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Agricultural Water Management 119 (2013) 65– 79

Contents lists available at SciVerse ScienceDirect

Agricultural Water Management

j ourna l ho me page: www.elsev ier .com/ locate /agwat

nvironmental and economic implications of various conservative agriculturalractices in the Upper Little Miami River basin

arawuth Naramngama, Susanna T.Y. Tongb,∗

Pollution Information Technology Group, Pollution Control Department, Bangkok 10400, ThailandDepartment of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, United States

r t i c l e i n f o

rticle history:eceived 16 April 2012ccepted 13 December 2012vailable online 18 January 2013

eywords:arming practiceslowater qualityodeling

arming economicsWAT

a b s t r a c t

Runoff from farmlands is often contaminated with excessive amounts of nutrients and bacteria. To miti-gate the non-point source pollution from farms, agricultural best management practices (BMPs) can beadopted. But, before the application of these practices, their implications for water resources have tobe examined. Although there are many field experiments on the hydrologic and water quality effects ofdifferent farming practices, most of them are conducted at a plot scale, and studies at a sub-watershedscale are lacking. To circumvent this problem, many researchers use hydrologic modeling. However, theirresults usually cannot be extrapolated to other geographical areas since the efficacy of the BMPs is specificto different cropping systems and environmental settings. Moreover, the economic returns under theseBMPs often are not addressed. To fill this knowledge gap, this study uses the Soil and Water AssessmentTool (SWAT) to model the impacts of different tillage (no-till, moldboard plowing), cropping (corn, soy-bean, corn–soybean rotation), and fertilization (0 kg/ha, 90 kg/ha, 170 kg/ha) practices over a 5-year anda 15-year period in a sub-watershed of the Little Miami River (LMR) in southwest Ohio, the Upper LMRbasin. Furthermore, the economic returns of these farming practices are examined.

The modeling results show that no-till system when practiced together with corn–soybean rotationseems to be the most feasible long-term conservative BMP for balancing the economic and environmentalbenefits in the cool temperate Upper LMR basin. It produces a lower amount of nitrogen (437.2 Mg/year)and phosphorus (9.6 Mg/year) loads and monthly fecal coliform concentrations (372 cfu/100 mL) thanother practices. It also provides a reasonable 2-year net profit, which is slightly less than the continuouscorn under moldboard (about $38 less) and continuous corn under no-till ($196 less).

. Introduction

In a watershed, the water quality is affected not only by natu-al factors, but also by human activities (Debrewer et al., 2000).or example, runoff from agricultural areas is one of the lead-ng sources of non-point source pollution (USDA NRCS, 1997).n farms, conventional practices, characterized by the intensiveses of herbicides, insecticides, fertilizers, continuous cropping,nd conventional tillage systems, often contribute to environmen-al deterioration, degrading soil and water resources (Burt, 2001;

arren et al., 2008). These farming practices disturb the top soil,xposing the soil surface to wind and water erosion as well asncreasing the surface water runoff and sediment loads. In the Mid-

est of the United States, water quality is usually degraded byxcessive amounts of nutrients (for example, nitrogen and phos-horus) and sediments from farmlands (Mitsch et al., 2001). These

∗ Corresponding author. Tel.: +1 513 556 3435; fax: +1 513 556 3347.E-mail address: [email protected] (S.T.Y. Tong).

378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agwat.2012.12.008

© 2012 Elsevier B.V. All rights reserved.

water pollutants can cause eutrophication, fish anomalies, and fishkills. In some places, the water quality can be so deterioratedthat the water is unfit for drinking, recreation, and even industry(USEPA, 1999, 2000; Rowe et al., 2004).

A major agenda in water management is to reduce non-pointsource pollution from farmlands. In this regard, alternative agri-cultural best management practices (BMPs) can be useful (Arabiet al., 2008). These are techniques and measures adopted to miti-gate non-point source pollution and protect water quality from thepotential adverse impacts of sediments and nutrients from farmlands (USEPA, 2002, 2004). The Conservation Technology Infor-mation Center (CTIC) in the United States, with support from theUnited States Department of Agriculture (USDA) and the UnitedStates Environmental Protection Agency (USEPA), has been pro-moting the use of four integrative BMPs in farmlands: conservationtillage, crop nutrient management, pest management, and conser-
vation buffers (CTIC, 2005). Conservation tillage keeps soil in placeand thus reduces soil erosion. Crop nutrient management increasesnutrient availability via the decomposition of crop residues. Byeliminating the excessive use of agrichemicals, pest management
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educes the accumulation of toxic elements. Conservative buffers,uch as vegetative filter strips, can provide an additional protectiony capturing pollutants from agricultural runoff before they enterhe receiving water bodies (Parajuli et al., 2008). Recently, theseonservative agricultural BMPs have been increasingly accepted asn alternative practice to conventional agricultural systems and arepplied to mitigate the adverse environmental impacts from farm-ng (Smith et al., 2001; Tebrugge and During, 1999; Uri, 1998; Urit al., 1998). In southwest Ohio, the BMPs are usually characterizedy conservation tillage (for example, no-tillage and ridge tillage)nd an increased use of organic materials and biological techniquesfor example, the application of manure and crop rotation) for pestontrol and fertilization. When used properly, these practices caneduce non-point source pollutants from farmlands, protect theoil from erosion, improve soil properties and soil organic mat-er, and decrease the need for fertilizer and pesticide use (Larneynd Lindwall, 1995; Miglierina et al., 2000; USEPA, 2002; USDARS, 1997). In addition to environmental benefits, they can alsoave economic advantages (CTIC, 2005; USEPA, 2002). In manyountries, such as Germany, the use of these conservative meas-res, especially the alterative tillage system, is widely supportedy many agro-environmental programs (Ullrich and Volk, 2009).owever, before implementing these practices, their efficacy in

educing pollution has to be evaluated.There are many studies on the environmental benefits of these

ractices (see, for example, Schmidt et al., 2001), but most ofhem investigate small geographical areas, at a plot or field scalesing field experiments. At a sub-watershed or watershed scale,he larger areal extent often precludes such intensive field stud-es. Hence, many researchers resolve in using hydrologic modelsnd water quality simulation to postulate the effects of conser-ative agricultural practices (see, for example, Ullrich and Volk,009; Pisinaras et al., 2010). An advantage of this type of modelingxercise is that it allows researchers to examine the water qualitympacts of different agricultural practices (Laurent and Ruelland,011), compare various management scenarios, and determinehe best management options (Arabi et al., 2008). Although theseesults can be useful to policy-makers and watershed managers,nabling them to make appropriate decisions for watershed pro-ection (Lam et al., 2010), they cannot be extrapolated to a differenttudy area. This is because the efficacy of agricultural BMPs ineducing pollutants varies according to different crop types andgricultural production systems (Nelson et al., 2006), as well ashe site characters, environmental settings, climate regimes, andoil types (Tuppad et al., 2010). The literature on the effective-ess of various agricultural BMPs in different geographical areas

s, therefore, inadequate (Tuppad et al., 2010). Besides, the eco-omic returns of these conservative practices are rarely considered

n tandem with the environmental benefits. However, their finan-ial feasibilities are one of the most influential criteria affecting theecisions of farmers in selecting between different types of cropotation, tillage, and fertilizer applications (Sahu and Gu, 2009).t is, therefore, the goal of this study to contribute to a moreetailed understanding of the benefits of agricultural BMPs in wateranagement by examining the impacts of different agricultural

onservative BMPs in terms of tillage, crop rotation, and rate ofertilization and comparing them with the conventional farmingechniques.

In an earlier pilot study (Tong and Naramngam, 2007), wenvestigated the water quality impacts of different cropping andillage systems in a sub-watershed of the Little Miami River (LMR),he Upper LMR, in southwest Ohio, where farm runoff is one
f the major sources of non-point source pollution. The resultsemonstrate that no-till with continuous soybean or corn–soybeanotation can reduce the amounts of sediments and nutrients in theeceiving waters.
ater Management 119 (2013) 65– 79

This paper further investigates the hydrologic and water qualityimpacts of not only different cropping and tillage systems, but alsofertilizer application rates. Besides, it investigates both the short-term and long-term (5- and 15-year) impacts of various farmingsystems on nitrate plus nitrite nitrogen (N), fecal coliform (FC),total phosphorus (P), and flow. Moreover, it discusses the economicreturns of these conservative farming practices in the study area.Specifically, this research focuses on the following objectives: (1)to explore the short-term and long-term environmental impacts ofdifferent BMP farming systems in ameliorating non-point sourcepollution from farmlands under a cool temperate climate, and (2)to determine the best farming practices that can help to balancethe economic and environmental benefits in southwest Ohio. It ishoped that this study will provide useful quantitative informationnot only to researchers but also to decision makers and farmersin the study area, and the findings can contribute to the efforts ofthe Conservation Effects Assessment Project (CEAP) of the USDAin examining the effects of conservative practices on water quality(Maresch et al., 2008).

2. Materials and methods

2.1. Study area

Originating in the southeastern Clark County in Ohio, the LMRtraverses southward to join the Ohio River in the Cincinnatimetropolitan area, Hamilton County (Fig. 1). Along its route of about172 km, the elevation drops from 341 to 137 m with an averagegradient of 1.2 m/km (Ohio EPA, 2000). The LMR is one of the mostbiologically diverse rivers in the region, home to 113 fish species,some of which are rare and endangered (Harrington, 1999). Des-ignated as a state scenic river and a national scenic river (OhioEPA, 1995), it is the longest stream segment in Ohio that has theExceptional Warm Water Habitat identification (Sanders, 2001).

The LMR watershed is a predominantly agricultural watershed.With intensive corn and soybean production, the basin contributesa high amount of nutrients and sediments to the waterway. Non-point source pollution from farmlands is one of the most significantsources of water quality impairment in the LMR. It is, therefore,essential to have a better understanding of the implications of dif-ferent farming practices on water quantity and quality.

Moreover, in the last 50 years, the basin has become moreintensively farmed (Liu et al., 2002). On the other hand, some farm-lands have been converted to urban/suburban areas and forests.Inevitably, these changes in land use can pose adverse impacts onwater quality and the aquatic ecosystems in the LMR (USGS, 2000).Studies have shown that the recent changes in land-use have con-tributed to a higher surface runoff, more frequent floods, and apoorer water quality (Liu et al., 2000; Liu, 2006; Tong, 1990; Tongand Chen, 2002; Wang, 2001).

Furthermore, the LMR watershed system is now under stress.According to the Ohio EPA (2000), the median concentration of totalphosphorus at the lowest pour point of the main stem of the LMRis 0.34 mg/L, which is higher than the levels commonly found ineutrophic lakes (USEPA, 1972). Nutrient enrichment has led to algalblooms and low dissolved oxygen concentrations. Escherichia coliand fecal coliform bacteria are widespread, and there is a high levelof fish anomalies in the river. The distribution of fish species has alsobeen changed, and more omnivores are found, an indication thatthe river is deteriorating. Toxic metals are detected in fish tissuesamples collected in the LMR (Janosy, 2003), and there is atrazine
contamination in the waterways (Miller, 2003).
There are a number of research projects on the impacts ofurbanization, land-use change, and climate change on flow andwater quality in the LMR, yet the knowledge about the impacts

S. Naramngam, S.T.Y. Tong / Agricultural Water Management 119 (2013) 65– 79 67

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processes are more difficult, and they typically take much longerthan in a smaller watershed. In a small watershed, like the UpperLMR, the easier processes of calibration and validation can enhance

Table 1Distribution of land use types in the Little Miami River and Upper Little Miami Riverbasins (% of total area of each watershed).

Land use LMR Upper LMR

1980s 1990s 1980s 1990s

Fig. 1. The Upper Litt

f farming practices in the study area is limited. Since farming isne of the primary industries in the LMR basin (Table 1), changingertilization rate, tillage, and cropping systems in the watershed

ay have significant environmental impacts on surface flow andater quality, as well as economic returns. Thus, more research iseeded to ascertain the effects of various farming practices on thenvironment and economy.

To this end, this study used the Upper LMR, a small sub-atershed of the LMR, as a case study for detailed analyses. Inydrologic and water quality modeling, spatial scale is often a keyonsideration. One of the main reasons for using a sub-watershedn this modeling exercise is because, in a smaller watershed, theata or parameters input (for example, weather data) into the
odel are more representative than those for a larger watershed.
n SWAT, the daily weather data are treated as if every calculatingnit has the same amount of rainfall and the same temperature;hile, in reality, those values are not always evenly distributed

mi River basin, Ohio.

throughout the area. As a result, the model generally produces amore realistic outcome in a smaller watershed than in a larger one.In addition, in a larger watershed, the calibration and validation

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he development of a more representative hydrologic and wateruality model, thereby providing a more reliable hydrologic andater quality simulation.

The Upper LMR is the headwater of the LMR. Its watershed isithin the uppermost part of the LMR watershed. With an area of

11 km2, it covers Clark County and Greene County in Ohio. Formedrom silts, alluvial, and residual materials from glacial deposits, theoils in the region are deep, fertile, and highly productive (Lercht al., 1975). The climate is cool-temperate. Summer is warm andumid with a maximum temperature of 30 ◦C and a minimumemperature of 15 ◦C. Winter is moderately cold with a high tem-erature of 0 ◦C and a low temperature of −10 ◦C. The averagennual snowfall is about 50–76 cm while the average annual rainfalls about 90–110 cm (Debrewer et al., 2000).

.2. Data sets

The data for this study were retrieved in 2009 and 2010 fromhe USEPA, USDA, and United States Geological Survey (USGS)atasets. The spatial geographic information systems (GIS) data

ayers included the land use data from the USGS Geographicnformation Retrieval and Analysis System (GIRAS) and the Multi-esolution Land Characterization Consortium (MRLC, 30 m × 30 mesolution), the soil map from the State Soil Geographic Dataase (STATSGO), the delineated watershed map and digital ele-ation models (DEMs) from the USGS, and weather informationtemperature and precipitation) at the Dayton Airport from theational Climatic Data Center (NCDC). The AVSWAT-X, an exten-

ion of the Soil and Water Assessment Tool (SWAT), was used torganize and process input datasets. The flow and water qualityata were acquired from the USGS’s National Water Informationystem (NWIS) and USEPA’s Storage and Retrieval (STORET). Allhese data layers were projected into the State Plane 1983 datum-hio south, and the map and distance units were set to meters.

.3. Model selection

In this research, the Soil and Water Assessment Tool (SWAT)Arnold and Fohrer, 2005) was chosen because (1) it is computa-ionally efficient, and it can provide reasonably accurate results at

watershed or sub-watershed scale (see, for example, Arnold andohrer, 2005; Chaplot et al., 2004; Heuvelmans et al., 2005; Liu,002), (2) it is based on a continuous time scale and is capablef simulating the effects of farming practices on water resourcesArnold and Fohrer, 2005; Shi et al., 2011), (3) it requires moder-te data input, uses readily available datasets, and needs minimalalibration for ungauged basins, and (4) it is available to the publicree of charge.

SWAT is reported to be a good tool for the determination ofhe impacts of best management practices (BMPs) and manage-

ent change on water use and water quality (Behera and Panda,006). It has been used by government agencies worldwide in theetermination of the Total Mass Daily Load (TMDL) (Kang et al.,006). Other researchers, including Rode et al. (2008), Volk et al.2009) and Tuppad et al. (2010), have used SWAT to address issueselated to point and non-point source pollution controls, land uselterations, and climate change. Some have employed it to simu-ate surface flow, sediment load, and nutrient and agrichemical lossGassman et al., 2007; SWAT, 2007). For example, Pisinaras et al.2010) used SWAT to simulate the effects of land use and agricul-ural crop management on flow, nitrate, and soluble phosphorus.
hey found that the model was capable of simulating different landse change and crop management scenarios. They asserted thathe model was a flexible and reliable tool for watershed manage-
ent analysis. Indeed, SWAT has been regarded as one of the most


suitable models for simulating the hydrology and water qualityimpacts of land management practices (Ullrich and Volk, 2009).

SWAT has five versions: SWAT98.1, SWAT99.2, SWAT2000,SWAT2005, and SWAT2009. The software is developed bythe USDA and is maintained and distributed via the internet(http://swatmodel.tamu.edu) by its Grassland, Soil, and WaterResearch Laboratory. All versions have various user interfaces (non-GIS and GIS), and they are incorporated in different GIS packages.The most commonly used GIS packages include BASINS, ArcSWAT,and AVSWAT. BASINS is a USEPA product, which contains utili-ties for data mining and display, as well as a few hydrologic andwater quality models. ArcSWAT is an ArcGIS – ArcView exten-sion developed by the USDA, and AVSWAT (ArcView SWAT) is anArcView extension developed by the Blackland Research Center(http://www.brc.tamus.edu/). Both SWAT2000 and SWAT2005 arecoupled in ArcSWAT and AVSWAT. The extension AVSWAT-X forSWAT2005 is used in this study since it has the additional capabilityto simulate bacteria transport.

2.4. SWAT model description

SWAT is a physically based, operational (lumped or concep-tual), distributed parameter, hydrologic and water quality model,which operates on a continuous time scale. By employing infor-mation about weather, soils, topography, vegetation, and landmanagement practices occurring in the watershed, it models thephysical processes (for example, water and sediment movement,and nutrient cycling) and can be used to predict the impacts of landmanagement practices on flow, sediment, agricultural chemicalyields, nutrients, and pathogenic transport in large river basins andungauged watersheds (Arnold et al., 1993; Gassman et al., 2007).

In SWAT, a watershed is divided into sub-watersheds basedon topography, and each sub-watershed is further subdivided intohydrologic response units (HRUs) that have homogenous land use,land management, and soils. In each HRU, the hydrologic cycle,nitrogen and phosphorus cycles, and crop growth are simulatedbased on climatic variables, hydrology, and the agricultural man-agement in that unit.

In SWAT, the flow simulation in the “land” phase is basedon the water balance equation. Using such information as dailyprecipitation, maximum/minimum air temperature, solar radia-tion, wind speed, relative humidity, and leaf area index, it modelsthe daily average soil temperature, evaporation from soils andplants, infiltration, and flow (Neitsch et al., 2002, 2005). In termsof surface runoff, SWAT uses either the distributed SCS curvenumber or the Green and Ampt infiltration method to estimate sur-face runoff. As the sub-hourly precipitation data required by theGreen and Ampt infiltration method are rarely available, the SCScurve number method is more commonly used. The curve numbermethod estimates the volume of surface runoff based on a nonlin-ear relationship between runoff versus rainfall, land use, and soilcharacteristics (USDA ARS, 1972).

For sediment transport, SWAT uses the modified UniversalSoil Loss Equation (MUSLE) to simulate soil erosion and sedi-ment transport (Williams, 1995). As for the nutrients, in mostfarms, phosphorus and nitrogen are added into the soil via plantresidue, animal manure, and/or inorganic fertilizers. Nitrogen isalso introduced to the soil by symbiotic or non-symbiotic bacteriafixation and rain. On the other hand, both nutrients are removedfrom the soil by mass flow, soil erosion, and plant uptake. Nitro-gen is further removed by volatilization and denitrification. Hence,to simulate the sediment transport of phosphorus and nitrogen,
SWAT uses a loading function developed by McElroy et al. (1976)and modified by Williams and Hann (1978). Since phosphorus isnot a mobile nutrient, the amount of soluble phosphorous runoff isestimated based on the solution phosphorus concentration in the
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op 10 mm of soil, runoff volume, and a partitioning factor (Neitscht al., 2002, 2005).

In terms of bacterial transport, fecal coliform is used in SWAT asn indicator of the pathogenic organism contamination. One or twopecies/strains of pathogens may be simulated in SWAT as persis-ent and less persistent bacteria with different die-off/re-growthates. In a watershed, bacteria are typically introduced via thepplication of animal manure, some of which will be interceptedy plant foliage, and the rest reaches the soil surface. Although bac-eria may be found in soil solution or attached to soil particles, onlyhose in soil solution are susceptible for leaching via surface runoffNeitsch et al., 2005). Thus, SWAT monitors bacteria populationsoth on plant foliage and in the top 10-mm of soil surface.

In addition to simulating pollutants, SWAT can also simulateanagement practices. SWAT allows users to define their scenar-

os of management practices in every HRU. Different managementractice scenarios regarding the beginning and the end of growingeasons, operations of grazing, harvest and kill, tillage practices,s well as the types of fertilizer and their application rates cane modeled. The effects of crop rotation can also be simulated byhanging different crops from one year to the next in the same fieldNeitsch et al., 2002, 2005).

.5. Compilation of the hydrologic model for the LMR

The steps for developing the hydrologic model for the UpperMR were as follows: the data were first retrieved from the meta-ata sets and prepared for input into the SWAT model. Second, thetudy area was delineated from DEM data and the Reach File ver-ion 3 (RF3). Third, the 1980s land use was reclassified into a SWATormat. It was then overlaid over a soil map (STATSGO) to gener-te a combined set of parameters based on the predominant landse and soil types. Fourth, the weather data were generated basedn the historical records of the Dayton Airport weather station.hen, all required input files and default parameters were writ-en based on the input map layers. Finally, the SWAT hydrologic

odel was executed. In this study, the variables to be simulatedere flow, N, P and FC. This is because many studies indicated that

hese variables were related to land use and management practicehanges in the LMR (Liu, 2002; Liu, 2006; Liu et al., 2000, 2002;ong and Chen, 2002; Wang, 2001; Tong et al., 2007; Liu and Tong,011).

.6. Model calibration and validation

The model for the Upper LMR basin was initially developedith the default values of input parameters. After the SWAT modelad been executed, the model was calibrated and validated byomparing its simulated outputs with observed data. This pro-ess is to ascertain the model’s reliability and accuracy and thatt can be used adequately to simulate the hydrologic and wateruality impacts of different management practices or land use con-itions under a different set of environmental conditions. A paired-test (two samples for means), the coefficient of determinationR2) from regression analysis, and the Nash–Sutcliffe coefficientNash and Sutcliffe, 1970) were used to compare the simulatedalues with the observed data and evaluate the performance ofhe model during calibrations and validations. These are commonarameters employed to assess model performance. A value closeo one for both the R2 and the Nash–Sutcliffe coefficient indi-ates a good relationship between simulated and observed data.
ccording to Tuppad et al. (2010), a Nash–Sutcliffe coefficientalue greater than 0.75 can be considered as being particularlyood in model performance, 0.65–0.75 as good, and 0.5–0.65 asatisfactory.
ater Management 119 (2013) 65– 79 69

In the calibration process, the 1980s land use and 1980–1984weather data were used. A few input parameters were adjustedunder the SWAT user interface by the trial-and-error method untilthe simulated results were statistically acceptable when comparedto observed data using the t-test, R2, and Nash–Sutcliffe coefficientcriteria.

In the surface flow model, calibration was done by adjus-ting parameters, such as the curve number (CN2), biomass(Biomix), available water capacity (SOL AWC), saturated hydraulicconductivity (SOL K), surface runoff lag time (SURLAG), ground-water evaporation coefficient (GW REVAP), groundwater delay(GW DELAY), and baseflow alpha factor (ALPHA BF), to approx-imate the observed monthly flow values collected in the gaugestations. After this step, these parameters were further adjusted byusing the daily flow values. Then, they were adjusted again usingthe monthly flow values, and the process was repeated until theresults of the simulation were reasonably close to the observed val-ues. The resultant monthly flow model was then used for furthercalibration and validation of N and P loads as well as FC concentra-tion.

As for the water quality calibration, the parameters usedincluded initial organic phosphorus concentration in soil layer(SOL ORGP), initial NO3 concentration in soil layer (SOL NO3),nitrogen and phosphorus percolation coefficient (NPERCO, PPECO),phosphorus soil partitioning coefficient (PHOSKD), phospho-rus sorption coefficient (PSP), bacteria partitioning coefficient(BACTKDQ), and temperature adjustment factor for bacteria die-off/growth (THBACT). However, the water quality calibration wasperformed only on a monthly basis due to the limitation of dailyobserved data.

After the model was successfully calibrated, it was validated. Inthe validation process, the 1990s land use and 1990–1994 weatherdata were used. Without adjusting any other parameters from thecalibration process, the model was run again. The simulated resultswere compared to the observed data, and the performance of themodel was assessed.

The results from the calibration and validation are promising(see Figs. 2 and 3). The relationships between observed and simu-lated data in the Upper LMR basin are in good agreement (R2 > 0.5;p-value <0.05). Besides, the mean observed and modeled values arenot significantly different (all paired t-test <1; p-value >0.05). TheNash–Sutcliffe coefficients are all above 0.51 in both the calibrationand validation periods (Table 2). Because of the satisfactory cali-bration and validation results, the monthly hydrologic and waterquality model was regarded adequate to simulate the hydrologicconditions of the study area. It was used to investigate the impactsof various scenarios of farming practices on flows and water quali-ties in the study area for both the short-term and the long-term (5-and 15-year) periods.

2.7. Generating farming practice scenarios

Based on the information suggested by the Ohio Department ofAgriculture (2006) and the Ohio State University Extension (OSUExtension, 2005), several farming management scenarios werechosen in this study for detailed analyses. These included twotillage systems, three cropping systems, and three nitrogen fer-tilization rates (Table 3). The tillage systems were no-tillage (NT)and moldboard plow (MP) as they are the most widely used tillagesystems in Ohio (USDA ERS, 1996), representing conservation andconventional tillage, respectively. Under NT, the harvest operationremoves the grain from the fields and leaves 100% of the crop
residues to cover the soil surface. The increased amount of residueon the soil surface helps to protect the soil after harvest. Conversely,under MP, less than 15% residues are left on the fields. Becausecorn and soybean account for the majority of crop production in

70 S. Naramngam, S.T.Y. Tong / Agricultural Water Management 119 (2013) 65– 79

Flow (cubic m/s)

0

20

40

60

80

100

120

140

Jan-80

Apr-80

Jul-80

Oct-80

Jan-81

Apr-81

Jul-81

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Jan-82

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Jul-82

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Jan-83

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Oct-84

Observed

Simulated

Fig. 2. Calibration results of daily flow in the Upper LMR.

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Fig. 3. Validation results

hio (Ohio Department of Agriculture, 2006) and corn–soybeanotation is the most common cropping sequence used in OhioOSU Extension, 2005), the three cropping systems used in thisesearch were continuous corn (CC), continuous soybean (SS), andorn–soybean (CS) rotation. Following the suggestions of the OSU
xtension for the Corn Belt Region (Vitosh et al., 2001; Johnsonnd Hudak, 1999), the fertilization application scenarios were
kg/ha for soybean, 90 kg/ha for corn, and 170 kg/ha for corn. Inrder to simulate the impacts on fecal bacteria in the Upper LMR,

able 2alibration and validation results for the Upper LMR basin SWAT model.

Observed value Simulated valu

Calibration (1980–1984)Monthly flow (m3/s) 4.078 4.082

Daily flow (m3/s) 4.070 4.083

N, NO3 + NO2 (mg/L) 4.095 4.096

P, phosphorus (mg/L) 0.136 0.137

FC, fecal coliform (cfu/100 mL) 702 691

Validation (1990–1994)Monthly flow (m3/s) 3.554 3.509

Daily flow (m3/s) 3.545 3.500

N, NO3 + NO2 (mg/L) 4.353 4.475

P, phosphorus (mg/L) 0.107 0.113

FC, fecal coliform (cfu/100 mL) 870 899

* The observed and modeled values are in good agreement (R2 > 0.5; p-value <0.05).** The mean observed and modeled values are not significantly different (all paired t-te

y flow in the Upper LMR.

fertilizer application consisted of 50% ammonium nitrate with anitrogen:phosphorus:potassium (N:P:K) ratio of 33:0:0 and 50%poultry manure with an N:P:K ratio of 1.5:1:1 (OSU Extension,2006). According to the information provided by Soupir et al. (2006)and Doyle et al. (1975), in this study, the amount of fecal coliform
in the poultry manure was estimated and set as 50,000 cfu/g ofmanure. In Ohio, the need for nitrogen is more crucial than phos-phorus and potassium for soils (Johnson and Hudak, 1999). Besides,manure application can introduce some phosphorus and potassium
e Nash–Sutcliffe efficiency R2 * t-Test**

0.74 0.77 −0.020.72 0.73 −0.140.70 0.72 −0.010.67 0.68 −0.140.65 0.67 0.11

0.70 0.73 0.210.71 0.65 0.360.55 0.58 −0.170.52 0.54 −0.400.58 0.60 −0.14

st <1; p-value >0.05).

S. Naramngam, S.T.Y. Tong / Agricultural W

Table 3Tillage, cropping system, and fertilization scenarios used in this study.

Scenario Tillage system Cropping system Nitrogen rate(kg/ha)a

1 Moldboard plow, MP Soybean–soybean, SS 02 Moldboard plow, MP Corn–corn, CC 903 Moldboard plow, MP Corn–corn, CC 1704 Moldboard plow, MP Corn–soybean, CS 905 Moldboard plow, MP Corn–soybean, CS 1706 No-till, NT Soybean–soybean, SS 07 No-till, NT Corn–corn, CC 908 No-till, NT Corn–corn, CC 1709 No-till, NT Corn–soybean, CS 90

10 No-till, NT Corn–soybean, CS 170

5

incsft

plVit

2

vutwn

ccaduWiff

TT

a

1

a N is only applied in corn planting and consists of 50% inorganic fertilizers and0% poultry manure.

nto the soils. Hence, in this simulation exercise, the 50% ammo-ium nitrate and 50% poultry manure fertilizer were applied only inorn planting. The fertilizer was not applied to soybean because theymbiotic bacteria in its root nodules can produce enough nitrogenor the crop (OSU Extension, 2006). The description of the agricul-ural operations in each scenario is presented in Table 4.

The impacts of farming practices may vary with different timeeriods. However, they are generally more consistent under a

onger term period, such as over 10 years (Al-Kaisi and Yin, 2005;arvel and Wilhelm, 2003). Because of this reason, the simulations

n this study were done over a 5-year period and a 15-year periodo ascertain the effects of different farming practices over time.

.8. Economic study

Since the overall 15-year SWAT simulation results have lessariation than the 5-year simulation, it seems that the 15-year sim-lation can provide a better representation of the water qualityrend. Consequently, in this study, the 15-year simulation resultsere used to determine the best practice for optimizing the eco-omic and environmental benefits in the Upper LMR basin.

In the economic study, the financial benefits of tillage andropping systems were considered. Since the applications and theosts of fertilization vary from farm to farm and season to seasonccording to the differences in soil nutrient contents, climatic con-itions, and targets for crop yield, the costs of fertilizer applicationsed in this study were only estimates. According to the data from
ard (2008a), the average cost of nitrogen fertilizer was $1.44/kg
n 2007–2008. In this economic analysis, for easier comparison,ertilizer application was set at a constant rate of 170 kg N/ha (50%ertilizer and 50% manure) for corn and 0 kg N/ha for soybean.

able 4he combined management practices for corn and soybean.

Crop Operation no.a Date Operatio

Corn 1 April 10 Tillage

2 April 14 Tillage

3 April 30 Fertilizat4 May 5 Planting

5 May 25 Tillage

6 June 1 Fertilizat7 November 15 Harvest

Soybean 1 April 5 Tillage

2 April 8 Tillage

3 May 1 Planting

4 May 25 Tillage5 October 1 Harvest

a No till has one operation, while moldboard plow has three operations: primary tillagnd soil-compact reduction.b The mixing efficiency (fraction) and the mixing depth (mm) of the soil caused by tilla

00 for moldboard plow-secondary, 0.10 and 60 for moldboard plow-tertiary (harrow for

ater Management 119 (2013) 65– 79 71

Hence, the cost for fertilizer application was $122.40/ha for a corncropping system.

In this analysis, the financial budget sheets and input data,including the variable and fixed costs of production, were acquiredfrom Ward (2008a). The price and other inputs used to produce thetargeted yield for each crop in the budget sheets were obtainedfrom several other studies, including those from University ofMinnesota Extension (2008), Ward (2007, 2008b), Ward et al.(2006) and Varvel and Wilhelm (2003). Based on these reports, thetargeted yields for Ohio were set to be 11,769 kg/ha for corn and3093 kg/ha for soybean, and the estimated prices were $0.18/kgfor corn and $0.43/kg for soybean. The incomes and total pro-duction costs of corn and soybean are shown in Table 5. In thisstudy, the 2-year net incomes and costs were used in economiccomparison because the 1-year data would not capture the over-all economic returns of corn–soybean crop rotations. However, itshould be noted that since the economic factors often vary consid-erably from year to year, the 2-year net revenues may not representthe real conditions. Nonetheless, it may still be a useful guideline forascertaining the economic and environmental benefits of differentfarming practices.

3. Results and discussion

The results of the 5-year and 15-year simulations in terms offlow, nitrogen and total phosphorus loads, and fecal coliform con-centrations under different conventional and conservative farmingpractices are listed in Table 6.

3.1. The impacts of tillage systems

Considering the impacts of different tillage systems under thesame cropping system and N application rate, NT yields lower flows,nitrogen and total phosphorus loads, and fecal coliform concentra-tions over the 5-year or 15-year periods than MP (Table 7). Mostdifferences between the two tillage systems, especially under the15-year simulations, are statistically significant based on paired t-tests (p < 0.05) (Table 7). This is because while MP leaves less than15% of residues on the field, NT leaves 100% of crop residues, butcrop residues can help to increase water infiltration as well as toreduce and slow down surface runoff (Geddes and Dunkerley, 1999;Takken et al., 2001; Tebrugge and During, 1999). Crop residues alsocan increase the organic matter content in soil. Moreover, it can
help to retain nutrients. Generally, soils under crop residues areless disturbed. Nutrients, such as phosphorus, which are naturallyabsorbed by soil particles, will not be removed in large amountsby leaching (Johnson et al., 1995). It is, therefore, reasonable
n type MP-parametersb NT-parametersb

Moldboard plow-primary No tillMoldboard plow-secondary

ion N application N applicationPlanting PlantingMoldboard plow-tertiary

ion N application N applicationHarvest Harvest

Moldboard plow-primary No tillMoldboard plow-secondaryPlanting PlantingMoldboard plow-tertiaryHarvest Harvest

e, secondary tillage for seedbed preparation, and tertiary tillage for weed controls

ge operation are 0.05 and 25 for no till, 0.95 and 150 for moldboard plow, 0.45 and weed control).


Table 5Income and costs ($/ha) under different tillage and cropping systems based on 170 kg/ha N fertilizer application rate and 15-year simulation.

MP/CC MP/CS NT/CC NT/CS MP/SS NT/SS

Year 1 Year 2 Year 1 Year 2 Year 1 Year 2 Year 1 Year 2 Year 1 Year 2 Year 1 Year 2

Yields (kg/ha) 11,768.72 11,768.72 11,768.72 3093.49 11,768.72 11,768.72 11,768.72 3093.49 3093.49 3093.49 3093.49 3093.49Prices ($/kg) 0.18 0.18 0.18 0.43 0.18 0.18 0.18 0.43 0.43 0.43 0.43 0.431 year income ($/ha) 2118.37 2118.37 2118.37 1330.20 2118.37 2118.37 2118.37 1330.20 1330.20 1330.20 1330.20 1330.202 year income ($/ha) 4236.74 3448.57 4236.74 3448.57 2660.40 2660.40

Variable costs ($/ha)a 942.90 942.90 841.19 481.42 941.88 941.88 840.18 470.60 481.42 481.42 470.60 470.60Fixed costs ($/ha)b 672.06 672.06 672.05 652.78 594.27 594.27 594.27 574.97 652.78 652.78 574.97 574.971 year costs ($/ha) 1614.96 1614.96 1513.24 1134.20 1536.15 1536.15 1434.45 1045.57 1134.20 1134.20 1045.57 1045.572 year costs ($/ha) 3229.92 2647.44 3072.30 2480.02 2268.40 2091.14

1 year profit ($/ha) 503.41 503.41 605.13 196.00 582.22 582.22 683.92 284.63 196.00 196.00 284.63 284.632 year profit ($/ha) 1006.82 801.13 1164.44 968.55 392.00 569.26Average profit ($/ha/year) 503.41 400.57 582.22 484.28 196.00 284.63

a Variable costs include costs for fertilizer (about 40–50% of variable costs), seeds, chemicals, and fuels, etc.b Fixed costs include costs for labor, machines, land, and management, etc.

Table 6The 5- and 15-year simulation results in terms of flow and water qualitiesa under different farming practicesb in the Upper LMR basin.

Scenario Tillage Cropping system N rate (kg/ha) Flow Nitrogen Phosphorus FC

5-Year 15-Year 5-Year 15-Year 5-Year 15-Year 5-Year 15-Year

1 MP SS 0 4.1 4.2 593.3 714.0 10.0 9.8 117 1122 MP CC 90 4.1 4.2 488.1 610.5 13.9 12.6 372 3943 MP CC 170 4.1 4.2 530.7 668.6 18.5 17.8 702 7394 MP CS 90 4.2 4.3 417.3 341.1 11.0 10.5 307 3105 MP CS 170 4.2 4.3 528.0 445.4 13.3 12.2 570 5806 NT SS 0 4.1 4.2 578.8 744.8 9.7 8.5 93 907 NT CC 90 4.1 4.2 471.7 585.1 12.0 10.9 359 3788 NT CC 170 4.1 4.2 528.9 635.0 13.6 13.5 678 7079 NT CS 90 4.1 4.3 408.2 339.3 9.9 8.7 209 199

10 NT CS 170 4.1 4.2 524.4 437.3 10.4 9.6 387 372

NO3 am value

em: S

t1ccnnlftsd

TTb

a Flow – mean monthly flow, m3/s; nitrogen – mean annual nitrogen load (NO2 +onthly fecal coliform concentration, cfu/100 mL; the mean values are the averageb For the tillage system: MP – moldboard plow, NT – no till; for the cropping syst

hat phosphorus loads are lower under NT than MP (Stonehouse,999). To some extent, crop residues can also reduce fecal coliformoncentrations. In this study, under the 15-year simulation, theoncentration differences between NT and MP are statistically sig-ificant, but under the 5-year simulation, the differences are mostlyot significant (Table 7). The results in the 5-year period are simi-

ar to a study in central Kentucky where the authors reported thatecal coliform concentrations were not significantly affected by the
illage systems (tilled or no-tillage) (Stoddard et al., 1998). Othertudies also reported similar results that there were no significantifferences of the amount of fecal coliform discharge between MP
able 7he comparisons of mean annual flow (m3/s), nitrogen load (Mg), total phosphorus load

ut with the same cropping system and N application rate (data show % change from the

Scenario Tillagea Cropa/N rate Flow

Base 5-Year 15-Year

1 MP SS/06 NT SS/0 −0.1 −0.5*

2 MP CC/907 NT CC/90 −0.2* −0.6*

3 MP CC/1708 NT CC/170 −0.1* −0.6*

4 MP CS/909 NT CS/90 −1.0* −1.2*

5 MP CS/17010 NT CS/170 −1.0* −1.2*

* Differences between the tillage systems are statistically significant at the 0.05 significa For the tillage system: MP – moldboard plow, NT – no till; for the cropping system: S

s nitrogen), Mg; phosphorus – mean annual total phosphorus load, Mg; FC – means over a 5 or 15 year period.S – soybean, CC – corn, CS – corn and soybean rotation.

and NT. They maintained that the bacterial concentrations weremore related to the flow volume and rainfall timing (Thiagarajanet al., 2007; Mishra et al., 2008). Another plot study in Virginiareported that the bacterial concentration increased with increas-ing surface flows, especially if the flow occurred soon after manureapplication (Soupir et al., 2006). Thus, in this study, since the flowis the prime factor on bacterial concentrations, it is conceivable tofind that NT has lower fecal coliform concentrations than MP as NT
generates lower flows than MP in both time periods.
For the average annual nitrogen load, an interesting observationis that N is found to be higher in NT than MP under SS/0 and 15-year

(Mg), and fecal coliform concentration (cfu/100 mL) under different tillage systems, base case*).

N load P load FC

5-Year 15-Year 5-Year 15-Year 5-Year 15-Year

−2.4 4.4* −3.2 −13.4* −20.5* −19.6*

−3.4* −4.1* −13.5 −13.6* −3.4 −4.0*

−0.4 −5.0* −26.4 −24.3* −3.4 −4.3*

−2.2 −0.4 −10.3 −16.6* −32.0 −35.6*

−0.7 −1.9 −22.3* −21.1* −32.2 −35.8*

ance level.S – soybean, CC – corn, CS – corn and soybean rotation.

ural W

slttmt1batWiwtgtaNt

3

sTwatfl

ta(fvbrtocnCeMctttntbacZttuldiit

ift


imulation. This is largely because under NT soil nitrogen accumu-ates over time, especially when the residues are soybeans sincehey have relatively higher nitrogen contents. In the 5-year simula-ion, because of the shorter time period, the amount of soil nitrogen

ay just equal to the crop demand, and less nitrogen is leached intohe receiving water under NT where soils are less disturbed. In the5-year simulation, however, the soil nitrogen content may haveeen accumulated to such an extent that it exceeds crop demand,nd more nitrogen will be leached and found in the water under NThan MP. This argument is supported by several studies. Varvel and

ilhelm (2003) reported that soybean yielded an annual 65 kg N/han the 10- and 20-year studies. Another study suggested that NT

ith residues significantly increased soil organic carbon after thehird year (Al-Kaisi and Yin, 2005). Studies also indicated that nitro-en from crop residues was available much later and at a slower ratehan nitrogen from inorganic fertilizer (Soon et al., 2001; Varvelnd Wilhelm, 2003). Therefore, nitrogen loads can be higher underT/SS/0 than under MP/SS/0 in a 15-year period as simulated in

his study.

.2. The impacts of cropping system

CC has significantly lower flows than CS under the same tillageystem and N application rate based on a paired t-test (Table 8).his may be because corn has a higher evapotranspiration andater consumption (Eichingera et al., 2006). It also leaves a larger

mount of residues than soybean (Jones et al., 1995), which helpso decrease surface runoff. Hence, it is likely that CC yields a lowerow than CS.

In terms of nutrient loads and fecal coliform concentrations,he values under CC are higher than under CS, and the differencesre statistically significant in most cases based on paired t-testsTable 8). These results are reasonable since the inorganic nitrogenertilizer applied to corn is extremely soluble. Hence, it is moreulnerable to leaching than the nitrogen produced by the sym-iotic bacteria in the soybeans. While the bacteria living in theoots of soybean can fix large amounts of nitrogen and increasehe available soil nitrogen in a long term (Peel, 1998), this typef nitrogen is organic in nature, and it is often adhered to soilolloids and plant residues. As a result, a smaller amount of soilitrogen will be washed away by surface runoff under soybean.onsequently, CS yields a smaller nitrogen load than CC. Sev-ral studies revealed concordant results. For example, Randall andulla (2001) reported that nitrogen losses were the highest under

ontinuous corn, followed by soybean rotated with other crops;hey were the lowest under alfalfa. Drury et al. (2001) reportedhat soybean–corn rotation yielded 11% less nitrogen (nitrate) losshan continuous corn. Randall and Vetsch (2005) also stated thatitrate loss was 54% under corn and 46% under soybean. In addi-ion, some researchers found that corn had a higher nitrogen lossecause of the wide-spaced rows, high nitrogen fertilizer inputs,nd limited root systems during the early growing season in theorn production system (Andraski et al., 2000; Toth and Fox, 1998).hu and Fox (2003) contended that, in their study, the main fac-ors affecting nitrogen leaching were the weather. They stated thathe differences in the leaching volume of nitrogen were minimalnder corn and soybean when annual precipitations were simi-

ar. Randall and Mulla (2001) also agreed that the leaching volumeepended largely on climate and soil properties. Therefore, depend-

ng on the weather conditions and soil properties of the study areas,t is plausible that nitrogen loads can be higher or lower under CShan CC.

The results that CC has higher phosphorus loads and fecal col-form concentrations than CS are probably attributed to the lack ofertilizers or phosphorus inputs under soybean. Conversely, a cer-ain amount of phosphorus and fecal coliform is applied to corn

ater Management 119 (2013) 65– 79 73

via animal manure from poultry. Intarapapong and Hite (2002)reported similar results that when compared to CC, total phos-phorus loads under CS and CCS (corn–corn–soybean) decreasedapproximately 4.46% and 0.81%, respectively. Thus, it is likely thatCC yields higher phosphorus loads and fecal coliform concentra-tions than CS.

3.3. The impacts of N application rate

For both simulation periods, an increase in N application rate isfound to be associated with a lower flow; however, such a decreaseis mostly not statistically significant (Table 9). Conversely, nitrogenand phosphorus loads and fecal coliform concentrations increasewith increasing N application rates (90–170 kg/ha). Besides, theincrease is mostly statistically significant for both time periods(Table 9).

These results are similar to a study in central Iowa, where theincrease of nitrogen inputs by 20% and 40% had led to higher nitro-gen loads in the water bodies by 25% and 49%, respectively (Chaplotet al., 2004). Yamoah et al. (1998) also reported that increasing Nrates had resulted in a residual nitrate increase in corn (or maize)and sorghum, but not in continuous soybean in a 12-year field studyin Nebraska. They found that soybean had the highest nitrogenremoval index (the ratio of nitrogen removed in the grain to totalnitrogen inputs, including nitrogen from both fertilizers and fix-ation of legumes), followed by corn, and the lowest in sorghum.Because of the lower nitrogen removal index, the increase of Napplication rates led to an increase of nitrogen residual in soilsunder corn and sorghum. Thus, it is logical to find an increase ofnitrogen loads in this study, especially under corn, where higher Napplication rates are applied than in soybean.

For our research, since 50% of the nitrogen inputs for corn arepoultry manure, which also contains phosphorus and a certainamount of bacteria, therefore, it is reasonable that a higher N rateyields greater phosphorus loads and fecal coliform concentrationsthan a lower N rate. This result concurs with another study in Iowa.In that study, Kanwar et al. (2004) found that the increase of poul-try manure application had resulted in the increase of nitrogen andphosphorus loads, as well as E. coli and fecal coliform.

3.4. Impacts of tillage and cropping system

When the combined impacts of tillage and cropping systems areconsidered, it seems that flows are highest under MP/CS, followedby NT/CS, MP/CC, and NT/CC in both time periods (see Table 10).Although the results are in concert with the individual impacts,the ANOVA shows that the differences among the testing practices(MP/CC, MP/CS, NT/CC, and NT/CS) in terms of the mean annual flowover the 5-year or 15-year simulation periods are not statisticallysignificant (Table 10).

The results of mean annual nitrogen and total phosphorus loadsand fecal coliform concentration simulations show that MP/CChas the greatest values, followed by NT/CC, MP/CS, and NT/CS(Table 10). These results seem reasonable as CC and MP have highernutrient loads than CS and NT, respectively. A study in the Mis-sissippi River Basin in Minnesota reported that cropping systemhad more influence on nitrogen loss than tillage systems (Randalland Mulla, 2001). In spite of a couple of appreciable differencesbetween different tillage/cropping practice and MP/CC (the basecase), the ANOVA results indicate that the overall mean differences
of nitrogen loads among all combined practices are not statisticallysignificant in the study area (Table 10). As for the phosphorus loadsand fecal coliform concentrations, the overall mean differencesamong all practices are statistically significant in the 15-year, but


Table 8The comparisons of mean annual flow (m3/s), nitrogen load (Mg), total phosphorus load (Mg), and fecal coliform concentration (cfu/100 mL) under different cropping systems,but with the same N rate and tillage system (data show % change from the base case*).

Scenario Crop systema Tillagea/N rate Flow N load P load FC

Base 5-Year 15-Year 5-Year 15-Year 5-Year 15-Year 5-Year 15-Year

2 CC MP/904 CS MP/90 2.1* 2.0* −14.5 −44.2* −20.8* −16.9* −17.3 −21.5*

3 CC MP/1705 CS MP/170 2.0* 1.9* −0.5 −33.3* −27.9 −31.7* −18.7 −21.4*

7 CC NT/909 CS NT/90 1.2* 1.4* −13.6* −42.0* −17.9* −19.8* −41.8* −47.3*

8 CC NT/17010 CS NT/170 1.0* 1.3* −0.9* −31.2* −23.8* −28.8* −43.0* −47.3*

* Differences between the cropping systems are statistically significant at the 0.05 significance level.a For the cropping system: SS – soybean, CC – corn, CS – corn and soybean rotation; for the tillage system: MP – moldboard plow, NT – no till.

Table 9The comparisons of mean annual flow (m3/s), nitrogen load (Mg), total phosphorus load (Mg), and fecal coliform concentration (cfu/100 mL) under different N applicationrates, but with the same cropping and tillage systems (data show % change from the base case*).

Scenario N rate Tillage/cropa Flow N load P load FC


2 90 MP/CC3 170 MP/CC −0.2 −0.1 8.8 9.5* 32.7* 41.6* 88.7* 87.4*

4 90 MP/CS5 170 MP/CS −0.3* −0.2* 26.5* 30.8* 20.9* 16.3* 85.6* 87.5*

7 90 NT/CC8 170 NT/CC −0.1 −0.1 12.1* 8.5* 12.8* 24.0* 88.7* 86.8*

9 90 NT/CS10 170 NT/CS −0.3 −0.2* 28.6* 28.7* 4.7* 10.0* 84.9* 86.8*

.05 sigem: S

nr

3

aaat(

mtTre

TTt

* Differences between the N application rates are statistically significant at the 0a For the tillage system: MP – moldboard plow, NT – no till; for the cropping syst

ot significant in the 5-year simulation (Table 10). These results areeasonable and in accordance with those of the individual impacts.

.5. Impacts of tillage and fertilization

Flows are the lowest under NT/170 and the highest under MP/90s both NT and 170 kg N/ha individually have lower flows than MPnd 90 kg/N/ha, respectively. However, the overall flow differencesmong all different combinations of tillage and N rate are not sta-istically significant (based on ANOVA results) in both time periodsTable 11).

In terms of nutrients and fecal coliform, MP/170 has the greatestean annual nitrogen and phosphorus loads and bacteria concen-

rations, and NT/90 has the lowest values in all cases (Table 11).hese results are appropriate and in accord with the simulationesults of individual impacts as MP is found to produce higher nutri-nt loads and coliform concentrations than NT, and N application


illage and cropping system (data show % change from the base case*).

Scenario Tillage/cropa N rate Flow

Base 5-Year 15-Year

3 MP/CC 1705 MP/CS 170 2.0 1.9

8 NT/CC 170 −0.1 −0.6

10 NT/CS 170 1.0 0.7

ANOVA for group means** x x

* Differences between each scenario compared to the base case are statistically signific** ANOVA results: y is statistically significant and x is not significant at the 0.05 significaa For the tillage system: MP – moldboard plow, NT – no till; for the cropping system: S

nificance level.S – soybean, CC – corn, CS – corn and soybean rotation.

rate of 170 kg/ha produces higher values than 90 kg/ha. Despite thelarge percent differences between each tillage/fertilization practiceand the NT/90 base case, the ANOVA results suggest that the over-all mean differences of nitrogen loads among all the combinedpractices are not significant. But the mean differences of bacteriaconcentrations under both 5- and 15-year simulations as well asphosphorous under 15-year simulation are significant (Table 11).

3.6. Impacts of cropping system and fertilization

As in the simulation of individual impacts, CS/90 has the high-est flow, followed by CS/170, CC/90, and CC/170 in all cases underboth MP and NT tillage systems (Table 12). Nonetheless, the ANOVA
results indicate that overall mean flows among these practices arenot significantly different (Table 12).
Regarding the nitrogen and phosphorus loads and coliform con-centrations, CC/170 has the highest values, and CS/90 has the lowest

(Mg), and fecal coliform concentration (cfu/100 mL) under the combined impacts of

N load P load FC

5-Year 15-Year 5-Year 15-Year 5-Year 15-Year

−0.5 −33.3* −27.9 −31.7* −18.7 −21.4−0.4 −5.0 −26.4 −24.3* −3.4 −4.3−1.2 −34.6* −43.9 −46.1* −44.9 −49.6*

x x x y x y

ant at the 0.05 significance level.nce level.

S – soybean, CC – corn, CS – corn and soybean rotation.


Table 11The comparisons of mean annual flow (m3/s), nitrogen load (Mg), total phosphorus load (Mg), and fecal coliform concentration (cfu/100 mL) under the combined impacts oftillage and nitrogen application rate (data show % change from the base case*).

Scenario Tillage/N ratea Cropa type Flow N load P load FC


7 NT/90 CC8 NT/170 CC −0.1 −0.1 12.1 8.5 12.8 24.0 88.7 86.8*

2 MP/90 CC 0.2 0.6 3.5 4.3 15.6 15.7 3.5 4.23 MP/170 CC −0.1 0.5 12.5 14.2 53.4 63.8* 95.3 95.3*

9 NT/90 CS10 NT/170 CS −0.3 −0.2 28.6 28.7 4.7 10.0 84.9 86.84 MP/90 CS 1.0 1.2 2.3 0.4 11.5 20.0 47.0 55.35 MP/170 CS 0.7 1.0 29.5 31.2 34.7 39.4* 172.8* 191.2*

ANOVA for group means** x x x x x y y y

ignificgnificaem: S

vttssa

3f

thttpfp(

rtbl(wHns

TTc

* Differences between each scenario compared to the base case are statistically s** ANOVA results: y is statistically significant and x is not significant at the 0.05 sia For the tillage system: MP – moldboard plow, NT – no till; for the cropping syst

alues (Table 12). These results are in agreement with those fromhe individual impacts, but again, the ANOVA results suggest thathe overall mean nitrogen loads among these practices are notignificantly different. Nevertheless, the ANOVA results indicateignificant differences under the 15-year period for phosphorus andlso under both simulation periods for fecal coliform (Table 12).

.7. Combined impacts of tillage, cropping system, andertilization

When considering the overall impacts of all three farming prac-ices, flows are the lowest under NT/CC/170 (scenario 8) and theighest under MP/CS/90 (scenario 4) in both the 5-year simula-ion and the 15-year simulation (Table 13). This is analogous tohe results from the individual impacts, but the orders among theractices vary considerably. The ANOVA results show that the dif-erences among all combinations of tillage/cropping/fertilizationractices are not statistically significant in both time periodsTable 13).

The combined impacts of tillage, cropping, and N applicationate on annual nitrogen load show complex results. The loads arehe lowest under NT/CS/90 (scenario 9) in both simulation periods,ut the highest under MP/SS/0 (in scenario 1) in the 5-year simu-

ation and under NT/SS/0 (scenarios 6) in the 15-year simulationTable 13). In general, the results are reasonable and compatible
ith the impacts of individual farming practice on nitrogen load.owever, SS is supposed to have the least nitrogen loads as there iso nitrogen application for soybean. A possible explanation is thatoybean yields about 65 kg N/ha/year (Varvel and Wilhelm, 2003).

ropping system and nitrogen application rate (data show % change from the base case*).

Scenario Cropa/N rate Tillage systema Flow

Base 5-Year 1

4 CS/90 MP5 CS/170 MP −0.3 −2 CC/90 MP −2.0 −3 CC/170 MP −2.3 −9 CS/90 NT10 CS/170 NT −0.3 −7 CC/90 NT −1.2 −8 CC/170 NT −1.4 −ANOVA for group means** x x

* Differences between each scenario compared to the base case are statistically signific** ANOVA results: y is statistically significant and x is not significant at the 0.05 significaa For the tillage system: MP – moldboard plow, NT – no till; for the cropping system: S



Over the years, under SS, soil nitrogen is accumulated (Varvel andWilhelm, 2003; Soon et al., 2001; Al-Kaisi and Yin, 2005), a higheramount of which is leached into the water than those under CCor CS as corn helps to decrease nitrogen accumulation. Nonethe-less, this assumption is realistic only for practices that leave cropresidues on the fields (for example, NT). Thus, it is plausible that,for a long-term period, NT/SS/0 has the highest loads. Also, withthe same crop and N rate, scenarios NT can have higher loads thanthose under MP (see Table 13). On the other hand, this assump-tion does not explain why MP/SS/0, from which most residues areremoved, has the highest nitrogen loads in the 5-year simulationand the second highest loads in the 15-year simulation. It should benoted that, based on ANOVA results, the overall mean differencesof nitrogen loads among all practices are not statistically significantin the 5-year simulation, but significant in the 15-year simulation(Table 13).

Regarding the combined impacts on phosphorus, the results aresimilar to the individual impacts, where MP/CC/170 (scenario 3) hasthe highest loads and NT/SS/0 (scenario 6) has the lowest loads inboth periods (Table 13). Nevertheless, the orders among other prac-tices do not exhibit a uniform pattern; the ANOVA results suggestthat the overall phosphorus load differences among all combinedpractices are significant only in the 15-year simulation period.

As for the fecal coliform, MP/CC/170 (scenario 3), NT/CC/170(scenario 8) and MP/CS/170 (scenario 5) have higher concentra-
tions while MP/CS/90 (scenario 4), NT/CS/90 (scenario 9), MP/SS/0(scenario 1), and NT/SS/0 (scenario 6) have lower concentrations(Table 13). In general, these results concur with the individualimpacts. ANOVA results confirm that the overall mean coliform
(Mg), and fecal coliform concentration (cfu/100 mL) under the combined impacts of

N load P load FC

5-Year 5-Year 15-Year 5-Year 15-Year 5-Year 15-Year

0.2 26.5 30.8 20.9 16.3 85.6 87.5*

2.0 17.0 79.1* 26.3 20.3 21.0 27.32.1 27.2 96.1* 67.6 70.3* 128.3 138.7*

0.2 28.6 28.7 4.7 10.0 84.9 86.8*

1.4 15.7 72.3* 21.8 24.6 71.9 89.8*

1.4 29.7 87.0* 37.4 54.6* 224.3* 254.5*

x x x y y y




Table 13The comparisons of mean annual flow (m3/s), nitrogen load (Mg), total phosphorus load (Mg), and fecal coliform concentration (cfu/100 mL) under the combined impacts oftillagea, cropping systema, and N application rate (data show % change from the base case*).

Scenario Base Compare to Flow N load P load FC

5-Year 15-Year 5-Year 15-Year 5-Year 15-Year 5-Year 15-Year

6 NT/SS/07 NT/CC/90 −0.2 −0.1 −18.6 −21.4 23.8 28.6* 286 320*

8 NT/CC/170 −0.4 −0.1 −8.7 −14.7 39.7 59.4* 629* 685*

9 NT/CS/90 1.0 1.3 −29.6 −54.4* 1.7 3.1 125 12210 NT/CS/170 0.7 1.1 −9.5 −41.3* 6.5 13.5 316 314*

1 MP/SS/0 0.1 0.5 2.5 −4.2 3.3 15.5 26 242 MP/CC/90 −0.0 0.6 −15.7 −18.1 43.2 48.8* 300 338*

3 MP/CC/170 −0.3 0.4 −8.4 −10.3 89.9* 110.6* 655* 721*

4 MP/CS/90 2.0 2.6 −28.0 −54.3* 13.4 23.7 231 244*

5 MP/CS/170 1.7 2.3 −8.8 −40.2* 37.0 43.8* 513* 545*

ANOVA for group means** x x x y x y y y

ignificgnificaem: S

ce

3

tpcn

2a(

3b

ttp

itpshctbi

TAr

m

* Differences between each scenario compared to the base case are statistically s** ANOVA results: y is statistically significant and x is not significant at the 0.05 sia For the tillage system: MP – moldboard plow, NT – no till; for the cropping syst

oncentrations among all testing practices are significantly differ-nt in both 5- and 15-year simulations.

.8. Economic returns

In terms of farming economics, corn provides on an average 2.49imes higher income than soybean. However, the total costs of cornroduction are roughly 1.40 times higher than soybean. Also, theosts of moldboard plowing are about 1.13 times as expensive aso-till (see Table 5).

When the income and production costs are considered, the-year net profits ($/ha) for these practices, from high to low,re NT/CC ($1164.44), MP/CC ($1006.82), NT/CS ($968.55), MP/CS$801.13), NT/SS ($569.26), and MP/SS ($392.00) (Tables 5 and 14).

.9. The best practice in terms of economic and environmentalenefits

Since the economic study was based on the 15-year simula-ion and 170 kg N/ha fertilizer application rate (see Section 2.8),he same criteria were also used in the determination of the bestractice.

From the simulation results, it seems that NT/CS is the best farm-ng practice to provide viable economic returns while minimizinghe impacts on the quality of the environment for the 15-yeareriod (Table 14). The reasoning is based on the fact that continuousoybean (NT/SS and MP/SS) yields very low 2-year net profits andas the highest nitrogen loads in a long-term period; so, these two
ombined practices are not recommended even though they arehe best practices to minimize the amount of phosphorus and fecalacteria. As for NT/CC, although it provides the highest net profit,
t has the second highest phosphorus load as well as fecal coliform

able 14 comparison of the 15-year simulation of the economic and water qualitya benefits undeate.

Practicesb 2-Year net profit ($/ha) Flow (m3/s)

NT/CC 1164.44 4.168

MP/CC 1006.82 4.192

NT/CSc 968.55 4.221

MP/CS 801.13 4.271

NT/SS 569.26 4.174

MP/SS 392.00 4.194

a Flow – mean monthly flow, m3/s; nitrogen – mean annual nitrogen load (NO2 + NO3 aonthly fecal coliform concentration, cfu/100 mL; the mean values are the average valueb For the tillage system: MP – moldboard plow, NT – no till; for the cropping system: Sc No till/corn–soybean rotation – the best practice for balancing the economic and env



concentration. MP/CC provides the second highest net revenue butthe highest phosphorus load and bacteria concentration. Therefore,both NT/CC and MP/CC are excluded from consideration too. Thisleaves NT/CS and MP/CS to be considered. Among the two, NT/CSis obviously a better practice as it yields a higher 2-year net profitthan MP/CS (it provides a slightly smaller revenue than MP/CC)and a lesser flow, smaller nitrogen and phosphorus loads, as wellas a lower fecal coliform concentration. To optimize the economicand environmental benefits in the study area, NT/CS is, therefore,the best farming practice. The second-best practice can be MP/CS ifthe quality of the environment is the priority, or it can be NT/CC ifeconomic returns are considered more important.

Under the same farming settings, NT is a better practice thanMP, and CS is a better practice than SS and CC in terms of the over-all environmental benefits. This is because CS produces a moderatefecal coliform concentration and a moderate phosphorus load, aswell as the lowest nitrogen load. While SS has the lowest fecalcoliform concentration and phosphorus load, it has the highestnitrogen load. CC yields the highest fecal coliform concentrationand phosphorus load, and it also has a moderate nitrogen load.The impacts on flows are less significant as the differences amongtesting practices are not statistically significant in the 15-year sim-ulation. In terms of economic returns, NT is also better than MP as itrequires less input, such as labor and machinery uses. However, CCis a better practice than CS and SS in economic returns. Therefore,NT is certainly the best tillage system in terms of the economic andenvironment benefits. Many field studies reported that CS was abetter practice in the long-term (Wesley et al., 2001; Cook, 1986;
USDA ERS, 1997). This is because crop rotation helps to improve soilproperties, soil fertility, and weed and pest control. As a result, itcan reduce fertilization and pesticide needs, as well as increase cropyields. On the contrary, CC was found to have the lowest net returns
r different tillage and cropping systems based on 170 kg/ha N fertilizer application

N (Mg) P (Mg) FC (cfu/100 mL)

635.0 13.5 707668.6 17.8 739437.2 9.6 372445.4 12.2 580744.8 8.5 90714.0 9.8 112

s nitrogen), Mg; phosphorus – mean annual total phosphorus load, Mg; FC – means over a 15 year period.S – soybean, CC – corn, CS – corn and soybean rotation.ironmental benefits.


Table 15Summary of the impacts of farming practices on the environment and economy.

Tillage system Crop system N rate (kg/ha)

Flow Moldboard plow > no till Corn–soybean > soybean > corn 90 > 170Nitrogen, N Moldboard plow > no tilla Soybean > corn > corn–soybean 170 > 90Phosphorus, P Moldboard plow > no till Corn > corn–soybean > soybean 170 > 90Fecal coliform, FC Moldboard plow > no till Corn > corn–soybean > soybean 170 > 90

amsacaivtreV

4

2vp

Pbarht1a

itcpaFTaas

vpmdaatotbfms

y

2-Year net profit No till > moldboard plow

a Except under SS/0 (soybean with no nitrogen application).

nd provided the poorest soil properties and soil fertility than otherixed rotations of corn, soybean, oat, and meadow in the 20-year

tudy in Iowa and Wisconsin (Karlen et al., 2006). In addition, cornnd soybean yields were found to increase when rotated with otherrops, but decline when planted in a continuous system (Pedersennd Lauer, 2002; Edwards et al., 1988). Thus, in the long term, its apparent that NT/CS is the best combined practice and the mostiable option to optimize environmental quality and crop produc-ivity in the study area. Nonetheless, it should be noted that NT caneduce crop growth and yields if the soil is poorly drained, and thenvironment is cold and wet (Lal et al., 2007; DeJong-Hughes andetsch, 2007).

. Conclusions

The results from the earlier pilot study (Tong and Naramngam,007) and this study show that agricultural BMPs are economicallyiable and can potentially reduce the water quality impacts of non-oint source pollution from farmlands (Table 15).

NT yields a higher 2-year net profit but lower surface flow, N and loads, as well as FC concentration than MP. CS has the highest flowut the lowest N, and moderate P, FC and 2-year net profit. SS yields

moderate flow, the highest N, and the lowest P, FC and 2-year netevenue; while CC produces the lowest flow, a moderate N, but theighest P, FC, and net profit. N application of 90 kg/ha produceshe greatest flow, but the lowest N, P, and FC when compared to70 kg/ha. The combined impacts of these three farming practicesre in accordance with the individual impacts.

In terms of environmental benefits, NT is better than MP, and CSs better than SS and CC. In terms of the economy, NT is also bet-er than MP while CC is better than CS and SS. For these reasons, itan be concluded that NT/CS is the most feasible long-term farmingractice for balancing the environment and economy. It provides

reasonable 2-year net profit, and lower flow, N and P loads, andC concentration. The second-best practice can be MP/CS or NT/CC.his information may be useful to farmers and water resource man-gers, especially those concerned with conserving the water qualitynd maximizing economic returns under a cool-temperate climate,uch as that in southwest Ohio.

While the simulation results in this study show that conser-ative farming system can be a viable option to balance croprofitability and environmental quality, a result concurring withany field studies, more information is still needed. Since the con-

itions of farmland (such as soil fertility, soil properties, and themount of productivity) vary considerably, each farm should benalyzed in more detail. Besides, the prices of crops differ from yearo year. There are also different advantages and disadvantages ofther farming practices. Hence, farmers should carefully examinehese factors before the best practice for an individual farmland cane determined. Indeed, there is no one “perfect” set of combinedarming practices for all cool-temperate farms as different soils, cli-

ate, and landscape may require different farming managementystems.

The results from this research show more variations in the 5-ear than 15-year simulations. Besides, while the environmental

Corn > corn–soybean > soybean –

impacts of some farming practices do not differ significantly fromothers under the 5-year period, the differences under the 15-yearsimulation are rather apparent. According to the literature, weathercondition plays a crucial role on flow volumes, nutrient leaching,and bacterial transportation. Higher precipitation usually leads tohigher flows and water pollutant loads. Therefore, if weather condi-tions vary each year, the flows and nutrients and bacterial leachingwill fluctuate, especially in a short-term study. Hence, the use of along-term time scale for simulation will be more advantageous.

Moreover, in this study, SWAT is found to be able to generaterealistic simulations of flow and water quality. The Nash–Sutcliffecoefficient, R2, and t-test of this study indicate that the simulatedand observed data are in good agreement for both the 1980–1984and the 1990–1994 periods.

Acknowledgements

We are grateful to the USEPA, USGS, and USDA for making a widevariety of useful dataset and modeling packages available for publicuse, as well as to members of the SWAT-user group for answeringquestions and providing helpful information on SWAT modelingand troubleshooting.

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