Speaker
Description
Email: di.he@csiro.au
[Introduction]
Within field variations of plant available water capacity (PAWC) of soil is one of the major causes of spatial yield variability in dryland agriculture systems, as PAWC interacts with pre-season and in-season rainfall and other climatic variables to determine crop growth and final yield. Quantification of such variations helps to better understand the changes in soil texture and subsoil constraints to inform spatially explicit management practice.
[Method]
We developed and tested a general inverse approach to estimate PAWC from crop yield and yield maps. The agricultural production systems model (APSIM) was used to simulate wheat yield on synthetic soils with contrasting PAWC and climates. The simulated results were used to develop an empirical model to relate simulated yield to PAWC. The empirical model was inversely used to predict PAWC from observed crop yield. Potential prediction ability was quantified using independently simulated wheat yield on actual soils. The actual ability was assessed with measured wheat yields and PAWC. We also further extend this approach to predict and map in-field variations of PAWC from yield maps of single and multiple crops. Soil PAWC maps were produced based on inversely predicted PAWC using crop yield maps together with in-field management information, and compared with: 1) available water capacity derived using laboratory-measured soil properties, and 2) soil types derived from proximally sensed soil spectra and ground geophysics for four representative farms in Australia.
[Result]
The approach had higher accuracy for sites with high rainfall or dominant summer rainfall. It could potentially provide acceptable PAWC predictions across contrasting climate regions (prediction error < 37 mm, 33.5%). The prediction error using crop yield against measured PAWC was <25 mm (26.5%). Our results demonstrate that soil PAWC can be reliably predicted from crop yield. The predicted PAWC maps matched well with within-field spatial variation of soil types, and well reflected the impact of soil constraints (e.g. salinity), and soil classifications from soil survey and local experience. This demonstrates that the predicted PAWC from crop yield using inverse modelling can reflect the soil physicochemical variations within-field.
[Discussion]
This approach provides an alternative way to predict PAWC rather than directly measuring it via soil sampling, with profound implications for reducing labour and costs. The generated PAWC maps can be combined with process-based modelling to predict crop yield and yield zones and to inform spatial field management and soil sampling.
| Keywords | Inverse modelling; APSIM (Agricultural Production Systems Simulator; Soil digital mapping; with-in field variability |
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