Phosphorus Management in the Agroecosystem: a Mental Models Analysis of Knowledge and Perceived Risk

Phosphorus Management in the Agroecosystem: A mental models analysis of knowledge and perceived risk

THESIS

Presented in Partial Fulfillment of the Requirements for the Master of Science in the Graduate School of the Ohio State University

Joshua David Ferry, B.A.

Graduate Program in Environment and Natural Resources

The Ohio State University

2011

Thesis committee:

Dr. Robyn Wilson, Advisor

Dr. Elena Irwin

Dr. Robert Mullen

Joshua David Ferry

2011

Abstract

Increasing evidence of phosphorus (P) contamination of surface water has precipitated numerous research efforts, both in government agencies and at academic institutions. These efforts have been overwhelmingly committed to characterizing this problem from a biophysical perspective, through fields such as limnology, aquatic ecology, soil science and agronomy—and have yielded great results. In many watersheds, particularly in the Eastern Cornbelt, phosphorus loading comes largely from row-crop agriculture. As a non-point source, phosphorus transport from individual agricultural operations cannot be directly monitored or regulated. Consequently, it behooves those interested in improving water quality to couple biophysical research with research devoted to understanding what social variables are animating the land management decisions ultimately responsible for contamination. Given this need, the objectives of this research were twofold. The first objective of this research was to characterize the current phosphorus risk assessment information available to farmers, in light of the emerging problem of soluble P. The second objective was to probe farmers‟ understanding of phosphorus management, which includes transport, fate and the risks associated with nutrient loss. Twenty-three farmers participated in in-depth interviews where they were asked questions pertaining to phosphorus management. Results were then coded and mapped onto the expert influence diagram illustrating key knowledge strengths and weaknesses. Individual farmers were assigned a knowledge score based on concept recognition. Knowledge scores were then correlated to self-reported risk ii

perception measures. The results indicate that farmers are thoroughly versed in the transport of P via direct soil loss, but have a much lower understanding of the transport of soluble P via surface runoff. Overall, risk perception is positively correlated to knowledge with risk perception levels relatively high. The risk perception results suggest that farmers are well positioned to receive and process a risk communication effort designed to address knowledge deficiencies specific to soluble P mitigation behavior.

iii

Dedication

To Grandma Ferry and Grandpa Trommater

Acknowledgments

First off, I want to thank my advisor, Dr. Robyn Wilson. Her instruction, encouragement, and patience throughout the research and writing process helped me more than I can put to words. Special thanks also to my committee members, Dr. Robert

Mullen and Dr. Elena Irwin: Dr. Irwin, for giving support and structure particularly in the early parts of my research development, and Dr. Mullen, for devoting so much time to critique my agronomy content.

I would also like to thank my friends and fellow graduate students. It‟s been fun to go through this process with friends, have such great conversation partners, and just be energized by what you all are doing.

I am indebted to the many interviewees who took time out of their very busy schedules to sit down with me. I had never interviewed anyone before and I am sure this was evident, but they were all very gracious.

Finally, my wife deserves all the credit in the world. We both know first-hand now what it means to be the proverbial “broke (and delirious) graduate student.” Yet, she remained steadfastly supportive. Thanks for sharing this journey!

Vita

October 1984……...…………………………………….Born Nashville, IN

2007………………………………………………...... B.A., Humanities, Milligan College

2007-2009……...……………………………………….Financial Services Representative First Tennessee Bank

2009-Present……...…………………………………….Graduate Research Assistant The Ohio State University

Field of Study

Major Field: Environment and Natural Resources

Table of Contents

Abstract………………………………………………………………………..…….…...ii

Dedication…..……………………………………………………………...……………iv

Acknowledgments..…...……………………………………………………...………….v

Vita…..……………………………………………………………...…………………...vi

List of Figures and Tables…………………….……………………….………….…...xi

Chapter 1: Review of Relevant Literature…………………………………………..…1

1.1: A “Coupled” Approach to Surface Water Contamination Mitigation………….....….1

1.2: A History of Freshwater Contamination……………………………………………...3

1.3: Risks of algae & limiting nutrients………………………………………….……..…6

1.3.1: Eutrophication……………………………………………………………....6

1.3.2: Risks from algae…………………………….……………………………...7

1.3.3 Limiting Nutrients………………………………………………………….10

1.4: Sources of P: A history of algae in Lake Erie………………………………………13

1.5: Mental models, risk perception and profit……………..……………………………19

1.5.1 Mental Models: Concept.……………………..……………………………19

1.5.2 Mental Models: Method…………….……………………………...………21

1.5.3 Risk Perception……………………………………….……………………23

1.5.4 Profit-maximization………………………………………………..………28

1.6: Outline of the thesis…………………………………………………………………31

1.6.1 Overarching Research Questions…..………………………………………31 vii

1.6.2 Research Objectives…………………………………………….…………32

Chapter 2: The Ohio Phosphorus Index Assessment: A viable tool for DRP risk communication?...... 33

2.1 Introduction………………………………………………………………………….33

2.1.2 Lake Erie pollution and its legacy…………………………………………35

2.1.3 Particulate Phosphorus……………………………………………………..36

2.1.4 Dissolved Reactive Phosphorus……………………………………………37

2.2 Research Approach…………………………………………………………………..42

2.3 The Expert Model for Phosphorus Risk……………………………………………...42

2.3.1 Phosphorus Transport and Impacts………………………………………...42

2.3.2 Characteristics of Phosphorus……………………………………………...44

2.3.3 Phosphorus in the soil……………………………………………………...44

2.3.4 Phosphorus in the water……………………………………………………46

2.3.5 Aquatic biomass growth…………………………………………………...47

2.3.6 Impacts of biomass growth………………………………………………..48

2.4 Land management strategies to prevent phosphorus loss……………………………51

2.4.1 Soil Test Risk Assessment (STRAP) ……………………………………...51

2.4.2 Phosphorus Risk Index……………………………………………………52

2.4.3 Rationale for a P-Index differentiation……………………………………56

2.4.4 Particulate Phosphorus Risk Index………………………………………..57

2.4.5 Dissolved Reactive Phosphorus Risk Index………………………………61

2.5 Discussion……………………………………………………………………………64

2.6 Conclusion…………………………………………………………………………...67 viii

Chapter 3: Phosphorus Management in the Agroecosystem: An analysis of knowledge and perceived risk………………………………………………………….69

3.1 Introduction…………………………………………………………………………..69

3.1.2 Importance of Individual Decision-making………………………………..72

3.1.3 Risk Communication………………………………………………………73

3.2 Research Approach………………………………………………………………….76

3.2.1 Expert Model………………………………………………………………76

3.2.2 Mental Model Interviews………………………………………………….77

3.2.3 Subjects…………………………………………………………………….78

3.2.4 Hypotheses…………………………………………………………………79

3.3 Analysis………………………………………………………………………………81

3.3.1 Coding and Measurement: Knowledge……………………………………81

3.3.2 Measurement and Analysis: Risk perception………………………………82

3.3.3 Measurement and Analysis: Demographics………………………………..82

3.3.4 Farming Goals: Profit and Utility…………………………………………83

3.4 Results and Discussion………………………………………………………………83

3.4.1 Farmer mental model results: Source and Transport Knowledge…………………………………………………………………83

3.4.2 Farmer mental model results: Knowledge of impacts from phosphorus loss…………………………………………………………………………89

3.4.3 Hypotheses results…………………………………………………………92

3.4.4 Lessons for risk communication…………………………………………...96

List of References………………………………………………………………………………98

Appendix A…………………………………………………………………………….106

Appendix B…………………………………………………………………………….111

List of Figures and Tables

Figure 1.1: Soil P holding capacity…………...... ……………………………………….17

Figure 1.2: Risk information and processing model……...……….…………………….27

Figure 2.1: The stability of TP loading into Lake Erie over time ….……………………………………………………………………...... 38

Figure 2.2: The decline of PP loading into Lake Erie from the Maumee and Sandusky rivers…………………………….……...……………………………....…...39

Figure 2.3: The increase of DRP loading into Lake Erie over time from the Maumee and Sandusky rivers………...…….……………………………………………...40

Figure 2.4: Phosphorus transport pathways……………………...……………….……..43

Figure 2.5: Impacts from phosphorus contamination of surface water………….…...…50

Figure 2.6: Ohio‟s Phosphorus Risk Index (PP and DRP undifferentiated)...... …...54

Figure 2.7: Ohio‟s Phosphorus Risk Index (PP emphasis)……………..……..………...59

Figure 2.8: Ohio‟s Phosphorus Risk Index (DRP emphasis)…………...……………….62

Figure 3.1: Ohio‟s Phosphorus Risk Index (PP emphasis): Farmer mental model Results……………………………………………...……………………….86

Figure 3.2: Ohio‟s Phosphorus Risk Index (DRP emphasis): Farmer mental model results…………………………………………………………...……………88

Figure 3.3: Risks from surface water contamination of phosphorus loss………………..90 Table 3.1: Results of the Spearman‟s Rank Order Correlation test between risk perception and knowledge…………………………………………………...93

Table 3.2: Results of the Mann-Whitney Test comparing differences in mean knowledge scores among dichotomous demographic groups…….….………………….95

Chapter 1: Review of Relevant Literature

1.1 A “Coupled” Approach to Surface Water Contamination Mitigation

Soil nutrient inputs are a necessary part of row crop agriculture. Most soil nutrients need to be applied to replace what is removed in the crop at harvest and what is lost through natural or human processes. If nutrient inputs are not carefully managed there can be various externalities, in the form of environmental, human health and economic impacts. One need not go beyond the borders of Ohio to see these impacts.

Toxic algae blooms have plagued both Grand Lake St. Mary‟s and Lake Erie, and are a byproduct of nutrient enrichment of surface water from sources such as agriculture, waste water treatment plants, urban runoff, and many others. Various state and local authorities are looking for ways to mitigate growing algae blooms by emphasizing the need for natural science research to further explain the biomass growth dynamics and its related water quality impacts, such as low dissolved oxygen and reduced aquatic biodiversity

(OEPA, 2010c). The author believes that this one-sided research emphasis will ultimately limit the success of any mitigation campaign.

The research presented here is nested in a broader project that includes both biophysical and social scientists. Specifically, agricultural economists, agronomists, land-use geographers, among others, are collaborating to addresses the problem of water quality impairment in the Ohio River watershed. In addition to being a part of this larger 1

project, the specific research presented here is multi-phased; and this thesis focuses on the first of two distinct data collection phases (i.e., interviews and survey research) that will be aimed at developing policy recommendations for agricultural land management.

The focus of this research is on nutrient loss in agriculture within a “coupled” systems framework. Coupled systems research evaluates the interrelated complex of both human and social systems (Liu, Thomas, Carpenter, 2007). Traditionally, the natural and social systems have been studied in isolation, but research suggests that understanding the dynamic feedbacks between these two systems is tantamount to recognizing the function of the system as a whole (Antle & Stoorvogel, 2006; Liu, Dietz, Carpenter, Folke, 2007;

Liu, Thomas, et al., 2007; Pickett, Cadenasso, Grove, 2005). Using spatial, ecological, and demographic data, for example, researchers have been able to correlate the connection between land resource degradation and poverty (Antle & Stoorvogel 2006).

The problem context of this research likewise necessitates researching these two systems as a unified complex. By delineating the interrelated dynamic between the natural system

(i.e., water quality & eutrophication) and the social system (i.e., land management, knowledge & risk) one can more fully understand the problem of freshwater contamination by agricultural inputs (OEPA, 2010c). Based on the limited interview sample size and inadequate spatial information (i.e., insufficient water quality data over time at this scale), the spatial data feedback tools cannot be incorporated into this particular research phase, but the premise of coupled systems research will still be operative. By better understanding the physical processes of nutrient loss as well as the social processes of knowledge seeking behavior and other individual behavioral

motivations, those responsible for addressing the threat to water quality will know both which management decisions are most detrimental and how to address the behavior itself.

This introductory chapter has two goals. The first is to provide an assessment of surface water contamination, particularly as it relates to phosphorus loss from agriculture in Ohio, incorporating research from the soil science and limnology disciplines. The second goal is to explain the utility of social science research in assessing and evaluating the land-management behavior in question, by using research from psychology, sociology and systems dynamics.

1.2 A History of Freshwater Contamination

Freshwater is a scarce and essential natural resource. For millennia, human populations have located and settled based on the availability of freshwater. Protecting this resource from overuse or contamination has been, therefore, an utmost priority throughout human history. In the past, freshwater contamination was mitigated using dilution—as the infamous saying goes, “the solution to pollution is dilution.” There are two reasons why this approach has been shown to fail. First, the second law of thermodynamics, and the law of entropy specifically, stipulates that without an energy source ordered compounds will tend toward disorder (Pepper, Gerba, Brusseau, 2006). In other words, contaminant compounds will dissolve and become benign. The confounding variable, however, is that living organisms can re-concentrate compounds, thus countering entropy. For example, polychlorinated biphenyls (PCBs) are lipophilic.

This means that PCB‟s readily bind to the fatty tissue of living organisms. Several

hundred drums of PCB can be dissolved in the ocean. Small invertebrates will consume the contaminant and because of the lipophilic nature of the compound the contaminant will not be excreted. As the chemical biomagnifies, or concentrates up the food chain, larger predators can have staggeringly high amounts of contamination (Asante-Duah,

2002). Second, dilution is also ineffective because of the dwindling freshwater supply to dilute the contaminants to safe levels. Over the past twenty years, food supplies have dropped 17%, largely due to the decrease in available freshwater for crop production

(Pimentel, Berger, Filiberto, Newton, Wolfe, 2004). With the world population fast approaching seven billion people, the pressure on freshwater is expected to see equivalent gains (Census, 2010; Pimentel et al., 2004). Not only is the global population burgeoning, but also the relative fraction of total water in the freshwater form is much smaller than one might expect. According to the United States Geological Survey

(USGS), only 3% of the Earth‟s water resources consist of freshwater, and of that freshwater fraction, 68.7% is contained in ice caps and glaciers and is thus unavailable for direct human use (USGS, 2010a). The groundwater fraction comprises 30.1% of the total freshwater, leaving less than 1% of the total freshwater fraction, or .00027% of the

Earth‟s water in the form of surface water (Gleick, 1996).

Surface water functions in a number of ways in society. It serves as a vital contributor to drinking water, a resource for recreation and a key component of ecological services, like natural water purification. Surface water is defined by the USGS as “any open body of water, such as a stream or lake” (USGS, 2010a). In Ohio, the most prominent surface water body is Lake Erie, but others like the Ohio River, Scioto River

and Grand Lake St. Mary‟s are also important to the state because of the financial revenues from recreation.

Contamination of surface water occurs when a hazardous substance either dissolves or physically mixes into the water body (EPA, 2010). There are a variety of ways that surface water can become contaminated. The most obvious mechanism is the direct discharge of a contaminant into the water body. Another mechanism is movement of contaminated groundwater upward into surface water. An often overlooked mechanism is the movement of sediments into the surface water. Due to their chemical nature, some contaminants sorb strongly to the soil. Once attached to the soil, the contaminant can be deposited into surface water, “desorb” and dissolve in the water (Pepper et al., 2006).

The list of contaminants is nearly endless. Some chemicals are safe and beneficial in small quantities, but can be quite hazardous in large quantities, while others are hazardous in small quantities (Asante-Duah, 2002). Contamination results from both human activity and natural processes. Anthropogenic sources such as industry, private households, agriculture, domestic sewage and many others can all contribute to surface water pollution. There are many notable examples of industrial chemicals (often organic compounds) contaminating water sources (Asante-Duah, 2002). Perhaps the most famous case of industrial contamination is the Hexavalent Chromium contamination of private wells in Hinkley, California upon which the movie Erin Brockovich was based.

Natural sources, such as radioactive isotopes, can also contaminate water. For example, Uranium238 and Radium226 are both unstable, radioactive isotopes. Uranium238, an element with a very low water solubility value, decays into Radium226, a highly water

soluble element (EPA, 2008). Uranium can be a confining material in an aquifer used to supply water for a municipal supply, but despite the success of preventing anthropogenic contamination, the water in the aquifer can become heavily contaminated by radionuclides upon the decay of Uranium238 into Radium226. Once the groundwater breaches the surface, it has an exposure potential in surface water. Acute radium exposure is believed to be carcinogenic to humans (EPA, 2008).

1.3 Risks of algae & limiting nutrients

1.3.1 Eutrophication

While all of these contamination mechanisms are important, this paper will focus exclusively on the anthropogenic contamination of surface water from nutrient loss in conventional row-crop agriculture. Phosphorus (P) and nitrogen (N) have been identified as contributing most prominently to surface water contamination in agriculture (Braig,

2010; Carpenter, Correll, Howarth, Sharpley, Smith, 1998; Mullins, 2009). Along with potassium (K), these nutrients are most often applied in row-crop agriculture (e.g., corn and soybeans) and can move off of the fields and into nearby streams and lakes

(Thomison, Lipps, Hammond, Mullen, Eisley, 2005). Once in the water, these nutrients can catalyze production in the aquatic system, as they are both necessary nutrients for plant growth. There are varying levels of productivity in an aquatic system. Low productivity is referred to as oligotrophic; moderate productivity is mesotrophic; and high productivity is known as eutrophic (Correll, 1998; USGS, 2010b). Eutrophication can also be defined as a high rate of supplied organic matter into an ecosystem (Nixon, 1995). 6

An aquatic ecosystem cannot easily or quickly transition between trophic states; therefore, once a system becomes eutrophic, it will likely remain so for quite some time

(Correll, 1998).

1.3.2 Risks from algae

Eutrophic conditions often lead to high levels of algae. To be clear, algae are a perfectly natural part of the aquatic ecosystem; as they are, for example, the primary food source for zooplankton, which are key constituents of the trophic food web. There are, however, several risks associated with excessive amounts of algae, three of which warrant more attention. First, excessive algae growth can lead to low levels of dissolved oxygen (DO) in the aquatic systems. The microbes that decompose the algae consume high levels of dissolved oxygen, leading to reduced levels, hypoxia (< 2ppm DO), or even completely depleted levels, anoxia (NOAA, 2003). Low levels of dissolved oxygen contribute to adverse ecological effects, such as fish and benthic macro-invertebrate kills

(Paerl, Pinckney, Fear, Peirls, 1998). In the United States, agriculture is believed to be primarily responsible for hypoxia in several surface water bodies. Perhaps the most salient example is the “dead zone” in the Gulf of Mexico. This “dead zone” is the second largest known hypoxic zone in the world, encompassing up to 7000 square miles in the peak summer season (Bruckner, 2008; Rabalais, Turner, Wiseman Jr., 2002). Research has traced excessive nutrient levels in the Gulf to intensive, Midwestern row-crop agriculture (Bruckner, 2008; Rabalais et al., 2002; Randall & Mulla, 2001; Staver &

Brinsfield, 2001). The affects of hypoxia on the aquatic ecology are well described by

Rabalais et al. (2002):

“A hypoxia-stressed benthos is typified by short-lived, smaller surface deposit-

feeding polychaetes and the absence of marine invertebrates such as pericaridean

crustaceans, bivalves, gastropods, and ophiuroids. The changes in benthic

communities, along with the low dissolved oxygen, result in altered sediment

structure and sediment biogeochemical cycles. Important fisheries are variably

affected by increased or decreased food supplies, mortality, forced migration,

reduction in suitable habitat, increased susceptibility to predation, and disruption

of life cycles” (p. 235).

The second serious risk from excessive algae is the threat to human health. Toxic algae have been shown to form in eutrophic conditions (Braig, 2010; Funari & Testai,

2008). Also known as Harmful Algal Blooms (HAB), toxic blue-green algae are a type of cyanobacteria (Braig, 2010). In reality, they are not a true algal species. Rather, they are identified as a group of photosynthetic procariota (Funari & Testai, 2008). Scientists distinguish the two blue-green algae and green algae based on their organismal classifications. Traditional “green algae” are comprised of eukaryotic organisms, which are organisms that have a nucleus. The cyanobacteria comprised blue-green algae are classified as prokaryotic organisms, which do not have a true nucleus (Rollins, n.d.).

While naturally occurring green algae function as a natural component of the food chain, fish and other organisms mostly avoid the consumption of blue-green “algae” because of its toxic composition (Braig, 2010).

Cyanobacteria excrete numerous secondary metabolites, known as cyanotoxins.

Cyanotoxins are categorized based on their toxicity and targeted organs or systems

(Funari & Testai 2008). There are two prominent cyanotoxins produced in the most recent algae blooms. This first is microcystins produced by the cyanobacteria

Microcystis aeruginosa and saxitoxins produced by the benthic, mat-forming cyanobacteria Lyngbya wollei (Funari & Testai, 2008; OEPA, 2010c) Cyanotoxin is known to be toxic to laboratory animals, with known effects as neurotoxins (nervous system), hepatotoxins (liver) and dermatoxins (skin) (Braig, 2010; Funari & Testai,

2008). The primary human exposure route is oral ingestion, such as eating organisms that have bioaccumulated the toxin. However, dermal and inhalation exposure are also possible for those spending large amounts of time near the cyanotoxins, such as professional fishermen (Funari & Testai, 2008). The most severe form of exposure is for patients receiving hemodialysis treatments. In this scenario, subjects receive the cyanotoxin directly into the bloodstream. In Brazil, Azevedo et al. (2002) reported 56 out of 130 patients died after receiving cyanotoxin-contaminated water in hemodialysis.

Acute exposure is often relegated to those drinking untreated water, such as unintentional ingestion while swimming or drinking water in undeveloped countries that lack basic water treatment technologies. Little is known, however, about the long-term, chronic effects from cyanotoxin exposure (Funari & Testai 2008).

A third risk posed by excessive algae growth is the risk of economic loss. Perhaps the most salient risk is that to tourism. The combination of foul water taste and odors, unsightly algae “scum” and swimming restrictions make visiting Lake Erie for recreation less appealing. This is significant because Lake Erie tourism supports roughly 10,000 9

jobs for the region and generates $10.7 billion dollars in economic activity, with $430 million alone going to tax revenues for the state (ODNR, 2009). Exacerbating the financial burden in Lake Erie is the correlative declines in the walleye hatch since the current Lake Erie blooms began appearing in 2003 (OEPA, 2010c). Claimed by some to be the “walleye capital of the world,” Lake Erie is famous for yielding both high numbers of walleye, as well as large fish. Sport fishing on Lake Erie alone accounts for $480 million in revenues (ODNR, 2009).

Water treatment is another potential economic risk. Cyanobacteria and actinomycete bacteria, both a product of eutrophication, create an organic chemical compound known as Geosmin (Gerber, 1965). The term Geosmin comes from “ge” the Greek work for earth and “osme” the Greek word for smell; in other words, “earthy smell.” At high concentrations, this chemical has a manurial odor, but at diluted concentrations it has a soil odor (Gerber, 1965). Geosmin can be tasted at very low levels, as low as 3 parts per trillion—the equivalent of .15% of 1 tablespoon fully dissolved into an Olympic-sized swimming pool (Legrini, 1993). Because most people correlate water taste and water quality, treating contaminated water is vitally necessary (Randtke & deNoyelles, Jr.,

1998). The treatment process is generally a two-step process. First, pure activated carbon is used to sorb the majority of the Geosmin. But given the sensitivity of the public to this compound, the remainder must be removed using ozonation treatment; combined together this is a very expensive process (Legrini, 1993; Randtke & deNoyelles, Jr., 1998).

1.3.3 Limiting Nutrients

What is the root of this biomass growth? The short answer is, “It depends.” In marine environments, nitrogen is generally the limiting nutrient for biomass growth, while in freshwater systems phosphorus is most often the limiting nutrient (Blomqvist,

2004). There are several explanations as to why these two systems are limited by different nutrients. First, the algae species that exist in freshwater systems are capable of fixing their own nitrogen from atmospheric N2, so phosphorus becomes the limiting nutrient (Howarth, Marino, Lane, 1988). Second, the characteristics of the ecosystem itself can determine nutrient deficiencies. One prominent characteristic of the ecosystem that contributes to nutrient limitation is bonding. Organic nitrogen is carbon bonded (C-

N), whereas organic phosphorous is ester-bonded (C-O-P). Many plant roots, fungi and algae have an enzyme that can “cleave” or split the ester bond, making the phosphorus available for uptake. Cleaving the carbon bond independent of decomposition of the carbon material requires more energy and multiple enzyme systems; therefore, the more slowly decomposing marine environment naturally cleaves less of the C-N bond than the higher decomposing freshwater environment creating yet another limitation on nitrogen availability in water (Vitousek & Howarth, 1991). Third, there are higher rates of nitrogen loss in shallow tidal areas due to denitrification, creating a nitrogen limitation

(Blomqvist, 2004). Anaerobic conditions require microbes to find another electron acceptor other then O2, which is often the nitrate. In doing so, the nitrate eventually becomes reduced to N2 gas, and lost from the aquatic system (Pepper et al., 2006).

Finally, salinity governs the availability of phosphorus. Specifically, a decrease in dissolved iron (Fe) leads to an increase in available phosphorus levels. The process is as 11

follows: The phosphorus precipitate (low bioavailability) is the product of the oxidative hydrolysis of dissolved iron. In other words, more dissolved iron means more precipitated phosphorus—less available phosphorus for plant growth. Two iron (Fe) atoms are needed to precipitate one phosphorus atom. In marine environments, iron is sequestered by sulfur, which is a key constituent of sea salt. Therefore, higher salinity

(and thus higher sulfur) means lower iron levels, which ultimately translate into higher available phosphorus levels (Blomqvist, 2004). As such, marine environments have less dissolved iron and less precipitated phosphorus. Therefore, there is more available in marine environment systems, and thus these systems are nitrogen limited (Blomqvist,

2004; Nagai, Imai, Matsushige, Yokoi, Fukoshima, 2007).

Understanding the difference between limiting nutrients in each surface water system is important, and this difference serves as the research emphasis of this paper. The research study area encompasses conventional row-crop producers in Ohio and the concomitant water quality problems in the state. The major surface water bodies of Ohio are all freshwater sources. As freshwater sources, they are all limited by phosphorus.

That is not to say that nitrogen loss does not present a unique set of risks. Many scientists have studied the human health risk from nitrate contamination. They have identified the correlation between drinking water with high nitrate levels and becoming oxygen deficient, a condition known as methemoglobinemia, particularly dangerous to infants (Avery & L‟hirondel, 2003; Dinnes, Douglas, Jaynes, 2002; Kapoor, 1997). Also, as evidenced above, even coastal marine waters can become contaminated by input from the Midwest, as evidenced by the eutrophication of the Gulf of Mexico and the

Chesapeake Bay. That said, the freshwater quality problems relating to phosphorus loss 12

in places like Lake Erie and Grand Lake St. Mary‟s are significant enough to warrant independent research. The chemical properties, associated risks from loss, and the management implications are different enough between phosphorus and nitrogen that they need to be differentiated in research. Given the limited scope of this research, it was unrealistic to consider both nutrients and their related management implications; therefore, phosphorus (P) will be the sole focus hereafter.

1.4 Sources of P: A history of algae in Lake Erie

The Federal Water Pollution Control Amendment of 1972 and the subsequent Clean

Water Act of 1977 were established to provide clean surface water by limiting the type and amount of hazardous substances that could be discharged into water bodies. The Act identifies several “point sources” that would be regulated and monitored as a consequence of the Act, such as industrial facilities, military bases and concentrated animal feeding operations (CAFO) (Congress, 1972). These point sources acquire permits from the National Pollutant Discharge Elimination System (NPDES) program to discharge hazardous waste. As such, the EPA can monitor and regulate the contaminants being discharged into the water bodies of the U.S. from all point sources. The Act was particularly successful in controlling toxic algae blooms in the western portion of Lake

Erie in the 1960‟s. The nutrient source for this particular bloom was attributed to P inputs discharged by lakeside wastewater treatment plants. In response to the toxic algae blooms, the EPA established target levels of 15μg/l of total phosphorus (TP) into the 13

western portion of the lake and 10μg/l TP for the central and eastern portions of the lake

(OEPA, 2010c). As a result of limiting the point source of P, by the end of the 1970‟s the blooms disappeared (Matisoff & Ciborowski, 2005; OEPA, 2010c). In this case, the EPA simply identified the responsible point source and modified the NPDES permit to reduce the amount of permitted P release and the problem was solved. Unfortunately not all P inputs are this easily monitored and regulated.

In addition to “point sources” there are “non-point sources.” Non-point sources are essentially anything not designated as a point source in the Clean Water Act. The Act specifically excludes any non-CAFO agricultural production facility from the point source designation (Congress, 1972). To understand the influence of non-point sources on water quality, it helps to step back and consider the history of P input to Lake Erie.

After the P input was controlled at the wastewater treatment plants, the algae blooms virtually disappeared and target loads were met. However, in the 1980‟s, the algae blooms returned to the western portion of the lake. By knowing the discharge from point sources had not changed over this time, scientists knew that some other source was responsible. After extensive water quality testing at Heidelberg University, scientists were able to identify particulate bound phosphorus as constituting 75%-90% of the TP in the lake (OEPA, 2010c). Particulate P (PP) is simply the P that is attached to the soil.

Particulate P is deposited into water bodies via (mostly soil) erosion. Erosion is primarily a byproduct of the conventional row crop operations in the watershed. Approximately

72% of the land draining into the western portion of Lake Erie is classified as row-crop production (OEPA, 2010c). Authorities understood that direct regulation was not an option for non-point sources. They would need to rely on voluntary behavioral change to 14

solve this problem. Their outreach campaign communicated the risks of erosion loss and water quality effects, as well as provided clear changes farmers could make to reduce soil loss. Their targeted management changes were to adopt conservation tillage and install buffer strips between managed fields and water bodies. By promoting the economic and environmental benefits of conservation tillage through education outreach programs, many farmers in the region decided to switch their tillage practice to conservation tillage.

In fact, by the late 1980‟s no-till for soybeans and wheat became the dominant tillage in the region (OEPA, 2010c). Consequently, the algae blooms subsided.

However, in 2003, scientists identified a new blue-green algae bloom in the western portion of the lake. Since 2003, this bloom has grown larger and larger every year, moving into deeper, more eastern parts of the lake. Understanding the severity of this bloom, the Ohio Environmental Protection Agency (OEPA) enlisted an “Ohio Lake Erie

Task Force” group. This group includes university scientists (e.g., limnologists, soil scientists), agency professionals (e.g., UDA, ODNR, etc.) and citizen groups (e.g., Ohio

Farm Bureau). They have been charged with identifying the source of the P and deciding how to effectively mitigate the input.

NPDES permits showed that discharge from point sources had not significantly changed throughout this time. Water quality analyses demonstrated that PP levels were steady, as conservation tillage was still the dominant form of tillage in the region.

Further, these analyses demonstrated that TP in the lake had remained relatively constant since the 1990‟s. Upon closer analysis, the major change in water chemistry was identified in the component parts of TP. Total phosphorus is comprised of PP and

dissolved reactive phosphorus (DRP). The proportion of TP that was in the DRP form had increased over the last decade. The primary difference between DRP and PP is their bioavailability, or ability for biological uptake; DRP is 100% bioavailable, while PP is only 30% bioavailable (OEPA, 2010c). In other words, TP levels could remain constant, while significantly higher amounts of P become available in the water because of an increase in the proportion of TP in the DRP form.

What is the source of DRP? More detail will be provided in the following chapter, but essentially it is an issue of supply and demand. Phosphorus is applied as a fertilizer in the form of phosphate. Carrying a negative charge, phosphate is the only form of P available for biological uptake. In Ohio soils, phosphate is attached to clay minerals or precipitated as primary/secondary minerals. Soil particles are classified based on size, and clays are smaller than fine sands. As the particle size decreases, the surface area increases. In other words, a unit volume of clay (i.e., 1 L3) will have more surface area than that same unit volume of sand. The clay particle carries a negative charge, but through a process called isomorphous substitution, the silicon particle in the clay material is replaced by the similarly sized and positively charged cations—often aluminum, calcium or magnesium. As a negatively charged and highly reactive material, phosphate will become strongly sorbed to the positively charged clay soils. Phosphate‟s affinity for soil is the primary reason reducing erosion has generally been the number one concern for reducing P loss in agricultural watersheds.

However, the soil‟s ability to chemisorb P is limited. Figure 1.1 illustrates the process of loading P into the soil. Once the soil-bound P threshold is achieved, any additional amount of P added to the soil will be converted into the DRP (soluble) form.

Figure 1.1: Soil P holding capacity (OEPA, 2010c).

As mentioned above, in the late 1980‟s and early 1990‟s state authorities conducted a very successful conservation tillage campaign. This resulted in a high percentage of farmers adopting no-tillage practices, particularly for soybeans and wheat. In these systems, the P fertilizer is placed within a two-inch depth of the soil, as opposed to conventional tillage, which fractures the soil down to an eight inch depth. Therefore, no- till management limits the available soil onto which the P can sorb. If the farmer does not test the soil for existing nutrients (soil test P) prior to application and the soil has reached the soil bound P threshold, the soil test P will become stratified in the upper two inches of the soil. Within this two-inch depth, the PP may react with the surface

conditions and transform into the soluble DRP form. As such, phosphorus can be transported through surface water runoff.

DRP contamination is a logical outcome of the erosion prevention campaign of the late 1980‟s and early 1990‟s. Farmers identified the severity of erosion, made adequate changes and have since been focused on this specific P transport mechanism. In the meantime, soil P may have reached sorption capacity on some farms and therefore has become more vulnerable to transport into nearby surface water bodies as soluble DRP.

While there are still research gaps in the physical processes of this problem, physical scientists like limnologists and soil scientists have effectively characterized the physical problem. However, as a non-point source, individual management behavior determines the success or failure of mitigating P-loss and the related algae blooms in surface waters.

In this regard, social science research can be of particular help, but may be either undervalued or unrecognized for this problem as the Task Force Report only fleetingly mentions this kind of work as a future research need (OEPA, 2010c). To be sure, the importance of individual behavior is not lost on experts familiar with this problem. Dr.

David Baker, an emeritus professor of biology at Heidelberg University, explained the complex social nature of the current algae problem by saying, "Farmers did respond positively to the first call to change. But [their] question is 'What do we do now?'"(Scott,

2010). As explained above, individual producers cannot be directly monitored or regulated. Therefore, mitigating nutrient loss is purely the choice of each individual farmer. Consequently, it is prudent to better understand farmer knowledge of nutrient management, of the DRP transport mechanism and the associated risks from DRP loss in

order to improve communication efforts with farmers and ultimately inform farmer decision making as it relates to nutrient management.

1.5 Mental models, risk perception and profit

1.5.1 Mental Models: Concept

The mental models research approach was developed as decision makers realized that simply reducing state policy and management decisions to the beliefs of a few experts was ineffective; subject knowledge and beliefs needed to also be considered (Morgan,

Fischhoff, Bostrom, & Atman, 2002). The importance of mental models research is even more pronounced for those trying to communicate to the public. Communicators believed that individuals ought to have the opportunity to be well-informed, but knowing how to inform required more fully understanding how subjects were piecing together system knowledge; in other words, more fully understanding their mental models

(Morgan et al., 2002). Traditionally, public communication has been limited to what experts say the public ought to know and then communicating those facts. For messages with a clear cause and effect relationship, this approach works. For example, public health officials can solicit the expert opinion of medical professionals about washing hands and reducing the spread of influenza—notice the clear cause and effect. But in complex and layered contexts, simply relying on expert knowledge is inadequate for informing outreach communications (NRC, 1989; NRC, 1996).

The mental models concept has been used in numerous academic fields; therefore its use in this research should be qualified. Used here, mental models are a cognitive schema for coping with incomplete knowledge by associating relevant beliefs or concepts to a unified system of knowledge. In doing so, individuals convert fragmentary beliefs into a unified cognitive scheme. Two fields in particular, system dynamics and psychology, developed the mental models concept. System dynamics researchers use mental models to develop complex decision analysis computer programs. At the core of a systems approach to mental models is the idea that people have limited information, and will use the mental models construct as a coping mechanism. Forrester (1961) argues that mental models “are models to substitute in our real system that which is represented” (p. 49). He refers to them as fuzzy, incomplete, and changing. Mental models are a way to cope with limited understanding. Forrester (1994) says, “The number of variables [people] can in fact relate to one another is very limited. The intuitive judgment of even a skilled investigator is quite unreliable in anticipating the dynamic behavior of a simple information-feedback system of perhaps five or six variables” (p. 60). Sterman (1994) posits a slightly nuanced definition by saying, “the term mental model also emphasizes the implicit causal maps of a system, the beliefs about the network of causes and effects that describe how a system operates, the boundary of the model (the exogenous variables) and the time horizon considered relevant for framing or articulating of a problem” (p. 294). Another system dynamics researcher, Peter Senge (1990) also uses mental models as a way of identifying key implicit concepts and frameworks. He describes mental models as “deeply ingrained assumptions, generalizations, or even pictures and images that influence how we

understand the world and how we take action. Very often, we are not consciously aware of our mental models or the effects they have on our behavior” (p. 8). Implicit knowledge is an important part of the mental models analyses. Mental models are not simply a recapitulation of what is consciously known, like a kind of test. Psychologists share this implicit function. Wilson et al. (2008) succinctly explain mental models as a

“network of deeply held beliefs that operate below the conscious level” (p. 340).

Psychologists believe that making explicit mental models is critical in risk communication in particular because of the “predictably irrational” behavior risk contexts promote (Ariely, 2008). Risk will be considered in more detail in the following section, but first a review of the mental model method is needed.

1.5.2 Mental Models: Method

The first step in analyzing nutrient management behavior involves assessing the current knowledge state of conventional row crop producers. Critical information gaps, generalizations, and misinformation as well as areas of knowledge sufficiency all need to be identified in order to design targeted outreach communications (Atman, Bostrom,

Fischhoff, & Morgan, 1994). The mental models method is both a qualitative and quantitative data collection and analysis method that is designed to assess a target population‟s knowledge about a subject (Morgan et al., 2002). The method is based on the assumption that people piece together independent items into a cohesive system of knowledge, or mental model. For example, farmers will connect independent objects like soil loss, rainfall, buffer strips, and tillage practice to form a mental model of nutrient

management or phosphorus loss. Through a carefully designed research process, the mental model can be illustrated as a map, or influence diagram. These maps illustrate how individuals are associating various concepts to form a system. Of course, mental maps are partly idiosyncratic, as individual maps might vary; but despite individual variation, research has shown that once a target population has been identified, which in this case is conventional row crop producers, twenty to thirty mental model interviews will elicit approximately eighty percent of the subject knowledge about the risk (Morgan et al., 2002).

The mental model method consists of five steps: an expert model, subject mental model interviews, a follow-up confirmatory survey, a risk communication effort and evaluation of the communication (Morgan et al., 2002). The scope of this research is limited to the first two steps of the method. The second chapter of this thesis provides the detailed expert model developed through an extensive review of the literature and expert consultation. The expert model serves as baseline for system knowledge and is presented in the form of an influence diagram, illustrating the relationship between individual P management and, ultimately, P loss.

The third chapter presents the results from the interviews, which were used to construct a farmer specific mental model. The mental model interviews are designed to be as open-ended as possible, so that subjects can associate concepts as they see fit with as little prompting as possible. By beginning with cues such as, “Tell me about the relationship between your farm management and water quality,” subjects will begin to associate the concepts that are most salient. They are then prompted for more specific

information, to identify knowledge of specific concepts from the expert model. Finally, the information from the interviews is used to create an influence diagram that illustrates how the subjects collectively understand the relationship between individual P management and P loss. In addition to revealing information deficiency of the subjects, comparing the expert and subject mental models might also reveal key omissions from the expert model.

1.5.3 Risk Perception

In addition to evaluating subject knowledge, this research begins the process of considering determinative behavioral variables. As mentioned above, the step following mental model interviews is a confirmatory survey administered to a larger sample size. It will be at this phase that a more detailed, pathway analysis will be possible, which more confidently maps out the relationship between numerous variables. The interview sample cannot fulfill the statistical rigor of a path analysis, but this data is instead used as a way to inform the survey. One behavioral variable that was isolated and evaluated in the interviews was risk perception. There is considerable research demonstrating the pervasive influences that risk perception has on knowledge seeking and related behavior.

Scientists have found that higher risk perception often leads to greater knowledge seeking and higher knowledge, which in turn leads to more stable attitudes and behaviors

(Griffin, Dunwoody, Neuwirth, 1999; Griffin, Neuwirth, Giese, Dunwoody, 2002).

Individuals “perceive” risk, or the likelihood of an unwanted event, in different ways. Risk perception can be characterized mostly as intuitive risk judgments, rather 23

than deliberative risk calculations (Slovic, 1987). Depending on the timing and severity of the risk, these risk judgments can also be made more slowly and analytically (Slovic,

Finucane, Peters, & MacGregor, 2004; Slovic, Fischhoff, & Lichtenstein, 2000). Risk is not some normative, objective truth that only experts understand. Rather, risk is a formed, subjective reality that individuals create based on their beliefs, values, and assessment of facts. Slovic (2001) says, “Risk does not exist „out there,‟ independent of our minds and cultures, waiting to be measured. Instead, human beings have invented the concept of risk to help them understand and cope with the dangers and uncertainties of life” (p. 19). For this study, the most salient risks as perceived by farmers were identified, because one cannot trust that the farmers will share what the experts identify as the most important risks.

Not only is risk perceived subjectively, but also the risk concern itself (i.e., water quality impairment) might not be directly associated with the risk event (i.e., DRP loss).

Slovic (1987) says, “Risk concerns may provide a rationale for actions taken on other grounds or they may be a surrogate for other social or ideological concerns. When this is the case, communication about risk is simply irrelevant to the discussion. Hidden agendas need to be brought to the surface for discussion” (p. 285; emphasis added).

“Agendas” imply some political motive, but a hidden agenda can simply refer to unique values or different frameworks used to assign risk. Some might think about water quality and P loss in reference to financial solvency and others might consider family succession as the framework through which risk is understood.

The concept of different frameworks is especially relevant in this study, because of the previous risk communication effort. As mentioned above, the current risk communication campaign aimed at reducing DRP losses might conflict with the ongoing risk communication effort targeted toward reducing erosion. Experts understand the differences between the two mechanisms for P loss and can appropriately gauge risks based on this information, but will farmers confuse the two P loss campaigns? Before one can craft a risk communication message, decision makers must understand exactly how these two campaigns are being understood together. There is one particular avenue of research in psychology that might help predict how subjects are likely to receive these two risk campaigns together. Social scientists have explored how individuals associate new or unknown risks (i.e., DRP loss through runoff) to old or known risks (i.e., PP loss through erosion) (Fischhoff & Downs, 1997). Visschers et al (2007) found that

“[subjects] often appeared to relate the information about an unknown risk presented … to a cognitive scheme for a known risk, based on similarity of characteristics. The unknown risk seemed to inherit the characteristics of the associated risk, so that the scheme of the associated risk determined the perception of the unknown risk” (p. 719).

In other words, risks that share some semantic identity (or shared meaning) tend to be the risks that become related. They found four reasons people make risk associations: to characterize the possible severity and consequences, to demonstrate that people had already tolerated such risks, to clarify benefits, and to illustrate that risks could be resolved. Without considering the cognitive scheme and semantic category of a risk, the risk manager will not be able to effectively account for potential risk associations

(Visschers et al., 2007).

It is clear that the semantic identity of the two phosphorus mitigation campaigns will be shared, at least for the majority of the subjects. What then are the implications for how the risk message for DRP might be interpreted or misinterpreted given the previous

PP campaign? The first risk association is used to calculate severity. In this instance, relating PP and DRP loss might undervalue the severity of DRP loss as compared to PP loss, because of the significant difference in bioavailability—and thus risk for high volumes of algae growth. On the other hand, risk associations are also used to understand consequences. In this regard, the consequences are the same, eutrophication; therefore, the outreach does not need to emphasize consequences and can target instead the management practices designed to mitigate DRP loss. The second, third and fourth benefits of risk comparisons pertain to previous experiences, benefits and the ability to manage risk. These comparisons may empower the success of a risk message regarding

DRP management due to the potential for individual self-efficacy to be high based on past PP mitigation and the opportunity farmers had to see the difference behavior change can make to improve water quality. Both PP and DRP risk can be managed collectively, but it is important for risk communicators to understand that individuals have a propensity to equilibrate unrelated risk contexts based on some putative similarity.

The mental model method is an effective tool for gauging subject knowledge, but one must also consider those forces influencing knowledge development. The risk information seeking and processing model helps elucidate the influence of risk perception and can give structure to some of the risk association concepts being discussed (Griffin, et al., 1999). At least one target for risk communication is to encourage people to seek out knowledge in order to make more informed management decisions. The assumption 26

is that those with a better understanding of the risk are more likely to respond to the risk appropriately.

Figure 1.2: Risk information and processing model; illustrates the risk-related variables known to influence knowledge (Griffin et al., 1999).

Griffin et al. (1999) argue that individuals will seek out and process information

(i.e., gain knowledge) based on a perceived gap between their existing knowledge and what they think their knowledge ought to be. As illustrated in Figure 1.2, they argue that

several variables influence this perceived knowledge gap, such as demographics, perceived hazard characteristics (i.e., perceived risk), and affective response (i.e., worry or concern) (Griffin et al., 1999). Chapter three will explore in more detail these particular variables in this model and present pertinent results from the farmer interviews as they relate to understanding the factors associated with increased knowledge.

1.5.4 Profit-maximization

When trying to understand land-management behavior, it is important to think about how profit might help or hinder an intended behavioral change. From a nutrient application standpoint, there is a negative relationship between profit and DRP loss. In other words, as DRP loss decreases, net revenue increases. However, mitigation can also require land to be taken out of production or new equipment to be purchased thus lowering net revenue. As such, profit maximization cannot be relied upon exclusively to guide mitigation behavior. Further, individuals have shown a pattern of deviating from the script of profit-maximization, and this research considers specifically how profit should be used in risk communication. Chapter three will explore these questions more specifically in the form of hypotheses.

Classical economics was based on the premise of normative rationality, which says that “rational” individuals should systematically adhere to certain axioms in forming profit maximizing judgments (Friedman & Savage, 1948). These rational choice theorists argue that decision makers will not systematically violate certain principles (one might err, just not systematically). Consider two principles that are fundamental to 28

classical economics, transitivity and invariance. Transitivity refers to the behavior of related choices. Specifically, it says that if an individual prefers choice A over B and choice B over C, then by virtue of transitivity that individual will prefer choice A over C

(Plous, 1993). Invariance refers to the assumption that decision makers should not change a preference for an alternative based on context, or framing [i.e., whether the alternative is framed as a loss or gain should not matter] (Plous, 1993). Kahneman and

Tversky (1979) famously set up various experiments demonstrating the failure for the theories of transitivity and invariance to describe actual individual behavior. These experiments were so revolutionary that their work garnered a Nobel Prize in Economics in 2002. Specifically, prospect theory argues that an individual will systematically choose the clear profit maximizing decision in one context, but when that same alternative (i.e., same probability and consequence) is framed as a gain rather than a loss, the individual will choose the less profitable alternative (Kahneman & Tversky, 1979).

These experiments were not isolated events, but have been duplicated by numerous scholars over time (Kahneman & Tversky, 1984; Slovic et al., 2000).

Normative rationality is not simply an exercise in semantics found in academic journals. Consider an example illustrating how normative rationality continues to dictate how some believe environmental problems should be confronted. At a recent graduate seminar about conservation education, a professor in the audience stood up and asked the lecturer a rhetorical question: “How can you convince Americans to conserve energy?”

Confounded by the complexity of this putatively simple question, the lecturer shrugged his shoulders. The professor answered his own question, “Easy. It‟s simple economics.

If you want people to conserve energy, make it more profitable than the energy-intensive 29

alternative.” Phosphorus management can easily be substituted for energy conservation and the professor‟s response would be the same: behavior is fundamentally reduced to profit maximization.

In fashioning a way forward, Krantz (1991) has this to say:

“The normative assumption that individuals should maximize some quantity may

be wrong. Perhaps…there exists nothing to be maximized. Ordering may be

partial…because the calculations are impossible in principal: People do and

should act as problem solvers, not maximizers, because they have many different

and incommensurable…goals to achieve” (p. 34; emphasis added).

Without a doubt, the vast majority (if not all) of farmers would identify profit as at least one goal of their operation. But it would be naïve to suggest that profit maximizing is the only goal. Lin et al (1974) argue that the profit maximization production function wrongly assumes, among other things, fixed price and technology; in other words, a “risk-less” environment. Agricultural economics, for example, has since demonstrated that a risk-less agricultural environment is not representative of the production context of decision-making (Moss, 2010). In fact, risk is ubiquitous in farming; and risk is inextricably related to problem solving. As evidenced by the data to be presented in chapter three, individual farmers demonstrate varying risk perceptions, as well as many different primary farming goals. A high income farmer might perceive the risk of taking forty acres out of production for a filter strip much differently than the small farmer where forty acres might represent a substantially higher percentage of his total land; thus relying exclusively on profit curves may not be telling the entire story.

To further complicate this issue, profit itself can be thought of differently based on one‟s temporal perspective. One decision might maximize profit in one year, but another decision might maximize profit over ten years.

So rather than addressing behavior exclusively as profit-maximization, perhaps it is more helpful to take Krantz‟s advice and evaluate farmer behavior as “problem solvers” (Krantz, 1991). Problems are inherently multi-dimensional and complex, and in the same way, phosphorus management behavior is multi-dimensional and complex.

Knowledge, environmental values, attitudes, family history, among many things help form one‟s idea of what it means to maximize profit, which might be closer to maximizing utility.

1.6. Outline of the thesis

1.6.1 Overarching Research Questions

1. How well do Midwestern, row-crop farmers understand phosphorus

management, specifically phosphorus loss in the DRP form?

2. What demographic and risk variables significantly relate to knowledge?

3. Is the current Ohio P-Index an efficient and effective tool for individual

farmers to assess their vulnerability of phosphorus transport into surface

water in the PP and DRP forms?

1.6.2 Research Objectives

The overarching objective of this research is to couple social data with abundant biophysical data to consider the determinative behavioral variables in row-crop land management. The majority of this research is committed to assessing knowledge; therefore chapter two will present the detailed expert model of phosphorus management.

This chapter is meant to explain what experts indicate should be known about the system.

This will include a basic chemical review of elemental phosphorus and a description of the different forms of phosphates promoting algae growth. This will also map out the various transport mechanisms for P loss, including PP and DRP. Also covered will be the various mitigation mechanisms, both natural and human controlled. Finally, this chapter will present a detailed risk characterization of toxic algae and eutrophication.

Chapter three will present the method and results from the farmer interviews to consider the extent to which current row-crop producers understand the dynamic nature of phosphorus management and its impacts on water quality. The relationship between knowledge, risk perception, farmer values and demographics will also be discussed to begin evaluating what can be done to increase knowledge and ultimately promote behaviors that mitigate phosphorus contamination of surface water.

Chapter 2: The Ohio Phosphorus Index Assessment: A viable tool for DRP risk communication?

2.1 Introduction

“You‟re glumping the pond where the Humming-Fish hummed!

No more can they hum, for their gills are all gummed.

So I‟m sending them off. Oh, their future is dreary.

They‟ll walk on their fins and get woefully weary

In search of some water that isn‟t so smeary.

I hear things are just as bad up in Lake Erie.” (Geisel, 1971)

Dissolved reactive phosphorus contamination of surface water is an emerging problem, particularly in agricultural intensive watersheds (OEPA, 2010c). The severity of the impacts caused by DRP contamination has catalyzed a plethora of research in a wide variety of fields, like soil science, agronomy, limnology and aquatic ecology. The

Natural Resources Conservation Service (NRCS) has used these research findings to create two land management mitigation plans, the Soil Testing Risk Assessment

Procedure (STRAP) and the Phosphorus Index Assessment Procedure (P-index). The

STRAP model relies exclusively on soil test results; while the P-Index incorporates a number of other field specific variables (i.e., slope, soil properties, etc.) to provide a more comprehensive risk assessment of phosphorus transport into surface water. Farmer can

use these tools to identify the vulnerability of specific fields to phosphorus loss. Neither program is mandated as policy, but is rather voluntary for those who choose to mitigate phosphorus loss on their farms. Consequently, it behooves those interested in widespread adoption of these tools to provide farmers with clear information about what they are, why they are important and how they apply to the emerging phosphorus problems of today.

In light of the voluntary land management context, this chapter seeks to provide the framework in which a risk communication campaign can be structured. There are three primary objectives. The first objective is to present an expert model of phosphorus management which can be used as a baseline for assessing farmer knowledge and risk perception as part of designing a risk communication process. This objective will be accomplished by providing an overview of phosphorus contamination, by outlining the phosphorus dynamics in the soil and water and the risks associated with excessive phosphorus in surface water. The second objective is to consider the land management implications for PP and DRP mitigation. This information will be used to assess farmer knowledge about the relevant land management choices to mitigate P-loss.

Characterizing the current Ohio P-index, as well as considering the possibility of separate

PP and DRP emphasized indices, will help to accomplish this objective. The third objective moves beyond characterizing knowledge and risk assessment, and addresses the utility of current phosphorus management tools, particularly in light of competing forms of phosphorus transport. This objective will be accomplished by assessing the clarity of the current P-index and making suggestions as to whether or not two P-indices might better communicate the risks of nutrient loss.

2.1.2 Lake Erie pollution and its legacy

In the 1960‟s, Lake Erie became a rallying cry for those concerned about environmental degradation. In 1969, massive algae blooms overwhelmed the western portion of Lake Erie. To compound the problem, a major river discharging into the lake, the Cuyahoga River famously caught on fire as a result of petroleum contamination from nearby industries. In fact, the river had caught on fire at least nine times before the 1969 event, but in 1969 Time magazine reporters were present to photograph the incident.

Their report covered more than the river. In March, the following excerpt appeared in their nationally distributed magazine:

“Some lake! Industrial wastes from Detroit's auto companies, Toledo's steel mills

and the paper plants of Erie, Pa., have helped turn Lake Erie into a gigantic

cesspool. Of 62 beaches along its U.S. shores, only three are rated completely safe

for swimming. Even wading is unpleasant; as many as 30,000 sludge worms

carpet each square yard of lake bottom. Each day, Detroit, Cleveland and 120

other municipalities fill Erie with 1.5 billion gallons of inadequately treated

wastes, including nitrates and phosphates. These chemicals act as fertilizer for

growths of algae that suck oxygen from the lower depths and rise to the surface as

odoriferous green scum. Commercial and game fish—blue pike, whitefish,

sturgeon, northern pike—have nearly vanished, yielding the waters to trash fish

that need less oxygen. Weeds proliferate, turning water frontage into swamp. In

short, Lake Erie is in danger of dying by suffocation.”(1969)

Three years later, in 1972, the Water Pollution Control Act passed; it was later revised and named the Clean Water Act. This major regulatory measure succeeded in abating the pollution in Lake Erie, most notably through the point-source regulation of phosphates discharged from wastewater treatment plants. As a result of the point source regulation, Lake Erie met its target phosphorus levels of 11,000 metric tonnes and the algae blooms subsided in the early 1980‟s (OEPA, 2010c). However, water quality problems have returned several times in Lake Erie primarily because of changing phosphate input types.

2.1.3 Particulate Phosphorus

By the late 1980‟s, algae growth was beginning to re-emerge in the western portion of the lake. Unlike the previous blooms, significant point source reductions indicate that non-point sources are now considered to be the major culprit. Water quality analyses revealed that between seventy-five to ninety percent of the phosphorus in the western basin was in the form of PP, which is simply phosphorus attached to sediments

(OEPA, 2010c). Water quality authorities knew that in order to reduce the algae growth in the lake, they needed to prevent soil loss within the watershed. Approximately eighty percent of land use in the western Lake Erie watersheds (Maumee and Sandusky) is committed to row crop agriculture. Further, deep tillage that inverts the field surface, thus eliminating field residues which absorb the erosive energy from rainfall, was identified as being the most important land management variable contributing to soil loss

(OEPA, 2010c). As a result, authorities (i.e., university extension, USDA-NRCS, etc.) embarked on a very successful conservation tillage campaign, and by the 1990‟s

conservation tillage was the dominant form of tillage in the region, particularly with soybeans and wheat (OEPA, 2010c).

Two financial assistance programs aided farmers in this conversion. First, the

Conservation Reserve Program (CRP) provided financial incentives to farmers who took erosion- prone and/or streamside land out of production to be used instead as a vegetative buffer to filter field sediments. The second program, the Conservation Reserve

Enhancement Program (CREP), is very similar to CRP except that the contract commitment is much longer and the payments are of higher value. In fact, farmers with marginal land can potentially make more money in CREP than in CRP or production

(Sohngen, 2000). The years following these land management changes saw substantially reduced PP inputs into Lake Erie and correlating declines in algae growth (OEPA,

2010c).

2.1.4 Dissolved Reactive Phosphorus

In the early 2000‟s, large algae blooms appeared yet again. In 2003, the blooms were exceptionally expansive. Each year thereafter, the blooms have grown larger and larger, moving eastward into deeper parts of the lake. As expected, in 2010 the algae blooms were as large as they have ever been (OEPA, 2010c). With point-source P and

PP stabilized, what source contributed to this most recent bloom?

The dilemma facing scientists was that total phosphorus (TP) levels had remained relatively stable since the 1990‟s (see Figure 2.1), suggesting that algae blooms should not have re-emerged on the scale that they have. However, total phosphorus is comprised of PP (sediment bound) and DRP (soluble phosphorus). While PP inputs have showed a

decline over time (see Figure 2.2), a response to the land management changes to reduce soil loss, DRP inputs have increased sharply over the past fifteen years (see Figure 2.3).

Figure 2.1: Graph illustrating the stability of TP loading into Lake Erie over time (OEPA, 2010c).

Figure 2.2: These graphs demonstrate the decline of PP loading into Lake Erie from the two major rivers that discharge into the lake (Richards, 2009).

Figure 2.3: Graphs illustrating the increase of DRP loading into Lake Erie over time from the Maumee and Sandusky Rivers (OEPA, 2010c).

It is likely that the sediment mitigation campaign successfully stabilized PP loading and because TP was mostly in the PP form, TP stabilized and algae blooms diminished. These trends suggest that the proportion of TP in the DRP form has been increasing while the PP proportion has been declining. Further, exchanging PP for DRP is not a one to one ratio because of the bioavailability of each phosphorus type. The bioavailable phosphorus from particulate phosphorus is estimated to be around 30%

(though these numbers can fluctuate), while the phosphorus availability from DRP is

100% bioavailable (Wortman, Helmers & Barden, 2005). This means that a unit volume of DRP results in much higher biomass production in the aquatic system than that same volume of PP.

Understanding and managing DRP loss is an emerging issue for scientists and farmers alike. Managing for DRP loss is not as simple as eliminating soil loss, as was the case with PP loss mitigation (Helmers, Isenhart, Kling, Moorman & Tomer, 2007). To be sure, there is likely not one simple solution for DRP mitigation, in part because of the dynamic nature of soil phosphorus. Some suggest that farmers need to be more diligent in testing their soil in order to prevent over-application of phosphorus, but research suggests that agronomic phosphorus soil tests do not correlate strongly with phosphorus transport; so while they are good agronomic tools, they may not entirely predict surface water contamination (Sharpley, et al., 2003). Others suggest that increased tillage and phosphorus incorporation would help mitigate the DRP problem; however, the suggested tillage changes might increase the risk of PP loss (Gilley, Eghball & Marx, 2007). Still others believe the current problem originates in the disparity between artificially “safe”

phosphorus levels of 150ppm promoted by the NRCS models and proven agronomic nutrient requirements (OEPA, 2010c).

2.2 Research Approach

The mental models method is an effective tool for diagnosing the knowledge deficiencies and risk perceptions of a targeted population that a risk communication would need to address. The method has five steps: create an expert model of the risk

(i.e., phosphorus loss); conduct subject interviews to determine the similarity between the subjects‟ representation of the system and the expert model; administer a survey to a much larger population to test the results from the interviews; draft a risk communication; and evaluate the effectiveness of the risk communication (Morgan et al.,

2002). Through an extensive review of pertinent literature, this article summarizes the expert model, against which subject knowledge can be measured. The expert model in part borrows the framework from the current Ohio P-index, but also expands upon the index to cover individual phosphorus transport pathways of PP & DRP.

2.3 The Expert Model for Phosphorus Risk

2.3.1 Phosphorus Transport and Impacts

There are two primary forms that phosphorus can take in surface water: particulate phosphorus (PP) and dissolved reactive phosphorus (DRP) (Daniel, Sharpley,

Edwards, Wedpohl & Lemunyon, 1994). Currently, there is a gap in information available to farmers that explicitly accounts for the similarities and differences between

PP and DRP transport. Though they each have similar water quality impacts, the

phosphorus management implications are different for mitigating either type. Farmers need to understand how each transport mechanism functions, what the impacts are and what they can do to concurrently mitigate these risks.

Figure 2.4 illustrates the various potential destinations of field applied phosphorus. The best scenario is that the nutrient is used by the plant or remains fixed in the soil. The bolded directional arrows in Figure 2.4 illustrate the two primary routes that

P takes to reach water, erosion and surface runoff; it is these pathways that the P-index is designed to assess. In order to fully understand the content of the phosphorus risk index, it is necessary to consider five topics that provide the chemical, physical, biological and social background for the P-index: the general characteristics of phosphorus; the behavior of phosphorus in soil; the behavior of phosphorus in water; aquatic biomass growth; and the impacts of excessive biomass growth.

Applied Phosphorus

High Risk Low Risk

1) Surface runoff 3) Remain in soil 2) Erosion 4) Uptake by plant

Phosphorus in surface water

Figure 2.4: Once P fertilizer is applied to the soil, it can move (or stay) via four primary mechanisms; emphasis on high risk pathways into surface water.

2.3.2 Characteristics of Phosphorus

Elemental phosphorus (P), a nonmetal, has a low electronegativity value (2.19), which means the phosphorus atom has a weak ability keep its electrons (Brown, Lemay

& Bursten, 2006). Phosphorus has five electrons in its valence shell, and is most often found sharing electrons with three or four neighboring atoms (Brown et al., 2006; Van

Wazer, 1973). Because of its abundant electrons and weak ability to keep them, it is highly reactive with atoms and molecules which need electrons; therefore, phosphorus does not exist in nature as a free element (Brown et al., 2006).

Phosphorus minerals, like rock phosphate, are mined for use in fertilizer for crops.

Most phosphorus sequestered in minerals is extremely insoluble, and thus has a limited bioavailability. Bioavailability is the ability of living organisms to use phosphorus for growth. In order to become bioavailable, phosphorus has to be converted into soluble

2- - orthophosphate [hydrogen phosphate (HPO4 ) or dihydrogen phosphate (H2PO4 )]

(Pepper et al., 2006). Using phosphoric acid and heat, the insoluble rock phosphate can be synthetically converted into soluble orthophosphate fertilizer.

2.3.3 Phosphorus in the soil

Once phosphorus fertilizer is field applied, there are three different forms, or pools, in which phosphorus can exist, each with different implications for surface water impairment: sequestered P (also called stable/non-labile P), labile P, and available P

(Wortman et al., 2005). Approximately ninety-five percent of phosphorus in the soil is

sequestered P, while four percent is labile P and one percent of phosphorus is available P

(Wortman et al., 2005). The most pertinent environmental difference between phosphorus pools is their bioavailability. Sequestered P is formed through precipitation and adsorption. When phosphorus precipitates in minerals with a very low solubility constant, the only way for the phosphorus to be released is for that mineral to become soluble. Since most of the minerals “hosting” phosphorus are virtually insoluble, sequestered P has almost no bioavailability. Adsorption is simply the adhesion of an atom or molecule to a surface (Pepper et al., 2006). Adsorption is not pH driven, but rather is determined by the available surface area oxide clays, like iron (Fe), aluminum

(Al) and manganese (Mn) (Dayton, 2011). These prominent oxide clays in the soil are positively charged, while the negative charge of phosphorus catalyzing non-specific sorption, or the attraction of particles because of an opposite charge. These bonds are mostly relatively weak and “desorption” of these bonds can occur when introduced to water (Wortman et al., 2005). Sorption can also be specific. Specific sorption, such as chemisorption, is much stronger and occurs not just because of particle charge but also because of size and surface area matching; breaking specific sorption bonds require a lot of energy.

Labile phosphorus is the fraction of phosphorus that can either remain sequestered or be converted into available-P. The available-P pool refers to the total phosphorus that is available to the plant (Watson & Mullen, 2007). Phosphorus in the soil is continuously trying to maintain an equilibrium between phosphorus pools, and if too much available P is lost or taken out of the system by plants, then the labile-P is positioned such that it can easily convert what is needed into the available-P form (Wortman et al., 2005). Like

sequestered-P, labile-P is bound to soil particles and minerals. The difference in this pool of phosphorus is that labile-P has a much weaker bond to the minerals than sequestered-

P, and are thus able to replenish available-P in the soil that has been removed by plant uptake or other losses (Wiederholt & Bridget, 2005). The soil conditions that determine the behavior of labile P are critical for understanding phosphorus loss, as available-P is dynamic and can change for a variety of reasons. Two different processes govern the availability of labile-P: precipitation/dissolution and adsorption/desorption. Precipitation is the formation of solids in the soil solution and is pH driven. In precipitation, phosphorus is often bonded with the common soil minerals aluminum and calcium, which are most soluble between pH 5 and pH 7. Consequently, in this pH range the phosphorus is released from the mineral and the highest levels of available-P are attained.

Conversely, pH levels above or below these values prevent these minerals from becoming soluble and significantly less phosphorus is plant available (Dayton, 2011).

This process is different from precipitation of sequestered-P insofar as the minerals of sequestered-P, like apatite, do not become soluble under 5-7 pH.

2.3.4 Phosphorus in the water

After phosphorus is applied to the soil, it can be transported off of a field and into surface water in two forms: as soluble phosphorus (SP) or PP; together called total phosphorus. Whether the phosphorus comes from the sequestered P, labile P, or available P pool has a significant influence as to its bioavailability in water. Particulate phosphorus will mostly be phosphorus from the sequestered pool, which has a very low bioavailability. Particulate-P can also be comprised by the labile P pool, which has a

higher bioavailability. Together, the PP is considered approximately thirty percent bioavailable (Wortman et al., 2005). These numbers vary, as a higher percentage of that phosphorus can be bioavailable depending on water pH and solubility of the mineral

(Holtan, Kamp-Nielsen & Stuanes, 1988). Particulate phosphorus can be manifest as precipitate P, organic material containing P, or sediment-bound P; simply put, it is phosphorus associated with some kind of solid (Wortman et al., 2005).

Soluble phosphorus is much more bioavailable in water than PP. Soluble P is the sum of dissolved reactive phosphorus (DRP) and dissolved unreactive phosphorus

(DUP). Dissolved reactive P is already fully converted into orthophosphate, while DUP simply needs a special enzyme to engage a process called hydrolysis that splits a water molecule to create on oxygen atom to form orthophosphate (Lehmann, Lan, Hyland,

Sato, Solomon & Ketterings, 2005). Soil phosphorus in the available P form contributes most directly to the SP fraction. Farmers who understand the unique dynamics of phosphorus in the soil and water are able to more accurately predict the kind of phosphorus most likely to be transported into surface water from their fields. The potency of DRP in particular warrants special attention.

2.3.5 Aquatic biomass growth

The major threat facing surface water bodies as a result of phosphorus inputs is excessive biomass growth. High production in an aquatic system is referred to as eutrophication. Often, these high production periods result in biomass that is not found in a stable, healthy aquatic system, such as the cyanobacteria, or “blue-green algae,” appearing in Lake Erie. Cyanobacteria are photosynthetic prokaryotes that use

phosphorus in photosynthesis. Phosphorus is a key constituent of adenosine triphosphate

(ATP), which is a form of energy converted from sunlight that can be used by the plant

(Pepper et al., 2006). Of course, phosphorus is one of many nutrients needed for biomass growth, so why does phosphorus catalyze growth in freshwater systems? The answer lies in the law of limiting nutrients. Similar to the laws of supply and demand, various aquatic organisms demand a certain proportion of nutrients. If the supply of those nutrients in the water body is low, then any addition of the most needed nutrient will catalyze growth (Alexander, Smith, Schwartz, Boyer, Nolan & Brakebill, 2008). For cyanobacteria in freshwater systems, phosphorus is the limiting nutrient in part because they can convert atmospheric N2 into bioavailable forms of nitrogen, another macronutrient for plant growth. There are other reasons for nutrient limitation, but for the purposes of this research it is important to understand that phosphorus drives biomass growth in freshwater systems (Alexander et al., 2008). In addition to nutrient inputs, cyanobacteria growth requires warm temperatures and adequate light, which is why algae growth occurs at its peak in the late summer months. Relative to other aquatic plants, cyanobacteria are quite resilient because they can convert low levels of sunlight for photosynthesis and can resist harsh temperatures because of a thick gelatinous shell

(Funari & Testai, 2008).

2.3.6 Impacts of biomass growth

There are three primary impacts related to large cyanobacteria blooms: ecological, human health and economic impacts (see Figure 2.5). First, aquatic biodiversity depends on adequate levels of dissolved oxygen (DO). Large algae blooms directly consume DO

as living organisms, and they also indirectly consume DO as a part of their decay process because the bacteria that consume the algae require high amounts of DO. Reduced DO in an aquatic system, known as hypoxia, leads to fish and macroinvertebrates kills (Pepper et al., 2006). Hypoxia is a widespread concern, particularly in the United States where the Gulf of Mexico hosts the world‟s second largest hypoxic “dead zone.” Many other coastal waters are subject to hypoxia, such as the Chesapeake Bay, Pensacola Bay, Long

Island Sound, among others (Jewett, Lopez, Kidwell & Bricker, 2010). The severity of these impacts has spawned the Harmful Algal Bloom and Hypoxia Research and Control

Act, passed in 2004 which provides opportunity and resources to study the processes associated with hypoxia (Jewett et al., 2010).

Second, human health impacts result from the toxicity of cyanobacteria. Large blooms of cyanobacteria are known as harmful algal blooms (HABs). They are considered harmful because they produce toxic metabolites, known as microcystins.

Various forms of microcystins are known to be toxic to the liver, nerves and skin and have consequently been issued very low maximum concentration levels (MCLs) in drinking water by the World Health Organization at 1 part per billion (ppb). For recreational contact, WHO considers levels over 20ppb to present a moderate risk (Braig,

2010). In the summer of 2010, the inland lake Grand Lake St. Mary‟s reported microcystin levels over 2000 ppb which prompted a no-contact advisory (OEPA, 2010a).

In addition to the cyanobacteria toxins, HAB‟s can support the dinoflagellate, Pfiesteria

Piscicida, which excretes a substance toxic to its aquatic prey as well as humans

(Burkholder & Glasgow, 1997). Though this has not yet become a major concern in

Lake Erie, there is a potential risk given its impact in some coastal environments (NOAA,

2010). Impacts from phosphorus contamination in surface water

High P risk Individual yield index P loss & profit loss

Eutrophication of surface water

Ecological Excessive algae Tourism and effect growth financial loss

Reduced aquatic Swimming Drinking water biodiversity Human health restrictions contamination effects

Fish and Geosmin invertebrate Pfiesteria Toxic “algae” kills piscicida (cyanobacteria)

Low dissolved Fisheries loss oxygen

Figure 2.5: Impacts from phosphorus contamination of surface water.

Third, the ecological and human health effect can be detrimental to the local economy and tourism industry. According to the National Oceanic and Atmospheric

Administration (NOAA), one HAB event can cost tens of millions of dollars for a local economy. Overall, in the United States NOAA declares that a conservative estimate of economic costs over the past two decades from HAB‟s is over one billion dollars

(NOAA, 2010). Another economic cost is associated with the chemical Geosmin, which often accompanies HAB‟s. Geosmin has a foul, “earthy” taste that humans can detect at

an astounding 3 parts per trillion (Gerber, 1965). Treating drinking water contaminated by Geosmin requires expensive ozone treatment, which can cost one water treatment facility over one million dollars between new facilities and shut down costs (Randtke, et al., 1998). Swimming restrictions, walleye reductions and aesthetic concerns all contribute to the final economic cost of HAB‟s: lost tourism revenues. Tourism generates over one billion dollars to the local economy around Lake Erie (ODNR, 2009).

In Lake Erie, walleye fishing is a critical part of the local economy and as a result of the low DO conditions the walleye hatch has been in a steady decline since 2003 (OEPA,

2010c).

2.4 Land management strategies to prevent phosphorus loss

2.4.1 Soil Test Risk Assessment (STRAP)

The accumulation of these risks has motivated those concerned with the current water quality conditions to determine how to best respond to these problems. As mentioned earlier, agriculture is considered one of the major contributors of phosphorus in surface water, especially in Midwestern states. In addition to the aforementioned algae growth impacts, farmers need to be attentive to phosphorus in their soils because of crop yield implications. In order to keep their marginal costs as low as possible, it behooves the farmer to apply only what the plant needs for maximum growth. Consequently, many farmers regularly test their soils as a means to informing application decisions. The

Natural Resources Conservation Service (NRCS) has suggested using the information from soil test phosphorus (STP) as a preliminary risk index to evaluate how susceptible a particular field is to phosphorus loss (NRCS, 2001). Based on a Bray-Kurtz P1

agronomical soil test, the soil test risk assessment procedure (STRAP) uses categories of low, medium, high and very high transport potential. The STRAP index then makes basic land management suggestions such as application amount, cover crop recommendations and to what extent the nutrient should be incorporated into the soil

(NRCS, 2001).

This approach assumes that using STP results alone can help individual farmers determine the transport potential based on a simple assessment of the phosphorus source.

For example, fields with high and very high STP levels are more likely to experience phosphorus transport or loss (if the recommendations of NRCS are not followed). The virtue of the STRAP model is that it clearly communicates to the farmers when their soil levels are agronomically too high and instructs them to stop applying what will likely become soluble P in surface water. This model communicates to farmers that if their fields generate high STP numbers, then there is no need for further application, as additional application will not lead to an increase in crop yield. However, because of the dynamic nature of soil phosphorus, research has shown that STP levels do not necessarily correlate to phosphorus transport (Sharpley et al., 1996). It is necessary, therefore, to create a risk assessment index that more specifically addresses transport characteristics rather than merely the source values.

2.4.2 Phosphorus Risk Index

Experts at the NRCS understand the limitations of the STRAP model and therefore are not suggesting that STRAP be used exclusively for determining phosphorus transport. For fields considered at risk for phosphorus loss, the NRCS has expanded

upon the concept of the STRAP model by creating the phosphorus index assessment procedure (P-index) (NRCS, 2001). The NRCS would prefer producers whose fields are susceptible to phosphorus loss based on STRAP results to consult further with the more comprehensive P index. Though updates and changes to the model are currently being considered, this index rather effectively elucidates the key transport vulnerabilities of individual fields.

At its core, the P index is the sum of phosphorus source and transport, as illustrated by Figure 2.6. The source component of the model is comprised by STP and fertilizer application. STP represents the available soil phosphorus prior to application.

STP results can be represented by a variety of laboratory analyses (i.e., Bray-Kurtz P1,

Mehlich III, Olsen), but in Ohio the recommendations are designed for Bray-Kurtz P1 results; therefore, conversions are provided within the index.

To provide a more complete description of the phosphorus source, farmers are also asked to report fertilizer application method and rates. Values are given separately for organic and synthetic fertilizers. Application rates for organic fertilizers are given a slightly higher multiplier value than synthetic fertilizers (.06 vs. .05), which indicates that organic fertilizers are slightly more vulnerable to becoming plant-available than synthetic fertilizers, despite the evidence demonstrating that organic phosphorus is much less soluble (NRCS, 2001; Wiederholt & Bridget, 2005).

Phosphorus Risk Index = Source + Transport

Source Transport

Soil Test P Connectivity to Water Fertilizer Application Presence of filter strip Soil erosion potential Amount Method (RUSLE)

Runoff Class

Synthetic Organic

Figure 2.6: Ohio’s Phosphorus Risk Index (PP and DRP undifferentiated)

On the other hand, the multiplier for application method of synthetic fertilizers is higher than that of organic fertilizers. The categories and values under application method are as follows (first synthetic, then organic values): no fertilizer applied (0,0); immediate incorporation or on eighty percent residue cover (0.75, 0.5); incorporation within a week or on fifty to eighty percent residue cover (1.5, 1.0); incorporation between one week and three months or on thirty to forty-nine percent residue cover (3, 2); no incorporation or incorporation over three months after application or on less than thirty 54

percent land cover (6, 4) (NRCS, 2001). One can infer from these values that synthetic fertilizers are more prone to move than organic fertilizers if not applied properly.

The transport piece of the model is comprised of four variables: connectivity to water, filter strips, soil erosion values and runoff class (see Figure 2.6). The first variable refers to a field‟s connectivity to water. It is found simply by determining how close concentrated flow (like a waterway, subsurface drainage or a stream) is to the field, if at all. The highest index value belongs to flows that are adjacent to and drain into perennial streams, with steadily lower values belonging to flows that drain into intermittent streams or are not adjacent to a stream. The lowest values belong to fields with no concentrated flow and not adjacent to a stream. Tile drainage, for example, has been shown to contribute to higher levels of DRP loss than surface runoff, because of its concentrated nature, with 1.4 times more DRP loss coming from tile drainage than surface runoff.

DRP losses in tile drainage are even more pronounced in conservation tillage situations

(Gaynor, 1995). The second transport variable (and only mitigating variable) in the risk index is the presence of a filter strip, or vegetative buffer that filters runoff water. An active filter strip earns the farmer a deduction of two points.

By using the well-known formula RUSLE, or the revised universal soil loss equation, one can calculate the third transport variable, soil erosion potential. The formula is as follows: A = RKLSCP. Where A (t ha-1 yr-1) is average annual net soil loss from an area, R (MJ mm hr-1 ha-1 yr-1) is rainfall erosivity, K (t hr MJ-1 mm-1) represents soil erodibility, L (unitless ratio) is the slope length factor, S (unitless ratio) is the slope steepness factor, C (unitless ratio) is the cropping factor, and P (unitless ratio) is the conservation practices factor. A detailed description of RUSLE is available from the

United States Department of Agriculture (Renard, Foster, Weesies, McCool & Yoder,

1997). The fourth and final transport variable, runoff class, refers to a field‟s hydraulic conductivity and slope. In other words, how easily can water be transmitted through the soil (infiltrate) and what is the slope of the field. Higher values are given to fields with a large slope and low hydraulic conductivity values; as such, the water will infiltrate into the soil more slowly which leads to more flow as surface runoff, increasing transport potential. Low values are given to fields with little or no slope and a high ability to absorb water.

2.4.3 Rationale for a P-Index differentiation

In light of the emerging DRP issues in Lake Erie, one objective of this research is to assess the risk information available to farmers. For as long as phosphorus has been identified as a major determinant of algae growth and reduced water quality, research and communication toward farmers has mostly been focused on the PP transport mechanism.

This is especially true in the greater Lake Erie watershed because, as mentioned above,

PP was the shown to be the primary contributor of available phosphorus for the previous major algae bloom outbreak. But given the increased loading of DRP (see Figure 2.3), it is important to consider how the P risk index might be presented differently to elucidate both PP and DRP mechanisms. Should there be two different models? Is the status quo sufficient? Or should the current model be expanded to include both mechanisms? As will be shown below, a substantial part of the P-index is relevant for both transport mechanisms, but the emphasis on each model is different. If farmers are to be expected to make land management decisions to mitigate a new form of phosphorus loss (DRP),

while maintaining their mitigation of the previous loss emphasis (PP), then there needs to be an explicit P index showing how they are similar and different. In fact, farmers are encouraged to continue making erosion prevention a priority because of a multitude of benefits associated with the organic content of the top part of the soil, while at the same time mitigating DRP loss. Will farmers easily juggle the two similar, yet different management initiatives? As a way to begin to grasp the distinctiveness of these two mitigation efforts, the next two sections will provide independent models of the P index, emphasizing both PP and DRP transport by considering the parts of the model that are unique to each transport mechanism and by expanding upon the existing model based on research not yet incorporated into the current P-index.

2.4.4 Particulate Phosphorus Risk Index

The P index for a particulate phosphorus emphasized model must clearly highlight the determinative variables of soil loss. As with the original model, the PP- index is the sum of transport and source, but illustrates the components most pertinent to

PP transport (see Figure 2.7). The transport conditions are threefold: soil erosion potential, surface runoff potential and connectivity to water. As with the generalized model, the RUSLE formula serves to determine total soil loss potential, and is unchanged with a PP-Index.

The next part of the transport model concerns the conditions favorable for surface runoff. Surface runoff is nested in the RUSLE formula as rainfall erosivity. It is important to make explicit the component parts of rainfall erosivity, because without

runoff there would be no erosion (unless the farmer irrigates, which is uncommon in the

Eastern Corn Belt; wind erosion is also common in the western United States, but less so in this region). This model elucidates the conditions that a farmer should be aware of as creating a favorable environment for erosive runoff. Rainfall intensity and duration can be anticipated using local soil forecasts. It is important for farmers to recognize that high antecedent soil moisture conditions will lower infiltration rates, and thus increase surface runoff and erosion (Luk, 1985).

(Expanded) Phosphorus Risk Index = Source + Transport

Source

High Soil Test P Difficult to Saturated infer Timing Application Rate ground available P from STP Deep Method incorporation (no residues)

Transport

Soil Erosion Surface Connectivity to (RUSLE) runoff Water Antecedent soil Pathway Cover crops Filter strips moisture characteristics Slope length from field to Rainfall Infiltration rate and steepness surface water erosivity Tillage method Runoff class Field drainage Soil (conservation Grassed erodibility preferred) Lowers Rainfall intensity waterways

Increases Conventional Rainfall duration tillage (long-term)

Figure 2.7: Ohio’s phosphorus index with an emphasis in PP transport.

Farmers who want higher long-term infiltration rates and less soil erodibility are encouraged to adopt conservation tillage. Over the long-term, conventional tillage will reduce infiltration two ways: 1) by increasing compaction as a result of more passes over

the field with heavy equipment and 2) by reducing the organic matter content in the soil

(Beare, 1994). Conventional tillage is an oft-used, yet ambiguous word. Here, it simply means the inverse of conservation tillage. In other words, it is a practice that results in mostly broken ground with field residues buried. By oxidizing the organic content on the surface, conventional tillage lowers the organic content in the upper layer of the soil (Six,

1999). In the soil, organic matter is a critical component of stable soil aggregates. These aggregates have increased pore space, and thus allow for more infiltration. By reducing infiltration, one will observe an increase in soil erodibility.

In addition to the effect of tillage on organic matter, a soil‟s texture (i.e., clay, silt, or sand) can also play a role in infiltration. Clay soils have a large surface area to bind with organic material, and they are tightly compacted contributing to less air circulation and therefore less decomposition.

With the surface runoff and erosion conditions assessed, the last piece is assessing the connectivity to water conditions. In other words, where will the eroded soil go?

These variables are unchanged from the undifferentiated P-index; the closer a field is to concentrated runoff, the higher the index score. The source conditions for a PP index are very similar to the original model as well. One simply needs to know the STP levels and what is applied for the crop to determine what could be transported with erosion. If all of the transport and source conditions coalesce, one will experience phosphorus rich soil loss, which is transported via surface runoff and deposited into surface water because of high connectivity conditions. The next task is to consider if a differentiated DRP-index model is significant enough to warrant independent models.

2.4.5 Dissolved Reactive Phosphorus Risk Index

The P index for a dissolved reactive phosphorus model must clearly highlight the key difference between application method and tillage when managing to mitigate DRP loss as opposed to PP loss (as evidenced, by the red circles in Figure 2.8). First, the transport section reflects one prominent difference in the DRP-index. DRP transport is solely a function of surface runoff, with no significance given directly to erosion; although some of the erosion-prone conditions are similar to runoff-prone conditions. As such, all transport functions are framed based on their relationship to surface runoff. Instead of an erosion category, perhaps it would be more useful to frame these variables as “field management.” Some of these variables are constant between the two models, like cover crops and slope properties, but for different reasons.

A second model difference, and perhaps the number one land management influence, is the tillage practice. Because of the reactive nature of the phosphorus in soil, it is very difficult for this nutrient to leach down through the soil profile; though no-till systems can increase preferential flow, and thus increase downward migration (Gaynor & Findlay, 1995).

(Expanded) Phosphorus Risk Index = Red circles denote differences from PP Source + Transport model

Source

High Soil Test P

Frozen > Soil ground Timing Application Rate sorption Saturated capacity ground

Shallow injection Method Broadcasting (~2”)

Transport

Erosion inconsequential Field Surface Connectivity to Management Runoff Water Pathway Cover crops Filter strips Antecedent soil moisture characteristics from field to Tillage method Slope length Infiltration rate (conventional and surface water preferred) steepness Rainfall intensity Field drainage Rainfall duration Grassed Conservation waterways Stratification Runoff Class tillage

Figure 2.8: Ohio’s Phosphorus Risk Index (DRP emphasis).

In most circumstances, though, phosphorus remains close to the soil it encounters at application. Therefore, if too much phosphorus fertilizer is applied over time with little or no incorporation, the phosphorus can accumulate within the top two inches of the soil, a process called stratification (Schwab, Whitney, Kilgore,

Sweeney, 2006). Once accumulated on the top part of the soil, the labile

phosphorus can easily dissolve in surface runoff (Uranga, 2002). The extent to which stratification can occur in rotational tillage systems (i.e., no-till for soybeans and conventional tillage for corn) is being challenged, but the OEPA, for now, is attributing a significant portion of DRP loss to stratification (OEPA,

2010c; Uranga, 2002). Consequently, the tillage method preferred for avoiding

DRP loss is generally a conventional tillage approach. By definition, conservation tillage could both incorporate nutrients and leave field residue, making it also a potential mitigating practice for DRP loss. The challenge, though, is determining how much residue needs to remain on the surface to mitigate erosion, and how much of the ground needs to be inverted to sufficiently incorporate the phosphorus.

The component parts of the connectivity to water transport variable are similar enough to the PP-index that there need not be any differentiation. As for the source component, the soil properties play an important part in mitigating

DRP loss as well as PP loss. Like PP mitigation, course sediments are problematic because of their potential to oxidize organic matter.

Those concerned with DRP loss must also recognize that a soil‟s sorption capacity will be determined by soil texture. Soil phosphorus will sorb with the available oxide clays, but the more course-grained sandy soils provide less surface area for this process (Silver, Neff, McGoddy, Veldkamp, Keller & Cosme, 1999).

Consequently, if the same unit volume of phosphorus fertilizer is applied to clay rich soil as it is to sandy soil, the capacity to tie up the phosphorus will be much

higher in the clay soil. The sandy soil will reach sorption capacity much sooner, thus any phosphorus applied beyond this limit will remain in the fully bioavailable DRP form.

2.5 Discussion

Overall, the P-Index adequately assesses the major pathways for phosphorus loss by accounting for individual field variability. To be sure, there is no one easy management solution. Realistically, farmers will be asked to mitigate both as best as they can. This might mean some compromises, like vertical tillage that leaves a high percentage of residues on the surface, such as mulch tillage. Also, the NRCS uses a

150ppm available P threshold to determine phosphorus movement off of a field. These values are much higher than would typically be needed by the plant, and perhaps a lower, more sensible agronomic value would reduce the DRP levels in Lake Erie. Further, using the index cannot be time or cost prohibitive. If it is, the STRAP model might be overused, placing an overburden on soil testing as a predictor of phosphorus transport; as opposed to it being used as intended by the NRCS—strictly as an agronomical, fertilizer application tool.

One primary question faces those advocating the use of the P-index as a means to informing nutrient management decisions in the western Lake Erie watersheds is, “How well does this model instruct farmers as to the appropriate mitigation behaviors for PP and DRP loss?” For the most part, the land management choices that mitigate phosphorus loss of PP and DRP mirror one another. In both circumstances farmers 64

should be attentive to the STP on each of their fields; an available P level that exceeds what the plant needs often leads to biomass growth in surface waters and the concomitant water quality associations. Course textured soils are troublesome in both loss scenarios, whether because of rainfall erosivity or soil erodibility. Also, if a field is closely connected to a concentrated flow, the loss potential is high for PP and DRP.

There are, however, a number of differences between the two models. First and foremost is fertilizer application method and tillage practice. To avoid PP loss, farmers need to avoid fracturing the soil and maintain residue cover. To avoid DRP loss, the management emphasis is on incorporating the phosphorus into the soil. This management disparity translates into ambiguity in the current P-index. As the model stands, a farmer can avoid a high index score by either immediately incorporating

(mitigate DRP loss) or applying fertilizer on a percentage of residue cover (mitigate PP loss), but the index does not qualify these values based on loss type. There are options to the farmer that protect against each loss mechanism. For example, strip tilling fractures the ground in strips, leaving unplowed strips of a designated distance in between the tilled strips. This enables the nutrient to be worked into the ground, while reducing erosion loss because of the field residues that act as a wall to sediment. Research is needed, however, to determine the balance of incorporation and residues; in other words, how much incorporation is needed to significantly reduce DRP loss and how much residue is needed to significantly reduce soil loss? Gaynor et al (1995) show that DRP loss does increase along the no-till to full soil inversion continuum, indicating that a land management tradeoff between DRP and PP loss will likely have to be made. Perhaps there could be some measured variable (like slope, soil properties, or STP) that indicates 65

to the farmer which loss mechanism they should be more concerned about, and then modify the index to value application method accordingly. This would seem better than indiscriminately giving the farmer a management choice.

Another key difference pertains to the point of emphasis in the model. As mentioned above, a PP model would illustrate the determinative role of erosion by carefully attending to the variables of the RUSLE equation. On the other hand, a DRP model would only need to attend to the runoff class variables and could dismiss the

RUSLE equation. Evidence suggests that the previous erosion prevention programs of the late 1980‟s and early 1990‟s have had a marked impact on the way farmers anticipate phosphorus loss (Ferry, 2010). A much higher percentage of farmers understand the role of erosion in phosphorus loss (91%) than soluble phosphorus loss (22%) (Ferry, 2010).

This means, at the very least, that an effective P-index tool targeted to prevent DRP loss must educate farmers about the unique transport variables of DRP. Ideally, the index (or indices) would also effectively assess the risk of DRP and PP loss.

There seem to be limitations and redundancies that might unnecessarily convolute the index. For example, the current P-index does not consider application timing.

Phosphorus transport is particularly vulnerable when applied to frozen, snow-covered or saturated ground (Mullen et al., 2005). Also, two variables in the RUSLE model account for soil erodibility and field slope characteristics. This information is then added to

“runoff class” information, which consists of slope characteristics and hydraulic conductivity. Could the index be simplified to simply reflect one slope variable?

Further, since hydraulic conductivity is simply a soil‟s ability to transmit water, otherwise

known as infiltration, could infiltration be solely represented by the soil erodibility values in RUSLE? These are not questions to be answered in the article, but might shed light on possible ways to simplify the index to make room for new, differentiated PP/DRP parts.

Another significant challenge facing the viability of STRAP and the P index is the depth to which the soil is tested. Agronomical soil tests are soil cores taken from 0 to 6 or 8 inches. As mentioned above, two primary mechanisms for phosphorus transport are erosion and direct surface runoff, both of which take place on or within a few inches of the surface. As such, STP levels below three inches and especially at a six to eight inch depth have little influence on transport. They do, however, influence crop uptake and productivity, which is why they are considered part of an agronomical soil test. In phosphorus stratified soils, Maguire et. al. (2002) demonstrated that an environmental soil test which measures only the amount of available phosphorus within the two inch soil profile predicts DRP transport slightly better than agronomical soil test. There is also some question about the appropriateness of the acid extraction models for soil tests (i.e.,

Bray-Kurtz P1). These tests are ultimately designed to predict plant uptake, rather than phosphorus transport. Researchers are considering whether or not a water-soluble extraction design would provide more accurate soil test information pertaining to phosphorus transport (Dayton & Basta, 2005).

2.6 Conclusion

Overall, the initiative to expand the assessment of phosphorus transport risk beyond an exclusive source-based model should yield benefits for surface waters facing 67

eutrophication problems in agriculture-intensive watersheds. Despite the questionable suitability of one general index to inform two very different transport pathways and the unclear tillage and application recommendations, the current index functions well to inform farmers about the PP phosphorus management system. Many of the problems associated with DRP mitigation, such as the uncertainty about the type and method of soil tests needed and the debate about what STP levels are best correlated to DRP transport, require substantial research, and only until then can one definitively promote a two model

P-index.

Using the current P-index, as well as expanded PP and DRP indices, this chapter delineates an expert model of phosphorus management for row crop agriculture that can be used as a baseline for evaluating the knowledge state of farmers. For example, do farmers understand the impact of tillage on different phosphorus transport mechanisms and phosphorus stratification? To what extent do farmers understand the specific risks that phosphorus presents to inland, freshwater bodies? The next chapter presents the results of the farmer interviews in an attempt to begin to answer these questions. A better understanding of farmer knowledge about these two pathways may shed additional light on whether or not there is a need to further specify the current P-index.

Chapter 3: Phosphorus Management in the Agroecosystem: An analysis of knowledge and perceived risk

3.1 Introduction

Nutrient contamination of surface water in agricultural watersheds presents a

variety of risks to the environment and human society. At face value, nutrient

contamination seems like an oxymoron. While nutrients benefit living organisms in

moderation, excessive nutrients in aquatic systems can lead to excessive aquatic biomass

growth; and too much of a good thing can become harmful. An excessive growth

environment, called eutrophication, will often lead to oxygen-deficient water because of

the oxygen demands from the biomass in growth and decay, leading to reduced aquatic

biodiversity (Correll, 1998). Further, eutrophic systems can produce toxic blue-green

algae, which has negative human health impacts and high water treatment costs.

Scientists have identified nitrogen and phosphorus as the two primary nutrients that limit

biomass growth in most water bodies, and therefore any addition of the most limiting

nutrient will catalyze biomass growth. In marine environments, nitrogen is most limiting,

but the research focus of this article, freshwater systems, are limited by phosphorus

(Correll, 1998).

Agricultural runoff has long been identified as a major contributor of phosphorus

in surface water. Upon mounting evidence that phosphorus was 1) a problem for surface

water and 2) a significant by-product of agricultural production, farmers and water quality authorities commissioned research groups and action committees to more specifically identify the characteristics of phosphorus loss and address the land management changes needed to mitigate loss. One such group, the Lake Erie Phosphorus

Task Force, was coordinated by the Ohio Environmental Protection Agency (OEPA). In

2010, the Task Force published a “Final Report” that summarized existing water quality data, outlined the history of phosphorus contamination in Lake Erie, identified the leading sources in Lake Erie‟s key watersheds and provided advice as to what changes in land management and individual behaviors might best address this problem. These recommendations included more frequent soil testing, more incorporation of phosphorus fertilizer, strict avoidance of application on frozen or saturated ground and more diligent use of current phosphorus transport assessment tools (OEPA, 2010c).

One key finding in the report pertained to phosphorus loading over time in Lake

Erie. In the water, phosphorus is measured as total phosphorus (TP), which is comprised of particulate phosphorus (PP) and soluble phosphorus, most of which is in the dissolved reactive phosphorus form (DRP). Particulate-P refers to the phosphorus that is bound to the soil and is approximately 30% available for uptake by living organisms, while DRP refers to the phosphorus that is dissolved in water and is 100% bioavailable. For as long as experts have known about the relationship between phosphorus and water quality impairment, the overwhelming focus in agriculture has been on preventing PP transport through erosion control measures; Lake Erie is certainly no exception. Water quality analyses in the 1970‟s and 1980‟s showed that approximately 90% of TP in the western part of the lake (where the algae blooms were most severe) was in the PP form. The 70

land-use in the western watersheds (Maumee and Sandusky) is 80% row crop agriculture; therefore, erosion control efforts in agriculture represented the opportunity for the highest reductions of TP. Through the late 1980‟s and early 1990‟s, these efforts achieved the intended goal of reducing algae blooms (OEPA, 2010c). However, by the early part of

2000, large algae blooms were beginning to reappear. Analyses revealed that TP levels had remained mostly stable throughout the previous decade and that the decline of PP was continuing. More importantly, though, they revealed that the proportion of TP in the

DRP form had been increasing (thus offsetting the PP decline). Due to its bioavailability,

DRP loading has enormous biomass growth potential, which was confirmed by the amount of algae in Lake Erie. Between 2000-2010, each year experienced an incremental growth in algae blooms, culminating with the largest bloom on record in

2010 (OEPA, 2010c).

The Lake Erie narrative has been used to contextualize this problem mostly because of the bounty of research committed to this particular lake, but other lakes throughout Ohio have also experienced high levels of algae growth. For example, in

2010, twenty-two lakes throughout Ohio received water quality advisories because of conditions suitable for toxic algae growth. Of these lakes, toxins were reported in eleven, two of which, Delaware Lake and Alum Creek Lake, were in the study area of this research (OEPA, 2010b). Further, the problem of toxic algae reaches well beyond Ohio‟s borders, as concentrations of algae toxins that exceed World Health Organization maximum concentration levels have been found in other U.S. states, like Indiana and

Florida, as well as other countries, such as Australia and India (Lembi, 2003).

3.1.2 Importance of individual decision-making

Surface water contamination of DRP is not unprecedented. Wastewater treatment plants formerly did not treat phosphorus, which had high levels of DRP because of its prevalent use in detergents. A policy mechanism, the Clean Water Act (CWA), was legislated which allowed direct regulation of WWTPs as a point source; consequently, algae growth was contained. Row crop agriculture, however, is specifically excluded from CWA regulation and is labeled a “non-point” source. As such, the success of a phosphorus mitigation campaign hinges on voluntary individual decision making by farmers. Farmers have shown a willingness to make the necessary changes in the past, if given suitable options. The questions facing those interested in finding a solution to this current problem are 1) “What land management practices need to change in order to prevent DRP and PP loading?” and 2) “What will be the most effective approach to communicate the risks of phosphorus loss, so that farmers can better understand why they need to make these changes?” Limnologists, soil scientists, aquatic ecologists among others, are actively working to address the first question. Question two is inextricably bound to social behavior as well as biophysical processes. Thus far, there is a significant disparity between social science research and its biophysical counterpart. Specifically, the Lake Erie Task Force Final Report mentioned previously thoroughly presents the biophysical context of the water contamination problem in Lake Erie, but does little to represent the current or needed research pertaining to the social variables that might accompany behavioral change (OEPA, 2010c). As a starting point, these variables

include assessing how well farmers understand phosphorus management and surface water contamination, identifying the perceived risks of phosphorus loss and considering the motivating values specific to these landowners. By addressing key knowledge gaps, points of disparity between farmer risk “perception” and expert risk “estimation”, and fundamental farmer values, experts can begin to couple biophysical data with social science data in order to confront the challenge of phosphorus loading, and DRP contamination specifically, in agricultural watersheds.

3.1.3 Risk communication

Risk communication can be defined as the “communication intended to supply laypeople with the information they need to make informed, independent judgments about risks to health, safety, and the environment” (Morgan et al., 2002). Historically, the content of risk communication messages was based on the opinions and judgments of a select few experts. However, the approach to risk communication used in this article involves both experts and the intended recipients of the message, or non-experts, throughout the risk communication process. The systematic, expert-developed “risk estimation” can look quite different from the intuitive “risk perception” of a non-expert stakeholder; as such, a risk communication effort developed solely by experts and one that accounts for risk perceptions of non-experts will potentially look very different—as well as differ in their respective success (Asante-Duah, 2002).

This article systematically evaluates four distinct social variables: current farmer knowledge, risk perception, values and demographics. An assessment of the current 73

knowledge state allows communicators to identify potential knowledge strengths and weaknesses so that an information campaign is efficient and effective. In addition to simply assessing farmer knowledge, this research begins evaluating the potential antecedent conditions for knowledge seeking behavior. The correlation between risk perception and knowledge has been demonstrated in many different contexts (NRC,

1996; Griffin et al., 1999; Griffin et al., 2002). Specifically, an increase in risk perception has been shown to increase knowledge seeking and information processing behavior (Griffin, et al., 2002). Individuals who spend more time and effort seeking out information are more likely to develop more stable risk cognitions, and perhaps more importantly, more stable risk-mitigating behaviors, such as necessary phosphorus mitigation land management behavior (Griffin, et al., 1999).

Risk perceptions are also closely related to how individuals deal with uncertainty and unknown risks. Research has shown that individuals often characterize an unknown risk based on the semantic attributes of a known risk (Visschers et al., 2007). An unknown risk may appear to be similar to a known risk, but in reality these two risk objects may be very much unrelated. For example, Visschers et al (2007) demonstrated how non-experts associated chlorine transport by railcar (unknown risk) to the transport of nuclear waste (known risk). Consequently, the public would perceive the risk posed by the transport of chlorine much higher than the experts, as the transport pathways and potential for acute or long-term harm are quite different between the two contaminants.

If the risk communication effort does not identify and address this risk comparison, but rather relies exclusively on expert risk judgments, there might be considerable backlash among the public regarding the related management decision. 74

For the risk communication context of this chapter, risk comparison research is particularly pertinent. As stated above, the prevention of phosphorus loss for several decades focused on soil erosion and PP. A risk communication effort that is designed to address both PP and DRP must recognize the potential for individual farmers to compare these risks. If these two processes are not differentiated, but considered together as merely phosphorus loss, one can expect knowledge about phosphorus management, and the risks therein, to predominantly reflect the characteristics of PP transport. For reasons to be explained below, a PP-centric management characterization might have negative consequences for DRP loss. Therefore, a risk communication campaign must pointedly address the key differences between the two phosphorus transport mechanisms.

To further explore the antecedent conditions to knowledge and land management behavior the primary farming goals, or values, were considered. Differing values and knowledge levels between experts and non-experts can also contribute to the disparity between technical risk estimations and lay risk perceptions, and thus limit the success of risk communication (NRC, 1996). For this context in particular, farmer values might not be reduced to purely profit-maximization. Research has shown that while maximizing profit is certainly a ubiquitous goal among farmers, but it is not the only primary farming goal (Lin, 1974). Policy makers and water quality authorities who are contemplating various outreach or policy tools to motivate behavioral change might focus exclusively on what will motivate behavior from the perspective of the farmer‟s bottom-line, but addressing economic motivations may not always produce positive behavioral change

(Lin, 1974). In order to explicate farmers‟ knowledge, risk perception and farming goals the mental models research method was used. Rather than relying on a few experts to 75

speculate about farmers‟ risk perceptions and knowledge of the phosphorus management system, the mental models approach provides a systematic, empirical tool for informing risk communication content (Morgan et al., 2002; Zaksek & Arvai, 2004). The mental models method diagrammatically represents how individuals piece together fragmented concepts into a system of knowledge, or a cognitive schema (Morgan et al., 2002). In doing so, communicators are able to identify and target critical knowledge gaps between experts and non-experts. The method has five steps: 1) develop an expert model of the risk; 2) conduct open-ended mental model interviews to assess target audience knowledge; 3) administer a confirmatory survey with a larger sample; 4) develop a risk communication message; 5) and evaluate the risk communication effort. The research reported here includes results from mental model interviews conducted to identify current risk perceptions as well as knowledge strengths and deficiencies among current agricultural producers.

3.2 Research Approach

3.2.1 Expert Model

The expert model was designed to provide an information baseline of land- management concepts that are most relevant to phosphorus contamination of surface water. Consequently, the expert model does not reflect details superfluous to land- management behavior (e.g., subjects were expected to understand that phosphorus in water generates algae growth, and that certain algae can be toxic to humans; but they were not expected to know the species of algae and specific targeted organs of their toxic 76

by-products). The expert model primarily consists of information from the current Ohio

Phosphorus Index Risk Assessment (P-Index), but is supplemented with information from peer-reviewed literature to more thoroughly reflect land management decisions that contribute to both PP and DRP losses, as the current P-Index emphasizes PP transport through erosion. The P-index, and the resulting expert model, identifies source and transport as the two variables most determinative of surface water contamination.

Having only source capability or transport capability does not present a threat to surface water. Phosphorus in the soil needs something to move it; conversely, the transport function can be very high, but without phosphorus to transport, there is no threat. Three expert models were created to illustrate the risk of PP contamination (see Figure 3.1),

DRP contamination (see Figure 3.2) and the primary impacts of surface water contamination by phosphorus (see Figure 3.3). These models are hereafter referred to as the farmer mental models, because they are presented to reflect the extent to which the farmers understand these concepts.

3.2.2 Mental Model Interviews

Following the development of the expert models (discussed in-depth in Chapter

2), a standardized, 33-question interview protocol was designed to elicit farmer knowledge of the concepts within these expert models (see Appendix A). The goal of a mental models interview is for subjects to exhaust as much information as they have about a given risk with as little prompting as possible from the interviewer (Morgan et al., 2002). Therefore, the structure of the interview protocol is to begin with broad

questions, and then ask more specific questions based on what is not mentioned without prompting. This allows the researcher to ensure that subjects have the opportunity to exhaust their knowledge, while also introducing new concepts that were not included in the expert model. For example, the leading prompt of the interview was “Tell me about the relationship between your farm and the environment.” If the interviewee failed to mention water quality, the interviewer asked: “Tell me about the relationship between your farm and water quality.”

3.2.3 Subjects

This research used a sample size of twenty-three subjects (n=23), based on the recommendation of twenty to thirty subjects for achieving an information asymptote; in other words, additional interviews are expected to yield limited new information from the targeted audience (Morgan et al., 2002). Marion and Delaware county auditor data were used to generate the initial list of potential subjects. Originally, this research was intended to explore the relationship between the distance of an actively managed land parcel to surface water and risk perception. The hypothesis was that the subjects who actively farmed land on or very close to surface water would exhibit higher risk perception levels, based on risk salience (e.g., they can see or catch the fish that might be effected). However, because of the large size of current row-crop operations very rarely will farmers manage land that is not immediately bordering surface water. The ambiguous definition of surface water further complicated this as a selection criterion.

For example, almost all farmers have some kind of “ditch” or grassed waterway which

channels water more efficiently off of their fields, be it from surface or subsurface water.

Therefore, subjects were selected based on three criteria: agricultural production type, land-management decision authority and location. The subjects were required to be actively managing a conventional row crop operation. Conventional farming is used here to mean the use of synthetic fertilizers. In this study, row crop refers to three crops: corn, soybeans and wheat. The farmers also needed to have the authority to make land management decisions. Subjects were split between Marion (n=12) and Delaware (n=13) counties in central Ohio. In this area, row crop agriculture is the dominant land use.

Further, the soil properties between these two counties (i.e., silty clays and loams) are representative of the broader region, though individual fields can vary. Most subjects farmed full-time (78%), but some were also educators, truck-drivers and certified crop consultants (22%).

3.2.4 Hypotheses

While the main objective of this research was to explicate farmer knowledge, there was a concerted effort to begin exploring the variables that relate to knowledge. This was done by testing the principle hypotheses regarding risk perception, knowledge and values, as well as testing several post-hoc hypotheses regarding knowledge and socio- demographics. The basis for the principle hypotheses is explained in detail in the preceding literature review. The principle hypotheses also served to inform the research design; in other words, it was with these variables in mind that the research commenced.

The post-hoc hypotheses did not shape the research design, but were analyzed after the

data collection process to evaluate whether common demographic factors explained any variance in knowledge.

Principle Hypotheses:

Hypothesis 1a: Farmer knowledge of phosphorus management is positively correlated to both environmental and financial risk perception. As risk perception scores increase, knowledge scores would also increase.

Hypothesis 1b: Farmer knowledge is more strongly correlated with financial risk perception than environmental risk perception.

Hypothesis 2: Primary farming goals will more commonly be represented by utility-maximizing/profit-maximizing values, as opposed to exclusively profit-maximizing values.

Post-hoc Hypotheses:

Hypothesis 3: Farmer knowledge differs with age. Specifically, younger farmers exhibit great knowledge than older farmers.

Hypothesis 4: Farmer knowledge differs with income. Specifically, farmers with a large annual income will exhibit higher knowledge scores than medium and small sized farmers.

Hypothesis 5: Farmer knowledge differs with education. Specifically, farmers with a higher education will exhibit higher knowledge scores than those less educated.

Hypothesis 6: Farmer knowledge is positively correlated with frequency of soil testing. As knowledge of phosphorus management increases, participation in frequent soil testing will increase. (This hypothesis was not testable as all of the subjects interviewed frequently soil tested, leaving no room to explore variance).

3.3 Analysis

3.3.1 Coding and Measurement: Knowledge

To describe farmer knowledge and develop the farmer mental models, the interviews were voice recorded and transcribed. The transcripts were then coded based on concepts from the expert model. The coding values were a simple, one-one ratio; a subject was given a value of one for each concept recognized in the expert model.

Specifically, using the MAXQDA coding software, an outline of the expert model

(Appendix B) was inserted into the software program and each subject transcript was uploaded and analyzed. For example, experts have identified fertilizer application

“timing” as vitally influential to phosphorus loss (Mullen et al., 2005). A “timing” code was then established, which incorporated concepts like applying fertilizer on saturated or frozen ground. Any timing-related response could then be assigned to this particular code. Once the coding was complete, individual subjects were assigned a knowledge score based on the number of concepts he or she identified from the expert model.

Knowledge scores could range from 0 to 25 based on the number of concepts in the expert model.

3.3.2 Measurement and Analysis: Risk perception

The mental model interviews were also used to elicit knowledge of the risks and impacts from nutrient loss. In addition to the open-ended prompt, “Tell me about the risks of phosphorus loss,” subjects were asked to report a risk perception measure. There were two risk perception measures using a ten-point scaled response (where 0 = not at all concerned and 10 = extremely concerned): 1) How concerned are you about the environmental risks of phosphorus loss? 2) How concerned are you about the financial risks of phosphorus loss? The knowledge scores were then correlated to environmental and financial risk perception using Spearman‟s Rank Order correlation.

3.3.3 Measurement and Analysis: Demographics

Farmers were asked to self-report four demographic variables:

1. Age, by decade.

2. Highest level of education: Pre-HS, HS Diploma or GED, Some college,

Bachelors degree or Post-graduate

3. Farm size: small, medium, or large (small being revenues under $50,000;

medium being revenues between $50,000 & $250,000; and large being over

$250,000)

4. Gender

The relationship between these variables and knowledge was then analyzed using a non- parametric, Mann-Whitney U test.

3.3.4 Farming Goals: Profit and Utility

In addition to knowledge and risk perception, subjects were asked to state their primary farming goals. The transcribed data was assessed by placing subjects into two groups: 1) strictly profit-related response (e.g. high yield, more income, etc.) and 2) mixed non-profit, or utility-based (e.g. lifestyle, stewardship, etc.) and profit response.

These codes were not designed to presuppose a strict dichotomy between profit- maximizers and utility-maximizers; individuals are surely some combination. This research simply explores the variety and extent of farmers who identify utility-based goals for farming.

3.4 Results and Discussion

3.4.1 Farmer mental model results: Source and Transport Knowledge

The farmer mental models depicted in figures 3.1 and 3.2 illustrate the variables influencing the risk of surface water contamination. The two models differentiate between the pertinent source and transport variables that contribute to surface water contamination by phosphorus in the PP form (see Figure 3.1) and in the DRP form (see

Figure 3.2). Specifically, the models propose that the timing, rate and method of nutrient application determine the P level in the soil (i.e., source), and that soil erosion, surface

runoff, and connectivity to water factors all determine whether or not phosphorus remains in the soil (i.e., transport). These factors in combination determine the overall risk of contamination. As explained above, PP transport depends primarily on the conditions suitable for soil loss, whereas DRP loss is closely associated with direct surface runoff conditions. Consequently, figures 3.1 and 3.2 elucidate some of the unique land management considerations that can mitigate either of the transport pathways. The parenthetical percent (%) response value in each figure represents how many farmers understood that concept.

Overall, farmer knowledge of phosphorus transport (as found in the “transport” node in each diagram) reflected a strong understanding of surface movement of soil as PP

(91%), but a much lower understanding surface runoff as DRP (22%). In fact, phosphorus transport through leaching was more commonly identified (30%) than surface runoff of DRP. Current research is considering the extent to which leaching contributes to contamination of water (particularly as a result of preferential flow pathways, like earthworm burrows, cracks in the soil and compacted soil layers), but agronomy experts typically emphasize that in most Eastern Cornbelt soils leaching is not a prominent phosphorus loss avenue (Djodjic, Borling & Bergstrom, 2003). Leaching is identified as a “misunderstanding” because these farmers identified leaching as either the only or at least the primary phosphorus transport pathway. While preferential flow could be one potential pathway, phosphorus does not leach through clay soils like nitrogen, another key applied nutrient in row crop production. Nitrogen does not react strongly with the soil and is known to “wash” or leach through the soil with heavy rain events. The farmers who mentioned leaching as a transport pathway for phosphorus, often explained 84

it as synonymous to nitrogen transport. Of those who identified leaching, 71% simply responded to the question, “How does phosphorus move within or off of your farm?” by saying “same as nitrogen.” This misunderstanding has important implications for water quality, as these individuals might ignore surface mitigation options, like incorporation and filter stripping, because phosphorus is believed to move through the subsurface.

Particulate-P contamination (see Figure 3.1), in particular, is well understood.

For example, farmers exhibited strong understanding of the importance of timing, rate, and method of application in regards to preventing surface water contamination (concepts mentioned by 75% or more of respondents). The low understanding nodes were “slope characteristics” and “pathway characteristics.” However, despite a low recognition of these concepts (13% and 9% respectively) there was a strong understanding of the variables that are meant to mitigate slope and pathway characteristics (e.g., filter strips,

65% and cover crops, 48%) In all, these results seem to substantiate the success of the anti-erosion campaigns of the 1980‟s and 1990‟s.

High risk for contamination of surface water by Particulate Phosphorus (PP) = Source + Runoff • (%) Recognition: Source (LOW) 25% and below (MED) 26% - 74% (HIGH) 75% and above Misunderstanding

Timing Rate Application (96%) (100%)

Method Frozen/Saturated (87%) Soil Testing Ground (100%) (96%)

PP Transport Leaching (91%) (30%)

Connectivity Soil Erosion to Water Factors Surface Runoff Grassed Rainfall Filter strips waterways characteristics (65%) Rainfall (96%) characteristics (26%) Cover crops (96%) Field drainage Soil erodibility (48%) (26%) Infiltration rate (26%) Tillage method (26%) Slope length (conservation Pathway characteristics and steepness preferred) from field to (13%) (70%) surface water (9%)

Figure 3.1: Ohio’s phosphorus index with an emphasis in PP transport where diamonds represent organizing concepts in the model and rectangles represent specific concepts that were coded for among subject responses.

The DRP contamination farmer mental model (Fig. 3.2) indicates that farmers have a poor understanding of DRP transport and source contributions to loss. As mentioned above, only 22% of farmers mentioned surface runoff of dissolved phosphorus. Further, many of the 22% respondents who were credited with understanding DRP likely were thinking simply about fertilizer pellets washing into streams. Though this would constitute a DRP loss, the Lake Erie “Final Report” refers more specifically to soil phosphorus transformations contributing to the DRP increases in the Lake Erie basin (e.g., phosphorus stratification of enriched P soils) (OEPA, 2010c).

Another key component of the model that was poorly understood and strongly emphasized in the “Final Report” was the role of tillage in determining how easily the nutrient can move (e.g., broadcasting on no-tilled fields). Only 17% of farmers identified the necessity of incorporating the fertilizer into the soil or avoiding continuous no-till because not doing so would increase phosphorus loss. These 17% did not necessarily refer to DRP loss; they simply identified the unique risks of conservation tillage.

While DRP transport as surface runoff is poorly understood, many of its component parts are not (Fig. 3.2). For example, individuals recognize the power of rainfall to influence phosphorus loss (96%), as well as the importance of application timing (96%) and rate (100%). While presently understood in the context of PP transport, a risk communication effort needs only to translate these salient concepts into the context of DRP loss and explain how behavior might need to change.

High risk for contamination of surface water by Dissolved Reactive Phosphorus (DRP) = Source + Transport •Black circles denotes model difference between DRP to PP • (%) Recognition: Source (LOW) 25% and below (MED) 26% - 74% (HIGH) 75% and above Misunderstanding

Timing Rate Application (96%) (100%)

Frozen/Saturated Method Ground (17%) Soil Testing (96%) (100%)

DRP Transport Leaching (22%) (30%)

Field Surface Connectivity Management Runoff to Water factors Cover crops Filter strips Rainfall Field drainage (48%) (65%) characteristics (26%) (96%) Tillage method Slope length Grassed waterways (conventional and Infiltration rate (26%) preferred) steepness (26%) Pathway (17%) (13%) characteristics from field to surface water (9%)

Figure 3.2: Ohio’s phosphorus index with an emphasis in DRP transport where diamonds represent organizing concepts and rectangles represent specific concepts that were coded for among subject responses. Black circles indicate concepts unique to a

DRP-emphasized model.

In some cases, the land management behavior designed to prevent PP and DRP loss is constant (and mostly well-understood by farmers): avoiding applying fertilizer on saturated soil (96%); and soil testing to ensure that the amount of phosphorus does not exceed what the plant needs (100%). In other cases, individuals will need to make trade- offs based on which transport mechanism he or she seeks to mitigate (as denoted by the black circles in Figure 3.2 that indicate points in the model where DRP differs from PP).

If PP is the primary concern, the farmer will seek to leave the ground as uncultivated as possible with plenty of residue cover, whereas DRP mitigation requires incorporating the fertilizer into the soil. There are tillage and fertilizer application alternatives (e.g., mulch-tillage) that enable farmers to incorporate the fertilizer with a minimal amount of soil disruption; thus targeting both PP and DRP loss. However, farmers must first identify PP and DRP contamination as a serious risk to water quality as well as a serious pathway through which their fertilizers can be lost.

3.4.2 Farmer mental model results: Knowledge of impacts from phosphorus loss

The final farmer mental model (Fig. 3.3) illustrates the primary impacts of phosphorus contamination. The model begins with phosphorus loss, which leads to individual profit and environmental losses, and then becomes more specific by illustrating the unique role of phosphorus to catalyze algae growth. Finally, the model highlights the three oft-mentioned risks of algae growth: to the ecology, human health and regional economy. 89

Individual yield & Phosphorus profit loss Loss (100%)

•% Recognition: (LOW) 25% and below (MED) 26% - 74% (HIGH) 75% and above General soil and water Excessive algae impairment growth (100%) (22%)

Tourism and regional financial loss Ecological effect (13%) (30%)

Swimming Drinking water Low dissolved restrictions contamination oxygen (9%) (0%) (17%) Human health/pet effects (9%) Reduced aquatic biodiversity (26%) Toxic “algae” Fisheries loss (cyanobacteria) (0%) (9%)

Figure 3.3: Risks from surface water contamination of phosphorus loss

The farmer mental model results indicate that farmers have a strong understanding of basic soil and water contamination (100%) and individual profit loss

(100%) from phosphorus loss. Despite extensive media coverage throughout Ohio pertaining to algae growth in surface water, only 22% of subjects identified algae growth as a risk of phosphorus loss. Understandably, even fewer recognized the specific risks of algae growth, such as toxic algae (9%), drinking water contamination (0%) and low dissolved oxygen or hypoxia (17%). Griffin et al. (1999) argue that those with higher

risk perceptions are more likely to seek out and process risk related information and develop more stable attitudes and behaviors. If so, this information suggests that risk communicators have a multitude of risk-related information that can be communicated in order to encourage more knowledge seeking behavior. For example, risks pertaining to human health and economic/tourism are both understood by less than 15% of the study sample. If risk information relating to these impacts is properly communicated, risk perceptions might be elevated which could lead to higher knowledge levels and more sustainable behavior change.

The challenge for risk communicators is determining the specificity of risk-related knowledge needed to motivate knowledge seeking behavior and land management change. On the one hand, the profit results are encouraging because individuals strongly understand certain risks, namely the risk of lost fertilizer and profits. On the other hand, though well intentioned, some individuals might be overlooking basic land management changes needed to mitigate DRP loss or might have a tempered sense of urgency because of their low understanding of the more specific risks of phosphorus; two of these risks, human health and water treatment, deserve further examination.

First, the human health impact from toxic algae growth was understood by merely

9% of the subjects. When discussing human health risks from agriculture, subjects often referred to the leaching of nitrates and pesticides into drinking water, but almost never cited exposure to toxic algae spawned by phosphorus contamination. Further, toxic algae exposure might have its harshest impact on house pets, a fact mentioned by only one

subject. In 2010, at least three dogs in Ohio were reported to have died from exposure to toxic algae (McGlade & Spencer, 2010).

Second, regionally born economic costs were poorly understood (13%). For example, drinking water treatment costs often rise upon phosphorus contamination of municipal drinking water supplies. The chemical Geosmin is the by-product of algae growth and can be tasted at very low concentrations. One growth event can cost local treatment facilities thousands of dollars. Not one subject identified this as a risk of phosphorus contamination of surface water. Further, tourism directly related to Lake Erie generates approximately one billion dollars in economic activity annually with millions of dollars contributing to state tax revenues (ODNR, 2009). Reduced sport fishing opportunities, as well as unsightly and odorous algae threaten what is now a very strong component of the Midwestern economy.

3.4.3 Hypothesis Results

The Spearman‟s Rank Order correlation results support hypothesis 1a, demonstrating a significant positive correlation between risk perception and knowledge

(see Table 3.1). However, hypothesis 1b was rejected, as environmental risk perception was more strongly correlated to knowledge than financial risk perception (see Table 3.1).

To be sure, both correlations are strong and these results are not meant to conclude that environmental risk is more strongly perceived than financial risk. As demonstrated by figure 3.3, when subjects were prompted by an open-ended question about risk they most often talked about personal profit losses. These results do support information seeking

and processing research, by demonstrating that those with higher risk perception have higher knowledge levels than those with lower risk perceptions.

Independent Variable Correlation strength Significance

Environmental Risk Perception .704 .001*

Financial Risk Perception .499 .035*

Table 3.1: Results - Correlation between environmental and financial risk perception with knowledge scores using Spearman’s Rank Order correlation

(*Significant at p < .05; 2-tailed)

The results indicate that those concerned about agricultural land management behavioral change might overlook opportunities to resonate with farmers if their message focuses exclusively on financial values. In other words, farmers are concerned about environmental damage from nutrient loss. Another way to look at the data is to consider the mean values between environmental and financial risk. On a scale from 0 to 10

(where 0 means no risk and 10 means a very high risk), the mean value for environmental risk perception was 6.6, while the mean financial risk perception score was 6.7. This is further evidence that non-profit risk-based messages (e.g., ecological and human health) would resonate with agricultural producers.

The primary farming goals results provide additional evidence that profit might not be the all-inclusive behavioral motivator; thus supporting hypothesis 2. Only 17% of

farmers referred to strictly profit-maximizing goals, with the remaining 83% describing utility based goals. Farmers mentioned stewardship of natural resources, a quiet country lifestyle, occupational autonomy, family succession and the status quo (e.g., “Farming is just what I‟ve always done”) as equally strong motivators of farming behavior. Unlike quantitative risk assessment or cost/benefit analyses, values are often qualitative, and thus harder for risk experts to systematically evaluate. However, the prevalence of utility- based values indicates that they ought to be considered alongside profit maximization as determinative behavioral variables.

To further explore variables associated with knowledge, demographic variables were analyzed as post-hoc hypotheses. Within each demographic category (age, income, and education), subjects were split into two evenly distributed groups (see Table 3.2).

Hypothesis 3 was rejected, as younger farmers were not significantly more knowledgeable than older farmers. Similarly, hypothesis 4 was rejected, as income was not significantly related to knowledge. This may seem surprising because large operations stand to lose more money (or gain more income from proper mitigation).

Upon further consideration, however, these results may be explained by the relationship between income and use of crop consultants. Many of the higher income farmers interviewed consult extensively with certified crop specialists before making phosphorus related decisions (a few hire them full-time). Consequently, farmers managing higher income operations might know less than the small or medium farmers who have to make many of these decisions with less help. Large farmers might not have significantly higher knowledge scores than their small and medium counterparts, but in this

demographic category, one should not try and associate farmer owner knowledge with mitigation behavior—they may simply be doing what their paid experts tell them to do.

Finally, the data from the interviews support hypothesis 5, as those more highly educated exhibited higher knowledge scores. None of the farmers received any formal education within the past ten years, and for many the time out of school is much longer.

On the one hand, that they would exhibit higher knowledge scores is perplexing because so much of the literature pertaining to phosphorus has changed. Perhaps this reveals that individuals with a higher formal training have the necessary foundational skills, or knowledge, to effectively process new information about phosphorus. Further, this might also suggest that these higher educated individuals have more access to the outlets that promulgate new information.

Independent Variable Sub-category Z Significance

Education Group One (High School and Some -2.15 .03* College) Group Two (Bachelor’s and above)

Age Group One (40-59) -1.53 .13 Group Two (60 & older)

Income Group One (Small, Medium) -1.36 .17 Group Two (Large)

Table 3.2: Results- Difference in mean knowledge scores between dichotomous demographic groups using Mann-Whitney Test (*Significant at p < .05)

3.4.4 Lessons for risk communication

The results of this research indicate two main opportunities exist to improve risk communication efforts targeted toward reducing non-point phosphorus inputs into surface water bodies from row-crop operations. First, future risk communication messages should target two critical gaps in knowledge: DRP transport and the unique impacts of phosphorus loss on surface water. In order to address the knowledge gap regarding DRP transport and mitigation, research has shown that individuals will cope with uncertainty by associating an unknown risk to a known risk (Visschers et al., 2007). In this circumstance, the known risk is PP transport and the unknown risk is DRP transport. As mentioned above, this is not entirely problematic because certain management implications are constant (e.g., application timing and rate), but in some ways they are different (e.g., application method and tillage practice). Consequently, individuals who focus solely on PP transport do not understand the comprehensive phosphorus management system and fail to recognize key land management behaviors that create a high risk for surface water impairment.

Second, future efforts should more consciously differentiate farmer audiences based on demographic differences. On one level, demographic variables are fixed, and a risk communication effort cannot change them. However, there might be opportunities to fashion a more effective message based on the results from this research; specifically with regard to education. Farmers with higher education demonstrated 40% higher knowledge scores than the less educated dichotomous group. Given the evolving literature surrounding phosphorus management, one would expect those with the most

access to this information to demonstrate higher knowledge scores, and perhaps there is a relationship between education and access to formal academic content as well as informal extension and electronic content. Future research should look to operationalize education by including informal education, such as frequency with which one reads university extension websites or attends educational seminars.

Age should also not be discounted entirely. Though not significant at p<.05, younger farmers demonstrated 32% higher knowledge scores than the older group.

Agricultural information and media content are changing rapidly by becoming more internet-based, and perhaps older farmers are not receiving the same quality of information as younger farmers. It might be necessary to make a conscious effort to continue to use traditional outlets, such as the radio and print media to reach the older demographic. Further, farmers of all age groups rely heavily on fertilizer suppliers and crop consultants for information, so whenever possible, these outlets should be used as well.

Future research must substantiate these mental model results on a larger scale by determining whether or not the misunderstandings and knowledge gaps, upon which these risk communication recommendations are based, are consistent throughout a broad agricultural region. Granted, the solution to phosphorus contamination of surface water might be as simple as developing policy-based incentives, such as financial support for specialized equipment that more fully incorporates phosphorus while leaving adequate field residues. But given strained federal and state budgets, these funds might not materialize, or farmers might already have the proper equipment, but are unfamiliar with

what specifically needs to be done to mitigate DRP because of their interaction with the previous PP-centric risk communication. A successful risk communication targeted toward phosphorus contamination of surface water will attend to the knowledge gaps revealed in this research and couch this message in terms of the profit and utility-based values identified throughout this research process.

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Appendix A: Interview Protocol

Record Subject Number: Record Subject Selection Parameters (County, Proximity to water, Distance from Columbus): Mental Models Interview This interview will have two main parts. The first part of the interview will highlight information about your day-to-day operations based on your land management decisions in the past, present and future. The second part of the interview requests your thoughts regarding the relationship between agriculture, soil and water. Part 1: Land Use/Land Management 1. Let’s begin by talking about your current operation. So, to start off,

 How long have you been farming?  What are your primary farming goals?  How many people (including family) do you employ? o How many are full time and how many are part time?  Do you or anyone in your household have off-farm employment? o If yes, who and what employment (type of employment, part-time or full-time)? For how long?  What type of product are you currently producing? o What is the acreage of your top three crops, in terms of acres planted?  How do you market your farm products? (categorize as contract with processor, cooperative marketing, individual sales at farm, wholesale, direct sales, other)  Is your farm (or any portion of the farm) leased to anyone? o If so, how is this land being used?  Do you rent additional land for your farming operations? o If so, how much and how are you using this land? Where is the additional land located? (e.g., in the county, distance from home farm)

2. Now, let’s talk about your farming operation in the past.

 How long have you farmed at this location?

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 Did you previously farm at a different location? If so, where? Did you stop farming at that location and if so, why?  Have you always had the same farm business? o If not, what type of operation did you have in the past? When did you change? What influenced this decision?  In the past, have you: o Sold land? . If so, how much and when? . Where was it located? . Why did you sell? . Do you know how this land is currently used? o Purchased land? If so, how much and when? Where is it located? Why did you purchase? How are you currently using this land?

3. We’ve talked about your current and past plans, let’s talk a bit about your future plans.

 Do you plan to continue farming? o What are the biggest factors that influence this decision?  Do you plan to maintain the same farming operations? o Under what conditions (or what factors) would you change your operation? . What would it take for you to consider farming for something else? o What are the largest barriers to implementing changes in the operation?  Do you (or any other member of the household) plan to seek off-farm employment (or continue off-farm employment)?  What are your plans for selling or purchasing land? For leasing out or renting land? o Would you sell your land to a developer at some point in the future? If yes, what factors will determine when you sell your land?

4. Now I’d like to ask you a few more questions about how the landscape is changing and what that means for your future agricultural decisions.

 How has the growth of cities and urban areas impacted your farming decisions?  How has the area around you changed during your farming tenure? o Have these changes made it harder to farm? If so, in what ways? o Have you had to adapt your operation in any way?  How do you expect the area around you to change in future? o How might this influence your future decisions about farming?

5. Let’s talk about how government impacts your farming operations. 109

 What do you think the role should be of government in agriculture?  Are you currently participating or have you participated in any government programs? (support programs & conservation programs) o If yes, which ones? Why? . Have these programs influenced how or what products you farm? o If no, why not?  How have government policies or regulations influenced your farming decisions? (i.e., zoning, right-to-farm—can‟t be sued from noise, smells, etc.)…prompt for local, etc. . Have these policies influenced how or what products you farm?

Part 2: Agriculture and Water Quality 6. Now I’d like to switch gears and let you tell me about the relationship between your farm and the surrounding environment. As mentioned above, we want to know how and why farmers make certain decisions to try and find innovative ways to meet the needs of farmers in light of a rapidly changing landscape. We began by talking about basic day-to-day economic data, and I’d now like to hear about your day-to-day agronomic decisions. We’ll begin with pretty broad questions, and move into more specific questions.

 So first, what comes to mind when you think about the relationship between agriculture and the environment? Anything further come to mind when you think about your farm in particular? o What is the role of soil in agriculture? o How does soil influence water quality in conventional grain agriculture?

Transport and Fate 7. Now, I’d like to ask a few more specific questions about two important nutrients found in the agro-ecosystem: nitrogen and phosphorus: o What happens to nitrogen after field application? . What are the major reasons for nitrogen loss? . If not mentioned: How might nitrogen end up in water? . Where would you rate your ability to prevent nutrient loss on a scale of 1 to 10 with 1 being no control at all and 10 being complete control?  Why? o What happens to phosphorus after field application? . If not mentioned: How might phosphorus end up in water? . Where would you rate your ability to prevent nutrient loss on a scale of 1 to 10 with 1 being no control at all and 10 being complete control?  Why?

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o Can you tell me more about where these nutrients end up or how they get there?

Impacts 8. Now let’s talk more about the risks and benefits associated with these important nutrients.  What are the benefits associated with nutrient application? . Can you think of anything else? o Are the benefits different for nitrogen and phosphorus?  What are the risks associated with nutrient loss? . Can you think of anything else? . In not mentioned, prompt for detailed explanation of the risks of P and N contamination o Are the risks different for nitrogen and phosphorus . In not mentioned, prompt for environment and/or financial risk  How concerned are you about the financial impacts from nutrient loss on your farm on a scale from 1 to 10 with 1 being not at all concerned and 10 being extremely concerned?

 How concerned are you about the environmental impacts from nutrient loss on a scale from 1 to 10?

Mitigation  What can you do to minimize potential risks and maximize benefits associated with nutrient application on your farm? o In not mentioned, prompt for specifics to P and N

9. I’d like to ask you a few questions regarding where you seek out information.  What are the most important sources of information that you use to make farming decisions? (for example, magazines, internet, radio)  Who are you most likely to go to for information? (e.g., extension agents, farm consultants, other farmers or forums) o Why these people? How do these people influence your farming decisions?  Can you easily find information about nutrient application when you need it? o Is this information useful? Why or why not?  Where do you perceive your understanding/knowledge of nutrient management on a scale from 0 to 10 with 0 being none and 10 being perfect understanding?  Using that same scale, where does understanding need to be to make the best decisions?

10. Now I would like to ask you about one specific decision related to nutrient management on your farm.

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 Do you test for nitrogen & phosphorus in your soil? o If Yes: . How often? . Why? . Is the information from the results understandable? . How do you use this information? o If No: . Why not?  What is preventing you from soil testing?  Do you intend to soil test, but just don‟t get around to it? Or do you not intend to soil test because the value doesn‟t outweigh the costs of testing?

You’ve been very helpful and I appreciate the time you’ve taken to speak with me today. Before we finish, I would like to ask a few more questions that will help me understand how participants in this project differ on a few basic characteristics…  Can I get your age, by decade is fine? o Prompt for approximate age by decade if necessary  What is the highest level of education that you have completed? o Prompt for Pre-HS, HS Diploma or GED, Some college, Bachelors degree or Post-graduate education if necessary  Would you consider your farm small, medium, or large; with small being revenues under $50,000; medium being revenues between $50,000 & $250,000; and large being over $250,000?  Record gender: Male / Female

11. Finally, is there anything else that came to mind while we were talking that you would like me to include in our research?

Thanks again for your time, I greatly appreciate it! I am leaving my contact information with you so that you can follow-up at any time with additional information or questions. If you would like to see the results of this study, let me know and we can get those to you.

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Appendix B: Coding Scheme (via MAXQDA)

 Nitrogen and Phosphorus o Mitigation . Influence of Nature  Soil properties  Rainfall timing and intensity . Influence of Management  Manure Storage  Tiling  pH  Meeting intended yield  Fertilizer tank diking  Soil testing  Application timing, amount and method  Crop rotation/cover crops o Impacts . Soil quality . Water quality . Wildlife . Yield loss  Phosphorus o Fate . Erosion  PP transport . Leaching . Surface runoff 113

 DRP transport o Impacts . Regional tourism  Water treatment  Swimming restrictions . Human health  Toxic algae . Ecological  Low dissolved oxygen  Reduced biodiversity o Mitigation . Influence of nature  Slope length and angle . Influence of management  Auto-steering  Filter strips  Tillage practices o Conservation o Conventional  Nitrogen o Fate . Leaching . Volatilization . Erosion o Impacts . Greenhouse gases . Human health o Mitigation . Influence of nature  Temperature

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. Influence of management  Wide root zone  Nitrogen stabilizer  Primary farming goals o Environmental stewardship o Profit o Autonomy o Family succession

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