Roger Stern

SUPPORTING AGRICULTURAL INNOVATION IN UGANDA TO RESPOND TO CLIMATE RISK: LINKING CLIMATE CHANGE AND VARIABILITY WITH FARMER PERCEPTIONS

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.

Analysing Daily Rainfall Measurements to Give Agronomically Useful Results. I. Direct Methods

Simple methods are described for the analysis of daily rainfall measurements. The distinctive feature is that each year provides one number for any event or characteristic of interest. The resulting observations are then analysed, assuming that they are a simple random sample from a single distribution. An estimate of the probability of an event can be made directly from its relative frequency of occurrence, or alternatively a distribution (such as the normal) can be fitted. The methods are applied to agronomic questions on dry spells, the start, end and length of the growing season, and the distribution of amounts of rainfall through the year. Examples are given from Nigeria and India.

Designing experiments for intercropping research.

The efficiency of many intercropping research programmes could be improved if research workers made fuller use of modern statistical knowledge about experimental design. Important statistical considerations for experimental design and plot sampling are reviewed and their relevance to intercropping research assessed. The general philosophy of factorial structure and blocking, and specific ideas on the use of systematic designs and monocrop plots, are discussed.

ASSESSING CLIMATE RISK AND CLIMATE CHANGE USING RAINFALL DATA – A CASE STUDY FROM ZAMBIA

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 Nino, La Nina and Ordinary years on rainfall distribution patterns.

ASSESSING AND ADDRESSING CLIMATE-INDUCED RISK IN SUB-SAHARAN RAINFED AGRICULTURE: LESSONS LEARNED

SUMMARY 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. 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. 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. 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. 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. 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.

Statistics and Agrometeorology: Introductory Issues and Cases

This chapter is problem-based, and hence starts with examples of problems. We then look at the climatic data and the statistical methods that are needed to solve the problems.