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A. Cleaning Yield Data:
Jon Kleinjan, Jiyul Chang, Jim Wilson, Dan Humburg, Gregg Carlson, Dave Clay and Dan Long
Summary: Combine yield monitors are increasingly being used by production agriculturalists across the world. A portion of the data acquired from yield monitors is erroneous. Erroneous data can result from rapid speed changes, extraneous vibration resulting from crossing bumps in the field, not cutting a full header width, erroneous position information and a yield sensor that is not calibrated properly. When using yield monitor data in a decision support system, any data that can be identified as erroneous data must be removed. This guideline discusses sources of problem data and methods of yield monitor file data cleaning.
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B. Estimating Corn Yield Losses From Unevenly Spaced Planting
Gregg Carlson, Tom Doerge and David Clay
Summary
· Agronomists and corn growers have long assumed that evenly spaced stands of corn have a greater yield potential than unevenly spaced stands.
· The uniformity of spacing between plants can easily be determined by using a commonly used statistic, standard deviation (SD). SD is a commonly used statistical function that is available within most spreadsheets.
· Estimated yield loss due to non-uniform plant spacing can be calculated as follows:
Yield loss = (Present Plant Spacing SD -2.0) X (4 bu/acre per inch of SD improvement)
· This guide discusses how to measure stand variability and develops criteria for determining if recalibration of planter meters is needed.
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C. Estimating Harvest Loss
Gregg Carlson, and David Clay
Executive Summary
Most farmers and agronomists feel a sense of pride and accomplishment while watching a combine harvesting a grain field. Would any one of these farmers or his agronomist feel the same pride of accomplishment if he knew that his combine was not setup/adjusted properly and was losing 3 to 5 bu/acre? Ensuring that a combine is setup/adjusted properly is both a science and an art. It requires an attention to detail and constant checking. The efficiency of a combine can be checked by estimating the amount of grain left on the ground behind the combine. The objective of this paper is to ensure that every farmer/agronomist is able to quickly and easily make an estimate of harvest loss on the ground behind a combine.
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D. On-Farm Test Trials
Gregg Carlson
On farm research is one way that a farmer can test to see if there are options out there that will improve his bottom line. Just as the most progressive managers in American Industry devote some of their profits to Research and Development (R&D), so it is important for farmers to do likewise. Industry management performs R&D because they believe that they will receive a good economic return for their investment. Farm managers should have the same expectation.
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E. A Field as a Profit Center
(An agronomist's simplified approach)
Gregg Carlson
Executive Summary
Sound agronomic management decisions are becoming increasingly important in determining success - survival of a farming business. Informal surveys conducted at extension meetings indicate that very few farm managers conduct profitability analysis of their fields. Conducting whole field profit center analysis will improve management decisions. Discussed in this guide is a most simple, cost effective (time and money), and understandable method that will enable most producers to analyze the profitability of a field. An Excel® spreadsheet is used as the profit center accounting analysis engine for the field cost accounting analysis. The Field Profit Center accounting method demonstrated in this guideline is a simplification of more complex approaches. As a farm manager becomes more familiar with profit center analysis, more complex accounting approaches may be more appropriate. |
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F. Yield Mapping
Gregg Carlson and David Clay
Why map? Mapping if used properly, provides the producer with a greater understanding of a given field. Our experience is that studying maps transforms producers from tractor drivers to "Why's Guys". What is a Why's Guy? He is a person that constantly asks the question WHY????. He is a person that manages information rather assets. To understand the value of information management, one must understand the perverse economics of information management.
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G. Plant Population
By Paul Gaspar, Gregg Carlson and David Clay
Summary
Many agronomists and producers conduct experiments that test the impact of rates of fertilizer, population, or pesticide on yield. To analyze this data we need to conduct a yield response analysis. Agronomists and or producers that understand yield response and how to use yield response to determine the point of optimum economic return will be able to make better management decisions. The point of optimum economic return is determined by:
1. Conducting a yield response experiment
2. Converting the yield response data to a functional relationship,
outputcorn yield = f(input the plant population)
3. Using calculus to determine where the change in the value of the input equals the change in the value of the output.
In site-specific farming, scientists have found that yield response relationships may change spatially and temporally.
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