A Predictive Model Of Climate Sensors Effectiveness On Sustainability Of Subsistence Agriculture: The Case Of Laikipia County

Abstract

In contrast to many areas of the globe where farmer posses adequate physical, economic and social resources to adapt to and moderate effects of climate variation and climate change, subsistence agriculture in the arid and semi-arid lands (ASALs) of Kenya are particularly affected in an unfavorable manner by the effects of climate change. This is more so because of the increasing dependency of a good number of the population on rain fed agriculture as a source of livelihood and economic income. An effective adaption mechanism to climate change for sustainability of subsistence agriculture in these areas using communication technologies is therefore highly important for food security and protection of livelihoods within the rural areas. The main aim of this study was to model and predict the effectiveness of climate sensors on the sustainability of subsistence agriculture in Laikipia County, one of the ASALs in Kenya. The study hypothesized that the current community based strategies applied by the local farmers are relevant and important to the present-day quest for climate change adaptation strategies, and that feedback from the stakeholders can generate insight used to generate an improved predictive model to further enhance this adaptation. The study therefore conducted a survey study of rural stakeholders in Laikipia farmlands and assessed the output through descriptive measures. Further, a logistic regression model of variables constructed from the survey study was used to predict the effectiveness of data communication technologies such as climate sensors that are currently employed on the sustainability of subsistence agriculture in these rural areas, using variables such as geographic extent, temporal scope, precision level, frequency of usage, and cost of acquisition. The model was be tested through standard measures of goodness of fit such as Chi-square and adjusted goodness-of-fit index. It is expected that results of this study will be useful in policy formulations regarding adaptation mechanisms to climate change for sustainability of rural-based subsistence agriculture.