Semiparametric geographically weighted response curves with application to site-specific agriculture

Lack of basic knowledge about spatial and treatment varying crop response to irrigation hinders irrigation management and economic analysis for site-specific agriculture. One model that has been postulated for relating crop-specific economic quantities to irrigation is a quadratic response curve of yield as a function of irrigation. Although this model has far reaching economic interpretations it does not account for spatial variation or possible nitrogen-irrigation interactions. To this end we propose a spatially treatment varying coefficient model that alleviates these limitations while providing measures of uncertainty for the estimated coefficient surfaces as well as other derived quantities of interest. The modeling framework we propose is of independent interest and can be used in many different applications. Finally, an example involving site-specific agricultural data from the U.S. Department of Agriculture-Agricultural Research Service demonstrates the applicability of this methodology.

[1]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[2]  David Ruppert,et al.  Variable Selection and Function Estimation in Additive Nonparametric Regression Using a Data-Based Prior: Comment , 1999 .

[3]  S. Fotheringham,et al.  Geographically Weighted Regression , 1998 .

[4]  M. Wand,et al.  Smoothing with Mixed Model Software , 2004 .

[5]  Christopher K. Wikle,et al.  Climatological analysis of tornado report counts using a hierarchical Bayesian spatiotemporal model , 2003 .

[6]  M. E. Johnson,et al.  Minimax and maximin distance designs , 1990 .

[7]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[8]  Chih-Ling Tsai,et al.  Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion , 1999 .

[9]  D. Ruppert Selecting the Number of Knots for Penalized Splines , 2002 .

[10]  C. Crainiceanu,et al.  Semiparametric Regression in Capture–Recapture Modeling , 2006, Biometrics.

[11]  Gerda Claeskens,et al.  Bootstrapping for Penalized Spline Regression , 2009 .

[12]  J. A. Millen,et al.  Spatial variation of corn response to irrigation , 2002 .

[13]  R. Kuehl Design of Experiments: Statistical Principles of Research Design and Analysis , 1999 .

[14]  L. M. Berliner,et al.  Hierarchical Bayesian space-time models , 1998, Environmental and Ecological Statistics.

[15]  Douglas W. Nychka,et al.  FUNFITS: data analysis and statistical tools for estimating functions , 2008 .

[16]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[17]  Grace Wahba,et al.  Spatial-Temporal Analysis of Temperature Using Smoothing Spline ANOVA , 1998 .

[18]  Douglas W. Nychka,et al.  Design of Air-Quality Monitoring Networks , 1998 .

[19]  R. Tibshirani,et al.  Varying‐Coefficient Models , 1993 .

[20]  G. Molenberghs,et al.  Linear Mixed Models for Longitudinal Data , 2001 .

[21]  L. Fahrmeir,et al.  PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE , 2004 .

[22]  M. Wand,et al.  Semiparametric Regression: Parametric Regression , 2003 .

[23]  M. Wand,et al.  General design Bayesian generalized linear mixed models , 2006, math/0606491.

[24]  Paul H. C. Eilers,et al.  Flexible smoothing with B-splines and penalties , 1996 .

[25]  R Fisher,et al.  Design of Experiments , 1936 .

[26]  Ciprian M. Crainiceanu,et al.  Bayesian Analysis for Penalized Spline Regression Using WinBUGS , 2005 .

[27]  Sudipto Banerjee,et al.  Coregionalized Single‐ and Multiresolution Spatially Varying Growth Curve Modeling with Application to Weed Growth , 2006, Biometrics.

[28]  C. F. Sirmans,et al.  Spatial Modeling With Spatially Varying Coefficient Processes , 2003 .

[29]  R. Assunção,et al.  A Bayesian space varying parameter model applied to estimating fertility schedules , 2002, Statistics in medicine.