Project Overview
One of the primary challenges in organic production systems is maintaining crop yields comparable to those obtainable in conventional production systems. Precision agriculture, a data-driven management practice that utilizes geo-referencing technology to estimate spatial and temporal variation in order to optimize input-use efficiency, is one potential strategy to improve yields and profits in organic systems. However, due to its complex nature (as well as technological and financial barriers), adoption of precision agriculture has remained low.
On Farm Precision Experimentation (OFPE) is a novel methodology to deploy precision agricultural techniques. Unlike traditional precision agriculture, which requires extensive soil sampling and the deployment of costly proximal sensors, OFPE only utilizes free data – openly-available data from satellite sensors and data already generated by the farmers’ own equipment. OFPE places an emphasis on experimentation, with the goal being that farmers (1) adjust their crop prediction models and input rates over time to maximize net returns, and (2) gain an understanding of the spatial variation in their fields.
This study, spearheaded by researchers at Montana State University, evaluated whether OFPE can be used to develop ‘optimal’ cover crop and cash crop seeding rates on organic grain farms in the Northern Great Plains to maximize farmer net returns.
Farmer Takeaways
- Tailoring seeding rates to specific, in-field variation can result in higher organic grain and cover crop yields than if seeding is performed uniformly.
- Investing in on-farm precision experimentation (OFPE) to develop input rate models that account for in-field variability can significantly improve net returns in organic grain systems in the Northern Great Plains region, both by enhancing yields and reducing overall seed costs.
Project Objectives and Approach
To use on-farm precision experimentation (OFPE) to spatially optimize seeding rates of rain-fed cash and cover crops on organic grain farms
- Experimental fields were established at five certified organic grain farms across the Northern Great Plains. Cash crops covered in the study included spring wheat, hemp, oats, and barley, and cover crops included winter wheat and a green pea green manure. Organic nitrogen (N) fertilization sources included chicken manure, bloodmeal, and green manure.
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- The workflow of the project involved on-field experimentation, data collection, machine learning modeling, and analysis of simulated outcomes to compare seeding rate strategies.
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- When developing the OFPE models (which would be used to calculate ‘optimum’ seeding rates for each field), data were compiled from satellite sources, farm machinery (tractor monitors and combine-mounted yield monitors), and historical farm records.
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- Satellite data included soil bulk density, sand content, clay content, pH, soil water content, carbon content, slope, elevation, and topographic position index (TPI).
- When available, yield and seeding rate data from previous years were incorporated into model development and analysis.
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- Seeding rate strategies included: (1) the farmer-chosen seeding rate (FCR), defined as the rate the farmer themselves expressed as their chosen rate, applied uniformly across the field, (2) optimum uniform seeding rates (OUR), calculated by OFPE models, and (3) optimum variable seeding rates (OVR), where seeding rates varied across fields, as calculated by OFPE models.
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- Seeding rate treatments were generally placed in a checkerboard design across the field; however, in several instances, farmers reverted to a simpler strip trial design.
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To determine if OFPE-simulated variable seeding rate strategies are consistently more or less profitable than uniform seeding rate strategies on organic grain farms, and to quantify the economic costs and benefits of implementing OFPE
- Economic data were collected for each seeding rate strategy, including (1) price received for winter wheat, spring wheat, oats, barley, and hemp, (2) seed costs for each of these crops, and (3) associated fixed costs over the years (custom services, fuel, electricity, repairs, straw baling, labor, insurance, general overhead). From these data, net returns were calculated.
Key Findings
Crop Yields
- Yields between seeding rate treatments were always highest for the optimum variable seeding rate (OVR) treatment, regardless of whether the calculated and farmer-chosen rates for a given field were high or low. This underscores the importance of identifying and understanding the spatial variability within a field and suggests that tailoring seeding rates to specific, in-field variation can result in higher yields than if seeding is performed uniformly across a field.
Net Returns & Profitability
- When calculating optimum seeding rates (both uniform and variable) for each field using the data-driven OFPE models, the calculated rates tended to be lower than the farmer-chosen seeding rates (FCR), particularly during drought conditions.
- Net returns were consistently higher for both optimum seeding rate treatments (uniform and variable) than for the farmer-chosen seeding rate (FCR), with the variable seeding rate treatment (OVR) performing best. This was likely due to a combination of factors, including increased yields and lower seeding rates (lower seed cost/ha).
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- The average increase in net return across all sites and years when adopting the variable seeding rate (OVR) over the farmer-chosen seeding rate was $50.01/ha. When compared to the average cost of implementing OFPE ($6.90/ha), these findings suggest that investing in on-farm precision experimentation to develop input rate models that account for in-field variability may be financially profitable.
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Resources
Loewen, S., & Maxwell, B. D. (2024). Optimizing crop seeding rates on organic grain farms using on farm precision experimentation. Field Crops Research, 318, 109593.
Read MoreLocation
MontanaCollaborators
Bruce Maxwell, Montana State University
Region
Northwest
Topic
Cropping Systems, Tools and Technology
Category
Grain and Field Crops
Year Published
2023
