Design of a decision support system...
 Example use of crop and soil...
 Table 1. General functional requirements...
 Table 2. Indicators of changes...
 Table 3. Soil water holding characteristics,...
 List of Figures

Title: Decision support systems for sustainable agriculture
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00080879/00001
 Material Information
Title: Decision support systems for sustainable agriculture
Physical Description: Book
Creator: Jones, J. W.
Bowen, W. T.
Boggess, W. G.
Ritchie, J. T.
Affiliation: Michigan State University -- East Lansing, MI
Publisher: Florida Agricultural Experiment Station, University of Florida
Place of Publication: Gainesville, Fla.
 Record Information
Bibliographic ID: UF00080879
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 183319024

Table of Contents
        Page 1
        Page 2
    Design of a decision support system for sustainable agriculture
        Page 3
        Page 4
        Functional requirements
            Page 5
        Data bases
            Page 6
            Page 7
        Analysis tools
            Page 8
            Page 9
            Page 10
    Example use of crop and soil models
        Page 11
        Page 12
        Soil and climate characteristics of the analysis site
            Page 13
        Simulating yield changes for an eroded soil
            Page 14
        Simulating losses in soil organic matter and crop yield
            Page 15
            Page 16
        Crop-soil modeling needs
            Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
    Table 1. General functional requirements of a decision support system for sustainable agriculture for use by researchers, planners, and policy makers
        Page 25
    Table 2. Indicators of changes in field-level cropping systems that may be useful in studying agricultural sustainability
        Page 26
    Table 3. Soil water holding characteristics, bulk density, pH, and initial soil organic carbon for the Oxisol from Brazil used in the simulations (from Bowen, 1990)
        Page 27
    List of Figures
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
Full Text


J. W. Jones, W. T. Bowen, W. G. Boggess, J. T. Ritchie
Univ. of Florida Univ. of Florida Univ. of Florida Michigan State Univ.
Gainesville, FL Gainesville, FL Gainesville, FL East Lansing, MI


1 Recent literature has focused attention on many of the issues that

2 jeopardize our ability to meet man's food, fuel and fiber needs for the

3 future. World population is continuing to increase, thereby creating

4 increasing demands on limited resources. Many agricultural practices threaten

5 the sustainability of our resource base and sometimes cause unacceptable

6 degradation of the environment. Clearly, agriculture is both affected by, and

7 affects, other components of our planet, hence a framework for developing or

8 improving agriculture must be based on long-term and broad-based perspectives.

9 A concept of "sustainable agriculture" is emerging as a philosophy to

10 guide agricultural research, development, and technology transfer. Embodied

11 in this concept is a recognition that agriculture is a hierarchy of systems

12 operating in and interacting with economic, ecological, social, and political

13 components of the earth. This hierarchy ranges from a field managed by a

14 single farmer to regional, national, and global scales where policies and

15 decisions are made that can influence production, resource use, economics, and

16 ecology at each of these levels. Because it is such a holistic philosophy,

17 1 Approved for publication as Journal Series No. by the Florida
18 Agricultural Experiment Station. This research was supported by the Institute
19 of Food and Agricultural Sciences, University of Florida, Gainesville, Florida,
20 and by the IBSNAT program of the US-AID, implemented by the University of Hawaii
21 under contract No. AID/DAN-4054-C-00-2071-00.


1 agricultural researchers who wish to develop sustainable agricultural systems

2 and policy makers who attempt to influence agriculture are confronted with

3 many difficulties. There are various goals embodied by the philosophy of

4 sustainable agriculture, some of which may be in conflict. Solutions that

5 work in one soil, climate, and socio-economic setting may not in others. The

6 multiplicity of problems that can prevent an agricultural system from being

7 sustainable and the many variations of practices and policies that could be

8 implemented create a situation in which rapid progress is difficult.

9 Indicators are needed to quantify the changes that occur in agricultural

10 systems over time at different hierarchical levels for studying agricultural

11 sustainability. These indicators can be grouped into four major categories:

12 productivity, socio-economic, resource, and ecology. More than one indicator

13 will be necessary to characterize the sustainability of a particular system.

14 Analysis of sustainability will then be based on measurement or prediction of

15 the long-term changes in the indicators appropriately defined for the level of

16 the system under study.

17 Suggestions on how to improve the sustainability of agricultural systems

18 include 1) reduce input use, 2) improve efficiency of resource use, and 3)

19 increase use of natural processes such as biological nitrogen fixation,

20 nutrient cycling, and integrated pest management. One approach to research is

21 to implement long-term crop management experiments with crop yields, soil

22 characteristics, environmental quality and other indicators periodically

23 measured. Although long-term studies are needed, the impracticality of

24 establishing enough field experiments to provide answers to all questions in

25 all locations should be apparent. Methods are needed, in addition to long-

26 term field experiments, to provide planners and policy makers with the


1 capability to analyze various practices and policies for improving

2 agricultural sustainability.

3 Modern computer technology can play a major role in the future

4 development of sustainable agricultural systems and in deciding on policies

5 and plans that help ensure their adoption. Decision support systems (DSS)

6 integrate data bases, models, expert systems, analysis and graphics programs,

7 and other information to assist users in planning and decision making. The

8 purposes of this paper are to present design concepts for a decision support

9 system for sustainable agriculture, to present an example of the use of an

10 existing DSS for studying sustainability issues, and to discuss enhancements

11 needed for existing models and data bases.


13 In agriculture, the terminology "Decision Support System" has been used

14 to describe a wide range of computer software designed to aid various types of

15 decision makers. Such systems have been developed for making site-specific

16 recommendations for pest management (Michalski et al., 1983; Beck et al.,

17 1989), fertilizer (Yost et al., 1988), farm financial planning (Boggess et

18 al., 1989), and general crop management (Plant, 1989). These decision aids

19 have been developed for use by farmers or by extension agents who are advising

20 farmers. Many have been based on expert system concepts and the programming

21 of rules and logic used by experts in making these decisions. Users provide

22 site-specific information on crop and field conditions, then the decision

23 support system provides one or more recommendations. As such, these decision

24 support systems are usually narrow in scope and may be applicable only within

25 the region where they were developed.


1 Other decision support systems provide information and analysis

2 capabilities to assist researchers and planners in determining the best

3 management practices for various crops, soils, and climate conditions. For

4 example, the Decision Support System for Agrotechnology Transfer (DSSAT)

5 provides data bases, crop and soil models, and analysis programs integrated

6 together to evaluate weather-related risks associated with user-specified

7 combinations of crops, locations, and management practices (IBSNAT, 1986;

8 Jones, 1989). This type of decision support system has some capabilities for

9 evaluating the sustainability of certain crop systems as will be demonstrated

10 below. Existing decision support systems, however, lack the scope and

11 functionality to address the range of problems that have to be considered in

12 assessing the sustainability of agricultural systems. They have usually been

13 site oriented, focused on either production or environmental quality, but not

14 on both, and have not dealt with long term changes in soils, weather,

15 resources and economics.

16 It is impractical to attempt to design a single decision support system

17 that will address all of the issues being discussed in the sustainable

18 agriculture literature or one that will meet the goals of every researcher,

19 planner, or policy maker. However, based on the successes of recent systems,

20 such as the DSSAT, and continuing improvements in technology, it is reasonable

21 to expect that within the next few years considerable progress will be made in

22 developing relevant and useful systems to assist researchers, planners, and

23 policy makers in developing more sustainable production systems. In this

24 section, the functional requirements, components, and preliminary design of

25 decision support systems for sustainable agriculture (DSSSA) are presented.


1 Functional Requirements

2 The proposed DSSSA will have the capabilities needed by researchers,

3 planners, and policy makers to identify agricultural systems that are not

4 likely to be sustainable in a region and to analyze the potential benefits of

5 alternative practices over long periods of time. Table 1 summarizes the

6 general functional requirements of the system. It will provide access to

7 information on soils, weather, ecology, and land use and provide maps of the

8 distributions of various attributes over space in the region as well as

9 statistics on their frequencies of occurrence. For example, all saline soils

10 could be located, or all areas within one km of lakes could be identified. A

11 Geographic Information System (GIS) will form the basis for storing data in

12 the system and linking them to specific areas in the region. Through an

13 interactive user-interface, DSSSA users will be able to interactively select

14 areas for analysis within a specified region as well as the type of

15 information to display.

16 The proposed system will also be able to identify potential problem areas

17 in a region, display them for users, and categorize them as to the type and

18 probable magnitude of the problem. For example, users may wish to identify

19 all non-agricultural areas in a region that would likely be highly erodible

20 under intensive crop production practices or areas in which highly sensitive

21 or endangered species of plants and animals exist. This part of the DSSSA

22 will be based on expert system types of programs using knowledge bases

23 collected from scientists in several fields. It will compare attributes of

24 the landscape with rules and logic in the knowledge base using diagnostic

25 operations and present interpretations to users using maps, graphics, and

26 tables.

6 -

1 The proposed DSSSA will also contain simulation models for evaluating the

2 effects of alternative practices on various indicators of sustainability over

3 time and space. For example, it will contain crop/soil models to simulate the

4 changes in crop yield, soil organic matter, and nutrients over long periods of

5 time under specified practices. It will also contain models to simulate soil

6 erosion and nutrient and pesticide contamination of ground and surface water

7 in the region. These models will need to simulate the desired set of

8 sustainability indicators for each combination of crop, management, soil, and

9 weather condition in the region.

10 Finally, the DSSSA will provide an interactive tool for use in developing

11 plans and policies that influence the mosaic of crops and practices over a

12 region. For example, a goal may bp set by planners to produce a certain crop

13 yield in a region with a high probability level, but at the same time

14 minimizing water pollution, soil loss, and destruction of natural forests.

15 To provide these functional requirements, the DSSSA will include three

16 major classes of components: data bases, analysis tools, and planning or

17 decision aids. The data bases will store characteristics of the region under

18 study in the computer, and the analysis tools will use the data to predict

19 various sustainability indicators. The planning or decision aids will provide

20 interactive dialogue for users to access any of the data bases and analysis

21 tools and perform any of the functions in the system. Figure 1 shows a

22 schematic of the organization of the proposed DSSA with its spatial and

23 attribute data bases, analysis tools, and decision aids.

24 Data Bases

25 The DSSSA will require both spatial and attribute data bases. The

26 spatial data bases will divide the region into areas that delineate soils,

7 -

1 weather regimes, land use, socio-economic conditions, ecology, hydrology, and

2 transportation. Each of these data types are referred to as "layers" in a

3 geographic information system. By overlaying the layers, every point in the

4 region can be identified by its soil, land use, and other layer

5 characteristics. These spatial data bases provide the information for

6 creating maps of the region in which each area on the map can be colored based

7 on its characteristics to provide users with a picture of their distributions

8 over space.

9 The attribute data bases will store specific information about each type

10 of soil, land use, farm type, and management practice in the region. For

11 example, soil characteristics such as slope, color, depth, and percent sand,

12 silt, and clay by horizon would be.stored in the soil attribute file for every

13 soil in the region. The attribute files will be related to the spatial files

14 so that the spatial distribution of any attribute, such as soil slope, can be

15 plotted on maps. A GIS will provide the capabilities for storing both spatial

16 and attribute data, creating maps, and performing many calculations that will

17 be required to aggregate and summarize regional or sub-regional information.

18 Finally, a second type of attribute data base will need to store, on a

19 temporary basis, results from various models as well as plans being considered

20 for changes in land use or practices. The permanent attribute files will

21 provide inputs to run various models, and the temporary attribute files will

22 store simulated yields, soil loss, organic matter, economics, and other

23 variables which can then be displayed on maps or printed in reports. The

24 temporary attribute file will also store possible changes in land use and

25 practices being analyzed during a session.


1 Analysis Tools

2 The main analysis tools of the proposed DSSSA will be simulation models

3 and expert systems. These tools will make use of the data bases and perform

4 the various diagnostic and analysis functions listed in Table 1. The major

5 role of the expert system component will be to identify areas in a region

6 where problems are likely to exist under current or proposed practices, and to

7 suggest alternatives for additional analyses. There will be a need for

8 several simulation models in the DSSSA, particularly site specific crop-soil

9 simulation models and hydrology and water quality models. The crop-soil

10 models must include the capabilities to simulate changes in soil properties

11 for given cropping systems and the associated changes in crop production with

12 time. There are many existing crop and soil models (Jones and Ritchie, 1991),

13 but most of these currently lack the capabilities to simulate long-term

14 changes in soil properties or to simulate the effects of various soil factors

15 on growth and yield.

16 Table 2 provides a list of some of the major indicators of change for

17 studying the sustainability of cropping systems at a field scale. The crop-

18 soil models must be able to simulate these indicators in response to crop

19 rotations and fallow periods, tillage, irrigation, residue management,

20 fertilizer management, and pest management. The crop-soil models must be

21 sensitive to weather variables, simulate the uptake of nutrients and water,

22 and be sensitive to fertility and pests. Existing crop-soil models have the

23 capabilities to perform some of the analyses required for studying sustainable

24 cropping systems. An example will be presented later in which maize and

25 soybean rotations, with fallow periods between the crops, will be simulated

26 taking into account irrigation, residue management, and nitrogen fertilizer

9 -

1 management. However, existing models do not have the capability to simulate

2 all of the indicators of change at the field scale nor the crop responses to

3 them.

4 Sustainability indicators at watershed or regional scales would have

5 broader scope than those in Table 2 for a field scale. For example, the

6 regional indicators would include characteristics of lakes, wetlands, and

7 natural forests. Various hydrology and water quality models exist (USDA,

8 1980; Beasley, 1977; Hornsby et al., 1990; Hahn et al., 1982). These models

9 simulate nutrient or pesticide losses from fields or watersheds to predict

10 changes in water quality and environmental contamination. By combining these

11 types of models with the cropping system models in the DSSSA, users will have

12 the capability to investigate the trade-offs between production and

13 environmental quality under alternative practices or plans.

14 Some work has been initiated in which different types of simulation

15 models are included in a regional decision support system (Lal et al., 1990).

16 This system, called the Agricultural and Environmental Geographic Information

17 System (AEGIS), uses a GIS, crop-soil models, and an erosion model to assist

18 researchers and regional agricultural planners in Puerto Rico and the

19 Caribbean.

20 Economic models will also be needed to simulate the changes in farm and

21 regional economies under various scenarios. Agricultural cropping systems

22 must be profitable over time. Changes in supply and demand of inputs and

23 outputs will affect prices and thus profits. Also, changes in agricultural or

24 environmental policies can have dramatic effects on the profitability of

25 cropping systems. Thus, the DSSSA will show profits varying in response to

26 changes in prices and management practices. Some work has been initiated to


1 link farm scale economic models with crop-soil models in a GIS framework for a

2 Guatemala study of bean and maize producing areas (Thornton et al., 1990). In

3 a related study, the results of general circulation and crop-soil models have

4 been used in a spatial equilibrium model to evaluate the economic effects of

5 climate change on U.S. agriculture (Adams, et al., 1990).

6 Planning/Decision Aids

7 A collection of programs in the DSSSA will be structured in a

8 hierarchical manner, providing analysis and diagnostic capabilities at field,

9 farm, and regional scales. They will provide information to the user, allow

10 options to be selected from pop-up menus on the screen, and provide for

11 flexible map, report, and graphic presentation of results. The site or field

12 scale user interface will allow users to select a site (soil and weather),

13 specify initial conditions and management inputs, simulate the cropping system

14 over a number of years, and provide graphical and numerical outputs of

15 sustainable indicators and their variabilities. The existing DSSAT (IBSNAT,

16 1989) has many of the features needed for this field scale analysis. An

17 expansion of the capabilities of this system to analyze long-term trends can

18 form the basis for this decision aid at the field scale.

19 The regional scale planning decision aid will also provide information to

20 users and allow for options to be selected from on-screen menus, but the

21 options will be different from the field scale. Users will be able to select

22 all or part of the region based on a wide range of options, select regional

23 socio-economic characteristics, select farm and cropping system

24 characteristics and simulate over time and space the different sustainability

25 indicators. It will provide a capability for "remembering" good combinations

26 of land use practices so that users can incrementally build a plan for the


1 region with acceptable characteristics of any of the predicted attributes.

2 This framework for spatial analysis and plan building is an extension of the

3 one described by Lal et al. (1990) for a prototype regional agricultural

4 planning system (AEGIS).

5 The development of the DSSSA will require a well coordinated,

6 multidisciplinary effort. It will require definitions for all of the data

7 bases, their data structures, and methods for data collection. It will

8 require considerably more detail in its design than is presented here before

9 it can be further developed. In addition, it will require input from a wider

10 range of scientific expertise (e.g., anthropologists, wildlife scientists,

11 etc.) to design the system to ensure that appropriate knowledge, data, and

12 analysis tools are included to proyide users with credible planning tools.

13 The structure of the proposed DSSSA would lend itself for use across

14 different regions by putting in data specific to a given region. The models

15 and expert systems must then also be applicable to the region under study.

16 The models and expert systems should be tested, and the expert systems may

17 need to be revised to include regional expertise.

18 The following section describes examples of the types of analyses that

19 the crop/soil models will need to perform as a part of the overall system.


21 The development of sustainable agricultural systems requires an

22 understanding of the physical, chemical, and biological responses to

23 management practices at a field production unit scale. The responses to

24 existing and proposed practices form the basis for quantitative statements

25 about the sustainability of production in the field itself and, by aggregation


1 over space, some of the sustainability indicators in a region. These

2 responses could be based on long-term experiments or on results from crop and

3 soil simulation models. Resource requirements, production, and chemical

4 losses from the fields in a region will be estimated by models and combined

5 with spatial information on socio-economics, ecology, and natural resources to

6 estimate regional sustainability indicators. Several existing models simulate

7 soil erosion and its effect on crop growth and yield. These two models

8 (Williams et al., 1984; Shaffer and Larson, 1982) also simulate the dynamics

9 of soil nitrogen and carbon under different tillage, fertilizer, and residue

10 management. The CENTURY model (Parton et al., 1987) simulates long-term

11 changes in soil organic carbon to study steady-state organic matter levels for

12 grasslands under different climatic zones. Wolf et al. (1989) developed a

13 model to study long-term crop response to fertilizer and soil nitrogen, taking

14 into account various soil, crop, weather, and management conditions. The

15 CERES-Maize model (Ritchie et al., 1989) contains a soil nitrogen and carbon

16 dynamics component described by Godwin and Vlek (1985). Therefore,

17 considerable experience has already been gained in modeling several of the

18 important changes that may occur to soils and affect their long-term

19 productivity.

20 Because of the critical role of crop-soil models for analyses at all

21 scales in the DSSSA, two brief examples are presented here to demonstrate some

22 of the features needed by these models. In the first example, models in an

23 existing decision support system are used to estimate changes in maize yield

24 that may occur if practices result in significant soil erosion over some

25 period of time. In a second example, existing maize and soybean models are

26 combined to simulate the effects of long term crop rotations on soil organic


1 carbon and nitrogen and on crop yield under different nitrogen fertilizer

2 levels. The first example is analogous to performing 10 years of experiments

3 at a site to measure maize response to N and then repeating this experiment 50

4 to 100 years later after which time 15 cm of topsoil have been lost. The

5 second example is analogous to a long-term, continuous experiment (60 years)

6 with two crop rotations at each of 2 N levels. Crop yield is the primary

7 indicator of sustainability in both examples. In the second example, soil

8 organic carbon is also simulated to show how different practices may affect

9 this resource and affect the basic productivity of the site.

10 Soil and Climate Characteristics of the Analysis Site

11 Weather and soil data for these hypothetical analyses represent a site in

12 the Cerrado Region of Central Brazil. The climate in this region is

13 characterized by wet summers and dry winters. Annual precipitation at the

14 site averages about 1500 mm, 80% of which falls during the usual growing

15 season from November to April. Monthly mean daily temperatures range from 19

16 to 230 C at the site (Goedert, 1983).

17 Five years of weather data from a site in Cerrado (160 23' S and 490 8'

18 W) were used with the WGEN weather generator (Richardson and Wright, 1984)

19 contained in the DSSAT (IBSNAT, 1989) to simulate long-term daily weather data

20 for the models.

21 The soil used in all simulation analyses was an Oxisol clayeyy, oxidic,

22 isothermic Typic Acrustoxes), having 65% clay, 20% silt, and 15% sand

23 throughout the profile. The soil profile was 1.20 m in depth, and properties

24 used as model inputs for water holding limits, organic carbon, pH, and bulk

25 density (Table 3) were obtained from field measurements following several

26 years of cultivation (Bowen et al., 1990).


1 Simulating Yield Changes for an Eroded Soil

2 The first simulation experiment was conducted using version 2.1 of CERES-

3 Maize contained in the DSSAT (Ritchie et al., 1989). In each of 10 years, the

4 maize variety Cargill 111 was planted on November 10 at a population of 6.2

5 plants/m2. For each year, seven N fertilizer rates (0, 50, 100, 150, 200,

6 250, and 300 kg/ha) were simulated to obtain a yield response to applied N.

7 The experiment was then repeated to estimate the N response if the soil was

8 eroded. The eroded soil profile was created by assuming that 15 cm of the

9 original topsoil were removed, organic carbon was reduced in the remaining

10 soil by 11%, and soil water holding capacities of the top two layers were

11 decreased by 0.03 cm3/cm3 each, representing a decrease in available water of

12 0.9 cm.

13 Results of the ten years of simulated experiments were averaged and

14 plotted in Figure 2. When no N fertilizer was added, the yield loss was about

15 50% for the eroded soil, or about 0.65 t/ha. The response curves indicated 15

16 to 20 kg/ha of N would be needed on the eroded soil to produce the same yield

17 as no N on the non-eroded soil. Under high levels of N, the yield decrease

18 expected due to changes in soil properties by erosion remained about 0.65

19 t/ha. However, due to the asymptotic yield responses at these high N levels,

20 the amount of extra N needed by the eroded soil to offset erosion increased to

21 40 to 60 kg/ha. The lower yields of the eroded soil in this example were due

22 to decreased N supply from organic matter in the soil as well as some increase

23 in water stress due to its lower water holding capacity.

24 Depending on the practices of the farmer, the magnitude of expected

25 losses in this hypothetical example may or may not be of major consequence.

26 If the normal practice is to apply 200 kg N/ha, then the yield loss would be


1 about 6.5%. By increasing N to 250 kg/ha for the eroded soil, yield could be

2 maintained. However, the resource use efficiency (yield per unit of N in this

3 case) would be lower. Furthermore, the cost of N may prevent the use of

4 additional N to maintain yield.

5 If the farmer did not use N, the 50% loss in yield could be devastating,

6 and it may not be possible to add 15 to 20 kg N to maintain the yield due to

7 lack of availability or high cost of N. Clearly, the magnitude of the impact

8 of this loss in soil productivity would depend upon the socio-economic

9 setting. Results from this field scale simulation provide information for

10 computing productivity, resource, and socio-economic indicators of

11 sustainability at the field scale.

12 Simulating Losses in Soil Organic Matter and Crop Yield

13 In contrast to the previous example, this study simulates the long-term

14 effect of various practices on soil properties fr v-.-u~a praeet-eeaz as well

15 as crop yield. In particular, losses in soil organic matter are simulated for

16 60-year sequences of cropping practices.

17 Maize (CERES-Maize V2.1) and soybean (SOYGRO V5.42) models were modified

18 to simulate long-term sequences of maize-fallow and maize-fallow-soybean-

19 fallow crop rotations under two nitrogen levels (0 and 150 kg N/ha). The

20 soybean model was modified by Hoogenboom et al. (1990) to simulate soil N

21 processes, using submodels from CERES-Maize, and symbiotic N fixation. The

22 soil N models in both CERES-Maize and SOYGRO were modified by incorporating

23 the method reported by Parton et al. (1987) to represent soil C and N pools

24 and their decomposition rates. Fresh organic matter dynamics as well as

25 inorganic N transformation, transport and uptake remained as they were in the


1 CERES-Maize model. The revised maize and soybean models were then modified to

2 run in sequence with fallow periods (bare soil) between crop seasons.

3 The treatments were crop rotation (maize each year or soybean-maize, each

4 with bare soil fallow during the dry season) and N fertilizer level for the

5 maize crop (0 or 150 kg N/ha). The maize variety, row and plant spacings, and

6 planting dates were the same as for the previous example. A tropical soybean

7 variety, Jupiter, was planted each year on November 10 in 0.9 m rows at a

8 density of 30 plants/m2. Residue from each crop was assumed to be

9 incorporated to 10 cm after harvest. The biomass residue and the C:N ratio

10 depended on growth during each year. Neither of the crops was irrigated in

11 any of the rotations.

12 Figure 3 shows the changes in~maize yields for the different rotations

13 and N treatments over the 60 years of simulated experiments. Year-to-year

14 variability in each treatment was due to differences in weather in each of the

15 years. In the fertilized treatments, maize yields ranged from about 5.5 to

16 12.2 T/ha, with only a small effect of soybean in the rotation. There was no

17 apparent trend in this sequence of maize yields under high N levels even

18 though soil organic carbon declined during this time period (Fig. 4). The

19 annual variability masked any apparent trends. However, additional analyses

20 similar to the first example may show some loss in productivity.

21 Results for both crop rotations without N fertilizer showed significant

22 yield declines during the first 10 to 15 years in addition to annual

23 variability. Without N, the yields of the maize-fallow sequence declined from

24 an average of about 1.0 T/ha over the first three years to no significant

25 yield after 15 years. This decline was attributed directly to the loss in

26 soil organic carbon (Fig. 4). Over this same period, maize yield in the


1 maize-fallow-soybean-fallow rotation treatment without N fertilizer declined,

2 but remained about 0.5 to 1.0 T/ha above the yield of the maize-fallow

3 -rotation.

4 Organic carbon dropped for all treatment combinations (Fig. 4), but more

5 so for the treatments without inorganic N fertilizer. Crop residue added back

6 to the soil was considerably higher in the fertilized treatments, resulting in

7 a less rapid decline. Such declines in soil organic carbon would not be

8 expected to be continuous and equal under all circumstances. Other

9 simulations with these and other models (Parton et al., 1987; Wolf et al.,

10 1989) show that soil organic carbon reaches steady state levels which depend

11 on management practices, soil, and climate.

12 Results from this study estimate that a decline in productivity by maize

13 will occur rather quickly without the replacement of N in this soil. Rotation

14 with soybean supplied some N to sustain productivity. Further improvements in

15 management could be investigated, such as timing of soybean residue

16 incorporation or growing green manure crops prior to each maize crop as shown

17 by Bowen et al. (1990). Other benefits of rotation may occur in cropping

18 systems but were not considered in this example.

19 Crop-Soil Modeling Needs

20 For use in studying sustainable agriculture issues, crop models must

21 respond to a wide range of soil conditions, such as water, nutrients, organic

22 matter, acidity, salinity, and soil organisms. Currently, most crop models

23 respond to weather and soil water, and some respond to soil N (Jones and

24 Ritchie, 1991). Research is needed to better understand the effects of other

25 soil conditions and how the plant responds to various combinations of stress

26 conditions. Understanding the mechanisms of genetic differences in responses


1 is also needed to simulate differences in cultivars in attempts to develop

2 sustainable crop systems. In addition, more work is needed to incorporate

3 these responses into existing crop models and to test them under a wide range

4 of conditions.

5 Within a single crop season, changes in many soil properties are small

6 enough to ignore them for predicting crop responses to soil, weather, and

7 management over a season. Analysis of sustainable cropping systems, however,

8 will require that the soil models simulate the slowly changing properties as

9 well. Changes in soil organic carbon, soil depth, salinity and other

10 variables must be included, and they must be coupled to crop models to

11 properly account for the feedback effects between these two components. These

12 models must also be tested under a,range of conditions and over long time

13 periods. It is for this reason that long-term experiments are so important.

14 Monitoring of these long-term experiments should include a minimum data set

15 required to run crop and soil models and to compare experimental and simulated

16 indicators of sustainability.


18 In this chapter, a systems approach to study sustainability is proposed.

19 This requires an explicit definition of the components to be considered, their

20 interrelationships, variables that characterize their response to practices

21 and environment, and external variables that act to influence the system under

22 study. To illustrate this approach, emphasis was placed on an agricultural

23 field, and indicators of the sustainability of a field production system were

24 proposed. Crop and soil simulation models were used to show how

25 sustainability at this scale would be affected by practices that contribute to


1 soil erosion and soil organic matter loss. A point was made that existing

2 crop/soil models need to be expanded to include other factors for broad

3 application in a DSSSA, and that these models need to be tested over time and

4 space.

5 This is only a starting point. The forces that prevent agricultural

6 sustainability extend beyond the soil and climate changes that may occur at a

7 field site. We do not live in a static world. Changes in populations,

8 resource availability, economics, and policies at regional, national, and

9 international scales create forces for change in agriculture. Therefore,

10 planners and policy makers must be able to respond to these changes and make

11 decisions that facilitate the development of sustainable agricultural systems

12 that adapt to the changes. A systems approach is needed to assist decision

13 makers understand the possible changes in agriculture due to changes that are

14 occurring or are likely to occur in socio-economic, ecological, and political

15 conditions and to allow them to evaluate different policies and plans.

16 Because the forces of change are variable over time and space, analysis tools

17 in a DSSSA must allow for complete specification of these variables to create

18 the appropriate setting in which analyses are to be performed.

19 Development of a DSSSA is not a trivial task. It will require a long-

20 term commitment by interdisciplinary research teams to fully design the system

21 and its needed models, data bases, and other analysis tools and to implement

22 and test the various components. It will require an international effort in

23 which scientists from different institutions and disciplines cooperate in the

24 creation of such a system. The logistics of the communications required for

25 accomplishing this goal far exceed those of any previous agricultural research

26 program.


1 The payoffs of a large scale effort to develop a DSSSA would more than

2 justify the efforts and costs involved. It would provide a scientific

3 framework for learning how to manage agricultural production in the larger

4 contexts of its interactions with other components of the earth. It would

5 provide context-sensitive analysis tools for planners and policy makers in

6 different socio-economic, political, agricultural, and ecological settings to

7 help them understand the many consequences of their decisions. It would also

8 provide for improved education across scientific disciplines, the general

9 public, farmers, and policy makers.



2 Adams, R. M., C. Rosenzweig, R. M. Peart, J. T. Ritchie, B. A. McCarl, J. D.

3 Glyer, R. B. Curry, J. W. Jones, K. J. Boote, and L. H. Allen. 1990.

4 Global climate change and U.S. agriculture. Nature 345:219-224.

5 Beasley, D. B. 1977. ANSWERS: A mathematical model for simulating the

6 effects of land use and management on water quality. Ph.D. Thesis.

7 Agricultural Engineering Department, Purdue University, West Lafayette,

8 IN.

9 Beck, H., Jones, P. H., and Jones, J. W. 1989. SOYBUG: An expert system for

10 soybean insect pest management. Ag. Systems 30(3):269-86.

11 Boggess, W. G., van Blokland, P. J,., and Moss, S. D. 1989. FinARS: A

12 financial analysis review expert system. Ag. Systems 31:19-34.

13 Bowen, W. T., J. W. Jones, and U. Singh. 1990. Simulation of green manure

14 nitrogen availability to subsequent maize crops. Agronomy Abstracts.

15 Am. Soc. of Agronomy, Madison, WI. p. 14.

16 Godwin, D. C. and P. L. G. Vlek. 1985. Simulation of nitrogen dynamics in

17 wheat cropping systems. In W. Day and R. K. Atkin (eds,), Wheat Growth

18 and Modeling. Plenum Press, New York.

19 Goedert, W. J. 1983. Management of the Cerrado soils of Brazil: A review.

20 J. Soil Sci. 34:405-428.

21 Hahn, C. T., H. P. Johnson, and D. L. Brakensilk (eds.). 1982. Hydrologic

22 Modeling of Small Watersheds. Amer. Society of Agr. Engineers, St.

23 Joseph, MI 49085.

:z.- .. ^ "


1 Hoogenboom, G., J. W. Jones, and K. J. Boote. 1989. Nitrogen fixation,

2 uptake and remobilization in legumes: A modeling approach. Agronomy

3 Abstracts. American Soc. of Agronomy, Madison, WI p. 16.

4 Hornsby, A. G., P. S. C. Rao, J. G. Breath, P. V. Rao, K. D. Pennell, R. E.

5 Jessup, and G. D. Means. 1990. Evaluation of models for predicting fate

6 of pesticides. Project Report. DER Contract No. WM-255, Soil Science

7 Department, University of Florida, Gainesville, FL 32611.

8 IBSNAT. 1986. Decision support system for agrotechnology transfer.

9 Agrotechnology Transfer 2:1-5. Department of Agronomy and Soils,

10 University of Hawaii, Honolulu, HI 96822.

11 IBSNAT. 1989. Decision Support System for Agrotechnology Transfer (DSSAT)

12 Version 2.1 Dept. of Agronomy and Soils, University of Hawaii, Honolulu,

13 HI 96822.

14 Jones, J. W. 1989. Integrating models with data bases and expert systems for

15 decision making. IN Climate and Agriculture: Systems Approaches to

16 Decision Making. A. Weiss (ed.). University of Nebraska, Lincoln, NE.

17 pp. 194-211.

18 Jones, J. W. and J. T. Ritchie. 1991. The use of crop models in irrigation

19 management IN Management of Farm Irrigation Systems. G. J. Hoffman,

20 T. Howell, and K. H. Solomon (eds.). ASAE, St. Joseph, MI (In Press).

21 Lal, H., J. W. Jones, F. H. Beinroth and R. M. Peart. 1990. Regional

22 agricultural planning information and decision support system. Proc. of

23 Conference on Application of GIS Simulation Models and Knowledge-Based

24 Systems for Land Use Management, VPI and State University, Blacksburg,

25 VA. Pg. 51-60.


1 Michalski, R. S., J. H. Davis, V. S. Visht and J. B. Sinclair. 1983. A

2 computer-based advisory system for diagnosing soybean diseases in

3 Illinois. Plant Disease 67:459-463.

4 Parton, W. J., D. S. Schimel, C. V. Cole, and D. S. Ojima. 1987. Analysis of

5 factors controlling soil organic matter levels in Great Plains

6 grasslands. Soil Sci. Soc. Am. J. 51:1173-1179.

7 Plant, R. E. 1989. An integrated expert decision support system for

8 agricultural management. Ag. Systems 29:49-66.

9 Ritchie, J. T., U. Singh, D. Godwin, and L. Hunt. 1989. A user's guide to

10 CERES-Maize-V2.10. International Fertilizer Development Center, Muscle

11 Shoals, Alabama 35662.

12 Shaffer, M. J. and W. E. Larson (ed.). 1982. Nitrogen-tillage-residue

13 management (NTRM) model: Technical documentation. Research Report.

14 USDA-ARS, St. Paul, MN.

15 Thornton, P. K. and G. Hoogenboom. 1990. Agrotechnology transfer using

16 biological and socio-economic modeling in Guatemala. Research Report.

17 The Edinburgh School of Agriculture, Edinburgh, UK. 40 pp.

18 USDA. 1980. CREAMS: A field-scale model for chemicals, runoff, and erosion

19 from agricultural management systems. IN W. G. Knisel (ed.),

20 Conservation Research Report 26., USDA, Washington, DC.

21 Williams, J. R., C. A. Jones, and P. T. Dyke. 1984. A modeling approach to

22 determining the relationship between erosion and soil productivity.

23 Trans. Am. Soc. Agric. Eng. 27:129-144.


1 Wolf, J., C. T. de Wit, and H. van Keulen. 1989. Modeling long-term crop

2 response to fertilizer and soil nitrogen. I. Model description and

3 application. Plant and Soil 120:11-22.

4 Yost, R. S., G. Uehara, M. Wade, M. Sudjadi, I. P. G. Widjaja-Adhi and Zhi-

5 Cheng Li. 1988. Expert systems in agriculture: Determining lime

6 recommendations for soils of the humid tropics. Research Extension

7 Series 089-03.88 College of Tropical Agriculture and Human Resources,

8 University of Hawaii, Honolulu, HI. 8 pp.


Table 1. General functional requirements of a Decision Support System for
Sustainable Agriculture for use by researchers, planners, and policy

1. Provide information to users on characteristics of a region under study,
such as land use, soil types, weather characteristics, farm types, and
socio-economic conditions.

2. Assist users in identifying areas in which existing production systems
are likely to be non-sustainable.

3. Suggest changes in practices that should be considered in fragile
agroecosystems to improve the sustainability of agricultural production
systems, natural resources, and the environment.

4. Analyze alternative practices to determine optimal practices to meet
short and long-term production, economic and environmental goals for a
site or farm within the region.

5. Provide policy makers with a consequence analysis tool for evaluating the
probable impacts of various policies and practices on productivity,
stability and sustainability of agriculture in a region.

6. Provide methods for developing regional agricultural plans that meet
desired economic, production, and environmental goals.


Table 2. Indicators of changes in field-level cropping systems that may be
useful in.studying agricultural sustainability.

Productivity Socio-Economic Resource Ecology

Yield Cost of Production Chemical Use Chemical Losses
Yield Quality Net Returns Water Use Water Quality
Yield Stability Soil Beneficial Organisms
Soil Properties Soil Toxins"
Soil Nutrients
Genetic Material


Table 3. Soil water holding characteristics (LL and ODUL, lower and drained
upper limits, respectively), bulk density, pH, and initial soil
organic carbon for the Oxisol from Brazil used in the simulations
(from Bowen, 1990).

Bulk Organic
Depth OLL BOL Density Carbon pH

0-15 0.170 0.250 0.93 1.70 5.6
15-30 0.237 0.337 1.03 1.35 5.4
30-45 0.250 0.330 1.01 1.10 5.3
45-60 0.256 0.329 0.95 0.93 5.2
60-75 0.259 0.325 0.93 0.81 5.1
75-90 0.250 0.319 0.91 -0.76 4.9
90-105 0.250 0.322 0.91 0.69 5.0
105-120 0.250 0.326 0.91 0.63 5.0



Schematic of data bases, models, and analysis tools for the
proposed Decision Support System for Sustainable Agriculture.

Simulated maize yield response to nitrogen fertilizer application
for the Cerrado Region of Brazil for the original soil (M) and
the same soil after 15 cm of topsoil erosion (A).

Simulated maize yields for the Cerrado Region of Brazil on an
oxisol over 60 years. Treatments were maize-fallow with 150
kgN/ha, 9 maize-fallow with no N applied, A soybean-fallow-
maize-fallow with 150 kgN/ha on maize, and soybean-fallow-
maize-fallow with no N applied.

Simulated organic carbon for the oxisol soil profile for 60 years
in the Cerrado Region of Brazil. (See Figure 3 for symbols

Figure 1.

Figure 2.

Figure 3.

Figure 4.

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