Spectro Agrometereological Maize Yield Forecast Model Using Remote Sensing and Gis in South Tigray Zone, Ethiopia

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Addis Ababa University


For a country like Ethiopia whose economy is strongly dependent on rainfed agriculture; reliable, accurate and timely information on types of crops grown, their acreage, crop growth and Yield forecast are vital components for planning efficient management of resources. Remote-sensing data acquired by satellite have a wide scope for agricultural applications owing to their synoptic and repetitive coverage. This study reports the development of an operational spectro-agrometeorological yield model for maize crop derived from time series data of SPOTVEGETATION, actual and potential evapotranspiration, rainfall estimate satellite data for the years 2003-2012 which were utilized as input data for the indices while official grain yield data produced by the Central statistical Agency of Ethiopia was used to validate the strength of indices in explaining yield (quintal per hectare). One obstacle to successful modeling and prediction of crop yields using remotely sensed imagery is the identification of image masks. This process allows to consider only information pertaining to the crop of interest. Therefore crop masking at crop land area was applied and further refined by using agro ecological zone suitable for crop of interest(maize).Correlation analyses were used to determine associations between crop yield, spectral indices and agrometeorological variables for the maize crop of the longest rainy season (Meher). Indices with high correlation with maize yield were identified and were ready for further analysis, accordingly rainfall and average Normalized Difference Vegetation Index (NDVIa) have high correlation with yield (85% and 80% respectively). Many studies reported that linear regression modeling is the most common method to produce yield predictions by using remote sensing derived indicators together with bio climatic information. Statistical multiple linear regression model has been developed using variables which have high correlation with yield. Accordingly, NDVIa and rainfall were bring to the regression and lastly a regression model with P- value of less than 0.05 at 95 % confidence level were developed. The developed spectro-agrometerological yield model was validated by comparing the predicted Zone level yields (quintal per hectare) with those estimated by CSA(quintal per hectare). Very encouraging results were obtained by the model (r2 0.88 , RMSE 1.4 quintal/ ha and 21% CV). From this study we found that crop yield forecasting is possible using remote sensing and GIS in the fragmented agricultural lands of south Tigray. Since the data range we used for analysis was small we recommend application of the model after testing by newly appeared data with a long range of time series data before using for operational purposes.



Remote Sensing, Yield Prediction, Ndvi, Maize Yield