Predicting germination in semi-arid wildland seedbeds. I. Thermal germination models

Rawlins, Jennifer K.; Roundy, Bruce A.; Davis, Scott; Egget, Dennis

doi:10.1016/j.envexpbot.2011.10.003

Cited by 24 publications

(31 citation statements)

References 26 publications

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“…1), and the number of hours spent under those conditions after sowing and before germination (Hardegree et al, 2017). For these scenarios, the optimal WPT was between −0.1 and −0.4 MPa, so in general, germination rate of these subpopulations would have potentially been overestimated for models based on the −0.5, −1.0 and −1.5 WPT values previously used by Roundy et al (2007) and Rawlins et al (2012aRawlins et al ( , 2012b. 2).…”

Section: Resultsmentioning

confidence: 99%

“…2 demonstrates, overestimation by the thermal model component in the 0 to −0.5 MPa Y range is offset by underestimation of the underlying HT response below a given WPT. Roundy et al (2007) and Rawlins et al (2012aRawlins et al ( , 2012b, however, noted that reliable germination predictions could be obtained using WPT values of −1.0 and −1.5. 3).…”

Section: Discussionmentioning

confidence: 95%

“…Data in this figure include germination time estimates for all years (44) and all fall planting dates (5) of the simulation. The relatively few instances noted by Rawlins et al (2012aRawlins et al ( , 2012b of WT models underestimating germination rate in the field could be due to other abiotic or physiological factors other than WT or HT germination response. 0 MPa and the WPT, and that this error would be exacerbated for seedlots and subpopulations that had a lower WPT ( Fig.…”

Section: Discussionmentioning

confidence: 97%

“…Roundy et al (2007) suggested that field germination of cheatgrass (Bromus tectorum L.) could be adequately described by a wet-thermal (WT) model that accumulated thermal time above a fixed Y threshold (WPT). Rawlins et al (2012aRawlins et al ( , 2012b tested WT models for cheatgrass and several other species in the field and found that a WPT of −1.5 MPa produced relatively accurate estimates of field germination response. Theoretically, one would expect that thermal accumulation models would overestimate germination rate in the environmental range where water stress reduced, but did not completely suppress, germination response.…”

Section: S Eedbed Microclimate In the Great Basin And Intermountainmentioning

confidence: 99%

“…Rawlins et al (2012aRawlins et al ( , 2012b tested WT models for cheatgrass and several other species in the field and found that a WPT of −1.5 MPa produced relatively accurate estimates of field germination response. Rawlins et al (2012aRawlins et al ( , 2012b, however, found that WT germination models with a WPT of −1.5 MPa underestimated germination rate in the field for some species and environmental conditions. Rawlins et al (2012aRawlins et al ( , 2012b, however, found that WT germination models with a WPT of −1.5 MPa underestimated germination rate in the field for some species and environmental conditions.…”

Section: S Eedbed Microclimate In the Great Basin And Intermountainmentioning

confidence: 99%

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Hydrothermal Germination Models: Assessment of the Wet‐Thermal Approximation of Potential Field Response

Hardegree

Roundy

Walters³

et al. 2018

Crop Science

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Hydrothermal (HT) models can be used to characterize seed germination response to field‐variable conditions of temperature (T) and water potential (Ψ). Hydrothermal response data are relatively difficult to generate, which limits their utility for large‐scale comparisons of inter‐ and intraspecies germination response. Previous studies have hypothesized that HT germination response can be estimated using a simple model for thermal‐time accumulation above a fixed threshold of environmental Ψ (wet‐thermal [WT] model). The purpose of this study was to test this hypothesis by explicitly comparing HT and WT germination rates and estimated cumulative germination response of 13 rangeland grass seedlots under simulated conditions of field‐variable T and Ψ. We used a 44‐yr weather record to parameterize a seedbed‐microclimate model for estimation of hourly T and Ψ at seeding depth for a sandy loam soil type at the Orchard field test site in southwestern Ada County, Idaho. Hydrothermal and WT germination responses for each hour of the simulation were estimated for 13 range grass seedlots. We found that overestimation of germination rate by thermal accumulation at low Ψ contributed relatively little to WT model error rates, and that >95% of the variability in predicted germination response could be explained by WT germination response above a Ψ threshold of −0.3 to −0.5 MPa. Given the pulse‐like nature of favorable germination conditions in the field, this modeling approach may have immediate and wide potential application, as there are a relatively large number of thermal‐germination datasets currently available for rangeland plant species.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 97%

Section: S Eedbed Microclimate In the Great Basin And Intermountainmentioning

confidence: 99%

Section: S Eedbed Microclimate In the Great Basin And Intermountainmentioning

confidence: 99%

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Hydrothermal Germination Models: Assessment of the Wet‐Thermal Approximation of Potential Field Response

Hardegree

Roundy

Walters³

et al. 2018

Crop Science

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

Richardson

Whitaker

Sant

et al. 2018

Ecology and Evolution

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

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Influence of an abscisic acid (ABA) seed coating on seed germination rate and timing of Bluebunch Wheatgrass

Richardson

Badrakh

Roundy

et al. 2019

Ecology and Evolution

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Semi‐arid rangeland degradation is a reoccurring issue throughout the world. In the Great Basin of North America, seeds sown in the fall to restore degraded sagebrush ( Artemisia spp.) steppe plant communities may experience high mortality in winter due to exposure of seedlings to freezing temperatures and other stressors. Delaying germination until early spring when conditions are more suitable for growth may increase survival. We evaluated the use of BioNik™ (Valent BioSciences LLC) abscisic acid (ABA) to delay germination of bluebunch wheatgrass ( Pseudoroegneria spicata ). Seed was either left untreated or coated at five separate rates of ABA ranging from 0.25 to 6.0 g 100 g −1 of seed. Seeds were incubated at five separate constant temperatures from 5 to 25°C. From the resultant germination data, we developed quadratic thermal accumulation models for each treatment and applied them to 4 years of historic soil moisture and temperature data across six sagebrush steppe sites to predict germination timing. Total germination percentage remained similar across all temperatures except at 25°C, where high ABA rates had slightly lower values. All ABA doses delayed germination, with the greatest delays at 5–10°C. For example, the time required for 50% of the seeds to germinate at 5°C was increased by 16–46 d, depending on the amount of ABA applied. Seed germination models predicted that the majority of untreated seed would germinate 5–11 weeks after a 15 October simulated planting date. In contrast, seeds treated with ABA were predicted to delay germination to late winter or early spring. These results indicate that ABA coatings may delay germination of fall planted seed until conditions are more suitable for plant survival and growth.

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Predicting germination in semi-arid wildland seedbeds. I. Thermal germination models

Cited by 24 publications

References 26 publications

Hydrothermal Germination Models: Assessment of the Wet‐Thermal Approximation of Potential Field Response

Hydrothermal Germination Models: Assessment of the Wet‐Thermal Approximation of Potential Field Response

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

Influence of an abscisic acid (ABA) seed coating on seed germination rate and timing of Bluebunch Wheatgrass

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