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/.
Olivia Godber1, Mart Ros1, Agustin Olivo1, Kristan Reed2, Mike van Amburgh2, Kirsten Workman1,3, and Quirine Ketterings1
1Nutrient Management Spear Program, 2Department of Animal Science, 3PRODAIRY, Cornell University, Ithaca, NY 14853
Introduction
Between 2017 and 2019, 110 New York dairy farms completed their whole farm nutrient mass balance assessment. Of the feed fed to the animals on the farms, almost 70% was homegrown, which means it was produced on the land-base operated by the farm itself (Figure 1a). Almost all this homegrown feed was forages such as corn silage, alfalfa, and grass. The farms averaged 0.56 mature cows per acre (weighted by tillable acres). How does this compare to average values in the United States and why is this important?
Comparison with Dairies in New York and Nationally
The share of homegrown feed for the New York farms was considerably high than typically reported across the US. The number of mature cows per acre was higher across the US (Figure 1b), while farmers in the assessment spent considerably less on feed costs per unit of milk produced than reported for the US (Figure 1c).
Figure 1: (a) The share of homegrown feed on New York dairy farms participating in the 2017-2019 nutrient mass balance assessment (histogram); (b) the average number of mature cows per acre of cropland on dairy farms in the US (boxplot) and on New York dairy farms (blue diamond) according to the 2017 USDA Census of Agriculture, and the average number of mature cows per tillable acre for New York dairy farms participating in the 2017-2019 nutrient mass balances (green diamond); (c) the average amount spent on purchased feed per ton of milk sold for US dairy farms (boxplot) and New York dairy farms (blue diamond) according to the 2017 USDA Census of Agriculture.
Importance of Optimizing Homegrown Forage Production
For most dairy farms, feed purchases are the largest annual expense, so growing forages on the farm’s land base reduces the costs of feed. However, there are also other reasons why optimizing homegrown feed is key.
Reducing the amount of feed that needs to be imported helps to avoid the carbon and energy footprint that imported feeds have (production elsewhere plus transport to the farm).
By minimizing feed imports, farms are also minimizing the risk of feed price fluctuations and economic uncertainty.
Farmers that grow feed have greater control over the quality of that feed. They can select what crops are needed and in which quantities to meet the needs of their animals.
Farmers can, to a certain extent, alter crop management practices as needed, and optimize nutrient use, thereby reducing nutrient losses to the environment.
By increasing nutrient recycling with the use of manure on the farm itself, farms are reducing their reliance on fertilizer use. This results in a smaller environmental footprint for feed production. Optimizing the use of manure over synthetic fertilizer can also help with managing volatile fertilizer prices and with the farm’s overall economic sustainability.
Farms with insufficient land base will need to export manure. By optimizing feed production and animal density, a farm can reap the benefits of using manure, thereby avoiding extra expenses incurred with manure export (if feasible at all) and avoiding carbon emissions associated with the transport of manure beyond the farm boundaries.
Acknowledgements
We thank the farmers, farm advisors and past and current NMSP team members who worked on the whole farm NMB project with us over the past 15+ years. This research is funded primarily by a gift from Chobani, in addition to Federal Formula Funds, and grants from the Northern New York Agriculture Development Program (NNYADP), Northeast Region Sustainable Agriculture and Education (NESARE), New York State Department of Environmental Conservation (NYDEC). For questions, contact Quirine M. Ketterings (qmk2@cornell.edu) or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/.
Sunoj, S.1, J. Cho1, J. Guinness2, J. van Aardt3, K.J. Czymmek1,4, and Q.M. Ketterings1
1Nutrient Management Spear Program, Department of Animal Science, 2Department of Statistics and Data Science, and 4PRODAIRY, Department of Animal Science, Cornell University, Ithaca, NY 14853; 3Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology
Introduction
Yield maps can help farmers identify high and low yielding areas in a field and customize management practices to maximize return on investment. Currently, most yield monitor systems on choppers and combines record yield and GPS location at 1-second intervals. With properly calibrated systems, and once data generated by these systems are cleaned of errors, accurate yield maps can be generated. However, calibration and data cleaning are required as extra steps prior to yield mapping, while sensors are expensive and can break during harvest, without the opportunity to redo the data collection. Approaches to estimate yield that reduce the risk of data loss, are less time consuming, and can be used by a larger number of farmers therefore could be beneficial. Here we report on a study using drones (commonly called “unmanned aerial systems” or UAS) to estimate yield at the subfield level.
Timing of N sidedress and UAS flights
Sidedress N treatments – The experiment was conducted at the Musgrave Research Farm in Aurora, NY in 2019. All N treatments received starter N (30 lbs N per acre). Six N treatments were implemented including zero N (NoN – only starter), N rich (NRich; 300 lbs N per acre at planting), and sidedress applications (180 lbs N per acre applied) at V4, V6, V8, or V10.
UAS flights – Weekly UAS flights (total of 12 flights) were done between VE and R5 using the QuantixTM mapper from AeroVironment Inc. The UAS payload consists of two cameras, one for color imagery (red, green, and blue bands) and one with a near-infrared (NIR) band. The reflectance values were used for calculating “vegetation indices”, which typically are used to highlight specific vegetation features or conditions. Although six different vegetation indices were tested, we only report here on models derived using the normalized difference vegetation index (NDVI), which was best-suited for yield estimation. NDVI is a combination of the red and near-infrared bands.
Results
Did a Delay in Sidedressing Impact Yield?
All N treatments that received more than just starter N produced higher yields (Figure 1A), with NRich and sidedressing at V4 and V6 producing the highest yields (~170 bushels per acre) and the NoN treatment producing the lowest yields (85 bushels per acre). Delay in sidedressing to V8 and V10 resulted in lower yields. These results were consistent with N sidedress experiments across four years at the same location (Link).
Did Timing of Sidedressing Impact Yield Model Accuracy?
The timing of N sidedress application had not just an effect on yield, but also on NDVI reflectance when sensed at the R4 growth stage (Figure 1A). Earlier sidedress N application (up to V6) produced a narrow range of NDVI values, while delaying the N sidedress application produced more variable NDVI signals (e.g., V8 and V10 in Figure 1A).
The performance of yield estimation models (Figure 1B) showed that models that used data from plots that were sidedressed at or before V6 did well (R2 > 0.90), whereas inclusion of data from plots that were sidedressed at V8 or V10 were much less reliable (R2 < 0.68). These findings suggest that estimation of yield for fields that were sidedressed later than V6 are much less reliable, even with inclusion of NoN and NRich NDVI data.
Figure 1. (A) Relationship between corn grain yield (from yield monitor system) and NDVI reflectance (from UAS) for different nitrogen (N) treatments; and (B) Yield model performance for different combinations of sidedress N application. Note: NoN = starter only; NRich = 300 lbs N per acre at planting; V4, V6, V8, and V10 = sidedressing of 180 lbs N per acre at the respective growth stages. The values above each bar indicate the coefficient of determination (R2) for models fitted with NDVI and corn grain yield. The R2 values range between 0 and 1, with 1 being the best model. Models were derived from flights at the R4 growth stage.
Does Timing of Flight in the Season (Growth Stage) Impact Yield Model Accuracy?
Flights at the R4 growth stage resulted in reliable models, as long as sidedressing took place at or before V6. But what about flying earlier in the season? Data shown in Figure 2 indicate a much lower estimation accuracy at all vegetative growth stages (up to VT) and after R4 (Figure 2). At R5 the canopy started to turn yellow and much of the reflectance signals were not reliable for yield estimation. The lower performance at R2 was attributed to cloudy conditions during the flight, highlighting one challenge with the use of passive sensors to capture NDVI. Our results suggest that flying on a sunny day, when corn is between R1 and R4, gives us the best yield estimation models.
Figure 2. Corn grain yield estimation performance of NDVI using the UAS images obtained throughout the growing season.
Conclusions and Implications
Yield estimation using drones is a promising approach provided we implement the following management strategies: (1) Avoid delay in sidedressing – Sidedressing after V6 not only reduced corn grain yield, but also produced variable NDVI values, resulting in poor estimations of corn grain yield; (2) Fly the drone between R1 and R4 – After R4, the canopy starts to turn yellow, which makes it unsuitable for yield estimation; and (3) Avoid cloudy days for flights – Flying on cloudy days can impact the images collected and the accuracy of yield estimation models derived from the imagery. Ongoing research at the NMSP is exploring an approach of scaling this work to larger fields and developing yield estimation models that can be applied across farm fields and different farms.
Full Citation
This article is summarized from our peer-reviewed scientific publication: Sunoj, S., J. Cho, J. Guinness, J. van Aardt, K.J. Czymmek, and Q.M. Ketterings (2021). Corn Grain Yield Prediction and Mapping from Unmanned Aerial System (UAS) Multispectral Imagery. Remote Sensing, 13(19), 3948. https://doi.org/10.3390/rs13193948
Acknowledgements
This research was funded with federal formula funds. We thank Greg Godwin for flying the Quantix drone and Paul Stachowski, farm manager of the Musgrave Research Farm at Aurora, NY, for help with field management. We also thank the many NMSP team members for help with harvest and sample processing over the years. This research was funded in part with Federal Formula Funds and through grants from the New York Farm Viability Institute (NYFVI) and New York State Department of Environmental Conservation (NYDEC). J.G. received support from the National Science Foundation under grant No. 1916208. 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/.
Sandra Wayman1, Lisa Kissing Kucek2, Virginia Moore3, Lais Bastos Martins4, Matt Ryan1 1Soil and Crop Sciences Section, SIPS, Cornell University, 2USDA ARS Dairy Forage Research Center, 3currently: NC State University. Feb 2021: Plant Breeding and Genetics Section, Cornell University, 4Crop and Soil Sciences, NC State University.
Legume cover crops have room for improvement Winter annual legume cover crops are essential management tools for organic farmers; they fix nitrogen, improve soil health, and suppress weeds. Winter annual cover crops are planted in the early fall, overwinter, then grow vigorously in the spring and complete their life cycle in the summer. However, many farmers struggle with these cover crops. Poor emergence, low vigor, and winter kill are basic challenges that could be addressed through plant breeding. Unlike cash crops, cover crops have received relatively little attention from plant breeders in the past. Thus, even modest investments in germplasm improvement could return large benefits. The Sustainable Cropping Systems Lab is taking advantage of this opportunity to improve legume cover crops for organic farmers by participating in the national Cover Crop Breeding Network (Fig. 1). Sites across the U.S. are developing cover crop lines best suited to each region. Our goal is to develop new varieties that boost the sustainability of organic farms, using classical plant breeding methods rather than genetic engineering. We are working with three species of winter annual legume cover crops: hairy vetch (Viciavillosa), crimson clover (Trifoliumincarnatum), and winter pea (Pisumsativum) (Fig. 2).
Figure 1. Sites participating in the legume cover crop breeding program.Figure 2. Left, hairy vetch; top right, crimson clover; bottom right, winter pea.
The traits farmers want To inform our breeding efforts, we conducted a national survey of organic and conventional farmers to learn which cover crop traits were important to them (Fig. 3, Wayman et al 2017). We received 417 responses to the survey, and 87% of the respondents reported they used cover crops. Organic farmers reported placing greater value on the ecosystem services from cover crops than did conventional farmers. The top four traits chosen by respondents as important for legume cover crops were nitrogen fixation, winter hardiness, early vigor and establishment, and biomass production (Fig. 3).
Figure 3. Percentage of farmers (organic and conventional together) who rated the given traits for four focus cover crops as “important” or “very important” out of total of five rating levels (“not at all important” to “very important”). Numbers above bars indicate count of farmer respondents for each cover crop and trait. Stars on bars indicate significant differences between conventional and organic farmers for that particular trait and cover crop (chi-square test, * is P < 0.05, *** is P <0.001).
Genetic improvement The steps in developing better cover crop varieties for farmers are 1) create better genotypes through breeding nurseries, and 2) select the best new varieties through advanced line trials. Researchers at different sites in the project are selecting for different legume traits based on their region. In the legume cover crop nurseries planted at Cornell University, we are selecting for winter-hardiness in addition to early-flowering.
We began the breeding program with seeds of hairy vetch, crimson clover, and winter pea from commercially available varieties, lines from worldwide breeding programs, landraces selected by farmers, and PI (plant introduction) lines from the U.S. National Plant Germplasm System Germplasm Resources Information Network (NPGS GRIN) seed bank.
For five seasons, we have planted breeding nurseries of the three legume cover crop species at our Cornell University site. We selected plants based on fall vigor, low winterkill, spring vigor, early maturity, and soft seed. We culled undesirable plants before flowering, and saved seeds from the best plants to replant in the following year. We selected between 2.8% and 4.6% of the hairy vetch individuals across the breeding seasons, and between < 0.01% and 2.8% of crimson clover individuals.
For winter pea, the first year of the breeding program evaluated the performance of accessions from the National Plant Germplasm System. The results informed what material to include in breeding nurseries. For the following three seasons, we planted and selected early generation breeding lines originating from the USDA-ARS Grain Legume Genetics Physiology Research Center in Pullman, WA. The best 0.5 to 1.4% of the winter pea plants were chosen as new breeding lines, based on winter survival and vigor. In 2019, the winter peas experienced severe winter conditions 900 feet above Cornell University’s campus, where almost all the winter peas died from winterkill.
Advanced line trials In the 2018-2019 and 2019-2020 seasons, our breeding lines were tested against commercial varieties in multi-environment advanced line trials. Sites across the country (Fig. 1) grew replicated plots of breeding lines and commercial checks of each legume cover crop species. Each trial grew the legume cover crops alongside triticale to simulate grass-legume cover crop mixes typically grown by farmers. Breeding lines of each crop were compared with commercial check varieties to assess if our breeding program has produced something better than what is currently available to growers on the market. Lines were evaluated for emergence, winter survival, fall and spring vigor, flowering timing, disease, and biomass. The best lines of each species will be tested again in the 2020-2021 season, and performance of these lines will determine variety release and commercialization.
Nursery and advanced line trial performance Testing variety performance is currently underway. An analysis of the advanced line trials will identify if any lines perform well across the U.S., or if certain lines excel in specific regions. Ideally, we would find a few breeding lines performing well across all sites. Such “broadly adapted” lines could be sold as varieties nationwide. If certain lines are excellent in specific regions, however, seed companies are interested in selling lines as “regionally adapted” varieties. In the meantime, data from the breeding nurseries indicated patterns in regional performance. The results suggest different trends among species, which are detailed below.
Hairy vetch We found no hairy vetch line that performed best in both the fall and spring (Kissing Kucek et al. 2019). Instead a tradeoff between fall growth and spring growth was observed. As a result, the breeding program is screening and selecting for vigor at both times of the year, with the goal of finding ideal lines that have the best overall seasonal performance.
Over two seasons and a dozen U.S. sites (Fig. 1), we tested 16 hairy vetch breeding lines and six checks. Breeding lines developed by the Cover Crop Breeding Network beat the commercial check lines in both years. Winning lines, however, differed among sites. Colder northern environments had different winning breeding lines than warmer southern and western sites. Our Cornell University site proved to be an intermediate winter environment compared with the harsh upper Midwest and mild southeast and west. In cold winters like 2018-2019, Cornell University shared winning lines with MN, WI, and NE. In contrast, during warm winters like 2019-2020, NY was more similar to southern and western sites. These results suggest that the best performing lines in NY may vary depending on weather conditions, with warmer years in NY mimicking southern and Mid-Atlantic sites, and colder winters grouping NY with the northern Midwest. To select for resilient lines that can handle variable winter conditions, Cornell University breeding nurseries include material from warm and cold regions of the U.S.
The 2018 hairy vetch line from Cornell University was the second highest seed yielding in our OR trials, demonstrating 25% more seed yield than checks (Hayes and Azevedo, 2019). High seed yield is a very desirable trait for seed growers and seed companies.
Crimson clover Two commercially available varieties of crimson clover, ‘Dixie’ and ‘Linkarus’ were included as checks in our trials. ‘Dixie’ is a variety developed in GA that exhibits high forage biomass production, ability to reseed, and high amounts of hard seed (Hollowell 1953). ‘Linkarus’ is a highly productive winter hardy crimson clover which was developed in Germany. In general, we have seen ‘Dixie’ perform well in the southern locations, while ‘Linkarus’ performs better at northern locations. In the harsh NY winter of 2018-2019, our breeding lines beat both ‘Dixie’ and ‘Linkarus’.
In two seasons, we also evaluated crimson clover breeding lines for biomass production at Cornell University. Biomass production is important for all farmers, who often use crimson clover as a green manure. The crimson clover lines with the highest biomass production were included in the next season’s nursery. At our Cornell University site in 2018, ‘Linkarus’ had the highest biomass production, with 1.5 to 2.9 times more biomass production than ‘Dixie.’ Additionally, to compare top-performing lines from nursery selections at a dozen sites across the country, we tested 13 crimson clover breeding lines and 2 checks over two seasons. In the first season, a soft-seeded MD breeding line produced the most biomass, followed by ‘Linkarus’ and a Cornell University breeding line. In the second season, ‘Dixie’ produced the most biomass, followed by a MD breeding line.
Breeding lines have also been tested for seed yield in OR, where most crimson clover seed is produced. The two checks beat all breeding lines for seed yield (Hayes and Azevedo 2019). As a result, we have increased our focus on selection for seed yield in the crimson clover breeding program.
Winter pea In NY, winter peas have often been challenging for farmers due to poor winter survival. In the 2017-2018 season, 0.5% of plants were selected based on winter survival and vigor. Their seed is currently being increased so they can be included in future advanced line trials. In the 2019-2020 season, our Cornell University site experienced optimal weather to discriminate cold tolerance. Data from 39 new and different genotypes helped us choose the entries with the best potential to be increased for the advanced line trial.
Over two seasons, we tested 21 winter pea lines and five checks in our advanced line trial. In the 2018-2019 season, the winter pea advanced line trials did not survive at Cornell University and in MN due to harsh winter conditions. Southern locations (CA, GA, NC, OR) of the advanced line trials had overall higher biomass production than did the northern locations (MD, MO, NE, WI) in 2018-2019. Across all sites, our breeding lines performed better than the checks. Indeed, one of our breeding lines was in the top five performers across five different locations, showing good potential for release as a variety. Many of our breeding lines performed better than the two commercially available cultivars in the trial.
An additional observation for winter pea is that lines with the highest vigor in fall may have poor biomass production in the spring. This is not uncommon in peas; if the plants grow too much in the fall their exposed above-ground biomass is susceptible to frost damage and winter kill.
Next steps As part of this project, we will release varieties of legume cover crops adapted to specific regions. Our next steps include selecting for high-vigor and improved material in our nurseries, continuing advanced line trials with this new material, planting seed increases, and inviting farmers and seed company representatives to the breeding sites to evaluate the lines. We planted a third year of advanced line trials in 2020, after which we will determine if any lines are consistently high performers and good candidates for variety release.
Cover Crop Breeding Network team member coming to Cornell In February 2021, a Postdoctoral Scholar with the team, Virginia Moore, will join Cornell as an Assistant Professor with SIPS in the Plant Breeding and Genetics Section. Virginia’s program will focus on breeding for sustainable cropping systems. Virginia has been involved in the national Cover Crop Breeding Network as a project manager since 2019. She is currently based at USDA-ARS in Beltsville, MD, and completed her graduate work at the University of Wisconsin, with a MS in Agroecology and Agricultural & Applied Economics and a PhD in Plant Breeding & Plant Genetics. She sees plant breeding as a powerful tool to increase sustainability of cropping systems, with goals like a) reducing pesticide inputs through breeding for pest resistance, b) increasing cover crop adoption by developing regionally adapted cultivars, c) selecting crops for organic systems, and d) diversifying cropping systems through rotations and intercropping. She is excited to continue working in cover crop breeding and to take on new crops including alfalfa and other forages, hemp, and switchgrass.
Acknowledgements: Thanks to Gerald Smith for sharing data and resources. Thanks to Chris Pelzer, Katherine Muller, Dylan Rodgers, James Cagle, Nina Sannes, and Matt Spoth for help planting the nurseries.
References Hayes, R and M. Azevedo. 2019. Seed yield of hairy vetch and crimson clover breeding lines. Raw data available upon request.
Hollowell, E. A. 1953. Registration of varieties and strains of crimson clover (Dixie crimson, Reg. No. 1). Agron. J. 45:318-320
Kissing Kucek, L.; H. Riday; et al. 2019. Environmental influences on the relationship between fall and spring vigor in hairy vetch (Vicia villosa Roth). Crop Science. 59:1-9
Wayman, Sandra & Kissing Kucek, Lisa & B. Mirsky, Steven & Ackroyd, Victoria & Cordeau, Stéphane & Ryan, Matthew. (2017). Organic and conventional farmers differ in their perspectives on cover crop use and breeding. Renewable Agriculture and Food Systems. 32. 376-385. 10.1017/S1742170516000338.
Elson Shields and Tony Testa, Department of Entomology, Cornell University
What are Biocontrol Nematodes?
Biocontrol nematodes are microscopic round worms in the soil which only attack insects in the soil or on the soil surface. Biocontrol nematodes are different from the plant parasitic nematodes which attack crops. The biocontrol nematodes discussed here are native to our Northern New York (NNY) soil where they were original collected. The nematode insect infective stage (called the Infective Juvenile or IJ) moves about in the soil in search of insect hosts, finding the insect using CO2 gradients and other chemical attractants. When an insect host is located, the IJ enters the insect through a breathing opening called a spiracle and enters the insect body cavity. Once inside, the nematode releases a bacteria which kills the insect. The nematodes then molt to adults and produce offspring on the nutrition provided by the dead insect. When the insect resources are consumed, a new set of IJs are released into the soil to search for additional insect hosts. An average sized insect larvae will produce between 100,000 and 200,000 new IJs.
What do these biocontrol nematodes attack?
This entire technology was developed to reduce snout beetle (ASB) populations to sub-economic levels in NNY. ASB is costly to the dairy farmer, commonly killing alfalfa stands in a single year. The economic costs of ASB on dairy farmers is very high and often hidden. Dairy farms are impacted by the high cost of replacing alfalfa fields and the high cost of purchasing replacement feed to replace the loss of alfalfa production. Estimates of these dual costs exceed $30,000 per 100 cows on the farm annually. To date, more than 150 NNY farms have applied biocontrol nematodes to >25,000 acres to successfully reduce snout beetle to a sub-economic level and increase stand life back to 3-5 years.
Corn Rootworm: During the research developing the use of native persistent biocontrol nematodes to reduce ASB populations in NNY to sub-economic levels, it was discovered that biocontrol nematodes applied in alfalfa for snout beetle control also carryover to attack corn rootworm when the field is rotated to corn. Not only are the biocontrol nematodes completely compatible with all of the Bt-RW traits, killing the Bt toxin survivors, but in NY, the biocontrol nematodes appear to be capable of being used alone if the farmer chooses to grow non-Bt-RW traited corn. Research has shown that after 4 years of corn, the populations of biocontrol nematodes in the field are high enough to attack alfalfa soil insects when the field is rotated back to alfalfa.
Wireworm and White grubs: Since NY alfalfa culture usually incorporates grass into the mix, NNY fields usually have a population of wireworms and native white grubs in the field when the field is rotated to corn. Often, these insects then cause stand problems in 1st year corn. If the field has been inoculated with biocontrol nematodes for control of either snout beetle or rootworm, the biocontrol nematodes also attack these insects and reduce their impact on seedling corn when rotated to corn.
Seed corn maggot: With our corn and soybean insecticide seed treatments under attack, the questions arises whether biocontrol nematodes present in the soil will be effective against seed corn maggot under NY spring conditions. Seed corn maggot is killed by biocontrol nematodes in the laboratory, but the question is whether the biocontrol nematodes can work fast enough in the field under the cool spring soil temperatures.
Does the soil type influence the species of biocontrol nematode applied?
NY research data indicates a mix of biocontrol nematode species gives better control of soil insects than a single species alone. The reason for these results is each nematode species has a preferred section of the soil profile where it is most effective. For example, Steinernema carpocapsae prefers the top 2-3” of the soil profile and dominates this region. If S. carpocapsae is the only nematode used, insect larvae below the 2” level escape attack. The addition of a second nematode species which prefers the low portions of the soil profile compliments the presence of S. carpocapsae and gives more complete control of soil insects throughout the plant root zone. In sandier soils, the top 2” often become too dry for a biocontrol nematode to move and attack insect larvae. In these soils, a nematode species mix which include S. carpocapsae would be ineffective and requires a different mix of nematode species.
Our recommendations for biocontrol nematode species mixes for soil types:
Clay loam – silt loam soils: S. carpocapsae + S. feltiae
Sandy loams – sand soils: S. feltiae + Heterorhabditis bacteriophora.
What are the differences between the entomopathogenic (biocontrol) nematodes purchased on the web from the Persistent NY strains mentioned here?
Biocontrol nematodes purchased from commercial sources have lost the ability to persist in the soil after application for a significant length of time. Many commercial strains persist in the soil for only 7-30 days and require application timing to be closely match with the presence of their target host, requiring an annual reapplication. In contrast, the NY persistent strains of Biocontrol Nematodes are carefully cultured to maintain their evolutionary ability to persist across hostile conditions such as the lack of available hosts and temperature extremes (dry soil conditions, winter). Additionally, NY persistent strains are re-isolated from the field every two years so the nematode cultures do not become “Lab strains”, but remain adapted for NY agricultural soil conditions. New York persistent strains are applied once and persist in the field for many years following application. Not surprising because they were isolated from NY soils where they have evolved for a few million years. If the NY persistent strains are cultured carelessly, they also quickly lose their ability to persist and are no better than the commercial strains purchased off the web.
How are biocontrol nematodes applied?
There are two major ways to apply biocontrol nematodes to NY fields.
Commercial Pesticide Sprayer: Thousands of acres have been inoculated using slightly modified pesticide sprayers of all sizes from 30’ booms to 100+’ booms. To use these sprayers, the following guidelines need to be followed.
A good washing of the sprayer (similar to changing pesticides)
All screens and filters removed (nematodes cannot pass through them
Nozzle change to a stream type nozzle to shoot a concentrated stream of water to the soil surface through any vegetation.
50 gpa minimum
Application in the evening or under cloudy/rainy conditions (nematodes are sensitive to UV)
Liquid Dairy Manure: This method was recently developed and offers some advantages over using a pesticide sprayer. The biggest limitation is the time between adding the nematodes to the liquid manure and field application. After adding the nematodes to the manure, the manure needs to be spread in the field within 20-30 minutes. Longer intervals results in the nematodes dying from the lack of oxygen.
The advantages of using liquid dairy manure as the carrier are 1) no extra trips over the field, 2) can be applied any time of the day and 3) no extra costs.
Application timing:
Biocontrol nematodes which are persistent, can be applied anytime during the growing season when soil temperatures are above 50 F. Ideally, nematodes should be applied when there are host in the soil so they can immediately go to work and reproduce. However, the NY persistent strains have the ability to sit and wait for months before needing to attack hosts and reproduce. We request that no nematode applications be made after September 15th due to cooling soil temperatures and limited time to find hosts before winter. Applications are made to the soil surface under conditions of low UV exposure (late in the day, rainy/overcast days, in cover crops where there is adequate ground shading). Field tillage has no impact on biocontrol nematodes. In addition, if nematodes are applied before field tillage, the movement of soil during tillage helps the nematodes redistribute throughout the field and help them fill in the gaps which may occur during application.
Where can I get Biocontrol Nematodes which are adapted to NY and will persist across growing seasons?
Currently, there are two sources to purchase biocontrol nematodes adapted to NY growing conditions with their persistent genes intact to persist across growing seasons (and winter) in NY.
Mary DeBeer, Moira, NY. cell: (518) 812-8565 email: md12957@aol.com
The full version of What’s Cropping Up? Volume 30 No. 2 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:
