Markets and Value Chain Study of Major Commodities in the Dryland Regions of Maharashtra

The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), in partnership with the Ground Water Surveys and Development Agency (GSDA), is conducting a collaborative study focused on improving agricultural systems and livelihoods in rural Maharashtra, India. The study emphasizes both resource efficiency and enhanced agricultural productivity. It also explores sustainable farming practices and effective market integration for smallholder farmers. The central goal is to support rural communities by strengthening natural resource management, introducing climate-resilient farming techniques, and improving market access for key agricultural commodities. This initiative adopts a comprehensive approach to agricultural development, combining economic advancement with ecological sustainability and social empowerment. By equipping farmers with improved knowledge and practices and strengthening linkages across the agricultural value chain—from production to market—the project seeks to improve farmers’ incomes and reduce their vulnerability to climate and market risks. A critical focus of the study is on optimizing the use of land and water resources while promoting farming systems that are both environmentally responsible and economically viable. The project also seeks to identify and promote region-specific innovations that align with agro-ecological conditions and local market opportunities. Improved market access is a core pillar of this intervention. By enhancing the efficiency of input supply chains and improving the terms under which farmers engage with output markets, the project aims to ensure that smallholder producers receive fair returns for their produce. Stronger value chain integration will also increase the availability of quality inputs and improve the competitiveness of rural farmers in regional markets

Biochar Application: A Sustainable Approach for Managing Stem Rot Disease in Peanut Caused by Sclerotium rolfsii Sacc.

Soilborne pathogens are often difficult to manage due to a wide host-range and long-term survival of pathogens. Inorganic and organic soil amendments are considered as one of the key management strategies. Biochar, produced through biomass pyrolysis under oxygen-limited conditions, has gained attention for enhancing soil structure, sequestering carbon, improving water retention, and boosting fertility. Beyond its role in soil enhancement, biochar shows potential in managing soilborne diseases. This study explores the multifaceted impact of biochar in managing stem rot disease in peanut, as well as its influence on soil properties and microbial communities. The effects of different biochar concentrations such as 0% (no biochar + S. rolfsii), 1%, 3% and 5% on Sclerotium rolfsii-induced stem rot were thoroughly assessed. Under laboratory conditions, biochar did not exhibit inhibitory effects on S. rolfsii at any concentration; however, it significantly reduced sclerotia formation, indicating a concentrationdependent suppression of pathogen resting structures. Moreover, biochar treatments effectively delayed disease onset and slowed disease progression in peanut plants, with notable variation observed among genotypes and biochar concentrations. Interactions involving genotypes ICGV 171002 and ICGV 181035 with BC2 + Sr (3% biochar + S. rolfsii) and BC3 + Sr (5% biochar + S. rolfsii) demonstrated superior disease suppression under controlled conditions. Field evaluations further validated these findings, revealing genotype-specific responses to biochar applications. However, no significant difference was observed between BC2 + Sr (3%) and BC3 + Sr (5%) in their ability to manage stem rot disease compared to controls. In addition to disease management, biochar improved soil fertility by increasing nitrogen, phosphorus, and potassium levels while enhancing soil organic matter, electrical conductivity and pH. These findings highlight biochar’s potential as a sustainable soil amendment, contributing to disease suppression and soil health improvement. Further research is needed to optimize biochar application strategies across diverse agricultural settings

Multi-locus Genome-Wide Association Study Uncovers Candidate Genes for Early Leaf Spot and Rust Resistance in Groundnut

Early leaf spot (ELS) and rust, caused by Cercospora arachidicola and Puccinia arachidis respectively, are major fungal diseases limiting global groundnut (Arachis hypogaea L.) production. Mini-core set was evaluated for ELS at 3 locations Senegal, Malawi and Mali and for rust at Dharwad for three seasons using the modified 9-point scale. High-density genotyping was performed using whole genome resequencing data to identify genomic regions associated with early leaf spot and rust resistance. After quality filtering, 561,009 high-confidence SNPs were obtained. To dissect the genetic architecture of resistance to these diseases, genome-wide association study (GWAS) was conducted on 184 diverse groundnut mini-core lines which revealed four marker trait associations (MTAs) significantly associated with ELS resistance and twelve MTAs associated with rust resistance, collectively explaining 10–59% of the phenotypic variance. The most significant SNPs were Ah01_48511619 and Ah17_133493906 for rust resistance, which were clearly differentiating the resistant and susceptible minicore lines and also validated in other diverse populations. These SNPs encode sterol c4-methyl oxidase 1-2 and MYB transcription factor. Sterol c4-methyl oxidase 1-2 is involved in brassinosteroids biosynthesis which inturn has a role in salicylic acid pathway and MYB transcription factor involved in activation of WRKY disease resistance genes to combat against fungal pathogens. For ELS resistance, the most significant SNP (Ah14_140021024) is associated with MYB transcription factor explaining phenotypic variance of 59%. The validated markers and the predicted candidate genes can be useful in integrating favorable alleles into elite cultivars through marker-assisted selection (MAS) for the development of cultivars with improved disease resistance. The ELS markers can be utilized in the breeding programs upon further validation

Differential impacts of regenerative agriculture practices on soil organic carbon: a meta-analysis of studies from India

Regenerative agriculture (RA) is heralded as a transformative solution to combat climate change, enhance biodiversity, and improve soil health. However, its effectiveness across diverse agroclimatic contexts remains underexplored. This meta-analysis synthesizes results from 147 peer-reviewed studies across India's major agro-ecological and agro-climatic regions. Using a random-effects model, we estimate the soil organic carbon (SOC) change attributable to a suite of RA practices, including organic amendments (farmyard manure, green manure, compost, and biochar), conservation tillage, crop residue retention, and fertilizer management. Biochar application resulted in the highest SOC gain, followed by farmyard manure, green manure, compost, and fertilizer management. Conservation tillage and crop residue retention demonstrated moderate, yet consistent, carbon benefits across time scales. The SOC gains were most significant over durations exceeding five years and varied across agro-ecological regions, with semi-arid and sub-humid regions showing particularly strong responses. The findings affirm that RA practices effectively sequester carbon, particularly when applied over longer durations and in regionally adapted combinations

Systems for Heterosis Breeding and Hybrid Seed Production in Pigeon Pea

Male sterility has provided new avenues for exploitation of heterosis and hybrid seed production in pigeon pea (Cajanus cajan L. Millspaugh). Finding of genic male sterility laid the foundation of commercial hybrid seed making in pigeon pea, resulting in commercial release of five hybrids in India. However, the Genetic Male Sterility (GMS) system was soon abandoned due to its high production cost. Later, identification of cytoplasmic male sterility (CMS) overcame crucial bottlenecks associated with the GMS system. The latest pigeon pea hybrids based on CMS system are IPH 15-03 and IPH 09-5, which are suitable for deployment in pigeon pea-wheat rotation system. The seed of newly released superior hybrid varieties should be available at the farmers doorstep before the onset of cropping season to ensure maximum benefits to the farming community. Multiplication of hybrid seed and its parental lines remains a herculean task. Successful seed production relies on several factors, and the present chapters describe crucial aspects of quality seed production of pigeon pea including floral biology, good agronomic practices, management of biotic and abiotic stresses, breeding behaviour, classes of seed and their requirement and post-harvest management and packaging of seed

Genetic variability studies for quantitative traits in pearl millet (Pennisetum glaucum L.) mapping population and maintainer lines under different phosphorus fertilizer rates in Sudano Sahelian Agro-Ecology of Nigeria

Low soil Phosphorus is considered to be one of the major limiting factors to pearl millet production in sub-Saharan Africa (SSA). This study seeks to establish the extent of genetic variability in the pearl millet mapping population and maintainers under varying levels of phosphorus. A set of twenty-four pearl millet mapping population and maintainer lines were evaluated at the International Institute of Tropical Agriculture (IITA) Minjibir experimental station under two levels of Phosphorus application to determine the genotype responses and to identify major traits involved in Phosphorus tolerance. The experiment was laid out in an alpha lattice with three replications. Analysis of variance revealed significant differences for all 11 characters studied. The study identified phosphorus-tolerant lines, P1449-2, PT732B-P2, ICMB 89111, ICMB 90111, and susceptible lines; H77/833-2 P5 (NT), Tift 186, and H77/833-2 (reselected), which could be utilized in developing QTLs to aid in marker-assisted breeding. Phosphorus fertilizer application resulted in significantly higher grain and grain yield components. Phosphorus application significantly increased heritability and genetic advance values in most of the yield and yield components. High heritability (60.12) coupled with high genetic advance (317.56) was found for head weight, followed by grain weight with high heritability (44.45) and high genetic advance (99.46), indicating that most likely the heritability is due to additive gene effects, and selection based on these characters would be effective for further pearl millet mapping population improvement program

Leveraging ML to predict climate change impact on rice crop disease in Eastern India

Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical factors for current and future scenarios. The nine biophysical parameters are precipitation (Pr), maximum temperature (Tmax), minimum temperature (Tmin), soil texture (ST), available water capacity (AWC), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference chlorophyll index (NDCI), and normalized difference moisture index (NDMI) by Random forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Artificial Neural Net (ANN), and Support vector Machine (SVM). The multicollinearity test Boruta feature selection techniques that assessed interdependency and prioritized the factors impacting crop disease. However, climatic change scenarios were created using the most recent Climate Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5-8.5 datasets. The rice crop disease validation was accomplished using 1105 field-based farmer observation recordings. According to the current findings, Purba Bardhaman district experienced a 96.72% spread of rice brown spot disease due to weather conditions. In contrast, rice blast diseases are prevalent in the north-western region of Birbhum district, affecting 72.38% of rice plants due to high temperatures, water deficits, and low soil moisture. Rice tungro disease affects 63.45% of the rice plants in Bankura district due to nitrogen and zinc deficiencies. It was discovered that the link between NDMI and NDVI is robust and positive, with values ranging from 0.8 to 1. According to SHAP analysis, Pr, Tmin, and Tmax are the top three climatic variables impacting all types of disease cases. The study’s findings could have a substantial impact on precision crop protection and meeting the United Nations Sustainable Development Goals

Shoot Fly Resistance in Sorghum: An Overview

Sorghum is an annual diploid C4 plant largely grown for food, fodder and feed purposes. Several insect pests pose major challenges to sorghum production from the seedling stage to maturity, among which the sorghum shoot fly Atherigona soccata (Rondani) is a major pest across Asia, Africa and Mediterranean Europe. Infestation by the pest is prevalent both during rainy and postrainy seasons. The exploitation of host-plant resistance can play a vital role in breeding for resistance to shoot flies. The shoot fly causes significant grain and fodder yield losses in sorghum in semi-arid regions. An integrated approach for host-plant resistance that combines morphological, genetic/molecular and agronomic approaches is key for the management of shoot fly infestations and the subsequent increase in sorghum productivity. To complement traditional breeding approaches, intervention in genomic approaches is required to enhance breeding efficiency. This review focuses on genetic approaches in sorghum for integrating shoot fly resistance and exploring genetic inheritance, variability and trait associations, including shoot fly resistance quantitative trait loci (QTLs)

A Comprehensive Review of Aflatoxin in Groundnut and Maize Products in Africa: Prevalence, Detection and Mitigation Strategies

Aflatoxins are a toxic secondary metabolite, mainly produced by the fungi Aspergillus flavus and A. parasiticus. Aflatoxin contamination of food is a global concern, as they are carcinogenic, mutagenic and teratogenic. Groundnuts and maize products are highly susceptible to aflatoxin contamination at both pre- and postharvest stages; this leads to a great risk for those countries that rely on these products for food and nutrition security as well as income. Groundnut and maize products have contributed a substantial amount of aflatoxin exposure to human and animal health risks, especially in countries that experience tropical climate and recurrent drought, favouring mould developments. Due to the strange health impacts of aflatoxin in agricultural commodities, different countries have set the acceptable limits for groundnut and maize products, whereas most of the countries use the same limit for both commodities. Detection and quantification of aflatoxins in groundnut and maize products are mainly through enzyme-linked immunoaffinity assay (ELISA) and high-performance liquid chromatography (HPLC), among others. However, currently rapid, accurate and cost-effective techniques are emerging to quickly monitor and enforce the regulation limits. Among the widely applied strategies for aflatoxin mitigation are biological control including atoxigenic Aspergillus strains, plant extracts, and chemical and physical methods of detoxification and decontamination. Aflatoxin decontamination using plant extracts is promising for most countries in sub-Saharan Africa owing to the availability, ease of access and affordability; however, there is a need for further screening to isolate the bioactive ingredients. This review could provide insight into the researchers, stakeholders and consumers on the prevalence of aflatoxin in groundnut and maize products as well as mitigation strategies to improve food safety

AI-Enabled UAV Borne Hyperspectral Imaging for Crop-Livestock Farm Management

Crop-livestock farming plays a crucial role in global agricultural communities by integrating crop production with livestock farming to create a sustainable and diversified farming system. However, this industry has become increasingly scrutinized due to environmental impact, climate change, and land degradation. As per present reports, crop residues, a significant livestock feed resource, are in shortage and have poor nutritional value. Moreover, various factors like heatwaves, drought, and diseases can negatively impact forage quality and reduce productivity. Conventional methods of assessing forage/crop residue quality face significant challenges, including labor-intensive, costly, time-consuming, and error-prone. UAV-based imaging can boost multi-dimensional crop improvement programs due to advantages like wider coverage, short revising times, high spatial resolutions, and ease of operation. Hyperspectral imaging (HSI) sensors provide enriched spectral information, enabling more precise investigations into feed quality evaluation, forage management, and livestock health. Artificial intelligence and machine learning (AI/ML) approaches can effectively analyze high-dimensional HSI data and extract meaningful insights. Integrating UAV-based HSI and AI/ML techniques is crucial to enhance crop-livestock farm management. This chapter explores the potential of UAV-based HSI and AI/ML for crop-livestock farm research and management, focusing on animal and forage health monitoring, and enhancing feed quality. We also emphasize AI/ML-based data analytics and algorithm development on UAV-borne HSI data to revolutionize crop-livestock farming