In agroclimatology, the rainy season onset and cessation dates are often defined from a combination of several empirical rainfall thresholds. For example, the onset may be the first wet day of N consecutive days receiving at least P millimeters without a dry spell lasting n days and receiving less than p millimeters in the following C days. These thresholds are parameterized empirically in order to fit the requirements of a given crop and to account for local-scale climatic conditions. Such local-scale agroclimatic definition is rigid because each threshold may not be necessarily transposable to other crops and other climate environments. A new approach is developed to define onset/cessation dates and monitor their interannual variability at the regional scale. This new approach is less sensitive to parameterization and local-scale contingencies but still has some significance at the local scale. The approach considers multiple combinations of rainfall thresholds in a principal component analysis so that a robust signal across space and parameters is extracted. The regionalscale onset/cessation date is unequally influenced by input rainfall parameters used for the definition of the local rainy season onset. It appears that P is a crucial parameter to define onset, C plays a significant role at most stations, and N seems to be of marginal influence.

ABSTRACT. In studying indigenous climate knowledge, two approaches can be envisioned. In the first, traditional knowledge is a cultural built-in object; conceived as a whole, its relevance can be assessed by referring to other cultural, economic, or technical components at work within an indigenous society. In the second, the accuracy of indigenous climate knowledge is assessed with western science knowledge used as an external reference. However, assessing the accuracy of indigenous climate knowledge remains a largely untapped area. We aim to show how accurate the culturally built indigenous climate knowledge of extreme climatic events is, and how amenable it is to fuzzy logic. A retrospective survey was carried out individually and randomly among 195 Eastern African farmers on climatic reasons for loss of on-farm crop diversity from 1961 to 2006. More than 3000 crop loss events were recorded, and reasons given by farmers were mainly related to droughts or heavy rainfall. Chisquare statistics computed by Monte Carlo simulations based on 999 replicates clearly rejected independence between indigenous knowledge of drought and heavy rainfall that occurred in the past and rainfall records. The fuzzy logic nature of indigenous climatic knowledge appears in the clear association of drought or heavy rainfall events, as perceived by farmers, with corresponding extreme rainfall values, contrasting with a fuzzy picture in the intermediate climatic situations. We discuss how the cultural built-in knowledge helps farmers in perceiving and remembering past climate variations, considering the specificity of the contexts where extreme climatic events were experienced. The integration of indigenous and scientific climate knowledge could allow development of drought monitoring that considers both climatic and contextual data.

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