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Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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Appendix A

Poster Sessions

Low-Input Sustainable Agriculture Farm Decision Support System

John E. Ikerd

U.S. farmers are faced with growing environmental concerns and rising costs associated with highly specialized farming operations. They are searching for farming systems that are ecologically sustainable as well as productive and profitable. Many are motivated by perceived risks that the inputs on which they depend today may not be available, may not be effective, or may cost much more in the future. Such farmers are searching for ways to reduce their dependence on external purchased inputs while maintaining their productivity and profits through more intensive management of their internal resources.

The current search for sustainability and profitability in U.S. agriculture is centered on helping farmers develop more ecologically sound and economically viable farming systems with existing technology while searching for even more sustainable and profitable alternatives for the future.

A short-term objective is to improve the input efficiency of current farming systems. However, long-term sustainability may require more diversified systems of farming that include commodities that can be produced with more ecologically benign systems. Diversified farming systems traditionally use crop rotations to control pests, conserve soil, and maintain productivity. Integrated cropping and livestock systems have been used to reduce feed costs, recycle waste, and stabilize the incomes of U.S. farmers.

The current hope for future success lies in finding ways of combining

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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new technologies such as microcomputers and biotechnology with the tried and proven principles of management by objectives and diversification —old principles with new technologies.

Such farming systems will be more complex and thus will require more intensive hands-on resource management than do higher-input, specialized systems. However, synergistic gains from effective integration of enterprises and activities in diversified farming systems represent the best hope for achieving long-term sustainability with a minimum of government regulation.

A microcomputer-based farm decision support system1 is being developed under a project funded jointly by the Extension Service and Cooperative State Research Service, U.S. Department of Agriculture, to integrate the concept of sustainability into farm planning and to implement farm management strategies for sustainability.

The Low-Input Sustainable Agriculture Farm Decision Support System (LISA-FDSS) project was approved in 1988 for funding through November 1990.

CHARACTERISTICS OF LISA-FDSS2

The LISA-FDSS system has six basic functions that are supported by two microcomputer-based program components, two farming systems data bases, and several specialized data bases to support the budgeting process. LISA-FDSS is designed to be compatible with a national financial planning project, FINPACK, and a national linear programming project that emphasizes labor and machinery management.

The six basic functions of the LISA-FDSS system are as follows: (1) resource management strategy (RMS) budgeting, (2) whole-farm planning, (3) environmental checking, (4) financial checking, (5) risk checking, and (6) resource checking. The two data bases are (1) default RMS budgets and (2) customized RMS budgets. Additional data bases include soil types and characteristics, fertilizer and pesticide characteristics, correlation coefficients, and energy conversion units.

RMS Budgeting

The RMS associated with a cropping system consists of a crop sequence or rotation, an irrigation system (if any), a tillage system, a fertility system, and a pest management system. An RMS budget reflects the resource requirements, input requirements, input costs, expected production, expected returns, potential conservation impacts, and potential environmental impacts of the individual crops as components of a cropping system. An RMS budget contains all non-site-specific information needed to calculate

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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expected soil losses, water-quality risks, resource use, gross margin over purchased inputs, and revenue risks.

The default RMS budget data base will contain budgets for cropping and livestock systems deemed appropriate for the geographic region of application. These data bases will be constructed by extension specialists using a basic FINPACK financial budget format augmented by additional resource and environmental (R&E) components. Development of R&E budget components will be facilitated by a budgeting program developed as a part of the LISA-FDSS project. The R&E budgeting program is one of the two basic microcomputer program components of the LISA-FDSS system.

Default data bases should include budgets for a wide range of cropping systems deemed appropriate for the geographic region where the LISA-FDSS program is to be used. A cropping system might include from 1 to 12 different crops. A monocrop system would have the same budget for each year. A given crop following different crops in different rotations might have a different budget for each rotational position. Different crops, of course, would have different budgets.

Each cropping system will be budgeted for up to four alternative input systems. An input system will reflect a specific fertility and pest management system. Most systems would be budgeted with unrestricted-input, reduced-input, and low-input RMS alternatives.

Unrestricted-input RMS budgets will reflect the use of typical fertilizer and pesticide inputs for a particular cropping system on fields with no significant fertilizer or pesticide leaching or runoff risk potential. Reduced-input RMS budgets will reflect some lower level of inputs suggested for fields with significant nutrient or pesticide risk potentials. Split applications and banding of fertilizers and pesticides might be a logical reduced-input system, for example. A low-input system should reflect minimum levels of external inputs that specialists deem feasible for commercial production on fields with high nutrient loading or pesticide risks.

Each cropping system will also be budgeted for alternative tillage levels. Tillage options will range from unrestricted tillage to minimum tillage. Unrestricted tillage would be the suggested system for fields without erosion problems, with minimum tillage suggested for highly erodible fields. Each tillage system should be matched with an appropriate complement of inputs. Consequently, some systems may have no low-input, minimum-tillage RMS, if such a combination of tillage and inputs is not considered feasible for a given cropping system.

In general, the alternative input systems will be designed to reduce water-quality and other environmental risks by moving to lower-input alternatives. In general, the alternative tillage systems will be designed to reduce soil erosion risks by moving to lower tillage levels. Irrigation systems, if any, will be specified as a part of each input system.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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Farmers who use an unrestricted system for one crop would likely use an unrestricted system for another crop in the same rotation, although an unrestricted system might imply different tillage and input regiments for different crops in the same rotation. Likewise, a farmer interested in a low-input commercial alternative for one crop likely would be interested in a similar system for other crops in the same rotation. Thus, the levels of inputs and tillage will be identified for whole cropping systems rather than individual crops.

Whole-Farm Planning

The Whole-Farm Planner (WFP) is a microcomputer-based decision support system that allows farmers to evaluate the potential impact of using various cropping systems or RMSs on their specific farms. The WFP is a field-based system. It allows farmers to plan their farms field by field and year by year and to assess the RMS implications for each field and each year for the whole farming system, including livestock as well as crops.

A typical FDSS user would begin with the whole-farm planner component of the system. An agent working with a farmer should have determined the basic rotations used by the farmer and have those RMSs available in the default data base at the time of the first planning session. Otherwise, the farmer and agent would have to add those budgets to the default data base before the planning process could begin. Most farmers will want to begin with an assessment of their current system before they begin to evaluate alternatives.

All site-specific information and the associated yield and environmental impact estimates are calculated within the whole-farm planner program. Thus, the whole-farm planning process begins with a field-by field inventory of the land or soil resources of the farm. Much of the information related to soil erosion and environmental vulnerability can be derived from the Soil Conservation Service (SCS) data base of soil types. Soil texture, pesticide leachability, pesticide surface loss potential, and average slope and slope length are identified in the SCS data base of U.S. soils. However, the farmer will be asked to verify yield potentials, soil characteristics, and environmental impact estimates in the planning process.

Environmental and conservation impacts will be evaluated field by field over a 12-year planning period. Thus, estimates of soil loss, water-quality risks from pesticides and fertilizers, and input toxicity will be evaluated for cropping systems rather than individual crops.

Financial and resource implications of alternative systems will be evaluated for the whole farm for each year in the planning period. The acreage of each crop, pasture, set-aside or conservation reserves, expected revenues, input costs, gross margins, revenue risks, corn equivalents produced and

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

needed, hay equivalents produced and needed, and nonrenewable energy use will be summarized for each year.

The ecological vulnerability of each field will be identified by color-highlighted codes for high, medium, and low levels of vulnerability to soil loss, pesticide leaching, and residue runoff. Each cropping system and RMS will likewise be color-coded with respect to its potential for soil loss and water-quality risks. These two sets of codes, one for the field and the other for the RMS, will be combined to yield a similar color-coded set of implications for using a given RMS on a given field.

Each combination of field and RMS will have a color-coded indicator of soil loss, water-quality risk from pesticide and nitrogen use, and input toxicity. A set of red H's for a given RMS on a given field, for example, could indicate severe ecological problems. Such problems would be associated with the use of a particular RMS on a particular field. The same RMS might be acceptable on another field, but a different RMS might be indicated for the particular field being examined.

There will be relatively few alternatives for correcting the ecological vulnerability of a given field. Exceptions would be to contour till, terrace, strip crop, or ridge till a field to reduce soil loss potential. In most cases, farmers will have to change RMSs to correct ecological problems.

Each RMS will be identified with a code indicating the tillage and input levels associated with the particular strategy. A farmer with an erosion problem might consider an RMS with less tillage. If, instead, the farmer is faced with a water-quality problem, he or she might select a lower-input RMS. If the farmer has an erosion and water-quality problem, he or she could select a longer crop rotation that included meadow or some other soil-conserving crop.

A similar approach will be used in the financial, risk, and resource sections of the program. An unacceptable income level for a given year would be color coded with a red H or some similar sign. The farmer could first consider shifting rotations to get more high-income crops in a given year, if the problem occurred only for 1 or 2 years. However, if the problem occurs for several years, he or she may consider some more intensive RMSs that will generate more income in more years.

Inconsistencies between labor needs and availability would be flagged. Seasonal labor problems may be addressed by shifting rotations, changing to lower-labor RMSs, or hiring labor during peak need periods, if it is feasible. Feed needs and production would be handled in a similar manner. An unacceptable level of risk might suggest that diversity be added by selecting alternative cropping systems, adding livestock to the system, or, possibly, considering off-farm employment for income stability.

Changes in RMSs to solve financial, risk, or resource problems may generate ecological problems. However, no attempt will be made to calcu-

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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late an optimum system for a given farm. Farmers will simply attempt to solve their ecological and economic problems by matching alternative resource management strategies (cropping systems with alternative tillage and input levels and livestock enterprises) with their internal resources (land, labor, and machinery).

Information describing each RMS, including any specialized machinery requirements, will be available from the whole-farm planner program. For example, a farmer may want to know what type of fertility program, tillage system, pest control system, and labor requirements are assumed for a low-input soybean alternative in a corn-soybean rotation in field number three in year 4 of the plan currently on the screen. He or she would indicate with some set of key strokes the basic data he or she wants to review for this particular alternative.

The whole-farm planner program assumes that a farmer has multiple objectives that include both ecologic and economic factors. The ecologic factors are soil loss, water quality, input toxicity, and nonrenewable energy use. Standards for the ecological factors will be predetermined. The economic factors are net returns or income; income risks; and utilization of land, labor, and machinery. Farmers will be asked to develop their own income objectives from overall farm financial information.

Some farmers may be willing to settle for a whole-farm plan with a large number of red, or warning, indicators on the ecologic factors to achieve green, or safe, indicators in the financial and resource areas. Others may be willing to tolerate lower economic results to achieve safe indicators (green color codes) in the ecologic areas. Others will continue to explore alternatives until they have all ecologic and economic indicators in acceptable ranges or they will not farm. These choices are to made by the individual farmer.

Custom Budgets

Each farmer would need to work with his or her agent or specialist in customizing the default RMS budgets to reflect inputs and resources for tillage and cropping systems that the farmer actually expects to use on his or her farm. The WFP program would allow the farmer to greatly narrow the range of budgets that might be considered to be logical for his or her operation. However, the customized alternatives need not be limited to those for a single best farm plan identified by the farm planner.

Changes from default values to customized values for environmental and economic impacts may significantly change the estimated outcomes of a given farm plan. Thus, once the customization process is completed, the farmer would be expected to return to the WFP program. He or she would simply repeat the earlier iterative planning process with the customized sets of budgets until a satisfactory customized plan is achieved.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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OBJECTIVES OF LISA-FDSS

The RMS budgeting process will allow agricultural specialists to reflect the full range of existing and future research results and information in a form that is readily usable by farmers. For example, ecologic and economic impacts of cover crops, intercropping, and relay cropping in various rotations can be reflected in alternative RMS budgets. Uses of legumes and livestock manure for fertilizers as well as alternative systems of fertilizer application can be included among the RMS alternatives to be considered.

Impacts of alternative tillage systems and residue management programs on potential soil loss will be an integral part of the budgeting process. Alternative weed, insect, and other pest control systems, including specific pesticide uses and their potential risks to humans and water quality, will be reflected directly in the environmental components of each RMS budget.

The whole-farm planning process will allow farmers to synthesize profitable and sustainable farming systems by integrating relevant RMSs with their particular set of land, labor, machinery, and management resources. They can select RMSs that are well-suited for their soils, climate, and location-specific pest problems. They can integrate systems of livestock and crop RMSs that tighten or complete nutrient cycles, facilitate energy flows, and enhance the ecologic and economic viability of their farming systems.

Farmers who use the whole-farm planner can evaluate potential impacts of using various levels of various chemical fertilizers and pesticides on specific fields. They can match tillage systems and soil-conserving practices with specific slope and soil characteristics of fields to reduce erosion. They can assess risks through evaluation of diversification effects of alternative farming systems and develop systems that are resistant, resilient, and regenerative.

The LISA-FDSS will not result in a recipe for success. LISA-FDSS is just a tool to facilitate farm planning and management. Farmers who choose an alternative to their current system will be advised to gather as much additional information as is available before they adopt a new farming enterprise or practice. Farmers will be strongly encouraged to talk with other farmers who have experience with the practice under consideration. They will be encouraged to visit other farms where the practice is used before they change their own operation. They will be advised to work into any new system slowly, so they can learn as they go.

The LISA-FDSS will not ensure a more profitable or sustainable farming system. However, it will allow farmers to evaluate the potential impact of alternative LISA technologies and strategies within the context of their

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

particular farming situation without doing the necessary research and testing on their own. Use of the LISA-FDSS will not ensure success, but the LISA-FDSS can be a valuable and important aid in taking the first step toward the goals of economic and ecologic sustainability.

NOTES

1. This farm decision support system was developed by the LISA-FDSS Task Force: John Ikerd, Columbia, Missouri; Richard Levins, St. Paul, Minnesota; Larry Bond, Logan, Utah; Mike Duffy, Ames, Iowa; Don Tilmon, Newark, Delaware; Tim Hewitt, Marianna, Florida; and Patrick Madden, Glendale, California; special funding was provided by the Extension Service of USDA.

2. LISA-FDSS has been renamed Sustaining and Managing Agricultural Resources for the Future—Farm Resource Management System (SMART-FRMS). Further development and support of the system is being carried out by the Center for Farm Financial Management, University of Minnesota, St. Paul, Minnesota.

Voisin Controlled Grazing Management: A Better Way to Farm

William M. Murphy

Permanent pastures in the northeastern United States typically have low productivity, producing only about 2 tons of moderate-to-poor-quality forage per acre during a 3- to 4-month grazing season. A proven method exists that enables these kinds of pastures to produce 4 tons or more of excellent-quality (23 percent crude protein, 0.72 Mcal/pound of net energy lactation) dry forage per acre during a 6- to 7-month grazing season. The method is controlled grazing management, as described by Andre Voisin (1959) (see also Murphy, 1987). This method, which is also known as short-duration grazing, intensive rotational grazing, and rational grazing, has been used for many years in New Zealand and for 8 years in Vermont. New Zealand's highly productive and profitable agriculture depends almost entirely on permanent pastures that are grazed under controlled management. New Zealanders raise 70 million sheep, 8 million cattle, 1 million deer, and 1 million goats, without grain supplements, on only 37 million acres of pastureland, which is the size of Iowa. This proves that the method works.

Many American dairy farmers, in contrast, use a system of zero pasturing or year-round confinement feeding that involves large amounts of purchased feed and supplements, huge capital investments in facilities and

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

equipment, large cash flows, and low profitability. Partly because of this, many U.S. dairy farmers are experiencing a financial crisis that may eliminate many family farms, because feeding of livestock in confinement can cost six times as much as it does on well-managed pastures.

American farmers who do not use year-round confinement feeding put their livestock to pasture, where the animals are grazed continuously or rotated through a few large pasture divisions with little control and less planning. Invariably, by late June or early July the pastures are depleted and worn out. These dairy farmers generally do not feel that their pastures have much feed value and use the same ration all year, regardless of what the pastures produce. Therefore, pastures have been a wasted resource in the United States.

One way to increase profitability of a farm is to reduce feed costs. The permanent pastures that exist on most farms produce biomass far below their potential because of poor grazing management. Pastures managed under controlled grazing conditions can be some of the most valuable areas on a farm, producing high yields of excellent-quality forage. When incorporated into livestock feeding programs, this homegrown forage can reduce feed costs and increase the profitability of many northeastern farms. First-year costs of materials, maintenance, and labor for the grazing management method range from $1,500 to $2,000 for a 40-cow herd. Its use has returned $3.75 in benefits for each $1 invested by dairy farmers (Jones and Burns, 1988; Pillsbury and Burns, 1989).

Voisin grazing management is a simple system of controlling grazing by dividing pastures into small areas (paddocks) that are grazed on a rotational basis. This method minimizes the waste of forage and protects the plants from overgrazing.

GUIDELINES FOR VOISIN CONTROLLED GRAZING MANAGEMENT IN VERMONT

The essentials of the Voisin grazing method and what its use has meant to three farmers in terms of increased profitability and improved quality of life are illustrated in a 33-minute video produced as part of a low-input sustainable agriculture (LISA) project (Murphy et al., 1989).

Recovery Periods

The recovery periods between grazings must vary with the plant growth rate. This usually means that recovery periods must increase as the plant growth rate decreases as the season progresses. In Vermont, for example, this means that a 10- to 18-day recovery period is needed during May and June. This gradually increases to 36- to 42-day recovery periods by

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

the end of September. Another way to look at it would be as follows: 10 to 12 days of recovery time in late April to early May, 15 to 18 days by June 1, 24 days by July 1, and 36 days by September 1. These are guidelines only; longer or shorter recovery times may be needed, depending on the local growing conditions.

Recovery periods reflect the pre- and postgrazing pasture mass (total forage) relationships shown in Figure A-1. Pasture mass influences most the net harvested forage production at the extremes of low postgrazing and high pregrazing masses. At a low pasture mass, the lack of leaf surface area limits solar interception and photosynthesis. At a high pasture mass, shading of lower leaf surfaces blocks solar interception, while respiration of shaded plant parts consumes the carbohydrates that are produced, until death and decomposition of the shaded parts occur, with consequent loss to net production.

Based on these relationships, the forage should not be taller than 6 to 8 inches when cows are turned into a paddock (the forage should not be taller than 4 inches for sheep, because sheep-grazed swards are more dense)

FIGURE A-1 Effect of pasture mass (as dry matter [DM]) on rates of new herbage formation, net forage production, and forage losses. Forage losses through death and decay result mainly from shading of lower plant parts, and these losses increase as pasture mass increases. Source: C. J. Korte, A. C. P. Chu, and T. R. O. Field. 1987. Pasture production. Pp. 7–20 in Feeding Livestock on Pasture, A. M. Nichol, ed. Occasional Publication No. 10. Hamilton, New Zealand: New Zealand Society of Animal Production.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

and should be grazed down to 1 to 1.5 inches from the soil surface before the animals are removed. If the animals do not eat enough to keep up with the rapidly growing forage in the spring, some of the paddocks should be removed from the rotation and should be harvested for hay or silage. Usually, about one-half of the pasture area must be saved for machine harvesting, because too much forage is produced in May and June. This means, for example, that if there are 20 paddocks, 10 of the most level ones should be saved for machine harvesting. After harvesting, the paddocks should be rested until the plants regrow adequately before they are included in the next rotation. The larger number of paddocks then available for grazing in late July and early August automatically increases the recovery periods of all paddocks.

If, at any time, the paddocks have not fully recovered by their turn in the rotation, all of the animals should be removed from the pasture and should temporarily be fed elsewhere (e.g., they could be grazed on the hayland aftermath or fed green chop, hay, or silage harvested from the excess earlier in the season) until recovery periods are adequate before the animals are turned back into the pasture system. By strictly observing this need for adequate recovery times, permanent pastures in areas such as Vermont may be able to be grazed from mid-April to mid-November. In contrast, pastures that are not under Voisin controlled management can be grazed for a much shorter time, typically from mid-May to mid-August.

Periods of Occupation

The total time that animals occupy a paddock in any one rotation must be less than 6 days, to prevent grazing of regrowth in the same rotation. Paddocks must be small enough so that all or most forage in each paddock is grazed down to about 1.5 inches from the soil surface within this time limit. If two separate groups of animals are grazed (e.g., milking cows or heifers, dry cows, and lambs and ewes), each group should not be in a paddock for longer than 3 days, because forage palatability and availability decrease too much after 3 days for each group.

In practice, the shorter are the periods of occupation, the better it is for optimum plant and livestock production. If animals are grazed as one group, they should not be in a paddock for longer than 2 days for the best livestock production. If two groups graze a paddock, each one should be in the paddock for only 1 day. Milking or fattening animals should not be in a paddock for longer than 1 day, so that they can be kept on a consistently high level of nutrition. Milking cows produce the most if they are given a fresh paddock after every milking. Growing lambs and beef cattle should be moved to a fresh paddock once a day for the best results.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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Paddock Size

Paddock sizes must be adjusted according to the desired intensity of management. For example, if milking cows are to be moved from a paddock every 12 hours and then dry cows and heifers are placed in that paddock for another 12 hours to eat the remaining forage, paddocks must be small enough so that all or most forage above 1.5 inches in each paddock can be eaten within the total 24-hour occupation period. On the other hand, under less intensive management, milking cows can graze a paddock for 1 to 2 days, followed by dry cows and heifers that graze the paddock for another 1 to 2 days to eat the remaining forage, giving a total occupation period of 2 to 4 days. If the herd is kept as one group of milking cows, dry cows, and heifers, then paddocks must be small enough so that all the forage is eaten within the 0.5-, 1-, or 2-day total occupation periods, depending on how often the animals are to be moved. Paddocks usually should be less than 2 acres in size, depending on pasture productivity and herd size. One-acre paddocks or smaller may be needed on excellent pasture for the highest pasture and animal productivity.

Paddock Number

Farms that feed livestock on pasture need 10 to 80 paddocks, depending on how frequently the animals are moved. For example, if animals are in each paddock for a total of 4 days per rotation, 10 paddocks will eventually be needed to provide 36- to 42-day recovery periods. If animals are in paddocks for a total of 12 hours, about 80 paddocks are needed.

Fencing

Electric fencing is the preferred method of dividing pasture into paddocks. Only one strand of smooth wire about 30 inches from the soil surface is needed to control dairy animals. Three strands of smooth wire or flexible net fencing is needed for sheep. Perimeter fence for sheep must have at least five strands to keep predators out. Cedar posts with insulators at the corners and gates and round fiberglass posts every 50 to 60 feet are commonly used. A better way is to use treated, self-insulating hardwood posts of various sizes to support high-tensile, spring-tightened smooth wire for perimeter fence and permanent paddock divisions. Portable fencing can be used for internal subdivisions to decrease the amount of permanent paddock fences that need to be built.

Ordinary fence chargers short out very easily and may not control livestock under intensive grazing management. The best way to charge an electric fence is to use a New Zealand-type energizer; they provide the

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

dependable high shocking power that is needed. A smooth-wire fence is a psychological barrier, not a physical one. Animals must know that they will always be shocked if they touch the fence.

Water

Ideally, drinking water should be available in the paddock where animals are grazing. Animals can then remain in the paddock, rather than having to walk back to the barn or other distant location to drink. Having adequate water in the paddock increases production efficiency. Another benefit is that manure is kept in the paddock, where it is needed to recycle nutrients for sustained plant production.

CONCLUSION

The use of Voisin controlled grazing management can make farming profitable again for many financially stressed dairy farmers in the northeastern United States. Increased profits combined with the labor and time savings that result from use of this method can give farmers the money, time, and energy to enjoy life more.

REFERENCES

Jones, C., and P. Burns. 1988. Economic Effects of Adoption of Rational Grazing. Orono, Maine: Soil Conservation Service.

Korte, C. J., A. C. P. Chu, and T. R. O. Field. 1987. Pasture production. Pp. 7–20 in Feeding Livestock on Pasture, A. M. Nicol, ed. Occasional Publication No. 10. Hamilton, New Zealand: New Zealand Society of Animal Production.

Murphy, W. M. 1987. Greener Pastures on Your Side of the Fence: Better Farming with Voisin Grazing Management. Colchester, Vt.: Arriba Publishing.

Murphy, B., B. Brigham, J. Brigham, A. Cleaves, D. Lockhar, and B. Lockhart. 1989. Voisin controlled grazing management: A better way to farm. A 33-minute video. Charlotte, Vt.: Perceptions. (Available from the Department of Plant and Soil Science, University of Vermont, Burlington, Vermont 05405.)

Pillsbury, B., and P. Burns. 1989. Economics of Adopting Voisin Grazing Management on a Vermont Dairy Farm. Winooski, Vt.: Soil Conservation Service.

Voisin, A. 1959. Grass Productivity. New York: Philosophical Library.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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Appendix B

Expert Systems: An Aid to the Adoption of Sustainable Agriculture Systems

Edwin G. Rajotte and Timothy Bowser

Agricultural production has evolved into a complex business requiring the accumulation and integration of knowledge and information from many diverse sources, including marketing, horticulture, insect management, disease management, weed management, accounting, and tax laws. This is especially true of emerging sustainable practices that require even more information (to substitute for purchased inputs) for implementation. Very seldom do farm managers have all available information in a usable form at their disposal when major management decisions must be made. Increasingly, the modern grower must become expert in the acquisition of information for decision making to remain competitive. However, integration and interpretation of information from many sources may be beyond the means of individual growers, so they use the expertise of agricultural specialists. Unfortunately, agricultural specialist assistance is becoming relatively scarce at the same time that the complexity of agriculture is increasing. To alleviate this problem, it is essential that current information be structured and organized into a system for easy access by growers and agricultural specialists. No organized structure is currently available for information storage and retrieval; consequently, technical information, both experimental and experiential, is often lost or unavailable to potential users. One way to make this information readily available is through the use of electronic decision support systems.

The development of an electronic decision support system requires the combined efforts of specialists from many fields of agriculture and must be developed with the cooperation of the growers who will use them. Specialists tend to be trained in rather narrow domains and are best at solving

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

problems within that domain. However, there is a growing realization that the complex problems faced by growers go beyond the abilities of individual specialists. Interdisciplinary teams of specialists must work in unison to formulate solutions to agricultural problems. Agriculture must be viewed as a system of interacting parts where the perturbation of one part affects many others.

The acquisition and utilization of information can be considered a means of reducing the amount of uncertainty in a given decision problem (Hey, 1979). Because high-quality information has not been easily accessible to growers when they are faced with important management decisions, decision making on the farm has been surrounded by a high degree of uncertainty. To compensate for the large degree of uncertainty, farm managers have increased inputs of chemical pesticides and fertilizers in an effort to minimize the variability in yield and quality that can occur from year to year. The price of this strategy, however, is reduction in potential profit and an increased threat to the environment because of the overuse of fertilizers and pesticides.

One way to alleviate these problems in agriculture is to substitute high-quality interpreted information for purchased production inputs such as fertilizer, labor, and pesticides. By providing farm business managers with up-to-date, interpreted information, the risk of decision making is reduced, the application of unnecessary inputs is eliminated, and profits are increased. The problem faced by land-grant colleges of agriculture and other providers of agricultural information has been how to deliver accurate information to farm managers rapidly in an integrated, interpreted fashion. Fortunately, several technologies are now available that can help overcome this problem: (1) data bases that include geographic information systems, (2) expert systems, (3) decision analysis tools, and (4) electronic communication through computer systems and telephone lines. A complication of this solution, however, is the fact that the adoption of computer technology by growers is predicated on a linkage between a particular farm operation and the access conditions of the particular technology (Audirac and Beaulieu, 1986). These access conditions are determined, in part, by the development of the technology and by private and public diffusion infrastructures. The development of diffusion strategies that consider growers' needs and capabilities relative to specific access conditions will accelerate the adoption of these new technologies.

DESCRIPTION OF EXPERT SYSTEMS

This discussion concentrates on defining expert systems, describing the development of an apple production expert system, and reporting some of the reactions of commercial apple growers to this new information delivery

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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technology. An expert system(s) is a computer program designed to simulate the combined problem-solving capabilities of a number of people who are experts in specialized disciplines or domains (Coulson and Saunders, 1987; Denning, 1986). Expert systems are able to draw and store inferences from information and are thus often called knowledge-based systems. A form of artificial intelligence, expert systems are capable of integrating and delivering quantitative information, much of which has been developed through basic and applied research, as well as heuristics (experientially based rules of thumb) to interpret quantitatively derived values, or for use when quantitative values do not exist.

Expert systems technology can be used as a delivery mechanism in a larger decision support system. By computing sequences of symbols that represent different levels in the solution of a problem, the expert system attempts to represent a common problem-solving pattern: “if conditions, then consequences” (Denning, 1986; Rajotte, 1987). Moreover, because an expert system remembers its logical chain of reasoning, a user may query the system about why a particular recommendation was given.

In agriculture, expert systems can be used to integrate the perspectives of individual disciplines (e.g., agronomy, horticulture, entomology, ecology, and economics) in a fashion that addresses the day-to-day, ad hoc decision-making processes required of modern farmers. Developed correctly, expert systems can become a powerful tool for providing farmers with the readily accessible, highly integrated decision support they need to practice a sustainable system of farming.

Unlike many industrial applications, most expert systems for agricultural production management are still in the developmental and testing phases (Schmisseur and Doluschitz, 1987). This chapter describes the creation of an expert system for apple production and provides the results of the first widespread field testing of expert systems by growers. Unlike most studies, this research has implemented an evaluation plan simultaneously with the beginning of adoption of the system. Thus, some of the problems with earlier research, such as lack of baseline data and the potential confounding of management ability and adoption (Wetzstein et al., 1985) can be avoided. The purpose of this study is to document the socioeconomic impact of expert systems in terms of changes in knowledge, skills, attitudes, and practices.

DECISION MAKING FOR APPLE ORCHARDS

Apple orchards are highly diversified and complex ecological, economic, and social systems. Apple production is affected by a wide variety of insect, mite, disease, weed, and mammalian pests and is subject to the same

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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economic and social constraints as any agricultural business enterprise. Moreover, orchardists are experiencing increased pressure from environmental and consumer groups to reduce their chemical use, particularly pesticides.

Apple producers have a need to utilize various sources of state-of-the-art agricultural knowledge as well as site-specific, on-farm information in a highly integrated fashion to reduce pesticide use and improve farm productivity and profitability. Alternative methods of pest management in apple production are needed in the face of increasing pesticide resistance and concerns about food safety and human health. The case for implementing integrated pest management (IPM) programs in apple production as one strategy to meet these requirements has been made previously (Rajotte et al., 1987).

However, the best means for effectively implementing IPM programs and other sustainable agriculture practices for widespread adoption are still being discovered. To overcome the initial complexities of converting to IPM, growers require more education, experience, and technical expertise. In addition, orchardists are confronted with an overwhelming amount of information that they need to assimilate in order to make decisions about production, harvesting, and the control of insects, diseases, and weeds. Traditional agricultural information and decision support delivery systems are discipline-oriented packages. Thus, growers must often integrate various disciplinary information and data for application to their own orchards (Rajotte et al., 1987). Rarely, if ever, do apple growers have the time or resources to compile and effectively assimilate all the required information involved in the daily decision-making process. An apple production expert system can provide an improved level of decision support in a timely and integrated fashion whenever and wherever growers require it.

THE PENN STATE APPLE ORCHARD CONSULTANT

An expert system known as the Penn State Apple Orchard Consultant (PSAOC) has been developed to help apple growers make better decisions about production and pest management. After 4 years of development and testing (including 2 years supported in part by a U.S. Department of Agriculture [USDA] low-input sustainable agriculture [LISA] grant), this system has recently been made available for sale to fruit growers in Pennsylvania through Penn State Cooperative Extension (Travis et al., 1990). The system integrates various facets of apple production. It gives the apple grower the information necessary to reduce some purchased inputs by substituting high-quality, integrated, information derived from three sources (state-of-the-art apple production and IPM knowledge;

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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site specific, farm level data; and weather records). A primary emphasis of the PSAOC expert system is to decrease the detrimental environmental impacts associated with pesticide and fertilizer use as well as input costs, thereby improving farm profitability and reducing economic risk.

PSAOC was designed to view the apple orchard from an ecological perspective as a complex and highly interdependent system where the alteration of one component results in changes in the entire system. The system mimics the way in which growers must approach problem-solving in their orchards. The goal is to consider the orchard as a whole organism, and to make management recommendations in a holistic fashion, rather than making individual recommendations based on independent components (Heinemann et al., 1989).

Two unique characteristics of the PSAOC program are (1) the relative user friendliness of the system, and (2) a built-in user feedback loop that facilitates the incorporation of grower and user suggestions for improving the system into updated versions of the program (Heinemann et al., 1989). The two versions of the PSAOC system, Macintosh (Apple Computers, currently available) and DOS (available in 1991), were designed so that a person who has never used a computer may operate it. Operation of the system can be accomplished without using the keyboard in the Macintosh version. Growers' use of the system is being continuously monitored and evaluated, which allows them to have direct input into how the system is being developed. The software shell being used (PennShell) allows modifications to be made quickly so that updated versions can be rapidly distributed to growers.

Developers of PSAOC felt that these two components (user friendliness and user feedback loop) were critical to attaining the goals (Bowser, 1990; Heinemann et al., 1989). These two components contribute prominently to the ability of growers to input into the system data specific to their own orchards as well as up-to-the-minute weather data. With these baseline data in the system, growers may query PSAOC about specific problems of pest management, soil fertility, and orchard planting. They may also request in-depth supplementary information (including pictures) about an individual insect, disease, or weed. The user may ask the system to explain the logic behind a given recommendation (Bowser, 1990; Crassweller et al., 1989; Heinemann et al., 1989).

Recommendations are usually given with a range of alternatives (where alternatives exist), thus allowing growers to combine their own preferences and experiences with the recommendation being offered by the system. This combined package of information is then used to support the decision-making process of the grower in planning a pest management or other strategy.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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Structure of the Penn State Apple Orchard Consultant

When the PSAOC expert system was first introduced to growers in the field test it consisted of three main components: insects, diseases, and horticulture. Since each program fits onto one disk, a top level calling module provided a main menu to call each of the three main modules. In the most recent version, the insect and disease module were fully integrated into an IPM module. PSAOC is further divided into profiles (long- and short-term memory of an orchard block) and various decision modules that utilize recent orchard observations.

Profiles

The apple producer's orchard management program is based on orchard blocks. A block is the largest unit of an orchard within which consistent decisions are made (generically known as a management unit). A typical orchard may consist of several blocks that are each managed differently. Information about the block is stored in two separate files, called long-term and short-term profiles, and each block has its own profiles. The use of profiles eliminates the need for the grower to repeatedly enter information about the orchard that changes infrequently. The long-term profile consists of details about the orchard block that would not change from day to day. For example, the location of the block will not change at all. The tree varieties in each block, the ages of the trees, and the history of insect problems remain fixed for an entire growing season. Projected harvest dates usually remain fixed until the end of the growing season, when they may be adjusted. The short-term profile contains information that either needs updating on a more frequent basis or else has the potential for changing. For example, weather history data that need daily updating are kept in this profile. Crop load and market destination may change because of a number of environmental factors that alter the quantity and quality of the crop.

Information (besides weather) that changes from hour to hour within a day must be entered by the user at the beginning of a new session and is not stored in a profile. For instance, disease incidence and insect and mite population changes may be assessed as often as once a day.

The management program either can be initiated directly from the profile, in which case all profile information will automatically be loaded into the program, or else the user will be asked if a profile should be loaded. The user either can choose a previously defined profile or the user can create a new one.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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The Integrated Pest Management Module

The user has the option of requesting a recommendation about an individual pest problem or running the IPM module, which considers all the orchard and pest characteristics as an integrated system when the management of each component will affect other components.

By considering site characteristics, horticultural parameters, weather conditions, pest severity, and predator density, for example, the program determines whether the insect and mite populations are over thresholds that signal the need for action to control these pests. It then calls a pesticide management module to establish pesticide application priorities. With the help of the expert system, the user then builds a recommendation by considering pesticide efficacy and appropriateness, timing, days to harvest, and tank compatibility. For instance, if the mite population is over the threshold level and predators are not sufficient to control the mites, miticide rates are determined. These rates will vary depending on the severity of the problem. Insecticide rates are then determined for the primary insect over the threshold level (i.e., most damaging). If the primary insect control is effective for all secondary insects, no more insecticide compounds will be considered. Otherwise, the module will determine other compounds and rates to control the secondary insects. Steps similar to those described in the preceding paragraph are taken to determine the disease control recommendations.

The program has now determined an array of miticides, insecticides, and fungicides that will control the pest problems in the orchard block. The array of pesticides is then checked against the days-to-harvest rules. Certain pesticides cannot be applied within a certain period of time before harvest, and that period varies between materials. The program checks the current date and the estimated harvest date and then eliminates any materials that are illegal to use during that time. Most growers mix pesticides into a single tank application. The final filter for the pesticide array is to determine tank mix compatibility between pesticides. Any incompatible chemicals are removed from the array. The user is given a choice of selecting from a list of the remaining pesticides.

Rates for the chosen pesticides are displayed on the computer screen. The program generally recommends a tank mix of a fungicide to control diseases, a miticide to control mites, a primary insecticide to control the most damaging insects, and a secondary insecticide to control any insects that are over threshold but that are not controlled by the primary insecticide. After reviewing the pesticides and rates, the user has the option of asking for a different combination of pesticides for the same pest problems. This option is offered because there are many pesticide combinations that may be suitable.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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PSAOC as a Tool for Sustainable Apple Production

Effective use of PSAOC provides growers with specific, IPM-oriented information that they may not have had in a usable form previously. This information may tell the grower that certain insect pests are present, but not at economically threatening levels that require application of a pesticide, or that conditions for a disease infection period have not been met, even though it is the proper season for disease infections. This information is substituted for the routine spraying practices that might have occurred without this knowledge. Thus, the ecosystem is spared the application of unnecessary pesticides, while the grower realizes an economic savings derived from not applying pesticides. Moreover, the yield and quality of the crop is maintained because pest problems are managed with a profitability objective.

PSAOC is a potentially effective tool for sustainable apple production for six reasons:

  1. it delivers IPM-derived information and solutions to pest management problems, the benefits of which are outlined above;

  2. it provides this information in a very up-to-date and site-specific fashion that is unattainable by traditional information delivery systems;

  3. this information is always readily available to any grower with access to a computer and the software, relieving dependence on the accessibility of literature or human experts, thus enabling the grower to make critical, timely decisions whenever necessary;

  4. when used effectively, it provides the apple grower with the opportunity to reduce the usage of chemical pesticides, thus reducing the negative impacts of apple production on the ecosystem and human health;

  5. it can increase grower profits; profitability is an essential condition for sustainable agriculture; and

  6. as additional low-input sustainable methods of production are developed, these can be easily incorporated into PSAOC.

It remains to be seen whether apple producers will successfully adopt this new agricultural innovation on a widespread basis. To address this question, a field test and evaluation of the expert system was conducted during 8 months of the 1988 and 1989 growing seasons. Some of the results of this field research are presented below.

Field Testing the System

During regular extension educational meetings in 1988, apple growers were asked to volunteer for on-farm field testing of the expert system. Over 140 growers volunteered to participate in the first phase of the evaluation.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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Of those volunteers, 26 apple growers were selected as a pilot test group. These growers were carefully selected to represent the spectrum of apple production characteristics in Pennsylvania, including farm size, geographic location, and experience with computers. These pilot test participants met with the study organizers for 1 day and were given instructional training and software. Fourteen growers who did not own computers were loaned Macintosh computers.

Growers agreed to use the system and to record their experiences with the system, suggestions for its improvement, and their usage patterns. A monthly telephone survey was used to collect the data being generated by the pilot group. Some results are discussed here. For a more complete discussion see Bowser (1990).

Grower Surveys: System Use and Practice Change

in this section grower usage of the PSAOC expert system is discussed, as are changes in farming practices resulting from this use of the system.

General System Usage Patterns

The number of times a grower uses the PSAOC expert system and the amount of time it is used during each session are indicators of the degree of adoption of the expert system. Table B-1 displays two measures of the frequency of use of the expert system: the total number of times that individual growers accessed PSAOC, and the total number of hours they used the system. Both measures are summations of the data from an 8-month period in 1988 and 1989 during which the study data were collected.

TABLE B-1 Penn State Apple Orchard Consultant Expert System Use Characteristics of Growers

Percentage of Growers

System Use Characteristics (n = 26)

Total no. of times system accessed by growers in 8 months

 

0

7.7

1–9

19.2

10–15

34.6

16–29

15.4

30–110

23.1

Total no. of hours system used by growers in 8 months

 

0

7.7

1–3

26.9

4–6

15.4

7–9

23.1

10–40

26.9

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

The first measure, the number of times that the growers accessed the system, represents the number of times an individual grower actually turned on and used the system, regardless of the duration of the session. This measure shows that 7.7 percent of the growers did not use the system at all, 53.8 percent of the growers used the system less than 16 times in 8 months (2 times per month), and 23.1 percent used it 4 times or more each month.

The second measure in Table B-1 represents the total number of hours that the system was actually used by the growers during those 8 months. Again, 7.7 percent did not use the system at all, 42.3 percent of the growers used it for less than 6 hours, and 26.9 percent used it 10 hours or more.

Total use of the system varied widely by year and time of year. Figure B-1 shows the percentage of growers who accessed the system each month. This variation is explained in two ways. The growers did not receive the system for use until late July 1988. A very high percentage of growers accessed the system during August 1988 (73.3 percent) because they were trying it for the first time. Use of the system in August 1989 was 3 1.8 percent, which more accurately reflects the need for information a grower would have just prior to harvest. The percentage of growers who

FIGURE B-1 Percentage of growers who accessed the Penn State Apple Orchard Consultant expert system each month.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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FIGURE B-2 Average number of hours per month the Penn State Apple Orchard Consultant expert system was accessed by growers.

used the system fell precipitously during October (34.8 percent) and November (10.3 percent) of 1988. During 1989, after the growers had the opportunity to review the system throughout the winter months, system use was high in the spring months. The spring is traditionally an intensive period of pest management because of favorable conditions for fungal and bacterial diseases caused by wet conditions. In addition, insect and mite populations begin to increase in the spring and are therefore more vulnerable to management actions. System use gradually decreases throughout the summer, as would be expected based on the declining informational needs of the growers.

Figure B-2 shows the average number of hours of use per month by growers who accessed the system. A pattern of variation similar to that described above occurred. On average, growers used the system fewer times but for longer durations earlier in the growing season than they did later in the growing season. This may be explained by the differences in types of information needed at different points in the growing season. Earlier in the season, growers were more involved in planning and scheduling for the season's work, which required more intensive and in-depth use of information sources. More importantly, pest problems (especially diseases) are much more complex in the spring than in the summer, requiring more time on the computer to extract a recommendation. During the summer months, growers are more involved in crop maintenance and

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

troubleshooting and may be doing more of the double checking of their own knowledge mentioned above.

These measures of system use taken together indicate one aspect of adoption: use of the innovation. While the number of times that the PSAOC system is accessed shows how frequently the system is being used, the actual amount of time spent using the system may be a more significant indicator of adoption of the innovation. Some growers reported that they used the system primarily as a quick validation of their own knowledge regarding a decision. These growers reported a relatively high number of accesses and a low number of hours used. Conversely, the growers who reported that they used the system for many hours were presumably more fully engaging the logic of the system in their decision-making process.

General Practice Change Characteristics

The degree to which growers follow the recommendations presented by the expert system is a second aspect of adoption. Table B-2 displays two measures of the frequencies of changes induced by use of the system: (1) any change in growers' production practices and (2) increased pest monitoring. Both measures were derived from the eight monthly surveys.

The first measure is a sum of the number of times that growers indicated that use of the expert system stimulated some change in their production practices. Over the course of the 8 survey months, 65.2 percent of the growers indicated that they had changed standard production practices in some way during at least 1 month. Of these growers, 17.4 percent indicated some change during 3 different months of the 8 survey months.

A significant number of those sampled (65.2 percent) engaged a new and untried technology and were stimulated to change production practices as a result.

The second of the practice change characteristics displayed in Table B-2 is a sum of the number of times that a grower was stimulated by the expert system to go to the orchard and scout for a pest (monitoring). Pest monitoring is seminal to any IPM program. A large majority of growers (82.6 percent) reported that the system stimulated them to increase their monitoring at least once. A total of 30.3 percent of growers were stimulated to monitor their orchards four or more times. As the majority of pest monitoring occurs during April, May, and June, these numbers take on more significance when viewed as a subset of the eight monthly observations.

Weekly Time Monitoring and Basic Economic Questionnaires

During the field test and evaluation process in the 1989 season, the economic impact of the apple expert system on cooperators' operations and net

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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TABLE B-2 Penn State Apple Orchard Consultant Expert System Adoption Characteristics of Growers

Production Practice Change Characteristic

Percentage of Growers (n = 23)

No. of times growers reported some change in practices, per grower

 

0

34.8

1

21.7

2

26.1

3

17.4

No. of times system stimulated increased pest monitoring, per grower

 

0

17.4

1

26.0

2

4.4

3

21.7

4

21.7

6

4.4

7

4.4

income was estimated. Many growers already maintain pesticide logs that contain most of the data needed for development of an apple enterprise budget. A basic economic survey questionnaire was developed from the pesticide record and crop history logsheet of a major commercial apple processor to collect orchard characteristics, apple yields, and prices received. Additional information to aid in the comparison between expert systems users and a control group of nonusers was incorporated into the questionnaire. A weekly time monitoring survey was designed to gather information on the amount of time each grower spent scouting (monitoring) his or her orchard each week as well as what pest problem was being looked for. Pesticide application records were also collected to provide information on the chemicals and rates that the chemicals were applied to each orchard. The survey questionnaire was subjected to three reviews: first, by the research team; next, by all the county agents involved in the project; and finally, by selected growers who had expressed interest in its development. This feedback was particularly helpful for developing the yield and price components of the questionnaire, which was a two-part format that was collected in the spring and the fall.

Results from the monitoring surveys are still being analyzed. While the findings reported here are preliminary and subject to change, they, too, indicate that the expert system is an effective teaching tool. In the past,

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

extension information has encouraged growers to monitor for mites at the time of bloom and thereafter (week 8 of the growing season). Both PSAOC users and nonusers performed scouting at similar frequencies in the postbloom period. However, a new prebloom monitoring practice is recommended by the expert system as an effective mite control strategy that may reduce pesticide usage later in the season. The nonusers of PSAOC were not as aware of this prebloom method. Figure B-3 shows that more PSAOC system users tended to monitor for European red mites earlier in the season than did the comparison group of nonusers. Similar behavior has been seen in PSAOC users who ended their monitoring processes sooner than did the control group, thus making more efficient use of limited time. This constitutes direct evidence that use of an expert system can stimulate measurable changes in farming practices.

A preliminary comparison of the farm-gate economics of expert system users versus those of expert system nonusers shows some trends. Even though Pennsylvania suffered through a poor apple-growing season in 1989, the preliminary results of the survey show that yields of PSAOC users and nonusers were roughly similar.

The cost of time spent monitoring the orchard for pests and using the expert system is also a component of the economic impact being examined. Specifically, the team is looking to answer the question of whether savings

FIGURE B-3 Monitoring for European red mites (ERM) by users and nonusers of the Penn State Apple Orchard Consultant expert system.

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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on pesticide applications were being offset by greater costs in management. A weekly time-monitoring survey was developed that provides a checklist for most of the common items monitored. Primarily, it asks how much time was spent monitoring each block and using the expert system. This checklist went through the same review process the basic economics survey did.

No clear results have yet been obtained from the pesticide records analyzed thus far, but some interesting trends have been noted. There is some indication that system users may have applied lower amounts of some insecticides than nonusers did. Further analysis of this information may indicate whether or not the expert system is changing growers' practices regarding pesticide use and will provide the basis for partial budget analysis.

Further Mechanisms to Obtain Grower Evaluation, Feedback, and Training
Cooperators' Planning and Review Meetings

The experiences with the PSAOC expert system during the 1988 and 1989 growing seasons were summarized during facilitated meetings of cooperating growers, researchers, and extension personnel in February 1989 and March 1990, respectively. The primary purposes of the meetings were to review the system's performance over the year to date, provide the growers with an opportunity for in-depth input and discussion about improvements in the program, and collectively plan for the upcoming year. In addition, a major benefit was to bring growers from 13 counties in Pennsylvania and researchers and extension agents from three states together to interact for the first time.

The nominal group technique was employed during working sessions with the growers group to solicit any suggestions that they had for improving either the software itself or the field evaluation process. Recommendations were distilled and ranked by growers according to importance during a later session.

Growers and extension agents also strongly suggested the inclusion of more economic information into the PSAOC expert system. A session devoted to procedures for collecting relevant budget data yielded an additional step in the proposed analysis of farm-level economic impacts.

Researchers and extension specialists from The Pennsylvania State University (University Park), University of Massachusetts (Amherst), University of Vermont (Burlington), and the Rodale Research Center (Maxatawney, Pennslyvania) also met for 1.5 days to plan and coordinate the following year's program. Additional responsibilities for expert systems

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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development and evaluation were outlined for the second- and third-year plans of work.

Midseason Grower Training Sessions

Based on feedback from growers as well as trends in the survey data, small-group training sessions were held at the Biglerville Fruit Laboratory and the Berks County Agriculture Center during the summer of 1989. It was determined that the newest version of PSAOC was not being comprehended adequately and therefore was not being used to its fullest efficiency. These training sessions sought to correct this problem by familiarizing the growers in-depth with the new aspects of the software.

Electronic Mail Network Among Growers and Researchers

Also in response to feedback from growers, an electronic mail users group was formed to improve communications between cooperating growers, researchers, and extension personnel. Using The Pennsylvania State University's PenMail system, the growers are able to communicate with each other, with county extension agents, and with specialists on campus via electronic mail. This communications link has helped to make growers more comfortable with the computer and the information they receive.

The electronic mail system was set up in March 1989. Grower communications have included questions about insects, pest trapping, use of the computer, and information on the new version of PSAOC. The project 's evaluation coordinator has sent out numerous informational and update bulletins. The growers are also receiving their own copy of the state horticultural newsletter by electronic mail. Half of the growers have accessed the system (for messages, responses, PenMail) roughly once a week, and the others have accessed the system about once a month. This system has worked well so far, and it is expected that usage will continue to grow.

Site Visits to Cooperating Orchards

Visits to field test sites were made by evaluation staff at various points during the growing season, to observe orchard management and expert systems use by grower. These visits also provided more opportunity for the growers to give input into the development and improvement of PSAOC. It was noted that the expert system was more often found in the business office of the orchard, residing on the computer the grower used for accounting.

Grower Panel at Professional Meetings

Three pilot study growers and the cooperating regional tree fruit exten-

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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sion agent presented a panel discussion on the Penn State Apple Orchard Consultant system to over 300 apple growers at a meeting of the Pennsylvania Horticultural Association in January 1990. Discussants provided insights into their experiences with testing of the system, citing both the problems and potentials of using the expert system in orchard management. Panelists were mostly supportive of the new technology, citing increased responsibility on the part of the grower to reduce environmental inputs and improve food safety while still maintaining profitability.

Involvement with Cooperative Extension Agents

Cooperative extension agents were directly involved in the organization and implementation of the project. In addition to consulting on the structure and content of the survey process, agents were primarily responsible for the selection of cooperating growers for the project.

County Extension Agents Survey on Expert Systems for Fruit Growers

A survey was distributed by electronic mail in January 1989 to measure the familiarity of county extension agents with fruit expert systems and to solicit feedback on the overall expert systems program. The survey was necessary for two reasons: (1) many extension personnel were not informed about expert systems development, thus indicating some training sessions were necessary; and (2) feedback was received that indicated agents in cooperating counties could be better served and utilized by the evaluation process.

The survey was sent by PenMail to agents with horticultural responsibilities in all 67 county extension offices in Pennsylvania. Additional questions were asked of agents in the counties where growers were cooperating in the pilot study to solicit feedback on improvements to the evaluation process.

A vast majority (84 percent) of county extension agents were at most only somewhat familiar with expert systems for fruit production. Seventy-six percent of agents indicated that they would attend an in-service training program on how to use this technology in their programs.

Extension Agent Expert System Training Session

In response to feedback from county extension agents, training sessions for county extension personnel were scheduled during the March extension in-service training programs at The Pennsylvania State University. Agents participated in a lecture and discussion of what expert systems are and how they work. In another session, participants received hands-on experience with expert systems in a computer laboratory. This training was provided

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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to help familiarize agents with expert systems and to lay the groundwork for the future diffusion of agricultural expert systems.

Local Experts Network

A proposal has been made to extension administration to initiate a network of extension agents to serve as local experts to support expert systems users within a specified region. The local expert is a person who learns a new technology quickly and is motivated to help others learn it (Landy et al., 1987). Scharer (1983) suggests that the individual is central to the ultimate success of the training effort. This process, which is often used in the diffusion of software technologies, provides a more rapid response to user problems and educational needs than is currently available through Cooperative Extension programs. It is expected that this network will facilitate a more efficient and effective adoption process.

CONCLUSIONS

The project reported here is the first in the literature of an agriculture-oriented expert systems being tested in the field with comparisons of user and nonuser practices. Evidence from this study supports the thesis of Audirac and Beaulieu (1986) that the access conditions of a technology need to be considered in the diffusion process. Those access conditions of the expert system derived from its technological development as well as its intrinsic characteristics are important variables in the diffusion process. In particular, two characteristics seem noteworthy based on the results of this study.

First, the Penn State Apple Orchard Consultant expert system is primarily an information delivery technology. While it contains data base production information (such as weather), it also requires the input of reliable, site-specific information in order to formulate recommendations for the user. The information requested as well as the resultant recommendations require the apple producer to form questions and to look at problems in a manner different from that of previous information delivery systems used in apple production. That this transition will not occur automatically is reflected by the fact that the test group exhibited various levels of use and that almost none of the changes in practices occurred until growers had sufficient time to develop some familiarity with the system's logic. Some growers indicated that they still do not trust the system to make decisions for them. This attitude is appropriate. PSAOC is not intended as a substitute for good management but as a source of information to guide and enlighten growers' decisions. Distrust of the PSAOC expert system could also be the result of incongruence between growers' perceptions of

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

the system versus those of their apple orchards. The expert system is an information technology that is intrinsically different from most information technologies that have previously been used by apple producers. The kinds of practical and educational experience a grower or user has may affect how well the system is understood and, thus, adopted.

Second, the expert system is a technology that is inherently connected to microcomputers. For a grower to make use of the decision support capabilities of PSAOC, they must (1) have access to a microcomputer capable of running the system and (2) be able to operate the computer proficiently. While the software was designed and developed to be used by people with little or no computer experience, results of the study indicate that growers with the least amounts of computer experience also had the lowest rates of system use. This would appear to be an example of the access conditions of the technology not being congruent with the farming operation. This technology is inherently computer based, and a farming operation must have access to a computer and a person who can operate it before the technology will be adopted.

By substituting information for some chemical inputs, the Penn State Apple Orchard Consultant expert system has the potential to contribute to the generation of more sustainable apple production systems in the northeastern United States. This trend can accelerate through the introduction of more information-intensive, low-input IPM practices into the farm production system. This study has provided some preliminary evidence that changes in usual production practices occur as growers and users substitute information for purchased inputs, in this case, pesticides. It was also demonstrated that the substitution of information for inputs was stimulated by the expert system, which enabled the grower or user to collect, integrate, and interpret the information rapidly. However, based on other evidence produced by the study, it appears that the potential for sustainable agriculture that this technology holds will be diminished without some attention to better linking of the access conditions of the technology to the farming operation.

RECOMMENDATIONS

More work will need to be done at the first stage of the diffusion process if the Penn State Apple Orchard Consultant is to become an effective tool for sustainable agriculture. This first stage concerns the set of activities which provide for the “establishment of diffusion agencies or a network of outlets from which the innovation is distributed to potential adopters” (Audirac and Beaulieu, 1986, p. 63).

In the present case, it is planned that this diffusion network will be the traditional Cooperative Extension Service network of university and county

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
×

extension offices and personnel. In addition to acting as the distributive agent for this innovation, this network must also provide new educational training programs in key areas identified by this research, if the effective adoption of this innovation and its potential for sustainable agriculture are to be realized. Some growers are not using the system very often, and others are not being stimulated to change production practices based on their use of the system. In some of these instances, perhaps no change is necessary or advisable. In other instances, change would be highly beneficial in terms of grower profits and reduced pesticide use. In the latter case, effective adoption is not occurring and the potential to reduce the amount of pesticide inputs being used is diminished.

To correct this situation when the system is offered for general use by growers, it is recommended that the diffusion agency provide new educational programming in the following areas:

  1. training in and basic orientation to computer use for farming operations in general and agricultural expert systems in particular; these training sessions should be held on a very localized basis and taught by people who are familiar with expert systems software and the cropping system being discussed;

  2. training that provides an overview of the gradual modification of existing production systems to incorporate reduced-input methods; this training should focus on societal-level needs and responsibilities for reducing pesticide use as well as the long-term farm-level benefits for doing so;

  3. establishment of a network of local experts to provide a resource for growers experiencing difficulties with the computer or expert system;

  4. continual updating of system capabilities, so that recommendations remain scientifically current and appropriate;

  5. training of extension specialists and agents to familiarize them with the possibilities and potentials of the system; and

  6. beginning the process by delineating the criteria and goals for sustainable agriculture attainable with expert systems as a tool. In this way scientists will be better able to begin to design production systems for agricultural operations of all sizes that provide more flexibility in responding to dynamic production conditions, thus enabling time and spatially specific recommendations for the expert system to be better implemented. In the long run this may be the greatest contribution of agricultural expert systems development toward a more sustainable system of global agriculture.

ACKNOWLEDGMENTS

The Penn State Apple Orchard Consultant expert system described here was developed by J. Travis and K. Hickey, Department of Plant Pathology;

Suggested Citation:"APPENDIXES." National Research Council. 1991. Sustainable Agriculture Research and Education in the Field: A Proceedings. Washington, DC: The National Academies Press. doi: 10.17226/1854.
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E. Rajotte and L. Hull, Department of Entomology; R. Crassweller, Department of Horticulture; P. Heinemann, Department of Agricultural Engineering; and R. Bankert, V. Esh, J. Kelley, and C. Jung, Integrated Pest Management computer programmers. Program evaluation was conducted by J. McClure and T. Bowser, Department of Entomology; C. Sachs, W. Musser, and D. Laughland, Department of Agricultural Economics and Rural Sociology; and W. Kleiner, Pennsylvania State University Cooperative Extension. Cooperators from other institutions include L. Berkett, Department of Plant Pathology, University of Vermont; D. Cooley, Department of Plant Pathology, University of Massachusetts; and S. Wolfgang, orchard leader, Rodale Research Center. Partial support for this work was provided by LISA project LNE88-8, “Implementation of Electronic Decision Support System for Apple Production.”

REFERENCES

Audirac, I., and L. J. Beaulieu. 1986. Microcomputers in agriculture: A proposed model to study their diffusion/adoption Rural Sociology 51(1):60–77.

Bowser, T. 1990. Adoption of Expert Systems by Apple Growers: A Test of a New Model Unpublished master's thesis. Pennsylvania State University, University Park, Pa.

Coulson, R. N., and M. C. Saunders. 1987. Computer-assisted decision-making as applied to entomology. Annual Review of Entomology 32:415–437.

Crassweller, R. M., P. H. Heinemann, and E. G. Rajotte. 1989. An expert system for determining apple tree spacing. Hortscience 24(1):148.

Denning, P.J. 1986. The science of computing: Expert systems. American Scientist 71:18–20.

Heinemann, P. H., E. G. Rajotte, J. W. Travis, and T. Bowser. 1989. An expert system for apple orchard management. Paper presented at the 1989 International Meeting of the American Society of Agricultural Engineers and the Canadian Society of Agricultural Engineering.

Hey, J. D. 1979. Uncertainty in Microeconomics. New York: New York University Press.

Landy, F. J., H. Rastegary, and S. Motowidlo. 1987. Human-computer interactions in the workplace: Psychosocial aspects of VDT use. In Psychological Issues of Human Computer Interaction in the Work Place Amsterdam: Elsevier/North-Holland Science Publishers B.V.

Rajotte, E. G. 1987. A reflective decision support system for Pennsylvania agriculture: Merging electronic information sources, artificial intelligence, and field experience. Agricultural Economics and Rural Sociology Staff Paper No. 144. University Park, Pa.: The Pennsylvania State University.

Rajotte, E. G., R. F. Kazmierczak, Jr., G. W. Norton, M. T. Lambur, and W. A. Allen. 1987. The national evaluation of extension integrated pest management (IPM) programs. Virginia Cooperative Extension Service Publication No. 491-010. Blacksburg, Va.: Virginia Cooperative Extension Service.

Scharer, L. L. 1983. User training: Less is more. Datamation 175–236.

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Schmisseur, E., and R. Doluschitz. 1987. Expert systems insights: Future decision tools for farm managers. Journal of the American Society of Farm Managers and Rural Appraisers 51(2):51–57.

Travis, J., K. Hickey, E. Rajotte, L. Hull, R. Crassweller, R. Bankert, P. Heinemann, V. Esh, and C. Jung. 1990. Penn State Orchard Consultant. University Park, Pa.: The Pennsylvania State University.

Wetzstein, M. E., W. N. Musser, D. K. Linder, and G. K. Douse. 1985. An evaluation of integrated pest management with heterogeneous participation Western Journal of Agricultural Economics 10(2):344–353.

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Sustainable Agriculture Research and Education in the Field: A Proceedings Get This Book
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Interest is growing in sustainable agriculture, which involves the use of productive and profitable farming practices that take advantage of natural biological processes to conserve resources, reduce inputs, protect the environment, and enhance public health. Continuing research is helping to demonstrate the ways that many factors—economics, biology, policy, and tradition—interact in sustainable agriculture systems.

This book contains the proceedings of a workshop on the findings of a broad range of research projects funded by the U.S. Department of Agriculture. The areas of study, such as integrated pest management, alternative cropping and tillage systems, and comparisons with more conventional approaches, are essential to developing and adopting profitable and sustainable farming systems.

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