IDENTIFYING YIELD-STABILITY MANAGEMENT ZONES FOR HIGHER YIELDS AND BETTER NITROGEN ALLOCATION

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 1016799
• Project Start Date: Oct 1, 2018
• Project End Date: Sep 30, 2021
• Program Code: [(N/A)]- (N/A)

Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853

Performing Department
Animal Science

Non Technical Summary
Zone management within fields can result in much better use of resources and/or more stabilized yields over time. The best indicator to design zones around is yield itself, and yield stability over time (consistency in yields from one year to another).In 2016 we introduced "yield stability zones" as a better way to differentiate fields on a farm (Long et al. 2016). In this approach, three or more years of yield data for a field are combined into one yield stability map with four zones (quadrants). The fields in quadrant 1 (Q1) yield above the farm average and do so consistently across years. The fields in Q4 are consistent as well over years but these are the low yielding fields. Fields in Q2 and Q3 are much more variable from year to year. If a farmer can determine what keeps fields or areas within fields in Q3 and Q4 from being higher yielding, there could be options to increase the overall yield of the farm over time.While managing fields differently based on yield stability determination at the whole field level is an improvement over single management for the entire farm, much can be gained by zone-based management within fields. This was a challenge until recently as there were no standardized protocols for data cleaning and processing and the number of farms with multi-year yield records was limited as well. These problems have been resolved and in 2016 we introduced "yield stability zones" as a better way to delineate management zones in a field than currently done with soil maps, EC zones, or average yield maps.With these improvements, if a farmer can determine what keeps fields or areas within fields in Q3 and Q4 from being higher yielding, there could be options to increase the overall yield of the farm over time. Similarly, if resource allocation (fertilizer, manure, seed, etc.) can be adjusted to reflect needs for the different management zones, great improvements in resource allocation can result. Here we propose to work with three farms per year to determine yield stability maps for the farm and implement N rich strips in such a way that side by side comparisons can be done for crop response to early season N addition (N rich strips) in each zone.Here we propose to work with three farms to determine yield stability maps for each farm and implement N rich strips in two fields per farm in such a way that side by side comparisons can be done for crop response to early season N addition (N rich strips) in each zone within the selected fields. This will allow us to answer the question: "Do we need to apply more (or less) N in the higher yielding zones?"Once yield stability zones are determined in year 1 for each field, zone maps will be updated (from year 1 to year 2 and 3) as extra years of yield data are added to evaluate consistency in yield-stability zones over time and impact of years of data included in the assessment on zone mapping to answer the question: "How many years of yield data should be included for mapping of yield stability zones?"Thirdly, the proposed work will involve soil and plant sampling within and outside of the N rich strip in each field and across zones, allowing for evaluation of targeted soil and plant sampling and hence more effective precision management of nutrients over time.

Animal Health Component

50%

Research Effort Categories

Basic

(N/A)

Applied

50%

Developmental

50%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
102	0199	1000	100%

Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships;

Subject Of Investigation
0199 - Soil and land, general;

Field Of Science
1000 - Biochemistry and biophysics;

Keywords

corn

soil health

yield monitoring

yield stability mapping

farmer on-farm research

nitrogen management

Goals / Objectives
Here we propose to work with three farms to determine yield stability maps for each farm and implement N rich strips in two fields per farm in such a way that side by side comparisons can be done for crop response to early season N addition (N rich strips) in each zone within the selected fields. This will allow us to answer the question: "Do we need to apply more (or less) N in the higher yielding zones?"Once yield stability zones are determined in year 1 for each field, zone maps will be updated (from year 1 to year 2 and 3) as extra years of yield data are added to evaluate consistency in yield-stability zones over time and impact of years of data included in the assessment on zone mapping to answer the question: "How many years of yield data should be included for mapping of yield stability zones?"Thirdly, the proposed work will involve soil and plant sampling within and outside of the N rich strip in each field and across zones, allowing for evaluation of targeted soil and plant sampling and hence more effective precision management of nutrients over time.

Project Methods
We will work with three farms that grow corn (at least one dairy and cash grain operation included) and have at least three years of reliable crop yield monitor data. We will process the yield data for all fields with at least three years of data (using a recently developed standardized data cleaning protocol documented in Kharel et al., 2018) to eliminate errors and then develop yield stability zones (quadrant maps) for each of those field. Of all fields with at least three years of yield data, we will select two fields with sufficient area in at least three of the yield stability zones (quadrants). We will implement an N-rich strip just prior to planting that goes through all three zones. This could be a fertilizer N application just before planting, a doubling of the manure rate across a field, or both, depending on farmer interest. Each strip will be field long and at 2-3 chopper/combine widths wide. By selecting smaller grid areas within the N rich strip and directly outside of it (sampling every 50-60 m, depending on field size), we will evaluate crop response to the extra N in each of the zones. Sampling will consist of soil sampling at two depth (0-20 cm and 0-30 cm depth) at the V6 growth stage, typically the time when farmers side-dress nitrogen to fields that are expected to benefit from the N application. The 0-30 cm samples will be analyzed for KCl extractable ammonium and nitrate according to Griffen et al. (1995). The 0-20 cm samples will be analyzed for general soil fertility including soil pH, organic matter, Cornell Morgan extractable P, K, Ca, Mg, Mn, Zn, Fe, and Al (Morgan, 1941), and the Illinois Soil Nitrogen Test (ISNT) according to Kahn et al. (2001) but modified by Klapwyk and Ketterings (2005).At silage harvest time, at each 50-60 m sampling point (consistent with the soil sampling), we will take corn stalk samples (15-35 cm above the ground) which will be analyzed for the corn stalk nitrate test (CSNT; Binford et al., 1990;1992).Advanced statistical approaches are being evaluated now as part of a USDA-NIFA grant and the expectation is to apply Bayesian statistical approaches to the dataset to determine percent crop yield increased in each of the management zones and to evaluate more targeted zone-based soil and crop sampling (soil nitrate and ammonium, ISNT, and CSNT).

Progress 10/01/18 to 09/30/21

Outputs
Target Audience:We have worked with farmers who supplied corn silage and grain yield data through 2020. Every farmwith at least three year of data received theirannual corn yield reports (silage and/or grain), their multi-year yield potential report, and their yield stability zone maps. In addition, on-farm research was developed and implemented to evaluate zone-specific management. Our main audience is corn growers (dairy farms and cash grain operations), with yield monitor data and an interest in turning yield data into actionable zone-management maps. We developed a new approach to on-farm research, called the SSEA (single-strip spatial evaluation approach) that derives statistically sound results from single-strip data in fields with yield history information (fields with at least three years of data). This approach will have benefits beyond New York. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?One graduate student, two postdoctoral researchers, and five undergraduate students contributed to the project. This is in addition to the work done by two staff members. Farmer extension talks were given and we developedfactsheets on yield data and their importance to farming, and the importance of data cleaning. The project gave two undergraduate student an opportunity to conduct an honor's thesis (one graduated last May, one graduates this May) and trained additional students on yield data processing and technology use in agriculture. How have the results been disseminated to communities of interest?Extension articles were published (What's Cropping Up?, Progressive Dairy), factsheets were published and shared with farmers and farm advisors, and extension talks were given at winter crop meetings and other events. Scientific talks were given at annual ASA/SSSA/CSSA conferences andthe 2021 First Conference on Farmer-Centric On-Farm Experimentation (OFE) that was held October 13-15. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? We developed protocols to clean raw corn grain and silage yield monitor data of errors,generate annual yield maps for farmers, and develop yield stability based zone maps (Kharel et al., 2019a,b; Cho et al., 2021). We also released a yield data cleaning manual (Kharel et al., 2018). Research with yield data from four farms with the longest history of reliable yield records showed that yield data should first be examined for the presence of a consistent yield trend over time when estimating average yield for zone delineation. Including only the most recent four to five years of data reduced the risk of misrepresenting expected average yield when there was a yield trend. Otherwise, both the temporal average and standard deviation in yield were most consistent when all years of data were included. We concluded from this research that farms interested in developing yield stability zones should use at least four to five years of yield data and continue to add new years of data for improved delineation of zones over time (Cho et al., paper in development). Development and use of reliable yield stability zone maps allows us to developed an on-farm research approach that is easy to implement for farmers, yet statistically sounds: the SSEA (single-strip spatial evaluation approach) (Cho et al., 2021). In this approach, single strips of a management change are implements across yield stability zones based on prior years. The analyses allows for determination of shifts in yield distribution and means resulting from the management change for each zone represented in the field. Research to date shows that higher and more stable yielding zones are unlikely to need additional N but additional trials will be needed in future years to evaluate this across more farms and growing seasons.

Publications

Progress 10/01/19 to 09/30/20

Outputs
Target Audience:We continued to work with the yield data from the farms that conducted 2018 and 2019 zone management evaluations, and added trials on two additional farms in 2020. Farmers collected yield data and shared those with us for processing.We continued to collaborate with colleagues in Iowa and Missouri on the statisticalapproaches to on-farm research and strengthened our collaborations with faculty at the University of Buffalo (geospatial analyses) and Rochester Institute of Technology (sensor technology). The specific focus of the work is on nitrogen management of corn across yieldstability based management zones in New York but our aim is to develop an on-farm research approach that replaced the complete randomized block design and instead uses single strip evaluations with spatial models and past yield stability information, which will have benefits beyond New York. Changes/Problems:With COVID19 forcing us to all work from home, it impacted our ability to visit farms and do on-site meetings. Thankfully, we were able to continue the work remotely with regular zoom calls. The graduate student on the project is making good progress, despite the many challenges faced this year. Would have liked to have more direct farmer contact but hopefully we can do farm visits again sometime later this year. What opportunities for training and professional development has the project provided?The graduate student presented all three of his projects (chapters) at ASA/SSSA/CSSA annual meetings. Part 1 was presented this November (virtual meeting), Jason Cho,the graduate student on the project, won the 1st price with his talk in the student presentation. Preliminary data for part 2 were presented at the 2018 meeting in San Antonio, Texas, in November of 2019. Preliminary results for part 3 were presented as a poster at the virtual meeting this fall as well. One of the undergraduate student on the project, Ben Lehman, presented his work at the Virtual meeting as well, receiving the 3rd price for his poster in the undergraduate student competition. He was awarded a Greenfield Scholarship as well. Four undergraduates are part of team that works with yield data cleaning, two of which were newly trained this year. How have the results been disseminated to communities of interest?The project also has a website:http://nmsp.cals.cornell.edu/NYOnFarmResearchPartnership/YieldStabilityZones.html.We have given presentations for extension audiences that includes discussions on zone management (in addition to the abstracts, posters, oral presentations at scientific meetings that werealready identified). Extension presentations included: Ketterings, Q.M, K.J. Czymmek, J. Cho, J. Guinness, B. Lehman, and S. Shajahan (2020). Creating and Managing Zones within Fields; a Pathway Forward. Certified Crop Advisor Annual Training. Virtual. December 2, 2020. 50 min. 126 people. Ketterings, Q.M. (2020). Know Your Yield for Nutrient Management Planning and Zone-Based Management. Aurora Field Day. August 6, 2020. Virtual. 20 min. ~80 people. Ketterings, Q.M., and K.J. Czymmek (2020). Nutrient Management Spear Program Update. Upper Susquehanna Watershed Coalition Retreat, Nichols, NY. January 30, 2020. 60 min. ~50 people. Ketterings, Q.M. (2020). Making use of yield data to make better crop management decisions. Oneida Country Crop Congress. Waterville, NY. January 7, 2020. 1 hr. 55 people. What do you plan to do during the next reporting period to accomplish the goals?We hope to wrap up the research for parts 2 and 3 and publish findings in journal articles, as well as continue to give extension talks and develop materials so farmers can evaluate zone-based management.

Impacts
What was accomplished under these goals? We added seven fields to the database this year, across three farms, where N application strips were applied across fields for which management zones have been developed. Main focus, in addition to the field work, has been on (1) development of scientifically sound protocols to convert raw yield data into reliable rasterized annual yield maps (publication accepted pending minor revision in Precision Agriculture); evaluation of the impact of years of data included in yield stability zone development on farm specific yield and variabiltiy categorization (setting of the foru quadrants of yield stability; work ongoing in collaboration with six farms); and (3) development of a spatial analyses approach that evaluates impact of the added N on yield distribution in each of the yield stability zones. Findings of part 1 shows that kriging with the Matern covariance function resultsin the most accurate corn silage and grain yield raster mapsfor both corn silage and grain, regardless of field size, year when data were obtained, or farm thaWe added seven fields to the database this year, across three farms, where N application strips were applied across fields for which management zones have been developed. Main focus, in addition to the field work, has been on (1) development of scientifically sound protocols to convert raw yield data into reliable rasterized annual yield maps (publication accepted pending minor revision in Precision Agriculture); evaluation of the impact of years of data included in yield stability zone development on farm specific yield and variabilty categorization (setting of the four quadrants of yield stability; work ongoing in collaboration with six farms); and (3) development of a spatial analyses approach that evaluates impact of the added N on yield distribution in each of the yield stability zones. Findings of part 1 shows thar kriging with the Matern covariance function resultsin the most accurate corn silage and grain yield raster mapsfor both corn silage and grain, regardless of field size, year when data were obtained, or farm that supplied the data. These results are beneficial to ensure accurate and precise spatial mapping of yield products toward optimized corn growth management and hence parts 2 and 3 use yield stabilty maps derived from multiple annual yield maps generates with kriging with the Matern covariance function. Initial findings for part 2 suggest the more years of data are included, the more stable the zone deliniation. A graduate student and a statistics undergraduate are working on parts 2 and 3 of the project supplied the data. These results are beneficial to ensure accurate and precise spatial mapping of yield products toward optimized corn growth management and hence parts 2 and 3 use yield stability maps derived from multiple annual yield maps generates with kriging with the Matern covariance function. Initial findings for part 2 suggest the more years of data are included, the more stable the zone delineation. A graduate student and a statistics undergraduate are working on parts 2 and 3 of the project.

Publications

• Type: Journal Articles Status: Under Review Year Published: 2021 Citation: Cho, J., J. Guinness, T.P. Kharel, S. Sunoj, D. Kharel, E.K. Oware, J. van Aardt, and Q.M. Ketterings (accepted pending minor revisions). Spatial estimation methods for mapping corn silage and grain yield monitor data. Precision Agriculture.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cho, J., A. Maresma, T.P. Kharel, G. Godwin, D. Kharel, K. J. Czymmek, E. Oware, J. van Aardt, J. Guinness and Q.M. Ketterings (2020). Analyzing Single Strip Nitrogen Trials for Corn through Yield Stability Based Management Zones. ASA/SSSA/CSSA Annual Meeting, November 9-13, 2020. Virtual. Abstract and Poster.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Lehman, B., J. Cho, B. Mace, C. Walden, K.J. Czymmek, and Q.M. Ketterings (2020). Do Corn Yield Stability Zones on a New York Dairy Farm Reflect within-Field Variability of Soil Characteristics? ASA/SSSA/CSSA Annual Meeting, November 9-13, 2020. Virtual. Abstract and Poster.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cho, J., A. Maresma, T.P. Kharel, G. Godwin, S. Shajahan, D. Kharel, E. Oware, Jan van Aardt, J. Guinness and Q.M. Ketterings (2020). Spatial Estimation Methods for Mapping Corn Yield Monitor Data. ASA/SSSA/CSSA Annual Meeting, November 9-13, 2020. Virtual. Abstract and Oral Presentation.

Progress 10/01/18 to 09/30/19

Outputs
Target Audience:This proposal was developed in collaboration with counterparts at DuPont Pioneer/Corteva, GrowMark FS, Champlain Valley Ag, Cornell Cooperative Extension educators and three farmers who participate in theevaluation of zone delineation based on yield and yield stability over time. All have been involved in field selection, development of zone maps, implementation of N rich strips, and collection of 2018 yield data. We collaborate with colleagues in Iowa and Missouri on the statistical approaches to on-farm data. This project is focused on development of a Bayesian approach to determining likeliness of a crop response to a treatment. The specific focus of the work proposed here is on nitrogen management of corn across yield stability based management zones in New York but the findings can have benefits for other states and internationally as well. Changes/Problems:The 2019 growing season was a very challenging season for many of the farmers in the Northeast, due to incredibly delays in planting. The season started out very wet, which caused delays in planting until the middle of June and even beyond (several fields planted in July, while normal planting month is May). Yields were late this year as a result of delay in maturing of the crop. Thus, we are only now processing 2019 yield data. What opportunities for training and professional development has the project provided?By working directly with farmers and crop consultants, we strengthen our on-farm research network, and ensure communication among participants and other farmers as well. This past year, we trained two additional consulting firms in the data cleaning protocol and several co-authored a new factsheet on data cleaning and one of zone management this past December. How have the results been disseminated to communities of interest?We established a project website:http://nmsp.cals.cornell.edu/NYOnFarmResearchPartnership/YieldStabilityZones.html. Talks were given at the ASA/SSSA/CSSA annual meeting in San Antonio, Texas: Cho, J., B. Lehman, T. Kharel, D. Kharel, K.J. Czymmek, J. Guinness, and Q.M. Ketterings. 2019. Year to Year Variability in Yield Stability Based Management Zone for Corn. ASA/SSSA/CSSA Annual Meeting, November 10-13, 2019. San Antonio, Texas. Abstract and Presentation. 64-4. Maresma, A., P. Berenguer, R. Breslauer, A. Tagarakis, T. Kharel, K.J. Czymmek, and Q.M. Ketterings. 2019. Corn Stalk Nitrate Test in-Field Spatial Variability. ASA/SSSA/CSSA Annual Meeting, November 10-13, 2019. San Antonio, Texas. Abstract and Presentation. 84-5. What do you plan to do during the next reporting period to accomplish the goals?We are working on the 2019 yield data (delayed season due to late planting across the region in 2019) to update zone maps and evaluate impact of N addition in strips on the farms. We are alsoadding a few farms with long-term records to evaluateconsistency in yield-stability zones over time and impact of years of data included in the assessment on zone mapping to answer the question: "How many years of yield data should be included for mapping of yield stability zones?" and expect to have this evaluation done in 2020.

Impacts
What was accomplished under these goals? We worked with two farms with long-term records as well to evaluateconsistency in yield-stability zones over time and impact of years of data included in the assessment on zone mapping to answer the question: "How many years of yield data should be included for mapping of yield stability zones?" The findings of this study were presented at the ASA/SSSA/CSSA annual meeting in San Antonio, Texas, in November of 2019. Work is ongoing to evaluate number of years across a larger number of farms, but initial results suggest 4-6 years of data is optimal, taking into account crop rotations of importance to New York (corn and alfalfa/grass hay rotations), and year to year weather variability. Evaluation of the relationship between yield and corn stalk nitrate test (CSNT) results suggest improvements can be made if CSNT sampling is targeted to yield zones. We are currently working on a publication to summarize those trials and findings. Yield stability zones were developed for the farms, and we are currently in the process of adding the 2019 growing season.

Publications

• Type: Journal Articles Status: Published Year Published: 2019 Citation: Kharel, T.P, A. Maresma, K.J. Czymmek, E.K. Oware, and Q.M. Ketterings (2019). Combining spatial and temporal corn silage yield variability for management zone development. Agronomy Journal (in press). doi: 10.2134/agronj2019.02.0079.
• Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Kharel, T.P., A. Maresma, K.J. Czymmek and Q.M. Ketterings (2018). Documenting Spatial and Temporal Variability in Corn Silage Yield in Farmers' Fields. ASA/SSSA/CSSA Annual Meeting, November 4-7, 2018. Baltimore, Maryland. Abstract and Poster.
• Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: 14. Ketterings, Q.M. and K.J. Czymmek (2018). 292-2 Development of an Adaptive Management Approach for Nutrient Management in New York. ASA/SSSA/CSSA Annual Meeting, November 4-7, 2018. Baltimore, Maryland. Abstract and Presentation.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Cho, J., B. Lehman, T. Kharel, D. Kharel, K.J. Czymmek, J. Guinness, and Q.M. Ketterings. 2019. Year to Year Variability in Yield Stability Based Management Zone for Corn. ASA/SSSA/CSSA Annual Meeting, November 10-13, 2019. San Antonio, Texas. Abstract and Presentation. 64-4.