Use of auto-germ to model germination timing in the sagebrush-steppe.

摘要: Germination timing has a strong influence on direct seeding efforts, and therefore is closely tracked demographic stage in wide variety of wildland agricultural settings. Predictive seed germination models, based soil moisture temperature data the zone are an efficient method estimating timing. We utilized Visual Basic for Applications (VBA) to create Auto-Germ, which Excel workbook that allows user estimate field wet-thermal accumulation models data. To demonstrate capabilities we calculated various indices modeled 11 different species, across 6 years, 10 Artemisia-steppe sites Great Basin North America identify planting date required 50% or more simulated population germinate spring (1 March later), when conditions predicted be conducive plant establishment. Both between within indicated there was high temporal spatial variability occur. However, some general trends were identified, with species falling roughly into three categories, where seeds could planted average either fall (Artemisia tridentata ssp. wyomingensis Leymus cinereus), early winter (Festuca idahoensis, Poa secunda, Elymus lanceolatus, elymoides, Linum lewisii), mid-winter (Achillea millefolium, wawawaiensis, Pseudoroegneria spicata) still not run risk during winter. These predictions made through Auto-Germ may optimal time period sowing most non-dormant if desired goal have spring.