TY - JOUR
T1 - Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data
T2 - Geospatial effects
AU - Gonzalez-Gonzalez, Miguel Angel
AU - Guertin, David Philip
N1 - Funding Information:
All persons who have made substantial contributions to the work reported in the manuscript (e.g. technical help, writing and editing assistance, general support), but who do not meet the criteria for authorship, are named in the Acknowledgements and have given us their written permission to be named. If we have not included an Acknowledgements, then that indicates that we have not received substantial contributions from non-authors.
Publisher Copyright:
© 2021 The Authors
PY - 2021/12/25
Y1 - 2021/12/25
N2 - Seasonal crop estimates at large scales are essential for national food security. Therefore, this study aimed to predict non-irrigated dry bean yields for 41 districts in the semi-arid region in central Mexico before estimations from crop census. 13-year period data included: bean yields from official statistics, climate data from local weather stations, and Remote Sensing Imagery. The study examined a suite of econometric modeling approaches to predict seasonal bean yields under different precipitation categorization scheme (normal-wet seasons and dry seasons), as well as predictand/predictor log-transformations. The method tested Ordinary least Squares (OLS) and OLS + Dummy (dummy variable with spatial regimes), and spatial regression methods: Spatial Lag (SAR) and Spatial Error (SER), and Geographically Weighted Regression (GWR). At first, exploratory OLS regressions were used to create a subset of specified models before testing the spatial regression models. The predictors that accounted for most of the bean yield variability were precipitation and Enhanced Vegetation Index. The models that incorporated explicit and implicit spatial effects (SAR and OLS + Dummy, respectively), with log-transformations of predictand and non-transformation of predictors, showed the best performances (r2 between 0.81 and 0.84 with an AIC between −81 and −99). Likewise, a prognosis of a13-year yield simulations (hindcasts) indicated that the latter models are adequate for normal-wet and dry seasons (mean absolute error less than 0.190 ton ha−1). Overall, spatial regression techniques have the potential to estimate bean yields as an early forecast for crop official statistics, eluding large amounts of monetary and human resources on gathering ground data in poor or developing countries, particularly.
AB - Seasonal crop estimates at large scales are essential for national food security. Therefore, this study aimed to predict non-irrigated dry bean yields for 41 districts in the semi-arid region in central Mexico before estimations from crop census. 13-year period data included: bean yields from official statistics, climate data from local weather stations, and Remote Sensing Imagery. The study examined a suite of econometric modeling approaches to predict seasonal bean yields under different precipitation categorization scheme (normal-wet seasons and dry seasons), as well as predictand/predictor log-transformations. The method tested Ordinary least Squares (OLS) and OLS + Dummy (dummy variable with spatial regimes), and spatial regression methods: Spatial Lag (SAR) and Spatial Error (SER), and Geographically Weighted Regression (GWR). At first, exploratory OLS regressions were used to create a subset of specified models before testing the spatial regression models. The predictors that accounted for most of the bean yield variability were precipitation and Enhanced Vegetation Index. The models that incorporated explicit and implicit spatial effects (SAR and OLS + Dummy, respectively), with log-transformations of predictand and non-transformation of predictors, showed the best performances (r2 between 0.81 and 0.84 with an AIC between −81 and −99). Likewise, a prognosis of a13-year yield simulations (hindcasts) indicated that the latter models are adequate for normal-wet and dry seasons (mean absolute error less than 0.190 ton ha−1). Overall, spatial regression techniques have the potential to estimate bean yields as an early forecast for crop official statistics, eluding large amounts of monetary and human resources on gathering ground data in poor or developing countries, particularly.
KW - Bean yield prediction
KW - Rainfall
KW - Spatial modeling
KW - Vegetation indices
UR - http://www.scopus.com/inward/record.url?scp=85121590131&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121590131&partnerID=8YFLogxK
U2 - 10.1016/j.jag.2021.102623
DO - 10.1016/j.jag.2021.102623
M3 - Article
AN - SCOPUS:85121590131
VL - 105
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
SN - 1569-8432
M1 - 102623
ER -