Agricultural College and Research Institute, Killikulam

Rice is a staple food crop in Tamil Nadu, cultivated in diverse ecosystems ranging from river delta plains to the Nilgiris hill valleys. The alarming climate change events are predicted to affect rice crop productivity across the globe. In this study, 76 newly developed breeding lines from inter-subspecific crosses, two commercial restorers, and checks were evaluated in four different temperature regimes of Tamil Nadu. Various stability methods were used to analyze genotype-environment interactions to identify lines with stable performance even under various temperature conditions. The stability methods applied in the study were classified under three models viz., the Uni-trait Stability Selection Model (Model – 1), the Uni-trait Mean Performance Stability Selection Model (Model – 2), and the Multi-trait Mean Performance Stability Selection Model (Model – 3). These models are primarily based on Additive Main-effects and Multiplicative Interaction (AMMI), Best Linear Unbiased Prediction (BLUP), and Genotype × Environment (G × E) statistical approaches. Further, molecular markers linked to the Rf3 and Rf4 fertility restorer genes were used to investigate their application in either three-line or two-line hybrid breeding systems. The analysis results revealed a significant genotype-environment interaction in the current study, with temperature being a key factor influencing genotype variation across environments. Various stability models were assessed for efficiency based on correlation and genetic gain results, which indicated that integrating yield performance with stability indices such as GGE (17.51), RPGV (17.51), HMGV (17.51), and WAASBY (166.32) led to higher genetic gain. Furthermore, combining all the models helps to identify lines that are both high-performing and also stable, more effectively than a single model approach. The integrated models identified breeding lines G- 17, G- 25, G- 30, G- 48, and G- 68 as potential candidates for use as restorers in developing hybrids suited to varied-temperature environments, with molecular analysis confirming their use in three-line breeding. Additionally, lines G- 39 and G- 50 are promising candidates for developing climate-smart two-line hybrids with enhanced heterosis.

Dwarfism is a major trait for developing lodging-resistant rice cultivars. Gamma irradiation-induced mutagenesis has proven to be an effective method for generating dwarf rice mutants. In this research, we isolated a dwarf mutant from Anna R (4) in the M2 generation and subsequently stabilized the trait through successive selfing of progeny across the M3-M7 generations. We then employed whole-genome re-sequencing (WGRS) and RNA sequencing (RNA-seq) analyses of Anna R (4) and the mutant (designated as ACM-20001) to elucidate the underlying mechanisms and identify candidate genes associated with dwarfness. Numerous genetic variations were identified between Anna (R) 4 and ACM-20001 through WGRS. In total, 2049 genetic variants, including 343 InDels and 1706 nonsynonymous SNPs, were identified across 697 genes. Additionally, RNA-seq analysis revealed 2,881 differentially expressed genes between the wild-type Anna (R) 4 and the mutant ACM-20001, with 1,451 genes up-regulated and 1,430 genes down-regulated in ACM-20001 compared to Anna (R) 4. By integrating WGRS and RNA-seq data with functional annotation analysis, we identified the most likely candidate genes (i.e., Os02g0506400, Os05g0515200, Os06g0154200 and Os08g0250900) related to dwarfness. Quantitative real-time PCR analysis verified the expression of these genes. Collectively, our study provides valuable insights in to the genes and mechanisms underlying dwarfness in rice. Further studies are required to elucidate the roles of these candidate genes in dwarfness, which contribute to advancements rice breeding programs.

Remote sensing has become a vital component of plant protection system, offering significant details about disease dynamics, insect infestations and crop health. The current review highlighted a standpoint on the innovative and past application of remote sensing and their application exclusively in insect pests, crop diseases, plant parasitic nematodes (PPNs), site-specific weed management (SSWM), soil microbes and environmental hazards. Consequently, it can identify pest and disease invasions as early and notify farmers to implement the appropriate countermeasures to ensure crop health and safeguard the crop yield from significant losses. PPNs can now be diagnosed in the field because of recent advancements in this technology. Pesticides application can be done precisely and accurately by using remote sensing to identify and map the pest infestation. Remote sensing-based SSWM has great potential, but there are several problems and restrictions to overcome. Geographical forecast of these bacterial taxa’s relative abundance, which is based on remote sensing that, reveals configurations of ecosystem function and soil microbiome richness within the environment. Farmers can improve decision making, create site-specific management plans and increase overall crop resilience by integrating remote sensing data with field observations and previous information. As agriculture enters a new era, agriculture 4.0, with smart agriculture technologies, enables farmers to stay connected to their farms virtually anywhere at any time. This contributes significantly to the world's agricultural sector by serving farmers with increased yield, lower costs and efficient management of their land. Undertaking research activities for the modernization of agriculture and assessing the socioeconomic effects of digitization is essential to building new digital solutions that will maintain sustainability for long-term agriculture.

In a metabolomics analysis using GC–MS, we explored the metabolic responses of moderately resistant West Coast Tall (WCT) and susceptible Chowghat Orange Dwarf (COD) coconut varieties to infestation by the exotic whiteflies Aleurodicus rugioperculatus and Paraleyrodes bondari. WCT exhibited a low to medium Infestation Grade Index (IGI) for both whitefly species, while COD displayed a medium to high IGI. Additionally, the study examined the preferential feeding behavior of the whiteflies, highlighting their tendency to predominantly infest the bottom leaves rather than the top leaves. GC–MS analysis of healthy top leaves and whitefly-infested bottom leaves from the WCT and COD coconut varieties identified 56 metabolites, categorized into carbohydrates, fatty acids, organic acids, amino acids, and secondary metabolites. The WCT coconut variety exhibited moderate resistance to exotic whitefly infestation through the accumulation of boric acid. Furthermore, the activation of the biosynthetic pathway for unsaturated fatty acids, leading to increased levels of docosahexaenoic acid and arachidonic acid, played a significant role in its defense response. In WCT, the uninfested top leaves showed higher levels of shikimic acid, stearic acid, threonic acid, lactic acid, and palmitic acid, suggesting these compounds contribute to its defensive strategy. The abundance of sugars in the bottom leaves of COD likely facilitated the feeding and development of the whiteflies, making it a more favorable host for the pest. This study highlights distinct metabolic responses to whitefly resistance and lays the foundation for future research aimed at developing pest-resistant coconut cultivars.

Effective microorganisms pose a great potential in wastewater treatment. In the present study, effective microorganisms’ formulations were developed using different organic substrates that support the growth of more beneficial microorganisms for sewage treatment. Based on the metagenomic analysis and biochemical profile information, the fish waste-based effective microorganisms’ formulation was identified as the effective formulation. Metagenomic analysis showed that fish-based effective microorganisms’ formulation had the Lactobacillus and Acetobacter groups of bacteria. The dominant groups were Lactobacillus pontis (64.85