Principal Investigator: Mike Duncan*, Ian McDonald, Nicole Rabe, and Ben Rosser
Research Institution: Niagara College* / Ontario Ministry of Agriculture, Food and Rural Affairs
Timeline: April 2014 – October 2017
- Develop a web-based portal for site-specific geospatial data storage and sharing and provide transparent mathematics as a teaching tool for the Ontario grain farmer audience.
- Develop a protocol for collecting various field characteristics and creating digital layers of farm field characteristics to enable comparisons/correlations.
- Provide an electronic infrastructure that can provide algorithms and protocols to define management zones.
- Determine and demonstrate the cost/benefit (economic and environment) value of precision agriculture and variable-rate application protocols through on-farm case studies.
- Develop methods that allow producers to validate precision agriculture practices on their farms.
- The assessment of the value of data layers that can be used to create management zones, in the absence of historical yield data on the farm, to allow producers to be more confident in their management zones for their fields or adopt new methods of producing management zones.
- The validation of precision agriculture practices on farms can facilitate greater adoption of the practices and increase farm business competitiveness both economically and environmentally.
GPS-enabled farm technology offers the opportunity to divide a farm field into multiple geographically separate areas, each defined by different management practices. The ultimate value of adopting precision agriculture technologies is producer empowerment. This includes finer control of their business, the ability to spatially control inputs by matching inputs to yield potential across the field. This is done with management zones, and it challenges the current ‘blanket application’ of inputs that many farm operations still use. Managing crop inputs site-specifically allows them to be used with optimal efficiency; for instance, it could allow for inputs to be applied in a manner that considers landscape limitations, such as topographic location and soil texture within each field. Precision application of crop inputs is possible at this time; however, the tools to define defendable management zones, and validate decisions are not currently robust.
The overall purpose of the project was to validate protocols that define management zones within farm fields, and the prescription maps produced by management zones through on-farm research. The project included: farmers and their fields that had the appropriate GPS enabled infrastructure, approximately 3 years of calibrated yield maps and associated data which provided the necessary base data layers for their fields. Through field assessment, data mining, algorithm development, and equipment deployment, the project tested the geospatial management theories developed by the partners involved in the project. The Crop Portal provided the central data repository for the project and allowed access to both the data and tools created for the project. The Crop Portal also had educational value by providing transparent mathematics as a teaching tool for the Ontario grain farmer audience. The research approach used data layers to delineate management zones with the best possible resolution, and these were compared with base data, management zone theories, and identified trends. The farm consultant partners in the project worked with each producer to provide a data-based perspective of their fields, exploit the characteristics of their fields, and use the data in order to create an acceptable (within the project scope) definition of management zones and protocols for field inputs. Annual observations, rigorous field testing and monitoring assessed the viability and validity of the management zone definitions.
The Precision Ag Adoption for Ontario (PAAO) team successfully established criteria for cooperator participation in consultation with the industry partners. Agronomic management, data and equipment criteria were used to establish 20 field sites that best represent the Ontario grain farm landscape. The systematic soil sampling was completed in the spring of 2015 by SGS labs. RTK-GPS elevation data was collected on all the fields that required it. Soil sensing and mapping (e.g., gamma ray, electrical conductivity) as a proxy for soil texture variability was collected on all fields. Further, McGill University partners helped model topography/electrical conductivity (EC) data into a handful of texture samples (as budget allowed) on all fields.
In 2015, the first set of management zone maps were created, and prescription maps were created for variable rate (VR) seed and fertilizer application (primarily nitrogen (N) on corn or wheat). In some cases, farmers had very few years of historical yield data to provide to the project for the analysis. This made the establishment of the first set of management zones difficult on those fields. The project team decided to include these farmers despite not having the required sets of data layers as it was important to represent grain farmers from across Ontario, to represent different soil and climate regions.
In spring 2016 and 2017, 20 fields had successful VR population and VR-N trials implemented based on Yield Probability Index (YPI) derived management zones. All the prescription maps had automated validation built into them (i.e., uniform rate strips or “learning blocks/stamps” for agronomic validation). Spring PSNT sampling for nitrogen trials and plant counts for population trials were completed on all fields. Aerial imagery was collected by Unmanned Aerial System (project partner Deveron UAS) at least once on all fields during the 2016 season, and then twice in the 2017 growing season. The imagery was processed by OMAFRA staff and used as a crop scouting tool to detect anomalies after planting and earlier spring activities (i.e., planter skips, emergence issues, weather variables – too wet to seed areas, compaction etc.). This kind of in-season imagery allowed for additional inquiries with farmers across Ontario to identify issues that would impact resulting yield data.
Statistical work through Dr. Steve Bowley and Ken Janovicek (Research Assistant) at the University of Guelph (U of G), previously explored the best method for analyzing the spatial yield data to determine statistically significant results for crop response. This resulted in fuller analysis for the 2015 and 2016 datasets across multiple data layers. The statistical analysis of precision ag data remains in its infancy and there continues to be no recognized method of scientific validation for this technology sector.
In January 2017, the Niagara College (NC) team released the new and revised Research Crop Portal (RCP) to the full PAAO project team, including farmers, CCAs/consultants, and OMAFRA. The new RCP retained the original functionality while supplying additional tools, including an advanced cleaning tool called Delta Clean, improved data hierarchy and organization, upgraded statistics, and evolved usability and functionality. The new and improved items were proactive to improve the user experience, and to incorporate PAAO project user feedback. LandMapR tools were added to the Crop Portal and used to analyze field topography. LandMapR uses topography/elevation maps, and it produces a series of landform and hydrology maps. The LandMapR output maps helped to provide a new water flow and dynamics perspective on management zones (e.g., prescription maps to figure out how much fertilizer is in each sub-field watershed & its potential to exit by overland flow with a significant precipitation event).
The above project achievements have improved awareness of the project work, promoted good farm data management processes, and progressed the state of precision agriculture in the Ontario grain farmer community. This has increased knowledge within the target group of GFO farmers and the CCA/consulting professionals. By working through data and creating tools, the PAAO project team is providing objective answers with respect to how to utilize farm data to create management zones and execute successful variable rate farming. Dr. Duncan and the NC team worked with the OMAFRA team, Ontario grain farmers, and Ontario crop consultants to develop a better understanding of the need for good farm data management in the grain farming community. This has involved outreach at farm-specific events, and articles written by the PAAO team via the GFO magazine. The feedback from farmers and the Certified Crop Advisor (CCA)/agricultural consultant community has been very positive.
In terms of the overall project deliverables, following the 2017 crop harvest, case studies for each field in the project will be completed by OMAFRA/ U of G staff. These case studies will enable GFO members to find project results that represent their geographic region (i.e., crop rotation, specific soil type, various machinery, and management practices etc.). This will provide tangible Ontario field examples of management zone and variable-rate practices that grain farmers and crop consultants can review and implement based on their needs.
The project has shown a workflow that can be used to establish management zones in farm fields and develop prescriptions to exploit the variation identified. While management zones as a concept were in place before the start of the project, growers in general were skeptical about the zone development process and the value (economically and environmentally) of investing in the technology. The findings of this project will give people confidence that management zones can be created, and prescriptions applied. The elusive factor remains whether the management zone by variable prescription applications of seed, fertilizer or other inputs can be validated. Further work is required in this area to ensure that investments required to adopt site specific farming require a documentable return on investment to spur more farmers down this path. A great deal has changed in the technology and awareness of precision ag over the course of the project. Despite this, the industry remains in its infancy, especially in Ontario, and further work is needed to fully understand the value to farmers, industry and the public at large. A series of articles in the Ontario Grain Farmer magazine were published to share the key learnings from this project. You can find references to each article in the “Project Related Publications” section below.
External Funding Partners:
This project was funded in part through Growing Forward 2 (GF2), a federal-provincial-territorial initiative. The Agricultural Adaptation Council assists in the delivery of GF2 in Ontario.
Project Related Publications:
Duncan, M. 2015. A simple exercise that will change your views on variable rate fertilizing. Better Farming Magazine. March.
Duncan, M. 2015. Creating accurate maps – the key to precision agriculture. Better Farming Magazine. March.
Duncan, M. and Lepp, S. 2015. The challenge of “cleaning” yield data from your fields. Better Farming Magazine. August / September.
Duncan, M., Willemse, R., and Lepp, S. 2015. A simple variable-rate prescription algorithm for your fields. Better Farming Magazine. November.
Quay, M. 2019. How to use the GFO PrecAg story map. Field Crop News. April 10.
Rabe, N. 2019. Precision Ag Advancement for Ontario. Field Crop News. April 10.
The following are a series of articles on ‘Understanding Precision Agriculture’ related to this project and that were published in Ontario Grain Farmer Magazine:
Aspinall, D., Rabe, N. and McDonald, I. 2015. Understanding precision agriculture: Prescription maps and validation. Ontario Grain Farmer Magazine. December.
Aspinall, D., Rabe, N. and McDonald, I. 2015. Understanding precision agriculture: Integrating soil, landscape, and yield maps. Ontario Grain Farmer Magazine. November.
Aspinall, D., Rabe, N. and McDonald, I. 2015. Understanding precision agriculture: Management zones development process highlights. Ontario Grain Farmer Magazine. October.
Aspinall, D., Rabe, N. and McDonald, I. 2015. Understanding precision agriculture: Remote sensing. Ontario Grain Farmer Magazine. September.
Aspinall, D., Rabe, N. and McDonald, I. 2015. Understanding precision agriculture: Soil type mapping. Ontario Grain Farmer Magazine. August.
Aspinall, D., Rabe, N. and McDonald, I. 2015. Understanding precision agriculture: Soil data. Ontario Grain Farmer Magazine. June.
Aspinall, D., Rabe, N. and McDonald, I. 2015. Understanding precision agriculture: Mapping elevation and topographic modelling. Ontario Grain Farmer Magazine. April.
Aspinall, D., Rabe, N., Stewart, G., and McDonald, I. 2015. Understanding precision agriculture: An introduction to management zones. Ontario Grain Farmer Magazine. February.
Aspinall, D., Rabe, N., Steward, G. and McDonald, I. 2015. Understanding precision agriculture: The importance of multi-year yield data. Ontario Grain Farmer Magazine. March.
Rabe, N. 2019. Precision Ag advancement for Ontario: New tool for understanding research. Ontario Grain Farmer Magazine. March.
Sabljic, J. 2017. Understanding precision agriculture: Why data collection matters to your farm. Ontario Grain Farmer Magazine. December.
Sabljic, J. 2017. Understanding precision agriculture: Data networks. Ontario Grain Farmer Magazine. November.
Sabljic, J. 2017. Understanding precision agriculture: Sustainable farming contributions. Ontario Grain Farmer Magazine. October.
Sabljic, J. 2017. Understanding precision agriculture: Are you and your equipment in sync? Ontario Grain Farmer Magazine. September.
Sabljic, J. 2017. Understanding precision agriculture: The challenges of on-farm precision ag research. Ontario Grain Farmer Magazine. August.
Sabljic, J. 2017. Understanding precision agriculture: How do you know what works and what doesn’t? Ontario Grain Farmer Magazine. April.
Sabljic, J. 2017. Understanding precision agriculture: Experiences from the field. Ontario Grain Farmer Magazine. March.
Sabljic, J. 2017. Understanding precision agriculture: A perfect prescription for every acre. Ontario Grain Farmer Magazine. February.
Sabljic, J. 2017. Understanding precision agriculture: Where do management zones make sense? Ontario Grain Farmer Magazine. January.
Sabljic, 2016. J. Understanding precision agriculture: Do something with your data. Ontario Grain Farmer Magazine. December.
Sabljic, J. 2016. Understanding precision agriculture: Starting to map yield and elevation data. Ontario Grain Farmer Magazine. November.
Sabljic, J. 2016. Understanding precision agriculture: Lessons from the field – Recapping the first year of the precision agriculture advancement for Ontario project. Ontario Grain Farmer Magazine. October.