Effects of tillage and traffic on crop production in dryland farming systems: I. Evaluation of PERFECT soil-crop simulation model
Introduction
In recent years, controlled traffic has been widely adopted by dryland farmers in Australia as a strategy for reducing soil compaction and reducing input costs. Controlled traffic with zero tillage provides better protection for both surface and subsurface soil, reducing runoff and improving crop production (Tullberg et al., 2001, Li et al., 2007). In spite of its potential importance, there have still been a few attempts at a broader exploration of traffic and tillage effects in terms of water balance and crop yield effects.
Soil, crop, tillage, wheel traffic and other environmental factors interact with each other and influence both crop performance and water regimes including runoff and soil water status (Tullberg et al., 2001, Li, 2001). These factors, particularly those that are weather related, are often difficult to isolate in experimental studies. An effective computer simulation model might be used to further investigate these factors and explore some interactions between traffic and tillage in cropping systems. Productivity erosion and runoff functions to evaluate conservation techniques (PERFECT, Littleboy et al., 1989, Littleboy et al., 1999) is one of the soil–crop models that integrates the dynamics of soil and crop processes. Unlike earlier simulation models such as CREAM (Knisel, 1980) and EPIC (Williams et al., 1984), PERFECT was designed to predict runoff, erosion and crop yield for some major management options, including sequences of planting, harvesting and residue management under different tillage practices. This model has been widely used in the dryland farming areas of Australia (Littleboy et al., 1992a, Littleboy et al., 1992b, Thomas et al., 1995), and in other countries such as China (Wang et al., 2003) and India (Littleboy et al., 1996a, Littleboy et al., 1996b). A residue cover-infiltration algorithm, derived under simulated rainfall developed by Glanville et al. (1984) and Littleboy et al. (1996b), has been incorporated into PERFECT, together with the effect of tillage induced soil surface roughness (Littleboy et al., 1996a). It appears to be an appropriate and well-tested model for the prediction of infiltration, runoff and crop performance outcomes of soil, crop and fallow management systems.
The objectives of the work reported here were to calibrate PERFECT for four tillage and traffic management practices, using daily weather, runoff, soil water and crop yield data obtained from experimental plots. USDA curve number was used in the water balance sub-model (Rallison and Miller, 1982). Changes in USDA curve number of bare soil at average soil water content (CNII) and the saturated hydraulic conductivity (Ksat) of the soil horizon were explored to determine the ability to explain effects of these management options on variations in measured runoff by use the PERFECT model.
Section snippets
Curve number and Ksat
Curve number is one of the major factors used, largely for runoff prediction, in the water balance sub-model of PERFECT. The curve number function is empirically derived from fitting measured runoff data and the physical process of infiltration is not represented (Boughton, 1989). The curve number approach does not require detailed information on soil properties, rainfall intensity or energy (Connolly, 1998). Previous experience with the USDA curve number method in Australia used antecedent
Description of experimental site
The PERFECT model was evaluated using data collected from 1995 to 1999 on a controlled traffic experiment at the University of Queensland, Gatton, Australia (27°34′ S, 152°20′ E). The experimental site was a black vertisol (sometimes spelled vertosol), an adhesive shrink–swelling clay soil as classified by Isbell (2002). This comprised a 0.6–1.0 m surface layer of black earth, exhibiting typical self-mulching characteristics and moderate cracking, overlaying a highly permeable gravel and sand
Weather and runoff data
On-site rainfall and runoff were measured at 1.0 min intervals and accumulated into daily totals to provide a daily rainfall and runoff record for the PERFECT model calibration (Tullberg et al., 2001, Li et al., 2007). There were four pluviometers at the experimental site, so the most reliable was selected to provide most daily rainfall values and the other three were used as backups in the case of missing data. Daily temperature, radiation and evaporation data were supplied by the
Optimization of CNII and Ksat values
Optimized values of CNII and Ksat for different layers, together with statistics of fit with daily runoff are given in Table 2.
The number of events during the 5-year experimental period which produced runoff was 22% greater for the wheeled treatments than for the controlled traffic treatments. The ranking of the four treatments in order of increasing CNII was CZT (78), CSM (89), WZT (92) and WSM (93) (Table 2). Ksat of the lower layer under controlled traffic was four times higher than under
CNII and Ksat calibrations
This study used both CNII and Ksat approach to identify which soil profile layers were controlling infiltration, using the PERFECT simulation model. For controlled traffic, surface conditions such as roughness due to tillage and cover from either crop or residue had a greater effect on runoff and infiltration. Saturated hydraulic conductivity (Ksat) below 100 mm for controlled traffic soil was four times greater than that for wheeled soil, indicating that subsurface compaction was a major impact
Conclusion
The PERFECT soil–crop simulation model was calibrated using 5 years of experimental data, and values of CNII established from rainfall simulation tests. Calibration was accomplished by adjusting Ksat, and CNII, to explore the capacity of PERFECT to model the effects of combinations of traffic and tillage management. In the calibrated model:
- (1)
The model explained 75–95% of variations of daily, monthly and annual runoff values. The model over predicted runoff from controlled traffic treatments in
Acknowledgements
This work was supported by the Australian Centre for International Agricultural Research (ACIAR), under project numbers 9209 and 96143. We thank Mr. M. Littleboy for his generous assistance in model calibration through all stages and Dr. R. Connolly for his help and advice in modeling. We also thank Mr. B. Jahnke and Mr. G. Groth for technical assistance throughout this work.
References (29)
Weather and other environmental factors influencing crop responses to tillage and traffic
Soil Till. Res.
(1988)Modelling effects of soil structure on the water balance of soil–crop systems: a review
Soil Till. Res.
(1998)- et al.
Wheel traffic and tillage effects on runoff and crop yield
Soil Till. Res.
(2007) Soil and water management and conservation—a review of the USDA SCS curve number method
Aust. J. Soil Res.
(1989)- Connolly, R.D., 2000. Improved methodology for simulating infiltration in soil–crop systems. Ph.D. Thesis. School of...
- et al.
Antecedent rainfall and tillage effects upon infiltration
Soil Sci. Soc. Am. J.
(1989) - et al.
Optimising soil surface management in response to climatic risk
Soil water
- et al.
Using curve numbers from simulated rainfall to describe the runoff characteristics of contour bay catchments
The Australian soil classification
(2002)
Traffic and residue cover effects on infiltration
Aust. J. Soil Res.
PERFECT (Version 1. 0): A Computer Simulation Model of Production Erosion Runoff Functions to Evaluate Conservation Techniques
Cited by (15)
Controlled traffic farming effects on productivity of grain sorghum, rainfall and fertiliser nitrogen use efficiency
2021, Journal of Agriculture and Food ResearchCitation Excerpt :These effects can have off-farm environmental impacts such as increased risk of diffuse pollution [4,5] and greenhouse gas emissions [6,7], and significantly reduce plant available water capacity (PAWC) [8]. These are important considerations for dryland cropping systems that rely on rainfall and soil water conservation for successful crop establishment [9]. Compaction is often persistent, particularly in the subsoil, and its alleviation through tillage is both energy-demanding and transient [10].
A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments
2020, Environmental Modelling and SoftwareCombinations of tall standing and horizontal residue affect soil water dynamics in rainfed conservation agriculture systems
2015, Soil and Tillage ResearchCitation Excerpt :Soil and crop management practices that enhance the quantity of soil water, and its availability, are likely to increase yield and overall productivity (Imaz et al., 2010). Indeed, water stress is the main limiting factor for crop production in many parts of Australia (Tullberg et al., 2007); therefore research on various aspects of soil water and management in conservation agriculture has received attention in recent years (Li et al., 2008). The architecture of crop residues (stubble)—defined here as the arrangement of standing and horizontally-distributed residues—can alter the surface microclimate and impact water storage in the soil (McMaster et al., 2000).
Considering cost accountancy items in crop production simulations under climate change
2014, European Journal of AgronomyCitation Excerpt :However, many approaches consider prices as the only driver when impacts on agricultural production are investigated (Fischer et al., 2005; Bathgate et al., 2009). Agricultural production at the plot scale is modelled to assess the impact of changing framework conditions, such as climate (Strauss et al., 2009), policies (Wei et al., 2009; Nendel, 2009; Shepherd and Chambers, 2007) or other factors (Li et al., 2008), to develop production strategies on the basis of alternative management practices (Bell et al., 2009; Launay et al., 2009; Saseendran et al., 2008; Timsina et al., 2008) or to investigate feedback relations with adjacent (sub-)systems, such as groundwater (Qureshi et al., 2008) or soil organic matter (Robertson et al., 2009). In these cases, the economic element is defined by gross margins and other farm accountancy figures.