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Insurance Abstract
A method for managing the risk of growing a crop having a defined
attribute. A grower is evaluated to determine whether the grower
is associated with a qualified geographic zone or zones for growing
a particular crop with a defined attribute. A performance predictor
estimates or predicts a grower performance for the particular grower
consistent with the geographic zone and desired crop attribute.
An evaluator determines if the grower as a qualified grower for
the particular crop with the defined attribute based on the estimated
grower performance meeting or exceeding a threshold value. The evaluator
designates the qualified grower as eligible for an attribute endorsement
to at least one of a yield-based crop insurance and a revenue-based
crop insurance.
Insurance Claims
1. A method for providing crop insurance for a crop associated with
a defined attribute, the method comprising: evaluating a grower
to determine whether the grower is associated with a qualified geographic
zone or zones for growing a particular crop with a defined attribute;
estimating or predicting a grower performance for the particular
grower consistent with the geographic zone and defined attribute;
determining if the grower is a qualified grower for the particular
crop with the defined attribute based on the estimated grower performance
meeting or exceeding a threshold value; and designating the qualified
grower as eligible for insurance coverage associated with the defined
attribute.
2. The method according to claim 1 wherein the grower performance
comprises an estimated grower yield.
3. The method according to claim 1 wherein the grower performance
comprises a yield index comprising a ratio of predicted grower yield
data to historical grower yield data for the geographic zone
4. The method according to claim 1 wherein the grower performance
comprises a yield index; the yield index being determined in accordance
with the following equation: Y.sub.I=Y.sub.H/Y.sub.P, where Y.sub.I
is the yield index, Y.sub.H is the historical grower yield data
for a particular crop in the particular zone and Y.sub.P is the
predicted grower yield data for the particular crop in the zone.
5. The method according to claim 1 wherein the threshold value
is based on at least one of an average performance and a mode performance
of other growers associated with one or more comparable geographic
zone that are substantially similar to the qualified geographic
zones.
6. The method according to claim 1 wherein the insurance coverage
comprises at least one of an endorsement to a yield-based crop insurance
and an endorsement to a revenue-based crop insurance.
7. The method according to claim 1 wherein the designating the
grower as eligible comprises designating the grower as eligible
for one or more of the following as the crop insurance: a Multiple
Peril Crop Insurance, Crop Revenue Coverage, and Group Risk Insurance.
8. The method according to claim 1 wherein qualified geographic
zone is selected based on at least one of soil characteristics,
weather characteristics, and climate characteristics for the corresponding
zone.
9. The method according to claim 1 wherein the estimating of the
grower performance comprises defining genetic characteristics of
the particular crop, assessing management practices of the grower,
reviewing historic yields of the grower for substantially similar
crops in the geographic zones.
10. The method according to claim 1 further comprising determining
whether a particular grower's land is associated with more than
one zone distinguished by different soil characteristics and different
weather characteristics and applying multiple zones to estimate
the yield for a crop grown across multiple zones.
11. The method according to claim 1 further comprising determining
whether the particular grower's land is geographically distributed
in such a manner to reduce risk of growing the crop with a particular
attribute.
12. The method according to claim 1 further comprising: determining
a premium level for the qualified grower based on at least one crop
genetics, grower management practices, and grower environment associated
with the geographic zones.
13. The method according to claim 12 further comprising: determining
a rating or variance level for the particular grower based on a
particular grower's compliance with an enhanced genetics requirement
for a particular crop with a defined attribute, compliance with
generally suitable environment for the particular crop, and compliance
with requisite management practices for growing a particular crop;
and determining a respective premium level for crop insurance coverage
of the particular grower for the particular crop based on the determined
rating or variance level.
14. A method for providing crop insurance for a crop associated
with a defined attribute, the method comprising: evaluating a grower
to determine whether the grower is associated with a qualified geographic
zone or zones for growing a particular crop with a defined attribute;
estimating or predicting a grower performance for the particular
grower consistent with the geographic zone and desired crop attribute;
determining if the grower is a qualified grower for the particular
crop with the defined attribute based on the estimated grower performance
meeting or exceeding a threshold value; and determining a particular
premium level or range for the qualified grower for insurance coverage
associated with the defined attribute, the particular premium level
derived from an estimating level of risk associated with the grower
based on at least one of crop genetics, grower management practices,
and grower environment associated with the geographic zones.
15. The method according to claim 14 wherein the grower performance
comprises an estimated grower yield.
16. The method according to claim 14 wherein the grower performance
comprises a yield index comprising a ratio of predicted grower yield
data to historical grower yield data for the geographic zone
17. The method according to claim 14 wherein the grower performance
comprises a yield index; the yield index being determined in accordance
with the following equation: Y.sub.I=Y.sub.H/Y.sub.P, where Y.sub.P
is the yield index, Y.sub.H is the historical grower yield data
for a particular crop in the particular zone and Y.sub.P is the
predicted grower yield data for the particular crop in the zone.
18. The method according to claim 14 wherein the threshold value
is based on at least one of an average performance and a mode performance
of other growers associated with one or more comparable geographic
zone that are substantially similar to the qualified geographic
zones.
19. The method according to claim 14 wherein the insurance coverage
comprises at least one of an endorsement to a yield-based crop insurance
and an endorsement to a revenue-based crop insurance.
20. The method according to claim 14 wherein the designating the
grower as eligible comprises designating the grower as eligible
for one or more of the following as the crop insurance: a Multiple
Peril Crop Insurance, Crop Revenue Coverage, and Group Risk Insurance.
21. The method according to claim 14 wherein qualified geographic
zone is selected based on at least one of soil characteristics,
weather characteristics, and climate characteristics for the corresponding
zone.
22. The method according to claim 14 wherein the estimating of
the grower performance comprises defining genetic characteristics
of the particular crop, assessing management practices of the grower,
reviewing historic yields of the grower for substantially similar
crops in the geographic zones.
23. The method according to claim 14 further comprising determining
whether a particular grower's land is associated with more than
one zone distinguished by different soil characteristics and different
weather characteristics and applying multiple zones to estimate
the yield for a crop grown across multiple zones.
24. The method according to claim 14 further comprising determining
whether the particular grower's land is geographically distributed
in such a manner to reduce risk of growing the crop with a particular
attribute.
25. The method according to claim 14 wherein the determining the
particular premium level comprises: determining a rating or variance
level for the particular grower based on a particular grower's compliance
with an enhanced genetics requirement for a particular crop with
a defined attribute, compliance with generally suitable environment
for the particular crop, and compliance with requisite management
practices for growing a particular crop; and determining a respective
premium level for crop insurance coverage of the particular grower
for the particular crop based on the determined rating or variance
level.
26. The method according to claim 25 wherein the determining of
a variance level comprises determining at least one of a lowest
variance level, an intermediate variance level and a highest variance
level; and wherein the determining of the respective premium level
comprises assigning a lowest premium level for the lowest variance
level, assigning an intermediate premium level for the intermediate
variance level, and assigning a highest premium level for the highest
variance level.
Insurance Description
[0001] This document (including all drawings) claims priority based
on U.S. provisional application Ser. No. 60/691,109, filed Jun.
16, 2005, and entitled METHOD FOR PROVIDING CROP INSURANCE FOR A
CROP ASSOCIATED WITH A DEFINED ATTRIBUTE under 35 U.S.C. 119(e).
FIELD OF THE INVENTION
[0002] This invention relates to a method of providing crop insurance
for a crop associated with a defined attribute, an insurance product
for managing the risk associated with crops with defined attributes,
and a risk management system.
BACKGROUND OF THE INVENTION
[0003] Farmers use crop insurance to reduce or manage various risks
associated with growing crops. Such risks include crop loss or damage
caused by weather, hail, drought, frost damage, insects, or disease,
for instance. A farmer or grower may desire to grow a crop associated
with a particular defined attribute that potentially qualifies for
a premium over similar commodity crops, agricultural products, or
derivatives thereof. The particular attribute may be associated
with the genetic composition of the crop, certain management practices
of the grower, or both. However, many standard crop insurance policies
do not differentiate between commodity crops and crops associated
with particular attributes. Accordingly, farmers have a need for
crop insurance to cover the risk of growing crops associated with
particular attributes.
SUMMARY OF THE INVENTION
[0004] A method for providing crop insurance for a crop associated
with a defined attribute facilitates insuring crops that are distinct
with respect to a commodity crop or another reference crop. A grower
is evaluated to determine whether the grower is associated with
a qualified geographic zone or zones for growing a particular crop
associated with a defined attribute. A performance predictor estimates
or predicts a grower performance (e.g., yield) for the particular
grower consistent with the geographic zone, the particular crop,
and the desired crop attribute. An evaluator determines if the grower
is a qualified grower for the particular crop with the defined attribute
based on the estimated grower performance (e.g., yield) meeting
or exceeding a threshold value. The evaluator designates the qualified
grower as eligible for the crop insurance (or an endorsement) associated
with a defined attribute or as eligible for crop insurance for a
certain premium level or range.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is a block diagram of a risk management system for
providing crop insurance associated with a defined attribute.
[0006] FIG. 2 is a flow chart of one embodiment of a method for
providing crop insurance for a defined attribute in accordance with
the invention.
[0007] FIG. 3 is a flow chart of another embodiment of a method
for providing crop insurance for a defined attribute in accordance
with the invention.
[0008] FIG. 4 is a flow chart of yet another embodiment of a method
for providing crop insurance for a defined attribute in accordance
with the invention.
[0009] FIG. 5 is a flow chart of still another embodiment of a
method for providing crop insurance for a defined attribute in accordance
with the invention.
[0010] FIG. 6 is a flow chart an embodiment of a method for providing
crop insurance for a defined attribute based on a rating or variance
level of a particular grower.
[0011] FIG. 7 is a graph of first yield distribution of a first
grower for a particular crop and a second yield distribution of
a second grower for a particular crop or group of growers for the
particular crop.
[0012] FIG. 8A through FIG. 8D, inclusive, are graphs that show
variance reduction in the yield distribution of a grower by applying
various techniques.
[0013] FIG. 9 is an illustrative example of a price versus yield
model for a grower to illustrate the targeted risk coverage of attribute
insurance.
DESCRIPTION OF THE PREFERRED EMBODIMENT
[0014] A crop may include, any type of edible or inedible agricultural
product, grain, oilseed, fiber, fruit, nut, seed, or vegetable or
any other material produced by a genetically modified plant or non-genetically
modified plant. A defined attribute may comprise one or more of
the following characteristics of any crop: organic, organically
grown, high oil, high protein, high starch, waxy, highly fermentable,
color, grade, classification, weight, nutritionally enhanced, pest
resistant, herbicide resistant, pesticide resistant, fungicide resistant,
drought tolerant, freeze tolerant, mildew resistant, bacterial resistant,
disease resistant, non-genetically modified, genetically modified,
genetically altered protein content, genetically altered enzyme
content, genetically altered sugar content, genetically altered
starch content, high protein, yield enhanced, pharmaceutical traits,
precursors or ingredients, pharmaceutical properties, medicinal
properties, genetically resistant to cross-pollination (e.g., a
teosinte gene cluster introduced into corn deoxyribonucleic (DNA)
acid) from neighboring genetically modified crops, and any other
crop attributes.
[0015] A defined attribute may represent any plant trait associated
with a crop, an agricultural product derived from the crop, or both.
Further, the defined attribute may comprise a characteristic that
is associated with a particular level or range of levels of any
of the following: a plant trait, protein, oil, starch in plant material
(e.g., harvested grain, fiber or oilseed). For example, soy meal
having a certain characteristic (e.g., a minimum percent protein
content by volume or weight) may be derived from soybeans as the
crop. The defined attribute may be, but need not be, defined with
respect to a corresponding attribute of a commodity crop, an agricultural
product derived from a commodity crop, or another reference.
[0016] Although any generally accepted grading standard (e.g.,
a standard adopted by the Chicago Board of Trade or elsewhere within
the marketplace) may be used to define a commodity crop, the generally
accepted grading standards may not address a defined attribute or
another particular characteristic of interest (e.g., corn with a
particular protein profile, starch content, or sugar content). Accordingly,
the defined attributes may be defined in accordance with one or
more of the following items: (1) reference genetic profile (e.g.,
for pharmaceutical crops or other genetically modified crops), (2)
identity of a gene cluster or sequence inserted into plant deoxyribonucleic
acid (DNA), (3) a characteristic of a plant resulting from the expression
of a genetic trait, or (4) reference growing practices (e.g., for
organic crops or specialty crops).
[0017] In the U.S., the government (e.g., Federal Grain Inspection
Service) establishes grain standards under the United States Grain
Standards Act that are suitable for defining commodity grains. The
detailed grain standards are currently set forth in 7 C.F.R. .sctn.
810.101 through .sctn. 810.2205. Under U.S. grain standards, corn
is divided into three classes: yellow, white and mixed; each class
may be associated with a grade ranging from U.S. No. 1 to U.S. No.
5. The grades of corn are generally based on minimum test weight
per bushel, percentage of heat damage, percentage of broken kernels,
and amount of foreign material. Similarly, in the U.S. for commodity
soybeans the applicable soybean grades (e.g., U.S. No. 1) are generally
based on test weight, heat damage, foreign material content, total
damage and splits (e.g., broken seeds).
[0018] In FIG. 1, the risk management system 11 comprises a data
processing system 12 coupled to a user interface 10 and a data storage
device 24. The risk management system 11 supports the provision
of crop insurance or an endorsement for a crop insurance associated
with a defined attribute. In one embodiment, the data processing
system 12 may communicate with one or more of the following via
a communications network 22 (e.g., the Internet): a soil data source
28, a historic yield data source, a weather and climate data source
30, and a grower data source 32. Alternately, the user may input
data via the user interface 10, where the inputted data is equivalent
to, similar to, or distinct from that available via any data source
accessible over the communications network 22.
[0019] The user interface 10 may comprise any device that enables
a user to enter or input data directly or indirectly into the data
processing system 12. The user interface 10 may comprise a keyboard,
a keypad, a pointing device (e.g., an electronic mouse), a display,
an optical drive, a magnetic disk drive, a magnetic reader, or another
device for inputting or outputting data.
[0020] The data processing system 12 comprises a zone definer 14,
a performance predictor 16, an evaluator 18, and a communications
interface 20. The user may enter a particular crop identifier, or
a particular crop associated with a defined attribute, via the user
interface 10. The crop identifier or defined attribute may identify
the genetic characteristics or genetic profile of the crop or the
user may enter the genetic specifications associated with the particular
crop.
[0021] The zone definer 14 defines suitable zones for the particular
crop (with a defined attribute) based on at least one of soil data,
weather data, climate data, other environmental data, genetic characteristics,
and the crop identifier. The zone definer 14 may define the boundaries
of each zone or points (e.g., coordinates) that lie on the boundaries
of each zone. Alternately, each zone may be defined by a group of
cells (e.g., polygonal cells). A county or a grower's field may
be subdivided into one or more zones (e.g., if the soil or weather
materially varies over the county or the grower's field).
[0022] The performance predictor 16 may estimate the performance
(e.g., yield, attribute expression, purity of the defined attribute,
or concentration of the defined attribute) of the particular crop
with a defined attribute. For example, the yield of corn may be
expressed in terms of predicted or estimated bushels per land unit
(e.g., acre) or range thereof. The performance predictor 16 may
consider one or more of the following to estimate the performance
of a particular crop associated with a defined attribute: the defined
zone or zones for the corresponding particular crop, the crop identifier,
genetic profile, or genetic characteristics of the particular crop,
historic climate data, historic weather data, soil data, genetic
factors for the particular crop, environmental factors for the zone
or zones, historic performance (e.g., yield) of the particular grower
or geographic area, and crop management factors (e.g., irrigation
and application of crop inputs) associated with the particular grower.
[0023] The evaluator 18 determines whether estimated performance
(e.g., yield) of the particular grower meets or exceeds a threshold
performance (e.g., a reference or benchmark yield). In a first example,
the threshold performance is based on the yield of a substantially
similar crop to the particular crop in a zone that is the equivalent
of or substantially the same as the particular grower's zone. In
a second example, the threshold performance is based on the yield
of a substantially similar crop to the particular crop in a county
or other geographic region that includes or encompasses the zone.
In a third example, the threshold performance may comprise a coefficient
of variation (e.g., a ratio of standard deviation of the yield to
mean yield) or another benchmark that considers both variance and
the mean performance of the grower within a season or over multiple
growing seasons. In a fourth example, the threshold performance
is based on the historic performance or yield associated with the
particular crop growing in a suitable zone or zones for the particular
crop. If the evaluator 18 determines that the estimated performance
of the particular grower meets or exceeds the threshold performance,
the grower may add that grower to an eligibility list of growers
eligible for attribute crop insurance or growers eligible for crop
insurance at a certain range of corresponding premium levels. The
eligibility list, premium levels, grower variance ratings for growing
particular crops, or other insurance data 26 is stored in a data
storage device 24.
[0024] The communications interface 20 manages communications via
the communications network 22. Although the soil data source 28,
weather and climate data source 30, historic yield data source 34,
and the grower data source 32 are shown as communicating to the
data processing system 12 through the communications network 22,
any soil data, weather data, climate data, historic yield data,
or grower data may be inputted via the user interface 10. That is,
the user may input soil data, weather and climate data, historic
yield data, grower data, and other input data into the data processing
system 12, as opposed to obtaining such input data via the communications
network 22 (e.g., Internet).
[0025] In one embodiment, the soil data source 28 comprises a server,
a database management system, or another data processing system.
The soil data source 28 makes available or provides any soil survey
data or other soil data from a governmental or nongovernmental entity.
For example, the soil survey data may comprise the soil survey data
that is available from the National Resources Conservation Service
(NRCS) in the United States. Typically, soil survey data is available
on a county-by-county basis within each state. Although the scale
of soil surveys may vary from county to county, the latest soil
survey maps are available in typical scales of 1:12000 or 1:24000.
The Natural Resources Conservation Services (NRCS) National Cartographic
Center may provide soil maps, text, tables, and spatial data in
various text data formats, digital formats, shape files, and other
file formats or data structures.
[0026] If the soil survey data from government sources does not
offer sufficient resolution (e.g., 1 m to 5 m resolution is typical)
or if soil survey data in multiple spatial dimensions, commercially
available soil surveyors or soil surveying services may collect
a database of soil data for a grower, an insurer, or both. Soil
survey data may be defined in terms of soil parameters that can
affect the performance or growth of a crop.
[0027] The soil survey data may include, but is not limited to,
the following soil parameters: soil texture, sand content, silt
content, clay content, soil structure, bulk density, soil organic
matter content, soil moisture, water holding capacity, available
water capacity, nitrogen (N) level, posphorus (P) level, potassium
(K) level, nutrient levels, micronutrient levels, trace element
levels, mineral levels, soil pH (e.g., level of acidity or alkalinity),
and cation-exchange capacity. The available water capacity is the
capacity of a soil to hold water available for plants. The available
water capacity may be expressed as inches of water per a certain
soil depth.
[0028] In one configuration, the weather and climate data source
30 comprises a server, a database management system, or another
data processing system. The weather and climate data source 30 makes
available or provides weather data or climate data available from
a governmental or nongovernmental entity. For instance, the weather
and climate data may comprise data that is available through the
National Oceanic and Atmospheric Administration, the U.S. Department
of Agriculture, the National Drought Mitigation Center or other
sources. Climate data refers to data on expected long-term weather
patterns. Predictive climate data may be based on historical data.
Climate data may include precipitation (e.g., rainfall per unit
time or rainfall per date of the year), degree days, growing degree
days, winds, and temperature statistics for a corresponding geographic
area. The temperature statistics may include a minimum temperature,
a maximum temperature, a mean temperature, and a mode temperature
for a corresponding geographic area. The growing degree day and
degree day are both based on temperature statistics. The growing
degree day is an index that may be used to express or predict crop
maturity. The growing degree day may be based on the minimum and
maximum temperature for a day with respect to a reference temperature
(e.g., 50 degrees for a corn growing degree day) for a corresponding
geographic location. A degree day is used to estimate the amount
of energy required to maintain a comfortable target indoor temperature
in a certain geographic area. A degree day represents that extent
that the daily mean temperature falls below or above an indoor target
temperature (e.g., 65 degrees).
[0029] Climate data may be used to determine or estimate an average
growing season duration for a corresponding geographic area, growing
zones suitable for particular crops (e.g., based on the genetic
composition of those crops), temperature range zones, or other climate
classifications for corresponding geographic areas (e.g., geographic
zones) that are useful for agronomic management, crop selection,
planting dates, and crop maturity estimation.
[0030] Weather data refers to forecasted, current, or historic
data concerning the weather associated with a geographic area (e.g.,
geographic zones) or location. Weather data may be time stamped,
and date stamped. The weather data may include measurements and
statistics related to temperature, precipitation, sunlight (e.g.,
visible or ultraviolet light intensity versus time or cumulative
light exposure), cloud cover, wind speed, wind direction, and barometric
pressure, for instance.
[0031] The weather data may be used to provide a drought assessment
or drought report for a corresponding geographic location or area
(e.g., geographic zone). A drought refers to a deficiency of precipitation
resulting from a short term or long-term weather pattern. The drought
bay be defined with reference to a drought severity index (e.g.,
Palmer Drought Severity Index, the Crop Moisture Index, and the
Z index). A weather forecast may be used to determine the probability
of ending or reducing the severity level of a drought in a given
geographic area.
[0032] The historic yield data source 34 may comprise publicly
available data such as the yield data that is available on a crop-by-crop
basis for various counties. For instance, the historic yield data
may comprise county yield data of the National Agricultural Statistics
Database.
[0033] The grower data source 32 may comprise a grower personal
computer, a grower terminal, or an insurance agent terminal that
communicates with the data processing system 12 via the communications
network 22. For example, a grower terminal may access the Internet
via an internet service provider (ISP) to complete or submit an
application for a crop insurance policy or endorsement to another
crop insurance policy. The grower may provide grower data to the
data processing system 12 via the application, via an electronic
interview process, or otherwise. Similarly, the grower may in person
or via a telecommunications network (e.g., telephone network) provide
data to an insurance agent or insurance worker who enters the data
into the data processing system 12.
[0034] FIG. 2 illustrates a method for providing crop insurance.
The method of FIG. 2 begins in step S100.
[0035] In step S100, a zone definer 14 defines one or more qualified
geographic zones for growing a particular crop associated with a
defined attribute. In general, each qualified geographic zone is
selected to be generally suitable for growing the particular crop
based one or more of the following: genetic factors for a particular
crop, environmental factors for the corresponding zone, and historic
performance (e.g., historic yield data) of growers of the particular
crop within the geographic zone. The genetic factors (e.g., date
to maturity or duration from planting date to harvest date) for
the particular crop may impact whether or not a particular crop
is suitable for growing in a particular geographic zone. For instance,
a particular crop may be qualified for a corresponding geographic
zone because the length of a growing season or growing degree days
meets or exceeds a minimum duration. Grower data may provide information
on the genetic profile of a particular crop or other genetic factors
(e.g., drought tolerance), for instance. Environmental factors may
include soil data, weather data, and climate data, among other things.
With respect to climate data or weather data, a particular crop
with above average drought tolerance may be favored for a corresponding
geographic zone with less rainfall than required by similar crops
with average or below average drought tolerance.
[0036] In step S102, the evaluator 18 or data processing system
12 determines if the grower is associated with the qualified geographic
zone or zones. The grower may provide his street address, county,
geographic coordinates, or reference to a legal description of his
property to determine what zone or zones the grower's land falls
within. If the grower is associated with the qualified geographic
zone or zones, the method continues with step S106. However, if
the grower is not associated with the qualified geographic zone
or zones, the method continues with step S104.
[0037] In step S104, the evaluator 18 or data processing system
12 rejects the grower and (if applicable) (a) selects another grower
for evaluation to determine of the grower's field or fields lie
within qualified geographic zone or zones, or (b) selects another
crop (e.g., different particular crop with a different or similar
defined attribute) of the grower for evaluation.
[0038] In step S106, the performance predictor 16 or data processing
system 12 estimates or predicts a grower performance for a particular
grower consistent with the geographic zone, the particular crop
and the defined attribute. For example, the performance predictor
16 estimates or predicts a grower yield for a particular grower
consistent with the geographic zone, the particular crop and defined
attribute based on genetic, environmental, and management factors.
[0039] The prediction of the performance of a grower growing a
crop with a specific attribute may be carried out in accordance
with several techniques, which may be applied alternatively or cumulatively.
Under a first technique, the performance predictor 16 or data processing
system 12 estimates the grower performance or grower yield based
on one or more of the following: defining the genetic characteristics
of the particular crop, assessing management practices of the grower,
and reviewing historic yields of the grower for substantially similar
crops in the geographic zones. The management practices of a grower
may be rated based on one or more of the following: (1) use of low-till
or no-till farming operations to reduce soil erosion or loss of
applied nutrients during the growing season, (2) use of buffer zones
or filter strips to reduce soil erosion or loss of applied nutrients
during the growing season, (3) use of crop rotations to reduce pesticide
requirements (e.g., dosage or application rates) over a multi-year
time span, (4) use of legumes to enrich soil with nitrogen for subsequent
crops (e.g., corn or wheat), (5) the use of organic growing practices
(e.g., consistent with the Organic Food Production Act of 1990,
7 U.S.C..sctn..sctn. 6501-6522), (6) use of buffer zones (e.g.,
one mile zone) around the particular crop to prevent cross-pollination
or contamination (e.g., lack of purity) of defined attributes from
adjacent fields growing different varieties of crops, and (7) use
of dedicated harvester, combine, pickers, or other harvesting machinery
and storage facilities to avoid cross-contamination of the particular
crop (e.g., pharmaceutical grade corn) with other crops (e.g., commodity
crops). For instance, organic growing practices may prohibit the
use of synthetic chemicals during a growing season and for three
(3) years immediately preceding the harvest of agricultural products,
unless an exception applies under applicable law or regulations.
Further organic growing practices may require buffer zones between
organically cultivated land and land that is not cultivated in accordance
with organic operations.
[0040] Under a second technique, the performance predictor 16 determines
whether a particular grower's land is associated with more than
one geographic zone distinguished by different soil characteristics,
different weather characteristics, or both. If the particular grower's
land is associated with more than one geographic zone, the performance
predictor 16 applies multiple geographic zones to estimate the yield
for a particular crop with a crop attribute grown across multiple
geographic zones.
[0041] Under a third technique, the performance predictor 16 determines
whether the particular grower's land is geographically distributed
(e.g., among multiple geographic zones) in such a manner to reduce
risk of growing the crop with a particular defined attribute. If
the grower's land is geographically distributed, the variance of
the yield may be reduced in accordance with empirical studies or
historical field measurements relating or correlating distributed
production to reduced production variance for a particular crop.
[0042] In step S108, a data processing system 12 or an evaluator
18 establishes a threshold performance value to judge the growing
or planned growing of the particular crop in the qualified geographic
zone or zones. For example, the threshold performance value may
be established based on an average historic performance, a mode
historic performance or both of other growers associated with a
comparable geographic zone or zones (e.g., farms within the same
county) to that or those of the grower. Further, the threshold performance
may include supplemental criteria based on the standard deviation
or other measure of variability of the mean or mode historic performance.
In one configuration, the threshold performance value is based on
historical yields of growers of particular crops (e.g., the same
or substantially similar crops) in the same county as the particular
grower. In another example, the threshold performance value may
be based on grower in the same county with a substantially similar
zone or zone composition to the subject grower, as opposed to more
general county historical yields. In yet another example, the threshold
performance is based on historic performance or yield associated
with the particular crop growing in a suitable zone or zones for
the particular crop. Although step S108 is shown as following step
S106, in practice steps S106 and S108 may be carried out in any
order or simultaneously.
[0043] In step S10, a data processing system 12 or an evaluator
18 determines whether the grower qualifies for the status of a qualified
grower for a particular crop with the defined attribute based on
the estimated grower performance (e.g., grower yield) meeting or
exceeding the threshold performance value. If the estimated grower
performance meets or exceeds the threshold performance value, the
method continues with step S112. However, if the estimated grower
performance does not meet or exceed a threshold value, the method
continues with step S114.
[0044] In step S112, an evaluator 18 designates the qualified grower
as eligible for crop insurance coverage (e.g., crop insurance policy
or endorsement) associated with a defined attribute. For example,
the crop insurance coverage may comprise an attribute endorsement
to at least one of a yield-based crop insurance and a revenue-based
crop insurance. In one example, the yield-based crop insurance comprises
Multiple Peril Crop Insurance (MPCI). In another example, the revenue-based
crop insurance Crop Revenue Coverage (CRC) as the revenue-based
crop insurance. In still another example, the yield-based crop insurance
comprises Group Risk Insurance.
[0045] In the U.S., the multiple Peril Crop Insurance (MPCI) program
protects against yield risk. Yield risk tends to be affected by
natural disasters, for instance. The desired level of protection
may be based upon a percentage of a particular grower's historic
yield. The grower's historic yield may be defined in terms of a
grower's actual production history (APH). Catastrophic Risk Protection
(CAT) is generally the lowest level of MPCI coverage. MPCI and other
policies based on APH may have the following coverage exclusions:
(1) hail and fire exclusion provisions and (2) high risk land exclusion
provisions. MPHCI and other policies based on APH may also have
the following coverage requirements or limitations: (1) late planting
provisions, (2) replant requirements, (3) replanting payment provisions,
(4) prevented planting provisions, (5) nonstandard classification
system, and (6) experience adjustment factors.
[0046] A gap policy, called crop-hail insurance, can fill the gap
for damage that is less than the deductible of a basic MPCI policy.
Crop-hail insurance may provide acre-by-acre coverage against hail
damage, whereas certain MPCI coverage may only protect against widespread
hail damage that materially effects a grower's overall yield.
[0047] In the U.S., the Crop Revenue Coverage (CRC) program protects
against both yield and price risk to facilitate the insured grower
earning a minimum revenue. The protection of the yield is based
on using a particular grower's actual production history (APH).
[0048] Group Risk Protection (GRP) is generally similar to the
MPCI program, except that the county yield or geographic region
yield is used, instead of the individual grower's historic yield.
GRP is a risk management tool that is an alternative to the MPCI
or other crop insurance based on Actual Production history. The
GRP may be used by growers with yields that tend to track the county
yield and where a drought or other natural disaster tends to affect
a substantial portion of a county. GRP indemnifies or pays out the
insured grower if the county average per acre yield (referred to
as the "payment yield") falls below the insured grower's
trigger yield. The Federal Crop Insurance Corporation (FCIC) may
publish or disclose the payment yield to insurance providers for
each county, following each growing season or crop year, or otherwise.
The trigger yield means the expected county yield listed in the
actuarial document multiplied by the coverage level percent listed
on the accepted application. The expected county yield may represent
an average of annual NASS county yields adjusted for yield trends.
The grower may select a coverage level of from approximately sixty
percent to approximately one hundred percent (or a lesser applicable
maximum percentage) of the maximum protection per acre. Unless allowed
by the GRP policy, the insured grower cannot insure the same crop
through both an MPCI policy and GRP policy. A grower is not required
to maintain or report yield history for GRP policies. For a GRP
policy, a grower may have a low yield on his farm and not receive
a payment under the GRP policy because the policy is based on county
yields, not individual grower yields.
[0049] In step S114, the data processing system 12 or evaluator
18 rejects the grower with respect to insuring or covering the particular
crop with the defined attribute. However, the rejection of the grower
does not necessarily equate to a rejection of the grower for all
crops with defined attributes. If applicable, the data processing
system 12 or insurer may select another grower or may evaluate another
particular crop (e.g., a suitable substitute or replacement) with
an initially rejected grower. The subsequently evaluated particular
crop may have the same, similar, or different defined attributes
with respect to the initially evaluated particular crop.
[0050] The method of FIG. 3 is similar to the method of FIG. 2,
except step S112 of FIG. 2 is replaced with step S111 of FIG. 3.
Like reference numbers in FIG. 2 and FIG. 3 indicate like procedures
or steps.
[0051] In step S111, a data processing system 12 determines a particular
premium level or range for the qualified grower for crop insurance
coverage associated with a defined attribute (e.g., an attribute
endorsement to at least one of yield-based crop insurance and a
revenue-based crop insurance). The particular premium is derived
from an estimated level of risk associated with the grower based
on at least one of the crop genetics, grower management practices,
and grower environment associated with geographic zones. The method
of FIG. 6, which is described later, provides a detailed explanation
on determination of a premium level or premium range for insurance
coverage.
[0052] The method of FIG. 4 is similar to the method of FIG. 2,
except steps S106, S108 and S110 of FIG. 4 are replaced with steps
S206, S208 and S210, respectively. Like reference numbers in FIG.
2 and FIG. 4 indicate like procedures or steps.
[0053] In step S206, the performance predictor 16 or data processing
system 12 estimates or predicts a grower yield index for the particular
grower consistent with geographic zone, the particular crop, and
the defined attribute. For example, the performance predictor 16
determines a yield index for the grower based on the geographic
zones (e.g., qualified geographic zones) in which the grower is
located. In general, the yield index may comprise a ratio of predicted
grower yield data to historical grower yield data for a geographic
zone (e.g., qualified geographic zone). In one example, the historical
grower yield data divided by predicted grower yield data for a particular
crop within a particular zone. That is, the yield index may be determined
in accordance with the following equation: Y.sub.I=Y.sub.H/Y.sub.P,
Y.sub.I is the yield index, Y.sub.H is the historical grower yield
data for a particular crop (or a substantially similar crop) in
the particular zone and Y.sub.P is the predicted grower yield data
for the particular crop in the zone. The historical grower data
and predicted grower yield data may represent an average or mean
per land unit (e.g., acre) yields. Where the yield index is Y.sub.H/Y.sub.P,
a lower yield index indicates better performance of the grower and
a yield index.
[0054] In step S208, the evaluator 18 or data processing system
12 establishes a threshold yield index value based on other growers
associated with a comparable geographic zone or zones (e.g., farms
in the same county) to that or those of the grower. The threshold
yield index value may be based on a reference value of the yield
index that reflects a minimum performance standard (e.g., above
average performance of a grower for a county, region, or crop type).
For instance, the reference value may be selected within a range
of five (5) to fifteen (15) percent above an average or mean county
yield, above an average or mean yield for a corresponding geographic
region (e.g., qualified geographic zone), or with reference to the
variance of standard deviation of yields with the corresponding
geographic region.
[0055] Depending upon the identity or characteristics of the defined
attribute, a particular crop with a defined attribute may or may
not have a lower predicted yield than the yield of a corresponding
closest commodity crop. In one example, a noncompliant yield amount
of the harvested crop may not meet a defined specification consistent
with the defined attribute; the noncompliant amount may be deducted
from the gross yield to determine a net yield of the crop having
the defined attribute. The grower may be compensated from the purchaser
based on the net yield, as opposed to the gross yield. The noncompliant
amount of the harvested crop may or may not comply with standards
of merchantability or sale as a commodity crop. In another example,
if the yield is contractually tied to an agricultural product or
derivative product derived from harvested crop, the defined attribute
yield is distinct from the harvested crop yield, but may be proportional
to or correlated with respect to the harvested crop yield. In another
example, where the defined attribute represents organic produce
or meat, the yield of the organic produce or meat is not necessary
lower than that of conventional produce or meat. Although step S208
follows step S206 in FIG. 9, step S206 and step S208 may be executed
in any order with respect to each other, or even simultaneously.
[0056] In step S210, the evaluator 18 or data processing system
12 determines whether the grower is qualified grower for the particular
crop with the defined attribute based on the estimated grower yield
index meeting or exceeding the threshold yield index value. For
example, if the yield index is defined as the historical grower
yield data divided by the predicted grower yield data, and if the
grower yield index is less than the threshold index value of the
yield index, the method continues in step S112. However, if the
grower yield index does not comply with the threshold yield index,
the method continues with step S114.
[0057] In step S112, the grower is qualified or eligible to obtain
coverage under the supplemental attribute insurance or other crop
insurance policy associated with a defined attribute.
[0058] The method of FIG. 5 is similar to the method of FIG. 3,
except steps S111 and step S114 are replaced by steps S118 and S116,
respectively. Like reference numbers in FIG. 5 and FIG. 3 indicate
like procedures or steps.
[0059] In step S110 of FIG. 5, a data processing system 12 or an
evaluator 18 determines whether the grower qualifies for the status
of a qualified grower for a particular crop with the defined attribute
based on the estimated grower performance (e.g., grower yield) meeting
or exceeding the threshold performance value. If the estimated grower
performance meets or exceeds the threshold performance value, the
method continues with step S118. However, if the estimated grower
performance does not meet or exceed a threshold value, the method
continues with step S116.
[0060] In step S118, a data processing system 12 determines a first
premium level or first range for the qualified grower for crop insurance
coverage associated with the defined attribute. The first premium
level may be based on an estimate level of risk associated with
the grower based on at least one of the crop genetics, grower management
practices, and grower environment associated with the geographic
zones. The method of FIG. 6, which is described later, provides
a detailed explanation of determining premium level or premium ranges.
[0061] In step S116, a data processing system 12 determines a second
premium level or a second range for the grower (e.g., the unqualified
grower) for crop insurance coverage associated with the defined
attribute. For example, the second premium for a crop insurance
policy may be greater than a first premium for the same or substantially
equivalent crop insurance policy. The first risk associated with
the qualified grower for growing a particular crop may be less than
the second risk associated with an unqualified or less qualified
grower. Further, the grower may be unqualified or less qualified
because of its management practices or any other factor that contributes
to noncompliance with a threshold performance value in accordance
with applicable laws and regulations.
[0062] FIG. 6 discloses a method for determining a rating or variance
level of a particular grower that may be applied to any method of
providing crop insurance disclosed hereunder, including those set
forth in FIG. 2 through FIG. 5, inclusive. The method of FIG. 6
may be used to set an appropriate premium level or premium range
(for insurance coverage) that corresponds to the variance level
associated with estimated grower performance. The method of FIG.
6 begins in step S600.
[0063] In step S600, a data processing system 12 or evaluator 18
determines whether or not a particular grower complies with an enhanced
genetics requirement for a particular crop with a defined attribute.
The enhanced genetics requirement may comprise using plants or seeds
(e.g., certified seeds) with a certain genetic make-up or a particular
gene sequence inserted or otherwise brought into the deoxyribonucleic
acid (DNA) of seed, plant tissue, embryo, or plant cell that is
(a) associated with adequate expression of the defined attribute,
(b) proven to perform reliably in field trials, tests, or through
sufficient historic records of use and (c) sufficiently compatible
with the environment (e.g., soil, weather and climate) of the geographic
zone or zones in which the crop is grown. If the particular grower
complies with the enhanced genetics requirement, the method continues
with step S602. However, if the particular grower does not comply
with the enhanced genetics requirement the method continues with
step S604.
[0064] In step S602, the data processing system 12 changes a register
value associated with the particular grower and the particular crop.
For example, a counter is incremented that is associated with the
particular grower and the particular crop, where a higher aggregate
register value (e.g., counter value) represents greater compliance
with one or more agronomic factors or lesser variance in grower
performance. Alternately, the counter may be decremented, where
a lower aggregate register value represents greater compliance with
one or more agronomic factors or lesser variance in grower performance.
Step S604 may be executed after step S602, as indicated in FIG.
6.
[0065] In step S604, the data processing system 12 or the evaluator
determines whether the particular grower grows (or plans to grow)
the particular crop in a generally suitable environment for the
crop. For example, the data processing system 12 or evaluator 18
may determine if the grower is associated with a qualified geographic
zone or zones for growing a particular crop with a defined attribute.
Regardless of whether the enhanced genetics requirement is satisfied
or not, the qualified geographic zone may consider the soil, weather,
climate, planned planting date, planned harvest date, and growing
degree days, among other agronomic information in step S604. The
grower may provide information on the geographic coordinates of
the fields to be used for growing the particular crop or other location
data (e.g., a street or mailing address of a farm, a state and county
location, or the nearest main roads) to register or align a location
of a particular grower with corresponding applicable soil, weather
and climate data. If the particular grower grows or plans to crop
the particular crop in a generally suitable environment, the method
continues with step S606. However, if the particular grower is noncompliant
with respect to a generally suitable environment for the particular
crop, the method continues with step S608.
[0066] In step S606, the data processing system 12 changes a register
value associated with the particular grower and the particular crop.
For example, a counter is incremented that is associated with the
particular grower and the particular crop, where a higher aggregate
register value (e.g., counter value) represents greater compliance
with one or more agronomic factors or lesser variance in grower
performance. Alternately, the counter may be decremented, where
a lower aggregate register value represents greater compliance with
one or more agronomic factors or lesser variance in grower performance.
Step S608 may be executed after step S606, as indicated in FIG.
6.
[0067] In step S608, the data processing system 112 determines
whether a particular grower complies with the requisite management
practices for growing the particular crop. The requisite management
practices may stem from actual or anticipated contractual requirements
associated with a purchaser or potential purchaser of the particular
crop associated with the defined attribute. Alternatively, the requisite
management practices may be based on best management practices,
scientific research, conventions, norms, regulations, laws, certifications
or standard industry or agricultural practices in absence of actual
or anticipated contractual requirements. The management practices
may be audited (remotely or in person) during the growing season
as a condition of continued coverage under any insurance policy
or crop insurance coverage issued, for example. If the particular
grower complies with the requisite management practices, the method
continues with step S610. However, if the particular grower is noncompliant
with respect to requisite management practices for the particular
crop, the method continues with step S612.
[0068] In step S610, the data processing system 12 changes a register
value associated with the particular grower and the particular crop.
For example, a counter is incremented that is associated with the
particular grower and the particular crop, where a higher aggregate
register value (e.g., counter value) represents greater compliance
with one or more agronomic factors or lesser variance in grower
performance. Alternately, the counter may be decremented, where
a lower aggregate register value represents greater compliance with
one or more agronomic factors or lesser variance in grower performance.
Step S612 may be executed after step S610, as indicated in FIG.
6.
[0069] In step S612, a data processing system 12 determines a rating
or variance level of grower performance (e.g., variance or probability
density function of estimate yield of the particular crop) based
on the register value. In one example, if the register value (or
aggregate register value) is incremented or increased (e.g., by
a weighted or unweighted amount) during any of the steps S602, S606
and S610, the higher register value (e.g., counter value) represents
one or more of the following: greater compliance with one or more
agronomic factors, a higher grower rating, lesser variance in grower
performance, and a lower premium level, and a lower premium range.
In contrast, if the register value (or aggregate register value)
is decremented or decreased (e.g., by a weighted or unweighted amount)
during any of the steps S602, S606 and S610, the lower register
value (e.g., counter value) represents one or more of the following:
greater compliance with one or more agronomic factors, a higher
grower rating, lesser variance in grower performance, and a lower
premium level, and a lower premium range.
[0070] In step S614, a data processing system 12 determines a respective
premium level for crop insurance coverage of the particular grower
for the particular crop based on the determined rating or variance
levels. Step S614 may be carried out in accordance with various
techniques that may be applied independently or cumulatively. Under
a first technique, if the register value (or aggregate register
value) is incremented or increased (e.g., by a weighted or unweighted
amount) during any of the steps S602, S606 and S610, a highest register
value is associated with a lowest premium level or lowest premium
range; an intermediate register value is associated with an intermediate
premium level or intermediate premium range; and a lowest register
value is associated with a highest premium level or highest premium
range.
[0071] Under a second technique, if the register value (or aggregate
register value) is decremented or decreased (e.g., by weighted or
unweighted amount) during any of the steps S602, S606 and S610,
a highest register value is associated with a highest premium level
or first premium range; an intermediate register value is associated
with an intermediate level or intermediate premium range; and a
lowest register value is associated with a lowest premium level
or lowest premium range.
[0072] Under a third technique for carrying out step S614, the
data processing system 12 may store a look-up table, a chart, a
database, or another file or group of records that contains respective
premium levels associated with corresponding ratings or variance
levels. For example, the look-up table, chart, database or file
may assign a lowest premium level to a corresponding lowest variance
level, an intermediate premium level to a corresponding intermediate
variance level, and may assign a highest premium level to the highest
variance level.
[0073] Under a fourth technique, the data processing system 12
may calculate a respective premium level based on an equations or
formula that has a variance level as a factor, such that the lower
the variance level the lower the premium level and the higher the
variance level the higher the premium level.
[0074] Under a fifth technique, step S612 and step S614 are merged
or integrated into a single step procedure in which there is a direct
relationship between register value and a corresponding premium
range or level, as opposed to a first relationship between a register
value and a variance level of step S612 and a second relationship
between a variance level and a corresponding premium range or level
as in step S614.
[0075] In FIG. 7, the graph shows yield distributions or variances
for a particular crop with respect to a first grower and a second
grower or group of growers. The variance may represent variances
in yield over a certain number of growing seasons or years or the
probability of achieving a given yield in any single year. The first
yield distribution 402 is shown as a solid curved line, whereas
the second yield distribution 400 is shown as a dashed curved line.
The horizontal axis shows a yield and the vertical axis shows a
probability (or frequency of occurrence over a defined time interval)
of the yield. The first yield distribution 402 has a lesser variance
than the second yield distribution 400 does. If the first grower
has a first yield distribution 402 or variance for the particular
crop, an insurer may regard the first grower as lower risk or within
a lower premium schedule. In contrast, the insurer may regard the
second grower or group of growers as higher risk, consistent with
a higher premium schedule for coverage comparable to that of the
first grower. If variance data or yield distributions are available
for a grower, such data may be applied to determine a premium. However,
if the variance or yield distributions may be affected by various
factors as illustrated in FIG. 8A through FIG. 8D, inclusive.
[0076] In each chart or graph in FIG. 8A through FIG. 8D, the vertical
axis represents probability (or frequency of occurrence) and a horizontal
axis represents yield of a particular crop with a defined attribute.
[0077] In FIG. 8A, the baseline yield distribution is shown for
a grower that may grower crops with improper genetics, a suboptimal
environment, or with poor agronomic or management practices. In
FIG. 8B, an enhanced-genetics yield distribution is shown where
the proper genetics are selected, but the environment remains suboptimal
and the grower applies poor management practices. An insurance policy
(associated with a defined attribute) or endorsement may include
genetic requirements for seed or plants to reduce the variance of
a crop attribute of the particular crop and minimize the risk to
the insurer. The first variance level or range of the enhanced-genetics
yield of FIG. 8B is less than the baseline yield distribution of
FIG. 8A. The first variance level or range of FIG. 8B may correspond
to the assignment of a primary premium level (lower than a baseline
premium level) consistent with the method of FIG. 6, or otherwise.
[0078] In FIG. 8C, enhanced genetics and an enhanced environment
is selected for growing a particular crop, but the grower management
practices are deficient or inconsistent in some material respect.
The insurance policy (associated with a defined attribute) or endorsement
may include environmental requirements, such as soil characteristics,
rainfall temperature, and climate. The second variance level or
range of FIG. 8C may correspond to the assignment of a secondary
premium level (lower than a baseline premium level and the primary
premium level) consistent with the method of FIG. 6, or otherwise.
[0079] The variance of the yield distribution of FIG. 8C is less
than that of FIG. 8B. In FIG. 8D, enhanced genetics, an enhanced
environment, and enhanced grower management practices are selected
for growing a particular crop. For example, the enhanced grower
management practices may call for irrigation, application of certain
crop inputs (e.g. pesticides, insecticides, fungicides) with defined
dosages at particular times. The variance of the yield distribution
of FIG. 8D is less than that of FIG. 8C. The third variance level
or range of FIG. 8D may correspond to the assignment of a tertiary
premium level (lower than a baseline premium level, the primary
premium level, and the secondary premium level) consistent with
the method of FIG. 6, or otherwise.
[0080] FIG. 9 illustrates the targeted risk coverage of crop insurance
or an endorsement for covering a crop attribute. The vertical axis
shows the market price for a particular crop and the horizontal
axis shows the yield. On the vertical axis, the conventional insured
crop price (P.sub.i) is lower than the commodity crop price (P.sub.c).
The conventional insured crop price (P.sub.i) may represent the
federally insured crop price, for example. The attribute crop price
(P.sub.v) is higher than both the commodity crop price (P.sub.c)
and the conventional insured crop price (P.sub.i). On the horizontal
axis, conventional insured yield (Y.sub.i) is lower than the commodity
crop yield (Y.sub.c) and the attribute crop yield (Y.sub.v). Although
the attribute crop yield is lower than the commodity crop yield,
in an alternative examples the attribute yield may be greater than
or equal to the commodity crop yield.
[0081] The conventional crop insurance provides protection against
a risk of loss for grower for a conventional insured crop price
at a corresponding conventional insured yield. Accordingly, for
an insured grower that falls within the terms of the conventional
crop insurance policy, the payment for a protected or covered loss
may be equal to the insured crop price multiplied by the insured
crop yield. The expected grower revenue from a commodity crop is
shown by the rectangular region 701. The expected grower revenue
for the attribute crop is shown by the rectangular region 702. However,
if the commodity crop price and commodity yield are greater than
the conventional insured crop price and conventional insured yield,
there is a coverage gap between the conventional crop insurance
and the actual damage or loss that the grower suffers. Aside from
the potentially lower yield from certain crops with certain defined
attributes, the coverage gap is generally even greater for crop
with a defined crop attribute because the attribute crop price is
generally greater than the commodity crop price. The grower may
not receive the attribute crop price unless the attribute crop (with
the defined attribute) substantially or fully conforms to desired
attribute specifications. The cross hatched rectangular 703 region
in FIG. 7 represents an illustrative example targeted risk coverage
for a crop insurance that covers an attribute crop.
[0082] Having described the preferred embodiment, it will become
apparent that various modifications can be made without departing
from the scope of the invention as defined in the accompanying claims.
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