Remote Sensing and GIS Based Spectro-Agrometeorological Maize Yield Forecast Model for South Tigray Zone, Ethiopia

Abiy Wogderes Zinna,Karuturi Venkata Suryabhagavan

doi:10.4236/jgis.2016.82024

Abiy Wogderes Zinna, Karuturi Venkata Suryabhagavan

Open Access

https://doi.org/10.4236/jgis.2016.82024

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Abstract

Remote-sensing data acquired by satellite imageries have a wide scope in agricultural applications owing to their synoptic and repetitive coverage. This study reports the development of an operational spectro-agrometereological yield model for maize crop derived from time series data of SPOT VEGETATION, actual and potential evapotranspiration and rainfall estimate satellite data for the years 2003-2012. Indices of these input data were utilized to validate their strength in explaining grain yield recorded by the Central Statistical Agency through correlation analyses. Crop masking at crop land area was applied and refined using agro-ecological zones suitable for maize. Rainfall estimates and average Normalized Difference Vegetation Index were found highly correlated to maize yield with the former accounting for 85% variation and the latter 80%, respectively. The developed spectro-agrometeorological yield model was successfully validated against the predicted Zone level yields estimated by Central Statistical Agency (r2 = 0.88, RMSE = 1.405 q·ha-1 and 21% coefficient of variation). Thus, remote sensing and geographical information system based maize yield forecast improved quality and timelines of the data besides distinguishing yield production levels/areas and making intervention very easy for the decision makers thereby proving the clear potential of spectro-agrometeorological factors for maize yield forecasting, particularly for Ethiopia.

Highlights

Agriculture is the backbone of Ethiopian economy providing livelihood to ~84% population besides contributing 45% to the Gross Domestic Product and 86% to export earnings [1]
Correlation between various spectro-agrometeorological parameters, namely, NDVI actual (NDVIa), NDVI cumulative (NDVIc), NDVI crop cycle (NDVIx), REF, water requirement satisfaction index (WRSI), Eta and ETa total were found out using individual correlation/linear regression statistics
Correlation between different NDVI variables and maize yield showed that NDVIa was significantly correlated to the yield (r = 0.80, p = 0.02) while NDVIc (r = 0.44, p = 0.28) and NDVIx (r = −0.03, p = 0.94) were not significantly correlated (Figures 3-5)

Summary

Introduction

Agriculture is the backbone of Ethiopian economy providing livelihood to ~84% population besides contributing 45% to the Gross Domestic Product and 86% to export earnings [1]. Reliable, accurate and timely information on various crops raised, their extent, growth and yield forecast form vital components of planning in efficient resources management Such knowledge is all the more important, especially in regions characterized by climatic uncertainties to enable planners and decision makers to assess the quantum of imports required in case of a shortfall or alternatively the volume of exports possible during surplus. The second method followed by the Ethiopian Government involves data collection from stakeholders on predicted crop yield and comparing it with previous year’s yield as recorded by the Central Statistical Agency [4] This data are widely used by decision makers official statistics is highly subjective and dependent on the agenda of stakeholders [3] [5]. It is desirable to develop and adopt scientifically sound and technologically advanced yield prediction techniques to arrive at dependable forecasting systems

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Discussion

Conclusion