CSIRO PUBLISHING www.publish.csiro.au/journals/sr
Soil Research, 2011, 49, 389–407
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 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:
[email protected]
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 threepredictor model based on clay, silt, and sg (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. Introduction The most important soil factor that controls yield in much of the Australian grain-production regions is the quantity of plantavailable water (e.g. Rab et al. 2009). Prediction of the spatial CSIRO 2011
distribution of soil water, and its availability to plants, will enable growers to make informed production decisions that maximise profitability (e.g. management of nutrients and crop canopies). The soil-water balance is central to many of the 10.1071/SR10160
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processes that influence plant growth and degradation of soil and water resources. Information on within-paddock variations in soil hydraulic characteristics is needed for the modelling of water and solute transport and for efficient allocation of resources in farming systems at the landscape unit. Biophysically based farming system models such as APSIM (McCown et al. 1996; Keating et al. 2003), CropSyst (Stöckle et al. 2003), GrassGro (Moore et al. 1997), CAT (Beverly et al. 2005, 2009), and SWAT (Arnold et al. 1998; Srinivasan et al. 1998) are currently used worldwide to predict crop yield, overland flows, deep drainage, and solute movement, and for the efficient allocation of resources (e.g. fertiliser input requirements) in farming systems. Field capacity (FC) and permanent wilting point (PWP) are two critical input parameters required in such biophysical models. Direct measurements of FC and PWP are time-consuming and expensive. Many attempts have been made to establish the relationship between routinely measured soil properties and FC and PWP. Those relationships are referred to as pedotransfer functions (PTF) (Bouma 1989). There are four types of pedotransfer function (PTF) that are generally used for predicting FC and PWP: (i) class PTF (Wösten et al. 1995), (ii) multiple linear regression (MLR, e.g. Minasny et al. 1999), (iii) extended nonlinear regression (ELR, Scheinost et al. 1997), and (iv) artificial neural network (ANN; Pachepsky et al. 1996; Schaap and Bouten 1996; Tamari et al. 1996; Schaap et al. 1998). Minasny et al. (1999) argued that because of unique soil properties, different cut-offs used in particle-size classification (Nemes et al. 1999), and the unavailability of data on some soil properties, most PTFs developed are not suitable to be applied elsewhere. For Australian soils, Minasny et al. (1999) and Minasny and McBratney (2002) developed PTFs for predicting soil-water contents at –10 kPa (FC) and –1500 kPa (PWP) and parameters for the van Genuchten function (van Genuchten 1980), using five datasets (Prebble 1970; Forrest et al. 1985; Geeves et al. 1995; Smettem and Gregory 1996; Bristow et al. 1997). The datasets used by Minasny et al. (1999) and Minasny and McBratney (2002) contain very limited data
from north-western Victoria, and the PTFs were not widely validated for this region. Therefore, PTFs developed for other soil types may be of limited use for the dryland cropping soils of the north-western Victoria. The Mallee and Wimmera regions in north-western Victoria cover ~20% (4.5 Mha) of the state’s area. These regions also generate ~80% of Victoria’s grain production (DPI, 2008). The landforms and soil types vary considerably in the dominant North Western Dunefields and Plains geomorphological region (VRO 2009). The three major soil types in these regions (based on Orders of the Australian Soil Classification, Isbell 2002) are Vertosols, Sodosols, and Calcarosols (VRO 2009). There are limited published data on FC and PWP of dryland cropping soils across these regions (Cock 1985; Geeves et al. 1995), and no published information is available on the relationship between routinely measured soil properties and FC and PWP. This study generated new data on FC, PWP, and other routinely measured soil properties, representative of the target region of north-western Victoria, Australia. Using these data, the investigation addressed three objectives. The first was to quantify the variation in FC, PWP, and routinely measured soil properties in terms of soil depths, geomorphological units (GMUs), soil types, and soil texture classes. The second objective was to model the functional relationships of FC and PWP with routinely measured soil properties. This was to identify predictive models of FC and PWP that may be used to predict FC and PWP in the target region of north-western Victoria, and possibly in other regions with similar characteristics. The third objective was to evaluate the adequacy of some published Australian PTFs in reliably predicting FC and PWP for the target region of north-western Victoria. Methods Target region The target region, ~450 km north-west of Melbourne (Fig. 1), comprises two GMUs within the greater North Western
North-west Victoria Victoria
Fig. 1. Location of sampling paddocks in the north-western Victoria, south-east Australia.
Field capacity and permanent wilting point of dryland cropping soils
Dunefields and Plains region: ‘Hummocky calcareous dunes subdominant’ (GMU 5.1.5) in the southern Mallee, and ‘Clay plain with subdued ridges’ (GMU 5.4) in the Wimmera region of north-western Victoria (Robinson et al. 2006; VRO 2009). These two GMUs represent two prominent dryland cropping agricultural landscapes of north-western Victoria. They were chosen over other GMUs due to their significant area of cropped land, their large total area within north-western Victoria (with respect to private land), and their high agricultural value (grain production). The two GMUs together represent >15% of the cropped land of north-western Victoria. The landforms in GMU 5.1.5 are described as plains on which there are scattered low hummocks (Rowan and Downes 1963). The relative relief across these landforms is much lower than neighbouring landforms or landscapes to the north. Gilgaied cracking clay soils (Vertosols) and gradational calcareous soils (Hypercalcic Calcarosols) are by far the most widespread soils, occupying the plains and the gentler dune slopes. Sodic, red, grey, and yellow texture contrast soils (Sodosols) are more prominent on dune slopes and hummocks. Surfaces of these soils are sandy loams with strong change to medium clay subsoils that are extremely sodic in nature. The GMU 5.4 soils are grey and brown cracking clays (often friable and self-mulching). These selfmulching clays (Vertosols) are essentially end products of latePleistocene to Holocene aeolian redistribution of lacustrine sediments from inter-ridge corridors (Robinson et al. 2006). The climate of north-western Victoria is semi-arid; average annual rainfall varies from 300 mm in the north to 550 mm in the south of the study region. Winters are mild and summers are hot. The climate differs from typical Mediterranean climates, as significant rain events can occur in summer. The general elevations are 90–105 m above sea level in the south, falling gently to 45–60 m in the north. Sampling design Four paddocks, two from each GMU, were selected to obtain a reasonably good representation of major soils and landforms in the target region (Fig. 1). In each paddock, three representative sampling locations were strategically established to capture the inherent range of soil variability. The spatial positions of the three sampling locations in a paddock were chosen on the basis of available information on the extent of the paddock’s variability in grain yield, electromagnetic induction (EM38 mapping), and elevation. At each sampling location in a paddock, three sampling points were randomly established. This balanced, nested sampling design provided a total of 36 sampling points, with 18 sampling points from each GMU. At each sampling point, both undisturbed and disturbed samples were taken from 0–0.10 and 0.20–0.30 m soil depths, providing a total of 72 undisturbed and 72 disturbed soil samples. Particle size distribution, PWP, and chemical properties were measured from the 72 disturbed samples, and FC and soil bulk density from the 72 undisturbed samples. Measurements of soil properties For determination of particle size and chemical properties, disturbed soil samples were air-dried at 408C for 48 h,
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ground, and sieved using a 2.0-mm sieve. Soil organic carbon (OC) content was determined using the Walkley– Black procedure (Nelson and Sommers 1989). Exchangeable cations (Na+, K+, Ca2+, Mg2+), pH in 1 : 5 H2O (pHw) and in 1 : 5 CaCl2 (pHc), and electrical conductivity (EC) were determined using standard techniques (Rayment and Higginson 1992). Values of exchangeable sodium percentage (ESP) were determined by dividing Na+ by the sum of the four exchangeable cations. Clay (
