CIMMYT Publications Repository
Not a member yet
7809 research outputs found
Sort by
An InDel insertion in the promoter of a UDP-ᴅ-glucuronate 4-epimerase 1 gene enhances maize resistance to Fusarium ear rot
No full textFusarium ear rot (FER), caused by Fusarium verticillioides, results in substantial yield losses and poses a significant threat to maize production worldwide. However, the genetic basis of FER resistance remains poorly understood. Utilizing QTL-seq and association analysis, we identified a gene encoding UDP-ᴅ-glucuronate 4-epimerase 1 (ZmGAE1). A 141-base pair insertion was revealed as the natural functional variation in the promoter of ZmGAE1, which decreases its expression and enhances resistance to FER. Functional validation confirmed that ZmGAE1 acts as a negative regulator of maize resistance to FER. Notably, reduced ZmGAE1 accumulation not only improved FER resistance but also lowered fumonisin content. This effect was attributed to increased cell density within the down-placenta chalaza region, accompanied by the accumulation of galacturonic acid and pectin. Crucially, lines lacking ZmGAE1 exhibited no adverse effects on key agronomic traits and showed resistance to multiple diseases, including maize stalk rot, southern leaf blight, and seed rot. These findings highlight ZmGAE1 as a promising candidate for improving FER resistance in maize, offering a novel approach for crop protection and sustainable agriculture
Proceedings of Adaptation, Demonstration and Piloting of Irrigated Wheat Project: August 2021 - June 2025
No full textThis study assessed the adoption and impact of irrigated wheat technologies introduced through the Adaptation, Demonstration, and Piloting of Wheat Technologies for Irrigated Lowlands of Ethiopia (ADAPT-Wheat) project. The analysis is based on baseline (2022) and endline (2024) survey data collected from three districts in the Arsi Zone—Sire, Merti, and Jeju. Key findings: » Adoption patterns: Beneficiaries exhibited a higher rate of adoption of improved wheat varieties compared to non-beneficiaries. While Kingbird remained the dominant variety, the endline data revealed a shift toward newer varieties such as Daka and Boru, particularly among beneficiaries. This suggests a gradual diversification of wheat varieties influenced by project interventions, farmer preferences, and seed availability. » Input use and productivity: Wheat yields improved over time, with beneficiaries generally outperforming non-beneficiaries. Beneficiaries also reported slightly higher usage of fertilizers and herbicides, pointing to the impact of technical support and input access. These results underscore the importance of targeted interventions in seed distribution, fertilizer management, and extension services for enhancing productivity in irrigated wheat farming. » Spillover effects: Evidence of spillover effects was observed, as non-beneficiaries also adopted improved wheat technologies—particularly improved seed varieties. Adoption rates increased further when irrigation agronomy training was combined with other inputs. However, mechanization continued to exhibit lower adoption levels, likely due to access barriers such as cost and availability. »» Impact evaluation: The difference-in-differences (DID) analysis of yield outcomes showed positive but inconclusive results. While beneficiaries demonstrated a higher level of yield improvement, the short timeframe between baseline and endline likely limited the full capture of long-term impacts. Nonetheless, the observed gains provide an encouraging indication of progress, with the expectation that more conclusive impacts would emerge over a longer implementation and observation period. The findings confirm that well-targeted agricultural interventions—particularly those combining improved inputs with technical training—can drive adoption and improve productivity in smallholder irrigated wheat systems. While some adoption differences remain between direct and indirect beneficiaries, the positive trends in input use, varietal diversification, and yield improvement are promising. Strengthening seed systems, improving mechanization access, and extending the duration of support will be key to achieving sustained impact and scaling success across similar agroecological zones.145 page
GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction
No full textGenomic selection (GS) accelerates plant breeding by predicting complex traits using genomic data. This study compares genomic best linear unbiased prediction (GBLUP), quantile mapping (QM)-an adjustment to GBLUP predictions-and four outlier detection methods. Using 14 real datasets, predictive accuracy was evaluated with Pearson's correlation (COR) and normalized root mean square error (NRMSE). GBLUP consistently outperformed all other methods, achieving an average COR of 0.65 and an NRMSE reduction of up to 10% compared to alternative approaches. The proportion of detected outliers was low (<7%), and their removal had minimal impact on GBLUP's predictive performance. QM provided slight improvements in datasets with skewed distributions but showed no significant advantage in well-distributed data. These findings confirm GBLUP's robustness and reliability, suggesting limited utility for QM when data deviations are minimal
Narrowing the ecological yield gap to sustain crop yields with less inputs
No full textSustainable production of sufficient and healthy food requires efficient use of agricultural inputs. In many regions of the world with intensive agriculture and relatively small yield gaps, this calls for a reduction of external inputs (fertilizers and pesticides) while maintaining yields. Ecological intensification, defined as the use of practices that enhance on-farm ecosystem services to reduce external input requirements, has been proposed as a strategy to help achieve this. However, the effects of ecological intensification are context- and input-dependent, creating uncertainty on its effectiveness and feasibility. Here, we introduce the concept of an ‘ecological yield gap’ to provide a common analytical framework to strengthen collaboration between agronomists and ecologists in assessing the contribution of ecosystem services within the wider array of inputs, management practices, technologies, and biophysical limits that determine on-farm crop yields. We define the ecological yield gap as the yield increase that could be achieved in a given context (climate x soil x cropping system), and at a given input level, by increasing the delivery of ecosystem services via ecological intensification practices that support crop growth and substitute external inputs. We provide empirical examples of such practices, including crop diversification, service crops, and organic amendments that can increase the use efficiency of mineral fertilizers and suppress pests, weeds and diseases. The potential of these practices to narrow the ecological yield gap and their feasibility at farm level depend on how the ecosystem services they provide interact with other aspects of the farming system and requires analysis at farm level. This perspective paper aims to facilitate a shared research agenda among agronomists and ecologists to develop complementarity between ecosystem services and inputs at field and farm levels
Improved maize yield and profitability in the humid tropics through permanent beds, crop residues, and bean rotation
No full textAntecedentes: El estado de Oaxaca tiene diversas zonas agroecológicas que requieren estudios locales para evaluar prácticas agrícolas sustentables. La región del Papaloapan se caracteriza por tener suelos degradados, situación que es agravada por las pendientes pronunciadas, las altas precipitaciones y la predominancia de suelos luvisólicos, susceptibles a erosionarse en la superficie y compactarse en el subsuelo (piso de arado). Una alternativa para reducir la degradación podría ser la agricultura de conservación, pero su exitosa implementación y adopción masiva requiere de estudios que adapten sus componentes (mínimo movimiento del suelo, cobertura permanente del suelo y diversificación de cultivos) a las condiciones locales. Objetivo. Evaluar los efectos combinados de los componentes de la agricultura de conservación en el rendimiento y rentabilidad del maíz (Zea mays L.). Metodología. De 2014 a 2019, se empleó un diseño de bloques completos aleatorizados (DBCA) para evaluar ocho tratamientos resultantes de la combinación de prácticas de labranza (labranza convencional, cero labranza y camas permanentes), manejo de rastrojos, rotaciones de cultivos (Mucuna pruriens y Phaseolus vulgaris L.), diferentes fórmulas de fertilización y mejoradores de suelo. Resultados. El maíz rotado con frijol en camas permanentes con retención de rastrojo presentó en promedio un rendimiento de 5.2 Mg ha-1, una utilidad neta de 7,721.00 MXN ha-1 de utilidad neta y 1.53 de relación Beneficio/Costo). Incluso sin rotación de cultivos, los sistemas con camas permanentes con rastrojo mostraron rendimientos de maíz superiores a los de cero labranza y similares a los de labranza convencional. Implicaciones. El maíz sembrado en primavera-verano y rotado con frijol en otoño-invierno en camas permanentes con rastrojo presenta un rendimiento similar al del sistema convencional y una mayor utilidad neta. Conclusión. La agricultura de conservación, en su variante de camas permanentes, con retención de rastrojo y rotación maíz-frijol es una opción viable para la producción agrícola sustentable en el trópico húmedo como en el Papaloapan.Background. Oaxaca is a Mexican state with diverse agroecological regions, necessitating local studies to evaluate sustainable agricultural practices. The region of Papaloapan, Oaxaca, is characterized by soil degradation, aggravated by steep slopes, high rainfall and the predominance of luvisolic soils, which are prone to erosion on the surface and compaction in the subsoil (plow sole). An alternative to reduce soil degradation could be conservation agriculture, but its successful implementation and mass adoption will require studies to adapt its components (minimum soil movement, permanent soil cover, and crop diversification) to local conditions. Objective. To evaluate the combined effects of conservation agriculture components on maize (Zea mays L.) yield and profitability. Methodology. From 2014 to 2019, a randomized complete block design (RCBD) was used to evaluate eight treatments resulting from the combination of tillage practices (conventional tillage, no-tillage, and permanent beds), crop residue management, crop rotation (Mucuna pruriens and Phaseolus vulgaris L.), different fertilization formulas, and soil amendments. Results. Maize rotated with beans in permanent beds with crop residue showed an average yield of 5.2 Mg ha-1, a net profit of 7,721.00 MXN ha-1, and 1.53). Even without crop rotation, systems with permanent beds and crop residue showed maize yields superior to zero tillage and like conventional tillage. Implications. Maize in spring-summer rotated with beans in autumn-winter in permanent beds with residue retention yields similar to the conventional system and produces higher net profit. Conclusion. Conservation agriculture in its variant of permanent beds with crop residue and maize-bean rotation is a viable option for sustainable agricultural production in the humid tropics such as the Papaloapan
Harnessing novel genetic markers for scald resistance from gene bank spring barley genotypes
No full textBackground: Scald caused by Rhynchosporium graminicola is a common foliar disease affecting barley production worldwide. Identifying and utilizing scald resistance genes and quantitative trait loci (QTL) to develop barley cultivars with durable and effective resistance to scald is crucial. Results: In the present study, we evaluated 275 spring barley genotypes together with 4 commercial check cultivars under controlled conditions and examined the underlying genetics of scald resistance in these genotypes. A significant genetic variation (P value < 0.0001) for scald resistance was observed among the tested barley germplasms. A genome-wide association study (GWAS) identified eight markers-trait associations (MTAs) forming seven QTL located on chromosomes 3H, 6H, and 7H, of which three are novel. The allelic effects of these MTAs were further examined, and favorable alleles associated with scald resistance were identified. Conclusions: The identification of QTL for scald resistance, along with favorable allele identification, will be crucial for marker-assisted breeding programs. These findings will facilitate the development of new scald-resistant cultivars and contribute to the sustainability of barley production. Further studies, such as fine-mapping of candidate genes within these identified QTL regions, will help to narrow down the potential causative genetic variants and understand their functional effects on scald resistance
Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis
No full textThe rapid development of unmanned aerial vehicles (UAVs) and imaging technologies has opened new research avenues for precision agriculture, particularly in the context of plant phenotyping where their utilization has been intensive over the last decade. This review focuses on the interplay of machine learning, UAV-based multispectral imagery and plant phenotyping. We systematically reviewed the current literature to catalog and assess the variety of machine learning methodologies applied to multispectral UAV data for the prediction of key phenotypic traits such as biomass, yield and nitrogen. In this study, we conducted a comprehensive meta-analysis to analyze the relationship between the machine learning model performance and variables such crop type, the type of aerial phenotyping platform, the phenological stage, etc A trait-based comparison of the efficiency and popularity of machine learning algorithms was conducted. Our findings showed that the multiple linear regression is the most effective model in predicting biomass while artificial neural networks showed up as the top performing algorithm in determining nitrogen content. Random forest was identified as the most popular algorithm in estimating those key phenotypic traits. The best combinations of UAV and sensors that significantly enhance model performance for predicting critical agronomic traits were thoroughly examined. Results highlighted, for instance, that pairing the DJI 2 UAV with Micasense sensor led to better machine learning performance in predicting biomass while Parrot Sequoia was identified as the most efficient multispectral sensor to phenotype leaf nitrogen content. Ultimately, the challenges and future research prospects of UAV-based predictions related to the phenotype data variability, the choice of UAV platform, the model complexity and interpretability are discussed. Since previous studies described the broad applications of UAVs and sensors in agriculture, this review aimed to provide a targeted, systematic and quantitative analysis of optimal use of machine learning algorithms and UAV-based multispectral imagery for plant phenotyping
Genetic dissection of triple rust resistance (leaf, yellow, and stem rust) in Kenyan wheat cultivar, “Kasuku”
No full textClimate change is driving the spread of transboundary wheat diseases, necessitating the development of resilient wheat varieties for sustainable agriculture. Wheat rusts, including leaf rust (LR), yellow rust (YR), and stem rust (SR), remain among the most economically significant diseases, causing substantial yield losses worldwide. Enhancing genetic diversity by identifying and deploying rust resistance genes is crucial for durable resistance in wheat breeding programs. This study aimed to identify quantitative trait loci (QTL) associated with rust resistance in the CIMMYT wheat line Kasuku, released in Kenya in 2018. A recombinant inbred line (RIL) population (181 lines) derived from Kasuku (triple rust-resistant) and Apav#1 (triple rust-susceptible) was evaluated under artificial LR and YR epidemics in Mexico and YR and SR in Kenya. QTL mapping using genotyping-by-sequencing (DArTSeq) and phenotypic data identified four major loci: QLrYrSr.cim-1BL (Lr46/Yr29/Sr58) on 1BL, conferring resistance to LR, YR, and SR; QLrYr.cim-2AS (Yr17/Lr37) on 2AS, providing LR and YR resistance; QLrYr.cim-3AL on 3AL; and QLrYrSr.cim-6AL on 6AL, representing novel loci associated with multiple rust resistances. Additionally, minor QTL were also identified: for LR (QLr.cim-2DS on 2DS, QLr.cim-6DS on 6DS), for YR (QYrKen.cim-3DS on 3DS, QYrKen.cim-6BS on 6BS), and for SR (QSr.cim-2BS on 2BS, QSr.cim-5AL on 5AL, QSr.cim-6AS on 6AS). RILs carrying these QTL combinations exhibited significant reductions in rust severity. Flanking markers for these loci are being used to develop Kompetitive Allele-Specific PCR (KASP) markers for fine mapping and marker-assisted selection (MAS). These findings contribute to the strategic deployment of rust resistance genes in wheat breeding programs, facilitating durable resistance to multiple rust pathogens
Improving wheat grain yield genomic prediction accuracy using historical data
No full textGenomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data from 6 selection environments were analyzed, with the populations of 5 years (2018-2023) as the validation population and earlier years (back to 2013-2014) as the training population. Generally, we observed that as the number of training years increased, the prediction accuracy tended to improve or stabilize. For instance, in the late heat stress selection environment (beds late heat stress), prediction accuracy increased from 0.11 (1 training year) to 0.23 (5 years), stabilizing at 0.26. Similar trends were observed in the intermediate drought selection environment (beds with 2 irrigations), with prediction accuracy rising from 0.12 (1 year) to 0.21 (4 years) but minimal improvement beyond that. Conversely, some selection environments, such as flat 5 irrigations (flat optimal environment), did not significantly increase, with the prediction accuracy fluctuating around 0.09-0.14 regardless of the number of training years used. Additionally, average genetic diversity within the training population and the validation population influenced prediction accuracy. Indeed, a negative correlation between prediction accuracy and the genetic distance was observed. This highlights the need to balance genetic diversity to enhance the predictive power of genomic selection models. These findings exhibit the benefits of using an extended historical dataset while considering genetic diversity to maximize prediction accuracy in genomic selection strategies for wheat breeding, ultimately supporting the development of high-yielding varieties
Resilient yet productive: maize that can thrive under stress and in optimal conditions
No full textIn the Asian tropics, maize is predominantly grown as a rainfed crop during the summer-rainy season, which often suffers significant yield losses due to the erratic distribution pattern of monsoon rain that causes intermittent dry spells and/or excessive moisture within the season. The climate-induced abiotic stresses, particularly drought and waterlogging, pose significant threats to rainfed maize cultivation in the Asian tropics, where erratic patterns of monsoon rain and associated high genotype-by-environment interaction (GEI) effects undermine yield stability. To address these challenges, this study evaluated 61 advanced-stage maize hybrids developed under the Asia Waterlogging and Drought Tolerant (AWDT) product profile, designed to deliver hybrids with stable grain yields under variable moisture regimes without yield penalties under optimal conditions. Multi-environment trials (METs) were conducted across 19 locations in South and Southeast Asia (India, Bangladesh, Vietnam, and Thailand) under four moisture regimes: optimal, rainfed/random stress, reproductive-stage drought, and vegetative-stage waterlogging. A stratified ranking approach was employed to identify superior hybrids that matched or exceeded commercial checks under optimal conditions and outperformed them under at least one stress environment. Several elite hybrids demonstrated broad or specific adaptation to targeted stress-prone environments. These findings underscore the importance of targeted breeding and MET-based selection strategies in developing high-performing stress-resilient maize cultivars for climate-vulnerable agroecologies, with implications for food security, farmer livelihoods, and sustainable cropping systems in the face of escalating climate variability