Summary
This paper addresses a practical challenge in commercial agriculture: determining cost-effective soil testing strategies that balance information density against management utility. Using data from the North Wyke Farm Platform, the authors developed computational frameworks to quantify the relationship between testing intensity and decision-making value, finding that sparse but spatially comprehensive sampling of a limited variable set is preferable to frequent spot sampling of many variables. The work suggests that 'half-hearted' soil testing programmes are unlikely to support optimal farm management decisions.
UK applicability
The study was conducted at a UK farm platform and directly informs evidence-based soil testing protocols for British commercial producers. Findings are immediately applicable to UK farm advisory services and policy development regarding soil health monitoring standards.
Key measures
Information value quantified through bootstrapping experiments; spatial sampling frequency; inter-variable and serial correlation; incremental value of additional soil measurements
Outcomes reported
The study evaluated the relationship between soil testing density (spatial frequency and number of variables) and the informational value for farm management decisions using high-resolution soil data from a commercial farm platform. Results indicated that information value follows a concave function of spatial sampling frequency, and that serial correlation among variables can render some measurements redundant.
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