7 Bare (61.4) (20.4) (18.2) (59.0) (21.5) (19.5) *Parenthesis figures are in percent
Table 4.17: Effect of different growing environment and varieties on extinction coefficients (k) over mustard 50 % flowering (P4)
2012-13 Start of seed filling (P6)
D1
0.97
0.86
0.75
0.68
0.66
0.52
D2
0.84
0.83
0.69
0.64
0.63
0.52
D3
0.82
0.77
0.64
0.62
0.62
0.47
V1
0.83
0.78
0.67
0.64
0.63
0.47
V2
0.87
0.80
0.69
0.65
0.64
0.48
V3
0.92
0.81
0.72
0.67
0.65
0.53
Particulars
End of seed filling (P7)
50 % flowering (P4)
Date of sowing
2013-14 Start of seed filling (P6)
End of seed filling (P7)
Varieties
4.7.3 Optical characteristics Optical characteristics of solar radiation viz., transmitted (T), reflected (R) and absorbed (A) mustard crop recorded at different growth intervals are presented in Table 4.18. The absorption of radiation increased from 50 % flowering (P 4) to start of seed
63
filling (P 6) and thereafter decreased from start of seed filling (P 6) to end of seed filling (P 7) in all the dates of the sowing in both the crop seasons i.e. 2012-13 and 2013-14. Absorption values of radiation were quite high (about 86.5 per cent) during 2012-13 but it was only 61.4 per cent during 2013-14. The fraction of absorbed radiation increased till the crop attained maximum LAI and thereafter declined towards maturity. Over the bare field, the transmitted radiation remained between 86 to 88 per cent. The absorption radiation was between 3.6 to 5.9 per cent and reflected radiation between 7.8 to 10.2 per cent throughout the season. Table 4.18: Effect of different growing environment and varieties on optical characteristics in mustard 2012-13 Sowing dates D1 D2 D3 Varieties V1 V2 V3 Bare field Sowing dates D1 D2 D3 Varieties V1 V2 V3 Bare field
50 % flowering R T A 11.2 7.3 81.5 11.5 8.7 79.8 11.3 10.4 78.3 11.4 11.3 10.6 9.0
9.3 6.9 7.1 86.5
Start of seed filling R T A 5.2 8.3 86.5 7.7 6.9 85.4 5.2 10.5 84.3
7.2 9.5 83.3 6.7 8.4 84.9 7.2 6.5 86.3 8.0 87.1 4.9 2013-14 50 % flowering Start of seed filling R T A R T A 11.8 9.3 78.9 8.3 8.8 82.9 13.3 9.3 77.4 6.9 12.8 80.3 14.2 9.2 76.6 10.5 10.4 79.1
11.5 12.1 11.9 9.8
12.7 11.2 12.2 10.2
16.7 15.9 19.0 7.8
10.5 11.3 8.9 86.2
79.3 81.8 82.3 4.5
Full pod filling R T A 11.2 15.0 73.8 11.7 16.8 71.5 11.9 18.5 69.6
76.8 77.5 78.9 3.6
9.5 10.4 8.5 8.2
Where, R- Reflectivity, T- Transmissivity, A- Absorbitivity
11.5 9.7 9.2 85.9
79.0 79.9 82.3 5.9
18.2 17.0 15.8 86.0
70.3 70.9 72.3 4.2
Full pod filling R T A 14.2 17.0 68.8 16.7 18.0 68.3 12.9 25.7 61.4 17.2 16.7 11.9 88.0
66.1 67.4 69.1 4.2
4.8 Yield attributes and yield The effect of yield, yield attributes and harvest index influenced by experimental treatments in two crop seasons (2012-13 and 2013-14) are presented in Table 4.19 and 4.20. Date of sowing and varieties influenced the yield and yield attributes significantly. The delayed sowing decreased significantly the yield attributes along with the seed yield and harvest index (HI) during both the crop seasons. The crop sown on D1 produced maximum number of primary and secondary branch at harvest, siliquae length (cm), number of siliquae per plant, number of siliquae per square meter, number of seed per siliquae, seed yield per plant (g), seed yield (g m -2), 1000-seed weight (g), seed yield (q ha -1), biological yield (q ha-1), harvest index, oil content and oil yield (q ha-1) in both the crop seasons.
64
Table 4.19: Effect of growing environments and varieties on yield and yield attributes in mustard during 2012-13 Number of primary branch
Number of secondary branch
Siliquae length (cm)
Number of siliquae per plant
Number of siliquae per m2
Number of seed per siliquae
Seed yield per plant (g)
Seed yield (g/m2)
D1
6.5
8.4
6.9
352.5
7832.6
21.8
23.5
522.6
D2
5.6
6.6
6.5
331.2
7360.4
20.7
21.0
465.9
D3
4.4
6.0
6.2
271.7
6038.2
18.4
16.7
371.5
CD at 5%
0.2
1.0
0.1
19.2
34.0
0.4
0.8
16.8
V1
5.3
6.3
6.2
305.2
6781.1
19.9
18.2
353.0
V2
5.4
7.0
6.4
312.8
6951.1
19.9
19.0
435.8
V3
5.6
7.6
7.0
337.5
7498.9
21.3
24.1
485.2
CD at 5%
0.3
1.1
0.05
11.8
44.9
0.1
0.4
17.8
CD at 5% (D x V)
0.2
0.8
0.1
13.9
36.7
0.6
0.4
19.3
CD at 5% \ (V x D)
0.2
0.7
0.1
11.3
30.9
0.4
0.3
17.3
Treatments
Contd…
65
1000 –Seed
Seed yield
Biological yield
HI
Oil content
Oil yield
weight (g)
(q/ha)
(q/ha)
(%)
(%)
(q/ha)
D1
6.5
33.6
151.3
18.9
40.8
13.7
D2
5.5
30.5
138.7
18.7
37.4
11.4
D3
4.8
25.0
122.6
17.3
34.9
8.7
CD at 5%
0.2
2.4
13.1
1.2
2.7
1.2
V1
5.5
28.2
132.2
18.1
39.1
11.0
V2
5.1
28.8
133.8
18.2
35.7
10.3
V3
6.4
32.2
146.6
18.6
38.3
12.3
CD at 5%
0.3
0.5
10.9
0.04
0.2
0.2
CD at 5% (D x V)
0.40
1.35
15.20
0.52
1.47
0.96
CD at 5% (V x D)
0.33
1.15
12.92
0.58
1.69
0.65
Treatments
66
Table 4.20: Effect of growing environment and varieties on yield and yield attributes in mustard during 2013-14 Primary branch at harvest
Secondary branch at harvest
Siliquae length (cm)
Number of siliquae per plant
Number of siliquae per m2
Number of seed per siliquae
Seed yield per plant (g)
Seed yield (g/m2)
D1
5.6
11.1
6.8
331.2
7360.0
21.2
21.4
474.5
D2
5.4
10.2
6.3
280.7
6238.5
19.7
20.6
457.7
D3
4.3
8.4
5.7
247.1
5490.4
18.2
16.2
359.8
CD at 5%
0.9
2.1
0.1
18.6
30.8
1.0
2.4
25.7
V1
5.0
9.3
6.0
258.6
5745.9
18.8
18.1
332.5
V2
5.0
10.0
6.2
288.6
6413.9
19.2
19.6
421.8
V3
5.4
10.5
6.7
311.8
6929.2
21.1
20.4
454.3
CD at 5%
0.4
1.5
0.05
17.9
32.7
1.0
1.3
28.7
CD at 5% (D x V)
0.6
1.2
0.3
15.2
33.5
0.7
2.6
23.0
CD at 5% (V x D)
0.6
1.0
0.2
13.0
28.2
0.6
2.2
20.8
Treatments
Contd…
67
1000 –Seed
Seed yield
Biological yield
HI
Oil content
Oil yield
weight (g)
(q/ha)
(q/ha)
(%)
(%)
(q/ha)
D1
6.5
29.2
147.7
18.4
39.6
11.6
D2
5.4
28.7
135.8
18.2
36.2
10.4
D3
4.8
23.7
120.0
17.2
33.3
8.0
CD at 5%
0.2
0.9
13.1
0.2
0.6
1.1
V1
5.5
25.6
129.0
17.8
37.8
9.7
V2
5.0
26.6
130.7
17.9
34.3
9.1
V3
6.3
31.7
143.9
18.2
37.0
11.7
CD at 5%
0.1
0.4
10.9
0.03
0.4
0.4
CD at 5% (D x V)
0.2
0.7
14.6
0.1
0.3
1.0
CD at 5% (V x D)
0.1
0.9
12.3
0.1
0.3
0.8
Treatments
68
Table 4.21: Interaction effect of growing environment and varieties on mustard seed yield (q/ha) of mustard during 2012-13 and 2013-14 2012-13 Treatments
V1
V2
V3
Mean
D1
32.0
32.6
36.0
33.6
D2
29.0
29.6
33.0
30.5
D3
23.5
24.1
27.5
25.0
Mean
28.2
28.8
32.2
29.7
CD at 5% (D x V)
1.4
CD at 5% (V x D)
1.2 2013-14
Treatments
V1
V2
V3
Mean
D1
27.0
31.1
31.6
29.2
D2
26.4
27.4
32.4
28.7
D3
21.3
22.3
27.5
23.7
mean
25.6
26.6
31.7
27.2
CD at 5% (D x V)
0.9
CD at 5% (V x D)
0.9
The subsequent sowing date of both seasons recorded significantly less in all the yield attributes and yield. Among the varieties, the highest seed yield and oil yield were recorded in RH 0749 (V 3 ) followed by Laxmi (V 2 ) and RH 30 (V 1) during both the years, because less number of siliquae per meter square, number of seed per siliquae, seed yield per plant (g) and seed yield (g m -2). Total biomass production and harvest index was highest in RH 0749 (V 3) followed by Laxmi (V 2) and it was lowest in RH 30 (V 1). The oil content was significantly higher in RH 30 (V 1 ), followed by RH 0749 (V 3) and lowest in Laxmi (V 2), but the oil yield in mustard varieties was higher in RH 0749 (V 3), followed by Laxmi (V 2) which is found at par with RH 30 (V 1), during 2013-14. The interaction between different sowing environment and varieties on seed yield in mustard is given in Table 4.21. The delay in sowing date decreased seed yield among the varieties significantly during both the years. The variety RH 0749 (V 3) attained the highest seed yield of 36.0 and 31.6 qha-1 in 10th and 21th Oct. sown, whereas, minimum seed yield was observed in variety RH 30 (V 1) (23.5 and 21.3 qha-1) in 8th and 10th Nov. sown during the year 2012-13 and 2013-14, respectively.
69
4.9 Relationship among different meteorological parameters with mustard growth and yield 4.9.1 Correlation among different meteorological parameters with mustard growth To reveal the interactions of different mustard growth parameters with various meteorological parameters and crop growth period was divided into two phases i.e. vegetative phase and reproductive phase. The correlation study among different meteorological parameters during the above phases and pooled data of mustard crop are shown in Table. 4.22. The growth of the mustard crop i.e. maximum LAI (LAImax) and dry matter (DM) were correlated with maximum temperature (Tmax), minimum temperature (Tmin), relative humidity morning (RHm), relative humidity evening (RHe), wind speed (WS), bright sunshine (SS) and pan evaporation (PE) . Table 4.22: Correlation coefficients of growth parameters with weather parameters during vegetative and reproductive phases of mustard Particulars
Tmax
Tmin
RHm RHe WS 2012-13 Vegetative phase 0.47 0.51 -0.30 -0.42 -0.15 LAImax 0.69* 0.74* -0.48 -0.61 -0.24 DM Reproductive phase 0.44 0.39 -0.40 -0.53 -0.39 LAImax 0.80** 0.79* -0.72* -0.87** -0.41 DM 2013-14 Vegetative phase 0.67* 0.74* -0.61 -0.59 -0.45 LAImax 0.56 0.67* -0.45 -0.47 -0.42 DM Reproductive phase 0.70* 0.72* -0.32 -0.64 -0.35 LAImax 0.71* 0.71* -0.04 -0.48 -0.23 DM Pooled Vegetative phase 0.52 0.35 -0.66 -0.32 -0.41 LAImax 0.65 0.61 -0.56 -0.50 -0.37 DM Reproductive phase 0.67* 0.13 -0.67* -0.83** -0.38 LAImax 0.79* 0.55 -0.50 -0.63 -0.37 DM *Significant at the 0.05 probability level, **Significant at the 0.01probability
SS
PE
0.41 0.72*
0.32 0.64
-0.06 0.18
0.14 0.52
-0.22 -0.37
0.64 0.60
-0.47 -0.28
0.21 0.52
-0.61 -0.28
0.78* 0.65
-0.47 -0.15 level
0.73* 0.48
The growth parameters showed a positive correlation with Tmax, Tmin and PE, whereas, negative correlations were observed with RHm, RHe, WS and SS in above mention phases. The significant results were reported with LAI for Tmax in 2013-14 in both the phases. In pooled data, correlation study showed that LAI significantly related with PE in vegetative phase, whereas in reproductive phase LAI shown significant results with Tmax, RHm, RHe and PE. During year 2012- 13, dry matter showed significantly results with Tmax, Tmin and SS at vegetative phase and, with Tmax,
70
Tmin, RHm and RHe in reproductive phase, whereas, during 2013-14 it showed significant results only with Tmax (reproductive phase) and Tmin (for both the phases). When pooled the data, dry matter had significant relationship with Tmax (reproductive phase) only. The WS showed no association with growth para meters. Similarly SS had no significantly relationship with growth parameters except dry matter in vegetative phase during 2012-13, where it found positively correlated. 4.9.2 Correlation among different meteorological parameters with mustard yield The interaction of various meteorological factors and mustard yield and yield attributes period was divided into two phases i.e. vegetative phase and reproductive phase. The correlation study among different meteorological parameters during the above phases and pooled data of mustard yield are shown in Tables 4.23, 4.24 and 4.25. Mustard yield parameters i.e. siliqua length, siliqua per plant, number of seed per siliqua, test weight, seed yield per plant, seed yield per square meter, seed yield, biological yield, oil content and oil yield were correlated with maximum temperature (Tmax), minimum temperature (Tmin), relative humidity morning (RHm), relative humidity evening (RHe), wind speed (WS), bright sunshine (SS) and pan evaporation (PE). Table 4.23: Correlation coefficients of yield and its attributes with weather parameters during vegetative and reproductive phases in mustard (2012-13) Particulars
Tmax
Tmin RHm RHe WS SS Vegetative phase 0.66 0.71* -0.49 -0.60 -0.27 0.62 Siliqua length 0.86** 0.85** -0.46 -0.85** -0.65 0.77* Siliquae/plant 0.86** 0.87** -0.55 -0.85** -0.60 0.77* No. of seed/siliqua 0.74* 0.80** -0.60 -0.69* -0.33 0.72* Test weight 0.66 0.70* -0.42 -0.63 -0.36 0.59 Seed yield/plant 0.66 0.70* -0.42 -0.63 -0.36 0.59 Seed yield /m2 0.84** 0.86** -0.50 -0.82** -0.55 0.77* Seed yield 0.83** 0.86** -0.55 -0.79 -0.49 0.78* Biological yield 0.81** 0.83** -0.61 -0.79 -0.53 0.80** Oil content 0.88** 0.90** -0.59 -0.85** -0.56 0.83** Oil yield Reproductive phase 0.70* 0.68* -0.63 -0.73* -0.41 0.07 Siliqua length 0.83** 0.81** -0.65 -0.84** -0.71* -0.17 Siliquae/plant 0.78* 0.78* -0.69* -0.81** -0.67* -0.19 No. of seed/siliqua 0.84** 0.86** -0.75* -0.81** -0.42 0.17 Test weight 0.73* 0.73* -0.57 -0.74* -0.44 0.06 Seed yield/plant 0.73* 0.73* -0.57 -0.74* -0.44 0.06 Seed yield /m2 0.82** 0.81** -0.69* -0.84** -0.67* -0.11 Seed yield 0.84** 0.83** -0.73* -0.85** -0.61 -0.04 Biological yield 0.87** 0.91** -0.78* -0.79* -0.51 0.07 Oil content 0.89** 0.89** -0.77* -0.86** -0.63 -0.04 Oil yield * Significant at the 0.05 probability level, **Significant at the 0.01probability level
PE
0.53 0.74* 0.75* 0.65 0.52 0.52 0.72* 0.72* 0.79* 0.79* 0.43 0.39 0.33 0.63 0.53 0.53 0.39 0.44 0.58 0.49
It is noted that Tmax, Tmin, PE, RHm and RHe were the important weather parameters during vegetative phase of crop which decided the fate of the yield attributes
71
and seed yield. However, during reproductive and maturity phase in addition to the Tmax, Tmin, RHm, RHe, PE and WS showed better relationship with yield of mustard. The positive relationship of yield parameters were found with the Tmax, Tmin and PE, whereas, RHm, RHe, WS and SS showed negative relationships except SS during vegetative phase during the year 2012-13. The pooled data of correlation revealed that the Tmax, Tmin has significant correlation with yield and other parameters during both the phases. The RHe found significantly negatively correlated with the seed yield. Table 4.24:
Correlation coefficients of yield and its attributes with weather parameters during vegetative and reproductive phases of mustard (2013-14) Particulars Tmax Tmin RHm RHe WS SS PE Vegetative phase
Siliqua length
0.72*
0.79*
-0.64
-0.64
-0.47
-0.26
0.72*
Siliquae/plant
0.65
0.62
-0.59
-0.67*
-0.57
-0.22
0.65
No. of seed/siliqua
0.69*
0.74*
-0.62
-0.64
-0.51
-0.15
0.70*
Test weight
0.71*
0.78*
-0.57
-0.67*
-0.60
-0.26
0.74*
0.26
0.40
-0.20
-0.14
-0.02
-0.17
0.37
0.26
0.40
-0.20
-0.14
-0.02
-0.17
0.37
Seed yield
0.68*
0.76*
-0.62
-0.60
-0.42
-0.28
0.70*
Biological yield
0.79*
0.86**
-0.70*
-0.72*
-0.55
-0.18
0.80**
Oil content
0.82**
0.86**
-0.71*
-0.79*
-0.65
0.04
0.87**
Oil yield
0.76*
0.83**
-0.67*
-0.70*
-0.54
-0.23
0.79*
Seed yield/plant Seed yield /m
2
Reproductive phase Siliqua length
0.78*
0.79*
-0.23
-0.65
-0.29
-0.48
0.35
Siliquae/plant
0.56
0.55
0.13
-0.64
-0.32
-0.63
-0.18
No. of seed/siliqua
0.73*
0.73*
-0.15
-0.64
-0.27
-0.52
0.27
Test weight
0.84**
0.82**
0.03
-0.62
-0.26
-0.46
0.43
Seed yield/plant
0.54
0.55
-0.29
-0.03
0.47
0.05
0.75*
Seed yield /m 2
0.54
0.55
-0.29
-0.03
0.47
0.05
0.75*
Seed yield
0.75*
0.76*
-0.23
-0.61
-0.22
-0.45
0.35
Biological yield
0.85**
0.86**
-0.22
-0.71*
-0.27
-0.56
0.37
Oil content
0.90**
0.89**
-0.04
-0.69*
-0.11
-0.63
0.47
Oil yield
0.85**
0.85**
-0.14
-0.67*
-0.22
-0.53
0.41
*Significant at the 0.05 probability level, ** Significant at the 0.01probability level
72
Table 4.25:
Correlation coefficients of yield and its attributes with weather parameters during vegetative and reproductive phases of mustard (pooled data) Particulars Tmax Tmin RHm RHe WS SS PE Vegetative phase
Siliqua length
0.69*
0.70*
-0.60
-0.57
-0.36
-0.17
0.58
Siliquae/plant
0.72*
0.62
-0.60
-0.66
-0.57
-0.22
0.65
No. of seed/siliqua
0.78*
0.76*
-0.59
-0.72*
-0.55
-0.06
0.61
Test weight
0.72*
0.78*
-0.51
-0.67*
-0.37
0.05
0.47
0.45
0.51
-0.30
-0.37
-0.23
-0.04
0.36
0.45
0.51
-0.30
-0.37
-0.23
-0.04
0.36
Seed yield
0.77*
0.77*
-0.58
-0.69*
-0.50
-0.10
0.60
Biological yield
0.81**
0.84**
-0.59
-0.74
-0.48
0.02
0.56
Oil content
0.80**
0.83**
-0.59
-0.77*
-0.50
0.14
0.56
Oil yield
0.83**
0.84**
-0.61
-0.76*
-0.53
-0.02
0.61
Seed yield/plant Seed yield /m
2
Reproductive phase Siliqua length
0.73*
0.60
-0.46
-0.56
-0.37
-0.18
0.33
Siliquae/plant
0.68*
0.48
-0.33
-0.62
-0.49
-0.36
0.34
No. of seed/siliqua
0.74*
0.64
-0.43
-0.54
-0.51
-0.30
0.28
Test weight
0.75*
0.79*
-0.29
-0.40
-0.35
0.00
0.18
0.59
0.55
-0.37
-0.24
-0.06
-0.02
0.25
0.59
0.55
-0.37
-0.24
-0.06
-0.02
0.25
Seed yield
0.77*
0.67*
-0.46
-0.54
-0.49
-0.25
0.29
Biological yield
0.79*
0.76*
-0.42
-0.50
-0.48
-0.18
0.22
Oil content
0.79*
0.84**
-0.32
-0.43
-0.35
-0.10
0.19
Oil yield
0.82**
0.77*
-0.43
-0.52
-0.48
-0.19
0.27
Seed yield/plant Seed yield /m
2
* Significant at the 0.05 probability level, ** Significant at the 0.01probability level
4.10 Calibration of Simulation models 4.10.1 Calibration of InfoCrop model for mustard crop under Hisar conditions The model calibration involved determining the values of the phenology coefficients initially and then the values of the coefficients describing growth and seed development. Minimum crop performance data set are required for those calculations include dates of sowing, anthesis, and physiological maturity, maximum leaf area index, test weight, grain yield, biological yield and harvest index. The procedure for determining genetic coefficients involved in running the model using a range of values of each coefficient, in the order indicated above, until the desired level of agreement between simulated and observed values was reached. Crop growth and yield data of Rabi 2012-13 and 2013-14 were used for calibration of InfoCrop model and genetic coefficients of varieties for RH 30, Laxmi and RH 0749 were determined based on the observed crop characteristics. Table 4.26
73
shows genetic coefficient values by variety obtained during the calibration analysis. In general, the values of the genetic coefficient parameters are in the ranges obtained by other studies conducted on mustard with the exception of parameters potential rate of growth (RGRPOT), potential rooting depth growth rate (ZRTPOT), all related to grain growth. Table 4.26: Genotypic characteristics of mustard cv. RH 30, Laxmi and RH 0749 used in InfoCrop model Genetic constants Acronym Unit RH 30 Laxmi RH 0749 Thermal time for germination
TTGERM
°C
105
110
120
Thermal time for seedling emergence to anthesis
TTVG
°C
850
880
920
Thermal time for anthesis to maturity
TTGF
°C
1020
1090
1140
Specific leaf area of variety
SLAVAR
Fraction
0.0022
0.0022
0.0024
Potential rate of growth
RGRPOT
Fraction
2.02
2.03
2.06
Potential rooting depth growth rate
ZRTPOT
mm d -1
44
45
48
Maximum number of grains per hectare
GNOMAX
grains per hectare
41397000
40383000
44657000
Potential weight of a grain
POTGWT
mg grain -1
6.52
5.14
6.07
4.10.2 Calibration of WOFOST model for mustard crop under Hisar conditions The WOFOST model can be applied for simulating the growth of many crop species under a wide range of environmental conditions. For each crop species, a separate input data file with specific data is used in WOFOST for the main environmental conditions in an area, representative climate and soil data files are used. In this way, WOFOST can easily be applied for assessments at regional or national scales of the yield potential of a large number of annual crops. To apply the WOFOST model for a specific crop species and under specific conditions with respect to climate and soil conditions, a model calibration is often required. WOFOST was developed as a generic model. However, it appears to achieve better crop growth simulation results if the model variables are calibrated for sitespecific conditions. In a model calibration, the number of model variables that can be varied, is enormous. But modification of a few important coefficients may help to calibrate the model for local conditions viz. phenology of the crop, radiation utilization
74
and assimilate distribution among the crop organs. Important crop coefficients modified to calibrate WOFOST for Hisar conditions are mentioned in Table 4.27. Table 4.27: Genotypic characteristics of mustard cv. RH 30, Laxmi and RH 0749 used in WOFOST model Genetic constant description
Acronyms
Unit
RH 30
Laxmi
RH 0749
Lower threshold temp. for emergence
TBASEM
°C
5
5
5
Temperature sum from sowing to emergence
TSUMEM
°C
83.7
88.7
95.0
Temperature sum from emergence to anthesis
TSUM1
°C
563.0
581.7
619.7
Temperature sum from anthesis to maturity
TSUM2
°C
664.3
618.0
758.7
Optimum day length for development
DLO
h
11
11
11
Initial total crop dry weight
TDWI
Kg ha-1
231.0
240.4
242.7
Leaf area index at emergence
LAIEM
0.147
0.152
0.152
Life span of leaves growing at 35 °C
SPAN
Day
26
26
29
Efficiency of conversion into leaves
CVL
Kg ha-1
0.562
0.613
0.626
Efficiency of conversion into storage organs
CVO
Kg ha-1
0.832
0.840
0.849
Efficiency of conversion into roots
CVR
Kg ha-1
0.681
0.693
0.709
Efficiency of conversion into stems
CVS
-1
0.696
0.710
0.721
Relative maintenance respiration rate of leaves
RML
KgCH 2 O kg -1 d -1
0.038
0.038
0.041
Relative maintenance respiration rate of storage organs
RMO
KgCH 2 O kg -1 d -1
0.013
0.014
0.018
Relative maintenance respiration rate of roots
RMR
KgCH 2 O kg -1 d -1
0.015
0.015
0.016
Relative maintenance respiration rate stems
RMS
KgCH 2 O kg -1 d -1
0.016
0.016
0.016
Kg ha
4.11 Validation of models performance for mustard One of the major goal of this study was to compare the performance of dynamic crop simulation models under Hisar conditions. Details comparison statistics of InfoCrop and WOFOST models for days taken to 50% flowering, days taken to maturity, leaf area index, test weight, seed yield, biological yield and harvest index are presented in Table 4.28 to 4.34. The overall performance of simulation was found satisfactory. 4.11.1 Validation of days taken to 50% flowering The days taken to 50% flowering was simulated by calibration of InfoCrop and WOFOST models. The mean measured days taken to 50% flowering of mustard ranged between 52.7 (D 3) to 55.5 days (D 1) and 51.1 (D 2 ) to 54.9 days (D 3) among sowing
75
dates, while, 53.0 (V 2) to 55.4 days (V 3) and 51.0 (V 1) to 54.2 days (V 3) among varieties during both the crop seasons, respectively. The InfoCrop model simulated days taken to 50% flowering ranged from 53.3 (D 1 and D2 ) to 54.3 days (D 3) and 54.0 (D 2) to 58.0 days (D 1) among sowing dates, while, 51.3 (V 1) to 55.0 days (V 3) and 51.0 (V 2) to 57.6 days (V 3) among varieties during 2012-13 and 2013-14, respectively. The WOFOST model simulated days taken to 50% flowering ranged from 54.3 (D 1 and D 2) to 55.0 days (D 3) and 50.0 (D 1) to 57.3 days (D 2) among sowing dates, while, 52.3 (V 1) to 56.0 days (V 3) and 54.9 (V 1) to 58.0 days (V 3 ) among varieties during both crop seasons, respectively (Table 4.28). Overall both the model overestimated the days taken to 50% flowering for both the crop seasons. The error percent (PE) in InfoCrop model was 8.64 (2012-13) and 8.78 (201314), while, for WOFOST model it was 10.36 (2012-13) and 10.20 (2013-14). So, the error percent was less in InfoCrop model as compared to WOFOST model. The calculated values of statistical indices viz., mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), ability of model (R), tendency of model (V) and index of agreement (D-index) were under the acceptance range for InfoCrop and WOFOST models for both the crop seasons. The comparison of both the models revealed that InfoCrop performance was better as compared to WOFOST model. 4.11.2 Validation of days taken to maturity The days taken to maturity were simulated by using InfoCrop and WOFOST models. The mean measured days taken to maturity of mustard crop ranged between 125.0 (D 3) to 139.9 days (D 1) and 119.0 (D 3) to 132.8 days (D 1) among sowing dates, while, 124.8 (V 2) to 135.8 days (V 3) and 120.4 (V 2 ) to 128.2 days (V 3) among varieties during 2012-13 and 2013-14, respectively. The InfoCrop model simulated days taken to maturity ranged from 126.3 (D 3) to 142.0 days (D 1) and 122.3 (D 3) to 142.7 days (D 1) among sowing dates, while, 126.2 (V 2) to 137.9 days (V 3 ) and 126.3 (V 3) to 132.7 days (V 1) among varieties during both the crop seasons, respectively. The WOFOST model simulated days taken to maturity ranged from 138.3 (D 2) to 157.7 days (D 1) and 127.0 (D 3) to 147.3 days (D 1) among sowing dates, while, 134.0 (V 2) to 146.0 days (V 3) and 133.7 (V 2) to 139.7 days (V 3) among varieties during both crop seasons, respectively (Table 4.29). Overall both the model overestimated also the days taken to maturity for both the crop seasons. The error percent (PE) in InfoCrop model was 4.27 (2012-13) and 5.45 (2013-14), while, for WOFOST model it was 6.30 (2012-13) and 6.22 (2013-14). So, the error percent was less in InfoCrop model as compared to WOFOST model. The calculated values of statistical indices viz., MAE, MBE, RMSE, R, V and D-index were under the acceptance range for InfoCrop and WOFOST models during both the crop
76
seasons. The comparison of both the models revealed that InfoCrop performance was better as compared to WOFOST model. 4.11.3 Validation of leaf area index (LAI) The leaf area index (LAI) was simulated by using InfoCrop and WOFOST models. The mean measured LAI of mustard ranged between 3.9 (D 3) to 4.2 (D 1) and 3.6 (D 3) to 4.0 (D 1) among sowing dates, while, 4.0 (V1 and V2) to 4.2 (V 3 ) and 3.7 (V 1) to 3.9 (V 3) among varieties during 2012-13 and 2013-14, respectively. The InfoCrop model simulated LAI ranged from 3.9 (D 1 ) to 4.0 (D 2 and D 3) and 3.5 (D 3) to 3.8 (D 1) among sowing dates, while, 3.9 (V 1) to 4.0 (V 2 and V3) and 3.5 (V 2) to 3.7 (V 1) among varieties during both the crop seasons, respectively. The WOFOST model simulated LAI ranged from 3.7 (D 3 ) to 4.0 (D 1) and 3.3 (D 3) to 3.9 (D 1) among sowing dates, while, 3.7 (V 1) to 4.0 (V 3) and 3.5 (V 2) to 3.6 (V1 and V3) among varieties during both crop seasons, respectively (Table 4.30). Overall both the model underestimated the LAI for both the crop seasons. The calculated values of statistical indices viz., mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), ability of model (R), tendency of model (V) and index of agreement (D-index) were under the acceptance range for InfoCrop and WOFOST models during both the crop seasons. The error percent (PE) in InfoCrop model was 4.79 (2012-13) and 5.59 (2013-14), while, for WOFOST model it was 7.58 (2012-13) and 7.00 (2013-14). So, the error percent was less in InfoCrop model as compare to WOFOST model. The comparison of both the models explained that InfoCrop performance was better as compare to WOFOST model during both the crop seasons. 4.11.4 Validation of 1000-seed weight (test weight) The test weight (g) was simulated by calibration of InfoCrop and WOFOST models. The mean measured test weight of mustard ranged between 4.8 (D 3 ) to 6.6 g (D 1) and 4.8 (D 3) to 6.5 g (D 1) among sowing dates, while, 5.1 (V 2) to 6.4 (V 3) and 5.0 (V 2) to 6.3 g (V 3) among varieties during 2012-13 and 2013-14, respectively. The InfoCrop model simulated test weight ranged from 5.3 (D 3) to 6.4 g (D 1) and 5.2 (D 3) to 6.2 g (D 1) among sowing dates, while, 5.1 (V 2) to 6.6 g (V 3) and 5.2 (V 2) to 6.4 g (V 3) among varieties during 2012-13 and 2013-14, respectively. The WOFOST model simulated test weight ranged from 5.4 (D 3) to 6.4 g (D 1) and 5.1 (D 3) to 6.5 g (D 1) among sowing dates, while, 5.3 (V 2) to 6.8 g (V 3) and 5.2 (V 2) to 6.4 g (V 3) among varieties during both crop seasons, respectively (Table 4.31). Overall both the model overestimated the test weight for both the crop seasons except WOFOST model during 2013-14. The calculated values of statistical indices viz., mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), ability of model (R), tendency of model (V) and index of agreement (D -index) were
77
under the acceptance range for InfoCrop and WOFOST models during both the crop seasons. The error percent (PE) in InfoCrop model was 8.39 (2012-13) and 7.88 (201314), while, for WOFOST model it was 9.77 (2012-13) and 10.58 (2013-14). So, the error percent was less in InfoCrop model as compare to WOFOST model. The comparison of both the models revealed that InfoCrop performance was better as compare to WOFOST model. 4.11.5 Validation of seed yield The seed yield (kg ha -1) was simulated by calibration of InfoCrop and WOFOST models. The mean measured seed yield of mustard ranged between 2499.0 (D 3) to 3357.5 kg ha -1 (D 1) and 2368.7 (D 3) to 3153.5 kg ha-1 (D 1) among sowing dates, while, 2816.3 (V 1) to 3215.8 kg ha -1 (V 3) and 2564.2 (V 1) to 3173.3 kg ha -1 (V 3) among varieties during 2012-13 and 2013-14, respectively. The InfoCrop model simulated seed yield ranged from 2563.1 (D 3) to 3358.8 kg ha -1 (D 1) and 2562.3 (D 3) to 3235.0 kg ha -1 (D 1) among sowing dates, while, 2870.0 (V 1) to 3116.7 kg ha -1 (V 3) and 2749.3 (V 1) to 3118.3 kg ha -1 (V 3) among varieties during 2012-13 and 2013-14, respectively. The WOFOST model simulated seed yield ranged from 2756.3 (D 3) to 3228.7 kg ha -1 (D 1) and 2738.3 (D 3) to 3085.7 kg ha -1 (D 1) among sowing dates, while, 2860.0 (V 1) to 3286.3 kg ha -1 (V 3) and 2702.7 (V 1) to 3368.7 Kg ha -1 (V 3) among varieties during both crop seasons, respectively (Table 4.32). Overall both the models overestimated the seed yield for both the crop seasons except InfoCrop model in 2012-13. The calculated values of statistical indices viz., mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), ability of model (R), tendency of model (V) and index of agreement (D -index) were under the acceptance range for InfoCrop and WOFOST models during both the crop seasons. The error percent (PE) in InfoCrop model was 3.85 (2012-13) and 5.90 (201314), while, for WOFOST model it was 6.42 (2012-13) and 9.09 (2013-14). So, the error percent was less in InfoCrop model as compare to WOFOST model. The comparison of both the models revealed that InfoCrop performance was better as compare to WOFOST model. 4.11.6 Validation of biological yield The biological yield (kg ha -1) was simulated by calibration of InfoCrop and WOFOST models. The mean measured biological yield of mustard ranged between 12262.7 (D 3) to 15127.2 kg ha -1 (D 1) and 12002.0 (D 3) to 14773.0 kg ha-1 (D 1) among sowing dates, while, 13220.3 (V 1) to 14656.8 kg ha -1 (V 3) and 12897.3 (V 1) to 14384.8 kg ha -1 (V 3) among varieties during 2012-13 and 2013-14, respectively. The InfoCrop model simulated biological yield ranged from 13187.8 (D 3) to 16676.3 kg ha -1 (D 1) and 12526.7 (D 3) to 14960.7 kg ha -1 (D 1) among sowing dates, while, 14595.7 (V 2) to
78
14798.8 kg ha -1 (V 3) and 13313.3 (V 2) to 14302.3 kg ha -1 (V 3) among varieties during 2012-13 and 2013-14, respectively. The WOFOST model simulated seed yield ranged from 14342.7 (D 3) to 15286.7 kg ha -1 (D 1) and 12784.7 (D 3) to 15078.7 kg ha -1 (D 1) among sowing dates, while, 14251.0 (V 1) to 15895.0 kg ha -1 (V 3) and 13155.0 (V 2) to 15216.7 kg ha -1 (V 3) among varieties during both crop seasons, respectively (Table 4.33). Overall both the models overestimated the biological yield for both the crop seasons. The calculated values of statistical indices viz., mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), ability of model (R), tendency of model (V) and index of agreement (D-index) were under the acceptance range for InfoCrop and WOFOST models during both the crop seasons. The error percent (PE) in InfoCrop model was 9.04 (2012-13) and 5.91 (2013-14), while, for WOFOST model it was 11.98 (2012-13) and 9.00 (2013-14). So, the error percent was less in InfoCrop model as compare to WOFOST model. The comparison of both the models revealed that WOFOST performance was better as compare to WOFOST model. 4.11.7 Validation of harvest index The harvest index (HI) was simulated by calibration of InfoCrop and WOFOST models. The mean measured HI of mustard ranged between 17.3 (D 3) to 18.9 % (D 1) and 17.2 (D 3) to 18.4 % (D 1) among sowing dates, while, 18.0 (V 1) to 18.6 % (V 3 ) and 17.7 (V 1) to 18.2 % (V 3) among varieties during 2012-13 and 2013-14, respectively. The InfoCrop model simulated HI ranged from 19.4 (D 3) to 20.6 % (D 2) and 19.3 (D 2) to 20.6 % (D 1) among sowing dates, while, 19.5 (V 1) to 21.0 % (V 3) and 18.3 (V 1 and V2) to 20.9 % (V 3) among varieties during 2012-13 and 2013-14, respectively. The WOFOST model simulated seed yield ranged from 19.2 (D 3) to 21.1 % (D 1) and 19.3 (D2 and D3) to 20.5 % (D 1) among sowing dates, while, 19.8 (V 2) to 20.7 % (V 3) and 17.4 (V 2) to 21.1 % (V 3) among varieties during both crop seasons, respectively (Table 4.34). Overall both the models overestimated the HI for both the crop seasons. The calculated values of statistical indices viz., mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), ability of model (R), tendency of model (V) and index of agreement (D-index) were under the accepted range for InfoCrop and WOFOST models during both the crop seasons. The error percent (PE) in InfoCrop model was 10.43 (2012-13) and 6.00 (2013-14), while, for WOFOST model it was 11.22 (2012-13) and 7.09 (2013-14). So, the error percent was higher in InfoCrop model as compare to WOFOST model. The comparison of both the models revealed that WOFOST performance was better as compare to InfoCrop model.
79
Table 4.28: Validation of InfoCrop and WOFOST model for days taken to 50 % flowering of mustard under different growing environment and varieties (2012-13 and 2013-14) 2012-13 Particulars
2013-14
InfoCrop model
WOFOST model
InfoCrop model
WOFOST model
O
P
%D
O
P
%D
O
P
%D
O
P
%D
D1
55.5
53.3
-4.0
55.5
54.3
-2.2
51.4
58.0
12.9
51.4
50.0
-2.7
D2
54.3
53.3
-1.7
54.3
54.3
0.1
51.1
54.0
5.6
51.1
57.3
12.1
D3
52.7
54.3
3.2
52.7
55.0
4.4
54.9
55.6
1.3
54.9
56.2
2.4
V1
54.1
51.3
-5.1
54.1
52.3
-3.2
51.0
53.2
4.4
51.0
54.9
7.6
V2
53.0
54.7
3.2
53.0
55.3
4.5
52.2
51.0
-2.3
52.2
56.7
8.6
V3
55.4
55.0
-0.8
55.4
56.0
1.0
54.2
57.6
6.2
54.2
58.0
6.9
MAE
4.11
4.78
3.56
4.04
MBE
2.78
3.67
2.44
2.84
RMSE
4.41
5.28
3.88
4.46
R
0.16
0.22
0.16
0.20
V
0.02
0.03
0.02
0.03
D-index
0.93
0.93
0.97
0.96
PE
8.64
10.36
8.78
10.20
Where, O: Observed, P: Predicted, %D: Per cent deviation, MAE: mean absolute error, MBE: mean bios error, RMSE: root mean square error, R: model’s ability to produce the observed growth and development pattern, V: model’s tendency, D-index: index of agreement and PE: Percent error.
80
Table 4.29: Validation of InfoCrop and WOFOST model for days taken to maturity of mustard under different growing environment and varieties (2012-13 and 2013-14) 2012-13 Particulars
2013-14
InfoCrop model
WOFOST model
InfoCrop model
WOFOST model
O
P
%D
O
P
%D
O
P
%D
O
P
%D
D1
139.9
142.0
1.5
139.9
157.7
12.7
132.8
142.7
7.5
132.8
147.3
11.0
D2
128.9
130.2
1.0
128.9
138.3
7.3
122.3
133.7
9.3
122.3
139.7
14.2
D3
125.0
126.3
1.0
125.0
142.3
13.9
119.0
122.3
2.8
119.0
127.0
6.7
V1
133.3
132.8
-0.3
133.3
138.0
3.6
125.5
132.7
5.7
125.5
135.7
8.1
V2
124.8
126.2
1.1
124.8
134.0
7.4
120.4
131.7
9.4
120.4
133.7
11.1
V3
135.8
137.9
1.6
135.8
146.3
7.8
128.2
126.3
-1.4
128.2
139.7
9.0
MAE
5.78
8.22
7.44
8.55
MBE
3.78
6.89
4.33
5.78
RMSE
6.39
9.40
8.13
9.26
R
0.08
0.14
0.09
0.12
V
0.01
0.01
0.01
0.01
D-index
0.97
0.97
0.97
0.96
PE
4.27
6.30
5.45
6.22
Where, O: Observed, P: Predicted, %D: Per cent deviation, MAE: mean absolute error, MBE: mean bios error, RMSE: root mean square error, R: model’s ability to produce the observed growth and development pattern, V: model’s tendency, D-index: index of agreement and PE: Percent error.
81
Table 4.30: Validation of InfoCrop and WOFOST model for Leaf Area Index (LAI) of m ustard under different growing environment and varieties (2012-13 and 2013-14) 2012-13 Particulars
InfoCrop model
2013-14 WOFOST model
InfoCrop model
WOFOST model
O
P
%D
O
P
%D
O
P
%D
O
P
%D
D1
4.2
3.9
-5.9
4.2
4.0
-5.2
4.0
3.8
-4.4
4.0
3.9
-2.9
D2
4.1
4.0
-2.9
4.1
3.9
-5.5
3.8
3.6
-6.1
3.8
3.6
-5.3
D3
3.9
4.0
2.1
3.9
3.7
-5.9
3.6
3.5
-3.7
3.6
3.3
-7.4
V1
4.0
3.9
-2.8
4.0
3.7
-6.3
3.7
3.6
-3.4
3.7
3.6
-2.7
V2
4.0
4.0
-0.7
4.0
3.8
-6.8
3.8
3.5
-6.2
3.8
3.5
-6.2
V3
4.2
4.0
-3.3
4.2
4.0
-3.6
3.9
3.7
-4.7
3.9
3.6
-6.4
MAE
0.17
0.25
0.19
0.26
MBE
-0.09
-0.22
-0.18
-0.19
RMSE
0.19
0.31
0.21
0.26
R
-0.07
-0.21
-0.23
-0.15
V
0.01
0.02
0.01
0.01
D-index
0.97
0.95
0.96
0.96
PE
4.79
7.58
5.59
7.00
Where, O: Observed, P: Predicted, %D: Per cent deviation, MAE: mean absolute error, MBE: mean bios error, RMSE: root mean square error, R: mode l’s ability to produce the observed growth and development pattern, V: model’s tendency, D-index: index of agreement and PE: Percent error.
82
Table 4.31: Validation of InfoCrop and WOFOST model for 1000-seed weight (g) of mustard under different growing environment and varieties (2012-13 and 2013-14) 2012-13 Particulars
InfoCrop model
2013-14 WOFOST model
InfoCrop model
WOFOST model
O
P
%D
O
P
%D
O
P
%D
O
P
%D
D1
6.6
6.4
-3.8
6.6
6.4
-2.5
6.5
6.2
-4.4
6.5
6.5
-0.6
D2
5.5
6.0
8.8
5.5
6.2
12.2
5.4
5.9
8.1
5.4
5.9
9.6
D3
4.8
5.3
10.9
4.8
5.4
12.1
4.8
5.2
9.4
4.8
5.1
7.9
V1
5.5
5.9
7.5
5.5
5.9
7.3
5.5
5.7
3.8
5.5
5.9
7.8
V2
5.1
5.1
1.4
5.1
5.3
3.8
5.0
5.2
5.6
5.0
5.2
5.3
V3
6.4
6.6
4.4
6.4
6.8
7.7
6.3
6.4
1.8
6.3
6.4
2.6
MAE
0.42
0.51
0.39
0.49
MBE
0.25
0.36
0.21
-0.04
RMSE
0.47
0.55
0.43
0.58
R
0.13
0.19
0.12
0.16
V
0.02
0.03
0.02
0.03
D-index
0.96
0.96
0.96
0.98
PE
8.39
9.77
7.88
10.58
Where, O: Observed, P: Predicted, %D: Per cent deviation, MAE: mean absolute error, MBE: mean bios error, RMSE: root mean square e rror, R: model’s ability to produce the observed growth and development pattern, V: model’s tendency, D-index: index of agreement and PE: Percent error.
83
Table 4.32:
Validation of InfoCrop and WOFOST model for seed yield (kg ha -1) of mustard under different growing environment and varieties (2012-13 and 2013-14) 2012-13
Particulars
2013-14
InfoCrop model
WOFOST model
InfoCrop model
WOFOST model
O
P
%D
O
P
%D
O
P
%D
O
P
%D
D1
3357.5
3358.8
0.0
3357.5
3228.7
-3.8
3153.5
3235.0
2.6
3153.5
3085.7
-2.2
D2
3051.5
2937.5
-3.7
3051.5
3070.3
0.6
2873.0
2902.7
1.0
2873.0
3062.0
6.6
D3
2499.0
2563.1
2.6
2499.0
2756.3
10.3
2368.7
2562.3
8.2
2368.7
2738.3
15.6
V1
2816.3
2870.0
1.9
2816.3
2860.0
1.6
2564.2
2749.3
7.2
2564.2
2702.7
5.4
V2
2875.8
2872.8
-0.1
2875.8
2909.0
1.2
2657.7
2832.3
6.6
2657.7
2814.7
5.9
V3
3215.8
3116.7
-3.1
3215.8
3286.3
2.2
3173.3
3118.3
-1.7
3173.3
3368.7
6.2
MAE
93.40
161.51
150.67
208.44
MBE
-16.02
49.28
100.67
196.00
RMSE
115.58
188.36
159.83
256.09
R
-0.01
0.05
0.12
0.21
V
0.00
0.01
0.01
0.03
D-index
0.99
0.99
0.99
0.98
PE
3.85
6.42
5.90
9.09
Where, O: Observed, P: Predicted, %D: Per cent deviation, MAE: mean absolute error, MBE: mean bios error, RMSE: root mean square error, R: model’s ability t o produce the observed growth and development pattern, V: model’s tendency, D-index: index of agreement and PE: Percent error.
84
Table 4.33:
Validation of InfoCrop and WOFOST model for biological yield (kg ha -1 ) of mustard under different growing environment and varieties (2012-13 and 2013-14) 2012-13
Particulars
2013-14
InfoCrop model
WOFOST model
InfoCrop model
WOFOST model
O
P
%D
O
P
%D
O
P
%D
O
P
%D
D1
15127.2
16676.3
10.2
15127.2
15286.7
1.1
14773.0
14960.7
1.3
14773.0
15078.7
2.1
D2
13869.2
14255.7
2.8
13869.2
15259.0
10.0
13574.5
13672.7
0.7
13574.5
14420.0
6.2
D3
12262.7
13187.8
7.5
12262.7
14342.7
17.0
12002.0
12526.7
4.4
12002.0
12784.7
6.5
V1
13220.3
14725.3
11.4
13220.3
14251.0
7.8
12897.3
13544.3
5.0
12897.3
13911.7
7.9
V2
13381.8
14595.7
9.1
13381.8
14742.3
10.2
13067.3
13313.3
1.9
13067.3
13155.0
0.7
V3
14656.8
14798.8
1.0
14656.8
15895.0
8.4
14384.8
14302.3
-0.6
14384.8
15216.7
5.8
MAE
1142.09
1358.28
742.67
1046.89
MBE
953.31
1209.50
271.78
646.22
RMSE
1226.30
1645.22
793.49
1208.87
R
0.21
0.26
0.06
0.14
V
0.03
0.04
0.01
0.02
D-index
0.98
0.98
0.98
0.99
PE
9.04
11.98
5.91
9.00
Where, O: Observed, P: Predicted, %D: Per cent deviation, MAE: mean absolute error, MBE: mean bios error, RMSE: root mean square error, R: model’s ability to produce the observed growth and development pattern, V: model’s tendency, D-index: index of agreement and PE: Percent error.
85
Table 4.34:
Validation of InfoCrop and WOFOST model for Harvest Index (%) of m ustard under different growing environment and varieties (2012-13 and 2013-14) 2012-13
Particulars
2013-14
InfoCrop model
WOFOST model
InfoCrop model
WOFOST model
O
P
%D
O
P
%D
O
P
%D
O
P
%D
D1
18.9
20.1
6.8
18.9
21.1
12.1
18.4
20.6
12.0
18.4
20.5
11.2
D2
18.7
20.6
10.3
18.7
20.1
7.7
18.2
19.3
6.3
18.2
19.3
6.3
D3
17.3
19.4
12.3
17.3
19.2
11.2
17.2
19.5
13.5
17.2
19.3
12.7
V1
18.0
19.5
8.2
18.0
20.1
11.3
17.7
18.3
3.4
17.7
19.5
10.2
V2
18.2
19.6
7.8
18.2
19.8
8.5
17.9
18.3
2.2
17.9
17.4
-2.4
V3
18.6
21.0
13.1
18.6
20.7
11.1
18.2
20.9
14.8
18.2
21.1
16.1
MAE
1.78
1.88
1.04
1.24
MBE
1.78
1.88
0.44
0.54
RMSE
1.91
2.05
1.25
1.46
R
0.29
0.31
0.07
0.05
V
0.03
0.04
0.01
0.02
D-index
0.98
0.97
0.98
0.98
PE
10.43
11.22
6.00
7.09
Where, O: Observed, P: Predicted, %D: Per cent deviation, MAE: mean absolute error, MBE: mean bios error, RMSE: root mean square e rror, R: model’s ability to produce the observed growth and development pattern, V: model’s tendency, D-index: index of agreement and PE: Percent error.
86
4.12 Sensitivity analysis of InfoCrop and WOFOST model to weather and non-weather parameters Tropical countries are more vulnerable to climatic effects in agricultural productivity. Simulation models provide a scientific approach to study the impact of climate change on agricultural production and world food security. Sensitivity test of the crop simulation models is the process by which various input parameters are evaluated with regard to their importance relative to simulation relations. Here, an effort has been made to test the models under two situations in terms of mathematical logic and stability to extreme values of weather and nonweather parameters. The first situation is combined effect of change in mean ambient temperature (±1 to ±5°C) and concentration of carbon dioxide (base value-360 ppm; 400, 450, 500, 550, 600, 650, and 700ppm) and; second situation is combined effect of mean ambient temperature (±1 to ±5°C) and change in rainfall (-30 to +40%) from the normal value. The models simulated the mustard seed yields under altered weather and non-weather parameters. 4.12.1 Effect of mean ambient temperature and carbon dioxide Sensitivity analysis was carried out for combined effect of change in mean ambient temperature and different levels of CO2 concentration (400, 450, 500, 550, 600, 650 and 700 ppm) using InfoCrop and WOFOST models in Figures 4.9 and 4.10. The results revealed that CO2 concentration of 400 ppm there was less reduction in seed yield either increased or decreased in mean ambient temperature (±1 to ± 5°C) in all three mustard varieties with both the models. The desirable effects for InfoCrop model were simulated under downscaling of the temperature, the total effect being -22 to 26% (RH 30), -22 to 29% (Laxmi) and -25 to 24% (RH 0749), whereas, using WOFOST model, the effect was -28 to 19% (RH 30), -28 to 22% (Laxmi) and -31 to 20% (RH 0749), respectively. At CO2 concentration 450 ppm, using InfoCrop model the net effect on yield was -29 to 36% (RH 30), -31 to 36% (Laxmi) and -31 to 35% (RH 0749), however, the effect under using WOFOST model was -35 to 22% (RH 30), -33 to 25% (Laxmi) and -34 to 23% (RH 0749), respectively. Under increasing of CO2 concentration 500 ppm scenario, increased yield levels were higher in RH 30 followed by Laxmi and lowest in RH 0749 using these models. The higher benefits was obtained at 500 ppm but further increase in CO2 (550, 600, 650 and 700 ppm) combined with one unit increase in mean ambient temperature reduced the per cent change in mustard yield. The interaction effect of temperature and CO2 concentration revealed that the response under variety Laxmi was showed high response followed by RH 0749 and RH 30. The per cent change in yield decreased in both ways i.e. either increasing or decreasing the
87
mean ambient temperature from mean value. The results indicated that the elevated CO2 concentrations at 550, 600, 650 ppm could alleviate and positive impact of temperature upto 3 to +1°C, however, 700 ppm CO2 concentration the negative impact of temperature was simulated only at -1°C in all the mustard varieties through both the models. 4.12.2 Effect of mean ambient temperature and rainfall Sensitivity analysis was carried out for combined effect of change in mean ambient temperature and percent change in rainfall (-30 to +40%) from actual rainfall of study period using InfoCrop and WOFOST models in Figures 4.11 and 4.12. The results revealed that rainfall change of +10%, less reduction in seed yield was observed either increased or decreased in mean ambient temperature (±1 to ±5°C) in all three mustard varieties with both the models. The desirable effects for InfoCrop model were simulated under downscaling of the temperature, the total effect being -18 to 27% (RH 30), -19 to 29% (Laxmi) and -21 to 31% (RH 0749), whereas, using WOFOST model, the effect was -17 to 26% (RH 30), -15 to 27% (Laxmi) and -18 to 27% (RH 0749), respectively. At +20% change in rainfall, using InfoCrop model the net effect on yield was -20 to 31% (RH 30), -19 to 31% (Laxmi) and -21 to 33% (RH 0749), however, the effect seen under using WOFOST model was -21 to 28% (RH 30), -24 to 29% (Laxmi) and -22 to 31% (RH 0749), respectively. By increasing the 20% rainfall scenario, the increase in yield levels are higher in RH 0749 followed by Laxmi and lowest in RH 30 using both crop simulation models. The higher benefits was obtained at +20% rise in rainfall but further decrease or increase (-30 to -10% and +30 to +40%) in rainfall combined with change from ±3 to ± 5°C in mean ambient temperature reduced the percent change in mustard yield. The interaction effect of temperature and rainfall change revealed that the response of the variety RH 0749 was quite high followed by RH 30 and Laxmi. The percent change in yield decreased in both ways i.e. either increasing or decreasing the mean ambient temperature from mean value. The results indicated that the change in rainfall from -30 to 10% and +30 to +40% could alleviate and positive impact of temperature upto -2 to +2°C on seed yield by InfoCrop model, however, rainfall change from -10 to +20% have positive impact of temperature was simulated from -3 to +3°C on seed yield in all the mustard cultivars by using WOFOST model.
88
Fig. 4.9: Interaction effect of change in mean ambient temperature (°C) and CO2 conc. (ppm) on simulated mustard seed yield (% change) using InfoCrop model
89
Fig. 4.10: Interaction effect of change in mean ambient temperature (°C) and CO2 conc. (ppm) on simulated mustard seed yield (% change) using WOFOST model
90
Fig. 4.11: Interaction effect of change in mean ambient temperature (°C) and ranfall change (%) on simulated mustard seed yield (% change) using InfoCrop model
91
Fig. 4.12: Interaction effect of change in mean ambient temperature (°C) and ranfall change (%) on simulated mustard seed yield (% change) using WOFOST model
92
4.13 Climatic variability and trend of maximum and minimum temperature and rainfall at Hisar 4.13.1 Maximum temperature The seasonal, annual and baseline period mean values along with standard deviation (S.D.) and coefficients of variation (C.V.) of maximum temperature over forty five years period (1970-2014) at Hisar are presented in the Table 4.35. The mean maximum temperature was found highest in summer (36.2ºC) and lowest in winter (21.3ºC), among different seasons. The Kharif season has experienced high maximum temperature, whereas, baseline and annual mean value were 31.5 and 31.4ºC, respectively. The values of standard deviation (S.D.) over the seasons i.e. winter (Dec. to Feb.), summer (March to June), monsoon (July to Sept.), post monsoon (Oct. to Nov.), Kharif (July to Oct.), Rabi (Oct. to March), baseline (1970 to 1990) and annual were 0.93, 1.16, 1.06, 1.05, 0.94, 0.80, 0.58 and 0.64ºC and the coefficient of variations were 4.37, 3.21, 3.00, 3.40, 2.71, 3.09, 1.83 and 2.05 per cent, respectively. The maximum temperature showed decreasing trends during winter, summer, postmonsoon, Kharif, Rabi and annual at the rate of -0.0231, -0.0038, -0.0150, -0.0051, -0.0162 and 0.0099ºC/year, whereas, during monsoon, baseline showed increasing trend i.e. 0.0012 and 0.0005ºC/year, respectively, under regression analysis. However, Theil-Sen’s analysis were shown decreasing trend during all the season and periods except monsoon, where it found increasing trend of 0.0033ºC/year. 4.13.1 Minimum temperature The seasonal, annual and baseline period mean values along with standard deviation (S.D.) and coefficient of variation (C.V.) of minimum temperature calculated over forty five years period (1970-2014) at Hisar are presented in Table 4.36. The mean minimum temperature was found highest in monsoon (24.9ºC) and lowest in winter (5.6ºC) seasons. Among agricultural seasons, Kharif season has high minimum temperature, whereas, baseline and annual mean value were 16.2 and 16.3ºC, respectively. The values of standard deviation (S.D.) over the seasons i.e. winter, summer, monsoon, post monsoon, Kharif, Rabi, baseline (1970 to 1990) and annual were 0.99, 1.82, 0.74, 1.21, 0.73, 0.74, 0.61 and 0.60ºC and the coefficient of variations were 17.71, 9.45, 2.98, 9.21, 3.23, 8.10, 3.75 and 3.68 percent, respectively. The minimum temperature showed increasing trends during winter, summer, monsoon, post-monsoon, Kharif, Rabi, baseline and annual; and their values were 0.2066, 0.0348, 0.0159, 0.0181, 0.0163, 0.0163, 0.0297 and 0.0122ºC/year, respectively, under regression analysis and Theil-Sen’s analysis were also showed increasing trend during all the season and periods and; their values were 0.0127, 0.0160, 0.0164, 0.0191, 0.0143, 0.0143, 0.0337 and 0.0100ºC/year, respectively.
93
Table 4.35: Variability and trend analysis of maximum temperature at Hisar (19702014) Maximum temperature (°C) Particulars Regression analysis Theil-Sen’s analysis Mean SD CV% 2 Slope/year R Slope/year SE Winter 21.3 0.93 4.37 -0.0231 0.1061 -0.0228 0.0109 (Dec.-Feb.) Summer 36.2 1.16 3.21 -0.0038 0.0018 -0.0123 0.0120 (March-June) Monsoon 35.3 1.06 3.00 0.0012 0.0002 0.0033 0.0098 (July-Sept.) Post-monsoon 30.9 1.05 3.40 -0.0150 0.0351 -0.0138 0.0096 (Oct.-Nov.) Kharif season 34.8 0.94 2.71 -0.0051 0.0051 -0.0031 0.0070 (July-Oct.) Rabi season 25.9 0.80 3.09 -0.0162 0.0707 -0.0107 0.0092 (Oct.-March) Baseline 31.5 0.58 1.83 0.00052 0.0031 -0.0058 0.0129 (1970-1990) 31.4 0.64 2.05 -0.0099 0.0404 -0.0115 0.0078 Annual Values significant at 90%.
Table 4.36: Variability and trend analysis of minimum temperature at Hisar (1970-2014) Minimum temperature (°C) Particulars
Mean
SD
CV%
Winter (Dec.-Feb.)
5.6
0.99
Summer (March-June)
19.1
Monsoon (July-Sept.)
Regression analysis
Theil-Sen’s analysis
Slope/year
R2
Slope/year
SE
17.71
0.02066
0.0128
0.0127
0.0116
1.82
9.45
0.0348
0.0635
0.0160
0.0141
24.9
0.74
2.98
0.0159
0.0792
0.0164
0.0079
Post-monsoon (Oct.-Nov.)
13.1
1.21
9.21
0.0181
0.0386
0.0191
0.0155
Kharif season (July-Oct.)
22.7
0.73
3.23
0.0163
0.0849
0.0143
0.0089
Rabi season (Oct.-March)
9.1
0.74
8.10
0.0163
0.0847
0.0143
0.0095
Baseline (1970-1990)
16.2
0.61
3.75
0.0297
0.0919
0.0337
0.0279
Annual
16.3
0.60
3.68
0.0122
0.0711
0.0100
0.0065
Values significant at 90%.
4.13.1 Rainfall The seasonal, annual and baseline period mean values along with standard deviation (S.D.) and coefficients of variation (C.V.) of rainfall calculated over forty
94
five years period (1970-2014) at Hisar are presented in Table 4.37. The mean rainfall was found highest in monsoon (312.8 mm) and lowest in post-monsoon (12.8 mm), among seasons. The Kharif season received highest rainfall (322.4 mm), whereas, baseline and annual mean value were 444.5 and 472.8 mm, respectively. The values of standard deviation (S.D.) over the seasons i.e. winter, summer, monsoon, post monsoon, Kharif, Rabi, baseline (1970 to 1990) and annual were 23.95, 65.84, 146.53, 28.72, 149.74, 47.02, 149.41 and 163.40 mm and the coefficient of variations were 68.62, 58.73, 46.83, 225.16, 46.45, 76.83, 33.61 and 34.56 percent, respectively. The rainfall showed increasing trends during winter, summer, monsoon, Kharif, Rabi, and annual; and their values were 0.0124, 1.6116, 0.5634, 0.7340, 0.4110, 2.3588 mm/year, respectively, whereas, decreasing trends during post-monsoon and baseline i.e. -0.0197 and -2.5380 mm/year, respectively in regression analysis. However, TheilSen’s analysis was also showed the increasing trend during all the season and periods except baseline (-2.4833 mm/year). The post-monsoon season has no trend of rainfall in Theil-Sen’s analysis. Table 4.37: Variability and trend analysis of rainfall at Hisar (1970-2014) Rainfall (mm) Particulars
Mean
SD
CV%
Winter (Dec.-Feb.)
34.9
23.95
Summer (March-June)
112.1
Monsoon (July-Sept.)
Regression analysis 2
Theil-Sen’s analysis
Slope/year
R
Slope/year
SE
68.62
0.0124
0.0270
0.0574
0.2795
65.84
58.73
1.6116
0.1034
1.4098
0.6551
312.8
146.53
46.83
0.5634
0.0025
0.3830
1.9574
Post-monsoon (Oct.-Nov.)
12.8
28.72
225.16
-0.0197
0.0000
0.0000
0.0000
Kharif season (July-Oct.)
322.4
149.74
46.45
0.7340
0.0041
0.5842
1.5716
Rabi season (Oct.-March)
61.2
47.02
76.83
0.4110
0.0132
0.1290
0.3995
Baseline (1970-1990)
444.5
149.41
33.61
-2.5380
0.0111
-2.4833
8.2238
Annual
472.8
163.40
34.56
2.3588
0.0359
2.2846
1.9143
Values significant at 90%.
4.14 PRECIS generated weather conditions during projected periods for Hisar The PRECIS projection output of scenario A1b 2030 (2020-2049), A1b 2080 (2070-2099), A2 2080 (2070-2099) and baseline (1970 to 1990) were considered for
95
projection of weather for Hisar. The baseline data generated by PRECIS showed marked differences with actual data recorded at Hisar station. So, the projected data were calculated considering actual data (1970 to 1990) of Hisar station. The difference between above-mentioned scenario and PRECIS baseline was added to actual data to get weather data for projected periods. Two approaches were adopted (i) day to day actual data of baseline and (ii) daily normal as baseline. The crop model InfoCrop was used to study the crop response with the weather data generated using first approach i.e., day to day sum of actual weather data of 1970-1990 and changes calculated using PRECIS baseline and projected periods (A1b 2030, A1b 2080 and A2 2080) data. The grid wise data of maximum, minimum temperature and rainfall have been separated for Hisar of Western Haryana. Subsequently based on monthly mean, daily data were generated and projected data of A1b 2030, A1b 2080 and A2 2080 scenarios for above-mentioned parameters. The InfoCrop model was run for individual year for the projected scenarios using daily weather data for the projected period for mustard growth, phenology and yield simulation for Rabi season under different dates of sowing and varieties. The summary figures and comparison with baseline have been prepared for temperature, rainfall and CO 2 for A1b 2030, A1b 2080 and A2 2080 scenarios and depicted in Figures 4.13, 4.14 and 4.15; and Table 4.38. 4.14.1 A1b 2030 projected climate change scenario The results showed that there would be mean rise of maximum and minimum temperature to the tune of 6.8 and 19.8 per cent, respectively against the baseline periods 1970-90 (Fig. 4.13). Average annual maximum temperature for the projected period A1b 2030 (2020-2049) is likely to be higher than the baseline period temperature by 2.2ºC with maximum value of 34.2ºC in the projected year 2026 and with minimum value of 30.9ºC in the projected year 2036. Similarly, the average annual minimum temperature for the projected period A1b 2030 is likely to be rise by 3.3ºC with maximum value of 19.7ºC in the projected year 2049 and with minimum value of 17.4ºC in the projected year 2040. The average rise during 2020-2049 will be 2.75ºC in both the maximum and minimum temperature as compared to their baseline temperatures (31.5 and 16.2ºC, respectively). The rate of rise of maximum and minimum temperature will be 0.0062 and 0.0075ºC/year, respectively. PRECIS generated rainfall results showed that Hisar will receive 16.1 per cent higher rainfall during projected period A1b 2030 against their respective baseline (1970-1990) period. Highest rainfall will be estimated of 962.1 mm for the year 2049. The years, which will be receiving higher rainfall, i.e. 2034, 2036, 2037, 2044 and 2049 with 675.0, 936.7, 763.3, 750.8 and 962.1mm rainfall, respectively. The lowest rainfall (drought) was estimated of 142.6mm for the year 2039. The average mean annual rainfall was estimated of 516.1 mm
96
for projected period. The CO2 concentration has significant relation with projected period. According to A1b 2030 scenarios CO2 concentration likely to increase 87.0 ppm from its baseline period and reach up to 447.0 ppm, which will be 24.2 per cent higher (Table 4.38).
Fig. 4.13: PRECIS projected maximum and minimum temperature; and rainfall in A1b 2030 scenario (2020-2049) and observed values of baseline periods 4.14.2 A1b 2080 projected climate change scenario Results showed that there would be mean rise of maximum and minimum temperature to the tune of 15.7 and 36.6 per cent, respectively against the baseline periods 1970-90 (Fig. 4.14). Average annual maximum temperature for the projected period A1b 2080 (2070-2099) is likely to be higher than the baseline period temperature by 5.0ºC with maximum value of 37.0ºC during projected year 2076 and the lowest value of 33.7ºC during projected year 2086. Similarly, the average annual minimum temperature for the projected period A1b 2080 is likely to be rise by 6.0ºC with maximum value of 22.4ºC in the projected year 2099 and with minimum value of 20.2ºC in the projected year 2090. The average rise during 2070-2099 will be 5.5ºC in both maximum and minimum temperatures as compared to their baseline temperatures. The rate of rise of maximum and minimum temperature will be 0.0061 and 0.0077ºC/year, respectively. The rainfall results showed that Hisar will receive 20.6 per cent higher rainfall during projected period A1b 2080 against, their respective baseline period. Highest rainfall will estimated of 984.9 mm for the year 2099. The years, which are getting higher rainfall, will be 2086, 2087, 2090, 2094 and 2099 with 942.7, 731.9, 787.5, 784.2 and 984.9 mm rainfall, respectively. The lowest rainfall will be estimated of 158.1 mm for the year 2089. The mean annual rainfall will be estimated of 535.9 mm for projected period. The CO2 concentration has significant relation with the projected period. According to A1b 2080 scenarios, CO2
97
concentration is likely to increase 279.0 ppm from its baseline period and reach up to 639.0 ppm, which is 77.5 per cent higher (Table 4.38).
Fig. 4.14: PRECIS projected maximum and minimum temperature; and rainfall in A1b 2080 scenario (2070-2099) and observed values of baseline periods 4.14.3 A2 2080 projected climate change scenario There will be mean rise of maximum and minimum temperature to the tune of 18.3 and 38.3 per cent, respectively against the baseline periods (Fig. 4.15). Average annual maximum temperature for the projected period A2 2080 (2070-2099) is likely to be higher than the baseline period temperature by 5.7ºC with maximum value of 37.9ºC in the projected year 2076 and with minimum value of 34.6ºC in the projected year 2086. Similarly, the average annual minimum temperature for the projected period A2 2080 is likely to be rise by 6.3ºC with maximum value of 22.7ºC in the projected year 2099 and with minimum value of 20.4ºC in the projected year 2090. The average rise during 2070-2099 will be 6.0ºC in both the maximum and minimum temperature as compared to their baseline temperatures. The rate of rise of maximum and minimum temperature will be 0.0061 and 0.0076ºC/year, respectively. PRECIS generated rainfall results showed that Hisar will receive -2.8 per cent lower rainfall during projected period A2 2080 against their respective baseline (1970-1990) period. Highest rainfall will be estimated of 765.6mm for the year 2086. The years, which will be receiving higher rainfall i.e. 2086, 2087, 2090, 2094 and 2099 with 765.6, 625.9, 716.4, 656.3 and 743.1mm rainfall, respectively. The lowest rainfall will be estimated of 142.9 mm for the year 2089. The average mean annual rainfall was estimated of 432.2mm for projected period. According to A2 2080 scenarios CO2 concentration is likely to increase 322.0 ppm from its baseline period and reach up to 682.0 ppm, which is 89.4 per cent higher (Table 4.38).
98
Fig. 4.15: PRECIS projected maximum and minimum temperature; and rainfall in A2 2080 scenario (2070-2099) and observed values of baseline periods Table 4.38: Percent change in weather parameters of different PRECISE projected periods as compared to baseline period Particulars
Weather parameters Baseline
A1b 2030
A1b 2080
A2 2080
CO2 (ppm)
360
447
639
682
Tmax (°C)
31.5
33.7
36.5
37.2
Tmin (°C)
16.2
19.5
22.2
22.5
Rainfall (mm)
444.5
516.1
535.9
432.2
Change in parameters CO2 (ppm)
-
87.0
279.0
322.0
Tmax (°C)
-
2.2
5.0
5.8
Tmin (°C)
-
3.3
6.0
6.2
Rainfall (mm)
-
71.7
91.4
-12.2
Per cent change in parameters CO2
-
24.2
77.5
89.4
Tmax
-
6.8
15.7
18.3
Tmin
-
19.8
36.6
38.3
Rainfall
-
16.1
20.6
-2.8
4.15 Impact on mustard phenological variation, yield attributing characters and yield under projected climate change scenarios 4.15.1 Days taken to 50% flowering The mean days taken to 50% flowering under baseline and projected periods for all three varieties (RH 30, Laxmi and RH 0749) and date of sowings for InfoCrop and WOFOST models are presented in Fig. 4.16 to 4.19. The fluctuation in days of 50% flowering were seen
99
during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods. The highest days taken to 50% flowering were recorded in D3 (10th Nov.) sowing followed by D2 (25th Oct.) and lowest in D1 (10th Oct.). Under A1b 2030, days taken to 50% flowering simulated almost similar to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario in all the sowing dates by both the models. Among the varieties, RH 0749 has taken more days to 50% flowering as compared to RH 30 and Laxmi under baseline and projected periods. Under A1b 2030, both the models overestimated the days for 50% flowering, whereas it was underestimated in A1b 2080 and A2 2080 projected climate change scenarios. Among the varieties, RH 30 has highest reduction in days taken to 50% flowering followed by Laxmi and least reduction simulated in RH 0749 with both the models under all the projected climate change scenarios. More number of days was taken for 50% flowering simulating InfoCrop model as compared to WOFOST model and presented in Figures 4.18 and 4.19. The comparison of both the models revealed that InfoCrop model overestimated the days taken to 50% flowering as compared to WOFOST; however, both the models simulated the 50% flowering under acceptable range. 4.15.2 Days taken to physiological maturity The mean days taken to physiological maturity under baseline and projected periods for all three varieties (RH 30, Laxmi and RH 0749) and date of sowings for InfoCrop and WOFOST models are presented in Fig. 4.20 to 4.23. The variation in days taken to reach physiological maturity were simulated during A1b 2030, A1b 2080 and A2 2080 of all the varieties with respect to baseline periods. The highest days taken to physiological maturity was recorded in D3 (10th Nov.) sowing followed by D2 (25th Oct.) and lowest in D1 (10th Oct.). Under A1b 2030, days taken to physiological maturity simulated almost similar to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario among all the sowing dates with both the models. Among the varieties, RH 0749 have taken more days to physiological maturity as compared to RH 30 and Laxmi under baseline and projected periods. Under A1b 2030, both the models overestimated the days for physiological maturity, whereas it was underestimated during A1b 2080 and A2 2080 projected climate change scenarios. The variety, RH 30 has highest reduction in cased days taken to reach physiological maturity followed by Laxmi and least reduction simulated in RH 0749 by both the models and under all the projected climate change scenarios. More number of days taken under the study of InfoCrop model as compare to WOFOST model presented in Figures 4.22 and 4.23. The comparison of both the models revealed that InfoCrop model overestimated the days taken for physiological maturity as compared to WOFOST model.
100
4.15.3 Leaf area index The mean maximum leaf area index (LAI), under baseline and projected periods of all three varieties (RH 30, Laxmi and RH 0749) and date of sowings using InfoCrop and WOFOST models are simulated and presented in Figures 4.24 to 4.27. The variations in LAI were simulated during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods. The highest LAI were recorded in D1 (10th Oct.) date of sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.). Under A1b 2030, the maximum LAI value were simulated almost similar to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario among all the sowing dates with both the models. Among the varieties, RH 0749 have more LAI as compared to RH 30 and Laxmi under baseline and projected periods. Under A1b 2030, both the models overestimated the LAI except RH-30 in WOFOST model, whereas it was underestimated during A1b 2080 and A2 2080 projected climate change scenarios. The variety RH 30 has highest reduction in maximum LAI followed by Laxmi and least reduction simulated in RH 0749 using both the models under all projected climate change scenarios. The higher LAI was simulated under the study of InfoCrop model as compare to WOFOST model presented in Figures 4.26 and 4.27. The comparison of both the models revealed that InfoCrop model overestimated the maximum LAI as compared to WOFOST model; however, both the models gave the results under acceptable range. 4.15.4 Test weight or 1000-seed weight The mean test weight (1000-seed weight) (g) under baseline and projected periods for all three varieties (RH 30, Laxmi and RH 0749) and date of sowings for InfoCrop and WOFOST models are simulated and presented in Figures 4.28 to 4.31. The variation in test weight were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties with respect to baseline periods. The highest test weight was recorded in D1 (10th Oct.) sowing date followed by D2 (25th Oct.) and lowest test weight found in D3 (10th Nov.). Under A1b 2030, the test weight were simulated less as compare to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario in all the sowing dates using both the models. Among the varieties, RH 0749 have found more test weight as compared to RH 30 and Laxmi under baseline and projected periods. It was underestimated during A1b 2030, A1b 2080 and A2 2080 projected climate change scenarios by both the models. Among the varieties, RH 30 has highest reduction in test weight followed by Laxmi and lowest reduction simulated in RH 0749 under the study by both the models and in all the projected climate change scenarios. The higher test weight was simulated by InfoCrop model as compare to WOFOST model presented in Figures 4.30 and 4.31. The comparison of both the models revealed that InfoCrop model overestimated the test weight as compared to WOFOST model; however, both the models gave the results under acceptable range.
101
4.15.5 Seed yield The mean seed yield (kg ha-1), under baseline and projected periods for all three varieties (RH 30, Laxmi and RH 0749) and date of sowings using InfoCrop and WOFOST models are presented in Figures 4.32 to 4.35. The disparity in seed yield were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods. The highest seed yield was recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.). Under A1b 2030, the seed yield were simulated almost similar to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario among all the sowing dates by both the models. Among the varieties, RH 0749 have more seed yield followed by Laxmi and lowest in RH 30 under baseline and projected periods. Under A1b 2030, both the models overestimated the seed yield except RH 30 in WOFOST model, whereas it was underestimated in A1b 2080 and A2 2080 projected climate change scenarios. Among the varieties, RH 30 showed highest reduction in seed yield followed by Laxmi and lowest reduction was simulated in RH 0749 using both the models and under all the projected climate change scenarios. The higher seed yield was simulated through InfoCrop model as compare to WOFOST model presented in Figures 4.34 and 4.35. The comparison of both the models revealed that InfoCrop model overestimated the seed yield as compared to WOFOST model; however, both the models gave the results within acceptable range. 4.15.6 Biological yield The mean biological yield (kg ha-1), under baseline and projected periods for all three varieties (RH 30, Laxmi and RH 0749) and date of sowings with InfoCrop and WOFOST models are simulated and presented in Figures 4.36 to 4.39. The variation in biological yield were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods. The highest biological yield was recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.). Under A1b 2030, the biological yield were simulated almost similar to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario in all the sowing dates by both the models. Among the varieties, RH 0749 have higher biological yield followed by Laxmi and lowest in RH 30 under baseline and projected periods. Under A1b 2030, both the models overestimated the biological yield except RH 30 using WOFOST model, whereas it was underestimated in A1b 2080 and A2 2080 projected climate change scenarios. Among the varieties, RH 30 showed highest reduction in biological yield followed by Laxmi and lowest reduction simulated in RH 0749 by both the models and in all the projected climate change scenarios. The higher biological yield simulated by InfoCrop model as compare to WOFOST model presented in Figures 4.34 and 4.35. The comparison of both the models revealed that InfoCrop model overestimated the biological yield as compared to WOFOST model; however, both the models gave the biological yield under acceptable range.
102
Fig. 4.16: Mean days taken to 50 % flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
103
Fig. 4.17: Mean days taken to 50 % flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
104
Fig. 4.18: Mean per cent change in days taken to 50% flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
Fig. 4.19: Mean per cent change in days taken to 50% flowering under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
105
Fig. 4.20: Mean days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
106
Fig. 4.21: Mean days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
107
Fig. 4.22: Mean per cent change in days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
Fig. 4.23: Mean per cent change in days taken to physiological maturity under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
108
Fig. 4.24: Mean Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
109
Fig. 4.25: Mean Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
110
Fig. 4.26: Mean per cent change to Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
Fig. 4.27: Mean per cent change to Leaf Area Index (LAI) under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
111
Fig. 4.28: Mean test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
112
Fig. 4.29: Mean test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
113
Fig. 4.30: Mean per cent change to test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
Fig. 4.31: Mean per cent change to test weight under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
114
Fig. 4.32: Mean seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
115
Fig. 4.33: Mean seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
116
Fig. 4.34: Mean per cent change to seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
Fig. 4.35: Mean per cent change to seed yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
117
Fig. 4.36: Mean biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using InfoCrop model
118
Fig. 4.37: Mean biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties grown in different environment using WOFOST model
119
Fig. 4.38. Mean per cent change to biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using InfoCrop model
Fig. 4.39: Mean per cent change to biological yield under baseline and different PRECIS projected climate change scenarios for mustard varieties using WOFOST model
120
4.16 Adaptation measures study The adaptation measures was tried for yield enhancement of different mustard cultivars by applying 50% extra dose of fertilizer, 50% extra dose fertilizer with organic manure, additional irrigation application at 50% flowering; and improved variety with irrigation management and increased fertilizer dose at normal sown (10th Oct.) during A1b 2080 and A2 2080 projected climate change scenarios are presented in Tables 4.39 and 4.40, respectively. 4.16.1 A1b 2080 projected climate change scenario Results showed that by applying additional 50% dose of fertilizer gave nearly 5.0, 6.9 and 5.0 per cent gain in seed yield of mustard crop by InfoCrop; and 2.9, 3.8 and 5.0 per cent gain in seed yield of mustard crop by using WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. This showed that there will be nearly 5.6 and 3.9 per cent gain in mustard yield by applying additional 50% dose of fertilizer using InfoCrop and WOFOST models under A1b 2080 projected climate change scenario. The percent gain in yield by 50% extra dose of fertilizer with organic manure under projected period of A1b 2080 was simulated by using InfoCrop and WOFOST models and are presented in Table 4.39. This adaptation measures gave 8.2, 8.9 and 9.0 per cent gain in seed yield of mustard varieties by using InfoCrop model and 7.4, 8.3 and 7.4 per cent gain in seed yield of mustard varieties by WOFOST model for RH 30, Laxmi and RH 0749, respectively. This showed that there will be 8.7 and 7.6 per cent gain in mustard yield by 50% extra dose of fertilizer with organic manure under A1b 2080 projected climate change scenario by using InfoCrop and WOFOST, respectively. The application of supplementary additional irrigation at 50% flowering to mustard crop resulted in given nearly 13.1, 12.4 and 14.8 per cent increase in seed yield by InfoCrop model, whereas, WOFOST model simulated 11.1, 11.0 and 12.3 per cent increase in seed yield for varieties RH 30, Laxmi and RH 0749, respectively. There was a gain in the seed yield of mustard crop by this adaptation measures using InfoCrop model (13.4 per cent) and WOFOST model (11.5 per cent) and; are under acceptable range. The InfoCrop model simulated more gain in seed yield than WOFOST model. The per cent gain in yield by improved variety with irrigation management and increased fertilizer dose under projected period of A1b 2080 was simulated through InfoCrop and WOFOST models and results are presented in Table 4.39. This adaptation measures gave 15.8, 17.5 and 19.4 per cent gain in seed yield of mustard varieties by InfoCrop model; and 12.0, 12.4 and 13.3 per cent gain by WOFOST model for RH 30, Laxmi and RH 0749, respectively. There was 17.6 and 12.6 per cent gain in mustard yield by using InfoCrop and WOFOST model under A1b 2080 projected climate change scenario by adopting improved varieties, irrigation management and increased fertilizer dose.
121
Table 4.39: Projected gains in seed yield of mustard varieties with different adaptation options during 2nd fortnight of Oct. sowing under A1b 2080 projected climate change scenario InfoCrop model Projected mean seed yield (kg ha-1) Particulars
RH 30
Laxmi
2076.9
2266.4
% reduction from baseline
RH 0749
RH 30
Laxmi
RH 0749
2525.3
-19.34
-17.22
-15.75
-1
Adaptation options
Gain in seed yield (kg ha ) after adaptation
% change in yield after adaptation
50 % extra dose of fertilizer
2180.7
2422.8
2651.6
5.0
6.9
5.0
50 % extra dose of fertilizer and organic manure
2247.2
2468.1
2752.6
8.2
8.9
9.0
Additional irrigation application at 50 % flowering
2348.9
2547.5
2899.1
13.1
12.4
14.8
With improved variety, irrigation management and increased fertilizer dose
2405.0
2663.0
3015.2
15.8
17.5
19.4
WOFOST model Projected mean seed yield (kg ha-1) Particulars
RH 30
Laxmi
1949.6
2082.2
% reduction from baseline
RH 0749
RH 30
Laxmi
RH 0749
2286.2
-22.97
-20.73
-20.01
-1
Adaptation options
Gain in seed yield (kg ha ) after adaptation
50 % extra dose of fertilizer
2006.1
2161.4
2400.5
2.9
3.8
5.0
50 % extra dose of fertilizer and organic manure
2093.9
2255.1
2455.4
7.4
8.3
7.4
Additional irrigation application at 50 % flowering
2166.0
2311.3
2567.4
11.1
11.0
12.3
With improved variety, irrigation management and increased fertilizer dose
2183.5
2340.4
2590.3
12.0
12.4
13.3
122
% change in yield after adaptation
4.16.2 A2 2080 projected climate change scenario The application of additional 50% dose of fertilizer under A2 2080 projected climate change scenario resulted in nearly 3.0, 3.0 and 2.2 per cent gain in seed yield of mustard in InfoCrop; and 2.4, 2.0 and 2.0 per cent gain by using WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively (Table 4.40). There will be nearly 2.7 and 2.1 per cent gain in mustard yield using InfoCrop and WOFOST models. The per cent gain in seed yield of mustard crop by 50% extra dose of fertilizer with organic manure under projected period of A2 2080 was simulated by InfoCrop and WOFOST models and results are presented in Table 4.40. This adaptation measures gave 7.0, 8.7 and 7.1 per cent gain in seed yield of mustard varieties by InfoCrop model and 3.5, 5.0 and 4.4 per cent gain by WOFOST model for RH 30, Laxmi and RH 0749, respectively. The results showed that by this adaptation 7.6 and 4.3 per cent gain in mustard yield by InfoCrop and WOFOST model under A2 2080 projected climate change scenario. The application of supplementary additional irrigation at 50% flowering to mustard crop resulted in nearly 10.1, 11.0 and 10.6 per cent gain in seed yield by InfoCrop, whereas, WOFOST model simulated 7.8, 8.1 and 9.8 per cent gain for varieties RH 30, Laxmi and RH 0749, respectively. There was a gain in the seed yield of mustard crop by this adaptation measures using InfoCrop model (10.6 per cent) and WOFOST model (8.7 per cent) and the results are under acceptable range. The InfoCrop model simulated more gain in seed yield than WOFOST model (Table 4.40). The per cent gain in seed yield of mustard crop by improved variety with irrigation management and increased fertilizer dose under projected period of A2 2080 was simulated by using InfoCrop and WOFOST models and results are presented in Table 4.40. This adaptation measures resulted in 12.2, 13.7 and 15.2 per cent gain in seed yield of mustard crop by InfoCrop model; and 9.9, 11.0 and 11.5 per cent gain by WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. There was 13.7 and 10.8 per cent gain in mustard yield using InfoCrop and WOFOST model by this adaptation measures under A2 2080 projected climate change scenario. It was concluded that improved variety with irrigation management and increased fertilizer dose prove more beneficial than other adaptation measures during critical growth stages of mustard during A1b 2080 and A2 2080 projected climate change scenarios at normal sown. The A2 2080 scenario has more reduction in yield even after adaptation as compared to A1b 2080 scenario.
123
Table 4.40: Projected gains in seed yield of mustard varieties with different adaptation options during 2nd fortnight of Oct. sowing under A2 2080 projected climate change scenario InfoCrop model Projected mean seed yield (kg ha-1) Particulars
% reduction from baseline
RH 30
Laxmi
RH 0749
RH 30
Laxmi
RH 0749
1612.7
1789.3
2009.2
-37.37
-34.65
-32.97
Adaptation options
Gain in seed yield (kg ha-1) after adaptation
50 % extra dose of fertilizer
1661.0
1843.0
2053.4
3.0
3.0
2.2
50 % extra dose of fertilizer and organic manure
1725.5
1945.0
2151.9
7.0
8.7
7.1
Additional irrigation application at 50 % flowering
1755.5
1986.2
2222.2
10.1
11.0
10.6
1809.4
2034.5
2314.6
12.2
13.7
15.2
With improved variety, irrigation management and increased fertilizer dose
% change in yield after adaptation
WOFOST model Projected mean seed yield (kg ha-1) Particulars Adaptation options
RH 30
Laxmi
1499.9
1593.4
% reduction from baseline
RH 0749
RH 30
Laxmi
RH 0749
1804.5
-40.74
-39.34
-36.86
-1
Gain in seed yield (kg ha ) after adaptation
% change in yield after adaptation
50 % extra dose of fertilizer
1535.9
1625.3
1840.6
2.4
2.0
2.0
50 % extra dose of fertilizer and organic manure
1552.4
1673.1
1883.9
3.5
5.0
4.4
Additional irrigation application at 50 % flowering
1616.9
1722.5
1981.4
7.8
8.1
9.8
1648.4
1768.7
2012.0
9.9
11.0
11.5
With improved variety, irrigation management and increased fertilizer dose
124
4.17 Net vulnerability analysis of climate change on mustard Once impact of climate change on mustard production system were analyzed, it was imperative that the adaptation capacity, which helps in assessing the vulnerability of the system, is also worked out and, are presented in Tables 4.41 and 4.42. The mustard yield at all adaptation measures was compiled to calculate the adaptation gains. For this, net yield gain due to best possible adaptation strategy at each scenario was calculated and then expressed as the relative change from the mean impact yield. Thereafter, net vulnerability in respective scenario was derived by subtracting the adaptation gains from the impact yield. 4.17.1 A1b 2080 projected climate change scenario The mustard yield is vulnerable to climate change impact after the application of 50% extra dose fertilizer nearly -14.3, -10.3 and 10.8 per cent using InfoCrop model; and -20.1, -16.9 and -15.0 per cent by WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively (Table 4.41). The results showed that there are net vulnerable nearly -11.8 and -17.3 per cent using InfoCrop and WOFOST models in mustard yield under A1b 2080 projected climate change scenario. The vulnerability analysis in adaptation of 50% extra dose of fertilizer with organic manure under projected period of A1b 2080 was simulated by using InfoCrop and WOFOST models and results are presented in Table 4.41. The vulnerability in mustard yield under this adaptation was -11.1, -8.3 and -6.8 per cent by using InfoCrop model and -15.6, -12.4 and -12.6 per cent by WOFOST model for RH 30, Laxmi and RH 0749, respectively. The average vulnerability of seed yield in mustard crop was -8.7 and -13.5 per cent by using InfoCrop and WOFOST model under A1b 2080 projected climate change scenario. The application of supplementary additional irrigation at 50% flowering to mustard against the climate change, the vulnerability in mustard yield was nearly -6.2, 4.8 and -0.9 per cent by InfoCrop, whereas, WOFOST model simulated -11.9, -9.7 and 7.7 per cent for varieties RH 30, Laxmi and RH 0749, respectively. Overall, net vulnerability by this adaptation measures using InfoCrop and WOFOST model are -4.0 and -9.8 per cent, respectively. The WOFOST model showed more net vulnerability in seed yield against the climate change (Table 4.41). The net vulnerability assessment of mustard yield by adaptation of improved variety with irrigation management and increased fertilizer dose under projected period of A1b 2080 was simulated by InfoCrop and WOFOST models and results are presented in Table 4.41. The vulnerability in mustard yield under this adaptation was -3.5, -0.3 and -3.7 per cent by using InfoCrop model and -8.0, -6.4 and -4.0 per cent by WOFOST model for RH 30, Laxmi and RH 0749, respectively. So, the net vulnerability of seed
125
yield in mustard crop was -2.5 and -6.1 per cent by using InfoCrop and WOFOST model under A1b 2080 projected climate change scenario. 4.17.2 A2 2080 projected climate change scenario The mustard seed yield is vulnerable to climate change impact after the application of 50% extra dose fertilizer nearly -34.4, -31.7 and -30.8 per cent using InfoCrop model; and -38.3, -37.3 and -34.9 per cent by WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively (Table 4.42). The net vulnerability in mustard seed yield is nearly -32.3 and -36.8 per cent using InfoCrop and WOFOST models under A2 2080 projected climate change scenario. The vulnerability analysis in adaptation of 50% extra dose of fertilizer with organic manure under projected period of A2 2080 was simulated by using InfoCrop and WOFOST models and results are presented in Table 4.42. The vulnerability in mustard yield under this adaptation was -30.4, -26.0 and -25.9 per cent by using InfoCrop model and -37.2, -34.3 and -32.5 per cent by WOFOST model for RH 30, Laxmi and RH 0749, respectively. The net vulnerability of seed yield in mustard was 27.4 and -34.7 per cent by using InfoCrop and WOFOST model under A2 2080 projected climate change scenario. The application of supplementary additional irrigation at 50% flowering to mustard against the climate change, the vulnerability was nearly -27.3, -23.7 and -22.4 per cent by InfoCrop, whereas, WOFOST model simulated -32.9, -31.2 and -27.1 per cent varieties cv. RH 30, Laxmi and RH 0749, respectively. The net vulnerability by this adaptation measures using InfoCrop and WOFOST model are -24.4 and -30.4 per cent, respectively. The WOFOST model observed more net vulnerability in seed yield against the climate change (Table 4.42). The net vulnerability assessment of mustard yield by adaptation of improved variety with irrigation management and increased fertilizer dose under projected period of A2 2080 was simulated by using InfoCrop and WOFOST models and results are presented in Table 4.42. The vulnerability in mustard yield under this adaptation was 25.2, -21.0 and -17.8 per cent by using InfoCrop model and -30.8, -28.3 and -25.4 per cent by WOFOST model for RH 30, Laxmi and RH 0749, respectively. The net vulnerability of yield was -21.3 and -28.2 per cent by using InfoCrop and WOFOST model under A2 2080 projected climate change scenario. The improved variety with irrigation management and increased fertilizer dose proved more beneficial measures than other adaptation measures and least vulnerable during A1b 2080 and A2 2080 projected climate change scenarios in normal sown mustard crops. The A2 2080 scenario has more vulnerability to mustard seed yield even after adaptation as compare to A1b 2080 scenario.
126
Table 4.41: Net vulnerability assessment of mustard crop against different adaptation measures during A1b 2080 projected climate change scenario Vulnerability of seed yield (kg ha-1) Vulnerability of seed yield (per cent) Particulars RH 30 Laxmi RH 0749 RH 30 Laxmi RH 0749 InfoCrop model 50 % extra dose of fertilizer -103.8 -156.4 -126.3 -14.3 -10.3 -10.8 50 % extra dose of fertilizer and organic manure -170.3 -201.7 -227.3 -11.1 -8.3 -6.8 Additional irrigation application at 50 % flowering -272.0 -281.1 -373.8 -6.2 -4.8 -0.9 With improved variety, irrigation management and increased fertilizer -328.1 -396.6 -489.9 -3.5 -0.3 -3.7 WOFOST model 50 % extra dose of fertilizer -56.5 -79.2 -114.3 -20.1 -16.9 -15.0 50 % extra dose of fertilizer and organic manure -144.3 -172.9 -169.2 -15.6 -12.4 -12.6 Additional irrigation application at 50 % flowering -216.4 -229.1 -281.2 -11.9 -9.7 -7.7 With improved variety, irrigation management and increased fertilizer -292.4 -297.8 -365.8 -8.0 -6.4 -4.0 Table 4.42: Net vulnerability assessment of mustard crop against different adaptation measures during A2 2080 projected climate change scenario Vulnerability of seed yield (kg ha-1) Vulnerability of seed yield (per cent) Particulars RH 30 Laxmi RH 0749 RH 30 Laxmi RH 0749 InfoCrop model 50 % extra dose of fertilizer
-48.3
-53.7
-44.2
-34.4
-31.7
-30.8
50 % extra dose of fertilizer and organic manure
-112.8
-155.7
-142.7
-30.4
-26.0
-25.9
Additional irrigation application at 50 % flowering
-142.8
-196.9
-213
-27.3
-23.7
-22.4
With improved variety, irrigation management and increased fertilizer
-196.7
-245.2
-305.4
-25.2
-21.0
-17.8
-36
-31.9
-36.1
-38.3
-37.3
-34.9
50 % extra dose of fertilizer and organic manure
-52.5
-79.7
-79.4
-37.2
-34.3
-32.5
Additional irrigation application at 50 % flowering
-117
-129.1
-176.9
-32.9
-31.2
-27.1
-148.5
-175.3
-207.5
-30.8
-28.3
-25.4
WOFOST model 50 % extra dose of fertilizer
With improved variety, irrigation management and increased fertilizer
127
CHAPTER-V
DISCUSSION The experimental findings of the investigation entitled the “Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana” embodied in preceding chapter are being discussed in the present chapter with the help of observations recorded in the present study and findings of other research workers. 5.1 Crop phenology Among growing environments, the treatment D1 (10th Oct. and 21th Oct.) and D2 (25th Oct. and 30th Oct.) took minimum time to complete emergence, whereas, D3 (8th Nov. and 10th Nov.) took maximum time during 2012-13 and 2013-14, respectively (Tables 4.1 and 4.2). This was resulted due to lower temperature experienced by late sowing dates. The days taken to attainment of P2 to P8 were higher in treatments D1 (10th Oct., 2012 and 20th Oct., 2013) as compared to other treatments. This was due to higher temperature experienced in middle and later phenophases under delayed sowing which caused reduction in duration of reproductive phases in both the years. The increase in bright sunshine hours in March, subsequently increased the temperature, which fulfilled its thermal unit requirement early. Moreover, higher temperature during reproduction stage caused supra optimal thermal stress which resulted into forced maturity and reduced crop duration (reproductive phase) significantly under late sown crop. Among the variety RH 0749 took more days to reach physiological maturity compared to other two varieties (Laxmi and RH 30). The effect of these variety in respect of sowing dates, there was not much significant different. The total crop duration of mustard phenophases was shorter in 2013-14 compare to 2012-13 because of delay in sowing dates during 2013-14. The results are in conformity with the findings of Liyong et al. (2007) and Jun et al. (2007). Adak et al. (2011a, b) also found that there was a reduction in total life span of mustard crop when sowing was delayed. 5.2 Plant height Among date of sowings, the plant height at different growth stages was recorded significantly higher in first sowing (D1) than others in 2012-13 and 2013-14 (Tables 4.3 and 4.4). This was probably due to more growing degree consumed by D1 sown crop as compared to delayed sowing. Temperature stress on delayed sown crop during both the crop seasons might lead to this effect. RH 0749 have observed highest plant height followed by Laxmi and lowest in RH 30 during the year 2012-13 and 2013-14. These variations in plant height
among different varieties were due to variation in their genetic constitution Weerakoon and Somaratne (2011) also supported these findings. 5.3 Agrometeorological indices 5.3.1 Growing degree days (GDD) Accumulated GDD were significantly different at emergence among sowing dates during both the crop seasons because number of days taken to emergence was lower in D1 (10th Oct. and 21st Oct.) sowing followed by D2 (25th Oct. and 30th Oct.) and D3 (8th Nov. and 10th Nov.) (Table 4.5, 4.6 and 4.7). These findings were supported by Roy et al. (2005) and Neogi et al. (2005). The accumulated GDD was less at emergence in late sowing in later sowing due to comparatively low temperature (Srivastava et al. (2011). Subsequently, heat unit accumulation was higher under early sown crop at all the growth intervals and phenophases till maturity. This can be due to more growing period available to early sown mustard. The accumulated growing degree days at maturity was recorded higher during 201213 probably due to increased maturity period because of supra-optimal thermal stress as compare to 2013-14. In case of varieties, maximum GDD consumed were by RH 0749 under both the crop seasons followed by Laxmi and RH 30, this may be due to their genetic characters (Si and Walton, 2004). 5.3.2 Photothermal units (PTU) The highest photothermal units were acquired in D1 (10th Oct. and 21th Oct.) and V3 (RH 0749) at all the phenophases among date of sowings and varieties during crop seasons, respectively, (Table 4.8 and 4.9). The photothermal unit values were higher during reproductive phase as compared to vegetative phase in both the crop seasons. This could have been happened because of increased accumulated GDD and less number of days was taken to transform one phase to another crop phase. The photothermal index increased from emergence to physiological maturity and the highest values were recorded at physiological maturity under all the treatments. Similar findings have also been reported by Srivastava et al. (2011). 5.3.3 Heliothermal units (HTU) Heliothermal units accumulated at all the phenophases were also varied in different sowing environments. HTU calculated during Rabi 2012-13 and 2013-14 (Tables 4.10 and 4.11) showed that D1 sown crop accumulated more HTU than D2 and D3 sown crop during both the study periods. In case of cultivars, maximum HTU value obtained in RH 0749 under both the years followed by Laxmi and lowest in case of RH 30. These findings were supported by Kumar et al. (2010a); Kingra and Kaur (2012) and Neogi et al. (2005). HTU accumulation was higher at all growth phases during 2012-13 as compared to 2013-14 due to availability of more mean numbers of sunshine hours in this year.
129
5.3.4 Radiation Use Efficiency (RUE) The highest RUE was recorded in earlier sown crop of D1 and lowest in D3 sowing date in both the crop seasons (Table 4.12 and 4.13). The continue increase in biomass upto 90 DAS resulted in increased RUE, however, a slight fall in RUE at 120 DAS and lowest at physiological maturity was because of slower gain in biomass during this period. The maximum RUE values were obtained in RH 0749 under both the years followed by Laxmi and lowest in case of RH 30. The highest RUE in the earlier sown crop and variety RH 0749 was due to the maximum PAR absorption and then converted into dry matter production, both of which decreased subsequently due to reduction in LAI with delayed sowing during both the crop seasons. The RUE values were accumulation was higher at all growth intervals during 2012-13 as compared to 2013-14. The results of the present investigation are akin to those reported earlier by Pidgeon et al. (2001) in mustard crop. 5.3.5 Thermal Use Efficiency (TUE) Among the varieties, the TUE was higher in RH 0749 followed by Laxmi and minimum in RH 30 during both the crop seasons. This was due to the reason that RH 30 had minimum biomass production among the cultivars under study. This was due was consumed in delayed sown crop. The decrease in TUE with delay in sowing was due to the fact that delayed sowing of mustard crop led to early reproductive phase due to low temperature, less number of accumulated GDD and shorter days experienced by late sown crop.
The
accumulation of TUE was higher at all growth intervals during 2012-13 as compared to 201314. These findings were supported by Roy et al. (2005); Neogi et al. (2005); Singh and Singh (2005); and Singh et al. (2014) and Kingra and Kaur (2012). 5.4 Biomass and its partitioning The partitioning of biomass into different components of mustard varieties as influenced by experimental treatments in two crop seasons are presented in Table 4.14 and 4.15. The dry matter accumulation increased till physiological maturity among all the treatments during both crop seasons. The increase in dry matter of plants was due to increase in plant height, growth and development of plant organs. The highest dry matter was accumulated in D1 (10th Oct. and 21st Oct.) at all the growth intervals till physiological maturity, whereas the minimum dry matter accumulation was recorded in D3 (8th Nov. and 10th Nov.) during both crop seasons. The crop of D1 date of sowing has utilized more solar radiation at early vegetative phase as well as more congenial environment at reproductive phase and grand growth phase which resulted in higher biomass, LAI and better partitioning. However, D3 late sown crop during vegetative phase suffered low temperature and consumed less radiation, whereas, at grand growth phase and reproductive phase due to higher temperature and terminal heat stress resulted into reduced reproductive phase and forced maturity too. The findings of Weerakoon and Somaratne (2011) are also supported these
130
results. Schwarte et al. (2005) also confirmed that delayed planting decrease the dry matter production. In 2013-14, the accumulation of dry matter and its allocation to different plant parts was drastically reduced due to delayed sowing (D3) because of abnormal weather conditions. The highest biomass in earlier sown crop and variety RH 0749 might be due to maximum LAI and more PAR absorption by this variety. Among the varieties, the biomass accumulation in different plant parts of RH 0749 was significantly higher followed by Laxmi and RH 30 in irrespective of the growth intervals during both the years. The highest biomass in earlier sown crop and variety RH 0749 might be due to maximum LAI and more PAR absorption by this variety. The varieties performed same trend of biomass allocation as the sowing dates for the different growth intervals. In all plant parts, biomass accumulation was higher by RH 0749 as compared to other two varieties during year 2012-13 and 2013-14. 5.5 Leaf Area Index (LAI) The LAI is very useful crop growth parameter in studying the radiation interception and quantification of dry matter accumulation. Leaf area index was highly influenced by different growing environments (Figure 4.8). Among three sowing dates, D1 produced highest LAI followed by D2 and D3 sown crop during 2012-13 and 2013-14. The D1 date of sowing has attained maximum value of LAI due to the elongated vegetative phase which added more foliage to the crop as compared to delayed sowing. Comparatively warmer temperature prevailed under delayed sowing induced force maturity by shortening duration of phenophases and ultimately life span of the crop. Among varieties, RH 0749 (V3) has produced maximum LAI followed by Laxmi (V2) and RH 30 (V1) in both the crop seasons. The more PAR absorption was recorded in timely sown crop and RH 0749 due to elongated vegetative phase as compared to delayed sowing and other varieties where shorter days led to the occurrence of reproductive stage quickly and life cycle of the crop became shorter. Gill and Bains (2008) confirms that early sowing enhanced leaf area index and dry matter accumulation over late sown mustard. Nanda et al. (1996), Kumari and Rao (2005) and Tripathi (2005) found that delayed sowing reduced leaf area. 5.6 Radiation studies 5.6.1 Energy balance components over mustard The diurnal pattern of energy balance components namely, net radiation (Rn), latent heat vaporization (LE), sensible heat flux (A) and soil heat flux (G) were studied at three growth stages viz. 50 % flowering (P4), start of seed flowering (P6) and end of seed filling (P7). The results showed that around 61.8 to 74.4 per cent of the net radiation was utilized as LE, 20.8 to 27.8 per cent as sensible heat flux and 3.6 to 10.9 percent as soil heat flux (Table 4.16). The lower values of soil heat flux over a cropped field could be because of the fact that only a small fraction of net radiation reaches to the soil surface as compared to the bare open exposed area in bare field. The values of latent heat flux component were at peak around noon
131
hours in absence of sensible heat advection and showed an increasing trend in the morning and decreasing trend in the evening hours. Sensible heat fluxes (A) remained positive (directed away from the surface) during the daytime. The study further showed that the values of sensible heat flux and soil heat flux were smaller than the latent heat flux throughout the day. Soil heat flux values were also positive except in morning hours due to loss of energy during night as long wave radiation. The sensible heat flux and soil heat flux values were higher in 2013-14. That might be due to more exposure of land area because of less crop canopy establishment and growth of crop as compared to 2012-13. The LE values were slightly higher at 50 % flowering (P4) other than start of seeds filling (P6) and end of seed filling stage (P7) in both the crop seasons this might be happened due to the lower green area due to leaf senescence at later crop growth stages in both seasons. The higher LAI, better crop growth and canopy development in the early sown crop (D1) than the late sown crop. The sensible heat flux (A) values were more in D3 because of poor canopy development owing inferior growth parameters resulting in open canopy which shown in the Table 4.16. These results were supported by Awal et al., 2006 and Singh et al., 2012. 5.6.2 Light extinction coefficient (k) The values of k calculated for different treatments are presented in Table 4.17. Among the different sowing dates, the highest values of k were observed in D1 sowing at 50% flowering stage (P4), whereas, lowest values of k were found in D3 sown crop at end of seed filling stage (P7). The k values were observed less in 2013-14 as compare to 2012-13 crop season. This can be expected because of the poorer crop establishment and lesser values of LAI in 2013-14. The k values increase till maximum LAI resulted into higher light interception and decrease till the crop reached to maturity. Among the varieties, RH 0749 have highest values of k as compared to other varieties during both the crop seasons. This might be due to the plant architecture and lower LAI attained by Laxmi followed by RH 30. Similar findings have also been reported by Singh et al., 2012. 5.6.3 Optical characteristic Optical characteristics of solar radiation viz., transmitted (T), reflected (R) and absorbed (A) of mustard crop recorded at different growth intervals are presented in Table 4.18. Absorption of radiation increased from 50 % flowering (P4) to start of seed filling (P6) and thereafter, decreased up to end of seed filling (P7) in all the sowing dates during 2012-13 and 2013-14. The poor crop growth and establishment because of delayed sowing and further, abnormal weather led to poor absorption of radiation during year 2013-14 as compare to 2012-13. Maximum absorption (%) and less transmission (%) were recorded in D1 date sown crop as comparedd to D2 and D3 dates of sowing which might be due to maximum leaf area index in D1 sown crop. The variety RH 0749 absorbed maximum PAR followed by Laxmi and RH 30. Over the bare field, the transmitted radiation remained between 86.0 to 88 per
132
cent. The absorption radiation was between 3.6 to 5.9 percent and reflected radiation between 7.8 to 10.2 percent. Monga (2009) also reported the same radiation pattern in tomato crop under different sowing environments. 5.7 Yield and yield attributes The yield of any crop species depends upon the source-sink relationship and is the cumulative function of various growth parameters and yield attributing characters viz., maximum number of primary and secondary branch at harvest, siliquae length (cm), number of siliquae per plant, number of seed per siliquae, seed yield per plant (g), 1000-seed weight (g), seed yield (q ha-1), biological yield (q ha-1), harvest index, oil content and oil yield (q ha1
). Stronger source is required to develop stronger sink. The partitioning of biomass in
economic and non-economic plant parts is affected by the change in environment of plant, due to treatment effect. The late planted crop was adversely affected during the reproductive phase because of supra-optimal thermal stress (ºC) during 2012-13 and 2013-14 (Fig. 4.2) and excess precipitation (mm) and cloudiness (SS) during 2013-14. The high temperature during reproductive phase with lesser days available for occurrence of various phenophases induced forced maturity. It might be probable reason for reduced number of primary and secondary branches, number of seed per siliquae, siliquae length and siliquae per plant under late sowing. The reason for increased number of siliquae and its size was the most sensitiveness to temperature (Weerakoon and Somaratne, 2011). Among the yield attributing characters i.e., primary and secondary branch at harvest, siliquae length, number of siliquae per m2, number of seed per siliquae, test weight, were found highest in D1 (10th Oct. and 21st Oct.) sowing treatment during both crop seasons (Table 4.19 and 4.20). This might be due to the fact that D1 sown crop produced higher LAI (Fig. 4.8) which resulted in higher values of agrometeorological indices (Table 4.5 and 4.13), thereby more biomass production as compared to other treatments (Table 4.14 and 4.15). Similar finding were also observed by Robertson et al. (2002), Poureisa and Nabipour (2007) and Singh et al. (2002) in B. napus. Among sowing dates, D1 (10th Oct. and 21st Oct.) recorded highest seed yield (33.6 and 29.2 q ha-1 in 2012-13 and 2013-14, respectively) and biological yield (151.3 and 147.7 q ha-1 in 2012-13 and 2013-14, respectively) (Table 4.19 and 4.20). Significantly, higher biomass production and harvest index in RH 0749 was due to comparatively longer crop mutually period and better partitioning of biomass to economic sink, respectively. The increased yield in D1 sowing attributed to early emergence, taller plants, higher LAI, and more number of siliquae per m2 in 2012-13 and 2013-14 than D2 and D3. Yield decreased due to delay in sowing was due to less PAR interception, low accumulated GDD and shorter seed filling period in both the years. The similar kind of finding by Poureisa and Nabipour (2007) and Singh et al. (2002) revealed that the yielding ability of a crop is dependent on investment
133
of a greater proportion of biomass and yield to variation in their edaphic and environmental conditions, which was achieved through change in sowing dates. 5.8 Correlation between different meteorological with mustard growth parameters and yield Correlation between different meteorological and mustard growth parameters (Table 4.22) and yield (Table 4.23 to 4.25) traits revealed positive related with maximum and minimum temperature, pan evaporation. More bright sunshine hours also directly related to higher air temperature and thus prove inverse relationship for crop production. Rest of the meteorological parameters (RHm, RHe, WS and SS) was negatively correlated with growth and yield parameters of mustard crop. Highest association was observed during reproductive phase for all the variables. This was due to more influence of weather parameters on mustard crop during reproductive phase. Above findings were supported by Roy et al., 2005; Singh and Singh 2005 and Kalra et al., 2008. 5.9 Validation of models performance for phenology, growth and yield parameters The 50% flowering, days taken to maturity, test weight, seed yield, biological yield and harvest index were over estimated, whereas, LAI was under estimated by the InfoCrop and WOFOST models for Hisar conditions among all treatments during 2012-13 and 2013-14 (Table 4.28 to 4.34). These different parameters were simulated higher in case of 2012-13 as compare to 2013-14. The different statistical indices viz., mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), ability of model (R), tendency of model (V) index of agreement (D-index) and per cent error (PE) were computed for both the models and found satisfactory under acceptable range. The InfoCrop perform better as compare to WOFOST model during both the crop seasons for Hisar. So InfoCrop model simulation was more reliable than WOFOST for all the mustard varieties because InfoCrop model simulates basic physiological processes and the genetic coefficients more adaptable to specific conditions. These validation results were supported by the findings of Kumar et al. (2015); Adak et al. (2009); Choudhary et al. (2014a,b) for InfoCrop model and Shekhar et al. (2008); Mukherjee et al. (2011); Confalonieri et al. (2009); Catalin et al. (2009); Mishra et al. (2013) for WOFOST model. 5.10 Sensitivity analysis of InfoCrop and WOFOST model to weather and non-weather parameters To check the sensitivity of InfoCrop and WOFOST models, combined effect of change in mean ambient temperature (±1 to ± 5°C) and different levels of CO2 concentration (base value, 400, 450, 500, 550, 600, 650 and 700 ppm) was carried out and presented in Figure 4.9 and 4.10. The positive benefits was obtained upto 500 ppm but further increase in CO2 (550, 600, 650 and 700 ppm) combined with one unit increase in mean ambient temperature reduced the percent change in mustard yield. The interaction effect of
134
temperature and CO2 concentration revealed that the response under variety Laxmi was quite higher followed by RH 0749 and RH 30. The rate of photosynthesis in relation to temperature forms a bell curve. Enzymes responsible for photosynthesis are inactive at low temperature and increasing of temperature resulted reduction of mustard yield due to energy loss in maintenance of respiration and other plant processes. Increasing the surface air temperature results in reducing the total duration of mustard crop by inducing early flowering and shortening the pod fill period (Butterfield and Morison, 1992). The shorter the crop duration, lower is the yield per unit area; a warmer atmosphere at later stage should therefore lead to reduced mustard productivity. At highest CO2 concentration (700 ppm), the negative impact of temperature was simulated only at -1°C in all the mustard varieties. Rising of carbon dioxide concentration can be sensed by plant tissues, which are directly in contact with atmosphere. In general an increase in carbon dioxide concentration was found to increase yield while increase in temperature reduced yield (Matthews et al., 2002). Increase in yield was due to the increase in photosynthesis resulting from higher carbon dioxide concentration. C3 crops (i.e. rice, wheat, mustard) respond more to carbon dioxide enrichment than C4 crops (i.e. maize, sorghum, sugarcane). But photosynthetically, these plants (C3) are underachievers because, on the one hand, they assimilate atmospheric carbon dioxide into CH2O but, on the other, part of the potential for CH2O production is lost by respiration in daylight, releasing carbon dioxide into the atmosphere, a wasteful process termed photorespiration (Ku, 2000). Several reviews have exposed that the above increase in photosynthetic rates is translated to increase in biomass production and grain yield of mustard crop (Lawlor and Mitchell, 1991; Jablonski et al., 2002). These analyses were match with the findings of Kadam et al. (2014); Akula (2005a); Mukherjee et al. (2011); Khan et al. (2009); Ruhil et al. (2015) and Mishra et al. (2015). Another option for analysis of these models sensitivity were check with the combined effect of change in mean ambient temperature (±1 to ±5°C) and percent change in rainfall (30 to +40%) from actual study periods (Figure 4.11 and 4.12). Under increasing of 20% rainfall scenario, increase in the yield levels are higher in RH 0749 followed by Laxmi and lowest in RH 30 using both crop simulation models. The higher benefits was obtained at +20% rise in rainfall but further decrease or increase (-30 to -10% and +30 to +40%) in rainfall combined with change from ±3 to ±5°C in mean ambient temperature reduced the percent change in mustard yield. The interaction effect of temperature and rainfall change revealed that the response of variety RH 0749 was quite higher followed by RH 30 and Laxmi. The results were supported by Catalin et al. (2009); Kadam et al. (2014) and Mishra et al. (2015).
135
5.11 Climatic variability; trend of maximum, minimum temperature and rainfall at Hisar The trends of maximum temperature decreasing during winter, summer, postmonsoon, Kharif, Rabi and annual period, whereas, increasing trend were evaluated during monsoon, baseline under regression analysis. However, Theil-Sen’s analysis shown decreasing trend during all the season and periods except monsoon season, where it found increasing trend. The above study of maximum temperature trend and variability were also evaluated by Lal (1993); Kothawale et al. (2005); Choudhary et al. (2014a, b); Dhorde et al. (2009) and Chinchorkar et al. (2015). The regression analysis shows that trends of minimum temperature increasing during winter, summer, monsoon, post-monsoon, Kharif, Rabi, baseline and annual were 0.2066, 0.0348, 0.0159, 0.0181, 0.0163, 0.0163, 0.0297 and 0.0122ºC/year, respectively. Theil-Sen’s analysis also shown increasing trend during all the season and periods were 0.0127, 0.0160, 0.0164, 0.0191, 0.0143, 0.0143, 0.0337 and 0.0100ºC/year, respectively. The similar results were also reported by Singh et al. (2008); Choudhary et al. (2014a, b); Bhutiyani et al., (2007); Dash and Hunt (2007). The values of standard deviation (S.D.) in rainfall over the seasons i.e. winter, summer, monsoon, post monsoon, Kharif, Rabi, baseline (1970 to 1990) and annual were 23.95, 65.84, 146.53, 28.72, 149.74, 47.02, 149.41 and 163.40 mm and the coefficient of variance were 68.62, 58.73, 46.83, 225.16, 46.45, 76.83, 0.34 and 34.56 percent, respectively (Table 4.37). The trends of rainfall increasing during winter, summer, monsoon, Kharif, Rabi, and annual were 0.0124, 1.6116, 0.5634, 0.7340, 0.4110, 2.3588 mm/year, respectively, whereas, decreasing during post-monsoon and baseline were -0.0197 and -2.5380 mm/year, respectively. However, Theil-Sen’s analysis also showed increasing trend during all the season and periods except baseline (-2.4833 mm/year). The post-monsoon season has no trend of rainfall in Theil-Sen’s analysis. The similar results were found for the rainfall trend and variability by Singh and Sontakke, 2004; Choudhary et al. (2014a, b); De and Rao, 2004; Chinchorkar et al. (2015); Kumar et al., (2010) and Dash and Hunt, 2007. 5.12 PRECIS generated weather conditions during projected periods Average annual maximum and minimum temperature for the projected period A1b 2030 (2020-2049) is likely to be higher than the baseline period temperature i.e. 2.2 and 3.3ºC, respectively. The rate of rise of maximum and minimum temperature was 0.0062 and 0.0075ºC/year, respectively (Fig. 4.13 and Table 4.38). The PRECIS generated rainfall results showed that Hisar will receive 16.1 per cent higher rainfall during projected period A1b 2030 against their respective baseline (1970-1990) period (Fig. 4.13). The average mean annual rainfall was estimated of 516.1mm for projected period. CO2 concentration has significant relation with projected period. The projected A1b 2030 scenarios CO2 concentration is likely
136
to increase 87.0ppm from its baseline period and reach up to 447.0ppm, which is 24.2 per cent higher (Table 4.38). Results showed that there will be mean rise of maximum and minimum temperature to the tune of 15.7 and 36.6 per cent, respectively for the projected period A1b 2080 against the baseline periods (1970-90) (Fig. 4.14). The rate of rise of maximum and minimum temperature was 0.0061 and 0.0077ºC/year, respectively (Table 4.38). The rainfall results showed that Hisar will receive 20.6 per cent higher rainfall during projected period A1b 2080 against. The average mean annual rainfall was estimated of 535.9mm for projected period. The CO2 concentration has significant relation with the projected period. According to A1b 2080 scenarios CO2 concentration is likely to increase 279.0ppm from its baseline period and reach up to 639.0ppm, which is 77.5 per cent higher. During projected period A2 2080, there will be mean rise of maximum and minimum temperature to the tune of 18.3 and 38.3 per cent, respectively against the baseline periods (Fig. 4.13). The rate of rise of maximum and minimum temperature was 0.0061 and 0.0076ºC/year, respectively. PRECIS generated rainfall results showed that Hisar will receive -2.8 per cent lower rainfall during projected period A2 2080. The average mean annual rainfall was estimated of 432.2mm for projected period. The CO2 concentration under A2 2080 scenarios is likely to increase 322.0 ppm from its baseline period and reach up to 682.0 ppm, which is 89.4 per cent higher (Table 4.38). Xu et al. (2006); Marengo and Ambrizzi (2006); Jones et al. (2004); Rupakumar et al. (2006); Taylor et al. (2007); Kumar et al. (2014); Fischer et al. (2012) and Karmalkar et al. (2008) were also evaluated the similar results for different RCM and PRECIS model for the projected climate change scenarios to generate the future climate. 5.13 Impact on mustard phenology, yield attributing characters and seed yield under projected climate change scenarios 5.13.1 Days taken to 50% flowering The fluctuation in days of 50% flowering were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods (Figures 4.16 to 4.19). The highest days taken to 50% flowering were recorded in D3 (10th Nov.) sowing followed by D2 (25th Dec.) and lowest in D1 (10th Oct.). The highest reduction was found in A2 2080 projected climate change scenario in all the sowing dates and by both the models. Among the varieties, RH 0749 have taken more days to 50% flowering as compared to RH 30 and Laxmi under baseline and projected periods. The InfoCrop model simulated the variation in days taken in 50% flowering in all the projected climate change scenarios and the varieties i.e. A1b 2030 (RH 30: 0.5, Laxmi: 1.4 and RH 0749: 2.3%); A1b 2080 (RH 30: -24.4, Laxmi: -22.6 and RH 0749: -20.8%) and A2 2080 (RH 30: -42.1, Laxmi: -40.1 and RH 0749: -38.0%), whereas, WOFOST model results were showed reduction i.e. A1b 2030 (RH 30: 0.7, Laxmi: 2.2 and
137
RH 0749: 3.0%); A1b 2080 (RH 30: -25.5, Laxmi: -24.4 and RH 0749: -21.9%) and A2 2080 (RH 30: -43.2, Laxmi: -40.4 and RH 0749: -39.4%) as compare to baseline period. The comparison of both the models revealed that InfoCrop model overestimated the days taken to 50% flowering as compared to WOFOST; however, both the models simulated the 50% flowering under acceptable range (Figures 4.18 and 4.19). Choudhary et al. (2014c, d) at Anand for Kharif and Rabi maize at Anand were also simulated the similar trend for anthesis. 5.13.2 Days taken to physiological maturity The variation in days of physiological maturity were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods (Figures 4.20 to 4.23). The highest days taken to physiological maturity was recorded in D3 (10th Nov.) sowing followed by D2 (25th Oct.) and lowest in D1 (10th Oct.). The highest reduction was found in A2 2080 projected climate change scenario in all the sowing dates and by both the models. Among the varieties, RH 0749 have taken more days to physiological maturity as compared to RH 30 and Laxmi under baseline and projected periods. The InfoCrop model simulated results showed fluctuation in days taken in physiological maturity in all the projected climate change scenarios and the varieties i.e. A1b 2030 (RH 30: 0.4, Laxmi: 1.3 and RH 0749: 2.2%); A1b 2080 (RH 30: -23.8, Laxmi: -19.6 and RH 0749: -18.9%) and A2 2080 (RH 30: 40.2, Laxmi: -39.2 and RH 0749: -36.3%), whereas, WOFOST model results were showed reduction i.e. A1b 2030 (RH 30: 0.6, Laxmi: 1.4 and RH 0749: 2.2%); A1b 2080 (RH 30: 27.2, Laxmi: -24.9 and RH 0749: -22.9%) and A2 2080 (RH 30: -42.2, Laxmi: -40.6 and RH 0749: -38.3%) as compare to baseline period. The comparison of both the models revealed that InfoCrop model overestimated the days taken to physiological maturity as compared to WOFOST model (Figures 4.22 and 4.23). Choudhary et al. (2014c, d) at Anand for kharif and rabi maize at Anand were also simulated the similar trend for days taken to physiological maturity. 5.13.3 Leaf Area Index The deviation in LAI were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods (Figures 4.24 to 4.27). The highest LAI were recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.). Under A1b 2030, the maximum LAI value was simulated almost similar to baseline. Among the varieties, RH 0749 has showed more LAI as compared to RH 30 and Laxmi under baseline and projected periods. The InfoCrop model simulated results showed fluctuation in LAI in all the projected climate change scenarios and the varieties i.e. A1b 2030 (RH 30: 0.9, Laxmi: 1.2 and RH 0749: 1.8%); A1b 2080 (RH 30: -23.7, Laxmi: -20.1 and RH 0749: 19.8%) and A2 2080 (RH 30: -42.2, Laxmi: -40.9 and RH 0749: -40.2%), whereas, WOFOST model results were also showed reduction in LAI i.e. A1b 2030 (RH 30: 0.0, Laxmi: 0.8 and RH 0749: 2.0%); A1b 2080 (RH 30: -26.4, Laxmi: -24.7 and RH 0749: -21.0%) and A2 2080
138
(RH 30: -43.7, Laxmi: -42.2 and RH 0749: -41.1%) as compare to baseline period (Figures 4.26 to 4.27). The comparison of both the models revealed that InfoCrop model has predicted higher maximum LAI value as compared to WOFOST model; however, both the models have given the results under acceptable range. Similar results for LAI were obtained by Rao et al. (2010) for maize at Andhra Predesh and Singh et al. (2010) for wheat at IARI, New Delhi. 5.13.4 Test weight (TW) The variation in test weight were seen during projected A1b 2030, A1b 2080 and A2 2080 in all the cultivars as compared to baseline periods (Figures 4.28 to 4.31). The highest test weight was recorded in D1 sowing followed by D2 and lowest in D3. Under A1b 2030, the test weight value was simulated almost less to baseline. Among the varieties, RH 0749 has showed more test weight as compared to RH 30 and Laxmi under baseline and projected periods. The InfoCrop model simulated results showed fluctuation in test weight in all the projected climate change scenarios and the varieties i.e. A1b 2030 (RH 30: -2.7, Laxmi: -2.4 and RH 0749: -0.7%); A1b 2080 (RH 30: -26.1, Laxmi: -25.0 and RH 0749: -22.8%) and A2 2080 (RH 30: -35.1, Laxmi: -33.5 and RH 0749: -30.3%), whereas, WOFOST model results showed reduction in test weight i.e. A1b 2030 (RH 30: -3.1, Laxmi: -2.8 and RH 0749: 0.9%); A1b 2080 (RH 30: -28.7, Laxmi: -27.3 and RH 0749: -25.8%) and A2 2080 (RH 30: 41.6, Laxmi: -40.1 and RH 0749: -39.2%) as compare to baseline period (Figures 4.30 to 4.31). The comparison of both the models revealed that InfoCrop model overestimated the test weight as compared to WOFOST model. Choudhary et al. (2014c, d) at Anand for Kharif and Rabi maize at Anand were also simulated the similar trend for test weight. Similar results for test weight were obtained by Singh et al. (2010) for wheat at IARI, New Delhi. 5.13.5 Seed yield The fluctuation in seed yield were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods. The highest seed yield was recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.) (Figures 4.32 to 4.35). Under A1b 2030, the seed yield were simulated almost similar to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario in all the sowing dates and by both the models. Among the varieties, RH 0749 has showed more seed yield followed by Laxmi and lowest in RH 30 under baseline and projected periods. The InfoCrop model simulated results showed fluctuation in seed yield in all the projected climate change scenarios and the varieties i.e. A1b 2030 (RH 30: 1.4, Laxmi: 2.3 and RH 0749: 3.2%); A1b 2080 (RH 30: -19.3, Laxmi: -17.2 and RH 0749: -15.7%) and A2 2080 (RH 30: -37.4, Laxmi: -34.6 and RH 0749: -33.0%), whereas, WOFOST model results showed reduction in seed yield i.e. A1b 2030 (RH 30: -0.2, Laxmi: 1.6 and RH 0749: 2.8%); A1b 2080 (RH 30: -23.0, Laxmi: -20.7 and RH 0749: -20.0%) and A2 2080 (RH 30: -40.7, Laxmi: -39.3 and RH 0749: -36.9%) as compare to baseline period. The comparison of both the models revealed that
139
InfoCrop model overestimated the seed yield as compared to WOFOST model; however, both the models have given the results under acceptable range. Similar results for yield reduction had been obtained by Chaudhari et al. (2009) reported similar results for wheat and rice at Rajasthan and M.P.; Singh et al. (2010) for irrigated maize and wheat crops respectively at IARI, New Delhi and Rao et al. (2010) at Hyderabad also reported same results for maize yield. Choudhary et al. (2014c,d) at Anand for kharif and rabi maize at Anand were also simulated the similar trend for seed yield. 5.13.6 Biological yield The variation in biological yield were seen during A1b 2030, A1b 2080 and A2 2080 in all the varieties as compared to baseline periods. The highest biological yield was recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Oct.) (Figures 4.36 to 4.39). Under A1b 2030, the biological yield were simulated almost similar to baseline, whereas, highest reduction was found in A2 2080 projected climate change scenario in all the sowing dates by both the models. Among the varieties, RH 0749 has showed higher biological yield followed by Laxmi and lowest in RH 30 under baseline and projected periods. The InfoCrop model simulated results showed fluctuation in biological yield in all the projected climate change scenarios and the varieties i.e. A1b 2030 (RH 30: 0.3, Laxmi: 1.1 and RH 0749: 2.3%); A1b 2080 (RH 30: -24.8, Laxmi: -22.8 and RH 0749: -21.6%) and A2 2080 (RH 30: -44.2, Laxmi: -42.1 and RH 0749: -40.5%), whereas, WOFOST model results showed reduction in biological yield i.e. A1b 2030 (RH 30: -1.0, Laxmi: 0.6 and RH 0749: 1.6%); A1b 2080 (RH 30: -26.3, Laxmi: -24.3 and RH 0749: -22.2%) and A2 2080 (RH 30: -46.1, Laxmi: -43.9 and RH 0749: -40.6%) as compare to baseline period. The comparison of both the models revealed that InfoCrop model overestimated the biological yield as compared to WOFOST model; however, both the models given the results under acceptable range. Similar results for biological yield reduction had been obtained by Singh et al. (2010) for irrigated maize and wheat crops respectively at IARI, New Delhi; Choudhary et al. (2014c, d) at Anand for maize; Rao et al. (2010) at Hyderabad for maize yield; and Chaudhari et al. (2009) reported similar results for wheat and rice at Rajasthan and M.P. 5.14 Adaptation measures study The different adaptation measures was tried for yield enhancement of different mustard varieties under sown 2nd fortnight of Oct. (10th Oct.) during A1b 2080 and A2 2080 projected climate change scenarios and results are presented in Table 4.39 and 4.40, respectively. 5.14.1 A1b 2080 projected climate change scenario Results showed that by applying additional 50% dose of fertilizer gave nearly 5.0, 6.9 and 5.0 per cent increase in seed yield in InfoCrop; and 2.9, 3.8 and 5.0 per cent by using WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. The adaptation
140
measures of 50% extra dose of fertilizer with organic manure has given 8.2, 8.9 and 9.0 per cent increase in seed yield by using InfoCrop model and 7.4, 8.3 and 7.4 per cent increase in seed yield by WOFOST model for RH 30, Laxmi and RH 0749, respectively. The application of supplementary additional irrigation at 50% flowering to mustard resulted in nearly 13.1, 12.4 and 14.8 per cent increase in seed yield by InfoCrop, whereas, WOFOST model simulated 11.1, 11.0 and 12.3 per cent increase in seed yield for varieties RH 30, Laxmi and RH 0749, respectively. The percent gain in yield by improved variety with irrigation management and increased fertilizer dose gave 15.8, 17.5 and 19.4 per cent increase in seed yield by using InfoCrop model; and 12.0, 12.4 and 13.3 per cent increase by WOFOST model for RH 30, Laxmi and RH 0749, respectively. The similar trend of results were found by Kumar et al. (2010b); Singh et al. (2010); Byjesh et al. (2010); Choudhary et al. (2015) and Khan (2009) for different agronomic adaptation measures. 5.14.2 A2 2080 projected climate change scenario The application of additional 50% dose of fertilizer has given nearly 3.0, 3.0 and 2.2 per cent increase in seed yield by InfoCrop; and 2.4, 2.0 and 2.0 per cent increase by using WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. The adaptation measures of 50% extra dose of fertilizer with organic manure resulted in 7.0, 8.7 and 7.1 per cent increase in seed yield by using InfoCrop model and 3.5, 5.0 and 4.4 per cent increase by WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. The application of supplementary additional irrigation at 50% flowering to mustard gave nearly 10.1, 11.0 and 10.6 per cent increase in seed yield by InfoCrop, whereas, WOFOST model simulated 7.8, 8.1 and 9.8 per cent increase for varieties RH 30, Laxmi and RH 0749, respectively. The improved variety with irrigation management and increased fertilizer dose adaptation measures gave 12.2, 13.7 and 15.2 per cent increase in seed yield by using InfoCrop model; and 9.9, 11.0 and 11.5 per cent increase by WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. Results were in good conformity with the findings of Kumar et al. (2010b); Henchanova (1988); Agustin (2006); Singh et al. (2010); Byjesh et al. (2010); Conde et al. (1997); Choudhary et al. (2015) and Khan (2009) for different adaptation measures. 5.15 Net vulnerability analysis of climate change on mustard The vulnerability analysis results for different mustard varieties under normal sown (10th Oct.) during A1b 2080 and A2 2080 projected climate change scenarios are presented in Table 4.39 and 4.40, respectively. 5.15.1 A1b 2080 projected climate change scenario The mustard yield is vulnerable to climate change impact after the application of 50% extra dose fertilizer nearly -14.3, -10.3 and -10.8 per cent using InfoCrop model; and -20.1, 16.9 and -15.0 per cent by WOFOST model for varieties RH 30, Laxmi and RH 0749,
141
respectively. The vulnerability in mustard yield under 50% extra dose of fertilizer with organic manure adaptation was -11.1, -8.3 and -6.8 per cent by using InfoCrop model and 15.6, -12.4 and -12.6 per cent by WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. The application of supplementary additional irrigation at 50% flowering to mustard not cope against the climate change and vulnerability was nearly -6.2, -4.8 and -0.9 per cent by InfoCrop, whereas, WOFOST model simulated -11.9, -9.7 and -7.7 per cent for varieties RH 30, Laxmi and RH 0749, respectively. The vulnerability in mustard yield under improved variety with irrigation management and increased fertilizer dose adaptation was 3.5, -0.3 and -3.7 per cent by using InfoCrop model and -8.0, -6.4 and -4.0 per cent by WOFOST model for RH 30, Laxmi and RH 0749, respectively. So, the net vulnerability of yield was -2.5 and -6.1 per cent by using InfoCrop and WOFOST model under A1b 2080 projected climate change scenario. 5.15.2 A2 2080 projected climate change scenario The mustard yield is vulnerable to climate change impact after the application of 50% extra dose fertilizer nearly -34.4, -31.7 and -30.8 per cent using InfoCrop model; and -38.3, 37.3 and -34.9 per cent by WOFOST model for varieties RH 30, Laxmi and RH 0749, respectively. The vulnerability in mustard yield under 50% extra dose of fertilizer with organic manure adaptation was -30.4, -26.0 and -25.9 per cent by using InfoCrop model and 37.2, -34.3 and -32.5 per cent by WOFOST model for RH 30, Laxmi and RH 0749, respectively. The application of supplementary additional irrigation at 50% flowering to mustard not cope against the climate change and vulnerability was nearly -27.3, -23.7 and 22.4 per cent by InfoCrop, whereas, WOFOST model simulated -32.9, -31.2 and -27.1 per cent for varieties RH 30, Laxmi and RH 0749, respectively. The vulnerability in mustard yield under improved variety with irrigation management and increased fertilizer dose adaptation was -25.2, -21.0 and -17.8 per cent by using InfoCrop model and -30.8, -28.3 and 25.4 per cent by WOFOST model for RH 30, Laxmi and RH 0749, respectively. So, the net vulnerability of yield was -21.3 and -28.2 per cent by using InfoCrop and WOFOST model under A2 2080 projected climate change scenario. Khan et al. (2009); Moriondo et al. (2010); Torriani et al. (2007); Byjesh et al. (2010); Bhoomiraj el al., (2010); Reidsma et al. (2008); Reidsma et al. (2010) were also evaluated the similar results for vulnerability analysis under projected climate change scenarios.
142
CHAPTER-VI
SUMMARY AND CONCLUSION This chapter describes the brief and precise summary of the procedures and techniques followed, findings achieved and conclusions drawn through the study entitled “Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana”. The experiment was conducted at Research Farm, Agricultural Meteorology of CCS Haryana Agricultural University, Hisar, during Rabi seasons of 2012-13 and 2013-14 was summarized in this chapter. The experiment was comprised of three sowing dates (main plot treatments) viz. D1- 10th October and 21st October; D2- 25th October and 30th October; and D3- 8th November and 10th November; three sub plot treatments comprising three different varieties viz. V1 (RH 30), V2 (Laxmi) and V3- (RH 0749) during 2012-13 and 2013-14, respectively. The experiment was laid out in split-plot design with four replications. The main objectives of the experiments were to find out the relationship between growth and yield parameters with weather variables for mustard crop; to validate and carryout sensitivity analysis of InfoCrop model for mustard crop and to assess the impact of A2 scenario on mustard crop. The silent findings of the experiments have been précised in the following text. The impact of climate change on agriculture sector is well known. Agricultural output of tropical region will be reduced due to increase in temperature and large uncertainties in monsoon pattern. In view of this fact it is the time to evaluate regional crop response to projected climate change. Various adaptations measures and introduction of temperature /drought tolerant varieties will alleviate the impact of climate change on agricultural crops. For climate change impact assessment on mustard yield, the PRECIS model output in A2 scenario and baseline data of Hisar station was used. Mustard yield simulation for projected and baseline period were assessed using InfoCrop model. The vulnerability analysis was carried out to check that the mustard copping ability against the projected climate change. 6.1 Crop phenology Among the dates of sowing, D1 (10th Oct. and 21st Oct.) and D2 (25th Oct. and 30th Oct.) took minimum days to attain emergence, whereas D3 (8th Nov. and 10th Nov.) sown crop took maximum during 2012-13 and 2013-14. Mustard crop had taken maximum number of days to attain physiological maturity in D1, followed by D2 and D3 date of sowings during both crop seasons. The varieties RH 0749 taken more days to reach physiological maturity followed by Laxmi and RH 30. The days taken to maturity were more in 2012-13 as compared to 2013-14.
143
6.2 Plant height Among dates of sowing, the plant height was slightly higher in D1 than other dates in 2012-13 and 2013-14. The variety RH 0749 was attained highest plant height followed by Laxmi and RH 30. 6.3 Agrometeorological indices The different agrometeorological indices i.e. GDD, PTU, HTU, RUE and TUE accumulation was significantly higher under D1 date of sowing at almost all the phenophases in comparison to the other dates of sowing. But among the varieties, RH 0749 attained highest values of agrometeorological indices followed by Laxmi and RH 30. The accumulated values of these indices were more in 2012-13 as compared to 2013-14. 6.4 Biomass and its partitioning The highest dry matter was accumulated under D1 date of sowing at all the crop growth stage till physiological maturity, whereas the minimum dry matter accumulation was recorded in D3 date of sowing during both the seasons. RH 0749 accumulated significantly higher dry matter at all growth intervals till physiological maturity. The Rabi season of 201213 accumulated higher dry matter as compared to 2013-14 and squeezed due to higher temperature. 6.5 Leaf Area Index (LAI) The highest LAI, at all the growth intervals recorded in D1 date of sowing and lowest in D3. During 30 to 60 DAS, rate of leaf area production was higher by the stiffness of the polynomial curves and attained maximum LAI at 90 DAS. Thereafter, it decreased till maturity. RH 0749 was produced higher LAI at all crop growth intervals as compare to other varieties. Varietal difference was more evident during maximum LAI stage under all growing environments. LAI was higher in 2012-13 as compared to 2013-14 in all the treatments. 6.6 Radiation studies The study of diurnal pattern of energy balance components showed that net radiation (Rn), latent heat flux (LE), sensible heat flux (A) and soil heat flux (G) reached their peak during noon hours. Further, it was noticed that on an average around 61.8 to 74.4 per cent of the net radiation was utilized as LE, 20.8 to 27.8 per cent as A and 3.6 to 10.9 per cent of G. Delay in sowing reduced the utilization of energy towards LE irrespective of the crop growth stage. The extinction coefficient (k) calculated by using PAR showed a range of 0.64 to 0.97 and 0.47 to 0.68 in different treatments during 2012-13 and 2013-14, respectively. The value of k kept on increasing till the crop attained maximum LAI and decreased thereafter. Among the varieties, the lowest k values were observed in RH 30. The values of transmitted reflected and absorbed PAR over canopy proportionate to the growth stages of the crop. However, over the bare field, the transmitted, radiation remained between 86 to 88 per cent. The absorption radiation was between 3.6 to 5.9 percent and reflected radiation between 7.8 to 10.2 percent.
144
6.7 Yield attributes and yield The yield attributes such as primary and secondary branch at harvest, siliquae length, number of siliquae per plant, number of siliquae per m2, number of seeds per siliquae, test weight and oil content (%) were significantly higher in D1 (10th Oct. and 21st Oct.) sowing date as compared to second and third sowing dates. The variety RH 0749 was attained highest yield attribute values followed by Laxmi and RH 30. The values of yield attributes were higher during 2012-13 than 2013-14. Seed yield per plant, seed yield per m2, seed yield, biological yield, harvest index and oil yield showed a significant difference among the three different sowing dates during both the crop seasons. The mean seed yield of mustard crop was higher under different dates of sowing during 2012-13. The variety RH 0749 attained highest yield value followed by Laxmi and RH 30. 6.8 Correlation among different meteorological parameters with mustard growth and yield The mustard growth parameters showed a positive correlation with Tmax, Tmin and PE, whereas, negative correlations were observed with RHm, RHe, WS and SS (except during 2012-13) in vegetative and reproductive phases. During the year 2012- 13, dry matter showed significantly positive correlated with Tmax, Tmin and SS in vegetative phase and, with Tmax, Tmin, RHm and RHe reproductive phase, whereas, during 2013-14 it showed significantly positive results only with Tmax (reproductive phase) and Tmin (for both the phases). The WS and SS had no significant relationship with growth parameters except dry matter (for SS) in vegetative phase during 2012-13. It is observed that Tmax, Tmin, PE, RHm and RHe were the important weather parameters during vegetative phase of mustard crop which decided the fate of the yield attributes and seed yield. However, during reproductive and maturity phase addition to the Tmax and Tmin; RHm, RHe, PE, WS showed better relationship with yield of mustard. The positive relationship of yield parameters were found with the Tmax, Tmin and PE, whereas, RHm, RHe, WS and SS showed negatively relationships except SS during vegetative phase of the year 2012-13. The pooled data of correlation revealed that the Tmax, Tmin has significant correlation with mustard yield. 6.9 Validation of models performance for phenology, growth and yield parameters The different parameters viz., 50% flowering, physiological maturity, LAI, test weight, seed yield, biological yield and harvest index were simulated higher in 2012-13 as compare to 2013-14. The different statistical indices viz., MAE, MBE, RMSE, R, V, D-index and PE were computed for both the models were found satisfactory under acceptable range. The InfoCrop perform better as compare to WOFOST model during both the crop season for Hisar. All parameters were over estimated by the InfoCrop and WOFOST models except LAI for Hisar conditions among all treatments during 2012-13 and 2013-14
145
6.10 Sensitivity analysis The higher per cent of benefits was obtained at 500 ppm but further increase in CO2 (550, 600, 650 and 700 ppm) combined with one unit increase in mean ambient temperature reduced the percent change in mustard yield. The interaction effect of temperature and CO2 concentration revealed that the response of variety Laxmi was quite higher followed by RH 0749 and RH 30. At highest CO2 concentration (700 ppm), the negative impact of temperature was simulated only at -1°C in all the mustard varieties. Under increasing of 20% rainfall scenario, the increase in the yield levels are higher in RH 0749 followed by Laxmi and RH 30 using both crop simulation models. The higher benefits was obtained at +20% rise in rainfall but further decrease or increase (-30 to -10% and +30 to +40%) in rainfall combined with change in mean ambient temperature ±3 to ± 5°C reduced the percent change in mustard yield. The interaction effect of temperature and rainfall change revealed that the response of RH 0749 variety was quite higher followed by RH 30 and Laxmi. 6.11 Climatic variability There was a decreasing trend of maximum temperature during winter, summer, postmonsoon, Kharif, Rabi and annual, whereas, increasing trend observed during monsoon and baseline periods with regression analysis. However, Theil-Sen’s analysis shown decreasing trend during all the season and periods except monsoon season, where it found increasing movement. The minimum temperature showed increasing trends during winter, summer, monsoon, post-monsoon, Kharif, Rabi, baseline and annual with regression analysis and Theil-Sen’s analysis also observed increasing trend in minimum temperature during all the season and periods. The rainfall showed increasing trends during winter, summer, monsoon, Kharif, Rabi, and annual, whereas, decreasing trends observed during post-monsoon and baseline period in regression analysis. However, Theil-Sen’s analysis also showed increasing trend during all the season and periods except baseline period. The post-monsoon season has not showed any trend of rainfall in Theil-Sen’s analysis. 6.12 PRECIS generated weather conditions during projected periods The rate of rise of maximum and minimum temperatures of projected period A1b 2030 were 0.0062 and 0.0075ºC/year, respectively, which is likely to be higher than the baseline period in case of temperature by 2.2 and 3.3ºC, respectively. PRECIS generated rainfall results showed that Hisar will receive 16.1% higher rainfall during A1b 2030. The CO2 concentration is likely to increase 87.0 ppm from its baseline period, which is 24.2 per cent higher under projected period A1b 2030. The rate of rise of maximum and minimum temperatures of projected period A1b 2080 were 0.0061 and 0.0077ºC/year, respectively, which is likely to be higher than the baseline period temperature by 5.0 and 6.0ºC, respectively. The PRECIS generated rainfall results showed that Hisar will receive 20.6 per cent higher rainfall during A1b 2080. The CO2 concentration is likely to increase 279.0 ppm
146
from its baseline period, which is 77.5 per cent higher under projected period A1b 2080. The rate of rise of maximum and minimum temperatures of projected period A2 2080 were 0.0061 and 0.0076ºC/year, respectively, which is likely to be higher than the baseline period temperature by 5.8 and 6.2ºC, respectively. The PRECIS generated rainfall results showed that Hisar will receive -2.8 per cent less rainfall during A2 2080. The CO2 concentration is likely to increase 322.0 ppm from its baseline period, which is 89.4 per cent higher during projected period A2 2080. 6.13 Impact of climate change on mustard under projected climate scenarios The highest days taken to 50% flowering were recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.). The highest reduction in days taken to 50% flowering was found in A2 2080 projected climate change scenario in all the sowing dates by both the models. Among the varieties, RH 0749 has taken more days to 50% flowering as compared to RH 30 and Laxmi under baseline and projected periods. The highest days taken to physiological maturity were recorded in D1 sowing followed by D2 and lowest in D3. The highest reduction in days taken to physiological maturity was found in A2 2080 projected climate change scenario in all the sowing dates by both the models. Among the varieties, RH 30 matured early followed by Laxmi and RH 0749 by both models under all the projected climate change scenarios. The comparison of both the models revealed that InfoCrop model overestimated the days taken to 50% flowering and physiological maturity as compared to WOFOST model. The highest LAI were recorded in D1 followed by D2 and lowest in D3. RH 30 has highest reduction in maximum LAI followed by Laxmi and least reduction simulated in RH 0749 using both models and under all the projected climate change scenarios. The highest test weight was recorded in D1 sowing followed by D2 and lowest in D3. Under A1b 2030, the test weight values were simulated almost less to baseline. Among the varieties, RH 0749 have more test weight as compared to RH 30 and Laxmi under baseline and projected periods. The comparison of both the models has showed that InfoCrop model overestimated the LAI and test weight as compared to WOFOST model. The highest seed yield was recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.). Under A1b 2030, the seed yield was simulated almost similar to baseline, whereas, highest reduction in seed yield was found in A2 2080 scenario. RH 30 has highest reduction in seed yield followed by Laxmi and lowest reduction was simulated in RH 0749 by both models under all the projected climate change scenarios. The highest biological yield was recorded in D1 (10th Oct.) sowing followed by D2 (25th Oct.) and lowest in D3 (10th Nov.). Under A1b 2030, the biological yield of mustard crop were simulated almost similar to baseline, whereas, highest reduction in biological yield was found in A2 2080 projected climate change scenario in all the sowing dates and by both the models.
147
Among the varieties, RH 0749 simulated has highest biological yield followed by Laxmi and RH 30 under baseline and projected periods. The comparison of both the models revealed that InfoCrop model overestimated the seed yield and biological yield of mustard crop as compared to WOFOST model. 6.14 Adaptation measures study It was concluded from results (Tables 4.39 and 4.40) that improved variety with irrigation management and increased fertilizer dose proved more beneficial than other adaptation measures during critical growth stages of mustard during A1b 2080 and A2 2080 projected climate change scenarios under normal sown (10th Oct.). The A2 2080 scenario has more reduction in seed yield even after adaptation as compare to A1b 2080 scenario. 6.15 Net vulnerability analysis of climate change on mustard The A2 2080 projected climate change scenario found most vulnerable against the climate change for seed yield even after taken different adaptation measures during critical growth stages of mustard crop as compare to A1b 2080 and A1b 2030. Conclusions Based on the results presented in Chapter IV and subsequent discussion in the previous chapter, the following conclusions are drawn:
The first date of sowing (10th Oct. and 21st Oct.) sown crop performed better in respect of agrometeorological indices, growth and yield parameters as compared to second date of sowing (25th Oct. and 30th Oct.) and third date of sowing (8th Nov. and 10th Nov.) in both the crop seasons. The delayed sowing led to increase the vegetative phase and reduced reproductive phase both of which are detrimental to a healthy sink development.
Among varieties, RH 0749 performed better with respect to agrometeorological indices, growth and yield parameters as compared to Laxmi and RH 30 in both the crop seasons.
Simulation results of mustard crop by using InfoCrop and WOFOST model was within the acceptable limit. So, both models proved to be valuable tool for predicting mustard yield for climate change study under projected climate change scenarios, whereas InfoCrop given the good response as compared to WOFOST model.
In the projected climate change scenario A1b 2030; the Tmax, Tmin, rainfall and CO2 concentration i.e. 2.2ºC (6.8%), 3.2ºC (19.8%), 71.7mm (16.1%) and 87ppm (24.2%) found higher as compare to baseline period. The Tmax, Tmin, rainfall and CO2 concentration found higher i.e. 5.0ºC (15.7%), 6.0ºC (36.6%), 91.4mm (20.6%) and 279.0ppm (77.5%) in the projected climate change scenario A1b 2080 as compare to baseline period. The variation in Tmax, Tmin, rainfall and CO2 concentration found i.e. 5.8ºC (18.3%), 6.2ºC (38.3%), -12.2mm (-2.8%) and 87ppm (24.2%) in the projected climate change scenario A2 2080 as compare to baseline period.
148
The highest reduction in mustard seed yield was found in A2 2080 projected climate change scenario as compared to A1b 2080 and A1b 2030.
Late sown (November) mustard crop showed more reduction in phenology, yield and yield attributes as compared to October sown crop, whereas among the varieties, RH 0749 showed resistant to projected climate change as compared to Laxmi and RH 30.
The improved variety with irrigation management and increased fertilizer dose found more helpful than other adaptation measures to enhance seed yield of mustard during A1b 2080 and A2 2080 projected climate change scenarios under normal sown (10th Oct.).
The improved variety with irrigation management and increased fertilizer dose gave 15.8, 17.5 and 19.4 per cent gain in seed yield using InfoCrop model; and 12.0, 12.4 and 13.3 per cent gain by WOFOST model for RH 30, Laxmi and RH 0749 under A1b 2080, respectively, whereas 12.2, 13.7 and 15.2 per cent gain by using InfoCrop model; and 9.9, 11.0 and 11.5 per cent by WOFOST model under A2 2080 scenario for cv. RH 30, Laxmi and RH 0749, respectively.
The net vulnerability of mustard yield was -2.5 and -6.1 per cent by using InfoCrop and WOFOST model under A1b 2080 projected climate change scenario, whereas -21.3 and 28.2 per cent by using InfoCrop and WOFOST model under A2 2080 projected climate change scenario.
149
BIBLIOGRAPHY Abdul Haris, A., Chhabra, V., Bhatt, B.P. and Sikka, A.K. (2015). Yield and duration of potato crop in Bihar under projected climate scenarios. J. Agromet., 17(1): 67-73. Adak, T., Chakravarty, N.V.K. and Rani Saxena (2009). Growth and yield prediction in mustard using InfoCrop simulation model. J. Agromet., 11(2): 156-161. Adak, T., Chakravarty, N.V.K., Muthukumar, M., Deshmukh, P.S., Joshi, H.C. and Katiyar, R.K. (2011a). Evaluation of biomass and thermal energy utilization efficiency of oilseed Brassica (Brassica juncea) under altered microenvironments. Biom. Bioener., 35: 2254-2267. Adak, T., Narjary, B., Chakravarty, N.V.K., (2011b). Response of Brassica to microenvironment modification under semi-arid agroecosystem. Ind. J. agric. Sci., 81: 744-750. Agustin (2006). Climate change and variability in the mixed crop/livestock production systems of the Argentinean, Brazilian and Uruguayan Pampas. A final report submitted to assessments of impacts and adaptations to climate change (AIACC), Project No. LA 27. Akula, B. (2003). Estimating wheat yields in Gujarat using WTGROWS and INFOCROP model. Ph.D. (Agri. Meteorology) thesis submitted to GAU, Sardar Krushinagar, pp: 50-68. Akula, B. and Shekh, A.M. (2005a). Sensitivity analysis of InfoCrop to weather and non-weather parameters. Ind. J. Plant Physiol., 10(3): 236-240. Akula, B., Shekh, A.M. and Parmar, R.S. (2005b). Estimation of variability in wheat production levels in middle Gujarat using simulation model. J. Agromet., 7(2): 208-213. Anonymous (2010). ICAR Network Project on Impact, Adaptation and Vulnerability of Indian Agriculture to Climate Change, Annual Progress Report (2009-2010), pp. 177-178. Anonymous (2012). DRMR VISION 2030. Directorate of Rapeseed-Mustard Research, Bharatpur 321 303 (Rajasthan), pp. 30. Anonymous (2014). http://www.smh.com.au/environment/climate-change/as-climate-changes-worldweather-agency-calls -for-new-baseline-20140710-zt34x.html. Anonymous (2015). Mustard crop survey report 2014-15.http://www.religareonline.com/research/ Disclaimerrcl.html Awal, M. A., Koshi, H. and Ikeda, T. (2006). Radiation interception and use by maize/peanut intercrop canopy. Agril. For. Meteorol., 139: 74-83. Bhutiyani, M.R., Kale, V.S. and Pawar, N.J. (2007). Long-term trends in maximum, minimum and mean annual air temperatures across the Northwestern Himalaya during the twentieth century. Climatic Change, 85(1-2): 159–177. Boomiraj, K., Byjesh, K., Lakshmi, K., Sritharan, N., Kumar, R.K. and Jawahar, D. (2013). InfoCrop – a crop simulation model for assessing the climate change impacts on crops. J. Agromet., 15(Spacial Issue-II): 26-31. Boomiraj, K., Chakrabarti, B., Aggarwal, P., Choudhary, R. and Chander, S. (2010). Assessing the vulnerability of Indian mustard to climate change. Agril. Ecosys. Environ., 138(3-4): 265–273.
i
Butterfield, R.E. and Morison, I.L.J. (1992). Modelling the impact of climatic warming on winter cereal development. Agril. For. Meteorol., 62(3-4): 241-261. Byjesh, K., Kumar, S.N. and Aggarwal, P.K. (2010). Simulating impacts, potential adaptation and vulnerability of maize to climate change in india. Mitig. Adapt. Strateg. Glob. Chan. 15: 413431. Catalin, L., Baruth, B., Fabio, M., and Anca, L.D. (2009). Adaptation of WOFOST model from CGMS to Romanian conditions. J. Plant Develop., 16: 97-102. Chand, M., Bangarwa, A.S., Sudep, Kumar, P, and Pannu, R.K. (1995). Crop weather relationship in Brassica species. Agric. Sci. Digest, 15(4): 197-200. Chatterjee, .A (1998) Simulating the impact of increase in carbon dioxide and temperature on growth and yield of maize and sorghum. M.Sc Thesis, Division of Environmental Sciences, IARI, New Delhi, pp: 32-56. Chaudhari, K.N., Oza, M.P. and Ray, S.S. (2009). Impact of climate change on yields of major food crops in India. Archives XXXVIII 8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture, pp. 100-105. Chinchorkar, S.S., Sayyad, F.G., Vaidya, V.B. and Pandye, V. (2015). Trend detection in annual maximum temperature and precipitation using the Mann Kendall test- A case study to assess climate change on Anand of central Gujarat. Mausam, 66(1): 1-6. Choudhary, D., Patel, H.R. and Pandey, V. (2014c). Assessment of climate change under A2 scenario and its effect on kharif maize (Zea mays L.) yield using InfoCrop model for Dahod District of middle Gujarat Agro climatic zone. Eco. Env. Cons., 20(Suppl.): S245-S250. Choudhary, D., Patel, H.R. and Pandey, V. (2015). Evaluation of adaptation strategies under A2 climate change scenario using InfoCrop model for Kharif maize in middle Gujarat region. J. Agrimet., 17(1): 98-101. Choudhary, D., Patel, H.R. Yadav, S.B. and Pandey, V. (2014d). Assessment of climate change under A2 scenario and its effect on rabi maize (Zea mays L.) yield using InfoCrop model for Dahod District of middle Gujarat Agro climatic zone. Annals Biol., 30(3): 509-514. Choudhary, D., Patel, H.R., and Pandey, V. (2014a). Calibration and validation of InfoCrop model v.1.0 for yield and yield attributing characters of rabi maize in middle Gujarat region. Annals Biol., 30(4): 658-660. Choudhary, D., Patel, H.R., and Pandey, V. (2014b). Calibration and validation of InfoCrop model v.1.0 for yield and yield attributing characters of kharif maize in middle Gujarat region. Trends Biosci., 7(13): 1528-1530. Conde, C., Liverman, D., Flores, M., Ferrer, R., Araujo, R., Betancourt, E., Villarreal, G. and Gay, C. (1997). Vulnerability of rainfed maize crops in Mexico to climate change. Clim. Res., 9: 1723. Confalonieri, R., Acutis, M., Bellocchi, G. and Donatelli, M. (2009). Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice. Ecological Modelling. journal homepage: www.elsevier.com/locate/ecolmodel/abstract. Das, D.K., Chakravarty, N.V.K. and Vashisth, A. (2009). Annual report 2008-2009. Division of Agricultural Physics, IARI, New Delhi. pp. 57-62. Dash, S.K. and Hunt, J.C.R. (2007). Variability of climate change in India. Curr. Sci., 93(6): 782–788.
ii
De, U.S. and Prakasa, Rao, G.S. (2004). Urban climatic trends- the Indian scenario. J. Ind. Geophyl. Uni., 8(3): 193-203. Denmead, O.T. and Mclory, I.C. (1970). Measurement of non-potentials evaporation from wheat. Agric. Meteorol., 7: 285-302. Dhorde, A., Dhorde, A. and Gadgil, A.S. (2009). Long-term temperature trends at four largest cities of India during the twentieth century. J. Ind. Geophyl. Uni., 13(2): 85-97. Diepen, C.A., van, Wolf, J. and van Keulen, H. (1989) WOFOST: a simulation model of crop production. Soil Use Manag., 5: 16-24 Fischer, A. M., Weigel, A. P., Buser, C. M., Knutti, R., Künsch, H. R., Liniger, M. A., Schär, C., Appenzeller, C. (2012). Climate change projections for Switzerland based on a Bayesian multimodel approach. Int. J. Clim., 32 (15): 2348–2371. Ghosh, R.K. and Chatterjee, B.N. (1988). Effect of date of sowing on the productivity of Indian mustard (Brassica juncea sub sp. juncea) in short and mid winter of the gangetic plains of West Bengal. Ind. J. Agric. Sci., 58(8): 589-596. Gill, K.K. and Bains. G.S. (2008). Growth-yield dynamics, radiation interception and RUE in Brassica carinata. J. Agromet., 10(Special Issue-Part 1): 72-74. Graf, B., Dingkuhn, M., Schnifer, F. and Coronel, V. (1991). A simulation model for the dynamics of rice growth and development: III. Validation of the model with high-yielding varieties. Agril. Syst., 36: 329-349. Gunasekera, C.P., Martin, L.D., Walton, G.H. and Siddique, K.H.M. (2003). Growth and seed yield components of Indian mustard (Brassica juncea L.) and canola (B. napus L.) in a low rainfall short season Mediterranean type environment. In: 13th Australian Research Assembly on Brassicas. Hedge, D.M. (2005). Oilseed scenario in India – past, present and future with special reference to rapeseed-mustard. In: Winter School on Advances in Rapeseed-Mustard Research Technology for Sustainable Production of Oilseeds, National Centre on Rapeseed-Mustard, Sewar, Bharatpur, December 15 to January 04, 2005, pp: 1–15. Henchanova, R.F. (1988). Effect of irrigation liming and depth of water table on the yield of corn. Presented In 4th scientific meeting of federation of crop science societies of the Philippines 1988. Philippine J. Crop Sci. 13 (suppl. No. 1): 21-24. Indiastat, (2014). Area, production and yield of rapeseed and mustard along with percentage coverage under irrigation in India (1950-1951 to 2012-2013) (targets). URL: http://www.indiastat.com/ agriculture/2/oilseed/17204/rapeseedandmustad/19578/stat.aspx. IPCC (2007). Climate Change 2007. Impacts, Adaptation and Vulnerability. Working Group II Contribution to Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E. (eds) Cambrisge University Press, Cambridge, UK. pp. 1-976. Jablonski, L.M., Wang, X.Z. and Currtis, P.S. (2002). Plant reproduction under elevated CO2 conditions: a meta-analysis of report on 79 crop and wild species. New Phytologist.156: 926. Jackson, M.L. (1973). Soil Chemical Analysis. Prentice Hall Pvt. Ltd., India. pp. 521-527. Jain, V.K., Chauhan, Y.S., Khandekar, M.P., Jain, P.C. and Rathore, D.R. (1986). Performance of raya varieties under late sown conditions. Ind. J. Agron., 31(4): 374-376.
iii
Jones, R.G., Noguer, M., Hassell, D.C., Hudson, D., Wilson, S.S., Jenkins, G.J. and Mitchell, J.F.B. (2004). Generating high resolution climate change scenarios using PRECIS. Met Office, Hadley Centre, Exeter, UK, 40pp April 2004. Jun, Z., Wancang, S., Yaxiong, M., Huiling, F., Yue, G., Jian, Y. And Jianmin, D. (2007). The effect of sowing date on plant survive rate and seed yield of winter rapeseed (B. rapa) in Northwest China. In: Proceedings of 12th International rapeseed congress Wuhan, China, March 26-30, III, pp. 186-187. Kadam, N.N., Xiao, G., Melgar, R.J., Bahuguna, R.N., Quinones, C., Tamilselvan, A., Prasad, P.V. and Jagdish, K.S.V. (2014). Agronomic and physiological responses to high temperature, drought and elevated CO2 interactions in cereals. Advan. Agron., 127: 111-156. Kalra, N., Aggarwal, P.K., Chander, S., Pathak, H., Choudhary, A., Chaudhary, A., Sehgal, M., Rai, H.K., Soni, U.A., Sharma, A., Jolley, M., Singh, U.K., Ahmed, O. and Hussain, M.Z. (2003). Impacts of climate change on agriculture. In: Climate Change and India: Vulnerability Assessment and Adaptation. Univ. Press (India) Pvt. Ltd., Hyderabad, pp. 193-226. Kalra, N., Chakraborty, D., Sharma, A, Rai, H.K., Jolly, M., Chander, S., Kumar, P.R., Bhadraray, S., Barman, D., Mittal, R.B., Lal, M. and Sehgal, M. (2008). Effect of increasing temperature on yield of some winter crops in northwest India. Curre. Sci., 94(1): 82-88. Kanth, R.H., Shah, M.H. and Bali, A.S. (2000). Effect of different sowing dates on heat-unit requirement for different phenophases of brown sarson (Brassica campestris) varieties and dynamics of aphid (Lipaphis erysimi) population. Ind. J. Agron. 45: 170-173. Karmalkar, A.V., Bradley, R.S. and Diaz, H.F. (2008). Climate change scenario for Costa Rican montane forests. Geo. Res. Let., 35: L11702. Khan, A.M., Khan, T., Marwat, B. and Arif, M. (2009). Organic and inorganic nitrogen treatments effects on plant and yield attributes of maize in a different tillage systems. Pak. J. Bot., 41(1): 99-108. Khan, S.A., Kumar, S., Hussain, M.Z. and Kalra, N. (2009). Climate Change, Climate Variability and Indian Agriculture: Impacts Vulnerability and Adaptation Strategies. In: S.N. Singh (eds.), Climate Change and Crops, Environmental Science and Engineering, DOI 10.1007/978-3540-88246-6 2, C _ Springer-Verlag Berlin Heidelberg 2009. Kingra, P.K. and Kaur, P. (2012). Effect of Dates of Sowing on Thermal Utilisation and Heat Use Efficiency of Groundnut Cultivars in Central Punjab. J. Agril. Phy., 12(1): 54-62. Koning, G.H.J., de Jansen, M.J.W., Boons-Prins, E.R., Diepen, C.A, van Penning and de Vries, F.W.T., (1993). Crop growth simulation and statistical validation for regional yield forecasting across the European Community. Simulation Reports CABO-TT, No. 31, ABDLO, Wageningen, The Netherlands. Kothawale, D.R. and Rupa Kumar, K. (2005). On the recent changes in surface temperature trends over India. Geophyl. Res. Let., 32(18): L18714. Ku, M.S.B. (2000). Metabolically Modified Rice Exhibits Superior Photosynthesis and Yield, ISBN New Report. http://www.biotech-info.net-metabolocally.html. Kumar, A., (2005). Rapeseed-mustard in India: current status and future prospects. In: Winter School on Advances in Rapeseed-Mustard Research Technology for Sustainable Production of Oilseeds, National Centre on Rapeseed-Mustard, Sewar, Bharatpur, December 15 to January 04, 2005, pp. 278–288.
iv
Kumar, A., Sharma, P., Thomas, L., Agnihotri, Abha., and Banga, S.S. (2009). Canola cultivation in India: scenario and future strategy. 16th Australian Research Assembly on Brassica. Ballarat Victoria 2009. 54b7c27d0cf24eb34f6eda10.pdf. Kumar, A., Singh, D.P. and Singh, P. (1994). Influence of water stress on photosynthesis, transpiration, water use efficiency and yield of Brassica juncea L. Field Crops Res., 37: 95-101. Kumar, A., Tripathi, P., Yadav, S.B., Singh, K.K. and Mishra, S.R. (2015). Validation of InfoCrop model for rice cultivars under eastern plain zone of Utter Pradesh. J. Agromet., 17 (1): 80-83. Kumar, G., Adak, T., Chakravarty, N.V.K., Chamola, R., Katiyar, R.K. and Singh, H.B. (2007). Effect of ambient thermal regime on growth and yield of Brassica cultivars. Brassica, 9(1-4): 47-52. Kumar, G., Chakravarty, N.V.K., Kurothe, R.S., Sena, D.R., Tripathi, K.P., Adak, T., Haldar, D. and Anuranjan (2010a). Effect of projected climate change on mustard (Brassica juncea). J. Agromet., 12(2): 168-173. Kumar, G., Kurothe, R.S., Sena, D.R., Vishwakarma, A.K., Madhu, M., Rao, B.K., Tripathi, K.P. and Anurajan (2010b). Sensitivity of wheat crop to projected climate change in non-traditional areas. J. Agromet., 12 (2): 161-167. Kumar, S.N., Aggarwal, P.K., Kumar, U., Jain, S., Rani, S., Chauhan, N. and Saxena, R. (2014). Vulnerability of Indian mustard (Brassica juncea (L.) Czernj. Cosson) to climate variability and future adaptation strategies. Mitig. Adapt. Strateg. Glob. Change. DOI: 10.1007/s11027014-9606-z. Kumar, V., Jain, S.K. and Singh, Y. (2010). Analysis of long-term rainfall trends in India. Hydrol. Sci. J., 55(4): 484-496. Kumari, C.R. and Rao, D.S.K. (2005). Effect of land treatment and dates of sowing on growth parameters of mustard, B. juncea L. Czern and Coss. J. Oilseed Res., 22(1): 188-189. Kundu, S.S., Skogerboe, G.V. and Walker, W.R. (1982). Using a growth simulation model for evaluating irrigation practices. Agric. Water. Manag., 5: 253-268. Lal, B. (1993). Trends and periodicities of surface air temperature of Delhi. Mausam, 44(1): 35-38. Lawlor, D.W. and Mitchell, R.A.C. (1991). The effects of increasing CO2 on crop photosynthesis and productivity: a review of field studies; commissioned review. Plant, Cell Environ., 14: 807818. Liyong, Hu., Zhiquang, Lu. And Tingdong, Fu. (2007). The effects of sowing date on fatty acid synthesis of rapeseed (B. napus. L). In: Proceeding of 12th International Rapeseed Congress. Wuhan, China. March 26-30. III, pp. 80-84. Mandal, N. (1998) Simulating the impact of climatic variability and climate change on growth and yield of chickpea and pigenonpea crops. M.Sc Thesis, Division of Environmental Sciences, IARI, New Delhi. pp: 30-69. Marengo, J.A. and Ambrizzi, T. (2006). Use of regional climate models in impacts assessments and adaptations studies from continental to regional and local scales. In: Proceedings of 8 ICSHMO, Foz do Iguacu, Brazil, April 24-28, 2006. INPE, pp: 291-296. Matthews R.B., and Stephens, W. (2002). Application of crop-soil simulation models in tropical. CABI Publishing. CAB International, Wallingford, Oxon, United Kingdom. pp: 80 -90. McCaffery, D., Bambach, R., and Haskins, B. (2009). Brassica juncea in north-western NSW. Prime facts. Retrieved from: http://www.dpi.nsw.gov.au/_data/assets/pdf_file/0018/222246/Brassica -juncea-in-north-western-NSW.pdf on 21 June 2012.
v
Mendham, N.J., Shipway, P.A. and Scott, R.K. (1981). The effect of delayed sowing and weather on growth, development and yield of winter oil-seed rape (Brassica napus). J. Agric. Sci., 96: 389-416. Mishra, S.K., Shekh, A.M., Pandey, V., Yadav, S.B. and Patel, H.R. (2015). Sensitivity analysis of four wheat cultivars to varying photoperiod and temperature at different phenological stages using WOFOST model. J. Agromet., 17(1): 74-79. Mishra, S.K., Shekh, A.M., Yadav, S.B., Kumar, A., Patel, G.G., Pandey, V. and Patel, H.R. (2013). Simulation of growth and yield of four wheat cultivars using WOFOST model under middle Gujarat region. J. Agromet., 15(1): 43-50. Monga, R. K., Singh, R., Prakash, V. and Narang, R. S. (2009). Assessment of spectral response of tomato (Lycopersicum esculentum L.) to varying microclimate in Western Ghats conditions. Ind. J. Agron., 35 (1 & 2): 153-158. Moriondo, M., Bindi, M., Kundzewicz, Z.W., Szwed, M., Chorynski, A., Matczak, P., Radziejewski, M., McEvoy, D., Wreford, A. (2010). Impact and adaptation opportunities for European agriculture in response to climatic change and variability. Mitiga. Adapt. Strate. Globl. Chang. 15 (7): 657–679. Morrison, J.I.L. (1996). Global environmental change impacts on crop growth and production in Europe. Implications of global environmental change for crops in Europe. Asp. Appl. Biol., 45: 62-74. Mukherjee, J., Singh, L., Singh, G., Bal, S.K., Singh, H. and Kaur, P. (2011) Comparative evaluation of WOFOST and ORYZA2000 models in simulating growth and development of rice in Punjab. J. Agromet., 13: 86-91. Nanda, R., Bhargava, S.C. and Tomar, D.P.S. (1994). Rate and duration of siliqua and seed filling period and their relation to seed yield in Brassica spp. Ind. J. Agric. Sci., 64: 227-232. Nanda, R., Bhargava, S.C., Tomar, D.P.S and Rawson, H.M. (1996). Phenological development of Brassica campestris, B. juncea, B. napus and B. carinata grown in controlled environments and from 14 sowing dates in field. Field Crop Res., 46(1-3): 93-103. Neogi, P. and Chakarvarty, N.V.K. (2005). Thermal time and phenological model for Brassica juncea. J. Agromet., 7(2): 174-181. Neogi, P., Chakravarty, N.V.K., Srivastava, A.K., Goutom, B., Katiyar, R.K. and Singh, H.B. (2005). Thermal time and its relationship with seed yield and oil productivity in Brassica cultivars. Int. J. Brassica, 7(1 & 2): 63-70. NOAA (2014). Climate change indicators in the United States: U.S. and Global Temperature. National Oceanic and Atmospheric Administration.2014.http://www.epa.gov/climatechange/indicatorsUpdated May 2014. Olsen, S.R., Cole, C.V., Walanafle, F.S. and Dean, L.A. (1954). Estimation of available phosphorus in soil extract with sodium bicarbonate. USDA Circular No. 939, Washington. Oram, R.N., Kirk, J.T.O., Veness, P.E., Hurlstone, C.J., Edlington, J.P. and Halsall, D.M. (2005). Brassica juncea Breeding. Aust. J. Agril. Res., 56: 581-596. Pidgeon, J.D., Werker, A.R., Jaggard, K.W., Richter, G.M., Lister, D.H. and Jones, P.D. (2001). Climatic impact on the productivity of sugar beet in Europe, 1961–1995. Agric. For. Meteorol., 109: 27–37. Piper, G.B. (1966). Soil and Plant Analysis. Hans Publishers, Bombay.
vi
Poureisa, M. and Nabipour, M. (2007). Effect of planting dates on canola phenology, yield and yield components. In: Proceedings of 12th International rapeseed congress. Wuhan, China. 26-30th March, 2007. III, 97-101. Pradhan, S., Sehgal, V.K., Das, Jain, A.K., Bandyopadhyay, K.K., Singh, R. and Sharma, P.K. (2014). Effect of weather on seed yield and radiation and water use efficiency of mustard cultivars in a semi arid environment. Agric. Water Manag., 139: 43-52. Pushpalatha, P., Singh, D.V., Sharma, N., Poonam and Ghildiyal, M.C. (2008). Differential susceptibility of photosynthesis and productivity of wheat cultivars to elevated temperature. Ind. J. Pl. Physiol., 13(4): 357-360. Ramaraj, A.P., Jagnnathan, R. and Dheebakaran, G. (2009). Impact of climate change on rice and groundnut yield using PRECIS Regional Climate Model and DSSAT crop simulation model. ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture. ISPRS Ahmedabad, Space Applications Centre (ISRO) Ahmedabad, India 17–18 December 2009. Rameshwar., Negi, P.S. and Sharma, S. (2000). Effect of planting date and row spacing on growth parameters, yield and economics of gobhi sarson (Brassica napus var. oleracea) under mid hill conditions of Himachal Pradesh. Crop Res. 20(1): 39-45. Rao, G.G.S.N., Vanaja, M., Rao, M.S., Rao, A.V.M.S., Rao, V.U.M., Desai, S., Rao, C.S. and Rao, K.V. (2010). ICAR network project on impact, adaptation and vulnerability of Indian agriculture to climate change Annual Progress Report (2009-2010), pp 29-60. Rawson, H.M. (1992). Plant responses to temperature under conditions of elevated CO2. Aust. J. Bot., 40: 473-490. Reidsma, P. and Ewert, F. (2008). Regional farm diversity can reduce vulnerability of food production to climate change. Eco. Soc., 13(1): 38. Reidsma, P., Ewert, F.Oude., Lansink, A., Leemans, R. (2010). Adaptation to climate change and climate variability in European agriculture: the importance of farm level responses. Eur. J. Agron., 32: 91–102. Richards, L.A. (1954). Diagnosis and Improvement of Saline and Alkaline Soils. USDA Handbook No. 60, Washington D. C. Robertson, M.J., Holland, J.F., Bambach, R. and Cawthray, S. (2002). Response of canola and Indian mustard to sowing date in risky Australian environments. Aust. J. Agric. Res., 53(10): 11551164. Rosenthal, W.D. and Gerik, T.J. (1991). Radiation use efficiency among cotton cultivars. Agron. J., 83(4): 653-258. Roy, S., Meena, R.L., Sharma, S.C., Vinod, K., Chattopadhyay, C., Khan, S.A., (2005). Thermal requirement of oilseed Brassica cultivars at different phonological stages under varying environmental conditions. Ind. J. Agril. Sci., 75(11): 717-721. Ruhil, K., Sheeba, Ahmad, A., Iqbal, M. and Tripathy, B.C. (2015). Photosynthesis and growth responses of mustard (Brassica juncea L. cv Pusa Bold) plants to free air carbon dioxide enrichment (FACE). Protoplasma, 252: 935–946. Rupakumar, K., Sahai, A.K., Krishna Kumar, K., Patwardhan, S.K., Mishra, P.K., Revadekar, J.V., Kamala, K. and Pant, G.B., (2006). High-resolution climate change scenarios for India for the 21st century. Cur. Sci., 90(3): 334-345.
vii
Sahoo, S.K. (1999) Simulating growth and yield of maize in the different agro-climatic regions. M.Sc Thesis, Division of Environmental Sciences, IARI, New Delhi, pp. 38-70. Schwarte, A.J., Gibson, L.R., Karlen, D.L., Liebman, M. & Jannink, Jean-Luc. (2005) Planting date effects on winter triticale dry matter and nitrogen accumulation. Agron. J., 97, 1333-1341. Scott, M. (2013). Arctic sea ice minimum compared to the new ‘normal’: Baseline period change. Accessed September 24, 2013. Sharma, D.K. and Kumar, A. (1989). Effect of water stress on plant water relations and yield of varieties of Indian mustard (Brassica juncea subsp. juncea). Ind. J. Agric. Sci., 59: 281–285. Shekhar, C., Singh, D., Singh, R. and Rao, V.U.M. (2008). Prediction of wheat growth and yield using WOFOST model. J. Agromet., 10: 400-402. Si, P. and Walton, G. H. (2004). Determination of oil concentration and seed yield in canola and Indian mustard in the lower rainfall areas of Western Australia. Aust. J. Agril. Res., 55(3): 367-377. Singh, D., Rao, V.U.M. and Bishnoi, O.P. (1993). Thermal requirements of Brassica species under three date of sowing. Ind. J. Agron., 38: 45-50. Singh, M.P., Lallu and Singh, N.B. (2014). Thermal requirement of Indian mustard (Brassica juncea) at different phenological stages under late sown condition. Ind. J. Pl. Physiol., 19(3): 238-243. Singh, N. and Sontakke, N.A. (2004). On Climatic Fluctuations and Environmental Changes of the Indo-Gangetic Plains, India. Climatic Change. 52(3): 287-313. Singh, P., Kumar, V., Thomas, T. and Arora, M. (2008). Basin-wide assessment of temperature trends in northwest and central India. Hydrol. Sci. J., 53(2): 421-433. Singh, P.K., Mishra, A.K. and Imtiyaz, M. (1991). Moisture stress and the water use efficiency of mustard. Agric. Water Mgt., 20, 245–253. Singh, R. and Singh, S. (2005). Wheat yield variability in relation with weather parameters. Haryana Agric. Univ. J. Res., 35: 23-26. Singh, R. Diwan Singh; Biswas, B. and Mani, J. K. (2012). Radiation interception and radiation use efficiency in barley. J. Agromet., 14(SI): 358-362. Singh, R., Rao, V.U.M. and Singh, D. (2002). Biomass partitioning in Brassica as affected by sowing dates. J. Agromet., 4(1): 59-63. Singh, S.D., Nagarajan, S., Choudhary, A., Pal, M., Chandra, S., Anand, A., Bhatia, A., Jain, N. and Benerjee, B. (2010). ICAR network project on impact, adaptation and vulnerability of Indian agriculture to climate change Annual Progress Report (2009-2010), pp. 29-60. Sinha, S.K. and Swaminathan, M.S. (1991). Deforestation, climate change and sustainable nutrition security: A case study of India. Climatic Change, 19: 201–209. Soora, N.K., Aggarwal, P.K., Saxena, R., Rani, S., Jain, S. and Chauhan N. (2013). An assessment of regional vulnerability of rice of climate change in India. Climate change, DOI: 10.1007/s10584-013-0698-3. Srivastava A.K., Chakravarty, N.V.K., Adak, T. (2011). Quantification of growth and yield of oilseed Brassica using thermal indices under semi-arid environment. J. Agromet., 13(2): 135-140. Stewart, D.W. (1970). A simulation of net photosynthesis of field corn Ph.D. Thesis. Crnell Univ. Ithaca, New York, N.Y. Comp. Amsterdam, pp. 210.
viii
Subhiah, B.V. and Asija, G.L. (1956). A rapid procedure for the estimation of available nitrogen in soils. Cur. Sci., 25: 172-194. Taylor, M.A., Centella, A., Charley, J., Borrajero, I., Benzanilla, A., Campbell, J., Rivero, R., Stephenson, T.S., Whyte, F. and Watson, R. (2007). Glimps of the future: A briefing from the PRECIS Caribbean Climate change project, Caribbean Climate Change Centre, Belmopan, Belize, pp. 1-24. Thurling, N. (1974) Morphological determinants of yield of rapeseed and mustard (Brassica campestris and B. juncea). Growth and morphological character. Aust. J. Agric. Res., 25(5): 692-710. Torriani, D., Calanca, P., Schmid, S., Beniston, M., and Fuhrer, J. (2007). Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland. Climate Res., 34 (1): 59–69. Tripathi, M.K. (2005). Quantification of micrometeorological variation in Indian mustard under different growing environments. Ph.D. Thesis., CCSHAU, Hisar. Tripathy, K.P., Sena, D.R., Kumar, G., Singh, H.B. and Patra, S., (2009). Annual Progress Report of the Network Project on Climate Change (for the year 2008-2009), pp: 126-141. Van Ittersum, M.K. and Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crop Res., 52: 197-208. Weerakoon, S.R. and Somaratne, S. (2011). Seasonal variation of growth and yield performance of mustard [Brassica juncea (L.) Czern and Coss)] genotypes in Sri Lanka. J. Agric. Sci. 6(1): 115. Wilby, R.L. and Wigley, T.M.L. (1997). Downscaling general circulation model output: a review of methods and limitations. Prog. Phyl. Geo., 21: 530-548. Willmott (1982). Validation of the model. Chapter no. 8 in CERES-Wheat book, Draft no. 1. Wolf, J. (1993). Effects of climate change on wheat production potential in the European Community. Eur. J. Agron., 2(4): 281-292. Xu, Y., Huang, X., Zang, Y., Lin, W. and Lin, E. (2006). Statistical analysis of climate change scenarios over China in the 21st Century. Adv. Clim. Chang. Res., 2 (Suppl.1): 50-53.
ix
ABSTRACT Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana Full name of student : DIVESH CHOUDHARY Admission No. : 2011A02D Title of degree : Doctor of Philosophy in Agricultural Meteorology Name and Address of Major : Dr. Raj Singh, Professor & Head Advisor Department of Agricultural Meteorology, CCS Haryana Agricultural University, Hisar-125 004 (Haryana), India Degree awarding University/ : CCS Haryana Agricultural University, Institute Hisar-125004 (Haryana), India Year of award of degree : 2016 Major Subject : Agricultural Meteorology Total number of pages in Thesis : 149 + ix Number of words in abstract : 495 Key words: Mustard, climate change, PRECIS, InfoCrop model, WOFOST model, sensitivity analysis, agrometeorological indices, adaptation measures, vulnerability Title of Thesis
:
Field experiment entitled “Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana” was conducted during Rabi season (2012-13 and 2013-14) at research farm of Department of Agricultural Meteorology, CCS HAU, Hisar (29° 10 N, 75° 46 E and altitude 215.2 m). The D1 (10th Oct., 2012-13 and 21st Oct., 2013-14) sown crop performed better in respect of agrometeorological indices, growth and yield parameters as compared to D2 (25th Oct., 2012-13 and 30th Oct., 2013-14) and D3 (8th Nov., 201213 and 10th Nov., 2013-14) date of sowings. The delayed sowing led to increase the vegetative and reduced reproductive phases are detrimental to healthy sink development. Among varieties, RH 0749 performed better in respect of agrometeorological indices, growth and yield parameters as compared to Laxmi and RH 30 in both the crop seasons. Simulation results of mustard crop by using InfoCrop and WOFOST model were simulated within the acceptable limit. So, both models proved to be valuable tool for predicting mustard yield for climate change study under projected climate change scenarios, whereas InfoCrop given the good response as compared to WOFOST model. In the projected climate change scenario A1b 2030; the Tmax, Tmin, rainfall and CO2 concentration i.e. 2.2ºC (6.8%), 3.2ºC (19.8%), 71.7mm (16.1%) and 87ppm (24.2%) found higher as compare to baseline period. The Tmax, Tmin, rainfall and CO2 concentration found higher i.e. 5.0ºC (15.7%), 6.0ºC (36.6%), 91.4mm (20.6%) and 279.0ppm (77.5%) in the projected climate change scenario A1b 2080 as compare to baseline period. The variation in Tmax, Tmin, rainfall and CO2 concentration found i.e. 5.8ºC (18.3%), 6.2ºC (38.3%), -12.2mm (-2.8%) and 87ppm (24.2%) in the projected climate change scenario A2 2080 as compare to baseline period. The highest reduction in mustard seed yield was found in A2 2080 projected climate change scenario as compare to A1b 2080 and A1b 2030. Late sown (November) mustard crop showed more reduction in phenology, yield and yield attributes as compared to October sown, whereas among the varieties, RH 0749 showed resistant to projected climate change as compare to Laxmi and RH 30. The improved variety with irrigation management and increased fertilizer dose found more helpful than other adaptation measures to enhance seed yield of mustard during A1b 2080 and A2 2080 projected climate change scenarios under normal sown (10th Oct.). The improved variety with irrigation management and increased fertilizer dose gave 15.8, 17.5 and 19.4% gain in seed yield using InfoCrop model; and 12.0, 12.4 and 13.3% gain by WOFOST model for RH 30, Laxmi and RH 0749 under A1b 2080, respectively, whereas 12.2, 13.7 and 15.2% gain by using InfoCrop model; and 9.9, 11.0 and 11.5% by WOFOST model under A2 2080 scenario for varieties RH 30, Laxmi and RH 0749, respectively. The net vulnerability of mustard yield was -2.5 and -6.1% by using InfoCrop and WOFOST model under A1b 2080 projected climate change scenario, whereas -21.3 and -28.2% by using InfoCrop and WOFOST model under A2 2080 projected climate change scenario.
MAJOR ADVISOR HEAD OF THE DEPARTMENT
SIGNATURE OF DEGREE HOLDER
CITATION: Divesh Choudhary, 2015. Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana. PhD. Thesis submitted to Department of Agricultural Meteorology. College of Agriculture, CCS Haryana Agricultural University, Hisar, Haryana - 125 004.
CURRICULUM VITAE (a) (b) (c) (d) (e) (f) (g) (h) (i) (j)
Name Date of birth Place of birth Mother’s name Father’s name Permanent address Telephone Mobile E-mail Academic qualification:
Degree
: : : : : : : : :
Divesh Choudhary 14th June, 1986 Rewari, Haryana Smt. Nirmala Devi Sh. Hari Singh Choudhary C-104, H.K.M. Nagar, Alwar-301001, Rajasthan Nil +91-8901275704
[email protected]
University/Board
Year of
Percentage
Passing
of Marks
Subjects
B. Sc. (Ag.) Hons.
Dr. Balasaheb Sawant Konkan Krishi Vidhyapeeth, Dapoli, (Maharashtra)
2009
72.20
Agriculture
M. Sc. (Ag.)
Anand Agricultural University, Anand (Gujarat)
2011
77.20
Agricultural Meteorology
Ph.D. (Ag.)
CCS Haryana Agricultural University, Hisar (Haryana)
2016
77.90
Agricultural Meteorology
(k) Co-curricular activities (l) Medals/Honours received
: :
Sketching, listening music, reading scriptures ICAR-NTS during B.Sc. (Agri.), ICAR-JRF during M.Sc. and DST INSPIRE fellow,GoI during Ph.D. CSIR-UGC NET and ICARASRB NET qualified.
(m) List of publications : Divesh Choudhary, Raj Singh, Parvender Sheoran and Anil Kumar. (2014) Energy balance and light interception over mustard cultivars in Hisar region. Journal of Agrometeorology. (paper in communication) Divesh Choudhary, Raj Singh, Diwan Singh, Parvender Sheoran and Anil Kumar. (2014) Validation and sensitivity analysis of InfoCrop model v.1.0 for phenology, yield and yield attributing characters of Indian mustar cultivars in Western Haryana region. Journal of Agrometeorology. (paper in communication) Divesh Choudhary, H. R. Patel and V. Pandey. (2015). Evaluation of adaptation strategies under A2 climate change scenario using InfoCrop model (v. 1.0) for kharif maize in middle Gujarat region. Journal of Agrometeorology, 17 (1): 98-101. Divesh Choudhary, H.R. Patel and V. Pandey. (2014). Assessment of climate change under A2 scenario and its effect on kharif maize (Zea mays L.) yield using InfoCrop model for Dahod District of middle Gujarat Agro climatic zone. Eco. Env. & Cons. 20 (Suppl.): S245-S250. D. Choudhary, H.R. Patel, S. B. Yadav, and V. Pandey. (2014) Assessment of climate change under A2 scenario and its effect on rabi maize (Zea mays L.) yield using InfoCrop model for Dahod District of middle Gujarat Agro climatic zone. Annals of Biology 30(3): 509-514. D. Choudhary, H.R. Patel and V. Pandey. (2014) Calibration and validation of InfoCrop model v.1.0 for yield and yield attributing characters of rabi maize in middle Gujarat region. Annals of Biology 30(4): 658-660.
UNDERTAKING OF COPY RIGHT I, Divesh Choudhary, Admission. No. 2011A02D undertakes that I give copy right to the CCS HAU, Hisar of my thesis entitled “Assessment of climate change effects on mustard yield using InfoCrop model for western Haryana”. I also undertake that patent, if any, arising out of the research work conducted during the programme shall be filled by me only with due permission of the competent authority of CCS HAU, Hisar.
SIGNATURE OF THE STUDENT
