University of Agricultural Sciences, Dharwad (UASD) is a state agriculture university established by the Government of Karnataka which imparts education, research and extension activities in the fields of agriculture, forestry, food science, agricultural marketing and home science. It is on Dharwad-Belgaum road (PB-4) on a campus with space for growing trees and fields for conducting experiments and research.
Little is known about the sustainability of an intercropping system comprising finger millet (Eleusine coracana) and pigeon pea (Cajanus cajan). We therefore sowed both cover crops and the above crops over 3 years in a seed mix of 8:2 by weight to assess their effects on soil properties, yield potential, and energy-use efficiency. Treatments consist of tillage intensity-conventional, reduced, or no tillage in combination with the cover crop horse gram or lablab bean versus no cover crop. Averaged over 3 years, conventional tillage required energy inputs 12% higher than reduced tillage and 4% higher than those without tillage. The sustainable yield index (89.6%) was highest in conventional tillage with horse gram as the cover crop with a mean of 51.6% and a variation of 42.3%. This combination also improved soil quality, although the energy index was greater under reduced tillage than under no-tillage or conventional tillage. In the wet year (2019), we had higher soil quality index values (8.45-9.97) than in the dry years (2018) (2.50-3.02). The right combination of a cover crop and the intensity of tillage may confer substantial environmental benefits with only minimal detrimental effects on yield.
Drought is a persistent environmental challenge with profound impacts on water resources, agriculture, and ecosystems, particularly in semi-arid regions. This study investigates the spatial and temporal dynamics of drought in the Southern Telangana Zone (STZ) using the Standardized Precipitation Evapotranspiration Index (SPEI) across 12 districts over the past 44 years. Results highlight considerable variability in drought intensity and frequency, with extreme value distribution analysis indicating an increasing risk of severe drought events in the future. To enhance predictive capability, several time series models were developed, including ARIMA, STARMA, and a novel two-stage STARMA-TDNN framework that integrates spatiotemporal linear modeling with nonlinear machine learning. The proposed triangular fuzzy STARMA-TDNN model achieved the highest efficiency, reducing training and testing mean squared error by more than 70 % compared to alternative approaches. While ARIMA captured only temporal linear patterns and STARMA addressed linear spatiotemporal dependencies, the two-stage STARMA-TDNN framework effectively represented both linear and nonlinear spatiotemporal drought dynamics. The Diebold-Mariano test further confirmed the statistical superiority of the two-stage model. These findings underscore the potential of advanced hybrid models for reliable drought forecasting and provide a scientific basis for location-specific drought management strategies in the STZ.
The fall armyworm (Spodoptera frugiperda), a highly invasive and destructive pest of maize, poses serious challenges to sustainable crop protection due to its rapid spread and widespread resistance to chemical insecticides. In this study, we evaluated the insecticidal and sublethal effects of two indigenous entomopathogenic bacterial symbionts, Xenorhabdus indica UASD_BidS and Photorhabdus luminescens UASD_KaH, isolated from Steinernema spp. and Heterorhabditis spp., respectively. Molecular identification based on 16 S rRNA sequencing confirmed the taxonomic placement of both isolates. Bioassays against third-instar S. frugiperda larvae revealed strong concentration-dependent mortality, with cell-free supernatants (CFS) consistently outperforming cell suspensions (CS). While both bacteria caused significant lethal effects at higher concentrations, P. luminescens exhibited greater potency, inducing faster mortality and stronger feeding deterrence. At sublethal concentrations, both bacteria significantly reduced larval feeding, larval weight and pupal mass, cuasing severe developmental abnormalities in emerging adults. Notably, extracellular metabolites mediated pronounced growth inhibition and feeding suppression even in the absence of immediate mortality. Comparative analysis demonstrated that P. luminescens exerted stronger sublethal effects than X. indica, particularly on larval growth and pupal development. These findings highlight the critical role of secreted bacterial metabolites in disrupting insect physiology and development. Overall, the study underscores the strong potential of X. indica and P. luminescens, especially their extracellular metabolites, as eco-friendly biocontrol agents for inclusion in integrated management strategies against fall armyworm.
Post-rainy season sorghum cultivation is significantly challenged by various biotic and abiotic stresses, with shoot fly (Atherigona soccata) emerging as the most destructive biotic constraint. This study aimed to identify and evaluate shoot fly-resistant introgression lines (ILs) from the bc(2)F(2) to bc(2)F(4) generations. These ILs were developed by crossing three elite recurrent parents-SPV2217, BJV44 and SVD0806-with two resistant donor lines, J2614-5 and J2799, known to possess QTLs linked to leaf glossiness and trichome density, respectively-traits associated with shoot fly resistance. Molecular screening of bc(2)F(2) ILs using SNP markers confirmed the successful introgression of shoot fly resistance QTLs in the progeny. Subsequent phenotypic evaluations of the bc(2)F(3) generation led to the identification of several high-yielding and resistant families across different crosses, notably family numbers 25, 30, 42 (SPV2217 x J2614-5); 8 (SPV2217 x J2799); 1, 8, 11 (BJV44 x J2614-5); 12, 13, 14, 21, 26 (BJV44 x J2799); 14, 21, 22, 29, 37 (SVD0806 x J2614-5) and 2, 9, 12, 23 (SVD0806 x J2799). In the bc(2)F(4) generation, 304 ILs were screened using the interlard fish meal technique, revealing several highly resistant lines across all crosses. These ILs consistently exhibited key resistance traits including reduced egg laying, lower dead heart incidence, enhanced leaf glossiness, higher trichome density and improved seedling vigour-indicating successful introgression of resistance traits from donor to recurrent backgrounds. AMMI and GGE biplot analyses further identified genotype SF 5 as a stable, high-yielding and shoot fly-resistant line. The tolerant genotypes identified in this study represent a valuable resource for developing shoot fly-resistant sorghum varieties for post-rainy season cultivation in future plant breeding programs.
Background factors such as vagaries in monsoon, unsuitable soil, inappropriate sowing time, non-adoption of recommended technologies, especially plant geometry and fertilizer use, are limiting cotton production at farmers’ fields. The yield gaps can be reduced with better crop management, such as optimum date of sowing, plant spacing, and nitrogen. Against this background, the current investigation was carried out to test and validate the model in the Raichur area of Karnataka, India for the dynamic simulation of cotton development, growth, and seed cotton yield under varied sowing times, plant densities, and nitrogen levels. The model was calibrated using observed data on phenology and yield components from the experiments conducted at the Main Agricultural Research Station, Raichur, during the kharif periods 2022–23 to 2023–24. The CSM-CROPGRO-Cotton model performed well under different dates of sowing, plant densities, and nitrogen levels for the simulation of phenology; the model performance was fair for the simulation of seed cotton yield, biomass, and nitrogen uptake for cultivar US7067. The model application through seasonal analysis was also used to confirm the results of the CROPGRO-Cotton model validation using the past 30 years of weather data. Optimum sowing time for predicting higher seed cotton yield was at the second fortnight of June under semi-arid conditions. In the case of plant population from 12,345 plants ha−1 (90 cm × 90 cm) to plant density of 74,074 plants ha−1 (90 cm × 15 cm), an increased seed cotton yield was predicted. The incremental increase in nitrogen level from 100 to 250 kg N ha−1 did not show much influence on predicted mean seed cotton yield. However, a higher mean seed cotton yield (1,682 kg ha−1) was predicted with higher levels of nitrogen application, i.e., 250 and 300 kg N ha−1. The CROPGRO-Cotton model applicability for the research area was evident from its calibration and validation in the Karnataka semi-arid environment. Using a seasonal analysis tool, the CROPGRO-Cotton model results demonstrated a clear path to increased seed cotton yield.
