Stalk Nitrate Test Results for New York Corn Fields from 2010 through 2024

Sanjay Gami¹, Juan Carlos Ramos Tanchez¹, Mike Reuter², and Quirine M. Ketterings¹

¹Cornell University Nutrient Management Spear Program (NMSP) and ²Dairy One

Introduction

            The corn stalk nitrate test (CSNT) is an end-of-season evaluation tool for N management for corn fields in the 2nd or more years after a sod. It allows for identification of situations where more N was available during the growing season than the crop needed (CSNT>2000 ppm). 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 2010-2024

            In 2024, 47% of all tested fields had CSNT-N greater than 2000 ppm, while 37% were over 3000 ppm and 28% exceeded 5000 ppm (Table 1). In contrast, 20% of the 2024 samples were low in CSNT-N. Two years of CSNT monitoring is recommended before making management changes unless CSNT’s exceed 5000 ppm, in which case one year of data is sufficient.
            Some of the variability in CSNT distribution over the years may be reflect differences in growing season (Figure 1). The percentage of samples testing excessive in CSNT-N across 2010-2024 was most correlated with the total precipitation in May-June with droughts in those months translating to a greater percentage of fields testing excessive. The year 2024 was classified as normal based on these criteria although some areas experienced drought conditions for parts of the season, possibly contributing to a higher percentage of stalks testing excessive in CSNT.

            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 Adaptive Nitrogen Management for Field Crops in New York lists targeted within-field CSNT sampling as one of five end-of-season evaluation tools. Samples received in more recent years may also reflect more targeted field sampling. 

A bar graph.
Figure 1: In drought years more samples test excessive in CSNT-N while fewer test low or marginal. The last 15 years included six drought years (2012, 2016, 2018, and 2020 through 2023), three wet years (2011, 2013, and 2017), and five years labelled normal (2010, 2014, 2015, 2019, and 2024) determined by May-June rainfall (less than 7.5 inches in drought years, 10 or more inches in wet years). Weather data are state averages; local conditions may have varied from state averages.

            Because crop and manure management history, soil type and growing conditions all impact CSNT results, conclusions about future N management should consider 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 in wet years, and any other stress factor that can impact crop growth and N status. 

Relevant References

   Instructions for CSNT Sampling: http://nmsp.cals.cornell.edu/publications/StalkNtest2016.pdf.
.  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.
.  Adaptive Nitrogen Management for Field Crops in New York (2025): http://nmsp.cals.cornell.edu/publications/extension/AdaptiveNitrogenManagement2025.pdf

Acknowledgments

We thank the farmers and farm consultants that sampled their fields for CSNT over the years.

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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Profitability of contrasting organic management systems from 2018-2021 in the Cornell Organic Cropping Systems Experiment

Kristen Loria1, Allan Pinto Padilla2, Jake Allen1, Christopher Pelzer1, Sandra Wayman1, Miguel I. Gómez2, Matthew Ryan1

1School of Integrative Plant Science, 2Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY 14853.

About the Cornell Organic Cropping Systems Experiment

The Cornell Organic Cropping Systems (OCS) experiment was established in 2005 at the Musgrave Research Farm in Aurora, New York to serve as a living laboratory for organic field crop management systems and provide practical insights to farmers. This ongoing long-term experiment compares four management systems along a dual spectrum of external inputs and soil disturbance over a multi-year crop rotation. An advisory board consisting of a dedicated group of organic farmers provides guidance on management decisions. The four systems are compared in terms of several sustainability indicators including yield, profitability, soil health and greenhouse gas emissions.

Both external input and soil disturbance gradients of the four treatment systems range from an extensive approach (low input) aimed at maximizing profitability by reducing costs via efficient resource use, to an intensive approach (high input), aimed at maximizing profitability by maximizing yield. Risk associated with low input management includes reduced crop production from inadequate soil fertility or weed competition, which can lead to decreased returns despite low input costs. Risk associated with high input management include diminishing returns where productivity increases are insufficient to justify additional cost.

The four management systems of OCS are: 1) High Fertility (HF), 2) Low Fertility (LF), 3) Enhanced Weed Management (EWM), and 4) Reduced Tillage (RT). In 2018, the crop rotation was modified from a three-year rotation to a  four-year rotation based on advisor input.: This article includes an economic analysis of the complete four-year crop rotation cycle from 2018-2021, which consisted of: 1) triticale / red clover, 2) corn / interseeded cover crop mix, 3) summer annual forage mix / cereal rye cover crop, 4) soybean (Figure 1).

Figure 1. Four-year crop rotation for the OCS phase 2018-2021.

Looking back: key takeaways from past OCS cycles

Caldwell et al. (2014) compared the yields and the profitability during and after the initial phase of organic transition in OCS following two three-year rotation cycles (corn-soybean-winter spelt/red clover) from 2005-2010. The first three years were considered as transitional production years in which crops could not be sold as certified organic, while crops produced from 2008 to 2010 could be sold as such. They used flexible interactive crop budgets to calculate relative net returns based on crop yields, tillage, weed management and fertility practices and, after the three-year transition period, compared relative net returns of organic production with concurrent organic price premiums to Cayuga County yield averages with conventional crop production inputs and prices. With a 30% organic price premium, the relative net return of organic production in all systems except RT was positive. The RT system was excluded from most analyses due to major challenges with experimental ridge-till practices resulting in decreased crop competitiveness. For both corn and soybean phases averaged across entry points, relative net return in the HF system was significantly lower than LF or EWM, due to higher input costs without corresponding higher yields in the HF system. For the spelt phase averaged across entry points, relative net return was higher in HF than LF and EWM (though not significantly so), with increased input cost in the HF system corresponding with a yield increase. The HF system led to higher weed biomass over time than the EWM and LF systems.

Trial design and system differences

The Cornell Organic Cropping Systems experiment uses a split-plot randomized complete block design with four blocks. The main plot treatments are the four management systems, whereas subplot treatments are two crop rotation entry points (A and B) . Entry points A and B represent different phases of the crop rotation. For example, in 2018 entry point A was planted to triticale while entry point B was planted to soybean.

Treatment systems are arranged along a fertility gradient as well as a soil disturbance gradient (Figure 3). For triticale, summer forage, and corn, the HF system had a 50% higher fertilization rate than RT and EWM. LF received fertilizer rates 50% lower than RT and EWM on the same crops. Intermediate fertilizer rates were applied to both EWM and RT. With respect to soil disturbance, EWM received additional weed management operations in several crops, while RT and LF incorporated an organic no-till soybean phase. Overall number of primary tillage events was not substantially different between systems, though mechanical cultivation was reduced in the soybean phase for RT and LF.

Figure 2. Contrasting management approaches in four systems.

Crop yields across management systems

No matter the management system, crop yield is a key component of profitability. Yields across all four years of the cycle comprising five harvested crops are summarized below. Ryelage was only harvested in EWM and HF systems as the cereal rye cover crop was rolled-crimped for no-till soybean in LF and RT systems. Triticale was grown as a grain crop in EWM and HF and taken for forage in the LF and RT systems. Organic no-till practices were implemented in RT and LF systems only, with soybean planted into tilled soil in HF and EWM. In entry point A soybean yields were comparable across systems, but in entry point B organic no-till soybean yields were nearly half of cultivated yields, likely due to dry conditions in the soybean phase in 2018.

Table 1. Mean yields for all harvested crops across four management systems and crop rotation entry point from 2018-2021. Within an entry point, systems sharing a letter were not significantly different (p < 0.05). Means were not compared between entry points. Triticale in RT and LF systems was harvested as forage (lbs DM/ac) while in HF and EWM it was harvested as grain (lbs/ac). Means were not compared.

Net return of management systems

Net return subtracts total variable costs (TVC) of production (inputs + labor + equipment-associated costs) from gross income (crop yield x price). Prices for corn and soybean were obtained from the USDA organic grain report (USDA National Organic Grain and Feedstuffs Report, February 4, 2022). As commodity price references for triticale grain, cereal rye forage and summer annual forage were unavailable, prices were based those typically fetched for organic forage in NY (MH Martens and P Martens, personal communications, 2022). All operation-related costs were taken from Pennsylvania’s 2022 Custom Machinery Rates (USDA NASS 2023). To correct the absence of an inflation adjustment, crop prices and input costs used in this study were converted to real values using the U.S. Consumer Price Index (CPI), with 2016 as the reference year.

All values are denominated in U.S. dollars and represent the average annual revenue, production costs, and net return over four years. In the case of crop rotation entry point A, the LF cropping system exhibited the lowest Total Variable Cost (TVC). Conversely, the HF system had the highest TVC, which despite higher grain and forage yields, resulted in lower net return than LF, EWM and RT systems (Figure 4).

Overall, across four years of the crop rotation and in both crop rotation entry points (i.e., temporal replications of the trial) the EWM system maximized net return via intermediate fertility rates and relatively high yields, though the HF system yielded higher in both entry points Net return for RT and LF systems was more variable between crops and entry points, possibly indicating higher weather-related risk associated with those system approaches, i.e. reliance on cover crops for fertility in LF, and use of organic no-till management for LF and RT (Figure 4).

Figure 4. Comparison of net return and components across four systems in entry point A.

In entry point A, LF demonstrated higher net return than both HF and RT despite lower yields due to reduced input costs. Net return in RT narrowly surpassed HF due to lower input costs as well. In entry point B, LF ranked lowest in net return due to low grain yields across the rotation. HF ranked second and RT ranked third, with RT characterized by intermediate to low yields with intermediate input costs.

Figure 5: Comparison of net return and components across four systems in entry point B.

When net return of each management system is summarized by entry point, high variability in profitability was observed across entry points, largely due to yield differences between growing seasons of the same crop. Because management was nearly identical for each crop within each system across entry points, temporal variation in net return can be attributed to yield response from seasonal environmental or climatic factors either directly or in interaction with management. This highlights the complexity of systems experiments given year-to-year variation (Figure 6).

Figure 6: Net return comparison of all four cropping systems and two entry points.

Conclusions

Differences in yield and subsequent net return between systems varied significantly across entry points, making it difficult to draw conclusions on the most profitable system overall. However, the HF system had the lowest net return across entry points, indicating that input levels were likely higher than optimum and yield gains to justify increased inputs were not realized. EWM had the highest net return across entry points, indicating that intermediate levels of fertility combined with additional cultivation passes in the row crop phases and full tillage soybean production “paid off” as a management strategy, with increased labor or fuel costs outweighed by increased yields. Of course, this assumes availability of labor required which may be out of reach for some farms, and can be challenged by finite weather-related windows conducive to field operations.

Variability in net return between entry points was particularly high for the LF and RT systems, largely driven by yield variation in the soybean phase between temporal replications. For entry point B, intermediate corn yields and low organic no-till soybean yields drove low profitability in LF, while relatively high corn yield in RT partially made up for low organic no-till soybean yield. This variation in soybean yield highlights a challenge with an organic no-till management approach that dry conditions can reduce yields to a greater extent compared to a tillage-based approach. However, in an extremely wet year where adequate weed control was not possible, no-till management may pay off.

By accounting for system profitability only, this article does not consider other tradeoffs between systems such as soil health outcomes or greenhouse gas emissions from contrasting management, additional sustainability metrics to evaluate organic production system success.

References

Caldwell, B; Mohler, CL; Ketterings, QM; and DiTommaso, A. (2014). Yields and profitability during and after transition in organic grain cropping systems. Agronomy Journal, 106(3):871–880.

Gianforte, L personal communication. 2022.

Jernigan, A. B., Wickings, K., Mohler, C. L., Caldwell, B. A., Pelzer, C. J., Wayman, S., and Ryan, M. R. (2020). Legacy effects of contrasting organic grain cropping systems on soil health indicators, soil invertebrates, weeds, and crop yield. Agricultural Systems, 177:102719.

USDA National Organic Grain and Feedstuffs Report, February 4 2022. Agricultural Marketing Service.

Martens, MH personal communication. 2022.

Martens, P personal communication. 2022.

Pennsylvania’s 2022 Machinery Custom Rates. USDA NASS.

For more results from the Cornell Organic Systems Experiment visit the Sustainable Cropping Systems Lab website.

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Value of Manure Calculator Cell Phone App Available Now

Juan Carlos Ramos Tanchez¹, Kirsten Workman¹?², Carlos Irias¹, and Quirine M. Ketterings¹ 

Cornell University Nutrient Management Spear Program¹, PRO-DAIRY²

Introduction

Quantification of nutrients in manure is essential to ensure efficient resource management, maximize agricultural productivity, and minimize negative environmental impact. A Value of Manure Calculator cell phone app was developed to estimate the agronomic and economic fertilizer replacement value of manure. To use the app, users will need to enter (1) the percent solids, N, P, and K nutrient content from a recent manure analysis, (2) the implemented or planned manure application rate, (3) crop nutrient needs, and (4) fertilizer costs. The app will then return past and current N credits, P and K credits, the fertilizer replacement value of the manure. Once the land application cost per gallon of manure is added, the tool also calculates the land application cost per extra mile hauled and the break-even hauling distance and costs. The app uses manure N credits that are in line with Cornell’s Nitrogen Guidelines for Field Crops in New York (2023). If you are in another state, consult your local land grant university guidance for manure N credits.

Calculator Access 

The Value of Manure app can be accessed at Value of Manure Calculator (https://valueofmanure-nmsp.glideapp.io/) with any browser or by scanning the QR code to the right. Once opened, the user will see the opening page of the calculator (Figure 1 left). The front page shows, at the bottom of the screen, seven tabs: Lab Analyses; Past Application; Current Application; Crop Needs; Fertilizer Value; Hauling; and Results (Figure 1 right).

Two phone screens.
Figure 1. Value of Manure App main page showing the manure analyses tab (left) and the results page (right).

Calculator User’s Guide

Recently a User’s Guide explaining what the app does and how to use it, was released (Figure 2). The guide walks the user through the different taps and explains in detail the following topics:

  1. Entering a Manure Analysis
  2. Calculating Nitrogen Credits from Past Applications
  3. Calculating Current Year Manure Nutrient Credits
  4. Entering Crop Needs
  5. Entering Fertilizer Value
  6. Calculating Break-Even Hauling Distances and Costs
  7. Understanding the Results
  8. Signing in to Save Results
An image of the cover of the Value of Manure project calculator user's guide.
Figure 2. Value of Manure Calculator User’s Guide.

The Value of Manure Calculator User’s Guide can be accessed at the NMSP website by clicking on the following link: http://nmsp.cals.cornell.edu/publications/extension/ValueManure2025.pdf.

Additional resources

Acknowledgments

The original manure crediting system was developed based on many years of field research under the leadership of S.D. Klausner, with contributions by D.J. Lathwell, D.R. Bouldin, and W.S. Reid of Cornell University, Department of Crop and Soil Sciences. This app was developed with financial support from the Northern New York Agricultural Development Program, the New York Agricultural Viability Institute, and USDA-NIFA. 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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New End-Of-Season Assessment Tool for Nitrogen Management of Corn Silage

Agustin J. Olivo1, Olivia F. Godber1, Kirsten Workman1,2, Karl J. Czymmek1,2, Kristan F. Reed1, Daryl V. Nydam3, Quirine M. Ketterings1

1Department of Animal Science, 2PRO-DAIRY, 3Department of Public and Ecosystem Health Cornell University, Ithaca, NY United States 

Introduction

            Effective nitrogen (N) management is an essential aspect of productivity and sustainability of corn silage production for dairies. In New York (NY), end-of-season evaluations that consider indicators like N balance (N supply – N removal) and ratio of N removal to N supply can be implemented to assess nutrient use efficiency. Comparing these results with feasible outcomes can help farmers identify opportunities to refine N management over time, and support field experimentation through the NY adaptive N management process. To identify target values for these indicators, characteristics of 994 corn silage field observations across eight NY dairies, together with land grant university guidelines for N management were used to create the “Green Operational Outcomes Domain” (GOOD) assessment framework. The GOOD combines feasible target values for field-level N balances, N removal/N supply, and an indicator related to manure inorganic N utilization efficiency. Indicators were derived using the method outlined in Agronomy Factsheet 125.

Key findings

The GOOD was defined by a 50% minimum N removal/N supply and a 142 lbs/acre maximum balance

A line graph depicting N balances and the "Green Operational Outcomes Domain."
Fig. 1. Feasible outcome values for maximum tolerable N balance and minimum N removal/N supply that define the GOOD framework.

            The GOOD framework was defined by comparing field N removal and available N supply (Fig. 1). Fields performing inside the GOOD (green area in Fig. 1) have an N removal/N supply that is at least 50%, and a field N balance of 142 lbs N/acre or less. The latter was defined based on the maximum balance that fields in the present dataset would display if managed according to land grant university guidelines. The GOOD was set to identify fields with large N balances and low efficiencies in the context of adaptive N management, without restricting application rates to less than annual P crop removal.

Average farm performance remained within the GOOD, but with large variability

            When considering actual farm management practices (“achieved” indicators) across all 994 fields, 66% of observations were within the GOOD and 34% outside. However, there was large variability across the eight farms evaluated.  The percentage of fields outside the GOOD ranged from only 1% for one farm (Fig. 2 left) and up to 54% for another farm (Fig. 2 right). The annual averages for achieved available N balance on all farms ranged between 4 and 192 lbs N/acre, and for N removal/available N supply between 38% and 95%.

Two line graphs describing the relationship between farm animal density and N balances.
Fig. 2. Nitrogen (N) removal and achieved available N supply as calculated from farm management data for corn silage fields of two different dairy farms. Percentages at the top of each graph represent the percentage of fields inside (green, left), and outside (red, right) the green operational outcomes domain (GOOD). Yellow diamonds represent the area-weighted average performance across all fields data was collected for in each farm.

Manure N use was efficient in this dataset, but with opportunities for refinement

            Forty-six percent of observations had spring manure injection or surface application followed by incorporation, whereas 32% received manure application but manure inorganic N contributions were zero (manure was either applied in fall, or in spring with no incorporation within five days). Twenty-six percent of observations were both within the GOOD and had manure inorganic N contributions larger than zero. This shows an overall efficient use of N for corn silage production. For 20% of the observations, manure injection or incorporation in the spring did take place, but the fields fell outside of the GOOD, reflecting opportunities to reallocate a portion of the nitrogen applied to other fields.

Additional graphical tools and indicators complement the GOOD framework well

A graph describing the relationships between yield and balances.
Fig. 3. Graphical tool displaying field achieved N balance vs corn silage yield, in the context of the feasible maximum tolerable N balance (142 lbs N/acre) and farm average yield. Q = quadrant.

            A series of additional graphical tools and numerical indicators were created to provide farms with more information to identify opportunities to refine N management in corn silage production. For example, one tool helps to identify fields with low yields and high N balances (Q3 in red, Fig. 3). These fields can represent the first target when attempting to refine N management in corn silage.

Conclusions

            The GOOD framework is introduced as an end-of-season assessment tool for farms to identify corn silage fields with large N balances and low N removal/N supply. This can be used in the context of the NY adaptive N management process, and/or to identify opportunities for N management refinement over time. On the latter, this study showed that the strategies with largest potential for refining N management and meeting the GOOD feasible targets included reducing N inputs, evaluating non-N yield barriers (e.g. drainage, pests) for fields with low yields and high balances, crediting N contributions from sod, and increasing manure N utilization efficiency (with spring injection or incorporation) and adjusting rates accordingly.

Full citation

            This article is summarized from our peer-reviewed publication: Olivo, A.J., O.F. Godber, K. Workman, K.J. Czymmek, K. Reed, D.V. Nydam, and Q.M. Ketterings (2024). Doing GOOD: defining a green operational outcomes domain for nitrogen use in NY corn silage production. Field Crops Research. https://doi.org/10.1016/j.fcr.2024.109676.

Acknowledgements

            We thank farmers and their certified crop advisors who shared farm data. This research was funded by a USDA-NIFA grant, funding from the Northern New York Agricultural Development Program (NNYADP), and contributions from the New York Corn Growers Association (NYCGA) managed by the New York Farm Viability Institute (NYFVI), and the Department of Animal Science, Cornell University. For questions about these results, contact Quirine M. Ketterings at qmk2@cornell.edu, and/or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/.

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Manure nutrient variability during land application in four New York dairies

Aidan Villanueva1, Carlos Irias1, Juan Carlos Ramos Tanchez1, Kirsten Workman1,2, Quirine Ketterings1

1 Department of Animal Science, Cornell University, Ithaca, NY, United States; 2PRO-DAIRY, Department of Animal Science, Cornell University, Ithaca, NY, United States

Introduction

               Dairy manure is a rich source of essential plant nutrients, making it an excellent natural fertilizer. When applied correctly, it can enhance soil health, boost crop yields, and reduce reliance on synthetic fertilizers, thereby increasing agriculture’s sustainability and contributing to a more circular economy. Unlike inorganic fertilizers that have a guaranteed analysis, manure dry matter and nutrient content can vary, influenced by numerous factors such as dairy rations, type and amount of bedding, rainfall and wash water, manure storage systems and handling. Manure sampling and analyses will be essential in determining the potential value of the manure as a nutrient source. Our objectives were to assess the variability in manure dry matter (DM), nitrogen (N), phosphorus (P), and potassium (K) content across farms, across different storage units within a farm, and across time (hourly versus daily sampling), and to document the impact of agitation on DM and manure nutrient content. 

How was the data collected?

               Four New York dairy farms participated in this study. Manure samples were collected during land application in the spring of 2023 for all four farms and repeated in the spring of 2024 for one of the farms. Manure management and storage practices (Table 1) varied from farm to farm. Storages were sampled in the spring across days (“daily sampling”), and for the 2023 sampling we also took samples every two hours on selected days (“intense sampling”) to compare variability across hours and across days.

Table indicating manure management of different farms.

A manure spreader moving in a field on the left and a bucket of liquid manure on the right.
Fig. 1. Manure collection from a spreader.

               Manure samples were collected by filling a five-gallon bucket directly at the pump or the manure spreader (Figure 1). For each sampling round, three subsamples were taken and submitted for nutrient analyses to ensure outliers could be captured. Samples were analyzed for DM, total N, inorganic N, organic N, P, and K. Means, standard deviation, and coefficient of variation (CV) were determined to assess variability in the results across farms, storages, spreading events, and sampling intensity.

What was found?

               Storages varied greatly from farm to farm (results not shown) and within a farm (Figure 2). This highlights the importance of sampling each storage unit individually and maintaining accurate storage-to-field application records. 

A bar graph indicating mean nutrient content.
Fig. 2. Mean nutrient content at farm D for dry matter, total nitrogen, inorganic nitrogen, organic nitrogen phosphorus (P2O5), and potassium (K2O) in manure samples collected from four manure storage units (S1, S2, S3, and S4) in 2023. Error bars are standard deviations.

               Composition varied as the manure storage was emptied (results not shown). In general, across storages and farms, K content showed lower variability compared to P and N. In general, variability in N (total, organic, and inorganic) and P among hours within a day was much smaller than the variability from day to day (Figure 3). Hourly sampling often resulted in CVs below 13% while daily sampling showed CVs up to 34%. Because of the much lower CVs for hourly sampling, sampling over multiple days is recommended instead of sampling within a day.

Bar graph showing manure variation.
Fig. 3. Coefficient of variation for daily versus hourly sampling at three dairy farms for total nitrogen, phosphorus (P2O5), and potassium (K2O) in manure samples collected in 2023. # = Agitation, + = solid-liquid separation.

               Manure agitation completed the day before and on the day of application resulted in higher nutrient content, specifically for total N and P (Figure 3), reflecting settling of manure solids without agitation. Dry matter content was correlated with total N and P with lower N and P content for the more liquid upper layers in the storage. Potassium did not show much variability reflecting that K is predominantly found in the liquid fraction of the manure. These results show the benefits of consistent agitation to ensure a greater homogeneity over time as manure is land applied.

Bar graph indicating the impact of agitation.
Fig. 4. Impact of agitation the day before land application, during land application, and no agitation on manure mean nutrient content at farm B24 for dry matter, total nitrogen, inorganic nitrogen, organic nitrogen, phosphorus (P2O5), and potassium (K2O).

Conclusions

               Manure nutrient composition and variability differed across farms and across storage units on the same farm. Variability was also present over time as storages were emptied, although there was little variability between samples taken just a few hours apart (same day sampling). Agitation helped reduce variability. We recommend sampling each storage unit separately, keeping storage-to-field application records, agitating storages where feasible prior to and during land application, sampling manure from pumps or spreaders during land application, and sampling every time a significant change in manure dry matter content is seen.

Additional Resources

Acknowledgements

               We thank Dairy Support Services as well as farmers and their certified crop advisors who worked with us to collect manure samples. This research was funded by a USDA-NIFA grant, funding from the Northern New York Agricultural Development Program (NNYADP), the New York Farm Viability Institute (NYFVI), New York State Department of Agriculture and Markets (NYSAGM) and Environmental Conservation (NYSDEC). For questions, contact Quirine M. Ketterings at qmk2@cornell.edu, and/or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/.

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Characterization of Soil Health in Suffolk County, Long Island

Deborah Aller1Kaitlin Shahinian2Joseph Amsili3, Harold van Es3
1Cornell Cooperative Extension of Suffolk County, 2Suffolk County Soil and Water Conservation District, 3Soil and Crop Sciences Section, Cornell University

Interest in soil health concepts, practices, and testing has grown rapidly across the United States as farmers, researchers, and the general public increasingly recognize the central role of soils in food production, water quality, environmental sustainability, and climate adaptation and mitigation. Further, it is well known that land managers have a tremendous capacity to either degrade or improve the health of the soil through their management decisions.

Acknowledging the importance of healthy soil for the long-term productivity and sustainability of agriculture on Long Island specifically, the CCE Agricultural Stewardship Program partnered with the Suffolk County-Soil and Water Conservation District to offer soil health testing free of charge to all farmers in the County. This program began in spring 2018 and in just three years over 60 farms have participated, and more than 200 soil samples have been collected. In 2020, the New York Soil Health Initiative (https://newyorksoilhealth.org/) published a report (https://newyorksoilhealth.org/soil-health-characterization/) characterizing soil health across New York State (NYS), which quantified the effects of different cropping systems on soil health. We additionally characterized soil health at a smaller regional scale within the state so that farmers can compare their soil health to similar production environments nearby.

We have summarized results from 231 soil samples collected from across Suffolk County that encompass a variety of soil types and cropping systems. The samples were approximately evenly split among sandy loam, loam, and silt loam texture classes. The County has a higher proportion of coarse-textured soils (higher percentage of sand) than much of the rest of the state. These coarser soils are indicated by the Psamment soil suborder (Figure 1). All soil samples were analyzed using the Standard Comprehensive Assessment of Soil Health (CASH) package at the Cornell Soil Health Laboratory.

map graphic
Figure 1. Map of soil suborders in Suffolk County.

Suffolk County hosts a great diversity of agriculture and remains the top producer of nursery crops, certain vegetable crops (pumpkins and tomatoes), and perennial fruits (grapes and peaches). There are also many small-scale diversified vegetable farms that largely grow fresh market vegetables and several pastured livestock operations. Additionally, the high value of land and the maritime climate creates much different conditions for agricultural production than the rest of NYS. Five cropping system categories were constructed by grouping similar crops (Figure 2). The Processing Vegetable category grouped fields where winter squash, potatoes, pumpkins, and tomatoes were grown. The Mixed Vegetable category grouped fields where several different vegetable crops were grown in the same field in a single season and sold as fresh market produce (and also tend to be smaller farms than with processing vegetables). The Perennial Fruit category grouped all small fruit (blueberries and brambles), tree fruit orchards (apples, peaches, cherries, etc.), and vineyards. Woody Plant Nurseries included all operations producing field-grown ornamental horticulture crops (oak trees, California privet, boxwood, holly, etc.), and Pastures included the livestock operations with perennial forage crops.

composite image containing plants and a cow
Figure 2. Cropping systems analyzed in Suffolk County.

The initial analysis focused on differences among cropping systems on silt loam soils, although it reinforced the concepts that soil texture and cropping system are dominant factors contributing to the overall soil health on farms (Figure 3).

colored bar graphs
Figure 3. Mean soil organic matter (A), active carbon (B), respiration (C), and aggregate stability (D) across cropping systems on silt loam textured soils.

For silt loams, the soil health indicators of active carbon, respiration, and aggregate stability showed differences across cropping system, whereas soil organic matter (OM) did not. This indicates that some of these more labile OM indicators (more directly related to biological activity in the soil) can better and earlier detect changes in soil health than the total soil OM level which generally changes slowly over time. Pastures had greater active carbon levels than Processing Vegetable systems. Respiration and aggregate stability were slightly more sensitive to cropping system than active carbon. Pastures had higher soil respiration than both Processing Vegetable and Mixed Vegetable systems. Furthermore, Pastures had more than twice the aggregate stability compared to all other systems, which highlights the importance of living roots year-round to build and stabilize soil aggregates (Figure 3).

Overall, different agricultural management practices associated with various cropping systems had a big impact on soil health status. They often reflect important differences in total carbon and nutrient balances and degrees of disturbance from tillage. Pasture and Perennial Fruit maintained the best overall soil health because these systems are largely undisturbed and have perennial vegetation (Figure 3). Pasture systems receive continuous root and shoot inputs year-round and some Perennial Fruit systems may receive woodchip mulch. This permanent cover further protects the soil from losses due to wind and water erosion. The Mixed Vegetable farms typically have diverse rotations, practice cover cropping, and utilize various soil amendments such as compost to supplement fertility and build OM. In contrast, Processing Vegetable systems are more intensively managed, and although they often practice cover cropping, typically don’t receive sufficient organic inputs to replace the OM that is lost annually from tillage and other management activities. Typically, 40-80% of the carbon and nutrients in the aboveground biomass are exported off the farm in the form of crop harvests, which needs be counterbalanced with soil management practices like cover cropping and organic amendment application to maintain and build soil health.

Stay tuned for the complete report that characterizes soil health across Suffolk County, which will examine the effects of soil texture, soil taxonomic unit, and cropping system on the suite of biological, physical, and chemical soil parameters included in the CASH test. Refer to the full Characterization of Soil Health in New York State (https://newyorksoilhealth.org/soil-health-characterization/) report as an example of what will be produced for Suffolk County.

References and further reading:

Amsili, J.P., H.M. van Es, R.R. Schindelbeck, K.S.M. Kurtz, D.W. Wolfe, and G. Barshad. 2020. Characterization of Soil Health in New York State: Technical Report. New York Soil Health Initiative. Cornell University, Ithaca, NY

Magdoff, F.R. and H.M. van Es. 2009. Building Soils for Better Crops: Sustainable Soil Management. Sustainable Agriculture Research and Extension, College Park, MD. (The fourth edition will be out in 2021).

Moebius-Clune, B.N., D.J. Moebius-Clune, B.K. Gugino, O.J. Idowu, R.R. Schindelbeck, A.J. Ristow, H.M. van Es, J.E. Thies, H.A. Shayler, M.B. McBride, K.S.M Kurtz, D.W. Wolfe, and G.S. Abawi, 2016. Comprehensive Assessment of Soil Health – The Cornell Framework. Ed. 3.2. Cornell University, Geneva, NY

Sustainable Agriculture Research and Education (SARE). 2007. Managing Cover Crops Profitably. 3rd Ed. Available for download at this link: https://www.sare.org/wp-content/uploads/Managing-Cover-Crops-Profitably.pdf

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