Field experiments consisting of two sowing time (early and timely), two tillage options (conventional tillage and conservation tillage) and ten genotypes were conducted with the aim to maximize the wheat productivity and profitability. The early sowing (second fortnight of October) produced 16.0% higher grain yield compared to timely sowing (mid-November) in northern Indian Plains. However, no significant yield differences were observed between conventional tillage (CT) and conservation tillage (CST) practices. Among genotypes, the better yielders were PBW 723, BISA 927 and HD 2967. The interaction of sowing time and genotype had a significant (p < 0.05) effect on wheat yield. However, the interaction of genotype and tillage did not produce any significant response on wheat yield. The experiments conducted at farmer's fields also demonstrated similar performance of wheat under CT and CST systems but CST offered the savings of more than Rs. 3500 (US $ 47) along with 125 kg ha lesser CO emissions over CT due to reduction in fuel consumption associated with tillage and seed bed operations. At farmers field also, early sown wheat yielded 5.5% higher over wheat sown in November. The results of present studies show that early sowing of high yielding wheat genotypes under CST practice enhanced the productivity and profitability of wheat under rice-wheat cropping system along with lesser noxious impact on the environment. Amidst climate vagary and its menace on the agriculture, the adoption of climate-resilient management practices such as advancing the sowing time and conservation tillage can improve the productivity of long duration wheat cultivars in sub-tropical humid conditions besides lesser deleterious consequences on the environment.

Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future.

Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000-2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85-1.2 t/ha which lies in the error range of 10-15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1-10 t/ha and 1-12.5 kg/m, respectively.