Modelling and prediction of soil water contents at field capacity and permanent wilting point of dryland cropping soils
M. A. Rab A E , S. Chandra A , P. D. Fisher A , N. J. Robinson B , M. Kitching C , C. D. Aumann A and M. Imhof D
+ Author Affiliations
- Author Affiliations
A Future Farming System Research Division, Department of Primary Industries, 255 Ferguson Road, Tatura, Vic. 3616, Australia.
B Future Farming System Research Division, Department of Primary Industries, Cnr Midland Highway and Taylor Street, Epsom, Vic. 3554, Australia.
C Future Farming System Research Division, Department of Primary Industries, 621 Sneydes Road, Werribee, Vic. 3030, Australia.
D Future Farming System Research Division, Department of Primary Industries, 1301 Hazeldean Road, Ellinbank, Vic. 3821, Australia.
E Corresponding author. Email: abdur.rab@dpi.vic.gov.au
Soil Research 49(5) 389-407 https://doi.org/10.1071/SR10160
Submitted: 3 August 2010 Accepted: 8 March 2011 Published: 12 July 2011
Abstract
Field capacity (FC) and permanent wilting point (PWP) are two critical input parameters required in various biophysical models. There are limited published data on FC and PWP of dryland cropping soils across north-western Victoria. Direct measurements of FC and PWP are time-consuming and expensive. Reliable prediction of FC and PWP from their functional relationships with routinely measured soil properties can help to circumvent these constraints. This study provided measured data on FC using undisturbed samples and PWP as functions of geomorphological unit, soil type, and soil texture class for dryland cropping soils of north-western Victoria. We used a balanced, nested sampling strategy and developed functional relationships of FC and PWP with routinely measured soil properties using residual maximum likelihood based mixed-effects regression modelling. Using the data, we also tested the adequacy of nine published pedotransfer functions (PTFs) in predicting FC and PWP.
Significant differences were observed among the three soil types and nine texture classes for most soil properties. FC and PWP were higher for Grey Vertosols (FC 43.7% vol, PWP 29.1% vol) than Hypercalcic Calcarosols (38.4%, 23.5%) and Red Sodosols (20.2%, 9.2%). Of the several functional relationships developed for prediction of FC and PWP, a quadratic single-predictor model based on dg (geometric mean particle size diameter) performed better than other models for both FC and PWP. It was nearly bias-free, with a root mean square error (RMSE) of 3.18% vol and an R2 of 93% for FC, and RMSE 3.47% vol and R2 89% for PWP. Another useful model for FC was a slightly biased, two-predictor quadratic model based on clay and silt, with RMSE 3.14% vol and R2 94%. For PWP, two other possibly useful, though slightly biased, models included a single-predictor quadratic model based on clay (RMSE 3.45% vol, R2 89%) and a three-predictor model based on clay, silt, and σg (geometric standard deviation of particle size diameter) (RMSE 3.27% vol, R2 90%). We observed a strong quadratic relationship of FC with PWP (RMSE 1.61% vol, R2 98%). This suggests the possibility to further improve the prediction of FC indirectly through PWP. These predictive models for FC and PWP, though developed for the dryland cropping soils of north-western Victoria, may be applicable to other regions with similar soil and climatic conditions. Some validation is desirable before these models are confidently applied in a new situation. Of the nine published PTFs, the multiple linear regression and artificial neural network based NTh5 for FC and NTh3 and CAM for PWP performed better on our data for the prediction of FC and PWP. The root mean square deviation of these PTFs, for both FC and PWP, was higher than the RMSE of our models. Our models are therefore likely to perform better under the dryland cropping soils of north-western Victoria than these PTFs. As a safeguard against arriving at optimistic inferences, we suggest that the modelling of functional relationships needs to account for the hierarchical structure of the sampling design using appropriate mixed effects regression models.
Additional keywords: mixed effects regression, nested sampling design, plant-available water capacity, PTFs, residual maximum likelihood, soil texture, soil type, soil water retention.
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