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Identification of diagnostic KASP‐SNP markers for routine breeding activities in yam (<i>Dioscorea</i> spp.)

AbstractMaintaining genetic purity and true‐to‐type clone identification are important action steps in breeding programs. This study aimed to develop a universal set of kompetitive allele‐specific polymerase chain reaction (KASP)‐based single nucleotide polymorphism (SNP) markers for routine breeding activities. Ultra‐low‐density SNP markers were created using an initial set of 173,675 SNPs that were obtained from whole‐genome resequencing of 333 diverse white Guinea yam (Dioscorea rotundata Poir) genotypes. From whole‐genome resequencing data, 99 putative SNP markers were found and successfully converted to high‐throughput KASP genotyping assays. The markers set was validated on 374 genotypes representing six yam species. Out of the 99 markers, 50 were highly polymorphic across the species and could distinguish different yam species and pedigree origins. The selected SNP markers classified the validation population based on the different yam species and identified potential duplicates within yam species. Through penalized analysis, the male parent of progenies involved in polycrosses was successfully predicted and validated. Our research was a trailblazer in validating KASP‐based SNP assays for species identification, parental fingerprinting, and quality control (QC) and quality assurance (QA) in yam breeding programs.

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Open Access
Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks.To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF).Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season.In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

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High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

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Open Access
Genome-Wide Association Study Reveals Novel Powdery Mildew Resistance Loci in Bread Wheat.

Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020-2021, 2021-2022, and 2022-2023), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K Infinium iSelect assay to obtain genome-wide single-nucleotide polymorphism (SNP) marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 marker-trait associations (MTAs) located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p ≤ 0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020-2021 and 2021-2022. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host-pathogen interaction-related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.

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Open Access
Opportunities to close wheat yield gaps in Nepal's Terai: Insights from field surveys, on-farm experiments, and simulation modeling

CONTEXTWheat (Triticum aestivum) is among the most important staple food crops in the lowland Terai region of Nepal. However, national production has not matched the increasing demand. From a South Asian regional perspective, average productivity is low with high spatial and temporal variability. OBJECTIVESThis study determines entry points for closing yield gaps using multiple diagnostic approaches, i.e., field surveys, on-farm experiments, and simulation models across different wheat production environments in the Terai region of Nepal. METHODOLOGYYield and production practice data were collected from 1745 wheat farmers' fields and analysed in tandem with over 100 on-farm experiments. These were complemented by long-term simulation modeling using the APSIM Next Generation to assess system production behavior over a range of climate years. RESULTS AND DISCUSSIONOn-farm survey data suggests that yield and profit gaps under farmers' management (difference between the most productive (top 10th decile) and average wheat fields) were 1.60 t ha−1 and 348 USD ha−1 in the Terai region. The potential yield gap (difference between simulated potential yield and surveyed population mean) estimated was 4.63 t ha−1, suggesting ample room for growth even for the highest-yielding fields. Machine learning diagnostics of survey data, and on-farm trials identified nitrogen rate, irrigation management, terminal heat stress, use of improved varieties, seeding date, seeding method, and seeding rate as the principal agronomic drivers of wheat yield. While fields in the top 10th decile yield distribution had higher fertilizer use efficiencies and irrigation and seeding rates with similar overall production costs as average-yielding farmers. Our results suggest a complementary set of agronomic interventions to increase wheat productivity among lower-yielding farms in the Terai including advancing the time of seeding by 7–10 days on average, increasing nitrogen fertilizer by 20 kg ha−1, and alleviating water stress by applying two additional irrigations. SIGNIFICANCEAlthough wheat yields in the Terai are among the lowest in the region, biophysical production potential is high and remains largely untapped due to sub-optimal agronomic management practices rather than intrinsic agroecological factors. Data from this study suggests that incremental changes in these practices may result in substantial gains in productivity and profitability.

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Open Access
Linking soil adsorption-desorption characteristics with grain zinc concentrations and uptake by teff, wheat and maize in different landscape positions in Ethiopia

AimZinc deficiencies are widespread in many soils, limiting crop growth and contributing to Zn deficiencies in human diets. This study aimed at understanding soil factors influencing grain Zn concentrations and uptake of crops grown in different landscape positions in West Amhara, Ethiopia.MethodsOn-farm experiments were conducted in three landscape positions, with five farmers’ fields as replicates in each landscape position, and at three sites. Available Zn from the soil (Mehlich 3, M3, Zn) and applied fertilizer (NET_FERT Zn, estimated based on adsorption/desorption characteristics and applied Zn) were related to the actual grain Zn concentration and uptake of teff, wheat, and maize. Zinc fertilizer treatments tested were Zn applied at planting (basal), basal plus side dressing and a control with no Zn applied.ResultsZn treatments had a significant effect on grain Zn concentration (increase by up to 10%) but the effect on grain yield was variable. Differences in crop Zn concentrations along the landscape positions were observed but not at all sites and crops. Trial results showed that soils with higher soil pH and Soil Organic Carbon (SOC) (typical of footslope landscape positions) tended to adsorb more applied Zn (reduce NET_FERT Zn) than soils with lower soil pH and SOC (typical of upslope landscape positions). Zn availability indicators (M3, NET_FERT Zn, clay%) explained 14-52% of the observed variation in grain Zn concentrations, whereas macronutrient indicators (Total N, exchangeable K) together with M3 Zn were better in predicting grain Zn uptake (16 to 32% explained variability). Maize had the lowest grain Zn concentrations but the highest grain Zn uptake due to high yields.ConclusionWe found that the sum of indigenous and fertilizer Zn significantly affects grain Zn loadings of cereals and that the associated soil parameters differ between and within landscape positions. Therefore, knowledge of soil properties and crop characteristics helps to understand where agronomic biofortification can be effective.

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Open Access