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.

Question: How are natural factors

concerned in the expansion of corn

& soybean land?

Study area:

Tools& Methods:

General:

1. Arability level:

Results:

Insufficient computing power• on the extant accessible equipment, we had to

reduce the area of study and simplify the arabilityfunction, which might have raveled the pattern.

Data• crops may be susceptible to more subtle changes

which are beyond the precision of the data.

Conclusion

Limitations:

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: USgeological survey

• This mapshows thedistribution ofarability level,namely thepercentage ofperenniallands;

• no explicitpattern can beobservedaccording tothis map.

Assumptions• Farmers know their

business: cultivated lands are suitable & land usage change isrational;

Precipitation

Slope

Solar radiation

Temperature

Table of polygons with different combinations of natural factors

Natural Factors

Elevation

Arability based on land usage

pattern

Surface analysis

Arability function

Land use rasters

(2000, 2006, 2012)

Prediction

Materials:

Corn & soybean land 2000 land 2000

Corn & soybean land 2006 land 2000

Corn & soybean land 2012 land 2000

Continuous plow land

Rastercalculato

r

Arability level

Fishnet+

Spatial join

2. Geographically weighted

regression:

Dependent variables: arability

level

Union

Explanatory variables: natural

factors

1.Arability

level:

2. Geographically weighted

regression:

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.

More sophisticated regression tool• Since the potential pattern may not follow a linear

function, using more sophisticated regression toolmay 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.

Explanations & values: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.

Corn & soybean lands Elevation

Precipitation Temperature

Notes• Slope and solar radiation layer

is created by elevation datausing TIN model;

• Arability level is measured bythe percentage of perennialplowlands.

Material dataProcessCategoriesTool

Geographically weighted

regression

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 solarradiation in natural factors set, but the relationshipremains un clear (with a regression of coefficientof 0.637).

• Grid study area

• Better lands prioritized: the perennial is more suitable than the rotary.