Anthropogenic factors are accelerating the climate change much faster than the evolutionary ability of organisms. Those not able to evolve or adapt keeping pace with the changing climates eventually become extinct. Crop species, identified by the humans for utilization, are no exception. Major crops such as rice, wheat and maize contribute to about 30% of food calories to more than half of global population. Towards 2030, projected increase in calorie contribution of agricultural commodities is 1.3% per year, growth in total agricultural production by 1.1% per year, and crop production growth mainly contributed by crop improvement (79%), cropland expansion (15%) and cropping intensity (6%) (OECD-FAO, 2023). India, being a tropical country, is in the forefront of climate change and is projected to face more climatic extremes that affect agricultural production. The manifestation of climate change impacts on various sectors including agriculture are becoming more evident, frequent and intensive causing severe losses and damages. Crop improvement has been the major factor for food security across globe in the past and will remain so in future. However, there is need to look beyond the approaches employed so far for crop improvement to outpace the climate change. Major productivity of crops in India is projected to be affected up to 18% by 2030s without adaptation. For instance, without adaptation, projected loss in all India wheat mean productivity is about 6% by 2030s (Naresh Kumar et al., 2023). Recent climatic extremes such as heat wave in March, 2022 and heavy rainfall events in March, 2023 significantly restricted wheat production to 107.74 Mt in 2021-22 and to 110.55 Mt in 2022-23, lower than the fourth estimates of respective years of production by 2 to 3 Mt.
The above analyses underline the importance of accelerating crop improvement efforts using integrated approaches. While using technologies such as CRISPR-Cas, speed breeding, etc. accelerate varietal improvement, use of simulation models provides insights into the performance of the improved varieties in existing and projected environments. In silico approaches have been increasingly used for guiding crop improvement targets, improved varietal performance in target environments, exploring germplasm performance in diverse environmental conditions, potential exploitation opportunities using trait combinations and so on. Indigenously developed InfoCrop, a computer based dynamic crop model that simulates the effects of weather and management (sowing time, nitrogen, irrigation, organic matter and pests) on the growth, development and yield of crops was used to simulate the performance of about 22,000 germplasm accessions of wheat. The trait value range of entire wheat germplasm performance under normal and late sown conditions during 2011-2014 crop seasons was used to simulate the performance of germplasm lines. Selected lines were evaluated under current and future climates and results indicated that some lines outperform the current major varieties of wheat. Further, specific range of major traits were combined in Monte Carlo simulations and InfoCrop model was used simulate the performance of these novel genotypes for their performance under varying climatic regimes. Analysis indicated that ideal trait combinations can significantly improve the wheat productivity despite increase in climatic stresses.
