Considering long-term ecological effects on future land-use options when making tactical break-crop decisions in cropping systems
Michael Renton A C , Roger Lawes B , Tess Metcalf A and Michael Robertson B
+ Author Affiliations
- Author Affiliations
A School of Plant Biology and Institute of Agriculture, The University of Western Australia, Stirling Highway, Crawley, WA 6009, Australia.
B CSIRO Agriculture Flagship, Private Bag 5, Wembley, WA 6913, Australia.
C Corresponding author. Email: michael.renton@uwa.edu.au
Crop and Pasture Science 66(6) 610-621 https://doi.org/10.1071/CP14135
Submitted: 9 May 2014 Accepted: 19 January 2015 Published: 29 May 2015
Abstract
In cropping systems where one type of crop dominates for economic reasons, farmers may employ alternative cropping or pasture options for strategic purposes such as controlling weed populations, reducing crop disease, and accumulating soil nitrogen. Tactical decisions regarding break crops often involve understanding the economic implications of several interacting bio-physical factors, along with complex trade-offs between short-term benefits, such as immediate profit, and long-term ecological problems, such as increased weed seedbank. Modelling analysis regarding tactical crop-sequencing and break-crop decisions has generally not addressed these longer term dynamic factors. In this study we adapted an analysis and modelling framework (LUSO), originally designed to aid understanding of the long-term strategic planning of agricultural crop and pasture rotations, so that it can be used to analyse immediate tactical decisions regarding break crops and sequencing, while still accounting for both short- and long-term implications of these decisions. We show how the revised framework was applied to two example scenarios and demonstrate that in both cases it can be used for simple decision-support, as well as more in-depth analysis and insight into the factors influencing the immediate decision.
Additional keywords: disease, optimal, rotation, sequence, simulation model, weeds.
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