CIMMYT Publications Repository
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Future on our plates: Rethinking food systems in South Asia
No full textSouth Asia is the most densely populated region in the world, with a current population of 1.8 billion (one-fourth of the global population), which is projected to increase to more than 2 billion by 2050. This rapid population growth will put significant pressure on agriculture and food systems. Despite being an agricultural powerhouse, South Asia has high levels of food insecurity, with many people suffering from malnutrition and hunger. It is also among the region’s most vulnerable to climate change. It relies heavily on intensive farming practices, leading to several challenges in agriculture and food systems that bear directly on food production and security. Further, the highest concentration of agrifood system workers is in South Asia, with 793 million people. South Asia is also home to more than 600 million young people (ages 15–35), who make up around one-half of all agrifood system workers.5 page
Yield from the shadows: beyond top layer photosynthesis to enhance crop productivity
No full textPhotosynthesis research in crops typically focuses on upper canopy layers, which is partly for convenience and partly for the sake of achieving stable datasets under high light conditions. This neglects significant contributions from light - limited portions of the canopy within the lower layers. This study aimed to provide an empirical quantification of the role of these hidden layers of wheat canopies in the context of canopy scale productivity. We demonstrate that light-saturated photosynthetic rates (Asat) in middle and bottom layers at key growth stages can be strong predictors of grain yield. Despite variability in architecture across layers, light interception remained similar and key associations between biomass accumulation and yield with Asat emerged. Yield showed positive associations with photosynthesis in all canopy layers but was stronger at the top layer during grain filling and at the bottom layer during booting. Whole canopy photosynthetic rates were influenced by top layer architecture, N availability in the middle and bottom layers and leaf angles at the bottom of the canopy. Our findings suggest that measurements within hidden layers are required, and that optimizing middle and bottom layer Asat during the vegetative period and top layer Asat during grain filling can boost food security
Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield
No full textGenomic selection is an extension of marker-assisted selection by leveraging thousands of molecular markers distributed across the genome to capture the maximum possible proportion of the genetic variance underlying complex traits. In this study, genomic prediction models were developed by integrating phenological, physiological, and high-throughput phenotyping traits to predict grain yield in bread wheat (Triticum aestivum L.) under three environmental conditions: irrigation, drought stress, and terminal heat stress. Model performance was evaluated using both five-fold cross-validation and leave-one-environment-out (LOEO) schemes. Under five-fold cross-validation, the model incorporating vegetation indices derived from spectral datasets from the grain-filling phase achieved the highest accuracy. In LOEO validation, the model that included days to heading performed best under irrigation, whereas under drought stress, the model utilizing vegetation indices from the vegetative stage showed the highest accuracy. Under terminal heat stress, three models performed best: one incorporating genotype by environment interaction, one using vegetation indices during the vegetative stage, and one integrating spectral reflectance data from both the vegetative and grain-filling phases. Although incorporating multiple covariates can improve prediction accuracy or reduce the normalized root mean square error, using an extended model with all available covariates is not recommended due to the marginal predictive accuracy gains, increases in phenotyping, costs and complexity of data collection analysis. Overall, our findings show the importance of tailored phenomic inputs to specific environmental contexts to optimize genomic prediction of wheat yield
Identification and validation of a major QTL on chromosome 2A for wheat-Parastagonospora nodorum interactions
No full textWheat (Triticum aestivum L.) is frequently affected by Septoria nodorum blotch (SNB), a fungal disease that significantly reduces wheat yields. In this study, two recombinant inbred line (RIL) populations, developed from crosses involving two elite CIMMYT breeding lines (WUYA and KATH) and a common susceptible male parent (CIANO T79), were used to detect quantitative trait loci (QTL) associated with SNB resistance. High-density genetic maps were constructed for these RIL populations by incorporating presence/absence variation (PAV) markers using the DArTseq genotyping platform. Three major and stable QTL linked to SNB resistance were identified on chromosomes 2A, 4B, and 5B. Among these, QSnb.cim-2A accounted for 22.16%–28.74% and 17.62%–19.71% of the phenotypic variation in the WUYA/CIANO T79 and KATH/CIANO T79 populations, respectively, and it was also validated in the CASCABEL/CIANO T79 RIL population. The remaining two QTL, QSnb.cim-4B and QSnb.cim-5B, were found to be associated with Rht-B1b and tsn1, respectively. The combined effect of these three QTL significantly improved SNB resistance while also reducing plant height, indicating their promising utilization in wheat breeding programs
Development of a composite core collection from 5,856 Sesame accessions being conserved in the Indian National Genebank
No full textObjectives: A composite core collection (CCC) in sesame (Sesamum indicum L.) will help utilize genetic resources efficiently. This study reports, using genomics tools, a representative minimal set (CCC) that capture maximal genetic diversity from a set of 5,856 sesame accessions being conserved at the National Genebank (NGB) of the ICAR-NBPGR. The CCC will serve as a valuable resource for researchers and breeders to facilitate sesame improvement for traits such as yield, disease resistance, stress resilience, and nutritional content. Ultimately, this work contributes to the broader goal of improving sesame for an ever-increasing demand for vegetable oil, to meet our food security challenges. Data description: This study presents ddRAD-seq data for a total of 5,856 sesame accessions that includes 2,496 accessions (a subset of 5,856 accessions) that was reported by us recently. Using next-generation sequencing (NGS) short-reads over 2.16 Terabases of sequence data were generated, with each sample averaging 1.2 million reads. The study identifies a set of 1,768 sesame accessions as the CCC that captures maximal diversity, genotypic and phenotypic. This will aid researchers in trait discovery, association studies, pre-breeding, and parental selection for complex traits viz., yield, disease resistance, stress resilience, and other economically important traits
Temporal sentinel-2 imagery for wheat mapping and monitoring: Analyzing phenological stages with machine learning to improve mapping precision for small farms
No full textPrecise mapping and tracking of wheat crops are crucial to improve agricultural management, particularly for small farms in challenging landscapes such as Nepal. By utilizing temporal Sentinel-2 imagery, this research maps wheat fields by examining phenological stages using machine learning methods, which enhances classification accuracy. Sentinel-2, a component of the Copernicus program by the European Space Agency, offers high-quality multispectral images for precise monitoring of crop growth over time. Two classification models, Random Forest (RF) and Support Vector Machine (SVM), were employed to distinguish wheat from non-wheat areas. The accuracy of classification was improved by integrating in-situ data collected with Kobo Toolbox. The findings showed that Random Forest performed better than SVM, reaching 99% accuracy in training and 86% in validation, with 56%of the study region classified as wheat. RF's outstanding performance is due to its capacity to manage temporal and spectral intricacies, particularly in capturing the phenological cycle of crops. This research showcases how machine learning, specifically Random Forest, can enhance the accuracy of wheat mapping for small farms by analyzing phenological stages effectively, with plans to apply these methods to rice and maize in the future.143-14
Sparse testcrossing for early-stage genomic prediction of general combining ability to increase genetic gain in maize hybrid breeding programs
No full textSparse testcrossing is an effective strategy for increasing both short- and long-term genetic gain in hybrid breeding programs. Maize hybrid breeding programs aim to develop new hybrid varieties by crossing genetically distinct parents from different heterotic pools, exploiting heterosis for improved performance. The programs typically consist of two main components: population improvement and product development. The population improvement component aims to enhance the heterotic pools through reciprocal recurrent selection based on general combining ability (GCA). However, especially in the early stages of testing, evaluating large numbers of hybrid combinations to estimate GCA is impractical due to considerable logistical challenges and costs. Therefore, breeders often evaluate the initial population of selection candidates using only a single tester to narrow down the candidate pool before further evaluation. Using a single tester, however, may not adequately represent the heterotic pool, leading to inaccurate GCA estimates and suboptimal selection decisions. To address this, we propose sparse testcrossing for early-stage testing, where subsets of candidate genotypes are testcrossed with different testers, connected through a genomic relationship matrix. We conducted stochastic simulations to compare various sparse testcrossing designs with a conventional testcross strategy using a single tester over 15 cycles of reciprocal recurrent genomic selection. Our results show that using 3-5 testers, sparsely distributed among full-sibs, sparse testcrossing offers breeders a practical balance between simple testcross designs, resource efficiency, and increased prediction accuracy for GCA, ultimately resulting in increased rates of genetic gain
Can short-term conditional subsidies sustain agroecology adoption? Evidence from Zimbabwe
No full textAlthough agroecology is widely regarded as critical for transforming agri-food systems, its adoption remains low. This raises important policy questions about what can be done to nudge adoption. Large-scale input subsidies (ISPs) implemented by most governments are a good avenue. How to implement ISPs to nudge agroecology remains unclear and under explored. We assess the extent to which the Zimbabwean government’s large-scale conditional ISP, Pfumvudza, can sustain agroecology adoption using framed economic field experiments. We found indicative evidence suggesting that the provision of conditional subsidies for two consecutive seasons increased adoption by 5% and sustained it even after the subsidy was withdrawn. Thus, well designed and targeted conditional subsidies have the potential to sustain agroecology adoption.7 page