Mapping the impact of subsoil constraints on soil available water capacity and potential crop yield
Mikaela J. Tilse
A * , Thomas F. A. Bishop A , John Triantafilis B and Patrick Filippi A
+ Author Affiliations
- Author Affiliations
A Precision Agriculture Laboratory, Sydney Institute of Agriculture, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia.
B Manaaki Whenau – Landcare Research, Lincoln, New Zealand.
Crop & Pasture Science 73(6) 636-651 https://doi.org/10.1071/CP21627
Submitted: 9 August 2021 Accepted: 25 January 2022 Published: 25 February 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Context: The depth-to a constraint determines how much of the soil profile, and the water it contains, can be accessed by plant roots. Information describing the impacts of soil constraints on available water capacity (AWC) and yield is important for farm management, but is rarely considered in a spatial context.
Aims and methods: The depth-to three yield-limiting constraints (sodicity, salinity, and alkalinity) was mapped across ∼80 000 ha in northern New South Wales, Australia using machine learning and digital soil mapping techniques. Soil AWC was calculated using soil data and pedotransfer functions, and water use efficiency equations were used to determine potential yield loss due to the presence of soil constraints. From this, the most-limiting constraint to yield was mapped.
Key results: One or more constraints were found to be present across 54% of the study area in the upper 1.2 m of the soil profile, overall reducing the AWC by ∼50 mm and potential yield by an average of 1.1 t/ha for wheat and 0.8 bales/ha for cotton. Sodicity (Exchangeable Sodium Percentage > 15%) was identified as the most-limiting constraint to yield across the study area.
Implications: The simplification of multiple sources of information into a single decision-making tool could prove valuable to growers and farm managers in managing soil constraints and understanding important interactions with available water and yield.
Keywords: decision trees, digital soil mapping, pedotransfer function, precision agriculture, Random Forest, soil constraints, soil water, yield potential.
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