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Empowering farmers with digital El Niño advisories: Insights from Malawi and Zambia
No full textClimate extremes, including droughts, floods, and cyclones, are becoming increasingly frequent, severe, and geographically extensive in Southern Africa, with devastating effects on populations heavily reliant on rain-fed agriculture and broader economies. Farmers urgently need guidance and support to build resilience against climate-related disasters. However, the national extension systems in the region lack adequate funding and the capacity to respond promptly and effectively during emergencies. This report analyzes a digital advisory campaign aimed at disseminating awareness and advisory messages to farmers during an El Niño event across Malawi and Zambia, leveraging Interactive Voice Response (IVR) technology accessible via basic, non-smart mobile phones. The campaign was rolled out in December 2023, prior to the start of the 2023/24 season. Our report builds on farmer feedback, particularly on how useful they found the campaign, their livelihood outcomes in the aftermath of the El Niño, and other notable trends in the data. The campaign reached over 1 million farmers in the two countries, with more than 90% of respondents finding the messages useful for awareness and preparedness. Both in Malawi and Zambia, radio emerged as the most popular channel for receiving extension messages. Moreover, results also confirmed that meteorological forecasts played a critical role in influencing farmers’ choices of crop and planting dates. For instance, dry spells of more than 21 days led to maize yield losses of 70-90%. Results further indicate that conservation agriculture and the adoption of drought-tolerant crops were the most popular mitigation measures employed by farmers. The findings suggest that Governments need access to appropriate information about the circumstances of their population to make informed decisions about how to efficiently extend support. Digital advisories provide an opportunity to gather real-time data on farm conditions and can be used to deploy support. Additionally, post-event analysis provides valuable evidence that can be used to enhance the design of future response interventions and reduce donor dependence.15 page
A Bayesian optimization R package for multitrait parental selection
No full textSelecting and mating parents in conventional phenotypic and genomic selection are crucial. Plant breeding programs aim to improve the economic value of crops, considering multiple traits simultaneously. When traits are negatively correlated and/or when there are missing records in some traits, selection becomes more complex. To address this problem, we propose a multitrait selection approach using the Multitrait Parental Selection (MPS) R package—an efficient tool for genetic improvement, precision breeding, and conservation genetics. The package employs Bayesian optimization algorithms and three loss functions (Kullback–Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. The software's functionality includes three main functions—EvalMPS, FastMPS, and ApproxMPS—catering to different data availability scenarios. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits
Gender, rainfall endowment, and farmers’ heterogeneity in wheat trait preferences in Ethiopia
No full textWheat is a vital cereal crop for smallholders in Ethiopia. Despite over fifty years of research on wheat varietal development, consideration of gendered trait preferences in developing target product profiles for wheat breeding is limited. To address this gap, our study used sex-disaggregated survey data and historical rainfall trends from the major wheat-growing regions in Ethiopia. The findings indicated heterogeneity in trait preferences based on gender and rainfall endowment. Men respondents tended to prefer wheat traits with high straw yield and disease-resistance potential, while women showed a greater appreciation for wheat traits related to good taste and cooking quality. Farmers in high rainfall areas seemed to prioritize high straw yield and disease resistance traits, while those in low rainfall areas valued good adaptation traits more highly. Most of the correlation coefficients among the preferred traits were positive, indicating that farmers seek wheat varieties with traits that serve multiple purposes. Understanding men's and women's preferences and incorporating them in breeding and seed systems could contribute to the development of more targeted and effective wheat varieties that meet the diverse needs of men and women farmers in Ethiopia
Opportunities and limitations to the irrigation-led sustainable intensification of mixed farming systems in Nepal’s mid-hills
No full text16 page
Genomic selection in plant breeding: key factors shaping two decades of progress
No full textGenomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP—theoretically reaching one when using the Pearson's correlation as a metric—is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.552-57
Social safety nets in Nepal: A review
No full textGovernments across the world have established social protection systems where they implement programs to eradicate extreme poverty, address rising inequality, foster inclusive growth, and provide crucial assistance to the most vulnerable segments of society. These programs involve in-kind or cash transfers through a system (World Bank, 2012), or public employment at guaranteed wages (Rahman and Pingali, 2024). Additionally, social protection programs provide poor communities, households, and individuals support to increase their resilience to economic risks and catastrophic events. Social protection programs can be classified into three broad categories based on their objectives and the risks they cover: • Social security programs include contributory programs that offer workers in formal and informal sectors of the economy and their dependents pensions, gratuities, health insurance and related support during employment and after retirement.• Labor market programs focus on individuals’ skill building in the job market through vocational training programs, connecting them to potential employers through employment service centers and employment management information systems, and providing financial and logistical support for foreign employment. • Non-contributory social assistance programs, or social safety net (hereafter, SSN) programs, often (but not always) support for poor and vulnerable individuals, households, and communities. These programs are in various forms including cash transfers or vouchers, in-kind transfers, school feeding and other targeted food assistance programs, labor-intensive public work programs, scholarships, and humanitarian and disaster relief programs.38 page
Agronomic monsoon onset definitions to support planting decisions for rainfed rice in Bangladesh
No full textThe usability gaps between climate information producers and users have always been an issue in climate services. This study aims to tackle the gap for rice farmers in Bangladesh by exploring the potential value of tailored agronomic monsoon onset definitions. Summer aman rice is primarily cultivated under rainfed conditions, and farmers rely largely on monsoon rainfall and its onset for crop establishment. However, farmers’ perception of the arrival of sufficient rains does not necessarily coincide with meteorological definitions of monsoon onset. Therefore, localized agronomic definitions of monsoon onset need to be developed and evaluated to advance in the targeted actionable climate forecast. We analyzed historical daily rainfall from four locations across a north-south gradient in Bangladesh and defined dynamic definitions of monsoon onset based on a set of local parameters. The agronomic onset definition was evaluated in terms of attainable yields simulated by a rice simulation model compared to results obtained using conventional meteorological onset parameters defined by the amount of rainfall received and static onset dates. Our results show that average simulated yields increase up to 7 – 9% and probabilities of getting lower yields are reduced when the year-to-year varying dynamic onset is used over the two drier locations under fully rainfed conditions. It is mainly due to earlier transplanting dates, avoiding the impact of drought experienced with early monsoon demise. However, no yield increases are observed over the two wetter locations. This study shows the potential benefits of generating “localized and translated” climate predictions
High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images
No full textTo ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning
Enhanced radiation use efficiency and grain filling rate as the main drivers of grain yield genetic gains in the CIMMYT elite spring wheat yield trial
No full textCommon wheat (Triticum aestivum L.) is a major staple food crop, providing a fifth of food calories and proteins to the world’s human population. Despite the impressive growth in global wheat production in recent decades, further increases in grain yield are required to meet future demands. Here we estimated genetic gain and genotype stability for grain yield (GY) and determined the trait associations that contributed uniquely or in combination to increased GY, through a retrospective analysis of top-performing genotypes selected from the elite spring wheat yield trial (ESWYT) evaluated internationally during a 14-year period (2003 to 2016). Fifty-six ESWYT genotypes and four checks were sown under optimally irrigated conditions in three phenotyping trials during three consecutive growing seasons (2018–2019 to 2020–2021) at Norman E. Borlaug Research Station, Ciudad Obregon, Mexico. The mean GY rose from 6.75 (24th ESWYT) to 7.87 t ha−1 (37th ESWYT), representing a cumulative increase of 1.12 t ha−1. The annual genetic gain for GY was estimated at 0.96% (65 kg ha−1 year−1) accompanied by a positive trend in genotype stability over time. The GY progress was mainly associated with increases in biomass (BM), grain filling rate (GFR), total radiation use efficiency (RUE_total), grain weight per spike (GWS), and reduction in days to heading (DTH), which together explained 95.5% of the GY variation. Regression lines over the years showed significant increases of 0.015 kg m−2 year−1 (p < 0.01), 0.074 g m−2 year−1 (p < 0.05), and 0.017 g MJ−1 year−1 (p < 0.001) for BM, GFR, and RUE_total, respectively. Grain weight per spike exhibited a positive but no significant trend (0.014 g year−1, p = 0.07), whereas a negative tendency for DTH was observed (− 0.43 days year−1, p < 0.001). Analysis of the top ten highest-yielding genotypes revealed differential GY-associated trait contributions, demonstrating that improved GY can be attained through different mechanisms and indicating that no single trait criterion is adopted by CIMMYT breeders for developing new superior lines. We conclude that CIMMYT’s Bread Wheat Breeding Program has continued to deliver adapted and more productive wheat genotypes to National partners worldwide, mainly driven by enhancing RUE_total and GFR and that future yield increases could be achieved by intercrossing genetically diverse top performer genotypes
Interaction of planting system with radiation-use efficiency in wheat lines
No full textRadiation-use efficiency (RUE) is an important trait for raising biomass and yield potential in plant breeding. However, the effect of the planting system (PS) on genetic variation in RUE has not been previously investigated. Our objectives were to quantify genetic variation in RUE, biomass and grain yield in raised-bed and flat-basin planting systems, and associations with canopy-architecture traits (flag-leaf angle and curvature). Twelve spring wheat (Triticum aestivum L.) cultivars were evaluated under irrigated conditions for 3 years in North West Mexico using raised-bed and flat-basin planting systems. Canopy architecture traits were measured at booting and anthesis + 7 days. Grain yield (10.6%), biomass (7.6%), and pre-grain-filling RUE (9.7%) were higher in raised beds than flat basins, while a significant planting system × genotype interaction was found for grain yield. Genetic variation in pre-grain-filling RUE was associated with biomass and grain yield in beds and basins. In flat basins, higher pre-grain-filling RUE was correlated with a more upright flag-leaf angle but not in raised beds. In raised beds, cultivars with less upright flag-leaf angle had greater fractional light interception pre-anthesis. Taller semi-dwarf cultivars intercepted relatively more radiation in the beds than the flats before anthesis, consistent with the taller cultivars showing relatively greater increases in yield in beds compared to flats. Our results indicated that the evaluation of genotypes for RUE and biomass in wheat breeding should take into account planting systems to capture genotype × PS effects. In addition, the results demonstrate how flag-leaf angle has a different effect depending on the planting system.314-33