Seedling emergence of tall fescue and wheatgrass under different climate conditions in Iran

Behtari, B; Luis, Martín de

doi:10.5424/sjar/2012101-486-10

Cited by 3 publications

(1 citation statement)

References 20 publications

(25 reference statements)

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“…Of the K subsamples, a single subsample was retained as the validation data for testing the model, and the remaining K -1 subsamples were used as training data. The goal of cross-validation is to find out whether the result is replicable or just a matter of random fluctuations (Behtari and de Luis, 2012). After fitting the models to the data, a comparison was made between the model-averaged prediction value ( i P ) and the nonused observation value (O i ) used in the following model evaluation criteria (Jalota et al, 2010):…”

Section: Validationmentioning

confidence: 99%

Predicting germination of Medicago sativa and Onobrychis viciifolia seeds by using image analysis

Behtari¹,

Luis²,

Nasab³

2014

Turk J Agric For

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IntroductionThe ultimate object of testing for germination is to gain information with respect to the field planting value of the seed and to provide results that can be used to compare the value of different seed lots (ISTA, 2005). However, the standard germination test often overestimates actual field emergence (Hampton, 2009). The major reason for this overestimation is that germination tests are conducted under conditions that are more favorable than typically encountered in the field. In order to provide a more accurate appraisal of seed quality for estimating field emergence, the concept of seed vigor was established to measure the quality of a seed lot, based on sampled observations of seedling growth (Sako et al., 2002).Vigor testing not only measures the percentage of viable seeds in a sample; it also reflects the ability of those seeds to produce normal seedlings under the less-than-optimum or adverse growing conditions that may occur in the field (ISTA, 2005). Low-vigor lots having poor field emergence may not necessarily be detected by standard germination (Demir and Mavi, 2008). Demir (2007a, 2007b) identified that vigor test regimens correlated well with field emergence potential.Although vigor testing provides useful information, most vigor tests are time-consuming and costly, and they produce variable results from laboratory to laboratory (Sako et al., 2002). In plants that have small seeds, such as Medicago sativa and Onobrychis viciifolia, these problems are serious and have prevented the widespread, standardized use of seed vigor testing. The vigor tests that have been proposed can be grouped into 3 categories (Bennett, 2002): stress tests (e.g., cold test, accelerated aging), biochemical tests (e.g., electric conductivity, tetrazolium test), and germination evaluation and seedling growth tests (e.g., first count of the germination test, normal seedling emergence in peat, image analysis).Accelerated aging is one of the 2 frequently used ISTArecommended vigor tests (

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Section: Validationmentioning

confidence: 99%

Predicting germination of Medicago sativa and Onobrychis viciifolia seeds by using image analysis

Behtari¹,

Luis²,

Nasab³

2014

Turk J Agric For

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Weed Emergence Models

Royo‐Esnal

Torra

Chantre

2020

Decision Support Systems for Weed Management

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Modeling weed seedling emergence for time-specific weed management: a systematic review

Marschner,

Colucci,

Stup

et al. 2024

Weed Sci

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Understanding the timing of weed emergence is crucial to effective management. Management practices implemented too early may fail to completely control late-emerging seedlings, whereas management practices implemented too late will suffer from low efficacy. Weed emergence times reflect biological factors, such as seed dormancy and germination requirements, as well as environmental conditions. We conducted a systematic review of studies that developed models to predict weed emergence temporal patterns. We screened 1,854 studies, 98 of which were included in the final dataset. Most included studies were conducted in North America (51) or Europe (30). A wide variety of weed species (102) and families (21) were included, and many studies modeled several weeds. Grass weeds (Poaceae) were modeled most frequently (83 instances). Most weeds (40%) had base temperature T b values between 0 to 5 C and 38% had base water potential ψ b ranging from -1.0 to -0.5 MPa. Most studies used empirical parametric models, such as Weibull (40%) or Gompertz (30%) models. Non-parametric and mechanistic models were also represented. Models varied in their biological and environmental data requirements. In general, empirical parametric models based on hydrothermal time (i.e., time above base temperature and water potential thresholds) represented a good balance between ease of use and prediction accuracy. Soft computing approaches such as artificial neural networks demonstrated substantial potential in situations with complex emergence patterns and limited data availability, although they can be susceptible to overfitting. Our study also demonstrated variability in model performance and limited generalizability across species and regions. This finding underscores the need for context-specific and well-validated weed emergence models to inform management, especially in the context of climate change.

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Seedling emergence of tall fescue and wheatgrass under different climate conditions in Iran

Cited by 3 publications

References 20 publications

Predicting germination of Medicago sativa and Onobrychis viciifolia seeds by using image analysis

Predicting germination of Medicago sativa and Onobrychis viciifolia seeds by using image analysis

Weed Emergence Models

Modeling weed seedling emergence for time-specific weed management: a systematic review

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