Agricultural rent in -use models: comparison of frequently used proxies Raja Chakir, Anna Lungarska

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Raja Chakir, Anna Lungarska. Agricultural rent in land-use models: comparison of frequently used proxies. Spatial Economic Analysis, Taylor & Francis (Routledge), 2017, 12 (2-3), pp.279-303. ￿10.1080/17421772.2017.1273542￿. ￿hal-01526265￿

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Distributed under a Creative Commons Attribution - ShareAlike| 4.0 International License Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 pta cnmcAayi nieo 7Fb21,aalbeonline: available 2017, Feb 17 on . online http://www.tandfonline.com/10.1080/17421772.2017.1273542 Analysis Economic Spatial hsi nAcpe aucito natcepbihdb alr&Facsin Francis & Taylor by published article an of Manuscript Accepted an is This Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of Comment citer cedocument:

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3 2 eeal,sao rcsare prices shadow Generally, Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of giutrlrnsadtermria mat) rdcinqaiy(RS normalized – (NRMSE quality prediction impacts), of marginal (significance their hypothesis and theoretical rents the agricultural with based consistency made are including: comparisons criteria These various proxies. on rent three the specifications using spatial model different land econometric compare the agricultural and of price rent: shadow land land the agricultural and present of income, the definitions farm price, In possible three 2000). on Wear, and focus and vague we Gottleib rather paper, Parks, to is (Hardie, is concept empirically here this define idea since to The rent difficult land 2008). agricultural al., deter- of et is definitions Lubowski use different 1989; land test (Lichtenberg, that rent and states land which paper, the theory our by economic in mined central on is based Causality is question effects. empirical causal considering our the of identifying expense the of at issue choice, crucial that speci- justify the model to tests best the of choosing batteries on conduct and focus fication spatial applied of field the in papers 2011). Irwin, and Brady 2007; (Anselin, models especially choice – discrete prediction of case and the several testing, in raises hypothesis dependence estimation, spatial econometric because to is related This issues 2011). Bateman, and Fezzi 2008; al., et ( it with nhg ult orastn oinr pta eedne ruea o methods hoc ad use or dependence, published spatial articles ignore by to and tend proposed journals Chakir models quality 2013; use high in land Bhat, econometric and recent Ferdous Most 2012; Bhat, 2009). Li, Parent, 2013; and Gallo, Sidharthan Le 2013; and apply Deng, Chakir studies 2016; and few Gallo, Wu a Le only and still, Chakir (Ay, (2011), tools Irwin econometric and spatial Brady to according increased has dence specifications. model spatial database, different sensing remote the from (CLC derived Cover are the Land data Corine cover pasture, use to agriculture, Land resolution, classes: other. use grid and land urban, cell five forestry, 8km consider We x 8km France. metropolitan homogeneous of a territory at models share use land vial nteln-s ieaue pta apig aiueadlniuea xgnu aibe,and variables, exogenous as longitude and latitude variables. sampling, geophysical spatial lagged literature: spatially land-use the in available 3 4 codn oGbosadOemn(02 n ord n igeo 21) most (2012), Fingleton and Corrado and (2012) Overman and Gibbons to According lhuhwr nteln s ieauewihtk xlctacuto pta depen- spatial of account explicit take which literature use land the in work Although o oeifraino CLC: on information more For ePnoadNlo 20)eueaetretpso dhccretosfrsaileet hc are which effects spatial for corrections hoc ad of types three enumerate (2007) Nelson and Pinto De e.g. ri,Bl n egea,20;CrinFoe n ri,20;Lubowski 2004; Irwin, and Carrion-Flores 2003; Geoghegan, and Bell Irwin, Comment citer cedocument: 3 .W oe pta eednebtengi el,adcompare and cells, grid between dependence spatial model We ). http://land.copernicus.eu/pan-european/corine-land-cover

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. 9 9 Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of hudb qa oteana giutrlrent. value) agricultural dual annual or the price to (shadow equal LM LM be this optimum, should proxies. the the returns use at agricultural that We us of tells study constraint. theory comparative Microeconomic area our total in constraint a this respecting allocate with farmers while associated model crops the different in to profits, their land maximize their to in order region In FADN the operates. is he/she location each which For concerning information costs). available variable publicly minus only (revenues the margins farmer gross grouped their farms maximizing representative Common are types the farm model for by the accounts in detailed and agents data a economic FADN The (for on Policy. AROPAj based Agricultural is model 2015) supply-side al., et economic Jayet The see agriculture, description France. for to model val- them programming the apply mathematical use Union and We European a activities. by on-farm estimated between ues interactions production, their complex to the The attribute for farmers models. accounts that share and value use non-market land the econometric captures in price application shadow such first the is this knowledge, price shadow Land regions. of group a or scale region the agricultural at small (Agreste) French provided Agriculture a are of of paper, Ministry French our the in of used department prices statistical land the on by consequently, data and annual use, The land the France. on use in change (2014) birds climate al. common of et Ay effects literature, the use assess land to the approach In Ricardian sharply increases. falls city that which the find premium from development (2003) distance high Wavresky the a as and in Cavailhès results France, city of a to case proximity development the immediate land In to is use). conversion urban profitable Doye to most and (switch The Brorsen formula. Guiling, this, NPV address the To extend (2009) assumption. strong rather a be might which seuvln otecs iiiainpolm o eea ecito e cadn(1978). McFadden see description problem general maximization a profit For duality the the problem. approach of minimization this application cost Following from the results problem. to rent equivalent maximization agricultural is profit the the and constraint to land theorem 400,000ha. total than the with more associated to 1,000ha land France, some of from varies case territory the In Their regions. question. in good the of particularity some include not do ones 11 10 hdwpie r sdwe elmre ausaentaalbe rwe existing when or available, not are values market real when used are prices Shadow giutrlrn stermnrto fln safco fpouto.Teeult ewe h LM the between equality The production. of administrative factor the a of as land subdivision of a remuneration is the and is rent division Agricultural territorial French a is region agricultural small A Comment citer cedocument: nti td,w etln hdwpieue sapoy oour to proxy; a as used price shadow land test we study, this In

10 11 10 Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of oetyrents Forestry level). the 2 namely NUTS scale, the original to their (corresponding at region considered FADN are French model AROPAj corresponding the the from prices for shadow value mean the use we eie rmsaitcldt.Terbooia oue lo ohmdl oaccount to models both allow modules biological Their data. growth) and statistical mortality from (tree parameters derived Launay, uses Brisson, FFSM++ 2003; while al., 2009), et Beaudoin, (Brisson and STICS Mary model crop generic the with coupled partly and Caurla con- Delacote, a Lobianco, under see types information, 2016). Barkaoui, more forest (for two hypothesis these area between forest switching forests, stant potential broadleaved and the coniferous for French for accounts the is and of revenue scale The the at (NUTS2). 2006, region in administrative starting Nancy. annually calculated in are (INRA) returns Institute expected Research The Agricultural French the and at Barthès Laboratory Forestry the Economics by Lecocq, developed 2015) Delacote, Barkaoui, and Caurla, Caurla Delacote, 2012; Lobianco, 2013; Barkaoui, Delacote, and (Caurla FFSM++ model six and five respectively of period Thus, over averaged scale. same are years. the prices on land data and obtain revenues to order farmers’ in necessary was aggregation Some years. land. with potentially associated they profits bounded, upper the are underestimate rents and can prices land As on-farm, market. consumed the on are valued products not Furthermore, are some 2012). where Temesgen, system complex and a (Dupraz is coincide agriculture not do prices rental observed the etlpie r diitrdb ulcauthorities, public by administered are prices rental r fe bie opyudrtecutrfre nsi re ooti ihso land. on rights obtain feeding. to animal order for in destined ones former under-the-counter pay to obliged often are org/indicator/NY.GDP.DEFL.KD.ZG 14 13 12 ohteAOA n h FM+mdl nld ilgclmdls RPjis AROPAj modules. biological include models FFSM++ the and AROPAj the Both different for and scales different at available are proxies rent agricultural the on Data nainetmtsfrtepro eeotie rmteWrdBank, World be the could from produced biomass obtained some were while period crops the on for fertilizer estimates a Inflation as used be tenants new could and manure circumvented is instance, regulation For this regions some In L411-11. Article Code, Rural French 14 nteasneo nomto nln rcsfragvnsalarclua region, agricultural small given a for prices land on information of absence the In r prxmtdb xetdrtrsetmtdb h partial-equilibrium the by estimated returns expected by approximated are Comment citer cedocument:

. 11 12 département n hs h hdwpieand price shadow the thus, and http://data.worldbank. h aao land on data The . 13 n thus, and Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of eas ftehg orlto ewe lp n liue lo lp ed obetter to leads slope fit. Also, model the altitude. of and terms slope slope in introduce between results only correlation we model high the the In of 1km). (approximately because seconds arc 30 of scale the at Relief and an Plantinga is Ahn, quality 2013; 2008). Land al., Gallo, et Le reference. Lubowski and the 2000; (Chakir as Alig, models used use is land 1, in level variable according quality, texture important lowest soil is The quality soil levels. for four use we to indicator further The and level. 1:1000000 cell of grid scale at the aggregated at 2012) Montanarella, and Jones Liedekerke, Van gos, Soils topography. land on data use We data Physical 3.4 that for fact the endogenous. proxy especially is to limitations, it its literature potentially highlight use proxy this land using the studies Most in populated rent. used densely urban more commonly in is higher density are Population land, urban pressures for locations. the rents that the thus, is and returns land, urban develop for to proxy to density population using French for the justification at by available provided and (INSEE) are institute indicators statistical Both French revenue. for the proxies two and use density, and population (2011) Sohngen rent: and urban Plantinga Alig, Haim, follow we paper this In Demography 3.3 scenarios. policy and price different of simulation allow is components economic models modules the the between Also, of linkage 2013). the Noblet-Ducoudré, AROPAj de of and Jayet case Leclère, the in (in detailed change climate of effects the for t rent. its 15 eas ftesrn orltosbtentedffrn lmtcadeahcvariables edaphic and climatic different the between correlations strong the of Because o ntne nolgtr e-sd lueicessdmn o o ult adadconsequently and land quality low for demand increases clause set-aside obligatory an instance, For r ersne ytedt rvddb h on eerhCnr JC (Pana- (JRC) Centre Research Joint the by provided data the by represented are attd n lp)i eie rmtedgtleeainmdlGOO available GTOPO, model elevation digital the from derived is slope) and (altitude Comment citer cedocument:

12 commune 15 cl.The scale. Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of h w aibe ecie bv hc eeietfida en h otinfluential. most the being as identified only retain were to which decided above we described analysis variables our two of indicators the results composite the retain on and Based variation parameters. of physical axes the for main analysis the correspondence determine multiple to order and in (PCA) (MCA) analysis component principal perform we lp lp % .6 .1 44.2 0 6.211 4.363 140.131 2.75 2.973 classes texture Soils’ 5.541 44.642 (%) Slope 0 TEXT 3.213 12.424 Slope density Population density Pop revenues ’ revenues ( Pop revenues Forestry revenue (k For land arable for Price Max price Land Min (k revenues Farmers’ dev. St. revenues Farmers’ (k Mean price shadow Land price Shadow Description use Land Variable al :Smaysaitc fln s hrsadteepaaoyvariables explanatory the and shares use land of statistics Summary 2: Table s s s s s ot ur fo pa ag Comment citer cedocument: ubro el 1945 88525 2858 4256 1179 Scale Source cells of Number Scale Source Scale Source Scale Source (k Scale Source 1 0.99 or gion Scale 0.989 0 0 Source 0 0.94 0.133 0.097 0 0.22 1 0.065 0.053 0.181 0.262 0 0.181 0.276 Scale 0.438 Source uses other of Share urban of Share forests of Share pastures of Share use agricultural of Share Scale Source Scale Source e er household) year/ / 1:1,000,000 : ( sec arc 30 : French : French : scale 2 NUTS : re- agricultural small French : 8km x 8km at aggregated : US2scale 2 NUTS : scale 2 NUTS : R,Pngse l (2012) al. et Panagos JRC, : 30 GTOPO : 1999 INSEE, : 2000 INSEE, : 2006 for results FFSM++, : 1995-2000 mean Agreste, : 2000 CLC : AN en1995-1999 mean FADN, : (2002) v.2 AROPAj : département

commune commune ∼ e e h)172 55 89 308.00 28.93 65.54 137.20 /ha) 1km) e h)06101301 0.975 0.19 0.153 0.651 /ha) h)05601701.029 0 0.197 0.576 /ha) e h)305145020.256 0 1.485 3.035 /ha) 13 4 3 2 1 Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of o l L oesteMoran’s the models OLS all For tests Specification 4.1 (elastic- effects rent prediction ities). land to of agricultural quality marginal according R-squared), of models interpretation and economic compare AIC and (log-likelihood, (NRMSE), then fit We of goodness specifications. criteria: these Hausman three compare and to LM and run estimated, are are proxies tests rents land agricultural different and GNS) (ap- materials The supplementary D). the prices. pendix in land presented are arable results and other revenue, and farmer’s coefficients price, estimated shadow - rents land agricultural for estima- model. GNS share and use SAC, land each SDEM, for SDM, spatial tors SAR, different SEM, with SLX, associated OLS, gains consider the we evaluate specifications, and estimations compare to order In results Estimation 4 21) ic h aeepaaoyvralsaeue nec qain ed o xetti oaffect to this expect not do we D18. equation, and each in used the are with variables significantly. coincide results explanatory not our same does the which since scale texture) (2013), a soil at processes and geological (slope to parameters subject Another physical are variables. they the since explanatory are the autocorrelation of of scale due the source is to possible dataset changes our than in other autocorrelation factors spatial to the mostly that conclude can we Thus, previously. Moran’s of forestry values the and and agricultural significant, highly excluding remain models statistics verify use to Moran’s land order run the In estimate and cells. we rents grid case, regular the the is of each this to if values rents regional forestry and the agricultural replicating we the by of instance, autocorrelation For spatial dataset. artificial our introducing rents. in land be autocorrelation agricultural might of for sources proxies possible three numerous the are for There rejected is autocorrelation spatial no of 17 16 oeswt ieetsailseictos(L,SM A,SM DM SAC, SDEM, SDM, SAR, SEM, (SLX, specifications spatial different with Models culvle ftets r rvddi h ulmnaymtras(pedxD,tbe 2 D10, D2, tables D), (appendix materials suplementary the in provided are test the Gallo of Le values and Actual Chakir in shown as However, correlation. inter-equation for account directly not do We Comment citer cedocument: I eto h eut o h ierseicto.Teresulting The specification. linear the for results the on test

I 16 score ahseicto setmtdfrtetreproxies three the for estimated is specification Each 17 ssgicn enn httenl hypothesis null the that meaning significant is 14 I r ls otoeobtained those to close are Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of a ecniee.Terslso h L n D oesso htw hudconsider should we that show models specification. SDM and GNS SLX the the of results The considered. rel- be are can specifications SEM and SAR both that for all show evant for results significance test 1% LM at dependent robust rejected lagged The are spatially models. term no error of autocorrelated hypotheses spatially both no tests, and results variable classic the robust the presents Using the 4 Table and tests. (1996). (1988) these Yoon Anselin and of Florax by Bera, proposed Anselin, test by LM proposed test classic LM the use we GNS) and SAC, spatial Other misspecified. considered. is be model to this need that specifications spec- indicates best test the Hausman is the SEM since that with ification conclude correlated cannot are we or Consequently, problem, variables. (2009) serious explanatory Pace a the and represent of LeSage variables hypothesis to omitted null According the the that 3). reject means (table this we estimates that OLS and show SEM test the this of for equality not results variables) Our explanatory SEM. specifica- the the of with in indication correlated present variables good a omitted is as test (such This problems SEM. tion and OLS from estimates coefficient the study. the in used grid regular apni D). (appendix 18 al :Humnts o h ieec ewe h L n h E estimates SEM the and OLS the between difference the for test Hausman 3: Table ocmaedffrn pta oe pcfiain OS L,SM A,SM SDEM, SDM, SAR, SEM, SLX, (OLS, specifications model spatial different compare To of equality the test to (2009) Pace and LeSage by proposed test Hausman the use We l h siae offiinsfrsailautocorrelation, spatial for coefficients estimated the All h opeerslso h et r rvddi alsD4adD5i h upeetr materials supplementary the in D45 and D44 tables in provided are tests the of results complete The amr’revenue Farmers’ prices Land rx nptoh naroh nfroh ln(urb/oth) ln(for/oth) ln(agr/oth) ln(pst/oth) dual Regional Proxy ln ( pst/oth Comment citer cedocument: ) and ln ( agr/oth .e1 .e1 .e1 2.2e-16 < 2.2e-16 < 2.2e-16 2.2e-16 < < 2.2e-16 2.2e-16 < < 2.2e-16 2.2e-16 < < 2.2e-16 2.2e-16 < < 2.2e-16 < 2.2e-16 < 0.983081 9.47113.4367 294.3477 115.9688 330.8311 289.641 303.3948 333.4682 106.8259 315.5557 287.0825 325.6753 312.7065

) . 18 hsmasta h A rSMspecifications SDM or SAC the that means This 15 λ o h E n SDEM, and SEM the for Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of al :Rslsfrtesailatcreaintssfrtearclua aduemodels use land agricultural the for tests autocorrelation spatial the for Results 4: Table outL a 79 * 36 * 09 *** 30.92 *** 6649.84 *** 43.61 *** 6648.84 *** 6651.46 *** 27.94 SLX lag LM Robust lag LM Merr748 * .94**762 *** 7.6126 *** 7.6944 *** *** 7.4987 6657.44 *** 6652.04 SDM *** error 6660.14 LM SAR error + lag LM Merr1.29**1.85**1.52*** 18.0562 *** 18.3835 *** 17.8209 error LM *** 7.59 *** 6626.52 *** 3.2 *** 6608.43 *** *** 6632.2 *** 6715.72 8.68 *** SLX 178.47 *** *** 6327.88 6720.92 error LM Robust *** *** *** 179.9 6334.06 error 6715.02 LM SLX *** *** *** 6326.48 387.84 6537.24 error *** + 175.08 lag LM OLS *** 6541.02 *** 386.85 lag LM Robust Price *** Land 6539.94 lag *** LM 388.54 OLS Price Shadow error LM Robust error LM Test OLS vs. vs. vs. vs. vs. vs. vs. vs. A (H SAC (H SAR E (H SEM D (H SDM (H SDEM N (H GNS A (H SAC N (H GNS Comment citer cedocument: 0 0 0 0 0 0 : : : 0 : : : : 0 λ ρ λ λ λ : ρ λ

0 = = = 0 = 0 = λ 0 = 0 = ρ 0 = ρ ) ) ) ) 0 = 0 = ) ) ) ) giutrlln etproxy rent land Agricultural 16 Revenues Farmers’ Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of infiati h te he qain.FrteGSwt hdwpie n adprices, land and prices shadow with GNS the ρ For equations. three other the in significant and odeso to lentv pta oe pcfiain,w rvd suoR-squared pseudo a provide we specifications, model spatial alternative of fit of goodness used. proxy rent the of independent is specification spatial best the of choice 40542 revenues, SAC, the of case the In mrvdfraltesailseictosecp h L.Frln hdwpiethese price shadow land For SLX. the from are go except AIC criteria specifications and spatial function the (LL) all results log-likelihood for These specification, improved models. OLS different the the to for compared fit that of show goodness for results the summarizes 5 Table fit of Goodness 4.2 multicollinearity the of result the variables. be lagged could the (the This through signs introduced negative). den- opposing is The with value but impact. coefficients lagged negative same of the a coefficient have have models values GNS lagged the the in density variables SDM of sity the variable in lagged the and SDEM, significant, the not the and is and SLX employed the proxy in rent However, agricultural the specification. of model regardless model urban the for signs tive population for and variable, slope lagged the for also significant valid density. The is positive. slope both are of values impact and lagged significant negative spatially its all and are variable the texture for the with signs for associated the and coefficients specification the this models, Under land the agricultural specification). improving OLS are the significant SLX with never the (compared are in somewhat coefficients variables spatial lagged two spatially these The results, our simultaneously. In models. two other the ssgicn nyi h ra oe while model urban the in only significant is 19 lhuhteei oeuvln fteRsurdfrsailmdl,t sesthe assess to models, spatial for R-squared the of equivalent no is there Although ouaindniyadhueodrvneaesgicn n aeteepce posi- expected the have and significant are revenue household and density Population uldtiso siae r rvddi alsD–2 ntesplmnaymaterials. supplementary the in D2–D25 tables in provided are estimates on details Full pta uoersiecoefficient autoregressive spatial hsrsl svldfrtetrearclua etpoiswihmasta the that means which proxies rent agricultural three the for valid is result This . λ ssgicn o h atrsadarcluemdl and models agriculture and pastures the for significant is LL OLS Comment citer cedocument: -26 and =-22363 λ ssgicn nyi h ra adueeuto while equation use land urban the in only significant is

AIC OLS ρ 17 o h A n D,aesgicn t1%. at significant are SDM, and SAR the for λ 44745 = ssgicn lehr.I euefarmers’ use we If elsewhere. significant is to LL GNS -05 and =-20251 ρ ssgicn for significant is AIC R 2 GNS scores ρ = 19 is Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of etseicto steGSmdlrgrls ftearclua etpoyused. proxy rent agricultural the the of criteria, regardless pseudo-R-squared model the GNS to the according is and specification use, best land introduced agricultural is of component case the spatial In When 15%. to close are the forest of coefficient the same high; the as score not do use, other vs. s n h ra s te s oesprombte ntrso xlie variance, explained of terms in better perform models other use vs. other with agricultural vs. the urban specification, the OLS and the use In (1991). Nagelkerke on based metric D2–D25. calculus the on details more elasticities. other provides these all C of proxy Appendix land agricultural constant. the held in change variables agricultural percent the explanatory 1 in a based change from percentage rents result the would agricultural which give the share elasticities to calculated The respect 4. with calculate equation shares we on land complexities, these agricultural the with of and deal elasticities variables To the dependent shares. use the land of of transformation is specification nonlinear material) log-odds supplementary the the both in by (reported complicated estimates parameter the of Interpretation Elasticities 4.3 use land agricultural the for specifications model different the model of share fit of Goodness 5: Table 20 hsadtefloigfiue r vial ntesplmnaymtras(pedxD,tables D), (Appendix materials supplementary the in available are figures following the and This R N .4 221452064-05 04 .4 22240544 -20252 40617 0.644 44546 -20290 40541 -22255 0.64 -20251 40644 0.439 40619 0.644 40615 -20310 44539 -20290 40542 -20288 0.639 -22252 0.64 -20251 40665 0.641 40643 0.439 0.644 -20321 40615 -20310 40614 44544 40737 0.638 -20289 0.639 -20288 -22254 -20358 GNS 44751 40665 0.641 40645 0.641 0.439 0.635 -22366 -20321 SDEM -20311 40618 40736 0.424 0.638 0.639 -20290 SLX -20357 44753 40666 0.64 0.635 -22367 SAC -20322 40734 0.424 0.638 SDM -20356 44745 0.635 -22363 SAR 0.425 SEM OLS Model R 2 2 oty(xetteSX mrv ral from greatly improve SLX) the (except mostly ls rspro o40%. to superior or close R 2 hdwpieLn rc amr’Revenues Farmers’ price Land price Shadow Comment citer cedocument: LAIC LL

20 h te w oes oetv.ohrueadpasture and use other vs. forest models, two other The R R 2 2 offiinso atr r oe hn3%and 30% than lower are pasture of coefficients 18 LAIC LL R OLS 2 0 = R . 425 2 to LAIC LL R GNS 2 0 = . 644 . Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of tbeaditiierslssneti etawy h oiieadsgicn maton impact significant and positive use. a land provides ahs proxy always agricultural rent price shadow this the since only results positive that intuitive are show and results they stable These revenue, SLX. farmers’ and For expected OLS GNS. the the have and for elasticities SDEM, only the SAC, proxy, SAR, price SEM, land for the the lower in of and signs case SEM the SLX, the In OLS, for SAC. the higher and are for SAR figures similar the The are price specifications. GNS shadow and the SDEM, to SDM, respect with use land agricultural of (2015). Elhorst and Vega Halleck in as effects specifications indirect spatial and different direct for These calculated proxy. are rent agricultural the to associated effects indirect al :Eatcte o h giutrlln s hr oe ihrsett agricultural brackets. to in respect intervals with confidence model 95% share use proxies. land rent agricultural land the for Elasticities 6: Table h lsiiyetmtsaerpre ntbe6 h eut hwta h elasticities the that show results The 6. table in reported are estimates The and effects direct the of sum the is rent agricultural the of impact total the where, N .15024 -0.1373 -0.0095 0.2447 0.2849 -0.5455 0.1278 -0.1529 0.3125 -0.0203 -0.4302 0.3248 0.2774 GNS 0.209 -0.0828 -0.0749 SDEM 0.0384 0.2156 SLX 0.148 0.1812 SAC 0.0817 SDM 0.5114 SAR SEM L .15-.730.266 -0.0743 Revenues Farmers’ 0.3105 Price Land Price Shadow OLS Model Arrent ∂Agr ∂s Comment citer cedocument: 008 .25 008;051][022;-0.0726] ; [-0.202 0.5518] ; -0.005] [0.018 ; [-0.014 0.5265] ; [0.0985 0.2475] ; 0.36] [0.0081 ; [0.1294 0.4673] ; [0.0875 -0.2884] -0.0097] ; ; [-0.8026 [-0.2961 0.3521] ; [0.0659 0.629] ; [0.0206 -0.0107] ; [-0.0299 0.0647] ; [0.0121 -0.0048] ; -0.2274] [-0.145 ; [-0.633 0.3053] ; [0.0571 0.4175] ; [0.0137 -0.0438] ; [-0.1218 0.1377] ; [0.0257 0.2866] ; [0.0094 0.3914] ; [0.1406 0.8615] ; [0.1613 -0.0047] ; [-0.1439 0.5231] ; [0.0979 ag ∗ g rent Agr

s ag = oa impact Total 19 agr _ rent ∗ g rent, Agr (4) Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of fiain ihdffrn etpoisw aclt h RS sfollows: as NRMSE the calculate we proxies rent different with ifications concerning information available variables. on response based is the which type 2007). first Prucha, the use and we Kelejian comparison, our variables 2014; In response Goulard, the and whom Laurent for individuals (Thomas-Agnan, variables of unknown response set are which a for to observations related of are which set but a available are on are variables based response is and first type estimated second The is The model the models. observed. when econometric obtained spatial values fitted for the predictors is of type types three generally are There Predictions 4.4 al 1i h pedxpoie h RS crsfrteohrln s shares. use land other the for scores proxy. NRMSE rent the agricultural provides the appendix of the regardless in B1 prediction model Table accurate GNS most the results, the our use, provide to land to According seems agricultural rent. agricultural predicting the when for proxy NRMSE the accounting of the that regardless reducing shows 7 is Table dependence share. spatial use for land agricultural the for specifications spatial al :Nraie otma-qaeerrrslsfrtearclua aduemodels use land agricultural the for results error root-mean-square Normalized 7: Table nodrt opr h ult ftepeito o h ieetsailmdlspec- model spatial different the for prediction the of quality the compare to order In al rsnstevle fteNMEfrtetrern rxe,adtedifferent the and proxies, rent three the for NRMSE the of values the presents 7 Table N .10017 0.1185 0.1232 0.2289 0.1285 0.1175 0.1230 0.1222 0.1264 0.2240 0.1190 0.1214 0.1277 0.2348 0.1222 0.1235 0.2289 0.1253 GNS 0.1288 SDEM 0.1204 0.1234 0.2294 SLX 0.1269 SAC 0.1218 SDM 0.2340 Revenues SAR Farmers’ Price SEM Land Price OLS Shadow Model Comment citer cedocument: RMSE

NRMSE k = r k = P 20 y i n =1 k max RMSE (ˆ y − n ik y − k min y ik ) 2 (5) (6) Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of iue2 bevdln s hrs(o o)adpeitd rx o h agricultural the for Proxy known) variables predicted. (dependent and fit row) in-sample (top price; shares shadow use rent: land Observed 2: Figure Comment citer cedocument:

21 Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of bevda eyageae cls n ontepanmc ftettlvraini land in variation total the are of proxies much shares. explain rent use not agricultural rent do three and agricultural the scales, that three aggregated very fact the at the observed for by similar explained be is might quality 2016). This prediction al., proxies. et that Ay 2013; also Gallo, show Le previous results and confirms (Chakir Our literature which use indicators) land aggregated (NRMSE the predictions in results the of quality the improves variables explanatory data. use the use we for land since scales and different case of our – because in errors and especially grids, correlated true land constructed spatially is artificially aggregated the This of to problem. context measurement mainly a data due In a is essentially autocorrelation, data. of our fits existence the best which use, criteria specification quality the prediction is and GNS AIC) and the log-likelihood, squared, goodness the (pseudo-R to criteria According fit considered. of be should models GNS and SAC, SDEM, SDM, the and (LL, tests) fit (LM of and tests goodness rents specification (NRMSE), agricultural quality of prediction (significance impacts), marginal explanation on their economic based of are comparisons quality compared These then criteria: proxies. We several rent three GNS). the SAC, using SDEM, specifications spatial SDM, model these SAR, spatial SEM, different SLX, the for (OLS, and elasticities specifications cells, estimated grid and demo- between accuracy and dependence prediction physical, spatial compared economic, modeled using We what uses investigated variables. alternative We explanatory in graphic other. land (5) of and shares urban, the (4) classes: determines forest, use (3) land five pasture, considered (2) and scale, agriculture, grid (1) 8km) models x share (8km use homogeneous a land at estimated different France We for three model. prices programming on land mathematical shadow based a and from prices, models derived land use revenues, - farmers’ land – compare rent land to agricultural was for proxies paper this of objective The Conclusion 5 ocrigtecmaio ewe h ieetrn rxe,fo h statistical the from proxies, rent different the between comparison the Concerning models share use land in dependence spatial including that also show results Our that show specifications SLX and SAR, SEM, OLS, the from tests LM of results The Comment citer cedocument: R 2 AIC). ,

22 Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of h,S,Patna .J n lg .J 20) rdcigFtr oetadAe : Area Forestland Future Predicting (2000). J. R. Alig, and J. A. Plantinga, S., Ahn, References The hospitality. their for team apply. ODR disclaimers US usual INRA Anna the to gratitude (ANR-11-BSH1-005). her project expresses Lungarska ModULand the and ORACLE (ANR-10-CEPL-011) the under project (ANR) Agency Research land National We French on rents. from forest funding data on acknowledge data providing the for INRA) providing (LEF, to Lobianco for Antonello like and INRA) would prices shadow Publique, authors The (Economie Jayet manuscript. Pierre-Alain the thank improve suggestions to and comments contributed valuable greatly their have for which reviewers anonymous the as well as advices Acknowledgments etc.). quality, water (greenhouse reduced biodiversity, LUC of of loss externalities emissions, to environmental gas mech- negative makers policy the policy evaluate reduce to for to and useful designed scenarios, anisms change be climate would LUC This future into insights policies. provide mitigation of effects the simulate use. land agricultural on it impact results; significant intuitive and and positive stable provides a proxy viewpoint has price always economic shadow an the from only and However, that show (NRMSE) results specifications. accuracy the spatial prediction different of of terms in fit results of same goodness the give they view, of point nei,L (1988). L. Anselin, nei,L 20) pta cnmtisi SE ersetadprospect. and Retrospect RSUE: in econometrics Spatial (2007). L. Anselin, nei,L,Br,A . lrx .adYo,M .(96.Sml igotctssfor tests diagnostic Simple (1996). J. M. Yoon, and R. Florax, K., A. Bera, L., Anselin, oprsno cnmti Approaches. Econometric of Comparison ihr,Dordrecht. lishers, cec n ra Economics Urban and Science pta dependence. spatial u oe ol eue osmlt lmt hneipcso adueadto and use land on impacts change climate simulate to used be could model Our Comment citer cedocument: pta cnmtis:MtosadModels and Methods : Econometrics Spatial einlSineadUbnEconomics Urban and Science Regional ewudlk otakPoesrPu los o i eyhelpful very his for Elhorst Paul Professor thank to like would We

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mrcnJunlo giutrlEconomics Agricultural of Journal American 30 eihn u aditcatudNation- und Landwirtschaft auf Beziehung mrcnEooi Review Economic American Hydrobiologia 5,3–18. 657, , 80(3), , , Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of iei etrsi o 00 ore get,Fec iityo agriculture of Ministry French farms’ Agreste, on Source: data The 2000. activity. for agricultural is per hectares 2005 in for size profits farmers’ Average A2: Table Data A al 1 xrc rmteCCcasfiainadtecrepnigL aggregation LU corresponding the and classification CLC the from Extract A1: Table ie amn 7048 25 34 75 46 58 12 13 27.0 7 17.6 36.6 33.1 24.2 28.8 13.1 12 68 10.5 30.7 ∗ farming Mixed other and poultry Pig, 52,9 other 24.1 and Sheep (mixed) Bovine (meat) Bovine surface (milk) farm Bovine Average others and Fruits tax before Profit wine Other label geographical under Wine Horticulture crops protein and Cereals activity Agricultural ae ois4,.. 4Other Other 44 39 ..., ..., 40, 35, Pastures Other Other Other Other 18 27 Other 26 28 29 Urban 34 ..., 30, Forest Agriculture class LU bodies 17 Water ..., 5 11 12, ..., vegetation 1, no Wetlands or 25 4 little and with value 24 spaces CLC 23, Open 3.3 woodland-shrub Transitional 3.2.4 vegetation Sclerophyllous 3.2.3 heathland and Moors 3.2.2 grasslands Natural Areas 3.2.1 Natural Semi and Forest 3 Pastures 2.3 Areas Agricultural 2 Surfaces Artificial 1 class Cover Land vrg o iiutr ngeneral in viticulture for Average Comment citer cedocument:

31 10 uo)(ha) euros) (1000 n 9 .,22 ..., 19, and ∗ ∗ Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of ausaecluae stedffrnebtentersdasadtemdlresponse model the and accompanying documentation residuals the the see details, fitted between more the For difference and variables. the 1), as table in calculated presented are are values equations (the variables response available the . NRMSE and Predictions B al 1poie h crsfrtei-apefi ftedpnetvralsaeknown. are variables dependent the if fit in-sample the for scores the provides B1 Table h te ausaeotie yepotn the exploiting by obtained are values fitted The amr’Revenues Farmers’ hdwPrice Shadow Comment citer cedocument: adprice Land DM007 .22008 .190.0531 0.1139 0.0781 0.1232 0.0775 SDEM DM007 .22007 .100.0533 0.1130 0.0772 0.1222 0.0774 SDEM DM007 .25007 .170.0531 0.1137 0.0776 0.1235 0.0772 SDEM D .71013 .75014 0.0540 0.1140 0.0775 0.1230 0.0771 SDM D .78012 .79013 0.0543 0.1133 0.0769 0.1222 0.0768 SDM D .77013 .79013 0.0539 0.1139 0.0769 0.1234 0.0767 SDM E .71011 .75014 0.0544 0.1144 0.0775 0.1214 0.0781 SEM E .71010 .78013 0.0546 0.1135 0.0768 0.1204 0.0781 SEM E .78011 .72014 0.0545 0.1141 0.0772 0.1218 0.0778 SEM N .79018 .75019 0.0517 0.1093 0.0745 0.1185 0.0739 GNS N .73017 .79018 0.0520 0.1086 0.0729 0.1175 0.0743 GNS N .71019 .76019 0.0515 0.1093 0.0736 0.1190 0.0741 GNS A .79016 .73017 0.0543 0.1177 0.0783 0.1264 0.0769 SAR A .70015 .79016 0.0542 0.1166 0.0779 0.1253 0.0770 SAR A .76016 .79017 0.0544 0.1176 0.0779 0.1269 0.0766 SAR A .72018 .76012 0.0583 0.1126 0.0746 0.1285 0.0742 SAC A .74017 .75012 0.0596 0.1120 0.0745 0.1277 0.0744 SAC A .71018 .74012 0.0583 0.1129 0.0744 0.1288 0.0741 SAC L .28024 .92012 0.0660 0.1921 0.1942 0.2348 0.1248 OLS L .20029 .80016 0.0667 0.1865 0.1850 0.2294 0.1230 OLS L .21024 .82018 0.0676 0.1882 0.1882 0.2340 0.1211 OLS L .21028 .97015 0.0663 0.1854 0.1917 0.2289 0.1241 SLX L .23024 .84010 0.0673 0.1805 0.1834 0.2240 0.1213 SLX L .17028 .80012 0.0675 0.1822 0.1860 0.2289 0.1197 SLX al 1 nsml siainfit estimation In-sample B1: Table

_tsa _asf s_ur s_fo s_pa s_ag s_ot 32 R package spdep R package h siae use estimates The . spdep . Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of iueB:Osre aduesae tprw n rdce.Poyfrteagricultural the known) for variables Proxy (dependent predicted. fit and in-sample row) price; (top land shares rent: use land Observed B1: Figure Comment citer cedocument:

33 Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of iueB:Osre aduesae tprw n rdce.Poyfrteagricultural the known) for variables Proxy (dependent predicted. fit and in-sample row) revenues; (top farmers’ shares rent: use land Observed B2: Figure Comment citer cedocument:

34 Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of aclsfrteelasticities. the for Calculus Elasticities C silvr rmnihos.Teedrc n nieteet r acltda nHalleck in as calculated (2015). are Elhorst effects and indirect Vega and direct These neighbors). from (spillovers h oa ffc ftearclua eti h u ftedrc n nieteffects indirect and direct the of sum the is rent agricultural the of effect total The Arrent ∂Agr rent ∂Agr Arrent ∂Agr ∂s ∂s ∂s Comment citer cedocument: ag ag Arrent ∂Agr ag ∂  s s ∗ ∗ ag Arrent ∂Agr rent ∂Agr ot ∗ ∂ln g rent Agr rent Agr  s 1 ∂s ot  s s

ag ag ag ∗ ∗ s s ag ot s s s s  ag ag ot ot ======oa effect Total s s effect Total effect Total effect Total ag ag ∗ ∗ 35 oa effect Total effect Total agr agr agr agr _ _ _ _ rent rent rent rent agr agr ∗ _ _ rent rent g rent Agr ∗ g rent Agr s ag (7) Version preprint frequently usedproxies. SpatialEconomic Analysis, 12(2-3),279-303. DOI:10.1080/17421772.2017.1273542 Chakir, R., Lungarska, A.(2017).Agricultural rentin land-usemodels: comparison of al 1 lsiiiso giutrlln ihrsett ieetarclua etproxies rent agricultural different to respect with land agricultural of Elasticities C1: Table amr’revenues Farmers’ price Land St.Dev Max Qu. 3rd Mean Median Qu. 1st Min. Model price Shadow rent Agr Comment citer cedocument: N 026-.6-.3-.3 018-.40.032 -0.04 0.129 -0.118 -0.16 -0.137 0.002 0.005 -0.47 -0.003 -0.006 -0.13 0.101 -0.546 -0.126 -0.008 -0.017 -0.515 -0.16 0.058 -0.01 -0.206 0.02 -0.02 0.063 -0.635 -0.37 0.367 -0.024 -0.009 -0.818 0.399 -0.019 GNS -0.43 0.133 0.285 -0.071 -0.011 -0.024 SAC 1.691 -0.406 -0.083 0.152 0.245 -0.014 0.31 -0.03 SDEM 1.927 -0.501 -0.078 0.266 0.346 0.06 0.231 -0.645 SDM -0.096 0.285 0.758 0.395 0.251 -0.124 0.211 SAR 0.101 0.325 0.035 0.259 0.155 0.072 0.229 0.069 SEM 1.279 0.128 0.295 0.078 0.201 0.878 SLX 0.262 0 0.229 0.116 OLS 0.216 0.072 0.18 -0.053 0 0.035 -0.075 0.148 0.09 0.196 0 0.107 GNS -0.068 0 0.134 0.152 0.559 0 SAC 0.013 -0.091 0 -0.108 0.104 -0.444 SDEM -0.153 0.069 -0.052 0.37 0.095 -0.074 0 SDM -0.139 0.312 0.496 0.062 0.045 -0.068 0 0.028 SAR -0.186 0.324 0.038 0.266 0.328 -0.907 0.146 SEM -0.09 0.277 0.033 0.214 -0.441 0.236 SLX 0.097 0.072 0.181 0.175 0.029 0.236 0.106 0.082 OLS 0.374 0.914 0.154 0.555 0 0.21 0.247 0.07 0.605 0 0.137 GNS 0.367 0.209 0.511 0.062 SAC 0.31 0 0.178 0.435 0 SDEM 0.264 0.158 0 SDM 0.387 0.235 0.001 SAR 0 SEM 0 SLX OLS

36