Key words: farm risk reduction, farm government intervention, El Niño Southern Oscillation (ENSO), whole farm simulation, forecast value.

Transcription

1 1 FARM PROGRAMS EFFECTS ON THE VALUE OF SEASONAL CLIMATE INFORMATION 1 Victo E. Cabea 1, David Letson 2, Guillemo Podestá 3 Postdoctoal Reseach Associate, Division of Maine Affais & Policy, Rosenstiel School of Maine and Atmospheic Science, Univesity of Miami, Miami, Floida. Coesponding autho addess: 256 Fazie Roges Hall, PO Box , Gainesville, FL 32611; phone: x 256; fax: ; v.cabea@miami.edu 2 Associate Pofesso, Division of Maine Affais & Policy, Rosenstiel School of Maine and Atmospheic Science, Univesity of Miami, Miami, Floida. 3 Reseach Associate Pofesso, Division of Meteoology and Physical Oceanogaphy, Rosenstiel School of Maine and Atmospheic Science, Univesity of Miami, Miami, Floida. ABSTRACT Pedictability of seasonal climate vaiations associated with ENSO suggests a potential to educe fam isk by tailoing agicultual management stategies to mitigate the impacts of advese conditions o to take advantage of favoable conditions. Fedeal fam policies may enhance o limit the usefulness of this climate infomation. A epesentative peanut-cotton-con non-iigated noth Floida fam was used to estimate the value of the ENSO-based climate infomation and examine impacts of fam pogams unde uncetain conditions of climate and pices and isk avesion levels. Yields fom cop model simulations and histoical seies of pices wee used to geneate stochastic distibutions that wee fed into a whole fam model, fist, to optimize management pactices, and then, to simulate uncetain outcomes unde isk avesion, with and without the use of climate infomation, and with and without the inclusion of fam pogams. Results suggest that seasonal climate foecasts have highe value fo moe isk-avese fames when La Niña o El Niño ENSO phases ae foecast. Highly isk-avese fames could benefit fom the foecast by taking advantage of potential favoable conditions (offensive esponses). The inclusion of Commodity Loan Pogams (CLP) and Cop Insuance Pogams (CIP) deceased the oveall value of the foecast infomation even to negative levels. Howeve, moe isk-avese fames could still benefit modeately fom El Niño and maginally fom La Niña foecasts when they paticipate in CLP and CIP. Key wods: fam isk eduction, fam govenment intevention, El Niño Southen Oscillation (ENSO), whole fam simulation, foecast value. INTRODUCTION Majo impovements in climate pedictions based on El Niño-Southen Oscillation (ENSO) phenomena call fo studies to estimate the value of this technology and its potential uses to educe fam isks (Phillips et al., 2002; Podestá et al., 2002). The agicultual secto, among the most vulneable to climate changes, can use seasonal foecasts to mitigate the impacts of advese conditions o to take advantage of favoable conditions (Bet et al., in pess; Chen et al., 2002; Jagtap et al., 2002). Howeve, fam decisions do not occu in isolation and may be 1 SECC Technical Seies SECC , Gainesville, FL 1

2 influenced by decision making institutions such as fedeal fam policies and egulations that enhance o limit the usefulness of climate infomation (Hansen, 2002). Seveal studies have estimated the value of agicultual foecasts (Hamme et al., 2001; Letson et al., 2005; Meza and Wilks, 2003), but few have included the impacts of govenment institutions on the value of the seasonal foecasts (Bosch, 1984; Mjelde and Hill, 1999; Mjelde et al., 1996). Mjelde et al. (1996) emains the state of the at analysis on how fam pogams might influence the value of climate infomation; but since this analysis was conducted fam legislation has undegone substantial changes and eseaches have leaned much moe about how to apply climate infomation and how to estimate the value of climate infomation. The use of biophysical cop simulation models togethe with stochastic weathe geneatos to chaacteize ENSO intaphase vaiations and confidence intevals have played a majo ole in the study of the value of the climate infomation in ecent yeas. Synegies and conflicts between fam pogams and climate infomation epesent citical knowledge gaps in how we think about climate foecast value. Fam pogams condition the use of climate infomation in at least two ways: they may limit the ange and efficacy of foecast esponses if fam pogams estict the cops fames can gow and how they may gow them; fam pogams may alte the iskiness of decision envionments because they ae intended to educe the yea-to-yea vaiability of a fam income The objective of this study was to estimate the impacts of USDA fam pogams on the value of ENSO-based foecasts in a ainfed peanut-cotton-con fam in Jackson County, Floida. We tested the hypothesis that govenment inteventions might enhance o limit the usefulness of the climate infomation. This study expands the famewok used by Letson et al. (2005) by including the impacts of govenment fam pogams into the estimations of the foecast value. MATERIALS AND METHODS Repesentative fam The study was conducted on a epesentative 129-ha ainfed fam in Jackson County, FL (30.774N, W) that gows peanut, cotton, and maize in soil type Dothan loamy sand. We selected this specific case study because it has simila envionment (e.g., climate, soils), esouces (e.g., fam size, cops gown), and technology (e.g., ainfed agicultue) to othe majo agicultual poduction aeas in the southeasten United States, which would suggest a boade elevance of ou findings. Jackson County has a median annual pecipitation of 1466 mm and an aveage tempeatue of 19.3 C ( Duing the gowing season (Febuay-Novembe) the ainfall is 1143 mm and the tempeatue is 21.7 C. ENSO phases influence pecipitation and tempeatue in Jackson County duing the gowing season, but not in a consistent manne. Fo example, highe total ainfall amounts ae expected duing El Niño events; howeve pobability of ainfall Apil, May, and Octobe is geate duing La Niña events. 2

3 The model We integated climatic, agonomic, economic, and policy components in a fam decision model. This model fist optimizes management pactices with and without foecasts and with and without fam pogams, and then simulates net magins ove long peiods of time. The climatic component uses 65 yeas of daily weathe ecods. The agonomic component stochastically geneates cop yields fo each ENSO phase by e-sampling simulated cop yields poduced by biophysical models. The economic component stochastically geneates distibutions of likely cop pices based on histoical pices and govenment fam pogams. To test ou hypothesis that USDA Fedeal fam policies may enhance o limit the usefulness of the climate infomation (Mjelde et al., 1996) we intoduced two fam pogams, a Commodity Loan Pogam (CLP) and Cop Insuance Pogam (CIP). The CLP included loan deficiency payments (LDP) and maketing loan benefits (MLB), while the CIP included multipeil cop insuance (MPCI) and cop evenue coveage (CRC). In the study aea, LDPs ae available fo cotton and MLBs ae available fo peanut and maize. Also, MPCI is available fo all thee cops, but CRC is available only fo cotton and maize Agonomic component Cops yield simulation by ENSO phase The longest histoical daily weathe ecod (including ainfall, T max, T min, and iadiation) epesentative fo Jackson County is 65 yeas ( ) fom the weathe station at Chipley (30.783N, W). Duing this peiod of time, 14 yeas wee El Niño and 16 wee La Niña (Table 1). -Table 1- These weathe seies wee used to simulate and classify cop yields of peanut, cotton, and maize by ENSO phase. Cops yields wee simulated using models in the Decision Suppot System fo Agotechnology Tansfe v4.0 (Jones et al., 2003). We adjusted outcomes fom cop model simulations to align yields with means epoted by local infomants: 3360 kg ha -1 fo peanut (J. Maois, Reseache, Noth Floida Reseach and Education Cente, Quincy, pesonal communication, Octobe 22, 2004), 730 kg ha -1 fo cotton, and 6270 kg ha -1 fo maize (J. Smith, Statistician, Noth Floida Reseach and Education Cente, Quincy, pesonal communication, Nov. 23, 2004). Cop simulations wee an using contempoay management pactices in the egion fo vaieties, fetilization, and planting dates (H.E. Jowes, Co. Extension Diecto IV, Jackson Co. Extension Office, Maianna; pesonal communication, Oct. 28, 2004); and the epesentative soil type Dothan loamy sand. Fo peanut we used the most popula vaiety in the aea, Geogia Geen (Univesity of Geogia), a unne type maket vaiety with medium matuity and modeate esistance to late tomato spotted wilt vius (TSWV) and to cylindicladium black ot (CBR). Fo cotton, we used a popula medium to full season Delta & Pine Land (DP) vaiety. Fo maize we used McCudy 84aa, a medium- to full-season vaiety simila to band name vaieties of Monsanto (Dekalb) o Pionee. 3

4 Nitogen fetilization was applied accoding to local extension ecommendations, namely, 10 kg at planting fo peanut, 110 kg in 2 applications fo cotton, and 135 kg in 3 applications fo maize. Maize was planted between mid-febuay and mid-apil, cotton was planted between mid-apil and ealy-may, and peanut was planted between mid-apil and mid- June (Table 2). Nine planting dates (about one-week apat) wee simulated fo peanut and maize and fou planting dates wee simulated fo cotton Geneation of synthetic cop yields Limited duation of daily weathe ecods povided elatively a few ealizations of the ENSO impacts to cop yields (i.e., only 14 El Niño ealizations). A thoough assessment of climate isk and foecast value equies lage numbe of ENSO events. Pevious appoaches have elied on the use of stochastic weathe geneatos to poduce synthetic weathe (Letson et al., 2005; Meza et al., 2003) and then used these weathe data to pedict agonomic and economic outcomes. We used a diffeent appoach consisting of a stochastic yield geneato based on simulated cops yields. Ou stochastic yield geneato employed e-sampling in thee steps. 1. Sot simulated cop yields simulated by ENSO phase and a planting date. 2. Calculate the best fitting function (logaithmic, exponential, quadatic, o linea; whicheve had a highe R 2 ) fo the data. We used a mathematical function in ode to avoid undeestimating potential exteme values in the distibution. 3. Geneate 990 stochastic yields by e-sampling the best fitting function. We epeated the pocedue fo each planting date, each cop, and each ENSO phase. Table 2 shows mean and standad deviation of synthetically geneated cop yields acoss ENSO phases and planting dates. -Table Economic component Geneation of synthetic pices In ode to match ou yields, we stochastically geneated distibutions of 2970 pice seies fo each cop (peanut, cotton, and maize) by simulating a multivaiate distibution especting pice covaiance among cops based on histoical pice vaiability. The pocedue followed seveal steps (fo moe details see Letson et al., 2005, Appendix B). 1. Obtained monthly aveage pices (Jan 1996 Jan 2005) eceived by Floida fames fo peanut, cotton, and maize fom the USDA National Agicultual Statistical Sevice ( and conveted them to $ Mg -1 units. 2. Study and gaph the data, estimate thei desciptive statistics, and exploe thei coelation stuctue. 3 Deflated pices to Jan 2005 dollas using the US Consume Pice Index. 4. De-tend the data fo seasonal diffeences by estimating monthly esiduals with espect to thei means. 4

5 5. Use pincipal component analysis to decompose the matix of pice esiduals into thee uncoelated time seies of amplitudes that wee sepaately sampled. 6. Combine sampled values combined and back tansfom them to econstuct cop pice esiduals. 7. Confim that the coelation stuctue of the synthetic pice esiduals was simila to that of the histoical data accoding to Kolgomoov-Sminov tests and that the histoical pice distibutions wee well epoduced accoding to quantile-quantile plots. 8, Re-intoduce seasonal pice aveages fo the havesting dates of the thee cops: Sep 2- Nov 6 fo peanut, Sep 22-Dec 28 fo Cotton, and Jul 1-Sep 30 fo Maize. Fo the case of cotton, we inceased its pice by 18.66% to account fo the seed value. Distibutions fo the synthetically geneated pices can be seen in Fig. 1. Note that the pice distibutions ae not histoical values, but ae distibutions consistent with histoical vaiability. In ode to study the impacts of potential futue peanut pice eductions in ou value of infomation potfolio, we ceated an scenaio of abitaily educing the pice of peanut by 33% and estimated the value of the infomation unde those conditions Poduction costs We conside vaiable and fixed poduction costs by cop in the model. Contempoay local costs of poduction and labo equiements fo the thee cops wee povided by the Noth Floida Reseach and Education Cente (J. Smith & T. Hewitt, Entepises Budgets, Quincy; pesonal communication, Nov. 23, 2004). The vaiable (fixed) costs ($ ha -1 ) wee: 1080 (344) fo peanut, 1122 (177) fo cotton, and 574 (87) fo maize Whole fam model We used a stochastic non-linea whole fam model to study the ole of climate foecasts in decision making and to estimate the value of these foecasts. We solved the whole fam model to identify optimal decisions and to simulate annual economic outcomes by constaining the model to the optimal settings with and without ENSO infomation, and with and without fam pogams Optimal fam decisions We sampled 325 yeas of ou synthetic yields and pices to find optimal land allocation decisions, assuming the chance of foecasting a given phase is its histoical fequency (14 El Niño, 35 neutal, and 16 La Niña phases) fo the peiod The model selected optimal combinations of 22 possible cop management pactices (Table 2) fo 70 El Niño events, 175 neutal yeas, 80 La Niña events, and the sum of all of them. The model maximized the expected utility (U) fo one yea subject to land and labo availability (Letson et al., 2005), whee utility was a powe function of wealth based on a constant elative isk avesion R (Hadake et al., 2004), Equations 1 to 4. 5

6 N max E{ U ( W f )} = qiu ( W0 + i, n) / N [Eq. 1] 22 x m= 1 10 j = 1 3 n= 1 i= 1 X = 1; 0 [Eq. 2] m X m X * L L j [Eq. 3] m m, j 1 R U ( W ) = W /(1 R ) f f [Eq. 4] Whee: i = ENSO phase (1=El Niño, 2=neutal, 3=La Niña), j = month of the labo constaint (1-10, Febuay to Novembe), m = management altenative fom Table 2, n = yea fo each optimization (1 to N); = income (US$ y -1 ), W 0 = initial wealth (US$ y -1 ), W f = final wealth (US$ y -1 ), q = histoical likelihood of an ENSO phase foecast (%), X = land allocation (ha), L = labo equiement (day). This model eplicates simila models defined by Letson et al. (2005) and Messina et al. (1999) fo Agentina. We constained the model to use all land each yea to account fo ealistic cop otations commonly used in the study aea. Local infomation indicates fames use diffeent plots of land to otate these thee cops in diffeent yeas (C.A. Smith, Extension Agent II, Jackson Extension Office, Maianna; pesonal communication, Nov. 12, 2004); the model does not distinguish among fam fields, but accounts fo land aea and management pactices on each one of them. We used the MINOS5 algoithm in GAMS (Gill et al., 2000) along with a andomized pocedue to alte stating values and assue global maxima solutions. Evey solution identified land allocation fo cop entepises that maximized expected utility fo each constant elative isk of avesion ( R ): 0, 0.5, 1, 2, 3, and 4, Hadake et al. (2004, p. 102) Fam simulation and Estimate of the Value of the Infomation (EVOI) We constained the fam model to optimal land allocations to simulate net magins fo 2970 yeas (990 fo each ENSO phase) using all synthetic yields and all synthetic pices geneated as descibed above. This pocedue was epeated fo each constant elative to isk avesion. We define foecast value (EVOI) as the monetay amount of change in the net income esulting fom incopoating seasonal climate foecast infomation and isk avesion levels in fam decision making. 6

7 We estimated EVOI by compaing the simulated net magins with and without foecast accoding to thei histoical popotion fequencies. To be consistent with pecedent liteatue, we estimated EVOI ove diffeent planning hoizons in cetainty equivalent units (US$) Intoduction of fam pogams Seveal fam pogams exist and diectly impact agicultual poduction isk in the United States. Among them, cop insuance, disaste assistance, fixed and countecyclical payments, and commodity loan pogams ae available fo fames in Jackson County, Floida. In ode to evaluate land allocation decisions fo ou thee cops, we wee inteested in fam pogams that depend on actual poduction and distinguish among commodities as is the case of commodity loan pogams and cop insuances. We wee not inteested in disaste assistance pogams, fedeal income taxes, and othe type of fam pogam povisions (fixed and countecyclical payments) because they eithe do not depend diectly on actual poduction o fames have limited o no contol ove them in thei annual decision making. In addition, vey few cases can be found fo fames in Jackson County that claimed disaste assistance (K. Nicodemus, Rual Community Insuance, Octobe 2004); also, Fedeal income taxes have been found to influence only modeately the value of the foecast (Mjelde et al., 1996); and pogam payments ae totally independent of poduction and fam decision making Commodity loan pogams The Fedeal Agicultue Impovement and Refom Act of 1996 (the 1996 FAIR Fam Act) initiated loan deficiency payment (LDP) pogams fo seveal cops, including cotton. The pupose of this LDP pogam is to povide poduces with financial help to maket thei cops thoughout the yea. The LDP fo a county is detemined by compaing the county's loan ate and posted county pice (PCP). If the PCP is below the loan ate, then poduces ae eligible fo LDPs. The payment amount is the diffeence between the loan ate and the PCP ( The USDA Fam Pogam in Jackson County sets a minimum pice of $1.14 kg -1 fo cotton LDPs. The Fam Secuity and Rual Investment Act of 2002 (the 2002 FSRIA Fam Act) eliminated the peanut quota, but ceated new foms of fam financial help fo peanut gowes ( peanutsecto.htm). Among the new souces of govenment payments is the maketing loan benefit (MLB), which entitles peanut gowes to eceive maketing assistance loans of $0.39 kg -1 on cuent poduction. Also the 2002 FSRIA Fam Act changed the maize MLB to $0.08 kg -1 ( /policy.htm). In ode to compae EVOI with and without the inclusion of fam pogams, we applied the LDP to cotton and MLB to peanut and maize in ou synthetically geneated pices by limiting the minima to at least the levels of the espective pogams. In the case of cotton, we fist applied the LDP and then added the value of the seed. Distibution of geneated synthetic pices befoe and afte the inclusion of pogams ae pesented in Fig. 1. 7

8 -Figue Cop insuance pogams Seveal cop insuance options ae available. To limit possible decisions to a logistically manageable numbe we used the most common insuance poducts used by Jackson County fames in 2004 accoding to the Economic Reseach Sevice ( Fo peanut we used multi-peil cop insuance (MPCI) at the 70% level; fo cotton cop evenue coveage (CRC) at 65% level; and fo maize, MPCI at 50% coveage. The MPCI coves yield loss to a selected level, while CRC coves loss value to a selected level (yield multiplied by a pice election). The pice election selected was the maximum in each one of the cases. It was ($ kg -1 ): fo peanut, fo cotton, and fo maize. The use of aveage levels fo yield coveage (peanut and cotton) and highest pice coveage is consistent with what poduces tend to insue (Mjelde et al., 1996). Insuance pemium costs by cop wee calculated by multiplying the pemium cost by the selected planted aea by cop inside the decision function of the model. The local pemium costs to the fame wee ($ ha -1 ): fo peanut, fo cotton, and 7.66 fo maize. An indemnity payment was calculated when yield (MPCI fo peanut and maize) o cop value (CRC fo cotton) was less than the insued theshold in a detemined yea. The indemnity payment was the amount the fame would eceive in compensation to aise the income of the cop to the insued level. The indemnity payment was added to the income in the objective function by multiplying the land aea by the pice base and by the amount of loss. 8

9 RESULTS AND DISCUSSION 3.1. Optimal land allocation without fam pogams Optimal cop and management choices by ENSO phase ae influenced by isk avesion. We pesent only the case of R =1 (Fig. 2). The popotion of cops on the fam did not change. Howeve, thee wee favoable management pactices fo diffeent ENSO phases that maximized the fam net income. Late peanut plantings wee pefeed in El Niño yeas, while vey ealy cotton plantings wee chosen fo La Niña phases. Medium to late maize plantings wee selected fo El Niño and La Niña yeas, but ealie plantings wee selected duing neutal yeas. Divesification deceased with isk avesion; e.g., only 2 management altenatives wee selected fo R =4 and only 3 managements altenatives wee selected fo R =0, compaed to 4 fo R =1 when optimized fo all yeas. Cop otations and land allocation fom optimizations ae consistent with the anges indicated by local infomants. Fo R =0, 0.5, and 1 the popotion was 35% of peanut, 36.7% cotton, and 28.3% maize; fo R =2, 3, and 4 the popotion was 0% of peanut, 37.8% of cotton, and 62.2% of maize Optimal land allocation with fam pogams Optimal land allocation with Commodity Loan Pogams Application of CLP only maginally affected the optimal decisions. Fo R =1, small popotions of planting date cop selection wee changed fo maize duing El Niño yeas and fo cotton duing neutal yeas (Fig. 2 A, B). Fo R =2, 3, and 4 the popotion was 0% of peanut, 93.6% of cotton, and 6.4% of maize. -Figue Optimal land allocation with Cop Insuance Application of CIP only modeately affected the optimal decisions. Fo R =1, small popotions of plating date cop selections wee changed fo maize duing El Niño yeas, and fo peanut and cotton fo neutal yeas (Fig. 2 A, C). Fo R =2, 3, and 4 the popotion was 0% of peanut, 37.8% of cotton, and 62.2% of maize, with vaiants in the planting dates Optimal land allocation with Commodity Loan and Cop Insuance Pogams The combined impact of CLP and CIP on the optimization of land allocation was also only modeate. Fo R =1, majo changes occued in the planting dates and popotions fo maize duing El Niño yeas and fo cotton and peanut fo neutal yeas. When both pogams ae pesent, the popotion of cop selection fo R =2, 3, and 4 wee as in the case of no fam pogams Foecast value without fam pogams Foecast value and isk pefeences 9

10 We used a single 2970-yea inteval weighted aveage of ENSO-phase histoical fequency to estimate cetainty equivalent to exploe and to compae the value of the infomation (EVOI) with pevious studies. Fig. 3 shows the elationship between ENSO phases, EVOI, and R. Risk toleant fames employ a defensive esponse, while isk avese fames use foecasts defensively. Foecast esponses in Jackson County combine defensive with offensive isk stategies. Unde nomal isk avesion ( R =1), when poduces ae pepaed to minimize income losses (defensively) and to take advantage of favoable conditions (offensively), the aveage EVOI was $2.90 ha -1, which inceased to $6.60 ha -1 fo El Niño events. The value of the infomation inceased consideably to aound $25 ha -1 fo the aveage of all yeas when R > 1. This was even moe valuable fo the case of moe isk advese fames when El Niño o La Niña events wee foecast ($48 ha -1 ). Fo less isk avese o isk toleant poduces ( R < 1), limited incease in the value of the infomation was obseved fo La Niña events and emained steady fo El Niño Events (Fig. 3A). Small-scale Jackson County fames, like the epesentative fame fo this study, ae typically isk avese fames that would use the foecast offensively by being moe esponsive to La Niña o El Niño events to take advantage of likely favoable conditions. Convesely, lage fames would use the foecast defensively by being moe esponsive to La Niña phases to avoid losses duing these events. Fo all yeas, EVOI is $2.40 ha -1 at R =0 and it is maximized at $24.60 ha -1 at R =2. Simila esults wee found by Letson et al. (2005) in Pegamino, Agentina. -Figue 3- Ou findings of EVOI, which show the best oppotunity of application of seasonal climate foecasts by highly isk avese poduces and encouages offensive foecast use, is consistent with pevious studies (Letson et al., 2005, 2001; Messina et al., 1999; Mjelde et al, 1998; and Katz s webpage ( Even a pefect foecast povides a distibution of possible weathe outcomes, which will affect cop yields and, with uncetain pices, will affect economic etuns. A fequency distibution of EVOI estimates is pesented in Fig. 4. -Figue 4- EVOI ange and pobability ae of pactical impotance because foecast uses may want to know the ange and likelihood of EVOI as well as the likelihood of negative EVOI estimates. The pobability of negative EVOI estimates in Fig. 4 is 831 out of 2970 (28%), which is not negligible. Negative EVOI occus because of the joint effect of weathe and pices. Pices of peanut ae still distoted by govenment egulations. Even afte the 2002 Fam Act that abolished the peanut quota, thee ae still quota buyout, base aceage, and othe peanut pice pogams. These peanut pice incentives ae likely to disappea in the nea futue. Consequently, it is highly likely that the final pices eceived by peanut fames will continue to decease in the nea futue. The Economic Reseach Sevice, ERS, ( estimates this eduction on the ode of 33% of cuent likely pices. Also, statistics and pojections fom ERS indicate that peanut expots fom the US will emain maginal and not have majo impacts on the domestic peanut pices. The esults of peanut pice eductions in ou value of infomation potfolio, by abitaily educing the pice of peanut by 33% indicated that thee wee no substantial changes in the 10

11 EVOI estimates. Fo example, compaed with the oiginal case, the aveage EVOI when the pice of peanut was 33% lowe only deceased $0.20 ha -1 unde isk nomality ( R =1), and it only deceased aound $2.40 ha -1 fo the aveage of all yeas when R > 1. The cuves when using 33% lowe peanut pices wee vey simila to Figue 3A Foecast value with fam pogams Foecast value with commodity loan pogams We followed simila analyses to the EVOI estimates when CLPs wee applied. Fig. 3B shows the elationship between ENSO phases, EVOI and R when CLPs ae included. Oveall, the value of the infomation is geatly educed when CLPs ae applied. Unde nomal isk avesion ( R =1), aveage EVOI was slightly highe than when not using CLP, $3.80 ha -1, which inceased to $6.80 ha -1 fo El Niño events. EVOI fo El Niño was the highest value of the infomation when including CLPs. Fo moe isk avese fames ( R > 1), the value of the infomation was substantially lowe than when not using CLP, on the ode of $1.50 ha -1 fo the aveage of all yeas. The EVOI was small but positive fo all yeas. The EVOI was zeo fo La Niña yeas and R = 1 as well as fo El Niño and neutal yeas and R > 1 because thee wee no diffeences between the optimal settings when using foecast infomation. Wheeas fo less isk avese o isk toleant fames ( R < 1) a defensive esponse could have slightly bette EVOIs than not using CLP, fo moe isk avese poduces ( R > 1) the value of the infomation is substantially lowe fo the case of using CLP. When using CLP, less isk avese fames (usually lage-scale fames) would slightly benefit with defensive esponses duing El Niño events, howeve moe isk avese fames (usually small fames) would not benefit by using ENSO foecast Foecast value with cop insuance pogams We followed simila analyses to the EVOI estimates when CIPs wee applied. Fig. 3C shows the elationship between ENSO phases, EVOI and R when CIPs ae included. When CIP is applied, the oveall value of the infomation is geatly educed to even negative values. Howeve, the EVOI fo all yeas unde less o nomal isk avesion levels was slightly inceased to moe than $5.60 ha -1. EVOI was negative ($-0.50 ha -1 ) fo all yeas and R > 2. Negative EVOI fo all yeas fo R > 2 occued because EVOI was highly negative (<$-11 ha -1 ) when neutal yeas, even when EVOI estimates fo El Niño yeas wee modeately high (>$ 22 ha -1 ). Still unde CIP conditions highly isk avese fames could benefit by potential favoable conditions when El Niño yeas ae foecast. Ove concened optimal decisions of highly isk avese decision makes ceate a geat diffeence in the potential gains. Negative EVOI is possible as epoted in pevious studies (Letson et al., 2005; Mjelde et al., 1996). Negative EVOI occus because of inta-phase vaiability: e.g., optimization selected a cop combination based on a sample of weathe ealizations and the actual weathe occuence diffeed in ways that impacted income. Moeove, the incidence of negative EVOI estimates inceased when stochastic pices, which ae ENSO independent, ae unfavoable fo a defined entepise poposition. Unde high isk avesion levels, entepises with etuns that have smalle 11

12 vaiability ae chosen ove entepises with oveall highe etuns. Fo all optimizations, peanut was not selected fo high isk avesion levels even though it was the most pofitable entepise. In addition, high fequency of negative EVOI may be a consequence of constaining the model to optimal settings obtained fom the sampled 325 yeas. Use of a seasonal climate foecast could esult in negative EVOI when exteme pice fluctuations and exteme weathe conditions coincide. High fequency and oveall highe negative values found in this study, including the case of not using fam pogams, diffe fom pevious studies. In ou model, Jackson County poduces ae equied to use all thei land with limited labo available. This fact makes poduces select even negative entepises, in ode to use labo as efficiently as possible. Fo example, cotton was a negative entepise fo all ENSO phases and no fam pogams, but it was always selected because it was needed in the natual otation of cops as descibed by local infomants Foecast value with commodity loan and cop insuance pogams We included both CLP and CIP at the same time and followed simila analyses to the EVOI estimates. Fig. 3D shows the elationship between ENSO phases, EVOI and R when CLP and CIP ae included. Although the inclusion of both fam pogams deceases the oveall value of a seasonal climate foecast, it also buffes the occuence of negative values as when is only CIP applied. The EVOI fo all yeas is negative fo R >1 vaying between $-0.10 ha -1 and $-0.90 ha -1. The value of the infomation was positive, but maginal fo La Niña yeas and fo R >1. It was always positive fo El Niño yeas and it had modeate values ($ 26 ha -1 ) fo R >1, indicating that highly isk avese fames would still benefit by using El Niño foecast offensively by taking advantage of potential advantageous situations when CLP and CIP ae in place. 4. Conclusions As hypothesized, fam pogams substantially affect the potential value of seasonal climate foecasts. Fam pogams such as commodity loan pogams and cop insuance pogams educe fam income vaiability and the iskiness of the fam entepises. Consequently, the inclusion of CLP and CIP tends to educe the oveall value of the climate infomation and incease the likelihood of negative values of infomation. Howeve, depending upon the isk avesion level of the fame, the value of the infomation could vay consideably. Decision making institutions and egulations such as fam pogams will always affect fam iskiness and fames decisions. They should be included in the analyses of decisions. Foecast value is inheently pobabilistic even fo pefect ENSO phase foecast and must be estimated and communicated as dispesion athe than a single point estimate. Ou numeous synthetic pices and yields allowed us to geneate pobabilistic distibutions of the value of the foecasts. Each estimate we epot is associated with its pobability of occuence. Within these distibutions, negative value of the foecast infomation exists and is not negligible (Letson et al., 2005). 5. Refeences 12

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