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Level 4: The aim, problems, procedure
Diagnostic evidence: The approach paths
Evaluation factors: Complex and component attributes
Criteria: Understanding cause and effect
AIM: The actions at this Level are intended to expose trends of change in the local environment and, by recognizing and explaining the causes of these trends in the past, project the pattern of future change and its effects on sustainability. Specifically to:
• identify which factors and component attributes (from amongst the evaluation factors chosen at Level 3) show trends of change in the recent past;
• establish the causes which effect these changes and determine whether interacting change is likely to continue in this or other directions in the future;
• develop an overall picture of the pattern of change in the local environment with time (the future scenario);
• develop criteria, based on understanding of cause and effect, to determine the likely status of different evaluation factors at future times.
PROBLEMS: The need to predict the future is problem enough at this stage!
PROCEDURES. At first sight nothing short of a crystal ball can achieve future prediction but, in practice, reasonably confident progress can be made using data drawn from each of four approaches:
• observing present evidence of trends (Observation)
• researching historical evidence of the site (Historical)
• comparing geographical evidence of similar sites (Spatial)
• theoretical projections (Modelling).
In each approach the intention is to assess the stability of the Evaluation Factors (identified in Level 3). The existence and direction of trends would be established first and then, if possible, the rate of change would be assessed. The most reliable conclusions can be expected when evidence from all four approaches converge, and all four should be attempted where feasible.
In most cases, the Evaluation Factors will, themselves, be complex-their character being a reflection of interactions between a number of component attributes. Very often it is the component attributes that can be measured, observed, or estimated most easily along the approach paths listed above. Sound understanding of cause/effect relationships is needed before component attribute values can be used as a guide to the status, and change of status, of the complex attributes (Evaluation Factors) to which they contribute. Table 6 provides examples of some complex and component attributes.
TABLE 6: Relationship between complex and component attributes: some examples
Complex Attribute (Level 3) |
Component Attribute (Levels 5 and 6) |
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1. |
Nutrient availability |
1.1 |
Topsoil nitrogen |
% |
1.2 |
Topsoil available P |
ppm |
||
1.3 |
Topsoil available K |
meq/100 g |
||
1.4 |
Soil acidity |
pH |
||
1.5 |
Subsoil weatherable minerals |
% |
||
1.6 |
Subsoil total P |
meq/100 g |
||
1.7 |
Subsoil total K |
meq/100 g |
||
2. |
Flood hazard |
2.1 |
Length of inundation at critical periods |
days |
2.2 |
Depth of inundation at critical periods |
m |
||
2.3 |
Frequency of damaging floods |
|||
3. |
Pests and diseases |
3.1 |
Severity of pest 'X' |
damage % |
3.2 |
Severity of disease 'Y' |
damage % |
||
3.3 |
Local factors favouring pest 'X'/disease 'Y' |
|||
4. |
Gross expenditures |
4.1 |
Cost of seeds |
money |
4.2 |
Cost of fertilizer |
money |
||
4.3 |
Labour costs |
money |
||
4.4 |
Fuel costs |
money |
||
4.5 |
Replacement costs of equipment |
money |
||
5. |
Gross returns |
5.1 |
Returns from land |
money/ha |
5.2 |
Returns from labour |
money/man/day |
||
5.3 |
Returns from capital |
% |
||
6. |
Land tenure |
6.1 |
Average size of holdings |
ha |
6.2 |
Form of ownership |
|||
6.3 |
Basis of acquisition/inheritance |
|||
7. |
Population |
7.1 |
Total numbers/rate of change |
|
7.2 |
Distribution by age/sex |
|||
7.3 |
Available labour force |
|||
7.4 |
Migration into/out of locality |
|||
The following notes may clarify the differences and limitations of the four approach paths:
• Present Evidence: A wide variety of on-site evidence of environmental degradation (instability) may be visual. The following are examples;
• erosion: rills, gullies, scars, downslope accumulation
• soil structure: surface crusting, poor emergence
• nutrient status: poor growth, deficiency symptoms
• excess water: waterlogging, plant communities
• salinity/toxicity: poor growth, plant communities, surface salts
• pests and diseases: poor growth, visible symptoms
• poor stewardship: neglect of equipment and buildings, weed growth, overgrown access, degraded terraces, in-filled drains etc.
• socio-economic problems: poverty, low morale, ill health, voiced complaints
More detailed site investigations (relationships of soils, topography, vegetation) - possibly as an extension or repetition of work done for site characterization or in suitability evaluation may reveal, explain or clarify present trends in attribute stability.
• Historic Evidence: Any historic records of the site, or of the immediate locality, may assist explanation of present observations or draw attention to potential problems. Past crop yields, profit margins, or social history may provide direct pointers to trends. Failing this, if the land has been newly brought into use, local climatic records (particularly rainfall) may assist interpretation of, and future projection from, present observations.
• Spatial Evidence: Experience of comparable forms of land use in different stages of development under comparable environmental conditions may provide clues to trends in change with time. Clearly, the very greatest care must be taken in making the initial comparisons. A seemingly minor difference, such as distance to markets, availability of supplies or social unrest, could completely invalidate a comparison between environments that appear physically identical.
• Theoretical Projections: At best, these will only be as good as the quality of the data available to feed them, and as reliable as the depth and breadth of the experience on which they are formulated. A practical objection to computer modelling in 'a black box' is that few but the author know, for sure, what pathways, connections and approximations exist within the box. Nevertheless, the development of projection systems are an important research objective, and in time we can expect to have increasingly reliable systems suited to a widening range of uses and conditions.
Many evaluation factors identified at Level 3 are complex - their influence reflects the interacting influences of their component attributes. Some examples of complex and component attributes are listed in Table 6.
Knowledge of the make up of complex attributes, and of the potential interactions of their components, forms part of the understanding of 'cause and effect' that needs to be established at Level 4.
In developing the FESLM, it will be necessary to explore and test a wide variety of complex and contributing attributes to decide which have most general use in the assessment of sustainability.
These examples are purely
illustrative. The attributes on the right do not represent a unique or complete breakdown
of the more complex attributes on the left but are chosen rather to underline the
heterogeneity of the attributes that can be tested for stability. The value of some of
these component attributes as indicators of stability is discussed briefly later.
Diagnostic Criteria have been defined as: 'Standards or rules (models, tests or measures) that govern judgements on environmental conditions'.
In sustainability analysis, criteria based on an understanding of cause and effect are needed to serve several functions:
• to interpret component factor relationships and interactions that determine the stability and direction of change in evaluation factors (see above)
• to provide predictions on the future status of factors so that they may be used as indicators of change
• to interpret the effect of interacting environmental changes on sustainability.
Fortunately, in our context, much is already known in general terms about cause and effect, especially in relation to the physical environment, so well known in many instances as to be second nature-a matter seemingly of 'common senses.
In many instances this understanding has been developed into numerical "criteria' (equations and other more complex mathematical models) that allow us to predict effects-given certain observations.
The Universal Soil Loss Equation (USLE) of Wischmeier and Smith (1978) is a very well known example of an equation that goes far to meet the first and second criteria-objectives above:
A = R x K x L x S x C x P
Having obtained experimental evidence of the relationship between the rate of erosion (A) and the rainfall factor (R) on a certain soil (K) under a certain use (C, P) on a certain slope (L, S) the equation can be used to predict the increase in erosion if the rainfall factor rises.
In recent years, computer modelers have explored widely the kinds of relationships in environmental systems that need to be understood to develop satisfactory sustainability criteria. Most of this work relates to the present environmental condition-to the 'matching' of widely separated environments for purposes of technology transfer, for example-but some modelers are beginning to tackle the prediction of trends and future values. A great deal more research needs to be done in this direction in support of sustainability analysis.
In the Framework diagram (Figure 3)
and in the FESLM Flow Chart (Chapter 5) the action of developing criteria - discerning
cause and effect - is shown as a circle surrounding the approach pathways. This form of
presentation is intended to stress the iterative nature of investigation at Level 4-two
steps forward, one back to revise!
'Indicators' and 'Thresholds' are defined within the FESLM as follows:
Indicators: environmental attributes that measure or reflect environmental status or condition of change.
Thresholds: levels of environmental indicators beyond which a system undergoes significant change; points at which stimuli provoke significant response.