Predicting Nitrogen Leaching Rates in a Clay Loam and Sandy Loam Soil in North Central Iowa

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Spring 2021

Predicting nitrogen leaching rates in a clay loam and sandy loam soil in north central Iowa

Allison Rigler

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Recommended Citation Rigler, Allison, "Predicting nitrogen leaching rates in a clay loam and sandy loam soil in north central Iowa" (2021). Creative Components. 792. https://lib.dr.iastate.edu/creativecomponents/792

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Predicting nitrogen leaching rates in a clay loam and sandy loam soil in north central Iowa

Allison Rigler

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Major: Agronomy

Program of Study Committee: Michael Thompson Major Professor Allen Knapp

The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred.

Iowa State University

Ames, Iowa

2021

TABLE OF CONTENTS

Page TABLE OF CONTENTS ...... ii NOMENCLATURE ...... iii ACKNOWLEDGMENTS ...... iv ABSTRACT ...... v CHAPTER 1. INTRODUCTION ...... 1 CHAPTER 2. RATIONALE ...... 5 CHAPTER 3. MATERIALS AND METHODS ...... 7 Soil Location ...... 7 Modeling System ...... 7 Weather Data ...... 8 The Maize Crop ...... 9 Last Crop ...... 10 Soils ...... 11 Management Variables ...... 12 CHAPTER 4. RESULTS ...... 13 CHAPTER 5. DISCUSSION ...... 19 CHAPTER 6. REFLECTION ...... 28 CHAPTER 7. CONCLUSION...... 29 REFERENCES ...... 30

iii

NOMENCLATURE

WTH File Weather File

CRM Comparative Relative Maturity

ACKNOWLEDGMENTS

I would like to thank my committee chair, Michael Thompson, and my committee member, Allen Knapp, for their thoughtful guidance and support throughout the course of this research.

In addition, I would also like to thank my friends, colleagues, the department faculty and staff for making my time at Iowa State University a wonderful experience. I want to also offer my appreciation to Haishun Yang, at the University of Nebraska, who was willing to engage in enabling conversation concerning Maize-N.

ABSTRACT

Maize-N, a modeling system developed at the University of Nebraska, provides predicted nitrogen leaching rates for agricultural soils based on climate, soil, and management practices.

Management practices may influence the rate at which nitrogen leaches from the field, and these practices are options that Maize-N provides to the user. The two soils used for this Maize-N simulation experiment are located near Kanawha, in north central Iowa. The first soil is a

Nicollet clay loam, and the second soil is an Estherville sandy loam. The Nicollet soil is located two miles south of the Estherville soil. The organic matter content was assumed to be 6% for the

Nicollet soil and 3.5% for the Estherville soil (United States Department of Agriculture, 1989).

Bulk density was assumed to be 1.1 and 1.4 Mg m3, respectively, for the Nicollet and Estherville soils, respectively (United States Department of Agriculture, 1989). The management practices chosen for simulation included tillage type (plow/disk, reduced till, and no-till), tillage timing

(fall/spring), fertilizer application timing (fall/spring), and residue amount (all or none).

Predicted nitrogen leaching rates were modeled for 2004, 2005 and 2008. In a given year, the

Nicollet and Estherville soils experienced similar trends in leaching rates in response to the various management practices. The Estherville soil had a higher predicted nitrogen leaching rate than the Nicollet soil for each year studied. For example, in 2004, when modeling a fall disk/plow treatment with a spring fertilization application and all crop residue, the Nicollet soil had a predicted leaching rate of 54 lb A-1 , compared to 145 lb A-1 predicted for the Estherville soil. Of the management practices, tillage timing had the greatest impact on the predicted nitrogen leaching rate. For cropping year 2004, when all crop residue was left and nitrogen fertilizer was applied in the spring, nitrogen leaching rates were less with fall tillage than with spring tillage by as much as 15% in the Nicollet clay loam soil and by 10% in the Estherville

vi soil. Nicollet clay loam had a predicted nitrogen leaching rate of 0 lb A-1 in cropping year 2005.

Nicollet soils can also have a loam texture in the rooting zone, so to better understand the impacts that texture could have on the prediction of nitrogen leaching a Nicollet loam was also modeled. When modeling a Nicollet loam instead of a Nicollet clay loam, the predicted leaching rates for the control scenario in 2005 increased from 0 to 25 lb A-1. Soil properties (e.g., texture and porosity) may influence nitrogen leaching more than management practices. Management practices may interact with soil properties to regulate nitrogen loss, yet soil properties appeared to play a dominant role in the comparisons of leaching in these two soils.

CHAPTER 1. INTRODUCTION

In 2004 fertilizer applications in the state of Iowa account for 25% of the state’s nitrogen inputs. Of this, 90% was applied to corn and soybean fields. This 25% equates to 984,000 tons of nitrogen applied each year in Iowa (Libra et al., 2004). Fertilizer applications influence soil nitrogen levels, but other agricultural practices, such as livestock production, can also influence the amount of nitrogen that can reach streams and water ways. A recent analysis showed that

29% of the long-term nitrate load of the Mississippi-Atchafalaya Basin can be attributed to Iowa

(Jones et al., 2018). In other words, one state contributes nearly one-third of the nitrogen load to a basin that receives water from 31 states.

Many different factors, including environmental and management practices, can influence the rate in which nitrogen can leach through soil. In many soils, leaching occurs when soil water concentrations are greater than the capacity of the soil to retain water. This causes water to flow through the soil in response to gravity. When water moves through soil, it can move nutrients with it. Soil water can be added to the soil by precipitation or irrigation.

Nitrogen in the soil, can be added through fertilizer applications or plant residues. Timing of nitrogen applications, climate, field conditions, and some management practices are all variables that may influence the rate in which nitrate can leach. Nitrogen is present in multiple forms within the soil and can also be lost through erosion and denitrification, in addition to leaching.

For example, it has been predicted that roughly 2.3 pounds of nitrogen are lost per ton of soil eroded, largely in the form of crop residues and soil organic matter (Duffy, 2012).

Soil nitrate leaching has become a large problem in the Midwest, specifically in Iowa. An increase in extreme precipitation events in Iowa has increased the amount water that enters the soil. Extreme precipitation years can increase nitrogen loads in waterways by 30% compared to

2 years of average levels of precipitation (Lu et al., 2020). Nitrogen loads increase due to the inability of saturated fields to hold elevated levels of water. Leaching rates in a field can vary drastically by year. One field example saw a 52 pounds of nitrate-N per acre loss in 1993 compared to 9 pounds nitrate-N per acre in 1994 (Duffy, 2012).

While there are ways to remove nitrate from waterways and drinking water, it is important to look at the source of the issue. If a single nitrogen application rate were applied across Iowa, crop demand for nitrogen would be exceeded on 66% of acres. Under-application would occur on only 4% of acres (Babcock and Pautsch, 1997). Variable-rate applications of fertilizer to a field are based on the soil characteristics and they may reduce nitrogen loss while ensuring that the fertilizer applied is effective in supplying nutrition to crop plants. The timing of application and precipitation patterns may also be important in influencing the rate in which nitrogen can move through the soil. Given the normal distribution of precipitation in Iowa, up to

70% of subsurface drainage may occur in a field before the month of July. So, a precipitation event after nitrogen application could result in a large rate of nitrogen in drainage water (Lawlor et al., 2011). Exploring different management practices may impact the amount of nitrogen that farmers apply and the amount that remains for crop growth.

Utilizing tools to predict the likelihood that nitrogen will be leached in soils can provide opportunities for producers to optimize their application practices. Maize-N (Yang et al., 2015) is a software program developed by Dr. Haishun Yang at the University of Nebraska. Maize-N allows for farmers, or others, to enter data that are directly related to their management practices, as well as soil characteristics and climate data specific to their field of interest, to generate predictions about the fate of nitrogen. Such information can then be used to select management

3 strategies that may reduce the loss of nitrogen through leaching. Reductions of nitrogen leaching can lead to economic benefits related to maximizing the efficiency of nitrogen applications.

In this study, four variables will be explored: tillage type, tillage timing, fertilizer application timing, and crop residue amounts. These variables, particularly the timing of fertilizer application and the amount of crop residue left in the field, may influence the amount of nitrogen present in the soil and thus can influence the rate at which nitrate-nitrogen leaches from the soil.

In Maize-N, the types of tillage options are plow/disk, reduced tillage and no tillage. The practice of tillage is used to agitate the top soil of a field. Tillage incorporates plant residues and weeds from the soil surface into deeper zones in the soils, creating a layer of soil free of debris at the surface that might inhibit planting. No-till management can be defined in several ways, but it basically refers to using as little disturbance of the soil between crops as possible. The study of no-till farming began in universities in the 1960s as a sustainability practice. When comparing no till versus plow/disk practices, it has been observed that higher nitrogen and carbon soil levels are often observed when no till practices were utilized, mainly because there was less erosion that removed organic matter along with mineral soil particles (Havlin et al., 1990). Increased levels of tillage can improve the ability for water to infiltrate soil. Tilled fields typically provide higher yields than no till fields, but tillage may also result in higher rates of soil erosion (Siemens and Oschwald, 1978).

Tillage may occur in either the spring or the fall. Spring tillage can be difficult for farmers due to the potential of a wet springs and thus limited flexibility of performing the tillage in addition to planting and other activities. Studies of tillage timing are also of interest because the loss of greenhouse gases, including nitrous oxide, can vary seasonally. Higher levels of

4 greenhouses gases are released with fall tillage than with spring tillage, possibly because of higher soil microbial activity (Reicosky, 1997).

Fertilizer can be applied to agricultural fields at a variety of times. Fall and spring applications are the most common. One study looking at the impact of fertilizer nitrogen timing in Nicollet soils, in southern Minnesota, found that fall applications could reduce grain yield by

20% compared to spring applications, and total N uptake for fall N applications was 45% compared to 87% with spring applications (Vetsch and Randall, 2004). Soil characteristics can also interact with the timing of fertilizer application. For example, observations on sandy loam and clay loam soils in New York found that fall nitrogen applications led to higher nitrogen leaching in the sandy loam soils than in clay loam soils (van Es et al., 2002).

Crop residues are the stalks, leaves, stems and adventitious roots that are left on the soil surface after crop harvest. Plant residues can reduce the rate at which water moves over the soil surface, allowing more time for water to infiltrate into the soil. In this way, residues can protect the soil from erosion. Residue can lead to cooler soil surfaces during the spring and summer, reducing nitrogen mineralization and therefore reducing leaching of nitrate. Residues that are incorporated into the soil can also increase water retention in the surface horizon.

CHAPTER 2. RATIONALE

This creative component tells a story of predicting nitrate leaching in a field in northern

Iowa. This story will be told using a modeling system called Maize-N. The field of primary interest is one that holds many memories for me; walking down rows looking for left behind corn ears, hearing stories from my great-grandparents, and envisioning my father learning to drive a tractor and grow into the man I know. When I was a child, this field was just an area where I could spend time with my family. The crops, the soil, and the climate, were only the background for my play.

I grew up outside of Iowa and didn’t think much of farming. However, as I attended

Iowa State University to obtain my bachelors’ degree, I began to understand the importance of agriculture and how I could fit into its overall picture in the state. My initial passion was the environment and helping people. I now see how I can combine my initial interests with my new passions, helping people through understanding agricultural practices.

Through my creative component I wanted to expand my understanding about farming practices and their influence on the environment. Both agriculture and the environment are important aspects of our current world and will continue to be important for future generations.

It can be cliché to hear that we need to save the planet but understanding how these important systems interact and what technology is available to assist was of interest to me.

I now live in Iowa and water quality is a concern that I have for myself and for future generations. Pesticides and fertilizer are two agricultural inputs that have increased over the past

100 years, and these amendments can be found in surface waters, sometimes in ground water, and sometimes in drinking water. From an economic perspective, I’m interested in understanding the movement of fertilizers so farmers can apply fertilizer at a rate correct for their

6 fields. Matching application rates with the needs of the crops can result in a reduction in costs in an economic sector where costs are increasing.

Modeling programs provide an opportunity for individuals to gain experience in a topic without the direct collection of raw data. Maize-N allows for researchers and farmers to understand past interactions between management practices, soil conditions, and weather. These past interactions can then educate a user and provide a foundation for future decision making.

Learnings can be gathered remotely, and an entire growing season can be observed in minutes verses days.

My first goal for this study was to use computer modeling systems to provide additional information into how climate and farming practices influence the leaching of nitrogen in a clay loam-texture soil on my great-grandparents farm in Wright County, northern Iowa. This soil is

Nicollet clay loam.

To test the capabilities of Maize-N, a second soil, a coarse-textured soil, was also chosen,

Estherville sandy loam. The coarser soil occurs in several northern counties and in some places the two soils can be found in the same field. The two soils were compared using the same management practices and climate. Bulk density, soil texture, and organic matter concentrations were entered in Maize-N to reflect values corresponding with the two soil types.

CHAPTER 3. MATERIALS AND METHODS

Soil Location

The location of one of the soils for this project, Nicollet clay loam, is 1102 Emmett Ave.,

Kanawha, IA 50447 latitude 42.89 deg, longitude -93.89 deg, in Wright County. This field was previously farmed by my great-grandfather, but for the past 30 years it has been rented out to various farmers with unknown management practices. It is known that the field has been in a corn and soybean rotation.

The location of the second soil, Estherville sandy loam, is two miles north of the first soil at latitude 42.91 deg, -93.89 deg, in Hancock County, and is owned and farmed by an unknown farmer. It is included in the study to provide an experimental contrast with the Nicollet soil.

Modeling System

For this project I used Maize-N. Specific data about the field and climate is required to provide meaningful predictions to the user. Due to a lack of knowledge of current management practices at the site, the information assumed for the data inputs system was based on common practices in north central Iowa and on soil data available in the Web Soil Survey.

Figure 1 provides a visualization of the Maize-N inputs page. Information within this page communicates the practices that may be used for the soil. This information provides data for the modeling prediction to be based upon.

Figure 1. Visualization of the Maize-N input page.

To determine many of the values included in Maize-N, different data sources were used to compile information based on traditional farming practices in Northern Iowa. The different data points and their rationales will be explained in greater detail below.

Weather Data

Three years of weather data, (2004, 2005 and 2008) were compiled from the Iowa

Environmental Mesonet data site. Data was provided on a daily interval and retrieved from the

Kanawha weather station. The data set included solar radiation, the minimum and maximum temperature, relative humidity, precipitation, and potential evapotranspiration. Maize-N requires a WTH file, which can be obtained by uploading the Environmental Mesonet data into the

WeatherAid Program, which is a subroutine in Maize-N. The WeatherAid program indicated that up to ten years of data could be used, but various errors were encountered, and only two years could be effectively included when creating the WTH file. Figure 2 is an example of how a text document must read prior to conversion to a WTH file.

Figure 2. Example of a text document prior to conversion to a WTH file.

The Maize Crop

Irrigation verses Rainfed: I assumed that the corn crop was rainfed. Irrigation in northern Iowa is uncommon.

Maturity: 110 CRM, comparative relative maturity, was chosen. CRM is a unit-less value provided by breeding companies to compare maturity timing for different hybrids. A smaller

CRM indicates fewer days or GDUs are required to reach maturity, while a larger CRM indicates more days or GDU are required to reach maturity. When planting prior to June 1st, it is expected that increased yields would be observed with a higher CRM variety, such as 111 compared to a

CRM of 95 or 104 (Licht et al., 2019).

Date of Planting: May 1st was chosen. May 1st was selected because when choosing a date prior to May 1st the program would provide an error. The original desired planting date was

April 21st. According to Iowa State University, to obtain a 98-100% yield potential in North

Central Iowa it is recommended to plant between April 12th and April 30th (Elmore, 2012).

April 21st was the average of the two extremes provided.

Plant Population: 32,000 seeds/acre was selected. In one study, the largest grain yields were observed for a population of 37,700 seeds/acre (Rusk & Sievers, 2010). Maize-N requires the

10 number of germinated seeds. If a population of 37,700 is seeded, it is predicted that roughly

32,000 will germinate (Anderson et al,. n.d.).

Price of Maize per Bushel: $3.50 per bushel was chosen. This number is based on a five-year span from 2015 to 2019 (Johanns, 2020b). A recent five-year span was chosen because it better reflects current corn prices. A ten-year average would include periods when corn prices were much higher (e.g. 2011 through 2013).

Average Yield of Last 5 years: 198 bushels per acre. This average maize yield was observed in

Wright County from 2015 through 2019 (Johanns, 2020a).

Last Crop

Type of Crop: Maize. For this study I assumed that corn was grown at the sites continuously.

Economic yield: $182, which was the default value in Maize-N.

Total N Applied, lb/acre: 184. This value was determined by using the Corn Nitrogen Rate

Calculator (Iowa State University Agronomy Extension and Outreach, 2021). Inputs for the calculator included selecting the single-price option, which only calculates the optimum fertilizer nitrogen rate based on one price of nitrogen.

Figure 3. Inputs for the Corn Nitrogen Rate Calculator.

N fertilizer recovery rate: 40% was selected. The nitrogen fertilizer recovery rate illustrates the difference in crop uptake of nitrogen between fields that have and have not had nitrogen applications. The default value for Maize-N is 40% and it coincided with data from Minnesota showing 37% recovery in maize crops (Davies et al., 2020).

Date of Maturity (approximation): September 10. This value was obtained from Iowa State

University that indicates that, based on multiple varieties in Iowa and typical growing-degree units in a cropping season, average maturity occurs between August 20th and October 20th based on variety. September 10th is average maturity date in which 50% of Iowa’s corn is mature and

50% is not (Todey and Taylor, n.d.).

Root zone soil moisture at corn maturity, % of field capacity: The default value for the

Maize-N, 25%, was chosen.

Soils

Nicollet is a clay loam soil typically found on slopes of 0-5%. The soil is considered somewhat poorly drained. Tiling is common in this soil type to aid with drainage. It was formed in glacial till. Long-term saturation can occur at depths below about 45 cm and runoff is typically low. Depth to carbonates ranges from about 50 cm to about 125 cm.

Estherville is a somewhat excessively drained soil that formed in 25-50 centimeters of loamy sediments over sandy and gravelly glacial outwash. It can be found on slopes up to 70%.

Carbonates can be found between 30 and 100 cm.

The soil characteristics assumed for these simulation experiments are shown in Table 1

(United States Department of Agriculture, 1989, 1992).

Table 1. Soil Characteristics Organic Matter Bulk Root Zone Soil pH Soil Content Density Texture % Mg m-3 Nicollet 6.0 1.1 Clay loam Neutral Estherville 3.5 1.4 Sandy loam Neutral

Root zone depth (in): For the Nicollet soil, 40 inches was assumed because tile drainage is commonly installed at this depth in these soils. For the Estherville soil, 48 inches was assumed because it is not likely to be tile drained and there are no significant impediments to root growth within that depth.

Management Variables

Tillage Type (Disk/Plow, Reduced Tillage, No-Till)

Timing of tillage (April 15th or October 15th in the previous year)

Amount of crop residue left (All, None)

Timing of fertilizer application (April 20th or October 20th in the previous year)

The selected soil management scenarios are shown in Table 2. Table 2. Soil management scenarios

Scenario Fertilizer Time Tillage Time Residue 1 (Control) Spring Fall All 2 Spring Fall All 3 Spring Fall None 4 Fall Fall All 5 Fall Fall None 6 Fall Spring None 7 Spring Spring None 8 Spring Spring All 9 Fall Spring All

CHAPTER 4. RESULTS

The predicted nitrogen leaching rates for Nicollet clay loam are presented in Table 3 for

2004, 2005 and 2008. For 2004 and 2008, the predicted N losses ranged from 46 to 54 lbs A-1 and from 42 to 52 lbs A-1, respectively. In contrast to 2004 and 2008, no N was predicted to leach from the rooting zone of the Nicollet clay loam in 2005 under any of the management practices.

Predicted losses decreased gradually from scenario 1 through scenario 9. Although there was little difference in the predicted N leaching among the three kinds of tillage practices, lower predicted losses were associated with spring tillage than with fall tillage in both years. When tillage was in the fall, the least predicted N loss was associated with fall application of N fertilizer; the presence of crop residues in the fall affected leaching loss very little. In contrast, when tillage was in the spring, the least predicted N loss was associated with leaving crop residues in the field. When tillage was in the spring, the impact of the timing of fertilizer application had little effect on predicted N loss in both 2004 and 2008.

Predicted nitrogen leaching rates for the Nicollet soil in 2005 were zero for all scenarios.

For example, the predicted 2005 leaching rate for scenario one was zero compared to the predicted leaching rate of 2004 and 2008 of 53 pounds per acre. This difference in predicted leaching rates across years led to an additional set of simulations for the Nicollet soil, adjusting the soil texture. Web Soil Survey identified the surface horizon of the Nicollet soil on my great- grandfather’s field to have a clay loam texture, and in the simulations run, the average root zone texture was also assumed to be clay loam. However, the surface horizons of Nicollet soils can also have loam textures. To further test the Maize-N model, the average root zone texture for the

Table 3. Predicted nitrogen leaching in Nicollet clay loam: 2004, 2005, and 2008 Year 2004 Tillage Practice Residue Fertilizer Reduced Scenario left timing Timing Plow/Disk tillage No-till ------lbs A-1 ------1 All Spring Fall 54 - - 2 All Spring Fall 54 54 53 3 None Spring Fall 54 54 53 4 All Fall Fall 52 52 51 5 None Fall Fall 52 51 51 6 None Fall Spring 51 50 50 7 None Spring Spring 50 49 49 8 All Spring Spring 47 47 47 9 All Fall Spring 46 46 46 Year 2005 Tillage Practice Residue Fertilizer Reduced Scenario left timing Timing Plow/Disk tillage No-till ------lbs A-1 ------1 All Spring Fall 0 - - 2 All Spring Fall 0 0 0 3 None Spring Fall 0 0 0 4 All Fall Fall 0 0 0 5 None Fall Fall 0 0 0 6 None Fall Spring 0 0 0 7 None Spring Spring 0 0 0 8 All Spring Spring 0 0 0 9 All Fall Spring 0 0 0 Year 2008 Tillage Practice Residue Fertilizer Reduced Scenario left timing Timing Plow/Disk tillage No-till ------lbs A-1 ------1 All Spring Fall 52 - - 2 All Spring Fall 52 52 52 3 None Spring Fall 52 52 52 4 All Fall Fall 50 50 49 5 None Fall Fall 49 49 48 6 None Fall Spring 48 47 47 7 None Spring Spring 46 46 46 8 All Spring Spring 43 43 44 9 All Fall Spring 42 42 43

Nicollet soil was changed from clay loam to loam, and all other soil characteristics were held constant. Table 4 provides the predicted leaching rates for the Nicollet loam soil.

Table 4. Predicted nitrogen leaching in Nicollet loam: 2005. Year 2005 Tillage Practice Residue Fertilizer Reduced Scenario left timing Timing Plow/Disk tillage No-till ------lbs A-1 ------1 All Spring Fall 25 - - 2 All Spring Fall 27 28 27 3 None Spring Fall 28 28 28 4 All Fall Fall 27 27 27 5 None Fall Fall 28 28 28 6 None Fall Spring 28 28 28 7 None Spring Spring 28 28 28 8 All Spring Spring 26 27 27 9 All Fall Spring 26 26 27

The modification from clay loam to loam texture in the rooting zone increased the predicted leaching rates considerably. For example, the loss of N in scenario one increased from zero to 25 lb/ac. The increase in predicted N leaching when the rooting zone texture was assumed to be loam instead of clay loam may be caused by the lower clay content in a loam compared to a clay loam soil. Clay particles retain more water than silt and sand particles, and clay promotes the development of stable aggregates that hold water, too. Because there was less clay in the soil, water may move through the Nicollet loam faster, leading to higher rates of predicted nitrogen leaching. Although predicted N loss in Nicollet loam in 2005 was greater than in Nicollet clay loam, it is noteworthy that none of the management scenarios appeared to have an impact on nitrogen leaching in 2005 (Table 4).

Predictions of nitrogen leaching rates derived from Maize-N can be compared with leaching rates measured in a long-term field experiment in central Iowa where about one-half of

16 the study site consisted of Nicollet soil with loam or clay loam textures in the rooting zone. The

Comparison of Biofuel Cropping Systems (COBS) is a large-scale cropping systems experiment where nitrate-N is monitored in tile drainage water throughout the growing season. Over five cropping seasons (2010 – 2015) [which included both a drought year (2012) and a “high- precipitation” year (2010)] plots in which corn followed soybean lost an average of ~26 lbs of N per acre in leaching to drainage tile (M. Helmers and M.L. Thompson, Iowa State University, personal communication, January 2021). Plots in which continuous corn was planted lost an average of ~21 lbs of N per acre to leaching. Therefore, the predicted N leaching losses for

Nicollet soil in the present study are the same order of magnitude as values actually measured in a comparable soil in central Iowa.

The predicted nitrogen leaching losses for 2004, 2005, and 2008 for Estherville sandy loam are presented in Table 5. Because Estherville is a much coarser soil than Nicollet, it was expected that predicted N leaching would be much greater than in the Nicollet soil, and it was.

The losses ranged from 129 to 145 lbs A-1, from 50 to 54 lbs A-1, and from 90 to 106 lbs A-1 in

2004, 2005, and 2008, respectively. For 2004, roughly three times more N was predicted to leach from the Estherville sandy loam than from the Nicollet clay loam; for 2008, about twice as much

N was predicted to leach from Estherville than from Nicollet.

As with the Nicollet simulations for 2004 and 2008, there was little difference in predicted N leaching as a result of different kinds of tillage. With minor exceptions, the primary differences in predicted N leaching corresponded to the timing of tillage in both years. When the soil was tilled in the spring, the amount of predicted leaching was considerably less than when the soil was tilled in the preceding fall. When tillage occurred in the fall of 2004 or 2008, N leaching was predicted to be less for fall fertilizer application than for spring applications.

In contrast to the 2005 predictions for Nicollet clay loam, the predicted N losses due to leaching in Estherville sandy loam in 2005 were notable. However, they varied in a narrow range

(50-54 lbs A-1), and they were not consistently associated with any of the management practices imposed in the simulation.

In summary, for both soils, scenarios with spring tillage predicted N losses less than or equal to those with fall tillage. For each year, predicted N loss was higher in the Estherville soil than in the Nicollet soil. In both soils and all three years, the predicted rate of N leaching was similar across the three tillage practices.

Table 5. Predicted nitrogen leaching in Estherville sandy loam: 2004, 2005, and 2008. Year 2004 Tillage Practice Residue Fertilizer Reduced Scenario left timing Timing Plow/Disk tillage No-till ------lbs A-1 ------1 All Spring Fall 145 - - 2 All Spring Fall 145 145 144 3 None Spring Fall 146 146 145 4 All Fall Fall 141 141 141 5 None Fall Fall 143 143 141 6 None Fall Spring 140 140 139 7 None Spring Spring 138 137 136 8 All Spring Spring 130 131 131 9 All Fall Spring 128 129 129 Year 2005 Tillage Practice Residue Fertilizer Reduced Scenario left timing Timing Plow/Disk tillage No-till ------lbs A-1 ------1 All Spring Fall 52 - - 2 All Spring Fall 52 53 53 3 None Spring Fall 54 54 54 4 All Fall Fall 52 53 53 5 None Fall Fall 54 54 54 6 None Fall Spring 54 54 54 7 None Spring Spring 54 54 53 8 All Spring Spring 51 51 52 9 All Fall Spring 50 51 51 Year 2008 Tillage Practice Residue Fertilizer Reduced Scenario left timing Timing Plow/Disk tillage No-till ------lbs A-1 ------1 All Spring Fall 106 - - 2 All Spring Fall 106 106 105 3 None Spring Fall 107 106 106 4 All Fall Fall 101 101 101 5 None Fall Fall 103 102 101 6 None Fall Spring 101 100 99 7 None Spring Spring 99 98 98 8 All Spring Spring 92 93 93 9 All Fall Spring 90 91 92

CHAPTER 5. DISCUSSION

One of the Maize-N outputs is a prediction of the loss of nitrogen through nitrate leaching. Nitrogen loss from soil may also occur with denitrification. Denitrification can be an important source of nitrogen loss from fine-textured soils, i.e., those dominated by silt and clay, while leaching may be the most important mechanism of nitrogen losses in coarse-textured soils

(Torbert et al., 1993). In the present study, the difference in texture between the two soils is likely to have led to the differences in predicted nitrogen loss. The model Nicollet soil had a clay loam texture in the rooting zone, while the model Estherville soil was a sandy loam in the rooting zone. Compared to coarse-textured soils, fine-textured soils have greater water-holding capacity and typically higher soil moisture contents. With more water in soil pores, there is less oxygen in the soil, promoting microbial activities that lead to denitrification. Nicollet soils are classified as somewhat poorly drained, which means that water moves through the soil slowly enough that “the soil is wet at a shallow depth for a significant period during the growing season” (Soil Survey Division Staff, 1993). Soils with coarse textures cannot retain as much water as fine soils, and more water is lost to leaching. If Maize-N were able to predict denitrification losses in addition to leaching losses, the predicted nitrogen loss rates for the

Nicollet soil could be higher than those presented in Table 3, where only leaching is accounted for. Because they are somewhat excessively drained, Estherville soils would likely not see as large of a nitrogen loss through denitrification.

Nitrogen management in the soils was modeled for three different years using Maize-N.

Over the three years, the predicted leaching rates differed. Precipitation and potential evaporation are two variables that influence soil moisture and the volume of soil water that will pass through a soil. The total precipitation and potential evapotranspiration for the three years

20 studied are showing in Figure 3. Annual totals, for calendar years, are similar for 2004 and

2005, while 2008 had less precipitation and evapotranspiration.

Annual Precipitation and Potential ET Totals 60 47.9 48.15 50 38.28 40 31.78 33.37 30 26.48

Inches Inches 20 10 0 1 2 3 Year Precipitation Potential ET

Figure 3. Annual totals of precipitation and potential evapotranspiration for 2004, 2005 and

2008.

While annual totals of precipitation did not vary greatly for 2004 and 2005, the predicted leaching rates did. Precipitation timing may impact the predicted rate of leaching. By observing the timing of precipitation, we may get a deeper understanding of the differences among the three years. Figure 4 shows the monthly precipitation totals for the three years. Leaching rates, for both soils, were predicted to be higher in 2004 than 2005. But it is still difficult to discern the impact of individual precipitation events on leaching when using monthly precipitation records.

Monthly Precipitation Totals by Year 9 8 2004 7 2005 6 5 2008 4 3 2 1

Precipitation (in) Precipitation 0

Jan Jan Jan

Oct Oct Oct

Apr Apr Apr

Feb Sep Feb Sep Feb Sep

July July July

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

June June June Month

Figure 4. Monthly precipitation totals, in inches, for 2004, 2005 and 2008.

The graphs generated by Maize-N provide valuable information and predictions about nitrogen dynamics in the soil. The annual graphs of predictions generated in Maize-N (Figures

5, 6 and 7) illustrate how different aspects of the nitrogen cycle were predicted to behave. The plotted parameter “Total N in soil” illustrates the amount of nitrogen in the soil from all sources.

“N from SOM” (soil organic matter) provides information about mineralization of nitrogen from native soil organic matter. “Applied N” indicates the timing of the nitrogen fertilizer applications. “N uptake” reports how much nitrogen the crop was predicted to remove from the soil (therefore, it is shown as a negative value in the graph). “N leaching” (also negative) predicts the amount of nitrogen that would leach from the soil (in lbs A-1). Lastly, ”Rainfall” outlines timing of precipitation and the amount of precipitation.

Figure 5 is the Maize-N generated graph for the 2003-2004 growing season for the

Nicollet clay loam in scenario one. It shows that there was little change in total N in soil or N in soil organic matter in the final months of 2003. However, with the spring fertilizer application

22 on April 20th, total N in the soil increased considerably. In addition, N from SOM began to increase in the spring when temperatures rose, and nitrogen was mineralized from the organic matter. A large precipitation event occurred around May 23rd, but this event did not cause leaching to occur. A later precipitation event, around June 15th, did promote leaching, and at that time the total nitrogen in the soil no longer increased but and began to drop. After May 15, little nitrogen was lost through leaching and additional nitrogen became available through mineralization from SOM. The prediction of nitrogen uptake demand illustrates that corn plants began to use the soil nitrogen around May 15th, and they continued to take it up throughout the growing season. The use of nitrogen by the crop is also illustrated in the drop of total nitrogen in the soil. Total N in the soil does decrease, however the reduction of total nitrogen in the soil is not as steep as loss of N from the soil by the nitrogen uptake demand because nitrogen continued to be mineralized from SOM.

Figure 5. Composite graphs of N dynamics in Nicollet clay loam, Scenario one, 2003 to 2004 growing season.

Figures 6 and 7 show N additions and losses as well as precipitation for both the Nicollet soil and the Estherville soil. There was a wet May in 2004, while 2005 had more sustained

23 precipitation over the growing season and at lower rates. To illustrate this point better, graphs created within Maize-N show how peak precipitation events corresponded to times when nitrate leaching increased (Fig. 6 and 7). Leaching rate increases can be seen in both soils after a precipitation event around June 15th, 2004 (Fig. 6 and 7). The impact of precipitation events on leaching in the Estherville soil was greater than for the Nicollet soil, possibly due to the increased pore space that allowed water to drain more freely. In 2004, the Nicollet soil had two precipitation events that resulted in leaching, while the Estherville soil had more than five precipitation events that led to leaching.

Figure 6. Composite graphs of N dynamics in Nicollet soil, 2004, Scenario 1.

Figure 7. Composite graphs of N dynamics in Estherville soil, 2004, Scenario 1.

The predicted rates of nitrogen leaching were larger in the Estherville soil than in the

Nicollet soil. In addition, the predicted N leaching values for Estherville in 2004 and 2005 varied considerably, with 2004 having the higher predicted rate (Fig. 8). The Maize-N prediction graphs provide some information about why these two years were different. In 2004, the largest precipitation event occurred after the spring nitrogen application. In contrast, in 2005, the largest precipitation event occurred prior to the spring fertilizer application, around May 1st

(Fig 8). Large precipitation events corresponded to larger rates of leaching that, in turn, led to drops in the total nitrogen in the soil. The largest precipitation event after nitrogen application in

2005 was roughly one inch less than the largest precipitation event of 2004. That 2005 event did correspond with a predicted loss of nitrogen through leaching (Fig. 8). But the growing season of 2004 had multiple large precipitation events that caused leaching, as indicated by the multiple steps in the N leaching line. The largest single precipitation event of 2005 occurred prior to spring nitrogen application, i.e. at a time when a significant amount of nitrogen was not available for leaching.

Temperatures may also influence the rate of nitrogen leaching. Similar annual temperature trends were observed in 2004 and 2005. Figure 9 illustrates the temperatures for

2004 and 2005. Temperature interactions with applications of nitrogen, in the form of anhydrous ammonia, could influence the rate of nitrogen leaching. This can be observed by comparing a two-week period after the spring nitrogen application. The temperatures in 2005 remained cooler longer than those in 2004, possibly reducing the conversion of ammonium into nitrate by microbial activity. Lower temperatures may also reduce the rate in which a plant grows and the rate of evapotranspiration, thus reducing the uptake of N.

Figure 8. Composite graphs of N dynamics in Estherville soil, 2004 and 2005, Scenario 1.

Figure 9. Growing-season air temperatures for the 2004 and 2005 growing seasons

Evapotranspiration represents the movement of water from soil to the atmosphere, both evaporated directly from the soil surface and transported through plants. Annual evapotranspiration rates for 2004 and 2005 were similar (Fig. 3), but Figure 10 illustrates the differences in evapotranspiration across months.

Monthly Potential ET by Year 9 8 2004 7 2005 6 2008 5 4 3

Potential ET (In) PotentialET 2 1

Jan Jan Jan

Oct Oct Oct

Sep Sep Feb Feb Feb Sep

Apr Apr Apr

Dec Dec Dec

Mar Mar July July Mar July

Aug Nov Aug Nov Aug Nov

May June May June May June Month

Figure 10. Potential evapotranspiration monthly totals for 2004, 2005 and 2008.

Both the timing of precipitation and evapotranspiration may influence the predicted nitrogen leaching rates. Precipitation in May of 2005 was high (~7.5 inches, Fig. 6) but May was followed by a higher rate of evapotranspiration in June, again ~7.5 inches. Higher rates of evapotranspiration indicate more soil moisture was used for plant growth, reducing the amount available for leaching. In contrast, the peak precipitation month in 2004, May, was followed by decreasing evapotranspiration rates, indicating that less soil water was being used for plants, perhaps resulting in more water being available for leaching.

The timepoint for tillage seems to be the most impactful management variable. While leaching rates did not vary greatly across tillage types, predicted leaching rates were observed to decrease for both soils when tillage occurred in the spring. When comparing spring tillage to fall

27 tillage in the Nicollet clay loam with scenario four and nine, a 11% reduction in leaching was predicted. When applying a spring fertilizer application, the reduction in predicted leaching for spring tillage (comparison of scenarios one and eight) was 15%. The Estherville soil, when comparing spring tillage to fall tillage saw predicted leaching rates decrease 10% and 9% for spring and fall fertilizer applications, respectively. Tillage can impact the drainage of the soil by modifying the amount of pore space available in the soil. This predicted difference between spring and fall tillage may be observed because lower soil moisture levels in the fall may allow for more efficient tillage, while spring tillage may occur when there is additional soil moisture, causing compaction. The additional pore space associated with fall tillage may assist with increasing soil temperature in the spring, aiding in the conversion of ammonium to nitrate and increasing the amount of nitrogen available for leaching from the soil. Soils with spring tillage or compaction may have less pore space available for air to assist in increasing soil temperatures to promote the conversion to nitrate.

The Maize-N predictions of nitrate leaching rates indicate that most management variables did not greatly impact the amount of nitrogen that leached from the soils. Only the timing of tillage had a modest impact on the prediction of nitrogen leaching. However, the two soils performed quite differently under the same seasonal weather patterns. This observation may indicate that soil properties (e.g., texture and porosity) may influence nitrogen leaching more than management practices. Management practices, specifically tillage timepoints, may interact with soil properties to regulate nitrogen loss and are essential for making predictions of nitrogen leaching. Yet soil properties appeared to play the dominant role in the comparisons of leaching in these two soils.

CHAPTER 6. REFLECTION

Utilizing Maize-N was an interesting experience that gave me a better understanding in how seasonal weather patterns, soil properties, and agricultural management practices could influence nitrogen loss through leaching. Agriculture is evolving with technology. Tools like

Maize-N are available for researchers and farmers to provide insight into how management strategies may play out, without having to make the physical decisions or observations in the field.

Comparing modeled N losses in the two soils, Nicollet and Estherville, was a helpful way to understand how differences in soil textures could influence nitrogen loss. While predictions of nitrogen leaching rates across management practices for a given growing season and soil were relatively consistent, the model helped to illustrate the importance that soil texture has on nitrogen leaching specifically.

Maize-N provides opportunities for all users to customize both fertilizer inputs and management decisions to better reflect the practices and field characteristics of a particular farm.

The flexibility of Maize-N allows for the generation of predictions for many scenarios. In addition, the depth of information required for modeling leads one to a more complete understanding of agriculture in Iowa.

CHAPTER 7. CONCLUSION

The Maize-N predictions of nitrogen leaching rates indicated that management variables may impact only modestly the amount of nitrogen that can leach from a soil. However, under similar management and seasonal weather patterns, nitrogen dynamics in the two soils of this study were quite different. This observation may indicate that soil properties (e.g., texture and porosity) may influence nitrogen leaching more than management practices. Management practices, specifically tillage timing, may interact with soil properties to regulate nitrogen loss and are essential for making predictions of nitrogen leaching. Yet soil properties appeared to play a dominant role in the comparisons of leaching in these two soils. Modeling predictions can provide an understanding of past interactions to users to assist with future decision making.

REFERENCES

Anderson, M., Vittetoe, R., Rieck-Hinz, A., Licht, M. Stand Assessments-Corn. Iowa State University Extension and Outreach, Integrated Crop Management. https://crops.extension.iastate.edu/encyclopedia/stand-assessments-corn Babcock, B.A., Pautsch, G.R. (1997). Moving from uniform to variable fertilizer rates on Iowa corn: effects on rates and returns. Journal of Agricultural and Resource Economics, 23, 385-600. https://www.jstor.org/stable/40986990?seq=1 Davies, B., Coulter, J.A., Pagliari, P.H. (2020). Timing and rate of nitrogen fertilization influence maize yield and nitrogen use efficacy. PLOS One. https://doi.org/10.1371/journal.pone.0233674 Duffy, M.D. (2012). Value of soil erosion to the land owner. Iowa State University. https://www.extension.iastate.edu/agdm/crops/html/a1-75.html Elmore, R. (2012). Best Corn Planting Dates for Iowa. Iowa State University Extension and Outreach, Integrated Crop Management. https://crops.extension.iastate.edu/cropnews/2012/03/best-corn-planting-dates- iowa#:~:text=Optimum%20Iowa%20corn%20planting%20dates,of%20Iowa%20(see%2 0Table) Halvin, J.L., Kissel, D.E., Maddux, L.D., Claassen, M.M., Long, J.H. (1990). Crop rotation and tillage effects on soil organic carbon and nitrogen. Soil Science Society of America Journal, 52, 448-452. https://doi.org/10.2136/sssaj1990.03615995005400020026x Iowa State University Agronomy Extension Outreach. (2021). Corn Nitrogen Rate Calculator [Software]. Iowa State University. http://cnrc.agron.iastate.edu/ Joanns, A. (2020a). Historic Corn Yields by County in Iowa. Iowa State University Extension. https://www.extension.iastate.edu/agdm/crops/pdf/a1-12.pdf Joanns, A. (2020b). Iowa Cash Corn and Soybean Prices. Iowa State University Extension. https://www.extension.iastate.edu/agdm/crops/pdf/a2-11.pdf Jones, C.S., Nielsen, J.K., Schiling, K.E., Weber, L.J. (2018). Iowa stream nitrate and the Gulf of Mexico. PLoS ONE, 13. https://doi.org/10.1371/journal.pone.0195930 Lawlor, P.A., Helmers, M.J., Baker, J.L., Melvin, S.W. (2011). Comparison of liquid swine manure and aqua ammonia nitrogen application timing on subsurface drainage water quality in Iowa. Iowa State University. https://core.ac.uk/download/pdf/38932971.pdf Libra, R.D., Wolter, C.F., Langel, R.J. (2004). Nitrogen and phosphorus budge for Iowa and Iowa Watersheds. Iowa Department of Natural Resources-Geological Survey. https://s- iihr34.iihr.uiowa.edu/publications/uploads/Tis-47.pdf

Licht, M., Baum, M., Archontoulis, S. (2019). Late Corn Planting Options. Iowa State University Extension and Outreach, Integrated Crop Management. https://crops.extension.iastate.edu/cropnews/2019/05/late-corn-planting-options Lu, C., Zhang, J., Tian, H., Crumpton, W.G., Helmers, M.J., Cai, W., Hopkinson, C.S., Lohrenz, S.E. (2020). Increased extreme precipitation challenges nitrogen load management to the Gulf of Mexico. Communications Earth & Environment, 1, 21. https://doi.org/10.1038/s43247-020-00020-7

Reicosky, D.C. (1997). Tillage-induced CO2 emission from soil. Nutrient Cycling in Agroecosystems, 49, 273-285. https://doi.org/10.1023/A:1009766510274 Rusk, R., Sievers, J.L. (2010). Corn Plant Population. Iowa State Research Farm Progress Reports. 412. https://lib.dr.iastate.edu/farms_reports/412 Siemens, J.C., Oschwald, W.R. (1978). Corn-soybean tillage systems: erosion control, effects on crop production, costs. American Society of Agricultural and Biological Engineers, 21, 293-302. doi: 10.13031/2013.35292 Soil Survey Division Staff. (1993) Soil survey manual. Soil Conservation Service. U.S. Department of Agriculture Handbook 18. Todey, D., Taylor, E. Crop and Climate Calendars. Iowa State University Department of Agronomy. http://agron- www.agron.iastate.edu/courses/Agron541/classes/541/lesson03a/541L3.pdf Torbert, H.A., Hoeft, R.G., Vanden Heuvel, R.M., Mulvaney, R.L., Hollinger, S.E. (1993). Short-term excess water impact on corn yield and nitrogen recovery. Journal of Production Agriculture, 6, 337-344. https://doi.org/10.2134/jpa1993.0337 United States Department of Agriculture. (1989). Soil Survey of Hancock County, Iowa. National Cooperative Soil Survey. https://www.nrcs.usda.gov/Internet/FSE_MANUSCRIPTS/iowa/IA081/0/hancock.pdf United States Department of Agriculture. (1992). Soil Survey of Wright County, Iowa. National Cooperative Soil Survey. https://www.nrcs.usda.gov/Internet/FSE_MANUSCRIPTS/iowa/IA197/0/wright.pdf Van Es, H.M., Czymmek, K.J., Ketterings, Q.M. (2002). Management effects on nitrogen leaching and guidelines for a nitrogen leaching index in New York. Journal of Soil and Water Conservation, 57, 499-504. https://www.jswconline.org/content/57/6/499/tab- article-info Vetsch, J.A., Randall, G.W. (2004). Corn production as affected by nitrogen application timing and tillage. Agronomy Journal, 96, 502-509. https://doi.org/10.2134/agronj2004.5020 Yang, HS; Dobermann, A; Cassman KG and Walters, DT. Hybrid-Maize: A Simulation Model for Corn Growth and Yield. 2015. Nebraska Cooperative Extension CD9, University of Nebraska-Lincoln.