The Effect of Climate Change on Thailand s Agriculture

Transcription

1 MPRA Munch Personal RePEc Archve The Effect of Clmate Change on Thaland s Agrculture Wtsanu Attavanch Kasetsart Unversty September 2013 Onlne at MPRA Paper No , posted 22 January :32 UTC

2 The Effect of Clmate Change on Thaland s Agrculture Wtsanu Attavanch 1 Abstract Agrculture s potentally affected by clmate change especally n developng countres where the agrcultural sector plays a crucal role ncludng Thaland. The objectves of ths study are to analyze the effect of clmate change on Thaland s agrculture and nvestgate mplcatons for greenhouse warmng under future clmate change scenaros usng the Rcardan approach allowng a varety of the adaptatons that farmers make n response to changng economc and clmate condtons. The study fnds that both temperature and precptaton sgnfcantly determne farmland values. Summer temperature, precptaton n the early rany and summer season negatvely affect the farmland values, whle wnter temperature, precptaton n the late rany and wnter season enhance the farmland values. Overall, the projected negatve mpacts of clmate change on Thaland s agrculture durng range from $24 to $94 bllon. By downscalng the analyss to the provnce level, ths artcle fnds that western, upper part of central, and the left part of northern regons are projected to be better off, whle southern, eastern regons, lower part of central, and the rght part of northern regons s projected to be worse off. Key words: Thaland s agrculture, clmate change, Rcardan analyss, regonal clmate model, farmland value 1 Wtsanu Attavanch s an assstant professor n the Department of Economcs, Faculty of Economcs, Kasetsart Unversty. Correspondence to be sent to Emal: attavanch.wtsanu@gmal.com.

3 1. INTRODUCTION Recent studes, ncludng those by the Intergovernmental Panel on Clmate Change (IPCC) (2001a; 2001b; 2007a; 2007b), ndcate that greenhouse gas (GHG) emssons and resultant atmospherc concentratons have led to changes n the world s clmate condtons, such as ncreases n temperatures, extreme temperatures, droughts, and ranfall ntensty. Such changes are expected to contnue and agrculture s potentally the most senstve economc sector to clmate change, gven that agrcultural producton s hghly nfluenced by clmatc condtons (e.g., IPCC 2007b; Mendelsohn, Nordhaus, and Shaw 1994; Deschenes and Greenstone 2007; McCarl, Vllavcenco, and Wu 2008; Schlenker and Roberts 2009). Compared wth developed countres, developng countres are more vulnerable to clmate change snce they are already n a hot clmate zone, depend on labor-ntensve technologes wth fewer adaptaton opportuntes, and a majorty of people n these countres rely heavly on the agrcultural sector (Mendelsohn et al. 2001). Thaland s one of developng countres that agrculture plays a crucal role. For example, n 2011 the agrcultural sector employed about mllon people, accountng for 38.7 percent of the Tha labor force (Natonal Statstcal Offce Thaland 2012) and agrcultural actvtes generate about $40 bllon, whch contrbuted to 12.8 percent of the gross domestc producton (Offce of the Natonal Economc and Socal Development Board 2012). Thaland s also a major exporter for many agrcultural commodtes such as rce, natural rubber, and cassava. Therefore, clmate change mpacts on agrculture are expected to sgnfcantly affect the economy and the lvelhood of the people n ths country. The objectves of ths study are to analyze the effect of clmate change on Thaland s agrculture and nvestgate mplcatons for greenhouse warmng under future clmate change scenaros on Thaland s agrcultural sector usng the Rcardan approach frstly proposed by Mendelsohn et al. (1994). Although there are many studes (Offce of Envronmental Polcy and Plannng 2000; Buddhaboon, Kongton, and Jntrawet 2005; Pannangpetch et al. 2009; Isvlanonda et al. 2009) analyzng the effect of clmate change on Thaland s agrculture, most studes (except for Khamwong and Praneetvatakul 2011) use the tradtonal producton functon approach, whch potentally overestmates the damage from clmate change snce the model allows lttle adaptaton of farmers (Mendelsohn et al. 1994).

4 3 The current artcle dffers from Khamwong and Praneetvatakul (2011) n several aspects. Frst, t expands the scope of the prevous research from the northeast regon to cover entre country. Second, the current paper uses the fner scale of dataset, a farm-level dataset, whch could mprove the estmated results snce t reflects the farmer s decson regardng the clmate adaptaton strateges more than the use of provncal-level data. Thrd, the current artcle employs the land value as a dependent varable smlar to the orgnal Rcardan approach, whch could address the potental problem from the use of the annual net farm revenue. Fourth, more constructed mportant explanatory varables determnng the farmland values collected from varous sources are ncluded to address the problem of endogenety bas. Lastly, the current artcle projects the mpacts of clmate change under clmate scenaros usng the unque dataset from regonal clmate models. 2. LITERATURE REVIEW In vew of ts mportance to economc well-beng, effects of clmate change on agrculture have been well research and documented, datng back at least 25 years (e.g. Smth and Trpak 1989; Mendelsohn et al. 1994; Adams et al. 1999; Relly et al. 2003; McCarl et al. 2008; Attavanch et al. 2013; and varous IPCC reports). Overall, the effect of clmate change on agrculture s mxed n developed countres, but negatve mpacts are found n developng countres. Moreover, n a country, the damage s heterogeneous across regons. Usng an agrcultural sector model, Adams et al. (1999) fnd that agrcultural welfare strctly ncreases n the Unted States (U.S.) wth a 1.5 C warmng and further warmng could decrease ths beneft at an ncreasng rate. The welfare gan from a 1.5 C warmng wth 7 percent precptaton s $55 bllon n Further warmng by 2.5 C could reduce these benefts to $47 bllon. Wth smlar approach, Relly et al. (2003) estmated the net effect n terms of economc welfare of the combned changes n crop yelds ncludng adaptaton and CO2 fertlzaton effects, water supply, rrgaton demand, pestcde expendtures, and lvestock effects was generally postve. The ncrease n economc welfare was ranged from $0.8-$7.8 bllon n 2030 and $3.2-$12.2 bllon n U.S. producers generally suffered ncome losses due to lower commodty prces whle consumers ganed from these lower prces. Usng the Rcardan analyss, Mendelsohn et al. (1994) fnd that hgher temperatures n all seasons except autumn reduce average U.S. farm values, whle more precptaton outsde of

5 4 autumn ncreases farm values. They estmate that a clmate change nduced loss n U.S. farmland value rangng from -$141 to $34.8 bllon. Schlenker, Hanemann, and Fsher (2005) do a smlar study and fnd an annual loss n U.S. farmland value n the range of $5-$5.3 bllon for dryland non-urban countes. Mendelsohn and Rensborogh (2007) fnd that U.S. farms are much more senstve to hgher temperature than Canadan farms and but are less senstve to precptaton ncreases. Deschenes and Greenstone (2007) fnd that clmate change wll lead to a long run ncrease of $1.3 bllon (2002$) n agrcultural land values. They ndcate that agrcultural land values n Calforna, Nebraska, and North Carolna wll be lowered substantally by clmate change, whle South Dakota and Georga wll have the bggest ncreases. For developng countres, Seo and Mendelsohn (2008) fnd that n South Amerca clmate change wll decrease farmland values except for rrgated farms. Moreover, they fnd small farms are more vulnerable to the ncrease n temperature, whle large farms are more vulnerable to ncreases n precptaton. Mendelsohn, Arellano-Gonzalez, and Chrstensen (2010) project that, on average, hgher temperatures decrease Mexcan land values by 4 to 6 thousand pesos per degree Celsus amountng to cropland value reductons of 42-54% by Wang et al. (2009) fnd that n Chna an ncrease n temperature s lkely to harm ran-fed farms but beneft rrgated farms. A small value loss s found n the Southeast Chna farms, whle the largest damage s dscovered n the Northeast and Northwest farms (Wang et al. 2009). In Thaland, several studes have nvestgated the effect of clmate change on agrculture. By usng the Crop Envronment Resource Synthess (CERES) model, Offce of Envronmental Polcy and Plannng (2000) reveals that rce grown under ranfed condtons was found to be hghly vulnerable to clmate change. Moreover, yelds of rce and maze are projected to declne as much as 57 and 44 percent as compared to the baselne, respectvely, although ther magntudes vary dependng on clmate condtons, sol types, and crop practces. Ther results are dfferent from Pannangpetch et al. (2009) who employ the Decson Support System for Agro Technology Transfer (DSSAT) model to analyze the mpacts of global warmng on rce, sugarcane, cassava, and maze producton n Thaland. They fnd a lttle mpact of rsng atmospherc CO2 concentraton and temperature on the rce, sugarcane and maze producton. However, cassava producton may drop as much as 43 percent as compared to the baselne. Buddhaboon, Kongton, and Jntrawet (2005) smulate the effect of clmate change on KDML 105 rce yeld n Tung Kula paddy feld by drect seedng method and set CO2

6 5 concentraton at 1.5 and 2.0 tmes of year (normal year) n the perod of and , respectvely. They reveal that clmate change lkely enhances overall rce yeld. Smlar fndng s founded n Isvlanonda et al. (2009) who conclude that clmate change enhances KDML 105 rce yeld n the north-eastern and the northern regons usng CropDSS smulaton model. Usng changes n yelds data, they fnd that the total producton of KDML 105 s project to ncrease approxmately 1.4 mllon ton, whch s equvalent about 14,195 mllon baht. However, Isvlanonda et al. (2009) project that clmate change could adversely affect Suphan Bur 1 rce yeld n central plan wth a reducton of about mllon ton, creatng a loss n value approxmately 2,029 mllon baht. Unlke prevous studes, Khamwong and Praneetvatakul (2011) apply the Rcardan model wth provnce-level data to analyze the mpacts of clmate change on agrculture n northeast regon. They fnd that rsng temperature n summer and early rany season and ncreased ranfall at the end of the rany season decrease net farm revenue. On the other hand, ncreased ranfall n summer and early rany season ncreases net farm revenue. Accordng to the above studes, we can classfy the model used to analyze the mpacts of clmate change on agrculture nto three categores (Mendelsohn et al. 1994; Wang et al. 2009) consstng of: 1) Tradtonal producton functon approach (e.g., Smth and Trpak 1989); 2) Rcardan approach (e.g., Mendelsohn et al. 1994); and 3) Agro-economc approach (e.g., Adams et al. 1999). Mendelsohn et al. (1994) crtcze that the tradtonal producton functon approach has a serous drawback snce the model tends to overestmate the damage from clmate change by omttng a varety of adaptatons that farmers can make n response to changng economc and envronmental condtons. Whle the agro-economc approach ncorporates the clmate change adaptaton of farmers, they are dffcult to buld especally n the developng countres due to data avalablty and complexty of the model. Mendelsohn et al. (1994) then ntroduce the Rcardan approach to brdge the gap between the tradtonal producton functon approach and the agro-economc approach. Recently the Rcardan approach s ganng popularty. Ths approach s appled to both developed countres such as U.S. and Canada (e.g., Mendelsohn et al. 1994, 2001; Rensborough 2003) and developng countres such as Brazl, Inda, Sr Lanka, and Chna (Dnar et al. 1998; Kumar and Parkn 2001; Mendelsohn et al. 2001; Seo, Mendelsohn, and Munasnghe 2005; Wang et al. 2009).

7 6 3. THEORETICAL FRAMEWORK The Rcardan approach developed by Mendelsohn, Nordhaus, and Shaw (1994) s the prmary method that we use n ths paper. In contrast to the tradtonal producton functon approach, the Rcardan approach allows a varety of the adaptatons that farmers make n response to changng economc and clmate condtons. By not permttng a complete range of adjustments, prevous studes could overestmate damages from clmate change. Instead of studyng yelds of specfc crops n the tradtonal producton functon approach, the Rcardan approach examnes how clmate n dfferent locatons affects the net rent or value of farmland. By drectly measurng farmland values, the approach account for the drect mpacts of clmate on yelds of dfferent crops as well as the ndrect substtuton of dfferent nputs, ntroducton of dfferent actvtes, and other potental adaptatons to dfferent clmates. The Rcardan approach assumes that each farmer maxmzes ncome subject to the exogenous condtons of ther farms. Specfcally, the farmer chooses the nputs and the combnaton of crop and/or lvestock, ndexed by j, whch maxmzes net revenue for each unt of land: Max P Q ( X C, H, S ) P X (1) j J j j j j J j j where s the net revenue of farm, Pj s a vector of nput and output prces, Qj s the producton functon for each crop or lvestock j, X j s a vector of endogenous nput choces such as seeds, fertlzer, pestcdes, rrgaton, hred labor and captal, varables, C s a vector of clmate H s a vector of economc control varables and S s a vector of sol characterstcs. Dfferentatng equaton (1) wth respect to each nput dentfes the set of nputs that maxmzes net farm revenue. The resultng locus of net revenues for each set of exogenous varables s the Rcardan functon shown n equaton (2). It descrbes how net revenue wll change as exogenous varable change. * ( C, H, S P ) (2) j

8 7 If land s traded n the perfectly compettve market, the land value (V ) wll be equal to the present value of the net revenue of each farm shown n equaton (3). V * rt e dt (3) 0 where r s the nterest rate. The welfare mpact (W) of clmate change s calculated by computng the dfference between the value of farmland under the new clmate (B) and the value of farmland under the current clmate (A) as llustrated n equaton (4). W t V t CB ) Vt ( C A Lt ( ) (4) where Lt s the amount of land at perod t of farm. 4. METHODOLOGY Emprcal Estmaton To answer the frst objectve and capture the expected non-lnear relatonshp between the farmland value and clmate, ths artcle specfes the followng model to examne the mpacts of clmate change on farmland values n Thaland: V 2 d Z 0 1sTs 2sTs 3sPs 4sPs k k e (5) 2 k where the dependent varable, V, s the farmland value (dollar) per ra 2, T and P represent a vector of seasonal temperature and precptaton varables, s s season ncludng: wnter (November-January); summer (February-Aprl); early rany (May-July); and late rany (August- October), Z s a vector of relevant control varables capturng characterstcs of prncpal operator (level of educaton), farm characterstcs (rrgaton status, whether the farm has man total sales from crop or lvestock, whether the farm has the flood problem, and whether the farm has the problem of steep slope), sol condtons (whether the farm has the problem of sol salnty and sandy sol) and locaton characterstcs (dstrct-level populaton densty, whether the farm 2 1 ra s equal to about acre.

9 8 locates n the plan area, Eucldan dstance of the farm to the cty of the provnce n whch farm s located, and percent of agrcultural land to total land area for provnce n whch farm s located), and e s an error term. Uncertanty of Clmate Change Impacts To answer the second objectve of the study, ths study nvestgates the mplcaton for greenhouse warmng on Thaland s agrculture by employng our estmated coeffcents n equaton (5) together wth future clmate projectons durng smulated by PRECIS (Provdng REgonal Clmates for Impacts Studes) regonal clmate model and used Global Crculaton Model (GCM) ECHAM4 dataset as ntal data for calculaton. The smulaton covers IPCC emsson scenaros A2 and B2 3, whch could account for the upper and lower lmt of clmate change mpacts on Thaland s agrculture. 5. DATA Data used n ths study are collected from varous sources. For the most part, the data are from the 2011/2012 natonal agrcultural household soco economcs survey at the farm level wth 6,701 completed farms sampled across 76 provnces from Offce of Agrcultural Economcs. 331 out of 6,701 farms, or about 5%, have been removed from calculaton to address the outlner problem 4 and ncomplete data on farmland values. In total, we have 6,370 farms. The gathered data consst of: the estmated current market value of farmland ncludng buldng expressed n dollars per ra; educaton level of the prncpal operator; sol condtons; whether the farm has the problem of steep slope and flood problem; rrgaton status; whether the farm has man total sales from crop. 3 A2 scenaro s characterzed by: a world of ndependently operatng, self-relant natons; contnuously ncreasng populaton; and regonally orented economc development. B2 scenaro s more ecologcally frendly. The B2 scenaro s characterzed by: contnuously ncreasng populaton, but at a slower rate than n A2; emphass on local rather than global solutons to economc, socal and envronmental stablty; and ntermedate levels of economc development (IPCC 2007a). 4 We have found that several farms located n the urban area, especally n large cty such as Bangkok, Nonthabur, and Changma provnces have very hgh land prces per ra wth small ncome generated from agrcultural actvtes. Includng these farms n the estmaton could bas the mpacts of clmate change on overall Thaland s agrculture.

10 9 Clmate data are obtaned from Thaland Meteorology Department, whch gathers data from 76 meteorologcal statons throughout Thaland. The data nclude nformaton on monthly temperature and precptaton from 1971 through Snce the purpose of ths study s to forecast the mpacts of clmate changes on agrculture, we focus on the long-run mpacts of temperature and precptaton on agrculture, not year-to-year varaton weather. We consequently analyze the normal clmatologcal varables the 42-year average of each clmate varable for every staton durng To capture seasonal effects of clmate on agrculture, we construct the seasonal clmate varables dvded nto four seasons ncludng: wnter (November-January); summer (February-Aprl); early rany (May-July); and late rany (August-December). In order to lnk the agrcultural data whch are organzed n the farm-level and the clmate data whch are organzed by staton, we assgn these clmate varables to each farm that locates close to the clmate staton usng the nearest dstance crteron even though the farm locates n dfferent provnce from clmate staton. To account for locaton characterstcs and potental of land for non-agrcultural development, we collect several varables ncludng: dstrct-level populaton densty; whether the farm locates n the plan area; Eucldan dstance of the farm to the cty of the provnce n whch farm s located; and percent of agrcultural land to total land area for provnce n whch farm s located from varous sources manly from the Natonal Statstcal Offce, Mnstry of Agrculture and Cooperatves, Mnstry of Interor, and Google Earth. Lastly, data of clmate projectons mentoned n the methodology secton are collected from Center of Excellence for Clmate Change Knowledge Management, Chulalongkorn Unversty. Table 1 summarzes varables used n the estmaton, ther defntons, and summary statstcs across the full sample of farms. For example, on average the farmland n Thaland has ts value equal to $2,945 per ra. The monthly temperatures averaged durng n the early rany, late rany, and summer seasons are around degree Celsus, whle n the wnter the month temperature drop to degree Celsus. Late rany season has the hghest level of monthly precptaton equal to mllmeters, whle wnter season has the lowest level of monthly precptaton equal to mllmeters.

11 10 Table 1. Descrpton of varables and summary statstcs Varable Defnton of Varables Mean Std. Dev. (N=6,370) farm value Estmate of the current market value of farmland 2,945 2,948 ncludng buldng (dollars per ra) early rany temperature Normal monthly mean temperature ( C) from to 2012 durng the early rany season (May-July) late rany temperature Normal monthly mean temperature ( C) from to 2012 durng the late rany season (August-October) summer temperature Normal monthly mean temperature ( C) from to 2012 durng the summer season (February-Aprl) wnter temperature Normal monthly mean temperature ( C) from to 2012 durng the wnter season (November-January) early rany precptaton Normal monthly precptaton temperature (mm) from to 2012 durng the early rany season (May-July) late rany precptaton Normal monthly precptaton temperature (mm) from to 2012 durng the late rany season (August-October) summer precptaton Normal monthly precptaton temperature (mm) from to 2012 durng the summer season (February-Aprl) wnter precptaton Normal monthly precptaton temperature (mm) from to 2012 durng the wnter season (November-January) educaton Whether the prncpal operator graduated at least grade (equal to 1 f yes) salt sol Whether the farm has the problem of sol salnty (equal to 1 f yes) sandy sol Whether the farm has the problem wth sandy sol (equal to 1 f yes) steep slope Whether the farm has the problem of steep slope (equal to 1 f yes) flood problem Whether the farm has the flood problem (equal to 1 f yes) rrgate Whether the farm s the rrgated farm (equal to 1 f yes) man sale from crop Whether the farm has man total sales from crop (equal to 1 f yes) plan Whether the farm locates n the plan area (equal to 1 f yes) dstance Eucldan dstance, n klometers, of the farm to the cty of the provnce n whch farm s located populaton densty Populaton densty per square klometer for dstrct n whch farm s located agrcultural land Percent of agrcultural land to total land area for provnce n whch farm s located Note: Values n Baht are converted wth the exchange rate of 32 Baht/US.

12 11 6. EMPIRICAL RESULTS As the Rcardan approach estmates the mportance of clmate and other varables on farmland values. Table 2 provdes the regresson results by regressng farmland values on varables of clmate, sol, operator characterstcs, farm characterstcs, and locaton characterstcs to estmate the best-value functon across dfferent farms n Thaland. There are 6,370 crosssectonal observatons. In order to gve a sense of the mportance of nonfarm varables n the model, we begn wth a model that contans only clmate varables. Specfcaton 1 n Table 2 s a quadratc model that ncludes the eght measures of clmate. For each varable, lnear and quadratc terms are ncluded to reflect the nonlneartes that are apparent from feld studes. For Specfcaton 2, we nclude varables capturng characterstcs of operator, sol, farm, and locaton to control for other factors nfluencng farmland values. Overall we fnd that all clmate varables statstcally affect farmland values and ther squared terms are sgnfcant, mplyng that the observed relatonshps are non-lnear as found n the feld studes. However, some of the squared terms are postve (.e., early rany temperature, wnter temperature, early rany precptaton, and summer precptaton) mplyng that there s a mnmally productve level of temperature and precptaton n that season and that ether more or less temperature and precptaton wll rase farmland values. The negatve coeffcent of squared terms mples that there s an optmal level of a clmate varable from whch the value functon decreases n both drectons. The overall mpact of clmate as measured by the margnal mpacts evaluated at the mean level of each varable s provded n Table 3. In general, we dscover that hgher summer temperatures and hgher early rany and summer precptaton are harmful for crops, whle hgher wnter temperatures and hgher late rany and wnter precptaton are benefcal for crops. The hgher summer temperatures by 1 C decrease the farmland values equal to $479 per ra, whle hgher wnter temperatures by 1 C ncrease the farmland values equal to $299 per ra. For precptaton, hgher early rany and summer precptaton by 1 mllmeter decrease the farmland values equal to $7 and $28 per ra, respectvely. On the other hand, the farmland values wll be ncreased $11 and $18 per ra, as late rany and wnter precptaton ncrease by 1 mllmeter, respectvely.

13 12 Table 2. Regresson models explanng farm values Specfcaton 1 Specfcaton 2 farm value Coef. Std. Err. Coef. Std. Err. Constant -471,422.50*** 129, ,252.10*** 136, early rany temperature -23,327.83** 11, ,941.65*** 11, early rany temperature^ ** *** late rany temperature 56,029.68*** 16, ,825.58*** 16, late rany temperature^2-1,014.24*** ,440.79*** summer temperature 15,099.00*** 2, ,444.66** 2, summer temperature^ *** *** wnter temperature -14,180.45*** 2, ,004.27*** 2, wnter temperature^ *** *** early rany precptaton *** *** 4.18 early rany precptaton^2 0.02** * 0.01 late rany precptaton 25.58*** *** 6.37 late rany precptaton^2-0.03*** *** 0.01 summer precptaton *** *** summer precptaton^2 0.22*** *** 0.08 wnter precptaton 25.64*** *** 3.27 wnter precptaton^2-0.05*** *** 0.01 educaton *** sol salt ** sandy sol steep slope *** flood problem * rrgate ** man sale from crop -1,010.18*** plan ** dstance -5.73*** 1.13 populaton densty 1.51*** 0.38 agrcultural land Adj. R squared Note: Standard Errors are calculated usng the Huber/Whte/sandwch estmator. ***,**,* are sgnfcant at 1, 5 and 10 percent, respectvely.

14 13 Table 3. Margnal effect of clmate varables farm value Coef. Std. Err. early rany temperature late rany temperature summer temperature *** wnter temperature ** early rany precptaton -6.74*** 2.43 late rany precptaton 10.68*** 3.44 summer precptaton *** 5.14 wnter precptaton 18.47*** 2.81 Note: ***,**,* are sgnfcant at 1, 5 and 10 percent, respectvely. 7. IMPLICATIONS FOR GREENHOUSE WARMING Ths secton nvestgates the mplcatons for greenhouse warmng durng on Thaland s agrculture. To project the clmate change mpacts, the estmated coeffcents from Specfcaton 2 n Table 3 are used together wth the clmate projectons from the regonal clmate model mentoned n the prevous sectons. In bref, future clmate projectons n both scenaros shows trend of ncreasng temperature throughout Thaland, especally n the central plan of Chao Phraya rver basn and lower part of northeastern regon. Total annual precptaton lkely fluctuates n the early part of the century. Snce scenaros A2 s assumed to have more carbon doxde emssons than scenaro B2, projected temperatures from scenaro A2 s hgher than those from scenaro B2 whle projected precptaton from scenaro A2 s slghtly lower than those from scenaro B2. By substtutng clmate projectons, ths study fnds that durng farmland values per ra are projected to decrease from $2,703 per ra to $2,068 and $2,538 per ra n clmate scenaros A2 and B2, respectvely. By multplyng the farmland values per ra to the total farmland area n Thaland (149 mllon ra), clmate change are projected to adversely affect Thaland s agrculture range from $24 bllon to $94 bllon as shown n Table 4. By downscalng the analyss to the provnce level, ths artcle fnds that western, upper part of central, and the left part of northern regons are projected to be better off, whle southern, eastern regons, lower part of central, and the rght part of northern regons s projected to be worse off under both clmate scenaros as llustrated n Fgure 1. As expected, scenaro A2 projects hgher negatve mpacts of clmate change on Thaland s agrculture more than scenaro B2.

15 14 Table 4. Implcatons for greenhouse warmng (natonal level) farmland value/ra total land values total change ($) ($1,000) ($bllons) Baselne 2, ,361,918 - A2 2, ,674, B2 2, ,838, Note: Agrcultural land s equal to 149,246,428 ra n Fgure 1. Implcatons for greenhouse warmng (projecton of ) Top ten provnces that are projected to adversely affect under both clmate scenaros are Surat Than, Chang Ma, Chumphon, Rayong, Chachoengsao, Songkhla, Chanthabur, Nakhon S Thammarat, Trang, Suphanbur, respectvely, wth the mpacts rangng from $3.48- $19.43 bllon. On the other hand, Kamphaeng Phet, Udon Than, Chayaphum, Phetchabun, Nakhon Ratchasma, Nongbua Lamphu, Burram, Bangkok, Khon Kaen, Sukhotha are top ten provnces that are projected to be better off wth the values rangng from $ $7.80 bllon.

16 15 8. CONCLUSION Agrcultural sector s potentally the most senstve economc sector to clmate change and Thaland s one of developng countres that agrcultural sector plays an mportant role. Ths study utlzes the Rcardan approach to analyze the effect of clmate change on Thaland s agrculture and nvestgate mplcatons for greenhouse warmng under future clmate change scenaros durng A unque farm-level dataset s constructed usng data from several sources manly from the 2011/2012 natonal agrcultural household soco economcs survey. The normal clmatologcal varables durng are constructed usng clmate data from Thaland Meteorology Department. Future clmate projectons are smulated by PRECIS regonal clmate model. The artcle fnds that both temperature and precptaton sgnfcantly determne farmland values. Overall, greenhouse warmng s projected to adversely affect Thaland s agrculture rangng from $24 bllon to $94 bllon. For the analyss n the provnce level, western, upper part of central, and the left part of northern regons are projected to be better off, whle southern, eastern regons, lower part of central, and the rght part of northern regons s projected to be worse off. Surat Than, Chang Ma, Chumphon, Rayong, Chachoengsao, Songkhla, Chanthabur, Nakhon S Thammarat, Trang, Suphanbur are top ten provnces adversely affected by clmate change, whle Kamphaeng Phet, Udon Than, Chayaphum, Phetchabun, Nakhon Ratchasma, Nongbua Lamphu, Burram, Bangkok, Khon Kaen, Sukhotha are top ten provnces that are benefcal under clmate change. Governmental organzatons related to the agrcultural sectors should support farmers on several ways such as provdng knowledge to farmers regardng adequate croppng technques, agrcultural resource management and encourage farmers to create adaptaton plan to reduce the damage from clmate change. At the same tme, polcy makers should develop plans or programs ahead to relef those who are projected to adversely affect by clmate change n provnces across Thaland.

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