Potential impacts of climate change and adaptation strategies for sunflower in Pakistan Muhammad Awais, Aftab Wajid, Muhammad Farrukh Saleem, Wajid Nasim, Ashfaq Ahmad, Muhammad Aown Sammar Raza, Muhammad Usman Bashir, et al. Environmental Science and Pollution Research ISSN 0944-1344 Environ Sci Pollut Res DOI 10.1007/s11356-018-1587-0
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Author's personal copy Environmental Science and Pollution Research https://doi.org/10.1007/s11356-018-1587-0
RESEARCH ARTICLE
Potential impacts of climate change and adaptation strategies for sunflower in Pakistan Muhammad Awais 1,2 & Aftab Wajid 2,3 & Muhammad Farrukh Saleem 2 & Wajid Nasim 4,5,6 & Ashfaq Ahmad 2,7 & Muhammad Aown Sammar Raza 1 & Muhammad Usman Bashir 1 & Muhammad Mubeen 4 & Hafiz Mohkum Hammad 4 & Muhammad Habib ur Rahman 8 & Umer Saeed 2 & Muhammad Naveed Arshad 2,9 & Jamshad Hussain 2 Received: 8 August 2017 / Accepted: 18 February 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract Growth, development, and economic yield of agricultural crops rely on moisture, temperature, light, and carbon dioxide concentration. However, the amount of these parameters is varying with time due to climate change. Climate change is factual and ongoing so, first principle of agronomy should be to identify climate change potential impacts and adaptation measures to manage the susceptibilities of agricultural sector. Crop models have ability to predict the crop’s yield under changing climatic conditions. We used OILCROP-SUN model to simulate the influence of elevated temperature and CO2 on crop growth duration, maximum leaf area index (LAI), total dry matter (TDM), and achene yield of sunflower under semi-arid conditions of Pakistan (Faisalabad, Punjab). The model was calibrated and validated with the experimental data of 2012 and 2013, respectively. The simulation results showed that phenological events of sunflower were not changed at higher concentration of CO2 (430 and 550 ppm). However LAI, achene yield, and TDM increased by 0.24, 2.41, and 4.67% at 430 ppm and by 0.48, 3.09, and 9.87% at 550 ppm, respectively. Increased temperature (1 and 2 °C) reduced the sunflower duration to remain green that finally led to less LAI, achene yield, and TDM as compared to present conditions. However, the drastic effects of increased temperature on sunflower were reduced to some extent at 550 ppm CO2 concentration. Evaluation of different adaptation options revealed that 21 days earlier (as compared to current sowing date) planting of sunflower crop with increased plant population (83,333 plants ha−1) could reduce the yield losses due to climate change. Flowering is the most critical stage of sunflower to water scarcity. We recommended skipping second irrigation or 10% (337.5 mm) less irrigation water application to conserve moisture under possible water scarce conditions of 2025 and 2050. Keywords Achene yield . OILCROP-SUN model . Helianthus annuus L. . Leaf area index . Total dry matter
Responsible editor: Philippe Garrigues * Muhammad Awais
[email protected] 1
Department of Agronomy, University College of Agriculture and Environmental Sciences, The Islamia University, Bahawalpur, Pakistan
2
Agro-Climatology Laboratory, Department of Agronomy, University of Agriculture, Faisalabad, Pakistan
3
College of Agricultural and Environmental Sciences, University of California, Davis, CA, USA
4
Department of Environmental Sciences, COMSATS Institute of Information Technology (CIIT), Vehari 61100, Pakistan
5
CIHEAM—Institute Agronomique Mediterraneen de Montpellier (IAMM), 34090 Montpellier, France
6
CSIRO Sustainable Ecosystem, National Research Flagship, Toowoomba, QLD 4350, Australia
7
Climate Change, U.S.-Pakistan Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad, Pakistan
8
Department of Agronomy, Muhammad Nawaz Sharif University of Agriculture, Multan, Pakistan
9
John Muir Institute of Environment, University of California, Davis, CA, USA
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Introduction Edible oil scarcity in Pakistan (Nasim et al. 2012) is becoming horrible (GOP 2017) due to increasing population. The country has to import 86% of the total edible oil requirement during 2015–2016 (Govt. of Pakistan 2017). Sunflower has ability to reduce the deviation between edible oil demand and its native production (Munir et al. 2007; Nasim et al. 2011; Awais et al. 2015). Sunflower crop was planted on an area of 0.09 million hectare with seed and oil production of 0.11 and 0.04 million tons, respectively (GOP 2017). The possible disastrous potential impacts of climate change on agricultural productivity have become a major challenge in many countries situated at lower latitudes like Pakistan. Agriculture contributed for 20.9% of the Gross Domestic Product (GDP), and it is a source of livelihood for a huge proportion of rural population (43.5%) (GOP 2015). The traditional farming practices render the most economical sector (agriculture) of Pakistan climate-dependent. The climate change drastically affects the agricultural resources of Pakistan (land and water) through increased evapotranspiration, glaciermelt, increased land-degradation, more emission of greenhouse gases, de-nitrification, ammonia immobilization, increasing crop-water requirements, and unavailability of plant-nutrients. The frequency of extreme weather events like drought, floods, heat and cold waves, tropical cyclones and tornadoes, dust and thunder storms, etc., is increasing with negative consequences to the crop productivity. An increase in average temperature (0.6 to 1.0 °C) since 1900 and decrease in precipitation (10 to 15%) in coastal regions of Pakistan during the previous 40 years have recorded. Rainfall will be increased in humid and subhumid areas during monsoon season; however, rainfall will be decreased in coastal and hyper-arid areas during winter and summer (Conway et al. 1994; Aguera et al. 1997; Garg et al. 2001; Cline 2007; IPCC 2007). Climate change effects on crop growth (Yano et al. 2007), development, yield (Lobell and Asner 2003; Peng et al. 2004; Tao et al. 2006; Lakho et al. 2017), and crop management are well known, globally. Changing climate has a distinct effect on production of many crops like soybean, rice, and wheat (Keeling et al. 1989; Liu and Teskey 1995; IPCC 1996; Krupa 2003; Lobell and Field 2007; Tao et al. 2008; Adeel et al. 2017; Rozina et al. 2017; Javaid et al. 2017). Certain crops indicate a positive response to enriched CO2, whereas many researchers reported reduced grain quality in response to increased CO2 (Wolf and Diepen 1995; Upreti 1999; Tubiello et al. 2000; Garg et al. 2001; Parry et al. 2004; Xiong et al. 2007; Malla 2008). Even a small fluctuation in temperature can cause more difficulties for crop production. Rainfall reduction also has a negative effect on field crops (Gbetibouo and Hassan 2005; Anjum et al. 2016; Qasim et al. 2016). Growth period of many crops was shortened with increasing temperature (Peiris et al. 1996; Singh et al. 1998; Giannakopoulos et al. 2009; Ishaq and Memon 2016; Khan et al. 2016). The changed climate
(high temperature) is probable to disturb sunflower yield both by accelerating phenological phases and by decreasing time of dry matter production (Moriondo and Bindi 2007; Ali et al. 2016; Mehmood et al. 2016; Jan et al. 2017; Abbas et al. 2017). Hence, climate smart agriculture (potential impact assessment, sustainable agriculture, and adaptation and mitigation strategies to climate change) is an important aspect to minimize the drastic effects of climate change on crop productivity. Decision Support System for Agro-technology Transfer (DSSAT v 4.6) is a windows-based computer software that contain tools and effective programs to manage genetic, soil, crop, pests, and weather data. The main objective of crop models is to appraise the variation in crops yield as a function of management practices (Boote et al. 2008; Hoogenboom et al. 2011) as well as soil (Jones et al. 2003) and weather conditions (Boote et al. 2010). The cropping system models present in DSSAT has already proved authentic in providing decision support to agricultural scientists in Pakistan (Mubeen et al. 2013, Mubeen et al. 2016; Nasim et al. 2016a; Awais et al. 2017a). OILCROP-SUN model, a part of DSSAT (Rinaldi et al. 2003; Awais et al. 2017a), has successfully used to simulate planting density, N fertilizer (Nasim et al. 2016b), and water requirement of sunflower crop (Awais et al. 2017a). This model can also predict the potential impacts of climate change on sunflower growth and yield (El-Marsafawy 2006; Awais 2015) and according to Nasim (2010) and Awais (2015), OILCROP-SUN model predict a positive influence of increased CO2 concentration and negative influence of increased temperature on sunflower productivity in Pakistan. There are lots of studies carried out all around the world but only a few studies have been reported about the climate change effects on agricultural crops in Pakistan. So, there is a need of effect identification and implementation of adaptation strategies to cope with vulnerabilities in agricultural sector of Pakistan. For that purpose, the current study was carried out to use OILCROP-SUN model for assessing the climate change effects on sunflower and to plan sustainable, environmental friendly, and long-term crop management practices to minimize the drastic effects of climate change on sunflower in Pakistan.
Materials and method An experiment was conducted during spring seasons of 2012 and 2013 at the Agronomic Research Farm, University of Agriculture Faisalabad, Pakistan (31o26' N, 73o06' E) to collect the required data for calibration and evaluation of OILCROP-SUN model. The seeds of hybrid Hysun-33 were sown on same date (1st March) during both study years. Awais et al. (2015) already revealed the soil properties and weather conditions of experimental site. The site-specific recommended production technology (Table 1) was adapted to maximize the sunflower yield.
Author's personal copy Environ Sci Pollut Res Table 1
Production operations performed in 2012 and 2013
Operations
Year 2012
2013
Sowing dates Plant spacing
01.03.2012 25 cm
01.03.2013 25 cm
Row spacing
60 cm
Seed rate Sowing method
7 kg ha−1 Ridge sowing
60 cm 7 kg ha−1 Ridge sowing
Crop establishment Fertilizer application
11.03.2012
11.03.2013
P (DAP) 60 kg ha−1 K (SOP) @ 60 kg ha−1 N (Urea) 1st Dose
01.03.2012 01.03.2012
01.03.2013 01.03.2013
N application in three splits 01.03.2012 01.03.2013
2nd Dose 3rd Dose
20.03.2012 21.04.2012
20.03.2013 18.04.2013
Thinning
19.03.2012
19.03.2013
Earthing up Irrigation scheduling 1
18.04.2012
15.04.2013
20.03.2012
20.03.2013
2 3 4
07.04.2012 21.04.2012 11.05.2012
05.04.2013 18.04.2013 06.05.2013
5 Harvesting
21.05.2012 12.06.2012
21.05.2013 08.06.2012
The model The model involves six genetic coefficients to estimate growth, development, and achene yield of sunflower. Awais et al. (2017a, b) reported the values of genetic coefficients for hybrid Hysun-33. The genetic coefficients include P1 (the value of juvenile phase duration), P2 (amount (days/h) that development is slowed if crop is grown in photoperiod smaller than the optimum (15 h)), P5 (expressed period of the first anthesis-physiological maturity stage), G2 (maximum possible achene number in one head), G3 (kernel growth rate during linear kernel filling stage), and O1 (oil contents related coefficient).
Model calibration The model was calibrated with the field experimental data obtained during 2012. This simulation with the present climatic conditions provides a baseline. The calibration of OILCROP-SUN model requires integrated effect of field experimental data, soil data, DSSAT software, historical weather data, and experts from different institutions and disciplines. Metrological data (daily solar radiation, precipitation, maximum, and minimum temperatures) for weather file were
obtained from the observatory located at Department of Agronomy, University of Agriculture Faisalabad. The soil file (SBuild) contains information related to soil like structure, color, texture, pH, etc. (Soltani and Hoogenboom 2007). We selected Lyallpur series that represents sandy clay loam soils. Flood irrigation method was selected in experimental data file of the model to avoid any stress during simulation.
Model evaluation The accuracy of OILCROP-SUN model was evaluated by comparing simulated values of anthesis days, maturity days, achene yield, LAI, TDM, and oil contents with their field observed values during 2013.
Statistical analysis Model performance during calibration for LAI and TDM at different stages was evaluated with index of agreement (d) as mentioned below: " # ∑ni¼1 ðPi −Oi Þ2 d ¼ 1− 0 0 2 ∑ n P þ O i¼1
i
i
where P indicates predicted value, n is the number of observation, and O shows experimental observation (Nasim et al. 2016b).
Climate change potential impacts assessment After successful calibration of model, Climate change effects on maturity duration, maximum leaf area index, TDM, and achene yield of sunflower hybrid Hysun-33 were estimated using OILCROP-SUN model (v 4.5). The model was run with a set of climate change scenarios attaining from General Circulation Model (GCM) to determine the effects of climate change (elevated CO2 and temperature) on phenology and yield of sunflower. The influence of climate change was accessed by comparing model predicted grain yield under current and changed climatic conditions.
Adaptation practices Some adaptation measures were tested using DSSAT model to determine the most favorable adaptation strategy for farmers to adjust the possible drastic effects of future climate change (2025 and 2050). The tested adaptation treatments included planting dates (8, 15, 22 February and 1, 8, 15, 22 March), plant populations (47619, 55555, 66666, 83333, and 111111 plants ha−1), irrigation amounts (225, 262.5, 300, 337.5, 375.5, 412.5, 450, 487.5, and 525 mm), irrigation regimes (six irrigations, i.e., irrigation application after every 15 days; five irrigations, i.e., irrigation at 20 days of sowing, 40 days of
Author's personal copy Environ Sci Pollut Res
sowing, at flowering, at seed formation, and at milking stage, missing first irrigation, missing second irrigation, missing third irrigation, missing fourth irrigation, and missing fifth irrigation), and N rates (30, 60, 90, 120, 150, 180, 210, 240, and 270 kg N ha−1).
Table 3
Observed and simulated data during model evaluation
Variable
Simulated
(%) Difference
Anthesis (days)
61
61
0
Maturity (days)
97
97
0
4.58 3045 9773
3.83 3059 10,354
− 19.58 0.46 5.61
39.75
42.15
5.69
Maximum LAI Achene yield (kg ha−1) Total Biomass (kg ha−1) Oil contents (%)
Results
Observed
Model calibration and evaluation Tables 2 and 3 indicate the simulation results of OIL CROPSUN model about anthesis and maturity days, leaf area index, total dry matter, and achene yield of sunflower during model calibration (2012) and evaluation (2013). Model estimated same number of days to anthesis (65 vs 65 in 2012 and 61 vs 61 in 2013) and maturity (101 vs 101 in 2012 and 97 vs 97 in 2013) as observed in the field conditions. The simulated LAI and TDM curves were very close to their corresponding field observed cure as Fig. 1 revealed the very low d-statistic values (0.96 in 2012 and o.95 in 2013 for LAI and 0.99 in 2012 and 0.98 in 2013 for TDM). Similarly, percent difference (PD) values were remained very low for maximum LAI (− 11.8 and − 19.58% during calibration and evaluation of model, respectively), TDM (10.07% in 2012 and 5.61% in 2013), achene yield (0.09 and 0.46% in 2012 and 2013, respectively), and oil contents (1.38% in 2012 and 5.69 in 2013).
Climate change effects on sunflower productivity Potential impact of elevated CO2 levels on sunflower Table 4 reveals the OILCROP-SUN model predictions about the effects of increased CO2 concentration (430 and 550 ppm) on sunflower phenology, leaf area index, achene yield, and total dry matter at existing temperature and precipitation. No change in crop duration was observed in response to increased CO2 concentration. However, increases in CO2 concentration have a little but positive effect on maximum LAI. LAI was increased by 0.24% with 430 ppm and 0.48% with 550 ppm Table 2
Observed and simulated data during model calibration
Variable
Observed
Simulated
(%) Difference
Anthesis (days) Maturity (days) Maximum LAI Achene yield (kg ha−1) Total biomass (kg ha−1) Oil contents (%)
65 101 4.66 3193 10,159 39.36
65 101 4.17 3196 11,297 39.91
0 0 − 11.8 0.09 10.07 1.38
CO2 levels, respectively. Promotive influences of increased CO2 were also found on achene yield, and it was increased from 3196 to 3275 kg ha −1 at 430 ppm CO 2 and to 3298 kg ha−1 at 550 ppm CO2 level. Similarly, changing CO2 concentration from 393 to 430 ppm has a slight increasing effect on total dry matter and TDM was increased by 4.67% over the current TDM. However, 550 ppm CO2 level produced considerably more TDM (9.87%) as compared to the current TDM.
Potential impact of elevated temperature on sunflower Tables 5 and 6 exhibited that 1 °C increase in temperature that is estimated in 2025 condensed crop duration by 3.96%, and same results were recorded at increased CO2 concentrations (430 and 550 ppm). This decrease in crop duration was higher (7.92%) with increase in temperature 2 °C (expected in 2050). Crop phenology was not changed with interactive effects of temperature and CO2. The increase in temperature had also negative effects on leaf area index (LAI), and these effects were more at present CO2 concentration (393 ppm). The negative effects of increasing temperature were reduced to some extent at higher CO2 concentrations (430 and 550 ppm). It means crop took benefit from increased CO2 even at high temperature (1 or 2 °C increase in current temperature). The reduction in LAI was 2.88 and 8.87% at 1 °C (in 2020) and 2 °C (in 2050) increase in temperature, respectively, under current CO2 concentration. Increase in CO2, however, reduced the negative potential impacts of high temperature. The reduction in LAI was 2.15% at 1 °C and 7.19% at 2 °C increase in temperature under 430 ppm CO2. Similarly, negative effects of increased temperature were reduced to some extent at 550 ppm CO2, but OILCROP-SUN model still predicted a reduction of 1.20 and 4.80% in LAI. Table 5 indicates reduction in achene yield with increasing temperature; however, these effects were lowest with 550 ppm CO2 as compared to 430 or 393 ppm CO2 (Table 6). The reduction in achene yield with 393 ppm CO2 was 9.11% at 1 °C and 18.71% at 2 °C increase in temperature. The achene yield was declined by 6.45 and 16.33% at 1 and 2 °C increase in temperature, respectively, under increased CO2 concentration (430 ppm).
Author's personal copy Environ Sci Pollut Res 5
(a)
d = 0.96
(b)
d = 0.95
Leaf area index
4
3
2
1
(2013)
(2012) 0 12000
(c)
d = 0.99
(d)
d = 0.98
Total dry matter (kg ha-1)
10000
8000
6000
4000
2000
(2012)
(2013)
0
0
20
40
60
80
100
0
20
40
60
80
100
Days after sowing
− = Simulated LAI and TDM,
= Observed LAI and TDM
Fig. 1 Observed and model-simulated LAI (a) and TDM (b) of sunflower during 2012 and 2013
OILCROP-SUN model predicted achene yield of 3066 kg ha−1 with increase of 1 °C at 550 ppm CO2 concentration, and it is 4.07% less over current yield (3196 kg ha−1). Similarly, an increase of 2 °C at 550 ppm CO2 also produced reduction in achene yield (11.89%). Total dry matter was also negatively affected by increase in present average temperature, and TDM was decreased from 11,297 kg ha−1 (current TDM) to 10,775 kg ha−1 (1 °C increase in temperature) and 9539 kg ha−1 (2 °C increase in temperature). The reduction in TDM as compared to current was ranged from 3.41 and 11.65% at 1 and 2 °C increase in temperature, respectively, with 430 ppm CO2 concentration, whereas the reduction in TDM with 550 ppm CO2 concentration was 2.33 and 6.15% at 1 and 2 °C increase in temperature, respectively.
Table 4 Impact of CO2 concentration on crop duration, growth, achene yield, and TDM of sunflower
Variables
Crop duration (days) Max. leaf area index Achene yield (kg ha−1) TDM (kg ha−1)
Current
101 4.17 3196 11,297
Evaluation of adaptation strategies Different sowing dates as an adaptation strategy Influence of planting time (as an adaptation strategy) on achene yield of sunflower was determined using DSSAT (V.4.5) (Table 7). The model predicted that a delay in sowing time as compared to current sowing date will further decrease the sunflower achene yield, while yield was enhanced with early planting of sunflower. A decline in sunflower seed yield of 12.78, 25.98, and 30.23% in 2025 and 11.36, 29.43, and 40.83% in 2050 was predicted with delayed sowing of 7, 14, and 21 days, respectively. On the other hand, 7, 14, and 21 days early sowing of sunflower would produce more seed
430 ppm
101 4.18 3275 11,851
Difference (%) 0 0.24 2.41 4.67
Current
101 4.17 3196 11,297
550 ppm
101 4.19 3298 12,534
Difference (%) 0 0.48 3.09 9.87
Author's personal copy Environ Sci Pollut Res Table 5 Impact of temperature on crop duration, growth, achene yield, and TDM of sunflower
Variables
Crop duration (days) Max. leaf area index Achene yield (kg ha−1) TDM (kg ha−1)
Current
101 4.17 3196 11,297
yield (3.61, 10.87, and 14.47% in 2025 and 3.29, 8.18, and 12.51% in 2050) as compared to current situation.
Varying plant populations as an adaptation strategy Studies on different plant population of sunflower were carried out with DSSAT model. Increasing plant population from current level (66,666 plants ha−1) to 83,333 plants ha−1 increased the grain yield by 3.61% in 2025 and 7.09% in 2050 (Table 8). However, further increase in plant population (1,11,111 plants ha−1) would have a very little positive effect on seed yield (4.41 and 7.91% in 2012 and 2013, respectively) (Fig. 2).
Different irrigation amounts as an adaptation strategy
1 °C 2025
97 4.05 2905
Difference (%) − 3.96 − 2.88 − 9.11 − 4.62
10,775
Current
101 4.17 3196 11,297
2 °C 2050
Difference (%)
93 3.8 2598
− 7.92 − 8.87 − 18.71
9539
− 15.56
improvement in sunflower seed yield as it was enhanced only by 0.76, 1.09, 0.93, and 0.53% in 2025 and 0.56, 0.42, 0.11, and 0.04% in 2050 with 10, 20, 30, and 40% more water application.
Different irrigation regimes as an adaptation strategy Different irrigation regimes were analyzed with DSSAT model to optimize irrigation requirements of sunflower crop in 2025 and 2050. Table 10 indicates that six irrigations treatment (irrigation after every 15 days) would increase seed yield only by 0.96% in 2025 and 0.18% in 2050. While all other treatments (missing of first, second, third, fourth, and fifth irrigation) would decrease the seed yield in both study years (2025 and 2050).
Different nitrogen doses as an adaptation strategy The model simulation revealed that sunflower sown at current planting date, supplied with 375 mm water and 150 kg ha−1 N would produce 2990 kg ha−1 in 2025 and 2816 kg ha−1 seed yield in 2050. Different amounts of water were tested to optimize the water requirements of sunflower under changed climatic conditions (2025 and 2050). Table 9 indicates that 10, 20, 30, and 40% reduced water application will decrease sunflower yield by 0.80, 9.26, 16.29, and 28.16% in 2025 and 0.60, 7.0, 14.7, and 26.42% in 2050. Similarly, more irrigation water application will not contribute any considerable Table 6 Impact of temperature and CO2 on growth, achene yield, and TDM of sunflower
Table 11 indicates the model simulation about the influence of different N rates on sunflower seed yield. DSSAT model predicted a seed yield of 2990 and 2816 kg ha−1 with current crop production technology in climatic conditions of 2025 and 2050, respectively. A decrease in N rate showed a successive negative potential impact on achene yield (Fig. 3), and minimum achene yield (1908 kg ha−1 in 2025 and 1792 kg ha−1 in 2050) was recorded with the lowest N rate (30 kg ha−1). An increase of 5.47, 9.20, 11.22, and 12.11% in 2025 and 4.61,
Variables
Current
1 °C
CO2 conc. Crop duration (days) Max. leaf area index
430 ppm 101 4.17
Achene yield (kg ha−1) TDM (kg ha−1) CO2 conc. CO2 conc. Crop duration (days) Max. leaf area index Achene yield (kg ha−1) TDM (kg ha−1)
3196 11,297 550 ppm
2990 10,911
− 6.45 − 3.41
3196 11,297
101 4.17 3196 11,297
97 4.12 3066 11,034
− 3.96 − 1.20 − 4.07 − 2.33
101 4.17 3196 11,297
97 4.08
Difference
− 3.96 − 2.15
Current
101 4.17
2 °C
93 3.87
Difference
− 7.92 − 7.19
2674 9981
− 16.33 − 11.65
93 3.97 2816 10,602
− 7.92 − 4.80 − 11.89 − 6.15
Author's personal copy Environ Sci Pollut Res Table 7 Adaptation strategies (different planting dates) to mitigate climate change impacts on sunflower in 2025 (430 ppm CO2 and 1 °C increased temperature) and 2050 (550 ppm CO2 and 2 °C increased temperature)
Planting dates
Grain yield (kg ha−1)
− 21 days (8 Feb.) − 14 days (15 Feb.)
3496 3355
14.47 10.87
− 7 days (22 Feb.)
3102
03.61
Present (1st March)
2990
+ 7 days (8 March) + 14 days (15 March)
2608 2213
− 12.78 − 25.98
2496 1987
− 11.36 − 29.43
+ 21 days (22 March)
2086
− 30.23
1666
− 40.83
2025
8.90, 12.03, and 13.38% in 2050 was predicted with 180, 210, 240, and 270 kg N ha−1.
Discussion OILCROP-SUN model already proved to have the capability to simulate the sunflower productivity at different planting dates (Rinaldi et al. 2003), plant population (Awais 2015), nitrogen (Ashfaq et al. 2013; Awais et al. 2017a), hybrids (Nasim et al. 2017, 2016c), and deficit irrigation (Awais et al. 2017b). So, a number of crop modelers proved the validity of this model in arid (Nasim et al. 2016a), semi-arid (Awais et al. 2017a, b; Nasim et al. 2016b), and sub-humid (Nasim et al. 2016a) conditions of Pakistan. Similarly, ElMarsafawy (2006) reported the robustness of this model for quantification of climate change effects on sunflower in Egypt. He further used this model to develop some adaptation practices to reduce the potential impacts of climate change on sunflower. So, it is the need of the time to evaluate this model to identify the potential impacts of climate change on sunflower crop in Pakistan. Current simulation revealed negative consequences of climate change on sunflower production in Pakistan. Model also suggested early sowing (up to 21 days) and increasing plant population (83,333 plants ha−1) of sunflower crop in order to sustain productivity under possible drastic climatic conditions. Data for the calibration and evaluation of the OILCROPSUN was collected from the field experiment that was carried out for 2 years (2012 and 2013) in Faisalabad, Pakistan. Good simulation outcomes during model calibration (first year Table 8 Adaptation strategies (different plant populations) to mitigate climate change impacts on sunflower in 2025 (430 ppm CO2 and 1 °C increased temperature) and 2050 (550 ppm CO2 and 2 °C increased temperature)
Diff. (%)
Grain yield (kg ha−1)
Diff. (%)
2050 3219 3067
12.51 8.18
2912
3.29
2816
simulation) and evaluation (second year simulation) confirmed the model robustness for semi-arid conditions. Percent difference (calculated between the model-estimated and field-collected data for different parameters) in current study was in agreement with Nasim et al. (2016a) who evaluate the same model in Pakistan and reported PD values of 1.1, 0.0, 9.3, and − 6.8% for anthesis, maturity, TDM, and oil contents simulation during calibration. While respective values of PD for abovementioned parameters were 3.8, 2.5, 4.3, and −3.8% during model evaluation. Regarding LAI and TDM simulation (at different stages) model made good estimation throughout the growing season. Nasim et al. (2016a) described very high d-stat for both of these parameters (0.96 for LAI and 0.99 for TDM) under sub-humid conditions of Pakistan. OILCROP-SUN model made positive prediction about the potential impact of increased CO2 concentration on sunflower crop. Reduction in transpiration rate due to partially closing of stomata at higher CO 2 concentration (Ganopolski and Rahmstorf 2001) finally led to higher water use efficiency. Photosynthesis and water-use efficiency were increased with increasing CO2 in atmosphere (Trnka et al. 2007) that ultimately produced maximum biomass. The simulation results revealed the stability of sunflower phenology with changed concentration of CO2, and this was in line with Nasim et al. (2016b) who reported that the fluctuation in CO2 has no effect on days to anthesis and days to physiological maturity. However, he described the positive and desirable effects of elevated CO2 on LAI, achene yield, and TDM of sunflower. Similar effects of increased CO2 concentration on different crops were also explained by Kimbal (1983), Bunce (2000),
Plant population (plants ha−1)
Grain yield (kg ha−1)
47,619 55,555 Present (66,666) 83,333 1,11,111
2025 2062 2466 2990 3102 3128
Diff. (%)
− 31.04 − 17.53 03.61 04.41
Grain yield (kg ha−1) 2050 1966 2351 2816 3031 3058
Diff. (%)
− 30.18 − 16.51 07.09 07.91
Author's personal copy Environ Sci Pollut Res 3200
Fig. 2 Response of achene yield of sunflower to different plant populations (adaptation practice)
Achene yield (kg ha-1)
3000
2025 2050
2800
2600
2400
2200
2000
1800 40
50
60
70
80
90
100
110
120
Plant populations (thousand ha-1)
Abraha and Savage (2006), Settle et al. (2007), Sultana et al. (2009), and Thornton et al. (2010). Crops are very sensitive to change in temperature, and their growth can be slowed down at too low or too high temperatures (Challinor and Wheeler 2008; Giannakopoulos et al. 2009). High temperature is the main reason of decreased sunflower seed yield with delay in sowing date. High temperature enhanced the capability of a crop to accumulate required photo thermal time for physiological maturity in less time that shortened the crop duration and subsequently photosynthates assimilation. When crop accumulates required thermal and photo-thermal time for a particular growth stage, the crop moves towards next stage (Xiao et al. 2013). Actually increased temperature would reduce the anthesis period of sunflower as compared to baseline. This will shift the grain formation stage towards the more hotter season as compared to usual; grain will accumulate lesser assimilates resulting into small grain size and ultimately lower achene yield. Reproductive stage of a crop, being a most critical stage, should take place under optimum environmental Table 9 Adaptation strategies (different irrigation amounts) to mitigate climate change impacts on sunflower in 2025 (430 ppm CO2 and 1 °C increased temperature) and 2050 (550 ppm CO2 and 2 °C increased temperature)
conditions (particularly temperature) (Visser and Both 2005). Similarly, duration of abovementioned stage should also be enough long for maximum transfer of assimilates into grain. As crop phenology is greatly temperature reliant, so a warmer condition is likely to influence both of abovementioned terms by progressing developmental stages to new climatic conditions and by decreasing duration for assimilate production. The growing duration of crops was decreased with increasing temperature (Bindi et al. 1996; Harrison and Butterfield 1996; Guerena et al. 2001; Alexandrov and Hoogenboom 2001; Ganopolski and Rahmstorf 2001; Giannakopoulos et al. 2009) that finally reduced biomass accumulation. The model predicted reduction in drastic potential impacts of increased temperature on sunflower productivity with elevated CO2, and it was in line with Nasim et al. (2016b). Crop modeling is an effective approach in order to assess climate change effect and to develop some economic adaptation practices (Challinor et al. 2005). The length of different
Irrigation amounts
Grain yield (kg ha−1)
− 40% present amount − 30% present amount − 20% present amount − 10% present amount Present (375 mm) + 10% present amount + 20% present amount + 30% present amount + 40% present amount
2148 2503 2713 2966
Diff. (%)
Grain yield (kg ha−1)
− 28.16 − 16.29 − 09.26 − 0.80
2072 2402 2619 2799
0.76 1.09 0.93 0.53
2832 2828 2819 2817
2025
2050
2990 3013 3023 3018 3006
Diff. (%)
− 26.42 − 14.70 − 07.00 − 0.60 2816 0.56 0.42 0.11 0.04
Author's personal copy Environ Sci Pollut Res Table 10 Adaptation strategies (different irrigation regimes) to mitigate climate change impacts on sunflower in 2025 (430 ppm CO2 and 1 °C increased temperature) and 2050 (550 ppm CO2 and 2 °C increased temperature)
Irrigation amounts
Grain yield (kg ha−1)
*Six irrigations **Present (5 irrigation)
3019
Diff. (%)
Grain yield (kg ha−1)
2025
Diff. (%)
2050 0.96
2821
2990
0.18 2816
Missing 1st irrigation
2896
− 3.14
2707
Missing 2nd irrigation
2939
− 1.71
2771
− 3.87 − 1.60
Missing 3rd irrigation Missing 4th irrigation
2611 2708
− 12.68 − 9.43
2410 2583
− 14.42 − 8.27
Missing 5th irrigation
2751
− 7.99
2597
− 85
*irrigation application after every 15 days, **irrigation at 20 days of sowing, 40 days of sowing, at flowering, at seed formation and at milking stage
developmental stages is an important determinant of the crop productivity (Sadras and Monzon 2006), and influence of high temperature may be increased or decreased depending on timing of phonological stage. Sunflower needs cold conditions (low temperature) at germination and during early growth stages, and hence, it is planted when weather is cool (low temperature). As the sunflower crop was usually planted during spring (after winter) in Pakistan, so an early sowing of this crop escaped the anthesis and grain formation stage from high-temperature stress (end of May and early June). Model simulation revealed a linear rise in sunflower seed yield with increasing plant population from 66,666 to 1,11,111 plants ha−1 which was not in agreement with the current field situations. Increased plant population (83,333 plants ha−1) produced a positive contribution towards the sunflower seed yield (Awais et al. 2013); however, further higher plant populations (1,20,000 plants ha−1) produced a negative impact on sunflower achene yield (Mojiri and Arzani 2003). Increased plant population beyond a certain level would have enhanced the competition between plant for space, light, water, nutrients, and radiation interception that subsequently produced decreased achene yield. It means that the model was not capable to consider the competition among plants at higher plant population (1,11,111 plants ha−1). Analysis indicated that seed yield of sunflower decrease with reduced plant populations Table 11 Adaptation strategies (different N rates) to mitigate climate change impacts on sunflower in 2025 (430 ppm CO2 and 1 °C increased temperature) and 2050 (550 ppm CO2 and 2 °C increased temperature)
(17.53 and 16.51% reduction with 55,555 plants ha−1 and 31.04 and 30.18% reduction with 47,619 plants ha−1 in 2025 and 2050, respectively). As high temperature will reduce the crop duration by 4 days in 2025 and 8 days in 2050, so 10% less water application will not produce any considerable negative effect on sunflower economic yield. Moreover, sunflower can tolerate short duration water scarcity (Turhan and Baser 2004). Maximum seed reduction (12.68% in 2025 and 14.42% in 2050) was predicted when irrigation application (third) was skipped at flowering stage. Sunflower productivity was reduced with the water deficiency at flowering stage (David et al. 2011; Goksoy et al. 2004). High temperature already shortened the crop growth duration in 2025 and 2050, so excess water application is only the wastage of precious water resources. Each successive decrease in N application (from 150 to 30 kg ha−1) reduced while increase in N application rate (from 150 to 270 kg ha−1) enhanced the sunflower productivity. However N application rate of 270 kg ha−1 did not produce significant increase over 240 kg N application rate for both 2025 and 2050 sunflower. Nitrogen application enhanced the green color of leaves (Tisdale et al. 2003), photosynthatic efficiency (Rouphael et al. 2007), anthesis and maturity days (Awais et al. 2013), leaf area index, total dry matter (Nasim et al. 2011), crop growth rate (Nasim et al. 2012), head
N rates (kg ha−1)
Grain yield (kg ha−1)
30 60 90 120 Present (150 kg ha−1) 180 210 240 270
1908 2373 2604 2816
Diff. (%)
Grain yield (kg ha−1)
− 36.19 − 20.64 − 12.91 − 5.82
1792 2162 2449 2661
5.47 9.20 11.22 12.11
2952 3091 3201 3251
2025
2050
2990 3163 3293 3368 3402
Diff. (%)
− 36.36 − 23.22 − 13.03 − 5.50 2816 4.61 8.90 12.03 13.38
Author's personal copy Environ Sci Pollut Res 3600
Fig. 3 Response of achene yield of sunflower to different N rates (adaptation practice)
3400 2025 2050
Achene yield (kg ha-1)
3200 3000 2800 2600 2400 2200 2000 1800 1600 0
50
100
150
200
250
300
N rates (kg ha-1)
diameter, thousand achene weight, and number of achenes per head (Awais et al. 2015) that finally positively contributed to seed yield of sunflower (Awais et al. 2017a, 2017b). However, excess N application reduced the nitrogen use efficiency (Fanjun et al. 2013) and oil contents of sunflower (Awais et al. 2013) enhanced nitrogen leaching loss and environmental pollution. It also increased the cost of production. So different nitrogen management tools like green seeker and soil moisture sensor should be used to optimize the quantity, timing, and method of nitrogen application and to increase nitrogen use efficiency under changed climatic conditions (2025 and 2050).
production. On the other hand, missing second irrigation can be recommended as an adaptation strategy without any considerable reduction in achene yield under water scarce conditions of 2025 and 2050 in Pakistan. Similarly, as the farmers in Pakistan usually irrigate their crops with excess water (75 mm or even more), a 10% reduction in this amount seems to be a best management practice to conserve water under climate change conditions (2025 and 2050). Further studies should focus on evaluation and application of OILCROP-SUN model for different agro-climatic regions where sunflower is an important crop, are needed, for adapting agricultural management strategies for better sunflower productivity as well as to improve the livelihood of small-holding farmers, which are mainstay of Agriculture in Pakistan.
Conclusions
Acknowledgements The first author is grateful to the worthy reviewers for the productive suggestions to improve the manuscript. Furthermore, the corresponding author is thankful to Higher Education Commission of Pakistan for funding of research project.
The climate change analysis indicated the negative effects of climate change on sunflower production in Pakistan. The OILCROP-SUN model predicted a decrease in crop duration, leaf area index, total dry matter, and achene yield at increased temperatures (1 °C in 2025 and 2 °C in 2050) as compared to the present. Although increased CO2 concentration showed a positive effect on sunflower but it did not compensate the negative effects of increased temperature. The model simulation revealed that early sowing of sunflower with increased plant population (83,333 plants ha−1) is one of the most favorable, long terms, environment friendly, easy to adopt, and economical adaptation practices to reduce vulnerability of sunflower to possible high temperature in feature. Flowering is the most delicate stage to water scarcity for sunflower crop plants. In case of normal water availability irrigation after every 15 days (six irrigation treatment) may reduce the negative effects of climate change on sunflower
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