Agribusiness, Agricultural and Food Policy, Production Economics,

Erickson, Kenneth W.

Featherstone, Allen M.

Hallahan, Charles B.

Harris, James Michael

Morehart, Mitchell J.

Williams, Robert P.

Wilson, Christine A.

English

Research Papers in Economics

Rating the Financial Health of U.S. Production Agriculture using Synthetic Credit Rating 
Methods 
 
Christine Wilson
3, Mitch Morehart
2, Allen Featherstone
1, Charlie Hallahan
2, Kenneth 
Erickson
2, Michael Harris
2 and Robert Williams
2 
 
1 Contact Author, Department of Agricultural Economics, Kansas State University, 313 Waters 
Hall, Manhattan, Kansas 66506-4011 
Phone: 785.532.4441, Fax: 785.532.6925, Email: afeather@ksu.edu  
2 United States Department of Agriculture, Economic Research Service, Washington, DC  
 
3 Department of Agricultural Economics, Kansas State University 
 
 
 
  
 
 
Poster prepared for presentation at the Agriculture & Applied Economics Association 2010 
AAEA, CAES, &WAEA Joint Annual Meeting, Denver, Colorado, July 25-27, 2010  
Copyright 2010 by Christine Wilson, Mitch Morehart, Allen Featherstone, Charlie Hallahan, 
Kenneth Erickson, Michael Harris and Robert Williams. All rights reserved. Readers may make 
verbatim copies of this document for non-commercial purposes by any means, provided that this 
copyright notice appears on all such copies.  Christine Wilson1, Mitch Morehart2, Allen Featherstone1, Charlie Hallahan2, Kenneth Erickson2, Michael Harris2 and Robert Williams2
1 Department of Agricultural Economics, Kansas State University, Manhattan, Kansas
Rating the Financial Health of U.S. Production Agriculture using Synthetic Credit Rating Methods
Introduction
The financial health of the agricultural economy has been 
Probability of Default Results Synthetic Credit Ratings
One way to think of credit models is to relate them to a well-
2 United States Department of Agriculture, Economic Research Service, Washington, DC
Synthetic  Average Probabilty of Default for ARMS Farms 1996 ‐ 2008
Definition of the Variables
Capital Debt Repayment Capacity (CDRC) – This variable is 
sed to determine repa ment capacit It meas res the abilit excellent for the past couple of years with farm income at record 
levels.  However, the overall U.S. economy has experienced a 
credit crisis, the largest recession since the Great Depression, 
and instability in input prices.  Although the U.S. farm sector has 
been largely shielded from the downturn in the economy, it may 
not be long before the agricultural sector enters a downturn.  In 
fact, livestock farms are already beginning to experience stress.  
Recent articles in the Wall Street Journal (May 19, 2009) and 
The New York Times (May 26, 2009) have indicated that 
traditional and organic dairies are financially vulnerable.  It was 
reported that a Wisconsin dairy farmer cashed out his IRA to 
purchase inputs this spring.  This leads to the question, “What is 
the frequency of these cases by farm type and geographic 
known benchmark such as Standard and Poor’s (S&P).  Relating 
a farm’s creditworthiness to the rating classes used by S&P 
benefits the research in several ways.  The S&P model has been 
established, used, and validated in the marketplace and the use 
of its classes provides a consistency in the marketplace.  This 
consistency allows the researcher the ability to compare results 
across studies.  Since the S&P model is one of the most 
commonly used, policymakers will have a better grasp of what 
the ratings indicate.
The largest percentage of ARMS farms have credit ratings of 
BB+ and BB.  Approximately 90% or more of ARMS farms have 
credit ratings from BBB- to B+. 
0.4
0.6
0.8
1
1.2
1.4
1.6
D
e
f
a
u
l
t
 
P
e
r
c
e
n
t
used to determine repayment capacity.  It measures the ability 
of the borrower to repay principal and interest on term loans by 
comparing their cash flow to their debt requirements.  The larger 
the ratio, the greater their ability to meet repayment needs.  
CDRC is calculated by dividing repayment capacity by the sum 
of annual principal and interest payments on term loans, 
working capital deficiency (WCD) and capital asset replacement 
(CAR).  Repayment Capacity is the result of net farm income 
from operations plus non-farm income plus term interest plus 
depreciation minus income taxes minus family living expenses 
minus non-farm expenses.
Owner Equity Percentage (OE) – This provides a measure of a 
’
qy y y p g g p
region?”
The last major downturn occurred in the early 1980s when farm 
bankruptcy issues dominated the news.  During that crisis, 
USDAs measurement of financial stress classified farms into 
four distinct categories (favorable, marginal solvency, marginal 
income, and vulnerable) based on a farm’s income and solvency 
position.  However, since the 1980s, lending institutions serving 
agriculture have moved to credit scoring models that provide 
more specificity in measuring risk.  
The probability of default ranged from 1.00 (2007) to 1.44 
(2002) and averaged 1.15 from 1996 to 2008.  From 2002 
though 2007, the probability of default declined before 
increasing in 2008.  
Convert Probability of Default to 
Synthetic Credit Ratings
g
0
0.2
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
15
20
25
30
35
40
P
e
r
c
e
n
t
 
o
f
 
F
a
r
m
s
Distribution of Synthetic Credit Ratings of ARMS Farms; 1996, 2002,  and 2008
borrower’s solvency.  This ratio is calculated by dividing net 
worth by total assets.  In this analysis, the OE will be restricted 
between 0 and 100%.
Working Capital Percentage (WC) – This ratio measures a firm’s 
liquidity position as it relates to its revenue.  It is calculated by 
dividing working capital by the adjusted gross income.  Working 
capital is the result of current assets minus current liabilities.  
Adjusted gross income is gross receipts minus purchases for 
resale.  This ratio is used to make sure the borrower has 
sufficient liquidity.
The above variables are graphed below from 1996 to 2008. Synthetic Credit Ratings
The probability of default for each farm was mapped into a S&P 
credit rating using the table below.
Conclusions
The probability of default was estimated for USDAs farms from 1996 to
Objectives
This paper will analyze the probability of default for USDA ARMs 
farms over time using the method proposed by Featherstone, 
Roessler, and Barry (2006).  Specifically, methods will be 
applied to estimate the probability of default for each ARMS 
farm.  With this new information, the results will be aggregated 
to geographic region, and product type to assess the farm 
sector’s financial health.
0
5
10
BBB+ BBB BBB‐ BB+ BB BB‐ B+ B B‐ CCC+
1996 2002 2008
40
60
80
100
120
140
160
180
P
e
r
c
e
n
t
Working Capital, Equity to Asset, and Debt Repayment Capacity Ratios 
for ARMS Farms; 1996 ‐ 2008
The Mapping of S&P Credit Ratings to KMV EDF Values1
S & P Rating KMV EDF Value (%)
AAA (0.00, 0.02]
AA+ (0.02, 0.03]
AA (0.03, 0.04]
AA- (0.04, 0.05]
A+ (0 05 0 07]
Data and Methods
This study used farm-level data from USDA’s Agricultural 
Resource Management Survey (ARMS). 
Credit scores are found using financial data from each farm.  
Each farm in the ARMS data is viewed as a potential borrower.  
This allows us to assess the risk that a loan will enter default 
status.  After assessing the risk of farm and assigning an 
appropriate credit rating, we are able to determine the riskiness 
of the sector by aggregating the individual farms. 
The probability of default was estimated for  USDAs farms from 1996 to 
2008.  Since 1996, the probability of default has generally trended down 
expect in 2002 when the financial condition of farms deteriorated.  Future 
research will examine differences among geographic and farm type. 
References
Featherstone, A.M., L.M. Roessler, and P.J. Barry.  “Determining the 
Probability of Default and Risk Rating Class for Loans in the Seventh 
Farm Credit District Portfolio.”  Review of Agricultural Economics, 
28(Spring 2006):4-23.
Lopez, J.A. “The Empirical Relationship Between Average Asset 
Correlation, Firm Probability of Default and Asset Size.”  Working Paper, 
Federal Reserve Bank of San Francisco San Francisco CA 2002
Measuring Probability of Default
The variables above were used to calculate the probability of 
default.
0
20
40
1994 1996 1998 2000 2002 2004 2006 2008 2010
Working Capital Equity to Assets Debt Repayment
A+ (0.05, 0.07]
A (0.07, 0.09]
A- (0.09, 0.14]
BBB+ (0.14, 0.21]
BBB (0.21, 0.31]
BBB- (0.31, 0.52]
BB+ (0.52, 0.86]
BB (0.86, 1.43]
BB- (1.43, 2.03]
B+ (2.03, 2.88]
B (2.88, 4.09]
B- (4.09, 6.94]
CCC+ (6 94 11 78]
The farm record data used for this study are expected to provide 
an accurate representation of the financial data received by a 
lender from a borrower.  These data are obtained from ARMS 
and are used to calculate the probability of default and the 
corresponding score for each farm.  The probability of default for 
each loan in the sample is calculated from an equation derived 
from a binary logit regression using actual loan origination data. 
Federal Reserve Bank of San Francisco, San Francisco, CA, 2002.  
http://www.frbsf.org/publications/economics/papers/2002/index.html probabiltyofdefault
ln -2.3643-0.00135(CDRC)- 0.0217(OE)- 0.00399(WC)
(1-probabiltyofdefault)
=
XB
XB
(e )
probabiltyofdefault
(1 e )
=
+
CCC+ (6.94, 11.78]
CCC (11.78, 14.00]
CCC- (14.00, 16.70]
CC (16.70, 17.00]
C (17.00, 18.25]
D (18.25, 20.00]
1 Source: Lopez 2002

Determining the Probability of Default and Risk Rating Class for Loans in the Seventh Farm Credit District Portfolio.” Review of Agricultural Economics,

Federal Reserve Bank of San Francisco,

The Empirical Relationship Between Average Asset Correlation, Firm Probability of Default and Asset Size.” Working Paper, Federal Reserve Bank of San Francisco San Francisco CA

Rating the Financial Health of U.S. Production Agriculture using Synthetic Credit Rating

Rating the Financial Health of U.S. Production Agriculture using Synthetic Credit Rating

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