Modelling the influence of soil properties on crop yields using a non-linear NFIR model and laboratory data

Date published

2021-02-16

Free to read from

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

MDPI

Department

Type

Article

ISSN

2571-8789

Format

Citation

Whetton RL, Zhao Y, Nawar S, Mouazen AM. (2021) Modelling the influence of soil properties on crop yields using a non-linear NFIR model and laboratory data. Soil Systems, Volume 5, Issue 1, February 2021, Article number 12

Abstract

This paper introduces a new non-linear correlation analysis method based on a non-linear finite impulse response (NFIR) model to study and quantify the effects of ten soil properties on crop yield. Two versions of the NFIR model were implemented: NFIR-LN, accounting for both the linear and non-linear variability in the system, and NFIR-L, accounting for linear variability only. The performance of the NFIR models was compared with a non-linear random forest (RF) model, to predict oilseed rape (2013) and wheat (2014) yields in one field at Premslin, Germany. The ten soil properties were used as system inputs, whereas crop yield was the system output. Results demonstrated that the individual and total contribution of the soil properties on crop yield varied throughout the different cropping seasons, weather conditions, and crops. Both the NFIR-LN and RF models outperformed the NFIR-L model and explained up to 55.62% and 50.66% of the yield variation for years 2013 and 2014, respectively. The NFIR-LN and RF models performed equally during yield prediction, although the NFIR-LN model provided more consistent results through the two cropping seasons. Higher phosphorus and potassium contributions to the yield were calculated with the NFIR-LN model, suggesting this method outperforms the RF model.

Description

Software Description

Software Language

Github

Keywords

DOI

Rights

Attribution 4.0 International

Relationships

Relationships

Supplements

Funder/s