An efficient geostatistical analysis tool for on-farm experiments targeted at localised treatment. (May 2021)

• Record Type: Journal Article
• Authors: Jin, Huidong; Shuvo Bakar, K.; Henderson, Brent L.; Bramley, Robert G.V.; Gobbett, David L.
• Is Part Of: Biosystems engineering. Volume 205(2021)
• Page Start: 121
• Page End: 136
• Publication Date: 2021-05
• Subjects: On-farm experimentation -- Precision agriculture -- Multivariate analysis tool -- Cokriging; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2021.02.009
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : On farm experimentation (OFE) has been a long-standing method for farmers to assess alternative management at scales relevant to their farming practices. Through the use of spatially distributed designs, whether simple strips or other 'whole-of-block' trials, OFE can provide information such as which treatment should be recommended at specific locations, and make important contributions to precision agriculture. However, when treatment response data sets become large, such as with tens of thousands of field observations that are readily collected using on-the-go sensors, existing geostatistical systems for analysing such experiments become computationally intensive, if not impossible. To enable farmers, or their consultants, to generate high-resolution treatment response and recommendation maps on their own computers within a reasonable time, we present a fast and adaptive local cokriging tool for non-colocated and non-stationary OFE data. It uses a spatially-varying neighbourhood radius. It has a graphical user interface accessible via QGIS, a free and open source software. The adaptive local cokriging is demonstrated on three OFE examples. It performs indistinguishably from global cokriging on a small data set, but for large data sets, for which global cokriging is impractical, it predicts significantly more accurately than spatial splines or sampling-based cokriging. It outperforms cokriging base on a fixed number of nearest neighbours when this fixed number is not carefully chosen. Highlights: We give a spatially-varying local cokriging method for large on-farm experimentation data. The new method could recommend high-resolution site-specific farming treatment. Its open source code is accessible via a user-friendly interface of Quantum GIS. It finishes computation within a reasonable time on farmers' normal PC. Its promising spatial prediction accuracy is compared with five other techniques.