Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand: a case study of maize-grain crop production in the Waikato region. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Jiang, Guopeng; Grafton, Miles; Pearson, Diane; Bretherton, Mike; Holmes, Allister
• Is Part Of: New Zealand journal of crop and horticultural science. Volume 49:Issue 1(2021)
• Page Start: 41
• Page End: 62
• Publication Date: 2021-01-02
• Subjects: Precision farming -- machine learning -- maize -- soil texture analysis -- GIS; Crops -- New Zealand -- Periodicals; Horticulture -- New Zealand -- Periodicals; Crops -- Periodicals; Horticulture -- Periodicals; Crops; Horticulture; New Zealand; Periodicals; 633.09905
• DOI: 10.1080/01140671.2020.1865413
• Languages: English
• ISSNs: 1175-8783

Abstract

ABSTRACT: Precision agriculture manages within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. Delineation of field-specific management zones (MZs), representing significantly different yield potentials prescribe the rates of a specific crop inputs within-field. This paper examines multiple-year maize grain yield maps (2014, 2015, 2017 and 2018) and their spatial and temporal variability of within-field datasets (soil electrical conductivity [EC], soil organic matter [OM], and elevation) and climate data. The research was undertaken on a non-irrigated field at New Zealand's Foundation for Arable Research (FAR) in the Waikato region, to provide a simple, heuristic method to delineate dynamic MZs for crop inputs. Supervised statistical learning models (stepwise multiple linear regression [SMLR], feedforward neural network (FFNN), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost) and Cubist regression) were implemented to predict spatial yield. Prediction accuracies of the trained models were evaluated by withholding one subset of data for testing. For internal 'split-sample' validation, CART, random forest and XGBoost produced slightly better statistical predictions (RMSE = 1.9–2.0 and R 2 = 0.60–0.63) than Cubist and FFNN (RMSE = 2.1–2.2 and R 2 = 0.52–0.57), whereas MLR produced the weakest prediction (RMSE = 2.3 and R 2 = 0.51). Spatial yield prediction of individual years, were poor ( R 2 = 0.07–0.36). Input data used is readily and inexpensive for small arable fields in New Zealand. The methods presented, could be applied to a wider range of arable crops for within-field management inputs, to respond to spatially diverse soil texture distribution and variable rainfall patterns.