Perception Biases and Land Use Decisions

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1 Percepton Bases and Land Use Decsons Hongl Feng a Tong Wang b Davd A. Hennessy c a Assocate Professor, Dept. of Agrcultural, Food & Resource Economcs, Mchgan State Unversty Contact: hennes65@msu.edu b Assstant Professor and Extenson Specalst, Dept. of Economcs, South Dakota State Unversty Contact: tong.wang@sdstate.edu c Professor, Dept. of Agrcultural, Food & Resource Economcs, Mchgan State Unversty Contact: hennes64@msu.edu Selected Paper prepared for presentaton at the 2017 Agrcultural & Appled Economcs Assocaton Annual Meetng, Chcago, Illnos, July 30-August [Ths paper s a prelmnary draft, please do not cte.] Copyrght 2017 by H. Feng, T. Wang, and D.A. Hennessy. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 Abstract A large lterature has shown that perceptons are not always consstent wth realty and can have sgnfcant mpacts on behavors. Percepton bases documented n the lterature often pertan to subject matters that are dffcult to observe or measure. In ths paper, we study percepton bases wth respect to an ndcator that s very concrete and can be objectvely measured land use changes n a local area. Despte ts potental sgnfcance, how perceptons about land use changes compare to objectve measurements has receved lttle attenton. In ths paper, we start to fll ths gap wth a farmer survey and satellte land use data. We found systematc bases n farmers perceved land use changes. Furthermore, the bases are assocated wth decsons made n the past. Farmers who had converted grassland to cropland had over-percepton, on average; they perceved larger cropland ncreases than there actually were. Farmers who had converted from cropland to grassland dd not exhbt such bases, on average. We also found evdence on a lnkage between percepton bases and ntended future land conversons. Our fndngs suggest that any communcaton strateges that are ntended to gude resource allocatons through addressng percepton bases have to recognze these lnkages. Keywords: behavoral economcs; grassland conversons; human dmensons; land use changes; percepton bases JEL codes: D03, D83, R14 2

3 1. Introducton An ndvdual s decsons are shaped by her perceptons and these perceptons are not always consstent wth the realty. Heterogeneous perceptons about qualtatve measures are ubqutous, e.g., people have dfferent opnons on how beautful a landscape s. But large heterogenety can also exst on perceptons about common quantfable objectve measures. Percepton bases have been documented n complex measures such as ncome dstrbuton and nflaton rate as well as n more concrete and observable measures such as average heght and weght of a specfc populaton (Cruces et al., 2013; Proto and Sgro, 2015; Matsumoto and Spence, 2016). For sound polcy makng t s mportant that we understand whether percepton bases exst and, f so, what factors contrbute to bas formaton. In ths paper, we study percepton bas n the context of land use changes whch are crtcal for the health of ecosystems as well as the human communtes who own these lands, and consume the land s outputs. As Brown et al. (2014) assert, t s ncreasngly recognzed that Land-use decson processes are nfluenced not only by the bophyscal envronment, but also by markets, laws, technology, poltcs, perceptons, and culture. We am to examne how perceptons regardng land use changes compare wth the realty and how perceptons relate to actual land use decsons. Our study contrbute to the decson makng lterature regardng percepton bases by dentfyng and assessng dfferent measures of percepton bases n the context of land use changes. People wth the same avalable nformaton can have systematcally dfferent belefs (Akerlof and Dckens 1982; Bénabou 2015). Understandng the drvng factors behnd percepton bases s a necessary step for any publc polces that are lkely to be affected by such bases. Percepton bases arse due to many dfferent context-dependent factors. Some bases are related to our past experence, our lmted nformaton as n the case of law of small numbers (Rabn 2002) or our psychology tendency to perceve ourselves as beng smart and nce. The latter s manfested n so-called confrmaton bas. For a representatve sample of German resdents aged 1

4 16 and above, Dohmen et al. (2009) found that almost a thrd of the respondents exhbt systematcally based perceptons of probablty and the perceptons bases are related to ndvdual economc outcomes. Botzen et al. (2015) found that homeowners percepton of the probablty of flood rsk and the magntude of damages could be based and that homeowners who have experenced a flood are lkely to overestmate the probablty of a future flood whle those who do not have such experence wll underestmate the probablty. Proto and Sgro (2015) showed evdence of a powerful and ubqutous bas n perceptons that are self-centered n the sense that those at extremes tend to perceve themselves as closer to the mddle of the dstrbuton than s actually the case. The self-centered percepton bases are lkely to be selfservng due to some form of subconscous strategc gnorance to meet ndvduals psychologcal needs. Whle percepton bases have been found n many contexts and measures, the underlyng reasons for bases may dffer n dfferent crcumstances. It s mportant that we understand percepton bases wthn the conceptual context n whch t arses. In ths analyss, we dentfy two measures of percepton bas that can be defned n the context of confrmaton bases. Frst, we can measure average percepton bas as the dfference between the average of a perceved outcome and the average actual outcome n a populaton. The premse s that f people do not have systematc bases, then ndvdual bases wll cancel out on average. Another measure of percepton bas s dentfed based on how percepton s lnked wth one s past behavor. If one takes an acton (e.g., to grow more corn or swtch to energy-effcent applances), then confrmaton bases can mply that one thnks that many other people have also taken the same acton. If so, percepton bases about the acton s general outcome can be ntroduced. Thus, we can compare percepton bases among acton takers wth the percepton bases among the nactve. The man objectve of our study s to examne percepton bases as appled to land use 2

5 changes. Even though a large body of lterature has examned the drvers and consequences of land use changes, few studes have focused on how perceptons are related to land use decsons. For example, Claassen et al. (2011) and Wrght and Wmberly (2013) dentfed the eastern Dakotas as an area of ntensve land converson from grass to crop producton. Such conversons are of ncreasng concern due to ther mplcatons for regonal ecosystems. Several studes have examned the drvers behnd the rapd land use changes n the regon (e.g., Rashford et al. 2010; Claassen et al. 2011; Feng et al. 2013). Hgh commodty prces, weather cycles, and government commodty market supports have been dentfed as the man drvers of land use changes n the regon. To our best knowledge, our study s the frst to focus on behavoral aspects of farmers land use decsons, whch can be a crtcal factor that contrbutes to land use changes. Understandng the extent and mpacts of percepton bases n the context of land use changes wll provde new nsghts nto drvers of land use changes and may n turn help polcymakers better target lmted conservaton resources. In ths paper, we form and test three hypotheses. We frst examne whether there are percepton bases n land use changes n our study regon. Next we conjecture that, f a farmer made a converson from one land use to another n the past then her current perceptons on land use changes s lkely to be based toward the new converted land use. Fnally, we post that farmers who currently perceve excessve changes n one land use are more lkely to express ntenton for smlar future land use changes. Whether perceptons about land uses s consstent wth realty may have mportant polcy mplcatons f farmers land use perceptons drectly affect ther land use change decsons. For example, land use changes n an area often mply other changes. If the percepton about land use changes are related to perceptons about other factors (e.g., nfrastructure support system related to a partcular type of producton), then perceptons about land use changes can have larger mpacts. Also, perceved land use changes may be a drvng force behnd polcy postons of nterest groups. Therefore, t s mperatve that 3

6 we understand the extent, f any, and the mplcatons of bases n these perceptons. 2. Conceptual framework of perceptons and land use decsons Suppose there are N farmers n a geographcal area where land s used for crops, grasses, and other uses such as forest and urban development. We focus on land use changes between crops and grasses because they are the man land uses n our study regon (descrbed n detal later), and these uses have very dfferent ecologcal outcomes. Grass cover s usually more benefcal n terms of water qualty, wldlfe habtat, and carbon sequestraton whle the man crops, and especally corn, are more nput ntensve wth hgher adverse mpacts on the envronment. To represent our study regon, we assume that all farmers have cropland whle some farmers have both grassland and cropland. To model changes over tme, we assume that there are three tme perods: past, present, and future, denoted as t = 0, t = 1, and t = 2, respectvely. Denote k,1 q as the actual land use changes that has occurred between tme t = 0 and t = 1 n farmer s local area, whch s defned as an area wthn a gven dstance (say, fve mle radus) of the farmer s locaton. Superscrpt k represents the new converted land use, whch could be ether crop or grass,.e., k {, cg} wth c for crop and g for grass. Smlarly, k,1 y represents farmer s perceved land use change towards land use k that has occurred between tme t = 0 and t = 1 n her local area. Our focus s on the dfference between perceved and actual land use changes, whch we refer to as percepton bas z y q. k,1 k,1 k,1 k,1 z,.e., In a world wth complete nformaton and perfect observaton, there would be no percepton bas,.e., k,1 z = 0. In a large populaton, we can assume that percepton has some dosyncratc errors wthn the populaton but where [] represents statstcal expectaton. When,1 [ z k ] = 0 4

7 k,1 systematc factors affect percepton errors n a gven populaton, we wll have [ z ] 0. For example, as we dscussed n the ntroducton, percepton mght be systematcally affected by farmers past experences but the magntude and drecton of percepton bases stll need to be tested. Followng the lterature (Bénabou 2015; Trole 2002), we model utlty as a weghted average of return and psychologcal adjustment. Suppose landowner has the followng utlty functon at t = 1: 1 k,0 k,0 k,1 k,1 k,0 U = f α* Π (a, W ) + (1 α) * Ω( q, y, a ) (1) where 0 α 1, Π (,) and Ω (,,) are functons of return from land use and psychologcal adjustment, respectvely. Note that changes farmer made for land use k ; and k,1 q represents actual land use changes;,0 a k represents W nclude farmer s characterstcs (such as years n farmng), her farm s attrbutes (such as total acreage), and some other control varables to be explaned n the emprcal part of the paper. Psychologcal adjustment ( Ω (,,)) could come from self-valdaton: f a past decson brngs addtonal postve utlty to a landowner, then selfvaldaton wll cause Ω (,,) to exceed zero. In our context, one form of self-valdaton s that a landowner perceves the land use change n a localty to be consstent wth her past decsons. In other words, f a landowner made a converson from grassland to cropland, she would perceve an ncrease n cropland area n the localty. Smlarly, f a converson from cropland to grassland s made, there could be a perceved ncrease n grassland area, whch can be expressed by the followng equatons: > q > = q = Ω (,,) = = 0 otherwse. k,1 k,0 k,1 k,1 0 f ether {( 0) (a 1)}, or {( 0) (a 0)}; (2) 5

8 The magntude of Ω (,,) and the weght on Ω (,,) are determned by psychologcal needs. A landowner wll try to maxmze 1 U n determnng her percepton about land use changes, and what weght to assgn to the actual realzed net return. Note that past so t s not a decson varable here. Also, not a decson varable.,0 a k s a decson made n the k,1 y s perceved land use change and so s also 1 k,0 k,1 { U } max a = 1, q (3) α, y k,1 We conjecture that the utlty from self-valdaton decreases as percepton bas (.e., the dfference between percepton and realty) ncreases. Thus, the two key hypotheses for our analyses concern about the exstence of perceptons bases and how the bases are lnked wth past decsons. The frst hypothess that we test s as follows, Hypothess I: There s sgnfcant percepton bas n land use changes n our study regon, that s, z. k,1 [ ] 0 Our perceptons are related to our experence and who we are. Perceptons are affected by objectve happenngs, the way we process such happenngs, and our (subconscous) psychologcal needs (e.g., Rabn 1998, Bénabou 2016). For example, there s evdence that supports motvated vsual percepton,.e, we see what we want to see (e.g., Balcets and Dunnng 2006). In our context, we post that farmers perceptons of land use changes n ther localty wll on average dffer from the actual land use changes. In addton, farmers perceptons may be affected by past land use decsons they made. Perhaps those who have converted grassland to cropland would lke to thnk what they dd was normal n that most others n ther localty have made smlar decsons. Addtonally, we post that farmers perceptons are affected by ther own characterstcs and stuatons. Wrtng perceptons as a functon, f (.), of all these factors, we have 6

9 y = f( q,a, W) +ν (4) k,1 k,1 k,0 k,1 We model,0 a k as an ndcator varable, representng whether farmer made a converson to land use k at t = 0 wth = and k,0 a 1 k,0 a 0 = denotng, respectvely, that there was and was not a converson. In ths paper, we are mostly nterested n assessng whether and how past land use decsons have affected perceptons of land use changes n a local area. One hypothess regardng ths ssue can be stated as follows, Hypothess II: If a farmer made a converson to land use k, then her percepton of land use changes s lkely to bas toward land use k. In other words, converson to land use k s lkely to k lead to hgher percepton of land use k n the local area,.e.,,1 k y,1,0 > y,0. k k a = 1 a = 0 Percepton feeds back to decson makng as has been shown n Arbuckle and Roesch- McNally (2015). In our context, ths means that current perceptons about local land use changes,2 may also affect a farmer s ntenton for future land use changes. Let a k be the land use ntenton for t = 2 that s expressed by farmer at t = 1. In partcular, we model the ntenton to convert to land use k from another land use. If k and f k = c, t means converson from grass to crop = g t means converson from crop to grass. In canoncal economc modelng, converson decson s determned by economc returns: f a crop s expected to brng hgher returns than grass, then converson from grass to crop wll be preferred; and vce versa (Claassen et al. 2011). In our analyss, we examne to what extent, f any, percepton of local land use changes plays a role n land converson ntentons. In partcular, we post the probablty of k,2 a 1 s determned by a functon of g (.) wth, = Here, k,2 Pr(a =1),2,0,1,1 Pr( a k 1) ( a k, k, ) k = = g y W +ψ.. (5) s affected not only by factors consdered n tradtonal analyses such as land 7

10 qualty ndcators and farmers characterstcs ( W ), (Claassen et al., 2011), but also by the farmer s percepton of land use changes n local areas ( k,1 y ) and her past decsons (,0 a k ). The hypothess regardng the role of perceptons on future conversons can be expressed as follows, Hypothess III: Farmers who currently perceve more land converson to k s more lkely to express ntenton of future converson to k,.e., = k,2 Pr(a 1) k,1 >0 y (6) 3. Data 3.1. Survey of farmers land use decsons and perceptons A survey was conducted n 2015 that asked farmer three types of questons regardng: () ther land use changes n the precedng 10 years ( ) and ntended land use changes n the subsequent 10 years ( ), () percepton on land use changes n ther local area, and () the rankng of factors that nfluence land use changes on ther ndvdual farms and n ther local area. Our analyss combnes these survey data wth satellte land use data and land qualty ndcators to assess to what extent farmers perceptons on land use changes are consstent wth satellte data and the lnkage between farmers past land use decsons, ther current land use perceptons, and ther stated future land use ntentons. The survey was conducted wth farmers n the eastern regon (the Prare Pothole Regon) of the Dakotas whch, as mentoned n the ntroducton, s a regon that have experenced dramatc land use changes n recent years and s therefore deal for our study. Fg. 1 shows a map of our study regon. A sample of 3,000 farms n the eastern Dakotas was purchased from Survey Sample Internatonal, a large company specalzng n survey servces. We only ncluded farms wth at least 100 acres of cropland because our focus was on farmers who make decsons on a substantal amount of land area. A stratfed samplng strategy was used so that countes wth 8

11 proportonally more farms overall had proportonally more farms ncluded n our sample. Iowa State Unversty s Survey Research Center was contracted to mplement the survey and data entry from returned surveys. Respondent malng addresses are avalable to researchers. The survey dstrbuton and collecton occurred from early March to early May of 2015 through a Dllman Protocol that nvolves an advance letter, an ntal malng, a postcard remnder, and a second malng to non-respondents. A two dollar ncentve was ncluded n the ntal malng that also contaned a cover letter, the 8-page questonnare, and the return envelope. A total of 1,050 completed surveys were receved, gvng a survey response rate of 36.2% n the elgble sample (.e., the orgnal sample excludng non-delverable addresses). 3.2 Land use data based on satellte mages We obtaned actual land use data from the Cropland Data Layer (CDL) of USDA Natonal Agrcultural Statstcs Servce (USDA-NASS). The CDL data s a geospatal, crop-specfc data product publcly avalable for use and free for download through CropScape (USDA-NASS, 2017). The CDL s assembled annually for the contnental Unted States usng satellte magery and extensve agrcultural ground truthng. The frst year when CDL became avalable vares by state. For South Dakota, CDL data are avalable commencng 2006 whle for North Dakota the data are avalable commencng Because t s based on hgh-resoluton satellte mages, the CDL data has hgh spatal resolutons. There s a growng lterature that uses the CDL data to analyze crop and land use trend (e.g., Wrght and Wmberly 2013). These data are most relable for corn and soybeans and less so for grass cover, see Klne et al. (2013), Langen (2015) and Retsma et al. (2016) for dscussons on relablty. 1 1 By conventon, user accuracy s used to project relablty of these data. User accuracy was over 95% for corn and soybeans for 2014 n the Dakotas (USDA-NASS 2016b). For grass, the CDL bascally takes the Natonal Land Cover Database (NLCD) grass-data and overlays ts agrcultural classes on t. So accuracy number s not avalable for grass and s consdered to be lower than the number for corn and soybean. It s very mportant to note that these accuracy 9

12 The CDL data are used to measure land use changes n a respondent s localty. Besdes questons regardng own land use changes, our survey asks questons about land use changes n the local area n the precedng 10 years and ntentons for the subsequent 10 years. The local area s defned as a 5-mle radus centered on the respondent s locaton. Changes n corn, soybean, and grassland areas are measured as the changes from 2004 to 2014 for North Dakota, and from 2006 to 2014 for South Dakota gven that 2006 was the frst year that CDL data were avalable. 3.3 A summary of survey data and Land use data based on satellte mages Key varables from survey and CropScape data are provded n Table 1, whch ncludes summary statstcs on three categores: 1) farmer and farm characterstcs; 2) land use and nfrastructure changes n surroundng areas; and 3) farmer converson hstores and converson decsons n the future. Categorcal data descrptons are provded as follows: For farmer s age, we have 1 = 19 to 34 ; 2 = 35 to 49 ; 3 = 50 to 59 ; 4 = 60 to 69 ; and 5 = 70 or over ; For land tenure status, we have 1 = ownng all operated acres, 2 = ownng most operated acres, 3 = ownng about half of operated acres, 4 = rentng most operated acres, and 5 = rentng all operated acres. ; For farmers prncpal occupaton (off-farm employment), we have 1 = farmng or ranchng, 2 = employment n off-farm job, 3 = own/operate a non-farm busness, 4 = retred, and 5 = other. Of the frst category, the average cropland acres s 1,226, accountng for more than 70% of the total farm acre, whch averaged 1,686 acres. Ths s consstent wth typcal land use patterns n the Eastern Dakotas, where crop producton s the major component of the regonal economy. Farmer s age, land tenure and off-farm employment are dscrete choce varables. 2 Average farm numbers are at the pxel-level (30 m X 30 m). The statstcs used n our analyss pertans to land cover for a 5-mle radus that s made up of over 200,000 pxels. At ths scale of spatal averagng, errors tend to wash out, especally for areas lke ours that are mostly composed of corn, soybeans and grass as major land uses. 10

13 age s 3.30, whch mples the average farmer age s somewhere between years old. Land tenure averaged 2.75, ndcatng most of the farmers own more than half of hs/her operated acres. Off-farm employment has average 1.24, mplyng that most survey respondents have farmng or ranchng as ther prncpal occupaton. The farm locaton varable ndcates that the survey area spans 4.93 degrees n lattude and 4.32 degrees n longtude. Sol qualty s measured by the land capablty classfcaton (LCC) system (Helms, D., 1992; Wang et al., 2017). Classes I and II sols have few lmtatons for crop producton, where lmtatons mght nclude slope, sol depth and composton and dsposton to eroson. Class III sols have moderate lmtatons for crop producton. Class IV sols are very margnal for crop producton although the may be cropped f convenent for farm operatons. Class V VIII sols are seldom cropped. On average, 92.7 percent of land of our surveyed farm has sol qualty under class III, whch s far from deal but s generally sutable for crop producton. Of the second category, perceved nfrastructure change, perceved change n cropland and grassland acres are dscrete choce varables descrbed as follows: for perceved nfrastructure change, we have 1 = much worse, 2 = somewhat worse, 3 = stayed about the same, 4 = somewhat better, and 5 = much better ; for perceved cropland and grassland change, we have 1 = decreased markedly (over 10%) ; 2 = decreased somewhat (5-10%) ; 3 = stayed about the same (less than 5%) ; 4 = ncreased somewhat (5-10%) ; and 5 = ncreased markedly (over 10%). These three perceved change varables captured the change between the survey year (2014) and 10 years earler. The perceved nfrastructure for corn s 3.83, whch means for most farmers the external envronment for corn producton somehow mproved. Noteworthy s that durng ths tme several ethanol plants moved nto the area (Gaurav et al., 2015). Soybean processng facltes have also moved nto the area. The perceved change n cropland acres averaged 4.31, whch means on average a respondent perceves a more than 5% ncrease n corn and soybean acres. For grassland acres, the perceved change averaged only 1.994, whch means 11

14 a perceved decrease of between 5% and 10%. As to actual changes, CropScape data ndcates that wthn 5 mle radus of the farm cropland acres shows a 5.76% ncrease, whle the grassland acres shows a 6.97% decrease from year 2006 to Grassland accounted for a quarter of the total land n 2006, whle the land of class no worse than III was over 90% and land slope of no larger than 3 also accounted for nearly 50%, whch demonstrated the potental for land use converson n the studed regon. Of the thrd category, a quarter of farmers have converted some porton of grassland to cropland n the 10 years pror to 2015, a decade n whch Chcago Board of Trade corn futures prces shfted from just over $2 per bushel durng to beyond $5 per bushel durng and back to about $3.50 per bushel by Lookng nto the future 10 years, the percentage of farmers who plan to convert more grassland to cropland dropped to 15.8%, whle 9.5% of farmers expressed an nterest n convertng cropland to grassland. The contrast between hstorcal actons and future plans mples that the aggregate grassland to cropland converson rate would slowng down n eastern Dakota regon. 4. Emprcal methods 4.1 Exstence of perceptons bases n the studed regon To formally test Hypothess I that there s no percepton bases n land use changes, we calculated the average bases by takng the dfference between land use changes as measured by CDL data and land use changes as perceved by farmers. As we noted earler, the perceved land use changes are measured as dscrete varables n fve categores whereas land use changes based on CDL data are contnuous varables. To calculate the dfference between perceved and measured land use changes, we chose the mddle pont of each percepton category as the perceved land use changes. For example, a postve 7.5% s chosen for the category ncreased by 5-10%, and a 0% s chosen for stayed about the same (-5% to 5%). For the category 12

15 decrease (ncrease) by more than 10%, we used 15% n our analyss. Besdes overall average, we wll next characterze percepton bases n: (a) (ms)matched categores of actual and perceved land use changes; and (b) the number of respondents fallng nto each category. 4.2 Relatonshp between current perceptons and past land use decsons Modelng percepton of land use change as a probablty wll allow us to make use of the categorcal nature of our survey data. In the survey, farmers responses regardng ther perceptons about land use changes n ther own localty take values that have an ntrnsc order and enable us to apply the ordnal logstc regresson model: over 10% decrease, 5-10% decrease, stayed wthn 5%, 5-10% ncrease, and over 10% ncrease. We label these responses, n the order gven, as 1, 2, 3, 4, and 5. Let m be the hghest category number, then n our case, m = 5. Let τ, j= Pr( Y= j) for j {1,2,..., m} be the probablty that respondent wll choose land use change category j. We defne the cumulatve probablty ( ϕ, j), the probablty of choosng a hgher degree of mpact, as ϕ, j= Pr( Y j) = τ, j+ τ, j τ, m, ϕm, = Pr( Y m) = τm,, and ϕ = Pr( Y 1) = 1. Clearly the cumulatve probablty functon, ϕ, j, (weakly) ncreases as,1 response value j decreases. Defne the cumulatve logt lnk as logt( ϕ, j) = log[ ϕ, j/ ( τ,1 + τ, τ, j 1)] = log[pr( Y j) / Pr( Y < j)]. Usng the control varables n Error! Reference source not found., the proportonal odds model can be specfed as: logt( ϕ ) = α + β q + β a + γw, j {2,3,..., m}. (7) k,1 k,0, j j 1 2 If other varables are fxed at 0, then α represents the log odds of choosng Y { j,..., m} j nstead of Y {1,..., j 1}. The model assumes that categorzaton does not affect the mpacts of 13

16 the explanatory varables on the odds. An mplcaton s that coeffcents for dfferent functons are held to be the same across regressons n system (7) and only the ntercept term dffers. These cross-equaton restrctons have been mposed n system (7). When compared wth the multnomal logt regresson model, the proportonal odds model s more parsmonous n that fewer coeffcents are estmated. Capuano et al. (2007) has an extensve dscusson on the strengths and weaknesses of the proportonal odds model. We consder two groups of varables for ncluson n W : (a) farm and farmer attrbutes whch nclude farmers age, farm sze and farm tenure status; and (b) land and land use characterstcs n a respondent s localty, whch nclude land class and percent of land n partcular classes wthn 5-mle radus of the respondents addresses. How these control varables affect a respondent s percepton s not apparent and, to our best knowledge, there s no theory that deals wth these ssues. We conjecture that farmers perceptons could be affected by these control varables. Operators years n farmng can account for nerta n the sense that farm operators have already nvested n establshng the farm operaton that they are comfortable wth. A tenancy ndex s also consdered that measures what fracton of the land operated s rented n. We expect that tenancy should ncrease the desre to convert but also reduces the ablty to convert, so we are not sure what sgn the varable should have. We also consder the respondents operated acreage, our pror regardng whch s that larger operatons mght lead to perceptons closer to the actual land use changes because the operators may devote more attenton to land use related ssues. Fnally, we consder a group of varables that reflect land characterstcs n a respondent s localty: the fracton of land wthn 5 mles Eucldan radus that was under grass n 2006, drectonal varables representng, respectvely, degrees lattude north and degrees longtude west; and the fracton wthn land capablty class 3 or better and the fracton that has slope three or lower of land wthn 5 mle radus. 14

17 4.3 Relatonshp between current perceptons and future land use ntentons We use a logstc regresson to capture the relatonshp between the probablty of ntended land converson n the future and current perceptons of land use changes along wth other control varables, Pr( a = 1) = α + δ a + h( y ) + λw + ε, k,2 k,0 k,1 0 1 (8) wth ε as the error term,,0 a k representng past land use decsons, and W representng a vector of control varables ncludng farm and farmer characterstcs, natural endowments and/or the lay of the land. Coeffcents α 0, δ 1 and λ are regresson coeffcents. Several alternatve forms wll be estmated regardng the perceptons component, hy k,1 ( ), whch s our prmary varable of nterest. We can model how perceptons bas affects land converson decsons,.e., hy ( ) = γ ( y q ). (9) k,1 k,1 k,1 1 A postve response would suggest that, operators are more lkely to convert to a certan land use f she has postve percepton bas towards that type of land use, whch may n turn create a postve feedback loop Results 5.1 The extent and characterstcs of percepton bases n land use changes Table 2 ndcate that the actual change n cropland area, as measured by CropScape data, s 5.76%. The perceved cropland change averaged at 9.80%, whch s 4.05 percentage ponts hgher than the actual CropScape data and s statstcally sgnfcant. Ths ndcates that regardng percentage ncrease n the cropland area, on average producers percepton rate s much hgher than the actual rate. For the extent of grassland area changes, the perceved changng rate s -7.55%, whch s slghtly below the actual change of -6.97%. Ths tme the 15

18 percepton bas s only 0.53 percentage ponts and s not statstcally sgnfcant. These percepton patterns seem to suggest that people tend to overemphasze the changes that drectly relate to ther own benefts whle underestmate the changes assocated wth ndrect socal benefts. In our context, more cropland acreage means ncreased profts to the farmers, whle less grassland acreage mples compromsed ecosystem servce to the overall socety. 5.2 Msmatched categores of actual and perceved land use changes. Fg. 2 provdes a box-and-whskers plot of CropScape assessed cropland changes n a fve-mle radus of a respondent s locaton durng , condtoned on the respondent percepton categores. Wth the excepton of the over 10% decrease percepton category, the 25%, 50% and 75% percentles 3 of CropScape-assessed cropland change, hereafter referred to as the actual percentage change, ncreased wth percepton categores. For the two lowest percepton categores, over 10% decrease and 5-10% decrease, the perceved cropland change was much lower than the actual percentage change. For the two hghest percepton categores, 5-10% ncrease and over 10% ncrease, the perceved changes were hgher than the medan value of actual change. Perceved change s most algned wth actual change for the medan percepton category, stayed wthn 5%. Smlar to Fg. 2, Fg. 3 compares grassland change perceptons wth the Cropscape assessment over the same perod. On grassland change, the medan of actual value was below 0 for each percepton category. However, there s no clear trend n how actual grassland change relates to perceved grassland change categores. Fg. 3 suggests that more than 75% of those n the two hghest percepton categores had over-perceved grassland change. Fgures 2 and 3 do not contan nformaton on the number of respondents n each category. It turns out that respondents are very unevenly dstrbuted across the percepton categores. In 3 The 50% percentle s denoted by the mddle bar of the box, the arthmetc mean s denoted by damond shape, whle the 25% and 75% percentle are denoted by the lower bar and upper bar of the box, respectvely, n the box-and-whskers plot. 16

19 partcular, the two lowest categores regardng cropland area change and the two hghest categores regardng grassland change had very few respondents. Accordng to panel A n Table 3A, only 14 producers, or 1.4% of all respondents, perceved more than 5 percent reducton n cropland area. Smlarly, Table 4B shows that only 31 producers (3.1% of total) perceved grassland area had ncreased by more than 5%. The majorty of producers, 33% and 50% of the total respondents perceved 5-10% ncrease and over 10% ncrease n cropland area, respectvely. As Fg. 2 llustrates, for about 50% of the producers respondng 5-10% ncrease, and over 75% of the producers respondng over 10% ncrease, the actual changes were below perceved changes. Ths suggests more than half of all producers over-perceved the ncrease n cropland area (33% x 50% + 50% x 75%=54%). For changes n grassland area, Table 3B shows that the majorty of respondents, 34% and 37% respectvely, belong to the over 10% decrease and 5-10% decrease categores. The rest 26% of respondents chose the stayed wthn 5% category. Whether there s under-percepton or over-percepton concernng grassland area changes s not clear from Fg. 2. To determne whether the dstrbutons of actual land use change reman the same when perceved land use changes dffer, A Ch-square test was conducted usng nformaton n Table 3. Note that Ch-square s not a vald test for Table 3A and 3B snce 55% of the cells have expected counts less than 5. Therefore, for cropland changes, we wll drop the two decreasng categores, whch has very few counts based on both percepton and CropScape crtera as ndcated by Table 3A. Smlarly, the two ncreasng categores wll be dropped for grassland changes. The Ch-square value for the remanng 3 x 3 tables are sgnfcant at 1% wth a value of for the cropland and for the grassland, both wth degree of freedom of 4. Ths ndcates that actual land use dstrbutons are not the same when perceved land use changes dffer. From the frequency table (3C and 3D) we can also see that f respondents perceve a hgher percentage of land use change surroundng ther farm, the actual percentage of land use change s more lkely 17

20 to be hgh as well. In other words, the perceved changes shows a certan degree of consstency wth the actual changes. 5.3 Characterstcs of over-percepton and under-percepton groups. To better understand the nature of dscrepances between perceved and actual land use change, we dvde the respondents nto three groups dependng on the extent of the dscrepances (Table 3A, B). Group I refers to the 5 dagonal yellow shaded boxes, on whch the respondents responses are consstent wth CropScape data. Group II refers to the 8 blue cross-shaded boxes, whch are only 1 cell off the dagonal, mplyng the responses only have slght dscrepances from the CropScape values. The remanng 12 non-shaded boxes belong to Group III, whch are at least two cells away from the correspondng Cropscape values,.e., the category of percepton s at least two categores away from CropScape values. Table 4A and 4B report Duncan s Multple Range tests of the three groups descrbed above on varables such as age, years operatng the land, educaton level, employment off-farm or not, acres operated, and land locatons. The tables reveal no clear trend on how percepton accuracy changes wth most of these varables. On off-farm employment status, the trend s apparent from crop sde, whch shows that farmers more nvolved n on-farm jobs are more lkely to make the rght predcton. Ths s probably because those who are more nvolved n farmng should have a better sense of what s happenng n ther neghborhood. In addton, locaton may play a role n predcton error too. On the cropland sde, t seems that producers are more lkely to make accurate predctons when they are located further south; whle on grassland sde, producers located further west generally make more accurate predctons Current percepton bases and past land use decsons Table 5 breaks down respondents n each percepton categores by past land use decsons. The left and rght panel are perceved changes n cropland and grassland area, respectvely. For 18

21 example, the table ndcates that among 500 respondents who perceved cropland area ncreased by >10%, 126 converted grassland to cropland and 374 dd not make such changes. Conversely, among the 16 who perceved grassland Increased by >10%, only 2 respondents converted grassland to crop and 14 dd not make such a converson. Table 6A and 6B present the test results for Hypo II., that s the relatonshp between perceptons and past land use decsons based on the ordnal logt model explaned earler n equaton (4). As ndcated by Table 3, for changes n cropland area, very few respondents chose the two decreasng categores, so we combned decreased by >10%, decreased by 5-10% and wthn 5% nto a sngle category, referred to as the non-ncrease of cropland category and labeled as 1 n the model. Consequently, ncreased by 5-10% and ncreased by >10%, are labeled as 2 and 3 respectvely. For smlar reasons, ncreased by >10%, ncreased by 5-10% were combned wth wthn 5% when t comes to changes n grassland area, referred to as the non-decrease of grassland category. Regardng perceved grassland change, decreased by >10%, decreased by 5-10%, and non-decrease of grassland are labeled as 1, 2 and 3 respectvely. Therefore movng to a hgher category means ether a larger ncrease n cropland acres, or a smaller decrease n grassland acres. Three dfferent models, from the most concse to the most complex, were estmated for both perceptons on cropland change and grassland change, wth the results summarzed n Tables 6A and 6B. The number of control varables ncreases from Model I to III. Whle Model I only ncludes the prevous converson varable, Model II ncludes the prevous converson acton, percepton on crop prce mportance and farmer/farm characterstcs. Model III ncludes all the varables n Model II and further extends to the varables related to farm surroundng areas. Table 6A ndcates that prevous converson decsons from grass to crop has a sgnfcantly postve mpact on perceved cropland area changes n all three models. Even after control varables such as characterstcs of the farm and farm surroundng areas are ncluded, the odds of 19

22 hgher percepton from producers who converted grassland to cropland s stll 1.30 hgher than producers who dd not convert. Therefore, n terms of cropland change, Hypothess II s confrmed and producers made grassland to cropland converson n the past 10 years are more lkely to have a hgher percepton on cropland area change. Perceved mpact of changng crop prces and tenure ndex are two varables n Model III that sgnfcantly ncrease the odds of hgher percepton on cropland area change. If changng crop prces on land use change s perceved to have a hgher mpact, e.g., 5: great mpact vs. 4: qute a bt of mpact, then the odds of hgher percepton ncreases by In addton, f the producer rent a hgher proporton of the farmed land, then the odds of hgher percepton ncreases by These two varables reman sgnfcant n Model III. Fve other control varables on farm surroundng areas are sgnfcant n Model III, and all ncrease the odds of percevng more converson to cropland. Those nclude CropScape recorded cropland change, percent of grassland area n 2006, percent of land of class I to III, change n nfrastructure for corn and lattude. Ths ndcates that respondent s percepton of cropland are generally consstent wth CropScape records. When the proporton of grassland area wthn 5 mle radus n 2006 ncreased by 1%, then the odds rato of hgher percepton ncreased by 1.025, gven other factors fxed. Ths shows that people generally perceved hgher grassland to cropland converson rate when there were orgnally hgher proporton of grassland avalable for converson. Consstent wth our ntuton, there were also hgher perceptons of cropland area change when a hgher percentage of land was sutable for crop producton and when greater mprovements n corn nfrastructure were reported. Lattude remans sgnfcant even after accountng for the other control varables, whch shows some regonal percepton bases. Producers n the North are more lkely to perceve a hgher cropland change, as ndcated by the odds rato ncrease when lattude ncrease by 1 degree. Regardng percepton on changes of grassland area, snce the queston on prevous cropland 20

23 to grassland converson decsons was not ncluded n the questonnare, Hypothess II cannot be tested as we dd for cropland area change percepton. However, smlar to Model I n Table 6A, Model I n Table 6B ndcates that for those farmers who made grassland to cropland converson are less lkely to have a hgher percepton of grassland change, whch means less decrease of grassland acres. In other words, ths smply means the cropland converters are more lkely to perceve more decrease n grassland acres. Interestngly, educaton and prncpal occupaton, though not sgnfcant n cropland area change percepton, play sgnfcant roles here. It ndcates that people wth more educaton and more nvolvement wth the farm are more lkely to perceve less decrease n grassland acres than ther counterparts. Intutvely, the varables that ncreases the odds of hgher cropland change percepton, such as percentage of grassland wthn 5 mles radus n 2006, and percentage of land n class III or better, decrease the odds of hgher grassland change percepton. It also makes sense that the perceved mprovement n cattle nfrastructure goes hand n hand wth the percepton of grassland area ncrease. The ncrease n lattude, however, also ncreases the percepton of grassland area change, but the odds rato of such ncrease here s 1.188, lower than the odds rato of n Table 6A. Ths means producers lve further north are lkely to overestmate cropland and grassland change, especally on the cropland land change Future land use ntentons, current perceptons and land use decsons Results n Table 7 support Hypohess III regardng cropland area percepton and future converson ntenton from grass to crop. If we dsregard the two decreasng categores (or we may combne those two wth the wthn 5%), among the rest 3 categores, those who perceved a hgher ncrease n cropland area are also more lkely to ntend grass to cropland converson n the next 10 years. However, Table 7 shows no postve relatonshp between perceved change n grassland area and ntenton to convert to grassland n the future, thus lendng no support to 21

24 Hypothess III regardng grassland change percepton. On the contrary, those who perceved a decrease n grassland area are more lkely to express a wllngness to convert to grassland n the future. Ths ndcates that most respondent s desre to convert to grassland arse from the concern of further grassland loss, rather than followng others producers converson pattern as n cropland case. Table 8A and 8B provde test for Hypothess III usng 3 dfferent models, from most parsmonous to the most complex, whch resembles the layouts of Table 6A and 6B. On the cropland sde, Model I shows that percepton bas sgnfcantly ncreases the odds for future converson ntenton. However, as more control varables are ncluded ths s no longer the case. Therefore Hypothess III s not supported. Prevous converson experence, nstead, has most sgnfcant mpact on future converson experence, as t ncreases the odds of future converson by Prncpal occupaton also plays a sgnfcant role, as those who are less nvolved wth farmng are more lkely to make future conversons to cropland. Not surprsngly, land qualty (land class I to III) ncreases the odds of future converson to cropland. In addton, we found that n areas where CropScape predcted hgher percentage ncrease n cropland, future converson s not lkely to occur. A lkely explanaton s that landowners expectaton about the future proftablty of cropland has dropped, as ths survey took place n Sprng 2015, a tme when commodty prces already started to declne. Longtude, too, has a sgnfcant postve mpact on future converson to cropland. It seems the focus of land use converson wll gradually shft to the west as most of the land n the east has already been converted. On grassland sde, smlar to the concluson n Table 7, Model III found the percepton bas decreases the odds for future converson, whch s contrary to Hypothess III. Besdes the mpact of percepton bas, only two factors that are sgnfcant n Model III and both mproves the odds of future converson to grassland. These factors are percentage of grassland n 2006 and percentage of hgh qualty land (Table 8B). A plausble explanaton s that n areas where most 22

25 grassland prevals n 2006, producers who learned the converson consequences may try to resume the prevous grassland and cropland balance n the future Perceptons bases and land use choce motves There s some nterestng connecton between percepton bases about local land use changes and the perceved man drvers of land use changes. In our study, we asked respondents about factors that motvated land use change over the pror ten years wthn 5 mles of ther operaton base. Wth the excepton of land unts at ssue, the queres were structured exactly as wth those for own-farm land use wth the summary statstcs provded n Table 9. Four tems stand out. One s that the response rate was lower for questons about ther neghborhood, lkely because growers were less sure of nformaton beyond ther operaton or because the queston was asked later n the survey. Secondly, the mean values are generally larger than correspondng own responses. A student s t test confrms the dfference at sgnfcance level of 1% for all varables except that for changng clmate patterns, for whch the dfference between one s own land and local area s not sgnfcant. We can conceve of two possble, related, reasons why these dfferences exst. Growers may only ascrbe economc motves to ther neghbors actons but see more complexty and nuance n ther own motves. For example, they may have weghed land stewardshp, famly crcumstances and legacy concerns n ther own response but not n those of other landowners. The other possble reason s that own response s lkely to be more varable than the average response of others. If non-economc motves are dosyncratc then they are lkely to average out over growers, leavng economc motves wth a stronger response on average. Ths brngs us to the thrd noteworthy tem, that the standard devaton of responses s lower for questons on local land than on own land, whch s consstent wth the dea that ownresponses contan dosyncratc motves that respondents assume to average out. Fnally, own 23

26 response to the wldlfe motve s, though stll low, hgher on average than s the respondent s vews on that motve among other owners. Perhaps respondents feel that they care more about wldlfe than do others, a possblty we cannot preclude because the survey response rate was less than 40% and response mght ndcate a more cvc-mnded dsposton. In any case, f farmers perceve economc forces to be a stronger motves for land use changes n ther localty than n ther own land use decsons, and the economc condtons were favorable n the study perod for converson from grassland to cropland, then t s reasonable that we observe overpercepton of grassland to cropland converson. 6. Concludng remarks The human dmensons of land use changes are a crtcal aspect n our understandng of the drvers underlyng the changes. Landowner characterstcs and regonal land use patterns are central n the study of human dmensons. In the exstng lterature, ths human dmenson s mostly reflected n economc motves, and there s lttle study wth respect to the behavoral and psychologcal factors underlyng land use changes. We provde an analyss on how landowners percepton of land use changes are related to actual land use changes. We found substantal overpercepton of the expanson of cropland. In partcular, farmers perceved changes are twce as large as the actual changes. Such over-percepton can lead to a snowballng effect when percepton of land use changes are also assocated the change n regonal nfrastructural support for crop producton. As our results show that farmer s percepton of land use changes are correlated wth ther perceptons of how nfrastructure has evolved. Larger perceved cropland ncreases are postvely correlated wth percepton of mproved nfrastructure condton for major crop producton. The latter mples lower producton costs and more convenent busness network that supports crop producton. Ths could eventually lead to addtonal converson from other land uses to cropland. 24

27 Even though, the extent and nature of a regon s agrculture s determned largely by the regon s clmate and sols. There s much more to an effcent producton system. An ncreasngly sophstcated complex of local servces supports all land n a localty that s under a gven producton system. Thus, the presence of such nfrastructure as elevators and machnery servces a) s the result of sgnfcant croppng presence and b) reduces the costs of croppng due to ts very presence. A farmer s percepton of land use changes mght be based on complex personal experence and neghborhood context and may not be accurate. Our results ndcate that ther percepton s sgnfcantly related to ther past land use decsons. Ths fndng ndcates that a sound land use management strategy need to recognze the underlyng reasons of percepton bases and accommodate farmers nsghts and ntellgence n publc polcy communcatons. The formaton of percepton s a complex process and s beyond ths analyss. We are not sure how people form ther perceptons, our study s only a begnnng n the context of land use changes. More can be done regardng the formaton of perceptons n future studes. 25

28 References Adesna, A.A., and J. Badu-Forson Farmers Perceptons and Adopton of New Agrcultural Technology: Evdence from Analyss n Burkna Faso and Gunea, West Afrca. Agrcultural Economcs 13(1), 1-9. Akerlof, G., and W.T. Dckens The Economc Consequences of Cogntve Dssonance. Amercan Economc Revew 72 (3), Arbuckle, G.J., L.W. Morton, and J. Hobbs Farmer Belefs and Concerns about Clmate Change and Atttudes Toward Adaptaton and Mtgaton: Evdence from Iowa. Clmatc Change 118(3), Arbuckle, J. Gordon, and G. Roesch-McNally Cover Crop Adopton n Iowa: The Role of Perceved Practce Characterstcs. Journal of Sol and Water Conservaton 70(6), Balcets, E., and D. Dunnng See What You Want to See: Motvatonal Influences on Vsual Percepton. Journal of Personalty and Socal Psychology 91(4), Bénabou, R Ideology. Journal of the European Economc Assocaton 6(2), Bénabou, R The Economcs of Motvated Belefs. Revue d Économe Poltque 125, Bleemer, Z., and B. Zafar Intended College Attendance: Evdence from an Experment on College Returns and Costs. September 1, FRB of New York Staff Report No Avalable at SSRN: or Botzen, W.J.W, H. Kunreuther, and E. Mchel-Kerjan Dvergence Between Indvdual Perceptons and Objectve Indcators of Tal Rsks: Evdence from Floodplan Resdents n 26

29 New York Cty. Judgment and Decson Makng 10(4), Brown, D.G., C. Polsky, P. Bolstad, S.D. Brody, D. Hulse, R. Kroh, T.R. Loveland, and A. Thomson. 2014: Ch. 13: Land Use and Land Cover Change. Clmate Change Impacts n the Unted States: The Thrd Natonal Clmate Assessment, J. M. Melllo, Terese (T.C.) Rchmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, do: /j05q4t1q. Capuano, A.W., J.D. Dawson, and G.C. Gray Maxmzng Power n Seroepdemologcal Studes Through the Use of the Proportonal Odds Model. Influenza and Other Respratory Vruses 1(3), Claassen R., F. Carrazo, J.C. Cooper, D. Hellersten, and K. Ueda Grassland to Cropland Converson n the Northern Plans: The Role of Crop Insurance, Commodty, and Dsaster Programs. Economc Research Report No. ERR-120 (U.S. Department of Agrculture Economc Research Servce, Washngton, DC. Cruces, G., R. Perez-Trugla, and M. Tetaz Based Perceptons of Income Dstrbuton and Preferences for Redstrbuton: Evdence from a Survey Experment. Journal of Publc Economcs 98(February), Dohmen, T., A. Falk, D. Huffman, F. Marklen, and U. Sunde Based Probablty Judgment: Evdence of Incdence and Relatonshp to Economc Outcomes from a Representatve Sample. Journal of Economc Behavor & Organzaton 72(3), DOI: Dramstad, W.E., M. Sundl Tvet, W.J. Fjellstad, and G.L.A. Fry Relatonshps Between Vsual Landscape Preferences and Map-based Indcators of Landscape Structure. Landscape and Urban Plannng 78(4),

30 Feng, H., D.A. Hennessy, and R. Mao The Effects of Government Payments on Cropland Acreage, Conservaton Reserve Program Enrollment, and Grassland Converson n the Dakotas. Amercan Journal of Agrcultural Economcs 95(2), Gaurav A., P.T. Wolter, H. Feng, and D.A. Hennessy Role of Ethanol Plants n Dakotas Land Use Change: Analyss Usng Remotely Sensed Data Selected paper presented at AAEA Annual Meetng, San Francsco, CA, July 26-28, Georganas, S., P.J. Healy, and N. L Frequency Bas n Consumers Perceptons of Inflaton: An Expermental Study. European Economc Revew 67(Aprl), Helms, D The Development of the Land Capablty Classfcaton. In D. Helms (Ed.), pp , Readngs n the Hstory of the Sol Conservaton Servce. Washngton, DC: Sol Conservaton Servce. Avalable at: Kahneman, D., Thnkng, Fast and Slow. Farrar, Straus and Groux, New York. Klne, K.L., N. Sngh, and V.H. Dale Cultvated Hay and Fallow/dle Cropland Confound Analyss of Grassland Converson n the Western Corn Belt. PNAS USA 110(31), E2863. Langen, C Measurng Cropland Change: A Cautonary Tale. Papers n Appled Geography 1(1), Lawler, J.J., D.J. Lews, E. Nelson, A.J. Plantnga, S. Polasky, J.C. Wthey, D.P. Helmers, S. Martnuzz, D. Pennngton, and V.C. Radeloff Projected Land-use Change Impacts on Ecosystem Servces n the Unted States. PNAS USA 111 (20) Matsumoto, M., and F. Spence Prce Belefs and Experence: Do Consumers Belefs Converge to Emprcal Dstrbutons wth Repeated Purchases? Journal of Economc Behavor & Organzaton 126,

31 Mao, R., D.A. Hennessy, and H. Feng Sodbustng, Crop Insurance, and Sunk Converson. Land Economcs 90(4), Proto, E., and D. Sgro Based Belefs and Imperfect Informaton. Insttute for the Study of Labor, IZA Dscusson Paper, No. 8858, February. Rabn, M Psychology and Economcs. Journal of Economc Lterature 36(1), Rabn, M Inference by Belevers n the Law of Small Numbers. Quarterly Journal of Economcs 117(3), Rashford, B.S., J.A. Walker, and C.T. Bastan Economcs of Grassland Converson to Cropland n the Prare Pothole Regon. Conservaton Bology 25(2), Retsma, K.D., B.H. Dunn, U. Mshra, S.A. Clay, T. DeSutter, and D.E. Clay Land Use Change Impact on Sol Sustanablty n a Clmate and Vegetaton Transton Zone. Agronomy Journal 107(6), Trole, J Ratonal Irratonalty: Some Economcs of Self-management. European Economc Revew 46(4-5), U.S. Government Accountablty Offce (USGAO) Agrcultural Conservaton: Farm Program Payments Are an Important Factor n Landowners Decsons to Convert Grassland to Cropland. Washngton, D.C., Report , September, 70 pages. USDA Natonal Agrcultural Statstcs Servce (USDA-NASS) 2016a. Cropland Data Layer. Publshed crop-specfc data layer. Avalable at USDA-NASS, Washngton, DC. (Accessed n June) 2016b. CropScape and Cropland Data Layer Metadata. Vscus, W.K Do Smokers Underestmate Rsks? Journal of Poltcal Economy 98(6), 29

32 Vscus, W.K., J Huber, and J. Bell The Prvate Ratonalty of Bottled Water Drnkng. Contemporary Economc Polcy 33(3), Wang, T., M. Lur, L. Janssen, D.A. Hennessy, H. Feng, M. Wmberly, and G. Arora Determnants of Motves for Land Use Decsons at the Margns of the Corn Belt. Ecologcal Economcs 134, Wrght, C.K., and M.C. Wmberly Recent Land Use Change n the Western Corn Belt Threatens Grasslands and Wetlands. PNAS USA 110(10),

33 Table 1. Summary statstcs of key varables from farmers survey. Varable N Mean Std. Dev. Mnmum Maxmum Farm sze Cropland acre Farmer s age Land tenure ndcator Off-farm employment Lattude Longtude Percent of land n land class III or lower Perceved change n nfrastructure for corn % change n corn and soybean acres, percepton % change n grassland acres, percepton % change n corn and soybean acres, CropScape % change n grassland acres, CropScape % of grassland acres, 5 m. radus, CropScape % of land class I-III, 5 m. radus, CropScape % of land Slope 3, 5 m. radus, CropScape Converson to cropland, past 10 years 1, Converson to cropland, next 10 years Converson to grassland, next 10 years Table 2. (Testng of Hypothess I). Average percepton bases (measured as the dfference between land use changes accordng to CropScape and land use changes perceved by respondents) Land use change varables N Mean Pr > t % change n corn and soybean acres, percepton <.0001 % change n grassland acres, percepton <.0001 % change n corn and soybean acres, CropScape <.0001 % change n grassland acres, CropScape <.0001 Perceptons bases of change n cropland area <.0001 Perceptons bases of change n grassland area

34 Table 3. Number of respondents n dfferent categores of perceved land use changes and actual grassland changes measured by CropScape data, wthn fve-mle radus of a respondent s address. Survey A. For changes n cropland area CropScape Decreased by > 10% Decreased by 5-10% Wthn 5% Increased by 5-10% Increased by > 10% Decreased by > % Decreased by % Wthn 5% Increased by 5-10% Increased by > 10% Survey B. For changes n grassland area CropScape Decreased by > 10% Decreased by 5-10% Wthn 5% Increased by 5-10% Increased by > 10% Decreased by > % Decreased by % Wthn 5% Increased by 5-10% Increased by > 10%

35 C. Row frequency tables for changes n cropland area (reduced to 3 x 3) Survey CropScape Wthn 5% Increased by 5-10% Increased by > 10% Wthn 5% 51.0% 45.2% 3.8% Increased by 5-10% 41.0% 54.3% 4.8% Increased by > 10% 33.9% 56.1% 10.0% D. Row frequency tables for changes n grassland area (reduced to 3 x 3) Survey CropScape Decreased by > 10% Decreased by 5-10% Wthn 5% Decreased by > 10% 51.7% 19.3% 29.0% Decreased by 5-10% 45.0% 20.0% 35.0% Wthn 5% 35.5% 23.4% 41.1% 33

36 Table 4. Duncan s Multple Range tests of the three groups n Table 3 (cropland). A. For changes n cropland area (groups defned n Table 3A) Mean Value Group I (5 dagonal boxes) Group II (8 cross-lne shaded boxes) Group III (12 non-shaded boxes) Age 3.24 ab 3.37 a 3.12 b Years operatng land 4.32 b 4.49 a 4.26 b Educaton level 2.95 a 2.93 a 3.06 a Off-farm employment status 1.16 b 1.25 ab 1.28 a Acres operated 1,883 a 1,546 a 1,734 a Longtude a a a Lattude b b a B. For changes n grassland area (groups defned n Table 3B) Mean Value Group I (5 dagonal boxes) Group II (8 cross-lne shaded boxes) Group III (12 non-shaded boxes) Age 3.26 ab 3.21 a 3.39 a Years operatng land 4.38 a 4.36 a 4.44 a Educaton level 2.92 b 3.06 a 2.87 b Off-farm employment status 1.27 a 1.14 b 1.27 a Acres operated 1,787 a 1,799 a 1,463 b Longtude a ab b Lattude a a a Note: Means wth the same letter are not sgnfcantly dfferent. 34

37 Table 5. (Testng of Hypothess II tabulaton) The number of respondents by past converson hstory and percepton categores Perceved change n cropland area Converson from grass to crop 0 (No) 1 (Yes) total Perceved change n grassland area Converson from grass to crop 0 (No) 1 (Yes) total Decreased by > 10% Decreased by > 10% Decreased by 5-10% Decreased by 5-10% Wthn 5% Wthn 5% Increased by 5-10% Increased by 5-10% Increased by > 10% Increased by > 10% total ,008 total ,003 35

38 Table 6. (Testng of Hypothess II) Estmaton results of perceptons as a functon of past land use decsons and other varables from the ordnal Logt model represented by equaton (7). (Dependent varable s the odds of choosng a hgher categores of changes. Alternatve model results were presented that ncorporated ncreasngly more varables.) A. Perceptons about changes of cropland area (Odds Rato Estmates) Parameters Model I Model II Model III Converson from grass to crop c c a Perceved mpact of changng crop prces c b Farm acre a Years operatng Tenure ndex b c Educaton Prncpal Occupaton CropScape cropland % change c % of grassland, 2006, 5 m. radus c % of land class I-III, 5 m. radus c % of land Slope 3, 5 m. radus Change n nfrastructure for corn c Lattude c Longtude Percent Concordant 29.1% 61.1% 72.6% B. Perceptons about changes of grassland area (Odds Rato Estmates) Parameters Model I Model II Model III Converson from grass to crop b a Perceved mpact of changng crop prces a c Farm acre Years operatng a Tenure ndex Educaton b a Prncpal Occupaton b b CropScape grassland % change % of grassland, 2006, 5 m. radus c % of land class I-III, 5 m. radus b Slope 3, 5 m. radus Change n nfrastructure for cattle b Lattude c Longtude Percent Concordant 26.8% 56.9% 62.1% Note: For all estmaton results, superscrpt a, b, c means statstcally sgnfcant at 10%, 5%, and 1%, respectvely. 36

39 Table 7. (Testng of Hypothess III tabulaton) The number of respondents by percepton and ntenton of land use changes n the future Perceved change n cropland area 0 (No) Intenton of converson from grass to crop 1 (Yes) total Percent of yes Perceved change n grassland area Intenton of converson from crop to grass 0 (No) 1 (Yes) total Percent of yes Decreased by > 10% % Decreased by > 10% % Decreased by 5-10% % Decreased by 5-10% % Wthn 5% % Wthn 5% % Increased by 5-10% % Increased by 5-10% % Increased by > 10% % Increased by > 10% % total % total % 37

40 Table 8. (Testng of Hypothess III) Estmaton results of ntended future land use changes as a functon of current percepton bas and other varables from the logstc model represented by equaton (8). A. Intenton of future converson from grassland to cropland (Odds Rato Estmates) Parameters Model I Model II Model III Percepton bas n cropland area change c b Converson from grass to crop b c Perceved mpact of changng crop prces c Farm acre Years operatng Tenure ndex a Educaton Prncpal Occupaton b CropScape cropland % change b % of grassland, 2006, 5 m. radus % of land class I-III, 5 m. radus b % of land Slope 3, 5 m. radus Change n nfrastructure for corn Lattude Longtude c Percent Concordant 59.0% 70.7% 74.9% B. Intenton of future converson from cropland to grassland (Odds Rato Estmates) Parameters Model I Model II Model III Percepton bas n grassland area change a Converson from grass to crop a Perceved mpact of changng crop prces Farm acre Years operatng Tenure ndex Educaton Prncpal Occupaton CropScape grassland% change % of grassland, 2006, 5 m. radus c % of land class I-III, 5 m. radus b % of land Slope 3, 5 m. radus Change n nfrastructure for cattle Lattude 1.03 Longtude Percent Concordant 47.1% 57.9% 69.9% Note: For all estmaton results, superscrpt a, b, c means statstcally sgnfcant at 10%, 5%, and 1%, respectvely. 38

41 Table 9. Summary of the mpact rankng of dfferent factors n own land use decsons versus n neghborhood land use changes (The rankng ranges from 1 to 5 wth 5 beng Great Impact and 1 beng no mpact.) Impact on agrcultural land use (own farm) Impact on agrcultural land use (local area) Varable N Mean Std Dev N Mean Std Dev Changng crop prces Changng nput prces Avalablty of crop nsurance polces Avalablty of drought-tolerant seed Development n pest management practces Improved crop yelds Development of more effcent croppng equpment Labor avalablty problems Improvng wldlfe habtat Changng weather/clmate patterns

42 Fgure 1. Cropland as a share of respondent acres and the number of survey respondents n each county. (Source: Wang et al., 2017) 40