72nd SWCS International Annual Conference July 30-August 2, 2017 Madison, WI

1. Costs and Benefits of Cover Crops: An
Econometric Analysis of the Impacts of
Cover Crops on Cash Crop Yield in
Central and Northeastern Indiana Farms
Stephen M. Lira
8/02/2017

2. Our Study and Purpose
• Wealth of anecdotal information on the benefits of cover
crops
• Farmers are still slow to adopt the practice
• Lack of relevant economic information on cover crop
benefits and costs
• We sought to estimate information on the economic value of
cover crops, particularly yield effects, so farmers could
make better-informed decisions

3. Previous Economic Studies
• Studies determining costs and benefits of cover crops often
conducted in short-term on a small scale
• Method used to asses effect on cash crop yield is most
commonly linear variance model, controlling for cover crop
use and varying N application
• While results are promising, not very applicable to farmers
and their practices
• Economic studies could be very influential to farmers’
decisions

4. How this Study Differs
• Worked with several farmers and gather data on
economic aspects of their management practices
• Some farms have been cover cropped for several
years, so benefits should be fully realized
• Include several variables to control for heterogeneity

5. Methods
• Framework for study developed by previous Purdue
Ag Econ grad student (Myriam Bounaffaa)
• Bounaffaa attempted a similar study, but suffered from
a high degree of heterogeneity
• Developed this study to be highly homogenous to
obtain conclusive results

6. Region of Study
Eligible Counties
Adams Johnson
Allen (Western) Madison
Benton Marion
Blackford Miami
Boone Montgomery
Carroll Morgan
Clinton Noble
Decatur Putnam
DeKalb Randolph
Delaware Rush
Fayette Shelby
Grant Tippecanoe
Hamilton Tipton
Hancock Union
Hendricks Wabash
Henry Wayne
Howard Wells
Huntington Whitley
Jay

7. Additional Specifications
• Further requirements to homogenize the data
• Corn or Soybeans
• Genetically modified (or not specialty)
• Non-irrigated
• At least five years of historic data

8. Data Collection
• Farmers compensated $1,000/year for 3 years for
participation
• Utilized several resources to recruit farmers
• Farmers send a spreadsheet to complete
• Spreadsheet contained sections for cash crop
information, cover crop information, and a
demographic survey
• All data contained in coded spreadsheet

9. Supplied and Collected Data
• All farmers asked information about field, cash crop
management, chemical use, and demographic survey
• Cover crop farmers asked to supply cover crop
specie(s), establishment and termination methods,
time, and cost
• Weather data (Temperature and Precipitation) from
Indiana State Climate Office

10. Current Dataset
• 20 farmers, and 95 fields, 640 field years split nearly
evenly between corn and soybeans
• Fell short of goal of 35 farmers
• Lack of longevity in cover cropping fields
• Most CC fields had only been cover cropped for about
3 years

11. Yield: Cover Crop vs. Non-Cover Crop
CC Corn Non-CC
Corn
All Corn CC Soy Non-CC
Soy
All Soy
Mean 157.39 173.75 170.25 52.05 55.55 54.82
Std. Dev 40.47 41.74 41.95 8.58 9.76 9.62
Min 47 20 20 25 18.72 18.72
Max 220 263 263 68 84.8 84.8

12. Slope Data
Mean Std. Dev Min Max
CC Fields 3.77 2.38 0.5 9
Non-CC
Fields
1.90 1.93 0.5 13
All Fields 2.30 2.17 0.5 13
• Shows interesting pattern, farmers with steeper
fields could be more likely to adopt cover crops

13. Nitrogen Fertilizer (lbs. N/acre)
CC Corn Non-CC
Corn
All Corn CC Soy Non-CC
Soy
All Soy
Mean 166.65 213.91 203.81 6.22 4.40 4.78
Std. Dev 54.65 117.02 108.47 8.31 12.88 12.09
Min 20.09 0.48 0.48 0 0 0
Max 234.50 641.20 641.20 32.58 160.68 160.68
• Similar patterns for P and K with corn
• P and K in Soybeans are higher for CC fields

14. Fixed Effects models
• Originally tested FE model with all unchanging
variables
• Weather was only consistent significant variables in
both models, N was significant in corn model
• Cover crops had no significant effect
• Decided to test parsimonious model using only cover
crop variable(s), temperature and precipitation, and N
application in the corn model

15. Parsimonious Results
Corn Soybeans
Coefficient P-Value Coefficient P-Value
Cover Crop 8.2962 0.377 2.6127 0.249
May Precipitation 0.5760 0.699 - -
June Precipitation 3.3593 0.001 -0.0233 0.924
July Precipitation -3.7837 0.003 0.2665 0.408
May Temperature -8.1278 0.000 - -
June Temperature 3.6128 0.001 0.5826 0.043
July Temperature -1.0847 0.000 -0.1641 0.004
N Application 0.1001 0.019 - -
Constant 155.0988 0.000 49.7380 0.000

16. 3 or more years of Cover Crop use model
Corn Soybeans
Coefficient P-Value Coefficient P-Value
1-2 Years of
Cover Crop Use
10.3966 0.249 1.4792 0.535
3 or more Years of
Cover Crop Use
9.4771 0.396 4.5384 0.118
May Precipitation 0.5194 0.749 - -
June Precipitation 3.3564 0.001 -0.0025 0.992
July Precipitation -3.7766 0.003 0.2029 0.535
May Temperature -8.1340 0.000 - -
June Temperature 3.5795 0.002 0.6049 0.036
July Temperature -1.0782 0.000 -0.1645 0.004
N Application 0.1000 0.020 - -
Constant 155.2373 0.000 49.4215 0.000

17. Cover Crop Adoption and Use Patterns
• Cover crop farmers had steeper slopes, indicating
those who have more to gain could be more likely to
use cover crops
• Could be verified further with soil data such as SOC
• All but 6 CC years were no-till, shows strong
correlation
• No-till may be capturing benefits cover crops can
provide

18. Other Takeaways
• No impact from cover crops in first two years
• Several farmers believe cover crops can decrease
yield in the initial years of CC use
• Cover crop farmers also used less fertilizer in corn
years
• We can’t say this is due to cover crop use

19. Questions for Further Research
• Funding for soil tests on all fields in study
• Look at SOC, other agronomic aspects and their
effects on cover crops and CC adoption
• Financial analysis on long-term variability of benefits
and costs
• Study impacts of individual CC species
• More closely look at why CC farmers used less N

20. Conclusions
• Five years of historic data may have been a limiting factor in
recruitment
• Larger dataset will allow us to more easily overcome selection
bias
• Lack of results show cover crops are not “one size fits all”
• Determining benefits and costs of CC use will be arduous and
data-heavy
• Market intervention may be necessary to encourage adoption

21. Sources
• Bounaffaa, M. 2015. "Benefits and Costs of Cover Crops: A Framework for Data Collection and
Analysis." Purdue University.
• NRCS (2013) "Cover Crop Conservation Practice Standard." In NRCS-Minnesota ed.
• Schlenker, W., Michael J. Roberts. 2009. "Nonlinear Temperature Effects Indicate Severe
Damages to U.S. Crop Yields under Climate Change." Proceedings of the National Academy of
Sciences 106:15594-15598.
• Snapp, S.S., S.M. Swinton, R. Labarta, D. Mutch, J.R. Black, R. Leep, J. Nyiraneza, K. O'Neil.
2005. "Evaluating Cover Crops for Benefits, Costs and Performance within Cropping System
Niches." Agronomy Journal 97:322-332.