Month: February 2019

Increase Yield Monitor Data Accuracy and Reduce Time Involved in Data Cleaning

Sheryl Swink1, Tulsi Kharel1, Dilip Kharel1, Angel Maresma1, Erick Haas2, Ron Porter2, Karl Czymmek1,2, and Quirine Ketterings1
1Cornell Nutrient Management Spear Program, 2Cazenovia Equipment Company, 3PRODAIRY

Introduction

Reliable yield maps allow farmers and farm consultants to analyze yields per field, within fields, across fields and across years. Yield maps can be used to develop yield stability zones, or to identify reason(s) for low/high yielding areas by overlaying them with other geospatially tagged data such as elevation maps, soil series maps, etc. For reliable data, pre-harvest calibration of yield monitors and sensors should be followed up by careful operation in the field and proper post-harvest data cleaning in the office (Figure 1). This article presents best practices (pre-harvest, in-field, and post-harvest) that minimize yield monitor data errors and noise, reduce loss of data, and speed up data cleaning.

Pre-Harvest

  1. Field naming. Develop a simple and consistent set of field IDs or names for each farm. Make sure all operators know and use the correct field identification. Using numbers eliminates spelling errors. Inconsistency in a field’s name from year to year results in extra, time consuming, post-harvest data clean-up.
  2. Field boundaries. Establish and load geo-spatially fixed/frozen field boundary files into the Yield Monitor prior to harvesting. This will assist in maintaining the accuracy of field IDs. Preloading fixed field boundaries facilitates assignment of harvest data to the correct fields as the harvester moves from field to field. Follow the procedures in your Yield Monitor manual to load boundary files before harvest begins.
Figure 1: Valuable data can be obtained when yield monitors are calibrated and yield data are properly cleaned. For instructions on corn silage and grain yield monitor data cleaning, see: http://nmsp.cals.cornell.edu/publications/extension/ProtocolYieldMonitorDataProcessing2_8_2018.pdf.

In-Field

  1. Calibrate. Calibration using accurate scale weights or a grain cart with load sensors will increase accuracy. When calibrating, harvest as you would normally do in average crop areas in the field (include variability in the field, not just the best part). Re-calibrate the yield monitor often – for each crop or even variety that is being harvested, and for significant changes in crop conditions (very dry to very wet). Check and zero the mass flow sensor every morning so that the sensor identifies crop flow accurately. Clean the lens of the moisture sensor and inspect for damage daily.
  2. Field name/ID. Check to be sure correct field name/ID is entered or displayed before harvester enters a new field. Avoid inventing field names “on the fly.” Carefully check spelling if manually entering a field ID while harvesting. Misspelled or variations in field names from season to season make it difficult to match field data files across years for yield comparisons and within-field variability analysis. Proper field naming will ensure that yield data are assigned to correct field files.
  3. Harvest speed. Maintain a steady harvest speed within the calibration range for your system. Yield data recorded outside of the calibration range will be less accurate (irregular and/or very slow or high velocities over parts of the field result in yield calculations errors).
  4. Header height. Be sure the monitor logs a start and stop for each directional pass across the field to ensure data and yield area are logged properly. In most cases, the operator must lift the header beyond a set height to trigger the “stop logging” signal when exiting a pass or turning in the field. For some equipment, material flow can also be used to log the end of passes when the header is not raised for turning or for driving in the field without harvesting. Correctly logged field passes expedite trimming of unrepresentative start and end pass data points (ramping effect) during the cleaning process and proper shifting of data when correcting for flow and/or moisture delays relative to GPS location.
  5. Swath width. Be sure the recorded swath width is the actual width harvested. If swath width is not recorded properly, the harvested area calculated is wrong and so is the yield value. If the GPS system of the yield monitor has a large positional error (e.g. WAAS), turn off the auto swath adjustment and manually enter the default swath/chopper width. When harvesting less than the default chopper width without auto-swath, manually adjust swath width of the pass in the yield monitor to avoid erroneous yield calculations.
  6. Short rows. For long, narrow fields, plant and harvest rows the length of the field rather than the width if practical and consistent with soil conservation and other farm objectives. Short harvest passes distort yield data due to ramping velocity and flow impacts at the beginning and end of a pass, leaving few or no accurate data points in very short passes.
  7. Multiple combines/choppers in the field. If using more than one combine or chopper on a field, harvest a discrete section of the field with each one rather than mixing their passes across the whole field. Differences between operators, equipment and sensors result in different flow and moisture delays. These factors, if interlaced across the field, make it difficult to properly clean data.

Post-Harvest

Do not risk losing the season’s data by just leaving it on your monitor or relying on the cloud to save it. Download the raw yield monitor data files periodically during the season. The data cleaning protocol requires raw data to be transferred into Ag Leader format. Save the original files, backing them up on thumb drives and on your computer.

In Summary

Reliable data are essential for making the right decisions in field management. Mitigating errors at the source reduces the amount of data loss when filtering out noise during the post-harvest data cleaning process. The accuracy of yield data depends not only on proper calibration of yield monitoring equipment prior to and during harvest, but also on operation in the field and post-harvest data cleaning. Data become more reliable and the data cleaning process can be accelerated with implementation of the pre-harvest, in-field, and post-harvest practices described in this article.

Acknowledgements

This work was co-sponsored by the United States Department of Agriculture, National Institute of Food and Agriculture, Agriculture and Food Research Initiative Bioenergy, Natural Resources and Environment program, grants from the Northern New York Agricultural Development Program (NNYADP), New York Farm Viability Institute, New York Corn Growers Association, and Federal Formula Funds. For questions about these results, contact Quirine M. Ketterings at 607-255-3061 or qmk2@cornell.edu, and/or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/.

Nitrogen Management of Brown Midrib Forage Sorghum in New York

Sarah E. Lyonsa, Quirine M. Ketteringsa, Greg Godwina, Debbie J. Cherneyb, Jerome H. Cherneyc, John J. Meisingerd, and Tom F. Kilcere

a Nutrient Management Spear Program, Department of Animal Science, Cornell University, Ithaca, NY, b Department of Animal Science, Cornell University, Ithaca, NY, c Soil and Crop Sciences Section of the School of Integrative Plant Science, Cornell University, Ithaca, NY, d USDA-ARS Beltsville Agricultural Research Center, Beltsville, MD, and e Advanced Agricultural Systems, LLC, Kinderhook, NY

Introduction

Forage sorghum is a drought and heat tolerant warm-season grass that can be used for silage on dairy farms. It can be a good alternative to corn silage in New York particularly during drought years or in the case of delayed planting in the spring. Forage sorghum requires soil temperatures of at least 60°F for planting, which normally occurs in early June in New York. Forage sorghum could also be a good fit for double cropping rotations because its later planting date gives time for an early May harvest of a forage winter cereal. Between 2013 and 2017, we conducted 13 N-rate trials across three regions of New York to evaluate nitrogen (N) needs for a brown midrib (BMR) forage sorghum variety (Alta Seeds AF7102).

Trial Set-Up

The trials were planted between early June and early July in central New York (eight trials) and northern New York (five trials). Of the northern New York trials, three were on commercial farms. The other trials were on Cornell research farms. Two of the three trials on commercial farms were conducted on fields with recent manure or legume histories. For eleven of the trials, sorghum was planted at a 1-inch seeding depth and 15-inch row spacing (15 lbs/acre seeding rate). The remaining two trials were planted either with a 30-inch or 7.5-inch row spacing. Five N-rates as Agrotain®-treated urea (Koch Agronomic Services, LLC, Wichita, KS) were broadcasted at planting (0, 50, 100, 150, and 200 lbs N/acre) with two additional N rates (250 and 300 lbs N/acre) for one of the central New York locations. The forage sorghum was harvested at the soft dough stage, which occurred between September 20 and October 14. Harvest was done using a 4-inch cutting height and dry matter (DM) yield was measured. This allowed for determination of the most economic rate of N (MERN), the N use efficiency (NUE), and the apparent N recovery (ANR). The NUE and ANR are measures of N efficiency. The NUE is the amount of N taken up in relation to yield, and is calculated by subtracting the yield when no N was applied in the spring from the yield when N was applied, and dividing that value by the N rate applied (NUE [lbs DM/lbs N] = [Triticale yieldN rate – Triticale yield­0 N]/N rate). A higher NUE means that more of the N that was applied was taken up by the sorghum. The ANR is the amount of fertilizer N recovered, calculated by subtracting the N in the forage when no N was applied from the N in the forage when N was applied, and dividing that value by the N rate applied (ANR [%] = [Forage N of Nrate – Forage N of N0]/N rate).

Results

The crop yield response to N could be separated into three yield response groups: (1) no response to N addition (MERN = 0; two trials), (2) no yield plateau (MERN > 200 kg N ha-1; four trials), and (3) a yield plateau between the lowest and highest N rates (seven trials) (Figure 1). The two trials on fields at commercial farms with a recent manure or legume history did not respond to N addition (group 1 trials, panel A). The trial in group 1 with the lowest yield (5.3 tons DM/ac) was planted with a 30-inch row spacing, which resulted in weed issues that likely impacted crop performance. Trials in group 2 (panel B) were either very responsive to N addition or had N uptake limitations, most likely reflecting weather or soil drainage issues. The trials in group 3 (panel C) had MERNs ranging from 134 to 234 lbs N/acre, averaging 181 lbs N/acre. Yields at the MERN for group 3 trials ranged from 6.7 to 10.4 tons DM/acre and averaged 8.9 tons DM/acre. On average, for responsive sites (so excluding group 1 trials), forage sorghum required approximately 20 lbs N/acre per ton DM. On average, for each ton of DM, 25 lbs of N was taken up by the sorghum. For group 3, higher N rates led to lower ANR and NUE (Figure 2). For these trials, NUE at the MERN averaged 56 lbs DM/lbs N and ANR at the MERN averaged 83%.

 

Figure 1: Impact of N application on forage sorghum yield for 13 trials from 2013 to 2017. Sorghum was harvested at the soft dough stage. Two trials did not respond to N (A), four trials did not have a yield plateau (B), and seven trials had a yield plateau between the lowest and highest N rates (C). Differences are likely due to sites native N supply, weather conditions, agronomic practices, and/or soil properties (see text for further details). Different symbols represent different sites within each group.
Figure 2: Forage sorghum nitrogen use efficiency (NUE, A) and apparent N recovery (ANR, B) as impacted by N application rate for seven trials with a most economic rate of N between the highest and lowest N rates. Different shapes represent different trials within each group.

Conclusions and Implications

Forage sorghum can be a good alternative to corn silage in years of drought, delayed corn planting, or as part of a double crop rotation with forage winter cereals. The BMR forage sorghum in this study, grown on N-limited sites, needed around 180 lbs N/acre, or around 20 lbs N per ton of DM, and yielded between 7 and 10 tons DM per acre. Fields with recent manure or legume histories supplied sufficient N, resulting in no crop response to additional N for the forage sorghum. Applying N beyond the N needs of the crop will result in reduced N use efficiencies. In addition, stands with row spacing greater than the recommended 15 inches may result in weed or other stand issues that could impact performance.

Acknowledgements

This work was supported by Federal Formula Funds, and grants from the Northern New York Agricultural Development Program (NNYADP), New York Farm Viability Institute (NYFVI), and Northeast Sustainable Agriculture Research and Education (NESARE). For questions about these results, contact Quirine M. Ketterings at 607-255-3061 or qmk2@cornell.edu, and/or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/.

Avipel Shield Seed Treatment Repels Birds and Improves Corn Establishment

Ken Wise & Jaime Cummings, NYS IPM, Cornell University

Many species of birds, including crows, ravens, black birds, starlings, grackles, Canada geese and wild turkeys, are a pest problem annually for corn growers in several areas in New York State. Many growers have issues with birds picking corn seed and seedlings out of the ground after planting.

Photos by Joe Lawrence (PRO-DAIRY, Cornell University)

Birds can greatly reduce corn plant populations in fields. Many farmers indicate that they do not achieve high yields in fields with high bird pressure. Bird damage is not easily predictable.  But small fields surrounded by roosting areas with soils that are compacted or gravelly, and where seed is planted shallow tend to be most susceptible. However, damage can be observed in any corn field where a random flock of birds decides to feast. Many farmers have this problem annually, and struggled to find effective options to keep birds out of the fields.

A biological seed treatment, called Avipel Shield, developed by Arkion Life Sciences, is marketed to repel birds from feeding on newly planted corn seed and seedlings. The active ingredient is “anthraquinone”, which is a plant extract found in aloe, rhubarb, buckthorn and more. The corn seed is coated with Avipel Shield, which is also compatible with other conventional seed treatments.  As it states on the product’s website, “Avipel Shield (AQ) creates a powerful negative intestinal reaction in all birds”. This product does not harm the birds, but causes them to forage elsewhere. The product can come pretreated on seed, or the farmer can apply it themselves.

Corn growers in NY were interested to know if this product really worked. Therefore, NYS IPM and CCE collaborators around the state conducted 3 years of research to determine the efficacy of this product for deterring birds from feeding on newly planted corn fields.

Methods and Procedures:

We worked cooperatively with nine CCE educators/specialists who organized 11 farms in eight counties (Schenectady, Delaware, Jefferson, Ulster, Green, Lewis, Oneida and Franklin) to implement this on-farm research project. Trials were established in fields that traditionally had a history of excessive bird damage to newly seeded field corn.  Each trial involved a split-field design on 5 acres. Half of each trial (2.5 acres) was treated with Avipel Shield and the other half was not. A 97-day, multi-purpose triple-stacked hybrid was selected with a typical insecticide and fungicide seed treatment package from Dairyland (HiDF 3197RA) in order to minimize other possible variables from interfering with the research. Any remaining acreage of each field was planted to a hybrid of the farmer’s choice. Data was collected at each trial from each treatment at the V3 growth stage from two random samples in four quadrants of each treatment area. Plant populations were measured in each of the quadrants in 100 ft lengths of two consecutive rows. Observations on crop damage from birds were recorded at this time. Yields were recorded, when possible, for both silage and grain trials. For silage trials, scales and wagons/trucks were used to measure the wet plant weight of the entire treatment area (2.5 acres), or were hand harvested at five random locations in each treatment block, cut a 20’ row length at 10” above the soil surface. For grain trials, yield monitors were used to determine bushels/acre.

Results:

The results of the five replicated trials in 2016 showed that the seed treatment significantly reduced feeding by birds. On average, the plant population in the Avipel treated plots was 30,237 plants/acre, compared to 27,604 plants/acre in the non-treated plots, resulting in 2,632 more plants/acre in the Avipel treated plots. In 2017, there were 16 replicated trials, and the Avipel treatment resulted in significantly higher plant populations overall when compared to the non-treated control, with an average of 612 more plants per acre.  In 2018, there were 20 replicated trials. Once again, the Avipel treatment resulted in significantly higher plant populations overall when compared to the non-treated control, with an average of 962 more plants per acre. With plant population data pooled from all three years of the study, the difference between the Avipel treatment and the control was highly significant (Figure 1). Despite the significant increase in plant populations in the Avipel treated plots, there was no significant difference in yield between the treatments. However, many factors account for end of season yields in field corn, including weather and other environmental factors.

Figure 1: Combined overall plant populations of the Avipel treated and non-treated seed.

Impacts and Observations:

In this study, crows were the main pest observed in the fields, but there were also turkeys, seagulls and red winged black birds observed. It is thought that the birds learn the effect of the product, and likely do not return to those fields in subsequent years, though this was not specifically measured in this study. The main impact of this research revealed that Avipel Shield helps maintain plant populations, especially in fields with high bird pressure. But, birds, like crows, are complicated in how they select where they want to roost and feed from year to year, making it difficult to predict bird damage.

One observation from this study is that there may have been an effect within the same field where Avipel-treated seed is planted next to the non-treated seed. The birds may have left and avoided the entire field after experiencing the Avipel, rather than seeking to feed on the non-treated half of the field. A second observation is that once the birds learned the taste of Avipel in certain fields, they did not return, and many of the fields used in this study were planted to the same trial each year. This may explain the low bird pressure in some fields.

Avipel Shield has since been registered for use in New York, and some of the growers involved with this project have decided to treat all of their corn with Avipel based on the results of participating in these trials.

This research was made possible with funding from the NYS Corn Growers Association and the NYS Farm Viability Institute, and with extensive assistance from CCE collaborators Aaron Gabriel, Kevin Ganoe, Jeff Miller, Mike Hunter, Dr. Kitty O’Neil, Joe Lawrence, Paul Cerosaletti, Dale Dewing and Dr. Paul Curtis.