A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Abstract

Effective site‐specific management requires an understanding of the soil and environmental factors influencing crop yield variability. Moreover, it is necessary to assess the techniques used to define these relationships. The objective of this study was to assess whether statistical models that accounted for heteroscedastic and spatial‐temporal autocorrelation were superior to ordinary least squares (OLS) models when evaluating the relationship between corn (Zea mays L.) yield and soil attributes in Brazil. The study site (10 by 250 m) was located in São Paulo State, Brazil. Corn yield (planted with 0.9‐m spacing) was measured in 100 4.5‐ by 10‐m cells along four parallel transects (25 observations per transect) during six growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. Ordinary least squares, generalized least squares assuming heteroscedasticity (GLShe), spatial‐temporal least squares assuming homoscedasticity (GLSsp), and spatial‐temporal assuming heteroscedasticity (GLShe‐sp) analyses were used to estimate corn yield. Soil acidity (pH) was the factor that most influenced corn yield with time in this study. The OLS model suggested that there would be a 0.59 Mg ha–1 yield increase for each unit increase in pH, whereas with GLShe‐sp there would be a 0.43 Mg ha–1 yield increase, which means that model choice impacted prediction and regression parameters. This is critical because accurate estimation of yield is necessary for correct management decisions. The spatial and temporal autocorrelation assuming heteroscedasticity was superior to the OLS model for prediction. Historical data from several growing seasons should help better identify the cause and effect relationship between crop yield and soil attributes.

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

“…A two-factorial field trial was laid out in a common randomized split-plot design on 27 May 2013, with the corn cultivar "NK Ravello". Four sowing densities (8)(9)(10)(11) seeds·m −2 ) were tested at four different levels of nitrogen fertilization (50, 100, 150 and 200 kg·N·ha −1 ) in a setup with four replicates.…”

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

“…This procedure was chosen because it takes into account the spatial and temporal variability of crop yields and soil attributes, as well as the heteroscedasticity of variables and because it shows better results in determining the yield-limiting factors than traditional regression analyses, using ordinary least square. A detailed description of this analysis for the present study area was given in RODRIGUES et al (2013).…”

Soil chemical attributes restricting grain yield in Oxisols under no-tillage system

“…The spatial variability of crop yield under the notillage system (NTS) is influenced by several factors, especially soil chemical attributes (Rodrigues et al, 2012(Rodrigues et al, , 2013Santi et al, 2012). Knowledge of these limiting factors is essential for commercial crop planning (Rodrigues et al, 2012;Santi et al, 2012) and for site-specific management (Rodrigues et al, 2013;Corassa et al, 2016).…”

“…bras., Brasília, v.53, n.11, p.1203-1212, Nov. 2018 DOI: 10.1590/S0100-204X2018001100002 (McBratney et al, 2005;Miao et al, 2006; Marques da Silva & Silva, 2008;Santi et al, 2012). The use of a large dataset aims to avoid substantial variation in grain yield throughout the years (McBratney et al, 2005;Rodrigues et al, 2013) and misguided management decisions (Rodrigues et al, 2013).…”