Note: Descriptions are shown in the official language in which they were submitted.
CA 02697608 2010-03-23
METHOD OF PREDICTING CROP YIELD
This invention is in the field of agriculture and in particular a method of
predicting the
yield of agricultural crops.
BACKGROUND
When a farmer plants a crop, the estimate of what that crop will yield varies,
depending a
number of factors, with weather being major factor. Long range weather
predictions with
at least some relevance are available today using satellite and like
technology.
Phenomenon such as La Nina and El Nino are detected and used attempt to
predict
weather during the coming growing season. The amount of rainfall, sunshine,
high and
low temperatures all heavily influence a growing crop.
The amount of nutrients used by a crop vary with the yield. A big crop
requires more
nutrients than a small crop. Since many of these nutrients must be
supplemented may be
lacking in the soil where the crop will be grown, they must be added to the
soil by the
farmer. It is therefore desirable to estimate as accurately as possible the
yield of the crop,
so that appropriate nutrients, generally applied as a commercial fertilizer,
can be
provided. Generally each crop nutrient is applied by the farmer based on a
combination
of factors including the nutrient cost and the expected yield increase due to
the particular
nutrient. The estimated market price of the crop is then used to determine the
economically feasible amount of nutrient to apply.
Similarly a farmer must decide whether it is warranted to use various
herbicides,
pesticides, or fungicide when weeds, insects, or diseases are present in
varying degrees in
a crop after it is seeded.
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Many management decisions are thus based on a prediction of crop yield, while
at the
same time directly influencing that yield. More accurately predicting crop
yield allow for
increasing the effectiveness of management decisions.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide a method of predicting
crop yield that
overcomes problems in the prior art.
In a first embodiment the present invention provides a method of predicting
crop yield in
a field. The method comprises determining categories affecting crop yield for
the field,
determining a rating score for each category, and then calculating the yield
based on the
rating scores for a variety of possible weather classes.
In one embodiment of the invention the categories comprise seed bed
preparation,
nutrition, seed quality, depth of seeding, seeding date, and pest control, and
a rating
score is determined for each category and the rating scores are added together
to
determine a final score.
Management decisions can be made based on the affect of these decisions on the
predicted crop yield.
DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS
In the present specification the ultimate yield is defined as the maximum
yield that could
have been achieved for any given environmental year. The goal is to eliminate
the yield
barriers through best management processes and thereby minimize the potential
each of a
specified number of yield determinants has on reducing yield. The farm manager
will
have a measurement of how management decisions can affect yield goals, and
thereby
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assess the cost-benefit ratio of each decision, such as those respecting
seeding practices,
nutrients, herbicides, and the like.
Similar to a gambling concern in the process of determining the odds of a
certain event
occurring, the present method breaks down the factors affecting crop yield,
rates them,
and then predicts the outcome. This is a similar process used in the art of
horse racing.
Seven different categories are evaluated that are accepted determinants in the
yield of a
crop. If none of the factors are dealt with or managed the odds against
success are 7!
(factorial) (7x6x5x4x3x2x l) = 5040 to. 1, very poor odds. If a farm manager
had satisfied
one yield determinant such as pest control then his odds will have gone up to
6!
(6x5x4x3x2xI) or 720 to 1, an impressive increase in odds compared to the
previous
example where no determinants are satisfied.
The present method considers each determinant category and scores the
management
decision or practice to determine the probability of achieving the ultimate
yield. The
seven categories of the present method are:
I . seed bed preparation
2. nutrition
3. seed quality
4. depth of seeding
5. seeding date
6. pest control weather
7. weather
Seed bed preparation has many considerations - crop rotation, residual
herbicide
history, method of seeding, straw management, and soil moisture management
play a key
role while maintaining a firm moist seed bed. Seed bed preparation begins with
proper
CA 02697608 2010-03-23
crop rotation, and deductions from ultimate yield occur when planting cereal
on cereal or
oilseed on oil seed and sometimes pulse on pulse. When crop rotation is
compromised for
cash flow management due to varied grain prices ultimate yield may be
sacrificed for
short term gain in return per acre.
Nutrition is determined by soil physical and chemical characteristics as well
as rotation
with pulses. The present method recognizes that plant growth and crop yield is
controlled
not by the total of resources available, but by the scarcest necessary
resource.
Nutrition regimes for ultimate yield include delivery by application of
nutrients to the
seed, the soil, or the plant foliage. Nutrition begins with a detailed
physical and chemical
analysis of the soil types within a field. The present method uses the soil
physical and
chemical makeup as the foundation for it's entire plan, and therefore in depth
analysis of
the soil's health and class are essential to building the entire program.
Ground
information systems, aerial photography, yield maps, and topo-grid maps are a
requirement at least on benchmark fields. The information is then utilized to
assist in
micro-management of differences within the field. The system assumes that the
field will
soon be managed in zones and allows growers to prepare for eventual use of
variable rate
technology.
Seed quality is rated from 1-10 . As ratings for each category drop below 10
ultimate
yield will be assumed to be sacrificed to varying degrees. The current seed
certification
system utilized does not always satisfy this method's ultimate yield
criterion. The
criterion for seed quality can largely be conducted in a laboratory. Therefore
lab analysis
is utilized for key criterion. Since seed quality determines seed rates and
ultimately plant
densities, 100 kernel weights and plant densities will be part of the
criterion when
evaluating ultimate yield.
Seed Quality Criterion are as follows:
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purity and genetics: considerations are given to generation and hybridity;
since
hybrid vigor is well documented extra ratings are given to hybrid seed
varieties.
germination
vigor
plumpness
protein Content
test weight
color
diseases
uniform size
mechanical damage
use of seed treatment
Depth of seeding directly impacts ultimate yield. Seeding deeper due to dry
soil
conditions will provide germination, however the decision will most certainly
limit yield
potential. The decision to seed deeper is one that admits to lower yield
however this is
better than no yield at all due to no germination. The ultimate yield
recognizes the
decision to be practical but one must measure the added stress of deep
seeding. Thus the
present method recognizes that seeding deeper to reach moisture will reduce
the yield of
the crop.
The coleoptile is the pointed protective sheath covering the emerging shoot in
monocotyledons such as oats, cereals and grasses. The impact of depth of
seeding on
yield is related to coleoptile length in cereals. Longer coleoptile lengths
generally allow
for deeper seeding. Modem cultivars of wheat have been selected to have a
coleoptile
length of about 1.5 - 2 inches. Seeding deeper than 1.5 adds stress to
emergence and
therefore ultimate yield.
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Large seeded crops such as peas tend to require large amounts of soil moisture
to initiate
germination and sustain emergence therefore seeding peas deeper is important
for
complete and speedy emergence.
In small seeded crops such as canola and mustard, seeding depth is generally
preferred to
be above 0.5 inches. Any deeper impacts ultimate yield. As a general rule
shallower
germination with good seed to soil contact is preferred.
Seeding date as a yield determinant is certainly a difficult parameter to
measure,
however generally the impact of seeding dates upon ultimate yield is more
related to soil
temperature and therefore emergence rate. Seeding date of course can impact
yield as it
relates to days to flower and typically flowering crops during high heat times
of the
season can yield lower. Seed date criterion generally are related to days to
maturity. The
goal in much of prairie agriculture in temperate climate zones is to complete
the
operation as soon as possible to avoid fall frost and flowering heat units.
This must be
weighted against soil temperature and stress related delay of emergence.
Pest control includes control of weeds, diseases and insects. A pest
management
strategy is laid out prior to seed emergence. Pre-seed pests are dealt with
prior to seed
emergence and fine-tuning of the plan. is required through crop scouting. Pest
control
involves early identification of pests so that control measures can be
implemented early
prior to impact upon ultimate yield. Crop tolerance to treatments, type of
control, mode
of action of control agent, crop stage and efficacy of pesticide need to be
considered.
Weather is evaluated on a scale of 0 - 10. Overall environmental conditions
are
somewhat subjective but are used in the post-mortem of the season to evaluate
how close
to ultimate yield we were able to come. For planning purposes the grower will
evaluate
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potential ultimate yield given a forecast of 3 environmental classes. The
classes of
environment we will use as a base will be a s follows:
CLASS 3 Environment - sub optimal quantities and/or timeliness of moisture
combined with high heat units during critical growth stages of crop
development.
CLASS 5 Environment - average quantities and/or timeliness of moisture
combined
with average heat units during critical growth stages of crop development.
CLASS 7 Environment - above average quantities and/or timeliness of moisture
combined with ideal heat units during critical growth stages of crop
development.
Since ultimate yield is the maximum yield potential for a given environmental
year, 3
yield targets will be utilized when evaluating economic risk in decision
making. The
outcome will provide a range of outcomes and therefore provide a 3 case
scenario that
can be used primarily for contingency planning given a class 3 environmental
year.
EXAMPLE
As an example it will be proposed that two different farmers (or a single
farmer on two
different fields) makes two series of choices, for various reasons, referred
hereafter as
farm A and farm B in Table 1 below.
TABLE 1
CATEGORIES
Seed Bed Preparation farm A farm B
crop rotation 5 10
straw managment 7.5 7.5
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trash % issue on emergence 7.5 8
herbicide residues 10 10
firm ness of seed bed 6 10
moisture management 10 10
6 factors x 10/ factor
60 total possible points 46 55.5
out of 10 7.7 9.3
Nutrition
soil build prior to crop year 0 10
minimum requirements meet 0 10
balanced program 7 10
placement 8 9
4 factors x 10/ factor =
40 total possible points 15 39
out of 10 3.8 9.8
Seed Quality
germination 9 9
plumpness 7 8
% protein 6 10
1000 k weight used for rate 6 8
disease 8 10
uniformity 7 8
mechanical damage 10 10
seed treatment used 8 8
purity 6 10
vigor 8 9
variety selected 10 10
variety match 10 10
12 factors x 10/ factor =
120 total possible points 95 110
out of 10 7.9 9.2
Seed Depth
even seed depth across field 3 9.5
average depth 3 10
does every plant have
emergence on same day ? 4 10
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3 factors x 10/ factor =
30 total possible points 10 29.5
out of 10 3.3 9.8
Seed Date
avg soil temp on seed date 6 6
1 factor 10/ factor
total possible points 6 6
out of 10 6.0 6.0
Pest Control
insects
insects premerge 2 10
insects in crop 7 10
weeds
historical issues
rotational issues
residual issues
were weeds an issue in:
fall 3 8
spring pre seed 5 9
spring in crop 6 9
summer in crop 4 10
disease
leaf disese prescence 7 10
flag health 6 9
root disease 7 9
9 factors x 10/ factor =
90 points total possible 47 84
out of 10 5.2 9.3
grand total score out of 350
factor points possible 219 324
% of possible points 63 93
sum of categories (out of 60) 33.9 53.3
final score (out of 100) 56.5 88.9 100
fail in one category (under 5) - 48.0 75.6
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reduce by 15%
fail in two categories reduce by
25% 42.4 66.7
thin black soil
Ultimate theoretical
weather class no fail score farm A farm B farm 3 yield
0 0.0 0.0 0
1 (.565x18)10.2 (.889x18)16.0 18
2 16.4 25.8 29
3 19.2 30.2 34
4 24.3 38.2 43
32.2 50,7 57
6 37.8 59,6 67
7 45.2 71.1 80
8 50.3 79.1 89
9 54.2 85.3 96
max genetic potential of variety 55.9 88.0 99
weather class 2 fail grades
0 0.0 0.0 0
1 (.424x18) 7.6 (667x18)12.0 18
2 12.3 19.3 29
3 14.4 22.7 34
4 18.2 28.7 43
5 24.1 38.0 57
6 28.4 44.7 67
7 33.9 53.3 80
8 37.7 59.3 89
9 40.7 64.0 96
max genetic potential of variety 41.9 66.0 99
Seed bed preparation in the above table has 6 factors for consideration to
determine
points: - crop rotation, straw management, trash % issue on emergence,
herbicide
residues, firmness of seed bed, and moisture management. A score out of a
possible 10 is
attached to each factor. Crop rotation for farm A scores only 5110 while for
farm B
scores 10/10. This may be because farm A is considering following a not
recommended
rotation, such as canola only one year after a prior canola crop on the same
farm, rather
than the recommended 3 year waiting period. Each of the other factors is
similarly
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considered and awarded a score. Farm A scores 46/60 or 7.7/10, while farm B
scores
55.5/60 or 9.3/10.
Nutrition in the above table has 4 factors for consideration to determine
points: soil build
prior to crop year, minimum requirements met, balanced program, placement of
nutrient.
Farm A again scores much lower - in fact farm A "fails" this category because
it scores
less than 5/10. Such failure is discussed below.
Seed quality in the above table has 12 factors for consideration to determine
points:
germination, plumpness, % protein, 1000 kernel weight (used to determine
seeding rate),
disease, uniformity, mechanical damage, seed treatment, purity, vigor, variety
selected,
variety match. Seed quality points are much closer for farms A and B.
Depth of seeding in the above table has 3 factors for consideration to
determine points:
even seed depth across the field, average seed depth, and plant emergence on
the same
day. Farm A fails this category as well, scoring only 3.3/10.
Seeding date in the above table has only I factor for consideration to
determine points:
average soil temperature on seeding date. This scores the same for farm A and
farm B.
Pest control in the above table has 9 factors for consideration to determine
points: two
with respect to insects - insects pre-emergence, insects in crops; four with
respect to
weeds - weeds present in fall, weeds present in spring before seeding, weeds
present in
spring in crop, and weeds present in summer in crop; and three with respect to
plant
diseases - leaf disease present, flag leaf health, and root disease present.
Again farm A
score much lower than farm B.
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Final Score is determined by totaling the sum of the 6 categories and then
converting
that number to a percent of the possible points for all categories. Thus farm
A scored
33.9/60 = 56.5/100 = 56.5%, while farm B scored 53.3/60 = 88.9/100 = 88.9%.
"Failure" in a category further diminishes the potential yield of a crop by
introducing a
failure factor. In the present example farm A fails in the categories of
Nutrition and
Depth of Seeding. Failure in one category results in a reduction of 15%, the
failure factor
for one failure, while failure in two categories results in a reduction of
25%, the failure
factor for one failure. The failure factor is determined by historical.
agronomic records.
This reduction represents a recognition that a drastic shortcoming in one area
can reduce
yields significantly by limiting the ability of good scores in other
categories to make up
for the shortfall. In this example a drastic failure in nutrition will limit
the yield no
matter how good the seed is, or the pest control, or any like factor. Here
farm A also fails
in seed depth, where the crop emergence will be so uneven as to delay harvest
so that
again the other categories cannot make up the difference - thus the total
further reduction
of 25%.
Weather - the potential of the crop of course is determined to a large extent
by the
weather, however the category scores are equally determinative. Once the
category
scores are determined, the weather is evaluated on a scale of 0 - I 0,as shown
in Table 1.
Where there is no rain for example, or where there is mid summer killing
frost, the
weather class is "0" at the bottom end of the scale and there is no crop. The
top end of
the scale at "10" is when the weather is an ideal mix of rain and sun and so
forth. At the
class 10 for weather then is the "maximum genetic potential yield of the
variety", in this
case 99.
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The "ultimate theoretical yields" are based on a "Final Score" of 100% at each
weather
class, and the projected yield for each faun are determined by multiplying the
final
percentage score for each farm by these yields, as shown in the table.
The foregoing is considered as illustrative only of the principles of the
invention.
Further, since numerous changes and modifications will readily occur to those
skilled in
the art, it is not desired to limit the invention to the exact construction
and operation
shown and described, and accordingly, all such suitable changes or
modifications in
structure or operation which may be resorted to are intended to fall within
the scope of
the claimed invention.
