Agronomic improvements can make future cereal systems in South Asia far more productive and result in a lower environmental footprint
2016
Ladha, Jagdish Kumar | Rao, Adusumilli Narayana | Raman, Anitha K. | Padre, Agnes Tirol | Dobermann, Achim | Gathala, Mahesh Kumar | Kumar, Virender | Saharawat, Yashpal S. | Sharma, Sheetal | Piepho, Hans-Peter | Alam, Muhammad Mehboob | Liak, Ranjan | Rajendran, Ramasamy | Reddy, Chinnagangannagari Kesava | Parsad, Rajender | Sharma, Parbodh Chander | Singh, Sati Shankar | Saha, Abhijit | Noor, Shamsoon
South Asian countries will have to double their food production by 2050 while using resources more efficiently and minimizing environmental problems. Transformative management approaches and technology solutions will be required in the major grain-producing areas that provide the basis for future food and nutrition security. This study was conducted in four locations representing major food production systems of densely populated regions of South Asia. Novel production-scale research platforms were established to assess and optimize three futuristic cropping systems and management scenarios (S2, S3, S4) in comparison with current management (S1). With best agronomic management practices (BMPs), including conservation agriculture (CA) and cropping system diversification, the productivity of rice- and wheat-based cropping systems of South Asia increased substantially, whereas the global warming potential intensity (GWPi) decreased. Positive economic returns and less use of water, labor, nitrogen, and fossil fuel energy per unit food produced were achieved. In comparison with S1, S4, in which BMPs, CA and crop diversification were implemented in the most integrated manner, achieved 54% higher grain energy yield with a 104% increase in economic returns, 35% lower total water input, and a 43% lower GWPi. Conservation agriculture practices were most suitable for intensifying as well as diversifying wheat–rice rotations, but less so for rice–rice systems. This finding also highlights the need for characterizing areas suitable for CA and subsequent technology targeting. A comprehensive baseline dataset generated in this study will allow the prediction of extending benefits to a larger scale.
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