BALZARINI MONICA GRACIELA
Artículos
Título:
Statistical Models of Yield in On-Farm Precision Experimentation
Autor/es:
PACCIORETTI, PABLO; GIANNINI-KURINA, FRANCA; CORDOBA, MARIANO A.; BRUNO, CECILIA; BULLOCK, DONALD; BALZARINI, MÓNICA G.
Editorial:
AMER SOC AGRONOMY
Resumen:
n-farm precision experimentation (OFPE) is increasingly conducted using variable-rate technology and precision agriculture equipment to measure the effect of changes in input application rates on yields and profits at specific fields. Classical linear regression models and new Bayesian and machine learning regressions for spatial data can be used to investigate site-specific crop response from georeferenced data. The objective of this work was to compare statistical models that can be used by researchers analyzing OFPE data to estimate crop response and better describe its spatial within-field variability. Three statistical models estimating the responses to N rates, seed rates, and site-specific soil properties from eight OFPEs were compared: 1) linear regression (LR) for spatially correlated errors, 2) Bayesian regression (BR) with random site effects, and 3) Random Forest regression (RF) with kriged residuals. Models were adjusted to account for spatial variation in yield response,