Best management practices that promote sustainable crop pollination: the role of crop rotations and tillage depth

Final Report for GW13-018

Project Type: Graduate Student
Funds awarded in 2013: $24,954.00
Projected End Date: 12/31/2015
Grant Recipient: University of California, Davis
Region: Western
State: California
Graduate Student:
Major Professor:
Neal Williams
University of California, Davis
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Project Information

Summary:

Wild, native pollinators contribute to crop pollination. Management practices to support these pollinators will need to include strategies that can be implemented across the entire farm, including crop fields. This project explored how tilling and crop rotation practices impact an important pollinator of squash and pumpkin, the squash bee (Peponapis pruinosa). We found that for this ground nesting bee tilling can have a negative impact on offspring emergence. In addition, the location of squash fields, through time, impacts squash bee densities in squash and pumpkin fields. Both management practices impact squash bee population growth according to the spatially explicit simulation model that we developed. We used a variety of outreach tools (workshops, fact sheets, an on-line tool, and videos) to raise grower understanding of squash bees and farm practices that can support these important crop pollinators.

Introduction

Pollinators are essential for the production of some crops. Farm management strategies to support pollinators have typically focused on resource intensive, field border practices such as seeding wildflower strips or planting hedgerows (Garibaldi et al. 2014). While field border practices are important, few studies have explored the value of within-field practices to support pollinators.

Crop fields can provide important resources for bees. For, example flowering crops provide pollen and nectar (e.g. Williams and Kremen 2007). Crop fields also provide nesting sites for ground nesting bees (Kim et al. 2006). While crop fields can benefit bees, practices associated with agricultural intensification negatively impact these important crop pollinators (Williams et al. 2010). For example, if bees prefer nesting in fields, tilling can kill overwintering offspring. In addition, annual crop rotations can create landscapes where resources provided by flowering crops can vary in space and time. If a bee is a specialist it will need to “track” these resources in order to persist in the landscape.

Squash and pumpkin (Cucurbita spp.) are dependent on pollinators to set fruit. Growers rent honey bees for pollination, but colony collapse disorder, pests, and diseases threaten this species. Wild, native bees also pollinate Cucurbita crops. For example, squash bees (Peponapis pruinosa) are as efficient as honey bees in pollinating squash (Tepedino 1981). Squash bees are ground nesting bees that specialize on plants in the genus Cucurbita. While a potentially important crop pollinator, the abundance of this species may be limited by tilling and crop rotation practices.

Project Objectives:

Objective 1: Conduct a manipulative experiment to determine overwintering survival of P. pruinosa under different tillage depth treatments

Objective 2: Conduct an observational survey to determine if crop rotations that promote between-year connectivity (i.e. how connected a focal Cucurbita field is to surrounding Cucurbita fields through time) have larger P. pruinosa populations than crop rotations that have low between-year connectivity

Objective 3: Build a spatially explicit simulation model that uses crop rotations and tillage practices to predict P. pruinosa abundance and use this model to identify optimal management strategies

Objective 4: Validate the model described in Objective 3 with data collected from squash fields in Yolo County (see Objective 1) and grower interviews

Objective 5: Communicate best-management practices using existing UC Cooperative Extension, Natural Resources Conservation Service, and non-profit (e.g. Xerces Society for Invertebrate Conservation) partnerships

Objective 6: Develop an interactive website that can be used to show growers how crop rotation practices and tillage practices impact squash bee populations

Objective 7: Publish the outcomes of Objectives 1-4 in three papers in peer-reviewed journals

Cooperators

Click linked name(s) to expand
  • Katharina Ullmann
  • Dr. Neal Williams

Research

Materials and methods:

Study system:

The study was carried out in Yolo County, California. There are no wild species of Cucurbita found in this county. Squash bees, therefore, depend on Cucurbita species grown as crops. Squash bees are univoltine, solitary bees that nest preferentially underneath or near Cucurbita vines (Julier and Roulston 2009). Squash bee offspring produced in one summer overwinter underground and emerge the following summer (Hurd et al. 1974). Individuals that survive tilling must locate squash, pumpkin, or gourd fields within their dispersal range from which to collect pollen to provision nests.

Tilling practices (Objective 1):

In summer 2012 we set up twenty 3m by 3m by 1.8m cages over three varieties of squash planted earlier in the season (Fig 1). We then released two female and two male squash bees into each sealed cage to establish artificial nesting aggregations. We monitored cages to count the number of nests observed and to ensure that bees were provisioning nests with pollen. In late fall 2012, after all adult bees had died, we removed cages. We then randomly selected ten cage locations to apply a tillage treatment. The tillage treatment included disking to 15.2 cm, ripping to 40.6 cm, and then disking again at 15.2 cm. In spring 2013 we placed the cages back over their previous locations. We collected all emerging offspring in blue vane traps (Springstar Inc, Woodinville, Washington, USA) filled with soapy water. In collaboration with Mathew Meisner (University of California, Davis), we used Bayesian statistics to quantify the relationship between tilling and the number of emerging offspring. We considered tillage to have a strong effect on the offspring emergence if the 95% highest posterior density (HPD) interval, a Bayesian analogue of a confidence interval, for the tillage parameter did not overlap zero (Severini 1991). Alternatively, if the HPD interval slightly overlapped zero, we used information about the posterior distribution to interpret the results. We fitted an overdispersed Poisson mixed model to quantify the effect of tillage on the number of emerging squash bee offspring trapped per cage. Treatment, number of nests per cage, and Cucurbita variety were included as fixed effects. Cage ID was included as a subject level random effect to account for overdispersion.

Crop rotations (Objective 2):

We sampled 65 squash fields in California that differed in their connectivity to surrounding squash fields over time. At each field we estimated the density of squash bees along two to four 50 square meter transects, for a total of 100-200 square meters per field. For each transect we observed and recorded all squash bees visiting squash flowers during a 10 minute interval. Prior to analysis we subsampled the data such that each site only had data from two transects (e.g. 100 square meters). Fields were sampled under similar weather conditions. Voucher specimens were collected at the end of each sample round to verify observational identifications. Sampling occurred between July 3, 2012 and September 4, 2012. Field size, in square meters, and distance between squash fields within and across years was calculated in ArcGIS using GIS data provided by the California Department of Pesticide Regulation and verified through ground surveys. Within-year connectivity was measured as the Euclidian distance between the focal field and all surrounding fields. We assumed that squash fields surrounding the focal field that fell within the foraging range of squash bees (e.g. 1600m based on intertegular distance) contributed more bees and were weighted accordingly (Greenleaf et al. 2007, Winfree et al. 2006). To measure between year connectivity we calculated the same measure but looked at distance between the focal field and the location of all squash fields grown in the previous year (e.g. 2011). We fit a generalized linear model with a negative binomial distribution to account for overdispersion in the data. Our response variable was squash bee density per field in 2012. Covariates included Julian date the field was sampled and either within-year or between-year connectivity. We used Akaike’s Information Criteria (AIC) to determine which model best explained the data ( AIC <2, Burnham & Anderson 2002).

Modelling P. pruinosa populations (Objectives 3-4):

In collaboration with Matt Loiacono and Eric Lonsdorf (both at Franklin and Marshall College) we developed a spatially explicit population model, modelling female squash bees only. The model assumed that squash bee population growth is a function of overwintering and tilling survival, dispersal, and number of eggs laid. We assumed that overwintering survival and number of eggs laid was constant across squash fields, but that tilling survival and dispersal survival varied across squash fields. We assumed that if the squash bee survived tilling and emerges, she will need to find a new squash field to nest in. Squash bee survival is therefore dependent on a field being within 1,600 meters of where the squash bee emerged. This distance estimate is based on squash bee intertegular distance (Greenleaf et al. 2007). We applied the model to a landscape that consisted of 540 squash fields using locations of squash fields over a five year period. Squash field locations were identified using GIS data provided by the California Department of Pesticide Regulation. The flowering pattern was repeated for any number of years the model ran for. We ran an initial 60 year simulation to evaluate population growth. Parameter values were chosen to ensure that the model ran stably. We then ran the model exploring the impact of tilling on the total population after a 30 year simulation. We ran 50 iterations of this simulation under different tilling conditions across the landscape, e.g. 0, 20%, 40%, 60%, 80%, or 100% of fields experienced tilling. In order to determine the impact of squash field connectivity over time, we ran a 30 year simulation without tilling. We quantified the number of squash bees per field at three different time intervals (after 5 years, 15 years, and 30 years) to determine if areas of high connectivity over time also had more squash bees over time.  

Research results and discussion:

Tilling practices (Objective 1):

Squash bees successfully nested within their cages; we found a total of 40 nests across the twenty cages. Our analysis found some evidence that tilling had a negative effect on offspring emergence (Fig 2a and 2b). There were 0.51 times fewer bees emerging in tilled cages compared to control cages. There was an 89% posterior probability that emergence was lower in tilled cages than in control cages. The parameter’s 95% HPD interval was -1.87, 0.49, slightly overlapping zero. The negative impact of tilling on offspring emergence was expected given that this species nests within the tilling zone. This agricultural practice may have negative consequences for squash population growth given that offspring emergence is reduced by 50% due to tilling.

Crop rotations (Objective 2):

We predicted that between-year connectivity would be more important than within-year connectivity in explaining squash bee density. However, according to AIC values, between-year connectivity and within-year connectivity were equivalent in explaining the number of bees observed in each squash field (change in AIC = 0.8). This suggests that the pattern of crop rotations may not important in explaining squash bee densities. This was surprising given that, in previous years (e.g. 2010 and 2011), the variation in squash bee density (see Fig 3a and 3b) was largely explained by between-year connectivity (Ullmann, unpublished data). This suggests that the connectivity between squash fields, across years, may only affect squash bee populations in some years. Crop rotations patterns should, therefore, be a consideration when managing squash fields for squash bees.  

Modelling P. pruinosa populations (Objectives 3-4):

In collaboration with Matt Loiacono and Eric Lonsdorf (both with Franklin and Marshall College) we found that highly connected fields over time had more bees at the end of the 30 year simulation than those with low connectivity (Fig 4). We have not yet validated the model analytically. However, anecdotal, visual comparisons suggest that the location of fields with high squash bee densities in 2010 and 2011 (Fig 3) match the location of fields with high squash bee population sizes at the end of the 30 year model simulation (Fig 4).  The frequency of tilling across the landscape had a negative effect on the regional squash bee population; the total number of squash bees after a 30 year simulation run declined as the proportion of tilled fields increased (Fig 5). These results matched our expectations that squash bee populations would be impacted by crop rotation patterns and tilling practices over time.

Participation Summary

Educational & Outreach Activities

Participation Summary:

Education/outreach description:

Communicating best management pracitces, buidling interactive website, and publishing results (Objectives 5-7):

We used multiple strategies to communicate our project to growers, University of California farm advisors, seed companies, and the broader scientific community. These strategies included personal communication during site visits with growers; presentations at grower workshops, scientific meetings, and a grower field day; submitting manuscripts to scientific journals; presenting posters at extension meetings; and writing grower fact sheets. In addition to these traditional extension methods, we developed a blog (www.pollinatorfarm.wordpress.com) to share information on farm management practices that support crop pollinators, including squash bees. The Pollinator Farm blog has had 1,258 views since it launched. We also launched a Native Bee Youtube Channel that currently includes three videos (1) Squash bee natural history, 675 views (https://www.youtube.com/watch?v=WobQObH4oDE), (2) Squash bee identification: squash bees and honey bees, 676 views (https://www.youtube.com/watch?v=a2UcgRx9ugE), (3) Squash bee identification: male and female squash bees, 448 views (https://www.youtube.com/watch?v=NZJcuEvJanA). We have an additional video explaining the squash bee modeling tool on the Youtube channel associated with the Pollinator Farm blog (https://www.youtube.com/channel/UCPEvCmcYhGFbANjAgcYsV2A).

We launched the interactive version of the squash bee population model developed with Eric Lonsdorf and Matt Loiacono. This on-line tool is meant to be used by extension professionals during workshops. This interactive model shows how squash bee populations respond to tilling and and different crop rotation practices over time. This model is currently housed on the University of California, Agriculture and Natural Resources website (http://ucanr.edu/squash_bees/) and featured on the Pollinator Farm blog.

Ullmann K., J. Aaronson, and M. Cruz. 2013. Pocket guide of common bees visiting California squash and pumpkin flowers (grower fact sheet, located at Pollinator Farm blog)

Ullmann K., J. Aaronson, and M. Cruz. 2013. Squash bee pamphlet (English and Spanish) (grower fact sheet, located at Pollinator Farm blog)

Ullmann K. 2013. Best management practices for squash and pumpkin pollinators. UCCE workshop on crop pollination for Yolo County Growers. (grower workshop slideshow)

Ullmann K. 2014. Squash bees and other crop pollinators. Good Humus Farm. (field day)

Ullmann, K., M. Mesner, N. Williams. 2014. Impact of tillage on a ground-nesting, crop pollinating bee. UCD-UCCE Farm Advisor Workshop (poster)

Ullmann K. 2015 Best management practices for squash and pumpkin pollinators. Colusa County Farm Show workshop on crop pollination (grower workshop)

Ullmann, K. 2014. Squash bee persistence in agricultural landscapes. Dept. of Entomology Seminar Series, University of California, Davis (scientific presentation) 

Ullmann, K. M. Meisner, N. Williams. 2014. Effects of tillage on a ground-nesting, crop-pollinating bee. Ecological Society of America (scientific presentation) 

Ullmann, K. M. Meisner, N. Williams. 2014. Tillage effects on the ground-nesting bee, Peponapis pruinosa. Entomological Society of America (scientific presentation) 

Ullmann, K. and N. Williams. Between-year connectivity explains bee population density in a dynamic landscape. (manuscript to be submitted) 

Ullmann, K. M. Meisner, and N. Williams. Impact of tillage on a ground nesting, crop-pollinating bee. (manuscript to be submitted)

Project Outcomes

Project outcomes:

We communicated our findings with growers, extension professionals, and seed companies using a variety of strategies outlined in the publications and outreach section below.

Tilling practices (Objectives 1,3, and 4):

We found that tilling had a negative impact on squash bee emergence in our field experiment and on regional squash bee populations in our simulation study. Squash bees are known to be as efficient as honey bees in pollinating Cucurbita crops. Previous research suggests that if enough squash bees are present, pollination can be completed before honey bees are active. However, squash bee densities vary between Cucurbita fields. We suggest that, in order to build squash bee populations, growers consider reducing tillage. Further research is need to determine how best to reduce the negative impact of tilling on squash bees (e.g. reducing the frequency, intensity, or depth of disturbance).

Crop rotations (Objective 2, 3, and 4):

The location of squash fields over time can impact squash bees. Our previous, unpublished research found that squash fields clustered spatially across years had higher squash bee densities than fields that were isolated across years. In addition, our simulation model suggests that fields in areas of high connectivity had larger squash bee populations than fields in areas of low connectivity. However, our 2012 observational study found no relationship between squash field pattern over time and squash bee densities. We therefore suggest that growers who want to support squash bees on their farm consider squash field crop rotation patterns in addition to other practices that support squash bees (e.g. reduced tilling). We caution, though, that more research should be conducted to quantify how squash and pumpkin pests respond to modified crop rotation patterns.

Economic Analysis

n/a

Farmer Adoption

Growers were interested in learning about native pollinators of squash and pumpkin and how tilling and crop rotation practices impacted squash bees. Many growers are already reducing the frequency of deep tilling as a result of changing irrigation practices (e.g. adopting buried drip lines to irrigate row crops) but were excited to learn the added benefits of reduced tilling. In addition, some growers were interested in choosing a crop rotation pattern that benefited squash bees. One seed company we worked with is now using squash bees for squash pollination in some of their cage trials.

Recommendations:

Areas needing additional study

Our tilling project was limited in that, due to the sample size, we were only able to compare a typical deep till for the region against a control. Future studies should consider the impact of tilling depth and implement type, including those associated with conservation tillage practices, on the squash bee and other ground nesting bee species. Our modelling work suggests that the pattern of squash fields across time can influence squash bee population growth. Future work should consider how mobile Cucurbita pests are impacted by the pattern of squash fields over time. Finally, although not a focus of this project, additional research should incorporate squash bee exposure to pesticides in future models of squash bee population growth in agricultural landscapes.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.