Category: Research

Brian Caldwell, Matthew Ryan, and Charles Mohler
Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University

In 2015, over 1000 certified organic farms were operating in New York State (NYS Dept. of Agriculture and Markets).   Nationwide, New York ranks third in number of organic farms and organic cropland harvested (USDA 2011).  Of the approximately 5000 dairy farmers in NYS, about 430 are currently certified organic.  This number is expected to rise to 500 within two years (Fay Benson, Cornell Cooperative Extension, personal communication).  Thus 10% of NYS dairy farms will then be organic.  However, organic grain production has not kept up with demand, and well over half of feed grains sold to New York livestock farmers are from out of state (Mary-Howell Martens, Lakeview Organic Grain, personal communication). Consequently, land-grant university research is needed to support more organic feed and forage production in NYS.

The Cornell Organic Cropping Systems Grain Experiment (OCS) was initiated in 2005 at the Musgrave Research Farm in Aurora, NY. The purpose of this long-term experiment is to compare four approaches to organic production. Results from 2005-2010, including the 3-year transition period, were documented previously (Caldwell et al. 2014).  This article discusses recent findings from the experiment and its future prospects.

Experimental Design

The OCS compares organic cropping systems: high fertility (HF), low fertility (LF), enhanced weed management (EWM) and reduced tillage (RT) organic cropping systems.  We consider them systems because they are different in multiple ways.  They have evolved over time to address production challenges with help from our organic farmer advisory board.  Currently, HF employs higher nutrient additions during each rotation than the others, and uses both belly-mounted and rear-mounted cultivators.  LF receives only corn starter fertilizer once during every 3-year rotation and only rear-mounted cultivators are used.  EWM has an intermediate nutrient regimen and employs both types of cultivators, short tilled fallows, and extra cultivation to reduce weeds.  In contrast to the moldboard plow-based tillage program of the other systems, RT uses a mixture of deep zone tillage, ridge tillage, and chisel plowing depending on the crop.  It has an intermediate soil nutrient regimen.

The experiment includes four replications and two rotation entry points of each system.  Plots are 30 x 100 feet and are managed with farm scale equipment. Soils are in the Lima series, relatively flat calcareous silt loams with fair internal drainage.  All systems started with a

 

rotation for the first six years (RT used other legumes instead of red clover in the spelt year).  A group of local organic farmers and extension educators advise on the management of this experiment.

Weed biomass in HF and RT systems was much higher than in LF and EWM by 2010 (Figure 1), and was reducing yields significantly.  It was decided by the OCS researchers based on advisory group input to change the rotation for HF and RT to address this issue.  The rotation for HF and RT was lengthened to six years:

 

In essence, a double crop of winter barley and buckwheat was substituted instead of corn at year 4 (2013 for EP A and 2014 for EP B).  This enabled extra mid-season tillage to reduce weeds, particularly perennials.  It also meant that no red clover was grown that year.  In the other years of the rotation, crops were similar to those in LF and EWM.

Results 2005-2010

Results from 2005 to 2010 were reported in Caldwell et al. (2014).  Briefly, applied organic chicken manure compost increased spelt yields but not corn yields.  The LF system had the best overall financial returns.  Corn was a poor choice during the transition period to certified organic production, but soybeans performed relatively well and spelt was intermediate.  After the transition period, corn yields increased and were similar to Cayuga County averages.  Organic crops with an arbitrary 30% price premium (chosen to reflect a conservative value) were more profitable than analogous conventional crops with County average yields.  In recent years, the organic premium for corn and soybeans has often been higher than 30%.  Currently (7/8/16) it is over 100% for corn and about 50% for soybeans (USDA, Chicago Board of Trade).

Results 2011-2016

Weather extremes

The current six-year cycle, starting in 2011, will finish at the end of this season for both entry points.  HF and RT will complete one 6-year rotation and LF and EWM will complete two, 3-year rotations.  The period 2011-15 was marked with a dry July (2011) and August (2012) and two very wet Junes (2013 and 2015).  OCS stands were poor and areas of crops were severely stunted in 2013 and 2015 due to insufficient drainage, but crops tolerated the dry spells.  Figure 2 shows yields of OCS crops over the period.  Yields were normalized as a percentage of Cayuga County (if available) or NYS average yields for each year and crop, then placed in two groups based on wet or “normal” years.  Buckwheat yields were not included due to lack of State or County averages.  In 2011, 2012, and 2014, yields were close to County averages for the conventionally tilled systems, whereas in 2013 and 2015 they were quite reduced.  The RT system had about 60% of County yields in all years, regardless of June precipitation.  In the wet years, the low fertility system was affected most severely.

In 2015, 8 inches of rain fell in June, whereas in 2016, the June total was only 0.74 inch, the lowest growing season monthly precipitation during this experiment.  It appears likely that such extreme weather periods will be common in the future.   Our results indicate that under organic management on this soil type, higher nutrients can ameliorate some of the negative effects of excess rainfall.  The extremely dry June of 2016 was preceded by a dry May, and drought continued into July.  Whereas the winter spelt crop looks excellent in HF, EWM, and RT as of this writing, corn growth has been slowed dramatically.  Corn harvest this fall will give us insight into whether any of these systems are better able to withstand severe dry spells.

New crop rotation

Weed biomass was reduced in HF and RT after the barley/buckwheat year in their expanded rotations (Figure 1).  Whether weed biomass will remain lower in these systems is not yet clear.  However, this strategy under our constraints was likely unprofitable.  The introduction of new crops such as winter barley and buckwheat into the crop mix often requires new equipment and knowledge.  Our buckwheat yields in particular were low because of equipment limitations and unfamiliarity with harvesting this crop. Local organic buckwheat farmers often use a swather and combine pickup head to harvest buckwheat.  The swather mows and gently windrows the buckwheat, allowing it to remain in the field to fully mature and dry. The windrows are then gathered into the combine using the pickup head.  Instead, we direct-harvested the crop, a method that can result in field losses (Bjorkman 2010).  Similarly, our inexperience with barley also resulted in some harvest losses.  Although we have not yet put together financial budgets for these crops, net returns for the barley and buckwheat with our yields would likely have been much lower than those from corn achieved in LF and EWM in corresponding years.  Our experiences mirrored those of many farmers when starting out with new crops.

Future plans

The OCS grain experiment begins a new phase in 2017.The first twelve years have yielded valuable insights into nutrient regimens, crop yields, and weed dynamics, but farmers are now facing additional challenges and attractive opportunities.  For example, climate change seems to make “normal” seasons rarer and rarer.  Extremes of rainfall and drought are encountered more frequently.  On the plus side, markets for organic dairy feed including balage and other forages are strengthening.  Buyers for crops such as sunflowers (Bob Gelser, personal communication) are looking for local producers.  Over the next year, we will work with our organic farmer advisory group to plan out the next 12 years of the experiment.  It will start in 2017 with a uniformity trial in which the same crop (sorghum sudangrass) will be grown over all plots.  This will allow us to assess cumulative effects on soil nutrients and weeds from 12 years of management using four different organic management systems.  In addition to updating management practices and data collection protocols, we will also work to improve the research site by installing new tile drainage in the alleyways between plots

This new phase of the Organic Cropping Systems Grain Experiment will explore scenarios and issues that we and our advisors anticipate will impact farmers in our region in coming years.

References
Bjorkman, T. 2010. Buckwheat Production: Harvesting.  Agronomy Fact Sheet 51.  Cornell Cooperative Extension.  Ithaca, NY.

Caldwell, B., C. L. Mohler, Q. M. Ketterings, and A. DiTommaso. 2014. Yields and Profitability during and after Transition in Organic Grain Cropping Systems. Agron. J. 106:871-880.

Chicago Board of Trade (accessed 7/25/16).    http://quotes.ino.com/exchanges/exchange.html?e=CBOT

NASS. USDA National Agricultural Statistics Service (accessed 7/25/16).https://quickstats.nass.usda.gov/

Schipanski, M.E. & Drinkwater, L.E. 2011. Nitrogen fixation of red clover interseeded with winter cereals across a management-induced fertility gradient.NutrCyclAgroecosyst 90: 105-119.

USDA. National Organic Grain Feedstuffs online price list(accessed 7/25/16).https://www.ams.usda.gov/mnreports/lsbnof.pdf

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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.

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.

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.

 

 

 

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).

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.

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).

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

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.

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.

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.