ZhengKuang_BernardoAdolfoBastien Olvera_poster

How are natural factors concerned in crop lands expansion:
An atypical suitability analysis for corn & soybean land in Iowa
Bernardo Adolfo Bastien Olvera,Zheng Kuang: final project_C188
Data source: US geological survey
Question: How are natural factors
Tools& Methods:
concerned in the expansion of corn
& soybean land?
General:
Background •  corn & soybean are two most important cash cops in the US, taking up over 50% of all agricultural yields in the whole country; •  Iowa, ranking first in the nation in corn and soybean production, is potentially an good sample for corn & soybean cultivation study. Client •  researchers interested in corn & soybean land expansion pattern and it underlying logic; •  Governmental land use planners. Study area:
Materials:
•  Grid study area Land use
rasters
(2000, 2006,
2012)
Explanations & values:
Precipitation
Elevation
Surface analysis
Slope
Solar radiation
Table of polygons with
different combinations
of natural factors
Arability based
on land usage
pattern
Union
Assumptions •  Farmers know their Temperature
business: cultivated lands are suitable & Natural Factors
Arability function
land usage change is Material data rational; Process •  Better lands prioritized: the Categories Tool perennial is more suitable than the rotary. 1. Arability level:
Corn & soybean
land
2000 land 2000
Notes Fishnet
Raster
•  Slope and solar radiation layer +
calculato
Spatial
r
is created by elevation data join
Corn & soybean land
Continuous plow
using TIN model; 2006 land 2000
land
•  Arability level is measured by the percentage of perennial Corn & soybean land
2012 land 2000
plowlands. Prediction
2. Geographically weighted
regression:
Dependent
variables: arability
level
Arability level
Geographically
weighted
regression
Explanatory
variables: natural
factors
Results:
2. Geographically weighted
regression:
1.Arability
level:
Corn & soybean lands Precipitation Elevation Temperature •  This map shows the distribution of arability level, namely the percentage of perennial lands; •  no explicit pattern can be observed according to this map.
Conclusion
Dominant human factors •  because Iowa has been long exploited for cash crops, the sophisticated modern agricultural technologies have supplanted the determinacy of natural factors; •  the natural situation in Iowa in generally advantageous so that natural factors are not the major limitation. A reconsideration of suitability analysis •  a suitability analysis should intuitively incorporate natural factors including but not limited to temperature, precipitation, slope etc., however this study shows this “intuition” is not absolute: for modern agriculture, human factors may well be more significant. Limitations:
Insufficient computing power •  on the extant accessible equipment, we had to reduce the area of study and simplify the arability function, which might have raveled the pattern. Data •  crops may be susceptible to more subtle changes which are beyond the precision of the data. Expectation:
More powerful computing power •  so that the study area can be scaled up or may vary to capture a pattern, since the existence of pattern often depends scale. No explicit pattern observed:
•  regression coefficient is 0.38; •  there is no explicit relation observed between natural factors and corn & soybean cultivations (surprisingly!); •  reduced variables:exclude slope and solar radiation in natural factors set, but the relationship remains un clear (with a regression of coefficient of 0.637). More sophisticated regression tool •  Since the potential pattern may not follow a linear function, using more sophisticated regression tool may be necessary for further research. Modeling and prediction •  As it shows in the chart flow (gray arrows), if a pattern relating natural factors and crop lands expansion was established, this model can be used for prediction, whereas a more explanatory model should incorporate human factors as well.