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

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School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
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Foundation for Arable Research, Christchurch 8441, New Zealand
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Author to whom correspondence should be addressed.
Agriculture 2019, 9(11), 237; https://doi.org/10.3390/agriculture9110237
Received: 30 September 2019 / Accepted: 30 October 2019 / Published: 4 November 2019
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. View Full-Text
Keywords: data fusion; precision agriculture; arable; satellite imagery data fusion; precision agriculture; arable; satellite imagery
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Jiang, G.; Grafton, M.; Pearson, D.; Bretherton, M.; Holmes, A. Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity. Agriculture 2019, 9, 237.

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