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Research Project: Multi-Objective Optimization of a Profitable and Environmentally Sustainable Agriculture to Produce Food and Fiber in a Changing Climate

Location: Forage Seed and Cereal Research

Title: Timing of slug emergence in new perennial ryegrass plantings

Author
item Mueller Warrant, George
item Trippe, Kristin
item MCDONNELL, RORY - Oregon State University

Submitted to: Seed Production Research at Oregon State University
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/21/2019
Publication Date: 4/30/2019
Citation: Mueller Warrant, G.W., Trippe, K.M., McDonnell, R.J. 2019. Timing of slug emergence in new perennial ryegrass plantings. Seed Production Research at Oregon State University. 2019:33-35.

Interpretive Summary: Slugs have long been viewed as a major threat to new fall plantings of grasses grown for seed in western Oregon. Growers routinely apply pelletized slug baits containing metaldehyde or iron-phosphate every several weeks from time of emergence of crop seedlings (mid-September through late October) through arrival of cold weather in December or January. Multiyear tests in grower fields often detected moderate damage to crop stands in areas where counts exceeded 10 slugs per standard "slug blanket" during the first month after crop emergence despite intensive baiting of slugs by growers. This paper reanalyzed raw data on slugs counts from 2014 to 2017 along with new information on weather conditions and previously unreported data on soil moisture content to develop predictive models of slug emergence patterns as driven by potential evaporation throughout the preceding summer and rainfall immediately prior to counting of slugs. The final model of slug emergence incorporated surface soil moisture measurements coincident with slug counts along with total rainfall in August through September. Wetter falls saw slugs emerge over wider periods of time and at broader ranges in surface soil moisture content. Drier falls saw slugs emerge in narrower windows of time and more restricted ranges in surface soil moisture content. Attempts to predict surface soil moisture content from cumulative evaporation during the summer and rainfall in the final 10 days before sampling were moderately successful, with fairly reliable predictions for the range from 25 to 40% soil moisture, a range corresponding to that most important in predicting slug emergence patterns.

Technical Abstract: Growers view slugs as one of the most serious pests of grass seed production in western Oregon. The first few weeks after emergence of new crop seedlings in the fall are especially critical, as feeding damage by newly emerging slugs that survived the summer hiding out deep underground as juveniles may actually destroy many of the young grass plants. A multiyear synthesis of slug emergence patterns in new plantings of perennial ryegrass grown for seed was developed using previously published data on slug counts and new information on both weather and gravimetric surface soil moisture content. Two separate models were developed in efforts to convert the studies from 2014 through 2017 into general knowledge capable of predicting slug behavior in future years. First, slug counts as percentage of the maximum number present on any date within a given field were modeled using third degree polynomial surface soil moisture and second degree polynomial August through September rainfall totals. Creation of this model required removal of three of the 15 test sites, two because they were no-till plantings into white clover with high numbers of slugs even at relatively low surface soil moisture levels rather than conventional till plantings, and one because of the nearly complete absence of slugs at that site. The second model predicted surface soil moisture as a function of evaporation at the Hyslop weather station for the 120-day period prior to the count combined with the observed rainfall during the last 10 days before counting slugs. As an alternative to this second model, soil moisture content could be derived from other sources, such as the Soil-Water-Assessment Tool (SWAT), and then fed into the slug emergence model to predict conditions in which slugs should first appear, along with their likely increases in numbers as conditions improve.