71st SWCS International Annual Conference July 24-27, 2016 Louisville, KY

1. North Carolina Agricultural and Technical State University
Modeling the dynamics of conservation tillage adoption:
effects of crop rotation and erodibility of the soil on
continuous conservation tillage adoption in Iowa
Dat Q. Tran1 and Lyubov A. Kurkalova2
1 PhD candidate, North Carolina A&T State University
2 Professor, North Carolina A&T State University

2. North Carolina Agricultural and Technical State University
This presentation
 Motivation for interest
 Statistical models
 Data, estimation, and results
 Conclusions and next steps

3. North Carolina Agricultural and Technical State University
Conservation tillage
 Tillage
»Conventional ‐ less than 30% crop residue left, after planting
»Conservation tillage (CT) – at least 30% crop residue left, after planting
»Continuous CT (CCT) – CT is used continuously over a period of years
 Continuous CT (CCT), and especially continuous NT provides
significant environmental benefits, when compared to
conventional till
»Reduction in soil erosion by water and wind
»Reduction in Nitrogen and Phosphorus run‐off
»Carbon sequestration

4. North Carolina Agricultural and Technical State University
Dynamics of tillage
 For carbon sequestration benefits to occur, CT needs to be
practiced continuously over several years in a row
» Even a single year of conventional till in between years of CT (NT)
releases most of the accumulated carbon back to atmosphere
(Manley et al., 2005; Conant et al., 2007)
 Theoretical economic studies: dynamic optimization
» McConnell, 1983; Wilman, 2011
 However, most of the empirical economic studies of tillage
choices did not account for the dynamics:
» Binary, single year choice between tillage regimes (e.g.,
Conventional vs. NT), conditional on the crop grown (Rahm and
Huffman, 1984; Soule at al., 2000; Pautsch et al., 2001; Vitale et al.,
2011; Druschke and Secchi, 2014, Knowler, 2014, VandenBygaart,
2016)

5. North Carolina Agricultural and Technical State University
Dynamics of tillage: Limited data
 Nation‐wide USDA ARMS
» Selected years, crops, states
» Limited attempts to gather information on continuous CT
 Nation‐wide CTIC
» Tillage systems by county and crop, yearly 1989 –1998, 2000, 2002, 2004
» Survey was not designed to track tillage from one year to another
 Nation‐wide CEAP
» Tillage systems, yearly 2003‐2006
» Each year, different set of farmers surveyed
 Regional, based on surveys of farmers:
» Hill, 2001; Napier and Tucker, 2001

6. North Carolina Agricultural and Technical State University
48%
31%
21% Till every year
Used no‐till 1‐3 years
Used no‐till in all 4 years
Claassen & Ribaudo, (2016, Choices)
 Field survey (ARMS data), 2009, 2010 and 2012
 Wheat in 2009, corn in 2010, soybeans in 2012
 Level: Nationwide

7. North Carolina Agricultural and Technical State University
Regional studies: Hill (2001, JSWC)
 Field survey
 Corn‐soybean, 1994 – 1999
 Level: IL, IN and MN
State/
counties
surveyed
% fields in NT continuously for the indicated number
of years
2 3 4 5 6
IL/ 18 44 30 22 19 13
IN/ 11 41 25 18 14 9
MN/ 10 9 7 3 3 n/a

8. North Carolina Agricultural and Technical State University
Research questions
 How often do farmers rotate CT with conventional tillage (CV) in
Iowa?
 How do CCT and alternating CT (ACT) vary spatially across Iowa?
 What factors contribute to the variability of CCT and ACT in Iowa?

9. North Carolina Agricultural and Technical State University
CTIC data, Iowa state
Crop‐tillage share, Source: CTIC
5%
15%
25%
35%
45%
55%
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Tillage‐crop share (%)
Year
CT corn CV corn
CT soybeans CV soybeans

10. North Carolina Agricultural and Technical State University
Statistical model used in present study
 Assume that crop‐tillage choice could be described as a
stationary 1st order Markov process
 Si, i = 1,2,3,4 is the share of state’s cropland in
1 – CT‐corn, 2 – CV‐corn, 3 – CT‐soybeans, 4 – CV‐soybeans
 Each transition probability pij represents the probability of crop‐
tillage category i after crop‐tillage category j the year before
   
11 21 31 41
1 12 22 32 42
1 2 3 4 1 2 3 4
13 23
14 24
0 0
0 0
t t
p p p p
p p p p
s s s s s s s s
p p
p p

 
 
 
 
 
 

11. North Carolina Agricultural and Technical State University
The 1st order Markov transition diagram
Notes: CT = conservation tillage, CV = conventional tillage. The four circles represent the four
tillage-crop states (choices) considered. The arrows represent transitions from one state to
another. The probabilities of the transitions are listed next to the corresponding arrows. Dashed
lines represent the transitions, for which the probabilities are all set to zero in the model: from
soybeans (CT or CV) to soybeans (CT or CV).

12. North Carolina Agricultural and Technical State University
Estimation approaches
 We apply Quadratic Programming to 1992‐1997 data for
99 counties in Iowa
»Estimate 99 transition matrixes
»Calculate the probability of CCT, ACT, CCV
 We use ANOVA (SAS, 1996) to analyze the effect of HEL
and crop rotation on the tillage dynamics

13. North Carolina Agricultural and Technical State University
Model performance
CT corn
r=0.82
CV corn
r=0.87
CT soybeans
r=0.80
CV soybeans
r=0.77

14. North Carolina Agricultural and Technical State University
Average probability of CCT, CCV and ACT over 99
counties
0%
20%
40%
60%
80%
100%
1‐year sequence 2‐year sequence 3‐year sequence
CCV
ACT
CCT

15. North Carolina Agricultural and Technical State University
HEL data (percentage acreage classified as HEL)

16. North Carolina Agricultural and Technical State University
Effect of HEL on probability of CCT
Regression line 2 year tillage-crop
sequence
3 year tillage-crop
sequence
Slope 0.19 0.13
P‐value 0.006 0.036
2 1 1 1
11 1 31 3 13 1
3 1 1 1 1 1
11 11 1 11 13 1 13 31 1 31 13 3 31 11 3
ˆ ˆ ˆ
ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ
year
cct
year
cct
p p s p s p s
p p p s p p s p p s p p s p p s
  
    

17. North Carolina Agricultural and Technical State University
Crop rotation effect on probabilities of CCT and ACT
Rotation CCT* ACT*
Less corn 0.13a 0.53a
More corn 0.03b 0.17b
P(T<=t) <0.001 <0.001
Less corn: 1 year of corn with in 3 years
More corn: 2 or 3 years of corn with in 3 years
3 1 1 1 1 1
22 22 2 22 24 2 24 42 2 42 22 4 42 24 4
3 1 1 1 1 1
11 11 1 11 13 1 13 31 1 31 13 3 31 11 3
ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ
ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ
1
year
ccv
year
cct
act cct ccv
p p p s p p s p p s p p s p p s
p p p s p p s p p s p p s p p s
p p p
    
    
  
*Within‐column simulated means followed by the same letter are not significantly different using Fisher’s LSD at P≤0.05.

18. North Carolina Agricultural and Technical State University
Conclusions and next steps
 Conclusions
»HEL and crop rotation/crop choice are found to have significant
effect on CCT and ACT
 Next steps:
»Extend the model to allow the Markov transition matrix to vary
across time
»Apply the Markov chain approach to cropping patterns data
derived from USDA/NASS‐Cropland Data Layer (CDL)

19. North Carolina Agricultural and Technical State University
Acknowledgements
This research was partially funded by the
USDA Forest Service Southern Research Station
agreement No. 15‐JV‐11330143‐010 and by the
USDA National Institute of Food and Agriculture,
award No. 2016‐67024‐24755. The views expressed
in this article are those of the authors and do not
necessarily reflect the views or policies of the USDA.

20. North Carolina Agricultural and Technical State University
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21. North Carolina Agricultural and Technical State University
Estimated mean CCT probabilities of alternative two-
year tillage-crop sequences
Current tillage‐crop, year t Previous tillage‐crop, year t‐1 probability*
CT corn CT corn 0.10a
CT corn CT soybeans 0.44b
CT soybeans CT corn 0.51b
LSD (0.05) 0.07
2 1 1 1
22 2 42 4 24 2
ˆ ˆ ˆ
1
year
ccv
act cct ccv
p p s p s p s
p p p
  
  

22. North Carolina Agricultural and Technical State University
Crop rotation effect on ACT probabilities of
alternative two-year tillage-crop sequences
Current tillage‐crop, year t Previous tillage‐crop, year t‐1 probability*
CT corn CV corn 0.12a
CV corn CT corn 0.13a
CT corn CV soybeans 0.40b
CV corn CT soybeans 0.56c
CT soybeans CV corn 0.48d
CV soybeans CT corn 0.27e
LSD (0.05) 0.07