Research Projects

Burgeoning population demands and industrial requirements, combined with climate change, freshwater will be the biggest threat to agriculture and hence food security. Saving water and improving productivity for every drop of water, therefore, are the most important and formidable challenges. Semi-irrigated aerobic cultivation with its less frequent surface irrigation is a potential water saving technology. Here, plants experience well-watered and water-limiting conditions periodically. Sustaining growth and productivity under such a scenario is possible when plants’ innate ability for stress reponses are combined with mechanisms governing metabolism. Plants respond to a gradually advancing stress and develop specific mechanisms that protect them when stress becomes severe. This propensity to respond include a range of processes that are collectively referred to as “Acquired Tolerance Traits” (ATT). The Drought simulator phenomics facility at UASB provided an excellent opportunity to phenotype for ATTs among germplasm and to demonstrate significant genetic variability in ATTs (Lekshmi et al., 2021; Pushpa et al., 2023). To harness the water saving advantages of aerobic cultivation, we propose to develop and deploy an Artificial intelligence and Machine learning (AI/ML) model that enhances the accuracy and efficiency of breeding. We envisage identifying robust markers by GWAS for both Constitutive and Acquired physiological traits, using a subset of the 3K RG panel of Rice germplasm. Determination of breeding value to each SNP associated with the mechanisms of ATTs will enhance the breeding success of trait introgression. Another novelty is the generation of mechanistic explanations for ATTs at physiological, molecular, biochemical and genetic levels, which is largely lacking at this stage. This proposal is based on our scientific hypothesis that when mechanisms of constitutive and acquired tolerance traits are deciphered at the molecular, biochemical and genetic levels, their introgression to improve physiological status at the whole plant level will be more effective. Genomic estimates of breeding value (GEBV) for such mechanisms would enable us to develop a precise AI based strategy for effective deployment of an focused trait-introgression breeding activity to develop rice cultivars that would sustain productivity under water-limited conditions. An excellent combination of expertise from the investigating partners is strongly complementary to achieve the anticipated goals. We deliver a genotyping chip comprising of validated SNPs linked to ATTs and constitutive traits. Further, genes, promoters, QTLs, etc governing ATTs will be made available for utilization. A precise breeding strategy through identifying most appropriate combinations of Haplotypes to improve productivity under water limitation and a SNP genotyping chip to be used by any rice breeder along with a set of specific trait donor genotypes will be delivered.