8
Soil Quality in Relation to Value and Sustainable Management

Francis J. Pierce

Crop and Soil Sciences Department Michigan Sate University, Lansing

INTRODUCTION

Soil is a vital natural resource, whose quality is inextricably linked to the human quality of life. Processes that affect the soil resource base impact the quality of life, either directly by affecting food and fibre production or indirectly by affecting other natural resources such as air, water and wildlife. The link to human quality of life gives soil value. My assigned task is to address how soils are valued, with specific emphasis on the "weaknesses in conventional measures of crop yields from a soil science perspective." It is my intention in this discussion paper to raise issues related to soil value in terms of the capacity of soil to produce food and fibre tempered by the need to account for impacts of land use on the quality of life, e.g., "green accounting," over the long-term.

I am suggesting in this paper that, in order to better reflect the depletion of the soil resource base and environmental damage associated with its use, crop yield alone must be considered inadequate as a measure of soil productivity, that the concept of maximum economic yield (MEY) of the 1970s and 1980s be replaced by maximum sustainable yield (MSY), and that deliberate efforts must be made to design management systems that are inherently sustainable and establish standards for soil quality as a measure of that sustainability.

SOIL PRODUCTIVITY AND CROP YIELDS

Point I. Crop yield alone is not a good measure of soil productivity because (1) long-term yield data by soil type are limited, and (2) maximum yield is not a sufficient measure of productivity because it does not reflect all costs of production, including environmental costs.

From an agricultural perspective, the value of soil has traditionally been measured in terms of its productivity, defined as the capacity of a soil to produce a plant or sequence of



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Assigning Economic Value to Natural Resources 8 Soil Quality in Relation to Value and Sustainable Management Francis J. Pierce Crop and Soil Sciences Department Michigan Sate University, Lansing INTRODUCTION Soil is a vital natural resource, whose quality is inextricably linked to the human quality of life. Processes that affect the soil resource base impact the quality of life, either directly by affecting food and fibre production or indirectly by affecting other natural resources such as air, water and wildlife. The link to human quality of life gives soil value. My assigned task is to address how soils are valued, with specific emphasis on the "weaknesses in conventional measures of crop yields from a soil science perspective." It is my intention in this discussion paper to raise issues related to soil value in terms of the capacity of soil to produce food and fibre tempered by the need to account for impacts of land use on the quality of life, e.g., "green accounting," over the long-term. I am suggesting in this paper that, in order to better reflect the depletion of the soil resource base and environmental damage associated with its use, crop yield alone must be considered inadequate as a measure of soil productivity, that the concept of maximum economic yield (MEY) of the 1970s and 1980s be replaced by maximum sustainable yield (MSY), and that deliberate efforts must be made to design management systems that are inherently sustainable and establish standards for soil quality as a measure of that sustainability. SOIL PRODUCTIVITY AND CROP YIELDS Point I. Crop yield alone is not a good measure of soil productivity because (1) long-term yield data by soil type are limited, and (2) maximum yield is not a sufficient measure of productivity because it does not reflect all costs of production, including environmental costs. From an agricultural perspective, the value of soil has traditionally been measured in terms of its productivity, defined as the capacity of a soil to produce a plant or sequence of

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Assigning Economic Value to Natural Resources plants under a physically defined set of management practices (Soil Survey Staff, 1951). Soil productivity, then, includes two aspects: the inherent productivity of soil and its response to managed inputs. Crop yield has been considered the best indicator of soil productivity as it integrates the inherent and managed components of soil productivity. However, using crop yields as a measure of soil productivity is difficult since yield data are limited, are spatially and temporally variable, and depend on the management and level of technology used (Pierce, 1991). Well-documented yield records are scarce and usually available only from farmers' long-term records, crop yield surveys, and plot experiments. Farmers' yield records are field-based, which actually represent averages of a number of soil map units and combined fields with varying management histories. As fields continue to increase in size, field yields increasingly average across management units, making identification of specific soil yield potentials more difficult. Even when available, a well-documented yield history will not necessarily be indicative of future effects of soil degradation processes. Therefore, alternatives to evaluate land and soil productivity have been intensively pursued. Land evaluation systems and soil productivity ratings have been developed in lieu of crop yields as a measure of potential productivity (Huddleston, 1984; Olson, 1974; Riquier, 1974; Stewart, 1968; Wagenet et al., 1991). Nix (1968) recognized three approaches. The most common is the analogue or transfer-by-analogy approach and is based on land and soil classifications. A second approach, the site-factor approach; seeks to relate key parameters to biological productivity within a given environment where yield is described by a multiple regression equation. A third approach, called the system-analysis-and-simulation approach, is concerned with resolution of a complex system into simple component processes that are synthesized into a mathematical model of the whole system. The simulation of crop yields utilizing soil databases is gaining in use in land evaluation (Wagenet et al., 1991) and productivity assessment (Williams and Renard, 1985). Each of these approaches has been used not only to assess soil productivity but also the effects of soil degradation, most commonly by accelerated soil erosion, on soil productivity (see Pierce, 1991, for a review). These evaluation techniques will become increasingly important in the design and evaluation of sustainable management systems, as discussed later. Regardless of the approach, however, land evaluation procedures or indices relate to the potential of land (soil) to produce food and fibre, and hence must correlate to crop yield. This must take into consideration, however, that weather and inputs are major contributors to crop yield, in addition to soil. Farmers consider the most productive and, therefore, valuable soils to be those that produce the consistently highest yields. While important in the equation, crop yields alone are no longer sufficient as a measure of productivity since the costs of production (economic, social, and environmental) increasingly alter the value of production.

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Assigning Economic Value to Natural Resources SHIFTING FROM MAXIMUM CROP YIELDS TO MAXIMUM SUSTAINABLE YIELDS Point II. Concerns over resource degradation and environmental damage from agriculture require a shift from crop and economic yields to sustainable yields. Crop yield as the measure of soil value is inappropriate because it ''simplifies" the soil by treating it as a closed system in which an array of managed inputs produces a single output, crop yield (Figure 8-1a). This closed system view of soils was exemplified by the "maximum yield" (MY) concept of the 1960s in which large quantities of inputs were used to achieve increases in crop yield, often realizing only incremental increases at high levels of input. Agricultural production systems were characterized by high productivity with huge energy and material subsidies of labor, fossil fuels, pesticides, fertilizers, and irrigation. The tendency was to simplify the agricultural ecosystem by creating monocultures with minimal diversity beyond what was needed to produce maximum yields—a very short-term perspective. Continuous monocultures, particularly corn, tend to require larger subsidies of materials and energy. Such simple ecosystems tend to be ecologically unstable in their characteristic patterns of energy flow, nutrient cycling, and structural change (in terms of species composition, biomass, and spatial organization) (Cox and Atkins, 1979). During the period of MY, soil erosion was a major national problem. The resulting nonpoint source pollution and soil productivity decline associated with soil erosion stimulated the enabling legislation for the protection of soil and water resources, which began with the Water Pollution Control Act of 1965. It was the energy crisis of the 1970s that moved agriculture to the "maximum economic yield" (MEY) concept in which inputs are limited to those that produce an economic crop yield response. Given typical input yield response curves, MEY would be less than MY. Higher production costs associated with increased energy costs and inflated land values resulted in increased interest in and use of conservation tillage, efficient nutrient management, and Integrated Pest Management (IPM). The shift from MY to MEY was relatively easy for farmers because it was value based at the farm level. The farmer needed to improve his profitability in the face of increased production costs and lower prices for his crops. Therefore, maximizing economic return was essential for those who would successfully compete. It was clearly demonstrated both by research and on-farm demonstration that decreased inputs could improve farm profitability. It was also clear, however, that agriculture did not buy into MEY completely since farmers continued to apply more inputs than necessary, in part, perhaps, due to their perception of risks involved. For example, yield goal is the key parameter in determining the optimum rate of fertilizer addition. However, farmers apply fertilizers even when soil tests indicate more than adequate levels of these nutrients are available in soil, a sort of insurance application. Some, in fact, ignore soil testing completely. The same is true of nitrogen (N) applications. Farmers will often overestimate their yield goal, apply more than the optimum rate, and/or inadequately account for N supplied from the soil or previous crop in their determination of N fertilizer needs. Schepers et al. (1986) reported that farmers in Hall County, Nebraska, overestimated their yield goal by 40 bushels per acre, resulting in an over application of 40 pounds of N per acre. The extent to which farmers adhered to the MEY concept probably relates to their perception of risks associated with the MEY management practices.

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Assigning Economic Value to Natural Resources FIGURE 8-1 (a) An illustration of soil as a closed system in which crop yield is the sole output. (b) An illustration of soil as an open system with all its component inputs, outputs, and transformations.

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Assigning Economic Value to Natural Resources While farmers have undoubtably shifted to the MEY approach, there is little evidence that national accounting systems have moved away from the MY concepts. The focus is still on yield potential without regard to efficiencies of production. This must certainly be regarded as a weakness in the conventional measure of crop yield. The concept of MEY was directed primarily at the economics of crop production over the short-term. The principles of MEY, however, also had implications for environmental issues which intensified during the 1970s and 1980s and proved useful in providing a basis for the development of best management practices (BMPs). BMPs refer to a variety of agronomic practices and structural practices that are designed to reduce the transport of sediment, nutrients, and toxics to water resources, while sustaining producer profitability. Currently, BMPs are voluntary and, therefore, must be economical if they are to be adopted. Therefore, some BMPs require financial subsidy to offset their costs, such as waterway structures. The point is that the management practices that accomplish MEY also improve water quality. Bock and Hergert's (1991) statement that high N-use efficiency is the main goal of a "best management practice" conforms completely to the MEY concept. However, the relevant question concerning MEY is whether it is sufficient for sustainability? For some soils and cropping systems, MEY may be sufficient; for others, it will not suffice. Although not formalized, the search for an optimum yield as a value for soil is headed towards what I will call maximum sustainable yield (MSY). The concept of MSY embraces the concerns of green accounting without abandoning the traditional view of soil productivity as a capacity to produce crop yield. What changes is the physical set of management practices under which that capacity is defined. Under MSY, the soil is treated as an open system of inputs, outputs, and transformations in which more emphasis is placed on transformations than external inputs to create desirable outputs of crop yield and minimal environmental impacts (Figure 8-1b). The reliance on transformations will require more biological diversity within the soil and thereby increase the complexity within the agroecosystem, resulting in increased stability and resiliency. Where managed inputs are required, they will be tailored to the location specific needs within a field and applied at the time and in a manner conducive to maximum use efficiency or efficacy. Thus, the MSY concept will incorporate the qualities of MEY and at the same time accommodate the goals of green accounting. The next step is to define MSY management systems and what standards will be used to evaluate them. The sustainable management systems that are emerging in the United States depend more on internal transformations within the soil and less on external inputs. Pierce and Lal (1991) envision the incorporation of ecological principles from natural ecosystems into agroecosystems as the key to developing sustainable soil management systems. They proposed a broad management principle to achieve sustainable management: "managing soils in space and time." Within this framework, they offer three specific management principles. The first is farming by soilscapes in which management practices are matched to specific soil and landscape characteristics within a field. Farmers farm fields which contain a range of soils and landforms that differ in inherent productivity and susceptibility to undesirable material flows, such as erosion and leaching. Technology exists to vary managed inputs as machinery moves across the field so that inputs can be matched to location-specific needs within the field and applied with precision. The net result will be to increase profitability for farmers and reduce soil and environmental deterioration (Robert et al., 1993). The second aspect of farming

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Assigning Economic Value to Natural Resources by soilscapes deals with the notion that controls to a problem occurring within agricultural fields are often located outside the field boundaries. Soil erosion extends beyond field and political boundaries and is best managed on a watershed basis. The second management principle is to manage zones within a field. This is not a new concept, as USDA has used it in the design of water and erosion control measures since the 1930s. New applications of this principle fit MSY as illustrated in the use of trap crops for insect control. Figure 8-2 illustrates the use of strips of potatoes and eggplant as trap crops to manage Colorado beetles in tomatoes. The trap crops attract the beetle, since they are a preferred host plant. Control is made easier when the beetles are concentrated in small strips near field borders. This avoids beetle damage on the tomato plants while minimizing the use of chemicals over the entire field. Alley cropping is another more recent example of managing zones within a field by combining ligneous species with field crops arranged spatially and temporally to provide food and fibre while preserving the soil resource base. The third management principle is managing the noncrop period. During the cropping period, soils are managed intensively. Outside the cropping period, little is done to manage the soil in spite of the fact that the major degradation processes, such as erosion, leaching, and compaction, are often most intense during the noncrop period. The noncrop period affords an opportunity for sustainable management practices that enhance MSY. A prime example is the use of cover crops during the noncrop period which provides for erosion control, weed suppression and fixation of atmospheric nitrogen for use in the succeeding crop, all beneficial to MSY. I would expect the nature of MSY to be quite diverse and variable but its yield level close to MEY, if the management system were properly designed and the actual management conformed to that design. The perspective of MSY is long-term and unlike either MY or MEY, it is predicated on what happens to the resource base, not just short-term gains. DESIGN AND EVALUATION OF SUSTAINABLE MANAGEMENT SYSTEMS Point III. Deliberate efforts must be made to design management systems that are inherently sustainable and establish standards for soil quality as a measure of that sustainability. Pierce and Larson (1993) recognize that a deliberate effort is required to design management systems that are inherently sustainable and ensure, through quality control measures, that the system processes conform to the design. In an analogy to statistical quality control in manufacturing, they suggest that quality must be built into both the design and the system processes in order to achieve the desired food or resource quality. The overall process of assessing sod quality and evaluating management systems is illustrated in Figure 8-3. A brief discussion of soil quality and statistical quality control is necessary to understand these concepts (based on Larson and Pierce, 1991, 1993; Pierce and Larson, 1993). The term "soil quality" is relatively new. Soils vary in quality and quality changes in response to its use and management. The changes in quality can be either positive (aggradation)

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Assigning Economic Value to Natural Resources FIGURE 8-2 Illustration of the concept of trap crops for the control of Colorado potato beetle in tomatoes. Source: Redrawn from Janice Elmhirst, O.M.A.F., Ontario, Canada.

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Assigning Economic Value to Natural Resources FIGURE 8-3 A flow diagram illustrating a procedure for evaluating the sustainability of land management systems. Source: From Pierce and Larson, 1993.

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Assigning Economic Value to Natural Resources or negative (degradation). If sustainable systems must maintain or improve soil quality, then a quantitative assessment of soil quality provides a measure of sustainable management. Thus, the performance of a management system can be determined by measuring the change in soil quality parameters. Larson and Pierce (1991) defined soil quality as the capacity of a soil to function, both within its ecosystem boundaries (e.g., soil map unit boundaries) and with the environment external to that ecosystem (particularly relative to air and water quality). Soil quality relates specifically to the ability of soil to function as a medium for plant growth (productivity), in the partitioning and regulation of water flow in the environment, and as an environmental buffer. As a simple operational definition, soil quality means ''fitness for use" (Pierce and Larson, 1993). In this concept, crop yield is not the sole value of soil. Larson and Pierce (1991) define soil quality, Q, as the state of existence of a soil relative to a standard or in terms of a degree of excellence. It is expressed as a function of attributes of soil quality, qi, defined as: (1) While Q is important in land evaluation, sustainable management requires knowledge about changes in soil quality, dQ/dt, defined as: (2) An aggrading soil would have a positive dQ/dt and a degrading soil would have a negative dQ/dt. The functional relationship in Equation (2) is difficult to define and it is impossible to describe Q in terms of all soil attributes. Therefore, Larson and Pierce (1991) proposed that a minimum data set (MDS), in combination with pedotransfer functions (PTF's), be designed to monitor changes in soil quality. An important aspect of an MDS is that it must include soil attributes in which quantitative attributes can be measured in a short time span in order to be useful in land use or management decisions. The components of an MDS are selected on the basis of their ease of measurement, reproducibility, and to what extent they represent key variables that control soil quality. It is important to note that any MDS represents a minimum set of attributes to be measured to assess soil quality. Other attributes may be part of an extended data set intended for certain investigations. An example of a minimum data set was described by Larson and Pierce (1991) and a summary is given in Table 8-1. Note that both the type of measurement and a measurement procedure should be standardized, at least within a geographic region. In addition, there are soil parameters that are too costly or difficult to measure that would be desirable in a MDS for soil quality. Fortunately, soil properties are interrelated and can be predicted from other properties using pedotransfer functions (PTFs). A

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Assigning Economic Value to Natural Resources TABLE 8-1 Soil Attributes and standard methodologies for their measurement to be included as part of a minimum data set (MDS) for monitoring soil quality (adapted from Larson and Pierce, 1991). Soil Attribute Methodology Nutrient availability for region Analytical soil test Total organic carbon (OC-T) Dry or wet combustion Labile organic carbon (OC-L) Digestion with KCl Texture Pipette or hydrometer method Plant-available water capacity (PAWC) Best determined in field or from water desorption curve Structure Bulk density from intact soil cores field measure permeability or Ksat Strength Bulk density or penetration resistance Maximum rooting depth Crop specific—depth of common roots or standard pH Glass electrode-calomel electrode pH meter Electrical conductivity Conductivity meter PTF is described by Bouma (1989) as a mathematical function that relates soil characteristics and properties with one another for use in the evaluation of soil quality (Larson and Pierce, 1991). Therefore, PTFs can be used to extend the utility of the MDS to monitor soil quality. Many PTFs occur in the literature and are statistical or empirical in nature. Selected PTFs were discussed by Larson and Pierce (1991) and are given in Table 8-2. There is no consensus on what a MDS for soil quality should contain. The MDS and PTFs given by Larson and Pierce (1991) represent a starting point. The soil quality MDS is measured over time to assess the dynamics of soil quality. There are two ways of assessing changes in soil quality: (1) through the use of computer models to determine how changes in the MDS impact the important functions of soil, such as productivity; and (2) using statistical quality control procedures, through which a MDS is repeatedly measured over time and the temporal pattern of variation of a MDS parameter or PTF is evaluated (Pierce and Larson, 1993). The use of models for dynamically assessing soil quality is illustrated by the use of productivity indices by Pierce et al. (1983) to quantify soil productivity and the loss in productivity with accelerated soil erosion (Pierce and Larson, 1992). Using a modification of a productivity model developed by Kiniry et al. (1983), Pierce et al. (1983) used the PTFs to calculate a normalized sufficiency of soil pH, bulk density and available water capacity (AWC) for root

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Assigning Economic Value to Natural Resources TABLE 8-2 A Limited Listing of Proposed Pedotransfer Functions (adapted from Larson and Pierce, 1991). PTF No. Estimate Relationship     Chemical 1 Phosphate-sorption capacity PSC + 0.4 (Alox + FEox) 2 Cation-exchange capacity CEC = A OC + b C 3 Change in organic matter ΔC = + b OR     Physical 4 Bulk density Db = b0 + b1 OC + b2 Si + b3 M 5 Bulk density Random packing model using particle size distribution 6 Bulk density Db = f(OC, clay) 7 Water retention q10 = b0 + b1 C + b2 Sy 8 Water retention q = b1 (%Sa) + b2 (%Si) + b3 (%Cl) + b4 (%OC) 9 Random roughness from moldboard plowing RR = f (soil morphology) 10 Porosity increase P = f (MR, IP, clay, Si, OC)     Hydraulic 11 Hydraulic conductivity Ks = f (texture) 12 Seal conductivity SC = f (texture) 13 Saturated hydraulic conductivity Ds = f (soil morphology)     Productivity 14 Soil productivity PI = f (Db, AWHC, pH, Ec, ARE) 15 Rooting depth RD = f (Db, WHC, pH)

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Assigning Economic Value to Natural Resources growth. Once determined, the product of the sufficiencies was weighted by a normalized rooting function to calculate a productivity index, PI as: where Ai is sufficiency of AWC, Ci is the sufficiency of bulk density, Di is the sufficiency of pH, WF is a weighting factor, and r is the number of soil horizons in the depth of rooting. Using soil survey data contained in the SOILS-5 database and land use and erosion data from the National Resources Inventory, the effect of erosion on soil productivity was estimated for the Corn Belt Region of the United States (Pierce et al., 1984). Both the quality (PI) and the change in quality (ΔPI) were estimated using the concepts given in Equations (1) and (2). A second approach to assess the dynamics of soil quality from the MDS is the use of statistical quality control. Statistical tools appropriate for assessing changes in soil quality may be found in the use of "control charts." Control charts are a standard device used in statistical quality control in the manufactured goods and services industry. The statistical basis for their use is well established and the types and uses of control charts are very diverse (Gilliland, 1990; Montgomery, 1985; Ryan, 1989). Control charts can be thought of as indicators of changing soil quality. The basic use of control charts is illustrated in Figure 8-4. Under this procedure, soils would be sampled over time for soil attributes (MDS) representing quality parameters or transformed using PTFs to other quality parameters. The upper control limit (UCL) and lower control limit (LCL) are set based on known or desired tolerances, or based on the mean variance obtained from past performance or known through some other means. It may be desirable to design the control limits to represent minimum levels for sustainability, beyond which management cannot be sustained, such as minimum soil organic matter content. In the simplest case, as long as the sample mean plots within the control limits, the process or system is considered in-control. When a sample mean plots outside the control limits, the process or system is considered out-of-control, i.e., soil quality is changing. Additionally, trends may occur within the control chart and statistical quality control procedures are. available to analyze these trends. Trends may be indicative of instability in the management system or merely characteristic of the process. For example, the data in Figure 8-5 would be indicative of a system with cyclic variation that operates within the control region (Pierce and Larson, 1993). It is worth noting here that the variance should be charted as well as the mean since changes in variance can be indicative of a system out of control. The concept of using control charts for each MDS and PTF parameter is useful in quantifying the dynamics of soil quality. It is likely that for a given management system some qi's may be stable, others out-of-control, and others showing trends. A sustainable management system will be characterized by qi's which are stable over time and if trends occur in the control charts, they are indicative of an aggrading soil quality, not degrading. An important aspect of process control is that it be in the hands of those managing the process. The manager should be able to interpret the control charts and take appropriate action to adjust the process and bring it back into control. Informing the manager that the outcome of

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Assigning Economic Value to Natural Resources FIGURE 8-4 The basic concept of a Shewhart control chart used for soil quality monitoring. SOURCE: From Pierce and Larson, 1993. After Montgomery, 1985, and Ryan, 1989.

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Assigning Economic Value to Natural Resources FIGURE 8-5 An example of a control chart with variation within the control limits but exhibiting a pattern in the variation of a soil quality parameter. Source: From Pierce and Larson, 1993.

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Assigning Economic Value to Natural Resources the process is unacceptable is useless in helping the manager achieve the desired quality of the output. Consider the management of crop residues for erosion control as an example of a management practice that relies on monitoring with little regard for the notion of design and process control (Pierce and Larson, 1993). Residue cover is a key factor influencing erosion control. Until recently, interest focused specifically on the amount of residue cover after planting and it was not uncommon that conservation tillage systems failed to meet the target residue coverage amount of 30 percent on the erosive landscape positions. The problem is that the standard measure of residue cover occurs after most of the management practices that affect residue cover are completed. At this time, it is not possible for the farm manager to alter those practices to ensure the proper residue cover. How would process control measures impact residue cover, if it can be assumed that the design of the system is correct? Since the harvest, tillage, and planting operations impact residue distribution, statistical process controls could be implemented to monitor machinery performance relative to residue management to detect when machinery adjustments are required to achieve desired residue coverage. This level of management is nearly achievable with the site-specific crop management technology currently available (Robert et al., 1993). Thus, if the system is designed to meet the intended output, then through process control the output can be reasonably assured. Consider the management system design procedure illustrated in Figure 8-3. In this system, soil survey data, MDSs, and PTFs provide input to simulation models to design sustainable management systems and establish standards for soil quality. Control charts of various qi's are monitored with time and used alone, or in combination with models, to detect quality control problems and identify improvement opportunities in the system. Control charts can also be used in combination with MDSs and PTFs to monitor soil quality and serve as thresholds and criteria for quality standards. A design criteria for an MEY system would certainly include the impact on undesirable system outputs to the environment. Figure 8-6 gives a hypothetical plot of profit versus an environmental output from soil such as nitrate-N. The MEY will allow for environmental outputs of nitrate-N that is greater than what might be considered sustainable loss. The MSY must be designed so that it does not exceed the sustainable loss value. As illustrated, MSY would be less profitable. The difference in profit between MEY and MSY would have to be born by the farmer or society. The following steps are important in the design and evaluation of sustainable management systems that fit the MSY concept (Pierce and Larson, 1993). Explicit identification of the desired outputs of a management system. Assessment of the design of the system to determine if it will produce the desired output. Identification of the soil quality parameters of importance and establishment of quality standards. Establishment of the starting point for evaluation of a management system. Knowledge of the condition of the soil at the initiation of the management change is required unless the historical record of the site is good. Assessment of the system output to determine if it results from the system design, the system process performance, or both.

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Assigning Economic Value to Natural Resources FIGURE 8-6 Illustration of a design criteria for sustainable management systems comparing the profitability of MSY management systems to MEY systems.

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Assigning Economic Value to Natural Resources Stabilization of a system process that is out-of-control. A stable system of variation is one in which the variation is solely a result of the system in place; there are no special causes of variation (Gilliland, 1990). Improvement of the sustainability of a stable management system by adjusting it with proper experimental design techniques (note: tampering with a stable system will make the system less stable) (Montgomery, 1985). SUMMARY AND CONCLUSIONS I have addressed weaknesses in conventional measures of crop yields from a soil science perspective. I have attempted to show that crop yields are important but alone are not sufficient as a measure of soil productivity or soil value. Crop yields by soil type are difficult to obtain and yields alone do not account for the cost of production. Farmers must operate on economic, and not biological yields, and have realized this for 'some time. It would appear that economic yields may not be the correct measure either as other costs of production (environmental, erosion loss, social, etc.) are realized. A shift to sustainable yields may be forthcoming. However, sustainable management systems must be designed and quality standards set for their proper evaluation. An approach to achieving sustainable management is presented that has utility in accounting for soil degradation and environmental damage from agricultural systems. This requires a shift in the agricultural paradigm from a short-term to a long-term view. REFERENCES Bock, B. R. and G. W. Hergert. 1991. Fertilizer nitrogen Management. pp. 139-164. In R. F. Follett, D. R. Kenny, and R. M. Cruse (eds.) Managing Nitrogen for Groundwater Quality and Farm Profitability. Soil Sci. Soc. Am., Inc., Madison, WI. 357 pp. Bouma, J. 1989. Using soil survey data for quantitative land evaluation. Advances in Soil Sci. 9:177-213. Cox, G. W. and M. D. Atkins. 1979. Agricultural Ecology: An Analysis of World Food Production Systems. W. H. Freeman and Company, San Francisco, CA. 721 pp. Gilliland, D.C. 1990. Experiences in Statistics. Kendall/Hunt Pub. Co., Dubuque, IA 52004. 104 pp. Huddleston, J. H. 1984. Development and use of soil productivity ratings in the United States. Geoderma 32:297-317. Kiniry, L. N., C. L. Scrivner, and M. E. Keener. 1983. A soil productivity index based upon predicted water depletion and root growth. Res. Bull. 1051. Mo. Agr. Exp. Sta., Columbia. Larson, W. E. and F. J. Pierce. 1991. Conservation and enhancement of soil quality. In Evaluation for Sustainable Land Management in the Developing World Vol. 2: Technical Papers. Bangkok, Thailand: International Board for Soil Research and Management, 1991. IBSRAM Proceedings No. 12(2).

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Assigning Economic Value to Natural Resources Larson, W. E. and F. J. Pierce. 1993. The dynamics of soil quality as a measure of sustainability. Proceedings of a Symposium on Soil Quality, ASA annual meeting, November 1-6, 1992, Minneapolis, MN. (in press). Montgomery, D.C. 1985. Introduction to Statistical Quality Control. John Wiley and Sons, New York. 520p. Nix, H. A. 1968. The assessment of biological productivity. In G. A. Stewart (ed). Land Evaluation. The MacMillan Company of Australia, Melbourne. pp. 77-87. Olson, G. W. 1974. Land Classifications. SEARCH Agriculture Vol 4, No.7. Cornell University Agricultural Experiment Station, Ithaca, N.Y. 33pp. Pierce, F. J. 1991. Erosion productivity impact prediction. p. 35-52. In R. Lal and F. J. Pierce (eds.) Soil Management for Sustainability. Soil and Water Conserv. Sot., Ankeny, IA 50021. Pierce, F. J. and R. Lal. 1991. Soil management in the 21st century. 175-179. In R. Lal and F. J. Pierce (eds.) Soil Management for Sustainability. Soil and Water Conserv. Soc., Ankeny, IA 50021. Pierce, F. J. and W. E. Larson. 1993. Developing threshold values, indicators, and criteria to evaluate sustainable land management. pp. 7-14. In J. M. Kimble (ed) Proceedings of the Eighth International Soil Management Workshop, Utilization of Soil Survey Information for Sustainable Land Use-May, 1993. USDA, Soil Conservation Service, National Soil Survey Center. Pierce, F. J., W. E. Larson, R. H. Dowdy, and W. A. P. Graham. 1983. Productivity of soils: Assessing long-term changes due to erosion. J. Soil Water Conserv. 38:39-44. Pierce, F. J., W. E. Larson, R. H. Dowdy, and W. A. P. Graham. 1984. Soil productivity in the Corn Belt: An assessment of erosion's long-term effects. J. Soil Water Conserv. 39:131-136. Riquier, J. 1974. A survey of parametric methods of soil and land evaluation. In Approaches to Land Classification. Soils Bulletin 22. Food and Agricultural Organization, Rome, Italy. pp. 47-53. Robert, P. C., R. H. Rust, and W. E. Larson (eds). 1993. Proceedings of soil specific crop management: a workshop on research and development issues: April 14-16, 1992, Sheraton Airport Inn, Minneapolis, MN. Am. Soc. Agron, Inc., Madison, WI. Ryan, T. P. 1989. Statistical Methods for Quality Control. John Wiley and Sons, New York. 446p. Soil Survey Staff. 1951. Soil Survey Manual. Agr. Handbk. No. 18. U. S. Dept. Agr., Washington, D.C. Schepers, J. S., K. D. Frank, and C. Bourg. 1986. Effect of yield goal and residual soil nitrogen considerations on nitrogen fertilizer recommendations for irrigated maize in Nebraska. J. Fert. Issues 3:133-139. Stewart. G. A. (ed.). 1968. Land Evaluation. The MacMillan Company of Australia, Melbourne. Wagenet, R. J., J. Bouma, and R. B. Grossman. 1991. Minimum data sets for use of soil survey information in soil interpretive models. pp. 161-182. In M. J. Mausbach and L. P. Wilding (eds) Spatial Variabilities of Soils and Landforms. SSSA Spec. Publ. Number 28. Soil Sci. Soc. Am., Inc., Madison, WI 53711.

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Assigning Economic Value to Natural Resources Williams, J. R. and K. G. Renard. 1985. Assessment of soil erosion and crop productivity with process models (EPIC). In R. F. Follett and B. A. Stewart (eds). Soil Erosion and Crop Productivity. Soil Sci. Soc. Am., Madison, WI. pp. 67-103.