The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is an international organization which conducts agricultural research for rural development, headquartered in Patancheru (Hyderabad, Telangana, India) with several regional centers (Bamako (Mali), Nairobi (Kenya) and research stations (Niamey (Niger), Kano (Nigeria), Lilongwe (Malawi), Addis Ababa (Ethiopia), Bulawayo (Zimbabwe).It was founded in 1972 by a consortium of organisations convened by the Ford and the Rockefeller foundations. Its charter was signed by the FAO and the UNDP.Since its inception, host country India has granted a special status to ICRISAT as a UN Organization operating in the Indian territory making it eligible for special immunities and tax privileges.ICRISAT is managed by a full-time Director General functioning under the overall guidance of an international Governing Board. The current Director General is Dr Jacqueline d’Arros Hughes who took the post in April 2020. The current chair of the Board is Prof. Prabhu Pingali.
The global population is expected to reach 9.7 billion by 2050, which will put pressure on food production systems that are already vulnerable to climate change. Crop diseases caused by pathogenic bacteria, fungi, and viruses cause significant yield losses in major food crops. Conventional breeding has contributed to developing resistant cultivars, but it is time-consuming and limited by genetic variability. The clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein (Cas) systems have emerged as transformative tools for the precise and rapid improvement of disease resistance in food crops. This review presents a comprehensive overview of the application of CRISPR/Cas-based genome editing platforms such as Cas nucleases, base editors, and prime editor systems for developing disease resistance in food crops. These technologies enable targeted knockout of susceptibility (S) genes, precise nucleotide substitutions, promoter engineering, and direct viral RNA interference for imparting disease resistance. Delivery of DNA-free ribonucleoprotein (RNP) complexes further supports the development of non-transgenic (GM-free) crops for quick regulatory approvals and field cultivation. The review highlights successful applications of CRISPR/Cas tools across major cereals, legumes, and tuber crops for developing disease resistance. Additionally, CRISPR-enabled diagnostics provide new prospects for rapid pathogen detection in field conditions. It also draws in roads for future adoption of CRISPR/Cas tools for developing disease resistance in major crops and extension to minor cereals such as millets. Prudent applications of CRISPR/Cas tools will play a pivotal role in promoting sustainable agriculture and achieving the United Nations Sustainable Development Goal of Zero Hunger.
Climate change is expected to significantly disrupt irrigation water availability, thereby threatening crop productivity and irrigators' livelihoods. Studies have shown that the smart water management tools (SWM), the Chameleon sensor and Full-Stop wetting front detector, in combination with Agricultural Innovation Platforms, can improve irrigation efficiency and decouple crop production from water consumption. However, there is a need to further test the effectiveness of these SWM tools under projected climate change scenarios. This study applies the Agricultural Production Systems sIMulator (APSIM) to assess soil moisture and nutrient dynamics for maize production at a smallholder irrigation scheme in Zimbabwe. The simulation incorporated improved and unimproved irrigation practices under moderate-and high-emission scenarios. Agreement between simulated and observed water productivity confirms the reliability of APSIM to guide agronomic decisions. Results show that improved irrigation practice (through use of SWM tools) significantly improves maize yields (by over 50%), enhances water productivity (from 5.2 kg/m3 to 6.2 kg/m3, and sustains higher grain yields. While yield and water productivity will both decrease under future climate scenarios, the decline is less severe with use of improved irrigation practice (7-9%) compared to unimproved practice (15-17%). A combined analysis of irrigation practice and fertiliser application shows improved irrigation can stabilise yields, but having an optimum fertiliser rate has a stronger influence on maize productivity. Policy and development approaches that support access to the SWM tools, participatory learning platforms and climate smart agricultural education packages can improve irrigation, optimise fertiliser application, and build the adaptive capacity of smallholder irrigation systems.
Declining soil fertility status and poor agronomic management practices are major factors of declining quality for malt barley in the Ethiopian highland area, particularly in the study area. To address these major challenges, a two-year (2022-2023) field experiment was conducted in experimental fields in the Welmera district to evaluate the effects of mixed-use mineral N fertilization and compost rates on malt barley quality parameters. A randomized complete block design with factorial arrangements of five N rates (0, 23, 46, 69, and 92 kg ha-1) and four compost rates (0, 2.5, 5, and 7.5 t ha-1) was tested in three replications. According to the results, both compost and mineral nitrogen fertilizer were significantly influenced thousand-seed weight, protein content, malt extract, beta-glucan content, malt friability and germination energy of malt barley grain, with seasonal variations. Increased mineral N levels enhanced seed weight and grain protein content but reduced malt extract yield and malt friability, while compost improved grain protein content and malt beta-glucan. These influences were improved by organic compost and mineral fertilization, which enhanced multiple quality parameters. The results clearly demonstrated that application of 69 kg N ha-1 and 5 t ha-1 of compost rate in moderation, which optimized the malt quality parameters, met industry standards without increasing protein concentration or diminishing malt extract yield of malt barley grain. These mixed management approaches not only enhance the quality of malt barley grain for the beer industry but also help soil fertility restoration and guarantee long-term production sustainability for smallholder farmers in the Ethiopian highlands. For robust and wide applicability, subsequent multiple-seasons and multiple-locations studies with additional quality assessments are recommended.
Grain amaranth (Amaranthus hypochondriacus) is a nutri-dense pseudocereal with dual-purpose potential as both grain and leafy vegetable. The exploration of grain amaranth types as leafy green could be useful in diversifying food systems, cropping systems and enhancing amaranth germplasm resources. The systematic nutritional profiling and multivariate analysis of grain amaranth leaves is limited, constraining targeted breeding efforts to improve nutritional quality. To establish its breeding potential for nutritious leafy greens, underscoring the compositional diversity of mineral nutrients and heavy metals accumulation in grain amaranth germplasm is vital. Plants were grown in an augmented randomized block design, and nutrient profiling was performed using digestion-based methods and microwave plasma atomic emission spectroscopy (MP-AES). Significant genetic variations were observed for essential mineral nutrients and heavy metals concentrations. Exotic accession EC519523 exhibited superior N and P content, while indigenous collections IC42356 and IC47434 showed promise for K-related nutritional quality. Vegetable checks, Arka Neelachal Bainishi and Arka Neelachal Ruchitha, along with grain type accessions, displayed potential for Fe and Mn biofortification. Notably, the comparative analysis of heavy metals with corresponding FAO/WHO permissible limits depicted significant genotypic variations where several accessions remained within limits, while the mean Cd and Pb values exceeded the referenced permissible thresholds. These findings indicated that suitability for consumption should therefore be considered genotype-dependent rather than universally safe. Multivariate analyses revealed five principal components explaining > 80
In-season, pre-harvest crop yield forecasts are essential for enhancing transparency in commodity markets and improving food security. They play a key role in increasing resilience to climate change and extreme events and thus contribute to the United Nations’ Sustainable Development Goal 2 of zero hunger. Pre-harvest crop yield forecasting is a complex task, as several interacting factors contribute to yield formation, including in-season weather variability, extreme events, long-term climate change, soil, pests, diseases and farm management decisions. Several modeling approaches have been employed to capture complex interactions among such predictors and crop yields. Prior research for in-season, pre-harvest crop yield forecasting has primarily been case-study based, which makes it difficult to compare modeling approaches and measure progress systematically. To address this gap, we introduce CY-Bench (Crop Yield Benchmark), a comprehensive dataset and benchmark to forecast maize and wheat yields at a global scale. CY-Bench was conceptualized and developed within the Machine Learning team of the Agricultural Model Intercomparison and Improvement Project (AgML) in collaboration with agronomists, climate scientists, and machine learning researchers. It features publicly available sub-national yield statistics and relevant predictors, such as weather data, soil characteristics, and remote sensing indicators, that have been pre-processed, standardized, and harmonized across spatio-temporal scales. With CY-Bench, we aim to: (i) establish a standardized framework for developing and evaluating data-driven models across diverse farming systems in more than 25 countries across six continents; (ii) enable robust and reproducible model comparisons that address real-world operational challenges; (iii) provide an openly accessible dataset to the earth system science and machine learning communities, facilitating research on time series forecasting, domain adaptation, and online learning. The dataset (https://doi.org/10.5281/zenodo.11502142, Kallenberg et al., 2025) and accompanying code (https://doi.org/10.5281/zenodo.20456375, Kallenberg et al., 2026) are openly available to support the continuous development of advanced data driven models for crop yield forecasting to enhance decision-making on food security.
