based on the principles of Site-Specific Nutrient Management (SSNM). It facilitates the ... âNutrient Management of Maize Genotypes under Conservation Agriculture Using Nutrient ...... Punjab, Haryana, TN,. Delhi ...... Malhi, S.S., Lemke, R., Wang, Z.H. and Chhabra, B.S. 2006. Tillage .... www.upcaronline.org/dynamic2.pdf.
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CHAPTER 1
INTRODUCTION
Maize (Zea mays L.) has been considered a unique plant since the time peoples developed it to be their staple food. It is an important food and feed crop of the world and often referred to as “Queen of cereals, back bone of America, miracle crop, king of grain crops”. Maize is 3rd most important cereal crop after only wheat and rice. It has very high nutritional value with starch (72%), protein (10%), oil (4.8%), fibre (8.5%), sugar (3%) and ash (1.7%) (Chaudhary., 1983). The expanded use of maize in industry gives it a prominent place in the agricultural economy. Refineries use maize crop for producing products as corn oil, gluten for animal feed, corn starch, syrup, dextrose (used mainly by pharmaceutical industry as the starting material for manufacturing vitamin C and penicillin), alcohol for beverages, ethanol, high fructose corn syrup (used mainly by soft drink industry), biodegradable chemicals and plastics, ready to eat snack food and breakfast cereals, corn meal, grits, flour and additives in paints and explosives. It is estimated that worldwide maize yields 4000 industrial products (Sprague et al., 1988). Maize, a crop of worldwide economic importance, together with rice and wheat, provides approximately 30% of the food calories to more than 4.5 billion people in 94 developing countries and the demand for maize in these countries is expected to double by 2050. In India, maize is third most important food crop among the cereals and contributes to nearly 9% of the national food basket (Dass et al., 2012). In India it occupy fourth place in area and third in production among cereals. It is grown in 8.78 million hectares with production of 21.76 million tonnes and productivity of 2.47 tonnes per hectare during 2011-12 (upcaronline.org). In India maize is gaining popularity as a rice-maize cropping system by replacing the second rice crop in the existing rice-rice or rice-rice-pulse cropping systems due to water scarcity in rice growing regions and prevalence of incidence of diseases in pulses. Now days, maize is gaining importance in conservation agriculture. Because maize is wider spaced crop, has slow growth rate in its early stage, which leads to more loss of water through evaporation and substantial reduction in grain yield due to heavy infestation of weed. To overcome this problem conservation agriculture is mostly adopted. But production is limited by low fertilizer efficiency, inadequacy in existing fertilizer recommendations and the ignorance of nutrients other than N, P, and K and hence is a serious concern.
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There exists significant opportunity to increase fertilizer efficiency and productivity of a crop by adopting Nutrient Expert-based field specific fertilizer recommendations. The Nutrient Expert for Hybrid Maize is a new, computer-based decision support tool developed to assist local experts to quickly formulate fertilizer guidelines for tropical hybrid maize based on the principles of Site-Specific Nutrient Management (SSNM). It facilitates the development of recommendation in the form of a quick guide for each region enabling local experts to run multiple scenarios to identify the most common characteristics or factors affecting fertilizer rates in the region. Originally in English, the Nutrient Expert for Hybrid Maize is also now available in Indonesian and Vietnamese languages. Moreover, nutrient requirements vary from field to field due to high variability in soil fertility across farmers’ fields and single homogenous and sub-optimal official state recommendations may not be very useful in improving maize yields. Also, the current scenario of escalating prices of fertilizers demands solution for optimized use of nutrients. Nutrient Expert, a new nutrient Decision Support System (DSS) offers solutions for providing field-specific fertilizer recommendations to improve the yield and economics of maize. Keeping all in view, it is considered worthwhile to carry out an investigation entitled “Nutrient Management of Maize Genotypes under Conservation Agriculture Using Nutrient Expert – Decision Support System” with the following objectives,
Objectives of the study:
1. To validate nutrient expert on hybrid maize under conservation agriculture with various hybrids. 2. To estimate resource-use efficiency of maize hybrids under conservation agriculture influenced by the nutrient expert based vis-à-vis current fertilizer recommendations. 3. To find out most economic nutrient management practice in various maize hybrids under conservation agriculture.
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CHAPTER 2
BACKGROUND
In this chapter an attempt has been made to review the work carried out on “Nutrient Management of Maize Genotypes under Conservation Agriculture Using Nutrient Expert – Decision Support System” in India and abroad. Cultivation of agricultural soils has until relatively recently predominantly been achieved by inverting the soil using tools such as the plough. Continual soil inversion can in some situations lead to a degradation of soil structure leading to a compacted soil composed of fine particles with low levels of soil organic matter (SOM). Such soils are more prone to soil loss through water and wind erosion eventually resulting in desertification, as experienced in USA in the 1930s (Biswas, 1984). To combat soil loss and preserve soil moisture soil conservation techniques were developed in USA known as conservation agriculture, this involves soil management practices that minimize the disruption of the soil’s structure, composition and natural biodiversity, thereby minimizing erosion and degradation, control annual weed and seed bank but also water contamination (Anonymous, 2001). Here the effect of nutrient management and conservation agriculture on growth, yield, nutrient uptake, carbon sequestration, soil chemical properties, soil fertility and economics has been discussed under different subheads. 2.1 Conservation agriculture and maize production Bunderson et al. (2013) Conservation agriculture (CA) practices are gaining importance in India. It aims to achieve sustainable and profitable agriculture and subsequently aims at improved livelihoods of farmers through the application of the three CA principles: minimal soil disturbance, permanent soil cover and crop rotations. CA holds tremendous potential for all sizes of farms and agro-ecological systems, but its adoption is perhaps most urgently required by small holder farmers, especially those facing acute labour shortages. It is a way to combine profitable agricultural production with environmental concerns, sustainability and it has been proven to work in a variety of agro-ecological zones and farming systems. Christian et al. (2013) studied effect of CA on soil parameters and maize yield over eight cropping seasons. Results showed that maize yields in CA systems were strongly affected by rainfall infiltration, which was 24–40% greater compared with the conventional
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ridge and furrow system. In some cases, maize yields in CA plots were double that of conventional tillage plots. The larger water infiltration observed in CA plots relative to conventional tillage indicated that CA systems may increase access to soil water by the crop and offset the negative effects of seasonal dry spells. 2.2 Effect of ZT vis-à-vis CT on performance of maize
Various on-farm participatory trials revealed little or no difference in yields performance of zero-till maize when compared to best managed conventional crop (Gupta et al., 2002). Paliwal (2003) demonstrated how a successful zero-till maize crop could be grown using different management practices. Jat et al. (2005) from Modipuram, Utter Pradesh reported that maize productivity was marginally higher with no-til (NT) than CT practices. Similarly, Srivastava et al. (2005) reported that the performance of QPM hybrids on a sandy loam soil was better under NT planting compared to CT with respect to yield, water productivity and profitability. A field experiment was conducted at PAU, Ludhiana by Ram et al. (2010) revealed that all the growth parameters (plant height, dry matter accumulation and leaf area index), yield attributes (cobs/plant, grains/cob and 1000-grain weight) and yields performance of maize under different conventional and zero tillage practices were observed statistically similar. The yields of wheat, maize and soybean obtained under zero tillage were comparable to conventional tillage provided the seeds were properly controlled (Mishra and Singh, 2005). Dhillon et al. (1987) reported from the response of maize and wheat in a sequence to different levels of tillage (zero tillage and one/four disking) for the last 12 years that different tillage treatments had non-significant effect on grain yield of both the crops. Ball-Coelho et al. (1998) reported higher root length density in 15-30 cm depth of soil profile in no-tillage than conventional tilled field but reported similar yield of corn in zero tillage and conventional tillage treatments. Kaputsa et al. (1996) reported similar grain yield of maize under no-till, chisel plough and mould board plough, but 15 % higher grain yield of soybean under notillage than under mould board plough treatment was reported by Hussain et al. (1999). However, the highest corn yield was observed with no-till system, indicating this as the most reliable conservation tillage system (Brar and Kumar, 2000; Torbert et al., 2001; Suleimenov and Pala, 2002).
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Similarly, Bachmann and Friedrich (2002) from Mongolia and Wall (2002) from Bolivia reported that no-tillage with direct seeding of crop significantly increased the yield of crops. There was an average of 17 % yield increase across soybean, maize and wheat crops compared to the conventionally tilled treatments. Pedersen and Lauer (2004) reported that the yields of wheat, maize and soybean obtained under zero tillage were comparable to conventional tillage provided the seeds were properly controlled. Contrarily, reduction in crop yield from no-tillage (Kaskarbayev et al., 2002) against deep tillage amounted for 16 %. Similarly, lower plant height, LAI, weight/cob and grain and stover yields of maize under minimum tillage than conventional tillage practices were reported by Singh et al. (2007a) from IARI, New Delhi. Jat et al. (2006) also reported marginally lower yields of maize under ZT than CT practices. The yield reduction in no-tillage had been explained by more weed infestation. However, water storage was higher in minimum or no-tillage treatments than conventionally tilled plots. Reducing the tillage practices resulted in lower grain yield of maize. Singh et al. (2011) reported that mean decrease under minimum tillage was 6.8–12.1 % in grain yield, and 5.9–17.1 % in stover yield compared with conventional tillage. 2.3 Effect of SSNM on maize yield Kumar et al. (2012) found that site-specific nutrient recommendations from recently developed Nutrient Expert decision support tool for wheat increased wheat yields and farmer profits over existing farmer fertilizer practices and generalized recommendations under both conservation agriculture and conventional tillage. Pampolino et al. (2012) found that SSNM has increased yield and profit in rice, maize, and wheat in major cropping systems in Asia, but, crop advisors have found it complex and difficult to implement in the field. Nutrient Expert (NE) was developed to provide crop advisors with a simpler and faster way to use SSNM. NE enables crop advisors to develop SSNM recommendations using existing site information. Nutrient Expert for Hybrid Maize (NEHM) increased yield of farmers in Indonesia and the Philippines. In Indonesia, NEHM increased yield by 0.9 t/ha. Where as in the Philippines, NEHM increased yield by 1.6 t/ha and profit by US$ 379/ha compared with farmer fertilizer practice. Satyanarayana et al. (2012) reported that conservation tillage practices are gaining importance for maize cultivation in southern India. Nutrient recommendations from NE were tested against farmer fertilizer practice and state recommendation under Conventional Tillage (CT) and Conservation Agriculture (CA) during both the growing seasons. Across seasons,
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NE recorded higher grain yield in CA (9.3 t/ha) in comparison to CT (8.4 t/ha). The magnitude of yield increase over CT was higher in Kharif (20%) than in the Rabi (3%) season, respectively. Yield improvement with NE-based fertilizer recommendation could primarily be attributed to a balanced application of nutrients rather than to increasing the nutrient rates. Sreelatha et al. (2012) showed that highest yield of both rice and maize and also the highest system productivity were obtained with SSNM. Jat et al. (2013) indicated significantly higher yield of maize under SSNM compared to state recommendations at most of the locations (Delhi, Bajaura, Udhampur, Dholi, Ludhiana, Pantnagar, Banswara and Ranchi). Meena et al. (2014) reported that SSNM improved maize productivity and micronutrients uptake in grain at Udaipur. It was revealed that application of SSNM treatment recorded significantly higher grain, stover and biological yield. This treatment recorded 62.40, 50.52 & 55.02 and 17.95, 17.35 & 17.59 % higher grain, stover and biological yields over control and state recommendation of nutrients. This SSNM treatment remained at par with SSNM-potash in respect to grain, stover and biological yields. Singh (2001) found that yield attributes increased with application of 150 kg N/ha. Kalpana and Krishnarajan (2002) reported that application of 150 kg K/ha in 3 split doses resulted in the highest cobs per plant (3.63), cob length (18.33 cm), cob width (3.16 cm). Muthukumar et al. (2005) reported that the application of N in split doses had significant influence on the yield parameters viz., length, diameter and weight of cob and corn. Vishram et al. (2006) stated that enhancement in yield attributes such as cob length, cob girth, grain weight per cob, shelling % and test weight as well as grain and stover yield was recorded with 180 kg N/ha applied through 100% chemical fertilizer. 2.4 Effect of Nutrient Expert based SSNM on Maize and Wheat South Asian agriculture is characterized by small land holdings for cultivation with high variability in plant nutrient availability between fields. However, fertilizer recommendations for crops in this region are usually done over large geographic areas that often fail to meet the demand of high yielding cops like hybrid maize. Site-specific nutrient management (SSNM), on the other hand, integrates information from different scales to make field specific decisions on N, P and K management. SSNM, which was developed for rice in Asia (Dobermann et al., 2002), was later adapted for maize in Asia (Pasuquin et al., 2010).
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The fertilizer requirement for a field or location is estimated from the expected yield response to each fertilizer nutrient, which is the difference between the attainable yield and the nutrient-limited yield. Nutrient-limited yields are determined from nutrient omission trials in farmers’ fields, while attainable yield is the yield in a typical year at a location using best management practices without nutrient limitation. The amount of nutrients taken up by a crop is directly related to its yield (Janssen et al., 1990). So that the attainable yield indicates the total nutrient requirement and the nutrient-limited yield is the yield supported only by the indigenous supply of the concerned nutrient without any external application (Dobermann et al., 2003). The yield response, which is the yield difference between an ample nutrient plot yield and the nutrient omission plot yield, is used as an indirect estimate of the nutrient deficit in soil that must be supplied by fertilizers. NE follows the SSNM guidelines for fertilizer application and split dressings to consider the crop’s nutrient demand at critical growth stages. In the absence of trial data for a specific location, NE estimates the attainable yield and yield response to fertilizer from site information using decision rules developed from onfarm trial data. Johnston et al. (2011) found that SSNM is, however, a knowledge intensive technology in which optimum fertilizer management for a crop field is tailored to specific local condition, growth duration of the variety, crop residue management, past fertilizer use, and input of nutrients from external sources. Such knowledge requirements have slowed the wide-scale promotion and adoption of SSNM by the farmers. Development of tools that consolidate the complex and knowledge-intensive SSNM information into simple delivery systems is the key for enabling farmers and their advisors to rapidly implement this technology on a large scale. IPNI in collaboration with CIMMYT has recently developed Nutrient Expert (NE), a new nutrient decision support system (DSS) for hybrid maize, based on SSNM principles. Nutrient Expert, while providing fertilizer recommendations, considers yield response and targeted agronomic efficiency in addition to the contribution of nutrients from indigenous sources. Pampolino et al. (2012b) worked on Nutrient Expert (NE) for wheat, a new nutrient decision support tool, is based on the principles of SSNM and recommends balanced application of nutrients based on crop requirement. It enables crop advisers to rapidly develop field-specific fertilizer recommendations for wheat using existing site information. Adaption of NE wheat resulted with slight increase in fertilizer N (+6 kg N/ha) but with large
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increase in fertilizer K (+63 kg K2O/ha). Adaption of NE wheat resulted in moderate increase in fertilizer N (+16 kg N/ ha) and substantial increase in fertilizer K (+33 kg K2O/ha) over state recommendation. Pampolino et al. (2012a) reported that NE also considers other important parameters of the growing environment affecting nutrient management recommendations in a particular location and enables crop advisors to provide farmers with fertilizer guidelines that are suited to individual farming conditions. The tool uses a systematic approach of capturing site information that is important for developing a location-specific recommendation. The tool has been successfully used to provide farmer specific fertilizer recommendations in the major maize growing ecologies across the country and improved yield and farmer profit as compared to existing fertilizer management practices. Satyanarayana et al. (2012) conducted recent study using the NE tool for maize in South India revealed that the N, P2O5 and K2O use by farmers varied from 80 to 550, 38 to 230 and 23 to 352 kg/ha, with an average of 193, 89 and 114 kg/ha, respectively. The corresponding NPK use based on NE recommendations varied from110 to 230, 17 to 81 and 18 to 104 kg/ha, with an average of 161, 39 and 48 kg/ha, respectively. The NE based fertilizer recommendations reduced N, P2O5 and K2O use by 32, 50, 66 kg/ha indicating 17, 56 and 58% reductions in fertilizer use over farmers’ practice (FP). Gilkes et al. (2010) based on 120 on farm experiment with hybrid maize during 2004-2008 at key production sites in Indonesia, the Philippines and Vietnam evaluate a new site-specific nutrient management approach for Asia. Results show that adaption of SSNM in maize increased the agronomic efficiency of N fertilizer by 53% compared to the FFP. Pasuquin et al. (2010) reported that SSNM and FFP treatments differed in the amount of N, P and K applied among the three countries. In the Philippines, N rates were 27.1 kg/ha higher in the SSNM compared to the FFP, but on average across the three countries, 16.1 kg/ha less N was applied in SSNM plots than in FFP. Fertilizer P and K rates with SSNM were slightly higher than in the FFP (+5 kg P/ha and +15 kg K/ha). SSNM led to large gains in N use efficiency. Jat et al. (2013) reported that precision agriculture is an emerging concept wherein the input variables such as fertilizers are applied in right amount, at the right place and at the right time (variable rate application) as per demand of the crop-plants, rather than prophylactic application. Nutrient expert decision support system is the one of the components of precision farming. It helps to improve input-use efficiency, economy and ensures sustainable use of natural resources, as it minimizes wastage.
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Johnston et al. (2009) found that SSNM is one such approach that utilizes fertilizer best management practices (FBMPs) for optimizing nutrient management in crops, including maize. SSNM is a widely used term in all parts of the world, generally with reference to addressing nutrient differences, which exist within and between fields and making adjustments in nutrient application to match these location or soil differences. 2.5 Effect of residue management on crop productivity and soil fertility Unger et al. (1988) reviewed the role of surface residues on water conservation and indicates that this association between surface residues, enhanced water infiltration and evaporation led to the adoption of conservation tillage after the 1930s dust bowl problem. Research since that time has documented beyond doubt the importance of surface residues on soil water conservation and reduction in wind and water erosion (Unger et al., 1988). Kumar and Goh (2000) reviewed the effect of crop residues and management practices on soil quality, soil nitrogen dynamics and recovery and crop yield. The review concluded that crop residues of cultivated crops are a significant factor for crop production through their effects on soil physical, chemical and biological functions as well as water and soil quality. Ram (2006) reported the higher values of plant height, dry matter accumulation, LAI, CGR and RGR of maize under permanent bed with residue than no-residue under both ZT and CT practices. Jat (2010) also reported the higher values of yield attributes of maize under residue applications with higher doses of N fertilizers. Ram et al. (2010) reported higher yields of wheat under ZT with residue due to the cumulative effects of higher light interception more dry matter production, low soil and canopy temperature, more soil moisture, tillers, grains/ear and 1000-grain weight than no-residue application under ZT practices, as well as CT practices. Bakht et al. (2009) reported that on average, crop residue incorporation increased the wheat grain yield by 1.31 times and straw yield by 1.39 times. Improved grain yield due to straw mulch in maize under no-tillage and permanent bed planting was also reported in earlier studies by Tolk et al. (1999); Govaerts et al. (2005). The similar results were also reported by Sen et al. (2002); Mahey et al. (2002); Brar et al. (2004) and Kumar et al. (2004) in earlier studies. However, Gajri et al. (1994) found that mulching increased maize grain yield in loamy sand soil for all the 10 years studies but it decreased yields in sandy loam soil some years while increased the yields in other years compared with bare surface. Tillage and straw treatments did not significantly affect seed yield or root mass of wheat grown during the year 2000, but straw and chaff yields were significantly increased with straw retained compared to straw removed by 6 % (Malhi et al., 2006).
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Ma and Han (1995) reported that application of wheat straw mulch in maize at 6.0 t/ha improved grains per ear, ear length, grain weight per ear and maize yields. But Opoku et al. (1997) reported that completely removing of all wheat residues increased no-till maize grain yield by 0.5 and 0.9 t/ha compared with baling and not baling straw, respectively. Opoku et al. (1997) reported that wheat and maize stands were better and emergence had reached 1-2 days earlier in permanent bed plus straw stubble through the removal of crop residue from the row area by attachment of row cleaner to planter. Borresen (1999) from Norway reported that chopped show residue on the soil surface increased the grain yield of spring barley, oats and wheat by 0.29 t/ha compared with means of other straw management treatments. Increased yield of spring crops might be due to reduction in evaporation since of a cover by straw mulch. Talukder et al. (2004) from Bangladesh reported that straw retention (50 or 100 %) or permanent beds produced higher crop yields than straw removal. The retention of 50 % straw significantly increased the grain yield of Maize by 32 % over straw removal. Similar effect was observed in rice and wheat. They suggested that straw retention on permanent bed planting could act as restorative management and would have positive impact on soil health. Based on 12 years experimentation, Govaerts et al. (2005) reported that permanent bed planting along with rotation and residue retention had the advantages in yield potential of wheat and maize. Thus residue management under permanent bed planting and zero tillage improved the productivity of crops. 2.6 Soil chemical parameters as influenced by Zero tillage 2.6.1 Organic carbon and carbon sequestration Long-term tillage can cause a loss of 20 to 50 % of original SOC levels, whereby most of this loss occurs at the beginning of tillage practices i.e., first years to decades. Eventually, a new equilibrium is reached, at a level which will depend on tillage frequency and intensity (Conant et al., 2007). Intensive tillage management has caused a significant loss of SOM and serious soil degradation (Liu et al., 2010). Tillage increases oxidation of SOM, while ZT reduces its oxidation because of less mixing with the soil. Therefore, one would expect a substantial increase of total organic carbon in soil under ZT compared to CT (Halvorson et al., 2002), especially in soils with relatively low initial organic matter content (Thomson et al., 2006).
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Tillage practice can also influence the distribution of SOC in the profile with higher SOM content in surface layers with ZT than with CT, but a higher content of SOC in the deeper layers where residue is incorporated through tillage (Dolan et al., 2006; Jantalia et al., 2007). Conservation agriculture, reduced/zero tillage, crop rotation and retention of a rational amount of residue is a practice that leads to soil organic C sequestration. C sequestration is a strategy to achieve food security through improvement of soil quality (Lal, 2004). Most comprehensive field studies have shown that zero tillage results in greater accumulation of soil organic matter in surface layers (0–20 cm) than conventional tillage (Kern and Johnson, 1993; Govaerts, et al., 2007). Zero-tillage, on the other hand, combined with permanent soil cover, has been shown to result in a build-up of organic carbon in the surface layers (Lal, 2005). Govaerts, et al. (2007) reported that permanent raised beds with full residue retention increased soil organic matter content 1.4 times in the 0–5 cm layer compared to conventionally tilled raised beds with straw incorporated and it increased significantly with increasing amounts of residue retained on the soil surface for permanent raised beds. No-tillage minimizes SOM losses and is a promising strategy to maintain or even increase soil C and N stocks (Bayer et al., 2000). As SOC changes are generally directly related to the quantity of crop residues returned to the land, agronomic practices that influence yield and affect the residues returned to soil are likely to influence SOC (Campbell et al., 2000). Returning more crop residues is associated with an increase in SOC concentration (Wilhelm et al., 2004; Dolan et al., 2006). It can be hypothesized that conventional tillage with all plant residues incorporated by disking, is actually a system that rapidly breaks down the organic C inputs, while C coming from roots in permanent raised beds with all residues removed maintains some C in the soil. Similar findings were also reported by Sarkar and Kar (2011). 2.7 Nutrient content, uptake and agronomic efficiencies Wells (1984) reported that in comparison to CT, no-till production systems is more efficient in utilization of fertilizer and resulted in higher yields in CA. Madhavi et al. (1995) stated that the grain and stover yield of maize were significantly increased by increasing level of NPK fertilizer from 0 to 100 per cent of the recommended rate of NPK fertilizer (120:60:60 NPK kg/ha). Bundi and Andraski (2001) found that the application of fertilizer at a higher rate (150:26:32 NPK kg/ha) significantly increased the grain and stover yield.
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Ashoka et al. (2008) observed that the application of RDF (150:75:40 kg N:P2O5:K2O -1
ha ) + 25 kg ZnSO4 + 10 kg FeSO4 recorded significantly higher yield and green fodder yield (232.33 q/ha).
Gilkes et al. (2010) conducted 120 on-farm experiments with
hybrid maize during 2004-2008 at 19 key production sites in Indonesia, the Philippines and Vietnam to develop and evaluate a new site-specific nutrient management approach for Asia. Compared to the farmers' practice, maize yield improved by about 0.9 to 1.3 Mg/ha across sites in each country. Pasuquin et al. (2010) reported that significantly greater yield (+16%) was achieved with SSNM across all crops and countries. Compared to the farmers practice, average grain yields in SSNM increased by 0.89 Mg/ha in Indonesia, 1.16 Mg/ha in the Philippines and 1.25 Mg/ha in Vietnam. Average straw yield increased significantly by 1.37 Mg/ha with SSNM, representing a potential benefit in areas where farmers use straw as fuel, forage, or for soil improvement. Satyanarayana et al. (2012) found that based on the data nutrient use in Kharif maize revealed that the lowest N use in farmer practices (FP) has increased from 80 to 110 kg/ha in NE, whereas, the maximum N use in FP has decreased from 550 to 230 kg/ha in the NE based recommendations. The varied yield response to N, P and K application suggests that single homogenous state recommendations may become inadequate for improving maize yields in the region. Thus, fertilizer N, P2O5 and K2O requirements determined by NE, varied among fields or locations, proved to be critical in improving the yield and economics of maize farmers in the region. In fact, use of the NE actually increased yields and profit, while reducing economic risk to the farmer, simply by providing some direction in the most appropriate fertilizer rate. Yield improvement with NE-based fertilizer recommendation could primarily be attributed to a balanced application of nutrients based on SSNM principles. The NE program recommended application of secondary and micronutrients especially S, Zn, Mn, Fe and B at 24 out of 32 locations in the study area. This clearly explains how NE helped in promoting balanced use of all the essential nutrients thereby improving yields and optimizing nutrient use in the maize growing areas of Southern India. Meena et al. (2014) found that application of SSNM recorded maximum content of Zn, Cu, Fe and Mn in grain and stover, as well as, their realization by grain, stover and total by the crop. However, this level also remained at par with SSNM-potash. This treatment recorded 89.40, 86.52, 59.45 and 100.30% higher in total Zn, Cu, Fe and Mn uptake by the maize crop. Xinpeng Xu et al. (2014) reported that a generic but flexible and locationspecific fertilizer recommendation method is necessary due to inappropriate fertilization in
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China. A new fertilizer recommendation method, Nutrient Expert (NE) for Hybrid Maize, was developed using maize datasets from 2000 to 2010 in main maize production areas. The results showed that the average of indigenous nutrient supply were 130, 41 and 124 kg/ha, the mean of yield response were 2.1, 1.2 and 1.2 t/ha and the average agronomic efficiency were 11.4, 15.7 and 11.8 kg/kg for N, P and K respectively. There was a significantly negative exponential relationship between yield response and indigenous nutrient supply and a significant negative linear relationship between yield response and relative yield. Analysis also indicated that the quadratic curve relation was obvious between yield response and agronomic efficiency. NE system was established based on yield response and agronomic efficiency (AE) through above analysis and on-farm field experiments were conducted in 408 farmers’ fields to validate this system at seven provinces in China. The results showed that fertilizer recommendation based on NE method could maintain grain yield and profitability and improve nutrient use efficiency through 4R nutrient stewardship and it is proved to be a promising approach for fertilizer recommendation when soil testing is not timely or not available. Average AEN under SSNM rose to 25.1 kg/kg, an increase of 53% compared to the FFP. Better timing and splitting of fertilizer N applications during the season was probably the major reason to the increase in N-use efficiency (Pasuquin et al., 2010). 2.8 Economics SSNM is a set of nutrient management principles combined with good crop management practices that will help farmers attain high yield and achieve high profitability both in the short and medium-term. Pampolino et al. (2012) reported that adoption of NE wheat in conservation agriculture increased gross return above fertilizer costs (GRF) by 180 and 112 USD/ha over farmer fertilizer practices (FFP) and state recommendation (SR), respectively. Satyanarayana et al. (2012) data pertaining to relative performance of NE over SR and FP for grain yield of maize, fertilizer cost and GRF. Across all sites (n=32) during the Kharif season, NE increased yield and economic benefit (i.e. Gross Return above Fertilizer costs) over FP and SR. Compared to FP, on average it increased yield by 1.06 t/ ha and GRF by 12,902 INR/ha with a significant reduction in fertilizer cost of 3,239 INR/ha. Recommendations from NE also increased yield (by 0.9 t/ha) and GRF (by 8,033 INR/ha) over SR with a moderate reduction in fertilizer cost (-1,041 INR/ha). Meena et al. (2014) recorded application of SSNM on productivity and micronutrients realization on maize crop demonstrated in Udaipur revealed that 65.17% significantly higher returns over control.
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Gilkes et al.(2010) reported that based on 120 on farm experiment with hybrid maize during 2004-2008 at key production sites in Indonesia, the Philippines and Vietnam evaluate a new site-specific nutrient management approach for Asia. Results show that adaption of SSNM added net benefit of 184 US$/ha/crop with maize was attributed to increased yield rather than reduced costs of inputs. Pampolino et al. (2012b) showed that field evaluation of a beta version of NE wheat was conducted at six sites under conventional tillage (CT) in the Indo-Gangetic Plains (IGP) representing five states with different cropping systems. Adaption of NE wheat resulted in increased economic benefit of 221 USD/ha over farmer fertilizer practices. NE Wheat also increased GRF by 68 USD/ ha over SR. Pampolino et al. (2012) found that adaption of Nutrient Expert for Hybrid Maize (NEHM) increased profits of farmers in Indonesia and the Philippines by US$ 270 ha -1 and US$ 379 ha-1 over farmer's fertilizer practice (FFP) respectively. Field evaluation showed that the location-specific nutrient recommendations from the tool increased yield and economic benefits of wheat farmers as compared to the existing practices. Chaudhary et al. (2002) found that the application of 120:90:0 N:P:K kg/ha turned out to be the cheapest alternative in terms of total costs, while in terms of net benefits, 120:90:90 N:P:K kg/ha was marginally more attractive than 120:90:0 NPK kg/ha. Ashoka et al. (2008) reported that the significantly higher gross returns of
96,838, net return of
76,889/ha and
B: C ratio of 3.85 was noticed in RDF+25 kg ZnSO4. Johnston et al. (2011) found that SSNM approach was successfully implemented by the IPNI that improved field specific recommendation to a farmer, in a cost effective and timely fashion. These reviews suggest that SSNM based on NE have potential to enhance the production and profitability of maize. However, the meager information is available on effect of SSNM with various hybrids under conservation agriculture.
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CHAPTER 3
MATERIALS AND METHODS
The experiment entitled “Nutrient Management of Maize Genotypes under Conservation Agriculture Using Nutrient Expert – Decision Support System” was conducted under field conditions during kharif 2013. The details of materials used and methods adopted during the course of investigation are presented in this chapter. 3.1 Experimental Site The experiment was conducted on sandy loam soil of the “Block 9B” research farm of the Division of Agronomy, Indian Agricultural Research Institute, New Delhi, situated at a latitude of 28°38' N and longitude of 77°11' E altitude of 228 m above the mean sea level. 3.2 Climate and Weather Conditions The climate of Delhi is of sub-tropical and semi-arid type with hot and dry summer and cold winter and falls under the agro-climatic zone ‘Trans-Gangetic plain’. During summer, May and June are the hottest with maximum temperature ranging between 41 and 46°C, while there is a drop in temperature from September onward. January is the coldest month of the year with a minimum temperature ranging from 5 to 7°C. Normally the mean annual rainfall is 652 mm, while July and August are the wettest months. The annual pan evaporation is about 850 mm. Sand storm during summer and frost during winter is common features over here. The summer season is intercepted by the south-west monsoon. Winter showers are received occasionally during January-February and sometimes accompanied by high wind velocity and hailstorm. The relative humidity attained the maximum value (70 to 77 per cent or even more) during south-west monsoon and the minimum of 30 to 45 per cent during summer month. The detailed weather data during crop growing season recorded at meteorological observatory of Indian Agricultural Research Institute, New Delhi, are given in Appendices I. 3.3 Soil Characteristics Representative soil samples were collected prior to experimentation from 0-30 cm depth using core sampler. The composite soil samples of each layer were analyzed for the available major plant nutrients and also for the physio-chemical properties. The soil was sandy loam in texture, poor in organic carbon and low in available N, medium in available P and high in available K concentration with the pH 7.6.
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3.4 Cropping history of the experimental field The cropping patterns followed in the field during the preceding years of commencement of the investigation are given below. Table 1: Cropping history of the experimental field Year 2005-06 2006-07 2009-10 2010-11 2011-12
2012-13
Crop season Rabi Wheat Wheat Wheat Wheat till Zero till Wheat with green maize residue
Kharif Maize Maize Maize Maize Conventional Maize with gram residue Zero till Maize with green gram residue (experiment)
Summer Zero till Green gram with wheat residue
Table 2: Schedule of field operations Name of operation Land preparation Seed sowing Residue application Fertilizer application (1st split) First weeding Fertilizer application (2st split) Second weeding First irrigation Second irrigation Third irrigation Harvesting of maize
Date 01.07.2013 03.07.2013 05.07.2013 13.07.2013 30.08.2013 15.08.2013 23.08.2013 05.09.2013 10.09.2013 19.09.2013 10.10.2013
3.5 Experimental Details The experiment was laid out in factorial randomized block design (FRBD) with twenty treatments combinations (four fertility level and five genotypes) and each treatment replicated thrice. In order to evaluate current nutrient management practices and farmer practices the general recommended dose of fertilizer and half of the recommended dose of fertilizer are taken respectively. Lay out of experiment was given in appendices II. Gross Plot size - 30.15 m2, Net Plot Size - 20.1 m2, Spacing – 67 cm X 20 cm.
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Table 3: Treatments Fertility levels
Genotypes
F0: Absolute control
H1: PMH-1
F1: 100% RDF (150:60:40 kg N:P2O5:K20)
H2: PMH-3
F2: 50% RDF (75:30:20 kg N:P2O5:K20)
H3: HQPM-1
F3: SSNM
H4: CMH-08-292 H5: S 6217 Table 4: Treatment combination T1
F0H1
T11
F2H1
T2
F0H2
T12
F2H2
T3
F0H3
T13
F2H3
T4
F0H4
T14
F2H4
T5
F0H5
T15
F2H5
T6
F1H1
T16
F3H1
T7
F1H2
T17
F3H2
T8
F1H3
T18
F3H3
T9
F1H4
T19
F3H4
T10
F1H5
T20
F3H5
3.6 Observations Recorded A. Growth parameters
Plant Height
Leaf area
SPAD
Dry matter accumulation at periodic intervals (30 days) and at harvest
Growth analysis – LAI, CGR, RGR
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Yield attributes
:
Cobs/plant
Grains/cob
Grain rows/cob
Test weight (1000 grain weight)
Shelling percentage (%)
Harvest Index (%)
B. Initial and final fertility status of soil (OC, N, P, and K)
C. Calculations and derivations 1. Nutrients uptake
: NPK
2. Resource use efficiency
: Nutrients use efficiency
3. Economics
: Cost of cultivation, gross returns, net returns and B: C ratio
3.7 Seed and sowing Maize seed was sown @ 20 kg ha-1 by kera method at inter row spacing of 67 cm and plant to plant spacing of 20 cm. sowing operation was carried out in the month of July (03-072013). Unfortunately there was frequent rain after sowing subsequently poor germination sown crop. In order to get optimum plant population gap filling was done two times. There was poor crop stand in early stage of the growth, how ever it was compensated in later stage of the growth. 3.8 Observations recorded Details of the collection of data during the experimental period are given as under: Five plants in each plot from 2nd and 3rd row were randomly selected and marked for recording plant height, tillers per hill, dry matter accumulation and leaf area index at 30, 60, days after transplanting (DAS) and at harvest. A. Plant height (cm) It was measured from the base of the plant (first nodal mark) to the tip of tassel at 30, 60, DAS and at harvest. The height of five plants was measured in centimeters (cm) and average values are reported.
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B. Dry matter accumulation The plants used for leaf area measurement were chopped into piece and sun dried. The sample then oven dried at 600 C for 24 hours and dry matter accumulation per plant was worked out at different crop growth stages. C. Leaf area The leaves were separated from the stem and cleaned with de-ionized water and then dried with tissue paper. The area of fresh green leaves for each treatment was measured by using leaf area meter (Model LICOR 3000, USA) and was expressed in cm2/plant. D. Leaf area index The leaf (petioles and lamina) of five randomly selected plants were separated, cleaned and leaf area per plant was recorded by using leaf area meter. The leaf area was recorded at 30, 60 DAS and at harvest. The leaf area index was calculated by using formula, suggested by Evans (1972): LAI= (Leaf area per plant cm2/Ground area covered by plant cm2) x 100 E. Relative Growth Rate (RGR) The following equations were used to calculate the different growth indices (Roderick, 1990) RGR = loge W2 - loge W1/ t2 – t1 Unit
g/g/day
Where, W1 and W2 = Plant dry weight at time t1 and t2, respectively. F. Net Assimilation Rate (NAR) W2 - W1 NAR =
loge L2 - loge L1 X
t2 – t1
L2 - L1
Where, L1 and L2 = Leaf area of plant at time t1 and t2 respectively. W1 and W2 = Plant dry weight at time t1 and t2 respectively. Unit g/m2(leaf area)/day G. Crop Growth Rate (CGR) The CGR was calculated by using the formula as suggested by Watson, 1958. CGR = W2 - W1/ P (t2 – t1)
g-1 m-1 day-1
Where, W1 and W2 = Plant dry weight at time t1 and t2, respectively. P = Spacing occupied by the crop
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H. Harvest Index Harvest index was calculated by using the following expression (Singh and Stoskoff, 1971). Economic yield (t/ha) Harvest index (%) =
x 100 Biological yield (t/ha)
Grain yield (t/ha) =
x 100 Grain yield (t/ha) + Straw yield (t/ha)
3.9 Post harvest study A. Number of cobs per plant The total number of cobs recorded from net plot was divided by number of plants present in net plot to get number of cobs/plant. B. Cob length (cm) Length of five randomly selected cobs was taken from base of ear to its tip and average length of cobs was worked out. C. Number of grains per cob The cobs selected for length measurement were shelled separately and grain was counted and fives cobs average was worked out. D. Number of grain row Total number of grain rows from previously selected five cobs were counted and averaged to get number of grain row per cob. E. 1000 grain weight (test weight) The weight of hundred grains drawn at random from the five cobs was recorded and expressed in grams. F. Harvesting and shelling Crop was harvested on 10/10/2013. The cobs were removed from the net plot area of each plot separately. Then stover was cut above ground level. Sample plants were first air dried and later the samples were oven dried to a constant temperature at 600C in hot air oven and weight was recorded. Shelling was done by maize sheller. Grain weight per plot was recorded at 14 per cent moisture. G. Grain yield (t/ha) After shelling the cobs, the grain yield per net plot was recorded and it was converted on per hectare basis. H. Stover yield (t/ha) The Stover yield per net plot area was weighed after complete sun drying and it was converted to Stover yield per hectare.
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I. Harvesting index Harvesting index was computed as the ratio of grain yield to the total biological yield i.e. the total dry matter. The product was multiplied by 100 to express on percentage basis. HI = (Economical yield/biological yield) x 100 3.10 Chemical analysis 3.10.1 Plant analysis Plant samples collected at harvest were dried in hot air oven at 60oC for 6 hours. The ovendried samples of plants and air-dried sample of grain were ground to pass through 40 mesh sieve in a Macro-Wiley Mill. From each replication 0.5 g grain and straw samples were taken for chemical analysis to determine the P, K, Zn, Cu and Fe concentrations. 3.10.1.1 Determination of total N concentration (%) in plant samples The N concentration in rice grain and straw samples were determined by modified Kjeldahl method (Prasad et al., 2006). The method has two main steps. 1. Digestion of the sample to convert the N compound in sample to NH4+ form and 2. Determination of NH4+ in digested samples. (1) Digestion The digestion of sample was done according to modified version of Kjeldahl procedure. Accurately weighed 0.5 g of finely ground samples was placed in digestion tube of 100 ml capacity. In each tube, 3-4 g of catalyst mixture (anhydrous sodium sulphate and copper sulphate pentahydrate in 10:1 ratio) and 10 ml concentrated H2SO4 were added. Samples were digested till digest got clean aliquot on a digestion unit. After cooling the volume was made up to 100 ml by adding distilled water. (2) Distillation The digested material was transferred into vacuum jacket of Macro-Kjeldahl distillation apparatus. Twenty ml of 4% boric acid solution was taken in a conical flask containing bromocresol green and methyl red indicator to which the condenser outlet of the flask was dipped. In the Kjeldahl flask, 100 ml of 40% NaOH solution was added. After completion of distillation, the boric acid was titrated against 0.1 N H2SO4. A blank was also carried out and titration was done to the same end point of sample. The nitrogen content of the sample was estimated as: (ml of acid for sample – ml of acid for blank) Amount of N in samples (S) =
x 14 x Normality of acid 10
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100 N (%) in samples = S x Weight of sample (g)
N uptake was calculated by using the following expression: N uptake (kg/ha) in grain/straw = [% N in grain/straw X grain/straw yield (kg/ha)] Total uptake of N (kg/ha) = N uptake in grain + N uptake in straw 3.10.1.2 Phosphorus content and uptake Phosphorus content in grain and straw was determined by vanadomolybdophosphoric acid yellow colour method (Prasad et al., 2006). Total P uptake (kg/ha) was calculated by following expression: P uptake (kg/ha) in grain/straw = [% P in grain/straw X grain/straw yield (kg/ha)] Total uptake of P (kg/ha) = P uptake in grain + P uptake in straw 3.10.1.3 Potassium content and uptake Potassium content in grain and straw was determined by flame photometer (Prasad et al., 2006). Potassium uptake was calculated by multiplying K content with the dry matter yield K uptake (kg/ha) in grain/straw = [% K in grain/straw X grain/straw yield (kg/ha)] Total uptake of K (kg/ha) = K uptake in grain + K uptake in straw 3.11 Soil chemical properties
3.11.1 Organic carbon The Soil samples (0–15 cm depth) were collected in small polythene bags from each plot of the experimental field at the initial and at the end of experimentation period. The samples were air dried and organic carbon content in soil samples were determined by Walkley and Black (1934). 3.11.2 Available N, P and K The soil samples were collected from 0–15 cm soil profile at the initial and at the end of experimentation period. The soil samples were air dried ground and passed through 2 mm mesh sieve and were analysed for available N, P and K. The available N was estimated by alkaline KMnO4 method suggested by Subbiah and Asija (1956) and expressed in kg/ha. The available P content in soil was estimated by Olsen’s method (Olsen et al., 1954). Available K was determined using neutral normal ammonium acetate extraction (flame photometer) method as described by Jackson (1973) and expressed
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in kg/ha. A balance sheet of N, P and K used of the crop sequence was prepared by comparing the net change in nutrient status of soil after maize–wheat system. 3.12 Nutrient use indices The estimated values of partial factor productivity (PFP), agronomic efficiency (AE), Apparent recovery (AR), physiological efficiency (PE) and harvest index (HI) of applied N, P and K were computed as suggested by Fageria and Baligar (2003) and Dobermann (2005) which are given as below: A. Partial factor productivity of applied N (PFP) = Yt/ Na where, Yt = Yield under treatment (kg/ha) Na = Amount of nutrient added (kg/ha) B. Agronomic nitrogen use efficiency (AE) = Yt – Y0/At (kg grain/kg nutrient applied) where, Yt = Yield under test treatment (kg/ha) Yo = Yield under control (kg/ha) At = Units of nutrient applied in the test treatment (kg/ha) C. Physiological efficiency (PE) = Yt - Yo / Ut - Uo where, Yt = Yield under test treatment (kg/ha) Yo = Yield under control (kg/ha) Ut = Uptake of nutrient in test treatment (kg/ha) Uo = Uptake of nutrient in control (kg/ha) D. Apparent recovery (%) = Nt - No / Na x 100 where, Nt = Amount of nutrient taken from test treatment plot (kg/ha) No = Amount of nutrient taken from the control plot (kg/ha) Na = Amount of nutrient added (kg/ha) F. Nitrogen harvest index (%) = Ns / Nt x 100 where, Ns = Nitrogen uptake by grain at harvest Nt = Nitrogen uptake by whole plant (grain + straw) at harvest 3.13 Economics Economics of different treatments was worked out by taking into account the cost of inputs and income obtained from grain and stover yield. The detailed account of common cost of cultivation and cost of cultivation as per treatment has been presented in table 26. From these values, the gross and net returns as well as benefit: cost ratio was worked out as followed for each treatment.
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Gross returns ( /ha) = Economic yield × market price of produce Net returns ( /ha) = Gross returns – cost of cultivation
3.14 Statistical Analysis The data collected during the experiment was subjected to statistical test by “Analysis of variance technique”. Wherever variance ratio (F value) was significant, critical differences (CD) values at 5% level of probability was computed for making comparison between treatments. 3.15 Nutrient Expert Decision Support System: The Nutrient Expert for Hybrid Maize will help a farmer increase his yield and profit by suggesting a meaningful yield goal for attainable location and by providing a fertilizer management strategy required to attain the yield goal. This software only requires information that can be easily provided by a farmer or local expert. This set of information include: • Current yield and nutrient management practice • Farmer’s current planting density • Characteristics of the growing environment or estimate of the attainable yield (if known) • Soil fertility indicators (e.g. soil texture and colour, historical use of organic inputs) or estimates of yield responses to fertilizer N, P, and K (if known) • Crop residue management, use of organic inputs, and nutrient carryover from previous crop are used to adjust fertilizer P and K requirements as merited. After answering a set of simple questions, the user will get guidelines on fertilizer management (and more) that are tailored to his location (i.e. maize environment) and locallyavailable fertilizer sources. The software also presents a simple profit analysis comparing costs and benefits between the farmer’s current practice and the recommended alternative improved practice. In addition, Nutrient Expert for Hybrid Maize was designed in such a way that it can be used as a learning tool—providing quick helps, instant summary tables and graphs, plus allowing a great amount of flexibility in navigating through the modules in the software.
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The Nutrient Expert for Hybrid Maize helps to: • Develop an optimal planting density for a location • Evaluate current nutrient management practices • Determine a meaningful yield goal based on attainable yield • Estimate fertilizer NPK rates required for the selected yield goal • Translate fertilizer NPK rates into fertilizer sources • Develop an application strategy for fertilizers (4R nutrient stewardship)
Table 5: Recommended dose of nutrient by NE decision support system Hybrids PMH 1 PMH 3 HQPM 1 CMH-08-292 S6217 OTHER TREATMENTS
Potential yield (t/ha) 9 9 8 9 9 F0 F1 F2
N 170 170 180 170 170 0 150 75
F0 – Control, F1 – 100% RDF, F2 – 50% RDF.
SSNM rates P2O5 40 40 33 40 40 0 60 30
K2O 48 48 40 48 48 0 40 20
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Table 6: Important Characteristics of Cultivars S. No
1
2
3
4
5
Name
PMH l
PMH 3
HQPM 1
CMH-08-292
S 6217
Parentage
Releasing
Year of
Centre
release
LM 13 X LM
PAU,
14
Ludhiana
LM 17 X
PAU,
LM 14
Ludhiana
HKI 193-1 X HKI 163
TNAU,
UMI-1230
Coimbatore
TO254 X TO202
2007
2008
Ltd.
resistance MLB, stalk rots, potential
Delhi, Punjab, Haryana
Late maturity, orange, flint, highly
and Western Uttar
responsive to inputs and potential yield
Pradesh
9 t/ha Late maturity, yellow, responsive to
2007
J&K, Uttarakhand, NE,
fertilizers, tolerance to frost/ cold,
HP, Assam
resistance to MLB and common rust and potential yield 8 t/ha
Rajasthan, Gujarat, 2013
Madhya Pradesh and Chhattisgarh Punjab, Haryana, TN,
Syngenta India Pvt.
Irrigated areas of Punjab
yield 9 t/ha
karnal
UMI-1200 X
Characteristics Late maturity, stem is zigzag,
CCS, HAU,
Area of adaptation
2013
Delhi, UK, UP, AP, MP, MH, Karnataka, Rajasthan and Gujarat.
Orange, yellow, semi dent, late maturity, potential yield 9 t/ha
Orange, semi flint and tolerant to MLB, TLB, PFSR and RDM, medium duration, potential yield
9 t/ha
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Chapter - 4
RESEARCH PAPER -1
Growth and development behaviour of hybrid maize under conservation agriculture with various genotypes ABSTRACT
A field experiment was conducted at New Delhi during rainy (kharif) season of 2013, to study the effect of nutrient management on growth and development behaviour of maize (Zea mays L.) hybrids. Normalized difference vegetation index (NDVI), SPAD value, dry-matter accumulation, leaf-area index, crop growth rate (CGR) and relative growth rate (RGR) were significantly higher with site-specific nutrient management (SSNM) over the recommended dose of fertilizer (RDF) under conservation agriculture. Of the maize–genotypes ‘CMH 08292’ recorded significantly highest dry-matter accumulation at various stages and CGR, while ‘PMH 1’ recorded higher NDVI, SPAD, leaf-area index and RGR. Cobs/plot, cob length and girth, grain rows/cob, grains/row, cob yield, shelling, harvest index and grain yield were significantly higher with SSNM over RDF. ‘PMH 3’ recorded significantly higher cobs/plot, cob length and girth, grains/row, cob yield and grain yield, whereas ‘S 6217’ recorded significantly higher grain rows/cob and harvest index, while significantly higher shelling was recorded with ‘CMH 08-292’.
Key words: CGR, Growth parameters, NDVI, RGR, SPAD and yield attributes. 4.1 Introduction Maize also known as queen of cereals, which is the now world’s highest producing cereal crop that surpassed wheat and rice. It is cultivated in India over 8.67 million ha with 22.26 million tonnes production having an average productivity of 2566 kg/ha, contributing nearly 8% in the national food basket (DACNET, 2014). Now-a-days, maize is gaining importance in conservation agriculture as it is a wide-spaced crop, having slow growth rate in its early stage, which leads to more loss of water, and nutrient through evaporation and heavy infestation of weeds. To overcome this problem, adoption of conservation agriculture- based crop-management practices are increasing in maize production area of peninsular India. But production is limited by low fertilizer efficiency, inadequacy in existing fertilizer
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recommendations and the ignorance of nutrients balance and hence pausing serious threat in maize production. The Nutrient ExpertTM for Hybrid Maize is a new, computer-based decision support tool developed to assist local experts to quickly formulate fertilizer guidelines for tropical hybrid maize based on the principles of site-specific nutrient management (SSNM). There exists significant opportunity to increase fertilizer efficiency and productivity of maize by adopting Nutrient Expert-based field specific fertilizer recommendations (Satyanarayana et al., 2013).
4.2 Materials and methods Keeping all in view, an investigation to evaluate the effect of SSNM on maize genotypes under conservation agriculture was undertaken on sandy-loam soil of the Indian Agricultural Research Institute, New Delhi 28o40’N, 77o12’E, 228 m above the mean sealevel during rainy (kharif) season 2013. This location has a typical semi-arid and sub-tropical climate characterized by hot dry summers and cool winters. The rainfall during cropping period was 1,207.2 mm, while the evaporation was 638 mm. The mean maximum and minimum temperature were 32.6 and 22.2oC respectively. The experimental soil was sandy loam having pH 8.2, low in organic carbon (0.3%) and available N, medium in available P and high in available K. The experiment was laid out in a randomized block design (RBD) with 20 treatments combinations having 4 nutrient management practices and 5 genotypes and replicated thrice. The 5 genotypes used in the experiment were; ‘PMH-1’, ‘PMH-3’, ‘HQPM 1’, ‘CMH 08-292’ and ‘S-6217’. In order to evaluate current nutrient-management practices and farmer practices the general recommended dose of fertilizer for Delhi region and half of the recommended dose of fertilizer (RDF) were taken, respectively. The nutrient-management practices were: absolute control, 100% RDF (150:60:40 kg/ha N:P2O5:K2O), 50% RDF, SSNM (‘PMH1’, ‘PMH 3’, ‘CMH 08-292’ and ‘S-6217’ (170:40:48 kg/ha N:P2O5:K2O), and with ‘HQPM 1’ (170:33:40 kg/ha N:P2O5:K2O). This experiment was taken under permanent conservation agriculture trial on maize– wheat–green gram started in the rainy season (kharif) 2012, in which green gram straw @ 1.5 t/ha was retained before maize planting. Five random were tagged for measuring plant height and SPAD reading, while leaf-area index, fresh and dry-matter accumulation were measured from 3 randomly sampled plants at 90 days after sowing. The SPAD reading was recorded with the help of SPAD meter as per the procedure.
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The normalized difference vegetation index (NDVI) was measured using hand-held green seeker sensors. The leaf-area index (LAI) was calculated by using formula: LAI= [Leaf area/plant (cm2)/Ground area/plant (cm2)] The crop growth rate (CGR) calculated using standard formula, as given below CGR= W2–W1/t2–t1 The Relative Growth Rate (RGR) calculated using standard formula, as given below RGR=Log w2–Log w1/t2–t1, Where W2 and W1 are weight per plant at time 2 and time 1 respectively. The yield attributes of cobs/plant was calculated from plant present in net plot divided by number of cobs. Length of 5 randomly selected cobs was taken from base of ear to its tip. Total numbers of grain row from these selected 5 cobs were counted. The cobs selected were shelled. The weight of 1,000 seeds drawn at random from the grains of 5 cobs was recorded to get 1,000-grain weight. The harvest index was computed as the ratio of grain yield to total biological yield. 4.3
Results
4.3.1 Growth attribute 4.3.1.1 Plant height Significantly higher growth characters were recorded with SSNM over 100% RDF. It seems that SSNM-based balanced dose provided nutrient as per the crop requirement, hence better plant growth was observed with SSNM. With regards to different hybrids at harvest, significantly highest plant growth was recorded by ‘CMH 08-292’ over all other hybrids and significantly lowest plant height (Table 7) was recorded by ‘HQPM 1’. Plant height is an indicator of crop plant which can be easily deciphered. Data on plant height recorded at various growth stages (30, 60 and at maturity) of maize as influenced by genotypes and fertility levels have been presented in Table 7. Significantly highest plant
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height was recorded with SSNM at all stages viz. 30, 60 DAS and at harvest over absolute control, 50% RDF and 100% RDF, respectively. It seems that SSNM based balanced dose provided nutrient as per the crop requirement, hence, better plant height was observed with SSNM. With regards to different hybrids at 30 DAS, the hybrid PMH 1 and PMH 3 recorded significantly higher plant height over CMH-08-292 and S-6217. However, at 60 DAS and at harvest, significantly highest plant height was recorded by CMH-08-292 over all other hybrids. At all stages significantly lowest plant height was recorded by HQPM-1. This hybrid (CMH-08-292) was found to be tall growing in nature due to which it resulted in higher plant height.
Table 7: Effect of nutrient management practices and genotypes on plant height Plant height (cm) Treatment 30 DAS
60 DAS
At harvest
53.98c 62.80b 61.19b 67.66a 3.441
