A 2010 map estimate of annually tilled cropland within the conterminous

United States

David M. Johnson

United States Department of Agriculture, National Agricultural Statistics Service, United States

article info

Article history:

Received 3 May 2012

Received in revised form 15 August 2012

Accepted 30 August 2012

Keywords:

Area

Classification

Cropland

Estimation

Mapping

Tillage

abstract

A ca. 2010, 30 m resolution map depicting annually tilled areas across the conterminous United States

was developed. Input sources included four years, spanning 2008–2011, of annual national-level coverage Cropland Data Layer (CDL) land cover classifications as produced by the National Agricultural Statistics Service. Derived total land area under tillage from the aggregate CDL product equaled 112.8 million

hectares (278.7 million acres). By comparison, the 2007 Census of Agriculture (CoA) produced an estimate of 122.9 million hectares, suggesting the map is under representing tilled area by 10.1 million hectares or 8.2%. Regression analysis using state-level summaries showed a strong, albeit biased, correlation

(r-squared = 0.99) between the CDL derived tilled area and the CoA information. Notable outliers were

North Dakota and Montana. Comparisons of the CDL tilled map were also made against the 2006 National

Land Cover Dataset (NLCD) land cover product’s Cultivated Crops category. Strong state-level regression

agreement (r-squared = 0.98) was also found between the NLCD and the CDL acreages, but the NLCD estimated 8.5% more area than the CDL and thus closely matched that of the CoA. However, significant pixel

level differences were found between the CDL and the NLCD. Nationally 5.6% of the maps were in disagreement as to whether cultivated or not, a large proportion considering around a seventh of the country’s land area is tilled. States of Arkansas, Montana and Wisconsin had the largest absolute discrepancies

between the NLCD and CDL. Accepting the CDL as reference showed a national level NLCD cropland commission error of 23.0% and omission error of 14.5%. Much of what is believed to be problematic in the

NLCD could be explained by definitional issues having included alfalfa hay into their cultivated category

for many areas. Ultimately, while it is likely that the CDL annually tilled area model is an underestimate

of the true total, taken contextually in map form and adjusted for undercount bias it likely is the best

available.

Published by Elsevier Ltd.

1. Introduction

1.1. Background

Understanding the area and extent of croplands is important for

a variety of societal and environmental reasons. They include biodiversity (Landis et al., 2008; Tscharntke et al., 2005), pest and disease spread (Margosian et al., 2009; Werling et al., 2011),

bioenergy management (Fargione et al., 2009; McDonald et al.,

2009), watershed runoff and chemical monitoring (King et al.,

2005; Meehan et al., 2011), wildlife management (Meehan

et al., 2010), food security (Brown and Funk, 2008), water balance

(Dappen et al., 2008) and weather (Raddatz, 2003; Steyaert and

Knox, 2008). Also included in the list is the grand topic of climate

change. Land use and land cover change, with the ramifications of

carbon storage (Fargione et al., 2008; Jung et al., 2006; West et al.,

2010) and greenhouse gas emissions (Searchinger et al., 2008), is

not a fully understood driver for altered future global and United

States (US) level climate scenarios (Feddema et al., 2005;

Mahmood et al., 2010; Pielke et al., 2011). Because of agriculture’s

relatively large areal footprint, it likely plays a very large role in the

climate system (Fall et al., 2009; Pielke et al., 2007). Impacts to the

US are likely variable depending on location (Fall et al., 2010), and

a shifting or more variable climate will likely alter what crops can

be grown where (Schlenker and Roberts, 2009). All of the listed

topics are interrelated and have complex feedback mechanisms

(Foley et al., 2005).

In a more applied setting, it is also important to know how

much land is utilized for resource allocation and economics (Rudel

et al., 2009; You et al., 2009). If, for example, one is trying to plan

for the transportation or utilization of crop products it is useful to

have a detailed picture of the cultivated areas. Likewise, having a

firm handle on the distribution of crop areas is helpful for natural

disaster mitigation, assessment and response. And because growing areas are highly anthropogenic lands, they are likely more sensitive to change versus other land cover types. Within the US for

0308-521X/$ - see front matter Published by Elsevier Ltd.

http://dx.doi.org/10.1016/j.agsy.2012.08.004

E-mail address: dave.johnson@nass.usda.gov

Agricultural Systems 114 (2013) 95–105

Contents lists available at SciVerse ScienceDirect

Agricultural Systems

journal homepage: www.elsevier.com/locate/agsy
example, farm policy or commodity pricing can have impacts on

what gets planted where over the span of just a year.

The distribution and extent of croplands has been described and

defined in many contexts. In tabular form, recent estimates are

that 1381.2 million hectares (10.6%) of the global land area is arable by the Food and Agricultural Organization (FAO) of the United

Nations (FAO, 2009). Within the conterminous US the FAO puts the

number at 162.8 million hectares, or about 11.8% of the world’s total arable land. Most countries independently estimate their

amount of cropland through surveys or a census. Cropland areas

of all types within the US are estimated at 164 million hectares

by the US Department of Agriculture (USDA, 2009a). This represents about 18.0% of the total land cover in the conterminous

US. Of that total, 125.3 million hectares (76.2%) is considered

harvested cropland by the USDA.

1.2. Mapping cropland areas

Statistics alone may not shed detailed enough information on

which areas of land cover are under cultivation or changing. Detailed maps, however, can also allow for extraction of the land cover statistics beyond what is typically provided at only national or

sub-national levels. The development of global and regional crop

map databases, primarily through remote sensing, is not new or

unique. DIScover is a global one kilometer squared resolution Advanced Very High Resolution Radiometer (AVHRR) based land cover data set (Loveland et al., 2000) with categories for ‘‘croplands’’

and ‘‘croplands/natural vegetation mosaic.’’ The University of

Maryland also developed a one kilometer global product from

AVHRR data, albeit with a different methodology (Hansen et al.,

2000). Global Land Cover 2000, also one kilometer, was developed

with SPOT 4 data (Bartholome ́ and Belward, 2005) with the land

cover classes ‘‘cultivated and managed areas,’’ ‘‘mosaic-Cropland/

Tree Cover/Other natural vegetation’’ and ‘‘mosaic-Cropland/Shrub

or Grass Cover.’’

More recent, occasionally updated, and at higher resolution, a

Moderate Resolution Imaging Spectroradiometer (MODIS) based

land cover product is available at 500 m (Friedl et al., 2010,

2002). It is named from the suite of MODIS derived products as

MCD12Q1. It has the data categorized in several manners so as

to be comparable to the one kilometer datasets mentioned previously. Embedded in MCD12Q1 are agricultural category names

such as ‘‘croplands,’’ ‘‘croplands/natural vegetation mosaic,’’ ‘‘cereal crops’’ and ‘‘broad-leaf crops.’’ Another global land cover classification product, and similar to MODIS, named GlobCover was

developed from ca. 2005 Envisat Medium Resolution Imaging

Spectrometer (MERIS) imagery (Arino et al., 2008). It is a finer

300 m resolution and has categories for crops including: ‘‘postflooding or irrigated croplands,’’ ‘‘rainfed croplands,’’ ‘‘mosaic cropland (50–70%)/vegetation (grassland, shrubland, forest) (20–50%)’’

and ‘‘mosaic vegetation (grassland, shrubland, forest) (50–70%)/

Cropland (20–50%).’’

Similar map products have been developed that are just focused

on US croplands. Chang et al. (2007) utilized MODIS 500 m data to

map the distribution of corn and soybeans within the Corn Belt region of the US for 2002. Wardlow and Egbert (2008) created crop

maps with 250 m data but focused instead on the US Great Plains.

Also, Shao et al. (2010) used MODIS data but focused on the Great

Lakes Basin region. All of these map products were shown to have

good area agreement with official statistics from the USDA.

Even with all these sources of information the data still can lack

the needed spatial detail, or accuracy for further analysis and often

do not agree (Giri et al., 2005; Hansen and Reed, 2000; Herold

et al., 2008). The timeliness or analyzed time period may not be

useful either depending on the application. Improvement can be

made by combining the map information with national surveys

(Monfreda et al., 2008) but the importance of even higher resolution datasets was shown by Nelson and Robertson (2007), even

when coarser scaled data are considered highly accurate.

Land cover products with finer resolve and information about

agriculture do exist but are less common, particularly over large regions. Thirty meter grid cell size is arguably the de facto standard

for these higher resolution products. Earliest derived was the Geocover LC dataset which has categories for ‘‘agriculture, general’’

and ‘‘agriculture, rice/paddy.’’ It was produced ca. 1990 and

encompassed the entire world (Cunningham et al., 2002). But,

while ambitious at the time, it is dated now since two decades

have passed from its analysis date.

The National Land Cover Database (NLCD) is a more recent and

visible product but only over the US domain. It is also 30 m in resolution. There are currently three epochs; 2006, 2001 and 1992

(respectively, Xian et al., 2009; Homer et al., 2004; Vogelmann

et al., 2001) and more are planned for the future. The NLCD is

not solely focused on mapping cropland agriculture but has categories in its classification scheme that are directly related. The two

most recent NLCD releases contained categories for ‘‘cultivated

crops’’ while the older version contained categories for ‘‘row crops’’

and ‘‘small grains.’’ The cultivated crops category specifically is defined as ‘‘areas used for the production of annual crops, such as

corn, soybeans, vegetables, tobacco and cotton, and also perennial

woody crops such as orchards and vineyards. Crop vegetation

accounts for greater than 20% of total vegetation. This class also

includes all land being actively tilled.’’

Analysis of the NLCD agricultural classes against official USDA

statistics has been performed in some contexts. Maxwell et al.

(2008) assessed differences in the 2001 NLCD for the Upper Midwest portion of the US and Goslee (2011) did similar for the New

England areas with the 2006 NLCD. Accuracy assessment results

of all of the categories of the 2006 NLCD are yet forthcoming.

1.3. The Cropland Data Layer

Beyond all the datasets previously mentioned, the timeliest and

arguably best crop tailored US land cover product is now generated

annually by the USDA’s National Agricultural Statistics Service

(NASS). The dataset itself is coined the Cropland Data Layer

(CDL). The CDL is typically a 30 m resolution raster-based grid

spanning the conterminous US with focus on agricultural cover

types. It goes into more category detail than just ‘‘cropland’’ or

‘‘cultivated crops’’ like found in the NLCD. Instead the CDL attempts

to identify specific crop types like corn, soybeans, wheat, cotton,

etc. Non-agricultural cover types are also documented but with a

lesser degree of thematic detail. A general description of the

2009 product is given by Johnson and Mueller (2010) while a more

in depth explanation of the CDL program is provided by Boryan

et al. (2011). In short, datasets were created using Landsat Thematic Mapper (TM), or similar, multispectral satellite data in concert with ‘‘ground truth’’ reference information through a

supervised classification methodology. There are currently US national level CDL coverages for each of the years spanning 2008–

2011. But in total, NASS has been researching and developing these

cropland tailored CDL land cover classifications over specific agriculturally intensive states for more than a decade.

The CDLs have several roles within NASS. The main use for the

products has been to derive within season acreage estimates of the

major commodity crops at state and county levels which help supplement data from ongoing and traditional NASS surveys. The CDLs

serve as reference land cover information in roles such as developing land cover stratification for the NASS June-based and annually

published Acreage survey (USDA, 2011). The CDLs help as a benchmark for defining cropland areas during times of natural disaster.

And finally, the CDLs are used as ground truth for defining crop

96 D.M. Johnson / Agricultural Systems 114 (2013) 95–105
types in concert with coarser scaled data, such as MODIS, for crop

progress monitoring and yield modeling. After the growing season

is fully completed and NASS published estimates solidified, the

CDLs are freely released for public consumption early the following

year (Han et al., 2012; USDA, 2012).

The NASS land cover classifications usually have high thematic

accuracies, particularly for the dominant commodity crops, but

each category in the CDL still likely contains a certain amount of

bias if used for estimating area from a simple ‘‘pixel counting’’

methodology. It is believed in general the CDLs have a tendency

to underestimate crop areas modestly, but this undercount is not

necessarily consistent year to year or region to region. Fortunately,

any area bias can be compensated for through a ‘‘regression estimator’’ (Battese et al., 1988). This is accomplished by comparing

area totals calculated at a sample of small areas within the CDL

against estimates collected at the same areas by NASS field enumerators. The area relationship of what the CDL estimated versus

the in situ estimates over the many samples form a linear relationship. Then the slope of the regression line providing the bias

adjustment for what each pixel is truly worth in terms of area.

Thus, acreage estimates NASS produces from the CDL are not simply a pure pixel area sum but rather the area of those pixels

summed and then multiplied by regression parameters.

Earliest CDL efforts were research based and focused on the core

Corn Belt states like Iowa and Illinois. But as the utility for the

products has grown, the classification methodologies become more

efficient, imagery datasets availability now at low cost or free, and

computational power increased, the land cover product became

national in scope. The year 2009 marked the first national coverage

that was completed during the same growing season. Since then

CDLs have been produced and released nationally for the 2010

and 2011 growing season. The year 2008 was not complete nationally initially but retrospective analysis was performed for the

states not originally undertaken. As a result, a full conterminous

US 2008 CDL now also exists.

Thus, there are four years of complete CDL coverage for the conterminous US spanning the years 2008–2011. The native pixel resolution of the 2010 and 2011 CDLs is 30 m with a map projection of

Albers Conic Equal-area. The 2009 product’s resolution is 56 m

which coincides with the at nadir sample size of the Resourcesat1 Advanced Wide Field Sensor (AWiFS) imagery that was used

primarily by the USDA during that period. 2008 is mostly 56 m

resolution, again due to the utilization of AWiFS. However, the

several states that were classified retrospectively utilized Landsat

as the primary data set and thus have native resolution of 30 m.

Those states are California, Florida, Idaho, Washington, Oregon,

Montana, Michigan and all those in the New England region.

Accuracy assessments of the crop category aspects of the CDLs

were undertaken at the state-level and available in the metadata

for each. Full discussion of the accuracies is beyond the scope here.

In summary though, state-level accuracies at the pixel-level for the

individual tilled crop type categories typically ranged from about

70.0% to 95.0%. For year 2011 the non-weighted, by state size, crop

accuracy average was 85.3%. Year 2010’s accuracies were a percentage point lower at 84.3%. The averages for 2009 and 2008 were

notably lower with overall crop accuracies around 80.0% and 76.9%,

respectively.

States with the highest accuracies tended to be those in the

Corn Belt region. This is likely a function of the regimented nature

of the cropping practice, availability of the most complete ground

truth, and premium by NASS placed on acquiring the best imagery

in that area since it makes up the bulk of US commodity production. CDL accuracies have steadily improved through the years

probably due to the ability to analyze larger volumes of time-series

imagery, more complete ground truth, and processing experience.

Additionally, the older 2009 and 2008 products were primarily

reliant on the lower resolution AWiFS imagery and thus a coarser

56 m versus 30 m grid cell size was used for most of those classifications. While it is believed the larger AWiFS pixels do not overly

impact crop statistics because most fields in the US are relatively

big in area and homogenous to begin with, there is still an expected

accuracy loss attributable to the decreased ability to map detailed

landscape features like complex field edges.

1.4. US cropland area non-map statistics

Other sources of US crop area statistics are important even

though they do not have a direct map component. The NASS Census of Agriculture (CoA) provides the most complete and detailed

inventory of agricultural land use available for the US. The CoA is

performed every five years with the most recent undertaking during the 2007 calendar year (USDA, 2009a). CoA statistics are primarily aggregated at county, state, and national levels.

Additionally, tabulation of the data was performed at watershed

and congressional district levels for 2007. Much of the CoA information is also provided in atlas form, usually through simple

choropleth maps, but with no additional detail beyond county

level. Agricultural and use statistics pertaining to the area tilled

or cultivated are not published directly by the CoA. They can however be calculated by reconciling the land use tables. Case in point,

total cropland is published but it includes by definition areas used

for pasture and grazing, forage crops, orchards, nursery stock, and

short rotation woody crops. Those subcategories have tables of

their own and thus can be subtracted from the overall totals however. The CoA also details in Appendix A (USDA, 2009b) overall

measures of quality and reliability. Because it is a census and not

a survey much of the potential error is based on under-coverage

or non-response. Farm-level adjustments and relative errors are

listed but do not translate directly to potential errors in the land

use information.

The other main NASS source of cropland utilization information,

and which is produced more timely and annually, is known as

Acreage (USDA, 2011). It is published the last business day of every

June and is the summation of results from the NASS area based

June Acreage Survey and the list based June Agricultural Survey.

They are both undertaken the first half of that same month. The

survey sampling methodology is large in that about 2.9 million

hectares of land, with unit sizes of about 260 ha each, is visited

and enumerated each year. The survey is tailored toward the US

and state-level area statistics of the large commodity crops. It also

does not include information at geographic levels more detailed

than US states. Furthermore, the statistics are not mutually exclusive for a given parcel of land over a growing season. This means

some areas with two or more crops per year on the same piece

of ground get double counted in the land use total. So for these

several reasons combined, it is not perfectly suited for spatially

detailed or unbiased total crop area estimates.

2. Methodology

2.1. Defining annually tilled

A clear definition of ‘‘annually tilled’’ was first derived. For this

work tilled included all areas that traditionally have annually

seeded crops with manipulation, either physically or chemically,

of the soil in between or during growing seasons. This included

all of the primary field crops like corn, soybeans, wheat, rice, and

cotton along with vegetable crops and non-tree fruit crops. Perennial crops such as hay were excluded from the definition. Orchard

and tree crops were also not included. Ultimately, usage of the

term tilled in this paper’s context is meant to be more exclusive

than the nearly synonymous and commonly used word

D.M. Johnson / Agricultural Systems 114 (2013) 95–105 97
‘‘cultivated.’’ Cultivated by some definitions would also include

tree, perennial crops, and/or livestock areas.

Potentially confusing the definition of tilled, some farmers practice ‘‘conservation tillage’’ or ‘‘no-till’’ soil management techniques.

These are fields, where the soil is not turned by plow and new

seeding is planted directly into the previous crop’s residue. However, these areas were indeed included in the definition of tilled

being undertaken here. And confusing the annual portion of the

definition, areas that typically sit idle or ‘‘summer fallow’’ for a

year because crops are planted only biennially were also included.

The most common example of this is for wheat crops in the dry

areas of the western Great Plains. Likewise, fields could have had

more than one planted crop per year were also included.

A couple of CDL crop categories demanded special attention as

to their tilled status or not. Alfalfa is found throughout the US and

often rotated with field crops, particularly corn, after several years

of residing perennially. It was not included in the tilled category for

this analysis although is some context it could be argued it should.

NASS also generalizes alfalfa as a hay crop and that is how it was

ultimately considered for this analysis. Conversely, the CDL

category seed/sod grass, most abundant in the Pacific Northwest

region, was included in the tilled definition. These grass areas are

heavily managed annually with similarities more in line with

field crops than hay. Furthermore, seed and sod grasses are not

categorized as field or hay crops by NASS. They are a special

category altogether.

2.2. Map derivation of tilled areas

With the tilled definition in place, four years of the national level CDLs spanning the years 2008–2011 were assimilated to derive

a best of ca. 2010 annually tilled area classification. The integration

of multiple years provided the opportunity to develop a more refined product than what a single years’ CDL could provide. This is

particularly true given every years’ CDL, like any thematic map,

has a certain amount of misclassification or noise.

A simple rule-set was constructed to build the tilled map from

the four input years. First, each CDL year was categorically recoded

to ‘‘tilled’’ or ‘‘not tilled’’ based on the appropriate raw input categories (Table 1). Then, all four years were ‘‘overlaid’’ and assessed

as for tilled status at each pixel location. If two or more of the four

total input years were in the tilled category then the output classification was called tilled.

One exception was not allowed – if only the pixels in years 2008

and 2009 were deemed tilled then the output pixel was not called

tilled. In other words, minimally either 2010 or 2011 had to have

been called tilled for the output to reflect it too. This modification

was implemented for two reasons. One, all of the 2009 and most of

the 2008 CDLs were coarser in spatial resolution and with less thematic accuracy than the 2010 and 2011 products. Thus, there was

somewhat less confidence in them which meant they were more

likely to propagate error. The second reason for the less weight applied to 2008 and 2009 was the overall goal to develop something

closest to a 2010 era product. Thus it was also desirable to give the

older 2009 and 2008 products less weight in the rule voting

decision.

Two special categories, alfalfa and orchards, were also labeled in

the output for later comparison and reconciliation against other

data sets, particularly the most similar NLCD. Alfalfa was carried

as unique category for two reasons. One, in some contexts it could

be considered a tilled crop because usually it gets rotated with

other crops, particularly corn, after a few years. Secondly, prior

observation of the NLCD by NASS has shown alfalfa suspect of often

being included in its NLCD cultivated crops category instead of pasture/hay, which is more appropriate by the NLCD’s own definition.

Orchards were carried as a second a unique category in order to

better compare to the NLCD as its cultivated croplands category

also includes orchards by NLCD definition. Ultimately, the voting

rule methodology for alfalfa and orchards were implemented in

the same manner as the tilled crops.

Finally, after the voting rules derived the new single output

layer, a minimum mapping unit of five pixels (about 0.4 ha or

one acre) was applied to the entire output image to reduce some

of the ‘‘speckling.’’ Single pixels or very small patches of contiguous

pixels had a high probability of being spatial noise since crop field

sizes tend to be relatively large in area. Moreover, groups of pixels

were not considered conjoined if they only touched on the corners.

This helped reduced ‘‘stringy’’ or linear features which are also

unlikely to be crops areas.

The analysis was performed independently for each state. There

were two reasons for this. One, because there was no way to best

harmonize the 2008 CDL into a single mosaic without compromise

given the mix of 30 m and 56 m resolutions. And two, application

of the minimum mapping unit is computational intensive and

was not possible to run over the entire US at once. Output projection was the same as common to all of the input CDLs- Albers

equal-area conic. The output grid was set to 30 m resolution and

nested the same as the 2011 and 2010 input CDLs. The state analysis boundaries were defined on the Census-based Shapefiles

‘‘dtl_st.shp’’ distributed by Environmental Systems Research Institute (ESRI) via their ArcGIS software suite. All processing steps

were performed in Erdas Imagine 2011.

Table 1

CDL numeric codes with category names placed into tilled classification output.

1. Corn 27. Rye 48. Watermelons 213. Honeydew melons

2. Cotton 28. Oats 49. Onions 214. Broccoli

3. Rice 29. Millet 50. Cucumbers 216. Peppers

4. Sorghum 30. Speltz 51. Chick Peas 219. Greens

5. Soybeans 31. Canola 52. Lentils 221. Strawberries

6. Sunflowers 32. Flaxseed 53. Peas 222. Squash

10. Peanuts 33. Safflower 54. Tomatoes 227. Lettuce

11. Tobacco 34. Rapeseed 55. Caneberries 229. Pumpkins

12. Sweet corn 35. Mustard 56. Hops 242. Blueberries

13. Popcorn/ornamental 38. Camelina 57. Herbs 243. Cabbage

corn 41. Sugarbeets 59. Sod/seed grass 244. Cauliflower

14. Mint 42. Dry beans 61. Fallow/idle cropland 245. Celery

21. Barley 43. Potatoes 205. Triticale 246. Radishes

22. Durum wheat 44. Other crops 206. Carrots 247. Turnips

23. Spring wheat 45. Sugarcane 207. Asparagus 248. Eggplants

24. Winter wheat 46. Sweet potatoes 208. Garlic 249. Gourds

25. Other small grains 47. Miscellaneous vegetables and fruits 209. Cantaloupes 250. Cranberries

a Not shown were the also included ‘‘double cropped’’ classes: 26, 225, 226, 230–241, and 254.

98 D.M. Johnson / Agricultural Systems 114 (2013) 95–105
3. Results

3.1. Simple metrics

Direct pixel counting of the ca. 2010 annually tilled classification resulted in a total area estimate of 112.8 million hectares

(278.7 million acres) within the conterminous US. This represents

about 14.7% of the total land area (i.e. not including water) available. An overview map showing the distribution of tilled areas is

shown in Fig. 1. Not surprisingly, the majority of the tilled area is

found in the mid-section of the US in the Corn Belt and Great Plains

regions. Significant density of tilled area is also found in the California Central Valley, The Delta (the Mississippi Alluvial Plain)

and the Intermountain region of the Northwest.

A complete list of state level area totals are provided in Table 2.

The top five states for annually tilled area are Iowa, Kansas, Illinois,

Texas and North Dakota. Two of those states, Iowa and Illinois, represent areas for which more than half of the land is utilized for

tilled cropping. The five states with the least amount of tilled area

are Rhode Island, New Hampshire, Connecticut, Nevada and West

Virginia. The first three are no surprise since their land area is very

small to begin with. Nevada is very large though and its tilled area

is the smallest percentage of any across the US at only 0.1%. West

Virginia and New Hampshire both also resulted in low areas of

tilled with less than 1.0% of the total land area. The District of

Columbia had no tilled cropland.

3.2. Comparisons to Census of Agriculture statistics

The 2007 CoA produced an estimate of 122.9 million hectares

(303.6 million acres) of tilled land after subtracting pasture and

grazing, forage crops, orchards, nursery stock, and short rotation

woody crops from the overall cropland category. Using the CoA

as truth, the suggestion is the CDL derived tilled map is under representing the actual area by 10.1 million hectares (24.9 million

acres), or about 8.2%. A scatter plot showing the relationship

between the CDL and CoA tilled areas tallied at the state-level is

depicted in Fig. 2. The linear relationship between the two variables overlaid is also shown and with a very strong correlation

with an r-squared of almost 0.99. The bias is graphically documented with a least-squares regression line overlaid the chart.

The CDL tilled likely underestimating the CoA tilled as the slope

of the line does not equal one. The calculated relationship is CoA

tilled = 1.088 CDL tilled. The root-mean-square error (RMSE) is

0.5 million hectares.

The most notable outliers undercounting are Montana (MT) and

North Dakota (ND). They are beneath the CoA counts by about

28.0% and 18.0%, respectively. Texas (TX) has the biggest departure

from the regression line on the other side. But it falls almost perfectly on the 1-to-1 line so there is ultimately no suggestion of bias

for it. But, it is probably an overestimate of tilled area given the

relationship to the rest of the points. For states with small tilled

areas it is difficult to assess the relative amount of difference from

Figs. 1 and 2 since they have little area to begin with. Delaware, a

relatively intensive cropland state, would be the biggest outlier

percentage area wise though with suggestion that CDL-based tilled

estimate statistics are over counting the true tilled area percentage

by about 20.0%.

3.3. Comparisons to NLCD statistics

The total conterminous US area from the 2006 NLCD ‘‘cultivated

crops,’’ category number 82, is 124.3 million hectares (307.3 million acres) obtained via direct pixel counting. The sum was derived

by using the same boundary extent file as for the CDL analysis. The

NLCD numeric is not directly comparable to the CDL estimate because it includes orchards in its definition. Thus, to make the comparison valid the tabulation of the special orchards CDL derived

class was added to the CDL tilled area summaries. The orchard adjusted CDL total is 114.5 million hectares (283.0 million acres)

which is about 1.5% larger than without. The resulting difference

between it and the NLCD is 9.8 million hectares, or about 7.9%,

and similar to what was found differentiating the CDL tilled from

the CoA tilled.

The state-level regression comparisons are shown in Fig. 3.

Again, the CDL estimates here include orchards in order to compensate for the NLCD definition which includes it. Henceforth the

term CDL cultivated will be used to reference it. The relationship

(NLCD cultivated = 1.055 CDL cultivated) is very strong with an

r-squared over 0.98. The RMSE is 0.4 million hectares. The largest

outlier differences, where the CDL cultivated area estimates are less

than the NLCD are for the states of Wisconsin (WI) and Montana

(MT). Conversely, Arkansas (AR) appears it may be overestimated

in the CDL cropland analysis. If the statistics are normalized for

land area, Wisconsin is still an outlier, underestimating cultivated

area, and both Arkansas and Delaware overestimating.

Fig. 1. Overview map of CDL derived tilled cropland areas in the conterminous US.

D.M. Johnson / Agricultural Systems 114 (2013) 95–105 99
3.4. Comparison to NLCD map

Aggregated total statistics do not necessarily tell the whole

story. A region could have very similar totals but may not match

geographically at the local, field, or pixel level. Overlay analysis

was thus performed to count the pixel level agreement between

the cultivated areas in NLCD and CDL. Direct comparison of the

two within a geographic information system was straightforward

and perfectly aligned since NLCD and CDL tilled share the same

map projection, cell size and reference grid origin. For all pixels,

US level agreement for non-cultivated pixels (agriculture or otherwise) was 81.7%. For the cultivated cover type the agreement was

12.7%. In the NLCD 3.4% of the pixels were labeled cultivated that

were not in the CDL. And finally, the remaining 2.1% were cultivated in the CDL but not in the NLCD. The total does not exactly

equal 100.0% due to rounding. If one were to assume the CDL cultivated product to be the truth or reference, then the commission

error in the NLCD cultivated crops is 23.0% and omission error

14.5%.

Pixel agreement result percentages by state are shown graphically in Fig. 4. Most states have total pixel level agreements above

90.0%. Largest disagreements are in the states of Wisconsin and

Delaware at near 17.0%. Wisconsin is particularly large discrepancy

in being non-cultivated in the CDL and cultivated in the NLCD. Delaware is the opposite with a large CDL component that is cultivated, where the NLCD is not. Less severe and more balanced

differences that are still notable are Indiana, Iowa, Kansas, Maryland, North Dakota, Ohio, and South Dakota. All are relatively heavy cropland states and have disagreements a bit above 10.0%.

Granted, the statistics can be misleading because by random

chance it is easiest to have agreement in states with little percentage of crop areas.

If one of the classifications is taken as truth, then it is possible to

compare the disagreement scenarios to obtain assessment of omission and commission errors and a sense of bias between them. For

example, Kansas which is one of the more intensive cropland

states, shows 5.1% of the pixels are labeled as cultivated in the

NLCD and not cultivated in the CDL, while 4.9% of the pixels are

the opposite. Thus, there is a total disagreement 10.0%. But because

the errors are nearly equal but contrasting there is little overall

bias. This is a prime example, where the state totals do not tell

the whole story. The state with the biggest difference between

the two error metrics is Wisconsin. 13.4% of the pixels there are

categorized as cultivated in the NLCD and non-cultivated in the

CDL. The opposite scenario is 4.0%. Thus, their errors do not cancel

and it suggests the NLCD opinion on cultivated area is much larger.

Conterminous US wide map differences between the NLCD and

CDL cultivated are shown in Fig. 5. Notable regional discrepancies

were found across the mid-Atlantic’s Delmarva Peninsula, southcentral Colorado and central Wisconsin. The latter area is shown

in detail in an example map subset in Fig. 6c. Many of the map differences appearing visually can be attributed to the inclusion of

areas of alfalfa hay into cultivated crops within the NLCD product.

Cross checking against the CDL derived alfalfa category areas helps

confirm this. While by some definitions alfalfa could be considered

a cultivated crop, by strict interpretation of the NLCD definition it

best fits into pasture/hay and thus is in error. For clarification, pasture/hay is defined by the NLCD as ‘‘areas of grasses, legumes, or

grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/

hay vegetation accounts for greater than 20% of total vegetation.’’

Also showing lots of disagreement against the NLCD are areas of

‘‘summer fallow’’ within the CDL cultivated map. Summer fallow

are fields that are kept out of production during a regular growing

season and thus may only be tilled every other year. But, many of

these summer fallow areas appear to be incorrectly as pasture/hay

in the NLCD.

4. Discussion

Tilled areas results from the CDL analysis aligned closely with

the CoA at the state level but showed a general undercount bias.

This is consistent with what is perceived as underestimation by

the CDL crop categories independently. By using the CoA as

reference though it allows for the tuning of these CDL tilled area

statistics through regression estimation. There were a total of

112.8 million hectares (out of 765.1 million hectares of total land

area) found to be annually tilled in the entire conterminous US.

Multiplying them by the CDL versus CoA regression slope of

1.0879 yields an adjusted area of 122.7 million hectares. This is just

0.1 million hectares below the CoA total. One can apply the 1.0879

multiplier to all pixels across a given study areas to come up with

what are likely more refined area totals, regardless of geographic

extent.

States appearing as outliers in the regression analysis between

the CDL and CoA make it suspect that there is an error in one,

Table 2

Estimates of tilled area and% tilled by US state from the CDL analysis.

State Tilled area (ha) Land% tilled

Alabama 522,813 4.0

Arizona 537,522 1.8

Arkansas 2,733,620 20.3

California 1,812,516 4.5

Colorado 2,755,078 10.3

Connecticut 21,769 1.7

Delaware 197,654 39.2

Florida 516,279 3.7

Georgia 1,294,885 8.6

Idaho 1,380,605 6.4

Illinois 8,679,430 60.5

Indiana 4,523,148 48.8

Iowa 9,296,238 64.4

Kansas 9,147,169 43.4

Kentucky 1,026,153 10.0

Louisiana 1,809,282 16.4

Maine 101,226 1.3

Maryland 501,222 20.0

Massachusetts 30,156 1.5

Michigan 2,437,007 16.6

Minnesota 7,086,247 34.5

Mississippi 1,634,770 13.5

Missouri 3,410,465 19.2

Montana 3,976,685 10.5

Nebraska 6,794,950 34.2

Nevada 21,761 0.1

New Hampshire 7,764 0.3

New Jersey 180,933 9.5

New Mexico 544,980 1.7

New York 792,801 6.5

North Carolina 1,658,771 13.2

North Dakota 8,033,671 45.4

Ohio 3,466,048 32.8

Oklahoma 2,940,988 16.6

Oregon 1,014,968 4.1

Pennsylvania 937,536 8.1

Rhode Island 3,025 1.1

South Carolina 738,112 9.5

South Dakota 5,484,602 28.1

Tennessee 1,036,037 9.7

Texas 8,272,463 12.2

Utah 242,765 1.1

Vermont 45,801 1.9

Virginia 526,695 5.2

Washington 1,901,730 11.0

West Virginia 25,391 0.4

Wisconsin 2,443,661 17.4

Wyoming 255,258 1.0

Conterminous US 112,802,649 14.7

100 D.M. Johnson / Agricultural Systems 114 (2013) 95–105
Fig. 2. Area estimates of CDL derived tilled cropland versus CoA tilled cropland.

Fig. 3. Area estimates of CDL derived cultivated cropland versus NLCD cultivated cropland.

D.M. Johnson / Agricultural Systems 114 (2013) 95–105 101
the other or both. It is probably more likely in the CDL analysis but

it could be interpreted as a weakness in the CoA data as well. The

states with the largest differences would be the first to investigate

if trying to gauge the reliability of either dataset. Montana, for

example, has a large absolute difference with the CDL tilled map

about 1.6 million hectares under the CoA area estimate, and of all

the states it falls farthest from the regression line. The discrepancy

may lie in the fact there is a lot of dryland type farming in which

many fields may sit for several years without being used. Thus,

the CoA may capture them as cropland, as that is how they were

reported by the farmer, but nothing was actually actively grown

on them during the 2008–2011 CDL years. A similar situation is

probably also occurring in neighboring North Dakota which

actually has the largest difference (nearly 1.8 million hectares)

between the tilled area analysis and the CoA.

Crop area estimate differences could be compounded by the

non-overlap of the years used in the analysis between the CDL

information and both the CoA and NLCD information. In the former

case there is a three year difference (2007–2010) and the later

four years (2006–2010). This is always a caveat between dataset

inter-comparisons when the timing does not coincide. And, the

larger the time differences between the analysis the larger the

uncertainty. While it is believed there were not significant changes

between the areas tilled from one year to the next, there were

certainly at least some areas that went into or out of crop production. This is particularly true since commodity prices have risen

significantly in the last few years and thus likely more total land

had gone into production as the demand for grain has increased.

The forthcoming 2012 CoA will best establish what direction land

change has occurred after 2007. To gain prior insight, change

analysis of the CDLs from 2008 and 2011 could be undertaken

and potentially help quantify the differences. But, careful attention

to the error rates of the different CDLs needs to be incorporated

so that any change found is truly reflective of what is occurring

on the ground and not just the compounding of inherent noise or

bias.

Fig. 4. State-level pixel agreement between cultivated in the CDL derived versus NLCD.

102 D.M. Johnson / Agricultural Systems 114 (2013) 95–105
When comparing the NLCD versus the CDL cultivated areas it is

reasonable to ask which is more correct. This is difficult to firmly

answer without robust ground reference information about tilled

versus non-tilled cover types from the concurrent year to validate

either product. The CDL crop category accuracies vary by crop type

but are generally in the 85–95% correct range for the major field

crops which dominate what was ultimately being labeled as tilled.

The combining of the individual crop categories should be improving those accuracies further. The assimilation of four years of CDLs

to derive a single product should even more improve the accuracy

over information from a single year product like the 2006 NLCD.

And finally, areas found here with large discrepancies were investigated and believed to be more likely in error in the NLCD than the

CDL. Thus, it is believed that in terms of cultivated areas the area

estimates and maps from the combined CDLs are more robust than

what the NLCD provides.

Many of the egregious differences between the CDL and NLCD

come from the initial categorization of alfalfa and fallow crop

areas. From the CDL perspective, which is also likely applicable

to the NLCD, these are two of the most difficult classes to map.

For alfalfa, and other hays constituting forage crops, the CDLs

only map to an estimated accuracy of roughly 50% and are often

highly confused with pasture and grasslands. In many regions this

is no better than a simple educated guess. For the fallow class, it

is really a usage category and thus may not be portraying a consistent cover type or age. For example, in some areas fallow may

represent bare soil, for a single season, while in other areas it may

be grass or trees if the ground has not been utilized for crops for

years. This variability makes the CDL’s classification process for

fallow a deeper challenge since it is based both on spectral reflectance and temporal properties in its decision process. Furthermore, even if starting with highly accurate land cover maps the

harmonization of different category definitions can be difficult

and there is no true standard. It is generally believed here that

both alfalfa and summer fallow are better represented in the

CDL but some of the differences could be perceived as definitional, particularly within the NLCD. And, it is also acknowledged

that both alfalfa and fallow areas could be arguably both be suited or not suited as cultivated crop categories given a particular

context.

Only state level regression estimates were given here. More robust analysis could be undertaken with the same methodology but

utilizing data aggregated to the county level. There are over 3000

counties nationally which would greatly expand the number of

data points. Analysis could be done nationally as a whole or regionally to fine tune regression estimates and better ascertain localized

differences between the tilled CDL area statistics and those from

the CoA or NLCD.

Fig. 5. Map differences between CDL cultivated cropland and NLCD cultivated cropland.

Fig. 6. Map subset example comparison of CDL and NLCD cultivated areas (central Wisconsin): (a) CDL derived cultivated (dark gray), (b) NLCD cultivated (dark gray) and (c)

difference image (see legend in Fig. 5).

D.M. Johnson / Agricultural Systems 114 (2013) 95–105 103
The multi-year CDL aggregation methodology chosen may not

be the best possible or could potentially be refined further. Less

weight in the voting rule was given to years 2008 and 2009 since

they had lower thematic accuracies to begin with and were usually

based on the 56 m AWiFS resolution. That decision was ultimately

determined subjectively, after some trial and error, with not only a

goal of accuracy but also of simplicity and consistency across the

entire US. It could be argued that the data from 2008 and 2009

should not have been included at all, voting rules made different

and more complex, or a more sophisticated spatial smoothing routine employed. Furthermore, it is reasonable to imagine that different regions may work better with different techniques so that a

single universal approach may not be a best fit for all areas.

The emphasis on this work was to document tilled versus nontilled areas. The analysis could easily be modified to target specific

crops or crop types. The weight or usage rules of the different years

could also be altered to better tailor the output results. Derived

data layers examples like ‘‘high probability corn areas,’’ ‘‘C5 plant

areas’’ or ‘‘small grain crops’’ could be easily constructed. Furthermore, analysis of crop rotation patterns could be undertaken now

that a time series of annual crop specific land cover information

has become available.

The analysis presented has been broad scaled. Admittedly, the

national and state-level statistics presented from the CDL probably

do not add much value to what is found in the CoA other than add

some caution in cases found to be in relatively wide disagreement.

However, the real appeal of the this type of derived dataset is when

statistics or land cover spatial analysis is needed at local or even

field level geographic scales. In those cases only a high quality land

cover product, like the annually tilled CDL layers accomplishes the

goal.

5. Conclusion

Area and map estimates of annually tilled croplands have been

presented for the conterminous US. The ca. 2010 product was derived by assimilating four years of CDL data to create the best

and most consistent national level map possible. These CDL data

have provided a new era of spatially detailed and timely land cover

of croplands available across the US. The ultimate goal of this work

was to derive the very best model of land considered to be under

annual tillage practice in the US and, alongside, understand the result’s errors or biases.

It was shown the CDL derived annually tilled area to be about

112.8 million hectares nationally. This underestimates the true

area by about 8.2% when using the CoA as reference. Regression

analysis at the state level showed the underestimate bias to be

about 8.8%. Either way, some added uncertainty of the difference

exists since the CoA was based on the 2007 crop year and the tilled

map more closely represents 2010. Thus, there may have been

some true land cover change during the same time confounding

the comparison of the results. States with the most notable differences from the CoA were Montana, North Dakota and Texas. Delaware was also an outlier when looked at from a percentage

difference standpoint.

An evaluation of the NLCD cultivated cropland category was

also undertaken. Area total statistics for cultivated cropland in

the NLCD closely matches that of the CoA at national and state levels. However, map differences, sometimes large, between the NLCD

and the CDL tilled were found which put into question the accuracy of the NLCD cultivated layer. Of particular issue is the inclusion of areas likely to be alfalfa into the NLCD cultivated

cropland areas which by definition should not include them. States

showing the largest differences by cultivated area percentage were

Arkansas, Delaware and Wisconsin.

Detailed and timely land cover information is usually difficult to

find and often lacking in quality, resolution, or scope. Datasets like

the annually updated 30 m CDL are changing this though. And

while it is likely that the CDL tilled model derived here is an underestimate of the true area total, taken contextually in map form and

adjusted for bias it should be considered state of the art. It is particularly suited to regional or local land cover analysis of croplands,

where high resolution maps or area statistics just do not exist.

Acknowledgements

Special thanks to colleagues whom provided suggestions and

editing help. Particularly, they were Jeff Bailey, Claire Boryan, Rick

Mueller, Bob Seffrin and Patrick Willis. Also thanks to the peer

reviewers each of whom added useful and thoughtful

improvements.

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