Foresee Transition to Agricultural Season by Integrating Indigenous Knowledge, Satellite Imagery, Weather Data and ARIMA Family Models to Enable Good Crop Establishment by Small-Scale Farmers in Swayamani Region, KwaZulu-Natal, South Africa

Abstract

Local farmers in Swayamani region relied heavily on rainfed agriculture and indigenous knowledge to perform cropping. These local farmers contribute towards food security and they are part of organic food producers here in South Africa since most of them relied only on natural resources to grow crops. However, many technological developments are not paying enough attention on them. They are not equipped with irrigation systems and agricultural machineries to help with cropping. Most of them are technological and educationally semi-literate and financially volatile. Because of rapid increase and unpredictability of climate, these local farmers are severely suffering. They are constantly experiencing poor crop yield because of season transition variations caused by climate change. Due to these variations, it becomes difficult for farmers to spot the onset of agricultural season to enable good establishment of crops. A transition to agricultural season can be foreseen by using indigenous knowledge indicators and observing the movement of temperature and vegetation cover. During the onset of the warm agricultural season, healthy vegetation cover increases as the temperature increases and vis-versa during the onset of the cold agricultural season. This is because many plants are vulnerable to cold temperature. In this paper we optimize local farmers' agricultural season transition predictions by integrating the indigenous knowledge with satellite imagery data, climate data and ARIMA family models.