Quantifying individual and collective influences of soil properties on crop yield
Rebecca Whetton A , Yifan Zhao B and Abdul M. Mouazen C D
+ Author Affiliations
- Author Affiliations
A Cranfield Soil and AgriFood Institute, Cranfield University, Bedfordshire MK43 0AL, UK.
B Through-life Engineering Services Institute, Cranfield University, Bedfordshire MK43 0AL, UK.
C Department of Soil Management, Ghent University, Coupure 653, 9000 Gent, Belgium.
D Corresponding author. Email: Abdul.Mouazen@UGent.be
Soil Research 56(1) 19-27 https://doi.org/10.1071/SR16264
Submitted: 3 October 2016 Accepted: 2 June 2017 Published: 20 July 2017
Abstract
Quantification of the agronomic influences of soil properties, collected at high sampling resolution, on crop yield is essential for site specific soil management. The objective of this study was to implement a novel Volterra Non-linear Regressive with eXogenous inputs (VNRX) model accounting for the linear and non-linear variability (VNRX-LN) to quantify causal factors affecting wheat yield in a 22-ha field with a waterlogging problem in Bedfordshire, UK. The VNRX-LN model was applied using high-resolution data of eight key soil properties (total nitrogen (TN), organic carbon, pH, available phosphorous, magnesium (Mg), calcium, moisture content and cation exchange capacity (CEC)). The data were collected with an on-line (tractor mounted) visible and near infrared spectroscopy sensor and used as multiple-input to the VNRX-LN model, whereas crop yield represented the single-output in the system. Results showed that the largest contributors to wheat yield were CEC, Mg and TN, with error reduction ratio contribution values of 14.6%, 4.69% and 1% respectively. The overall contribution of the soil properties considered in this study equalled 23.21%. This was attributed to a large area of the studied field having been waterlogged, which masked the actual effect of soil properties on crop yield. It is recommended that VNRX-LN is validated on a larger number of fields, where other crop yield affecting parameters e.g., crop disease, pests, drainage, topography and microclimate conditions should be taken into account.
Additional keywords: nonlinear parametric modelling, proximal soil sensing, VNRX-LN, yield-limiting factors.
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