Rainfed agriculture in sub-Saharan Africa (SSA) is the mainstay of the continent's food and feed production. Nearly 90% of staple food and feed production comes from, and will continue to come from, rainfed agriculture (Rosegrant et al., 2002). In spite of this, investment in this vital production system, and hence its productivity, has stagnated. There are many complex and interrelated issues that contribute to this state of affairs. The outcomes of lack of investment and low production of rainfed agriculture reinforce each other leading to poverty traps and increased vulnerability of livelihoods to climatic and other shocks (World Bank, 2000). This has become well recognized and an emerging political will, both within and outside SSA, to support increased investment in rainfed agriculture appears to be gaining momentum (Sanchez et al, 2009).

Global food security is under threat by climate change, and the impacts fall disproportionately on resource-poor small producers. With the goal of making agricultural and food systems more climate-resilient, this paper presents an adaptation and mitigation framework. A road map for further agricultural research is proposed, based on the CGIAR Research Program on Climate Change, Agriculture and Food Security. We propose a holistic, integrated approach that takes into account trade-offs and feedbacks between interventions. We divide the agenda into four research areas, three tackling risk management, accelerated adaptation and emissions mitigation, and the fourth facilitating adoption of research outputs. After reviewing specific technical, agronomic and policy options for reducing climate change vulnerability, we acknowledge that science and good-faith recommendations do not necessarily translate into effective and timely actions. We therefore outline impediments to behavioural change and propose that future research overcomes these obstacles by linking the right institutions, instruments and scientific outputs. Food security research must go beyond its focus on production to also examine food access and utilization issues. Finally, we conclude that urgent action is needed despite the uncertainties, trade-offs and challenges.

We review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

Rainfall variability, both within and between seasons, is reflected in highly variable crop growth and yields in rainfed agriculture in sub-Saharan Africa and results in varying degrees of weather-induced risk associated with a wide range of crop, soil and water management innovations. In addition there is both growing evidence and concern that changes in rainfall patterns associated with global warming may substantively affect the nature of such risk. Eighty-nine years of daily rainfall data from a site in southern Zambia are analysed. The analyses illustrate approaches to assessing the extent of possible trends in rainfall patterns and the calculation of weather-induced risk associated with the inter- and intra-seasonal variability of the rainfall amounts. Trend analyses use monthly rainfall totals and the number of rain days in each month. No simple trends were found. The daily data were then processed to examine important rain dependent aspects of crop production such as the date of the start of the rains and the risk of a long dry spell, both following planting and around flowering. The same approach is used to assess the risk of examples of crop disease in instances when a ‘weather trigger’ for the disease can be specified. A crop water satisfaction index is also used to compare risks from choices of crops with different maturity lengths and cropping strategies. Finally a different approach to the calculations of these risks fits a Markov chain model to the occurrence of rain, with results then derived from this model. The analyses shows the relevance of this latter approach when relatively short daily rainfall records are available and is illustrated through a comparison of the effects of El Niño, La Niña and Ordinary years on rainfall distribution patterns.

This study examines farmers’ perceptions of short- and long-term variability in climate, their ability to discern trends in climate and how the perceived trends converge with actual weather observations in five districts of Eastern Province in Kenya where the climate is semi-arid with high intra- and inter-annual variability in rainfall. Field surveys to elicit farmers’ perceptions about climate variability and change were conducted in Machakos, Makueni, Kitui, Mwingi and Mutomo districts. Long-term rainfall records from five meteorological stations within a 10 km radius from the survey locations were obtained from the Kenya Meteorological Department and were analysed to compare with farmers’ observations. Farmers’ responses indicate that they are well aware of the general climate in their location, its variability, the probabilistic nature of the variability and the impacts of this variability on crop production. However, their ability to synthesize the knowledge they have gained from their observations and discern long-term trends in the probabilistic distribution of seasonal conditions is more subjective, mainly due to the compounding interactions between climate and other factors such as soil fertility, soil water and land use change that determine the climate's overall influence on crop productivity. There is a general tendency among the farmers to give greater weight to negative impacts leading to higher risk perception. In relation to long-term changes in the climate, farmer observations in our study that rainfall patterns are changing corroborated well with reported perceptions from other places across the African continent but were not supported by the observed trends in rainfall data from the five study locations. The main implication of our findings is the need to be aware of and account for the risk during the development and promotion of technologies involving significant investments by smallholder farmers and exercise caution in interpreting farmers’ perceptions about long-term climate variability and change.

This paper investigates farmers’ perceptions of climate change and variability in southwest Uganda and compares them with daily rainfall and temperature measurements from the 1960s to the present, including trends in daily rainfall and temperature, seasonality, changing probability of risk and intensity of rainfall events. Statistical analyses and modelling of rainfall and temperature were performed and contrasted with qualitative data collected through a semi-structured questionnaire. The fieldwork showed that farmers perceived regional climate to have changed in the past 20 years. In particular, farmers felt that temperature had increased and seasonality and variability had changed, with the first rainy season between March and May becoming more variable. Farmers reported detailed accounts of climate characteristics during specific years, with recent droughts in the late 1990s and late 2000s confirming local perceptions that there has been a shift in climate towards more variable conditions that are less favourable to production. There is a clear signal that temperature has been increasing in the climate data and, to a lesser extent, evidence that the reliability of rains in the first season has decreased slightly. However, rainfall measurements do not show a downward trend in rainfall amount, a significant shift in the intensity of rainfall events or in the start and end of the rainy seasons. We explore why there are some differences between farmers’ perceptions and the climate data due to different associations of risk between ideal rainfall by farmers, including the amount and distribution needed for production, meteorological definitions of normal rainfall or the long-term statistical mean and its variation, and the impact of higher temperatures. The paper reflects on the methodological approach and considers the implications for communicating information about risk to users in order to support agricultural innovation.

In sub-Saharan Africa (SSA), rainfed agriculture is the dominant source of food production. Over the past 50 years much agronomic crop research has been undertaken, and the results of such work are used in formulating recommendations for farmers. However, since rainfall is highly variable across seasons the outcomes of such research will depend upon the rainfall characteristics of the seasons during which the work was undertaken. A major constraint that is faced by such research is the length of time for which studies could be continued, typically ranging between three and five years. This begs the question as to what extent the research was able to ‘sample’ the natural longer-term season-to-season rainfall variability. Without knowledge of the full implications of weather variability on the performance of innovations being recommended, farmers cannot be properly advised about the possible weather-induced risks that they may face over time. To overcome this constraint, crop growth simulation models such as the Agricultural Production Systems Simulator (APSIM) can be used as an integral part of field-based agronomic studies. When driven by long-term daily weather data (30+ years), such models can provide weather-induced risk estimates for a wide range of crop, soil and water management innovations for the major rainfed crops of SSA. Where access to long-term weather data is not possible, weather generators such as MarkSim can be used. This study demonstrates the value of such tools in climate risk analyses and assesses the value of the outputs in the context of a high potential maize production area in Kenya. MarkSim generated weather data is shown to provide a satisfactory approximation of recorded weather data at hand, and the output of 50 years of APSIM simulations demonstrate maize yield responses to plant population, weed control and nitrogen (N) fertilizer use that correspond well with results reported in the literature. Weather-induced risk is shown to have important effects on the rates of return ($ per $ invested) to N-fertilizer use which, across seasons and rates of N-application, ranged from 1.1 to 6.2. Similarly, rates of return to weed control and to planting at contrasting populations were also affected by seasonal variations in weather, but were always so high as to not constitute a risk for small-scale farmers. An analysis investigating the relative importance of temperature, radiation and water availability in contributing to weather-induced risk at different maize growth stages corresponded well with crop physiological studies reported in the literature.

Climate change and land use change are two forces influencing the hydrology of watersheds and their ability to provide ecosystem services, such as clean and well-regulated streamflow and control of soil erosion and sediment yield. The Soil Water Assessment Tool, SWAT, a distributed, watershed-scale hydrological model was used with 18 scenarios of rainfall, temperature and infiltration capacity of land surface to investigate the spatial distribution of watershed services over the 3587 km2 Nyando basin in Western Kenya and how it is affected by these two forces. The total annual water yield varied over the 50 sub-basins from 35 to 600 mm while the annual sediment yield ranged from 0 to 104 tons ha−1. Temperature change had a relatively minor effect on streamflow and sediment yield compared to change in rainfall and land surface condition. Improvements in land surface condition that result in higher infiltration are an effective adaptation strategy to moderate the effects of climate change on supply of watershed services. Spatial heterogeneity in response to climate and land use change is large, and hence it is necessary to understand it if interventions to modify hydrology or adapt to climate change are to be effective.

This paper seeks to establish the concept that the analysis of high temporal resolution meteorological data adds value to the investigation of the effect of climatic variability on the prevalence and severity of agricultural pests and diseases. Specifically we attempt to improve disease potential maps of root rots in common beans, based on a combination of inherent susceptibility and the risk of exposure to critical weather events. We achieve this using simulated datasets of daily rainfall to assess the probability of heavy rainfall events at particular times during the cropping season. We then validate these simulated events with observations from meteorological stations in East Africa. We also assess the utility of remotely sensed daily rainfall estimates in near real time for the purposes of updating the risks of these events over large areas and for providing warnings of potential disease outbreaks. We find that simulated rainfall data provide the means to assess risk over large areas, but there are too few datasets of observed rainfall to definitively validate the probabilities of heavy rainfall events generated using rainfall simulations such as those generated by MarkSim. We also find that selected satellite rainfall estimates are unable to predict observed rainfall events with any power, but data from a sufficiently dense network of rain gauges are not available in the region. Despite these problems we show that remotely sensed rainfall estimates may provide a more realistic assessment of rainfall over large areas where rainfall observations are not available, and alternative satellite estimates should be explored.

This paper makes the case for innovative risk management approaches in pastoral settings, which may include adjustments to the traditionally practiced approaches that have become progressively less effective. We use recent data from studies in Kenya and southern Ethiopia to confirm that traditional pastoral risk management approaches are increasingly futile against increasing external pressures, seasonal rainfall variability and future climate change. Some pioneering approaches and ideas, with potentially wider application to African pastoral settings, appear to offer greater hope; these include pilot studies designed to demonstrate the efficacy of index-based risk transfer products in pastoral systems, improvements in the management of food insecurity response for pastoralists and the recasting of development interventions as risk management. The International Livestock Research Institute, in collaboration with a wide range of partners, is currently testing these ideas.

A defining characteristic of many rainfed tropical agricultural systems is their vulnerability to weather variability. There is now increased attention paid to climate-agriculture links as the world is focused on climate change. This has shown the need for increased understanding of current and future climate and the links to agricultural investment decisions, particularly farmers’ decisions, and that integrated strategies for coping with climate change need to start with managing current climate risk. Research, largely from an Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA) project to demonstrate the value of such increased understanding, is presented in this issue of the journal. Key lessons from this research are as follows:

1. 1. Statistical methods of analysis of historical climate data that are relevant to agriculture need not be complex. The most critical point is to describe the climate in terms of events of direct relevance to farming (such as the date of the start of a rainy season) rather than simple standard measures (such as annual total rainfall).

2. 2. Analysis requires access to relevant data, tools and expertise. Daily climate data, both current and historical, are primarily the responsibility of national meteorological services (NMS). Accessing such data, particularly daily data, is not always easy. Including staff from the NMS as research partners, not just data providers, can reduce this problem.

3. 3. Farmers’ perceptions of climate variation, risk and change are complex. They are keenly aware of variability, but there is evidence that they over-estimate risks of negative impacts and thereby fail to make use of good conditions when they occur. There is also evidence that multiple causes of changes are confounded, so farmers who observe decreasing crop production may not be distinguishing between rainfall change and declining soil fertility or other conditions. Hence any project working with farmers’ coping and adaptation to climate must also have access to analyses of observed climate data from nearby recording stations.

4. 4. Mechanisms for reducing and coping with risks are exemplified in pastoral systems that exist in the most variable environments. New approaches to risk transfer, such as index-based insurance, show potential for positive impact.

5. 5. Skilful seasonal forecasts, which give a better indication of the coming season than a simple average, would help farmers take decisions for the coming cropping season. Increasing meteorological knowledge shows that such forecasting is possible for parts of Africa. There are institutional barriers to farmers accessing and using the forecast information. Furthermore, the skill of the forecasts is currently limited so that there are maybe still only a few rational choices for a farmer to make on the basis of a forecast.

With the justified current interest in climate and agriculture, all stakeholders including researchers, data providers, policy developers and extension workers will need to work together to ensure that interventions are based on a correct interpretation of a valid analysis of relevant data.