During the past decade, the agricultural sector generated massive amounts of data, which included every aspect of crop production ranging from multiple management practices and biotic yield limiting factors, to final crop yield. When these data sources are coupled with environmental data (e.g., field soil properties, weather conditions), large, robust, and valuable datasets can be created. Recently, there have been several attempts by universities (e.g., Rattalino Edreira et al., 2017; Mourtzinis et al., 2018; Andrade et al., 2019) and private sector (e.g., The Climate Corporation) to gather, aggregate, and analyze data from diverse sources to identify practices and factors that can be manipulated by farmers to increase soybean yield. Most of the approaches successfully generalized the results of the analysis across large regions; however, they were not able to identify and advise optimum cropping systems at the field level. Being able to down-scale such information to each individual farmer would increase the usefulness as applied to specific farms and cropping systems.
An optimal pre-plant pest management strategy is difficult to define with confidence. This is due to the lack of knowledge of the anticipated pest-mitigated yield pressure that may arise during the growing season, primarily because of annually variable weather conditions. As a result, many farmers apply inputs prophylactically in the absence of pest pressure, which often leads to a negative return of investment (ROI) and increases risk of pest resistance and other negative environmental impacts. Currently, there are no attempts to develop systems that will evaluate the pre- and post-plant risk of pest pressure at the field level and provide advance notification to farmers for potential pest management action. A tool capable of better predicting when and where sustainable pest management options can reliably increase ROI and thus would be useful to soybean farmers.
Another important issue with current research approaches is that the highly variable and farm-specific management costs (e.g., different farmers often pay variable prices for similar seed) is not considered. Therefore, the effect of a management practice (e.g., seeding rate) on yield is disconnected from its associated cost (e.g., $/seed bag). Although this element does not negatively affect the recommended rate for optimum yield, it makes fine-tuning of profit optimization at the farm level impossible. Consequently, input over-application, or simply input application when there is no need, reduces net returns in the short-term and can have negative economic and environmental consequences in the long-term.
This proposal will address these issues by developing large databases that incorporate farmer management decisions (rates and costs) with the presence of yield-limiting biotic factors, yield data, soil properties, in-season weather, and remote sensing data. By amassing three years of data (2022-2024) on important variables of a field’s productivity, variable data can be related to farm profitability outcomes. Specifically, the objective of this proposal is to develop a tool to optimize soybean management practices that will allow for profitability optimization at the field level. Additionally, within field variability will be quantified to further increase farmer profit. The proposed approach is an opportunity for U.S. soybean farmers to increase profitability because it will allow for efficient input/management application at the field level. Currently, a similar approach using farmer data does not exist.

We propose a 3-year interdisciplinary and collaborative regional project, that will be Co-directed by PIs at the University of Wisconsin-Madison and The Pennsylvania State University, with collaborators in OH, MI, IA, MN, NE, and ND. The primary goal of the project is to analyze farmer data (yield, management practices and associated costs) in fields located across the NCSRP region. Our primary focus will be on increased profitability through improved knowledge of yield-limiting factors and best management practices. We believe that the proposed project fits well within the following three NCSRP targeted research areas: (i) “Basic and applied research that addresses soybean response to water, nutrients, climate, soil and environmental conditions specific to the North Central Region”, (ii) “Management of weeds and weed resistance to herbicides for species of common occurrence and threat across the North Central Region” and (iii) “Soybean production practices & crop management for increased yields and profitability in an environmentally sustainable manner”.

The proposed project has five major components:
(1) Data collection via surveys. Data collection is a key component of the proposed project. Based on our previous experience working with farmer self-reported data, a large number of reporting fields, which are well distributed across soybean production regions, are needed to account for large management/soil/weather variability. Collaborators in each state working with graduate and undergraduate students and technicians will be responsible for collecting the field level data. Requested information will include yield (captured by yield monitor), field location, and detailed information on crop/field/input management, such as planting date, soybean variety, tillage method. A primary focus, which is novel from previous studies, will be on pest management decisions and costs of major inputs (seed, fertilizer, foliar product + application cost etc.).

(2) Data collection via in-season scouting. Boots on the ground field scouting in a subset of soybean fields (minimum of 8-10 different farmers; can have multiple fields per farm if they are willing) selected from diverse environmental regions (TEDs) will take place on a weekly basis. Selected fields will be chosen specifically to allow for large environmental variability and expected pest pressure. Efforts will occur on an annual basis and continue for all three years of the project in order to examine year-to-year weather and pest presence variation. Individual field data and farmer contact information will be kept strictly confidential.

(3) Data assimilation. Collected data will be standardized into a single, consistent format, error-checked, and then inputted into a digital database. We will also retrieve soil data for each individual field (using its GPS coordinates) from readily available websites (e.g., USDA-NRCS SSURGO database). Daily weather data and satellite imagery will be collected. Ultimately, for each field-year data point supplied by an individual farmer, we will have a detailed description of weather, soil, management, and health progress (via the use of in-season reflectance data and scouting), which, when taken together, will help us to identify management factors that affect yield. Ultimately, by accounting for input costs, analyzing the data will allow us to identify opportunities for improving profitability regionally and more importantly, at and across the field level.

(4) Data analysis. We will use well-known methods to analyze the data and new techniques for information extraction. We will base our approach on the use of already developed robust protocols that have resulted in a new online decision tool (see screenshot of the user-friendly interface below) which was developed by Agstat (https://www.agstat.com/cropping-system-optimizer/), a private company that we will collaborate with in the proposed work.

However, according to the developer, it uses algorithms trained on “synthetic and simulated datasets” which may not align with what farmer’s experience in their fields. At the moment, no in-season crop health information is considered nor does above model provide pre-or post-plant in-season pest pressure risk alerts. Therefore, a similar tool will be developed using farmer’s data and will be providing in-season recommendations, which we believe can result in increased accuracy and profit optimization compared to tools and products that currently exist. Given that the collected data will not have the attributes of traditional replicated field trials, where we can control potential confounding factors by replicated blocking, a combination of novel statistical approaches (e.g., multilevel models, repeated measures for spatial and temporal correlation) and machine learning algorithms (e.g., convolutional and recursive neural networks) will be used to analyze the data. We have worked with these methods to explore complex data sources, in particular to examine interactions among different variables which cannot be easily explored in traditional statistical methods (Mourtzinis et al., 2021-in review; Shah et al., 2021-in preparation). The methods we propose to use are also being applied by the industry as part of their digital agriculture programs, but our approach and analysis will differ because it will be based on the aggregated farmer database. The product of this work will be a tool (combination of sequential models and algorithms) that will have the potential to generate field-specific recommendations and early-season pest risk alerts using farmer’s data. The outcomes will provide information for optimal cropping systems and management practices that can increase farm profitability at the field level.

(5) Communication and dissemination of results. Due to the large variability in management practices and costs among different farms, results will be communicated with farmers following a 2-stage approach. In the first stage (at the end of the second year), we will perform a large-scale profit optimization across all fields and years in the database. The objective will be to identify the optimum management practices for increased profitability and report the estimated profit difference across the NC US. In the second stage (during the last growing season), a subset of farms will be selected to demonstrate the potential of the developed tool to increase farm profitability compared to what farmers typically use. We will work with our collaborators in every state to identify the demonstration farms. All results from this 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, grower associations, and proprietary organizations that market their products to soybean producers.

Update:
We held a project meeting in State College, PA, on November 8-11, 2023. We discussed project progress, objectives, outputs, and logistics. Additionally, we discussed and refined the approach for the between-season data collection and the protocol for data collection for the in-season field scouting part of the project. The between-season survey can be accessed here: https://survey123.arcgis.com/share/7a5e999e1832473a8b5fd2a2d21d234f. During the subsequent three months, all state collaborators contacted farmers to contribute to our project with their local field data. We aim to collect data from at least 10 scouted fields per state. The first two years of the project (2022 and 2023) we scouted the following number of fields per state: IA (25), MI (13), MN (3), NE (10), ND (12), OH (11), WI (25), PA (36) for a total of 135 fields. These data will serve as ground truth for subsequent analysis based on remote sensing data. Additionally, we aim to collect data from at least 100 fields with between-season data. This is currently an ongoing task for the team.

One objective of the project is the development of a script to allow real-time monitoring of fields with satellite images to detect areas, potentially linked with low yield. By utilizing the well-established relationship of normalized vegetative index (NDVI) and yield, (low NDVI values are associated with reduced chlorophyl content and therefore, reduced productivity), we will be able to detect the potentially problematic areas within each field and notify growers every 7-10 days or whenever the next cloud-free image will be available. The attached (Figure 1) is an example of the result of the script for four different NDVI thresholds.

We are finalizing the three major functions in the Open Crop Manager (OCM) platform; scouting reports (Image 2), field registration, and the production survey, to maximize data quality and ease of use for farmers. Data security features will continue to be developed to combat emerging threats, and privacy policy notices have been implemented to help farmers manage their data appropriately. Development of value-adding features such as a disease diagnostic tool, stressor occurrence alerts, and comprehensive field summaries (Image 3) is ongoing, with the diagnostic aid tool and comprehensive field summaries scheduled to be launched in spring of 2024. The OCM mobile application, which allows for off-line reports in more remote regions, is currently in beta testing and is scheduled to launch in spring of 2024. In the meantime, the OCM browser version (found at open-crop.vmhost.psu.edu) will remain optimized for mobile use, with the option of using Epicollect5 for offline mobile reporting. In testing of the tool in 2022 and 2023, we generated 7,890 scouting observations and reports. The OCM includes 32 pests, 48 weeds, and 28 abiotic issues. The scouting tool also result in the collection of 7,743 images, which we will use to develop new algorithms for image assessment.

Project PIs Dr. Shawn Conley (WI) and Dr. Paul Esker (PA), along with Dr. David Kramar (ND), Dr. Santosh Sanjel (PA), John Gaska (WI), and Dr. Spyridon Mourtzinis (WI), will supervise data collection and will be responsible for quality control of the data. The WI-PA-ND core team retrieves soil, satellite, and weather information for each field each year from existing databases. The WI-PA-ND core team holds bi-monthly virtual meetings to discuss and monitor project progress.
At the end of this 3-year project, we will have validated a novel tool (combination of models and algorithms) that utilizes self-reported on-farm production practices with associated costs to identify management practices (single and cropping systems) that can result in increased profit. A “beta version” of the online tool will be ready for in-depth, extensive validation in the growing season following project completion. We will also strengthen state-to-state research collaboration through the managed coordination of the on-farm network. The potential impact of the outcomes derived from this study is significant, including helping farmers adopt new approaches and understand the effect of management decisions on soybean yield and profitability.

The development of the tool proposed in this project should prove of significant value to producers as they will better understand the impact of decisions made throughout the production cycle in terms of outcomes at the end of the year. Additionally, as a bonus, they will recognize the value of participating in such efforts and further promote the value of this research by showcasing its benefits.

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Update:
We held a project meeting in State College, PA, on November 8-11, 2023. We discussed project progress, objectives, outputs, and logistics. Additionally, we discussed and refined the approach for the between-season data collection and the protocol for data collection for the in-season field scouting part of the project. The between-season survey can be accessed here: https://survey123.arcgis.com/share/7a5e999e1832473a8b5fd2a2d21d234f. During the subsequent three months, all state collaborators contacted farmers to contribute to our project with their local field data. We aim to collect data from at least 10 scouted fields per state. The first two years of the project (2022 and 2023) we scouted the following number of fields per state: IA (25), MI (13), MN (3), NE (10), ND (12), OH (11), WI (25), PA (36) for a total of 135 fields. These data will serve as ground truth for subsequent analysis based on remote sensing data. Additionally, we aim to collect data from at least 100 fields with between-season data. This is currently an ongoing task for the team.

One objective of the project is the development of a script to allow real-time monitoring of fields with satellite images to detect areas, potentially linked with low yield. By utilizing the well-established relationship of normalized vegetative index (NDVI) and yield, (low NDVI values are associated with reduced chlorophyl content and therefore, reduced productivity), we will be able to detect the potentially problematic areas within each field and notify growers every 7-10 days or whenever the next cloud-free image will be available. The attached (Figure 1) is an example of the result of the script for four different NDVI thresholds.

We are finalizing the three major functions in the Open Crop Manager (OCM) platform; scouting reports (Image 2), field registration, and the production survey, to maximize data quality and ease of use for farmers. Data security features will continue to be developed to combat emerging threats, and privacy policy notices have been implemented to help farmers manage their data appropriately. Development of value-adding features such as a disease diagnostic tool, stressor occurrence alerts, and comprehensive field summaries (Image 3) is ongoing, with the diagnostic aid tool and comprehensive field summaries scheduled to be launched in spring of 2024. The OCM mobile application, which allows for off-line reports in more remote regions, is currently in beta testing and is scheduled to launch in spring of 2024. In the meantime, the OCM browser version (found at open-crop.vmhost.psu.edu) will remain optimized for mobile use, with the option of using Epicollect5 for offline mobile reporting. In testing of the tool in 2022 and 2023, we generated 7,890 scouting observations and reports. The OCM includes 32 pests, 48 weeds, and 28 abiotic issues. The scouting tool also result in the collection of 7,743 images, which we will use to develop new algorithms for image assessment.

Project PIs Dr. Shawn Conley (WI) and Dr. Paul Esker (PA), along with Dr. David Kramar (ND), Dr. Santosh Sanjel (PA), John Gaska (WI), and Dr. Spyridon Mourtzinis (WI), will supervise data collection and will be responsible for quality control of the data. The WI-PA-ND core team retrieves soil, satellite, and weather information for each field each year from existing databases. The WI-PA-ND core team holds bi-monthly virtual meetings to discuss and monitor project progress.
At the end of this 3-year project, we will have validated a novel tool (combination of models and algorithms) that utilizes self-reported on-farm production practices with associated costs to identify management practices (single and cropping systems) that can result in increased profit. A “beta version” of the online tool will be ready for in-depth, extensive validation in the growing season following project completion. We will also strengthen state-to-state research collaboration through the managed coordination of the on-farm network. The potential impact of the outcomes derived from this study is significant, including helping farmers adopt new approaches and understand the effect of management decisions on soybean yield and profitability.

The development of the tool proposed in this project should prove of significant value to producers as they will better understand the impact of decisions made throughout the production cycle in terms of outcomes at the end of the year. Additionally, as a bonus, they will recognize the value of participating in such efforts and further promote the value of this research by showcasing its benefits.

By the end of this 3-year project we will have validated a novel tool (combination of models and algorithms) that utilizes self-reported on-farm production practices with associated costs, to identify management practices (single and cropping systems) that can result in increased profit. At the end of the project, a “beta version” of the online tool will be ready for in-depth extensive validation in following growing seasons. We will also strengthen state-to-state research collaboration through the managed coordination of the on-farm network. The potential impact of the outcomes derived from this study are significant.