This project is supported by US Department of Agriculture, National

Agricultural Statistics Service (Grant No.: 58-3AEU-0-0067, PI: Dr. Liping

Di). 

Exploring Continuous Corn Cropping Patterns and 

Their Relationship with Geographic Factors 

Weiguo Han, Liping Di, Ali L. Yagci 

Center for Spatial Information Science and Systems 

George Mason University 

4400 University Drive, MS 6E1, Fairfax, VA 22030, USA 

whan@gmu.edu, ldi@gmu.edu 

Zhengwei Yang 

US Department of Agriculture 

National Agricultural Statistics Service 

3251 Old Lee Highway, Fairfax, VA 22030, USA 

zhengwei.yang@nass.usda.gov

Abstract— Continuous cropping is one of main planting methods 

in the cropping systems. Discovering the continuous planting 

patterns and their relationship with geographic factors is a very 

meaningful research subject to support agricultural decision 

making. The Cropland Data Layer (CDL) data is one of useful 

land cover data resources to study the crop planting patterns of 

any area of interest in the Contiguous United States. In this 

paper, the CDL data of Iowa for the years of 2006-2012 are 

retrieved directly from CropScape, and planting patterns of 

continuous corn cropping is discovered from these CDL files. The 

raster layer of these planting patterns will be overlaid and 

analyzed in combination with other geospatial datasets, like 

digital elevation model data, average annual temperature, 

average annual precipitation data, and soil classification data to 

identify the important physical factors that impact on the 

patterns. Spatial relationships between continuous corn planting 

patterns and these geographic factors are also investigated and 

presented in this paper. 

Keywords- Cropland Data Layer; CropScape; Planting Pattern; 

Continuous Cropping; Overlay Analysis 

I. INTRODUCTION

Continuous cropping and crop rotation are two main 

planting methods in the cropping systems [1]. Continuous 

cropping means that the same crop is continuous cultivated for 

multiple years on the same land [2], like wheat in the Central 

and Southern Plains. Crop rotation is to plant specific groups of 

crops over years on the same land [3], like corn-soybean 

rotation in the Midwest. Discovering these planting patterns 

and their relationships with geographic factors is a very 

important issue in the research fields of water resources 

assessment, soil fertility maintenance, crop yield estimation, 

pesticide control, land cover monitoring, agricultural 

sustainability, etc. 

The Cropland Data Layer (CDL) data is one of useful land 

cover data resources to study the planting patterns of any area 

in the Contiguous United States (CONUS). The CDL data 

product is produced from mid-resolution satellite data and high 

quality ground truth data [4]. This geospatial product presents 

the detailed information on agricultural and non-agricultural 

landscapes [5]. 

In this paper, the state of Iowa is selected as the study area 

because it is the No. 1 corn producing state in the United 

States. The CDL data for the crop years of 2006-2012 of Iowa 

are extracted directly from the popular Web portal of 

CropScape [6]. Next, the planting pattern of continuous 

cropping is generated from these CDL files. Like in other 

Midwest states, high value crop of corn is continuously 

cropped over years in many areas of Iowa. The continuous corn 

cropping patterns will be discovered through the method of 

spatial analysis. Then, these spatial patterns will be overlaid 

and analyzed in combination with other geospatial datasets like 

digital elevation model (DEM) data from National Elevation 

Datasets (NED), average annual temperature data, average 

annual precipitation data, and soil classification data from 

Digital General Soil Map of the United States. And the 

important physical factors that impact on planting patterns will 

be identified. These datasets are freely obtained from United 

States Department of Agriculture (USDA), United States 

Geological Survey (USGS), and other federal agencies or 

organizations. Finally, spatial relationships between continuous 

corn planting patterns and these environmental factors will be 

investigated and presented in thematic maps, graphs, as well as 

tabular reports. Geoprocessing tools of Spatial Analyst in 

ArcGIS® software will be utilized and executed to analyze 

spatial relationships. 

The remainder this paper is organized as the following: the 

geographic datasets and spatial analytics functions are 

introduced in Section 2. The continuous corn planting patterns 

and their relationships with other geographic factors are 

discussed in Section 3. Finally, future works are described and 

the conclusions are summarized in Section 4. 

II. DATA AND METHODS

2.1 Geospatial Datasets 

1) CDL Data 

USDA National Agricultural Statistics Service (NASS) has 

produced the CDL data annually since 1997. This georeferenced cropland product provides specific land cover 

classifications crossing the CONUS. It covers all 48 

conterminous states and District of Columbia from the crop 

year of 2008. This valuable geospatial data has been served as 

one of the important sources in the official reporting process of 

agricultural statistics information, and can be leveraged to 

explore land cover and land use changes and agricultural geoinformation of the CONUS. 

Users can access, visualize, retrieve, and analyze the CDL 

data within area of interest (AOI) at any geographic level 

through CropScape to answer cropland related questions 
having local, regional, state, or national imp

top corn producing state in the United States,

as a case study to investigate the continuou

pattern in the state. User can explore the CDL

state directly by inputting CropScape link plu

(i.e. http://nassgeodata.gmu.edu/CropScape/IA

internet browsers. For the reason of original d

the CDL data for the years of from 2006 th

downloaded from CropScape easily for further

2) NED Data 

The study shows that crop yield is correla

terrain attributes, like elevation, slope, and cur

are numerous sources of DEM datasets share

Web. USGS NED data is selected in this 

provides the best raster elevation data of 

research and GIS applications [8]. 

The Geospatial Data Gateway (GDG) o

environmental and natural resources data to ge

including NED data at 3 meters, 10 meters, an

state of Iowa includes 1137 3-meter maps 

MB), 1141 10-meter maps (size 9306.911 M

meter maps (size 1571.143 MB) covering the 

DEM data will be reclassified to define as m

categories, so 26 30-meter DEM tiles are

GeoTIFF format from GDG, and will be

rasterized into one DEM file. The primary ter

aspect and slope will be calculated from this D

3) Temperature Data 

The weather variables like temperature 

impacts on crop growth, development, and 

annual average temperature data by state can

ESRI® Shapefile format from GDG. 

The latest 1981-2010 annual aver

temperature data for Iowa is customized, order

from GDG portal. It contains 11 polygons wh

areas of average maximum temperature from 

seen in Figure 1-1). 

4) Precipatation Data 

The yield of corn, especially planting in th

is highly dependent on precipitation [11]. GD

1961-1990, 1971-2000, and 1981-2010 mon

average precipitation data by state in ESRI® S

These data are derived from point precipitati

data for the 30-year period in a model. 

The 1981-2010 Iowa annual precip

downloaded from GDG portal. There are 15

vector file which represent the areas of 

precipitation from 25 inches to 39 inches, as se

5) Soil Type Data 

The soil types affect how well corn respon

the surface of the soil [12]. The U. S. Ge

(STATSGO) data and the Soil Survey Spatial a

(SSURGO) data are records of map unit lo

classifications collected and produced by Nati

Soil Survey (NCSS). The former one i

geographic display and analysis at the state, re

plications. As the 

 Iowa is selected 

us corn planting 

L data within this 

s its abbreviation 

A) in the common 

data quality, only 

hrough 2012 are 

r analysis. 

ated with several 

rvature [7]. There 

ed freely over the 

study because it 

the CONUS for 

of USDA offers 

eneral public [9], 

nd 30 meters. The 

(size 74266.332 

MB), and 26 30-

whole state. The 

multiple elevation

 downloaded in 

e mosaiced and 

rrain attributes of 

DEM file. 

have important 

yield [10]. The 

n be obtained in 

rage maximum 

red, and retrieved 

hich represent the 

53°F to 63°F (as 

he dry-land areas, 

DG provides the 

nthly and annual 

Shapefile format. 

ion and elevation 

itation data is 

5 features in this 

annual average 

een in Figure 1-2. 

nds to residues on 

eneral Soil Map 

and Tabular Data 

ocations and soil 

onal Cooperative 

is designed for 

egional, and state 

level, it is composed of general ma

features. 

The STATSGO data for Iowa is

GDG portal. The compressed file in

in pure text which can be import

databases and spatial information i

Figure 1-3 illustrates that the map u

covered by 83 different types of soil

(1) Annual Average Maxim

(2) Annual Average P

(3) General Soil 

Figure 1. Geographic D

2.2 Data Processing 

In this study, ArcGIS® software

analyze geospatial data. The CDL 

2009 are at 56m resolution, and th

So the re-sampling processing is re

the same resolution (56m) for raste

basis. Raster Processing of Resa

Management Tools is utilized to fulf

The 26 NED DEM files are m

using Mosaic of Raster Datas

Management Tools. And the single 

state boundary of Iowa using Clip o

same toolset. The boundary file ca

ap units associated with soil 

 ordered and retrieved from 

ncludes tabular information 

ted in Microsoft

® Access®

in ESRI® Shapefile format. 

units in this state are mainly 

l. 

mum Temperature 

Precipitation

Types 

Datasets of Iowa 

e is utilized to process and 

files for the years of 2006-

he others at 30m resolution. 

equired to make all files at 

er calculation on a per-cell 

ample in the Raster Data 

fill this operation. 

mosaiced into a single file 

set in the Raster Data 

file is rasterized within the 

of Raster Processing in the 

an be obtained from United 
States Census Bureau website [13]. The wh

seen in Figure 2. 

Figure 2. DEM Data Processing 

2.3 Spatial Analysis 

Spatial analysis functions of slope, aspect, 

reclassification, and overlay analysis are pe

above geospatial datasets and the derived file

continuous corn platting patterns during the 

2012 in the state of Iowa and investigate th

with geographic factors. Spatial Analyst To

provide the corresponding geoprocessing t

toolsets, including Map Algebra, Surface, Rec

to perform these analytical functions. 

Raster Calculator in Map Algebra too

generate the raster file of continuous corn p

during the seven years from the re-sampled CD

Slope and Aspect in Surface toolset are p

processed DEM file to calculate the terrain at

and aspect of the entire state. 

Reclassify in Reclass toolset is executed 

DEM data and the derived slope data into s

which stand for the specified value ranges 

slope. Other geospatial data mentioned above 

do this because they have been grouped 

datasets. 

Zonal Statistics in Zonal toolset is utilize

analysis tool to calculate the raster value

continuous corn cropping within the categ

several geographic datasets. 

III. RESULTS

3.1 Continuous Corn PlantingAreas 

Figure 3. 7-year Continuous Corn Planting Ar

hole process is as 

raster calculator, 

erformed on the 

es to explore the 

period of 2006-

heir relationships 

ools in ArcGIS®

tools in several 

class, and Zonal, 

olset is used to 

planting patterns 

DL files. 

performed on the 

ttributes of slope 

to reclassify the 

everal categories 

of elevation and 

are not needed to 

into categorical 

ed as an overlay 

s of the 7-year 

gorized areas of 

reas in Iowa 

An expression with logical ope

and Boolean And "&") and numeric

using the 2006-2012 Iowa CDL dat

executed in Raster Calculator t

representing the 7-year continuous

output raster layer is shown in Fig

means that the crop values of this

2006-2012 are always the one of c

and is set as NoData value. It c

continuous corn planting areas are

middle of Iowa. 

3.2 Terrain Attributes 

(1) Reclssfied Ele

(2) Reclssfied S

(3) Aspect

Figure 4. Terrain Attri

The DEM file is reclassified into

to elevation value ranges: [119, 21

[291, 324], [325, 356], [357, 387], 

(unit: meter). The reclassified DEM 

The output slope data is grouped

the intervals: [0, 1.21], [1.22, 2.9],

[6.77, 9.41], [9.42, 12.8], [12.9, 17

61.6] (unit: degree). Figure 4-2 disp

data of slope. 

erators (i.e. Equal To: "==" 

cal value (i.e. 1, corn value) 

ta as the inputs is built and 

to create the raster data 

s corn planting areas. The 

gure 3. In this file, value 1 

s cell during the period of 

corn, value 0 means not all 

can be seen that most of 

e located in the north and 

evation

Slope 

t

ibutes of Iowa 

o eight categories according 

14], [215, 252], [253, 290], 

[388, 420], and [421, 509] 

file is shown in Figure 4-1. 

d into nine classes based on 

, [2.91, 4.83], [4.84, 6.76], 

.6], [17.7, 24.4], and [24.5, 

plays the reclassified raster 
The output aspect data contains nine catego

the aspect of each cell, including Flat, North,

Southeast, South, Southwest, West, and Nort

in Figure 4-3). 

3.3 Overlay Analysis 

Overlay analysis combines the featu

geospatial datasets together to create the deta

have the specified attributes. ArcGIS® offers 

overlay tools to overlay multiple vector a

respectively. 

In this study, Zonal Statistics as Table is ad

the raster layer of the 7-year continuous co

other categorized vector and raster layers and

values within the categorized zones of th

statistics results are outputted into tables, a

graphs. The pie charts in Figure 5 illustrate 

geographic factors impact on continuous c

Iowa. 

 

 (1) By Elevation Ranges (2) By 

 (3) By Slope Ranges (4) By Annual Ma

(5) By Annual Precipitation (6) B

Figure 5. Display of Zonal Statistics Re

ories representing 

, Northeast, East, 

thwest (as shown 

ures of several 

ailed areas which 

vector and raster 

and raster layers 

dopted to overlay 

orn planting with 

d summarizes its 

hese layers. The 

and visualized in 

how much these 

corn cropping of 

y Aspect 

aximum Temperature 

By Soil Types 

esults 

1) Relationship with elevation a

Figure 5-1 displays that the el

continuous corn planting areas are b

meter. The areas with elevation ran

387m, and 291m-324m are the top th

Unlike other charts with obv

categories, the percentage of contin

each aspect is roughly similar, as s

can be concluded that there is no 

and continuous corn planting. 

Overall, about a very large m

planting areas are located in the lo

4.83 degree slope), as seen in Figur

<1.21 degree) is the biggest segment

2) Relationship with climate fac

 At first glance of Figure 5-4, 

average maximum temperature o

continuous corn planting areas is be

remaining segments account for 

cropping areas with annual average

56°F and 57°F are roughly half of th

The continuous corn planting ar

precipitation between 31 inches to 3

87% of the total areas, as shown in F

of areas with 34 inches, 35 in

approximately 19%, 23%, and 22% 

3) Relationship with soil types 

Figure 5-6 shows the propo

planting areas by soil types. The bi

areas are s1750 (about 23%) that re

drained soil types (Webster-Nicolle

(nearly 12%) that indicates one of th

soil types (Kenyon-Floyd-Clyde), 

means another poorly drained so

Canisteo), and s1696 (around 6%) w

well drained soil types (Fayette-Dow

IV. DISCUSSIONS AND

This paper explores the continu

during the period of 2006-2012 

investigates these patterns relation

factors, including elevation, asp

precipitation and soil type. To g

multiple geographic datasets are col

software. These data are processed

continuous corn planting patterns a

geographic factors to become appa

generated maps and graphs sho

relationships between continuous c

slope, temperature, precipitation, an

CDL data provided by CropSca

agricultural spatial data sources 

patterns. Open access geospatial dat

be served as explanatory variables o

However, the relationships wi

further investigated. The detailed so

will be utilized in the analysis. Oth

attributes 

evations of nearly 85% of 

between 253 meter and 420 

nges of 325m-356m, 357mhree ones. 

vious differences between 

nuous corn planting areas in 

shown in Figure 5-2. So, it 

correlation between aspect 

majority of continuous corn 

wer slope areas (0 through 

re 5-3. The flat areas (slope 

t, about 55% of total areas. 

ctors 

it is clear that the annual 

of approximately 97% of 

etween 55°F and 60°F. The 

about 3% in total. The 

e maximum temperature of 

he total ones. 

eas with the annual average 

36 inches account for about 

Figure 5-5. The proportions 

nches, and 36 inches are 

respectively. 

rtion of continuous corn 

iggest four soil types of the 

epresents one of the poorly 

et-Clarion-Canisteo), s1759

he somewhat poorly drained 

s1751 (almost 6%) which 

oil type (Nicollet-Clarionwhich stands for one of the 

wns) . 

D CONCLUSIONS

uous corn planting patterns 

in the state of Iowa, and 

nships with environmental 

pect, slope, temperature, 

generate the relationships, 

lected and organized in GIS 

d and analyzed to allow the 

and their relationships with 

arent. In a conclusion, the 

ow that there are strong 

orn planting and elevation, 

nd soil types. Moreover, the 

ape is one of the useful 

to discover crop planting 

ta from multiple sources can 

of spatial analysis. 

ith soil types need to be 

oil type data (i.e. SSURGO) 

her geographic factors, like 
national wetland inventory data, water resource points and 

stream network, will be considered as explanatory variables. In 

addition, the relationships between crop patterns census, social 

and economic data, such as crop production, yield and price, 

will also be explored, and a classification tree will be built from 

the relationships of crop planting patterns with these various 

types of factors. Furthermore, new standard geoprocessing 

services of raster calculator and overlay analysis will be 

implemented and integrated in CropScape to extend its 

functionality. These geoprocessing services will facilitate users’ 

analysis in their research and applications, and can be 

integrated in their scientific workflow. 

REFERENCES

[1] C. C. Sheaffer, K. M. Moncada. Introduction to Agronomy: Food, Crops 

and Environment. Delmar Cengage Learning. New York: Clifton Park, 

2008. 

[2] A. Deberman, D. Dawe, R. P. Roetter, and K. G. Cassman, “Reversal of 

rice yield decline in a long-term continuous cropping experiment”, 

Agronomy Journal, Vol. 92, pp. 633-643, 2000. 

[3] M. Liebman, E. Dyck, “Crop rotation and inter-cropping strategies for 

weed management”, Ecological Applications, Vol. 3, pp. 92-122, 1993. 

[4] United States Department of Agriculture National Agricultural Statistics 

Service, “Cropland Data Layer”, http://nassgeodata.gmu.edu/CropScape, 

2013. 

[5] C. Boryan, Z. Yang, R. Mueller, and M. Craig, “Monitoring US 

agriculture: the US Department of Agriculture, National Agricultural 

Statistics Service, Cropland Data Layer Program”, Geocarto 

International, Vol. 26, No. 5, pp. 341-358, 2011. 

[6] W. Han, Z. Yang, L. Di, and R. Mueller, “CropScape: A Web service 

based application for exploring and disseminating US conterminous 

geospatial cropland data products for decision support”, Computers and 

Electronics in Agriculture, Vol. 84, pp. 111-123, 2012. 

[7] T. C. Kaspar, T. S. Colvin, D. B. Jaynes, D. L. Karlen, D. E. James, D. 

W. Meek, D. Pulido, and H. Butler, “Relationship between six years of 

corn yields and terrain attributes”, Precision Agriculture, Vol. 4, No. 1, 

pp. 87-101, 2003. 

[8] United States Geological Survey, “National Elevation Dataset”, 

http://ned.usgs.gov, 2013. 

[9] United States Department of Agriculture National Resources 

Conservation Service, “Geospatial Data Gateway”, 

http://datagateway.nrcs.usda.gov, 2013. 

[10] D. T. Coelho, and R. F. Dale, “An energy-crop growth variable and 

temperature function for predicting corn growth and development: 

planting to silking”, Agronomy Journal, Vol. 72, No. 3, pp. 503-510, 

1980. 

[11] D. C. Nielsen, M. F. Vigil, and J. G. Benjamin, “The variable response 

of dryland corn yield to soil water content at planting”, Agricultural 

Water Management, Vol. 96, No. 2, pp. 330-336, 2009. 

[12] J. A. Tolk, T. A. Howell, and S. R. Evett, “Effect of mulch, irrigation, 

and soil type on water use and yield of maize”, Soil and Tillage 

Research, Vol. 50, No. 2, pp. 137-147, 1999. 

[13] Uinted States Census Bureau, “The 2010 TIGER/Line® Shapefiles”, 

http://www.census.gov/cgi-bin/geo/shapefiles2010/main, 2013.