Understanding canola cropping systems for enhancing stakeholder management strategies: from pests to pollinators and beneficial arthropods

Commodities

• Agronomic: rapeseed
• Animals: bees, other

Practices

• Animal Production: feed/forage, stocking rate
• Crop Production: beekeeping, cropping systems, pollination, pollinator habitat, pollinator health, varieties and cultivars
• Education and Training: demonstration, display, extension, on-farm/ranch research, participatory research, workshop
• Farm Business Management: budgets/cost and returns, value added
• Natural Resources/Environment: habitat enhancement, wildlife
• Pest Management: integrated pest management, traps
• Production Systems: holistic management
• Sustainable Communities: quality of life, sustainability measures

Canola contributes over $1 billion annually to the U.S. economy. With over 80,000 acres in the region, canola acreage has been steadily increasing in the Pacific Northwest. While canola can self-seed, pollinators have been shown to improve crop yield, with reports suggesting bees are essential for canola hybrid seed production. Canola also produces pollen and nectar and is used as supplemental forage plants for bees. Increasingly, beekeepers and growers seek to understand the nutritional value of pollinated crops, such as canola, as well as the pests and beneficial arthropods in such cropping systems for the most efficient management strategies. To optimize canola production systems and enhance cooperation among canola growers and beekeepers, this proposal, spanning two years, seeks funding to address three stakeholder needs and concerns: (1) Examine the impacts of canola bloom on bee nutrition. We will select three distinct canola production landscape types and assess the impacts of the surrounding landscape on pollination and bee nutrition. (2) Examine the impacts of different canola cultivars on bee nutrition. We will assess the quantity and quality of canola pollen and nectar available to bees across different canola cultivars. (3) Enhance management strategies in canola production systems by mapping the beneficial arthropods and pests. We will map the presence and abundance of pests and beneficial arthropods across selected landscapes over time to understand management practices. Ultimately, our aim is to improve canola yield by understanding the pest-pollinator-beneficial arthropod dynamics to suggest integrated pest and pollinator management practices.

Objective 1. Examine the impacts of canola bloom on bee nutrition.

For this objective, we will work closely with the beekeepers who have existing pollination contracts with the participating growers. Honey bee colonies will be placed at the canola fields across the three field sites. Stocking rates for honey bee colonies in fields will follow the standard pollination contract for these locations. We will place front porch pollen traps at the entrances of four random honey bee colonies in each field site based on standard methods³⁰. The traps will be activated for 48 hours at each timepoint (pre, peak, and post-bloom) and only on days conducive to foraging activity (i.e. above 55 °F and without precipitation or excessive wind).

We will measure the total amount of pollen trapped from each colony at each timepoint and sort the pollen pellets by color³⁰. Each distinct color will represent a different plant, and color sorting will allow for identification of the plant origins. We will perform pollen acetolysis ³¹ and DNA metabarcoding²⁶ to identify the plant origins in the pollen samples. Acetolysis is a process through which pollen grains are stained and mounted to a microscope slide, allowing for visual identification using a taxonomic key. This method usually allows identification to plant genus, though its success can depend on the experience of the observer, the quality of reference keys, and the availability of reference pollen samples collected from the field. To improve identification success and provide greater taxonomic resolution, we will extract DNA from pollen samples and identify pollen origins based on the sequence of the ITS2 and rbcL genes via DNA metabarcoding ²⁶. Accurate identification of pollen collected from colonies at each site will provide valuable information for commercial beekeepers and growers regarding the forage availability based on various landscape types. Once the pollen is sorted, we will utilize standard chemical assays and multi-omic techniques to evaluate the macro- and micronutrient profiles of composite (mixed origin) and color-sorted (single origin) pollen samples (details provided after objectives). All nutritional analyses will be performed after the harvest season is complete each experimental year. These data will allow us to test whether the macro- and micronutrient profiles of pollen collected by colonies are influenced by landscape effects and bloom progress.

Objective 2. Examine the impacts of different canola cultivars on bee nutrition.

To analyze the differences between cultivars, commercially available (popular) canola cultivars will be tested utilizing the WSU and U of I canola variety trials. Each year, three winter and three spring cultivars will be tested for a total of twelve cultivars being tested over the entire project. For nutritional quality, pollen and nectar will be hand-collected from multiple flowers using established laboratory methods and then pooled for each cultivar and stored for analysis. Pollen will be hand-collected using vacuums, and nectar collection will be done via 4µL microcapillary tubes. Pollen and nectar will be collected during each field's peak bloom of winter and spring canola. For pollen and nectar quantity testing, budding canola blooms will be cut, brought to the laboratory, and allowed to bloom, where each cultivar will be tested in ten flower biological triplicates. Pollen will be collected via a buzz pollinator and weighed ^29,32,33. Nectar will be collected via 0.25 µL microcapillary tubes, imaged on a Canon Macropod camera ³⁴, and the volume will be measured using ImageJ. We will utilize standard chemical assays and multi-omic techniques to evaluate the micronutrient profiles of the hand-collected pollen. Nectar will be tested via a refractometer for Brix percentage of sugar ²⁶. All nutritional analyses will be performed after the harvest season is complete for each experimental year.

Objectives 1&2 Nutritional Analysis

For macronutrients, total pollen protein content will be determined using a colorimetric bicinchoninic acid (BCA) assay, and total pollen lipid content will be quantified with a sulfo-phospho-vanillin (SPV) assay ^29,32,33. To characterize micronutrients, we will perform untargeted metabolomics using liquid chromatography mass spectrometry (LCMS) methods ³⁵. All nutritional analyses will be performed after the harvest season is complete each experimental year. Based on the results from objectives 1 and 2, beekeepers can either strategize supplemental feeding based on landscape, location, and cultivar-specific nutrition, or can adjust stocking rates for commercial pollination contracts across different canola cropping systems.

Objective 3. Enhance management strategies in canola production systems by mapping the beneficial arthropods and pests.

The arthropod field surveys will be done pre, peak, and post-bloom using five random blocks in the canola fields. We will monitor pest and beneficial arthropod populations using pan traps, blue vein traps, and pit fall traps, allowing pests and beneficials to be trapped for 48 hours (pan traps and blue vein traps) and seven days (pit fall traps) at every time period, and sweep netting will be performed on each field in 5 double strokes twice a day, once a week at pre-, peak, and post bloom ^17,36,37. Arthropods will be preserved, pinned, and identified in the laboratory at WSU. The insect pests, natural enemies, and pollinators identified via these methods will be classified to the lowest taxonomic level possible, allowing us to create educational extension guides for growers for their population dynamics over time and bloom intensities. A generalized linear mixed modeling approach will be used in R to evaluate the effect of landscapes and bloom progress on the abundances of pests and beneficial predators. Once the arthropods are identified, their location and time of collection are documented, and R statistics are performed, abundance charts and arthropod maps will be created unique to each field. This will help growers better understand pests and other arthropod population dynamics across different landscape effects and bloom times. Sample identification and population dynamics maps will be made after the harvest season for each of the experimental years.