ASSESSING THE RELATIONSHIP BETWEEN CROP CHOICE AND LAND USE CHANGE USING A MARKOV MODEL. Wei Hua Ohio State University Contact:

Transcription

1 ASSESSING THE RELATIONSHI BETWEEN CRO CHOICE AND LAND USE CHANGE USING A MARKOV MODEL We Hua Oho Sae Unversy Conac: Hua.22@osu.edu Dane He, Auburn Unversy Bren Sohngen, Oho Sae Unversy Absrac There s wdespread concern among envronmenal and agrculural neres ha land use change wll affec he fuure producvy of he agrculural ndusry by ulzng hghly producve land for developmen. Ths paper consders he lnks beween land use change and crop choces n order o analyze wheher land use change s nfluencng crop choces. In order o accoun for poenal endogeney beween crop choces and land use choces, we develop a Markov Model ha allows us o capure poenal endogeney beween hese wo choces land use and crop choce. The Markov model s developed for he 2 Mdwesern U.S. Saes usng USDA NRI daa a he couny level. Seleced aper prepared for presenaon a he Amercan Agrculural Economcs Assocaon Annual Meeng, rovdence, Rhode Island, July 24-27, 2005 Copyrgh 2004 by We Hua, Dane He, and Bren Sohngen. All rghs reserved. Readers may make verbam copes of hs documen for non-commercal purposes by any means, provded ha hs copyrgh noce appears on all such copes. Acknowledgemen: Fundng for hs research has been obaned from he USDA Naonal Research Inave. The auhors hank parcpans n research semnars a Oho Sae Unversy for her commens.

2 ASSESSING THE RELATIONSHI BETWEEN CRO CHOICE AND LAND USE CHANGE USING A MARKOV MODEL Inroducon Over he years, a number of auhors have explored land use change decsons Harde and arks, 997; lannga, 999; Cho e al., 200; Hseh, 2000; Bhaara e al., 2004; Templeon, 2004, and many acreage response models have been esmaed o analyze crop choce decsons Wu and Adams, 200; Wu and Segerson, 995; Lchenberg, 989 among he man feld crops lke corn, soybeans, whea, ec. Whle hese papers provde mporan nsghs, hey do no consder he neracons beween land use change decsons and crop choce decsons, alhough s lkely ha hese decsons nfluence each oher n dfferen ways. On one hand, land use change and crop choce decsons are deermned smulaneously. In each me perod, a farm landowner may decde how much land o sell for developmen, whle a he same me, he/she may decde how much land o grow crops, and how much land o grow lvesock, and how much land for mber harves, and more specfcally, he/she may decde wha knds of crops o grows and allocae hs/her land among all of hose land use caegores. Therefore, he shares of developed land and he shares of dfferen crop choces are deermned smulaneously. On he oher hand, here are recursve relaonshps beween he wo decsons. For example, here s evdence ha curren crop choce decsons affec fuure sol producvy Orazem and Mranowsk, 994 and ha developmen uses he leas producve land frs when occurs n rural areas Hseh e al., 2000; He e al., 200, Cho e al., 200. A he same me, land use changes n rural areas appear o nfluence crop choces as landowners adjus capal and labor usage and poenally shf remanng agrculural lands owards less labor nensve uses.e., conservaon llage, 2

3 or hgher value crops. The changng crop choces may lead o dfferen profably of farmng operaons, whch may hen nfluence land converson hemselves. Ths sudy models he crop choces and land use change decsons n one framework, assumes ha crop choces are endogenous wh he land use change decson, and seeks o denfy how crop choces affec land use change decsons, and vce versa. In parcular, hs sudy begns o explore he process or hsores of land use change, and answer he queson: wha successon of land uses ulmaely leads o loss of agrculural land? Ths objecve wll be accomplshed by buldng a Markov Chan model of croppng and land use decsons ha focuses on major gran and vegeable crops n he Mdwes, as well as fores, grazng, and urban uses. Usng he Markov Chan model, equlbrum land use shares are predced, whch have polcy mplcaons for fuure land use plannng. Leraure Revew There has been consderable research no he causes or deermnans of land use change Harde and arks, 997; lannga e al., 999; Cho e al., 200; Hseh, 2000; Bhaara e al., 2004; Templeon, Mos sudes consder general land use caegores: agrculure, fores, urban uses; whle, some only focus on urban use e.g. Hseh, 2000; Templeon, There are also many sudes on opmal mng of developmen e.g. He, e al., 2003; Irwn and Bocksael, Besdes economc research, here have been a number of geographc sudes on land use change e.g. Qhao Weng, 2002, whose analyss are done n dynamc framework. Ther analyss, however, are sascally based, relyng on saelle remoe sensng echnques and Geographc 3

4 Informaon Sysem GIS, and do no explore he underlyng economc reasons behnd he phenomenon of land use change. Facors ha have been found o nfluence land use change nclude land qualy, land rens, populaon densy, growh raes of populaon, per capa ncome, ransporaon, accessbly o urban ceners, demographc characerscs such as age and educaon, and polcy conrols, ec. Specfcally, lannga 999 uses a mulnomal log model and fnds ha populaon densy does no have a sgnfcan effec on he rao of agrculural o foresland, bu sgnfcanly nfluences he rao of urban o foresland. Counes wh lower average land qualy and hgher ransporaon coss end o have less agrculural land relave o foresland. The resuls from a modfed mulnomal log model esmaed by Harde and arks 997 ndcae ha populaon densy and per capa ncome have a sgnfcanly negave nfluence on farmland and foresland, and share of hgh qualy land s posvely relaed o farmland, bu negavely relaed o foresland. Landowner s age s posvely relaed o foresland, bu negavely relaed o farmland. The resuls from a spaal model by Cho e al., 200 sugges ha urbanzaon rends, measured by populaon densy and dsance from he neares cy, affec manly he levels of change o urban from fores and agrculural land, bu he decson beween agrculure and foresland depends manly on land rens and land qualy. Usng GIS daa for wes Geoga, Bhaara e al sugges ha agrculural use s negavely relaed o per capa ncome bu posvely relaed o educaon level. Travel me o work has a posve effec on developed land use, bu negavely affecs agrculure and foresry. Whle hese papers provde mporan nsghs, hey do no consder he neracons beween land use change decsons and crop choce decsons, alhough s 4

5 lkely ha hese decsons nfluence each oher n dfferen ways. On one hand, land use change and crop choce decsons are deermned smulaneously. In each me perod, a farm landowner may make land allocaon decsons among dfferen land use caegores: sellng for developmen, growng crops, rasng lvesock, mber harvesng, and more specfcally, wha knds of crops o grow. Therefore, he shares of developed land and he shares of dfferen crop choces are deermned smulaneously. On he oher hand, here are recursve relaonshps beween he wo decsons. Frs, here s evdence ha curren crop choce decsons affec fuure sol producvy Orazem and Mranowsk, 994. The resulng sol producvy could nfluence he course of land developmen, and poenally shf developmen owards he leas producve land. There s evdence ha when developmen occurs, uses he leas producve land among alernaves Hseh e al., 2000; Sohngen e al., 200, Cho e al., 200, suggesng ha farmers wh he leas producve land are he frs o sell for developmen. Ths s probably because s cheaper o buld on he land wh leas producvy, due o lower land prces. However, may also be he case ha less producve land s hller, whch could ncrease consrucon coss, hereby slowng urban developmen somewha. Furher, s also possble ha landowners wh more producve land are more able o ake advanage of new opporunes ha arse as developmen occurs n once rural areas. Second, land use changes n rural areas appear o nfluence crop choces as landowners adjus capal and labor usage and poenally shf remanng agrculural lands owards less labor nensve uses.e., conservaon llage, or hgher value crops. Besdes, he land use converson process rarely nvolves a dscree sep from farmer-owner o a house or developmen projec. Insead, ofen nvolves a number of seps, from farmer-owner, o non-farm 5

6 owner poenally a developer who may ren he land, o developer, o urban use. Along hs converson pahway, land enure ssues could nfluence crop choces. The changng crop choces may lead o dfferen levels of profably of farmng operaons, whch may hen nfluence land converson decsons. Dfferen analycal mehods have been appled o sudy land use change. Some are performed n a sac framework Harde and arks, 997; lannga e al., 999; Cho e al., 200; Hseh, 2000; Bhaara e al., 2004; Templeon, 2004, whle ohers are dynamc sudes on opmal mng of developmen e.g. He, e al., 2003; Irwn and Bocksael, Besdes economc research, here have been a number of geographc sudes on land use change e.g. Qhao Weng, 2002, whose analyss are done n dynamc framework. Ther analyss, however, s sascally based, relyng on saelle remoe sensng echnques and Geographc Informaon Sysem GIS, and do no explore he underlyng economc reasons behnd he phenomenon of land use change. Ths sudy dffers from he prevous ones by nvesgang he process of land use change n a dynamc framework usng he Markov Chan model, whch can accommodae boh he smulaneous and recursve relaonshps beween land use change and crop choces. The Markov Chan model has been appled n oher areas of economcs. Early economc applcaons have ncluded descrbng and predcng srucures of ndusry or marke Adelman, 958; Ehrdge e al., 985; Mellor, 984; Dsney e al., 988; modelng of consumer brand choce behavor Telser 962; and labor force analyss Heckman and Wlls, 977. More recenly, Markov modelng has been used n recreaon demand Haab 200; fnancal applcaons Ello e al, 200, and busness cycle applcaons Konolems, 200, whle Skaggs and Ghosh 999 employ a Markov 6

7 analyss o nvesgae changes n sol eroson. Markov modelng has also been used n geography for land use change analyss, ogeher wh remoe sensng and GIS echnologes e.g., Weng, However, here have been few economc applcaons of Markov modelng on land use change. The only known sudy s McMllen and McDonald 99, who used ndvdual rac daa from Chcago o esmae a Markov Chan Model of land use zonng n an urban seng. Ths sudy dffers from McMllan and McDonald by usng a Markov Chan model wh aggregae daa a couny level, whch employs a dfferen esmaon process. There are several mers of usng he Markov framework o model land use change. Frs, he Markov Chan model can capure he sae dependence he propery of land use change process, or rgdes n he zonng process,.e. he nfluence of he pas on he presen. For example, consder a couny wh 20% urban land, and 80% agrculure land. And suppose he land s worh more n urban use han n agrculural use. The enre couny mgh evenually be convered o urban use n a compeve marke, bu he adjusmen lkely akes place over a number of me perods raher han mmedaely, due o zonng resrcons or because of speculave land hold up a he urban rural frnge, for example. Therefore, f a couny has a hgher percenage of land n agrculure use n any gven year, hen one would expec a hgher percenage of agrculure land n year +. The dagonal elemens of he Markov model can measure he level of dependency beween me perods. Second, he Markov Chan model can ake care of he endogenous relaonshp beween crop choce and land use change decsons. In he Markov model, a share of one knd of land use n he curren perod depends on no only s own share n he prevous perod, bu also he shares of all he oher knds of land use n he prevous 7

8 perod. Those lagged shares, herefore, serve as nsrumenal varables, whch can help elmnae endogeney among land use choces whn he same perod. In addon, he lagged shares serve as nsrumens for analyzng he recursve relaonshp beween land use change and crop choces. Thrd, wh he Markov framework, a rcher se of facors ha affec land use change can be modeled, compared o sac analyss usng a mulnomal log model. Specfcally, raher han smply analyzng change from agrculural o urban uses, we exend he leraure by nvesgang nermedae land use saes ha precede he ulmae change o urban use. For example, we are able o nfer how changes n proporons from owned o rened farmland affec he speed of urbanzaon. Economerc Model I s dffcul o collec dealed daa racng he movemen of ndvdual racs or farms among land use caegores for he 2 saes mdwesern regon, and only he aggregae proporon of land n each caegory s avalable. Wh aggregae daa, he general Markov Model s gven by + = u u u u y y y y y y y y J j J j JJ kj Jj J j J j J k J k J j or smply 2, u y y j k k j + = J j, = 8

9 where and are he proporons of land n couny n use j a me and a y j y j me - respecvely. Generally, here are J knds of land use caegores. s he ranson probably ha a parcel of land n he h couny s n use j a me, gven ha he same parcel s n use k a me -, and u j s he assocaed error erm. The marx of erms represens he Markov marx. Two resrcons need o be sasfed n esmang he Markov model: R: Every elemen of s bounded by zero and one,.e., 0, k, j R2: Each column of mus sum o one,.e., =, k. Inuvely, he model saes ha he proporon of land n a gven use a me depends on s use a me -, and on he ransonal probables gven by he Markov marx. The ransonal probables may depend on varous characerscs of a couny; le he vecor comprsng hese explanaory varables. Therefore, s a funcon of X. In many prevous Markov sudes, has been assumed o be a lnear funcon J j= X be of X Mellor, 984; Dsney e al., 988; McMllen and McDonald, 99. Mellor 984 and Dsney e al. 988 use lnear regresson, whle McMllen and McDonald 99 use prob regresson o oban parameer esmaes. However, her model esmaon are all based on lnear probably assumpon, whch canno guaranee ha he predced probables sasfy he frs resrcon R, no o say he second one. In hs sudy, s assumed ha s a mulnomal log funcon of X, as n MacRae 977, such ha 9

10 3 = J exp X + exp X m= β β km, k=,,j and j=,,j- and 4 = J + exp X m= β km k=,,j and j=j β s a vecor of unobserved parameers o be esmaed, β kj s normalzed o zero Green 2000, The logsc specfcaon resrcs he ransonal probables o sasfy he wo condons R and R2. luggng equaons 3 and 4 no 2, hen equaon 2 becomes 5 y F y, X, u = β, j=,,j. j k + where y j s now a nonlnear funcon of y j, X, and j β. Snce for all k, one only need o esmae he frs J- equaons of 5. Suppose K vecor, hen he number of parameers ha need o be esmaed s J J K + J j= X = s a, whch requres a large amoun of compuaonal operaons. Usng Nonlnear Leas Square eraon on he frs J- equaons of 5, one can oban he parameer esmaes βˆ. In hs sudy, we use NRI daa, whch s survey daa sufferng from samplng error. Accordng o MacRae 977, leas squares esmaon appled o mperfecly observed daa wll gve nconssen esmaes, whch can be remeded by usng nsrumenal varable esmaon or lmed nformaon maxmum lkelhood esmaon. Bu as MacRae 977 also menons, he nconssency problem may no be serous f he sample sze s large. NRI daa conss of several hundreds of sample plos n 0

11 each couny, and for now, we assume ha he sample sze s large enough for he nconssency o be a problem. Laer, conssen esmaors wll be employed n he model. The obaned parameer esmaes βˆ can be plugged back no 3 and 4 o ge he predced ranson probables ˆ for a represenave observaon. The marx of ˆ represens he one-perod ranson probably marx. The -perod ranson probably marces Q are smply he marx powers of. In equlbrum, he mulperod ranson marces Q wll converge o a marx Q as goes o nfny. In ha case, all rows of Q are dencal, represenng he seady-sae dsrbuon, whch n hs case are he equlbrum land use shares. olcy smulaon can also be performed wh he predced Markov marces, and he margnal effecs of explanaory varables xm on pk Greene, p86, where J pk 6 = pk β km pj β jm x m j= Daa The sudy area n hs paper s he 2 Mdwes saes Oho, Indana, Illnos, Mchgan, Mnnesoa, Wsconsn, Iowa, Mssour, Kansas, Nebraska, Norh Dakoa, Souh Dakoa. See Appendx I for a map of hs regon. A oal of,054 counes are examned. Ths regon s neresng because s quckly urbanzng, bu sll has sgnfcan agrculural producon. Land Use Shares Daa used n hs sudy are obaned from varous sources. In parcular, he couny level land use proporons are obaned from Naonal Resource Invenory NRI

12 daabase for he years 982, 987, 992 and 997. The NRI has been conduced by he Naural Resource Conservaon Servce of he U.S. Deparmen of Agrculure every fve years snce 982. I s a scenfcally based, longudnal panel survey ha conans nformaon on nearly 800,000 sample ses across he connenal Uned Saes USDA, Esmaes from hose sample ses are vald for only when aggregaed o he couny level, whch we hus use n hs sudy. A range of en land managemen caegores are examned n hs sudy: hgh value crops, row crops such as corn, soybeans, close grown crops such as whea, oas, hay land, oher cropland, grazng land, fores, CR land, oher rural land, and developed land. The changes n he acreage of he en land use caegores are shown n able. The defnons of he land use caegores based on 997 NRI are as follows: hgh value crops nclude fru, nus, vneyard, bush fru, berres, flowers, vegeable and ruck crops ncludng melons; 2 row crops nclude corn, sorghum, soybeans, coon, peanus, obacco, sugar bees, poaoes, all oher row crops, sunflowers. Vegeable and ruck crops are defned as row crops n he 997 NRI, bu n hs sudy, hey are grouped ogeher wh horculure crops no he hgh value crops caegory. 3 Close grown crops nclude whea, oas, rce, barley, and all oher close grown crops. 4 Hay land ncludes grass, legumes, and mx of legumes and grass. 5 Oher cropland ncludes summer fallow, aquaculure n a crop roaon, and oher cropland no planed. 6 Grazng land nclude pasureland, rangeland, and grazed foresland. 7 Fores s defned as ungrazed fores land. 8 Oher rural land ncludes farmseads and ranch headquarers, oher land n farms no assocaed wh farmseads, barren land, permanen snow and ce felds, marshland, and all oher land. 9 Conservaon reserve program CR land s Includes agroforesry and ree planaons, arplane landng srps, commercal feedlos, duck farms, feld wndbreaks, greenhouses, hog facles, mnk farms, mushroom farms, nurseres, poulry facles. 997 NRI 2

13 separaed ou from oher rural land as a sngle land use caegory, because alhough boh CR land and oher rural land as defned n caegory egh are ou of producon land, dfferen paerns of land use change may occur beween CR land and oher rural land. From able, we can see ha over he years, oher rural land has been decreasng and CR land has been ncreasng. 0 Developed land ncludes urban and bul-up areas and rural ransporaon land. Table ndcaes ha here has been a 20% ncrease n developed land n he mdwesern regon beween 982 and 997. Alhough he oal acreage of hgh value crops s relavely small compared o oher land use caegores, almos doubled durng he same perod. There has been an ncrease n row crops land, hay land, foresland, bu a decrease n close grown crops, oher cropland, graze land, and oher rural land. Land Use Rens Land use rens are mporan land use change deermnans. In hs sudy, populaon densy and populaon growh are used as a proxy for urban land values. Dfferen approaches have been used n prevous leraure o esmae agrculural rens. Harde and arks 997 use farm revenue and coss as wo varables separaely; Lchenberg 989 uses agrculural commody prces; lannga e al. 999 uses presen dscouned value of he sream of annual ne revenues, whle Cho e al. 200 use annual revenue above varable cos. In hs sudy, he followng formula s used o esmae crop rens from couny level daa: 7 Annual crop ren $/acre = [rce $/un - varable cos $/un] yeld un/acre Couny level crop yeld daa and sae level crop prces daa are obaned from he USDA Agrculural Sascs Daa Base USDA-NASS. Regonal level varable cos daa are 3

14 from USDA Economc Research Servce commody coss and reurns daase USDA- ERS. Because ERS employs dfferen regonal defnons and dfferen measures of producon coss before and afer 995, adjusmens are made n order o make he measure of varable cos as conssen as possble across he four years. Crop rens for corn, soybeans, whea, and oas are esmaed for each couny based on formula 7. Snce on average, corn and soybeans accoun for 86% of land n row crops n he sudy regon, couny level esmaes of row crop rens are hen deermned by weghng he rens for corn and soybeans by he number of acres n he wo crops n he couny for each perod. Smlarly, because whea and oas occupy 9% of land n close grown crops, weghed crop ren beween whea and oas s used as he close grown crop ren for each couny. Annual fores rens s esmaed as he annual share of dscouned ne presen value of mber revenue per acre, usng daa from USDA Fores Invenory Mapmaker Verson.7. Ne presen value of mber revenue per acre s obaned wh he Fausmann formula Johansson and Logfren, 985: NV p f a exp ra c =, exp ra where a s roaon age, p s mber prce, r s neres rae, c s harves cos. Annual fores ren s hen he annual share of NV r NV. Urban land values are measured by populaon densy O_DEN and populaon growh rae O_GROWTH. opulaon densy s measured as populaon per acre a couny level. Couny level populaon esmaes are from he Bureau of Economc Analyss USDC BEA, and he oal area of each couny s from NRI daa. Oher measuremens of urbanzaon level wll be ncluded laer, for example, dsance from each couny o he neares major cy wll be calculaed from ZIFI developed by USDA. 4

15 Oher Varables Daa for sol characerscs are from NRI daa. Average land capably class ALCC for each couny s esmaed o measure average land qualy. The lower he value s, he hgher he average land qualy. The share of land whn he frs wo capably classes LCC_2 s also ncluded n he regressons. Oher characerscs such as waer holdng capacy, sol permeably can be obaned by lnkng NRI o he SOIL5 daabase developed by he NRCS Tanaka and Wu, Clmae daa are ncluded o conrol for ecologcal realy, such as unfness of some areas for foresry. Couny level seasonal precpaon and emperaure daa are obaned from Dr. Bren Sohngen. Capal nvesmen nensy s measured by wo varables, one s marke value of buldngs and land per acre BLDGAC, he oher s marke value of machnes and equpmen per acre MACHAC. The cale nvenory CATTLE_N s ncluded as a measure of lvesock operaon sze. Share of owned farmland OWN_ER s ncluded as a measure of land enure. Daa for all hese varables are from Census of Agrculure USDA- NASS. Oher daa wll need o be colleced as follows: Couny level unemploymen rae wll be obaned from he Bureau of Economc Analyss; llage pracce daa wll be obaned from Conservaon Technology Informaon Cener CTIC, whch wll be suded n combnaon wh crop choce laer. Non-agrculural wage raes need o be colleced o replace he general wage rae, n order o ge a beer measuremen of opporuny cos of workng on he farm. The full daa se s based on four years of observaons a couny level 982, 987, 992, and 997. All moneary values were 5

16 deflaed for base year Specfcally, curren dollars were convered no consan dollars by usng he formula: IDB 8 Year Z consan dollar value=year Z curren dollar value IDZ, where ID s base year ID number and ID s year Z ID number. 2 B Based on he daa currenly colleced, he sascs of he varables used n he regressons are shown n able 4. Z Emprcal Resuls and Dscusson In order o check esmaon resuls, a nonparamerc Markov ranson marx s esmaed based on sascal frequency, wh p k = J n j= k n j, where s he ranson p k probably of land use change from o k, and n k s he number of ranson from land use o land use k Table 2. For he sake of smplcy, we llusrae he full economerc model based on four land use caegores: agrculural land 2 fores land 3 developed land, and 4 oher land. The average share of each land use s dsplayed n able 3. We can see ha agrculural use s sll he predomnan use n he mdwesern regon, bu s average land use share decreases by 5% beween 982 and 997. Foresland ncreases by abou %. Developed land ncreases by.2%, and oher land uses ncrease by 3.2%. The descrpve sascs of varables used n he analyss are lsed n able 4. 2 Implc prce deflaor ID, or GN deflaors, for year 982, 987, 992, and 997 are calculaed based on quarerly ID publshed by he BEA n March 2004 on he bass year of

17 Only hree rows of he Markov marx need o be esmaed snce =. Usng he esmaed model sysem from Equaon 5, he predced one-perod ranson probably marx evaluaed a he sample mean of explanaory varables s presened n able 5. As expeced, he dagonal elemens are he larges elemens n he Markov marx. For a represenave couny, he probably of land use change from agrculure o fores s , and he probably of land use change from agrculure o developed land s , mplyng ha agrculural land s slghly more lkely o shf o developed land han o fores land. The probably of land use change from fores o agrculure, and from fores o developed land s and respecvely, mplyng ha fores land ends o more lkely o shf o agrculural land han o developed land. The probably of land use change from developed land o agrculure s The probably of land use change from developed land o fores and oher uses are boh zero. If we wan o rereve he process of land use change,.e. examne he way agrculural land evenually converge o developed land, frs look a he hrd lne of he ranson marx, whch represens he condonal probables ha land use changes o developed land. Excep, he larges elemen s p, whch mples ha he oher uses caegory s more p33 43 lkely o conver o developed land han agrculure land and foresland. Then we look a he fourh lne. Excep, he larges elemen s p, whch mples ha agrculure land p44 4 s more lkely o conver o oher uses han foresland and developed land. Based on hose seps, one can nfer roughly he land use change process,.e. from agrculural land o oher uses, and hen o developed uses. In order o undersand wha knd of agrculural 4 j= 7

18 land are more lkely o be convered o oher uses, I wll subdvde he agrculural land no dealed crop choce caegores. p The equlbrum land use shares are p p p =, mplyng ha n he long run, a represenave couny wll have 53.3% of foresland, 25.94% of developed land,.% of oher land, and only 9.65% of agrculural land. Ths s no surprsng, because from he Markov marx, we see ha alhough he dagonal elemens are he larges elemens, p, he probably of remanng n agrculure, s he smalles one among he dagonal elemens. In oher words, agrculural land s mos vulnerable, and more lkely o change o oher land use caegores. I s mporan o noe ha he above Markov marx and he equlbrum land use shares are obaned a he means of explanaory varables, and hey can vary by couny. In oher words, dfferen characerscs of dfferen counes wll lead o dfferen ranson probables and equlbrum land use shares. The margnal effec of explanaory varables on he ranson probables wll be calculaed, whch wll yeld more neresng resuls. For example, we would expec ha hgher populaon densy would lead o hgher. p 33 Conclusons and Furher Research In he demonsraon example, we develop a Markov model for general land use caegores, agrculural, fores, and developed land, and oher land uses. By addng more dealed land managemen caegores whn agrculural land, we can ake a furher sep o analyze he nfluence of crop choce on land use change, whch knd of agrculural 8

19 land managemen s more lkely o be convered o non-agrculural land, and fnally o developed land. In addon, he sudy can be expanded by ncorporae llage nformaon no crop choce daa. Tllage pracce daa wll be obaned from Conservaon Technology Informaon Cener CTIC. By combnng crop choce and llage nformaon, we can explore more n deal how crop choce and llage pracces ogeher nfluence land use change process. More explanaory varables need o be colleced and ncorporaed no he model. Crop ren wll be esmaed wh crop budge daa. Some polcy varables, such as governmen paymens, need o be added o he model, n order o make polcy smulaon analyss. For example, by consderng he dfferen magnudes of margnal effec of governmen paymen on he ranson probably, and he dfferen magnudes of ranson probables hemselves, we can compare he effcency of governmen paymens o dfferen knds of operaons. For example, f he land use ranson probably from corn and soybeans o developed land s, and he margnal effec of governmen paymen on s. We expec s negave, so ha ncreasng governmen paymen o farmers may reduce he probably ha agrculural land beng convered o developed land. Smlarly, suppose he land use ranson probably from whea o developed land s, and he margnal effec of governmen paymen on s M w cd w M cd. If he absolue value of s bgger han ha of, ha means he ranson M cd probably s more sensve o governmen paymen han. Combned wh he cd magnude of ranson probably self and he land use shares, he model may help gude polcy makers n allocaon funds. For example, gven lmed resources, we may M cd cd M w w w 9

20 be able nfer whch knds of farmers should be funded frs n order o preserve agrculural land. Some resrcons can be mposed on he parameers o make he model f realy. For example, we can resrc he ransonal probables from developed land o oher land use caegores as zero o reflec he land rreversbly. Ths wll help o make any polcy predcons more useful. 20

21 Table. Changes n oal acreage of he Ten Land Use Caegores n Mdwesern U.S. 00 acres Varables ercen change Hgh Value Crop % Row Crops % Close Grown Crops % Hayland % Oher Cropland % Grazeland % Foresland % Oher Rural Land % CR land Developed Land % Sources: 997 NRI Table 2. Land Use Change Frequences Land Use Caegores Hgh Vaue Crops Row Crops Close Grown Crops Hay Land Lag Land Use Caegores Oher Cropland Grazng Land Fores Oher Rural Land CR Developed Land Hgh Vaue Crops Row Crops Close Grown Crops Hay land Oher Cropland Grazng Land Fores Oher Rural Land CR Developed Land Toal *All numbers n he able are n percenage %. 2

22 Table 3. Average Share of Each Land Use Year Agrculure Fores Developed Oher Uses % 5.5% 5.5% 7.8% % 5.7% 5.8% 8.9% % 5.9% 6.2%.% % 6.2% 6.7%.0% Table 4. Descrpve Sascs of Varables Used In The Analyss Varable Mean Sd. Dev. Descrpon Y Share of land n agrculural use Y Share of land n fores use Y Share of land n urban use Y Share of land n oher uses LAGY Lag value of agrculural land use share LAGY Lag value of fores land use share LAGY Lag value of developed land use share LAGY Lag value of oher land use share ALCC Average land capably class LCC_ Share of land n LCC I and II % OWN_ER Share of owned farmland % O_DEN opulaon densy persons/acre O_GROWTH opulaon growh rae Average marke value of land and buldngs per acre BLDGAC $000 Average marke value of all machnery/equpmen per farm $000 MACHAC ROWCRO_R Land ren for row crops CLOSECRO_R Land ren for close grown crops CATTLE_N Cale nvenory 000 Table 5. redced One-perod Transon robably Marx* Land Use Lag Land Use Caegores Caegores Agrculure Fores Developed Oher Uses Agrculure Fores Developed Oher Uses *Evaluaed a he means of explanaory varables. 22

23 Reference: Adelman, Irma G. A Sochasc Analyss of he Sze Dsrbuon of Frms. Journal of he Amercan Sascal Assocaon. 284 Dec., 958: Bhaara, Gandh, Upon Hach, and Daowe Zhang. Socoeconomc Influences on Land Use Dsrbuon a Waershed Level: A Mulnomal Log Analyss of Land Use Dsrbuon n Wes Georga. AAEA Annual Meeng, 2004, Denver, Colorado Cho, Suk-won, Bren Sohngen, and Ralph Alg. Land-Use Change and Carbon Sequesraon n he Foress of Oho, Indana, and Illnos: Sensvy o opulaon and Model Choce. AAEA Annual Meeng, 200, Chcago, IL Dsney, W. Terry, arca A. Duffy, and Wllam E. Hardy,Jr. A Markov Chan Analyss of ork Farm Sze Dsrbuons n he Souh. Souhern Journal of Agrculural Economcs December 988: Ello, Rober J., Wllam C. Huner and Barbara M. Jameson. Fnancal Sgnal rocessng: A Self-Calbrang Model. Inernaonal Journal of Theorecal and Appled Fnance 44: Ehrdge, Don E., Suj K. Roy, and Davd W. Myers. A Markov Chan Analyss of Srucural Changes n he Texas Hgh lan Coon Gnnng Indusry. Souhern Journal of Agrculural Economcs December 985: -20 Greene, Wllam H. Economerc Analyss. Fourh Edon rence Hall Inernaonal, Inc. Haab, T.C. Temporal correlaon n recreaon demand models wh lmed daa. Journal of Envronmenal Economcs and Managemen : Harde, Ian W., and eer J. arks. Land Use wh Heerogeneous Land Qualy: An Applcaon of an Area Base Model. Amer. J. Agr. Econ 79 May 997: Heckman, James J., and Rober J. Wlls. A Bea-logsc Model for he Analyss of Sequenal Labor Force arcpaon by Marred Women. The Journal of olcal Economy 85 Feb., 977: He, Dane, Bren Sohngen, and Josh Templeon. Zonng, Developmen Tmng, and Agrculural Land Use a he Suburban Frnge: A Compeng Rsks Approach. Amer. J. Agr. Econ 76 Aprl 2003: Hseh, Wen-hua, Elena G. Irwn, and Lynn Foreser. Spaal Dependence among Couny-Level Land Use Changes. AAEA Annual Meeng, 2000, Tampa, FL 23

24 Irwn, E. G., and N. E. Bocksael Ineracng Agens, Spaal Exernales, and he Endogenous Evoluon of Resdenal Land Use aerns. Journal of Economc Geography, 2: MacRae, Elzabeh Chase. Esmaon of Tme-Varyng Markov rocesses wh Aggregae Daa. Economerca, 45Jan., 977: McMllen, Danel., and John F. McDonald. A Markov Chan Model of Zonng Change. Journal of Urban Economcs 30 99: Mellor., C.J. An Applcaon and Exenson of he Markov Chan Model o Cereal roducon. Journal of Agrculural Economcs 352 May 984: Orazem, eer F., and John A. Mranowsk. A Dynamc Model of Acreage Allocaon wh General and Crop-Specfc Sol Capal. Amer. J. Agr. Econ 76 Aug 994: lannga, Andrew J., Thomas Mauldn, and Douglas J. Mller. An Economc Analyss of The Coss of Sequeserng Carbon n Foress. Amer. J. Agr. Econ 8 Nov 999: Qhao Weng. Land use change analyss n he Zhujang Dela of Chna usng saelle remoe sensng, GIS and sochasc modelng. Journal of Envronmenal Managemen : Skaggs, Rhonda, and Soumen Ghosh. Assessng Changes n Sol Eroson Raes: A Markov Chan Analyss. Joural of Agrculural and Appled Economcs 3 December 999: Sohngen, Bren, Dane He, and Josh Templeon. Land Use Change and ropery Taxes: An Emprcal Sudy of he Effec of ropery Taxes on he Tmng of Land Conservaon from Agrculure o Developmen. AAEA Annual Meeng, 200, Chcago, IL Tanaka, Kasuya, and Junje Wu. An Evaluaon of Conservaon olces for Reducng Nrogen Loads o he Msssspp Rver and Gulf of Mexco. AAEA Annual Meeng, 2004, Denver, Colorado Telser, Leser G. The Demand for Branded Goods as Esmaed from Consumer anel Daa. Revew of Economcs and Sascs 443 Aug.,962: Templeon, Sco R. Demographc, Economc, and olcal Deermnans of Land Developmen n he U.S. AAEA Annual Meeng, 2004, Denver, Colorado USDA -- ERS: US Deparmen of Agrculure Economc Research Servce, Commody coss and reurns daa. hp:// 24

25 USDA NASS: US Deparmen of Agrculure Naonal Agrculural Sascal Servce Census of Agrculure, hp:// USDA-NRI: US Deparmen of Agrculure. Naonal Resource Invenory Daa NRI, USDC BEA: US Deparmen of Commerce Bureau of Economc Analyss hp:// 25