Manure Continues to Offset Nitrogen Fertilizer Needs and Increase Corn Silage and Grain Yields: Value of Manure Project 2024 Update

Juan Carlos Ramos Tanchez¹, Carlos Irias¹, Allen Wilder², Janice Degni³, Paul Cerosaletti³, Dale Dewing³, Kirsten Workman¹𝄒⁴, and Quirine M. Ketterings¹ 

¹Cornell University Nutrient Management Spear Program, ²Miner Agricultural Research Institute, ³Cornell Cooperative Extension, and ⁴PRO-DAIRY

Introduction

              Manure contains all seventeen nutrients a plant needs, making it a tremendously valuable nutrient source. In addition, manure can help build soil organic matter, enhance nutrient cycling, and improve soil health and climate resilience when managed appropriately. Previous research in New York State indicates that these manure benefits contribute to crop yield beyond just the fertilizer value of manure.  The Value of Manure Project, part of the New York On-Farm Research Partnership, is funded by the New York Farm Viability Institute (NYFVI) and the Northern New York Agricultural Development Program (NNYADP), with additional support from the New York State Departments of Agriculture and Markets and Environmental Conservation, and the US Department of Agriculture’s National Institute for Food and Agriculture. This statewide project evaluates nitrogen (N) and yield benefits of various manure sources and application methods to corn silage and corn grain. Eight trials were conducted in 2024, adding to eleven trials conducted in 2022 and 2023. Here we summarize the findings of the 2024 trials.

What we did in 2024

              Trials were implemented within commercially farmed corn fields in western (1 trial), northern (2 trials), central (4 trials), and southeastern (1 trial) New York. Each trial had three strips that received manure and three that did not, for six strips per trial (Figure 1a).

Figure 1. Layout of a 2024 Value of Manure study plot. Three strips received manure before planting corn (1a). At the V4-V6 stage each of the six strips received six different inorganic N sidedress rates (1b).

              Four trials (D, E, F, G) received manure in spring 2023 but not in 2024. For these trials, we tested yield and fertilizer offset carryover benefits of 2023 manure into the 2nd year (2024) after manure application. For all other trials, manure was applied in spring 2024 before planting corn. Dairy manure treatment and application methods varied across trials (Table 1).

*Note: manure was applied in the spring of 2023 in trials D, E, F, and G so we tested its carryover value for 2024. For all other trials, manure was applied in the spring of 2024. SMG = soil management group (http://nmsp.cals.cornell.edu/publications/factsheets/factsheet19.pdf).

              Strips were 1200-1800 ft long and 35-120 ft wide for all but one trial where strips were 300 ft long. When corn was at the V4-V6 stage, each strip was divided into six sub-strips (Figure 1b) and subplots were sidedressed at a rate ranging from 0 to 200 pounds N per acre. Sidedress rates were trial-specific, based on the expected N needs for that field stemming from its specific characteristics and history. For each trial, manure was analyzed, and samples were taken for general soil fertility, Pre-Sidedress Nitrate Test (PSNT), Corn Stalk Nitrate Test (CSNT), yield, and forage quality. Soil test phosphorus (P) levels in the trials were in the medium to very high category (Table 2). Soil test potassium (K) was optimum or very high for five of the trials, while trials D, F, and G tested medium in K. Magnesium soil test values were high or very high for all trials. Soil test zinc (Zn) was medium for trials C, D, and G and high for all other trials. Manganese (Mn) and iron (Fe) were in the normal category.

What we have found so far

              As we also found in 2022 and 2023, trials differed in their responses to manure and sidedress inorganic N (Figure 2). Common among all trials in 2024 was that yield responded to N sidedress application in all eight trials. In seven of the eight trials (A to G), manure increased yield to levels not achievable with fertilizer alone by 0.3 to 2.7 tons/acre and 13 bushels/acre (Table 3). Out of these seven trials, in one of the trials with medium levels of K (F), manure applications increased yield to such elevated levels (2.3 tons/acre) that it also increased the crop’s need for fertilizer N, similar to what was observed for two trials in 2023. Yield in trial H only responded to manure in the lower N rates, likely reflecting the low application rate of this trial and low manure N contribution (3,840 gallons/acre, surface applied without incorporation). In trials A, C, and E manure did not replace inorganic N fertilizer, but still resulted in a yield increase.

Figure 2. Most Economic Rate of Nitrogen (MERN) in eight Value of Manure trials conducted in 2024. Orange text boxes are the MERN and yield at MERN for manured plots; gray text boxes are MERN and yield at the MERN for no-manure plots. Corn silage yields are in tons/acre at 35% dry matter (DM). Corn grain yields are in bushels/acre at 84.5% DM.
*Note: manure was applied in the spring of 2023 in trials D, E, F, and G so we tested its carryover value for 2024. For all other trials, manure was applied in spring 2024.

              The PSNT levels where liquid or digested manure was applied in 2024, were higher than their no-manure counterparts for all but one trial, showing that manure supplied crop available N to the soil (Table 4). The exception was trial C, where the application rate was relatively low (5,200 gallons/acre), which may have contributed to a lack of a response in PSNT. The PSNT results in the no-manure plots incorrectly identified trials B, C, and E as not needing additional N. In the manure plots, PSNTs also incorrectly suggested trials A, C, E, and H did not need sidedress N. For the manure carry-over trials (D, E, F, and G), PSNT levels were similar in manure and no-manure strips (Table 4), suggesting limited to no carryover into the second year, although each of these trials had a yield increase where manure was applied the previous year. 

              In all eight trials, CSNT levels of the plots that did not receive manure or sidedress fertilizer N were low, consistent with the yield response to N. In the plots that received manure but no N fertilizer, only trial B was in the excess category, consistent with the lack of a sidedress-induced yield response in trial B manured strips (trial B manure MERN = 0 pounds N/acre, Table 3). In the other trials (A, C, D, E, F, G, H) the manure strips without N fertilizer addition were in the low CSNT category, accurately reflecting the need for additional N.

              In 2024 we documented “yield bumps” resulting from manure application beyond what could be obtained with fertilizer only in seven of the eight trials, consistent with observations for seven of the eleven trials in 2022 and 2023. These yield bumps were also present in all four “carry-over” trials, signaling that manure applied in 2023 continued to be beneficial to crop yield in 2024. Those yield increases in the trials with optimal or high fertility status show that manure has additional benefits beyond its nutrient contributions. The CSNT results consistently reflected where N was needed and allowed for documentation of the N contributions of manure. The PSNT results showed inconsistencies this year with five trials where corn yield still responded to sidedress N even though the PSNT values were high. 

Next steps in 2025

              To re-evaluate the current N crediting system and learn how to predict and take into account yield bumps, the Value of Manure project requires the addition of more trials beyond the nineteen trials completed so far. Thus, the Value of Manure Project will continue in 2025. We will be testing additional manure types and application methods in various soil types and weather conditions. Join us and obtain valuable insights about the use of manure on your farm! If you are interested in joining the project, contact Juan Carlos Ramos Tanchez at jr2343@cornell.edu.

Additional resources

Acknowledgments

              We thank the farms participating in the project and their collaborators for their help in establishing and maintaining each trial location, and for providing valuable feedback on the findings. For questions about this project, contact Quirine M. Ketterings at 607-255-3061 or qmk2@cornell.edu, and/or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/.

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Greenhouse gas emissions and nutrient use efficiency assessment of six New York organic dairies

Agustin J. Olivo¹, Olivia F. Godber¹, Kristan F. Reed¹, Daryl V. Nydam², Michel A. Wattiaux³ and Quirine M. Ketterings¹

¹Department of Animal Science, Cornell University, Ithaca, NY United States ²Department of Public and Ecosystem Health, Cornell University, Ithaca, NY United States, ³Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States.

Introduction

Improving nutrient use efficiency and reducing greenhouse gas (GHG) emissions are important environmental priorities for organic-certified dairy operations. Regular assessment of key performance indicators (KPIs) via decision-support tools can help monitor farm performance and identify opportunities for improvement in these areas. Multiple decision-support tools have recently emerged to evaluate these indicators. However, these tools vary in complexity, required data inputs, scope and aggregation. A study was recently conducted at Cornell University to evaluate nutrient use efficiency and GHG emissions in six organic dairies, and to analyze the impact of alternative farm management practices on GHG emissions. Three decision support tools were used: Cornell nutrient mass balance (NMB) calculator, for whole-farm nutrient use efficiency, and Cool Farm Tool (CFT) and COMET, for GHG emissions. Farms had between 30 and 138 cows, 76 and 266 acres, and were certified organic (Farms 1-6) and grass-fed (Farms 3 and 4). Evaluations were done for two years.

Key findings

Farms showed high whole-farm nutrient use efficiency, primarily driven by low nutrient imports.

Farm-gate NMBs ranged from -5 to 17 lbs N/acre for nitrogen (N), and -2 to 7 lbs P/acre for phosphorus (P). Of the six farms, four met the feasible levels for N per hundredweight (cwt) of milk and per acre of cropland, versus two farms for P and five farms for K (Figure 1). Feasible levels are NMB performance ranges that previous research has shown farms in New York can operate within. Balances were generally low, explained by low animal densities and low nutrient imports. For N, inputs from legume fixation were equivalent to or larger than nutrients imported with feed and organic fertilizer purchases for all farms. Legume stands in sod and pasture fields played an important role in the sustainability of the farms when it comes to N management. For P, low inputs resulted in negative P balances in three of the six farms. Continuously operating under negative P balances may compromise the long-term sustainability of farms by reducing soil test P levels and ultimately crop yields. It is therefore relevant that farms continue tracking farm-gate NMBs, as well as changes in soil test P and plan fertility programs accordingly.

Fig. 1. Farm-gate N and P balances (lbs/acre) vs milk production (1000 lbs/acre) for six case-study farms. The green area indicates the optimal operational zone, where balances per acre (blue square) and per cwt (yellow triangle) are within feasible levels defined for dairies in NY. F=farm.

Whole-farm GHG emissions intensity showed variability and were directionally in agreement between CFT and COMET.

Estimations from CFT for GHG emissions intensity, a common way to report farm GHG emissions, ranged from 0.98 to 2.10 lbs of CO2-equivalent (CO2-eq, a common unit to aggregate all GHGs generated in the farm), per lbs of fat and protein corrected milk (FPCM). This value did not include carbon sequestration in soils (Figure 2). Baseline estimations from COMET (that consider different farm GHG sources to CFT) ranged from 0.69 to 2.48 lbs CO2-eq/lbs FPCM. Ranking of farms was similar between the two tools, suggesting that both tools can help identify, among multiple farms, those with the greatest emissions and need for implementing GHG mitigation measures. Enteric fermentation was the single largest source of GHG emissions, followed by energy and fuel use, and feed production or cropland emissions. Manure management emissions were larger for farms with liquid manure storages (Farms 4 & 5), compared to farms with solid manure handling (Figure 2).

A bar graph.
Fig. 2. Annual average GHG emissions intensity from Cool Farm Tool and COMET across six organic dairies. Numbers above each bar correspond to aggregation of all emission sources. FPCM: fat and protein corrected milk.

Cow milk productivity and manure management strategies showed opportunities to reduce farm GHG emissions.

A scatterplot.
Fig. 3. Average GHG emissions intensity calculated with Cool Farm Tool and COMET for farms 1-6, as related to average milk production per cow. F=farm.

Milk production per cow ranged from 4,400 (Farm 3) to 22,000 (Farm 5) lbs/cow per year and was negatively associated with GHG emissions intensity. The greater the milk production per cow, the lower the whole-farm GHG emissions intensity (Figure 3). Statistical analysis showed that, for farms with similar characteristics, increasing milk production from 4,000 lbs/cow/year to 11,000 lbs/cow/year could decrease GHG emissions intensity by almost 1 lbs CO2-eq/lbs FPCM. 

Analysis of alternative management strategies in the areas of crop management, manure management and farm energy use showed changes in GHG emissions intensity ranging from -8% to +8% compared to current management, when considered alone. For example, theoretical implementation of solid-liquid manure separation and/or anaerobic digestors in farms with liquid slurry storages resulted in an average reduction between 6 and 8% in whole-farm GHG emissions intensity (Figure 4). Implementing strategies such as composting or piling of manure resulted in an increase in GHG emissions intensity for most farms, given the starting practice of daily spread is associated with low GHG emissions (Figure 4). Other strategies such as replacing 50% of the farms’ grid energy use with a solar source and reducing 20% the farm fuel use resulted in a 2% decrease in whole-farm GHG emissions intensity.

A bar graph.
Fig. 4. Average change in GHG emissions intensity across farms and years compared to baseline estimations, with the implementation of alternative management strategies related to manure management. SLS: manure solid-liquid separator.

Conclusions

Farm-gate NMBs were low or negative, particularly for P. Additional nutrient imports, coupled with nutrient management planning, adequate legume stands, and diet balancing may help improve nutrient balances. GHG emissions varied largely across farms, with enteric fermentation, feed production, fuel and energy use, and manure management representing the largest sources. Management changes that resulted in the greatest GHG emissions intensity reductions included increasing milk production per cow and implementing manure treatment systems in farms with liquid slurry storages.

Full citation

This article is summarized from our peer-reviewed publication: Olivo, A.J., O.F. Godber, K. Reed, D.V. Nydam, M. Wattiaux, and Q.M. Ketterings (2024). Greenhouse gas emissions and nutrient use efficiency assessment of six New York organic dairies. Journal of Dairy Science https://doi.org/10.3168/jds.2024-25004. 

Acknowledgements

We thank the participating farmers, Cornell Cooperative Extension (CCE) Educators Janice Degni, April Lucas, Paul Cerosaletti, Dale Dewing, and CCE intern Mikala Anderson for sharing, collecting and processing data, and giving feedback on findings. We appreciate the support of the COMET outreach team at Colorado State University. This project was funded by The Sustainability Foundation at Cornell University, a gift from Chobani, and the Department of Animal Science at 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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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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Enhancing nitrogen management in corn silage: insights from field-level nutrient use indicators

Agustin J. Olivo1, Kirsten Workman1,2, Quirine M. 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

              Optimizing nitrogen (N) management in corn silage production can help improve farm profitability while reducing potential environmental impacts derived from N losses in dairy farms. One strategy to monitor and improve nutrient management at the field level is the calculation of end-of-season field balances, the difference between nutrients supplied to the crop, and what is removed with harvest. Ideal field-level N balances are positive, but not excessively large.

Fig. 1. Nitrogen pools considered for N supply and N uptake when calculating field-level N balances.

              To assess the use of field N balances as an evaluation tool, field-level N balances (N supply – N uptake) and associated N use indicators were derived for 994 field observations from eight NY dairy farms across NY. Available and total N balances per acre, which differed only in the fraction of manure N accounted for (plant-available N or total N), yield-scaled N balances, and N uptake/N supply were calculated (Fig. 1).

Key findings

Nitrogen use indicators varied widely

              The median balance across all fields was 99 lbs/acre for available N and 219 lbs/acre for total N. Excluding soil N contributions reduced these medians to 26 lbs/acre for available N and 145 lbs/acre for total N. Median N uptake/N supply were 0.60 (available N) and 0.41 (total N). Balances varied by farm, ranging from 41 to 145 lbs/acre for available N and from 126 to 338 lbs/acre for total N (Fig. 2).

Two bar graphs.
Fig. 2. Relative frequency distributions for available nitrogen (N) balances per ac (A), and total N balances per ac (B), for all observations across farms and years.

Nitrogen supply considerably affected N use indicators

              Nitrogen supply was a bigger driver for N use indicators than N uptake (Fig. 3), suggesting that decisions on N inputs influence N use indicators more than yield itself. Larger balances were associated with high N supply and low-yielding fields, indicating that for those fields factors other than N supply limited yield. These could be in-season factors that prevent a field from achieving its yield potential (such as extreme weather events and pest problems), or (semi) permanent limitations (such as shallow depth to bedrock, subsurface compaction, and drainage issues) not acknowledged in N application planning.

Manure-N availability impacted N use efficiency

              The database showed a wide range of manure and fertilizer N supply to fields. Available manure organic and inorganic N played the largest roles in explaining the variability of N use indicators, with available N balances increasing with an increase in manure N supply. The study showed a 0.2 unit decrease in fertilizer N application on average in corn fields, with a 1 unit increase in available N from manure. This suggests that manure is valued as an N source, but its N content is not credited to the full extent possible, resulting in larger N balances at the end of the season.

Scatter plot.
Fig. 3. Available nitrogen (N) balances per acre, as related to N uptake and available N supply. Each data point represents a field*year observation in the database.

Sod-N crediting impacted N use efficiency

              First year corn fields showed reduced fertilizer and manure N applications than 2nd through 4th year fields (Fig. 4). Average available N balances (black dots in Fig. 4) for 1st year corn were, however, slightly larger than for fields with no sod N credits, suggesting opportunities for further reductions in nutrient allocation to 1st year corn.

A bar graph.
Fig. 4. Area-weighted average available nitrogen (N) from fertilizer and manure applications (colored bars), across all farms and years and for different stages of the crop rotation. Blue numbers (line one) on top of the graph represent number of observations in each category, and green numbers (line two), the area-weighted average N credits from sod for observations in each rotation stage. Black bolded numbers on top of each bar represent the sum of the area-weighted average available N from fertilizer and manure. COS1, COS2, COS3 = first, second and third crop year of corn silage after sod.

Farm animal density was associated with N use indicators

              At the whole-farm level, N balances per acre were positively related to animal density (animal units per acre) and impacted by farm crop rotations and within-farm allocation of manure N (Fig. 5).

Fig. 5. Relationship between farm animal density and (A) area-weighted farm averages for available nitrogen (N) balance, and (B) total N balance. Dotted horizontal gray lines represent the area-weighted average for each dependent variable across farms and years. AU = animal unit = 1,000 lbs of live animal weight.

Conclusions

              Nitrogen supply impacted N balance indicators more than N uptake (yield) and N balances tended to increase with larger farm animal density. Adjusting N supply based on realistically attainable yield, fully crediting manure and sod N contributions, improving manure inorganic N utilization efficiency, optimizing animal density, and/or exporting manure can aid in improving field N use indicators over time.

Full citation

              This article is summarized from our peer-reviewed publication: Olivo A.J., K. Workman, and Q.M. Ketterings (2024). Enhancing nitrogen management in corn silage: insights from field-level nutrient use indicators. Frontiers in Sustainable Food Systems 8. https://doi.org/10.3389/fsufs.2024.1385745.

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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