Project Details:

Boots on the Ground: Validation of Benchmarking Process Through an Integrated On-Farm Partnership

Parent Project: Boots on the Ground: Validation of Benchmarking Process Through an Integrated On-Farm Partnership
Checkoff Organization:North Central Soybean Research Program
Categories:Crop management systems
Organization Project Code:MSN240704
Project Year:2021
Lead Principal Investigator:Shawn Conley (University of Wisconsin)
Co-Principal Investigators:

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

Analysis of producer survey data performed during our previous 3-year NCSRP-funded benchmarking project revealed: (1) an average yield gap of 20-30% between current farmer yield and potential yield as determined by climate, soil, and genetics, and (2) a number of agronomic practices that, for a given soil-climate context, can be fine-tuned to close the gap and improve soybean producer profit (Rattalino Edreira et al., 2017, Mourtzinis et al., 2018). We propose here to leverage on the previous investment made by NCSRP by working with on-farm groups to bring the yield gap analysis “back to the farm”. This project will focus on showing how the producer survey data can be used to identify and strategically evaluate management changes in on-farm research settings across the US NC region. Some of these settings can also be used in highly publicized field day events, where soybean producers can observe how a key yield-enhancing management practice often cannot be fully optimized without some adjustment needed in other management practices a producer also uses (i.e., exploiting synergies).

Traditional field research has relied too much on trial and error (i.e., random allocation of trials, subjective decisions on what technology to evaluate, etc.), with little capacity to extrapolate results to farmer fields and quantify impact relative to enhancing net profit beyond extra cost of any required production input. Clearly, there is an opportunity to make field research more efficient and impactful and, by doing so, increase coordination and exploit synergisms among on-farm research groups. Such an optimization requires a robust spatial framework that delineates geographic areas with similar climate and soil and, hence, where a similar yield response to a given set of technologies is expected. This framework can definitively help determine the number and location of trials, guide extrapolation of results from research plots to farmer fields, quantify impact at local and regional levels, and, ultimately, increase the return on investment (ROI) made on on-farm research. For example, there may be an on-farm research group evaluating the same technology at multiple locations with similar climate and soil. This offers an opportunity to be more efficient, for example, by testing a different technology at some of these locations or by reallocating some of the experiments somewhere else to evaluate the same technology in an environment with different climate and soil. To summarize, we believe that, in addition to more “boots on the ground”, there is need for a framework that can help make on-farm research more efficient and impactful.
On-farm trials can evaluate a small number of technologies due to logistics and cost constraints. Hence, it is crucial to choose technologies that are most likely to improve soybean producers’ yield and profit. On-farm research has relied too much on unrealistic expectations from researchers/industry and anecdotic evidence rather than on a clear understanding of producers’ realities and problems. We believe that our previous analysis of producer survey data provides an excellent starting point to prioritize on-farm research because we have now identified more than 10 management practices that explain yield gaps in producer soybean fields in the US NC region. Another problem associated with traditional on-farm research is the strong focus on evaluating changes in single factors (e.g., late versus early planting) without sufficient attention on ‘background’ management (that is, other managements practices besides the one evaluated). A major problem with this one-factor-at-a-time approach is that the potential benefit of a technology may not be fully realized if other management factors need some adjustment for full optimization of that technology. Thus, a robust evaluation of agricultural technologies should include tactical changes in other management practices in order to exploit the synergisms among them. For example, evaluation of early planting date should include application of seed treatment, monitoring of soil temperature and weather forecasts to ensure optimal plant stand establishment, and optimization of MG. To summarize, there is an urgent need for a shift from the current ‘single-factor comparison’ model to a more meaningful and farmer-oriented “system comparison”.

Project Objectives

We propose a 3-year collaborative (interdisciplinary and interuniversity) regional project that will be co-directed by PIs at the University of Wisconsin-Madison (UW) and University of Nebraska-Lincoln (UNL), partnering with on-farm research networks in NE, WI, OH, MI, IA, MN, and ND. The primary goal of the proposed project is to evaluate agronomic practices with greatest potential for increasing soybean yields for a given combination of climate and soil (hereafter called a “technology extrapolation domain [TED]”). Such an evaluation will help to demonstrate those KEY management factors in each state (and across the US NC region) that can be used by individual producers to increase on-farm soybean yield, input-use efficiency, and net profit while minimizing the environmental footprint. We believe that the proposed project fits well within the following NCSRP key research area: “Soybean production practices, crop management and conservation through on-farm research and similar for increased yields and profitability in an environmentally sustainable manner. This may include basic and applied research that addresses soybean response to water, nutrients and water quality, climate, soil and environmental conditions specific to the North Central Region.” Because on-farm trials will be run across a wide range of climates and soils, results from the project will also be useful to producers in states other than those included in the proposal.

Project Deliverables

Briefly, the proposed project will have four major activities:
(1) Design on-farm trials. In the proposed project, we will follow a novel approach that will allow us to (i) efficiently locate field experiments, (ii) determine which practice(s) to evaluate, (ii) scale out results from these experiments to other farmer fields, and (iii) quantify regional production and ROI impact from targeted production changes. The project will leverage from the outputs of our previous 3-year benchmarking project. First, our approach will determine trial locations using the Technology Extrapolation Domain (TED) spatial framework that delineates geographic regions with similar climate-soil conditions (Rattalino Edreira et al., 2018). Second, we will determine which practice(s) to test in each TED based on a list of candidate explanatory factors for yield gaps derived from our previous analysis of farmer survey data (Mourtzinis et al, 2018). We will first use legacy data (existing data from on-farm research groups and previous NCSRP-funded benchmarking project) to evaluate the robustness of the proposed approach. Such evaluation will compare the ROI in on-farm trials following the current “business-as-usual” model versus the prescient selection model proposed here.

Our prescient approach will strategically locate on-farm trials to represent TEDs with largest soybean planted area in each state. In other words, we will prioritize environments where the potential impact of on-farm research is the largest. Fields will be chosen to be representative of the “average” farmer in each TED, that is, with yields and practices that do not deviate substantially from the average in the region. We will use data from our previous project as a benchmark to determine average yield and dominant set of practices for each TED (Mourtzinis et al, 2018). We will ensure that fields are located near an automatic weather station—understanding on-site weather conditions will allow proper interpretation of the results. Once we have determined the location of the field trials, we will determine what practice to test for (or to omit) based on (i) what the farmer is currently doing, versus a (ii) list of candidate management practices explaining yield gaps in each TED derived from our previous NCSRP benchmarking project (Rattalino Edreira et al 2016; Mourtzinis et al 2017). When designing the specific treatment for each TED, the aim will be to have a ‘system comparison’ in which we modify a management practice, but we also fine-tune other practices so that we fully capture the yield benefit associated with that change. The treatment will aim to increase farmer profit by increasing yield, or by reducing costs, or both, and doing so in a way that maximize profit and minimize environmental footprint.

The map, in the proposal, shows a (preliminary) selection of TEDs where we would like to conduct on-farm trials. These TEDs account for the majority of soybean acreage in the US NC region, and we have already identified a set of candidate explanatory factors for yield gaps in each of them through our previous NCSRP-funded project. The TED framework will help disseminate results from the on-farm trials to other producer analog soybean fields with similar climate-soil conditions. As we mentioned previously, because on-farm trials will be run across a wide range of climates and soils, results from the project will also be useful to producers in states other than those included in the proposal. Using the TED framework as basis for site selection will help on-farm research groups to better complement and coordinate their field trials to make sure that they will not have an excessive number of field trials located in one single region with similar climate and soil, while other important regions (in relation with soybean area) are not covered.

(2) Conduct on-farm trials. Based on our previous experience conducting field experiments, a large (but not excessive) number of field trials (that are not concentrated too much in a local area) is needed to detect statistically significant effect of changes in management practices and make robust recommendations. We would like to conduct a minimum of 8 field trials (one trial per farmer field) in each year and each state. Each field trial will consist of 3-4 replicated strips (size: ~40 by 500 ft) where the treatment determined by the UW-UNL core team, in consultation with the state collaborator, will be implemented. The goal is to compare the yield and profit measured for that treatment against the one attained by the producer for the rest of the field using his/her average management. We will conduct the on-farm trials over 3 years to account for year-to-year weather variation, particularly, in-season precipitation, which can be locally variant. The collaborator in each state and his/her technician will be responsible for conducting field trials following UW-UNL guidelines, input results into a digital file, and send it to the core team. Our collaborators will also request farmers to report information about their yield, field location, and detailed information on crop/field/input management, such as planting date, soybean variety, tillage method, etc. as well as to submit grain samples to UW for seed composition. Individual field data and producer contact information will remain confidential. Indeed, the TED framework will ensure confidentiality of producer data because, once a field has been contextualized relative to its climate and soil, the exact field location has no value. Participating farmers will also complete a grower production survey to get input costs for economic analysis. Given the funding cycle of NCSRP we propose to initiating a third year for the project where we can establish research plots and identify farms to serve as on-farm learning laboratories where collaborators can sponsor field days and events to communicate results locally. Collaborators will request a no-cost extension till Dec 2021 to ensure that experiments are concluded satisfactorily and data are processed. PIs will also evaluate results from years 1-2 to decide how/what to evaluate in the third year.

(3) Data analysis. Once the data are provided, they will need to be standardized into a single, consistent format, error-checked, and then inputted into a digital database. We will use a range of state-of-art methods to analyze the data from field experiments, including remote sensing, crop modeling, spatial analysis, and advanced statistical techniques (e.g., machine learning). We will make use of the expertise on analysis of farmer data acquired by the UW-UNL team during the previous benchmarking project to retrieve detailed data on weather, soil, and topography for each field-year trial. UW and UNL will be responsible for data analysis and will collaborate with faculty at the Statistical Departments at UNL, UW and ISA to validate our statistical analyses. Our analysis will be based on the aggregated database and results of the analysis will not specifically pinpoint individual producer fields. The database will be saved in a secured server, which will be accessible only to those involved in the project. After the end of the project, the state-specific databases (yield, management, soil, weather) will be (with NCSRP permission) turned over to the on-farm groups for use by them, particularly if they want to continue the annual on-farm trials to build longer term databases for their use in knowing more about their producer constituents.

(4) Communication and dissemination of results. Results of the proposed project will be disseminated to producers and public via peer-reviewed scientific and Extension publications, presentations at scientific conferences and Extension events sponsored by universities, natural resources districts, growers’ associations, and proprietary organizations that market their products to soybean producers. Individual state and combined regional reports will be posted and distributed through various webpage portals such as, SRRI, and the NE CropWatch website ( UW will organize team meetings at the ASA/CSSA/SSSA meetings and/or Commodity Classic. Moreover, participating farmers will be actively involved with this research project. Given the project timeline, additional funding will be required to see this third year of research through harvest.

Progress of Work

Updated March 19, 2021:
We had an annual kick-off meeting with project collaborators in November 7, 2020. At this meeting we discussed project objectives, outputs and logistics, and we agreed on minor protocol changes to enhance the experimental design and resulting output. Project PIs Grassini (NE) and Conley (WI), along with Dr. Juan Ignacio Rattalino Edreira (NE), Dr. Jose Andrade (NE), Dr. Spyridon Mourtzinis (WI), Mr. Adam Roth (WI) and Mr. John Gaska (WI) continue to supervise data collection and are responsible to quality control the data and input them into a digital database. The NE-WI core team has had bi-monthly Skype calls to discuss and monitor project progress. The core team has also developed and distributed detailed field protocols and data collection methods to ensure consistency in the experiments conducted across states. State collaborators were requested to identify fields before April 15, 2021. The number of collected fields until March 19th 2021 are 18.

The NE-WI core team actively promotes and distributes output from this project. In-season live Twitter interviews with participating growers occurred in the 2019 growing season with farmers in IA, WI, IL, and OH. The core team also developed an Extension publication with year 1 and 2 results that was widely distributed and is housed on SRII and In short, the “Improved system” increased yield by 3.2 bu/ac (5.5 bu/a in 2019) and profit by $31/a ($51/a in 2019). Protein, oil, and AA data were also collected. For more information, please see the full publication entitled: Boots on the Ground: Validation of benchmarking process through an integrated on-farm partnership: 2020 on-farm trials report. This is posted on

The NE-WI core team has been actively utilizing the legacy data from the initial NCSRP project and others on-farm networks across the NC US region. To date, we have published seven manuscripts from these legacy data (listed below). We have also synthesized all of the data from the original Benchmarking project into an Extension publication entitled: Benchmarking Soybean Production Systems in the North Central US. This publication has been shared with all collaborators and published through multiple venues.

• Azzari, G. et al. 2019. SATELLITE MAPPING OF TILLAGE PRACTICES IN THE NORTH CENTRAL US REGION FROM 2005-2016. Remote Sensing of Environment 229: 417-429.
• Andrade, J.F. et al, 2019. Assessing the influence of row spacing on US soybean yield using experimental and producer survey data. Field Crops Research 230: 98-106.
• Rattalino Edreira, IR et al. 2020. From sunlight to seed: assessing limits to solar radiation capture and conversion in agro-ecosystems. Agricultural and Forest Meteorology. 280 107775. doi:
• Matcham, E., S. Mourtzinis, S. P. Conley, J. I. Rattalino Edreira, P. Grassini, A. Roth, S. N. Casteel, I. A. Ciampitti, H. J. Kandel, P. M. Kyveryga, M. A. Licht, D. S. Mueller, E. D. Nafziger, S. L. Naeve, J. Stanley, M. J. Stanton, and L. E. Lindsay. 2020. Management Considerations for Early and Late-Planted Soybean in the North Central US. Agronomy Journal 1-16 doi:10.1002/agj2.20289.
• Rattalino Edreira, J. I., S. Mourtzinis, G. P. Azzari, J. Andrade, S. P. Conley, J. E. Specht, and P. Grassini. 2020. Combining field-level data and remote sensing to understand impact of management practices on producer yields. Field Crops Research 257 107932 doi:
• Mourtzinis, S et al. 2020. Assessing approaches for stratifying producer fields based on biophysical attributes for regional yield-gap analysis. Field Crops Research. 254 107825
• Mourtzinis, S., J. Andrade, P. Grassini, J. I. Rattalino Edreira, H. J. Kandel, S. L. Naeve, K. Nelson, M. Helmers, and S. P. Conley. 2020. Assessing benefits of artificial drainage on soybean yield in the North Central US region. Agricultural Water Management 243 106425 doi:

Despite the reduced funding, by the end of this 3-year project, we will have validated a novel research approach that utilizes self-reported on-farm production practices, together with on-farm validation, to identify management practices with greatest impact on farm yield and profit. Consequently, we will strengthen state-to-state research collaboration through the managed coordination of the on-farm partnership and identify and communicate key management practices that increase soybean productivity and return of investment.

View uploaded report PDF file

Final Project Results

Benefit to Soybean Farmers

By the end of this 3-year project, we will have validated a novel research approach that utilizes self-reported on-farm production practices, together with on-farm validation, to identify management practices with greatest impact on farm yield and profit. We will also strengthen state-to-state research collaboration through the managed coordination of the on-farm partnership, build farmer-to-farmer networks and identify and communicate key management practices that increase soybean productivity and ROI. The potential impact of the outcomes derived from this study is significant. For example, on-farm validation of the identified management strategies across all examined regions, will impact 60 million acres of soybean across the North Central region. Potential impact of this research project is tremendous as it can be estimated based on the preliminary analysis of the results from Year 1 shown. For example, the regions examined, which includes 19.5 million acres planted with soybean, could potentially increase their production and profit by nearly 100M bushels and 1 billion US$ by tuning their management practices as done in our ‘improved’ treatment.

Performance Metrics

We proposed the following measurable Key Performance Indicators and deliverables for each component of the project for each semester: Year 3

Oct 2020- April 2021: Trial data is collected and processed. Work with collaborators to discuss year 1 & 2 results & implement plans for field labs. Compilation of year 2 results. Database populated with soil, weather and yield data for each field. Data analysis. Presentation of results at scientific and Extension events. Writing of scientific and extension articles.

May 2021- Sept 2021: Establish on-farm research and field laboratories. Analysis of entire database to determine difference in yield and profit between treatments and farmer field.

Annual: Each collaborator establishes 8 on-farm trials and identifies “learning laboratories” for field day events Final report with thorough assessment of entire database and validation of producer-data benchmarking approach. Final database with 3 years of on-farm yield, management, soil and weather for each field. Scientific and extension publications to document results of the proposed project as well as presentation at scientific and extension events.

Project Years