Quirine Ketterings1, Karl Czymmek1,2, Sanjay Gami1, Mike Reuter3 Cornell University Nutrient Management Spear Program1, PRO-DAIRY2, and Dairy One3
Introduction The corn stalk nitrate test (CSNT) is an end-of-season evaluation tool for N management for 2nd or higher year corn fields that allows for identification of situations where more N was available during the growing season than the crop needed. Research shows that the crop had more N than needed when CSNT results exceed 2000 pm. Results can vary from year to year but where CSNT results exceed 3000 ppm for two or more years, it is highly likely that N management changes can be made without impacting yield.
Findings 2010-2018 The summary of CSNT results for the past ten years is shown in Table 1. For 2019, 33% of all tested fields had CSNT-N greater than 2000 ppm, while 24% were over 3000 ppm and 11% exceeded 5000 ppm. In contrast, 31% of the 2019 samples were low in CSNT-N. The percentage of samples testing excessive in CSNT-N was most correlated with the precipitation in May-June with droughts in those months translating to a greater percentage of fields testing excessive. As crop history, manure history, other N inputs, soil type, and growing conditions all impact CSNT results, conclusions about future N management should take into account the events of the growing season. This includes weed pressure, disease pressure, lack of moisture in the root zone in drought years, lack of oxygen in the root zone due to excessive rain, and other stress factors that can impact the N status of the crop.
Within-field spatial variability can be considerable in New York, requiring (1) high density sampling (equivalent of 1 stalk per acre at a minimum) for accurate assessment of whole fields, or (2) targeted sampling based on yield zones, elevations, or soil management units. The 2018 expansion of adaptive management options for nutrient management now includes targeted CSNT sampling as a result of findings that targeted sampling generates more meaningful information while reducing the time and labor investment into sampling. Two years of CSNT data are recommended before making any management changes unless CSNT’s exceed 5000 ppm (in which case one year of data is sufficient).
Figure 1: In drought years (determined in this analysis by May-June rainfall below 7.5 inches; which occurred in 2012, 2016, and 2018), more samples test excessive in CSNT-N while fewer test low or marginal.
Agronomy Factsheets #31: Corn Stalk Nitrate Test (CSNT); #63: Fine-Tuning Nitrogen Management for Corn; and #72: Taking a Corn Stalk Nitrate Test Sample after Corn Silage Harvest. http://nmsp.cals.cornell.edu/guidelines/factsheets.html.
Acknowledgments We thank the many farmers and farm consultants that sampled their fields for CSNT. 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/.
Jerry Cherney and Bill Cox, Soil and Crop Sciences Section, Cornell University
Research has been conducted on a range of corn silage management topics in NYS over the past few decades. This summary is based on research that has included multiple sites and/or multiple years. Issues can be divided into two basic categories: Concerns prior to planting and concerns after planting. First, we prepare for the season, and then we have a limited number of options to react to the specific weather conditions of each season.
Pre-season
Hybrid selection While there are a number of options regarding plant genetics, of primary concern is the selection of the correct maturity group for your area and for the particular season (Fig. 1). You can expect to increase yield by about one ton/acre for every 10 additional maturity group days. The greater the maturity group, the higher the moisture content will be on any given fall date. Keep in mind that moisture loss per day keeps shrinking through the fall period (Fig. 2), making it increasingly difficult to reach optimum moisture in late fall.
Fig. 1. Effect of Maturity group selection on moisture and yield of corn silage, assuming all are harvested on the same day.Fig. 2. Approximate moisture loss per day through the fall.
Maturity group selection is influenced by your planting date and by your normal first frost date. 1) Plant full season hybrids from late April (if soil is dry) to late May, 2) Adjust hybrid relative maturity to planting date – mid-season hybrid from late May-~June 10, or 3) Plant early-season hybrids after June 10.
Brown-midrib (BMR) corn hybrids continue to improve in comparison to other options, and are worth considering. BMR hybrids will yield about 90% of a normal hybrid, plus or minus a few percent. On the plus side, BMR hybrids can be up to 25% higher in fiber digestibility (NDFD) than normal hybrids. Dairy feeding trials have shown that a small increase in ration fiber digestibility produces a significant increase in milk production.
While there still may be some increased potential for lodging, current BMR hybrids are much improved over the original BMR offerings. However, one issue that remains is dry down, BMR hybrids tend to retain moisture significantly longer than normal hybrids in the same maturity group. Also, there are indications that BMR types are somewhat lower in starch digestibility than floury types.
Seeding rate Numerous seeding rate trials over several decades in NY produced relatively consistent results (Fig. 3). Soil type has some impact on suggested seeding rates. Deep, well-drained soils => 36,000; Moist silt loam soils => 34,000, and Droughty soils => 30,000. Seeding at a rate of 35,000 is now very common in NY.
Fig. 3. Yield as influenced by plant density.
Row spacing Trials comparing narrow rows (15”) with standard 30” rows have shown increased yield with narrow rows, if fertilized adequately with N (Fig. 4). Narrow rows have a more equidistant plant spacing, resulting in full sunlight being intercepted earlier in the season. The possible downside to narrow rows includes purchase of a new planter, and a row-independent harvesting head if not already on-hand. Any post-emergence applications will result in some wheel traffic damage, which can be minimized by large-width application equipment. Partial budget analysis, assuming the purchase of new equipment, shows a significant increase in returns when converting to narrow row corn.
Fig. 4. Narrow rows can increase yield, if adequately fertilized.
During the Season
Side-dress N There is an optimum amount of N for a given field that will maximize yield, excess N application beyond the optimum amount will have negative environmental consequences. There are several alternative methods for determining side-dress N application to corn, and also methods for evaluating the relative success of the chosen application rates.
Whatever method is used to determine N application rates, it is critical to have confidence in that process, as it must adhere to federal conservation practice standards, which are mandatory for concentrated animal feeding operations (CAFOs). The process for development of Land Grant University guidelines for fertility management and for evaluating environment risk is currently being formulated.
Harvest stubble height If excess silage yield is anticipated, corn may be harvested at a higher stubble height to increase silage quality. Yield decreases linearly and NDFD increases linearly, with increased harvest stubble height (Fig. 5). A corn plant is basically a low-quality fiber pole that holds a high-quality grain bin. Cutting the plant higher up loses some fiber (lower yield), but it concentrates the impact of the grain bin (higher quality). Conversely, cutting perennial grasses or legumes high to improve quality ends in failure, it only reduces yield.
Fig. 5. Relationship between harvest stubble height of corn silage and yield or quality.
If you are routinely cutting high to increase corn silage quality, consider switching to BMR hybrids. The typical yield loss with BMR is in the same range as the yield loss due to high stubble height. The increase in fiber digestibility with BMR, however, can be three times as great as the NDFD increase due to high stubble height.
Determining optimum moisture at harvest Harvesting at optimum moisture content is more important than hybrid genetics selection. Optimum moisture content for making silage is between 60 and 70%, although generally the ideal moisture content is in the upper 60’s. It is nearly impossible to estimate whole plant moisture content visually. There are several methods of estimating moisture content to optimize moisture at harvest.
It is very difficult to estimate whole plant moisture visually.
Growing Degree Days. On average, a 100-day hybrid will have 1200 GDD from planting to silking. Another 800 GDD is the average from silking to silage harvest. The current year’s local weather data can be used to determine the actual GDD up to the present time, and then long-term average weather data can be used to predict into the future. The shorter the prediction time period, the more accurate a GDD prediction will be.
Chop and measure moisture. A more accurate method of determining whole plant moisture is to use the harvester to chop a few hundred feet into the field and either dry a sample or use an on-board NIRS instrument to determine moisture content. While this can be very accurate, it only provides a measure of moisture for the current day. An estimate of future field drying is required (Fig. 2) to reach optimum moisture.
Cut a sample by hand. Another method is to walk through a portion of the field and cut a number of plants by hand, then chopping the plants and drying to determine moisture content. The number of plants required depends in part on the uniformity of the field. During the fall of 2019 we cut 40-50 individual plants/field out of a number of fields across central and northern NY. Since we measured individual plant moisture content, it was possible to determine the number of plants required to get a representative field moisture value. For a very uniform field, 5 plants are likely to provide a good field moisture estimate. Even with more variable fields (uneven emergence issues, etc.) 10 plants is likely to provide a reasonable estimate of field moisture.
NIRS on whole plant corn. Since estimating moisture content for harvest is critical, we are evaluating small hand-held NIR units to determine if it is possible to estimate standing whole plant corn moisture. One major issue with standing whole plants is that ear moisture changes at a very different rate than stover moisture. Ears begin drying down much earlier than the stalk, so estimates of standing plant moisture may need to include information on both ear and stover moisture status. A fast and accurate method of estimating field moisture would be very beneficial for the large acreage of corn silage in NY.
There are many important management decisions regarding corn silage that must be made prior to the start of the season. The relatively few management decisions that can be made during the growing season, particularly moisture content at harvest, can make the difference between profit and loss.
Karl J. Czymmek1,2, Quirine M. Ketterings2, Mart Ros2, Sebastian Cela2, Steve Crittenden2, Dale Gates3, Todd Walter4, Sara Latessa5, and Greg Albrecht6
1PRODAIRY, 2Nutrient Management Spear Program (NMSP), Department of Animal Science, Cornell University, 3United States Department of Agriculture Natural Resources Conservation Service (USDA-NRCS), 4Department of Biological and Environmental Engineering, Cornell University, 5New York State Department of Environmental Conservation (NYSDEC), 6New York State Department of Agriculture and Markets (NYSDAM)
After more than 15 years of field use, version 1 of the New York Phosphorus Index (NY-PI) has been updated. The new version (NY-PI 2.0) incorporates new science and does a better job of addressing P loss risk while still giving farm managers options for recycling manure nutrients. The process of updating the NY-PI was led by the NMSP at Cornell in partnership with NYSDAM, NYSDEC, and NRCS and in consultation with certified planners and farmers. Farms that are regulated as concentrated animal feeding operations (CAFOs) will need to start using the new NY-PI when the CAFO Permit is updated (current permits are due to be renewed in 2022). Farms that are in state or federal cost share programs will need to use the tool based on NRCS determination. Agency discussions are in progress to make sure the roll-out is as smooth as possible.
Figure 1: the new NY-PI has a transport × BMP approach.
Here is how it works: a farm field is rated based on an assessment of its runoff risk-related transport features, including those observed directly during a field visit and others from normal soil survey information (most of these factors are the same as those used in the old NY-PI). For example, being close to a stream or watercourse, poorly drained soil, or higher levels of soil erosion are some of the risk factors that can lead to a high transport score. For fields with a high transport score, manure and P fertilizer application practices can be selected to reduce the transport risk. These best/beneficial management practices (BMPs) cover a combination of changes in application timing (close to planting) and method (placing P below the soil surface), and more vegetation on the soil surface when P is applied. Thus, implementation of BMPs will reduce the final PI score. Field practices include setbacks, ground cover (sod or cover crops) or placing manure below the soil surface (injection or incorporation). Combined with information about soil test P levels, the final NY-PI score results in a management implication: if risk is classified as low or medium, manure may be used at N-based rates; if classified as high, manure rate is limited to expected P uptake by the crop, and if very high, no P from manure or fertilizer may be applied. This transport × BMP approach is shown in Figure 1.
Coefficients were set for the new NY-PI using a database of more than 33,000 New York farm fields supplied by certified nutrient management planners and a second dataset that included data for PI assessment and whole-farm nutrient P balance assessments for 18 New York AFO and CAFO farms. While some farm fields had to have manure diverted, in almost all situations, the NY-PI 2.0 provided a pathway for farms with an adequate land base to both reduce risk and apply the manure generated from their herd. The full NY-PI 2.0 can be seen in Table 1.
Jaime Cummings and Ken Wise, NYS Integrated Pest Management Program; Mike Hunter, Mike Stanyard, Aaron Gabriel and Kevin Ganoe, Cornell Cooperative Extension; Michael Dorgan, NYS Dept. of Agriculture and Markets
The soybean cyst nematode (SCN) is considered the number one pest of economic concern of soybeans nationally and globally, potentially causing 10-30% yield loss in the absence of above ground symptoms. In 2017, national estimates reported over 109 million bushels lost to this pest in the U.S. alone. Considering that this pest is confirmed in surrounding states and provinces, and given its potential to spread, statewide survey efforts have been underway since 2013 to determine the presence or absence of the soybean cyst nematode in NY. From 2013-2016, numerous fields in 17 counties were sampled and tested as part of a statewide soybean disease survey led by Cornell’s Field Crops Pathology program, funded by Northern NY Agricultural Development Program and NY Corn and Soybean Growers Association. In 2016, SCN was confirmed in one field in Cayuga County by Cornell’s USDA ARS Nematology lab, albeit at very low levels. Since then, survey efforts have continued, because it is widely assumed that SCN is much more prevalent in NY.
In 2019, the NYS Integrated Pest Management Program was commissioned by NYS Department of Ag and Markets to coordinate a Cooperative Agricultural Pest Survey (CAPS) in soybeans with Cornell Cooperative Extension specialists to maintain vigilance against potentially invasive species. For more information about the CAPS program and this survey effort, please refer to this article. As part of this survey, 25 soil samples were collected from fields in 16 counties across NYS and were submitted for testing at the SCN Diagnostics laboratory. Of those 25 samples, seven of them were positive for SCN in six different counties, confirming our suspicions that this pest is potentially widespread throughout soybean production areas in NY. This brings us to a total of seven counties in NY with at least one field positive for SCN. The counties identified with fields positive in 2019 include Columbia, Dutchess, Jefferson, Monroe, Tompkins and Wayne (Fig. 1).
Figure 1. Soybean cyst nematode survey efforts in NY since 2013. Counties colored in green had fields tested with negative results, and counties colored in red had one or more fields that tested positive. The first positive result was in Cayuga County in 216. In 2019, six more counties tested positive as a result of the soybean Cooperative Agricultural Pest Survey.
Thankfully, the egg counts in these positive samples were all below 500 eggs per cup of soil (250 cc of soil). Although that may sound like a lot, these are very low numbers compared to the 10,000-80,000 egg counts that some growers struggle with in other states. This means that we are in a good position to proactively manage for this pest before it gets out of hand and starts causing economic losses.
An integrated management approach will help NY soybean growers stay ahead of the soybean cyst nematode. This involves continued testing efforts to monitor your fields for SCN. Determining if you have the pest is the first step toward management. For detailed information and recommendations on how to collect samples for SCN testing and where to send those samples to, please refer to this article. If you get a positive result, keep records of your egg counts for each individual field. Implement the following tactics when managing for this pest:
SCN can be moved among fields on soil, whether it be via wind, water, equipment, or boots. Consider improving sanitation of equipment coming from fields with known SCN infestation to avoid spreading it to others.
Crop rotation is the number one tool for managing SCN. Rotating to a non-host crop, such as corn, small grains, alfalfa, forage grasses and mixes for one year can reduce the nematode population by up to 50%. Continuous soybean production in an infested field can increase nematode populations exponentially, since this pest can have up to three life cycles per season in NY.
Select and plant soybean varieties with resistance to SCN, and rotate those resistant varieties that you plant. The nematode quickly develops resistance to the resistant varieties when exposed to the same varieties over and over, in the same way that weeds develop resistance to over-used herbicides.
Consider nematicidal seed treatments if your SCN populations start causing economic damage (Fig. 2). Research has shown that these products are only cost-effective with high SCN population levels causing significant damage.
Keep testing. Continue to test fields that you get negative results from, and especially continue to test fields that you get positive results from. Keep track of your egg counts in each field to know how your populations are changing, as that may affect your management strategy. It is recommended that as long as egg counts remain below 30,000 eggs per cup of soil, crop rotation with SCN-resistant soybean varieties is the best approach.
Figure 2. Nematicidal seed treatments available for managing soybean cyst nematode.
Crop rotation is the most important tool, and we are lucky to have a number of non-host crops already in our rotations. But, SCN has a fairly wide host range, including a number of our common weeds and cover crops. Some of these weed and cover crop hosts include chickweed, some clovers, common mullein, henbit, pokeweed, vetch and purslane (Table 1). That’s just another thing to remember as you plan your crop rotations and weed management strategies.
Keep in mind that testing for SCN can be tricky, since it can be difficult to detect at low population densities, and populations can be quite variable within a field (Fig. 3). Focus your testing efforts on fields with unexplained lower yields, or fields with a history of Sudden Death Syndrome (SDS) or Brown Stem Rot. It is well known that there is a strong correlation between the presence of SCN and SDS. If you see patches of SDS in your field, that would be an ideal location to pull soil samples for testing for SCN. For more information on the relationship between SDS and SCN, please refer to this article.
Figure 3. Grid sampling reveals high variability in soybean cyst nematode population densities within a single field. (Image courtesy of Iowa State University)
For more information on this pest and recommendations, please visit the Soybean Cyst Nematode Coalition website. There you will find numerous resources explaining the resistance issues with this pest, how and where to test for it, management recommendations, and success stories. Expanded SCN testing efforts will commence in 2020, supported by the NY Corn and Soybean Growers Association. If you suspect SCN in your fields, contact your area Cornell Cooperative Extension specialist for assistance, they may be able to offer you free testing on suspect fields as part of the expanded testing efforts in 2020.
Jaime Cummings and Ken Wise (NYS Integrated Pest Management Program), Mike Hunter, Mike Stanyard, Aaron Gabriel and Kevin Ganoe (Cornell Cooperative Extension), and Michael Dorgan (NYSDAM)
Image courtesy of Purdue University CAPS website
Annual funding in the Plant Protection Act 7721 supports the Cooperative Agricultural Pest Survey (CAPS) pest detection program, led by the USDA Animal and Plant Health Inspection Service (APHIS), to safeguard against introductions of potentially harmful plant pests and diseases. These surveys ensure the early detection of potentially invasive species that could negatively impact U.S. agriculture and/or environmental resources. The NYS Department of Agriculture and Markets (NYSDAM) works with APHIS to prioritize the potentially invasive species to monitor in economically important commodities in NY each year. In 2019, NYSDAM partnered with the NYS Integrated Pest Management (IPM) program to coordinate a soybean CAPS survey to monitor for two potentially invasive moth species, as well as to expand monitoring of the soybean cyst nematode across New York soybean production areas.
The overarching goal of the CAPS program is to monitor for species that shouldn’t be here, and to confirm that they still aren’t in NY or even the U.S. These surveys are often the result of cooperation among state and federal employees, such as APHIS pest inspectors, NYSDAM inspectors and extension specialists. This ‘boots on the ground’ approach allows for broad coverage of the surveys across the state involving many individuals with agricultural and pest identification expertise.
Figure 1. Golden twin spot moth and looper larva. (photos by S. Hatch and P. Hampson, Bugwood.org)
For the 219 soybean CAPS survey, two moth species that are already problematic elsewhere in the world, but not known to exist in the U.S. were selected. The Golden Twin Spot moth (Chrysodeixis chalcites), which currently causes yield losses in Africa, Europe, the Middle East and Canada, has a larval stage known as a ‘looper’ which can cause significant damage to soybeans, tomato, cotton, tobacco, beans and potatoes (Fig. 1). Feeding by the loopers can result in defoliation, and they can also cause foliar damage due to rolling leaves with webbing for nests. The Silver Y moth (Autographa gamma), which is already a concern in many countries in Asia, Europe and Africa, also has a caterpillar larval state that can cause significant damage to soybeans and many other agronomically important crops, including beets, cabbage, hemp, peppers, sunflower, tomato, potato, wheat, corn and wheat (and many more) (Fig. 2). These caterpillars also defoliate and harm leaves through rolling and webbing. Given how potentially damaging an introduction of these pests could be to U.S. agriculture, it’s important that we are vigilant in our efforts to monitor for them and ensure they aren’t in NY.
Figure 2. Silver Y moth and caterpillar larva. (photos by P. Mazzei and J. Brambila, Bugwood.org)
In addition to monitoring for these two moth species, we also prioritized a pest that has very high potential to affect soybean yields in NY, and one that has thus far only been confirmed in one field in NYS. The soybean cyst nematode (SCN) is considered the number one pest of soybeans nationally and globally, causing an estimated 109 million bushels of yield loss in the U.S. in 2017. Extensive collaborative sampling for this pest from 2014-2017, supported by the NY Corn and Soybean Growers Association and Northern NY Agricultural Development Program, was coordinated by Cornell University and Cornell Cooperative Extension programs. Over the four years of the SCN survey, numerous fields in 17 counties were sampled, and one field in Cayuga County was identified as positive for SCN in 2016, albeit at very low levels (Fig. 3). Though it’s promising that SCN wasn’t identified widely across NY, we are fairly confident that it is very likely in many more than just one field in one county. Given the potential impact this pest could have on NY soybean (and dry bean) production, we decided to include this pest in the 2019 CAPS survey.
Figure 3. Soybean cyst nematode survey efforts in 17 counties in NY from 2014-2017, with one positive ID in Cayuga County in 2016, and information from the SCN Coalition on why you should test for SCN.
Six collaborators (Jaime Cummings and Ken Wise of NYS IPM, and Mike Stanyard, Mike Hunter, Aaron Gabriel and Kevin Ganoe of CCE) spent part of their typical summer soybean scouting efforts from western, to central, to eastern and northern New York setting up and checking pheromone traps intended to monitor for the Golden Twin Spot moth and Silver Y moth (Fig. 4). They communicated the importance of these surveys to cooperating farmers who agreed to host these traps in 25 fields across the state. Any suspicious moths caught in the traps are submitted to the Cornell Insect Diagnostic Clinic for thorough identification. Thus far, we have not caught any Silver Y or Golden Twin Spot moths. And that’s good news! As the growing season winds down, we will collect soil samples from the same 25 fields for SCN testing at the SCN Diagnostics laboratory.
Figure 4. Distribution of the 2019 soybean CAPS survey.
