- Email: [email protected]
The response of low-input agricultural systems to environmental variability. A theoretical approach
Agricultural Systems 44 (1994) 1-17
The Response of Low-Input Agricultural Systems to Environmental Variability. A Theoretical Approach Ernesto F. Viglizzo INTA-Centro Regional La Pampa-San Luis, CC 152, 6300 Santa Rosa, La Pampa, Argentina (Received 27 March 1991; accepted 24 February 1993)
A BSTRA CT The aims of this paper are: (a) to analyze the response of low-input agricultural systems to environmental variability; and (b) to suggest a theoretical framework to explain such behavior. Available evidence suggests that low-input systems are, in general, more sensitive than high-input systems to changes in the climatic environment. However, on the one hand this is not necessarily the case with respect to the economic environment, but, on the other hand, low-input systems of any region can differ in their response to similar environmental changes. Thus, when different levels of productivity and degrees of environmental sensitivity are combined, different patterns of performance can arise. A theoretical approach is presented in which the variations in system performance are related to system structure. It is suggested that a key feature is the degree of linkage between the system components. The 'articulation hypothesis' proposed here associates different patterns of articulation among essential components of the system with its performance. It is argued that, sensitivity in systems shows a negative relationship with the degree of internal articulations among different farming activities. The hypothesis also suggests that, the higher the internal articulation, the higher the maintenance cost, and hence, the lower the productivity of the system. Articulation appears to be a powerful risk-dissipative structural feature, reducing sensitivity at the price of lower productivity. In principle, the hypothesis could be successfully tested in marginal environments, but not in non-limiting or very extreme conditions. INTRODUCTION L o w external input systems are usually defined as farm production systems that have a low support energy requirement per hectare or per 1 Agricultural Systems 0308-521X/93/$06.00 © 1993 Elsevier Science Publishers Ltd, England. Printed in Great Britain
2
Ernesw F. Viglizzo
livestock unit (Wagstaff, 1987). They use substantially lower levels of manufactured fertilizers, agrochemicals, fuels, and concentrate feeds than is typical of modern high-input systems, and resemble natural ecosystems more than industrial systems (Gliessman, 1984). To simplify the analysis, it is considered that low utilization of support energy and money are sufficient conditions to describe low-input systems. As human intervention is lowered, such systems increasingly react to changes in climatic and economic conditions, and different patterns of response to the environment can appear (Viglizzo et al., 199i). In conventional higher-input farming, high levels of productivity often can be obtained without any appreciable attention to environmental sensitivity. But sensitivity can not be ignored if low-input systems, sustainable in the long term, are to be developed (Pimentel et al., 1989). Since sensitivity frequently is associated with farming risk, it is not a desirable condition in the agricultural business. Farmers and agronomists aim, in general terms, for system designs that can perform well over a wide range of environmental conditions. Studies on the general response of systems to environment have not traditionally been included in agricultural systems research. In an attempt to bridge this gap, the purposes of this paper are: (a) to analyze the response of low-input agricultural systems to environmental variability; and (b) to propose and discuss a theoretical approach to explain such behavior. The environment as a source of disturbance
The state of a system such as a low-input farm changes continuously with time in response to the natural rhythms and random fluctuations of the environment, especially under limiting conditions. Environmental conditions which cause disturbance include: (a) the regular seasonal rhythm of climate and prices; and (b) the unpredictable disturbance due to weather, pest outbreaks or economic forces. Climatic variability can explain a large part of the agricultural yield variance under extensive conditions. Evidence was reported by Brown and Merril (1958), Knetsch (1959), Parks and Knetsch 0959), Englestad and Doll (1961), Fuller (1965), Oury (1965), Russell (1968a,b), Schneider and Bach (1981), Swindale et al. (1981), Kogan (1986), Solomou (1986), de Wit (1986) and Thompson (1988). According to McQuigg (1981), the three major components of yield variability over a period of years are technological change, climate variability, and a random 'noise' that cannot be specified and is usually included in the statistical error term in an analysis of variance.
Response of low-input agricultural systems to environmental variability
3
The economic environment is another important source of disturbance. During the last part of this century in market oriented economies, agricultural producers have been operating under the pressure of a constantly changing economic environment. Variability in input-output as well as output-output price rates, has a considerable effect on the selection of different agricultural activities, affecting the overall performance of systems (Penna, 1983). Climate and economics appear to be the two main sources of variability, making risks in farming higher than in the nonfarming business (Calkins & DiPietri, 1983). As climatic and economic variations are not independent, extreme prices of agricultural products historically coincided with marked climatic anomalies in a year or group of years (Flohn, 1981). Solomou (1986) made a valuable study of the impact of climate variations on British economic growth during the period 1856-1913, showing that climatic and economic factors interact in affecting agricultural productivity. An analysis of system response to environment
Productivity, stability and sustainability appear to be three relevant system properties which combine a large number of processes into single, highlyaggregated expressions of performance (Marten, 1988). These expressions can, in turn, be used to analyze the system response to environmental variability. Conway (1987) has defined productivity as, the output of valued product per unit of resource input, both in biological and economic terms. He considered stability as the constancy of productivity in the face of small, normal and cyclical forces from the surrounding environment, and sustainability as the maintenance of productivity when subject to major disturbing forces that may be infrequent and unpredictable. It must be also considered that no system is sustainable if the disturbing forces are great enough. Both stability and sustainability involve longterm measurements of productivity, and can be considered as indices of the sensitivity of the system to external disturbing forces. Studies on the sensitivity of living organisms to the environment are not unusual in agricultural research. There is a general agreement amongst plant breeders, for example, that genotype-environment interaction ((3 X E) is of major importance in developing improved varieties. Some of the techniques developed by the plant breeders to assess the G × E may be adapted to estimate the system response to environment. Following the original approach of Yates and Cochran (1938), later modified by Finlay and Wilkinson (1963) and Eberhart and Russell (1966), regression analysis was adapted by Viglizzo (1983, 1986) and Viglizzo et al. (1991) to system studies. When an environmental
4
Ernesto F. Viglizzo
variable (e.g. rainfall, prices, etc.) is related to the system productivity (e.g. energy yield, economic income, etc.) along a period of consecutive years, the regression provides two stability parameters, namely the linear regression coefficient and the deviation from the regression mean square. In their earlier works, those authors estimated the biological and economic stability of farming systems by means of their variability about a trend. A time--sequence analysis was used to estimate the percent average oscillation amplitude of system outputs around the regression line, which represents the average trend in a series of consecutive years. Later, an alternative approach was utilized (Viglizzo et al., 1991) taking into account the sensitivity of system outputs in response to the changes in two environmental indices: rainfall and meat price. Sensitivity was estimated by means of the linear regression coefficients, or slope of the regression line. Rainfall and meat price were the selected variables because they usually have determinant effects on system performance, and do not impose limitations of availability and reliability of data. A high linear regression coefficient was taken as indicative of high sensitivity. By means of indices of productivity and output sensitivity to environment, Viglizzo et al. (1991) evaluated the biological and economic performance of 34 farms of the semi-arid pampas of Argentina during a period of six consecutive years. They found that mixed production systems were more productive, but more sensitive to environment, than cattle production systems. When each group was scattered in a two-axes model, corresponding to bio-economic productivity and sensitivity indices, it was possible to obtain a clear separation of the farms according to their specific patterns of performance. This study (Viglizzo et al., 1991) allowed the proposal of a tentative model for the classification of systems according to their patterns of response to environment. Five different types of systems were classified (Fig. 1) in relation to their productivity (P) and sensitivity (S) to a wide range of environmental conditions: high P-high S; high P-low S; low P-high S; low P-low S; and very high S. The last group was a special case of very high productivity under favorable external conditions, but with a dramatic fall-off when conditions worsened. This was a typical feature of farms devoting a high proportion of land to crop activities in a semi-arid ecosystem that is essentially marginal in terms of water supply. Low S systems were in general designed to avoid undesirable sensitivity to the environment, in spite of their low response to improved conditions. Those linear models must be considered as an oversimplification of reality, because in the real world the number of possible patterns of response varies more widely.
Response of low-input agricultural systems to environmental variability
5
1
High 2
I
, 4
Poor ~
F.MlmnmenlalCond#lo~ m~ Good
Fig. 1. Generalized models of the performance of agrcecosystems under different environmentalconditions: 1. High productivity-highsensitivity;2. high productivity-low sensitivity; 3. low productivity-high sensitivity; 4. low productivity-low sensitivity; 5. high productivity-veryhigh sensitivity,with no account of risk.
System configuration and response The term 'configuration' is used here to describe the internal structure of an agricultural system, comprising the number of farming activities as well as their respective interconnections. Diversity and connectance are properties which can affect the overall performance of the system. In a changing environment, overall system performance will be the result of the integrated activity of the various interrelated components. The question of the basic causes of system response will not have a simple answer. But it may be possible to reduce the complexity to simple expressions which are useful in practical terms. Biologically, different farming activities show quite varied patterns of response to the environment. For example, in energy terms, at similar levels of rainfall, the productivity of herbage and grain (primary production) clearly exceeds that of milk and beef (secondary production). But those secondary products generally appear to be less sensitive to external disturbance, although that difference in sensitivity tends to disappear when the environmental conditions improve (Viglizzo, 1986). Thus, when crop and animal activities are combined in different ways or proportions, the system productivity, as well as its environmental sensitivity, can change dramatically. Activities varying in their patterns of productivity and sensitivity provoke, when combined in different ways, great variability in the overall performance of the system (Roberto et al., 1984). Diversity appears to be an important property of ecological, as well as, agricultural systems. Evidence suggests that, some degree of diversification of activities can lessen the effect of external disturbance in low-input
6
Ernesto F. Viglizzo
agriculture, both in biological and economic terms 0¢iglizzo et al., 1984; Diaz et al., 1986; Viglizzo & Roberto, 1989). However, the effect tends to decrease with increasing diversity, and extreme diversification eventually may impose an additional management cost without extra benefit. In ecology, relationships between diversity and stability are still open to some debate. The general view, which has come to be recognised as ecological dogma, is that diversity is closely related to stability (MacArthur, 1955; Elton, 1958; Odum, 1971; Fjelddalon, 1968; Margalef, 1968; Hanson, 1972; Nickel, 1973; Wayne, 1987). But some reports (Gardner & Ashby, 1970; May, 1971, 1972, 1973) seem to contradict this traditional idea. Nevertheless, the dogma has developed into an important theoretical principle, used for making general predictions. In many cases, the theory anteceded its testing by experiments. Thus, the prediction that insect pest outbreaks will be more frequent in simple, than in diversified habitats became firmly established in ecological theory, but it was only later that experimental evidence became available to support it (Pimentel, 1961; Tahvanainen & Root, 1972; Root, 1973; Cromartie, 1975). Priestley and Bayles (1982) studied the effectiveness of diversification of varieties in reducing the spread of yellow rust and mildew in wheat. Their results showed that a benefit was gained from diversification because combined virulences occurred less frequently than the more simple virulences. From an agroecological point of view, diversity may appear in a variety of ways: temporal diversity, spatial diversity, diversity of species and varieties, or genetic diversity within populations (O'Neill & Reichle, 1979). Genetically homogeneous populations in agriculture, such as pure line varieties or single crosses, are usually vulnerable to disease and environmental extremes. Allard and Bradshaw (1964) considered that stability can be improved by increasing the genetic diversity of populations. According to this view, heterozygosity, as well as a mixture of heterogeneous populations, can transfer buffering effects to crop production. Connectance is another relevant property in agroecology. Ecologists assume that system diversity includes subsystems and components that are interconnected and interact in different ways. As far as the association of diversity with stability goes, the ecological view generally accepts that natural systems can maintain a relative homeostatic state by means of multiple connections and interactions amongst their components. However, some theoretical studies by Gardner and Ashby (1970) and May (1972, 1973) showed that large randomly connected systems have a decreasing probability of being stable as the number and strength of connections and interactions increase. Those results were important to ecology because they contradicted traditional ideas, but those mathematical
Response of low-input agricultural systems to environmental variability
7
artifacts only revealed a potential biotic feedback instability inherent in complex natural systems, ignoring other destabilizing influences, such as large environmental fluctuations (DeAngelis & Waterhouse, 1987). Systems frequently can fail to demonstrate stable behavior, due either to the disruptive effects of stochastic environmental elements, or to the influence of strong biotic internal feedbacks or 'overconnectance', exemplified by herbivores or predators which over-exploit their food resources (Ellis & Swift, 1988). But overconnectance is not a general rule for natural systems; there are examples that demonstrate that connectance is accompanied by increased stability (DeAngelis, 1975).
The system-articulation hypothesis Can the ecological principles of diversity and connectance be useful to explain the behavior of agricultural systems? An hypothesis relating structure and performance appears to be necessary. In a simple manner, the structure of a system can be described by the number and type of energy and money connections among different farming activities. The articulation of activities will be central to the following proposal, summarised in what can be called the 'system-articulation' hypothesis. In brief, this hypothesis assumes that: (a) the system sensitivity to the environment has a negative relationship with the diversity and connection of the farming activities; and (b) the greater the articulation among activities, the lower the productivity of the system. For the purposes of this discussion, it is assumed that activities are biologically articulated in cases in which energy is transferred from one activity to others after being processed. Similarly, they are, and will be, economically articulated in cases in which there are flows of money. Assuming different patterns of diversity and connectance, three basic models can be identified, as represented in Fig. 2: (a) a non-articulatedhomogeneous model, in which biologically uniform activities are essentially functioning in parallel, e.g. winter crops growing in the same year without overlapping their areas; (b) a non-articulated-heterogeneous model, in which the parallel configuration is maintained, but activities are not biologically uniform, e.g. winter and summer crops growing in the same farming year without overlapping their areas; and (c) an articulated model in which farming activities---uniform or not--are interconnected through energy and/or money flows, e.g. a pasture-animal production process, in which energy or money is used in a sequential way, and in the same area. The hypothetical scheme of Fig. 2 describes the performance of different system configurations, in terms of productivity and sensitivity in response to different environmental conditions. The simpler configurations
8
Ernesto F.. Viglizzo
Environmental
System Configuration
A Non articulated Homogeneous
Non articulated
Heterogeneous (diversified)
I
A I
A I
q)OQ I
I
I
A
A
A
I
I
I
@®® A I
/~ I
A
System Productivity
Sen,~tivity to Environment
S~e~
Low
Exl~'eme
Madem~
Mid
w~y High
Good
x i ~ or Very H~h
Severe
Low
Moderate
Mid
H~
Good
High
Mid
or Mid Very High
I
Very Low
Articulated
System
Conditions
?
Moderate
Low
Mid
Good
Mid
N~l~ible
Fig. 2. Hypotheticalschemeof system response to environmentunder differentsystem configurations. are highly sensitive to severe environmental conditions, in contrast to the more complex, articulated forms, which can better resist such unfavorable environments. Under more favorable conditions, the relatively low sensitivity of the more complex systems is no longer a condition of survival, and their inherently low productivity puts them at a disadvantage compared with the simpler forms. Evidence is needed to test this hypothesis. But some theoretical arguments, analytical research and empirical evidence may be adduced to support it. Biological articulation
It is evident that the quality of the environment affects system productivity. In a comprehensive study, in which a large sample of data, collected at 9500 different sites of the United States, was analyzed, Sala et al. (1988) confirmed the influence of water availability on aboveground net primary production of grasslands. A regional pattern of production was largely explained by annual rainfall. At a site level, variation in production was accounted for by annual rainfall, soil water-holding capacity, and the interaction between them, as expressions of environmental quality. In the Argentine pampas, a noticeable increase in the regional pattern of productivity was recorded in the transition from dry to wet zones, and annual precipitation was the major factor explaining yield variance (Viglizzo & Roberto, 1985).
Response of low-input agricultural systems to environmental variability
9
15
0
7.6
~I 6.0 "-o~,
I
0
I
I
1
I
I
2
I
|
s
Articula~)n index of eztlvlt~
Fig. 3. Degree of articulation of activities in a sample of farms of the Argentine pampas, and its association with mean productivity and its variability in two different environments; - - . moderate; . . . . , severe. (Viglizzo& Roberto, unpublished.) In many agricultural systems, productivity tends to decline as the articulations among activities increase. Data collected in the Argentine pampas (Viglizzo, 1986) demonstrate that, at similar levels of rainfall, production of edible energy was, in all cases, higher in the non-articulated configurations involved in primary production of herbage and grain, than in the articulated forms of secondary production of milk and beef. The lower inherent productivity of articulated models may be associated with a higher maintenance cost of their more complex internal structure. The addition of extra components to the energy flow chain requires extra energy for their maintenance. In addition, as the number of components increases the number of possible connections also increases very rapidly. The energy requirement increases in proportion to the number of components; but the energy required for connective organization increases more rapidly, since the number of possible combinations is increasing
10
Ernesto F. Viglizzo
also (Odum, 1983). The higher the energy diverted to structural maintenance, the lower the energy that can be devoted to production. For example, green plants capture solar energy by photosynthesis, and energy then flows through a complex structure of consecutive trophic levels, from primary consumers (herbivores) to secondary and tertiary consumers (carnivores), suffering very high energy losses in each transference (Krebs, 1972). Thus, the net productivity of living biomass in ecosystems decreases according to the number of metabolic steps involved in energy utilization structures. The disadvantage of lower productivity in articulated systems seems to be counteracted by a low sensitivity to the environment. The metabolic steps that increase energy loss may act, at the same time, as filtering stages essential to maintain the system in a state of low sensitivity to external change (Jameson, 1976; Coughenor et al., 1985). Thus, one can expect that productivity as well as sensitivity, will decline in the following sense: non-articulated homogeneous systems non-articulated heterogeneous systems articulated systems natural, highly articulated, systems An analysis of a sample of farms in a semi-arid environment of Argentina (E. F. Viglizzo & Z. E. Roberto, unpublished data) provides some evidence showing that energy yield, as well as sensitivity, are negatively related to an index of biological articulation amongst the farming activities. Figure 3 shows that relationship in two contrasting environmental conditions. In a pasture-cattle articulated system, at the same time that energy is used and dispersed in each transfer process, e.g. cow to suckling steer, the quality or concentration value of the retained energy increases. This can be demonstrated when the energy value of plants, mainly carbohydrates, is compared with the energy value of animal tissues, mainly proteins and body fats. The more concentrated forms of energy can, in turn, be used to damp out external fluctuations of the environment, and thus, to keep the system in a low state of sensitivity to the environment (Odum, 1983). For example, in grazing animal production systems, a cow can produce milk to feed a steer by mobilizing her own body fat reserves in a rather independent way with respect to currently available food (Broster, 1976). Thus, the growth of the steer does not depend directly on current external conditions that affect pasture production, but on internal body fat reserves of the cow. In non-articulated systems, heterogeneity (diversification) of farming activities can reduce the sensitivity of systems to the environment. As diversity in systems is concerned with the component parts and their
Response of low-input agricultural systems to environmental variability
11
connections, MacArthur 0955), on the one hand, suggested that, a perturbation affecting the overall system may be channelled and absorbed along different interconnected pathways, whereas Brockington (pers. comm.), on the other hand, suggested that, the buffering effect of diversification would depend on the size and residence time of the state variables. If size and residence time are small, e.g. in annual crops, the damping effect in relation to external factors would also be small. But that effect would be greater in long-cycle activities with a high residence time, e.g. in cattle production. In a moderate semi-arid environment of Argentina, Viglizz0 and Roberto (1989) analyzed a sample of 38 farms to study the links between diversification, productivity and stability. Diversification reduced the system sensitivity to the environment; but when diversification was increased to the point of excess, it imposed an extra management cost which was not matched by extra benefit. The interpretation of system sensitivity to extreme environments may be more controversial. Studies on desert ecosystems (Noy-Meir, 1979/80) and arid pastoral systems of Africa (Ellis & Swift, 1988), demonstrate a huge influence of climate on primary production, and a minimal influence of livestock on forage availability. They suggest that these ecosystems are structured mainly on the basis of independent reaqtions of each population to the environment. Since desert populations are sparse, with short periods of activity, the probability of articulation among them is low or negligible in comparison with the independent response of each population to the environment. Thus, the damping effect of articulation is unlikely to occur. Economic articulation
There appears to be less evidence that economic articulation is related to the behavior of agricultural systems than is the case with biological articulation. However, some supporting arguments may be noted: (1) The economic performance of agricultural systems can be directly related to the quality of the economic environment. According to FAO (1987), farmers of many development countries suffered a fall in their economic productivity during the 1980s because of the deterioration of international trade prices in agricultural products. (2) In bulk agricultural production, economic productivity per hectare tends to decline as articulation of activities is more intense. Perhaps this is a consequence of the lower biological productivity of articulated systems. Viglizzo et al. (1991) reported lower economic productivity in the more articulated cattle systems than in the cattle-crop systems. (3) In terms of economic gross margin per hectare, it was shown in the
Ernesto F. Viglizzo
12
same study (Viglizzo et al., 1991) that cattle-crop systems have a greater sensitivity to environmental variability than cattle only production systems. (4) It is evident that the same principles of energy loss and concentration that occurr in each transfer, from one system component to another, cannot be applied to money transfers between system activities. However, articulated structures can compensate for the economic variability of the environment by distributing the risk amongst different farming activities. At the same time, the economic productivity of the whole system will show a loss with respect to the maximum attainable, because each activity cannot attain a maximum at the same time. Thus, as Odum (1983) pointed out, a kind of parallelism can be seen in the biological and economic behavior of systems. In this case, articulation may reduce sensitivity in biological and economic terms, but with penalties in terms of reduced productivity and profit. In a study on bulk agricultural products of Argentina, Viglizzo and Roberto (unpublished data) found that, as the energy value of products increase, their economic value on a dry matter basis also increases
t
::f/ C;"f
0.0-
I
i
" I
I
I
I
I
I
4,0
I 0,,5
Gross Energyvalue (mcJ) per kOD.M. AodcuamllmXlUm: 1 p a m m h w , 2omOn~n, Soornomdn, 4~ra~/~, s v , ~ o , ~ , e ~ o l , 7tne, e m t .
Fig. 4. Relationships between energy and economic value on dry matter basis of different products in Argentina. Regression lines of all products (- - - -) and all products less wool ( ). (Viglizzo & Roberto, unpublished.)
Response of low-input agricultural systems to environmental variability
13
(Fig. 4). Accepting the basic differences between biological and economic units, it appears that if this trend could be verified in different places and times, some principles of the hypothesis might be applied to money, as well as energy flows. (5) Heterogeneity or diversification in the less articulated systems appears to reduce sensitivity to the economic variability of the environment. Various studies (Viglizzo et aL, 1984; Diaz et al., 1985, 1986; Viglizzo & Roberto, 1989) demonstrate increasing economic stability as the diversity of activities increases. But when a certain level of diversification is obtained, there is no further response in terms of economic stability.
CONCLUSIONS The understanding and management of basic agroecological mechanisms and processes seems to be fundamental to design low-input agricultural systems with a low sensitivity to environmental change. The investigation of the internal interactions that occur naturally in agroecosystems, and their links with the system response to the environment, offer a new and interesting possibility to be explored. House and Brust (1989) considered that, as energy and material inputs are lowered, a concomitant increase in fundamental knowledge of ecological processes in agriculture is needed. Low-input agricultural systems must retain some basic structural features of natural ecosystems in order to be persistent, and as Marten (1988) pointed out, to be effective they must have a structured diversity characterized by co-adaptation of their components. A controlled development of internal interactions seems to be a way to attenuate the impact of the environment upon the whole system, and this may be consistent with some basic ecological principles. Is a theory on the behavior of low-input systems necessary or useful? Common sense suggests that such a theory could be necessary or useful when systems react in response to environmental variability, and such behavior affects their performance. The articulation hypothesis is an attempt to orientate studies that relate structure to system response in low-input agricultural systems and, at the same time, may provide a basis to elaborate a more general theory on the behavior of such systems. Available evidence suggests that articulated configurations can operate as powerful risk-dissipative structures, because external disturbance can be channelled and absorbed among many interacting components. Nevertheless, the cost of maintenance of these structures can be very high, due to the multiple metabolic steps that are involved. As a result, productivity
14
Ernesto F. Viglizzo
is necessarily reduced. Accordingly, these structures would not be justified in the less limiting environments, where non-articulated configurations appear to be more efficient in the utilization of environmental resources, reaching higher productivity levels. Finally, a special apraisal may be required to explain the behavior of the overall system and its components under very extreme conditions.
ACKNOWLEDGMENTS The author gratefully acknowledges the discussions, useful comments and considerable assistance of Professor C. R. W. Spedding (University of Reading), Professor J. B. Dent (University of Edinburgh), Dr N. R. Brockington (EMBRAPA, Brazil), Dr D. Swift (Colorado State University, U S A ) , and one member of the Editorial Board of Agricultural Systems in preparing this manuscript. The financial assistance provided by the National Research Council of Argentina, CONICET, is gratefully acknowledged.
REFERENCES Allard, R. W. & Bradshaw, A. D. (1964). Implications of genotype environment interactions. Crop Sci., 4, 503-8. Broster, W. H. (1976). Plane of nutrition for the dairy cow. In Principles of Cattle Production, eds H. Swan & W. H. Broster. Butterworth & Co. Ltd, London, pp. 271-92. Brown, W. G. & Merril, M. O. (1958). Production functions from data over a series of years. J. Farm Econ., 40, 451-7. Calkins, P. H. & DiPietri, D. D. (1983). Farm Business Management, Macmillan Publishing Company, Inc., New York, 442 pp. Conway, G. R. (1987). The properties of agroecosystems. Agric. Syst., 24, 95-117. Coughenor, M. B., Ellis, J. E., Swift, D. M., Coppock, D. L., Galvin, K., McCabe, J. T. & Hart, T. C. (1985). Energy extraction and use in a nomadic pastoral ecosystem. Science, 230, 619-25. Cromartie, W. J., Jr. (1975). The effect of stand size and vegetational background on the colonization of cruciferous plants by herbivorous insects. J. Appl. Ecol., 12, 513-33. DeAngelis, D. L. (1975). Stability and connectance in food web models. Ecology, 56, 238-43. DeAngelis, D. L. & Waterhouse, J. C. (1987). Equilibrium and nonequilibrium concepts in ecological models. Ecol. Monogr., 57(1), 1-21. de Wit, C. T. (1986). Introduction. In Modelling of Agricultural Production: Weather, Soils and Crops, eds H. van Keulen & J. Wolf. Pudoc, Wageningen, pp. 3-10.
Response of low-inputagriculturalsystems to environmentalvariability
15
Diaz, M. J., Roberto, Z. E. & Viglizzo, E. F. (1985). El uso de modelos para valorar la estabilidad de sistemas de produceion. II: la relation ganaderiaagricultura y sus ef~tos sobre la estabilidad e¢on6mica del sistema. Rev. Arg. Prod. Anita., 5, 607-12. Diaz, M. J., Roberto, Z. E. & Viglizzo, E. F. (1986). Diversificaci6n productiva y estabilidad econ6mica de sistemas con distintas relaciones agroganaderas. Rev. Arg. Prod. Anita., 6, 603-8. Eberhart, S. A. & Russell, W. A. (1966). Stability parameters for comparing varieties. Crop Sci., 6, 36--40. Ellis, J. E. & Swift, D. M. (1988). Stability of African pastoral ecosystems. Alternate paradigms and implications for development. Z Range Management, 41(6), 450-9. Elton, C. S. (1958). The Ecology of Invasions by Animals and Plants. Methuen, London, 244 pp. Englestad, O. P. & Doll, E. E. (1961). Corn yield response to applied phosphorus as affected by rainfall and temperature variables. Agron. J., 53, 389-92. Englestad, O. P. & Parks, W. L. (1971). Variability in optimum N rates for corn. Agron. J., 63, 21-3. FAO (1987). Agriculture: Toward 2000, 24th Session, Rome, 7-26 November 1987. Conference Acts of the Food and Agriculture Organization of the United Nations, Rome, 263 pp. Finlay, K. W. & Wilkinson, G. N. (1963). An analysis of adaptation in a plant breeding program. Aust. J. Agric. Res., 14, 742-54. Fjelddalon, J. (1968). Planterermilder og moderne plante produksjion. Opuse Entomol., 33, 38-42. (Cited by Nickel, J. L. (1973).) Flohn, H. (1981). Climatic variability and coherence in time and space. In Food-Climate Interactions, eds W. Bach, J. Pankrath & S. H. Schneider. D. Reidel Publishing Co., Dordrecht, pp. 423-41. Fuller, W. A. (1965). Stochastic fertilizer production functions for continuous corn. J. Farm Econ., 47, 105-19. Gardner, M. R. & Ashby, W. R. (1970). Connectance of large dynamical (cybernetic) systems: critical values of stability. Nature, 228, 784. Gliessman, S. R. (1984). An agroecological approach to sustainable agriculture. In To Meet the Expectations of the Land, eds W. Jackson & B. Colman. Northpoint Press, Berkeley, CA. (Cited by Wagstaff, 1987.) Hanson, A. A. (1972). A directed ecosystem approach to pest control and environmental quality. J. Environ. Qual., 1, 45-54. House, G. J. & Brust, G. E. (1989). Ecology of low-input, no-tillage agroecosystems. Agric. Ecosyst. Environ., 27, 331-45. Jameson, D. A. (1976). Management of ecosystems: information supplied by simulation models. In Critical Evaluation of Systems Analysis in Ecosystems Research Management, eds C. W. Arnold & C. T. de Wit. Centre for Agricultural Publishing and Documentation, Wageningen, pp. 30-7. Knetsch, J. L. (1959). Moisture uncertainties and fertility response studies. J. Farm Econ., 41, 70-6. Kogan, F. N. (1986). Climate constraints and trend in global grain production. Agric. Forest Meteor., 37, 89-107. Krebs, Ch. J. (1972). Community metabolism. I: secondary production. In Ecology. The Experimental Analysis of Distribution and Abundance. Harper International Editors, New York, 694 pp.
16
Ernesto F. Viglizzo
MacArthur, R. (1955). Fluctuations of animal populations and a measure of community stability. Ecology, 36, 533-6. Margalef, R. (1968). Perspectives in Ecological Theory. University Chicago Press, IL, 111 pp. Marten, G. G. (1988). Productivity, stability, sustainability, equitability and autonomy as properties for agroecosystems assessment. Agric. Syst., 26, 291-316. May, R. M. (1971). Stability in multispecies communities. Math. BioSci., 12, 59-79. May, R. M. (1972). What is the chance that a large complex system will be stable? Nature, 238, 413-14. May, R. M. (1973). Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton, N J, 315 pp. McQuigg, J. D. (1981). Climate variability and crop yield in high and low temperature regions. In Food-Climate Interactions, eds W. Bach, J. Pankrath & S. H. Schneider. D. Reidel Publishing Co., Dordrecht, pp. 121-37. Nickel, J. L. (1973). Pest situations in changing agricultural systems. A review. Bull. Entomol. Sac. Am., 19, 136-42. Noy-Meir, I. (1979/80). Structure and function of desert ecosystems. Isr. J. Bot., 28, !-19. Odum, E. P. (1971). Fundamentals of Ecology, 3rd edn. Saunders, Philadelphia, PA, 574 pp. Odum, H. T. (1983). System Ecology: An Introduction. John Wiley & Sons, New York, 644 pp. O'Neill, R. V. & Reichle, D. E. (1979). Dimensions of ecosystem theory. In Forest. Fresh Perspectives from Ecosystem Analysis, ed. R. H. Waring. Oregon State University Press, OR, pp. 11-26. Oury, B. (1965). Allowing for weather in crop production model building. J. Farm Econ., 47, 270-83. Parks, W. L. & Knetsch, J. L. (1959). Corn yield as influenced by nitrogen level and drought intensity. Agron. J., 51, 363-4. Penna, J. A. (1983). El crecimiento del sector agropecuario pampeano durante las ultimas dos decadas: algunas consideraciones. Mimeo Seminary on 'El problema alimentario en America Latina y el Caribe: sus implicancias sociales'. CIDES (OEA). Pimentel, D. (1961). Species diversity and insect population outbreaks. Ann. Entomol. Sac. Am., 54, 76-86. Pimentel, D., Culliney, Th. W., Butler, I. W., Reinemann, D. J. & Beckmann, K. B. (1989). Low-input sustainable agriculture using ecological management practices. Agric. Ecosyst. Environ., 27, 3-24. Priestley, R. H. & Bayles, R. A. (1982). Evidence that varietal diversification can reduce the spread of cereal diseases. J. Natn. Inst. Agric. Bot., 16, 31-8. Roberto, Z. E., Diaz, M. J. & Viglizzo, E. F. (I984). E1 uso de modelos para valorar la estabilidad de sistemas de production. I: La relation ganaderiaagricultura y sus efectos sobre la estabilidad de los rendimientos del sistema. Rev. Arg. Prod. Anita., 5, 353-8. Root, R. B. (1973). Organization of a plant-arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea). Ecol. Monogr., 43, 94-125.
Response of low-input agricultural systems to environmental variability
17
Russell, J. S. (1968a). Nitrogen fertilizer and wheat in a semi-arid environment. Part 2: Climatic factors affecting response. Aust. J. Exp. Agric. Anita. Hush., 8, 223-31. Russell, J. S. (1968b). Nitrogen fertilizer and wheat in a semi-arid environment. Part 3: Soil and cultural factors affecting response. Aust. J. Exp. Agric. Anita. Hush., 8, 340-8. Schneider, S. H. & Bach, W. (1981). Interactions of food and climate: issues and policy considerations. In Food--Climate Interactions, eds W. Bach, J. Pankrath & S. H. Schneider. D. Reidel Publishing Co., Dordrecht, pp. 1-19. Sala, O. E., Patton, W. J., Joyce, L. A. & Lauenroth, W. K. (1988). Primary production of the central grassland region of the United States. Ecology, 69, 40-5. Solomou, S. (1986). The impact of climatic variation on British economic growth, 1856-1913. Climate change, 8, 53-67. Tahvanainen, J. O. & Root, R. B. (1972). The influence of vegetational diversity on the population ecology of a specialized herbivore, Phyllotreta cruciferae (Coleoptera: Chrysomelidae). Oecoiogia, 10, 321--46. Thompson, L. M. (1988). Effects of changes in climate and weather variability on the yields of corn and soybeans. J. Prod. Agric., 1, 20-7. Viglizzo, E. F. (1983). Produetividad y estabilidad produetiva de distintos ecosistemas de la region pampeana subhumeda y semiarida. Agrarius, 1, 3-15. Viglizzo, E. F. (1986). Agroecosystems stability in the Argentine pampas. Agric. Ecosyst. Environ., 16, 1-12. Viglizzo, E. F. & Roberto, Z. E. (1985). Estabilidad produetiva en distintos ambientes del area pampeana. Rev. Arg. Prod. Anita., 5, 103-11. Viglizzo, E. F. & Roberto, Z. E. (1989). Diversification, productivity and stability of agroecosystems in the semi-arid pampas of Argentina. Agric. Syst., 31, 279-90. Viglizzo, E. F., Roberto, Z. E. & Peluffo, L. (1984). Efecto de la diversification productiva del agroecosistema sobre sus rendimientos y estabilidad. Rev. Arg. Prod. Anita., 4, 1167-76. Viglizzo, E. F., Roberto, Z. E. & Brockington, N. R. (1991). Agroecosystems performance in the semi-arid pampas of Argentina and their interactions with the environment. Agric. Ecosyst. Environ., 36, 23-36. Wagstaff, H. (1987). Husbandry methods and farm systems in industrialized countries which use lower levels of external inputs. A review. Agric. Ecosyst. Environ., 19, 1-27. Wayne, R. (1987). Crop mixtures for enhanced yield and stability. Rural Research ( Csiro), 135, 16-19. Yates, F. & Cochran, W. G. (1938). The analysis of groups of experiments. J. Agric. Sci., 28, 556--80.
The Response of Low-Input Agricultural Systems to Environmental Variability. A Theoretical Approach Ernesto F. Viglizzo INTA-Centro Regional La Pampa-San Luis, CC 152, 6300 Santa Rosa, La Pampa, Argentina (Received 27 March 1991; accepted 24 February 1993)
A BSTRA CT The aims of this paper are: (a) to analyze the response of low-input agricultural systems to environmental variability; and (b) to suggest a theoretical framework to explain such behavior. Available evidence suggests that low-input systems are, in general, more sensitive than high-input systems to changes in the climatic environment. However, on the one hand this is not necessarily the case with respect to the economic environment, but, on the other hand, low-input systems of any region can differ in their response to similar environmental changes. Thus, when different levels of productivity and degrees of environmental sensitivity are combined, different patterns of performance can arise. A theoretical approach is presented in which the variations in system performance are related to system structure. It is suggested that a key feature is the degree of linkage between the system components. The 'articulation hypothesis' proposed here associates different patterns of articulation among essential components of the system with its performance. It is argued that, sensitivity in systems shows a negative relationship with the degree of internal articulations among different farming activities. The hypothesis also suggests that, the higher the internal articulation, the higher the maintenance cost, and hence, the lower the productivity of the system. Articulation appears to be a powerful risk-dissipative structural feature, reducing sensitivity at the price of lower productivity. In principle, the hypothesis could be successfully tested in marginal environments, but not in non-limiting or very extreme conditions. INTRODUCTION L o w external input systems are usually defined as farm production systems that have a low support energy requirement per hectare or per 1 Agricultural Systems 0308-521X/93/$06.00 © 1993 Elsevier Science Publishers Ltd, England. Printed in Great Britain
2
Ernesw F. Viglizzo
livestock unit (Wagstaff, 1987). They use substantially lower levels of manufactured fertilizers, agrochemicals, fuels, and concentrate feeds than is typical of modern high-input systems, and resemble natural ecosystems more than industrial systems (Gliessman, 1984). To simplify the analysis, it is considered that low utilization of support energy and money are sufficient conditions to describe low-input systems. As human intervention is lowered, such systems increasingly react to changes in climatic and economic conditions, and different patterns of response to the environment can appear (Viglizzo et al., 199i). In conventional higher-input farming, high levels of productivity often can be obtained without any appreciable attention to environmental sensitivity. But sensitivity can not be ignored if low-input systems, sustainable in the long term, are to be developed (Pimentel et al., 1989). Since sensitivity frequently is associated with farming risk, it is not a desirable condition in the agricultural business. Farmers and agronomists aim, in general terms, for system designs that can perform well over a wide range of environmental conditions. Studies on the general response of systems to environment have not traditionally been included in agricultural systems research. In an attempt to bridge this gap, the purposes of this paper are: (a) to analyze the response of low-input agricultural systems to environmental variability; and (b) to propose and discuss a theoretical approach to explain such behavior. The environment as a source of disturbance
The state of a system such as a low-input farm changes continuously with time in response to the natural rhythms and random fluctuations of the environment, especially under limiting conditions. Environmental conditions which cause disturbance include: (a) the regular seasonal rhythm of climate and prices; and (b) the unpredictable disturbance due to weather, pest outbreaks or economic forces. Climatic variability can explain a large part of the agricultural yield variance under extensive conditions. Evidence was reported by Brown and Merril (1958), Knetsch (1959), Parks and Knetsch 0959), Englestad and Doll (1961), Fuller (1965), Oury (1965), Russell (1968a,b), Schneider and Bach (1981), Swindale et al. (1981), Kogan (1986), Solomou (1986), de Wit (1986) and Thompson (1988). According to McQuigg (1981), the three major components of yield variability over a period of years are technological change, climate variability, and a random 'noise' that cannot be specified and is usually included in the statistical error term in an analysis of variance.
Response of low-input agricultural systems to environmental variability
3
The economic environment is another important source of disturbance. During the last part of this century in market oriented economies, agricultural producers have been operating under the pressure of a constantly changing economic environment. Variability in input-output as well as output-output price rates, has a considerable effect on the selection of different agricultural activities, affecting the overall performance of systems (Penna, 1983). Climate and economics appear to be the two main sources of variability, making risks in farming higher than in the nonfarming business (Calkins & DiPietri, 1983). As climatic and economic variations are not independent, extreme prices of agricultural products historically coincided with marked climatic anomalies in a year or group of years (Flohn, 1981). Solomou (1986) made a valuable study of the impact of climate variations on British economic growth during the period 1856-1913, showing that climatic and economic factors interact in affecting agricultural productivity. An analysis of system response to environment
Productivity, stability and sustainability appear to be three relevant system properties which combine a large number of processes into single, highlyaggregated expressions of performance (Marten, 1988). These expressions can, in turn, be used to analyze the system response to environmental variability. Conway (1987) has defined productivity as, the output of valued product per unit of resource input, both in biological and economic terms. He considered stability as the constancy of productivity in the face of small, normal and cyclical forces from the surrounding environment, and sustainability as the maintenance of productivity when subject to major disturbing forces that may be infrequent and unpredictable. It must be also considered that no system is sustainable if the disturbing forces are great enough. Both stability and sustainability involve longterm measurements of productivity, and can be considered as indices of the sensitivity of the system to external disturbing forces. Studies on the sensitivity of living organisms to the environment are not unusual in agricultural research. There is a general agreement amongst plant breeders, for example, that genotype-environment interaction ((3 X E) is of major importance in developing improved varieties. Some of the techniques developed by the plant breeders to assess the G × E may be adapted to estimate the system response to environment. Following the original approach of Yates and Cochran (1938), later modified by Finlay and Wilkinson (1963) and Eberhart and Russell (1966), regression analysis was adapted by Viglizzo (1983, 1986) and Viglizzo et al. (1991) to system studies. When an environmental
4
Ernesto F. Viglizzo
variable (e.g. rainfall, prices, etc.) is related to the system productivity (e.g. energy yield, economic income, etc.) along a period of consecutive years, the regression provides two stability parameters, namely the linear regression coefficient and the deviation from the regression mean square. In their earlier works, those authors estimated the biological and economic stability of farming systems by means of their variability about a trend. A time--sequence analysis was used to estimate the percent average oscillation amplitude of system outputs around the regression line, which represents the average trend in a series of consecutive years. Later, an alternative approach was utilized (Viglizzo et al., 1991) taking into account the sensitivity of system outputs in response to the changes in two environmental indices: rainfall and meat price. Sensitivity was estimated by means of the linear regression coefficients, or slope of the regression line. Rainfall and meat price were the selected variables because they usually have determinant effects on system performance, and do not impose limitations of availability and reliability of data. A high linear regression coefficient was taken as indicative of high sensitivity. By means of indices of productivity and output sensitivity to environment, Viglizzo et al. (1991) evaluated the biological and economic performance of 34 farms of the semi-arid pampas of Argentina during a period of six consecutive years. They found that mixed production systems were more productive, but more sensitive to environment, than cattle production systems. When each group was scattered in a two-axes model, corresponding to bio-economic productivity and sensitivity indices, it was possible to obtain a clear separation of the farms according to their specific patterns of performance. This study (Viglizzo et al., 1991) allowed the proposal of a tentative model for the classification of systems according to their patterns of response to environment. Five different types of systems were classified (Fig. 1) in relation to their productivity (P) and sensitivity (S) to a wide range of environmental conditions: high P-high S; high P-low S; low P-high S; low P-low S; and very high S. The last group was a special case of very high productivity under favorable external conditions, but with a dramatic fall-off when conditions worsened. This was a typical feature of farms devoting a high proportion of land to crop activities in a semi-arid ecosystem that is essentially marginal in terms of water supply. Low S systems were in general designed to avoid undesirable sensitivity to the environment, in spite of their low response to improved conditions. Those linear models must be considered as an oversimplification of reality, because in the real world the number of possible patterns of response varies more widely.
Response of low-input agricultural systems to environmental variability
5
1
High 2
I
, 4
Poor ~
F.MlmnmenlalCond#lo~ m~ Good
Fig. 1. Generalized models of the performance of agrcecosystems under different environmentalconditions: 1. High productivity-highsensitivity;2. high productivity-low sensitivity; 3. low productivity-high sensitivity; 4. low productivity-low sensitivity; 5. high productivity-veryhigh sensitivity,with no account of risk.
System configuration and response The term 'configuration' is used here to describe the internal structure of an agricultural system, comprising the number of farming activities as well as their respective interconnections. Diversity and connectance are properties which can affect the overall performance of the system. In a changing environment, overall system performance will be the result of the integrated activity of the various interrelated components. The question of the basic causes of system response will not have a simple answer. But it may be possible to reduce the complexity to simple expressions which are useful in practical terms. Biologically, different farming activities show quite varied patterns of response to the environment. For example, in energy terms, at similar levels of rainfall, the productivity of herbage and grain (primary production) clearly exceeds that of milk and beef (secondary production). But those secondary products generally appear to be less sensitive to external disturbance, although that difference in sensitivity tends to disappear when the environmental conditions improve (Viglizzo, 1986). Thus, when crop and animal activities are combined in different ways or proportions, the system productivity, as well as its environmental sensitivity, can change dramatically. Activities varying in their patterns of productivity and sensitivity provoke, when combined in different ways, great variability in the overall performance of the system (Roberto et al., 1984). Diversity appears to be an important property of ecological, as well as, agricultural systems. Evidence suggests that, some degree of diversification of activities can lessen the effect of external disturbance in low-input
6
Ernesto F. Viglizzo
agriculture, both in biological and economic terms 0¢iglizzo et al., 1984; Diaz et al., 1986; Viglizzo & Roberto, 1989). However, the effect tends to decrease with increasing diversity, and extreme diversification eventually may impose an additional management cost without extra benefit. In ecology, relationships between diversity and stability are still open to some debate. The general view, which has come to be recognised as ecological dogma, is that diversity is closely related to stability (MacArthur, 1955; Elton, 1958; Odum, 1971; Fjelddalon, 1968; Margalef, 1968; Hanson, 1972; Nickel, 1973; Wayne, 1987). But some reports (Gardner & Ashby, 1970; May, 1971, 1972, 1973) seem to contradict this traditional idea. Nevertheless, the dogma has developed into an important theoretical principle, used for making general predictions. In many cases, the theory anteceded its testing by experiments. Thus, the prediction that insect pest outbreaks will be more frequent in simple, than in diversified habitats became firmly established in ecological theory, but it was only later that experimental evidence became available to support it (Pimentel, 1961; Tahvanainen & Root, 1972; Root, 1973; Cromartie, 1975). Priestley and Bayles (1982) studied the effectiveness of diversification of varieties in reducing the spread of yellow rust and mildew in wheat. Their results showed that a benefit was gained from diversification because combined virulences occurred less frequently than the more simple virulences. From an agroecological point of view, diversity may appear in a variety of ways: temporal diversity, spatial diversity, diversity of species and varieties, or genetic diversity within populations (O'Neill & Reichle, 1979). Genetically homogeneous populations in agriculture, such as pure line varieties or single crosses, are usually vulnerable to disease and environmental extremes. Allard and Bradshaw (1964) considered that stability can be improved by increasing the genetic diversity of populations. According to this view, heterozygosity, as well as a mixture of heterogeneous populations, can transfer buffering effects to crop production. Connectance is another relevant property in agroecology. Ecologists assume that system diversity includes subsystems and components that are interconnected and interact in different ways. As far as the association of diversity with stability goes, the ecological view generally accepts that natural systems can maintain a relative homeostatic state by means of multiple connections and interactions amongst their components. However, some theoretical studies by Gardner and Ashby (1970) and May (1972, 1973) showed that large randomly connected systems have a decreasing probability of being stable as the number and strength of connections and interactions increase. Those results were important to ecology because they contradicted traditional ideas, but those mathematical
Response of low-input agricultural systems to environmental variability
7
artifacts only revealed a potential biotic feedback instability inherent in complex natural systems, ignoring other destabilizing influences, such as large environmental fluctuations (DeAngelis & Waterhouse, 1987). Systems frequently can fail to demonstrate stable behavior, due either to the disruptive effects of stochastic environmental elements, or to the influence of strong biotic internal feedbacks or 'overconnectance', exemplified by herbivores or predators which over-exploit their food resources (Ellis & Swift, 1988). But overconnectance is not a general rule for natural systems; there are examples that demonstrate that connectance is accompanied by increased stability (DeAngelis, 1975).
The system-articulation hypothesis Can the ecological principles of diversity and connectance be useful to explain the behavior of agricultural systems? An hypothesis relating structure and performance appears to be necessary. In a simple manner, the structure of a system can be described by the number and type of energy and money connections among different farming activities. The articulation of activities will be central to the following proposal, summarised in what can be called the 'system-articulation' hypothesis. In brief, this hypothesis assumes that: (a) the system sensitivity to the environment has a negative relationship with the diversity and connection of the farming activities; and (b) the greater the articulation among activities, the lower the productivity of the system. For the purposes of this discussion, it is assumed that activities are biologically articulated in cases in which energy is transferred from one activity to others after being processed. Similarly, they are, and will be, economically articulated in cases in which there are flows of money. Assuming different patterns of diversity and connectance, three basic models can be identified, as represented in Fig. 2: (a) a non-articulatedhomogeneous model, in which biologically uniform activities are essentially functioning in parallel, e.g. winter crops growing in the same year without overlapping their areas; (b) a non-articulated-heterogeneous model, in which the parallel configuration is maintained, but activities are not biologically uniform, e.g. winter and summer crops growing in the same farming year without overlapping their areas; and (c) an articulated model in which farming activities---uniform or not--are interconnected through energy and/or money flows, e.g. a pasture-animal production process, in which energy or money is used in a sequential way, and in the same area. The hypothetical scheme of Fig. 2 describes the performance of different system configurations, in terms of productivity and sensitivity in response to different environmental conditions. The simpler configurations
8
Ernesto F.. Viglizzo
Environmental
System Configuration
A Non articulated Homogeneous
Non articulated
Heterogeneous (diversified)
I
A I
A I
q)OQ I
I
I
A
A
A
I
I
I
@®® A I
/~ I
A
System Productivity
Sen,~tivity to Environment
S~e~
Low
Exl~'eme
Madem~
Mid
w~y High
Good
x i ~ or Very H~h
Severe
Low
Moderate
Mid
H~
Good
High
Mid
or Mid Very High
I
Very Low
Articulated
System
Conditions
?
Moderate
Low
Mid
Good
Mid
N~l~ible
Fig. 2. Hypotheticalschemeof system response to environmentunder differentsystem configurations. are highly sensitive to severe environmental conditions, in contrast to the more complex, articulated forms, which can better resist such unfavorable environments. Under more favorable conditions, the relatively low sensitivity of the more complex systems is no longer a condition of survival, and their inherently low productivity puts them at a disadvantage compared with the simpler forms. Evidence is needed to test this hypothesis. But some theoretical arguments, analytical research and empirical evidence may be adduced to support it. Biological articulation
It is evident that the quality of the environment affects system productivity. In a comprehensive study, in which a large sample of data, collected at 9500 different sites of the United States, was analyzed, Sala et al. (1988) confirmed the influence of water availability on aboveground net primary production of grasslands. A regional pattern of production was largely explained by annual rainfall. At a site level, variation in production was accounted for by annual rainfall, soil water-holding capacity, and the interaction between them, as expressions of environmental quality. In the Argentine pampas, a noticeable increase in the regional pattern of productivity was recorded in the transition from dry to wet zones, and annual precipitation was the major factor explaining yield variance (Viglizzo & Roberto, 1985).
Response of low-input agricultural systems to environmental variability
9
15
0
7.6
~I 6.0 "-o~,
I
0
I
I
1
I
I
2
I
|
s
Articula~)n index of eztlvlt~
Fig. 3. Degree of articulation of activities in a sample of farms of the Argentine pampas, and its association with mean productivity and its variability in two different environments; - - . moderate; . . . . , severe. (Viglizzo& Roberto, unpublished.) In many agricultural systems, productivity tends to decline as the articulations among activities increase. Data collected in the Argentine pampas (Viglizzo, 1986) demonstrate that, at similar levels of rainfall, production of edible energy was, in all cases, higher in the non-articulated configurations involved in primary production of herbage and grain, than in the articulated forms of secondary production of milk and beef. The lower inherent productivity of articulated models may be associated with a higher maintenance cost of their more complex internal structure. The addition of extra components to the energy flow chain requires extra energy for their maintenance. In addition, as the number of components increases the number of possible connections also increases very rapidly. The energy requirement increases in proportion to the number of components; but the energy required for connective organization increases more rapidly, since the number of possible combinations is increasing
10
Ernesto F. Viglizzo
also (Odum, 1983). The higher the energy diverted to structural maintenance, the lower the energy that can be devoted to production. For example, green plants capture solar energy by photosynthesis, and energy then flows through a complex structure of consecutive trophic levels, from primary consumers (herbivores) to secondary and tertiary consumers (carnivores), suffering very high energy losses in each transference (Krebs, 1972). Thus, the net productivity of living biomass in ecosystems decreases according to the number of metabolic steps involved in energy utilization structures. The disadvantage of lower productivity in articulated systems seems to be counteracted by a low sensitivity to the environment. The metabolic steps that increase energy loss may act, at the same time, as filtering stages essential to maintain the system in a state of low sensitivity to external change (Jameson, 1976; Coughenor et al., 1985). Thus, one can expect that productivity as well as sensitivity, will decline in the following sense: non-articulated homogeneous systems non-articulated heterogeneous systems articulated systems natural, highly articulated, systems An analysis of a sample of farms in a semi-arid environment of Argentina (E. F. Viglizzo & Z. E. Roberto, unpublished data) provides some evidence showing that energy yield, as well as sensitivity, are negatively related to an index of biological articulation amongst the farming activities. Figure 3 shows that relationship in two contrasting environmental conditions. In a pasture-cattle articulated system, at the same time that energy is used and dispersed in each transfer process, e.g. cow to suckling steer, the quality or concentration value of the retained energy increases. This can be demonstrated when the energy value of plants, mainly carbohydrates, is compared with the energy value of animal tissues, mainly proteins and body fats. The more concentrated forms of energy can, in turn, be used to damp out external fluctuations of the environment, and thus, to keep the system in a low state of sensitivity to the environment (Odum, 1983). For example, in grazing animal production systems, a cow can produce milk to feed a steer by mobilizing her own body fat reserves in a rather independent way with respect to currently available food (Broster, 1976). Thus, the growth of the steer does not depend directly on current external conditions that affect pasture production, but on internal body fat reserves of the cow. In non-articulated systems, heterogeneity (diversification) of farming activities can reduce the sensitivity of systems to the environment. As diversity in systems is concerned with the component parts and their
Response of low-input agricultural systems to environmental variability
11
connections, MacArthur 0955), on the one hand, suggested that, a perturbation affecting the overall system may be channelled and absorbed along different interconnected pathways, whereas Brockington (pers. comm.), on the other hand, suggested that, the buffering effect of diversification would depend on the size and residence time of the state variables. If size and residence time are small, e.g. in annual crops, the damping effect in relation to external factors would also be small. But that effect would be greater in long-cycle activities with a high residence time, e.g. in cattle production. In a moderate semi-arid environment of Argentina, Viglizz0 and Roberto (1989) analyzed a sample of 38 farms to study the links between diversification, productivity and stability. Diversification reduced the system sensitivity to the environment; but when diversification was increased to the point of excess, it imposed an extra management cost which was not matched by extra benefit. The interpretation of system sensitivity to extreme environments may be more controversial. Studies on desert ecosystems (Noy-Meir, 1979/80) and arid pastoral systems of Africa (Ellis & Swift, 1988), demonstrate a huge influence of climate on primary production, and a minimal influence of livestock on forage availability. They suggest that these ecosystems are structured mainly on the basis of independent reaqtions of each population to the environment. Since desert populations are sparse, with short periods of activity, the probability of articulation among them is low or negligible in comparison with the independent response of each population to the environment. Thus, the damping effect of articulation is unlikely to occur. Economic articulation
There appears to be less evidence that economic articulation is related to the behavior of agricultural systems than is the case with biological articulation. However, some supporting arguments may be noted: (1) The economic performance of agricultural systems can be directly related to the quality of the economic environment. According to FAO (1987), farmers of many development countries suffered a fall in their economic productivity during the 1980s because of the deterioration of international trade prices in agricultural products. (2) In bulk agricultural production, economic productivity per hectare tends to decline as articulation of activities is more intense. Perhaps this is a consequence of the lower biological productivity of articulated systems. Viglizzo et al. (1991) reported lower economic productivity in the more articulated cattle systems than in the cattle-crop systems. (3) In terms of economic gross margin per hectare, it was shown in the
Ernesto F. Viglizzo
12
same study (Viglizzo et al., 1991) that cattle-crop systems have a greater sensitivity to environmental variability than cattle only production systems. (4) It is evident that the same principles of energy loss and concentration that occurr in each transfer, from one system component to another, cannot be applied to money transfers between system activities. However, articulated structures can compensate for the economic variability of the environment by distributing the risk amongst different farming activities. At the same time, the economic productivity of the whole system will show a loss with respect to the maximum attainable, because each activity cannot attain a maximum at the same time. Thus, as Odum (1983) pointed out, a kind of parallelism can be seen in the biological and economic behavior of systems. In this case, articulation may reduce sensitivity in biological and economic terms, but with penalties in terms of reduced productivity and profit. In a study on bulk agricultural products of Argentina, Viglizzo and Roberto (unpublished data) found that, as the energy value of products increase, their economic value on a dry matter basis also increases
t
::f/ C;"f
0.0-
I
i
" I
I
I
I
I
I
4,0
I 0,,5
Gross Energyvalue (mcJ) per kOD.M. AodcuamllmXlUm: 1 p a m m h w , 2omOn~n, Soornomdn, 4~ra~/~, s v , ~ o , ~ , e ~ o l , 7tne, e m t .
Fig. 4. Relationships between energy and economic value on dry matter basis of different products in Argentina. Regression lines of all products (- - - -) and all products less wool ( ). (Viglizzo & Roberto, unpublished.)
Response of low-input agricultural systems to environmental variability
13
(Fig. 4). Accepting the basic differences between biological and economic units, it appears that if this trend could be verified in different places and times, some principles of the hypothesis might be applied to money, as well as energy flows. (5) Heterogeneity or diversification in the less articulated systems appears to reduce sensitivity to the economic variability of the environment. Various studies (Viglizzo et aL, 1984; Diaz et al., 1985, 1986; Viglizzo & Roberto, 1989) demonstrate increasing economic stability as the diversity of activities increases. But when a certain level of diversification is obtained, there is no further response in terms of economic stability.
CONCLUSIONS The understanding and management of basic agroecological mechanisms and processes seems to be fundamental to design low-input agricultural systems with a low sensitivity to environmental change. The investigation of the internal interactions that occur naturally in agroecosystems, and their links with the system response to the environment, offer a new and interesting possibility to be explored. House and Brust (1989) considered that, as energy and material inputs are lowered, a concomitant increase in fundamental knowledge of ecological processes in agriculture is needed. Low-input agricultural systems must retain some basic structural features of natural ecosystems in order to be persistent, and as Marten (1988) pointed out, to be effective they must have a structured diversity characterized by co-adaptation of their components. A controlled development of internal interactions seems to be a way to attenuate the impact of the environment upon the whole system, and this may be consistent with some basic ecological principles. Is a theory on the behavior of low-input systems necessary or useful? Common sense suggests that such a theory could be necessary or useful when systems react in response to environmental variability, and such behavior affects their performance. The articulation hypothesis is an attempt to orientate studies that relate structure to system response in low-input agricultural systems and, at the same time, may provide a basis to elaborate a more general theory on the behavior of such systems. Available evidence suggests that articulated configurations can operate as powerful risk-dissipative structures, because external disturbance can be channelled and absorbed among many interacting components. Nevertheless, the cost of maintenance of these structures can be very high, due to the multiple metabolic steps that are involved. As a result, productivity
14
Ernesto F. Viglizzo
is necessarily reduced. Accordingly, these structures would not be justified in the less limiting environments, where non-articulated configurations appear to be more efficient in the utilization of environmental resources, reaching higher productivity levels. Finally, a special apraisal may be required to explain the behavior of the overall system and its components under very extreme conditions.
ACKNOWLEDGMENTS The author gratefully acknowledges the discussions, useful comments and considerable assistance of Professor C. R. W. Spedding (University of Reading), Professor J. B. Dent (University of Edinburgh), Dr N. R. Brockington (EMBRAPA, Brazil), Dr D. Swift (Colorado State University, U S A ) , and one member of the Editorial Board of Agricultural Systems in preparing this manuscript. The financial assistance provided by the National Research Council of Argentina, CONICET, is gratefully acknowledged.
REFERENCES Allard, R. W. & Bradshaw, A. D. (1964). Implications of genotype environment interactions. Crop Sci., 4, 503-8. Broster, W. H. (1976). Plane of nutrition for the dairy cow. In Principles of Cattle Production, eds H. Swan & W. H. Broster. Butterworth & Co. Ltd, London, pp. 271-92. Brown, W. G. & Merril, M. O. (1958). Production functions from data over a series of years. J. Farm Econ., 40, 451-7. Calkins, P. H. & DiPietri, D. D. (1983). Farm Business Management, Macmillan Publishing Company, Inc., New York, 442 pp. Conway, G. R. (1987). The properties of agroecosystems. Agric. Syst., 24, 95-117. Coughenor, M. B., Ellis, J. E., Swift, D. M., Coppock, D. L., Galvin, K., McCabe, J. T. & Hart, T. C. (1985). Energy extraction and use in a nomadic pastoral ecosystem. Science, 230, 619-25. Cromartie, W. J., Jr. (1975). The effect of stand size and vegetational background on the colonization of cruciferous plants by herbivorous insects. J. Appl. Ecol., 12, 513-33. DeAngelis, D. L. (1975). Stability and connectance in food web models. Ecology, 56, 238-43. DeAngelis, D. L. & Waterhouse, J. C. (1987). Equilibrium and nonequilibrium concepts in ecological models. Ecol. Monogr., 57(1), 1-21. de Wit, C. T. (1986). Introduction. In Modelling of Agricultural Production: Weather, Soils and Crops, eds H. van Keulen & J. Wolf. Pudoc, Wageningen, pp. 3-10.
Response of low-inputagriculturalsystems to environmentalvariability
15
Diaz, M. J., Roberto, Z. E. & Viglizzo, E. F. (1985). El uso de modelos para valorar la estabilidad de sistemas de produceion. II: la relation ganaderiaagricultura y sus ef~tos sobre la estabilidad e¢on6mica del sistema. Rev. Arg. Prod. Anita., 5, 607-12. Diaz, M. J., Roberto, Z. E. & Viglizzo, E. F. (1986). Diversificaci6n productiva y estabilidad econ6mica de sistemas con distintas relaciones agroganaderas. Rev. Arg. Prod. Anita., 6, 603-8. Eberhart, S. A. & Russell, W. A. (1966). Stability parameters for comparing varieties. Crop Sci., 6, 36--40. Ellis, J. E. & Swift, D. M. (1988). Stability of African pastoral ecosystems. Alternate paradigms and implications for development. Z Range Management, 41(6), 450-9. Elton, C. S. (1958). The Ecology of Invasions by Animals and Plants. Methuen, London, 244 pp. Englestad, O. P. & Doll, E. E. (1961). Corn yield response to applied phosphorus as affected by rainfall and temperature variables. Agron. J., 53, 389-92. Englestad, O. P. & Parks, W. L. (1971). Variability in optimum N rates for corn. Agron. J., 63, 21-3. FAO (1987). Agriculture: Toward 2000, 24th Session, Rome, 7-26 November 1987. Conference Acts of the Food and Agriculture Organization of the United Nations, Rome, 263 pp. Finlay, K. W. & Wilkinson, G. N. (1963). An analysis of adaptation in a plant breeding program. Aust. J. Agric. Res., 14, 742-54. Fjelddalon, J. (1968). Planterermilder og moderne plante produksjion. Opuse Entomol., 33, 38-42. (Cited by Nickel, J. L. (1973).) Flohn, H. (1981). Climatic variability and coherence in time and space. In Food-Climate Interactions, eds W. Bach, J. Pankrath & S. H. Schneider. D. Reidel Publishing Co., Dordrecht, pp. 423-41. Fuller, W. A. (1965). Stochastic fertilizer production functions for continuous corn. J. Farm Econ., 47, 105-19. Gardner, M. R. & Ashby, W. R. (1970). Connectance of large dynamical (cybernetic) systems: critical values of stability. Nature, 228, 784. Gliessman, S. R. (1984). An agroecological approach to sustainable agriculture. In To Meet the Expectations of the Land, eds W. Jackson & B. Colman. Northpoint Press, Berkeley, CA. (Cited by Wagstaff, 1987.) Hanson, A. A. (1972). A directed ecosystem approach to pest control and environmental quality. J. Environ. Qual., 1, 45-54. House, G. J. & Brust, G. E. (1989). Ecology of low-input, no-tillage agroecosystems. Agric. Ecosyst. Environ., 27, 331-45. Jameson, D. A. (1976). Management of ecosystems: information supplied by simulation models. In Critical Evaluation of Systems Analysis in Ecosystems Research Management, eds C. W. Arnold & C. T. de Wit. Centre for Agricultural Publishing and Documentation, Wageningen, pp. 30-7. Knetsch, J. L. (1959). Moisture uncertainties and fertility response studies. J. Farm Econ., 41, 70-6. Kogan, F. N. (1986). Climate constraints and trend in global grain production. Agric. Forest Meteor., 37, 89-107. Krebs, Ch. J. (1972). Community metabolism. I: secondary production. In Ecology. The Experimental Analysis of Distribution and Abundance. Harper International Editors, New York, 694 pp.
16
Ernesto F. Viglizzo
MacArthur, R. (1955). Fluctuations of animal populations and a measure of community stability. Ecology, 36, 533-6. Margalef, R. (1968). Perspectives in Ecological Theory. University Chicago Press, IL, 111 pp. Marten, G. G. (1988). Productivity, stability, sustainability, equitability and autonomy as properties for agroecosystems assessment. Agric. Syst., 26, 291-316. May, R. M. (1971). Stability in multispecies communities. Math. BioSci., 12, 59-79. May, R. M. (1972). What is the chance that a large complex system will be stable? Nature, 238, 413-14. May, R. M. (1973). Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton, N J, 315 pp. McQuigg, J. D. (1981). Climate variability and crop yield in high and low temperature regions. In Food-Climate Interactions, eds W. Bach, J. Pankrath & S. H. Schneider. D. Reidel Publishing Co., Dordrecht, pp. 121-37. Nickel, J. L. (1973). Pest situations in changing agricultural systems. A review. Bull. Entomol. Sac. Am., 19, 136-42. Noy-Meir, I. (1979/80). Structure and function of desert ecosystems. Isr. J. Bot., 28, !-19. Odum, E. P. (1971). Fundamentals of Ecology, 3rd edn. Saunders, Philadelphia, PA, 574 pp. Odum, H. T. (1983). System Ecology: An Introduction. John Wiley & Sons, New York, 644 pp. O'Neill, R. V. & Reichle, D. E. (1979). Dimensions of ecosystem theory. In Forest. Fresh Perspectives from Ecosystem Analysis, ed. R. H. Waring. Oregon State University Press, OR, pp. 11-26. Oury, B. (1965). Allowing for weather in crop production model building. J. Farm Econ., 47, 270-83. Parks, W. L. & Knetsch, J. L. (1959). Corn yield as influenced by nitrogen level and drought intensity. Agron. J., 51, 363-4. Penna, J. A. (1983). El crecimiento del sector agropecuario pampeano durante las ultimas dos decadas: algunas consideraciones. Mimeo Seminary on 'El problema alimentario en America Latina y el Caribe: sus implicancias sociales'. CIDES (OEA). Pimentel, D. (1961). Species diversity and insect population outbreaks. Ann. Entomol. Sac. Am., 54, 76-86. Pimentel, D., Culliney, Th. W., Butler, I. W., Reinemann, D. J. & Beckmann, K. B. (1989). Low-input sustainable agriculture using ecological management practices. Agric. Ecosyst. Environ., 27, 3-24. Priestley, R. H. & Bayles, R. A. (1982). Evidence that varietal diversification can reduce the spread of cereal diseases. J. Natn. Inst. Agric. Bot., 16, 31-8. Roberto, Z. E., Diaz, M. J. & Viglizzo, E. F. (I984). E1 uso de modelos para valorar la estabilidad de sistemas de production. I: La relation ganaderiaagricultura y sus efectos sobre la estabilidad de los rendimientos del sistema. Rev. Arg. Prod. Anita., 5, 353-8. Root, R. B. (1973). Organization of a plant-arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea). Ecol. Monogr., 43, 94-125.
Response of low-input agricultural systems to environmental variability
17
Russell, J. S. (1968a). Nitrogen fertilizer and wheat in a semi-arid environment. Part 2: Climatic factors affecting response. Aust. J. Exp. Agric. Anita. Hush., 8, 223-31. Russell, J. S. (1968b). Nitrogen fertilizer and wheat in a semi-arid environment. Part 3: Soil and cultural factors affecting response. Aust. J. Exp. Agric. Anita. Hush., 8, 340-8. Schneider, S. H. & Bach, W. (1981). Interactions of food and climate: issues and policy considerations. In Food--Climate Interactions, eds W. Bach, J. Pankrath & S. H. Schneider. D. Reidel Publishing Co., Dordrecht, pp. 1-19. Sala, O. E., Patton, W. J., Joyce, L. A. & Lauenroth, W. K. (1988). Primary production of the central grassland region of the United States. Ecology, 69, 40-5. Solomou, S. (1986). The impact of climatic variation on British economic growth, 1856-1913. Climate change, 8, 53-67. Tahvanainen, J. O. & Root, R. B. (1972). The influence of vegetational diversity on the population ecology of a specialized herbivore, Phyllotreta cruciferae (Coleoptera: Chrysomelidae). Oecoiogia, 10, 321--46. Thompson, L. M. (1988). Effects of changes in climate and weather variability on the yields of corn and soybeans. J. Prod. Agric., 1, 20-7. Viglizzo, E. F. (1983). Produetividad y estabilidad produetiva de distintos ecosistemas de la region pampeana subhumeda y semiarida. Agrarius, 1, 3-15. Viglizzo, E. F. (1986). Agroecosystems stability in the Argentine pampas. Agric. Ecosyst. Environ., 16, 1-12. Viglizzo, E. F. & Roberto, Z. E. (1985). Estabilidad produetiva en distintos ambientes del area pampeana. Rev. Arg. Prod. Anita., 5, 103-11. Viglizzo, E. F. & Roberto, Z. E. (1989). Diversification, productivity and stability of agroecosystems in the semi-arid pampas of Argentina. Agric. Syst., 31, 279-90. Viglizzo, E. F., Roberto, Z. E. & Peluffo, L. (1984). Efecto de la diversification productiva del agroecosistema sobre sus rendimientos y estabilidad. Rev. Arg. Prod. Anita., 4, 1167-76. Viglizzo, E. F., Roberto, Z. E. & Brockington, N. R. (1991). Agroecosystems performance in the semi-arid pampas of Argentina and their interactions with the environment. Agric. Ecosyst. Environ., 36, 23-36. Wagstaff, H. (1987). Husbandry methods and farm systems in industrialized countries which use lower levels of external inputs. A review. Agric. Ecosyst. Environ., 19, 1-27. Wayne, R. (1987). Crop mixtures for enhanced yield and stability. Rural Research ( Csiro), 135, 16-19. Yates, F. & Cochran, W. G. (1938). The analysis of groups of experiments. J. Agric. Sci., 28, 556--80.