Indian Agricultural Statistics Research Institute

Sesame (Sesamum indicum L.), an important oilseed crop, requires genetic improvement to ensure food security. Exploitation of a small fraction of genetic diversity is a major limiting factor for this crop. ICAR-NBPGR holds a large sesame base collection (6,000 unique accessions), originating from India and other countries. The study aims at the characterization of a sesame germplasm collection and development of working core set representing the variability of entire germplasm collection in the National Genebank. Characterization was carried out in an Augmented Block Design (ABD) under multi-environmental conditions (six locations representing five agro-ecological zones of India) for two consecutive years based on 33 agro-morphological traits, different levels of biotic stress (phyllody, dry root rot), abiotic stress (water logging, drought), and seed quality parameters (oil, protein, lignan, fatty acids). For core set preparation, the coincidence rate (CR%) for range and the variable rate (VR%) for coefficient of variation were maximum. Levene’s test and Newman-Keuls tests were performed to compare the entire and core collection. Diversity between the two (entire and core collection) was compared using Shannon–Weaver diversity index. We developed a sesame core set of 773 accessions, which consists of 12.88% of the entire collection. Developed core set exhibited a Coincidence rate (CR)% of 99.8% and a variable rate (VR%) of CV 103.59%. A multipurpose core set of sesame germplasm was assembled to benefit the plant breeding community (for trait introgression), farmers, and researchers to develop climate-resilient sesame cultivars.

The case for mitigating greenhouse gas emissions from rice production systems is well recognised. Among different strategies, alternate wetting and drying (AWD) has emerged as a low-tech, water-saving approach with strong emission mitigation potential. Intermittent drying of rice fields creates an aerobic environment that suppress methane production. We present a multi-season, landscape-scale assessment of AWD in intensive rice cultivation systems of Telangana state in Southern India. Using a robust experimental design, we quantified irrigation water use, crop productivity, and methane emissions, measuring methane fluxes with static closed chambers. Across seasons, AWD consistently lowered methane emissions relative to conventional continuously flooded systems while maintaining grain and biomass yield. Irrigation demand was reduced under AWD, with water savings ranging from ~14% to ~37%. When conservatively scaled, emission reductions correspond to mitigation potentials of 2.5-3.5 Mt CO2-eq ha-1 season-1. These observations enabled the calibration and validation of process-based biogeochemical crop models, which were subsequently extrapolated to the project region using a remote-sensing-based framework. Overall, this work highlights AWD as a scalable lever for reducing emissions, conserving water and improving regional inventories, while strengthening voluntary carbon market accounting and sustainability assessments in rice production systems.

Pepper (Capsicum annuum L.) holds significant global importance as both a spice and a vegetable, extensively cultivated for its unique pungency, nutritional value, and diverse pharmaceutical uses. Being a warm tropical fruit vegetable, it is highly sensitive to heat stress (HS). However, temperature above 33 °C impairs its reproduction, subsequently impacting fruit yield and seed quality. Despite this vulnerability, pepper harbors substantial genetic diversity for key morphological, fruit, and seed traits. Therefore, studying this genetic diversity and identifying genome-wide candidate genes through GWAS for these traits under heat-stress conditions is crucial for advancing heat-resilient pepper improvement. We assessed the genotypic and phenotypic variance among 146 diverse accessions in two seasons (S1: 2022 and S2: 2023) under HS condition. Key morphological, fruit, and seed traits exhibited significant genotypic effects (P < 0.001) across seasons. A pooled ANOVA revealed significant genotype × season interactions for a number of traits, including leaf length (LL), number of primary branch (NPB), average fruit width (AFW), average fruit weight (AF Wt), and hundred-seed weight (HSW). The majority of traits exhibited high heritability (>60%) and high genetic advance (>20%) suitable for selection. Genome-wide SNP screening identified 292.91 million biallelic SNPs, with 1,04,354 high-quality SNPs chosen for GWAS. Population structure and PCA revealed three genetic subpopulations, with LD decay estimated at 580.21 kb, showing extended LD. The BLINK model with Bonferroni correction (−log₁₀(p) > 7.01) identified 58 significant MTAs across all chromosomes, with chr01 having highest (13) followed by chr03 (10), chr11 (6), chr05 (5), chr10 (5), chr06 (4), chr07 (4), chr12 (4), chr04 (3), chr08 (2) and chr09 (2). Fourteen MTAs were consistent across seasons, including one pleiotropic SNP that affects six traits. In-silico annotation revealed that the majority of potential SNPs were found in gene-rich locations. Our study offers significant insights into phenotypic and genetic diversity of pepper for heat tolerance. The BLINK model detected 14 consistent MTAs. Annotated putative genes were mainly associated with cell differentiation, hormonal signalling, flowering, fruit and seed biology, making them useful targets for climate-resilient breeding programs and MAS strategies.

The green revolution enhanced global food production, ensuring food security for a rapidly growing population. However, overreliance on traditional fertilizers has led to environmental degradation and poor nutrient use efficiency. This necessitates a paradigm shift toward sustainable agriculture. Nanofertilizers are a frontier technology having the potential to overcome such challenges by optimizing nutrient delivery and reducing environmental footprint. This review critically assesses the mechanistic basis of nanofertilizers, beginning with synthesis methods and formulation techniques which enable their controlled and targeted nutrient release. It further discusses, the fate and transformation of nanofertilizers in soil and atmospheric matrices. Also, interaction with microbial populations, along with mechanisms of uptake, translocation, and cellular internalization in plants. The review also explores pivotal areas of nanotoxicology, detailing phytotoxicity mechanisms, defining dose-limiting toxicity thresholds, and deliberation on mitigation options. A brief overview of the developing regulatory schemes is also presented, coupled with the present landscape of innovation and future prospects. Such an integration emphasizes the significance of multidisciplinary approach, linking strong regulatory controls with technological development to safely and efficiently harness nanofertilizers for a sustainable and climate-resilient food future.

Reliable crop yield estimates at fine spatial resolution are essential for precision agriculture, food security planning, and insurance schemes. However, yield statistics are reported at coarser administrative levels, limiting their applicability for field-scale analysis. This study proposes a multi-stage hybridized framework that integrates deep learning (DL) models with geostatistical residual kriging to disaggregate village-level crop yield statistics to the pixel level. The proposed methodology is demonstrated using wheat and mustard crops as case study in the semi-arid districts, Haryana, India. The study identifies suitable data combination by evaluating multiple combinations of soil, weather, Sentinel-1, and Sentinel-2 bands data for yield disaggregation. Results show that datasets combining spectral and weather information consistently outperform other data combinations. Validation results showed that the strongest numerical accuracy was observed for machine learning algorithms, e.g., random forest, with an R2 of 0.9949, but it lacks spatial realism. On the other hand, DL models had comparable numerical accuracy and also produced smoother and more realistic spatial transitions but exhibited spatially structured residuals. To mitigate these spatial biases, residual kriging was applied to DL outputs, resulting in RMSE reduction of 35-45% and generating smoother pixel-level maps that preserved fine-scale heterogeneity and aligned with reported village yields. Moran's I analysis confirmed significant residual spatial autocorrelation for DL models, justifying the use of geostatistical correction. Thus, the proposed hybridized framework emerged as best for balancing statistical accuracy with spatially realistic yield disaggregation. This study provides one of the first empirical demonstrations of village-to-pixel yield disaggregation using the identified weather and satellite band data combination.

Introduction The multiple nutritional disorders producing the early decline of citrus productivity are commonly observed across the citrus belts of northeast India. This situation is further compounded by a mismatch between annual addition and consumption of fertilizers, in the backdrop of an erroneous diagnosis of nutrient imbalance. In this background, we attempted to diagnose nutrient balance in Khasi mandarin ( Citrus reticulata Blanco) orchards using several diagnostic tools comprising machine learning (ML) tools. Methods A database of soil available nutrients (KMn0 4 -N, Brays-P, NH 4 0Ac-K, DTPA-Fe, DTPA-Mn, DTPA-Cu, DTPA-Zn) and fruit yield documenting 180 Khasi mandarin orchards of seedling origin (10–30 years old with row-to-row distance of 4 m and trees 6 m apart) raised under rainfed conditions in the Meghalaya state of northeast India. Diagnosis methods were compared: the sufficiency level of available nutrients (SLAN), the basic cation saturation ratio (BCSR), the compositional nutrient diagnosis (CND), the diagnosis and recommendation integrated system (DRIS) and ML tools like random forest and xgboost. Results Soil test interpretation of a low-yielding and nutritionally imbalanced orchard differed among diagnostic methods. DRIS predicted deficient-to-low concentrations of Zn, Ca, P, N, and K; other nutrients like Fe, Cu, Mn, and Mg were at optimum-to-high concentrations. CND standards diagnosed Zn deficiency and Cu excess with potential agronomic manifestations for early decline in productivity. SLAN interpretation was highly skewed for Ca; moderately skewed for Mg, Cu and Zn, and unskewed for N, P, K, Fe and Mn. The accuracy of ML regression models relating nutrient expressions to fruit yield was invariably high, followed by SLAN. The ML xgboost regression model exhibited the highest accuracy in predicting fruit yield from soil test. Conversely, the BCSR, which considers only three cationic dual ratios, was inaccurate. There were distortions when relating concentration values to DRIS indices to determine ‘optimum’ concentration ranges. The ML classification models showed that concentration values were also less accurate than clr to classify data as true negative (TN) or true positive (TP). The xgboost classification model showed optimum ranges for N, P, Ca, Mg, Mn, and Cu; whereas K, Fe, and Zn fell below the lower limits. Conclusion ML, hence, as a classification approach, aided in discarding cases of poor yields and high yields showing luxury nutrient consumption or suboptimal nutrient levels. The soil test standards and site-by-site comparisons can further support site-specific nutrient management and precision fertilization. Considering Zn as the most deficient nutrient, Zn biofortification interventions are recommended to increase fruit yield and quality in Khasi mandarin.

Introduction Teaching effectiveness is a critical determinant of learning quality, skill development, and academic performance in agricultural higher education. However, empirically grounded assessments remain limited in Indian agricultural universities, where challenges related to pedagogy, student engagement, and institutional support persist. This study develops a multidimensional, statistically derived Teaching Effectiveness Index (TEI) to evaluate student-perceived teaching effectiveness across three premier agricultural institutions. Methods Data were collected from 180 postgraduate students using a structured questionnaire comprising 75 sub-indicators across three dimensions: Pedagogical Proficiency, Learning Engagement, and Educational Environment Dynamics. Principal Component Analysis (PCA) was used to derive empirical weights for constructing the TEI, and institutional differences were assessed through non-parametric tests. The Analytic Hierarchy Process (AHP) was applied in parallel to identify and prioritize student-perceived constraints affecting teaching effectiveness. Results The Indian Agricultural Research Institute (IARI) achieved the highest TEI score (0.74), followed by GBPUAT and BHU (0.71 each). PCA indicated that innovative teaching strategies, technology integration, meaningful internships, and resource adequacy contributed most strongly to teaching effectiveness. AHP analysis ranked research-related constraints as the most critical barriers (priority weight = 0.267), followed by knowledge-development (0.229) and course-related constraints (0.206). Discussion Prominent challenges included the non-provision of research funds, inadequate laboratory facilities, limited software-related guidance, and insufficient practical exposure. This study demonstrates the value of integrating PCA-based empirical weighting with AHP-based prioritization to produce a comprehensive, scalable framework for evaluating teaching effectiveness. The findings underscore the need for targeted reforms to enhance pedagogical innovation, strengthen research and digital infrastructure, and expand experiential learning opportunities in agricultural universities.