modeling dynamics of tillage adaption tran

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 IowaDat Q. Tran1 and Lyubov A. Kurkalova2

1 PhD candidate, North Carolina A&T State University2 Professor, North Carolina A&T State University


This presentation Motivation for interest

Statistical models

Data, estimation, and results

Conclusions and next steps


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


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)


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


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


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 6IL/ 18 44 30 22 19 13IN/ 11 41 25 18 14 9MN/ 10 9 7 3 3 n/a


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?


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


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 in1 – 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 32 421 2 3 4 1 2 3 4

13 23

14 24

0 00 0

t t

p p p pp p p p

s s s s s s s sp pp p


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).


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


Model performance

CT cornr=0.82

CV cornr=0.87

CT soybeansr=0.80

CV soybeansr=0.77


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

CCVACTCCT


HEL data (percentage acreage classified as HEL)


Effect of HEL on probability of CCT

Regression line 2 year tillage-crop sequence

3 year tillage-crop sequence

Slope 0.19 0.13P‐value 0.006 0.036

2 1 1 111 1 31 3 13 1

3 1 1 1 1 111 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

yearcct

yearcct

p p s p s p s

p p p s p p s p p s p p s p p s


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 yearsMore corn: 2 or 3 years of corn with in 3 years

3 1 1 1 1 122 22 2 22 24 2 24 42 2 42 22 4 42 24 4

3 1 1 1 1 111 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 ˆ1

yearccv

yearcct

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 sp p p

*Within‐column simulated means followed by the same letter are not significantly different using Fisher’s LSD at P≤0.05.


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)


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.


Thank You for your attention!

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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 1 122 2 42 4 24 2ˆ ˆ ˆ

1

yearccv

act cct ccv

p p s p s p sp p p


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

modeling dynamics of tillage adaption tran

Environment