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Agroclimatic shifts directly influence water availability, crop productivity, and food security in vulnerable regions. By characterizing rainfall variability, crop season onset, length of growing period (LGP), and water deficit in Bihar, this study provides actionable insights for optimizing water management and building climate-resilient agriculture in the Middle Gangetic Plains. The study analyzes rainfall data (1984–2003, 2004–2023) across 38 districts of Bihar (~7.95 mha cultivated area), integrating potential evapotranspiration and soil water-holding capacity using agroclimatic tools to evaluate spatial-temporal changes in key parameters. The results highlight substantial shifts in agroclimatic conditions of Bihar, including rainfall climatology, sowing onset, growing period length (LGP), and water surplus. During 2004–2023, compared to 1984–2003, most districts experienced delayed sowing onset and reduced LGP, with East Champaran recording the maximum reduction of 36 days. Water-stressed zones expanded considerably, particularly in northwestern Bihar, while even high-rainfall areas showed declining water surplus. Concurrently, water deficits increased across the state, signalling growing irrigation demand. These changes underscore the urgency of adaptive water management strategies to sustain agricultural productivity, enhance resilience, and ensure long-term food security in the Middle Gangetic Plains. Adaptation strategies in Bihar focus on diversified climate-smart cropping system revisions and water-smart technologies such as direct-seeded rice (DSR), alternate wetting and drying (AWD), precision land levelling, micro-irrigation, and residue mulching. The study highlights the urgent need to revisit and realign agricultural planning and policy decisions in light of the evolving agroclimatic realities.
A comprehensive understanding of crop phenology and light dynamics, particularly the spatial variability of these interactions within the canopy, is critical for developing new strategies to enhance field-scale productivity. In pursuit of this, an innovative field experiment on winter maize was conducted during 2021–2023, aiming to elucidate the complex relationships among phenological development, canopy light balance components, and light use efficiency across diverse microenvironments. This study offers new insights into optimizing yield potential under real-world field conditions. Winter maize was sown on five dates at 10-day intervals, viz., 1st November, 10th November, 20th November, 30th November, and 10th December in two consecutive winter seasons (2021–22 and 2022–23) at Pusa (25.7°N, 87.5°E, 52 m), Bihar, situated in the middle Gangetic plains of India. The results revealed notable variations in the crop’s phenological responses across sowing dates. Delayed sowing extended the emergence phase but shortened the vegetative period, leading to an accelerated progression to reproductive stages. Moreover, key phenophases such as tasseling, silking, and milking occurred more rapidly in later sowings, likely due to variations in temperature and day length. Incident photosynthetically active radiation (PARin) over the canopy was significantly affected by sowing date. This caused differences in intercepted, transmitted, and absorbed PAR depending on the crop stage and canopy density. Among all sowing dates, the 20th November sowing recorded the highest levels of intercepted and absorbed PAR, attributed to the maximum leaf area index. The fraction of absorbed PAR (fAPAR) consistently remained lower than the fraction of intercepted PAR (fIPAR) throughout the phenological stages. Additionally, fIPAR and the light extinction coefficient (k) exhibited logarithmic and linear relationships with leaf area index, respectively. The highest light use efficiency (5.72 g MJ− 1) was achieved with the 20th November sowing, indicating effective utilization of the prevailing resource environment to maximize maize yield.
Codon usage bias (CUB) is a phenomenon that shows variation both within the genes of a species and across different species, wherein particular codons are preferred over their synonymous counterparts. Genomic nucleotide configuration provides insights into the molecular basis of genes and their evolutionary relationships in distinct plant species. In this study, the VTE4 gene, responsible for enhancing the nutritional quality and oil stability of oilseed Brassica , sequences from six Brassica species were extracted through the NCBI GenBank database. Subsequently, various CUB-related parameters, including nucleotide composition (AT and GC content), relative synonymous codon usage (RSCU), effective number of codons (ENC), frequency of optimal codons (Fop), relative codon usage bias (RCBS), neutrality plot (GC12 vs. GC3), parity rule-2 [(A3/(A3 + T3) vs. (G3/(G3 + C3)], and correspondence analysis, were assessed to analyse codon biasness within the U’s triangle of Brassica species. The results showed AT bias across the Brassica species and moderate codon usage frequency for specific amino acids based on RSCU values in the VTE4 gene. An evolutionary study confirmed a similarity in codon usage preference among species clustered together. The elevated ENC value, along with low Fop and RSCU values, indicated a low level of gene expression and a moderate bias in codon usage preference within the Brassica genus. Moreover, the results from the neutrality plot, parity rule, and correspondence analysis revealed that natural selection pressure had a more significant influence on shaping codon usage bias (CUB) in the VTE4 gene than mutation pressure. This research contributes to understanding codon optimization, enhancing exogenous gene expression, and advancing transgenic engineering for improved α-tocopherol content profiling in Brassica species to enhance seed oil quality.
Developing high-yielding and climate-resilient wheat genotypes is a primary objective of breeding programs. Spike characteristics are a primary candidate for grain yield improvement in wheat. In the present study, the allelic variation of 49 functional and linked markers for spike-related traits genes was assessed in 32 genotypes of bread wheat. Based on 34 polymorphic markers, 81 alleles with an average of 2.76 alleles per locus were amplified. Polymorphic information content varied from 0.11 to 0.62, with a mean of 0.34. Based on the Heatmap analysis, the studied genotypes were assigned to two main groups. The first group consisted of breeding lines and cultivars, while the second group included cross-derived genotypes, a landrace, and an amphiploid wheat. Bayesian model-based clustering assigned the genotypes into two distinct groups consistent with the major clusters identified by the Heatmap. Principal component analysis also separated breeding lines and commercial cultivars. Several spike-form–linked simple sequence repeats (SSRs) delineated yield-component contrasts: Xgwm155 (142 bp) homozygotes showed higher spikelet and grain numbers (P < 0.05), and Xwmc453 classes differed by 1.6 spikelets per spike (P = 0.006). Height variation was captured by GA-pathway diagnostics (e.g., DG118 alleles reduced plant height; P = 0.0001), whereas TaCwi-A1 and TaGW2-6A alleles did not consistently differentiate thousand-kernel weight under our conditions, and Vrn-B1 spring alleles were not associated with earlier heading/flowering. This study highlights the efficiency of functional and linked markers in deciphering the genetic relationships of wheat genotypes with different spike morphology and related traits.
Understanding the evolution of extreme rainfall patterns due to climate change is essential for climate-resilient agricultural planning and hydrological risk management. This study examined long-term (1965–2022) daily rainfall data from 44 stations in the coastal, Western Ghats, and arid agroclimatic regions of southern Karnataka, utilizing fifteen extreme precipitation indices according to the India Meteorological Department (IMD) and Expert Team on Climate Change Detection and Indices (ETCCDI) standards. Trend detection was conducted utilizing the Mann–Kendall (MK) test and the Innovative Trend Analysis (ITA) method, facilitating the evaluation of both monotonic changes and transitions among low-, medium-, and high-intensity rainfall categories. The results indicated significant regional variability in extreme rainfall patterns. The coastal zone demonstrated considerable reductions in southwest monsoon heavy rainfall (HRE: -0.045 days/year), very heavy rainfall (VHRE: -0.031 days/year) and maximum 1-day and 5-day rainfall (RX1: -0.39 mm/year, RX5: -1.94 mm/year), alongside an increase in light (LRE: 0.136 days/year) and moderate rainfall (MRE: 0.092 days/year) occurrences and reduced wet spell durations (WN: 0.046 days/year). This indicates a fragmentation of monsoonal precipitation, probably affected by diminishing westerlies and modified moisture transport from the Arabian Sea. Conversely, the arid region witnessed substantial rises in heavy precipitation occurrences (HRE: 0.005 days/year), RX1 (0.181 mm/year), RX5 (0.269 mm/year), and the duration of wet spells, aligning with intensified convective activity attributable to land surface warming and augmented atmospheric moisture availability. The Western Ghats exhibited predominantly stable extreme rainfall indices, with only increases observed in rainy days (RD: 0.187 days/year) and light rainfall events (LRE: 0.161 days/year), indicating that orographic processes continue to mitigate intense climatic variations in the region. The integrated MK–ITA framework identified indices demonstrating monotonic trends versus those displaying non-monotonic or category-specific behavior, providing more refined insights than traditional trend tests alone. The results underscore evolving climatic changes with direct consequences for water management, agriculture, infrastructure development, and disaster risk mitigation throughout southern Karnataka.
