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Enhancing the efficiency of Sorghum and Groundnut improvement in Nigeria
No full textStrong collaborations with ADCIN, CIMMYT, private sector stakeholders, and research institutions have facilitated knowledge exchange, technology adoption, and the development of innovative solutions tailored to farmers' needs and industry demands.1 graphi
Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction
No full textThe popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in genotyping. Ridge regression is one ofthe most popular methods used for genomic prediction; however, its efficiency (in terms of prediction performance) depends on the appropriate tunning of the penalization parameter. In this paper we propose a novel, more efficient method to select the optimal penalization parameter for Ridge regression. We compared the proposed method with the conventional method to select the penalization parameter in 14 real data sets and we found that in 13 of these, the proposed method outperformed the conventional method and across data sets the gains in prediction accuracy in terms of Pearson's correlation was of 56.15%, with not-gains observed in terms of normalized mean square error. Finally, our results show evidence of the potential of the proposed method, and we encourage its adoption to improve the selection of candidate lines in the context of plant breeding
Transforming farming in Uganda: The journey of four farmers and their demonstration plots
No full text9 page
Vision to Action report for Mbire and Murehwa, Zimbabwe
No full textDeveloping a vision-to-action plan is essential for guiding the transition to agroecology in Mbire and Murehwa. This plan, grounded in agroecological principles, aims to minimize conflicts and promote participation, equity, and the integration of scientific and local knowledge throughout the engagement process. The key objectives included articulating a vision, identifying behavior changes, understanding their drivers, co-designing transition pathways, and formulating action plans and Monitoring, Evaluation, and Learning (MEL) systems for Agroecological Living Landscapes (ALLs) to track progress effectively. The methodology emphasizes inclusiveness, diversity, and representativeness, ensuring genuine stakeholder engagement. Workshops involved group discussions among different demographic segments to capture varied perspectives, fostering inclusive decision-making. The transition towards sustainable agroecology in Mbire and Murehwa requires key behavior changes among farmers and communiies. These include adopting improved agricultural practices, enhancing knowledge and skills through continuous learning and training, increasing women's participation in leadership and income-generating activities, and improving market access. Additionally, crucial elements include enhancing environmental stewardship, coexisting with wildlife, promoting social cohesion, and reducing gender-based violence. Shifting towards entrepreneurial mindsets by exploring new business models and value addition opportunities is also essential. These changes aim to build resilient, sustainable, and equitable local food systems, thereby improving agricultural productivity and overall community well-being. Findings were integrated with the Local Indicators Selection Process (LISP) to develop a comprehensive monitoring and evaluation framework. This approach ensures that selected indicators reflect local needs and priorities, supporting robust monitoring, evaluation, and adaptive management. Ultimately, this process aims to cultivate a shared understanding, community engagement, and coherent progress toward building sustainable agroecological systems in Zimbabwe.36 page
Developing genotyping capacity and establishing an end-to-end genotyping workflow at CSIR-SARI
No full text1 graphi
Genomic prediction of kernel water content in a hybrid population for mechanized harvesting in maize in northern China
No full textGenomic prediction enables rapid selection of maize varieties with low kernel water content (KWC), facilitating the development of mechanized maize harvesting and reducing costs. This study evaluated and characterized the KWC and grain yield (GY) of hybrid maize in northern China and used genomic prediction to identify superior hybrid combinations with low kernel water content at maturity (MKWC) and high GY adapted to northern China. A total of 285 hybrids obtained from single crosses of 34 inbred lines from Stiff Stalk and Non-Stiff Stalk heterotic groups were used for genomic prediction of KWC and GY. We tested 20 different statistical prediction models considering additive effects and evaluating the impact of dominance and epistasis on prediction accuracy. Employing 10-fold cross-validation, it showed that the average prediction accuracy ranged drastically from 0.386 to 0.874 across traits and models. Eight linear statistical methods displayed a very similar prediction accuracy for each trait. The average prediction accuracy of machine learning methods was lower than that of linear statistical methods for KWC-related traits, but the random forest model had a high prediction accuracy of 0.510 for GY. When genetic effects were incorporated into the prediction model, the prediction accuracy for each trait was improved. Overall, the model with dominant and epistatic effects (G:AD(AA)) performed best. For the same number of markers, predictions using trait-specific markers resulted in higher prediction accuracy than randomly selected markers. When the number of trait-specific SNPs was set to 100, the prediction accuracy of GY increased by 33.27%, from 0.406 to 0.541. Out of all the 561 potential hybrids, the TOP 30 hybrids selected by genomic prediction would lead to a 1.44% decrease in MKWC compared with Xianyu335, a hybrid with a fast kernel water dry-down, and these hybrids also had higher GY simultaneously. Our results confirm the value of genomic prediction for hybrid breeding low MKWC suitable for maize mechanized harvesting in northern China. In conclusion, this study highlights the potential of genomic prediction to optimize maize hybrid breeding, enhancing efficiency and providing insights into genotype-accuracy relationships. The findings offer new strategies for hybrid design and advancing mechanized harvesting in northern China
Rice–wheat comparative genomics: Gains and gaps
No full textRice and wheat provide nearly 40% of human calorie and protein requirements. They share a common ancestor and belong to the Poaceae (grass) family. Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits. Several wheat genes and gene families have been characterized based on their rice orthologs. Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops. Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping, deduce their putative functions and biochemical pathways, and develop molecular markers for marker-assisted breeding. A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering, gene editing, or wide crossing.656-66
Yield gap decomposition: quantifying factors limiting soybean yield in Southern Africa
No full textSoybean production in Sub-Saharan Africa (SSA) is increasing as its demand for food, feed, cash, and soil fertility improvement soars. Yet, the difference between the smallholder farmers’ yield and either the attainable or the potential is large. Here, we assessed the contribution of various crop management practices to yield gap, and the major factors limiting soybean yield increase per unit area. This study showed that besides soil nutrients and plant nutrition, soybean variety is the most limiting factor in Malawi and Zambia, whereas, in Mozambique, seed rate is significant. Overall, in the Southern Africa region (Malawi, Zambia, and Mozambique) the major soybean yield gap contributors are: variety (63.9%), seed rate (49.7%), and disease damage (36.3%), especially soybean rust. An indication that through yield gap decomposition, interventions could be prioritized to target the most yield-limiting factors with the minimum resources available to smallholder farmers and immensely narrow the yield gap