For our final project of the course, we were to either choose an independent project or work with the Oaxaca Valley data from module 6 for further catchment analyses. As I could not find a suitable topic (or at least one for which data were readily available), I went with the Oaxaca Valley data. Looking through the data provided for the northern portion of the valley (grid rows N11 through N14), there appeared to be oscillations in population density from the south to the north and back again through the time periods. My project was to calculate maize consumption estimates based on population and compare this with estimated maize yields based on 1 km catchments.
To create the Thiessen polygons as described in the module video for catchment analysis, I set up a model in ModelBuilder to create the final 1 km catchment buffer using the Buffer, Feature to Point, Thiessen Polygons, and Intersect tools. I also used the model to create additional buffers of 2 km and 5 km, although I did not end up making use of them for this project.
The population data for my collection units for each period was acquired by joining the Excel sheets provided in our module 6 data. Using the high and low consumption estimates provided in our readings (.16 metric tons and .29 metric tons respectively), I added four fields to the attribute tables for each period and used the Field Calculator to compute consumption estimates for each collection unit.
Arriving at maize yield estimates for the 1 km catchments was also a multi-step process. The Identity tool was used to create shapefiles for each land type corresponding to the catchment areas. A new field as added to calculate the area in hectares (since the estimates used in our readings are based on hectares). Based upon data provided by Anne Kirkby (1973, The use of land and water resources in the past and present Valley of Oaxaca, Mexico, University of Michigan Museum of Anthropology Memoir 5), the yields of maize through time were multiplied by the area of each land type (again using the Field Calculator) to arrive at potential yields.
Microsoft Excel's Data Analysis correlation tool was used to determine if there was a correlation between population consumption estimates and maize yield estimates. Unfortunately, no such correlation was seen. The resulting data does show an oscillation of population from south to north and back, but significant variables remain unknown. We do not know the fallowing strategy that was applied, nor do we know the potential degradation in catchment yields as the population grew and cultivation was intensified. We also do not know the details of rainfall variation. The majority of the study area (excepting one grid square) is considered to feature low average rainfall (below 700 mm), and the potential yields of the land types in the region (Type II and Type III) are highly susceptible to precipitation variation. While this is suggestive as a potential cause for population movement through time, the lack of more detailed rainfall data prevents me from offering more than a mere suggestion.
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