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Evaluation of bread wheat (Tritium aestivum L.) genotype in multi-environment trials using enhanced statistical models
No full textIn varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance. Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties.67-7
Genomic prediction from multi-environment trials of wheat breeding
No full textGenomic prediction relates a set of markers to variability in observed phenotypes of cultivars and allows for the prediction of phenotypes or breeding values of genotypes on unobserved individuals. Most genomic prediction approaches predict breeding values based solely on additive effects. However, the economic value of wheat lines is not only influenced by their additive component but also encompasses a non-additive part (e.g., additive x additive epistasis interaction). In this study, genomic prediction models were implemented in three target populations of environments (TPE) in South Asia. Four models that incorporate genotype x environment interaction (G x E) and genotype x genotype (GG) were tested: Factor Analytic (FA), FA with genomic relationship matrix (FA + G), FA with epistatic relationship matrix (FA + GG), and FA with both genomic and epistatic relationship matrices (FA + G + GG). Results show that the FA + G and FA + G + GG models displayed the best and a similar performance across all tests, leading us to infer that the FA + G model effectively captures certain epistatic effects. The wheat lines tested in sites in different TPE were predicted with different precisions depending on the cross-validation employed. In general, the best prediction accuracy was obtained when some lines were observed in some sites of particular TPEs and the worse genomic prediction was observed when wheat lines were never observed in any site of one TPE
Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system
No full textAgricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar’s anthesis and physiological maturity, with observed value falling within 5% of the model’s predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9–16%. The study also demonstrated the model's ability to accurately capture soil nitrate–N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop’s growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further
Novel resistance loci for quantitative resistance to Septoria tritici blotch in Asian wheat (Triticum aestivum) via genome-wide association study
No full textBackground Septoria tritici blotch (STB) disease causes yield losses of up to 50 per cent in susceptible wheat cultivars and can reduce wheat production. In this study, genomic architecture for adult-plant STB resistance in a Septoria Association Mapping Panel (SAMP) having 181 accessions and genomic regions governing STB resistance in a South Asian wheat panel were looked for. Results Field experiments during the period from 2019 to 2021 revealed those certain accessions, namely BGD52 (CHIR7/ANB//CHIR1), BGD54 (CHIR7/ANB//CHIR1), IND92 (WH 1218), IND8 (DBW 168), and IND75 (PBW 800), exhibited a high level of resistance. Genetic analysis revealed the presence of 21 stable quantitative trait nucleotides (QTNs) associated with resistance to STB (Septoria tritici blotch) on all wheat chromosomes, except for 2D, 3A, 3D, 4A, 4D, 5D, 6B, 6D, and 7A. These QTNs were predominantly located in chromosome regions previously identified as associated with STB resistance. Three Quantitative Trait Loci (QTNs) were found to have significant phenotypic effects in field evaluations. These QTNs are Q.STB.5A.1, Q.STB.5B.1, and Q.STB.5B.3. Furthermore, it is possible that the QTNs located on chromosomes 1A (Q.STB.1A.1), 2A (Q.STB_DH.2A.1, Q.STB.2A.3), 2B (Q.STB.2B.4), 5A (Q.STB.5A.1, Q.STB.5A.2), and 7B (Q.STB.7B.2) could potentially be new genetic regions associated with resistance. Conclusion Our findings demonstrate the importance of Asian bread wheat as a source of STB resistance alleles and novel stable QTNs for wheat breeding programs aiming to develop long-lasting and wide-ranging resistance to Zymoseptoria tritici in wheat cultivars
“Did you control for rainfall?”: Geospatial weather data, measurement error and the consistency of farm model estimates in Ethiopia
No full textThe availability of spatially-explicit time-series estimates of rainfall and other weather outcomes has expanded rapidly over the past decade and a half. This proliferation of publicly-accessible rainfall data has changed how empirical analysis of farm production and productivity takes place: fortifying survey-derived data (e.g., on farm management and production outcomes) with spatial estimates of seasonal rainfall outcomes is now standard practice, much to the benefit of applied agricultural economic analysis. Yet guidance on which dataset to use, among the many available alternatives, has largely been lacking: while there have been some studies comparing rainfall data quality across alternative datasets, relative to some accuracy benchmark, there has not yet been systematic analysis of whether the choice of rainfall dataset may affect econometric model performance, or the estimation of other (non-rainfall) model parameters. Such a concern would arise from cases where any mismeasurement of rainfall is correlated with model error terms or other model covariates. To investigate this, we use panel data on plot-level maize yield outcomes in Ethiopia from 2018 and 2021, along with fifteen alternative spatio-temporal rainfall data products, including gauge-based, satellite-derived, and reanalysis datasets. We estimate yield response models, in which our primary interest is on the estimate of nitrogen use efficiency (NUE). Estimation results from alternative specifications and panel estimators indicate that, while coefficient estimates for rainfall vary considerably with alternative rainfall estimates, the coefficient estimates on nitrogen fertilizer (and consequently, our estimates of NUE) do not vary in a statistically significant way. Our results suggest that the choice of rainfall dataset does not significantly affect analytical conclusions about fertilizer response.24 page
How much is enough? A Spatial Bioeconomic Model for Agroecology (SpBiMA) trade-off assessments
No full textAgroecology is gaining traction across the global south as part of the potential solutions to food insecurity, soil health loss and degradation and for managing climatic stresses. Yet, there are many sceptics arguing that agroecology cannot sustain the food security needs of a growing population and that probably much more input intensification on the existing lands would be the best alternative as it will allow conservation of crop and forest biodiversity. In this paper, we develop a virtual landscape model (the spatial bioeconomic model for agroecology, SpBiMA) that maximizes economic value from crop production and pixel level crop biodiversity while meeting food consumption needs, and restricting cropland expansion. We then apply this stylized model to a case study in Malawi: a country plagued with very high deforestation rates due to cropland expansion. The spatially explicit model allows us to pinpoint the changes in the spatial footprint of crop production, at landscape level, that will attain the economic and biodiversity goals and simulate if an agroecology practice (e.g., doubled up legume or others) can help in getting to the pareto frontier. We use the model to suggest the different locations where agroecological practices can be targeted in future agricultural and biodiversity investments to minimize the environmental costs while maximizing economic value.19 page
Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi-temporal UAV imagery
No full textThis study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late-season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispectral relationship matrix (M-matrix), and vegetation indices (VIs) were considered. GP involved the genomic relationship matrix (G), extended to multi-kernel predictors by incorporating environmental and genotype–environment interaction effects, complemented with multispectral reflectance data. Two different models including PLSR (partial least square regression) and Bayesian genomic best linear unbiased prediction regressor were applied. The phenological stage of spectral data collection impacted the trait prediction accuracy correlating with the relationship between multispectral data and measured traits. Higher correlations resulted in higher PP prediction accuracy. The results revealed that spectral bands and M-matrix outperformed VIs by 10%–40% across different timepoints and all timepoints together for grain yield (GY) prediction. The single-kernel GP model (G) outperformed PP by 28% (using Bayesian) and 29% (using PLSR). The integration of multi-kernel GP models with spectral data improved GY prediction by up to 4%. In terms of days to maturity (DM) prediction, phenomic methods excelled, surpassing the single-kernel GP (G: r = 0.63) model by 11% (Bayesian). In conclusion, this study underscores the effectiveness of phenomics prediction for traits like DM and its potential to enhance predictions for complex traits such as GY while highlighting the importance of correlation between measured traits and spectral data, kernel combinations, and model selection for prediction accuracy
Genetic trends in the Kenya highland maize breeding program between 1999 and 2020
No full textOptimization of a breeding program requires assessing and quantifying empirical genetic trends made through past efforts relative to the current breeding strategies, germplasm, technologies, and policy. To establish the genetic trends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade (1999-2020) historical dataset from the Preliminary Variety Trials (PVT) and Advanced Variety Trials (AVT) was analyzed. A mixed model analysis was used to compute the genetic gains for traits based on the best linear unbiased estimates in the PVT and AVT evaluation stages. A positive significant genetic gain estimate for grain yield of 88 kg ha-1 year-1 (1.94% year-1) and 26 kg ha-1 year-1 (0.42% year-1) was recorded for PVT and AVT, respectively. Root lodging, an important agronomic trait in the Kenya highlands, had a desired genetic gain of -2.65% year-1 for AVT. Results showed improvement in resistance to Turcicum Leaf Blight (TLB) with -1.19% and -0.27% year-1 for the PVT and AVT, respectively. Similarly, a significant genetic trend of -0.81% was noted for resistance to Gray Leaf Spot (GLS) in AVT. These findings highlight the good progress made by KHMP in developing adapted maize hybrids for Kenya's highland agroecology. Nevertheless, the study identified significant opportunities for the KHMP to make even greater genetic gains for key traits with introgression of favorable alleles for various traits, implementing a continuous improvement plan including marker-assisted forward breeding, sparse testing, and genomic selection, and doubled haploid technology for line development
Empowering smallholder farmers with blockchain-enabled digital identities: The case of CIMMYT for traceability, financial inclusion and value chain integration
No full textThis paper examines the transformative potential of blockchain-enabled digital identities in empowering smallholder farmers, with a specific focus on CIMMYT’s initiatives in the Global South. By providing farmers with secure, verifiable credentials and data wallets, these technologies address critical challenges in financial inclusion, supply chain traceability, and data governance. Leveraging case studies from CIMMYT’s partnerships with Bluenumber and Identi, the paper explores the application of blockchain to enhance data ownership, improve market access, and foster transparency within agrifood systems. Findings highlight how digital identities enable farmers to control and monetize their data, access financial services, and comply with traceability standards, thereby strengthening their position in global value chains. Despite significant potential, challenges such as digital literacy gaps, infrastructure limitations, and regulatory disparities persist. The paper concludes with recommendations for scaling these solutions, emphasizing region-specific adaptations, collaborative frameworks, and robust data governance to maximize impact and inclusivity.24 page
Nurturing the sustainable food systems: crafting policies and practices for crop diversification in Bangladesh
No full textBangladesh's agriculture is highly rice-centric. Although economically rational, this is also risky, and arguably unsustainable. As a result, there is increasing interest in crop diversification (CD). This study examines the policy environment and the implementation of projects promoting CD in Bangladesh from 1971 to the present. An integrated analytical framework, developed by the International Wheat and Maize Improvement Center (CIMMYT) was used. Despite numerous policies and projects aimed at promoting CD, progress remains limited due to historical biases and various challenges. This research identifies a significant gap in existing approaches, which primarily focus on production aspects while neglecting market systems for new crops. Additionally, inadequate coordination among government agencies has impacted the effectiveness of projects implemented by development partners. The study highlights that CD efforts have been largely project-driven and short-lived, emphasizing the need for mainstreaming CD with dedicated annual funding to ensure long-term sustainability. Key challenges in funding, market development, and implementation are identified. The study recommends mainstreaming CD through annual budgets and enhancing market linkages. Furthermore, it provides actionable guidelines for policymakers and practitioners to effectively promote and sustain crop diversification in Bangladesh's agriculture