Keith A. Janssen

Soil loss predictions with three erosion simulation models

Abstract Quantification of soil loss is one of the greatest challenges in natural resources and environmental planning. Computer simulation models are becoming increasingly popular in predicting soil loss for various land use and management practices. In this study, three soil erosion prediction models — the Water Erosion Prediction Project (WEPP), the Erosion Productivity Impact Calculator (EPIC), and the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS) were used for simulating soil loss and testing the capability of the models in predicting soil losses for three different tillage systems (ridge-till, chisel-plow, and no-till). For each model, the most sensitive model parameters were calibrated using measured soil erosion data. After calibration, models were run and predicted soil loss values were compared with the measured soil loss values. The measured soil erosion data were collected from an erosion experiment field of Kansas State University at Ottawa (Kansas), USA. Field experiments were conducted from 1995 to 1997 on small plots to measure runoff and soil losses under all three tillage systems. All three models were evaluated on the basis of individual event, total yearly, and mean event-based soil loss predictions. Results showed that all the three models performed reasonably well and the predicted soil looses were within the range of measured values. For ridge-till and chisel-plow systems, WEPP and ANSWERS gave better predictions than those by EPIC model. For no-till system, WEPP and EPIC predictions were better than those by ANSWERS. The overall results indicate that WEPP predictions were better than those by the other two models in most of the cases, and it can be used with reasonable degree of confidence for soil loss quantification for all the three tillage systems.

Soil and crop response to stover removal from rainfed and irrigated corn

Excessive corn (Zea mays L.) stover removal for biofuel and other uses may adversely impact soil and crop production. We assessed the effects of stover removal at 0, 25, 50, 75, and 100% from continuous corn on water erosion, corn yield, and related soil properties during a 3‐year study under irrigated and no‐tillage management practice on a Ulysses silt loam at Colby, irrigated and strip till management practice on a Hugoton loam at Hugoton, and rainfed and no‐tillage management practice on a Woodson silt loam at Ottawa in Kansas, USA. The slope of each soil was <1%. One year after removal, complete (100%) stover removal resulted in increased losses of sediment by 0.36–0.47 Mg ha−1 at the irrigated sites, but, at the rainfed site, removal at rates as low as 50% resulted in increased sediment loss by 0.30 Mg ha−1 and sediment‐associated carbon (C) by 0.29 kg ha−1. Complete stover removal reduced wet aggregate stability of the soil at the irrigated sites in the first year after removal, but, at the rainfed site, wet aggregate stability was reduced in all years. Stover removal at rates ≥ 50% resulted in reduced soil water content, increased soil temperature in summer by 3.5–6.8 °C, and reduced temperature in winter by about 0.5 °C. Soil C pool tended to decrease and crop yields tended to increase with an increase in stover removal, but 3 years after removal, differences were not significant. Overall, stover removal at rates ≥50% may enhance grain yield but may increase risks of water erosion and negatively affect soil water and temperature regimes in this region.

Modeling runoff and sediment yields from combined in-field crop practices using the Soil and Water Assessment Tool

Cropland best management practice recommendations often combine improvements to both tillage and fertilizer application practices to reduce sediment losses with surface runoff. This study evaluated the impact of conventional-till and no-till management practices with surface or deep-banded fertilizer application in sorghum-soybean rotation on runoff and sediment-yield predictions using the Soil and Water Assessment Tool (SWAT) model. The model was calibrated using USDA Natural Resources Conservation Service runoff curve number for antecedent moisture condition II (CNII), saturated hydraulic conductivity, and available water capacity parameters for runoff and USLE cropping factor (Cmin) for sediment-yield predictions for three field plots (0.39 to 1.46 ha [0.96 to 3.6 ac]) with different combinations of practices and validated for three field plots (0.40 to 0.56 ha [1.0 to 1.4 ac]) over a period of 2000 to 2004. Surface runoff calibration required CNII values greater than the recommended baseline values. No-till treatments required slightly greater curve number values than the till treatment, and this difference was similar to that associated with increasing the soil hydrologic group by one classification. Generally the model underpredicted the sediment yield for all management practices. Baseline Cmin values were adequate for treatments with soil disturbance, either by tillage or fertilizer deep-banding, but best-fit Cmin values for field conditions without soil disturbance (no-till with surface-broadcast fertilizer) were 2.5 to 3 times greater than baseline values. These results indicate current model limitations in modeling undisturbed (no-till) field management conditions, and caution that models calibrated for fields or watersheds predominated by tilled soil conditions may not function equally well in testing management scenarios without tillage.

A field-based assessment tool for phosphorus losses in runoff in Kansas

Nonpoint P sources from the agricultural landscape are a significant environmental problem for surface water bodies because of the promotion of eutrophication. Many states have developed P assessment tools to help differentiate land uses and their potential for P losses to surface water. Kansas has developed such a P index (PI), and the purposes of this paper are to report on the calibration of that index against data collected from four runoff studies and to explore the modification of the PI as a means to improve the predictive capability of the PI. The PI includes soil test P, rate and application method for P from fertilizers and manure, soil erosion, runoff class, distance from surface water bodies, and irrigation erosion as inputs. As originally proposed, the PI was well correlated with soluble P (r = 0.93) and bioavailable P (r = 0.94) losses but was less correlated with total P (r = 0.79). By modification of the PI, the r values improved to 0.97 for bioavailable P, 0.95 for soluble P, and 0.89 for total P. Of the 90 plots at four different sites, plots from Neosho and Franklin-1 and Franklin-2 sites were ranked as having very low and low vulnerability to P loss (82%) whereas plots in the Riley County site were ranked as high and very high vulnerability to P loss (18%) due to manure applications. Therefore, for only the Riley site, P management strategies need to be modified to reduce P losses. Moreover, additional P applications are not warranted for this site. Using soil test P as a single factor to predict P losses in runoff for our sites produced results similar to using the modified PI.

Modeling Nutrient Runoff Yields from Combined In-Field Crop Management Practices Using SWAT

Cropland best management practice recommendations often combine tillage and nutrient application improvements to reduce nutrient losses with surface runoff. This study used the Soil and Water Assessment Tool (SWAT) model to evaluate nutrient runoff yields from conventional-till and no-till management practices with surface and deep-banded fertilizer application in a sorghum-soybean rotation. The model was calibrated for three field plots (0.39 to 1.46 ha) with different combinations of practices and validated for three field plots (0.40 to 0.56 ha) during 2001 to 2004. Daily performance of the calibrated SWAT model in simulating total N for all treatments was satisfactory for median-based Nash-Sutcliffe model efficiency (Ef* of 0.54 to 0.64), good to very good for percent bias (PBIAS of 31% to 7%), and satisfactory to good for median-based root mean square error to observations standard deviation ratio (RSR* of 0.72 to 0.62). Performance was slightly lower and more variable for total P calibration (Ef* of 0.42 to 0.62, PBIAS of -48% to 2%, and RSR* of 0.76 to 0.62). Monthly statistics improved for total P runoff yield compared to daily performance, but changed little for total N runoff yields, probably due to the stronger influence of outliers in the N data. Based on validation results, SWAT was more robust in simulating total N runoff yields from the treatment with less soil disturbance (NT/SB) and total P for the two treatments with more soil disturbance (NT/DB and TILL). A major concern was that SWAT predicted greater annual average total N runoff yields for no-till treatments than for tilled treatments, which was contrary to measured values at the study site. This reinforces a fundamental research issue that tillage system effects on nutrient losses are still very much uncertain and thus may not be properly modeled. The SWAT model generally underpredicted monthly total N yields for all treatments in the higher-precipitation months of May and June and overpredicted total N and total P yields from September through November. Calibration for N and P resulted in identical calibration parameters for NPERCO (1.0), RSDCO (0.05), BIOMIX (0.2), PPERCO (10), PHOSKD (175), and UBP (50) regardless of tillage practice or fertilizer application method. Together with results that calibrated parameters for runoff (CN, Ksat, AWC) and erosion (Cmin) differed among the treatments, this study found that differences in nutrient yields among tillage and fertilizer management may be adequately modeled with SWAT by calibrating runoff and sediment yields only, and that further calibration of nutrient parameters may not improve model results.

Calibration of the APEX Model to Simulate Management Practice Effects on Runoff, Sediment, and Phosphorus Loss

Process-based computer models have been proposed as a tool to generate data for Phosphorus (P) Index assessment and development. Although models are commonly used to simulate P loss from agriculture using managements that are different from the calibration data, this use of models has not been fully tested. The objective of this study is to determine if the Agricultural Policy Environmental eXtender (APEX) model can accurately simulate runoff, sediment, total P, and dissolved P loss from 0.4 to 1.5 ha of agricultural fields with managements that are different from the calibration data. The APEX model was calibrated with field-scale data from eight different managements at two locations (management-specific models). The calibrated models were then validated, either with the same management used for calibration or with different managements. Location models were also developed by calibrating APEX with data from all managements. The management-specific models resulted in satisfactory performance when used to simulate runoff, total P, and dissolved P within their respective systems, with > 0.50, Nash-Sutcliffe efficiency > 0.30, and percent bias within ±35% for runoff and ±70% for total and dissolved P. When applied outside the calibration management, the management-specific models only met the minimum performance criteria in one-third of the tests. The location models had better model performance when applied across all managements compared with management-specific models. Our results suggest that models only be applied within the managements used for calibration and that data be included from multiple management systems for calibration when using models to assess management effects on P loss or evaluate P Indices.

Corn Response as Affected by Planting Distance from the Center of Strip-Till Fertilized Rows

Strip-till has been used at a large scale in east central Kansas as an alternative to earlier planting dates under a no-till system. To determine the effects of planting corn (Zea mays) under previously established strip-tilled fertilized rows, experiments were conducted on an Osage silty clay loam soil in 2006 and 2008 and on a Woodson silt loam soil in 2009, 2010, and 2011 using three different planting distances from the strip-tilled fertilized rows (0, 10, 20, and 38 cm) with a strip-till operation performed between 1 and 73 days before planting. The depth of the strip-till fertilizer application was 13–15 cm below the soil surface. Corn that was planted 10 cm from the fertilized row showed greater early season growth, higher plant population, and grain yield. Planting 20 and 38 cm from the center of the fertilized rows showed none of the benefits that are typically associated with strip-tillage system. Enough time should be allowed between the strip-till operation and planting to reach satisfactory soil conditions (e.g., moist and firm seedbed). Our results suggest that the best location for planting strip-tilled fertilized corn vary depending on soil and climatic conditions as well as the time between fertilizer application with the strip-till operation and planting. With fewer number of days, planting directly on the center of fertilized strip-till resulted in decreased plant population and lower grain yield. However, the greatest yield benefit across different planting conditions was attained when planting within 10 cm of the strip.

Calibration and Validation of SWAT for Field-scale Sediment-Yield Prediction

Numerous best management practices (BMPs) are available to reduce soil erosion and sediment in runoff from cropland, where non-point source pollution is a serious problem. Water quality models are being extensively used to simulate agricultural practices. The objective of this study was to calibrate and validate sediment-yield predictions by identifying the best USLE C factor using SWAT water quality model for field-scale till and no-till management practices over a period of 2000-2004. USLE C factor, selected as the calibration parameter, was varied within ± 200% from the “text book” (baseline) value for each plot (including 3 calibration and 3 validation plots). The SWAT model under predicted the sediment-yield compared to measured sediment-yield for all the treatments. No-till deep banded application resulted in the highest daily modeling efficiency (Ef* = 0.60). Generally, model performed well in simulating the validation plots (R2: 0.21 to 0.44 and Ef*: -0.81 to 0.49). No-till deep-banded application gave the best simulation statistics. The results of this study can be used to help modeling other fields and watersheds where no measured data are available for simulating best management practices.

Sensitivity Analysis of SWAT Nutrient Modeling under different Management Practices

Water quality due to agricultural management practices is an issue, where non-point source pollution such as nutrient losses from cropland is a major concern in the U.S. Soil and Water Assessment Tool (SWAT) model is one of the most cost effective tools used to simulate numerous best management practices (BMPs) to reduce agricultural pollutants especially nutrients in crop lands. The objectives of this study were to assemble all necessary field data from the Integrated Agricultural management Systems (IAMS) Franklin site in Kansas to calibrate SWAT using sensitivity analysis approach, and validate the model performance to evaluate the effectiveness of BMPs for nutrient reduction. In this study, SWAT model was calibrated using measured data over the field plots in sorghum-soybean cropping sequence for a period of 2001-2004. Combination of tillage and nutrient application methods tested were: till (chisel) with fertilizer broadcast then incorporated, no-till with fertilizer broadcast without incorporation, and no-till with fertilizer incorporated (knifed). The model predicted total-N and total-P were compared with the measured values at each plot for daily and monthly time step using median based Nash-Sutcliffe efficiency (Ef) and coefficient of determination (R2). Calibrated daily total-N model predicted well for no-till surface broadcast with higher Ef>0.60 and R2>0.65. In all the plots, calibrated monthly total-N losses were over predicted during high precipitation months of May, June, and July. Model performed well in simulating the validation plots with best daily statistics for no-till surface broadcast. Calibration results showed reduced total-P losses for no-till with fertilizer incorporated. The model performed well in simulating the validation plots and no-till with fertilizer incorporated gave the highest daily Ef of 0.58. Sensitivity analysis of the model showed that the nutrients transported from cropland in southeastern Kansas were most sensitive to the parameters like, nitrogen percolation coefficient, residue decomposition coefficient, biological mixing coefficient for total-N and biological mixing coefficient, phosphorous uptake distribution parameter, phosphorous percolation coefficient for total-P. Overall, the results showed that SWAT was able to predict the effects of BMPs on nutrient losses and model is a useful tool for simulating BMPs in other fields and watersheds where no measured nutrients data are available.