C.O.R.N. Newsletter: 2014-24
Scout Corn for Western Bean Cutworm and Western Corn RootwormAuthor(s): Andy Michel
As we are entering tasseling and pollination for most of our corn, there are two insects that we should watch out for. However, both need to be scouted before tank mixing an insecticide with a fungicide spray gives a good return.
First, the amount of western bean cutworm adults in our traps is still on the rise, although we have probably reached peak flight (we won’t know for sure until the numbers go down). This is the time period when producers will want to scout for egg masses on the upper 2-3 leaves. Remember that economic threshold is only 5-8% of corn with egg masses. Although we have not yet had economic damage from WBC, it is still important to look for the presence of eggs before any applications should be made.
Second, growers may be seeing more western corn rootworm adults. For the past few years, we haven’t had to deal much with this pest, but we have received a fair amount of calls regarding leaf stripping and the potential for silk clipping. Both types of damage on corn are rare for Ohio. For leaf stripping, this is usually not economic, but could be an indication of population sizes the following year.
There are a few considerations before spraying to prevent silk clipping. First—silks must still be viable, in other words have not begun to turn brown; second—5 or more beetles per silk mass must be present; third—silks are being clipped to less than 1/2 inch. If all three of these conditions are met, control may be needed.
Corn Yield PredictionAuthor(s): Peter Thomison
Two procedures that are widely used for estimating corn grain yields prior to harvest are the YIELD COMPONENT METHOD (also referred to as the "slide rule" or corn yield calculator) and the EAR WEIGHT METHOD. Each method will often produce yield estimates that are within 20 bu/ac of actual yield. Such estimates can be helpful for general planning purposes.
THE YIELD COMPONENT METHOD was developed by the Agricultural Engineering Department at the University of Illinois. The principle advantage to this method is that it can be used as early as the milk stage of kernel development, a stage many Ohio corn fields have probably achieved. The yield component method involves use of a numerical constant for kernel weight which is figured into an equation in order to calculate grain yield. This numerical constant is sometimes referred to as a "fudge‑factor" since it is based on a predetermined average kernel weight. Since weight per kernel will vary depending on hybrid and environment, the yield component method should be used only to estimate relative grain yields, i.e. "ballpark" grain yields. When below normal rainfall occurs during grain fill (resulting in low kernel weights), the yield component method will OVERESTIMATE yields. In a year with good grain fill conditions (resulting in high kernel weights) the method will underestimate grain yields.
In the past, the YIELD COMPONENT METHOD equation used a "fudge factor" of 90 (as the average value for kernel weight, expressed as 90,000 kernels per 56 lb bushel), but kernel size has increased as hybrids have improved over the years. Dr. Bob Nielsen at Purdue University suggests that a "fudge factor" of 80 to 85 (85,000 kernels per 56 lb bushel) is a more realistic value to use in the yield estimation equation today. Moreover, given the exceptionally favorable growing conditions we have experienced in 2014, a fudge factor of 75 (75,000 kernels per 56 lb bushel) may be appropriate for some fields . For more on this checkhttp://www.agry.purdue.edu/ext/corn/news/timeless/YldEstMethod.html.
Step 1. Count the number of harvestable ears in a length of row equivalent to 1/1000th acre. For 30‑inch rows, this would be 17 ft. 5 in.
Step 2. On every fifth ear, count the number of kernel rows per ear and determine the average.
Step 3. On each of these ears count the number of kernels per row and determine the average. (Do not count kernels on either the butt or tip of the ear that are less than half the size of normal size kernels.)
Step 4. Yield (bushels per acre) equals (ear #) x (avg. row #) x (avg. kernel #) divided by 85.
Step 5. Repeat the procedure for at least four additional sites across the field. Keep in mind that uniformity of plant development affects the accuracy of the estimation technique.
The more variable crop development is across a field, the greater the number of samples that should be taken to estimate yield for the field.
Example: You are evaluating a field with 30‑inch rows. You counted 29 ears (per 17' 5" = row section). Sampling every fifth ear resulted in an average row number of 16 and an average number of kernels per row of 33. The estimated yield for that site in the field would be (29 x 16 x 33) divided by 85, which equals 180 bu/acre.
THE EAR WEIGHT METHOD can only be used after the grain is physiologically mature (black layer), which occurs at about 30‑35% grain moisture. Since this method is based on actual ear weight, it should be somewhat more accurate than the yield component method above. However, there still is a fudge factor in the formula to account for average shellout percentage.
Sample several sites in the field. At each site, measure off a length of row equal to 1/1000th acre. Count the number of harvestable ears in the 1/1000th acre. Weigh every fifth ear and calculate the average ear weight (pounds) for the site. Hand shell the same ears, mix the grain well, and determine an average percent grain moisture with a portable moisture tester.
Calculate estimated grain yield as follows:
Step A) Multiply ear number by average ear weight.
Step B) Multiply average grain moisture by 1.411.
Step C) Add 46.2 to the result from step B.
Step D) Divide the result from step A by the result from step C.
Step E) Multiply the result from step D by 1,000.
Example: You are evaluating a field with 30‑inch rows. You counted 24 ears (per 17 ft. 5 in. section). Sampling every fifth ear resulted in an average ear weight of 1/2 pound. The average grain moisture was 30 percent. Estimated yield would be [(24 x 0.5) / ((1.411 x 30) + 46.2)] x 1,000, which equals 135 bu/acre.
Because it can be used at a relatively early stage of kernel development, the Yield Component Method may be of greater assistance to farmers trying to make a decision about whether to harvest their corn for grain or silage.
Reference: Nielsen, RL. 2013. Estimating Corn Grain Yield Prior to Harvest. Corny News Network, Purdue University. http://www.agry.purdue.edu/ext/corn/news/timeless/YldEstMethod.html. (URL checked July 2014).
Our new Corn, Soybean, Wheat, and Alfalfa Field Guide can be ordered here: http://estore.osu-extension.org/productdetails.cfm?PC=2841.
Yield Forecasts for CornAuthor(s): Peter Thomison
According to the National Agricultural Statistics Service (http://www.nass.usda.gov/Statistics_by_State/Ohio/Publications/Crop_Prog...) for the week ending 7-27-14, 69% of the state’s corn was silking and 9% was in the dough stage. Questions are being asked about how this year’s weather has impacted expected yield to date, and how growing conditions during grain fill, the 8 to 9 weeks following silking, may affect final yields.
To help estimate yield and the impact of weather can impact those yields, OSU is collaborating with a team from the University of Nebraska and Robert B. Daugherty Water for Food Institute to use the Hybrid-Maize model (http://hybridmaize.unl.edu) to forecast potential corn yields across the Corn Belt. Other universities collaborating in this effort include Kansas, Iowa, Illinois and Wisconsin
The Hybrid-Maize model estimates yield based on current and historical weather parameters with the assumption that plants stands are uniform; flooding and hail did not occur; and that typical management of nutrients, insects, diseases, and weeds are not limiting (Licht, 2014). Yield estimates resulting from Hybrid-Maize become less variable as the season progresses because of less reliance on historical weather data. This helps in understanding how current season weather conditions affected corn growth up to the date of the simulation but also gives some projections of yield estimates for the remainder of the growing season.
The July 20 simulation indicate a high chance of above-average dryland yields in almost all simulated locations in the central and eastern Corn Belt (Iowa, Illinois, and Ohio). In the case of dryland corn, above-normal rainfall, coupled with low rates of daily water use due to low daytime temperature, are factors that have the largest contribution to above-average yield potential forecasts by Hybrid-Maize across the entire Corn Belt. Factors in 2014 that may result in lower yields than these forecasts even with optimal management include hail or flood damage as well as greater likelihood of foliar diseases. Also, given the large amount of rain in some areas, nitrogen leaching and/or denitrification may limit yields due to nitrogen deficiency if additional nitrogen was not applied to affected areas. Cooler than normal weather could increase the probability of an early killing frost at locations that were planted late, which would result in yields lower than currently forecast.
For more information concerning the July 20 simulation for the 25 Corn Belt locations considered, check the following “2014 Forecasted Corn Yields Based on July 20 Hybrid Maize Model Simulations” at http://cropwatch.unl.edu/archive/-/asset_publisher/VHeSpfv0Agju/content/2014-forecasted-corn-yields-based-on-hybrid-maize-model-simulations-as-of-july-20th.
In-season yield potential forecasts for the three Ohio test sites, Custar, S. Charleston, and Wooster, considered in this simulation are shown in Table 2.
Acknowledgements: The data presented here is part of larger yield forecasting project coordinated by Patricio Grassini, Haishun Yang, Roger Elmore and Kenneth Cassman from the Department of Agronomy and Horticulture, University of Nebraska-Lincolnand the Robert B. Dougherty Water for Food Institute.
Other sources: Licht, M. 2014. Corn Yield Predictions. Iowa State University Integrated Crop Management News. http://www.extension.iastate.edu/CropNews/2014/0725Licht.htm.
Downy Mildew and Bacterial Pustule on SoybeanAuthor(s): Anne Dorrance
During field surveys last week we found the usual culprits, Phytophthora stem rot, some brown spot and frogeye leafspot. Two finds, downy mildew and bacterial pustule, were found in several fields. Both are considered minor diseases as yield reductions have been minor or difficult to document. Reports from other states and some parts of Ohio, Downy mildew may be at higher incidence. I expect that some of this is due to the cool nights, misty weather we have had this summer.
Symptoms of downy mildew on the top of the leaves are yellow chlorotic spots, almost round in shape. If the humidity is high, on the underside of the leaf is a necrotic spot (very small) with white “fuzz”. This “fuzz” are the fruiting structures (sporangia) of the oomycete fungus Peronospora manshurica. If in doubt, place the leaf in a plastic bag with a moist (not sopping wet) paper towel and wait about 6 hours. The sporangia will form and that is the diagnostic feature of this pathogen.
Symptoms of bacterial pustule are dark brown, necrotic areas surrounded by yellow. On the underside of the leaf, inside these necrotic areas are raised bumps. Also on the underside of the leaf we can see watersoaking around the new lesions, very classic for symptoms for a bacterial disease. For bacterial blight, there is still the yellow halo and water soaking but the lesions are flat. To separate the symptoms of soybean rust from bacterial pustule, with high humidity, spores would be present in the pustule, and the necrotic area around the pustule tends to be much smaller.
Again, for both of these pathogens, downy mildew and bacterial diseases in general, there are no products that have demonstrated efficacy in field conditions. Enjoy the scouting there is lots to find this year.
PrecisionAg Big Data Conference: Managing Your Most Elusive Farm Asset is Set for August 25, 2014Author(s): Greg LaBarge, CPAg/CCA
Matt Darr, Agricultural and Biosystems Engineering, Iowa State University
Did you know that over two-thirds of every dollar spent in agriculture is spent on decisions focused on seed selection, fertility, and land access? Producers annually compile new information around input selections, farming practices, and risk management to implement an improved production plan each year. Recently, advances in machine data availability, improved climate modeling, and new technologies including high resolution crop imagery have enabled new industries around the concept of Big Data which is aimed to help producers better navigate their annual decision process and result in more on-farm productivity and profitability.
Iowa State University Extension and Outreach, in partnership with national precision ag leaders and Meister Media, will hold an Ag Big Data Conference on August 25, 2014 on the Iowa State University (ISU) campus. Big Data is a concept of data driven and value added decisions that has been growing in use across agriculture. As new Big Data products and services are available to producers, questions exist around data privacy, producer value, and best practices to engage with this new industry.
The agenda is packed with knowledge from both university leaders in the area of Big Data as well as producers who are actively using Big Data as part of their day-to-day on-farm decision making process:
· The morning will be kicked off by a message from Bill Northey, Secretary of Agriculture, state of Iowa, about the importance of this new industry in the Midwest.
· Dr. Matt Darr, ISU, will discuss the producer value opportunity of working with data. He will be joined by producers that will share their story of how they have leveraged data into value added decisions.
· Dr. Shannon Ferrell, Oklahoma State University, will be on-hand to share key details regarding data ownership and privacy, and help educate attendees on how to navigate these issues when engaging in data sharing and data management.
· After lunch Catherine Campbell will share how data is being infused into sourcing decisions with major food processors and retailers like General Mills and Walmart and what data producers should expect to provide in order to meet these future requirements.
· Dr. John Fulton, Auburn University, and Dr. Scott Shearer, The Ohio State University, will jointly lead a discussion on who the major players and partners are within the Big Data space. They will discuss pros and cons of different data services models and provide best management practices for producers to decide what type of data partner is best for their business.
· The day will wrap up with a panel discussion by experienced growers who are actively using Big Data in their decision process. They will share their success stories and help stimulate ideas for how producers integrated Big Data into farm management practice.
· All sessions will have planned time for Q&A so bring along your top Big Data questions and get them answered by leaders in this emerging field.
Additional information and registration can be found here:
This article was submitted by Dr. Scott Shearer and Greg LaBarge.
2014 Manure Science ReviewAuthor(s): , Glen Arnold, CCA
The 2014 Ohio Manure Science Review (MSR) will be held in Wayne County on August 14, hosted by Rupp Vue dairy farm near Sterling at 14636 Seville Rd. The MSR is an educational program designed for those involved in any aspect of manure handling, management, or utilization. The MSR consists of both classroom style presentations, including a farmer panel, and field demonstrations of equipment and other demonstration plots. Registration opens at 8:30 am and the program begins at 9:00 am. The day concludes out in the field at 3:30 pm.
This year’s MSR will focus on aspects of manure management related to dairy farms and handling poultry litter. Topics that will be covered in the morning program include Nutrient Management Practices at Rupp Vue Farm, Nutrient Variability of Liquid Manure in Storage, Economic Value of Manure, Planning and Managing Manure Storage (farmer panel), Growing the Application Window, the Effectiveness of Setbacks in Preventing Winter Nutrient Runoff, and Subsurface Band Application of Poultry Litter.
Field demonstrations in the afternoon will feature a new poultry litter applicator; a mobile solar unit; cover crops’ benefits to soils; calibration of solid manure spreaders; effects of manure application rates on yields; forages and extending manure application windows; and applicators for injecting liquid manure, side-dressing liquid manure, and dragline systems on corn.
Participants in the event are eligible for the following continuing education credits: ODA Certified Livestock Manager, 5.5 continuing education hours; Certified Crop Adviser, 3.0 Soil and Water Management continuing education units and 2.5 Nutrient Management CEUs; and Professional Engineer, 2.0 continuing professional development hours.
Pre-registration is requested. Early registration by August 6 is $25/person and registration after August 6 or the day of the event is $30/person. Morning coffee, juice, donuts, and lunch catered by Omahoma Bob’s BBQ is included in registration. In addition to the program, there will be sponsor exhibits and displays.
More information, including details on program topics and field demonstrations, registration forms, and flyers, are available on the Wayne County Extension web site at http://go.osu.edu/MSR2014 or contact the Wayne County Extension office at 330-264-8722.
Ohio Manure Science Review collaborators include OSU Extension, the Ohio Department of Agriculture, Ohio Federation of Soil and Water Conservation Districts, Ohio Department of Natural Resources’ Division of Soil and Water Resources, Natural Resources Conservation Service, U.S. Department of Agriculture’s Agricultural Research Service, and Cooper Farms.
Event sponsors include the Ohio Livestock Coalition, the Ohio Poultry Association, Case Farms, Gerber’s Poultry, Ag Credit, Hubner Seed, and Farm Credit Mid-America.
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