1000 Ballpark Way, Suite 314, Arlington, TX 76011

(817) 261-7000 / (817) 261-7002 (Fax)

 

 

FEASIBILITY STUDY FOR INSURING 

DEDICATED ENERGY CROPS 

PHASE 2: DELIVERABLE 2.4.2.2 

FINAL SUBMISSION 

SOLICITATION # N10PC18199 

 

May 9, 2011

PREPARED FOR:

Risk Management Agency

USDA/RMA/RED

6501 Beacon Drive

Kansas City, MO 64133

(816) 926-3854

COTR: Janet Naglich 

 

 

POINT OF CONTACT:

Joe Davis

President

jdavis@agrilogic.com

(817) 261-7000 ext. 4250
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report i

Contract # N10PC18199

CONTENTS 

EXECUTIVE SUMMARY_________________________________________________________ 1

PROGRAM EVALUATION TOOL...................................................................................................................................1

ENVIRONMENTAL POLICY INTEGRATED CLIMATE MODEL (EPIC).............................................................................1

DEDICATED ENERGY CROPS ......................................................................................................................................2

Camelina ...............................................................................................................................................................2

Switchgrass...........................................................................................................................................................2

Energy Cane..........................................................................................................................................................2

CAMELINA __________________________________________________________________ 3

CROP DESCRIPTION....................................................................................................................................................3

Introduction...........................................................................................................................................................3

Economic Importance ...........................................................................................................................................3

Marketing and Utilization .....................................................................................................................................7

Agronomic and Botanical Characteristics.............................................................................................................9

Chemical Composition..........................................................................................................................................9

Production Operations.........................................................................................................................................10

Fertilizer and Moisture Requirements.................................................................................................................11

Varieties..............................................................................................................................................................11

Adaptation and Distribution................................................................................................................................11

Insects and Diseases............................................................................................................................................12

REVIEW OF OTHER PROGRAMS ................................................................................................................................13

State and Federal Programs.................................................................................................................................14

Private Products ..................................................................................................................................................15

Contracting Clauses ............................................................................................................................................15

Saskatchewan Policy...........................................................................................................................................15

DATA AVAILABILITY AND PRICE METHODOLOGIES.................................................................................................15

Yield Data ...........................................................................................................................................................15

Price Data............................................................................................................................................................15

LISTENING SESSIONS................................................................................................................................................16

Montana ..............................................................................................................................................................16

Oregon/Washington ............................................................................................................................................17

CAMELINA RISK EVALUATION.................................................................................................................................18

Introduction.........................................................................................................................................................18

Montana Program Evaluation Tool Summary.....................................................................................................18

Montana Program Evaluation Tool Results ........................................................................................................23

Oregon/Washington Program Evaluation Tool Summary ..................................................................................24

Oregon/Washington Program Evaluation Tool Results......................................................................................30
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ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR MONTANA.....................................................................30

MONTANA CAMELINA INSURANCE PREMIUM CALCULATIONS.................................................................................33

Camelina Yield Distribution ...............................................................................................................................34

ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR OREGON/WASHINGTON.................................................37

SPRING CAMELINA INSURANCE PREMIUM CALCULATIONS......................................................................................40

Spring Camelina Yield Distribution....................................................................................................................41

WINTER CAMELINA INSURANCE PREMIUM CALCULATIONS.....................................................................................44

Winter Camelina Yield Distribution ...................................................................................................................44

FEASIBILITY RECOMMENDATION .............................................................................................................................48

Crop Insurance Program Design Options............................................................................................................48

Recommendation ................................................................................................................................................49

Impact Analysis ..................................................................................................................................................54

SWITCHGRASS ______________________________________________________________ 55

CROP DESCRIPTION..................................................................................................................................................55

Introduction.........................................................................................................................................................55

Economic Importance .........................................................................................................................................55

Marketing and Utilization ...................................................................................................................................59

Agronomic and Botanical Characteristics...........................................................................................................59

Production Operations.........................................................................................................................................61

Susceptibility to Pests and Diseases....................................................................................................................62

Adaptation and Distributions ..............................................................................................................................63

Varieties..............................................................................................................................................................63

REVIEW OF OTHER PROGRAMS ................................................................................................................................65

State and Federal Programs.................................................................................................................................65

Private Products ..................................................................................................................................................66

DATA AVAILABILITY AND PRICE METHODOLOGIES.................................................................................................66

Yield Data ...........................................................................................................................................................66

Price Data............................................................................................................................................................67

LISTENING SESSIONS................................................................................................................................................67

Tennessee............................................................................................................................................................67

SWITCHGRASS RISK EVALUATION ...........................................................................................................................68

Introduction.........................................................................................................................................................68

Tennessee Program Evaluation Tool Summary ..................................................................................................68

Tennessee Program Evaluation Tool Results......................................................................................................73

ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR TENNESSEE SWITCHGRASS ...........................................73

SWITCHGRASS INSURANCE PREMIUM CALCULATIONS.............................................................................................77

Establishment Year Switchgrass Yield Distribution ...........................................................................................78

Mature Switchgrass Yield Distribution...............................................................................................................81

FEASIBILITY RECOMMENDATION .............................................................................................................................84
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Recommendation ................................................................................................................................................84

ENERGY CANE ______________________________________________________________ 86

CROP DESCRIPTION..................................................................................................................................................86

Introduction.........................................................................................................................................................86

Economic Importance .........................................................................................................................................86

Marketing and Utilization ...................................................................................................................................87

Agronomic and Botanical Characteristics...........................................................................................................87

Chemical Composition........................................................................................................................................88

Production Operations.........................................................................................................................................88

Susceptibility to Pests and Diseases....................................................................................................................90

Adaptation and Distribution................................................................................................................................93

Varieties..............................................................................................................................................................94

REVIEW OF OTHER PROGRAMS ................................................................................................................................95

State and Federal Programs.................................................................................................................................95

Private Products ..................................................................................................................................................95

DATA AVAILABILITY AND PRICE METHODOLOGIES.................................................................................................95

Yield Data ...........................................................................................................................................................95

Price Data............................................................................................................................................................95

LISTENING SESSIONS................................................................................................................................................96

Louisiana.............................................................................................................................................................96

ENERGY CANE RISK EVALUATION...........................................................................................................................97

Introduction.........................................................................................................................................................97

Louisiana Program Evaluation Tool Summary ...................................................................................................97

Louisiana Program Evaluation Tool Results.....................................................................................................103

ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR LOUISIANA ENERGY CANE..........................................103

ENERGY CANE INSURANCE PREMIUM CALCULATIONS ..........................................................................................107

Establishment Year Energy Cane Yield Distribution........................................................................................108

Plant Cane Year Energy Cane Yield Distribution.............................................................................................112

Ratoon Crop Energy Cane Yield Distribution ..................................................................................................115

FEASIBILITY RECOMMENDATION ...........................................................................................................................118

Recommendation ..............................................................................................................................................119

REFERENCES_______________________________________________________________ 120
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Contract # N10PC18199

LIST OF FIGURES AND TABLES 

TABLE 1: MONTANA CAMELINA PRODUCTION DATA, 2007-2009..................................................................................3

TABLE 2: MONTANA COUNTY LEVEL CAMELINA PRODUCTION DATA, 2008-2009........................................................4

TABLE 3: NORTH DAKOTA CAMELINA ACRES FOR SELECT NORTHWESTERN COUNTIES AND STATEWIDE TOTAL.........5

TABLE 4: ENTERPRISE BUDGET SUMMARY FOR SPRING PLANTED CAMELINA. ..............................................................6

TABLE 5: OILSEED PROFITABILITY MONTANA STATE UNIVERSITY DRYLAND CROP FARM...........................................6

TABLE 6: NO-TILL DRYLAND CAMELINA TRIALS: HAVRE, MT, 2005............................................................................7

TABLE 7: ESTIMATING BIODIESEL YIELD PER ACRE OF CAMELINA................................................................................7

FIGURE 1: ANNUAL GROWTH CYCLE .............................................................................................................................9

TABLE 8: GROWTH STAGES OF CAMELINA.....................................................................................................................9

TABLE 9: MONTANA‘S CAMELINA CROP DEVELOPMENT, 2009 ...................................................................................10

TABLE 10: SUSTAINABLE OILS CAMELINA VARIETY CHARACTERISTICS .....................................................................11

FIGURE 2: DISTRIBUTION OF CAMELINA.......................................................................................................................12

TABLE 11: CAMELINA PRODUCER YIELDS ...................................................................................................................15

TABLE 12: CAMELINA CONTRACT PRICES....................................................................................................................16

FIGURE 3: CHOUTEAU, GLACIER, PONDERA, & TETON COUNTIES 10 YEAR CROP YIELD RISK COMPARISON .............20

FIGURE 4: MONTANA CANOLA CAUSE OF LOSS OBSERVATIONS OF LOST COST RATIO IN EXCESS OF 25%.................20

FIGURE 5: MONTANA CAMELINA DEMAND SHIFTER AND PRODUCT DESIGN SIGNALS.................................................24

FIGURE 6: MORROW, UNION, & UMATILLA COUNTIES 10 YEAR CROP YIELD RISK COMPARISON...............................26

FIGURE 7: WHITMAN COUNTY, WASHINGTON 10-YEAR CROP YIELD RISK COMPARISON...........................................26

FIGURE 8: OREGON CANOLA CAUSE OF LOSS OBSERVATIONS OF LOST COST RATIO IN EXCESS OF 25% ....................27

FIGURE 9: OREGON/WASHINGTON CAMELINA DEMAND SHIFTER AND PRODUCT DESIGN SIGNALS ............................30

FIGURE 10: CORRELATION OF SIMULATED YIELDS TO PRODUCER AVERAGE YIELDS ..................................................32

TABLE 13: CAMELINA YIELD PAIRED T-TEST...............................................................................................................32

FIGURE 11: PDF OF CAMELINA YIELDS IN MONTANA..................................................................................................33

FIGURE 12: CDF OF CAMELINA YIELDS IN MONTANA .................................................................................................33

TABLE 14: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF CAMELINA IN MONTANA ................................34

TABLE 15: NORMALITY TEST FOR THE YIELD DISTRIBUTION OF CAMELINA IN MONTANA..........................................34

TABLE 16: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF CAMELINA IN MONTANA .........35

TABLE 17: YIELD INSURANCE PREMIUMS FOR CAMELINA IN MONTANA; ASSUMING ALTERNATIVE YIELD COVERAGE 

LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 1,241 POUNDS PER ACRE, AND GUARANTEED PRICE OF 

$0.15 PER POUND.................................................................................................................................................36

FIGURE 13: PDF OF ALTERNATIVE DISTRIBUTIONS OF CAMELINA YIELDS IN MONTANA ............................................36

TABLE 18: COMPARISON OF CAMELINA PREMIUM ESTIMATES TO CANOLA PREMIUM ESTIMATES ..............................37

FIGURE 14: CORRELATION OF SIMULATED CAMELINA YIELDS TO PRODUCER AVERAGE YIELDS................................38

TABLE 19: OREGON/WASHINGTON CAMELINA PAIRED T-TEST....................................................................................39

FIGURE 15: PDF OF SPRING CAMELINA YIELDS IN OREGON/WASHINGTON (POUNDS/ACRE).......................................39

FIGURE 16: CDF OF SPRING CAMELINA YIELDS IN OREGON/WASHINGTON (POUNDS/ACRE).......................................39

FIGURE 17: PDF OF WINTER CAMELINA YIELDS IN OREGON/WASHINGTON (POUNDS/ACRE)......................................40

FIGURE 18: CDF OF WINTER CAMELINA YIELDS IN OREGON/WASHINGTON (POUNDS/ACRE) .....................................40

TABLE 20: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF SPRING CAMELINA IN OREGON/WASHINGTON41

TABLE 21: NORMALITY TEST FOR THE YIELD DISTRIBUTION OF SPRING CAMELINA IN OREGON/WASHINGTON ........41

TABLE 22: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF SPRING CAMELINA IN 

OREGON/WASHINGTON........................................................................................................................................42

TABLE 23: YIELD INSURANCE PREMIUMS FOR SPRING CAMELINA IN OREGON/WASHINGTON; ASSUMING 

ALTERNATIVE YIELD COVERAGE LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 1,311 POUNDS PER 

ACRE, AND GUARANTEED PRICE OF $0.15 PER POUND ........................................................................................43

FIGURE 19: PDF OF ALTERNATIVE DISTRIBUTIONS OF SPRING CAMELINA YIELDS IN OREGON/WASHINGTON ...........43

TABLE 24: COMPARISON OF CAMELINA PREMIUM ESTIMATES TO CANOLA PREMIUM ESTIMATES FOR WASHINGTON 44

TABLE 25: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF WINTER CAMELINA IN OREGON/WASHINGTON

.............................................................................................................................................................................45

TABLE 26: NORMALITY TEST FOR THE YIELD DISTRIBUTION OF WINTER CAMELINA IN OREGON/WASHINGTON........45
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TABLE 27: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF WINTER CAMELINA IN 

OREGON/WASHINGTON........................................................................................................................................45

TABLE 28: YIELD INSURANCE PREMIUMS FOR WINTER CAMELINA IN OREGON/WASHINGTON; ASSUMING 

ALTERNATIVE YIELD COVERAGE LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 982 POUNDS PER 

ACRE, AND GUARANTEED PRICE OF $0.15 PER POUND ........................................................................................47

FIGURE 20: PDF OF ALTERNATIVE DISTRIBUTIONS OF WINTER CAMELINA YIELDS IN OREGON/WASHINGTON..........47

FIGURE 21: SWITCHGRASS PRODUCTION CHART..........................................................................................................56

FIGURE 22: TOTAL SWITCHGRASS GROWERS...............................................................................................................56

TABLE 29: COSTS OF PRODUCTION FOR SWITCHGRASS IN ILLINOIS .............................................................................57

TABLE 30: ANNUALIZED COSTS OF PRODUCTION FOR SWITCHGRASS IN ILLINOIS .......................................................58

TABLE 31: SUMMARY OF FIVE ECONOMIC ANALYSES THAT ESTIMATE THE FULL ECONOMIC COST TO PRODUCE 

SWITCHGRASS AS A DEDICATED ENERGY CROP...................................................................................................58

TABLE 32: EISA CELLULOSIC ETHANOL MANDATE ....................................................................................................59

FIGURE 23: SWITCHGRASS GROWTH CYCLE.................................................................................................................60

FIGURE 24: SWITCHGRASS CHEMICAL COMPOSITION...................................................................................................61

TABLE 33: UPLAND SWITCHGRASS CULTIVARS ...........................................................................................................64

FIGURE 25: YIELD PROJECTIONS FOR UPLAND VARIETIES ...........................................................................................64

TABLE 34: LOWLAND SWITCHGRASS CULTIVARS ........................................................................................................65

FIGURE 26: YIELD PROJECTIONS FOR LOWLAND VARIETIES ........................................................................................65

TABLE 35: SWITCHGRASS AVERAGE PRODUCER YIELDS .............................................................................................67

FIGURE 27: TENNESSEE COMBINED COUNTY CROP YIELD RISK COMPARISON ............................................................70

FIGURE 28: TENNESSEE SWITCHGRASS DEMAND SHIFTER AND PRODUCT DESIGN SIGNALS........................................73

FIGURE 29: CORRELATION OF SIMULATED YIELDS TO PRODUCER AVERAGE YIELDS ..................................................75

TABLE 36: SWITCHGRASS YIELD PAIRED T-TEST.........................................................................................................76

FIGURE 30: PDF OF MATURE SWITCHGRASS YIELDS IN SE TENNESSEE ......................................................................76

FIGURE 31: CDF OF MATURE SWITCHGRASS YIELDS IN SE TENNESSEE......................................................................76

FIGURE 32: PDF OF SWITCHGRASS YIELDS FOR ESTABLISHMENT YEAR IN SE TENNESSEE.........................................77

FIGURE 33: CDF OF SWITCHGRASS YIELDS FOR ESTABLISHMENT YEAR IN SE TENNESSEE ........................................77

TABLE 37: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF ESTABLISHMENT YEAR SWITCHGRASS IN SE

TENNESSEE...........................................................................................................................................................78

TABLE 38: NORMALITY TEST OF THE YIELD DISTRIBUTION FOR THE ESTABLISHMENT YEAR OF SWITCHGRASS IN SE

TENNESSEE...........................................................................................................................................................79

TABLE 39: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF THE ESTABLISHMENT YEAR OF 

SWITCHGRASS IN SE TENNESSEE..........................................................................................................................79

TABLE 40: YIELD INSURANCE PREMIUMS FOR THE ESTABLISHMENT YEAR OF SWITCHGRASS IN SE TENNESSEE;

ASSUMING ALTERNATIVE YIELD COVERAGE LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 3.5 TONS 

PER ACRE, AND GUARANTEED PRICE OF $75 PER TON ........................................................................................80

FIGURE 34: PDF OF ALTERNATIVE DISTRIBUTIONS OF SWITCHGRASS YIELDS FOR ESTABLISHMENT YEAR IN SE

TENNESSEE...........................................................................................................................................................81

TABLE 41: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF MATURE SWITCHGRASS IN SE TENNESSEE.....81

TABLE 42: NORMALITY TEST FOR THE YIELD DISTRIBUTION OF MATURE SWITCHGRASS IN SE TENNESSEE...............82

TABLE 43: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF MATURE SWITCHGRASS IN SE

TENNESSEE...........................................................................................................................................................82

TABLE 44: YIELD INSURANCE PREMIUMS FOR MATURE SWITCHGRASS IN SE TENNESSEE; ASSUMING ALTERNATIVE 

YIELD COVERAGE LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 8 TONS PER ACRE, AND GUARANTEED 

PRICE OF $75 PER TON .........................................................................................................................................83

FIGURE 35: PDF OF ALTERNATIVE DISTRIBUTIONS OF MATURE SWITCHGRASS YIELDS IN SE TENNESSEE.................84

FIGURE 36: U.S. SUGARCANE: TEN-YEAR CAUSES OF LOSS ........................................................................................90

FIGURE 37: ENERGY CANE DISTRIBUTION ...................................................................................................................94

TABLE 45: ENERGY CANE VARIETY COMPARISON (COMMERCIAL TYPE)....................................................................94

TABLE 46: ENERGY CANE YIELDS................................................................................................................................95

FIGURE 38: LOUISIANA STATE TOTAL CROP YIELD RISK COMPARISON.......................................................................99

FIGURE 39: LOUISIANA SUGARCANE CAUSE OF LOSS OBSERVATIONS OF LOST COST RATIO IN EXCESS OF 25% ........99

FIGURE 40: U.S. COMMODITY PRICES RECEIVED BY FARMERS 10 YEAR VARIATION ................................................100
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FIGURE 41: LOUISIANA ENERGY CANE DEMAND SHIFTER AND PRODUCT DESIGN SIGNALS......................................103

FIGURE 42: CORRELATION OF SIMULATED YIELDS TO PRODUCER AVERAGE YIELDS...................................................105

TABLE 47: ENERGY CANE YIELD PAIRED T-TEST.......................................................................................................105

FIGURE 43: PDF OF ESTABLISHMENT YEAR 1 ENERGY CANE YIELDS IN LOUISIANA.................................................106

FIGURE 44: CDF OF ESTABLISHMENT YEAR 1 ENERGY CANE YIELDS IN LOUISIANA ................................................106

FIGURE 45: PDF OF PLANT CANE YEAR 2 ENERGY CANE YIELDS IN LOUISIANA.......................................................106

FIGURE 46: CDF OF PLANT CANE YEAR 2 ENERGY CANE YIELDS IN LOUISIANA ......................................................107

FIGURE 47: PDF OF RATOON YEARS 3-6 ENERGY CANE YIELDS IN LOUISIANA ........................................................107

FIGURE 48: CDF OF RATOON YEARS 3-6 ENERGY CANE YIELDS IN LOUISIANA........................................................107

TABLE 48: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF ESTABLISHMENT YEAR ENERGY CANE IN 

LOUISIANA .........................................................................................................................................................109

TABLE 49: NORMALITY TEST OF THE YIELD DISTRIBUTION FOR ESTABLISHMENT YEAR ENERGY CANE IN LOUISIANA

...........................................................................................................................................................................109

TABLE 50: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF ESTABLISHMENT YEAR ENERGY 

CANE IN LOUISIANA ...........................................................................................................................................110

TABLE 51: YIELD INSURANCE PREMIUMS FOR THE ESTABLISHMENT YEAR OF ENERGY CANE IN LOUISIANA;

ASSUMING ALTERNATIVE YIELD COVERAGE LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 18.4 TONS 

PER ACRE, AND GUARANTEED PRICE OF $16.25 PER WET TON .........................................................................111

FIGURE 49: PDF OF ALTERNATIVE DISTRIBUTIONS OF ENERGY CANE YIELDS FOR ESTABLISHMENT YEAR IN 

LOUISIANA .........................................................................................................................................................111

TABLE 52: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF PLANT YEAR CANE ENERGY CANE IN 

LOUISIANA .........................................................................................................................................................112

TABLE 53: NORMALITY TEST FOR THE YIELD DISTRIBUTION OF PLANT YEAR CANE ENERGY CANE IN LOUISIANA..112

TABLE 54: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF PLANT YEAR CANE ENERGY 

CANE IN LOUISIANA ...........................................................................................................................................113

TABLE 55: YIELD INSURANCE PREMIUMS FOR PLANT YEAR CANE ENERGY CANE IN LOUISIANA; ASSUMING 

ALTERNATIVE YIELD COVERAGE LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 46 TONS PER ACRE,

AND GUARANTEED PRICE OF $16.25 PER WET TON...........................................................................................114

FIGURE 50: PDF OF ALTERNATIVE DISTRIBUTIONS OF PLANT YEAR CANE YEAR 2 ENERGY CANE YIELDS IN 

LOUISIANA .........................................................................................................................................................114

TABLE 56: SUMMARY STATISTICS FOR BASE YIELD DISTRIBUTION OF RATOON CROP ENERGY CANE IN LOUISIANA

...........................................................................................................................................................................115

TABLE 57: NORMALITY TEST FOR THE YIELD DISTRIBUTION OF RATOON CROP ENERGY CANE IN LOUISIANA.........115

TABLE 58: UNIVARIATE PARAMETER ESTIMATION FOR THE YIELD DISTRIBUTION OF RATOON CROP ENERGY CANE IN 

LOUISIANA .........................................................................................................................................................116

TABLE 59: YIELD INSURANCE PREMIUMS FOR RATOON CROP ENERGY CANE IN LOUISIANA; ASSUMING ALTERNATIVE 

YIELD COVERAGE LEVELS AND YIELD DISTRIBUTIONS, AN APH YIELD OF 43 TONS PER ACRE, AND 

GUARANTEED PRICE OF $65 PER TON................................................................................................................117

FIGURE 51: PDF OF ALTERNATIVE DISTRIBUTIONS OF RATOON CROP ENERGY CANE YIELDS IN LOUISIANA ...........117

TABLE 60: COMPARISON OF ENERGY CANE PREMIUM ESTIMATES TO SUGARCANE PREMIUM ESTIMATES ................118

TABLE 61: SUSTAINABLE OILS PLANT BACK REQUIREMENTS FOR CAMELINA...........................................................143

FIGURE 52: MONTANA STATE HAIL INSURANCE RATES.............................................................................................147

TABLE 62: MONTANA STATE HAIL INSURANCE RATES BY ZONE ...............................................................................147

FIGURE 53: PROGRAM EVALUATION TOOL DECISION TREE .......................................................................................149

FIGURE 54: MONTANA CAMELINA DECISION TREE....................................................................................................150

FIGURE 55: OREGON/WASHINGTON CAMELINA DECISION TREE................................................................................151

FIGURE 56: TENNESSEE SWITCHGRASS DECISION TREE .............................................................................................152

FIGURE 57: LOUISIANA ENERGY CANE DECISION TREE .............................................................................................153
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LIST OF APPENDICES 

APPENDIX A: WILLAMETTE BIOMASS PROCESSORS, INC.-CAMELINA PRODUCTION CONTRACT FOR OREGON 2008

APPENDIX B: SUSTAINABLE OILS 2011 CAMELINA PRODUCTION CONTRACT

APPENDIX C: THE UNIVERSITY OF TENNESSEE – FIXED PRICE CONTRACT WITH SWITCHGRASS PRODUCERS

APPENDIX D: SUSTAINABLE OILS PLANT BACK REQUIREMENTS FOR CAMELINA

APPENDIX E: SASKATCHEWAN CROP INSURANCE CORPORATION: 2010 TERMS AND CONDITIONS FOR CAMELINA

APPENDIX F: UNITED STATES STANDARDS FOR CANOLA

APPENDIX G: MONTANA STATE HAIL INSURANCE RATES

APPENDIX H: MATHEMATICAL DESCRIPTION OF CDFDEV

APPENDIX I: PROGRAM EVALUATION TOOL DECISION TREE

APPENDIX J: MONTANA CAMELINA PROGRAM EVALUATION TOOL DECISION TREE

APPENDIX K: OREGON/WASHINGTON CAMELINA PROGRAM EVALUATION TOOL DECISION TREE

APPENDIX L: TENNESSEE SWITCHGRASS PROGRAM EVALUATION TOOL DECISION TREE

APPENDIX M: LOUISIANA ENERGY CANE PROGRAM EVALUATION TOOL DECISION TREE

APPENDIX N: PROGRAM EVALUATION TOOL DIAGNOSTIC INSTRUMENTS
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 1

Contract # N10PC18199

EXECUTIVE SUMMARY 

―The Food, Conservation, and Energy Act of 2008 (2008 Farm Bill) directed the Risk Management Agency (RMA) 

to  ̳offer to enter into one or more contracts with qualified entities to carry out research and development regarding a 

policy to insure dedicated energy crops.‘ The 2008 Farm Bill states that research and development shall evaluate the 

effectiveness of risk management tools for the production of dedicated energy crops, including policies and plans of 

insurance that (i) are based on market prices and yields, (ii)evaluate, to the extent that insufficient data exist to 

develop a policy based on market prices and yields, the policies and plans of insurance based on the use of weather 

or rainfall indices to protect the interest of crop producers, and (iii) provide protection for production or revenue 

losses or both.‖ (USDA; RMA, 2010a) This draft feasibility report represents Phase 2 listed in section 2.4.2.1 of the 

Statement of Work (SOW) for the overall project entitled ―Data Collection Report and Feasibility Research Report

for Insuring Dedicated Energy Crops.‖

The submitted Data Collection Report representing Phase 1 of the SOW found camelina and switchgrass to be the 

only crops that meet the following criteria: they are commercially grown and dedicated to energy production, as 

defined in the SOW. RMA added energy cane to the feasibility report after the submission of the ―Initial Data 

Collection Report‖. The report evaluates the insurability of camelina, switchgrass, and energy cane based on the 

current demand for an insurance product, production practices, market prices, yields, and data availability. Two 

principal tools were used to provide a quantitative analysis that aids in the feasibility determination. The Program 

Evaluation Tool was utilized to evaluate risk exposure, and the Environmental Policy Integrated Climate model or 

EPIC was utilized to simulate and quantify yield data series.

PROGRAM EVALUATION TOOL 

The RMA‘s Program Evaluation Tool found in the USDA RMA Program Evaluation Handbook (FCIC-2210 (PEH) 

(http://www.rma.usda.gov/FTP/Publications/directives/22000/06_22010.pdf) was used by the Contractor as a 

supplement to assist in the development of the overall recommendation for each dedicated energy crop. This tool 

creates a better understanding of the risk of exposure and potential for various products to transfer a portion of that 

risk to an insurance pool. The tool also assists in gauging the demand for the insurance product and identifying 

potential design issues that may arise. 

The instrument was applied separately to each of the four regions where listening sessions were held. These regions 

include Tennessee for switchgrass, Louisiana for energy cane, and Montana and Oregon/Washington for camelina. 

The program evaluation tool was completed with the information obtained through listening sessions with the

producers, insurance agents, FSA personnel, university extension personnel, crop consultants, and conversations 

with RMA Regional Office personnel, as well as the Contractor‘s independent research and analysis of the current 

production and market conditions for each crop in each region and that of comparable crops. Each completed tool 

was then utilized to work through a decision tree process for determining a recommended course of action.

ENVIRONMENTAL POLICY INTEGRATED CLIMATE MODEL (EPIC) 

EPIC (Potter, Atwood, Kellog, & Williams, 2004) is a daily time-step model capable of simulating a wide array of 

crop production and environmental processes that include plant growth, crop yields, plant competition, and soil 

erosion as well as water, and nutrient balances. Biomass growth is related to solar radiation intercepted by the plant 

canopy, vapor pressure deficit, CO2 concentration, and other physiological stresses including water, temperature, N, 

P, and soil aeration deficits/surpluses.

Among the model‘s many features is a stochastic weather generator. Weather can be input from historical records or 

it can be estimated stochastically using precipitation, air temperature, solar radiation, wind, and relative humidity 

parameters developed from historical records. Utilizing the stochastic weather generator, the model simulates the

growth and development of a crop each day from emergence to harvest. Producer collected data facilitated the

setting up of the model for each crop and the simulating of 100 years of stochastically generated yields for 

developing a probability distribution. The yield distributions as developed using the EPIC model were then used to 
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Contract # N10PC18199

estimate yield insurance premiums for the best four distributions. These distributions were simulated in Simetar© 

for 25,000 iterations to estimate ―fair insurance premiums‖ for eight yield coverage levels ranging from 50 percent 

to 85 percent of the average yield. Where possible these premiums are compared to a proxy crop to better quantify 

yield data and to better determine the potential for rating each crop.

DEDICATED ENERGY CROPS 

Camelina 

An oilseed crop in the Brassicaceae family, Camelina is currently grown in Montana, Oregon, and Washington on 

over 20,000 acres. Camelina is a short seasoned, annual plant that grows one to three feet tall at maturity and 

requires low inputs. It has been grown for thousands of years and has been called multiple names including 

leindotter, false flax, wild flax, German sesame, Siberian oilseed, gold of pleasure, and camelina. It is a member of 

the mustard family and a distant relative to canola. The crop was recently found to be suitable for energy production, 

utilizing the extracted seed oil for processing to biofuels.

Listening sessions held in Montana and the Oregon/Washington region with producers indicated that camelina was a 

low input, low risk crop that was easy to grow. These producers understand the benefits of a crop insurance program 

and suggested that an APH type insurance plan would be advantageous to manage their risk. After utilizing the 

Program Evaluation Tool it was determined that enough significant risk existed to develop and insure the product. 

As determined by the results of the research and analysis for camelina, currently commercially produced for 

dedicated energy, the contractor determined that a crop insurance program for camelina is feasible. As discussed in 

the recommendation section of this report, the recommended course of action is to develop an APH crop insurance 

program for camelina.

Switchgrass 

Switchgrass is a perennial grass native to most of North America except for the west coast region--specifically 

California, Oregon, and Washington. This warm-season grass is well suited for (a) conservation uses such as erosion 

control and wildlife habitat, and (b) pasture, forage, and hay due to its deep root system and ability to thrive on 

marginal soils. It was recently identified as an ideal crop dedicated to energy production due to its native status and 

high biomass yields from relatively low inputs on marginal soils. 

Commercial production of switchgrass for dedicated energy is currently isolated to Eastern Tennessee. The only 

commercial biorefinery utilizing switchgrass is located in Vonore, Tennessee, which was built by Genera Biofuels 

LLC, DuPont Danisco Cellulosic Ethanol LLC, and the University of Tennessee. Production is limited to Genera 

Biofuels‘ contracted producers who grew 6,000 acres of switchgrass in 2010. The producers are contracted with 

Genera Biofuels via acreage contracts that exhibit no revenue risk. Because of the lack of significant revenue risk 

and other sources of revenue risk, the Program Evaluation Tool decision tree framework suggests that no new 

product be developed or that no action is required. Combined with the remainder of research results, the contractor 

recommends that, for the eastern Tennessee region, it is not feasible to develop a crop insurance program for 

switchgrass at this time.

Energy Cane 

Energy cane is simply sugarcane that is bred for higher fiber content. Most of the energy cane acreage is in test plots 

for further research and development; however, BP Alternative Energy has contracted two growers in Louisiana for 

their demonstration plant in Jennings, Louisiana and one grower in Florida for a plant that is currently under 

construction. Current production in Louisiana and Florida is less than 1,000 acres and 400 acres respectively. With 

no significant commercial market for energy cane, it is determined that it is not feasible to develop a crop insurance 

program for energy cane at this time.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 3

Contract # N10PC18199

CAMELINA 

CROP DESCRIPTION 

Introduction 

Camelina is an oilseed crop grown in the northern and western plains regions of the United States. Camelina is a 

short seasoned, annual plant that grows one to three feet tall at maturity and requires low inputs. It has been grown 

for thousands of years and has been called multiple names including leindotter, false flax, wild flax, German sesame, 

Siberian oilseed, gold of pleasure, and camelina. It is a member of the mustard family and a distant relative to 

canola. Each camelina seed contains approximately 30 to 40 percent oil compared to the 20 percent contained in 

soybeans. (Stratton, Klienschmit, & Kenney, 2007)

Recently, camelina has been produced for biofuels and bio-based products from the crops‘ oilseed. Most notably, the 

U.S. Navy is using camelina biodiesel in its jet fuels, providing significant market potential. A few private 

companies have entered into the market as processors or first handlers, and most sign contracts with growers for 

oilseed production at a fixed price. Also, the camelina meal leftover from biodiesel production was recently 

approved by the United States Food and Drug Administration (FDA) for use in livestock feed rations.

Economic Importance 

SUPPLY

United States

As the camelina industry is in its infancy, production data are limited. Discussions with industry leaders and the data 

collected for this report indicated that production is concentrated in the Northwestern United States, specifically 

Idaho, Montana, North Dakota, Washington, and Oregon. Publicly available data have been obtained for Montana 

and North Dakota, while published data for Idaho, Washington, and Oregon are not publicly available.

Conversations with processors and first handlers indicate that less than 1,000 acres of camelina are currently 

produced in Oregon and Washington. Further, Scott Johnson with Sustainable Oils also indicated that Camelina was 

produced under contract in Idaho prior to 2010, but actual acreage estimates are not made publicly available. 

Montana

Montana is currently the leading state for camelina production with 20,800-planted acres in 2009. Acreage,

production, and crop value at a state and county level for 2007 through 2009 are illustrated in Table 1 and Table 2.

Table 1: Montana Camelina Production Data, 2007-2009 

Year Acreage Production Value

Planted Harvested Yield/Ac 

(lbs.)

Total 

Production 

(000)

Price/Cwt. Value of 

Production 

$(000)

2009 20,800119,500 615 11,998.0 -- --

2008 12,200 9,100 569 5,181.5 -- --

2007 22,500 20,400 598 12,197.0 9.18 1,112

Source: (USDA; NASS, 2010)

 

1 Correction: Montana published inconsistent data. Planted acres were confirmed to be 20,800 in 2009. Source: Montana NASS. Telephone 

Conversation. April 6, 2011.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 4

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Although the crop is produced statewide, the county level data in Table 2 shows a concentration of production in the 

North Central and North Eastern regions. Counties in the North Central region produced 30 percent of the state‘s 

total production, and the counties in the North Eastern region produced 37 percent in 2009. 

Table 2: Montana County Level Camelina Production Data, 2008-2009. 

County and 

District

2008 2009

Planted 

Ares

Harvested 

Acres

Yield/Ac 

(lbs)

Production 

(lbs)

Planted 

Acres

Harvested 

Acres

Yield/Ac 

(lbs)

Production 

(lbs)

Other 200 200 540 108,000 100 100 500 50,000

Northwest 200 200 540 108,000 100 100 500 50,000

Chouteau 700 700 473 331,100 800 700 1,070 749,000

Glacier - - - - 700 600 640 384,000

Liberty - - - - 900 900 615 553,000

Phillips - - - - 500 300 770 231,000

Pondera 1,200 1,200 1,581 1,897,200 1,800 1,800 760 1,368,000

Teton 1,200 700 768 537,600 1,400 1,300 100 130,000

Other 1,700 1,500 301 451,100 600 600 475 285,000

North 

Central

4,800 4,100 785 3,217,000 6,700 6,200 597 3,700,000

Dawson - - - - 900 900 1,000 900,000

Garfield - - - - 600 600 750 450,000

McCone - - - - 2,300 2,300 750 1,725,000

Sheridan - - - - 1,500 1,400 420 588,000

Other 1,800 1,600 238 381,500 800 700 1,180 828,000

Northeast 1,800 1,600 238 381,500 6,100 5,900 761 4,491,000

Broadwater - - - - 500 500 250 125,000

Fergus 1,300 400 443 177,200 - - - -

Other 2,200 1,500 496 744,100 500 300 30 100,000

Central 3,500 1,900 485 921,300 1,000 800 281 225,000

Southwest 500 500 508 254,200 800 500 614 307,000

Big Horn - - - - 5,100 5,100 550 2,805,000

Stillwater - - - - 500 400 450 180,000

Other 1,000 400 394 157,500 - - - -

South 

Central

1,000 400 394 157,500 5,600 5,500 543 2,985,000

Southeast 400 400 355 142,000 500 500 480 240,000

Montana 12,200 9,100 569 5,181,500 20,800 19,500 615 11,998,000

Source: (USDA; NASS, 2010)

North Dakota

A 2009 publication from North Dakota State University (NDSU) Area Extension Specialists, Chet Hill, reported 

camelina acreage in counties concentrated in northwest North Dakota; as well as a statewide total. As illustrated in 

Table 3, Hill reported a statewide total of 47 camelina acres in 2008. The statewide acreage reported in 2009 was 

987; representing a 2000% increase from 2008. Williams County and Divide County reported 540 acres and 46 

acres respectively in 2009 and all other counties in the state reported 401 acres in 2009. (Hill, 2009)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 5

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Table 3: North Dakota Camelina Acres for Select Northwestern Counties and Statewide Total 

County 2006 2007 2008 2009

Divide - - - 46

Mountrail 120 - - -

Ward 5 - - -

Williams - 39 - 540

Other - 8 47 401

Statewide Total 125 47 47 987

Source: (Hill, 2009)

Oregon

Currently, publicly published data are not available for camelina production in Oregon. Private companies such as 

Sustainable Oils and Willamette Biomass Processors have contracted with several growers for production in 2009 

and 2010, but the data are not published. Tomas Endicott with Willamette Biomass Processors stated that the 

estimated current acreage for Oregon and Washington camelina is less than 1,000 acres. (Personal Communication; 

Oregon and Washington Listening Session, 2011)

In 2007, OSU received a grant through the Sun Grant initiative to develop camelina as an oilseed crop in Oregon, 

Idaho, and Washington. The study is ongoing and seeks to determine the optimum planting date, varieties, and 

inputs for production in the Pacific Northwest. Currently, the information available from this study is limited to brief 

reports without a yield data series. (Oregon State University, n.d.)

Washington

Currently, publicly available data from acceptable sources are not available for the state of Washington. Two 

producers who attended the listening sessions produced camelina in Whittman and Yakima Counties; although, their 

acreage was not reported. However, estimates of less than 1,000 acres for Oregon and Washington were reported at

the Oregon/Washington listening session by Tomas Endicott, a sales manager from Willamette Biomass Processors.

(Personal Communication; Oregon and Washington Listening Session, 2011)

Canada

In Saskatchewan, camelina production is being contracted, largely by Great Plains Oil & Exploration and 

Sustainable Oils. The Saskatchewan Ministry of Agriculture reported that 20,000 acres of camelina was planted in 

2009 and 15,000 in 2010. The actual reason for the decrease in planted acres in 2010 has not been disclosed but may 

be attributed to lower production levels due to excess moisture. (Saskatchewan Ministry of Agriculture, 2011)

PRODUCTION COSTS

The production cost of camelina varies by geographic location and soil qualities. Table 4 contains a spring planted 

production budget for the Willamette Valley of Oregon (Jaeger & Siegel, 2008). At a 1,600-pound yield and a price 

of $0.095, the revenue net of variable costs is projected to be slightly over $40 per acre. However, Sustainable Oils 

sets their price to have a net return of $50 to $75 for the producer (Johnson S. , 2011). 

A second set of camelina cost information for a Montana State University model (CropFarm) was reported using 

555 acres of camelina. The data in Table 5 illustrates that an expected price of $0.205 and yield of 1,141 pounds per 

acre would have a total production cost of $101.35 and a net return of $132.56 per acre. (Schanczenski, 2009)

Table 6 illustrates the agronomic data for camelina trials in Havre, Montana. Depending upon variety and planting 

date, yields were as high as 1,789 pounds per acre for camelina planted on March 15 and as low as 731 pounds per 

acre for camelina planted on April 20. Priced at $0.09 per pound, revenues net of variable cost were also highest and 

lowest for those varieties and planting dates as well, ranging from $27.85 per acre to $123.04 per acre. (Johnson D. , 

2007)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 6

Contract # N10PC18199

Table 4: Enterprise Budget Summary for Spring Planted Camelina. 

Oilseed crop

Costs and revenues Camelina

Variable cost ($/acre) 111.81

Fixed cost ($/acre) 166.81

Land rent ($/acre) 125.00

Other ($/acre) 41.81

Total costs ($/acre) 278.62

Yield (lb./acre) 1,600

Seed price ($/lb.) 0.095

Cost ($/lb.) 0.174

Total revenue ($/acre) 152.00

Net revenue ($/acre) -126.62

Revenue net of variable costs ($/acre) 40.19

Adapted from: (Jaeger & Siegel, 2008)

Table 5: Oilseed Profitability Montana State University Dryland Crop Farm 

Crop: Camelina

Acres in Crop 555

Income (per Acre):

Government Payment $0

Expected Yield (lb. per acre) 1141 lbs.

Expected Price per bu./lb. $0.205

Total Income/acre $233.91

Costs (per Acre):

Seed and Treatments $3.00

Total Chemicals $10.00

Total Fertilizers $16.50

Crop Insurance $ -

Other Misc. Costs $ -

Machinery Operating Costs $19.08

Interest on Operating Costs $0.85

Total Operating Costs/Acre $49.43

Total Ownership Costs/Acre $51.92

Total Costs $101.35

Returns over All Costs/Acre $132.56

Source: (Schanczenski, 2009)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 7

Contract # N10PC18199

Table 6: No-Till Dryland Camelina Trials: Havre, MT, 2005 

Celine 

Camelina

Celina 

Camelina

MT 1 Camelina MT 5 Camelina

Planting Date March 15 April 20 March 15 March 15

Yield (lb./acre) 1585 731 1789 1590

Seed price ($/lb.) 0.09 0.09 0.09 0.09

Total revenue ($/acre) 142.61 65.85 161.04 143.14

Variable Costs ($/acre) 37.00 38.00 38.00 38.00

Revenue Net of Variable Costs ($/acre) 105.61 27.85 123.04 105.14

Adapted from: (Johnson D. , 2007)

DEMAND

Recent efforts to reduce dependency on foreign oil have led to several alternative energy initiatives. The Department 

of the Navy plans to use eight billion gallons of renewable fuels by 2020, largely by switching to renewable jet fuels

(Tindal, 2010). Camelina is among the alternative energy sources for jet fuel. As other potential markets may exist, 

the Navy‘s alternative energy goals provide a significant demand driver for camelina.

Although little data are available for camelina, the acreage needed to satisfy the Navy‘s initiative can be estimated. 

Previous research assumes a 1:1 conversion ratio for seed oil to biodiesel (Isom & Booker, 2008). The National 

Center for Appropriate Technology reported that 54.33 gallons of oil could be produced from one acre of camelina 

that yields 598 pounds, which is a conversion ratio of 0.090852843. Using the Contractor‘s simulated average 

production level of 1,241 pounds and the estimated 112 gallons of biodiesel produced per acre of camelina

calculated in Table 7, it would take over 70 million acres of camelina to achieve the 8 billion gallons of renewable 

fuels that the Navy plans to use in 2020. In comparison, 88 million acres of corn were planted in 2010. (USDA; 

NASS, 2010)

Table 7: Estimating Biodiesel Yield per Acre of Camelina 

Camelina to Biodiesel Conversion

2007 Per Acre Yield

in Montana2

Gallons of oil per 

acre (From a Yield 

of 598 lbs./acre)

Ratio of Yield to 

Gallons Per Acre3

Average Simulated 

Yield in Montana

(lbs./acre)4

Estimated

Biodiesel 

Production/Acre 

Capability5

598 54.33 0.090852843 1241 112.7483779

Adapted From: (The National Center for Appropriate Technology, 2009)

Marketing and Utilization 

Camelina is marketed to oilseed crushing plants that use the oil to produce biodiesel; however, the industry is 

currently in its infancy. Seed companies, processors, first handlers, and researchers formed the industry‘s only trade 

organization, the North American Camelina Trade Association, in 2009. There are currently seven processing

facilities in the U.S with an unknown production capacity and two marketing companies. The marketing companies 

include Sustainable Oils and Great Plains Oil and Exploration – The Camelina Company.

Accelergy Pilot Facility (Grand Forks County, ND)

University of North Dakota; Energy & Environmental Research Center (EERC)

15 North 23rd Street, Stop 9018

Grand Forks, ND 58202-9018

 

2

Source: (USDA; NASS, 2010)

3 Camelina Yield to Gallons of Biodiesel Ratio = (54.33 Gallons of Oil Per Acre)/(598 Pounds Per Acre)

4 Reference: Estimation of Yield Probability Distributions for Montana

5 (Biodiesel Conversion)(Average Simulated Yield) = Estimated Biodiesel Production/Acre Capability
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 8

Contract # N10PC18199

Anacortes Refinery (Skagit County, WA)

10200 West March Point Rd.

Anacortes, WA 98221 

Great Plains Oil & Exploration – The Camelina Company

1 Enfield Street

Cincinnati, OH 45218

Northwest Crush Plant (Erie County, PA)

Union City, PA

Natural Selection Farms (Yakima County, WA)

6800 Emerald Road

Sunnyside, WA 98944-9708

Willamette Biomass Processors (Polk County, OR)

1055 South Pacific Highway West

Rickreall, OR 97371

Montana Specialty (Cascade County, MT)

300 3rd Ave NW

Great Falls, MT 59404

Sustainable Oils

2815 Eastlake Avenue E, Suite 300

Seattle, WA 98102

West Central Cooperative (Carroll County, IA)

406 1st St

Ralston, IA

Great Plains Oil & Exploration-The Camelina Company, Sustainable Oils, and Willamette Biomass Processors are 

the primary camelina companies. Currently, growers are contracted by processors or first handlers at a fixed price. 

An example of a production contract with Willamette Biomass Processors is presented in Appendix A and 

Sustainable Oils (first handler) in Appendix B. The market is currently driven by the demand for biodiesel jet fuel. 

Recently, Japan Airlines completed a flight on January 30, 2010 using a camelina based jet fuel (Sustainable Oils, 

2009). In addition, the Department of Navy‘s initiative discussed earlier is driving up demand.

In 2010, the U.S. Food and Drug Administration (FDA) approved camelina meal as a livestock feed at specified 

portions of the diet. Camelina can be fed to cattle at no more than 10 percent of the diet, broiler chickens at no more 

than 10 percent, laying hens at no more than 10 percent, and growing swine at no more than two percent of the diet

(Montana Deptment of Agriculture, 2010). This could potentially add economic value to the crop as the meal would 

be sold for animal feed, thereby increasing the amount paid to the grower.

GRADING STANDARDS

Currently, there are no USDA grading standards for camelina. However, grades for camelina would likely be similar 

to the canola standards found in Appendix F. The current canola grades are U.S. Numbers 1, 2, and 3, using kernel 

quality and the amount of conspicuous admixture as characteristics for evaluation. Loss of kernel quality can be due 

to damage from heat or kernels that are distinctly green. (USDA; Grain Inspection, Packers, and Stockyards 

Administration, 1992)

Camelina processors and first handlers expressed that quality standards were not a major factor for processing 

camelina seed; however, Willamette Biomass Processor‘s producer contract guidelines provide an outline of 

standards for the crop. Further, producers at listening sessions indicated that the crop is docked in price at a rate less 

than 1% for excess foreign matter. (Personal Communication; Montana Listening Session, 2011)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 9

Contract # N10PC18199

Agronomic and Botanical Characteristics 

INTRODUCTION

Camelina sativa is a member of the Brassicaceae family and originated in Central Asia. Brassicaceae plants are 

commonly referred to as crucifers or mustards, which include common crops such as cabbage and mustard. 

Camelina grows up to 90 centimeters tall and has branched smooth or hairy woody stems. The leaves are arrowshaped, sharp-pointed, five to eight centimeters long with smooth edges. It grows pods or bolls similar to flax and its 

seeds have a relatively high oil content of approximately 30-40 percent oil per seed with each pod holding eight to 

10 seeds. (Saskatchewan Ministry of Agriculture, n.d.)

PHENOLOGICAL STAGES OF GROWTH

Camelina is an annual crop that mimics the exact stages of growth of flax (Wysocki, 2011). The growth cycle,

shown in Figure 1, illustrates the basic stages of development of the plant during its 85-100 day lifespan. The cycle 

follows a seedling, vegetative growth, flower, and death pattern. The stages of growth are further broken down into 

seedling, leafing, blossom, boll, boll ripening, and maturity as illustrated in Table 8.

Figure 1: Annual Growth Cycle 

Source: (Oregon State University, n.d.)

Table 8: Growth Stages of Camelina 

Stage Description Timeline 

Seedling Emergence of basal leaves in early spring to sixth leaf 0-14 Days

Leafing Flowering shoots form and first blossom 15-45 Days

Blossom From first blossom to first boll 46-58 Days

Green Boll Bolls forming through development of seeds 59-77 Days

Boll Ripening When the bolls begin to turn color until seeds reach maturity 78-100

Maturity Seeds are mature 100

Source: Adapted from (USDA; RMA, 2010c)

Chemical Composition 

As an oilseed crop, the camelina seed is the principal portion of the plant and is used for analyzing the crop‘s 

chemical composition. Each seed can contain up to 30 to 40 percent oil, leaving 60 to 70 percent germplasm that can 

be utilized as livestock meal. (Stratton, Klienschmit, & Kenney, 2007)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 10

Contract # N10PC18199

Production Operations 

PLANTING

Camelina is planted no deeper than 1⁄4 to 1⁄2 inch by broadcasting the seed onto a clean seed bed that is then followed 

up by a harrow or rollers. Other practices include direct seeding into a minimum tillage or no-tilled seedbed via a 

seed drill. Seed to soil contact and soil compaction is vital and planting too deep will cause poor or no 

establishment. 

Planting dates are variable across regions, and are still undergoing trials to find the optimal date in each region. In 

Oregon and Washington, camelina can be planted from August through the end of April. However, most fall seeding 

does not begin planting until October, while spring seeding occurs no later than the end of April (Personal 

Communication; Oregon and Washington Listening Session, 2011). In North Central Montana, spring planting dates 

range from late March through the end of April; however, Eastern Montana ranges from late April through early 

June. (Personal Communication; Montana Listening Session, 2011). The results from a 2009 survey conducted by 

Montana NASS indicate the average planting date in 2009 occurred statewide from early April to early June

(Montana Agriculture Statistics Service, 2010).

Illustrated in Table 9 are the results of a 2009 camelina survey from Montana Agriculture Statistics Service. 

Montana NASS data indicate the following crop development timeline. Listening session feedback in West Central

Montana (Conrad) also indicates that a month difference between planting and harvesting occurs between western 

Montana and Southeastern Montana (Billings) (Personal Communication; Montana Listening Session, 2011).

Table 9: Montana’s Camelina Crop Development, 2009 

Stage Beginning End

Planted Early April Early June

Emerged Mid April Mid June

Blooming Mid June Early July

Turning Late June Late July

Harvested Mid July Late August

Source: (Montana Agriculture Statistics Service, 2010)

SITE SELECTION AND WEED CONTROL

Camelina should be planted in fields with limited weed pressure to reduce competition. The history of herbicide use 

should be reviewed, as camelina is susceptible to long-term residual herbicides such as Maverick, Glean, Ally, 

Harmony, Affinity, Agility, Express, Finesse, and Tordons. Camelina is also susceptible to IMI herbicides such as 

Beyond and Clearmax. Camelina should not be planted on fields where these herbicides are used within the plantback date on the herbicide label. A list of plant-back requirements is provided in Appendix D.

Producers in both Montana and Oregon saw better yield results when they planted camelina in fields that were 

previously fallow or growing wheat, barley, peas or lentils (Personal Communication; Montana Listening Session, 

2011) & (Personal Communication; Oregon and Washington Listening Session, 2011). Camelina does not perform 

well when grown back-to-back or when planted in fields following other Brassica crops such as rapeseed, canola, 

and brown mustard.

Throughout the vegetative growth period of camelina, weeds need to be closely monitored. A burn down of 

broadleaves and grassy weeds utilizing RoundUp (glyphosate) is recommended prior to planting to lessen weed 

competition during establishment. The herbicide Poast provides good post emergence grassy weed control; however,

nothing is labeled for post emergence broadleaf weed control in camelina. 

HARVEST

Camelina is harvested from June to August depending on weather conditions, planting dates, and the time of 

maturity. However, in the case of Oregon producers, the planting dates have little to no effect on the harvest date. 

Growers will harvest in June or July despite varying planting dates. 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 11

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Camelina needs to be harvested within a few days of maturity, as the pods shatter much easier at this stage. When 

the pods mature, they become pale brown in color and the seed shells easily from the pod. The seed moisture content 

must be less than eight percent to ensure proper storage quality. (Sustainable Oils, 2010)

ROTATION AND ISOLATION REQUIREMENTS

As camelina is a short seasoned and hearty crop, producers are able to plant camelina in several different rotation 

patterns. Camelina has shown poor performance when planted consecutively or following canola or another Brassica 

such as mustard or rapeseed, but growers have the opportunity to use camelina as a replacement of the fallow 

portion of their crop rotation system. Some producers in Montana are currently replacing their fallow with camelina 

between wheat crops in a wheat/camelina/wheat pattern. Since camelina is susceptible to herbicide residuals from 

commonly used wheat herbicides, plant back restrictions must be monitored. Plant-back restrictions for canola are 

currently the best guideline available for camelina production. 

Fertilizer and Moisture Requirements 

Camelina is touted as a low input crop due to its relatively small nutrient requirements. Sustainable Oils 

recommends that soil nitrogen should be no less than 90 pounds per acre and no than less than 32 pounds of 

Phosphorous. Growers are encouraged to apply a fertilizer blend of 20-20-0 at seeding and maintain the required 

nutrient levels by applying top-dress Nitrogen or Phosphorous as needed. (Personal Communication; Montana 

Listening Session, 2011)

Camelina is grown in semi-arid regions on dryland, as it produces a crop with little rainfall. Golden Plains Area 

Extension Service in Akron, Colorado discovered that camelina has the highest water use efficiency of all oilseed 

crops evaluated. (Lafferty, Rife, & Foster, 2009)

Varieties 

Sustainable Oils‘ plant breeding program has conducted extensive research on camelina varieties. The results from

the variety tests are listed in Table 10. Other varieties, such as Calena, Celine and Lagina, are imported to the U.S. 

from Europe. Montana State University has released its Blaine Creek and Suneson varieties to the public. (Lafferty, 

Rife, & Foster, 2009)

Table 10: Sustainable Oils Camelina Variety Characteristics 

Variety Flowering 

Days

Plant 

Height

Seed 

Filling

Grain Yield Test 

Weight

Seed 

Weight

Oil Content

Days After 

Planting

Inches Days Lbs./Acre Lbs./Bu G/1000 %

SO-10 69.79 33.30 39.25 1466.85 52.34 1.12 36.69

SO-20 70.00 31.84 37.75 1443.37 51.36 1.07 36.76

SO-30 69.04 33.07 40.25 1489.62 52.43 1.11 37.12

Celine 71.75 36.06 37.37 1302.94 51.78 0.92 36.50

CV (%) 2 6 4 14 1 7 3

LSD (0.05) 0.81 0.94 1.54 76.65 0.33 0.003 0.50

Mean 69.58 33.14 38.31 1369.40 51.84 1.01 36.54

Source: (Sustainable Oils, 2011)

Adaptation and Distribution 

There are little public data on the distribution of camelina; however, the crop is suited best for the northern United 

States and southern Canada. In most cases, it will be found growing in areas where production of canola, flax, and 

mustard are also produced such as Idaho, Montana, Minnesota, Oregon, North Dakota, South Dakota, and 

Washington. Figure 2 illustrates the states, in green, where camelina is presently able to grow according to USDA 

PLANTS. Currently, processors and first handlers contract camelina production in Montana, North Dakota, Oregon,

and Washington. While the USDA PLANTS database does not graphically illustrate that camelina is distributed in 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 12

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California, conversations with Scott Johnson from Sustainable Oils suggest ―Camelina acreage will be contracted 

beginning in 2012‖ (Johnson S. , 2011).

Figure 2: Distribution of Camelina 

Source: (USDA, 2011)

Insects and Diseases 

―Camelina is generally considered to be a disease resistant crop. It is resistant to some of the common pests and 

diseases of Brassica oilseeds. The greater resistance of camelina is attributed to the production of antimicrobial 

compounds in the roots, including two phytoanticipins and the phytoalexins camalexin and methoxycamalexin. 

Research suggests that camelina is considered to be alternaria blackspot resistant and is highly resistant to a wide 

range of blackleg isolates (Leptosphaeria maculans), which are major diseases of canola or Brassica crops.‖

(Saskatchewan Ministry of Agriculture, n.d.)

DISEASES

Downy Mildew

There have been a few reports of downy mildew in camelina in Saskatchewan, and it is common among other 

brassica crops. An infection can be localized or systemic. Symptoms on camelina include grayish-white mycelial 

growth on lower-leaf surfaces, stems, and pods. Severely infected plants may be malformed. Researchers have found 

some resistance to this disease that would allow the development of downy mildew resistant cultivars in the future.

Sclerotinia Stem Rot

Sclerotinia stem rot is caused by the fungus Sclerotinia sclerotiorum. Symptoms on camelina are similar to those on 

canola. Infection begins as a soft, watery rot on leaves or stems. Lesions develop up and down the stem from the 

point of infection, eventually girdling the stem and causing the plant to wilt and die. The disease affects many other 

crops, including sunflowers, potatoes, safflower, beans, peas, and alfalfa. These crops should only be grown once 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 13

Contract # N10PC18199

every three to four years on the same field to help reduce the disease. Agriculture and Agri-Food Canada research 

revealed that the development of stem rot resistant cultivars is feasible.

White Rust

White rust is caused by Albugo candida. It is a major disease of brassica crops worldwide. Symptoms on camelina 

include powdery white pustules containing sporangia on lower leaf surfaces and hypertrophied siliques or entire 

inflorescences (staghead) that are conspicuous later in the growing season. Stagheads are green at first, but become 

brown and brittle at maturity. They contain thick-walled oospores that may survive in soil for several years before 

germinating to produce motile zoospores that infect cotyledons and rosette leaves. So far, no resistance to white rust 

has been observed in camelina germplasm.

Clubroot

Camelina was found to be highly susceptible to clubroot. Research trials in Canada have revealed that the symptoms 

on camelina were typical to those observed on canola. To date, no resistance was found in camelina for clubroot 

disease. Therefore, this crop will not provide a viable rotation alternative in areas where clubroot is prevalent. 

Aster Yellows

Aster yellows are the most diverse and widespread phytoplasma diseases worldwide. It was quite commonly 

observed in camelina trials. Stems, leaves, and siliques of plants exhibiting aster yellows symptoms were greenishyellow or red, often with distorted inflorescences. Stunting was observed in most symptomatic plants. Small, 

flattened siliques containing small and misshapen seeds were observed on all infected plants. Small misshapen seeds 

were also observed in normal-looking siliques sampled from asymptomatic but infected plants. However, 

researchers have found some resistance in germplasm that could result in resistant cultivars.

Other diseases

Research trials have found other diseases on camelina such as damping-off and root rots caused by Rhizoctonia 

solani and Pythium debaryanum, grey mold caused by Botrytis cinerea, bacterial blight caused by Pseudomonas 

syringae and black rot caused by Xanthomonas campestris. Camelina was also found to be susceptible to viral 

diseases like turnip crinkle virus and turnip rosette virus that are transmitted by flea beetles.

In summary, camelina is highly resistant to alternaria black spot and blackleg of brassicas. It exhibits variation for 

resistance to sclerotinia stem rot, brown girdling root rot, and downy mildew, suggesting that disease resistant 

cultivars can be developed. The susceptibility of camelina to clubroot, white rust, and aster yellows disease will 

limit the adaptation of this crop in areas where these diseases are prevalent. (Saskatchewan Ministry of Agriculture, 

n.d.)

INSECTS

Few insects have been found to cause damage to camelina and the use of insect control measures is rarely reported. 

Flea beetles, cabbage seed pod weevil, and Brassica aphids may be observed on camelina, but the potential for 

damage is much less than that on canola or mustard (Saskatchewan Ministry of Agriculture, n.d.). Growers in the 

U.S. have not reported damage caused by flea beetles or other insects at this point.

REVIEW OF OTHER PROGRAMS 

Currently a few state and federal programs support or subsidize producers of camelina. Federal programs such as 

direct payments and counter-cyclical payments are unavailable for camelina producers, but some available federal 

and state programs provide payments or tax credits to eligible producers of biofuel feedstocks. Further, one private 

insurance product sold by an agency was identified for camelina. This private product insures only hail and transit 

but the agency‘s carriers are developing named-peril insurance policies that will cover frost/freeze, wind, and fire in 

addition to hail and transit coverage. Despite the existence of the hail and transit insurance product, producers 

attending the listening sessions indicated that they were either unaware of the program or did not participate in it. 

Details of available programs are discussed below.
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Contract # N10PC18199

State and Federal Programs 

BIOMASS CROP ASSISTANCE PROGRAM 

The Biomass Crop Assistance Program (BCAP) supports establishing and producing eligible crops for the 

conversion to bioenergy in project areas and in contracts on land for up to five years. Through a matching payment 

program BCAP assists agricultural and forest land owners and eligible material owners with collecting, harvesting, 

storing, and transporting eligible materials for use in qualified Biomass Conversion Facilities (BCF). These 

payments will be available at the rate of $1 for each dollar per dry ton paid by the BCF to the eligible material 

owners, limited to a maximum of $45 per dry ton and limited to a two-year payment duration. 

Camelina falls under the non-edible fats, oils, and greases derived from plants category for eligible materials along 

with kenaf. United States producers of camelina and kenaf combined have received $162,067 in BCAP payments as 

of October 20, 2010. (USDA; FSA, 2010b)

BIOENERGY PROGRAM FOR ADVANCED BIOFUELS

United States Department of Agriculture, Rural Development provides payments to producers of feedstocks for 

biofuels production under the Bioenergy Program for Advanced Biofuels. Eligible producers enter into a contract 

and are paid based on the quantity and quality of advanced biofuels production and on the net nonrenewable energy 

content of the advanced biofuels. The payment amount will depend on the number of producers participating in the 

program, the amount of advanced biofuels being produced, and the amount of funds available (National Sustainable 

Agriculture Information Service, 2010). Mandatory funding for this program has been set at $85 million in fiscal 

year 2011, $105 million in fiscal year 2010, and discretionary funding of up to $25 million each from fiscal year

2009-2012 (Crooks, 2010).

SUN GRANT RESEARCH INITIATIVE 

The Sun Grant Research Initiative authorizes the United States Department of Agriculture, Department of 

Transportation, and Department of Energy to issue grants to five regional Sun Grant Centers and one sub-center. 

Each center is tasked to coordinate bioenergy efforts in their region to ―develop, distribute, and implement biobased 

technologies, promote diversification and environmental sustainability through biobased energy and product 

technologies, promote diversification of rural areas through biobased energy, enhance efficiency of bioenergy and 

biomass research and development through collaborations among the United States Department of Agriculture, 

Department of Energy, and land grant universities.‖ The initiative is awarded $75 million in annual funding through 

2012 (Dohlman, Caswell, & Duncan, 2008). Although this program is not a direct subsidy to Camelina producers, it 

plays a vital role in the research in and development of camelina. 

MONTANA STATE HAIL INSURANCE PROGRAM

The Montana state insurance program was created to provide basic hail insurance coverage on any crop grown in 

Montana. It is directed by the Montana Board of Hail Insurance and administered by the Montana Department of 

Agriculture. The maximum coverage is $50 per acre on non-irrigated crops and $76 per acre on irrigated crops. The 

insurance program rates can be found in Appendix G. (Montana Department of Agriculture, n.d.)

OREGON INCOME TAX CREDIT FOR BIOMASS PRODUCERS AND COLLECTORS

The state of Oregon passed House Bill 2210 during November 2007, providing incentives for biomass production 

and collection for energy use. Producers or collectors of Oregon are eligible for tax credit incentives based upon the 

volume of production or collection. The credit provided under House Bill 2210 as it applies to camelina (oilseed 

crops) allows producers to claim tax credits of $0.05 per pound produced. (Oregon Department of Energy, 2007)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 15

Contract # N10PC18199

Private Products 

PRAIRIE MOUNTAIN INSURANCE COMPANY

Prairie Mountain Insurance is a full line independent agency specializing in crop and farm/ranch insurance. They 

offer a standalone hail and transit insurance product for camelina; however, agent Lewis Zanto stated ―no policies 

have been sold in the last 3 years.‖ (Personal Communication; Prairie Mountain Insurance, 2011)

Contracting Clauses 

SUSTAINABLE OILS

Sustainable Oils provides contracts with a ―shared risk‖ program bonus. The shared risk program pays a guaranteed 

dollar amount to the producer regardless of delivery, but only if an established and confirmed stand of 12 plants per 

square foot is achieved. In the instance of a loss of the crop by an ―Act of God: the farmer is guaranteed the dollar 

amount from the shared risk program (Sustainable Oils, 2011)

Saskatchewan Policy 

The Canadian province of Saskatchewan currently provides an insurance policy for camelina through the 

Saskatchewan Crop Insurance Corporation. Yield risk coverage is available at 50, 60, or 70 percent levels. The 

terms and conditions of this policy are included in Appendix E.

DATA AVAILABILITY AND PRICE METHODOLOGIES 

Yield Data 

As previously discussed in the Economic Importance section, little production data is publicly available for 

camelina. The only public yield data from an acceptable data source was published by NASS and can be found in

Table 1 and Table 2. Given the limited data, yield was simulated for the purposes of this report in the Estimation of 

Yield Probability Distributions for Montana and the Estimation of Yield Probability Distributions for

Oregon/Washington. The methodology for simulating yield data includes developing a production schedule of 

tillage, planting, fertilization, pesticide applications, and harvesting operations along with management decisions 

regarding typical dates of each operation, seeding rates, and application rates of fertilizers and pesticides. Camelina 

producers were utilized to assimilate as much information regarding production and yields as possible. The farm 

level yields obtained from a producer from the states of Washington and Montana can be found in Table 11.

Table 11: Camelina Producer Yields 

Camelina (Pounds Per Acre)

Year 1 Year 2 Year 3 Year 4

Washington 1100 1200 1135

Montana 1150 2295 1135 1378

Sources: (Personal Communication; Oregon and Washington Listening Session, 2011) & (Personal Communication; Montana 

Listening Session, 2011)

Price Data 

Currently, processors and first handlers whom contract production with producers set prices. Example contracts 

from Willamette Biomass Processors and Sustainable Oils are included in Appendix A and Appendix B, which list a 

price of $0.11 per pound and $0.15 per pound, respectively. While no price data set is publicly available, 

conversations with Mike Waring and Scott Johnson from Sustainable Oils and Tomas Endicott from Willamette 

Biomass Processors provided some historical contract prices (2011). Table 12 illustrates prices paid by Sustainable 

Oils and Willamette Biomass Processors for 2007, 2008, 2009, and 2011. Alternative pricing methods other than 

contracts currently do not exist and pricing methodologies are mostly proprietary among processors and first 

handlers; however, industry representatives indicated that prices would be set based on factors such as a floating 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 16

Contract # N10PC18199

Chicago Board of Trade soybean oil basis, processor/first handler profitability, and/or farmer crop margins (Waring, 

Johnson, & Endicott, 2011). General Manager of Sustainable Oils and President of the North American Camelina 

Trade Association, Scott Johnson, stated ―the contract price for camelina is established around September 1 and will

not change for the crop year. For insurance purposes, Sustainable Oils would have no problem telling RMA what the 

contract price is at this time.‖ (Personal Communication; Pricing Methodology, 2011)

Table 12: Camelina Contract Prices 

Camelina (Cents per Pound)

2007 2008 2009 2010 2011

Sustainable Oils $0.09 - $0.145 - $0.15

Willamette Biomass Processors - $0.11 - - $0.17

Sources: (Waring, Johnson, & Endicott, 2011)

LISTENING SESSIONS 

As per requirement of section 2.4.2.1 of the Statement of Work, the Contractor conducted listening sessions with 

camelina producers. Locations in Montana and Oregon were chosen because processors and first handlers have 

contracted with producers in these regions, allowing for a higher likelihood of producer turnout by working with the 

processors and first handlers to promote the meetings. These processors and first handlers consisted of Great PlainsThe Camelina Company, Natural Selection Farms, Inc., Sustainable Oils, Inc., and Willamette Biomass Processors, 

Inc.

Utilizing member contacts from the North American Camelina Trade Association (NACTA), the Contractor worked 

with NACTA President and general manager of Sustainable Oils; Scott Johnson and Sales Manager Tomas Endicott

from Willamette Biomass Processors, Inc., to identify appropriate regions to host listening session meetings for 

producers of camelina. Locations were selected based on feedback from each member of the organization and their 

experience with the producers they have worked with. 

Montana 

Mike Waring with Sustainable Oils, Inc. assisted the Contractor in further determining the location and date of the 

Montana meeting; as well as, notifying and encouraging all available contract producers to attend the meeting. A 

flyer was developed and emailed to Sustainable Oils contract producers and personnel from Great Plains-The 

Camelina Company. The listening session was held on Wednesday, January 20, 2011 (12:00 pm) at the Home Café 

located in Conrad, Montana. Eight participants attended and included one agent, one agronomist, three producers, 

one processing manager, one RMA risk management specialist, and one elevator operator.

LISTENING SESSION SUMMARY

The meeting location in western Montana produced very good feedback on the perception of insurance for camelina 

and the demand drivers for camelina insurance. The producers attending the listening session indicated that they 

have been growing camelina for approximately four years and have had very good yield results with less risk than 

canola. Producers in this region praised the crop as being a low input, low risk crop that yielded well and fit well in a 

rotation with other crops. Specifically, camelina was regarded as a very water efficient crop that worked well 

following or replacing summer fallow ground. One producer mentioned that because of the water use efficiency of 

camelina he was using camelina in replacing the fallow period. Instead of a rotation of spring wheat and fallow, 

camelina can replace the fallow allowing for continuous cropping of spring wheat, camelina, and spring wheat 

allowing the producer to get three crops in three years versus two crops in three years. 

The overall results of the feedback suggest that camelina is less risky than many of the current crops in these 

producers‘ rotations. Comparing camelina with relatively low yield risk crops in the area such as wheat and barley, 

camelina was determined to be much easier to grow with less risk. The crop was labeled as being very resistant to 

freeze/frost, heat and drought. More specifically, the crop was stated as being more tolerable to heat than lentils and 

canola. Perils that concern growers were those that could affect the crop just prior to harvest. Specifically, shattering 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 17

Contract # N10PC18199

of the seed pods by severe weather (hail and wind) was of concern. It was stated that shatter is more of a concern for 

camelina than that of canola, with one producer experiencing a production loss of about 70 percent due to shatter 

from wind and hail. The risk for hail and wind is greater approximately 10 days prior to harvest.

Quality and price risk were of less concern to these producers. Quality risk was not important to producers, as they 

were not docked for quality losses beyond foreign matter. Price risk was of no concern as the price was a fixed 

contract price set by the processor. Other sources of revenue risk such as prevented planting or replanting issues that 

were discussed did not seem to be a major concern.

The consensus was that camelina was a relatively low risk crop to grow that worked well in Montana. Yet all the 

attendees felt that a risk management program such as crop insurance was important to the overall success of the 

producers growing the crop. 

Oregon/Washington 

Scott Johnson with Sustainable Oils, Inc. and Tomas Endicott with the Willamette Biomass Processors Inc. assisted 

the Contractor in further determining the location and date of the Oregon/Washington meeting; as well as, notifying 

and encouraging all of their contract producers to attend the meeting. The listening session was held on Friday, 

January 22, 2011 (12:00 pm) at the Hermiston Agricultural Research and Extension Center located in Hermiston, 

Oregon. This location was selected to facilitate a meeting for producers in the Washington/Oregon/Idaho region. 

Eight participants attended and included one agent, two producers, two processing personnel, one RMA risk 

management specialist, and two extension agronomist.

LISTENING SESSION SUMMARY

Feedback from the Oregon/Washington listening session producers indicates that they very much want an insurance 

program for camelina. While, producers were very favorable towards camelina production and its characteristics for 

tolerating freeze/frost, heat, and drought, they understand that mitigating catastrophic yield risk is crucial to the 

longevity of producing the crop in this region. 

As in the case of Montana, attendees praised the crop as being a low input, low risk crop that yielded well and fit 

well in a rotation with other crops. Both producers attending the meeting were in their 4th year of camelina 

production and felt that camelina could only gain popularity among the masses if a crop insurance policy were 

available for the crop. These producers felt that while the crop was easy to grow, producers seek out the safety net of 

crop insurance for other crops to fit in a rotation. These producers suggest that a simple yield based policy would be 

perfect for camelina.

While the perception of crop insurance for camelina in this region was similar to Montana, attendee comments 

indicated that production practices are different. In Washington, 30-40 percent of the crop will be fall planted, which 

was determined to be between August and January, while central Oregon is planted more often in the spring. The 

crop in the Washington region can be broadcast seeded and then harrowed or packed using a roller/packer during 

planting or conventionally seeded with a drill. In one instance the crop was even broadcast on top of snow and was 

successfully established. Typical rotations would consist of winter wheat, camelina, spring wheat; or camelina, 

spring wheat, camelina. Regardless of spring or fall planted camelina, yields risk for camelina were determined to be 

lower compared to winter wheat, spring wheat, barley, and canola. In terms of management, camelina was 

determined to be similar to the management required for spring barley.

Pricing contracts also differed in this region compared to Montana. While Montana producers were not docked for 

oil quality parameters, producers who signed with Willamette Biomass Processors in Oregon/Washington receive a 

price reduction when the quality of oil does not meet contract requirements. Nonetheless, attendees indicated that 

price and quality were not a concern and presented very little risk. Concerns with fall planting were discussed as 

related to dry conditions in the fall presenting the potential for competition from volunteer wheat that could cause 

stand reduction. While many of the demand drivers such as yield, price, quality and other sources of revenue risk 

were determined by attendees to be low, the consensus was that a crop insurance program was needed to protect 

against catastrophic events.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 18

Contract # N10PC18199

CAMELINA RISK EVALUATION 

Introduction 

The RMA‘s Program Evaluation Tool (Diagnostic Instrument) found in the in USDA RMA Program Evaluation 

Handbook FCIC-2210 (PEH) (http://www.rma.usda.gov/FTP/Publications/directives/22000/06_22010.pdf) was 

used by the Contractor as a supplement to assist in the development of the overall recommendation for each 

dedicated energy crop. This tool creates a better understanding of the risk exposure and potential for various 

products to transfer a portion of that risk to an insurance pool. The tool also assists in gauging the demand for the 

insurance product and potential design issues that may arise. 

The instrument was applied separately to each of the four regions where listening sessions were held. These regions 

included Tennessee for switchgrass, Louisiana for energy cane, and Montana and Oregon/Washington for camelina. 

The program evaluation tool was completed from information obtained through listening sessions with producers,

insurance agents, university extension personnel, crop consultants, and conversations with RMA Regional Office

personnel, as well as the Contractor‘s independent research and analysis of the current production and market 

conditions for each crop in each region and that of comparable crops. Results of the Program Evaluation Tool are 

summarized below with the completed Diagnostic Instrument for each region included in Appendix N

Montana Program Evaluation Tool Summary 

―The Program Evaluation Tool uses a series of questions to elicit information on production processes, market 

characteristics, availability of federally facilitated insurance products, as well as eight demand signals of which five 

are ―Demand Shifter Categories‖ such as yield risk, quality risk, price risk, other sources of revenue risk, the 

sufficiency of non-insurance available to cope with risk, while three are ―Product Design Categories‖ such as 

potential and realized risk classification challenges, potential and realized moral hazard and monitoring issues, and 

other problems that may affect insurance participation. Overall assessment questions are answered for the eight 

categories using a Likert scale of 1 to 5, where higher numbers for the ―Demand Shifter‖ category indicate higher 

demand for insurance products while lower numbers suggest relatively lower demand. For the ―Product Design 

Issues‖ categories, higher numbers indicated either a lack of product design problems or a high likelihood of being 

able to address any existing product design problems, while low numbers indicate more serious product design 

problems and/or problems that cannot be easily addressed. Using the overall assessment scores from each of the 

eight diagnostic categories for each region, the results have been graphically summarized for each region. Based on 

the overall scores assigned to each of the eight diagnostic categories, assessments are made and used to work 

through a generalized decision tree framework (See Appendix I), intended to facilitate decision-making. However, 

the diagnostic instrument may be used independently of the decision tree.‖ (USDA; RMA, 2005)

The following is a summary of the completed Diagnostic Instrument for Montana. Note that the completed 

Diagnostic Tools in Appendix N provide the completed answers to the questions while the summary below is meant 

to provide summary level answers to the eight demand signal questions used for Likert scale ratings. This summary 

will be best understood by reviewing the Program Evaluation Tool and the completed Diagnostic Instrument in 

Appendix N.

MARKETING

Great Plains Oil & Exploration (Great Plains – The Camelina Company) is a renewable fuels energy company 

founded with the purpose of manufacturing and marketing biodiesel produced from camelina (Great Plains, The 

Camelina Company, 2010). Great Plains currently contracts with producers utilizing a production contract in 

Montana. Sustainable Oils, Inc., also currently contracts for camelina utilizing production contracts in Montana 

(Sustainable Oils, 2011).

RMA-FACILITATED INSURANCE PRODUCTS

There are no RMA-facilitated insurance products currently available for camelina utilized for dedicated energy in 

Montana or any other region in the United States.
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Contract # N10PC18199

YIELD RISK

Relative risk is used to adjust absolute magnitudes that vary across crops to a relative level to facilitate 

comparability of crop risk. Because NASS reported yield data for Montana camelina is only reported for four years, 

assuming that the relative yield of camelina is similar to the relative yield of canola (both brassica crops); we can 

use canola yields to assist in better quantifying the yield risk for Montana camelina compared to other crops. 

Utilizing the coefficient of variation (CV) helps to facilitate the comparison across crops with different expected 

yields. Calculated as the standard deviation divided by the mean, this allows the comparison of variation of unlike 

data series. As illustrated in Figure 3, yield risk for canola in Montana is higher than all other crops reported by 

NASS over the past ten years. The coefficient of variation for canola of 0.44 for both canola and peas suggests that 

these crops in this region may be perceived as having very high yield risk. Listening session attendees confirmed 

that these crops would be crops with the highest yield risk. Utilizing a Likert scale rating from 1 to 5 for noncatastrophic yield risk, where ―1‖ is very low relative yield risk and ―5‖ is very high relative yield risk, listening 

session participants indicated that canola, dry peas, and spring wheat were crops that would have the highest risk 

(rating of ―5‖), while camelina would have the lowest risk (rating of ―1‖). Listening session producers stated that 

camelina is very easy to grow, requires low inputs, and has very little risk and would have much less yield risk than 

canola or flax. Although listening session producers suggest that camelina has very little yield risk, the analyzed data 

suggests that camelina may more closely resemble the risk of canola and spring wheat. Utilizing this information, 

the Likert scale rating from 1 to 5 for non-catastrophic yield risk was rated at a ―3‖; thus, associating the crop with 

crops having moderate yield risk.

The risk for a catastrophic yield loss is hard to gauge for camelina but utilizing the canola cause of loss data from 

RMA, we can make a more informed estimate. Utilizing the loss cost ratio as a way of measuring severity of causes

of loss, the number of observations for the loss cost ratio above 25 percent (captures a minimum 50 percent yield 

loss based on assumption that guarantee is established at 75 percent coverage level for all policies) was recorded. 

Analysis of these observations for all Montana canola suggest that cold wet weather, cold winter, drought, excess 

moisture, freeze and frost, hail, heat, hot winds, insects, plant disease, other (snow-lightning, etc.) and wind/excess 

wind can cause losses to canola in excess of 50 percent. The observations illustrated in Figure 4 suggest that drought 

has the potential to occur 52 percent of the time over 25 years6. Several other causes of loss might occur less 

frequently at a loss greater than 50 percent for canola. Using canola as a comparable crop, these observations are 

likely to occur for camelina. As such, catastrophic yield risk for camelina results in a moderate relative yield risk 

rating of ―3‖ on the Likert scale.

 

6Where 25 years of Summary of Business Cause of Loss Data from RMA did not exist, the contractor estimated the observations over 25 years by 

taking the ratio of observations divided by years in dataset and multiplied by 25. 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 20

Contract # N10PC18199

0.08 0.08 0.09

0.19

0.24 0.25

0.29 0.29 0.29

0.44 0.44

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Hay All Alfalfa Hay

Other

Barley Wheat

All

Wheat

Winter

Oats Wheat

Duram

Wheat

Spring

Dry Peas Canola

Coefficient of Variation

Chouteau, Glacier, Pondera & Teton Counties 10-Year Crop Yield Variation

Figure 3: Chouteau, Glacier, Pondera, & Teton Counties 10 Year Crop Yield Risk Comparison 

Adapted from USDA; NASS, 2011

Figure 4: Montana Canola Cause of Loss Observations of Lost Cost Ratio in Excess of 25% 

Adapted from (USDA; RMA, 2010b) Cause of Loss Summary of Business
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 21

Contract # N10PC18199

QUALITY RISK

Camelina, to be utilized as a feedstock for dedicated energy, does not exhibit significant quality risk at the time of 

harvest and thus can be characterized as having very low quality risk. Sustainable Oils does not dock the price paid 

to a producer based on oil quality below specific parameters but does dock for excessive foreign matter (1 percent

dockage). These quality risks are insignificant in terms of insurance design, and it is appropriate to characterize 

quality risk as very low (Likert scale ―1‖).

PRICE RISK

Camelina in Montana is primarily grown under a production contract that is established before crop planting. The 

production contract pays a fixed amount per pound of camelina and sometimes includes storage, transportation, and,

in some contracts, a ―shared risk‖ program bonus. The shared risk program pays a guaranteed dollar amount to the 

producer regardless of delivery, but only if an established and confirmed stand of 12 plants per square foot is 

achieved. In the instance a grower loses the crop by an ―Act of God‖, the farmer is guaranteed the dollar amount 

from the shared risk program (Sustainable Oils, 2011). Based on the contract pricing, price risk for camelina in this 

region of Montana is determined to be rated very low (Likert scale ―1‖).

OTHER SOURCES OF REVENUE RISK

Camelina is a small seeded crop that prefers shallow planting, a firm seedbed and low competition. A planting depth 

of less than a 1⁄4 of an inch is required, so this presents challenges for getting seed in good soil moisture and good 

seed to soil contact. Therefore, establishing stands is tricky and does present some levels of risk in terms of drought 

and/or poor management. Some contracts include technical assistance for growing camelina and include shared risk 

bonuses for established and confirmed stands, yet un-established stands would not qualify for such a bonus. 

Prevented planting is much less of a concern, but the crop will not tolerate standing water. Thus, assessing all other 

sources of revenue risk other than yield, quality, and price risk, this risk is rated as low (Likert scale ―2‖).

SUFFICIENT NON-INSURANCE COPING MECHANISMS

The demand for various crop insurance products (existing and potential) is influenced by non-insurance coping 

mechanisms, such as government price and income support programs; government disaster programs; marketing 

contracts including futures and options on futures for exchange-traded commodities; crop portfolio and spatial 

diversification; risk reducing production technologies and practices; and lenders‘ attitudes, expectations, and rulesof-thumb.

Federal commodity programs tend to reduce farmer exposure to price risk and thus, revenue risk. For camelina, no 

government crop programs are available for this crop in terms of marketing loans and counter cyclical payments; 

however, the Biomass Crop Assistance Program is available. The Biomass Crop Assistance Program (BCAP) 

supports establishing and producing eligible crops for the conversion to bioenergy through project areas and through 

contracts on land of up to five years for annual and non-woody perennial crops or up to 15 years for woody 

perennial crops. Through a matching payment program BCAP assists agricultural and forest land owners and 

eligible material owners with collection, harvest, storage, and transportation of eligible material for use in qualified 

Biomass Conversion Facilities (BCF). These payments will be available to eligible material owners at the rate of $1 

for each $1 per dry ton paid by the BCF to the eligible material owners, limited to a maximum of $45 per dry ton 

and limited to a 2-year payment duration. No listening session attendees indicated that they currently utilized the 

program. (USDA; FSA, 2010b)

Other programs such as NAP (Noninsured Crop Disaster Assistance Program) are available. However, there has not 

been a history of federal disaster payments as issued by the Crop Disaster Program, which is available to producers 

for losses in excess of 35 percent at a payment rate of 65 percent for insured crops and non-insurable crops; and 60 

percent for uninsured crops. 

Production contracts often reduce a farmer‘s exposure to some, but not all, risks. In terms of price mechanisms, 

approximately 100 percent of the crop in this region is under production contract with a first handler or processor 

and is priced before harvest and usually priced prior to establishment. Under the terms of the contract the grower is 

not exposed to contract risk (grower does not have to deliver on the contract under production shortfalls), the grower 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 22

Contract # N10PC18199

is exposed to quality risk (price penalties if the product does not meet the quality characteristics specified in the 

contract), and the grower is not exposed to price risk (prices for specific quality characteristics are not specified in 

the contract). For this reason, these mechanisms may reduce demand for crop insurance. Further, if price and yield 

are significantly negatively correlated, revenue variability is reduced and, all other things equal, the demand for crop 

insurance products will be reduced. Camelina yield and price are expected to be independent of each other because 

contracted production (supply) will always be adjusted to the appropriate demand level. Thus, the producer is not 

exposed to significant price risk or volatility, which alleviates risk exposure.

Financial leverage, growth strategies, and recent events all impact a farmer‘s ability to self-insure. Most listening 

session attendees currently purchase crop insurance on other commodities and indicated that they would feel more 

comfortable with their level of risk exposure under FCIC insurance rather than self-insuring; however, currently 

they are self-insuring.

Diversifying the farm enterprise across multiple commodities (crops and/or livestock) has the potential to 

significantly reduce whole farm revenue variability. Yield shortfalls on one crop may be partially offset by high 

yields on a different crop. In addition, a carefully diversified portfolio of crop enterprises can help farmers manage 

the revenue effects of price risk when other means of managing price risk are limited. For example, if yield risk for a 

crop (or crops) is small but price risk is significant, a farmer might choose to have no yield insurance and manage 

revenue risk due to price variation through diversification. Typically, in this region, for growers of camelina, 15-25

percent of total farm revenue can be attributed to this crop, while other commodities produced include barley, 

canola, peas, hard red winter wheat, and spring wheat with winter wheat and spring wheat being more prevalent for 

those attending listening sessions. For diversification to generate whole farm revenue risk reduction, the correlation 

between the commodities must be low (negatively correlated). Commodities with highly positive correlated revenue 

streams act as if they were a single commodity and, as a result, diversification will not significantly reduce revenue 

risk and the demand for crop insurance. Because barley, canola, peas, spring wheat and winter wheat all possess the 

same growth period, their yield risk and revenue risk are deemed to be positively correlated, hence this creates a 

higher demand for crop insurance.

Farmers at the camelina listening sessions indicated that they were all full-time farmers. Part-time farmers are 

typically less likely to focus on risk management strategies, including crop insurance. Since full-time farmers 

produce camelina in this region, the demand for crop insurance products is greater. Further spatial diversification, 

like commodity diversification, reduces whole farm revenue variability if the yield correlation across farm parcels is 

low. Typically, camelina farms in this region are not spatially diversified; thus, farms are more exposed to yield risk, 

increasing the demand for insurance.

Private-sector insurance products can have a mixed impact on the demand for RMA facilitated crop insurance 

products. If the private-sector products have features that complement or require the use of underlying RMAfacilitated crop insurance, they potentially increase demand for RMA-facilitated insurance products. On the other 

hand, some products may be substitutes or partial substitutes for RMA-facilitated crop insurance. Currently there is 

one private product offered through Prairie Mountain Insurance in Montana, but the amount of sales in this region is 

unknown and listening session participants were unaware of it, which helps to increase the demand for a FCIC 

camelina crop insurance policy.

Lenders can have a substantial impact on farmers‘ use of crop insurance products. Often, the insured‘s value on 

growing crops is treated as a current asset on the balance sheet. Lenders‘ awareness, understanding of, and attitudes 

toward crop insurance have an impact on demand, particularly under circumstances where farmers are highly 

leveraged. In the case of Montana farmers, lenders favor producers purchasing insurance.

Overall, based on the availability of sufficient non-insurance coping mechanisms for producers of camelina in this 

region Montana, the expectation of demand for insurance is very high (rated a ―5‖ on Likert scale).

RISK CLASSIFICATION

Risk classification is a serious challenge in rating crop insurance products. Non-insureds and insureds have different 

perspectives on the cost of crop insurance coverage. Of course, some individuals choose not to insure because they 

utilize the non-insurance coping mechanisms discussed in the previous category. In other cases, however, the 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 23

Contract # N10PC18199

amount of insurance purchased is limited because some individuals perceive the premium rate as being ―too high.‖ 

However, it is possible that existing classification methods will result in premium rates that are appropriate (or even 

too low) for one group but too high for another group. Unfortunately, for camelina in this region, no FCIC insurance 

products exist nor have private products been purchased so classification cannot be measured. As such, no Likert 

scale measurement was provided for this category and it is assumed non-applicable in assisting the Contractor in 

following the decision tree process.

MORAL HAZARD

This category attempts to assess whether moral hazard may cause higher crop insurance indemnities. If so, the 

higher indemnities may be reflected in higher premium rates that could limit the purchase of insurance. By gauging 

the potential for ―gaming the system,‖ a quantitative measure may be used to help assess whether insuring camelina

will likely be prone to significant moral hazard problems.

A measurement of variation in yield caused by unavoidable ―acts of nature‖ or avoidable ―acts of management‖ 

suggest that the yield variation would almost exclusively be due to ―acts of nature‖ (potential for gaming is low), 

while quality variation caused by unavoidable ―acts of nature‖ or avoidable ―acts of management‖ would be 

exclusively due to ―acts of nature‖ (potential for gaming is low). Since potential for gaming both yield and quality is 

low and very low respectively, then overall the extent to which moral hazard is a concern is small (Likert scale ―4‖).

PROBLEMS AFFECTING INSURANCE PARTICIPATION

The previous categories dealt with both the potential for, and realization of problems associated with risk 

classification and moral hazard. This category focuses on other problems that may limit demand for RMA-facilitated 

crop insurance products for this crop in this region. Because no FCIC programs currently exist for camelina, no 

Likert scale measurement was provided for this category and it is assumed non-applicable in assisting the Contractor 

in following the decision tree process.

Montana Program Evaluation Tool Results 

As indicated above and illustrated in Figure 5 below, Montana camelina quality risk, price risk and other risk are 

rated very low (―1‖), very low (―1‖), and low (―2‖) respectively, while yield risk is rated as moderate (―3‖). Other 

non-insurance coping mechanisms are rated very high (low availability). By placing more weight on the yield 

component and other coping mechanisms, these demand signals indicate moderate demand for an insurance product 

and/or a moderate potential market. Product design issues such as moral hazard are assigned a rating of high (―4‖), 

indicating the extent of potential problems is small. Risk classification was not applicable at this time because no 

premium rates are available to gauge the program. Other problems affecting insurance participation were also not 

applicable because no programs exist to facilitate an answer to the questions. Nonetheless, the tool assisted in 

establishing a decision tree framework to help determine a recommended course of action for insuring camelina as a 

dedicated energy crop.

See Appendix J, to step through the decision tree process for Montana camelina. Highlighted red arrows mark the 

decision tree path. Because there is large potential market in Montana (greater than 20,000 acres), the path leads to 

the significant revenue risk node. Assuming that the relative yield risk rating of moderate justifies significant 

revenue risk, the path then evaluates the insufficient non-insurance coping mechanisms available. Since there is a 

low availability of non-insurance coping mechanisms, and there are no classification problems, there is low potential 

for moral hazard and other problems are not applicable, the decision tree suggests that a new product may be 

developed.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 24

Contract # N10PC18199

Figure 5: Montana Camelina Demand Shifter and Product Design Signals 

Oregon/Washington Program Evaluation Tool Summary 

The following is a summary of the completed Diagnostic Instrument for Oregon/Washington. Note that the 

completed Diagnostic Tools in Appendix N provide the completed answers to the questions while the summary 

below is meant to provide summary level answers to the eight demand signal questions used for Likert scale ratings. 

This summary will best be understood by reviewing the Program Evaluation Tool and the completed Diagnostic 

Instrument in Appendix N.

MARKETING

Great Plains Oil & Exploration (Great Plains – The Camelina Company) is a renewable fuels energy company 

founded with the purpose of manufacturing and marketing biodiesel produced from camelina (Great Plains, The 

Camelina Company, 2010). Great Plains currently contracts with producers utilizing a production contract in 

Oregon. Also Willamette Biomass Processors, Inc. and Natural Selection Farms, Inc. currently contracts for 

camelina utilizing production contracts in Oregon and Washington. Although Sustainable Oils does not contract 

production in Oregon, often it will purchase contracted production from Willamette Biomass Processors to help 

meet its demand (Johnson S. , 2011).

RMA-FACILITATED INSURANCE PRODUCTS

There are no RMA-facilitated insurance products currently available for camelina utilized for dedicated energy in 

Oregon or any other region in the United States.

YIELD RISK

Relative risk is used to adjust absolute magnitudes that vary across crops to a relative level to facilitate 

comparability of crop risk. Although camelina in Oregon and Washington are not reported by NASS, assuming that 

relative yield of camelina is similar to the relative yield of canola (both brassica crops); we can use canola yields to 

assist in quantifying the yield risk for Oregon‘s camelina in comparison to other crops. Canola is not reported by 

NASS in Oregon, so data from Oregon State‘s Agricultural Information Network are used for the analysis. Whitman 

County in Washington does not report canola, so we analyzed the NASS data for other crops in the region to 

illustrate the risk for canola. Whitman County was specifically selected due to the producer attending the Oregon 

listening session who grows camelina in that county.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 25

Contract # N10PC18199

Utilizing the coefficient of variation (CV) helps to facilitate the comparison across crops with different expected 

yields. Calculated as the standard deviation divided by the mean, this allows the comparison of variation of unlike 

data series. As illustrated in Figure 6, yield risk for canola in Northeast Oregon (Morrow, Umatilla, and Union 

Counties) is comparable to that of wheat, Kentucky bluegrass, and other hay over the past ten years. Alfalfa, corn, 

peppermint, potatoes, and onions appear to have less risk; however, we suspect that the majority of these crops are 

under irrigation, while camelina, canola, and wheat would be non-irrigated crops. Also, as illustrated in Figure 7, the 

other hay and spring wheat CV‘s in Whitman County, Washington, are very similar to those crops in Morrow, 

Umatilla, and Union Counties, Oregon. It is therefore possible to conclude that canola would perform similarly to

other hay and spring wheat in Whitman County. Utilizing a Likert scale rating from 1 to 5 for non-catastrophic yield 

risk where ―1‖ is very low relative yield risk and ―5‖ is very high relative yield risk, listening session participants 

indicated that barley, canola, corn for grain, winter wheat, and spring wheat were crops that would have the highest 

risk (Likert scale rating of ―5‖), while camelina would have the lowest risk (Likert scale rating of ―1‖). Listening 

session producers stated that camelina is very easy to grow, requires low inputs, and has very little risk and would 

have much less yield risk than canola or flax. Although listening session producers suggest that camelina has very 

little yield risk, the analyzed data suggests that camelina may more closely resemble the risk of canola and wheat. 

Utilizing this information, the Likert scale rating from 1 to 5 for non-catastrophic yield risk was rated at a ―3‖; thus, 

associating the crop with crops having moderate yield risk (Likert scale ―3‖).

An assessment of catastrophic yield risk results in a moderate relative yield risk rating of ―3‖ on the Likert scale. 

The risk for a catastrophic yield loss is hard to gauge for camelina but utilizing the canola cause of loss data from 

RMA, we can make a more informed estimate. Utilizing the loss cost ratio as a way of measuring severity of causes 

of loss, the number of observations for the loss cost ratio above 25 percent (captures a minimum 50 percent yield 

loss based on assumption that guarantee is established at 75 percent coverage level for all policies) was recorded. 

Analysis of these observations for all Oregon canola suggest that cold wet weather, cold winter, drought, excess 

moisture, freeze and frost, hail, heat, hot winds, and insects can cause losses to canola in excess of 50 percent or 

more. These observations are illustrated in Figure 8, and the data suggest that as much as 60 percent of the time over 

25 years7, losses for any given cause of loss greater than 50 percent have occurred for canola, and could occur for 

camelina.

 

7 Where 25 years of Summary of Business Cause of Loss Data from RMA did not exist, the contractor estimated the observations over 25 years 

by taking the ratio of observations divided by years in dataset and multiplied by 25.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 26

Contract # N10PC18199

0.05 0.06 0.07 0.07 0.07

0.14

0.17

0.19 0.19

0.22 0.22

0.00

0.05

0.10

0.15

0.20

0.25

Coefficient of Variation

Morrow, Umatilla, & Union Counties 10-Year Crop Yield Variation

Figure 6: Morrow, Union, & Umatilla Counties 10 Year Crop Yield Risk Comparison 

Adapted from OAIN, 2011

Figure 7: Whitman County, Washington 10-Year Crop Yield Risk Comparison 

Adapted from USDA; NASS, 2011
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 27

Contract # N10PC18199

2 2 2 2

4 4

6

8

10

15

0

4

8

12

16

20

Number of Observation

Oregon Canola Cause of Loss with LCR Greater than 25% 

Adjusted for 25-Year Time Period 

Figure 8: Oregon Canola Cause of Loss Observations of Lost Cost Ratio in Excess of 25% 

Adapted from USDA; RMA Cause of Loss Summary of Business, 2011

QUALITY RISK

Camelina to be utilized as a feedstock for dedicated energy does not exhibit significant quality risk at the time of 

harvest and thus can be characterized as very low in quality risk. Willamette Biomass Processors in Oregon docks

the price paid to a producer based on oil quality below specific parameters and excessive foreign matter (1 percent

dockage); however, listening session attendees indicated that the dockage and oil quality was insignificant and not 

much of a problem. Therefore, quality risks are insignificant in terms of insurance design and it is appropriate to 

characterize quality risk as low (Likert scale ―2‖).

PRICE RISK

Camelina in Oregon and Washington is primarily grown under a production contract established before crop 

planting. The production contract pays a fixed amount per pound of camelina and sometimes includes storage, 

transportation and in some contracts a ―shared risk‖ program bonus. The shared risk program pays a guaranteed 

dollar amount to the producer regardless of delivery, but only if an established and confirmed stand of 12 plants per 

square foot is achieved. In the instance a grower loses the crop by an ―Act of God‖, the farmer is guaranteed the 

dollar amount from the shared risk program (Sustainable Oils, 2011). Based on the contract pricing, price risk for 

camelina in this region of Oregon and Washington is determined to be rated low (Likert scale ―2‖).

OTHER SOURCES OF REVENUE RISK

Camelina is a small seeded crop that prefers shallow planting, a firm seedbed and low competition. A planting depth 

of less than a 1⁄4 of an inch is required, so this presents challenges for getting seed in good soil moisture and good 

seed to soil contact. Therefore, establishing stands is tricky and does present some levels or risk in terms of drought 

and/or poor management. Some contracts include technical assistance for growing camelina and include shared risk 

bonuses for established and confirmed stands, yet un-established stands would not qualify for such a bonus. 

Prevented planting is much less of a concern, but the crop will not tolerate standing water. Thus, assessing all other 

sources of revenue risk other than yield, quality and price risk, this risk is rated as low (Likert scale ―2‖).
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 28

Contract # N10PC18199

SUFFICIENT NON-INSURANCE COPING MECHANISMS

The demand for various crop insurance products (existing and potential) is influenced by non-insurance coping 

mechanisms, such as: government price and income support programs; government disaster programs; marketing 

contracts including futures and options on futures for exchange-traded commodities; crop portfolio and spatial 

diversification; risk reducing production technologies and practices; and lenders‘ attitudes, expectations, and rulesof-thumb.

Federal commodity programs tend to reduce farmer exposure to price risk and thus, revenue risk. For camelina, no 

government crop programs are available for this crop in terms of marketing loans and counter cyclical payments; 

however, the Biomass Crop Assistance Program is available. The Biomass Crop Assistance Program (BCAP) 

supports establishing and producing eligible crops for the conversion to bioenergy through project areas and through 

contracts on land of up to 5 years for annual and non-woody perennial crops or up to 15 years for woody perennial 

crops. Through a matching payment program BCAP assists agricultural and forest land owners and eligible material 

owners with collection, harvest, storage, and transportation of eligible material for use in qualified Biomass 

Conversion Facilities (BCF). These payments will be available to eligible material owners at the rate of $1 for each 

$1 per dry ton paid by the BCF to the eligible material owners, limited to a maximum of $45 per dry ton and limited 

to a 2-year payment duration. No listening session attendees indicated that they currently utilized the program.

(USDA; FSA, 2010b)

Other programs such as NAP (Noninsured Crop Disaster Assistance Program) are available. However, there has not 

been a history of federal disaster payments as issued by the Crop Disaster Program, which is available to producers 

for losses in excess of 35 percent at a payment rate of 65 percent for insured crops and non-insurable crops; and 60 

percent for uninsured crops. 

Production contracts often reduce a farmer‘s exposure to some, but not all, risks. In terms of price mechanisms, 

approximately 100 percent of the crop in this region is under production contract with a first handler or processor 

and is priced before harvest and usually priced prior to establishment. Under the terms of the contract the grower is 

not exposed to contract risk (grower does not have to deliver on the contract under production shortfalls), the grower 

is exposed to quality risk (price penalties if the product does not meet the quality characteristics specified in the 

contract), and the grower is not exposed to price risk (prices for specific quality characteristics are not specified in 

the contract). For this reason, these mechanisms may reduce demand for crop insurance. Further, if price and yield 

are significantly negatively correlated, revenue variability is reduced and, all other things equal, the demand for crop 

insurance products will be reduced. Camelina yield and price are expected to be independent of each other because 

contracted production (supply) will always be adjusted to the appropriate demand level. Thus, the producer is not 

exposed to significant price risk or volatility, which alleviates risk exposure.

Financial leverage, growth strategies, and recent events all impact a farmer‘s ability to self-insure. Most listening 

session attendees currently purchase crop insurance on other commodities and indicated that they would feel more 

comfortable with their level of risk exposure under FCIC insurance rather than self-insuring.

Diversifying the farm enterprise across multiple commodities (crops and/or livestock) has the potential to 

significantly reduce whole farm revenue variability. Yield shortfalls on one crop may be partially offset by high 

yields on a different crop. In addition, a carefully diversified portfolio of crop enterprises can help farmers manage 

the revenue effects of price risk when other means of managing price risk are limited. For example, if yield risk for a 

crop (or crops) is small but price risk is significant, a farmer might choose to have no yield insurance and manage 

revenue risk due to price variation through diversification. Typically, in this region, for growers of camelina 15-25

percent of total farm revenue can be attributed to this crop, while other commodities produced include alfalfa, other 

hay, hard red winter wheat, spring wheat, and barley, with winter wheat and spring wheat being more prevalent for 

those attending listening sessions. For diversification to generate whole farm revenue risk reduction, the correlation 

between the commodities must be low (negatively correlated). Commodities with highly positively correlated 

revenue streams act as if they were a single commodity and, as a result, diversification will not significantly reduce 

revenue risk and the demand for crop insurance. Because other hay, barley, winter wheat and spring wheat all 

possess the same growth period, their yield risk and revenue risk are deemed to be positively correlated, hence this 

creates a higher demand for crop insurance.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 29

Contract # N10PC18199

Farmers at the camelina listening sessions indicated that they were all full-time farmers. Part-time farmers are 

typically less likely to focus on risk management strategies, including crop insurance. Since full-time farmers 

produce camelina in this region, the demand for crop insurance products is greater. Further spatial diversification, 

like commodity diversification, reduces whole farm revenue variability if the yield correlation across farm parcels is 

low. Typically, camelina farms in this region are not spatially diversified; thus, farms are more exposed to yield risk, 

increasing the demand for insurance.

Private-sector insurance products can have a mixed impact on the demand for RMA facilitated crop insurance 

products. If the private-sector products have features that complement or require the use of underlying RMAfacilitated crop insurance, they potentially increase demand for RMA-facilitated insurance products. On the other 

hand, some products may be substitutes or partial substitutes for RMA-facilitated crop insurance. Currently there is 

one private product offered through Prairie Mountain Insurance in Montana, but the amount of sales in this region is 

unknown and listening session participants were unaware of it, which helps to increase the demand for a FCIC 

camelina crop insurance policy.

Lenders can have a substantial impact on farmers‘ use of crop insurance products. Often, the insured‘s value on 

growing crops is treated as a current asset on the balance sheet. Lenders‘ awareness, understanding of, and attitudes 

toward, crop insurance have an impact on demand, particularly under circumstances where farmers are highly 

leveraged. In the case of Oregon and Washington farmers, lenders favor producers purchasing insurance.

Overall, based on the availability of sufficient non-insurance coping mechanisms for producers of camelina in this 

region Oregon and Washington, the expectation of demand for insurance is very high (rated a ―5‖ on Likert scale).

RISK CLASSIFICATION

Risk classification is a serious challenge in rating crop insurance products. Non-insureds and insureds have different 

perspectives on the cost of crop insurance coverage. Of course, some individuals choose not to insure because they 

utilize the non-insurance coping mechanisms discussed in the previous category. In other cases, however, the 

amount of insurance purchased is limited because some individuals perceive the premium rate as being ―too high.‖ 

However, it is possible that existing classification methods will result in premium rates that are appropriate (or even 

too low) for one group but too high for another group. Unfortunately, for camelina in this region, no FCIC insurance 

products exist nor have private products been purchased so classification cannot be measured. As such, no Likert 

scale measurement was provided for this category and it is assumed non-applicable in assisting the Contractor in 

following the decision tree process.

MORAL HAZARD

This category attempts to assess whether moral hazard may cause higher crop insurance indemnities. If so, the 

higher indemnities may be reflected in higher premium rates that could limit the purchase of insurance. By gauging 

the potential for ―gaming the system,‖ a quantitative measure may be used to help assess whether insuring camelina

will likely be prone to significant moral hazard problems.

A measurement of variation in yield caused by unavoidable ―acts of nature‖ or avoidable ―acts of management 

―suggest that the yield variation would almost exclusively be due to ―acts of nature‖ (potential for gaming is low), 

while quality variation caused by unavoidable ―acts of nature‖ or avoidable ―acts of management‖ would be 

exclusively due to ―acts of nature‖ (potential for gaming is low). Since potential for gaming both yield and quality is 

low, then overall the extent to which moral hazard is a concern is small (Likert scale ―4‖).

PROBLEMS AFFECTING INSURANCE PARTICIPATION

The previous categories dealt with both the potential for, and realization of problems associated with risk 

classification and moral hazard. This category focuses on other problems that may limit demand for RMA-facilitated 

crop insurance products for this crop in this region. Because no FCIC programs currently exist for camelina, no 

Likert scale measurement was provided for this category and it is assumed non-applicable in assisting the Contractor 

in following the decision tree process.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 30

Contract # N10PC18199

Oregon/Washington Program Evaluation Tool Results 

As illustrated in Figure 9 below, Oregon camelina yield risk, quality risk, price risk and other risk are rated 

relatively moderate (―3‖), low (―2‖), low (―2‖) and low (―2‖) respectively, while sufficient non-insurance coping 

mechanisms are rated very high (very low availability). The quality, price and other risk demand signals suggest or 

indicate lower demand for an insurance product and/or a low potential market, yet yield risk and other coping 

mechanisms may suggest moderate to high demand. Product design issues such as moral hazard are rated a higher 

number (―4‖) indicating smaller potential for problems or an ability to address problems. Risk classification was not 

applicable at this time because no premium rates are available to gauge the program. Other problems affecting 

insurance participation were also not applicable because no programs exist to facilitate an answer to the questions. 

Nonetheless, the tool assisted in establishing a decision tree framework to help determine a recommended course of 

action for insuring camelina as a dedicated energy crop.

See Appendix K, to step through the decision tree process for Oregon/Washington camelina. Highlighted red arrows 

mark the decision tree path. Because listening sessions attendees suggest that the potential market is greater than 

10,000 acres in the next 3-5 years the path leads to the significant revenue risk node. Assuming that the relative 

yield risk rating of moderate justifies significant revenue risk, the path then evaluates the insufficient non-insurance 

coping mechanisms available. Since there is a low availability of non-insurance coping mechanisms, and there are 

no classification problems, there is low potential for moral hazard and other problems are not applicable, the 

decision tree suggests that a new product may be developed.

Figure 9: Oregon/Washington Camelina Demand Shifter and Product Design Signals 

ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR MONTANA 

Estimating yields and yield risks of relatively new crops in regions heretofore unutilized as production areas raises 

questions regarding expected yields, yield variability, and profitability. With little or no production history, USDA‘s 

Risk Management Agency is faced with uncertain production capabilities when developing crop insurance 

programs. Agronomists, soil scientists, hydrologists, engineers, and others have developed tools for evaluating yield 

possibilities. Many of these tools are incorporated into computerized crop simulation models. One such model that 

began development in the mid-80s is EPIC, the original acronym for the Erosion Productivity Impact Calculator 

model.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 31

Contract # N10PC18199

EPIC has evolved over time to include many impacts of crop production along with climatic factors on the 

environment. The model has been utilized by scientists worldwide and EPIC is now the acronym for Environmental 

Policy Integrated Climate model. One of the principal developers is Dr. J.R.Williams, Blackland Research and 

Extension Center, Texas AgriLife Research, Temple, Texas. The EPIC model has been applied at regional 

(Williams, 1995) and national scales (Thompson, Izaurralde, Rosenberg, & He, 2006) & (Izaurralde, Rosenberg, 

Brown, & Thompson, 2003).

EPIC (Potter, Atwood, Kellog, & Williams, 2004) is a daily time-step model capable of simulating a wide array of 

crop production and environmental processes including plant growth, crop yields, plant competition, and soil erosion 

as well as water, and nutrient balances. Biomass growth is related to solar radiation intercepted by the plant canopy, 

vapor pressure deficit, CO2 concentration, and other physiological stresses including water, temperature, N, P, and 

soil aeration deficits/surpluses. Similarly, root growth is affected by bulk density, temperature, and aluminum 

content. Soil carbon algorithms calculate carbon balance including losses of carbon from water and wind erosion. 

Plant growth and development is influenced by temperature during the growing season, expressed within the model 

as heat units. The quantity of heat units necessary for the crop to reach maturity varies by latitude.

Among the model‘s many features is a stochastic weather generator. Weather can be input from historical records or 

it can be estimated stochastically using precipitation, air temperature, solar radiation, wind, and relative humidity 

parameters developed from historical records. 

The model simulates growth and development of a crop each day from emergence to harvest. Initially, a crop 

production schedule is developed from producers indicating their dates (and application rates) of operations prior to 

and during the growing season for tillage, planting, pesticides, irrigations, fertilizers, and harvesting. Adding the 

rates and dates of crop production inputs and other management information facilitates the simulation of tillage, 

irrigation, and fertilizer applications. Each operation combined with climate can affect crop production; either 

through growth, erosion, soil water, or nutrients, and most times a combination of these elements. 

The primary objective of this analysis of camelina in Montana is to utilize the EPIC model, which has been adapted 

to a Windows® application, WinEPIC, to estimate an array of stochastic yields from which to develop a probability 

distribution. Thereby, yield variability (risk) can be quantified and variations in revenues inferred. Sub-objectives 

include: a) by utilizing local weather data along with producer‘s camelina yields and management data from a 

designated area in west central Montana, calibrate crop coefficients to best represent the current yields and 

production conditions, and b) to utilize the calibrated model for producing 100 years of stochastic camelina yield 

observations utilizing successive 3-year rotations of winter wheat/fallow/spring camelina. 

The methodology includes developing a production schedule of tillage, planting, fertilization, pesticide applications, 

and harvesting operations along with management decisions regarding typical dates of each operation, seeding rates, 

and application rates of fertilizers, and pesticides. Camelina producers were utilized to assimilate as much 

information regarding production and yields as possible. 

They reported that fertilization of camelina typically included 80-100 pounds/acre Nitrogen and 15-20 pounds/acre 

Phosphorus. Planting, utilizing no-tillage or direct seeding into stubble, occurred in late March through early April 

and the crop was harvested in 105 days after planting in July or August.

A local producer provided his best estimate of historical non-irrigated yields on his farm from 2007-2010. Though 

the 2008 yield was actually known to be reduced significantly by shattering and 2009 yields suffered from 

harvesting losses through the combine, his best yield estimates were 1,150, 2,295, 1,135, and 1,378 pounds/acre for 

the four years, respectively. Each year of camelina production followed a summer fallow period for that specific 

field. Thus, a rotation of minimum-till winter wheat/no-till fallow/direct-seeded spring seeded camelina was utilized 

for calibrating the EPIC model to simulate the historical camelina yields as closely as possible. Daily maximum and 

minimum temperatures and rainfall for each year for Conrad, Montana, from the National Weather Service were 

used since on-site weather records were not available.

Upon calibrating the model‘s crop physiological coefficients, simulated yields were 832, 2,380, 1,264, and 1,395 

pounds/acre for 2007-2010, respectively. The 4-year simulated average yield was 1,468 pounds/acre compared with 

the actual average yield of 1,490 pounds/acre, a difference of only 22 pounds/acre or 1.5 percent. 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 32

Contract # N10PC18199

The following graph depicts the relationship of simulated to producer yields year-by-year; thereby producing a 

regression line having a slope of 1.14 with an R-squared=0.92. A slope of 1.0 with a zero intercept, depicted by the 

dashed line, indicates a perfect relationship or perfect correlation of simulated yields to producer yields. R-squared

signifies the accuracy of the black regression line in predicting them; R-squared=1.0 being a perfect prediction. 

y = 1.1431x - 234.74

R2 = 0.9183

0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 3000

Simulated Yields (lbs/ac)

Producer yields (lbs/ac)

Figure 10: Correlation of Simulated Yields to Producer Average Yields 

Further statistical evidence that the simulated yields approximate the producer yields is illustrated by the paired ttest (Table 13). A difference of zero is being tested between the means of both sets of fresh weight yields, 1,489.5 

vs. 1,467.9 pounds/acre for the producer mean versus the simulated mean, respectively. In this case, t = 0.21 which 

is less than the critical t value of 5.84 indicates the means are not significantly different at the 1 percent level.

Table 13: Camelina Yield paired t-Test 

Item Simulated Producer

Mean 1,489.5 1,467.853

Variance 300,731 427,868.2

Observations 4 4

Hypothesized Mean Difference 0

df 3

t Stat 0.214

t Critical two-tail 5.841

Utilizing the calibrated coefficients that were developed by simulating the producer yields above, 100 years of 

stochastically generated yields were simulated for developing a probability distribution. Two changes were made 

from the calibration simulations above: 1) the original soil profile was maintained throughout the long-term 

simulation by stopping erosion and 2) daily weather parameters were generated and were based on historical records 

for 1960-2010 from the nearby weather station at Conrad, Montana. This generated daily weather coupled with the 

production practices over the long-term simulation period produced a probability distribution, Figure 11 and Figure 

12. The 100-year average grain yield was 1,241 pounds/acre. The range of yearly yields varied from a low of 249 

pounds/acre to a high of 3,590 pounds/acre.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 33

Contract # N10PC18199

200 700 1200 1700 2200 2700 3200

Figure 11: PDF of Camelina Yields in Montana 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

200 700 1200 1700 2200 2700 3200 Prob

Figure 12: CDF of Camelina Yields in Montana 

MONTANA CAMELINA INSURANCE PREMIUM CALCULATIONS 

The yield distributions for camelina grown in West Central Montana, as developed using the EPIC model, were used 

to estimate yield insurance premiums. There is only one distribution for camelina. The analysis procedure used for 

the camelina yield distribution is the following:

The summary statistics to describe the distribution are calculated. Probability density function (PDF) and 

cumulative distribution function (CDF) charts for the distribution are provided.

The distribution was tested for normality. 

Parameters for 15 parametric distributions are estimated using a maximum likelihood procedure in 

Simetar©. The parameters for an empirical distribution are estimated as well.

The parameters for the 16 distributions are used to simulate the yield distribution for 500 iterations. 

Summary statistics for the distributions are compared to the original distribution.

The CDFDEV8function in Simetar© are used to determine how closely the 16 distributions reproduced the 

original distribution. Based on the CDFDEV criteria the four distributions that most closely simulate the 

original distribution are selected for analysis.

 

8 The goodness-of-fit criteria selected for testing how closely the simulated PDFs compare to the original 

distribution is a weighted cumulative distribution comparison function (CDFDEV) available in Simetar©. The 

CDFDEV criteria is calculated as the sum of the squared distance between two distribution functions with penalty 

weights increasing in value as the observations move away from the mean. If a simulated PDF is identical to the 

original distribution, the CDFDEV value equals zero. When comparing two or more distributions as to their 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 34

Contract # N10PC18199

The best four distributions were simulated in Simetar© for 25,000 iterations to estimate ―fair insurance 

premiums‖ for eight yield coverage levels ranging from 50 percent to 85 percent of the average yield 

reported by the farmers in a focus group interview. The random number generator used for the analysis 

uses a Latin hypercube procedure and generates pseudo random numbers based on a fixed seed. 

The no load and fully loaded fair premiums are reported as $/acre values for the four assumed probability 

distributions for eight levels of possible year coverage.

The no load fair insurance premium is calculated by multiplying the probability of each state of nature 

(indemnity) by its respective probability. Using a Monte Carlo simulation approach each insurance policy 

was simulated for 25,000 yields drawn at random from assumed probability distributions. 

The loaded insurance premium is calculated using a 0.90 unit division load factor, a 0.88 FCIC disaster 

reserve factor, and a 1.30 qualitative load factor.

Camelina Yield Distribution 

The summary statistics in Table 14 report that the average yield as 1,241.12 pounds/acre and the minimum expected 

yield is 249 pounds/acre. The distribution is slightly skewed to the right given a skewness statistic of 1.19. This 

shape is confirmed in the PDF and CDF charts for the Montana camelina yield distribution (Figure 11 and Figure 

12).

Table 14: Summary Statistics for Base Yield Distribution of Camelina in Montana 

Base Yield

Mean 1,241.12

Standard Deviation 707.54

Min 249.29

Median 1,075.25

Max 3,589.97

Skewness 1.19

Kurtosis 1.25

Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 15). All five tests reported that statically the distribution is not 

distributed normal at the alpha equal 5 percent level of significance.

Table 15: Normality Test for the Yield Distribution of Camelina in Montana 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.906 0.000 Reject the Ho that the Distribution is Normally Distributed*

Anderson-Darling 2.596 0.000 Reject the Ho that the Distribution is Normally Distributed*

Cramer von Mises 0.401 0.000 Reject the Ho that the Distribution is Normally Distributed*

Kolmogorov-Smirnov 0.117 NA Consult Critical Value Table

Chi-Squared 38.400 0.005 Reject the Ho that the Distribution is Normally Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 16 were simulated using a common uniform 

standard deviate to insure the results are directly comparable. The summary statistics for these distributions and the 

goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are summarized in Table 

 

goodness-of-fit, the distribution with the smallest CDFDEV is considered ―best‖ for the purposes of this study. A 

mathematical description of the CDFDEV formula is provided in the Appendix.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 35

Contract # N10PC18199

16. The 16 distributions were simulated 500 iterations with a common uniform standard deviate and the resulting 

summary statistics are reported beside the parameters. All of the distributions reproduce the mean, as expected but a 

number of them fail to reproduce the range of the distribution. The CDFDEV criteria indicate the four best 

distributions for simulating the distribution are empirical, beta, gamma, and Weibull; with the empirical distribution 

being best.

Table 16: Univariate Parameter Estimation for the Yield Distribution of Camelina in Montana 

Distribution Parameters Parm. 1 Parm. 2 Mean Std. Dev. Min Max CDFDEV Rank

Beta α, β; A≤x≤B, α,β>0 1.068 2.397 1,279 731 251 3,424 20,000 Second

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 1075.245 526.603 1,075 745 (2,256) 4,443 356,854

Exponential μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 249.290 991.835 1,241 992 250 7,280 546,105

Gamma α, β; 0≤x<∞, α,β>0 3.387 366.466 1,241 675 97 4,461 33,191 Fourth

Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 1161.592 380.917 1,161 691 (1,512) 3,861 187,152

Log-Log μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 931.362 506.802 1,224 650 (56) 4,524 46,469

Log-Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 3.091 1069.069 1,276 914 110 10,585 1,524,252

Lognormal μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 6.969 0.567 1,248 766 181 6,316 235,573

Normal μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 1241.125 703.997 1,241 705 (957) 3,454 110,938

Pareto α, β; α≤x<∞, α,β>0 249.290 0.690 23,050 330,532 250 7,268,992 1.584195E+12

Uniform a, b ; a≤x≤b 249.290 3589.970 1,920 965 252 3,587 485,925

Weibull α, β; 0≤x<∞, α,β>0 1.888 878698.998 1,248 688 34 3,967 22,665 Third

Geometric p ; x=1,2,...; 0≤p≤1 0.001 1,242 1,241 2 8,798 1,281,170

Poisson λ ; x=0,1,...; 0≤λ<∞ 1240.590 1,240 35 1,130 1,351 963,982

Negative Binomial s, p ; x=1,2,...; 0≤p≤1 3.000 0.002 1,265 674 3 3,494 43,320

Empirical Si, F(x) 1,229 670 249 3,567 1,808 Best

MLEs Statisitcs Goodness of Fit

INSURANCE PREMIUMS FOR MONTANA CAMELINA

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 1,241 pounds/acre (APH) assuming a price 

guarantee of $0.15/pound. The eight policies are defined in terms of fraction from the APH and are 0.50, 0.55, 0.60, 

0.65, 0.70, 0.75, 0.80, and 0.85. The APH yield was assumed to equal the average for the base yield distributions 

from the EPIC runs.

The calculated insurance premiums are reported in Table 17. Based on the assumption that the yield for camelina is 

distributed empirically, the no load premiums are less than $10.50/acre for policies that insure 50 percent-65 percent

of the APH. The no load premium at 70 percent APH is $13.56/acre. It increases rapidly thereafter with a 

$16.85/acre premium for 75 percent APH coverage, $20.58/acre for 80 percent APH coverage, and reaches 

$24.84/acre for 85 percent APH coverage.

The calculated insurance premiums for the other three probability distribution assumptions are generally higher than 

those for the empirical distribution. For example, for the 85 percent APH policy, the Weibull distribution indicates a 

$26.95/acre premium, the beta distribution has a $28.36/acre premium, and the gamma distribution has a 

$24.97/acre premium. 

The difference in premiums for each yield insurance policy differs across probability distributions due to the weight 

the distribution places on the insured range of the yield distribution. This relationship can be seen in Figure 13, a 

PDF of the original yield distribution and the four selected distributions. The beta distribution is associated with the 

highest premiums for the 70 percent-85 percent APH policies because it has more weight in the higher yield values 

over the range of 250 to 500 pounds/acre.

The fully loaded premium for the 85 percent APH coverage ranges from $40.77/acre for the empirical distribution to 

$46.54/acre for the beta distribution (Table 17). The fully loaded premium was calculated by dividing the no load 

fair premium by 0.90 (the unit division load factor) and then dividing that result by 0.88 (the FCIC disaster reserve

factor) and multiplying by 1.3 (the qualitative load factor). The qualitative load factor of 1.3 is used to adjust for the 

lack of risk on the regression equations for physical relationships and production functions in the EPIC model. 

EPIC‘s only risk component is from the weather variables so it lacks the risk normally associated with simulating a 

regression equation used to predict production based on the values for the independent variables. 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 36

Contract # N10PC18199

Table 17: Yield Insurance Premiums for Camelina in Montana; Assuming Alternative Yield Coverage Levels and 

Yield Distributions, an APH Yield of 1,241 Pounds Per Acre, and Guaranteed Price of $0.15 Per Pound 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 93.08 102.39 111.70 121.01 130.32 139.63 148.93 158.24

No Load Fair Premium ($/acre)

Empirical 3.43 5.30 7.73 10.48 13.56 16.85 20.58 24.84

Beta 6.11 8.33 10.88 13.76 16.95 20.45 24.26 28.36

Weibull 6.46 8.40 10.66 13.24 16.16 19.42 23.01 26.95

Gamma 4.62 6.38 8.51 11.02 13.92 17.22 20.90 24.97

Fully Loaded Premium ($/acre)

Empirical 5.63 8.70 12.69 17.20 22.25 27.66 33.79 40.77

Beta 10.02 13.67 17.86 22.58 27.82 33.57 39.82 46.54

Weibull 10.60 13.79 17.50 21.74 26.53 31.87 37.78 44.24

Gamma 7.58 10.48 13.97 18.09 22.85 28.26 34.30 40.98

Loss Cost (%)

Empirical 3.68% 5.18% 6.92% 8.66% 10.40% 12.07% 13.82% 15.70%

Beta 6.56% 8.13% 9.74% 11.37% 13.01% 14.65% 16.29% 17.92%

Weibull 6.94% 8.20% 9.54% 10.94% 12.40% 13.91% 15.45% 17.03%

Gamma 4.96% 6.23% 7.62% 9.11% 10.68% 12.33% 14.03% 15.78%

Fully Loaded Base Premium (%)

Empirical 6.0% 8.5% 11.4% 14.2% 17.1% 19.8% 22.7% 25.8%

Beta 10.8% 13.3% 16.0% 18.7% 21.3% 24.0% 26.7% 29.4%

Weibull 11.4% 13.5% 15.7% 18.0% 20.4% 22.8% 25.4% 28.0%

Gamma 8.1% 10.2% 12.5% 15.0% 17.5% 20.2% 23.0% 25.9%

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Base Yield Empirical Beta Weibull Gamma

Figure 13: PDF of Alternative Distributions of Camelina Yields in Montana 

The loss cost ratios for each of the eight yield insurance policies were calculated as the ratio of the expected 

indemnity or loss and the liability. The loss cost ratio for the 85 percent APH policy ranges from 15.7 percent to 

17.92 percent based on the distribution assumed (Table 17). 

Fully Loaded Base Premium Rate

As illustrated in Table 18, the calculated premiums for camelina are compared to those from non-irrigated spring 

canola in Teton county Montana. RMA‘s Cost Estimator for 2011 was utilized to determine the estimated liability 

and total premium for canola. Parameters used for the canola estimate included an APH equivalent to the reference 

yield, 100 percent price election and a basic unit. The base rate for each crop is illustrated in the table and is 

calculated by dividing the total premium by the liability for each coverage level. The results demonstrate that 

estimated premiums of camelina at the higher coverage levels track very close to those of canola. Specifically for the 

empirical distribution the difference in the estimated base rates at the 85 percent coverage level are 20.18 percent 

lower for camelina. At the 85 percent coverage level, the base rate for 2011canola is 32.28% while the empirical 

base rate estimate for camelina is 25.76%. While the exact canola rating methodology is unknown, these results 

demonstrate that the potential may exist to utilize canola data as a proxy for camelina rating, and perhaps the EPIC 

model.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 37

Contract # N10PC18199

Table 18: Comparison of Camelina Premium Estimates to Canola Premium Estimates 

Canola 0015 85% 80% 75% 70% 65% 60% 55% 50%

Liability Amount $158.00 $149.00 $139.00 $130.00 $121.00 $112.00 $102.00 $93.00 

Total Premium Amount $51.00 $44.00 $37.00 $32.00 $27.00 $23.00 $20.00 $17.00 

Calculated Base Premium Rate 32.28% 29.53% 26.62% 24.62% 22.31% 20.54% 19.61% 18.28%

Camelina 85% 80% 75% 70% 65% 60% 55% 50%

Liability Amount $158.24 $148.93 $139.63 $130.32 $121.01 $111.70 $102.39 $93.08 

Total Premium Amount:

Empirical $40.77 $33.79 $27.66 $22.25 $17.20 $12.69 $8.70 $5.63 

Beta $46.54 $39.82 $33.57 $17.82 $22.58 $17.86 $13.67 $10.02 

Weibull $44.24 $37.78 $31.87 $26.53 $21.74 $17.50 $13.79 $10.60 

Gamma $40.98 $34.30 $28.26 $22.85 $18.09 $13.97 $10.48 $7.58 

Calculated Base Premium Rate:

Empirical 25.76% 22.69% 19.81% 17.07% 14.21% 11.36% 8.50% 6.05%

Beta 29.41% 26.74% 24.04% 13.67% 18.66% 15.99% 13.35% 10.76%

Weibull 27.96% 25.37% 22.82% 20.36% 17.97% 15.67% 13.47% 11.39%

Gamma 25.90% 23.03% 20.24% 17.53% 14.95% 12.51% 10.24% 8.14%

% Difference from Proxy Crop:

Empirical -20.18% -23.17% -25.58% -30.64% -36.30% -44.68% -56.67% -66.91%

Beta -8.88% -9.46% -9.68% -44.45% -16.38% -22.14% -31.91% -41.11%

Weibull -13.39% -14.10% -14.25% -17.30% -19.49% -23.71% -31.31% -37.70%

Gamma -19.77% -22.01% -23.97% -28.77% -33.01% -39.10% -47.80% -55.45%

Non-Irrigated Spring Canola: Montana, Teton County Yield Protection

Parameters: Non-Irrigated APH 713, Reference Yield 713, Basic Unit, Price Election 100% @ $0.299

Non-Irrigated Spring Camelina: Montana, Teton County

Parameters: Non-Irrigated APH 1241, Basic Unit, Price Election 100% @$ 0.150

SUMMARY FOR MONTANA CAMELINA YIELD DISTRIBUTION

The yield distribution has a mean of 1,241 pounds/acre and a range of 249 pounds/acre to 3,589 pounds/acre. The 

yield distribution is not normally distributed. Among 16 analyzed distributions, four of the best yield distributions 

were utilized to estimate premiums. These four distributions facilitated comparisons of simulation results for eight 

possible yield insurance policies. These estimates indicate that fully loaded premium rates for camelina would be 

less than those of canola and provide further support for gauging the potential yield risk and demand for a camelina 

crop insurance program.

The biggest problem with insuring camelina production is the lack of information to calculate the farmer‘s APH or 

establish transitional yields. There does not appear to be sufficient yield history for the farmer to calculate the APH

or establish actuarially sound premium rates. Results from the EPIC model suggest that EPIC may facilitate 

development of yield data until sufficient results across years and fields are accumulated to develop a more 

appropriate estimate of the APH and its distribution.

ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR 

OREGON/WASHINGTON 

The primary objective of this analysis of camelina in Washington is to utilize the EPIC model, to estimate an array 

of stochastic yields from which to develop a probability distribution. Thereby, yield variability (risk) can be 

quantified and variations in revenues inferred. Sub-objectives include: a) by utilizing local weather data along with 

producer‘s camelina yields and management data from a designated area in southeast Washington, calibrate crop 

coefficients to best represent the current yields and production conditions, and b) to utilize the calibrated model for 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 38

Contract # N10PC18199

producing 100 years of stochastic camelina yield observations utilizing successive 2-year rotations of both spring 

wheat/spring camelina and spring wheat/winter camelina. 

The methodology includes developing a production schedule of tillage, planting, fertilization, pesticide applications, 

and harvesting operations along with management decisions regarding typical dates of each operation, seeding rates, 

and application rates of fertilizers and pesticides. Camelina producers were utilized to assimilate as much 

information regarding production and yields as possible. 

They reported that fertilization of camelina typically included 30 pounds/acre N since about 80 pounds/acre N was 

obtained from wheat crop residue. Planting, utilizing no-tillage or direct seeding into stubble, occurs in the fall and 

in the springtime with harvest in July. 

A local producer near LaCrosse, Washington, provided his best estimate of historical yields on his farm from 2008-

2010. His yield estimates were 1,100, 1,200, and 1,135 pounds/acre for the three years, respectively. The 2008 and 

2010 crops were seeded in early and later springtime months, respectively, while the 2009 crop was seeded in 

November 2008. Thus, a 4-year rotation of minimum-till spring wheat/direct-seeded spring camelina/minimum-till 

spring wheat/direct seeded winter camelina was utilized for calibrating the EPIC model to simulate the historical 

camelina yields as closely as possible. Daily maximum and minimum temperatures and rainfall for each year for 

LaCrosse, Washington from the National Weather Service were used since on-site weather records were not 

available. 

Upon calibrating the model‘s crop physiological coefficients, simulated yields were 1,107, 1,201, and 1,135 

pounds/acre for 2008-2010, respectively. The 3-year simulated average yield was 1,148 pounds/acre compared with 

the actual average of 1,145 pounds/acre, a difference of only 3 pounds/acre. 

Figure 14 depicts the relationship of simulated to producer yields year-by-year; thereby producing a regression line 

having a slope of 0.95 with an R-squared=0.997. A slope of 1.0 with a zero intercept, depicted by the dashed line, 

indicates a perfect relationship or perfect correlation of simulated yields to producer yields. R-squared signifies the 

accuracy of the black regression line in predicting them: R-squared=1.0 being a perfect prediction. 

y = 0.9502x + 59.504

R2 = 0.9968

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000 1200 1400

Simulated Yields (lbs/ac)

Producer Yields (lbs/ac)

Figure 14: Correlation of Simulated Camelina Yields to Producer Average Yields 

Further statistical evidence that the simulated yields approximate the producer yields is illustrated by the paired ttest in Table 19. A difference of zero is being tested between the means of both sets of fresh weight yields, 1,145 vs. 

1,147.5 pounds/acre for the producer mean versus the simulated mean, respectively. In this case, t = 1.65 which is 

less than the critical t value of 5.84 indicates the means are not significantly different at the 1 percent level.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 39

Contract # N10PC18199

 Table 19: Oregon/Washington Camelina Paired t-Test 

Item Simulated Producer

Mean 1,147.51 1,145

Variance 1,554.995 1,716.667

Observations 4 4

Hypothesized Mean Difference 0

df 3

t Stat 1.653

t Critical two-tail 5.841

Utilizing the calibrated coefficients for both spring and winter camelina that were developed by simulating the 

producer yields above, 100 years each of stochastically generated successive yearly yields were simulated in four 

sets of 4-year rotations identical to those indicated by the producer for developing probability distributions. Two 

changes were made from the calibration simulations above: 1) the original soil profile was maintained throughout 

the long-term simulation by stopping erosion and 2) daily weather parameters were generated and were based on 

historical records for 1960-2010 from the nearby weather station at LaCrosse, Washington. This generated daily 

weather coupled with the production practices over the long-term simulation period produced two probability 

distributions for spring and winter camelina, Figure 15 and Figure 17

In the case of spring-seeded camelina, the 100-year average grain yield was 1,311 pounds/acre. The range of yearly 

yields varied from a low of 536 pounds/acre to a high of 2,908 pounds/acre. These were generally higher than fall–

seeded camelina, which averaged 983 pounds/acre with a range of 289 to 1,418 pounds/acre for the 100-year 

simulation. 

700 1200 1700 2200 2700

Figure 15: PDF of Spring Camelina Yields in Oregon/Washington (pounds/acre) 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

700 1200 1700 2200 2700 Prob

Figure 16: CDF of Spring Camelina Yields in Oregon/Washington (pounds/acre) 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 40

Contract # N10PC18199

300 500 700 900 1100 1300 1500

Figure 17: PDF of Winter Camelina Yields in Oregon/Washington (pounds/acre) 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

300 500 700 900 1100 1300 1500 Prob

Figure 18: CDF of Winter Camelina Yields in Oregon/Washington (pounds/acre) 

SPRING CAMELINA INSURANCE PREMIUM CALCULATIONS 

The yield distributions for camelina grown in southeastern Washington, as developed using the EPIC model, were 

used to estimate yield insurance premiums. There are two yield distributions for camelina: spring planted and winter

planted. The analysis procedure used for both camelina yield distributions is the following:

The summary statistics to describe the distribution are calculated. Probability density function (PDF) and cumulative 

distribution function (CDF) charts for the distribution are provided.

The distribution was tested for normality. 

Parameters for 15 parametric distributions are estimated using a maximum likelihood procedure in 

Simetar©. The parameters for an empirical distribution are estimated as well.

The parameters for the 16 distributions are used to simulate the yield distribution for 500 iterations. 

Summary statistics for the distributions are compared to the original distribution.

The CDFDEV function in Simetar© are used to determine how closely the 16 distributions reproduced the 

original distribution. Based on the CDFDEV criteria the four distributions that most closely simulate the 

original distribution are selected for analysis.

The best four distributions were simulated in Simetar© for 25,000 iterations to estimate ―fair insurance 

premiums‖ for eight yield coverage levels ranging from 50 percent to 85 percent of the average yield 

reported by the farmers in a focus group interview. The random number generator used for the analysis 

uses a Latin hypercube procedure and generates pseudo random numbers based on a fixed seed. 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 41

Contract # N10PC18199

The no load and fully loaded fair premiums are reported as $/acre values for the four assumed probability 

distributions for eight levels of possible coverage.

The no load fair insurance premium is calculated by multiplying the probability of each state of nature 

(indemnity) by its respective probability. Using a Monte Carlo simulation approach each insurance policy 

was simulated for 25,000 yields drawn at random from assumed probability distributions. 

The loaded insurance premium is calculated using a 0.90 unit division load factor, a 0.88 FCIC disaster 

reserve factor, and a 1.30 qualitative load factor.

Each of the EPIC distributions is presented separately because they are completely different distributions.

Spring Camelina Yield Distribution 

The summary statistics in Table 20 report that the average yield for spring planted camelina is 1,311 pounds/acre, 

the minimum expected yield is 536 pounds/acre, and the maximum yield is 2,908 pounds/acre. The distribution is 

skewed to the right given a skewness statistic of 1.47. This shape is confirmed in the PDF and CDF charts for the 

spring planted yield distribution (Figure 15 and Figure 16). 

Table 20: Summary Statistics for Base Yield Distribution of Spring Camelina in Oregon/Washington 

Base Yield

Mean 1,311.36

Standard Deviation 424.26

Min 536.41

Median 1,188.25

Max 2,908.06

Skewness 1.47

Kurtosis 2.77

Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 21). All five tests reported that statically the distribution is not 

distributed normal at the alpha equal 5 percent level of significance.

Table 21: Normality Test for the Yield Distribution of Spring Camelina in Oregon/Washington 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.883 0.000 Reject the Ho that the Distribution is Normally Distributed*

Anderson-Darling 3.481 0.000 Reject the Ho that the Distribution is Normally Distributed*

Cramer von Mises 0.592 0.000 Reject the Ho that the Distribution is Normally Distributed*

Kolmogorov-Smirnov 0.143 NA Consult Critical Value Table

Chi-Squared 42.400 0.002 Reject the Ho that the Distribution is Normally Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 22 were simulated using a common uniform 

standard deviate to insure the results are directly comparable. The summary statistics for these distributions and the 

goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are summarized in Table 

22. The 16 distributions were simulated 500 iterations with a common uniform standard deviate and the resulting 

summary statistics are reported beside the parameters. All of the distributions reproduce the mean as expected, but a 

number of them fail to reproduce the range of the distribution. The CDFDEV criteria indicate that the four best 

distributions for simulating the distribution are empirical, lognormal, log-log, and gamma. The empirical distribution 

as measured by the CDFDEV has the best fit.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 42

Contract # N10PC18199

Table 22: Univariate Parameter Estimation for the Yield Distribution of Spring Camelina in Oregon/Washington 

Distribution Parameters Parm. 1 Parm. 2 Mean Std. Dev. Min Max CDFDEV Rank

Beta α, β; A≤x≤B, α,β>0 1.757 3.425 1,340.50 452.17 551.68 2,692.96 21,604.6

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 1188.250 293.852 1,188.14 415.75 (670.53) 3,067.52 105,755.6

Exponential μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 536.410 774.952 1,311.44 774.73 537.10 6,029.63 493,263.9

Gamma α, β; 0≤x<∞, α,β>0 11.221 116.870 1,311.34 391.93 416.27 2,892.98 16,186.6 Fourth

Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 1260.260 216.626 1,260.18 393.23 (259.98) 2,795.63 58,663.1

Log-Log μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 1129.993 308.356 1,307.97 395.64 529.14 3,315.63 13,295.3 Third

Log-Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 6.240 1230.916 1,284.30 391.49 399.77 3,832.72 39,146.5

Lognormal μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 7.134 0.295 1,308.99 394.56 499.28 3,165.52 12,705.1 Second

Normal μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 1311.362 422.129 1,311.30 422.75 (6.96) 2,638.33 42,396.0

Pareto α, β; α≤x<∞, α,β>0 536.410 1.178 2,605.55 10,991.41 536.82 219,889.89 1,432,621,871

Uniform a, b ; a≤x≤b 536.410 2908.060 1,722.20 685.38 538.53 2,906.08 277,728.8

Weibull α, β; 0≤x<∞, α,β>0 3.102 6571137183 1,306.36 461.42 152.09 2,746.27 39,759.4

Empirical S, F(x) 1,304.74 394.18 546.96 2,894.89 1,383.1 First

Geometric p ; x=1,2,...; 0≤p≤1 0.001 1,312.06 1,311.06 2.00 9,297.00 2,520,873.3

Poisson λ ; x=0,1,...; 0≤λ<∞ 1310.930 1,309.99 36.26 1,196.93 1,423.82 344,982.4

Negative Binomial s, p ; x=1,2,...; 0≤p≤1 11.000 0.008 1,310.38 397.51 71.00 2,558.00 41,202.8

MLEs Statisitcs Goodness of Fit

INSURANCE PREMIUMS FOR SPRING CAMELINA

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 1,311 pounds/acre (APH) assuming a price 

guarantee of $0.15/pound. The eight policies are defined in terms of fraction from the APH and are: 0.50, 0.55, 0.60, 

0.65, 0.70, 0.75, 0.80, and 0.85. APH yield was assumed to equal the average for the base yield distributions from 

the EPIC runs.

The calculated insurance premiums are reported in Table 23. Based on the assumption that the spring planted yield 

for camelina is distributed empirically, the no load premiums are less than $1.00/acre for policies that insure 50

percent - 65 percent of the APH. The no load premium at 70 percent of the APH is $1.33/acre. It increases rapidly 

thereafter with a $2.77/acre premium for 75 percent APH coverage, $4.82/acre for 80 percent APH coverage, and 

reaches $7.63/acre for 85 percent APH coverage.

The calculated insurance premiums for the other three probability distribution assumptions are generally higher than 

those for the empirical distribution. For example, for the 85 percent APH policy, the lognormal distribution indicates 

a $12.66/acre premium, the gamma distribution has a $9.78/acre premium, and the log-log distribution has a 

$9.32/acre premium. 

The difference in premiums for each yield insurance policy differs across probability distributions due to the weight 

the distribution places on the insured range of the yield distribution. This relationship can be seen in Figure 19, a 

PDF of the original yield distribution and the four selected distributions. The lognormal distribution is associated 

with the highest premiums for all eight APH policies because it has more weight (the PDF line is higher) for yields 

below the mean.

The fully loaded premium for the 85 percent APH coverage ranges from $12.52/acre for the empirical distribution to 

$20.77/acre for the lognormal distribution (Table 23). The fully loaded premium was calculated by dividing the no 

load fair premium by 0.90 (the unit division load factor) and then dividing that result by 0.88 (the FCIC disaster 

reserve factor) and multiplying by 1.3 (the qualitative load factor). The qualitative load factor of 1.3 is used to adjust 

for the lack of risk on the regression equations for physical relationships and production functions in the EPIC 

model. EPIC‘s only risk component is from the weather variables so it lacks the risk normally associated with 

simulating a regression equation used to predict production based on the values for the independent variables. 

The loss cost ratios for each of the eight yield insurance policies were calculated as the ratio of the expected 

indemnity or loss and the liability. The loss cost ratio for the 85 percent APH policy ranges from 4.56 percent to 

7.57 percent based on the distribution assumed (Table 23). 
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Table 23: Yield Insurance Premiums for Spring Camelina in Oregon/Washington; Assuming Alternative Yield 

Coverage Levels and Yield Distributions, an APH Yield of 1,311 Pounds Per Acre, and Guaranteed Price of 

$0.15 Per Pound 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 98.35 108.19 118.02 127.86 137.69 147.53 157.36 167.20

No Load Fair Premium ($/acre)

Empirical 0.09 0.21 0.38 0.61 1.33 2.77 4.82 7.63

Lognormal 0.21 0.66 1.49 2.73 4.44 6.66 9.39 12.66

Log-Log 0.08 0.23 0.57 1.21 2.30 3.95 6.27 9.32

Gamma 0.13 0.34 0.76 1.50 2.68 4.39 6.74 9.78

Fully Loaded Premium ($/acre)

Empirical 0.14 0.34 0.62 1.00 2.18 4.55 7.91 12.52

Lognormal 0.34 1.09 2.44 4.48 7.30 10.93 15.42 20.77

Log-Log 0.13 0.38 0.94 1.99 3.77 6.48 10.28 15.30

Gamma 0.21 0.56 1.25 2.46 4.39 7.21 11.06 16.05

Loss Cost

Empirical 0.09% 0.19% 0.32% 0.47% 0.96% 1.88% 3.06% 4.56%

Lognormal 0.21% 0.61% 1.26% 2.14% 3.23% 4.51% 5.97% 7.57%

Log-Log 0.08% 0.22% 0.48% 0.95% 1.67% 2.67% 3.98% 5.58%

Gamma 0.13% 0.31% 0.65% 1.17% 1.94% 2.98% 4.28% 5.85%

Fully Loaded Base Premium (%)

Empirical 0.1% 0.3% 0.5% 0.8% 1.6% 3.1% 5.0% 7.5%

Lognormal 0.3% 1.0% 2.1% 3.5% 5.3% 7.4% 9.8% 12.4%

Log-Log 0.1% 0.4% 0.8% 1.6% 2.7% 4.4% 6.5% 9.2%

Gamma 0.2% 0.5% 1.1% 1.9% 3.2% 4.9% 7.0% 9.6%

400 900 1400 1900 2400 2900 3400

Base Yield Empirical Lognormal Log-Log Gamma

Figure 19: PDF of Alternative Distributions of Spring Camelina Yields in Oregon/Washington 

Fully Loaded Base Premium Rate

As illustrated in Table 24, the calculated premiums for spring planted camelina are compared to those from nonirrigated spring canola in Whitman county Washington. RMA‘s Cost Estimator for 2011 was utilized to determine 

the estimated liability and total premium for canola. Parameters used for the canola estimate included an APH 

equivalent to the reference yield, 100 percent price election and a basic unit. The base rate for each crop is illustrated 

in the table and is calculated by dividing the total premium by the liability for each coverage level. The results 

demonstrate that estimated premiums for the lognormal distribution of camelina at the higher coverage levels track 

very close to those of canola. Specifically, for the lognormal distribution, the difference in the estimated base rates at 

the 85 percent coverage level are 17.53 percent lower for camelina than canola. At the 85 percent coverage level, the 

premium rate for 2011canola is 15.06 percent while the lognormal base rate for camelina is 12.42 percent. The 

difference in spring planted camelina premium rates for the empirical distribution is 50.29 percent less than canola 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 44

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at the 85 percent coverage level. Further, the larger percent difference in the base rates for Washington versus 

Montana is assumed to be attributed to a smaller variation in yields for the base data in Washington. In regards to 

the lognormal distribution, these results suggest that the EPIC model and a proxy crop such as canola may prove 

useful in establishing rates for camelina as more data becomes available.

Table 24: Comparison of Camelina Premium Estimates to Canola Premium Estimates for Washington 

Canola 0015 85% 80% 75% 70% 65% 60% 55% 50%

Liability Amount $239.00 $225.00 $211.00 $197.00 $183.00 $169.00 $155.00 $141.00 

Total Premium Amount $36.00 $31.00 $26.00 $21.00 $16.00 $14.00 $11.00 $9.00 

Calculated Base Premium Rate 15.06% 13.78% 12.32% 10.66% 8.74% 8.28% 7.10% 6.38%

Camelina 85% 80% 75% 70% 65% 60% 55% 50%

Liability Amount $167.20 $157.36 $147.53 $137.69 $127.86 $118.02 $108.19 $98.35 

Total Premium Amount:

Empirical $12.52 $7.91 $4.55 $2.18 $1.00 $0.62 $0.34 $0.14 

Lognormal $20.77 $15.42 $10.93 $7.30 $4.48 $2.44 $1.09 $0.34 

Log-Log $15.30 $10.28 $6.48 $3.77 $1.99 $0.94 $0.38 $0.13 

Gamma $16.05 $11.06 $7.21 $4.39 $2.46 $1.25 $0.56 $0.21 

Calculated Base Premium Rate:

Empirical 7.49% 5.03% 3.08% 1.58% 0.78% 0.53% 0.31% 0.14%

Lognormal 12.42% 9.80% 7.41% 5.30% 3.50% 2.07% 1.01% 0.35%

Log-Log 9.15% 6.53% 4.39% 2.74% 1.56% 0.80% 0.35% 0.13%

Gamma 9.60% 7.03% 4.89% 3.19% 1.92% 1.06% 0.52% 0.21%

% Difference from Proxy Crop:

Empirical -50.29% -63.52% -74.97% -85.15% -91.05% -93.66% -95.57% -97.77%

Lognormal -17.53% -28.88% -39.88% -50.26% -59.92% -75.04% -85.80% -94.58%

Log-Log -39.25% -52.58% -64.35% -74.31% -82.20% -90.39% -95.05% -97.93%

Gamma -36.27% -48.99% -60.34% -70.09% -77.99% -87.21% -92.71% -96.65%

Non-Irrigated Spring Canola: Washington, Whitman County Yield Protection

Non-Irrigated Spring Camelina: Washington, Whitman County

Parameters: Non-Irrigated APH 1071, Reference Yield 1071, Basic Unit, Price Election 100% @ $0.263

Parameters: Non-Irrigated APH 1311, Basic Unit, Price Election 100% @$ 0.150

SUMMARY FOR SPRING CAMELINA YIELD DISTRIBUTION

The yield distribution has a mean of 1,311 pounds/acre and a range of 536 pounds/acre to 2,908 pounds/acre. 

Statistically the yield distribution is not normally distributed. Among 16 analyzed distributions, four of the best yield 

distributions were utilized to estimate premiums. These four distributions facilitated comparisons of simulation 

results for eight possible yield insurance policies. These estimates indicate that fully loaded premium rates for 

camelina would be less than those of canola and provide further support for gauging the potential yield risk and 

demand for a camelina crop insurance program.

The biggest problem with insuring spring planted camelina production is the lack of information to calculate the 

farmer‘s APH. There is no long-term yield history for the farmers to calculate the APH. Using the average yield 

from the EPIC model will have to suffice until sufficient results across years and fields are accumulated to develop a 

more appropriate estimate of the APH and its distribution.

WINTER CAMELINA INSURANCE PREMIUM CALCULATIONS 

Winter Camelina Yield Distribution 

The summary statistics in Table 25 report that the average camelina yield for a winter planted stand is 982 

pounds/acre and the minimum expected yield is 289 pounds/acre. The distribution is slightly skewed to the left 

given a skewness statistic of -0.721. This shape is confirmed in the PDF and CDF charts for the winter planted 

camelina yield distribution (Figure 17 and Figure 18).
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 45

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Table 25: Summary Statistics for Base Yield Distribution of Winter Camelina in Oregon/Washington 

Base Yield

Mean 982.455

Standard Deviation 245.555

Min 289.060

Median 1,024.805

Max 1,418.140

Skewness -0.721

Kurtosis 0.154

Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 26). All five normality tests reported that statically the distribution is 

not distributed normal. 

Table 26: Normality Test for the Yield Distribution of Winter Camelina in Oregon/Washington 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.956 0.002 Reject the Ho that the Distribution is Normally Distributed*

Anderson-Darling 1.370 0.001 Reject the Ho that the Distribution is Normally Distributed*

Cramer von Mises 0.232 0.002 Reject the Ho that the Distribution is Normally Distributed*

Kolmogorov-Smirnov 0.104 NA Consult Critical Value Table

Chi-Squared 37.200 0.007 Reject the Ho that the Distribution is Normally Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 27 were simulated for 500 iterations using a 

common uniform standard deviate to insure the results are directly comparable. The summary statistics for these 

distributions and the goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are 

summarized in Table 27. The 16 distributions were simulated 500 iterations and the resulting summary statistics are 

reported beside the parameters. All of the distributions reproduce the mean as expected, but a number of them fail to 

reproduce the range of the distribution. The CDFDEV criteria indicate the four best distributions for simulating the 

distribution are empirical, beta, Weibull, and normal; with the empirical distribution being best.

Table 27: Univariate Parameter Estimation for the Yield Distribution of Winter Camelina in Oregon/Washington 

Distribution Parameters Parm. 1 Parm. 2 Mean Std. Dev. Min Max CDFDEV Rank

Beta α, β; A≤x≤B, α,β>0 2.146 1.485 956.44 258.30 321.08 1,413.29 2,137.9 Second

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 1024.805 191.730 1,024.73 271.27 (188.00) 2,250.98 38,437.0

Exponential μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 289.060 693.395 982.52 693.19 289.68 5,204.16 837,084.1

Gamma α, β; 0≤x<∞, α,β>0 12.740 77.116 982.44 275.57 339.72 2,079.23 24,065.3

Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 1001.615 137.970 1,001.56 250.45 33.36 1,979.50 16,540.0

Log-Log μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 852.770 272.559 1,010.08 349.71 321.67 2,784.68 99,681.3

Log-Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 6.339 980.500 1,021.68 306.14 324.06 2,999.52 102,294.2

Lognormal μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 6.850 0.304 988.78 307.77 365.43 2,454.67 55,261.6

Normal μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 982.455 244.324 982.42 244.68 219.42 1,750.49 6,653.5 Fourth

Pareto α, β; α≤x<∞, α,β>0 289.060 0.845 5,907.04 59,114.43 289.37 1,273,093.09 4.815192E+10

Uniform a, b ; a≤x≤b 289.060 1418.140 853.58 326.29 290.07 1,417.20 23,558.4

Weibull α, β; 0≤x<∞, α,β>0 4.916 7.918679E+14 984.15 229.33 257.38 1,598.07 2,935.9 Third

Empirical S, F(x) 985.08 233.33 296.38 1,416.32 244.2 First

Geometric p ; x=1,2,...; 0≤p≤1 0.001 983.13 982.22 1.00 6,965.00 1,928,139.1

Poisson λ ; x=0,1,...; 0≤λ<∞ 982.020 982.00 31.38 884.13 1,080.51 91,578.3

Negative Binomial s, p ; x=1,2,...; 0≤p≤1 13.000 0.013 981.47 274.55 125.00 1,843.00 11,482.6

MLEs Statisitcs Goodness of Fit

INSURANCE PREMIUMS FOR WINTER CAMELINA

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 982 pounds/acre (APH) and assume a price 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 46

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guarantee of $0.15/pound. The eight policies are defined in terms of fraction from the APH and are 0.50, 0.55, 0.60, 

0.65, 0.70, 0.75, 0.80, and 0.85. APH yield was assumed to equal the average for the base yield distributions from 

the EPIC runs.

The calculated insurance premiums are reported in Table 28. Based on the assumption that the yield for a winter

planted stand of camelina is distributed empirically, the premiums are less than $2.00/acre for policies that insure 50

percent - 65 percent of the APH. The fair premium at 70 percent APH is $2.68/acre. It increases rapidly thereafter 

with a $3.64/acre premium for 75 percent APH coverage, $5.03/acre for 80 percent APH coverage, and reaches 

$6.81/acre for 85 percent APH coverage

The calculated insurance premiums for the beta probability distribution are generally higher than those for the 

empirical distribution. For example, for the 85 percent APH policy, the beta distribution indicates an $8.98/acre 

premium. The Weibull distribution generates lower premiums per acre for each level of coverage than the empirical 

distribution, with a premium of $5.71/acre for an 85 percent of APH policy. 

The difference in premiums for each yield insurance policy differ due to the weight the distribution places on the 

insured range of the distribution. This relationship can be seen in Figure 20, a PDF of the original yield distribution 

and the four selected distributions. The beta distribution is associated with the highest premiums for the 70 percent -

85 percent APH policies because it has more weight for the yield values over the range of 300 to 700 pounds per 

acre.

The fully loaded premium for the 85 percent APH coverage ranges from $11.18/acre for the empirical distribution to 

$14.74/acre for the beta distribution (Table 28). The fully loaded premium was calculated by dividing the no load 

fair premium by 0.90 (the unit division load factor) and then dividing that result by 0.88 (the FCIC disaster reserve

factor) and multiplying by 1.3 (the qualitative load factor). The qualitative load factor of 1.3 is used to adjust for the 

lack of risk on the regression equations for physical relationships and production functions in the EPIC model. 

EPIC‘s only risk component is from the weather variables so it lacks the risk normally associated with simulating a 

regression equation used to predict production based on the values for the independent variables. 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 47

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Table 28: Yield Insurance Premiums for Winter Camelina in Oregon/Washington; Assuming Alternative Yield 

Coverage Levels and Yield Distributions, an APH Yield of 982 Pounds Per Acre, and Guaranteed Price of $0.15 

Per Pound 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 73.68 81.05 88.42 95.79 103.16 110.53 117.89 125.26

No Load Fair Premium ($/acre)

Empirical 0.46 0.74 1.31 1.97 2.68 3.64 5.03 6.81

Beta 0.43 0.85 1.47 2.34 3.49 4.95 6.78 8.98

Weibull 0.27 0.47 0.78 1.24 1.90 2.82 4.07 5.71

Normal 0.30 0.51 0.83 1.32 2.03 3.02 4.37 6.15

Fully Loaded Premium ($/acre)

Empirical 0.76 1.21 2.14 3.24 4.39 5.98 8.26 11.18

Beta 0.71 1.39 2.41 3.83 5.72 8.13 11.12 14.74

Weibull 0.44 0.76 1.27 2.03 3.12 4.63 6.68 9.38

Normal 0.50 0.84 1.37 2.17 3.33 4.96 7.18 10.09

Loss Cost (%)

Empirical 0.63% 0.91% 1.48% 2.06% 2.59% 3.29% 4.27% 5.44%

Beta 0.59% 1.05% 1.66% 2.44% 3.38% 4.48% 5.75% 7.17%

Weibull 0.36% 0.57% 0.88% 1.29% 1.84% 2.55% 3.45% 4.56%

Normal 0.41% 0.63% 0.94% 1.38% 1.97% 2.73% 3.71% 4.91%

Fully Loaded Base Premium (%)

Empirical 1.0% 1.5% 2.4% 3.4% 4.3% 5.4% 7.0% 8.9%

Beta 1.0% 1.7% 2.7% 4.0% 5.5% 7.4% 9.4% 11.8%

Weibull 0.6% 0.9% 1.4% 2.1% 3.0% 4.2% 5.7% 7.5%

Normal 0.7% 1.0% 1.5% 2.3% 3.2% 4.5% 6.1% 8.1%

250 450 650 850 1050 1250 1450 1650

Base Yield Empirical Beta Weibull Normal

Figure 20: PDF of Alternative Distributions of Winter Camelina Yields in Oregon/Washington 

Fully Loaded Base Premium Rates

The base premium rate for winter camelina ranges from 7.5% to 11.8% for the 85% level of yield coverage. At the 

65 percent level of yield coverage, the base premium is between 2.1% and 4%. The winter planted camelina rates 

are lower than the spring planted camelina rates, due to lower variability of the simulated winter planted camelina 

yields. Further, since canola is not winter planted in Washington, no comparisons between canola and winter planted 

camelina are made.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 48

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SUMMARY FOR WINTER CAMELINA YIELD DISTRIBUTION

The yield distribution has a mean of 982 pounds/acre and a range of 289 pounds/acre to 1,418 pounds/acre. Among 

16 analyzed distributions, four of the best yield distributions were utilized to estimate premiums. These four 

distributions facilitated comparisons of simulation results for eight possible yield insurance policies. Statistically the 

yield distribution is not distributed normal, yet it is the fourth best distribution for simulating the EPIC generated 

yield distribution.

At the highest levels of yield coverage (85 percent of APH), the fully loaded insurance premiums range from $11.18 

to $14.74/acre, or 7.5 percent to 11.8 percent of liability. These fully loaded premium estimates provide support for 

gauging the potential yield risk for camelina and assist in assessing the demand for a camelina crop insurance 

program.

The biggest problem with insuring camelina production is the lack of information to calculate the farmer‘s APH or 

establish transitional yields. There does not appear to be sufficient yield history for the farmer to calculate the APH

or establish actuarially sound premium rates. Results from the EPIC model suggest that EPIC may facilitate 

development of yield data until sufficient results across years and fields are accumulated to develop a more 

appropriate estimate of the APH and its distribution.

FEASIBILITY RECOMMENDATION 

As the aforementioned ―Camelina Risk Evaluation‖ section indicated, the demand signals for camelina in Montana, 

Oregon and Washington suggest that moderate demand for an insurance product and/or a moderate potential market 

for camelina exist. Specifically, the Diagnostic Instrument demand signals are stronger for Oregon/Washington than 

Montana; however, the potential market is currently greater in Montana. Premium estimates in Montana also suggest 

that yield risk may in fact be greater for the area analyzed versus the area in Washington; thus, suggesting greater 

demand for an insurance program in Montana. Nonetheless, the RMA Program Evaluation Tool Decision Tree for

both areas suggests that a new product be developed. Further, listening session comments from producers and 

attendees for both regions are in favor of an insurance product for camelina and expressed interest in an actual 

production history policy.

Crop Insurance Program Design Options 

The following are potential options for designing a crop insurance program for camelina in the Northwest region of 

the United States.

1. Create an actual production history (APH) Multi-Peril Crop Insurance Program (MPCI) utilizing the 

current FCIC APH crop insurance program.

2. Create a revenue based Multi-Peril Crop Insurance Program (MPCI) utilizing the current FCIC revenue 

crop insurance program.

3. Create a weather or rainfall based index (Section 2.1 of SOW: ―RMA does not wish to evaluate the policies 

and plans of insurance based on weather or rainfall indices at this time.‖).

4. Create an actual revenue history (ARH) Crop Insurance Program (MPCI) utilizing the current FCIC ARH 

crop insurance program.

The actual production history (APH) type program seems to be the most logical choice as; the characteristics of 

camelina are that of similar row crops currently insured in these regions. Specifically, those crops such as canola and 

mustard that are in the same genus as camelina are insurable in Montana, Oregon, and Washington. In discussions 

with industry representatives and listening session attendees, it was noted that camelina would have a similar growth 

stage of that of flax, also insurable in Daniels, Roosevelt, Sheridan, and Valley counties in Montana. While no 

known loss adjustment methodology has been developed, it is likely that the flax and canola Loss Adjustment 

Standards Handbook would serve as a preliminary guide for establishing such methodology. Further, the Crop 

Provisions would be modeled after the APH policy materials for canola and flax, while the Special Provisions of 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 49

Contract # N10PC18199

Insurance could be modeled from the APH policy material for canola in each region, as planting dates are very 

similar for both fall and spring seeded canola and camelina.

Because camelina does not trade on a commodity exchange, it poses challenges for the formulation of an expected 

price for camelina. Currently the majority of camelina in all regions of the United States is produced under contract 

with a processor or first handler. While the price of the crop may be transparent between processor and producer, 

details on pricing methodology from some processors and first handlers remain proprietary and are not typically

disclosed (Waring, Johnson, & Endicott, 2011). The lack of a price discovery mechanism presents potential design 

problems for a revenue policy such as the FCIC ―Revenue Protection‖ insurance program. This further supports the 

development of an APH based insurance program with the basis of guarantee established from a contract price.

As measured by the demand signals from the Program Evaluation Tool, yield was rated as having higher risk than

quality, price and other sources of revenue risk. With the price of camelina established prior to planting and fixed for 

the production and delivery spectrum, the risk for revenue variability becomes less than that of many of the row 

crops that can be produced in these regions. Very low quality risk also reduces the risk of price variability, hence 

significant revenue risk become a function based entirely on yield. While a program such as ARH could be 

developed for camelina, the ARH program is designed more to protect against revenue risk caused by losses from 

low yields, low prices, low quality, or any combination of these occurrences.

Recommendation 

Based on the research and analysis of camelina commercially produced for dedicated energy in Montana, Oregon,

and Washington; it is determined that the development of a crop insurance program for camelina grown for 

dedicated energy is feasible. Camelina possesses characteristics of other currently insured crops in the Northwest 

insured under the FCIC APH crop insurance programs; thus, potential exist for adding camelina to the existing APH 

crop insurance program. Based on conversations with industry representatives, producers, processors, and first 

handlers; an APH based crop insurance program would be viewed favorably to help mitigate production risk. 

Further, while the majority of the research results were based on specific geographic locations in Montana, Oregon 

and Washington, processors and first handlers suggest appropriate regions for a pilot program should include 

counties in Idaho, Montana, North Dakota, Oregon, and Washington (Waring, Johnson, & Endicott, 2011).

Sustainable Oils also plans to contract production in California in 2012 (Johnson S. , 2011).

PROGRAM OUTLINE

Guarantee

The guarantee would be that of the current FCIC APH plan of insurance program. Specifically, the guarantee would 

be equal to the producer‘s actual production history (APH) x selected coverage level.

Preliminary Premiums

As indicated in the aforementioned research, data availability is limited, yet two selected producers provided as 

much as four years of individual yields through 2010. For purposes of establishing rates for camelina, the limited 

data set may be of concern. However, as discussed in the ―Estimating Yield Probability Distributions‖ section of the 

report, utilizing the EPIC model the Contractor demonstrated that unsubsidized no-load and loaded fair premium 

estimates could be developed. These premiums are illustrated in Table 17, Table 23, and Table 28.

The premium per acre would be equal to the production guarantee x contract price x applicable premium rate x 

applicable option factors.

Expected Price

Camelina is typically only produced under contract with a processor or first handler, since the current market for 

camelina is still being developed. The Contractor envisions camelina grown under a contract would be set as a basic 

insurability requirement. As such, the established price for camelina would be the contract price. RMA also 

currently list dry peas, dry beans, mustard, and buckwheat as crops grown under contract as a basic insurability 

requirement. If an established base price were preferred, the expected base price for camelina would likely be 

established as a contract price. Because producers can plant in the fall or spring, the price for camelina is set in the 

fall by processors and first handlers. This would allow RMA to contact the appropriate entities in the fall to 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 50

Contract # N10PC18199

determine a base price whereby the producer would then only be able to insure at that price or a contract price not to 

exceed a set percentage of the base price. Scott Johnson with Sustainable Oils stated, ―The contract price is set by 

September 1 just prior to planting and will be fixed for the remainder of the crop year‖ (Personal Communication; 

Pricing Methodology, 2011). Further, Johnson stated, ―I would not have a problem with the RMA calling me to 

determine our contract price for the crop year‖ (Personal Communication; Pricing Methodology, 2011).

Yield Determination

Approved yields for insurance will be determined from the producer‘s actual production history (APH) on a practice 

basis (fall and spring). While many producers may not have a sufficient set of yield data, transitional yields (Tyields) would need to be developed as part of the rating methodology. One option available to minimize the number 

of T-yields required would be to establish a single unit for the county that would include all acreage in which the 

insured has a share (see unit discussion). Additional benefits may result (program simplification, improved program 

integrity, etc.).

Year of Implementation

It is anticipated that development of an actual production history crop insurance program for camelina would take 

approximately 1 year or less. A reasonable estimate for implementing such a program would be the 2013 crop year.

Units of Exposure

The Contractor envisions a unit structure for camelina of one county unit (all acreage in the county in which the 

insured has an interest). Implementation would require the modification of the definition of the basic unit.

Types

Separate types will not be applicable.

Practices

Producers in Idaho, Montana, North Dakota, Oregon, and Washington can plant camelina in both the fall preceding 

the harvest year or the spring of the harvest year. Therefore, separate planting practices can be defined for fall 

camelina and spring camelina. The yield of spring and fall planted camelina may vary but the extent has not been

clearly established; as additional yield data is accumulated, separate yields including T-yields may be appropriate by 

planting practice. Separate yield databases for fall planting and spring planting practices should also be considered 

until more data is available to quantify yield differences by practice. The fall and spring planting practices for 

camelina will be further identified into Irrigated, Non-Irrigated, Organic (Certified) Irrigated, Organic (Transitional) 

Irrigated, Organic (Certified) Non-Irrigated, and Organic (Transitional) Non-Irrigated. Standard APH procedures 

and reporting requirements will apply.

Insurance Dates

Camelina can be seeded anywhere from October 1 through early June. Generally, fall seeded camelina is seeded in 

most regions between October 1 through the end of November; while spring seeded camelina is planted between 

Mid-March through the end of April in western Montana and closer to early April to early June for eastern Montana 

and North Dakota. In the instance of utilizing both fall and spring planting practices, the sales closing date and final 

planting dates for fall seeded camelina might be set similar to those listed in the wheat special provisions of 

insurance for counties in Oregon/Washington, while spring seeded camelina might be set similar to spring nonirrigated canola in Oregon/Washington. Fall and spring planted camelina sales closing dates and final planting dates 

in Montana might be set similar to the wheat special provisions of insurance. The acreage reporting date for fall 

seeded camelina in all regions might be set to 12/15/20XX; while spring seeded camelina in all regions might be set 

to 6/30/20XX.

Initial Insurability Requirements

Insurability requirements would be those similar to the Basic Provisions, the Crop Provisions and Special provisions 

of Insurance with the addition of the requirement that insured camelina must be grown under a processor contract 

and applicable rotation provisions.
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Contract # N10PC18199

Pilot Counties

The counties listed below are identified as eligible to pilot test a camelina insurance program. This determination 

was made using counties with existing production according to NASS, NDSU, and counties identified by 

Sustainable Oils as having existing or potential production (Johnson S. , 2011).

*Indicates counties with known production as identified by Sustainable Oils, NASS, and North Dakota State 

University.

Idaho

Blaine

Bonner

Boundary

Butte

Cassia

Clark

Gooding

Lincoln

Minidoka

Twin Falls

(Johnson S. , 2011)

Montana

Big Horn*

Blaine

Broadwater*

Carbon

Carter

Cascade

Chouteau*

Custer

Daniels

Dawson*

Fallon

Fergus*

Flathead

Gallatin

Garfield*

Glacier*

Hill
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Judith Basin

Lewis and Clark

Liberty*

McCone*

Meagher

Musselshell

Petroleum

Philips*

Pondera*

Powder River

Prairie

Richland

Roosevelt

Rosebud

Sheridan*

Stillwater*

Sweetgrass

Teton*

Toole

Treasure

Valley

Wheatland

Wibaux

Yellowstone

(USDA; NASS, 2010)

North Dakota

Adams

Billings

Bowman

Burke

Divide*

Dunn

Golden Valley

Grant

Hettinger
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McKenzie*

McLean

Mercer

Morton

Mountrail*

Oliver

Renville

Sioux

Stark

Ward*

Williams*

(Hill, 2009)

Oregon

Baker

Crook*

Deschutes*

Grant

Harney*

Jackson*

Klamath*

Lake*

Malheur*

Morrow

Umatilla*

Union*

Wallowa*

Wheeler

(Personal Communication; Oregon and Washington Listening Session, 2011)

Washington

Adams

Asotin

Benton

Chelan

Columbia

Douglas
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Ferry

Franklin

Garfield

Grant

Lincoln

Okanogan

Pend Oreille

Spokane

Stevens

Walla Walla

Whitman*

Yakima*

(Personal Communication; Oregon and Washington Listening Session, 2011)

Insured Cause of Loss

Insured causes of loss will be those as specified in the Common Crop Insurance Policy, Basic Provisions, and 

similar to those contained small grains and canola crop provisions.

Uninsured Causes of Loss

See the Insured Causes of Loss section above.

Description of What Triggers Loss and Indemnity Calculation

Any production to count not meeting the producer guarantee due to an insurable cause of loss will trigger an 

indemnity. Indemnities will be calculated as specified in the established Crop Provisions. It is anticipated that a 

combination of the canola and rapeseed crop provisions and the small grain crop provisions will be utilized to 

develop the loss crop provisions for camelina.

Availability of Loss Adjustment Procedures

Loss adjustment methods found in the small grains LASH and the canola and rapeseed LASH would serve as 

models for loss adjustment procedures for camelina. Researchers at listening sessions indicated that since camelina 

is in the brassica family, losses should be similar to that of canola and rapeseed. They also indicated that camelina 

matches up almost exactly to the growth stages of flax, and that the small grains LASH could also facilitate 

developing a set of camelina loss adjustment procedures.

Impact Analysis 

The impacts of implementing a crop insurance program for camelina in the Northwest are currently hard to gauge 

quantitatively. While there would be additional cost to develop and administer the program, the overall benefits to 

producers would be far reaching. Listening session comments indicated that the sustainability of the camelina 

industry would be dependent upon a crop insurance program. The demand signals from the Program Evaluation 

Tool suggest that the demand for camelina insurance is moderate; however, the full extent of producer participation 

is unknown. Listening Session producers all indicated a need for the program and the consensus was that they all 

would purchase a properly rated program. Based on estimated premium rates from the EPIC model and 

approximately 20,000 acres, total premium in the range of $540,000 and liability in the range of $2.6 million would 

become available to AIP‘s. Further, this crop insurance program would fit within the mission of the Risk 

Management Agency: ―Serving America‘s agricultural producers through effective, market-based risk management 

tools and solutions to strengthen the economic stability of agricultural producers and rural communities― (USDA; 

RMA, 2011).
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 55

Contract # N10PC18199

SWITCHGRASS 

CROP DESCRIPTION 

Introduction 

Switchgrass (Panicum virgatum L.) is a perennial grass native to most of North America except for the West Coast 

region, specifically California, Oregon, and Washington. This warm-season grass is well suited for (a) conservation 

uses such as erosion control and wildlife habitat, and (b) pasture, forage, and hay due to its deep root system and 

ability to thrive on marginal soils.

In 1991, the U.S. Department of Energy identified switchgrass as a model crop for development to be dedicated to 

energy production. Within the last four years, switchgrass has been utilized as a low input biomass crop for 

cellulosic ethanol production. Factors including wide commercial seed availability, geographic adaptation, high 

biomass production, relative ease of planting, low input requirements, and compatibility with current equipment and 

production practices make switchgrass, if profitable, an ideal energy crop. 

Economic Importance 

SUPPLY

The commercial switchgrass industry is currently in its infancy, with limited alternative markets for growers to sell 

the crop for conversion to biofuels. The only market opportunity for growers to profit from growing switchgrass is 

to contract production with biomass processing firms. Currently, the only commercial switchgrass production is in 

east Tennessee, centered around the Vonore, Tennessee biorefinery built by Genera Biofuels LLC, DuPont Danisco 

Cellulosic Ethanol LLC, and the University of Tennessee. 

Producers within an economically feasible distance from the biorefinery are contracted with Genera Energy for 

switchgrass production at a fixed price. An example contract from Genera Energy can be found in Appendix C. 

Figure 21 and Figure 22 illustrate the total commercially-contracted acres for switchgrass and the number of 

growers, respectively. From 2008 to 2010 the number of commercial acres increased by 730% from 723 acres to 

6,000 acres.

As Genera Energy‘s biorefinery in Tennessee is the only commercial market for switchgrass producers, the 

production data shown in Figure 21 and Figure 22 represents the total commercial switchgrass production in the 

United States. However, there is significant potential for future commercial switchgrass production in other regions.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 56

Contract # N10PC18199

Figure 21: Switchgrass Production Chart 

Source: (Genera Biomass, LLC, 2010)

Figure 22: Total Switchgrass Growers 

Source: (Genera Biomass, LLC, 2010)
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PRODUCTION COSTS

As a perennial crop, switchgrass is planted once during its lifespan of ten or more years. The costs incurred during 

this period include the cost of establishment, and the cost of annual management activities such as nutrient inputs, 

pest control, harvest, and storage. A breakdown of production costs for switchgrass in Illinois by crop year can be 

found in Table 29. Switchgrass does not reach its maximum yield potential until three years after establishment;

therefore, it is important to note that costs incurred during the first three years of production will likely see a low 

return on investments. Notable costs during the first three years include seed, fertilizer, and bailing. Seeding 

required $106.67 per hectare alone, and fertilizer inputs were 32% of the first-year operating cost. The total breakeven price needed to cover the cost of production at delivery was found to be $64.84 per ton in Illinois, and $98.19 

when including the opportunity cost of land rent, as shown in the annualized costs in Table 30. 

Data is not available for commercial production costs; however, several university studies have been conducted on 

the costs of producing switchgrass. A summary of the current research can be found in Table 31. Based on these 

studies, production costs per ton at the farm gate are approximately $36 to $44 across all the regions studied;

suggesting that the limiting factor in each region is the opportunity cost of land rent. 

Table 29: Costs of Production for Switchgrass in Illinois 

Cost Items/hectare

Switchgrass ($/hectare)

Year 1 Year 2 Year 3-10 PV (10 yrs.)

Fertilizer 86.98 60.96 52.29 484.10

- Nitrogen 0.00 37.07 49.42 355.57

- Phosphorous 16.31 4.66 0.78 25.81

- Potassium 13.84 5.03 2.09 32.22

- Lime 56.83 14.21 0.00 70.49

Herbicide 17.89 17.89 0.00 35.09

Seed 106.67 26.67 0.00 132.31

Interest on Operating Inputs 14.81 7.39 3.66 45.61

Pre-harvest Machinery 44.12 23.55 17.65 181.06

- Disking 16.80 0.00 0.00 16.80

- Harrowing 9.06 0.00 0.00 9.06

- Potato planter (for miscanthus planting) - - - 0.00

- Airflow / fertilizer spreader 10.59 15.89 17.65 140.17

- Spraying chemicals (Atrazine and 2-4 D) 7.66 7.66 0.00 15.03

Harvesting 0.00 146.35 271.28 1896.93

- Mowing / conditioning 0.00 24.09 32.12 231.12

- Ranking / Swathing 0.00 6.30 8.40 60.45

- Baling 0.00 68.72 136.75 951.35

- Staging and loading 0.00 32.67 65.02 452.35

- Storage 0.00 14.57 28.99 201.66

Operating Costs at Farmgate ($ ha-1) 270.47 282.80 344.88 2775.10

Transportation 0.00 27.83 55.39 385.36

Operating Cost Including Transportation ($ ha-1) 270.47 310.64 400.28 3160.46

Delivered Yield (t ha-1) 0.00 3.52 7.01 48.75

Breakeven Farmgate Price Excluding Land Rent ($ t-1) 56.93

Breakeven Delivered Price Excluding Land Rent ($ t-1) 64.84

Source: Khanna, et al., 2008
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Table 30: Annualized Costs of Production for Switchgrass in Illinois 

Cost Items ($ ha -1) Switchgrass

Fertilizer 57.39

Chemicals 4.16

Seed 15.69

Interest on Operating Inputs 5.41

Storage/Drying/Crop Insurance 23.91

Machinery 222.44

Transportation 45.68

Annualized Operating Cost 374.67

Annualized Yield (t ha-1) 5.78

Opportunity Cost of Land 192.76

Breakeven delivered cost including opportunity cost of land ($ t-1 DM) 98.19

Source: (Khanna, Basanta, & Clifton-Brown, 2008)

Table 31: Summary of Five Economic Analyses That Estimate the Full Economic Cost to Produce Switchgrass 

as a Dedicated Energy Crop 

Study State

Yield 

Level(s) 

Assumed

Stand 

Lifespan(s) Land Cost

Harvest 

Method

Estimated Cost of 

Production

Tons/acre Years $/acre Yes/No $/ton

Khanna et al. 

(2008) Illinois 9.4 10 $78

Large 

rectangular 

bales

$44 (farmgate, w/o 

land cost) 

$89 (delivered)

Mooney et 

al. (2009) Tennessee 6.2 – 7.9 5 and 10 $68

Large round 

bales

$42 – $63 

(farmgate, 10-year 

lifespan)

Perrin et al. 

(2008)

North Dakota, 

South 

Dakota, 

Nebraska 2.6 – 3.5 5 and 10 Various Mixed

$42 – 71 

(farmgate, 10-year 

lifespan)

Epplin et al. 

(2007) Oklahoma NS NS $60

Large 

rectangular 

bales

$36 – $52 

(farmgate) 

$49 – $65 

(delivered)

Wang (2009) Tennessee 6.0 – 7.8 NS

Varied by 

productivity Mixed

$66 – $77 

(delivered)

Source: (Mooney & English, 2009)

DEMAND

As a commercially grown crop, switchgrass has the opportunity to fulfill the biomass needs for an emerging biofuels 

industry. Using the cellulosic process, the biomass from switchgrass can be converted to ethanol. The Energy 

Independence and Security Act (EISA) of 2007 mandates that the United States produce 36 billion gallons of 

ethanol by 2022, with 16 billion gallons coming from cellulosic biofuels. The biorefinery in Vonore, Tennessee has 

a 250,000 gallon per year capacity for ethanol production, which is half of the mandate for 2012 production set by 

EISA‘s timetable, shown in Table 32. 

Given this mandate, there is considerable opportunity for producers to enter the market. Assuming that one ton of 

biomass from switchgrass can produce 103 gallons of ethanol, and that an average yield would be approximately

eight tons per acre (Estimation of Yield Probability Distributions for Montana), it would take over 22 million acres 

of switchgrass to meet the 16 billion gallon cellulosic ethanol mandate. (Wang, Saricks, & Santini, 1999)
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Contract # N10PC18199

As mentioned in the camelina description, The United States Department of the Navy has made a commitment to 

have half of their energy consumption come from alternative sources by the year 2020. To accomplish this, the Navy 

plans to use a stair-step approach, increasing the amount of alternative energy consumption to eight billion barrels in 

2020, giving switchgrass producers another market opportunity. (Tindal, 2010)

Table 32: EISA Cellulosic Ethanol Mandate 

Energy Independence and Security Act Applicable Volume for Cellulosic Ethanol

Calendar Year Volume (Billions of Gallons)

2010 0.1

2011 0.25

2012 0.5

2013 1

2014 1.75

2015 3

2016 4.25

2017 5.5

2018 7

2019 8.5

2020 10.5

2021 13.5

2022 16

Source: (One Hundred Tenth Congress of the United States of America, 2007)

Marketing and Utilization 

Currently, switchgrass is primarily utilized for biomass production and marketed for conversion to ethanol using the 

cellulosic process. It is also being explored as a fuel to be co-fired with coal in electricity production plants. To date, 

co-firing switchgrass is in the research and development stage. (U.S. Department of Energy, 2003)

The only commercial production is located near the Genera Biofuels biorefinery in Vonore, Tennessee. This is the 

only facility for producers to market their crop. Genera Biomass has contracted with producers at a fixed price of 

$450 per planted acre in previous years. A sample contract is included in Appendix C. Genera plans to move its

pricing toward a production-based price once area growers have established switchgrass stands. (Personal 

Communication; Tennessee Listening Session, 2010)

Agronomic and Botanical Characteristics 

PHENOLOGICAL STAGES OF GROWTH

Switchgrass is a warm-seasoned native grass that requires few inputs and generates high yields. A herbaceous 

perennial grass that follows the life cycle illustrated in Figure 23, it is grown for its tall, dense shoots that produce 

high amounts of biomass. Commercial plantings take two to three years to reach maturity, but have a productive life 

of 10 years or more. 
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Figure 23: Switchgrass Growth Cycle 

Source: (Oregon State University, n.d.)

Germination Stage

The germination stage encompasses the events occurring after a seed is placed in the soil through its emergence 

from the soil. Under ideal soil and climate conditions, this process will take five to seven days. (Wolf & Fiske, 

2009) This stage is critical for production, as the stand is at risk of yield loss due to weed competition. This can be 

managed by having a weed-free seedbed. 

Vegetative Stage

The vegetative stage refers to the developmental period comprising leaf growth and development. This begins with 

the emergence of the first leaf. Each successive sub-stage refers to the number of fully emerged live leaves currently 

present. Leaves are considered fully emerged when collared. Once stem elongation commences, the elongation stage 

begins and the vegetative stage ceases. During this stage, the stand is still susceptible to competition from weeds.

Elongation Stage

Elongation is the stage during which culm or stem elongation occurs, and is often referred to as jointing. Sub-stages 

of the elongation stage are defined by the number of nodes that have become visible as the result of stem elongation. 

The elongation stage ceases when the inflorescence is enclosed in the uppermost leaf sheath, which is commonly 

referred to as the boot stage. Additional nodes may become visible after this time. This stand is considered to be 

well established at this stage and can withstand weather related perils.

Reproductive Stage

The reproductive stage begins with emergence of the inflorescence and continues through fertilization. Seed

ripening begins when the developing seed becomes visible and ends when it is ripe. Growth is completed at this 

stage. (Moore, Moser, Vogel, Waller, Johnson, & Pedersen, 1991)

CHEMICAL COMPOSITION

Switchgrass is composed of lignin, cellulose, hemicellulose, and other compounds known as extractives. A 

breakdown of the amount of chemical characteristics in switchgrass can be found in Figure 24. The cellulose and 

hemicellulose are polysaccharides that can be hydrolyzed to sugars and then fermented to ethanol. On average, 66%

of switchgrass‘ dry matter is cellulose and hemicellulose, which can be utilized for conversion to ethanol. The lignin 

cannot be utilized for fermentation.
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Figure 24: Switchgrass Chemical Composition 

Source: (DoKyoung, Owens, Boe, & Jeranyama, 2007)

Production Operations 

As the market for switchgrass is new and underdeveloped, production practices are currently being researched. The 

current practices are the result of experience through forage production and limited research. Production is separated 

into three phases: establishment, management, and harvest. Switchgrass does not require replanting once a 

successful stand is established.

ESTABLISHMENT

From planting the crop to the second year of growth, the establishment of a switchgrass stand is paramount to the 

success of the crop. This initial phase of production often presents the most challenges to the producer in the form of

seed dormancy and competition from other grasses and weeds. Producers have seen yields ranging from zero to five 

tons per acre in the first year of production.

Planting occurs early May to mid-June using a no-till practice. If planting takes place after a row crop, the seedbed 

must be smoothed out and firm. The seed has a period of dormancy; therefore, the highest level of pure live seed is 

desirable, and is often sold by its ―Pure Live Seed‖ percentage9(USDA; NRCS, 2009). At this stage, the seedling is 

vulnerable to weed competition, especially in fields with perennial weed infestations. Proper field preparation is 

necessary to avoid competition. Field condition before planting is preferably smooth with little or no vegetation. The 

field is typically grazed heavily, and RoundUp (glyphosate) needs to be applied before seeding. 

Seeds are planted from one-quarter to one-half inch deep at a rate of five to six pounds of pure live seed per acre. 

Switchgrass takes 15-20 days to germinate, and during this period, the field is typically sprayed with Round-Up

(glyphosate) to kill weeds and grasses that compete for establishment. Nitrogen will hinder establishment by 

producing additional weed competition, but 40 to 80 pounds of phosphorus can be applied. (Personal 

Communication; Tennessee Listening Session, 2010) Currently, there are no approved herbicides to aid in weed 

control for switchgrass. 

 

9

Pure Live Seed Calculation: %PLS = (% purity × % viability) × 100; % Viability = % germination + % dormant seed
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Contract # N10PC18199

MANAGEMENT

Once a switchgrass stand is established, it requires a relatively small amount of inputs and is susceptible to few 

perils. During this stage of growth, switchgrass develops a large root system that can reach more than 10 feet below 

ground, giving it an increased ability to withstand drought and wind (Bransby, n.d.).

During this phase, yield can reach ten tons per acre. The highest-yielding results were from producers using 40-60 

pounds of nitrogen, but producers using applications beyond 60 pounds did not see any yield increase. Over the 

years of production, little variation in yield occurs. 

Weed control is necessary until the stand becomes very dense, in order to mitigate yield loss. Once a stand becomes 

mature, the base can reach 20 inches in diameter, which will prevent weeds from becoming established.

A successful switchgrass stand is assumed to last more than ten years before seeing any reduction in yield. However, 

it is likely that a higher-yielding variety will be developed in the next ten years. Should a higher-yielding variety be 

developed, producers would likely reestablish a stand with the new variety. (Personal Communication; Tennessee 

Listening Session, 2010)

HARVEST

Switchgrass is typically harvested after the second or third hard freeze of the year. The goal is to reduce the moisture 

content to below 15 percent for baling immediately following the windrower. The switchgrass is harvested at six 

inches from the ground, and the entire cutting is baled in round or square bales and may be chopped. (Teel, 1998)

Once the crop is harvested, switchgrass can be stored either covered or uncovered. There is a cost trade-off between 

the amount invested in storage and the amount of money lost due to higher moisture content (Searcy, 2010).

As of 2010, yield is determined in the field by portable scales, which eliminates shrinkage. This system is likely to 

change in the future. Yield and moisture content will be measured at delivery to the plant in order to determine if the 

product is acceptable to the plant based on its standards. Moisture management of switchgrass is critical, as ethanol 

production is generally higher from properly stored switchgrass. (Personal Communication; Tennessee Listening 

Session, 2010)

ROTATION AND ISOLATION REQUIREMENTS

As switchgrass is a perennial grass, there is no need to replant or rotate the crop after establishment. (Teel, 1998)

The economic life of the crop is usually over ten years without replanting. However, new varieties are expected to 

be released before the current crop‘s economic life is over, which would require a replant.

SOIL REQUIREMENTS

Switchgrass can be produced in a wide variety of soil types and is tolerant of poor soil conditions without irrigation. 

Higher yields are seen in soils such as loam and sandy soils, which allow roots to spread easily. Planting in a 

smooth, firm, clod-free seedbed and ensuring good seed-to-soil contact at a consistent depth provides the best 

establishment results. In addition, greater soil depth gives roots more access to soil water and nutrients. (Caddel & 

Redfearn, 2008) Switchgrass is most productive in soils with a pH at 5.0 or higher. (Garland, n.d.)

Susceptibility to Pests and Diseases 

At this time, the impact of disease and pest on switchgrass production is not known. However, occurrences of rust 

and smut were reported in South Dakota and Iowa test plots, respectively. As acres increase, biomass yield 

reductions could be attributable to pests and diseases in the future. (Nyoka, Jeranyama, Owens, Boe, & Moechnig, 

2007)

DISEASES

Rust (Puccinia emaculata)

Rust is a type of airborne spore that germinates and infects plants it lands on. At first, light yellow flecks on the 

surface of the leaves or on culms can be observed. As the disease progresses, numerous lesions containing mature 
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small brown spores erupt through the leaf surface. Upland varieties have shown more severe symptoms. Control is 

achieved by planting resistant varieties and possibly by the use of foliar-applied fungicides.

Smut (Tilletia maclagani)

Smut is a fungi that survives as spores on seed and in infested soils. Spores infect coleoptiles before seedling 

emergence in the spring. The fungus grows through the plant and primarily colonizes at points of plant growth. In 

the developing seed head, the fungus displaces the kernel and upon maturity, bunt balls emerge, releasing spores that

are dispersed by wind, rain, and healthy seed. The disease can cause reduced yields and stand decline. Control is 

facilitated through clean seed, plant resistance, and fungicidal seed treatments.

PESTS

Grasshoppers are known to feed on switchgrass, but the extent of their damage has not been quantified. Commonly 

found insects on switchgrass include aphids, leafhoppers, blister beetles, chinch bugs, grasshoppers, stem bores, and 

wireworms. There are also several beneficial insects found on switchgrass, including ants, rove beetles, ground 

beetles, parasitoid wasps, and spiders.

Weeds

Both weedy grasses and broadleaf weeds can cause serious problems during switchgrass establishment, but usually 

only minor problems occur after the first year. The array of weeds found in any field depends on the previous use of 

the land and any nearby plants whose seeds may have been blown or washed onto the site. (Caddel & Redfearn, Pest 

Management, n.d.)

Weed competition can be controlled during establishment by eliminating weeds before planting and not applying 

nitrogen until the stand is established. An application of RoundUp (glyphosate) applied before seeding will help 

reduce competing weeds (Personal Communication; Tennessee Listening Session, 2010).

Adaptation and Distributions 

As a native grass to North America, switchgrass is adaptable to most areas of the United States with suitable soils. 

Areas with sandy to clay loam soils have seen the highest yields, whereas areas with rich, heavy soils tend to have 

poor yields (USDA; PLANTS, 2011). Figure 25 and Figure 26 illustrate the projected yield for switchgrass 

throughout the United States. The far western states are shown to be unsuitable for switchgrass production, and the 

highest-yielding areas are in the central to eastern parts of the country.

Varieties 

Switchgrass varieties are typically categorized by their ecotype, as either an upland variety or lowland variety. The 

upland varieties, discussed in Table 33, are best suited for production north of 40° latitude (north of Kansas) and 

require less precipitation. 

While these varieties can endure the colder, drier climates, they have lower yields than lowland varieties. Of the two 

lowland varieties shown in Table 34, Alamo has the highest yield, and the highest demand for seed in Tennessee. 

Producers contracted with Genera Energy in the Madisonville, Tennessee area had yields as high as ten tons per acre 

with the Alamo variety. In the near future, Genera Energy expects yields to reach 15 tons per acre from their 

producers. (Personal Communication; Tennessee Listening Session, 2010)
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Table 33: Upland Switchgrass Cultivars 

Varieties Description

Trailblazer This cultivar was released in 1984 from USDA and the University of Nebraska, and is adapted to 

the Central Great Plains. 

Blackwell Blackwell was developed by USDA in Manhattan, Kansas in 1944 for the Kansas, Oklahoma, 

southern Nebraska, and northern Texas areas. This cultivar is adapted to those regions provided 

they have 20 inches or more of annual precipitation.

Cave-In-Rock This variety was released by NRCS-USDA and the Missouri Agricultural Experiment Station in 

1973 to be tolerant to flooding and adapted to the Midwest.

Pathfinder The Pathfinder variety was developed by Nebraska Agricultural Experiment Station and ARSUSDA in 1967 to be winter-hardy and mature late.

Caddo This cultivar recovers well after mowing and can be high yielding using irrigation. It was 

developed by Oklahoma Agricultural Experiment Station and ARS-USDA.

Source: Adapted from (Rinehart, 2006)

Figure 25: Yield Projections for Upland Varieties 

Source: (Wright, et al., 2009)
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Table 34: Lowland Switchgrass Cultivars 

Varieties Description

Alamo In 1978, Texas Agricultural Experiment Station and NRCS-USDA released the Alamo variety. It is 

typically high yielding and is the premier lowland variety.

Kanlow This variety was developed by Kansas Agricultural Experiment Stations and ARS-USDA for soil 

conservation.

Source: Adapted from (Rinehart, 2006)

Figure 26: Yield Projections for Lowland Varieties 

Source: (Wright, et al., 2009)

REVIEW OF OTHER PROGRAMS 

State and Federal Programs 

BIOMASS CROP ASSISTANCE PROGRAM

The Biomass Crop Assistance Program (BCAP) supports switchgrass establishment and management through 

project areas and contracts on land for up to five years. Switchgrass is listed as an eligible material under the 

renewable plant material category of herbaceous resources. Through a matching payment program, BCAP assists 

switchgrass owners with collection, harvest, storage, and transportation for use in qualified Biomass Conversion 

Facilities (BCF). These payments are available to switchgrass growers at the rate of $1 for each dollar per dry ton 
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paid by the BCF to the eligible material owners, limited to a maximum of $45 per dry ton and a 2-year payment 

duration. (USDA; FSA, 2010a)

BIOENERGY PROGRAM FOR ADVANCED BIOFUELS

Payments are made to switchgrass producers for the purpose of biofuel production. Eligible producers entering into 

a contract are paid based on the quantity and quality of advanced biofuel production and on the net nonrenewable 

energy content of the advanced biofuel. The payment amount depends on the number of producers participating in 

the program, the amount of advanced biofuels being produced, and the amount of funds available. (National 

Sustainable Agriculture Information Service, 2010)

Mandatory funding for this program has been set at, $105 million in fiscal year 2010, $85 million in fiscal year 

2011, and discretionary funding of up to $25 million each year from fiscal year 2009 - 2012. 

RURAL ENERGY FOR AMERICA PROGRAM 

The Rural Energy for America Program (REAP) established a grant and loan guarantee program to assist producers 

in purchasing renewable energy systems and making energy efficiency improvements. (Crooks, 2010)

BIOREFINERY ASSISTANCE PROGRAM

This program provides grants for construction of demonstration-scale biorefineries and loan guarantees for 

commercial-scale biorefineries. The demonstration-scale biorefineries are funded up to 50 percent of their project 

cost. Each loan is guaranteed up to $250 million for commercial-scale biorefineries. Although assistance does not go 

directly to the grower, this program assists in developing the industry and indirectly benefits the producer. (Crooks, 

2010)

SUN GRANT RESEARCH INITIATIVE 

This research initiative authorizes the United States Department of Agriculture, Department of Transportation, and 

Department of Energy to issue grants to five regional Sun Grant Centers and one sub-center. Each center is tasked to 

coordinate bioenergy efforts in their region to ―develop, distribute, and implement bio-based technologies, promote 

diversification and environmental sustainability through bio-based energy and product technologies, promote 

diversification of rural areas through bio-based energy, enhance efficiency of bioenergy and biomass research and 

development through collaborations among United States Department of Agriculture, Department of Energy, and 

land grant universities.‖ The Initiative is awarded funding of $75 million annually through 2012 (Dohlman, Caswell, 

& Duncan, 2008). Although this program will not provide a direct payment, it will provide future benefits to 

producers through research and development.

Private Products 

To date, there are no private insurance products available for switchgrass in Tennessee or any other states. 

DATA AVAILABILITY AND PRICE METHODOLOGIES 

Yield Data 

Currently, yield data for switchgrass is not publically available; however, producers who attended the listening 

sessions provided the Contractor with yield estimates. These yield estimates were then utilized to simulate a 

historical data series. The methodology includes developing a production schedule of tillage, planting, fertilization, 

pesticide applications, and harvesting operations, along with management decisions regarding typical dates of each 

operation, seeding rates, and application rates of fertilizers and pesticides. Illustrated in Table 35 is the collected 

yield data.
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Table 35: Switchgrass Average Producer Yields 

Tennessee Switchgrass (Tons Per Acre)

Year 1 Year 2 Year 3

3.33 5.21 7.53

Source: (Personal Communication; Tennessee Listening Session, 2010)

Price Data 

Currently, first handlers who contract with growers for switchgrass production solely determine switchgrass price 

(i.e., contract price). Genera Energy and University of Tennessee currently price their contracted production at $450 

per year for each acre planted. Dr. Sam Jackson of Genera Energy indicated that pricing will move to a 

production/yield-based price in the future, but would not release the pricing methodology for proprietary reasons

(Jackson, 2010). An example contract can be found in Appendix C.

LISTENING SESSIONS 

Tennessee 

As per requirement of section 2.4.2.1 of the Statement of Work, the Contractor conducted a listening session with 

switchgrass producers in Southeast Tennessee. Tennessee was chosen because of the role the University of 

Tennessee has played in developing a cellulosic biofuels industry in the state. As part of the Tennessee Biofuels 

Initiative, Genera Energy, a limited liability company wholly owned by the University of Tennessee Research 

Foundation, was created. Genera partnered with DuPont Danisco Cellulosic Ethanol, LLC to construct and operate a 

demonstration-scale cellulosic ethanol biorefinery in Vonore, Tennessee using 6,000 acres of switchgrass produced 

in the region. (Genera Energy, 2010) & (Tiller, 2010)

Genera President Dr. Kelly Tiller and Dr. Sam Jackson assisted the Contractor in determining the location and date 

of the meeting, and notified and encouraged all their contract producers to attend the meeting. The listening session 

was held on Friday, November 19, 2010 (7:30 am) at Donna‘s Old Town Café located in Madisonville, Tennessee. 

Twenty-three participants attended, including producers, bankers, extension agronomist, and university professors, 

with the majority in attendance being producers.

LISTENING SESSION SUMMARY

The majority of the attendees at the listening session were landowners who have not previously participated in FCIC 

insurance. Among the total in attendance, only two switchgrass producers had bought FCIC crop insurance to cover 

other crops. These two producers were the most vocal of the group, and indicated that FCIC crop insurance was 

essential to their risk management practices on their farms. They indicated that based on their experience with the 

crop, switchgrass was a relatively low-risk crop that can be grown on marginal land. Color Wheel Farm owner Brad 

Black stated, ―20 bushel soybean ground can make 10 tons of switchgrass, and class 8 ground will make 8 tons.‖ 

(Personal Communication; Tennessee Listening Session, 2010). Producers also indicated that crop insurance would 

be an effective risk management tool for switchgrass, given that potential changes of the current contract structure

will be implemented in the near future.

Additional comments confirmed that switchgrass is a low input and low maintenance crop that yields well across 

various areas and diverse environmental conditions. At present, the majority of switchgrass producers were 

indifferent to insuring switchgrass since current contracts are paying $450 per acre to the growers, with no risk of 

loss for revenue. However, acreage contracts would gradually phase into production/yield contracts, under which the 

growers will be paid at a fixed dollar amount per ton of switchgrass produced. It is reasonable to believe that more 

interest for a FCIC crop insurance program for switchgrass will develop as revenue risk becomes a more relevant 

concern to the growers. Current factors driving the demand for crop insurance such as yield, price and quality risk 

for switchgrass are relatively low in Tennessee, which helps explain the attitudes of producers that are indifferent to

purchasing crop insurance for switchgrass. Nonetheless, the two producers with existing FCIC crop insurance 

provided useful information from the perspective of current insured growers, and emphasized the importance of a 

FCIC crop insurance program as an effective risk management tool in the near future.
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SWITCHGRASS RISK EVALUATION 

Introduction 

The RMA‘s Program Evaluation Tool found in the USDA RMA Program Evaluation Handbook FCIC-2210 (PEH)10

was used by the Contractor as a supplement to assist in the development of the overall recommendation for each 

dedicated energy crop. This tool creates a better understanding of the risk exposure and the potential for various 

insurance products to transfer a portion of that risk to an insurance pool. The tool also assists in gauging the demand 

for insurance products and potential design issues that may rise. 

This instrument was applied separately to the four regions where the listening sessions were held. These regions 

included Tennessee for switchgrass, Louisiana for energy cane, and Montana and Oregon/Washington for camelina. 

The program evaluation tool was completed based on information obtained through (a) listening sessions with 

producers, insurance agents, FSA personnel, university extension personnel, crop consultants, (b) conversations with 

RMA Regional Office, and (c) the Contractor‘s independent research and analysis of the current production and 

market conditions for each crop in each region and that of comparable crops. Results of the Program Evaluation 

Tool are summarized below and the completed Diagnostic Instrument for each region is included in Appendix N.

Tennessee Program Evaluation Tool Summary 

―The Program Evaluation Tool uses a series of questions to elicit information on production processes, market 

characteristics, availability of federally facilitated insurance products, and eight demand signals, of which five are 

―Demand Shifter Categories‖ such as yield risk, quality risk, price risk, other sources of revenue risk, and the 

sufficiency of non-insurance available to cope with risk. The remaining three are ―Product Design Categories‖, such 

as potential and realized risk classification challenges, potential and realized moral hazard and monitoring issues, 

and other problems that may affect insurance participation. Overall assessment questions are answered for the eight 

categories using a Likert scale of 1 to 5, where higher numbers for the ―Demand Shifter‖ category indicate higher 

demand for insurance products, while lower numbers suggest relatively lower demand. For the ―Product Design‖ 

categories, higher numbers indicate either a lack of product design problems or a high likelihood of being able to 

address any existing product design problems, while low numbers indicate more serious product design problems 

and/or problems that cannot be easily addressed. Using the overall assessment scores from each of the eight 

diagnostic categories, the results have been graphically summarized for each region. Based on the overall scores 

assigned to each of the eight diagnostic categories, assessments are made and used to work through a generalized 

decision tree framework (See Appendix I), intended to facilitate decision-making. However, the diagnostic 

instrument may be used independently of the decision tree.‖ (USDA; RMA, 2005)

The following is a summary of the completed Evaluation Tool for Tennessee switchgrass. The completed Evaluation 

Tool in Appendix N provides the completed answers to the aforementioned questions, while the sections below 

provides summary level answers to the eight demand signal questions used for Likert scale ratings. This summary 

will be better understood by concurrently reviewing the Program Evaluation Tool in Appendix N.

BACKGROUND INFORMATION

Switchgrass is a perennial plant that grows in most areas of the United States. Its distribution spans south from 

Canada over most of the United States to east of the Rocky Mountains. Switchgrass is a hardy and deep-rooted,

perennial rhizomatous grass that begins growth in late spring through early fall, then it becomes dormant and 

unproductive during colder months. Once established, a switchgrass stand can survive for ten years or longer. Unlike 

corn, switchgrass can grow on marginal lands and requires relatively modest levels of chemical fertilizers.

MARKETING

Genera Energy was formed in 2007 to execute the capital construction projects and business elements of the 

Tennessee Biofuels Initiative. Genera Energy partnered with DuPont Danisco Cellulosic Ethanol to jointly construct 

 

10 The document can be downloaded from http://www.rma.usda.gov/FTP/Publications/directives/22000/06_22010.pdf.
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and operate a demonstration scale cellulosic biorefinery. As part of the Tennessee Biofuels Initiative, University of 

Tennessee developed a local farm-based switchgrass energy crop industry. Currently University of Tennessee 

provides acreage contracts to farmers for $450 per acre of switchgrass which began in 2009 and lasts three years. A

yield-based component will be added in 2010, under which Genera Energy will provide a yield-based production 

contract for $50-$75 per ton of switchgrass going forward. (Tiller, 2010)

RMA-FACILITATED INSURANCE PRODUCTS

Currently there are no RMA-facilitated insurance products available for switchgrass utilized for dedicated energy in 

Tennessee or any other region in the United States.

YIELD RISK

Switchgrass in Tennessee is exposed to fire as a catastrophic risk. It was noted that, at the Tennessee listening 

session, this risk may be caused by a catastrophic event such as lightening or more so by a spark from equipment 

during harvest or even a hot bearing. Under the occurrence of fire, yields for switchgrass could quickly be reduced 

to a 100 percent loss which has occurred at least once since 2009 as a result of an equipment fire. Although the risk 

for a catastrophic fire would be hard to gauge, utilizing the cause of loss data from RMA for the counties within a 50 

mile radius of the DuPont Danisco plant enables us to make a more informed estimate. For Anderson, Blount, 

Bradley, Cumberland, Hamilton, Knox, Loudon, McMinn, Meigs, Monroe, Morgan, Polk, Rhea, Roane, and Seiver 

Counties fire has occurred as an insurable cause of loss two times in the last 25 years (1985-2009). Both instances 

occurred to tobacco and, although significantly different from switchgrass, it provides the best quantitative measures

to evaluate the yield risk for switchgrass.

On a Likert scale from 1 to 5, ―1‖ being very low relative yield risk and ―5‖ being very high relative yield risk, noncatastrophic yield risk was deemed to be very low (―1‖). Although switchgrass data are not reported by NASS, 

assuming similarity of switchgrass to all dry hay yields, all dry hay yields for the aforementioned counties in 

Tennessee and the coefficient of variation for each crop can be used to assist in quantifying the yield risk associated 

with switchgrass.

The coefficient of variation, calculated as the standard deviation divided by the mean, facilitates the comparison 

across crops with different expected yields and allows the comparison of variation in unlike data series. As 

illustrated in Figure 27, yield risk for all dry hay in the aforementioned counties in Tennessee has been significantly 

lower than most of the other crops produced over the past ten years. The coefficient of variation for all dry hay is 

0.10, while corn for grain and soybeans are 0.21 and 0.28. This suggests that switchgrass is a relatively low risk crop 

to compared to corn for grain and soybeans. However, switchgrass used for dedicated energy may encounter 

significant biomass yield losses during storage. In addition, improper storage may decrease the quality of biomass so

that it lowers the yield potential for conversion to ethanol. (Jackson, 2010)
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Figure 27: Tennessee Combined County Crop Yield Risk Comparison 

Adapted from (USDA; NASS), 2011

QUALITY RISK

Switchgrass utilized as a feedstock for dedicated energy does not exhibit significant quality risk at the time of 

harvest and therefore can be characterized as very low (―1‖) in quality risk. However, the quality of the switchgrass 

may decline in storage resulting in significant biomass losses and actual enzymatic hydrolysis (EtOH), which in turn 

lowers ethanol yield from cellulosic conversion. Producers are currently not docked for quality losses and will be

paid based on switchgrass as measured by weighing bales coming directly from the field. Therefore, quality risk is 

insignificant in terms of insurance design and it is reasonable to characterize quality risk as very low (Likert scale 

―1‖).

PRICE RISK

As part of the Tennessee Biofuels Initiative, University of Tennessee developed a local farm-based switchgrass 

energy crop industry. Currently, University of Tennessee provides acreage contracts to farmers for $450 per acre of 

switchgrass, which began in 2009, and lasts three years. Genera Energy will then provide a yield based production 

contract for $50-$75 per ton going forward. (Tiller, 2010) Based on the current contract pricing, price risk for 

switchgrass in the aforementioned counties in Tennessee is rated as very low (Likert scale ―1‖).

OTHER SOURCES OF REVENUE RISK

Switchgrass is a small seeded crop that prefers shallow planting, a firm seedbed and low weed competition. 

Establishing stands is difficult and does bear some levels of risk. Currently producers are provided seed and 

technical assistance for establishing switchgrass stands. The technical assistance has been instrumental and the free 

seed has lowered production cost, making it less risky for a producer to produce switchgrass. When a replanting is 

needed, University of Tennessee provides the seed, while the farmer pays for land preparation and seeding. 

(Jackson, 2010) Since the free seed and technical assistance help to reduce other sources of revenue risk, this 

demand signal is assigned a rating of low (Likert scale ―2‖).
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SUFFICIENT NON-INSURANCE COPING MECHANISMS

The demand for various crop insurance products (existing and potential) is influenced by non-insurance coping 

mechanisms including government price and income support programs, government disaster programs, marketing 

contracts including futures and options on futures for exchange-traded commodities, crop portfolio and spatial 

diversification, risk reducing production technologies and practices, and lenders‘ attitudes, expectations, and rulesof-thumb.

Federal commodity programs tend to reduce farmers‘ exposure to price risk and revenue risk. For switchgrass, no 

government crop programs are available in terms of marketing loans and counter cyclical payments; however, the 

Biomass Crop Assistance Program (BCAP) is available. BCAP supports establishing and producing eligible crops 

for the conversion to bioenergy through project areas and contracts on land for annual and non-woody perennial 

crops up to 5 years or for woody perennial crops up to 15 years. Through a matching payment program BCAP 

assists agricultural and forest land owners and eligible material owners with collection, harvest, storage, and 

transportation of eligible material for use in qualified Biomass Conversion Facilities (BCF). These payments will be 

available to eligible material owners at the rate of $1 for each dollar per dry ton paid by the BCF, limited to a 

maximum of $45 per dry ton and a 2-year payment duration. No listening session attendees indicated their current 

utilization of the program. (USDA; FSA, 2010a)

Other programs such as Noninsured Crop Disaster Assistance Program (NAP) are not available, nor has there been a 

history of federal disaster payments issued by the Crop Disaster Program11. Producers who (a) elect not to obtain 

federal crop insurance on non-insurable crops or crops for which the producer received crop loss assistance and (b)

elected not to participate in NAP for the year in which benefits are received, must purchase crop insurance at a level 

greater than the catastrophic coverage for insurable crops for the next two crop years in the administrative county 

where the crop was produced or prevented from being produced. (USDA; FSA, 2011)

Production contracts often mitigate farmers‘ exposure to some, but not all risks. Almost 100 percent of the crops in 

these counties are under production contracts with a first handler or processor and are priced prior to harvest and 

establishment. Under the terms of the contracts the growers are not exposed to (a) contract risk (i.e., growers do not 

have to deliver on the contract under production shortfalls) (b) quality risk (i.e., no significant price penalties if the 

product does not meet the quality characteristics specified in the contract), and (c) price risk (i.e., prices for specific 

quality characteristics are not specified in the contract). Therefore, these mechanisms may reduce the demand for 

crop insurance products. Furthermore, if price and yield are significantly negatively correlated, revenue variability

and the demand for crop insurance products will be reduced, ceteris paribus. Switchgrass yield and price are 

expected to be independent of each other because contracted production (supply) will always be adjusted to the 

appropriate demand level. Therefore, producers are not exposed to significant price risk or volatility.

It should be noted that financial leverage, growth strategies, and recent events all impact farmers‘ ability to selfinsure. Most listening session attendees have no previous experience purchasing crop insurance since they are

comfortable with their level of risk exposure and choose to self-insure. Farmers and lenders show little interest in 

crop insurance until they experience a few catastrophic events, which cause them to reevaluate their risk bearing 

capacity and willingness to take risk.

Diversifying the farm enterprise across multiple commodities (crops and/or livestock) has the potential to 

significantly reduce farm revenue variability. Yield shortfalls on one crop may be partially offset by high yields on a 

different crop. Also, a carefully diversified portfolio of crop enterprises can help farmers manage the revenue effects 

of price risk when other means of managing price risk are limited. For example, if yield risk for a crop (or crops) is 

small but price risk is significant, a farmer might choose to have no yield insurance and manage revenue risk due to 

price variation through diversification. In this region (i.e., the aforementioned counties), 15-25 percent of total farm 

revenue can be attributed to switchgrass, while other commodities produced including alfalfa, other hay, corn, 

soybeans and livestock, with other hay and livestock are more prevalent for the attendees of the listening sessions. 

For diversification to generate farm level revenue risk reduction, the correlation between the commodities must be 

 

11 The Noninsured Crop Disaster Program is available to producers for losses in excess of 35 percent at a payment rate of 65 

percent for insured crops and non-insurable crops; and 60 percent for uninsured crops.
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low (negatively correlated). Commodities with highly positively correlated revenue streams act as if they were a 

single commodity and, as a result, diversification will not significantly reduce revenue risk and the demand for crop 

insurance. Because alfalfa, other hay, corn, and soybeans all possess the same growth period, their yield risk and 

revenue risk are deemed to be positively correlated, therefore creating a higher demand for crop insurance.

Farmers at the Tennessee switchgrass listening sessions indicated that they were all full-time farmers. Part-time 

farmers are typically less likely to focus on risk management strategies, including crop insurance. Since switchgrass 

in this region is produced by full-time farmers, the demand for crop insurance products is greater. In addition, spatial 

diversification such as commodity diversification reduces farm level revenue variability if the yield correlation 

across farm parcels is low. Typically, switchgrass farms in this region are not spatially diversified and farmers are 

therefore exposed to higher yield risk, which creates higher demand for insurance.

Private-sector insurance products can have a mixed impact on the demand for RMA facilitated crop insurance 

products. If the private-sector products have features that complement or require the use of underlying RMAfacilitated crop insurance, they potentially increase demand for RMA-facilitated insurance products. On the other 

hand, some products may be substitutes or partial substitutes for RMA-facilitated crop insurance products. Currently 

there are no private products for switchgrass, which may increase the demand for a FCIC switchgrass crop insurance 

policy.

Lenders can have a substantial impact on farmers‘ adoption of crop insurance products as well. Often, the insured‘s

value on growing crops is treated as a current asset on the balance sheet. Lenders‘ awareness, understanding of, and 

attitudes toward crop insurance have an impact on demand for crop insurance, particularly under circumstances 

where farmers are highly financially leveraged. In the case of Tennessee Switchgrass, lenders may be indifferent to 

producers purchasing crop insurance.

Overall, based on the low availability of sufficient non-insurance coping mechanisms for producers of switchgrass 

in this region of Tennessee, the expectation of demand for insurance is very high (Likert scale rating ―5‖).

RISK CLASSIFICATION

Risk classification represents a serious challenge in rating crop insurance products. Non-insureds and insureds have 

different perspectives on the cost of crop insurance coverage. Some individuals choose not to insure because they 

utilize the non-insurance coping mechanisms discussed in the previous section. In other cases, however, the amount 

of insurance purchased is limited because some individuals perceive the premium rate as being ―too high.‖ It is 

possible that existing classification methods may result in premium rates that are appropriate (or even too low) for 

one group too high for another. Unfortunately for switchgrass, no FCIC insurance products or private products exist 

so risk classification cannot be measured. As a result, Likert scale measurement is not provided for this category and 

is assumed to be non-applicable in assisting the Contractor in following the decision tree process.

MORAL HAZARD

This category attempts to assess whether moral hazard may cause higher crop insurance indemnities. If so, the 

higher indemnities may be reflected in higher premium rates that could limit the purchase of insurance. By gauging 

the potential for ―gaming the system‖, a quantitative measure may be used to assess whether insuring switchgrass 

will likely be prone to significant moral hazard.

A measurement of variation in yield and quality caused by unavoidable ―acts of nature‖ or avoidable ―acts of 

management‖ suggests that the yield variation and quality variation are almost exclusively due to ―acts of nature‖ 

(potential for gaming is low). Since potential for gaming both yield and quality is low (Likert scale ―4‖), the extent 

to which moral hazard is a concern is small.

PROBLEMS AFFECTING INSURANCE PARTICIPATION

The previous categories dealt with both the potential for, and actualization of problems associated with risk 

classification and moral hazard. This category focuses on other problems that may limit demand for RMA-facilitated 

crop insurance products for switchgrass in this region. Because no FCIC programs currently exist for switchgrass, a
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Likert scale assessment is not provided for this category and is assumed non-applicable in assisting the Contractor in 

following the decision tree process.

Tennessee Program Evaluation Tool Results 

As illustrated in Figure 28 below, Tennessee switchgrass yield risk, quality risk, and price risk are all ―very low‖

(Likert scale ‖1‖), other sources of revenue risk are ―low‖ (Likert scale ―2‖), and sufficient non-insurance coping 

mechanisms are rated as ―very high or low availability‖ (Likert scale ―5‖). These demand signals suggest a very low 

demand for an insurance product and/or a low potential market. Product design issues such as moral hazard are rated 

a higher number (Likert scale ―4‖), indicating smaller potential for problems or an ability to address problems. Risk 

classification is not applicable at present because no premium rates are available to gauge the program. Other 

problems affecting insurance participation are also not applicable because no programs exist to facilitate the answers

to the questions. Nonetheless, the Evaluation Tool assisted in establishing a decision tree framework to help 

determine a recommended course of action for insuring switchgrass as a dedicated energy crop.

See Appendix L to step through the decision tree process for Tennessee switchgrass. The decision tree path is 

highlighted by red arrows. Currently there is a very small market for switchgrass (6,000 acres) grown for dedicated 

energy, yet as much as 50,000 acres may be grown in the next few years establishing a significant potential market. 

Regardless of the size of the potential market for switchgrass, the demand signals suggest low underlying demand. 

The low revenue risk and the relatively limited other risks in conjunction with the decision tree results suggest that 

no new product will be developed or no action is required.

Figure 28: Tennessee Switchgrass Demand Shifter and Product Design Signals 

ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR TENNESSEE 

SWITCHGRASS 

Estimating yields and yield risks of relatively new crops in regions heretofore unutilized as production areas raises 

questions regarding expected yields, yield variability, and profitability. With little or no production history, USDA‘s 

Risk Management Agency is faced with uncertain production capabilities when developing crop insurance 

programs. Agronomists, soil scientists, hydrologists, engineers, and others have developed tools for evaluating yield 

possibilities. Many of these tools are incorporated into computerized crop simulation models. One such model that 
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began development in the mid-80s is EPIC, the original acronym for the Erosion Productivity Impact Calculator 

model.

EPIC has evolved over time to include many impacts of crop production along with climatic factors on the 

environment. The model has been utilized by scientists worldwide and EPIC is now the acronym for Environmental 

Policy Integrated Climate model. One of the principal developers is Dr. J.R.Williams, Blackland Research and 

Extension Center, Texas AgriLife Research, Temple, Texas.

EPIC is a daily time-step model capable of simulating a wide array of crop production and environmental processes 

including plant growth, crop yields, plant competition, and soil erosion as well as water, and nutrient balances. 

Biomass growth is related to solar radiation intercepted by the plant canopy, vapor pressure deficit, CO2

concentration, and other physiological stresses including water, temperature, N, P, and soil aeration 

deficits/surpluses. Similarly, root growth is affected by bulk density, temperature, and aluminum content. Soil 

carbon algorithms calculate carbon balance including losses of carbon from water and wind erosion. Plant growth 

and development is influenced by temperature during the growing season, expressed within the model as heat units. 

The quantity of heat units necessary for the crop to reach maturity varies by latitude.

Among the model‘s many features is a stochastic weather generator. Weather can be input from historical records or 

it can be estimated stochastically using precipitation, air temperature, solar radiation, wind, and relative humidity 

parameters developed from historical records. 

The model simulates growth and development of a crop each day from emergence to harvest. Initially, a crop 

production schedule is developed from producers indicating their dates (and application rates) of operations prior to 

and during the growing season for tillage, planting, pesticides, irrigations, fertilizers, and harvesting. Adding the 

rates and dates of crop production inputs and other management information facilitates the simulation of tillage, 

irrigation, and fertilizer applications. Each operation combined with climate can impact crop production; either 

through growth, erosion, soil, water, or nutrients, and most times a combination of these elements. 

The EPIC model has been applied at regional (Thompson, Izaurralde, Rosenberg, & He) and national scales 

(Izaurralde, Rosenberg, Brown, & Thompson), (Potter, Atwood, Kellog, & Williams). (Brown, Rosenberg, Hays, 

Easterling, & Mearns) and (Thompson, Izarraulde, West, Parrish, Tyler, & Williams)utilized EPIC to simulate 

switchgrass response to climate change in the central United States. 

The primary objective of this analysis of switchgrass is to utilize the EPIC model, which has been adapted to a 

Windows® application, WinEPIC, to estimate an array of stochastic yields from which to develop a probability 

distribution function. Thereby, yield variability (risk) can be quantified and variations in revenues inferred. Subobjectives include: a) by utilizing local weather data along with producer‘s switchgrass yields and management data 

from a designated area in Tennessee, calibrate crop coefficients to best represent the current yields and production 

conditions, and b) to utilize the calibrated model for producing 100 successive 11-year rotations of switchgrass, 

making a total of 1100 simulations (years) of stochastic yield observations. 

The methodology includes developing a production schedule of tillage, planting, fertilization, pesticide applications, 

and harvesting operations along with management decisions regarding typical dates of each operation, seeding rates, 

and application rates of fertilizers and pesticides. Feedback from seven switchgrass producers at Tennessee listening 

sessions was utilized to assimilate as much information regarding switchgrass production as possible. 

For most of the producers, three years of production experience had occurred by mid-November 2010. They 

reported that fertilization typically included 40-80 pounds/acre P applied before seeding and 60 pounds/acre N the 

two following years. Planting occurred in early spring around May 1 and harvest of each crop occurred after the first 

or second frost, about November 1. Two quarts of glyphosate was applied as a preplant burn down and another two 

quarts for weed and grass control just prior to germination, 2-3 weeks after seeding. Excluding the seeding year, the 

switchgrass rotation is expected to last for ten production years and possibly longer. 

Producers reported forage yields to be the lowest in the seeding year, averaging 3.33 tons/acre for three producers 

who seeded and harvested in 2008. The average yield of six of the seven producers climbed to 5.21 tons/acre in 

2009 and in 2010, it increased to 7.53 tons/acre. A production schedule was developed to simulate their typical 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 75

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operations and was coupled with actual National Weather Service daily weather data for Athens, Tennessee, since 

on-site weather records were not available. 

Using EPIC to simulate the three years of historical production by the producers, yields, after calibrating crop 

physiological coefficients, were 3.38, 5.50, and 7.44 tons/acre for years 1-3, respectively. The following graph 

depicts the relationship of simulated to producer yields; thereby producing a regression line having a slope of 0.9629 

with an R-squared=0.9927. A slope of 1.0 with a zero intercept, depicted by the dashed line, indicates a perfect 

relationship or perfect correlation of simulated yields to producer yields. R-squared signifies the accuracy of the 

black regression line, developed from the three producer yields, in predicting them; R-squared=1.0 being a perfect 

prediction. 

y = 0.9629x + 0.2778

R2 = 0.9927

0

2

4

6

8

10

0 1 2 3 4 5 6 7 8 9 10

Simulated Yield (t/ac)

Producer Average Yield (t/ac)

Figure 29: Correlation of Simulated Yields to Producer Average Yields 

Further statistical evidence that the simulated yields approximate the averages of all producer yields is illustrated by 

the paired t-test in Table 36. A difference of zero is being tested between the means of both sets of yields, 5.36 vs. 

5.44 tons/acre for the producer mean and the simulated mean, respectively. In this case, t = 0.72 which is less than 

the critical t value of 9.92 indicating the means are not significantly different at the 1 percent level.

Utilizing the calibrated coefficients that were developed by simulating producer yields, a single run of 1100 years 

was then simulated to produce one hundred 11-year rotations (10 production years plus the first seeding year). Three 

changes were made from the calibration simulations: 1) the original soil profile was maintained throughout the longterm simulation by stopping erosion, 2) fertilizer rates were adjusted to maintain adequate plant nutrition throughout 

the 1100 years, and 3) daily weather parameters were generated and were based on historical records for 1962-2010 

from the nearby weather station at Athens, Tennessee. The daily weather coupled with the production practices over 

the long-term simulation period produced a probability distribution. 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 76

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Table 36: Switchgrass Yield Paired t-Test 

Item Simulated Producer

Mean 5.439 5.360

Variance 4.134 4.426

Observations 3 3

Hypothesized Mean Difference 0

df 2

t Stat 0.720

t Critical two-tail 9.925

Annual yields for years 2 through 11 of each of 100 rotations, excluding the seeding year, are illustrated in Figure 30

and Figure 31. The 1000-year average stochastically generated forage yield was 8.04 tons/acre. The range of yearly 

yields varied from a low of 4.08 tons/acre to a high of 12.81 tons/acre.

4 5 6 7 8 9 1 0 1 1 1 2 1 3

Figure 30: PDF of Mature Switchgrass Yields in SE Tennessee 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

4 5 6 7 8 9 1 0 1 1 1 2 1 3 Prob

Figure 31: CDF of Mature Switchgrass Yields in SE Tennessee 
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Contract # N10PC18199

1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50

Figure 32: PDF of Switchgrass Yields for Establishment Year in SE Tennessee 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 Prob

Figure 33: CDF of Switchgrass Yields for Establishment Year in SE Tennessee 

SWITCHGRASS INSURANCE PREMIUM CALCULATIONS 

The yield distributions for switchgrass grown in Southeastern Tennessee, as developed using the EPIC model, were 

used to estimate yield insurance premiums. There are two yield distributions for switchgrass: establishment year and 

a mature stand of switchgrass (post establishment years). Insufficient data are available to model the correlation 

between the establishment year yields and mature crop yields. The analysis procedure used for both switchgrass 

yield distributions is the following:

 The summary statistics to describe the distribution are calculated. Probability density function (PDF) and 

cumulative distribution function (CDF) charts for the distribution are provided.

 The distribution was tested for normality.

 Parameters for 15 parametric distributions are estimated using a maximum likelihood procedure in 

Simetar©. The parameters for an empirical distribution are estimated as well.

 The parameters for the 16 distributions are used to simulate the yield distribution for 500 iterations.

Summary statistics for the distributions are compared to the original distribution.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 78

Contract # N10PC18199

 The CDFDEV12 function in Simetar©are used to determine how closely the 16 distributions reproduced the 

original distribution. Based on the CDFDEV criteria the four distributions that most closely simulate the 

original distribution are selected for analysis.

 The best four distributions were simulated in Simetar©for 25,000 iterations to estimate ―fair insurance 

premiums‖ for eight yield coverage levels ranging from 50 percent to 85 percent of the average yield 

reported by the farmers in a focus group interview. The random number generator used for the analysis 

uses a Latin hypercube procedure and generates pseudo random numbers based on a fixed seed. 

 The no load and fully loaded fair premiums are reported as $/acre values for the four assumed probability 

distributions for eight levels of possible year coverage.

 The no load fair insurance premium is calculated by multiplying the probability of each state of nature 

(indemnity) by its respective probability. Using a Monte Carlo simulation approach each insurance policy 

was simulated for 25,000 yields drawn at random from assumed probability distributions. 

 The loaded insurance premium is calculated using a 0.90 unit division load factor, a 0.88 FCIC disaster 

reserve factor, and a 1.30 qualitative load factor.

Each of the EPIC distributions is presented separately because they are completely different distributions.

Establishment Year Switchgrass Yield Distribution 

The summary statistics in Table 37 report that the average yield in the establishment year is 3.5 tons/acre and the 

minimum expected yield is 1.5 tons/acre. The distribution is slightly skewed to the left given a skewness statistic of -

0.47. This shape is confirmed in the PDF and CDF charts for the year one yield distribution (Figure 32 and Figure 

33).

Table 37: Summary Statistics for Base Yield Distribution of Establishment Year Switchgrass in SE Tennessee 

Base Yield

Mean 3.521

Standard Deviation 0.635

Min 1.500

Median 3.510

Max 5.250

Skewness (0.474)

Kurtosis 0.644

Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 38). All five tests reported that statically we cannot reject the null 

hypothesis that the distribution is distributed normal at the alpha equal 5 percent level of significance.

 

12 The goodness-of-fit criteria selected for testing how closely the simulated PDFs compare to the original distribution is a weighted cumulative 

distribution comparison function (CDFDEV) available in Simetar©. The CDFDEV criteria is calculated as the sum of the squared distance 

between two distribution functions with penalty weights increasing in value as the observations move away from the mean. If a simulated PDF is 

identical to the original distribution, the CDFDEV value equals zero. When comparing two or more distributions as to their goodness-of-fit, the 

distribution with the smallest CDFDEV is considered ―best‖ for the purposes of this study. A mathematical description of the CDFDEV formula 

is provided in the Appendix.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 79

Contract # N10PC18199

Table 38: Normality Test of the Yield Distribution for the Establishment Year of Switchgrass in SE Tennessee 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.981 0.168 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Anderson-Darling 0.513 0.190 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Cramer von Mises 0.070 0.720 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Kolmogorov-Smirnov 0.061 NA Consult Critical Value Table

Chi-Squared 9.800 0.958 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 39 were simulated using a common uniform 

standard deviate to insure the results are directly comparable. The summary statistics for these distributions and the 

goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are summarized in Table 

39. The 16 distributions were simulated 500 iterations with a common uniform standard deviate and the resulting 

summary statistics are reported beside the parameters. All of the distributions reproduce the mean, as expected but a 

number of them fail to reproduce the range of the distribution. The CDFDEV criteria indicate the four best 

distributions for simulating the distribution are: empirical, Weibull, normal, and beta; with the empirical distribution 

being best.

Table 39: Univariate Parameter Estimation for the Yield Distribution of the Establishment Year of Switchgrass 

in SE Tennessee 

Distribution Parameters Parm. 1 Parm. 2 Mean Stnd Dev Min Max CDFDEV Ranking

Beta α, β; A≤x≤B, α,β>0 3.200 2.881 3.47 0.70 1.71 5.11 0.02717 fourth

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 3.510 0.494 3.51 0.70 0.38 6.67 0.13690

Exponential μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 1.500 2.021 3.52 2.02 1.50 15.83 7.11008

Gamma α, β; 0≤x<∞, α,β>0 27.296 0.129 3.52 0.67 1.78 6.03 0.05973

Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 3.548 0.354 3.55 0.64 1.06 6.06 0.04677

Log-Log μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 3.190 0.706 3.60 0.91 1.81 8.19 0.54565

Log-Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 9.560 3.537 3.60 0.70 1.70 7.42 0.22866

Lognormal μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 1.240 0.200 3.53 0.71 1.85 6.47 0.11505

Normal μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 3.521 0.635 3.52 0.64 1.54 5.52 0.01673 third

Pareto α, β; α≤x<∞, α,β>0 1.500 1.198 6.96 28.02 1.50 557.56 9310.92

Uniform a, b ; a≤x≤b 1.500 5.250 3.37 1.08 1.50 5.25 0.38515

Weibull α, β; 0≤x<∞, α,β>0 6.410 5006.121 3.52 0.64 1.26 5.13 0.00659 second

Binomial n, p ; x=0,1,2,...,n; 0≤p≤1 14.000 0.217 3.04 1.55 0.00 8.00 1.95276

Geometric p ; x=1,2,...; 0≤p≤1 0.248 4.05 3.50 1.00 25.00 27.76166

Poisson λ ; x=0,1,...; 0≤λ<∞ 3.040 3.04 1.75 0.00 10.00 3.19032

Empirical Si, F(x) 3.53 0.59 1.59 5.19 0.00136 best

MLEs Summary Statistics Goodness of 

INSURANCE PREMIUMS FOR ESTABLISHMENT YEAR SWITCHGRASS

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 3.5 tons/acre (APH) assuming a price guarantee 

of $75/ton. The eight policies are defined in terms of fraction from the APH and are 0.50, 0.55, 0.60, 0.65, 0.70, 

0.75, 0.80, and 0.85. APH yield was assumed to equal the average for the base yield distributions from the EPIC 

runs.

The calculated insurance premiums are reported in Table 40. Based on the assumption that the establishment year 

yield for switchgrass is distributed empirically, the no load premiums are less than $1.00/acre for the policies that 

insure 50 percent - 65 percent of the APH. The no load premium at 70 percent APH is $1.27/acre. It increases 

rapidly thereafter with a $2.13/acre premium for 75 percent APH coverage, $3.46/acre for 80 percent APH 

coverage, and reaches $5.36/acre for 85 percent APH coverage.
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Table 40: Yield Insurance Premiums for the Establishment Year of Switchgrass in SE Tennessee; Assuming 

Alternative Yield Coverage Levels and Yield Distributions, an APH Yield of 3.5 Tons Per Acre, and Guaranteed 

Price of $75 Per Ton 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 132.00 145.20 158.40 171.60 184.80 198.00 211.20 224.40

No Load Fair Premium ($/acre)

Empirical 0.03 0.07 0.19 0.65 1.27 2.13 3.46 5.36

Weibull 0.13 0.27 0.51 0.92 1.59 2.62 4.18 6.42

Normal 0.04 0.10 0.22 0.47 0.95 1.79 3.20 5.40

Beta 0.01 0.07 0.27 0.73 1.61 3.04 5.20 8.23

Fully Loaded Premium ($/acre)

Empirical 0.04 0.12 0.32 1.07 2.08 3.50 5.68 8.80

Weibull 0.22 0.44 0.84 1.52 2.61 4.31 6.86 10.54

Normal 0.06 0.16 0.36 0.77 1.56 2.94 5.26 8.87

Beta 0.01 0.11 0.44 1.20 2.63 5.00 8.54 13.51

Loss Cost Ratio (%)

Empirical 0.02% 0.05% 0.12% 0.38% 0.68% 1.08% 1.64% 2.39%

Weibull 0.10% 0.19% 0.32% 0.54% 0.86% 1.33% 1.98% 2.86%

Normal 0.03% 0.07% 0.14% 0.27% 0.51% 0.91% 1.52% 2.41%

Beta 0.01% 0.05% 0.17% 0.43% 0.87% 1.54% 2.46% 3.67%

Fully Loaded Base Premium (%)

Empirical 0.03% 0.08% 0.20% 0.62% 1.12% 1.77% 2.69% 3.92%

Weibull 0.17% 0.30% 0.53% 0.88% 1.41% 2.18% 3.25% 4.70%

Normal 0.05% 0.11% 0.23% 0.45% 0.84% 1.49% 2.49% 3.95%

Beta 0.01% 0.08% 0.28% 0.70% 1.43% 2.52% 4.04% 6.02%

The calculated insurance premiums for the other three probability distribution assumptions are generally higher than 

for the empirical distribution. For example, for the 85 percent APH policy, the Weibull distribution indicates a 

$6.42/acre premium, the normal distribution has a $5.40/acre premium, and the beta distribution has an $8.23/acre 

premium. 

The difference in premiums for each yield insurance policy differs across probability distributions due to the weight 

the distribution places on the insured range of the yield distribution. This relationship can be seen in Figure 34, a 

PDF of the original yield distribution and the four selected distributions. The beta distribution is associated with the 

highest premiums for the 70 percent - 85 percent APH policies because it has more weight in the higher yield values 

over the range of 2 to 3.2 tons/acre.

The fully loaded premium for the 85 percent APH coverage ranges from $8.80/acre for the empirical distribution to 

$13.51/acre for the beta distribution (Table 40). The fully loaded premium was calculated by dividing the no load 

fair premium by 0.90 (the unit division load factor) and then dividing that result by 0.88 (the FCIC disaster reserve

factor) and multiplying by 1.3 (the qualitative load factor). The qualitative load factor of 1.3 is used to adjust for the 

lack of risk on the regression equations for physical relationships and production functions in the EPIC model. 

EPIC‘s only risk component is the weather variables, so it lacks the risk normally associated with simulating a 

regression equation used to predict production based on the values for the independent variables. 

The loss cost ratios for each of the eight yield insurance policies were calculated as the ratio of the expected 

indemnity and the liability. The loss cost ratio for the 85 percent APH policy ranges from 2.39 percent to 3.67

percent based on the distribution assumed (Table 40).
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 81

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0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 Axis Title

Base Yield Empirical Weibull Beta

Figure 34: PDF of Alternative Distributions of Switchgrass Yields for Establishment Year in SE Tennessee 

Base Premium Rates for Establishment Year Switchgrass

The base premiums for the 85% APH coverage level ranges from 3.9% to 6% and at the 65% level of coverage, the 

base premium ranges from 0.45% to 0.88%. 

SUMMARY FOR ESTABLISHMENT YEAR SWITCHGRASS YIELD DISTRIBUTION

The yield distribution has a mean of 3.52 tons/acre and a range of 1.5 tons/acre to 5.25 tons/acre. The yield 

distribution is not normally distributed, yet it is the third best distribution for simulating the EPIC generated yield 

distribution. Simulation results for eight possible yield insurance policies indicate low fully loaded premiums (less 

than $2.63/acre) for insured yield less than 70 percent of an APH equal to 3.5 tons per acre and a price election of 

$75/ton. At the highest levels of yield coverage (85 percent of APH) the fully loaded insurance premiums range 

from $8.80 to $13.51/acre.

The biggest problem with insuring switchgrass production for the establishment year is the lack of information to 

calculate the farmer‘s APH. There is no 10 year yield history for the farmer to calculate the APH. Using the average 

yield from the EPIC model will have to suffice until sufficient results across years and fields are accumulated to 

develop a more appropriate estimate of the APH and its distribution.

Mature Switchgrass Yield Distribution 

The summary statistics in Table 41 report that the average Switchgrass yield for a mature stand is 8.0 tons/acre and 

the minimum expected yield is 4.08 tons/acre. The distribution is slightly skewed to the right given a skewness 

statistic of 0.1069. This shape is confirmed in the PDF and CDF charts for the mature crop yield distribution (Figure 

30and Figure 31).

Table 41: Summary Statistics for Base Yield Distribution of Mature Switchgrass in SE Tennessee 

Base Yield

Mean 8.043

Standard Deviation 1.277

Min 4.082

Median 8.055

Max 12.814

Skewness 0.107

Kurtosis 0.608
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Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 42). The first four normality tests reported that statically the 

distribution is not distributed normal. The Chi-Square test failed to reject the null hypothesis that the yield 

distribution is distributed normal at the alpha equal 5 percent.

Table 42: Normality Test for the Yield Distribution of Mature Switchgrass in SE Tennessee 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.99 0.001 Reject the Ho that the Distribution is Normally Distributed*

Anderson-Darling 1.03 0.010 Reject the Ho that the Distribution is Normally Distributed*

Cramer von Mises 0.15 0.024 Reject the Ho that the Distribution is Normally Distributed*

Kolmogorov-Smirnov 0.03 NA Consult Critical Value Table

Chi-Squared 20.70 0.354 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 43 were simulated for 500 iterations using a 

common uniform standard deviate to insure the results are directly comparable. The summary statistics for these 

distributions and the goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are 

summarized in Table 43. The 16 distributions were simulated 500 iterations and the resulting summary statistics are 

reported beside the parameters. All of the distributions reproduce the mean, as expected but a number of them fail to 

reproduce the range of the distribution. The CDFDEV criteria indicate the four best distributions for simulating the 

distribution are empirical, logistic, normal, and gamma; with the empirical distribution being best.

Table 43: Univariate Parameter Estimation for the Yield Distribution of Mature Switchgrass in SE Tennessee 

Distribution Parameters Parm. 1 Parm. 2 Mean Stnd Dev Min Max CDFDEV Rank

Beta α, β; A≤x≤B, α,β>0 4.398 5.380 8.01 1.33 4.72 11.74 0.0524

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 8.055 0.991 8.05 1.40 1.79 14.39 0.0930

Exponential μ, σ; -∞<x<∞, -∞<μ<∞, σ>0 4.082 3.962 8.04 3.96 4.09 32.17 16.7265

Gamma α, β; 0≤x<∞, α,β>0 38.501 0.209 8.04 1.30 4.59 12.74 0.0295 Fourth

Logistic μ, σ; 0≤x<∞, -∞<μ<∞, σ>0 8.041 0.712 8.04 1.29 3.04 13.09 0.0118 Second

Log-Log μ, σ; -∞<x<∞, -∞<μ<∞, σ>0 7.408 1.276 8.14 1.64 4.92 16.45 0.5135

Log-Logistic μ, σ; 0≤x<∞, -∞<μ<∞, σ>0 11.075 7.983 8.09 1.35 4.24 15.14 0.0807

Lognormal μ, σ; 0≤x<∞, -∞<μ<∞, σ>0 2.072 0.164 8.05 1.33 4.76 13.28 0.0464

Normal μ, σ; -∞<x<∞, -∞<μ<∞, σ>0 8.043 1.277 8.04 1.28 4.06 12.06 0.0257 Third

Pareto α, β; α≤x<∞, α,β>0 4.082 1.503 11.60 25.01 4.08 456.08 1358.5197

Uniform a, b ; a≤x≤b 4.082 12.814 8.45 2.52 4.09 12.81 2.8183

Weibull α, β; 0≤x<∞, α,β>0 6.586 1411993.854 8.01 1.43 2.96 11.56 0.1178

Binomial n, p ; x=0,1,2,...,n; 0≤p≤1 107.000 0.071 7.56 2.66 1.00 17.00 3.7838

Geometric p ; x=1,2,...; 0≤p≤1 0.117 8.56 8.03 1.00 58.00 102.1045

Poisson λ ; x=0,1,...; 0≤λ<∞ 7.556 7.56 2.76 1.00 18.00 4.3875

Empirical Si, F(x) 8.04 1.27 4.33 12.81 0.0047 Best

MLEs Summary Statistics Goodness of Fit

INSURANCE PREMIUMS FOR MATURE SWITCHGRASS

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 8.0 tons/acre (APH) and assume a price 

guarantee of $75/ton. The eight policies are defined in terms of fraction from the APH and are: 0.50, 0.55, 0.60, 

0.65, 0.70, 0.75, 0.80, and 0.85. APH yield was assumed to equal the average for the base yield distributions from 

the EPIC runs.

The calculated insurance premiums are reported in Table 44. Based on the assumption that the yield for a mature 

stand of switchgrass is distributed empirically, the premiums are less than $1.00/acre for policies that insure 50

percent - 65 percent of the APH. The fair premium at 70 percent APH is $1.12/acre. It increases rapidly thereafter 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 83

Contract # N10PC18199

with a $2.52/acre premium for 75 percent APH coverage, $4.89/acre for 80 percent APH coverage, and reaches 

$8.72/acre for 85 percent APH coverage.

The fully loaded premium for the 85 percent APH coverage ranges from $14.32/acre for the empirical distribution to 

$14.84/acre for the logistic distribution (Table 44). The fully loaded premium was calculated by dividing the no load 

fair premium by 0.90 (the unit division load factor) and then dividing that result by 0.88 (the FCIC disaster reserve

factor) and multiplying by 1.3 (the qualitative load factor). The qualitative load factor of 1.3 is used to adjust for the 

lack of risk on the regression equations for physical relationships and production functions in the EPIC model. 

EPIC‘s only risk component is from the weather variables so it lacks the risk normally associated with simulating a 

regression equation used to predict production based on the values for the independent variables. 

The calculated insurance premiums for the logistic and normal probability distributions are generally higher than 

those for the empirical distribution. For example, for the 85 percent APH policy, the logistic distribution indicates a 

$9.04/acre premium; the normal distribution has an $8.85/acre premium. The gamma distribution generates lower 

premiums per acre for each level of coverage than the empirical distribution, with a premium of $7.90/acre for an 85

percent of APH policy. 

The difference in premiums for each yield insurance policy differ due to the weight the distribution places on the 

insured range of the distribution. This relationship can be seen in Figure 35, a PDF of the original yield distribution 

and the four selected distributions. The logistic distribution is associated with the highest premiums for the 70

percent - 85 percent APH policies because it has more weight in the yield values less than 5 tons per acre.

Table 44: Yield Insurance Premiums for Mature Switchgrass in SE Tennessee; Assuming Alternative Yield 

Coverage Levels and Yield Distributions, an APH Yield of 8 Tons Per Acre, and Guaranteed Price of $75 Per Ton 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 301.63 331.79 361.96 392.12 422.28 452.45 482.61 512.77

No Load Fair Premium ($/acre)

Empirical 0.00 0.02 0.12 0.41 1.12 2.52 4.89 8.72

Logistic 0.19 0.33 0.58 1.02 1.78 3.09 5.33 9.04

Normal 0.02 0.06 0.18 0.46 1.09 2.36 4.74 8.85

Gamma 0.00 0.01 0.04 0.17 0.57 1.59 3.78 7.90

Fully Loaded Premium ($/acre)

Empirical 0.00 0.03 0.20 0.67 1.85 4.14 8.02 14.32

Logistic 0.31 0.54 0.96 1.67 2.92 5.08 8.75 14.84

Normal 0.03 0.11 0.30 0.76 1.78 3.87 7.79 14.52

Gamma 0.00 0.01 0.07 0.28 0.93 2.60 6.21 12.96

Loss Cost (%)

Empirical 0.00% 0.00% 0.03% 0.10% 0.27% 0.56% 1.01% 1.70%

Logistic 0.06% 0.10% 0.16% 0.26% 0.42% 0.68% 1.10% 1.76%

Normal 0.01% 0.02% 0.05% 0.12% 0.26% 0.52% 0.98% 1.73%

Gamma 0.00% 0.00% 0.01% 0.04% 0.13% 0.35% 0.78% 1.54%

Fully Loaded Base Premium (%)

Empirical 0.00% 0.01% 0.06% 0.17% 0.44% 0.91% 1.66% 2.79%

Logistic 0.10% 0.16% 0.26% 0.43% 0.69% 1.12% 1.81% 2.89%

Normal 0.01% 0.03% 0.08% 0.19% 0.42% 0.86% 1.61% 2.83%

Gamma 0.00% 0.00% 0.02% 0.07% 0.22% 0.58% 1.29% 2.53%
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3 4 5 6 7 8 9 1 0 1 1 1 2 1 3

Base Yield Empirical Logistic Normal Gamma

Figure 35: PDF of Alternative Distributions of Mature Switchgrass Yields in SE Tennessee 

Base Premium Rates for Mature Switchgrass

The base premium for insuring 85 percent of the APH ranges from 2.5% to 2.9% across the four yield distributions 

tested. Base premium rates at the 65 percent coverage level range from 0.07% to 0.4%.

SUMMARY FOR MATURE SWITCHGRASS YIELD DISTRIBUTION

The yield distribution has a mean of 8 tons/acre and a range of 4 tons/acre to 12.8 tons/acre. The yield distribution is 

not normally distributed, yet the normal distribution is the third best for simulating the EPIC generated yield 

distribution. Simulation results for eight possible yield insurance policies indicate low fully loaded premiums (less 

than $2.95/acre) for insured yield less than 70 percent of an APH equal to 8 tons per acre and a price election of 

$75/ton. At the highest levels of yield coverage (85 percent of APH) the fully loaded insurance premiums range 

from $12.96 to $14.84/acre or 2.83 percent to 2.89 percent of liability. 

The biggest problem with insuring switchgrass production for a mature stand is the lack of information to calculate 

the farmer‘s APH. Producers do not have sufficient years of experience growing switchgrass to have 10 years of 

yields for establishing an APH. Using the average yield from the EPIC model will have to suffice until sufficient 

results across years and fields are accumulated to develop a more appropriate estimate of the APH and its 

distribution.

FEASIBILITY RECOMMENDATION 

As the aforementioned ―Switchgrass Risk Evaluation‖ section indicated, the demand signals for switchgrass in 

Tennessee suggest that low demand for an insurance product and/or a low potential market for switchgrass exist. 

Specifically, the Diagnostic Instrument demand signals in Tennessee indicate that yield, quality and price risk are 

very low. The results of the ―Switchgrass Insurance Premium Calculations‖ section also suggest that yield risk may 

be low in this region. Estimated loaded premium rates are less than 1 percent for the 75 percent coverage level for 

mature switchgrass stands. Utilizing the RMA Program Evaluation Tool Decision Tree for switchgrass in 

Tennessee, the results indicate that either no actions are required or no new product need be developed. While, 

listening session comments from producers and attendees in the region are in favor of an insurance product for 

switchgrass, many factors indicate that the program may not be viable in Tennessee. 

Recommendation 

Since commercial production of switchgrass grown for a dedicated energy resource is currently confined to

Tennessee, the Contractor implies that this recommendation is valid only for switchgrass currently commercially 

grown in Tennessee and does not imply feasibility to develop coverage for commercially produced switchgrass for 

dedicated energy that might be grown in other geographic regions in the future. Based on the research and analysis 

of switchgrass commercially produced for dedicated energy in Tennessee, it is determined that the development of a 

crop insurance program for switchgrass grown for dedicated energy is not feasible at this time. Current contracting 
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structures, demand signals, estimated premium rates, and concern for risk being spread over an acceptable pool of 

insureds and acres in the region of Tennessee lend support for this recommendation. While this recommendation is 

based solely on current commercial production located in Tennessee and given the expected growth of the dedicated 

energy industry, potential may exist for a crop insurance program for commercial production of switchgrass for 

dedicated energy in other regions within the United States.
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ENERGY CANE 

CROP DESCRIPTION 

Introduction 

During the oil crisis of 1970‘s, high gas prices spurred research in renewable energy resources. Sugarcane was 

identified as a potential source of energy by converting the sugar and biomass into ethanol. In 1976, USDA began 

developing high fiber sugarcane varieties or energy cane to serve as a crop dedicated for energy production

(Legendre, 2011). As fuel price decreased, interest in energy cane production decreased as well; however, the 

variety development process had already been initiated. Now that fuel prices and global warming have become 

prominent social and political issues, interest in energy cane production has increased once again.

Despite the considerable duration of research and development, there are no commercial producers for energy cane 

as a dedicated energy crop (Personal Communication; Energy Cane Conference Call, 2011). Private companies such 

as Verenium and British Petroleum (BP) have shown interest in entering the market as energy cane processors by 

contracting production with growers. Verenium Corporation owned a pilot plant and demonstration scale facility in

Jennings, Louisiana. In 2010, BP Biofuels North America acquired the Jennings facilities from Verenium. These

facilities are used for research and development of the cellulosic biofuels. Currently, two energy cane producers are 

under contract to provide the facilities with feedstocks (Musial, 2011). BP plans to construct another cellulosic 

ethanol plant on a commercial scale with a 20 to 30 million-gallon capacity within the next year. (BP Alternative 

Energy, 2010)

Economic Importance 

SUPPLY

There are a limited number of energy cane growers. Most of the energy cane acreage is in test plots for further 

research and development; however, BP Alternative Energy has contracted two growers in Louisiana for their 

demonstration plant in Jennings and one grower in Florida for a plant that is currently under construction. In 

Louisiana and Florida, energy cane production takes place on less than 1,000 acres and 400 acres, respectively. It is 

expected that the Florida facility will have commercial capacity and will require 20,000 acres of energy cane. 

(Musial, 2011)

Research suggests that cellulosic conversion of energy cane will yield 1,800 to 2,000 gallons of ethanol per acre 

annually, compared to conventional conversion of sugarcane sucrose that yields only 800 gallons per acre. 

Currently, sugarcane grows on 830,000 acres in Florida, Hawaii, Louisiana, and Texas. Research efforts are directed 

towards developing energy cane varieties that will grow outside of the traditional sugarcane growing states including

Mississippi, Oklahoma, Arkansas, California, Alabama, Georgia, and northern Florida. (USDA; NASS, 2010)

PRODUCTION COSTS

Currently, other than the BP Biofuels demonstration facility, energy cane is not commercially produced and there 

are no commercial cellulosic processing facilities in operation for energy cane in the United States. Therefore, 

production costs are estimated based on current sugarcane costs and experimental yields. Researchers at Louisiana 

State University estimated the total production costs of energy cane to be $73.95 per dry ton (Mark, Darby, & 

Salassi, 2009). Others estimated $50 per dry ton to break even and a total per acre production cost to be 

approximately $600. (Gravois, et al., 2011)

DEMAND

The market demand for biofuels is driven by the aforementioned government mandates. The Energy Independence 

and Security Act (EISA) of 2007 mandates that the United States will produce 36 billion gallons of ethanol by 2022 
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with 16 billion gallons coming from cellulosic biofuels. BP Biofuels anticipates that biomass from energy cane will 

be utilized to meet the mandate for cellulosic biofuels. (Musial, 2011)

Marketing and Utilization 

Although energy cane is not commercially produced, it is reasonable to assume that it will be utilized as the plant 

biomass to produce ethanol through lignocellulosic fermentation and/or as the extracted sugar through the traditional 

fermentation process. BP Alternative Energy has contracted two producers in Louisiana to supply its demonstration 

plant with biomass from their energy cane production. (Musial, 2011)

Agronomic and Botanical Characteristics 

Sugarcane is classified as a sugar or sweetener crop. As energy cane is a variety of sugarcane, its agronomic 

classification can be defined as a sugar crop dedicated to energy production (Bareja, n.d.). ―Energy cane and 

sugarcane are from the same genus, Saccharum, and the only difference between them is energy cane is bred for 

high fiber content whereas sugarcane is bred for low fiber content but high sugar content.‖ (Mark, Darby, & Salassi, 

2009)

PHENOLOGICAL STAGES OF GROWTH

As a variety of sugarcane, the same morphology and growth stages apply to energy cane. The stages include 

germination and establishment, tillering, grand growth, and maturation and ripening. Commercial sugarcane is 

seeded by vegetative propagation using cuttings of the stalk sometimes called seed cane. Each cutting usually 

contains two or more nodes with buds. The cane root system enables the intake of water and nutrients from the soil 

and anchors the plant. Two kinds of roots will develop from a planted seed piece. The set roots, which rise from the 

root band, are thin and highly branched, while the shoot roots, originating from the lower root bands of the shoots, 

are thick, fleshy, and less branched. (Miller & Gilbert, 2009)

Germination and Establishment Phase

The inception of this phase starts at seven to ten days after planting and lasts an additional 30 to 35 days. During this 

time, the crop completes the germination of its buds. The germination denotes activation and subsequent sprouting 

of the vegetative bud. This phase results in an increase of respiration (and good soil aeration is important). 

Therefore, open structured porous soils facilitate better germination. (Netafim)

Before shoots form, the germinating seed piece must depend entirely on the set roots for water and nutrients. The set 

roots, however, are only temporary and their function is eventually taken over by the shoot roots as they develop. 

The life of the shoot roots is also limited. Each new tiller (shoot) develops its own roots that eventually take over the 

function of the original shoot roots. This rejuvenation, guided by the periodicity of tillering, is important because it 

allows the plant to adjust to changing environmental conditions. 

A longitudinal section of a root tip consists of four parts: the root cap, the growing point, the region of elongation, 

and the region of root hairs. The root cap protects the tender tissues of the growing point as the root pushes through 

the soil. The growing point consists of an apical meristem, where cell division takes place. In the region of 

elongation, the cells increase in size and diameter until they reach their ultimate size. The region of root hairs is 

characterized by epidermal cells forming outgrowths (hairs), which dramatically increase the root-absorbing surface. 

The bud— a miniature stalk with growing point, root, and leaf primordia— forms a new shoot. In addition, a seed 

piece contains root primordia within its root band, which develops into set roots, and functions until the young shoot 

develops its own roots. 

The transition from the dormant into the active stage constitutes a complex phenomenon characterized by changes in 

the activity of enzymes and growth regulating substances (e.g., hormones and auxins). Maximum germination and 

shoot vigor will result when both internal and external factors are optimal. (Miller & Gilbert, 2009)
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Tillering Phase

Tillering is the physiological process of developing secondary shoots, which takes place at 40 days after planting 

and lasts up to 120 days (Netafim). Ultimately, tillering is related to the phenomenon known as "apical dominance" 

and plant hormones are involved in the process of tillering. The most important external factors influencing tillering 

include light, temperature, nutrition, moisture, and the spacing of the plantings. Among these factors, experiments 

have shown that light plays the most significant role. Increasing light intensity and duration, in general, greatly 

increases tillering. Sunlight reaching the base of the plant is pivotal, as approximately 50 percent of the tillers will 

die due to inadequate light (Miller & Gilbert, 2009). The tillers forming earlier have thicker stalks, thus generating a 

higher yield than the later forming tillers.

Once the crop has been planted for 90-120 days, the tiller population reaches its maximum of 1.5 to 2 tillers per bud 

and forms into canes. A ratoon crop begins its tillering phase much earlier than a planted crop; therefore, a ratoon 

crop can expect to see higher tillers. (Netafim)

Grand Growth Phase

At approximately the 120-day mark, the grand growth stage begins. It is aptly named as the stalks can grow four to 

five internodes per month during this period. The actual canes are formed and elongated, producing yield. 

Water is critical during this period for optimal yields. Therefore, the crop is susceptible to yield loss due to drought 

at this time. (Netafim)

Ripening and Maturation Phase

The ripening and maturation of the crop is the final stage of growth, which begins at the 270th day and lasts until the 

crop is harvested. During this period, the crop, which rapidly accumulates sugar and vegetative growth, is reduced. 

The monosaccharide, fructose and glucose, or simple sugars, are converted to disaccharide or cane sugar. The 

ripening begins at the base of the plant and moves upward; therefore, the base contains more sugar.

At this point, the crop reaches its reproductive stage. The plant stops growing new leaves and produces a red to 

white colored inflorescence or tassel. The tassel is the flowering portion of the plant, which produces several 

thousand flowers. Each flower is only capable of producing one small seed, which is too small to be used in 

commercial crop production. Thus, vegetative propagation is the only viable means of planting energy cane.

(Rainbolt & Gilbert, 2008)

Portions of the mature cane are reserved for vegetative propagation. Once the cane is harvested, the reserved 

sections of cut stalks can be replanted, which sprout genetically identical daughter plants. (Baucum & Rice, 2009)

Chemical Composition 

The chemical composition of energy cane can be broken down into two parts, the juice, and bagasse. The juice of 

energy cane comprises a smaller percentage of the plants composition than sugarcane with 9.8 percent fermentable 

sugars. The bagasse is 43 percent cellulose, 24 percent hemicelluloses, and 22 percent lignin. (Kim & Day, 2010)

Production Operations 

ESTABLISHMENT

Energy cane is planted from August through October. The seed cane should be placed three to four inches above the 

final water furrow or higher in poorly drained soils in order to avoid water damage. Typically, a sugarcane field is 

replanted every two years; researchers expect, however, a stand of energy cane to extend several years beyond the 

traditional sugarcane cycle to the third or fourth ratoon. (Personal Communication; Energy Cane Conference Call, 

2011)

Energy cane plants are propagated by horizontally planting sections of the stalk in the soil. The stem has joints or 

nodes as in other grasses. These nodes range from four to 10 inches apart along the aboveground section of the stem. 

At each node, a broad leaf, consisting of a sheaf or base and a leaf blade, rises. The sheaf is attached to the stem at 
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the node and surrounds the stem with overlapping edges. The sheath from one node encircles the stem up to the next 

node above and may overlap the base of the leaf on the next higher node. 

The leaf blade is very long and narrow and varies from one to three inches in width and up to five feet or more in 

length. At each node along the stem is a bud, protected under the leaf sheath. Stem sections are planted by laying 

them horizontally and covering them with soil; a new stem grows from the bud, and roots grow from the base of the 

new stem. The stem branches below the ground so that several stems may raise as the clump forms the growth of the 

bud at a node.

In planting cane fields, mature cane stalks are cut into sections and laid horizontally in furrows. In the continental 

United States, sections with several nodes are laid in a furrow, while in tropical countries sections with two or three 

nodes are commonly used since temperatures for growth are more favorable. Usually only one node on a stem piece 

develops a new plant because of the polarity along the stem piece.

Planting is done in rows approximately six feet apart to make cultivation and use of herbicides for early weed 

control possible. As plants become taller, lower leaves along the stems are shaded and die. These lower leaves 

ultimately drop off and only leaves toward the top remain green and active. Between the nodes, the stems have a 

hard, thin, outer tissue or rind and a softer center. Complete shading of the ground occurs five to eight months after 

planting and is referred to as ―close in‖ or ―lay-by.‖

No-till system is used in ratoon fields where the gaps in rows are mechanically replanted. For sugarcane production, 

a maximum of two ratoon crops are grown, but the norm is one ratoon crop. More than 70 percent of sugarcane 

fields are tilled and planted every two years to improve stands by eliminating soil compaction, improving soil 

aeration and drainage, changing cultivars, incorporating soil amendments or field residues, minimizing insect and 

other pests, and eliminating perennial weeds. Since energy cane is heartier, multiple ratoon crops are grown, 

decreasing the need for the aforementioned stand improvements that sugarcane requires. (Markle, Baron, & 

Schneider, 1998)

HARVEST

Energy cane harvest occurs from October to January, although researchers are developing varieties that will last up

to January and through to March in order to increase biomass growth. The crop should be allowed to dry to reduce 

the moisture content to approximately 60 – 75percent. The field should be dry as well to minimize foreign matter, 

such as mud from harvest. Fields are mechanically harvested, using all parts of the plant for biomass and sugar 

extraction. During the first crop year, the crop may be harvested twice. Cutting the crop in January of the first crop 

year is a standard cultural practice to achieve higher yield. This cutting may produce enough crop growth to be 

harvested. After the first crop is harvested, subsequent crops are grown from the stubble or ratoon. In the continental 

United States, where winter freezing is a hazard, the leftover stubble is susceptible to freeze damage, which can 

cause considerable yield loss for the following crop year. (Gravois, et al., 2011)

ROTATION AND ISOLATION REQUIREMENTS

Energy cane is a perennial that typically is grown successively (back-to-back). The stubble from the previous crop is 

utilized for the next crop year; therefore, rotation is not required. If the harvest is late, another crop such as rice or 

sweet corn may be substituted for the next growing season. This is termed fallow planting. (Baucum & Rice, 2009)

SOIL REQUIREMENTS

Research on energy cane indicates the same soil requirements as sugarcane. Ideally, soils for energy cane should be 

medium to heavy with a pH of 5.0 to 8.5. Liming is required if the pH is less than 5.0, and gypsum should be used if 

the pH is greater than 9.5. The organic muck soils of the Florida Everglades are rich in nitrogen and support 

vigorous cane growth. In Louisiana, sugarcane production has been concentrated in the Delta region where soils are 

fertile. In Texas, soils typically need an application of 150-200 pounds per acre of Nitrogen to achieve comparable 

yields as in Florida and Louisiana. (Rainbolt & Gilbert, 2008)
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Susceptibility to Pests and Diseases 

WEATHER-RELATED PERILS

Given that sugarcane is grown in the same regions as energy cane, and given energy cane is a variety of the same 

plant, analyzing the historical cause of loss data of sugarcane gives an accurate assessment of the perils that affect 

energy cane production. Figure 36 illustrates the significance of each peril. The historical data shows that weather 

related perils account for 83 percent of sugarcane indemnities during a ten-year period, with drought and excess 

moisture having the highest cause of loss percentages. (USDA; RMA, 2010b)

Figure 36: U.S. Sugarcane: Ten-Year Causes of Loss 

Source: (USDA; RMA, 2010d)

Frost/Freeze

For young plant cane and young ratoon cane, most or all of the above ground primary and secondary shoots that 

have growing points above ground will be killed by a freeze event. Post-freeze regrowth will come from secondary 

shoots whose growing points are below ground and protected from the cold. The killing of the young cane shoots by 

freeze may result in poor stands. Freeze can also cause loss of stalk weight over time, after the freeze. 

Freezing temperature causes leaf damage first. A very light freeze, between 29° and 32°F for a few hours, will cause 

burning of the leaf tips. A slightly harder freeze, between 25° and 28° F for a few hours will cause extensive leaf 

browning and terminal bud death. A few lateral buds will be killed and the freeze may damage tissue in the top few 

internodes. Responses to freezing temperatures of different varieties differ. 

A more severe freeze, between 23° and 24° F for a few hours, will completely brown leaves and partially or entirely 

freeze stalks. The terminal bud and all or nearly all the lateral buds will be killed. (Personal Communication; Energy 

Cane Conference Call, 2011)
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PESTS

Sugarcane Borer (Diatrea saccharalis)

The sugarcane borer is known to be a major pest of sugarcane, and therefore energy cane. The sugarcane borer is a 

crambid species whose larvae tunnel grows in the stalks of many gramineous plants including, sugarcane, corn, rice, 

and sorghum in Louisiana. Its egg masses are deposited on the sugarcane leaves and newly hatched larvae migrate to 

the leaf sheath and feed around that area until about half grown. At the internodes, the larvae bore into and tunnel 

along the stalk. The sugarcane borer completes development inside the stalk then emerges through an exit hole 

carved earlier by the larvae. There are five to seven instars. The reduction in stalk weight is the most severe damage, 

but sugarcane borer also stunts growth, weakens stalks, kills terminal buds and causes suckering, destroys some 

vegetative buds of seed cane, and increases plant susceptibility to pathogens. The sugarcane borer is monitored by 

visually inspecting the stalks. Economic treatment threshold is reached when five percent of the crop has live larvae 

feeding in the leaf sheaths on the outside of the stalk.

Wireworm

When present in the sugarcane field, wireworms can cause significant damage and are difficult to control. During 

winter, these insects mature from the larval or adult stage in the cane field. The adults emerge in the spring and 

appear to stay in the same area to lay eggs. The larvae hatch within a few days and feed on the roots of the plant. 

(USDA; Integrated Pest Management Centers, 1999)

Mexican Rice Borer

Mexican rice borer (MRB), Eoreuma loftini, represents 98 percent of insect damage in Texas sugarcane. The 

economic damage due to MRB is estimated to be $10 to $20 million annually. No known chemical treatment is fully 

effective because the larvae enter the stalk early hindering insecticide effectiveness. Damages include stunted 

growth, lodging, breakage, reduced juice quality, and ―deadhearts‖ (green shoots with dead whorl centers). (ReayJones, n.d.)

New Guinea Sugarcane Weevil

Since the 1880‘s, the New Guinea sugarcane weevil or sugarcane beetle borer has been a chronic and persistent pest 

of sugarcane. It is most damaging in regions where temperatures are warm and rainfalls are high. The adult female 

bores a hole with its long beak into young cane and lays an egg in either this puncture in splits, fractures, cuts, and 

rat bites on matured stalks. The larva or grub feeds on the tissue within the stalks for 45 to 80 days and causes 

extensive tunneling. The adult weevil can live for six months to one year. Each female lays about 120 to 150 eggs 

during this period. For a two-year crop, there can be five or six weevil generations before the cane is harvested. 

Effective control of the weevil can be achieved by integrating biological control and growing resistant cultivars 

without the use of insecticides. An example of biological control is the tachinid fly (Lixophaga sphenophori), a 

parasitoid of the weevil, introduced to Hawaii from New Guinea in early 1910‘s and again in early 1970‘s. To date, 

this parasitoid exerts substantial control of the weevil. 

Lesser Cornstalk Borer

In Hawaii, the lesser cornstalk borer (LCB) is considered the most destructive pest of young sugarcane, killing 

shoots up to six weeks of age. It damaged thousands of acres of sugarcane on the Island of Maui from 1986 to the 

present. It is more abundant during spring and summer in dry, hot areas. LCB population decreases after shoots are 

30 cm or taller. Infestations of the LCB may cause a delay in crop development resulting in a small reduction of 

stand the extreme cases where the entire field must be replanted. Two parasitoid species, Orgilus elasmopalpi and 

Horismenus elineatus, have been introduced from Texas and Bolivia, respectively. H. elineatus provided control that 

is more effective. Both species have been recovered from field collected LCB larvae. Integrated practices of 

biological control, resistant cultivars, and frequent irrigation prior to close-in have been effective against this pest. 

Since 1993, LCB damage has been reduced but remains heavy in localized areas.

Yellow Sugarcane Aphid

The yellow sugarcane aphid (YSCA) has been established in Hawaiian sugarcane fields since 1989. Its population 

increases during the spring and continues through the summer. The aphids colonize the lower surface of the leaves, 

generally the lower to middle leaves on a stalk. When large outbreaks occur, even upper leaves may become heavily 
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infested. Feeding results in a reddish stippling followed by a general yellowing with irregular purplish areas along 

the leaves. Severely affected leaves die. (USDA; Integrated Pest Management Centers, 1999)

White Grubs Sugarcane

The grub, Ligyrus subtropicus, feeds on the underground roots and stems causing stunted growth and, in some cases,

death of the plant. In an area with a high infestation, it can cause a 39 percent reduction in yield. A heavily infested 

crop may not be harvested. The damage is more significant in ratoon crops. (Cherry, 2008)

DISEASES

Eyespot

Eyespot is a fungal disease. It causes the appearance of leaf lesions with necrotic centers surrounded by a yellow 

halo that may extend into a long streak parallel to leaf venation. The fungus is spread through airborne spores. In the 

past, it caused severe plant necrosis, stunting, and death in susceptible cultivars. The destructiveness of the pathogen 

is due to its ability to produce a toxin that kills plant cells. The disease is no longer prevalent because resistant 

cultivars have been planted for many years. Nonetheless, screening for resistance is still undertaken in the breeding 

program and any clones found susceptible are discarded. Fungicides are not necessary for control of eyespot disease.

Ratoon Stunting Disease

Ratoon stunting disease (RSD) is caused by a fastidious, xylem-limited bacterium. The disease is insidious since it

has no visible symptoms other than stunting and thus spreads without detection through plantations. Over time, RSD 

can cause severe yield reduction in lost cane tonnage. The bacterium is spread from plant to plant by cutting tools so 

that the percentage infection increases with each ratoon. New field areas are infected through planting of infected 

seed pieces. There are no pesticides available for the control of this systemic bacterial disease. The disease is kept 

under control in Hawaii by continuous monitoring of seed fields with serological testing. New seed fields are 

established with seed pieces that have been treated with a two-hour hot water treatment to eliminate the bacterium. If 

RSD is suspected, harvesting knives are sanitized with sodium hypochlorite (bleach) before moving to a new field.

Leaf scald

The symptom of leaf scald disease is characterized by colorless streaks in leaves, which eventually become necrotic 

giving a characteristic scorched look to foliage. Severe yield loss or complete crop loss can happen. The causal 

organism is a bacterium that is spread through infected seed pieces. There are no pesticides available for control of 

this disease. 

Yellow Leaf Syndrome

This viral disease recently originated in Hawaii and causes severe leaf yellowing. It begins with the midrib and leaf 

tip, then progresses down the leaf blade. The amount of yield loss is not well documented at this time and may vary 

with cultivars and cultural practices. These symptoms occur in stressed cane. The virus is vectored and transmitted 

by the sugarcane aphid (Melanaphis sacchari). The only control method practiced at this time is to discard any new 

sugarcane clones that show symptoms.

Pineapple Disease

Pineapple disease is caused by a fungal pathogen that has soil and air-borne spores. It enters stalk pieces cut for seed 

through the cut ends of the seed pieces. There it spreads rapidly through the stalk, killing the buds and preventing 

germination. The disease is ubiquitous in Hawaii and causes high mortality in newly planted fields. There are no 

resistant commercial cultivars and the only available treatment is pre-plant dip of the seed pieces in fungicide. 

Smut

Smut is a fungal disease caused by a basidiomycete (Ustilago scitaminea). This fungus invades plants directly by 

means of airborne spores that are produced on long sori called "whips.‖ The fungus systemically invades standing 

cane and can be spread through planting of infected seed pieces. Genetic resistance to the disease exists and resistant 

cultivars are planted when possible. Fungicides have no effect on the pathogen. Seed stocks are routinely inspected 

for the presence of the whips and infected fields are not used for planting material. Seed stock fields are first planted 

with seed pieces treated in a hot water dip, which greatly reduces any systemic fungus present. Through the 

continuous use of these control measures, losses from the disease are kept to a minimum.
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Rust

Rust is a fungal disease of leaves that is caused by an obligate parasite. Airborne spores from the leaf lesions 

germinate and infect new leaves during periods of high humidity. In susceptible sugarcane cultivars, the leaf lesions 

may be so severe that most of the leaves on the plants become necrotic and non-functional. This results in crop 

stunting and loss in yield. While copper sprays are used in other crops for control of rust diseases, copper is not used 

on sugarcane in Hawaii. Instead, the disease is controlled using resistant cultivars. During development of new 

cultivars in the breeding program, any rust susceptible clones are discarded. (USDA; Integrated Pest Management 

Centers, 1999)

Pokkah Boeng Disease of Sugarcane

Pokkah Boeng is caused by the fungus, Fusarium moniliforme, which can cause yield losses in certain cases. 

However, losses are rare and do not require any preventative measures. (Raid, 2009)

Sugarcane Mosaic Virus Disease

Sugarcane Mosaic Virus (SCMV), commonly known as simply ―mosaic,‖ is caused by a viral pathogen. Louisiana 

and Florida have both seen outbreaks in the past, with an outbreak in Louisiana causing a detrimental amount of loss 

across the state during the 1920s. Losses caused by SCMV vary depending on the time period and regions involved. 

The most distinctive symptom of the virus is the varying island-shaped shades of green on the leaves. This 

discoloration can turn yellow-green to red with varying degrees of necrosis. Younger plants are more susceptible to 

the disease. SCMV is mainly spread by aphid vectors and infected seed cane. SCMV resistant varieties have been 

developed and are essential to the prevention of the disease. (Comstock & Gilbert, 2009)

WEEDS

Weeds potentially cause more economic loss in sugarcane than all other pests combined. Weeds cause loss of 

tonnage in the field, reduce sucrose recovery in the mill, and shorten ratoon lives. Weeds compete with energy cane 

in many ways by reducing solar heating of soil for early growth/re-growth, extracting soil water, reducing light, 

robbing the crop of nutrients, and serving as reservoirs for numerous insect and disease pests. Along with 

harvesting, weed control is a costly component of production. The sugarcane industry is highly dependent on 

herbicides for profitable, environmentally sound production.

The most common and troublesome weeds are the sunflower, pigweed (Careless weed), guinea grass, johnsongrass, 

winter annual broadleaf weeds, annual grasses (Colorado or Jungle), morning glories, vines, nut sedge, and Bermuda 

grass. (USDA; Integrated Pest Management Centers, 1999)

Adaptation and Distribution 

Energy cane, like sugarcane, is adapted to the southern tropical regions of the United States, specifically southern 

Louisiana, Texas, Hawaii, and Florida. Texas sugarcane production is confined to an area classified as a subtropical 

climate - long hot summers and short mild winters. Killing freeze is a recurrent threat, and hurricanes and drought 

have significantly reduced production in some years as well. The sugarcane production in Hawaii is on the islands of 

Maui and Kauai. Maui has consistent weather with mild temperatures and modest annual rainfall. Kauai is slightly 

cooler with considerably less rainfall. Most of the Florida sugarcane is produced along the southern and southeastern 

shore of Lake Okeechobee in Southern Florida where the growing seasons are long and winters are generally warm. 

Florida is a hot-humid region. Abundant sunshine, large bodies of water and 60 inch per year rainfall contribute to 

Florida's hot-humid climate. Louisiana is the northern most sugarcane growing state in the contiguous United States

and the climate can be characterized as warm, moist, and almost tropical. Because of this, the sugar industry in 

Louisiana has been expanding both northward and westward into non-traditional sugarcane growing areas. Figure 37

illustrates the distribution and adaptation according to USDA PLANTS. Currently, researchers are working on 

developing energy cane varieties that are adaptable to areas farther north than the traditional sugarcane growing 

areas. (USDA; PLANTS, 2011)
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Figure 37: Energy Cane Distribution 

Source: (USDA; PLANTS, 2011)

Varieties 

Research on energy cane varieties began in the mid-1970s in Louisiana. The variety L79-1002 was an outcome of 

that research project and is currently the variety used for energy cane production. The development process for new 

sugarcane or energy cane varieties normally takes 12 years. Of the current commercial varieties, L79-1002 yields 

the highest amount of fermentable solids. However, it is highly susceptible to smut, creating a need for a disease 

resistant variety. A variety released in 2010, Ho 02-113, has been developed with resistance to smut and biomass 

yields compare to those of L79-1002. (Legendre, 2011)

At the Southern Agricultural Economics Association Annual Meeting in 2009, Mark, Darby, and Salassi compared 

the attributes of commercial sugarcane varieties in their estimation of feedstock costs. A summary of their findings 

can be found in Table 45. The evaluation concluded that Ho 00-961 produces the highest amount of fiber per acre of 

the commercial type sugarcane varieties evaluated (Mark, Darby, & Salassi, 2009). However, an interview 

conducted with Dr. Ben Legendre indicated that the variety L79-1002 produces approximately a 10 percent more 

fiber per acre than the Ho 00-961 variety. (Personal Communication; Energy Cane Varieties, 2011)

Table 45: Energy Cane Variety Comparison (Commercial Type) 

Variety Gross Cane Brix Fiber 

Solids 

Brix Fiber Total

(tons/ac) ( % cane) (tons/ac) (tons/ac) 

Ho 00-961 34.6 17.7 15.9 6.1 5.5 11.6

HoCP 91-552 38.9 16.8 15.2 6.6 6 12.6

LCP 85-384 31.5 18.2 14 5.6 4.4 10

Source: (Mark, Darby, & Salassi, 2009)
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REVIEW OF OTHER PROGRAMS 

State and Federal Programs 

BIOMASS CROP ASSISTANCE PROGRAM

The Biomass Crop Assistance Program (BCAP) supports establishing and producing eligible crops for the 

conversion to bioenergy through project areas and contracts on land up to five years. Energy cane bagasse qualifies 

for assistance as an eligible crop residue. Through a matching payment program, BCAP assists eligible material 

owners with collection, harvest, storage, and transportation of eligible material for use in qualified Biomass 

Conversion Facilities (BCF). These payments will be available to eligible material owners at the rate of $1 for each 

dollar per dry ton paid by the BCF, limited to a maximum of $45 per dry ton and a 2-year payment duration. 

(USDA; FSA, 2010b)

BIOREFINERY ASSISTANCE PROGRAM

This program provides grants for demonstration scale biorefineries and loan guarantees for commercial scale 

biorefineries. The pilot or demonstration scale biorefineries are funded up to 50 percent of the project cost. Each 

loan is guaranteed up to $250 million for commercial scale biorefineries. Although assistance does not go directly to 

the grower, this program assists in developing the industry and indirectly benefiting the producer. (Crooks, 2010)

BIOENERGY PROGRAM FOR ADVANCED BIOFUELS

Payments are made to producers of feedstocks for the purpose of biofuels production. Eligible producers entering 

into a contract are paid based on the quantity and quality of advanced biofuels production and on the net 

nonrenewable energy content of the advanced biofuels. The payment amount will depend on the number of 

producers participating in the program, the amount of advanced biofuels being produced, and the amount of funds 

available. (National Sustainable Agriculture Information Service, 2010) Mandatory funding for this program has 

been set at $105 million in fiscal year 2010, $85 million in fiscal year 2011, and discretionary funding of up to $25

million each year from fiscal year 2009-2012 (Crooks, 2010).

Private Products 

To date, there are no private insurance products available for energy cane. 

DATA AVAILABILITY AND PRICE METHODOLOGIES 

Yield Data 

Currently, there is no commercial production of energy cane; therefore, there are no published public commercial 

yield data available. For the purposes of this report, research yields from Louisiana State University were utilized to 

simulate a yield data series. The collected data are illustrated in Table 46 and are reported in fresh (wet) weight. The 

methodology for simulating the yield data is described in the section of this report titled ―Estimation of Yield 

Probability Distributions for Louisiana Energy Cane.‖ (Gravois, et al., 2011)

Table 46: Energy Cane Yields 

Louisiana Energy Cane (Tons Per Acre)

Year 1 Year 2 Year 3 Year 4

35.91 46.1 34.2 33.8

Source: (Personal Communication; Energy Cane Conference Call, 2011)

Price Data 

Given that no commercial markets currently exist for energy cane, no pricing data are publicly available. However,

Gravois, et al., (2011) determined that based on cost of production studies, prices paid would need to range from $50 
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– $74 per dry ton in order to breakeven. This range is equivalent to $12.50 – $18.50 per wet ton, assuming an energy 

cane crop at 75% moisture (Personal Communication; Energy Cane Conference Call).

LISTENING SESSIONS 

Louisiana 

Subcontractors Dr. James Richardson and Dr. Joe Outlaw assisted in setting up the meetings for energy cane. Since

there is currently no commercial production of energy cane, a conference call was conducted with researchers at

Louisiana State University, USDA‘s Agriculture Research Service, and Texas A&M Agriculture Research Center in 

Beaumont in order to gather feedback on Thursday, January 13, 2011 at 9:00 a.m. There were seven researchers on 

the call as well as two subcontractors and two employees from the Contractor.

While the conference call provided critical energy cane production information, the lack of producer input prevented 

assessing the producer‘s perception of insurance. Researches did elaborate on risk and causes of loss that they have 

encountered for sugarcane and indicated that these risks would most likely be applicable to energy cane. 

Specifically, researchers recalled freeze events in 1976, 1983, and 1989 that caused significant damage to sugarcane 

ratoon crops for the following years‘ crops. Fifty percent losses were recorded in 1990 following the 1989 freeze. 

Un-harvested sugarcane suffered little damage because the growth acted as insulation, while stubble cane suffered

significant damage and losses were seen on the following year‘s crop. 

Where the epicenter of production was described as being around Jefferson Davis Parish, drought constituted

substantial risk that could reduce yields of sugarcane and energy cane. These soils were said to be soggy in the 

winter and dry in the summer, resulting in droughty conditions during growth in the summer. These conditions favor 

irrigation, which can increase yield by 30 to 40 percent, although not all production is irrigated. The other risk that 

was of concern was the establishment of plant cane. Plant cane is vegetatively propagated (recommended practice) 

and some varieties (particularly F1 hybrids) have characteristics (undefined buds) that cause poor germination. Plant 

cane is propagated in August to December, with a low yield of approximately 6 tons achievable between January to 

March. It is a common practice to harvest this initial biomass in January to March. This harvest is followed by the 

harvest of the actual plant cane crop in the fall and will carry on production from ratoon crops for approximately 4 

years. Timing of harvest each year affects stand life and harvesting too early in consecutive years can cut the 

following years yield in half. The harvest window runs from October through January. A variety known as L79 -

1002 would last 10 years if cut in December each year, but would only last to the third ratoon if cut in October each 

year. Although the Contractor probed for further discussions on relative yield risk of energy cane, researchers did 

not indicate the perceived yield risk in relation to other crops.

Further discussions with the energy cane researchers indicated that yield trials exist in Louisiana in a commercially 

scaled field. These trials are in small plots that run the length of the non-irrigated field. Each plot has three to four 

replications. It was stated that 200 acres of energy cane are currently being transported to the demonstration plant in 

Jennings, Louisiana. Although the Jennings plant is taking in the aforementioned 200 acres of energy cane, no actual 

market for energy cane exist. Growers are currently looking for buyers to contract production; however, BP Biofuels 

is at capacity for the demonstration plant in Jennings and does not envision expansion at this plant. Additional 

details were unavailable as BP Biofuels keeps much of their information confidential.

Since no producers were interviewed, the consensus for developing a crop insurance program for energy cane in 

Louisiana is hard to evaluate. Therefore, comments received from researchers are employed to form the basis of the 

perception of an energy cane crop insurance program. Researchers perceptions were that crop insurance on 

sugarcane acted as a very effective tool for managing yield loss. Since the production practices and growth of energy 

cane is identical to sugarcane, a crop insurance program for energy cane is expected to be as effective and well

received by producers who grow this crop.
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ENERGY CANE RISK EVALUATION 

Introduction 

The RMA‘s Program Evaluation Tool (Diagnostic Instrument), found in the USDA RMA Program Evaluation 

Handbook FCIC-2210 (PEH)13, was used by the Contractor as a supplement to assist in the development of the 

overall recommendation for each dedicated energy crop. This tool creates a better understanding of the risk exposure 

and the potential for various insurance products to transfer a portion of that risk to an insurance pool. The tool also 

assists in gauging the demand for insurance products and potential design issues that may arise. 

This instrument was applied separately to the four regions where listening sessions were held. These regions 

included Tennessee for switchgrass, Louisiana for energy cane, and Montana and Oregon/Washington for camelina. 

The program evaluation tool was completed based on information obtained through listening sessions with 

producers, insurance agents, university extension personnel, crop consultants, conversations with RMA Regional 

Offices, and the Contractor‘s independent research and analysis of the current production and market conditions for 

each crop in each region and those for comparable crops. Results of the Program Evaluation Tool are summarized 

below and the completed Diagnostic Instrument for each region is included in Appendix N.

Louisiana Program Evaluation Tool Summary 

―The Program Evaluation Tool uses a series of questions to elicit information on production processes, market 

characteristics, availability of federally facilitated insurance products, as well as eight demand signals of which five 

are ―Demand Shifter Categories‖ such as yield risk, quality risk, price risk, other sources of revenue risk, the 

sufficiency of non-insurance available to cope with risk, while three are ―Product Design Categories‖ such as 

potential and realized risk classification challenges, potential and realized moral hazard and monitoring issues, and 

other problems that may affect insurance participation. Overall assessment questions are answered for the eight 

categories using a Likert scale of 1 to 5, where higher numbers for the ―Demand Shifter‖ category indicate higher 

demand for insurance products while lower numbers suggest relatively lower demand. For the ―Product Design 

Issues‖ categories, higher numbers indicated either a lack of product design problems or a high likelihood of being 

able to address any existing product design problems, while low numbers indicate more serious product design 

problems and/or problems that cannot be easily addressed. Using the overall assessment scores from each of the 

eight diagnostic categories for each region, the results have been graphically summarized for each region. Based on 

the overall scores assigned to each of the eight diagnostic categories, assessments are made and used to work 

through a generalized decision tree framework (See Appendix I), intended to facilitate decision-making. However, 

the diagnostic instrument may be used independently of the decision tree.‖ (USDA; RMA, 2005)

The following is a summary of the completed Diagnostic Instrument for Louisiana energy cane. Note that the 

completed Evaluation Tool in Appendix N provides the completed answers to the aforementioned questions while 

the section below provides summary level answers to the eight demand signal questions used for Likert scale 

ratings. This summary will best be understood by concurrently reviewing the Program Evaluation Tool in Appendix 

N.

BACKGROUND INFORMATION

Sugarcane is classified as a sugar or sweetener crop. As energy cane is a variety of sugarcane, its agronomic 

classification can be defined as a sugar crop dedicated to energy (Bareja, n.d.). The botanical classification is species 

of the genus Saccharum. Cultivars or complex hybrids are bred from six to thirty-seven species (depending on 

taxonomic system) of tall perennial grasses of the genus Saccharum. ―Energy cane and sugarcane are from the same 

genus, saccharum, and the only difference between them is that energy cane is bred for high fiber content and that

sugarcane is bred for low fiber content but high sugar content.‖ (Mark, Darby, & Salassi, 2009)

Energy cane, like sugarcane is planted as a perennial crop. It is harvested only once per year beginning as early as 

October and ending as late as the end of January. It is typically planted in August via vegetative propagation and, as 

 

13 The document can be downloaded from http://www.rma.usda.gov/FTP/Publications/directives/22000 /06_22010.pdf.
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a standard practice for any biomass, should be removed in January of the first crop year in which yields are about 

one-quarter of the regular yield wet weight. Yields are at peak production in the following year and stands are

harvested for 4-5 years before replanting. (Personal Communication; Energy Cane Conference Call, 2011)

MARKETING

Louisiana is home to Verenium‘s pilot plant operating in Jennings using bagasse in a cellulosic ethanol process 

(Mark, Darby, & Salassi, 2009). Verenium is a Massachusetts-based company that was formed in June 2007 through 

the merger of Diversa Corporation, a global leader in enzyme technology, and Celunol Corporation, a leading 

developer of cellulosic ethanol process technologies and projects. In February 2007, Verenium broke ground with a 

1.4 million gallon-per-year demonstration plant right next to its Jennings pilot site. (Zimmerman, 2008) In July 

2010, BP bought the Verenium plant and technology to accelerate delivery with its low cost, low carbon, sustainable 

biofuels (LaMonica, 2010). To date no information has been obtained on contracting or pricing mechanisms for this 

pilot site. Dr. Ben Legendre suggested that the plant may not ―go full scale‖ and no current commercialized 

production is in place at the Jennings plant; however, production may commence at a Florida based plant.

RMA-FACILITATED INSURANCE PRODUCTS

Currently there are no RMA-facilitated insurance products available for energy cane utilized for dedicated energy in 

Louisiana or any other region in the United States.

YIELD RISK

Relative risk is used to adjust absolute risk magnitudes, which vary across crops, to a relative level in order to 

facilitate comparability of crop risk. Although data for energy cane are not reported by NASS, (assuming similarity

of energy cane to sugarcane in relative yield) sugarcane yields can be used to assist in quantifying the yield risk

associated with energy cane. 

The coefficient of variation, calculated as the standard deviation divided by the mean, facilitates the comparison 

across crops with different expected yields and allows the comparison of variation of unlike data series. As 

illustrated in Figure 38, yield risk for sugarcane in Louisiana is lower than most of the other crops produced over the 

past twenty years. The coefficient of variation for sugarcane is 0.13, while winter wheat is 0.24. This suggests that 

energy cane is a relatively low risk crop to produce, compared to winter wheat and other small grains. Utilizing this 

information, a Likert scale rating from 1 to 5 for non-catastrophic yield risk, ―1‖ being very low relative yield risk 

and ―5‖ being very high relative yield risk, can be assigned. This non-catastrophic yield risk of energy cane was 

rated a ―1‖ on the Likert scale; therefore, the crop has a relatively low yield risk.

Factoring in catastrophic yield risk results in a higher relative yield risk rating of ―3‖ on the Likert scale. The risk for 

a catastrophic yield loss is hard to measure for energy cane, but a more informed estimate can be obtained by 

utilizing the cause of loss data for sugarcane from RMA. Utilizing the loss cost ratio as a way of measuring severity 

of causes of loss, the number of observations with the loss cost ratio above 25 percent14 was recorded. Analysis of 

these observations for Louisiana sugarcane suggest that cold wet weather, drought, excess moisture, freeze and frost, 

heat, hurricane, insects, other (e.g., snow, lightning, etc.), and plant disease can cause sugarcane losses in excess of 

50 percent or more. These observations are illustrated in Figure 39, and the data suggest that as much as 40 percent

of the time over 25 years, losses greater than 50 percent have occurred for sugarcane due to drought and excess 

moisture; therefore, for energy cane, potential exist for a similar frequency and severity of loss for these types of 

events.

 

14 It captures a minimum 50% yield loss based on assumption that guarantee is established at 75 percent coverage level for all 

insurance policies.
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Figure 38: Louisiana State Total Crop Yield Risk Comparison 

Source: Adapted from USDA; NASS, 2011

1 1 1 1

2 2 2

3

6

10

11

0

3

6

9

12

15

Number of Observation

Louisiana Sugarcane Cause of Loss with LCR Greater than 25% 

Adjusted for 25-Year Time Period 

Figure 39: Louisiana Sugarcane Cause of Loss Observations of Lost Cost Ratio in Excess of 25% 

Source: Adapted from USDA; RMA Cause of Loss Summary of Business, 2011
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QUALITY RISK

Two processes to convert energy cane from a feedstock to dedicated energy can be utilized: (1) convert the sugar to 

ethanol and/or (2) use bagasse and/or biomass in a cellulosic ethanol process. Because low sucrose content would 

lower ethanol yield, quality risk may be of concern when utilizing the sugar to convert to ethanol. A more typical 

scenario is to use bagasse and/or all the energy cane biomass in a cellulosic process to convert to a dedicated energy 

product. Since the pilot plant process in Jennings focuses on using bagasse and all of the energy cane biomass, 

discussions are focused on this process for evaluating quality risk. This results in a very low quality risk (Likert 

rating of ―1‖) assessment for energy cane in Louisiana.

PRICE RISK

There are no operational commercial cellulosic processing facilities operating in the United States beyond the 

demonstration plant in Jennings; therefore, price risk is difficult to quantify, as no publicly available price data exist. 

Growers contracted with BP Biofuels are paid solely based on total biomass (i.e. dry ton) delivered to the 

processors. Production practices are the same for sugarcane and energy cane; therefore, to attract acres of energy 

cane, BP Biofuels provides growers with prices that will help return similar net margins of sugarcane. While pricing 

data for BP Biofuels remains confidential, Matt Musial (2011) indicated that producers are paid per dry ton for all 

biomass, with a guaranteed payment based on an unspecified yield. Further, establishment and transportation cost to 

the demonstration plant are paid for by BP (Personal Communication; BP Biofuels, 2011).

Utilizing ―United States Prices Received by Farmers‖ published by USDA ERS, a quantitative assessment of price 

risk can be performed. The ten-year average price and standard deviation of prices for each commodity were 

calculated and a coefficient of variation was utilized to determine the crops with the least variation. In Louisiana, 

sugarcane is the primary crop and it exhibits less variation in price than many other commodities grown as 

illustrated in Figure 40. Sugarcane and cotton exhibited the least price variation with a coefficient variation of 0.18 

and 0.21 respectively, while corn, grain sorghum, soybeans, winter wheat and rough rice in ascending order were 

substantially more variable year to year. As previously mentioned, if processors provide comparable prices to 

motivate energy cane production, the data suggest that the price risk of energy cane from year to year would be less 

than that of other commodities. As such, price risk is rated very low (Likert rating of ―1‖).

Figure 40: U.S. Commodity Prices Received by Farmers 10 Year Variation 

Source: Adapted from USDA; ERS, 2011
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OTHER SOURCES OF REVENUE RISK

Production practices of energy cane are similar to sugarcane and are not impacted to an extensive degree by other 

sources of revenue risk such as prevented planting or replanting. Therefore, other sources of revenue risk are rated 

very low (Likert rating of ―1‖).

SUFFICIENT NON-INSURANCE COPING MECHANISMS

The demand for various crop insurance products (existing and potential) is influenced by non-insurance coping 

mechanisms including government price and income support programs, government disaster programs, marketing 

contracts including futures and options on futures for exchange-traded commodities, crop portfolio and spatial 

diversification, risk reducing production technologies and practices, and lenders‘ attitudes, expectations, and rulesof-thumb.

Federal commodity programs tend to reduce farmers‘ exposure to price risk and therefore, revenue risk. For energy 

cane, no government crop programs are available in terms of marketing loans and counter cyclical payments; 

however, the Biomass Crop Assistance Program (BCAP) is available. BCAP supports establishing and producing 

eligible crops for the conversion to bioenergy through project areas and contracts on land for annual and non-woody 

perennial crops up to five years or for woody perennial crops up to 15 years. Through a matching payment program 

BCAP assists agricultural and forest land owners and eligible material owners with collection, harvest, storage, and 

transportation of eligible material for use in qualified Biomass Conversion Facilities (BCF). These payments will be 

available to eligible material owners at the rate of $1 for each dollar per dry ton paid by the BCF, limited to a 

maximum of $45 per dry ton and a 2-year payment duration. (USDA; FSA, 2010b)

Other programs such as Noninsured Crop Disaster Assistance Program (NAP) are not available, nor has there been a 

history of federal disaster payments issued by the Crop Disaster Program15. Producers who (a) elect not to obtain 

federal crop insurance on non-insurable crops or crops for which the producer received crop loss assistance and (b)

elected not to participate in NAP for the year in which benefits are received, must purchase crop insurance at a level 

greater than the catastrophic coverage for insurable crops for the next two crop years in the administrative county 

where the crop was produced or prevented from being produced (USDA; FSA, 2011).

Production contracts often mitigate farmers‘ exposure to some, but not all, risks. Although no contracting guidelines 

have been acquired, it is anticipated that in terms of pricing mechanisms, approximately 100 percent of the crops

would be produced under a production contract with a first handler or processor and would be priced before harvest 

and establishment. Under the terms of the contract the grower is (1) not exposed to contract risk (the grower is not 

required to deliver on the contract under production shortfalls); (2) the grower is not exposed to quality risk (no 

significant price penalties if the product does not meet the quality characteristics specified in the contract); and (3)

the grower is not exposed to price risk (prices for specific quality characteristics are not specified in the contract). 

For this reason, these mechanisms may reduce demand for crop insurance. In addition, if price and yield are 

significantly negatively correlated, revenue variability is reduced and, all other things equal, the demand for crop 

insurance products will be reduced. However, energy cane yield and price are expected to be independent of each 

other because contracted production (supply) will always be adjusted to the appropriate demand level. Thus, the 

producer is not exposed to significant price risk or volatility, which alleviates risk exposure.

Financial leverage, growth strategies, and recent events all impact farmers‘ ability to self-insure. Since no energy 

cane producers were interviewed, a perception of producers ability to self-insure was in between ―strongly disagree‖

and ―strongly agree.‖

Diversifying the farm enterprise across multiple commodities (crops and/or livestock) has the potential to 

significantly reduce whole farm revenue variability. Yield shortfalls on one crop may be partially offset by high 

yields on a different crop. In addition, a carefully diversified portfolio of crop enterprises can help farmers manage 

the revenue effects of price risk when other means of managing price risk are limited. For example, if yield risk for a 

crop (or crops) is small but price risk is significant, a farmer might choose to have no yield insurance and manage 

 

15 The Noninsured Crop Disaster Assistance Program is available to producers for losses in excess of 35 percent at a payment rate 

of 65 percent for insured crops and non-insurable crops; and 60 percent for uninsured crops.
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revenue risk due to price variation through diversification. In this region there are two growers of energy cane; 

however, no assessment can be made on the total farm revenue attributed to this crop. For diversification to generate 

whole farm revenue risk reduction, the correlation between the commodities must be low (i.e., negatively 

correlated). Commodities with highly positively correlated revenue streams act as if they were a single commodity 

and, as a result, diversification will not significantly reduce revenue risk as well as the demand for crop insurance. 

Assuming growers also grow sugarcane on their farm, since the crops possess the same growth period, their yield 

risk and revenue risk are deemed to be strongly positively correlated which creates a higher demand for crop 

insurance.

It is assumed that 75 percent of farmers that would grow energy cane would be full-time farmers. Part-time farmers 

are typically less likely to focus on risk management strategies, including crop insurance. Since energy cane in this 

region is assumed to be produced by more full-time farmers, the demand for crop insurance products is greater. 

Further spatial diversification, like commodity diversification, reduces whole farm revenue variability if the yield 

correlation across farm parcels is low. The assumption is that energy cane farms in this region are not spatially 

diversified; thus, farms are more exposed to yield risk, increasing the demand for insurance.

Private-sector insurance products can have a mixed impact on the demand for RMA facilitated crop insurance 

products. If the private-sector products have features that complement or require the use of underlying RMAfacilitated crop insurance, they potentially increase the demand for RMA-facilitated insurance products. On the 

other hand, some products may be substitutes or partial substitutes for RMA-facilitated crop insurance. Currently 

there are no private products for energy cane; therefore, this may increase the demand for a FCIC crop insurance 

policy.

Lenders can have a substantial impact on farmers‘ use of crop insurance products. Often, the insured‘s value on 

growing crops is treated as a current asset on the balance sheet. Lenders‘ awareness, understanding of, and attitudes 

toward crop insurance have an impact on demand, particularly under circumstances where farmers are highly 

leveraged. In the case of energy cane, it is assumed that lenders may be indifferent to producers purchasing 

insurance.

Overall, based on the low availability of sufficient non-insurance coping mechanisms for producers of energy cane

in Louisiana, the expectation of demand for insurance is high.

RISK CLASSIFICATION

Risk classification is a serious challenge in rating crop insurance products. Non-insureds and insureds have different 

perspectives on the cost of crop insurance coverage. Some individuals choose not to insure because they utilize the 

non-insurance coping mechanisms discussed in the previous category. In other cases, however, the amount of 

insurance purchased is limited because some individuals perceive the premium rate as being ―too high.‖ However, it 

is possible that existing classification methods will result in premium rates that are appropriate (or even too low) for 

one group but too high for another group. Unfortunately, since there are no FCIC insurance products or private 

products for energy cane, risk classification cannot be measured. Therefore, no Likert scale measurement was 

provided for this category and it is non-applicable in assisting the Contractor in following the decision tree process.

MORAL HAZARD

This category attempts to assess whether moral hazard may cause higher crop insurance indemnities. If so, the 

higher indemnities may be reflected in higher premium rates that could limit the purchase of insurance. A

quantitative measure is used to help assess whether insuring energy cane will likely be prone to significant moral 

hazard problems.

A measurement of variation in yield and quality caused by unavoidable ―acts of nature‖ or avoidable ―acts of 

management‖ suggests that the yield variation and quality variation are almost exclusively due to ―acts of nature‖ 

(potential for gaming is low). Since potential for gaming both yield and quality is low, overall concern for moral 

hazard is small (Likert scale rating ―4‖).
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PROBLEMS AFFECTING INSURANCE PARTICIPATION

The previous categories dealt with both the potential for, and realization of problems associated with risk 

classification and moral hazard. This category focuses on other problems that may limit demand for RMA-facilitated 

crop insurance products for this crop in this region. Because no FCIC programs currently exist for energy cane, no 

Likert scale measurement was provided for this category and it is non-applicable in assisting the Contractor in 

following the decision tree process.

Louisiana Program Evaluation Tool Results 

As illustrated in Figure 41 below, Louisiana energy cane quality risk, price risk, and other sources of revenue risk

are rated very low (Likert scale ―1‖), while yield risk is rated moderate (Likert scale ―3‖). Sufficient non-insurance 

coping mechanisms are rated high (low availability). Weighting the yield component as a higher demand variable, 

these demand signals indicate a moderate demand for this insurance product and a potential market. Product design 

issues such as moral hazard are assigned a higher rating (very low moral hazard), indicating that the extent of 

potential problems is small. 

See Appendix M for a graphical representation of the decision tree process for Louisiana. Highlighted red arrows 

mark the decision tree path. Since currently there is no significant market, the decision tree suggests that no 

insurance policy needs to be developed.

 

Figure 41: Louisiana Energy Cane Demand Shifter and Product Design Signals 

ESTIMATION OF YIELD PROBABILITY DISTRIBUTIONS FOR LOUISIANA 

ENERGY CANE 

Estimating yields and yield risks of relatively new crops in regions heretofore unutilized as production areas raises 

questions regarding expected yields, yield variability, and profitability. With little or no production history, USDA‘s 

RMA is faced with uncertain production capabilities when developing crop insurance programs. Agronomists, soil 

scientists, hydrologists, engineers, and others have developed tools for evaluating yield possibilities. Many of these 

tools are incorporated into computerized crop simulation models. One such model that began development in the 

mid-80s is EPIC, the original acronym for the Erosion Productivity Impact Calculator model.

EPIC has evolved over time to include the many impacts of crop production along with climatic factors on the 

environment. The model has been utilized by scientists worldwide and EPIC is now the acronym for Environmental 
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Policy Integrated Climate model. One of the principal developers is Dr. J.R. Williams, Blackland Research and 

Extension Center, Texas AgriLife Research, Temple, Texas. The EPIC model has been applied at regional 

(Williams, 1995) and national scales (Thompson, Izaurralde, Rosenberg, & He), (Izaurralde, Rosenberg, Brown, & 

Thompson).

EPIC (Potter, Atwood, Kellog, & Williams) is a daily time-step model capable of simulating a wide array of crop 

production and environmental processes including plant growth, crop yields, plant competition, and soil erosion as 

well as water, and nutrient balances. Biomass growth is related to solar radiation intercepted by the plant canopy, 

vapor pressure deficit, CO2 concentration, and other physiological stresses including water, temperature, N, P, and 

soil aeration deficits/surpluses. Similarly, root growth is affected by bulk density, temperature, and aluminum 

content. Soil carbon algorithms calculate carbon balance including losses of carbon from water and wind erosion. 

Plant growth and development is influenced by temperature during the growing season, expressed within the model 

as heat units. The quantity of heat units necessary for the crop to reach maturity varies by latitude.

Among the model‘s many features is a stochastic weather generator. Weather can be input from historical records or 

it can be estimated stochastically using precipitation, air temperature, solar radiation, wind, and relative humidity 

parameters developed from historical records. 

The model simulates growth and development of a crop each day from emergence to harvest. Initially, a crop 

production schedule is developed from producers indicating their dates (and application rates) of operations prior to 

and during the growing season for tillage, planting, pesticides, irrigations, fertilizers, and harvesting. Adding the 

rates and dates of crop production inputs and other management information facilitates the simulation of tillage, 

irrigation, and fertilizer applications. Each operation combined with climate can impact crop production; either 

through growth, erosion, soil water, or nutrients, and most times a combination of these elements. 

Among its many features is a stochastic weather generator. This weather generator utilizes historical weather data 

from a given site including maximum and minimum daily temperatures, humidity, rainfall, wind velocity, and solar 

radiation. The model simulates growth and development of a crop each day from emergence to harvest. Initially, a 

crop production schedule is developed from producers indicating their dates (and application rates) of operations 

prior to and during the growing season for tillage, planting, pesticides, irrigations, fertilizers, and harvesting. Each 

operation combined with climate can impact crop production; either through growth, erosion, soil water, or 

nutrients, and most times a combination of these elements. 

The primary objective of this analysis of energy cane in Louisiana is to utilize the EPIC model, which has been 

adapted to a Windows® application, WinEPIC, to estimate an array of stochastic yields from which to develop a 

probability distribution function. Thereby, yield variability (risk) can be quantified and variations in profits 

revenues. Sub-objectives include: a) by utilizing local weather data along with producer‘s energy cane yields and 

management data from a designated area in southern Louisiana, calibrate crop coefficients to best represent the 

current yields and production conditions, and b) to utilize the calibrated model for producing three probability 

distributions of 1) the establishment year, 2) the plant cane year, and 3) the four ratoon years. These 600 years of 

stochastic yield observations were developed utilizing 100 successive 6-year rotations. 

The methodology includes developing a production schedule of tillage, planting, fertilization, pesticide applications, 

and harvesting operations along with management decisions regarding typical dates of each operation, seeding rates, 

and application rates of fertilizers and pesticides. 

Four years of production experience were utilized from the period 2005-2008. Yields were averaged each year over 

four energy cane varieties. At harvest, the fresh weight yields were 35.91, 46.1, 34.2, and 33.8 tons/acre in 2005 -

2008, respectively. Fertilization typically included 90 pounds/acre N the plant cane year and 120 pounds/acre N for 

subsequent ratoon years. Energy cane was established in the late summer 2004 and harvested in December to 

remove first year growth. Harvest dates for the plant cane and ratoon crops were a few days before the milling dates 

of Dec. 13, 2005, Dec. 12, 2006, Dec. 16, 2007, and Nov. 23, 2008. 

The energy cane rotation is expected to be for five to six years, including the establishment year. A production 

schedule was developed to simulate typical operations and was coupled with actual National Weather Service daily 

weather data for Jennings, Louisiana, since on-site weather records were not available. 
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Using EPIC to simulate the historical fresh weight yields, after calibrating crop physiological coefficients the 

simulated yields were 36.3, 43.0, 33.8, and 33.7 tons/acre for 2005-2008, respectively. The following graph depicts 

the relationship of simulated to producer yields; thereby producing a regression line having a slope of 0.916 with an 

R-squared=0.9869 (Figure 42). A slope of 1.0, depicted by the dashed line, indicates a perfect relationship or perfect 

correlation of simulated yields to the average producer yields for the four years.

y = 0.916x + 2.142

R2 = 0.9869

0

1 0

2 0

3 0

4 0

5 0

0 1 0 2 0 3 0 4 0 5 0

Simulated (t/ac)

Producer (t/ac)

Figure 42: Correlation of simulated yields to producer average yields 

Further statistical evidence that the simulated yields approximate the producer yields is illustrated by the paired ttest in Table 47. A difference of zero is being tested between the means of both sets of fresh weight yields, 36.71 

versus 37.51 tons/acre for the producer mean versus the simulated mean, respectively. In this case, t = 1.03 which is 

less than the critical t value of 5.84 indicates the means are not significantly different at the 1 percent level.

Table 47: Energy Cane Yield Paired t-Test 

Item Simulated Producer

Mean 37.505 36.708

Variance 33.425 19.093

Observations 4 4

Hypothesized Mean Difference 0

df 3

t Stat 1.025

t Critical two-tail 5.841

Utilizing the calibrated coefficients that were developed by simulating producer yields, a single run of 600 years was 

then simulated to produce 100 six-year rotations (the first year for establishment, the second is the plant cane year, 

and four ratoon crops). Daily weather based on historical records for 1960-2010 from the nearby weather station at 

Jennings, Louisiana, was utilized for the stochastic simulation. Three factors were adjusted in the long-term 

simulation: 1) the initial soil profile was maintained each year by stopping erosion, 2) N and P rates were adjusted 

from those in practice above to provide adequate plant nutrition over the long-term, and 3) weather parameters were 

developed from maximum and minimum temperatures and rainfall from daily weather records at Jennings, 

Louisiana. Wind speed records were obtained from Crowley, Louisiana. Daily weather coupled with the production 
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practices over the long-term simulation period produced three probability distributions of 1) the establishment year, 

2) the plant cane year, and 3) the four ratoon years. The following graphs depict the probability distributions. 

The 100-year probability distribution of the fresh weight yield with typical 75 percent water content at harvest for 

the establishment year is illustrated in Figure 43 and Figure 44. The average was 18.48 tons/acre fresh weight. 

Yields varied from a low of 5.38 tons/acre to a high of 28.54 tons/acre.

5 1 0 1 5 2 0 2 5

Figure 43: PDF of Establishment Year 1 Energy Cane Yields in Louisiana 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

5 1 0 1 5 2 0 2 5 Prob

Figure 44: CDF of Establishment Year 1 Energy Cane Yields in Louisiana 

The average fresh weight yield of the 100 stochastic observations for the plant cane year was 45.99 tons/acre. The 

range was 33.18 to 65.12 tons/acre. The probability distribution is illustrated in Figure 45 and Figure 46.

33.00 38.00 43.00 48.00 53.00 58.00 63.00

Figure 45: PDF of Plant Cane Year 2 Energy Cane Yields in Louisiana 
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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

3 3 3 8 4 3 4 8 5 3 5 8 6 3 Prob

Figure 46: CDF of Plant Cane Year 2 Energy Cane Yields in Louisiana 

The probability distribution for all four ratoon years of 100 stochastically generated yields per year is illustrated in 

Figures Figure 47 and Figure 48. The average yield was 42.94 tons/acre fresh weight with a range of 12.71 to 63.8 

tons/acre.

1 0 2 0 3 0 4 0 5 0 6 0

Figure 47: PDF of Ratoon Years 3-6 Energy Cane Yields in Louisiana 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 0 2 0 3 0 4 0 5 0 6 0 Prob

Figure 48: CDF of Ratoon Years 3-6 Energy Cane Yields in Louisiana 

ENERGY CANE INSURANCE PREMIUM CALCULATIONS 

The yield distributions for energy cane grown in southern Louisiana, as developed using the EPIC model, were used 

to estimate yield insurance premiums. There are three yield distributions for energy cane: Establishment Year 1, 

Plant Cane Year 2, and Ratoon Crop Energy Cane. These three yields distributions are best defined as:
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Establishment Year 1 – the crop is planted in August and it will produce a small crop that year which has to 

be harvested to make room for the next year‘s crop. This short harvest is pure biomass and because that is 

the purpose of the crop, we have estimated a probability distribution for the yield.

Plant Cane Year 2 – in the sugar cane industry the first crop harvested after planting a crop is called the 

―plant cane year.‖ We adopted that terminology to describe the distribution for energy cane yields 

obtained from the first full year of production.

Ratoon Crop – is the yield we calculated for energy cane in years 3-6.

The analysis procedure used for all three Energy Cane yield distributions is the following:

The summary statistics to describe the distribution are calculated. Probability density function (PDF) and 

cumulative distribution function (CDF) charts for the distribution are provided.

The distribution was tested for Normality. 

Parameters for 15 parametric distributions are estimated using a maximum likelihood procedure in 

Simetar©. The parameters for an empirical distribution are estimated as well.

The parameters for the 16 distributions are used to simulate the yield distribution for 500 iterations. 

Summary statistics for the distributions are compared to the original distribution.

The CDFDEV function in Simetar© are used to determine how closely the 16 distributions reproduced the 

original distribution. Based on the CDFDEV criteria the four distributions that most closely simulate the 

original distribution are selected for analysis.

The best four distributions were simulated in Simetar© for 25,000 iterations to estimate ―fair insurance 

premiums‖ for eight yield coverage levels ranging from 50 percent to 85 percent of the average yield 

reported by the farmers in a focus group interview. The random number generator used for the analysis 

uses a Latin hypercube procedure and generates pseudo random numbers based on a fixed seed. 

The no load and fully loaded fair premiums are reported as $/acre values for the four assumed probability 

distributions for eight levels of possible year coverage.

The no load fair insurance premium is calculated by multiplying the probability of each state of nature 

(indemnity) by its respective probability. Using a Monte Carlo simulation approach each insurance policy 

was simulated for 25,000 yields drawn at random from assumed probability distributions. 

The loaded insurance premium is calculated using a 0.90 unit division load factor, a 0.88 FCIC disaster 

reserve factor, and a 1.30 qualitative load factor.

During the conference call with energy cane, breeders and experimentation sugar cane growers we were 

told that energy cane yield could be susceptible to freeze damage. The EPIC model is not capable of 

simulating freeze damage so a separate Bernoulli distribution with a 10 percent probability was used to 

augment the EPIC generated yield distributions during the insurance simulation step. At the beginning of 

each iteration, a uniform random deviate (0, 1) was drawn at random and if the value was less than 10

percent, the simulated yields for the four probability distributions were reduced by 50 percent. The 50

percent yield reduction was based on the scientists‘ experience with sugar cane under freeze conditions. 

The 10 percent probability of a yield reducing freeze event was calculated by counting the number of 

times the minimum daily temperature fell below 29 degrees for five consecutive days in December and 

January over the 121 year period from 1889 to 2010.

Each of the EPIC distributions is presented separately because they are completely different distributions.

Establishment Year Energy Cane Yield Distribution 

The summary statistics in Table 48 report that the average yield in the establishment year is 18.4 tons/acre and the 

minimum expected yield is 5.38 tons/acre. The distribution is slightly skewed to the left given a skewness statistic of 
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-0.41. This shape is confirmed in the PDF and CDF charts for the establishment year (year one) yield distribution 

(Figure 43 and Figure 44). 

Table 48: Summary Statistics for Base Yield Distribution of Establishment Year Energy Cane in Louisiana 

Base Yield

Mean 18.43

Standard Deviation 4.27

Min 5.38

Median 18.90

Max 28.54

Skewness (0.41)

Kurtosis 0.04

Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 49).All five tests reported that statically we cannot reject the null 

hypothesis that the distribution is distributed normal at the alpha equal 5 percent level of significance.

Table 49: Normality Test of the Yield Distribution for Establishment Year Energy Cane in Louisiana 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.983 0.226 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Anderson-Darling 0.541 0.162 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Cramer von Mises 0.088 0.841 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Kolmogorov-Smirnov 0.078 NA Consult Critical Value Table

Chi-Squared 26.200 0.125 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 50 were simulated using a common uniform 

standard deviate to insure the results are directly comparable. The summary statistics for these distributions and the 

goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are summarized in Table 

50. The 16 distributions were simulated 500 iterations with a common uniform standard deviate. The resulting 

summary statistics are reported beside the parameters. All of the distributions reproduce the mean, as expected but a 

number of them fail to reproduce the range of the distribution. The CDFDEV criteria indicate the four best 

distributions for simulations: empirical, Weibull, normal, and beta; with the empirical distribution being best.
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Table 50: Univariate Parameter Estimation for the Yield Distribution of Establishment Year Energy Cane in 

Louisiana 

Distribution Parameters Parm. 1 Parm. 2 Mean Std. Dev. Min Max CDFDEV Coefficient Rank

Beta α, β; A≤x≤B, α,β>0 2.842 2.336 18.09 4.64 6.48 28.03 0.8666 Fourth

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 18.900 3.479 18.90 4.92 (3.10) 41.15 10.0144

Exponential μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 5.375 13.057 18.43 13.05 5.39 97.93 296.1777

Gamma α, β; 0≤x<∞, α,β>0 15.974 1.154 18.43 4.62 7.29 36.39 3.9075

Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 18.595 2.448 18.59 4.44 1.42 35.95 3.1404

Log-Log μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 16.234 4.579 18.88 5.87 7.31 48.69 22.8514

Log-Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 6.996 18.256 18.88 5.08 6.70 50.28 19.5618

Lognormal μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 2.883 0.266 18.50 5.01 7.78 41.21 9.0547

Normal μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 18.433 4.254 18.43 4.26 5.15 31.80 0.8395 Third

Pareto α, β; α≤x<∞, α,β>0 5.375 0.833 119.98 1,237.79 5.38 26,709.11 21,401,740.0980

Uniform a, b ; a≤x≤b 5.375 28.540 16.96 6.69 5.40 28.52 12.6028

Weibull α, β; 0≤x<∞, α,β>0 5.061 3915773.381 18.44 4.18 5.02 29.55 0.2544 Second

Geometric p ; x=1,2,...; 0≤p≤1 0.053 18.88 18.37 1.00 131.00 684.2553

Poisson λ ; x=0,1,...; 0≤λ<∞ 17.880 17.89 4.24 6.00 33.00 1.3368

Empirical Si, F(x) 18.47 4.02 5.98 28.28 0.0647 Best

MLEs Statisitcs Goodness of Fit

INSURANCE PREMIUMS FOR ESTABLISHMENT YEAR ENERGY CANE

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 18.4 wet tons/acre (APH) assuming a price 

guarantee of $65/dry ton or $16.25 per wet ton16. The eight policies are defined in terms of fraction from the APH 

and are 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, and 0.85. APH yield was assumed to equal the average for the base 

yield distributions from the EPIC runs.

The calculated insurance premiums are reported in Table 51. Based on the assumption that the Plant Year yield for 

energy cane is distributed empirically, the no load premiums are less than $11.00/acre for policies that insure 50

percent-65 percent of the APH. The no load premium at 70 percent APH is $10.70/acre. It increases rapidly 

thereafter with a $14.32/acre premium for 75 percent APH coverage, $18.74/acre for 80 percent APH coverage, and 

reaches $23.75/acre for 85 percent APH coverage. 

The calculated insurance premiums for the other three probability distribution assumptions are all higher than those 

for the empirical distribution. For example, for the 85 percent APH policy, the Weibull distribution indicates a 

$24.23/acre premium, the normal distribution has a $23.92/acre premium, and the beta distribution has a $28.44/acre 

premium, while the empirical distribution calculated a premium of $23.57/acre. 

The difference in premiums for each yield insurance policy differs across probability distributions due to the weight 

the distribution places on the insured range of the yield distribution. This relationship is illustrated in Figure 49, a 

PDF of the original yield distribution and the four selected distributions. The beta distribution is associated with the 

highest premiums for the 70 percent - 85 percent APH policies because it has more weight in the higher yield values 

over the range of 5.5 to about 15 tons/acre.

The fully loaded premium for the 85 percent APH coverage ranges from $38.69/acre for the empirical distribution to 

$46.69/acre for the beta distribution (Table 51). The fully loaded premium was calculated by dividing the no load 

fair premium by 0.90 (the unit division load factor) and then dividing that result by 0.88 (the FCIC disaster reserve

factor) and multiplying by 1.3 (the qualitative load factor). The qualitative load factor of 1.3 is used to adjust for the 

lack of risk on the regression equations for physical relationships and production functions in the EPIC model. 

EPIC‘s only risk component is from the weather variables so it lacks the risk normally associated with simulating a 

regression equation used to predict production based on the values for the independent variables. 

The loss cost ratios for each of the eight yield insurance policies were calculated as the ratio of the expected 

indemnity or loss and the liability. The loss cost ratio for the 85 percent APH policy ranges from 9.26 percent to 

11.17 percent based on the distribution assumed (Table 51). 

 

16 A typical moisture content of 75 percent at harvest was utilized to convert from dry weight to wet weight.
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Table 51: Yield Insurance Premiums for the Establishment Year of Energy Cane in Louisiana; Assuming 

Alternative Yield Coverage Levels and Yield Distributions, an APH Yield of 18.4 Tons Per Acre, and Guaranteed 

Price of $16.25 Per Wet Ton 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 149.77 164.74 179.72 194.70 209.67 224.65 239.63 254.60

No Load Fair Premium ($/acre)

Empirical 2.11 3.58 5.55 7.95 10.70 14.32 18.74 23.57

Weibull 2.33 3.84 5.87 8.43 11.51 15.12 19.33 24.23

Normal 2.27 3.75 5.73 8.20 11.18 14.73 18.94 23.92

Beta 2.80 4.50 6.79 9.73 13.35 17.67 22.69 28.44

Fully Loaded Premium ($/acre)

Empirical 3.46 5.88 9.11 13.05 17.56 23.51 30.76 38.69

Weibull 3.82 6.30 9.64 13.84 18.89 24.82 31.72 39.76

Normal 3.72 6.16 9.41 13.46 18.36 24.18 31.08 39.26

Beta 4.59 7.39 11.15 15.96 21.91 29.01 37.24 46.69

Loss Cost (%)

Empirical 1.41% 2.17% 3.09% 4.08% 5.10% 6.37% 7.82% 9.26%

Weibull 1.56% 2.33% 3.27% 4.33% 5.49% 6.73% 8.07% 9.52%

Normal 1.51% 2.28% 3.19% 4.21% 5.33% 6.56% 7.90% 9.39%

Beta 1.87% 2.73% 3.78% 4.99% 6.37% 7.87% 9.47% 11.17%

Fully Loaded Base Premium (%)

Empirical 2.3% 3.6% 5.1% 6.7% 8.4% 10.5% 12.8% 15.2%

Weibull 2.6% 3.8% 5.4% 7.1% 9.0% 11.0% 13.2% 15.6%

Normal 2.5% 3.7% 5.2% 6.9% 8.8% 10.8% 13.0% 15.4%

Beta 3.1% 4.5% 6.2% 8.2% 10.5% 12.9% 15.5% 18.3%

5 1 0 1 5 2 0 2 5 3 0

Base Yield Empirical Weibull Normal Beta

Figure 49: PDF of Alternative Distributions of Energy Cane Yields for Establishment Year in Louisiana 

Base Premium Rates for Establishment Year Energy Cane

The base premiums calculated for the fully loaded premium range from 15.2% to 18.3% for the 85% APH yield 

coverage policy. At the 65% coverage level, the base premium ranges from 6.7% to 8.2%. At the 50% coverage 

level, the base premium rates range from 2.3 to 3.1%.

SUMMARY FOR ESTABLISHMENT YEAR ENERGY CANE YIELD DISTRIBUTION

The yield distribution has a mean of 18.4 tons/acre and a range of 5.3 tons/acre to 28.5 tons/acre. Simulation results 

for eight possible yield insurance policies indicate high fully loaded premiums (greater than $21/acre) for insured 

yield equal to 70 percent of an APH equal to 18.4 tons/acre and a price election of $16.25/ wet ton. At the highest 

levels of yield coverage (85 percent of APH), the insurance premiums range from $46.69 to $38.69/acre, or 15.2 

percent to 18.3 percent of liability. 

The biggest problem with insuring energy cane production for the establishment year is the lack of information to 

calculate the farmer‘s APH. By definition of an establishment year there is no yield history for the farmer to 
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calculate the APH on that field. Using the average yield from the EPIC model will have to suffice until sufficient 

results across years and fields are accumulated to develop a more appropriate estimate of the APH and its 

distribution.

Plant Cane Year Energy Cane Yield Distribution 

The summary statistics in Table 52 report that the average energy cane yield for a plant year cane (year 2) stand is 

45.99 tons/acre and the minimum expected yield is 33.18 tons/acre. The distribution is slightly skewed to the right 

given a skewness statistic of 0.11. This shape is confirmed in the PDF and CDF charts for the plant year cane crop 

yield distribution (Figure 45 and Figure 46).

Table 52: Summary Statistics for Base Yield Distribution of Plant Year Cane Energy Cane in Louisiana 

Base Yield

Mean 45.99

Standard Deviation 5.91

Min 33.18

Median 46.21

Max 65.12

Skewness 0.11

Kurtosis 0.51

Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 53). All five tests reported that statically we cannot reject the null 

hypothesis that the distribution is distributed normal at the alpha equal 5 percent level of significance.

Table 53: Normality Test for the Yield Distribution of Plant Year Cane Energy Cane in Louisiana 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.985 0.306 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Anderson-Darling 0.339 0.494

Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Cramer von Mises 0.039 0.705 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

Kolmogorov-Smirnov 0.054 NA Consult Critical Value Table

Chi-Squared 17.600 0.549 Fail to Reject the Ho that the Distribution is Normally 

Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 54 were simulated for 500 iterations using a 

common uniform standard deviate to insure the results are directly comparable. The summary statistics for these 

distributions and the goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are 

summarized in Table 54. The 16 distributions were simulated 500 iterations and the resulting summary statistics are 

reported beside the parameters. All of the distributions reproduce the mean, as expected but a number of them fail to 

reproduce the range of the distribution. The CDFDEV criteria indicate the four best distributions for simulating the 

distribution are empirical, gamma, normal, and lognormal; with the empirical distribution being best.
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Table 54: Univariate Parameter Estimation for the Yield Distribution of Plant Year Cane Energy Cane in 

Louisiana 

Distribution Parameters Parm. 1 Parm. 2 Mean Std. Dev. Min Max CDFDEV Coefficient Rank

Beta α, β; A≤x≤B, α,β>0 2.021 3.066 45.87 6.34 33.58 63.10 2.2398

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 46.212 4.583 46.21 6.48 17.22 75.52 12.9588

Exponential μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 33.178 12.811 45.99 12.81 33.19 123.99 203.0698

Gamma α, β; 0≤x<∞, α,β>0 60.243 0.763 45.99 5.93 29.68 66.89 1.3845 Second

Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 46.041 3.296 46.04 5.98 22.91 69.40 4.3479

Log-Log μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 43.056 5.724 46.36 7.34 31.90 83.63 19.5668

Log-Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 13.689 45.974 46.38 6.21 27.53 77.15 7.4599

Lognormal μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 3.820 0.130 45.99 6.01 30.40 68.61 1.8447 Fourth

Normal μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 45.989 5.880 45.99 5.89 27.63 64.47 1.5265 Third

Pareto α, β; α≤x<∞, α,β>0 33.178 3.143 48.61 23.53 33.19 316.53 2,324.9090

Uniform a, b ; a≤x≤b 33.178 65.122 49.15 9.23 33.21 65.09 33.3208

Weibull α, β; 0≤x<∞, α,β>0 8.068 4.033020E+13 45.76 6.75 20.35 61.92 7.8436

Geometric p ; x=1,2,...; 0≤p≤1 0.022 46.52 46.02 1.00 327.00 4,838.7948

Poisson λ ; x=0,1,...; 0≤λ<∞ 45.510 45.51 6.75 26.00 68.00 4.0444

Empirical Si, F(x) 45.95 5.56 33.28 64.66 0.2161 Best

MLEs Statisitcs Goodness of Fit

INSURANCE PREMIUMS FOR PLANT YEAR CANE ENERGY CANE

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 46 tons/acre (APH) assuming a price guarantee 

of $65/ton. The eight policies are defined in terms of fraction from the APH and are: 0.50, 0.55, 0.60, 0.65, 0.70, 

0.75, 0.80, and 0.85. APH yield was assumed to equal the average for the base yield distributions from the EPIC 

runs.

The calculated fully loaded insurance premiums are reported in Table 55. Based on the assumption that the yield for 

a Plant Year Cane stand of energy cane is distributed empirically, the premiums are less than $20.00/acre for 

policies that insure 50 percent-65 percent of the APH. The fully loaded premium at 70 percent APH is $26.41/acre. 

It increases rapidly thereafter with a $33.86/acre premium for 75 percent APH coverage, $42.96/acre for 80 percent

APH coverage, and reaches $54.30/acre for 85 percent APH coverage. The fully loaded insurance premiums for the 

normal probability distribution are higher than those for the empirical distribution. For example, for the 85 percent

APH policy, the normal distribution indicates a $54.41 premium per acre versus $54.30 for the empirical 

distribution. Overall, the premium rates are very close for each distribution.

The difference in premiums for each yield insurance policy differ due to the weight the distribution places on the 

insured range of the distribution. This relationship is illustrated in Figure 50, a PDF of the original yield distribution 

and the four selected distributions. The normal distribution is associated with the highest premiums for the 65

percent - 85 percent APH policies because it has more weight in the low yield values over the range of 27 to 37 

tons/acre.

Base Premium Rates for Plant Year Cane Year 2 Energy Cane

The base premiums calculated for the fully loaded premium range from 8.3% to 8.6% for the 85% APH yield 

coverage policy. At the 65 percent APH coverage level, the base premium is 4.1% for all four distributions. At the 

50% level of coverage, the base premium for the empirical distribution ranges between 0.8% and 0.9%.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 114

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Table 55: Yield Insurance Premiums for Plant Year Cane Energy Cane in Louisiana; Assuming Alternative Yield 

Coverage Levels and Yield Distributions, an APH Yield of 46 Tons Per Acre, and Guaranteed Price of $16.25 

Per Wet Ton 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 373.66 411.02 448.39 485.76 523.12 560.49 597.85 635.22

No Load Fair Premium ($/acre)

Empirical 1.90 4.50 8.16 12.09 16.09 20.33 26.17 33.08

Gamma 2.02 4.67 8.18 12.08 16.16 20.53 25.65 32.39

Normal 2.00 4.61 8.15 12.12 16.33 20.88 26.24 33.15

Lognormal 2.03 4.70 8.21 12.08 16.13 20.43 25.44 32.12

Fully Loaded Premium ($/acre)

Empirical 3.12 7.39 13.39 19.85 26.41 33.36 42.96 54.30

Gamma 3.31 7.66 13.43 19.83 26.53 33.71 42.10 53.17

Normal 3.29 7.56 13.38 19.90 26.80 34.28 43.07 54.41

Lognormal 3.34 7.72 13.47 19.83 26.47 33.53 41.76 52.72

Loss Cost (%)

Empirical 0.51% 1.10% 1.82% 2.49% 3.08% 3.63% 4.38% 5.21%

Gamma 0.54% 1.14% 1.82% 2.49% 3.09% 3.66% 4.29% 5.10%

Normal 0.54% 1.12% 1.82% 2.50% 3.12% 3.73% 4.39% 5.22%

Lognormal 0.54% 1.14% 1.83% 2.49% 3.08% 3.64% 4.26% 5.06%

Fully Loaded Base Premium (%)

Empirical 0.8% 1.8% 3.0% 4.1% 5.0% 6.0% 7.2% 8.5%

Gamma 0.9% 1.9% 3.0% 4.1% 5.1% 6.0% 7.0% 8.4%

Normal 0.9% 1.8% 3.0% 4.1% 5.1% 6.1% 7.2% 8.6%

Lognormal 0.9% 1.9% 3.0% 4.1% 5.1% 6.0% 7.0% 8.3%

2 7 3 2 3 7 4 2 4 7 5 2 5 7 6 2

Base Yield Empirical Gamma Normal Lognormal

Figure 50: PDF of Alternative Distributions of Plant Year Cane Year 2 Energy Cane Yields in Louisiana 

SUMMARY FOR PLANT YEAR CANE YEAR 2 ENERGY CANE YIELD DISTRIBUTION

The yield distribution has a mean of 46 tons/acre and a range of 33.18 tons/acre to 65.12 tons/acre. We fail to reject 

that the yield distribution is distributed normal and it is the third best distribution for simulating the EPIC generated 

yield distribution. Simulation results for eight possible yield insurance policies indicate high fully loaded premium

rates (less than 5%) for insured yield less than 70 percent of an APH equal to 46 tons per acre and a price election of 

$65/ dry ton. At the highest levels of yield coverage (85 percent of APH), the fully loaded insurance premiums range 

from 8.3 percent to 8.6 percent of liability.

The biggest problem with insuring energy cane production for a Plant Year Cane stand is the lack of information to 

calculate the farmer‘s APH. Producers do not have sufficient years of experience growing energy cane to have 10 

years of yields for establishing an APH. Using the average yield from the EPIC model will have to suffice until 

sufficient results across years and fields are accumulated to develop a more appropriate estimate of the APH and its 

distribution.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 115

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Ratoon Crop Energy Cane Yield Distribution 

The summary statistics in Table 56 report that the average energy cane yield for a ratoon crop stand is 42.94 

tons/acre and the minimum expected yield is 12.71 tons/acre. The distribution is slightly skewed to the left given a 

skewness statistic of -0.56. This shape is confirmed in the PDF and CDF charts for the ratoon crop yield distribution 

(Figure 47 and Figure 48).

Table 56: Summary Statistics for Base Yield Distribution of Ratoon Crop Energy Cane in Louisiana 

Base Yield

Mean 42.94

Standard Deviation 5.82

Min 12.71

Median 43.25

Max 63.80

Skewness -0.56

Kurtosis 4.08

Five statistical tests for normality were performed: Shapiro-Wilks, Anderson-Darling, Cramer von Mises, 

Kolmagarov-Smiroff, and Chi-Square (Table 57). All five normality tests reported that statically the distribution is 

not distributed normal. 

Table 57: Normality Test for the Yield Distribution of Ratoon Crop Energy Cane in Louisiana 

Confidence Level 95.000%

Procedure Test Value p-Value

Shapiro-Wilks 0.941 0.000 Reject the Ho that the Distribution is Normally 

Distributed*

Anderson-Darling 4.942 0.000 Reject the Ho that the Distribution is Normally 

Distributed*

Cramer von Mises 0.821 0.000 Reject the Ho that the Distribution is Normally 

Distributed*

Kolmogorov-Smirnov 0.075 NA Consult Critical Value Table

Chi-Squared 39.650 0.004 Reject the Ho that the Distribution is Normally 

Distributed*

*Based on approximate p-values

Parameters for the 15 parametric distributions reported in Table 58 were simulated for 500 iterations using a 

common uniform standard deviate to insure the results are directly comparable. The summary statistics for these 

distributions and the goodness of fit criteria (CDFDEV) for the 15 distributions and the empirical distribution are 

summarized in Table 58. The 16 distributions were simulated 500 iterations and the resulting summary statistics are 

reported beside the parameters. All of the distributions reproduce the mean, as expected but a number of them fail to 

reproduce the range of the distribution. The CDFDEV criteria indicate the four best distributions for simulating the 

distribution are Weibull, empirical, beta, and normal; with the Weibull distribution being best.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 116

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Table 58: Univariate Parameter Estimation for the Yield Distribution of Ratoon Crop Energy Cane in Louisiana 

Distribution Parameters Parm. 1 Parm. 2 Mean Std. Dev. Min Max CDFDEV Rank

Beta α, β; A≤x≤B, α,β>0 7.685 5.491 42.51 6.70 22.43 59.41 80.97 Third

Double Exponential α, β; α≤x<∞, -∞<α<∞, β>0 43.251 4.070 43.25 5.76 17.51 69.28 99.65

Exponential μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 12.708 30.237 42.95 30.23 12.73 227.04 2,461.04

Gamma α, β; 0≤x<∞, α,β>0 47.522 0.904 42.94 6.24 26.09 65.23 100.62

Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 43.107 2.984 43.11 5.42 22.16 64.26 87.86

Log-Log μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 39.878 7.731 44.34 9.92 24.81 94.67 277.63

Log-Logistic μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 13.543 42.909 43.30 5.86 25.56 72.42 116.08

Lognormal μ, σ ; 0≤x<∞, -∞<μ<∞, σ>0 3.749 0.153 42.99 6.62 26.37 68.70 114.38

Normal μ, σ ; -∞<x<∞, -∞<μ<∞, σ>0 42.944 5.814 42.94 5.82 24.79 61.22 85.60 Fourth

Pareto α, β; α≤x<∞, α,β>0 12.708 0.828 293.42 3,061.34 12.72 66,104.93 1.312158E+08

Uniform a, b ; a≤x≤b 12.708 63.801 38.25 14.77 12.75 63.76 233.23

Weibull α, β; 0≤x<∞, α,β>0 7.708 5.885879E+12 42.65 6.56 18.25 58.50 73.05 First

Binomial n, p ; x=0,1,2,...,n; 0≤p≤1 13300.000 0.003 42.44 6.51 24.00 64.00 91.25

Geometric p ; x=1,2,...; 0≤p≤1 0.023 43.45 42.95 1.00 305.00 5,008.14

Poisson λ ; x=0,1,...; 0≤λ<∞ 42.445 42.44 6.52 24.00 64.00 91.11

Empirical Si, F(x) 42.96 5.58 15.50 63.67 79.91 Second

MLEs Statisitcs Goodness of Fit

INSURANCE PREMIUMS FOR RATOON CROP ENERGY CANE

Eight yield loss insurance policies were simulated for stochastic yields using the four best probability distributions. 

The eight policies are expressed as a fraction of the average yield of 43.0 tons/acre (APH) assuming a price 

guarantee of $65/ton. The eight policies are defined in terms of fraction from the APH and are 0.50, 0.55, 0.60, 0.65, 

0.70, 0.75, 0.80, and 0.85. APH yield was assumed to equal the average for the base yield distributions from the 

EPIC runs.

The calculated insurance premiums are reported in Table 59. Based on the assumption that the yield for a ratoon 

crop of energy cane is distributed via the Weibull distribution, the premiums are less than $21.00/acre for policies 

that insure less than 65 percent of the APH. The fully loaded premium at 70 percent APH is $28.41/acre. It increases 

rapidly thereafter with a $37.32/acre premium for 75 percent APH coverage, $47.98/acre for 80 percent APH 

coverage, and reaches $61.26/acre for 85 percent APH coverage.

The calculated insurance premiums for the Weibull probability distribution are higher than those for the other 

distributions for five of the eight coverage levels. For example at the 70 percent APH coverage level the Weibull‘s 

fully loaded premium is $28.41/acre while the next highest premium is from the beta distribution with a $27.29/acre 

premium.

The difference in premiums for each yield insurance policy differ due to the weight the distribution places on the 

insured range of the distribution. This relationship is illustrated in Figure 51 , a CDF of the original yield distribution 

and the four selected distributions. The Weibull distribution is associated with the highest premiums for the 60

percent-70 percent and 80 percent-85 percent APH policies because it has more weight in the low yield values less 

than 38 tons/acre.

Base Premium for Ratoon Crop Energy Cane

The base premiums calculated for the fully loaded premium range from 8.8% to 10.3% for the 85% APH yield 

coverage policy. At the 65% APH coverage level, the base premium ranges from 4.1% to 4.6%. At the 50 percent

level of coverage, the base premium for the Weibull distribution is 1.2% of liability.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 117

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Table 59: Yield Insurance Premiums for Ratoon Crop Energy Cane in Louisiana; Assuming Alternative Yield 

Coverage Levels and Yield Distributions, an APH Yield of 43 Tons Per Acre, and Guaranteed Price of $65 Per 

Ton 

0.5 of APH 0.55 of APH 0.6 of APH 0.65 of APH 0.7 of APH 0.75 of APH 0.8 of APH 0.85 of APH

Liability

($/acre) 348.92 383.81 418.70 453.60 488.49 523.38 558.27 593.16

No Load Fair Premium ($/acre)

Weibull 2.45 4.90 8.39 12.58 17.31 22.74 29.23 37.32

Empirical 1.87 4.48 8.21 12.34 16.61 21.06 26.17 32.23

Beta 2.51 4.91 8.18 12.11 16.63 21.97 28.61 37.20

Normal 1.98 4.39 7.66 11.38 15.39 19.81 25.09 31.93

Fully Loaded Premium ($/acre)

Weibull 4.02 8.04 13.77 20.65 28.41 37.32 47.98 61.26

Empirical 3.08 7.36 13.47 20.26 27.27 34.56 42.96 52.90

Beta 4.12 8.06 13.42 19.87 27.29 36.06 46.97 61.06

Normal 3.25 7.21 12.58 18.68 25.25 32.52 41.19 52.41

Loss Cost (%)

Weibull 0.70% 1.28% 2.00% 2.77% 3.54% 4.34% 5.24% 6.29%

Empirical 0.54% 1.17% 1.96% 2.72% 3.40% 4.02% 4.69% 5.43%

Beta 0.72% 1.28% 1.95% 2.67% 3.40% 4.20% 5.13% 6.27%

Normal 0.57% 1.14% 1.83% 2.51% 3.15% 3.79% 4.49% 5.38%

Fully Loaded Base Premium (%)

Weibull 1.2% 2.1% 3.3% 4.6% 5.8% 7.1% 8.6% 10.3%

Empirical 0.9% 1.9% 3.2% 4.5% 5.6% 6.6% 7.7% 8.9%

Beta 1.2% 2.1% 3.2% 4.4% 5.6% 6.9% 8.4% 10.3%

Normal 0.9% 1.9% 3.0% 4.1% 5.2% 6.2% 7.4% 8.8%

1 0 2 0 3 0 4 0 5 0 6 0

Base Yield Weibull Empirical Beta Normal

Figure 51: PDF of Alternative Distributions of Ratoon Crop Energy Cane Yields in Louisiana 

As illustrated in Table 60, the calculated premiums for energy cane are compared to those from sugarcane in 

Jefferson Davis parish Louisiana. RMA‘s Cost Estimator for 2011 was utilized to determine the estimated liability 

and total premium for sugarcane. Parameters used for the sugarcane estimate included an APH equivalent to the 

reference yield, 100 percent price election, and a basic unit. The base rate for each crop is illustrated in the table and 

is calculated by dividing the total premium by the liability for each coverage level. The results demonstrate that 

estimated premiums for the Weibull distribution of sugarcane at the higher coverage levels track very close to those 

of sugarcane. Specifically for the Weibull distribution, the difference in the estimated base rates at the 75 percent 

coverage level is 75.26 percent higher for energy cane. At the 75 percent coverage level, the total base rate for 

2011sugarcane is 4.07% while the Weibull premium estimate for energy cane is 7.13%. The estimate for the 

empirical distribution is 62.30 percent higher for energy cane at the 75 percent coverage level. These results suggest 

that the EPIC model and a proxy crop such as sugarcane may prove useful in establishing rates for energy cane.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 118

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Table 60: Comparison of Energy Cane Premium Estimates to Sugarcane Premium Estimates 

Sugarcane 75% 70% 65% 60% 55% 50%

Liability Amount $467.00 $436.00 $405.00 $374.00 $343.00 $311.00 

Total Premium Amount $19.00 $15.00 $11.00 $9.00 $7.00 $5.00 

Calculated Base Premium Rate 4.07% 3.44% 2.72% 2.41% 2.04% 1.61%

Energy Cane 75% 70% 65% 60% 55% 50%

Liability Amount $523.38 $488.49 $453.60 $418.70 $383.81 $348.92 

Total Premium Amount:

Weibull $37.32 $28.41 $20.65 $13.77 $8.04 $4.02 

Empirical $34.56 $27.27 $20.26 $13.47 $7.36 $3.08 

Beta $36.06 $27.29 $19.87 $13.42 $8.06 $4.12 

Normal $32.52 $25.25 $18.68 $12.58 $7.21 $3.25 

Calculated Base Premium Rate:

Weibull 7.13% 5.82% 4.55% 3.29% 2.09% 1.15%

Empirical 6.60% 5.58% 4.47% 3.22% 1.92% 0.88%

Beta 6.89% 5.59% 4.38% 3.21% 2.10% 1.18%

Normal 6.21% 5.17% 4.12% 3.00% 1.88% 0.93%

% Difference from Proxy Crop:

Weibull 75.26% 69.05% 67.61% 36.67% 2.64% -28.34%

Empirical 62.30% 62.26% 64.45% 33.69% -6.04% -45.09%

Beta 69.34% 62.38% 61.28% 33.19% 2.90% -26.56%

Normal 52.72% 50.25% 51.62% 24.86% -7.95% -42.06%

Sugarcane: Louisiana, Jefferson Davis Parish APH

Parameters: APH 5170, Reference Yield 5170, Basic Unit, Price Election 100% @ $0.1205

Ratoon Energy Cane: Louisiana, Jefferson Davis Parish APH

Parameters: Non-Irrigated APH 43 tons, Basic Unit, Price Election 100% @$65/dry ton ($16.25/wet ton)

SUMMARY FOR RATOON CROP ENERGY CANE YIELD DISTRIBUTION

The ratoon crop energy cane yield distribution has a mean of 42.94 tons/acre and a range of 12.7 tons/acre to 63.8 

tons/acre. Statistically the yield distribution is not distributed normal yet it is the fourth best distribution for 

simulating the EPIC generated ratoon crop yield distribution. Simulation results for eight possible yield insurance 

policies indicate fully loaded premium rates greater than those of sugarcane. At the highest levels of sugarcane yield 

coverage (75 percent of APH) the fully loaded insurance premium rates for energy cane range from 6.21 percent to 

7.13 percent of liability versus 4.07 percent for sugarcane. While estimated rates are higher for energy cane than 

those of sugarcane, the results of EPIC yield modeling suggest that the methodology may prove useful for 

developing rates and transitional yields for areas with limited data sets.

FEASIBILITY RECOMMENDATION 

Currently there are only two producers and less than 1,000 acres of energy cane grown for a demonstration scale 

bio-refinery in Jennings, LA (Musial, 2011). While demand drivers for energy cane suggest that an acceptable 

demand for energy cane insurance is present, the lack of a potential market may cause concerns for program 

acceptance by RMA, due to the lack of geographic diversity and the lack of current producers to spread risk over an 

acceptable pool of insureds.

Further, the RMA Program Evaluation Tool Decision Tree results for energy cane in Louisiana indicate that no 

action is required or no new product be developed. See Appendix M for a graphical representation of the decision 

tree process for Louisiana. The decision tree path is marked by highlighted red arrows. Because there are currently
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 119

Contract # N10PC18199

few commercial producers of energy cane (no significant market), the decision tree suggests that no insurance policy 

needs to be developed.

Recommendation 

Based on the research and analysis of energy cane, it is determined that the development of a crop insurance product 

for commercially grown energy cane for dedicated energy is not feasible at this time. The recommended course of 

action is to not develop a crop insurance program for energy cane. While this recommendation is based solely on the 

lack of current commercial production, potential may exist for a crop insurance program for commercial production 

of energy cane in the future.
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 120

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REFERENCES 

Bareja, B. G. (n.d.). Classifications of Agricultural Crops. Retrieved October 25, 2010, from Crops Review: 

http://www.cropsreview.com/support-files/agriculturalcrops-classifications.pdf

Baucum, L. E., & Rice, R. W. (2009, August). An Overview of Florida Sugarcane. Retrieved October 24, 

2010, from University of Florida IFAS Extension: http://edis.ifas.ufl.edu/sc032

Black, B. (2010, 11 19). Switchgrass Listening Session. (S. Hardison, W. Dowtin, & J. Richardson, 

Interviewers)

BP Alternative Energy. (2010, July 15). Press Release: BP and Verenium Announce Pivotal Biofuels 

Agreement. Retrieved 12 2011, April, from British Petroleum: 

http://www.bp.com/genericarticle.do?categoryId=9024973&contentId=7063769

Bransby, D. (n.d.). Switchgrass Profile. Retrieved November 2010, from Auburn Univeristy: 

http://bioenergy.ornl.gov/papers/misc/switchgrass-profile.html

Brown, R., Rosenberg, N., Hays, C., Easterling, W., & Mearns, L. (2000). Potential production and 

environmental effects of swithgrass and traditional crops under current greenhouse-altered 

climate in the central United States: a simulation study. Agric. Ecosyst. Environ., 78, 31-47.

Caddel, J., & Redfearn, D. (2008). What is Switchgrass? Retrieved January 9, 2011, from Department of 

Plant and Soil Sciences - Oklahoma State University: 

http://switchgrass.okstate.edu/whatisswitchgrass/index.htm

Caddel, J., & Redfearn, D. (n.d.). Pest Management. Retrieved January 9, 2011, from Department of 

Plant and Soil Science - Oklahoma State University: 

http://switchgrass.okstate.edu/pestmanagement/index.htm

Camelina Producers. (2011, January 20). Personal Communication; Montana Listening Session. (S. 

Hardison, W. Dowtin, & D. Richardson, Interviewers)

Camelina Producers. (2011, January 21). Personal Communication; Oregon and Washington Listening 

Session. (S. Hardison, W. Dowtin, & D. Richardson, Interviewers)

Cherry, R. H. (2008, February). White Grubs in Florida Sugarcane. Retrieved January 12, 2011, from 

University of Florida IFAS Extension: http://edis.ifas.ufl.edu/sc012
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 121

Contract # N10PC18199

Comstock, J. C., & Gilbert, R. A. (2009, December). Sugarcane Mosaic Virus Disease. Retrieved 12 

2011, January, from University of Florida IFAS Extension: http://edis.ifas.ufl.edu/sc009

Crooks, T. (2010). Biorefinery Business Models and USDA Rural Development Funding Presentation. 

Renewable Energy Biomass Field Days. Knoxville.

Dohlman, E., Caswell, M., & Duncan, M. (2008, August 20). 2008 Farm Bill Side-By-Side. Retrieved 

November 2010, from USDA Economic Research Service: 

http://www.ers.usda.gov/farmbill/2008/titles/titleixenergy.htm#infrastructure

DoKyoung, L., Owens, V. N., Boe, A., & Jeranyama, P. (2007). Composition of Herbaceous Biomass 

Feedstocks. Brookings: North Central Sun Grant Center.

Garland, C. (n.d.). Growing and Harvesting Swtichgrass for Ethanol Production in Tennessee. Retrieved 

January 9, 2011, from University of Tennessee Extension: 

https://utextension.tennessee.edu/publications/Documents/SP701-A.pdf

Genera Biomass, LLC. (2010). Switchgrass Production Program. Retrieved February 9, 2011, from 

Genera Energy Web Site: 

http://www.generaenergy.net/what_we_do/genera_biomass_llc/switchgrass_production_program.

aspx

Genera Energy. (2010). History of Genera Energy LLC. Retrieved 12 3, 2010, from Genera Energy: 

http://www.generaenergy.net/about_genera/history_of_genera_energy_llc.aspx

Gravois, K., Richard, E., Salassi, M., Legendre, B., Viator, R., Way, M., et al. (2011, January 13). 

Personal Communication; Energy Cane Conference Call. (S. Hardison, W. Dowtin, & J. 

Richardson, Interviewers)

Great Plains, The Camelina Company. (2010). About Great Plains. Retrieved February 4, 2011, from 

Great Plains, The Camelina Company: 

http://www.camelinacompany.com/Marketing/AboutGreatPlains.aspx

Hill, C. (2009). 2009 MonDak Camelina Acres. Retrieved March 14, 2011, from NDSU Williston Research 

Extension Center: http://www.ag.ndsu.edu/WillistonREC/documents/ne-montana-nw-northdakota-crop-acres/09%20MondakCamelina.xls/view

Isom, L., & Booker, W. (2008). Growing Crops for Better Biodiesel. Retrieved February 17, 2011, from 

Nebraska Renewable Energy Association: 

http://www.nebraskarea.org/downloads/growingcropsforbiodiesel.pdf
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 122

Contract # N10PC18199

Iverson, G. W. (2006, June 30). CAMELINA SATIVA: A MULTIUSE OIL CROP FOR BIOFUEL, OMEGA3 COOKING OIL, AND PROTEIN/OIL SOURCE FOR ANIMAL FEED . Retrieved February 4, 

2011, from Research, Education and Economics Information System, USDA: 

http://www.reeis.usda.gov/web/crisprojectpages/203181.html

Izaurralde, R., Rosenberg, N., Brown, R., & Thompson, A. (2003). Integrated assessment of Hadley 

Center (HadCM2) climate-change impacts on agricultureal productivity and irrigation water suply 

in the conterminous United States Part II. Regional agricultural production in 2030 and 2095. 

Agric.Forest Meterol., 117(1-2), 97-122.

Jackson, D. S. (2010, November 16-19). Personal Communication; Switchgrass Production. (S. Hardison, 

Interviewer)

Jaeger, W. K., & Siegel, R. (2008). Economics of Oilseed Crops and Their Biodiesel Potential in Oregon's 

Willamette Valley, Special Report 1081. Corvallis: Department of Agriculture and Natural 

Resources, Oregon State University.

James, Glyn (editor). (2004). Sugarcane, 2nd Edition. Wiley-Blackwell.

Johnson, D. (2007). Camelina. Havesting Clean Energy Conference VII. Creston: Montana State 

University, Northwestern Agricultural Research Center.

Johnson, S. (2011, March 9). Personal Communication; Pricing Methodology. (S. Hardison, Interviewer)

Khanna, M., Basanta, D., & Clifton-Brown, J. (2008). Costs of Producing Miscanthus and Switchgrass for 

Bioenergy in Illinois. Biomass & Bioenergy (Department of Agricultural and Consumer 

Economics, University of Illinois, Urbana-Champaign), 482-493.

Kim, M., & Day, D. (2010). Composition of sugar cane, energy cane, and sweet sorghum suitable for 

ethanol production at Louisiana sugar mills. Journal of Industrial and Microbiology & 

Biotechnology, Abstract.

Lafferty, R. M., Rife, C., & Foster, G. (2009, December). Blue Sun Energy. Retrieved January 31, 2011, 

from Spring Camelina Production Guide: 

http://www.colorado.gov/cs/Satellite?blobcol=urldata&blobheader=application%2Fpdf&blobkey=id

&blobtable=MungoBlobs&blobwhere=1251616501820&ssbinary=true

LaMonica, M. (2010, July 15). BP to buy ethanol fueld plant from Verenium. Retrieved January 31, 2011, 

from CNet News: http://news.cnet.com/8301-11128_3-20010648-54.html
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 123

Contract # N10PC18199

Lane, J. (2009, September 10). Sustainable Oils wins Navy camelina jet fuel contract; 40K gallons, option 

for 150k more. Retrieved January 28, 2011, from Biofuels Digest: 

http://www.biofuelsdigest.com/blog2/2009/09/10/sustainable-oils-wins-navy-camelina-jet-fuelcontract-40k-gallons-option-for-150k-more/

Legendre, B. (2011, February 10). Personal Communication; Energy Cane Varieties. (W. W. Dowtin, 

Interviewer)

Mark, T., Darby, P., & Salassi, M. (2009). Energy Cane Usage for Cellulosic Ethanol: Estimation of 

Feedstock Costs. Southern Agricultural Economics Association Annual Meeting, (pp. 1-20). 

Atlanta.

Markle, G. M., Baron, J. J., & Schneider, B. A. (1998). Food and Feed Crops of the United States. Meister 

Publishing Company.

Miller, J. D., & Gilbert, R. A. (2009, July). Sugarcane Botany: A Brief View. Retrieved February 11, 2011, 

from University of Florida IFAS Extension: http://edis.ifas.ufl.edu/sc034

Montana Agriculture Statistics Service. (2010, March 10). 2008 Camelina Production. Retrieved 2 8, 

2011, from Montana Agriculture Statistics Service: 

http://www.nass.usda.gov/Statistics_by_State/Montana/Publications/Press_Releases_Crops/cam

elina.htm

Montana Department of Agriculture. (n.d.). Montana State Hail Insurance Rates. Retrieved February 24, 

2011, from Montana's Official State Website: http://agr.mt.gov/crops/hailRates.asp

Montana Deptment of Agriculture. (2010, September 2). Currently Allowed Practices for the Use of 

Camelina sativa meal as a Commercial Feed in Montana. Retrieved February 4, 2011, from 

Montana Department of Agriculture: http://agr.mt.gov/camelina/FDA_exception9-2-10.pdf

Mooney, D., & English, B. C. (2009). Economics of the Switchgrass Supply Chain: Enterprise Budgets 

and Production Cost Analyses. Knoxville: Univeristy of Tennessee.

Moore, K. J., Moser, L. E., Vogel, K. P., Waller, S. S., Johnson, B. E., & Pedersen, J. F. (1991). 

Describing and Quantifying Growth Stages of Perennial Forage Grasses. Retrieved February 8, 

2011, from University of Nebraska - Lincoln: Agronomy and Horticulture Department: 

http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1507&context=agronomyfacpub

Musial, M. (2011, March 10). Personal Communication; BP Biofuels. (S. Hardison, & W. Dowtin, 

Interviewers)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 124

Contract # N10PC18199

National Sustainable Agriculture Information Service. (2010, January 26). Bioenergy Program for 

Advanced Biofuels. Retrieved November 2010, from National Sustainable Agriculture Information 

Service: http://attra.ncat.org/guide/a_m/bpab.html

Netafim. (n.d.). Germination and Establishment Phase. Retrieved October 22, 2010, from Sugarcane: 

http://www.sugarcanecrops.com/crop_growth_phases/germination_establishment_phase/

Nyoka, B., Jeranyama, P., Owens, V., Boe, A., & Moechnig, M. (2007, June). Management Guide for 

Biomass Feedstock Production from. Retrieved November 2010, from SunGrant Initiative North 

Central Center South Dakota State University: 

http://ncsungrant.sdstate.org/uploads/publications/SGINC2-07.pdf

OAIN. (2011). Oregon Agricultural Information Network (OAIN). Retrieved February 4, 2011, from Oregon 

State University Extension Service: http://oain.oregonstate.edu

One Hundred Tenth Congress of the United States of America. (2007). Energy Independence and 

Security Act. House Resolution 6, Title II: Energy Security Through Increased Production of 

Biofuels.

Oregon Department of Energy. (2007, October 31). Biofuel Consumer Income Tax Credits - House Bill 

2210. Retrieved March 1, 2011, from ODOE: Renewable Energy: 

http://www.oregon.gov/ENERGY/RENEW/docs/HB2210Advisory103107.pdf

Oregon State University. (n.d.). Plant Life Cycles: Annuals, Biennials, Perennials. Retrieved January 31, 

2011, from Oregon State University Extension: 

http://extension.oregonstate.edu/mg/botany/cycles.html

Oregon State University. (n.d.). Sun Grant Camelina Project. Retrieved January 28, 2011, from Oregon 

State: http://cropandsoil.oregonstate.edu/bioenergy/sungrant

Potter, S., Atwood, J., Kellog, R., & Williams, J. (2004). An approach for estimating soil carbon using the 

national nutrient loss database. Environ Management, 33, 496-506.

Raid, R. N. (2009, January). Pokkah Boeng Disease of Sugarcane. Retrieved January 12, 2011, from 

University of Florida IFAS Extension: http://edis.ifas.ufl.edu/sc004

Rainbolt, R., & Gilbert, R. (2008, January). Production of Biofuel Crops in Florida: 

Sugarcane/Energycane. Retrieved January 12, 2011, from University of Florida IFAS Extension: 

http://edis.ifas.ufl.edu/ag303
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 125

Contract # N10PC18199

Reay-Jones, F. P. (n.d.). INTEGRATED PEST MANAGEMENT OF THE MEXICAN RICE BORER IN 

LOUISIANA AND TEXAS SUGARCANE AND RICE. Retrieved February 2011, from Louisiana 

State University: http://etd.lsu.edu/docs/available/etd-07132005-180922/unrestricted/ReayJones_dis.pdf

Rinehart, L. (2006). Switchgrass as a Bioenergy Crop. Retrieved November 2010, from National 

Sustainable Agriculture Information Service: http://attra.ncat.org/attra-pub/PDF/switchgrass.pdf

RMA. (2010). About RMA: Our Mission and Vision. Retrieved 11 18, 2010, from Risk Management 

Agency: http://www.rma.usda.gov/aboutrma/what/vision.html

Saskatchewan Ministry of Agriculture. (2011, January). 2010 Specialty Crop Report: Statistics. Retrieved 

February 18, 2011, from Saskatchewan Ministry of Agriculture: 

http://www.agriculture.gov.sk.ca/Default.aspx?DN=71bbc952-b3fe-4601-9ece-a666fc87b425

Saskatchewan Ministry of Agriculture. (n.d.). Camelina. Retrieved Janauary 31, 2011, from Government 

of Saskatchewan: http://www.agriculture.gov.sk.ca/Default.aspx?DN=67a5b5a3-b4fc-402b-9edeabcebb2b64b8

Schanczenski, J. (2009, October). Economics of Oilseeds for Biofuels in Montana. Retrieved July 29, 

2010, from The National Center for Appropriate Technology: 

http://www.ncat.org/special/oilseeds_econ.php

Searcy, S. (2010). Harvest, Handling, Transportation and Storage. Renewable Energy Field Days, (p. 

Presentation). Knoxville.

Stratton, A., Klienschmit, J., & Kenney, D. (2007). Camelina. Minneapolis: Institute for Agriculture and 

Trade Policy, Rural Communities Program - BioEnonomy.

Sustainable Oils. (2009, March 3). New Trade Association Formed for North America Camelina Industry. 

Retrieved February 4, 2011, from Sustainable Oils: Industry News and Events: 

http://www.susoils.com/dynamic-content/csArticles/articles/000000/000041.htm

Sustainable Oils. (2010). 2010 Sustainable Oils Grower Protocol - U.S. Sustainable Oils.

Sustainable Oils. (2011). Camelina Production Services Contract. 2011 Camelina Production ContractU.S. Seattle, WA, USA: Sustainable Oils, LLC.

Sustainable Oils. (2011). New Camelina Varieties and Yields. Retrieved January 31, 2011, from 

Sustainable Oils: http://www.susoils.com/camelina/varieties.php
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 126

Contract # N10PC18199

Switchgrass Producers. (2010, November 19). Personal Communication; Tennessee Listening Session. 

(S. Hardison, W. Dowtin, & D. Richardson, Interviewers)

Teel, A. (1998). Management Guide for the Production of Switchgrass for Biomass Fuel Production in 

Southern Iowa. Retrieved November 10, 2010, from Bioenergy Feedstock Information Network: 

http://bioenergy.ornl.gov/papers/bioen98/teel.htm

The National Center for Appropriate Technology. (2009, September). The Economics of Oilseeds for 

Biodiesel in Montana. Retrieved February 17, 2011, from The National Center for Appropriate 

Technology: http://www.ncat.org/special/oilseeds_econ.php

Thompson, A., Izarraulde, R., West, T., Parrish, D., Tyler, D., & Williams, J. (2009, December). Simulating 

potential switchgrass production in the United States. PNNL-19072, Joint Global Change 

Research Institute, Pacific Northwest Laboratory.

Thompson, A., Izaurralde, R., Rosenberg, N., & He, X. (2006). Climate change impacts on agriculture and 

soil carbon sequestration potential in the Huang-Hai Plain of China. Agric. Ecosyst. Environ., 

114(2-4), 195-209.

Tiller, D. (2010, Nobember 16). Switchgrass Production Contracting. (S. Hardison, Interviewer)

Tindal, C. (2010). Renewable Energy Biomass Education Field Day. Knoxville, Tennessee.

U.S. Department of Energy. (2003, July). Chariton Valley Biomass Project: Final Environmental 

Assessment and Finding of No Significant Impact. Retrieved November 2010, from DOE National 

Environmental Policy Act (NEPA) Website: http://nepa.energy.gov/nepa_documents/ea/EA1475/

Uchytil, R. J. (1993). Panicum virgatum . Retrieved February 8, 2011, from Fire Effects Information 

System Online: 

http://www.fs.fed.us/database/feis/plants/graminoid/panvir/all.html#DISTRIBUTION AND 

OCCURRENCE

USDA. (2011, February 14). PLANTS Profile for Camelina sativa. Retrieved February 17, 2011, from 

PLANTS Database: 

http://plants.usda.gov/java/profile?symbol=CASA2&mapType=large&photoID=casa2_001_avd.tif

USDA; FSA. (2010a, October 19). BCAP CHST Componenet Report FY 2009 and FY 2010. Retrieved 

February 4, 2011, from Farm Service Agency: 

http://www.fsa.usda.gov/Internet/FSA_File/bcap_chst_component_report.pdf
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 127

Contract # N10PC18199

USDA; FSA. (2010b, October). Biomass Crop Assistance Program - - Final Rule Provision. Retrieved 

February 4, 2010, from FSA Newsroom Fact Sheets: 

http://www.fsa.usda.gov/FSA/newsReleases?area=newsroom&subject=landing&topic=pfs&newst

ype=prfactsheet&type=detail&item=pf_20101021_consv_en_bcap.html

USDA; FSA. (2011, January 28). Noninsured Crop Disaster Assistance Program for 2009 and 

Subsequent Years. Retrieved January 28, 2011, from USDA FSA: 

http://www.fsa.usda.gov/FSA/newsReleases?area=newsroom&subject=landing&topic=pfs&newst

ype=prfactsheet&type=detail&item=pf_20090318_distr_en_nap.html

USDA; GIPSA. (1992, February 28). United States Standards for Canola. Retrieved February 4, 2011, 

from Grain, Rice and Pulses: Official U.S. Standards for Grain: 

http://archive.gipsa.usda.gov/reference-library/standards/810canola.pdf

USDA; Grain Inspection, Packers, and Stockyards Administration. (1992, February 28). United States 

Standards for Canola. Retrieved November 2010, from United States Standards for Grain: 

http://www.gipsa.usda.gov/GIPSA/webapp?area=home&subject=grpi&topic=sq-ous

USDA; Integrated Pest Management Centers. (1999, April 26). Crop Profile for Sugarcane in Louisiana.

Retrieved November 2010, from National Site for the USDA: 

http://www.ipmcenters.org/cropprofiles/docs/LAsugarcane.pdf

USDA; NASS. (2010, March 31). 2009 Camelina Crop Press Release. Retrieved November 2010, from 

National Agricultural Statistics Service: 

http://www.nass.usda.gov/Statistics_by_State/Montana/Publications/Press_Releases_Crops/cam

elina.pdf

USDA; NASS. (2010, April 8). Camelina Acreage, Yield, and Production, Montana, USA. Retrieved March 

14, 2011, from NASS: 

http://www.nass.usda.gov/Statistics_by_State/Montana/Publications/crops/camelayp.htm

USDA; NASS. (2010). U.S. & All States Data - Crops - Sugarcane for Sugar. Quick Stats.

USDA; NASS. (2010). U.S. & All States Data - Crops Planted, Harvested, Yield, Production, Price (MYA), 

Value of Production.

USDA; NASS. (n.d.). Quick Stats; U.S. & All States County Data-Crops. Retrieved 1 11, 2011, from 

USDA; NASS: http://www.nass.usda.gov/QuickStats/Create_County_All.jsp
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 128

Contract # N10PC18199

USDA; NRCS. (2009). Planting and Managing Switchgrass as a Bioenergy Crop. Natural Resources 

Conservation Service - USDA.

USDA; PLANTS. (2011, February 21). PLANTS Database. Retrieved 2 22, 2011, from Natural Resources 

Conserversation Services: http://plants.usda.gov/java/profile?symbol=SASP

USDA; PLANTS. (2011, April 11). Plants Profile: Switchgrass. Retrieved November 2010, from Natural 

Resources Conservation Service: http://plants.usda.gov/java/profile?symbol=PAVI2

USDA; RMA. (2005, September). Bulletins and Handbooks: 22000 - Progarm Evaluations. Retrieved 11 

14, 2010, from United States Department of Agriculture; Risk Managment Agency: 

http://www.rma.usda.gov/handbooks/22000/index.html

USDA; RMA. (2010a). Statement of Work for Data Collection Report and Feasibility Research Reports for 

Insuring Dedicated Energy Crops. USDA.

USDA; RMA. (2010b, September 16). Cause of Loss Historical Data Files. Retrieved January 24, 2011, 

from USDA; RMA: http://www.rma.usda.gov/data/cause.html

USDA; RMA. (2010c, July 28). Small Grain Loss Adjustment Standard Handbook. Retrieved February 2, 

2011, from Risk Management Agency: http://www.rma.usda.gov/handbooks/25000/index.html

USDA; RMA. (2010d, September 16). Cause of Loss Historical Data Files. Retrieved February 15, 2011, 

from Risk Management Agency: http://www.rma.usda.gov/data/cause.html

USDA; RMA. (2011, 3 14). Our Mission and Vision. Retrieved 3 12, 2011, from USDA; RMA: 

http://www.rma.usda.gov/aboutrma/what/vision.html

Wang, M., Saricks, C., & Santini, D. (1999, January). Effects of Fuel Ethanol Use on Fuel-Cycle Energy 

and Greenhouse Gas Emissions. Retrieved February 20, 2011, from Argonne Transportation 

Technology R & D Center: http://www.transportation.anl.gov/pdfs/TA/58.pdf

Waring, M., Johnson, S., & Endicott, T. (2011, January 21-22). Personal Communication; Contract Pricing

Methodology. (S. Hardison, Interviewer)

Williams, J. (1995). THe EPIC Model in Singh, V.P. ed. Computer Models of Watershed Hydrology. Water 

Resources Publications, 909-1000.
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Contract # N10PC18199

Wolf, D. D., & Fiske, D. A. (2009, May 1). Planting and Managing Switchgrass for Forage, Wildlife, and 

Conservation. Retrieved February 18, 2011, from Virginia Cooperative Extension: 

http://pubs.ext.vt.edu/418/418-013/418-013.html

Wright, L. L., Gunderson, C. A., Davis, E. B., Perlack, R. D., Baskaran, L. M., Eaton, L. M., et al. (2009, 

October). Switchgrass Production Potential and Use for Bioenergy in North America. Retrieved 

November 2010, from Bioenergy Feedstock Information Network: 

http://bioenergy.ornl.gov/main.aspx

Wysocki, D. (2011, January 21). Associate Professor. (AgriLogic, Interviewer)

Zanto, L. (2011, May 6). Personal Communication; Prairie Mountain Insurance. (S. Hardison, Interviewer)

Zimmerman, C. (2008, January 30). Verenium Plant Almost Finished. Retrieved 1 31, 2011, from 

DomesticFuel.com: http://domesticfuel.com/2008/01/30/verenium-plant-almost-finished/
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APPENDIX A: WILLAMETTE BIOMASS PROCESSORS, INC.-

CAMELINA PRODUCTION CONTRACT FOR OREGON 2008 

Camelina Program - Partnering with Willamette Biomass Processors, Inc. (WBP) will require the planting of

camelina during 2008 and committing all production yield from those acres to WBP. All WBP camelina must be

contracted before March 1, 2008 and will be planted between March 1 and no later than April 15, 2008 unless

otherwise approved in writing by WBP.

Camelina Production – The grower must purchase all camelina seed necessary to satisfy the terms of this agreement

from either WBP or its agent at $1.00/lb. Once harvested, Grower agrees to store seed product on farm or to deliver

seed product to a designated point for transportation or storage. Other non-WBP designated storage must be agreed

to by WBP in writing and available for pickup by a date to be determined on a case by case basis. WBP will 

reimburse or take responsibility for all shipping costs to a designated storage or processing facility with a WBP

written approved load minimum. Seed Product is to be stored in a manner that will result in its moisture content not

exceeding 8% based upon a canola scale in a grain moisture meter.

Planting - WBP asks that planting should occur between February 1, 2008 and April 15, 2008 unless a written

deviation is approved. Grower must notify WBP within 10 days of planting. Notification can be made through

WBP‘s website at www.willamettebiomass.com or by calling WBP directly at 503.559.3513. Grower must also 

confirm the exact location of where the seed product has been planted (Field ID). Grower may use either a grain 

drill or broadcast method depending upon their available seeding equipment. Grower agrees to give Willamette 

Biomass Processors, Inc. and its‘ agents access to fields where seed product is being grown and locations where 

seed product is being stored.

Payment – WBP will pay Grower $ .11/per pound of seed product grown under this contract plus early incentives. 

The $.11/lb. will be paid as $.055/lb. at harvest and $.055/lb. when delivered to the WBP processing facility in

Rickreall, Oregon. Also, all Oregon Growers are eligible to receive an additional $.05/lb. Oregon Tax Credit as part

of Oregon HB-2210*. Additionally, all growers that contract for the 2008 planting season will receive a $15/acre

sign-on incentive to front the cost of planting. This $15/acre planting incentive will be paid out as $10/acre at 

contract signing plus an additional $5/acre at emergence when confirmed by a WBP agent/service representative. 

WBP will pay unpaid balance to grower within 30 days after delivery to a WBP processing facility.

**Please see example of payment schedule attached at the end of the contract**

Market Sharing Partnership - WBP makes this contract offer assuming a predetermined raw vegetable oil and

crushed meal target price. This contract price of $.11/lb. is the minimum per lb. price that WBP will offer. WBP

will offer to share in the increase in price margin should the raw vegetable oil and/or meal price market price

increase after the signing of this contract. The starting market price for vegetable oil and meal will be set on April

15, 2008. The market price will be again assessed August 1, 2008. The new paid contract price/lb. to the grower

will be the average between the April 15, 2008 market pricing and the August 1, 2008 market pricing.

$.11/LB PLUS 33% OF THE DIFFERENCE BETWEEN THE PRICE ON APRIL 15, 2008 AND AUGUST 1,

2008.

Storage - If WBP and the Grower agree that Camelina seed will be stored on the farm instead of a WBP elevator,

WBP agrees to pay the Grower a storage fee of $.025/bushel (50lbs per bushel) for every month on, a prorated basis,

of needed storage. This agreement makes the Grower responsible for the quality of the stored Camelina Seed.

Camelina Quality After Screening – The Grower and WBP will take a representative sample of seed when the

Grower delivers the seed to WBP for processing to determine dockage. All seed received will have a moisture

content of <11% (based on canola moisture standard) with additional moisture weight deducted from calculated seed

weight.
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Contract # N10PC18199

All seed will be received with 40% oil content and 98% pure, no stones or rocks, dockage will be assessed for all 

other seed quality based on the attached following schedule a:

Other - Grower agrees in good farmer like manner to mature, harvest, and deliver said crop and cooperate with

company to deliver crop on a timely basis to WBP. Disputes will be settled by a three man board consisting of a 

representative of the Grower, a representative of WBP and a representative selected by the representatives of the

Grower and WBP.

_____________________________________ _____________________________________

Grower Signature Willamette Biomass Processors, Inc. Officer

_____________________________________ _____________________________________

Grower Name (Print) Date

_____________________________________ _____________________________________

_____________________________________ Grower Phone#

Grower Address

_____________________________________ _____________________________________

Grower Email Address Grower Cell Phone#

_____________________________________ _____________________________________

Fax Number (if available) Acres Contracted
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Contract # N10PC18199

Fax Number (if available) Acres Contracted Example: Grower agrees to plant 100 acres of camelina in 2008

assuming a 1500lb. yield. He would be paid as follows:

Paid to Grower

1. At Contract Signing $10/acre $ 1,000.00

2. At Emergence in approx. May 2008 $5/acre $ 500.00

3. Harvest $.055 x (1,500lb yield x 100 acres) $ 8,250.00

4. Delivery to WBP Processing Facility $.055 x (1500lb.yield x 100 acres) $ 8,250.00

*5. $.05/lb. Oregon Tax Credit $ 7,500.00

Sub-Total $25,500.00

6. Transportation Reimbursement $??

7. Storage Reimbursement $??

Total $25,500.00+

========

* Grower must apply with Oregon Department of Energy
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Contract # N10PC18199

APPENDIX B: SUSTAINABLE OILS 2011 CAMELINA 

PRODUCTION CONTRACT 

Camelina Production Services Contract

Sustainable Oils, LLC

2815 Eastlake Ave. E, Suite 300

Seattle, WA 98102

Producer: ________________________ Effective Date: _________

Farm Name ____________________________ Contract No: ____________

Contact Name ____________________________

Address ________________________________

City and State ___________________________

Phone _________________________________

E-mail address __________________________

(―Producer‖)

NOW, THEREFORE, in consideration of the payments by Sustainable Oils and the Services to be provided, and 

other good and valuable consideration, the receipt and adequacy all of which is acknowledged, and in consideration 

of the mutual agreements, covenants and obligations hereinafter set forth, the parties agree as follows:

This Camelina Production Contract (―Contract‖), and the Exhibit A Protocol, which is a part of this Contract, 

describes the production agreement between Sustainable Oils, LLC, a Delaware limited liability company 

(―Sustainable Oils‖), and Producer. 

1. Planting Seed Payment. Producer agrees to plant, produce and maintain with acceptable agronomic 

practices, to harvest and deliver a Camelina grain crop ("Crop") on behalf of Sustainable Oils (the ―Services‖). The 

Services shall include costs of all pesticide and fertilizer inputs into the production of the Crop: the cost of the Seed 
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Contract # N10PC18199

planted; herbicide treatments prior to planting, during the growing period, and one post-harvest, plus fertilizer 

applied up to the levels recommended by the production Protocol. Sustainable Oils, or its designated agent, agrees 

to Sell ________ pounds of Camelina Planting Seed ("Seed") to Producer for the sole purpose of the Services. 

Producer shall deliver the Crop at a date and place designated by Sustainable Oils. Producer agrees that the Services 

to be provided are a ―work for hire.‖ Producer agrees that this Contract is a contract for services. Payments shall be 

made in US Dollars.

2. Planting Acreage. Producer agrees to plant Seed prior to April 30, 2011 for Crop production, on 

_________ acres located in ___________________ County, State of ________________. Producer agrees to notify 

Sustainable Oils, or its designee, within 30 days after planting 1) the planting date; 2) the total number of acres 

planted; 3) the amount of Seed planted per acre; and 4) the precise planting location. Any unused Seed after 

planting will be returned to Sustainable Oils at Producer‘s expense or will be disposed of in a manner expressly 

approved by Sustainable Oils.

3 Limited Use of Seed and Crop. Producer agrees that Producer will not use, or permit to be used, the Seed 

to produce Crop for any person or entity other than Sustainable Oils. The Producer will not sell, transfer, or 

distribute any Seed, or any portion of the Crop, including seed pods, cuttings, volunteer Seed, or other genetic 

material that grows or is harvested from the Seed to any person or entity other than Sustainable Oils. Producer will 

not save any portion of the Seed or Crop for any future use unless approved in writing by Sustainable Oils.

4. Producer Responsibility. Producer will be solely responsible for the Services and the cost and management 

of the planting, maintenance, agronomy practices, and harvesting, except for the payments to be made under this 

Contract.. Producer agrees to complete the agronomic practices form ―Exhibit B‖ and return to Sustainable Oils.

5. Ownership of Seed and Crop. 

A. Producer acknowledges and agrees that Sustainable Oils is the owner of all right, title and interest in and to the 

Seed, and to the intellectual property related to the Seed and to any subsequent Crop and that this Contract is not a 

license or grant of any right in or to the Seed or Crop, except for the right to provide the Services. 

B. Producer acknowledges and agrees that it will not acquire any property rights in or to the Seed or the Crop. 

C. Producer agrees and acknowledges that Producer is being paid a fee for Services to produce a Crop from the 

Seed. 

D. Producer acknowledges that Sustainable Oils is the owner of all right, title and interest in and to the Crop and to 

any Seed form or embodiment thereof and is also the owner of the goodwill attached and which shall become 

attached to the Seed. 

E. Producer will not challenge Sustainable Oils‘ ownership of or the validity of the Seed or any intellectual property 

right or grant thereof, or any copyright or trademark relating thereto, or any other rights to the Seed. 

F. Producer will not knowingly, at any time, do or suffer to be done any act or thing which will in any way 

jeopardize, dilute or adversely affect any registration of the Seed or Sustainable Oils‘ ownership of the Seed. 

G. Producer expressly covenants and agrees to maintain the Seed and the Crop free and clear of any and all liens 

and encumbrances, and will indemnify and hold harmless Sustainable Oils from a breach of this provision. 

H. Producer expressly represents and warrants that no other party or person has any claim or right in the Seed or the 

Crop. 

I. Producer grants to Sustainable Oils a security interest in the Seeds and the Crop. 

6. Crop Payment for Services and Quality Parameters

Sustainable Oils will pay the Producer $0.15/lb. for all delivered crop that meets Sustainable Oils standards. Crop 

will be delivered, at its or his sole cost and expense, to designated collection point _______________________. 
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Contract # N10PC18199

Upon verification by Sustainable Oils of a commercial stand of at least 12 plants per sq. ft. avg., and delivery of 

Exhibit B (Sustainable Oils Grower Information Sheet), Producer will qualify for the $50 per acre ―shared risk‖ that 

will be paid at crop settlement time. . After a stand has been established and confirmed, if the grower should lose 

the crop by an ―Act of God‖, the farmer is guaranteed the fifty dollars as part of the ― Shared risk ― program. 

Sustainable Oils will take delivery from the Producer before March 1, 2012, or will ―Advance‖ 80 percent of the 

projected bin total as determined by Sustainable Oils, not later than March 30, 2012. 

Protocol. Exhibit ―A‖ describes the Protocol that a Camelina Producer shall follow to participate in this Contract.

Crop Rejection. The Crop may be rejected by Sustainable Oils in part or in whole if moisture is greater than 8%, or 

for any reason that the Crop is not merchantable, including a Crop or any part that is sprouting, heated, or spoiled. 

Sustainable Oils may then dispose of the rejected Crop, in whole or in part, as it deems necessary. 

Crop Dockage. Normal dockage is screening dockage over 1% and will be deducted by weight for Services‘ 

payment calculations.

7. Crop Storage Fees. If Producer and Sustainable Oils agree that the Crop will be stored by the Producer 

after harvest, Sustainable Oils will pay Producer 3 cents ($0.03) per fifty-pound (50 lb.) bushel for each 30 days of 

storage, prorated, starting October 1, 2011 The Producer agrees to be responsible for Crop insurance, quality 

maintenance, and loss of Crop during storage. Producer shall not dispose of or transfer the Crop other than in 

accordance with this Contract by delivery to Sustainable Oils.

8. Term. This Contract shall be effective as of the Effective Date after execution and delivery, and will 

continue for an initial Term of one Crop season (―Initial Term‖). This Contract may be renewed at the end of the 

Initial Term and any subsequent Term for a renewal Term of one Crop season upon the written agreement of the 

parties. Notwithstanding the foregoing, this Contract may be terminated earlier as provided herein. 

9. Termination by Sustainable Oils. In addition to any other remedies available to Sustainable Oils, 

Sustainable Oils may, by written notice to Producer, terminate this Contract upon the occurrence of any one of the 

following events (―Event of Default‖): (1) Producer breaches any material provision of this Contract which breach is 

not cured within ten (10) days after Sustainable Oils gives notice to Producer of such breach; (2) Producer (a) 

terminates or suspends its business activities, (b) becomes insolvent, admits in writing its inability to pay its debts as 

they mature, makes an assignment for the benefit of creditors, or becomes subject to control of a trustee, receiver or 

similar authority, or (c) becomes subject to any bankruptcy or insolvency proceeding which is not rescinded within 

ten (10) days. 

10. Effect of Termination. Upon termination of this Contract, all rights granted to Producer shall immediately 

cease and terminate; provided, however, that unfulfilled obligations shall not terminate.

11. Replant / Crop Destruction. The Producer shall not plow down or reseed the acreages planted with Seed 

without the prior consent of Sustainable Oils, which consent shall not be unreasonably withheld; provided, however, 

that Producer shall not harvest any volunteer Seed Crop produced from the Seed.

Non-Assignment. This Contract and the Services are unique and personal and may not be transferred, conveyed, 

assigned or delegated by Producer, in whole or in part, whether by (1) independent agreement, (2) acquisition by 

another party of a Producer‘s stock or assets, (3) lease, (4) mortgage, pledge or other assignment as security, (5) 

merger, consolidation or other reorganization, or (6) the succession by another party to the Producer‘s business by 

operation of law, or whether as a consequence of any transaction that results in a change in the ownership of or right 

to control the management of Producer, or otherwise, unless Sustainable Oils has expressly consented in writing. 

The occurrence of any of the foregoing events shall be grounds for immediate termination of this Contract by 

Sustainable Oils.

Grant of Access. Producer grants Sustainable Oils or its designee the right of access to the Crop, to land on which 

the Crop is grown, and the location in which the Crop is stored after harvest. Producer will maintain Seed planting, 

Crop harvesting, and production records, including pesticide and fertilizer application records in sufficient detail and 

appropriate for purposes of confirming Producer‘s compliance with this Contract. Sustainable Oils will have the 
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right, during normal business hours and upon reasonable notice, to inspect and copy all such records of Producer to 

the extent reasonably required in order for Sustainable Oils to confirm Producer‘s obligations. 

Indemnity. Each party will indemnify, defend, and hold the other party, its directors, officers, employees, and 

affiliates harmless from and against all claims, demands, liabilities, damages and expenses, including reasonable 

attorneys‘ fees and costs (―Liabilities‖) arising out of (i) the negligence, recklessness or intentional wrongful acts or 

omissions of such indemnifying party, in connection with the performance of any work or Services for or in 

connection with this Contract; or (ii) breach of this Contract.

Force Majeure. A party may be excused from the performance of this Contract if performance is hindered by Acts 

of God, strikes, embargoes, earthquakes, war, terrorism, or events beyond the reasonable control of such party. The 

party claiming force majeure shall give notice to the other party as soon as reasonably practicable. In the event that 

a force majeure event exists for more than 30 days, Sustainable Oils may terminate this Contract and have no further 

obligations to Producer.

Confidentiality. The terms of this Contract shall be kept confidential by Producer.

Applicable Law. Any disputes between the parties relating to this Contract shall be determined by the laws and 

Courts in the State of Montana, notwithstanding Montana‘s conflicts of laws rules. The parties agree that only 

courts located in the state of Montana shall have jurisdiction to settle any dispute between them arising under this 

Contract. 

Entire Agreement. This Contract, including all exhibits and attachments, is the entire agreement between Producer 

and Sustainable Oils relating to the subject matter hereof, and may not be altered, changed, or amended unless in 

writing and signed by both Producer and Sustainable Oils. This Contract many not be amended by course of 

dealing, course of performance or usage of trade. 

Non-Waiver. Any waiver by a party of any condition, or of the breach of any provision, term, covenant, 

representation or warranty contained in this Contract, in any one or more instance, shall not be deemed to be nor 

construed as a further or continuing waiver of any such condition, or of the breach of any other provision, term, 

covenant, representation or warranty of this Contract. No failure or delay on the part of a party in exercising any 

right under this Contract will operate as or be deemed to be a waiver of or limitation on any such right, and no single 

or partial exercise of any such right will preclude any other or further exercise thereof or the exercise of any other 

right.

No Agency or Joint Venture Relationship. Except for Sustainable Oils‘ accrued and owing obligations for payment 

for the Services, Producer shall perform all its undertakings and obligations under this Contract strictly as an 

independent contractor and at Producer‘s own risk and expense, and shall exercise control over the Services. Except 

for Liabilities retained by Sustainable Oils and Producer, respectively, Producer assumes and discharges, and shall 

indemnify Sustainable Oils and hold it harmless from, all Liabilities arising from or relating to all Servicers and 

activities by and of Producer. Nothing in this Contract shall be construed as constituting either party as the legal 

representative, agent, franchisee, or partner of or a joint venture with the other party or any of its affiliates, or as 

constituting either party liable for any debts or obligations of the other party, except as otherwise provided by 

indemnity obligations.

Severability. If any provision of this Contract, or the application of such provision to any person or circumstance, is 

declared by a court of competent jurisdiction to be invalid or unenforceable, such invalidity or unenforceability shall 

not affect the remaining provisions of this Contract or the application of such provisions to persons or circumstances 

other than those to which it is held invalid or unenforceable; and if such invalidity or unenforceability is due to the 

court‘s determination that the provision‘s scope is excessively broad or restrictive under applicable law then in 

effect, then such court shall construe such provision by modifying its scope so as to be enforceable to the fullest 

extent compatible with applicable law then in effect.

SUSTAINABLE OILS DOES NOT MAKE ANY EXPRESS OR IMPLIED WARRANTIES TO PRODUCER 

REGARDING THE SEED, ITS VIABILITY OR ABILITY TO THRIVE, ITS FITNESS FOR ANY PURPOSE, 
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OR ITS MERCHANTABILITY, OR ELIGIBILITY OF SEED PRODUCTS FOR COMMERCE. UNINTENDED 

CONSEQUENCES, INCLUDING FAILURE TO GERMINATE, OR INABILITY TO THRIVE, MAY RESULT 

BECAUSE OF SUCH FACTORS AS THE PRESENCE OR ABSENCE OF CHEMICALS USED IN PLANTING 

AND GROWING, OR PRODUCER‘S AGRONOMIC PRACTICES, PEST INFESTATION, OR THE WEATHER, 

ALL OF WHICH ARE BEYOND THE CONTROL OF SUSTAINABLE OILS AND ASSUMED BY 

PRODUCER.

Authorized Producer Signature

________________________________ 

Printed Name____________________________

Date ____________________________

Authorized Sustainable Oils, LLC Signature

Printed Name___________________________ 

Date ____________________________

NOTICE: Only designated employees of Sustainable Oils are authorized to execute contracts or make any 

representation or warranty in the name of Sustainable Oils, LLC. Any purported execution, representation, or 

warranty on behalf of Sustainable Oils, LLC by any other person shall be null and void.
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APPENDIX C: THE UNIVERSITY OF TENNESSEE – FIXED 

PRICE CONTRACT WITH SWITCHGRASS PRODUCERS 

THE UNIVERSITY OF TENNESSEE CONTRACT

This Contract, made and entered into on ________________, documents the agreement between The University of 

Tennessee (hereinafter University) and ________________________________________________ (hereinafter 

Contractor). 

This Contract consists of this cover page, two pages containing the University's Standard Terms and Conditions and 

two pages of Additional Terms and Conditions. Terms contained on this cover page and the University‘s Standard 

Terms and Conditions shall prevail over those of any attachment unless otherwise stated under "Other terms" below. 

Contractor agrees to grow and harvest switchgrass for the University to be used in the University‘s Biofuels 

Initiative, such switchgrass to be grown on that portion of real property located at ___________________________, 

in _______________ County (―Contractor‘s Property‖), indicated on the field layout map attached hereto and 

incorporated herein as Exhibit A, and which is comprised of ___________ acres (the ―Switchgrass Contract 

Acreage‖), for the term of this Contract pursuant to both the Standard and the Additional Terms and Conditions 

attached hereto and incorporated herein, and using management practices defined by University of Tennessee 

Extension. 

The period of performance under this contract is from March 15, 2009 through March 15, 2012. However, the 

University may terminate this Contract, in its discretion, with or without cause, by giving the Contractor at least 

thirty (30) days written notice before the effective termination date. If the Contract is terminated by University for 

cause, including but not limited to breach by the Contractor, all obligations of the University, including payment 

obligations in excess of fair compensation for completed services, shall be terminated. If the Contract is terminated 

by the University other than for cause, the Contractor shall be entitled to receive equitable compensation for 

satisfactory authorized work completed as of the termination date. 

During the term hereof, the University will compensate Contractor $450 per acre of Switchgrass Contract Acreage 

per year for three consecutive switchgrass production years. For the purposes of calculating payments due to 

Contractor under this Contract, the University shall use a global positioning system (GPS) to calculate the number of 

acres (to the nearest tenth of an acre) included in the Switchgrass Contract Acreage. The level of Switchgrass 

Contract Acreage payments may be adjusted each year. Current guideline University Extension switchgrass budgets 

were based on farm diesel prices as of October 1, 2007. An annual adjustment to the Switchgrass Contract Acreage 

compensation rate will be based on the change in the U.S. Gulf Coast No. 2 Diesel Low Sulfur average price FOB 

(cents per gallon) for the first week in October. The Contractor will be notified of the adjustment by November 1 

for the current year of the contract. The first year adjustment will be based on 40.65 gallons of diesel equivalents 

per acre. Contract year 2 and 3 adjustments will be based on 32.4 gallons of diesel equivalents per acre each year. 

Payments will not be adjusted below $450 per acre. 

The Energy Company will be responsible for pick-up of switchgrass on the Contractor‘s Property and delivery to the 

point of intermediate or end use. The Energy Company may negotiate a mutually agreeable switchgrass storage 

arrangement and compensation with the Contractor in the second and third production years. University assumes no 

responsibility for switchgrass not picked up by the Energy Company within 12 months of harvest, and Contractor 

shall be free to dispose of switchgrass remaining on the Contractor‘s Property after 12 months. 

Other payment terms: All such payments shall be made no later than sixty (60) days after receipt of invoice from 

Contractor. Contractor shall send University a single invoice each year following certification by University 

Extension that the grower followed production and harvesting management practices defined by University of 

Tennessee Extension. In the event the Contractor followed prescribed management practices and did not establish 

an acceptable stand of switchgrass on all or part of the acreage in any production year, $450 per acre of Switchgrass 
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Contract # N10PC18199

Contract Acres will be paid to the Contractor and the Contractor will replant the affected acreage during the next 

following production year. 

The University‘s maximum liability under this Contract is $____________________________. 

Other terms: See Additional Terms and Conditions and Field Layout Map, Exhibit A, attached hereto and 

incorporated herein. I have reviewed and agree to all requirements in the Request for Contract Proposal for the 2009 

Biofuels Program and, knowing the University of Tennessee will rely upon my responses to questions, certify that 

my responses to the questions are true and correct to the best of my knowledge. The Request for Contract Proposal 

is attached as Exhibit B hereto and incorporated herein. 

authorized representatives.

FOR CONTRACTOR: FOR UNIVERSITY: 

Signature Office of Bioenergy Programs 

Name (Printed) ____________________________________ ___________________________________ 

Responsible Account Address 

Administrative Signature (Optional) 

___________________________________ Authorized Official Telephone Number 

1. The University is not bound by this Contract until it is approved by the appropriate University official(s) 

indicated on the signature page of this Contract. 

2. This Contract may be modified only by a written amendment which has been executed and approved by the 

appropriate parties as indicated on the signature page of this Contract. 

3. The Contractor shall not assign this Contract or enter into a subcontract for any of the services performed 

under this Contract without obtaining the prior written approval of the University. 

4. Unless otherwise indicated on the reverse, if this Contract provides for reimbursement for travel, meals or 

lodging, such reimbursement must be made in accordance with University travel policies. 

5. The Contractor warrants that no part of the total Contract amount shall be paid directly or indirectly to an 

employee or official of the State of Tennessee as wages, compensation, or gifts in exchange for acting as 

officer, agent, employee, subcontractor, or consultant to Contractor in connection with any work 

contemplated or performed relative to this Contract, and that no employee or official of the State of 

Tennessee holds a controlling interest in the Contractor. If the Contractor is an individual, the Contractor 

certifies that he/she is not presently employed by the University or any other agency or institution of the 

State of Tennessee; that he/she has not retired from or terminated such employment within the past six 

months; and that he/she will not be so employed during the term of this Contract. 

6. The Contractor shall maintain documentation for all charges against the University under this Contract. 

The books, records and documents of the Contractor, insofar as they relate to work performed or money 

received under this Contract, shall be maintained for a period of three (3) full years from the date of the 

final payment, and shall be subject to audit, at any reasonable time and upon reasonable notice, by the 

University or the Comptroller of the Treasury, or their duly appointed representatives. These records shall 

be maintained in accordance with generally accepted accounting principles. 

7. No person on the grounds of disability, age, race, color, religion, sex, national origin, veteran status or any 

other classification protected by Federal and/or Tennessee State constitutional and/or statutory law shall 

be excluded from participation in, or be denied benefits of, or be otherwise subjected to discrimination in 

the performance of this Contract. The Contractor shall, upon request, show proof of such nondiscrimination, and shall post in conspicuous places, available to all employees and applicants, notice of 

nondiscrimination. 
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8. The Contractor, being an independent contractor, agrees to carry adequate public liability and other 

appropriate forms of insurance, and to pay all taxes incident to this Contract. The University shall have no 

liability except as specifically provided in this Contract. 

9. The Contractor shall comply with all applicable Federal and State laws and regulations in the performance 

of this Contract. 

10.This Contract shall be governed by the laws of the State of Tennessee, which provide that the University 

has liability coverage solely under the terms and limits of the Tennessee Claims Commission Act. 

11.The Contractor shall avoid at all times any conflict of interests between his/her duties and responsibilities 

as a Contractor and his/her interests outside the scope of any current or future Contracts. The following 

principles define the general parameters of a conflict of interests prohibited by the University: 

a. A Contractor=s outside interests shall not interfere with or compromise his/her judgment and 

objectivity with respect to his/her duties and responsibilities to the University. 

b. A Contractor shall not make or influence University decisions or use University resources in a 

manner that results in: $ Financial gain outside any current or future Contracts for either the 

Contractor or his/her relatives or $ Unfair advantage to or favored treatment for a third party 

outside the University. 

c. A Contractor=s outside financial interests shall not affect the design, conduct, or reporting of 

research. 

The Contractor certifies that he/she has no conflicts of interests and has disclosed in writing the following: 

A. Any partners or employees of the Contractor who are also employees of the University. 

B. Any relatives of the Contractor=s partners or employees who work for the University. 

C. Any outside interest that may interfere with or compromise his/her judgment and objectivity with respect to 

his/her responsibilities to the University.

1. If the Contractor fails to perform properly its obligations under this Contract or violates any term of this 

Contract, the University shall have the right to terminate this Contract immediately and withhold 

payments in excess of fair compensation for completed services. The Contractor shall not be relieved of 

liability to the University for damages sustained by breach of this Contract by the Contractor. 

2. It is understood by the Contractor that the University will possess all rights to any creations, inventions, 

other intellectual property, and materials, including copyright or patents in the same, which arise out of, 

are prepared by, or are developed in the course of the Contractor‘s performance under this Contract. The 

Contractor and the University acknowledge and agree that the Contractor=s work under this Contract shall 

belong to the University as "work-made-for-hire" (as such term is defined in U.S. Copyright Law). 

3. The Contractor hereby attests that the Contractor shall not knowingly utilize the services of any illegal 

immigrant in the United States in the performance of this contract and shall not knowingly utilize the 

services of any subcontractor who will utilize the services of an illegal immigrant in the United States in 

the performance of this Contract. 

WHEREAS, the University is performing scientific research on the production of biofuels from switchgrass as part 

of Tennessee‘s Biofuels Initiative and the production of switchgrass by a select number of Tennessee farmers for use 

in research to be performed by or at the discretion of the University is a necessary component of this research; and 

WHEREAS, Contractor has agreed to participate in this research by growing switchgrass on the Switchgrass 

Contract Acreage pursuant to the terms and conditions hereof. 

1. The University shall furnish Contractor with a quantity of seed sufficient to sow the acreage under contract. 

While the University agrees to use every reasonable effort to obtain reliable seed, THE UNIVERSITY 

GIVES NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE QUALITY OR PRODUCTIVENESS 

OF THE SEED FURNISHED. This is a research project. Therefore, UNIVERSITY MAKES NO 
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Contract # N10PC18199

WARRANTY, EXPRESS OR IMPLIED, AS TO THE VALIDITY OR EFFICACY OF 

MANAGEMENT PRACTICES DEFINED BY UNIVERSITY EXTENSION. The Energy Company will 

be responsible for loading and hauling the contract switchgrass from the Contractor‘s farm to the 

biorefinery.

a) Contractor agrees to: 

b) Prepare all the land within the Switchgrass Contract Acreage and plant the switchgrass on all such land in a 

timely fashion, as weather conditions permit. In the event the University determines that the Switchgrass 

Contract Acreage needs to be re-seeded in the second or third year, the University shall furnish such seed 

and Contractor shall plant such seed in a timely fashion, as weather conditions permit. 

c) Manage carefully and in a timely manner the growing switchgrass, including weed control, fertilizing and 

all other matters relating to the production of switchgrass, at the proper season and in the best manner 

possible, in consultation with, and subject to the approval and satisfaction of, University Extension or the 

University‘s designated representatives. Contractor specifically agrees to use reasonable efforts to control 

weeds in the Switchgrass Contract Acreage, including such methods as are recommended by University 

Extension or the University‘s designated representatives. Contractor specifically agrees to use soil 

samples that are collected, processed, analyzed, and interpreted according to guidelines recommended by 

University Extension or the University‘s designated representatives. 

d) Refrain from using the Switchgrass Contract Acreage for any other purposes during the term of this 

Contract without the University‘s prior written consent, which consent shall not be unreasonably withheld. 

e) Harvest and bale the switchgrass, using mesh wrap or triple tying the bales and otherwise, at a time and in a 

manner mutually agreeable to the University and the Contractor. 

f) Assemble the bales of switchgrass at a place on or near Contractor‘s Property, mutually agreed upon by the 

parties. Switchgrass bales must be assembled in a manner mutually agreed upon by the parties, and 

located near an all-weather road that can be accessed by an over-the-road semi tractor trailer pulling a 53‘ 

trailer or low-boy trailer. 

g) Comply with all local, state, and federal laws and regulations related to groundwater contamination, the 

application of herbicides, fungicides, pesticides and fertilizers, hazardous waste storage or disposal, and 

the cultivation of crops. Contractor shall also follow label directions and all applicators‘ licensing 

requirements in the handling and application of all chemicals. 

h) Keep records of the dates and amounts of any and all applications of seed, fertilizers, pesticides, herbicides, 

fungicides or other chemicals or materials to the Switchgrass Contract Acreage, and allow use of the 

records submitted by Contractor and all such other information pertaining to Contractor‘s Property and the 

production of switchgrass thereon as is reasonably necessary to perform the research and publish and 

otherwise disseminate the results of such research. 

i) Permit the University or its representatives to enter upon Contractor‘s Property at any reasonable time for 

the purpose of (i) consulting with Contractor, (ii) viewing, monitoring, measuring, inspecting or analyzing 

the Switchgrass Contract Acreage, the switchgrass growing thereon or the switchgrass harvested 

wherefrom, (iii) ensure that Contractor is fulfilling the terms and conditions of this Contract, and (iv) for 

such other purposes as are reasonably necessary to complete work outlined in the contract, and conduct 

research, provided that such entry does not interfere with Contractor‘s ability to carry out regular farming 

operations on Contractor‘s Property. 

a. Permit the Energy Company and its representatives and contract haulers to enter upon 

Contractor‘s Property at any reasonable time for the purpose of (i) loading and transporting 

harvested switchgrass, (ii) consulting with Contractor, (iii) viewing, monitoring, measuring, 

inspecting or analyzing the switchgrass harvested, and (iv) for such other purposes as are 

reasonably necessary to complete work outlined in the contract, provided that such entry does not 

interfere with Contractor‘s ability to carry out regular farming operations on Contractor‘s 

Property. 
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Contract # N10PC18199

1. In the event Contractor fails to properly manage or harvest the switchgrass being grown on the Switchgrass 

Contract Acreage, the University reserves the right, personally or through its designated agents, to enter upon 

Contractor‘s Property and properly care for and harvest the switchgrass, deducting the costs of such care and 

harvest from any amounts payable to Contractor under this Contract. Neither this reservation of right, nor the 

exercise thereof, shall limit any other remedies provided by this Contract or that the University may have at law 

or in equity. Nothing in the contract creates any obligation on the part of University to care for or harvest 

Contractor‘s switchgrass. 

2. Contractor certifies that he/she is not presently debarred, proposed for debarment, suspended or declared 

ineligible for covered transactions by any federal agency or department. Contractor also certifies that within the 

past three years he/she has not been convicted of or had civil judgment rendered against them for a fraudulent 

contract or transaction, violation of federal or state antitrust laws, or the commission of embezzlement, theft, 

forgery, bribery, falsifying or destroying records, receiving stolen property, or making false statements. 

3. University reserves the right to substitute varieties of switchgrass, as it determines appropriate for its 

demonstration purposes. 

4. CONTRACTOR MAKES NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE PRODUCTIVITY, 

QUALITY OR SUITABILITY OF THE SWITCHGRASS FOR USE IN THE UT BIOFUELS INITIATIVE.

Contractor agrees to use the management practices defined by University Extension, but CONTRACTOR MAKES 

NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE VALIDITY OR EFFICACY OF MANAGEMENT 

PRACTICES OR CONTRACTOR‘S AGRICULTURAL PRACTICES IN PRODUCING SWITCHGRASS 

UNDER THIS RESEARCH PROGRAM. 
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APPENDIX D: SUSTAINABLE OILS PLANT BACK 

REQUIREMENTS FOR CAMELINA 

Table 61: Sustainable Oils Plant Back Requirements for Camelina 

Crop Rotation Restrictions For Camelina

Product Months

Accent 18

Acetochlor Next Cropping Season

Ally Extra 22

Amber Bioassay

Assert 15

Atrazine 22

Balance Pro 18

Basis 18

Beacon 18

Callisto Next Cropping Season

Camix 18

Celebrity Plus 18

Chlopyralid 0

Chlopyralid, 2,4-D 5

Dicamba 4

Everest 9

Far-Go Next Cropping Season

Glean/Finesse Bioassay

Matrix 18

Maverick Bioassay

Metribuzin 12

Metsulfuron 34

Milestone 24

Olympus 22

Option 2

Paramount 10

Peak 22

Permit 15

Plateau 48

Prowl 0

Pursuit 26

Raptor 18

Rave Bioassay

Rimfire 10

Silverado 10

Sonalan 0

Spartan 24
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Status/Distinct 1

Tordon 24

Trifluralin 0

Valor/Chateau 8

Widematch 4
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APPENDIX E: SASKATCHEWAN CROP INSURANCE 

CORPORATION: 2010 TERMS AND CONDITIONS FOR 

CAMELINA 

This program was developed as a result of co-operative efforts between the Saskatchewan Crop Insurance 

Corporation (SCIC), industry specialists, Saskatchewan Ministry of Agriculture, Agriculture and Agri-Food Canada 

and individual camelina producers.

These terms and conditions set out special conditions of camelina insurance to help you understand the coverage 

provided. Unless otherwise described below, all other aspects of the multi-peril Crop Insurance program also apply. 

Please refer to your endorsement, coverage detail, and statement of insurance forms for specific premium and 

production guarantee information.

Insurance Features:

Insurance is provided on established stands only. There is no establishment benefit. All fields will be inspected to

determine eligibility based on SCIC criteria. Minimum of 120 plants per square yard.

Coverage of 50, 60, or 70 per cent is available.

Production guarantees for all producers are determined using the provincial average yield with no 

distinction for soil class, risk zone, or individual yield histories.

Insurance coverage is for yield loss only and no coverage is provided for quality loss. Premium discounts 

and surcharges do not apply.

Camelina seeded in the brown soil zone will have a seeding deadline of May 21. See Your Complete Guide 

to Understanding Crop Insurance for a soil zone map.

Areas outside the brown soil zone must consider the average days to maturity and the normal first fall frost 

for the area when determining the final seeding date for the variety seeded

Liability will not be accepted on camelina seeded after June 20.

Agronomic Practices:

Producers are advised to consult Saskatchewan Ministry of Agriculture Camelina Farm Facts for detailed agronomic

practices, including weed control, seed rate, crop rotations, and other agronomic considerations. Failure to follow

recommended practices may result in the reduction or denial of any claim, should a loss occur. Producers are

advised of the following specific consideration:

Pre-seed weed control is important to establishing a good camelina stand. If weed pressure is a cause of 

loss and no method of pre-seed weed control was implemented, claims may be reduced or denied.

Camelina is susceptible to herbicide residues in the soil. Do not seed on land where herbicide residue could 

be an issue.

Seed at five pounds an acre to target a plant population greater than 150 plants per square yard.

Camelina should be seeded shallow (1⁄4 to 1⁄2 inch depth) with good seed-to-soil contact .Insurance will not 

be available to camelina stands that do not have an adequate plant population.

The Saskatchewan Crop Insurance Corporation is committed to developing a financially sound insurance package 

to meet the needs of camelina producers. Producer input and co-operation is essential to the growth of this 

insurance program. SCIC encourages all camelina producers to discuss their needs and concerns with the 

Corporation.

01/10
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APPENDIX F: UNITED STATES STANDARDS FOR CANOLA 

Grades and Grade Requirements § 810.304 Grades and grade requirements for canola. 

Grading factors 

Grades, U.S. Nos. 

1 2 3 

Damaged kernels: 

Heat damaged 0.1 0.5 2.0

Distinctly green 2.0 6.0 20.0

Total 3.0 10.0 20.0

Conspicuous admixture:

Ergot 0.05 0.05 0.05

Sclerotinia 0.05 0.10 0.15

Stones 0.05 0.05 0.05

Total 1.0 1.5 2.0

Inconspicuous admixture 5.0 5.0 5.0

Maximum count limits of:

Other material: 

Animal filth 3 3 3

Glass 0 0 0

Unknown foreign substance 1 1 1

U.S. Sample grade Canola that: 

(a) Does not meet the requirements for U.S. Nos. 1, 2, or 3; or 

(b) Has a musty, sour, or commercially objectionable foreign odor; or 

(c) Is heating or otherwise of distinctly low quality. 

Special Grades and Special Grade Requirements

§ 810.305 Special grades and special grade requirements. 

Garlicky canola. Canola that contains more than two green garlic bulblets or an equivalent quantity 

of dry or partly dry bulblets in approximately a 500 gram portion.

Nongrade Requirements

§ 810.306 Nongrade requirements.

Glucosinolates. Content of glucosinolates in canola is determined according to procedures prescribed 

in FGIS instructions.

Source: (USDA; Grain Inspection, Packers, and Stockyards Administration, 1992)
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APPENDIX G: MONTANA STATE HAIL INSURANCE RATES 

Figure 52: Montana State Hail Insurance Rates 

Source: (Montana Department of Agriculture, n.d.)

Table 62: Montana State Hail Insurance Rates by Zone 

Crops Rate as a percentage of coverage

Wheat, barley, rye, oats, flax, durum, 

safflower, sunflowers, triticale, sorghum, 

speltz, corn, hay and millet

Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 Zone 7

11 % 10 % 9 % 8 % 7 % 6 % 5 %

Lentils, peas, beans, canary seed, grass 

seed and alfalfa seed 14 % 13 % 12 % 11 % 10 % 9 % 6 %

Canola, mustard, rape, sainfoin and alfalfa 

seed 16 % 15 % 14 % 13 % 12 % 11 % 7 %

Potatoes and sugar beets

(Up to maximum coverage of $100/acre) 6.6 % 6 % 5.4 % 4.8 % 4.2 % 3.6 % 3 %

Mixed fields Charge for mixed fields is the highest rate crop in the mix
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APPENDIX H: MATHEMATICAL DESCRIPTION OF CDFDEV 

For random variable with i.i.d. observations for , let be the empirical distribution function 

and be a known cumulative distribution function (possibly based on estimated parameters) hypothesized to be 

the true underlying distribution for . Let be the ith order statistic in the observed data series and let be the 

corresponding - quantile from the hypothesized distribution where (if a sample of size n from the 

hypothesized distribution is provided, then would be the ith order statistic). The CDFDEV criterion is defined as

For a group of J alternative hypothesized distributions, or variations on parameterizations of these distributions, with 

respective cumulative distribution functions , the alternative which the minimum CDFDEV value should be 

considered the best hypothesized distribution among the given alternatives. In the case where two or more 

alternatives produce the same minimum value CDFDEV, the distribution with the smallest moment bias should be 

considered the better of the two. Note that if then 
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APPENDIX I: PROGRAM EVALUATION TOOL DECISION TREE 

Significant Potential Market?

Yes No

Significant Revenue Risk? No Action Required

Yes No Other Significant Risk?

Sufficient Non-Insurance Coping 

Mechanisms?

Yes No

Yes No

New Product? No Action Required

No Action 

Required

Classification Problems?

Fix Problem Withdraw Product?

Yes

No Moral Hazard Problems?

Yes No

Fix 

Problem

Withdraw 

Product? Yes

Fix Problem

Is if Fixable? Yes No

Benefit/Cost?

No

Other Problems?

Withdraw 

Product

Yes No

Is it Fixable? 

Benefit/Cost?

Yes No

Wait and See 

or New 

Product

Is it Fixable? 

Benefit/Cost?

Figure 53: Program Evaluation Tool Decision Tree 

Source: (USDA; RMA, 2005)
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 150

Contract # N10PC18199

APPENDIX J: MONTANA CAMELINA PROGRAM EVALUATION 

TOOL DECISION TREE 

Significant Potential Market?

Yes No

Significant Revenue Risk? No Action Required

Yes No Other Significant Risk?

Sufficient Non-Insurance Coping 

Mechanisms?

Yes No

Yes No

New Product? No Action Required

No Action 

Required

Classification Problems?

Fix Problem Withdraw Product?

Yes

No Moral Hazard Problems?

Yes No

Fix 

Problem

Withdraw 

Product? Yes

Fix Problem

Is if Fixable? Yes No

Benefit/Cost?

No

Other Problems?

Withdraw 

Product

Yes No

Is it Fixable? 

Benefit/Cost?

Yes No

Wait and See 

or New 

Product

Is it Fixable? 

Benefit/Cost?

Figure 54: Montana Camelina Decision Tree 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 151

Contract # N10PC18199

APPENDIX K: OREGON/WASHINGTON CAMELINA PROGRAM 

EVALUATION TOOL DECISION TREE 

Significant Potential Market?

Yes No

Significant Revenue Risk? No Action Required

Yes No Other Significant Risk?

Sufficient Non-Insurance Coping 

Mechanisms?

Yes No

Yes No

New Product? No Action Required

No Action 

Required

Classification Problems?

Fix Problem Withdraw Product?

Yes

No Moral Hazard Problems?

Yes No

Fix 

Problem

Withdraw 

Product? Yes

Fix Problem

Is if Fixable? Yes No

Benefit/Cost?

No

Other Problems?

Withdraw 

Product

Yes No

Is it Fixable? 

Benefit/Cost?

Yes No

Wait and See 

or New 

Product

Is it Fixable? 

Benefit/Cost?

Figure 55: Oregon/Washington Camelina Decision Tree 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 152

Contract # N10PC18199

APPENDIX L: TENNESSEE SWITCHGRASS PROGRAM 

EVALUATION TOOL DECISION TREE 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significant Potential Market?

Yes No

Significant Revenue Risk? No Action Required

Yes No Other Significant Risk?

Sufficient Non-Insurance Coping 

Mechanisms?

Yes No

Yes No

New Product? No Action Required

No Action 

Required

Classification Problems?

Fix Problem Withdraw Product?

Yes

No Moral Hazard Problems?

Yes No

Fix 

Problem

Withdraw 

Product? Yes

Fix Problem

Is if Fixable? Yes No

Benefit/Cost?

No

Other Problems?

Withdraw 

Product

Yes No

Is it Fixable? 

Benefit/Cost?

Yes No

Wait and See 

or New 

Product

Is it Fixable? 

Benefit/Cost?

Figure 56: Tennessee Switchgrass Decision Tree 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 153

Contract # N10PC18199

APPENDIX M: LOUISIANA ENERGY CANE PROGRAM 

EVALUATION TOOL DECISION TREE 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significant Potential Market?

Yes No

Significant Revenue Risk? No Action Required

Yes No Other Significant Risk?

Sufficient Non-Insurance Coping 

Mechanisms?

Yes No

Yes No

New Product? No Action Required

No Action 

Required

Classification Problems?

Fix Problem Withdraw Product?

Yes

No Moral Hazard Problems?

Yes No

Fix 

Problem

Withdraw 

Product? Yes

Fix Problem

Is if Fixable? Yes No

Benefit/Cost?

No

Other Problems?

Withdraw 

Product

Yes No

Is it Fixable? 

Benefit/Cost?

Yes No

Wait and See 

or New 

Product

Is it Fixable? 

Benefit/Cost?

Figure 57: Louisiana Energy Cane Decision Tree 
Phase 2-Deliverable 2.4.2.2 Final Feasibility Report 154

Contract # N10PC18199

APPENDIX N: PROGRAM EVALUATION TOOL DIAGNOSTIC 

INSTRUMENTS 
SEPTEMBER 2005 53 FCIC-22010 (PEH)

Program Evaluation Diagnostic Questions

Region Montana

Crop Camelina

Market 

(fresh, processed, sold for animal feed, etc.) Sold for Dedicated Energy

Background Information

Production Processes

Annuals

1. Is the crop planted multiple times during a crop production year? If yes, explain: Yes No

2. For a single planting, is the crop harvested multiple times during a crop production year? If 

yes, explain: Yes No

3. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as double crop, fallow, irrigation, regional differences in climate or soils, etc.). Discuss, 

particularly, features that are critical in assessing potential demand including potential issues with 

practices and types. 

Camelina is planted as a rotation crop and typically follows a wheat, wheat, fallow rotation with 2-3 years 

between Camelina crops. Rotation is essential and camelina should not follow camelina or other brassica 

crops. In addition, growers should not use wheat herbicides with residual action which may cause carry-over 

damage. These include those in the ALS-inhibitor type herbicides. Avoid SU‘s and Imy-family herbicides. 

Common brand names to avoid include: Glean, Finesse, Ally, Amber, Rave, Maverick, Olympus.

Biennials

4. Is the crop harvested multiple times during a crop production year? Yes No

5. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types.

N/A
SEPTEMBER 2005 54 FCIC-22010 (PEH)

Perennials

6. Is the crop harvested multiple times during a crop production year? If yes, explain: Yes No

7. Is the crop alternate bearing? Yes No

8. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types. 

9. What is the economic life of the capital stock (trees, vines, etc.)? N/A years

10. Over its economic life, what is the likelihood that 10 percent or more of the capital stock 

would be lost due to natural causes? Describe: N/A % 

(probability 

of loss)

11. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

starts producing salable output? N/A years

12. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

is at peak production? N/A years

Nursery

13. Describe distinguishing characteristics of prevailing production system(s) for nursery crops in this 

region. Discuss, particularly, features that are critical in assessing potential demand including potential 

issues with practices and types. 

N/A
SEPTEMBER 2005 55 FCIC-22010 (PEH)

Marketing

14. Describe typical marketing channels and/or contracting structures for this crop.

Producers in Montana are under a production contract that pays $0.15/lb.

15. In this region are there critical time periods (i.e., marketing windows) when producers hope to market 

this crop? If so, describe.

No, because camelina production is contracted with a fixed price. Therefore, there is no price risk in 

marketing camelina.

16. Within the marketing channels and/or contracting structures mentioned above describe how quality 

variations are handled (e.g., off-grade apples in a fresh market system may be processed for juice).

The crop is rejected if the moisture is greater than 8%. The crop can also be used as a meal for livestock 

feed.

17. In this region, do federal supply control marketing orders exist for production of this crop?

Describe:

Yes No

18. In this region, do state quality marketing orders exist for production of this crop? 

Describe:

Yes No
SEPTEMBER 2005 56 FCIC-22010 (PEH)

RMA–Facilitated Insurance Products

19. In this region, what RMA-facilitated insurance products are currently available for this crop? List all:

None

Yield Risk

20. In this region what are examples of crops with very low relative yield risk? Relative risk is used to 

adjust absolute magnitudes that vary across crops to a relative level to facilitate comparability (roughly, 

a measure of variation divided by the mean level).

Hay, Oats

21. In this region what are examples of crops with very high relative yield risk?

Canola, Peas

22. Is this crop exposed to catastrophic risks that would reduce yields by 50 percent or more? Yes No

23. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic losses to 

occur?

Description Years (or crop 

cycles) out of 25

Cold Wet Weather, Cold Winter, Other (Snow-Lightning, Etc.), Plant Disease 2, 10, 2. 4. 2

Failure of Irrigation Supply, Hot Wind, Wind/Excess Wind, Freeze, Frost 15, 4, 2, 6, 8

Excess Moisture/Precip/Rain, Insects, Hail, Heat, Drought 7,7,8,8,13

This analysis is based on canola, which is the closest related crop.

24. Characterize yield risk for this crop ignoring the catastrophic yield risk(s) described earlier. On a scale 

from one to five, if the low relative yield risk crops identified earlier were one, and the high relative 

yield risk crops identified earlier were five, what number would you assign to the non-catastrophic 

yield risk associated with this crop in this region?

1

very low 

relative yield 

risk

2 3 4 5

very high 

relative yield 

risk
SEPTEMBER 2005 57 FCIC-22010 (PEH)

25. In this region, do producers tend to experience multiple-year sequences of good yields or bad 

yields for this crop? If yes, describe what causes these multiple-year sequences.

There is not enough yield data to determine.

Yes No

26. On a scale from one to five, where one is very low yield risk and five is very high yield risk, provide an 

overall assessment of yield risk faced by producers of this crop in this region.

1

very low yield 

risk

2 3 4 5

very high yield 

risk

Quality Risk

27. In this region what are examples of crops with very low quality risk?

Hay, Oats, Wheat

28. In this region what are examples of crops with very high quality risk?

Peas, Barley, Alfalfa

29. Is this crop exposed to catastrophic quality risks that would reduce the average price received 

by 20 percent or more? Yes No

30. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic quality 

losses to occur?

Description Years (or crop 

cycles) out of 25

31. We now want to characterize quality risk for this crop ignoring the catastrophic quality risk(s) 

described earlier. On a scale from one to five, if the crops with very low risk of quality problems 

identified earlier were one, and the crops with very high risk of quality problems identified earlier 

were five, what number would you assign to the quality risk associated with this crop in this region?

1

very low 

quality risk

2 3 4 5

very high 

quality risk
SEPTEMBER 2005 58 FCIC-22010 (PEH)

32. On a scale from one to five, if one is very low quality risk and five is very high quality risk, provide 

an overall assessment of quality risk faced by producers of this crop in this region.

1

very low 

quality risk

2 3 4 5

very high 

quality risk

Price Risk

33. In this region what are examples of crops with very low relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or equivalent for perennials) and sale. 

(Similar concept to IP and RA for crops with futures markets).

Alfalfa, Hay

34. In this region what are examples of crops with very high relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or, equivalent for perennials) and sale. 

(Similar concept to IP and RA for crops with futures markets).

Peas, Soybeans, Wheat

35. On a scale from one to five, if the low price risk crops identified earlier were one and the high price 

risk crops identified earlier were five, what number would you assign to the relative price risk (within 

the production cycle) associated with this crop in this region?

1

low price risk 

crop

2 3 4 5

high price risk 

crop

36. In this region, do producers tend to experience multiple-year sequences of high prices or low 

prices for this crop?

If yes, describe. No established market.

Yes No

37. On a scale from one to five, where one is very low price risk and five is very high price risk, provide 

an overall assessment of price risk (within the production cycle) faced by producers of this crop in 

this region.

38. 39.

1

very low price 

risk

2 3 4 5

very high price 

risk
SEPTEMBER 2005 59 FCIC-22010 (PEH)

Other Sources of Revenue Risk

38. For this region, describe other factors that affect revenue risk for this crop (e.g., prevented planting).

The Camelina seed is very small and planting should be less than a 1/4 inch in depth. Because of this it is 

tough to get the seed in good moisture and tough to get good seed to soil contact causing problems for 

good stand establishment. Therefore drought during planting periods or during stand establishment are 

problematic and may cause losses. Prevented planting does not seem to be as much as an issue as the 

planting period is very long (fall or spring seeding). The crop does not tolerate standing water so well 

drained soils are a requirement.

39. 40.

39. On a scale from one to five, where one is very low risk and five is very high risk, provide an overall 

assessment of risk sources other than yield, quality, and price risks faced by producers of this crop in 

this region.

40. 41.

1

very low risk

2 3 4 5

very high risk

Sufficient Non-Insurance Coping Mechanisms

40. On a scale from one to five, where one is very low and five is very high, assess the extent to which 

producers of this commodity in this region use risk-reducing inputs as a substitute for crop insurance.

1

very low 

2 3 4 5

very high

41. Are government crop programs (e.g., marketing loans and counter-cyclical payments) 

available for this crop?

Describe: The Biomass Crop Assistance Program (BCAP) supports establishing and 

producing eligible crops for the conversion to bioenergy through project areas and through 

contracts on land of up to 5 years for annual and non-woody perennial crops or up to 15 years 

for woody perennial crops. Through a matching payment program BCAP assists agricultural 

and forest land owners and eligible material owners with collection, harvest, storage, and 

transportation of eligible material for use in qualified Biomass Conversion Facilities (BCF). 

These payments will be available to eligible material owners at the rate of $1 for each $1 per 

dry ton paid by the BCF to the eligible material owners, limited to a maximum of $45 per dry 

ton and limited to a 2-year payment duration. No listening session attendees indicated that 

they utilized the program. NAP is also available.

Yes No
SEPTEMBER 2005 60 FCIC-22010 (PEH)

42. In this region, is there a history of federal disaster payments for this crop?

Describe:

Yes No

43. Approximately what percentage of the total production of this crop is under production 

contract with a first handler or processor? 100 __%

Describe contracts:

a. Under the terms of a typical production contract for this crop, is the grower exposed 

to production risk (i.e., the grower must deliver on the contract even if production 

shortfalls occur)?

Yes No

b. Under the terms of a typical production contract for this crop, is the grower exposed 

to quality risk (i.e., there are significant price penalties if the product does not meet 

the quality characteristics specified in the contract).

Yes No

c. Under the terms of a typical production contract for this crop, is the grower exposed 

to price risk (i.e., prices for specific quality characteristics are not specified in the 

contract)?

Yes No

44. In this region, approximately what percentage of the total production of this crop is priced 

prior to harvest (may or may not be tied to a production contract)?

Describe: All camelina production is contracted prior to planting

100 __%

45. When corn farmers in the Midwest experience low (high) yields, they can often expect higher (lower) 

market prices (i.e., prices and yields are very negatively correlated). This moderates the revenue 

impacts of low yields. In contrast, for corn farmers in the Southeast there is very little relationship 

between their yields and market prices (i.e., prices and yields are independent). In this region the price 

and yield for this crop are (circle one):

Independent Somewhat Negatively Correlated Highly Negatively Correlated

Describe: It is anticipated that camelina yields and price will be Independent of each other. This is 

because the commercialization of camelina for bioenergy is in its infancy and in its current form, the 

demand is being met by the current supply. However, going forward this may change greatly.

46. On a scale from one to five, where one is ―strongly disagree‖ and five is ―strongly agree,‖ provide your 

reaction to the following statement:

―In this region, producers of this crop are financially able to self-insure against production losses.‖

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Producers ranked crop insurance as the second highest priority for the industry, next to 

getting a higher price. They are currently exposed to production losses, but have a fixed production 

price.
SEPTEMBER 2005 61 FCIC-22010 (PEH)

47. For a typical grower of this crop, approximately what percentage of the total farm revenue 

would be attributable to this crop? 15-25 %

48. What other commodities would typically be produced on a farm that produces this commodity? What is 

the correlation between revenue from these other commodities and the revenue from this commodity? 

For correlation use a scale of one to five, where 1 is ―strongly negatively correlated,‖ 2 is ―negatively 

correlated,― 3 ―independent,― 4 is ―positively correlated,― and 5 is ―strongly positively correlated.‖

List:

Correlation

(assign a number 

between 1-5)

Winter Wheat, Spring Wheat 4,4

Canola 4

Barley 4

Peas 4

49. In this region, approximately what percentage of the total production of this crop is produced 

by part-time farmers who have full-time employment off the farm? 0 %

50. On a scale from one to five, where one is ―strongly disagree― and five is ―strongly agree,― provide your 

reaction to the following statement:

―In this region, producers of this crop attempt to manage production risk by spreading their production 

over several geographic locations.‖

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: One producer stated that he was not spread over several geographic locations, while another producer 

stated that he was spread over several geographic locations.

51. In this region, what private-sector insurance products (if any) are currently available for this crop?

List all: Prairie Mountain Insurance Agency in Great Falls Montana offers a hail and transit policy

for camelina producers.
SEPTEMBER 2005 62 FCIC-22010 (PEH)

52. Characterize how agricultural lenders in this region view the available RMA-facilitated insurance 

products for this crop. ―Unfavorable‖ implies that lenders actually discourage borrowers from 

purchasing the product while ―favorable‖ implies that lenders strongly encourage and often require 

borrowers to purchase the product. If multiple insurance products are offered, answer for each 

product.

Unfavorable Indifferent Favorable

Describe: Hard to determine as no lenders were present at the listening sessions. Yet, listening session 

attendees typically say that their lenders prefer they have crop insurance for production loans.

53. On a scale from one to five, where one is very high and five is very low, assess the sufficiency of noninsurance coping mechanisms for producers of this crop in this region.

1

high

availability

2 3 4 5

low

availability

Risk Classification

54. On a scale from one to five, where one is strongly disagree and five is strongly agree, provide your 

reaction to the following statement:

―In this region, no producers of this crop are really any more or less risky than any others. They all 

face about the same risk of loss.‖

54. 55.

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Listening session feedback indicated that they all faced about the same risks of loss, with 

the exception of those closer to the mountain region. These producers stated that they face more harsh 

weather conditions due to wind and precipitation caused by the mountains.

55. In this region, for those who are currently not insured, would you say that premium rate on the 

existing RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or 

―much too high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A
SEPTEMBER 2005 63 FCIC-22010 (PEH)

56. In this region, for those who currently are insured, would you say that premium rate on the existing 

RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or ―much too 

high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A

57. For this region, to what extent does the system used to establish the guarantee (e.g., APH yield or 

expected revenue) for this crop match the true value of the production at risk? An answer of one 

indicates that the system used to establish the guarantee does a very poor job of matching the true 

value of the production at risk. An answer of five indicates that the system used to establish the 

guarantee does a very good job of matching the true value of the production at risk.

1

very poor job

2 3 4 5

very good job

58. On a scale from one to five, where one is very low and five is very high, assess the effectiveness of 

existing RMA-facilitated insurance products in accurately classifying potential policyholders 

according to their loss exposure (i.e., higher risk growers pay higher premiums while lower risk 

growers pay lower premiums).

59. 60.

1

very low

2 3 4 5

very high
SEPTEMBER 2005 64 FCIC-22010 (PEH)

Moral Hazard and Monitoring

59. Yield variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ In 

practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential for 

gaming the RMA-facilitated crop insurance product for this crop on a scale from one to five, where 

one implies that variation in yield is almost exclusively due to ―acts of nature‖ (potential for gaming 

is low) and five implies that yield variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

60. 61.

1

very low

2 3 4 5

very high

60. To the extent that management affects yield loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: It is not too hard to monitor the insured's behavior in growing camelina; however, it may be 

challenging to monitor the producers practices in relation to chemical residuals from previous crops.

61. 62.

61. Quality variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ 

In practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential 

for gaming the RMA-facilitated insurance product for this crop on a scale from one to five, where one 

implies that variation in quality is almost exclusively due to ―acts of nature‖ (potential for gaming is 

low) and five implies that quality variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

62. 63.

1

very low

2 3 4 5

very high

62. To the extent that management affects quality loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: Producers cannot change the quality characteristics of the oil; therefore, it is not difficult.

63. On a scale from one to five, where one is very large and five is very small, assess the extent of moral 

hazard problems with existing RMA-facilitated insurance products for this crop.

63. 64.

1

very large

2 3 4 5

very small
SEPTEMBER 2005 65 FCIC-22010 (PEH)

Problems Affecting Insurance Participation

64. Have significant problems occurred (either past or current) with policy provisions on existing 

RMA-facilitated insurance products for the crop? If multiple insurance products are offered, 

answer for each product.

Yes No

65. If the answer to the previous question is no, go to next question. If yes, for each significant problem:

a. Briefly describe the problem.

b. What has been the impact of the problem (e.g., high loss ratios, reduced demand, etc.)?

c. Have policy provisions since been changed to adequately address the problem?

d. If policy provisions have not been changed, what changes in policy provisions do you think would 

increase insurance demand for this crop?

66. In this region, do reinsured companies have sufficient incentives to aggressively market 

existing RMA-facilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

67. In this region, do agents have sufficient incentives to aggressively market existing RMAfacilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

68. List any perils that concern growers of this crop but are not covered by the existing RMA-facilitated 

insurance products (e.g., business interruption due to unavailability of irrigation water, disease 

quarantines, etc.). For each peril assess the extent of growers‘ concerns about this peril on a scale from 

one to five where one is minor concern and five is major concern.

List all: 1

minor concern

2 3 4 5

major concern
SEPTEMBER 2005 66 FCIC-22010 (PEH)

69. Briefly describe the potential for insuring these currently uninsured perils? In answering this, consider 

the following questions:

Can hidden action/moral hazard and classification/adverse selection problems be avoided?

Can clearly stated policy provisions be developed and accurate premium rates established?

N/A

70. On a scale from one to five, where one is very high and five is very low, assess the likelihood that 

problems affecting participation can be adequately addressed by product or policy modifications.

71.72.

1

very low

2 3 4 5

very high
SEPTEMBER 2005 53 FCIC-22010 (PEH)

Program Evaluation Diagnostic Questions

Region Oregon/Washington

Crop Camelina

Market 

(fresh, processed, sold for animal feed, etc.) Sold for Dedicated Energy

Background Information

Production Processes

Annuals

1. Is the crop planted multiple times during a crop production year? If yes, explain: Yes No

2. For a single planting, is the crop harvested multiple times during a crop production year? If 

yes, explain: Yes No

3. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as double crop, fallow, irrigation, regional differences in climate or soils, etc.). Discuss, 

particularly, features that are critical in assessing potential demand including potential issues with 

practices and types. 

Camelina is grown as a rotation crop and typically follow a hard red winter wheat, camelina, spring wheat, 

summer fallow rotation with two to three years between camelina crops. Rotation is essential and camelina 

should not follow camelina or other brassica crops. In addition, growers should not use wheat herbicides 

with residual action which may cause carry-over damage. These include those in the ALS-inhibitor type 

herbicides. Avoid SU‘s and Imy-family herbicides. Common brand names to avoid include: Glean, Finesse, 

Ally, Amber, Rave, Maverick, Olympus.

Biennials

4. Is the crop harvested multiple times during a crop production year? Yes No

5. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types.

N/A
SEPTEMBER 2005 54 FCIC-22010 (PEH)

Perennials

6. Is the crop harvested multiple times during a crop production year? If yes, explain: Yes No

7. Is the crop alternate bearing? Yes No

8. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types. 

9. What is the economic life of the capital stock (trees, vines, etc.)? N/A years

10. Over its economic life, what is the likelihood that 10 percent or more of the capital stock 

would be lost due to natural causes? Describe: N/A % 

(probability 

of loss)

11. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

starts producing salable output? N/A years

12. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

is at peak production? N/A years

Nursery

13. Describe distinguishing characteristics of prevailing production system(s) for nursery crops in this 

region. Discuss, particularly, features that are critical in assessing potential demand including potential 

issues with practices and types. 

N/A
SEPTEMBER 2005 55 FCIC-22010 (PEH)

Marketing

14. Describe typical marketing channels and/or contracting structures for this crop.

Great Plains Oil & Exploration (Great Plains – The Camelina Company) is a renewable fuels energy 

company founded with the purpose of manufacturing and marketing biodiesel produced from camelina. 

Great Plains currently contracts with producers in Oregon. Also the Willamette Biomass Processors, Inc. 

currently contracts with producers of camelina in Oregon.

15. In this region are there critical time periods (i.e., marketing windows) when producers hope to market 

this crop? If so, describe.

No, because camelina production is contracted with a fixed price. Therefore, there is no price risk in 

marketing camelina.

16. Within the marketing channels and/or contracting structures mentioned above describe how quality 

variations are handled (e.g., off-grade apples in a fresh market system may be processed for juice).

Currently there are no quality concerns.

17. In this region, do federal supply control marketing orders exist for production of this crop?

Describe:

Yes No

18. In this region, do state quality marketing orders exist for production of this crop?

Describe:

Yes No
SEPTEMBER 2005 56 FCIC-22010 (PEH)

RMA–Facilitated Insurance Products

19. In this region, what RMA-facilitated insurance products are currently available for this crop? List all:

None
SEPTEMBER 2005 57 FCIC-22010 (PEH)

Yield Risk

20. In this region what are examples of crops with very low relative yield risk? Relative risk is used to 

adjust absolute magnitudes that vary across crops to a relative level to facilitate comparability (roughly, 

a measure of variation divided by the mean level).

Alfalfa, Barley, Sugarbeets, Potatoes

21. In this region what are examples of crops with very high relative yield risk?

Winter Wheat, Spring Wheat, Corn for Grain, Oats

22. Is this crop exposed to catastrophic risks that would reduce yields by 50 percent or more? Yes No

23. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic losses to 

occur?

Description Years (or crop 

cycles) out of 25

Cold Wet Weather, Drought, Excessive Moisture/Precip/Rain, Hail, Hot Wind 2, 10, 2. 4. 2

Freeze, Frost, Insects, Cold Winter, Heat 15, 4, 2, 6, 8

This analysis is based on canola which is the most closely related insurable crop.

24. Characterize yield risk for this crop ignoring the catastrophic yield risk(s) described earlier. On a scale 

from one to five, if the low relative yield risk crops identified earlier were one, and the high relative 

yield risk crops identified earlier were five, what number would you assign to the non-catastrophic 

yield risk associated with this crop in this region?

1

very low 

relative yield 

risk

2 3 4 5

very high 

relative yield 

risk

25. In this region, do producers tend to experience multiple-year sequences of good yields or bad 

yields for this crop? If yes, describe what causes these multiple-year sequences.

Yes No

26. On a scale from one to five, where one is very low yield risk and five is very high yield risk, provide an 

overall assessment of yield risk faced by producers of this crop in this region.

1

very low yield 

risk

2 3 4 5

very high yield 

risk
SEPTEMBER 2005 58 FCIC-22010 (PEH)

Quality Risk

27. In this region what are examples of crops with very low quality risk?

Alfalfa, Wheat

28. In this region what are examples of crops with very high quality risk?

Barley, Sugarbeets, Potatoes

29. Is this crop exposed to catastrophic quality risks that would reduce the average price received 

by 20 percent or more? Yes No

30. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic quality 

losses to occur?

Description Years (or crop 

cycles) out of 25

31. We now want to characterize quality risk for this crop ignoring the catastrophic quality risk(s) 

described earlier. On a scale from one to five, if the crops with very low risk of quality problems 

identified earlier were one, and the crops with very high risk of quality problems identified earlier 

were five, what number would you assign to the quality risk associated with this crop in this region?

1

very low 

quality risk

2 3 4 5

very high 

quality risk

32. On a scale from one to five, if one is very low quality risk and five is very high quality risk, provide 

an overall assessment of quality risk faced by producers of this crop in this region.

1

very low 

quality risk

2 3 4 5

very high 

quality risk

Price Risk

33. In this region what are examples of crops with very low relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or equivalent for perennials) and sale. 

(Similar concept to IP and RA for crops with futures markets).

Alfalfa, Hay Other
SEPTEMBER 2005 59 FCIC-22010 (PEH)

34. In this region what are examples of crops with very high relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or, equivalent for perennials) and sale. 

(Similar concept to IP and RA for crops with futures markets).

Corn for Grain, Soybeans, Hard Red Winter Wheat, Spring Wheat

35. On a scale from one to five, if the low price risk crops identified earlier were one and the high price 

risk crops identified earlier were five, what number would you assign to the relative price risk (within 

the production cycle) associated with this crop in this region?

1

low price risk 

crop

2 3 4 5

high price risk 

crop

36. In this region, do producers tend to experience multiple-year sequences of high prices or low 

prices for this crop?

If yes, describe.

Yes No

37. On a scale from one to five, where one is very low price risk and five is very high price risk, provide 

an overall assessment of price risk (within the production cycle) faced by producers of this crop in 

this region.

38. 39.

1

very low price 

risk

2 3 4 5

very high price 

risk

Other Sources of Revenue Risk

38. For this region, describe other factors that affect revenue risk for this crop (e.g., prevented planting).

The Camelina seed is very small and planting should be less than a 1/4 inch in depth. Because of this it is 

tough to get the seed in good moisture and tough to get good seed to soil contact causing problems for 

good stand establishment. Therefore drought during planting periods or during stand establishment are 

problematic and may cause losses. Prevented planting does not seem to be as much as an issue as the 

planting period is very long (fall or spring seeding). The crop does not tolerate standing water so well 

drained soils are a requirement.

39. 40.
SEPTEMBER 2005 60 FCIC-22010 (PEH)

39. On a scale from one to five, where one is very low risk and five is very high risk, provide an overall 

assessment of risk sources other than yield, quality, and price risks faced by producers of this crop in 

this region.

40. 41.

1

very low risk

2 3 4 5

very high risk

Sufficient Non-Insurance Coping Mechanisms

40. On a scale from one to five, where one is very low and five is very high, assess the extent to which 

producers of this commodity in this region use risk-reducing inputs as a substitute for crop insurance.

1

very low 

2 3 4 5

very high

41. Are government crop programs (e.g., marketing loans and counter-cyclical payments) 

available for this crop?

Describe: The Biomass Crop Assistance Program (BCAP) supports establishing and 

producing eligible crops for the conversion to bioenergy through project areas and through 

contracts on land of up to 5 years for annual and non-woody perennial crops or up to 15 years 

for woody perennial crops. Through a matching payment program BCAP assists agricultural 

and forest land owners and eligible material owners with collection, harvest, storage, and 

transportation of eligible material for use in qualified Biomass Conversion Facilities (BCF). 

These payments will be available to eligible material owners at the rate of $1 for each $1 per 

dry ton paid by the BCF to the eligible material owners, limited to a maximum of $45 per dry 

ton and limited to a 2-year payment duration. No listening session attendees indicated that 

they utilized the program. NAP is also available.

Yes No

42. In this region, is there a history of federal disaster payments for this crop?

Describe:

Yes No

43. Approximately what percentage of the total production of this crop is under production 

contract with a first handler or processor? 100 __%

Describe contracts:

d. Under the terms of a typical production contract for this crop, is the grower exposed 

to production risk (i.e., the grower must deliver on the contract even if production 

shortfalls occur)?

Yes No

e. Under the terms of a typical production contract for this crop, is the grower exposed 

to quality risk (i.e., there are significant price penalties if the product does not meet 

the quality characteristics specified in the contract).

Yes No

f. Under the terms of a typical production contract for this crop, is the grower exposed 

to price risk (i.e., prices for specific quality characteristics are not specified in the 

contract)?

Yes No
SEPTEMBER 2005 61 FCIC-22010 (PEH)

44. In this region, approximately what percentage of the total production of this crop is priced 

prior to harvest (may or may not be tied to a production contract)?

Describe: All camelina production is contracted prior to planting

100 __%

45. When corn farmers in the Midwest experience low (high) yields, they can often expect higher (lower) 

market prices (i.e., prices and yields are very negatively correlated). This moderates the revenue 

impacts of low yields. In contrast, for corn farmers in the Southeast there is very little relationship 

between their yields and market prices (i.e., prices and yields are independent). In this region the price 

and yield for this crop are (circle one):

Independent Somewhat Negatively Correlated Highly Negatively Correlated

Describe: It is anticipated that camelina yields and price will be Independent of each other. This is 

because the commercialization of camelina for bioenergy is in its infancy and in its current form, the 

demand is being met by the current supply. However, going forward this may change greatly.

46. On a scale from one to five, where one is ―strongly disagree‖ and five is ―strongly agree,‖ provide your 

reaction to the following statement:

―In this region, producers of this crop are financially able to self-insure against production losses.‖

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Indications from the listening session meeting suggest that most producers wanted crop 

insurance on camelina so they are not exposed to large production losses, even though most producers 

had assistance in establishing the crop and were being paid a fixed price for their production.

47. For a typical grower of this crop, approximately what percentage of the total farm revenue 

would be attributable to this crop? 15-25 %

48. What other commodities would typically be produced on a farm that produces this commodity? What is 

the correlation between revenue from these other commodities and the revenue from this commodity? 

For correlation use a scale of one to five, where 1 is ―strongly negatively correlated,‖ 2 is ―negatively 

correlated,― 3 ―independent,― 4 is ―positively correlated,― and 5 is ―strongly positively correlated.‖

List:

Correlation

(assign a number 

between 1-5)

Hard Red Winter Wheat, Spring Wheat 4,4

Alfalfa 3

Barley 4

49. In this region, approximately what percentage of the total production of this crop is produced 

by part-time farmers who have full-time employment off the farm? 0 %
SEPTEMBER 2005 62 FCIC-22010 (PEH)

50. On a scale from one to five, where one is ―strongly disagree― and five is ―strongly agree,― provide your 

reaction to the following statement:

―In this region, producers of this crop attempt to manage production risk by spreading their production 

over several geographic locations.‖

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Most producers stated that they were not spread over several geographic locations.

51. In this region, what private-sector insurance products (if any) are currently available for this crop?

List all:

52. Characterize how agricultural lenders in this region view the available RMA-facilitated insurance 

products for this crop. ―Unfavorable‖ implies that lenders actually discourage borrowers from 

purchasing the product while ―favorable‖ implies that lenders strongly encourage and often require 

borrowers to purchase the product. If multiple insurance products are offered, answer for each 

product.

Unfavorable Indifferent Favorable

Describe: Hard to determine as no lenders were present at the listening sessions. Yet, listening session 

attendees typically say that their lenders prefer they have crop insurance for production loans.

53. On a scale from one to five, where one is very high and five is very low, assess the sufficiency of noninsurance coping mechanisms for producers of this crop in this region.

1

high

availability

2 3 4 5

low

availability
SEPTEMBER 2005 63 FCIC-22010 (PEH)

Risk Classification

54. On a scale from one to five, where one is strongly disagree and five is strongly agree, provide your 

reaction to the following statement:

―In this region, no producers of this crop are really any more or less risky than any others. They all 

face about the same risk of loss.‖

56. 57.

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Most producers attending the listening sessions indicated that those producers further to the 

west were probably less at risk due to drought, while those further east are more exposed to less 

rainfall and higher risk.

55. In this region, for those who are currently not insured, would you say that premium rate on the 

existing RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or 

―much too high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A

56. In this region, for those who currently are insured, would you say that premium rate on the existing 

RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or ―much too 

high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A

57. For this region, to what extent does the system used to establish the guarantee (e.g., APH yield or 

expected revenue) for this crop match the true value of the production at risk? An answer of one 

indicates that the system used to establish the guarantee does a very poor job of matching the true 

value of the production at risk. An answer of five indicates that the system used to establish the 

guarantee does a very good job of matching the true value of the production at risk.

1

very poor job

2 3 4 5

very good job

58. On a scale from one to five, where one is very low and five is very high, assess the effectiveness of 

existing RMA-facilitated insurance products in accurately classifying potential policyholders 

according to their loss exposure (i.e., higher risk growers pay higher premiums while lower risk 

growers pay lower premiums).

59. 60.

1

very low

2 3 4 5

very high
SEPTEMBER 2005 64 FCIC-22010 (PEH)

Moral Hazard and Monitoring

59. Yield variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ In 

practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential for 

gaming the RMA-facilitated crop insurance product for this crop on a scale from one to five, where 

one implies that variation in yield is almost exclusively due to ―acts of nature‖ (potential for gaming 

is low) and five implies that yield variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

60. 61.

1

very low

2 3 4 5

very high

60. To the extent that management affects yield loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: It is not too hard to monitor the insured's behavior in growing camelina; however, it may be 

challenging to monitor the producers practices in relation to chemical residuals from previous crops.

61. 62.

61. Quality variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ 

In practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential 

for gaming the RMA-facilitated insurance product for this crop on a scale from one to five, where one 

implies that variation in quality is almost exclusively due to ―acts of nature‖ (potential for gaming is 

low) and five implies that quality variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

62. 63.

1

very low

2 3 4 5

very high

62. To the extent that management affects quality loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: Producers cannot change the quality characteristics of the oil; therefore, it is not difficult.

63. On a scale from one to five, where one is very large and five is very small, assess the extent of moral 

hazard problems with existing RMA-facilitated insurance products for this crop.

65. 66.

1

very large

2 3 4 5

very small
SEPTEMBER 2005 65 FCIC-22010 (PEH)

Problems Affecting Insurance Participation

64. Have significant problems occurred (either past or current) with policy provisions on existing 

RMA-facilitated insurance products for the crop? If multiple insurance products are offered, 

answer for each product.

Yes No

65. If the answer to the previous question is no, go to next question. If yes, for each significant problem:

a. Briefly describe the problem.

b. What has been the impact of the problem (e.g., high loss ratios, reduced demand, etc.)?

c. Have policy provisions since been changed to adequately address the problem?

d. If policy provisions have not been changed, what changes in policy provisions do you think would 

increase insurance demand for this crop?

66. In this region, do reinsured companies have sufficient incentives to aggressively market 

existing RMA-facilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

67. In this region, do agents have sufficient incentives to aggressively market existing RMAfacilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

68. List any perils that concern growers of this crop but are not covered by the existing RMA-facilitated 

insurance products (e.g., business interruption due to unavailability of irrigation water, disease 

quarantines, etc.). For each peril assess the extent of growers‘ concerns about this peril on a scale from 

one to five where one is minor concern and five is major concern.

List all: 1

minor concern

2 3 4 5

major concern
SEPTEMBER 2005 66 FCIC-22010 (PEH)

69. Briefly describe the potential for insuring these currently uninsured perils? In answering this, consider 

the following questions:

Can hidden action/moral hazard and classification/adverse selection problems be avoided?

Can clearly stated policy provisions be developed and accurate premium rates established?

N/A

70. On a scale from one to five, where one is very high and five is very low, assess the likelihood that 

problems affecting participation can be adequately addressed by product or policy modifications.

71.72.

1

very low

2 3 4 5

very high
SEPTEMBER 2005 53 FCIC-22010 (PEH)

Program Evaluation Diagnostic Questions

Region Tennessee

Crop Switchgrass

Market 

(fresh, processed, sold for animal feed, etc.) Sold for Dedicated Energy

Background Information

Production Processes

Annuals

1. Is the crop planted multiple times during a crop production year? If yes, explain:

N/A Yes No

2. For a single planting, is the crop harvested multiple times during a crop production year? If 

yes, explain:

N/A

Yes No

3. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as double crop, fallow, irrigation, regional differences in climate or soils, etc.). Discuss, 

particularly, features that are critical in assessing potential demand including potential issues with 

practices and types. 

N/A

Biennials

4. Is the crop harvested multiple times during a crop production year? Yes No

5. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types.

N/A
SEPTEMBER 2005 54 FCIC-22010 (PEH)

Perennials

6. Is the crop harvested multiple times during a crop production year? If yes, explain:

This is uncommon but it has been done. The most common practice would be to wait until after 

November 1 or 2 to 3 hard freezes to get crop dried down as much as possible. One cutting would 

be the common cultural practice.

Yes No

7. Is the crop alternate bearing? Yes No

8. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types. 

Currently much of the switchgrass is grown on marginal soils land that was taken away from pasture 

production and put into switchgrass; however, some was converted from cropland. Switchgrass is 

determined to be easy to grow and the need for intensive management is unnecessary, yet it does respond 

well to fertility.

9. What is the economic life of the capital stock (trees, vines, etc.)? 10 years

10. Over its economic life, what is the likelihood that 10 percent or more of the capital stock 

would be lost due to natural causes? Describe:

Hard to determine, as this is a very hardy grass that thrives natively. Introduced high yielding 

cultivars have been developed for regions such as Tennessee and no pest or disease issues have 

been prevalent. However, the crop has only been in commercial production for 3 years specifically 

for dedicated energy purposes.

0 % 

(probability 

of loss)

11. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

starts producing salable output? 1 years

12. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

is at peak production? 

3 years
SEPTEMBER 2005 55 FCIC-22010 (PEH)

Nursery

13. Describe distinguishing characteristics of prevailing production system(s) for nursery crops in this 

region. Discuss, particularly, features that are critical in assessing potential demand including potential 

issues with practices and types. 

N/A

Marketing

14. Describe typical marketing channels and/or contracting structures for this crop.

Genera energy was formed to execute the capital construction projects and business elements of the 

Tennessee Biofuels Initiative. Genera energy partnered with DuPont Danisco Cellulosic Ethanol to jointly 

construct and operate a demonstration scale cellulosic biorefinery. As part of the Tennessee Biofuels 

Initiative, the University of Tennessee developed a local farm-based switchgrass energy crop industry. 

Currently the University of Tennessee provides acre contracts to farmers for $450/acre which began in 2009 

and will run for 3 years; however a yield based component will be added in 2010. Genera energy will then 

provide a yield based production contract for $50-$75 per ton going forward.

15. In this region are there critical time periods (i.e., marketing windows) when producers hope to market 

this crop? If so, describe.

No, there are not really critical time periods because the harvest window is large and it is anticipated that 

switchgrass will be stored. The producers are also paid based on contract price, so there is no price 

variability or market timing to be concerned with.

16. Within the marketing channels and/or contracting structures mentioned above describe how quality 

variations are handled (e.g., off-grade apples in a fresh market system may be processed for juice).

Currently there are no quality concerns.
SEPTEMBER 2005 56 FCIC-22010 (PEH)

17. In this region, do federal supply control marketing orders exist for production of this crop?

Describe:

Yes No

18. In this region, do state quality marketing orders exist for production of this crop?

Describe:

Yes No

RMA–Facilitated Insurance Products

19. In this region, what RMA-facilitated insurance products are currently available for this crop? List all:

None
SEPTEMBER 2005 57 FCIC-22010 (PEH)

Yield Risk

20. In this region what are examples of crops with very low relative yield risk? Relative risk is used to 

adjust absolute magnitudes that vary across crops to a relative level to facilitate comparability (roughly, 

a measure of variation divided by the mean level).

All Dry Hay, Tobacco

21. In this region what are examples of crops with very high relative yield risk?

Alfalfa, Corn for Grain, Soybeans

22. Is this crop exposed to catastrophic risks that would reduce yields by 50 percent or more? Yes No

23. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic losses to 

occur?

Description Years (or crop 

cycles) out of 25

Hard to quantify, but RMA insurance cause of loss experience from 1985-2009 presents 2 2

causes of loss from fire for tobacco. Although it is significantly different from switchgrass, it

may be the best quantitative measure available.

24. Characterize yield risk for this crop ignoring the catastrophic yield risk(s) described earlier. On a scale 

from one to five, if the low relative yield risk crops identified earlier were one, and the high relative 

yield risk crops identified earlier were five, what number would you assign to the non-catastrophic 

yield risk associated with this crop in this region?

1

very low 

relative yield 

risk

2 3 4 5

very high 

relative yield 

risk

25. In this region, do producers tend to experience multiple-year sequences of good yields or bad 

yields for this crop? If yes, describe what causes these multiple-year sequences.

Based on NASS estimates of All Dry Hay yields back to 1990, it appears that for the counties in 

which DuPont Danisco Ethanol will source their switchgrass, data indicates that yields are above 

the 17 year average 25 times and below average 20 times, with a sequence of 9 consecutive years 

above average and 2 consecutive years below average.

Yes No
SEPTEMBER 2005 58 FCIC-22010 (PEH)

26. On a scale from one to five, where one is very low yield risk and five is very high yield risk, provide an 

overall assessment of yield risk faced by producers of this crop in this region.

1

very low yield 

risk

2 3 4 5

very high yield 

risk

Quality Risk

27. In this region what are examples of crops with very low quality risk?

Corn, Wheat

28. In this region what are examples of crops with very high quality risk?

Alfalfa, Tobacco

29. Is this crop exposed to catastrophic quality risks that would reduce the average price received 

by 20 percent or more? Yes No

30. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic quality 

losses to occur?

Description Years (or crop 

cycles) out of 25

31. We now want to characterize quality risk for this crop ignoring the catastrophic quality risk(s) 

described earlier. On a scale from one to five, if the crops with very low risk of quality problems 

identified earlier were one, and the crops with very high risk of quality problems identified earlier 

were five, what number would you assign to the quality risk associated with this crop in this region?

1

very low 

quality risk

2 3 4 5

very high 

quality risk

32. On a scale from one to five, if one is very low quality risk and five is very high quality risk, provide 

an overall assessment of quality risk faced by producers of this crop in this region.

1

very low 

quality risk

2 3 4 5

very high 

quality risk
SEPTEMBER 2005 59 FCIC-22010 (PEH)

Price Risk

33. In this region what are examples of crops with very low relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or equivalent for perennials) and sale. 

(Similar concept to IP and RA for crops with futures markets).

Alfalfa, All Hay

34. In this region what are examples of crops with very high relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or, equivalent for perennials) and sale. 

(Similar concept to IP and RA for crops with futures markets).

Corn for Grain, Soybeans

35. On a scale from one to five, if the low price risk crops identified earlier were one and the high price 

risk crops identified earlier were five, what number would you assign to the relative price risk (within 

the production cycle) associated with this crop in this region?

1

low price risk 

crop

2 3 4 5

high price risk 

crop

36. In this region, do producers tend to experience multiple-year sequences of high prices or low 

prices for this crop?

If yes, describe.

Yes No

37. On a scale from one to five, where one is very low price risk and five is very high price risk, provide 

an overall assessment of price risk (within the production cycle) faced by producers of this crop in 

this region.

38. 39.

1

very low price 

risk

2 3 4 5

very high price 

risk
SEPTEMBER 2005 60 FCIC-22010 (PEH)

Other Sources of Revenue Risk

38. For this region, describe other factors that affect revenue risk for this crop (e.g., prevented planting).

Switchgrass is a perennial crop that is planted in May or June at the rate of 5-6 lbs of Pure Live Seed per 

acre. Seed cost $15/lbs; therefore seeding cost run $75-$90 per acre. Drought in this region caused 20% of 

producers to replant in 2009. Drought presents challenges for establishment; so replant payments may be 

needed; however, no indication of prevented planting issues were mentioned. Other revenue risks for 

switchgrass are production loss from storage, and risk of fire in storage.

39. 40.

39. On a scale from one to five, where one is very low risk and five is very high risk, provide an overall 

assessment of risk sources other than yield, quality, and price risks faced by producers of this crop in 

this region.

40. 41.

1

very low risk

2 3 4 5

very high risk

Sufficient Non-Insurance Coping Mechanisms

40. On a scale from one to five, where one is very low and five is very high, assess the extent to which 

producers of this commodity in this region use risk-reducing inputs as a substitute for crop insurance.

1

very low 

2 3 4 5

very high

41. Are government crop programs (e.g., marketing loans and counter-cyclical payments) 

available for this crop?

Describe: The Biomass Crop Assistance Program (BCAP) supports establishing and 

producing eligible crops for the conversion to bioenergy through project areas and through 

contracts on land of up to 5 years for annual and non-woody perennial crops or up to 15 years 

for woody perennial crops. Through a matching payment program BCAP assists agricultural 

and forest land owners and eligible material owners with collection, harvest, storage, and 

transportation of eligible material for use in qualified Biomass Conversion Facilities (BCF). 

These payments will be available to eligible material owners at the rate of $1 for each $1 per 

dry ton paid by the BCF to the eligible material owners, limited to a maximum of $45 per dry 

ton and limited to a 2-year payment duration. No listening session attendees indicated that 

they utilized the program. NAP is not available.

Yes No
SEPTEMBER 2005 61 FCIC-22010 (PEH)

42. In this region, is there a history of federal disaster payments for this crop? Describe: Yes No

43. Approximately what percentage of the total production of this crop is under production 

contract with a first handler or processor? 100 __%

Describe contracts:

g. Under the terms of a typical production contract for this crop, is the grower exposed 

to production risk (i.e., the grower must deliver on the contract even if production 

shortfalls occur)?

Yes No

h. Under the terms of a typical production contract for this crop, is the grower exposed 

to quality risk (i.e., there are significant price penalties if the product does not meet 

the quality characteristics specified in the contract).

Yes No

i. Under the terms of a typical production contract for this crop, is the grower exposed 

to price risk (i.e., prices for specific quality characteristics are not specified in the 

contract)?

Yes No

44. In this region, approximately what percentage of the total production of this crop is priced 

prior to harvest (may or may not be tied to a production contract)?

Describe:

100 __%

45. When corn farmers in the Midwest experience low (high) yields, they can often expect higher (lower) 

market prices (i.e., prices and yields are very negatively correlated). This moderates the revenue 

impacts of low yields. In contrast, for corn farmers in the Southeast there is very little relationship 

between their yields and market prices (i.e., prices and yields are independent). In this region the price 

and yield for this crop are (circle one):

Independent Somewhat Negatively Correlated Highly Negatively Correlated

Describe: It is anticipated that switchgrass yields and price will be Independent of each other. This is 

because the commercialization of switchgrass for bioenergy is in its infancy and in its current form, the 

demand is being met be the current supply and it is anticipated that it will continue to be met going 

forward.

46. On a scale from one to five, where one is ―strongly disagree‖ and five is ―strongly agree,‖ provide your 

reaction to the following statement:

―In this region, producers of this crop are financially able to self-insure against production losses.‖

1

strongly

disagree

2 3 4 5

strongly

agree
SEPTEMBER 2005 62 FCIC-22010 (PEH)

Describe: Only 2-3 listening session attendees have ever purchased federal crop insurance. Indications 

from the meeting suggest that most producers were comfortable with their level of risk suggesting that 

they are willing and able to self-insure their crop. Although most producers had assistance in 

establishing the crop and were being paid a fixed contract amount for their production.

47. For a typical grower of this crop, approximately what percentage of the total farm revenue 

would be attributable to this crop? 15-25 %

48. What other commodities would typically be produced on a farm that produces this commodity? What is 

the correlation between revenue from these other commodities and the revenue from this commodity? 

For correlation use a scale of one to five, where 1 is ―strongly negatively correlated,‖ 2 is ―negatively 

correlated,― 3 ―independent,― 4 is ―positively correlated,― and 5 is ―strongly positively correlated.‖

List:

Correlation

(assign a number 

between 1-5)

Alfalfa, Other Hay 4,4

Corn 4

Soybeans 4

Livestock 3

49. In this region, approximately what percentage of the total production of this crop is produced 

by part-time farmers who have full-time employment off the farm? 0 %

50. On a scale from one to five, where one is ―strongly disagree― and five is ―strongly agree,― provide your 

reaction to the following statement:

―In this region, producers of this crop attempt to manage production risk by spreading their production 

over several geographic locations.‖

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Currently in a 50 mile radius there are only 5,200 acres of switchgrass devoted to being 

grown for commercial production of bioenergy. Each producer typically has 1 or 2 fields in or near 

the same region.

51. In this region, what private-sector insurance products (if any) are currently available for this crop?

List all: None
SEPTEMBER 2005 63 FCIC-22010 (PEH)

52. Characterize how agricultural lenders in this region view the available RMA-facilitated insurance 

products for this crop. ―Unfavorable‖ implies that lenders actually discourage borrowers from 

purchasing the product while ―favorable‖ implies that lenders strongly encourage and often require 

borrowers to purchase the product. If multiple insurance products are offered, answer for each 

product.

Unfavorable Indifferent Favorable

Describe: Hard to determine as no lenders were present at the listening sessions. Yet, since the 

amount of revenue per acre currently exceeds the production cost, lenders would probably be 

indifferent to an insurance program for switchgrass.

53. On a scale from one to five, where one is very high and five is very low, assess the sufficiency of noninsurance coping mechanisms for producers of this crop in this region.

1

high

availability

2 3 4 5

low

availability

Risk Classification

54. On a scale from one to five, where one is strongly disagree and five is strongly agree, provide your 

reaction to the following statement:

―In this region, no producers of this crop are really any more or less risky than any others. They all 

face about the same risk of loss.‖

58. 59.

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Most producers attending listening sessions thought that they all faced about the same risks 

of loss.

55. In this region, for those who are currently not insured, would you say that premium rate on the 

existing RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or 

―much too high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High
SEPTEMBER 2005 64 FCIC-22010 (PEH)

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A

56. In this region, for those who currently are insured, would you say that premium rate on the existing 

RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or ―much too 

high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A

57. For this region, to what extent does the system used to establish the guarantee (e.g., APH yield or 

expected revenue) for this crop match the true value of the production at risk? An answer of one 

indicates that the system used to establish the guarantee does a very poor job of matching the true 

value of the production at risk. An answer of five indicates that the system used to establish the 

guarantee does a very good job of matching the true value of the production at risk.

1

very poor job

2 3 4 5

very good job

58. On a scale from one to five, where one is very low and five is very high, assess the effectiveness of 

existing RMA-facilitated insurance products in accurately classifying potential policyholders 

according to their loss exposure (i.e., higher risk growers pay higher premiums while lower risk 

growers pay lower premiums).

59. 60.

1

very low

2 3 4 5

very high
SEPTEMBER 2005 65 FCIC-22010 (PEH)

Moral Hazard and Monitoring

59. Yield variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ In 

practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential for 

gaming the RMA-facilitated crop insurance product for this crop on a scale from one to five, where 

one implies that variation in yield is almost exclusively due to ―acts of nature‖ (potential for gaming 

is low) and five implies that yield variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

60. 61.

1

very low

2 3 4 5

very high

60. To the extent that management affects yield loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: It is not too hard to monitor the insured's behavior. Most producers should have receipts for 

any chemical management practices that could influence yield one way or the other. However, it may 

be hard to monitor yield risk exposure from intentional fire.

61. 62.

61. Quality variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ 

In practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential 

for gaming the RMA-facilitated insurance product for this crop on a scale from one to five, where one 

implies that variation in quality is almost exclusively due to ―acts of nature‖ (potential for gaming is 

low) and five implies that quality variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

62. 63.

1

very low

2 3 4 5

very high

62. To the extent that management affects quality loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: The impact of quality of switchgrass for ethanol is measured via enzymatic hydrolysis 

(EtOH). Variation of EtOH during production is very small and would only be caused by acts of 

nature; although, in storage acts of management could affect biomass losses and EtOH potential. 

However, it would be easy to monitor storage of switchgrass bales.

63. On a scale from one to five, where one is very large and five is very small, assess the extent of moral 

hazard problems with existing RMA-facilitated insurance products for this crop.

67. 68.

1

very large

2 3 4 5

very small
SEPTEMBER 2005 66 FCIC-22010 (PEH)

Problems Affecting Insurance Participation

64. Have significant problems occurred (either past or current) with policy provisions on existing 

RMA-facilitated insurance products for the crop? If multiple insurance products are offered, 

answer for each product.

Yes No

65. If the answer to the previous question is no, go to next question. If yes, for each significant problem:

a. Briefly describe the problem.

b. What has been the impact of the problem (e.g., high loss ratios, reduced demand, etc.)?

c. Have policy provisions since been changed to adequately address the problem?

d. If policy provisions have not been changed, what changes in policy provisions do you think would 

increase insurance demand for this crop?

66. In this region, do reinsured companies have sufficient incentives to aggressively market 

existing RMA-facilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

67. In this region, do agents have sufficient incentives to aggressively market existing RMAfacilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

68. List any perils that concern growers of this crop but are not covered by the existing RMA-facilitated 

insurance products (e.g., business interruption due to unavailability of irrigation water, disease 

quarantines, etc.). For each peril assess the extent of growers‘ concerns about this peril on a scale from

one to five where one is minor concern and five is major concern.

List all: 1

minor concern

2 3 4 5

major concern
SEPTEMBER 2005 67 FCIC-22010 (PEH)

69. Briefly describe the potential for insuring these currently uninsured perils? In answering this, consider 

the following questions:

Can hidden action/moral hazard and classification/adverse selection problems be avoided?

Can clearly stated policy provisions be developed and accurate premium rates established?

N/A

70. On a scale from one to five, where one is very high and five is very low, assess the likelihood that 

problems affecting participation can be adequately addressed by product or policy modifications.

71.72.

1

very low

2 3 4 5

very high
SEPTEMBER 2005 53 FCIC-22010 (PEH)

Program Evaluation Diagnostic Questions

Region Louisiana

Crop Energy Cane

Market 

(fresh, processed, sold for animal feed, etc.) Sold for Dedicated Energy

Background Information

Production Processes

Annuals

1. Is the crop planted multiple times during a crop production year? If yes, explain:

N/A Yes No

2. For a single planting, is the crop harvested multiple times during a crop production year? If 

yes, explain:

N/A

Yes No

3. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as double crop, fallow, irrigation, regional differences in climate or soils, etc.). Discuss, 

particularly, features that are critical in assessing potential demand including potential issues with 

practices and types. 

N/A

Biennials

4. Is the crop harvested multiple times during a crop production year? Yes No

5. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types.

N/A
SEPTEMBER 2005 54 FCIC-22010 (PEH)

Perennials

6. Is the crop harvested multiple times during a crop production year? If yes, explain:

During the establishment year, the energy cane is cut in January or after approximately six of 

growth as a cultural practice. This first cutting can be harvested if per acre yields are high enough. 

Once the stand is established, the crop is harvested once per production year.

Yes No

7. Is the crop alternate bearing? Yes No

8. Describe distinguishing characteristics of prevailing production system(s) for this crop (e.g., practices 

such as irrigation, regional differences in climate or soils, etc.). Discuss, particularly, features that are 

critical in assessing potential demand including potential issues with practices and types. 

Planted in sandy soils in southern Louisiana. Test plots do not use irrigation, but water is essential during the 

grand growth phase so irrigation would potentially reduce yield variability.

9. What is the economic life of the capital stock (trees, vines, etc.)? 4

th ratoon

years

10. Over its economic life, what is the likelihood that 10 percent or more of the capital stock 

would be lost due to natural causes? Describe:

Using sugarcane as a comparable crop, NASS data shows that sugarcane in Louisiana has an 11% 

probability of a yield loss over 10% from the previous year.

11 % 

(probability 

of loss)

11. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

starts producing salable output? 1 years

12. If capital stock is lost, how long will it take to reestablish the capital stock to a point where it 

is at peak production? 

NA years
SEPTEMBER 2005 55 FCIC-22010 (PEH)

Nursery

13. Describe distinguishing characteristics of prevailing production system(s) for nursery crops in this 

region. Discuss, particularly, features that are critical in assessing potential demand including potential 

issues with practices and types. 

N/A

Marketing

14. Describe typical marketing channels and/or contracting structures for this crop.

To date there is not an established market for energy cane, although 200 acres of cane are under a production 

contract in Louisiana by a refinery previously owned by Verenium and now British Petroleum (BP). The 

details of the contracts and current status of production is not known at this time.

15. In this region are there critical time periods (i.e., marketing windows) when producers hope to market 

this crop? If so, describe.

The marketing window for energy cane is at harvest, which is typically between November and January to 

allow for the cane to dry. During the establishment year the cane is cut twice as a cultural practice, giving 

producers an additional window to market their crop. The yield from the first cut must be high enough to 

cover logistics costs for farmers to sell the cutting.

16. Within the marketing channels and/or contracting structures mentioned above describe how quality 

variations are handled (e.g., off-grade apples in a fresh market system may be processed for juice).

Long freezes have an unknown effect on the chemical composition of the cane. This could potentially be a 

quality issue for processors. Also, the stalk must be free of mud for processors.
SEPTEMBER 2005 56 FCIC-22010 (PEH)

17. In this region, do federal supply control marketing orders exist for production of this crop?

Describe:

Yes No

18. In this region, do state quality marketing orders exist for production of this crop?

Describe:

Yes No

RMA–Facilitated Insurance Products

19. In this region, what RMA-facilitated insurance products are currently available for this crop? List all:

None
SEPTEMBER 2005 57 FCIC-22010 (PEH)

Yield Risk

20. In this region what are examples of crops with very low relative yield risk? Relative risk is used to 

adjust absolute magnitudes that vary across crops to a relative level to facilitate comparability (roughly, 

a measure of variation divided by the mean level).

Rice and sugarcane have a low yield risk in the State of Louisiana, but historic sugarcane yields have lower 

risk than both.

21. In this region what are examples of crops with very high relative yield risk?

Corn, Cotton, Grain Sorghum, Wheat

22. Is this crop exposed to catastrophic risks that would reduce yields by 50 percent or more? Yes No

23. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic losses to 

occur?

Description Years (or crop 

cycles) out of 25

Cold Wet Weather, Drought, Excess Moisture/Precip/Rain, Flood, Freeze and Frost 3,10,11,1,7

Hail, Heat, Hurricane/Tropical Depression, Insects, Other (Snow-Lightning-Etc.) 0,1,2,2,2

Plant Disease, Wind/Excess Wind 1,0

24. Characterize yield risk for this crop ignoring the catastrophic yield risk(s) described earlier. On a scale 

from one to five, if the low relative yield risk crops identified earlier were one, and the high relative 

yield risk crops identified earlier were five, what number would you assign to the non-catastrophic 

yield risk associated with this crop in this region?

1

very low 

relative yield 

risk

2 3 4 5

very high 

relative yield 

risk

25. In this region, do producers tend to experience multiple-year sequences of good yields or bad 

yields for this crop? If yes, describe what causes these multiple-year sequences.

Yes No

26. On a scale from one to five, where one is very low yield risk and five is very high yield risk, provide an 

overall assessment of yield risk faced by producers of this crop in this region.

1

very low yield 

2 3 4 5

very high yield 
SEPTEMBER 2005 58 FCIC-22010 (PEH)

risk risk

Quality Risk

27. In this region what are examples of crops with very low quality risk?

Corn, Oats, Wheat, Grain Sorghum

28. In this region what are examples of crops with very high quality risk?

Peaches, Sweet potatoes

29. Is this crop exposed to catastrophic quality risks that would reduce the average price received 

by 20 percent or more? Yes No

30. If the answer to the previous question is yes, describe these risks. If no, proceed to the next question. 

Over 25 years (or crop cycles) approximately how often would you expect such catastrophic quality 

losses to occur?

Description Years (or crop 

cycles) out of 25

31. We now want to characterize quality risk for this crop ignoring the catastrophic quality risk(s) 

described earlier. On a scale from one to five, if the crops with very low risk of quality problems 

identified earlier were one, and the crops with very high risk of quality problems identified earlier 

were five, what number would you assign to the quality risk associated with this crop in this region?

1

very low 

quality risk

2 3 4 5

very high 

quality risk

32. On a scale from one to five, if one is very low quality risk and five is very high quality risk, provide 

an overall assessment of quality risk faced by producers of this crop in this region.

1

very low 

quality risk

2 3 4 5

very high 

quality risk

Price Risk

33. In this region what are examples of crops with very low relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or equivalent for perennials) and sale. 

(Similar concept to IP and RA for crops with futures markets).

Sugarcane, Cotton

34. In this region what are examples of crops with very high relative price risk within the production 

cycle? That is, variation in price between pre-plant for annuals (or, equivalent for perennials) and sale. 
SEPTEMBER 2005 59 FCIC-22010 (PEH)

(Similar concept to IP and RA for crops with futures markets).

Corn, Rice, Sorghum, Soybeans, Wheat

35. On a scale from one to five, if the low price risk crops identified earlier were one and the high price 

risk crops identified earlier were five, what number would you assign to the relative price risk (within 

the production cycle) associated with this crop in this region?

1

low price risk 

crop

2 3 4 5

high price risk 

crop

36. In this region, do producers tend to experience multiple-year sequences of high prices or low 

prices for this crop?

If yes, describe. No established market.

Yes No

37. On a scale from one to five, where one is very low price risk and five is very high price risk, provide 

an overall assessment of price risk (within the production cycle) faced by producers of this crop in 

this region.

38. 39.

1

very low price 

risk

2 3 4 5

very high price 

risk

Other Sources of Revenue Risk

38. For this region, describe other factors that affect revenue risk for this crop (e.g., prevented planting).

None

39. 40.

39. On a scale from one to five, where one is very low risk and five is very high risk, provide an overall 

assessment of risk sources other than yield, quality, and price risks faced by producers of this crop in 

this region.

40. 41.

1

very low risk

2 3 4 5

very high risk

Sufficient Non-Insurance Coping Mechanisms

40. On a scale from one to five, where one is very low and five is very high, assess the extent to which 

producers of this commodity in this region use risk-reducing inputs as a substitute for crop insurance.

1 2 3 4 5
SEPTEMBER 2005 60 FCIC-22010 (PEH)

very low very high

41. Are government crop programs (e.g., marketing loans and counter-cyclical payments) 

available for this crop?

Describe: The Biomass Crop Assistance Program (BCAP) supports establishing and 

producing eligible crops for the conversion to bioenergy through project areas and through 

contracts on land of up to 5 years for annual and non-woody perennial crops or up to 15 years 

for woody perennial crops. Through a matching payment program BCAP assists agricultural 

and forest land owners and eligible material owners with collection, harvest, storage, and 

transportation of eligible material for use in qualified Biomass Conversion Facilities (BCF). 

These payments will be available to eligible material owners at the rate of $1 for each $1 per 

dry ton paid by the BCF to the eligible material owners, limited to a maximum of $45 per dry 

ton and limited to a 2-year payment duration.

Yes No

42. In this region, is there a history of federal disaster payments for this crop?

Describe:

Yes No

43. Approximately what percentage of the total production of this crop is under production 

contract with a first handler or processor?

100 __%

Describe contracts:

j. Under the terms of a typical production contract for this crop, is the grower exposed 

to production risk (i.e., the grower must deliver on the contract even if production 

shortfalls occur)?

Yes No

k. Under the terms of a typical production contract for this crop, is the grower exposed 

to quality risk (i.e., there are significant price penalties if the product does not meet 

the quality characteristics specified in the contract).

Yes No

l. Under the terms of a typical production contract for this crop, is the grower exposed 

to price risk (i.e., prices for specific quality characteristics are not specified in the 

contract)?

Yes No

44. In this region, approximately what percentage of the total production of this crop is priced 

prior to harvest (may or may not be tied to a production contract)?

Describe:

100 __%
SEPTEMBER 2005 61 FCIC-22010 (PEH)

45. When corn farmers in the Midwest experience low (high) yields, they can often expect higher (lower) 

market prices (i.e., prices and yields are very negatively correlated). This moderates the revenue 

impacts of low yields. In contrast, for corn farmers in the Southeast there is very little relationship 

between their yields and market prices (i.e., prices and yields are independent). In this region the price

and yield for this crop are (circle one):

Independent Somewhat Negatively Correlated Highly Negatively Correlated

Describe: There is no established market, but it is expected that prices and yields will be independent.

46. On a scale from one to five, where one is ―strongly disagree‖ and five is ―strongly agree,‖ provide your 

reaction to the following statement:

―In this region, producers of this crop are financially able to self-insure against production losses.‖

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Data is not available for production loss, as the crop is not yet commercially produced.

47. For a typical grower of this crop, approximately what percentage of the total farm revenue 

would be attributable to this crop? 0 %

48. What other commodities would typically be produced on a farm that produces this commodity? What is 

the correlation between revenue from these other commodities and the revenue from this commodity? 

For correlation use a scale of one to five, where 1 is ―strongly negatively correlated,‖ 2 is ―negatively 

correlated,― 3 ―independent,― 4 is ―positively correlated,― and 5 is ―strongly positively correlated.‖

List:

Correlation

(assign a number 

between 1-5)

Sugarcane 5

49. In this region, approximately what percentage of the total production of this crop is produced 

by part-time farmers who have full-time employment off the farm? 0 %
SEPTEMBER 2005 62 FCIC-22010 (PEH)

50. On a scale from one to five, where one is ―strongly disagree― and five is ―strongly agree,― provide your 

reaction to the following statement:

―In this region, producers of this crop attempt to manage production risk by spreading their production 

over several geographic locations.‖

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Not enough data to strongly disagree or strongly agree.

51. In this region, what private-sector insurance products (if any) are currently available for this crop?

List all: None

52. Characterize how agricultural lenders in this region view the available RMA-facilitated insurance 

products for this crop. ―Unfavorable‖ implies that lenders actually discourage borrowers from 

purchasing the product while ―favorable‖ implies that lenders strongly encourage and often require 

borrowers to purchase the product. If multiple insurance products are offered, answer for each 

product.

Unfavorable Indifferent Favorable

Describe: The views of lenders cannot be determined as there is no established market for producers. 

Lenders would be indifferent to insurance for producers under contract because revenue would be 

guaranteed.

53. On a scale from one to five, where one is very high and five is very low, assess the sufficiency of noninsurance coping mechanisms for producers of this crop in this region.

1

high

availability

2 3 4 5

low

availability
SEPTEMBER 2005 63 FCIC-22010 (PEH)

Risk Classification

54. On a scale from one to five, where one is strongly disagree and five is strongly agree, provide your 

reaction to the following statement:

―In this region, no producers of this crop are really any more or less risky than any others. They all 

face about the same risk of loss.‖

60. 61.

1

strongly

disagree

2 3 4 5

strongly

agree

Describe: Listening session feedback indicated that they all faced about the same risks of loss.

55. In this region, for those who are currently not insured, would you say that premium rate on the 

existing RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or 

―much too high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A

56. In this region, for those who currently are insured, would you say that premium rate on the existing 

RMA-facilitated insurance products for this crop are ―much too low,‖ ―about right,‖ or ―much too 

high‖? If more than one RMA insurance product is offered, answer for each product.

Much Too Low About Right Much Too High

If you answered that premium rates are ―much too high,‖ explain why (or how) you think this 

happened.

N/A

57. For this region, to what extent does the system used to establish the guarantee (e.g., APH yield or 

expected revenue) for this crop match the true value of the production at risk? An answer of one 

indicates that the system used to establish the guarantee does a very poor job of matching the true 

value of the production at risk. An answer of five indicates that the system used to establish the 

guarantee does a very good job of matching the true value of the production at risk.

1

very poor job

2 3 4 5

very good job

58. On a scale from one to five, where one is very low and five is very high, assess the effectiveness of 

existing RMA-facilitated insurance products in accurately classifying potential policyholders 

according to their loss exposure (i.e., higher risk growers pay higher premiums while lower risk 

growers pay lower premiums).

59. 60.

1

very low

2 3 4 5

very high
SEPTEMBER 2005 64 FCIC-22010 (PEH)

Moral Hazard and Monitoring

59. Yield variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ In 

practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential for 

gaming the RMA-facilitated crop insurance product for this crop on a scale from one to five, where 

one implies that variation in yield is almost exclusively due to ―acts of nature‖ (potential for gaming 

is low) and five implies that yield variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

60. 61.

1

very low

2 3 4 5

very high

60. To the extent that management affects yield loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: It is not too hard to monitor the insured's behavior. The first harvest of the establishment 

year could be difficult to monitor as it is typically a small yield and may or may not be sold.

61. 62.

61. Quality variation can be caused by unavoidable ―acts of nature‖ or avoidable ―acts of management.‖ 

In practical parlance, what is the potential for ―gaming‖ the insurance product? Evaluate the potential 

for gaming the RMA-facilitated insurance product for this crop on a scale from one to five, where one 

implies that variation in quality is almost exclusively due to ―acts of nature‖ (potential for gaming is 

low) and five implies that quality variation is almost exclusively due to ―acts of management‖ 

(potential for gaming is high). If multiple insurance products are offered, answer for each product.

62. 63.

1

very low

2 3 4 5

very high

62. To the extent that management affects quality loss risk exposure, how difficult is it to monitor the 

insured‘s behavior?

Extremely Difficult Difficult Not too Difficult

Explain: Management affects quality loss risk exposure in terms of not applying proper control 

measures for insects or disease; however, an act of nature such as hail can lead to quality loss which is 

easy to identify. Receipts should be available for control measures in the case of proper management; 

therefore, it would not be too difficult to monitor.

63. On a scale from one to five, where one is very large and five is very small, assess the extent of moral 

hazard problems with existing RMA-facilitated insurance products for this crop.

69. 70.

1

very large

2 3 4 5

very small
SEPTEMBER 2005 65 FCIC-22010 (PEH)

Problems Affecting Insurance Participation

64. Have significant problems occurred (either past or current) with policy provisions on existing 

RMA-facilitated insurance products for the crop? If multiple insurance products are offered, 

answer for each product.

Yes No

65. If the answer to the previous question is no, go to next question. If yes, for each significant problem:

a. Briefly describe the problem.

b. What has been the impact of the problem (e.g., high loss ratios, reduced demand, etc.)?

c. Have policy provisions since been changed to adequately address the problem?

d. If policy provisions have not been changed, what changes in policy provisions do you think would 

increase insurance demand for this crop?

66. In this region, do reinsured companies have sufficient incentives to aggressively market 

existing RMA-facilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

67. In this region, do agents have sufficient incentives to aggressively market existing RMAfacilitated insurance products for the crop?

If yes, go to next question. If no, explain.

N/A

Yes No

68. List any perils that concern growers of this crop but are not covered by the existing RMA-facilitated 

insurance products (e.g., business interruption due to unavailability of irrigation water, disease 

quarantines, etc.). For each peril assess the extent of growers‘ concerns about this peril on a scale from 

one to five where one is minor concern and five is major concern.

List all: 1

minor concern

2 3 4 5

major concern
SEPTEMBER 2005 66 FCIC-22010 (PEH)

69. Briefly describe the potential for insuring these currently uninsured perils? In answering this, consider 

the following questions:

Can hidden action/moral hazard and classification/adverse selection problems be avoided?

Can clearly stated policy provisions be developed and accurate premium rates established?

N/A

70. On a scale from one to five, where one is very high and five is very low, assess the likelihood that 

problems affecting participation can be adequately addressed by product or policy modifications.

71.72.

1

very low

2 3 4 5

very high