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Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model\ud

机译:使用CROPGRO-大豆模型分析印度雨养大豆的潜在产量和产量缺口\ ud

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摘要

To assess the scope for enhancing productivity of soybean (Glycine max L. Merr.), the CROPGRO-Soybean model was calibrated and validated for the diverse soybean-growing environments of central and peninsular India. The validated model was used to estimate potential yields (water non-limiting and water limiting) and yield gaps of soybean for 21 locations representing major soybean regions of India. The average water non-limiting potential yield of soybean for the locations was 3020 kg ha−1, while the water limiting potential was 2170 kg ha−1 indicating a 28% reduction in yield due to adverse soil moisture conditions. As against this, the actual yields of locations averaged 1000 kg ha−1, which was 2020 and 1170 kg ha−1 less than the water non-limiting potential and water limiting potential yields, respectively. Across locations the water non-limiting potential yields were less variable than water limited potential and actual yields, and strongly correlated with solar radiation during the season (R2 = 0.83, p ≤ 0.01). Both simulated water limiting potential yield (R2 = 0.59, p ≤ 0.01) and actual yield (R2 = 0.33, p ≤ 0.05) had significant but positive and curvilinear relationships with crop season rainfall across locations. The gap between water non-limiting and water limiting potential yields was very large at locations with low crop season rainfall and narrowed down at locations with increasing quantity of crop season rainfall. On the other hand, the gap between water limiting potential yield and actual farmers yield was narrow at locations with low crop season rainfall and increased considerably at locations with increasing amounts of rainfall. This yield gap, which reflects the actual yield gap in rainfed environment, is essentially due to non-adoption of improved crop management practices and could be reduced if proper interventions are made. The simulation study suggested that conservation of rainfall and drought resistant varieties in low rainfall regimes; and alleviation of water-logging and use of water-logging tolerant varieties in high rainfall regimes will be the essential components of improved technologies aimed at reducing the yield gaps of soybean. Harvesting of excess rainfall during the season and its subsequent use as supplemental irrigation would further help in increasing crop yields at most locations.
机译:为了评估提高大豆生产率的范围(Clypgro-Soybean模型),该模型已针对印度中部和印度半岛的多种大豆种植环境进行了校准和验证。经验证的模型用于估计代表印度主要大豆地区的21个地区的潜在单产(非限水和限水)和大豆单产缺口。该地区大豆的平均非限水潜力产量为3020 kg ha-1,而限水潜力为2170 kg ha-1,表明由于不利的土壤水分条件,单产降低了28%。与此相反,该地区的实际平均产量平均为1000 kg ha-1,这分别比非限水潜力和限水潜力的产量分别低2020和1170 kg ha-1。在各地,水的非限制性潜在产量比水限定的潜在产量和实际产量变化少,并且与季节中的太阳辐射密切相关(R2 = 0.83,p≤0.01)。模拟的限水潜力产量(R2 = 0.59,p≤0.01)和实际产量(R2 = 0.33,p≤0.05)与各地的作物季节降雨都具有显着但正的和曲线的关系。非作物限水潜力和非作物限水潜力之间的差距在作物季节降雨较低的地区非常大,而在作物季节降雨数量增加的地区则有所缩小。另一方面,限水潜力产量与农民实际产量之间的差距在作物季节降雨少的地区较窄,而在降雨量增加的地区则大大增加。该产量差距反映了雨养环境中的实际产量差距,这主要是由于未采用改进的作物管理方法,如果采取适当的干预措施,则可以减少这一差距。模拟研究表明,在低降雨条件下,保持降雨和抗旱品种是可行的。减少涝灾和在高降雨条件下使用耐涝品种将是旨在减少大豆单产差距的改良技术的重要组成部分。在该季节收获多余的降雨并随后用作补充灌溉,将进一步帮助大多数地方提高作物产量。

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