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There is little knowledge available on the best techniques for transferring spatial information such as stochastic interpolation and multivariate analyses for black pepper. This study applies multiple linear and spatial regression to estimate black pepper productivity based on physical and chemical properties of the soil. A multiple linear regression including all properties of a Latosol was performed and followed by variance analysis to verify the validity of the model. The adjusted variograms and data interpolation by kriging allowed the use of spatial multiple regression with the properties that were significant in the multiple linear regression. The forward stepwise method was used and the model was validated by the F-test. The influence of the Latosol properties was greater than the residual on the prediction of productivity. The model was composed by the physical properties fine sand (FS), penetration resistance (PR), and Bulk density (BD), and by the chemical properties K, Ca, and Mg (except for Mg in the spatial regression). The physical properties were of greater relevance in determining productivity, and the maps estimated by ordinary kriging and predicted by the spatial multiple regression were very similar in shape.

Multiple linear and spatial regressions to estimate the influence of Latosol properties on black pepper productivity