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Market and retail food environments in South Asia: A comparative analysis of five rural districts
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Using systems thinking for agricultural research for development to transform food systems in the global South
No full textThis study investigates the role of systems thinking tools in bridging the gap between theoretical knowledge and practical implementation for sustainable transformation, with a focus on the agri-food sector. Drawing from an assessment of various toolkits and guidelines, it highlights the potential of systems approaches in translating complex sustainability theories into actionable strategies. While these tools provide valuable frameworks for understanding and engaging with multifaceted challenges, such as novelty, innovation, and collaboration, they also reveal certain limitations and gaps. The analysis highlights the diversity in available toolkits, tailored to different sectors and stakeholders, and underscores their role in fostering organizational change and facilitating collaboration. However, it identifies challenges such as the predominance of tool development in the Global North, linguistic barriers, and a lack of consensus on defining systems and addressing long-term processes and uncertainties. Furthermore, the study explores opportunities and challenges for the application of systems thinking tools within a case study of CGIAR, emphasizing the need for a paradigm shift towards integrated, inclusive, and locally-relevant approaches. It calls for the institutionalization of systems thinking approaches and the development of comprehensive yet accessible tools to guide researchers in examining their innovations within broader systems, fostering collaboration, and maximizing impact. Despite limitations in sample size and scope, this research offers insights into the potential of systems thinking toolkits and guidelines as part of the processes to drive sustainable transformations in agri-food systems. It underscores the importance of comprehensive tailored-made toolkits that can guide researchers and agricultural development practitioners in applying systems approaches to advance agricultural innovation for sustainability transitions in their own fields, contexts and sectors.28 page
Hymenopteran parasitoid complex and fall armyworm: a case study in eastern India
No full textFall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) has significantly affected maize crop yields, production efficiency, and farmers’ incomes in the Indian Eastern Gangetic Plains region since it was first observed in India in 2018. A lack of awareness by maize growers of the appropriate selection, method, and timing of insecticide application not only creates a barrier to sustainable FAW control but also contributes to increased environmental pollution, reduced human health and increased production costs. We demonstrated that FAW inflicted the most damage in early whorl growth stage of maize, regardless of whether chemical insecticides were applied. FAW egg masses and larvae collected from maize fields in which no insecticides had been sprayed showed high parasitism rates by parasitoid wasps; in contrast fields that had been sprayed had much lower rates of parasitism on FAW. Ten hymenopteran parasitoids were observed in maize fields across the study region, suggesting a diversity of natural methods to suppress FAW in maize at different growth stages. These included two FAW egg parasitoids and eight FAW larval parasitoids. Microplitis manilae Ashmead was the most abundant FAW larval parasitoid species, and Telenomus cf. remus was the dominant FAW egg parasitoid species. Endemic FAW parasitoids such as those observed in this study have great potential as part of a sustainable, cost-effective agroecological management strategy, which can be integrated with other methods to achieve effective control of FAW
Promoting access to and use of quality finger millet seeds using behaviour-led interventions through youth and women quality centres in Busia, Kenya: workshop technical brief
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FieldSimR: an R package for simulating plot data in multi-environment field trials
No full textThis paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR's capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR's value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids' genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments
Advancing crop disease early warning in South Asia by complementing expert surveys with internet media scraping
No full textWheat contributes one-fifth of the global food supply with an estimated 29% of global production in low and lower-middle income countries. As production expands across southern Asia, yields are often negatively impacted by outbreaks of fungal rust diseases. A wheat rust early warning and advisory system comprising surveillance, near real-time disease risk forecasts and advisory dissemination has been established in two target countries in South Asia, including Nepal and Bangladesh. However, as wheat rust spores can be aerially transmitted over long distances, near real-time estimates of disease incidence are required from sources of infection in neighbouring regions. To address this challenge, we developed and tested a novel algorithm to generate proxy observations of infection sources using online media reports in two neighbouring countries, India and Pakistan. Media sampling could provide an effective alternative where data from ground surveys are not readily available in near real-time. Our results show that west Nepal was exposed to a substantial inoculum pressure from aerially dispersed stripe rust spores originating from India and Pakistan. There were no outbreaks of stripe rust disease in Bangladesh with only very low levels of cross-border dispersion and generally unfavourable environmental conditions for infection. We further describe how proxy observations informed farmer decision-making in near real-time in Nepal and filled a knowledge gap in identifying early sources of infection for a major outbreak of stripe rust during 2020 in Nepal. Our results highlight the importance of international cooperation in mitigating transboundary plant pathogens
CIMMYT and Kenya
No full textCIMMYT has long worked in Kenya with Kenyan researchers, policymakers, crop scientists, agricultural organizations and farmers. These collaborations date back to 1964 when CIMMYT and Kenyan scientists developed the first maize hybrid variety in 1964. Since then, CIMMYT has been committed to collaborating with Kenyan partners to improve the quality, quantity, and dependability of Kenya’s production systems and cereal crops.4 page
Genotype performance estimation in targeted production environments by using sparse genomic prediction
No full textIn plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations. Various sparse testing methods have been explored to optimize resource use. This study employed Incomplete Block Design (IBD) to allocate lines to environments, ensuring not all lines were tested in every environment. We compared IBD to Random line allocation, maintaining a consistent number of environments per line across both methods. The primary objective was to estimate grain yield performance of lines using Genomic Estimated Breeding Values (GEBVs) computed through six Genomic Best Linear Unbiased Predictor (GBLUP) methods. In the first five methods, missing values were predicted before cross-environment adjustment; in the sixth, adjustment was performed directly. Using the Bayesian GBLUP model, we analyzed genotype performance under both IBD and random allocation. Results indicate that computing GEBVs for a target population of environments (TPE) using available phenotype and marker data is effective for selection. The IBD method showed superior performance with less variability compared to random allocation. These findings suggest that using IBD designs can enhance selection accuracy and efficiency, and that pre-adjustment prediction of missing lines may not necessarily improve selection outcomes
Assessment of genetic biodiversity and association of micronutrients and agronomic traits using microsatellites and staining methods which accelerates high-micronutrients variety selections within different wheat groups
No full textEvaluation of genetic biodiversity for micronutrients is crucial for breeding high-quality crops and addressing the negative impacts of mineral deficiencies. The objectives of this research were to assess genetic variation and the relationship between grain Fe and Zn levels and agronomic traits in a diverse collection of wheat varieties. Additionally, the study aimed to determine the correlation between microsatellite markers (SSR) and micronutrient quantities. A total of 42 genotypes (Iranian commercial cultivars, landraces, and Afghan and Swiss varieties) were evaluated over a two-year period. Fe and Zn levels were measured using two semi quantitative staining assays and atomic absorption spectrophotometry (AAS) facility. Semi-quantitative staining methods and AAS showed high correlations for micronutrient contents. Landraces exhibited higher Fe (63.79 mg/kg) and Zn (44.76 mg/kg) but lower grain yield compared with commercial cultivars. Heritability estimates ranged 53%-79.43%, suggesting that genetic variance played a higher contribution in the phenotypic variation of traits than environmental factors. Notably, Fe content displayed significant correlations with days to maturity. Canonical correlation analysis (CCA) revealed that Zn content was correlated with four agronomic traits. Evaluation of genetic diversity using SSR markers demonstrated high genetic variation among the genotypes tested. The analysis of polymorphism information content (PIC) indicated that SSR primers had an average PIC of 0.75, with the Xgwm192 primer exhibiting higher PIC than others. Several SSR markers revealed association with micronutrient content that can be used in marker-assisted selection (MAS) programs aimed at selection of high micronutrient genotypes. In conclusion, the findings underscored the substantial genetic diversity present in micronutrient levels among global wheat genotypes, the potential of landraces for micronutrients biofortification of wheat cultivars through cross hybridization, the utility of staining methods for screening high/low micronutrient genotypes, and use of microsatellite markers for marker-assisted breeding aiming to micronutrient improvement in breeding programs