干旱区生态环境知识资源中心(Arid): Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects

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DOI	10.1016/j.jag.2021.102623
	Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects
	Angel Gonzalez-Gonzalez, Miguel; Guertin, David Philip
通讯作者	Gonzalez-Gonzalez, MA (corresponding author), Inst Nacl Invest Forestales Agri Colas & Pecuaria, Lab Nacl Modelaje & Sensores Remotos, Campo Expt PabellOn, Km 32-5 Carr Ags Zac, Pabellon De Arteaga 20660, Ags, Mexico.
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2021
卷号	105
英文摘要	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 logtransformations 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.
英文关键词	Bean yield prediction Rainfall Vegetation indices Spatial modeling
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000724334100001
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; CROP YIELD ; AGRICULTURE ; IMPACT ; MODEL ; PRECIPITATION ; SIMULATION ; PREDICTION ; PHENOLOGY ; MAIZE
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374150
作者单位	[Angel Gonzalez-Gonzalez, Miguel] Inst Nacl Invest Forestales Agricolas & Pecuarias, Campo Expt Pabellon, Km 32-5 Carr Ags Zac, Pabellon De Arteaga 20660, Ags, Mexico; [Guertin, David Philip] Univ Arizona, Environm & Nat Resources 2, 1064 East Lowell St, Tucson, AZ 85721 USA
推荐引用方式 GB/T 7714	Angel Gonzalez-Gonzalez, Miguel,Guertin, David Philip. Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects[J],2021,105.
APA	Angel Gonzalez-Gonzalez, Miguel,&Guertin, David Philip.(2021).Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,105.
MLA	Angel Gonzalez-Gonzalez, Miguel,et al."Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 105(2021).