Simulation of Tillage Systems Impact on Soil Biophysical Properties Using the SALUS Model

Basso, Bruno; Ritchie, J. T.; Grace, Peter; Sartori, Luigi

doi:10.4081/ija.2006.677

Cited by 57 publications

(38 citation statements)

References 25 publications

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“…Example soil management strategies include conventional tillage versus no-till farming (e.g., Ghimire et al, 2012;Hobbs et al, 2008), crop rotations (e.g., Johnston, 1986;Odell et al, 1984), and off-season cover crops (e.g., Allen et al, 2005;Havlin et al, 1990). Conservation agriculture incorporates these land management strategies to increase soil fertility by preserving surface organic carbon, protecting soil from water runoff, and reducing soil loss by eliminating bare exposure (Basso et al, 2006;Hobbs et al, 2008). Managing soils to improve fertility reduces the demand for additional water applications.…”

Section: Soil Managementmentioning

confidence: 99%

Complex water management in modern agriculture: Trends in the water-energy-food nexus over the High Plains Aquifer

Smidt

Haacker

Kendall

et al. 2016

Science of The Total Environment

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115

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In modern agriculture, the interplay between complex physical, agricultural, and socioeconomic water use drivers must be fully understood to successfully manage water supplies on extended timescales. This is particularly evident across large portions of the High Plains Aquifer where groundwater levels have declined at unsustainable rates despite improvements in both the efficiency of water use and water productivity in agricultural practices. Improved technology and land use practices have not mitigated groundwater level declines, thus water management strategies must adapt accordingly or risk further resource loss. In this study, we analyze the water-energy-food nexus over the High Plains Aquifer as a framework to isolate the major drivers that have shaped the history, and will direct the future, of water use in modern agriculture. Based on this analysis, we conclude that future water management strategies can benefit from: (1) prioritizing farmer profit to encourage decision-making that aligns with strategic objectives, (2) management of water as both an input into the water-energy-food nexus and a key incentive for farmers, (3) adaptive frameworks that allow for short-term objectives within long-term goals, (4) innovative strategies that fit within restrictive political frameworks, (5) reduced production risks to aid farmer decision-making, and (6) increasing the political desire to conserve valuable water resources. This research sets the foundation to address water management as a function of complex decision-making trends linked to the water-energy-food nexus. Water management strategy recommendations are made based on the objective of balancing farmer profit and conserving water resources to ensure future agricultural production.

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Section: Soil Managementmentioning

confidence: 99%

Complex water management in modern agriculture: Trends in the water-energy-food nexus over the High Plains Aquifer

Smidt

Haacker

Kendall

et al. 2016

Science of The Total Environment

Self Cite

115

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show abstract

“…Therefore N management tools that simultaneously consider dynamics in soil organic carbon mineralization, crop growth, weather conditions, and agronomic practices may greatly improve site- and year-specific EONR estimates (Basso et al, 2012, 2016; Dumont et al, 2016). Dynamic cropping system simulation models such as Agricultural Production Systems sIMulator (APSIM; Holzworth et al, 2014), DSSAT (Jones et al, 2003), RZWQM (Ahuja et al, 2000), CropSyst (Stockle et al, 2003), SALUS (Basso et al, 2006), and others have been used to investigate soil-crop-weather dynamics, however, model use has been limited to address long-term optimum N rates (Ma et al, 2007; Basso et al, 2010). The scientific literature is also rich with examples of model applications to improve our understanding of N dynamics and to answer questions that cannot be addressed with field research due to time and cost constraints (Batchelor et al, 2002; Schnebelen et al, 2004; Fountas et al, 2006; Malone et al, 2010; Basso et al, 2012, 2016; Anapalli et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

et al. 2016

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Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

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“…RCP 2.6 represents a scenario where stringent mitigation plans would be implem-ented and future global temperature is no more than 2°C higher than preindustrial temperatures. 2 Overview of the SALUS model (Basso et al, 2006). We chose the most stringent mitigation scenario (RCP 2.6) and one stabilization pathway (RCP 6.0).…”

Section: Salus Model Inputs Used In This Studymentioning

confidence: 99%

Spatial evaluation of switchgrass productivity under historical and future climate scenarios in Michigan

Liu

Basso

2016

GCB Bioenergy

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Switchgrass (Panicum virgatum) productivity on marginal and fertile lands has not been thoroughly evaluated in a systematic manner that includes soil–crop–weather–management interactions and to quantify the risk of failure or success in growing the crop. We used the Systems Approach to Land Use Sustainability (SALUS) model to identify areas with low risk of failing to having more than 8000 kg ha−1 yr−1 switchgrass aboveground net primary productivity (ANPP) under rainfed and unfertilized conditions. In addition, we diagnosed constraining factors for switchgrass growth, and tested the effect of nitrogen fertilizer application on plant productivity across Michigan for 30 years under three climate scenarios (baseline climate in 1981–2010, future climate with emissions using RCP 2.6 and RCP 6.0). We determined that <16% of land in Michigan may have at least 8 Mg ha−1 yr−1 ANPP under rainfed and unfertilized management with a low risk of failure. Of the productive low‐risk land, about 25% was marginal land, with more than 80% of which was affected by limited water availability due to low soil water‐holding capacity and shallow depth. About 80% of the marginal land was N limited under baseline conditions, but that percentage decreased to 58.5% and 42.1% under RCP 2.6 and RCP 6.0 climate scenarios, respectively, partly due to shorter growing season, smaller plants and less N demand. We also found that the majority of Michigan's land could have high switchgrass ANPP and low risk of failure with no more than 60 kgN ha−1 fertilizer input. We believe that the methodology used in this study works at different spatial scales, as well as for other biofuel crops.

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Simulation of Tillage Systems Impact on Soil Biophysical Properties Using the SALUS Model

Cited by 57 publications

References 25 publications

Complex water management in modern agriculture: Trends in the water-energy-food nexus over the High Plains Aquifer

Complex water management in modern agriculture: Trends in the water-energy-food nexus over the High Plains Aquifer

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Spatial evaluation of switchgrass productivity under historical and future climate scenarios in Michigan

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