July/August 2000
Volume 2, Issue 4
Missouri Precision
Agriculture Center
257 Agricultural Engineering Building
University of Missouri
Columbia, MO 65211
Phone: (573) 882-1138
Fax: (573) 882-1138
E-mail: mpac@missouri.edu
Internet: http://fse.missouri.edu/mpac
MPAC is a partnership
of:
MU College of Agriculture, Food and Natural Resources
University Outreach and Extension
USDA Agricultural Research Service
-- Cropping Systems and Water Research Unit
MPAC is supported in part by Outreach Development Funds, University Outreach and Extension
In This Issue
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Rate of Precision Ag Adoption Lags Behind Innovation
While researchers presented their latest findings at the 5th International Precision Ag Conference in Minneapolis, July 16-19, 2000, suppliers talked about the slow adoption of precision agriculture technologies by producers. Reasons abound for the lack of adoption but the main ones are the dismal prices for commodities, technologies that are too complicated, and a learning curve that is too steep. Producers also say that they think precision agriculture suppliers are trying to control them. There is too much "wow" and not enough payoff.
This trend is not unique to the United States. Dr. John Stafford of Silsoe Solutions, United Kingdom, reported that in Europe the rate of precision agriculture adoption is also slow. Only 1,250 farmers in Europe currently use yield maps. In the United Kingdom just 500-600 farmers out of 25,000 actively use precision ag technologies.
But slow adoption doesn’t mean no adoption. Progressive farmers are learning that information about their farms that is properly developed and maintained is an asset they can literally take to the bank when they need to borrow or when it’s time to sell.
On a producer panel at the conference, a corn and soybean farmer from Minnesota cited a savings of $80,000 he directly attributes to using precision agriculture. Three other farmers who are also enthusiastic supporters of site-specific farming joined him on the panel. Although many of the benefits of precision ag aren’t as easily attributed to the bottom line, these producers believe that precision agriculture yields better information that can lead to reduced input costs and increased strength in marketing their crops and farms. One presenter even suggested that producers should spin off their data acquisition and management activities into separate companies in order to capitalize their asset value.
Producer to producer recruitment may be the key to increased adoption, but the panel said that for now their non-precision ag using neighbors see new technologies as a luxury they cannot afford.
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Are You Calibrating Your Yield Monitor Correctly?
Harvest season is in full swing, and that means it is time to check the calibration of your yield monitor. While this is often a dreaded task, it is an essential one. To get the most accurate information from your yield monitor it has to be calibrated correctly. It may not be possible to calibrate it perfectly, with zero- percent error, but you can minimize as many errors as possible. One step often overlooked in the calibration procedure is collecting calibration loads over a full range of operating conditions.
At the 2000 American Society of Agricultural Engineers Annual International Meeting held July 9-12 in Milwaukee, Wisconsin, Paul Jasa, University of Nebraska extension agricultural engineer, reported about a study he completed in the fall of 1999 that looked at the effects of low flow rates on yield monitor accuracy. He conducted the study with Bobby Grisso, also an University of Nebraska extension engineer, and in cooperation with Successful Farming magazine.
The study involved 13 producers from eastern Nebraska. Eight of the producers used either an AgLeader or a Case IH AFS monitor while the other five used a John Deere GreenStar monitor.
Before participating in the study, each of the cooperators calibrated their yield monitors, according to their own method. Several had calibrated properly using the operator’s manual while others either calibrated improperly or biased their calibration by inputting additional calibration loads without using loads across a full range of operating conditions.
A weigh wagon was used to weigh each load during the evaluation. All of the cooperators were harvesting corn with field average yields ranging from 150 to 220 bushels/acre. Each producer harvested one load at full width and at his usual harvest speed. Though not full combine capacity, this was assumed to be "full flow". The combine was then operated at two reduced loading rates, one at about two-thirds flow by harvesting 4 rows of 6 (or 5 of 8) and the other at about one-third flow by harvesting 2 rows of 6 (or 3 of 8). Throughout, the combine was operated at the same constant speed as during a normal harvest.
Results from the Study
At first glance, the GreenStar monitors seemed to be better than the AgLeader/AFS monitors, especially at reduced flow rates (Figure 1). The GreenStar monitor uses only one calibration load, usually at full flow, and assumes that reduced flows can be measured using a linear response from the flow sensor. If the AgLeader/AFS monitors are calibrated using only full flow calibration loads, the percentage of errors that occur at reduced flow increases (Figure 2), which indicates that the grain flow sensors on these models do not react in a linear fashion. Thus the GreenStar appears better for a simple, fixed, full flow calibration. However, when the AgLeader/AFS monitors are calibrated using several loads at varying flow rates as recommended by the monitor operator’s manual, their accuracy is greatly improved (Figure 2).
Figure 1
Figure 2
Conclusions of the Study
Calibration at various grain flow rates is important for accurate measurement and is part of the recommended calibration procedure for AgLeader and Case IH AFS yield monitors. When calibrated properly, errors for the AgLeader/AFS yield monitors averaged 1.9 percent and 4.5 percent for two-thirds and one-third flows, respectively. If calibrated only with one flow rate (full flow), the errors averaged 9.8 and 15.9 percent, respectively. The errors for reduced flow rates for the GreenStar yield monitors, which use a single point calibration procedure (usually at full flow), averaged 5.4 percent and 5.3 percent for two-thirds and one-third flows, respectively. It should also be mentioned that you should check the calibration of a yield monitor periodically throughout the harvest season.
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Variable-Rate Corn Seeding -- Will It Work In Missouri?
The AGCO Corporation and the Missouri Precision Agriculture Center partnered in 1998 to study variable-rate corn seeding for 3 years. The objectives of the project were 1) to evaluate the yield benefits of variable-rate seeding and 2) to investigate the factor(s) that can help guide variable-rate seeding strategies. Research was conducted at four locations in central and northeast Missouri. Results from the previous two seasons have been very interesting.
An example from 1999 at the Columbia, MO location will be used in this article for the purpose of discussion. Results from the other sites in both 1998 and 1999 showed similar results. Although this particular site was under drought conditions most of the growing season, it did receive rainfall in July that contributed to yields that were better than other area farms.
By using a combine-mounted population sensor, it was shown that factors other than seeding rate have a strong influence on plant populations. Uneven germination, emergence, and plant survival rates can lead to complications when deciding whether to adopt a variable-rate seeding strategy unless there are consistent patterns from year to year. At the 1999 Columbia location, plant survival appeared to be influenced by soil properties measured by apparent soil electrical conductivity (soil ECa) (Figure 1).
Figure 1. Soil ECa
and Plant Survival
Results revealed that in most fields there is a relationship between the optimum at-harvest plant population and soil ECa because different optimum plant populations occurred at different soil Eca levels.
Figure 2 shows both the optimum at-harvest plant populations and the seeding rates required to reach these populations based upon 1999 results. Under different seed germination conditions these seeding rates could be different. Overall, there was little variation in the optimum plant densities required for maximum economic return. However, there were large differences in seeding rates. Comparison of Figure 2 with the map of plant survival in Figure 1 shows the strong influence of plant survival on optimum seeding rates.
Figure 2. Optimum
Plant Populations and Seeding Rates
Economic comparison of several seeding strategies shows a possible benefit to a variable-rate seeding strategy. Table 1 shows a comparison of a variable rate seeding strategy when the economic optimum seeding rate is known for a field compared to two uniform seeding rate strategies. This example uses the 5-year average corn price of $2.51/bu and seed corn cost of $116/bag.
Table 1. Comparison of
seeding strategies for Columbia 1999 example
In conclusion, results showed yield and economic benefits from using a variable rate seeding strategy on Missouri soils. Soil EC was a useful indicator of productivity and a potential guide for variable-rate seeding. However, the spatial variability of seed germination, emergence, and plant survival also play and important role in variable-rate seeding strategies.
Mace Bauer, a graduate student working on his Masters Degree in Agronomy, is responsible for the results discussed in this article.
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