Nitrogen mineralisation in sugarcane soils in Queensland, Australia: II. From laboratory to field-based prediction
T. G. Orton
A B C , D. E. Allen A B and P. M. Bloesch A
+ Author Affiliations
- Author Affiliations
A Landscape Sciences, Department of Environment and Science, Queensland Government, Ecosciences Precinct, GPO Box 2454, Brisbane, Qld 4001, Australia.
B 'The University of Queensland, School of Agriculture and Food Sciences, St Lucia, Qld 4072, Australia.
C Corresponding author. Email: Thomas.Orton@des.qld.gov.au
Soil Research 57(7) 755-766 https://doi.org/10.1071/SR19032
Submitted: 13 February 2019 Accepted: 1 July 2019 Published: 23 September 2019
Abstract
Using Australian sugarcane regions as a case study, we present an approach for prediction of in-field nitrogen (N) mineralisation over a crop season. The approach builds on the statistical modelling applied in Allen et al. 2019, which demonstrated good predictive ability on data from a laboratory incubation study (an external R2 of 0.84 in a cross-validation exercise), and adjusts those mineralisation rates according to soil moisture and temperature factors. The required field soil temperature and moisture conditions were simulated using a mechanistic model for the response of soil conditions to input climate data. We investigate drivers of variability in the predicted in-season mineralised N, and compare predictions with currently implemented N fertiliser discounts, which are based on a relationship with soil organic carbon content. The main purpose of this paper is to illustrate the potential use of the results in Allen et al. (2019) for calculating predictions of in-season mineralised N that could be applicable under field conditions in the Australian sugarcane regions. A thorough test to properly validate predictions has not yet been conducted, but collecting data to do so should be the focus of further work.
Additional keywords: Soil N supply, statistical models, simulation models, soil indices, climate data, climate forecasts.
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