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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Guopeng Jiang & Miles Grafton & Diane Pearson & Mike Bretherton & Allister Holmes, 2019. "Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity," Agriculture, MDPI, vol. 9(11), pages 1-22, November.
    • Handle: RePEc:gam:jagris:v:9:y:2019:i:11:p:237-:d:283527
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    References listed on IDEAS

    as
    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Bergmeir, Christoph & Benítez, José M., 2012. "Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i07).
    3. Xinbing Wang & Yuxin Miao & Rui Dong & Zhichao Chen & Yanjie Guan & Xuezhi Yue & Zheng Fang & David J. Mulla, 2019. "Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China," Sustainability, MDPI, vol. 11(3), pages 1-26, January.
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    Cited by:

    1. Tinghui Wu & Jian Yu & Jingxia Lu & Xiuguo Zou & Wentian Zhang, 2020. "Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis," Agriculture, MDPI, vol. 10(7), pages 1-14, July.
    2. Diane Pearson, 2020. "Key Roles for Landscape Ecology in Transformative Agriculture Using Aotearoa—New Zealand as a Case Example," Land, MDPI, vol. 9(5), pages 1-25, May.
    3. Ha Quang Thinh Ngo & Thanh Phuong Nguyen & Hung Nguyen, 2020. "Research on a Low-Cost, Open-Source, and Remote Monitoring Data Collector to Predict Livestock’s Habits Based on Location and Auditory Information: A Case Study from Vietnam," Agriculture, MDPI, vol. 10(5), pages 1-26, May.

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