Soil sensing and machine learning reveal factors affecting maize yield in the Mid-Atlantic USA
Article
Article Title | Soil sensing and machine learning reveal factors affecting maize yield in the Mid-Atlantic USA |
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ERA Journal ID | 5298 |
Article Category | Article |
Authors | Kinoshita, Rintaro (Author), Tani, Masayuki (Author), Sherpa, Sonam (Author), Ghahramani, Afshin (Author) and van Es, Harold (Author) |
Journal Title | Agronomy Journal |
Journal Citation | 115 (1), pp. 181-196 |
Number of Pages | 16 |
Year | 2023 |
Publisher | John Wiley & Sons |
Place of Publication | United States |
ISSN | 0002-1962 |
1435-0645 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/agj2.21223 |
Web Address (URL) | https://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/agj2.21223 |
Abstract | In large-scale arable cropping systems, understanding within-field yield variations and yield-limiting factors are crucial for optimizing resource investments and financial returns, while avoiding adverse environmental effects. Sensing technologies can collect various crop and soil information, but there is a need to assess whether they reveal within-field yield constraints. Spatial data regarding grain yields, proximal soil sensing data, and topographical and soil properties were collected from 26 maize (Zea mays L.) growing fields in the Mid-Atlantic USA. Apparent soil electrical conductivity (ECa) collected by an on-the-go sensor (Veris) was an effective method for estimating subsoil textural variation and water holding capacity in the Coastal Plain region, which was also the best predictor of spatial yield pattern when combined with surface pH and topographic wetness index in a Random Forest (RF) model. In the Piedmont Plateau region, proximal soil sensors showed a lower correlation to measured soil properties, while topographical properties (aspect and slope) were important estimators of spatial yield patterns in an RF model. In locations where the RF model failed to predict yield variation, soil compaction appeared to be limiting crop yields. In conclusion, the application of RF models using ECa sensors and topographical properties was effective in revealing within-field yield constraints, especially in the Coastal Plain region. On the Piedmont Plateau, the calibration of proximal sensor information needs to be improved with a particular focus on soil compaction. |
Keywords | soil sensing; maize |
ANZSRC Field of Research 2020 | 300207. Agricultural systems analysis and modelling |
410605. Soil physics | |
410601. Land capability and soil productivity | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Cornell University, United States |
Obihiro University, Japan | |
Centre for Sustainable Agricultural Systems | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7v8q/soil-sensing-and-machine-learning-reveal-factors-affecting-maize-yield-in-the-mid-atlantic-usa
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