Category: Soil Health

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 Aller1, Kaitlin Shahinian2, Joseph 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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Manure injection for corn silage in conservation till, strip till and no-till conditions

Martin L. Battagliaa, Quirine M. Ketteringsa, G. Godwina, Karl J. Czymmeka,b
a Nutrient Management Spear Program, b PRODAIRY, Department of Animal Science, Cornell University

Introduction

Conservation tillage practices and incorporation and injection of manure have increased in New York State over the last 20 years. In the future, it is expected that dairy farmers will need to make significant further progress toward no-till practices to minimize soil erosion losses and maximize soil health and carbon sequestration. Compared to surface application of manure, incorporation and injection can reduce ammonia volatilization, odor emissions and nutrient losses, particularly phosphorus (P), in water runoff. However, shallow incorporation of manure with an aerator tool or similar full-width tillage implements, while effective at retaining nitrogen (N) and P (Place et al., 2010), does not meet no-till practice standards as defined by USDA-NRCS. Injection of manure is only compatible with no-till and reduced tillage if low disturbance equipment is used. One central question is: are conservation tillage practices, including no-till planting and zone building, compatible with systems where manure is spring-injected in New York.

Field studies

manure injection systemTwo types of studies were conducted on dairy farm fields in western New York. The first study (2012-2013) evaluated the impact of zone tillage depth (0, 7 and 14 inches). This study was completed on one field in 2012 and two fields in 2013. An aerator was used for seedbed preparation. The second study (2014-2016) evaluated three intensities of conservation tillage, including no-tillage, reduced tillage (aerator without zone tillage), and intensified reduced tillage (aerator plus zone tillage at 7 inches depth). This study was conducted on two fields each year.

All fields had a zone tillage and a winter cereal cover cropping history of more than 10 years. Fields were in a dairy rotation of typically 3-4 yr corn alternated with 3-4 yr alfalfa/grass. Liquid manure was used as the primary source of soil fertility. It was injected (6-inch depth; 30 inches between injection bands) in March at a rate of about 13,000 gallons per acre (2012 through 2015) or 8,000 gallons per acre (2016) using a manure injector with chisel and sweep tools (Figure 1). Average total N content in manure ranged from 20 to 25 pounds of N per 1000 gallons. Manure P content ranged from 5 to 11 pounds of P2O5 per 1,000 gallons, while solids content varies from about 5 to 10%.

In both types of studies, zone tillage was performed in late April using an 8 row (30 inch) zone builder with subsoiler shanks and a 20-foot wide aeration tool set at a 15 degree angle pulled in tandem. Corn was planted at 15-inch corn row spacing at a rate between 34,000 and 35,000 seeds per acre between April 30 and May 13. No sidedressing of N was done given practical limitation of 15-inch corn row spacing. Each year, we measured early growth parameters (plant biomass, leaves per plant, stand density, and plant height at V5), and took soil samples at V5 that were analyzed for the pre-sidedress nitrate test (PSNT). At harvest we took corn stalks and analyzed them for the corn stalk nitrate test (CSNT), determined silage yield and dry matter content as well as forage quality parameters including crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF).

Results

Average plant density at V5 ranged between ~31,600 and 32,700 plants per acre (between 90 and 96% of the seeding rate). Reduced tillage and even omitting tillage altogether did not impact early corn silage stand density (Table 1).

In both types of studies, and for all fields, the PSNT-N exceeded 21 ppm NO3-N, indicating sufficient N from manure and soil organic matter mineralization. The PSNT results also indicate no impact of tillage practice or depth on mid-season N availability (Table 1).

Silage yield averaged about 25 tons per acre (at 35% dry matter) in the tillage depth study, with 7.8% CP. In the tillage intensity studies, yields averaged about 23 tons per acre with 7.3% CP. The results should not be compared between the two types of studies as trials were conducted on different fields and across different growing seasons. Tillage depth or intensity did not impact yield or CP content in either of the studies (Table 1).

The CSNT-N ranged between 3,235 and 3,589 ppm NO3-N in the zone tillage depth, and between 2,315 and 2,753 ppm NO3-N in the tillage intensity study, above the 2,000 ppm NO3-N optimum range. Zone tillage depth and different tillage intensities did not impact CSNT-N and both PSNT-N and CSNT-N show N was not limiting plant growth (Table 1).

Plant density and pre-sidedress nitrate test (PSNT) at V5, and corn stalk nitrate test (CSNT), silage yield [35% dry matter (DM)], crude protein (CP), and acid and neutral detergent fiber (ADF, NDF).

Conclusions and Implications

All types of tillage systems and depths performed equally well in terms of plant growth, N availability, corn silage yield and quality suggesting that reduced tillage and no-till can both be viable options to more intensive tillage for this farm. Results might be different for fields with limited history of zone building and other efforts to improve soil health. We conclude that at this farm that has made significant efforts to adopt soil health practices, manure injection followed by no-till planting or zone building can sustain yields and conserve N. No-till planting has the additional benefit that it reduces soil disturbance, risk of P runoff, as well as tillage-associated fuel, equipment, and labor costs.

Additional Resources

Full Citation

This article is summarized from our peer-reviewed publication: Battaglia, M.L., Ketterings, Q.M., Godwin, G., Czymmek, K.J. 2021. Conservation tillage is compatible with manure injection in corn silage system. Agronomy Journal. https://doi.org/10.1002/agj2.20604 (in press).

Acknowledgements

Cornell, NMSP, and Pro-Dairy logosIn memory of Willard DeGolyer, whose dedication to on-farm research inspired us all. This study was funded by the New York Farm Viability Institute (NYFVI), a USDA Conservation Innovation Grant (69-3A75-17-26), supplemented by federal formula funds. We thank the owners and the farm crew for their collaboratively work and dedication to the success of this research over the 5 years where the field studies were conducted. 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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Soil Health in New York State: Establishing Aspirational Goals and Soil Organic Carbon Sequestration Opportunities

Joseph Amsili, Harold van Es, Robert Schindelbeck, Kirsten Kurtz, and David Wolfe
Soil and Crop Sciences and Horticulture Sections, Cornell University

The soil is a foundational resource for life on earth and its health is critical to the sustainability of agriculture and food systems. Healthy soils can lead to increased profitability and resilience to extreme weather for farmers, while also contributing to many off-farm benefits, including improved water quality and climate change mitigation. Defining realistic soil health targets and goals for farmers, professionals, and policymakers is a critical step to making progress towards New York State’s water quality and climate mitigation goals.

In order to establish aspirational soil health goals and assess soil organic carbon sequestration potential (for carbon farming programs), we summarized results from 542 soil samples from across New York State with both cropping system and soil texture information (Figure 1). Each composite soil sample went through the Standard Comprehensive Assessment of Soil Health (CASH) package at the Cornell Soil Health Laboratory.

Figure 1. Cropping system and soil texture groups used to establish aspirational soil health goals.

These reports include a first attempt at developing aspirational soil health goals for NYS by soil texture and cropping system. It is based on the 75th percentile of the distribution for each biological and physical soil health indicator in each cropping system and texture grouping (Figure 2). We’ve known that soil texture determines the amount of organic matter a soil can hold and needs to be considered when defining soil health goals. But our results also indicate that cropping systems should be considered when defining soil health goals. For example, it’s not realistic or useful to hold a 1,000-acre annual grain operation to the same standards as a five-acre organic vegetable farm. These aspirational soil health goals provide realistic targets for NYS farmers within the context of their own production environment.

Figure 2. Aspirational soil health goals for soil organic matter, active carbon, soil protein, soil respiration, and aggregate stability for different cropping systems on loam textured soils. The full table of aspirational soil health goals by soil texture and cropping system are available in the reports below.

Soil health practices, including reduced tillage, cover crops, organic amendments, and perennial crops, have the potential to build and maintain soil organic carbon levels, which can help remove some atmospheric carbon dioxide (CO2). An important consideration is that soils have a limited capacity to store soil organic carbon, mostly based on texture and mineralogy. As a soil approaches its saturation point, carbon inputs in the form of plant residues or organic amendments have decreased efficiency at further increasing soil organic carbon. Once this is saturated, soil organic carbon can only build up in more labile fractions that are less protected, more readily decomposed and returned to the atmosphere as carbon dioxide, i.e., the carbon gains in that case are not permanent.

Our results show that most fields under Annual Grain and Processing Vegetable cropping systems have less soil organic carbon than their capacities based on texture class in a grassland system (Figure 3). Therefore, these cropping systems have the greatest potential to stabilize additional soil organic carbon. Conversely, many fields in Pasture and Mixed Vegetable systems are closer to their saturation levels and therefore have less potential to sequester more carbon. Dairy Crop fields are intermediate. Annual Grain and Processing Vegetable systems can build soil organic carbon by incorporating the types of management practices that make Dairy Crop, Mixed Vegetable, and Pasture soils healthier. This includes applications of composts and manure, integration of livestock, better rotations, cover cropping and reduced tillage.

Figure 3. Soil organic carbon as a fraction of the saturation potential of the silt and clay fraction across different cropping systems (based on grassland systems). Carbon sequestration potential is greater at lower saturation levels.

The results of these reports will enable New York State policy makers, agricultural professionals, and farmers to set goals for improved soil health and carbon farming within the context of their soil type and cropping system. Additionally, relative carbon saturation metrics can be used to optimize carbon allocations for soil sequestration and thereby also improve soil health.

Figure 3. The Characterization of Soil Health in New York State Summary (left) and Technical Report (right) are now available.

For more information, please visit our website: newyorksoilhealth.org

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Soil Health in New York State: Effects of Soil Texture and Cropping System

Joseph Amsili, Harold van Es, Robert Schindelbeck, Kirsten Kurtz, David Wolfe, and Galia Barshad
Soil and Crop Sciences and Horticulture Sections, Cornell University

Soil health concepts, practices, and testing have generated a growing awareness of the soil’s central role and highlights that sustainable soil management requires an understanding of biological, physical, and chemical processes and their interrelationships. Furthermore, it is recognized that human management can significantly degrade or improve the quality of the soil.

New York State (NYS), through Cornell University, has been a global leader in the development of soil health programs, including the development of testing methodologies. NYS land managers are becoming increasingly excited about improving the health of their soils. As progress is made in characterizing the health of soils nationwide, researchers will be able to develop regionally specific interpretive metrics that are shaped by the interplay of soil management with soil types and climate.

As part of that effort, we have summarized results from 1,456 soil samples from across New York State from a variety of soil types and cropping systems (Figure 1). Each composite soil sample went through the Standard Comprehensive Assessment of Soil Health (CASH) package at the Cornell Soil Health Laboratory.

Figure 1. Distribution of soil health samples by county across New York State.

The report demonstrated the important effects of soil texture and cropping system on soil health parameters (Figure 2). For many biological soil health indicators, soils with higher amounts of silt and clay showed higher values, which needs to be accounted for when interpreting test results. Overall, human management through cropping system had a big impact on soil health status, and cropping system differences often reflected the cycling and flows of carbon and nutrients. Pasture systems maintained the best overall soil health because these fields are seldom disturbed by tillage and receive year-round root and shoot inputs. Mixed Vegetable systems typically involve certified organic practices with diverse rotations, cover cropping, and significant quantities of organic nutrient amendments such as compost. Dairy Crop systems can maintain soil health due to cycling of carbon and nutrients through manure inputs and rotations with perennial legume and grass sods. In contrast, Annual Grain and Processing Vegetable systems are intensively managed, and typically don’t apply enough organic amendments to replace the organic matter that is lost each year. Typically, 40-80% of the carbon and nutrients in the aboveground biomass are exported off the farm in the form of crop harvests, which is generally not counterbalanced with regenerative soil management practices like cover cropping and organic amendment application. The results of this study (available in the Reports below) will enable New York State policy makers, agricultural professionals, and farmers to interpret soil health data within the context of soil type and cropping system (Figure 3).

Figure 2. Mean soil organic matter across soil texture groups (left, A). And mean soil organic matter across cropping systems on loam textured soils (right, B).
Figure 3. The Characterization of Soil Health in New York State Summary (left) and Technical Report (right) are now available.

For more information, please visit our website: newyorksoilhealth.org

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