Research – Page 5 – What's Cropping Up? Blog

July 27, 2016

Winthrop Square Park Project: Using Cornell University’s Comprehensive Assessment of Soil Health in an Urban Environment

Lindsay Fennell, Bob Schindelbeck, Aaron Ristow, and Harold van Es
Soil and Crop Sciences Section, Cornell University

Chuck Sherzi Jr.
Sherzi & Company, LLC

Introduction

Soil health has recently captured the attention of farmers in the U.S. and internationally, yet there are many applications that expand beyond the field of agriculture. The green urban landscape has great potential for improving soil quality. In city parks and greenways, compaction from both human and machine activity, and the mixing of topsoil and subsoil during routine park maintenance can affect soil functions such as plant growth, water infiltration, and the support of biological life. Although soil degradation is visible in many parks, a systematic approach to characterize soil health has only recently been applied to urban landscapes.

Cornell University’s Comprehensive Assessment of Soil Health (CASH) provides an assessment of soil health relative to important soil physical, chemical and biological processes. In both rural and urban settings, chemical indicators such as pH and macro/micro nutrients are often at optimal levels due to lime and fertilizer applications, yet the soil itself can still be physically and biologically degraded. CASH provides a full picture of what’s going on below the surface. It outlines the interconnected processes causing constraints, which in turn empowers land managers to make informed decisions about soil amelioration and future maintenance.

By promoting urban soil health, cities can create positive environmental outcomes such as flood protection, groundwater recharge, and sequestration of dust and carbon, while providing a more comfortable urban climate through healthy plants. Recently, CASH was evaluated as a tool for a renovation project in Boston, Massachusetts, which is a notable example of the application of the test for soil health management in city parks.

Case Study: Winthrop Square Park

Pocket parks (or mini parks) play an important role in city life, whether it’s sitting on a park bench, strolling through a bit of green in an urban jungle, or the neighborhood kids playing a game of kickball. These small parks, sometimes no larger than ¼ acre, provide a safe and inviting environment for community members. They support the overall ecology of the surrounding environment, landscape and heritage, and empower local residents to make decisions that affect their community. Boston has hundreds of these outdoor parks all over the city, and one of the oldest and most beloved is Charlestown’s Winthrop Square Park.

Figure 1 1852 McIntyre map of Boston showing the Training Field in relation to City Square and the Bunker Hill monument. By this time the Training Field site was becoming more park-like. Photo taken from the Cultural Landscape Plan for Winthrop Square Park.

The park, also known as the “Training Field”, is a .89 acre green space with a 400 year history. It was a training ground for colonial militia in the 1640’s, was witness to the Battle of Bunker Hill in 1775, served multiple functions as a civic space throughout the centuries, and most recently became a hotspot for Charlestown residents and a stop along the Freedom Trail. Although this space is deeply significant to the community and the city itself, it has been many years since restorations took place. Along with other much needed rehabilitation work, it faced drainage and erosion problems, and the overall soil health was lacking in many areas.

The park renovation project developed out of a cultural landscape report, prepared by Kyle Zick, Landscape Architect, and Shary Page Berg, Landscape Preservationist, in partnership with two local community groups, the Charlestown Preservation Society and the Friends of the Training Field. The Boston Parks and Recreation Department led the way and a park renovation proposal was approved by the City, along with $690,000 from its Capital Budget.

Site Analysis

Figure 2 Aerial view of the park and the six unique sampled areas

Sherzi & Company LLC was part of the consulting team brought in to address the drainage and soil health issues at the site. Owner Chuck Sherzi had extensively used the CASH approach in previous projects, and recommended to employ this holistic approach to address the concerns facing the park. The complete diagnostic report included data analysis and interpretations from the Cornell Soil Health lab, site observations, and a detailed summaries of suggested recommendations and appropriate construction materials. Since the site is naturally divided into six unique areas and each had a specific set of challenges (foot traffic, grade elevation, water flow, etc.), separate soil samples were collected from each area (Figure 2).

The Comprehensive Assessment of Soil Health

The CASH approach emphasizes the integration of soil biological, physical, and chemical measurements. These include soil texture, available water capacity, soil penetration resistance (compaction), wet aggregate stability, organic matter content, soil proteins, respiration, active carbon, and macro- and micro-nutrient content (see soilhealth.cals.cornell.edu/ for more details). The results are synthesized into a comprehensive soil health report with indicator scores, constraint identification, and management suggestions. The report can be used by consultants and managers as a baseline assessment and to guide soil amelioration and future management.

Winthrop Square Soil Health Results

The entire site showed soil health concerns, with overall quality scores of low to medium. Specifically, the CASH reports (Figure 3) showed:

Physical Indicators

Figure 3 Summary page from the Area 5 Soil Health report

Aggregate stability (indicating soil resistance to disintegration from rainfall) and available water capacity (indicating the soil’s ability to store water) scored high on all six assessments, possibly a result of minimal soil disturbance at the site over the years.
Surface (0 – 6 inches) and subsurface (6 – 18 inches) hardness (indicating compaction that limits root growth, water transmission and plant access to nutrients and water) scored mostly low and medium throughout the site.

Biological Indicators

The indicators generally scored medium or low, suggesting marginal soil biological activity. Although several areas tested within range for total organic matter, active carbon was constrained at four sites, indicating a lack of biologically available food and energy within the organic matter.
All six areas scored medium in the root health rating, most likely due to compaction.

Chemical Indicators

pH and minor elements scored low or medium in five of the six areas.
Heavy metals in all areas were found to be within the allowable concentrations for garden soil and were therefore not a concern for this project.

Example of Detailed Problem Spot: Area #5 (Figure 3)

Area 5 was the second largest of the areas assessed, experiencing a high amount of foot traffic and patchy consistency throughout. It had the lowest soil health score, with the major constraints being surface and subsurface compaction. While the organic matter scored high, other biological indicators were relatively low. The sandy loam texture could be a factor in the loss of nutrients through leaching, and the low nutrient base cations Mg++ and Ca++ could be associated with a low pH. A layered approach was proposed for Area 5 and all other areas assessed.

Recommendations and Implementations

Results of the assessment highlighted soil compaction as the major underlying constraint common to all six of the Winthrop Square areas. Issues with the physical structure of soil eventually leads to negative impacts of both biological and chemical components. Compacted soils have decreased pore space that can limit infiltration, increase runoff and erosion potential, and allow for anaerobic conditions that are unfavorable for beneficial microbial communities. They can also limit plant access to nutrients and water. Given these results, recommendations for the site were focused on decompaction measures followed by incorporation of organic amendments to improve nutrient cycling, pH, and the overall biological health of the soil. Sherzi also recommended amending the sandy loam soil with biochar and a green manure to break up the surface hardness, aid in nutrient retention and further prevent runoff and erosion.

Soil Decompaction Techniques

A multi-tiered approach was used to addressing soil compaction. Due to the natural slope at the site, any remediation effort had to be careful not to destabilize the existing upper soil profile. The project was done in phases where Areas 1, 2, 3 and 4 were done together as they do not impact the daily flow of foot traffic in the park. Areas 5 and 6 were addressed separately as they are the two largest spaces and have the most foot traffic (Figure 2). The compaction issues were addressed using the following tools/techniques:

Figure 4 Air spade used to aerate and break up soil at Winthrop Square Park.

Air Spading: Useful for tree root collar work and excising of girdling roots. The specialized tool uses compressed air to dislodge, breakup, and aerate compacted soil. Soil amendments are then added and “stirred” into the existing soil using the air tool.

Figure 5 Example of vertical composting.

Vertical Composting: Utilizes air tools to open up holes in the soil along a predetermined grid pattern in turf areas. Soil amendments are then added to these holes and graded over. The compaction layer is slowly broken up by the microbes as the holes begin to coalesce, reducing compaction and improving the overall soil health.

Figure 6 Radial trenching pattern around tree at Winthrop Square.

Radial Trenching: much like the root collar technique, this approach works on a pattern of trenches, radiating from the trunk of the tree, either dug by hand, by machine or by air tool. Soil amendments are then added to help stimulate fibrous roots of the tree.

In addition to the physical decompaction work, incorporating organic matter, both dry and liquid, was critical for maintaining balanced soil biological communities. Dry and liquid soil amendments used in conjunction with the de-compaction work: compost, calcitic lime, bio-char, fish hydrolysate.

Maintenance Recommendations

Potential long term management solutions for the park include top dressing with compost, adding fertilizer, limestone for pH, and fungal foods, grass mowing at 3.5-4” height, and keeping pedestrian foot traffic on walkways. An irrigation system was installed along with moisture sensors to determine proper watering amounts.

Project Progress

The Winthrop Park Project is scheduled for completion in summer 2016, including attention to the hardscape –new concrete, relining the ‘Freedom Trail’, fencing, etc. CASH, with its site-specific soil health analysis and holistic management approach, proved to be effective for soil testing in the urban environment. Using this method, a cherished piece of green-scape for the Charlestown community set an example for how cities can approach future soil health monitoring in city parks.

For more information about CASH, please visit soilhealth.cals.cornell.edu. For more information about the project contact Chuck Sherzi Jr. at csherzi@comcast.net.

July 22, 2016

Precision Ag Project Examines Topographical and Soil Nutrient Influences on Yield

Authored by: Savanna Crossman, Research Coordinator, CCA
Statistical Analysis by: Margaret Krause, Cornell University PhD Student

*This article is part of an ongoing series. Previous articles can be found at www.nycornsoy.org/research*

Model Validation Underway in NYS

This Precision Ag Research Project debuted its variable rate seeding prescription model on over 500 acres of corn and soybean this spring. The prescriptions were designed to maximize yield using each field’s specific soil, topographical, and historical characteristics. Though this project is focused on using the highest resolution grid management, the data can also be used to guide growers at various levels of precision ag adoption.

Field Level Management

Growers in the Northeast know the challenges of farming the highly varied soils of the region. It is not atypical for one grower to be managing loams, mucks, and gravelly soils across their farm or even across one field. Loamy ground is traditionally treated as the best ground, where growers might try to drive up seeding rates to increase their yields. Other, less productive fields, are usually sown with lower seeding rates as an insurance policy against flooding or other expected hindrances to yield.

Figure 1. The yield of P0216 for Field 8 at the four experimental seeding rates.

The data collected over the past four years of this project are beginning to suggest that this traditional knowledge does not always hold true. For example, Field 8 located in Sackets Harbor, is considered by the grower to be highly productive land. The Pioneer recommended seeding rate for the hybrid P0216 is 30,800 sds/ac in order to reach the typical yield in this field. In 2015, this field yielded the same at 27,000 sds/ac as it did at 42,000sds/ac (Figure 1).

In this field, driving up the seeding rate did not result in driving up yields. If the grower had planted the entire field at 27,000 sds/ac instead of the recommended rate, the grower would have saved $13.30/acre in seed cost or about $1000 in this 75 acre field.

This emphasizes the importance of knowing the productivity of the land and managing each field individually. There are dramatic gains to be made if growers can begin to manage the extreme variations of the landscape instead of the falling back on whole farm averages. Once a grower has become comfortable with this type of management, thinking at a subfield level will yield a further economic advantage.

Grid Level Management

Grid level management is the highest resolution of management being used in the Northeast. This involves using a combination of precision ag technologies such as grid soil sampling, multi-hydraulic drive planters, and RTK GPS.

Figure 2. As-planted data layer displaying elevation acquired via RTK-GPS (field: SW 1). Credit: M. Krause

For this case study, the analysis is focused on a 117 acre field in Union Springs, NY. The field, SW1, exhibits large variations in the topographical landscape with elevations ranging from 512 to 638 feet above sea level and slopes as high as 14.3 degrees (Figure 2). Given these known factors, it is expected that topography will have a significant impact on yield as well as the variation in soil characteristics. To investigate these hypotheses the field was sown with a split planter containing the corn hybrids P0216AM and P0533AM1 in 2015. In the same year, fourteen soil characteristics were measured and geo-located on ½ acre grids across the field.

The hybrid P0533AM1 planted at 27,000 sds/ac resulted in the highest average yield while P0216AM planted at 42,000 sds/ac resulted in the lowest average yield. However, the planting rate was only able to explain 3.1% of the variation in yield here. This serves as strong indication that other factors in the field are contributing to the variation in yield.

As previously mentioned, elevation and slope were expected to have a signification effect on variation in yield. In order of importance, slope, elevation, and curvature exhibited significant correlations with yield and were able to explain 15.5% of yield variation.

The model then added the fourteen soil characteristics to the topographic factors and was able to explain 45.6% of the yield variation. The typical nutrients and soil properties, organic matter, P, K, Ca, and Mg, that are known to support corn growth exhibited significant positive correlations with yield.

Figure 3. Correlations between soil characteristics evaluated via grid soil sampling and topographical characteristics at SW 1.

The strongest relationships indicated that higher levels of magnesium (r=0.54) and pH were found in areas of higher elevation, most likely due to shallower depth to bedrock (Figure 3). The bedrock is more susceptible to weathering and releases nutrients as it broken down by physical disintegration and chemical decomposition. Given the soil type and location, it is likely that the bedrock in this area is limestone dominated by the dolomite mineral (CaCO³ × MgCO³). During the weathering processes, the bedrock releases magnesium and calcium, as well as other minerals, into the soil. Given their stronger charge than other cations, Mg²⁺ and Ca²⁺ take the place of other nutrients on negatively charged soil particles. This results in a more basic pH and higher Mg and Ca concentrations on hilltops and side slopes where the depth to bedrock is lower.

Most other soil nutrients were found in lower concentrations in areas of high slope or elevation. This aligns with the general knowledge that soil, and thus soil nutrients, can be easily eroded away from the tops of hills and along steep hillsides. Not surprisingly, phosphorus was the most prone to exhibit a negative relationship with slope. Phosphorus can easily be lost from the soil profile from water runoff or soil erosion. (Figure 3)

High ground and hillsides tended to yield the highest in 2015, which was expected given the above average rainfall throughout the year. It is likely that water was pooling in the low flat areas of the field, negatively impacting root growth and nutrient uptake, thus accounting for lower yields. When compared to a year with dry to average rainfall, it would not be surprising to see these relationships switch.

Figure 4. The distribution of planting rates predicted to be optimal at SW1.

Relationships such as these that are heavily driven by climactic conditions make multiyear testing and historical data of the utmost importance when creating a variable rate seeding prescription. Based on the data available, the model generated a seeding prescription that calls for much lower seeding rates than the grower’s typical practice of 35,000 sds/ac (Figure 4). This prescription is estimated to save the grower approximately $1800 in seed cost without sacrificing yield, however, the 2016 research harvest will test the true performance of the model.

The extreme variability of the Northeast has presented challenges to growers for decades, and finally the research and technology is here to capitalize on this variability. The data analysis has already begun to call into question agronomic theories that were once considered fact. Most notably, the demonstration that the most productive ground can support equally high yields at seeding rates four to five thousand below standard practice. The analysis emphasizes how yield is driven by the interaction between many factors such as elevation, slope, and soil nutrients. SW1 demonstrated how topographic factors can drive yield in field and also how it can influence the spatial distribution of soil nutrients. As the project begins to test these relationships through model validation, it remains critical that the geographic reach of testing locations continues to expand. Any interested parties are encouraged to contact Savanna Crossman at 802-393-0709 or savanna@nycornsoy.com .

May 26, 2016

Weed Seedbanks on Local Organic Farms

Sandra Wayman, Brian Caldwell, Chris Pelzer, and Matthew Ryan
Sustainable Systems Cropping Lab, Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University

The goal of this project was to quantify soil weed seedbanks in the fields of four local organic farms. In particular, we have heard from organic farmers that red clover (Trifolium pratense L.) often volunteers profusely in some fields after tillage. This may be due to the common practice of harvesting red clover for seed on these farms, during which a substantial number of seeds shatter and are dropped on the field. Under these conditions, red clover could act either as a valuable nitrogen-fixing resource or perhaps as a weed.

We asked four organic grain farmers to identify “weedy”, “clean”, and “high clover” fields on their farms. We took soil samples from these fields, did greenhouse germination bioassays to allow weed seeds to germinate from the soil samples, and then counted the seedlings of each species. This report details the results from the first year of sampling, which is being repeated this year.

Sampling Protocol
Fields were sampled in the spring of 2015, before spring weed germination. Our sampling protocol was to divide each field into four evenly sized quadrants and take 30 soil cores from inside each quadrant. We avoided sampling from headlands. We used a 5/8^th inch diameter soil probe and sampled to a depth of 8 inches. Field conditions generally made for easy soil sampling. Soil samples were kept in a cooler until greenhouse weed seedbank bioassay. We also dried separate subsamples of the soil to calculate gravimetric water content and estimate bulk density.

Greenhouse Germination Bioassay

Figure 1. Flats of germinating weed seedlings in the greenhouse for the weed germination bioassay.

In the greenhouse, 1 kg of soil from each quadrant was spread out in flats (10 x 10 inches) over a thin layer of vermiculite and watered daily (Figure 1). Weed seedlings were identified to species (or at least genus, if unknown), counted, and removed from the flat. After all weed seedlings were removed, the flats were left to dry for a few weeks, soil from each flat was mixed, and then watering began again to encourage a second flush of weeds. Data presented are from both flushes of weeds.

Red Clover Seedbank
Red clover was the 6^th most common species accounting for approximately 5% of all emerging seedlings. Our results show that these organic farmers do in fact have red clover in their seedbanks, bolstering their observations that the red clover seedbank likely increased via intentional planting and losses from seed harvest. The average density of red clover ranged from 0 to 23 seedlings per kg soil across all 12 fields (Figure 2). Half of the fields had over 4 seedlings per kg of soil, which is quite a lot. For example, one seedling per kg of soil equates to over 100,000 seeds per acre in the top inch of soil—enough for a good stand of red clover.

Figure 2. Average red clover seedling counts standardized per kg of soil from three fields on four organic farms. Error bars indicate standard error.

Interestingly, across all four farms, red clover populations were greater in the field chosen as “weedy” compared with the field chosen as “high clover.” This suggests that these organic farmers might not be as good at identifying which fields actually have the highest red clover populations. The red clover seedbank was densest in the fields from Farm 3 (Figure 2). This farm has been under organic management for over 30 years, and occasionally they harvest red clover for seed. Compared with Farm 3, red clover seedling emergence was much lower in the “high clover” fields on the other farms. In the spring of 2016 on Farm 2 we observed an abundant stand of red clover in the “high clover” field, even though red clover was not planted the previous year (Figure 3). Oats had been the previous crop in this field in 2015.

Figure 3. Red clover volunteers in the “high clover” field at Farm 2 on March 25, 2016. Oats were grown in 2015 and no red clover seed was planted in 2015.

Weed Seedbank Density
On Farm 3, total weed seedlings were comparable to the other farms and similar among the three different fields chosen by the farmers (Figure 4). Specifically, the “weedy” field on Farm 3 was no weedier than either the “clean” field or the “high clover” field.

Total weed seed density in the “weedy” fields on farms 1, 2, and 4 tended to be higher than those in the “clean” fields. This indicates that the farmers were pretty good at knowing what fields were weedy and what fields were not. Although the total seedbank size might seem large, these densities are comparable to other studies in organic and non-organic fields. More importantly, experienced organic farmers are typically able to manage competition from weeds and grow high yielding crops, so even high weed seedbank densities are not necessarily a problem.

Figure 4. Total seedling counts from four organic farms by field, standardized per kg soil, from greenhouse bioassay.

Weed Species Diversity
Most weed species observed across the four farms were summer and winter annuals and there were fewer perennials (Figure 5). The top 12 species accounted for over 75% of all seedlings counted in the greenhouse bioassay. Pigweed (redroot and Powell), giant foxtail, common ragweed, and common lambsquarters dominated weed counts and are warm-season species. The winter annuals purslane speedwell and wild mustard also occurred at high density. Path rush, yellow woodsorrel, and broadleaf plantain are low-growing, relatively non-competitive perennials. Fleabanes (Erigeron spp.) include both annuals and perennials.

Because of the relatively diverse rotations on these farms, which include warm season grains, winter cereals, and perennial legumes, a high diversity of weed species was expected. The weed community that we observed would persist by reproducing and replenishing the soil weed seedbank when conditions allowed.

Figure 5. Total counts of the top 12 most frequently occurring weed species on all four farms.

Average weed species richness (i.e. number of species per kg soil) ranged from 6 species per kg soil in the “high clover” field from farm 1 to 27 species per kg soil in “weedy” field from farm 4 (Figure 6). As some weed species might provide benefits similar to cover crops, and greater biodiversity is typically assumed to be beneficial, high species richness can be good, especially when combined with low weed seedbank density. However, as the number of weed species in a field increases, so does the probability that a highly competitive/problematic weed species will be present in the community.

Figure 6. Total weed species richness (i.e., number of species) standardized per kg of soil in three fields on four farms, from greenhouse bioassay.

As not all weed species are equal in terms of their potential to reduce crop yields, we list the top species accounting over 50% of all individual seedlings counted on each farm below. Species are listed in order of abundance.

Although no single weed was part of the top 50% across all farms, common ragweed was dominant at three farms and several other species were dominant at two farms. This indicates that these weeds are likely common on other organic farms in the region. We are currently repeating this study and will be evaluating the correlation between weed abundance and weed community composition between 2015 and 2016. We also analyzed a subsample of soil for soil health properties and will be testing the relationship between soil properties and weeds.

This work was supported by grant proposal titled Agroecological Strategies for Balancing Tradeoffs in Organic Corn and Soybean Production from the Organic Transitions Program (ORG) National Institutes for Food and Agriculture (NIFA) U.S. Department of Agriculture (Project: 2014-51106-22080). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture.

Special thanks goes to Scott Morris for weed identification assistance and to the four anonymous farmers for the use of their fields.

September 17, 2015September 17, 2015

What’s Cropping Up? Volume 25, Number 4 – September/October

The full version of What’s Cropping Up? Volume 25, No. 4 is available as a downloadable PDF and on issuu. Individual articles are available below:

September 16, 2015

NYCSGA Precision Ag Research Project Update: Variable Rate Seeding Prescription Model to Begin Testing in 2016

By: Savanna Crossman, Research Coordinator, CCA

Multiple years of data collection and research have led to the creation and testing of a variable rate seeding model customized to the conditions of New York State. Thanks to the statistical analysis by Cornell Professor, Dr. Michael Gore, and PhD student, Margaret Krause, the Precision Ag Project is gearing up to test this model on select fields in 2016.

The Cornell research team has spent the summer analyzing the 2014 project data. The team has been examining the data using different statistical techniques to create a model that will select hybrids and population rates given certain soil properties and characteristics.

The Data Types

For the first round of preliminary analysis, the team has focused in on six major data types; seeding rate, hybrid, topographical information, NRCS soil survey maps, Veris soil sampling data, and grid soil sampling data.

It is important to note that four of the above data types are accessible to growers who are involved in precision farming at almost any level. The variables of seeding rate, hybrid, and topographical information can all be taken from the display monitor in the form of as-applied data. Though the accuracy of NRCS Soil Survey maps can be highly varied across the State, they were included in the analysis as they are publically available and easily accessed online.

Figure 1. Veris data takes nearly continuous measurements as it travels across the field. Here, organic matter data points are shown.

The two types of precision soil sampling data used in the analysis are services that growers can purchase from several companies. Of the two methods, the Veris soil sampler provides the highest resolution of soils data for pH, electrical conductivity, and organic matter. The Veris takes these measurements every few seconds as it is pulled across a field (Figure 1). It is a very slow process, however, and requires near perfect field conditions and a highly skilled operator to obtain accurate data. For these reasons, the project only has Veris data on approximately 600 acres.

Figure 2. Grid soil samples taken on 1/2 acre grids must be interpolated to create a heat map which assigns a value to each point in the field (shown on bottom).

By contrast, grid soil sampling is significantly more time efficient and forgiving of field conditions. The grid sampler collects and geo-locates soil samples in ½ acre grids. Each sample then receives a standard fertility test which yields approximately twenty soil characteristics. As the samples are done in grids and not continuously like the Veris, the values must be interpolated to assign each point in the field a value (Figure 2).

Analysis Process

The project is in its third year of data collection with 2700 acres and ten growers involved over that time. This article will only discuss the preliminary analysis of one field in 2014 for demonstrative purposes. The project will release more thorough results and analysis as it is completed.

Figure 3. The experimental prescription divides the field into two acre blocks and randomly assigns each block one of four seeding rates.

This section will walk through the model analysis of the field and demonstrate how the model generates a variable rate seeding prescription. The analysis presented is focused on a 2014 grain corn field located in Sackets Harbor, NY. The field is in a corn-soybean rotation with conventional tillage and 30” rows. It was planted with a split planter using the hybrids P9675AMXT and P9690AM and the project’s randomized design prescription (Figure 3).

Figure 4. The percent of corn yield explained by each data type.

When the model was run, it was determined that hybrid and seeding rate together only accounted for 4.2% of the variation in yield (Figure 4). Some growers would have expected to see these two factors contributing more to the yield picture, however, this simply tells us that there are several other important variables that need to be included in the model for this field.

Figure 5. Topography data shows a largely uniform field in slope and elevation.

Topography is one such variable. Due to the glaciation of the soils across New York State, topography can be highly varied in a single field. As this field is located in Sackets Harbor, the topography happens to be fairly uniform. There are some low spots, but the field is largely flat with some downward sloping around the edges (Figure 5). As a result of its uniformity, the topography data of elevation, slope, aspect, and curvature only account for 11.2% of the yield variation (Figure 4).

Figure 6. The NRCS soil survey map shows six soil types in this field.

figure6_legend — Figure 6. The NRCS soil survey map shows six soil types in this field.

The NRCS soil survey maps also represented this field as fairly uniform. Though the map shows five different soil types in this sixty acre field, the soil types are fairly similar in terms of their sand, silt, and clay content (Figure 6). The model determined that in this case, the NRCS soil survey map explained less than 1% of the yield variation and was therefore dropped from the model.

In the next phase of analysis, the model has added in the precision soil sampling results. As expected, these data help to explain more of the yield picture. The Veris samples captured organic matter and cation exchange capacity at this location. While only capturing two soil properties, the Veris data was able to explain 8.3% of the yield variation (Figure 4).

When the grid soil sample results were run in the model, it was determined that they accounted for 40.4% of the yield variation (Figure 4). As this method of sampling captures over twenty soil parameters, this result is not unexpected. It does, however, emphasize the huge potential that grid sampling holds in the development of variable rate seeding prescriptions.

The full model combines all of the above variables and determines how much they collectively contribute to yield. In this field, 50% of the yield variation was explained using just these four data types (Figure 4). As the model combined the data types, it was able to progressively explain more yield. This suggests that these data, the grid sampling in particular, are capturing a large amount of the variation in the field. As the analysis continues, more data types such as precision weather and crop health will be included in the model to explain more yield.

Generating a Variable Rate Seeding Prescription

Figure 7. The model generated variable rate seeding prescription for each of the hybrids.

The end goal of this model is to use it to make predictive variable rate seeding prescriptions for growers. This is done by breaking the field into small grids and running the model in reverse. Now, the model is given all the soil and topographical characteristics and asked which seeding rate will optimize yield, or profit, for that grid and hybrid (Figure 7).

It is important to recognize that a prescription written for maximum yield can look quite different from one written for maximum profit. These prescriptions can shift dramatically as the commodity prices change which emphasizes the importance of watching the markets and input costs to make the best management decisions.

Figure 8. The model generated variable rate seeding prescription for a multi-hybrid planter using P9675AMXT and P9690AM.

As a result of multi-hybrid planters coming onto the market, the model was also run to determine the optimal seeding rate and hybrid choice for each grid (Figure 8). In this scenario, a visible planting rate-hybrid interaction can be seen between the two maps as each hybrid has optimal seeding rates given certain field and soil conditions. These example prescriptions showcase the potential that the project has to evolve and meet grower needs as agricultural technology continues to rapidly advance. To demonstrate the cost benefit of utilizing these technologies, a brief economic analysis was done comparing the model to grower practice.

Table 1. Economic Analysis of the Model Predicted Prescriptions

The economic analysis of the variable seeding rate prescriptions is promising for this field. The grower typically plants a flat rate of 35,000 seeds/ac at this location but the model predicts that it can dramatically increase that with the variable seeding rate prescriptions it has generated. For P9690AM the model predicts an increase of $24.18/ac and $60.46/ac for P9675AMXT (Table 1). For hypothetical purposes, if the grower was to use a multi-hybrid planter with these two hybrids, the model predicts a profit increase of $91.56/ac.

Looking Forward

These results show a huge potential for the success of variable seeding rate technology as well as precision soil sampling in New York State. As it is still in its pilot state, the model generated variable rate seeding prescriptions will begin testing on select fields in 2016. As the analysis continues, additional statistical techniques will be used and additional years of data will be incorporated to make the model more robust. It can be expected that in 2017 the model will be released on a larger scale.

This first pass of the data has demonstrated that as more data types are added, more of the yield is explained. As a result, the project would like to expand the breadth and resolution of the collected data types through precision weather data, precision UAV data, and expanded precision soil sampling.

Looking to the 2016 season, the project is aiming to expand grower involvement to help further the development of the model. Increasing total acreage across the State will help to capture more climatic and topographical variation. This is critical in creating a model that can be used accurately by growers in all regions of the State.

A key to this expansion will be increasing the acreage with precision soil sampling data. In order to facilitate this, the project offers the grid soil sampling at 50% cost-share on all acres that are committed to participate in 2016. This Fall, there will be a round of post-harvest grid soil sampling, and those interested in sampling at that time are encouraged to contact the project as soon as possible.

Anyone interested in participating in the research project or the precision soil sampling is encouraged to contact Savanna Crossman at savanna@nycornsoy.com or (802) 393-0709.

July 13, 2015September 29, 2015

Double Cropping Winter Cereals for Forage Following Corn Silage: Costs of Production and Expected Changes in Profit for New York Dairy Farms

Hanchar¹, J.J., Q.M. Ketterings², T. Kilcer^2,3, J. Miller⁴, Kitty O’Neil⁵,M. Hunter⁶, B. Verbeten¹, S.N. Swink², and K.J. Czymmek⁷

¹Northwest New York Dairy, Livestock, and Field Crops Program, Cornell University, ²Nutrient Management Spear Program, Department of Animal Science, Cornell University, Ithaca, New York, ³Advanced Ag Systems, ⁴Cornell Cooperative Extension of Oneida County,⁵Cornell Cooperative Extension of St Lawrence, Franklin, Clinton, Essex Counties, ⁶Cornell Cooperative Extension of Jefferson and Lewis County, and ⁷PRO-DAIRY, Cornell University

Weather extremes in 2012 and 2013 impacted corn silage and hay yields for many dairy farms in New York, prompting a growing interest in double cropping of winter cereals for harvest as high quality forage in the spring. From 2012 to 2014, forage yields were measured for 19 cereal rye fields and 44 triticale fields in New York where the winter cereal for forage followed corn. Yields averaged 1.62 and 2.18 tons of dry matter (DM) per acre for cereal rye and triticale, respectively, and 71% of all fields in the study exceeded 1.5 tons DM/acre (Ketterings et al., 2014). To learn from farmers’ experiences, 30 New York farm managers that had grown winter cereals for forage were interviewed. Surveyed farmers planted, on average, 8% of their tillable acres to winter cereals with the intent to harvest as forage. Triticale was most frequently used (70%), typically seeded with a drill (57%). Farmers identified timely fall seeding as the biggest challenge with double cropping of winter cereals. Despite challenges and production questions, 83% of the surveyed farmers planned to continue to grow double crops.

Examining the Economics of Double Cropped Winter Cereals for Forage

The economic analysis sought to answer three questions: (1) What are the costs of production associated with double cropped winter cereals for forage following corn silage?; (2) What are the expected changes in profit associated with double cropping?; and (3) What yield levels ensure that adoption of a double cropped winter cereal will be a profitable change? For this analysis, five general scenarios were defined (Table 1).

Producers helped to describe the machinery complement, including size of tillage, planting and harvesting machinery, tractors, and self-propelled units for three dairy farm sizes: 100, 500 and 1,000 cows (Table 2). Scenarios reflected cultural practices, hours per acre by task, input use, and other factors typical or recommended for the region. Cost concepts, including variable and fixed costs, machinery costs based upon hours of use per acre, and others, were used to estimate costs of production for different scenarios. Lazarus (2014) provided machinery ownership and operating cost per hour estimates. All analyses reflect 2014 price levels.

Partial budgeting was used to estimate changes in profit associated with the double crops versus no winter crop, where profit equaled value of production, income minus the costs of inputs used in production. A partial budget analysis answers four questions: (1) What increases in value of production are expected?; (2) What decreases in costs are expected?; (3) What decreases in value of production are expected?; and (4) What increases in costs are expected? The first two items combine to increase profit, while the third and fourth items combine to decrease profit.

Costs and changes in profit resulting from key variable alternatives were estimated. For costs of production estimates, N application at spring green-up was set at 0 or 75 pounds per acre (two scenarios, based on early research), while expected winter cereal forage yield was 1, 1.5, 2, 3, or 3.5 tons DM per acre (five scenarios, reflecting yield distribution realistic for New York). To estimate the sensitivity of expected changes in profit, we used a forage value of $130, $180, $200, or $220 per ton DM (four scenarios), and defined expected change in corn silage yield following the winter cereal crop in the rotation as 0 (no decline), -0.25, or -1 ton DM per acre (three scenarios).

Costs of Production

Costs of production per ton of winter cereal DM varied by scenario and by other key factors, including expected winter cereal yield and N needs for the winter cereal. For scenarios where the winter forage averaged 2 ton DM per acre without the need for extra N at green-up, costs of production estimates averaged $94 per ton DM and ranged from $83 for no-till in Northern NY to $118 per ton DM for conventional tillage scenarios also in Northern NY. When 75 lbs of N per acre were needed to obtain the same 2 tons DM/acre winter forage yield, costs of production estimates averaged $122 per ton DM and ranged from $111 for no-till in Northern NY to $145 per ton DM for Northern NY conventional tillage scenarios.

Expected Changes in Profit and Breakeven Yields

Expected changes in income included the value of production assigned to the winter cereal harvested as forage and expected change in value of corn silage production where appropriate. Expected increases in costs included labor; machinery repairs and maintenance; fuel, oil and grease; fertilizer where appropriate; seeds; spray and other crop expenses. The analyses also reflect depreciation, but only when it was considered to be use-related, that is, when use affects the expected years owned and/or expected salvage value. Expected changes in profit averaged across the three farm sizes varied by scenario and by other key factors (Table 3).

Where 75 lbs of N was needed at green-up to generate 2 tons of DM per acre and a 1 ton DM per acre yield decline occurred for the corn seeded after the winter cereals due to a delayed planting date, expected changes in profit ranged from -$44 to +$16 per acre for the Northern NY conventional and Western NY no-till scenarios, respectively, and averaged -$2 per acre across all scenarios. Where corn yield deceased by 1 ton DM per acre and 75 lbs N per acre was needed at green-up for the winter cereal, the break-even winter forage yields averaged 2 tons DM per acre, and ranged from 2.3 to 1.9 tons DM per acre for the NNY conventional and the two no-till scenarios, respectively (Table 3). If in the same scenario, corn silage yield was not impacted, the break-even winter forage yield averaged 1 ton DM per acre. This was further reduced to 0.7 tons of DM if no N was needed at green-up of the winter forage (Table 3).

Conclusions

Economic analyses suggest that double cropping a winter cereal for forage following corn silage has the potential to be an economically attractive, beneficial change in practice for dairy farms in NY. This includes double cropping’s role in successfully managing risks related to meeting forage needs of the herd over time. Costs of production analyses suggest that double cropped winter cereals likely compare favorably to costs and/or values of alternative forages over a range of expected winter cereal yields. Partial budget analyses suggest that adoption of double cropped winter cereals as forages could be an economically beneficial change in practice for dairy farms (expected changes in profit exceed zero over a range of key factors). Break-even analyses suggest that producers have to obtain yields around 2 tons DM per acre to ensure that a double cropped winter cereal’s expected benefits are greater than or equal to expected changes in costs under the most demanding, least favorable set of assumptions (i.e., 75 lbs N/acre at green-up and a corn silage yield reduction of 1 ton DM per acre). Results are sensitive to a number of factors including expected winter cereal yield, expected value of forage, spring N addition needed, expected effect on corn silage yield and others.

References

Ketterings, Q.M., S. Ort, S.N. Swink, G. Godwin, T. Kilcer, J. Miller, W. Verbeten, and K.J. Czymmek (2014). Winter cereals as double crops in corn rotations on New York dairy farms. What’s Cropping Up? 25(3): 31-33. Accessible at: https://blogs.cornell.edu/whatscroppingup/2015/06/16/winter-cereals-as-double-crops-in-corn-rotations-on-new-york-dairy-farms/.

Lazarus, W.F. (2014). Machinery Cost Estimates. University of Minnesota, Extension. Accessible at: http://wlazarus.cfans.umn.edu/william-lazarus-spreadsheet-decision-tools/.

Acknowledgments

Funding sources included the Northern New York Agriculture Development Program (NNYADP), a USDA-NRCS Conservation Innovation Grant, Federal Formula Funds, and Northeast Sustainable Agriculture Research and Education (NESARE). For questions about the double crop 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/.

Category: Research