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Pal.(.ily..c.1v.‘ .. vla‘. .. . v.. .... ‘ . a .. ,3VolsinID-‘t....:.!o.r .3. . ll . .... f o .y\.. I vl A 7 Tx‘. . \ n‘ «I \ - I- It It'll I (#18355 STATE UNIVERSITY LIBRARIES ‘Ii'iii’iifliiii minim | 3 1293 00881 1592 L This is to certify that the thesis entitled Energy and economic analyses of comparative sustainability in low-input and conventional farming systems presented by Tiang-Hong Chou has been accepted towards fulfillment of the requirements for Master degree in Science “mm. Major professor Date May 24, 1993 O-7639 MS U is an Affirmative Action/Equal Opportunity Institution . V.._._. _._ - -_._.__ 4 _ is...-—-4 l _' LIBRARY Michlgan State University PLACE IN RETURN BOX to remove this checkout trom your record. TO AVOID FINES return on or before date due. DATE DUE DATE DUE DATE DUE I it 7"” W0 J ____1 MSU Is An Affirmative Action/Equal Opportunity Institution cMMms-DJ ENERGY AND ECONOMIC ANALYSES OF COMPARATIVE SUSTAINABILITY IN LOW-INPUT AND CONVENTIONAL FARMING SYSTEMS BY Tiang-Hong Chou A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Resource Development 1993 ABSTRACT ENERGY AND ECONOMIC ANALYSES OF COMPARATIVE SUSTAINABILITY IN LOW-INPUT AND CONVENTIONAL FARMING SYSTEMS BY Tiang-Hong Chou The sustainability of two low-input (LIP) cropping systems and one conventional system, all from the Rodale Farming Systems Trial, is compared from 1981 to 1992 using energy and economic indicators. The low-input animal system (LIP-A) spread manure, while the low-input cash grain system (LIP-CG) grew green manure crops for nutrients. The conventional system (CONV) used commercial fertilizers and pesticides. Results of these analyses show that both LIP systems required only 50% of CONV nonrenewable energy consumption. Food and biomass energy production was highest for LIP-A. Although LIP—CG generated about 75% of CONV food energy production, it was the most stable system from energy and profitability viewpoints. LIP systems were less profitable than CONV under current policy and economic circumstances. The results demonstrate that LIP are more energy sustainable than CONV. Adjustments to social and economic settings are proposed that could make LIP operations as profitable and economically sustainable as CONV. ACKNOWLEDGEMENTS I would like to express my sincere appreciation to the following professors, colleagues, and friends without whom this thesis could never have been completed. Dr. Tom Edens chaired my thesis committee and helped me through the whole research process. His encouragement and advice are embodied in this thesis. Dr. Jon Bartholic served as my advisor and is the key person who helped me overcome the major barriers of studying and living over these past two years. I am grateful to have had Dr. Richard Harwood on my thesis committee and appreciate his insights into the subjects of this thesis. Researchers at Rodale Institute Research Center provided not only their valuable data but also their thoughtful suggestions and help. I would like to thank Mr. Steve Peters, Dr. Rhonda Janke, and Mr. Jeff Moyer for providing me with detailed information on the experiment, and valuable comments on this thesis. Thanks is also extended to my colleagues in the Department of Resource Development. Robert Pigg proofread the draft of this thesis; Roberta Miller and Shawn Lock provided needed computer editing techniques; Tracy Dunbar iii provided her thesis for my reference; Judith Pedersen-Benn helped edit the proposal of this thesis in addition to some class papers. Their kindness and help are greatly appreciated. My friends in the MSU Taiwanese Student Association generously helped me in many aspects of living here on the IMSU campus. They also shared with me the same concerns and visions for our country. One part of my progress in these two years is attributed to them. Last but not least, I want to thank my family for their endless support and love which are the ultimate source of my energy. I would like to dedicate this thesis to them and to show my commitment to the environment and agriculture in Taiwan in order to make them more sustainable for our future families. iv TABLE OF CONTENTS LI ST OF TABLES O O O O O O O O O I O O O O O O O 0 LIST OF FIGURES . . . . . . . . . . . . . . . . . . . CHAPTER 1 INTRODUCTION 0 O O O O O O O O O O O O O O O O O O O BaCkground O O O O O O O O O O O O O O O O O O O Risks to conventional agriculture . . . . . Additional options for agriculture and agricultural research . . . . . . . . The Study I O O O O O I O O O O O O O 0 Organization of the Study . . . . . . . . . . . CHAPTER 2 LITERATURE REVIEW AND DESCRIPTION OF THE RODALE FARMING SYSTEMS TRIAL . . Discussion of Agricultural Sustainability . . . Literature review . . . . . . . . . . . . . Implications for this study . . . . . . . Energy Analysis of Agriculture . . . . . . . . . Literature review . . . . . . . . . . . . . Implications for this study . . . . . . . . Comparative Studies of Low-input and Conventional Farming Systems . . . . . . . . . Description of the Rodale Farming Systems Trial Background . . . . . . . . . . . . Field condition . System designs . Objectives . . . Major findings . . . . . . . Results from economic studies . Needs for further studies . . . . CHAPTER 3 PROBLEM STATEMENT AND HYPOTHESES . . . . . . . . . . Chapter Introduction . . . . . . . . . . . . . . Problem Statement . . . . . . . . . . . . . Definitions and Measurement of Main Variables . Nonrenewable energy . . . . . . . . . . . V . viii IJhJH tom» Comparable production level Long-term stability . . . . . . . . . . . . Hypotheses . . . . . . . . . . . . . . . . Nonrenewable Energy Consumption . . . . . . Comparable Energy Production Levels . Energy Productivity Stability . . . . Economically Comparable Production Levels . Economic Stability . . . . . . . . . . . . CHAPTER 4 RESEARCH APPROACH . . . . . . . . . . . . . . . . . . Chapter Introduction . . . . . . . . . . . . . . Sources of Information . . . . . . . . . . Field data . . . . . . . . . . . . . . . . Results of previous studies . . . . . . . . Energy Estimates and Analysis . . . . . . . . . Conversion ratios . . . . . . . . . . . . Basis for the calculation and comparison . Inputs excluded from energy calculation . . Energy embodied in machinery . . . . . . Fuel . . . . . . . . . . . . . . . . . Fossil energy embodied in seeds . . . . . . Energy embodied in commercial fertilizers . Energy embodied in synthetic pesticides . . Energy contained in crops . . . . . . Human labor . . . . . . . . . . . . . . . . Economic analysis . . . . . . . . . . Agricultural prices . . . . . . . . . . Estimates of operation costs . . . . . . . Basis for economic comparison . . . CHAPTER 5 RESULT AND DISCUSSION . . . . . . . . . . . . . . . . Chapter Introduction . . . . . . . . . . . . . . Energy Analysis . . . . . . . . . . . . Energy input and test of hypothesis 1 . . Energy output and test of hypothesis 2 . . Energy productivity . . . . . . . . . . Energy stability and test of hypothesis 3 . Economic Analysis . . . . . . . . . . . . . . . Costs . . . . . . . . . . . . . . . . . . Revenue . . . . . . . . . . . . . . Returns above variable and amortized equipment costs and test of hypothesis Profitability . . . . . . . . . . Economic stability and test of hypothesis 5 vi 40 41 43 43 44 44 44 45 68 68 68 68 77 83 85 90 90 99 101 106 107 CHAPTER 6 SUMMARY, CONCLUSION, AND RECOMMENDATIONS Summary . . . Major Findings Conclusions Recommendations Recommendations for policy Recommendations for future research LIST OF REFERENCES vii 109 109 111 113 114 114 115 117 Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table LIST OF TABLES 2.1 Treatment design and rotation schedule of the Rodale Farming Systems Trial, 1981-1985 . 4.1 Conversion factors used in the analysis . 4.2 Average energy production of the low-input treatments in Rodale Farming Systems Trial 4.3 Energy embodied in diesel fuel and machinery 4.4 Machinery used in the Rodale Farming Systems Trial 0 O O O O O O O O O O O O O O O O O O 4.5 Machinery used on a 500-acre standardized fam O O O O O O O O O O O O O O O O O O O 4.6 Estimated fossil energy costs of field seed production, processing and distribution . . 4.7 Energy inputs for chemical fertilizers 4.8 Energy inputs (production, formulation, packaging, transport) for various pesticides 4.9 Food energy in various cash crops . . . . 4.10 Prices of various agricultural commodities, 1981-1992 . . . . . . . . . . . . . . . 4.10 (cont'd) . . . . . . . . . . . . . . . . . 4.11 Indexes of prices received and paid by farmers, United States, 1981-1992 . . . 5.1 Energy budget in LIP-A system . . . . . . 5.2 Energy budget in LIP-CG system . . . . . 5.3 Energy budget in CONV system . . . . . . . 5.4 Summary of energy budgets . . . . . . . . . 5.5 Energy and economic stability indexes . . . viii 29 48 51 53 55 56 57 58 59 60 62 63 65 69 70 71 76 89 Table 5.6 Economic balance in LIP—A system . . Table 5.7 Economic balance in LIP-CG system . . . Table 5.8 Economic balance in CONV system . . . . Table 5.9 Summary of economic balances, ix 1981-1992 91 92 93 98 Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure 2.4 2.5 5.1 5.4 LIST OF FIGURES System diagram of the conventional system (CONV) in the Rodale Farming Systems Trial . . . . . . . . . . . . System diagram of the low-input with animal cropping system (LIP-A) in the Rodale Farming Systems Trial . . . . System diagram of the low-input cash grain cropping system (LIP-CG) in the Rodale Farming Systems Trial . . . . . Treatment design and rotation schedule of the Rodale Farming Systems Trial, 1986-1992 O O O O O O O O O O O O O O O (cont'd) . . . . . . . . . . . . . . . Rodale Farming Systems Trial field layout . . . . . . . . . . . . . . . . . Total nonrenewable energy consumption in the Rodale Farming Systems Trial . . . . Share of nonrenewable energy consumption in the Rodale Farming Systems Trial, 1981-1992 O O O O O O O O O O O O O O Total nonrenewable energy consumption on a BOO-acre standardized farm . . . . . . Total energy output in Rodale Farming Systems Trial . . . . . . . . . . . . Energy productivity in the Rodale Farming Systems Trial . . . . . . . Energy productivity on a 500-acre standardized farm . . . . . . . . . . . Total variable and amortized equipment costs in the Rodale Farming Systems Trial O O O O O O O O O O O O O O O O O X 26 27 28 30 31 32 72 74 78 81 86 87 94 Figure 5.8 Figure 5.9 Figure 5.10 Share of costs in the Rodale Farming Systems Trial . . . . . . . . . . . . . 96 Total revenue from crop sales in the Rodale Farming Systems Trial . . . . . . 100 Returns above variable and amortized equipment costs in the Rodale Farming Systems Trial . . . . . . . . . . . . . 104 xi CHAPTER 1 INTRODUCTION Mam After decades of development of conventional agriculture, farmers and researchers are seeking new paradigms of food production which are sustainable over a long period. Since the 19408, U.S. agriculture has constantly innovated and adapted farming techniques. This contributed to dramatic increases in per acre yield and overall agricultural production, and led to the wide use of machinery, mono-cultural practices, irrigation, and synthetic fertilizers and pesticides in the United States and many other countries (National Research Council, 1989: 25). It is estimated that American farmers' expenditures for petroleum fuel rose five-fold from 1940 to 1974. During the same period, fertilizer and pesticide inputs increased by more than twelve-fold in the U.S. (Stout, 1984: 13). Risks to conventional agriculture. This energy-intensive agricultural system has experienced at least four problems. First, in the long term, the conventional system is not 2 sustainable because of the foreseeable uncertainty of fossil fuel supplies (Lockeretz, 1984: 78). Most recent world estimates show that there are about 1500 billion barrels of recoverable petroleum remaining. These will last about 60- 65 years at current rates of consumption (See Cutter et al., 1991: 357-358; Edens and Haynes, 1982: 364). It is speculated that the decrease in petroleum supplies will cause substantial increases in oil prices in the foreseeable future. Second, due to high dependence on fossil energy, current farms are sensitive to external forces. An increase in the price of a raw material or commodity may cause major economic difficulty for farmers, especially those small operators who lack sufficient capital. The impact of the 1973 OPEC oil embargo on agriculture reflected the sensitivity of conventional production systems to its external forces. From 1973 to 1978, major farm inputs showed the following price increases: land, 167%; farm machinery, 137%; fertilizers, 65%; and fuel and oil, 234% (Stout, 1984: 13). Total farm debt rose from $52.8 billion in 1970 to $206.5 billion in 1983 (National Research Council, 1989: 91). The impact has been felt not only at the farm level but at the societal level as well. Studies estimated that in the late 19708, American farmers invested $3 billion in pesticides in order to save $12 billion in U.S. crops, and 3 the entire country spent at least $1 billion to cover environmental and health costs associated with pesticides each year (Pimentel et al., 1986). A similar assessment carried out in 1992 concluded that the environmental and social costs of pesticides alone had increased four-fold, to $5 billion dollars, while direct pesticide costs remained unchanged, and the value of crops saved increased only 33% to $16 billion (Pimentel et al., 1992). Although the assessment was thought to underestimate the social and environmental costs because the data was not complete, it is fairly evident that society may be even more sensitive than the farm to the use of pesticides. Third, public concern about the stress of agriculture on the environment has been rising rapidly. Numerous studies have linked conventional farming to degradation of agricultural ecosystems, such as erosion and salinization of soils, underground and surface water contamination, and the reduction of wildlife and natural enemies of agricultural pests (National Research Council, 1989: 97-130). Conventional practices have also resulted indirectly in pollution problems like climatic warming, destruction of the ozone layer, acid rain, and others (Conway, 1990). Finally, because of the reduction of farming ecosystem linkages, the conventional production system is losing its internal stability. For instance, the increasing reliance on chemicals for pest control has resulted in rising 4 resistance to pesticides and the decline of natural pest control mechanisms. These changes have ultimately increased the susceptibility of the systems to insects and diseases (Edens and Koenig, 1980: 697). In Southern and Southeastern Asia, “hopperburn” (a severe rice damage caused by Brown Planthopper) in rice fields has occurred more frequently since the 19603, when high yielding varieties and relating cropping patterns were introduced into the region (Dyck et Additional options for agriculture and agricultural research. Actually, these problems are related to each other. The situation common to these problems is the structural dependence of farming systems on fossil energy. Therefore, many have suggested that future innovations should be based on reducing nonrenewable energy resources in agriculture (Pimentel et al., 1973: 446; Edens and Koenig, 1980: 697; Harwood, 1985: 64). In Francis and King's words, to achieve more sustainable farming systems in the future, the farming paradigm should be shifted from a ”reliance on external resources“ to an “utilization of internal farm- derived, renewable resources“ (1988: 67). Two major approaches are leading farmers and agricultural researchers toward alternative agriculture. The first approach focuses mostly on t;aditiggal_§grming philgggphy_gng_me§hgg§. In this country, studies have been 5 conducted to examine energy saving, soil conservation, and the sustainability of Amish agriculture (Jonson et al., 1977; Jackson, 1988; Stinner et al., 1989). In addition, farming systems research increasingly emphasizes the exploration of indigenous farming knowledge in many parts of the world (Chambers, 1992). Although valuable, the traditional practices are seen as subject more to their particular cultures and religions. In the case of Amish farming, for example, it was pointed out that most of the energy savings resulted from the frugal lifestyles of the Amish, and not from their farming practices (Kaffka, 1984: 15). Also, Edens and Haynes (1982: 388) argued that "system structures are not reversible in any literal sense...future adjustments must be understood in the context of our current state and the forces most influential in directing future changes." Therefore, knowledge of only traditional farming has limitations in helping us to define the “paradigm transformation.“ The second approach enlightens agricultural specialists and operators to es' test a mana a ter tive fa;ming_§y§;em§ using current ecological and agronomic knowledge. The term “Agroecosystem Integrated Management" (AIM) by Edens and Koenig (1980) provides an useful concept for describing this approach. The AIM perspective looks at a farming system, not only at production, or any particular subsystem, but at all its components, and the relationships 6 between these components in the context of the system. It focuses on rational design and management of a farming system while recognizing the natural constraints of the agroecosystem. It tries to overcome the constraints through its designed mechanisms, using on-farm renewable resources instead of depending on external nonrenewable resources. AIM seeks the long-term stability of a cropping system and the maintenance of balance between human activities and the agroecosystem in a closed-loop farming structure with feedback. Organic farming and low-input/sustainable agriculture (LISA) program are two typical examples in this area. A United States Department of Agriculture (USDA) research team on organic farming (USDA, 1980: 9) defines organic farming as: Organic farming is a production system which avoids or largely excludes the use of synthetically compounded fertilizers, pesticides, growth regulators, and livestock feed additives. To the maximum extent feasible, organic farming systems rely upon crop rotations, crop residues, animal manures, legumes, green manures, off—farm organic wastes, mechanical cultivation, mineral-bearing rocks, and aspects of biological pest control to maintain soil productivity and tilth, to supply plant nutrients, and to control insects, weeds, and other pests. Also, Harwood (1984: 3) provides a brief description for organic farming: An organic system is one which is structured to minimize the need for off-farm soil or plant-focused inputs. Because of lack of information on the 7 disruptive effect of synthetic inputs, none are used. “Natural" sources of inputs are used with discretion. Although the term “organic farming" has been widely accepted among researchers and operators, some have suggested other terms for the farming philosophy and practice, such as regenerative (Rodale, 1983), ecological (Luo and Han, 1990; Soule and Piper, 1992), biodynamic (Pettersson, 1977; Harwood, 1990), low-input farming (Madden and Dobbs, 1990), etc. In this thesis, “low-input" farming is used because the term presents objectively the basic feature of the philosophy and operation from the standpoint of nonrenewable energy use. The Low-Input/Sustainable Agriculture (LISA) program is an education and research program of organic farming developed in the 1987 by the USDA. It has funded many studies, both on-farm and on-station, to design sustainable farm productions systems for various environments and agricultural products (Parr et al., 1990). Additionally, the approach of design and management of sustainable farming systems has led many researchers and operators to carry out low-input practices in the U.S. and Europe. The processes and results of some of the experiments and implementations are shown in Balfour, 1977; Pettersson, 1977; Eggert, 1977; Harwood, 1984; Kaffka, 1984; Sahs and Lesoing, 1985; National Research Council, 1989; Liebhardt et al., 1989; Peters et al., 1992; Cunningham et al., 1992; and Chou, 1992. The Study Among these efforts, researchers in the Rodale Institute Research Center started a farming systems experiment in 1981 to explore the yield performance and other processes of two low-input farming systems and to compare them with a conventional systems from the perspectives of biophysical and environmental sustainability. In addition to the Institute's studies, several economic analyses have been done to evaluate the economic potential of the low-input practices of the Rodale experiment. Due to the critical role of energy in future agricultural development, and to the fact that energy analysis might be a viable tool in exploring the cropping systems performance, this study is designed t9 measure and us i ' ' both the w-‘n s tem a d 0! e9 ._0!a, S = "111 he 3093.5 31.1. : =t‘1= W This study will analyze energy budgets and economic balances of the three farming systems for the 12-year period from 1981 to 1992. 'ec ' e o ' t d ' w-‘n ut farmi s stems 'n od e s e ' a e e s t ' able ' m'c ’ u s o ' a1 8 e . It is hoped that this study will provide a useful and 9 appropriate design for the comparison of agricultural sustainability between various farming systems. Organization of the Study This chapter has included an introductory discussion and an overview of the development of low-input farming systems researches and practices. In the following chapter, I will discuss the literature related to agricultural sustainability, energy analysis, and comparative studies of low-input and conventional farming systems, followed by a description of the Rodale farming systems trial. In the third chapter of this thesis, the objective and hypotheses of the study will be fully described. Important concepts like sustainability, productivity, and stability are quantitatively defined in the chapter. Methods and materials used in the analysis and comparison of the systems, and some associated assumptions in the calculation of the energy and economic budgets are presented in the fourth chapter. Chapter five shows the study's main findings followed by a detailed discussion of the results and the validity of the hypotheses. The last chapter contains a summary of the key points presented from chapter two to five. Finally, conclusions and recommendations for the future development of low-input agriculture are addressed based on the findings of the study. CHAPTER 2 LITERATURE REVIEW AND DESCRIPTION OF THE RODALE FARMING SYSTEMS TRIAL Disgnssign of Agricultural Sustainability Literature review. Agricultural sustainability (AS) has been discussed increasingly since the 19803. Most of the discussions focused on the qualitative characteristics of AS. Edens and Haynes (1982: 372) described sustainability as ” long-term stability" in agricultural production systems. Since stability is somewhat ambiguous and not well defined, they suggested that sustainability might be a more appropriate criterion for evaluating long-term human impacts on renewable resources (p.383). Another noted description of AS was given by Douglass (1984 and 1985) who stated a sustainable agriculture (SA) includes: agricultural methods which will generate needed levels of production with the least amount of damage to the physical and human communities on which sustainable societies must depend” (1984: 5). He pointed out that the definition had led to three 10 11 different approaches to a SA. The igggznniiigiengy_§gnggi emphasizes economic scarcity, attempting to expand the food supply by increasing the agricultural resource base and productive efficiency. The stewagdsnip schogl is concerned about the ecological balance and natural constraints, concentrating on the need for population control, restructuring of agriculture, or cutting down on hazards to sustainable production. For this school, sustainable production means "the average level of output over an indefinitely long period which can be sustained without depleting the renewable resources on which it depends" (1985: 10). The communigy schogl focuses on the effects of different production systems on the social organization and culture of rural life, suggesting a socially holistic perspective in addressing the issues of agricultural sustainability, rather than depending only on scientific or technological efforts. Similarly, Crosson defined a sustainable agricultural system as: one that can indefinitely meet demands for food and fiber at socially acceptable economic and environmental costs (See Harrington, 1992: 565). According to Harwood (1988), a sustainable agriculture represents: an agriculture that can evolve indefinitely toward greater human utility, greater efficiency of resource use and a balance with the environment that is favorable both to humans and to most other species. 12 In addition, some authors have had intensive discussions on the relationships of productivity, stability, sustainability, and equitability in agroecosystems (Conway, 1986, 1990; Marten, 1988). Their definitions of the concepts are generally close. The basic concept is nggngniyiny, which was defined as "... the net increment in valued product per unit of resource“ (Conway, 1986: 23). Then they went further to define SLéDlllLX in terms of the consistency of productivity under normal and/or small scale fluctuations in environmental variables, and to identify snstninabiiity as the ability of a system to maintain a specific level of productivity over the long term. Although these properties were mutually defined, the authors argued that there are trade-offs among them. For example, the dramatic increase of labor productivity in agriculture through the wide use of agrichemicals and farm machinery in the past decades has threatened the stability and sustainability of the system now and for the future. Also, in order to stabilize crops yields in the short term, farmers applied large amount of pesticides in pest control which have inversely affected AS in the long term. These arguments are critical because they show the significance of the time factor and the perspectives that we use to consider these properties. For instance, because productivity is the foundation on which the definitions of other concepts were developed, it is important to select an appropriate index 13 for productivity. Lowrance et al. (1986) tried to incorporate different definitions of AS by proposing a hierarchical definition of sustainability. They indicated four levels of AS: * agronomic sustainability in the field system, * microeconomic sustainability in the farm system, * ecological sustainability in the watershed/ landscape system, * macroeconomic sustainability in the national/regional system. The importance of their points is that in evaluating AS, we should determine which levels of sustainability we wish to address, and we should fully consider the interactions among various hierarchical levels (also see Seetisarn, 1988: 7). These suggestions therefore provide a good scope for the analysis of AS. In addition to the qualitative definitions of AS, however, there have been few studies associated with quantifying and measuring AS (MacKay, 1989). There is an urgent need to develop operational definitions of As so that more concrete indexes of AS can be formed for evaluating agroecosystems. Farming systems researchers like Charoenwatana and Rambo (1988) have pointed out the lack of comparative analyses focused on identifying common or unique factors in sustainability of various agroecosystems. Comparative analysis of ecosystem sustainability has also been viewed as an important objective of future researches. 14 Implications for this study. A definition of agricultural sustainability for this study is derived from the discussion above. The definition centers on physical long-term stability (in Edens and Haynes' term), efficiency of nonrenewable energy resource use (in Harwood's definition), economically acceptable production level (in Crosson's words), and sufficient level of food production (in Douglass’s definition) of a farming system. In this thesis, five hypotheses are developed to compare the sustainability of the systems. The first studies the nonrenewable energy consumption. The second is associated with relative food energy production for examining the sufficient food production level. The third hypothesis focuses on returns above variable costs for economically acceptable production level. The remaining two examine energy and economic long-term stability of low-input and conventional farming systems, respectively. Conway's definition of stability, the constancy of productivity, can be converted easily into an operational definition if the constancy is measured through the concept of statistical variation. An alternative indicator of productivity from the energy perspective is applied in this study. Energy productivity is defined as the ratio of food energy output to the nonrenewable energy input of a system. The energy productivity could also be considered as the efficiency of nonrenewable energy use in the farming system. 15 In fact, the approach of this study incorporates the food- efficiency and stewardship schools described in Douglass's article. The system boundaries of this study are at the farm level or microeconomic level of the sustainability hierarchy discussed by Lowrance et al. (1986). Energy Analysis of Agricuiture Literature review. The evolution of energy analysis (EA) has been related closely to the recognition of the important position of energy in the world's development. Although engineers in process technologies had been traditionally trained to manage energy functions in the process systems, an overall concern for energy economy did not occur until the 19703, or more exactly, 1973, when OPEC imposed an oil embargo which resulted in a worldwide shortage of petroleum (IFIAS, 1974). In agriculture, classical economists defined the three. elements of production as land, capital, and labor. In the 19703, research from an energy perspective took place, and new alternatives to traditional approaches were developed. As Doyle has described (1990: 92), some started discussing the resources of agricultural production in terms of land, energy, and labor. Traditionally identified inputs, such as machinery, fuels, and chemicals were replaced by a proxy of 16 the fossil energy required to operate and produce them (de Wit, 1979: 281). Moreover, Pimentel and Pimentel (1979: 13) went further and calculated labor in the form of energy. These efforts laid a basis for further development of EA in which units of energy instead of dollars constitute the indicator of production. Many researchers have pointed out the ggnnginnnign§_nng WWW—mm as the relevant unit of account. First of all, Wilson (1974: 7) discussed the risk of reliance on monetary prices in an imperfect market with government interventions, lack of information, and other imperfections. He suggested that energy might be a more sensitive and concrete indicator in guiding us to better resource allocation. According to Wilson, EA could be a more value-free tool which could provide valuable additional information for decision making. This argument could be supported by the examples shown in the Edens and Koenig article (1980). The authors strongly criticized the FAO's estimate of “self- sufficiency ratios" by deducing the dollar value of the difference between exports and imports of major agricultural products, and argued that the FAO completely ignored imports of fossil fuel, which should be included in the calculation of self-sufficiency. They also pointed out the fact that the price of energy has failed to reflect its real cost in the national economy. 17 Third, Axinn and Axinn (1984) found it difficult and inappropriate to apply cash-dominated economic analysis to a rural area where households and communities are primarily self-sufficient. Cash flow may not be significant or even exist in a village if the villagers tend to recycle materials rather than to trade products for cash income. They thus developed an energy recycling ratio as an analytical and comparative index for addressing Nepal's farming systems. One point implied in their findings was that a technique of EA such as the recycling ratio might provide better comparisons between systems, especially those with quite different cultures. Fourth, one powerful function of EA is its ability to identify the constraints and boundary conditions of a production system (IFIAS, 1974: 15). Unlike contents of money used in modern economy which are manipulated by humans and could change over time, laws of energy generation, storage, and transformation are natural phenomena and cannot be altered by humans. For instance, humans may overcome an economic crisis but they can never increase world fossil fuel storage. EA could lead researchers and operators to better understanding of the carrying capacity and mechanisms of agroecosystems, and to rationally and sustainably design and manage the systems. Finally, the authors of the IFIAS's report (1974) identified EA as "a mean of injecting physical variables 18 into economic theory." EA can thus contribute to the integration of agronomists, entomologists, and economists working for the development of sustainable agriculture. Renborg (1981), Norum (1983), Jones (1989) and others discussed the gnegnign§_nng_iininnnign§ of the methodology of EA in agriculture. Renborg focused on the problems related to the exclusion of solar energy and land in EA. Norum emphasized the danger associated with the aggregation of energy resources in both input and output sides. Jones indicated the distinction between solar energy and support energy, and suggested different systems boundaries for different levels of analysis. More significantly, they all pointed out the difficulties and conflicts of EAs in dealing with human labor, but came to separate conclusions. Renborg tended to consider the life support system of a farmer in calculating human labor. Norum concluded that labor should be separated from other inputs and expressed by number of hours. Due to the fact of competition and substitution between resources in production, he suggested that energy analysts should clarify their values as a guide to decision making among the alternatives. Jones argued that the decisions in EA depend on the purpose of the analysis; however, he concluded that EA may be able to serve a descriptive function rather than an analytical function. It is important that energy analysis proponents 19 recognize both the strengths and limits of EA. They should base decisions to use, or not to use, EA on the objectives of the study. In conducting EA, the analyst should make clear the major assumptions and limitations of the analysis, and interpret its results carefully. Implications for this study. It can be concluded from the literature that energy analysis is an appropriate technique for this study. As mentioned earlier, this study includes an examination of economic profitability and physical constraints in various farming systems, including low-input systems which tend to recycle intensively their internal resources. According to the authors, energy analysis has unique benefits and strength in exploring these phenomena. Also, farming is a human activity that is closely related to the use of natural resource. EA can be highly useful in allocating resources, especially when dealing with nonrenewable resources, because the renewable and nonrenewable characteristics of resources can be distinguished relatively clearly from an energy perspective. The weakness and limitations of EA described in the previous literature should have only a minor impact on this study. This analysis will emphasize the utilization and constraints of nonrenewable energy in various farming systems. The exploration of interactions between renewable resources like solar, land, water, etc, and the nonrenewable 20 energy is not the object of this analysis. Human labor will be estimated in terms of time (hours) following the suggestion of Norum (1983). Hence, a straightforward EA can be validly carried out to meet the objective of this study. Also, this study will combine energy and economic analyses, which could be complemented by one another, and making the result more comprehensive. Finally, some needed assumptions will be provided and the results will be interpreted carefully to overcome the possible flaw of EA. Compazative Studies of Low-input and Qonyentignsi Farming Systems Numerous studies have been designed to compare mechanisms of conventional and low-input farming systems. Some were done from the viewpoints of biophysical relations within the systems, such as nest activity (Motyka and Edens, 1984), soil erosion (Reganold et al., 1987; Sahr and Lesoing, 1985), and nntsient flow (Patten, 1982; Eggert and Kahrmann, 1984; Heichel and Barnes, 1984). Economic ggmnnnisgns have also attracted much attention in seeking information on transitions from conventional to low-input production systems (Berardi, 1978; Lockeretz, 1981; Dabbert, 1986). Berardi concluded that although total costs were higher on the low-input organic farms than on the 21 conventional, the low-input farms had lower operating costs (total costs excludes unpaid family labor) than the conventional group. Also, the low-input farms were economically comparable to the well managed conventional farms in New York because the organic farmers compensated their lower yields by receiving a price premium. Lockeretz et al. found in their study from 1974 to 1977 that low-input organic farms produced less market values as well as lower operating costs than the conventional farms, resulting in an approximately equal returns (crop sales minus operating costs) in the groups. Both the studies of Berardi and Lockeretz et al. were also designed to compare the enengy efficiency snd nggducsivity of low-input and conventional production systems. The former author pointed out that conventional farms consumed 48% more energy, yet produced only 29% higher yields than did the low-input farms. Lockeretz et al. (1981) concluded that between 1974 and 1978, the energy consumed to produce a dollar's worth of crops on organic farms was about 40% as great as on conventional farms. Kaffka (1984) showed that his study farm, which practiced low-commercial-input farming methods, used fossil energy more efficiently per unit of milk and crop production than average New York state dairy farms. Pimentel et al. (1984) found that organic farms in Iowa produced corn and wheat 26— 70% more efficiently than did the conventional farms. 22 Description of the Rodale Farming Systems Trial Background. The ongoing cropping systems experiment was initiated in 1981 by a group of researchers at Rodale Institute Research Center, located in southeastern Pennsylvania, near Kutztown. The trial, which includes two low-input systems and one conventional system, was initially designed to study the transition from conventional to low- input production methods. In 1986, it was assumed that the low-input systems had reached a new equilibrium after five years of transition and the study was shifted from addressing conversion difficulties to examining long-term systems operations and environmental conditions in the post- transition phase. During the past twelve years, the trial has generated much information about yield performance, rotation effects, weed impact, nutrient situation, and soil conditions in the farming systems. Hence, it is helpful in understanding holistically the biophysical mechanisms of different systems and their impacts on the environment. Additionally, the data provide good material for an analysis concerning the comparative sustainability of the farming systems. Field condition. According to a personal communication with Steve Peters (April 29, 1993), the soil of the 13-acre site (3% south—facing slope) is mainly a Berks shaley silty klay 23 loam that is well drained, with lesser amounts of Commly silt loams and Duffield silt loams. Portions of the silt loams, however, may generate a perched water table. The climate provides 180 frost-free days, 3000 growing days (based on 50° F), and an average of 42 inches of rainfall which is relatively evenly distributed through out the year. Prior to the establishment of the trial, the field was farmed mostly in corn and a small portion of wheat with chemical fertilizers and pesticides. After the harvest of winter wheat in the summer of 1980, the site was fallow until all of it was plowed in March, 1981; followed by the start of the experiment. Foxtail was growing widely on the site during the fallow period (Liebhardt et al. 1989: 152). System designs. The three farming systems are as follows: 1. The Low-Input system with animal (LIP-A) simulated a beef operation by practicing a five-year rotation including red clover/alfalfa hay, oats, winter wheat, corn grain and silage, and soybeans. Nitrogen was provided both by steer manure from a farm adjunct to the research center, and by third-year legume hay crops plowed down just prior to planting corn. 2. The Low-Input/Cash Grain system (LIP-CG) did not include an animal enterprise. It produced a cash grain every year, 24 such as corn, soybeans, oats, winter wheat, and spring barley. Nitrogen was provided by short-term legume hay and green manure crops. Weed control for corn and soybeans in both low input systems was accomplished mechanically with a rotary hoe and ridge cultivator, and culturally through crop rotation, green manuring and relay cropping. 3. The Conventional Cash Grain system (CONV) was operated through a corn-soybean rotation using commercial fertilizers and synthetic pesticides recommended by Pennsylvania State University. Conventional tillage, including moldboard plow, disk, harrow, and cultipack, was applied in all three cropping systems. All these jobs were conducted in the spring with the exception of a fall plowing for winter wheat. Pest control in both low-input systems was accomplished through crop rotation, while insecticides were used on the conventional system, respectively. It should also be noted that adjustments were made in the system designs during the study. For the LIP-A system, the whole rotation pattern stayed the same but some minor adjustments occurred. First, different cover crop species were used for competition with weeds in different period. From 1981 to 1985 and 1989, pure red clover was grown in the LIP-A plots; in 1986 and 1987 red clover was combined with 25 alfalfa hay; and in 1991 red clover and orchardgrass were used. Second, since 1991, ryegrass and rye grain were added into the LIP-A system as additional cover crops. For the LIP-CG system, the rotational pattern changed. First, prior to 1986, the system used a 5-year rotation. After that a 3 year rotation was practiced. Second, from 1986 to 1990, LIP-CG soybeans were grown by relay cropping. In addition, in this period, mono-cultural practices were followed for the production of LIP-CG soybeans. The remaining two changes in the LIP-CG system oCcurred in 1991 when hairy vetch replaced red clover as the cover crop of the system, and ryegrass was added into the system (Peters, April 29, 1993, personal communication). Figures 2.1, 2.2, and 2.3 are system diagrams which symbolize LIP-A, LIP-CG, and CONV farming systems in the Rodale FST respectively. The crop rotation schedules are presented in Table 2.1 and Figure 2.4. Every farming system in the Rodale FST was distributed randomly into 8 of 24 main plots (60 ft * 300 ft) with three subplots (20 ft * 300 ft) within each of the main plots. Every subplot represents one rotational entry point of a farming system. Therefore, in the experiment, there are 9 treatments (3 farming systems * 3 rotational entries), and 8 replications (8 main plots for each system), resulting in a total of 72 plots (Figure 2.5). Grass buffer strips (5 ft wide) were maintained between the main plots to minimize 26 . dough. mEouEwm emESw Azania; fleece—coca I II ail ...... .. _O.=COU A BOP..— moEozmon— H . a shaman mcoem .Hcflua meoummm oceanom eaccom ecu cw Amimaqv Ecumwm mcwmmouo assess cuw3 ucmcwiaoa on» no Ecumoac Seesaw 27 z / “‘ V‘s. ... u 1 w A A w 05:2: :35 Im. 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I «In . .la . . ml. . . «I. .l. :3 :1. _ .I. .g ...: .5 . :8 :5 .cl ... ..U ...: .3. .i :8 .i ..i . .abufi a 2.8... 86.5.23 3...: 33 1. £33m 5...... 3&8 =8. _ as... _ m8. _ Ba. _ 8... 5:5... .uoucou noncomom ousufiumcH eaccom "coucOm .NmmHloomH 31 ..e.unouv ¢.~ ousm.m .8 .>: ...: ..S. ...5 .>§ ...: .Rs ...5 :4: ... .3 ...: :3. ...: Z :5 ...t .5 ...: Elf _ 5.26 :35 .m:c..:o>:c0 an Eon—mam m i mwm 326m moo ”IN . _ . 85.81;. E . _ «la _ .53. 2:00 is}: ; 5.20 £ch .5...— Bow. "N Egmhm zqmmSm «m a. t . Ifi . .. < 3.2135 0.19.36 33. a .3 .8 m ..sz 1” ! _ Nuln >51 34.531.553.56 nun .... 2.8 mt. mmamouE wlw 2326.. 2.18 . 2.25.3.5...» ...—...: 3c: ”— 533nm dunno“: stroll. m8. :3. 2.: 2.11.: 3.: 5.2.5.: 32 ..Hc um muouom NounsOm .mmmN .ucONcN macaw HoNua mEoumam chENch mascox $200. ...-.0 .16 78.8.5.8“. u n .82... ...:u ....u .1... ...... u N 25312.3( .1... so. u . ....st 9.9.80 m.~ ousmfim x .’ JV“ N N _ N m . e _ N N . N m m N N N . N e . N N . N N N .N N _ _N N N N e _ _ _ N _ N N N — n J . N N . _ _ . N N N N 4 N . N_ m n . _ n _ . . N _ N _ N N N N . ._ . N N_ N _N .N _ . _N _. _ N . N _. N 4 _N N N_ N _ _ i4 . _ N _ N N ._ N . N_ N _ n i— . _ N J N . . FL N _ .N N N _ z N‘ N _ yr .. N . . N N_ III... II... lunatic Ilsa FIFO 33 Objectives. The objectives in the first five-year phase of the trial were: * to define yield-limiting factors that occur during the transition process, * to identify methods of minimizing yield reductions, and * to identify physical, chemical, and biological processes that occur during conversion to low- input methods" (Liebhardt et al., 1989: 151). As the experiment entered its second phase in 1986, it focused on the long term economic reliability, sustainability, and environmental impact of low-input and conventional techniques. Therefore, the study in this phase retained the same concern about yields in different systems, while it differed from the interests of the first phase in that more attention was focused on biological and chemical impacts of the different practices on their ecosystems, especially the 3011's condition (Peters et al., 1992). Major findings. They found that corn grain yields in the low-input systems were 75% of the conventional in 1981 to 1984, largely due to weed competition and insufficient nitrogen in the low-input fields. But in 1985 corn yields increased to the same level as the conventional system. Soybean production was at the same level or greater in the low-input systems than in the conventional system. It was 34 suggested that a favorable transition from input-intensive to low-input systems is feasible if crop rotations are applied with crops that demand less nitrogen and are competitive with weeds (Liebhardt et al., 1989). The major findings in the second (1986-1990) phase of the study showed that corn yields were nearly the same in all systems from 1986 to 1990. Moreover, in a dry year (1988) corn in a CONV treatment, which had also grown corn in 1987, was outyielded by both low-input systems. However, average LIP-CG soybean yields from 1986 to 1990 were about 85% of those in the other systems, resulting partly from the intercropping with either wheat or barley in the system. Weed levels were generally greater in the LIP systems compared to the CONV, causing yield reduction in two corn treatments and two soybean treatments. Ear leaf nitrogen concentration at corn silking in all treatments usually equalled or exceeded the sufficiency level. Soil nitrate- nitrogen levels in all corn treatments were always higher in the LIP-A system than in the other systems. Water infiltration rates and organic matter levels were higher in the low-input systems than in the CONV system after ten years. Since 1981, soil phosphorus levels had remained high; soil nitrogen levels had increased in LIP-A, unchanged in LIP-CG, and slightly decreased in CONV; but potassium levels had dropped constantly in all systems until the 35 application of potassium fertilizer to all treatments in 1989 (Peters et al., 1992) According to the results of the study from 1981 to 1990, it has been concluded that low-input farming either with use of animal or green manures could be promising if well-designed crop rotations are applied. Also, low-input systems can provide a favorable soil environment to sustain the growth of healthy crops in the long term. Results from economic studies. Hanson et al. (1990) carried out a whole-farm study to compare the profitability of the low-input cash grain and conventional systems in the Rodale FST. They considered the influence of various government programs and concluded that the low-input approach is advantageous for risk-averse farmers. They also found that the profit trend was upward for the low-input scenario, but that the economic transitional period was longer than the biological one. They suggested that soil improvement in the low-input fields might have contributed to higher profitability for the low-input operation in the latter years of the study. Another economic analysis of the PST (Duffy et al., 1989) pointed out that the LIP-A and CONV systems are significantly superior to the LIP-CG system from the farm return point of view. The result of the study reveals that 36 farmers in transition to low-input practices are well advised to avoid row crops, i.e., corn and soybeans. If the crops are to be grown, the authors suggest the use of intermediate levels of commercial fertilizers and pesticides to avoid major loss of profits during the conversion period. An economic study, conducted by Dunbar (1991) to evaluate the profitability of the Rodale farming systems, showed that returns above variable costs were slightly higher in conventional production than in low-input production. The author concluded that low-input practices are promising due to: 1) lower or no chemical costs, 2) more effective labor use, and 3) more profitable corn production. Needs for further studies. Some points remain unclear and demand further study: 1. Because the trial was divided into at least two stages with various foci, and currently remains in operation, an overall examination of the twelve-year experiment might help combine the separated parts into one continuous process and make the entire study more comprehensive. 2. The practicality of the low-input systems in the Rodale EST on real farms should be documented because success in research fields does not necessarily guarantee success in a 37 real farming situation. An assessment should be conducted before the alternative systems and management practices are applied on farms. A whole-farm production analysis and comparison should also be included. For example, in the reports (Peters et al., 1992 and Liebhardt et al., 1989) of the farming systems trials, the corn and soybean yields are compared on the basis of the productivity of the crop- growing plot (i.e., production per acre or per hectare of the corn and soybean-growing plots), not the productivity of the whole cropping system. In a real farm context, the farm incomes are dependent on the productivity of the entire farming system. Therefore, an comparison of the whole system productivity might help farmers make decisions in a more realistic context. 3. Sustainability is an important concept but was not clearly defined in the reports. It could help to develop indices of sustainability which could be used to examine some basic and unanswered questions in the trials, such as " are the low-input systems more sustainable than the conventional one?“ 4. Although some fundamental variations between the low- input and conventional farming practices remain, the systems were all operated with identical machinery. Fossil fuel 38 consumption was reported to be higher in 1981 in the low- input systems, and total energy consumption was higher in the conventional system (Harwood, 1985: 65). No additional evidence is available for characterizing the consumption in other years. The analysis of fuel consumption over all years would help us evaluate the performance of the alternative farming systems. CHAPTER 3 PROBLEM STATEMENT AND HYPOTHESES Chapter Intsoduction The problem and design of this study is based on the discussion in the last chapter. A main definition of agricultural sustainability, with four associated factors, is developed to analyze the problem. Finally, four quantitative hypotheses are described in the last section of this chapter, providing the research structure of this thesis. St ent This study addresses comparative sustainability in each of the three Rodale farming systems through energy and economic analyses of the 12-year experimental data. The major objective of this study is to test Lh§_h¥DQ§h§§i§_Lh§L 1‘ ow-fiy- _-9-'!- = : en: a ‘ tore 8 = a'reb.‘ a! 2‘ '1‘! .!i 3 3t’ll3 _°ll !' ’ 9'_ 3 ’ ‘!‘ 9| 3' 3!! W 39 40 Definitions and Measurement of Main Variables In this study, a farming system is defined to be more energy sustainable compared to other farming systems if it uses less nonrenewable energy while it maintains or increases productivity on or above an acceptable level over a long period of time. Nonrenewable energy. The nonrenewable energy of farming systems is the fossil fuel-based energy embodied in gasoline, diesel, machinery, seeds, commercial fertilizers, and pesticides. It is also assumed that renewable resources such as solar energy, water, green and animal manures and biomass can be regenerated and thus are not exhaustible. Comparable production level. There are a number of different methods to determine the acceptable production level of a farming system. One approach used in this study examines the biomass and food energy production of a system. If the food energy production levels in the low-input systems are comparable to those of the conventional system, they are acceptable. The second approach examines tne isvsi oi nst fszn LESBID- It is generally noted that low-input practices produce lower crop yields than conventional operations, but low-input practices also cost less because they use few external inputs. If the loss resulting from reduction of yields in a low-input system can be recovered by cost 41 savings, leading to similar net returns for the low-input and conventional systems, then the low-input production level is acceptable. Long-term stability. Long-term stability is another major criterion for a sustainable production system. In this study, two types of systems stability will be analyzed. One is st b'lit , the other is economic staniiity. A definition of systems stability by Conway (1990: 219) is used. He defined stability as: the constancy of productivity in the face of small disturbing forces arising from the normal fluctuations and cycles in the surrounding environment. Among many techniques of statistical analysis, ggsfiigisnn gfi_yn;isnign is mostly commonly used in measures of relative dispersion among several sets of observed values (Stockton and Clark, 1980: 93-94; Thomas, 1983: 15). The coefficient of variation is defined as the satio oi a standard deviation to 9‘ u‘a! 0. 1‘ cat: or W 'C! 96 S a 1; 2e fa f0! wa ut Comprehensively stated, the coefficient of variation presents a standard deviation as'a percentage of the mean of a set of values. It provides a standardized basis for a cross comparison of the variability of various sets of data with different average values. The concept of constancy is exactly opposite to the concept of variability. In mathematics, this opposite 42 relation can be expressed as a reciprocal one. Therefore, in this thesis, the relative stability of system productivity will be measured by the reciprocal of the coefficient of variation of the twelve productivity values in each system. In this definition of stability, the concept of productivity is especially important. As Conway (1990: 219) stated: Productivity is the output of valued product per unit of resource input. Based on this statement, definitions of energy productivity and economic productivity can be developed and described as: Energy productivity of a farming system is defined to be the ratio of the food energy output to the nonrenewable energy input to the system in a particular period. and similarly, Economic productivity of a farming system will be defined in terms of the ratio of the income obtained from valued output to the investment in the system needed to generate the output in the in a particular period. Actually, farm management economists use the term p:gfitnbility_ingsx to describe the concept of economic productivity defined in this thesis. Harsh et al. (1981: 247) defined the profitability index to be £h§_I§LiQ_Q£_Lh§ 43 present value of an investment to the cost of the investment. Hence, economic stability in this thesis can be appropriately described as the stability of profitability of a farming system. In this study, one year is used as a time unit in calculating productivity and profitability for each cropping system. Long-term stability is a major concern in this analysis, but the question of "long term" is a subjective judgement. In theory, the longer the period to be studied, the more valid the study is in addressing long-term effects. For this analysis, twelve years is the maximum period for which data are available. HYQO§11886§ As discussed earlier, the main hypothesis of the study includes five properties: nonrenewable energy consumption, energy production, returns, energy stability, and economic stability. This study has five specific quantitative hypotheses presented below. Nonrenewable Energy Consumption. The low-input cropping systems consume less nonrenewable energy embodied in machinery, fuels, seeds, commercial fertilizers, and synthetic pesticides than the conventional system. Formally 44 stated, the hypothesis to be tested is: H1: The total nonrenewable energy consumption in the low- input cropping systems will be less than that of the conventional system. Comparable Energy Production Levels. The low-input farming systems are able to produce as much food as conventional systems. Formally stated, the hypothesis to be tested is: H2: The food energy production per hectare in the low-input farming systems will be greater than or equal to that of the conventional system. Energy Productivity Stability. The two low-input systems are more stable in terms of food-energy productivity than the conventional system. Formally stated, the hypothesis to be tested is: H3: The reciprocals of coefficients of variation of energy productivity values (ratios of yearly food energy production to nonrenewable energy input) in both low- input systems are greater those that in the conventional system. Economically Comparable Production Levels. The dollars saved by reducing energy input in each low-input system compared to the conventional, is greater than or equal to the dollar losses which result from lower yields compared to the conventional in a particular time period. Formally 45 stated, the hypothesis to be tested 13: H4: The returns above variable and amortized equipment costs, measured by the difference between total revenue from valued crops (crop sales) and the costs in each low-input system will be greater than or equal to that of the conventional system. Economic Stability. The two low—input systems are more stable in terms of profitability than the conventional system during the same time period. Formally stated, the hypothesis to be tested is: H5: The reciprocals of coefficients of variation of profitability values (ratios of total yearly revenue from crop sales to total variable and amortized equipment costs) in both low-input systems are greater than those in the conventional system. These hypotheses will be tested for each cropping system in the Rodale Farming Systems Trial on an annual basis, over periods covering the first five years, the last seven years, and the total 12 year period respectively. CHAPTER 4 RESEARCH APPROACH W The research design of this thesis has been provided through the description of the hypotheses in the last chapter. Energy and economic analyses are two major sectors of this study. The former uses energy as an indicator in the exploration of a system while money is used by the latter. Both are quantitative approaches to a system analysis and cannot be carried out without sufficient quantified data. Additionally, some important assumptions need to be established in conducting the analyses. This information will be fully described in this chapter. All calculations in this study were done using a Lotus 1-2-3 for Windows spreadsheet package. MW Field data. Field data used in this study were provided by the Rodale Institute Research Center, including records of 46 47 crop yields for every replication in each system, and documents of field inputs such as the date, type, and amount of seed, fertilizer, animal manure, and pesticide applied to the field for each of the three systems. Data on field operations were also collected, including types and number of tillage operations, planting, harvesting, and machinery used. These data were produced and organized on an annual per-acre basis for each of the nine treatments by the project group from 1981 to 1992. The yield data were recorded for each replication plot. Information about the rotational pattern, climate and nutritional content and moisture of manure and crops was provided in two major published reports of the trial, by Liebhardt et al. (1989), and Peters et al. (1992). These two documents also presented detailed data, process, and findings in the first five years and the following five years of the project respectively. Results of previous studies. As mentioned earlier in the literature chapter, the Rodale cropping systems trial has been studied intensively since 1981 by individuals and research groups not associated with the Rodale Institute. Part of this information has been collected and is available for economic analysis and comparison in this study. These published articles and reports include Dabbert, 1986; Duffy et al., 1989; Hanson et al., 1990; and Dunbar, 1991. 48 Energy Estimates and Analysis Conversion ratios. Energy conversion factors of various input materials and output crops were obtained largely from a energy handbook edited by D. Pimentel (1980). This publication is probably the most widely-used data source for energy analysis in agricultural production. Other data needed for energy estimates can be found in other energy handbooks of particular agricultural industries, such as the fertilizer and pesticide sectors. Other conversion factors used in this study are listed in Table 4.1. Table 4.1 Conversion factors used in the analysis. 1 mile = 5280 ft 1 inch = 2.54 cm 1 hectare = 0.4 acre 1 square feet = 2.3 E -5 acre 1 gallon = 3.785 liter 1 pound = 0.454 kg 1 short ton = 909 kg 1 bushel of corn grain = 56 pounds of corn grain 1 bushel of wheat = 60 pounds of wheat 1 bushel of soybeans = 60 pounds of soybeans l bushel of oats = 32 pounds of oats 1 bushel of barley = 48 pounds of barley 1 bushel of rye = 60 pounds of rye* 1 kcal = 4186 joules * Assuming the same as wheat. 49 Estimates in the literature of the energy embodied in particular products or inputs are generally average values which were derived under various assumptions. As Kaffka (1984: 3?) pointed out, the energy embodied in synthetic fertilizer may differ depending on the type of fertilizer, its manufacturing process, efficiency of the factory, and other factors. Therefore, efforts have been made to choose values carefully based on the criterion of consistency among several authors. The details of energy calculations are discussed later in this chapter. Basis for the calculation and comparison. All the process and results of energy estimates in this study are shown by mean values in units of kilo-calories (kcal) per hectare per year for a cropping system. As mentioned in the description of the Rodale Farming Systems Trial (Rodale FST) in Chapter Two, there are three rotation entry points in each of the systems, resulting in nine treatments in the trial. This study will focus on the analysis of the whole system, and comparisons between systems, instead of treatments. This is because the level of sustainability that this thesis examines is the farm/microeconomic level. This study is most concerned about the practicality of low-input practices in a real farm situation. On a real organic farm, a farmer may not operate by using only one rotational entry point, or by growing only one crop at a time. 50 Another reason for not considering the effect of rotation entry points in this study is provided. Although it is recognized that the overall energy production in the low-input systems was lower for the treatments started with corn than the others, no consistent and significant difference in energy production between treatments with different rotation entry points was noted for the low-input systems (See Table 4.2). For example, a comparison of energy production between treatments shows that in the low- input with animal system, although the second treatment which started its rotation with corn produced less energy than the first treatment started with oats and clover, the third treatment started with corn silage had equal energy output of the first treatment in the first five years. In the low-input cash grain system, the third treatment started with corn averaged higher energy production than one of the other two treatments started without corn in the meantime. The difference between treatments seemed more obvious in the comparison of corn leaf tissue nitrogen concentration as discussed in the literature of Liebhardt et al. (1989), but not in yields. Stated another way, energy analysis based on yield data might have difficulties to examine the micro difference between treatments with distinct rotation entry points. Therefore, the field data for both inputs and crop yields of the three subsystems within a system are first summed and then converted into mean values, giving a system 51 basis for comparison. Table 4.2 Average energy production of the low-input treatments in Rodale Farming Systems Trial, 1981- 1985. Source: Rodale Institute Research Center. Cropping Entry Energy production Initial crop ll system (point (million kcals/ha/yr) LIP—A 1 21.40 oat, clover LIP-A 2 13.43 corn LIP-A 3 21.40 corn (silage) LIP-CG 1 19.20 oat, clover LIP-CG 2 13.61 soybean LIP-CG 15.03 corn There are at least four types of soil (Peters, November 13, 1992, personal communication) which vary in productive ability and which might result in different levels of productivity from one system to another. Nevertheless, the effect of this uncontrolled variable is assumed to be minimized by random distribution of a system into eight mainplots (replications) before the trial was initiated. Inputs excluded from energy calculation. Some researchers have pointed out that low-input practices may use human labor, manure, or information more intensively than do conventional practices (Francis and King, 1988). Nevertheless, human labor, animal manure, and information 52 will not be included in energy calculations. This decision was based on two considerations. First, labor and manure are considered to be renewable from a physical perspective, thus they are treated like other renewable resources as water, solar energy, etc. Second, although creating information (largely through research) may involve consumption of much nonrenewable energy, once the information is generated, it can be repeatedly applied on indefinite number of times. Calculus suggests that the nonrenewable energy consumed to generate one information application approaches zero as the number of applications of the information increases. Additionally, transportation of manure from the beef farm for the low-input with animal system is neglected in this analysis because of the short distance (about one mile) between the farm and the experimental site. Some indirect inputs such as buildings, and hardware are not included in this study, largely due to lack of information and partly because of the variation of these inputs farm one farm to another. Therefore, this analysis will focus only on the estimates of direct inputs. Energy embodied in machinery. This energy input is calculated by multiplying the amount of machinery consumed per hectare (in terms of kg) by a conversion factor, 18000 kcal/kg (Table 4.3), which represents the average energy 53 Table 4.3 Energy embodied in diesel fuel and machinery. Unit Real/unit Source Diesel* liter 11414 Cervinka, 1980: 15 Machinery kg 18000 Pimentel and Pimentel, 1979 * Diesel fuel consumption is assumed to be 0.053 gallon of diesel fuel per drawbar HP hour (Fuller et al., 1992: 1). embodied in a kilogram of farm machinery. The amount of machinery consumed in an operation is estimated from the operation, and the size, weight, and life of the machinery used for the operation. For example, the machinery consumed in a moldboard plow (MP) carried out by a 8-16“ moldboard plow of 1400 kg and a 160—bp tractor of 5789 kg with 2000 and 10000 hours of life respectively is calculated as following: According to the data provided in "Minnesota farm machinery economic cost estimates for 1992”, work performed by a 8-16" MP is approximately 4.65 acre per hour. One hectare is 2.5 acres. It thus requires 2.5 * 1/4.65, i.e., 0.54 hour, to moldboard plow a one-hectare field. The average machinery consumed per hour for equipment is calculated by dividing its weight by its life hours, resulting in 0.7 and 0.58 kg for the MP equipment and the tractor respectively. Therefore, it will consume (0.7 + 0.58)* 0.54 which is 54 approximately 0.69 kg of machinery to plow a hectare with a moldboard plow. Due to the fact that machinery utilization varies greatly from farm to farm, farms of significantly different size may use different machinery. Some have argued that it may not be reasonable to estimate the energy consumed in machinery by using the machinery information from the Rodale FST because the equipment was mainly for experimental purposes (Peters, November 13, 1992; and Harwood, January 1991, personal communication). To tackle this problem, this study will conduct one calculation and analysis according to the machinery used in the Rodale FST. Another calculation will be carried out assuming that the operation in each of systems of the trial is practiced on a standardized 500-acre farm with commonly-used machinery, because some organic farms of that size have been reported (USDA, 1980). This decision was also based upon the hope that the information provided by the trial and the analysis could be useful to operators in a more realistic situation. For the sake of the comparison between the three systems, it is also assumed in this study that all systems were operated on farms of the same size and with the same machinery. The machinery designs and associated information for Rodale EST and the standardized farm are shown in Table 4.4 and Table 4.5 respectively. Table 4.4 Machinery used in the 55 Rodale Farming Systems Trial. ‘ Machine Size Operation Weight "'Total costs ++the Work Perform (kg) lhour (hour) (acre/hr) Tractor JD 2840 80 hp M.P.. Chisel 3950 10000 Tractor JD 2640 70 hp Disk, Harrow, 2238 10000 Field Cultivate Spread manure Cut hay/silage TractorJDZO-to 4011p Plant corn/sybn 1945 10000 Drill. Spray Herbicide Tractor lH 584 50 hp Cultivate. Rotary hoe 2318 10000 Rotary mower cultipack Tractor iH 140 30 hp Sidedress 1376 10000 Tractor Oliver 1750 80 hp Bale. Rake, Ted 4915 10000 Combine JD 6620 small Combine 7718 74.11 2000 3.58 Moidboerd plow 3-18 Moidboard plow '750 +295 2000 1.96 Tandem disk 10ft Disk. Harrow. +1250 24.14 2000 4.85 Field cultivate Chisel plow 10ft Chisel plow ”800 27.48 2000 4.36 Planter JD 71 Flex 4-30 Plant com/soybean 92 +3592 1200 3.28 Drill JD 7100 12ft Drill small grain 864 37.81 1200 4.78 Cultivator 4-30 Cultivate “400 +2059 2000 3.88 Rotary hoe JD 415 6-30 Rotary hoe 599 +2682 2000 +1018 Manure spreader 150w Spread manure 753 +29.17 1200 3.49 Fertilizer spreader 20ft Sldedress ”300 +3802 1200 19.4 Lime truck 2 tons Spread lime “3700 +2781 10000 +146 Sprayer 208 Spray Herbicide “200 +2524 1500 9.45 Forage harvester NH 717 Bit, 2R Cut hay/com silage 726 +4802 2000 +1.5 Rotary mower Wd's MB4p 7ft, 2-30 Rtry mow. cultipack 563 +2584 2000 3.19 Baler Case Int'l 8420 Sale hay/straw 1444 +3363 2000 3.78 Source: RIRC; NAEDA OI'Ilcal Guide. spring 1992; Fuller et al.. 1992. + Estimates ++ AmericanSocletyoiAariculturai EngineersStandards,1991. ‘ John Deer Co. “ SoottandKrummei.1980. 120 "' indududepmdauon.m,lmunnce.hmming.mpah.m,hbu.mdlubrlcants. 56 Table 4.5 Machinery used on a SOD-acre standardized farm. Machine Size Operation Weight '"Total costs ++Lite Work Perform (kg) [hour (hour) (acre/hr) Tractor JD 8050 160hp M.P.. Chisel 5789 10000 Disk. Harrow, Field Cultivate Tractor JD 2840 80hp Spread manure 3950 10000 Cut hay/silage Plant. Drill, Cultivate Rotary mower. Rake Sidedress. Bale Ted, Cuitipack TractorJD2040 40hp Rotaryhoe 1945 10000 Spray Herbicide Combine Massey-Ferguson 8 Med. 20 it Combine 8626 88.64 2000 4.14 Moldhoard plow 8—16 Moidboard plow +1500 58.26 2000 4.65 Tandem disk 32ft Disk. Harrow, +3000 61.68 2000 15.52 Field cultivate Chisel plow 20ft Chisel plow +1500 46.73 2000 8.73 Planter JD 7000 8-30 Plant corn/soybean 1791 62.08 1200 6.55 Drill JD 7100 201i, 8-30 Drill small grain 1352 56.88 1200 7.96 Cultivator 8-30 Cultivate 550 30.46 2000 7.76 Rotary hoe 16ft Rotary hoe 550 26.38 2000 10.86 ManurespreaderCase Int’l53150bu Spread manure 753 29.17 1200 3.49 Fertilizer spreader 40ft. 4 ton Sidedress 400 +64.67 1200 38.79 Lime truck 2 tons Spread lime '3700 27.81 10000 +14.6 Sprayer 30ft Spray Herbicide +350 27.72 1500 14.18 Forage harvester NH 717 Bit, 2R Cut hay/corn silage 726 +4802 2000 +1.5 Rotary mower BH 3108-01 9ft Rtry mow. cultipack 977 (+4185 2000 4.64 Baler Cass int'l 8420 14'18“ Bale hay/straw 1823 +3495 2000 4.84 Source: RIRC; NAEDA Officai Guide, spring 1992; and Fuller et al., 1992; with lamhdgable assistance of Dr. G. Schwab. + Estimates ++ American Society of Agricultural Engineers Standards. 1991: 299. ' Scott and Kmmmel, 1980: 120 " Includes depreciation. interest, insurance, houshg, repairs, fuel. labor, and lubricants. 57 Fuel. In this study, all powered equipment is assumed to consume diesel fuel. Diesel fuel consumption is calculated by 0.053 gallons of diesel fuel per drawbar HP hour (Fuller et al., 1992). For instance, a 50—hp tractor consumes 50*0.053 gallons of diesel fuel in one hour of operation. The energy embodied in a liter of diesel fuel is 11414 kcal (Table 4.3) which has been used in many energy analyses. Fossil energy embodied in seeds. Seeds are one of the major inputs in agriculture. In a modern seed industry, a large amount of fossil energy is consumed to produce seeds. In this study, the energy embodied in seeds includes the energy needed in the production, processing, and distribution of seeds. The amount of seed energy varies from crop to crop. Table 4.6 is a list of the values for various seeds used in the Rodale FST. Table 4.6 Estimated fossil energy costs of field seed production, processing and distribution. Crop Kcal/kg Source Seed oats 4108 Heichel (1980: 32) Clover, Red 37604 Heichel (1980) Seed corn, hybrid 24806 Heichel (1980) Soybean seed 7584 .Heichel (1980) Seed wheat, spring 3002 Heichel (1980) Seed barley 3318 Heichel (1980) Ryegrass 12166 Heichel (1980) Orchardgrass 21646 Heichel (1980) Rye seed 3340 Reeves (1980: 100) Hairy vetch 14421* * Estimates. 58 Energy embodied in commercial fertilizers. Table 4.7 shows the energy embodied in various types of commercial fertilizer. Each number listed in the table is an average value and includes the energy needed to manufacture, transport, and distribute the final product. Table 4.7 Energy inputs for chemical fertilizers. Fertilizers Kcal/kg Source Urea 14300 Lockeretz (1980: 24) Ammonium nitrate 14700 Lockeretz (1980) Liquid N (UAN)* 14700 Crushed limestone 315 Terhune (1980: 26) Starter fertilizer 272 Mudahar and Hignett (1987: 52) Potassium sulfate 1600 Lockeretz (1980) * Assuming the same as ammonium nitrate. Energy embodied in synthetic pesticides. Rodale FST has used various herbicides and one insecticide recommended by Pennsylvania State University in the conventional cropping system. Information on the energy embodied in different products is not available. In this study, average values of energy inputs, including the energy in the production, formulation, packaging, and transportation of herbicides and insecticides were used. For the purpose of calculation, the herbicide and insecticide purchased for Rodale FST were assumed to be in the forms of miscible oil and granules, respectively. The energy values for the pesticides are shown in Table 4.8. 59 Table 4.8 Energy inputs (production, formulation, . packaging, transport) for various pestic1des. Source: Pimentel, 1980: 47. Pesticides Kcal/kg Herbicide Miscible oil 99910* Wettable powder 62770 Granules 86600 Insecticide Miscible oil 86910 Wettable powder 61470 Granules 74300* Dust 74300 * Used for this analysis. Energy contained in crops. Some studies have been done by separating nutritional content and caloric content in calculating energy contained in crops (Burnett, 1978). This is especially necessary for a production system with high- protein products like milk and meat. In Rodale FST, the products of the systems are crops, thus only the caloric content is considered in this study. Table 4.9 shows the energy contained in the crops produced in Rodale FST. It should be noted that feed crops like corn silage produced in the low-input with animal system, and hay and straw in both low-input systems in Rodale FST will be included in the energy calculation. Although these products are not consumed directly by human, resulting in lower economic values than those of food grains like corn and 60 wheat, the energetic content in the feed crops might not be distinct from that in the food crops from physical point of view. Table 4.9 Food energy in various cash crops. Crop Kcal/kg Source Corn grain 3550 Burnett (1978: 145-148) Soybean 4030 Burnett (1978) Wheat 3300 Burnett (1978) Barley 3480 Burnett (1978) Oats 3900 Burnett (1978) Rye 3340 Burnett (1978) Corn silage 1085* Pimentel (1984: 9) Hay 2713** Pimentel (1984: 9) * for corn silage with 65% moisture. ** includes alfalfa, straw, and hay. Human labor. As mentioned earlier in Chapter 2, human labor estimates will not be included in the energy calculation. The human labor needed in the three cropping systems is measured and discussed in terms of hours. W Agricultural prices. Information about the prices of inputs and products of Rodale FST in the past twelve were obtained primarily from various issues of “Agricultural Prices“ published by USDA. Prices in either March, April, or May 61 were collected depending on the availability of the data. The prices of seeds, diesel fuel, commercial fertilizers, and pesticides were average prices paid by farmers in the U.S.; the crop prices were the average prices received by U.S. farmers. Various herbicides and starter fertilizers were used in the Rodale conventional system, and not all of these agrichemicals could be found in the statistical document. Therefore, an average value was derived for total herbicide use in a year, and for starter fertilizer. It was found that the prices of Lasso and 13-13-13 can reasonably represent the average prices of herbicide and starter fertilizer, respectively. Hence, prices of the two products were used for herbicides and starter fertilizers in this thesis. The information about prices of manure, rye seed, hairy vetch seed, corn silage, and straw was not available in the USDA documents. Other sources were used to determine these prices. The total price data needed in this study and their sources are shown in Table 4.10. Estimates of operation costs. Operation costs in this study include the costs of human labor, machinery, and fuel. These costs will be calculated together rather than individually, because they are all machinery-related costs. Stated another way, the cost of operating machinery for one hour includes not only the costs of the machinery itself, 62 Table 4.10 Prices of various agricultural commodities, 1981- 1992. Year 1981 1982 1983 1984 1985 1986 Commodity Unit Prices Prices Prices Prices Prices Prices Slunit Slunit Slunit Slunit Slunit Slunit Diesel gallon 1 .16 1 .1 1 0.98 1 .00 0.97 0.70 Manure+ ton 11.00 12.00 12.00 12.00 12.00 12.00 SEEDS corn bu 80.00 63.70 84.60 70.20 67.30 86.80 soybeans bu 14.00 10.70 10.10 13.40 11.90 11.80 wheat bu 7.22 6.89 6.69 6.37 6.10 5.94 oats bu 4.42 4.51 4.37 4.52 4.18 3.63 barley bu 5.95 5.60 5.22 5.31 5.10 4.82 rye++ bu 7.25 ryegrass 100 lbs 37.80 37.20 39.20 39.00 37.30 38.10 orchardgrass 100 lbs 98.00 101.00 96.10 94.70 80.90 86.90 clover. red 100 lbs 117.00 126.00 180.00 145.00 121.00 138.00 hairy vetch+++ 100 lbs 57.20 58.40 58.40 62.60 63.40 61.30 FERTILIZERS Urea ton 237.00 240.00 213.00 227.00 217.00 174.00 A. nitrate ton 185.00 195.00 184.00 198.00 189.00 171.00 UAN ton 150.00 158.00 148.00 153.00 146.00 132.00 limestone ton 14.60 15.50 16.00 18.30 16.00 15.90 N-P-K' ton 188.00 191 .00 184.00 187.00 187.00 165.00 K suIate ton 152.00 155.00 143.00 147.00 128.00 111.00 PESTICIDES Insecticides 50 lbs 43.40 46.80 51.20 72.10 78.30 77.00 Herbicides“ 5 gals 85.10 93.10 99.12 105.00 105.00 101.92 CROPS Corn bu 3.16 2.41 3.03 3.36 2.70 2.25 Soybeans bu 7.10 5.88 6.06 8.24 5.88 5.13 Wheat bu 3.93 3.00 3.75 3.59 3.43 3.16 Oats bu 2.03 1.96 1.54 1.88 1.68 1.07 Barley bu 2.97 2.36 2.37 2.80 2.16 1.86 Corn slIage'" ton 26.96 22.46 26.18 28.16 24.20 21.50 Baled hey ton 71.80 70.90 83.90 84.90 72.50 69.20 Strauf'" ton 71 .60 70.90 83.90 84.90 72.50 89.20 Indexes at prices Pricesraceivedby 1977- 134 121 128 138 120 107 iarmerstoralicrope 100 Pricespaidbyiarmers 1977- 150 159 161 164 162 159 torcomrnodltlesal 100 services. interest. taxes. Swagerates SweezAa'lculharalPrices. 1981-1992. AlprlcesereUSaveragepricesotMarch.April .orMay breachyeeroucepttortheindlceted. + Cuflketal..1983:53.andestimate ++ Dabbert.1986:108 +++Agicultural Prices. 1972 Annual Summary. endestimate. ' Pnceoi13-13-13 " Priceothsso "‘ Pnceotcomgrain'6+8 Robbins.1986 ”"Assumingthesameeshay 63 Table 4.10 (cont’d). Year 1987 1988 1989 1990 1991 1992 Commodity Unit Prices Prices Prices Prices Prices Prices $lunit slunit slunit $lunit $lunit $Iunit Diesel gallon 0.70 0.75 0.80 0.81 0.82 0.79 Manure+ ton 12.00 13.00 13.00 14.00 14.00 14.00 SEEDS corn bu 64.90 64.20 71.40 69.90 70.20 71.80 soybeans bu 11.30 11.90 14.70 12.50 12.80 12.40 wheat bu 5.56 5.89 6.71 6.05 4.72 6.06 oats bu 3.99 4.37 5.89 4.19 3.71 4.26 barley bu 4.47 4.58 5.91 5.25 4.55 5.10 rye++ bu 9.00 ryegrass 100 lbs 45.10 47.90 54.30 50.50 46.80 43.80 orchardgrass 100 lbs 115.00 116.00 117.00 102.00 101.00 100.00 clover, red 100 lbs 160.00 143.00 143.00 145.00 134.00 122.00 hairy vetch+++ 100 lbs 61.30 62.20 68.40 68.40 67.50 67.50 FERTILIZERS ' Urea ton 161.00 183.00 212.00 184.00 212.00 198.00 A nitrate ton 157.00 166.00 189.00 180.00 184.00 178.00 UAN ton 109.00 135.00 147.00 134.00 139.00 137.00 limestone ton 16.30 15.90 15.80 16.40 18.00 17.70 N-P—K‘ ton 162.00 181.00 187.00 181.00 184.00 180.00 K sulfate ton 115.00 157.00 163.00 155.00 156.00 150.00 PESTICIDES Insecticides 50 lbs 71.80 70.20 71.30 73.30 77.90 81.30 Herbicides“ 5 gals 96.88 101.92 108.08 113.96 122.92 127.12 CROPS Corn bu 1.52 1.85 2.56 2.52 2.42 2.43 Soybeans bu 4.90 6.36 7.29 5.62 5.77 5.61 Wheat bu 2.63 2.81 4.03 3.51 2.60 3.66 Date bu 1.50 1.66 2.24 1.37 1.16 1.39 Barley bu 1.69 1.58 2.73 2.17 2.10 2.08 Corn silage” ton 17.12 19.10 23.36 23.12 22.52 22.58 Baled hay ton 64.10 72.90 101.00 91.60 87.30 73.00 Strawm‘ ton 64.10 72.90 101.00 91.60 87.30 73.00 Indexes of prices Prices received by 1977= 106 126 134 128 131 131 farmers for all crops 100 Prices paid by farmers 1977= 162 170 178 184 188 190 for commodifies 8 100 services. interest. taxes, 8wagerates 64 but also the costs of the human labor and fuel needed to operate the machine for an hour. Additionally, the three costs are all proportional to the use of machinery; thus, they can be estimated together. The operation costs per hour for various types of machinery are listed in Table 4.4 and Table 4.5, and were obtained or derived from the data provided in "Minnesota farm machinery economic cost estimated for 1992.“ Because information for estimating operation costs in other years is not available, the costs were all estimated according to the prices in 1992. In order to construct a basis for calculation and comparison of the prices and costs, the adjustment of all other prices to 1992 is necessary. Two series of price indexes will be use to make the adjustment. First, the price indexes of all crops from 1981 to 1991 were used for the price adjustment of the crops produced in the Rodale FST. Second, the indexes of prices paid by farmers for commodities and services, interest, taxes, and wage rates in the same period were used for making the adjustment for all inputs in the Rodale FST. The data were obtained from "Agricultural Prices“ and are listed in Table 4.11. Basis for economic comparison. Although it was mentioned in the description of the Rodale FST that the low-input with animal system was design to simulate beef production, this economic comparison was conducted assuming that all three 65 GOQUH omo3 a .umououcw .moUH>uom .moxou .mofluflUOEEoo omH mmH emH aha omH «ma mmH NwH ema flea mmH omH Has "Odom mooflum . macho Had HmH HmH mma «ma mma moa hoe oNH mma mNH HNH emH "oo>floomu moofium mm HmmH ommH mmmH mama mmma mama mme emma mmma mmmH HmmH xoocH — .doms .mmofium HonouHsOHumd ”cousom .Acoaussmav mmmHiHmmH .moumum condos .muoeucm mo owed one om>wmoou wooded mo moxoocH HH.¢ canoe 66 farming systems are commercial cash-crop farms. There are two reasons for this assumption. First of all, actually the low-input with animal system is a cropping system without a cattle sector within it. The focus of the Rodale experiment was also the crop performance and the systems environments. More significantly, the assumption provides a simple basis for the comparison of the economic performance between the three systems using available data. If the calculations of low-input with animal system are operated based on a beef operation, additional variables such as animal protein conversion factors, and equipment and operations for cattle must be added into the analysis. However,these data are not available; thus, an economic assessment of the low-input with animal system using the beef operation cannot be accomplished in this study. Under the assumption of commercial cash-crop operation for all systems, manure spread in the low-input with animal system is considered to be purchased from external markets and was included in costs. All crops produced in Rodale FST, including corn silage, hay, and straw in low-input systems were marketed to generate income. In the economic analysis and comparison, the conventional system is selected to be the base farm. The profitability of the low-input systems is judged on the basis of their economic performance compared to that of the conventional system. 67 Similar to the method used in calculating and presenting the energy budgets, the economic estimates and analyses will be conducted on an annual per-hectare basis within a whole system. CHAPTER 5 RESULT AND DISCUSSION Chapter Introduction Results of energy and economic calculations of this study are presented in this chapter, followed by discussions of the findings. Similarity or difference between the results of this study and those of previous studies are also addressed. The presentation and analysis of the results will be first organized for the energy analysis, then for the economic analysis. n r An sis Energy input and test of hypothesis 1. Tables 5.1, 5.2, and 5.3 show the results of energy calculations for the low- input with animal farming system (LIP-A), the low-input cash grain farming system (LIP-CG), and the conventional farming system (CONV) in the Rodale Farming Systems Trial (Rodale FST), respectively. It can be seen from Figure 5.1 that CONV nonrenewable energy consumption in each year was considerably higher than those in both low-input (LIP) 68 69 85552585505338233338358205 .. .§s§8§3§§§c.¥0038020§h 5.8858385289888880825083909520808022 . 8.2 8.0. 8.2 8.2 8.... .02 8.2 2.8 00.: 8.2 8.0. 8.2 0.2 .. 030888 .085. .00... 8.2 8.8 8.8 8.8 8.8 2.. .N 0.0.. 8.8 2.8 .000 8... 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RN 8.. 8... 8... 8.« 8... 83...}. 3... 2.... 8... ...... a... 3... ...... 8... ...... 9... 9... 8... 3... ...-m .... ...... 2... 8... ...... 3.... 8... ...... .... 8... 2.... a... 8... 18.0 8... 8... 8... 8... 8... 8... 8... 8... 8... ...... ...... 8... 8... is... «5.3.. 88.2. 8... .8. 8... 8... 8... .8. 8... 8... 3... 8... «8. .8. 8.» 3:95369iimaii .mnwuuo: you mamox GOHHHHE .Emumam >zov ca ammusn hmumcm m.m manna Energy input M11110n kcals per hectare 1—1 N w A U. 0‘ \I on \O C Figure 5.1 72 Energy Input Rodale F ST 1981 1982 1983 1984 1985 1986 1937 1988 1989 1990 1991 1992 + LIP-A _._ LIP-CG _,._ CONV Total nonrenewable energy consumption in the Rodale Farming Systems Trial. 73 systems. On average, CONV consumed more than 4.5 million kcals of nonrenewable energy per hectare per year, which was more than double that of each LIP system. Qne could conclude from the comparison that the CONV used substantially more nonrenewable energy than the LIP systems from 1981 to 1992. In the LIP systems, LIP-CG consumed approximately 2 million kcals and LIP-A accounted for 1.9 million kcals of energy consumption. The difference in energy consumption between LIP-A and LIP-CG is not significant. If manure were considered, LIP—A has consumed more off—farm energy than LIP-CG. Fertilizer utilization in CONV accounted for its high energy consumption. Without the fertilizer input (about 2.9 million kcals/hectare/year), only 1.7 million kcals of energy per hectare annually was consumed by CONV. The energy input of fertilizer alone in CONV exceeded the total energy input of each LIP system. The share of total energy consumption in the LIP and CONV systems by different inputs was different. In CONV, the following percentages are shown: fertilizer 63%; diesel fuel, 15%; seed, 13%; pesticides, 7%; and machinery, 2%. The patterns in LIP-A and LIP-CG were similar. Diesel fuel accounted for the greatest part of energy consumption, followed by seed, machinery, and fertilizer (Figure 5.2). The combination of diesel fuel and seed in LIP systems 74 Share of Energy Consumption Rodale FST, 1981-1992 § 3.5 >5 6T% 3'3 CL 3 Q) g 2.5 8 A: 2 a1 8. 0,15 ‘8 0 ;g l 8 a3' '8 g 15 — 'o 8 8 Q. @310 — L. Q) :5 5 _ O l l l L l l l l l l l J 1981 1982 1983 1984 1985 1986» 1987' 1988 1989 1990 1991 1992 _._LIP-A _._LIP-CG ...CONV Figure 5.6 Energy productivity on a SOD-acre standardized farm. 88 in Table 5.5. Overall, LIP—CG was the most stablg system among the three farming systems in Rodale FST, followed by LIP-A, and CONV. The energy stability index5.is 3.7 for LIP-CG, 3.4 for LIP-A, and 3.2 for CONV. Both the ratios in LIP systems were greater than that of CONV. Hence, nnp o 's t t L P s ems were more ener s ab e the gpny ln the twelve-year period is supponted. However, for different study periods, comparative energy stability also differed. In the conversion phase F (1981-1985), the stability index of LIP-A was much greater than those of LIP-CG and CONV. CONV was the least stable system in this period. In the post-conversion period (1986- 1992), the stability of CONV increased to be the highest among the systems. Energy stability of both LIP systems declined for the same period, particularly for LIP-A, which decreased by nearly 77%. Inis resulted in the nejection of tng hypothesis for the second period. There were some factors which contributed to the dramatic drop of energy stability in LIP systems in the post-conversion period: the production peak of 1986 in LIP- A, and the input increases in 1989, causing valleys on the curve for both energy productivity in LIP systems. l. Energy stability index was measured by the reciprocal of the coefficient of variation of energy productivity in the twelve- year period. Table 5.5 Energy and economic stability indexes, 1981- 89 1992. System Period I Period II 12-year Period 1981-1985 1986-1992 1991-1992 Rodale Farming Systems Trial Eneggy stability* LIP-A 12.0 2.8 3.4 LIP-CG 5.0 3.3 3.7 CONV 2.3 6.9 3.2 Economic stnbility** LIP-A 5.7 3.2 3.6 LIP-CG 4.7 4.5 4.5 CONV 2.6 6.9 3.8 SOD-acre Standardized Farm bil't * LIP-A 10.9 2.8 3.4 LIP-CG 5.2 3.3 3.8 CONV 2.3 6.9 3.2 Eggnonic stnbillty** LIP-A 5.4 3.2 3.6 LIP-CG 4.7 4.5 4.5 CONV 2.6 6.9 3.8 * Energy stability index is calculated by the reciprocal of coefficient of variation of energy productivity. ** Profitability stability index is calculated by the reciprocal of coefficient of variation of profitability index. 90 E2232mis.Analxai§ Costs. Tables 5.6, 5.7, and 5.8 show the balance of costs5 and revenue6.in LIP—A, LIP-CG, and CONV, respectively. On average, total annual variable and amortized equipment costs were approximately $453 per hectare in LIP-A, $316 in CONV, and $297 in LIP-CG. The higher cost of LIP-A compared to LIP-CG was due to manure costs in LIP-A. The curves of the costs in the three systems during the twelve-year period are shown in Figure 5.7. Although CONV consumed the greatest amount of nonrenewable energy, its total variable and amortized equipment costs were substantially less than those of LIP-A. One reason for this was the extremely inexpensive prige of fertilizer_ang_pe§figide- Cost8 per million cals of nonrenewable energy were estimated to be 0.21 dollar for the operation, 0.09 for seed, and only 0.03 and 0.04 for the fertilizer and pesticide, respectively. Obviously, the costs per unit of energy of the agrichemicals were less than one-half of the seed costs, and were only 14-19% of the operation costs from the perspective of energy use. 5 Costs include the costs of operation (i.e., labor, fuel, and amortized equipment), seeds, manure, commercial fertilizers, and pesticides. 6 Revenue include the sales of all crops (i.e. , corn grain, soybeans, wheat, oats, barley, corn silage, hay, and straw) produced in the three systems of Rodale Farming Systems Trial. 91 .§§.:88§2o389.assfisgsfisggtseésgiflngaanfi ... .ggvcunguaeobggcg.Bstgosoofifgaflfltssgtflzasggggégatm 8 .82.:885885888983 . 8.. 8. .... .... ...... 8.. 8. 8.. 8.. .o. 8.. 8.. 8.. ....6. 82.. 8.8 8.8. 9.8. 8.8 ...... 9.8 8.88 8.8. 9 .99 9 89 8.8. 9.8 9.8 32:33.. 9 .8. 8.8 8.8. 9.8 8.98 8.98 9.88 8.8... 8... 9.88 8.8 8.. .. 8.9 8:92 .8. 8.9 9.8 8.90 9.9 8.8 8...... 8.8. 8.8m 2.9 8.89 8.89 8.8. 8.8 .88 18.. 86 86 86 86 86 86 86 86 86 86 86 86 86 anon-ea 8.» 8... 8... 8... 8.8. 8... 8... 8... 8... 8... 8... 8... 8... 88.2.... ...8 8.8 9.... 9.8 9... 8... 8.9 8. .. 8.8 8.8 9.8 9.. 8.9 88 5.9. 8... 8.8 8.9 8.8. 9.8. 8... 8.8 8.9 8.9. 8.9 9.... 8.8. 83...... 8.8. 8.8. 8.8 8.8. 8.8. 8.8. 0.8. 9.8. 9.8 8.. .. 9.8. 8.8. 8.8. 8..-.80 9500 ES“— ue§ 0% 8.. .... 8.. 8.. 9.. .o. .... .... .o. 8.. 8.. 8.. 9.. 3.x... 82.. 8.8. ...... 8.9. 8.8 8.8. 8.9 9.9... 9.8 8.8 8.99 8.9. 9.8 9.... :25... 8:0)... 9 .8. 8.8 8.8. 9.... 0..... 8.9... 9.8 8.8... 8.... 9.8 8.8 8.. .. 8.9 .8... 9... 8... o... 8.0 8.9 8... 9.... 8. ... 8... 8... 8.0 8... 8... 328 8.9 8.8 8.8 8.8. 8.9 8. ... 9.8. 9.8. 9.... 8.8 8..... 8.8. 8.8 5.. 8.8. 8... 8.8. 2.8.. 8... 9.8. 8... 8.8.9 8.8 8... 8.9. 8... 8.8. .8... 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E9... 8.9... 2.9 ...anm 32: 8.2. 883. 88.. 8.8 3o. 8.. 8... 3.8 8o 8... ...: 8... 8... 8..... E8 8... 8... 3.8 8... 8o ...0 8... 8o :22. 8.5 8 8. 289.8 9.5. 8.8 58 843 8 8.” 88.” .88 .88. 2.2 2w. 8.08:. 8.: 8.8. .838“. 8.2 8.: Bow 8... 8... 22.: 8.... 2..: 8:880 «Soc «8. .8. 3:. Ifinitfipiscsfii .mumuown “mm HMHHOU .Eoumhm >zou ca moccawn UHEocoom m.m manna 94 Total Variable and Amortized Equipment Co Rodale FST 700 600 — 1513,500 — .n a 400 — a E 300 L “" ‘ 200 ~ 100 l l l l 1 l l l 4 l l 1 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 Figure 5.7 Total variable and amortized equipment costs in the Rodale Farming Systems Trial. mo: we: CG, C0! Opt 95 A second reason is that the LIP systems generally used more machinery, fuel, and seed than CONV. Operation costs were about $246 per hectare per year in LIP-A, $217 in LIP- CG, and $168 in CONV. Seed costs were $72 in LIP—CG, $55 in CONV, and $53 in LIP-A. Because costs associated with operation and seed were fairly high compared to the costs of chemicals, LIP systems tended to cost more. In other words, the r e er av' s of t e s stems w ot reflected in their total variable costs. In terms of the share of total variable and amortized equipment costs, operation costs accounted for the largest share of the costs in each system. In LIP-A, operation costs were 54%, manure 32%, seed 12%, and fertilizer 2% of the total costs. LIP-CG had a pattern similar to that of LIP-A except for no manure costs (Figure 5.8). Although fertilizer accounted for the greatest energy input in CONV, it accounted for 26% of its total variable costs, second to operation costs and followed by seed costs. Pesticide costs accounted for 4% of the total variable cost of CONV. Average operation costs were the greatest in LIP-A, followed by LIP—CG, and CONV. The costs of LIP-CG was 29% higher than that of CONV. This is relatively in accord with the findings by Hanson et al.(1990) who reported that labor costs were 28% higher for LIP-CG. From this comparison, LIP systems can be concluded to use labor, machinery, and fuel more intensively from both energy and economic viewpoints. 96 Share of Costs Rodale FST, 1981-1992 w 0 O 5¢% N u. o I h: 8 § 4%5 M O l Proportion of variable costs crnwv I Operation I Manure E] Seed I Fertilizer Pesticide Figure 5.8 Share of costs in the Rodale Farming Systems Trial, 1981-1992. to to th tc 1c 97 As shown in Table 5.9, total variable and amortized equipment costs in LIP-A increased by 10% from the transition period to the post-transition period partly due to the growing operation costs. In the meanwhile, LIP-CG's total variable costs increased by 13%, largely because of the gradual increase of seed costs. 0n the contrary, the total variable cost in CONV decreased by 19%, mostly due to lower fertilizer costs in the second period. These patterns of change in total variable and amortized equipment costs corresponded with those of energy input in the three systems. It has been shown in the Figure 5.7 that manure costs greatly affected the variable and amortized equipment costs of LIP-A. In 1987 and 1992 , in which no manure was spread, and in 1990, in which only a limited amount of manure was spread, the total annual costs of LIP-A dropped to three valleys. The effect of the application of fertilizer on the costs was also observed. In 1989, the application of additional fertilizer in the three systems, particularly in the LIP systems, caused a peak of the costs in each system. The highest peak of the costs among the systems occurred in LIP-A in 1991, when the costs of operation, manure, and seed reached their maximum values during the twelve year study. Total variable and amortized equipment costs on a 500- acre standardized farm were calculated to be slightly lower than those of the Rodale eXperiment in each system, due to Ta filo— Thu TU C Ti. TL 1 C TIA-IIII‘ 98 Table 5.9 Summary of economic balances, 1981-1992. Dollars per hectare per year. System Period I Period II 12-year period 1981-1985 1986-1992 1981-1992 Rodale Farming Systems Trial Total variable and amortized equipment costs LIP-A 427 472 453 LIP-CG 277 312 297 CONV 356 288 316 Tota revenue from cro sales LIP-A 722 835 788 LIP-CG 595 683 646 CONV 791 765 776 W* LIP-A 295 363 335 LIP-CG 319 371 349 CONV 435 476 459 Profitability index** LIP-A 1.7 1.9 1.8 LIP-CG 2.1 2.2 2.2 CONV 2.3 2.7 2.5 SOD-acre Standardized Farm W LIP-A 410 455 436 LIP-CG 263 299 284 CONV 345 278 306 Total revenge (Same as Rodale FST.) Returns aboye gasts* LIP-A 312 381 352 LIP-CG 333 384 363 CONV 446 487 470 ** LIP-A 1.8 2.0 1.9 CONV 2.4 2.8 2.6 * Difference of total revenue and total variable and amortized equipment costs. ** Profitability index is calculated by the ratio of total revenue to total variable and amortized equipment costs. 99 the lower operation costs. The average total annual costs shows the following decrease from Rodale FST to the standardized farm: LIP-A, 3.7%; LIP-CG, 4.4%; and CONV, 3.2% (Table 5.9). Revenue. Overall, total average revenue from crop sales was the greatest, about $788 per hectare per year in LIP-A, followed by $776 in CONV, and $646 in LIP-CG. This result is similar to the report of Hanson et al. (1990) that LIP-CG total revenue of sales was at average 19% lower each year than that of CONV. Also this relationship is similar to that of energy production among the systems, which means the total crop revenue have reflected energy production in Rodale FST. Transition effects in LIP systems have also been shown to effect the total income from crop sales. From the first five-year period to the last seven—year period, total income increased by 16% in LIP-A, and 15% in LIP-CG, but decreased slightly in CONV. Analyzed another way, the revenue of CONV was the highest in the first phase while LIP-A generated the greatest revenue in the second period. Both energy production and total revenue show that LIP systems were less productive in the transition period than they would be after the conversion was completed. Another finding illustrated by the curves in Figure 5.9 is that total revenue from crop sales was more sensitive to 1400 1300 1200 1100 was soo 700 Dollar per hectare 600 500 400 300 100 Total Revenue from Crop Sales Rodale FST l l V’v l l l l l l l l l l l l 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 _._LIP-A _._LIP-CG +CONV Figure 5.9 Total revenue from crop sales in the Rodale Farming Systems Trial . 101 weather than was energy production. 1988 was a dry year in which total revenue in all systems dropped significantly to the same level and recovered in the following year. Values of total energy production for 1988 in the systems were not significantly lower compared to those in other years. Similar results were found for 1981 and 1983 which were also dry years (See Figure 5.4, showing energy production curves.). The reason for the sensitivity is not clear. Lockeretz et al. (1981) used the ratio of total energy consumption to total income from crop sales to compare various systems and found that the value on low-input/ organic farms was only 40% of the ratio for conventional farms. A comparison of the same ratio between systems in this study agreed closely with this figure. The energy consumed to produce a dollar's worth of crops in LIP-A and LIP-CG was about 40% and 52% of that in CONV, respectively. The revenue on a standardized farm remains unchanged from the Rodale FST. Returns above variable and amortized equipment costs and test of hypothesis 4. As revealed in Table 5.9, 99H! had 0 ta - _- ,. : ssov- Va ';- e ild a ort'_-q e-L'om-s c-=t= gnan_L12_ayaaama. Values of the returns in LIP systems were close to each other, but considerably lower than those in CONV, by more than $110 per hectare per year. This relationship was the same for both study periods. 102 Therefore, the hypothesis that LIP systems would ganerate eguai or higher returns above variable costs than CONV is not sppported. Stated another way, the returns abova variable and amortized equipment costs of both LIP systems were significantly lower than those of CONV! based on a commerciai cash grain farm operation in the twelve years. The major reason for the lower returns above costs in LIP systems is the inexpensive fertilizer and pesticide, which greatly reduced the costs of CONV. Other factors in LIP systems could also contribute to their lower returns. As described earlier, LIP systems generally consumed more machinery, seed, and fuel, resulting higher costs for these inputs. Individually, LIP-A had very high manure costs and production of LIP-CG was lower than those of the other systems. Some previous economic studies also concluded that LIP— CG was less profitable than CONV under current U.S. agricultural policies and market operation (Duffy et al., 1989; Hanson et al., 1990; Dunbar, 1991). Their findings correspond to those of this study. However, Dunbar pointed out that LIP systems returns above variable costs were slightly less than those of CONV, but a large difference in the returns between LIP and CONV systems was found in this analysis. Additionally, this analysis revealed that LIP-CG generated slightly higher returns above variable and 103 amortized equipment costs than LIP-A. Nevertheless, according to the economic analysis by Duffy et al. (1989), LIP-A, which was equal to CONV in net returns, was superior to LIP-CG from a farm net return viewpoint. One cannot identify from this study whether the results would remain the same if the economic analysis of LIP-A was conducted on a beef farm basis. An examination of returns above variable and amortized equipment costs year by year shows that LIP-A generated higher returns than CONV in 1981, 1983, 1986, 1987, and 1990; as did LIP-CG in 1981, 1983, and 1987 (Figure 5.10). In 1981, 1983, and 1987, both LIP systems had higher returns than CONV. However, except for 1987, the differences were limited. The less unfavorable impacts of dry weather on LIP energy production were not as significant as on LIP returns. In comparing LIP-A and LIP-CG, 1982, 1984, 1986, 1989, and 1990 were the years in which LIP-A had higher returns than LIP-CG. It seems that wetter weather conditions tended to favor LIP-A more than LIP-CG, because 1982, 1984, 1986, and 1989 were wetter years. Dry weather had a significant negative impact on returns for all systems. As shown by the curves in Figure 5.10, the returns in each system dropped greatly in 1981, 1983, and 1988, which were the three driest years of the study. No such declines in energy production were noted. 104 Returns Above Variable and Amortized Equipment Co 900 800 700 600 U1 0 O > \ ~z ’9 A O 0 Dollar per hectare 300 200 100 Figure 5.10 Rodale FST l l l 1 l l l l l L 1981 1982 1983 1984 1985 1986 1987' 1988 1989 1990 1991 1992 Returns above variable and amortized equipment costs in the Rodale Farming Systems Trial. 105 In dry years crop production and sales were generally low. If total energy input and costs were also high in the same years, dramatically low returns could occur. This is the reason why the returns of LIP-A in 1988 and those of CONV in 1981 were particularly low. A system with more constant and lower inputs like LIP-CG could insulate itself from such shocks to some degree. It can be seen in Figure 5.10 that in these dry years, returns above variable and amortized equipment costs of LIP-CG never dropped as much as LIP-A and CONV, and they were maintained above $200 in Rodale FST during these dry years. As shown in Table 5.9, the average returns above variable costs to LIP-A increased by 23% from the transition period to the post-transition period. The increase for LIP- CG was 16% for the same period. Both increases were due to increases in total revenue from crop sales. Hanson et al. (1990) reported an 46% increase of profits for LIP-CG from the 1981-1984 period to the 1985-1989 period. In this thesis, a 62% increase of returns above variable costs for LIP-CG was measured during the same study period of Hanson et al. For CONV, a 9% increase in the returns was shown, due largely to decreases in total costs. The greater increases of the returns from the first phase to the second phase in LIP systems serves to illustrate the transition effects on the systems. All returns above variable and amortized equipment 106 costs measured on the standardized farm showed slight increases from Rodale FST. The average increase is 5%, 4%, and 2.4% in LIP-A, LIP-CG, and CONV respectively. Although only minor, LIP systems have both greater absolute and relative increases of returns than CONV. Profitability. Another technique to identify and compare the profitability of the systems is to calculate a profitability index7. As shown in Table 5.9, average profitability index in the twelve years was 2.5 for CONV, 2.2 for LIP-CG, and 1.8 for LIP-A. The CONV profitability index was considerably greater than those of LIP systems. This result corresponds with the relationship of returns above variable and amortized equipment costs among the systems. One could thus conclude from this analysis that C031 is the moar profitable aystem amppg rhe threa systems. Analyzing profitability with the profitability index and the returns showed one difference between the two methods. The average returns of LIP-CG were only slightly greater than LIP-A's returns. However, the LIP-CG's profitability index was substantially greater than that of LIP-A. This is primarily due to the lower costs for LIP-CG. 7 In this study, profitability index was measured by the ratio of total revenue from crop sales to total variable and amortized equipment costs of a farming system. 107 Overall, the profitability of the operation on a 500- acre standardized cash grain farm is improved sightly compared to that in Rodale FST. Economic stability and test of hypothesis 5. Table 5.5 shows the economic stability analysis in Rodale FST. The overall economic stability index8 was 4.5, 3.8, and 3.6 for LIP-CG, CONV, and LIP-A respectively. Profits for LIP-CG were significantly more stable than for the other systems. This result corresponds to the energy stability analysis. However, LIP-A was not economically more stable than CONV, as it was in energy stability; actually, it was slightly less stable than CONV in profitability. Therefore, aha hypothaaia that LLP system prpfits are more stahia thah CQNV 's o 0 IP- G but no 0 L P-A. This might be due partly to the commercial cash grain farm basis on which the economic analysis was conducted; it is biased against LIP-A, because on a real dairy farm there are no manure costs and silage and hay sales, all of which greatly affect LIP-A economic performance in this study. Additionally, LIP systems economic stability decreased while that of CONV increased from 1981-1985 to 1986-1992. The same trends were observed in energy stability analysis, suggesting that LIP systems, particularly LIP-A, were less Profitability stability index was calculated by the reciprocal of the coefficient of variation of the profitability index. 108 stable after entering the post—conversion phase. On the contrary, CONV improved its stability during the study. This problem might result from the current agricultural policy or market operation, but further study is needed to explore the causes. CHAPTER 6 SUMMARY, CONCLUSION, AND RECOMMENDATIONS §EEEQ£¥ F The central question this study seeks to answer is whether or not the two low-input cropping systems in the Rodale Farming Systems Trial, described in chapter Two, are more sustainable than the conventional system, from the vieWpoint of energy and economic performance. In the two low-input farming (LIP) systems, low-input with animal system (LIP-A) is a simulated beef farm operation, using steer manure and producing corn, soybeans, wheat, oats, corn silage and hay through a five-year rotation. The low-input cash grain system (LIP—CG) was operated without an animal sector, planting green manure crops as its nutrient source and growing corn, soybeans, wheat, and oats in a five-year crop rotation. No agrichemical products (i.e., commercial synthetic fertilizers and pesticides) were applied in LIP systems except for the application of commercial fertilizers in 1989. The conventional system (CONV) practiced a corn- corn-soybeans rotation with recommended amounts of agrichemicals. 109 110 In this study, an operational definition was formulated and five associated hypotheses of comparative sustainability were developed and tested. The definition includes three major components: (1) less nonrenewable energy consumption, (2) acceptable production levels, and (3) higher long-term stability. Two hypotheses were constructed based on each of the second and third characteristics of the definition from energy and economic perspectives, respectively. In addressing the acceptable production level of LIP systems, two approaches were used: (1) for energy analysis - energy production, and (2) for economic analysis - returns above variable costs. To explore long-term stability, two types of stability were included: (1) energy productivity stability, and (2) economic stability. These four hypotheses served as the central theme of this thesis. The analytical methods used to examine the relationships listed above are energy and economic analyses. They are generally parallel to one another methodologically and similar in their approach, but different in terms of analytical indicators. Economic analysis analyzes a system by tracing money flows and by calculating financial balances, while energy analysis explores a system through its energy flows (both inputs and outputs). In addition to testing the hypotheses, energy input, output, energy productivity, costs, total revenue from crop sales, returns above variable costs, and profitability index were also 111 compared and analyzed. hajpr Fihdings The major findings of the analyses are summarized as follows: * LIP systems consumed less than 50% of the CONV nonrenewable energy consumption in each year of the study period from 1981 to 1992. LIP—CG used the least amount of off-farm energy. Commercial fertilizers in CONV alone exceeded total energy consumption in each LIP system and accounted for the greatest amount of energy inputs to the system. In general, LIP systems consumed slightly more off-farm energy in the form of seeds, machinery, and fuel than did the CONV system. * LIP-A total energy production was above those of CONV and LIP-CG. If only food grains were considered, CONV had the greatest average gross energy production among the systems. LIP-CG food energy production was about 75% that of CONV. * LIP systems were more stable overall than CONV from the energy performance perspective. LIP-CG profits were much more stable than those of LIP-A and CONV. Economic stability levels for CONV and LIP-A were generally close when compared on a commercial cash grain basis. The energy and economic stability of the CONV system increased significantly from the period of 1981-1985 to that of 1986-1992. In contrast, the stability of the LIP-A decreased greatly during the same period. In addition to the unusually high production in 1986, and the application of fertilizer in 1989, there was a tradeoff between productivity and stability for LIP-A during the post—transition period. * Both LIP average returns above variable and amortized equipment costs were significantly lower than those of CONV. Profitability indexes also show that LIP operations were not as profitable as CONV based on a cash grain farm operation. This was due primarily to the fact that larger total nonrenewable energy 112 consumption in CONV was a relatively small portion of its total variable costs, because fertilizers and pesticides were very inexpensive compared with other inputs on a per unit of energy basis. Energy productivity in LIP systems is significantly higher than CONV productivity. On average, LIP-CG was 62% more productive or efficient than CONV in producing food energy per unit of nonrenewable energy input. If each of the three practices in Rodale FST were operated on a 500-acre standardized farm, all three systems' energy consumption per unit of land would be slightly lower, energy productivity would be improved, but energy stability would be close to the same, because the energy savings are limited. Operation costs were measured to be lower on the 500- acre standardized farm, as were total variable and amortized equipment costs; returns above variable and amortized equipment costs were higher on the standardized farm. This difference is not significant enough to alter the relationships of economic performance among the systems. Total variable and amortized equipment costs of LIP-A were considerably higher than those of CONV and LIP-CG, due largely to the application of manure in LIP-A. LIP-CG had the lowest total costs because it did not have manure and fertilizer costs, the two major variable costs in LIP-A and CONV, respectively. Transition effects in LIP systems were observed. Energy production, total revenue from crop sales, and returns above variable and amortized equipment costs increased significantly between period one, 1981-1985, to period two, 1986-1992. Substantially small increases in these measures were also noted for the CONV system. Energy productivity and stability, and economic stability in CONV increased significantly from the 1981-1985 period to the 1986-1992 period, resulting in a part from the decrease of fertilizer inputs in the latter period. Dry weather hurt LIP energy production less than CONV energy production, thus favoring LIP systems. In other words; under dry conditions, energy production in LIP systems was more stable CONV. No similar effect was noted for returns above variable and amortized equipment costs because dry conditions caused 113 proportional drops in returns for each system. Economic factors like total revenue from crop sales seemed more sensitive to dry weather than were biophysical ones, such as energy production for all systems. gonclusiona In conclusion, LIP systems are more sustainable than CONV from the energy perspective. Both LIP systems overall consumed much less nonrenewable energy and had higher stability than CONV. Individually, LIP-A also produced more energy than CONV during the study period. The criteria of being more sustainable in energy analysis have all been satisfied for the LIP-A system. Although the LIP-CG system produced about three-fourths of CONV food energy production, stability of the system was the greatest among the systems; also, its significant increased rate of production has been noted and might raise its energy production to be comparable to that of CONV and make it more sustainable than CONV in the near future. However, LIP systems may not be more sustainable than CONV under current economic circumstances. Although LIP-CG may have higher long-term economic stability than CONV, its profitability is significantly lower than that of CONV. Based on a commercial cash grain operation, LIP-A was not economically sustainable, due mostly to its lower profitability. 114 gaapmmenaations Recommendations for policy. Two types of sustainability have been addressed in this thesis: Energy sustainability (a bio-physical phenomenon) and economic sustainability (a social-political phenomenon). Humans cannot change laws of physics, so overcoming biophysical barriers may be more difficult than addressing social-political barriers. By understanding this, one could state that energy sustainability is above economic sustainability in long-term priority. Therefore, what one needs to do is to make economic sustainability correspond with energy sustainability. Stated another way, because LIP systems are more energy sustainable, efforts should be undertaken to make LIP systems more economically sustainable. One important step to achieve an economically sustainable low- input system is to make it more profitable. Two ways to make LIP systems' profitability levels comparable to CONV are: 1) increase in agrichemical prices - if prices of chemicals go up or the external costs of the chemicals are internalized and reflected in the commodities' prices, total costs for CONV operation would increase and profitability would decrease; 2) increase in prices of crops produced by low-input operation could increase its income as well as net returns. However, in a free market country like the U.S., these r "““T- .'— _ I 115 two possibilities can hardly be achieved without significant government regulation and increased social awareness. More environmentally-sound or energy-conserving regulations and policies should be implemented to internalize the environmental and social costs of agrichemical products on the one hand, and to provide financial encouragement for the development and adoption of low-energy-input, organic, or I other alternative farming practices. Social awareness of the side effects of conventional H—J farming practices and public appreciation of low-input operations is also critical. Realization of these goals will increase public willingness to support and purchase organic or low-input farm products, even though these products may be priced higher. Community support for organic farming could be a key factor influencing the future development of sustainable agriculture in the U.S. Recommendations for future research. There are at least two types of questions that are not fully understood and thus need further study. First, because the LIP-A system was originally designed for a beef farm operation, the energy and economic comparisons based on a commercial cash grain farm in this study may not be able to actually portray the system. Therefore, analyses based on a beef farm operation should be carried out to evaluate the energy performance and profitability of the LIP-A system. 116 Second, analyses of a longer term are needed to fully understand the systems dynamics in the Rodale Farming Systems Trial. For example, problems and reasons associated with the decline and increase of stability in LIP-A and CONV, respectively, over the study time period are not clear. One can not identify from this thesis if the changes are simply temporary phenomena or long-term trends. Also, efforts should be made to determine the factors (e.g., weather, crop rotation, soil structure, etc.) that resulted in the phenomena or trends based on the data of further experiment. It is thus suggested that the Rodale farming systems trial continue to provide additional data for these studies. Finally, as the interest of farmers and researchers in conducting on-farm experiments and studies on alternative farming practices increases, energy analyses could be included to provide a more comprehensive understanding of these systems. It is hoped that the straightforward design, method, and data presented in this thesis could be easily applied by researchers studying alternative farming systems and that the results can be transmitted to farmers to help them achieve greater sustainability on their farms. LIST OF REFERENCES American Society of Agricultural Engineers (ASAE). 1992. ASAE Standards: Standards, Engineering Practices and Data Adopted by the American Society of Agricultural Engineers. ASAE, St. Joseph, Michigan. Axinn, G.H. and N.W. Axinn. 1984. Energy and food relationships in developing countries: a perspective from the social sciences. In D. Pimentel and C.W. Hall (eds). Food and Energy Resources. Academic Press, Inc., Orlando, Florida. pp. 121-146. Balfour, L.E. 1977. The living soil. In Towards a Sustainable Agriculture. International Federation of Organic Agriculture Movements. pp. 18-27. Berardi, G.H. 1978. Organic and conventional wheat production: examination of energy and economics. Agro- Ecosystems 4:367-376. Burnett, M.S. 1978. Energy analysis of intermediate technology agricultural systems. M.S. thesis, University of Florida. Gainesville, Florida. Cervinka, V. 1980. Fuel and energy efficiency. In D. Pimentel (ed). Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, Florida. pp. 15-21. Chambers, R. 1992. Methods for analysis by farmers: the professional challenge. In Toward a New Paradigm for Farming Systems Research/Extension: Working Papers Set for the 12th Annual Farming Systems Symposium, Michigan State University, East Lansing, Michigan, September 13- 18, 1992. Charoenwatana, T. and A.T. Rambo. 1988. Preface. In T. Charoenwatana and A.T. Rambo (eds). Sustainable Rural Development in Asia: Proceedings of the Fourth SUAN Regional Symposium on Agroecosystem Research held at Khon Kaen University, July 4-7, 1988. Farming Systems Research Project and Southeast Asian Universities Agroecosystem Network (SUAN), Khon Kaen, Thailand. pp. Vii-xi. 117 118 Chou, T.H. 1992. Farming systems research: a case study of the Triple Tree Farm. Unpublished draft. Conway, G.R. 1986. Agroecosystem Analysis for Research and Development. Winrock International, Bangkok, Thailand. Conway, G.R. 1990. Agroecosystems. In J.G.W. Jones and P.R. Street (eds). Systems Theory Applied to Agriculture and the Food Chain. Elsevier Applied Science, New York. pp. 205-233. Culik, M.N., J.C. McAllister, M.C. Palada, and S. Rieger. 1983. Kutztown Farm Report: A Study of a Low-input Crop/Livestock Farm. Regenerative Agriculture Library Technical Bulletin. Rodale Research Center, Kutztown, Pennsylvania. Cunningham, L., J. Doll, J. Hall, D. Mueller, J. Posner, R. Saxby, and A. Wood. 1992. The Wisconsin Integrated Cropping Systems Trial: First Report. Lakeland Agricultural Complex Arlington Research Station. Cutter, S.L., H.L. Renwick, and W.H. Renwick. 1991. Exploitation, Conservation, Preservation: A Geographic Perspective on Natural Resource Use. John Wiley & Sons, New York. Dabbert, S. 1986. A dynamic simulation model of the transition from conventional to organic farming. M.S. thesis, The Pennsylvania State University. State College, Pennsylvania. DeWit, C.T. 1979. The efficient use of labor, land and energy in agriculture. Agricultural Systems 4:279-287. Douglass, G.R. 1984. The meanings of agricultural sustainability. In G.R. Douglass (ed). Agricultural Sustainability in a Changing World Order. Westview Press, Boulder, Colorado. pp. 3-29. Douglass, G.R. 1985. When is agriculture “sustainable“? In T.C. Edens and C. Fridgen (eds). Sustainable Agriculture and Integrated Farming Systems: 1984 Conference Proceedings. Michigan State University, East Lansing, Michigan. pp. 10-21. Doyle, C.J. 1990. Application of systems theory to farm planning and control: modelling resource allocation. In J.C.W. Jones and P.R. Street (eds). Systems Theory Applied to Agriculture and the Food Chain. Elsevier Applied Science, New York. pp. 89-112. 119 Duffy, M., R. Ginder, S. Nicholson. 1989. An economic analysis of the Rodale conversion project: overview. Staff paper No. 212, Department of Economics, Iowa State University, Ames, Iowa. Dunbar, T.V. 1991. An economic evaluation of alternative agriculture production methods in south central Pennsylvania. M.S. Thesis, The Pennsylvania State University, State College, Pennsylvania. Dyck, V.A., B.C. Misra, S. Alam, C.N. Chen, C.Y. Hsieh, and R.S. Rejesus. 1979. Ecology of the brown planthopper in the tropics. In Brown Planthopper: Threat to Rice Production in Asia. International Rice Research Institute, Manila. pp. 61-98. Edens, T.C., and D.L. Haynes. 1982. Closed system agriculture: resource constraints, management options and design alternatives. Annual Review of Phytopathol 20:363-395. Edens, T.C., and H.E. Koenig. 1980. Agroecosystem management in a resource-limited world. BioScience 30:697-701. Eggert, F.P. 1977. Preliminary results from plot trials to compare the efficacy of several soil management systems as determined by a number of soil parameters and the yields of some vegetable crops. in Towards a Sustainable Agriculture. International Federation of Organic Agriculture Movements. pp. 77-86. Eggert, F.P. and C.L. Kahrmann. 1984. Response of three vegetable crops to organic and inorganic nutrient sources. In Organic Farming: Current Technology and Its Role in a Sustainable Agriculture. American Society of Agronomy Special Publication No. 46, Madison, Wisconsin. pp. 97-109. Fluck, R.C. 1977. Energy productivity: a measure of energy utilisation in agricultural systems. Agricultural Systems 4:29-37. Francis, C.A. and J.W. King. 1988. Cropping systems based on farm-derived, renewable resources. Agricultural Systems 27:67-75. Fuller, E., B. Lazarus, L. Carrigan, and C. Green. 1992. Minnesota farm machinery economic cost estimates for 1992. AG-FO-2308-C. Minnesota Extension Service, University of Minnesota, St. Paul, Minnesota. 120 Hanson, J.C., D.M. Johnson, S.E. Peters, and R.R. Janke. 1990. The profitability of sustainable agriculture on a representative grain farm in the Mid-Atlantic region, 1981-89. Northeastern Journal of Agricultural and Resource Economics 19(2):90-98. Harrington, L. 1992. Sustainability in perspective: strengths and limitations of FSRE in contributing to a sustainable agriculture. In Toward a New Paradigm for Farming Systems Research/Extension (FSRE): Working Papers Set for the 12th Annual Farming Systems Symposium, Michigan State University, East Lansing, Michigan, September 13-18, 1992. pp. 562-584. Harsh, S.B., L.J. Connor, and G.D. Schwab. 1981. Managing the Farm Business. Prentice-Hall, Inc., Englewood Cliffs, New Jersey. Harwood, R.R. 1984. Organic farming research at the Rodale Research center. In Organic Farming: Current Technology and Its Role in a Sustainable Agriculture. American Society of Agronomy Special Publication No. 46, Madison, Wisconsin. pp. 1-17. Harwood, R.R. 1985. The integrated efficiencies of cropping systems. In T.C. Edens and C.Fridgen (eds). Sustainable Agriculture and Integrated Farming Systems: 1984 Conference Proceedings. Michigan State University, East Lansing, Michigan. pp. 64-75. Harwood, R.R. 1988. History of sustainable agriculture: US and international perspective. International Conference on Sustainable Agricultural Systems. September 9, 1988, Ohio State University, Columbus, Ohio. pp. 1-20. Harwood, R.R. 1990. A history of sustainable agriculture. In C.A. Edwards, R. Lal, P. Madden, R.H. Miller and G. House (eds). Sustainable Agricultural Systems. Soil and Water Conservation Society. Ankeny, Iowa. pp. 3-19. Heichel, G.H. 1980. Assessing the fossil energy costs of propagating agricultural crops. In D. Pimentel (ed). Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, Florida. pp. 27-33. Heichel, G.H. and D.R. Barnes. 1984. Opportunities for meeting crop nitrogen needs from symbiotic nitrogen fixation. In Organic Farming: Current Technology and Its Role in a Sustainable Agriculture. American Society of Agronomy Special Publication No.46, Madison, Wisconsin. pp. 49-59. 121 International Federation of Institutes of Advanced Study (IFIAS). 1974. Energy Analysis: Workshop Report No. 6. International Federation of Institutes of Advanced Study, Stockholm. Jackson, M. 1988. Amish agriculture and no-till: the hazards of applying the USLE to unusual farms. Journal of Soil and Water Conservation 43:483-486. Jones, M.R. 1989. Analysis of the use of energy in agriculture - approaches and problems. Agricultural Systems 29:339-355. Jonson, W.A., V. Stoltzfus, and P. Craumer. 1977. Energy conservation in Amish agriculture. Science 198:373-378 Kaffka, S. 1984. Dairy farm management and energy use efficiency: A case study with comparisons. M.S. thesis, Cornell University, Ithaca, New York. Liebhardt, W.C., R.W. Andrews, M.N. Culik, R.R. Hardwood, R.R. Janke, J.K. Radke, and S.L. Rieger-Schwartz. 1989. Crop production during conversion from conventional to low-input methods. Agronomy Journal 81(2):150-159. Lockeretz, W. 1980. Energy inputs for nitrogen, phosphorus, and potash fertilizers. In D. Pimentel (ed). Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, Florida. pp. 23-24. Lockeretz, W. 1984. Energy and the sustainability of the American agricultural system. In G.R. Douglass (ed). Agricultural Sustainability in a Changing World Order. Westview Press, Boulder, Colorado. pp. 77-88. Lockeretz, W., G. Shearer, and D.H. Kohl. 1981. Organic farming in the Corn Belt. Science 211:540-547. Lowrance, R., P.F. Hendrix, and E.P. Odum. 1986. A hierarchical approach to sustainable agriculture. American Journal of Alternative Agriculture 1(4):169- 173. Luo, S and C. Han. 1990. Ecological agriculture in China. In C.A. Edwards, R. Lal, P. Madden, R.H. Miller and G. House (eds). Sustainable Agricultural Systems. Soil and Water Conservation Society. Ankeny, Iowa. pp. 299-322. 122 MacKay, H.T. 1989. Sustainable agricultural systems: issues for farming systems research. In S. Sukmana, P. Amir, and D.M. Mulyadi (eds). Developments in Procedures for Farming Systems Research: Proceedings of an International Workshop; 13-17 March, 1989, Puncak, Bogor, Indonesia. pp. 105-118. Madden, J.P. and T.L. Dobbs. 1990. The role of economics in achieving low-input farming systems. In C.A. Edwards, R. Lal, P. Madden, R.H. Miller and G. House (eds). Sustainable Agricultural Systems. Soil and Water Conservation Society. Ankeny, Iowa. pp. 459-477. Marten, G.G. 1988. Productivity, stability, sustainability, equitability and autonomy as properties for agroecosystem assessment. Agricultural Systems 26:291- 316. Motyka, G. and T.C. Edens. 1984. A comparison of heterogeneity and abundance of pests and beneficial across a spectrum of chemical and cultural controls. Pest Management Technical Report #41, Department of Entomology, Michigan State University. 44pp. Mudahar, M.S. and T.P. Hignett. 1987. Energy requirements, technology, and resources in the fertilizer sector. In Z.R. Helsel (ed). Energy in Plant Nutrition and Pest Control. Elsevier, Amsterdam. pp. 25-61. National Research Council. 1989. Alternative Agriculture. National Academy Press, Washington, D.C. North American Equipment Dealer Association (NAEDA). 1992. Official Guide: Tractors and Farm Equipment, Spring 1992. NAEDA, St. Louis, Missouri. Norum, L. 1983. Problem formulation and quantification in energy analysis. Energy in Agriculture 2:1-10. Odum, H.T. 1983. Systems Ecology: An Introduction. John Wiley & Sons, New York. Parr, J.F., R.I. Papendick, I.G. Youngberg, and R.E. Meyer. 1990. Sustainable agriculture in the United States. In C.A. Edwards, R. Lal, P. Madden, R.H. Miller and G. House (eds). Sustainable Agricultural Systems. Soil and Water Conservation Society. Ankeny, Iowa. pp. 50-67. Patten, A.G.W. 1982. Comparison of nitrogen and phosphorus flows on an organic and conventional farm. M.S. thesis, Washington State University. Pullman, Washington. 123 Peters, 8., R. Janke, and M. Bohlke. 1992. Rodale's Farming Systems Trial: 1986-1990. Rodale Institute Research Center, Kutztown, Pennsylvania. 45pp. Pettersson, B.D. 1977. A comparison between the conventional and biodynamic farming systems as indicated by yields and quality. In Towards a Sustainable Agriculture. International Federation of Organic Agriculture Movements. pp. 87-94. Pimentel, D. 1980. Energy inputs for the production, formulation, packaging, and transport of various pesticides. In D. Pimentel (ed). Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, Inc., Florida. pp. 45-48. Pimentel, D. 1984. Energy flow in the food system. In D. Pimentel and C.W. Hall (eds). Food and Energy Resources. Academic Press, Inc., Orlando. pp. 1-24. Pimentel, D. and L. Levitan. 1986. Pesticides: amounts applied and amounts reaching pests. BioScience 36:86- 91. Pimentel, D. and M. Pimentel. 1979. Food, Energy, and Society. John Wiley & Sons, New York. Pimentel, D., L.E. Hurd, A.C. Bellotti, M.J. Forster, I.N. Oka, O.D. Sholes, and R.J. Whitman. 1973. Food production and the energy crisis. Science 182:443-449. Pimentel, D., G. Berardi, and S. Fast. 1984. Energy efficiencies of farming wheat, corn, and potatoes organically. In Organic Farming: Current Technology and Its Role in a Sustainable Agriculture. American Society of Agronomy Special Publication No. 46, Madison, Wisconsin. pp. 151-161. Pimentel, D., H. Acquay, M. Biltonen, P. Rice, M. Silva, J. Nelson, V. Lipner, S. Giordano, A. Horowitz, and M. D'Amore. 1992. Environmental and economic costs of pesticide use. BioScience 42(10):750-760. Reganold, J.P., L.E. Elliott, and Y.L. Unger. 1987. Long- term effects of organic and conventional farming on soil erosion. Nature 330: 370-372. Reeves, D.L. 1980. Energy requirements in rye production. In D. Pimentel (ed). Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, Inc., Florida. pp. 99-101. 124 Renborg, U. 1981. Energy analysis of agriculture: biology or economics - a survey of approaches, problems and traps. In C. Johnson and A. Maunder (eds). Rural change - The Challenge for Agricultural Economists. Proceedings 17th International Conference of Agricultural Economists, 3-12 September 1979, Banff, Canada. pp. 231-241. Robbins, R.D. 1986. A comparison of alfalfa haylage and corn silage and their effects on digestibility, feedlot performance, and economic value. M.S. thesis, Michigan State University. East Lansing, Michigan. Rodale, R. 1983. Breaking new ground: the search for a sustainable agriculture. The futurist 1: 15-20. Sahs, W.W. and G. Lesoing. 1985. Crop rotations and manure versus agricultural chemicals in dryland grain production. Journal of Soil and Water Conservation 40:511-516. Scott, W.O., and J. Krummel. 1980. Energy used in producing soybeans. In D. Pimentel (ed). Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, Inc., Florida. pp. 117-121. Seetisarn, M. 1988. Sustainable rural development in a systems perspective. In T. Charoenwatana and A.T. Rambo (eds). Sustainable Rural Development in Asia: Proceedings of the Fourth SUAN Regional Symposium on Agroecosystem research held at Khon Kaen University, July 4-7, 1988. Farming Systems research Project and Southeast Asian Universities Agroecosystem Network (SUAN), Khon Raen, Thailand. pp. 3-8. Soule, J.D. and J.R. Piper. 1992. Farming in Nature's Image: An Ecological Approach to Agriculture. Island Press, Washington, D.C. Stinner, D.H., M.G. Paoletti, and B.R. Stinner. 1989. In search of traditional farm wisdom for a more sustainable agriculture: A study of Amish farming and society. Agriculture, Ecosystems and Environment 27:77-90. Stockton, J.R. and C.T. Clark. 1980. Introduction to Business and Economic Statistics. South-Western Publishing Co., Cincinnati. Stout, B.A. 1984. Energy Use and Management in Agriculture. Breton Publishers, North Scituate, Massachusetts. 125 Terhune, E.C. 1980. Energy used in the United States for agricultural liming materials. In D. Pimentel (ed). Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, Inc., Florida. pp. 25-26. Thomas, J.J. 1983. An Introduction to Statistical Analysis for Economists. Weidenfeld and Nicolson, London. United States Department of Agriculture. National Agricultural Statistics Service, Agricultural Statistics Board. Agricultural Prices. Various Issues, 1981-1992. United States Department of Agriculture. 1980. Report and Recommendations on Organic Farming. U.S. Government Printing Office, Washington, D.C. Wilson, C.L. 1974. Initial session. In Energy Analysis: Workshop Report No. 6. International federation of Institutes of Advanced Study, Stockholm. pp. 5-9. HICHI RN STQTE UNIV. LIBRARIES ill/111111ill/11111ill/ll1111111111 3129300881 1592