National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Technical Report 

NREL/TP-510-37500 

May 2005 

Quantifying Cradle-to-Farm 

Gate Life-Cycle Impacts 

Associated with Fertilizer 

Used for Corn, Soybean, and 

Stover Production 

Susan E. Powers
Quantifying Cradle-to-Farm Gate 

Life Cycle Impacts Associated with Fertilizer 

used for Corn, Soybean, and Stover Production 

Final Report Prepared by 

Susan E. Powers, PhD, P.E. 

Clarkson University 

Potsdam NY 13699-5715 

This work was completed during a sabbatical leave at the 

DOE National Renewable Energy Laboratory, 

Golden CO 80401. 

January - May, 2004 

Prepared under Task No. BB05.7530 
NOTICE

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recommendation, or favoring by the United States government or any agency thereof. The views and 

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iii

Table of Contents

List of Tables v

List of Figures vii

Executive Summary ix

1. Introduction 1

Consequences of Increased Agricultural Activity for Biomass Fuel and Products 1

General Problems Associated with Nutrient Pollution 1

Objectives of Study 2

2. Background 4

Nutrient Use for Corn-Soybean Production 4

Environmental Fate and Impacts Associated with Nutrient Flows 4

Nitrogen cycling and fate in the environment 4

Phosphorus Cycling and Fate in the Environment 7

Water Quality Impacts Associated with Nutrients 8

Variables Affecting Nutrient Leaching to Surface Water 17

Nitrogen 17

Phosphorus 18

Modeling Fertilizer Nutrient Discharges to Surface Water 19

Emission Factor Approaches 19

Mass Balance Approaches 20

Mechanistic Models 21

3. Methods for Quantifying Lifecycle Inventory Data for Nutrient Flows 

Associated with Corn-Soybean Production 23

General Approach 23

System Boundaries 23

Stover Removal Model 27

Nitrogen Model Development and Input Data 28

Ammonia Sources 29

Ammonia Sinks 30

Nitrate Sources 31

Nitrate Sinks 31

Other Nitrogen Flows 36

Calibration of Nitrate Leaching Model 38

Phosphorus Flow Model Development 41

Fertilizer application 42

Removal with Crop 42

Leaching to Surface Waters 42

Potassium Model 44

Energy Use and Emissions on the Farm 44

Other Flows Included in the LCI 46
iv

Model Implementation 48

Base Case 48

Stover Collection Scenarios 48

Allocating Flows Among Crops 49

4. Results of Quantitative Lifecycle Inventory 52

Nutrient Flow Model Results 52

Base Case: C-S System with No Stover Harvest 52

C-S and C-C systems with stover harvest 55

Phosphorus Flows 63

Erosion 66

Quantifying Flows for Life Cycle Inventory 67

5. Results – Lifecycle Impact Assessment 84

Eutrophication Potential 84

Acidification Potential 87

Water Toxics 89

Green House Gas Emissions 90

Summary of Life Cycle Impacts 91

6. Summary and Recommendations 93

7. References 96

Appendix A – Definition and Quantification of Variables Used in Nutrient Flow 

Model 103

Appendix B – Files comprising LCA model 108
v

List of Tables 

Table 1. Nitrogen Transformation Reactions in an Agricultural System ................................. 5 

Table 2. Reference Points to Identify Excessive Levels of Nutrients in Midwest Surface 

Waters ...................................................................................................................... 10 

Table 3. Nutrient contamination in surface waters in the Eastern Iowa NAWQA study ....... 11 

Table 4. Sources of Nitrogen to the Mississippi River ........................................................... 12 

Table 5. Characteristics of Eastern Iowa Watersheds............................................................. 24 

Table 6. Range of Estimates - Percent of FN that Leaches with Water.................................. 32 

Table 7. Regression Equations Defining the Fraction of Fertilizer Nitrogen that Leaches into 

Tiles and Drains ....................................................................................................... 34 

Table 8. Denitrification Factors for Surface Water................................................................. 36 

Table 9. TN Loads (mt/yr) to the Mississippi River from E. Iowa Watersheds ..................... 38 

Table 10. Values Used to Calibrated the Leaching/Denitrification Model............................... 40 

Table 11. Measured TP Loads (mt) from the E. Iowa Watersheds to the Mississippi River... 43 

Table 12. Energy Used in Farming Activities in Iowa ............................................................. 45 

Table 13. Coefficients for stover harvest fuel use equation...................................................... 46 

Table 14. Emission factors for Fuel Combustion in Farming Tractors .................................... 46 

Table 15. DEAM Modules used to Calculate Upstream Flows and Emissions........................ 47 

Table 16. Pertinent LCA Flows from DEAM Modules............................................................ 47 

Table 17. Summary of Systems Considered and Major Assumptions...................................... 50 

Table 18. Comparison of Emission Factors Determined from This Study and Those Used by 

Others....................................................................................................................... 54

Table 19. Comparison of Nitrogen Yields Determined from This Study and Those Measured 

by Others.................................................................................................................. 54 

Table 20. Incremental Percent Change in Nitrogen Emissions Due to the Harvest of Corn 

Stover in Comparison to the Base Casea.................................................................. 63 

Table 21. Important Inputs for Farm stage of Life Cycle Inventory: C-S rotation in E. Iowa – 

Base case - no stover collection............................................................................... 70 

Table 22. Important Inputs for Farm-stage of Life Cycle Inventory: C-S rotation in E. Iowa – 

with stover collection............................................................................................... 72 

Table 23. Important Inputs for Farm-stage of Life Cycle Inventory: C-C Rotation in E. Iowa 

with Stover Collection ............................................................................................. 73 
vi

Table 24. Summary of Averagea

 Farm Inputs and Incremental Percentage Differences among 

Scenarios.................................................................................................................. 74 

Table 25. Important Nutrient Outflows for Farm stage of Life Cycle Inventory: C-S rotation 

in E. Iowa – Scenario 1: Base case - no stover collection ....................................... 75 

Table 26. Important Nutrient Outflows for Farm Stage of Life Cycle Inventory: Scenario 2: CS rotation in E. Iowa with Stover Collection ........................................................... 77 

Table 27. Important Nutrient Outflows for Farm Stage of Life Cycle Inventory: Scenario 3: CC rotation in E. Iowa with Stover Collection........................................................... 78 

Table 28. Overall Annual Cradle-to-Farm Gate LCI Flows and Distributions among Processes, 

Scenario 1: Base Case, C-S, no Stover Collectiona ................................................. 79 

Table 29. Overall Annual Cradle-to-Farm Gate LCI Flows and Distributions among Processes, 

Scenario 2: C-S with Stover Collection................................................................... 81 

Table 30. Overall Annual Cradle-to-Farm Gate LCI Flows and Distributions among Processes, 

Scenario 3: C-C with Stover Collection................................................................... 82 

Table 31. Summary of Changes in Flows and Incremental Percent Differences in LCI Flows 

Among Scenariosa

.................................................................................................... 83 

Table 32. Summary of Life Cycle Impact Analysis.................................................................. 92 
vii

List of Figures 

Figure 1. General impacts of excess nutrients in our water supplies ......................................... 2 

Figure 2. Historical rates of nitrogen, phosphorus and potassium fertilizer use on soy and corn 

fields in Iowa.............................................................................................................. 5 

Figure 3. Major nitrogen transformation processes and flows in an agricultural system .......... 7 

Figure 4. Major phosphorus transformation processes and flows in an agricultural system ..... 8 

Figure 5. Yields of nitrogen and phosphorus contributing to Hypoxia in the Gulf of Mexico 14 

Figure 6. Total discharge of (a) nitrogen and (b) phosphorus from the Mississippi River to the 

Gulf of Mexico are highly correlated to the total river discharge............................ 15 

Figure 7. The size of the hypoxic zone varies substantially among years and is correlated to 

the TN load from the Mississippi River................................................................... 16 

Figure 8. During a carefully controlled field study, Weed and Kanwar (1996) found that there 

were no statistically significant differences in the fraction of nitrate leached from CS and C-C rotations under different tilling operations. (MP – moldboard plow).... 18 

Figure 9. Map of Iowa identifies the watershed for the four major river systems in Eastern 

Iowa and the corresponding area included in the LCI based on county boundaries.24 

Figure 10. Annual average stream flow (a) for Eastern Iowa watersheds and (b) regional 

average and example county precipitation values ................................................... 26 

Figure 11. System boundary for cradle-to-gate life cycle assessment for corn and soybean 

production ................................................................................................................ 27 

Figure 12. Fraction of nitrogen leached as a linear function of annual rainfall. ....................... 33 

Figure 13. Actual TN flux to the Mississippi River and calibrated model including leaching of 

FN and animal manure and in-stream denitrification. ............................................. 40 

Figure 14. Percent error in rainfall-based regression analysis and GREET models for 

predicting TN discharged to the Mississippi River.................................................. 41 

Figure 15. The actual TN load determined from water quality data (Table 9) in 1990 is 

significantly lower than the general linear increasing trend with rainfall. .............. 41 

Figure 16. The fraction of FP that leaches to surface waters can be expressed as a linear 

function of rainfall. Limited data available from Klatt et al. (2003) ...................... 43 

Figure 17. Actual TP flux to the Mississippi River and calibrated TP leaching model, which 

includes soluble and particulate P............................................................................ 44 

Figure 18. Nitrogen flows (mt/yr) into and out of the E. Iowa C-S system.............................. 53 

Figure 19. Fractions of nitrogen flows for C-S system that are allocated to corn. Fractions 

represent averages over 13-year study period with error bars indicating one standard 

deviation................................................................................................................... 55 
viii

Figure 20. Phosphorus flows (mt/yr) into and out of the E. Iowa C-S system.......................... 56 

Figure 21. Total mass of stover harvested from C-C and C-S systems..................................... 57 

Figure 22. Stover harvest yields for C-C and C-S systems....................................................... 57 

Figure 23. Distribution of the number of counties with harvestable stover.............................. 58 

Figure 24. Distribution of corn stover production in the E. Iowa system. Black and gray 

striped bars indicate those counties that cannot reliably harvest stover at the 

maximum yield due to erosion constraints. ............................................................. 58 

Figure 25. Overall nitrogen flows through the C-S system with stover harvest ....................... 59 

Figure 26. Overall nitrogen flows through the C-C system with stover harvest....................... 60 

Figure 27. Nitrogen leached and discharged to E. Iowa surface waters in the three scenarios. 61 

Figure 28. Incremental additional nitrogen leached to the surface water – above base case 

scenario (C-S) - due to the collection of stover in C-C and C-S systems................ 61 

Figure 29. Nitrogen leached to ground water in each of the scenarios ..................................... 62 

Figure 30. Incremental additional nitrogen leached to the ground water due to the collection of 

stover in C-C and C-S systems ................................................................................ 63 

Figure 31. Phosphorus flows into (1) and out of (2) the C-S scenario with stover collection (a) 

and the C-C scenario with stover collection (b)....................................................... 65 

Figure 32. Phosphorus leached to the surface water for the three scenarios (top) and 

incremental additional TP leached due to the collection of stover in C-C and C-S 

systems (bottom)...................................................................................................... 66 

Figure 33. Soil erosion estimates for the three scenarios incorporating variability in crop yields 

but not rainfall.......................................................................................................... 67 

Figure 34. Eutrophication potential from TN and TP discharged from E. IA watersheds to the 

Mississippi River. Base case included in each as solid regions. Hash marks include 

the incremental addition associated with scenario 2 (bottom) and scenario 3 (top) 85 

Figure 35. Base case eutrophication potential discharged to the Mississippi River compared 

with suitable benchmarks......................................................................................... 86 

Figure 36. Average eutrophication potentials for the three scenarios....................................... 87 

Figure 37. Cradle-to-farm gate acidification potential for Scenario 1: C-S with no stover 

harvest base case. The bottom figure provides a detailed view of the same NOx and 

SOx data included in the top figure.......................................................................... 88 

Figure 38. Average acidification potentials for the three scenarios.......................................... 89 

Figure 39. Average global warming potential for the base case scenario................................. 91 

Figure 40. Average global warming potentials for the three scenarios..................................... 91 
ix

Executive Summary 

Fertilizers used to increase the yield of crops used for food or bio-based products can migrate through the 

environment and potentially cause adverse environmental impacts. Nitrogen fertilizers have a complex 

biogeochemical cycle. Through their transformations and partitioning among environmental 

compartments, they can contribute to eutrophication of surface waters at local and regional scales, 

groundwater degradation, acid rain, and climate change. Phosphate fertilizers have a simpler fate in the 

environment, although leaching of soluble and bound phosphorus is an important contributor to 

eutrophication. 

Eutrophication is considered one of the most pervasive problems affecting water quality in the United 

States, especially in the Midwest where fertilizers are used extensively for agriculture. In the process of 

eutrophication, the presence of excess N and P nutrients allows over production of plant biomass in 

waterways. The eventual degradation of this biomass consumes oxygen resulting in hypoxic conditions 

(low oxygen concentrations) in the most severe cases of eutrophication. Fertilizer use on corn and 

soybean farms in the Midwest is considered one of the primary contributors to the growing hypoxic zone 

in the Gulf of Mexico. Through a combination of excessive nutrient loads and hydrodynamic conditions, 

a region along the coast of Louisiana that is approximately the size of the State of Massachusetts is 

considered ecologically dead most summers. This results in the death of species that are not sufficiently 

mobile and changes in biodiversity and food webs throughout the region as larger species migrate to other 

locations. Researchers for federal agencies have suggested that reducing the average nitrogen load by 

30% will help to limit the hypoxic zone to acceptable levels. Other researchers predict that a 40-45% 

reduction in TN would be required to meet this goal. 

With an increase interest in the use of corn, soybeans and corn stover for bio-based products and fuels, it 

is important to understand the relative environmental benefits and deleterious impacts associated with this 

growing market. A team of researchers lead by NREL completed a life cycle assessment (LCA) for 

stover harvest and conversion to ethanol for transportation fuels. Their report focused on the green house 

gas emission benefits associated with biofuels as balanced by potential detriments to soil health (carbon 

content and erosion). Eutrophication was identified as an important issue by stakeholders involved with 

this project, but limited resources prevented this environmental impact category from being addressed. 

Thus, the goal of the work presented here was primarily to fill that gap, thereby providing a more 

complete picture of the overall environmental impacts associated with bio-based products. 

The nutrient leaching model developed here was coupled with LCA data describing emission occurring 

during fertilizer manufacture and energy production and consumption, providing a cradle-to-farm gate life 

cycle inventory (LCI) for corn, soybeans and stover. Three separate scenarios were considered: 

• Scenario 1: the base case, considered corn-soybean rotations (C-S) with conventional till and no 

stover collection; 

• Scenario 2: C-S with no till and stover collection at a maximum rate allowable with acceptable 

erosion levels; and, 

• Scenario 3: the same as 2 except for continuous corn (C-C) rather than C-S. 

The LCI for each of these scenarios was quantified and used to determine eutrophication, acidification 

and global warming potentials for the three scenarios. Eutrophication was calculated for each year in a 

13-year study period to incorporate variability with rainfall. Other impacts were only calculated as 

averages. 

The total nitrogen (TN) and total phosphorus (TP) leaching models that correlate the fraction of fertilizer 

nitrogen (FN) and fertilizer phosphorus (FP) that leaches with annual rainfall in each county provides a 

good representation of the measured variability in nutrient loads discharged from eastern Iowa watersheds 

to the Mississippi River (Figures ES1 and ES2). The ability to calibrate this model reduces the 
x

uncertainty in the leaching estimates for the base case scenario more than would be possible without the 

site-specific water quality data. The calibrated leaching model (solid line in Figs. ES-1 and ES-2) 

captures the wide variability in the annual TN and TP loads to the Mississippi River from the eastern 

Iowa watersheds. In contrast, a standard method for estimating nutrient leaching in other lifecycle 

assessments based on a constant fraction of the nutrients leached (▪ symbols in Fig. ES-1), provides a 

reasonable average, but does not capture the annual variability. 

There are significant uncertainties associated with the allocating these total nutrient loads among the 

various crops planted in rotation. The allocation process is an important step in an LCA to define of how 

much of the total environmental emissions can be attributed to corn versus soybean versus stover 

products. Two radically different approaches were used to estimate the allocation of nitrogen flows 

between corn and soybeans. Estimates of the TN leached from the overall C-S system that can be 

allocated to corn range from 60 and 99%, with the balance allocated to soybeans. This difference stems 

from a poorly understood symbiosis of nitrogen flows within the C-S rotation that is difficult to integrate 

into an LCA. 

The primary focus of this research was the eutrophication potential. The eutrophication potentials 

presented here include only nutrient emissions from the agricultural activities described in the three 

scenarios. Other agricultural inputs (e.g., animal feed lots) and point sources were not included in these 

totals. TN and TP flows to the Mississippi River and Gulf of Mexico were aggregated into an overall 

eutrophication potential, which is expressed 

as an equivalent mass of NO3-N. The 

eutrophication loads predicted through the 

nutrient flow model developed here were 

compared to the target 30% reduction in TN 

flows required to limit the size of the Gulf of 

Mexico hypoxic zone and recommended 

water quality standards to control 

eutrophication at a local level. 

The results of this analysis show that the 

eutrophication potential for the base case 

(scenario 1) already exceeds acceptable limit 

(solid line, Fig. ES-3). TN and TP 

discharges from C-S lands also exceed the 

maximum loads defined by the proposed 

water quality standards in each of the 13 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

Scenario 1 Scenario 2 Scenario 3

Average Eutrophication Potential (mt as NO3-N)

(w) TP

(w) TN

Fig. ES-3: Average eutrophication potential for the three 

scenarios (solid line represents goal for limited 

hypoxia; 30% reduction of ’80–‘96 TN average) 

0

50000

100000

150000

200000

250000

300000

1988 1990 1992 1994 1996 1998 2000

TN load to MSR from E. IA (mt)

Water quality data

Calibrated TN leaching model

24%(FN+manure)

 0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1988 1990 1992 1994 1996 1998 2000

TP Load to MSR from E. IA (mt)

Water quality data

Calibrated TP Leaching Model

Fig. ES-1: Calibrated TN leaching model compared 

with actual measured TN loads to the Mississippi River 

Fig. ES-2: Calibrated TP leaching model compared 

with actual measured loads to the Mississippi River 
xi

years of this study. Limits established by the goal of a 30% reduction in the average TN load are also 

exceeded in approximately half of the years. If additional sources of nutrients discharged from the study 

area are considered, it is estimated that the eutrophication potential is higher than recommended except 

under drought conditions. 

Changing the current C-S rotation to also harvest stover for biofuel production increases the 

eutrophication potential (scenario 2; Figure ES-3). With the assumptions used in this analysis, the C-Sstover scenario (2) results in a 21% increase in the eutrophication potential, while the C-C-stover scenario 

(3) almost tripled the total load of TN and TP (as equivalent NO3-N). In either case, the increased load is 

to a system that has already exceeded its assimilative capacity for nutrients. For the C-S system, it is 

likely that careful management of fertilizers used at the farm could help to limit nutrient leaching. With 

the very high nitrogen demand in a C-C system, however, it is not likely that management practices could 

be sufficient to overcome the detrimental effects of eutrophication resulting from the high leaching rates. 

The high nitrogen use in the C-C scenario also increases the use of fossil fuels for fertilizer production 

and associated emissions of species contributing to acid rain and global warming (GWP) potentials. The 

increase in acidification potential (+6% over the base case) is attributed to increases in NO production in 

soils with increased FN use and increased NOx from fossil fuel consumed to generate the increased 

energy necessary for fertilizer manufacture. Increases in the global warming potential (+71% over the 

base case, not including benefits of carbon sequestered in soil or crop) are attributed to methane emissions 

from natural gas used in FN manufacture and N2O emissions from nitrification and denitrification of the 

additional FN. 

Scenario 2 (C-S) actually reduces the global warming potential relative to the base case (-3%) and has 

essentially no impact on acidification. The reduction in the GWP is related to the reduced level of soil 

mineralization with no till and the resulting reduction in N2O emissions. 

There is some uncertainty in the quantitative results presented here due to assumptions used throughout 

the model to describe fertilizer use and their fate. The uncertainties in the life cycle and impact results are 

the least for scenario 1, for which site specific data and mass balance checks were used to verify the 

model as much as possible. There are greater uncertainties with scenario 2 and 3. Assumptions required 

to quantify differences in fertilizer needs and leaching under no-till practices were required. There has 

not yet been any analysis of the sensitivity of the conclusions to the uncertainty in these parameter values. 

Based on the results presented thus far and the acknowledged limitations of the model – the following 

recommendations for continued analyses can be made: 

• Consider stover only from C-S rotations in further analyses. 

• Improve the erosion modeling to incorporate variability with rainfall. This could help to improve 

leaching models by quantifying changes in sediment-bound nutrient loads in a no till versus 

conventional till system. 

• Use the framework developed here that utilizes water quality data to generate leaching models for 

herbicides. LCI data for herbicide manufacture also need to be developed. 

• Utilize additional resources to improve predictions of nutrient fate in a no till versus conventional 

till system and perform a sensitivity analysis on these processes. 

• Compare results to related studies and guidelines currently used to define the acceptability of biobased products, especially for federal procurement. 

The generation of this life-cycle data was required to provide a more comprehensive understanding of the 

environmental impacts associated with increased use of biomass for fuels and other products. The LCA 

quantifies impacts on very different components of the environment, but does not judge which of these 

components are more important. The LCA results are necessary, but not sufficient to allow decisions 

regarding future energy sources to be made. In the United States, the goal of current policies is to reduce 
xii

our dependence on imported energy. The LCA results can be used to quantify the environmental benefits 

and detriments associated with this goal and can help identify key concerns that should be addressed as 

this project is continued (e.g., improved nutrient management to reduce water quality degradation). At 

some point, a balance needs to be defined between the various goals of different interest groups in a 

manner that then overall environmental impact from biomass fuels and products is considered acceptable, 

leading to a sustainable materials and energy source for the future. 
1

1. Introduction 

Consequences of Increased Agricultural Activity for Biomass Fuel and Products 

Corn, soybeans and corn stover are all valuable feedstocks for conversion of biomass into consumer 

goods. Utilizing these agricultural products and residues creates a potential for both environmental 

benefits and deleterious impacts. The national use of these products could have a disproportionate 

negative impact in the Midwestern states on the soil and water resources, while having positive impacts 

on air quality and global climate change over a wider geographic scale. Many studies completed to date 

that have quantified the environmental impacts of bio-based products have focused on the air quality and 

greenhouse gas (GHG) benefits (Wang, 1999; Sheehan et al., 2002 Heller et al., 2003). There are clear 

benefits to using bio-based materials, especially in terms of greenhouse gas generation. Plant growth 

consumes atmospheric carbon dioxide is transformed to plant matter. Eventually, the carbon is released 

back to the environment at the end-of-life stage of a bio-based product or fuel. However, that release 

results in a near zero net GHG emission. In comparison, combustion of fossil fuels cause carbon 

sequestered in the subsurface for millennia to be added to our atmospheric carbon dioxide load. 

Studies have not been completed that adequately evaluate the balance between these benefits - GHG and 

other air emissions - and potential soil and water quality degradation. Sheehan et al. (2002) completed a 

life cycle assessment (LCA) designed to evaluate the environmental impacts of corn stover harvest in 

Iowa and subsequent conversion to fuel-grade ethanol. Their study included a thorough analysis of the 

agricultural activity on the long-term sustainability of soil health, as defined by erosion and soil carbon 

concentrations. They found that a fraction of the stover can indeed be harvested while maintaining 

tolerable levels of soil erosion and steady-state concentrations of soil carbon over a long-term. Thus, the 

GHG and air emission benefits of using stover for ethanol production far outweigh problems associated 

with soil degradation. 

The application of nutrients to croplands is critically important for improving crop yields and productivity 

of farmland. Only approximately 50% of the applied nutrients are integrated into plant mass, however. 

The remaining nutrients accumulate in soil, or are emitted to the atmosphere (NO, NH3, N2O) or water 

bodies as soluble components (NO3, PO4) or as a component of soil that is eroded. Agricultural activities 

are a significant contributor to the substantial increase of both reactive nitrogen and phosphorus (Smil, 

2000; Galloway, 1998) in our environment. These increases have contributed to the degradation of both 

air and water resources. Life cycle studies on agricultural processes and products that integrate all of 

these flows are very limited. Nutrients used on cropland are non-point sources of pollution to water 

bodies. This necessarily makes it more difficult to quantify these flows in a quantitative life cycle 

inventory. 

Through stakeholder meetings, the importance of nutrification – or eutrophication - of surface waters was 

recognized as a critical impact for the stover LCA (Sheehan et al., 2002). Resource limitations prevented 

these lower priority issues from being completed as part of the original LCA. A general goal of this report 

is to fill that void. 

General Problems Associated with Nutrient Pollution 

Nutrient enrichment frequently ranks as the top cause of impairment to our Nation’s water resources 

(USEPA, 2000a), especially in the Midwest where agricultural practices increase the rate of 

anthropogenic nitrogen and phosphorus in the environment. These non-point sources create far higher 

loads to surface and groundwater bodies than most point sources (e.g., wastewater treatment plants). 

Nutrients in water systems can cause problems ranging from aesthetic impacts to serious human health 

problems. 
2

While some nitrogen and phosphorus is required in all surface water bodies as nutrients, too much 

nitrogen can cause excess biological productivity, which leads to other problems (Figure 1). Algal 

blooms, for example, choke out other species and reduce light penetration into the water body causing 

underwater vegetation to die and altering habitat. As the algae die and sink, the biological oxygen 

demand that they impart can significantly reduce oxygen concentrations, thereby creating conditions that 

interfere with the recreational use of lakes and estuaries, increase costs to treat the water to drinking water 

quality standards, and the decrease the health and diversity of indigenous fish, plant, and animal 

populations. This condition is termed eutrophication. 

Excess nutrients

in water body

Changes in and 

increased growth

rates of 

macrophytes

Increased growth 

rates of algae

(Algal blooms)

Aesthetic issues

Impaired recreational uses

Altered habitats,

Reduced biodiversity

Taste and odor in drinking water

Reduced D.O. concentrations

Reduced visibility / light 

penetration

Toxic releases from cyanobacteria

Formation of carcinogenic THMs

Altered habitat

Coral reef destruction

Reduced species diversity

Fish and wildlife kills

Reduced harvestable 

fish/shellfish populations

Nutrients in

drinking water

Blue baby syndrome (MCL=10 mg/L)

Non-Hodgkin lymphoma (4 mg/L)

Excess nutrients

in water body

Changes in and 

increased growth

rates of 

macrophytes

Increased growth 

rates of algae

(Algal blooms)

Aesthetic issues

Impaired recreational uses

Altered habitats,

Reduced biodiversity

Taste and odor in drinking water

Reduced D.O. concentrations

Reduced visibility / light 

penetration

Toxic releases from cyanobacteria

Formation of carcinogenic THMs

Altered habitat

Coral reef destruction

Reduced species diversity

Fish and wildlife kills

Reduced harvestable 

fish/shellfish populations

Nutrients in

drinking water

Blue baby syndrome (MCL=10 mg/L)

Non-Hodgkin lymphoma (4 mg/L)

Figure 1. General impacts of excess nutrients in our water supplies 

(adapted from Carpenter, 1998, US EPA, 2000a) 

Low levels of oxygen in the Gulf of Mexico near the mouth of the Mississippi River are a form of severe 

eutrophication. Large areas of the Gulf, often similar in size to the State of Massachusetts, are considered 

ecologically dead. Mobile species leave this area, while smaller and immobile species succumb to the 

inhabitable low oxygen concentrations. This hypoxic zone was initially observed in the mid 1980s 

(Turner and Rablais, 1994) and has been the subject of numerous studies since (e.g., Scavia et al., 2003). 

The use of large quantities of inorganic fertilizers for row crop production (mostly soybeans and corn) 

and animal husbandry operations in the Midwest have contributed substantially to the nutrient load 

delivered to the Gulf of Mexico and the subsequent eutrophication processes that cause hypoxia. 

Objectives of Study 

The goal of this research is to understand and quantify the impacts that the increased utilization of 

dedicated biomass crops (corn, soybeans) and agricultural residues (corn stover) for bio-based energy and 

products will have on our Nation’s water resources. This research extends the stover LCA work already 

completed (Sheehan et al., 2002) by incorporating water resources impacts, especially focusing on 

nutrient use during the feedstock side of the LC. Specific objectives of the overall research project 

include: 
3

1. Quantify the loads of agrichemicals to ground and surface water bodies expected from cornsoybean rotations and continuous corn, with and without stover collection, on eastern Iowa farms. 

2. Identify and quantify the most suitable impact categories, characterization and normalization 

methods to incorporate these water quality impacts into an LCA 

3. Determine the LCA water resources impacts for a site specific watershed-scale case for which 

existing water quality data are available 

The report focuses on the use of nutrient agrichemicals only. By completing this work first, a framework 

has been established to extend the study to other agrichemicals and/or crops. 

The stover LCA (Sheehan et al., 2002) was developed for the State of Iowa using county and state 

average data for agricultural practices and soil characteristics. For consistency, the work presented here 

also focuses on Iowa. A smaller subset of Iowa counties is considered here, however, to include a set of 

specific watersheds in eastern Iowa. These eastern Iowa watersheds have similar geologic and hydrologic 

properties. Additional details about this region are provided later. 
4

2. Background 

Nutrient Use for Corn-Soybean Production 

Agricultural practices in the Midwestern United States rely heavily on chemicals to boost the productivity 

of the farmland. Inorganic nitrogen fertilizer (FN) use in the United States has increased from 10 million 

tons per year in the mid-1970s to over 45 million tons per year in the mid 1990s, with an average of 102 

kg N/ha cropland applied in 1998 (Burkart and Stoner, 2002). Animal manures also contribute to the 

nitrogen load to the environment. In North America, however, manure application rates (kg/ha cropland) 

in 1999 were less than 50% of the inorganic fertilizer application rates (Burkart and Stoner, 2002) and 

less than 20% of corn fields receive organic fertilizer. These increases in FN are a significant contributor 

to the recent tripling of reactive nitrogen in the global environment (Galloway et al., 2003) 

Burkart and Stoner (2002) identified 9 separate agricultural regions within the United States. The “corn, 

soybeans, hogs” category has by far the highest rate of nitrogen fertilizer application rates (median = 60 

kg/ha, n=605). In Iowa, application of nitrogen fertilizers to corn were much higher than the average for 

this region (Figure 2). Nitrogen fertilizer is used on almost all cornfields and approximately 10% of soy 

fields. The rate of fertilizer use has declined somewhat on cornfields since the high levels used in the 

1980s when the environmental and economic costs of over fertilizing became apparent. FN use on soy, 

however, is quite variable and at its highest level in the year 2000. 

Like nitrogen, global mobilization of reactive P in the environment has roughly tripled compared to 

natural flows (Smil, 2000). The application of inorganic phosphorus fertilizers (FP) for crops is a 

substantial contributor to this overall increase in P flows. Global consumption of FP reached 

approximately 5 Mt P/yr in 1960 and increased to a peak of 16.5Mt/yr in 1988. Total consumption has 

since dropped and leveled at approximately 12Mt/yr (Smil, 2000) 

Substantially lower rates of FP and FK are used compared with FN, especially on corn. However, FP 

application rates still generally exceed the rate at which P is removed from agricultural systems in the 

form of grain, leading to a long term increase in soil P concentrations (Sharpley et al., 2003). In 2000, 

61% of the soil samples collected in Iowa near P-sensitive waters had extractable P concentrations greater 

than optimal concentrations required for crop growth. This percentage was up from 49% in 1997, ranking 

Iowa among the states with the highest percent of samples with P concentrations greater than optimum, 

and among the states with the greatest increase in that percentage between 1997 and 2000 (Sharpley et al., 

2003). Figure 2 quantifies the rates of FP application in Iowa for soy and corn. 

Environmental Fate and Impacts Associated with Nutrient Flows 

Nitrogen cycling and fate in the environment 

As the primary component in air, nitrogen is transformed into many different forms that are required for 

life. Industrial, physical (lightening) and biological (e.g., legumes) processes transform non-reactive 

nitrogen (N2) into reactive forms, among them, ammonia and/or nitrate that can be used directly in living 

systems (US EPA 2000a, Galloway et al., 2003). These living systems then release mineralized forms of 

nitrogen (ammonia in urea, proteins etc.) as waste products or during decomposition after death 

(Galloway et al., 2003). Soil bacteria convert ammonium compounds into nitrate (nitrification). Under 

anaerobic conditions and in the presence or organic carbon, denitrifying bacteria transform nitrate into N2, 

with nitrous oxides (NO, N2O) as intermediate products that can be released to the atmosphere. Although 

this denitrification process is often considered to be an undesirable loss in terms of N available for plant 

growth, it is one of the few natural processes that convert reactive nitrogen back to its non-reactive state 

(N2) (Galloway et al., 2003). Chemical reactions involved in these processes are summarized in Table 1. 
5

0

10

20

30

40

50

60

70

80

90

100

1960 1965 1970 1975 1980 1985 1990 1995 2000

Fertilizer Use (% fields)

% fields use FN - corn

% fields use FP - corn

% fields use FK - corn

0

20

40

60

80

100

120

140

160

180

1960 1965 1970 1975 1980 1985 1990 1995 2000

Fertilizer Use (kg/ha applied)

kg-N/ha - corn

kg-P/ha - corn

kg-K/ha - corn

0

10

20

30

40

50

60

70

80

90

100

1960 1965 1970 1975 1980 1985 1990 1995 2000

Fertilizer Use (% fields)

% fields use FN - soy

% fields use FP - soy

% fields use FK - soy

0

20

40

60

80

100

120

140

160

180

1960 1965 1970 1975 1980 1985 1990 1995 2000

Fertilizer Use (kg/ha applied)

kg-N/ha - soy

kg-P/ha - soy

kg-K/ha - soy

 

Figure 2. Historical rates of nitrogen, phosphorus and potassium fertilizer use on soy and corn 

fields in Iowa 

(USDA Economic Research Service Fertilizer Use and Price Statistics Report (86012) 

(http://usda.mannlib.cornell.edu/) for 1964-1990 data and USDA NASS Quick Stats for 1990-

2000 data (http://www.nass.usda.gov:81/ipedb/)) 

Table 1. Nitrogen Transformation Reactions in an Agricultural System 

Type Chemical Reactiona Comments 

Nitrification NH4

+

 + 1.5O2 Æ NO2

-

 + 2H+ + H2O 

NO2

-

 + 0.5O2 Æ NO3

-

Nearly all NH4

+

 nitrified quickly. 

NO and N2O generated as byproducts 

Denitrification 

5(CH2O) + 4 NO3

-

 + 4H+ Æ 5CO2 + 2 N2 + 7 H2O 

(really a series of several steps - 

NO3

- Æ NO2-

Æ NO Æ N2O Æ N2) 

Requires organic matter and an 

anaerobic environment. NO and 

N2O generated as by-products 

Ammonification NH2•CO•NH2 + H2O Æ 2NH3 +CO2

Urea used here as an example of 

N-containing plant matter. 

Combination of ammonification 

and nitrification often defined as 

“mineralization.” 

Fixation N2 + plant matter Æ organic-N 

Process limited to a few plant 

and microbial species, including 

soy. a

 after Canter (1997), Mitsch et al. (1999) 
6

Nitrogen released from agrosystems as NOx or NH3 to the atmosphere returns quickly (hours of days) to 

the earth’s surface, typically as HNO3, NH4NO3, or (NH4)2SO4 (Galloway et al., 2003). In addition to 

contributing to air quality problems, this reactive nitrogen can contribute to the total flux of NO3

-

 and acid 

to surface water bodies, thus contributing to regional eutrophication and acidification problems. 

The natural nitrogen cycle has been significantly altered by human activities, including substantial 

increases in the fixation of N2 by industrial processes into ammonium products that are applied to soil to 

increase crop production rates. The industrial transformation of non-reactive to reactive forms of nitrogen 

by the Haber-Bosch process increased from 0 before 1910 to more than 100 Tg N/yr in 2000. 85% of this 

was used for fertilization (Galloway et al., 2003). 

Nitrogen found in surface or groundwater supplies in the Midwest has likely come from commercial 

fertilizer application or manure associated with animal feed lots. Nitrogen is typically applied to row 

crops in the spring as anhydrous ammonium or ammonium nitrate. Past farming practices included fall 

fertilization when the fields were dry. Due to mineralization and leaching losses, however, this practice 

has largely been abandoned. The overall fate of the nitrogen in an agricultural setting is shown in Figure 

3. Within a few weeks, most of the ammonia added to the soil is converted to nitrate, which can be taken 

up by plants, denitrified, or leached into water. The highest leaching rates occur in the spring after 

fertilizer is applied and rainfall is heaviest. Later in the summer, the growing crops effectively uptake 

much of the nitrate causing water and soil concentrations to decrease. Denitrification rates are also 

highest during these warmer months. Denitrification can reduce nitrate concentrations in both ground and 

surface waters. A USGS study in Eastern Iowa showed that 92% of the total nitrogen found in surface 

water is in the form of nitrate. Nitrate is considered to have a relatively short half-life in surface water 

bodies due to its transformation by denitrifying bacteria and uptake into plant matter where it then 

cascades through the food web. Galloway et al. (2003) estimate that 30-70% of nitrate that enters rivers 

and streams is eventually denitrified (1-20% in a particular reach of a river). 
7

Plants

Soil – N in org. matter

and sorbed (accumulation)

immobilization

mineralization

nitrification

immobilization

denitrification

FN applied (Nf)

f

Nf, vol

f

NO3,,de

f

de, NO

f

de,N2O

wet deposition (NWD,NH3)

dry deposition (NDD,NH3)

Baseflow / 

drains GW

f

NO3,SW

river

Gulf

f

de,dr

(dr+ri+Gu+GW) 

f

de

f

de, NO

f

de, N2O

erosion

f

om,NO3

f

NO3,GW

Hc

f

c,N

Hst

f

st,N

Hsoy

f

soy,,N

Ammonia /

ammonium

food / feed / energy

To 

deposition

denitrification

f

ni, NO

f

ni,N2O

Nsenes,NH

3

N2 fixation

Sources: Canter (1997), deVries (2003),

Blackmer (1987)

mineralization

uptake

uptake

Soil/plant system

atmospheric system

aqueous system

wet deposition (NWD,NO3)

dry deposition (NDD,NO3)

Soil solution

(NO3 + some NO2)

f

de,Gu

f

de,ri

Ms,er, fom

f

f,imm

Ns,min

Ncrop,min

ffix

Figure 3. Major nitrogen transformation processes and flows in an agricultural system 

(Ni

 – Flows (kg/ha), fi – fraction of flow)

Phosphorus Cycling and Fate in the Environment 

Phosphate in the environment occurs most abundantly as calcium phosphate minerals, such as 

hydroxyapatite (Ca10(PO4)6(OH)2), which have a very low solubility (Smil 2000). Because phosphorus is 

often a limiting nutrient for plant growth, man-made inorganic phosphate fertilizers are now produced 

from these phosphate rocks and strong acids for application to agricultural lands (Smil 2000). A 

commonly used fertilizer, triple superphosphate (Ca(H2PO4)2), contains 20% P. Global consumption of P 

fertilizers increased to over 15 Mt P yr-1 in 1990, but has decreased some in the last decade. This use of 

inorganic P fertilizer has contributed to Smil’s (2000) estimate that the global mobilization of P tripled by 

the year 2000 relative to its natural flows. 

Once phosphate fertilizer (FP) is applied to the soil, chemical equilibria with Al, Ca and Fe minerals in 

the soil dictates that much of FP reacts first to form insoluble mineral complexes that are very stable 

(Hansen et al., 2002). This sink must be satisfied to enable availability of excess P in the soluble and 

reactive pools necessary for plant uptake. Figure 4 identifies these forms of P and the important flows 

that distribute P among environmental compartments. Soil testing that extracts and measures only the 

bioavailable P (in soil solution and reactive pools) is required to determine optimal rates for adding FP to 

a field. 
8

Phosphorus cycling and transformations in the environment are less complex than nitrogen cycling. 

There is only a minuscule amount of P in the atmosphere, so this environmental compartment can be left 

out of analysis of P cycling (Smil 2000). There are, however, also far less data available to quantify flows 

between these compartments, including weathering, dissolution and immobilization, in comparison with 

that available for nitrogen (Smil 2000). 

Plants

P2O5

Soil/plant system

food / feed / energy corn stover soy

Reactive P in Soils

- organic

-inorganic

Stable P in Soils

- organic

-inorganic

P in soil solution

Baseflow / 

drains

river

Gulf

aqueous system

Leaching

Erosion

FP

Plants

P2O5

Soil/plant system

food / feed / energy corn stover soy

Reactive P in Soils

- organic

-inorganic

Stable P in Soils

- organic

-inorganic

P in soil solution

Baseflow / 

drains

river

Gulf

aqueous system

Leaching

Erosion

FP

Figure 4. Major phosphorus transformation processes and flows in an agricultural system 

(adapted from Smil, 2000; Hansen et al., 2002; Sharpley et al., 2003)

An estimated 1-2% of P fertilizers dissolve into water (range of 0.1 – 5%) (Smil, 2000). Leaching of 

excess fertilizer into water results in transport of the soluble form – orthophosphate (PO4

-3), primarily to 

surface waters (Becher et al., 2001). Most phosphorus sorbs to sediments and 70-95% of the total 

phosphorus (TP) load is transported through surface water bodies with suspended solids (Becher et al., 

2001). Although this phosphorus is slowly desorbed into the water column, the bound form is generally 

not directly biologically available. 

P is an essential mineral requirement for life and often a limiting nutrient for crop or algae growth. Corn 

has one of the highest intake rates, as much as 45 kg P ha-1 (Smil 2000). In soil microorganisms, the ratio 

N:P is on the order of 12:1 and for marine phytoplankton it is ~16:1 (Smil, 2000), based on an average 

composition of aquatic organisms (C106H263O11N16P) (Stumm and Morgan, 1990). In eutrophic waters, 

however, the N:P molar ratio in algae can be as high as 22:1 (Kalff, 2003). Thus, small additions of P can 

add sufficient nutrients for a large quantity of excess growth in an aqueous system. 

Water Quality Impacts Associated with Nutrients 

Hydrologic Conditions in Midwestern United States 

The flow of water through the agricultural systems in the Midwestern United states plays a significant 

role in the transport of nutrient pollution to water bodies and its distribution between ground and surface 

water. The eastern half of Iowa receives an average of 800 to >900 mm rainfall each year. The highest 

rainfall rates are in the southeastern region of the state with less rainfall in the northwest (Brenner et al., 
9

2001). Rain is sufficient in the eastern part of the state so that irrigation generally is not needed for row 

crop production. 

The drainage of water from agricultural fields in the Midwest has been substantially altered by the 

installation of subsurface tiles and drains. The installation of these drains began in the early 1900’s to 

make the region more suitable for crop growth. Overall, 25% of Iowa cropland is drained by subsurface 

tiles (USDA, 1987). Essentially, the tile drainage system results in most water entering streams and creeks 

results from ground water base flow and tile drainage rather than surface runoff. 

There are two main reasons agricultural drainage systems are installed in the Midwestern and Great Lakes 

states: (1) to allow timely seedbed preparation, planting, harvesting, and other field operations; and (2) to 

protect field crops from extended periods of flooded soil conditions. Factors that make drainage a 

necessity for agricultural production include low soil permeability, flat or pot-holed topography, or 

periods of excess precipitation. (Zucker and Brown, 1998). Subsurface drainage can be considered a 

management tool to reduce the potential for erosion and phosphorus enrichment of surface waters. 

However, nitrate-N loadings exported from drainage conduits to surface waters continue to be a major 

water quality concern. 

The extensively studied Walnut Creek watershed in central Iowa is a good example of the flat, pot-holed 

topography with extensive tile drainage. In the period 1992 – 1997, a total of 27% of the total 

precipitation drained through the subsurface tiles (Bakhsh et al., 2004). On an annual basis, between 12 

and 50% of the total annual rainfall was transported to surface water through the tile drainage system. 

The higher percentages correspond to wetter years with corresponding very high nitrogen leaching rates. 

In contrast to the low permeability of much of eastern Iowa’s croplands, the major river systems in Iowa 

create valuable and high permeability alluvial aquifers. These sand flood plain aquifers are hydraulically 

connected to the rivers, leading to transport of contaminants back and forth between these systems 

depending on recharge versus discharge conditions. The floodplain aquifers are often prime agricultural 

land. The use of agrichemicals, rapid infiltration rates and shallow unconfined nature of alluvial aquifers 

makes them very susceptible to contamination by nutrients and herbicides (Canter, 1997). 

Surface Water Contamination 

Reference points to define potential impacts

Since both nitrogen and phosphorus are required nutrients in surface water bodies, some reference point is 

required to allow us to understand if anthropogenic inputs are truly a problem. Background 

concentrations, ambient water quality criteria, and total maximum daily loads (TMDLs) are all reasonable 

reference points to identify the severity or impact of anthropogenic nutrient loads. Table 2 identifies 

reference points that can be used to determine if the nutrient concentrations in Midwest ground or surface 

waters are unacceptably high. 

Water quality and drinking water standards have been defined for nitrogen and phosphorus and can be 

used as a reference point to define the extent of impact from nutrients used in agriculture. The US EPA 

set a drinking water quality Maximum Contaminant Level (MCL) of 10 mg/L, primarily to protect infants 

from methemogloinemia (blue baby syndrome). The US EPA water quality standard for total phosphorus 

has been set at 0.1 mg/L to prevent excessive growth of aquatic plants in water bodies. Water quality 

criteria for nutrients were recommended by the US EPA in 2000 (2000a, 200b) for different eco-regions 

within the United States. These recommended criteria are presented in Table 2 for the Cornbelt and 

Northern Great Plains nutrient eco-region that encompasses much of Iowa. Although not directly 

comparable due to differences in units, the nitrogen water quality criteria for ecosystem health (TN) are 

less than the drinking water MCL (NO3-N) that was set for human health. Since nitrate is the most 

common form of nitrogen in water bodies, the recommended water quality criterion for ecosystem health 

can be considered to be significantly more stringent than the MCL for human health. The 

recommendations are slightly less for TP than the current ambient water quality standard. Water quality 
10

criteria are consistent with concentrations typically used as the cut off for defining a river or stream as 

eutrophic (TN > 1.5 mg/L and TP > 0.075 mg/L (Porter, 2003)). 

Recent efforts to identify background concentrations and loads to surface water bodies have been 

undertaken to support and justify recommended ambient water quality criteria. Nutrients are added to 

waterways in several non-anthropogenic ways – nutrient cycling by native plants and animals, increased 

loads after forest fires, and atmospheric deposition. Smith et al. (2003) used statistical analyses to 

estimate the natural background concentrations of nutrients in the 9 eco-regions identified for the nutrient 

water quality criteria (US EPA 2000a). Table 2 summarizes the estimated background concentrations and 

fluxes for the Corn Belt and N. Great Plains eco-region that encompasses much of Iowa. 

Table 2. Reference Points to Identify Excessive Levels of Nutrients in Midwest Surface Waters 

Concentration (mg L-1) Potential reference point 

TN TP 

Background levels 0.13 (0.105 - 0.19) a,b 0.021 (0.019 - 0.022) a

Recommended water quality criteriac 2.18 0.0763 

Ambient water quality standard -- 0.100 

Drinking water quality MCL 10 (as NO3-N) -- 

Definition of eutrophicd 1.5 0.075 a

 Frequency distribution (median (25% - 75%)) of background nutrient concentrations in the Corn Belt 

and N. Great Plains Eco-region (from Smith et al., 2003) b

 corrected to remove atmospheric deposition 

c Recommended water quality criteria, rivers and streams in Corn Belt Eco-region (US EPA 2000a, 

200b) d

 Porter (2003) 

Extent of nutrient contamination in surface water

Surface water in the Midwest generally has concentrations of nutrients that are much higher than 

considered acceptable based on background concentrations or water quality criteria or standards. These 

concentrations are also highly correlated to agricultural practices and the high rates of fertilizer used on 

row crops. 

The USGS sponsored a NAWQA (National Ambient Water Quality Assessment) study in Eastern Iowa 

during 1996-1998 (Becher et al., 2001). Samples were collected from both small watersheds (~300 km2

) 

to larger river watersheds, including the Cedar (20,200 km2

), Iowa (32,400 km2), Wapsipinicon (6,050 

km2

) and Skunk (11,200 km2) Rivers. The area includes 64% row crop agricultural lands and 88% 

agriculture overall. Representative results are included in Table 3. In summary, the studies showed: 

• NO3-N concentrations: 

o ranged from <0.05 to 22 mg/L, with a median concentration 6.6 mg/L 

o 22% of the nitrate samples exceeded the MCL of 10 mg/L 

o samples collected directly from tile discharges were consistently > 10 mg/L 

• Phosphorus concentrations: 

o ranged from 0.01 – 3.4 mg/L TP 

o 75% of the all of the samples exceeded the water quality limit of 0.1 mg/L for TP. 

Both median N and P concentrations increased annually over the three-year study period in a manner that 

was highly correlated to increases in stream flow over the same period. Monthly variations showed the 

highest concentrations in May and June, presumably due to recent fertilizer application and high rainfall 

rates. These concentrations were often higher than the MCL. 
11

Table 3. Nutrient contamination in surface waters in the Eastern Iowa NAWQA study 

Median concentration (mg L-1 System description ) a Drainage 

area (km2

) 

% Agric. 

land TN TP 

NAWQA – Eastern Iowa 

basin 1996-1998 

49,650 88.4% 7.2 

(4.4 – 10.0) b

0.22 

 (0.1 – 0.34) b

Wapsipinicon River 6,050 87.4% 6.7 0.23 

Iowa River 32,400 89.0% 6.0 0.30 

Skunk River 11,200 87.1% 6.8 0.33 

Flood Creek 320 95.3% 8.5 0.095 

South Fork Iowa 580 95.1% 10.0 0.072 

Wolf Creek 770 95.6% 10.5 0.13 a Becher et al. (2001) examples among 12 watersheds analyzed 

b

 (25th – 75th percentile) 

A longer-term study of nitrate concentrations in the Raccoon River in central Iowa provides a more clear 

understanding of the variability of nitrate concentrations and loads as a function of season and rainfall 

(Lucey and Goolsbey, 1993). Nitrate concentrations generally varied on a seasonal cycle with nitrate 

loads (typically 0 - ~300 mt/d with high of 780 mt/d) highly correlated to specific rainfall events, as 

characterized by daily variation in the streamflow. 

In the Iowa River, NO3-N concentrations have tripled since 1907, when they were <1 mg/L (Libra, 2001). 

Both the land area dedicated to row crops and the rate of nitrogen fertilizer used have increased 

significantly over that time. Others have also recognized that, over a wide range of scales, the mean NO3-

N concentrations (mg/L) (as calculated over several years in the 1990’s) are proportional to the 

percentage of total area that is planted in row crops (RC) in both small and large watersheds (Goolsby et 

al., 1999; Schilling and Libra, 2000; Becher et al., 2001). For example, for small watersheds (47 - 775 

km2

) in Iowa, Schilling and Libra (2000) determined that the nitrate concentration could be estimated as: 

NO3-N = 0.111 RC + 0.217 (R2

=0.94) [1] 

The slope of the line, which is generally around 11%, decreases with increasing scale of the watershed, 

presumably due to dilution and denitrification processes that occur within the stream (Schilling and Libra, 

2000). 

Due to the low solubility of phosphorus in water, phosphorus concentrations for the NAQWA study area 

were observed to be most closely associated with the concentration of suspended solids in the sample 

(Becher et al., 2001): 

TP = 0.0005 Csed + 0.2317 (R2

=0.62) [2] 

where the TP and sediment (Csed) concentration are in mg/L. The suspended solids concentration is in 

turn related to agricultural management practices and the intensity of rainfall events. 

Hypoxia

High nutrient concentrations contribute to eutrophic rivers and lakes at a local scale as well as at the 

National scale. Hypoxia is the condition defined by low oxygen levels (< 2 mg/L) that is caused by excess 

biological growth and decay. This condition often occurs in deeper water as the result of a coupling of the 

algae decay and seasonal water stratification due to temperature gradients. The region affected by 

hypoxia off the Louisiana coastline has increased from less than 10,000 km2

 in the mid-1980s to an 

average of 18,000 km2

 in the mid-1990s (Goolsby and Battaglin, 2000). 
12

The hypoxia zone in the Gulf of Mexico has been recognized as a significant environmental impact. In 

general, biological productivity and fishery yields drop dramatically after a water body enters into a 

hypoxic condition. One study completed by the 1999 Integrated Assessment of Hypoxia in the Gulf of 

Mexico task force assessed the environmental and economic consequences of hypoxia (Diaz and Solow, 

1999). Although there was not a lot of research completed to support this study, they observed hypoxia 

related stress in this region, including benthos mortality, elimination of larger longer lived species (who 

are suspected to have fled the area), and a shift in biological activity to non-hypoxic seasons. The 

economic assessment, however, did not find any direct effects attributable to hypoxia, although it is 

acknowledged that these effects might be masked by the general variability in fishery yields (Diaz and 

Solow, 1999). 

Nutrient loads to the Gulf of Mexico are largely attributed to high nitrogen fluxes from agricultural 

practices in the Midwestern United States, coupled with increased total water discharges (+30%) from the 

Mississippi River and decreased residence time due to channel straightening and floodplain/wetland 

losses. The changes in the hydrologic system decrease the opportunity for denitrification and uptake of N 

in organic matter that accumulates in the sediments (Galloway et al., 2003), thus contributing to the 

overall increased transport of nitrate to the Gulf of Mexico. Eventually, most of the nitrate transported 

through rivers, lakes and coastal waters is denitrified (Galloway, 1998). 

Howarth (1998) estimates increases in the nitrate loads of 2.2 – 6.5 times the estimate for pristine 

conditions. Goolsby and Battaglin (2000) estimate a similar increase; nitrate-nitrogen loads discharged 

with the Mississippi River have tripled from ~0.3x106

 metric tons in the late 1950s to an average of 

approximately 1x106

 metric tons per year since 1980. An additional 0.58 x106 metric tons are discharged 

in the form of organic nitrogen, for a total average annual load of 1.6 x106

 metric tons (Goolsby et al., 

1999). These increases in the reactive nitrogen load are correlated to increases in crop production and 

associated chemical fertilization rates in the heartland of the US. Fertilizer is clearly the most significant 

anthropogenic source of nitrogen to the Mississippi River (Table 4). Note that the sum of the total fluxes 

to the Mississippi River greatly exceeds the total load to the Gulf of Mexico due to denitrification and 

other nitrogen sinks that decrease concentrations as water flows from the source towards the Mississippi 

and the Gulf. 

Table 4. Sources of Nitrogen to the Mississippi River

Source Nitrogen Input a

(millions metric tons)

Soil mineralization 6.8 

Fertilizer 6.8 

Legumes and pasture 4.4 

Animal manure 2.8 

Atmospheric deposition of nitrate 1.1 

Atmospheric ammonia 0.7 

Municipal and industrial point sources 0.3 

a

 (averages for 1990-1996; Goolsby and Battaglin 2000) 

Phosphorus loads to the Gulf of Mexico are substantially less than nitrogen loads. The average annual 

discharge from 1980 to 1996 was 136,000 metric tons. Most of this (69%) is transported as part of the 

sediment load, the remainder is primarily as dissolved orthophosphate (Goolsby et al., 1999). These loads 

have not changed substantially since 1970. 

Row crop production in the Midwest contributes the highest fluxes of nitrogen and phosphorus to the 

Mississippi River basin. As shown in by the area denoted as region 6 in Figure 5, Eastern Iowa, Illinois 

and the surrounding vicinity, have the highest average yields (mass per land area per year) within the 
13

Mississippi drainage basin of nitrate (650-1150 kg km-2 yr-1), total nitrogen (1000-1690 kg km-2 yr-1), total 

phosphorus (96-129 kg km-2 yr-1), and orthophosphate (35-36 kg km-2 yr-1). These yields represent 

average calculated yields over the period 1980 to 1996. These years include both very wet (1993) and 

very dry (1988) years. 

Variability in the nutrient loads to the Gulf of Mexico is highly dependent on the annual mean river 

discharge (Figure 6), which in turn is a direct consequence of the variability in the annual rainfall in the 

Midwest each year. The higher nutrient loads observed during wet years are also somewhat correlated to 

the size of the hypoxic zone in the Gulf of Mexico (Figure 7). The trends are very apparent for 1985 – 

1993. The hypoxic zone remained large, however, after the heavy flooding in 1993. 

A model developed by Scavia et al. (2003) suggests that the size of the hypoxic zone in the Gulf of 

Mexico is correlated to the TN load to the Gulf during May – June. The size is estimated based on this 

TN load and simplified ocean mixing dynamics. The model predicts that a 40-45% reduction in the 

average TN load (1980-1996) would be required to maintain the hypoxic zone at <5000 km2

 – a goal 

specified by the NOAA Integrated Assessment of Hypoxia in the Gulf of Mexico (Brezonik et al., 1999). 

This required percentage reduction is greater than the 20-30% reduction in TN load suggested by the task 

force itself (Brezonik et al., 1999). 
14

Figure 5. Yields of nitrogen and phosphorus contributing to Hypoxia in the Gulf of Mexico 

(Goolsby et al., 1999) 
15

a)

0

500

1,000

1,500

2,000

2,500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Years, 1973 - 1996

TN load (1000 mt)

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

average annual flowrate (m3/s)

TN Load

Flowrate

b)

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Years, 1973 - 1996

TP load (1000 mt)

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

average annual flowrate (m3/s)

TP Load

Flowrate

Figure 6. Total discharge of (a) nitrogen and (b) phosphorus from the Mississippi River to the 

Gulf of Mexico are highly correlated to the total river discharge 

(data from Goolsby et al., 1999, NOAA report 

http://co.water.usgs.gov/hypoxia/html/nutrients_cenr.html)) 
16

0

2

4

6

8

10

12

14

16

18

20

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Size of hypoxic zone (1000km^2)

800

1,000

1,200

1,400

1,600

1,800

2,000

2,200

2,400

TN Load (1000 mt)

Size of hypoxic zone

TN load

Figure 7. The size of the hypoxic zone varies substantially among years and is correlated to the 

TN load from the Mississippi River. 

Note – no data are available for 1989. (data from Goolsby and Battaglin, 2000 (USGS Fact 

Sheet 135-00); NOAA report, http://co.water.usgs.gov/hypoxia/html/nutrients_cenr.html)

Ground Water Contamination 

The impacts of groundwater contamination in the Midwest include contaminated drinking water supplies 

and subsequent surface water contamination as shallow groundwater discharges to surface water bodies. 

Thus, there are several different reference points that could be used to define this extent of contamination. 

• Natural background nitrate levels in groundwater for Iowa <2 mg/L NO3-N 

(Kross et al., 1993) 

• Drinking water quality MCL (10 mg/L NO3-N) 

• Recommended ambient water quality criteria (Table 2) 

Based on any of these reference points, the ground water in Iowa and much of the Midwest is considered 

highly contaminated. A statewide analysis of the quality of drinking water in private drinking water wells 

was sponsored by the Iowa Dept. Natural Resources in the late 1980s. Kross et al. (1993) report many 

wells exceed the MCL of 10 mg/L NO3-N. These include: 

• 18.3% of all private wells; 

• 35% of all shallow wells (<15 m) (with 15.6% of shallows well having NO3-N >20 mg/L); and, 

• 9.2 – 11.6% of wells in eastern Iowa and 38.2% of wells in NW Iowa. 

Nitrate, the nitrogen form that is most abundant in ground water systems, is now considered nearly 

ubiquitous in shallow Midwestern groundwater at concentrations considered to be anthropogenic (>3 

mg/L). It was detected 13 times as often as NH4

+

 or organic nitrogen in ground water samples (Nolan and 

Stoner, 2000). Drinking water supplies from deeper ground water are generally much safer due to the 

increased age of ground water. In eastern Iowa, with wells for public drinking water supplies at 130-190 

ft deep, median NO3-N concentrations are low (<0.1 – 1.3 mg/L). Long residence times, which allow for 

denitrification and/or the possibility of ground water age pre-dating significant commercial fertilizer 

applications, contribute to these lower concentrations (Nolan et al., 1997). 

Burkart et al. (1999) used multivariate analyses and data from over 300 wells in the Midwest to identify 

site characteristics that are most closely correlated with nitrate concentrations. Low nitrate concentrations 
17

were most significantly correlated to a seasonally high water table. This condition is generally indicative 

of poorly drained soils that cause an increase percent of water to flow to surface water bodies and 

anaerobic conditions that increase denitrification rates. 

Results from the USGS National Ambient Water Quality Assessment (NAWQA) program showed that 

areas with high nitrogen yields (>21 kg/ha), well drained soils, and low woodland to cropland ratios 

(<0.3) are the most susceptible to nitrate pollution in shallow groundwater (Nolan et al., 1997). Areas 

with all three of these factors are defined as the highest risk group. This group had median concentrations 

of 4.8 mg/L NO3-N, with 25% of samples exceeding the MCL. In Iowa, the entire state was considered at 

risk due to high nitrogen inputs. With differences in the nature of the infiltration rates, however, 

approximately 50% of the state is in the highest risk category and 50% in the next highest. Poorly drained 

soils and the agricultural practice of constructing subsurface tiles greatly reduce the potential for the 

applied nitrogen to enter ground water systems in much of Iowa. 

Burkart and Stoner (2002) add irrigation to the list of factors that contribute to high risk. Irrigated lands 

are often well drained and the combination of a large amount of transport medium (irrigation water) and 

rapid transportation rates results in a significant fraction of the applied nitrogen reaching groundwater 

resources. Nebraska and other row crop agricultural areas have a much higher risk of ground water 

contamination by nitrates than eastern Iowa. 

The contamination of groundwater systems by nitrate represents a long-term loss of this water resource. 

Nitrate concentrations are slow to respond to measures instituted to prevent further contamination, and 

may take decades to recover. Tomer and Burkart (2003) measured NO3-N concentrations in two small 

watersheds in SW Iowa. The watersheds were nearly identical geologically and both had continuous corn 

grown from 1964-1974. One watershed, however, had very high nitrogen application rates for 1969-1974 

(446 kg ha-1 yr-1). In 1996, the crop rotations were changed and a study instituted to determine the 

impacts of this change on groundwater quality. Through analysis of the age of ground water sampled, 

they show, however, that the ground water concentrations are still greatly influenced by the high nitrogen 

application rates in the early 70s. Decades might be required to experimentally verify and changes in 

agricultural practices on groundwater quality due to the long-term impacts of past practices on ground 

water quality. 

Variables Affecting Nutrient Leaching to Surface Water 

Fertilizer application rates, tillage practices, crop rotations and rainfall have all been considered as 

potential causes of variability in the amount of nutrients that leach as non-point source pollution from 

agricultural fields to surface water bodies. Since the solubility of nitrates and phosphates are quite 

different, it is possible that these other variables will affect the loss of these nutrients to surface water 

differently. 

Nitrogen 

Early efforts to identify the most important variables for nitrate runoff suggested that the amount leached 

was directly proportional to the fertilizer application rate (e.g., Hallberg 1987). These results were based 

on short-term experiments (2-3 years) with very wide ranges of fertilizer application rates (0 – >400 kgN/ha) (e.g., Gast et al., 1978). Over a more realistic fertilizer application rate, at least for corn, of 100-

200 kg/ha, other variables have been found to be more important than the fertilizer application rate. 

Studies of variability in nitrate losses through subsurface drains indicate that differences in rainfall and 

the amount of water transported through subsurface drains are significantly more important variables than 

differences due to cropping or tillage practices. Figure 8 illustrates the results from Weed and Kanwar 

(1996), who show there is no consistent trend or statistically significant differences in the fraction of FN 

that leached into drainage tiles between the C-S rotation with conventional till and either continuous corn 

(C-C) or C-S with a no till (NT) operation. At this site near Nashua IA, much more substantial 
18

differences occur among the years due to differences in rainfall than among the crop rotations or tillage 

practices. 

Later studies at the same site also show that differences in the amount of nitrate leached among the years 

(5-59% of FN applied for C-S, chisel plow (CP) system) due to rainfall are more significant than due to 

fertilizer application or tillage practices (Bakhsh et al., 2002). They observed no statistically significant 

differences between corn and soy rotations for nitrate leached (kg/ha). A significantly higher average 

fraction of nitrate was leached from chisel plow fields (21%) than no till (NT) fields (14%) when nitrogen 

was applied in the spring at a rate based on soil test results. For fall application of FN at the same rate on 

all fields, however, the average fraction of FN leached from the no till plots (41%) was significantly 

higher than for CP fields (24.5%). 

With the most significant overall variability in nitrate leaching rates among the years due to differences in 

rainfall, Bakhsh et al. (2002) found that they could describe 79% of the total variability based on linear 

relationships that show the total nitrate leaching loss as a direct function of the subsurface drainage. 

Drainage, in turn was linearly related to rainfall. The net relationship shows: 

NO3-N loss (kg/ha) = 0.070 * rain (mm) - 41.72 [ 3] 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1990 1991 1992

Fraction FN leached 

C-S, CP/MP

C-S, NT

C-C, NT

Figure 8. During a carefully controlled field study, Weed and Kanwar (1996) found that there 

were no statistically significant differences in the fraction of nitrate leached from C-S 

and C-C rotations under different tilling operations. (MP – moldboard plow)

Phosphorus 

Data quantifying TP losses at field and small watershed scales are scarce. One study by Klatt et al. 

(2003) measured P inflows to Clear Lake in NE Iowa over a two-year period. Clear Lake is within the 

geographic boundary considered here and is a good example of a water body adversely impacted by 

eutrophication. The TP load to the lake and TP used within the watershed are presented by Klatt et al. 

(2003). As was observed with nitrogen loads to surface water, the TP loads were significantly higher 

during the year with a higher total rainfall. 

Statistically significant differences have been observed for phosphorus loads as a function of tillage 

practices, with higher loads often observed from no till plots. Gaynor and Findlay (1995) observed an 

factor of two increase in the TP loads from fields with no till practices compared to conventional till. 
19

McIsaac et al. (1995) also observed this increase for soluble phosphorus loads. They observed increases 

in SP to be 1.6 to 5.4 times higher for no till plots. The range of values was a function of different soil 

types. Daverede et al. (2003) observed a 3-fold increase in dissolved reactive phosphorus loads for NT, 

but no statistically significant difference for TP loads. 

These increases in phosphorus loads were unexpected. In general, NT reduces erosion substantially 

compared with CP practices. And, since most P is bound to eroded soils, it was thought that NT would 

reduce TP loads. The increased availability of P from crop residue decaying at the ground surface and the 

formation of root channels and other macro pores in the subsurface provide avenues for increased 

transport of soluble phosphate that is more substantial than the beneficial effects of reduced erosion. 

In general, phosphorus losses at the field scale are estimated in an empirical fashion based on a 

combination of P source factors (reactive P concentration in the soil, FP application rate and method) and 

transport potential (erosion, runoff, percolation) (Birr and Mulla, 2001; McDowell and Sharpley, 2001; 

Sharpley et al., 2001). The Mehlich-3 extraction process is typically used to define reactive P 

concentrations. This empirical approach provides a relative index to define vulnerability; it is not intended 

to predict phosphorus loads to surface or ground water bodies. 

Modeling Fertilizer Nutrient Discharges to Surface Water 

A wide variety of approaches has been used to estimate non-point source nutrient discharges from 

agricultural fields to surface water bodies. These approaches can be broadly classified as: 

• Empirical emission factors that base nutrient release on a fraction of the nutrient input, with or 

without stochastic distributions of the emission factors and input parameters 

• Mass balance approaches that assume nitrogen, which is not already incorporated into plant 

matter or soils, or the atmosphere, has the potential to leach 

• Mechanistic modeling of nutrient flows and reactions within and among environmental 

compartments. 

Others have also used hybrid approaches that combine aspects of the various approaches. Clearly, these 

approaches encompass a diversity of model complexity, suitable scale of application, and input data 

requirements. The empirical emission factor approach is the easiest to apply, and could be appropriate for 

simple systems such as phosphate discharges to surface water. However, it is least likely to capture the 

complex interactions among various transformations and partitioning of nitrogen among environmental 

compartments, especially as a function of the variety of agricultural practices and geologic, hydrologic 

and weather conditions among different farming regions. At the other extreme, mechanistic modeling 

efforts require significant amounts of site specific data, but provide more accurate estimates of nutrient 

fate and flows and their variability. The integration of GIS (geographic information systems) with these 

mechanistic models provides improved data management capabilities, allowing mechanistic models to be 

applied over larger scale systems. 

Emission Factor Approaches 

The model developed by Wang (1999) to estimate greenhouse gas and other air emissions associated with 

fuel use for transportation is a good example of a model based on emission factors. This model, GREET, 

estimates most nitrogen flows associated with feedstock preparation of biomass fuels as a fraction of the 

fertilizer nitrogen used. This includes atmospheric emissions of NH3, NOx and N2O. Fertilizer leaching 

is estimated as 24% of the FN used. This fraction was determined from literature defining nitrogen 

leaching rates measured at several different field test plots. Summaries of these data (e.g., Gast et al., 

1978; Hallberg, 1987), support the use of an emission factor that describes nitrogen leaching based on the 

rate of FN application. The GREET emission factor for FN was employed only to allow subsequent 

estimation of N2O emissions as the leached nitrate is denitrified. The use of a distribution of values for 
20

the fraction of FN ultimately emitted as N2O provides the only means of integrating variability in the 

leaching rates into the GREET model (triangular distribution from 1 to 2% with maximum 1.5% of FN 

transformed into N2O). 

The nitrogen flow modeling by the International Panel on Climate Control (IPCC, 1996) also focused on 

air emissions, and recognized the need to quantify leaching to surface waters due to the subsequent 

denitrification and release of N2O. The IPCC suggests that nitrogen leached to surface waters could be 

estimated as 30% of the applied fertilizer nitrogen that remains after volatilization, although they 

recognize values could range from 10-80%. 

Mass Balance Approaches 

Many of the original mass balance approaches generally tried to estimate potentially leachable nitrogen 

based on the difference between quantifiable nitrogen inputs, outputs and accumulation of nitrogen in the 

soil system at a field scale (Meisinger and Randall, 1991; Schepers and Moiser, 1991; Follett, 1995; 

Shaffer et al., 1995). Inputs include fertilizer (inorganic and manure), atmospheric deposition, irrigation, 

soil and crop mineralization, and nitrogen fixation by legumes. Quantifiable outputs include nitrogen 

removed with the harvested crop, volatilization, erosion, plant senescencing (direct loss of NH3 from 

plant tissue). Application of the mass balance approach to determine leaching by difference requires an 

assumption that the soil nitrogen concentration is at a steady state or direct measurement of soil-N 

concentrations to quantify the accumulation or depletion of soil nitrogen. 

The mass balance approach can identify potentially leachable nitrogen, but does not adequately identify 

the distribution of the leached nitrogen between surface and ground water. Systems for which all of the 

water flow can be assumed to go to either surface or ground water are required for a mass balance to be 

used. For example, Puckett et al. (1999) was able to assume that all of the leached nitrogen was 

transported to a highly permeable outwash aquifer. The leaching rates were determined assuming a 

steady state between inflows and outflows and that no nitrogen was lost by runoff or base flow. 

Mass balance approaches to identify nitrogen flows have also been applied to large watershed scale 

systems around the world. In many of these cases, the quantity of nitrogen and/or phosphorus leached 

was based on integration over time of measured river concentrations and flow rates. Several such studies 

have attempted to define empirical models for the river flux of N or P based on some measure of nutrient 

inputs (Howarth et al., 1996; Carpenter et al., 1998; Caraco and Cole, 1999; Goolsby et al., 1999; David 

and Gentry, 2000; Carey et al., 2001; McIsaac et al., 2001; McIsaac et al., 2002). The anthropogenic 

nitrogen inputs (ANI) or net anthropogenic inputs (NANI) are often used as the independent variable in 

these models. The models range from globally applicable (Caraco and Cole, 1999), to those developed 

for a specific large watershed. McIsaac et al (2001, 2002) for example, developed a regression equation 

for the Mississippi River Basin. Their regression recognizes the importance of historical nitrogen inputs 

as well annual variation in rainfall: 

NLM = 0.66 · W0.93 · exp(0.13 · NANI2-5 + 0.06 · NANI6-9) [4] 

where, W is the annual water yield (stream flow / watershed area) and NANI, the net anthropogenic 

nitrogen input (FN + atm. dep. + fixation – N in crops and feed), is quantified for the previous 2-5 years 

and 6-9 years. The model accounts for 95% of the variation in annual nitrate flux (NLM, kg ha-1 yr-1) at the 

monitoring station in the lower Mississippi River just before discharge to the Gulf of Mexico. 

Similarly, Goolsby and Battaglin (2001) estimated nitrate fluxes to the Gulf of Mexico as: 

NLM = 0.049 · Lf,Y2 + 36 · Q – 0.094 · RY1 [ 5] 

where Lf,Y2 is the nitrogen fertilizer load (mt) 2 years previous, Q is the mean annual stream flowrate 

(m3

s

-1), and RY1 is the residual nitrogen (inputs minus outputs) the previous year. 
21

The highly non-linear nature of these equations makes it difficult to allocate the total nitrogen flux to 

specific sources. 

On a much smaller scale, Tomer et al. (2003b) used similar types of regression analysis to estimate nitrate 

fluxes from tile drainage systems in Walnut Creek, central Iowa from 1992 through 2000. They found a 

log linear relationship between stream flow (Q, mm hr-1) and the flux (kg ha-1 yr-1). 

NLM = a · Qb [6] 

where a ~ 4 and b ~ 1.1. Regression coefficients were statistically similar for the two tile systems, but 

different for the downstream sampling point. For each individual system, the R2

 values were very high 

(>0.99). Application of this equation at a larger scale would require flow data and spatial integration of 

this non-linear relationship over all tiles drainage systems. 

The work of deVries et al. (2003) provides a hybrid approach based on both mass balance concepts and 

emission factors to quantify both aqueous and air nitrogen emissions. They essentially describe nitrogen 

cycling from agricultural and non-agricultural lands as a series of steps. Losses at each step then reduce 

the mass available for loss at the next step. The processes – in the order of assumed occurrence – include: 

ammonia volatilization, crop uptake, immobilization/mineralization, nitrification/denitrification, leaching 

to ground water, leaching to surface waters and subsequent denitrification, outflow to the sea. If at any 

point, the losses from previous steps exceed the nitrogen inputs, then there are no additional losses for the 

last processes in this series of steps. The flows and fractions lost at each of these steps were estimated for 

application in the Netherlands. 

Mechanistic Models 

Mechanistic models for nutrient flows attempt to explicitly model flows and interrelationships between 

these flows and nutrient use, crop productivity, weather patterns, and geographic conditions. Thus, the 

most comprehensive models are composed of submodels defining land use, soil quality, crop growth, 

hydrology, erosion and nutrient transformation and transport. Application of this type of models requires 

a vast set of site-specific parameters to quantify all of these interrelationships. 

SWAT (soil water assessment tool) developed by the USDA Agricultural Research Service (Arnold et al., 

1998) is one of the most comprehensive watershed models currently available, especially when linked to a 

model such as EPIC (erosion productivity impact calculator) that estimates field-scale crop growth, N and 

P cycling, runoff and erosion (Williams et al., 1989). SWAT, which models water, sediment and 

chemical flows in large watersheds, has been applied to watersheds in Story (Walnut Creek) and 

Powesheik (Buck Creek) Counties in central Iowa to predict the effectiveness of changes in agricultural 

practices on the reduction in erosion and nitrate loads to surface waters (Vache et al., 2002). It is also a 

component of the computation tool currently recommended by the US EPA to help establish total 

maximum daily loads (TMDLs) to maintain adequate water quality (USEPA, 2001; DiLuzio et al., 2002), 

and has been used in this capacity for a growing number of regions (e.g., Santhi et al., 2001). 

Wauchope et al. (2003) present a review of the numerous other mechanistic models could also be used to 

estimate the flow of nutrients or herbicides from agricultural fields. Some other examples include: 

• GLEAMS – Groundwater Loading Effects of Agricultural Management Systems. This model 

includes hydrology, erosion, pesticide and nutrient transport for a homogeneous field scale 

system. It was originally developed to estimate leaching of pesticides and nutrients within and 

below the root zone. It has recently been used to estimate nutrient and herbicide losses to tile 

drains at a field scale in Iowa (Bakhsh and Kanwar, 2001). Although this model is no longer 

supported by the USDA, its equations describing the movement of pesticides through the 

subsurface were integrated into SWAT (Wauchope et al., 2003) 
22

• RZWQM – Root Zone Water Quality Model. This model provides a one-dimensional dynamic 

tracking of nutrient and pesticide leaching to ground and surface water. This model has been 

successfully applied to a field in the Walnut Creek watershed to predict nitrate losses to 

subsurface drains (Bakhsh et al., 2004) 

• AnnAGNPS Annualized Agricultural Non-Point Source model. This model extends AGNPS, 

which was developed to estimate non-point source discharges at a watershed scale based on the 

hydrology and erosion with individual storm events (Young et al., 1989). The new annualized 

version can use actual annual weather data or such data can be generated with an internal 

stochastic climate module. It is characterized by the developers as requiring less data than models 

such as SWAT. (USDA, 2004a) 

• NAPRA WWW – National Pesticide Risk Analysis model for the WWW. Lim and Engel (2003) 

extended NAPRA to include nutrient leaching (from GLEAMS) and developed a WWW interface 

that provides relatively easy access to the required soils and hydrology data for any county in the 

United States. Output from this model includes a distribution of probabilities that water quality 

standards will be exceeded for a set of input agricultural practices. 

Mechanistic models such as those described above have not been used in agricultural life cycle 

assessments to date. This is most likely due to the extensive parameterization required (soil types, 

weather, agricultural practices etc.), even for field scale systems. 
23

3. Methods for Quantifying Lifecycle Inventory Data for Nutrient Flows 

Associated with Corn-Soybean Production 

General Approach 

The research reported here quantifies lifecycle inventory flows for corn and corn-soybean rotations, with 

and without stover removal. The focus of this work is on nutrients, although additional flows were also 

quantified to complete a cradle to farm-gate LCI for these systems. Inventory parameters required to 

assess environmental impacts associated with eutrophication, acidification, and greenhouse gas emissions 

were calculated to enable a more complete understanding of the relative environmental costs and benefits 

associated with the preparation of feedstocks for bio-based fuels and products. 

The mathematical models describing the flows and distribution of nutrients among the various 

environmental compartments uses a hybrid mass balance and emission factor approach. That is, these 

flows are based on some fraction of a suitable nutrient inflow. Two differences, however, advance these 

techniques for application in an LCI: 

• To the extent possible, the fractions integrate knowledge of the physical, chemical and biological 

mechanisms that control the flow. For example, NO emissions are based on a fraction of the 

nitrogen that undergoes nitrification or denitrification reactions, rather than a fraction of the total 

fertilizer nitrogen applied. 

• Variability in the distribution of nitrogen among environmental compartments, especially 

leaching to surface waters, as a function of rainfall rates is included. 

Data for many of the agricultural flows were taken from a specific geographic region that includes three 

major watersheds in eastern Iowa. The choice of this region allowed the calibration of nutrient leaching 

rates against water quality data available from the USGS. Temporal variation in rainfall is integrated 

through the inclusion of several years of data. 

System Boundaries 

Applying an agricultural LCA to a specific geographic location provides the increased accuracy by using 

site-specific data. A balance must be made, however between a site that is so small that it is not 

representative of the broader system versus a region that is so large that input data and nutrient flows vary 

substantially over the region and are difficult to characterize. The eastern Iowa region selected for this 

study encompasses approximately 50 thousand km2

. It was chosen based on its high productivity of corn 

and soybeans, the availability of agricultural practice, yield and erosion data, and its alignment with three 

major watersheds for which several years of water quality data exist. These data were essential for the 

development and calibration of nutrient leaching models. 

The system also included thirteen individual years – 1988 – 2000. Both very wet (1993) and very dry 

(1988) years were observed in this period. Data for the 1990s was readily available electronically through 

the USDA National Agricultural Statistics Service (USDA NASS) databases. Depending on the nature of 

the data, information for earlier years was available through NASS reports or databases. The inclusion of 

a thirteen-year period allowed variability of crop yields and nutrient flows as a function of rainfall to be 

incorporated. The use of average data would have lost the important environmental impacts associated 

with extreme climatic conditions. Employing average values with statistical distributions in the values 

used could have integrated some of this variability, but it would be harder to capture the correlations 

between nutrient fate and rainfall. 

The eastern Iowa region is shown in Figure 9. County boundaries were used to define the geographic 

system boundary to approximate the overall area encompassing the Wapsipinicon, Skunk, Iowa and 

Cedar River watersheds (Table 5). The Cedar/Iowa river systems provide the greatest contributions in 
24

terms of drainage area and the stream flow in this Eastern Iowa watershed region. There is over one order 

of magnitude variation in stream flow among years considered. For all the rivers, the lowest flows were 

recorded in 1989 and the highest in 1993. These trends correspond to annual rainfall extremes (Figure 

10). This entire region is considered to be humid, especially in the southeastern sections where there is 

significantly more rain and warmer temperatures compared with the northern regions of Iowa. 

Table 5. Characteristics of Eastern Iowa Watersheds 

Watershed 

System 

Drainage 

area (km2

)

a

Annual average flow rate 

(m3

/s) b (average and 

range 1988-2000) 

% drainage 

basin used for 

agriculture a

% drainage 

basin used for 

row crops a

Wapsipinicon R. 6,050 57.6 (10.7 – 149) 87.4 70.3 

Iowa R. 

(including Cedar) 32,400 300 (51.8 – 850) 89.0 65.3 

Skunk R. 11,200 88.6 (13.8 – 275) 87.1 57.2 

Entire watershed 49,650 446 (76.3 –1274) 88.4 64.1 

Modeled system c 49,720 -- -- 64.9 

a

Becher et al., 2001 

b

 USGS water resources data for Iowa, http://nwis.waterdata.usgs.gov/ia/nwis/ c based on sum or acreage-weighted average of county data 

Mississippi River

Wapsipinicon

Cedar

Iowa

Skunk

Approximate watershed boundary

Area included in LCI based

on county boundaries

IOWA

Mississippi River

Wapsipinicon

Cedar

Iowa

Skunk

Approximate watershed boundary

Area included in LCI based

on county boundaries

Mississippi River

Wapsipinicon

Cedar

Iowa

Skunk

Approximate watershed boundary

Area included in LCI based

on county boundaries

IOWA

Figure 9. Map of Iowa identifies the watershed for the four major river systems in Eastern Iowa 

and the corresponding area included in the LCI based on county boundaries. 

(Watershed definition based on Iowa DNR map, 

http://www.iowadnr.com/water/tmdlwqa/wqa/303d/1998/map1.gif) 
25

The geology, hydrology and land use practices in this region are described extensively by Becher et al., 

(1999; 2001). Overall, approximately 64% of the region is used for row crop production. This is mostly 

dedicated to corn-soybean rotations (C-S), although some farms, especially in the northeast and east 

central regions, practice corn-corn-soybean rotations (Brenner et al., 2001). This accounts for the slightly 

greater row crop land dedicated to corn (59%) every year versus soybeans (41%). 

There are four major landforms in eastern Iowa (Becher et al. 2000). The Des Moines lobe in north 

central Iowa was formed by glaciation and is characterized by very low topographical relief and thick 

loamy till soils. The Iowa surface in NE Iowa also has low relief and loamy till, but with a thin cover of 

loess on ridges or alluvium near streams. A portion of this region is defined as the Iowa karst subsection 

with typical sinkhole topography. Southeastern Iowa is defined by the southern Iowa Drift Plain. It has 

greater relief and steep rolling terrain with flat tabular divides between the hills. The soil is composed of 

glacial loess. Because of the low relief and till soils in the northern two-thirds of the study area, extensive 

tile drainage is required to maintain proper soil moisture for crop health. 

The lifecycle boundaries considered here include cradle-to-farm gate flows, resulting in the production of 

corn, soybeans or corn stover that could later be used for bio-based products or fuels. As shown in Figure 

11, the system considers: 

• Land area involved with corn or soy production 

• All nutrients (N, K, P) used to improve crop yields 

• Nutrient flows (NO3, NOx, N2O, TN as nitrogen, TP) to water and air compartments (as 

applicable) 

• N and P flow to the Mississippi River and the Gulf of Mexico 

• Carbon flows related to plant uptake and harvest, energy consumption 

• Diesel fuel, gasoline and motor oil used on the farm for crop planting and harvesting 

• Upstream generation of fertilizers and energy along with their LC nutrient and energy related 

emissions 

• Eutrophication, acidification and greenhouse gas impacts 

The system does not include flows related to 

• Other farm related energy uses 

• Farm or upstream processing equipment manufacture and maintenance 

• Other agrichemicals used on the farm (e.g., herbicides) 

• Other LC impacts (most notably – toxicity related to nitrates, fossil fuel emissions, or herbicides) 
26

a)

0

100

200

300

400

500

600

700

800

900

1988 1990 1992 1994 1996 1998 2000

Annual Average Stream flow (m3/s)

Wapsipinicon

Skunk

Iowa

b)

400

600

800

1000

1200

1400

1600

1988 1990 1992 1994 1996 1998 2000

Annual Rainfall (mm)

Winnebago

average

Johnson

Figure 10. Annual average stream flow (a) for Eastern Iowa watersheds and (b) regional average 

and example county precipitation values 

(USGS water resources data for Iowa, http://nwis.waterdata.usgs.gov/ia/nwis/ ; Spatial 

Climate Analysis Service http://www.ocs.orst.edu/prism/ ) 
27

Crop

production

FK

production

FP 

production

FN 

production

Fossil Fuel

Production

- gasoline

- diesel

- motor oil

Electricity

Generation

Crop

- corn

- stover

- soy

Atm.

emissions

- CO2, N2O

- NOx

Aqueous 

emissions

- TN

- TP

Soil erosion

System Boundary

Coal

Crude oil

Natural gas

Water

KCl

PO4 rock

Cropland

Crop

production

FK

production

FP 

production

FN 

production

Fossil Fuel

Production

- gasoline

- diesel

- motor oil

Electricity

Generation

Crop

- corn

- stover

- soy

Atm.

emissions

- CO2, N2O

- NOx

Aqueous 

emissions

- TN

- TP

Soil erosion

System Boundary

Coal

Crude oil

Natural gas

Water

KCl

PO4 rock

Cropland

Figure 11. System boundary for cradle-to-gate life cycle assessment for corn and soybean 

production 

Stover Removal Model 

The stover harvest rate was defined based on residue production in a given year and the minimum stover 

residue required limiting erosion to an acceptable level. Some residue is required to remain on the field to 

prevent excessive erosion. Minimum stover residue requirements for conventional, mulch and no till 

practices were calculated by Richard Nelson for an NREL report (Nelson 2004) for C-S rotations and for 

the stover LCA (Sheehan et al., 2002). Nelson used standard erosion models (RUSLE) with long-term 

average values for rainfall and grain yields to determine required remaining residue. His values for 

minimum residue requirements do not integrate variability among years associated with the weather. 

For the C-C system, the amount of residue that can be harvested in each county was estimated as: 

Hst,harv = Hres,c – Hres,min [ 7] 

where, Hst,harv is the yield of harvestable stover (kg/ha corn) and Hres,min is the crop residue that needs to 

remain on the field to limit erosion to a tolerable level. The amount of residue generated is defined as 

Hres,c = Hc · HIc ,where Hc is the corn grain yield and HIc is the harvest index for corn. In the study 

reported here, stover harvest values vary among years due to the use of annual Hc values for each of the 

13 years considered. 

Determining the quantity of stover that can be harvested in a C-S rotation is more complicated (Nelson, 

2004). In this case, the minimum amount of residue remaining on the field must be compared to a twoyear average of crop residue generated during a complete C-S rotation and actual amounts for each of the 

crops. The average residue generated (Hres,avg) was defined as: 

Hres, avg = Hc · HIc + Hres,soy,t-1 · HIsoy [ 8] 

where Hres,soy,t-1 is the soy residue generated in the previous year. 

For the C-S system, the amount of stover that could be harvested in a given year was determine by the 

following algorithm (Nelson, 2004): 

• If both the corn and soy residue amounts exceed the minimum required remaining residue over the 

two-year period, the maximum amount of stover can be harvested: 
28

IF Hres,c > Hres,min AND Hres,soy,t-1 > Hres,min THEN Hst,harv = Hres,c – Hres,min [ 9] 

• If the amount of stover generated is less than the required amount remaining, no stover can be 

harvested: 

IF Hres,c < Hres,min THEN Hst,harv = 0 [ 10] 

• If the average amount of residue generated over a two-year period is less than the required amount 

remaining, no stover can be harvested: 

IF Hres, avg < Hres,min THEN Hst,harv = 0 [ 11] 

• If the average residue generated is greater than the allowable, but the soy residue is less than the 

allowable, some stover can be collected, but at a yield less than the maximum: 

IF Hres,avg > Hres,min AND Hres,soy,t-1 < Hres,min THEN Hst,harv = (Hres,avg – Hres,min) · 2 [ 12] 

Data required for estimating the stover yields include: corn and soybean yields for each county and each 

year (USDA NASS); harvest indices (Gupta, 1978); and required minimum residue values for each 

county, which have been calculated and provided by Richard Nelson (2004; Sheehan et al., 2002). 

Nitrogen Model Development and Input Data 

An empirical approach is employed here that defines individual flows of nitrogen through the system as 

fractions of the known nitrogen inputs to the overall system (field, atmosphere, water). Key species of 

interest include ammonia/ammonium, nitrate (NO3

-

) (small quantities of nitrite present are included in this 

term), nitrous oxide (N2O) and nitrogen oxides (NOx, mostly as NO). All flows are presented in terms of 

their nitrogen mass (kg-N). All input flows, rates and fractions are defined and quantified in Tables A-1 

through A-4. Symbols are shown on Figure 3. 

The approach used here is most similar to that employed by deVries et al. (2003), and includes substantial 

ideas based on the work of Burkart and James (1999), Galloway et al. (2003), IPCC (1996), Meisinger 

and Randall (1991), and Goolsby et al. (1999). The model tracks individual nitrogen species and their 

particular fate rather than lumping species and processes together before fractioning to define the 

distribution of the nitrogen in various compartments. The deVries et al. (2003) work applies fractions 

suitable for the Netherlands and all types of land use. The model developed here uses a range of literature 

sources and field data to better quantify the flows and fractions of flows that are more applicable to corn 

and soybean production in eastern Iowa. 

Anthropogenic reactive nitrogen is applied directly as chemical or animal fertilizer and indirectly through 

atmospheric deposition. We assume that animal manures are infrequently used in Eastern Iowa and that 

anhydrous ammonia is used most widely as a commercial inorganic fertilizer (Hanna et al., 2002). 

Nitrogen available for crops and leaching is also supplied by nitrogen fixation by soybean plants and 

release of nitrate from crop residues and soil organic matter (SOM) during mineralization. 

Except for a small fraction of the ammonia fertilizers applied that are lost by volatilization, most of the 

remainder is converted through the microbial nitrification process into nitrate. Small, but important 

quantities of NO, N2O and NO2 are produced in the nitrification process. The nitrate produced is 

predominantly available for plant uptake. It is also stored in SOM, leached into ground and surface water, 

and anaerobically denitrified to the unreactive form of nitrogen – N2, with N2O and NO generated as byproducts. Although most nitrates are eventually denitrified, they remain in the soil and aqueous systems 

long enough to cause deleterious ecosystem and human health impacts. 

The flows included in this model include: 
29

Key Input flows 

• Fertilizer application 

• Atmospheric deposition (wet and dry) 

• Mineralization of SOM 

• Mineralization of remaining crop residues 

• N2 fixation 

Key transformation and output flows 

• Ammonia volatilization (fast) 

• Leaching to SW (through tiles and base flow expressed as TN – including soluble and particulate 

forms) 

• Leaching to groundwater 

• Nitrification (NH3Æ NO3

-

, including losses as NO and N2O) 

• Denitrification (NO3

- Æ N2, including losses as NO and N2O) 

• Crop harvest (including stover) 

• Nitrogen immobilization within SOM 

• Plant senescencing 

Ammonia Sources 

The total ammonia load (LNH3, kg yr-1) added to this system is the sum of fertilizer use (Lf), 

ammonification of organic nitrogen (Lsoil min + Lcrop min), and atmospheric deposition (Latm,NH3): 

LNH3 = Lf + Latm,NH3 + Lsoil min + Lcrop min [ 13] 

The fertilizer load includes that applied to corn (Lf,c), soy (Lf,soy) and the additional fertilizer required to 

replace the nitrogen lost from the system with the stover harvest the previous year (t-1). 

 A · N ·f

1-f

H ·f

L A · N ·f A · soy,p f,soy f,soy

vol

st,N

(t-1)

st,har

f c,p f,c f,c c,H + ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + [ 14] 

Appropriate historical values for the area planted (Ai,p) are multiplied by the application rates (Nf,i) and 

fraction of acres fertilized (ff,i) to quantify the loads due to fertilization. Subscripts c, st, and soy refer to 

corn, stover and soybeans, respectively. The middle term accounts for fertilizer nitrogen that must be 

added to replace the nitrogen removed with the stover harvest the previous year (Hst,harv

 (t-1)) (see eqn. 7-

12). The fraction of nitrogen in the stover (fst,N) is required for this calculation. As suggested by IPCC, 

FN needs are adjusted to account for the fraction that is rapidly lost via volatilization (fvol). 

Wet deposition can be defined by rainfall (Qrain) and NH3 concentration in rain (CNH3). Aws is the total area 

of the watershed. Dry deposition data are scarcer so this term is typically estimated as a fraction of the 

wet deposition (fDD). 

-5

atm,NH ws DD,NH3 rain NH L A ·(1 f )·(Q ·C )·10 3 3 = + [ 15] 

Concentration data are summarized by the National Atmospheric Deposition Program for wet deposition 

are available at three sites around the eastern Iowa watershed system (NADP, http://nadp.sws.uiuc.edu). 

These three sites do not include dry deposition measurements though. The closest site with dry 

deposition data is in east central Illinois (Bondville). Goolsby et al. (1999) summarize average data for 

this site for 1992 – 1993. They found fDD,NH3= 0.11, with higher values further east. and fDD,NO3= 0.41 

Organic matter in the system, which includes both SOM and crop residues, can undergo ammonification 

reaction (Canter, 1997). As described below, this ammonia is then typically quickly mineralized to nitrate. 
30

The extent of these reactions depends on the quantity of organic matter, how much of it is mineralized in 

a given year, and its nitrogen content. Burkart and James (1999) quantify soil mineralization as: 

Lsoil min = (Ac,p + Asoy,p) · Nsoil,min [16a] 

where Nsoil,min = ρb · Vsoil · fom · fom,N · fmin [16b] 

where Vsoil is the volume of soil in the top 30 cm, ρb is the bulk density (~1480 kg/m3

), fom quantifies the 

fraction of organic matter in the soil, fom,N is the fraction of nitrogen in the SOM (3%), and fmin is the 

fraction of SOM that is mineralized in a given year, typically 1-3%. The fraction mineralized varies with 

cultivation, with lower values for no-till soils (Schepers and Moiser, 1991). Using this approach, Goolsby 

et al. (1999) estimated >50 kg/ha of nitrogen are released annually in Iowa and other corn belt states due 

to soil mineralization. Gentry et al. (2001) measured annual mineralization rates of 72 to 112 kg-N/ha in a 

high organic content soil in Illinois (fom=0.045 – 0.05), with the variation attributed to the previous year’s 

crop (low values for corn, high values for soy). Calibrating eqn. [16b] to these data (fom=0.045) provides 

estimates of fmin,c(t-1) = 0.014 and fmin,s(t-1) = 0.02, where the term (t-1) in the subscript indicates that corn or 

soy was grown in the previous year. 

In the absence of such data, other researchers have used a range of approaches, including an assumption 

that this soil mineralization is negligible for all but soils with very high SOM (e.g., peat, deVries et al., 

2003). 

We assume that all crop residues that are left on the field from the previous year (t-1) are mineralized. 

This includes all of the soy straw and the corn stover that is not harvested. The soy straw actually 

degrades very quickly after harvest, and would really be released in the modeled, not following year 

(Gentry et al., 2001). Corn on the other hand, degrades more slowly, with 10-20% remaining after 2 

years (Wilhelm et al., 2004). 

( ) soy soy-st,N

t-1

soy

t-1

st,N soy,H

t-1

c st,harv

t-1

c

t-1

crop min c,H L = A · H ⋅HI - H ·f + A · H · HI ·f [ 17] 

where the area of concern here is the harvested area (Ai,H), not planted area, and fi,N is the mass fraction of 

nitrogen in the plant material. 

Ammonia Sinks 

Ammonia exists as a gas and can readily volatilize quickly after fertilizer is applied. The extent of this 

loss is quantified as: 

Lvol = Lf · fvol [ 18] 

The fraction that is volatilized (fvol) can range from 0-50% depending on the chemical formulation of the 

inorganic nitrogen fertilizer, the manner in which it is applied, and the soil pH. The injection of 

anhydrous ammonia results in the lowest loss rates (fvol = 0.0 – 0.05) (Meisinger and Randall, 1991). We 

assume that ammonia entering the system as atmospheric deposition or via ammonification does not 

volatilize. 

Ammonia gas is also lost directly from plant leaves at the end of the growing season. Plant senescencing 

can contribute Nsenes,c ~ 50 kg/ha/yr for corn and Nsenes,soy ~ 45 kg/ha/yr for soy (Burkart and James, 1999). 

Goolsby et al. (1999) used Nsenes,c = 60 kg/ha/yr for corn and the same value for soy as Burkart and 

James. The total flow can be estimated as: 

Lsenes = Ac,H · Nsenes,c + Asoy,H · Nsenes,soy [ 19] 

Data are insufficient to estimate this flow on a more appropriate basis of plant yield, climate or other 

factors (Bouwman et al., 1997). 
31

Most of the ammonia applied or deposited on cropland stays within the system but is transformed by 

nitrification, primarily into nitrate. 

Lni = (Lf ·(1 – fvol) + Latm,NH3 + Lsoil min + Lcrop min) · fni [ 20] 

where fni is the fraction of available ammonia that is nitrified. This assumes that volatilization occurs 

quickly so that that fraction that volatilizes is not available for nitrification. The small fraction of 

remaining ammonia, which is incorporated into plant tissue and SOM, is not quantified. 

Nitrate Sources 

Most of the nitrate in the system is transformed from ammonia through the nitrification process (eqn. 

[20]), although atmospheric deposition also contributes to the total amount of nitrate that is available for 

plant growth and leaching. 

LNO3 = Lni · (1 – fni,NO – fni,N2O) + Latm,NO3 [ 21] 

The ammonia loss by nitrification (Lni) is adjusted to quantify the amount of nitrate generated by 

recognizing that some is lost as NO and N2O. Atmospheric deposition (Latm,NO3) provides additional 

sources in both dry and wet forms. These flows are calculated in the same manner as described for 

ammonia (Eqn. [15]). 

Latm,N03 = Aws· (1+fDD,NO3) · (Qrain · CNO3) · 10-5 [ 22] 

The analysis by Goolsby et al. (1999) provides an estimate of fDD,NO3= 0.41 for the closest site (east 

central IL) with both wet and dry deposition data. Concentration data are available for three NADP sites 

surrounding the Eastern Iowa watershed system. 

Nitrate Sinks 

Most of the nitrate is taken into the plant mass and subsequently leaves the system as a harvested crop 

(Lharv) or remains as residue on the soil (Lres), to be recycled the following year as a nitrate source (eqn. 

[17]). Significant amounts are lost, however, through leaching (Lsw, Lgw), immobilization as organic 

nitrogen (Limm), and denitrification (Lde). 

Crop Harvest 

Agricultural data quantifying crop yields and nitrogen content in plants can be used to define the nitrogen 

leaving the system with the plant matter. 

Lharv = Ac,H · (Hc · fc,N + Hst,harv · fst,N) + Asoy,H · Hsoy · fsoy,N [ 23] 

Where H indicates the mass harvested, fi,N is the mass fraction of nitrogen in the material harvested. 

The nitrate bound in crop residues is defined in a similar manner: 

Lres = Ac,H · (Hc · HIc - Hst,harv) · fst,N + Asoy,H · Hsoy · HIsoy · fsoy-st,N = Lcrop min (t=t+1 yr) [ 24] 

This assumes that the minimum required stover and all of the soy straw is left on the field. Note that this 

equation is identical to the crop residue mineralization term. The mineralization is assumed, however, to 

occur in the following year. 

Leaching 

Nitrate species are very soluble and are often found in water leaching to surface or groundwater. The 

extent of leaching depends on both the total flow of water through the system and the amount of nitrate 

available for leaching. Numerous studies assume that the amount of nitrogen leached can be estimated 
32

directly as a fraction of the fertilizer nitrogen (Table 6) or net anthropogenic nitrogen1 applied (Howarth 

et al., 1996). However, this does not capture the substantial variation in the leaching during years of light 

versus heavy rainfall that has been observed at several field sites (Lucey and Goolsby,1993; Puckett et al., 

1999; Weed and Kanwar, 1996; Jaynes et al., 1999; Bakhsh et al. 2002). 

Table 6. Range of Estimates - Percent of FN that Leaches with Water 

Source Average % FN 

that leaches Comments 

GREET (Wang, 1999) 24% Used in LCA for subsequent denitrification and N2O 

emission calculations for transportation fuels 

IPCC (1996) 30% Global average value used for subsequent denitrification 

and N2O emission calculations 

Puckett et al. (1999) 27% Measured - Leached into groundwater in highly permeable 

outwash aquifer (west MN) (assumed no runoff) 

Lucey and Goolsby 

(1993) 

25% 

(1.5 – 64%) 

Measured values at downstream location. Based on 

cumulative load in Raccoon River (central IA) over 11 years 

versus cumulative FN used. Range in %FN leached 

consistent to range of annual rainfall. (Note – the down 

stream collection site for this study would lump in-stream 

denitrification into this fraction, concentrations at field would 

be higher) 

Based on data from 

Weed and Kanwar 

(1996) a

36% 

(8 - 59%) Measured values – 3 yr. field scale study. Nashua IA 

Based on data in 

Bakhsh et al. (2002) 

26% 

(5 – 76%) 

Measured values – 6 yr. field scale study. Nashua IA (NE 

IA). Rainfall rates most significant variable. 

Based on data in 

Jaynes et al. and 

Hatfield et al. (1999)a

42% 

(9 – 85%) 

Measured - Walnut Creek watershed study, central IA, 

average of several drainage-scale systems 

Based on data 

summarized by Bakhsh 

et al. (2004) 

47% 

Overall 6 year cumulative average fraction FN leached 

from single C-S rotation field in Walnut Creek watershed, 

central IA, (1992 – 1997) 

a

 data used for calibration of leaching model presented in Figure 12. 

Figure 12 presents the analysis of leaching data for agricultural research stations in Iowa and nearby 

regions to provide a means of estimating FN leaching in eastern Iowa. These data quantify the fraction of 

fertilizer nitrogen applied to the field that was lost with tile drainage as a function of the annual rainfall 

(mm). FN is used as a surrogate variable to quantify all nitrate leaching sources associated with row crop 

agriculture. In reality, atmospheric deposition, nitrogen fixation and soil mineralization also contribute to 

the total measured nitrate loads in the drains. The inclusion of terms to quantify these nitrogen sources as 

well as FN was attempted. The large uncertainties, especially with soil mineralization, prevented the 

development of meaningful relationships. 

The regression equation for corn-soybean fields includes both field scale (Nashua IA) and drainage scale 

(Walnut Creek) data with a range of tillage (CP- chisel plow, NT – no till) and nitrogen application 

practices (SA – single fall application, LS – late spring). For the field data, the fraction of fertilizer 

nitrogen leached was estimated over a two-acre system that assumed equal acreage in soy and corn. 

 

1

 Net anthropogenic input: NAI = fertilizer + crop N fixation + food/feed imports – food exports 
33

Fertilizer was only applied over 50% of this system, but nitrate leaching was similar for both soy and corn 

farmland. This indicates that there is residual FN remaining after the corn harvest and/or that other 

sources of nitrate contribute substantially to nitrate leaching during the soy rotation. One regression 

equation for C-S was determined for these two different sets of data even though there are statistically 

significant differences in their individual regression equations. Significantly higher leaching rates were 

observed at the larger scale Walnut Creek site. The cause of differences among these data sets can be 

attributed to geographical differences (hydrology, geology) or differences in the scale of measurement 

and, therefore, in the mechanisms that affect the nitrate fate. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

500 700 900 1100 1300 1500

Annual Rainfall (mm)

Fraction FN Leached into tiles

watershed-Walnut Creek

field-Nashua IA

Nashua IA - CP, SA

Nashua IA - NT, LS

Nashua IA - NT, SA

regression, C-S (R^2=0.64)

So. MN - C-C

Nashua IA - C-C

regression, C-C (R^2=0.65)

Figure 12. Fraction of nitrogen leached as a linear function of annual rainfall. 

Data from Walnut Creek (Jaynes et al., 1999; Hatfield et al., 1999), Nashua IA (Weed and 

Kumar, 1996; Bjorneberg et al., 1996; Bakhsh et al., 2002), and southern MN (Randall and 

Iragavarapu, 1995). 

Data for continuous corn operations were only available for field-scale systems. The regression analysis 

suggests that a lower fraction is leached from C-C than C-S (this does not imply that less total mass of 

TN is leached from C-C rotations). It is acknowledged that the primary difference here is the type of data 

available between these two crop rotations. Table 7 also includes a regression equation for a hillier and 

naturally drained field site in western Iowa (Steinheimer et al., 1998) for comparison. 

The right hand column in Table 7 compares the percentage FN that leaches based on the regression 

equations applied at an average rainfall rate for Cedar Rapids in eastern IA (917 mm; 

http://mesonet.agron.iastate.edu/climodat/index.phtml). The variability in these values suggests a wide 

range of estimated fractional losses due to differences in geographic location, crop rotation and/or tillage. 

Analysis of data from Weed and Kanwar (1996) at a single site, however, shows little differences among 

the various crop rotations and tillage methods used compared with differences among the three years 

studied, which included a range of precipitation rates. Thus, much of the differences seen among the data 

and between the regression lines in Figure 12 can be attributed to the scale of the measurement and/or 

geographic location; it is difficult to attribute these differences to just differences in the C-C versus C-S 

rotations. Care must be used to not apply leaching rates and or regression equations such as shown in 

Figure 12 over a broad geographic region. Differences in geology and hydrology could result in much 

different leaching rates. Data pertinent to the regional characteristics could be acquired and analyzed in a 

fashion similar to what was done here to apply this approach to other areas. 
34

Table 7. Regression Equations Defining the Fraction of Fertilizer Nitrogen 

that Leaches into Tiles and Drains 

System Slopea

( ± 95% C.I.) (mm-1)

Intercept 

( ± 95% C.I.) (--) R2 % FN 

leachedb

C-S, field and watershed scale, 

eastern IA, range of tillage 

practices 

0.00103 

(± 0.00029) 

-0.480 

(± 0.255) 0.643 46% 

C-C, chisel plow, E. IA 0.000779 

(± 0.000378) 

-0.483 

(± 0.361) 0.652 23% 

C-C, chisel plow, W. IA 0.000675 

(± 0.000473) 

-0.222 

(± 0.346) 0.536 40% 

a fNO3,SW = slope * rainfall (mm) + intercept b using long-term average annual rainfall (917 mm) at Cedar Rapids, IA 

The simple models presented in Figure 12 capture some of the annual variability in nitrogen leaching 

rates and thus are an improvement over those that use a single fraction of the FN to estimate the amount 

leached. This approach, however, is still subject to a large amount of uncertainty and fails to incorporate 

variability that stems from longer-term accumulation and release of soil nitrogen that occurs with a series 

of dry or wet years. A more mechanistic model would have to be used to incorporate these variables. 

Based on the field data and analysis presented above, it is assumed that the amount of nitrogen leached to 

surface water can be quantified as: 

LSW = Lf · fNO3,SW [ 25] 

where fNO3,SW is defined by a linear function of the annual rainfall. 

There is significantly less data to quantify the flux of nitrate into groundwater systems below the depth of 

the tiles or drains. In the absence of sufficient data to estimate the variation in this flow with rainfall and 

percolation rates, a straight fraction of the net nitrate input can be used to estimate the flow of nitrate to 

the groundwater. The net anthropogenic nitrate inputs here are approximated as the anthropogenic inputs 

minus the crop harvest. 

LGW = (Lf + Latm,NO3 – Lharv) · fNO3,GW [ 26] 

Based on an analysis of nitrogen cycling over the intensively cultivated areas within the United States, 

Howarth et al. (1996) estimate that approximately 3% of net anthropogenic nitrogen leaches to 

groundwater. In a more geographically applicable analysis, David and Gentry (2000) assumed that no 

nitrate enters and remains in groundwater in Illinois. The extensive use of tile drains in Illinois helps to 

substantiate this assumption, although nitrate contamination of groundwater systems has been observed. 

Thus, although this sink might be small from an overall mass balance perspective, it could be significant 

from a resource degradation standpoint. In fact, application of eqn. [25] to eastern Iowa data showed that 

in some years there were negative amounts of net available nitrogen as defined in this way. Thus, 

groundwater leaching is considered negligible here from a mass balance perspective. Higher fractions 

might be observed for western Iowa and Nebraska where the hydrology is influenced by irrigation and 

sandier soils than in E. IA and IL. 

Denitrification 

In an anaerobic environment and in the presence of sufficient organic matter, nitrates are denitrified to N2, 

with NO and N2O generated as by-products (Canter 1997). Denitrification can occur in the soil, 

groundwater and surface waters. Rates are highest in poorly drained soils and slowly moving and/or 

shallow surface water bodies (e.g., shallow lakes or wetlands), although some denitrification also occurs 

in shallow streams as well. The total extent of denitrification can be defined as the sum of losses in each 

of the environmental compartments: 
35

Lde = Lde,s + Lde,gw + Lde,dr + Lde,ri + Lde,gu [ 27] 

where subscripts s, gw, dr, ri and gu refer to the soil solution, deeper groundwater, drainage system, 

rivers, and Gulf of Mexico, respectively. Only the first three terms are important when considering the 

nitrate losses within the geographic boundary of Eastern Iowa. The first four terms are important for 

determining the contributions of fertilizer use in Iowa on the hypoxia condition in the Gulf of Mexico, 

and all the terms are important for quantifying NO and N2O emissions associated with the agricultural 

activity. 

Unfortunately, most researchers estimate only the overall fraction of applied reactive nitrogen, typically 

fertilizer + atmospheric deposition, that is denitrified (Howarth et al., 1996; Meisinger and Randall, 

1991): 

Lde = fde · (Latm,NO3 + Lf) [ 28] 

where the fraction denitrified (fde) is generally defined between 6 and 25%. This approach reduces the 

value of knowing the amount on nitrate in the system at intermediate steps as it is transported to a coastal 

system. 

Most denitrification reactions occur in the soil since this represents the largest pool of nitrogen. The 

fraction of nitrate in the soils that denitrifies can range from 2-20% (Meisinger and Randall, 1991, 

Burkart and James, 1999) of the available nitrate. Higher fractions are expected in more poorly drained 

and/or organic rich soils. For the average organic content of soils in Iowa (fom=0.035), Goolsby et al. 

(1999) used fde,s=0.20 applied to available fertilizer nitrogen (adjusted for volatile losses). 

DeVries et al. (2003) used a mass balance approach to estimate the nitrogen remaining in soil which is 

available for denitrification: 

Lde,s = fde,s · (Latm,NO3 + Lf · (1-fvol) + Lfix – (Limm + Lharv + Lres)) [ 29a] 

where the fde,s varies with soil type and moisture. For Loess soils, deVries et al. (2003) define fde,s ~ 0.40 – 

0.80 for moist soils and 0.60-0.95 for wet soils. These fractions are higher than others have used, but they 

are applied to a smaller mass of nitrate remaining in the soil. Thus, use of these emission factors should 

be carefully coordinated with the correct nitrogen mass. 

Similar equations can be developed for other compartments: 

Lde,gw = fde,gw · Lgw [29b] 

Lde,dr = fde,dr · Lsw [29c] 

Lde,ri = fde,ri · (Lsw – Lde,dr) [29d] 

Lde,Gu = fde,Gu · (Lsw – Lde,dr – Lde,ri) [29e] 

deVries et al. (2003) assumed fde,gw and fde,dr were equal to the value of fde,s for wet soils and fde,ri ~ 0.6-1.0 

for a set of rivers in the Netherlands. Galloway et al. (2003) cite a value for fde,gw = 0.4 for So. MN and 

general fractions fde,ri ~ 0.3 – 0.7, fde,est = 0.1 – 0.8 (estuaries), and fdeGu ~ >0.8. An additional term for 

estuaries was defined by Galloway since this is an important point in the surface water flow where the 

residence times decrease and the denitrification potential increases. The data used here to generate 

regression equations to estimate Lsw were generally collected at the point where the tile or drain 

discharged to a creek or river. Thus, any denitrification that occurred in the drains is already accounted 

for by this data, so fde,dr is set to zero since it is already lumped into the calculation of Lsw. 

Like leaching, denitrification factors are known to depend on environmental conditions and should be 

adjusted to account for annual variability in climatic conditions. Smith et al. (1997) define first order 

denitrification rates for US river stretches as a function of the annual flow rate. They define the lowest 

decay rates for rivers with annual flow rates > 283 m3

/s (>10,000 ft3/s) and the highest decay rates for 
36

rivers with annual flow rates <28.3 m3/s (>1,000 ft3/s). Although applying these decay constants would 

require sophisticated water quality models such as the SPARROW model employed by Smith et al 

(1997), we can use these ranges to choose reasonable initial values of fde,ri for the different watersheds 

depending on their actual annual flow rates. This assumes that the overall nature of the watershed systems 

are similar, including the number, depth and length of reaches contributing to the river and the overall 

residence time. The major watersheds in Eastern Iowa have annual discharges ranging from <28.3 m3

/s to 

>283 m3

/s (Table 8). The Mississippi River at Clinton Iowa has an annual flow rate generally greater than 

1000 m3

/s. It is typically assumed that there is no denitrification between entry of the nitrate into the 

Mississippi River and its discharge into the coastal waters of Louisiana (McIsaac et al. 2002). Others have 

completed experimental investigations to confirm that nitrogen losses in the Mississippi River are low 

(Battaglin et al. 2001; Richardson et al., 2004). 

Table 8. Denitrification Factors for Surface Water 

Years between 1988 and 2000 when watershed 

Watershed annual flow rate within specified range 

<28.3 m3

/s 28.3 – 283 m3/s > 283 m3/s 

Wapsipinicon ’88 – ‘89 ’90 – ‘00 -- 

Iowa 

(including Cedar) -- ’88 – ’89 ’94 –‘97 

’90 – ‘93 

’98 – ‘00 

Skunk ’88 – ‘89 ’90 – ‘00 -- 

Initially assumed rangea

 for fde,ri 0.5 – 0.7 0.4 – 0.6 0.3 – 0.5 

a

 based on overall range provided by Galloway et al. (2003) and the specific river flow rates 

Immobilization 

Nitrates can be slowly converted to organic nitrogen compounds and immobilized in the SOM. These 

compounds can be mineralized at a later date releasing the nitrogen for leaching or plant uptake. Over a 

period of several years, the soil nitrogen content has been relatively stable, suggesting that the 

immobilization and denitrification terms cancel each other out. On a year-to-year basis, however, there 

can be substantial changes in the soil nitrogen content due to reduced plant uptake during dry or years or 

increased leaching during wet years. Goolsby et al. (1999) suggest that the amount of nitrogen 

immobilized be estimated as a fraction of the anthropogenic nitrogen inputs: 

Limm = Lf · ff,imm + Latm,NO3 · fdep,imm [ 30] 

The fractions ff,imm and fdep,imm were both set to 0.4 by Goolsby et al. (1999), with an estimated 25-50% 

uncertainty. There is very limited data to improve these estimates based on physical measurements or 

mechanistic modeling. 

Other Nitrogen Flows 

Nitrogen Fixation 

Soybeans and other legumes can directly convert nitrogen from the atmosphere into nitrogen required for 

plant matter. Meisinger and Randall (1991) suggest that the nitrogen fixation rates can be estimated as a 

fraction of the nitrogen content in the plant material: 

Lfix = Asoy,p · Hsoy · (fsoy,N + HIsoy · fsoy-res,N) · ffix,N [31] 

where HI is the harvest index for soy and ffix,N is the fraction of the plant nitrogen content that is 

attributable to nitrogen fixation. Plants tend to use readily available soil nitrogen first and then fix 

additional nitrogen as needed (Gentry et al., 1998). ffix,N can range from 0.5 to 0.8 and decreases with 
37

increasing availability of nitrogen from commercial fertilizer or soil reserves (Meisinger and Randall, 

1991). 

Alternatively, a simpler approach is based solely on the acreage of soy and other legumes (Meisinger and 

Randall, 1991, Goolsby et al., 1999): 

Lfix = Asoy,p · Nfix,N [32] 

where Nfix,N is the rate at which nitrogen is fixed on soy cropland. Jordan and Weller (1996) provide an 

estimate for this parameter Nfix,N = 78 kg/ha with a range of 15-310 kg/ha. Applying typical value for soy 

yield in Iowa (2.74 Mg/ha) and the fractions quantifying nitrogen content of soy and soy straw (Table A3), eqn. [31] predicts a nitrogen fixation rate of 98 kg/ha soy cropland. This is within the range of values 

for Nfix,N summarized by Goolsby et al. (1999). 

Erosion 

The mass of soil eroded (Msoil,er) varies with soil type, agricultural practices, land use and rainfall 

intensity. Nelson (2002, 2004) used the generally accepted RUSLE soil erosion model to estimate soil 

erosion for each soil type in Iowa as a function of crop rotation and tillage practices for long-term average 

rainfall data. An area-weighted average of soil types and their associated erosion estimates were used here 

to determine the average mass of soil lost from each county in E. Iowa for conventional till practices. 

Not all eroded soil gets to waterways and degrades water quality. The sediment delivery ratio (fer,sed) 

defines that fraction of the total eroded soil that moves into waterways. Sheehan et al. (1998) estimated 

average sediment delivery ratios for major soybean producing states based on the work of Ribaudo 

(1989). They estimate fer,sed=0.512 for Iowa. 

Nitrogen, mostly in the form or organic-N in the SOM is transported to surface water bodies with eroded 

soil. This load is estimated as: 

Ler = (Ac,p + Asoy,p) · Msoil,er · fom · fom,N · fer,sed [33] 

Nitrogen associated with eroded sediments is already integrated into the TN leaching model. The data 

used to calibrate that model included all soluble and particulate forms of nitrogen. Thus, although the 

nitrogen flux with eroded sediments can be calculated separately as described by eqn. [33], it is not 

included as a separate term in the implementation of this model. 

N2O Emissions 

Nitrous oxide, a major contributor to greenhouse gas emissions, is released from agricultural systems 

during nitrification and denitrification reactions. Most studies lump together all sources of nitrous oxide 

to predict that ~1.25 - 1.5% of the total fertilizer nitrogen used is emitted as N2O (e.g., Matthews, 1994, in 

Cary et al., 2002; Brentup et al., 2000; Wang, 1999), with additional emissions from soil mineralization 

process. Alternatively, it can be estimated more directly as a fraction of the total amount of nitrogen that 

undergoes nitrification (note – Lni includes soil mineralization in this model) and denitrification reactions 

(deVries et al., 2003). This approach already accounts for variable denitrification rates due to soil type 

and moisture content. 

LN2O = fde,N2O · Lde + fni,N2O · Lni [ 34] 

For sand/loess/clay soils, these factors are estimated by deVries et al. (2003) as fni,N2O = 0.005 – 0.02 and 

fde,N2O =0.01 – 0.06. Higher values within this range of fde,N2O would be appropriate for peat soils. 

NOx Emissions 

Nitrogen oxide (NO) is also released during nitrification and denitrification reactions. An approach 

similar to that described above for N2O can be used to estimate NO emissions. 
38

LNO = fde,NO · Lde + fni,NO · Lni [ 35] 

deVries et al. (2003) suggests factors fni,NO = 0.01 – 0.03 and fde,NO =0.01 – 0.02, independent of soil type 

or moisture content. 

NOx, predominantly NO2, is also emitted during farming activities when diesel fuel and gasoline are 

combusted by tractors. These emissions are described in the section on energy inputs and use. 

Nitrogen Emissions 

Although nitrogen gas is not an environmental concern, quantifying this flow is required to provide 

closure of a mass balance. It is assumed here that all nitrogen that is denitrified, but not emitted as NO or 

N2O is converted into nitrogen gas. 

LN2 = (1-fde,N2O – fde,NO) · Lde [ 36] 

Calibration of Nitrate Leaching Model 

The eastern Iowa watershed system was selected as a geographic boundary due to the extensive data for 

TN and NO3-N fluxes from E. IA rivers to the Mississippi. The availability of these data provides an 

excellent means of calibrating our nitrogen flow model against real data. Leaching from fields and instream denitrification processes were both considered in this calibration process. 

Data sets quantifying the total nitrogen (TN) load are available from the USGS National Ambient Water 

Quality Assessment (NAWQA) (Becher et al., 2001) and the NOAA/USGS Hypoxia in the Gulf of 

Mexico study (http://co.water.usgs.gov/hypoxia/html/nutrients.html). Specific data that quantify the total 

annual loads of nitrate and/or TN from the three primary watersheds in eastern Iowa that discharge to the 

Mississippi River were used. Neither of these data sets provides both TN and NO3-N fluxes for all three 

watersheds of interest, however. TN loads are presented in Table 9. These include both those directly 

available through the datasets as well as those estimated based on the relative contributions of the 

different watersheds to the total. Values for the Cedar River – which is included within the overall Iowa 

River watershed – are included because they provided the best correlation to the fraction of TN load 

associated with the Wapsipinicon River. For the three years that a complete data set for all four 

watersheds was available (Becher et al., 2001), it was determined that the Wapsipinicon TN loads were 

approximately one-third (0.35±0.06) of the Cedar River values. This fraction was used to estimate the 

total nitrogen load to the Mississippi River during the period 1988-1995. 

Table 9. TN Loads (mt/yr) to the Mississippi River from E. Iowa Watersheds 

Year Wapsipincon Iowa Skunk Cedar 

1988 2,200 20,179 5,087 6,290

1989 1,227 7,616 2,189 3,508

1990 9,514 69,219 22,483 27,196

1991 19,618 96,879 22,729 56,079

1992 13,841 88,422 23,860 39,564

1993 35,784 199,911 51,287 102,290

1994 9,336 46,969 8,026 26,688

1995 8,284 54,321 15,877 23,680

1996 9,990 57,600 30,000 31,900

1997 21,700 75,100 23,500 52,200

1998 31,100 154,000 49,400 97,000

1996 – 1998 data from Becher et al. (2001) 

1988 – 1995 data for Skunk, Iowa and Cedar Rivers from NOAA/USGS Hypoxia study 

1988 – 1995, Wapsipinicon R. – Estimated based on 0.35 * TN(Cedar)
39

As an average over the years, nitrates contribute 78, 91, and 90% of the nitrogen in the TN flux for the 

Iowa, Skunk and Cedar Rivers, respectively, although the percentages range from 50-100%. With this 

range of uncertainty, it was difficult to predict NO3-N fluxes for the Wapsipinicon for the years no NO3-

N data were available (1988-1995). Thus, we could not directly calibrate potential leaching model (Table 

7) to available NO3-N data, and chose to focus on TN instead. 

Becher et al. (2000) provide an estimate of nitrogen sources in Eastern Iowa. They show that a 

combination of FN and animal manures contribute over 90% of the TN discharged to the Iowa and Skunk 

Rivers. Thus, we assumed here that the inclusion of animal manures as a TN source would enable us to 

perform the most accurate calibration. Becher et al.’s (2000) estimate for the animal manure generated in 

this region is used to identify potential sources of TN to the watershed system (Lam = 59,600, 21,800 and 

12,800 mt TN/yr applied to Iowa, Skunk and Wapsipinicon watersheds, respectively in 1996). The total 

that the animal manures contribute (92,400 mt TN) adds approximately 34-40% additional nitrogen to the 

watershed in addition to the inorganic FN application. FN used in this region was calculated from 

published county totals for corn and soy acreage, and statewide average amounts of FN used and fraction 

of farms applying FN to their crops (see additional details in Table A-2). Two key assumptions were 

made in this analysis: 

1. The same fraction of nitrogen leaches from animal manures as inorganic commercial fertilizer in 

a given year. This assumption is also use in the IPCC nitrogen flow model. 

2. Denitrification of NO3 in rivers also varies some with rainfall. The NO3 fraction of the TN was 

required to estimate denitrification. This fraction was defined as an average for watershed 

discharge data available 1988 – 1998, with a weighted average between the three rivers (fTN,NO3 = 

0.82). In contrast, Goolsby et al. (2000) report that nitrate accounts for 61% of the total nitrogen 

discharged from the Mississippi River to the Gulf of Mexico. 

The model defining the load of TN to the Mississippi River (LMS) can be represented as: 

 LMS = {(Lf + Lam) · fTN,SW} – {(Lf + Lam) · fTN,SW} · fTN,NO3 · fde,ri [ 37] 

Where the term in the {} is the TN leached within the watershed and the second term defines the NO3-N 

fraction that is lost due to denitrification within the watershed. As described above, fTN,SW was estimated 

as: 

fTN,SW = intercept + slope * rain(mm) [ 38] 

where the intercept and slope were determined by regression analysis of field and drainage basin scale 

data from the eastern Iowa region. Initially rainfall data from Cedar Rapids, which is centrally located 

within the region, was assumed representative of the region. Due to the very high level of sensitivity for 

this parameter, however, the calibration process was changed to use average rainfall values for each 

county to estimate the fraction of FN leached on a county-by-county basis. 

Values for the calibrated model parameters used are included in Table 10. The model estimates for LMS

were very sensitive to the slope, intercept and rain data used in the regression equation. Very small 

modifications in the slope and intercept helped substantially to improve the quality of the fit. Application 

of the high denitrification rates (fde,ri) suggested by deVries et al (2002) for the Netherlands resulted in 

very low estimates of LMS. These rates were lowered for high flowrate years to account for the high TN 

fluxes discharging to the MS river. The model was not very sensitive to fTN,NO3, so this parameter was not 

adjusted. The model was calibrated by minimizing the error in the difference between actual and 

modeled cumulative discharge to the Mississippi River over the period 1988-1998. The calibrated model 

estimates are within 1% of the actual cumulative TN load. 
40

Table 10. Values Used to Calibrated the Leaching/Denitrification Model 

Value 

Parameter 

Initial Calibrated

Comments 

Intercept -0.457 -0.48 Highly sensitive to small changes 

Slope 0.000881 0.00103 Highly sensitive to small changes 

fTN,NO3 0.82 0.82 Not sensitive 

fde,ri 0.3 0.2 

For years with high flow (Table 2). 

Sensitive, required lower value to estimate high TN 

loads 

fde,ri 0.4 0.4 For years with low flow (Table 2). 

Figure 13 presents the TN load to the Mississippi River estimated with the calibrated model (Table 10) as 

compared with data from water quality measurements. With a few exceptions, the calibrated model 

represents both the trends among years and the absolute value of the TN load. 

For individual years, the greatest percent errors were for 1988 (-57%) and 1989 (+266%) (Figure 14). 

Estimates for six of the years were within 25% of the loads estimated from water quality data. The highest 

absolute error and the largest deviation in the overall trends in the water quality data are associated with 

the estimate for 1990. The model estimate for 1990 is consistent with observations from Walnut Creek 

(Jaynes et al., 1999) and the overall middle Mississippi River loads that suggest that the nitrate load that 

year was high due to a wet year following two dry years with poor yields and little or no leaching. The 

data presented in Figure 15 shows that 1990 was indeed an anomalous year. The total nitrogen load to the 

Mississippi River was much lower than the general linear trend between loads and rainfall that fits other 

years quite well. Thus, the linear model between nitrogen discharge and rainfall does not adequately 

represent all years. 

0

50000

100000

150000

200000

250000

300000

1988 1990 1992 1994 1996 1998 2000

TN load to MS River (mt)

estimated-regression

based on WQ data

24%(FN+manure)

Figure 13. Actual TN flux to the Mississippi River and calibrated model including leaching of FN 

and animal manure and in-stream denitrification. The method used by GREET, which 

employs a constant fraction (24%) of FN to estimate leaching (modified here to also 

include animal manure), is included for comparison. 

The approach used by models such as GREET (Wang 1999) (leaching = 0.24 · Lf) provides an excellent 

estimate for “average” years (1994-1997) (Figure 14). However, they do not capture the wide variability 
41

in leaching rates as correlated to rainfall. Errors with constant value for the fraction leached are as high as 

700% overestimate (1989), although the overall cumulative error is only 13%. 

-200

-100

0

100

200

300

400

500

600

700

800

1986 1988 1990 1992 1994 1996 1998 2000

% Error

regression analysis

GREET

Figure 14. Percent error in rainfall-based regression analysis and GREET models for predicting 

TN discharged to the Mississippi River. 

y = 286.2x - 149421

R2

 = 0.88

0

50000

100000

150000

200000

250000

300000

350000

0 200 400 600 800 1000 1200 1400 1600 1800

Annual Rainfall in Cedar Rapids (mm)

TN load to MS River from E. Iowa (mt)

Figure 15. The actual TN load determined from water quality data (Table 9) in 1990 is significantly 

lower than the general linear increasing trend with rainfall. 

Phosphorus Flow Model Development 

The quantification of key phosphorus flows thought the C-S agricultural system is much simpler than for 

the nitrogen flows. It is assumed that inorganic fertilizer phosphorus (FP) is the only inflow. Outflows 

include P lost with the crop harvest and P leached to surface waters. Due to the strong sorption of P to 

soil minerals, it is assumed that no P leaches into groundwater. 

1990 
42

Fertilizer application 

The phosphorus fertilizer load is analogous to that described for nitrogen (eqn. [14]). This equation 

includes that applied to corn, soy and the additional fertilizer required to replace the phosphorus lost from 

the system with the previous year’s stover harvest. 

( ) P

f,soy

P

st,P soy,p f,soy

(t-1)

c,H st,har

P

f,c

P

c,p f,c L = A · N ·f + A · H ·f + A · N ·f Pf [ 39] 

Appropriate historical values for the area planted (Ai,p) are multiplied by the application rates (Nf,i) and 

fraction of acres fertilized (ff,i) to quantify the loads due to fertilization. Subscripts c, st, and soy refer to 

corn, stover and soybeans, respectively and the superscript P defines these as phosphorus-specific values. 

Removal with Crop 

Agricultural data quantifying crop yields and phosphorus content in plants can be used to define the 

phosphorus leaving the system with the plant matter. 

c,H c c,P st,harv st,P soy,H soy soy,P

P

harv L = A ·(H ·f + (H )·f ) + A · H ·f [ 40] 

Where H indicates the mass harvested, fi,P is the mass fraction of phosphorus in the material harvested. 

Leaching to Surface Waters 

An approach similar to that described above for nitrogen leaching was used to estimate the amount of 

phosphorus leached as a function of rainfall and to calibrate this model to actual data for phosphorus 

discharged from the three E. Iowa watersheds to the Mississippi River. Flows are quantified as total 

phosphorus (TP), which includes P in both soluble and particulate forms. 

The smaller-scale study by Klatt et al. (2003) provides some data used as a basis for the leaching model 

developed here. They measured P inflows to Clear Lake in NE Iowa over a two-year period. Clear Lake 

is within the geographic boundary considered here and is a good example of a water body adversely 

impacted by eutrophication. The TP loads were significantly higher during the year with a higher total 

rainfall (Figure 16). This suggests that model of the same form developed for nitrogen can be used to 

determine the TP load from this system to surface water bodies. 

TP,SW

P

f

P LSW = L ·f [ 41] 

Where, the fraction leached is defined as a linear function of rainfall: 

fTP,SW = interceptP

 + slopeP * rain(mm) [ 42] 

Although these data are too few to generate a reliable leaching model, they did provide a starting point to 

compare the fraction of TP inputs to the E. Iowa watershed system that are leached based on measured 

water quality data provided for the Wapsipinicon, Iowa ands Skunk Rivers. TP loads from these rivers to 

the Mississippi River are presented in Table 11. Values for the Wapsipinicon (1988-1995) were 

estimated as a fraction of the total loads from other rivers. 

Additional TP inputs from animal manure were available for the Iowa and Skunk Rivers in 1997 

(Goolsby et al., 1999). Extrapolating these to the entire area considered here provided an average 

estimate of 32,088 mt P from manure generated in this area every year. 
43

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0 200 400 600 800 1000 1200 1400

Annual Rainfall (mm)

Fraction FP Leached

Clear Lake IA

calibrated model

Figure 16. The fraction of FP that leaches to surface waters can be expressed as a linear function 

of rainfall. Limited data available from Klatt et al. (2003) 

Table 11. Measured TP Loads (mt) from the E. Iowa Watersheds to the Mississippi River 

Year Wapsipinicon 

River 

Cedar 

River 

Iowa River Skunk 

River 

1988 81 236 913 146 

1989 52 155 557 118 

1990 354 844 3108 1711 

1991 416 1778 3777 1112 

1992 326 1027 2893 1295 

1993 1158 3520 10341 4673 

1994 172 683 1711 365 

1995 248 653 2177 1140 

1996 540 1560 2360 3960 

1997 440 1860 3120 990 

1998 476 2470 3930 4140 

1996 – 1998 data from Becher et al. (2001) 

1988 – 1995 data for Skunk, Iowa and Cedar Rivers from NOAA/USGS Hypoxia study 

1988 – 1995, Wapsipinicon R. – Estimated based on 0.062 * TP(Cedar+Skunk+Iowa)

The calibrated model relationship between the fraction of TP leached and rainfall is 

fTP,SW = -0.097 + 0.000182* rain(mm) [ 43] 

This compares quite well with the limited data available from Klatt et al. (2003) (Figure 16). The 

predicted lower fractions leached relative to Klatt et al.’s (2003) measurements could indicate losses of 

TP in rivers between the discharges from the field and ultimate discharge to the Mississippi River. 

Settling of particulate forms of P or consumption of bioavailable forms by aquatic species would both be 

expected. The overall fraction of the TP leached ~ 1-14% includes the range typically assumed (<1-5%; 

Smil 2000), although fractions predicted by eqn. [43] also includes much higher values to account for 

increased leaching rates during wet years. 
44

Estimates of the TP load to the Mississippi River from the three eastern Iowa watersheds based on eqn 

[43] are shown in comparison with measured data on Figure 17. Trends in the estimated TP load track 

well with observations. The final slope and intercept presented in eqn. [43] were adjusted to minimize the 

errors between observed and estimated TP loads. The errors between the estimated and observed loads 

range from –80% (1988) to +91% (1989), although it is apparent that the absolute errors in these years 

was small. The cumulative 11-year error was +2.1%. The predicted TP load to the Mississippi River is 

extremely sensitive to the slope in this equation. 

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1988 1990 1992 1994 1996 1998 2000

TP Load to MSR from E. IA (mt)

Water quality data

Calibrated TP Leaching Model

Figure 17. Actual TP flux to the Mississippi River and calibrated TP leaching model, which 

includes soluble and particulate P. 

Potassium Model 

The quantification of key potassium fertilizer flows accounts only for the potassium added as fertilizer 

and that removed with the grain and stover. It is assumed that the potassium does not partition to air or 

water environmental compartments in any appreciable quantity. 

The fertilizer load equation is identical to that for nitrogen and phosphorus. It includes that applied to corn 

and soy, and the additional fertilizer required to replace the phosphorus lost from the system with the 

previous year’s stover harvest. 

( ) K

f,soy

K

st,K soy,p f,soy

(t-1)

c,H st,har

K

f,c

K

c,p f,c L = A · N ·f + A · H ·f + A · N ·f Kf [44] 

Agricultural data quantifying crop yields and potassium content in plants can be used to define the 

potassium leaving the system with the plant matter. 

c,H c c,K st,harv st,K soy,H soy soy,K

K

harv L = A ·(H ·f + (H )·f ) + A · H ·f [45] 

The terms in both these equations have been defined previously, with super and subscript K used to 

identify potassium specific values. 

Energy Use and Emissions on the Farm 

Energy used in farming activities and their associated emissions were quantified. This information was 

required to define the total nitrogen emissions to the atmosphere and the acidification impact. The energy 
45

flows are also used to define greenhouse gas emissions from the field so the relative benefits of GHG 

reductions can be compared to the detrimental effects of eutrophication and acidification. 

The energy flows in the crop production stage of the life cycle (e.g., farming) only include the diesel, 

gasoline and motor oil used in the planting, cultivation and harvesting of corn, soybeans and stover. Fuel 

used for corn and soybean farm activities is included in Table 12. These data were developed specifically 

for current conditions in Iowa (Shapouri et al., 2002, Sheehan et al., 1998). Shapouri et al. (2002) 

included additional energy sources for corn production beyond those shown in Table 12. The inclusion of 

a very wide range of electricity use though between 1991 (12 kWh/ha) and 1996 (380 kWh/ha), however, 

increases the uncertainty in these data. The authors indicate that substantially more electric power was 

used in 1996 for drying purposes than in 1991. It is questionable what the best representative value for 

electricity use for corn would be and, thus, it is not included here. Electricity usage has not been included 

in prior LCAs that include soybean production (Sheehan et al., 1998). 

Table 12. Energy Used in Farming Activities in Iowa 

Fuel use (l/ha) Crop Diesel Fuel Motor oil Gasoline

Reference, comments 

Corn 51.42 2.14a 28.05 

Shapouri et al.(2002). Energy balance for 

corn ethanol. Fuel use taken as average of 

IA data for 1991 and 1996. 

Soybeans 49.46 2.06 25.09 Sheehan et al. (1998), LCA for biodiesel 

a

 motor oil not included in Shapouri et al. (2002). The same ratio of diesel to motor oil was assumed as used by 

Sheehan et al. (1998) 

Lower fuel inputs are required for no till practices compared with conventional till. Data in Table 12 for 

corn and soy are based on current practices, which are predominantly conventional till. Detailed 

information provided by West and Marland (2002) were used to define a ratio of fuel used for 

conventional versus no till practices. They provide detailed 1990 national average data for diesel fuel 

consumed in each step - plowing, cultivating planting, fertilizer/pesticide application, and harvesting. 

The ratio of the total diesel fuel used for planting, fertilizer/pesticide application, and harvesting (NT) to 

the fuel used for these activities plus plow, disk and cultivation activities (CT) is 0.619. This ratio was 

used to adjust diesel fuel and motor oil used in the scenarios considered here with no till operations. 

The diesel fuel and motor oil required to harvest stover is a function of the stover yield. It is more energy 

efficient to harvest higher stover yields. Data from Sheehan et al. (2002) was used to generate functions 

for fuel and motor oil used to collect the stover. Equations of the form: 

Fuel (l/mt) = A · stover yieldB

 [ 46] 

adequately fit the data. Coefficients for this equation are included in Table 13. Gasoline is assumed to not 

be necessary for stover harvesting operations. 

Standard emission factors were used to estimate atmospheric emissions associated with the fuel use on the 

farm (Table 14). Only those emissions that are associated with acidification and GHG impact categories 

were accounted for in this LCA. 
46

Table 13. Coefficients for stover harvest fuel use equation 

(data from Sheehan et al., 2002)

Regression Coefficients Applicable conditions A B R2

Diesel Fuel: 

for stover collection < 2 mt/ha 13.40 -0.651 0.9887 

stover collection ≥ 2 mt/ha 15.95 -0.414 0.9904 

Motor Oil: 

for stover collection < 2 mt/ha 0.066 -0.666 0.9893 

stover collection ≥ 2 mt/ha 0.080 -0.433 0.9882 

Table 14. Emission factors for Fuel Combustion in Farming Tractors 

(as summarized by Sheehan et al.,1998)

Emission factors (g/MJ)a

Fuel 

CO NOx SO2 CH4 N2O CO2

Dieselb 0.32 0.89 0.12 0.0042 0.0019 75.5 

Gasoline 1.14 0.63 0.0046 0.032 0.0019 67.7 

a

 required conversion factors include density (ρg=0.72 g/mL; ρd=0.84 g/mL) and heat content (LHV) of the fuel 

(LHVg=44.7 MJ/kg; LHVd=43.5 MJ/kg) b assumed high sulfur fuel used in farm tractors, which are considered off-road vehicles 

Other Flows Included in the LCI 

All energy inputs and emissions associated with fertilizer manufacture and the generation of electricity 

and the fuels themselves are also included. The materials and energy inputs and emissions from these 

upstream lifecycle stages were determined with data included in Ecobilian’s DEAM database. Specific 

DEAM modules used are summarized in Table 15. Not all flows included in the DEAM accounting were 

considered in this LCA. Those that were considered here are listed in Table 16. 

Carbon (CO2, CH4), nitrogen (N2O, NOx) and sulfur (SOx) emissions to the atmosphere associated with 

energy use and upstream processes were quantified to provide a perspective of their contributions to 

global warming and acidification relative to emissions and impacts from fertilizer use and soil processes. 

Carbon dioxide and methane emissions associated with crop uptake and soil mineralization were not 

quantified here. They have been studied thoroughly and documented by Sheehan et al. (2002). 

Data from the DEAM database was integrated into the MS Excel workbook model developed here. 

Several of these modules include electricity as an input. These were modified to integrate electricity 

related inflows and outflows as necessary. The Ecobilian TEAM software was not employed. 
47

Table 15. DEAM Modules used to Calculate Upstream Flows and Emissions 

 DEAM Module 

401 Electricity (US, MAPP): Production.2 

v1 Diesel Oil: Production 

232I Gasoline (unleaded): Production 

232 Liquefied Petroleum Gas (US, LPG): Production.2 

403S Liquefied Petroleum Gas (US, Propane, Industrial Boiler): Combustion.2 

111I Lube Oil: Production 

v1 Natural Gas: Production 

v1 403S Natural Gas: Combustion (Industrial Boiler) 

241I Potash (KCl): Production 

241I Superphosphate (Triple): Production 

241 Ammonia (NH3): Production 

Table 16. Pertinent LCA Flows from DEAM Modules 

Flowa Units 

Inflows 

(r) Coal (in ground) kg 

(r) Natural Gas (in ground) kg 

(r) Oil (in ground) kg 

Water Used (total) l 

Outflows: 

(a) Ammonia (NH3-N) kg 

(a) Carbon Dioxide (CO2, biomass) kg 

(a) Carbon Dioxide (CO2, fossil) kg 

(a) Carbon Monoxide (CO) kg 

(a) Hydrogen Chloride (HCl) kg 

(a) Hydrogen Fluoride (HF) kg 

(a) Methane (CH4) kg 

(a) Nitrogen Oxides (NOx as NO2) kg 

(a) Nitrous Oxide (N2O) kg 

(a) Sulfur Oxides (SOx as SO2) kg 

(w) Acids (H+) kg 

(w) Ammonia (NH4

+

, NH3, as N) kg 

(w) Nitrate (NO3-N) kg 

(w) Nitrites (NO2-N) kg 

(w) Nitrogenous Matter (unspecified, as N) kg 

(w) Phosphates (as P) kg 

(w) Phosphorus (as P) kg 

(w) Suspended Matter (unspecified) kg 

(w) Water: Chemically Polluted l a

 letters in parentheses identify these flows as resources (r), air emissions (a) and water emissions (w). 
48

Model Implementation 

Base Case 

Equations ([13] - [43]) describing the nutrient flows and transformations, farm energy flows, and 

materials and energy flows associated with the manufacture of fertilizers and energy products were 

integrated into a set of Excel spreadsheet models. Input data included DEAM flows for upstream 

manufacturing, all fractions (Table A-3) describing the distribution of nutrients within the system, and 

site-specific data for the Eastern Iowa agricultural system (Table A-2). These data were collected for the 

period 1988 – 2000, primarily from the USDA National Agricultural Statistics Service (NASS). 

The model was initially applied to the present system with no stover harvest. In this base case scenario, it 

was assumed that 100% of the farms use a C-S rotation with the actual land area dedicated to these crops 

and crop yields available at the county level. In reality, ~10% of the farms use C-C or C-C-S rotations 

(Brenner et al., 2001). Data were not sufficient to adequately integrate these variations into the model. 

Annual fertilizer application rates were only available as a state average and were assumed to apply 

across the entire system. 

Values of some of the fractions identifying the distribution of nitrogen were adjusted to calibrate the 

model to the observation that long-term nitrogen content in row crop soils has been at a steady state. 

Thus, although there might be accumulation or depletion of nitrogen within the system in a given year, 

the cumulative inputs should balance cumulative outputs. This criterion was met by adjusting parameters 

(fimm, ffix, Nsenes,i) to minimize the difference between these cumulative flows. These parameters had both a 

high degree of uncertainty in their initial values as well as significant impact on the total mass of nitrogen 

flowing into and out of the system. 

Stover Collection Scenarios 

The model was also applied to two systems (C-S and C-C) with stover harvest. These systems and the 

major assumptions employed are summarized in Table 17. Justification for these assumptions is included 

below. 

To maximize stover harvest, it is assumed that all farmers switch to a no till practice. Tilling soil greatly 

increases soil erosion. Thus, practices that reduce tillage can be used to compensate for the potential 

erosion increases associated with stover removal. The change to a no till practice could, however, affect 

other variables as well. Grain yield and leaching rates could also be correlated to this switch in tilling 

practices. It has been observed that leaving residue on the field slows the warming of soil in the spring 

and can delay seed germination (Bjorneberg et al., 1996). However, in our case, much of the residue is 

removed, so the soil temperature and seed germination should not be adversely affected as in situations 

with no till operations and no residue removal. It is assumed that the yield is not impacted by this change. 

It is assumed here that there is no difference in the fraction of FN leached between C-C, NT and C-S, CT 

systems. Insufficient data are available to really identify a true correlation between tilling practices or 

crop rotations and nitrogen leaching rates. In the carefully controlled study of Weed and Kanwar (1996), 

no consistent and statistically significant differences in leaching rates among tillage systems or crop were 

observed. Bakhsh et al. (2002) show differences in tillage, but these results are conflicting depending on 

the fertilization practices. In comparison with chisel plow, higher leaching rates were observed for NT 

practices when the fertilizer was applied in the fall, but lower leaching rates were observed when FN 

applied in the late spring at a rate defined by soil nitrogen concentrations. This is one of many instances 

where there is uncertainty in the model as it is used to forecast leaching for systems we do not have data 

available for calibration. 

There are consistent and statistically significant data available that show that FP leaching rates are higher 

for NT practices. McIsaac et al. (1995) and Gaynor and Findlay (1995) both observed higher phosphate 

leaching rates from fields that had not been tilled. Factors of 1.6 to 5.4 times higher leaching rates have 
49

been observed. Differences among these values depend mostly on the type of soil. Given this weight of 

evidence, a factor is included here to increase the estimated rate of TP leaching for NT scenarios. 

Although the fraction of fertilizer that leaches is assumed to not change among the scenarios, the total 

amount of FN applied is different in each system. The harvest of stover is assumed to be early enough in 

the fall that none the nitrogen is mineralized and returned to the soil. Thus, additional FN was added to 

replace this loss. This amount was adjusted to account for the additional loss of some FN immediately to 

volatilization as was assumed by Sheehan et al. 2004. 

The switch from a C-S to a C-C rotation results in the loss of benefits associated with nitrogen fixation by 

the soy plants, requiring additional FN applied in a C-C system in comparison with the corn years of a CS rotation. Gentry et al. (2001) identify two processes by which the presence of soy in the preceding year 

benefits corn: 1) there is greater mineralization of soil nitrogen in the year following soy; and, 2) there is a 

greater quantity of residual inorganic nitrogen in the soil associated from residue of soy nodules created 

during nitrogen fixation. These differences were accounted for in the model by adjusting the rate of soil 

mineralization (eqn. [16]) and adding additional FN. The general guideline suggested by Kurtz et al. 

(1984) was used here – FN requirements for corn following soy are 45 kg/ha less than for corn following 

corn. 

Soil mineralization rates are also impacted by the tilling practices. Organic carbon in soil is mineralized 

much more quickly when it is actively tilled, which exposes more of the soil to oxygen required for 

mineralization. Although this has not been documented sufficiently for nitrogen release during 

mineralization, it has been studied extensively for CO2 release from soil during the mineralization 

process. Brenner et al. (2001) estimated the carbon accumulated due to agricultural activities in Iowa as a 

function of several different variables. Comparing their estimate of the accumulation of carbon in the soil 

in 1998 between conventional till practices (1.57 x 106

 mt C) and no till practices (2.42 x 106 mt C), there 

is approximately 46% less mineralization associated with no till practices in comparison with 

conventional till. This percentage was used to reduce the fraction of SOM mineralized each year (fmin, 

eqn. [16]). 

Allocating Flows Among Crops 

In an LCA, it is important to not only quantify flows through the environmental compartments, but also to 

allocate specific fractions of the total flows among the various products and by-products generated. This 

is particularly difficult in an agricultural system when there are synergistic affects associated with crop 

rotation. Van Zeijts et al. (1999) address this issue for allocating fertilizer among various crops in the 

Netherlands. Their overall approach is used here as well. They assume: 

• All nitrogen can be allocated to the crop for which it is applied in the year that it is applied 

• All P and K fertilizers should be allocated among the crops over the entire crop rotation in 

proportion to the fraction of nutrient that is harvested with each of the grains. 

They justify the differences in these approaches based on the intent of the fertilizer application and the 

environmental fate. Nitrogen, for example, is typically applied separately for each crop in proportion to 

that crops needs. Excess fertilization is avoided due to the recognition that this excess will likely be lost 

to leaching. Phosphate and potassium, however, behave differently. Excess nutrients applied as fertilizer 

for one crop conserved in the soil over a longer period. They are often applied only to one crop with the 

intent that they be consumed by the plants over the entire rotation. The consistency in the percentage of 

fields onto which FN, FP, and FK are applied (Figure 2) illustrates that indeed, many farmers chose not to 

apply FP and FK to fields in a soy rotation year, even though the soy beans need these for growth. These 

farmers rely on FP and FK remaining in the soil from the previous year. 
50

Table 17. Summary of Systems Considered and Major Assumptions 

System Major Assumptions 

Base Case – C-S 

rotation, conventional 

tillage and no stover 

collection 

All cropland is in a C-S rotation, conventional till 

Nitrogen flows adequately described by model equation ([13] – [36]) and values of input 

variables (Table A-1 – A-4) 

Phosphorus flows adequately described by model equation ([39] – [43]) and values of 

input variables (Table A-1 – A-4) 

Erosion for all years described by RUSLE equation that uses average precipitation across 

years but integrates variability due to crop cover and tillage practices. 

Nitrogen flows associated with cornfields and those associated with soy fields in an 

individual year are allocated to these crops, respectively. 

Total system phosphorus and potassium flows are allocated between corn and soy based 

on their respective proportion of the total uptake and removal with the grain. 

Maximum possible 

stover collection – C-S 

rotation, no till 

All cropland is in a C-S rotation, no till 

Nelson (2004) analysis of average minimum required stover can be applied to estimate 

the allowable stover collection 

Additional FN, FP, and FK are required to replace that removed with stover the previous 

year. The FN addition must be adjusted for immediate volatilization 

The fraction of FN that leaches to SW and GW does not change due to the stover harvest 

or tillage practices 

Phosphorus leaching is higher with no till practice than CT 

Nitrogen, phosphorus and erosion flows associated with the stover harvest can be 

defined as the difference between flows in this scenario and the base case. 

Less diesel fuel and motor oil required for NT operations 

Maximum possible 

stover collection – C-C 

rotation, no till 

All cropland planted in corn plus soybeans is now planted in continuous corn, no till 

Nelson (2004) analysis of average minimum required stover can be applied to estimate 

the allowable stover collection 

FP and FK application rates per year are less than for C in a C-C rotation than in a C-S 

rotation since some of the fertilizer applied to corn was allocated to soy. FP and FK rates 

determined by allocation procedure in the C-S rotation are used for C-C system. 

Additional FN, FP and FK are required, replacing that removed with stover the previous 

year. The FN addition must be adjusted for account for that lost immediately by 

volatilization 

Additional FN (45 kg/ha) required due to the loss of the benefits of rotation with soy (Kurtz 

et al., 1984) 

The fraction of FN that leaches to SW and GW does not change due to the stover harvest 

or tillage practices 

Phosphorus leaching is higher with no till practice than CT 

Overall mineralization rates are less in a C-C vs. C-S system (Eqn. [16]) (Gentry et al., 

2001, West and Marland, 2002) 

Nitrogen, phosphorus and erosion flows associated with the stover harvest can be 

defined as the difference between flows in this scenario and the base case. 

Less diesel fuel and motor oil required for NT operations 
51

The suitability of the Van Zeijts et al. (1999) approach for nitrogen allocation between corn and soybeans 

could be argued. Nitrogen fixation by the soy increases the nitrogen content in the soil and the soil 

mineralization rate during the corn years, thus providing extra nitrogen that would not otherwise be 

available (Gentry et al., 2001). Likewise, excess nitrogen applied to the soil during a corn year and 

immobilized with in soil organic matter could become available to soy plants in the following year (Baker 

and Timmons, 1994). The “carry over” of nitrogen fertilizer is indeed observed in leaching rates that are 

generally no different on a kg/ha basis in years that corn is grown versus years that soy is grown (Weed 

and Kanwar, 1996; Bakhsh et al., 2002). Thus, the approach used by van Zeijts et al. (1999) for nitrogen 

allocation is simplistic. 

An alternative approach for allocating the amount of FN leached from corn and soy fields could be based 

on field data that quantifies actual leaching rates from cornfields versus soy fields. Data for leaching rates 

from the Nashua IA agricultural testing facility were used to determine a suitable allocation of nitrates 

leaching from corn fields and soybean fields (Weed and Kanwar, 1996; Bjornberg et al., 1996; Bakhsh et 

al., 2002). Data from each of these sources indicate that even though little to no FN is applied to soy 

fields, nitrate leaching rates (kg/ha) in a given year are approximately equal from each type of field. 

Statistical analysis showed that there are no trends in the fraction of nitrate leaching from corn as a 

function of year or tillage practices (α=0.05). Thus, all data were lumped together (n=42) to show that 

fN-leach,c=51.1% (standard deviation = ±7.18) of the total amount of nitrate that leached on a per hectare 

basis could be allocated to the corn. Incorporating differences in the land planted in corn and soy, the 

total mass of nitrate nitrogen leached to the surface water (Lsw,cN

; mt) can be determined as: 

N

sw

soy p

N leach c

N leach c

c p

N c p

sw c L

A

f

f A

A

L ⋅

⋅ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − −

=

−

−

,

,

,

,

,

,

1

 [ 47] 

where LswN

 is the total nitrate leached to surface water (mt, eqn, [25]) and Ai,p are the areas (ha) planted in 

i=soy and corn. With the larger area planted in corn each year, this results in approximately 60% of the 

total nitrate leached from the C-S system allocated to the corn. With the method proposed by van Zeijts 

et al. (1999), over 98% of the total nitrate leached is allocated to corn. This difference in allocation 

method also impacts the allocation of NO and N2O between corn and soybeans due to the subsequent 

denitrification of nitrates in surface water. 

The excess corn stover available for harvest is treated here as a waste material. Thus, only those flows 

that are directly attributable to the stover harvest are allocated to the stover. This includes the extra 

nutrients required to replace the amount removed with the stover and tractor activity associated with the 

stover harvest. 
52

4. Results of Quantitative Lifecycle Inventory 

Nutrient Flow Model Results 

Base Case: C-S System with No Stover Harvest 

The estimated nitrogen inputs and outputs from the C-S system with no stover harvest are shown in 

Figure 18. These plots clearly show the variability in the magnitude of these flows among the years. For 

example, 1988 was a very dry year with low grain yields and very small amounts of nitrogen leached. The 

year 1993 was recorded as a record high rainfall. Yields were low this year due to too much soil moisture 

and the amount of nitrate leached to surface waters was very high. This year was also the only year that 

sufficient excess nitrogen was available for denitrification and significant groundwater leaching. The fact 

that denitrification occurred is consistent with the known higher rates of denitrification in wet years, when 

anaerobic conditions in the soil are more likely to occur. 

The nitrogen inputs and outputs balance within 8% for most of the years studied. The extreme dry year, 

1988 is one exception. The difference between inflows and outflows this year (~200,000 mt; ~28% of the 

inputs), suggests that nitrogen was stored in the soil after this crop year. Overall, the 12-year cumulative 

inflows were within 1% of the outflows, illustrating that this model, with the set of input variables 

included in Table A-3 meets the constraint that we have a long-term steady state for the nitrogen flows. 

The model used here for nitrogen flows attempts to be more mechanistic than many other models that use 

“emission factors” to define nitrogen flows as a direct fraction of the applied fertilizer nitrogen. These 

factors, however, provide a basis for comparing the results obtained here to other studies. Table 18 

presents emission factors calculated from the nitrogen flow results presented in Figure 18. Values used in 

other studies are also included for comparison. The N2O results are quite similar, but all other flows 

calculated by this model tend to be higher than the factors used by other researchers. We would expect 

this, especially for our (a) NH3 LCI flow, which includes direct FN volatilization as well as plant 

senescencing, whereas the IPCC value only attributes (a) NH3 flows to direct volatilization. The (a) NO 

emission factor could be reduced to be closer to the value used by GREET by reducing the value of fni,NO. 

This fraction is set at 0.01 in the calibrated model, which is at the low end of the range (0.01 – 0.03) 

suggested by deVries et al. (2003). Insufficient data are available to assess the accuracy of the value used 

in GREET and that calculated here. Thus, fni,NO= 0.01 is used regardless of the differences with other 

studies. 

Of most interest here, the fraction of the FN that leaches from the fields and eventually to the Mississippi 

River appears to be high. On average, our model would estimate the load of TN to surface waters through 

tile and base flow to be 28-60% higher than estimates using the IPCC or GREET emission factors for 

leaching. However, when the amount of nitrogen leached is normalized to the area of cropland instead of 

the amount of FN applied, the estimated amount of TN leached from our model is quite comparable to 

other studies from the Midwestern United States (Table 19). Vanni et al. (2001) and Bakhsh et al. (2002) 

directly relate the variation in the values they measure to variability in annual rainfall, as was done in our 

study, and show similar ranges in the fraction of FN that is leached between dry and wet years. Given the 

consistency of the yields predicted with the nitrogen model used here and those measured by others, and 

the accuracy of the calibrated TN leaching model (Figure 13), the estimated discharges of TN from C-S 

fields and ultimately to the Mississippi River and Gulf of Mexico are considered realistic. 
53

0

300000

600000

900000

1200000

1500000

1988

198919901991

1992

199319941995

1996

1997

1998

19992000

Total N inflows (mt)

N2 Fixation

Soil and crop mineralization

Atm. Deposition - NH3

Atm. Deposition, NO3

Fertilizer

0

300000

600000

900000

1200000

1500000

19881989199019911992

19931994

1995

1996

1997

1998

1999

2000

Total N outflows (mt)

NO (ni+de)

N2O (ni+de)

N2 (denitr)

Immobilization

Plant senescencing (NH3 to atm)

Volatilization (NH3 to atm)

Leaching-GW

Leaching-SW

Crop residue - harvest

Crop residues - field

Crop-corn

Crop-Soy

Figure 18. Nitrogen flows (mt/yr) into and out of the E. Iowa C-S system. 

The distribution of nitrogen outflows between corn and soybeans is illustrated in Figure 19. This plot 

shows the fraction of the total C-S system flows that should be allocated to corn. Two methods were 

considered for this allocation: method 1 assumes all FN applied to a cornfield should be allocated to corn; 

and, method 2 assumes that approximately the same amount of nitrogen is leached from cornfields as soy 

fields (eqn. [47]). There is clearly a very large discrepancy between these two methods for several of the 

emissions. Thus, there is significant uncertainty in the best method to use to allocate flows for specific 

crops in a system with a symbiotic relationship between two crops. The most probable accurate 

allocation lies somewhere between these two extremes. Additional field research that provides a more 

mechanistic understanding of the symbiosis between crops in the C-S system is required to more 

accurately allocate nitrogen flows between these crops. 
54

Table 18. Comparison of Emission Factors Determined from This Study 

and Those Used by Others 

Emission Factor - Fraction of FN 

LCI flow This Study 

median range typical value Reference 

(a) NH3 0.346 0.295 - 0.373 0.100 IPCC - fraction of FN volatilized 

(a) NO 0.019 0.010 - 0.030 0.008 GREET (Wang, 1999) 

(a) N2O 0.013 0.005 - 0.026 0.015 

(0.01 - 0.02) GREET (Wang, 1999) 

0.24 GREET (Wang, 1999) (w) TN - SW 0.385 0.061 - 0.850 0.30 IPCC (1996) 

(w) NO3-N - GW 0.000 0.000 - 0.010 

0.250 Howarth et al., 1996 (w) TN to MSR/Gu 0.308 0.037 - 0.680 0.160 Cary et al., 2002 

Table 19. Comparison of Nitrogen Yields Determined from This Study 

and Those Measured by Others 

Range of Values 

(kg-N/ha 

cropland/yr) 

Comments, references 

5.8 - 64 This study (median: 32 kg/ha) 

38 - 64 NO3-N in tile drain water, IL 30-ha field (David et al., 1997) 

11 - 107 NO3-N in tile waters, 4 different tillage systems in IA (Bjorneberg 

et al., 1996) 

4 - 46 NO3-N in tile waters, field study in IA (Bakhsh et al., 2002) 

2 - 60 NO3-N in tile waters, watershed study, IA, 1992-2000 (Tomer et 

al., 2003) 

10 - 80 TN from OH watershed (Vanni et al., 2001) 

10 -70 NO3-N from OH watershed (Vanni et al., 2001) 
55

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

(a) NH3 (a) NO (a) N2O (w) NO3 - SW (w) NO3-GW (w) NO3 to

MSR/Gu

Fraction of total allocatted to corn

Method 1

Method 2

Figure 19. Fractions of nitrogen flows for C-S system that are allocated to corn. Fractions 

represent averages over 13-year study period with error bars indicating one standard 

deviation. 

Phosphorus flows into and out of the C-S system in E. IA are shown in Figure 20. The phosphorus flow 

model is much simpler than the nitrogen model allowing the flows and allocations among the crops to be 

shown in Figure 20. In this case, the allocation between crops is based on the fraction of the total 

phosphorus removed with the harvested grain. Thus, although almost all of the FP is applied to 

cornfields, the distribution of FP use is more equally distributed between the crops than the nitrogen 

flows. 

Two points should be made about this figure. First, due to the low solubility of phosphates, the amount of 

phosphate that is leached is very small compared with the phosphorus removed with grain. Second, there 

is currently a very troubling disparity between the total phosphorus inflows and outflows. Sharpley et al. 

(2003) and most other researchers studying phosphorus in agricultural systems indicate that FP inflows 

are greater than P outflows, leading to an increasing amount of P accumulated in soils. The data shown in 

Figure 20 suggest the opposite. The cumulative 13-year outflows in this case are almost 50% higher than 

the inflows. Further efforts to verify the P content in grain and FP application rates are required. 

C-S and C-C systems with stover harvest 

Harvestable Stover 

The model was adapted to include stover harvest at the maximum possible rate to maintain acceptable 

levels of soil erosion. In reality, it is unlikely that the methods used to collect stover will be sufficiently 

efficient to harvest stover at this rate. Practical levels of stover harvest are expected to be below the rates 

discussed here. 

Minimum required stover residue rates for the C-C system were taken from Sheehan et al. (2002). These 

residue requirements allowed stover harvest in all counties, all years, except 3 counties (Iowa, Keokuk 

and Washington) in 1988, due to the very low corn yield. The total amounts of stover harvested by year in 

the E. Iowa system are presented in Figure 21. The average harvestable stover yield for the C-C system 

was 5.66 mt/ha corn, with a range of 2.26 – 7.27 (Figure 22). This amount is less than that estimated by 

Sheehan et al. (2002) (6.18 mt/ha corn). They used average crop yields for 1995 – 1997, which are higher 

than the longer-term average, thus leading to a harvestable stover collection rate greater than determined 
56

by the analysis here, which extends over a longer period of time and includes some extreme weather 

years. 

0

20000

40000

60000

80000

100000

120000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

P outputs from E. Iowa System (mt)

P leached

P in corn

P in soy

0

20000

40000

60000

80000

100000

120000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

P inputs to E. Iowa System (mt)

corn

soy

(allocated amounts based on fraction of output with grain)

Figure 20. Phosphorus flows (mt/yr) into and out of the E. Iowa C-S system. 

For C-S, the Nelson (2004) model to estimate maximum allowable removal rates was applied to each 

year, using the two-year average residue between the corn (t=t) and soy (t=t-1). Due to this averaging, 

often between years with low and high crop yields, there was sufficient reside left to allow stover harvest 

in most counties, all years. The stover harvest yields for the E. Iowa C-S system are included in Figure 

22. The average yield for this system was 5.20 mt stover/(ha of corn harvested), with a range among the 

years of 2.07 – 6.74 mt/ha of corn. Stover yields for the C-C system are, on average, 10% higher than for 

the C-S system. 

The distribution of number of counties with harvestable stover is shown in Figure 23. Stover could not be 

harvested in Jefferson Co. during 4 of the 13 years included in this study. No stover could be harvested 

from Iowa or Powesheik Counties 3 of the years, and Henry 2 of the years. Additional counties did not 

have sufficient residue to allow stover harvest in the extreme dry year of 1988. Of the counties with 

limited stover harvest, only Jasper and Jones are major contributors to the overall corn harvest and, 

therefore, stover harvest in this E. Iowa system (Figure 24). Limited stover harvest in other counties does 
57

not affect the overall yields substantially, although the proximity of these counties to each other in the 

S.E. part of the state could affect the optimal location of stover collection facilities. 

0

5000

10000

15000

20000

25000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Total Residue Harvest (1000 mt)

C-C, NT

C-S, NT

Figure 21. Total mass of stover harvested from C-C and C-S systems 

0

1

2

3

4

5

6

7

8

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Harvestable Residue Yield (mt/ha corn harvested)

C-C, NT

C-S, NT

Figure 22. Stover harvest yields for C-C and C-S systems 
58

0

5

10

15

20

25

30

35

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

# counties in E. Iowa system

full stover harvest

some harvest

no harvest

Figure 23. Distribution of the number of counties with harvestable stover. 

0

100

200

300

400

500

600

700

800

900

Clinton

Hamilton

Hardin

Franklin

Cedar

Benton

Buchanan

Jasper

Tama

Story

Cerro Gordo

Grundy

Jones

Black Hawk

Marshall

Linn

Butler

Scott

Washington

Mahaska

Iowa

Floyd

Winnebago

Mitchell

Chickasaw

Bremer

Poweshiek

Johnson

Keokuk

Worth

Muscatine

Henry

Louisa

Des Moines

Jefferson

Total corn stover generated ( 1000 mt), 1996

gray - five or more years with less than maximum stover harvest 

black - 2-4 years with no stover harvest 

Figure 24. Distribution of corn stover production in the E. Iowa system. Black and gray striped 

bars indicate those counties that cannot reliably harvest stover at the maximum yield 

due to erosion constraints. 

Nitrogen Flows 

The nitrogen flow results for a C-S and C-C rotation with stover harvest are included in Figures 25 and 

26. Overall, these results look similar to the system without stover harvest (Figure 18). However, there 

are some important differences. 

Over the 13 years studied, there is a net loss of nitrogen in both of these the systems due to the stover 

harvest; it appears that the outputs are 17% higher for C-S and 25% higher for C-C than the inputs. Extra 

fertilizer was added to replace the nitrogen content in the stover that is no longer mineralized in the field 

and recycled. This amount of extra FN was adjusted to account for that lost by volatilization, as was done 

by Sheehan et al., (2002), but not lost by other sources. The amount leached into surface water is also 

directly correlated to the rate of FN applied. This extra nitrogen sink can account for some of the overall 

loss in this system. This suggests that we should modify equation [14] to increase the amount of extra FN 

added to replace that removed with the stover harvest to account for TN losses from the soil system due to 

leaching. 
59

0

300000

600000

900000

1200000

1500000

1988

19891990199119921993199419951996

1997

199819992000

Total N inflows (mt)

N2 Fixation

Soil and crop mineralization

Atm. Deposition - NH3

Atm. Deposition, NO3

Fertilizer

0

300000

600000

900000

1200000

1500000

198819891990

199119921993

19941995

199619971998

1999

2000

Total N outflows (mt)

NO (ni+de)

N2O (ni+de)

N2 (denitr)

Immobilization

Plant senescencing (NH3 to atm)

Volatilization (NH3 to atm)

Leaching-GW

Leaching-SW

Crop residue - harvest

Crop residues - field

Crop-corn

Crop-Soy

Figure 25. Overall nitrogen flows through the C-S system with stover harvest 

A more significant factor – and an artifact of the way the “system” was defined here – is the reduced 

amount of soil mineralized in the no till scenarios. Essentially, some of the nitrogen is “lost” to the soil 

through immobilization, which is considered outside of the system defined here. In the base case 

scenario, most of the nitrogen immobilized is balanced over the years with nitrogen released through 

mineralization. The reduction in soil mineralization reduces nitrate inputs available to the plants, although 

it is still a component of agricultural soil. 
60

0

300000

600000

900000

1200000

1500000

1988198919901991

1992

19931994

1995199619971998

1999

2000

Total N inflows (mt)

N2 Fixation

Soil and crop mineralization

Atm. Deposition - NH3

Atm. Deposition, NO3

Fertilizer

0

300000

600000

900000

1200000

1500000

198819891990

199119921993

19941995

199619971998

1999

2000

Total N outflows (mt)

NO (ni+de)

N2O (ni+de)

N2 (denitr)

Immobilization

Plant senescencing (NH3 to atm)

Volatilization (NH3 to atm)

Leaching-GW

Leaching-SW

Crop residue - harvest

Crop residues - field

Crop-corn

Crop-Soy

Figure 26. Overall nitrogen flows through the C-C system with stover harvest 

Due to the need for an increase in the FN application when stover is harvested, there is a corresponding 

increase in the mass of nitrates that leach to surface water and, eventually the Gulf of Mexico (Figure 27). 

The very substantial increase for the C-C system is in part because the land area on which corn is grown 

is much higher under this scenario, thus, nearly twice as much area is treated with high FN rates 

associated with corn rather than soy. On average, this incremental addition above the base case system 

(C-S, no stover harvest) is +13% N leached to SW for the C-S-stover system (range – 3.6 – 32%) and 

+150% for the C-C-stover system (range - 116-215%). The incremental increase varies among the years 

depending on rainfall rate and the amount of stover harvested the previous year. In general, the increased 

nitrate leaching associated with the stover harvest is strongly correlated to years with high rainfall. 
61

0

100000

200000

300000

400000

500000

600000

1988 1990 1992 1994 1996 1998 2000

FN leached to SW (mt)

C-S

C-S, w/ stover

C-C, w/ stover

Figure 27. Nitrogen leached and discharged to E. Iowa surface waters in the three scenarios. 

Normalizing these data to show the incremental additional amount of nitrogen leached per mass of stover 

harvested indicates that switching to a C-C system still has a more significant adverse impact in terms of 

nitrate pollution compared with a C-S system with stover harvest (Figure 28). The amount of nitrogen 

leached in the C-C system is highly dependent on the assumed requirement of extra FN due to the switch 

from a C-S rotation to a C-C rotation. Gentry et al. (2001) suggests that 45 kg/ha extra FN is required for 

corn if it is not preceded by soy the previous year. The soy provides a benefit both in terms of the soil 

mineralization rate and presence of soy rhizomes that release nitrogen during the corn rotation. Even 

without this extra FN (solid triangles in Figure 28), the amount of nitrogen leached to the surface water is 

substantially higher than for the C-S system with stover harvest, especially in wet years when a greater 

fraction of the FN is leached. 

0.00

0.01

0.02

0.03

0.04

1988 1990 1992 1994 1996 1998 2000

Incremental Added FN leached to SW (mt/mt stover)

C-S, w/ stover

C-C, w/ stover

CC, no extra N for soy credit

Figure 28. Incremental additional nitrogen leached to the surface water – above base case 

scenario (C-S) - due to the collection of stover in C-C and C-S systems 
62

The amount of nitrogen leached to ground water (Figure 29) is very low compared to that discharged to 

surface waters. In fact, due to the constraints of the model used here – leaching to ground water occurs 

only when there is excess nitrogen in the system after crop uptake – results in a prediction that nitrate 

leaches to the groundwater in the base case scenario (C-S, no stover) only in those years with very low 

crop yields (1988, 1993). 

In the C-S scenario with stover harvest, leaching to groundwater occurred only in the same two low yield 

years, but at a higher rate (54-69%) than the base case scenario (Figures 29 and 30). In the C-C system, 

the large increases in FN use resulted in sufficient nitrate available for leaching to GW in all years (Figure 

29). As described above for leaching to surface water, the magnitude of the increase for the C-C system 

over the base case scenario is highly dependent of the amount of extra FN assumed necessary due to the 

lack of beneficial effects of soy (Figure 30; solid triangles – no extra FN, open triangles, 45kg/ha extra N 

required). 

Unlike the leaching model for surface water that was calibrated with water quality data, no such 

calibration was possible with the estimates of leaching to groundwater. A factor of 3% of the available 

nitrogen was assumed (fertilizer + atmospheric – crop uptake) (eqn. [26]). This factor was based on the 

work of Howarth et al. (1996) who reviewed nitrogen balances on a global scale. The low value used here 

is reasonable considering the influence of tile drains and low permeability of the soil in eastern Iowa. In 

contrast, Puckett et al. (1999) assumed that all leached nitrogen infiltrated to the ground water at a more 

highly permeable site in Minnesota and none was discharged to the surface water (at least not directly). 

Overall, there is considerable uncertainty in this factor for this particular region and variability expected 

as a function of geological and hydrological conditions. 

0

5000

10000

15000

20000

1988 1990 1992 1994 1996 1998 2000

FN leached to GW (mt)

C-S

C-S, w/ stover

C-C, w/ stover

Figure 29. Nitrogen leached to ground water in each of the scenarios 

Other nitrogen flows also change when the system is expanded to include stover harvest (Table 20). The 

percentage increase is small for most flows in the C-S with stover collection scenario; they are much 

more substantial for the C–C system. These values are not normalized to account for differences in the 

amount of stover collected between these two scenarios (C-C has nearly 2x the total stover harvest of CS). However, it is clear that in scenarios with stover collection, emissions from the C-C scenario are still 

substantially higher than the C-S scenario. Results for the C-C scenario are highly dependent on the 

assumed extra FN required to account for the loss of the soy credit, with the worst-case value used here. 
63

0.000

0.001

0.002

0.003

1988 1990 1992 1994 1996 1998 2000

Incremental Added FN leached to GW (mt/mt stover)

C-S, w/ stover

C-C, w/ stover

CC, no extra N for soy credit

Figure 30. Incremental additional nitrogen leached to the ground water due to the collection of 

stover in C-C and C-S systems 

The benefits in NO and N2O emissions for the C-S with stover collection scenario are primarily the result 

of the switch to a no till practice. It is assumed that soil mineralization is roughly 50% more when the soil 

is actively broken up and exposed to oxygen during conventional till practices. With NT, the reduction in 

the transformation of soil organic nitrogen to ammonia and subsequently to nitrate, results in a concurrent 

reduction in NO and N2O released as by products. The reduction in crop mineralization due to stover 

harvest also contributes some to the NO and N2O emission benefits. These benefits of NT are also 

included in the C-C scenario with stover collection. In this case, however, the nitrification and 

denitrification associated with the much higher FN rates causes the net production of NO and N2O to be 

higher than the base case. 

Table 20. Incremental Percent Change in Nitrogen Emissions Due to the 

Harvest of Corn Stover in Comparison to the Base Casea

Scenario 2: C-S w/ stover Scenario 3: C-C w/ stoverb

Nitrogen Flow average min max average min max 

(a) NH3 1.1 0.3 2.6 28 23 36 

(a) NO -23 -41 -1.6 22 -36 90 

(a) N2O -14 -34 8.4 60 -13 156 

(w) NO3 - SW 13 3.6 32 152 117 217 

(w) NO3-GW 46 0.0 51 92 0 676 

(w) NO3 to MSR/Gu 13 3.6 32 154 119 217 a only considering flows associated with soil, plants and fertilizer. Emissions associated with farm energy 

use not included here. b assuming 45 kg/ha extra FN required for loss of soy credit 

Phosphorus Flows 

The phosphorus flows into and out of the two systems with stover collection are shown in Figure 31. 

Additional detail showing just TP leaching rates and the incremental change in these rates compared to 

the base case scenario are included in Figure 32. As discussed above, outflows are currently greater than 

inflows. The incremental amount of phosphorus that is removed with the stover harvest is very small. It 
64

does not substantially impact the need for additional FP. The most important change in these scenarios 

compared with the base case is related more to the switch to a no till practice from conventional till. 

There is consistent and statistically significant data available that show that FP leaching rates are higher 

for NT practices. Based on the observations of McIsaac et al. (1995) and Gaynor and Findlay (1995), a 

factor of 1.6 was used here to estimate the higher leaching rates for these no till scenarios in comparison 

with the base case. A range of values from ~0 – 5 could be considered. This is critically important. It is 

the only significant cause for the observed higher TP leaching rates between the base case and the C-S 

scenario with stover collection. For the C-C scenario with stover collection, the greater FP usage for corn 

versus soy also contributes to the incremental increase in TP loads. Overall, there is less incremental 

increase in TP loads per harvestable stover mass in the C-C scenario versus the C-S scenario. This is the 

opposite of the trends observed in the TN loads between these two scenarios. The sensitivity of this result 

to the factor used to estimate increased TP loads in the no till scenario needs to be investigated. 
65

0

20000400006000080000

100000120000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

P outputs from E. Iowa System (mt)

P leachedP in stoverP in cornP in soy

0

20000400006000080000

100000120000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

P Inputs to E. IA System (mt)

stovercornsoy

a1)

a2)

0

20000400006000080000

100000120000

1988 1989 1990 1991 1992 1993 1994 1995 1996 

P outputs from E. Iowa System (mt)

P leachedP in stoverP in cornP in soy

0

20000400006000080000

100000120000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1

P inputs to E. IA System (mt)

b1)

b2)

Figure 31. Phosphorus flows into (1) and out of (2) the C-S scenario with stover collection (a) and the C-C scenari

collection (b). 
66

0

2000

4000

6000

8000

10000

12000

14000

1988 1990 1992 1994 1996 1998 2000

TP leached (mt)

C-S, CT

C-S, NT, w/stover

C-C, NT, w/ stover

0.0E+00

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

1988 1990 1992 1994 1996 1998 2000

Incremental TP leached (mt/mt stover)

C-C, NT, w/stover

C-S, NT, w/stover

Figure 32. Phosphorus leached to the surface water for the three scenarios (top) and incremental 

additional TP leached due to the collection of stover in C-C and C-S systems (bottom) 

Erosion 

Erosion is calculated here as the total mass of sediments discharged to local surface water bodies. Unlike 

the nutrient models, the erosion estimates currently are based only on average rainfall data. 

Figure 33 illustrates the estimated total erosion losses from C-S or C-C fields in eastern Iowa. 

Approximately 51% of this reaches surface water bodies and degrades water quality. Variability in 

estimates of erosion for the base case scenario are attributed only to variability in crop yields, not rainfall 

rates. 

In contrast to the increase in nutrient loads associated with a switch to scenarios with stover collection, 

the mass of eroded soils is substantially reduced with this change. The difference is almost entirely due to 

the change to no till practices. The base case scenario assumes that all fields are tilled in a conventional 
67

manner. In reality, many farmers in Iowa practice some conservation tillage, so this base case is not truly 

representative of current conditions. 

The near constant estimated erosion losses with the no till scenarios are based on the limitation of this 

model to include only average rainfall data. Based on an area-weighted average of the tolerable soil 

losses, an average of 10.8 mt soil erosion/ha is acceptable. These soil-specific tolerable soil losses were 

used to determine acceptable stover harvest rates each year based on an assumed average rainfall rate. 

The constant soil erosion shown in Figure 33 shows only the tolerable amount of erosion. It does not 

account for rainfall variability among years. Improvement in this model is necessary to integrate rainfall 

variability. The results will, however, still show substantial reductions in erosion with a switch to the 

stover collection scenarios with no till agricultural practices. 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Mass Soil Eroded (1000 mt)

C-S, CT

C-S, NT, w/ stover

C-C, NT, w/ stover

Figure 33. Soil erosion estimates for the three scenarios incorporating variability in crop yields 

but not rainfall. 

Quantifying Flows for Life Cycle Inventory 

The model generated here was used in conjunction with data from the DEAM database to quantify flows 

important for a lifecycle assessment of the C-S system without stover harvest and the C-C and C-S 

systems with stover harvest. Flows are presented as total (mt) mass flows. Normalization of these flows 

will depend on how they are used. Both yields (grain and stover) and land use information are included to 

provide data necessary for normalization at a future time. 

The following tables were generated for each of the three scenarios. 

• Input flows for farm processes for each of the 13 years (Tables 21-23), with averages summarized 

in Table 24. 

• Output flows associated with nutrient use on the farm (13 years) (Tables 25-27). 

• Overall LCI flows pertinent to this analysis as determined for the 13-year average farm inputs 

(Tables 28-30), with a summary and incremental changes among the scenarios in Table 31. 

The average flows are aggregated by processes into five categories for each crop: “Farm,” including soil 

and plant processes, fertilizer transformation and flows; FN production; FK+FP production; production of 

fossil fuels used on the farm; and, emissions from fossil fuel use on the farm. 
68

There are a couple of key differences in the nitrogen flow numbers presented in these tables versus 

Figures 18, 25 and 26 due to differences in the system boundaries. The mass balance approach presented 

in these figures only applies to the agricultural land in the E. Iowa watershed system. Water 

contaminated by nitrates flows out of this system, contributing to hypoxia in the Gulf of Mexico and 

eventually releasing additional NO and N2O as it eventually denitrifies. These additional emissions are 

included for the lifecycle inventory. Other flows (e.g., N2 emissions), that are important for the mass 

balance of nitrogen flows are not important flows for the LCI and are not included in Tables 21 - 23. 

Although most of the analysis of differences among the scenarios is discussed in the next section 

describing the impact assessment, there are a few points that should be made here to identify the various 

processes within the cradle-to-farm gate life cycle that contribute most to individual lifecycle flows. 

Important points, Base Case Scenario: 

• Ammonia releases are dominated (>99.9%) by activities associated with FN use and, more 

importantly, plant senescencing. The rate of senescencing used in this model is less than used by 

others (e.g., Goolsby et al., 1999) and there is significant uncertainty in this term. 

• Carbon dioxide emissions from fossil fuels are highest for the manufacture of FN and fossil fuel 

consumed on the farm. 

• N2O released at the farm due to the use and transformation of FN is the only significant source of 

this greenhouse gas. 

• Methane emissions are associated almost entirely with FN production, which uses natural gas. 

• NOx emissions are primarily the result of FN use and transformation, although fossil fuel 

combustion on the farm also contributes substantial amounts of NOx. 

• Fertilizer use can be considered the only source of TN and TP discharged to water bodies. 

• FP production is the only significant source of acids discharged to water. 

• Coal is use predominantly to generate electricity used to manufacture potassium and phosphate 

fertilizers. 

• Oil consumption is used predominantly on the farm for planting, tilling and harvesting corn and 

soybeans. 

Important differences among the scenarios: 

• Increased fertilizer use is required for the stover collection scenarios. The incremental increase is 

especially high for FK because of the relatively high potassium content in stover relative to other 

nutrients. 

• Reductions in NO and N2O emissions in scenario 2 are related to the reduction in soil 

mineralization with no till practices. In scenario 3, increased use of FN and the associated 

nitrification is sufficient to overwhelm the benefits of reduced soil mineralization. 

• Increases in the loss of total phosphorus to water bodies is the result of the assumed factor for 

increased TP leaching with no till practices. There is considerable uncertainty with this factor. 

• Reductions in diesel fuel and its associated emissions in Scenarios 2 and 3 are the result of the no 

till practices. 

• Substantially more fossil fuels (75%, based on total mass coal, NG and oil) are used in the overall 

cradle-to-farm gate generation of stover in scenario 3 – continuous corn with stover collection, 

versus the base case, C-S with no stover collection. The substantial increase in fertilizer use, 
69

especially FN, contributes most to the associated increase in fossil fuel consumption. In contrast, 

there is only a 6.2% increase in fossil fuel consumption with scenario 2. 

• Increases in TN discharges to surface water, natural gas consumption and methane generation are 

all most closely correlated with increased FN use. 

The sensitivity of these conclusions to uncertainty in the model parameters has not yet been analyzed. 

Although several of the trends noted above are expected to hold, the quantitative analysis presented in the 

LCI tables would be better represented by ranges and distributions. Scenario 1, the corn-soybean rotation 

with conventional till practices is expected to be most accurate. This scenario is relatively close to actual 

farming practices over the period considered so that quantities of fertilizer use, plant yields and nutrient 

loads from the three watersheds to the Mississippi River were all historically known quantities. Mass 

balance verification and calibration of the nutrient leaching component of the overall model both helped 

to provide assurances of the adequacy of this approach and the quantitative results. 

Extrapolation to the other two scenarios for which site specific historical data do not exist provides 

significant sources of uncertainty. Unlike Scenario 1, there are no means of verifying the results of these 

analyses. The quantitative analyses in these scenarios are based on a wide variety of literature sources 

that provide some justification for the values used. This approach inherently creates uncertainty in the 

model predictions due to the necessary application of parameter values measured for one system to a 

system with different scale, hydrology, farming practices and geology. The specific parameters for which 

values are most uncertain include: 

• Allocation of nitrogen flows between corn and soybeans 

• Ammonia released during plant senescencing 

• TN and TP leaching models, especially contributions of manure to total load to the MSRB 

• Fertilizer use and fate in a NT system versus convention till 

• Fertilizer requirements for a C-C versus C-S system 

• Actual fertilizer required to replace nutrients removed with stover 

• Actual erosion rates 

• Soil mineralization rates in a no till versus conventional till system. 

The sensitivity of the quantitative and qualitative conclusions presented in this report to these sources of 

uncertainty has not been determined yet. Further research is required to identify distributions of values 

for these parameters that can then be used in Monte Carlo predictions of LCI results. 
70

Table 21. Important Inputs for Farm stage of Life Cycle Inventory: C-S rotation in E. Iowa – Base case - no 

Scenario 1: C-S, no stover collectionInputs units Allocat 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 

land ha corn 1,760,940 1,958,175 1,988,145 1,948,455 2,037,150 1,859,355 1,988,550 1,879,605 1,950,480 1,855,467 1,882,157 1

FN mt-N corn 271,402 277,917 271,482 256,635 258,772 231,693 265,171 241,403 280,670 249,426 257,852 FP mt-P corn 21,664 25,477 30,103 30,255 39,183 33,116 35,827 28,675 38,842 30,517 32,030 FK mt-K corn 57,540 58,966 61,998 54,459 69,735 43,162 61,138 59,211 87,644 62,572 75,938 seed mt corn 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 Gasoline mt corn 37,659 41,877 42,518 41,669 43,566 39,764 42,527 40,197 41,712 39,681 40,251 Diesel mt corn 74,882 83,269 84,543 82,856 86,627 79,067 84,561 79,928 82,942 78,901 80,036 Motor oil mt corn 3,125 3,476 3,529 3,458 3,616 3,300 3,529 3,336 3,462 3,293 3,341 land ha soy 1,130,841 1,160,690 1,109,052 1,213,988 1,158,705 1,224,720 1,255,500 1,316,655 1,349,460 1,534,829 1,526,688 1FN mt-N soy 2,736 1,872 4,223 5,507 3,054 1,707 2,812 4,186 3,102 4,977 3,354 FP mt-P soy 10,944 12,247 12,606 14,254 12,561 18,502 16,279 14,783 11,509 16,884 17,396 FK mt-K soy 48,983 45,925 42,927 37,591 37,709 51,781 43,300 44,958 37,722 59,802 47,263 seed mt soy 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 Gasoline mt soy 22,330 22,919 21,900 23,972 22,880 24,184 24,791 25,999 26,647 30,307 30,146 Diesel mt soy 46,251 47,472 45,360 49,652 47,391 50,091 51,350 53,851 55,193 62,775 62,442 Motor oil mt soy 1,930 1,981 1,893 2,072 1,978 2,091 2,143 2,248 2,304 2,620 2,606 land ha stoverFN mt-N stoverFP mt-P stoverFK mt-K stoverseed mt stoverGasoline mt stoverDiesel mt stoverMotor oil mt stoverTOTAL INPUTSland ha 2,891,781 3,118,865 3,097,197 3,162,443 3,195,855 3,084,075 3,244,050 3,196,260 3,299,940 3,390,296 3,408,845 3FN mt-N 274,137 279,789 275,705 262,142 261,827 233,400 267,983 245,589 283,772 254,403 261,206 FP mt-P 32,607 37,724 42,709 44,508 51,744 51,618 52,106 43,457 50,351 47,401 49,425 FK mt-K 106,523 104,891 104,925 92,050 107,443 94,943 104,438 104,170 125,366 122,375 123,200 seed mt 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 Gasoline mt 59,989 64,796 64,417 65,641 66,446 63,947 67,318 66,196 68,359 69,987 70,397 Diesel mt 121,133 130,741 129,904 132,508 134,018 129,158 135,911 133,779 138,135 141,676 142,478 Motor oil mt 5,056 5,457 5,422 5,531 5,594 5,391 5,673 5,584 5,766 5,913 5,947 
71

Scenario 1: C-S, no stover collection (cont.) method 2 - allocation of leaching based on fraction of total leachingInputs units Allocat 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 

land ha corn 1,760,940 1,958,175 1,988,145 1,948,455 2,037,150 1,859,355 1,988,550 1,879,605 1,950,480 1,855,467 1,882,157 1

FN mt-N corn 169,794 178,525 179,751 164,226 169,544 143,162 167,053 147,030 170,734 141,999 147,058 FP mt-P corn 21,664 25,477 30,103 30,255 39,183 33,116 35,827 28,675 38,842 30,517 32,030 FK mt-K corn 57,540 58,966 61,998 54,459 69,735 43,162 61,138 59,211 87,644 62,572 75,938 seed mt corn 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 Gasoline mt corn 37,659 41,877 42,518 41,669 43,566 39,764 42,527 40,197 41,712 39,681 40,251 Diesel mt corn 74,882 83,269 84,543 82,856 86,627 79,067 84,561 79,928 82,942 78,901 80,036 Motor oil mt corn 3,125 3,476 3,529 3,458 3,616 3,300 3,529 3,336 3,462 3,293 3,341 land ha soy 1,130,841 1,160,690 1,109,052 1,213,988 1,158,705 1,224,720 1,255,500 1,316,655 1,349,460 1,534,829 1,526,688 1FN mt-N soy 104,344 101,263 95,954 97,916 92,283 90,238 100,930 98,560 113,038 112,404 114,149 FP mt-P soy 10,944 12,247 12,606 14,254 12,561 18,502 16,279 14,783 11,509 16,884 17,396 FK mt-K soy 48,983 45,925 42,927 37,591 37,709 51,781 43,300 44,958 37,722 59,802 47,263 seed mt soy 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 Gasoline mt soy 22,330 22,919 21,900 23,972 22,880 24,184 24,791 25,999 26,647 30,307 30,146 Diesel mt soy 46,251 47,472 45,360 49,652 47,391 50,091 51,350 53,851 55,193 62,775 62,442 Motor oil mt soy 1,930 1,981 1,893 2,072 1,978 2,091 2,143 2,248 2,304 2,620 2,606 land ha stoverFN mt-N stoverFP mt-P stoverFK mt-K stoverseed mt stoverGasoline mt stoverDiesel mt stoverMotor oil mt stoverTOTAL INPUTSland ha 2,891,781 3,118,865 3,097,197 3,162,443 3,195,855 3,084,075 3,244,050 3,196,260 3,299,940 3,390,296 3,408,845 3FN mt-N 274,137 279,789 275,705 262,142 261,827 233,400 267,983 245,589 283,772 254,403 261,206 FP mt-P 32,607 37,724 42,709 44,508 51,744 51,618 52,106 43,457 50,351 47,401 49,425 FK mt-K 106,523 104,891 104,925 92,050 107,443 94,943 104,438 104,170 125,366 122,375 123,200 seed mt 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 Gasoline mt 59,989 64,796 64,417 65,641 66,446 63,947 67,318 66,196 68,359 69,987 70,397 Diesel mt 121,133 130,741 129,904 132,508 134,018 129,158 135,911 133,779 138,135 141,676 142,478 Motor oil mt 5,056 5,457 5,422 5,531 5,594 5,391 5,673 5,584 5,766 5,913 5,947 

note – flows modified by the allocation method are highlighted 
72

Table 22. Important Inputs for Farm-stage of Life Cycle Inventory: C-S rotation in E. Iowa – with stov

Scenario 2: C-S, NT, stover collectionInputs units Allocati 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 

land ha corn 1,760,940 1,958,175 1,988,145 1,948,455 2,037,150 1,859,355 1,988,550 1,879,605 1,950,480 1,855,467 1,882,157 1

FN mt-N corn 271,402 277,917 271,482 256,635 258,772 231,693 265,171 241,403 280,670 249,426 257,852 FP mt-P corn 21,664 25,477 30,103 30,255 39,183 33,116 35,827 28,675 38,842 30,517 32,030 FK mt-K corn 57,540 58,966 61,998 54,459 69,735 43,162 61,138 59,211 87,644 62,572 75,938 seed mt corn 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 41,568 Gasoline m^3 corn 37,659 41,877 42,518 41,669 43,566 39,764 42,527 40,197 41,712 39,681 40,251 Diesel m^3 corn 46,380 51,575 52,364 51,319 53,655 48,972 52,375 49,505 51,372 48,869 49,572 Motor oil m^3 corn 3,125 3,476 3,529 3,458 3,616 3,300 3,529 3,336 3,462 3,293 3,341 land ha soy 1,130,841 1,160,690 1,109,052 1,213,988 1,158,705 1,224,720 1,255,500 1,316,655 1,349,460 1,534,829 1,526,688 1FN mt-N soy 2,736 1,872 4,223 5,507 3,054 1,707 2,812 4,186 3,102 4,977 3,354 FP mt-P soy 10,944 12,247 12,606 14,254 12,561 18,502 16,279 14,783 11,509 16,884 17,396 FK mt-K soy 48,983 45,925 42,927 37,591 37,709 51,781 43,300 44,958 37,722 59,802 47,263 seed mt soy 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 78,526 Gasoline mt soy 22,330 22,919 21,900 23,972 22,880 24,184 24,791 25,999 26,647 30,307 30,146 Diesel mt soy 28,647 29,403 28,095 30,753 29,353 31,025 31,805 33,354 34,185 38,881 38,675 Motor oil mt soy 1,930 1,981 1,893 2,072 1,978 2,091 2,143 2,248 2,304 2,620 2,606 land ha stover 0 0 0 0 0 0 0 0 0 0 0 FN mt-N stover 59,332 21,870 37,356 21,948 14,292 49,000 9,146 79,237 18,957 29,127 38,513 FP mt-P stover 940 346 720 1,018 814 1,307 412 1,313 961 1,132 1,127 FK mt-K stover 254,394 94,649 212,345 261,590 221,884 328,114 119,122 349,309 260,814 308,519 328,860 seed mt stover 0 0 0 0 0 0 0 0 0 0 0 Gasoline mt stover 0 0 0 0 0 0 0 0 0 0 0 Diesel mt stover 15,490 13,953 12,173 14,232 11,524 14,661 11,021 11,707 11,378 10,617 10,520 Motor oil mt stover 84 74 64 76 61 78 58 62 60 56 55 TOTAL INPUTSland ha 2,891,781 3,118,865 3,097,197 3,162,443 3,195,855 3,084,075 3,244,050 3,196,260 3,299,940 3,390,296 3,408,845 3FN mt-N 333,470 301,659 313,061 284,090 276,119 282,399 277,129 324,826 302,730 283,530 299,720 FP mt-P 33,547 38,070 43,429 45,527 52,558 52,925 52,518 44,770 51,312 48,533 50,553 FK mt-K 360,917 199,540 317,270 353,640 329,328 423,057 223,560 453,478 386,180 430,893 452,060 seed mt 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 120,094 Gasoline mt 59,989 64,796 64,417 65,641 66,446 63,947 67,318 66,196 68,359 69,987 70,397 Diesel mt 90,517 94,931 92,632 96,304 94,532 94,658 95,200 94,566 96,935 98,368 98,767 Motor oil mt 5,140 5,531 5,486 5,606 5,654 5,469 5,731 5,646 5,825 5,969 6,002 
73

Table 23. Important Inputs for Farm-stage of Life Cycle Inventory: C-C Rotation in E. Iowa with Stover 

Scenario 3: C-C, NT, stover collectionInputs units Alloca 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 land ha corn 2,891,781 3,118,865 3,097,197 4,376,430 3,195,855 3,084,075 3,244,050 3,196,260 3,299,940 3,390,296 3,408,845 3,FN mt-N corn 575,950 583,137 562,431 773,561 549,910 523,224 578,716 554,476 623,497 608,463 620,552 FP mt-P corn 35,576 40,578 46,895 49,105 61,470 54,930 58,448 48,761 65,715 55,760 58,010 FK mt-K corn 99,418 97,163 101,258 92,806 115,613 80,054 107,620 109,947 163,186 127,059 153,795 seed mt corn 66,004 72,487 71,489 73,546 74,579 69,421 75,663 74,438 76,950 78,842 79,587 Gasoline mt corn 61,843 66,699 66,236 93,593 68,346 65,955 69,376 68,354 70,572 72,504 72,901 Diesel mt corn 76,164 82,145 81,574 115,267 84,173 81,229 85,442 84,183 86,914 89,294 89,782 Motor oil mt corn 5,133 5,536 5,497 7,768 5,672 5,474 5,758 5,673 5,857 6,017 6,050 land ha soyFN mt-N soyFP mt-P soyFK mt-K soyseed mt soyGasoline mt soyDiesel mt soyMotor oil mt soyFN mt-N stover 107,057 39,831 73,462 37,588 25,968 82,011 17,624 140,568 34,300 52,924 74,905 

FP mt-P stover 1,696 1,696 1,696 1,696 1,696 1,696 1,696 1,696 1,696 1,696 1,696 

FK mt-K stover 254,394 254,394 254,394 254,394 254,394 254,394 254,394 254,394 254,394 254,394 254,394 

seed mt stover 00000000000

Gasoline mt stover 00000000000Diesel mt stover 29,152 21,757 19,454 21,896 18,815 25,418 18,642 20,770 20,177 20,274 20,008 

Motor oil mt stover 145 106 95 107 91 125 90 101 98 98 97 

TOTAL INPUTSland ha 2,891,781 3,118,865 3,097,197 4,376,430 3,195,855 3,084,075 3,244,050 3,196,260 3,299,940 3,390,296 3,408,845 3,

FN mt-N 683,006 622,968 635,893 811,149 575,878 605,235 596,340 695,044 657,798 661,388 695,456 FP mt-P 37,272 42,274 48,591 50,801 63,166 56,626 60,144 50,457 67,411 57,456 59,706 FK mt-K 353,812 351,558 355,653 347,200 370,008 334,449 362,015 364,342 417,581 381,454 408,190 seed mt 66,004 72,487 71,489 73,546 74,579 69,421 75,663 74,438 76,950 78,842 79,587 Gasoline mt 61,843 66,699 66,236 93,593 68,346 65,955 69,376 68,354 70,572 72,504 72,901 Diesel mt 105,316 103,902 101,029 137,163 102,988 106,646 104,085 104,953 107,091 109,568 109,791 Motor oil mt 5,278 5,642 5,592 7,875 5,764 5,599 5,848 5,774 5,955 6,116 6,147 
74

Table 24. Summary of Averagea Farm Inputs and Incremental Percentage Differences among Scenarios

Scenario 1: Base Case, C-S, no stover (allocation method 1)

( avg. mt)

Corn Soy Stover TOTAL

FN 259,505 4,216 263,721FP 31,070 14,598 45,668FK 62,459 47,078 109,537Gasoline 40,759 26,057 66,816Diesel 81,045 53,972 135,018Motor oil 3,383 2,253 5,635

Scenario 2: C-S, w/ stover (allocation method 1)

( avg. mt) Incremental % Change compared with Base Case

Corn Soy Stover TOTAL Corn Soy Stover TOTAL

FN 259,505 4,216 33,257 296,978 0 0 100 12.6FP 31,070 14,598 964 46,632 0 0 100 2.1FK 62,459 47,078 265,392 374,929 0 0 100 242.3Gasoline 40,759 26,057 0 66,816 0 0 100 0.0Diesel 50,197 33,429 12,137 95,763 -38.06 -38.06 100 -29.1Motor oil 3,383 2,253 64 5,700 0 0 100 1.1Scenario 3: C-C, w/stover

( avg. mt) Incremental % Change compared with Base Case

Corn Soy Stover TOTAL Corn Soy Stover TOTAL

FN 601,192 60,789 661,981 131.7 -100 100 151.0FP 52,607 1,696 54,303 69.3 -100 100 18.9FK 115,532 254,394 369,926 85.0 -100 100 237.7Gasoline 70,977 0 70,977 74.1 -100 100 6.2Diesel 87,413 21,270 108,683 7.9 -100 100 -19.5Motor oil 5,891 104 5,994 74.1 -100 100 6.4

a Averaged over 13 year period (from Tables 21-23) 
75

Table 25. Important Nutrient Outflows for Farm stage of Life Cycle Inventory: C-S rotation in E. Iowa – Scenario 1stover collection

Scenario 1: C-S, no stover collection Method 1: Allocation of N leaching to crop FN appliedOutputs units Allocati 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 grain mt corn 7,462,069 13,212,182 15,602,329 13,434,921 18,448,394 8,763,835 18,677,006 14,388,681 16,469,321 16,129,678 17,223,270 1erosion 1000 mt- corn 47,100 39,848 34,879 39,374 30,625 44,518 27,354 34,247 31,818 28,913 27,767 

(a) NH3 mt-N corn 58,352 65,688 66,173 65,041 67,906 59,329 66,559 64,665 65,843 62,079 63,279 (a) NO mt-N corn 1,974 2,932 5,291 4,311 4,426 5,936 4,166 3,345 3,843 3,349 4,833 (a) N2O mt-N corn 1,153 1,890 4,326 3,421 3,310 5,388 2,898 2,484 2,904 2,396 3,875 (w) TN - SW mt-N corn 16,594 45,101 182,646 137,595 119,271 196,841 86,694 86,304 104,548 76,743 158,488 (w) NO3-GW mt-N corn 2,457 0 0 0 0 1,924 0 0 0 0 0 (w) TN to MSR/Gu mt-N corn 9,956 27,060 146,117 110,076 95,417 157,473 52,017 51,782 62,729 46,046 126,790 (w) TP-SW mt-P corn 66 435 3,045 2,269 2,477 4,110 1,521 1,312 1,695 1,148 2,737 grain mt soy 2,175,206 3,025,679 3,218,404 3,415,251 3,450,599 2,630,838 4,323,526 4,085,288 4,027,441 4,879,986 5,203,268 

erosion 1000 mt- soy 30,247 23,620 19,456 24,532 17,419 29,323 17,271 23,990 22,014 23,917 22,523 

(a) NH3 mt-N soy 22,530 23,238 22,010 24,249 23,184 23,949 25,121 27,039 27,007 30,551 30,502 (a) NO mt-N soy 815 939 977 1,059 1,008 1,105 1,152 1,163 1,166 1,362 1,411 (a) N2O mt-N soy 409 472 514 558 517 742 585 596 594 695 725 (w) TN - SW mt-N soy 172 299 2,820 3,081 1,388 1,456 925 1,518 1,151 1,527 2,052 (w) NO3-GW mt-N soy 25 0 0 0 0 14 0 0 0 0 0 (w) NO3 to MSR/Gu mt-N soy 103 180 2,256 2,465 1,111 1,165 555 911 691 916 1,642 (w) TP-SW mt-P soy 45 250 1,511 1,412 1,104 3,012 855 906 1,005 850 2,027 grain mt stovererosion 1000 mt- stover(a) NH3 mt-N stover(a) NO mt-N stover(a) N2O mt-N stover(w) TN - SW mt-N stover(w) NO3-GW mt-N stover(w) TN to MSR/Gu mt-N stover(w) TP-SW mt-P stoverTOTAL OUTFLOWSgrain mt corn 7,462,069 13,212,182 15,602,329 13,434,921 18,448,394 8,763,835 18,677,006 14,388,681 16,469,321 16,129,678 17,223,270 1

grain mt soy 2,175,206 3,025,679 3,218,404 3,415,251 3,450,599 2,630,838 4,323,526 4,085,288 4,027,441 4,879,986 5,203,268 grain mt stover 0 0 0 0 0 0 0 0 0 0 0 erosion 1000 mt-soil 77,347 63,468 54,335 63,906 48,043 73,842 44,625 58,237 53,832 52,830 50,290 (a) NH3 mt-N 80,882 88,925 88,183 89,290 91,090 83,279 91,680 91,705 92,850 92,630 93,781 (a) NO mt-N 2,789 3,871 6,268 5,370 5,434 7,041 5,318 4,509 5,009 4,711 6,245 (a) N2O mt-N 1,562 2,362 4,840 3,979 3,827 6,130 3,483 3,080 3,498 3,091 4,599 (w) TN - SW mt-N 16,766 45,400 185,466 140,675 120,659 198,297 87,620 87,822 105,699 78,270 160,540 (w) NO3-GW mt-N 2,482 0 0 0 0 1,938 0 0 0 0 0 (w) TN to MSR/Gu mt-N 10,060 27,240 148,372 112,540 96,527 158,638 52,572 52,693 63,420 46,962 128,432 (w) TP-SW mt-P 112 685 4,556 3,681 3,581 7,122 2,376 2,218 2,700 1,998 4,764 
76

Scenario 1: C-S,no stover collection (cont.) method 2 - allocation of leaching based on fraction of total leachingOutputs units Allocati 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 grain mt corn 7,462,069 13,212,182 15,602,329 13,434,921 18,448,394 8,763,835 18,677,006 14,388,681 16,469,321 16,129,678 17,erosion 1000 mt-scorn 47,100 39,848 34,879 39,374 30,625 44,518 27,354 34,247 31,818 28,913 

(a) NH3 mt-N corn 58,352 65,688 66,173 65,041 67,906 59,329 66,559 64,665 65,843 62,079 (a) NO mt-N corn 1,856 2,627 4,136 3,386 3,657 4,512 3,560 2,708 3,069 2,724 (a) N2O mt-N corn 1,036 1,586 3,181 2,504 2,547 3,967 2,293 1,848 2,132 1,773 (w) TN - SW mt-N corn 10,385 28,968 120,918 88,130 78,132 121,631 54,620 52,577 63,595 43,688 (w) NO3-GW mt-N corn 2,457 0 0 0 0 1,924 0 0 0 0 (w) TN to MSR/Gu mt-N corn 6,231 17,381 96,734 70,504 62,506 97,305 32,772 31,546 38,157 26,213 (w) TP-SW mt-P corn 66 435 3,045 2,269 2,477 4,110 1,521 1,312 1,695 1,148 grain mt soy 2,175,206 3,025,679 3,218,404 3,415,251 3,450,599 2,630,838 4,323,526 4,085,288 4,027,441 4,879,986 5,erosion 1000 mt-ssoy 30,247 23,620 19,456 24,532 17,419 29,323 17,271 23,990 22,014 23,917 (a) NH3 mt-N soy 22,530 23,238 22,010 24,249 23,184 23,949 25,121 27,039 27,007 30,551 (a) NO mt-N soy 933 1,244 2,131 1,984 1,777 2,529 1,759 1,801 1,940 1,987 (a) N2O mt-N soy 526 776 1,659 1,475 1,280 2,163 1,190 1,232 1,366 1,319 (w) TN - SW mt-N soy 6,382 16,431 64,548 52,545 42,527 76,667 33,000 35,245 42,105 34,582 (w) NO3-GW mt-N soy 25 0 0 0 0 14 0 0 0 0 (w) TN to MSR/Gu mt-N soy 3,829 9,859 51,638 42,036 34,022 61,333 19,800 21,147 25,263 20,749 (w) TP-SW mt-P soy 45 250 1,511 1,412 1,104 3,012 855 906 1,005 850 grain mt stovererosion 1000 mt-sstover(a) NH3 mt-N stover(a) NO mt-N stover(a) N2O mt-N stover(w) TN - SW mt-N stover(w) NO3-GW mt-N stover(w) TN to MSR/Gu mt-N stover(w) TP-SW mt-P stoverTOTAL OUTFLOWSgrain mt corn 7,462,069 13,212,182 15,602,329 13,434,921 18,448,394 8,763,835 18,677,006 14,388,681 16,469,321 16,129,678 17,grain mt soy 2,175,206 3,025,679 3,218,404 3,415,251 3,450,599 2,630,838 4,323,526 4,085,288 4,027,441 4,879,986 5,grain mt stover 0 0 0 0 0 0 0 0 0 0 erosion 1000 mt-soil 77,347 63,468 54,335 63,906 48,043 73,842 44,625 58,237 53,832 52,830 (a) NH3 mt-N 80,882 88,925 88,183 89,290 91,090 83,279 91,680 91,705 92,850 92,630 (a) NO mt-N 2,789 3,871 6,268 5,370 5,434 7,041 5,318 4,509 5,009 4,711 (a) N2O mt-N 1,562 2,362 4,840 3,979 3,827 6,130 3,483 3,080 3,498 3,091 (w) TN - SW mt-N 16,766 45,400 185,466 140,675 120,659 198,297 87,620 87,822 105,699 78,270 (w) NO3-GW mt-N 2,482 0 0 0 0 1,938 0 0 0 0 (w) TN to MSR/Gu mt-N 10,060 27,240 148,372 112,540 96,527 158,638 52,572 52,693 63,420 46,962 (w) TP-SW mt-P 112 685 4,556 3,681 3,581 7,122 2,376 2,218 2,700 1,998 

note – flows modified by the allocation method are highlighted 
77

Table 26. Important Nutrient Outflows for Farm Stage of Life Cycle Inventory: Scenario 2: C-S rotation in E. Iowa 

Scenario 2: C-S, NT, stover collection (C-S allocation by method 1)Outputs units Allocat 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 grain mt corn 7,462,069 13,212,182 15,602,329 13,434,921 18,448,394 8,763,835 18,677,006 14,388,681 16,469,321 16,129,678 17,223,270 1erosion 1000 mt-scorn 12,182 12,499 11,944 13,077 12,482 13,192 13,522 14,170 14,531 16,533 16,444 

(a) NH3 mt-N corn 58,352 65,688 66,173 65,041 67,906 59,329 66,559 64,665 65,843 62,079 63,279 (a) NO mt-N corn 1,974 2,932 5,291 4,311 4,426 5,936 4,166 3,345 3,843 3,349 4,833 (a) N2O mt-N corn 1,153 1,890 4,326 3,421 3,310 5,388 2,898 2,484 2,904 2,396 3,875 (w) TN - SW mt-N corn 16,594 45,101 182,646 137,595 119,271 196,841 86,694 86,304 104,548 76,743 158,488 (w) NO3-GW mt-N corn 2,457 0 0 0 0 1,924 0 0 0 0 0 (w) TN to MSR/Gu mt-N corn 9,956 27,060 146,117 110,076 95,417 157,473 52,017 51,782 62,729 46,046 126,790 (w) TP-SW mt-P corn 66 435 3,045 2,269 2,477 4,110 1,521 1,312 1,695 1,148 2,737 grain mt soy 2,175,206 3,025,679 3,218,404 3,415,251 3,450,599 2,630,838 4,323,526 4,085,288 4,027,441 4,879,986 5,203,268 erosion 1000 mt-ssoy 18,951 21,077 21,400 20,967 21,925 20,014 21,404 20,252 20,994 19,973 20,258 (a) NH3 mt-N soy 22,530 23,238 22,010 24,249 23,184 23,949 25,121 27,039 27,007 30,551 30,502 (a) NO mt-N soy 815 939 977 1,059 1,008 1,105 1,152 1,163 1,166 1,362 1,411 (a) N2O mt-N soy 409 472 514 558 517 742 585 596 594 695 725 (w) TN - SW mt-N soy 172 299 2,820 3,081 1,388 1,456 925 1,518 1,151 1,527 2,052 (w) NO3-GW mt-N soy 25 0 0 0 0 14 0 0 0 0 0 (w) TN to MSR/Gu mt-N soy 103 180 2,256 2,465 1,111 1,165 555 911 691 916 1,642 (w) TP-SW mt-P soy 45 250 1,511 1,412 1,104 3,012 855 906 1,005 850 2,027 grain mt stover 3,464,615 7,198,571 10,183,065 8,141,262 13,069,415 4,121,022 13,126,850 9,609,870 11,319,570 11,272,459 12,361,503 1erosion 1000 mt-sstover 0 0 0 0 0 0 0 0 0 0 0 (a) NH3 mt-N stover 1,780 656 1,121 658 429 1,470 274 2,377 569 874 1,155 (a) NO mt-N stover -1,066 -1,343 -676 -945 -1,403 -111 -1,741 -712 -1,221 -1,291 -934 (a) N2O mt-N stover -490 -654 -17 -358 -645 521 -841 -94 -546 -565 -255 (w) TN - SW mt-N stover 4,057 1,894 24,861 12,440 6,099 40,733 3,179 27,874 6,885 8,527 23,060 (w) NO3-GW mt-N stover 1,144 0 0 0 0 1,204 0 0 0 0 0 (w) TN to MSR/Gu mt-N stover 2,434 1,136 19,889 9,952 4,879 32,586 1,907 16,725 4,131 5,116 18,448 (w) TP-SW mt-P stover 73 415 2,855 2,352 2,231 4,555 1,458 1,436 1,700 1,271 3,027 TOTAL OUTFLOWSgrain mt corn 7,462,069 13,212,182 15,602,329 13,434,921 18,448,394 8,763,835 18,677,006 14,388,681 16,469,321 16,129,678 17,223,270 1grain mt soy 2,175,206 3,025,679 3,218,404 3,415,251 3,450,599 2,630,838 4,323,526 4,085,288 4,027,441 4,879,986 5,203,268 grain mt stover 3,464,615 7,198,571 10,183,065 8,141,262 13,069,415 4,121,022 13,126,850 9,609,870 11,319,570 11,272,459 12,361,503 1erosion 1000 mt-soil 31,133 33,576 33,344 34,044 34,407 33,206 34,927 34,422 35,525 36,506 36,703 (a) NH3 mt-N 82,662 89,582 89,304 89,948 91,518 84,749 91,954 94,082 93,419 93,504 94,936 (a) NO mt-N 1,723 2,528 5,592 4,425 4,031 6,930 3,577 3,796 3,788 3,420 5,311 (a) N2O mt-N 1,072 1,708 4,823 3,621 3,182 6,650 2,642 2,986 2,952 2,526 4,344 (w) TN - SW mt-N 20,823 47,294 210,327 153,115 126,758 239,030 90,799 115,696 112,585 86,797 183,599 (w) NO3-GW mt-N 3,626 0 0 0 0 3,142 0 0 0 0 0 (w) TN to MSR/Gu mt-N 12,494 28,376 168,261 122,492 101,406 191,224 54,479 69,418 67,551 52,078 146,879 (w) TP-SW mt-P 185 1,100 7,410 6,033 5,811 11,677 3,835 3,654 4,400 3,269 7,791 
78

Table 27. Important Nutrient Outflows for Farm Stage of Life Cycle Inventory: Scenario 3: C-C rotation in E. Iowa 

Scenario 3: C-C, NT, stover collectionOutputs units Allocati 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 

grain mt corn 12,619,734 21,227,138 24,395,565 21,955,518 29,128,265 14,681,650 30,635,079 24,605,727 28,036,781 29,602,371 31,326,206 3

erosion 1000 mt-s corn 31,133 33,576 33,344 34,044 34,407 33,206 34,927 34,422 35,525 36,506 36,703 

(a) NH3 mt-N corn 88,013 103,727 97,231 112,343 107,004 91,968 110,678 94,650 111,400 110,435 108,868 

(a) NO mt-N corn 1,508 2,753 8,532 9,715 5,707 10,958 4,719 4,504 5,304 4,467 8,233 (a) N2O mt-N corn 1,143 2,277 8,391 9,732 5,187 11,398 4,152 4,162 4,862 4,013 7,831 (w) TN - SW mt-N corn 35,770 92,292 377,394 424,840 251,782 445,031 189,681 199,361 231,801 186,657 380,309 

(w) NO3-GW mt-N corn 13,079 8,287 8,798 15,851 4,198 13,008 2,337 9,136 7,620 6,085 7,509 (w) TN to MSR/Gu mt-N corn 21,462 55,375 301,915 339,872 201,426 356,025 113,809 119,617 139,080 111,994 304,247 

(w) TP-SW mt-P corn 203 1,173 7,981 6,610 6,772 12,146 4,277 4,007 5,629 3,758 8,929 grain mt soyerosion 1000 mt-s soy

(a) NH3 mt-N soy(a) NO mt-N soy(a) N2O mt-N soy(w) TN - SW mt-N soy(w) NO3-GW mt-N soy(w) TN to MSR/Gu mt-N soy(w) TP-SW mt-P soygrain mt stover 6,309,940 14,156,331 17,439,362 14,792,296 21,874,281 7,941,495 23,287,245 17,387,602 20,567,908 21,924,007 23,588,884 2

erosion 1000 mt-s stover 0 0 0 0 0 0 0 0 0 0 0 

(a) NH3 mt-N stover 16,360 7,085 12,700 5,459 5,053 14,415 3,371 23,995 6,128 9,606 13,141 

(a) NO mt-N stover 280 188 1,114 472 270 1,718 144 1,142 292 389 994 (a) N2O mt-N stover 212 156 1,096 473 245 1,786 126 1,055 267 349 945 (w) TN - SW mt-N stover 6,649 6,304 49,293 20,644 11,890 69,755 5,777 50,541 12,752 16,236 45,906 (w) NO3-GW mt-N stover 2,431 566 1,149 770 198 2,039 71 2,316 419 529 906 (w) TN to MSR/Gu mt-N stover 3,989 3,782 39,434 16,515 9,512 55,804 3,466 30,325 7,651 9,741 36,725 

(w) TP-SW mt-P stover 10 11 241 238 125 481 58 191 147 135 334 TOTAL OUTFLOWSgrain mt corn 12,619,734 21,227,138 24,395,565 21,955,518 29,128,265 14,681,650 30,635,079 24,605,727 28,036,781 29,602,371 31,326,206 3

grain mt soy 0 0 0 0 0 0 0 0 0 0 0 grain mt stover 6,309,940 14,156,331 17,439,362 14,792,296 21,874,281 7,941,495 23,287,245 17,387,602 20,567,908 21,924,007 23,588,884 2

erosion 1000 mt-soil 31,133 33,576 33,344 34,044 34,407 33,206 34,927 34,422 35,525 36,506 36,703 

(a) NH3 mt-N 104,373 110,812 109,931 117,802 112,057 106,383 114,049 118,645 117,528 120,040 122,009 (a) NO mt-N 1,789 2,941 9,647 10,187 5,977 12,675 4,862 5,645 5,596 4,856 9,227 (a) N2O mt-N 1,355 2,433 9,487 10,205 5,432 13,184 4,278 5,218 5,129 4,362 8,776 (w) TN - SW mt-N 42,419 98,596 426,687 445,483 263,672 514,786 195,458 249,902 244,553 202,893 426,215 (w) NO3-GW mt-N 15,510 8,853 9,947 16,621 4,396 15,047 2,408 11,452 8,039 6,614 8,415 (w) TN to MSR/Gu mt-N 25,452 59,158 341,350 356,387 210,938 411,829 117,275 149,941 146,732 121,736 340,972 (w) TP-SW mt-P 214 1,184 8,222 6,848 6,896 12,628 4,335 4,198 5,777 3,892 9,263 
79

Table 28. Overall Annual Cradle-to-Farm Gate LCI Flows and Distributions among Processes, 

Scenario 1: Base Case, C-S, no Stover Collectiona 

( Scenario 1: Base case, C-S allocation method 1)

Cradle to Farmgate Lifecycle Flows Corn Corn Corn Corn Corn Soy Soy Soy Soy Soy 

(in mt unless noted) Farm FN prod FP+FK prod energy prod energy farm Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 6.37E+04 2.60E-01 1.33E-02 2.79E-01 0.00E+00 2.64E+04 4.22E-03 7.46E-03 1.86E-01 0.00E+00 (a) Carbon Dioxide (CO2, fossil) 6.62E+05 1.01E+05 5.00E+04 3.89E+05 1.08E+04 5.54E+04 3.31E+04 2.56E+05 (a) Carbon Monoxide (CO) 1.58E+01 4.79E+01 3.63E+01 1.47E+03 2.56E-01 2.75E+01 2.41E+01 9.71E+02 (a) Methane (CH4) 5.36E+03 1.95E+02 1.08E+02 0.00E+00 8.70E+01 1.13E+02 7.18E+01 0.00E+00 (a) Nitrogen Oxides (NOx as NO2) 4.01E+03 8.82E+02 8.98E+00 1.40E+02 3.15E+03 1.15E+03 1.43E+01 2.13E+02 9.28E+01 2.09E+03 (a) Nitrous Oxide (N2O) 3.06E+03 0.00E+00 8.98E+00 4.57E+00 6.70E+00 6.07E+02 0.00E+00 5.09E+00 3.01E+00 4.46E+00 (a) Sulfur Oxides (SOx as SO2) 3.15E+02 1.90E+03 4.16E+02 4.64E+02 5.12E+00 9.20E+02 2.76E+02 2.61E+01 (w) Acids (H+) 1.58E-01 4.73E+03 1.04E-01 0.00E+00 2.56E-03 2.22E+03 6.90E-02 0.00E+00 (w) TN 1.09E+05 3.15E-01 2.73E-01 1.60E+01 0.00E+00 1.78E+03 5.12E-03 1.68E-01 1.05E+01 0.00E+00 (w) TP 1.84E+03 0.00E+00 5.65E-01 1.94E-02 0.00E+00 1.17E+03 0.00E+00 2.66E-01 1.24E-02 0.00E+00 (r) Land (ha) 1.91E+06 1.32E+06 (r) Coal (in ground) 7.54E+03 2.53E+04 4.23E+03 1.23E+02 1.42E+04 2.78E+03 (r) Natural Gas (in ground) 2.39E+05 4.32E+04 1.16E+04 3.89E+03 2.09E+04 7.61E+03 (r) Oil (in ground) 9.80E+02 1.11E+04 9.29E+04 1.59E+01 5.56E+03 6.19E+04 (r) Potassium Chloride (KCl, as K2O, in ground) 6.37E+04 4.80E+04 (r) Phosphate Rock (in ground) 2.31E+05 1.09E+05 Water Used (total) (m^3) 3.47E+06 2.67E+05 4.49E+05 5.63E+04 1.28E+05 2.93E+05 

Corn Corn Corn Corn Corn Soy Soy Soy Soy Soy

% of total Farm FN prod FP+FK prod energy prod energy farm Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 70.7 0.0 0.0 0.0 0.0 29.3 0.0 0.0 0.0 0.0(a) Carbon Dioxide (CO2, fossil) 42.5 6.5 3.2 25.0 0.7 3.6 2.1 16.4(a) Carbon Monoxide (CO) 0.6 1.8 1.4 56.7 0.0 1.1 0.9 37.4(a) Methane (CH4) 90.3 3.3 1.8 0.0 1.5 1.9 1.2 0.0(a) Nitrogen Oxides (NOx as NO2) 34.1 7.5 0.1 1.2 26.8 9.8 0.1 1.8 0.8 17.8(a) Nitrous Oxide (N2O) 82.7 0.0 0.2 0.1 0.2 16.4 0.0 0.1 0.1 0.1(a) Sulfur Oxides (SOx as SO2) 7.3 44.0 9.6 10.7 0.1 21.3 6.4 0.6(w) Acids (H+) 0.0 68.0 0.0 0.0 0.0 32.0 0.0 0.0(w) TN 98.4 0.0 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0(w) TP 61.2 0.0 0.0 0.0 0.0 38.8 0.0 0.0 0.0 0.0(r) Land (ha) 59.1 0.0 0.0 0.0 40.9 0.0 0.0 0.0(r) Coal (in ground) 13.9 46.8 7.8 0.2 26.2 5.1(r) Natural Gas (in ground) 73.3 13.2 3.5 1.2 6.4 2.3(r) Oil (in ground) 0.6 6.5 53.9 0.0 3.2 35.9(r) Potassium Chloride (KCl, as K2O, in ground) 57.0 43.0(r) Phosphate Rock (in ground) 68.0 32.0Water Used (total) (m^3) 74.4 5.7 9.6 1.2 2.7 6.3

a using average of 13 year inflow data (Table 24) 
80

Table 28 (cont.). Overall Annual Cradle-to-Farm Gate LCI Flows and Distributions among Processes, 

Scenario 1: Base Case, C-S, no Stover Collection 

(Scenario 1: Base case, C-S allocation method 2)

Cradle to Farmgate Lifecycle Flows Corn Corn Corn Corn Corn Soy Soy Soy Soy Soy 

(in mt unless noted) Farm FN prod FP+FK prod energy prod energy farm Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 6.37E+04 1.59E-01 1.33E-02 2.79E-01 0.00E+00 2.64E+04 1.05E-01 7.46E-03 1.86E-01 0.00E+00 (a) Carbon Dioxide (CO2, fossil) 4.06E+05 1.01E+05 5.00E+04 3.89E+05 2.67E+05 5.54E+04 3.31E+04 2.56E+05 (a) Carbon Monoxide (CO) 9.66E+00 4.79E+01 3.63E+01 1.47E+03 6.35E+00 8.39E+00 2.41E+01 9.71E+02 (a) Methane (CH4) 3.28E+03 1.95E+02 1.08E+02 0.00E+00 2.16E+03 1.13E+02 7.18E+01 0.00E+00 (a) Nitrogen Oxides (NOx as NO2) 3.21E+03 5.41E+02 8.98E+00 1.40E+02 3.15E+03 1.95E+03 3.56E+02 2.13E+02 9.28E+01 2.09E+03 (a) Nitrous Oxide (N2O) 2.27E+03 0.00E+00 8.98E+00 4.57E+00 6.70E+00 1.39E+03 0.00E+00 5.09E+00 3.01E+00 4.46E+00 (a) Sulfur Oxides (SOx as SO2) 1.93E+02 1.90E+03 4.16E+02 4.64E+02 1.27E+02 9.20E+02 2.76E+02 2.61E+01 (w) Acids (H+) 9.66E-02 4.73E+03 1.04E-01 0.00E+00 6.35E-02 2.22E+03 6.90E-02 0.00E+00 (w) TN 6.69E+04 1.93E-01 2.73E-01 1.60E+01 0.00E+00 4.39E+04 1.27E-01 1.68E-01 1.05E+01 0.00E+00 (w) TP 1.84E+03 0.00E+00 5.65E-01 1.94E-02 0.00E+00 1.17E+03 0.00E+00 2.66E-01 1.24E-02 0.00E+00 (r) Land (ha) 1.91E+06 1.32E+06 (r) Coal (in ground) 4.62E+03 2.53E+04 4.23E+03 3.04E+03 1.42E+04 2.78E+03 (r) Natural Gas (in ground) 1.47E+05 4.32E+04 1.16E+04 9.64E+04 2.09E+04 7.61E+03 (r) Oil (in ground) 6.01E+02 1.11E+04 9.29E+04 3.95E+02 5.56E+03 6.19E+04 (r) Potassium Chloride (KCl, as K2O, in ground) 6.37E+04 4.80E+04 (r) Phosphate Rock (in ground) 2.31E+05 1.09E+05 Water Used (total) (m^3) 2.12E+06 2.67E+05 4.49E+05 1.40E+06 1.28E+05 2.93E+05 

Corn Corn Corn Corn Corn Soy Soy Soy Soy Soy

% of total Farm FN prod FP+FK prod energy prod energy farm Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 70.7 0.0 0.0 0.0 0.0 29.3 0.0 0.0 0.0 0.0(a) Carbon Dioxide (CO2, fossil) 26.1 6.5 3.2 25.0 17.1 3.6 2.1 16.4(a) Carbon Monoxide (CO) 0.4 1.9 1.4 57.1 0.2 0.3 0.9 37.7(a) Methane (CH4) 55.4 3.3 1.8 0.0 36.4 1.9 1.2 0.0(a) Nitrogen Oxides (NOx as NO2) 27.4 4.6 0.1 1.2 26.8 16.6 3.0 1.8 0.8 17.8(a) Nitrous Oxide (N2O) 61.4 0.0 0.2 0.1 0.2 37.7 0.0 0.1 0.1 0.1(a) Sulfur Oxides (SOx as SO2) 4.5 44.0 9.6 10.7 2.9 21.3 6.4 0.6(w) Acids (H+) 0.0 68.0 0.0 0.0 0.0 32.0 0.0 0.0(w) TN 60.4 0.0 0.0 0.0 0.0 39.6 0.0 0.0 0.0 0.0(w) TP 61.2 0.0 0.0 0.0 0.0 38.8 0.0 0.0 0.0 0.0(r) Land (ha) 59.1 0.0 0.0 0.0 40.9 0.0 0.0 0.0(r) Coal (in ground) 8.5 46.8 7.8 5.6 26.2 5.1(r) Natural Gas (in ground) 44.9 13.2 3.5 29.6 6.4 2.3(r) Oil (in ground) 0.3 6.5 53.9 0.2 3.2 35.9(r) Potassium Chloride (KCl, as K2O, in ground) 57.0 43.0(r) Phosphate Rock (in ground) 68.0 32.0Water Used (total) (m^3) 45.6 5.7 9.6 30.0 2.7 6.3
81

Table 29. Overall Annual Cradle-to-Farm Gate LCI Flows and Distributions among Processes, 

 Scenario 2: C-S with Stover Collection 

( Scenario 2: C-S w/ stover, allocation method 1)

Cradle to Farmgate Lifecycle Flows Corn+Stover Corn+Stover Corn+Stover Corn+Stover Corn+Stover Soy Soy Soy Soy Soy 

(in mt unless noted) Farm FN prod FP+FK prod energy prod energy farm Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 6.47E+04 2.93E-01 3.16E-02 2.84E-01 0.00E+00 2.64E+04 4.22E-03 7.46E-03 2.50E-03 0.00E+00 (a) Carbon Dioxide (CO2, fossil) 7.47E+05 2.24E+05 4.08E+04 2.88E+05 1.08E+04 5.54E+04 7.02E+03 1.08E+05 (a) Carbon Monoxide (CO) 1.78E+01 1.24E+02 2.88E+01 1.21E+03 2.56E-01 2.75E+01 3.45E+00 3.13E+02 (a) Methane (CH4) 6.04E+03 5.12E+02 8.63E+01 0.00E+00 8.70E+01 1.13E+02 1.30E+01 0.00E+00 (a) Nitrogen Oxides (NOx as NO2) 2.93E+03 9.95E+02 2.22E+01 1.18E+02 2.42E+03 1.15E+03 1.43E+01 2.13E+02 2.47E+01 9.34E+01 (a) Nitrous Oxide (N2O) 2.68E+03 0.00E+00 2.22E+01 3.84E+00 5.15E+00 6.07E+02 0.00E+00 5.09E+00 8.91E-01 1.86E-01 (a) Sulfur Oxides (SOx as SO2) 3.55E+02 2.37E+03 3.34E+02 3.66E+02 5.12E+00 9.20E+02 3.10E+01 3.34E+01 (w) Acids (H+) 1.78E-01 4.88E+03 1.06E-01 0.00E+00 2.56E-03 2.22E+03 2.74E-08 0.00E+00 (w) TN 1.23E+05 3.55E-01 8.66E-01 1.40E+01 0.00E+00 1.78E+03 5.12E-03 1.68E-01 6.24E+00 0.00E+00 (w) TP 3.75E+03 0.00E+00 5.85E-01 1.94E-02 0.00E+00 1.17E+03 0.00E+00 2.66E-01 1.59E-02 0.00E+00 

0.00E+00

(r) Land (ha) 1.91E+06 1.32E+06 (r) Coal (in ground) 8.51E+03 6.00E+04 3.59E+03 1.23E+02 1.42E+04 2.08E+03 (r) Natural Gas (in ground) 2.70E+05 5.37E+04 9.82E+03 3.89E+03 2.09E+04 5.68E+03 (r) Oil (in ground) 1.11E+03 1.64E+04 7.25E+04 1.59E+01 5.56E+03 3.94E+04 (r) Potassium Chloride (KCl, as K2O, in ground) 3.35E+05 4.80E+04 (r) Phosphate Rock (in ground) 2.39E+05 1.09E+05 Water Used (total) (m^3) 3.91E+06 3.10E+05 4.50E+05 5.63E+04 1.28E+05 2.90E+05 

Corn+Stover Corn+Stover Corn+Stover Corn+Stover Corn+Stover Soy Soy Soy Soy Soy

% of total Farm FN prod FP+FK prod energy prod energy farm Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 71.0 0.0 0.0 0.0 0.0 29.0 0.0 0.0 0.0 0.0(a) Carbon Dioxide (CO2, fossil) 50.4 15.1 2.8 19.4 0.7 3.7 0.5 7.3(a) Carbon Monoxide (CO) 1.0 7.2 1.7 70.2 0.0 1.6 0.2 18.1(a) Methane (CH4) 88.2 7.5 1.3 0.0 1.3 1.6 0.2 0.0(a) Nitrogen Oxides (NOx as NO2) 36.7 12.5 0.3 1.5 30.3 14.4 0.2 2.7 0.3 1.2(a) Nitrous Oxide (N2O) 80.6 0.0 0.7 0.1 0.2 18.3 0.0 0.2 0.0 0.0(a) Sulfur Oxides (SOx as SO2) 8.1 53.6 7.6 8.3 0.1 20.9 0.7 0.8(w) Acids (H+) 0.0 68.7 0.0 0.0 0.0 0.0 31.3 0.0 0.0(w) TN 98.6 0.0 0.0 0.0 0.0 1.4 0.0 0.0 0.0 0.0(w) TP 76.2 0.0 0.0 0.0 0.0 23.7 0.0 0.0 0.0 0.0(r) Land (ha) 59.1 0.0 0.0 0.0 40.9 0.0 0.0 0.0(r) Coal (in ground) 9.6 67.8 4.1 0.1 16.0 2.3(r) Natural Gas (in ground) 74.2 14.8 2.7 1.1 5.7 1.6(r) Oil (in ground) 0.8 12.2 53.7 0.0 4.1 29.2(r) Potassium Chloride (KCl, as K2O, in ground) 87.4 12.6(r) Phosphate Rock (in ground) 68.7 31.3Water Used (total) (m^3) 76.0 6.0 8.8 1.1 2.5 5.6
82

Table 30. Overall Annual Cradle-to-Farm Gate LCI Flows and Distributions among Processes, 

Scenario 3: C-C with Stover Collection 

( Scenario 3: C-C w/ stover)

Cradle to Farmgate Lifecycle Flows Corn+Stover Corn+Stover Corn+Stover Corn+Stover Corn+Stover TOTAL

(in mt unless noted) Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 1.15E+05 6.62E-01 4.10E-02 4.94E-01 0.00E+00 1.15E+05(a) Carbon Dioxide (CO2) 1.69E+06 2.95E+05 7.11E+04 5.71E+05 2.63E+06(a) Carbon Monoxide (CO) 4.02E+01 1.58E+02 5.02E+01 2.11E+03 2.36E+03(a) Methane (CH4) 1.37E+04 6.49E+02 1.50E+02 0.00E+00 1.45E+04(a) Nitrogen Oxides (NOx as NO2) 6.60E+03 2.25E+03 2.85E+01 2.06E+02 4.22E+03 1.33E+04(a) Nitrous Oxide (N2O) 6.25E+03 0.00E+00 2.85E+01 6.69E+00 8.98E+00 6.29E+03(a) Sulfur Oxides (SOx as SO2) 8.04E+02 3.72E+03 5.83E+02 6.38E+02 5.75E+03(w) Acids (H+) 4.02E-01 8.27E+03 1.84E-01 0.00E+00 8.27E+03(w) TN 2.82E+05 8.04E-01 1.06E+00 2.44E+01 0.00E+00 2.82E+05(w) TP 5.71E+03 0.00E+00 9.90E-01 3.38E-02 0.00E+00 5.71E+03(r) land (ha) 3.23E+06 3.23E+06(r) Coal (in ground) 1.92E+04 7.78E+04 6.26E+03 1.03E+05(r) Natural Gas (in ground) 6.10E+05 8.46E+04 1.71E+04 7.12E+05(r) Oil (in ground) 2.50E+03 2.43E+04 1.26E+05 1.53E+05(r) Potassium Chloride (KCl, as K2O, in ground) 3.77E+05 3.77E+05(r) Phosphate Rock (in ground) 4.05E+05 4.05E+05Water Used (total) (m^3) 8.84E+06 5.02E+05 7.84E+05 1.01E+07

Corn+Stover Corn+Stover Corn+Stover Corn+Stover Corn+Stover

% of total Farm FN prod FP+FK prod energy prod energy farm

(a) Ammonia (NH3-N) 100.0 0.0 0.0 0.0 0.0(a) Carbon Dioxide (CO2, fossil) 0.0 64.3 11.2 2.7 21.8(a) Carbon Monoxide (CO) 0.0 1.7 6.7 2.1 89.5(a) Methane (CH4) 0.0 94.5 4.5 1.0 0.0(a) Nitrogen Oxides (NOx as NO2) 49.6 16.9 0.2 1.5 31.7(a) Nitrous Oxide (N2O) 99.3 0.0 0.5 0.1 0.1(a) Sulfur Oxides (SOx as SO2) 0.0 14.0 64.8 10.1 11.1(w) Acids (H+) 0.0 0.0 100.0 0.0 0.0(w) TN 100.0 0.0 0.0 0.0 0.0(w) TP 100.0 0.0 0.0 0.0 0.0(r) Land (ha) 100.0(r) Coal (in ground) 18.6 75.3 6.1(r) Natural Gas (in ground) 85.7 11.9 2.4(r) Oil (in ground) 1.6 15.9 82.5(r) Potassium Chloride (KCl, as K2O, in ground) 0.0 100.0 0.0(r) Phosphate Rock (in ground) 0.0 100.0 0.0Water Used (total) (m^3) 87.3 5.0 7.7
83

Table 31. Summary of Changes in Flows and Incremental Percent Differences in LCI Flows Among Scenariosa

Scenario 2: C-S with stover collection (allocation method 1)

Cradle to Farmgate Lifecycle Flows Corn+Stover Corn+Stover Corn+Stover Corn+Stover Corn+Stover Soy Soy Soy Soy 

(in mt unless noted) Farm FN prod FP+FK prod energy prod energy farm Farm FN prod FP+FK prod energy prod ene

(a) Ammonia (NH3-N) 9.98E+02 3.33E-02 1.83E-02 5.17E-03 0.00E+00 0 0 0 0 (a) Carbon Dioxide (CO2, fossil) 8.48E+04 1.24E+05 -9.19E+03 -1.01E+05 0 0 -2.61E+04 -1(a) Carbon Monoxide (CO) 2.02E+00 7.61E+01 -7.53E+00 -2.60E+02 0 0 -2.07E+01 -6(a) Methane (CH4) 6.87E+02 3.17E+02 -2.20E+01 0.00E+00 0 0 -5.88E+01 0(a) Nitrogen Oxides (NOx as NO2) -1.07E+03 1.13E+02 1.33E+01 -2.22E+01 -7.24E+02 0 0 0 -6.82E+01 -2(a) Nitrous Oxide (N2O) -3.84E+02 0.00E+00 1.33E+01 -7.28E-01 -1.55E+00 0 0 0 -2.12E+00 -4(a) Sulfur Oxides (SOx as SO2) 4.04E+01 4.67E+02 -8.12E+01 -9.77E+01 0 0 -2.45E+02 7(w) Acids (H+) 2.02E-02 1.47E+02 1.97E-03 0 0 0 0.00E+00 -6.90E-02 (w) TN 1.40E+04 4.04E-02 5.93E-01 -2.01E+00 0 0 0 0.00E+00 -4.23E+00 (w) TP 1.91E+03 0.00E+00 2.03E-02 2.46E-07 0 0 0 0.00E+00 0 (r) Land (ha) 0 0 (r) Coal (in ground) 9.66E+02 3.47E+04 -6.39E+02 0 0 -7.01E+02 (r) Natural Gas (in ground) 3.06E+04 1.05E+04 -1.76E+03 0 0 -1.93E+03 (r) Oil (in ground) 1.26E+02 5.28E+03 -2.04E+04 0 0 -2.25E+04 (r) Potassium Chloride (KCl, as K2O, in ground) 2.71E+05 0 (r) Phosphate Rock (in ground) 7.18E+03 0 Water Used (total) (m^3) 4.44E+05 4.30E+04 1.65E+03 0 0 -3.13E+03 Scenario 3: C-C w/ stover collectionCradle to Farmgate Lifecycle Flows Corn+Stover Corn+Stover Corn+Stover Corn+Stover Corn+Stover TOTAL(in mt unless noted) Farm FN prod FP+FK prod energy prod energy farm change % change(a) Ammonia (NH3-N) 5.15E+04 4.02E-01 2.77E-02 2.15E-01 0.00E+00 2.51E+04 27.5(a) Carbon Dioxide (CO2, fossil) 1.03E+06 1.95E+05 2.11E+04 1.82E+05 1.07E+06 72.2(a) Carbon Monoxide (CO) 2.44E+01 1.10E+02 1.39E+01 6.39E+02 -2.35E+02 -13.6(a) Methane (CH4) 8.31E+03 4.54E+02 4.22E+01 0.00E+00 8.53E+03 124.5(a) Nitrogen Oxides (NOx as NO2) 2.60E+03 1.37E+03 1.96E+01 6.56E+01 1.08E+03 1.56E+03 19.5(a) Nitrous Oxide (N2O) 3.19E+03 0.00E+00 1.96E+01 2.12E+00 2.28E+00 2.59E+03 78.0(a) Sulfur Oxides (SOx as SO2) 4.89E+02 1.82E+03 1.67E+02 1.74E+02 1.43E+03 32.3(w) Acids (H+) 2.44E-01 3.54E+03 8.00E-02 0 1.32E+03 18.5(w) TN 1.73E+05 4.89E-01 7.83E-01 8.39E+00 0 1.71E+05 136.9(w) TP 3.87E+03 0.00E+00 4.25E-01 1.44E-02 0 2.70E+03 54.9(r) Land (ha) 1.32E+06 0.00E+00 0.0(r) Coal (in ground) 1.17E+04 5.25E+04 2.03E+03 4.91E+04 55.5(r) Natural Gas (in ground) 3.71E+05 4.14E+04 5.54E+03 3.85E+05 105.9(r) Oil (in ground) 1.52E+03 1.32E+04 3.35E+04 -1.92E+04 -14.3(r) Potassium Chloride (KCl, as K2O, in ground) 3.14E+05 2.66E+05 69.5(r) Phosphate Rock (in ground) 1.73E+05 6.43E+04 18.5Water Used (total) (m^3) 5.38E+06 2.34E+05 3.35E+05 5.47E+06 106.3

a change and percent increase relative to base case scenario
84

5. Results – Lifecycle Impact Assessment 

The environmental implications for C-S agriculture and stover harvesting were evaluated through 

assessment of three specific environmental impacts: eutrophication, acidification and green house gas 

generation. The impact of nitrate as a toxic constituent in groundwater was also evaluated, although it is 

recognized that this analysis integrates only one of many potential toxic components that would be 

released from the cradle-to-farm gate stages of the C-S or C-C lifecycles. 

Eutrophication Potential 

The relationship between fertilizer use in row crop agriculture and local and regional scale eutrophication 

problems has been recognized for decades (USEPA, 2000a). Both nitrogen an phosphorus contribute as 

nutrients used in the growth of excess aquatic biomass, although in certain cases, one or the other is the 

truly limiting nutrient. 

In life cycle assessments, equivalency factors are used to aggregate nitrogen and phosphorus species to 

quantify the overall eutrophication potential. The molar ratio (16:1) for the N:P content in aquatic cell 

mass is used to quantify the equivalency factors (Hauschild and Wenzel, 1998). If the higher N:P ratio of 

22:1 for eutrophic waters is used (Klaff, 2003), results would show greater contributions from TP and 

higher total eutrophication potentials with this higher ratio. 

The eutrophication potential calculated here includes only the (w) TN-SW and (w) TP LCI flows 

presented in Tables 21-23. None of the other nitrogen or phosphorus flows are significant relative to 

these releases. The eutrophication potential (mt as NO3-N equivalents) discharged to local streams and 

then the Mississippi River is compared to several different benchmarks to define the potential impact of 

these discharges. Bench marks include: 

• Background concentration of nitrate in streams and rivers (Table 2) 

• Recommended water quality standards for TN and TP (Table 2) (as total equivalent NO3-N) 

• Recommended 30% reduced load of 1980-1996 average TN load to the Gulf of Mexico (NOAA 

Integrated Assessment of Hypoxia in the Gulf of Mexico, Brezonik et al., 1999) 

Concentration limits were multiplied by the actual annual mean stream flow rate and converted to a 

eutrophication potential (TN+TP, as NO3-N) to determine a load. It was assumed that the eastern Iowa 

watersheds would reduce their average TN loads at the same percentage as the entire Mississippi River 

watershed. A target load that would limit the hypoxic zone to an acceptable size was determined as a 

30% reduction in the average TN load from E. Iowa (1980-1996) for comparison purposes. 

The calculated eutrophication potential for the 13 years of the study period is shown on Figure 34. The 

base case scenario is represented by the solid region of the bars, with the incremental increases associated 

with a switch to scenario 2 or 3 included as the diagonal hashed region of each bar. Many of the 

conclusions that can be drawn from this figure parallel those already discussed for individual LCI flows. 

• The eutrophication potential varies substantially by year, due primarily due to the increased 

rainfall and means of transporting nitrogen and phosphorus from the field to surface water. 

• TN loads contribute more to the total eutrophication potential than TP loads. 

• A switch to scenario 3 more than doubles the total eutrophication potential (~150% increase over 

the base case). Both the increased FN required due to the loss of the soy credit and the assumed 

higher TP leaching rate in the NT system contribute to this significant rise in the eutrophication 

potential. 
85

• Increases in the eutrophication potential for scenario 2 are less substantial (~20% increase over 

the base case) than in scenario 3. 

0

500

1000

1500

2000

2500

3000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Discharge to MSR, C-C+stover (1000 mt NO3- eq.)

TP stover

TN stover

TP base

TN base

0

500

1000

1500

2000

2500

3000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Discharge to MSR, C-S+stover (1000 mt NO3- eq.)

TP stover

TN stover

TP base

TN base

Figure 34. Eutrophication potential from TN and TP discharged from E. IA watersheds to the 

Mississippi River. Base case included in each as solid regions. Hash marks include 

the incremental addition associated with scenario 2 (bottom) and scenario 3 (top) 

The eutrophication potential for the base case is compared to the applicable benchmarks in Figure 35. 

The estimated discharge, expressed as an eutrophication potential, is much higher in all years than the 

load at a concentration equal to the recommended water quality standards. This indicates that 

unacceptably high levels of biomass growth, decay, and oxygen depletion will occur in the streams and 

rivers in eastern Iowa. As summarized in Figure 1, habitat destruction, loss of biodiversity, and an 

increase in water toxics all result from eutrophication. 

As the TN and TP are transported downstream in the Mississippi River, they have additional 

eutrophication consequences in the Gulf of Mexico where they contribute to hypoxia. The goal of a 30% 

reduction in the average TN load established by a federally endorsed task force on hypoxia (Brezonik et 

al., 1999) is shown as a straight line at ~500,000 mt as NO3-N. 
86

The E. IA contributions to the total eutrophication potential exceed this goal in 5 of the 13 years in this 

study. Modeling work by Scavia et al. (2003) suggest that the goal set by Brezonik et al. (1999) is too 

low and that a 40-45% reduction in average TN loads would be required to limit the size of the hypoxic 

zone. The 45% reduction in average TN loads would lower the acceptable eutrophication potential to 

421,000 mt as NO3-N. TN and TP contributions from C-S agriculture in E.IA would exceed this goal in 7 

of the 13 years. 

In contrast, the use of a constant fraction of FN (24%) to estimate the nitrate load to surface water, such as 

used in other LCAs, with average TP loads would predict that the eutrophication potential discharged 

from E. Iowa to the MSR is always less than the suggested ~500,000 mt NO3-N target required for a 30% 

load reduction to reduce hypoxia. This example clearly shows the necessity of incorporating the climatic 

variability in loads on actual environmental impact. 

It should be noted that the discussion above addresses only one source of TN and TP discharges within 

the eastern Iowa watersheds. Animal manure can also contribute additional nutrients to water bodies. 

Based on the work Becher et al. (2000), animal manures can contribute an additional 34% above the loads 

from FN. Adding this to the base case scenario shown in Figure 35 would cause the 1994-1997 loads to 

also exceed the TN load target of a 45% reduction. 

Increasing the nutrient loads to the Mississippi River and the Gulf of Mexico under scenarios 2 or 3 will 

move us further away from meeting necessary levels to limit local eutrophication or hypoxia conditions. 

For the C-S scenario with stover removal (#2), the eutrophication potential would be ~20% higher than 

the base case. The total load (including animal manure) would be above 421,000 mt as NO3-N (45% 

reduction goal) in all years except extreme drought. The very substantial increases in eutrophication 

potential loads associated with scenario 3 would certainly increase these loads above acceptable levels (30 

or 45% reductions in TN) in all years except 1988 and 1989. 

0

100

200

300

400

500

600

700

800

900

1000

1988 1990 1992 1994 1996 1998 2000

Discharge from E. IA to MSR (1000 mt NO3 eq.)

base case est., C-S only

background

maximum load - WQS

hypoxia goal - 30% reduced avg. TN load

Figure 35. Base case eutrophication potential discharged to the Mississippi River compared with 

suitable benchmarks 

Figure 36 compares the average discharge of TN and TP, expressed as eutrophication potential for the 

three scenarios. Any changes in the present system that result in increased fertilizer use or increased 

leaching will only exacerbate local eutrophication and hypoxia problems. Thus, any changes in 

agricultural practices that would allow stover collection should be designed to minimize greater levels of 

leaching. For the C-C scenario, the much higher discharge of TN and TP to a system that has already 
87

exceeded its capacity to assimilate nutrients makes it difficult to recommend this option for increased 

stover harvest rates. 

The much lower TN and TP discharges from C-S rotations with stover harvest makes this scenario much 

more attractive than scenario 3. Careful farming practices that limit the fertilizer addition to just that 

amount deemed necessary from quantitative soil analysis would probably be sufficient to further reduce 

the nutrient loads to surface water bodies to levels acceptable from a hypoxia standpoint in most years. It 

is likely that recommended water quality standards designed to limit local eutrophication problems would 

still be exceeded, however. 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

Scenario 1 Scenario 2 Scenario 3

Average Eutrophication Potential (mt as NO3-N)

(w) TP

(w) TN

Figure 36. Average eutrophication potentials for the three scenarios 

The analysis presented here is limited by several assumptions. Most importantly, 

• It was assumed that 45 kg/ha additional FN would be required for C-C rotations due to the loss of 

the symbiotic relationship with soy. 

• TP leaching rates were 1.6 times higher in the no till system 

• TN and TP discharges were assumed to not be correlated to erosion rates, which are substantially 

lower with no till practices. 

Each of these assumptions contributes to high-end estimates for incremental increases in nutrient 

discharges for the stover collection scenarios. None of these assumptions, however, would impact the 

predicted high nutrient discharges associated with the base case. The quality of predictions for the base 

case scenario is impacted most by the nitrate and phosphorus leaching models. Uncertainties in how 

animal manure and denitrification were included in these models could impact the conclusions drawn 

here. Additional work is required to quantify these uncertainties. 

Acidification Potential 

In this cradle-to-farm gate LCA, air emissions that contribute to acidification include NH3 and NO from 

nutrient use and plant growth at the farm and NOx and SOx from fertilizer and energy production. HCl 

and HF emissions were also quantified, but they were insignificant. Air emissions contributing to 

acidification were aggregated into an overall acidification potential for each of the scenarios using the 

equivalency factors presented by Hauschild and Wenzel (1998). Average values over the 13-year period 

were used to quantify air emissions associated with the farming activities. 
88

The overall acidification potential for scenario 1 is shown in Figure 37. Ammonia releases from the farm 

are the predominant contributor to the overall acidification potential. Volatilization of FN contributes a 

small fraction to this release. Plant senescencing is the biggest source of ammonia emissions. As 

described above, there are significant uncertainties quantifying this source. The values used here are 

approximately half those used in some studies, although others ignore this term completely. Thus, the 

range for ammonia could range from near zero to almost twice the emissions shown in Figure 36. 

NO released during soil processes of nitrification and denitrification contributes 44% of the total NOx (as 

SO2). Much of this is released in the necessary nitrification of ammonia fertilizers into nitrate. Some 

additional NO is released during soil mineralization. The 44% estimate here is much lower than Sheehan 

et al’s (2002) estimate that 84% of the total NOx is due to soil processes. Based on IPCC guidelines, they 

estimated that 5% of the total nitrogen in FN is emitted as NO. The NO emissions estimated with the 

model used here, which more directly relates NO to actual nitrification and denitrification processes, were 

determined to be ~2% of the FN (Table 19). 

Other sources of NOx and SOx are mostly related to the combustion of fossil fuels for energy production 

and farming activities. The apparent contributions of FK+FP production to these emissions are dominated 

by fossil fuel combustion for the required electricity. 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

(a) NOx (a) SOx

Acidification potential (NOx and SOx) (mt as SO2)

Corn farm Soy farm

corn FN prod corn FP+FK prod

corn energy prod corn energy farm

soy FN prod soy FP+FP prod

soy energy prod soy energy farm

0

50,000

100,000

150,000

200,000

250,000

(a) NH3 (a) NOx (a) SOx total

Acidification potential (mt as SO2)

Corn farm Soy farm

corn FN prod corn FP+FK prod

corn energy prod corn energy farm

soy FN prod soy FP+FP prod

soy energy prod soy energy farm

Figure 37. Cradle-to-farm gate acidification potential for Scenario 1: C-S with no stover harvest 

base case. The bottom figure provides a detailed view of the same NOx and SOx data 

included in the top figure. 
89

Figure 38 provides a comparison in the average acidification potential for the three scenarios. There is 

less difference in acidification among the scenarios compared with eutrophication. This is due to the very 

high contribution of plant senescencing to the total ammonia emissions. This process occurs regardless of 

the farming practices. The higher acidification potential for scenario 3 is due to the higher senescing rate 

from corn versus soy. The slightly lower NOx contribution in scenario 2 is due to the reduced soil 

mineralization – and associated NO emissions – with the no till practice. This benefit does not contribute 

substantially to the overall acidification potential. 

0

50,000

100,000

150,000

200,000

250,000

Scenario 1 Scenario 2 Scenario 3

Average Acidification Potential (mt as SO3)

(a) SOx

(a) NOx

(a) NH3

Figure 38. Average acidification potentials for the three scenarios 

Water Toxics 

Nitrate is the only toxic species considered in the analysis conducted here. Herbicides and slightly 

soluble species from fuel spills at the farm are important toxic constituents that should also be considered 

in a more comprehensive analysis of this impact category. 

The MCL for nitrate-nitrogen (10 mg/L) can be considered a reasonable benchmark for defining the 

impact of nitrate on water quality from a toxic perspective. This impact is especially important for ground 

water ingestion due to the high percentage of people in rural areas that use ground water as drinking 

water. 

The model used for the LCI predicts that 4,420 mt of NO3-N are leached into the groundwater over the 13 

year period in the base case. Although concentration data were not predicted in this model, the total NO3-

N load can be used to estimate the volume of water contaminated by the leaching of FN. Assuming water 

is contaminated at the maximum allowable concentration, the nitrate could pollute 4.4 x 108

 m3 water. 

This assumes that none of the nitrate is denitrified and that groundwater flows are slow relative to the 13-

year period of this study. Assuming the elevated nitrate concentrations remain in the top 2 m of the 

saturated zone and an average porosity of 0.25, this water could include an area of 8.8 x 104

 ha. This 

represents 3.1% of the total C-S farmland. This is a low estimate compared with the 9 - 11.6% of the 

shallow groundwater wells in E. Iowa that Kross et al. (1993) observed to have concentrations greater 

than the MCL. Uncertainty in this analysis includes the leaching rate (3% of excess nitrate; eqn. [26]) and 

the depth of water contaminated over the 13-year period. Uncertainty in both of these factors could result 

in greater areas with groundwater contamination at a level greater than acceptable. 
90

This same analysis can be applied to the much higher predicted NO3-N mass leached to groundwater from 

the continuous corn scenario (#3). In this case, 121,108 mt NO3-N are predicted to leach into the 

groundwater over the 13-year period. The orders of magnitude increase (x27.4) is due to the increased 

FN requirements for corn versus soybeans. This also includes 45 kg-N/ha additional nitrogen required to 

replace the symbiotic benefits of the C-S rotation. The total leaching rate would be lower without this 

additional FN, but still substantially higher than in the base case. With the rough estimation approach 

applied above, this would result in 1.2 x 1010 m3

 water over an area of 2.4 x 106 ha, which is 84% of the 

total farmland in the eastern Iowa region that could be estimated to have NO3-N concentrations greater 

than the MCL. 

Although there are numerous uncertainties in this analysis, it is clear that switching to scenario 3 (C-C) 

could lead to a substantial rise in exposure of rural populations to unacceptably high nitrate 

concentrations through their drinking water. 

The major uncertainties in this analysis include: 

• The required FN rate for C-C, NT operation 

• Quantity of nitrate that leaches into the saturated groundwater zone in this region with 

significant tile drainage 

• Extent of denitrification of the nitrate in the groundwater over the 13-year period 

• Depth of contaminated groundwater 

The application of a numerical model that improves the nitrate leaching estimates (e.g., RZWQM, 

GLEAMS, AGNPS) could substantially reduce the uncertainties associated with this analysis. 

Green House Gas Emissions 

Green house gas emissions associated with fossil fuel use and soil processes generating N2O were 

calculated. Equivalency factors for 100-year global warming potential (GWP) were used to convert CH4

and N2O emissions to CO2 equivalents (Hauschild and Wenzel, 1998). The total global warming potential 

for the base case scenario is shown in Figure 39. Carbon dioxide emissions from fossil fuel combustion 

are the predominant GHG. N2O from soil processes is also significant, however, contributing 41% of the 

total GWP. 

Data in Figure 39 does not include carbon dioxide removed from the system as biomass or a net increase 

in the soil carbon. Brenner et al. (2001) used the CENTURY model to estimate increases in carbon 

sequestered in soil in each Iowa county. These data were used to estimate the carbon sequestered on C-S 

acreage in each of the counties considered here. On average, 1.1 (±0.5) mt CO2/ha are sequestered under 

current conditions. This translates to 3.2 million mt/y CO2 sequestered in the soil on C-S cropland. 

Comparing this value to the GHG emissions shown in Figure 40, it can be concluded that the carbon 

sequestered in the soil is sufficient to balance the GWP associated with fossil fuel energy combustion and 

soil processes for scenarios 1 and 2. 

Assuming an average carbon content of 0.4 kg C/kg dry mass in the corn and soy grain, another 30 

million mt/y CO2 is sequestered in grain that leaves the system as defined here. Much of this is released 

in a relatively short time after the grain is used for feed or fuel production, so has little impact on the net 

GWP. 
91

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

CO2 fossil CH4 N2O Total

Global Warming potential (mt as CO2)

Corn farm Soy farm

corn FN prod corn FP+FK prod

corn energy prod corn energy farm

soy FN prod soy FP+FP prod

soy energy prod soy energy farm

Figure 39. Average global warming potential for the base case scenario 

Figure 40 illustrates the average GWP for each of the three scenarios. The substantial increases in GWP 

noted for scenario 3 are related to the increase in FN use. This increase translates into an increase in 

energy consumed to manufacture the FN as well as N2O released in nitrogen transformation processes. 

The slight reduction in GWP for scenario 2 is due to the assumed reduction in soil mineralization with no 

till practices. This reduction would also be accompanied by carbon sequestration at rates even higher 

than estimated by Brenner et al. (2001) for current conditions. 

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

Scenario 1 Scenario 2 Scenario 3

Average global warming potential (mt as CO2)

N2O

CH4

CO2 fossil

Figure 40. Average global warming potentials for the three scenarios 

Summary of Life Cycle Impacts 

The section above quantifies changes in eutrophication, global warming and acidification potentials for 

these three impact categories to provide a clearer picture of the net impacts associated with stover 

collection. The qualitative discussion of the toxicity of nitrate in groundwater does not provide a complete 
92

accounting of the human toxicity potential and is not considered further. Table 32 summarizes the total 

impacts for each of these three categories as well as the percent change for each of the stover collection 

scenarios relative to the base case. 

Table 32. Summary of Life Cycle Impact Analysis 

Scenario 1 Scenario 2 Scenario 3 Impact Category Total Loads from E. Iowa (1000 mt) 

Eutrophication Potential (as NO3-N) 587 711 1,699 

Acidification Potential (as SO2) 219 219 232 

Global Warming Potential (as CO2) 2,886 2,810 4,924 

 

% increase over base case 

Eutrophication Potential (as NO3-N) -- 21 189 

Acidification Potential (as SO2) -- 0 6 

Global Warming Potential (as CO2) -- -3 71 

 

Normalized Loadsa

 

(mt/mt stover harvested) 

Eutrophication Potential (as NO3-N) -- 0.072 0.093 

Acidification Potential (as SO2) -- 0.022 0.013 

Global Warming Potential (as CO2) -- 0.286 0.271 

a

 using 13-year average stover harvest yields: ~9.8 million mt for scenario 2 and 18.2 

million mt for scenario 3 

When compared on a normalized basis (per mt stover harvested), scenario 3 looks slightly better than 

scenario 2 in terms of acidification and global warming. The eutrophication potential for scenario 3, 

however, is almost 30% higher than scenario 2. 

Of these three categories, eutrophication could be considered the most important because the discharge of 

nutrients even under the base case already exceeds measures of acceptable discharge levels, whereas the 

global warming potential is balanced with agricultural benefits of carbon sequestration in soil. Based on 

eutrophication, it is clear that switching to a continuous corn scenario could be very detrimental to water 

quality. Increases in local eutrophication and, more importantly, hypoxia in the Gulf of Mexico would be 

much higher for C-C that C-S, even when normalized to the amount of stover collected. 

There are significant uncertainties in the numerical values presented in Table 32 due to the assumptions 

required in this analysis. The LCI for scenario 1 was calibrated and verified as much as possible with 

mass balances and historical data for farming and water quality. Several assumptions were required to 

extrapolate the nutrient fate model to the other two scenarios. For example, the quantitative results from 

the eutrophication potential in scenario 2 depends on the assumed increased phosphate leaching in a no 

till scenario and the additional fertilizer requirements due to the removal of corn stover. In addition, 

estimates for scenario 3 are affected by the assumption that more fertilizer is required for corn in a 

continuous corn system versus a corn-soybean rotation. The importance of nitrogen cycle symbiosis in a 

C-S system is known, however, the quantitative amount of extra FN required in a C-C system to replace 

this symbiosis is not well understood. Processes such as this should be studied in more detail to 

determine the sensitivity of the conclusions to these uncertainties. 
93

6. Summary and Recommendations 

Fertilizers used to increase the yield of crops used for food or bio-based products can migrate through the 

environment and potentially cause adverse environmental impacts. Nitrogen fertilizers have a complex 

biogeochemical cycle. Through their transformations and partitioning among environmental 

compartments, they can contribute to eutrophication of surface waters at local and regional scales, 

groundwater degradation, acid rain, and global warming. Phosphate fertilizers have a simpler fate in the 

environment, although leaching of soluble and bound phosphorus is an important contributor to 

eutrophication. 

Eutrophication is considered one of the most pervasive problems affecting water quality in the United 

States, especially in the Midwest where fertilizers are used extensively for agriculture. In the process of 

eutrophication, the presence of excess N and P nutrients allows over production of plant biomass in 

waterways. The eventual degradation of this biomass consumes oxygen resulting in hypoxic conditions 

(low oxygen concentrations) in the most severe cases of eutrophication. Fertilizer use on corn and 

soybean farms in the Midwest is considered one of the primary contributors to the growing hypoxic zone 

in the Gulf of Mexico. Through a combination of excessive nutrient loads and hydrodynamic conditions, 

a region along the coast of Louisiana that is approximately the size of the State of Massachusetts is 

considered ecologically dead most summers. This results in the death of species that are not sufficiently 

mobile and changes in biodiversity and food webs throughout the region as larger species migrate to other 

locations. Researchers for federal agencies have suggested that reducing the average nitrogen load by 

30% will help to limit the hypoxic zone to acceptable levels. Other researchers predict that a 40-45% 

reduction in TN would be required to meet this goal. 

With an increase interest in the use of corn, soybeans and corn stover for bio-based products and fuels, it 

is important to understand the relative environmental benefits and deleterious impacts associated with this 

growing market. A team of researchers lead by NREL completed a life cycle assessment (LCA) for 

stover harvest and conversion to ethanol for transportation fuels. This report focused on the green house 

gas emission benefits associated with biofuels as balanced by potential detriments to soil health (carbon 

content and erosion). Eutrophication was identified as an important issue by stakeholders involved with 

this project, but resources prevented this environmental impact category from being addressed. Thus, the 

goal of the work presented here was primarily to fill that gap, thereby providing a more complete picture 

of the overall environmental impacts associated with bio-based products. 

The mathematical model developed here to track the flows of nutrients through the environment and 

harvested crops quantifies the distribution of nutrients as a fraction of the available N or P. Knowledge of 

specific processes was integrated into this model as much as possible to correlate emissions to the specific 

processes that affect them. For example, N and P leaching are correlated to rainfall and NO and N2O 

emissions vary between conventional and no till practices due to reduced soil mineralization. 

The nutrient flow model was applied to a set of watersheds covering most of the eastern half of Iowa for a 

13-year period (1988 – 2000) that includes both drought and flood years. Water quality data were 

available for these watersheds allowing the TN and TP leaching estimates to be calibrated to actual 

measured discharges. This approach has not been used before with LCAs for non-point source pollutants 

and provides a much better approach for quantifying the variability in leaching rates than approaches used 

by other researchers. The resulting leaching model incorporates variability in TN and TP loads due to the 

natural variability in annual rainfall. Although this approach helps to improve the estimates and 

variability in non-point emissions from nutrient leaching, it also makes the model and approach very site 

specific. Thus, the specific linear model presented here to estimate the fraction of fertilizer that leaches 

should not be applied to other locations. For example, western Iowa has sandier soil, which would result 

in greater quantities of nutrients to infiltrate to groundwater and less surface runoff. Although the specific 

values cannot be used for other sites, the general approach and framework is valuable and can be used at 

other sites that have suitable water quality data. 
94

The nutrient leaching model was coupled with LCA data describing emission occurring during fertilizer 

manufacture and energy production and consumption, providing a cradle-to-farm gate life cycle inventory 

for corn, soybeans and stover. Three separate scenarios were considered: Scenario 1, the base case, 

considered corn-soybean rotations (C-S) with conventional till and no stover collection; Scenario 2 was 

C-S with no till and stover collection at a maximum rate allowable with acceptable erosion levels; and, 

Scenario 3 was the same as 2 except for continuous corn (C-C) rather than C-S. The LCI for each of 

these scenarios was quantified and used to determine eutrophication, acidification and global warming 

potentials for the three scenarios. 

The TN and TP leaching models that correlate the fraction of FN and FP that leaches to annual rainfall in 

each county provides a good representation of the measured variability in nutrient loads discharged to the 

Mississippi River. The ability to calibrate this model reduces the uncertainty in the leaching estimates for 

the base case scenario than would be possible without the site-specific water quality data. There are, 

however, more significant uncertainties associated with the allocation of the total load between corn and 

soy and the predictions of loads when moving to the no till, stover collection scenarios. Two radically 

different approaches were used to estimate the allocation of nitrogen flows between corn and soybeans. 

For example, between the two methods, between 60 and 99% of the TN leached from the overall C-S 

system would be allocated to corn, with the balance allocated to soybeans. This difference stems from a 

poorly understood symbiosis with the C-S rotation. It is not apparent how to best integrate this into an 

LCA. 

Analysis of the LCI data identified several variables and processes for which the model is very sensitive. 

These include: 

• Allocation of nitrogen flows between corn and soybeans 

• Ammonia released during plant senescencing 

• TN and TP leaching models, especially contributions of manure to total load to the MSRB 

• Fertilizer use and fate in a NT system versus convention till 

• Fertilizer requirements for a C-C versus C-S system 

• Actual erosion rates 

• Soil mineralization rates in a no till versus conventional till system. 

Further work to improve the values of the parameters is required, as well as quantification of the 

sensitivity of the conclusions drawn here to uncertainties in these processes. The overall conclusions 

presented below do not integrate any sensitivity analysis at this point. 

The primary focus of this research was the eutrophication potential associated with the three scenarios. 

TN and TP flows to the Mississippi River and Gulf of Mexico were aggregated into an overall 

eutrophication potential, expressed as equivalent mass of NO3-N. The eutrophication loads predicted 

through the nutrient flow model developed here were compared to the target reduction in TN flows to 

limit the size of the Gulf of Mexico hypoxic zone and recommended water quality standards to control 

eutrophication at a local level. 

The results of this analysis show that the eutrophication potential for the base case already exceeds 

acceptable limits. TN and TP discharges from C-S lands exceed the maximum loads defined by the 

proposed water quality standards each of the 13 years of this study. Limits established by the goal of a 

30% reduction in the average TN load are also exceeded in approximately half of the years. If additional 

sources of nutrients discharged from the study area are considered, it is estimated that the eutrophication 

potential is higher than recommended except under drought conditions. 

Changing the current conditions to harvest stover for biofuels production increases the eutrophication 

potential above the base case. The C-S scenario (2) results in a 21% increase in the eutrophication 

potential, while the C-C scenario (3) causes almost triple the total load of TN and TP (as equivalent NO3-

N). In either case, the increased in load is to a system that has already exceeded its assimilative capacity 
95

for nutrients. For the C-S system, it is likely that careful management of fertilizers used at the farm could 

help to limit nutrient leaching and allow stover collection. With the very high nitrogen demand in a C-C 

system, however, it is not likely that management practices could be sufficient to overcome the 

detrimental effects of eutrophication resulting from the high leaching rates. 

The high nitrogen use in the C-C scenario also increases the use of fossil fuels for fertilizer production 

and associated emissions of species contributing to acid rain and global warming (GWP) potentials. The 

increase in acidification potential (+6% over the base case) is attributed to increases in NO production in 

soils with increased FN use and increased NOx from fossil fuel consumed to generate the increased 

energy necessary for fertilizer manufacture. Increases in the global warming potential (+71% over the 

base case, not including benefits of carbon sequestered in soil or crop) are attributed to methane emissions 

from natural gas used in FN manufacture and N2O emissions from nitrification of the additional FN. 

Scenario 2 (C-S) actually reduces the global warming potential relative to the base case (-3%) and has 

essentially no impact on acidification. The reduction in the GWP is related to the reduced level of soil 

mineralization with no till and the resulting reduction in N2O emissions. 

Human and ecological toxicity are also important impact categories for agricultural activities. Nitrates 

can contribute to human health impacts through drinking water from ground or surface water sources. 

Herbicides, which were not considered here, are as effective in waterways in killing plant life as they are 

on fields. The eco-toxicity of these agrichemicals should also be considered to provide the most 

comprehensive understanding of the environmental impacts associated with bio-based products. It is 

expected that higher herbicide application rates will be used with the no till practices required for 

sufficient stover collection. 

Based on the results presented thus far and the acknowledged limitations of the model – the following 

recommendations for continued analyses can be made: 

• Consider stover only from C-S rotations in further analyses 

• Improve the erosion modeling to incorporate variability with rainfall. This could help to improve 

leaching models by quantifying changes in sediment-bound nutrient loads in a NT versus 

conventional till system. 

• Use the framework developed here that utilizes water quality data to generate leaching models for 

herbicides. LCI data for herbicide manufacture will also need to be developed. 

• Utilize additional resources to improve predictions of nutrient fate in a NT versus CT system and 

perform a sensitivity analysis on these processes. 

• Compare results to related studies and guidelines currently used to define the acceptability of biobased products, especially for federal procurement. 

The generation of this life-cycle data was required to provide a more comprehensive understanding of the 

environmental impacts associated with increased use of biomass for fuels and other products. The LCA 

quantifies impacts on very different components of the environment, but does not judge which of these 

components are more important. The LCA results are necessary, but not sufficient to allow decisions 

regarding future energy sources to be made. In the United States, the goal of current policies is to reduce 

our dependence on imported energy. The LCA results can be used to quantify the environmental benefits 

and detriments associated with this goal and can help identify key concerns that should be addressed as 

we move forward with this goal (e.g., improved nutrient management to reduce water quality 

degradation). At some point, a balance needs to be defined between the various goals of different interest 

groups in a manner that then overall environmental impact from biomass fuels and products is considered 

acceptable, leading to a sustainable materials and energy source for the future. 
96

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Appendix A – Definition and Quantification of Variables Used in Nutrient 

Flow Model 

Table A-1: Parameters describing flows through the systema

Symbol Definition Sources of information 

LNH3 Total ammonia load added to the system Calculated, equation [13] 

Lf Total ammonia fertilizer load Calculated, eqn. [14] 

Lf

P

 Total phosphorus fertilizer load Calculated, eqn. [39] 

Lf

K

 Total potassium fertilizer load Calculated, eqn. [44] 

Latm,i Atmospheric deposition of i=NH3/NH4+ or 

NO3/HNO3 Calculated, eqn. [15] and [22] 

Lsoil min NH3-N load generated by soil mineralization Calculated, eqn. [16] 

Lcrop min

NH3-N load generated by crop residue 

mineralization Calculated, eqn. [17] 

Lvol

Total NH3-N volatilized from ammonia 

fertilizer Calculated, eqn. [18] 

Lsenes Total NH3-N lost during plant senescencing Calculated, eqn. [19] 

Lni nitrate load generated though nitrification Calculated, eqn. [20] 

LNO3 Total nitrate load from all sources Calculated, eqn. [21] 

Lharv

Nitrogen removed from the system with the 

crop and stover harvest Calculated, eqn. [23] 

LP

harv

Phosphorus removed from the system with 

the crop and stover harvest Calculated, eqn. [40] 

LK

harv

Potassium removed from the system with the 

crop and stover harvest Calculated, eqn. [45] 

Lres Nitrogen bound in crop residues left on field Calculated, eqn. [24] 

LSW

Nitrogen leached to surface water via runoff 

and tile/base flow drainage Calculated, eqn. [25], [38] 

LN

SW,C

Nitrogen leached to SW that is allocated to 

corn, allocation method 2 Calculated, eqn. [47] 

LP

SW

Phosphorus leached to surface water via 

runoff and tile/base flow drainage Calculated, eqn. [41], [43] 

LMS Nitrogen discharged to the Mississippi River Calculated, eqn. [37] 

LGW

Nitrate leached to groundwater systems 

(below zone influenced by tiles) Calculated, eqn. [26] 

Lde Total nitrate lost via denitrification Calculated, eqn. [27] or [28] 

Lde,i

Nitrate lost due to denitrification in a specific 

compartment I=dr, ri, gw, Gu) Calculated, eqn. [29a – 29e] 

Limm Nitrate immobilized as organic-N in SOM Calculated, eqn. [30] 

Lfix

Nitrogen added to the system through 

fixation by soy Calculated, eqn. [31] or [32] 

Ler

Nitrogen transported to surface water 

through soil erosion Calculated, eqn. [33] 

LN2O Nitrous oxide emissions Calculated, eqn. [34] 

LNO Nitrogen oxide emissions Calculated, eqn. [35] 

LN2 Nitrogen emissions Calculated, eqn. [36] a

 all flows expressed as N, P or K (mt yr-1) 
104

Table A-2: Rates required for nutrient flow calculations (kg ha-1 yr-1 unless otherwise noted) 

Symbol Definition Sources of information 

Nf,i Annual historical fertilizer rate per 

area fertilized, i=corn or soy 

USDA NASS data base and USDA Census reports, IA 

average values (http://agcensus.mannlib.cornell.edu/, 

http://www.usda.gov/nass/pubs/histdata.htm (Higher 

values - +45 kg/ha used for C-C systems, Gentry et al., 

2001) 

Nf,st

Additional NH3-N added to replace 

amount removed with corn stover Calculated, eqn. [1b] 

Nsenes,i

Ammonia loss through plant 

senescencing (i=corn, soy) 

Literature values used initially (Burkart and James, 1999, 

Goolsby et al., 1999) (Nsenes,ci =50-60; Nsenes,soy = 45). 

Calibrated values much lower (Nsenes,ci =30; Nsenes,soy = 20) 

Hi Harvest yield for i=corn or soy 

USDA NASS data base and USDA Census reports for 

each county (http://agcensus.mannlib.cornell.edu/, 

http://www.usda.gov/nass/pubs/histdata.htm, converted 

with bulk densities – 56 lb/bu, corn and 60 lb/bu soy 

(USDA PLANTS database) 

Hres,min Stover that must remain on the field Sheehan et al. (2002) and Nelson (2004) – estimates for 

C-C and C-S rotations for IA counties, average rain 

Qrain Annual rainfall (mm yr-1) 

Data for centroid of each county determined from PRISM 

Spatial Climate Analysis Service, 1988 – 2000. Varies by 

county, year http://www.ocs.orst.edu/prism/ 

Nfix,N Soy nitrogen fixation rate Jordan and Keller (1996) Nfix,N =78 (range = 15-310) 

Ni Grain yield (i=corn, soy) 

USDA NASS Quick Stats database for bu/ac yields – 

varies by country, year 

(http://www.nass.usda.gov:81/ipedb/). With bulk densities 

use for conversion (56 dry-lb corn grain/bu; 60 dry-lb 

soy/bu) (USDA plants database) 

Nst Stover harvest yield 

Calculated based on required residue to meet tolerable 

erosion (Sheehan et al., 2002; Nelson, 2004), varies by 

county, year 

Table A-3: Other parameters 

Ai,j

Crop area (ha) of i = corn or 

soybeans, j=planted, harvested 

USDA NASS data base and USDA Census reports for 

each county (http://agcensus.mannlib.cornell.edu/, 

http://www.usda.gov/nass/pubs/histdata.htm

Aws Area of watershed (ha) 

Approximated by county boundaries. County areas from 

US Census (2000). 

http://quickfacts.census.gov/qfd/maps/iowa_map.html 

Ci

Concentration of i=ammonia, nitrate 

or sulphate in wet deposition (mg/L) 

Concentrations from historical data at three locations near 

Eastern Iowa, averaged over system for each year. 

National atmospheric deposition program, 

http://nadp.sws.uiuc.edu/

Msoil Mass of soil in 1 ha, 30 cm deep Schepers and Moiser, 1991 

(assuming bulk density = 1480 kg/m3

) 
105

Table A-4. Fractions describing distribution of nutrient flows (unitless) 

Symbol Definition Value Range Source of 

information 

ff,i,j

Fraction of crop acreage (i=corn or 

soy) that fertilizer (j=N, K, P) is 

applied 

Historical data used (USDA NASS) 

fi,j

Fraction of crop (i=corn, soy, 

stover) that is nutrient (j=N, K, P) See Table A-4 

fvol

Fraction of ammonia fertilizer that 

volatilizes 0.03 0.0 – 0.15 

Meisinger and 

Randall (1991) for 

injected anhydrous 

ammonia. Puckett et 

al. (1999) 5-15%, for 

AN NH3. 

fDD,NH3

Ratio dry deposition to wet 

deposition, NH3/NH4 0.11 0.13 – 0.7 

(all N species) 

Goolsby et al., 1999 

(MRB) 

fDD,NO3

Ratio dry deposition to wet 

deposition, NO3, HNO3 0.41 0.13 – 0.7 

(all N species) 

Goolsby et al., 1999 

(MRB) 

fni Fraction of ammonia that is nitrified 0.95 0.90 – 1.00 

deVries et al. (2003) 

value for moist to 

wet Loess soils 

fni,N2O

Fraction of nitrogen that is nitrified 

and emitted as N2O 0.005 0.005 – 0.02 deVries et al. (2003) 

fni,NO

Fraction of nitrogen that is nitrified 

and emitted as NO 0.01 0.01 – 0.03 deVries et al. (2003) 

fom

Fraction of soil that is organic 

matter 

County-specific data from Brenner et al. (2001) used 

(average = 2.7%, range 2.4 – 3.1%) Goolsby et al. 

(1999) used 3.5% for IA average value 

fom,N

Fraction of organic matter that is 

nitrogen 0.03 

Meisinger and 

Randall, 1991; 

Schepers and 

Moiser, 1991 

fmin,c(t-1)

Fraction of soil org-N that is 

mineralized in a year after corn was 

grown 

0.014 0.01 – 0.03 

Range from 

Schepers and 

Moiser, 1991, 

specific values from 

Gentry et al. (2001) 

data. Lower values 

used for NT.a

fmin,soy(t-1)

Fraction of soil org-N that is 

mineralized in a year after soy was 

grown 

0.02 0.01 – 0.03 

Range from 

Schepers and 

Moiser, 1991, 

specific values from 

Gentry et al., data. 

HIc

Harvest index for corn – amount of 

corn stover generated per harvest 

of corn grain 

1.0 0.75 – 1.4 

Gupta et al. (1979) ; 

range from 

Pordesimo et al., 

2004 (higher values 

for early harvest). 

Linden et al. (2000) 

0.783 ±0.11 

HIsoy

Harvest index for soy – amount of 

soy straw generated per harvest of 

soybean harvest 

1.5 Gupta (1978) 
106

fi,SW

Fraction of total fertilizer (i=N, P) 

that is leached into tiles and drains See regression analyses, Table 10, eqn [43] 

f

P

sw,NT

Factor to account for increased 

leaching of TP from NT practices 1.6 0 – 5.4 

Gaynor and Findlay 

(1995); McIsaac et 

al. (1995) 

fNO3,gw

Fraction of net anthropogenic 

nitrogen that is leached into 

groundwater 

0.03 -- Howarth et al., 1996 

fde

Fraction of anthropogenic nitrate 

that is denitrified 0.1 0.06 – 0.25 

Meisinger and 

Randall, 1991; 

Howarth et al., 1996 

fde,s

Fraction of available soil nitrate that 

is denitrified 0.5 0.4 – 0.95 

deVries et al. (2003), 

higher values for 

wetter soils 

fde,gw

Fraction of nitrate leached to GW 

that is denitrified 0.4 0.4 – 0.95 

Galloway (2003); 

deVries et al. (2003), 

higher values for 

wetter soils 

fde,dr

Fraction of nitrate in tile drains that 

is denitrified 0.0 0.4 – 0.95 

deVries et al. (2003) 

(loss already 

integrated into Lsw

calculation) 

fde,ri

Fraction of nitrate discharged to 

rivers that is denitrified * 0.3 – 0.7 

deVries et al. (2003) 

* see narrower 

application of ranges 

based on river flow 

rate (Table 8) 

fde,Gu

Fraction of nitrate discharged to the 

Gulf of Mexico that is denitrified 0.9 > 0.8 deVries et al. (2003) 

fde,NO

Fraction of nitrogen, which is 

denitrified, that is emitted as NO 0.01 0.01 – 0.02 deVries et al. (2003) 

fde,N2O

Fraction of nitrogen, which is 

denitrified, that is emitted as N2O 0.015 0.01 – 0.06 

deVries et al. (2003), 

higher values for 

peat soils 

ffix

Fraction of N in soy plant that is 

attributed to N2 fixation 0.65 0.5-0.8 

Range for soy with 

60-100 kg/ha 

available N in soil. 

Lower end of range 

for fom > 0.03 

(Meisinger and 

Randall, 1991) 

fi,imm

Fraction of nitrate (i=fertilizer or atm 

dep.) that is immobilized as org-N 0.25 0.4±25-50% Goolsby et al. (1999) 

fer,sed

Fraction of soil eroded that is 

delivered to surface water 0.51 Sheehan et al. 

(1998) 

a

 lower mineralization rates for no till result in smaller fraction of total nitrogen in SOM being released by 

mineralization. Based on data quantifying increased carbon sequestration rates in Iowa for NT 

(Brenner et al., 2001) (2.42 million mt C sequestered in Iowa with NT, 1.57 million mt C sequestered 

with conventional till, suggests that 54% difference to NT. fmin,i reduced by this factor for NT. 
107

Table A-4. Nutrient content in cropsa

Plant 

material 

frac N frac P frac K 

corn grain 0.0150b 0.00267c 0.0034

corn stover d 0.00010 0.0150

soy bean 0.0650e 0.00653 c 0.0154

soy straw 0.0085e 0.00060 0.0057 a from USDA, NRCS, The PLANTS Database unless otherwise noted (Version 3.5, http://plants.usda.gov. National 

Plant Data Center, Baton Rouge, LA, 2004) b

 0.015 used by Sheehan et al. (2002), typical range 0.0135 – 0.0175 (Meisinger and Randall, 1991), 0.0164 in 

PLANTS database c

 value in table from Goolsby et al. (1999). These lower values used to help with mass balance. Values from 

PLANTS data base are fc,P = 0.0031 and fs,P = 0.0067. d

 0.0045 used by Sheehan et al. (2002), 0.0098 in PLANTS database. Regression equation used here to determine 

variability as a function of rainfall. Regression equation determined from data and approach in Balcom et al. (2003): 

( )

1000

13.4*exp(-0.009 rain) 1.3 fst,N

⋅ + = [48] 

values ranged from 0.0017 to 0.0061 with median =0.0025. e

 value from Meisinger and Randall (1991). Values from PLANTS data base, fs,N=0.0657; fstraw,N=0.0.0083 
108

Appendix B – Files comprising LCA model 

File Name Description 

summary - input.xls Includes most input parameter data sets (crop yields, fertilizer use, rain, 

etc) 

nitrate model-C-S.xls Nitrogen flow model for Scenario 1 -base case (C-S, no stover) 

nitrate model-C-S-stover.xls Nitrogen flow model – Scenario 2 (C-S w/ stover) 

nitrate model-C-C-stover.xls Nitrogen flow model – Scenario 3 (C-C w/ stover) 

phosphate.xls Phosphorus flow model for Scenario 1 -base case (C-S, no stover) 

phosphate-stover.xls Phosphorus flow model – Scenario 2 (C-S w/ stover) 

phosphate-C-C-stover.xls Phosphorus flow model – Scenario 3 (C-C w/ stover) 

potassium.xls Potassium flow model for Scenario 1 -base case (C-S, no stover) 

potassium-C-S-stover.xls Potassium flow model – Scenario 2 (C-S w/ stover) 

potassium-C-C-stover.xls Potassium flow model – Scenario 3 (C-C w/ stover) 

fuel use.xls Fuel use calculations – on farm 

nutrient impact.xls Eutrophication impact analysis for all scenarios 

flows from DEAM.xls Downloaded files from DEAM database and reorganization for models 

used here 

overall LCA.xls Summary of all LCI flows, all scenarios, with impact analysis 

summary-graphs.xls Includes most final graphs used in report, all linked to original 

worksheets where values calculated 
F1147-E(12/2004) 

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May 2005 

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January 2004 - May 2004 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Quantifying Cradle-to-Farm Gate Life-Cycle Impacts Associated with 

Fertilizer Used for Corn, Soybean, and Stover Production 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/TP-510-37500 

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BB05.37500 

6. AUTHOR(S)

Susan E. Powers 

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National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

Fertilizer use can cause environmental problems, particular eutrophication of water bodies from excess nitrogen or 

phosphorus. Increased fertilizer runoff is a concern for harvesting corn stover for ethanol production. This modelling 

study found that eutrophication potential for the base case already exceeds proposed water quality standards, that 

switching to no-till cultivation and collecting stover increased that eutrophication potential by 21%, and that switching 

to continuous-corn production on top of that would triple eutrophication potential. 

15. SUBJECT TERMS

eutrophication; stover; life-cycle analysis; nitrogen; phosphorus; no-till; continuous corn; fertilizer; ethanol 

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