Spatial Comparison and Quality Check of Farmer-recorded Daily Rainfall Data; A Case of Nyakach and Soyain-sigowett, Ken

Abstract

Abstract: Small scale farming is currently still heavily dependent on rainfall in developing nations. With the challenge of climate change, many innovations are proposed to help the farmers mitigate and adapt. The use of historical data provides a starting point in development of decision support tools for them. However, most climate data are not local, but far from the farmers. Thus, the challenge of representability of the data is questioned. In order to use the decision support tools with farmers at Nyakach and Soin-Sigowett, Kenya, historical data was used from a synoptic station 20 km away. The locals felt it was not representative enough, hence the need to look for more local data. In 2014, a CCAFS project empowered 100 farmers from the region with low cost rain gauges to collect and record their own data for use in decision support tools. In this paper, we look at the quality of the data comparing it to the KMS data. Line graphs were used to compare the total seasonal rain for more than 30 years with the farmers perception. In addition, pairwise t-tests have been used to compare difference in farmers recorded rain to the value at the synoptic station. Data from volunteer stations have also been used to confirm the validity of the spatial difference in the data. The results showed that quality of the farmers data is adequate for use. Further, data from farmers deviated from the main synoptic station half of the time. The results clearly show that there is need to allow locals collect their own data to help capture the spatial differences in climate. The farmers recorded data was good quality hence can be used in decision support tools to help them adapt to possible climate change