Agribusiness, Resource /Energy Economics and Policy,

Beckman, Jayson F.

Borchers, Allison

Stenberg, Peter L.

English

Research Papers in Economics

 
 
 
 
 
 
The Determinants of On-Farm Renewable Energy Adoption 
 
Jayson Beckman 
ERS/USDA 
JBeckman@ers.usda.gov 
 
Allison Borchers 
ERS/USDA 
ABorchers@ers.usda.gov 
 
Peter Stenberg 
ERS/USDA 
STENBERG@ers.usda.gov 
 
 
 
 
Poster prepared for presentation at the Agricultural & Applied Economics  Association’s 
2011 AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24-26, 
2011. 
 
 
 
 
 
 
 
 
 
 
 
The views expressed here are solely the authors’ and do not necessarily represent those of 
ERS or USDA. 
 Introduction 
Renewable energy generation capacity is appearing on more and more farms across the 
US. In 2007, 10,408 farms reported producing energy from wind turbines, solar panels, or 
methane digesters (NASS, 2011), but by 2009 this had more than doubled (table 1). 
Higher energy prices, environmental stewardship, technology early adapters, and 
favorable policies all likely contribute to this significant increase. For example, 
•  Renewable portfolio standards have mandated renewable energy production by 
electricity suppliers in 30 states (including DC) (DESIRE, 2011).   
•  Forty-three states have adopted net metering policies to encourage electricity 
consumers to install renewable energy generation (DESIRE, 2011).  
•  USDA conservation incentive programs offer assistance for the installation of 
solar and wind technologies for livestock watering systems which reduce 
streambank erosion.  
•  Electricity is a critical input in certain types of farming (e.g. poultry farms), thus 
the recent increases in electricity prices are making on-farm generation more 
attractive (Bazen and Brown, 2009). 
•  Due to renewable energy’s low carbon footprint this generation method may 
appeal to farming operations inherently interested in implementing other 
conservation practices as well.  
 
Technology adoption by farmers has been previously investigated.  
•  Knowler and Bradshaw (2007) and Daberkow and McBride (2003) conclude that 
farm and farm household characteristics, farm biophysical characteristics, farm 
financial/management characteristics are good determinants of adoption.  
 
Data: On-Farm Renewable Energy Production Survey 
The 2009 On-Farm Renewable Energy Survey is the first national survey of farm 
operators to obtain information on renewable energy production (NASS, 2011). The 
survey provides data about the type, size, cost, incentives and estimated savings of the 
renewable energy production. This data was merged with the 2007 Census of Agriculture 
to provide information about the farm operation and primary operator (USDA, 2007). 
 
This new dataset offers new insight into operations with renewable energy generation. 
For example, 
•  Solar energy production is the most prevalent form of renewable energy (93% of 
farms reporting renewable generation capacity have solar panels). Map 1 shows 
the distribution of these operations across the US. 
•  Wind is the second most prevalent generation type; with 17% of farms reporting 
renewable energy generation reporting wind turbines (see Map 2). However, 66% 
of these farms also report having solar panels (see figure 1). 
•  Cattle producers are the most frequent farm type to have wind or solar 
technologies (see figure 2). 
•  All types of renewable energy producers were more likely to be organic farmers 
and practice conservation techniques than the average US farmer (see figure 3). •  The amount of land owned by the average renewable energy producer is much 
more than that for the average US farmer (see figure 3).  
•  Methane digesters were far less common than either wind or solar (1% of farms), 
most likely due to being livestock specific and having high installation cost ($1.7 
million on average, compared to $32,000 for solar). 
•  23% of farms reporting renewable energy production are located in California 
(see Maps 1 and 2). 
 
Objective and Model  
This study examines the determinants of renewable energy (solar and wind) adoption by 
farmers. We exclude operations with methane digesters because the characteristics of 
these farms are much different than for all other renewable energy producers (see figure 
2).   
 
Our binary-choice model is used to test if there are characteristics which significantly 
increase the likelihood of operations adopting renewable energy. The logit model is 
specified as, 
Prob( REi = 1 ) = f(FCi, ICi, CPi, Statei) 
•  RE is an indicator variable for the adoption of wind and/or solar generation on the 
i
th farm. 
•  FC are farm characteristics (acres owned by the operation; value of machinery, 
farm type, e.g., primarily row crop). 
•  IC are operator characteristics (a measure of land tenure, how long the farmer has 
been farming, if the farmer lives on-farm; and if farming is the main occupation 
for the farmer). 
•  CP is conservation practice characteristics (if the operation is organic; if the 
operation uses conservation practices). 
•  State is a dummy variable for California, the state with the greatest number of 
renewable energy operations  
 
Discussion 
Table 2 presents the results of the logit model.   
•  Larger farms (in acres) are more likely to produce renewable energy (suggested 
by figure 2). Wind turbines and solar panels both require and are increasingly 
useful as operating acres increase. 
•  The coefficient on the value of machinery is negative suggesting farms with more 
expensive machinery are less likely to have wind and solar technologies. This is 
anticipated from the descriptive statistics. Solar producers have an average value 
of $76,571 for machinery, compared to $88,346 for the census average.   
•  Row crop farmers are less likely to produce renewable energy (only 1 of 10 types 
of adopting farms specializes in row crop production). 
•  The primary operator characteristics influence the likelihood of adoption in the 
expected ways. Operators with their primary residence on-farm and with farming 
as their main occupation are more likely to report renewable energy production.  •  Organic operations and operations using conservation practices are more likely to 
have renewable energy generation. This is in agreement with expectations and 
other literature showing environmental stewardship as a significant indicator of 
adoption.  
•  California has the most renewable energy producers, and this is likely a result of 
favorable policies offering high incentives for adoption. This should be further 
explored.   
 
Sources 
Bazen, E. and M. Brown. 2009. “Feasibility of Solar Technology (Photovoltaic) 
Adoption: A Case Study on Tennessee’s Poultry Industry”. Renewable Energy 34: 748-
745. 
 
Daberkow, S. and W. McBride. 2003. “Farm and Operator Characteristics Affecting the 
Awareness and Adoption of Precision Agriculture Technologies in the U.S.” Precision 
Agriculture 4 :163-177. 
 
Database of State Incentives for Renewables & Efficiency (DESIRE). 2011. Available at 
http://www.dsireusa.org/ Downloaded April 11, 2011.   
 
Knowler, D. and B. Bradshaw. 2007. “Farmer¹s Adoption of Conservation Agriculture: A 
Review and Synthesis of Recent Research”. Food Policy 32: 25-48. 
 
U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). 2011. 
On-farm Renewable Energy Production Survey (OFREPS): 2009. Washington DC.  
 
U.S. Department of Agriculture, National Agricultural Statistics Service (USDA). 2007.  
Census of Agriculture, Washington DC.  
 
 
 Table 1.  
Farms reporting renewable energy 
production
2007 10408
2008 17184
2009 25605  
Source: 2007: OFRES; 2008 and 2009: ARMS 
 
Figure 1.  
Solar: 7,030
Wind, Solar & 
Methane: 1
Wind & Solar: 931
Solar & Methane: 6
Wind: 487
Wind & Methane: 1
Methane: 113
Number of farms reporting generation, by source, 2009
 
 
Map 1.  
Number of Operations
1-49
50-99
100-199
200-499
500 or more
Farms Producing Solar Energy, 2009
 Map 2.  
Number of Operations
Up to 10
11-25
26-50
51-75
75 or more
Farms Producing Wind Energy, 2009
 
 
Figure 2.  
0 500 1000 1500 2000 2500
Beef Cattle Ranching and Farming
Hay Farming
Other Noncitrus Fruit Farming
Horses and Other Equine Production
All Other Miscellaneous Crop Farming
Grape Vineyards
All Other Animal Production
Other Vegetable (except Potato) and Melon Farming
Nursery and Tree Production
Sheep Farming
Number of Farms
Top 10 NAICS(six-digit) categories reporting wind or solar generating 
capacity, 2009
Source:  NASSOFRESurvey 2009; Census 2007
29% 8%
 
 
 
 
 
 
 
 
 
 
 Figure 3.  
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
Primarily crop production
Owns more than 80% of operating land
Primary residence on farm
Uses conservation practices
Farming is primary occupation
Uses organic production methods
Census Average
Solar Generating Farms
Wind Generating Farms
Methane Generating Farms
0 200 400 600 800 1,000 1,200 1,400 1,600
Methane Generating Farms
Solar Generating Farms
Wind Generating Farms
Census Average
Acres Owned
 
Table 2.  
Parameter    Estimate Standard Error Pr > ChiSq
Intercept -6.776 0.052 <.0001
The Amount of Land the Farmer Operates 0.001 1.22E-04 <.0001
The Value of Farm Machinery -0.001 1.00E-04 <.0001
If the Farmer is Predominately a Crop Farmer -0.354 0.027 <.0001
If the Farmer Owns 80% of Their Farmland 0.594 0.035 <.0001
If the Farmer Lives On-Farm 0.598 0.038 <.0001
If Farmer Practices Conservation Techniques 1.264 0.026 <.0001
If Farming is Main Occupation 0.192 0.027 <.0001
If Organic Farmer 1.809 0.043 <.0001
California 1.898 0.031 <.0001
The Number of Years the Farmer has been Farming -0.021 0.001 <.0001  

The Determinants of On-Farm Renewable Energy Adoption

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