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Genomic analysis of modern maize inbred lines reveals diversity and selective breeding effects
No full text578–58
Breeding zinc crops for better human health
No full textThis open access book discusses Micronutrient Malnutrition (MM) is a major obstacle to the development of human resources in developing nations in Asia and Africa and has been identified as a public health concern. Globally, more than 2 billion people suffer from MM. The most susceptible groups are women and young children under five. The main cause of the persistence of MM prevalence is the inaccessibility, unavailability, or high cost of diets rich in zinc. Typically, staple foods are poor in minerals. An inadequate intake of zinc (Zn) compromises human nutrition and immunological function. Breeding staple crops with enhanced critical nutrients offers a long-term solution for populations dependent on single staples or diets with less diversity. The significance of zinc in human nutrition, high throughput zinc phenotyping techniques, breeding product profile design, new varieties and germplasm that are rich in zinc, and the genetics and genomics of zinc are all covered in this book. For the first time, the publicly available standards (PAS) under the BSI standards for zinc crops were reviewed with an eye toward future global commercial production and commercialization. This edited volume will function as a scientific knowledge base and reference for researchers, graduate students, and other professionals in a comparable field. The goal is to enable biofortification science to help the poor world achieve food and nutrition security
Chapter 8. What do we know about the future of crop pests and diseases in relation to food systems?
No full textCrop pests and diseases (P&D) can cause substantial yield losses and pose a threat to global food security. Losses at a regional level can even exceed 40 percent for crops like maize and rice. Most studies show that a warmer climate creates a conducive, albeit spatially variable, environment for P&D spread. However, existing foresight research is largely biophysical in nature and focuses on individual pathosystems, examined mostly at the national level. As such, projections of the magnitude of economic impacts of changing patterns of P&D are missing. Global assessment of model-based historical and future P&D impacts on food systems remains constrained by the small number of available models that can estimate yield losses under contrasting climate and agroecological conditions. Efforts are needed to improve data accessibility, model versatility, and simulation platforms and to establish international observation and modeling networks. Artificial intelligence (AI) and related methods can assist in the development of robust and adaptable models to capture the impacts of P&D on food systems.45-4
Measuring the impact of COVID-19 on climate smart agriculture strategies of smallholder farmers in coastal Bangladesh
No full textContext: Climate-smart agriculture (CSA) strategies of smallholder farmers include intensification, diversification, alteration of farming practices, and transformation to other enterprises, along with mitigation of greenhouse gases and temporary or permanent migration. The determination of CSA strategies relies on livelihood capitals, namely physical, natural, human, social, financial, information, and technological capitals of smallholder farmers. The COVID-19 pandemic has prompted shifts in CSA strategies, with variations dependent on these capitals. Objective: The study aims at developing a conceptual and methodological framework to understand external disruptions like the impact of COVID-19 on CSA adoption and how the livelihood capitals and environmental conditions influence these impacts. Methods: The study develops a composite indicator of the changes in CSA strategies and links them to indicators of human, physical, financial, social, natural, and information capital through quantitative regression models. Results and conclusion: The results unraveled varied impacts of the COVID-19 pandemic on climate-smart agriculture in Bangladesh. The findings revealed that among the climate-smart agricultural strategies, intensification efforts were least affected, diversification showed a mixed picture, whereas seasonal migration experienced a significant negative impact. Ownership of physical capital, such as machinery, enhanced intensification, given the shortage of hired machinery and labor services during the pandemic period. Similarly, information capital, as reflected in mobile phone ownership, played a decisive role in improving farm productivity and income. Level of income loss during COVID period and difficulties in accessing credit increased migration while size of loan increased diversification. Canal irrigation access increased intensification and migration and reduced diversification. Significance: In response to the COVID-19 crisis, the government of Bangladesh has supported activities that favored the intensification of production of major crops. It appears to be successful in maintaining the intensity of staple crop cultivation despite the substantial impacts of COVID-19. It is observed that the rate of change in uptake of resilience-building agricultural practices, greenhouse gas mitigation practices and transformation to aquaculture from agriculture were stalled during the COVID-19 pandemic years. The results argue for policies to improve how finance is provided, extension services are delivered, and cellular mobile access is ensured, especially for marginal and women farmers. Difficulties in accessing loans increased migration and larger sized loans pushed farmers to diversification. Therefore, schemes to restructure farm credit need to be explored. To enhance migration as an adaptation strategy during pandemic time, it is necessary to impart additional skills and provide support schemes to offset income loss
SCASI Review and Planning Workshop Report
No full textThe SCASI Review and Planning Workshop, held on June 24 and25, 2025, at the ILRI Campus in Addis Ababa, served as a crucial strategic forum to critically assess the progress of the Scaling Conservation Agriculture-based Sustainable Intensification in Ethiopia (SCASI) initiative since 2022. Organized by CIMMYT and CFGB, the event brought together 41 diverse stakeholders, including government bodies, implementing partners, and donor representatives, with the objective of consolidating key lessons, reviewing bottlenecks, and collaboratively developing actionable plans for the remainder of 2025. This multi-stakeholder engagement successfully validated the project's performance and renewed the shared commitment essential for sustained scaling efforts. The project review confirmed SCASI's high success and relevance. The Endline Impact Assessment revealed that CASI practices achieved an exceptional 87.5% adoption rate and resulted in significant productivity gains, most notably a maize yield increase of up to 67.7%. While this impact validates the CASI package as a highly effective response to soil and climate challenges, the review also identified critical limitations. Results for wheat and teff were not significant, signaling the need for further package optimization. More urgently, two major sustainability threats were highlighted: high government staff turnover, which risks institutional memory, and the cost and scarcity of inputs, which makes scaling financially challenging. The resulting action plan focuses on immediate institutional transition to secure long-term gains. The strategy prioritizes embedding CASI within formal government extension systems and utilizing local by-laws to secure ownership and mitigate staff turnover. To directly counteract high input costs, the plan promotes low-cost, local alternatives (vermicomposting, green manure, cover crops). Finally, the plan calls for a re-analysis of impact data for strategic refinement and utilizing mass media, farmer-to-farmer training, and a central CASI knowledge hub to actively integrate lessons into the national agricultural extension system.25 page
Is newer better? The effect of varietal age on real-world maize yield in Kenya
No full textMaize varietal turnover is widely promoted across Sub-Saharan Africa to improve crop productivity and increase food security, yet its impact on yields remains poorly understood amid heterogeneous agroecological and socioeconomic conditions. This study quantifies the yield effects of varietal age in Kenya using a three-wave panel survey (2023-2024) of 4,160 smallholder households across Kenya. Using entropy balancing and weighted regression models to isolate the effect of varietal age on maize yield, we find a strong and consistent relationship between varietal age and yield. New varieties yield 147 kg/ha more than old ones in the long rains and 91 kg/ha more in the short rains. Finer age categorization reveals that switching to ultra-new varieties (0-5 years) delivers the highest gains-360 kg/ha over ultra-old varieties (21+ years) in the long rains and 269 kg/ha in the short rains. These findings suggest that slow varietal turnover carries significant opportunity costs in the form of forgone yield gains. While farmers generally perceive new varieties favorably-particularly for yield potential, early maturity and grain quality-concerns around labor intensity and resilience remain, potentially dampening adoption. Providing farmers with clear, locally relevant performance data and opportunities for on-farm experimentation can help shift perceptions and support wider uptake. Policies and programs that expand access to newer, better-performing varieties and strengthen seed quality assurance are essential for translating genetic gains into productivity improvements across Kenya's bimodal maiz
Assessing the potential of drone remotely sensed data in detecting the soil moisture content and taro leaf chlorophyll content across different phenological stages
No full textSoil moisture content is an important determinant of crop productivity, especially in agricultural systems that are dependent on rainfall. Climate variability has introduced water management challenges for smallholder farmers in Southern Africa. The emergence of unmanned aerial vehicle (UAV)-borne remote sensing offers modern solutions for monitoring soil moisture, plant health and overall crop productivity in real-time. This study evaluated the utility of UAV-acquired data in conjunction with random forest regression in predicting soil moisture content and chlorophyll across different growth stages of taro. The estimation models achieved R2 values up to 0.90 with rRMSE as low as 1.25%, demonstrating the robust performance of random forest in concert with different spectral datasets in estimating soil moisture and chlorophyll. Correlation analysis confirmed the association between these two variables, with the strongest correlation observed during the vegetative stage (r = 0.81, p < 0.05) and the weakest during the late vegetative stage (r = 0.78, p < 0.05). The results showed that UAV bands were crucial in predicting soil moisture and chlorophyll across all stages. These results demonstrate the utility of remote sensing, particularly UAV-borne sensors, in monitoring crop productivity in smallholder farms. By employing UAV-borne sensors, farmers can improve on-farm water management and make better and more informed decisions
Near-infrared spectroscopy-based phenomics data can improve genomic prediction of agronomic and grain quality traits across multi-environment sorghum hybrid trials
No full textIn recent years, phenotyping approaches in plant breeding have expanded in both methodology and data collection capacity. One such tool, Near-Infrared Spectroscopy (NIRS) generates a wealth of reflectance values for biological samples. To test the potential of NIRS-based predictions, a hundred grain sorghum hybrids generated from a 10 x 10 factorial mating design were evaluated across eight environments. Hybrids were phenotyped for grain yield, days to anthesis, plant height, kernel hardness index, kernel diameter, and kernel weight. Hybrid grain samples were scanned with NIRS to generate phenomic data while parental lines were genotyped using genotyping by sequencing. Three different predictive models: genomic prediction (GP), phenomic prediction (PP), and GP + PP were fitted. Three different cross-validation schemes of untested hybrids in characterized environments (CV1), tested hybrids in uncharacterized environments (CV2), and untested hybrids in uncharacterized environments (CV3) were completed. GP + PP significantly improved over GP for days to anthesis, kernel hardness index, kernel diameter, and kernel weight for CV1. Prediction accuracy of GP + PP was also significantly improved for the kernel hardness index and kernel weight for CV2 and CV3. Depending on logistics, phenomic prediction has the potential to complement or supplement genomic data for predictive strategies in sorghum