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RESEARCH ARTICLE
Contents Vol 58(8)

Simulating the effects of saline and sodic subsoils on wheat crops growing on Vertosols

Zvi Hochman A H , Yash P. Dang B , Graeme D. Schwenke C , Neal P. Dalgliesh A , Richard Routley D , Michael McDonald E F , Ian G. Daniells C , William Manning G and Perry L. Poulton A
+ Author Affiliations
- Author Affiliations

A CSIRO Sustainable Ecosystems/APSRU, PO Box 102, Toowoomba, Qld 4350, Australia.

B Queensland Department of Natural Resources and Water/APSRU, PO Box 102, Toowoomba, Qld 4350, Australia.

C NSW Department of Primary Industries, Tamworth Agricultural Institute, 4 Marsden Park Road, Callala, NSW 2340, Australia.

D Queensland Department of Primary Industries and Fisheries, Emerald, Qld 4390, Australia.

E Queensland Department of Primary Industries and Fisheries, LMB 2, Goondiwindi, Qld 4390, Australia.

F Department of Agriculture, Fisheries and Forestry, Canberra, ACT 2600, Australia.

G NSW Department of Primary Industries, PO Box 546, Gunnedah, NSW 2380, Australia.

H Corresponding author. Email: zvi.hochman@csiro.au

Australian Journal of Agricultural Research 58(8) 802-810 https://doi.org/10.1071/AR06365
Submitted: 21 November 2006  Accepted: 20 April 2007   Published: 30 August 2007

Abstract

Soils with high levels of chloride and/or sodium in their subsurface layers are often referred to as having subsoil constraints (SSCs). There is growing evidence that SSCs affect wheat yields by increasing the lower limit of a crop’s available soil water (CLL) and thus reducing the soil’s plant-available water capacity (PAWC). This proposal was tested by simulation of 33 farmers’ paddocks in south-western Queensland and north-western New South Wales. The simulated results accounted for 79% of observed variation in grain yield, with a root mean squared deviation (RMSD) of 0.50 t/ha. This result was as close as any achieved from sites without SSCs, thus providing strong support for the proposed mechanism that SSCs affect wheat yields by increasing the CLL and thus reducing the soil’s PAWC.

In order to reduce the need to measure CLL of every paddock or management zone, two additional approaches to simulating the effects of SSCs were tested. In the first approach the CLL of soils was predicted from the 0.3–0.5 m soil layer, which was taken as the reference CLL of a soil regardless of its level of SSCs, while the CLL values of soil layers below 0.5 m depth were calculated as a function of these soils’ 0.3–0.5 m CLL values as well as of soil depth plus one of the SSC indices EC, Cl, ESP, or Na. The best estimates of subsoil CLL values were obtained when the effects of SSCs were described by an ESP-dependent function.

In the second approach, depth-dependent CLL values were also derived from the CLL values of the 0.3–0.5 m soil layer. However, instead of using SSC indices to further modify CLL, the default values of the water-extraction coefficient (kl) of each depth layer were modified as a function of the SSC indices. The strength of this approach was evaluated on the basis of correlation of observed and simulated grain yields. In this approach the best estimates were obtained when the default kl values were multiplied by a Cl-determined function. The kl approach was also evaluated with respect to simulated soil moisture at anthesis and at grain maturity. Results using this approach were highly correlated with soil moisture results obtained from simulations based on the measured CLL values.

This research provides strong evidence that the effects of SSCs on wheat yields are accounted for by the effects of these constraints on wheat CLL values. The study also produced two satisfactory methods for simulating the effects of SSCs on CLL and on grain yield. While Cl and ESP proved to be effective indices of SSCs, EC was not effective due to the confounding effect of the presence of gypsum in some of these soils. This study provides the tools necessary for investigating the effects of SSCs on wheat crop yields and natural resource management (NRM) issues such as runoff, recharge, and nutrient loss through simulation studies. It also facilitates investigation of suggested agronomic adaptations to SSCs.

Additional keywords: subsoil constraints, simulation, crop lower limit, salinity, sodicity, chloride.


Acknowledgments

This work was made possible by funding from the Grains Research and Development Corporation. The authors acknowledge the contribution made by the farmers who collaborated in this study. We thank our colleagues Merv Probert, Anthony Whitebread, Jeremy Whish, and two anonymous referees for their comments on a draft manuscript. Some of the concepts explored in this research were advanced by discussions in August 2005 at a workshop on modelling subsoil constraints. We acknowledge the contributions of Julianne Lilley, Bill Bowden, Senthold Asseng, Phil Newton, Yvette Oliver, John Kirkegaard, and particularly Kirsten Verburg for suggesting the concept of keeping the CLL constant while varying kl in response to subsoil constraints.


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