Christine Lamanna

Information on the effects of changing agricultural management on crop and livestock performance is critical for developing evidence-based policies, investments, and programs. Evidence for Resilient Agriculture (ERA) v1.0.1 presents a dataset that harmonizes and aggregates 112,859 observations from 2,011 agricultural studies taken place in Africa between 1934 and 2018. The dataset includes information on the effect of 364 combinations of management practices and technologies on 87 environmental, social, and economic indicators of outcomes. Observations are geolocated and temporally tagged and thus can be linked to other datasets such as historical weather, soil properties, and road networks. ERA offers a new resource for understanding the impacts of changing farming practices under diverse environmental contexts, providing data to support strategic interventions aimed to enhance productivity, resilience, and sustainability of African agriculture.

Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce thetraitstrapR package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow,trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Agrivoltaics address energy, food, and water security concomitantly, yet little research has addressed the opportunities and barriers for this technology in East Africa, a region facing substantial challenges across the water-energy-food nexus. Here, we identify possible use cases for the technology in five East African nations and determine the factors influencing delivery of potential benefits, before assessing the spatial suitability of the technology for two of the emergent use cases: agribusiness and marginal farming. Our findings show there is a moderate to high suitability for AV across both use cases, with a range of biophysical and social factors driving successful outputs; land tenure and solar radiation were the most important for determining suitability. Our analysis indicates where AVs would be best developed to improve access to, and resilience of, energy and food production in East Africa, and our methods could be applied to other locations to support agrivoltaics development internationally.

Both global poverty and hunger have increased in recent years, endangering progress towards accomplishing Sustainable Development Goals (SDGs) 1 and 2. The regression has been most pronounced in Sub-Saharan Africa (SSA). Meeting the SDG targets requires achieving resilient farm productivity. Although many farm management technologies exist to improve yields, farmers in SSA largely have not adopted these approaches. A long-standing literature about technology adoption identifies multiple hypotheses as to why farmers may or may not adopt new agricultural technologies, culminating in numerous micro-econometric studies. We analyse a metadata set capturing the findings of 164 published studies specifically focusing on SSA and show that 20 out of 38, or 53%, of the determinants commonly believed to influence technology adoption lack empirical support. Eighteen determinants—primarily related to information access, wealth, group membership and social capital, and land tenure—consistently influence adoption across studies. Wealth remains a significant determinant of fertilizer adoption, despite long-running subsidies in most countries, although it is decoupled from the adoption of improved seeds and alternative crop and nutrient management technologies. We highlight the foundational determinants of adoption and offer guidance to design effective interventions that can decrease poverty and hunger towards 2030.

Despite progress in fighting undernutrition, Africa has the highest rates of undernutrition globally, exacerbated by drought and conflict. Mobile phones are emerging as a tool for rapid, cost effective data collection at scale in Africa, as mobile phone subscriptions and phone ownership increase at the highest rates globally. To assess the feasibility and biases of collecting nutrition data via computer assisted telephone interviews (CATI) to mobile phones, we measured Minimum Dietary Diversity for Women (MDD-W) and Minimum Acceptable Diet for Infants and Young Children (MAD) using a one-week test-retest study on 1,821 households in Kenya. Accuracy and bias were assessed by comparing individual scores and population prevalence of undernutrition collected via CATI with data collected via traditional face-to-face (F2F) surveys. We were able to reach 75% (n = 1366) of study participants via CATI. Women’s reported nutrition scores did not change with mode for MDD-W, but children’s nutrition scores were significantly higher when measured via CATI for both the dietary diversity (mean increase of 0.45 food groups, 95% confidence interval 0.34–0.56) and meal frequency (mean increase of 0.75 meals per day, 95% confidence interval 0.53–0.96) components of MAD. This resulted in a 17% higher inferred prevalence of adequate diets for infants and young children via CATI. Women without mobile-phone access were younger and had fewer assets than women with access, but only marginally lower dietary diversity, resulting in a small non-coverage bias of 1–7% due to exclusion of participants without mobile phones. Thus, collecting nutrition data from rural women in Africa with mobile phones may result in 0% (no change) to as much as 25% higher nutrition estimates than collecting that information in face-to-face interviews.