International Crops Research Institute for the Semi-Arid Tropics
ICRISAT Open Access RepositoryNot a member yet
12134 research outputs found
Sort by
The magnitude of crop yield improvement with different soil acidity management practices in the Ethiopian highlands: a meta-analysis
No full textSoil acidity affects over 44% of Ethiopia’s farmland, yet knowledge of acid soil management practices (ASMPs) and their impact on crop yields remains limited. Quantitative syntheses of studies on ASMPs and their role in yield improvement are also lacking in Ethiopia. This meta-analysis aimed to: (1) compile datasets to estimate yield improvements from ASMPs; and (2) provide a quantitative synthesis for evidence-based acid soil management in the Ethiopian highlands. Meta-analysis of 30 experiments across 48 locations with pH < 5.5 was conducted to quantify ASMP effects on crop yields. Linear mixed-effects model was applied using the response ratio (RR), calculated using treatment yield to respective no-input control. Sub-group analyses quantified variability within and between ASMPs. The weighted mean RR was 1.67 indicating that implementing ASMPs can increase yield by 67% relative to the control. However, significant residual heterogeneity (I2 = 98%, τ2 = 0.37) was also noted. Lime + phosphorus (P) fertiliser achieved the highest yield increase (RR = 2.94), followed by P fertiliser alone (RR = 1.60) and lime alone (RR = 1.36). Sub-group analysis revealed greater yield increases following compost + N (RR = 2.48), biochar + compost + N (RR = 2.29), biochar + N (RR = 2.25), biochar + compost (RR = 1.55), biochar alone (RR = 1.51), farmyard manure (FYM) (RR = 1.49), and FYM + P + N (RR = 1.28) relative to the control. It is concluded that implementing ASMPs increases crop yields by 28–194% relative to the control. Compared to the other ASMPs, lime + P achieved significantly higher yield increases across all agroecological zones, soil texture and slope classes. These findings emphasize ASMPs’ potential to enhance crop production in Ethiopia, providing valuable guidance for effective acid soil management policies and practices
Entropy-Based Streamflow Estimation in Data-Scarce Rivers Using MODIS–Landsat Fusion Imagery
No full textFew river systems around the world are adequately gauged. Remote sensing (RS) technology shows great potential for improving the estimation of stream characteristics in regions where field data is limited. However, challenges such as ground validation and sensor limitations remain. In this study, we hypothesize that integrating entropy theory with remote sensing-based techniques can effectively address these limitations. Using a fused moderate resolution imaging spectroradiometer (MODIS)–Landsat pixel, we derive surface velocity at the deepest river sections. This RS-derived velocity, applied to an entropy-based velocity model, enables the estimation of river discharge using the established stage-area relationship at the gauging sites where stage data is recorded. This fused-pixel of 30m×1-day resolution represents the land/water pixel ratio, derived after the spectral fusion of coarse resolution MODIS and fine resolution Landsat pixels using enhanced spatiotemporal adaptive reflectance fusion model. This novel framework is field-tested in the Brahmani River at three gauging stations and found promising with a Nash-Sutcliffe model efficiency of 0.79, 0.76, and 0.79 for Jenapur, Gomlai, and Panposh gauging stations under scanty data availability conditions, respectively. We believe, the developed entropy theory-RS-based integrated framework has the potential to be used in many world-rivers with many defunct streamflow gauging stations
Realized genetic gain for yield and yield attributes in groundnut breeding at ICRISAT from an ERA trial
Get PDFIntroduction: Groundnut or peanut (Arachis hypogaea L.) is an important food and oilseed crop with a global production of >50 m t from ~34 m ha. The ICRISAT groundnut breeding program, established in 1976, has significantly contributed to varietal development, resulting in the release of >240 varieties in 39 countries. Estimating realized genetic gain (RGG) in a breeding program helps to measure the progress made for agronomic traits and identify gaps to guide the breeding strategy.
Materials and methods: This study was conducted to estimate realized genetic gain using an Elite Replicated Agronomic (ERA) trial, with five ERA trials representing three product concepts across market types and maturity durations. These trials included improved germplasm developed over a span of 15–20 years at ICRISAT and were evaluated for three key traits: pod yield (PY), 100 seed weight (HSW), and shelling outturn (SP).
Results and discussion: Among these, PY and HSW exhibited high repeatability and genetic advance as the percentage of mean, whereas SP showed lower values. Realized genetic gain varied from 8.37 kg ha-¹ year−1 (0.48%) to 54.85 kg ha-¹ year−1 (3.91%) for PY. The Spanish Bunch germplasm recorded a higher realized GG of 46.45 kg ha-¹ year−1 (2.95%) for pod yield, compared to the Virginia Bunch germplasm with a marginal gain of 5.97 kg ha-¹ year−1 (0.23%). Higher RGG is realized in medium-duration and late-maturing germplasm with 27.1 kg ha-¹ year−1 (1.62%) and 25.32 kg ha-¹ year−1 (1.52%), respectively, while realized GG in early-maturing germplasm was 8.37 kg ha-¹ year−1 (0.5%). Among the traits, RGG was the highest for PY across all the trials. Higher RGG for PY and HSW was observed during the rainy season as compared to the post-rainy season, while SP showed a decline. This study helps breeders to optimize selection methods and design breeding strategies to enhance realized genetic gain for SP across two market types and three maturity durations. The study suggests a need for breeding strategies to enhance the rate of RGG for PY in early-maturing germplasm
Landsat-Derived Rainfed and Irrigated-Area Product for Conterminous United States for the Year 2020 (LRIP30 CONUS 2020) Using Supervised and Unsupervised Machine Learning on the Cloud
No full textAccurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed- and irrigated-area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel-based supervised random forest classifier, and pixel-based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer’s accuracies of 90.2% and 95.7%, respectively, and user’s accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (69.3%) was classified as rainfed and 44.5 Mha (30.7%) was classified as irrigated. State-level summaries highlight regional differences and their implications for national and global food and water security
Little Millet
No full textLittle millet (Panicum sumatrense) is an important crop of the Poaceae family, originating from India, and is predominantly cultivated in marginal soils with minimal input. This crop is highly nutritious and resilient to climate change, offering substantial potential to bolster food and nutrition security. Two cultivated races of little millet are nana and robusta. A considerable variability has been conserved in various gene banks. Utilizing this diversity is crucial for crop improvement. Improved little millet genotypes are developed through primary selection from landraces, while hybridization techniques are currently being explored for the new varieties development and release. Evaluating germplasm for different traits and employing genomic tools could expedite the development of improved cultivars that are high-yielding, nutrient-rich, and stress-tolerant. Phenotypic evaluation of landraces for various traits and use of genomic tools could accelerate the development of improved cultivars with high yield, nutrient density, and stress tolerance. Little millet may play a significant role in food security as the need for nutrient-dense and climate-adaptive crops grows globally. However, greater consumer awareness, regulatory support, and targeted breeding techniques are required for wider adoption. Highlight of this chapter is a comprehensive view on the significance of little millet as a nutritious food, along with its origin, history, genetic resources, breeding, genomics, cropping system, value addition, etc. Such insights will assist researchers in developing high-performing genotypes with enhanced tolerance to biotic and abiotic stresses, as well as in leveraging advanced genomic tools for this underutilized crop
A Ralstonia effector RipAU impairs peanut AhSBT1.7 immunity for pathogenicity via AhPME-mediated cell wall degradation
Get PDFBacterial wilt caused by Ralstonia solanacearum is a devastating disease affecting a great many crops including peanut. The pathogen damages plants via secreting type Ш effector proteins (T3Es) into hosts for pathogenicity. Here, we characterized RipAU was among the most toxic effectors as ΔRipAU completely lost its pathogenicity to peanuts. A serine residue of RipAU is the critical site for cell death. The RipAU targeted a subtilisin-like protease (AhSBT1.7) in peanut and both protein moved into nucleus. Heterotic expression of AhSBT1.7 in transgenic tobacco and Arabidopsis thaliana significantly improved the resistance to R. solanacearum. The enhanced resistance was linked with the upregulating ERF1 defense marker genes and decreasing pectin methylesterase (PME) activity like PME2&4 in cell wall pathways. The RipAU played toxic effect by repressing R-gene, defense hormone signaling, and AhSBTs metabolic pathways but increasing PMEs expressions. Furthermore, we discovered AhSBT1.7 interacted with AhPME4 and was colocalized at nucleus. The AhPME speeded plants susceptibility to pathogen via mediated cell wall degradation, which inhibited by AhSBT1.7 but upregulated by RipAU. Collectively, RipAU impaired AhSBT1.7 defense for pathogenicity by using PME-mediated cell wall degradation. This study reveals the mechanism of RipAU pathogenicity and AhSBT1.7 resistance, highlighting peanut immunity to bacterial wilt for future improvement
Forced to Sell Early? Smallholder Farmers’ Access to Credit and Commodity Marketing Behaviour in Rural Ethiopia
No full textLarge and predictable seasonal crop price variations in local markets offer African farmers substantial intertemporal arbitrage opportunities. But smallholder farmers are not commonly observed taking advantage of these arbitrage opportunities. In this paper, we study the effect of rural credit on shaping farmers’ commodity marketing behaviour as related to the timing of crop sales. Using a large dataset from Ethiopia and an instrumental variables (IV) approach, we find that households who accessed credit are more likely to sell their crops early at substantially depressed prices. Households pay a financial penalty for selling early, as they forego an expected 31% increase in crop revenues over three to six months. Our results highlight a hidden but potentially relevant cost of farmers’ participation in credit markets. Overall, current designs of rural credit products exacerbate the negative impacts of binding liquidity constraints and seasonal crop price cycles on poor households
Identification of miRNAs associated with Aspergillus flavus infection and their targets in groundnut (Arachis hypogaea L.)
Get PDFBackground
The quality of groundnut produce is adversely impacted due to aflatoxin contamination by the fungus Aspergillus flavus. Although the transcriptomic control is not fully understood, the interaction between long non-coding RNAs and microRNAs in regulating A. flavus and aflatoxin contamination remains unclear. This study was carried out to identify microRNAs (miRNAs) to enhance the understanding of in vitro seed colonization (IVSC) resistance mechanism in groundnut.
Result
In this study, resistant (J 11) and susceptible (JL 24) varieties of groundnut were treated with toxigenic A. flavus (strain AF-11–4), and total RNA was extracted at 1 day after inoculation (1 DAI), 2 DAI, 3 DAI and 7 DAI. Seeds of JL 24 showed higher mycelial growth than J 11 at successive days after inoculation. A total of 208 known miRNAs belonging to 36 miRNA families, with length varying from 20–24 nucleotides, were identified, along with 27 novel miRNAs, with length varying from 20–22 nucleotides. Using psRNATarget server, 952 targets were identified for all the miRNAs. The targeted genes function as disease resistant proteins encoding, auxin responsive proteins, squamosa promoter binding like proteins, transcription factors, pentatricopeptide repeat-containing proteins and growth regulating factors. Through differential expression analysis, seven miRNAs (aly-miR156d-3p, csi-miR1515a, gma-miR396e, mtr-miR2118, novo-miR-n27, ptc-miR482d-3p and ppe-miR396a) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in J 11, whereas ten miRNAs (csi-miR159a-5p, csi-miR164a-3p, novo-miR-n17, novo-miR-n2, osa-miR162b, mtr-miR2118, ptc-miR482d-3p, ptc-miR167f-3p, stu-miR319-3p and zma-miR396b-3p) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in JL 24. Two miRNAs, ptc-miR482d-3p and mtr-miR2118, showed contrasting expression at different time intervals between J 11 and JL 24. These two miRNAs were found to target those genes with NBS-LRR function, making them potential candidates for marker development in groundnut breeding programs aimed at enhancing resistance against A. flavus infection.
Conclusion
This study enhances our understanding of the involvement of two miRNAs namely, ptc-miR482d-3p and mtr-miR2118, along with their NBS-LRR targets, in conferring resistance against A. flavus-induced aflatoxin contamination in groundnut under in vitro conditions
In-silico optimization of peanut production in India through envirotyping and ideotyping
Get PDFPeanut (Arachis hypogaea L.) is an important cash crop with significant yield gaps, especially in developing countries. Optimizing peanut production could foster economic growth for a significant number of smallholder farmers across the globe. In this study, we used an in-silico cropping system model to simulate and optimize genotype × crop management (G × M) across India that would narrow the existing peanut yield gaps. For that, we simulated diverse G × M combinations across range of environments (E) in India, considering three irrigation regimes typical for managing peanut production systems. Covering whole India in a 0.5°×0.5° resolution, we simulated 60,480 G × M combinations for each grid, summing up to a total of 2.3 billion simulations and 1.02 TB output data. This required well-structured high-performance computing (HPC) approaches, data management, and analytical capacities. For this, we present the concept of a re-usable HPC system with interoperable modules, which can be readily adapted for different simulation setups. We introduced the novel way of analyzing simulation outputs − “Index of Goodness” (IoG) − that aggregates key peanut production characteristics (grain and haulm production) and production risk failure. IoG is a simple way to evaluate the suitability of simulated GxM options from the perspective of end-users, including primary producers and crop improvement programs. The generated output was used to identify the geographic regions (environmental clusters, EC) with high degree of similarities within each of the tested irrigation regimes. For each cluster, we identified a specific suite of GxM to benefit peanut production and prioritize G targets for breeding. In principle, irrigated cropping systems would benefit from high planting densities, long duration and vigorous crop types. With diminishing water availability (particularly in the Thar Desert and SE India), the optimal production included shorter duration crop types which could quickly respond to drought stimuli (i.e. close stomata and conserve soil water upon soil and atmospheric drought exposure). These traits should also be considered in phenotyping strategies to support context-specific breeding
Molecular mapping and transfer of sheath blight resistance QTLs from PAU-shb8 to cultivated rice PR-121
Get PDFSheath blight, caused by Rhizoctonia solani, severely affects rice, causing 20–69% yield losses in tropical and temperate regions. Key challenges include the pathogen’s broad host range, persistent sclerotia, climate change, and the reliance on semi-dwarf varieties. The disease’s complex inheritance and lack of highly resistant cultivars hinder management, making resistant variety breeding a sustainable solution. This study mapped sheath blight resistance Quantitative trait locus (QTLs) from PAU-shb8, a moderately resistant rice line. This line exhibited moderate resistance with a disease score of 3 (RLH 60%). Screening of 1160 plants from BC1F5 and BC1F6 populations revealed 50.34% as moderately resistant, 37.76% moderately susceptible, and 11.88% susceptible. (QTL) mapping using 4622 SNP markers identified 20 QTLs across eight traits, with significant loci on chromosomes 2, 4, 6, 8, 9, 10, 11, and 12. Chromosome 12 harbored a cluster of QTLs associated with multiple traits, including RLH, lesion height, and disease score, while chromosome 8 exhibited a major QTL for RLH with a LOD score of 9.8 and 9.2% phenotypic variance. Genomic analysis pinpointed candidate genes related to resistance, such as leucine-rich repeat proteins and calcium/calmodulin-dependent protein kinases. Promising genotypes 7168, 7183, and 7152 demonstrated moderate resistance, combining key QTLs for RLH, disease severity, and lesion height with favorable agronomic traits. These backcross inbred lines are pivotal for breeding sheath blight-resistant rice varieties and for the expansion of resistance gene pool of sheath blight