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Social and behaviour change and communication strategy for enhancing access to and use of quality finger millet seed of improved varieties through youth and women quality centres in Busia County, Kenya
No full text29 page
Artificial intelligence in plant breeding
No full textHarnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype–phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.891-90
CIMMYT and Zambia: working towards ‘Vision 2030’
No full textCIMMYT’s research and innovation efforts align with Zambia’s medium-term goal of “Socio-Economic Transformation for Improved Livelihoods” and its vision of becoming “A Prosperous Middle-Income Nation by 2030.”4 page
Characterizing dryland crop-based livelihoods: Insights from surveys in Kenya and Uganda
No full textThis report examines food security and farming systems among sorghum and millet cultivators in Kenya and Uganda, based on a survey of 2,701 households conducted in May–June 2023. The study reveals high rates of food insecurity (measured as food poverty) among these farmers, 53.2% in Kenya and 78.1% in Uganda, exceeding national averages. Cereals provide the majority of calories and dietary diversity is limited, with about half of the households surveyed consuming fewer than five food groups daily. The analysis shows that sorghum and millet farming is characterized by low use of inputs, limited market participation, and significant production challenges from drought, pests, and diseases. Farmers operate with minimal farm expenditures (US 12.40–15.07 in Uganda annually), and adoption of improved varieties remains low except in Eastern Kenya. Using decision tree analysis, the study identifies key determinants of food insecurity, including household size, education levels, land holdings, and crop diversification. The findings suggest that targeted interventions that take into consideration farming system characteristics, market access, and household demographics are crucial for improving food security among dryland cereal farmers in East Africa82 page
Deep learning methods improve genomic prediction of wheat breeding
No full textIn the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding
First Report of Ug99 Wheat Stem Rust Caused by Puccinia graminis f. sp. tritici in South Asia
No full text257
Sorghum & Finger millets Breeding Process and capacity improvement conducted during 2024 in Zimbabwe
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Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning
No full textTimely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yield estimation at a sub-district scale (1,000-meter spatial resolution). However, a significant gap exists in the application of remote sensing methods for government-reported rice yield estimation for food security management at high spatial resolution. Current methods are limited to specific regions and primarily used for research, lacking integration into national reporting systems. Additionally, there is no consistent yearly boro rice yield map at a sub-district scale, hindering localized agricultural decision-making. This workflow leveraged MODIS and annual district-level yield data to train a random forest model for estimating boro rice yields at a 1,000-meter resolution from 2002 to 2021. The results revealed a mean percentage root mean square error (RMSE) of 8.07% and 12.96% when validation was conducted using reported district yields and crop-cut yield data, respectively. Additionally, the estimated yield of boro rice varies with an uncertainty range between 0.40 and 0.45 tons per hectare across Bangladesh. Furthermore, a trend analysis was performed on the estimated boro rice yield data from 2002 to 2021 using the modified Mann-Kendall trend test with a 95% confidence interval (p < 0.05). In Bangladesh, 23% of the rice area exhibits an increasing trend in boro rice yield, 0.11% shows a decreasing trend, and 76.51% of the area demonstrates no trend in rice yield. Given that this is the first attempt to estimate boro rice yield at 1,000-meter spatial resolution over two decades in Bangladesh, the estimated mid-season boro rice yield estimates are scalable across space and time, offering significant potential for strengthening food security management in Bangladesh. Furthermore, the proposed workflow can be easily applied to estimate rice yields in other regions worldwide
The economics of seed use and production for farmers and seed enterprises: The case of groundnut, millet and sorghum
No full textThis report examines the economic viability of seed businesses for dryland crops, with a special focus on sorghum, millet, and groundnut, in sub-Saharan Africa. It explores the profitability and growth potential of seed enterprises (including community-based ones), highlighting key challenges including limited access to quality seeds, low private-sector investment, and insufficient adoption of improved varieties by smallholder farmers. The analysis uses profitability metrics, including the ratio of seed cost to crop value and seed-to-grain increase ratios, to assess the financial sustainability of seed systems across multiple countries. Case studies from East and West Africa provide insights into the profitability of early-generation and certified seed production, demonstrating the importance of integrating private and public sector strategies to enhance economic seed production. The report offers policy recommendations aimed at strengthening seed systems through public-private partnerhips, improving seed quality, and encouraging market development to support food security and sustainable agricultural growth in the region.iv, 48 page