Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

Guopeng Jiang, Miles Grafton, Diane Pearson, Mike Bretherton and Allister Holmes
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Guopeng Jiang: School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
Miles Grafton: School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
Diane Pearson: School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
Mike Bretherton: School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
Allister Holmes: Foundation for Arable Research, Christchurch 8441, New Zealand

Agriculture, 2019, vol. 9, issue 11, 1-22

Abstract: Spatial variability in soil, crop, and topographic features, combined with temporal variability between seasons can result in variable annual yield patterns within a paddock. The complexity of interactions between yield-limiting factors such as soil nutrients and soil water require specialist statistical processing to be able to quantify variability, and thus inform crop management practices. This study uses multiple linear regression models, Cubist regression and feed-forward neural networks to predict spatial maize-grain ( Zea mays ) yield at two sites in the Waikato Region, New Zealand. The variables considered were: crop reflectance data from satellite imagery, soil electrical conductivity, soil organic matter, elevation, rainfall, temperature, solar radiation, and seeding density. This exercise explores methods which may be useful in predicting yield from proximal and remote sensed data with higher resolution than traditional low spatial resolution point sampling using soil testing and yield response curves.

Keywords: data fusion; precision agriculture; arable; satellite imagery (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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