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Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains
No full textKnowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP.100-11
Africa Dryland Crops Improvement Network
No full textThe Africa Dryland Crops Improvement Network (ADCIN), established in August 2023, is a collaborative network initiated after a consultation meeting in Senegal in February 2022 and a network members’ meeting in Ghana in January 2023. It comprises more than 17 countries and over 200 scientists in various agricultural disciplines and organizations. Our vision is to establish a robust and sustainable crop improvement network for dryland crops in sub-Saharan Africa, with the ultimate aim of accelerating the rate of genetic gain on farms for these crops.2 page
Natural variation in maize gene ZmSBR1 confers seedling resistance to Fusarium verticillioides
No full textMaize seedling blight caused by Fusarium verticillioides is a widely occurring maize disease, but the genetics and mechanisms of resistance are not well understood. In this study, GWAS performed by MLM and 3VmrMLM identified 40 and 20 QTNs, associated with seedling blight resistance. These methods identified 49 and 36 genes, respectively. Functional verification of candidate gene ZmSBR1 identified by both methods showed that the resistance of a mutant line to seedling blight decreased by 0.37 grade points after inoculation with F. verticillioides, compared with the WT. The length of the stem rot lesion caused by F. verticillioides increased by 86% in mutant seedlings, and the relative length of the adult plant stalk rot increased by 35% in mutant plants compared to the wild type after inoculation with Fusarium graminearum. Transcriptome analysis showed that expression of defense-related genes after inoculation was down-regulated in the mutant compared to the wild type, synthesis of secondary metabolites associated with resistance was reduced, and the immune response triggered by PAMP decreased, resulting in decreased resistance of mutant maize seedlings. Candidate gene association analysis showed that most maize inbred lines carried the susceptible haplotype. A functional PCR marker was developed. The results demonstrated that ZmSBR1 conferred resistance to multiple Fusarium diseases at the seedling and adult growth stages and had important application value in breeding.836-84
Enhancing across-population genomic prediction for maize hybrids
No full textIn crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals that are genetically similar to the training population. Therefore, exploring possibilities and effective strategies for across-population prediction becomes an attractive avenue for applying GS technology in breeding practices. In this study, we used an existing maize population of 5820 hybrids as the training population to predict another population of 523 maize hybrids using the GBLUP and BayesB models. We evaluated the impact of optimizing the training population based on the genetic relationship between the training and breeding populations on the accuracy of across-population predictions. The results showed that the prediction accuracy improved to some extent with varying training population sizes. However, the optimal size of the training population differed for various traits. Additionally, we proposed a population structure-based across-population genomic prediction (PSAPGP) strategy, which integrates population structure as a fixed effect in the GS models. Principal component analysis, clustering, and Q-matrix analysis were used to assess the population structure. Notably, when the Q-matrix was used, the across-population prediction exhibited the best performance, with improvements ranging from 8 to 11% for ear weight, ear grain weight and plant height. This is a promising strategy for reducing phenotyping costs and enhancing maize hybrid breeding efficiency
Motivations and incentives for mechanization in Zambia: A mixed methods analysis
No full textThis study examines smallholder farmers' preferences for ownership models and mechanization in Zambia using mixed methods from a quantitative survey of 208 farmers, 18 focus group discussions, and 28 key informant interviews from farmers who own mechanized equipment. Productivity enhancement and income generation motivated tractor ownership. Being a male farmer is correlated with preference for an individual ownership model over group ownership. Female-headed households and increase in oxen owned is associated with preference for group ownership and individual ownership, respectively. Risk-contingent credit (RCC), and RCC combined with repair insurance were the most preferred incentives, highlighting farmers' need for comprehensive risk management in mechanization investments.36 page
Influence of genotypes and crop growth stage at time of infection on seed transmission of Maize chlorotic mottle virus
No full textUnderstanding the effect of seed transmission of Maize chlorotic mottle virus (MCMV) is important in managing the spread of the disease especially secondary transmission caused by vectors. This study was set out to investigate the effect of different genotypes and crop growth stage at the time of infection to rate of MCMV transmission from seeds to seedlings. The experiments were laid out in randomised complete block design with three replications. Detection of MCMV in the grow outs was determined using double antibody sandwich enzyme-linked immunosorbent assay and real time reverse transcription polymerase chain reaction. The results showed low rates of seed transmission from the genotypes in which their was detection of MCMV in their samples (0.08-1%). Similarly, crop growth stage at time of infection also showed low transmission rate of up to 0.1%, with highest transmission rate at the four-leaf stage.95-11
Is agricultural lime a profitable investment for African smallholders? Evidence from Rwanda
No full textSoil acidity is a major constraint to crop production in tropical regions. Although agricultural lime is one option to remediate acid soils, there is limited information on the potential returns on investments to liming by smallholders. Using survey data collected from 261 households in Rwanda, we estimated the crop -specific yield response to lime application and associated financial benefits. The estimated average yield gain from lime ranged from 941 kg/ha to 1 579 kg/ha for Irish potato, 562 kg/ha to 709 kg/ha for maize, and 453 kg/ha to 520 kg/ha for beans. With the existing lime and farmgate crop prices, reliable returns on investment from lime were observed for Irish potato, while applying lime to maize and bean was only profitable at a 50% lime price subsidy. As maize and beans are the major staple crops in Rwanda, the subsidy for ag-lime use in improving crop productivity is highly justifiable. The results inform policy decisions in considering market -oriented crops and subsidies when promoting agricultural lime in acid soils under smallholder conditions.1-1
