Definición de los casos de uso

The standard version of both the AMARC2 and AMARC3 models run using behavioural attitude values retrieved from a series of weight matrices that represent the probability of a particular agent class originating in a particular location migrating to a given destination on the basis of the rainfall characteristics of that year. The data-fitting performed on the EMIUB data gave migration probability values that defined each year between 1970 and 1999 as dry, average or wet according to the total rainfall of the year in question ( ). A single year of relatively high rainfall could therefore potentially have a considerable impact upon modelled migration.

Using the final full-demographic AMARC3 model an alternative approach to analysing the EMIUB migration data was also employed to provide another means of investigating the role of changes in rainfall variability. Rather than calculating migration probability values where each model year is defined as dry, average or wet according to the total rainfall of the year in question ( ), the alternative approach classes each year on the basis of the year in question and the two previous years ( ). A single year of relatively high rainfall could therefore have potentially little impact upon modelled migration if it was preceded by two years of below average rainfall. Using a three year rainfall trend such as this acknowledges the longer-term, slow-onset, cumulative nature of the impacts of rainfall change upon subsistence farmers such as the majority of individuals represented in the EMIUB data. Furthermore, Henry et al. (2004a) use a three year rainfall precursor in their study of the influence of rainfall upon migration in Burkina Faso. In order to further investigate the role of changes in rainfall variability in the migration decision, the AMARC3 model is run using the behavioural attitude weight matrices. In both the data fitting process and model runs, each year is therefore defined as dry, average or wet according to the rainfall of the year in question and the two preceding years.

Table 2 of Appendix 6 displays the averaged results of five runs of the AMARC3 model using 3 year rainfall weight matrices. When directly compared to elucidate the impact of using one-year and three-year rainfall matrices upon modelled migration, the data show very little disparity. A total of 6,409 agents are modelled as migrating between 1970 and 1999 by the AMARC3.ppt3.mp30 model, less than 1% more than are modelled by the version of the model that uses the standard weight matrices (AMARC3.ppt.mp30. Similarly, the proportional breakdown of these total flows shows very similar zonal distributions of migrants. Figure 7.2 displays the total numbers of migrants modelled by both versions of the AMARC3 model between 1970 and 1999.

Figure 7.2: Annual populations of agents modelled by the one-year ( ) and three-year ( ) rainfall classification versions of AMARC3 model as leaving each origin zone between 1970 and 1999. RMSD = 17 (7.07%).

It can be seen from Figure 7.3 that there is little difference between the total migration flows simulated by the two rainfall classifications of the AMARC3 model, particularly for the first 12 years (1970-1982). The RMSD residuals calculated between the two sets of data are only 17 (7.07%) over the thirty data points. In order to further investigate the nature of the two modelled flows, Figures 7.4 to 7.8 display the migration flows simulated from each of the five origin

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

AMARC3

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

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Each of the migrant flows displayed in Figures 7.3 to 7.7 reveal a slightly different pattern when one-year and three-year rainfall classifications are compared. The and AMARC3 modelled flows appear very similar with RMSD residuals between the two sets of data ranging from 4 to 6 (9.10% to 13.07%) in all zones apart from Bobo Dioulasso where the RMSD is 14

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1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

AMARC3

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

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1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

AMARC3

(18.51%). It is evident therefore that in the case of all zones apart from Bobo Dioulasso the difference between the AMARC3 and modelled flows is small. In the case of Bobo Dioulasso, two periods of significantly increased migration are modelled by the version of the model compared to the standard. The differences evident in the modelled migration of Bobo Dioulasso occur during the periods 1982 to 1984 and 1996 to 1997.. Although no such clear differences appear to be present in modelled migration flows from Ouagadougou or Sahel, the migration flows modelled from Centre and Southwest also show increased modelled migration during the early to mid eighties (1984 and 1985 in Centre and 1984 in Southwest).

Furthermore, modelled migration from Southwest using the weight matrices appears to be lower than the equivalent throughout the period 1995 to 1997. Figures 7.8 to 7.12 display the classification of each model year in each zone as dry (1), average (2) or wet (3) under the and classifications.

Figure 7.8: 1970-1999 and Ouagadougou rainfall classifications. Years with rainfall of below average/dry = 1, average rainfall = 2, and above average/wet = 3.

Figure 7.9: 1970-1999 and Bobo Dioulasso rainfall classifications. Years with rainfall of below average/dry = 1, average rainfall = 2, and above average/wet = 3.

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Figure 7.10: 1970-1999 and Sahel rainfall classifications. Years with rainfall of below average/dry = 1, average rainfall = 2, and above average/wet = 3.

Figure 7.11: 1970-1999 and Centre rainfall classifications. Years with rainfall of below average/dry = 1, average rainfall = 2, and above average/wet = 3.

Figure 7.12: 1970-1999 and Southwest rainfall classifications. Years with rainfall of below average/dry = 1, average rainfall = 2, and above average/wet = 3.

By considering the different approaches to rainfall classification used by the and versions of the AMARC3 model it is possible to investigate the origin of some of the slight differences between the two sets of modelled migration flows. Differences between the migration flows modelled using the two distinct rainfall approaches have been identified as being most evident in Bobo Dioulasso, Centre and Southwest during the early to mid eighties (between 1982 and 1985). Across all five zones both and rainfall approaches record some or all of this period as below average rainfall. In the case of Bobo Dioulasso, both rainfall approaches categorise the years 1982-1984 as below average rainfall. However, the

version classes the preceding six years (1976-1981) as having average rainfall while the version identifies these same years as being a mix of average, above and below. As the specific years during which different migration patterns are evident have been categorised as experiencing the same class of rainfall by both versions of the model, the different migration flows in Bobo Dioulasso can likely be attributed to the different levels of migration experience of agents leading up to this period of drought and the different tendencies to migration being communicated by each agent’s networked peers following different drought precursors.

In Centre and Southwest, the other two zones within which migration patterns are seen to be different during the extended period of drought witnessed across Burkina Faso in the early 1980s, similar causal components are thought to be at play. While the model identifies the years 1982-1984 as having below average rainfall, the version identifies this drought period as extending from 1981 to 1986, likely impacting upon the migration flows modelled.

While the drought periods identified by the two models in Southwest are of the same duration, the dry period finishes later in the version of the model perhaps leading to the lower migration flows modelled by that version in 1985.

Not only is each year in the 1970-1999 running period of the AMARC3 model interpreted on a different basis by the two different versions of the model, the probability values used to calculate the likelihood of migration used by agents in forming their behavioural attitude towards migration options are different. As it could potentially take a longer or more extreme period of low rainfall to result in a specific year being categorised as below average under the model conditions imposed within the version, it could be anticipated that the modelled migration flows might be more sensitive to such prolonged or extreme periods of drought. Such an occurrence is however only loosely evident from the five run averaged results displayed above. As a means to further understand the role of changes in rainfall variability upon the migration decision and therefore migration modelled as occurring in Burkina Faso the different ways of interpreting model rainfall as average, wet or dry are further considered.