Machine learning for optimizing complex site-specific management

Yuji Saikai, Vivak Patel and Paul Mitchell

No 305238, 2020 Conference (64th), February 12-14, 2020, Perth, Western Australia from Australian Agricultural and Resource Economics Society

Abstract: Despite the promise of precision agriculture for increasing the productivity by implementing site-specific management, farmers remain skeptical and its utilization rate is lower than expected. A major cause is a lack of concrete approaches to higher profitability. When involving many variables in both controlled management and monitored environment, optimal site-specific management for such high-dimensional cropping systems is considerably more complex than the traditional low-dimensional cases widely studied in the existing literature, calling for a paradigm shift in optimization of site-specific management. We propose an algorithmic approach that enables farmers to efficiently learn their own site-specific management through on-farm experiments. We test its performance in two simulated scenarios---one of medium complexity with 150 management variables and one of high complexity with 864 management variables. Results show that, relative to uniform management, site-specific management learned from 5-year experiments generates $43/ha higher profits with 25 kg/ha less nitrogen fertilizer in the first scenario and $40/ha higher profits with 55 kg/ha less nitrogen fertilizer in the second scenario. Thus, complex site-specific management can be learned efficiently and be more profitable and environmentally sustainable than uniform management.

Keywords: Research; and; Development/Tech; Change/Emerging; Technologies (search for similar items in EconPapers)
Pages: 25
Date: 2020-09-16
New Economics Papers: this item is included in nep-bec, nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:ags:aare20:305238

DOI: 10.22004/ag.econ.305238

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