Creating a New York Soybean Yield Database

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.

Graph of soybean yield density plots by soil type.
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/.

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Stalk Nitrate Test Results for New York Corn Fields from 2010 through 2022

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).

Relevant References

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/.

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Soybean cyst nematode in soybeans and dry beans: new research and renewed sampling efforts in 2022

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.

soybean roots with nematode cysts
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.

soybeans dried by SCN with healthy surrounding crop
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).

bBroome, Genesee, Oneida, Schenectady (not previously sampled), Tioga, and Yates (not previously sampled).

NYS map
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.

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Headlands often reduce overall field yield. Are they worth fixing?

S. Sunoja, Dilip Kharela, Tulsi Kharela, Jason Choa, Karl J. Czymmeka,b, Quirine M. Ketteringsa
aNutrient Management Spear Program, bPRODAIRY, Department of Animal Science, Cornell University

Introduction

Headland areas are defined as the outer edges of the field where farm equipment turns during field operations such as planting, sidedressing and harvest and where hedgerows or other physical features separate a field from adjacent fields or other land uses. The equipment traffic areas can be compacted which can cause considerable yield loss. Beyond compaction, yield loss in headland areas may also reflect edge-feeding of pests such as birds, rodents and deer, and competition for light, water, and nutrient resources with adjacent tree lines. Better decisions about headland management including investments to improve production potential, planting of other crops, or reductions in fertility or other crop inputs can be made when we know how much yield is given up on headlands. In the past several years, we have provided farm specific yield reports to farmers who have shared their corn silage and grain yield data with us.  The reports included yield by field with and without headland areas included.  Here we put all that information together, across farms, to evaluate how much corn grain and silage yield may be lost on headland areas across fields.

Corn Grain and Silage Yield Data

Corn yield data from 2648 fields representing ~49000 acres across 63 farms in New York were analyzed. This included 1281 corn grain fields and 1367 corn silage fields across two years (2017 & 2018). The yield data from each field were processed and cleaned using Yield Editor (free software from USDA-ARS) following the cleaning protocol developed by the Nutrient Management Spear Program at Cornell University (Kharel et al., 2020). Headland removal was performed in Yield Editor by manually selecting the outer edge passes and deleting the data points (Figure 1).

maps of headlands
Figure 1. Headland areas were removed using Yield Editor. Shown are (a) cleaned yield data including headland areas, (b) selected headland areas represented in black, and (c) yield in non-headland areas (i.e. after removal of headlands). Adapted from Sunoj et al. (2020).

Average field size ranged from 18.5 acres per field for grain and 19.3 acres per field for silage. Corn grain yields averaged 181 ± 33 bu/acre versus 22 ± 5 tons/acre for corn silage. We calculated optimal production, defined as production that could be obtained if the headland portion had yielded the same as the non-headland portion. We calculated production gain as the percentage increase between the actual and optimal production.

Results

Across all fields, the yield in the headland area was lower than the yield of the non-headland area (Figure 2A) for 94% of the grain fields and 91% of the silage fields. For some fields, the headland area yielded more than the non-headland area, possibly due to: (1) within-field features (e.g., trees, wet spots, alley ways), (2) irregular shapes of fields with short passes (as typically seen in New York agriculture), and (3) multiple directions of harvest within a field. The average yields were 188 bu/acre (non-headland area) and 161 bu/acre (headland areas) for corn grain. For silage, the average yields were 22.6 tons/acre (non-headland area) and 18.9 tons/acre (headland areas). Thus, headland yields were 14% (grain) and 16% (silage) lower than yields in the non-headland areas.

yield in scatter plot and bar graph
Figure 2. (A) Field scale yield in headland versus non-headland for corn grain and silage; and (B) distribution of production gain across all fields. Each circular marker in (A) represents a field. Adapted from Sunoj et al. (2020).

If the headlands yielded as much as the non-headland area, the production gain ranged from -8 to 32% for corn grain, and from -17 to 42% for corn silage (Figure 2B). The negative production gains reflected field that yielded more on the headland areas than the non-headland areas (points below the 1:1 line in Figure 2A). Averaging across all fields, the production gain amounted to about 4% for both corn grain and silage fields. However, 1% of the grain and silage fields had a potential production gain that exceeded 20%; 25% of the grain fields and 28% of the silage fields had gains between 5 and 20%, while for the rest of the fields (74% and 71%) potential yield gains were less than 5%. Production gains exceeding 20% were obtained on fields with the total field area was less than 25 acres, and with corn grain yields less than 143 bu/acre and silage yield less than 24 tons/acre. Such yield differences can, depending on the farm, reflect a considerable loss of yield and opportunity to improve total returns per cropland area.

Conclusions and Implications

Yield in headland areas was, on average, 14% (grain) and 16% (silage) lower than in the non-headland areas of the field. Taking into account the total percentage of a field in headland, at the field and farm levels, the potential yield gain amounted to 4%. The overall averages conceal the wide range of production gain values obtained in New York fields, from negative up to 32% for corn grain and up to 42% for some corn silage fields. Based on production gain for specific fields, farmers can either choose to ‘repair’ the headland with management (e.g., vertical tillage or subsoiling) to increase overall productivity and return on investment in seed and crop inputs, reduce crop inputs without further loss of yield in headlands, or ‘retire’ the headland from main crop farming and opt for perennial hay crop and conservation uses.

Additional Resources

Full Citation

This article is summarized from our peer-reviewed publication: Sunoj, S., D. Kharel, T.P. Kharel, J. Cho, K.J. Czymmek, and Q.M. Ketterings (2020). Impact of headland area on whole field and farm corn silage and grain yield. Agronomy Journal (in press). https://doi.org/10.1002/agj2.20489.

Acknowledgements

This research was funded with grants from the Northern New York Agricultural Development Program (NNYADP), New York State Corn Growers Association (NYSCGA), and federal formula funds. We thank the farmers and crop consultants for sharing whole-farm corn silage and grain yield data. 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/.

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What’s Cropping Up? Volume 30, No. 3 – May/June 2020

The full version of What’s Cropping Up? Volume 30 No. 3 is available as a downloadable PDF on issuu. This issue includes links to COVID-19 resources on the back page. And as always, individual articles are available below:

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What’s Cropping Up? Volume 30 No. 2 – March/April 2020 Now Available!