One could expect that the pre-emptive setting of priorities (or goals) would be difficult to specify, especially when dealing with group decision-making. A unique method to facilitate the participatory allocation decision-making process further, set the land use priorities and reach some sort of consensus on judgement was thus imperative in this case. As required by the research plan, the objective weights provided by the ten participants ultimately represented the relative weights entered for each objective during the application of the MOLA module in IDRISI.
31 As no reliable and detailed large-scale soil data of any kind was available for the study area, soil potential was essentially gauged by assuming that crop production is best on available deep, well-drained fertile soils on relatively flat terrain.
2.6.1 The method in practice
To extract functional values that prioritise each land use objectives from the final group, the researcher developed a technique relying on symbolism (to simplify) and a basic value system (to prioritise). First, as depicted in Figure 2.4, each land use objective was represented with a familiar object: forestry with a bundle of firewood, agriculture with a bag of maize, communal use with a calf, conservation with two buckets of water from a nearby stream, the built-up objective with a small stack of cement building blocks.
Figure 2.4: Participatory decision-making in land use evaluation
After a lengthy explanation each participant received ten tennis balls to proportionally allocate to each objective in terms of the rural development framework presented thus far. Tennis balls were appropriate objects to ‘weight’ the objectives because they could not be associated with pervasive local activity, nor could it be readily associated with currency or power. A container, placed in front of each object/ objective, received the weight or ‘value units’ each participant perceived it was worth earning. Basically, the instruction went: “Considering the land use objectives available and symbolized in front of you, distribute your quota of equal ‘value units’ (tennis balls) to the land use objectives in such a manner that the use(s) of perceived higher value – in terms of current use (or retention) AND possible improvement (through investment) – will receive the most weight.
Conversely, the use(s) with no or the least value units will be ranked lowest in terms of development priority as related to the future objectives.” Land use objectives were placed in random order so as to not fortify the initial ranking. Members were taken two at a time to allocate their allotted quota of balls to the objectives. This took place some distance away to prevent
members from possibly influencing each other’s judgements. After allocation, the results were recorded, all the balls were removed and handed to the next person and the process was repeated.
2.6.2 The results
Compared to the initial ranking, the priorities given to each objective this time around differed as shown in Table 2.2, particularly in the case of the conservation and agriculture objectives.
Table 2.2: Scoring and relative weight per land use objective.
Score per land use objective
Decision-makers Gender FOR AGR COM CSV BLT Total
1) HERDER #1 M 2 2 3 3 0 10
FOR=Forestry; AGR=Agriculture; COM=Communal Use; CSV=Conservation; BLT=Built-up
Conservation received the largest score or relative weight (0.25) with the least deviation among members, and thus relegating forestry to second place (0.24). Nevertheless, agroforestry systems can be superior to other land uses at the watershed scale because they optimise tradeoffs between increased food production, poverty alleviation, and environmental conservation (Sanchez 2000).
Communal use (0.20) remained the third priority with the largest deviation, but agriculture (0.19) moved down to fourth position. Built-up land remained, as expected now, the lowest priority (0.12).
The variance or standard deviation reflected the consistency of judgments among the participants.
With respect to the importance of each land use objective, a zero variance implies complete agreement or consensus among the decision-making group. Higher standard deviation indicates diverging opinions; the larger the standard deviation, the more varied the opinions or judgments.
Consensus on prioritising conservation seemed the most uniform, stressing the negative effect of environmental degradation. Slightly less uniformity was observed with the built-up objective. On
the other hand, and in the light of the current troublesome situation in agriculture mentioned earlier, agreement on agricultural allocation was relatively varied. Yet it was, as expected, the communal use objective that indicated the highest diversions in judgement.
In essence then, this spatial multiple criteria decision problem involves five geographically defined alternatives (land uses) from which a choice of (one or more) alternatives is made, yet how they will perform with respect to a given set of evaluation criteria is still undetermined. We now know what to do, but still need to know where to do it. To attain the appropriate suitability maps for each land use objective, the next phase of the research plan, Steps 2 and 3, required the selection and mapping of a variety of decision-criteria for each objective for the impending MCE procedure. The next chapter deals with this phase in the study design.
CHAPTER 3 PREPARING SPATIAL DECISION FACTORS: SELECTION AND MAPPING
This study engaged in finding solutions to decision problems characterised by multiple-choice alternatives, which could be evaluated by means of performance characteristics called decision criteria (Jankowski, Andrienko & Andrienko 2001). For any given objective then, several different attributes were necessary to provide complete assessment of the degree to which each objective might be achieved. The results of the analysis depended not only on the geographical distribution of events (attributes), but also on the value judgements involved in the decision-making process.
To aid the land suitability assessment and allocation processes at hand spatial decision factors were carefully selected, prioritised where necessary, and prepared for MCE analysis. The group members completed the latter (Step 2 in the research plan) and the GIS-expert the manipulation and mapping (Step 3).