CA2697608A1

CA2697608A1 - Method of predicting crop yield

Info

Publication number: CA2697608A1
Application number: CA2697608A
Authority: CA
Inventors: Dennis Bulani
Original assignee: Individual
Current assignee: Individual
Priority date: 2010-03-23
Filing date: 2010-03-23
Publication date: 2011-09-23

Abstract

A method of predicting crop yield in a field 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. 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 that is used to predict yields based on weather potential yields.

Description

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.

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 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 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:

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.

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

8 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.

CATEGORIES
Seed Bed Preparation farm A farm B

crop rotation 5 10 straw managment 7.5 7.5

9 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 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 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 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.

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.

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.

Claims

What is claimed is:

1. A method of predicting crop yield in a field, the method comprising 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.

2. The method of claim 1 wherein the categories comprise seed bed preparation, nutrition, seed quality, depth of seeding, seeding date, and pest control, and wherein a rating score is determined for each category and the rating scores are added together to determine a final score.

3. The method of claim 2 comprising, where a rating score in a category falls below a failure score, further reducing the final score by a failure factor.

4. The method of any one of claims 2 and 3 wherein the final score is multiplied by an ultimate theoretical yield for each weather class to determine the crop yield for that weather class.