Estimation of the Crop-Tillage Choices with Aggregate Data - Kurkalova

North Carolina Agricultural and Technical State University

Estimation of Markov transition matrices with aggregate data:

An application to modeling no-till frequency

Lyubov A. Kurkalova and Dat Quoc Tran


This presentation Motivation for interest in dynamics of tillage choices Markov chain model of crop-tillage choice Data, estimation, and results Conclusions and next steps


Conservation tillage Tillage

»Conventional - leaves less than 30% the soil covered with crop residue after planting

»Conservation (CT) – 30% or more residue coverNo-till (NT) – only minimal amount of soil disturbed

CT, and especially 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 (Lal at al., 1999, Lal et al., 2004)


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)


Dynamics of tillage: Limited data Nation-wide USDA ARMS (Agricultural Resources

and Management Survey)» Selected years, crops, states» Limited attempts to gather information on continuous CT

Nation-wide CTIC (Conservation Technology Information Center)»Tillage systems by county and crop, yearly 1989 –1998, 2000, 2002,

2004» Survey was not designed to track tillage from one year to another

Regional, based on surveys of farmers: » Hill, 2001; Napier and Tucker, 2001


Data: regional studies: Hill (2001, JSWC)

Continuous NT Corn-soybean, 1994 - 1999 Randomly selected 230 fields in

each surveyed county

State/ counties surveyed

% fields in NT continuously for the indicated number of years

2 3 4 5 6

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


Rotational tillage

Anecdotal evidence from Conservation Effects Assessment Project (USDA CEAP studies):» Farmers rotate tillage from one year to another»Tillage rotation is closely associated with crop rotation

Iowa and central Illinois:Soybeans: high probability of NTCorn: lower probability of NTCorn-after-corn: even lower probability of NT

Question: can we estimate these probabilities with the yearly county-level data from CTIC?


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 – NT-corn, 2 – till-corn, 3 – NT-soybeans, 4 – till-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 0

0 0

t t

p p p p

p p p ps s s s s s s s

p p

p p


NT cornTill corn

NT soybeansTill

soybeans

The 1st order Markov transition diagram


CTIC data and assumption for the model

No-till crop-tillage share, Source: CTIC


Estimation of Markov probability matrix

If the transitions from one crop-tillage category to another (field-level) are observed, then Maximum Likelihood can be used (Anderson and Goodman, 1957)

Time-ordered spatially aggregated data»Restricted Least Squares (RLS) (MacRae, 1977; Kelton, 1981;

Kelton, 1994 and Jones, 2005)» Studies of land use (Howitt and Reynaud, 2003; You and Wood,

2006; Chakir, 2009; Papalia, 2010; Aurbacher and Dabbert, 2011)

We apply RLS to 1992-1997 data»Transition matrix assumptions: stationary and 1st order


http://www.csrees.usda.gov/Extension/index.html http://www.icip.iastate.edu/maps/refmaps/crop-districts

State of Iowa: 9 Crop Reporting Districts (CRDs), 99 counties

Iowa


NT-corn Till-corn

NT-soyb

Till-soyb

NT-corn 0.00 0.00 0.40 0.60

Till-corn

0.11 0.21 0.14 0.54

NT-soyb

0.48 0.52 0 0

Till-soyb

0.00 1.00 0 0

Current year

Previous year

Estimated transition matrix, state of Iowa


Observed vs. predicted shares at state level

1993 1994 1995 1996 19970

5

10

15

20

25

Predicted NT-corn adoption rate Observed NT-corn adoption rate

Year

Ad

opti

on r

ate

(%)


Observed vs. predicted shares at state level

1993 1994 1995 1996 19970

5

10

15

20

25

30

Predicted NT-soybeans Observed NT-soybeans

Year

Ad

opti

on r

ate

(%)


IL, Mercer county

Probability of a field being in NT 2 years in a row• Hill (2001):

58.1%• Markov model:

54.1%

0.00 0.06 0.62 0.32

0.25 0.23 0.23 0.29

0.46 0.54 0 0

0.27 0.73 0 0


Conclusions and next steps Conclusions:

»Markov model provides a useful way of describing the dynamics of crop-tillage choices in Iowa

»Estimates of the probabilities of transition between alternative crop-tillage categories are consistent with qualitative data on dynamics of tillage in Iowa

Next steps:»Analyze the effect of soil productivity in the estimated Markov transition

matrices»Analyze the within-state variation in the estimated Markov transition

matrices»Extend the model to allow the Markov transition matrix to vary across

time»Apply the Markov process approach to cropping patterns data derived

from USDA/NASS-collected CDL


Acknowledgements

Partial support from USDA/ERS/Evans-Allen is gratefully acknowledged

The views expressed in this presentation are those of the authors and do not necessarily reflect the views or policies of the USDA


Thank You!

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Estimation of the Crop-Tillage Choices with Aggregate Data - Kurkalova

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