Remote Sensing for Sugarcane Crop Yield Estimation in Eswatini: Case of Lower Usuthu Smallholder Irrigation Project Sugarcane Farms
Early estimation of sugarcane crop yield is a key requirement for maximising profits in sugarcane production because most operations are based on these estimations. Such operations include planning for the season management of labour, transportation, storage and marketing. This study explored the potential for the use of NDVI to estimate sugarcane crop yield in LUSIP sugarcane farms at Siphofaneni, Eswatini, using Landsat 8 OLI satellite imagery for the years 2013 – 2016. Computed NDVI values were correlated with the respective sugarcane crop yield data sourced from Eswatini Water and Agricultural Development Enterprise (ESWADE), to establish the relationship between them, using regression analysis. The study concluded that the relationship between NDVI and sugarcane crop yield for the whole of LUSIP project at Siphofaneni was strongest for the month of August when considered at overall farms level (R2 = 0.973), rather than at individual farmer company scale (R2 = 0.134). Furthermore, it concluded that Landsat imagery was appropriate for sugarcane crop yield estimation in the country, especially in the month of August, even though at large scale than over small, individual project areas.
Crop Yield Estimation, Landsat, NDVI, Remote Sensing, Sugarcane
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