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Effects of Everglades Restoration on Sugarcane Farming in the Everglades Agricultural Area

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PAGE 1

EFFECTS OF EVERGLADES RESTORAT ION ON SUGARCANE FARMING IN THE EVERGLADES AGRICULTURAL AREA By JENNIE MARIA VARELA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by Jennie Maria Varela

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This thesis is dedicated to my parents, Carlos and Janet, and my sister, Carmen.

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iv ACKNOWLEDGMENTS I extend my deepest gratitude to my supervisory committee chair, Dr. Donna Lee, and committee members, Dr. Clyde Kiker and Dr. Alan Hodges, for their guidance and assistance over the course of my thesis res earch. I am very thankful to Dr. Rick Weldon for his advisement regarding the analysis pres ented in this document and to Barry Glaz and Forest Izuno for their personal cooperation and contributions to this project. I also wish to express my appreciation to the faculty and staff members in the Food and Resource Economics Department, and to my fellow graduate students for their support and encouragement throughout my course of study. Finally, I would like to thank my extended family and friends for their constant support and unwavering confidence. I am esp ecially grateful to the community of St. Augustine Church and Catholic Student Center whose friendship, love, and prayers made possible my success at the University of Florida.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT.......................................................................................................................ix CHAPTER 1 INTRODUCTION........................................................................................................1 Geography and Land Use.............................................................................................2 Economic Characteristics: EAA...................................................................................5 Restoration and Cons ervation History..........................................................................5 Comprehensive Everglad es Restoration Plan...............................................................7 Focus of Present Work..................................................................................................8 Problem Statement.................................................................................................8 Hypotheses............................................................................................................8 Maintaining a Higher Water Table Lowers Average Sugarcane Production for an EAA Farm......................................................................8 The EAA Sugarcane Operation Will Experience a Reduction in Profit Under the Changed Water Conditions........................................................9 Research Objectives..............................................................................................9 2 PRODUCTION THEORY AND ITS APPLICATION TO FLORIDA AGRICULTURE........................................................................................................11 Theory of the Firm......................................................................................................11 Interrelationships of Econo mic and Agronomic Concepts.........................................12 Diminishing Returns...................................................................................................15 Modeling Production..................................................................................................16 Modeling Production and Cost...................................................................................20 Economics of Water Use............................................................................................20 South Florida Agriculture and Ecosystem Restoration..............................................21 Sugarcane response to high wa ter tables and flooding...............................................22 3 METHODOLOGY.....................................................................................................26

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vi Introduction and Overview of Analysis......................................................................26 Agronomic Model.......................................................................................................27 Rainfall Model............................................................................................................29 4 DATA SOURCES......................................................................................................31 Empirical Research on Sugarcane Response..............................................................31 Water Table and Flooding Conditions........................................................................32 Historical Production..................................................................................................34 Climatic Data..............................................................................................................35 Costs of Production.....................................................................................................36 Sugarcane Prices.........................................................................................................37 5 RESULTS AND DISCUSSION.................................................................................39 Empirical Model Results............................................................................................39 Rainfall Model Results...............................................................................................41 Comparison of Model Scenarios................................................................................42 Evaluation of Hypotheses...........................................................................................43 Maintaining a Higher Water Table Lowe rs Average Sugarcane Production for an EAA Farm...................................................................................................43 The EAA Sugarcane Operation Will Expe rience a Reduction in Profit Under the Changed Water Conditions........................................................................44 6 SUMMARY AND CONCLUSIONS.........................................................................45 Summary.....................................................................................................................45 Conclusions.................................................................................................................45 Implications for Future Analysis................................................................................48 APPENDIX A SIMULATION OUTPUT FOR SCENARIO 1..........................................................50 B SIMULATION OUTPUT FOR SCENARIO 2..........................................................54 C SIMULATION OUTPUT FOR SCENARIO 3..........................................................57 D SIMULATION OUTPUT FOR SCENARIO 4..........................................................60 LIST OF REFERENCES...................................................................................................63 BIOGRAPHICAL SKETCH.............................................................................................66

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vii LIST OF TABLES Table page 3-1. Key output variables and probability distributions for empirical model....................28 4-1. Total monthly rainfall in inches for the EAA 1979-2000 ........................................35 4-2. Average EAA rainfall.................................................................................................36 4-3. Florida sugarcan e production expenses......................................................................37 5-1. Summary statistics for simula ted model output, pr ofit in dollars..............................40 5-2. Summary statistics for simulated model output, yield in tons per acre.....................41

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viii LIST OF FIGURES Figure page 1-1. Original Everglades, surrounding wetlands, and south Florida watershed.................3 1-2. Current map of the Everglades regi on, including the Everglades Agricultural Area........................................................................................................................... .4 4-1. Distribution of water leve l for Hendry County, FL 1977-1995.................................33 4-2: Total sugarcane production for Hendry County, FL from 1994-2004.......................34 4-3: Season average price: suga rcane for sugar and seed 1980-2003................................38

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ix Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EFFECTS OF EVERGLADES RESTORAT ION ON SUGARCANE FARMING IN THE EVERGLADES AGRICULTURAL AREA By Jennie Maria Varela December 2005 Chair: Donna Lee Major Department: Food and Resource Economics Sugarcane production is a $700 million business in the Everglades Agricultural Area. Beginning in the 1920s, this portion of the Everglades region of South Florida was drained, leaving rich muck soils to be used by agriculture. Productivity led to diminishing water quality until, in 1994, th e Everglades Forever Act called for the wetland to be restored. As part of the la rger Comprehensive Ev erglades Restoration Project (CERP), the current drainage system in Florida Everglades is being altered or removed and best management practices requir e that less water be dr ained out of the area to reduce phosphorus loads. It is expected that maintaining a higher water table lowers sugarcane production for an EAA farm and that the EAA sugarcane operation will experience economic losses under the changed water conditions. The analysis assumed a hypothe tical 640-acre sugarcane farm with a high level of management, operating to maximize profit, a nd independent of processors. Using an approach based on agronomic yield models, this analysis estima ted yield and profit

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x change for this typical farm under four scenarios. Functions from a 2002 study on sugarcane cultivar response to water tables were used to determine an expected yield function that included a parameter for flood even ts along with historical distributions for water table depth. That function, along with acreage, overhead cost, variable cost, and price, was then simulated using Simeta r, varying the input factors. The mean values for yield changes, and consequently for profit changes, in the simulations were significantly different across the baseline and post-re storation scenarios. A zero-profit estimation found that water ta ble depth of 31.83cm with 6 flood cycles resulted in a yield of 31.1 tons per acre, just a 7 % differenc e from the historical mean. All post-restoration simulations resulted in average losses over the estimated period including a decrease in yi eld to approximately 27 and 24 tons per acre. If basing decisions on the mean values of these scenar ios, one would expect that the 640-acre farm, even keeping all acreage in production, w ould see an average loss of up to $135,000 for the year.

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1 CHAPTER 1 INTRODUCTION Agriculture is one of Florid as largest industries and a ma jor sector of the economy. This industry alone accounts for over six bi llion dollars annually. Perhaps most well known commodities are citrus and sugarcane, vegetables, berries and melons While farmland may not be as expansive as in other st ates, much of it is in use for these types of high value crop production. Much of this pr oduction takes place in south Florida, which includes the Everglades Ag ricultural Area (EAA). For many, the Florida environment is just as valuable as its industries. Florida has diverse ecosystems including lakes and rive rs. Citizens and lawmakers alike have worked to restore fragile wetlands and greenwa ys. Programs such as Florida Forever set aside vulnerable parcels of land so that natural areas can be preserved. Public awareness has influenced initiatives for pr otected wildlife, sens itive land, and water resources in many forms, but perhaps none grea ter than the task of restoring the Florida Everglades. With strong citizen support, st ate and federal agenci es came together to develop the Comprehensive Everglades Restor ation Plan (CERP). It is this multi-stage effort that is bringing about great challenges in balanci ng the interests of producers, environmentalists, and developers. It was the establishment of agriculture th at motivated the creation of a system for flood control and began the series of cha nges in the Everglades area. Later on, population booms and urban growth further ch anged the landscape of the state. This expansion put further pressures on water qual ity and management. Construction and land

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2 development show no signs of slowing, and t hus water management will continue to be an issue for the foreseeable future. Due to these changes, current producers have a number of immediate concerns: keeping production profitable, adjusting their practices to meet envi ronmental standards, and making decisions with an uncertain future in their industry. The EAA is just a small piece of the larger picture. Th is area represents jut one sect or of one of the most complex and long-range wetland restora tion projects ever undertake n. Within this area, the primary concerns are maintaining flood contro l, while also ensuring water availability, and controlling runoff into the Everglades Protection Area. The case is unique as the EAA falls within a particular watershed, is home to crops that may not be produced in many areas, a nd has been subject to specific water management measures for so many years. Howeve r, Florida is not the only state trying to balance agricultural and environmental intere sts, nor is South Fl orida the only region struggling to manage development and water needs as well as wanting to preserve as much of the natural beauty as possible. As these challenges are approached, the results of these programs will surely se rve as indicators for future projects around the country. Geography and Land Use The Florida Everglades region is historica lly known as one of the most unique and productive ecosystems in the world Marjory Stoneman Douglas describes the region in her 1947 book, Everglades: River of Grass : The grass and the water together make the ri ver as simple as it is unique. There is no other river like it. Yet within that simplicity, enclosed within the river and bordering and intruding on it from each side there is a subtlety and diversity, a crowd of changing forms, of thrusting teem ing life. All that becomes the region of the Everglades.

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3 It is the defining ecosystem of South Flor ida, a hydrological ne twork of saw grass plains and swamps that once covered nearly three million acres (USGS 2002). Figure 1-1. Original Everglades, surroundi ng wetlands, and south Florida watershed. Source: USGS. From the extensive Everglades marsh, approximately 700,000 acres were drained to provide rich farmland (B ottcher and Izuno 1994). This area is now known as the Everglades Agricultural Area (EAA) and sits in Hendry and Palm Beach counties between Lake Okeechobee to the north and the Ev erglades Protection Area to the south.

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4 Figure 1-2. Current map of the Everglades regi on, including the Everglades Agricultural Area. Source: IFAS Agricultural development of the area wa s made possible by the establishment of a drainage and irrigation system to regulate the amount of water available, especially during the wet and dry seasons. The area r eceives enough annual rainfall to sustain its crops, however, most of that ra infall comes in June through September, while winter and spring are dry (EREC 2005). The irregularity of these rainfall patterns makes water management the EAA = s greatest challenge.

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5 The EAA has been able to regulate its wa ter levels by using a system of canals, pumps, and levees first put in place in the 1920's. Currently, approximately 80% of the area pumps its excess water into storm wate r treatment areas and water conservation areas and a few drainage di stricts still drain runoff in to Lake Okeechobee (FFWP). While agriculture thrived over the decades the water quality of the Everglades diminished with nutrient leve ls steadily increasing. Economic Charac teristics: EAA The EAA is still one of the most productiv e agricultural areas in the country. The agricultural industries occupying the EAA are responsible for an estimated $1.5 billion of sales each year (Aillery et al 2001). As of 1997, Palm Beach and Hendry counties had over 730,000 acres of cropland making up appr oximately 1200 farms. The leading crops in production are sugarcane, rice, sod, and winter vegetables. By 2002, the EAA had about 500,000 acres in production, with 90% of its acreage in Palm Beach County. Over 5,000 people in this area are em ployed either directly in ag riculture or in associated businesses as reported in the 2000 ce nsus (USACE and SFWMD 2003.) Sugarcane dominates production in the EAA covering 86% of its acreage and bringing in sales of over $762 million in 2001. N early a quarter of domestic sugar is produced in the region. The EAA also boast s a rice industry with sales of over $9 million. Winter vegetable production is also a profitable practice in the EAA, second only to sugarcane. Row crops cover over 16,000 acr es and represent about 16% of EAA sales in 2001 (USACE and SFWMD 2003). Restoration and Conservation History Regulation of the Everglades region bega n in 1934 when Congress authorized the acquisition of land for a park that would pr eserve natural conditi ons in south Florida

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6 (SFWMD 2004). After thirteen years, Pr esident Truman officially dedicated the Everglades National Park in December of 1947 (ENP), the same year Douglas published her account of the wetlands. Florida continued to grow, however, and in order to establish a system for flood control, agricultural and urba n water supply, and preservation of wildlife, Congress in 1948, set forth the Central & Southern Florida (C&SF) Project. Reaching from central Florida to the Florida Keys, it took the form of canals, levees, storage areas and other water control structures. The drainage pr ojects for controlling flooding were begun by the State of Florida and even tually continued by the Corp s of Engineers (USACE and SFWMD 1999). Successful drainage of th e area made the land south of Lake Okeechobee suitable for agricultural development, creating the EAA. However, this control system altered the na tural ecosystem to such an extent, that even the protected area was being dama ged by changes in water flows and the phosphorus running off of the agricultural lands. In 1991, the State of Florida established the Douglas Everglades Protection Act (F .S. 373.4592, 1991) which called for a Surface Water Improvement and Management (SWIM) plan and a change in regulatory procedures for the EAA. Many felt there wa s not enough available information to make such decisions and lawsuits began, delaying the restoration efforts. In 1994, the Florida state le gislature passed the more comprehensive Everglades Forever Act which established new storm wa ter treatment areas and Best Management Practices (BMP) for the EAA in an effort to re store the natural flow of the Everglades as well as improving water quality (Bottcher and Izuno 1994). Comb ining short and long term projects, this plan includes more rese arch and monitoring in the decision-making

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7 process than previous laws had allowed. The state followed up with the Water Resources Development Act (WRDA) of 1996, authorizing the Corps of E ngineers to develop a plan by 1999 that would encompass all aspects restoration. The Water Resources Development Act of 2000 set forth the paramete rs for the final restoration project and laid out the key points of the comprehensive plan. Comprehensive Everglades Restoration Plan The Comprehensive Everglades Restorati on Plan (CERP) was the result of the WRDA of 2000. With a budget of $7.8 billi on, the plan includes thirty years of construction and an additional twenty years of maintenance. There are more than sixty components of the plan, being carried out by both the SFWMD and the Corps of Engineers. The costs are to be evenly divi ded between the state and federal governments. The primary goals are to develop ecological values as well as increasing economic and social values. By restoring a more natura l hydrological flow, it is expected that water quality would be improved throughout the ar ea and threatened and endangered species would also see improvement in their habitat. Under CERP, new water quality treatment facilities will be establis hed along with over 200,000 acres of reservoirs and wetland water treatment areas being constructed. Agricultural concerns have been considered a major part of the plan, because of the proximity of natural and ag ricultural areas. In November of 1999, the South Florida Action Plan for the Applied Behavioral Sc iences, drafted to bring socioeconomic concerns into the restoration planning pro cess brought these concerns to light. A number of CERP projects take place within or adja cent to current agricultural areas. Though there are pockets of agriculture spread over south Florida, the ar ea concentrated around

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8 Lake Okeechobee, including the EAA, would be the most affected by CERP implementation (USACE and SFWMD 2001). Focus of Present Work This study seeks to provide insight into th e role that the Everglades restoration program will have on the immediate area, specifically, sugarcane farming in the EAA. EAA farms are crucial to Floridas agri cultural industry and indeed are major contributors to the greater economy. Problem Statement Under CERP, some major drainage canals wi ll be removed and more water is being retained as part of BMP. In light of thes e conditions, a sugarcane producer in the EAA is likely to face higher water tables and longe r flood durations, as pumping is limited both voluntarily and by regulation. To examine these conditions requires looking at many parts of the situation: options for mode ling agricultural production, the relationship between agriculture and the Everglades restor ation effort itself, th e economics of water use, and previous work on sugarcane yields. By examining the current status of sugarcane production in the EAA and the current strategies in water management, this st udy aims to provide an informed picture of the impacts CERP will have on the agricultura l industry in the long run. Looking at the impacts of water on those crops will assist in filling in some of the gaps in information Hypotheses Maintaining a Higher Water Table Lowers Average Sugarcane Production for an EAA Farm. Though each year brings a season of heavy rain to South Florida, farmers have been able to manage their risk by planting a lternate crops or letting the land lie fallow

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9 during the rainy season. Under the restoration plan, it is e xpected that there will be a significant difference, as more water is bei ng kept on the land to re duce nutrient loading in addition to removal of some of the dr ainage system. Assuming that producers are currently operating at an optimal level, incr easing the available wate r would hinder yield. Some crops, such as rice, require flooding a nd as such may not show a decreased yield. In the case of sugarcane, lower yield among the current cultivars is anticipated and in response new cultivars are being bred for water tolerance. The EAA Sugarcane Operation Will Experience a Reduction in Profit Under the Changed Water Conditions. Maintaining more water on farmland is a strategy being employed by sugarcane growers in order to reduce soil subsidence as well as limiting phosphorus loads, major objectives of the restoration effort. Literatu re suggests that production is constrained not only by the amount of water applied, but also by limitations on the amount of water that can be pumped out of the system under a ce rtain time. The producer, having to choose which fields to drain, may find that less acr eage can be harvested. The consequential decline in production cuts into the profits of that operation. Research Objectives The first objective of the analysis is to provide a descriptive framework for analyzing changes in sugarcane production for a representative farm in the Everglades Agricultural Area. This includes determini ng the current production practices in the area as well as estimating future production possibiliti es. In particular, the focus of the work is to describe the crop response to water management such that a producer would have knowledge of how changes in t hose practices could affect hi s operation. This requires

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10 not only gathering information on the crop yiel d itself, but also determining how much water is coming in and out of the system. The other major objective is to gauge the economic impact of the water flow change. In order to meet this objective, there must first be a determination of future production. By simulating the production rela tionship, a range of possibilities can be outlined. Along with future estimations, there must also be a determination of cost structure. Once these factors are combined, the resulting analysis can be useful for a producer in weighing future decisions.

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11 CHAPTER 2 PRODUCTION THEORY AND ITS APPLIC ATION TO FLORIDA AGRICULTURE Theory of the Firm As modern economic theory developed, there developed a need for specific definitions and assumptions. The focus of analysis shifted in the early twentieth century from industry perspective to that of the indi vidual unit. Coase, specifically, set forth his Nature of the Firm (1937) in order that cont inued analysis would ha ve a well-defined and consistent basis when performed at firm leve l. At the same time, it was hoped that the resulting definition would be bot h realistic and useful within the analytic methods of the time. Any definition, he stated, would need to conform to the most powerful economic instruments of economics: the ideas of marginal analysis and substitution. An initial step in creating the basic a ssumptions is differentiating the firm from the larger economic system. The basics of the system are well defined and lead the economist to assume that resources will be allo cated based on prices and that the system will continue working on its own through mark et transactions. Wh en the perspective changes to the firm level, however, there is not an internal market to allocate resources. Instead the firm has to have a coordinator, someone who will be the decision-maker and direct production. Through this coordinator, the firm avoids the costs that come with operating a market. The firm is then define d as a system of interactions in which resources are allocated by a particular entrepreneur. Among the most important characteristics of the theoretical firm are that it has an upward sloping cost curve and that it will not pay to produce output beyond the point at

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12 which marginal cost is equal to marginal re venue. Additionally, the firm will continue to grow until the marginal cost of creating a transa ction within the firm is equal to the cost of having that transactio n in the marketplace. Alchian and Demsetz (1972) do not refute Coases definitions, but take a more specific look at what constitutes a classical firm. They examine the structure and lay out six major qualifications of a firm. First, it must produce using joint inputs. Second, these inputs come from a number of input owners. Next, all th e contracts for joint inputs have a party in common. That party must also have the authority to renegotiate the input contracts independent of each other. Additiona lly, that party holds claim to the residuals and finally, must have the right to sell the central contractual residual status. Similar to Coase, Alchian and Demsetz see the existence of a central decisionmaker as imperative to the existence of th e firm. This decision-maker is owner and employer. They feel that this structure is the result of necessity. That is, that in attempting to align productivity to the marginal costs of inputs, the most efficient method is to operate as a classical firm. Interrelationships of Economic and Agronomic Concepts Agricultural decision-making involves combining both economic and biological factors in order to maximize outcome. This combination becomes crucial in examining yield responses, optimal output, and the ove rall input-output rela tionship (Redman and Allen, 1954). What is consid ered optimal they find, is greatly influenced by the particular concepts being employed. That is when constants are changed, other factors (e.g., the factor-product price ra tio) may become more or less important in determining the most profitable choice.

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13 Crop yield is certainly one area in whic h these two schools of thought intersect. Agronomic production functions give the econo mist a quantitative look at the changes in production under various conditions Yield is a result of numer ous factors, from the plant itself to the soil in use, to the surrounding clim ate, to nutritional i nputs. Early theorists concentrated on the food of plants: water, nitrogen, earth, fire. As agronomy has developed however, there is a more clear understanding of plant processes such as photosynthesis, which incorporates energy into the relationship. Some of these basic factors, however (water, nitrog en, and other fertilizers) are still the subject of economic analysis, especially under changing conditions. The Law of the Minimum argued by von Liebig was one of the earliest models of production and still carries some influen ce, even if it no longe r stands alone. Von Liebig began with the concept of a minimum factor, the factor of yield that is most scarce. In this theory, yield will change only when this minimum factor is changed. Consequently, the production curve would increa se at a constant rate until it reached the limit determined by the minimum factor. At that point, von Liebigs curve becomes horizontal, as adding more of the ot her factors does not change yield. While von Liebigs concept resonated w ith early farm economists, it was not an especially accurate model of plant response. Later experime nts provided data that would be used to modify the concept, moving away from the idea of a linear relationship with constant returns to scale. In trying to improve the production model, Mitscherlich assumed that there was a maximum yield under ideal conditions and that it was the shortage of any one factor that

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14 would cause yield to decrease proportionally. His result was a curve concave to the given factor axis such that dy/dx = (A y) C where y is the yield while x is the factor in question and A is the maximum yield. Though an improvement over the von Liebig eq uation, Mitscherlichs model still did not adequately represent possible yields since each factor had its own constant, C and did not account for factors influencing each other. Ba ule expanded this model to include variable growth factors, making the case that yiel d is dependent upon the interaction of many factors. Overall yield is then expressed as Y = (1-10c1x1)(1-10c2x2)(1-10cnxn) with c representing the effect of the corresponding x factors. At the same time, Spillman was developing another estimation of yield. Basing his model on the expectation that increments of a growth factor could be the terms of a geometric series. Using the example of fe rtilizer application, Spillman expressed the yield relationship as: Y= A(1-Rx) In this expression, R would i ndicate the ratio of incremen ts in yield and suggests a sigmoid curve. Both Mitscherlich and S tillman agreed that the law if diminishing increment would not apply once the input quant ity was large enough to damage the plant. Redman and Allen in their overview of these principles raise the concern that economists must be careful in using data from farm crops on the basis that the functions drawn from these data are not necessarily true of all plan t growth. Fixed factors and decision-making may be different for separate sets of data and as such, the economist

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15 attempts to find an expression of best fit. Once such an expression is created, it can then be used under various scenarios to forecast the profitability of choices. Diminishing Returns One of the most relevant parts of th e economics/agronomic relationship is the theory of diminishing returns. Th is idea was first set forth in the 18th century by Turgot. In describing expenditures and production, he noted that while return s initially increased, effects would eventually di minish. Early in the 19th century, Malthus, Ricardo, West, and Torrens all described the same phenomenon in their publications on land rent. Ricardo was perhaps most accurate in describing th e phenomenon within intensive farming. His analysis however was indicative of dimini shing average returns and not explicitly marginal returns. Malthus tried to use th e diminishing returns concept to make his arguments regarding population growth, speci fically, that the food supply was limited to arithmetic growth. The concept remained pa rt of economic thinking of the time, even incorporated into the four propositions stated by Senior as he began the study of political economy. It was not until the twentieth century drew near that the distinctions were made between average and marginal products. Clark presented a paper that applied the idea of diminishing returns to all factors of pr oduction. His major assumption was that all factors of production remain pe rmanent except for one factor that would be changed. Under these assumptions, if more units of the one varying factor were added, the marginal and average products associated with that factor would eventually decrease. Edgeworth in 1911 assumed that land was a fixed input and created a table that included variable levels (refe rred to as doses of the othe r inputs and their resulting output. Though he was arguing that these concepts would appl y to any industry (in this

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16 case, railways), his work was based on agricu ltural examples. Citi ng his observation that at some point there is a transition from incr easing to diminishing returns, Edgeworth was one of the first recognize the marginal product of the input as well as the average product, creating columns for each in his table. Modeling Production Thompson 1988 describes the flexible functi onal forms (FFF) as a way to relax the restrictions one gets when using Cobb-Dougl as functions. They can be expressed as quadratics, Box-Cox, and numerous other forms. It is important that empirical studies state clearly the reasons a pa rticular form was chosen. FFF are very useful when using duality th eory. If the function satisfies certain conditions (convexity, monotonici ty, homogeneity), there is no longer a need for a selfdual function. It is also possi ble to derive supply or dema nd from these functions and to use them for comparative statics. Additionally, these functions can be used to estimate equations (or systems) that are nonlinear in their parameters. One of the main problems with the FFF is collinearity. Also, it is often difficult to meet the above conditions over the entire se t of observations. Estimating nonlinear systems also limits interpretation, as much statistical theory as sumes linearity in the parameters. Deiwert (1973) defines flexib ility as a local property. Using an arbitrary function, he makes a second order approximation. The pa rameters of the FFF then must give it first and second derivatives equal to those of the arbitrary function. This definition of flexibility is often applied b ecause the conditions are easier to meet than those of other definitions.

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17 Another measure of flexibility is the S obolev norm, which is a global definition. This approach measures the average error of approximation and consequently estimates elasticities very close to the true elasticity. This also gives the model nonparametric properties including small average bias a pproximations (Gallant 1981); consistent estimations of substitution elasticities (Elbadawi, Gallant, Souza 1983); and asymptotically size testing procedures (Gallant 1982). Many FFF can be looked at as derivations from mathematical expansions, but the definitions do not limit them to only such derivations. Some commonly used expansions are the Taylor, Laurent, and Fourier expansions. The firs t two are flexible under the Diewert definition, while the Fourier follows the Sobolev definition. Some functions, like the generalized Mc Fadden and Barnett f unctions are not derived from an expansion (Diewert and Wales 1987). In Thompsons analysis, four types of studies were used to look at the various FFF: Monte Carlo, parametric modeling, Bayesian Analysis, and nonnested hypothesis testing. The FFF are useful in both production and c onsumer applications of the Monte Carlo studies, but depend upon the type of data, the si ze of the sample, and other properties that vary. The parametric model used Box-Cox tes ting and therefore could only be applied to some of the FFF. The Bayesian analysis allo wed very different models to be compared based on their data on both the production a nd consumer levels. The nonnested testing includes all the proposed models and is also based on the data. Of these methods, the Bayesian analysis and nonnested testing were the most useful in comparing FFF. It is noted that in either case, it is important to be able to compare models with various transformations of the dependent variable.

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18 One must look at the duality of the be havioral model and test the behavioral assumptions to make sure the data are consis tent with the assumptions. The data should then be tested for theoretical properties such as returns to scale. The chosen form should fit the assumptions and can be compared with other forms through Bayesian analysis or nonnested hypothesis testing. Af ter the form has been chose n, it is important that it be compared to other measures (in order to gauge sensitivity of that form). In an examination of The Cottonwood River Watershed, Apland, Grainger, and Strock (2004) describe a framework for cr eating a farm model that accounts for both agricultural production as well as water quality concerns. In trying to model these tradeoffs, Apland notes that mathematic al programming models can incorporate economic and agricultural factors but become very complex. A deterministic farm model forms the basis of Aplands work, which is designed to be expanded to include risk. The mode l is made up of 18 production periods for the year in order to represent harvest and planti ng activities in all combinations. Land, labor, and machines are held fixed so that the analysis can focus on the various harvesting, planting, and tillage dates and fe rtilizer application is endogenous. To carry the model forward, the auth ors discuss a discrete stochastic programming model (DSP). This type of model is useful in that it can capture alternative practices as part of the risk an alysis. Further, risk can be included in the constraints and as par of the sequential decision process. Ho wever, this method requi res a great deal of data to be useful and can be costly. Risk is a significant factor in mode ling agricultural produc tion. Just and Pope (1979) note that risk affected by price, market phenomena, technology, and policy.

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19 Traditional evaluations of pr oduction were drawn from experi mental data, but the authors argue that using continuous res ponse functions give better esti mates. This analysis uses neoclassical log-linear production functions. Some of the specific problems with previ ously used (and popular) models are that increasing input always has positive marginal effect on output and th at it also reduces variability of marginal productivity. In orde r to separate the eff ects of input on output and variability, the authors propose that a good stochastic specificat ion has two functions; the first modeling the effects of input on mean output and th e second modeling the effects of input on variance of output: y = f(X) + h1/2(X), E() = 0, V()=1 The mean and variance of output can then been seen independently as E(y) = f(X) and V(y) = h(X) The procedure proposed for such and estima tion is a three-step regression. The first is a nonlinear leas t squares (NLS) regression of yiel d. Second, the exp ected error is regressed against X using ordinary least s quares (OLS). The final step is a weighted NLS regression of y In using experimental data, the au thors focus on Cobb-Douglas and translog functions. The basic equation is then modified to include an error term, time, and plot to capture time effects. They conclude that the simple two-part pr oduction function remains within the bounds of traditional economi c thought while removing some of the constraints that hinder decision-making.

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20 Modeling Production and Cost In a static situation, Pari s and Caputo (2004) state, a ny estimation of the economic relationships of a price-taking firm should in clude both primal and dual relations. Their proposed model uses a generalized additive error (GAE) approach to make a nonlinear estimation. Their sample firm is at once risk -neutral and cost minimizing. The system is then represented as a set of equations: th e primal production and input price functions, and the dual input demand function. This analysis does face some challenges. The planning and decision-making data is generally not recorded by producers and thus has to be estimated. Also, the choice of production function can pose a dditional challenges. The Cobb-Douglas and constant elasticity of substitution appr oaches have the same functi onal form as their respective cost functions, but that may not be the case wi th other forms. Once quantities and prices are estimated using NLS, they are put in to a nonlinear seemingl y unrelated (NSUR) model. Economics of Water Use Water use, an important factor in pro duction, is often modeled as water applied. However, Kim and Schaible (2000) challenge the assumption that water (or any of the variable factors) is completely engaged in crop production. Noting that the production process does not consume not all inputs applie d, whether water or fertilizer, the authors seek to provide a measure of the overestima tion of economic benefits from water use. The authors observe that economic benef its in agriculture ar e often modeled as normalized-quadratic functions, but that the derived factor demands are sometimes linear and sometimes in Cobb-Douglas form. As such, both linear and nonlinear cases are examined. Under both scenario s, the total economic benefits were overestimated when

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21 using applied water rather than consumptive water. In the application of these methods to corn production in three Nebraska c ounties, the overestimation was 28.9% and estimate even higher when l ooking at agriculture overall. In one of the most traditional models of water use, the Von Liebig production function as described by Bogge ss et al (1993), uses evapor ation and transpiration to estimate the changes in yield. This type of function describes a linear output relationship until some maximum. From that equation, ac tual yield can then be estimated as a function of the ratio of actual to potential evapotraspiration. Similar to Kim and Schaible, Boggess et al make the distinction between effective water, actually used by plants, and the tota l amount of applied water. In modeling irrigation, they point out that it is fundamental to incor porate the concept of hydrologic balance. The principle of hydrologic balance st ates that there must be equality between the amount of water that enters a specific ar ea and the amount of wate r that leaves that area. That is, all water entering the partic ular area, through preci pitation, irrigation, or from the soil, and all water leaving the ar ea whether through evapot ranspiration, runoff, or percolation must be considered. South Florida Agriculture and Ecosystem Restoration Restoring the water flow of the Everglades will create a need fo r water retention in the northern part of the watershed. By 1978, over three million acres of land had been drained in South Florida (W eisskoff 2005). This region, es pecially the Everglades Agricultural Area (EAA) will require a great de al of water to meet needs during the dry season. Development of the EAA created a system if irrigation and drainage that prevents most water retention during the we t season. Increasing the amount of water retained may lessen agricultural profitability. Authors Aillery, Shoemaker, and Caswell

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22 attempt to model the economic effects of water table management and surface water retention scenarios. The authors measured the tradeoffs unde r two conditions, the first being that resource use is determined by agricultu ral producers alone, the second using joint maximization of agricultural and environmen tal objectives. Using both objectives resulted in higher marginal costs, but also significantly increased the benefits of lowering the water table (Aillery, et al 2001). Whether the benef its will outweigh the cost is dependent upon the specific agricultural and environmental demands. Three scenarios of water policy were simu lated: water-table restrictions, surfacewater development (including land acquisition), and water-retention targets. The first showed an increase in water retention, but at a high opportunity cost and the inability of the soils to retain the desired amount of wate r. Surface water devel opment also increased water retention, but comes at the cost of production foregone by retiring those lands. A moderate change to a target water-table depth was considered the best option (Aillery, et al 2001). The authors are up-front about two main conc erns with this article. The first is that a true cost-benefit analysis would need more empirical evidence from the agricultural sector and is difficult to gene ralize. The second is that su gar prices, the major component in estimating agricultural gains reflect price supp ort levels that could change in the future and thus alter these findings. Sugarcane response to high water tables and flooding Glaz, et al (2002) note that the EAA is dependent upon the canal system to maintain suitable water levels for sugar cane and other crops. Pointing to a study by Omary and Izuno (1995), ideal water levels fall within 40 and 95 cm below the soil

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23 surface. Keeping the water within this range has become more difficult as the farmers are dealing with soil subsidence. As soil is lost, the remaining soil cannot store as much water. Additionally, best management prac tices put in place to limit Phosphorus entering the Everglades have resulted in farmers pum ping less water and thus maintaining higher water levels. The researchers conducted two experiments, the first being planted in February and harvested in three cycles (plant cane and tw o ratoon crops). The second experiment was planted in January of the follo wing year and harvested in two cycles. In both cases, two fields were planted and receiv ed water treatments from June to October (the months with highest rainfall). The wetter field was treated to have a water level between flooding and 15 cm BSS. The drier field was kept with a water leve l between 15 and 38cm BSS. Over the period of study, the researcher s found that the soil profile was very sensitive to rainfall. They estimated that for every cm of rain, the soil profile rose 10cm. As a result, even the drier field had some da ys in which the water level was higher than 15cm BSS, suggesting that duri ng a normal year, fields with the drier target would still see flooding. For the plant cane crops, the average cane yield for the drier field was 5.8% higher than the wetter field. For the first-ratoon ha rvests, the drier field average cane yield was 4.3% higher than the wetter field. For the se cond-ratoon harvest, the average cane yield was 8.4% higher in the drier field. The researchers also measured the sugar yields from each harvest. The average sugar yield for the plant cane crops was 6.6% hi gher in the drier field than in the wetter field. The average sugar yield in the first-ra toon crops was 8.3% higher then in the wetter

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24 field. In the second-ratoon crop, the averag e sugar yield was 11.5% higher than in the wetter field. The project suggests that there are some cu ltivars that may continue to yield well if the water tables were maintained at a higher le vel. They suggest th at increasing the water table incrementally would be the best option considering profit and the need to reduce phosphorus discharge. In 2004, Glaz et al published their findings after experimenting with two different sugarcane genotypes. This study was to ex amine periodic flo oding, lasting no longer than one week and then draining to a wate r depth of about 50 cm below the surface. Flooding was set at 7 days to simulate the l ongest flood period a comm ercial field in the EAA might experience. The areas were treated fo r five and nine cycles in different years. It was noted that in the EAA, it is often diffi cult to drain to the desired level after a flood. One of the genotypes developed arenchyma (a ir cavities) at the roots, which seems to have impacted the yield response. Th ese did not show a significant response to changes in depth over the three years. The other genotype however, showed a 21% cane yield increase and an 18% sugar yield increase in the fully drained case as compared to the flooded specimens in 2000. The 2002 experime nt resulted in a 28% increase in both sugar and cane yields in the drained area comp ared to the flooded plants. The authors point out that using additiona l flooding periods might result in a nonlinear flood response. Such information would be of use to farmers th at are not able to dr ain all fields at once due to limitations on total drainage to the canals. Another consideration in sugarcane response is the possible benefit of flooding during certain stages of growth, specifically in trying to control for wireworm (Glaz

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25 2002, 2003). Flooding the seed cane after planting could replace the practice of applying insecticide to the soil. Before the practice could be commercially adopted, however, the feasibility and cost of ma intaining the flood condition and then draining would need further examination. There is also a concer n that the shortening of the growing season would lead to reduced yield.

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26 CHAPTER 3 METHODOLOGY Introduction and Overview of Analysis The analysis assumed a hypot hetical 640-acre sugarcane farm. Using two different approaches, one based on agronomic yield m odels and historical groundwater levels; the other based on historical yield and rainfall, this analysis estimated profits foregone for this typical farm. Both approaches assu me that the operation is independent of a processor and profit maximizing. In the first approach, the agronomic func tions measuring response to flooding/high water tables were combined to give an expect ed yield function that included a parameter for flood events. That function, along with acreage, overhead cost, variable cost, and price were then simulated, varyi ng the input factors. This appr oach also used a historical distribution along with a range of likely water table levels as described in Water Management for Florida Sugarcane Production (2002) and Agriculture and Ecosystem Restoration in South Florida: Assessing Trade-offs from Wa ter-Retention Development in the Everglades Agricultural Area (2001). The second approach utilized historical yi eld and rainfall, determining relationship between the two based on the most sensitive growth period. Future rainfall will be based on historical records and used to provide possible yields. From the previous Water Management article, the EAA Storage Reservoir Phas e 1 Existing Flood Control Conditions Documentation and the 2001 study on drainage uniformity, this approach will assume that the system w ill drain up to 48% of rainfall

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27 Agronomic Model Taking the findings of the empirical res earch on yield response by Glaz et al (2002), two equations were combined in or der to incorporate a parameter for flood events. The results for the two experiments were as follows: Y x 146016 .. (1) Y x 17625 .. (2) The year 2000 experiment with 5 flood cycles ( Eq. 1 ) was defined as a base ( Z =0) and the 2001 experiment with 9 flood cycles ( Eq. 2 ) was defined as Z =1. The following, then, represents flood events, Z, as Z = -1.25 + .25 F where F is the number of flood cycles, and Z is a qualitative variable. The number of flood cycles was simulated as part of the analysis. The simulation of flood cycles was first attempted using a uniform distribu tion (between 0 and 9), but ultimately, F was determined using a triangular distributi on from which pseudo-random numbers were generated. The boundaries of the distribution remained 0 to 9 in keeping with the experimental conditions. Equations (1) and (2), can then be combined as Y = 14.6 + 3 (Z) + .16X +.09( Z)X. Where Y is yield in kilograms per meter sq uared. Sugarcane yi eld is historically measured in tons per acre, so the resulting yi eld (in kg) must be c onverted by a factor of approximately 4.5 to be expressed in tons per acre (see Table 3-1). In order to translate the empirical data to practical terms, a calib ration factor was also included. This factor (0.267) was determined by setting the mean valu e of the empirical yield data equal to the historical mean yield.

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28 The other key variable in this model is th e groundwater level. Ta ble 1 illustrates all key output variables (KOV) and their distributions. In orde r to simulate the probable range of groundwater levels, th e historical data determine th e distribution from which the simulated values will be chosen. A normal pr obability plot suggested that the data were very close to a normal distri bution. The ANOVA procedure was used to determine the mean and standard deviation for the groundwater variable based on these data. The mean would be adjusted in order to simulate various scenarios. Table 3-1. Key output variables and probability distributions for empirical model Key Output Variables Specifications Acres 640 Flood Events "Z" Z = -1.25 + .25F Flood Cycles "F" Triangular distribution: 0 to 9, Depth X cm Varied by scenario Yield (kg per meter sq.) Y = 14.6 + 3(Z) + .16X +.09(Z)X Yield (tons per acre) Y 4.460947/3.74 Price (per ton) $31.70 Variable Cost (per acre) $760.94 Total Fixed Cost (dollars) $144,000 Profit (Price* Yield)-Total Costs Using the Simetar (Richardson, 2001) simu lation tool, these data were simulated for 100 iterations for each scenario. The output for each could then be compared in order to determine the effects of new water conditions Five different scenarios were simulated using this tool. Scenario 1: A representation of current conditions, this scenario assumed a mean water table depth of 85.2cm and a standard deviation of 43.23 based on USGS data. Scenario 2: Also represents current conditi ons, but with the mean depth adjusted to 76.2cm as described in Water Management for Flor ida Sugarcane Production Scenario 3:A model of post-restoration c onditions by raising water table depth to 54.78cm as suggested by Aillery, Shoemake r, and Caswell (Scenario I-5, 2001). Scenario 4: Alternative model of postrestoration includi ng a truncated normal distribution for water table depth with mean 50cm, minimum -27.1272cm

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29 (historical minimum), and maximum 92.8cm (95% Upper Confidence Interval for historical data). Scenario 5: Identifies conditions under which the hypothetical operation exhibits zero profit. Rainfall Model This approach began with the ANOVA pro cedure on monthly rainfall data over a twenty-year period in order to find variation for further simulation. Also included were the values for pan evaporation ( Evap ) and average temperature ( Temp ) for each month over the same growing period. The data were aggregated such that the annual yields were matched with the previous growing period. For example, Rainfall summed from August of 1990 to January 1991 corresponded to the 1991 production data. Maximum drainage ( Drain ) was set at 48% of the rainfall for each of the months included. A relationship between total production ( TP ) and these factors was determined by using an Ordinary Least Squares (OLS) regression: TP 2464440. 7 2947.3Eva p -299037.5Au g Rn-333598.7 S e p tRn-266891OctRn 303408.3NovRn309797.5DecRn258229.4JanRn-7455.9Temp+645862.7Drain Using this relationship, normal distributi ons for rainfall were specified for the simulation based on historical mean and variance In order to represent the changes in draina ge practices, the post-restoration scenario changes the percentage of water drained was varied while other climatic factors were held constant. The results of the two scenar ios could then be compared to each other and ultimately to the previous model. The rainfall model was designed to capt ure the concept of waster balance as presented in the literature rega rding water use. It was anticip ated that that in defining the amount of water coming into or out of the given system, future water flows could be

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30 estimated and consequently changes in produc tion could be predicted. In this case, varying the drainage capacity could give distinct scenarios for comparison.

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31 CHAPTER 4 DATA SOURCES Empirical Research on Sugarcane Response Glaz et al (2004) examined water table effects on two sugarcane genotypes in experiments from 2000-2002. Previous research they noted, was inconsistent regarding sugarcane response to water tabl es. EAA farmers specifically have to deal with periodic floods (less than a week) and cannot always drain the desired amount of water. To simulate these conditions, the experiment evaluated periodic flooding followed by drainage to depths of 50, 33, and 16 cm below the soil surface (BSS). To carry out the study, lysimeters made of polyethylene (1.5m x 2.6m x 0.6m) were set up with Pahokee muck soil from an uncropped EAA field. Each lysimeter had well water flowing in each day and a pump to get ri d of excess water. Additionally, each had a valve that drained the lysimeter to the target water table level. Two sugarcane genotypes were planted, both being chosen b ecause of high yield and similarity to commercially produced varieties. After plan ting, the water level remained at 50cm BSS until the actual treatments started. There were four total treatments; one a control and the others being flooded for the first week of a three-week cycle. After the 7 days of flooding, these treatments were drai ned to the aforementioned depths. Water height from the actual soil surface up to 2.5cm above the surface was considered a flood condition in this expe riment. The length of the flood, 7 days, was set to simulate the longest period of flooding one could expect in the EAA. Similarly, the 50cm control depth was based commercial prac tices. The experiment was repeated for

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32 three years; the 2000 experiment used fi ve flooding cycles while the 2001 and 2002 experiments used nine flooding cycles. The resulting response functions were Y x 146016 .. (2000, r 0992 .) Y x 17625 .. (2001, r 0942 .) Where Y is equal to cane yield in kg m-2 and x is equal to water table depth (cm) during drainage. The difference in flood cycl es can then be used as a factor in incorporating the number of floods in to the yield response analysis. Water Table and Flooding Conditions To be consistent with the cultural practices of the EAA, establishing the possible range of water tables included water tabl e levels as described in Lang et als Water Management for Florida Sugarcane Production (2002). They noted that 30 inches (76.2cm) was optimal depth in terms of sugar cane yield and stated that the recommended target level would be a depth of 23-30 inches (58.42-76.2cm) to the surface. They also noted that variation in EREC studies ranged from 39 inches (99.06cm) to surface level. Historical water table levels for the ar ea were available from the USGS from October of 1977 to September of 1995. The variation here ranged from a maximum depth of 206.95 cm below the surface to a mini mum of just over 27 cm above the surface. The mean depth was just over 85 cm below the surface. The 167 observations, however, are at irregular intervals, which made the information useful only in determining the variation in water table levels. The complete distribution of these data is represented in Figure 4-1. These data were collected fr om a well at Latitude 26'45", Longitude 81'07" in Hendry County, Florida.

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33 PDF Approximation -50.000.0050.00100.00150.00200.00250.00 CM below surface Figure 4-1. Distribution of water level for Hendry County, FL 1977-1995. Source: USGS Additionally, a more qualita tive source of information on water management was a summary of meetings with EAA sugar grow ers in November 2003 to get a consensus on flooding conditions. Coordinated by the Sout hwest Florida Water Management District, the EAA Storage Reservoir Phase 1 Existing Fl ood Control Conditions Documentation provided insight into the growers major conc erns. The participating groups included US Sugar, the Sugar Farms Cooperative, and Florid a Crystals, all producers within the EAA. The documentation of the three meetings reve aled a number of comm on points. Some of these key statements included: Farmers have not kept regular records of crop losses due to flooding thus far The sugarcane crop is most sensitive to flooding during early stages of growth Receiving more than 4 inches of rainfa ll in a 24-hour period is considered problematic There was also consensus among the grower s that heavy rainfall and flooding are of most concern to the areas near the Bolle s and Cross Canals which provide water flows to the east and west of the EAA. There was a concern that these canals to no have the capacity to carry water out as needed. From the 2001 study on drainage uniform ity it was noted that sites normally drained an average of only 48% of the rainfall input into the system (Garcia, Izuno, Scarlatos). When looking at the flow of wate r in and out of the EAA farm system, this

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34 average will used to determin e how much water is being drai ned out of the system rather than contributing to the groundwater level. Historical Production Through the National Agricultural Statisti cs Service (NASS), the US Department of Agriculture (USDA) provides historical production information down to a county level. Using the Quick Stats website allows users to s earch production history for all major crops. Under the category Sugarcane for Sugar, county-level data are available for acres harvested, yield per acre and total producti on. Figure 4-2 illustrates the total annual production for the Hendry County over the past ten years. 0 0.5 1 1.5 2 2.5 3Millions of tons 19941995199619971998199920002001200220032004Annual Sugarcane Production in Hendry County Figure 4-2: Total sugarcane production for Hendry County, FL from 1994-2004. Source: NASS The production values are listed annually and are available from 1977-2004. For this analysis, however, data from 1980-2004 we re considered. The area harvested over that time ranged from 35,000-76,000 acres. The av erage yield per acre varied from a low of 28.6 tons in 1981 to a maximum of 40.1 tons in 1998. Total annual sugarcane production in the county peaked in 2002 at over 25.8 million tons.

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35 Climatic Data The Everglades Research and Education Center includes an automated weather station that provides cl imatological data as a cooperative project of the University of Florida / IFAS and the South Florida Wate r Management District. The station was provides data on temperature, rainfall, solar radiation, and evaporati on at the coordinates 26.6567N and 80.6299W. The time series for rainfa ll goes back as far as 1924, but for the purposes of this analysis, only data fro m 1979 2000 were considered. The total evaporation and average temperature over the growing season were also noted. Within the area of interest, Hendry County, FL, sugarcane planting takes place from August through January and this period is considered the most sensitive to excess water. Table 4-1 illustrates the tota l rainfall for those crucial months. Table 4-1. Total monthly rainfall in inches for the EAA 1979-2000 Year JanuaryAugustSeptemberOctoberNovemberDecember 1979 5.39 14.24 2.24 4.80 2.89 1980 6.09 3.73 7.46 1.02 3.42 0.73 1981 0.68 17.37 4.87 0.67 3.08 0.85 1982 0.63 8.11 12.41 2.75 0.67 0.80 1983 3.91 6.26 6.77 5.16 1.16 4.42 1984 0.23 4.04 8.15 0.40 2.37 0.11 1985 0.75 5.52 9.63 3.39 1.54 2.20 1986 3.59 6.21 4.04 4.50 1.58 3.99 1987 1.18 4.20 4.49 3.14 8.04 0.30 1988 3.02 8.89 2.47 0.11 1.31 0.89 1989 0.97 6.92 8.91 3.49 1.24 1.95 1990 2.47 7.57 2.96 4.22 0.39 1.11 1991 8.24 2.83 6.27 3.54 2.45 0.46 1992 1.20 11.85 10.82 0.69 4.03 0.62 1993 10.16 6.19 5.56 8.00 1.75 0.79 1994 5.60 9.74 5.47 12.16 5.93 7.13 1995 1.91 10.51 8.76 9.60 0.65 0.89 1996 1.35 9.75 3.04 4.23 0.80 0.38 1997 1.23 4.56 5.47 0.65 4.44 5.77 1998 1.47 9.37 11.64 2.20 11.25 1.00 1999 1.95 5.04 8.18 7.69 0.72 0.45 2000 0.74 3.58 7.00 4.77 0.54 0.24

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36 The normal distributions for rainfall duri ng these months, specified through OLS regression, were used to in a simulation to predict the possible rainfall in each of the crucial months (Table 4-2). Table 4-2. Average EAA rainfall. Month Mean Rainfall (inches) Standard Deviation January 2.73 2.69 August 7.25 3.45 September 6.87 2.84 October 3.92 3.21 November 2.73 2.77 December 1.67 1.96 Source: EREC Weather Costs of Production Cost and Returns for Sugarcane on Muck Soils in Florida (Alvarez and Schuneman) provides a framework for looki ng at the production costs for farming sugarcane in the EAA. This work provides a number of key assumptions including: a profit maximizing management, independent gr ower status (non-producer), a small farm unit, and a three-crop cycle, that is, the hypot hetical farm crop includes first planting and first and second ratoon crops. However, cultivation and harvesting practi ces have changed and the most recent data regarding production costs comes fr om the USDA Economic Research Service (ERS). These data (see Table 4-2) from th e Farm Business Economics Report take into account the additional Everglades Restora tion tax that began in 1995. The 1995-1996 values were the final values published in this form. For the purposes of this analysis, it will be assumed that all acreage in the model is harvested.

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37 Table 4-3. Florida sugarcane production expenses Item Dollars per Harvested Acre 1995 1996 Variable Expenses Seed 27.95 27.95 Fertilizer 61.33 57.99 Chemicals 59.10 61.15 Custom operations 106.65 104.81 Fuel, lube, electric 21.67 23.79 Repairs 80.12 80.84 Labor 406.54 396.76 Irrigation water purchased6.70 7.07 Miscellanous 0.56 0.58 Total Variable Expenses770.62 760.94 Fixed Expenses General farm overhead 114.79 107.45 Taxes and insurance 59.27 59.93 Interest 9.61 9.49 Total Fixed Expenses 183.67 176.87 Total expenses 954.29 937.81 Source: USDA, ERS Sugarcane Prices The Florida Agricultural Statistics Servi ce (FASS) maintains an annual record of acreage, yield, production, season average pr ice and the overall value of production. From these field crop summaries, prices we re recorded from 1980-2003. These prices reflect sales of sugarcane for sugar and seed. As illustrate d in Figure 4-3, there has not been a great deal of volatility in price. The maximum price, $39.40 per ton, occurred in 1980 and was followed by a 27% drop to 28.60. After that initial fall, prices have remained close to $30 a ton. In contrast, the lowest season average price was $27.20 in 1999.

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38 Annual Price of Florida Sugarcane0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 19801985199019952000Dollars season avg price Figure 4-3: Season average price: sugar cane for sugar and seed 1980-2003. Source: FASS It is likely that a major factor in maintain ing this price stability is the tariff-rate quota system in place on sugar imports. Th is analysis, however assumes that these conditions will not change and thus do not factor into the modeled scenarios.

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39 CHAPTER 5 RESULTS AND DISCUSSION The analysis involved creati ng two stochastic models based on agronomic studies and historical data regarding sugarcane production and growing conditions in the Everglades Agricultural Area. Once each m odel was specified, the key output variables were identified and their respective probability distributions defined. The Simetar simulation application generated pseudo-random numbers, based on the given probability distributions, which were then used to upda te the model equations 100 times. Each iteration of the simulation genera ted new values for the every key variable. However, the values for water table depth, yield, and prof it (on the basis of hypothetical 640-acre farm) selected as the output of the simulation, as these were of most interest. Empirical Model Results Five scenarios using an empirical yield m odel were completed in order to compare production possibilities with and without the restoration conditions, emphasizing maintenance of a higher water table. These included: Scenario 1: Current conditions, assuming mean water table depth and standard deviation based on USGS data. Scenario 2: Current conditions, but with the mean depth adjusted to 76.2cm as recommended in Sugarcane Production literature. Scenario 3: Post-restorat ion conditions with water table depth to 54.78cm as suggested by Aillery, Shoemaker, and Caswell (Scenario I-5, 2001). Scenario 4: Post-restorati on incorporating a truncated normal distribution for water table depth. Scenario 5: Zero-profit condition The complete outputs for these simulations are illustrated in Appendices A-D. A summary of the simulated prof its is illustrated in Table 5-1, recalling that the fifth

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40 scenario was the zero-profit c ondition. The output series were also compared in pairs in order to determine whether the results were statistically different across the scenarios. Table 5-1. Summary statistics for simu lated model output, profit in dollars* Scenario 1 Scenario 2 Scenario 3 Scenario 4 Mean 26,722.33 (5,370.24) (85,485.58) (135,442.80) Standard Deviation 215,148.12 209,707.29 199,273.51 142647.91 Minimum Value (378,281.63)(388,859.30)(550,047.54) (383,743.63) Maximum Value 656,710.50 642,406.47 656,041.87 239,375.77 For 640 acres of production In terms of the simulated profits, it was surprising that the mean value for Scenario 2, which incorporated the curren t conditions, recommended in the Sugarcane Production literature. In all other respect s, the two base scenarios (1 and 2) were expectedly similar. There is still a large range in the output, poten tial losses of over $500,000 to profits of over $640,000, but the truncated di stribution of Scenario 4 appears to have been most successful in reducing the extreme values. It must be noted, however, that even that scenario exhibits more varia tion than should be expected. As the model stands, economic losses ar e probable. Scenario 5 was indeed a zero-profit scenario, in which the original sp ecifications were solved to determine the point at which total revenue equaled total co st for the sugarcane operation. The zeroprofit conditions were: water table de pth of 31.83cm, 6 flood cycles (for a Z value of .24), resulting in a yield of 31.1 t ons per acre, just a 7 % difference from the historical mean. The mean yield for Scenario 1, 32.42 tons per acre as stated in Table 5-2, is indeed comparable to the historical average of 33.48 tons per acre produced in Hendry County from 1980-2001. The postrestoration scenarios showed a substantial drop in yield to approximately 27 and 24 tons per acre. Howeve r, the variation within this model is still greater than one sees across the historical data. Specifically, the standard deviation is not consistent with the histori cal standard deviation of 2.67

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41 Table 5-2. Summary statistics for simulate d model output, yield in tons per acre* Scenario 1 Scenario 2 Scenario 3 Scenario 4 Mean 32.42 30.84 26.89 24.43 Standard Deviation 10.60 10.34 9.82 7.03 Minimum Value 12.46 11.94 3.99 12.19 Maximum Value 63.47 62.77 63.44 42.90 *Rounded to two decimal places Regarding the modeling for water table dept h, the historical m ean and distribution were successful in providing model results that were reasonable considering both the historical information as well as the range suggested in the water management guidelines and literature. In this instance it seems it wa s successful to maintain variability while adjusting the means. Rainfall Model Results As previously discussed, multiple regr ession using the historical data on production, rainfall during crucial months, ev aporation, temperature, along with likely drainage levels resulted in the follo wing relationship betw een total production ( TP ) and the climatic factors: T P 2464440.72947.3Eva p -299037.5AugRn-333598.7SeptRn-266891OctRn 303408.3NovRn309797.5DecRn258229.4JanRn-7455.9Temp+645862.7Drain The simulation was to provide outputs in cluding yield per ac re, total production, and profit for the 640-acre farm. Analysis of th is model, however, revealed that it lacked the explanatory power necessa ry to be of use in decision-making. While it was not unexpected that the historical data would have a great deal of error, selected variables explained only 48% of the variation in total production from 1980-2001. When regressing the same variables against the histor ical yields, the result s explained even less

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42 variation, with an R2 value of approximately .26. Though the historical model was not suitable for this analysis as specified, it ma y be useful in decision-making if modified. Comparison of Model Scenarios While the current and post-restoration scenarios remained linear models, the constraint on the distribution of water le vels changed the slope of the trend when comparing water levels with profit for the hypothetical sugarcane farm. The range of simulated values was wider than historically expected across all the scenarios. The change in water table levels, constraining the possible distribution, also had a great impact on the possible production range simulate d. The variation in scenarios 3 and 4 was predictably more limited across all variables. Only Scenario 1 yielded a positive aver age profit once simulated. It was more surprising, however that the other current scenario, using recommended optimal depth would average in the negative. Compared to the zero-profit case, Scenarios 3, 4, and 5 produced a mean water table that was higher than the zero-profit solution as would be expected. There remained a great deal of variation within the simulation results across all cases. Even Scenario 4, designed to reign in some of this variation by using a truncated distribution produced results ranging from losses of over $380,000 to profits of nearly $240,000. Tracing the variation back to the simula ted yield, one must note that Scenarios 1, 2, and 3 resulted in maxima that are far be yond current or historic al yield levels. On the other end of the spectrum, Scenario 3 produced a minimum that was similarly improbable, a mere four tons per acre.

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43 Evaluation of Hypotheses The two hypotheses for this study were analyzed to determine whether there was a significant difference in the mean or variance using a two-sample t-test and an F-test. The tests compared the output series of the simulations from both scenarios of the empirical model. The following hypotheses were tested: Maintaining a Higher Water Table Lowers Average Sugarcane Production for an EAA Farm. A distribution comparison of yi eld results indicated that both the mean and (and in most cases) variance of the base model and th e post-restoration scenarios are statistically different. Given a confidence level of 95%, the null hypothesis can be rejected. The two base scenarios, however, were not statistically different from each other in terms of the production results. Additionally, the variances of each base scenar io compared with Scenario 3 were not statistically different. Though the difference between scenarios was significant, it is essential to note that the post-restorati on scenario did provide some yields at or above the cu rrent production levels. The basic functions used at the beginning of the analysis indicated that yield would be responsive to water changes. Once modifi ed to represent an EAA farm, this model indicated that a shift in the range of maintained water levels affects the possible yields for that farm. The rejection of th e null hypothesis in this case in dicated that for this type of farm, the adjustment to higher water tables resu lts in lower yield on average. As there is still variation in those water levels, the possi bility exists that the farm could achieve greater yields. However, it is more likely that the sugarcane farm, under these conditions, will see lower yields.

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44 The EAA Sugarcane Operation Will Experience a Reduction in Profit Under the Changed Water Conditions. In examining the assumed sugarcane opera tion, analysis of the simulation series for profit indicated that these results were al so statistically different for both mean and variance across most scenarios. The null hypothesis can then be rejected with a confidence level of 95% except in compari ng the mean results between the two base scenarios; that is we can state that there is a significant difference in expected profit between each base scenario and the post-restoration scenarios. It should also be noted that between scenarios 1 and 3 and also between 1 and 4, the F-tests ar e such that the null hypothesis for variance cannot be rejected. As in the previous case, though the series are statistically different, they were not mutually exclusive. Of partic ular importance regarding the di fference in profit is that the given simulations kept harvested acreage cons tant, a variable that directly impacts the costs of production. Similar to the yield hypo thesis, it is likely that the operation will experience losses, but not absolutely certain. Looking at the assumed EAA sugar operati on, failure to reject the null hypothesis demonstrates that this typical operation will in deed see changes in profit in response to varying water conditions.

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45 CHAPTER 6 SUMMARY AND CONCLUSIONS Summary The purpose of this research was to provi de an economic perspective in examining the impacts of the Comprehensive Everglades Restoration Plan within in the Everglades Agricultural Area. As sugarcane is the domin ant crop in the area, a farm-level analysis was developed in order to examine the imp acts of water regime changes on sugarcane production. For the purposes of this analys is, those changes were limited in scope; defined as flood events and a higher average water table. In keeping with previous authors work on cots of production, the hypot hetical farm was modeled assuming that 640 acres in production, the firm employs profit maximizing management, and independent grower status. The analysis incorporated an empirical water response function and simulations of five scenarios represent the possible water re gimes. In order to better align with historical sugarcane producti on, the empirical data were calibrated. Additionally, the model incorporated historical cost and wate r distributions based on USGS records. A model based on historical yi elds and weather data was also developed, but lacked explanatory power and conseque ntly was not used in a simulation. Only one of the modeled scenarios showed an average pr ofit over the course of the simulation. Conclusions There was a significant difference between the simulated bases and the postrestoration models, confirmed by a two-sample t-test and an F-test. Upon examining the

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46 mean values of these scenarios, one coul d expect an EAA sugarcane farm to incur monetary losses. Though these results are specific to a small, independent farm, the probable losses are important on a regional scale. These results illustrate the additional costs of CERP implementation beyond cons truction and planning. This one farm situation is representative of the tradeo ffs farmers are facing in the EAA. Best Management Practices help fight soil subs idence and excess nutrient runoff, which are beneficial to all in the long term. However, these immediate losses are considerable. If this hypothetical opera tion is indeed indicative of EAA farming, the sheer volume of sugarcane production magnifies the imp act. It is important to remember that sugarcane is the primary crop in the EAA, making up 86% of the areas acreage in agricultural production. The EAA generate s nearly $800 million in producing nearly 25% of domestic sugar. While this analysis did not attempt to specify total regional implications, it stands to reas on that the entire area woul d be impacted. It is highly unlikely that all farms will experience the same degree of loss, but the high value of this crop and its regional importance suggests that there are many stakeholders who would be adversely affected. The results of the simulations were not completely negative, however. Even in the constrained post-restoration model, there were profitable iterations. That is, even under changed conditions, part of the range of conditions is such that yields would remain high enough to be profitable. In pr actical applications, the profit-maximizing producer will do everything in his power to rema in in that profitable range. It is also important to remember that while many conditions in the model were fixed, there is a great deal of research going into new cultiv ars of sugarcane, some of which are being

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47 bred for resistance to flooding and/or high wa ter tables. The producer facing a scenario as described would do well to examine the pos sibility of using su ch cultivars. There were, however, a number of limitati ons on the study. One of the primary limitations was a lack of available, uniform data. In some cases, such as in trying to determine flood information, it was clear that producers did not generally keep records of specific flood events or the resulting dama ge. Similarly, the analysis would have benefited from having more specific historical yield information rather than relying on and annual average. Additionally, the assumptions made in defining the study farm may hinder the application of information gaine d. Few sugarcane operations are independent of sugar processors and the di vision of first planting and cons equent ratoon crops is most likely not uniform as in this analysis. The estimation of production costs is a nother limitation to the relevance of the study findings. As many of the sugarcane fa rms are part of vert ically integrated production and processing operati on, it is difficult to get accu rate estimates of production revenue. In order for this analysis to be useful to producers or water management officials, it would be nece ssary to update the cost portion of the model. A new publication on costs and returns will soon be available (J. Alvarez, personal communication) and those would greatly im prove the value of the analysis. The complexity and scope of issues surrounding production in the EAA make it difficult to incorporate all factors into a singl e analysis. For example, a more intricate analysis would have to consider the intr oduction of flood resist ant cultivars and the interaction of price s upports on domestic sugar The entire restoration effort is a multidisciplinary project and though the analysis incorporated a variety of perspectives

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48 and sources of information, the problem calls for the attention and evaluation of experts from many disciplines. Implications for Future Analysis It is intended that this resear ch might be an initial inquiry that could lead to further study by those in water management or perhap s even producers. As a first step, this study sets forth a framework that identifies key variables, such as ma intained water level, which are essential in the planning process. By relaxing some of the assumptions and updating certain data, this type of analysis could be easily replicated and used by those most interested in making efficient wa ter management or production decisions. With updated cost and inputs, additional s cenarios can be simulated using the same model, which lends itself to repeated anal ysis throughout the deci sion-making process. Water managers could benefit form the additional information when adjusting the capacity for water storage or drainage. W ith improvements, even the climatic model could be of use to producers looking to anal yze the balance of wa ter in the production system, taking into account rain fall, evaporation, and drainage Such a model would also have to account for irrigation, which was not available for this particular analysis. It had been hoped that this analysis c ould capture the current production practices in the area, describing the crop response such that a producer woul d have knowledge of how changes in those practices could affect his operation. However, the large range of output is an indication th at this particular anal ysis is not yet ready fo r field application. Considering the economic importance of this indu stry, it follows that further analysis and continued data collec tion are warranted. Over the course of this study, it became evident that decision-makers in the EAA have to make policy and production decisions without complete information. With

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49 shared information, such as that from th e referenced growers meeting on flooding, and tools like spreadsheet simulation, future work should be able to better refine the problems to be researched and provide releva nt information to all stakeholders.

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50 APPENDIX A SIMULATION OUTPUT FOR SCENARIO 1 Iteration "Z" "F" X(depth) Yld tons/ac Profit 1 -0.140874.43652 36.4292922.99319-164516 2 0.2625966.0503842.02068918.53983-254866 3 0.2931226.17248988.7911437.68041133458.6 4 0.8092498.23699799.8425947.38158330275.8 5 0.6217387.486952136.119253.95415463620.3 6 -0.707492.17003542.6749719.51556-235070 7 0.2672686.06907471.5351833.6095950869.81 8 0.1366345.54653724.5370122.63242-171835 9 0.0033095.01323684.0090633.0310539132.38 10 0.5517557.20702291.0650641.58563212687.7 11 0.0984925.39396959.7626429.39677-34600 12 -0.441813.232743107.856730.87399-4630.05 13 -0.494173.02333756.7793223.14934-161348 14 -0.372663.509378114.788232.9391337267.45 15 -0.596782.612878120.938730.19318-18442.4 16 -0.774561.90174 5.29597115.00526-326575 17 0.0285065.11402538.8461824.70655-129755 18 -0.880571.47773554.8854719.28265-239795 19 -0.625962.49617882.9440325.08287-122120 20 -0.0305 4.877987145.657444.01623261999.8 21 -0.173734.305074111.110135.4342687888.72 22 -0.320123.719529157.697140.38569188343.3 23 -1.125190.49925581.7806118.8561 -248449 24 0.4562356.824941118.418 46.7976 318428.2 25 0.1947425.77896795.7680537.86543137212.3 26 0.1810855.72434161.6945630.6116 -9953.39 27 -0.516092.935652109.561829.99144-22535.3 28 -0.657762.36897566.5491522.74708-169509 29 0.4671126.86844917.3875 22.95801-165229 30 -0.103474.58614 28.5407621.87101-187283 31 -0.281593.873635132.786237.21858124088.9 32 0.2156715.862686100.834639.22092164712.5 33 -0.398793.404844153.035238.11374142250 34 0.0753465.30138348.074 26.87515-85758.6 35 0.10727 5.42908 77.2488232.9734237963.11

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51 51 36 -1.070270.71892586.1637919.85382-228207 37 0.5490717.196284139.929653.58913456214.8 38 0.4316036.72641351.2168 30.68115-8542.42 39 -0.098074.607739147.786343.11443243704 40 -0.693122.227535104.081726.68361-89644.6 41 0.7207187.88287419.6473924.91783-125469 42 -0.527382.89046953.6702722.42078-176129 43 -0.067184.73127285.0789 32.3490125295.11 44 -0.2085 4.16600546.2928424.13256-141400 45 0.3541436.41657392.1438639.22628164821.2 46 0.0558095.22323658.0341928.64046-49943.9 47 -0.467383.130461111.865931.04799-1100.05 48 -0.738162.04736993.2824224.8395 -127058 49 -0.539832.84068596.6335227.93762-64203.2 50 -0.836571.653734113.737 25.55865-112468 51 0.0116865.04674449.7688326.64755-90376.2 52 -0.356213.575153125.091634.7478973963.49 53 -0.267093.931622104.706332.9820538138.32 54 -0.805711.77717364.6825620.99026-205151 55 -0.251793.99282876.2338328.60526-50658.1 56 0.1144165.45766531.3574523.86276-146874 57 -0.418223.32713544.8087322.15079-181606 58 -0.161714.35314451.9219225.49021-113856 59 -0.4478 3.20881795.0678428.98362-42981.9 60 -0.114474.54210370.3193329.1567 -39470.6 61 0.5908947.363576108.422346.45435311464.2 62 -0.049154.80339636.6258 23.70666-150041 63 -0.131214.47517 228.228 56.50328515336.9 64 -0.842681.629292119.930826.07675-101957 65 -0.177744.28905163.2265127.26075-77935.6 66 -0.2419 4.03239197.4099 32.1637 21535.54 67 -0.193044.227823102.965433.7722654169.92 68 -0.314753.740993121.914234.9511578087.31 69 0.0276285.11051141.3474725.17799-120191 70 -0.006384.97447561.0254128.59985-50767.8 71 0.0748135.299251168.175950.42934392108.8 72 0.1679525.67180878.8510434.0140159074.64 73 0.8763458.505379130.018556.80787521516.4 74 -0.301263.794974164.664241.8564 218181.1 75 0.4811296.92451874.9378236.79808115557.9 76 -0.602932.588292-14.481113.24711-362244 77 -0.076854.692617142.317742.53647231978.3 78 0.3235226.29408880.2128536.16492102712.2 79 0.0490925.196367116.747339.93254179149.7

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52 52 80 0.3917336.566934124.204547.09045324369.5 81 0.5064037.025613127.400749.77619378857.8 82 0.6472727.589089177.724165.09517689649.3 83 -0.345713.61717267.4786926.18819-99695.6 84 0.3117036.24681287.9368337.73167134498.5 85 0.4137136.65485289.9799839.51561170691 86 -0.053314.78676924.3405921.4327 -196175 87 -0.021324.914725134.810242.17295224603.3 88 -0.385763.45694898.9566330.40015-14243.3 89 -0.217734.129074131.042138.05372141032.3 90 -0.565242.73903433.3856619.46776-236040 91 0.7840158.13606 151.438461.02104606993.3 92 0.6826437.73057174.0646838.88077157811.4 93 -1.007850.96858666.2222219.01787-245167 94 0.2210465.884183-19.403813.85001-350013 95 0.1413845.565535105.690839.1524 163322.3 96 -0.928271.28692713.5677515.12062-324234 97 0.3397256.35890178.2518135.9200497744.22 98 0.1599755.6399 87.2670935.6460192184.63 99 0.2499835.99993369.4871332.9779438054.75 100 0.3796026.51840773.0436435.2014983166.14

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53 53

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54 APPENDIX B SIMULATION OUTPUT FOR SCENARIO 2 Iteration "Z" "F" X(depth) Yld tons/ac Profit 1 -0.140874.43652 27.3705621.42314-196369 2 0.2625966.050384-7.0380416.58278-294570 3 0.2931226.17248979.7324235.6940993160.07 4 0.8092498.23699790.7838644.90021279933.8 5 0.6217387.486952127.060551.65264416927.1 6 -0.707492.17003533.6162418.48899-255897 7 0.2672686.06907462.4764531.6480711074.38 8 0.1366345.54653715.4782820.79619-209088 9 0.0033095.01323674.9503331.322714473.455 10 0.5517557.20702282.0063439.35124167356.3 11 0.0984925.39396950.7039227.59713-71111.1 12 -0.441813.23274398.7979929.59259-30627.1 13 -0.494173.02333747.7205921.91815-186326 14 -0.372663.509378105.729431.591399924.574 15 -0.596782.612878111.88 29.06041-41423.9 16 -0.774561.90174 -3.7627614.04302-346097 17 0.0285065.11402529.7874522.97404-164904 18 -0.880571.47773545.8267418.42208-257254 19 -0.625962.49617873.8853 23.97809-144534 20 -0.0305 4.877987136.598642.34032227998.8 21 -0.173734.305074102.051433.8957356674.89 22 -0.320123.719529148.638438.98756159978.1 23 -1.125190.49925572.7218818.23016-261148 24 0.4562356.824941109.359344.65483274955.6 25 0.1947425.77896786.7093235.9734798828.18 26 0.1810855.72434152.6358428.73274-48071.7 27 -0.516092.935652100.503128.78128-47087 28 -0.657762.36897557.4904221.6728 -191304 29 0.4671126.8684498.32877520.8048 -208914 30 -0.103474.58614 19.4820320.26508-219864 31 -0.281593.873635123.727535.7835 94973.99 32 0.2156715.86268691.7758337.30889125921.1 33 -0.398793.404844143.976536.79107115415.7 34 0.0753465.30138339.0152725.0977 -121819 35 0.10727 5.42908 68.1900931.165361281.158

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55 55 36 -1.070270.71892577.1050719.1752 -241975 37 0.5490717.196284130.870951.35732410935.6 38 0.4316036.72641342.1580728.562 -51535.7 39 -0.098074.607739138.727641.50332211017.8 40 -0.693122.22753595.0230225.64324-110752 41 0.7207187.88287410.5886622.52138-174088 42 -0.527382.89046944.6115421.22145-200461 43 -0.067184.73127276.0201730.70828-7992.1 44 -0.2085 4.16600537.2341122.62737-171937 45 0.3541436.41657383.0851437.18143123335.3 46 0.0558095.22323648.9754626.88176-85624.5 47 -0.467383.130461102.807129.79111-26599.5 48 -0.738162.04736984.2237 23.84233-147288 49 -0.539832.84068587.5747926.75023-88293 50 -0.836571.653734104.678324.65588-130783 51 0.0116865.04674440.7101 24.93116-125198 52 -0.356213.575153116.032933.3843846300.63 53 -0.267093.93162295.6476 31.533078741.292 54 -0.805711.77717355.6238320.05789-224067 55 -0.251793.99282867.1751 27.1416 -80352.9 56 0.1144165.45766522.2987222.04784-183695 57 -0.418223.32713535.75 20.84675-208063 58 -0.161714.35314442.8631923.94015-145304 59 -0.4478 3.20881786.0091127.70796-68862.6 60 -0.114474.54210361.2606 27.56132-71837.5 61 0.5908947.36357699.3635644.18241265371.2 62 -0.049154.80339627.5670722.04863-183679 63 -0.131214.47517 219.169254.92395483295.5 64 -0.842681.629292110.872125.17984-120153 65 -0.177744.28905154.1677825.72605-109071 66 -0.2419 4.03239188.3511730.69055-8351.71 67 -0.193044.22782393.9067232.2522423331.91 68 -0.314753.740993112.855533.5478749617.67 69 0.0276285.11051132.2887423.44632-155323 70 -0.006384.97447551.9666826.9008 -85238.2 71 0.0748135.299251159.117248.65241356058.4 72 0.1679525.67180869.7923132.1477521211.86 73 0.8763458.505379120.959854.26214469868.7 74 -0.301263.794974155.605440.44018189448.9 75 0.4811296.92451865.8790934.6314371600.87 76 -0.602932.588292-23.539812.12024-385106 77 -0.076854.692617133.258940.90501198879.2 78 0.3235226.29408871.1541234.1494361822.11 79 0.0490925.196367107.688538.18028143599.9

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56 56 80 0.3917336.566934115.145845.00954282152 81 0.5064037.025613118.341947.5853 334409 82 0.6472727.589089168.665362.76917642459.2 83 -0.345713.61717258.4199624.81461-127563 84 0.3117036.24681278.8781 35.7275293838.36 85 0.4137136.65485280.9212537.41362128045.9 86 -0.053314.78676915.2818619.77865-229732 87 -0.021324.914725125.751440.48823190423.6 88 -0.385763.45694889.8979129.06499-41331.1 89 -0.217734.129074121.983436.55739110674.7 90 -0.565242.73903424.3269318.30474-259635 91 0.7840158.13606 142.379758.56387557142.3 92 0.6826437.73057165.0059536.52083109933 93 -1.007850.96858657.1634918.27939-260149 94 0.2210465.884183-28.462511.93282-388909 95 0.1413845.56553596.6320437.31162125976.5 96 -0.928271.2869274.50901814.30581-340765 97 0.3397256.35890169.1930933.8890256538.82 98 0.1599755.6399 78.2083633.7874 54477.07 99 0.2499835.99993360.4284 31.03299-1404.32 100 0.3796026.51840763.9849133.1322141184.76

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57 APPENDIX C SIMULATION OUTPUT FOR SCENARIO 3 Iteration "Z" "F" X(depth) Yld tons/ac Profit 1 -0.140874.43652 5.94928517.71041-271693 2 0.2625966.050384-28.459311.95494-388460 3 0.2931226.17248958.3111430.99701-2134.3 4 0.8092498.23699769.3625939.03248160889.4 5 0.6217387.486952105.639246.21021306511.2 6 -0.707492.17003512.1949716.06143-305147 7 0.2672686.06907441.0551827.00963-83030.3 8 0.1366345.546537-5.9429916.45405-297182 9 0.0033095.01323653.5290627.28296-77484.9 10 0.5517557.20702260.5850634.0675460160.66 11 0.0984925.39396929.2826423.34149-157449 12 -0.441813.23274377.3767226.56245-92102.7 13 -0.494173.02333726.2993219.00675-245393 14 -0.372663.50937884.3081628.40439-54733.4 15 -0.596782.61287890.4586926.38175-95768.6 16 -0.774561.90174 -25.184 11.7676 -392261 17 0.0285065.1140258.36618318.87714-248022 18 -0.880571.47773524.4054716.38708-298540 19 -0.625962.49617852.4640321.36561-197536 20 -0.0305 4.877987115.177438.37726147596.3 21 -0.173734.30507480.6301 30.25753-17136.8 22 -0.320123.719529127.217135.6813992902.54 23 -1.125190.49925551.3006116.74999-291178 24 0.4562356.82494187.9379839.58779172155.4 25 0.1947425.77896765.2880531.499538060.889 26 0.1810855.72434131.2145624.28978-138211 27 -0.516092.93565279.0818425.91959-105145 28 -0.657762.36897536.0691519.13244-242843 29 0.4671126.868449-13.092515.71309-312214 30 -0.103474.58614 -1.9392416.46751-296909 31 -0.281593.873635102.306232.3899426125.6 32 0.2156715.86268670.3545632.7874834190.73 33 -0.398793.404844122.555233.6633451960.25 34 0.0753465.30138317.594 20.89457-207093 35 0.10727 5.42908 46.7688226.88981-85461

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58 58 36 -1.070270.71892555.6837917.57048-274532 37 0.5490717.196284109.449646.07971303863.5 38 0.4316036.72641320.7368 23.55083-153202 39 -0.098074.607739117.306337.69351133724.3 40 -0.693122.22753573.6017523.18308-160663 41 0.7207187.882874-10.832616.85445-289059 42 -0.527382.89046923.1902718.38539-257999 43 -0.067184.73127254.5989 26.82841-86706.7 44 -0.2085 4.16600515.8128419.06804-244149 45 0.3541436.41657361.6638632.3459525232.97 46 0.0558095.22323627.5541922.72294-169999 47 -0.467383.13046181.3858726.81896-86898.5 48 -0.738162.04736962.8024221.48434-195127 49 -0.539832.84068566.1535223.94239-145258 50 -0.836571.65373483.2570322.52108-174094 51 0.0116865.04674419.2888320.87242-207542 52 -0.356213.57515394.6116 30.16007-19114 53 -0.267093.93162274.2263328.10664-60774.2 54 -0.805711.77717334.2025617.8531 -268798 55 -0.251793.99282845.7538323.68046-150572 56 0.1144165.4576650.87744717.75609-270766 57 -0.418223.32713514.3287317.76309-270624 58 -0.161714.35314421.4419220.2747 -219669 59 -0.4478 3.20881764.5878424.69138-130063 60 -0.114474.54210339.8393323.78873-148376 61 0.5908947.36357677.9422938.80995156374.6 62 -0.049154.8033966.14580118.12787-263223 63 -0.131214.47517 197.748 51.18931407527.1 64 -0.842681.62929289.4508123.0589 -163183 65 -0.177744.28905132.7465122.09695-182699 66 -0.2419 4.03239166.9299 27.20698-79026.4 67 -0.193044.22782372.4854428.65785-49591 68 -0.314753.74099391.4342330.22953-17704.8 69 0.0276285.11051110.8674719.35141-238400 70 -0.006384.97447530.5454122.88303-166751 71 0.0748135.299251137.695944.45048270809.8 72 0.1679525.67180848.3710427.73457-68322.7 73 0.8763458.50537999.5385 48.24223347736.8 74 -0.301263.794974134.184237.09124121505.4 75 0.4811296.92451844.4578229.50793-32344.8 76 -0.602932.588292-44.96119.455519-439168 77 -0.076854.692617111.837737.04706120609.2 78 0.3235226.29408849.7328529.3834 -34871.1 79 0.0490925.19636786.2672734.0366959534.82

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59 59 80 0.3917336.56693493.7245540.0888 182320 81 0.5064037.02561396.9206642.40447229300.3 82 0.6472727.589089147.244157.26882530868.3 83 -0.345713.61717236.9986921.56648-193461 84 0.3117036.24681257.4568330.9883 -2311.01 85 0.4137136.65485259.4999832.4430227202.42 86 -0.053314.786769-6.1394115.86732-309085 87 -0.021324.914725104.330236.50434109598.5 88 -0.385763.45694868.4766325.90771-105386 89 -0.217734.129074100.562133.0189938887.7 90 -0.565242.7390342.90565715.55455-315431 91 0.7840158.13606 120.958452.75338439259 92 0.6826437.73057143.5846830.94027-3285.47 93 -1.007850.96858635.7422216.5331 -295578 94 0.2210465.884183-49.88387.399221-480886 95 0.1413845.56553575.2107732.9587 37664.54 96 -0.928271.286927-16.912312.37901-379856 97 0.3397256.35890147.7718129.08624-40900 98 0.1599755.6399 56.7870929.39231-34690.4 99 0.2499835.99993339.0071326.43375-94713.6 100 0.3796026.51840742.5636428.23899-58089

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60 APPENDIX D SIMULATION OUTPUT FOR SCENARIO 4 Iteration "Z" "F" X(depth) Yld tons/ac Profit 1 -0.140874.43652 3.47584117.28171-280390 2 0.2625966.050384-17.604414.30002-340883 3 0.2931226.17248946.1128 28.32226-56399.7 4 0.8092498.23699754.7722835.0359 79806.66 5 0.6217387.48695278.2070139.24061165111.9 6 -0.707492.1700358.29570515.61955-314112 7 0.2672686.06907431.9596825.04014-122987 8 0.1366345.546537-5.0549616.63406-293530 9 0.0033095.01323642.2463825.15521-120653 10 0.5517557.20702247.9293330.94592-3170.76 11 0.0984925.39396922.1666921.92781-186130 12 -0.441813.23274360.7219224.20654-139899 13 -0.494173.02333719.6993718.10973-263591 14 -0.372663.50937865.5735825.6171 -111282 15 -0.596782.61287869.6037423.77391-148677 16 -0.774561.90174 -16.140412.72824-372771 17 0.0285065.1140255.31851718.29427-259848 18 -0.880571.47773518.1399115.79186-310616 19 -0.625962.49617841.3778920.01357-224966 20 -0.0305 4.87798782.4888332.3297 24903.42 21 -0.173734.30507463.0367727.26948-77758.4 22 -0.320123.71952986.5880229.41068-34317.7 23 -1.125190.49925540.4265 15.99861-306422 24 0.4562356.82494167.9859434.8682876406.07 25 0.1947425.77896751.6323828.64748-49801.6 26 0.1810855.72434123.7698222.74567-169537 27 -0.516092.93565261.9430723.63001-151596 28 -0.657762.36897527.8088818.15285-262717 29 0.4671126.868449-9.6314816.53575-295524 30 -0.103474.58614 -2.2959516.40427-298192 31 -0.281593.87363576.4983 28.30146-56821.5 32 0.2156715.86268655.5258929.65757-29308.7 33 -0.398793.40484485.1705928.20479-58782.8 34 0.0753465.30138312.5939519.9135 -226997 35 0.10727 5.42908 36.6978424.87971-126242

PAGE 71

61 61 36 -1.070270.71892543.9956616.69489-292296 37 0.5490717.19628480.0271838.83084156798.5 38 0.4316036.72641315.1387922.24126-179771 39 -0.098074.60773983.3189531.6487911089.11 40 -0.693122.22753557.9602321.38671-197108 41 0.7207187.882874-8.2375717.54096-275131 42 -0.527382.89046917.1426717.58472-274243 43 -0.067184.73127243.1162824.74866-128901 44 -0.2085 4.16600511.1646518.2957 -259818 45 0.3541436.41657348.7854929.43888-33745.7 46 0.0558095.22323620.7357721.39918-196855 47 -0.467383.13046163.5652324.34639-137062 48 -0.738162.04736949.6848920.04039-224422 49 -0.539832.84068552.3050122.12717-182086 50 -0.836571.65373464.8577320.68745-211295 51 0.0116865.04674413.9629519.86331-228015 52 -0.356213.57515372.1588326.78051-87678.6 53 -0.267093.93162258.4224425.57873-112060 54 -0.805711.77717326.2547117.03507-285394 55 -0.251793.99282835.8585822.08163-183009 56 0.1144165.457665-0.2820617.52378-275479 57 -0.418223.3271359.98186617.13734-283319 58 -0.161714.35314415.7133 19.29446-239556 59 -0.4478 3.20881751.0860722.79004-168637 60 -0.114474.54210330.9481822.22287-180144 61 0.5908947.36357661.1288534.5931370823.78 62 -0.049154.8033963.62450817.66639-272586 63 -0.131214.47517 92.7359532.8812336092.71 64 -0.842681.62929268.9627421.03036-204338 65 -0.177744.28905125.0431920.79188-209176 66 -0.2419 4.03239152.9058724.92636-125296 67 -0.193044.22782357.1291926.08115-101867 68 -0.314753.74099370.2165626.94273-84387.4 69 0.0276285.1105117.25601118.66104-252406 70 -0.006384.97447523.2141721.50799-194648 71 0.0748135.29925189.0536734.9089877231.89 72 0.1679525.67180838.0198325.60203-111588 73 0.8763458.50537974.9992441.3461 207828 74 -0.301263.79497488.3316629.92277-23928.5 75 0.4811296.92451834.7852 27.19444-79280.7 76 -0.602932.588292-22.999912.1874 -383744 77 -0.076854.69261781.0938831.510158276.34 78 0.3235226.29408839.1404 27.02668-82684.3 79 0.0490925.19636766.8873530.28797-16519.2

PAGE 72

62 62 80 0.3917336.56693471.6250335.0122579327.03 81 0.5064037.02561373.5167536.74413114463.3 82 0.6472727.58908990.5583742.71363235572.6 83 -0.345713.61717228.5830320.2904 -219350 84 0.3117036.24681245.4264328.3267 -56309.5 85 0.4137136.65485247.0644529.55748-31339.5 86 -0.053314.786769-5.1869416.04124-305557 87 -0.021324.91472577.5487 31.523588548.78 88 -0.385763.45694854.0955 23.78808-148389 89 -0.217734.12907475.5619628.88944-44892.7 90 -0.565242.7390341.20164615.33578-319869 91 0.7840158.13606 84.6364142.90109239375.8 92 0.6826437.73057134.0610728.45922-53620.9 93 -1.007850.96858627.5366215.86416-309149 94 0.2210465.884183-24.027412.87146-369865 95 0.1413845.56553559.1467929.69442-28561.3 96 -0.928271.286927-11.866812.83283-370649 97 0.3397256.35890137.5258426.78903-87505.8 98 0.1599755.6399 44.8869426.95071-84225.5 99 0.2499835.99993330.2554824.55474-132835 100 0.3796026.51840733.2134126.10313-101421

PAGE 73

63 LIST OF REFERENCES Aillery, M., R. Shoemaker, and M. Caswell. A griculture and Ecosystem Restoration in South Florida: Assessing Trade-offs fro m Water-Retention Development in the Everglades Agricultural Area. Amer. J. Agr. Econ 83 (1) (February 2001):183195. Alchian, A.A. and H. Demsetz. Production, Information Costs, and Economic Organization. Amer. Econ. Rev 62(December 1972):777-795. Alvarez, J. and T. J. Schuneman. Costs a nd Returns for Sugarcane Production on Muck Soils in Florida, 1990-91. Economics Info rmation Report EI 91-3. Institute of Food and Agricultural Sciences, The Univer sity of Florida, (revised) June 1998. Apland, J., Grainger, C., and Strock, J. M odeling Agricultural Production Considering Water Quality and Risk, Department of Agricultural and Applied Economics, University of Minnesota, Sta ff Paper P04-13. November, 2004. Boggess, W., R. Lacewell and D. Zilberman. E conomics of Water Use in Agriculture," in Agricultural and Environmen tal Resource Economics eds. Gerald A. Carlson, David Zilberman and John A. Miranowski, Oxford Series in Biological Resource Management, Oxford University Press, New York, 1993: Bottcher, A.B. and F.T. Izuno, eds. Everglades Agricultural Area(EAA): Water, Soil, Crop, and Environmental Management : Gainesville, FL: University Press of Florida, 1994. Brue, S.L. Retrospectives: The Law of Diminishing Returns. J. of Econ. Persp 7(Summer, 1993):185-192. Coase, R. H. The Nature of the Firm. Economica 4(November 1937):386-405. Diewert, W.E. Functional Forms for Profit and Transformation Functions, J. Econ Theory 6(1973):284-316. Diewert, W.E. and T.J. Wales. Flexi ble Functional Forms and Global Curvature Conditions, Econometrica 55(1987):43-68. Douglas, M.S. Everglades: River of Grass : St. Simons Island, GA: Mockingbird Press, 1947 (p.18).

PAGE 74

64 64 Edgeworth, F.Y. Contributions to the Theory of Railway Rates. The Econ. J 21(September 1911):346-370. Elbadawi, I., R. Gallant, and G. Souza. A n Elasticity Can Be Estimated Consostently Without A Priori Knowledge of Functional Form, Econometrica 51(1983):173151. Everglades Research and Education Center (EREC), 2005. EREC Weather Station. http://erec.ifas.ufl.edu/WD/EWDMAIN.HTM Accessed July 8, 2004. Gallant, R. On the Bias in Flexible Func tional Forms and an Essentially Unbiased Form, J. Econometrics 15(1981):211-45. Gallant, R. Unbiased Determination of Production Technologies, J. Econometrics 20(1982):285-323. Glaz, B. Sugarcane Emergence after Long Duration under Water Soil Crop Sci. Florida Proc 62(2003):51-57. Glaz, B. and R. Cherry. Wireworm Eff ects on Sugarcane Emergence after ShortDuration Flood Applied at Planting J. Entomol. Sci 38 (July 2003):449-456. Glaz, B., S. Edme, J. Miller, S. Milligan, and D. Holder. Sugarcane Cultivar Response to High Water Tables in the Everglades Agron J 94(2002):624-629. Just, R.E. and R.D. Pope. Production Function Estimation and Related Risk Considerations. Amer. J. Agr. Econ 61(May 1979):276-284. Kim, C.S. and Glenn D. Schaible. Econom ic Benefits Resulting From Irrigation Water Use: Theory and an App lication to Groundwater Use Environ. and Res. Econ 17(September 2000):73-87. Lang, T.A., S. H. Daroub and R. S. Lentini. Water Management for Florida Sugarcane Production Circular SS-AGR-231. Institute of Food and Agricultural Sciences, The University of Florida, May 2002. Omary, M. and F Izuno. Evaluation of Sugar cane Evapotranspiration from Water Table Data in the Everglades Agricultural Area. Agric Water Manage. 27(1995):309319. Paris, Q. and M.R. Caputo. A Primal-Dual Estimator of Production and Cost Functions Within an Errors-in-Variables Context Department of Agricultural and Resource Economics University of California, Da vis Working Paper No. 04-008. September, 2004. Redman, J.C. and S.Q.Allen. Some Interre lationships of Economic and Agronomic Concepts. J. of Farm Econ 36 (August 1954):453-465.

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65 65 South Florida Water Management District (SFWMD). Florida Forever Work Plan, 2004 Annual Update. West Palm Beach, FL, February 2004. South Florida Water Management District (SFWMD). Everglades Agricultural Area Storage Reservoir Phase 1 Existing Fl ood Control Conditions Documentation. West Palm Beach, FL, 2004. Thompson, Gary D. Choice of Flexible Func tional Forms: Review and Appraisal Western J. of Agr. Econ ., 13(December 1988):169-183. U.S. Army Corps of Engineers (USACE) and South Florida Water Ma nagement District (SFWMD). Central and Southern Florid a ProjectComprehensive Review Study: Integrated Feasibility Report and Programm atic Environmental Impact Statement . USACE Jacksonville, District, FL, April 1999. U.S. Army Corps of Engineers (USACE) and South Florida Water Ma nagement District (SFWMD). Environmental and Economic Equity Program Management Plan. USACE Jacksonville District, FL, August 2001. U.S. Army Corps of Engineers (USACE) and South Florida Water Ma nagement District (SFWMD). Regional Economic ImpactEverglades Agricultural Area Storage ReservoirsPhase 1. USACE Jack sonville District, FL, October 2003. U.S. Geological Survey, 2002. Land and Pe ople: Finding a Balance; Everglades http://interactive2.usgs.gov/learning web/pdf/landpeople/evergladesst.pdf Accessed February 6, 2005. Weisskoff, R.. The Economics of Everglades Restoration: Missing Pieces in the Future of South Florida. Northhampton, MA: Edward Elgar Publishing, 2004.

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66 BIOGRAPHICAL SKETCH Jennie Varela was born and rais ed in St. Petersburg, Florid a. She was a graduate of the International Baccalaureate Program at St. Petersburg High School where she was named a National Merit Scholar. She contin ued her education at the University of Florida in Gainesville, Florid a. She graduated with a Bach elor of Science in food and resource economics in May 2002 and completed a Master of Science program in the same department in 2005. She has accepted a position as an Agricultural Economist in Dairy Programs for the Agricultural Marketing Service of USDA.


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Physical Description: Mixed Material
Copyright Date: 2008

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EFFECTS OF EVERGLADES RESTORATION ON SUGARCANE FARMING IN
THE EVERGLADES AGRICULTURAL AREA















By

JENNIE MARIA VARELA


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2005



































Copyright 2005

by

Jennie Maria Varela

































This thesis is dedicated to my parents, Carlos and Janet, and my sister, Carmen.















ACKNOWLEDGMENTS

I extend my deepest gratitude to my supervisory committee chair, Dr. Donna Lee,

and committee members, Dr. Clyde Kiker and Dr. Alan Hodges, for their guidance and

assistance over the course of my thesis research. I am very thankful to Dr. Rick Weldon

for his advisement regarding the analysis presented in this document and to Barry Glaz

and Forest Izuno for their personal cooperation and contributions to this project.

I also wish to express my appreciation to the faculty and staff members in the Food

and Resource Economics Department, and to my fellow graduate students for their

support and encouragement throughout my course of study.

Finally, I would like to thank my extended family and friends for their constant

support and unwavering confidence. I am especially grateful to the community of St.

Augustine Church and Catholic Student Center whose friendship, love, and prayers made

possible my success at the University of Florida.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TA BLES .................................. ........ ................................ .. vii

LIST OF FIGURES ...................................................... ................... viii

ABSTRACT .............. .......................................... ix

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

G geography and L and U se ............................................................. ....................... 2
Econom ic Characteristics: EA A ........................................................ ............. ..5
R restoration and Conservation H istory................................... .................................... 5
Com prehensive Everglades Restoration Plan.................................... .....................7
Focus of Present W ork .................. ................................ ........ .. ........ .. ..
P problem Statem ent........... .............................................................. ........ ... .... .8
H ypotheses ..................... ..................................... .... .. ..............8
Maintaining a Higher Water Table Lowers Average Sugarcane
Production for an EA A Farm ..................................................................8
The EAA Sugarcane Operation Will Experience a Reduction in Profit
Under the Changed W ater Conditions........................................ .......... 9
R research O objectives ................................................... .. ........ ...............

2 PRODUCTION THEORY AND ITS APPLICATION TO FLORIDA
A GR ICU LTU RE .......................................................... .. ................11

T theory of the F irm .............................. .. ........................... .............. .... 11
Interrelationships of Economic and Agronomic Concepts.................................12
D im in ish in g R etu rn s ....................................................................... .......... .. .... 15
M modeling P reduction ........................ .. .......................... .. .. ..... .......... 16
M odeling Production and Cost ........................................................ ............. 20
Econom ics of W ater U se ................... ...................................................... 20
South Florida Agriculture and Ecosystem Restoration ...........................................21
Sugarcane response to high water tables and flooding.............................................22

3 M E T H O D O L O G Y ............................................................................ ................... 26


v









Introduction and Overview of Analysis................................................................... 26
A gronom ic M odel ....................................................... .............. ... 27
R rainfall M odel ................................................................2 9

4 D A TA SO U R CE S ................. ................... .............................. .. .. .. .. 31

Empirical Research on Sugarcane Response............... .............................................31
W ater Table and Flooding Conditions............................................... .................. 32
H historical Production .................. ...................................... .......... .... 34
C lim atic D ata ................................................................. ..... .........35
C osts of P reduction ........... ... ............................................................ ....... ......36
S u g arc an e P ric e s ................................................................................................... 3 7

5 RESULTS AND DISCU SSION ........................................... .......................... 39

E m pirical M odel R results .................................................. .............................. 39
R rainfall M odel R results ........................ ............ ............... ..... ........ 41
Comparison of M odel Scenarios .................................. ........ .................. 42
E valuation of H ypotheses .................................................................................. .... 43
Maintaining a Higher Water Table Lowers Average Sugarcane Production for
an E A A F arm ............. .... .. ......... .......... ..........................................4 3
The EAA Sugarcane Operation Will Experience a Reduction in Profit Under
the Changed W ater Conditions.......................................................................44

6 SUMMARY AND CONCLUSIONS.....................................................................45

S u m m a ry ............................................................................................................... 4 5
Conclusions.................................45
Im plications for Future A analysis ........................................ .......................... 48

APPENDIX

A SIMULATION OUTPUT FOR SCENARIO 1 ................... ......................... 50

B SIMULATION OUTPUT FOR SCENARIO 2 ................. .............................. 54

C SIMULATION OUTPUT FOR SCENARIO 3 ................... ......................... 57

D SIMULATION OUTPUT FOR SCENARIO 4 ................. .............................. 60

L IST O F R E FE R E N C E S ......... .. ............. ................................................................63

BIOGRAPH ICAL SKETCH ...................................................... 66
















LIST OF TABLES


Table pge

3-1. Key output variables and probability distributions for empirical model ..................28

4-1. Total monthly rainfall in inches for the EAA 1979-2000 ....................................35

4-2 A average E A A rainfall ........................................................................ ...................36

4-3. Florida sugarcane production expenses...................................................................37

5-1. Summary statistics for simulated model output, profit in dollars............................40

5-2. Summary statistics for simulated model output, yield in tons per acre...................41
















LIST OF FIGURES


Figure pge

1-1. Original Everglades, surrounding wetlands, and south Florida watershed. ..............3

1-2. Current map of the Everglades region, including the Everglades Agricultural
A rea. .................................................................................. 4

4-1. Distribution of water level for Hendry County, FL 1977-1995 ............................33

4-2: Total sugarcane production for Hendry County, FL from 1994-2004 .....................34

4-3: Season average price: sugarcane for sugar and seed 1980-2003.............................38















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

EFFECTS OF EVERGLADES RESTORATION ON SUGARCANE FARMING IN
THE EVERGLADES AGRICULTURAL AREA

By

Jennie Maria Varela

December 2005

Chair: Donna Lee
Major Department: Food and Resource Economics

Sugarcane production is a $700 million business in the Everglades Agricultural

Area. Beginning in the 1920s, this portion of the Everglades region of South Florida was

drained, leaving rich muck soils to be used by agriculture. Productivity led to

diminishing water quality until, in 1994, the Everglades Forever Act called for the

wetland to be restored. As part of the larger Comprehensive Everglades Restoration

Project (CERP), the current drainage system in Florida Everglades is being altered or

removed and best management practices require that less water be drained out of the area

to reduce phosphorus loads. It is expected that maintaining a higher water table lowers

sugarcane production for an EAA farm and that the EAA sugarcane operation will

experience economic losses under the changed water conditions.

The analysis assumed a hypothetical 640-acre sugarcane farm with a high level of

management, operating to maximize profit, and independent of processors. Using an

approach based on agronomic yield models, this analysis estimated yield and profit









change for this "typical" farm under four scenarios. Functions from a 2002 study on

sugarcane cultivar response to water tables were used to determine an expected yield

function that included a parameter for flood events along with historical distributions for

water table depth. That function, along with acreage, overhead cost, variable cost, and

price, was then simulated using Simetar, varying the input factors.

The mean values for yield changes, and consequently for profit changes, in the

simulations were significantly different across the baseline and post-restoration scenarios.

A zero-profit estimation found that water table depth of 31.83cm with 6 flood cycles

resulted in a yield of 31.1 tons per acre, just a 7 % difference from the historical mean.

All post-restoration simulations resulted in average losses over the estimated period

including a decrease in yield to approximately 27 and 24 tons per acre. If basing

decisions on the mean values of these scenarios, one would expect that the 640-acre farm,

even keeping all acreage in production, would see an average loss of up to $135,000 for

the year.














CHAPTER 1
INTRODUCTION

Agriculture is one of Florida's largest industries and a major sector of the economy.

This industry alone accounts for over six billion dollars annually. Perhaps most well

known commodities are citrus and sugarcane, vegetables, berries and melons While

farmland may not be as expansive as in other states, much of it is in use for these types of

high value crop production. Much of this production takes place in south Florida, which

includes the Everglades Agricultural Area (EAA).

For many, the Florida environment is just as valuable as its industries. Florida has

diverse ecosystems including lakes and rivers. Citizens and lawmakers alike have

worked to restore fragile wetlands and greenways. Programs such as "Florida Forever"

set aside vulnerable parcels of land so that natural areas can be preserved. Public

awareness has influenced initiatives for protected wildlife, sensitive land, and water

resources in many forms, but perhaps none greater than the task of restoring the Florida

Everglades. With strong citizen support, state and federal agencies came together to

develop the Comprehensive Everglades Restoration Plan (CERP). It is this multi-stage

effort that is bringing about great challenges in balancing the interests of producers,

environmentalists, and developers.

It was the establishment of agriculture that motivated the creation of a system for

flood control and began the series of changes in the Everglades area. Later on,

population booms and urban growth further changed the landscape of the state. This

expansion put further pressures on water quality and management. Construction and land









development show no signs of slowing, and thus water management will continue to be

an issue for the foreseeable future.

Due to these changes, current producers have a number of immediate concerns:

keeping production profitable, adjusting their practices to meet environmental standards,

and making decisions with an uncertain future in their industry. The EAA is just a small

piece of the larger picture. This area represents jut one sector of one of the most complex

and long-range wetland restoration projects ever undertaken. Within this area, the

primary concerns are maintaining flood control, while also ensuring water availability,

and controlling runoff into the Everglades Protection Area.

The case is unique as the EAA falls within a particular watershed, is home to crops

that may not be produced in many areas, and has been subject to specific water

management measures for so many years. However, Florida is not the only state trying to

balance agricultural and environmental interests, nor is South Florida the only region

struggling to manage development and water needs as well as wanting to preserve as

much of the natural beauty as possible. As these challenges are approached, the results of

these programs will surely serve as indicators for future projects around the country.

Geography and Land Use

The Florida Everglades region is historically known as one of the most unique and

productive ecosystems in the world Marjory Stoneman Douglas describes the region in

her 1947 book, Everglades: River of Grass:

The grass and the water together make the river as simple as it is unique. There is
no other river like it. Yet within that simplicity, enclosed within the river and
bordering and intruding on it from each side, there is a subtlety and diversity, a
crowd of changing forms, of thrusting teeming life. All that becomes the region of
the Everglades.










It is the defining ecosystem of South Florida, a hydrological network of saw grass

plains and swamps that once covered nearly three million acres (USGS 2002).
































Figure 1-1. Original Everglades, surrounding wetlands, and south Florida watershed.
TEHSource: USGS.




S,,I I
f. ^ :"--- .'
....rial ^.




Figure 1-1. Original Everglades, surrounding wetlands, and south Florida watershed.
Source: USGS.

From the extensive Everglades marsh, approximately 700,000 acres were drained

to provide rich farmland (Bottcher and Izuno 1994). This area is now known as the

Everglades Agricultural Area (EAA) and sits in Hendry and Palm Beach counties

between Lake Okeechobee to the north and the Everglades Protection Area to the south.









































Figure 1-2. Current map of the Everglades region, including the Everglades Agricultural
Area. Source: IFAS

Agricultural development of the area was made possible by the establishment of a

drainage and irrigation system to regulate the amount of water available, especially

during the wet and dry seasons. The area receives enough annual rainfall to sustain its

crops, however, most of that rainfall comes in June through September, while winter and

spring are dry (EREC 2005). The irregularity of these rainfall patterns makes water

management the EAA=s greatest challenge.









The EAA has been able to regulate its water levels by using a system of canals,

pumps, and levees first put in place in the 1920's. Currently, approximately 80% of the

area pumps its excess water into storm water treatment areas and water conservation

areas and a few drainage districts still drain runoff into Lake Okeechobee (FFWP).

While agriculture thrived over the decades, the water quality of the Everglades

diminished with nutrient levels steadily increasing.

Economic Characteristics: EAA

The EAA is still one of the most productive agricultural areas in the country. The

agricultural industries occupying the EAA are responsible for an estimated $1.5 billion of

sales each year (Aillery et al 2001). As of 1997, Palm Beach and Hendry counties had

over 730,000 acres of cropland making up approximately 1200 farms. The leading crops

in production are sugarcane, rice, sod, and winter vegetables. By 2002, the EAA had

about 500,000 acres in production, with 90% of its acreage in Palm Beach County. Over

5,000 people in this area are employed either directly in agriculture or in associated

businesses as reported in the 2000 census (USACE and SFWMD 2003.)

Sugarcane dominates production in the EAA covering 86% of its acreage and

bringing in sales of over $762 million in 2001. Nearly a quarter of domestic sugar is

produced in the region. The EAA also boasts a rice industry with sales of over $9

million. Winter vegetable production is also a profitable practice in the EAA, second only

to sugarcane. Row crops cover over 16,000 acres and represent about 16% of EAA sales

in 2001 (USACE and SFWMD 2003).

Restoration and Conservation History

Regulation of the Everglades region began in 1934 when Congress authorized the

acquisition of land for a park that would preserve natural conditions in south Florida









(SFWMD 2004). After thirteen years, President Truman officially dedicated the

Everglades National Park in December of 1947 (ENP), the same year Douglas published

her account of the wetlands.

Florida continued to grow, however, and in order to establish a system for flood

control, agricultural and urban water supply, and preservation of wildlife, Congress in

1948, set forth the Central & Southern Florida (C&SF) Project. Reaching from central

Florida to the Florida Keys, it took the form of canals, levees, storage areas and other

water control structures. The drainage projects for controlling flooding were begun by

the State of Florida and eventually continued by the Corps of Engineers (USACE and

SFWMD 1999). Successful drainage of the area made the land south of Lake

Okeechobee suitable for agricultural development, creating the EAA.

However, this control system altered the natural ecosystem to such an extent, that

even the protected area was being damaged by changes in water flows and the

phosphorus running off of the agricultural lands. In 1991, the State of Florida established

the Douglas Everglades Protection Act (F.S. 373.4592, 1991) which called for a Surface

Water Improvement and Management (SWIM) plan and a change in regulatory

procedures for the EAA. Many felt there was not enough available information to make

such decisions and lawsuits began, delaying the restoration efforts.

In 1994, the Florida state legislature passed the more comprehensive "Everglades

Forever Act" which established new storm water treatment areas and Best Management

Practices (BMP) for the EAA in an effort to restore the natural flow of the Everglades as

well as improving water quality (Bottcher and Izuno 1994). Combining short and long

term projects, this plan includes more research and monitoring in the decision-making









process than previous laws had allowed. The state followed up with the Water Resources

Development Act (WRDA) of 1996, authorizing the Corps of Engineers to develop a plan

by 1999 that would encompass all aspects restoration. The Water Resources

Development Act of 2000 set forth the parameters for the final restoration project and

laid out the key points of the comprehensive plan.

Comprehensive Everglades Restoration Plan

The Comprehensive Everglades Restoration Plan (CERP) was the result of the

WRDA of 2000. With a budget of $7.8 billion, the plan includes thirty years of

construction and an additional twenty years of maintenance. There are more than sixty

components of the plan, being carried out by both the SFWMD and the Corps of

Engineers. The costs are to be evenly divided between the state and federal governments.

The primary goals are to develop ecological values as well as increasing economic

and social values. By restoring a more natural hydrological flow, it is expected that water

quality would be improved throughout the area and threatened and endangered species

would also see improvement in their habitat. Under CERP, new water quality treatment

facilities will be established along with over 200,000 acres of reservoirs and wetland

water treatment areas being constructed.

Agricultural concerns have been considered a major part of the plan, because of the

proximity of natural and agricultural areas. In November of 1999, the South Florida

Action Plan for the Applied Behavioral Sciences, drafted to bring socioeconomic

concerns into the restoration planning process brought these concerns to light. A number

of CERP projects take place within or adjacent to current agricultural areas. Though

there are pockets of agriculture spread over south Florida, the area concentrated around









Lake Okeechobee, including the EAA, would be the most affected by CERP

implementation (USACE and SFWMD 2001).

Focus of Present Work

This study seeks to provide insight into the role that the Everglades restoration

program will have on the immediate area, specifically, sugarcane farming in the EAA.

EAA farms are crucial to Florida's agricultural industry and indeed are major

contributors to the greater economy.

Problem Statement

Under CERP, some major drainage canals will be removed and more water is being

retained as part of BMP. In light of these conditions, a sugarcane producer in the EAA is

likely to face higher water tables and longer flood durations, as pumping is limited both

voluntarily and by regulation. To examine these conditions requires looking at many

parts of the situation: options for modeling agricultural production, the relationship

between agriculture and the Everglades restoration effort itself, the economics of water

use, and previous work on sugarcane yields.

By examining the current status of sugarcane production in the EAA and the

current strategies in water management, this study aims to provide an informed picture of

the impacts CERP will have on the agricultural industry in the long run. Looking at the

impacts of water on those crops will assist in filling in some of the gaps in information

Hypotheses

Maintaining a Higher Water Table Lowers Average Sugarcane Production for an
EAA Farm.

Though each year brings a season of heavy rain to South Florida, farmers have

been able to manage their risk by planting alternate crops or letting the land lie fallow









during the rainy season. Under the restoration plan, it is expected that there will be a

significant difference, as more water is being kept on the land to reduce nutrient loading

in addition to removal of some of the drainage system. Assuming that producers are

currently operating at an optimal level, increasing the available water would hinder yield.

Some crops, such as rice, require flooding and as such may not show a decreased yield.

In the case of sugarcane, lower yield among the current cultivars is anticipated and in

response new cultivars are being bred for water tolerance.

The EAA Sugarcane Operation Will Experience a Reduction in Profit Under the
Changed Water Conditions.

Maintaining more water on farmland is a strategy being employed by sugarcane

growers in order to reduce soil subsidence as well as limiting phosphorus loads, major

objectives of the restoration effort. Literature suggests that production is constrained not

only by the amount of water applied, but also by limitations on the amount of water that

can be pumped out of the system under a certain time. The producer, having to choose

which fields to drain, may find that less acreage can be harvested. The consequential

decline in production cuts into the profits of that operation.

Research Objectives

The first objective of the analysis is to provide a descriptive framework for

analyzing changes in sugarcane production for a representative farm in the Everglades

Agricultural Area. This includes determining the current production practices in the area

as well as estimating future production possibilities. In particular, the focus of the work

is to describe the crop response to water management such that a producer would have

knowledge of how changes in those practices could affect his operation. This requires









not only gathering information on the crop yield itself, but also determining how much

water is coming in and out of the system.

The other major objective is to gauge the economic impact of the water flow

change. In order to meet this objective, there must first be a determination of future

production. By simulating the production relationship, a range of possibilities can be

outlined. Along with future estimations, there must also be a determination of cost

structure. Once these factors are combined, the resulting analysis can be useful for a

producer in weighing future decisions.














CHAPTER 2
PRODUCTION THEORY AND ITS APPLICATION TO FLORIDA AGRICULTURE

Theory of the Firm

As modern economic theory developed, there developed a need for specific

definitions and assumptions. The focus of analysis shifted in the early twentieth century

from industry perspective to that of the individual unit. Coase, specifically, set forth his

Nature of the Firm (1937) in order that continued analysis would have a well-defined and

consistent basis when performed at firm level. At the same time, it was hoped that the

resulting definition would be both realistic and useful within the analytic methods of the

time. Any definition, he stated, would need to conform to the "most powerful economic

instruments" of economics: the ideas of marginal analysis and substitution.

An initial step in creating the basic assumptions is differentiating the firm from

the larger economic system. The basics of the system are well defined and lead the

economist to assume that resources will be allocated based on prices and that the system

will continue working on its own through market transactions. When the perspective

changes to the firm level, however, there is not an internal market to allocate resources.

Instead the firm has to have a coordinator, someone who will be the decision-maker and

direct production. Through this coordinator, the firm avoids the costs that come with

operating a market. The firm is then defined as a system of interactions in which

resources are allocated by a particular "entrepreneur."

Among the most important characteristics of the theoretical firm are that it has an

upward sloping cost curve and that it will not pay to produce output beyond the point at









which marginal cost is equal to marginal revenue. Additionally, the firm will continue to

grow until the marginal cost of creating a transaction within the firm is equal to the cost

of having that transaction in the marketplace.

Alchian and Demsetz (1972) do not refute Coase's definitions, but take a more

specific look at what constitutes a "classical firm". They examine the structure and lay

out six major qualifications of a firm. First, it must produce using joint inputs. Second,

these inputs come from a number of input owners. Next, all the contracts for joint inputs

have a party in common. That party must also have the authority to renegotiate the input

contracts independent of each other. Additionally, that party holds claim to the residuals

and finally, must have the right to sell the central contractual residual status.

Similar to Coase, Alchian and Demsetz see the existence of a central decision-

maker as imperative to the existence of the firm. This decision-maker is owner and

employer. They feel that this structure is the result of necessity. That is, that in

attempting to align productivity to the marginal costs of inputs, the most efficient method

is to operate as a classical firm.

Interrelationships of Economic and Agronomic Concepts

Agricultural decision-making involves combining both economic and biological

factors in order to maximize outcome. This combination becomes crucial in examining

yield responses, optimal output, and the overall input-output relationship (Redman and

Allen, 1954). What is considered "optimal" they find, is greatly influenced by the

particular concepts being employed. That is, when constants are changed, other factors

(e.g., the factor-product price ratio) may become more or less important in determining

the most profitable choice.









Crop yield is certainly one area in which these two schools of thought intersect.

Agronomic production functions give the economist a quantitative look at the changes in

production under various conditions. Yield is a result of numerous factors, from the plant

itself to the soil in use, to the surrounding climate, to nutritional inputs. Early theorists

concentrated on the "food" of plants: water, nitrogen, earth, fire. As agronomy has

developed however, there is a more clear understanding of plant processes such as

photosynthesis, which incorporates energy into the relationship. Some of these basic

factors, however (water, nitrogen, and other fertilizers) are still the subject of economic

analysis, especially under changing conditions.

The "Law of the Minimum" argued by von Liebig was one of the earliest models

of production and still carries some influence, even if it no longer stands alone. Von

Liebig began with the concept of a minimum factor, the factor of yield that is most

scarce. In this theory, yield will change only when this minimum factor is changed.

Consequently, the production curve would increase at a constant rate until it reached the

limit determined by the minimum factor. At that point, von Liebig's curve becomes

horizontal, as adding more of the other factors does not change yield.

While von Liebig's concept resonated with early farm economists, it was not an

especially accurate model of plant response. Later experiments provided data that would

be used to modify the concept, moving away from the idea of a linear relationship with

constant returns to scale.

In trying to improve the production model, Mitscherlich assumed that there was a

maximum yield under ideal conditions and that it was the shortage of any one factor that









would cause yield to decrease proportionally. His result was a curve concave to the given

factor axis such that

dy/dx = (A y) C

where y is the yield while x is the factor in question and A is the maximum yield.

Though an improvement over the von Liebig equation, Mitscherlich's model still did not

adequately represent possible yields since each factor had its own constant, C and did not

account for factors influencing each other. Baule expanded this model to include variable

growth factors, making the case that yield is dependent upon the interaction of many

factors. Overall yield is then expressed as

Y = (1-10clxl)(1-10c2x2)...(1-10cnxn)

with c representing the effect of the corresponding x factors.

At the same time, Spillman was developing another estimation of yield. Basing

his model on the expectation that increments of a growth factor could be the terms of a

geometric series. Using the example of fertilizer application, Spillman expressed the

yield relationship as:

Y= A(1-RX)

In this expression, R would indicate the ratio of increments in yield and suggests a

sigmoid curve. Both Mitscherlich and Stillman agreed that the law if diminishing

increment would not apply once the input quantity was large enough to damage the plant.

Redman and Allen in their overview of these principles raise the concern that

economists must be careful in using data from farm crops on the basis that the functions

drawn from these data are not necessarily true of all plant growth. Fixed factors and

decision-making may be different for separate sets of data and as such, the economist









attempts to find an expression of "best fit." Once such an expression is created, it can

then be used under various scenarios to forecast the profitability of choices.

Diminishing Returns

One of the most relevant parts of the economics/agronomic relationship is the

theory of diminishing returns. This idea was first set forth in the 18th century by Turgot.

In describing expenditures and production, he noted that while returns initially increased,

effects would eventually diminish. Early in the 19th century, Malthus, Ricardo, West, and

Torrens all described the same phenomenon in their publications on land rent. Ricardo

was perhaps most accurate in describing the phenomenon within intensive farming. His

analysis however was indicative of diminishing average returns and not explicitly

marginal returns. Malthus tried to use the diminishing returns concept to make his

arguments regarding population growth, specifically, that the food supply was limited to

arithmetic growth. The concept remained part of economic thinking of the time, even

incorporated into the "four propositions" stated by Senior as he began the study of

political economy.

It was not until the twentieth century drew near that the distinctions were made

between average and marginal products. Clark presented a paper that applied the idea of

diminishing returns to all factors of production. His major assumption was that all

factors of production remain permanent except for one factor that would be changed.

Under these assumptions, if more units of the one varying factor were added, the

marginal and average products associated with that factor would eventually decrease.

Edgeworth in 1911 assumed that land was a fixed input and created a table that

included variable levels (referred to as "doses" of the other inputs and their resulting

output. Though he was arguing that these concepts would apply to any industry (in this









case, railways), his work was based on agricultural examples. Citing his observation that

at some point there is a transition from increasing to diminishing returns, Edgeworth was

one of the first recognize the marginal product of the input as well as the average product,

creating columns for each in his table.

Modeling Production

Thompson 1988 describes the flexible functional forms (FFF) as a way to relax the

restrictions one gets when using Cobb-Douglas functions. They can be expressed as

quadratics, Box-Cox, and numerous other forms. It is important that empirical studies

state clearly the reasons a particular form was chosen.

FFF are very useful when using duality theory. If the function satisfies certain

conditions convexityy, monotonicity, homogeneity), there is no longer a need for a self-

dual function. It is also possible to derive supply or demand from these functions and to

use them for comparative statics. Additionally, these functions can be used to estimate

equations (or systems) that are nonlinear in their parameters.

One of the main problems with the FFF is collinearity. Also, it is often difficult to

meet the above conditions over the entire set of observations. Estimating nonlinear

systems also limits interpretation, as much statistical theory assumes linearity in the

parameters.

Deiwert (1973) defines flexibility as a local property. Using an arbitrary function,

he makes a second order approximation. The parameters of the FFF then must give it

first and second derivatives equal to those of the arbitrary function. This definition of

flexibility is often applied because the conditions are easier to meet than those of other

definitions.









Another measure of flexibility is the Sobolev norm, which is a global definition.

This approach measures the average error of approximation and consequently estimates

elasticities very close to the true elasticity. This also gives the model nonparametric

properties including "small average bias approximations (Gallant 1981); consistent

estimations of substitution elasticities (Elbadawi, Gallant, Souza 1983); and

asymptotically size a testing procedures (Gallant 1982)."

Many FFF can be looked at as derivations from mathematical expansions, but the

definitions do not limit them to only such derivations. Some commonly used expansions

are the Taylor, Laurent, and Fourier expansions. The first two are flexible under the

Diewert definition, while the Fourier follows the Sobolev definition. Some functions,

like the generalized Mc Fadden and Barnett functions are not derived from an expansion

(Diewert and Wales 1987).

In Thompson's analysis, four types of studies were used to look at the various FFF:

Monte Carlo, parametric modeling, Bayesian Analysis, and nonnested hypothesis testing.

The FFF are useful in both production and consumer applications of the Monte Carlo

studies, but depend upon the type of data, the size of the sample, and other properties that

vary. The parametric model used Box-Cox testing and therefore could only be applied to

some of the FFF. The Bayesian analysis allowed very different models to be compared

based on their data on both the production and consumer levels. The nonnested testing

includes all the proposed models and is also based on the data. Of these methods, the

Bayesian analysis and nonnested testing were the most useful in comparing FFF. It is

noted that in either case, it is important to be able to compare models with various

transformations of the dependent variable.









One must look at the duality of the behavioral model and test the behavioral

assumptions to make sure the data are consistent with the assumptions. The data should

then be tested for theoretical properties such as returns to scale. The chosen form should

fit the assumptions and can be compared with other forms through Bayesian analysis or

nonnested hypothesis testing. After the form has been chosen, it is important that it be

compared to other measures (in order to gauge sensitivity of that form).

In an examination of The Cottonwood River Watershed, Apland, Grainger, and

Strock (2004) describe a framework for creating a farm model that accounts for both

agricultural production as well as water quality concerns. In trying to model these

tradeoffs, Apland notes that mathematical programming models can incorporate

economic and agricultural factors but become very complex.

A deterministic farm model forms the basis of Apland's work, which is designed

to be expanded to include risk. The model is made up of 18 production periods for the

year in order to represent harvest and planting activities in all combinations. Land, labor,

and machines are held fixed so that the analysis can focus on the various harvesting,

planting, and tillage dates and fertilizer application is endogenous.

To carry the model forward, the authors discuss a discrete stochastic

programming model (DSP). This type of model is useful in that it can capture alternative

practices as part of the risk analysis. Further, risk can be included in the constraints and

as par of the sequential decision process. However, this method requires a great deal of

data to be useful and can be costly.

Risk is a significant factor in modeling agricultural production. Just and Pope

(1979) note that risk affected by price, market phenomena, technology, and policy.









Traditional evaluations of production were drawn from experimental data, but the authors

argue that using continuous response functions give better estimates. This analysis uses

"neoclassical log-linear production functions".

Some of the specific problems with previously used (and popular) models are that

increasing input always has positive marginal effect on output and that it also reduces

variability of marginal productivity. In order to separate the effects of input on output

and variability, the authors propose that a good stochastic specification has two functions;

the first modeling the effects of input on mean output and the second modeling the effects

of input on variance of output:

y =f(X) + hl/2(X)0, E(O) = O, V(O) =

The mean and variance of output can then been seen independently as E(y) =f(X)

and V(y) = h(X).

The procedure proposed for such and estimation is a three-step regression. The

first is a nonlinear least squares (NLS) regression of yield. Second, the expected error is

regressed against Xusing ordinary least squares (OLS). The final step is a weighted NLS

regression ofy.

In using experimental data, the authors focus on Cobb-Douglas and translog

functions. The basic equation is then modified to include an error term, time, and plot to

capture time effects. They conclude that the simple two-part production function remains

within the bounds of traditional economic thought while removing some of the

constraints that hinder decision-making.









Modeling Production and Cost

In a static situation, Paris and Caputo (2004) state, any estimation of the economic

relationships of a price-taking firm should include both primal and dual relations. Their

proposed model uses a generalized additive error (GAE) approach to make a nonlinear

estimation. Their sample firm is at once risk-neutral and cost minimizing. The system is

then represented as a set of equations: the primal production and input price functions,

and the dual input demand function.

This analysis does face some challenges. The planning and decision-making data

is generally not recorded by producers and thus has to be estimated. Also, the choice of

production function can pose additional challenges. The Cobb-Douglas and constant

elasticity of substitution approaches have the same functional form as their respective

cost functions, but that may not be the case with other forms. Once quantities and prices

are estimated using NLS, they are put into a nonlinear seemingly unrelated (NSUR)

model.

Economics of Water Use

Water use, an important factor in production, is often modeled as water applied.

However, Kim and Schaible (2000) challenge the assumption that water (or any of the

variable factors) is completely engaged in crop production. Noting that the production

process does not consume not all inputs applied, whether water or fertilizer, the authors

seek to provide a measure of the overestimation of economic benefits from water use.

The authors observe that economic benefits in agriculture are often modeled as

normalized-quadratic functions, but that the derived factor demands are sometimes linear

and sometimes in Cobb-Douglas form. As such, both linear and nonlinear cases are

examined. Under both scenarios, the total economic benefits were overestimated when









using applied water rather than consumptive water. In the application of these methods

to corn production in three Nebraska counties, the overestimation was 28.9% and

estimate even higher when looking at agriculture overall.

In one of the most traditional models of water use, the Von Liebig production

function as described by Boggess et al (1993), uses evaporation and transpiration to

estimate the changes in yield. This type of function describes a linear output relationship

until some maximum. From that equation, actual yield can then be estimated as a

function of the ratio of actual to potential evapotraspiration.

Similar to Kim and Schaible, Boggess et al make the distinction between effective

water, actually used by plants, and the total amount of applied water. In modeling

irrigation, they point out that it is fundamental to incorporate the concept of hydrologic

balance. The principle of hydrologic balance states that there must be equality between

the amount of water that enters a specific area and the amount of water that leaves that

area. That is, all water entering the particular area, through precipitation, irrigation, or

from the soil, and all water leaving the area whether through evapotranspiration, runoff,

or percolation must be considered.

South Florida Agriculture and Ecosystem Restoration

Restoring the water flow of the Everglades will create a need for water retention in

the northern part of the watershed. By 1978, over three million acres of land had been

drained in South Florida (Weisskoff 2005). This region, especially the Everglades

Agricultural Area (EAA) will require a great deal of water to meet needs during the dry

season. Development of the EAA created a system if irrigation and drainage that

prevents most water retention during the wet season. Increasing the amount of water

retained may lessen agricultural profitability. Authors Aillery, Shoemaker, and Caswell









attempt to model the economic effects of water table management and surface water

retention scenarios.

The authors measured the tradeoffs under two conditions, the first being that

resource use is determined by agricultural producers alone, the second using joint

maximization of agricultural and environmental objectives. Using both objectives

resulted in higher marginal costs, but also significantly increased the benefits of lowering

the water table (Aillery, et al 2001). Whether the benefits will outweigh the cost is

dependent upon the specific agricultural and environmental demands.

Three scenarios of water policy were simulated: water-table restrictions, surface-

water development (including land acquisition), and water-retention targets. The first

showed an increase in water retention, but at a high opportunity cost and the inability of

the soils to retain the desired amount of water. Surface water development also increased

water retention, but comes at the cost of production foregone by retiring those lands. A

moderate change to a target water-table depth was considered the best option (Aillery, et

al 2001).

The authors are up-front about two main concerns with this article. The first is

that a true cost-benefit analysis would need more empirical evidence from the agricultural

sector and is difficult to generalize. The second is that sugar prices, the major component

in estimating agricultural gains reflect price support levels that could change in the future

and thus alter these findings.

Sugarcane response to high water tables and flooding

Glaz, et al (2002) note that the EAA is dependent upon the canal system to

maintain suitable water levels for sugarcane and other crops. Pointing to a study by

Omary and Izuno (1995), ideal water levels fall within 40 and 95 cm below the soil









surface. Keeping the water within this range has become more difficult as the farmers are

dealing with soil subsidence. As soil is lost, the remaining soil cannot store as much

water. Additionally, best management practices put in place to limit Phosphorus entering

the Everglades have resulted in farmers pumping less water and thus maintaining higher

water levels.

The researchers conducted two experiments, the first being planted in February and

harvested in three cycles (plant cane and two ratoon crops). The second experiment was

planted in January of the following year and harvested in two cycles. In both cases, two

fields were planted and received water treatments from June to October (the months with

highest rainfall). The wetter field was treated to have a water level between flooding and

15 cm BSS. The drier field was kept with a water level between 15 and 38cm BSS.

Over the period of study, the researchers found that the soil profile was very

sensitive to rainfall. They estimated that for every cm of rain, the soil profile rose 10cm.

As a result, even the drier field had some days in which the water level was higher than

15cm BSS, suggesting that during a normal year, fields with the drier target would still

see flooding.

For the plant cane crops, the average cane yield for the drier field was 5.8% higher

than the wetter field. For the first-ratoon harvests, the drier field average cane yield was

4.3% higher than the wetter field. For the second-ratoon harvest, the average cane yield

was 8.4% higher in the drier field.

The researchers also measured the sugar yields from each harvest. The average

sugar yield for the plant cane crops was 6.6% higher in the drier field than in the wetter

field. The average sugar yield in the first-ratoon crops was 8.3% higher then in the wetter









field. In the second-ratoon crop, the average sugar yield was 11.5% higher than in the

wetter field.

The project suggests that there are some cultivars that may continue to yield well if

the water tables were maintained at a higher level. They suggest that increasing the water

table incrementally would be the best option considering profit and the need to reduce

phosphorus discharge.

In 2004, Glaz et al published their findings after experimenting with two different

sugarcane genotypes. This study was to examine periodic flooding, lasting no longer

than one week and then draining to a water depth of about 50 cm below the surface.

Flooding was set at 7 days to simulate the longest flood period a commercial field in the

EAA might experience. The areas were treated for five and nine cycles in different years.

It was noted that in the EAA, it is often difficult to drain to the desired level after a flood.

One of the genotypes developed arenchyma (air cavities) at the roots, which seems

to have impacted the yield response. These did not show a significant response to

changes in depth over the three years. The other genotype however, showed a 21% cane

yield increase and an 18% sugar yield increase in the fully drained case as compared to

the flooded specimens in 2000. The 2002 experiment resulted in a 28% increase in both

sugar and cane yields in the drained area compared to the flooded plants. The authors

point out that using additional flooding periods might result in a nonlinear flood response.

Such information would be of use to farmers that are not able to drain all fields at once

due to limitations on total drainage to the canals.

Another consideration in sugarcane response is the possible benefit of flooding

during certain stages of growth, specifically in trying to control for wireworm (Glaz






25


2002, 2003). Flooding the seed cane after planting could replace the practice of applying

insecticide to the soil. Before the practice could be commercially adopted, however, the

feasibility and cost of maintaining the flood condition and then draining would need

further examination. There is also a concern that the shortening of the growing season

would lead to reduced yield.














CHAPTER 3
METHODOLOGY

Introduction and Overview of Analysis

The analysis assumed a hypothetical 640-acre sugarcane farm. Using two different

approaches, one based on agronomic yield models and historical groundwater levels; the

other based on historical yield and rainfall, this analysis estimated profits foregone for

this "typical" farm. Both approaches assume that the operation is independent of a

processor and profit maximizing.

In the first approach, the agronomic functions measuring response to flooding/high

water tables were combined to give an expected yield function that included a parameter

for flood events. That function, along with acreage, overhead cost, variable cost, and

price were then simulated, varying the input factors. This approach also used a historical

distribution along with a range of likely water table levels as described in Water

Managementfor Florida Sugarcane Production (2002) and Agriculture and Ecosystem

Restoration in Snlih Florida: Assessing Trade-offs from Water-Retention Development in

the Everglades Agricultural Area (2001).

The second approach utilized historical yield and rainfall, determining relationship

between the two based on the most sensitive growth period. Future rainfall will be based

on historical records and used to provide possible yields. From the previous Water

Management article, the EAA Storage Reservoir Phase 1 Existing Flood Control

Conditions Documentation, and the 2001 study on drainage uniformity, this approach

will assume that the system will drain up to 48% of rainfall.









Agronomic Model

Taking the findings of the empirical research on yield response by Glaz et al

(2002), two equations were combined in order to incorporate a parameter for flood

events. The results for the two experiments were as follows:

Y = 14.6+ 0.16x (1)
Y= 17.6+.25x (2)

The year 2000 experiment with 5 flood cycles (Eq. 1) was defined as a base (Z=0)

and the 2001 experiment with 9 flood cycles (Eq. 2) was defined as Z=1. The following,

then, represents flood events, Z, as

Z= -1.25 +.25F,

where F is the number of flood cycles, and Z is a qualitative variable. The number of

flood cycles was simulated as part of the analysis. The simulation of flood cycles was

first attempted using a uniform distribution (between 0 and 9), but ultimately, F was

determined using a triangular distribution from which pseudo-random numbers were

generated. The boundaries of the distribution remained 0 to 9 in keeping with the

experimental conditions.

Equations (1) and (2), can then be combined as

Y= 14.6 + 3(Z) + .16X +.09(Z)X.

Where Y is yield in kilograms per meter squared. Sugarcane yield is historically

measured in tons per acre, so the resulting yield (in kg) must be converted by a factor of

approximately 4.5 to be expressed in tons per acre (see Table 3-1). In order to translate

the empirical data to practical terms, a calibration factor was also included. This factor

(0.267) was determined by setting the mean value of the empirical yield data equal to the

historical mean yield.









The other key variable in this model is the groundwater level. Table 1 illustrates all

key output variables (KOV) and their distributions. In order to simulate the probable

range of groundwater levels, the historical data determine the distribution from which the

simulated values will be chosen. A normal probability plot suggested that the data were

very close to a normal distribution. The ANOVA procedure was used to determine the

mean and standard deviation for the groundwater variable based on these data. The mean

would be adjusted in order to simulate various scenarios.

Table 3-1. Key output variables and probability distributions for empirical model
Key Output Variables Specifications
Acres 640
Flood Events "Z" Z = -1.25 + .25F
Flood Cycles "F" Triangular distribution: 0 to 9,
Depth "X" cm Varied by scenario
Yield (kg per meter sq.) Y = 14.6 + 3(Z) + .16X +.09(Z)X
Yield (tons per acre) Y 4.460947/3.74
Price (per ton) $31.70
Variable Cost (per acre) $760.94
Total Fixed Cost (dollars) $144,000
Profit (Price* Yield)-Total Costs

Using the Simetar (Richardson, 2001) simulation tool, these data were simulated

for 100 iterations for each scenario. The output for each could then be compared in order

to determine the effects of new water conditions. Five different scenarios were simulated

using this tool.


* Scenario 1: A representation of current conditions, this scenario assumed a mean
water table depth of 85.2cm and a standard deviation of 43.23 based on USGS data.
* Scenario 2: Also represents current conditions, but with the mean depth adjusted to
76.2cm as described in Water Management for Florida Sugarcane Production
* Scenario 3:A model of post-restoration conditions by raising water table depth to
54.78cm as suggested by Aillery, Shoemaker, and Caswell (Scenario 1-5, 2001).
* Scenario 4: Alternative model of post-restoration including a truncated normal
distribution for water table depth with mean 50cm, minimum -27.1272cm









(historical minimum), and maximum 92.8cm (95% Upper Confidence Interval for
historical data).
* Scenario 5: Identifies conditions under which the hypothetical operation exhibits
zero profit.

Rainfall Model

This approach began with the ANOVA procedure on monthly rainfall data over a

twenty-year period in order to find variation for further simulation. Also included were

the values for pan evaporation (Evap) and average temperature (Temp) for each month

over the same growing period. The data were aggregated such that the annual yields

were matched with the previous growing period. For example, Rainfall summed from

August of 1990 to January 1991 corresponded to the 1991 production data. Maximum

drainage (Drain) was set at 48% of the rainfall for each of the months included.

A relationship between total production (TP) and these factors was determined by

using an Ordinary Least Squares (OLS) regression:

TP = 2464440.7 2947.3Evap 299037.5AugRn 333598. 7SeptRn 266891OctRn
303408.3NovRn 309797.5DecRn 258229.4JanRn 7455.9Temp + 645862.7Drain

Using this relationship, normal distributions for rainfall were specified for the

simulation based on historical mean and variance

In order to represent the changes in drainage practices, the post-restoration scenario

changes the percentage of water drained was varied while other climatic factors were

held constant. The results of the two scenarios could then be compared to each other and

ultimately to the previous model.

The rainfall model was designed to capture the concept of waster balance as

presented in the literature regarding water use. It was anticipated that that in defining the

amount of water coming into or out of the given system, future water flows could be






30


estimated and consequently changes in production could be predicted. In this case,

varying the drainage capacity could give distinct scenarios for comparison.














CHAPTER 4
DATA SOURCES

Empirical Research on Sugarcane Response

Glaz et al (2004) examined water table effects on two sugarcane genotypes in

experiments from 2000-2002. Previous research, they noted, was inconsistent regarding

sugarcane response to water tables. EAA farmers specifically have to deal with periodic

floods (less than a week) and cannot always drain the desired amount of water. To

simulate these conditions, the experiment evaluated periodic flooding followed by

drainage to depths of 50, 33, and 16 cm below the soil surface (BSS).

To carry out the study, lysimeters made of polyethylene (1.5m x 2.6m x 0.6m) were

set up with Pahokee muck soil from an uncropped EAA field. Each lysimeter had well

water flowing in each day and a pump to get rid of excess water. Additionally, each had

a valve that drained the lysimeter to the target water table level. Two sugarcane

genotypes were planted, both being chosen because of high yield and similarity to

commercially produced varieties. After planting, the water level remained at 50cm BSS

until the actual treatments started. There were four total treatments; one a control and the

others being flooded for the first week of a three-week cycle. After the 7 days of

flooding, these treatments were drained to the aforementioned depths.

Water height from the actual soil surface up to 2.5cm above the surface was

considered a "flood" condition in this experiment. The length of the flood, 7 days, was

set to simulate the longest period of flooding one could expect in the EAA. Similarly, the

50cm control depth was based commercial practices. The experiment was repeated for









three years; the 2000 experiment used five flooding cycles while the 2001 and 2002

experiments used nine flooding cycles. The resulting response functions were

Y = 14.6 + 0.16x (2000, r2 = 0.99)

Y= 17.6+.25x (2001, r2 = 0.94)

Where Yis equal to cane yield in kg m-2 and x is equal to water table depth (cm)

during drainage. The difference in flood cycles can then be used as a factor in

incorporating the number of floods into the yield response analysis.

Water Table and Flooding Conditions

To be consistent with the cultural practices of the EAA, establishing the possible

range of water tables included water table levels as described in Lang et al's Water

Managementfor Florida Sugarcane Production (2002). They noted that 30 inches

(76.2cm) was optimal depth in terms of sugarcane yield and stated that the recommended

target level would be a depth of 23-30 inches (58.42-76.2cm) to the surface. They also

noted that variation in EREC studies ranged from 39 inches (99.06cm) to surface level.

Historical water table levels for the area were available from the USGS from

October of 1977 to September of 1995. The variation here ranged from a maximum

depth of 206.95 cm below the surface to a minimum of just over 27 cm above the surface.

The mean depth was just over 85 cm below the surface. The 167 observations, however,

are at irregular intervals, which made the information useful only in determining the

variation in water table levels. The complete distribution of these data is represented in

Figure 4-1. These data were collected from a well at Latitude 26038'45", Longitude

81026'07" in Hendry County, Florida.










PDF Approximation


-50.00 0.00 50.00 100.00 150.00 200.00 250.00

-CM below surface

Figure 4-1. Distribution of water level for Hendry County, FL 1977-1995. Source: USGS

Additionally, a more qualitative source of information on water management was a

summary of meetings with EAA sugar growers in November 2003 to get a consensus on

flooding conditions. Coordinated by the Southwest Florida Water Management District,

the EAA Storage Reservoir Phase 1 Existing Flood Control Conditions Documentation,

provided insight into the growers' major concerns. The participating groups included US

Sugar, the Sugar Farms Cooperative, and Florida Crystals, all producers within the EAA.

The documentation of the three meetings revealed a number of common points. Some of

these key statements included:

* Farmers have not kept regular records of crop losses due to flooding thus far
* The sugarcane crop is most sensitive to flooding during early stages of growth
* Receiving more than 4 inches of rainfall in a 24-hour period is considered
problematic
There was also consensus among the growers that heavy rainfall and flooding are

of most concern to the areas near the Bolles and Cross Canals which provide water flows

to the east and west of the EAA. There was a concern that these canals to no have the

capacity to carry water out as needed.

From the 2001 study on drainage uniformity it was noted that sites normally

drained an average of only 48% of the rainfall input into the system (Garcia, Izuno,

Scarlatos). When looking at the flow of water in and out of the EAA farm system, this










average will used to determine how much water is being drained out of the system rather

than contributing to the groundwater level.

Historical Production

Through the National Agricultural Statistics Service (NASS), the US Department

of Agriculture (USDA) provides historical production information down to a county

level. Using the "Quick Stats" website allows users to search production history for all

major crops. Under the category Sugarcane for Sugar, county-level data are available for

acres harvested, yield per acre and total production. Figure 4-2 illustrates the total annual

production for the Hendry County over the past ten years.



Annual Sugarcane Production in Hendry County

3
2.5
C
o 2
W 1.5


0.5


1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004


Figure 4-2: Total sugarcane production for Hendry County, FL from 1994-2004. Source:
NASS

The production values are listed annually and are available from 1977-2004. For

this analysis, however, data from 1980-2004 were considered. The area harvested over

that time ranged from 35,000-76,000 acres. The average yield per acre varied from a low

of 28.6 tons in 1981 to a maximum of 40.1 tons in 1998. Total annual sugarcane

production in the county peaked in 2002 at over 25.8 million tons.









Climatic Data

The Everglades Research and Education Center includes an automated weather

station that provides climatological data as a cooperative project of the University of

Florida / IFAS and the South Florida Water Management District. The station was

provides data on temperature, rainfall, solar radiation, and evaporation at the coordinates

26.6567N and 80.6299W. The time series for rainfall goes back as far as 1924, but for the

purposes of this analysis, only data from 1979 2000 were considered. The total

evaporation and average temperature over the growing season were also noted.

Within the area of interest, Hendry County, FL, sugarcane planting takes place

from August through January and this period is considered the most sensitive to excess

water. Table 4-1 illustrates the total rainfall for those crucial months.

Table 4-1. Total monthly rainfall in inches for the EAA 1979-2000.
Year January August September October NovemberDecember
1979 5.39 14.24 2.24 4.80 2.89
1980 6.09 3.73 7.46 1.02 3.42 0.73
1981 0.68 17.37 4.87 0.67 3.08 0.85
1982 0.63 8.11 12.41 2.75 0.67 0.80
1983 3.91 6.26 6.77 5.16 1.16 4.42
1984 0.23 4.04 8.15 0.40 2.37 0.11
1985 0.75 5.52 9.63 3.39 1.54 2.20
1986 3.59 6.21 4.04 4.50 1.58 3.99
1987 1.18 4.20 4.49 3.14 8.04 0.30
1988 3.02 8.89 2.47 0.11 1.31 0.89
1989 0.97 6.92 8.91 3.49 1.24 1.95
1990 2.47 7.57 2.96 4.22 0.39 1.11
1991 8.24 2.83 6.27 3.54 2.45 0.46
1992 1.20 11.85 10.82 0.69 4.03 0.62
1993 10.16 6.19 5.56 8.00 1.75 0.79
1994 5.60 9.74 5.47 12.16 5.93 7.13
1995 1.91 10.51 8.76 9.60 0.65 0.89
1996 1.35 9.75 3.04 4.23 0.80 0.38
1997 1.23 4.56 5.47 0.65 4.44 5.77
1998 1.47 9.37 11.64 2.20 11.25 1.00
1999 1.95 5.04 8.18 7.69 0.72 0.45
2000 0.74 3.58 7.00 4.77 0.54 0.24










The normal distributions for rainfall during these months, specified through OLS

regression, were used to in a simulation to predict the possible rainfall in each of the

crucial months (Table 4-2).

Table 4-2. Average EAA rainfall.
Mean
Rainfall Standard
Month (inches) Deviation
January 2.73 2.69
August 7.25 3.45
September 6.87 2.84
October 3.92 3.21
November 2.73 2.77
December 1.67 1.96
Source: EREC Weather

Costs of Production

Cost and Returns for Sugarcane on Muck Soils in Florida (Alvarez and

Schuneman) provides a framework for looking at the production costs for farming

sugarcane in the EAA. This work provides a number of key assumptions including: a

profit maximizing management, independent grower status (non-producer), a small farm

unit, and a three-crop cycle, that is, the hypothetical farm crop includes first planting and

first and second ratoon crops.

However, cultivation and harvesting practices have changed and the most recent

data regarding production costs comes from the USDA Economic Research Service

(ERS). These data (see Table 4-2) from the Farm Business Economics Report take into

account the additional Everglades Restoration tax that began in 1995. The 1995-1996

values were the final values published in this form. For the purposes of this analysis, it

will be assumed that all acreage in the model is harvested.










Table 4-3. Florida sugarcane production expenses
Dollars per Harvested
Item Acre
1995 1996
Variable Expenses
Seed 27.95 27.95
Fertilizer 61.33 57.99
Chemicals 59.10 61.15
Custom operations 106.65 104.81
Fuel, lube, electric 21.67 23.79
Repairs 80.12 80.84
Labor 406.54 396.76
Irrigation water purchased 6.70 7.07
Miscellanous 0.56 0.58
Total Variable Expenses 770.62 760.94
Fixed Expenses
General farm overhead 114.79 107.45
Taxes and insurance 59.27 59.93
Interest 9.61 9.49
Total Fixed Expenses 183.67 176.87

Total expenses 954.29 937.81
Source: USDA, ERS

Sugarcane Prices

The Florida Agricultural Statistics Service (FASS) maintains an annual record of

acreage, yield, production, season average price and the overall value of production.

From these field crop summaries, prices were recorded from 1980-2003. These prices

reflect sales of sugarcane for sugar and seed. As illustrated in Figure 4-3, there has not

been a great deal of volatility in price. The maximum price, $39.40 per ton, occurred in

1980 and was followed by a 27% drop to 28.60. After that initial fall, prices have

remained close to $30 a ton. In contrast, the lowest season average price was $27.20 in

1999.











Annual Price of Florida Sugarcane

45.00
40.00
35.00
30.00
8 25.00
20.00
15.00
10.00
5.00
0.00
1980 1985 1990 1995 2000

-season avg price

Figure 4-3: Season average price: sugarcane for sugar and seed 1980-2003. Source:
FASS

It is likely that a major factor in maintaining this price stability is the tariff-rate

quota system in place on sugar imports. This analysis, however assumes that these

conditions will not change and thus do not factor into the modeled scenarios.














CHAPTER 5
RESULTS AND DISCUSSION

The analysis involved creating two stochastic models based on agronomic studies

and historical data regarding sugarcane production and growing conditions in the

Everglades Agricultural Area. Once each model was specified, the key output variables

were identified and their respective probability distributions defined. The Simetar

simulation application generated pseudo-random numbers, based on the given probability

distributions, which were then used to update the model equations 100 times. Each

iteration of the simulation generated new values for the every key variable. However, the

values for water table depth, yield, and profit (on the basis of hypothetical 640-acre farm)

selected as the output of the simulation, as these were of most interest.

Empirical Model Results

Five scenarios using an empirical yield model were completed in order to compare

production possibilities with and without the restoration conditions, emphasizing

maintenance of a higher water table. These included:

* Scenario 1: Current conditions, assuming mean water table depth and standard
deviation based on USGS data.
* Scenario 2: Current conditions, but with the mean depth adjusted to 76.2cm as
recommended in Sugarcane Production literature.
* Scenario 3: Post-restoration conditions with water table depth to 54.78cm as
suggested by Aillery, Shoemaker, and Caswell (Scenario 1-5, 2001).
* Scenario 4: Post-restoration incorporating a truncated normal distribution for water
table depth.
* Scenario 5: Zero-profit condition
The complete outputs for these simulations are illustrated in Appendices A-D. A

summary of the simulated profits is illustrated in Table 5-1, recalling that the fifth









scenario was the zero-profit condition. The output series were also compared in pairs in

order to determine whether the results were statistically different across the scenarios.

Table 5-1. Summary statistics for simulated model output, profit in dollars*
Scenario 1 Scenario 2 Scenario 3 Scenario 4
Mean 26,722.33 (5,370.24) (85,485.58) (135,442.80)
Standard Deviation 215,148.12 209,707.29 199,273.51 142647.91
Minimum Value (378,281.63) (388,859.30) (550,047.54) (383,743.63)
Maximum Value 656,710.50 642,406.47 656,041.87 239,375.77
For 640 acres of production
In terms of the simulated profits, it was surprising that the mean value for Scenario

2, which incorporated the current conditions, recommended in the Sugarcane Production

literature. In all other respects, the two base scenarios (1 and 2) were expectedly similar.

There is still a large range in the output, potential losses of over $500,000 to profits of

over $640,000, but the truncated distribution of Scenario 4 appears to have been most

successful in reducing the extreme values. It must be noted, however, that even that

scenario exhibits more variation than should be expected.

As the model stands, economic losses are probable. Scenario 5 was indeed a

zero-profit scenario, in which the original specifications were solved to determine the

point at which total revenue equaled total cost for the sugarcane operation. The zero-

profit conditions were: water table depth of 31.83cm, 6 flood cycles (for a Z value of .24),

resulting in a yield of 31.1 tons per acre, just a 7 % difference from the historical mean.

The mean yield for Scenario 1, 32.42 tons per acre as stated in Table 5-2, is indeed

comparable to the historical average of 33.48 tons per acre produced in Hendry County

from 1980-2001. The post-restoration scenarios showed a substantial drop in yield to

approximately 27 and 24 tons per acre. However, the variation within this model is still

greater than one sees across the historical data. Specifically, the standard deviation is not

consistent with the historical standard deviation of 2.67









Table 5-2. Summary statistics for simulated model output, yield in tons per acre*
Scenario 1 Scenario 2 Scenario 3 Scenario 4
Mean 32.42 30.84 26.89 24.43
Standard Deviation 10.60 10.34 9.82 7.03
Minimum Value 12.46 11.94 3.99 12.19
Maximum Value 63.47 62.77 63.44 42.90
*Rounded to two decimal places


Regarding the modeling for water table depth, the historical mean and distribution

were successful in providing model results that were reasonable considering both the

historical information as well as the range suggested in the water management guidelines

and literature. In this instance it seems it was successful to maintain variability while

adjusting the means.

Rainfall Model Results

As previously discussed, multiple regression using the historical data on

production, rainfall during crucial months, evaporation, temperature, along with likely

drainage levels resulted in the following relationship between total production (TP) and

the climatic factors:


TP = 2464440.7- 2947.3Evap 299037.5AugRn 333598.7SeptRn 26689]OctRn
303408.3NovRn 309797.5DecRn 258229.4JanRn 7455.9Temp + 645862. 7Drain

The simulation was to provide outputs including yield per acre, total production,

and profit for the 640-acre farm. Analysis of this model, however, revealed that it lacked

the explanatory power necessary to be of use in decision-making. While it was not

unexpected that the historical data would have a great deal of error, selected variables

explained only 48% of the variation in total production from 1980-2001. When

regressing the same variables against the historical yields, the results explained even less









variation, with an R2 value of approximately .26. Though the historical model was not

suitable for this analysis as specified, it may be useful in decision-making if modified.

Comparison of Model Scenarios

While the current and post-restoration scenarios remained linear models, the

constraint on the distribution of water levels changed the slope of the trend when

comparing water levels with profit for the hypothetical sugarcane farm. The range of

simulated values was wider than historically expected across all the scenarios. The

change in water table levels, constraining the possible distribution, also had a great

impact on the possible production range simulated. The variation in scenarios 3 and 4

was predictably more limited across all variables.

Only Scenario 1 yielded a positive average profit once simulated. It was more

surprising, however that the other current scenario, using recommended optimal depth

would average in the negative. Compared to the zero-profit case, Scenarios 3, 4, and 5

produced a mean water table that was higher than the zero-profit solution as would be

expected.

There remained a great deal of variation within the simulation results across all

cases. Even Scenario 4, designed to reign in some of this variation by using a truncated

distribution produced results ranging from losses of over $380,000 to profits of nearly

$240,000. Tracing the variation back to the simulated yield, one must note that Scenarios

1, 2, and 3 resulted in maxima that are far beyond current or historical yield levels. On

the other end of the spectrum, Scenario 3 produced a minimum that was similarly

improbable, a mere four tons per acre.









Evaluation of Hypotheses

The two hypotheses for this study were analyzed to determine whether there was a

significant difference in the mean or variance using a two-sample t-test and an F-test.

The tests compared the output series of the simulations from both scenarios of the

empirical model. The following hypotheses were tested:

Maintaining a Higher Water Table Lowers Average Sugarcane Production for an
EAA Farm.

A distribution comparison of yield results indicated that both the mean and (and in

most cases) variance of the base model and the post-restoration scenarios are statistically

different. Given a confidence level of 95%, the null hypothesis can be rejected. The two

base scenarios, however, were not statistically different from each other in terms of the

production results. Additionally, the variances of each base scenario compared with

Scenario 3 were not statistically different. Though the difference between scenarios was

significant, it is essential to note that the post-restoration scenario did provide some

yields at or above the current production levels.

The basic functions used at the beginning of the analysis indicated that yield would

be responsive to water changes. Once modified to represent an EAA farm, this model

indicated that a shift in the range of maintained water levels affects the possible yields for

that farm. The rejection of the null hypothesis in this case indicated that for this type of

farm, the adjustment to higher water tables results in lower yield on average. As there is

still variation in those water levels, the possibility exists that the farm could achieve

greater yields. However, it is more likely that the sugarcane farm, under these conditions,

will see lower yields.









The EAA Sugarcane Operation Will Experience a Reduction in Profit Under the
Changed Water Conditions.

In examining the assumed sugarcane operation, analysis of the simulation series

for profit indicated that these results were also statistically different for both mean and

variance across most scenarios. The null hypothesis can then be rejected with a

confidence level of 95% except in comparing the mean results between the two base

scenarios; that is we can state that there is a significant difference in expected profit

between each base scenario and the post-restoration scenarios. It should also be noted that

between scenarios 1 and 3 and also between 1 and 4, the F-tests are such that the null

hypothesis for variance cannot be rejected.

As in the previous case, though the series are statistically different, they were not

mutually exclusive. Of particular importance regarding the difference in profit is that the

given simulations kept harvested acreage constant, a variable that directly impacts the

costs of production. Similar to the yield hypothesis, it is likely that the operation will

experience losses, but not absolutely certain.

Looking at the assumed EAA sugar operation, failure to reject the null hypothesis

demonstrates that this "typical" operation will indeed see changes in profit in response to

varying water conditions.














CHAPTER 6
SUMMARY AND CONCLUSIONS

Summary

The purpose of this research was to provide an economic perspective in examining

the impacts of the Comprehensive Everglades Restoration Plan within in the Everglades

Agricultural Area. As sugarcane is the dominant crop in the area, a farm-level analysis

was developed in order to examine the impacts of water regime changes on sugarcane

production. For the purposes of this analysis, those changes were limited in scope;

defined as flood events and a higher average water table. In keeping with previous

authors' work on cots of production, the hypothetical farm was modeled assuming that

640 acres in production, the firm employs profit maximizing management, and

independent grower status.

The analysis incorporated an empirical water response function and simulations of

five scenarios represent the possible water regimes. In order to better align with

historical sugarcane production, the empirical data were calibrated. Additionally, the

model incorporated historical cost and water distributions based on USGS records. A

model based on historical yields and weather data was also developed, but lacked

explanatory power and consequently was not used in a simulation. Only one of the

modeled scenarios showed an average profit over the course of the simulation.

Conclusions

There was a significant difference between the simulated bases and the post-

restoration models, confirmed by a two-sample t-test and an F-test. Upon examining the









mean values of these scenarios, one could expect an EAA sugarcane farm to incur

monetary losses. Though these results are specific to a small, independent farm, the

probable losses are important on a regional scale. These results illustrate the additional

costs of CERP implementation beyond construction and planning. This one farm

situation is representative of the tradeoffs farmers are facing in the EAA. Best

Management Practices help fight soil subsidence and excess nutrient runoff, which are

beneficial to all in the long term. However, these immediate losses are considerable.

If this hypothetical operation is indeed indicative of EAA farming, the sheer

volume of sugarcane production magnifies the impact. It is important to remember that

sugarcane is the primary crop in the EAA, making up 86% of the area's acreage in

agricultural production. The EAA generates nearly $800 million in producing nearly

25% of domestic sugar. While this analysis did not attempt to specify total regional

implications, it stands to reason that the entire area would be impacted. It is highly

unlikely that all farms will experience the same degree of loss, but the high value of this

crop and its regional importance suggests that there are many stakeholders who would be

adversely affected.

The results of the simulations were not completely negative, however. Even in

the constrained post-restoration model, there were profitable iterations. That is, even

under changed conditions, part of the range of conditions is such that yields would

remain high enough to be profitable. In practical applications, the profit-maximizing

producer will do everything in his power to remain in that profitable range. It is also

important to remember that while many conditions in the model were fixed, there is a

great deal of research going into new cultivars of sugarcane, some of which are being









bred for resistance to flooding and/or high water tables. The producer facing a scenario

as described would do well to examine the possibility of using such cultivars.

There were, however, a number of limitations on the study. One of the primary

limitations was a lack of available, uniform data. In some cases, such as in trying to

determine flood information, it was clear that producers did not generally keep records of

specific flood events or the resulting damage. Similarly, the analysis would have

benefited from having more specific historical yield information rather than relying on

and annual average. Additionally, the assumptions made in defining the study farm may

hinder the application of information gained. Few sugarcane operations are independent

of sugar processors and the division of first planting and consequent ratoon crops is most

likely not uniform as in this analysis.

The estimation of production costs is another limitation to the relevance of the

study findings. As many of the sugarcane farms are part of vertically integrated

production and processing operation, it is difficult to get accurate estimates of production

revenue. In order for this analysis to be useful to producers or water management

officials, it would be necessary to update the cost portion of the model. A new

publication on costs and returns will soon be available (J. Alvarez, personal

communication) and those would greatly improve the value of the analysis.

The complexity and scope of issues surrounding production in the EAA make it

difficult to incorporate all factors into a single analysis. For example, a more intricate

analysis would have to consider the introduction of flood resistant cultivars and the

interaction of price supports on domestic sugar. The entire restoration effort is a

multidisciplinary project and though the analysis incorporated a variety of perspectives









and sources of information, the problem calls for the attention and evaluation of experts

from many disciplines.

Implications for Future Analysis

It is intended that this research might be an initial inquiry that could lead to further

study by those in water management or perhaps even producers. As a first step, this

study sets forth a framework that identifies key variables, such as maintained water level,

which are essential in the planning process. By relaxing some of the assumptions and

updating certain data, this type of analysis could be easily replicated and used by those

most interested in making efficient water management or production decisions.

With updated cost and inputs, additional scenarios can be simulated using the same

model, which lends itself to repeated analysis throughout the decision-making process.

Water managers could benefit form the additional information when adjusting the

capacity for water storage or drainage. With improvements, even the climatic model

could be of use to producers looking to analyze the balance of water in the production

system, taking into account rainfall, evaporation, and drainage. Such a model would also

have to account for irrigation, which was not available for this particular analysis.

It had been hoped that this analysis could capture the current production practices

in the area, describing the crop response such that a producer would have knowledge of

how changes in those practices could affect his operation. However, the large range of

output is an indication that this particular analysis is not yet ready for field application.

Considering the economic importance of this industry, it follows that further analysis and

continued data collection are warranted.

Over the course of this study, it became evident that decision-makers in the EAA

have to make policy and production decisions without complete information. With






49


shared information, such as that from the referenced growers' meeting on flooding, and

tools like spreadsheet simulation, future work should be able to better refine the problems

to be researched and provide relevant information to all stakeholders.















APPENDIX A
SIMULATION OUTPUT FOR SCENARIO 1

Yld
Iteration "Z" "F" X(depth) tons/ac Profit
1 -0.14087 4.43652 36.42929 22.99319 -164516
2 0.262596 6.050384 2.020689 18.53983 -254866
3 0.293122 6.172489 88.79114 37.68041 133458.6
4 0.809249 8.236997 99.84259 47.38158 330275.8
5 0.621738 7.486952 136.1192 53.95415 463620.3
6 -0.70749 2.170035 42.67497 19.51556 -235070
7 0.267268 6.069074 71.53518 33.60959 50869.81
8 0.136634 5.546537 24.53701 22.63242 -171835
9 0.003309 5.013236 84.00906 33.03105 39132.38
10 0.551755 7.207022 91.06506 41.58563 212687.7
11 0.098492 5.393969 59.76264 29.39677 -34600
12 -0.44181 3.232743 107.8567 30.87399 -4630.05
13 -0.49417 3.023337 56.77932 23.14934 -161348
14 -0.37266 3.509378 114.7882 32.93913 37267.45
15 -0.59678 2.612878 120.9387 30.19318 -18442.4
16 -0.77456 1.90174 5.295971 15.00526 -326575
17 0.028506 5.114025 38.84618 24.70655 -129755
18 -0.88057 1.477735 54.88547 19.28265 -239795
19 -0.62596 2.496178 82.94403 25.08287 -122120
20 -0.0305 4.877987 145.6574 44.01623 261999.8
21 -0.17373 4.305074 111.1101 35.43426 87888.72
22 -0.32012 3.719529 157.6971 40.38569 188343.3
23 -1.12519 0.499255 81.78061 18.8561 -248449
24 0.456235 6.824941 118.418 46.7976 318428.2
25 0.194742 5.778967 95.76805 37.86543 137212.3
26 0.181085 5.724341 61.69456 30.6116 -9953.39
27 -0.51609 2.935652 109.5618 29.99144 -22535.3
28 -0.65776 2.368975 66.54915 22.74708 -169509
29 0.467112 6.868449 17.3875 22.95801 -165229
30 -0.10347 4.58614 28.54076 21.87101 -187283
31 -0.28159 3.873635 132.7862 37.21858 124088.9
32 0.215671 5.862686 100.8346 39.22092 164712.5
33 -0.39879 3.404844 153.0352 38.11374 142250
34 0.075346 5.301383 48.074 26.87515 -85758.6
35 0.10727 5.42908 77.24882 32.97342 37963.11










36 -1.07027 0.718925 86.16379 19.85382 -228207
37 0.549071 7.196284 139.9296 53.58913 456214.8
38 0.431603 6.726413 51.2168 30.68115 -8542.42
39 -0.09807 4.607739 147.7863 43.11443 243704
40 -0.69312 2.227535 104.0817 26.68361 -89644.6
41 0.720718 7.882874 19.64739 24.91783 -125469
42 -0.52738 2.890469 53.67027 22.42078 -176129
43 -0.06718 4.731272 85.0789 32.34901 25295.11
44 -0.2085 4.166005 46.29284 24.13256 -141400
45 0.354143 6.416573 92.14386 39.22628 164821.2
46 0.055809 5.223236 58.03419 28.64046 -49943.9
47 -0.46738 3.130461 111.8659 31.04799 -1100.05
48 -0.73816 2.047369 93.28242 24.8395 -127058
49 -0.53983 2.840685 96.63352 27.93762 -64203.2
50 -0.83657 1.653734 113.737 25.55865-112468
51 0.011686 5.046744 49.76883 26.64755 -90376.2
52 -0.35621 3.575153 125.0916 34.74789 73963.49
53 -0.26709 3.931622 104.7063 32.98205 38138.32
54 -0.80571 1.777173 64.68256 20.99026 -205151
55 -0.25179 3.992828 76.23383 28.60526 -50658.1
56 0.114416 5.457665 31.35745 23.86276 -146874
57 -0.41822 3.327135 44.80873 22.15079 -181606
58 -0.16171 4.353144 51.92192 25.49021-113856
59 -0.4478 3.208817 95.06784 28.98362 -42981.9
60 -0.11447 4.542103 70.31933 29.1567 -39470.6
61 0.590894 7.363576 108.4223 46.45435 311464.2
62 -0.04915 4.803396 36.6258 23.70666-150041
63 -0.13121 4.47517 228.228 56.50328 515336.9
64 -0.84268 1.629292 119.9308 26.07675 -101957
65 -0.17774 4.289051 63.22651 27.26075 -77935.6
66 -0.2419 4.032391 97.4099 32.1637 21535.54
67 -0.19304 4.227823 102.9654 33.77226 54169.92
68 -0.31475 3.740993 121.9142 34.95115 78087.31
69 0.027628 5.110511 41.34747 25.17799 -120191
70 -0.00638 4.974475 61.02541 28.59985 -50767.8
71 0.074813 5.299251 168.1759 50.42934 392108.8
72 0.167952 5.671808 78.85104 34.01401 59074.64
73 0.876345 8.505379 130.0185 56.80787 521516.4
74 -0.30126 3.794974 164.6642 41.8564 218181.1
75 0.481129 6.924518 74.93782 36.79808 115557.9
76 -0.60293 2.588292-14.4811 13.24711-362244
77 -0.07685 4.692617 142.3177 42.53647 231978.3
78 0.323522 6.294088 80.21285 36.16492 102712.2
79 0.049092 5.196367 116.7473 39.93254 179149.7










80 0.391733 6.566934 124.2045 47.09045 324369.5
81 0.506403 7.025613 127.4007 49.77619 378857.8
82 0.647272 7.589089 177.7241 65.09517 689649.3
83 -0.34571 3.617172 67.47869 26.18819 -99695.6
84 0.311703 6.246812 87.93683 37.73167 134498.5
85 0.413713 6.654852 89.97998 39.51561 170691
86 -0.05331 4.786769 24.34059 21.4327 -196175
87 -0.02132 4.914725 134.8102 42.17295 224603.3
88 -0.38576 3.456948 98.95663 30.40015 -14243.3
89 -0.21773 4.129074 131.0421 38.05372 141032.3
90 -0.56524 2.739034 33.38566 19.46776 -236040
91 0.784015 8.13606 151.4384 61.02104 606993.3
92 0.682643 7.730571 74.06468 38.88077 157811.4
93 -1.00785 0.968586 66.22222 19.01787 -245167
94 0.221046 5.884183 -19.4038 13.85001 -350013
95 0.141384 5.565535 105.6908 39.1524 163322.3
96 -0.92827 1.286927 13.56775 15.12062 -324234
97 0.339725 6.358901 78.25181 35.92004 97744.22
98 0.159975 5.6399 87.26709 35.64601 92184.63
99 0.249983 5.999933 69.48713 32.97794 38054.75
100 0.379602 6.518407 73.04364 35.20149 83166.14






53


























































53















APPENDIX B
SIMULATION OUTPUT FOR SCENARIO 2

Yld
Iteration "Z" "F" X(depth) tons/ac Profit
1 -0.14087 4.43652 27.37056 21.42314-196369
2 0.262596 6.050384 -7.03804 16.58278 -294570
3 0.293122 6.172489 79.73242 35.69409 93160.07
4 0.809249 8.236997 90.78386 44.90021 279933.8
5 0.621738 7.486952 127.0605 51.65264 416927.1
6 -0.70749 2.170035 33.61624 18.48899 -255897
7 0.267268 6.069074 62.47645 31.64807 11074.38
8 0.136634 5.546537 15.47828 20.79619 -209088
9 0.003309 5.013236 74.95033 31.32271 4473.455
10 0.551755 7.207022 82.00634 39.35124 167356.3
11 0.098492 5.393969 50.70392 27.59713 -71111.1
12 -0.44181 3.232743 98.79799 29.59259 -30627.1
13 -0.49417 3.023337 47.72059 21.91815 -186326
14 -0.37266 3.509378 105.7294 31.59139 9924.574
15 -0.59678 2.612878 111.88 29.06041-41423.9
16 -0.77456 1.90174 -3.76276 14.04302-346097
17 0.028506 5.114025 29.78745 22.97404 -164904
18 -0.88057 1.477735 45.82674 18.42208 -257254
19 -0.62596 2.496178 73.8853 23.97809 -144534
20 -0.0305 4.877987 136.5986 42.34032 227998.8
21 -0.17373 4.305074 102.0514 33.89573 56674.89
22 -0.32012 3.719529 148.6384 38.98756 159978.1
23 -1.12519 0.499255 72.72188 18.23016 -261148
24 0.456235 6.824941 109.3593 44.65483 274955.6
25 0.194742 5.778967 86.70932 35.97347 98828.18
26 0.181085 5.724341 52.63584 28.73274 -48071.7
27 -0.51609 2.935652 100.5031 28.78128 -47087
28 -0.65776 2.368975 57.49042 21.6728 -191304
29 0.467112 6.868449 8.328775 20.8048 -208914
30 -0.10347 4.58614 19.48203 20.26508 -219864
31 -0.28159 3.873635 123.7275 35.7835 94973.99
32 0.215671 5.862686 91.77583 37.30889 125921.1
33 -0.39879 3.404844 143.9765 36.79107 115415.7
34 0.075346 5.301383 39.01527 25.0977 -121819
35 0.10727 5.42908 68.1900931.16536 1281.158










-1.07027
0.549071
0.431603
-0.09807
-0.69312
0.720718
-0.52738
-0.06718
-0.2085
0.354143
0.055809
-0.46738
-0.73816
-0.53983
-0.83657
0.011686
-0.35621
-0.26709
-0.80571
-0.25179
0.114416
-0.41822
-0.16171
-0.4478
-0.11447
0.590894
-0.04915
-0.13121
-0.84268
-0.17774
-0.2419
-0.19304
-0.31475


0.718925
7.196284
6.726413
4.607739
2.227535
7.882874
2.890469
4.731272
4.166005
6.416573
5.223236
3.130461
2.047369
2.840685
1.653734
5.046744
3.575153
3.931622
1.777173
3.992828


5.457665 22.29872 22.04784 -183695
3.327135 35.75 20.84675 -208063
4.353144 42.86319 23.94015 -145304
3.208817 86.00911 27.70796 -68862.6
4.542103 61.2606 27.56132 -71837.5
7.363576 99.36356 44.18241 265371.2
4.803396 27.56707 22.04863 -183679
4.47517 219.1692 54.92395 483295.5


1.629292
4.289051
4.032391
4.227823
3.740993


0.027628 5.110511
-0.00638 4.974475
0.074813 5.299251
0.167952 5.671808
0.876345 8.505379
-0.30126 3.794974
0.481129 6.924518
-0.60293 2.588292
-0.07685 4.692617
0.323522 6.294088
0.049092 5.196367


110.8721 25.17984 -120153
54.16778 25.72605 -109071
88.35117 30.69055 -8351.71
93.90672 32.25224 23331.91
112.8555 33.54787 49617.67
32.28874 23.44632 -155323
51.96668 26.9008 -85238.2
159.1172 48.65241 356058.4


69.79231
120.9598
155.6054
65.87909
-23.5398
133.2589
71.15412
107.6885


32.14775 21211.86
54.26214 469868.7
40.44018 189448.9
34.63143 71600.87
12.12024 -385106
40.90501 198879.2
34.14943 61822.11
38.18028 143599.9


77.10507 19.1752 -241975
130.8709 51.35732 410935.6
42.15807 28.562 -51535.7
138.7276 41.50332 211017.8
95.02302 25.64324 -110752
10.58866 22.52138 -174088
44.61154 21.22145 -200461
76.02017 30.70828 -7992.1
37.23411 22.62737 -171937
83.08514 37.18143 123335.3
48.97546 26.88176 -85624.5
102.8071 29.79111 -26599.5
84.2237 23.84233 -147288
87.57479 26.75023 -88293
104.6783 24.65588 -130783
40.7101 24.93116-125198
116.0329 33.38438 46300.63
95.6476 31.53307 8741.292
55.62383 20.05789 -224067
67.1751 27.1416 -80352.9










80 0.391733 6.566934 115.1458 45.00954 282152
81 0.506403 7.025613 118.3419 47.5853 334409
82 0.647272 7.589089 168.6653 62.76917 642459.2
83 -0.34571 3.617172 58.41996 24.81461 -127563
84 0.311703 6.246812 78.8781 35.72752 93838.36
85 0.413713 6.654852 80.92125 37.41362 128045.9
86 -0.05331 4.786769 15.28186 19.77865 -229732
87 -0.02132 4.914725 125.7514 40.48823 190423.6
88 -0.38576 3.456948 89.89791 29.06499 -41331.1
89 -0.21773 4.129074 121.9834 36.55739 110674.7
90 -0.56524 2.739034 24.32693 18.30474 -259635
91 0.784015 8.13606 142.3797 58.56387 557142.3
92 0.682643 7.730571 65.00595 36.52083 109933
93 -1.00785 0.968586 57.16349 18.27939 -260149
94 0.221046 5.884183 -28.4625 11.93282 -388909
95 0.141384 5.565535 96.63204 37.31162 125976.5
96 -0.92827 1.286927 4.509018 14.30581 -340765
97 0.339725 6.358901 69.19309 33.88902 56538.82
98 0.159975 5.6399 78.20836 33.7874 54477.07
99 0.249983 5.999933 60.4284 31.03299 -1404.32
100 0.379602 6.518407 63.98491 33.13221 41184.76















APPENDIX C
SIMULATION OUTPUT FOR SCENARIO 3

Yld
Iteration "Z" "F" X(depth) tons/ac Profit
1 -0.14087 4.43652 5.949285 17.71041 -271693
2 0.262596 6.050384 -28.4593 11.95494 -388460
3 0.293122 6.172489 58.31114 30.99701 -2134.3
4 0.809249 8.236997 69.36259 39.03248 160889.4
5 0.621738 7.486952 105.6392 46.21021 306511.2
6 -0.70749 2.170035 12.19497 16.06143 -305147
7 0.267268 6.069074 41.05518 27.00963 -83030.3
8 0.136634 5.546537 -5.94299 16.45405 -297182
9 0.003309 5.013236 53.52906 27.28296 -77484.9
10 0.551755 7.207022 60.58506 34.06754 60160.66
11 0.098492 5.393969 29.28264 23.34149 -157449
12 -0.44181 3.232743 77.37672 26.56245 -92102.7
13 -0.49417 3.023337 26.29932 19.00675 -245393
14 -0.37266 3.509378 84.30816 28.40439 -54733.4
15 -0.59678 2.612878 90.45869 26.38175 -95768.6
16 -0.77456 1.90174 -25.184 11.7676 -392261
17 0.028506 5.114025 8.366183 18.87714 -248022
18 -0.88057 1.477735 24.40547 16.38708 -298540
19 -0.62596 2.496178 52.46403 21.36561 -197536
20 -0.0305 4.877987 115.1774 38.37726 147596.3
21 -0.17373 4.305074 80.6301 30.25753 -17136.8
22 -0.32012 3.719529 127.2171 35.68139 92902.54
23 -1.12519 0.499255 51.30061 16.74999 -291178
24 0.456235 6.824941 87.93798 39.58779 172155.4
25 0.194742 5.778967 65.28805 31.49953 8060.889
26 0.181085 5.724341 31.21456 24.28978 -138211
27 -0.51609 2.935652 79.08184 25.91959 -105145
28 -0.65776 2.368975 36.06915 19.13244 -242843
29 0.467112 6.868449 -13.0925 15.71309 -312214
30 -0.10347 4.58614 -1.93924 16.46751 -296909
31 -0.28159 3.873635 102.3062 32.38994 26125.6
32 0.215671 5.862686 70.35456 32.78748 34190.73
33 -0.39879 3.404844 122.5552 33.66334 51960.25
34 0.075346 5.301383 17.594 20.89457 -207093
35 0.10727 5.42908 46.7688226.88981-85461










36 -1.07027 0.718925 55.68379 17.57048 -274532
37 0.549071 7.196284 109.4496 46.07971 303863.5
38 0.431603 6.726413 20.7368 23.55083 -153202
39 -0.09807 4.607739 117.3063 37.69351 133724.3
40 -0.69312 2.227535 73.60175 23.18308 -160663
41 0.720718 7.882874 -10.8326 16.85445 -289059
42 -0.52738 2.890469 23.19027 18.38539 -257999
43 -0.06718 4.731272 54.5989 26.82841 -86706.7
44 -0.2085 4.166005 15.81284 19.06804 -244149
45 0.354143 6.416573 61.66386 32.34595 25232.97
46 0.055809 5.223236 27.55419 22.72294 -169999
47 -0.46738 3.130461 81.38587 26.81896-86898.5
48 -0.73816 2.047369 62.80242 21.48434 -195127
49 -0.53983 2.840685 66.15352 23.94239 -145258
50 -0.83657 1.653734 83.25703 22.52108 -174094
51 0.011686 5.046744 19.28883 20.87242 -207542
52 -0.35621 3.575153 94.6116 30.16007-19114
53 -0.26709 3.931622 74.22633 28.10664 -60774.2
54 -0.80571 1.777173 34.20256 17.8531 -268798
55 -0.25179 3.992828 45.75383 23.68046 -150572
56 0.114416 5.457665 0.877447 17.75609 -270766
57 -0.41822 3.327135 14.32873 17.76309 -270624
58 -0.16171 4.35314421.4419220.2747 -219669
59 -0.4478 3.208817 64.58784 24.69138 -130063
60 -0.11447 4.542103 39.83933 23.78873 -148376
61 0.590894 7.363576 77.94229 38.80995 156374.6
62 -0.04915 4.803396 6.145801 18.12787 -263223
63 -0.13121 4.47517 197.748 51.18931407527.1
64 -0.84268 1.629292 89.45081 23.0589 -163183
65 -0.17774 4.289051 32.74651 22.09695 -182699
66 -0.2419 4.032391 66.9299 27.20698 -79026.4
67 -0.19304 4.227823 72.48544 28.65785 -49591
68 -0.31475 3.740993 91.43423 30.22953 -17704.8
69 0.027628 5.110511 10.86747 19.35141 -238400
70 -0.00638 4.974475 30.54541 22.88303 -166751
71 0.074813 5.299251 137.6959 44.45048 270809.8
72 0.167952 5.671808 48.37104 27.73457 -68322.7
73 0.876345 8.505379 99.5385 48.24223 347736.8
74 -0.30126 3.794974 134.1842 37.09124 121505.4
75 0.481129 6.924518 44.45782 29.50793 -32344.8
76 -0.60293 2.588292-44.9611 9.455519-439168
77 -0.07685 4.692617 111.8377 37.04706 120609.2
78 0.323522 6.294088 49.73285 29.3834 -34871.1
79 0.049092 5.196367 86.26727 34.03669 59534.82










80 0.391733 6.566934 93.72455 40.0888 182320
81 0.506403 7.025613 96.92066 42.40447 229300.3
82 0.647272 7.589089 147.2441 57.26882 530868.3
83 -0.34571 3.617172 36.99869 21.56648-193461
84 0.311703 6.24681257.45683 30.9883 -2311.01
85 0.413713 6.654852 59.49998 32.44302 27202.42
86 -0.05331 4.786769-6.13941 15.86732-309085
87 -0.02132 4.914725 104.3302 36.50434 109598.5
88 -0.38576 3.456948 68.47663 25.90771 -105386
89 -0.21773 4.129074 100.5621 33.01899 38887.7
90 -0.56524 2.739034 2.905657 15.55455 -315431
91 0.784015 8.13606 120.9584 52.75338 439259
92 0.682643 7.730571 43.58468 30.94027 -3285.47
93 -1.00785 0.96858635.74222 16.5331 -295578
94 0.221046 5.884183 -49.8838 7.399221 -480886
95 0.141384 5.565535 75.21077 32.9587 37664.54
96 -0.92827 1.286927-16.9123 12.37901-379856
97 0.339725 6.358901 47.77181 29.08624 -40900
98 0.159975 5.6399 56.7870929.39231-34690.4
99 0.249983 5.999933 39.00713 26.43375 -94713.6
100 0.379602 6.518407 42.56364 28.23899 -58089















APPENDIX D
SIMULATION OUTPUT FOR SCENARIO 4

Yld
Iteration "Z" "F" X(depth) tons/ac Profit
1 -0.14087 4.43652 3.475841 17.28171 -280390
2 0.262596 6.050384 -17.6044 14.30002 -340883
3 0.293122 6.172489 46.1128 28.32226 -56399.7
4 0.809249 8.236997 54.77228 35.0359 79806.66
5 0.621738 7.486952 78.20701 39.24061 165111.9
6 -0.70749 2.170035 8.295705 15.61955 -314112
7 0.267268 6.069074 31.95968 25.04014 -122987
8 0.136634 5.546537 -5.05496 16.63406 -293530
9 0.003309 5.013236 42.24638 25.15521 -120653
10 0.551755 7.207022 47.92933 30.94592 -3170.76
11 0.098492 5.393969 22.16669 21.92781 -186130
12 -0.44181 3.232743 60.72192 24.20654 -139899
13 -0.49417 3.023337 19.69937 18.10973 -263591
14 -0.37266 3.509378 65.57358 25.6171 -111282
15 -0.59678 2.612878 69.60374 23.77391 -148677
16 -0.77456 1.90174 -16.1404 12.72824-372771
17 0.028506 5.114025 5.318517 18.29427 -259848
18 -0.88057 1.477735 18.13991 15.79186 -310616
19 -0.62596 2.496178 41.37789 20.01357 -224966
20 -0.0305 4.877987 82.48883 32.3297 24903.42
21 -0.17373 4.305074 63.03677 27.26948 -77758.4
22 -0.32012 3.719529 86.58802 29.41068 -34317.7
23 -1.12519 0.499255 40.4265 15.99861 -306422
24 0.456235 6.824941 67.98594 34.86828 76406.07
25 0.194742 5.778967 51.63238 28.64748 -49801.6
26 0.181085 5.724341 23.76982 22.74567 -169537
27 -0.51609 2.935652 61.94307 23.63001 -151596
28 -0.65776 2.368975 27.80888 18.15285 -262717
29 0.467112 6.868449 -9.63148 16.53575 -295524
30 -0.10347 4.58614 -2.29595 16.40427-298192
31 -0.28159 3.873635 76.4983 28.30146-56821.5
32 0.215671 5.862686 55.52589 29.65757 -29308.7
33 -0.39879 3.404844 85.17059 28.20479 -58782.8
34 0.075346 5.301383 12.59395 19.9135 -226997
35 0.10727 5.42908 36.6978424.87971-126242










36 -1.07027 0.718925 43.99566 16.69489 -292296
37 0.549071 7.196284 80.02718 38.83084 156798.5
38 0.431603 6.726413 15.13879 22.24126 -179771
39 -0.09807 4.607739 83.31895 31.64879 11089.11
40 -0.69312 2.227535 57.96023 21.38671 -197108
41 0.720718 7.882874 -8.23757 17.54096 -275131
42 -0.52738 2.890469 17.14267 17.58472 -274243
43 -0.06718 4.731272 43.11628 24.74866 -128901
44 -0.2085 4.166005 11.16465 18.2957 -259818
45 0.354143 6.416573 48.78549 29.43888 -33745.7
46 0.055809 5.223236 20.73577 21.39918 -196855
47 -0.46738 3.130461 63.56523 24.34639 -137062
48 -0.73816 2.047369 49.68489 20.04039 -224422
49 -0.53983 2.840685 52.30501 22.12717 -182086
50 -0.83657 1.653734 64.85773 20.68745 -211295
51 0.011686 5.046744 13.96295 19.86331 -228015
52 -0.35621 3.575153 72.15883 26.78051 -87678.6
53 -0.26709 3.931622 58.42244 25.57873 -112060
54 -0.80571 1.777173 26.25471 17.03507 -285394
55 -0.25179 3.992828 35.85858 22.08163 -183009
56 0.114416 5.457665 -0.28206 17.52378 -275479
57 -0.41822 3.327135 9.981866 17.13734 -283319
58 -0.16171 4.353144 15.7133 19.29446-239556
59 -0.4478 3.208817 51.08607 22.79004 -168637
60 -0.11447 4.542103 30.94818 22.22287 -180144
61 0.590894 7.363576 61.12885 34.59313 70823.78
62 -0.04915 4.803396 3.624508 17.66639 -272586
63 -0.13121 4.47517 92.73595 32.88123 36092.71
64 -0.84268 1.629292 68.96274 21.03036 -204338
65 -0.17774 4.289051 25.04319 20.79188 -209176
66 -0.2419 4.032391 52.90587 24.92636 -125296
67 -0.19304 4.227823 57.12919 26.08115 -101867
68 -0.31475 3.740993 70.21656 26.94273 -84387.4
69 0.027628 5.110511 7.256011 18.66104 -252406
70 -0.00638 4.974475 23.21417 21.50799 -194648
71 0.074813 5.299251 89.05367 34.90898 77231.89
72 0.167952 5.671808 38.01983 25.60203 -111588
73 0.876345 8.505379 74.99924 41.3461 207828
74 -0.30126 3.794974 88.33166 29.92277 -23928.5
75 0.481129 6.924518 34.7852 27.19444 -79280.7
76 -0.60293 2.588292-22.9999 12.1874 -383744
77 -0.07685 4.692617 81.09388 31.51015 8276.34
78 0.323522 6.294088 39.1404 27.02668 -82684.3
79 0.049092 5.196367 66.88735 30.28797 -16519.2










80 0.391733 6.566934 71.62503 35.01225 79327.03
81 0.506403 7.025613 73.51675 36.74413 114463.3
82 0.647272 7.589089 90.55837 42.71363 235572.6
83 -0.34571 3.617172 28.58303 20.2904 -219350
84 0.311703 6.24681245.42643 28.3267 -56309.5
85 0.413713 6.654852 47.06445 29.55748 -31339.5
86 -0.05331 4.786769-5.18694 16.04124-305557
87 -0.02132 4.914725 77.5487 31.52358 8548.78
88 -0.38576 3.456948 54.0955 23.78808 -148389
89 -0.21773 4.129074 75.56196 28.88944 -44892.7
90 -0.56524 2.739034 1.201646 15.33578 -319869
91 0.784015 8.13606 84.63641 42.90109 239375.8
92 0.682643 7.730571 34.06107 28.45922 -53620.9
93 -1.00785 0.968586 27.53662 15.86416 -309149
94 0.221046 5.884183 -24.0274 12.87146 -369865
95 0.141384 5.565535 59.14679 29.69442 -28561.3
96 -0.92827 1.286927-11.8668 12.83283-370649
97 0.339725 6.358901 37.52584 26.78903 -87505.8
98 0.159975 5.6399 44.8869426.95071-84225.5
99 0.249983 5.999933 30.25548 24.55474 -132835
100 0.379602 6.518407 33.21341 26.10313 -101421















LIST OF REFERENCES


Aillery, M., R. Shoemaker, and M. Caswell. "Agriculture and Ecosystem Restoration in
South Florida: Assessing Trade-offs from Water-Retention Development in the
Everglades Agricultural Area". Amer. J. Agr. Econ 83 (1) (February 2001):183-
195.

Alchian, A.A. and H. Demsetz. "Production, Information Costs, and Economic
Organization." Amer. Econ. Rev. 62(December 1972):777-795.

Alvarez, J. and T. J. Schuneman. "Costs and Returns for Sugarcane Production on Muck
Soils in Florida, 1990-91." Economics Information Report El 91-3. Institute of
Food and Agricultural Sciences, The University of Florida, (revised) June 1998.

Apland, J., Grainger, C., and Strock, J. "Modeling Agricultural Production Considering
Water Quality and Risk," Department of Agricultural and Applied Economics,
University of Minnesota, Staff Paper P04-13. November, 2004.

Boggess, W., R. Lacewell and D. Zilberman. "Economics of Water Use in Agriculture,"
in Agricultural and Environmental Resource Economics, eds. Gerald A. Carlson,
David Zilberman and John A. Miranowski, Oxford Series in Biological Resource
Management, Oxford University Press, New York, 1993:

Bottcher, A.B. and F.T. Izuno, eds. Everglades Agricultural Area(EAA): Water, Soil,
Crop, and Environmental Management: Gainesville, FL: University Press of
Florida, 1994.

Brue, S.L. "Retrospectives: The Law of Diminishing Returns. J. ofEcon. Persp.
7(Summer, 1993):185-192.

Coase, R. H. "The Nature of the Firm." Economica. 4(November 1937):386-405.

Diewert, W.E. "Functional Forms for Profit and Transformation Functions," J. Econ
Theory 6(1973):284-316.

Diewert, W.E. and T.J. Wales. "Flexible Functional Forms and Global Curvature
Conditions," Econometrica 55(1987):43-68.

Douglas, M.S. Everglades. River of Grass: St. Simons Island, GA: Mockingbird Press,
1947 (p.18).









Edgeworth, F.Y. "Contributions to the Theory of Railway Rates." The Econ. J.
21(September 1911):346-370.

Elbadawi, I., R. Gallant, and G. Souza. "An Elasticity Can Be Estimated Consostently
Without A Priori Knowledge of Functional Form," Econometrica 51(1983):1731-
51.

Everglades Research and Education Center (EREC), 2005. "EREC Weather Station.
http://erec.ifas.ufl.edu/WD/EWDMAIN.HTM. Accessed July 8, 2004.

Gallant, R. "On the Bias in Flexible Functional Forms and an Essentially Unbiased
Form," J. Econometrics 15(1981):211-45.

Gallant, R. "Unbiased Determination of Production Technologies," J. Econometrics
20(1982):285-323.

Glaz, B. "Sugarcane Emergence after Long Duration under Water" Soil Crop Sci. Florida
Proc. 62(2003):51-57.

Glaz, B. and R. Cherry. "Wireworm Effects on Sugarcane Emergence after Short-
Duration Flood Applied at Planting" J. Entomol. Sci. 38 (July 2003):449-456.

Glaz, B., S. Edme, J. Miller, S. Milligan, and D. Holder. "Sugarcane Cultivar Response
to High Water Tables in the Everglades" Agron J. 94(2002):624-629.

Just, R.E. and R.D. Pope. "Production Function Estimation and Related Risk
Considerations." Amer. J. Agr. Econ 61(May 1979):276-284.

Kim, C.S. and Glenn D. Schaible. "Economic Benefits Resulting From Irrigation Water
Use: Theory and an Application to Groundwater Use" Environ. andRes. Econ.
17(September 2000):73-87.

Lang, T.A., S. H. Daroub and R. S. Lentini. "Water Management for Florida Sugarcane
Production" Circular SS-AGR-231. Institute of Food and Agricultural Sciences,
The University of Florida, May 2002.

Omary, M. and F Izuno. "Evaluation of Sugarcane Evapotranspiration from Water Table
Data in the Everglades Agricultural Area." Agric Water Manage. 27(1995):309-
319.

Paris, Q. and M.R. Caputo. "A Primal-Dual Estimator of Production and Cost Functions
Within an Errors-in-Variables Context" Department of Agricultural and Resource
Economics University of California, Davis Working Paper No. 04-008. September,
2004.

Redman, J.C. and S.Q.Allen. "Some Interrelationships of Economic and Agronomic
Concepts." J. ofFarm Econ. 36 (August 1954):453-465.









South Florida Water Management District (SFWMD). "Florida Forever Work Plan, 2004
Annual Update." West Palm Beach, FL, February 2004.

South Florida Water Management District (SFWMD). "Everglades Agricultural Area
Storage Reservoir Phase 1 Existing Flood Control Conditions Documentation."
West Palm Beach, FL, 2004.

Thompson, Gary D. "Choice of Flexible Functional Forms: Review and Appraisal"
Western J. ofAgr. Econ., 13(December 1988):169-183.

U.S. Army Corps of Engineers (USACE) and South Florida Water Management District
(SFWMD). "Central and Southern Florida Project- Comprehensive Review Study:
Integrated Feasibility Report and Programmatic Environmental Impact Statement ."
USACE Jacksonville, District, FL, April 1999.

U.S. Army Corps of Engineers (USACE) and South Florida Water Management District
(SFWMD). "Environmental and Economic Equity Program Management Plan."
USACE Jacksonville District, FL, August 2001.

U.S. Army Corps of Engineers (USACE) and South Florida Water Management District
(SFWMD). "Regional Economic Impact- Everglades Agricultural Area Storage
Reservoirs- Phase 1." USACE Jacksonville District, FL, October 2003.

U.S. Geological Survey, 2002. "Land and People: Finding a Balance; Everglades"
http://interactive2.usgs.gov/learningweb/pdf/landpeople/evergladesst.pdf
Accessed February 6, 2005.

Weisskoff, R.. The Economics ofEverglades Restoration: Missing Pieces in the Future of
.S,,tlu Florida. Northhampton, MA: Edward Elgar Publishing, 2004.















BIOGRAPHICAL SKETCH

Jennie Varela was born and raised in St. Petersburg, Florida. She was a graduate of

the International Baccalaureate Program at St. Petersburg High School where she was

named a National Merit Scholar. She continued her education at the University of

Florida in Gainesville, Florida. She graduated with a Bachelor of Science in food and

resource economics in May 2002 and completed a Master of Science program in the same

department in 2005. She has accepted a position as an Agricultural Economist in Dairy

Programs for the Agricultural Marketing Service of USDA.