An Assessment of Health and Sustainability of a Smallholder-Dominated Tropical Highlands Ecosystem - Integrated Assessment of Health and Sustainability of Agroecosystems - page 182

phenomena within a self-organizing, goal-seeking complex system. While these phe-
nomena are controlled by feedback mechanisms, they present mainly as stochastic
processes with a high level of unpredictability and further complicated by differen-
tial effects across scales and time spans. Second, agroecosystems often have multi-
ple, sometimes-competing goals, and the objective of the system is goal optimization
rather than maximization. Furthermore, the process of goal optimization involves a
series of trade-offs and balances within the system and between the system and the
external environment. To obtain managerially useful information from indicators,
there needs to be a systemically generated conceptual framework that delineates the
expectations from system goals in terms of both their impact and the inputs required
to achieve them. The health status of the system can then be obtained by assessing
the implication of various indicator values (outcomes) with regard to generic health
attributes such as integrity, adaptability, resilience, efficiency, efficacy, effectiveness,
vigor, and productivity.
Predictions on the long-term sustainability and health of the systems rely on an
analysis of spatial and temporal trends of the indicators (Rapport and Regier, 1980;
Odum, 1985; Rapport et al., 1985). Interpretations of these trends require a systems
approach as well. A potentially useful approach is to use dynamic models such
as pulse processes to assess generic system attributes of the system given the trends
portrayed by the indicator data. Using contrasts between point measurements and
targets or thresholds, scenarios at different spatial and time spans can be re-created
and evaluated relative to a set of goals. Trends in indicators can be modeled as trends
in pulses within such models. Graphical techniques—especially plots in multidi-
mensional Euclidean space—provide intuitive tools for summarizing and presenting
data in forms that aid identification of such trends. Simple correspondence analysis
(SCA) and multiple correspondence analysis (MCA) are especially attractive tools
for exploring trends in indicators (Gitau et al., 2000) by enabling the categorization
of data based on predefined cutoffs and thresholds while not requiring any distribu-
tional assumptions.
This chapter describes how community participation, cognitive maps, and cor-
respondence analysis were used to evaluate indicator data. The objective was to gen-
erate managerially useful information that can be used to guide practical human
activity in the Kiambu agroecosystem.

7.2 PRocess and metHods

7.2.1 s p A t i A l A n D t e m p of r A l t r e n D s i in t h e i in D i C A t of r s
The objective was to determine, based on indicator measurements, what were the
most significant differences among the villages and in each village along the time
line of the project. In addition, the response of the holons to the project as an exter-
nal “stress” was compared across the six intensive study sites (ISSs) and along the
project time line. The extensive study sites were included in some of the analyses as
controls, to increase statistical power, and in the calculation of cutoffs, ranges, and
thresholds for indicators.