A New Webtool to Support On-Farm Decision Making with Single-Strip Evaluation Trials

Srinivasagan N. Subhashree1, Rahul Goel2, Manuel Marcaida III1, Juan Carlos Ramos-Tanchez1, and Quirine M. Ketterings1

1 Nutrient Management Spear Program (NMSP) and 2Department of Electrical and Computer Engineering, Cornell University

Simplifying On-Farm Research with Single-Strip Trials

On-farm research is a powerful tool to advance crop management, providing practical, field-specific insights. However, traditional designs such as the randomized complete block design, often referred to as replicated strip trials, are often difficult to implement on-farm due to time, equipment, and labor demands. With more farms collecting yield data with monitor systems, there is now an opportunity to conduct on-farm research using the Single-Strip Spatial Evaluation Approach (SSEA), which compares yield from one field-length treatment strip (at least two harvester widths) to two control strips using a spatial model. This new approach takes into account current year yield and pre-existing field variability, and reports results per yield stability zones: Q1 (high and stable), Q2 (high and variable), Q3 (low and variable), and Q4 (low and stable).  As one farmer noted, “Multiple years with the SSEA has helped us tune in where we could be improving nutrient placement… The practicality and ease of use makes it a welcome trial on a busy farm.”

Until recently, SSEA analyses required support from NMSP staff. To help farmers and advisors conduct these evaluations independently, we developed a new web-based tool (https://ssea-nmsp-tool.shinyapps.io/SSEA-tool-CornellNMSP/) that automates the analysis and provides result visuals and downloadable reports (Figure 1).

A screenshot of the SSEA tool showing the control and treatment strips.
Figure 1. Overview of the SSEA tool displaying the uploaded inputs such as the strip location and yield stability zone maps.

New Webtool to Support Spatial Evaluation of Single-Strip Trials

A free web-based tool was developed to automate the spatial analysis and provide interpretations of trial results without the need for statistical expertise. The tool was developed with feedback from a statewide advisory committee, who helped refine the visual outputs and ensure the tool met user’s needs. The interface includes four tabs: Inputs & Analysis, Results, Report, and About. When users first open the tool, only the Inputs & Analysis and About tabs are visible; the Results and Report tabs appear once the necessary files are uploaded, and the analysis is complete.

The Inputs & Analysis tab requires four inputs: (1) treatment and control strip locations, (2) a yield stability zone map, (3) temporal average yield layers, and (4) current-year yield data. The interface is designed to be simple and intuitive, with a satellite basemap, zoom tools, and checkboxes that allow users to view each layer individually. For farmers who share yield monitor data with the NMSP as part of the New York On-Farm Research Partnership, all tool inputs other than the strip location(s) are already prepared and shared in a ready-to-use format. Detailed instructions for creating strip shapefiles are available in the user guidelines found in the About tab.

Once the inputs are uploaded, the tool generates two key visuals: (1) a donut plot showing the distribution of yield stability zones in the field and in the strips, and (2) a confidence chart that summarizes the likelihood of yield benefit or loss as a result of the management change implemented in the treatment strip. These outputs appear in the Results tab. The tool then auto-interprets these results and compiles them into editable text boxes in the Report tab, allowing users to refine the language before downloading a polished, two-page PDF report. By delivering fast and easy-to-interpret results, the tool enables farmers to evaluate more trials and helps reduce key barriers to the adoption of on-farm research.

Case Study Results

A farm in central New York partnered with NMSP to test an agricultural product in a 23.5-acre corn silage field using the single strip approach. The treatment strip was two chopper widths wide, placed away from field edges, and positioned to allow equal-width control strips on either side. Strip locations, yield stability zone maps (derived from three years of historical yield data), temporal average yield, and current-year yield data were uploaded into the SSEA webtool.

A donut plot.
Figure 2: Single-strip spatial evaluation approach (SSEA) analysis results show a donut plot for zone distribution in the field and in the treatment strips (center strip and the control areas on both sides).

The zone distribution donut plot (Figure 2) confirmed that the treatment and control strips captured the major yield stability zones present in the field. In this field, the consistently low-yielding zone (Q4) represented the largest large portion (43%). The placement of the treatment strip was such that all four zones were represented but with more datapoints for zone Q2 (35%) and Q1 (34%) than for Q2 (20%) and Q3 (11%).

Figure 3: Single-strip spatial evaluation approach (SSEA) analysis results show a confidence chart that lists the probability of yield response of a certain size from the treatment that was implemented in the strip.

The confidence chart produced by the SSEA webtool showed a high confidence (dark purple, 81-100% confident) of a yield benefit in lower yielding zones with increases of 0.5 to 0.75 tons/acre for Q3 and 0.75 to 1 tons/acre for Q4; however, for high yielding zones, Q1 and Q2, this yield increase was not seen. Thus, the product that was tested by the farmer helped improve yields in the lower yielding zones only. The economic value of applying the product can be assessed by combining the information from the confidence chart, the distribution of yield stability zones in the farm, and the costs involved with applying the product versus the value of a yield increase. If the yield benefits outweigh the costs for Q3 and Q4, any field with a substantial area of these two zones could be targeted for use of the product while applications to fields with mostly Q1 and Q2 where a yield benefit is not expected. If targeted use in portions of the field is an option, the product would be used for Q3 and Q4 zones within different fields as well. For results to stand the test of time, it is highly recommended to test products or management changes across years and across multiple fields. The SSEA approach can combine information for multiple fields and years.

Conclusions

The single-strip approach provides a practical way to evaluate management practices on-farm while accounting for within-field variability and minimizing disturbance of field operations. The SSEA webtool provides a platform to evaluate single-strip trials using yield monitor data and yield stability zone maps.

Full Citation

This article is summarized from: Subhashree, S.N., R. Goel, M. Marcaida III, J.C. Ramos-Tanchez, and Q.M. Ketterings (2025). Enhancing on-farm research with a web-based single-strip spatial evaluation tool: Design, features, and applications. Agronomy Journal, 56(3): e70264. DOI: 10.1002/agj2.70264

Acknowledgments

This research was supported (in part) by Cornell Atkinson’s Center for Sustainability, Northern New York Agricultural Development Program (NNYADP), New York Farm Viability Institute (NYFVI), New York State Department of Agriculture and Markets (NYSAGM), New York State Department of Environmental Conservation (NYSDEC), and by intramural research program of the U.S. Department of Agriculture, National Institute of Food and Agriculture, Hatch NYC‐127459. The findings and conclusions in this publication have not been formally disseminated by the U.S. Department of Agriculture and should not be construed to represent agency determination or policy. 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/.

LakeEffect: New Cornell barley primed to take craft brewing world by storm

Craig Cramer

The Cornell Small Grains Breeding Program has announced the release of LakeEffect, the first winter malting barley released by the program in its 118-year history.

“We’re excited about LakeEffect because it couples the agronomic performance farmers want with the superior malting qualities brewers and distillers are looking for,” said Mark Sorrells, professor in Cornell’s School of Integrative Plant Science (SIPS), who led the breeding effort.

“What’s truly remarkable is that we took this from first cross to commercial release in just seven years – which is incredibly fast to move a new variety to market,” he added.

Certified seed growers are expected to harvest seed crops for commercial growers in summer 2026 for fall 2026 planting. For more information on seed availability, contact the New York Seed Improvement Program at (607) 255-9869 or nysip@cornell.edu.

sorrells in barley field
Mark Sorrells with LakeEffect winter barley.

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To Grid or Not to Grid: Precision Soil Sampling for Lime, P, and K Management of Corn Fields

Manuel Marcaida III1, Kirsten Workman1,2, and Quirine M. Ketterings1

1Cornell University Nutrient Management Spear Program (NMSP) and 2PRO-DAIRY

Introduction

Soil fertility often varies within a single field, impacting crop yield and efficiency of crop inputs. Grid soil sampling offers more detailed fertility information than whole-field sampling, enabling more targeted lime and nutrient applications. But is this added precision worth the investment and what grid size should be used? In collaboration with farmers and crop consulting firms, we analyzed the results of 20 New York corn silage fields (1149 total acres) with grid sample data at 0.5-, 1.0- and 2.5-acre resolution, to assess within-field variability in soil pH, phosphorus (P), and potassium (K) levels. Recommendations based on grid sampling were compared to those derived from conventional, whole-field, composite samples.

Key Findings

Grid sampling revealed substantial variability in soil nutrient levels

Although most fields had 2-8 (average 4) different soil series represented, soil series within a field all belonged to the same soil management group. However, each field showed a considerable range in pH, soil test P and soil test K (Figure 1). For some fields, grid sampling revealed low pH, P or K areas while for other fields, hot spots were identified (Figure 2).

Figure 1. Measured pH (A, B), phosphorus (C), and potassium (D) levels across fields, based on New York guidelines. Bar length shows total field area, and color changes indicate varying nutrient levels. The percentages at the end of each bar show the area needing lime or P or K fertilizer.
Figure 2. Field maps from two sample fields (A3, A1) showing the changes in lime (A), phosphorus (B), and potassium (C) recommendations based on the size of the soil sampling grid. This suggests that grid sampling helped identify areas that were suboptimal in pH and/or phosphorus-deficient, which would have been overlooked using whole-field averages. Management classifications were based on New York’s nutrient management guidelines (http://nmsp.cals.cornell.edu/guidelines/nutrientguide.html).

Lime and fertilizer recommendations vary depending on soil nutrient variability

For lime and P, grid sampling increased the recommended amount of lime for corn in alfalfa rotations (rotation target pH of 7.0) for many of the fields (Figure 3). This suggests that grid sampling helped identify areas that were suboptimal in pH and/or P-deficient that would be overlooked with use of whole-field averages. In contrast, for several fields, grid sampling revealed areas with sufficient K, which could result in K fertilizer savings (Figure 3).

Figure 3. Comparing the total cost or potential savings when using grid-based sampling at various grid sizes versus traditional whole-field recommendations for corn in rotation with alfalfa. All prices for lime and fertilizers were based on current rates from the USDA Agricultural Marketing Service at the time of our analysis.

Grid sampling increased total fertilizer or lime recommendations for 12 out of 20 fields, discovering low-pH or soil test P and/or K deficiencies that would have been missed using whole-field averages. On the flip side, seven fields had lower lime and nutrient input costs with grid sampling because there was no need to apply lime and fertilizer in already optimally limed or fertilized areas within the fields.

Half-acre grid size provides more detailed fertility insights, but it is also more costly (both in sampling costs and analytical costs). A cost-effective long-term strategy is to start with high-resolution (0.5-acre) sampling to characterize soil fertility and establish fertility-based zones (such as low, medium, optimum, high, very high). In subsequent years, sampling can then be done per fertility zone at a lower grid resolution of 2.5 acre within each zone, significantly reducing costs while still maintaining the benefits of precision nutrient management. This approach aims to homogenize the field over time through targeted applications, potentially leading to more uniform soil conditions.

Conclusions

Grid soil sampling enables more precise fertilizer and lime application by identifying within-field nutrient variability. Although results of this study suggested that for many of the 20 fields, grid-based sampling added to the cost of production and crop input needs, it should be recognized that detection of deficient areas allows a farmer to address yield barriers. Whether grid sampling leads to higher costs or significant savings, the long-term value is in applying fertilizer and lime only where needed. A cost-effective approach could be to begin with high-resolution (0.5-acre) sampling to define zones of low, medium, optimum, high or very high fertility, followed by lower-resolution (e.g., 2.5-acre) sampling within each fertility zone in future years.

Full citation

This article is summarized from our peer-reviewed publication: Marcaida, M., K. Workman, and Q.M. Ketterings (2025). Implication of Soil Grid Sampling on Lime, Phosphorus, and Potassium Management of Corn. Agronomy Journal 117: e70074. https://doi.org/10.1002/agj2.70074.

Acknowledgments

The authors would like to thank the staff of Champlain Valley Agronomics, Western New York Crop Management Association, and participating farmers for field selection and sampling. Funding came from the Northern New York Agricultural Development Program, the New York Corn and Soybean Growers Association via the New York Farm Viability Institute, the New York State Department of Environmental Conservation and the New York State Department of Agriculture. This research was also supported, in part, by the intramural research program of the U.S. Department of Agriculture, National Institute of Food and Agriculture, Hatch 2021-22-210. The findings and conclusions in this publication have not been formally disseminated by the U.S. Department of Agriculture and should not be construed to represent agency determination or policy.

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/.

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/

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.

New York Dairies Show the Way to Reduce Greenhouse Gas Emissions

Olivia F. Godber¹, Karl J. Czymmek², Michael E. van Amburgh³ and Quirine M. Ketterings¹

¹Nutrient Management Spear Program, ²PRO-DAIRY, and ³Dairy Nutrition, Department of Animal Science, Cornell University, Ithaca, NY 14853

Introduction

              In a recent study, 36 medium to large dairy farms (>300 cows) located across New York state were assessed for greenhouse gas (GHG) emissions for the 2022 calendar year using The Cool Farm Tool. Cows were predominantly Holstein. Dairies ranged in animal density from 0.71 to 1.96 animal units per acre (one animal unit is 1000 pounds of live weight). Herds produced an average fat and protein corrected milk (FPCM) yield of 29 000 lbs per cow per year using 64% homegrown feed. Total FPCM production was 1.92 billion lbs, sold to four dairy cooperatives. This milk production represented approximately 12% of total NY milk production in 2022. 

Findings

              The GHG emission intensity ranged from 0.63 to 1.06 lb COeq per lb of FPCM (mean GHG emission intensity = 0.86 lb CO₂eq per lb FPCM). Methane was the biggest contributor, accounting for 60% of total GHG emissions on average, with enteric methane as the largest contributor (45% of total farm emissions). With several studies suggesting the US average GHG intensity of around 1lb CO₂eq per lb FPCM, this study shows these New York dairies to be leaders in sustainability.  

              The relatively low GHG emission intensity achieved by the farms in this study reflect high quality and quantity of home-grown feed, careful nutrient management, quality nutrition and high animal productivity, and for several of the farms also the installation of more advanced manure management systems such as solid-liquid separation with cover and flare, and anerobic digesters. These characteristics allow optimization of milk production through high feed efficiency, demonstrate the recognition of the value of manure offsetting synthetic fertilizer use, and the farm’s ability to take advantage of the dilution of maintenance concept through high milk yields and components. 

              Another important finding of the study is that many of the key drivers of GHG emission intensity for these farms were related to homegrown feed production and manure management, two main areas of management that also impact whole farm nutrient use efficiency. Reducing fertilizer and feed purchases not only benefits the GHG emission intensity of the farm but also contribute to improvements in whole farm nitrogen and phosphorus balances and improves farm economics. 

Highlights

•  Medium to large New York dairy farms in a recent study averaged a GHG intensity of 0.86 lb COeq per lb of fat and protein corrected milk, much lower than the national average.  

•  Manure management system (implementation of solid-liquid separation with cover and flare, and anaerobic digesters), was a major driver of lower GHG emissions on the farms.

•  Homegrown feed (both total amount and quality), heifer/cow ratio, and feed efficiency all impacted emissions with reduced emissions for integrated farms that grow a large portion of the forages fed to the cows on the farm itself, have lower heifer/cow ratios, and for farms that implemented precision feed management. 

Invitation

              The farms in this study represent a considerable proportion of New York’s milk production but expansion of the database will be needed to develop additional understanding of drivers of emissions and opportunities for improvements over time. Many of the farms that participated with 2022 data are continuing to participate now with 2023 and 2024 data. We welcome additional farms to join and would particularly also invite more farms with under 300 cows to participate to better represent the diverse New York dairy industry.  

Full Citation

              This article is summarized from our peer-reviewed publication: Godber, O.F., K.J. Czymmek, M.E. van Amburgh, and Q.M. Ketterings (2025). Farm-gate greenhouse gas emission intensity for medium to large New York dairy farms.  Journal of Dairy Science.  https://www.journalofdairyscience.org/article/S0022-0302(25)00124-9/fulltext.

Acknowledgments

              We thank the farmers and farm advisors and coops that participated in the assessment. For questions about these results or inquiries about participating, 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/

Logos of associated partners, from left to right, NMSP, Cornell University, Cornell CALS and PRO-DAIRY.