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Impacts of Agricultural Practices and Policies on Potential Nitrate Water Pollution in the Midwest and Northern Plains of the United States

JunJie Wu, P. G. Lakshminarayan, and Bruce A. Babcock

Working Paper 96-WP 148

February 1996

Center for Agricultural and Rural Development

Iowa State University Ames, Iowa 50011-1070

For content questions please contact Bruce Babcock (babcock@iastate.edu) or P. G. Lakshminarayan (plakshm@iastate.edu). JunJie Wu is a visiting scientist, CARD; P.G. Lakshminarayan is an associate scientist and manager of CEEPES, CARD; and Bruce A. Babcock is an associate professor of economics and head of the Resource and Environmental Policy Division, CARD. This research was partially supported by EPA Cooperative Agreement CR822045-01-1. The authors wish to thank Phil Gassman, Mark Siemers, Alicia Carriquiry, Paul Mitchell, and Toshitsugu Otake for assisting with data collection and programming.

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ABSTRACT

An empirical model is developed to estimate the effects of alternative farming practices on

potential nitrogen runoff and leaching in 128,591 National Resources Inventory sites across the

Midwest and the Northern Plains of the United States. This model integrates the effects of soils,

climate, crops, and management practices on nitrogen loss. The model is applied to evaluation of

two policy scenarios. The first scenario reduces N fertilizer application rates by 25 percent through

the soil N test. The second replaces continuous cropping practices with crop rotations. The results

show that policy effects vary widely across the study region. This analysis emphasizes the

importance of conducting policy analysis on a disaggregated scale.

Key words: farming practices, nitrogen runoff and leaching, spatial heterogeneity, disaggregated

policy analysis

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IMPACTS OF AGRICULTURAL PRACTICES AND POLICIES ON POTENTIAL NITRATE WATER POLLUTION IN THE MIDWEST AND NORTHERN PLAINS

OF THE UNITED STATES

Public concern that use of fertilizers, especially nitrogen, is contributing to the

contamination of the nation’s surface water and groundwater is widespread. Nitrate-N is the most

commonly detected agricultural chemical in groundwater. In a recently released report, the U.S.

Geological Survey (USGS) found that nitrate concentrations in 21 percent of samples collected

beneath agricultural land exceeded the 10 mg/l maximum contamination level set by the U.S.

Environmental Protection Agency (Mueller et al.). Reflecting the increased awareness of the scope

and diversity of nonpoint source water pollution, several national inventories have been conducted

to determine the status, trend, or spatial patterns of nitrate concentrations in groundwater or surface

water (Smith, Alexander, and Wolman; Mueller et al.). While a few studies have evaluated

groundwater contamination potential from nitrogen use at the regional or national levels (Nielsen

and Lee; Kellogg, Maizel, and Goss), many have examined the impact of farming practices on

nitrate water pollution at the field, farm, or watershed levels (see Hallberg for a review). Although

these studies have linked nitrate water pollution to land use, nitrogen application rates, management

practices, and hydrogeologic settings, they have not provided adequate results that confirm the

effectiveness of alternative farming practices and policies in reducing agricultural water pollution.

Because of difficulties in measuring discharges from individual sources, the literature on

nonpoint pollution control policies has focused on instruments applied to inputs and management

practices (Shortle and Dunn). Specifically, a number of empirical studies in recent years have

evaluated nitrogen-use taxes and restrictions for controlling nonpoint-source water pollution

(Johnson, Adams, and Perry; Taylor, Adams, and Miller; Mapp et al.; Helfand and House; Wu et

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al.). Given the political difficulty and high monitoring cost of imposing direct controls on farmers,

researchers are exploring other ways to reduce nitrate water pollution. Improved farming

techniques, such as N testing and precise placement of fertilizers, show some promise of reducing

fertilizer applications, but not farm returns (Babcock and Blackmer; Swanson; Shortle et al.; Bosch,

Fuglie, and Keim).

Adoption of conservation practices, such as crop rotations, has been suggested as another

way to reduce nonpoint pollution (Diebel et al.; Braden et al.). There is evidence that eliminating

the base acreage concept from farm programs would increase crop rotations. Hennessy, Babcock,

and Hayes found that, under a free-market scenario, continuous corn producers in Iowa would shift

to a corn-soybean rotation because of the yield-enhancing rotation effects. Williams, Llewelyn, and

Barnaby compared wheat and grain sorghum rotations grown continuously, in fallow systems, and

in rotation under conventional and no-till methods for western Kansas. They found that current

commodity programs encourage continuous wheat as opposed to a wheat-sorghum-fallow rotation.

Little is known, however, about how effective these alternative farming practices would be in

reducing nitrate water pollution.

The primary objective of this study is to evaluate the impacts of alternative management

practices and policies on nitrate water pollution in the Corn Belt, Lake States, and Northern Plains

of the United States. An empirical model is developed by combining U.S. Department of

Agriculture (USDA) data from the 1992 National Resources Inventory (NRI), the SOILS 5 (Soil

Interpretation Record System), and the 1992 USDA Cropping Practices Survey. This model

incorporates much more detailed physical and production system information than previous

national or regional assessments. The model is used to identify high-risk areas in the study region

and to evaluate the effects of two policy scenarios on nitrate water pollution in the high-risk areas.

The first scenario reduces N fertilizer application rates by 25 percent through the soil N test. The

second replaces continuous cropping practices with crop rotations. The spatial variation of the

policy effects are demonstrated.

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Study Region

The study region includes 12 states in the Corn Belt, Lake States, and Northern Plains. The

region accounted for 57 percent of the nation’s cropland in 1992 (USDA/Soil Conservation

Service), and produced 89 percent of the nation’s corn, 81 percent of the nation’s soybeans, 56

percent of the nation’s sorghum, and 56 percent of the nation's wheat in 1991. The total nitrogen

and phosphate use in the study region are 6.12 and 2.41 million nutrient tons, in 1993, or 54 percent

of total U.S. application (USDA/Economic Research Service 1994).

This study focuses on nitrate water pollution from production of corn, soybeans, sorghum,

wheat, and alfalfa. These five crops and summer fallow account for about 90 percent of cropland in

the study region according to the 1992 NRI. Corn and soybeans are the major crops in the Corn

Belt and Lake States, accounting for 72 percent of cropland. In the Northern Plains, wheat and corn

are the major crops and account for 51 percent of cropland.

Fourteen major crop rotations were identified using the 1992 NRI (Table 1). The most

commonly used rotation in the Corn Belt and Lake States was corn-soybean, while the most

commonly used rotations in the Northern Plains were wheat-fallow and wheat-sorghum-fallow.

About 17.4 percent of cropland was cultivated with conservation tillage, and 10.6 percent was

cultivated using conservation practices such as contouring, terracing, and strip-cropping. Irrigation

is another major factor influencing nutrient leaching. In 1992, 6.7 percent of the region’s cropland

was irrigated, with most of these irrigated acres in Nebraska and Kansas.

The Empirical Model

A major challenge for evaluating nonpoint source water pollution at the regional level is to

account for the spatial heterogeneity of agriculture. Which crops are grown and how they are

grown varies from farm to farm, and affects nitrogen use and pollution. Because physical attributes

of the land are not homogeneous, nitrate water pollution also can vary dramatically across farms

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that grow the same crop and use identical farming practices. Climate also plays a large role in

determining water-quality impacts of farming practices. Heterogeneous land and climate

complicate the design of models intended to capture the impacts of agriculture on water pollution

since they add a spatial dimension to the analysis. Thus, the analyst must know not only how

cropping patterns are distributed across farms, but also the location of crops, cropping practices,

and the physical attributes of the land on which the crops are grown.

To capture spatial heterogeneity, agricultural land in the region is divided into plots or

microunits. Each microunit is homogeneous in production systems and resource settings. A

production system is defined by crop choice, rotation, tillage system, conservation practice,

irrigation, and other management practices; a resource setting is determined by soil type, weather,

and hydrogeologic properties. Total nitrogen runoff or leaching loss in an area within the study

region is estimated by

(1) NL A Zi ii

= ∑ ,

where NL is the total nitrogen runoff or leaching loss in the area, Ai is the acreage of microunit i,

and Zi is the per acre nitrogen runoff or leaching loss in microunit i. Zi depends on the production

system and the resource setting in the microunit:

(2) Z G X Ri i i= ( , ) ,

where Xi is a vector of variables that describes the production system in microunit i, and Ri is a

vector of variables that describes the resource setting in microunit i. Given cropping patterns, the

impact of a change in management practices on nitrogen runoff and leaching is estimated by

(3) A G X R G X Ri i i i ii

[ ( , ) ( , )]−∑ 1 ,

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where Xi1 is a vector of variables that describe the management practices on microunit i.

This model indicates that two types of data are needed for evaluating nitrogen runoff and

leaching potential. One is the spatial distribution of production systems and resource settings, and

the other is the nitrogen runoff and leaching losses under each combination of production systems

and resource settings. A four-step procedure is used to derive and analyze these data. The first step

is to identify the important combinations of production systems and resource settings in the study

region and their spatial distributions. The second step is to estimate nitrogen runoff and leaching

equations from (2). In the third step, we predict nitrogen runoff and leaching potential in each

microunit by using the estimated nitrogen runoff and leaching equations. Because a vast amount of

site-specific nitrogen loss data is generated, a geographic information system (GIS) is used to

present and interpret the results. Spatial patterns of nitrogen runoff and leaching potential as well as

high-risk areas also are identified. In the final step, we evaluate the effectiveness of conservation

farming practices and nitrogen use reductions in reducing nitrogen runoff and leaching losses in the

high-risk areas.

Identifying Production Systems and Resource Settings

The 1992 NRI and the SOILS 5 databases are the two primary data sources used to identify

the spatial distribution of production systems and resource settings in the study region. The 1992

NRI is the latest in a series of inventories conducted by the Natural Resource Conservation Service

(formerly, the Soil Conservation Service) of the U.S. Department of Agriculture. The purpose of

the NRI is to determine the status, condition, and trend of the nation’s soil, water, and related

resources (Goebel and Dorsch). Information on nearly 200 attributes was collected at each NRI

sample point. The attributes include land use and cover, cropping history, irrigation type, tillage

practices, conservation practices, topography, hydrology, and soil type.

Data for the 1992 NRI were collected for more than 800,000 nonfederal locations. The

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sampling design guarantees that inferences at the national, regional, state, and substate levels can be

made in a statistically reliable manner. Each NRI point is accompanied by an expansion factor that

assigns each point the appropriate weight under the design. In our study region, there are 128,591

NRI points growing corn, soybeans, sorghum, wheat, or legume hay. Of these points, 55,024 are in

the Corn Belt, 21,600 in the Lake States, and 51,967 in the Northern Plains.

We linked the 1992 NRI to the SOILS 5 database to identify soil profile properties at each

point. Climatic data were derived from long-term historical weather records at weather stations

across the United States. Each point is linked to a weather station using the Thiessen polygon

technique within ARCINFO, a Geographic Information System (GIS). Based on cropping practice,

soil profile property, and weather at each point, unique combinations of production systems and

resource settings were identified. The 2,141 general soils (each with a maximum of six soil

profiles), 14 crop rotations, four tillage practices, four conservation practices, two irrigation

systems, and many weather conditions in the study region constituted hundreds of thousands of

unique combinations of production systems and resource settings. The NRI expansion factors

determined the distribution of these combinations.

Estimation of Nitrogen Loss Equations

The relationship between agricultural practices and nitrogen losses as stylized in equation

(2) reflects the knowledge of many disciplines. The relationship has been represented by simulation

models in previous field- or watershed-level analyses. Although simulation models are analogs of

real processes, their direct application to regional analyses is generally prohibitive (Bouzaher et al.).

Because hundreds of thousands of unique combinations of production systems and resource settings

are identified in our study region, it is impractical to simulate nutrient runoff and leaching under

each and every combination. To simplify our task, metamodeling is used (Bouzaher et al.).

A metamodel approximates outcomes of a complex simulation model with regression

analysis. Previous applications include Bouzaher et al., who estimated loadings of 16 major

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herbicides in surface water and groundwater in the Corn Belt and Lake States; Teague and

Bernardo, who examined the impact of irrigation and nitrogen application levels on nitrogen

leaching in the Central High Plains; and Wu, Mapp, and Bernardo, who evaluated the impact of

alternative water quality policies on crop choices and irrigation investment decisions in the

Oklahoma panhandle. Applications of metamodels in management, industrial, and computer fields

are documented in Kleijnen.

For our analysis, a representative sample was selected from all combinations of production

systems and resource settings in the study region. Nitrogen runoff and leaching were then

simulated for each combination in the sample with a widely accepted biogeophysical process

model, the Water Quality and Erosion Productivity Impact Calculator (EPIC-WQ). Nitrogen runoff

and leaching equations were then estimated based on the simulation results.

EPIC-WQ Simulations

The 1992 NRI provides an ideal database for selecting a sample of production systems and

resource settings for EPIC-WQ simulations. To obtain a representative sample, NRI points within

each major land resource area (MLRA), as defined by the USDA, were first grouped according to

production system. Then 10 percent of the NRI points were randomly selected from each group.

A total of 11,403 NRI points were selected and EPIC-WQ simulations were run at each point. 1

EPIC-WQ was developed to simulate the effects of agricultural practices on crop yield and

nutrient losses by surface runoff, sediment movement, and leaching below the root zone. EPIC-

WQ has nine major components: hydrology, weather, erosion, nutrients, plant growth, soil

temperature, tillage, economics, and plant environment. The model has been validated and

calibrated for a wide variety of conditions, particularly for those prevalent in this study region. For

a detailed discussion of the EPIC-WQ model and its validation, see Williams, Jones, and Dyke and

Jones et al.

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Three major categories of data are needed for EPIC-WQ simulations: production systems,

soil profile properties, and weather. Production system data were obtained from several sources.

Crop rotation at each NRI point was determined by using cropping history information at the point.

The NRI also reports whether conventional or conservation tillage was practiced at each point. We

randomly assigned conservation tillage points into reduced and no-till categories based on crop-

specific and state-level distributions of reduced and no-till acres estimated from data published by

the Conservation Technology Information Center (CTIC).

The nitrogen and phosphorus application data for the EPIC-WQ simulations were collected

from several sources. Preliminary estimates for each state in the study were estimated from the

Federal Enterprise Data System (FEDS). These preliminary estimates were distributed to soil

fertility experts attending a soil fertility workshop in St. Louis, Missouri, on October 27, 1994. The

experts were asked to review the FEDS estimates and, if they felt these preliminary assessments

were unreasonable, to provide their own best estimates for their states. In addition, the experts were

asked to estimate how crop rotation, tillage, and irrigation might affect fertilizer rates. Based on the

survey results, nitrogen and phosphorus application rates by crop, rotation, tillage, and irrigation

were then estimated.

Timing and methods of nitrogen application were identified through the USDA 1992

Cropping Practices Survey (USDA). Fertilizer data compiled by the Tennessee Valley Authority

facilitated identification of nitrogen forms used in each state. For simplicity, it was assumed that all

phosphorus fertilizer was applied at planting as mineral phosphorus. Planting and harvesting dates

for all crops were obtained from the USDA’s 1972 Agriculture Handbook (Statistical Reporting

Service, USDA). The dates were updated from the USDA 1992 Cropping Practices Survey.

Soil and weather data also were needed for the EPIC-WQ simulations. The soil profile

properties at each NRI point were obtained by linking the 1992 NRI with the SOILS 5 database.

Daily weather inputs to EPIC-WQ were obtained from the EPIC-WQ weather generator (Sharpley

and Williams), which generates daily values of precipitation, maximum temperature, minimum

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temperature, solar radiation, wind speed, and relative humidity. These daily values are generated

for each selected NRI point from the long-term historical weather records at the weather station to

which the NRI point was linked.

An automatic input file builder and control program written in the C++ programming

language simplified the task of linking all databases and constructing the EPIC-WQ input files for

every selected NRI point. The same program automates execution of the EPIC-WQ simulations

and extraction of the pertinent output data from the standard EPIC-WQ output file. A 30-year

EPIC-WQ simulation run was conducted for each of the 11,403 selected NRI points. Each run

provides daily estimates of nitrogen runoff and leaching. Nitrogen runoff is measured as NO - N3

leaving the field through surface runoff, and nitrogen leaching is measured as NO - N3 leaving the

root zone by percolation. 2

Empirical Specification of the Metamodels

The 30-year averages of simulated nitrogen runoff and leaching loadings provide the data

for the dependent variables of the nitrogen runoff and leaching metamodels. 3 Preliminary analysis

of the data showed that the loadings varied across management practices and watersheds. The

distributions of both runoff and leaching loadings were nonnormal and positively skewed (to the

right). Initial results from a linear regression of loadings on management practices, physical

attributes, and their interaction terms yielded a poor fit and wedge-shaped residual patterns,

indicating existence of heteroskedasticity.

The heteroskedasticity was corrected using procedures similar to the generalized Box-Cox

power transformation to find a variance-stabilizing transformation of the dependent variables (Box

and Hill). The transformed dependent variables take the form of NR NPα δ and , where NR and NP

are, the 30-year average of the simulated nitrogen runoff and leaching, and α δ and are the power

transformation parameters. The normal scores ranking method proposed by Blom was used to rank

alternatives α δ and and select the best transformation. Thus, these nitrogen runoff and leaching

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metamodels are empirically estimated:

(3) NR Xi i i1 3 5/ . ,= +β ν

(4) NP Xi i i1 2 5/ . = +η υ ,

where i = 1, ..., 11,403 denotes the 11,403 selected NRI points for EPIC-WQ simulations,

Xi represents the independent variables listed in the first column of Table 2, and ν υi i and are the

error terms. Equations (3) and (4) were estimated separately.

Estimation Results

Table 2 presents estimates of the metamodel coefficients. Overall, the metamodels fit the

data very well and most of the model coefficients are significant at the 1 percent level. The

metamodels were validated with cross-validation methods, which are useful for testing in-use

prediction accuracy (Snee). The metamodels were also validated by comparing the frequency

distributions of nitrogen losses predicted by EPIC-WQ and by the metamodels. The results indicate

the metamodels approximate the nitrogen-loss predictions of EPIC-WQ very well.

The impacts of management practices and resource settings on nitrogen runoff and leaching

are reflected by both the coefficient of the relevant variable and the coefficient on the interaction

term between the relevant variable and nitrogen rate. Thus, an alternative production practice may

affect nitrogen runoff and leaching in one of these ways: (a) it increases the intercept but reduces

the coefficient of N, (b) it reduces the intercept but increases the coefficient of N, (c) it increases

both the intercept and the coefficient of N, or (d) it reduces both the intercept and the coefficient of

N. These four cases are shown, as panels a, b, c, and d of Figure 1.

If an alternative production practice does not change the nitrogen application rate, then it

would reduce nitrogen losses if and only if the nitrogen application rate is greater than Na in case a

and less than Nb in case b (Figure 1). The alternative would always increase nitrogen loss in case c

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and reduce nitrogen runoff or leaching in case d if the nitrogen application rate is not changed.

Many management practices and resource settings, however, do affect nitrogen application rates.

When this occurred, the impact of the alternative practice was determined. First, we estimated the

nitrogen runoff and leaching under the referenced practice or resource setting. We then estimated

the nitrogen application rate ( N * in Figure 1) that would result in the same runoff or leaching level

under the alternative. The alternative would result in a larger nitrogen runoff or leaching if and only

if more than N * pounds of nitrogen per acre are used.

Based on the estimated coefficients of the nitrogen runoff and leaching metamodels,

determination was made of the case (a, b, c, or d) to which each management practice and resource

setting belonged, the values of N N Na b, , * or were estimated, and the impacts of alternative

management practices and resource settings on nitrogen runoff and leaching were determined. The

results are reported in Table 3.

Because nitrogen application rates generally are not changed for different tillage systems

(Randall and Bandel), 4 the direction of the impact of tillage practices on nitrogen runoff and

leaching can be determined by comparing the nitrogen application rate to N Na b or . The results

suggest that the effect of reduced tillage on nitrogen runoff and leaching depends on the nitrogen

application rate of the cropping system to which they are applied and that no till reduces nitrogen

runoff when the nitrogen application rate is higher than 132 pounds per acre but has little effect on

nitrogen leaching. These results are generally consistent with previous studies. Baker performed a

comprehensive review of the literature addressing the effects of conservation tillage on nitrogen

losses in surface runoff water. He found that nitrogen losses for conservation tillage estimated by

previous studies ranged from 9 to 109 percent of those for conventional tillage. 5 Several studies

have examined the effect of tillage systems on nitrogen leaching, but reached different conclusions.

Tyler and Thomas, and Thomas, Wells, and Murdock estimated that more nitrogen leached below

90 centimeters in a killed-sod/no-till system than in a conventional treatment. In contrast, Kanwar,

Baker, and Laflen, working on a loam soil in Iowa, observed less leaching in no-till plots than in

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moldboard plow plots. Other research (Kitur et al.) found no difference between tillage systems in

nitrogen leaching. Gilliam and Hoyt used an N-balance approach to examine N leaching. After a

comprehensive literature review, they concluded that conservation tillage may increase or decrease

N leaching, depending upon soil and weather conditions.

No coefficients on dummies for contouring and strip-cropping and their interaction terms

with N rates are significant at the 10 percent level in either the nitrogen runoff or the leaching

metamodels (table 2). The other conservation practice, terracing, however, has statistically

significant coefficients in both the runoff and leaching metamodels. The impact of terracing on

nitrogen runoff belongs to case a, with Na = 29 pounds/acre. The impact of terracing on nitrogen

leaching belongs to case b, with Nb = 368 pounds/acre. Because average annual nitrogen

application rates for most cropping systems are between 29 and 368 pounds/acre, terracing would

reduce nitrogen runoff and leaching for most cropping systems. The results in Table 3 also indicate

that, although irrigation always causes more nitrogen leaching, it may not always cause more

nitrogen runoff, particularly when nitrogen application rates are high.

Our analysis suggests that the relative effects of alternative cropping systems on nitrogen

losses depend on soil, climate, and hydrologic properties. A cropping system may cause more

nitrogen loss on one soil, but less on another soil. Table 3 shows the relative effects of cropping

systems on Clarion soil in Story County, Iowa. The results reveal that production of continuous

corn on Clarion soil would cause more nitrogen runoff than a five-year rotation of two years of corn

and three years of alfalfa, but would cause less nitrogen runoff than production of continuous

soybeans or any rotation system that involves only corn and soybeans. This finding is consistent

with the fact that soybeans is the most erosive of the crops under study. Production of continuous

corn on Clarion soil would cause more nitrogen leaching than the corn-soybean rotation or the corn-

corn-soybean rotation, but would cause less nitrogen leaching than continuous alfalfa or any

rotation that involves more than one year of soybeans or alfalfa. This result is not surprising

because soybeans and alfalfa are nitrogen-fixing crops.

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The soil hydrologic group dummies and their interaction terms with the nitrogen application

rate also perform well. The coefficients for these variables indicate that for a given cropping

system, production on the two well-drained soils (hydrologic groups A and B) would cause less

nitrogen runoff and more nitrogen leaching than on the two poorly drained soils (hydrologic groups

C and D). This result is consistent with the findings of Mueller et al. that median nitrate

concentrations ranged from 4.7 mg/l in groundwater beneath hydrologic group A soils to 0.17 mg/L

in groundwater beneath hydrologic group D soils. Soil hydrologic groups C and D are less

susceptible to nitrogen leaching for two reasons (Mueller et al.). First, fine-grained deposits in

group C and D soils retard the downward movement of water, and therefore of nitrogen, to

groundwater. Second, poorly drained soils are usually anaerobic, favoring ammonia as the stable

form of nitrogen and thus preventing nitrate from forming.

Spatial Patterns of Potential Nitrate Water Pollution

Nitrogen runoff and leaching losses were estimated for all of the 1992 NRI sites that were

growing corn, sorghum, soybeans, wheat, or legume hay by using the nitrogen runoff and leaching

metamodels. For the entire study region, the average annual runoff and leaching, were estimated to

be 3.99 pounds/acre and 4.07 pounds/acre, which accounted for about 5.5 percent and 5.6 percent

of total nitrogen applied. These estimates are consistent with the findings of Turner and Rabalais,

who examined changes in water quality in the Mississippi River during this century and estimated

that nitrogen loading through all processes (including runoff, leaching, and subsurface flow)

represents no more than 22 percent of total nitrogen applied. Table 4 presents estimates of average

annual nitrogen runoff and leaching by state and USDA farm production region. The results

indicate that the Corn Belt has the highest average runoff per acre, while the Lake States region has

the highest average leaching per acre.

Of the 12 states in the study, Missouri is most vulnerable to nitrogen runoff. This finding

reflects the production systems and physical attributes of that state. Missouri has the lowest

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average permeability and the highest clay percentage and water erosion rate in the region. Missouri

also has the second highest mean annual precipitation. These physical and climatic conditions,

along with its large acreage of nitrogen-intensive crops, make Missouri more vulnerable to nitrogen

runoff overall than any other state in the study.

The results in Table 4 show that Michigan, Ohio, Indiana, and Wisconsin are most

vulnerable to nitrogen leaching. High nitrogen leaching potential in these states also reflects their

production systems and resource settings. Wisconsin, Michigan, and Indiana have the highest

average permeability among all states. Indiana also has the largest annual precipitation in the

region. In addition, these states all have large acreages of corn and/or alfalfa, the two most

leaching-prone crops.

Spatial heterogeneity of soil, climate, and production systems leads to large variations in

nitrate water pollution. In many cases, it is more important to present the distribution of

environmental outcomes rather than aggregate outcomes, because aggregate measures do not

identify regional situations of extreme severity. The spatial patterns of average nitrogen runoff and

leaching potential for each county in the study region are shown in Figure 2.

There are significant spatial variations in potential nitrogen runoff (panel A of Figure 2). It

is highest in the Ohio and Missouri River drainage basins and the northern Lake States. Although

per acre runoff is high in the northern Lake States, total runoff is relatively low because of the small

cropland acreage in the area. The pattern seems consistent with previous findings. Smith,

Alexander, and Lanfear estimated the amount of nitrogen delivered to streams from different land

use. They found corn and soybean fields delivered much more nitrogen to the nearby streams than

did wheat fields, forest land, rangeland, or urban areas per square mile. Omernik examined the

nonpoint nutrient runoff by using the U.S. Environmental Protection Agency National

Eutrophication Survey data. He found that nitrate concentrations downstream from agricultural

areas were highest in the Corn Belt.

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Nitrate-N (lbs/acre)

.63 to 2.80

2.80 to 3.68

3.68 to 4.13

4.13 to 4.61

4.61 to 5.26

5.26 to 13.18

Missing

Miles

2001000

(A) Average Nitrate-N RunoffCEEPES-NRI92 Baseline

Map Source: Center for Agricultural and Rural Development, Iowa State University

Nitrate-N (lbs/acre)

.00 to .96

.96 to 1.77

1.77 to 3.54

3.54 to 5.76

5.76 to 10.85

10.85 to 29.70

Missing

Miles

2001000

(B) Average Nitrate-N LeachingCEEPES-NRI92 Baseline

Map Source: Center for Agricultural and Rural Development, Iowa State University

Figure 2. Average nitrate-N runoff and leaching in the baseline

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Panel B of figure 2 shows the spatial patterns of nitrogen leaching potential. Nitrogen

leaching potential is highest in Ohio, Indiana, Nebraska, and a large portion of the Lake States; it is

lowest in the Dakotas. High leaching potential in Ohio and Indiana may be from a combination of

high nitrogen application rates, large annual precipitation, and relatively vulnerable soil conditions.

According to our survey, the average nitrogen application rate to continuous corn is about 150

pounds/acre in Ohio and Indiana, which is 25 pounds higher than the rate in Iowa. High leaching

potential in the Nebraska Central Platte can be attributed to widespread irrigation in the area. High

leaching potential in a large part of the Lake States may result from high soil permeability and large

alfalfa and corn acreage. Low leaching potential in the Dakotas can be attributed to fallow-based

crop enterprises, low nitrogen application rates, and low annual precipitation.

A few national assessments have been conducted to determine the spatial patterns of

nitrogen leaching potential. Huang, Westernbarger, and Mizer estimated the distribution of

cropland potentially vulnerable to nitrogen leaching and found that the Corn Belt states have most

of this cropland. Kellogg, Maizel, and Goss developed a groundwater vulnerability index for

nitrogen fertilizer (GWVIN). Relative to our study region, they found that nitrogen leaching

potential was highest in Indiana, Illinois and Ohio and was lowest in the Dakotas. One notable

discrepancy between their findings and ours is that Michigan and Wisconsin were ranked low in

leaching according to their GWVIN index, but were ranked high according to our results. The

discrepancy may reflect that GWVIN does not incorporate essential information on soil profile

properties and production systems that was included in our EPIC simulations. The GWVIN index

was measured as the product of a percolation factor and excess nitrogen. The only soil profile

properties incorporated into the percolation factor consisted of the soil hydrologic group

information. Furthermore, excessive nitrogen was estimated only for corn, wheat, and cotton.

Thus, high nitrogen leaching from alfalfa production was not reflected in the results of that study.

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The Impacts of Changing Crop Management Practices

The empirical model we developed was applied to evaluate two crop management practices:

(a) a 25 percent reduction in nitrogen application rate, and (b) a switch from monoculture to crop

rotations. The 25 percent reduction in nitrogen application rates is assumed to be achieved through

improved farming techniques such as soil N testing, split application, and precise fertilizer

placement. Therefore, the reduction in nitrogen use is assumed not to change cropping patterns.

Switches to crop rotations presumably result from perceived changes in current commodity

programs. One main theme of proposed changes to current commodity programs for the 1995 Farm

Bill is to increase planting flexibility. Based on studies by Hennessy, Babcock, and Hayes and

Williams, Llewelyn, and Barnaby, we assumed that in the Corn Belt and Lake States, continuous

corn will be switched to the corn-soybeans rotation, and continuous wheat and continuous sorghum

will be switched to the wheat-sorghum rotation without farm program constraints; in the Northern

Plains, continuous corn will be switched to a corn-soybeans rotation, and continuous wheat and

continuous sorghum will be switched to the wheat-sorghum-fallow rotation.

For the nitrogen use policy simulation, nitrogen application rates were reduced by 25

percent for all NRI points, and nitrogen runoff and leaching were reestimated using the

metamodels. The new estimates were compared with baseline results to determine policy impact.

Likewise, to evaluate the crop rotation policy, the cropping systems at all NRI points that were

growing continuous corn, sorghum, or wheat were switched into one of the region-specific crop

rotation systems. Nitrogen runoff and leaching losses at each NRI point were then reestimated

using the metamodels.

Reducing Nitrogen Application Rates by 25 Percent

Reduced nitrogen losses in runoff and leaching by state and region under this policy are

shown in Table 4. The reduction in runoff ranges from a low of 13.8 percent in Ohio to a high of

25.2 percent in North Dakota. The spatial variability in leaching reduction is even more dramatic,

21

ranging from insignificant reduction in the Dakotas to 19.2 percent in Nebraska. 6 Reducing

nitrogen application rates has little effect on leaching in the Dakotas because very little nitrogen

percolates out of the root zone, even under current application rates. The Lake States respond well

in reducing both runoff and leaching. Runoff losses are reduced by 21.5 percent and leaching

losses by 11.3 percent. Runoff and leaching are least responsive to nitrogen use reduction in the

Corn Belt, where runoff is reduced by 17.2 percent and leaching by only 6.9 percent.

The policy impacts at the county level are shown in panels A and B of Figure 3. Panel A

shows percentage changes in average runoff per acre, and panel B shows percentage changes in

average leaching per acre. In general, absolute reductions in runoff and leaching are larger in areas

where the loading rates are higher, but percentage reductions are smaller in these areas. Exceptions

are some counties in the northern Lake States where both absolute and percentage reductions are

relatively large. Overall, the predicted changes in nitrate losses suggest that a 25 percent reduction

of nitrogen application rates can significantly reduce nitrogen loss.

Table 5 shows the changes in total net returns when nitrogen application rates are reduced

by 25 percent through soil N testing. Assuming crop yields are not affected, the changes in total net

returns are equal to savings in nitrogen costs minus soil N testing costs. Nitrogen cost savings were

estimated for each state by multiplying nitrogen use reduction by the 1992 state-level nitrogen

prices. Total soil N testing costs were estimated by multiplying total crop acreage by the per acre

soil N test costs. Soil N testing costs are taken from Bosch, Fuglie, and Keim. The results indicate

total net returns would increase significantly in all three major production regions.

Crop Rotation Policy

Under this policy, 10.1 million acres would be switched from monoculture to crop rotation

in the Corn Belt, 9.5 million acres in the Lake States, and 24.6 million acres in the Northern Plains.

The effects of this policy on runoff and leaching by state and region are shown in Table 4. The

Lake States region responded with the maximum reduction in total nitrogen losses, even though

22

23

they had the fewest acres switched. Similar results were obtained under the nitrogen use policy for

the Lake States. In Nebraska, switching from monoculture to crop rotations would increase runoff

by 3.14 percent but would reduce leaching by 23.1 percent, the largest reduction among all states.

Given that the Platte River drainage basin in Nebraska has significant concentrations of nitrate-N in

groundwater (Mueller et al.), encouraging farmers to implement more crop rotations would

alleviate some of the problem. In Kansas and the Dakotas, switching continuous wheat and

sorghum into the wheat-sorghum-fallow rotation would increase nitrogen leaching. This may be

caused by the lack of a cover crop and by higher moisture retention in the fallow year of a wheat-

sorghum-fallow system (Williams, Llewelyn, and Barnaby).

The policy impacts at the county level are shown in panels C and D of Figure 3. Comparing

these two panels with those in Figure 2 indicates that areas with highest runoff have a relatively

small percentage reduction, even though absolute reductions in these areas are relatively large.

Exceptions are parts of the northern Lake States, where both absolute and percentage reductions are

large. Many areas of Kansas and the Dakotas would have increased nitrogen leaching under the

rotation policy, although the magnitude would be small (see table 4). The largest reductions in

nitrogen leaching were predicted only in some of the high-risk areas. The other high-risk areas,

especially those in Indiana and southern Michigan and Ohio, would experience only a small

reduction in leaching. The spatial heterogeneity suggests that uniform policies and aggregate

measures of policy impacts may not be very useful.

Changes in total net returns after switching to crop rotations are reported in the last column

of Table 5. The changes in per acre net returns when continuous corn is switched to the corn-

soybeans rotation were estimated for each state from yields and production costs under these two

cropping systems. The data on crop yields, prices, and costs are taken from the 1992 CARD/RCA

budgets. Changes in per acre net returns in the Corn Belt and Lake States when continuous wheat

and sorghum are switched to the wheat-sorghum rotation were estimated using data from the

CARD/RCA budgets. Changes in per acre net returns in the Northern Plains when continuous

wheat and sorghum are switched to the wheat-sorghum-fallow rotation were taken from Williams,

24

Llewelyn, and Barnaby. The results indicate that, although switching to crop rotations would not be

as effective as a 25 percent reduction in nitrogen application rates, it would increase net returns by a

much larger amount in each of the three major production regions.

Concluding Remarks

We have developed an empirical model for estimating the effects of alternative

management practices on potential nitrogen loss in runoff and leaching. The effects of two policy

scenarios on nitrate-N losses in the Corn Belt, Lake States, and the Northern Plains of the United

States were evaluated. The first scenario reduces N fertilizer application rates by 25 percent

through the soil N test. The second replaces continuous cropping practices with crop rotations.

The analytical tool used to evaluate these scenarios is a set of metamodels that summarize the

impacts of soil, climate, crops, and management practices on nitrogen runoff and leaching. The

metamodels were estimated using the EPIC-WQ simulation results for a stratified, randomly

selected sample of the 1992 NRI points. The potential for surface water and groundwater pollution

for each geographic unit in the study region was determined by combining the 1992 NRI with the

metamodels. The measurements were made for a total of 128,591 sites. The 1992 NRI and several

other databases were used to determine the joint distribution of soil, climate, crop, rotation, tillage,

and irrigation method. The metamodels estimated nutrient losses in runoff and leaching under each

of these combinations. Our findings show that if current trends of reduced agricultural subsidies continue and

producers become increasingly reliant on precision farming techniques, a significant reduction in

nitrogen losses can be expected. However, the effects of these changes vary widely across the study

region. On average, it appears that the largest absolute changes in N losses occur in areas that have

the highest losses, but the percentage changes in these areas are sometimes small. However, there

are some problem areas with small absolute changes and some with large percentage reductions.

Of particular note, the rotation policy would seem to increase leaching losses in parts of Kansas

25

where such losses may already be high. This policy evaluation emphasizes the importance of

conducting policy analysis on a disaggregated scale.

26

Tab

le 1

. C

ropl

and

Dis

trib

utio

n by

Cro

p R

otat

ion

in th

e U

.S. M

idw

est,

1992

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

_

Stat

e C

ropl

and

CC

C

SSS

WW

W

GG

G

C-S

C

CS

CSW

SS

C

W-F

W

GF

W-S

W

-G

AA

A

CC

AA

A

OT

H

CR

P

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

_

(1

00 a

cres

)

---

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

- per

cent

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

-

Ohi

o 12

2,15

3 7.

78

2.44

0.

56

0.00

28

.79

5.12

27

.07

4.79

0.

00

0.00

1.

02

0.00

10

.28

2.23

7.

34

2.58

Indi

ana

138,

624

12.8

5 3.

21

0.24

0.

02

43.2

6 12

.96

11.2

1 3.

31

0.00

0.

00

0.51

0.

00

2.98

0.

51

5.97

2.

99

Illin

ois

247,

937

9.96

1.

11

0.06

0.

02

55.7

7 8.

31

14.2

4 2.

11

0.00

0.

00

0.20

0.

00

2.30

1.

17

1.89

2.

87

Iow

a 26

9,47

3 15

.10

1.69

0.

04

0.01

49

.80

11.5

5 0.

21

2.25

0.

00

0.00

0.

02

0.00

5.

86

1.98

3.

73

7.77

Mis

sour

i 14

7,72

0 2.

96

9.99

1.

14

0.53

17

.88

5.00

21

.76

4.84

0.

00

0.00

1.

09

0.30

7.

18

0.83

15

.64

10.8

5

Mic

higa

n 92

,075

23

.03

2.00

1.

81

0.10

12

.23

5.50

10

.97

2.25

0.

02

0.00

0.

79

0.00

16

.05

2.15

20

.33

2.77

Wis

cons

in

114,

431

24.9

7 0.

93

0.31

0.

11

4.58

2.

94

1.11

0.

72

0.00

0.

00

0.10

0.

00

30.6

7 11

.93

15.8

1 5.

81

Min

neso

ta

231,

139

10.2

0 2.

31

8.52

0.

03

28.7

3 4.

70

7.31

3.

65

0.44

0.

00

1.66

0.

00

8.78

2.

85

12.9

9 7.

83

Kan

sas

294,

026

4.84

0.

88

15.5

4 0.

12

2.34

0.

00

0.00

0.

00

16.6

6 24

.49

6.13

13

.00

2.25

0.

29

3.72

9.

74

Neb

rask

a 20

5,78

6 29

.01

1.16

3.

43

0.57

18

.44

0.00

0.

00

0.00

7.

86

5.94

2.

28

8.09

6.

77

1.65

8.

17

6.62

S. D

akot

a 18

1,58

5 11

.38

1.37

9.

58

0.62

16

.10

0.00

0.

00

0.00

8.

61

7.43

7.

41

2.21

11

.48

1.25

12

.87

9.68

N. D

akot

a 27

6,36

5 2.

83

0.50

25

.48

0.04

0.

59

0.00

0.

00

0.00

16

.66

9.95

4.

89

0.12

4.

99

0.25

23

.20

10.5

0

Tot

al

2,32

1,31

4 11

.75

2.01

7.

12

0.17

23

.81

4.42

6.

24

1.73

5.

51

5.40

2.

52

2.57

7.

50

1.82

10

.22

7.22

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

_

Not

es: C

CC

= c

ontin

uous

cor

n, S

SS =

con

tinuo

us s

oybe

ans,

WW

W =

con

tinuo

us w

heat

, GG

G =

con

tinuo

us s

orgh

um, C

-S =

cor

n-so

ybea

n, C

CS

= co

rn-c

orn-

soyb

eans

, CSW

= c

orn-

soyb

eans

-whe

at,

SSC

= s

oybe

ans-

soyb

eans

-cor

n, W

-F =

whe

at-f

allo

w, W

GF

= w

heat

-sor

ghum

-fal

low

, W-S

= w

heat

-soy

bean

s, W

-G =

whe

at-s

orgh

um, A

AA

= c

ontin

uous

alf

alfa

, CC

AA

A =

cor

n-co

rn-a

lfal

fa-a

lfal

fa

-alf

alfa

, OT

H =

oth

er c

ropp

ing

syst

ems,

CR

P =

Con

serv

atio

n R

eser

ve P

rogr

am la

nds.

27

Table 2. Estimates of Coefficients for Nitrogen Runoff and Leaching Metamodels ___ Nitrogen Runoff ___ ___Nitrogen Leaching___ Variable Parameter t-Valuea Parameter t-Value Intercept -1.1621 -12.38 1.0158 2.92 N application rate (kg/ha) 0.0212 23.77 0.0083 2.51 Tillage, conservation, and irrigation practice dummies (Reference: Conventional tillage) Reduced tillage 0.0147 1.82 -0.0455 -1.52 No till 0.0593 4.54 -0.0552 -1.14 Contouring -0.0072 -0.39 0.0212 0.31 Strip-cropping -0.0393 -1.58 0.0538 0.58 Terracing 0.0192 1.73 -0.1298 -3.16 Irrigation 0.3478 29.09 1.1430 25.79 Interaction between the N application rate and tillage, conservation and irrigation practices N*Reduced tillage -0.0001 -1.32 0.0008 2.09 N*No till -0.0004 -2.96 0.0006 1.11 N*Contouring -0.0001 -0.42 -0.0006 -0.66 N*Strip-cropping 0.0005 1.40 0.0016 1.07 N*Terracing -0.0006 -3.45 0.0003 0.36 N*Irrigation -0.0030 -21.84 -0.0004 -0.80 Rotation practice dummies (Reference: Continuous alfalfa) Continuous corn -0.0223 -0.90 -0.9538 -10.36 Continuous soybeans 0.5134 20.94 -0.3710 -4.08 Continuous wheat -0.2303 -12.72 0.1368 2.04 Continuous sorghum -0.0996 -2.62 0.2282 1.62 Corn-Soybeans rotation 0.1748 7.26 -0.4840 -5.43 Corn-Corn-Soybeans rotation 0.0204 0.51 -1.3416 -9.08 Corn-Soybeans-Wheat rotation -0.0911 -2.91 0.3975 3.43 Soybeans-Soybeans-Corn rot. 0.1826 4.03 -0.3819 -2.27 Wheat-Fallow rotation 0.1653 11.29 1.1225 20.69 Wheat-Sorghum-Fallow rot. -0.1317 -4.54 0.6107 5.68 Wheat-Soybeans rotation 0.2806 6.57 1.2817 8.10 Wheat-Sorghum rotation -0.0487 -1.64 0.4716 4.29 Corn-Corn-3 years Alfalfa 0.1869 4.04 -0.4000 -2.33 Interaction between the N application rate and rotation practices N*Continuous corn -0.0135 -20.99 -0.0091 -3.79 N*Continuous soybeans -0.0080 -7.61 -0.0026 -0.67 N*Continuous wheat -0.0112 -17.17 -0.0128 -5.26 N*Continuous sorghum -0.0135 -13.27 -0.0155 -4.11 N*Corn-Soybeans -0.0106 -14.95 -0.0093 -3.54 N*Corn-Corn-Soybeans -0.0114 -15.21 -0.0020 -0.71 N*Corn-Soybeans-Wheat -0.0088 -11.34 -0.0155 -5.42 N*Soybeans-Soybeans-Corn -0.0094 -9.83 -0.0077 -2.17

28

Table 2. Continued ___ Nitrogen Runoff ___ ___Nitrogen Leaching___ Variable Parameter t-Valuea Parameter t-Value N*Wheat-Fallow -0.0136 -20.58 -0.0203 -8.28 N*Wheat-Sorghum-Fallow -0.0080 -9.08 -0.0147 -4.52 N*Wheat-Soybeans -0.0142 -11.04 -0.0353 -7.38 N*Wheat-Sorghum -0.0129 -18.47 -0.0139 -5.37 N*Corn-Corn-3 years Alfalfa -0.0151 -19.53 -0.0012 -0.43 Rainfall and soil properties Rainfall (mm) 0.0007 24.88 0.0042 39.59 Slope 0.0033 2.53 -0.0533 -11.11 Clay percentage -0.0044 -8.90 -0.0209 -11.45 Organic matter percentage -0.0015 -1.18 0.0335 7.15 Bulk density 0.1107 4.38 1.1027 11.76 Soil pH -0.0211 -3.90 -0.0434 -2.17 Permeability -0.0079 -3.27 -0.0162 -1.78 Available water capacity 0.6129 4.78 -5.9605 -12.54 Interaction between the N application rate and soil properties and rainfall N*Rainfall -0.00001 -13.15 0.0000 -1.21 N*Slope -0.00002 -1.46 0.0002 2.90 N*Clay percentage 0.00003 4.14 0.0001 2.33 N*Organic matter percentage 0.00004 2.65 0.0001 1.94 N*Bulk density -0.0020 -5.94 0.0016 1.27 N*Soil pH 0.0002 3.59 0.0010 3.91 N*Permeability 0.0001 2.69 0.0002 2.17 N*Available water capacity 0.0009 0.54 0.0001 0.02 Hydrologic group dummies (Reference: Hydrologic group A) Hydrologic group B 0.1974 7.92 0.0224 0.24 Hydrologic group C 0.4255 15.40 -0.4201 -4.10 Hydrologic group D 0.6178 21.03 -0.8977 -8.25 Interaction between the N application rate and hydrologic groups N*Hydrologic group B -0.0006 -2.20 -0.0047 -4.74 N*Hydrologic group C -0.0006 -2.01 -0.0063 -5.57 N*Hydrologic group D -0.0003 -0.94 -0.0074 -5.92 Latitude 0.0083 10.27 0.0067 2.23 Longitude 0.0113 18.36 -0.0285 -12.44 R2 0.75 0.73 aThe 1%, 5%, and 10% critical values of the t-distribution are 2.58, 1.96, and 1.65.

29

Table 3. The Impacts of Alternative Management Practices and Resource Settings on Nitrogen Runoff and Leaching Nitrogen Runoff Nitrogen Leaching .

Variable Case a Na/Nb/N* Change in Case Na/Nb/N

* (lb/acre) runoff (lb/acre) Tillage, Conservation, and Irrigation Practices (Relative to Conventional Tillage or No Irrigation) Reduced tillage a Na = 131 Reduced if N > 131 b Nb = 51 Increased if N > 51 No till a Na = 132 Reduced if N > 132 - --- Contouring - --- Little effect - --- Strip-cropping - --- Little effect - --- Terracing a Na = 29 Reduced b Nb = 386 Reduced Irrigation a Na = 103 Reduced if N > 104 a Na = 2,551 Increased Soil Hydrologic Group (Relative to Group A) Group B a Na = 294 Increased a Na = 4 Reduced Group C a Na = 633 Increased d --- Group D a Na = 1,838 Increased d ---

Rotation (Relative to Continuous Corn) b Corn-Soybeans c N* = 27 Increased a N* = 69 Reduced Corn-Corn-Soybeans c N* = 61 Increased b N* = 87 Reduced Soybeans-Soybeans-Corn c N* = 21 Increased c N* = 45 Increased 2-yr Corn-3 years Alfalfa a N* = 138 Reduced c N* = 26 Increased Continuous Soybeans c N* = 0 Increased c N* = 27 Increased if N >27 Continuous Alfalfa c N* = 19 Increased if N > 19 c N* = 3 Increased a Case a: the alternative increases the intercept but reduces the coefficient of N; Case b: the alternative reduces the intercept but increases the coefficient of N; Case c: the alternative increases both the intercept and the coefficient of N; and Case d: the alternative reduces both the intercept and the coefficient of N. b N* and the changes in nitrogen losses are determined based on the following assumptions: (a) the crops are dryland produced on Clarion soil (hydrologic group A) in Story County, Iowa, by using conventional tillage, and (2) the annual nitrogen application rate for continuous corn is 125 pounds/acre.

30

Tab

le 4

. A

vera

ge A

nnua

l per

Acr

e N

O3

-N L

osse

s in

Run

off a

nd L

each

ing

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

___

N

Run

off

N

Lea

chin

g

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

--

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

--

Stat

e B

asel

ine

25%

N R

educ

tion

Rot

atio

n Po

licy

Bas

elin

e 25

% N

Red

uctio

n R

otat

ion

Polic

y

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

___

(l

b/ac

re)

(% c

hang

e)

(% c

hang

e)

(lb/

acre

) (%

cha

nge)

(%

cha

nge)

C

orn

Bel

t 4.

84

-17.

2 -1

4.1

5.33

-6

.9

-5.4

I

llino

is

4.80

-1

7.5

-15.

5 4.

57

-7.7

-7

.0

I

ndia

na

4.77

-1

5.3

-10.

9 9.

62

-7.9

-6

.8

I

owa

4.19

-1

9.6

-15.

1 1.

74

-18.

2 -1

5.0

M

isso

uri

6.51

-1

7.4

-15.

9 2.

63

-2.2

0.

7

O

hio

4.79

-1

3.8

-10.

1 12

.50

-2.9

-1

.4

Lak

e St

ates

4.

08

-21.

5 -1

3.9

6.30

-1

1.3

-6.2

M

ichi

gan

4.11

-1

9.3

-6.8

15

.44

-8.3

-3

.7

M

inne

sota

4.

05

-23.

0 -2

0.9

2.39

-1

6.8

-12.

7

W

isco

nsin

4.

12

-20.

1 -5

.7

7.07

-1

2.7

-5.9

Nor

ther

n Pl

ains

3.

03

-18.

6 -9

.7

1.69

-8

.7

-3.0

K

ansa

s 2.

64

-15.

7 -8

.1

2.30

-2

.3

6.6

N

ebra

ska

3.55

-1

5.4

3.1

4 2.

91

-19.

2 -2

3.1

N

orth

Dak

ota

3.00

-2

5.2

-23.

0 0.

55

0

28.2

S

outh

Dak

ota

3.03

-1

9.2

-13.

7 0.

52

0

17.5

St

udy

Reg

ion

3.99

-1

8.4

-12.

7 4.

07

-8.4

-5

.2

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

31

Table 5. Changes in total net return under the two policy scenarios __________________________________________________________ Region 25% N Reduction Crop Rotation __________________________________________________________ ----------million dollars---------------

Corn Belt 77.53 196.16 Lake States 11.99 117.22 Northern Plains 24.43 387.94 ___________________________________________________________

32

ENDNOTES

1. We compared the population and sample frequency distributions of four key soil properties: clay percentage, bulk density, pH, and organic matter percentage. The result indicated the population and sample frequency distributions were almost identical, implying that a representative sample was obtained. 2. The water balance simulated by EPIC version 5,125 (used in this study) produced less than normal percolation of water below the root zone and above normal evapotranspiration rates. Therefore, the percolation results of EPIC version 5,125 were calibrated based on version 3040, whose water balance simulation was at expected levels. 3. Average annual nitrogen losses tend to be influenced by a small number of weather-related effluent spikes. It is the probability of these effluent spikes that dominates the human health concern of the public and policy makers. Conservation practices which reduce expected nitrogen losses might not reduce the probability that a high rainfall event will cause large nitrogen runoff and leaching. However, effluent spikes, particularly when they are measured at the bottom of the root zone, are not transferred to consumers because groundwater acts as a buffer. Thus, mean nitrogen runoff and leaching are useful indicators of surface and groundwater contamination potential. 4. Randall and Bandel surveyed agronomists in each of the 48 contiguous states regarding N management with conservation tillage. They found that there was no difference in N recommendations for various tillage systems in most states. Where an extra amount was suggested, the justification for the increase was to compensate for volatilization and immobilization. Proper placement was stressed more than N rate when surface residues exist. 5. In independent studies, Baker and Laflen, and Wendt and Burwell concluded that on average, conservation tillage reduces nitrogen runoff, with a probable average reduction of 20 to 25 percent. 6. Coincidentally, soil N tests have been conducted for a larger proportion of crop acres in Nebraska than in other areas of the study region (ERS, USDA 1993).

33

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