Boundary line models for soil nutrient concentrations and wheat yield in national-scale datasets

Lark, Richard M.; Gillingham, Vincent; Langton, David; Marchant, Ben P.. 2020 Boundary line models for soil nutrient concentrations and wheat yield in national-scale datasets. European Journal of Soil Science, 71 (3). 334-351. https://doi.org/10.1111/ejss.12891

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Official URL: http://dx.doi.org/10.1111/ejss.12891

Abstract/Summary

In boundary line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within‐field soil management zones. We considered boundary line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood‐based methods, that the boundary line model was preferable in most cases. The boundary line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within‐field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guidelines. Furthermore, there was evidence that the boundary line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1111/ejss.12891
ISSN:	13510754
Date made live:	13 Jan 2020 16:22 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/526445

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1111/ejss.12891

ISSN:

13510754

Date made live:

13 Jan 2020 16:22 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/526445