Effects of tillage and traffic on crop production in dryland farming systems: I. Evaluation of PERFECT soil-crop simulation model

doi:10.1016/j.still.2008.04.004

Soil and Tillage Research

Volume 100, Issues 1–2, July–August 2008, Pages 15-24

https://doi.org/10.1016/j.still.2008.04.004 Get rights and content

Abstract

Agricultural production systems are complex involving variability in climate, soil, crop, tillage management and interactions between these components. The traditional experimental approach has played an important role in studying crop production systems, but isolation of these factors in experimental studies is difficult and time consuming. Computer simulation models are useful in exploring these interactions and provide a valuable tool to test and further our understanding of the behavior of soil–crop systems without repeating experimentation.

Productivity erosion and runoff functions to evaluate conservation techniques (PERFECT) is one of the soil–crop models that integrate the dynamics of soil, tillage and crop processes at a daily resolution. This study had two major objectives. The first was to calibrate the use of the PERFECT soil–crop simulation model to simulate soil and crop responses to changes of traffic and tillage management. The second was to explore the interactions between traffic, tillage, soil and crop, and provide insight to the long-term effects of improved soil management and crop rotation options. This contribution covers only the first objective, and the second will be covered in a subsequent contribution.

Data were obtained from field experiments on a vertisol in Southeast Queensland, Australia which had controlled traffic and tillage treatments for the previous 5 years. Input data for the simulation model included daily weather, runoff, plant available water capacity, and soil hydraulic properties, cropping systems, and traffic and tillage management. After model calibration, predicted and measured total runoffs for the 5-year period were similar. Values of root mean square error (RMSE) for daily runoff ranged from 5.7 to 9.2 mm, which were similar to those reported in literature. The model explained 75–95% of variations of daily, monthly and annual runoff, 70–84% of the variation in total available soil water, and 85% of the variation in yield. The results showed that the PERFECT daily soil–crop simulation model could be used to generate meaningful predictions of the interactions between crop, soil and water under different tillage and traffic systems.

Ranking of management systems in order of decreasing merit for runoff, available soil water and crop yield was (1) controlled traffic zero tillage, (2) controlled traffic stubble mulch, (3) wheeled zero tillage, and (4) wheeled stubble mulch.

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 (CN_II) and the saturated hydraulic conductivity (K_sat) 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 K_sat

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 CN_II and K_sat values

Optimized values of CN_II and K_sat 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 CN_II was CZT (78), CSM (89), WZT (92) and WSM (93) (Table 2). K_sat of the lower layer under controlled traffic was four times higher than under

CN_II and K_sat calibrations

This study used both CN_II and K_sat 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 (K_sat) 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 CN_II established from rainfall simulation tests. Calibration was accomplished by adjusting K_sat, and CN_II, 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)

F.R. Boone
Weather and other environmental factors influencing crop responses to tillage and traffic
Soil Till. Res.
(1988)
R.D. Connolly
Modelling effects of soil structure on the water balance of soil–crop systems: a review
Soil Till. Res.
(1998)
Y.X. Li et al.
Wheel traffic and tillage effects on runoff and crop yield
Soil Till. Res.
(2007)
W.C. Boughton
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...
D.M. Freebairn et al.
Antecedent rainfall and tillage effects upon infiltration
Soil Sci. Soc. Am. J.
(1989)
D.M. Freebairn et al.
Optimising soil surface management in response to climatic risk
E.A. Gardner
Soil water
S.G. Glanville et al.
Using curve numbers from simulated rainfall to describe the runoff characteristics of contour bay catchments
R. Isbell
The Australian soil classification
(2002)

Knisel, W.G., 1980. CREAMS: a field-scale model for chemicals, runoff, and erosion from agricultural management...

Y.X. Li et al.

Traffic and residue cover effects on infiltration

Aust. J. Soil Res.

(2001)

Li, Y.X., 2001. Traffic and tillage effects on dryland cropping systems: a field and simulation study. Ph.D. Thesis....

M. Littleboy et al.

PERFECT (Version 1. 0): A Computer Simulation Model of Production Erosion Runoff Functions to Evaluate Conservation Techniques

(1989)

Cited by (15)

Controlled traffic farming effects on productivity of grain sorghum, rainfall and fertiliser nitrogen use efficiency
2021, Journal of Agriculture and Food Research
Citation 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].
Controlled traffic farming (CTF) is a mechanisation system in which all machinery has the same (or modular) working and track width so that field traffic can be confined to the least possible area of permanent traffic lanes. CTF enables productivity of non-compacted crop beds to be optimised for given energy, fertiliser and water (rainfall) inputs. This study investigated the agronomic response and economic return of grain sorghum grown in compacted and non-compacted soils to represent the conditions of non-CTF and CTF systems, respectively. Yield-to-nitrogen (N) responses were derived following application of urea, 3,4-dimethyl pyrazole phosphate-treated urea (DMPP), and urea ammonium nitrate (UAN, 32% N) at rates between 0 and 300 kg ha⁻¹ N. Selected soil properties were measured to guide parametrisation of the Agricultural Production Systems Simulator (APSIM), which was used to assess long-term (55 years) effects of CTF and non-CTF soil conditions on crop productivity, rainfall use efficiency (RUE) and develop rainfall-runoff relationships. Grain yield and yield components (harvest Index, grain thousand-grain weight, number of grains) were significantly higher in CTF compared with non-CTF. On average, the most economic N rates, and corresponding grain yields, were 144 and 3428 kg ha⁻¹, and 100 and 1796 kg ha⁻¹ for CTF and non-CTF, respectively. When N inputs were optimised, agronomic efficiency calculations showed 18% increase in CTF compared with non-CTF. Nitrogen use efficiency (NUE) was 1.75 times higher in CTF than in non-CTF. Rainfall-use efficiency was about 65% higher in CTF, which concurrently reduced the amount of runoff compared with non-CTF. Average rainfall season (330–450 mm in-crop) grain yield was 30% lower in non-CTF compared with CTF. For subtropical conditions of Australia, long-term APSIM simulations showed that increased productivity and inter-season yield stability can increase gross margin of grain sorghum by AUD74 ha⁻¹ or greater depending on the adopted tillage system and in-crop rainfall. In non-CTF systems, improvements in NUE and RUE are constrained by soil compaction. Enhanced efficiency fertilisers, such as DMPP-treated urea, cannot compensate for other stresses caused by soil compaction and therefore cannot achieve the same NUE and RUE as the CTF system. Adoption of CTF delivers improved resource-use efficiency and profitability in rainfall-limited environments.
A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments
2020, Environmental Modelling and Software
Australian and Queensland Government's Reef 2050 Water Quality Improvement Plan has set targets for improving the water quality entering the Great Barrier Reef lagoon. Given the large public investment and the deficit of data linking on-farm land management to changes in environmental outcomes, there is a need for a robust and efficient methods of quantifying links between land management and water quality. This paper explores a pragmatic approach to making this link using available data. We demonstrate that a simple parameterisation process is suitable for estimating hydrology and water quality across a wide range of land uses and management practices in agricultural landscapes. However, a manually calibrated model may still require the analysis of parameters to reduce error variances and evaluate uncertainties. Confidence in estimating hydrology and water quality in descending order is: runoff, sediment, nitrogen, phosphorous, and pesticide losses, reflecting the availability of data and inherent error propagation.
Combinations of tall standing and horizontal residue affect soil water dynamics in rainfed conservation agriculture systems
2015, Soil and Tillage Research
Citation 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).
Residue retention is a key component of modern conservation agriculture systems. However, conflicting evidence exists on the role of residue and its architecture on the capture of rainfall and subsequent water storage in the soil in rainfed environments. Experiments were conducted in 2010 and 2011, on a sandy soil, to investigate the effect of crop residue height and architecture on the capture of rainfall and spatial variability of soil water across the standing-residue rows. We hypothesised that: (1) high residue rates, particularly horizontal residue, prevent water reaching the ground during small rainfall events, but maximise soil water retention during larger rainfall events; and (2) tall standing-residue increases the capture of rainfall and reduces evaporation from the soil surface, but increases spatial variability of soil water. To test this we investigated the effect of combinations of vertical (0–0.3 m) and horizontal (0–4.0 t ha⁻¹) residues on water capture (using simulated rainfall, sprinkler irrigation and rainfall events), and the quantity and spatial distribution of water stored in the soil. The quantity of water intercepted by residue in the inter-row position increased with rate of horizontal residue and standing-residue height. However, the quantity of stored water concurrently increased under and adjacent to the standing-residue row, suggesting that some of this ‘lost’ water was intercepted by the high-cut residue, much of which infiltrated deeper into the soil, where it was less prone to evaporation. The quantity of stored soil water increased with increasing residue cut-height and horizontal residue, with bare soil having the least water. Most soil water was stored with straw cut high (0.25–0.3 m) in combination with at least 2 t ha⁻¹ horizontal residue, with soil directly under the standing straw row having the most water. Thus, retaining tall standing-residue in combination with horizontal residues can increase soil water capture in the no-till system.
Considering cost accountancy items in crop production simulations under climate change
2014, European Journal of Agronomy
Citation 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.
A Farm Economy Coefficient Generator (FECG) is presented as a post-processing tool for agro-ecological simulation models (ASMs) that deliver yields and consumption figures for nitrogen fertiliser and irrigation water use. The FECG carries out simple cost accountancy calculations using typical production techniques, costs and prices for the selected area. The default settings can be adjusted by the user to specifically describe local farm management. Together with various ASMs, the FECG is embedded in a web-based, geo-referenced decision support system to enable users to extract information on future yields and net returns on a regional or farm-level basis. Using the example of a winter wheat–winter barley–silage maize crop rotation in Germany and downscaled A1B climate data it was demonstrated how the ASM–FECG tandem can be used to analyse the cost and benefit of irrigation. In this example the irrigation costs would still outweigh the yield increase today, while in around 2070 the increased difference between potential and water-limited yield is likely to make irrigation profitable, provided that costs and price levels remain constant. The susceptibility of these results to changing market conditions is discussed.
Effects of tillage and traffic on crop production in dryland farming systems: II. Long-term simulation of crop production using the PERFECT model
2008, Soil and Tillage Research
Soil water conservation is critical to long-term crop production in dryland cropping areas in Northeast Australia. Many field studies have shown the benefits of controlled traffic and zero tillage in terms of runoff and soil erosion reduction, soil moisture retention and crop yield improvement. However, there is lack of understanding of the long-term effect of the combination of controlled traffic and zero tillage practices, as compared with other tillage and traffic management practices.
In this study, a modeling approach was used to estimate the long-term effect of tillage, traffic, crop rotation and type, and soil management practices in a heavy clay soil. The PERFECT soil–crop simulation model was calibrated with data from a 5-year field experiment in Northeast Australia in terms of runoff, available soil water and crop yield; the procedure and outcomes of this calibration were given in a previous contribution. Three cropping systems with different tillage and traffic treatments were simulated with the model over a 44-year-period using archived weather data.
Results showed higher runoff, and lower soil moisture and crop production with conventional tillage and accompanying field traffic than with controlled traffic and zero tillage. The effect of traffic is greater than the effect of tillage over the long-term. The best traffic, tillage and crop management system was controlled traffic zero tillage in a high crop intensity rotation, and the worst was conventional traffic and stubble mulch with continuous wheat. Increased water infiltration and reduced runoff under controlled traffic resulted in more available soil water and higher crop yield under opportunity cropping systems.
Effects of farming systems, tillage, and traffic practices on deep drainage and soil salt loads in the Queensland Murray-Darling and Fitzroy Basins using soil chloride
2022, Soil Research

View all citing articles on Scopus

View full text

Effects of tillage and traffic on crop production in dryland farming systems: I. Evaluation of PERFECT soil-crop simulation model

Abstract

Introduction

Section snippets

Curve number and Ksat

Description of experimental site

Weather and runoff data

Optimization of CNII and Ksat values

CNII and Ksat calibrations

Conclusion

Acknowledgements

Soil Till. Res.

Soil Till. Res.

Soil Till. Res.

Soil and water management and conservation—a review of the USDA SCS curve number method

Aust. J. Soil Res.

Antecedent rainfall and tillage effects upon infiltration

Soil Sci. Soc. Am. J.

Optimising soil surface management in response to climatic risk

Soil water

Using curve numbers from simulated rainfall to describe the runoff characteristics of contour bay catchments

The Australian soil classification

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

Curve number and K_sat

Optimization of CN_II and K_sat values

CN_II and K_sat calibrations