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Multistakeholder engagement to scale soil health globally: The coalition of action 4 soil health
No full textHealthy soil is critical for ecosystem restoration, climate change mitigation and adaptation, biodiversity conservation, water cycling, farmer livelihoods, and food and nutrition security. Despite its importance, soil health has often been overlooked, but momentum is growing as evidenced by recent high-level initiatives such as the Nairobi Declaration as part of the Africa Fertiliser and Soil Health Action Plan and the European Union Soil Mission: A Soil Deal for Europe. The UN Decade on Ecosystem Restoration was launched on 5 June 2021 to galvanise local, national and global action to restore degraded ecosystems. In the same year, the UN Food Systems Summit (UNFSS) initiated a call for coalitions of action to champion integrated, systemic approaches to transform food systems. The Coalition of Action 4 Soil Health (CA4SH) was launched to bring soil health into focus with participation across sectors and scales to ultimately raise awareness about this critical ecosystem that we depend on, but which is being degraded at unprecedented rates. Since 2021, CA4SH has grown to include nearly 200 members (as of January 2025) representing the public and private sectors, research institutions, non-governmental organisations, farmer organisations and cooperatives, individuals, youth-led organisations, and indigenous organisations to mention some. The initiative has also had a strong focus on gender equity and social inclusion (GESI) in soil and landscape restoration. The Coalition promotes soil as a unifier across a diverse set of stakeholders, building partnerships to overcome critical economic, technical and institutional barriers to the adoption and scaling of healthy soil practices. Furthermore, CA4SH facilitates evidence-based policy and practice action for the scaling of restoration practices that improve soil health. The Private Sector Guiding Group, launched as part of the UNFSS, developed a call to action to support increased investments in healthy soil, and continues to support the actions of the Coalition. Its four working groups focus on communication, soil health monitoring and implementation, policy, and financial investment. In the first three years since its launch, the Coalition has engaged in multinational dialogues and contributed to the adoption of soil health in the outcomes from the UN Framework Convention of Climate Change (UNFCCC) 27th Conference of the Parties (COP27) through the Koronivia joint work on agriculture (now the Sharm El-Sheikh Declaration), the UNFCCC COP28 UAE Declaration on Food Systems and Agriculture, the UNCCD COP16 Riyadh Action Agenda and also launched the Soil Health Resolution. Leveraging on the enabling policy environment, the Coalition catalyses public and private sector action with outcomes for economic returns and growth, productivity and rural livelihoods, climate and nature. The positioning of the Coalition in the current global environmental transition is pivotal to drive the multifaceted benefits that soil health improvement offers to food systems transformation and global adaptation to and mitigation of climate change
Identification of gray leaf spot–resistant donor lines in tropical maize germplasm and their agronomic performance under artificial inoculation
No full textGray leaf spot (GLS) disease is caused by two fungal pathogens, Cercospora zeae-maydis and Cercospora zeina. The current study evaluated 427 elite tropical/subtropical lines for their responses to GLS under artificial inoculation in Kakamega in western Kenya for 4 years. Furthermore, a subset of 140 lines was used for a high-resolution genome-wide association study (GWAS) for GLS resistance. Among the 427 lines evaluated, 14 were identified as resistant on the basis of a <4 (on a scale of 1–9) GLS disease severity score. Among these 14 lines, three lines, namely CML540, CML559, and CML566, are also known for resistance to MSV, tolerance to drought, and resistance to MLN, respectively. The phenotypic evaluation revealed significant (P < 0.01) genotypic and genotype x environment interaction variances and moderate to high heritability for GLS disease severity, area under disease progress curve (AUDPC), and other agronomic traits. GLS disease severity traits were negatively and significantly correlated (P < 0.01) with anthesis date, silking date, plant height, and ear height. A subset of 140 lines was genotyped with 33,740 DART-GBS SNP markers. Population structure and principal component analysis grouped the lines into two major clusters with moderate structure in the population. GWAS revealed 13 and 11 SNPs significantly associated with GLS disease severity and AUDPC values. Six among the 13 SNPs detected for GLS resistance are overlapped with earlier studies, which can be used for fine mapping and improvement of GLS resistance through marker-assisted selection. However, SNPs on chromosomes 9 and 10 were unique to the present study. Genomic prediction on GLS traits revealed moderate to high prediction correlations, suggesting its usefulness in the selection of desirable candidates with favorable alleles for GLS resistance. Overall, 14 GLS resistance lines identified in this study can be used as donor lines in both genetic studies and resistance breeding programs
Patterns of livelihood diversification in farming systems of the Eastern Gangetic Plains
No full textCONTEXT: The Eastern Gangetic Plains (EGP) is a region characterized by smallholder-dominated farming systems, facing rapid socio-economic and environmental changes. Livelihood diversification away from traditional agriculture is increasingly seen as a strategy to enhance resilience, income stability, and food security among these smallholders. However, comprehensive understanding of diversification patterns and their drivers within the EGP remains limited. OBJECTIVE: This study aims to elucidate the patterns of livelihood diversification in farming systems across the EGP, and identify the key factors driving diversification. METHODS: Utilizing data from the Rupantar project involving a baseline survey of 1400 households across India, Nepal, and Bangladesh, and a subsequent analysis employing the Simpson's Index of Diversity (SID) to quantify diversification levels. Multivariate regression models were used to explore the determinants of diversification, while disaggregating the analysis by country and diversification components (crop plot, crop non-plot, and non-crop non-plot). RESULTS AND CONCLUSION: The study revealed moderate levels of diversification across the EGP, with significant geographical and contextual variability. Key drivers of diversification included access to resources, gender, education, market access, and institutional support, with notable differences across countries and diversification types. Specifically, non-ownership of irrigation pumps, female household headship, and engagement in off-farm and non-farm activities emerged as significant predictors of higher diversification levels. SIGNIFICANCE: This study contributes to a nuanced understanding of livelihood diversification in the EGP, highlighting the complexity of diversification patterns and the multifaceted nature of its determinants and impacts. By identifying specific drivers of diversification, the findings provide valuable insights for policymakers, development practitioners, and researchers aiming to support rural livelihoods in the region. Emphasizing the role of gender, resource access, and institutional support, the study underscores the importance of tailored interventions to enhance the resilience and sustainability of smallholder farming systems in the face of changing environmental and socio-economic conditions
Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids
No full textGenotype, environment, and genotype-by-environment (GxE) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data for improved accuracy and efficiency in genetic analyses and genomic predictions. Dimensionality-reduced environmental parameters (RD_EPs) aligned with developmental stages are applied to establish linear relationships between RD_EPs and traits to assess the influence of environment on phenotype. Genome-wide association study identifies 539 phenotypic plasticity trait-associated markers (PP-TAMs), 223 environmental stability TAMs (Main-TAMs), and 92 GxE-TAMs, revealing distinct genetic bases for PP and GxE interactions. Training genomic prediction models with both TAMs and RD_EPs increase prediction accuracy by 14.02% to 28.42% over that of genome-wide marker approaches. These results demonstrate the potential of utilizing environmental data for improving genetic analysis and genomic selection, offering a scalable approach for developing climate-adaptive maize varieties
Modelling groundwater futures under climatic uncertainty for local policy and planning: a case of quantification of groundwater resources at sub-regional level in the Ganges basin
No full textStudy region: Nalanda district, Bihar, India, a sub-tropical region, and part of middle Ganga River basin. Study focus: Assessing the impacts of climate change on aquifers' seasonal replenishment is thus crucial for planning for future local food and water security. This study looks at how future groundwater levels will be affected by climate change in relation to important functioning thresholds that are typical for aquifers that replenish periodically. New hydrological insights for the region The result shows the projected groundwater levels from 2018 to 2060 using the CMIP6 global climate model, using rainfall data from three GCMs selected based on their different projected scenarios of levels of high intensity rainfall. Given the key role of low intensity rainfall in groundwater recharge, we find that incorporating rainfall intensity in groundwater models can be crucial for more robust projections. Our findings also show that higher total rainfall does not necessarily equate to higher groundwater recharge or lesser groundwater declines. Instead, the least groundwater declines were found in projections, where relatively higher total rainfall was also associated with lower high intensity rainfall periods, highlighting the need for combining and comparing varied SSPs and climate models for accurate future trends. At the sub-regional level, we find that climate change could lead to maximum groundwater loss of ∼ 0.8 km3 in 42 years in Nalanda district. Current trend analysis (2000–2018) already shows a negative annual groundwater balance. Even assuming no changes to current groundwater extraction rates, climate change will result in decreased groundwater levels and storage. The projection trends also reveal distinct short-term, medium-term, and long-term shifts which offer different policy windows for managing and governing the groundwater resources
Ensuring sustainable crop production when yield gaps are small: A data-driven integrated assessment for wheat farms in Northwest India
No full textNorthwest India achieved remarkable wheat productivity gains during the past decades. However, this has been accompanied by increasing input levels and intensive production practices, raising questions about the economic and environmental sustainability of current cropping systems. A multicriteria integrated assessment is required for wheat farms in the region to understand the scope for cleaner wheat production in the future. Production practices from irrigated wheat fields (n = 3928) were evaluated for multiple sustainability indicators, namely yield gap, nitrogen (N)-use efficiency, profitability, and greenhouse gas emissions. Stochastic frontier analysis was combined with simulated potential yield (Yp) data to identify the causes of wheat yield gaps in the region. N-use efficiency was estimated by calculating the partial factor productivity of N, profitability was computed based on reported input-output amounts and prices, and greenhouse gas emissions were quantified using the Mitigation Options Tool (MOT). These indicators were subjected to a multicriteria assessment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under different scenarios (i.e., different weights for different indicators). For each scenario, farmers’ fields were classified as most efficient, efficient, less efficient, and least efficient, and random forest was used to identify the most important management practices governing the field classification. Wheat yield gaps were small (25–30 % of Yp or 2.4 t ha−1) and mostly attributed to the technology yield gap (ca. 20 % of Yp or 1.5 t ha−1). Ranking and grouping the farmers’ fields in the scenario with equal weights for all indicators revealed that at least 25 % of the fields had very high greenhouse gas emissions (>1500 kg CO2-eq ha−1) at a productivity level of 80 % most efficient fields adopting zero tillage) to achieve an overall objective of higher yield, lower greenhouse gas emissions, more profit and higher N-use efficiency, whereas residue retention and tillage intensity would need to be prioritized for minimizing greenhouse gas emissions. For the most efficient fields the decrease in greenhouse gas emissions was always associated with a decline in yield level. The most important management practices governing the field classification included the crop establishment method used for the previous rice crop, the number of tillage operations, residue retention, and the N fertilizer rate for wheat. The study provides a data-driven approach to screen trade-offs between performance indicators and to identify the management practices that can deliver sustainable and cleaner crop production in the future
Ukama Ustawi (UU)- Annual Project report CGIAR Initiative on Diversification in East and Southern Africa 2024 in Ethiopia
No full text12 page
Assessment of genetic diversity and heterotic alignment of CIMMYT and IITA maize inbred lines adapted to sub-Saharan Africa
No full textDespite the breeding efforts by many institutions, maize (Zea mays L.) productivity in sub-Saharan Africa is still low. A limited number of productive maize hybrids have been developed partly due to a lack of knowledge on the diversity and heterotic relationship of the germplasm, especially in public breeding programs. Understanding the extent of diversity, structure, and heterotic grouping of available maize germplasm originating from different breeding programs is important to enhance long-term genetic gain in hybrid maize breeding programs by optimizing heterotic pools using modern breeding tools. Information about the genetic structure of the available germplasm could help breeders design effective breeding strategies to improve yield. This study was conducted to determine the genetic diversity, population structure, and heterotic alignment among 187 elite maize inbred lines from the IITA (International Institute of Tropical Agriculture) and CIMMYT (International Maize and Wheat Improvement Center) maize breeding programs. The inbred lines were genotyped with 9857 Diversity Array Technology sequencing based single nucleotide polymorphism markers. Hierarchical clustering revealed three major groups, with some subgroups consistent with the selection history, and pedigree of the inbred lines. Three broad groups were detected: two consisting of CIMMYT lines and a mixed group consisting of both CIMMYT and IITA inbred lines. The STRUCTURE analysis revealed six subpopulations (fixation index value = 0.58), which depicts a moderate genetic diversity among the materials. Population 2 comprises the highest number of genotypes (102) from both programs. More than 89% of the elite lines had homozygosity exceeding 95%, with the remaining lines requiring further inbreeding through repeated self-pollination. There was inconsistency in the predetermined heterotic groups' alignment between CIMMYT and IITA elite inbred lines. Analysis of molecular variance revealed that 96% of the total variation was accounted for by differences within groups, with the remaining 4% representing the variation between groups. This suggests that the two programs can benefit from germplasm exchange for the improvement of maize productivity
Dryland Crops Program: Tackling the 50:20 challenge
No full textIn 2024, CIMMYT’s Dryland Crops Program (DCP) laid the foundations to advance its vision and address the 50:20 challenge. This is possible through the efforts of over 200 scientists from partner institutions, working collaboratively to deliver impact.2 page
Identification of gray leaf spot resistant donor lines in tropical maize germplasm and their agronomic performance under artificial inoculation
No full textGray leaf spot (GLS) disease is caused by two fungal pathogens Cercospora zeae-maydis and Cercospora zeina. The current study evaluated 427 elite tropical/subtropical lines for their responses to GLS under artificial inoculation in Kakamega in western Kenya for four years. Further, a subset of 140 lines was used for a high-resolution genome-wide association study (GWAS) for GLS resistance.Among the 427 lines evaluated, 14 were identified as resistant on the basis of <4 (on a scale of 1-9) GLS disease severity score. Among these 14 lines, three lines namely, CML540, CML559, and CML566 are also known for resistance to MSV, tolerance to drought, and resistance to MLN, respectively. The phenotypic evaluation revealed significant (P <0.01) genotypic and genotype x environment interaction variances and moderate to high heritability for GLS disease severity, area under disease progress curve (AUDPC), and other agronomic traits. GLS disease severity traits were negatively and significantly correlated (P <0.01) with anthesis date, silking date, plant height, and ear height. A subset of 140 lines were genotyped with 33,740 DART-GBS SNP markers. Population structure and principal component analysis grouped the lines into two major clusters with moderate structure in the population. GWAS revealed 13 and 11 SNPs significantly associated with GLS disease severity and AUDPC values. Six among the 13 SNPs detected for GLS resistance are overlapped with earlier studies which can be used for fine mapping and improvement of GLS resistance through markerassisted selection. However, SNPs on chromosomes 9 and 10 were unique to the present study.Genomic prediction on GLS traits revealed moderate to high prediction correlations, suggesting its usefulness in the selection of desirable candidates with favorable alleles for GLS resistance. Overall, 14 GLS resistance lines identified in this study can be used as donor lines in both genetic studies and resistance breeding programs