- Vegetables: carrots
- Crop Production: nutrient management
- Education and Training: on-farm/ranch research
Effective nitrogen management is an area of major concern to all farmers, and its impacts extend far beyond final yield. Excess nitrogen fertilization can cause losses in quality and even yield for a variety of crops, and N leaching is associated with environmental damage and contamination. Still, many farmers tend to over-apply to avoid yield losses. Both the environmental and plant nutrition problems are exacerbated in sandy soils, where leaching can occur rapidly. Processing carrots are grown in such soils in the North Central region, and due to their long season many growers apply three or more topdresses during the summer and fall. The proper timing of these applications is uncertain. Some carrot growers have adopted petiole nitrate sampling as a means of timing these applications, applying N when the sampled nitrate values fall below a threshold. This method is labor intensive, however, and has poor spatial and temporal resolution relative to what is required for decision-making. Further, the thresholds at which fertilization should occur are not well-established. Remote sensing using unmanned aerial systems (UAS, “drones”) comes with the advantages of being relatively low-cost and rapid to implement, yielding spatially continuous image data which can be collected on-demand. The objective of this study is to evaluate the impact of topdress application timing on carrot yield, and to compare the cost, accuracy, and reliability of drone-based remote sensing methods to petiole nitrate sampling. A two-year on-farm trial (2019-2020) will be established with a grower collaborator in Michigan’s Oceana County to complete this objective. Treatments will include a zero-topdress control and a gradient of topdress N rates (50.4, 100.9, 151.3 kg N ha-1, split into three applications) as well as two treatments in which the recommended rate is applied on a split schedule offset from the other treatments by two weeks (early, late). Three additional treatments will have the three season-total N rates applied entirely at the typical first topdress date. These treatments will allow analysis of the impact of rate and timing on carrot yield and characterize how petiole nitrate and carrot imagery evolve over the course of the season. Results will be communicated through publications in peer-reviewed journals, as well as through presentations at scientific and grower conferences. Outcomes will include an improved understanding of carrot nutrient management, as well as drone technology and its potential uses for vegetable farmers in the region.
Project objectives from proposal:
The main goal of this study is to evaluate how drone-based remote sensing technology can be used for fertilizer application timing decision support in processing carrots. Concomitant with this goal will be an evaluation of how application timing and rate impact yield. The results of this study will give a realistic picture of the potential of this new technology for carrot nitrogen management, as well as information regarding the importance of timing as a management consideration. The specific outcomes we anticipate are as follows:
- Carrot farmers will have a more complete understanding of the importance of nitrogen topdress application timing as a management consideration, and the potential utility of drone-based decision-support relative to current practices (petiole sampling).
- Vegetable farmers and researchers will have both an improved awareness and greater understanding of drone technology and its strengths and limitations.
- Farmers and agricultural researchers will be better equipped to critically evaluate scientific or sales publications regarding the use of drones in agriculture.
- Carrot farmers will improve their nitrogen management practices through more robust decision support relative to current practices.
- Some carrot farmers will be able to integrate drones into their scouting and monitoring programs for nitrogen management.
- Carrot farmers and researchers will be better able to experiment with drone-based remote sensing for different applications more specific to their needs.