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 (r 2 = 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.
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 [
In Ethiopia, two methods are followed to monitor and forecast crop yields. The first is Crop Yield Monitoring and Forecasting System (CYMFS) run by the Ethiopian National Meteorological Agency (NMA) in conjunction with the European Union’s Joint Research Council (JRC) and the Food and Agricultural Organization (FAO). The system relies on empirical Crop Specific Water Balance (CSWB) model of FAO rather than process based crop simulation model. The model is less likely to capture complex interactions between climate and crop and hence considered as deficient [
Vegetation indices derived from remote sensing are considered as potential tools to improve simulations in real-time. Though remote sensing data alone have been used in different parts of the world to estimate crop yields [
The study area, South Tigray Zone lying at an altitude of 1156 to 3671 m asl is situated between 12˚15'16"N - 13˚38'45"N and 38˚59'33"E - 39˚53'20"E extending to 9432 km2 (
Annual mean rainfall in the area varies from 10 mm in November to 210 mm in August and is characterized by a bimodal pattern represented by short rainy season during March-April and prolonged rainy season from June to September with a peak in August.
The present work was carried out based on satellite imageries, Ethiopian Mapping Agency maps and other collateral data obtained from various organizations of the Federal Government of Ethiopia besides verified ground truths. The data were analyzed and maize yield forecast areas mapped using Remote Sensing (RS) and Geographical Information System (GIS). Data integration and further processing in developing the spectro-agrome- teorological model adopted during the present work are represented in the form of a flow chart (
Different satellite imageries and models, viz., SPOT VEGETATION (SPOT VEG), rainfall estimates (RFE),
water requirement satisfaction index (WRSI) and SPOT 5 imagery were made use of in the study. Satellite data obtained were digitally rectified and processed using ERDAS Imagine 9.2 version.
SPOT VEGETATION was launched in March 1998 on board SPOT 4 satellite to monitor surface parameters on global basis at daily intervals at 1 km resolution [
Rainfall data, currently used by Famine Early Warning System (FEWSNET), FAO and World Food Programme (WFP) for agricultural monitoring in several African countries were derived from rainfall estimate (RFE) product of National Oceanic and Atmospheric Administration’s (NOAA) climate prediction center. Rainfall estimates are available for two different time spans in two versions, viz., RFE 1.0 and RFE 2.0. RFE 1.0 relying on interpolation method to combine Meteosat and Global Telecommunication System data is available for the period 1995-2000. RFE 2.0 imbibing additional techniques along with cold cloud duration and station rainfall to refine precipitation estimates is available from 2001 onwards and is more reliable than rainfall data of European Center for Medium Range Weather Forecast (ECMWF) [
Water requirement satisfaction index was calculated as the ratio of seasonal actual evapotranspiration (ETa) to the seasonal crop water requirement (WR) based on the water availability and crop requirement during growing season [
Actual evapotranspiration (ETa), as opposed to the potential evapotranspiration (PET); represents actual amount of water withdrawn from soil water reservoir. When soil water hold remains above the maximum allowable depletion (MAD) level (based on crop type), ETa equals WR and no water stress is experienced. But, when soil water level goes below MAD level, ETa will be lower than WR in proportion to the remaining soil water volume indicating water stress [
Correlation between various spectro-agrometeorological parameters, namely, NDVI actual (NDVIa), NDVI cumulative (NDVIc), NDVI crop cycle (NDVIx), REF, 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). As NDVIa also satisfied the assumption of linearity, the same was selected for multiple linear regression model development.
Rainfall estimate and the yield were highly significantly correlated (r = 0.85, p = 0.01) to each other while assuming linear relationship and therefore selected for multiple linear regression model development (
Correlation between WRSI and the yield reflected no significant correlation between them (r = 0.44, p = 0.28) and hence not considered for multiple linear regression model development (
Correlation between average or total ET and the yield indicated no significant correlation in either case (r = 0.45, p = 0.26; r = 0.58, p = 0.13; respectively) and hence not considered for multiple linear regression model (
From the above seven variables, the significantly correlated factor NDVIa and the highly significantly correlated
parameter REF were used to create a MLRM. This multiple regression generated the following equation.
This resultant model was validated on the basis of coefficient of determination (R2), root mean square error (RMSE) and coefficient of variation (CV) with values each of 0.88 (adjusted R2 = 0.84), 1.405 (q∙ha−1) and 0.94, respectively at 99% confidence level (p = 0.005) evidently indicating that the maize yield prediction of the model is very good (
Further, the parameter estimates of the model confirmed that RFE bears high predictive capability than NDVIa as found earlier from high significant correlation between maize yield and REF as against significant
Source | DF | Sum of squares | Mean of squares | F | p |
---|---|---|---|---|---|
Model | 2 | 75.00 | 37.50 | 18.99 | 0.005** |
Error | 5 | 9.87 | 1.98 | ||
Total | 7 | 84.87 | **Highly significant |
DF―Degrees of freedom, F―Fisher ratio, p―Probability.
correlation between maize yield and NDVIa (
Evaluation of conventional crop yield forecast using the developed model showed close alliance between the two (
Based on the developed prediction model, highest maize yield for 2013 is expected to be 20.63 q∙ha−1 and lowest 11.84 q∙ha−1 with a mean of 16.2 q∙ha−1. The prediction also indicates that maize yield in 64.1% of the study area will be 12 - 16 q∙ha−1 and in 26.3% of the area to be 17 - 18 q∙ha−1 while the rest of 9.6% area is likely to yield 19 - 21 q∙ha−1 (
Spatial distribution of the production levels in South Tigray zone reveal that certain pockets of south-western part of the study area (Ofla Woreda) are most productive with 19 - 21 q∙ha−1 of yield while many stations in the north-west and south-east are intermediately productive with 17 - 18 q∙ha−1 of output. The entire eastern half of the study area also hosts least productive pockets giving only 12 - 16 q∙ha−1 of grains (
Spatial information derived from physiological crop models had demonstrated the expected accuracy of crop yield estimates to be ±10% - 15% [
Term | Estimate | Standard error | t | p |
---|---|---|---|---|
Intercept | −1.06 | 3.47 | −0.31 | 0.77 |
NDVIa | 21.99 | 8.25 | 2.67 | 0.05* |
RFE | 0.24 | 0.07 | 3.29 | 0.02* |
t―Student’s ratio, p―Probability, *―Significant.
Production level | Crop coverage area (%) | Yield forecast (q∙ha−1) |
---|---|---|
I | 9.6 | 19 - 21 |
II | 26.3 | 17 - 18 |
III | 64.1 | 12 - 16 |
all cereal crops. Thus, timelines issue can also be addressed effectively by remote sensing based approach even after considering all cereals included by CSA. Meteorological information from CSWB model, Crop Production System Zone (CPSZ) and real time satellite data are highly useful for crop yield forecast depicting the potential of spectro-agrometeorological factors [
Evapotranspiration total and NDVIc were the most suitable factors for developing a multiple linear regression model in Kenya [
Thus, remote sensing and geographical information system based maize yield forecast improved quality of the data, timelines of the prediction, facilitated differentiation of yield production levels and aided in discrimination of productive areas thereby paving the way easy for administrators to intervene in timely decision making further to demonstrating the clear potential of spectro-agrometeorological factors in yield forecasting.
In order to ease natural resources management practices in South Tigray Zone, a maize yield forecast model was developed from two most relevant spectro-agrometeorological variables, viz., REF and NDVIa. The developed model has a predictive capability of 0.88 with RMSE 1.405 q∙ha−1 and is quite encouraging, especially in view of the existence of fragmented cultivation plots. Preparation of a forecast map indicating productive areas and production levels for the ensuing year was also feasible. Through this model, forecast is possible in advance in September during flowering season itself as against that in December by the conventional method. Decision makers can identify relative productive areas coupled with yield quantum well in advance of harvest. So, development of agriculture yield prediction tools based on state-of-the-art technologies is crucial for timely interventions to safeguard the interests of the nation and its populace.
The authors wish to express their sincere thanks to the School of Earth Sciences and the College of Natural Sciences, Addis Ababa University for providing all the necessary facilities and support during the study period. The authors would like to express appreciation to the reviewers for their comments to the manuscript.
Abiy Wogderes Zinna,Karuturi Venkata Suryabhagavan, (2016) Remote Sensing and GIS Based Spectro-Agrometeorological Maize Yield Forecast Model for South Tigray Zone, Ethiopia. Journal of Geographic Information System,08,282-292. doi: 10.4236/jgis.2016.82024