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Beans have long been a cornerstone of East African diets, providing nearly 25% of daily protein intake for millions across the region. Yet, traditional varieties often require over 3 hours of cooking, exacerbating issues of fuel scarcity and health impacts from pollution, while iron and zinc deficiencies contribute to widespread malnutrition. The Rapid Cooking Bean Project (RCBP) is tackling these urgent challenges head-on, breeding new bean varieties that not only cook faster but are fortified with essential nutrients like iron and zinc. By reducing cooking times and improving nutritional content, the RCBP is enhancing food security and promoting better health outcomes, especially for women and children.
A crucial component of the project’s success has been the integration of ASReml-R, a powerful statistical tool, into the breeding process. This case study explores how ASReml-R has transformed data analysis, enabling faster breeding cycles, improving the accuracy of genetic selection, and driving measurable gains in both nutritional content and cooking efficiency, with far-reaching implications for food security and public health in East Africa.
A Collaborative Approach to Breeding Success
The Rapid Cooking Bean Project (RCBP) emerged from the urgent need to address two critical challenges affecting bean consumers in East Africa: excessive cooking times and widespread nutritional deficiencies, particularly in women and children. As a staple food in the region, beans are a crucial source of protein, iron, and zinc, but their preparation demands substantial time and fuel resources, creating both environmental and health concerns. The prevalence of iron and zinc deficiencies across the population, leading to issues such as stunting and anaemia, highlighted the urgent need for biofortified varieties.
The initiative began in early 2017, when Dr. Clare Mukankusi, Global Lead Bean Breeding for the Alliance-CIAT in CGIAR in Uganda and Professor Wallace Cowling from The University of Western Australia (UWA) identified an opportunity to combine their expertise to tackle the pressing challenges of food security and malnutrition. Together, they secured funding from the Australian Centre for International Research (ACIAR) to support the project. Now in its sixth year, RCBP represents a unique partnership between Australian and African breeders and agronomists in 6 East African countries under the umbrella of the Pan Africa Bean Research Alliance (PABRA).
Objectives
This multinational effort aimed to develop biofortified beans that cook faster, higher yielding, and are rich in essential nutrients like iron and zinc, which are critical for combating stunting and anaemia in African women and children.
With clear, measurable objectives, the project aimed to:
- Reduce cooking time by 30%, addressing fuel consumption, health and environmental concerns
- Increase iron content by 15% and zinc content by 10% to combat widespread anaemia
- Achieve these improvements within a five-year timeframe
Accelerating Breeding with the BRIO Approach
The project employed the Breeding for Impact and Outcome (BRIO) approach to achieve its ambitious goals. BRIO, developed by Professor Cowling’s research group at UWA, focuses on accelerating genetic gain by strategically selecting traits with the highest potential for impact and optimising the outcome in the long-term.
BRIO represents:
B: Accurate breeding values from advanced data analyses in ASReml-R
R: Rapid cycles of recurrent selection
I: Index of multiple traits weighted for desired genetic gains
O: Optimal contributions selection for optimized crossing designs
By employing the BRIO approach, the RCBP is able to focus on the traits that provide the highest potential for real-world impact. Index trait weighting allows breeders to prioritize traits like reduced cooking time, increased nutrient content, grain yield and disease resistance. This strategy ensures that the breeding program is not only faster but also highly targeted, leading to significant gains in the most crucial areas. ASReml-R plays a pivotal role in this process by helping breeders manage complex datasets, predicting accurate genomic breeding values for multiple traits and ensuring that genetic selection aligns with these priorities.
ASReml-R: A Key Tool for Breeding Decisions
The choice of ASReml-R as the project’s data analysis tool was clear from the outset. Known for its ability to handle complex genetic data, ASReml-R was integrated into every stage of the breeding process, from the early analysis of breeding trials to the final selection of new varieties.
ASReml-R was selected for its ability to support breeders with the following key advantages:
- Efficient performance on laptops, which is vital for African breeders working in the field
- Standalone functionality, critical for areas with limited or unreliable internet access
- Seamless integration with R programming, offering comprehensive data management and analysis capabilities
- A proven ability to handle multiple environment trials (MET) and factor analysis, making it ideal for the diverse conditions encountered across East Africa
One of ASReml-R’s key strengths is its ability to integrate data from different environments, such as various trial sites across East Africa. By accounting for environmental variability, it ensures that genetic predictions are accurate and relevant to the region’s diverse growing conditions. This integration allows breeders to select bean lines that will perform optimally across a wide range of environments, ensuring resilience and adaptability in the face of changing climates.
Other key features of ASReml-R, such as mixed model analysis and incorporating genomic and pedigree information, allowed breeders to gain deeper insights into the performance of various bean lines under different environmental conditions. The tool’s ability to accurately model genetic relationships and predict breeding values gave the project a significant edge in selecting varieties that not only met cooking time and nutritional targets but were also resilient and adaptable to local growing conditions.
Data Analysis and Insights
ASReml-R was used to analyse a range of data types, including phenotypic, genetic, and environmental data from breeding trials conducted across East Africa. The tool enabled breeders to account for variability between trial sites and environmental factors, ensuring more accurate predictions of how different bean lines would perform in real-world conditions.
ASReml-R enabled the analysis of diverse breeding data, including:
- Laboratory traits: cooking time, water absorption capacity, seed size, iron and zinc content
- Field traits: grain yield, growth habit, flowering date, maturity date, disease resistance
- Genomic data: whole genome data on approximately 1,000 progeny per year
- Pedigree data: pedigree information spanning 5 generations back to founders, plus 1000 new progeny per year.
Key improvements include:
- Identification of significant machine effects in cooking time laboratory tests
- Detection and correction of spatial effects in field trials
- Enhanced heritability and reduced error variance
- Improved accuracy of genomic estimated breeding values (GEBV) and pedigree-based estimated breeding values (EBV)
- Early identification of detrimental correlations of GEBV across traits, such as the association of lower iron and zinc content with higher yielding and faster cooking genotypes.
- Improved crossing designs through optimal contributions selection in the software MateSel, based on accurate GEBV and trait correlations identified in ASReml-R.
One key example of ASReml-R’s impact was its ability to accurately predict cooking times and other traits based on multi-trait genomic analysis, enabling breeders to select genotypes for crossing early in the breeding cycle, thus accelerating genetic gain.
Building Local Capacity for Sustainable Breeding
An essential aspect of the project was training African breeders in the use of ASReml-R. This capacity-building effort ensured that the skills needed to maintain and expand the breeding program were developed locally. Workshops and hands-on training sessions were held across the region, introducing breeders to advanced data analysis techniques and the specific features of ASReml-R. While the learning curve was steep, the project succeeded in transferring knowledge and building the confidence of local breeding teams, who are now independently conducting their own analyses using the tool.
Driving Impact: Key Achievements in Breeding and Beyond
After five years of breeding, the RCBP made significant strides towards its breeding goals:
- Significant genetic gain was achieved from cycle 1 to cycle 2 in shortening cooking time (decreased by more than 6% p.a.) and grain yield (increased by more than 3% p.a.). This rate of genetic gain achieved the project goal of 30% reduction in cooking time over five years.
- Small-seeded bean types were shown to have elevated iron and zinc content
- Detrimental trait correlations were identified between rapid cooking and high iron and zinc, so that breeders could adjust optimized breeding strategies to achieve both goals
“From the beginning of the project, ASReml-R has been the cornerstone of our analysis of bean breeding data. We were able to summarise several years of bean breeding genomic and phenotypic data in the African Bean Panel in ASReml-R, as a spring-board to begin the RCBP. This was published in the Plant Genome in 2021. We have continued to develop ASReml-R multivariate genomic analysis to estimate genetic gain in the project from 2019 – 2024, and this has revealed world-leading genetic gain in a crop breeding program.”
Dr Renu Saradadevi, Research Associate, ACIAR Project CROP/2018/132, The University of Western Australia
Impact on Food Security and Health
The RCBP’s contribution to food security and health improvements in Africa extends far beyond simple crop enhancement. The dry bean is an essential component of East African nutrition, providing essential nutrients including protein, iron, and zinc. However, despite their nutritional potential, current bean consumption falls short of meeting the region’s needs, particularly for women and children who suffer from stunting and anaemia due to nutritional deficiencies. The traditional preparation process, requiring many hours of soaking followed by 2-3 hours of cooking, creates a cascade of challenges. Families must rely on wood, charcoal, or gas for fuel, leading to multiple adverse effects: women and children face health risks from smoke inhalation during cooking, resulting in lung disease; they must undertake the time-consuming, expensive, and often dangerous task of collecting cooking fuel; and the extended burning of these fuels contributes significantly to carbon pollution.
Through the development of rapid-cooking biofortified beans, the RCBP addresses these interconnected challenges. By reducing cooking times whilst enhancing nutritional content, the project aims to make beans more accessible and practical for daily consumption, potentially increasing both production and consumption of these vital legumes. This comprehensive approach not only promises to improve the nutritional status of vulnerable populations but also addresses broader social and environmental challenges facing East African communities.
Looking Ahead: Expanding Breeding Efforts and Future Goals
The success of the RCBP has laid the groundwork for the ongoing development of new bean varieties with enhanced traits. The project continues to explore ways to further reduce cooking times and increase micronutrient content, while also expanding the breeding program to other regions in East Africa.
Additionally, there is potential to apply the learnings from this project to other crops, using ASReml-R to accelerate breeding programs focused on improving both nutritional content and crop performance.
Conclusion: A Path Toward Greater Food Security
As the Rapid Cooking Bean Project progresses, the combination of innovative breeding techniques with powerful analytical tools like ASReml-R has been instrumental in its success. By streamlining complex data and enhancing accuracy, ASReml-R has helped drive significant progress in reducing bean cooking times and boosting nutritional value. The potential long-term benefits for food security, public health, and sustainable crop breeding in East Africa are profound. As the project evolves, ASReml-R remains a crucial tool in accelerating these critical improvements.
“The Rapid Cooking Bean Project has been a remarkable success, and ASReml-R has been instrumental in overcoming key challenges we faced in the breeding process. Its ability to accurately detect machine effects in our laboratory tests allowed us to make necessary corrections that other tools might have overlooked, ensuring the reliability of our data. Moreover, ASReml-R’s genomic selection capabilities have accelerated our results by enabling us to rapidly predict accurate genomic breeding values for traits related to cooking time and nutrient content, and to identify detrimental genetic correlations between these traits. The insights we’ve gained through this tool have enabled us to make faster and more informed decisions, ultimately accelerating the development of biofortified beans that can make a real difference to food security in East Africa.”
Professor Wallace Cowling, Project Lead, ACIAR Project CROP/2018/132, The University of Western Australia
VSNi wishes to thank Professor Wallace Cowling and Dr Renu Saradadevi for their time spent assisting with this case study.
One Response
This is a powerful example of how data science can drive real-world impact. I like how the project combines advanced analytics with practical goals like reducing cooking time and improving nutrition. The focus on local capacity building is especially important it ensures the benefits are sustainable and long-lasting. visit us at Telkom University Jakarta for the latest news