AGRICULTURE, ECOLOGY, AND ENVIRONMENT AS A COMPLEX, INTERACTING, HIERARCHICAL
2 ASPECTS OF THE SYSTEM ANALYSIS .1 The National Level
Disregarding supranational interdependences we can give a crude overview of factors on the national level which are relevant to agriculture-ecology-environment problems (see Figure 1). In Figure 1, R stands for natural resources with the output w (including ore deposits, energy sources, water supply, etc.). The water supply is, of course, often directly used in agriculture for irrigation purposes. I stands for industrial production with the outputs. In the framework of this paper we are only interested in industry as the producer of fertilizers, pesticides, petrol, tools, machinery, buildings, etc., used for agri- cultural production. A stands for agricultural production (if necessary including the food industry). Fishing and forestry are ignored here. Agriculture receives resources, products of industry, and labor and is influenced by the weather M (i.e. climate), which is described by meteorological parameters m. We mainly consider the production of foodstuffs de- noted by the yield y. H stands for the human population, which is affected by agriculture directly through food production and, mediated by the natural environment E, through pollution p. H includes the human labor supply 1 for agriculture and industry.
In the following we take into account only the aforementioned key processes (interactions) related to agriculture. The social value system, based on the nation's way of life, imposes certain population demands on, for example, the supply of foodstuffs and environmental quality. These demands may be expressed as restrictions on yield
@gr)
and pollution @ z ) (both to be considered as real vectors). Any policy realized in the decision hierarchy has to take into account these constraints. The maximum acceptable pollution l e v e l p z is to be calculated not only t o guarantee a certain recreational quality for the environment but also to cut down negative feedback from the environment to industry and agriculture. In addition to these restrictions, the social system of values also implies a set of goals. Considering only agricultural goals we can formulate two of them:
(1) agricultural-benefit maximization, B@, y) + maximum, where B is a utility function of p and y ; (2) agricultural-cost minimization, C@) +minimum. Goal (2) is subject to the restriction C < Cm7, depending on industrial capacity, allocation of the gross national product, etc. The costs could also be written as a function of labor and capital (both agricultural and industrial) spent for agricultural production.
Up t o now we have not considered the question of different time horizons. With a presumed time preference function F(t) the step to long time horizons could be reduced to, for example,
Systems representation o f agriculture, ecology, and environme~t
d Material and information flows ----+ Flows considered in detail
FIGURE 1 Some relevant interdependences of agriculture at the national level (the parameters are defined in the text).
But this is only part of the truth. One long-term goal is the restoration of the environ- mental quality of E (where necessary). This implies the goal
which can easily be reformulated as "the cost of obtaining 'optimal pollution' at time t"
by means of appropriate goal functions which are increasing functions of time. It should be noted that in the long run all restrictions are functions of time.
As an example of agricultural production factors we consider the chemical sub- s t a n c e s ~ produced by industry I and used in the agricultural sector A as fertilizers and pesticides; at the least, the minimum yield y z ? must be produced using s E . The supply of chemicals for agriculture is restricted by industrial capacity and import capabilities t o a maximum value s z . Imports could also diminish the demand for agricultural yield yz?. Of course home-produced fertilizers as well as imported fertilizers or foodstuffs
should be taken into account in the cost function.
In our hierarchical approach we do not assign t o each economic sector a certain production function (e.g. a function fA for agriculture, which gives y = fA(l, W , s)) as is
160 K . Bellrnann e t al.
usual in single-level modeling. We split up each sector of the national level into sectors on the regional level (e.g. regional agricultural sectors) embedded in regional ecosystems.
After the establishment of regions, which in itself can be seen as a decision process (optimal regionalization), the decision problem at the national level consists in (1) finding optimal allocations of restrictions and resources for each region and (2) deriving regional goal functions. In more economic language, this should result in the optimal allocation of capital and labor to the regions and the optimization of interregional exchange. This means that one has to find the following for any region.
(1) A set of restrictions y T n , s-, p-, 11-, Ci-,
. . .
, (where i is the region index) with aggregation conditions for the extensive variablesSince we consider the pollution density (i.e. p per unit area) no such equation holds for pollution.
(2) A set of available production factors si, with Xi si = s.
(3) A set of goals such that the overall national goals may be fulfilled. Thus the sum of the actual yields of the regions should equal the national yield: Xi y i = y . The same should be true of costs: Xi Ci = C.
2.2 The Regional Level
Historically, agricultural regions have been distinguished by soil and climate types;
this guarantees a certain homogeneity of the natural production conditions within a region (e.g. a district with sandy soil and high rainfall or a district with heavy soils and an arid climate). Determined by these natural conditions, each region has its special collec- tion of optimal crop species (optimal implies high yield and low cultivation costs).
On the regional level the decision problem has much in common with the problem at the national decision level. Often even the goal functions are of the same type:
Bi(yi,pi) + maximum CiOi) + minimum For the allocations the following conditions hold:
C
Yij = Yi, iwhere j is the farm index. All the remarks concerning nation-to-region allocation may be applied also to farm allocations. However, on the regional level the restrictions on the use of chemical substances are not only subject to economic considerations but also to eco- logical ones. s- is determined taking into account long-term ecological predictions (e.g.
the future level of pollution in the region). To obtain these long-term predictions and insights into the behavior of the ecosystem under different policies it is necessary to
Systems representation of agriculture, ecology, and environment 161
develop an appropriate simulation model of ecosystems. This simulation model is named SONCHES (see the next paper (this volume, pp. 167-178) Section 2). SONCHES maps arbitrary ecosystem structures on the basis of so-called compartments. Natural and societal regional conditions should also be taken into account together with aspects of regional planning.
2.3 The Farm Level
Agricultural production takes place at the farm level. Figure 2 shows the various relevant inputs and outputs of the yield production process in a schematic way. The parameters of the yield production process are soil parameters and genetic parameters of crops and other species (weeds, pests) of the agroecosystem. The costs are a function of the input:
where wij stands for irrigation and 0 stands for parameters of the function Cii. For the farmer, the benefits are the yield output y i j . Therefore the decision-control problem on the farm level results as
Cij + minimum yij + maximum subject t o
Cij
<
C l y , S i j<
S T , lij<
l l Y , Yij Yij min P i j<
P-This is of course a polyoptimization-multiattribute decision problem. Its special compli- cation lies in the uncertainty of the system dynamics, i.e. the influence of weather and pests (the weather has a very short prediction horizon unfortunately). The simulation system SONCHES (see the next paper (this volume, pp. 167-178)) being developed in our
my (weather)
Inputs Ecological
environment
Outputs
FIGURE 2 The yield production process (the parameters are d e f i e d in the text).
162 K . Bellmann et al.
institute is capable of simulating the dynamics of almost any ecosystem, including agroecosystems. Given the control policy of the yield production process and the weather dynamics it predicts the annual dynamics of the agroecosystem and the yield. T o over- come the problems of environment (weather) uncertainty methods of risk assessment may be applied, based on simulation studies. SONCHES can be used as a tool in the optimization of any agricultural-biological-environmental decision problem which involves ecological processes on the regional and the farm level.
There is another way to overcome environmental uncertainty: namely through industrialization of agriculture, which implies a reduction of environmental influences on the yield production process (e.g. the use of greenhouses). This method needs special investments which usually are not available at the farm level. However, they can be allo- cated at the regional level.
Even at the farm level long-term goals are of special importance. Quite generally, they may be subsumed under the overall goal of maintenance of ecosystem productivity.
This implies, for example, the prevention of soil deterioration (soil pollution, erosion, etc.). Ecological considerations result in a second important long-term subgoal: the main- tenance of an optimal composition and low level of pests. Concerning the pest compo- sition of agroecosystems, the following observation should be made: unrestricted appli- cation of pesticides can almost completely exterminate various pest species native to the original ecosystem. As a consequence new pest species, naturally resistant or adapted t o the chemicals usually applied, can be expected to immigrate into the vacant ecological niches.
Agroecosystems are forced systems in the physical sense. Only sensible and balanced application of pesticides (and other chemicals) maintains their "stability". The simu- lation system SONCHES can also be used for optimization of pest control. One of the fundamental strategies for pest control and maintenance of soil fertility is crop rotation, i.e. the selection of an optimal time sequence of annual agroecosystem structures. Some- times it is possible to derive from long-term or medium-term (one-year) goals operational goals to be used for the calculation of operative controls. An example of such an operative goal is the optimal hydration of the plant as a physiological condition for maximal dry- matter production. The hydration can be controlled by irrigation.
In Figure 3 a goal tree relevant to agriculture is proposed. The long-term goals also relate to the regional and national levels whereas the short-term goals are realized only on the farm level. I t should be noted that these goals are occasionally in conflict and that only some of the relevant goals and goal dependences can be given.