ICRISAT Archival Report 2006 - The seedlings of success in the ...

Milestones contributing to Output 9A in 2006
9A.1 Tools for assessing and managing climate variability, Eastern and Southern Africa
KPC Rao
In general, agricultural research treats climate as static by targeting the recommendations to the mean annual
rainfall. Though mean annual or seasonal rainfall is a valuable statistic in planning various operations, it does not
take into account the dynamic nature of climate. Several researchers have suggested that the more appropriate index
for agricultural purposes is the “probability of occurrence” of a given amount of rainfall. The probability
information can be derived easily from an analysis of the frequency distribution of annual rainfall but requires longterm
observed data. The analysis carried out using historical climate data for 20 locations in Machakos and Makueni
districts of Kenya has suggested that minimum of 25 or more years data is required to capture the long-term trends
in rainfall fairly well (Figure 9A.4).
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100
Probability (%)
80
60
40
10 years
15 years
20 years
25 years
30 years
35 years
40 years
45 yeasr
20
0
0 100 200 300 400 500 600 700 800 900
Rainfall (mm)
Figure 9A.4: Relationship between probability distribution of annual rainfall and number of years of observed data
Most trials involving development and testing of agricultural technologies are normally conducted over 3 or 5
seasons. In case of rainfed farming the same is not sufficient to capture the effect of high variability associated with
the amount and distribution of rainfall and the results can under or over estimate the treatment effects depending on
the seasonal conditions that prevailed during the test period. Analysis of the data from a trial conducted at Katumani
over 20 crop seasons showed considerable variation in runoff and maize yield. Under such conditions three and five
year averages, could lead to relatively large errors in the assessments. For example three season average runoff can
be estimated either as 42 mm or 159 mm depending on the period during which the trial was conducted (Table
9A.2). Crop simulation models provide an opportunity to better characterize the effects of variable climate if longterm
climatic data is available.
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