Vipan Kumar1, Lynn Sosnoskie2, Mike Hunter3, Mike Stanyard4
1School of Integrative Plant Sciences -Soil and Crop Sciences Section, Cornell University, Ithaca, NY 14853, 2School of Integrative Plant Sciences – Horticulture Section, Cornell AgriTech, Geneva, NY 14456, 3Cornell Cooperative Extension North County Regional Ag Team, 4Cornell Cooperative Extension Northwest New York Dairy, Livestock, and Field Crops Program
With recent rainfall events and a new flush of summer annual weeds, NY producers are busy applying postemergence herbicide applications in row crops. If you have planted dicamba-tolerant soybeans and are planning for postemergence applications of dicamba-containing products (Xtendimax, Engenia, or Tavium), the following points need to be considered.
Xtendimax, Engenia and Tavium are the only dicamba-containing products that are labelled in dicamba-tolerant soybeans (Roundup Ready 2 Xtend or XtendFlex soybeans).
Be sure of your trait technology! Do not confuse Xtend traits with Enlist traits. Enlist traits provide crop resistance to 2,4-D but not to dicamba.
As per the revised labels in 2021, the legal last day of postemergence applications of Xtendimax, Engenia and Tavium in dicamba-tolerant soybeans is June 30.
Only certified applicators with dicamba training are allowed to apply these products.
Spray records need to be created within 3 days of applications of these products and should be maintained for 2 years. (Note: In New York State all applications of restricted use pesticides must be maintained for at least three years)
An approved drift reduction agent (DRA) and volatility reduction agent (VRA) should be included.
Only approved nozzles and tank-mix partners should be used for these products.
Wind speed at boom height should range from 3 to 10 miles per hour at the time of application.
As per the labels, maximum ground speed of sprayer should not exceed 15 miles per hour and maximum boom height above target pest or crop canopy should not exceed 24 inches.
Survey surrounding fields ahead of dicamba applications for sensitive crops (e.g., grapes, fruit trees, snap beans, fruiting vegetables (e.g., tomatoes, peppers), soybeans without dicamba-tolerance trait technology, etc…).
DO NOT apply these products if sensitive crops are in a downwind field or a run-off producing rain event is in the forecast in the next 48 hours.
After determining no adjacent sensitive crops are downwind, maintain a 240-feet downwind buffer.
Stop spraying if winds change direction towards sensitive crops.
DO NOT apply dicamba products during temperature inversions. Only spray between one hour after sunrise and two hours before sunset.
Ensure the entire sprayer system is properly cleaned before and after dicamba applications are made.
Applicator should consult Bulletins Live Two website to make sure no endangered species will be affected by these dicamba applications.
Disclaimer: Brand names appearing in this publication are for product identification purposes only. Persons using such products assume responsibility for their use in accordance with current label directions of the manufacturer.
1Nutrient Management Spear Program, 2PRODAIRY, Cornell University, Ithaca, NY 14853, 3Cornell Cooperative Extension North Country Regional Ag Team, 4USDA-Natural Resources Conservation Service, NY
Introduction
Tillage practices can impact soil greenhouse gas (GHG) emissions, soil carbon (C) sequestration and overall soil health. Tools are available to estimate whole farm GHG inventories (N2O, CO2, and CH4 emissions), field-based emissions, and C sequestration or loss. These tools often require a user to classify tillage intensity. The Natural Resources Conservation Service (NRCS) GHG accounting system uses the Revised Universal Soil Loss Equation (RUSLE2) to calculate a Soil Tillage Intensity Rating (STIR) that can be used to classify the intensity of various tillage practices. This tool also classifies tillage intensity based on percent residue surface cover and soil disturbed. This article explains what a STIR factor is, describes how to determine percent surface residue cover, and categorize different tillage practices into tillage intensity classifications.
Soil Tillage Intensity Rating (STIR)
The STIR value of a field can range from 0-200 with high values for intense tillage (Table 1). The STIR is based on four components: tillage type, depth of operation, operational speed, and percent of soil surface area disturbed.
Tillage type: This describes how a tillage pass mixes soil and crop residue. Tillage disturbance operations can include inversion and some mixing of soil, mixing and some (limited) inversion, lifting and fracturing, mixing only, and soil compression.
Depth of operation: The depth to which soil disturbance and residue incorporation occur.
Operational speed of tillage: This is the recommended operating speed of each tillage operation. The forward speed of a tillage implement impacts soil disturbance and mixing; faster speeds result in more significant forces and broader disturbance.
Percent of soil surface area disturbed: The percentage of surface soil disturbed by the tillage pass.
Estimating Percent Residue Cover
Percent residue cover remaining on the surface following a tillage operation can be determined using the RUSLE2 equation or measured in the field using the line transect method. The line transect uses a line measuring tool (a rope or tape measure) that has 100 equally distributed and easily viewed marks (Figure 1). Typically, the measuring tool is 100 feet long with markings at 1-foot intervals or 50 feet long with marking at 6-inch intervals. To determine the percent residue surface cover of a field, stretch the tool diagonally across crop rows in a direction that is at least 45 degrees off the row direction and count the number of markings that have crop residue directly present beneath. Residue smaller than 1/8 inch in diameter should not be counted. The total count (markings with residue beneath them) is the percent residue cover for the field. This process should be repeated at least three times in different areas of the field and percentages should be averaged.
Fig. 1: A simple measuring tape can be used to easily determine percent residue surface cover of a field (Picture credit: https://www.sdsoilhealthcoalition.org/ soil-health-assessment-card/).
NRCS Tillage Intensity Classes
The NRCS tool groups tillage practices into six categories; intensive, reduced, mulch, ridge, strip, and no-till:
Intensive tillage is full width tillage that inverts soil with high disturbance. Common equipment includes a moldboard plow.
Reduced tillage occurs at full width without soil inversion, using a point chisel plow, field cultivator and/or tandem disk.
Mulch tillage a single pass across the field using tools such as a tandem disk followed by field or row cultivator or similar implement.
Ridge tillage creates soil ridges in the field that are rebuilt during cultivation by disturbing up to 1/3 of the row width. The soil is then undisturbed from harvest to planting.
Strip tillage leaves the soil between crop rows undisturbed (Figure 2). To create a seedbed, up to 1/3 of the row width is disturbed.
No-till operations plant crop seeds directly through residue of the previous crop using a no-till planter or drill.
Figure 2: Strip tillage leaves the soil between crop rows undisturbed.
Full vs. Reduced vs. No-Till
Some GHG footprint assessment tools categorize tillage practices differently, using three main categories; full, reduced, and no-till:
Full tillage contributes to significant soil disturbance, fully inverting the soil (as is done with moldboard plowing) and/or performing tillage operations frequently in the same year using tools like chisel plows or row cultivators.
Reduced tillage also disturbs the soil but does not fully invert the soil. Examples include onetime use of chisel plows, field cultivators, tandem disks, row cultivators, or strip-tillers.
No-till practices directly drill crop seed through the residue layer with little to no disturbance to the soil. Minimal disturbance occurs in the area where seeds are planted. Common operations use a no-till planter.
In Summary
Tools available to assess whole-farm and field-based GHG inventories and C sequestration or loss require the user to classify tillage intensity. Choosing the tillage description that best fits the producers’ management practices is essential for accurately assessing GHG emissions.
Julianna Lee1, Manuel Marcaida III1, Jodi Letham2, and Quirine Ketterings1 1Cornell University Nutrient Management Spear Program and 2Cornell Cooperative Extension Northwest New York Dairy, Livestock and Field Crops
Soybeans acres and yield
Soybeans are an important crop for New York with a total land base of 325,000 acres harvested in 2022. Average yields are reported each year by the United States Department of Agriculture, National Agricultural Statistics Service (USDA-NASS) in New York’s Agricultural Overview. Their records these past 14 years show a range in yield from a low of 41 bu/acre in 2016 to a high of 53 bu/acre in 2021, averaging 46.5 bu/acres at 87% dry matter. While state averages are reported yearly, there is little documentation of yield per soil type. In the past three years, we have worked with soybean growers to collect soybean yield monitor data and determine the first soil type specific yield records. This project was started because knowing soil- and field-specific yield potentials for soybean can help farmers make better informed crop management and resource allocation decisions, including fertilizer and manure use decisions.
What’s Included in the Soybean Database so Far?
Whole-farm soybean yield monitor data, shared by farmers in central and western New York, were cleaned using Yield Editor prior to overlaying of soil types as classified by the Web Soil Survey. To generate soil type-specific yield distributions, analyses were limited to soil types with yield data for at least: (1) 3 acres of total area within an individual field; (2) 150 acres total across all fields and farms; and (3) in three different farms. These qualifiers resulted in a database (to date) of 9,653 acres of yield data collected across 13 farms in New York with information for 14 soil types. Of the total acres, about 90% was from 2017-2021 (with data going back to 2009). Density plots were generated to determine yield distributions per soil type. Varietal differences were not considered in the analysis.
What Did we Find?
The calculated area weighted average yield for New York was 56 bu/acre with a standard deviation of 14 bu/acre. This average is considerably higher than the 46.5 bu/acre reported in New York’s Agricultural Overview for the same time period. Soil type specific means ranged from 40 bu/acre (Lakemont) to 66 bu/acre (Conesus) but yield distributions showed large ranges (from low to high) for all 14 soil types (Figure 1). For some soil types, the density plots showed multiple peaks which may reflect farm-to-farm, field-to-field, variety, management, as well as weather differences. Except for 2014, the mean yield based on farmer data exceeded state averages reported in New York’s Agricultural Overview.
What’s Next?
Knowing soil- and field-specific yield potentials for soybean can help a farmer make crop management and resource allocation decisions, including use and rate of fertilizer and manure. With more farmers sharing their soybean yield data, this summary will become more representative for the state and additional soil types for which too few acres of yield data are available currently, may be included in future years. We invite New York soybean growers to share they yield data with us to build on this data summary. Farmers who share data obtain their farm-specific yield report. This includes an annual update that summarized their cleaned yield data, a multiyear report once three years are collected, and yield stability-based zone maps for all fields with at least three years of soybean yield data.
Figure 1. Soybean yield density plots based on the different soil types from the cleaned soybean yield monitor database from 2009 to 2021.
Acknowledgments
We thank the farmers who shared their yield monitor data with us. This project is sponsored by the New York Corn and Soybean Association and USDA-NIFA Federal Formula Funds. We thank Abraham Hauser and Anika Kolanu for help with cleaning and processing yield monitor data. For questions about this project, 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/.
Juan Carlos Ramos Tanchez1, Kirsten Workman1,2, Allen Wilder3, Janice Degni4, and Quirine Ketterings1 Cornell University Nutrient Management Spear Program1, PRO-DAIRY2, Miner Agricultural Research Institute3, and Cornell Cooperative Extension4
Introduction
Manure is a tremendously valuable nutrient source. When used appropriately (right rate, right timing, right placement method), it can help build soil organic matter, enhance nutrient cycling, and improve soil health and climate resilience. Sound use of manure nutrients can decrease the need for synthetic fertilizer, thus, lowering whole farm nutrient mass balances and contributing to reduced environmental footprints.
Current guidance for nitrogen (N) credits from manure recognize that N availability depends on the solids content of the manure (lower first year credits for manure with >18% solids than for liquid manure). It also recognizes that the amount of N in manure is affected by how it is collected, stored, treated (solid liquid separated, composted, digested, etc.), and land-applied (timing and method). Higher shares of manure N will be available to crops when manure is applied closer to when crops need it and if manure is injected or incorporated into the soil right after it is applied versus left on the surface.
In the past two decades since manure crediting systems were developed, many different manure treatments technologies have been implemented on farms and re-evaluation of the N crediting system for manure is needed. Furthermore, recent studies have shown that manure can increase yield beyond what could be obtained with N fertilizer only. Thanks to funding from New York Farm Viability Institute (NYFVI) and the Northern New York Agricultural Development Program (NNYADP), we initiated the “Value of Manure” statewide project to evaluate the N and yield benefits of various manure sources and application methods. Three trials were conducted in 2022. Here we summarize the initial findings.
What we did in 2022
Trials were implemented on three farms. Each trial had three strips that received manure and three that did not, for a total of six strips (Figure 1a). Strips were 1200-1800 ft long and 50-80 ft wide. When corn was at the V4-V6 stage, each strip was divided into six sub strips (Figure 1b) and sidedressed at a rate ranging from 0 to up to 192 pounds N/acre, depending on the farm. All three farms applied liquid untreated manure, ranging from 7,525 to 15,000 gallons/acre in the spring.
Figure 1. Layout of a Value of Manure study plot. Three strips received manure before planting (1a). At the V4-V6 corn stage each of the six strips received six different inorganic N sidedress rates (1b).
Soils on farm A were Lima and Honeoye (Soil Management Group [SMG] 2), farm B had a Hogansburg soil (SMG 4), and farm C had Valois and Howard soils (SMG 3). The farms implemented and harvested the trial. The Cornell team sampled for general soil fertility, Pre-Sidedress Nitrate Test (PSNT), Corn Stalk Nitrate Test (CSNT), and silage quality. Each trial was harvested with a yield monitor.
What we have found so far
Corn silage had a different response to manure and inorganic N sidedress in each of the study farms (Figure 2). Farm A responded to both the application of manure and inorganic N fertilizer. In that farm manure application was able to offset 58 lbs N/acre and presented a 0.6 ton/acre yield advantage at the Most Economic Rate of N (MERN), the rate of N that maximizes economic returns, compared to plots with inorganic N fertilizer application only (Figure 3). The application of inorganic N fertilizer and manure had no impact on the yield of farm B, showing that the field already had enough N and did not need any N addition (fertilizer or manure). At farm C, yield did not respond to the application of inorganic N sidedress (the field by itself provided enough N to the crop), but yield was higher when manure was applied: on average manured plots yielded 1.5 ton/acre higher than the no-manure plots. The MERN for farms B and C was 0 lbs N/acre both with manure and without it.
The PSNT and CSNT levels of the manured plots were higher than their no-manure counterparts for all three studies, showing that manure supplied N (Table 1). Both manure and no manure plots in farm A had optimum CSNT levels at the MERN, showing that manure was able to offset 58 lbs N/acre.
Figure 2. Effect of manure application and different nitrogen sidedress rates on corn silage yields in three New York farms. Error bars are standard deviations.Figure 3. Most economic rate of N (MERN) in farm A. Without manure, the MERN was 114 lbs N/acre with a yield at the MERN of 28.5 tons/acre. With manure, the MERN was 56 lbs N/acre, with a yield at the MERN of 29.1 tons/acre.
Conclusions and Implications (and Invitation)
The trials of 2022 show the range of possible responses, with one trial not showing a yield or N benefit of the manure, one trial showing a yield increase when manure was applied that was not due to N addition, and one showing both a yield and N fertilizer benefit from manure. This shows the importance of targeting manure application to fields with low past N credits, where it will be most likely to cause a yield respond. Additional trials are needed with various manure sources (raw manure, separated liquids, solids, digestate, etc.) before we can draw conclusions about the N and yield benefits of manure. Join us for the 2023 Value of Manure project and obtain valuable insights about the use of manure in your farm! If you are interested in joining the project, contact Juan Carlos Ramos Tanchez at jr2343@cornell.edu.
We thank the farms participating in the project for their help in establishing and maintaining each trial location, and for providing valuable feedback on the findings. For questions about this project, 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/.
Quirine Ketterings1, Sanjay Gami1, Juan Carlos Ramos Tanchez1, and Mike Reuter2 Cornell University Nutrient Management Spear Program1 and Dairy One2
Introduction
The corn stalk nitrate test (CSNT) is an end-of-season evaluation tool for N management for corn fields in the 2nd year or more 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 values exceed 3000 ppm for two or more years, it is highly likely that N management changes can be made without impacting yield.
Findings 2010-2022
In 2022, 43% of all tested fields had CSNT-N greater than 2000 ppm, while 35% were over 3000 ppm and 21% exceeded 5000 ppm (Table 1). In contrast, 29% of the 2022 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. Because crop and manure management history, 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 and disease pressure, lack of moisture in the root zone in drought years, lack of oxygen in the root zone due to excessive rain in wet years, and any other stress factor that can impact crop growth and N status.
Note: Data prior to 2013 reflect corn stalk nitrate test submissions to NMSP only; 2013, 2014, and 2017-2022 data include results from NMSP and Dairy One; 2015-2016 includes samples from NMSP, Dairy One, and CNAL. Yield data are from the USDA – National Agricultural Statistics Service. Rainfall data obtained from CLIMOD 2 (Northeast Regional Climate Center).
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 because 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 more samples test excessive in CSNT-N while fewer test low or marginal. The last 11 years include six drought years (2012, 2016, 2018, and 2020 through 2022), three wet years (2011, 2013, and 2017), and four years labelled normal (2010, 2014, 2015, 2019) determined by May-June rainfall (less than 7.5 inches in drought years, 10 or more inches in wet years).
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/.
E. Smith1, M. Zuefle2, X. Wang3, K. Wise2, J. Degni1, A. Gabriel1, M. Hunter1, J. Miller1, K. O’Neil1, M. Stanyard1, G. Bergstrom4
1Cornell Cooperative Extension, 2New York State Integrated Pest Management, 3United States Department of Agriculture – Agricultural Research Service, 4Cornell University
Soybean cyst nematode (SCN) is a plant parasitic roundworm and is the most damaging pest of soybean crops worldwide. Yield losses can reach 30% before above-ground symptoms manifest, leaving growers unaware that they have an infestation until it’s too late. With soybean prices the highest they’ve been in a decade, this translates to a loss of more than $13,000 per fifty acres in a field that would otherwise produce a yield of 55 bu/acre. We are only now beginning to understand the spread and damaging effect of SCN on dry bean crops, for which financial losses would almost certainly be greater due to their higher value.
In addition to legume crops, SCN can infest and reproduce on several weed species such as chickweed, purslane, clover, pokeweed, and common mullein. Overwintering SCN eggs hatch in spring when soil temperatures reach approximately 50°F (10°C). Females colonize roots to feed, eventually allowing the lower half of their bodies to protrude through the root wall and become visible as small white cysts (Figure 1). Eventually, the female dies and the cyst dries, hardens, and darkens in color, concealing up to 400 eggs. While we can expect at least three generations of SCN each growing season, these cysts can survive for years in the soil until the right conditions allow them to hatch. Because of their hardiness, longevity, and their relatively broad host range, once a field has been infested with SCN is it considered impossible to eradicate. SCN cysts can spread via wind, soil, water, tires and farm equipment, contaminated seeds or plants, and through birds or other animals.
Figure 1. Soybean cyst nematode cysts on soybean roots. Photo: Craig Grau, University of Wisconsin
This is an extremely hardy and pernicious pest, but populations can be managed using an integrated approach including scouting, soil sampling, host resistance, and crop rotation. The first step is of course scouting and identification using soil sampling.
If SCN infestation is not known in a field, the roots of symptomatic plants (stunting or premature yellowing compared with the surrounding crop) may be inspected for cysts (Figure 1). Otherwise, soil samples should be collected near harvest or just after. Samples should be taken from the root zone in field entrances and sections of the field that showed stunting or premature yellowing/death compared with the surrounding crop (Figure 2). If a field is known to have an SCN infestation, soil samples should be taken across the field in a zig-zag or grid pattern because SCN infestations are unevenly distributed.
Figure 2. Soybeans infested with SCN drying down prematurely compared with the surrounding crop. Photo: Erik Smith, Cornell Cooperative Extension
From 2017 to 2020, 134 soybean and dry bean fields in 42 counties were sampled for SCN, yielding positive samples in 30 counties (SCN+). In 2021, further testing revealed 6 more counties with infestations (Table 1, Figure 3).
Table 1. Soybean cyst nematode sampling results in 2021.
Fields tested
Fields SCN+
Counties sampled
Counties SCN+
New SCN+ counties
98
30a
37
15
6b
aMostly low populations (<500 eggs/cup of soil). Moderate egg counts (500-10,000 eggs/cup) were found in Western NY, the North Country, and the Southern Tier (no geographic trend).
Figure 3. Counties with known infestations of soybean cyst nematode (red), counties that have been sampled but have not yielded positive samples (green), and counties that have not been sampled (gray).
To scout for damage and sample soil more efficiently, researchers from New York State IPM are investigating the effectiveness of using soil electrical conductivity (EC) mapping technology. Soil EC mapping can determine field distribution for many nematode species but has not been tested on SCN. Nematode population density, if present, has a strong positive correlation with the proportion of sand in the soil because of increased mobility in looser, sandier soils. EC measurements can be used to detect the variability in sand content in a field and thereby create a map of areas with higher likelihood of SCN. This map is then used to target soil sampling to those areas. Preliminary data collected in 2021 using an EC machine shows there is variation in SCN distribution within fields. Results from 2022 (funded by the NY Dry Bean Industry) will be used to seek additional funding to expand our mapping, and to utilize existing EC maps from growers of dry beans, soybeans, and snap beans to further validate this approach.
While we have many SCN-resistant soybean varieties, the majority (>95%) are derived from a single resistant cultivar, PI 88788. The extensive use of this cultivar in soybean breeding has led to the emergence of SCN populations that can overcome PI 88788-type resistance. For example, recent SCN surveys conducted in major soybean producing states including Missouri and Minnesota all reported an increased level of adaptation to PI 88788-type resistance. In contrast with our current soybean varieties, SCN field populations exhibit great genetic diversity. During the fall of 2022, researchers from the USDA-ARS will be collecting soil samples to conduct a comprehensive study on SCN distribution, density, and virulence phenotypes across New York state. Regular monitoring of SCN densities and virulence phenotypes is essential for developing effective management plans based on the use of resistant cultivars.
With the current infestation levels in NY, crop rotation is our most valuable management tool. Rotating out of soybeans for even one year can reduce SCN populations by 50% or more. Continuing to rotate crops allows us to keep populations low, reducing the likelihood that growers will have to resort to more costly management strategies.
Please contact your local Cornell Cooperative Extension agent if you would like your field(s) to be sampled for SCN. This year, the NY Corn and Soybean Growers Association (NYCSGA) is providing funding for up to 75 soybean fields to be tested, while the NY Dry Bean Industry is funding EC mapping of three dry bean fields and nine soil samples per field (27 total samples). With continued scouting, soil sampling, and race-typing by Cornell University, USDA-ARS, and NYSIPM, New York’s soybean and dry bean growers are in position to continue making the best management decisions for this pest.
