Data processing: measuring – calibrating – or testing – Measurement system – Measured signal processing
Reexamination Certificate
1999-09-24
2003-01-07
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Measured signal processing
C701S050000, C340S991000
Reexamination Certificate
active
06505146
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to the field of agriculture. More particularly, it concerns methods and compositions for the spatial evaluation of field characteristics including crop performance and environmental conditions.
2. Description of Related Art
Today's farmer has available a wide array of methods, machines, chemicals, and crops that make farming more efficient. Due in part to improvements in technology, the long-term trend in farming has been a steady increase in farm size as more land is brought into production, and as new farm machinery and methods have made a farmer capable of caring for more and more land. Large machinery is available which can till, fertilize, and harvest larger acreages than ever before. The need for efficiency has been the impetus behind these trends.
A fairly recent development in farming efficiency is precision or site-specific farming. Precision farming uses information and control technology, such as the global positioning system (GPS), for acquiring information for pest eradication, fertilization, planting, and harvesting. Precision farming allows feedback and control on a small scale, with farmers able to determine trouble spots of small sizes and in scattered locations. Farmers gain the ability to treat small regions of a field differently and as needed, and not as one completely uniform tract of land.
One efficiency factor that has long been of importance to farmers is the genetic crop type. Farm crops have been selectively bred or engineered for a wide variety of traits. It is well known that the genetic crop type can greatly affect yield. Some genetic crop types are more suitable for drier soils, some for wet, some are more resistant to specific diseases or pests, some require a longer or shorter growing season, and some are more resistant to cold or frost. Other factors also may be selected or eliminated through a choice of a genetic crop type, such as height of the crop, amount of foliage, flavor or nutritional value of a crop, soil type and local soil conditions, etc. Yields can therefore be maximized if farmers can evaluate and control genetic crop performance on a small scale. However, traditional analysis of crop performance are confounded by genotype by environment (G×E) variance, which can prevent accurate predictions regarding the heritable component of crop performance. Accounting for such G×E variation would allow a farmer to make accurate predictions regarding which crop variety to use under any particular set of conditions.
Traditional evaluations of genetic performance for a crop such as corn are done on a fairly large-scale basis. There are many methods such as yield plots, strip trials, and side-by-side comparisons. The adoption of yield monitors and GPS technology provided a new method of tabulating yield data and calculating the average yield. Prior methods, for example, as described in U.S. Pat. No. 5,771,169, employed spatial referencing to analyze different treatment sections organized into geometric shapes or curves, and typically, polygons. Such structures are not amenable to direct comparisons with adjacently located regions of small enough scale to limit environmental variance, and thus, not optimally suited for the accounting of G×E variation. In particular, environment can vary significantly enough, even within a single field, such that crop performance information will be of little use due to G×E variation. Therefore, a method is needed that will allow comparisons between adjacently located, environmentally homogeneous regions. Such comparisons require a technique that will analyze samples that are equivalent in all aspects other than treatment. Using the prior art method, variations in field layout caused, for example, by planting pattern error, are not taken into account. This would lead to non-equivalent samples were a uniform cell-based comparative analysis attempted. Therefore, what is needed in the art is a method amenable to comparisons between adjacent cells and which takes into account field variations. In particular, what is needed is a pass-based method for the analysis of field characteristic data from adjacent field regions.
SUMMARY OF THE INVENTION
In one aspect, the current invention provides a method of determining a difference in the value of a first spatially-variable field characteristic, the method comprising the steps of: a) preparing a field, wherein the field comprises a first and a second crop test area; b) obtaining spatially-referenced field characteristic data for a designated region of the field, where the region comprises the first and the second crop test areas; c) designating the spatially-referenced field characteristic data to the first or the second crop test area; d) defining cells in the first and the second crop test area by designating length units within the crop test areas; and e) comparing the field characteristic data for a first cell from the first crop test area to an adjacent first cell from the second crop test area to identify a difference in the value of the field characteristic data.
The method may further comprise creating a visual representation of the difference in field characteristic data. The difference may be displayed using colors corresponding to the magnitude of the difference in the field characteristic data, or may be displayed using symbols, wherein the symbols correspond to the magnitude of the difference in the field characteristic data. Alternatively, the difference may be displayed alphanumerically. The step of creating a visual representation may comprise creating a graph. The data in the visual representation may be either spatially oriented relative to the designated region of the field, or may be non-spatially oriented relative.
The step of obtaining spatially-referenced field characteristic data may comprise measuring crop yield. There additionally may be a third, fourth or fifth crop test area, or still further, from about 6 to about 30 crop test areas, including 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 , 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, and 29 crop test areas. Where desired, a larger number of crop test areas also could potentially be used.
The first and said second crop test areas may differ in potentially any aspect, the effects of which one desires to analyze. In one embodiment of the invention, the first and second crop test areas differ in the genotype of seed planted. Such a difference in genotype may comprise different species of crops, or alternatively, may comprise seed of the same species but of different varieties. Such varieties may differ in only a single locus, for example, where a transgene has been backcrossed into a particular inbred line. The varieties may be hybrid varieties, and may differ only in a single transgene that has been introduced into one of the parents of the hybrid. Alternatively, the varieties may be inbred varieties. Potentially any species may be analyzed with the invention, including wheat, maize, rye, rice, oat, barley, turfgrass, sorghum, sugarcane, millet, tobacco, tomato, potato, soybean, cotton, canola, alfalfa, sunflower, sugarbeets, peanuts, broccoli, carrots, peppers, raspberry, banana, apple, pear, forage grass and hay, and peach. In a preferred embodiment of the invention, the species is maize and the first and second crop test areas are planted with different hybrid varieties of maize. In another embodiment, the species is soybean.
In addition to differences in crop variety or genotype, the crop test areas may differ in the method of cultivation. Such differences may include application of plant nutrient factors or soil amendments, application of insecticide, or application of herbicide.
The step of obtaining spatially-referenced field characteristic data may comprise measuring any information for which one wishes to compare, for example, a crop performance variable. Crop performance variables include measurements of grain moisture, protein content, oil content, starch content, plant he
Charioui Mohamed
Fulbright & Jaworski LLP
Hoff Marc S.
Monsanto Company
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