Providing New York Vegetable, Fruit and Dairy Farmers with Greenhouse Gas and Carbon Management Tools

<p>NON-TECHNICAL SUMMARY:<br/> Many best management practices (BMPs) for agricultural greenhouse gas (GHG) mitigation and soil carbon sequestration could increase farmer profits if properly implemented, could lead to a new source of revenues if carbon markets develop, and have environmental, food safety, and sustainability co-benefits. However, most information currently available was developed for major world food crops and will require modification for relevance to New York's important vegetable, fruit, and dairy production systems. Preparing for climate change and new energy and climate change policies will require new tools for quantifying potential benefits of BMPs in relation to costs and risks on individual farms. We propose a comprehensive research and education outreach effort focused on vegetable, fruit crop and dairy industries of New York that will provide: (1)
BMP fact sheets for GHG mitigation and soil carbon sequestration; (2) reliable low-cost guidelines for monitoring and verification of soil carbon stocks; (3) interactive web-based GHG and carbon auditing tools for use by farmers and Extension educators; and (4) a state-wide analysis to identify key opportunities for agricultural GHG mitigation and carbon sequestration. Our BMP fact sheets (Obj 1) will be derived after compiling, evaluating, and prioritizing existing relevant information from the scientific literature, credible websites, databases and other sources. A small expert-panel focus group of faculty, Extension staff and farmers in each commodity area will contribute to this effort. To develop new approaches to low-cost soil carbon assessment (Obj 2) we will intensively sample vegetable, fruit crop and dairy field sites and use these "maximal" data sets to develop statistical
models to predict soil carbon at points and depths in the landscape not measured, and thus reduce total samples required for future sampling. We will also develop guidelines on how to use USDA soil survey, land use, crop management, and remote sensed data to prioritize and strategically choose sampling sites. We will evaluate existing and emerging generic GHG accounting tools (Obj 3) for their reliability and ease of use, and modify or redesign them for application to New York production systems. This will be an iterative process and will integrate results from Objectives 1 and 2, and other on-going research. We will use our focus groups for feedback on the web-based tools. We will use geospatial data on soil characteristics, land use, cropping history, and management, and GHG and soil carbon accounting and management tools to identify key opportunities for mitigation and soil carbon
sequestration within the state (Obj 4). Ultimately, our project will provide vegetable, fruit and dairy farmers in New York and the region with new GHG and carbon management tools that will leave them better prepared for emerging energy and climate change policies, including the possibility of carbon offset payments for carbon markets. State policy-makers will have better information on the potential for mitigation and carbon sequestration across commodity areas and regions.
<p>APPROACH:<br/> Objective 1- BMP fact sheets: We will first compile, evaluate, and prioritize existing, credible, and relevant information from the peer-reviewed literature, technical reports, websites, existing databases, and other sources. Our BMP fact sheets will include approaches to: reduce emissions (e.g., better manure management, improved nitrogen use efficiency); enhance carbon sequestration (e.g., reduced tillage, better woodlot management); and avoid or displace emissions (e.g. using crop or manure bioenergy sources).We will identify a small focus group (e.g. 5 - 10 individuals) of faculty, Extension staff and growers in each commodity area to provide input and feedback as we develop and finalize BMP fact sheets for dissemination. Objective 2- Low-cost approaches for soil carbon assessment: We will identify several vegetable, fruit crop and dairy field sites
for intensive soil sampling each year, including Cornell field research facilities and commercial farms. USDA soil survey, land use, crop management, and remote sensed data will be used to create geographic information system (GIS) maps to prioritize and strategically choose sampling sites. Soil samples will in general be collected to a 60 cm depth in 20-cm increments and samples measured for: total carbon and nitrogen (Leco combustion analyzer, St. Joseph, MI), active carbon (permanganate oxidation, Weil et al. 2003), soil organic carbon by loss-on-ignition (LOI), and visible and near infrared reflectance (VNIR) spectrometry (ASD FieldSpec Pro, Boulder, CO). We have budgeted for 175 cores (525 samples) in years 1 and 2, and 50 cores (150 samples) in year 3. Adjacent undisturbed cores will be collected for bulk density determination (to convert carbon concentration to carbon per acre). A
nested sampling design will be used to characterize field variability. Intensive sampling at some sites (10 to 15 cores per acre) will provide a "maximal" data set to develop various geospatial statistical modeling approaches to predict soil carbon at points and depths in the landscape not measured, and thus reduce total samples required. Objective 3- Develop web-based GHG accounting tools: We will evaluate existing and emerging generic on-line farm GHG accounting tools, such as COMET-Farm, derived from COMET-VR, and the USDA Dairy Greenhouse Gas Model for their reliability and ease of use, and modify or redesign them for application to New York production systems. This will be an iterative process that also will integrate new information and data gained from Objectives 1 and 2, communication with developers of COMET-Farm, and Cornell researchers. We will use our focus groups for
feedback on the web-based tools. Objective 4- Identify key opportunities for mitigation and soil carbon sequestration in NY: We will use geospatial data on soil characteristics, land use, cropping history, and management, and GHG and soil carbon accounting and management tools to identify cost-effective mitigation approaches within each commodity area. On-going research at Cornell on carbon economics and policy (A. Bento, collaborator) will be utilized for our analysis.
<p>PROGRESS: 2010/10 TO 2013/09<br/>Target Audience: New York vegetable, fruit and field crop and dairy farmers will be better educated and better informed regarding best management practices (BMPs) for energy efficiency, greenhouse gas (GHG) mitigation, and soil carbon (C) sequestration as a result of the project. These farmers will be better prepared for, and more resilient to, fluctuating energy prices and new energy and climate change regulations or incentives, including the possibility of C offset payments through emerging C markets. The results of this project feed directly into Cornell Cooperative Extension Program Work Team efforts on energy conservation and climate change. Results are also relevant to NYS Agriculture and Markets and NYSERDA interests in agricultural energy efficiency, mitigation, and the potential of C markets for farmers. This project addresses
interests of the American Farm Bureau Federation (AFBF). New York State will be better poised to develop a state-wide plan for GHG mitigation within the agriculture sector and have better information on existing inventories and databases for state planning. The entire Northeast region will benefit from our educational outreach on this topic. Our results provide an important contribution to advancing the science and methodology for low-cost soil C assessments, which could influence national and international standards for agriculture, forestry and other land uses in the C economy. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Two recently completed M.S. theses (Sam Bosco, Sonam Sherpa, see Publications) were closely linked with the soil C assessment goal, although funded separately. The lab manager partially
funded through this project, Jeff Beem-Miller, has gained considerable experience in soil analyses and statistical procedures as part of this project, and has recently submitted applications to graduate programs in soil and environmental science at UC Berkeley, UC Davis, Michigan State, Colorado State, and several other universities. Over the course of the project four undergraduates have worked with our group and gained experience in field and lab methods, GIS mapping, and other skills. How have the results been disseminated to communities of interest? During the course of this project 2 M.S. Theses, 4 peer-reviewed journal articles, 4 book chapters, 4 abstracts for scientific conferences, 2 technical reports, and 7 extension and popular press articles associated with the project have been published. One other journal publication and one other extension fact sheet are in preparation. In
addition, Wolfe has given over 30 presentations related to the project, and van Es and his group have given a similar number of presentations related to the Adapt-N web tool in particular. The extension materials, video of some of the presentations, and other outputs from the project have reached a broader audience through our www.climatechange.cornell.edu website. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported
<p>PROGRESS: 2011/10/01 TO 2012/09/30<br/>OUTPUTS: We evaluated geostatistical modeling techniques and Visible to Near Infrared (VNIR) spectroscopy for reducing soil C assessment costs for a dairy farm in Harford, NY. A Partial Least Squares Regression (PLSR) model for predicting dry combustion percent C from VNIR was first developed from 525 samples collected from other NY locations, representing a range of soil types and dairy cropping systems. This was then locally calibrated by adding 250 samples collected from a 1 hectare area of the Harford farm site to the PLSR model. A high-resolution percent C map across the entire 232 hectare area of the Harford farm was subsequently developed from this PLSR model and VNIR measured at 360 sample points. Using ordinary kriging, we found that the number of local calibration samples from the Harford farm could be reduced by 50 to
75% with minimal increase in Root Mean Square Error of percent C or decrease in spatial correlation. Soil C assessment sampling in 2012 included 24 cores at a long-term apple ground cover management study (Cornell Orchard, Lansing, NY) and 72 cores at a long-term vegetable crops-soil health study (Cornell Gates Farm, Geneva, NY). Analyses of cores (5 depth increments to 75 cm) for VNIR, total C and nitrogen (combustion analyzer), active C (permanganate oxidation), bulk density, and soil texture are in progress. Analyses for organic matter (OM) percent (loss-on-ignition) are completed. In the apple orchard site (Hudson-Cayuga soil), the pre- and post-emergent herbicide groundcover treatments showed a slight decline in OM in the top 30 cm from 1992 to 2012 (from 4.4 to 3.81 and from 3.9 to 3.5 percent, respectively), while the sod and bark mulch treatments showed increases (from 3.7 to 4.2
and from 4.3 to 7.14, respectively). All of the gain in OM in the long-term mulch treatment occurred in the top 20 cm, with no significant effect at 20 - 75 cm. In the vegetable crops site we found a significantly higher OM in no-till and ridge till plots compared to plow-till in the upper 30 cm, but no effect from winter cover crop treatments (winter rye, vetch, or none), and no effect at lower depths (to 75 cm). The rotation sequence had a significant effect on Honeyoe and Kendaia soils, but not on the Lima soil. In 2012 we began evaluating existing and emerging GHG accounting tools, such as the "Cool Farm" tool (http://www.coolfarmtool.org), and we have participated in developing and beta-testing of the COMET-Farm tool (http://cometfarm.nrel.colostate.edu/), for application for NY fruit, vegetable, and dairy production systems. Soils data to provide COMET-Farm with soil physical
parameters and initial soil C stocks needed in the DayCent crop-soil simulation model have been compiled for all mapped USDA Soil Survey (SSURGO) scale (ca. 1:16,000) map units in the US. In addition to publications (see below), in 2012 Wolfe gave 18 invited presentations (over 800 contact hours), and 22 news media interviews, including outreach through major newspapers (e.g., New York Times), radio (e.g. National Public Radio), and trade magazines, (e.g., The American Gardener). PARTICIPANTS: Nothing significant to report during this reporting period. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
<p>PROGRESS: 2010/10/01 TO 2011/09/30<br/>OUTPUTS: We addressed Objective 1 in our proposal by conducting a comprehensive search of the literature and existing websites to develop an initial educational fact sheet focused on farm energy conservation, greenhouse gas mitigation, and soil carbon (C) sequestration. This has been published and also is available on our website, www.climatechange.cornell.edu , along with other new fact sheets produced this year, such as one on adaptation to climate change (see impacts and publications). We addressed Objective 2 of our proposal by focusing in this first year on strategies for low cost soil C assessment on a 650 ha dairy farm in southern New York with corn and alfalfa rotations as well as pasture (Cornell's Harford Research and Training Center). We evaluated correlations between measured soil C and other properties and explored
options for predicting soil C through proxy measures such as percent organic matter (OM), or by using values from the USDA National Resource Conservation Service's (NRCS) Soil Survey Geographic (SSURGO) database. We collected soil cores to 60cm in 20 cm intervals across 10 combinations of crop rotation, manure, and soil type, which encompassed about 25 percent of the farm acreage. We measured bulk density (BD), soil C, nitrogen (N), OM, permanganate oxidizable active C (AC) and texture. Total soil C in the top 60 cm ranged from xx to xx t C ha-1 across the 10 cropping system combinations, with manured, continuous alfalfa on Howard (Hd) soil having the highest value, 68 percent more t C ha-1 compared to non-manured alfalfa on the same soil type (p less than 0.001). The coefficient of variation (CV) for most soil properties more than doubled at 40-60 cm compared to 0-20 cm (significantly
different at p less than 0.001), except for BD, which had a similar CV at all depths. Bulk density and OM had the lowest coefficient of variation (CV) compared to other soil properties at all depths, which reduced calculated minimum sample requirements for any desired level of confidence or magnitude of detectable difference between treatment means. We developed significant (pIMPACT: 2010/10/01 TO 2011/09/30Our new fact sheets on climate change mitigation and adaptation (see Publications) are a unique resource for farmers, Extension staff, and policy makers. The information contained in these have been distributed at several conferences and workshops in the Northeast, including Cornell's Agriculture In-Service training in November 2011. These are also available at our new website: www.climatechange.cornell.edu. During this year, in our evaluation of soil C variability on a 650 ha dairy farm with corn-alfalfa rotations and pasture identified several approaches to reduce sampling requirements especially at deeper soil depths. Specifically, we found that sample requirements for a given desired confidence level were less for
bulk density compared to C concentration. We also found that a simple linear regression model could be used to predict soil C in the entire 0-60 cm profile from measurements in the 20-40 cm depth with an r2 of 0.89. We also found that OM from the USDA SSURGO data base could be improved to better match field measurements with a simple linear regression model derived from field measurements. Some of this work has been summarized in a M.S. Thesis (see Publications), and we also plan to submit some of this work for publications in a peer-reviewed journal in the coming year. These results will help to better inform strategic sampling and reduce costs for future soil C assessments.