The aim of this project is to document the adoption and impact of improved maize germplasm at poor, maize-dependent farmers’ level, in combination with understanding how access to affordable quality maize seed can be achieved through seed sector development. The assumption is that understanding the challenges, opportunities and implications of changes in the maize seed value chain will improve research results, and support the higher adoption and impact of research-derived maize germplasm. For this project, independent studies were carried out in four areas ( Mexico/Chiapas, India/Bihar, Malawi and Zambia).
The observation of crop types is paramount for a myriad of agricultural and environmental remote sensing applications, including land use mapping, crop growth monitoring, and yield forecasting. Accurate georeferenced and quality-controlled crop type data is integral to the calibration of machine learning algorithms. However, publicly available field data remains scarce, particularly within the dynamic smallholder farming systems of sub-Saharan Africa. To address this gap, the EthCT2020 dataset was established for the 2020/21 cropping season in Ethiopia. This dataset comprises 2,793 harmonized and quality-controlled in-situ samples of annual crops, representing seven crop groups and 22 classes across the diverse agricultural landscape of Ethiopia, with a specific emphasis on wheat-based farming systems. The data collection process involved a nationwide campaign implemented through a stratified sampling approach grounded in the wheat crop calendar, resulting in the acquisition of 1,263 samples from this campaign (GDCC; Source 1). Additionally, the dataset was enhanced by incorporating 1,530 wheat samples sourced from specialized databases dedicated to wheat disease surveillance, specifically the Wheat Rust Toolbox (WRTB; Source 2), which contributed 734 samples, and from an internal farm household survey database (FHSD; Source 3), which added 796 samples. The field data were meticulously labeled in accordance with the guidelines established by the Joint Experiment for Crop Assessment and Monitoring (JECAM) and the FAO Indicative Crop Classification for the definition and collection of agricultural land data. Extensive processing of the EthCT2020 dataset was conducted to ensure its quality and accuracy, which included data harmonization and mixed pixel assessment through visual interpretation of satellite imagery, coupled with quality control measures based on Sentinel-2 NDVI homogeneity analysis. The EthCT2020 dataset is distinctive in its diversity and spatial precision, and it represents a valuable resource for further development of innovative remote sensing technologies and predictive models within the Ethiopian agricultural context. Additionally, it serves as a foundational input for climate models and forecasting systems for crop diseases and pests.
At present nearly half of the world’s population is under some form of government restriction to curb the spread of COVID-19, an extremely contagious disease. In Bangladesh, in the wake of five deaths and 48 infections from COVID-19, between March 24 and May 30, 2020 the government imposed a nationwide lockdown. While this lockdown restricted the spread of COVID-19, in the absence of effective support, it can generate severe food and nutrition insecurity for daily wage-based workers. Of the 61 million employed labor force in Bangladesh, nearly 35% of them are paid on a daily basis. This study examines the food security and welfare impacts of the COVID-19 induced lockdown on daily wage workers both in the farm and nonfarm sectors in Bangladesh. Using information from more than 50,000 respondents complied from 2016-17 Household Income and Expenditure Survey (HIES) in Bangladesh, this study estimates daily wage rates as Bangladesh Taka (BDT) 272.2 in the farm sector and BDT 361.5 in the nonfarm sector. Using the estimated daily wages earnings, this study estimates that a one-day complete lockdown generates a US$64.2 million equivalent economic loss only considering the wage loss of the daily wage workers. After estimating the daily per capita food expenditure separately for farm and nonfarm households, this study estimates a minimum compensation package for the daily wage-based farm and nonfarm households around US $ 1 per day per household to ensure minimum food security for the daily wage-based worker households.
Agriculture in eastern Africa is predominantly rainfed and maize is a major food crop, primarily produced for home consumption and the market by small-scale family farms. The study characterized farm households in the drought prone maize growing areas of eastern Africa synthesizing data from parallel household surveys in Ethiopia, Kenya, Uganda and Tanzania. The study provides a comparative analysis of the farm households’ assets, livelihood strategies and crop management practices, with an emphasis on maize and maize seed. This illustrates how farmers in a similar agro-ecological environment but with different socio-economic and institutional settings have variously adapted to living with drought and how the inherent weather risk co-determines the livelihood portfolio, agricultural intensification incentives and system development pathways. The study thereby illustrates the challenges for agricultural intensification in such drought prone environments and the scope for drought tolerant maize varieties and explores the research and development implications.
