U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2011–1303

Derived Crop Management Data 

for the LandCarbon Project

Derived Crop Management Data for the 

LandCarbon Project

By Gail Schmidt, Shuguang Liu, and Jennifer Oeding

Open-File Report 2011–1303

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2011

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Suggested citation:

Schmidt, Gail, Liu, Shuguang, and Oeding, Jennifer, 2011, Derived crop management data for the LandCarbon Project: 

U.S. Geological Survey Open-File Report, 2011–1303, 15 p.
iii

Contents

Introduction.....................................................................................................................................................1

Crop Type.........................................................................................................................................................2

Crop Harvest...................................................................................................................................................2

Manure ............................................................................................................................................................4

Fertilizer...........................................................................................................................................................6

Tillage Type .....................................................................................................................................................6

Crop Cover Type.............................................................................................................................................9

Special Considerations.................................................................................................................................9

References Cited..........................................................................................................................................12

Appendix........................................................................................................................................................13

Figures

1. Map showing derived crop type for Ecoregion 9.2 .................................................................3

2. Map showing derived crop harvest for Ecoregion 9.2 ...........................................................5

3. Map showing derived manure Carbon rates for Ecoregion 9.2 ............................................7

4. Map showing derived fertilizer rates for Ecoregion 9.2 .........................................................8

5. Map showing derived tillage types for Ecoregion 9.2 ..........................................................10

6. Map showing derived cover crop for Ecoregion 9.2.............................................................11

Tables

1. Crop types for crop harvest ......................................................................................................14

2. Practice types for crop harvest data.......................................................................................14

3. Land cover classes used in crop type data............................................................................14

4. Crop types for manure data ......................................................................................................14

5. Crop types for fertilizer data .....................................................................................................14

6. Nutrient types for fertilizer data ...............................................................................................14

7. Crop types for tillage data .........................................................................................................15

8. Tillage types for tillage data......................................................................................................15

9. Cover crop types for cover crop data .....................................................................................15
iv

Conversion Factors and Abbreviations

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

square foot (ft2) 0.09290 square meter (m2)

Mass

ounce (oz) 28.35 gram (g) 

ounces per square yard (oz/yd2) 33.9057473586 grams per square meter (g/m2)
Introduction

The LandCarbon project is assessing potential carbon 

pools and greenhouse gas fluxes under various scenarios and 

land management regimes to provide information to support the formulation of policies governing climate change 

mitigation, adaptation and land management strategies. The 

project is unique in that spatially explicit maps of annual land 

cover and land-use change are created at the 250-meter pixel 

resolution. The project uses vast amounts of data as input to 

the models, including satellite, climate, land cover, soil, and 

land management data. Management data have been obtained 

from the U.S. Department of Agriculture (USDA) National 

Agricultural Statistics Service (NASS) and USDA Economic 

Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and 

cover crop (U.S. Department of Agriculture, 2011a, b, c). The 

LandCarbon team queried the USDA databases to pull historic 

crop-related management data relative to the needs of the 

project. The data obtained was in table form with the County 

or State Federal Information Processing Standard (FIPS) and 

the year as the primary and secondary keys. Future projections 

were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on 

Emissions Scenarios (SRES) scenarios using the historic data 

values along with coefficients generated by the project. The 

PBL Netherlands Environmental Assessment Agency (PBL) 

Integrated Model to Assess the Global Environment (IMAGE) 

modeling framework (Integrated Model to Assess the Global 

Environment, 2006) was used to develop coefficients for 

each IPCC SRES scenario, which were applied to the historic 

management data to produce future land management practice 

projections.

The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, 

manure, fertilizer, tillage, and cover crop products are used as 

input to the LandCarbon models to represent the historic and 

the future scenario management data.

The overall algorithm to generate each of the gridded 

management products is based on the land cover and the 

derived crop type. For each year in the land cover dataset, the 

algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop 

type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that 

crop type. The following is the overall algorithm used for the 

set of derived grids. The specific algorithm to generate each 

management dataset is discussed in the respective section for 

that dataset, along with special data handling and a description 

of the output product.

Inputs: start_year, nyears, landcover_filename, county_fips_filename, crop_filename, 

manure_filename, fertilizer_filename, tillage_filename, base_output_filename

Outputs: crop_type dataset, crop_harvest 

dataset, manure dataset, fertilizer dataset, tillage dataset and cover crop dataset

Obtain the input arguments

Read the county FIPS map

For each year

 Read the agriculture land cover for 

 the current year

 For each line

 For each sample

 Read the county FIPS for this 

 pixel

 Read the crop harvest informa 

 tion for the county FIPS 

 and year

 Read the manure information 

 for the county FIPS 

 and year

 Read the fertilizer informa

 tion for the county FIPS 

 and year

 Read the tillage information 

 for the county FIPS 

 and year

 Read the cover crop informa

 tion for the county FIPS 

 and year

Derived Crop Management Data for the 

LandCarbon Project

By Gail Schmidt1, Shuguang Liu2, and Jennifer Oeding1

 1Stinger Ghaffarian Technologies (SGT), contractor to the U.S. Geological Survey, Earth Resources and Observation Science (EROS) Center, 47914 

252nd Street, Sioux Falls, SD 57198, USA; worked performed under USGS 

contract G10PC00044.

2

U.S. Geological Survey, Earth Resources and Observation Science (EROS) 

Center, 47914 252nd Street, Sioux Falls, SD 57198, USA.
2 Derived Crop Management Data for the LandCarbon Project

 If this is an agriculture pixel

 Derive crop type for cur-

 rent pixel

 Derive crop harvest for 

 current pixel and 

 crop type

 Derive manure for current 

 pixel and crop type

 Derive fertilizer for cur

 rent pixel and crop type

 Derive tillage for current 

 pixel and crop type

 Derive cover crop for cur-

 rent pixel and crop type

 End if

 Write the crop type, crop har

 vest, manure, fertilizer, 

 tillage and cover crop values 

 for current year to separate 

 gridded netCDF4 files

 End for sample

 End for line

End for year

The land cover and gridded county FIPS datasets are the 

key inputs, in addition to the crop harvest, manure, fertilizer, 

tillage, and cover crop tables. A C-based application has been 

developed by the LandCarbon team, using the main algorithm 

and the algorithms described in the Crop Type, Crop Harvest, 

Manure, Fertilizer, Tillage Type, and Cover Crop sections, to 

generate derived crop management grids for the historic and 

A1B, A2, B1, and B2 future scenarios. These derived grids 

are generated for each of the ecoregions or regional study 

areas for each of the years being processed by the LandCarbon 

model.

Crop Type

The crop harvest data used by the LandCarbon project 

include the county FIPS, year, total acres planted, total acres 

harvested and yield for each crop and practice type within the 

county. It includes the percentage of total acres harvested and 

the converted yield for each crop. Crop types and practice 

types for crop harvest data for the represented crop types and 

practice types in the crop harvest table are shown in tables 1 

and 2 in the appendix. The table 1 and 2 crop harvest values, 

along with the gridded county FIPS map, allows for a gridded crop type dataset to be derived for each of the agriculture 

pixels.

To derive the crop type, the percent of composition (total 

acres harvested) for each crop (table 1in the appendix) was 

used. Based on a random number, the crop type is returned 

once the accumulated percent of composition exceeds the 

random number. The total crop practice was used to obtain the 

crop harvest data values for the current county FIPS and year. 

Inputs: percent_composition for all crops 

from crop harvest data for the current 

county and year

Generate random number, r (float from 0.0 

to 1.0)

Loop through the represented crop types

 Accum_percent = Accum_percent + per-

 cent_composition of current crop

 If Accum_percent >= r then

 Return current crop type

 End if

End loop

There are cases in the crop harvest data where the total 

accumulated percentage of composition was zero, therefore, 

no valid data were provided for that county FIPS and year. In 

this case, the user is asked to define a representative county 

FIPS value to be used [for example, the representative county 

used for Ecoregion 9.2 is Wright County, Iowa (county FIPS 

of 19197); Ecoregion 9.3 is Stark County, North Dakota 

(county FIPS of 38089); and Ecoregion 9.4 is Gove County, 

Kansas (county FIPS of 20063)]. Another option would be to 

use values for the current FIPS and a representative year, however, this project used the representative county FIPS versus 

the need to come up with a potentially different representative 

year for various counties. The crop type (and all other management data) is then derived using the crop harvest information 

from the representative county instead of the actual county for 

the current pixel.

The future scenario values for the crop harvest data are 

provided in 5-year increments instead of yearly increments. 

When processing the future scenarios, the crop harvest values 

are interpolated as needed to produce an annual derived crop 

type dataset.

The crop type data output is a three-dimensional product where each layer represents a year of data. The basis of 

the crop type product is the land cover dataset, and the crop 

type is then derived for agriculture class pixels. Therefore, 

the actual output pixel values are the same as the land cover 

dataset for non-agriculture pixels. See table 3 in the appendix 

for the land cover class values and associated class meanings. 

The agriculture pixels (currently a class value of 11) contain 

a value that represents the crop type (table 1 in the appendix) 

plus an offset of 100. This offset was added so that the crop 

type values could be distinguished from the land cover classes. 

Therefore, any pixel value greater than or equal to 100 represents an agriculture pixel. That pixel value minus 100 then 

represents the derived crop type. An example of the derived 

crop type data for 2000 is provided in figure 1.

Crop Harvest

The crop harvest value is a simple flag of one or zero to 

represent whether the crop was harvested. The crop harvest 

value is only derived for agriculture pixels, and is based on 
Crop Harvest 3

Figure 1. Derived crop type for Ecoregion 9.2. The red pixels represent non-agriculture areas. 

The yellow, green, and blue pixels represent agriculture areas of various derived crop types 

(tables 1 and 3 in the appendix).

102° 100° 98° 96° 94° 92° 90

48°

46°

44°

42°

40°

38°

36°

U.S. Geological Survey LandCarbon Project, 

 2011, 250 meter resolution

Albers Conical Equal area projection

Standard parallels = 29.5°, 45.5°

Longitude of central meridian = -96°

Latitude of projection origin = 23°

0

0 50 100 150 200 MILES

75 150 225 300 KILOMETERS
4 Derived Crop Management Data for the LandCarbon Project

the derived crop type for the current pixel. To derive the crop 

harvest value, the planted and harvested acres for each crop 

(table 1 in the appendix) were used. The planted and harvested 

values are obtained from the crop harvest table described in 

the Crop Type section. The probability harvested is computed 

using the fraction of harvested acres to planted acres. Based 

on a random number, the crop was harvested if the probability 

harvested is larger than the random number. Otherwise the 

crop was not harvested. The total crop practice was used to 

obtain the crop harvest data values for the current county FIPS 

and year.

Inputs: planted_acres and harvested_acres 

values for derived crop type from the crop 

harvest data for the current county and 

year

Calculate probability_harvested = har-

 vested_acres / planted_acres

Generate random number, r (float from 0.0 

 to 1.0)

If probability_harvested >= r then

 Return harvested

Else

 Return not_harvested

End if

The LandCarbon project determined that the planted 

acres in a few counties and years were not reliable. In the 

event that the planted acres value was zero and the harvested 

acres value was nonzero, the probability harvested was set to 

1.0. Furthermore, when working with the future scenario with 

crop harvest data, the harvested acres values were the only 

variables projected. Therefore, the planted acres values were 

not available (that is, equal to 0.0). The algorithm was modified to use the average of the last five years from the historic 

crop harvest planted acres for the projected planted acres when 

processing the future scenario data. As specified with the crop 

type, the future scenario areas and amounts for the crop harvest data are provided in 5-year increments instead of yearly 

increments. When processing the future scenarios, the crop 

harvest areas are interpolated as needed to produce an annual 

derived crop harvest dataset.

The crop harvest data output is a three-dimensional product where each layer represents a year of data. The actual pixel 

values are either 0 (not harvested) or 1 (harvested). Pixels 

outside of the ecoregion and pixels that are non-agriculture 

pixels are considered fill pixels. An example of the derived 

crop harvest data for 2000 is provided in figure 2.

Manure

The manure data value represents the amount of manure 

applied per unit area, and is based on the derived crop type for 

the current pixel. To derive the manure value, only the manure 

dataset is used. The manure data used by the LandCarbon 

project include the state FIPS, year, total planted area, percentage of planted area treated with manure, the amount of manure 

applied, rate of manure applied, rate of Nitrogen in manure 

applied, and rate of Carbon in manure applied for each crop 

type within the state. These manure values along with the gridded county FIPS map, allows for a gridded manure dataset to 

be derived for each of the agriculture pixels.

The manure value is computed using the percent of 

planted area treated with manure for the derived crop. Based 

on a random number, manure is considered to be applied if the 

percentage exceeds the random number. The manure values 

returned are the rate of carbon in manure applied and the rate 

of nitrogen in manure applied for the current crop type, state 

FIPS and year.

Inputs: percent_treated and manure nitrogen and carbon rates for the current crop

NOTE: make sure the units should be returned in gramsN/m^2 and gramsC/m^2

Generate a random number between 0 and 1

If percent_treated >= r then

 return manure rate (for both N and C)

Else

 return 0 (no manure)

End if

Looking at the represented crops for crop harvest and 

manure data (tables 1 and 4 in the appendix), there is a difference in the crops represented for each dataset. Crop type is 

derived using the crop harvest data, however there are many 

crops from that dataset (for example, beans, canola, flaxseed, 

and other similar crops) not represented in the manure dataset. 

If the particular crop type is not represented, then the winter 

wheat crop type is used to derive the manure value since it 

typically offers average management values versus outlier 

values. For example, if the derived crop type for the current 

pixel is rye, then manure data will be read and processed for 

the winter wheat crop type. The manure dataset also contains 

crops that are a superset of crops represented in the crop 

harvest data (that is, corn in the manure dataset and grain 

corn or silage corn in the crop harvest dataset). In this case, 

the superset crop type is used. Therefore, if the derived crop 

type is grain corn, then the corn crop type is used to derive the 

manure value. These crop type “mismatches” occur with the 

fertilizer and tillage data as well. The same solutions apply, 

except that wheat is used for the default crop for fertilizer data, 

since winter wheat is not represented.

As with the crop harvest data, the future scenario areas, 

amounts and rates for the manure data are provided in 5-year 

increments instead of yearly increments. When processing the 

future scenarios, the manure areas and rates are interpolated as 

needed to produce an annual derived manure dataset.

The output manure data is a three-dimensional product 

where each layer represents a year of data. The manure pixel 
Manure 5

Figure 2. Derived crop harvest for Ecoregion 9.2. Gray pixels represent unharvested pixels and 

yellow pixels represent harvested pixels.

102° 100° 98° 96° 94° 92° 90°

48°

46°

44°

42°

40°

38°

36°

U.S. Geological Survey LandCarbon Project, 

 2011, 250 meter resolution

Albers Conical Equal area projection

Standard parallels = 29.5°, 45.5°

Longitude of central meridian = -96°

Latitude of projection origin = 23°

0

0 50 100 150 200 MILES

75 150 225 300 KILOMETERS
6 Derived Crop Management Data for the LandCarbon Project

values are in grams per meter squared. Pixels outside of the 

ecoregion and pixels that are non-agriculture pixels are considered fill pixels. An example of the derived manure data for 

2000 is provided in figure 3.

Fertilizer

The fertilizer data value represents the amount of nitrogen applied per unit area, and is based on the derived crop 

type for the current pixel. To derive the fertilizer value, the 

crop harvest and the fertilizer datasets are used. The planted 

and harvested values are obtained from the crop harvest table 

described in the Crop Type section. The table of fertilizer data 

used by the LandCarbon project includes the state FIPS, year, 

total planted area with fertilizer applied, percentage of total 

area with each nutrient, rate of application for each nutrient, 

and total nutrient amount applied for each crop type within the 

state. Crop types used for fertilizer data are shown in table 5 in 

the appendix, and table 6 in the appendix shows the represented nutrient types for fertilizer data. These crop harvest 

and fertilizer values along with the gridded county FIPS map, 

allow for a gridded fertilizer dataset to be derived for each 

of the agriculture pixels. The fertilizer value returned is the 

amount of nitrogen fertilizer applied per unit area for the current crop type, state FIPS and year.

Inputs: state level fertilizer value for 

current crop, total harvested area for the 

crop type by state

NOTE: units should be returned in gramsN/

m2

Return total fertilizer (nitrogen nutrient) divided by total harvested area for 

crop type by state

Looking at the represented crops for crop harvest and 

fertilizer data (tables 1 and 5 in the appendix), there is a difference in the crops represented for each dataset. The differences in represented crop types are handled the same as for 

the manure dataset, except the default crop type for fertilizer is 

wheat since it typically offers average fertilizer values of the 

available crop types versus outlier values.

The algorithm derives the fraction of the total fertilizer 

used by the harvested area. The fertilizer dataset contains the 

total nitrogen applied and the crop harvest data contains the 

total harvested area per crop. However, this total harvested 

area does not represent total harvested area where nitrogen 

was applied. To obtain an estimate of the total harvested area 

with nitrogen applied, the fraction of planted area with nitrogen applied to total planted area for this crop was calculated. 

That planted fraction was applied to the total harvested area 

of the crop to obtain the approximate harvested area with 

nitrogen applied.

When using the total planted and harvested area from 

the crop harvest data, those values are represented at the 

county level; however, fertilizer data are represented at the 

state level. Therefore, the total planted and total harvested 

areas were summed from each county per crop to obtain a 

state level representation of total planted and harvested area. 

Similarly, all the subsets of the fertilizer crop type in the crop 

harvest data need to be summed into this total, if applicable. 

For example, when processing the wheat fertilizer crop type, 

winter wheat, spring wheat and durum wheat values all need 

to be accumulated for each county in the state. In addition, the 

crop harvest area units are in acres, whereas the fertilizer area 

units are in 1,000 square meters, therefore a unit conversion of 

acres to square meters is needed. Finally, the future scenario 

areas and amounts for the fertilizer data are provided in 5-year 

increments instead of yearly increments. When processing 

the future scenarios, the crop harvest areas, fertilizer areas, 

and amounts are interpolated as needed to produce an annual 

derived fertilizer dataset.

The fertilizer data output is a three-dimensional product 

where each layer represents a year of data. The fertilizer pixel 

amounts are in grams of nitrogen per meter squared. Pixels 

outside of the ecoregion and pixels that are non-agriculture 

pixels are considered fill pixels. An example of the derived 

fertilizer data for 2000 is provided in figure 4.

Tillage Type

The tillage type is derived only for agriculture pixels, and 

is based on the derived crop type for the current pixel. The 

table of tillage data used by the LandCarbon project includes 

the state FIPS, year, total planted area tilled, total percentage of residue for all tilled areas, planted area for each tillage 

type, and percentage of residue for each tillage type for each 

crop type within the state. Crop types and tillage types for 

the represented crop types and represented tillage types in the 

tillage table are shown in tables 7 and 8 in the appendix. These 

tillage values along with the gridded county FIPS map, allows 

for a gridded tillage type dataset to be derived for each of the 

agriculture pixels.

The tillage algorithm accumulates the planted area for 

each of the tillage types. The fraction of each tillage type is 

calculated by dividing the planted area for each tillage type 

by the accumulated planted area. Based on a random number, 

each tillage fraction is accumulated until the sum exceeds the 

random number. The tillage fraction that allows the sum to 

exceed the random number is the tillage type returned for the 

current crop type, state FIPS and year.
Tillage Type 7

Figure 3. Derived manure Carbon rates for Ecoregion 9.2. Brown values represent pixel areas 

where no manure was applied. Yellow and green pixel values represent the derived manure 

applied in grams of carbon per meter squared, with rates up to 517 grams per meter squared.

102° 100° 98° 96° 94° 92° 90°

48°

46°

44°

42°

40°

38°

36°

U.S. Geological Survey LandCarbon Project, 

 2011, 250 meter resolution

Albers Conical Equal area projection

Standard parallels = 29.5°, 45.5°

Longitude of central meridian = -96°

Latitude of projection origin = 23°

0

0 50 100 150 200 MILES

75 150 225 300 KILOMETERS
8 Derived Crop Management Data for the LandCarbon Project

Figure 4. Derived fertilizer rates for Ecoregion 9.2. Green pixels represent areas where no fertilizer 

was applied. Light to deep orange pixel values represent the derived fertilizer amounts of nitrogren 

applied in grams of nitrogen per meter squared, in order of increasing rates up to 18 grams per 

meter squared.

102° 100° 98° 96° 94° 92° 90°

48°

46°

44°

42°

40°

38°

36°

U.S. Geological Survey LandCarbon Project, 

 2011, 250 meter resolution

Albers Conical Equal area projection

Standard parallels = 29.5°, 45.5°

Longitude of central meridian = -96°

Latitude of projection origin = 23°

0

0 50 100 150 200 MILES

75 150 225 300 KILOMETERS
Special Considerations 9

Inputs: state level tillage values for 

current crop

Accumulate the total planted area values 

 for each tillage type

Compute the fraction of each tillage 

 planted acreage value divided by the

 total accumulated area

Generate random number, r (float from 0.0 

 to 1.0)

Loop through the tillage types

 Accum_fraction = Accum_fraction + 

 fraction of current tillage

 If Accum_fraction >= r then

 Return current tillage type

 End if

End loop

Looking at the represented crops for the crop harvest and 

tillage data (tables 1 and 7 in the appendix), there is a difference in the crops represented for each dataset. The differences 

in represented crop types are handled the same as for the 

manure dataset, with winter wheat being the default crop type 

since it typically offers average tillage values of the available 

crop types versus outlier values.

Unlike the other management products, projection coefficients for the tillage data were not available for the IPCC 

SRES scenarios. To allow tillage types to be derived for the 

future scenarios, an average of the last five years of historic 

tillage data was used to represent each year in the future scenario tillage data.

The tillage type data output is a three-dimensional 

product where each layer represents a year of data. The pixel 

values represent the derived tillage type (table 8 in the appendix) for the agriculture pixels. Pixels outside of the ecoregion 

and pixels that are non-agriculture pixels are considered fill 

pixels. An example of the derived tillage type data for 2000 is 

provided in figure 5.

Crop Cover Type

Like all the other variables, the cover crop is only derived 

for agriculture pixels and is based on the derived crop type 

for the current pixel. The table of cover crop data used by the 

LandCarbon project is a single representation of historic data 

and includes the state FIPS, percentage of crop area with a 

cover crop, the percentage of the cover crop that is rye, and 

the percentage of the cover crop that is winter wheat. Cover 

crops are represented in this dataset only for corn, cotton, and 

soybeans. Therefore only corn, cotton, and soybeans can be 

transitioned to a cover crop. Rye and winter wheat are the only 

two types of cover crops reported in the USDA data. These 

cover crop percentages, along with the gridded county FIPS 

map, allow for a gridded cover crop type dataset to be derived 

for each of the agriculture pixels.

The cover crop algorithm first determines if a cover crop 

exists, based on a random number. If the percentage of crop 

area with a cover crop is larger than the random number, then 

a cover crop will be represented in the derived data. Otherwise no cover crop is represented for that pixel. To derive the 

particular cover crop type, another random number is used 

to compare to the percentage of rye and winter wheat as the 

cover crop for the current pixel. A value of no cover crop, rye 

or winter wheat will be returned for the current crop type and 

state FIPS.

Inputs: state level cover crop values, 

current crop

If crop is Corn, Cotton, or Soybeans

 Generate a random number, r, between 

0 and 100%

 If percent crop area with cover crop 

 for this crop >= r then

 Generate a random number between 

0 and 100%

 If % rye as cover crop / total 

 percent cover crop for this 

 crop <=r1

 Return rye as cover crop

 Else

 Return winter wheat as cover 

 crop

 Else

 Return no_cover_crop 

 End if

Else

 Return no_cover_crop 

End if

The output cover crop data output is a three-dimensional 

product where each layer represents a year of data. The pixel 

values represent the derived cover crop (table 9 in the appendix) for the agriculture pixels. Pixels outside of the ecoregion 

and pixels that are non-agriculture pixels are considered fill 

pixels. An example of the derived cover crop data for 2000 is 

provided in figure 6.

Special Considerations

Processing this immense amount of data for each pixel 

in the ecoregion takes some time. Heavy input/output from 

data files is expensive in processing time, as is computing the 

state averages and 5-year averages. The C-based version of the 

derived grids application developed by the LandCarbon team 

has gained substantial efficiency by implementing a couple of 

key elements. First, when the management data are read for 

the current county ID or state ID, the values are read for all the 

represented crops, nutrients, and tillage types in the dataset. 

This eliminates the need to reread the management data as the 
10 Derived Crop Management Data for the LandCarbon Project

Figure 5. Derived tillage types for Ecoregion 9.2. Values represent the derived tillage types as 

defined in table 8 in the appendix. Deep brown areas represent conventional tillage and mulch 

tillage practices. Moderate brown areas represent mulch tillage and no tillage practices. Light 

brown areas represent reduced tillage practices. Green shades represent area where no tillage 

data were available or areas where tillage was not applied because of non-agriculture landcover.

102° 100° 98° 96° 94° 92° 90°

48°

46°

44°

42°

40°

38°

36°

U.S. Geological Survey LandCarbon Project, 

 2011, 250 meter resolution

Albers Conical Equal area projection

Standard parallels = 29.5°, 45.5° Longitude of central meridian = -96° Latitude of projection origin = 23°

0

0 50 100 150 200 MILES

75 150 225 300 KILOMETERS
Special Considerations 11

Figure 6. Derived cover crop for Ecoregion 9.2. Blue pixels represent area where no cover crop 

was used. Red pixels represent the winter wheat cover crop as defined in table 9 in the appendix. 

Rye cover crop is not used in this ecoregion.

102° 100° 98° 96° 94° 92° 90°

48°

46°

44°

42°

40°

38°

36°

U.S. Geological Survey LandCarbon Project, 

 2011, 250 meter resolution

Albers Conical Equal area projection

Standard parallels = 29.5°, 45.5°

Longitude of central meridian = -96°

Latitude of projection origin = 23°

0

0 50 100 150 200 MILES

75 150 225 300 KILOMETERS
12 Derived Crop Management Data for the LandCarbon Project

crop type changes from pixel to pixel. Second, the previous 

county and state ID are stored for each pixel. As the application loops from pixel to pixel, the same management data 

often can be reused since adjacent pixels are usually from the 

same county or state or both. This further eliminates the need 

to reread data and compute the 5-year averages.

In addition, static variables are used to keep track of the 

index and step values are used when handling the interpolation 

of each crop type for all future scenario management datasets. 

This eliminates the need to recompute the interpolation step 

and index values for each pixel within the same county or state 

or both. Lastly, the output data values are written 1 year at a 

time for each management type, to minimize the number 

of writes that occur. A verbose flag allows the user to obtain 

processing information as desired and status information 

(10 percent, 20 percent, ... 100 percent) is written for each 

year in the dataset.

References Cited

Integrated Model to Assess the Global Environment (IMAGE), 

2006, Integrated modelling of global environmental 

change—An overview of IMAGE 2.4 ©: Netherlands 

Environmental Assessment Agency, no. 500110002, 228 p. 

Publication accessed August 16, 2011 at http://www.rivm.nl/

bibliotheek/rapporten/500110002.pdf.

U.S. Department of Agriculture, Economic Research Service & Agricultural Resource Management Survey, 2011a, 

ARMS Farm Financial and Crop Production Practices—Tailored Reports: database accessed August 16, 2011 at http://

www.ers.usda.gov/Data/ARMS/app/. 

U.S. Department of Agriculture, Economic Research Service, 

2011b, Fertilizer Use and Price: database accessed August 

16, 2011 at http://www.ers.usda.gov/Data/FertilizerUse/. 

U.S. Department of Agriculture, National Agricultural Statistics Service, 2011c, Quick Stats: database accessed 

August 16, 2011 at http://www.nass.usda.gov/QuickStats. 
Appendix
14 Derived Crop Management Data for the LandCarbon Project

Table 1. Crop types for crop harvest.

ID Name

0 Barley

1 Beans

2 Canola

3 Corn, grain

4 Corn, silage

5 Cotton

6 Flaxseed

7 Forage

8 Hay

9 Lentils

10 Sorghum, silage

11 Oats

12 Peanuts

13 Peas

14 Potatoes

15 Rice

16 Rye

17 Safflower

18 Sorghum

19 Soybeans

20 Sugarbeets

21 Sugarcane

22 Sunflowers

23 Sweet corn

24 Tobacco

25 Tomatoes

26 Wheat, durum

27 Wheat, spring

28 Wheat, winter

Table 2. Practice types for crop harvest data.

ID Name

0 Total crop

1 Irrigated

2 Non-irrigated

3 Non-irrigated, continuous cropping

4 Non-irrigated, following summer fallow

5 Following another crop (double cropping)

6 Not following another crop

Table 3. Land cover classes used in crop type data.

ID Name

1 Water

2 Developed

3 Mechanically disturbed

4 Mining

5 Barren

6 Deciduous forest

7 Evergreen forest

8 Mixed forest

9 Shrubland

10 Grassland

11 Agriculture

12 Hay or pasture

13 Woody wetlands

14 Herbaceous wetlands

15 Perennial ice, snow, or both

Table 4. Crop types for manure data.

ID Name

0 Barley

1 Corn

2 Cotton

3 Oats

4 Peanuts

5 Soybeans

6 Wheat, spring

7 Wheat, durum

8 Wheat, winter

Table 5. Crop types for fertilizer data.

ID Name

0 Corn

1 Cotton

2 Soybeans

3 Wheat

Table 6. Nutrient types for fertilizer data.

ID Name

0 Nitrogen

1 Phosphate

2 Potash
Appendix 15

Table 7. Crop types for tillage data.

ID Name 0 Barley, feed 1 Barley, malted 2 Corn 3 Cotton 4 Oats 5 Peanuts 6 Rice 7 Sorghum 8 Soybeans 9 Wheat, spring

10 Wheat, winter

Table 8. Tillage types for tillage data.

ID Name 0 Conventional tillage 1 Mulch tillage 2 No tillage 3 Reduced tillage 4 Ridge tillage 5 Unknown tillage

99 Tillage N/A

1

1

This class denotes that no tillage data were 

available for this state and crop, therefore it is 

unknown if tillage practices were used. 

Table 9. Cover crop types for cover crop data.

ID Name 0 No cover crop 1 Rye cover crop 2 Winter wheat cover crop
Publishing support provided by:

Rolla Publishing Service Center

For more information concerning this publication, contact:

U.S. Geological Survey Earth Resources Observation 

 and Science (EROS) Center

47914 252nd Street

Sioux Falls, South Dakota, 57198

(605) 594-6151

gmted2010@usgs.gov

Or visit the EROS Center web site at:

http://eros.usgs.gov/

Schmidt and others

—Derived Crop Management Data for the LandCarbon Project

—Open-File Report 2011–1303