From Eco Pathology' to ‘agroecosystem Health

From ècopathology' to àgroecosystem health'$

B. Fayea,*, D. Waltner-Toewsb, J. McDermotta,b

a CIRAD-EMVT, BP 5035, 34032 Montpellier, Cedex 1, Franceb Department of Population Medicine, University of Guelph, Guelph, Ont., Canada, N1G 2W1

Accepted 6 December 1997

Abstract

The èpidemiologic revolution' of the 1960s arose in response to the inability of reductionist

methods to provide practical solutions to the complex problems of health and production in

livestock systems. In a farm, there are not only interactions between animal factors and herd

husbandry factors such as feeding, housing, and microbiological environment, but also with a

number of other `non-animal' factors. For this reason, a `global' or `holistic' approach, aimed at

explaining animal health status within the overall dynamic of a livestock production system, was

developed in France under the title of ècopathology'. In ecopathology, the discipline of

epidemiology is integrated into a systemic approach, including: the development of a preliminary

conceptual model, sampling based on the structure of the livestock production system, the

establishment of a field study by a multidisciplinary team, the organization and management of the

animal health and production information, data analysis, the distribution of results to all participants

and the development of a preventive medicine programme.

The farm is also influenced by the social, economic and environmental setting to which it

belongs. To account for this, a change of scale is necessary. The three elements of the livestock

production system considered in ecopathology (farmer, herd and resources), at the level of the

agroecosystem become a human community (farmers, consumers, decision-makers), an animal

population, and the complex of human, social and economic conditions within the system. The

concept of agroecosystem health is closely linked to the overall principle of improving the

sustainability of the system. This and other measures of the health status of an agroecosystem can

be assessed with methods developed by epidemiologists and other disciplines within a system's

perspective. In this systems view, ecopathology provides a basis for assessing herd health whereas

agroecosystem health develops the broader context into which ecopathology contributes. # 1999

Elsevier Science B.V. All rights reserved.

Keywords: Ecopathology; Agroecosystem health; Epidemiology; Agricultural systems; Sustainability

Preventive Veterinary Medicine 39 (1999) 111±128

* Corresponding author. Tel.: +33-73-62-42-63; fax: +33-73-62-45-48.$Presented originally as a Plenary paper at the VIII International Symposium on Veterinary Epidemiology

and Economics, Paris, France, July 8±11, 1997.

0167-5877/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 7 - 5 8 7 7 ( 9 8 ) 0 0 1 4 9 - 4

1. The epidemiological revolution

In an overview paper published in the first issue of Preventive Veterinary Medicine,

Schwabe (1982) presents a perspective on the developments of epidemiology and

economics in veterinary practice in the latter half of this century. In Schwabe's view, four

crises became apparent in the 1950s: (i) the persistence of health problems in some herds

even after many named infectious diseases had been substantially controlled; (ii) the

increasing demands of governments to estimate the economic costs and benefits of animal

health; (iii) the absence of appropriate research methods to understand and control

etiologically complex diseases affecting production; and (iv) the inability of private

veterinarians and producers to develop programs to control health and production

constraints associated with intensive farming practices. Because the `germ-theory'

approach failed in these contexts, there was an impetus for an `̀ holistic and

epidemiologic approach of the causality of the diseases'', fostering the initiation and

progression of what Schwabe called an èpidemiological revolution' in the 1960s to better

identify, quantify and analyze the multiple and interacting causes of many animal health

and production problems.

2. System theory

These changes mirrored a similar revolution among agronomists who were developing

the concepts of farming systems theory (Von Bertalanffy, 1970). Faced with numerous

failures of agricultural development projects in both industrialized and developing

countries, many agronomists questioned the efficacy of their traditional reductionist

research methods (SeÂbillotte, 1978) and searched for more holistic paradigms. Central to

farming systems theory was the socio-economic principal that `̀ farmers' practices

respond to certain aims and constraints, and the ignorance of those is the principal

source of the failure of technical solutions developed from research'' (Tourte, 1965). This

expansion of the traditional technical domains of animal health and production practice to

include consideration of the economic, social, and technical objectives of farmers has

been applied in practice by many veterinary epidemiologists (e.g. Bigras-Poulin et al.,

1985), who have wisely noted that without this integration, the objectives of the

epidemiological revolution would remain unfulfilled.

Formally, farming systems research considers the interactions between three elements:

the farmer, the herd and available resources. The farmer makes decisions in accord with

both technical and/or behavioral criteria. These decisions are made operational through

farming practices which by direct or indirect pathways and feed-back loops influence

both the performance of the herd and the quantity and quality of animal products.

Farmers also influence and are influenced by the resources available in their particular

farming system which of course greatly influence herd productivity. The performance of

the farming systems have been assessed by various measurable indicators which can be

used in whole-farm decision-making.

Landais (1994) described a conceptual model of a livestock production system from his

perspective as an animal scientist (see Fig. 1), but its lack of explicit accounting for

112 B. Faye et al. / Preventive Veterinary Medicine 39 (1999) 111±128

animal health as a component of the system is not acceptable to many veterinary

epidemiologists. In fact, the basis of the epidemiologic approach to studying animal

health and production problems developed in France, called ecopathology (Tuffery, 1977;

Barnouin and Brochard, 1981; Tillon, 1982), aims precisely to explain health within the

farming system.

3. Ecopathology ± epidemiologic assessment of livestock systems

Just as farmers make decisions based on technical criteria within the farming systems

paradigm, ecopathologists seek to define herd health `referentials' as criteria for

supporting preventive medicine and herd management decisions to improve health

performance. Both animal-level (e.g. relative risk of diseases related to physiological

factors) or herd-level (e.g. the relative incidence of animal diseases in comparison to

other farms in the same area) referentials can be used depending on the type of decision

to be made. These decisions will also depend, as in the farming systems paradigm, on the

farmer's behavioral attitudes with respect to disease occurrence and risk and they will be

implemented through various practices (e.g. management, culling, feeding practices,

vaccination) which, separately or in combination, will influence the risk of `disease'. In

Fig. 1. Working diagram for the farming system adapted from Landais (1994).

B. Faye et al. / Preventive Veterinary Medicine 39 (1999) 111±128 113

ecopathology, disease or health performance is interpreted as just another output of the

farming system, like production performance, which can vary depending on environ-

mental conditions (animal housing, climatic factors) and available resources (feeding,

inputs). For routine decision-making, it can be beneficial to define easily measurable and

important health indicators to reflect both health performance (e.g. somatic cell count as

a subclinical mastitis indicator) and risk status (e.g. cleanliness score as a hygiene

indicator). However, as in any complex system, the development of useful indicators can

be both very rewarding as well as difficult. In some circumstances, health indicators have

been particularly useful in identifying, at an early stage, imbalances and malfunctions in

livestock production systems (Barnouin et al., 1994).

Ecopathologists integrate epidemiologic principles into a systematic method for

studying health status within farming systems using an ecopathological survey. Such a

survey consists of several distinct but complementary stages:

� The sampling of farms by type of farming system. Since we consider the disease as an

output of the system controlled by inputs and farmer decisions, the farming system is

the operational basis for future actions.

� The use of rigorous observational study methods by multidisciplinary teams

(veterinarians, epidemiologists, zootechnicians, farm technicians, professional organi-

zations, statisticians, computer scientists and others according to the needs of the

survey). This allows for the collection of a diversity of parameters, enlarging the frame

of epidemiologic information with concepts and methods from non-medical disciplines

(animal husbandry, economics, sociology). In terms of herd health delivery, this also

allows veterinary inputs to be better integrated into herd management (feeding,

reproduction, and production). Thus, the ecopathological approach is both multi-

disciplinary and multiprofessional.

� The management and organization of an information system in a specific data base.

The collection of data in this context is characterised by a high degree of complexity

and linkages and with a great diversity of studied factors. This requires that careful

attention be paid to developing preliminary conceptual models based on prior

biological knowledge and subsequently revised by analysis of the assembled database.

This approach has been advocated for the analysis of complex biological systems with

a priori knowledge gaps (Mallows and Tukey, 1982). From this, empirical models are

developed using prior concepts refined in an iterative approach. Thus, unlike

experiments, data processing and analysis cannot be precisely defined at the beginning

of the study, since the analysis at any stage will be guided by the results of the previous

step. As a consequence, a fixed framework for data processing will be impossible to

construct and thus the data organization needs to be sufficiently flexible to support

unexpected applications (Lescourret et al., 1992).

� A staged approach to data analysis. This will emphasize exploratory (especially

graphical methods), followed by methods to develop `structured' disease models

reflecting both previous theory and exploratory results and finally modelling to

estimate the relative importance of different risk factors and health indices within the

`structure' developed. However, these analyses may be complicated by a number of

problems including multicollinearity, confounding and interaction (Dohoo et al., 1996).


The general strategy for dealing with multicollinear data revolve around reducing and/

or re-categorizing the usually large number of independent variables prior to

investigating associations with the disease. This screening of associations between

independent and dependant variables can be done by various multivariable techniques,

such as correlation analysis, linear or logistic regression, correspondence analysis and

others.

� The dissemination of results to all stakeholders and key players in the system. A

comprehensive programme to transfer the results of the survey includes: (i)

information transfer through brochures, technical documents, newspaper and magazine

articles, and workshop papers; (ii) skills transfer through training, development of

intervention guides and prevention manuals; (iii) raising awareness through discussion

groups, meetings and other publicity; and (iv) follow-up actions for assessing opinion,

refining materials and evaluating and measuring the impact of the proposed

modifications (Rosner, 1993).

The main objective of ecopathology is very practical, to identify the risk factors and

their interactions which most influence health performance in specific priority farming

systems. This approach has been used in tropical countries (Faye et al., 1994a) but the

main studies have been conducted in France (Barnouin, 1992; Faye, 1995; Faye and

Barnouin, 1996). The ecopathological study conducted in Brittany, France is a good

example of the latter. The study was conducted over four years (1986±1990) among

intensive dairy farms. This particular farming system was chosen since these farms were

supposed to represent the trend of French dairy farming towards increased farm size and

intensification (Faye and Barnouin, 1987). The study farms consisted almost exclusively

of Holstein±Friesian cattle with mean annual milk yields of at least 6500 kg, practiced

winter feeding based on silage, and had cubicles or free-stalls as the main animal housing.

Data were collected by a working group using a protocol developed by a multi-

disciplinary team. Data were collected at the cow (e.g. clinical diseases, production and

reproductive performance, cleanliness and body score, individual feed supplementation,

behaviour, and test results (e.g. milk bacteriology and blood metabolic profile)) and farm

(e.g. farming practices and conditions, baseline diet, eco-climatic parameters, inputs

assessment) levels.

A total of 4129 dairy cows (Holstein breed) contributing 8945 lactations and belonging

to 47 intensive dairy farms were involved. Farmers and veterinarians volunteered to

participate in the study. The participating farmers were purposively sampled as being

representative of the main dairy farming system in Brittany and all were members of the

Dairy Herd Improvement Association (DHIA). They were selected for their ability to

detect and record diseases efficiently, as judged by their veterinarians and confirmed

through a pre-study period. The State Veterinary Services from three French departments

(FinisteÁre, Ille-et-Vilaine and Morbihan) and the Veterinary Technical Association

participated in this survey. Pre-study training and discussions about data collection and

study design methods were used to help standardize the survey process among the

collaborators.

Veterinarians and farmers were given a glossary that included definitions of the main

clinical diseases of the dairy cow. Every six weeks, nine specialized surveyors from the


State Veterinary Services visited the farms to collect observational and management data,

to measure individual body condition and cleanliness scores for cows around calving, and

to collect milk samples for bacteriological analysis. Every month, individual milk

samples were collected from all lactating cows by DHIA technicians to estimate

parameters for milk yield and individual SCC. These data were accessible from DHIA

computer records (Fig. 2).

A database named Gwen-Ha-Du (`black-and-white') was developed. This database

included five basic data structures (Lescourret et al., 1993). The first comprised

identification and unique life histories for each cow, including identification, genealogy,

and culling. The second and third contained data collected throughout the cow's life,

including individual morbidity events and production and reproductive performance

measures (i.e. milk production, milk composition, bacteriological quality of milk, drying

off, calving and services). The last two data structures contained herd-level data on herd

management and feeding practices.

Results concerning all main elements of this dairy farming system were disseminated

and published. At the farmer level, a typology of farmer behaviour was developed using

the temperament of their animals as an indicator (Faye, 1996). At the animal level, the

factors affecting the distribution of clinical mastitis in space and time (Lancelot et al.,

1997), the importance of intramammary infections (Faye et al., 1994b), placental

retention (Chassagne et al., 1996), mortality (Faye and PeÂrochon, 1995), and the

association between diseases and culling (Beaudeau et al., 1994a, b) were studied. At the

herd environment and resources level, the dietary factors associated with somatic cell

count (SCC) (Barnouin et al., 1995) and the housing factors associated with udder health

(Faye et al., 1994c) were the main research components.

Fig. 2. Integration of the conceptual data models (thin framed boxes) with specific submodels (thick framed

boxes) into the global model of an epidemiological survey on dairy cows (adapted from Lescourret et al., 1993).


After reflection and discussion of the results, it was considered that periparturient

diseases were the most important health factor. Thus, health management programmes

targeted at changes in feeding practices, which most influenced periparturient disease

incidence, were developed and extended (Barnouin et al., 1994).

In ecopathology, the diversity of disciplines involved both strengthens the contribution

of epidemiologists (Sabatier et al., 1994) and enlarges the frame of the epidemiological

survey (Calavas et al., 1996). This is done both by including non-medical disciplines (e.g.

animal production, economics, sociology) and by mobilizing the practical knowledge of

extensionists and farmers in management, reproduction, and feeding. These experiences

in expanding the scope of studies and mobilizing stakeholders can provide a useful

starting point for evaluating livestock health and production in even broader contexts.

4. From farming systems to agroecosystems

Agroecosystem health is a professional application of systems approaches which have

been successful at animal, herd and farm level. Just as we can talk meaningfully about

animal, herd, farm, community and public health, it is possible to talk about the health of

an agroecosystem.

Although ecopathology enlarges the scope of veterinary investigations to consider the

complexities of farming systems, agroecosystem health both enlarges the window of

observation and changes its focus. Like ecopathology, agroecosystem health is an

application of systems theories. Thus, research and its applications are part of the same

iterative process, where management interventions become part of the research process

itself. Unlike ecopathology, the unit of interest is the entire complex socio-ecological

system within which agricultural activities take place. Like both herd health and

ecopathology, and unlike epidemiology, the ultimate aim of this process is more synthetic

than analytic.

It is useful to think about the science and practice of agroecosystem health in terms of

the processes of reasoning which are applied in herd health management programs. One

might think of agroecosystem health management as comprising at least five (not

necessarily linear) steps: (1) a description of the agroecosystem in systemic terms; (2)

identification of decision-makers and/or stakeholders; (3) establishment of goals, that is,

perceived attributes of a healthy system, operational objectives related to those goals at

various scales and time horizons, and determination of measurable indicators which will

give information about whether or not those objectives are being achieved; (4)

identification and implementation of desirable and feasible changes, which often involves

resolving conflicts among goals set by different decision-makers at different levels; and

(5) monitoring of the selected indicators, and adapting to changing circumstances or

unexpected or undesirable outcomes. This may mean revisiting the description of the

system, the objectives selected, and the management options chosen.

4.1. Describing the system

An agroecosystem can be defined as `̀ a socio-ecological system managed primarily for

the purpose of producing food, fiber and other agricultural products comprising


domesticated plants and animals, biotic and abiotic elements of the underlying soils,

drainage networks, and natural vegetation and wildlife'' (Gallopin, 1995; Waltner-

Toews, 1996). Such systems are self-organizing, that is, through internal autocatalytic

feedback loops, they re-create themselves in particular forms. Furthermore, agroecosys-

tems exist in nested hierarchies from farms and their sub-units up to regional and global

levels. Such hierarchies, in which larger units are comprised of, but with characteristics

which are greater than, the units within them, have been called holarchies, with each unit

being referred to as a holon (Checkland, 1981). These holons are both bounded by, and

necessarily interdependent with, other holons. Farms, subwatersheds and regions are

inextricably linked in a socio-ecological web (Giampietro, 1994).

Since considerable work has already been done in studying and assessing farming

systems, most recent work on agroecosystem health has focussed on scales larger than the

farm (Waltner-Toews, 1996). For example, at the sub-watershed level, the three

constitutive elements of the livestock farming system from an ecopathology view (farmer/

herd/resources) become: (i) a human community including different actors of livestock

activity (farmers and their organizations, political, economic and environmental

managers), (ii) an animal population (domestic herds, wild animals) and (iii) the

biophysical environment (water, nutrients, energy).

Land-time cross budgets have been proposed to link operational objectives of farmers

(for instance, minimization of risk, maximization of income) and activities to achieve

those objectives (such as rotations, livestock systems) with both ecological and economic

variables (Giampietro and Pastore, 1997). This also links the internal workings of farm

activities to the larger energy and matter flows across watersheds.

Another way to understand agroecosystems at a particular level is to create attractor

models in several dimensions. This is based on the observation that many socio-

ecological systems fall into a few patterns of behaviour and resist pressures for change. In

some cases, these appear to represent what Gilberto Gallopin (1997) has called `perverse

resilience', leaving communities in states of economic stagnation and environmental

degradation despite our best efforts to reverse their decline. In other cases,

agroecosystems appear to resist such degradation. Often, such complex systems will

experience sudden, discontinuous changes which can be described in terms of the

catastrophe theory first developed by Rene Thom (see Kay, 1991). If conceptual models

of such attractors and potential `flips' can be created, we can begin to identify their

determinants. These are similar to what have been called determinants of health in the

population health literature.

4.2. Identification of responsible decision-makers and stakeholders

In herd health programs and ecopathology, management and governance are integrated.

Once one moves beyond the farm gate however, not only are conflicting goals and

perspectives associated with different people, but governance functions and management

functions are separated. This is further complicated by the occurrence of multiple

perspectives across multiple scales.

Institutional and stakeholder analysis can be carried out to determine who should be at

the table, and how to involve them while recognizing political and economic power


differentials. In some cases, new organizations can be created to bridge economic,

environmental, public health or political jurisdictions. The International Joint Commis-

sion for the Great Lakes is one such example, but there are many other economy-

environment round tables and non-governmental multi-stakeholder groups that have

emerged throughout the world in the past few years, at levels ranging from local

communities to the biosphere, in what sociologist George Francis (1996) has called

`virtual governance'.

In order to better understand complex problems, some researchers have found

influence diagrams, or signed digraphs (Caley and Sawada, 1995) to be helpful.

These identify feedback loops which reinforce or weaken particular system states, and

clarify who has the power to influence those connections. In these diagrams, which

some have termed `spaghetti diagrams', various components of the system are connected

by arrows which are labelled as positive if they co-vary in the same direction, or

negative if they co-vary in opposite directions. For instance, a line between income and

meat-eating might be positive, while that connecting water quality with human disease

would be negative. By examining these diagrams one can judge whether particular

relationships are likely to be amplified or dampened when changes are made; when time-

lags are considered, one can predict whether to expect stabilization or oscillation of

patterns. In the context of such diagrams one can begin to explore why, for instance, the

education and empowerment of women might have a greater long-term effect on the

health of agroecosystems (both directly through increasing available knowledge to the

system and indirectly by decreasing demands on the system through self-directed

population control) than technological interventions which may appear to be more

attractive in the short run.

Even as one branch of agroecosystem health management is based on a scientific

description of the system, this cannot proceed without reference to the other branch, the

articulation of what kind of system is considered to be desirable by decision-makers and

stakeholders (Boyle et al., 1996). Waltner-Toews and Nielsen (1995) proposed a

classification strategy for agroecosystem health concerns, which includes hierarchical

(holarchical) level and dimension (economic, social, biophysical) as two classification

axes of a three-dimensional matrix, with the third axis used to describe the desired

attributes (e.g. integrity and effectiveness) of health (Fig. 3).

4.3. Setting goals and operational objectives, and selecting indicators

Having set out a holarchy and several dimensions, one must derive some criteria by

which to judge health. Most definitions of health combine a notion of a harmonious

balance (which might be termed integrity, measured in terms of the current functioning of

the system) and the notion of capacity to achieve some goals (sometimes measured in

terms of various forms of social, economic or natural capital). In complex system terms,

one would say that the goal of health management is to foster the ability of the system to

self-organize and evolve over time to adapt to environmental change (Kay and Schneider,

1994). More specifically, one might ask: are the quality and quantity of internal and

external resources sufficient, and is their organization appropriate, for the agroecosystem

to meet its goals (Waltner-Toews and Wall, 1997)?


In order for holons to self-organize and adapt, the boundaries of each holon should

remain intact while at the same time fostering interchange among them. Thus, it is

important for farmers' livelihoods to be able to trade, but not to become absorbed into

larger and larger farms; similarly, rural communities cannot remain isolated; however, to

give global free traders an absolute carte blanche to remove materials and energy from a

local community is tantamount to peeling the skin off a live cow and letting its life

exchange freely with the environment.

Since agroecosystems are human re-arrangements of ecological systems, these goals

are fundamentally human ones reflecting a desire to enhance the quality of life for

ourselves and our descendants. Even the desire to maintain biodiversity can be seen as a

way of protecting future options for human community development. In general, one

moves from articulating general goals, to setting operational objectives, to selecting

measurable indicators which can be used to assess progress. Furthermore, just as

variables of interest to those studying the health of farming systems incorporate measures

of longevity, disease, productivity and capacity to respond in relation to goals, so

indicators of agroecosystem health must reflect social, economic and biophysical

dimensions of the system at various levels (Fig. 3).

What emerges from this combination of holarchic system description and goal-setting

by decision-makers is a process which incorporates the participation of farmers,

communities and regional governing organizations in defining and monitoring the health

status of the system of which they are a part. Because the capacity to adapt to

unpredictable future changes is an essential part of health, and local empowerment is an

element in this capacity, selection of indicators a priori by outside experts is counter-

productive. The operational objectives which reflect agroecosystem goals, and indicators

which measure their achievement, can only be identified through a process of negotiation

among those who live in and/or have a stake in the health of those agroecosystems. The

role of researchers and agroecosystem health practitioners in this context shifts from the

Fig. 3. A classification framework for agroecosystem health initiatives (adapted from Waltner-Toews and

Nielsen, 1995).


conventional view of experts giving advice, to that of facilitators helping to explore the

possible consequences of alternative choices.

4.4. Implementing desirable and feasible changes

In herd health, ecopathology and agroecosystem health, every management interven-

tion must be viewed as a kind of experiment. Furthermore, while epidemiological

techniques inherited from human medicine are designed to isolate the effects of

individual components, systemic approaches are designed to assess the success of the

overall, interacting system.

In ecopathology, the passage from an object `herd' to the object `farm' has

required a conceptual change to integrate the multiple purposes of farming and the

multiples objectives of the farmer (Bawden, 1995; Faye, 1996). The further general-

izations made in agroecosystem health framework better allow the assessment of the

interrelationships between farming systems and their environment which were recognized

but largely ignored in farming systems studies. First, the complexity of relationships

between farms and other components of the agroecosystem (related in part to spatial and

temporal feedback loops) is added by enlarging the context of study. In a full

agroecosystem situation, it has become clear that there are not only conflicts

between different dimensions at any given scale (e.g. trade-offs between the richness

of social life with many small farms and the increased income associated with economies

of scale) but also across scales. Thus, the goal of a farmer to retain as much income as

possible is in conflict with the national goal of paying off foreign debts; a country may

want to increase milk production for food security reasons even as the farmer decides to

reduce production for personal reasons. Furthermore, whereas a farm family may

negotiate goals and implement better management practices for a farm, it is not always

clear who should, or who can, do this at larger scales. Niels Roling of Wageningen

Agricultural University has identified a meaningful integration of the multiple

perspectives of stakeholders as the central problem facing all agroecosystems approaches

(Roling, 1996).

In view of the fact that hypotheses about agroecosystem health relate to the system as a

whole, they are necessarily complex and multi-faceted. Patterns of self-organization to

which the system is drawn are strongly determined by system goals and their

implementation. For example, globalized agricultural markets, facilitated through unified

road-building and transportation networks, apply increasingly steep external gradients to

local agroecosystems, sometimes with undesired and even tragic consequences. In

seeking to maximize achievement of a single goal, such as productivity or economic

efficiency, many unstated but highly cherished goals like equity, democracy, public health

and sustainability may be critically undermined. The occurrence of Bovine Spongiform

Encephalopathy or vero-toxin producing E. coli in beef and Salmonella in poultry is a

natural consequence of systemic changes based on the single-goal attractor of increasing

supplies of cheap meat. In fact, many emerging diseases in humans and animals are a

consequence of policy decisions based on achieving single (albeit desirable) goals.

It is at this stage of deciding on appropriate management programs that it becomes

necessary to facilitate negotiations between various decision-makers to accommodate


goals that may be conflicting. The creation of platforms for negotiation or other fora is

thus an essential part of agroecosystem health management.

4.5. Monitoring and adaptation

The monitoring of indicators, reflecting goals and objectives set by decision-makers

and stakeholders through a process of negotiation informed by science, is the most

feasible way to test agroecosystem health hypotheses. Management, in this context is

research; management programs should be considered as `natural' experiments which

should be designed and monitored as rigorously as laboratory experiments but with

important differences. Agroecosystem health management demands that decision-makers

become part of the research process, and that new programs at various scales be formally

implemented in a way that allows for monitoring and adjustment. By incorporating

human communities into the research process, by negotiating trade-offs between multiple

goals, and by making policy and management changes part of the research process,

agroecosystem health management is simultaneously research and management. Since

Fig. 4. A research process for agroecosystem health (adapted from Rowley et al., 1997).


the stakeholders are intimately involved in this process, one is not left, in the end, trying

to sell experimental results to recalcitrant farmers or policy-makers.

Agroecosystem health management, being both participatory and structured in a

looping fashion which mimics the system itself (Fig. 4), does not result in a single

definable outcome. In this, agroecosystem health management is quite unlike a simple

experimental approach which asks a simple question under controlled conditions and

receives a simple answer, or industrial management, which may focus on a simple

outcome such as producing more pigs more cheaply. This may be frustrating to those of

us used to well-defined experiments and business managers accustomed to the stable

world of the 1960s and 1970s. Many executives of multi-enterprise corporations,

however, have already recognized the significance of similar approaches in devising

adaptive strategies to a rapidly changing world (Checkland, 1981). It is none too soon for

public institutions to follow suit. The outcome of this approach is a sustainable web of

learning organizations, from farms to communities to global institutions, which are

capable of acting, monitoring and adapting in a world characterized by perpetual change.

5. Epidemiology and agroecosystem health

Although ecopathology and agroecosystem health seem to have a natural comple-

mentarity, it is not entirely clear how these ideas relate to what we have come to think of

as epidemiology. One could make an argument that agroecosystem health merely

introduces epidemiologists to a new, and larger, unit of concern ± agroecosystems as large

cows, as it were.

For those who are working in herd health programs, the shift to ecopathology and then

to agroecosystem health seems to represent merely a broadening of vision and an increase

in complexity. A complex system such as an agroecosystem may be legitimately

described in different ways (e.g. economically, by energy flows, by the health of the rural

community health, by the happiness of the agricultural workers) which may be rooted in

different paradigms and result in different (sometimes contradictory) goal-priorities.

While a farmer may balance income losses from disease against preventive program

costs, in agroecosystems, we may find ourselves balancing drinkable water against cheap

food, or sustainable rural communities against high commodity prices or overproduction.

The problem is more intractable than this, however. By thinking of reality in terms of a

holarchy, and by putting people inside the system, we simultaneously reduce the sample

size, and the reference population, and blur the separation between conventional science

and management. One role for epidemiology makes use of conventional tools to select,

compare and study the dynamics among smaller holons (animals, herds, farms). This is

certainly a necessary component of systems approaches.

If we consider that epidemiology is the study of the determinants of health and disease

in populations, we might also say, however, that ecopathology and agroecosystem health

represent simply different ways of understanding these determinants, which are not based

on statistical, quantitative methods. In this role, conventional analytical tools, while

useful for studying short-time linear components of the system, are of little use in

studying long-term, multi-level feed-back loops with multiple outcomes. Some


researchers in this field have stated flatly that in this arena there is no global truth, only

local truth; that is, every solution is unique, and the only thing that can be generalized is

the process of assessment and adaptation (Kay and Schneider, 1994). Unlike

ecopathology, agroecosystem health has a very short history, and it is still not entirely

clear what is context-specific and what can be generalized.

Currently, researchers from the University of Guelph are involved in agroecosystem

studies in Canada (The Agroecosystem Health Project, University of Guelph), Peru

(Rowley et al., 1997), Honduras and Kenya, with plans on the table for Ethiopia and

Nepal. Over the next several years, these studies should identify many of the problems,

and, hopefully, many of the solutions, necessary to apply agroecosystem health concepts.

6. Final words

Ecopathology may be considered as an enrichment of epidemiology by the concepts of

systemic ecology. In that ecologic context, the herd may be considered as an anthropo-

biocenose, i.e. a specific community where micro-organisms (which could be pathogens)

and animals live together and in which their reciprocal relationships often exhibit a high

level of organization. The farm, or physical space occupied by the herd could then be

considered as an anthropo-biotope. Agroecosystem health studies further this process by

examining the larger milieu in which these ecologic units exist, not only with broader

ecologic discipline studies but also with ecologic studies in a much broader sense

including concepts from the agricultural, health, epidemiologic and social sciences. In

this context, agroecosystem health studies are to ecopathology what ecopathology is to

herd medicine ± a broader context in which diagnostic medicine can be understood and

evaluated. Ecopathology provides the context for herd medicine and agroecosystem

health provides the context for ecopathology.

Systemic views of complex reality are open to many different legitimate perspectives,

which is why at least one of the authors (Waltner-Toews) sees poetry as being

complementary to scientific understanding in the search for resolutions to some of our

most intractable health and environmental problems.

Mad Cows and a Bill from the Power Company

It begins in New Guinea

with the fear of your ancestors.

It begins with the love of wisdom and intelligence.

It begins with respect for the dead,

with women and children first.

It begins with our cleverness

and our envy.

It begins with eating the brains

of those whom you admire.

It ends with Kuru, a spongy Jacuzzi

of laid back prions, engulfing the brain.

It ends with a young woman

throwing herself into the fire.


It begins in America with a dust bowl,

with a world war's devastation.

It begins with hungry children

in Europe and Africa.


and our lust for power and our tractors.

It begins with the cows coming in from the green

wilderness in droves.

It begins with the praise of hamburgers

on every tongue.

It begins with some spare change

after shopping for food,

and the thrill of a new car.

It begins with recycling, with efficiency,

with cows consuming cows.

It ends with old men

hungry for power.

It ends with a mob of mad cows

fed to power stations,

shimmering up in smoke

from the incinerators.

It begins with the love of life.

It begins in a white coat.

It begins with cutting and sewing.

It begins with new drugs.


and our fear of death.

It begins with ingesting those

who have what we want.

It begins with blood transfusions,

with hearts, kidneys, and corneal transplants,

with bone marrow and dura mater.

It ends with rabies, with AIDs,

with a slow toppling of prions

across the brain.

It ends with a young man,

unable to walk, unable to speak

his own name.

It begins with flowers and wine.

It begins with clever conversation.

It begins with the love of children,

of making them, and, surprisingly, caring for them.

It begins with a house and a car


and a school and a computer.

It ends with paying

the power bills.

It comes round

to huddling by a campfire

singing plaintive melodies

with old guitars,

and, in the ash-black darkness at our backs,

the sound of cows or bears foraging,

and the slow sigh of a rising moon.

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Documents

From Eco Pathology' to ‘agroecosystem Health