Information and services delivered through mobile phones, 'm-services', have transformative potential to provide rural African farmers with important agro-meteorological information. However, a greater understanding is needed regarding the types of m-services available to farmers, how farmers access that information, and possible factors affecting m-service use. With a study of smallholder farming communities in central Kenya, we examine factors affecting the likelihood of Kenyan farmers' adoption of m-services specifically related to agriculture and livestock information, buying and selling products, and alerts about agricultural or livestock activities. According to a survey of 577 farming households, 98% of respondents own a mobile phone. Approximately 25% use it to access information about agriculture and livestock, 23% access information about buying and selling products, and 18% receive alerts. Personal smartphone ownership increases the likelihood of m-services use (p < .001), as does membership in farmer organizations (p < .001). We find that age and income are not significantly related to m-service use, and we discuss this result in terms of intersections between smartphone ownership, gender, and education. Consistent with prior qualitative research, our quantitative results further support the need for m-services providers to design for basic or feature phone users for improved dissemination of agro-meteorological information. ARTICLE HISTORY

No abstract

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the “Crisis” phase of AFI—requiring emergency food assistance—at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards.

No abstract

No abstract

We examine relationships between the start of rainy season (SOS) and sub-national grain (white maize) market price movements in five African countries. Our work is motivated by three factors: (a) some regions are seeing increasing volatility SOS timing; (b) SOS represents the first observable occurrence in the agricultural season and starts a chain reaction of decisions that influence planting, labor allocation, and harvest—all of which can have direct impacts on local food prices and availability; and (c) pre- and post-harvest price movements provide key insights into supply-and-demand issues related to food insecurity. We start by exploring a number of different SOS definitions using varying reference periods to define whether an SOS is ‘on-time’ or ‘late’. We then compare how those different definitions perform in seasonal price forecasting models. Specifically, we examine if SOS indicators can predict price means over 6 and 9 month periods, or roughly the length of time from planting to market. We use different reference periods for defining ‘early’ versus ‘late’ seasonal starts based on the previous year’s start date, or median start dates over the past 3, 5, and 10 year periods. We then compare the out-of-sample forecast performance of univariate time-series models (autoregressive integrated moving average (ARIMA)) with time-series (ARIMAX) models that include various SOS definitions as exogenous predictors. We find that using some form of SOS indicator (either an SOS anomaly or 1st month’s rainfall anomaly) leads to increased predictive power when examining prices over a 6 months window. However, the results vary considerably by country. We find the strongest performance of SOS indicators in central Ethiopia, southern Kenya, and southern Somalia. We find less evidence in support of the use of SOS indicators for price forecasting in Malawi and Mozambique.

In semiarid and arid regions of sub-Saharan Africa rising temperatures and shifting rainfall patterns are projected to negatively impact agricultural output (Downing et al., 1997;Branca et al., 2011;Müller et al., 2011;Slingo et al., 2005). Changes in rainfall associated with climate variability directly impact crop growth as storms are projected to become more intense with longer periods between rainfall events (Donat et al., 2016;Harrison et al., 2019;Meehl et al., 2007). The stochastic nature of rainfall during the growing season leaves crops susceptible to water stress during critical stages of development and can lead to crop failure (Sah et al., 2020;Salgado-Aguilar et al., 2020). Small rainfed farms cultivated by single families on plots less than 5 ha represent the most prevalent form of agriculture in sub-Saharan Africa and are particularly vulnerable to climate variability and change (Samberg et al., 2016). Because of their dependence on rainfed agriculture (Dinar et al., 2008), smallholder livelihoods are susceptible to climate shocks that affect food prices (Ray et al., 2012), variability in production and supply (Lobell et al., 2011;Slingo et al., 2005), and farmer incomes (Reidsma et al., 2010).Farmers make a variety of choices before and during the growing season that impact their agricultural production and thus food security and livelihood. Cultivar choice is one of the most critical choices a smallholder makes (Kalanda-Joshua et al., 2011). Because of the uncertainty associated with climate variability, farmer decision-making is becoming increasingly complex and uncertain at the expense of input use efficiency and profitability (Hansen et al., 2011;Guido et al., 2020;Waldman et al., 2019). Management options that were optimal for past or average climatic conditions may no longer be suited for increasingly common growing season weather. Additionally, traditional crop varieties may no longer be best suited for a farmer's environment, which has led to the development of hybrid and fast growing varieties (Smale & Jayne, 2010). Given ongoing changes in rainfall patterns, farmers need to select cultivars well suited for their local context that can lead to the greatest payoff in terms of yield while also minimizing the risk of crop failure. To date,

No abstract