Replaced with revised version of poster 9/2/11.Agricultural and Food Policy,

Cooper, Joseph C.

Goodwin, Barry K.

Piggott, Nicholas E.

English

Research Papers in Economics

 
 
 
 
 
Modeling Acreage Response and US Farm Policy In a New Market 
Environment  
 
 
 
 
Joseph Cooper, USDA-Economic Research Service, Washington, DC  
Barry K. Goodwin and Nicholas E. Piggott, Department of Agricultural and Resource 
Economics, North Carolina State University. Raleigh, NC 
 
  
E-mail: jcooper@ers.usda.gov, barry_goodwin@ncsu.edu, nick_piggott@ncsu.edu 
 
 
  
 
 
 
Poster prepared for presentation at the Agricultural & Applied Economics Association 2011 
AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24-26, 2011  
 
 
 
 
 
 
 
 
 
 
 
 
Direct correspondence to Nick Piggott at Box 8109, North Carolina State University, 
Raleigh, NC, 27695. The views expressed herein are those of the authors and not 
necessarily those of ERS or USDA.  
   printed by 
www.postersession.com 
METHODOLOGY 
RESULTS 
Figures 3 & 4: US Major Row Crop Acreage and New Crop Futures at Planting 
Figures 1 & 2: US Major Row Crop Nearby Futures Prices Levels and Implied Volatilities 
Figure 5 & 6: Tests for Structural Change for Corn and Soybeans, 1960-2010 
ABSTRACT 
 
AGGREGATE MODELS 
 Poor fit for the corn equation (low R2 and statistically insignificant parameters estimates suggest 
that standard acreage model may not be suitable. 
 Structural change tests reveal that there appears to be different structures after the mid 1980’s 
(Figure 5). However, given the poor model fit, this may not be very robust. 
 Better  fit  for  the  soybean  equation  (high  R2  and  statistically  significant  parameter  estimates) 
suggests that the standard acreage model fits reasonably well. 
 Structural change tests confirm that the model fits well, with less evidence of structural change 
after the early 1970’s (Figure 6). 
MICRO-LEVEL  MODELS 
 Examination  of  more  micro–level  data  seems  appropriate  to  better  understand  the  acreage 
allocation decisions in the corn market and to also evaluate  the robustness of the soybean acreage 
response. 
 An empirical analysis of two important dimensions of the current farm legislation has also been 
initiated.  This includes: an analysis of the factors associated with participation in the ACRE 
program and of the 2002 base acreage updating decision. Both issues are becoming critical in the 
ongoing Farm Bill deliberations.   
 
IMPLICATIONS 
This study investigates the unprecedented degree of price volatility in recent years and its 
impact  on  major  row  crop  acreage.  This  significant  volatility  and  instability  in  markets 
resulted  from  the  combination  of  many  factors,  the  most  prominent  of  which  relates  to 
fundamental changes in bio-energy policies that took place in 2007 and subsequent crop 
years. In particular, the U.S. Energy Independence and Security Act of 2007 established 
significant increases in mandated renewable fuel standards. The legislation mandated use of 
renewable fuels of at least 36 billion gallons by 2022—a level that was nearly 5 times bigger 
than the existing 7.5 billion gallon renewable fuel mandate for 2012 that had been established 
in the Energy Policy Act of 2005. Commodity markets reacted with unprecedented decreases 
in  relative  stocks  and  corresponding  spikes  in  prices  for  all  major  crops  (Figure  1).  In 
addition, very high levels of price volatility (Figure 2) were realized in response to these 
shocks.  Planted acres of the major U.S. row crops has increase by 12.9 million acres in 2011 
compared with 2006 (Figure 3).    
 
 
Background  
 Statutory price supports, which largely adhere to the levels established under the 2002 
and  2008  Farm  Bills,  are  at  levels  that  are  far  below  market  prices  for  most 
commodities. For most major commodities, no loan deficiency payments or counter-
cyclical payments have been made in recent years. This raises important questions 
regarding the extent to which these policies impact current production decisions.  
 The Average Crop Revenue Program (ACRE) that eligible farmers could first elect to 
participate  in  the  2009  crop  year  does  provide  payments  for  revenue  losses  that 
automatically re-equilibrate to a considerable extent to recent prices, but in return for 
these payments, the farmer must give up some other program support, including a 
portion of the fixed Direct Payments that he would have received if he stayed with the 
“traditional”  commodity  support.  Even  with  a  meaningful  possibility  of  ACRE 
revenue payments for 2009, only 13-14% of eligible farmers chose to enroll in ACRE.  
 While significant paperwork and other transaction costs are one reason, the other is 
that once enrolled, the farmer is locked into ACRE through the 2012 crop year. Hence, 
the farmers’ opinions on price movements over this period will play into the decision 
to forego a portion of Direct Payments with certainty in return for the possibility of 
payment based on losses with respect to expected revenue. Now that farmer’s have a 
choice between “traditional” payment linked to statutory price targets and one whose 
revenue target is based on (an albeit simplified measure of) expected market prices, 
certainly  the  dynamic  of  the  relationship  between  farm  policy, market  prices,  and 
farmer decisions is different than before the current Farm Act. This new relationship 
is likely to continue and, perhaps, be magnified with the deliberations over the 2012 
Farm Bill. 
 This paper reports on research that attempts to provide a better understanding of the 
role of farm policy, market prices, and price volatility in shaping and affecting acreage 
planting decisions by individual farmers.  
 
 
The Central Question 
Can we identify recent periods of structural change in US 
major row crop acreage plantings? 
 
• Employ aggregate annual data, 1960-2010, soybean and corn acreage 
• Standard acreage response models that include 
 expected price (harvest time futures) (Figure 4),  
 expected price of competing crops for acreage (harvest time futures),  
 an index of input prices,  
 acreage in the previous period 
• Apply a number of statistical approaches to test and date change 
 Andrews sup(F) test 
 Ploberger and Kramer OLS CUMSUM 
• Initial application to annual data, to be applied to more micro-level data  
 We intend to employ data that will contain the following (for 2009 and 2010 
and by crop) county-level total base acreage, acreage enrolled in the ACRE 
program, and acreage in the SURE program 
Direct correspondence to Nick Piggott at Box 8109, North Carolina State University, Raleigh, NC, 27695 
E-mail: nick_piggott@ncsu.edu. The views expressed herein are those of the authors and not necessarily those of 
ERS or USDA. 
Modeling Acreage Response and US Farm Policy In a New Market Environment 
Joseph Cooper, USDA-Economic Research Service, Washington, DC  
Barry K. Goodwin and Nicholas E. Piggott, Department of Agricultural and Resource Economics, North Carolina State University. Raleigh, NC 
 
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US Corn, Soybean, Wheat, and Cotton Acreage 
1960-2011 
corn
soybeans
wheat
cotton
combined
2007 significant increase  in 
corn acres at the expense of  
soybean acres in response to 
bio-energy policies
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Nearby Futures Weekly Average 7/2/1959 to 
11/1/2010
Corn ($/bu)
Soybean ($/bu)
Wheat ($/bu)
Cotton (cents/lbs)
2007 was a period of across 
the board high prices for 
corn, soybeans, and wheat
Structural Change Testing: Corn
Poor fit for corn may suggest structural change and need for different model
Structural Change Testing: Soybeans
Less evidence of change for soybeans, especially in later years
RESULTS FOR STANDARD AGGREGATE ACREAGE RESPONSE  
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New Crop Futures at Planting 1960-2010
corn ($/bu)
soybeans ($/bu)
wheat ($/bu)
cotton (cents/lbs)
After the significant increase in 
corn acres at the expense of 
soybean acres in 2007 new crop 
soybean prices had to bid and 
buy back acres
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Implied Price Volatities for Corn, Soybeans, Wheat, 
and Cotton 1990-2010
Wheat
Corn
Cotton
Soybean
Implied volatilities doubled during 
2007 and stayed above historical 
previous levels until more recently 
in 2010 with the exception of 
wheat which remains high to due 
low international stocks   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Variable Estimate Std. Error t-ratio Pr(>|t|) Variable Estimate Std. Error t-ratio Pr(>|t|)
Intercept 8.478 1.279 6.629 0.000 Intercept 9.355 1.032 9.061 0.000
E(Corn Price) 0.101 0.132 0.765 0.448 E(Corn Price) -0.341 0.098 -3.471 0.001
E(Soybean Price) -0.249 0.160 -1.561 0.125 E(Soybean Price) 0.314 0.113 2.773 0.008
Input Prices 0.606 0.283 2.143 0.037 Input Prices 0.084 0.233 0.360 0.720
Acreage (t-1) 0.030 0.016 1.848 0.071 Acreage (t-1) 0.315 0.017 18.473 0.000
R
2 0.255 R
2 0.9595
F-statistic: 3.937 F-statistic: 272.1000
p-value: 0.00787 p-value: 0.0000
Corn Acreage Soybean Acreage

Modeling Acreage Response and US Farm Policy In a New Market Environment

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