Presented by:

A Probabilistic Approach for Estimating 

the Spatial Extent of Pesticide 

Agricultural Use Sites and Potential Cooccurrence with Listed Species for Use in 

Ecological Risk Assessments

Katie Budreski1, Michael Winchell1, Lauren Padilla1, 

JiSu Bang2, Richard Brain2

1. Stone Environmental, Inc. 2. Syngenta Crop Protection

EMPM, April 28th, 2015

1
Background

 An important component of endangered species assessments 

(ESAs) is the definition of crop footprints that represent potential 

sites for pesticide applications based on labeled uses. 

 Historically, crop 

footprints have often 

been based on 

generalized land use 

datasets, (i.e., 

NLCD), without 

information 

concerning specific 

crops or historic use. 

2
Background

 The Endangered Species Act and the National Academy of 

Sciences (NAS) recommends using best available data in ESAs 

and have listed the Cropland Data Layer (CDL, NASS) as a 

source of best available crop/land cover information (NAS 2013). 

 The NAS report also recommends incorporating probabilistic 

approaches within ESAs to address uncertainty. 

 The current approach proposed by EPA and the Services for 

crop footprint development uses multiple years of best available 

land cover data (CDL) to account for crop rotation and 

uncertainty, resulting in a deterministic crop footprint.

3
Motivation: Probabilistic Crop Footprint 

for use in ESAs

 Previous crop footprint methods based on generalized land use 

datasets (e.g., NLCD) are overly conservative in the 

representation of crop footprints for individual crops.

 Proposed crop footprint methods are unable to quantify the 

likelihood of co-occurrence of potential pesticide use sites and 

habitat areas, which may be advantageous at Step 2 and Step 3 

of the proposed ESA process.

 Proposed crop footprint methods do not account for all 

uncertainty information available with the land cover datasets.

4
Objectives

 Develop a methodology that uses publically available, high 

resolution geospatial datasets to create refined crop footprints 

representing the probability of crop presence and thus potential 

pesticide use in any given year. 

− Account for misclassification of crop classes in the Cropland Data 

Layer (CDL) by incorporating accuracy assessment information by 

state, year, and crop, and include 5 years of data.

− Adjust CDL misclassification probability based on accuracy 

assessment information from the National Land Cover Dataset 

(NLCD) by land cover class, 

− Scale crop probabilities at the state level by comparing against 

NASS surveys of reported planted acres by crop 

5
Example Case Study

 Study Area

− The continental 

US was 

evaluated to test 

the proposed 

approach.

− Soybean was 

used as the 

example target 

crop for pesticide 

applications

6 6
Probabilistic Crop Footprint Methodology: 

5-Year CDL, Bayesian Probability Method

7 7
Probabilistic Crop Footprint Methodology

5-Year CDL, Bayesian Probability Method

8

Commission Adjustment: Adjusting for errors associated with 

pixels that were incorrectly classified as soybean.

NonSoyNonSoyNonSoyNonSoyNonSoy

Soy 

(T)

Soy 

(T)

NonSoyNonSoyNonSoy

Soy 

(T)

Soy 

(T)

NonSoySoy 

(F)

Soy 

(F)

Omission Adjustment: Adjusting for errors associated with pixels that 

are soybean but incorrectly classified as another crop or land use. 

NonSoy

Soy 

(T)

Soy

(F)

All pixels not classified as soybean

Classified as soybean and is soybean 

on the ground

Classified as soybean, but is not 

soybean on the ground

Legend:

Soy

NonSoy (T)

NonSoy (F)

Classified as soybean

Not classified as soybean and are not 

soybean on the ground

Not classified as soybean, but IS 

soybean on the ground

Legend:

NonSoy 

(T)

NonSoy 

(T)

NonSoy 

(T)

NonSoy 

(T)

NonSoy 

(T)

Soy Soy 

NonSoy 

(F) 

NonSoy 

(T)

NonSoy 

(T)

Soy Soy

NonSoy 

(F)

Soy Soy

8
Probabilistic Crop Footprint Methodology

5-Year CDL, Bayesian Probability Method

 Step 1: Base-layer Development

− CDL Footprint By Year (Target Crop with Original Class 

Preserved)

9

Example: 2012 CDL for Anderson County, Kansas

Single Year CDL Single Year CDL: Soybean Classes Extracted 

9
Probabilistic Crop Footprint Methodology

5-Year CDL, Bayesian Probability Method

 Step 2: Base-layer Development

− Overlay CDL Footprint for Target Crop by Year with NLCD (All 

Original Classes Preserved)

10

Example: 2012 CDL and 2006 NLCD for Anderson County, Kansas

Single Year CDL: 

Soybean Classes 

Extracted 

NLCD

Single Year CDL and NLCD Overlay

10
Probabilistic Crop Footprint Methodology

5-Year CDL, Bayesian Probability Method

 Step 3: Commission Adjustment

− Calculate Bayesian probability based on CDL accuracy by 

year, state, and class; and NLCD accuracy by NLCD class.

− Bayes theorem is used to determine the posterior probability a 

pixel is soybean assuming a prior probability from CDL user’s 

accuracy and conditional probabilities from NLCD overlap. 

Posterior probabilities may both increase or decrease 

depending on the NLCD overlap.

11 11

Results: 

-Pixel with CDL ‘soy’ AND NLCD ‘cultivated crops’  higher probability 

of being soy

-Pixel with CDL ‘soy’ AND NLCD other class  lower probability of 

being soy
Probabilistic Crop Footprint Methodology

5-Year CDL, Bayesian Probability Method

 Step 4: Omission Adjustment

− Omission Error Adjustment based on 2007 Ag Census or 

NASS Quick Stats. 

− Assumption: Survey data from AgCensus and NASS Quick 

Stats represents the “true” crop acreage.

− In all omission adjustment cases, no pixel is added or removed 

from the overall 5-Year CDL Crop Footprint.

− Pixel probabilities are scaled to meet AgCensus/QuickStats

acreage

12 12
Probabilistic Crop Footprint Methodology

5-Year CDL, Bayesian Probability Method

 Step 5: Final Yearly 

Crop Footprint

− Combine 

Commission 

Adjusted Footprint 

with Omission 

Adjusted Footprint

13

EXAMPLE

Commission 

Adjusted

Footprint

Omission 

Adjusted

Footprint

Final Yearly Crop Footprint

Combine

13
Probabilistic Crop Footprint Methodology

5-Year CDL, Bayesian Probability Method

 Step 6: Combine Yearly Crop Footprints

− Average 5 Years of Probabilistic Crop Footprints

 Step 7: Final Crop Footprint Check

− For Original CDL Crop Pixels with a Final Probability of 0, 

assign 0.0001

14

Take Average 

Yearly Probability 

of Crop Footprints

14
Comparison with Alternative Methods

 Compared Bayesian Probabilistic Method with 4 Alternate 

Methods

− NLCD 2006, Cultivated Crop Class

− 1-Year CDL, All Soybean Classes

− 5-Year CDL, All Soybean Classes

− 5-Year CDL, All Soybean Classes, Probability Based on # of Years 

Soybean is Present (e.g., soybean in 1 of 5 years would equal a 

probability of 0.20).

15
Results: Crop Footprint Comparison

National Crop Footprint Acreage

 Results

16
Results: Crop Footprint Comparison

Crop Footprint Extent

 Results

Example: Dallas County, Iowa 17
Results: Crop Footprint Comparison

Spurious Pixels 

Example: Iron County, Missouri
Results: Crop Footprint Comparison

Crop Footprint Extent

 Crop footprint EXTENT is the same for all 5-year CDL methods 

(5-Year CDL, 5-Year CDL Simple Probability, and 5-Year CDL 

Bayes Probability)

− NO pixels removed for probabilistic methods

 Spurious pixels are not removed, but assigned lower 

probabilities, due to overlap with non-agricultural NLCD 

classes.

 Final acreages are within the error bounds of known 

soybean acreages, based on NASS statistics.

19
Results: Crop Footprint Comparison

Co-occurrence Analysis

 All 5-Year CDL methods result in the same number of species 

that have co-occurrence with soybean crop footprint 

− NO pixels removed for probabilistic methods

NLCD NLCD 1-Year CDL 1-Year CDL 5-Year CDL 5-Year CDL

5-Year CDL, 

Simple 

Probability

5-Year CDL, 

Simple 

Probability

5-Year CDL, 

Bayesian 

Probability

5-Year CDL, 

Bayesian 

Probability

(Number of 

Species)

(% of All 

Species 

Evaluated)

(Number 

Species)

(% of All 

Species 

Evaluated)

(Number 

Species)

(% of All 

Species 

Evaluated)

(Number 

Species)

(% of All 

Species 

Evaluated)

(Number 

Species)

(% of All 

Species 

Evaluated)

511 48.99% 210 20.13% 276 26.46% 276 26.46% 276 26.46%

20
Results: Crop Footprint Comparison

Co-occurrence Analysis, Single EOs

 5-Year CDL, Bayesian Probability: 59% of EOs have less than 1% 

overlap with soybean.

 5-Year CDL: 34% of EOs have less than 1% overlap with soybean

21
Results: Crop Footprint Comparison

Co-occurrence Analysis, Single EOs

 5-Year CDL, Bayesian Probability: 55% of the habitat area has 

a crop presence probability exceeding 0.300

22
Summary of Probabilistic Approach

 Use of best available, crop-level land cover data (Cropland 

Data Layer, USDA NASS)

 Use of multiple years of crop-level land cover data to account 

for crop rotation and uncertainty 

 Probabilistic crop presence based on well established Bayesian 

approach and known uncertainty of land cover datasets

 The number of species that co-occur with the crop footprint and 

potential pesticide use is the same as all other 5-year CDL 

methods (can be used in Step 1 of proposed ESA method)

 However, the likelihood of co-occurrence of species habitat and 

potential pesticide use sites is better understood (can be used 

in Step 2 and Step 3 of proposed ESA approach)

23
Conclusions

 There are known and quantifiable uncertainties in land cover datasets that 

can be accounted for using probabilistic methods.

 The proposed method accounts for both commission and omission errors.

 Using yearly data can help understand and account for crop rotation and 

changes in land use over time. 

 The probabilistic crop footprints represent the same amount of acreage as 

NASS reported acreages by state (+/- 95% CI)

 The probabilistic crop footprints help to reduce the influence of ‘spurious’ 

pixels without removing them from the analysis.

 The probabilistic crop footprint allows for conservative estimates of May 

Affect for ‘Step 1’ of the proposed ESA process, but allows for more detailed 

analysis and review of habitat level information for ‘Step 2’ and ‘Step 3’ of the 

proposed ESA process

24
References

Budreski, K., M. Winchell, L. Padilla, J. Bang, and R. Brain. A probabilistic approach for 

estimating the spatial extent of pesticide agricultural use sites and potential co-occurrence with 

listed species for use in ecological risk assessments. IEAM, in review.

Fry, J., G. Xian, S. Jin, J. Dewitz, C. Homer, L. Yang, C. Barnes, N. Herold, and J. Wickham, 

2011. Completion of the 2006 National Land Cover Database for the Conterminous United 

States, PE&RS, Vol. 77(9):858-864.

USDA National Agricultural Statistics Service Cropland Data Layer. 2008-2012. Published 

crop-specific data layer [Online]. Available at http://nassgeodata.gmu.edu/CropScape/. USDANASS, Washington, DC.

USDA National Agricultural Statistics Service Quick Stats. 2008-2012. Published crop-specific 

statistics [Online]. Available at http://www.nass.usda.gov/Quick_Stats/. USDA-NASS, 

Washington, DC.

USDA National Agricultural Statistics Service 2007 Census of Agriculture. December 2009. 

Available at http://www.agcensus.usda.gov/Publications/2007/Full_Report/. USDA-NASS, 

Washington, DC.

Wickham, J.D., Stehman, S.V., Gass, L., Dewitz, J., Fry, J.A., and Wade, T.G. 2013. Accuracy 

assessment of NLCD 2006 land cover and impervious surface, Remote Sensing of 

Environment, Vol. 130, pp. 294-304. 25