Wheat Yield Forecast Using Remote Sensing and Gis in East Arsi Zone, Ethiopia

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Addis Ababa Universty


Ethiopia’s agriculture is involving substantial variations in crops grown across in different regions and agro ecological zones. The core crop season is the long rainy season, with harvests between Novembers to February. Five major cereals (teff, wheat, maize, sorghum and barley) are the major agricultural crops of the country, accounting for about three-quarters of total area cultivated and 29% of agricultural GDP in 2005/06 (i.e. 14% of the country total GDP). This thesis reports the development of an operational agrometeorological yield model for wheat crop derived from time series data of SPOTVEGETATION, actual and potential evapotranspiration, rainfall estimate and satellite data for the years 2004−2013. Official grain yield data maintained by the Central statistical Agency of Ethiopia was used to validate the strength of the indices in explaining the yield. Crop masking at crop land area was applied and refined by using agroecological zones suitable for the crop of interest. Correlation analyses were used to determine associations among crop yield, spectral indices and agrometeorological variables for wheat crop of the long rainy season (Meher). Indices with high correlation with wheat yield were identified. Average Normalized Difference Vegetation Index (NDVIa) and rainfall have high correlation of wheat yield with 96% and 89%, respectively. That means there variables are positively strong related with wheat yield. Multicollinearity was assessed among the independent variables. Many studies reported that linear regression modeling is the most suitable method to produce yield predictions by using remote sensing derived indicators together with bioclimatic information. Accordingly, NDVIa was used to the regression models with P- value of less than 0.0069 at 95% confidence level were derived. Very encouraging results were obtained by the model (r² =0.93, RMSE= 0.99 quintal /ha and 16.01% CV). The result of ANOVA table shows that there is a linear relationship between the dependent and independent variables. Policy makers need accurate and timely information on crop production and for that matter multiple linear regression model and remote sensing method are appropriate approach for crop yield forecasting. This study has revealed that crop yield forecasting is possible using remote sensing and GIS, and this method can be used as a modern tool for similar analysis. For future studies, more researches can be done using longer period of time serious data to enhance the model results. Keywords: NDVI, Remote sensing, RFE 2.0, SPOT VEGETATION, Wheat yield



NDVI, Remote sensing, RFE 2.0, SPOT VEGETATION, Wheat yield