Indian Council of Agricultural Research

The development of stable and high-yielding fodder pearl millet genotypes with improved quality traits is crucial for enhancing livestock productivity under diverse environments. In this study, twenty-six elite genotypes, including brown midrib (bmr) lines and two check cultivars, were evaluated across four locations, which fall broadly under two agro-climatic zones of India, during the summer season of 2024 to assess their stability for yield and fodder quality traits. Significant genotypic differences and genotype × environment interactions (GEIs) were observed for all traits, indicating substantial genetic variability and environmental influence on trait expression. Additive Main Effects and Multiplicative Interaction (AMMI) and Weighted Average of Absolute Scores (WAAS) analyses identified IGPM 100 as a high-yielding and stable genotype across environments, whereas Baif Bajra 1 and IGBV 97 exhibited specific adaptation. Among quality traits, ICMbmr 2401, ICMbmr 2402, and ICMbmr 2404 recorded consistently low lignin content, confirming their potential for improving forage digestibility. Further, ICFPM 05 recorded high tillering and longer leaves, while ICMFV 2308 exhibited late flowering across locations, indicating their potential for use in developing leafy, late-flowering genotypes. The multi-trait stability index (MTSI) efficiently identified IGPM 100, ICFPM 02, ICMbmr 2404, and IGBV 9 as superior and stable genotypes across multiple traits. High selection differentials for green fodder yield and negative differentials for lignin and fibre fractions highlight the possibility of a simultaneous improvement in yield and quality traits. Overall, the integration of AMMI, WAAS, and MTSI models facilitated the identification of broadly adapted and trait-specific genotypes, which, after evaluating their combining ability, can be used for developing fodder pearl millet composites and hybrids.

Agricultural marketing in India has undergone several reforms to improve efficiency, transparency and price realization for farmers. Despite the establishment of regulated markets under the Agricultural Produce Market Committee (APMC) system, farmers continued to face problems such as limited competition, lack of transparency and dependence on intermediaries. To address these issues, the Government of India launched the electronic National Agriculture Market (e-NAM) in 2016 to integrate Agricultural Produce Market Committees (APMCs) across the country through a unified online trading platform. The present study examines the progress of e-trading in Telangana and identifies the constraints faced by farmers in participating through the e-NAM platform. Both primary and secondary data were used for the study. Secondary data relating to stakeholder participation and commodity-wise trade were collected for the period 2016-17 to 2023-24 from the e-NAM portal and the Department of Agricultural Marketing, Telangana. Primary data were collected from 100 farmers across five selected e-NAM markets using a structured interview schedule. The data were analysed using descriptive statistics, Compound Annual Growth Rate (CAGR) and the Garrett ranking technique. The results revealed that out of 197 regulated markets in Telangana, only 57 markets (28.93%) have been integrated under e-NAM. The platform has registered 18.23 lakh farmers, 6,371 traders, 5,161 commission agents and 137 Farmer Producer Organizations (FPOs). From 2016-17 to 2023-24, a total of 64.68 lakh tonnes of commodities valued in Rs 23,637.6 crore were traded through e-NAM. Commodity-wise analysis shows that paddy was found to be high in traded volume, whereas turmeric, chilli and cotton account for a higher share in trade value. The compound annual growth rate of the value of trade was positive and significant (8.69%). Inter-mandi trade was more active compared to interstate trade, which remained limited. The study also identified lack of digital literacy, inadequate understanding of e-NAM procedures and dependence on commission agents as the major constraints faced by farmers. The findings suggest that strengthening digital awareness, improving infrastructure and expanding market integration are essential for enhancing the effectiveness of the e-NAM platform in Telangana.

The organic heterocyclic 4-((4-nitrophenyl)-diazenyl)-3,5-dimethyl-1H-pyrazole was synthesized and used as a corrosion inhibitor in a saline solution containing mild steel. The corrosion inhibitor efficiency was evaluated using the weight-loss method. The inhibition efficiency of the inhibitor increases with its concentration. At a higher concentration of 600 ppm, it showed good inhibition efficiency. Characterization of pyrazole is performed using FTIR. The surface morphology of the inhibitor was investigated using SEM and EDAX techniques. This paper represented the inhibition of the inhibitor on the surface of the mild steel. The protective monolayer of the inhibitor formed on the metal surface

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.

This study employs a hybrid methodology that integrates a physical process-based radiative transfer (RT) model and machine learning regression to assess three key wheat crop traits: leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC). The non-imaging hyperspectral data collected proximally using the ASD FieldSpec Spectroradiometer were spectrally resampled to 269 spectral bands ranging from 400 to 1000 nm for the retrieval of these crop traits. Upon validating against in situ measurements, good accuracies in terms of NRMSE values, 10.65%, 11.63%, and 13.85%, were achieved for LAI, LCC, and CCC, respectively.

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.

Aspergillus flavus infection and accumulation of carcinogenic aflatoxins are detrimental to maize (Zea mays) production and consumption. We investigated lncRNA-RBP interactions during maize-A. flavus crosstalk using transcriptomic profiling, structural analysis, molecular docking simulations, and machine learning approaches. Analysis of 18 RNA-seq datasets identified 2104 lncRNAs in maize, of which 461 were differentially expressed under A. flavus infection. Distinct lncRNAs were preferentially induced under infection (e.g., Zm00001eb303170) or normal germination (e.g., Zm00001eb144150, Zm00001eb406410). RNA secondary structure predictions indicated high structural heterogeneity and thermodynamic stability, consistent with dynamic regulatory potential. Docking simulations with six key RNA binding proteins (RBPs)-including branch point bridging protein (BPB), KH domain protein, and pentatricopeptide repeat (PPR) proteins-demonstrated strong lncRNA-protein binding, with the lncRNA1-BPB complex exhibiting the highest binding affinity. ML algorithms identified the crucial role of tryptophan in determining interactions, while lncRNA17-KH and lncRNA1-BP complexes were found to have the best interaction under normal germination and A. flavus infection, respectively. The lncRNA-miRNA-mRNA regulatory network highlighted lncRNAs functioning as decoys or precursors of stress-responsive miRNAs (e.g., zma-miR156, zma-miR164, zma-miR399). These interactions targeted transcriptional regulators, splicing factors, and metabolic enzymes implicated in stress tolerance, seed germination, and systemic acquired resistance. The maize lncRNAs are active regulatory molecules embedded in complex RBP and miRNA interaction networks that fine-tune gene expression during A. flavus infection. The study provides novel insights into lncRNA-mediated resistance mechanisms and offers potential molecular targets for breeding or gene editing to mitigate aflatoxin contamination.

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.