Pérez-Gutiérrez, J. D., & Kumar, S. 2019.Simulating influence of integrated crop-livestock systems

Journal of Environmental Management 239 (2019) 385–394

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Research article

Simulating the influence of integrated crop-livestock systems on water yield at watershed scale

T

Juan D. Pérez-Gutiérrez, Sandeep Kumar∗ Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Edgar S. McFadden Biostress Laboratory, Brookings, SD 57006, USA

ARTICLE INFO

ABSTRACT

Keywords: SWAT model Grazing Cover crops Integrated crop-livestock systems Water yield Runoff Groundwater Lateral flow Corn-soybean-oats Barley

Integrated crop-livestock (ICL) systems are being promoted as environmentally favorable alternatives to traditional crop agriculture and livestock production. There are few, if any, evaluation studies of the hydrologic response of watersheds to the implementation of ICL systems. Thus, we applied the Soil and Water Assessment Tool (SWAT) model to simulate the potential impacts of ICL systems on water yield and its hydrological components using a large agricultural dominated watershed. In this study, the integration of grazing operations with cropping systems represented cattle grazing under three typical crop rotations: (i) continuous corn (Zea mays L.; 1-year rotation), (ii) conventional (corn-soybean [Glycine max (L.) Merr.]; 2-year rotation), and (iii) winter cover crops (corn-soybean-oats (Avena sativa L.)/winter barley (Hordeum vulgare L.); 3-year rotation). Modeling results showed a significant reduction in water yield over a long-term period simulation (31 years) when grazing of corn residue or winter barley was scheduled within the rotations. When compared to scenarios without grazing operations, the reduction in water yield was 14.7% under corn-soybean rotation (corn as the forage grazed), 12.5% under continuous corn rotation, 6.4% under corn-soybean-oats/winter barley rotation (corn as the forage grazed), and 3% under corn-soybean-oats/winter barley rotation (winter barley as the forage grazed). Of the three components that constitute water yield (i.e., surface runoff, lateral and groundwater flow), only surface runoff was reduced when integrating grazing into the cropping system. Instead, lateral and groundwater flows increased when ICL systems were scheduled in the watershed. Groundwater flow was the hydrological component with the highest relative impact on streamflow. These results indicate that ICL systems can positively affect processes involved in soil water storage and transit. Runoff reduction benefits of ICL systems might be helpful in improving the environmental quality of receiving waterbodies and in reducing flood-risk potential.

These systems over the long-term could benefit the watershed's hydrological cycle through increased baseflow.

Overall, this study suggests new watershed-scale benefits of ICL systems with important hydrological implications that might be of interest for agricultural watershed planners.

1. Introduction Recent projections indicate that the world population could be increased to 12.3 billion people by 2100 (Gerland et al., 2014). To feed this population, research has suggested that agriculture must double yields (Tilman et al., 2002, 2011; Tilman and Clark, 2015). The increased agricultural yields may be an achievable task through the intensification and expansion of farming accompanied by sufficient water supply to ensure improved crop growth and livestock survival (McNeill et al., 2017). This inextricable link between food production and water resources is vital to the subsistence of humankind. However, agricultural production drives unacceptable environmental impacts that include soil degradation, greenhouse gas emissions, and water quality

∗

impairment (Swain et al., 2018). Therefore, production systems that can increase agricultural yields while reducing adverse impacts on the environment, especially preserving water quantity and quality, are in the research spotlight.

In addition to intensification and expansion, specialization of agriculture plays an important role in enhancing agricultural yields.

Although economically successful, the specialized production systems based on undiversified, continuous, and short-term crop rotations are linked to a number of negative environmental effects (Lemaire et al., 2014) such as reduction or loss of crop diversity, soil organic matter and soil physical characteristics; increase of reservoirs' sedimentation, waterbodies’ eutrophication and pollution, insect and disease issues and many others (Sulc and Tracy, 2007). By looking back at agricultural

Corresponding author.

E-mail address: sandeep.kumar@sdstate.edu (S. Kumar).

https://doi.org/10.1016/j.jenvman.2019.03.068 Received 23 August 2018; Received in revised form 22 February 2019; Accepted 14 March 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Environmental Management 239 (2019) 385–394

J.D. Pérez-Gutiérrez and S. Kumar

approaches established in the U.S. before the World War II, research has suggested that diversification of agriculture can provide new opportunities to tackle undesirable effects triggered by current agricultural systems (Lemaire et al., 2014; Sulc and Tracy, 2007). Integration of crops with livestock is one example of diversification in agricultural systems which is gaining popularity among researchers, producers, and agricultural land managers because it can improve both soil productivity and environmental quality (Rakkar and Blanco-Canqui, 2018; Sulc and Franzluebbers, 2014).

Integrated crop-livestock (ICL) systems are being promoted as environmentally favorable alternatives to traditional cropping systems, especially across the U.S. Northern Great Plains. While there are opportunities to adopt a variety of ICL systems across the U.S. (Sulc and Franzluebbers, 2014), the typical ICL systems in the Northern Great Plains consist of grazing crop residues and grazing cover crops within crop rotations. Both crop residues and cover crops can be a valuable grazing forage source although corn residue grazing has been more commonly practiced (Rakkar and Blanco-Canqui, 2018; Schmer et al., 2017). Despite what type of forage source is grazed, there is a generalized concern among producers about the potential negative impacts of ICL systems that include soil compaction, water and wind erosion, reduced water infiltration, reduced soil fertility and carbon storage, increased greenhouse gas emissions, and lower agricultural yields. Yet, research has offered better insights into the soil properties and crop yield response to the implementation of ICL systems. For example, Rakkar and Blanco-Canqui (2018) synthesized a comprehensive review to study the impacts of crop residue grazing on soil properties and crop production. Their main findings were that grazing corn residue could (1) increase penetration resistance with no apparent crop yield reduction, (2) have little or no effect on soil bulk density and hydraulic properties, and (3) positively affect soil nutrients. Unlike grazing crop residues, grazing cover crops is still a research field that remains in its infancy. Grazing winter cover crops provides opportunities to reduce elevated feed costs during the cold season to grassland-based livestock producers (Schoonmaker et al., 2003; Sulc and Tracy, 2007). Therefore, ICL systems might be an eco-friendly and cost-effective strategy to achieve food security goals that can result in the increased implementation of these systems in agricultural lands in the near future.

Although the potential effects of ICL systems on soil properties and crop yields have been investigated, much less attention has been paid to evaluating the hydrologic response of watersheds to ICL systems implementation. Yet, this evaluation is critical to stakeholders, action agencies, and watershed managers to better identify where these systems can be implemented to preserve water quantity and quality and other ecosystem services. Thus, the specific objective of this study was to simulate the long-term impacts of ICL systems on water yield at a large watershed dominated by agricultural land use. To accomplish this objective, we used the Soil and Water Assessment Tool (SWAT) model to simulate the potential influence of ICL systems on water yield and its hydrological components at watershed scale, using the Big Sioux River (BSR) in South Dakota, USA as the case study.

1993).

According to climate data, derived from the National Oceanic and Atmospheric Administration (NOAA) databases for 1970 to 2009, the average daily temperature in the BSR watershed ranges from a minimum of −25 °C to a maximum of 28 °C observed during January and July, respectively. The watershed receives rain during 210 days on average every year (Fig. 2). This pattern results in average annual rainfall of 634 mm, with some dry and wet years adding up as little as 295 mm and as much as 849 mm of rain, respectively. Average monthly rain shows a unimodal shape that groups 74% of the rain depth from April to September. During this period, typical extreme events (99th percentile) are characterized by total rain depth between 21 and 60 mm day−1, mainly occurring during June.

Streamflow levels peak during March–July partly matching the rainy season in the watershed. Extreme levels (99th percentile) have been recorded between 420 and 1433 m3 s−1 during April and June due to snowmelt and maximum rainfall, respectively. Overall, base flow as low as 2.5 m3 s−1 governs the discharge pattern from September to February with most events occurring during the last two months of this period.

2.2. SWAT model SWAT is designed to evaluate the impact of land management practices on hydrological and water quality response of watersheds over long-term periods (Arnold et al., 1998, 2012; Neitsch et al., 2011).

This physically-based and semi-distributed model runs efficiently on a daily time step with low computational demand using readily available inputs. In general, the model partitions a watershed into smaller units to account for the spatial variability and complexity of watershed landscapes. The partition routine results in the delineation of several subwatersheds that are divided into areas defined as hydrological response units (HRUs). These units consist of unique combinations of land use and management, soil type, and topographical attributes. Hydrological processes occurring in land are calculated for each HRU and then aggregated to estimate the subwatershed loadings of water, sediments, nutrients, and other forms of nonpoint source (NPS) pollution to streams. A soil water balance is the basis of the in-land hydrological cycle simulation as expressed in Equation (1): t

SWt = SW0 +

(Rday

Qsurf

Ea

wseep

Qgw )

i=1

(1)

where SWt is the final soil water content (mm), SW0 is the initial soil water content on day i (mm), t is the time (days), Rday is the amount of precipitation on day i (mm), Qsurf is the amount of surface runoff on day i (mm), Ea is the amount of evapotranspiration on day i (mm), wseep is the amount of water entering the vadose zone from the soil profile on day i (mm), and Qgw is the amount of return flow on day i (mm). In addition, SWAT simulates plant growth and the associated soil water losses due to transpiration and plant uptake including nutritional requirements. NPS pollution loadings washed off by overland flow can also be estimated in SWAT simulations. Finally, in-streams processes modeled by SWAT route the loadings to the watershed outlet throughout the channel network. Additional details of the in-land and in-stream phases and other components of the SWAT model can be found in Neitsch et al. (2011).

2. Materials and methods 2.1. Study area The BSR watershed is located in the upper U.S. Midwest and covers a drainage area of approximately 21,375 km2, of which 67.6%, 18%, and 14.4% extend over the states of South Dakota, Minnesota, and Iowa, respectively (Fig. 1a). The landscape of the watershed is relatively flat with elevation ranging from 342 to 650 m. Most of the area in the watershed is used for corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] production although a significant portion remains covered by pasture (Fig. 1b). Loamy soils dominate the watershed and primarily belong to the hydrologic soil group B, indicating that these soils have moderately low runoff potential when thoroughly wet (USDA-NRCS,

2.3. SWAT model set up, calibration, and validation The ArcSWAT graphical user input interface (version 2012.10.4.19) was used to set up the SWAT model for the BSR watershed streamflow simulation. Major SWAT input data usually include digital elevation model (DEM), land use, soil, and climate. We used a 30 m resolution DEM provided by the U.S. Geological Survey through the National Elevation Dataset (https://viewer.nationalmap.gov), land use 386

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J.D. Pérez-Gutiérrez and S. Kumar

Fig. 1. The Big Sioux River watershed and its main characteristics: a) elevation, b) land use, and c) hydrologic soil group. * U.S. Department of Agriculture – National Agricultural Statistics Service – 2008 Cropland data layer (https://www.nass.usda.gov). ** U.S. Department of Agriculture – Natural Resources Conservation Service – Soil Survey Geographic Database (SSURGO) (< u > https://datagateway.nrcs.usda.gov)〈/u〉.

information from a crop-specific land cover layer of 30 m resolution derived from satellite imagery that can be obtained from the U.S. Department of Agriculture (USDA) through the National Agricultural Statistics Service (NASS) (https://www.nass.usda.gov), soil map and tabular data from the Soil Survey Geographic Database developed by the USDA Natural Resources Conservation Service (https:// datagateway.nrcs.usda.gov), and SWAT-format climate data derived from the NOAA databases, provided by the USDA Agricultural Research Service (https://www.ars.usda.gov).

The SWAT model was calibrated and validated using daily streamflow measurements (m3 s−1) from a United States Geological Survey (USGS) gauge station located at Akron, Iowa (Station No. 06485500) that matches the outlet location defined for the BSR watershed delineation. Streamflow data from 1992 through 2001 were selected for calibration while data from 1980 through 1986 were selected for validation. For both periods, five years (1987–1991 for calibration; 1975–1979 for validation) were established as a “warm up” phase to allow the model for appropriately initializing the watershed hydrological cycle. In this study, the split-time series approach was implemented as part of the calibration and validation strategy due to the continuity and quality of the hydrological and climate data available.

The accuracy of the SWAT model to simulate streamflow at the BSR watershed outlet was assessed using subjective and objective techniques. The subjective techniques compared the measured and simulated streamflow in time series plots. This comparison consisted of describing the main characteristics of the hydrographs including over and underprediction of peak and base flows as well as the timing and rising and falling limbs. The objective techniques required the use of efficiency criteria to estimate the degree of fit between the measured and

simulated streamflow at each time step. In this study, Nash-Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), coefficient of determination (R2), and percent bias (PBIAS) (Gupta et al, 1999) were the criteria used for objective evaluation of the model. Additional details regarding the SWAT model set up, calibration, and validation can be found in the supplementary material section.

2.4. Scenario definition and statistical analysis SWAT can simulate the impact of several management practices on water quantity and quality. However, only fertilizer application, planting, harvesting, killing, and grazing operations were used in this study. Fertilizer application operation controls the type, amount, and timing of fertilizer applied to crops. Planting operation begins the crop growth according to the specified planting date and the total number of heat units required for the crop to reach maturity. Harvesting operation removes plan biomass and killing operation terminates the crop growth.

SWAT conceives grazing as a management operation that simulates daily plant biomass consumption and manure deposition by farm animals per hectare over a specified period. The input variables that control this operation in the model are the amount of biomass ingested, the amount of biomass trampled, the minimum biomass for grazing to occur, and the duration of grazing. Additionally, the amount of manure applied can be included into the SWAT grazing operation module. More information about these management practices can be found in Neitsch et al. (2011).

After calibration and validation, the SWAT model was used to evaluate the potential changes in water yield due to different ICL systems adoption for a long-term period (31 years; 1980–2010) in the BSR 387

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Fig. 2. Rainfall and streamflow patterns of the Big Sioux River watershed. a) Time series of the daily rainfall (mm day−1; inverted blue bars) and streamflow (m3 s−1; black-edge color and grey area plot). b) Average monthly rainfall (mm month−1; inverted blue bars) and streamflow (mm month−1; black-edge color and grey bars). Note: rainfall data from the NOAA databases provided by the U.S. Department of Agriculture – Agricultural Research Service (https://www.ars.usda.gov) were averaged for 23 weather stations (as shown in Fig. 1); streamflow data was measured by the U.S. Department Geological Survey at the watershed outlet (gauge station No. 06485500 located at 96.56W–42.84N in Akron, IA; https://waterdata. usgs.gov). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Table 1 Crop and grazing management operations used in the SWAT model of this study.

Agricultural practice

Management practice

Management scheduled

Growing corn

Fertilizer

Urea (46-0-0) - 168 kg ha−1 Di-ammonium phosphate (16-46-0) - 168 kg ha−1 Mono-ammonium phosphate (11-52-0) - 56 kg ha−1 No till May 5th October 5th (without grazing) October 5th and December 30th (with grazing) No till May 15th October 15th No till April 23rd August 23rd No till September 6th April 23rd November 1st 55 days 34.8 kg ha−1 day−1 9.0 kg ha−1 day−1 24.2 kg ha−1 day−1

Tillage Planting Harvest and kill Growing soybean Growing oats Growing winter barley Grazing

Tillage Planting Harvest and kill Tillage Planting Harvest and kill Tillage Planting Harvest and kill Initiation Duration Plant biomass consumed Amount of manure applied Plant biomass trampled

watershed. Baseline conditions (crop rotations without integration of grazing operations) and ICL systems implementation (crop rotations with integration of grazing operations) were simulated according to three typical crop rotation practices including continuous corn (corncorn; 1-year rotation), conventional (corn-soybean; 2-year rotation), and winter cover crops (corn-soybean-oats (Avena sativa L.)/winter barley (Hordeum vulgare L.); 3-year rotation). In this study, the integration of grazing operations with the cropping systems represent cattle consuming corn residue and/or winter barley. The agricultural management schedule used in the SWAT simulations is presented in Table 1. Corn, soybean, and oats were established to be the cash crops

growing during mid spring to mid fall. Winter barley was planted in fall after harvesting oats and killed at the beginning of the next growing season. All grazing operations initiated on November 1st at a biomass removal constant rate of 34.75 kg ha−1 day−1 for 55 days. In addition, our approach assumed that ICL systems do not affect soil hydrological properties, and forage source is lightly grazed [i.e. one beef cow per hectare (500 kg ha−1) grazing during winter for 55 days]. The details of this operation were obtained from extension personnel experts on grazing practices of the region. Adding grazing operations into the crop rotations resulted in three ICL system scenarios. These scenarios along with their respective baseline scenarios summed up six simulation 388

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where %Change is the percent of change (%), YBS is the median water yield for the baseline scenario (mm), and YICLs is the median water yield for the ICL system scenario (mm). The boxplots were set at 90th (the upper whisker), 75th (the upper quartile), 50th (the median), 25th (the lower quartile), and 10th (the lower whisker) percentiles. Outliers were considered those observations 1.5 times beyond the 25th and 75th percentiles.

To further understand the long-term influence of the ICL systems implementation on water yield in the BSR watershed, the hydrologic components that contribute to streamflow were examined. Surface runoff contributes to streamflow when rainfall or/and irrigation inputs to the soil exceed the rate of infiltration. Lateral flow contributes to streamflow through the soil profile mediated by hydraulic conductivity.

Groundwater or baseflow is the amount of water from a shallow aquifer that contributes to streamflow. Transmission losses and pond abstractions were subtracted from the water yield balance because of their negligible effect in this watershed. The SWAT monthly output of each hydrologic component was therefore analyzed following the same approach previously described for water yield scenario comparison.

Table 2 Simulation scenarios used to evaluate the potential changes in water yield due to different ICL system adoption in the BSR watershed.

Group

Scenario

A (1-year rotation) B (2-year rotation) C (3-year rotation)

Rotation Corn-corn Corn/grazing-corn Corn-soybean Corn/grazing-soybean Corn-soybean-oats/winter barley Corn/grazing-soybean-oats/winter barley/grazing

Description Baseline A ICL system A Baseline B ICL system B Baseline C ICL system C

ID BSA ICLA BSB ICLB BSC ICLC

scenarios (Table 2). Then, each scenario was run in SWAT for the period of 1980–2010 using a monthly time step.

The Soil Conservation Service Curve Number (SCS-CN) method was used to calculate surface runoff as a function of the soil's permeability, land use, and antecedent soil water conditions (USDA-SCS, 1985; USDA-SCS, 1986). This method suggests the runoff CN values that can be used to simulate different hydrologic conditions based on the ground cover and the grazing intensity of continuous forage. Previous studies have followed this approach to simulate differences in ground cover conditions under various grazing practices with SWAT model (e.g., Chaubey et al., 2010; Park et al., 2017a; Park et al., 2017b; Park et al., 2017c; Wilson et al., 2014). In the present study, CN values suggested for 50–75% ground cover and not heavily grazed were used in all HRUs where corn grazing was established, and CN values recommended for > 75% ground cover and lightly or only occasionally grazed were assigned to all HRUs where winter barley grazing was scheduled.

Table 3 shows a summary of the runoff CN values used for simulation of the ICL systems in the BSR watershed.

Statistical analysis tested the significance of detected changes in water yield throughout the BSR watershed at the HRUs where the grazing operation was scheduled. Water yield is the total amount of water leaving the HRU and entering the main channel during the specified time step. In this study, the nonparametric Wilcoxon signed rank test was applied to compare each set of matched HRUs between scenarios within the same group (baseline vs. ICL system). First, the monthly average water yield for all HRUs planted with corn or winter barley was grouped by subwatershed. Subsequently, the median of each group was compared between the baseline and the ICL systems scenario. The changes were examined using the median because it is a resistant measure of the center of frequency in the presence of outliers.

P-values ≤ 0.05 were considered statistically significant. Matlab® and the Statistics and Machine Learning Toolbox™ (The Mathworks, Inc., Natick, MA) were used to perform all the mathematical and statistical calculations.

In addition to conducting statistical analysis, boxplots were used as summary plots to show the comparison of water yield between scenarios and to calculate the percentage of the detected change in the median of each set of matched grouped HRUs according to the following equation:

%Change =

YBS

YICLs YBS

× 100

3. Results 3.1. Calibration and validation of the SWAT model Results of the subjective and objective techniques used to assess the accuracy of the SWAT model to simulate streamflow at the BSR watershed outlet are shown in Fig. 3. Model performance is usually judged as satisfactory if R2 and NSE > 0.5 and −15 < PBIAS < 15 (Engel et al., 2007; Moriasi et al., 2007); therefore, the model developed in this study is appropriate for streamflow simulation as R2, NSE, and PBIAS values were 0.72, 0.72, −3.3, and 0.73, 0.71, −13.8 for calibration and validation periods, respectively. In addition, the uncertainty analysis indices showed that the simulation is good enough. The P-factor indicates that the 95% prediction uncertainty band encloses 93% and 90% of the measured data for the calibration and validation periods.

The strength of the calibration and validation outputs was also confirmed by the adequate thickness (r-factor) of the prediction band.

Additional inspection of the agreement between measured and simulated flows revealed different model performance at different streamflow levels. Streamflow for the calibration period shows a good fit at the extremes (high and low) and the recession flow segments. However, for the validation period, the agreement seemed to be better for high flow and recession flow segments.

3.2. Changes in water yield During the long-term simulation (1980–2010), all HRUs scheduled with the implementation of ICL systems yielded significantly less water to streams (Fig. 4a). Regarding the magnitude of the yield reduction relative to baseline ranked from highest to lowest, we found scenario group B; corn-soybean rotation (14.7%; corn as the forage grazed) > scenario group A; continuous corn rotation (12.5%) > scenario group C; corn-soybean-oats/winter barley rotation (6.4%; corn as the forage grazed) > scenario group C (3%; winter barley as the forage grazed) (Fig. 4b). SWAT output obtained for the scenario group B showed that

(2)

Table 3 Runoff curve number values used for simulation of the ICL system in the BSR watershed according to USDA-SCS (USDA-SCS, 1985; USDA-SCS, 1986).

Forage grazed

Scenario

Hydrologic condition

Curve number value Hydrologic soil group

Corn Winter barley

ICLA/ICLB/ICLC ICLC

50–75% ground cover and not heavily grazed > 75% ground cover and lightly or only occasionally grazed

389

A

B

C

D

49 39

69 61

79 74

84 80

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Fig. 3. Time series of measured and simulated daily streamflow for the (a) calibration and (b) validation period. Annotations inside the plots show the resulting indices (p-factor and r-factor) for uncertainty analysis and the efficiency criteria (coefficient of determination – R2; Nash-Sutcliffe efficiency – NSE; percent bias – PBIAS) for accuracy assessment of the SWAT model to simulate streamflow at the BSR watershed outlet.

the HRUs planted with corn received median annual precipitation of 702 mm. Of this precipitation input, 18.7% (131.7 mm) contributed to water yield when no grazing practices were incorporated into the cropping system. In contrast, 16% (112.3 mm) of the precipitation contributed to water yield under the corn-soybean rotation and grazing corn residue scenario. HRUs planted with corn under the scenario group A received median annual precipitation of 702 mm, of which 20.5% (144 mm) and 18% (126 mm) counted as water yield in continuous corn without grazing and continuous corn with grazing scenarios, respectively. Under the scenario group C, the median annual precipitation available to all HRUs planted with corn was 742.6 mm. Twenty percent and 18.5% of the precipitation contributed to water yield in HRUs without grazing operations and with grazing operations, respectively.

Over the HRUs planted with winter barley, precipitation input was estimated as 658.7 mm of which 15.5% and 15% contributed to water yield for no grazing and grazing scenarios.

system B (corn-soybean rotation), followed by ICL system A (continuous corn rotation), and ICL system C (corn-soybean-oats/winter barley rotation). While runoff was reduced under scenarios of grazing incorporation into the cropping systems, lateral and groundwater flows were increased. The increase in lateral flow varied between 2.7% and 6.7%, with winter barley-planted HRUs showing the lowest increase relative to baseline and continuous corn showing the highest increase.

Although the magnitude of groundwater flow contribution to streamflow was lower when compared to lateral flow, the percentage change was substantially higher for groundwater flow with values ranging from 33.3% to 77.1%. Scenario simulations of grazing corn under the cornsoybean rotation resulted in the highest contribution of groundwater to water yield, followed by grazing corn under the corn-soy-oats/winter barley rotation, grazing corn under continuous corn rotation, and grazing corn under the corn-soy-oats/winter barley rotation.

4. Discussion

3.3. Changes in hydrological components of water yield

4.1. Water yield under ICL scenarios

The comparison between hydrological components contributing to streamflow from HRUs according to the SWAT simulations is shown in Fig. 5a–c. Results showed that all HRUs scheduled with the implementation of ICL systems produced significantly less runoff. The highest reduction (28.7%) in surface runoff was observed under the ICL

We used the SWAT model to evaluate the potential impacts of ICL systems on the hydrology of the BSR agriculture-dominated watershed.

According to the SWAT simulations, significant reduction in water yield was detected over a long-term period simulation (31 years) when cattle 390

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Fig. 4. Water yield comparison between baseline and ICL systems scenarios for three crop rotations. a) Boxplots summarizing the detected change in the median of each set of matched grouped HRUs. Values between parentheses correspond to the median of each boxplot. The p – value resulting from the Wilcoxon signed rank test is shown in the plot by each scenario group. b) Percent of the detected change between medians of each scenario group.

23rd). In the BSR watershed, only 26% of the annual rainfall occurs from September to April which suggests that less runoff volume is produced. Another possible explanation might be the increased size and the number of soil surface depressions caused by grazing operations through hoof traffic of livestock. If the infiltration rate of soil is exceeded by the water input and surface depressions have filled, surface runoff is triggered. With more depressions to be filled, runoff would

grazing of corn residue or winter barley was incorporated into HRUs scheduled with rotations such as continuous corn (1-year rotation), corn-soybean (2-year rotation), and corn-soybean-oats/winter barley (3-year rotation with cover crop). The reduction in water yield was higher under cattle grazing of corn residue than the grazing of winter barley. This effect might be explained by the rainfall regime observed during the time that cover crops were planted (i.e. September 1st – April 391

Fig. 5. Hydrological components comparison between baseline and ICL systems scenarios for three crop rotations. a-c) Boxplots summarizing the detected change in the median of each set of matched grouped HRUs for surface runoff, lateral flow, and groundwater. Values between parentheses correspond to the median of each boxplot. The p – value resulting from the Wilcoxon signed rank test resulted in < 0.0001 for all tested cases. df) Percent of the detected change between medians of each scenario group.

J.D. Pérez-Gutiérrez and S. Kumar

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take longer to occur (Ponce and Lindquist, 1990). Livestock-hoof traffic may also influence topsoil characteristics that in turn can affect soil hydrological properties (Sulc and Tracy, 2007). For example, grazing cover crops may (Franzluebbers and Stuedemann, 2008) or may not (Tollner et al., 1990) affect soil bulk density. According to the results of a study conducted in Iowa, grazing corn residue during winter did not impact soil bulk density (Clark et al., 2004). There is limited research conducted which can document the impacts of ICL systems on soil bulk density and to better understand the advantages and disadvantages of implementing ICL systems in agricultural lands (Franzluebbers and Stuedemann, 2008).

lateral flow also indicates that the soil water content surpassing the field capacity might be a frequent condition under ICL systems implementation.

4.3. ICL systems significantly impact baseflow While both lateral and groundwater flows increased with ICL systems adoption, the relative increase of groundwater was substantially higher. This means that groundwater flow was the hydrological component with the highest relative impact on streamflow. Groundwater contributes baseflow to streamflow and comes directly from the shallow aquifer. Therefore, integration of grazing operations into the watershed cropping systems could benefit the watershed's hydrological cycle through increased baseflow over the long term. Benefits of increased baseflow includes: (1) greater summer flows, (2) healthier riparian areas, (3) increased channel and bank stability, (4) decreased erosion and sediment transport, (5) improved water quality, (6) enhanced fish and wildlife habitat, (7) lower stream temperature, and (8) improved stream aesthetics (Ponce and Lindquist, 1990).

4.2. ICL systems reduce runoff while increase lateral and groundwater flows Of the three components that constitute water yield, only surface runoff was reduced when integrating grazing into the cropping system.

This indicates that the simulated reduction in water yield under the ICL systems scenarios was mainly due to reduced runoff. SWAT-simulated runoff volume was estimated according to the SCS-CN method. To compute runoff volume, the method suggests the use of CN values that attempt to represent the possible hydrological condition based on cover types and antecedent moisture condition. In general, from low to high CN values, the runoff volume produced was increased. The CN values used to represent the long-term hydrological condition of cattle grazing of corn residue and winter barley were lower than the values used to represent cropping systems without integration of grazing. Therefore, a reduction in the surface runoff volume generated under the ICL systems scenarios was anticipated. Reducing runoff from farming and livestock systems has been the cornerstone of agricultural drainage management.

Agricultural runoff transports chemical inputs and sediments that can deteriorate downstream water quality and consequently degrade aquatic ecosystems (Carpenter et al., 1998; Smith et al., 1999). Runoff reduction by using the ICL systems might be helpful in improving the environmental quality of receiving waterbodies although grazing intensity should be managed properly. For example, high intensity of grazing can result in greater runoff loss likely due to increased soil compaction (Clary, 1995; Clary and Medin, 1990; Orr, 1975). Soil compaction directly affects water movement through soil by diminishing infiltration mechanisms (Bohn and Buckhouse, 1985; Gifford and Hawkins, 1978; Orr, 1975). A recent modeling study showed that overgrazing of pasture areas can produce high runoff and nutrient losses and highlighted that overgrazing should be avoided for improving the water quality (Chaubey et al., 2010). Another benefit of ICL systems might occur at the watershed scale through lessening the incidence of downstream flooding. The ICL system-induced runoff reduction translated into improved capacity of soil to capture and store water might play an important role as an adaptation measure to more frequent high-intensity rainfall events (Liebig et al., 2011). Therefore, flooding managers and watershed planners might value the potential of runoff reduction brought by the adoption of ICL systems as an ecosystem service at watershed scale.

Unlike surface runoff, lateral and groundwater flows were increased when ICL systems were scheduled in the watershed HRUs. This result suggests that incorporation of cattle grazing into cropping systems may enhance the mechanisms through which water is stored in and enters the soil. Research has shown how crop rotations that increase soil organic matter may improve water infiltration by enhancing soil structure, soil aggregation, and decrease bulk density (Bullock, 1992). These benefits seem to be magnified by increased soil organic matter under cattle grazing of corn residue or winter barley because of manure deposition and higher cover crops residue (Rakkar et al., 2017). However, lateral and groundwater flow mechanisms greatly depend on the local soil characteristics. In the study watershed, soils having a moderate transmission and infiltration rate cover slightly more than 84% of the study area. This soil predominance might favor water movement in the soil profile through lateral flow to streamflow contribution. Increased

4.4. Limitations of this study and future direction Our study offers new insights into the potential changes in water yield due to different long-term ICL systems adoption at the agricultural dominated watershed. However, the study had two main limitations.

The first one is related to the estimation of runoff by using the SCS-CN method. The CN values are listed for continuous forage for grazing of pasture, grassland, or rangeland. The CN values used in this study were selected based on the possible similarity between the ground cover percentage, the hydrologic condition linked to the listed CN numbers, and the typical condition observed for grazing of corn residue or cover crops in the study region. Future research is needed which can focus on determining the most accurate CN values to represent runoff volume response to ICL systems. The second limitation is that the change in soil physical characteristics is not simulated in SWAT (e.g., Chaubey et al., 2010). Therefore, representing the impact of ICL systems on soil properties is a challenging task that deserves attention from water resources researchers and watershed-scale model developers. This might be an effective approach to simulate the effects of possible soil compaction caused by livestock grazing in ICL systems, which is not discussed in this study and need to be studied in the future.

5. Summary and conclusions The objective of this study was to evaluate the potential changes in water yield due to different ICL systems adoption for a long-term period (31 years; 1980–2010) in the BSR watershed using SWAT simulations.

The ICL systems simulated in this study involved grazing corn residue or winter barley under three traditional crop rotations: continuous corn, corn-soybean, and corn-soybean-oats/winter barley. In general, when grazing operation was integrated into the cropping system, water yield decreased due to a reduction in runoff volume. While runoff volume was reduced, lateral and groundwater flow increased under ICL system scenarios. This indicates that incorporation of cattle grazing of corn residue or winter barley might positively affect processes involved in soil water storage and transit. Of the three components contributing to water yield (i.e. runoff volume, lateral and groundwater flow), groundwater flow had the largest relative impact on streamflow through increased baseflow. Ecosystem services derived from the adoption of ICL systems would be valuable to flooding managers and watershed planners. Overall, our study suggests that research should focus on determining the CN values to represent runoff volume response to ICL system implementation at the watershed scale. In addition, efforts need to be placed in providing the SWAT model with the capability of simulating the change in soil physical characteristics as discussed in previous studies (e.g., Chaubey et al., 2010). Also, we 393

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Acknowledgments This work was supported by the USDA National Institute of Food and Agriculture, Coordinated Agricultural Projects (CAP) (Award No. 2016-68004-24768) and the project entitled ‘‘Back to the Future: Enhancing food security and farm production with integrated croplivestock production systems.’’ Authors would like to thank Mr. Anthony Bly and Dr. Peter Sexton for their suggestions regarding the grazing and cover crops scenarios in this study. We thank the three anonymous reviewers for their comments and suggestions that improved our manuscript.

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jenvman.2019.03.068.

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