Crop Insurance for Energy Grasses

Transcription

1 Crop Insurance for Energy Grasses Ruiqing Miao Energy Biosciences Insiue Universiy of Illinois a Urbana-Champaign miaorong@illinois.edu Madhu Khanna Deparmen of Agriculural and Consumer Economics and Energy Biosciences Insiue Universiy of Illinois a Urbana-Champaign khanna@illinois.edu Seleced Paper prepared for presenaion a he Agriculural & Applied Economics Associaion s 203 Crop Insurance and he Farm Bill Symposium, Louisville, KY, Ocober 8-9, 203. Copyrigh 203 by Ruiqing Miao and Madhu Khanna. All righs reserved. Readers may make verbaim copies of his documen for non-commercial purposes by any means, provided his copyrigh noice appears on all such copies.

2 Crop Insurance for Energy Grasses Ruiqing Miao and Madhu Khanna Absrac This sudy compares he efficiency of hree policy insrumens (i.e., crop insurance, esablishmen cos share, and biomass price subsidy) in promoing energy crop producion. The efficiency is measured by energy crop acreage increased due o he policy insrumen for a given amoun of governmen expendiure supporing he insrumen. Based on a unique daase of couny-level miscanhus yield over across he rainfed region of he Unied Saes, our resuls show ha if here is no credi consrain in financing esablishmen coss hen crop insurance is mos efficien and biomass price subsidy is leas efficien among he hree policy insrumens. If here is credi consrain in financing esablishmen coss, however, hen crop insurance is more (respecively, less) efficien han esablishmen cos share for small (respecively, large) expendiure. Geographical disribuions of energy crop acreage under differen policy insrumens are sudied as well. Keywords: crop insurance, energy crops, esablishmen coss, miscanhus, yield JEL codes: Q5, Q6, Q28

3 Crop Insurance for Energy Grasses. Inroducion Increasing concerns abou energy securiy, dependence on non-renewable fossil fuels, and climae change have led o significan policy suppor for biofuels in recen years. For insance, he Energy Independence and Securiy Ac (EISA) of 2007 mandaes ha he annual use of biofuel increases o 36 billion gallons by 2022, of which 6 billion gallons mus be cellulosic biofuel. Miscanhus (Miscanhus giganeus) and swichgrass (Panicum virgaum) have long been considered promising cellulosic biofuel crops (Heaon e al. 2008). Unlike convenional crops such as corn and soybeans, perennial energy crops are new and unfamiliar and heir producion involves significan upfron invesmens (i.e., esablishmen coss) in esablishmen which akes one or wo years before hey yield an income and a 0 o 5-year commimen of land o he crop. Energy crop producion exposes farmers o price and yield risks ha may differ from convenional crops, o he exen ha weaher variaions affec yields of energy crops differenially and oupu prices are affeced by demand condiions in differen markes ha may no be correlaed. Moreover, he significan amoun invesmens required for perennial energy crop producion make credi consrain a relevan issue. The commercial producion of hese crops will depend no only on heir expeced reurns relaive o ha of exising annual crops bu also on he riskiness of hese crops relaive o annual crops. In he presence of crop insurance for convenional crops, farmers will be relucan o swich o energy crops wihou similar proecion from down-side risks. Therefore, insurance programs for energy crops are expeced o be criical o induce farmers o conver land from annual crops currenly covered by crop insurance, o bioenergy crops as commercial producion of cellulosic biofuels commences. Such a program would reduce he risks associaed wih energy crop producion and hus he risk premium ha refineries will need o pay farmers o induce producion of energy crops. This can reduce he overall cos of

4 cellulosic biofuels and make hem more compeiive wih corn ehanol and gasoline. Alhough crop insurance programs for energy crops are no being proposed for he 203 farm bill, such programs are expeced o be criical o induce farmers o conver land from annual crops currenly covered by crop insurance, o energy crops as commercial producion of cellulosic biofuels commences. Such a program would reduce he risks associaed wih energy crop producion and hus he risk premium ha refineries will need o pay farmers o induce producion of energy crop. This could also reduce he overall cos of cellulosic biofuels and make hem more compeiive wih corn ehanol and gasoline. Various public inervenions have been esablished or proposed for he biofuel indusry argeed owards farmers and refineries. These include a Cellulosic Biofuel Producion Tax Credi (CBPTC) ha provides blenders a ax credi of $.0 per gallon of cellulosic biofuels and he Biomass Crop Assisance Program (BCAP) ha provides growers wih a 75% cosshare of esablishmen coss, a subsidy of $45 per on of biomass for wo years o cover coss of collecion, harvesing, sorage and ransporaion of biomass, and an annual paymen up o 5 years for woody biomass and up o 5 years for non-woody biomass. Bioenergy crops have o compee wih convenional crops like corn and soybeans ha are covered by federally subsidized crop insurance programs ha reduce he down-side risks due o weaher and marke condiions for farmers. In 20, he USDA had proposed o sudy he feasibiliy of providing crop insurance o producers of biofuel feedsock, including crop residues and woody biomass. Provision of energy crop insurance would miigae heir riskiness and he disincenives for allocaing land o heir producion. The effeciveness of argeing federal subsidies owards such an insurance program insead of direcly o biofuel and biomass producion programs needs o be invesigaed o design he socially opimal mix of policy inervenions ha can simulae he ransiion o a bio-based economy in he U.S. 2

5 The purpose of his sudy is o provide a comprehensive economic evaluaion of a poenial crop insurance program for energy grasses. Specifically, we are invesigaing he following aspecs of a poenial crop insurance program for energy grasses: ) comparison of he efficiency beween crop insurance and Biomass Crop Assisance Program (BCAP) ype policies in 2008 farm bill in enhancing farmers adopion of energy crops; 2) coss for federal governmen o suppor such insurance; and 3) effecs on growers land-use decisions when hey are considering wheher o plan radiional row crops or energy crops. Crop insurance coverage is currenly available o more han 350 commodiies in all 50 saes and more han 80% of eligible acres are enrolled in various insurance programs (Babcock 202). The exisence and scope of he federal crop insurance program in U.S. provides a preceden for developing insurance programs o provide comparable risk proecion for farmers of bioenergy crops. Previous sudies have analysed crop insurance design and he impac of crop insurance for convenional crops on farmers welfare, land-use decisions, and echnology adopion. Several sudies have examined he effec of crop insurance on cropping decisions. Goodwin e al. (2004) showed ha crop insurance subsidies affeced cropping decisions bu his effec was no large. This finding is similar o ha obained by more recen sudies by Claassen e al. (20), Rashford e al. (20), Miao e al. (202), and Feng e al. (203) which use finer resoluion daa and recognize ha he value of crop insurance subsidies increase in direc proporion o he oupu price level. However, under a real opion framework ha akes swiching coss of convering beween cropland and grassland ino accoun, Miao e al. (203) show ha he incenivizing effec of crop insurance on naive grassland conversion can be very large. Furhermore, Woodard e al. (202) found ha he insurance rules of he Risk Managemen Agency impeded adopion of skip-row echnology. 3

6 The impac of crop insurance on farmer welfare has also araced much aenion. For example, by comparing revenue insurance wih 990 deficiency paymen program, Hennessy e al. (997) showed ha revenue insurance is more efficien in enhancing farmers welfare. Their simulaions show ha revenue insurance wih 75% coverage level can provide farmers wih abou he same benefi bu a a fourh of he cos of he deficiency paymen program. Coble and Dismukes (2008) also showed ha revenue insurance can be more efficien in reducing risk han separae price risk and yield risk insrumens. Unlike previous sudies ha have analysed he effecs of crop insurance for convenional crop choice, his sudy will examine he effeciveness of crop insurance in inducing he adopion of new crops. A survey of farmers by Fewell e al. (20) suggess ha availabiliy of insurance programs for bioenergy crop producion will be a key facor in incenivizing farmers o grow bioenergy crops. We will examine he design of a yield insurance program o induce conversion of land o energy crops. We hen examine he implicaions of federal governmen subsidies supporing such insurance and compare he efficiency of providing a crop insurance subsidy wih ha of providing an esablishmen cos share subsidy or a biomass price subsidy and analyze heir implicaions for farmers welfare and coss of cellulosic biofuel producion. This sudy conribues o he lieraure by sudying he effeciveness crop insurance for inducing invesmen in energy crop producion. Based on he yield disribuions of energy grasses we can quanify he acuarially fair premiums for yield insurance conracs a differen coverage levels. Governmen budgeary effec of subsidizing crop insurance programs for bioenergy crops is sudied. We will examine he effecs of differen levels of insurance premium subsidy by he governmen on he profiabiliy of producing energy crops and biofuels a given ehanol prices. 4

7 The aricle proceeds as follows. In he nex secion we provide a concepual framework under which key facors ha influence growers land allocaion decisions are analyzed. In Secion 3 we describe daa and simulaion approach we employ for his sudy. Simulaion resuls are presened and discussed in Secion 4. Secion 5 concludes. 2. Concepual Framework Suppose ha a grower has L amoun of homogenous land ha can be devoed o convenional row crops and energy crops. Le x 0 and x denoe he quaniy of land devoed o convenional crops and energy crops, respecively. Clearly we have x0 + x L. Also suppose 0 he life-span of he energy crop is T years. Le π and π denoe he profi per acre from growing row crops and energy crops, respecively. Here we assume consan reurns o scale. For simpliciy we also assume ha once a parcel of land is devoed o an energy crop hen i will be covered under he energy crop during he energy crop s enire life-cycle, i.e., T years. Tha is, we assume away he possibiliy ha he grower may abandon he energy crop in a year < T when marke siuaions are favorable o growing row crops. Given he above assumpions, he grower s problem is o opimally allocae he land beween row crop and energy crop so ha he discouned expeced uiliy over he T years is maximized. Specifically, we have max () x0, x T = ( β E u( x π + x π ) 0 0 s.. x + x = L and x, x 0, 0 0 where β is a uiliy discoun facor, and u ( ) is an insananeous uiliy funcion such ha u '( ) 0 and u ''( ) 0. In his aricle we assume ha u ( ) is a consan absolue risk aversion i (CARA) uiliy funcion wih form u( π ) = e απ. i ) Le i y and i p are yield and price for crop i {0,} in year, respecively. Here superscrip 0 sands for he row crop and for he energy crop. We assume ha row crops are 5

8 covered under revenue crop insurance. Then he indemniy of he row crop in year {,..., T} is, (2) I = max[ φ E( p y ) p y,0], where φ is he crop insurance coverage level for he row crop, and E( ) is expecaion operaor. We furher assume ha he variable cos and fixed cos of row crop producion is and 0 0 c, respecively. Therefore, he profi of planing he row crop, π, can be wrien as, 0 v (3) where π = p y v y c + I ( s )E( I ), s [0,] is premium subsidy rae provided by he governmen o row crop insurance. To model he hree policy insrumens (crop insurance, esablishmen cos share, and biomass subsidy), exra noaions are in order. For energy crop producion, le s [0,], s ρ [0,], and p 0 denoe insurance premium subsidy rae, esablishmen cos share endured by he governmen, and biomass price subsidy o he energy crop, respecively. Since he energy crop producion will likely occur under a long-erm conrac beween a grower and a bio-refinery in which biomass price is fixed (Yang e al. 203), in his aricle we assume ha biomass, p, is a consan over he T-year period. Therefore, he indemniy of he energy crop in year is, (4) I = p max[ φ E( y ) y,0], where φ and y are he energy crop s insurance coverage level and yield, respecively. Then he profi from planing he energy crop in year is (5) π = ( p + p ) y v y ( ρ) e c + I ( s )E( I ), s where e is he energy crop s esablishmen cos, v is variable cos, and c is oher fixed coss excep esablishmen coss of he energy crop producion. Based on equaion (5), i is readily checked ha s π / p > 0, π / ρ > 0, and π / s > 0. Tha is, he hree policy 6

9 insrumens will increase grower s profis. We can also show ha π / p > 0 (see Appendix A), which means ha an increase in biomass price will increase energy crop profi even hough he premium of crop insurance will increase in biomass price. Since planing miscanhus involves a large amoun of esablishmen coss, a grower may have difficuly o finance on heir own. Therefore, one purpose of his sudy is o invesigae how credi consrain on esablishmen coss a grower faces may affec he land-use decisions. Equaion (5) presens a grower s profi from planing energy crops under credi consrain because he grower finances he esablishmen cos by herself. Following Bocquého and Jacque (200), we model he scenario of wihou credi consrain by assuming ha he grower has access o a loan o pay for he esablishmen coss in he firs wo years. Saring from he hird year he farmer will pay back he loan wih an annuiy ill he end of year T. Assume he annuiy o be w. Then he grower s profi from planing miscanhus when here is no credi consrain becomes (6) π = s ( + ) + ( )E( ), if p p y v y c I s I {, 2} s ( ) ( )E( ) p + p y v y c + I s I w, if {3,..., T}. The relaionship beween e, {, 2} and w is e2 (7) ( )( ) w ρ e + = ( ) ( ), + T r + r + r r where r is annual ineres rae. Equaion (9) saes ha a) he ne presen value (NPV) of he loan is equal o he NPV of he annuiies; and b) he grower only obain he loan o pay for he esablishmen coss ha are no covered by governmen subsidy. The Kuhn-Tucker necessary condiions of problem () can be wrien as (8) T = ( β E u '(( x ) π + x π )( π π ) λ 0 ( = 0 if x > 0) and * 0 * 0 * λ 0 ( = 0 if x < L), * ) 7

10 where λ is he Lagrange muliplier. For an inerior soluion (i.e., 0 < x < L ) he necessary * condiions can be simplified as (9) T = ( β E u'(( x ) π + x π )( π π )) = 0 * 0 * 0 Given he assumpion of CARA uiliy funcion, i is readily checked ha x * ρ > and / 0 x >. These wo inequaliies sae ha boh increasing esablishmen cos share and * / s 0 increasing premium subsidy rae of energy crop insurance will enlarge he opimal acreage of energy crop. Wihou furher informaion regarding he uiliy funcion and he join disribuions of crop yields and row crop price, however, we canno idenify he signs of x p and * / * s x p To see his, by implici funcion heorem we have /. (0) x p * = π π T T 0 * β E ( u''( Π)( π π ) x + E ( '( ) β u Π = p = p T 0 2 β E ( u''( Π)( π π ) ) = ) ), where we define Π ( x ) π + x π o save noaion. The sign of * 0 * x p is he same as * / he sign of he numeraor of equaion (0) because he denominaor is posiive by u ''( ) < 0 and π π ) > 0. The second erm in he numeraor of equaion (0) is posiive. However, 0 2 ( wihou furher informaion on he disribuions of crop yields and row crop prices, we canno decide he sign of he firs erm in he numeraor. Since u ''( ) < 0, x > * 0, and 0 we conjecure ha a) when π π is very small or even negaive (e.g, when π / p > 0, p is very low), hen 0 x p is more likely o be posiive, and b) when π π is large (e.g., when * / p is very high) hen x p is more likely o be negaive. In our simulaion we find evidence * / supporing his conjecure. Similar analysis abou equaion (0) applies for * s x p If /. u ''( ) = 0, i.e., he grower is risk neural, hen we have * and x / 0 p * s x p which / 0, 8

11 means ha an increase in biomass price or biomass price subsidy will increase, a leas weakly, he opimal acreage of energy crop. In he nex secion we employ simulaion o furher invesigae how he risk aversion parameer and he hree policy insrumens affec he growers land allocaion decisions. 3. Daa and Simulaion Approach 3.. Daa Our numerical analysis is based on hisorical daa on crop yields and prices of convenional crops and simulaed daa for yields of energy crops and projeced price of biomass o esimae crop-specific yield and price disribuions. Yields of convenional crops (corn and soybeans in his sudy) over and across he rain-fed area of U.S. have been obained from Naional Agriculural Saisics Service (NASS) of he U.S. Deparmen of Agriculure (USDA) (hp:// In he absence of observed daa from commercial producion of miscanhus in he U.S., several sudies have used crop growh models o simulae miscanhus yield based on daa obained from experimenal fields. Kiniry e al. (992) developed a general crop growh model, namely ALMANAC, ha has been used in several sie-specific sudies o simulae he yield of swichgrass (Kiniry e al. 996, 2005; McLaughlin e al. 2006). Originally developed for Ireland o predic miscanhus yield, MISCANMOD has been used o simulae he yield of miscanhus across Europe (Clifon-Brown, Sampfl, and Jones 2004). Mos recenly, Miguez e al. (202) developed a sophisicaed semi-mechanisic dynamic crop growh and producion model, BIOCRO, o simulae he yield of miscanhus in he Unied Saes. Incorporaing he biochemical, physiological, and environmenal biophysical mechanisms ha affec plan growh, BIOCRO simulaes hourly leaf-level phoosynhesis and somaal conducance and scales up o he crop level using a muli-layer canopy archiecure. The predicive abiliy of he model was esed wih 30 previously published sudies and he mean 9

12 bias is only a 0.62 meric onne/ha. BIOCRO was used o simulae yields over a 32 year period using climae and soil daa for , on a 32 by 32 km grid for he coninenal US. Annual yield esimaes for miscanhus in he rain-fed area were obained for each grid over period and were aggregaed o he couny-level. Figure SM in he Supplemenal Maerials (SM) of his aricle paper shows maps of he couny-level average yield of hese wo energy crops and of corn and soybeans. Noe ha in Figure SM he scales used o depic yields differ across he maps o illusrae spaial variaion in yields for each crop. The spaial variabiliy in he yields suggess ha he economic benefis of swiching from corn/soybeans o miscanhus will differ across locaions. Figure SM2 in SM shows he variabiliy in hese yields wih he climaic condiions by calculaing he coefficien of variaion (defined as he raio of sandard deviaion of yield o mean yield) for each couny in he rain-fed area of he U.S. Higher values indicae greaer variabiliy in yields. The figure shows ha miscanhus yield is more variable in he norhern region and he Grea Plains region while corn is relaively more risky in he Alanic saes. Sae-level received prices for corn and soybean over are obained from NASS as well. Producion coss of miscanhus in 30 saes of he rain-fed US has been well documened by Khanna e al. (2008) and Jain e al. (200). Oher echno-economic informaion including coss of biofuel producion, conversion efficiency of biomass o biofuel, coss of ransporaion of biomass are obained from relaed lieraure (see Huang e al. 203 for a review on he lieraure). As we have discussed under our concepual framework, biomass price will likely be fixed under producion conracs beween growers and bio-refineries. Therefore, in our simulaion we consider wo scenarios regarding biomass price: a low price scenario under which he biomass price is $48/Mg and a high price scenario under which he biomass price 0

13 is $8/Mg. Here $48/Mg and $8/Mg are corresponding o crude oil prices a $00/barrel and $20/barrel, respecively. In his aricle we assume ha ehanol price and biomass price are deermined by crude oil price. We uilize he conversion parameers in Tyner and Taheripour (2007) and Jain e al. (200) o conver crude oil prices ino ehanol prices and hen o biomass prices Join yield-price disribuions To calculae he expeced profi from growing he row crops and miscanhus requires join yield-price disribuions. Copula approach is employed o obain he join disribuions. Because of heir flexibiliy, copula approach is becoming increasingly popular when modeling join disribuions (Yan 2007). Copula approach can be implemened by he Inference Funcion for Margins (IFM) mehod, which esimaes marginal disribuions firs and hen, by aking hese marginals as given, esimaes he copula. For a crop, we firs selec he bes fiing yield disribuion by Kolmogorov-Smirnov es from eigh disribuions ha are ofen used o model yield disribuions. These eigh disribuions are: Bea, Gamma, Logisic, Lognormal, Normal, Reverse gamma, Reverse lognormal, and Weibull. We hen use he bes fiing yield disribuion as he marginal yield disribuions in he copula esimaion. Price marginal disribuions for convenional crops are obained by esimaing a normal disribuion for he derended sae-level received prices. Once we obain he join disribuions, yield-price join draws from he disribuions can be made, on which he simulaion is based upon. For procedures of performing copula esimaion and make random draws from he esimaed disribuion, we refer readers o Miao, Hennessy, and Feng (202) Efficiency Comparison beween Policy Insrumens We define he efficiency of a policy insrumen o be he energy crop acreage increased by he insrumen for a given amoun of governmen expendiure supporing he policy insrumen. As we have discussed in Inroducion, in his sudy we focus on hree policy

14 insrumens: subsidized crop insurance, esablishmen cos share, and a direc biomass price subsidy. To compare he efficiency, we firs map he relaionship beween energy crop acreage and he governmen expendiure under a policy insrumen. Then we compare he acreage across all hree insrumens for a given amoun of expendiure and deermine which insrumen is more efficien han he ohers. Here we ake subsidized crop insurance as an example o illusrae how we obain he acreage-expendiure relaionship for a policy insrumen. Similar procedures apply for obaining he acreage-expendiure relaionship for esablishmen cos share and biomass price subsidy. For simpliciy we assume ha governmen expendiure under a subsidized crop insurance program only consiss of premium subsidies. We do no consider he adminisraion and operaing (A&O) coss ha is usually a componen of governmen expendiure supporing crop insurance. Under a given premium subsidy rae, we solve he grower s maximizaion problem and obain he opimal acreage allocaed o energy crop and he governmen expendiure in he form of premium subsidies. Therefore, we have one pair of acreage-expendiure values. To obain he acreage-expendiure relaionship under energy crop insurance, we vary premium subsidy rae from 0 o 00% and obain many pairs of acreageexpendiure values from which we can consruc he acreage-expendiure relaionship. 4. Resuls We specify four key scenarios in our simulaion. They are: ) he baseline scenario under which no policy inervenion is in presence; 2) energy crop insurance scenario under which only crop insurance is considered and he insurance coverage level, φ, is assumed o be 75%; 3) esablishmen cos share scenario under which only he policy insrumen esablishmen cos share is considered; and 4) biomass price subsidy scenario under which only he policy Governmen s oal A&O coss for crop insurance may no change very much when energy crops are covered by crop insurance. This is because a) A&O cos is proporional o premium (Shield 202), and b) an increase in energy crop acreage implies a decrease in row crop acreage and hence he oal premium may no change very much. 2

15 insrumen of biomass price subsidy is considered. Table presens a specific se of simulaion resuls regarding energy crop acreage and expendiure under each scenario where insurance premium subsidy rae, s, is assumed o be 55%, esablishmen cos share, ρ, is assumed o be 75%, and biomass price subsidy rae, s p, is assumed o be $5/Mg. 4.. Acreage and Expendiure under Each Scenario From Table we can see ha when here is credi consrain and when biomass price is a $48/Mg, hen he acreage devoed o miscanhus in he sudied area under he baseline scenario is abou.5 million acres. To pu he acreage number in perspecive, consider a miscanhus yield a 7.2Mg/acre (he sample mean of our daa) and a conversion rae a 87.2 gallon per Mg of biomass (see Jain e al. (200)). Then he.5 million acres devoed o miscanhus producion will generae 0.94 billion gallons of cellulosic ehanol per year. Relaxing credi consrain will significanly increase miscanhus acreage. For example, when biomass price is $48/Mg, he miscanhus acreage under each scenario excep esablishmen cos share scenario is abou 0 imes larger han ha when here is credi consrain. The effec of credi consrain on energy crop acreage is smaller under he esablishmen cos share scenario. This is inuiive because under ha scenario he esablishmen cos is subsidized. The effec of biomass price on miscanhus acreage is also significan. When biomass price increases from $48/Mg o $8/Mg, hen miscanhus acreage under he baseline scenario wih credi consrain increases from.5 million acres o 24.5 million acres. Regarding governmen expendiure, from Table we can see ha he expendiure under increases when biomass price increases. This is because he expendiure under he hree policy insrumens is proporional o he acreage ha increases when biomass price jumps from $48/Mg o $8/Mg. For he same reason, he expendiure under each policy insrumen increases as credi consrain is relaxed. 3

16 Figures o 4 include maps of couny-level miscanhus acreage under each scenario and differen assumpions of biomass price and credi consrain. These four figures show ha when biomass price is low (i.e., $48/Mg), hen under he baseline scenario he acreage of miscanhus is mainly locaed in souh-easern saes, such as Virginia, Norh Carolina, Souh Carolina, Georgia, Alabama, and Mississippi. However, when biomass price is high (i.e., $8/Mg), hen a few Midwesern saes, such as Wisconsin, Illinois, and Nebraska, will see an increased acreage of miscanhus. Especially, when biomass price is high and when here is no credi consrain, Midwesern saes become he major producion region for miscanhus even under he baseline scenario (see Figure 4). From he maps in Figures o 4 we can see ha a 75% esablishmen cos share will make he Midwesern saes a major producion region of miscanhus even if biomass price is low and if growers are facing credi consrain. This is because he 75% esablishmen cos share is a large suppor o miscanhus growers given he magniude of he esablishmen cos of miscanhus producion. When biomass price is low, hen a $5/Mg biomass price subsidy can double miscanhus acreage han ha under he baseline scenario. However, he effec of biomass price subsidy on miscanhus acreage becomes weaker when biomass price is high Efficiency of he Three Policy Insrumens Figures o 4 and Table show ha he 75% esablishmen cos share has he larges effec on increasing miscanhus acreage. However, his does no mean ha esablishmen cos share is he more efficien han energy crop insurance or biomass price subsidy. To compare he efficiency beween hese hree policy insrumens, we need o compare miscanhus acreage for a given amoun of governmen expendiure under each insrumen. Figures 5 and 6 depic he relaionship beween miscanhus acreage and program expendiure for each insrumen, under credi consrain and under no credi consrain, respecively. In Figures 5 and 6, we consider four cases of risk aversion and biomass price combinaions. The low and high risk 4

17 aversion parameers are corresponding o risk premiums a 0. and 0.2, respecively. Following Babcock e al. (993), a farmer wih risk premium equal o 0. is willing o pay 0% of he sandard deviaion of farm revenue o eliminae income risk. The low and high biomass prices are $48/Mg and $8/Mg, respecively. From Figures 5 and 6 we can see ha biomass price subsidy is always dominaed by esablishmen cos share and energy crop insurance. When here is credi consrain, hen energy crop insurance firs dominaes, and hen is dominaed by, esablishmen cos share as expendiure increases (Figure 5). When here is no credi consrain, hen crop insurance is always he mos efficien policy insrumen while biomass price subsidy is he leas efficien one (Figure 6). Price subsidy is dominaed by crop insurance and esablishmen cos share because, unlike biomass price subsidy, crop insurance and esablishmen cos share provide suppor o growers when hey need he suppor mos. Specifically, crop insurance provides growers wih indemniies when yield of miscanhus is lower han a guaraneed level. Esablishmen cos share, on he oher hand, provides suppor during he esablishmen period when here is no harves of biomass and hence he ne reurn from miscanhus is negaive. Tha is, a subsidy in esablishmen cos provides financial suppor when ne reurns form miscanhus is negaive. Therefore, such subsidy is very valuable. Biomass price subsidy, however, provides suppor ha is proporional o biomass yield. The oal price subsidy a farmer obains is larger in a good year (a year wih high yield) and smaller in a bad year (a year wih low yield). Therefore, biomass price subsidy does no have he effec of smoohing reurns across years ha boh crop insurance and esablishmen cos share have. As a resul, crop insurance and esablishmen cos share are more efficien han biomass price subsidy. The reason ha crop insurance firs dominaes and hen is dominaed by esablishmen cos share as expendiure increases under credi consrain is as follows. Noice ha even 5

18 here is no premium subsidy and hence no governmen expendiure, crop insurance sill has he effec of smoohing reurns across years over miscanhus lifespan. However, esablishmen cos share does no have his propery. I affecs growers land allocaion decision only when i provides paymen o he growers. As he esablishmen cos share paymen becomes larger, he effec of his paymen on land allocaion decision increases. When growers can have access o a loan ha finances he esablishmen coss, hen he effec of he esablishmen cos share on smoohing reurns becomes less imporan because he loan has he same effec. This is why when here is no credi consrain hen esablishmen cos share is sricly dominaed by crop insurance Marginal Effecs To invesigae how a change in he magniude of he hree policy insrumens and in grower s risk aiude affecs he share of energy crop acreage for a given biomass price, we simulae miscanhus acreage share for each couny under 625 combinaions of risk premium θ Θ {0., 0.2, 0.3, 0.4, 0.5}, crop insurance premium subsidy rae 65%}, share of esablishmen cos ρ R s S {45%, 50%, 55%, 60%, {0%, 20%, 30%, 40%, 50%}, and biomass price subsidy rae s p Ω {$5/Mg, $0/Mg, $5/Mg, $20/Mg, $5/Mg} for a given biomass price p P {$48/Mg, $8/Mg} and for a given credi consrain siuaion δ {credi consrain, no credi consrain}. Parameer vecor vecor of policy insrumen parameers and vecor s ( s,, p ) ρ S R Ω can be viewed a (, ) p δ P can be viewed as a vecor of marke condiion parameers. I is readily checked ha se Θ S R Ω has 625 elemens. In oal, for each couny we have 2,500 values of miscanhus acreage share because se Θ S R Ω P has 2,500 elemens. Under each vecor of marke condiion parameers, ( p, ) δ P, we firs fix a risk premium parameer or a policy insrumen parameer, hen we calculae he opimal 6

19 miscanhus acreage share under each combinaions of he remaining parameers for each couny and ake simple average of hese acreage shares across all counies. For example, he average energy crop share when ) biomass price is $48/Mg, 2) credi consrain is in presence, and 3) risk premium is 0. is calculaed as he simple average of 25 share values ha are corresponding o 25 vecors of policy insrumen parameers, s ( s,, p ) ρ S R Ω while seing biomass price a p = $48/Mg, risk premium a θ = 0., and credi consrain is in presence. Table 2 summarizes he calculaion resuls. The value of land share of miscanhus in Table 2 should be explained in he following way. Here we use 9.5, he number of land share corresponding o risk premium a 0. in he upper-lef panel of Table 2, as an example. This number means ha he simple average of land share of miscanhus across all counies is 9.5% when ) he biomass price is $48/Mg, 2) growers face credi consrain, 3) he risk premium is 0., and 4) he parameers of he hree policy insrumens, s ρ p S R Ω, ake values from heir 25 combinaions. s (,, ) From Table 2 we can see ha when marke siuaion and policy insrumen parameers ake values described above, hen he average land share of miscanhus decreases as risk premium increases. This may indicae ha miscanhus is more risky han row crops (corn and soybean in his sudy), which is consisen wha we have shown in Figure SM2. As we have shown in he concepual framework, an increase in crop insurance subsidy rae or in esablishmen cos share increases he land share. Corresponding o he findings in he concepual framework, from Table 2 we do observe ha an increase in biomass price subsidy can eiher increase or decrease he opimal land share of miscanhus. When biomass price is $48/Mg and when here is no credi consrain, hen he land share firs increases and hen decreases as biomass price subsidy increases from $5/Mg o $25/Mg. When biomass is $8/Mg and when here is no credi consrain, we observe ha he land share decreases as biomass price subsidy increases from $5/Mg o $25/Mg. One reason for he fac ha higher 7

20 biomass price subsidy may decrease miscanhus land share is ha he increase in price increases he variance of miscanhus profi and hence decreases risk averse growers willingness o plan miscanhus. 5. Conclusions In his paper we develop a concepual framework of growers opimal land allocaion decisions beween row crops and energy crops. Based on he couny-level yield daa of corn, soybean and miscanhus, we simulae and compare he efficiency of crop insurance, esablishmen cos share, and biomass price subsidy in erms of incenivizing growers adopion of energy crops. We find ha when here is no credi consrain, hen crop insurance is he mos efficien and biomass price subsidy is he leas efficien among he hree policy insrumens. However, when growers face credi consrain on financing esablishmen coss, hen crop insurance is more efficien han esablishmen cos share for relaively small expendiure bu is less efficien han esablishmen cos share for relaively large expendiure. We also find ha under cerain condiions an increase in biomass price subsidy may no necessarily increase he opimal miscanhus acreage. Geographical disribuions of miscanhus acreage under various scenarios are discussed. When here are no policy inervenions and when biomass price is low, hen he souheasern Unied Saes will be he major producion region of miscanhus. When here is no credi consrain and when biomass price is high, however, hen he Midwesern saes will become he major region for miscanhus producion. The policy insrumens we discussed in his sudy generally will increase miscanhus acreage. When compared wih souheasern saes, Midwesern saes have larger poenial o respond o incenives from he policies. Appendix A In his appendix we show ha π / p > 0 where π is defined in equaion (5). By equaion (4) we have 8

21 (A) π I E( I ) ( ). p p p = y + s Assume he cumulaive disribuion funcion (CDF) of energy crop yield is G y wih ( ) suppor [0, ). Then we have I (A2) p = max[ φ E( y ) y, 0], and (A3) I dg( y ) I 0 I = = dg( y ) max[ E ),0] = φ y y dg y 0 0 E( ) p p p E( y ) φ = ( φ y y dg( y 0 E ( ) ) ). ( ( ) Plugging equaions (A2) and (A3) ino equaion (A) we can obain, (A4) π p = y φ E( y ) + max[ φ E( y ) y,0] ( s ) φ E( y ) y ) dg y 0 φ E( y ) φ E( y ) y y s φ y dg y s y dg y 0 0 = max[ φ E( ), ] ( ) E( ) ( ) + ( ) ( ) φ = max[ E( y ), y ] ( s ) E( y ) >0, b φ y s [0, ] and G( φ E( y )) <. ( ( ) φ E( y ) + y dg y 0 G( φ E( y )) ( s ) ( ) References Babcock, B.A The Poliics and Economics of he U.S. Crop Insurance Program. in J.S. Graff Zivin and J.M. Perloff eds. The Inended and Uninended Effecs of U.S. Agriculural and Bioechnology Policies. Universiy of Chicago Press. Babcock, B.A., K.E. Choi, and E. Feinerman Risk and Probabiliy Premiums for CARA Uiliy Funcions. Journal of Agriculural and Resource Economics, 8():

22 Claassen, R., J.C. Cooper, and F. Carriazo. 20. Crop Insurance, Disaser Paymens, and Land Use Change: The Effec of Sodsaver on Incenives for Grassland Conversion. Journal of Agriculural and Applied Economics, 43(2):95-2. Clifon-Brown, J.C., P. Sampfl, M.B. Jones Miscanhus biomass producion for energy in Europe and is poenial conribuion o decreasing fossil fuel carbon emissions. Global Change Biology. 0: Coble, K.H., R. Dismukes Disribuional and Risk Reducion Effecs of Commodiy Revenue Program Design. Review of Agriculural Economics. 30(3): Goodwin, B.K., M.L. Vandeveer, and J.L. Deal An Empirical Analysis of Acreage Effecs of Paricipaion in he Federal Crop Insurance Program. American Journal of Agriculural Economics. 86(4): Feng, Hongli, David A. Hennessy, and Ruiqing Miao The Effecs of Governmen Paymens on Naive Grassland Conversion, he CRP, and Corn Acreage Changes in he Dakoas. American Journal of Agriculural Economics. 95(2): Fewell, J., J. Bergold, and J. Williams. 20. Farmers Willingness o Grow Swichgrass as a Cellulosic Bioenergy Crop: A Saed Choice Approach. Paper presened a he 20 Join Annual Meeing of he Canadian Agriculural Economics Sociey & Wesern Agriculural Economics Associaion, Banff, Albera, Canada. Heaon, E., F. Dohleman, and S. Long Meeing US biofuel goals wih less land: The poenial of miscanhus. Global Change Biology. 4: Hennessy, D.A., B.A. Babcock, and D.J. Hayes Budgeary and Producer Welfare Effecs of Revenue Insurance. American Journal of Agriculural Economics. 79(3):

23 Huang, H. M. Khanna, M, H. Onal and X.Chen Sacking Low Carbon Policies on he Renewable Fuel Sandard: Economic and Greenhouse Gas Implicaions. Energy Policy. 56: 5-5. Jain, A. K., M. Khanna, M. Erickson, and H. Huang An Inegraed Biogeochemical and Economic Analysis of Bioenergy Crops in he Midwesern Unied Saes. Global Change Biology Bioenergy. 2: Khanna, M., B. Dhungana, and J. Clifon-Brown Coss of Producing Miscanhus and Swichgrass for Bioenergy in Illinois. Biomass and Bioenergy. 32: Kiniry, J.R., J.R. Williams, P.W. Gassman, P. Debaeke A general process-oriened model for wo compeing plan species.transacions of he ASAE, 35, Kiniry, J.R., K.A. Cassida, and M.A. Hussey e al Swichgrass simulaion by he ALMANAC model a diverse sies in he souhern U.S. Biomass and Bioenergy, 29, Kiniry, J.R., M.A. Sanderson, J.R. Williams Simulaing Alamo swichgrass wih he ALMANAC model. Agronomy Journal. 88: McLaughlin, S.B., J.R. Kiniry, C. Taliaferro, D. Ugare Projecing yield and uilizaion poenial of swichgrass as an energy crop. Advances in Agronomy. 90: Miao, Ruiqing, Hongli Feng, and David A. Hennessy The Effec of Crop Insurance Subsidies and Sodsaver on Land Use Change. Cener for Agriculural and Rural Developmen, Iowa Sae Universiy. Working Paper 2-WP 530. Miao, Ruiqing, David A. Hennessy, and Hongli Feng Naive Grassland Conversion: The Roles of Risk Inervenion and Swiching Coss. Cener for Agriculural and Rural Developmen, Iowa Sae Universiy. Working Paper 3-WP

24 Miguez, F.E., M. Maughan, G.A. Bollero, and S.P. Long Modeling spaial and dynamic variaion in growh, yield, and yield sabiliy of he bioenergy crops Miscanhus giganeus and Panicum virgaum across he conerminous Unied Saes. Global Change Biology: Bioenergy. 4(5): Rashford, B.S., J.A. Walker, and C.T. Basian. 20. Economics of Grassland Conversion o Cropland in he Prairie Pohole Region. Conservaion Biology, 25(2): Shields, D.A Federal Crop Insurance: Background. Congressional Research Service. CRS Repor for Congress, December 6. Tyner, W.E. and F. Taheripour Fuure Biofuels Policy Alernaives. Working paper. Deparmen of Agriculural Economics, Purdue Universiy. Woodard, J.D., A.D. Pavlisa, G.D. Schnikey, P.A. Burgener, and K.A. Ward Governmen Insurance Program Design, Incenive Effecs, and Technology Adopion: The Case of Skip-Row Crop Insurance. American Journal of Agriculural Economics. 94(4): Yan, J Enjoy he Joy of Copulas: wih a Package copula. Journal of Saisical Sofware. 2(4):-2. Yang, X., N.D. Paulson, and M. Khanna Opimal Conracs o Induce Biomass Producion under Risk. Paper presened a he Agriculural & Applied Economics Associaion s 202 AAEA&NAREA Join Annual Meeing, Seale, Washingon, Augus 2-4,

25 Table. Acres Devoed o Energy Crops and Governmen Expendiures under Differen Policy Insrumens (unis: boh acres and expendiure are in,000) Wih Credi Consrain Wihou Credi Consrain Biomass Price = $48/Mg. Biomass Price = $8/Mg. Biomass Price = $48/Mg. Biomass Price = $8/Mg. acres expendiure acres expendiure acres expendiure acres expendiure Baseline Scenario, , , ,626 0 Energy Crop Insurance,776 8,694 25,597 2,85,573 9,808,465,437 08,33 2,864,00 Esablishmen Cos Share 55,345 56,30,664 03,35 05,047,362 88,872 90,22, 0,74 2,057,878 Biomass Price Subsidy 4,063,937,697 26,55 2,363,869 42,768 20,400,908 06,906 48,984,829 Noe: Expendiures are he oal amoun of expendiures over 5 years (i.e., a life-circle of miscanhus). Energy crop insurance is wih 75% of coverage level and 55% premium subsidy rae. Esablishmen cos share rae is 75%. Biomass price subsidy rae is $5/Mg. Here Mg. sands for million gram. 23

26 Table 2. Share of Cropland Devoed o Miscanhus under Various Model Specificaions wih credi consrain Biomass Price = $48/Mg. Biomass Price = $8/Mg. Risk Premium Land Share of Miscanhus (%) Crop Insurance Premium Subsidy (%) Land Share of Miscanhus (%) Share of Esablishmen Cos (%) Land Share of Miscanhus (%) Biomass Price Subsidy ($) Land Share of Miscanhus (%) wihou credi consrain Risk Premium Land Share of Miscanhus (%) Crop Insurance Premium Subsidy (%) Land Share of Miscanhus (%) Share of Esablishmen Cos (%) Land Share of Miscanhus (%) Biomass Price Subsidy ($) Land Share of Miscanhus (%) Noe: Biomass price is in 2006 dollars. Mg. sands for million gram. 24

27 Baseline Energy Crop Insurance Esablishmen Cos Share Biomass Price Subsidy Legend 0 acre (0, 5,000] (5,000, 0,000] (0,000, 30,000] (30,000, 50,000] (50,000, 00,000] (00,000, 50,000] >50,000 Noe: Risk premium and biomass price are assumed o be 0. and $48/Mg., respecively. The coverage level of energy crop insurance is 75% and he subsidy rae for insurance premium is 55%. The rae of esablishmen cos share is 75% and biomass price subsidy rae is $5/Mg. Figure. Couny Level Acres Devoed o Miscanhus under Low Biomass Price and Credi Consrain 25

28 Baseline Energy Crop Insurance Esablishmen Cos Share Biomass Price Subsidy Legend 0 acre (0, 5,000] (5,000, 0,000] (0,000, 30,000] (30,000, 50,000] (50,000, 00,000] (00,000, 50,000] >50,000 Noe: Risk premium and biomass price are assumed o be 0. and $8/Mg., respecively. The coverage level of energy crop insurance is 75% and he subsidy rae for insurance premium is 55%. The rae of esablishmen cos share is 75% and biomass price subsidy rae is $5/Mg. Figure 2. Couny Level Acres Devoed o Miscanhus under High Biomass Price and Credi Consrain 26

29 Baseline Energy Crop Insurance Esablishmen Cos Share Biomass Price Subsidy Legend 0 acre (0, 5,000] (5,000, 0,000] (0,000, 30,000] (30,000, 50,000] (50,000, 00,000] (00,000, 50,000] >50,000 Noe: Risk premium and biomass price are assumed o be 0. and $48/Mg., respecively. The coverage level of energy crop insurance is 75% and he subsidy rae for insurance premium is 55%. The rae of esablishmen cos share is 75% and biomass price subsidy rae is $5/Mg. Figure 3. Couny Level Acres Devoed o Miscanhus under Low Biomass Price and No Credi Consrain 27

30 Baseline Energy Crop Insurance Esablishmen Cos Share Biomass Price Subsidy Legend 0 acre (0, 5,000] (5,000, 0,000] (0,000, 30,000] (30,000, 50,000] (50,000, 00,000] (00,000, 50,000] >50,000 Noe: Risk premium and biomass price are assumed o be 0. and $8/Mg., respecively. The coverage level of energy crop insurance is 75% and he subsidy rae for insurance premium is 55%. The rae of esablishmen cos share is 75% and biomass price subsidy rae is $5/Mg. Figure 4. Couny Level Acres Devoed o Miscanhus under High Biomass Price and No Credi Consrain 28

31 Energy Crop Acres 2.4 x Low Risk Aver. and Low Price Crop Insurance Subsidy Esab. Cos Share Price Subsidy Energy Crop Acres 2.9 x Low Risk Aver. and High Price Crop Insurance Subsidy Esab. Cos Share Price Subsidy Energy Crop Acres Program Expendiure ($/5 years) x 0 8 x High Risk Aver. and Low Price Crop Insurance Subsidy Esab. Cos Share Price Subsidy Energy Crop Acres Program Expendiure ($/5 years) 5.5 x 06 5 High Risk Aver. and High Price Crop Insurance Subsidy Esab. Cos Share Price Subsidy x Program Expendiure ($/5 years) x Program Expendiure ($/5 years) Figure 5. Acreage-Expendiure Relaionship under Various Policy Insrumens and Scenarios (wih credi consrain) x 0 8

32 2.2 x 07 Low Risk Aver. and Low Price.085 x 08 Low Risk Aver. and High Price Energy Crop Acres Crop Insurance Subsidy Esab. Cos Share Price Subsidy Program Expendiure ($/5 years) x 0 9 Energy Crop Acres Crop Insurance Subsidy Esab. Cos Share Price Subsidy Program Expendiure ($/5 years) x x 07 High Risk Aver. and Low Price 7 x 07 High Risk Aver. and High Price Energy Crop Acres Crop Insurance Subsidy Esab. Cos Share Price Subsidy Program Expendiure ($/5 years) x 0 9 Figure 6. Acreage-Expendiure Relaionship under Various Policy Insrumens and Scenarios (wihou credi consrain) 30 Energy Crop Acres Crop Insurance Subsidy Esab. Cos Share Price Subsidy Program Expendiure ($/5 years) x 0 9

33 Supplemenal Maerials for Crop Insurance for Energy Grass Miscanhus Legend < >20 Corn Legend < >30 Soybean Legend < >45 Fig. SM: Couny-level Average Yield of Miscanhus (meric onne/ha.) and Corn and Soybeans (bu./acre) Corn Miscanhus Legend < >0.5 Soybeans Fig. SM2: Coefficien of Variaion of Crop Yields ( )