Predicting Seedling Emergence of Three Canarygrass (Phalaris) Species under Semi-Arid Conditions Using Parametric and Non-Parametric Models

Sousa‐Ortega, Carlos; Royo‐Esnal, Aritz; Urbano, J. M.

doi:10.3390/agronomy11050893

Cited by 5 publications

(3 citation statements)

References 31 publications

(46 reference statements)

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“…The models for the three Phalaris species decreased their accuracy in the current study compared to the validation results of Sousa‐Ortega et al 28 . The RMSE values increased from 11.1 to 15.9, from 14.1 to 15.9 and from 17.5 to 27.7 for Phalaris paradoxa , Phalaris minor and Phalaris brachystachys , respectively.…”

Section: Resultscontrasting

confidence: 69%

“…These algorithms enable the daily download of weather data, estimate soil moisture via a water balance approach, and subsequently calculate soil water potential using the equations proposed by Saxton and Rawls 39 based on the estimated soil moisture. This methodology has been described by Sousa‐Ortega et al 20,24,28 . Finally, mathematical models are applied to predict the percentage of emergence for each weed species.…”

Section: Methodsmentioning

confidence: 99%

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Validation of a new tool to predict the weed emergence in Spain

Sousa‐Ortega

Alcántara

2023

Pest Management Science

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BackgroundThe seedling stage is the most vulnerable period of growth and development for annual weeds and an important target for weed management operations. To address this, several weed emergence models have been developed, but none are commercially available. Therefore, this study aims to develop a web application that implements predictive weed emergence models for eight different weed species, utilizing weather data sourced from public weather stations.ResultsLolium rigidum Gaudin presented a mean root mean squared error (RMSE) value of 8.9, achieving an RMSE value below 15 (success rate) in 84.5% of cases. This result may be attributed to the use of a water potential base, set at −0.4 MPa, to evaluate water availability. Centaurea diluta Aiton achieved an RMSE value below 15 in all situations, with an average value of 9.0. This weed showed higher accuracy at southern sites than northern sites. Conversely, Avena sterilis ssp. ludoviciana (Durieu) Gillet & Magne achieved higher precision at northern sites where no dry periods occurred. The newly developed model for Bromus diandrus Roth. achieved an average RMSE value of 7.7 and a 100% success rate. Papaver rhoeas L. and the three Phalaris species exhibited lower accuracy in this study than in previous ones. Nonetheless, the success rates for Papaver rhoeas and Phalaris paradoxa L. were still above 70%.ConclusionModels for C. diluta, B. diandrus, L. rigidum, Papaver rhoeas and Phalaris paradoxa have demonstrated potential for adoption in commercial production, while Phalaris minor and Phalaris brachystachys models require refinement. © 2023 Society of Chemical Industry.

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Section: Resultscontrasting

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Validation of a new tool to predict the weed emergence in Spain

Sousa‐Ortega

Alcántara

2023

Pest Management Science

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“…Crop models that utilize the information on molecular markers can be critically important tools [16]. The effect of weather conditions on the daily rate of progression to the next phenological phase is quantitatively characterized using concepts like Heat Unit Index (HUI) [17], Crop Heat Units (CHI), hydrothermal degrees (DHD) [18], Degree Days (DD), or Biological Days (BD) [19]. Both DD and BD could depend on temperature, water content and photoperiod.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Flowering Time in Vigna radiata with Approximate Bayesian Computation

et al. 2021

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Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering time. The functions for daily progression of the plant from planting to flowering are obtained in analytic form and depend on daily values of climatic factors and genetic information. The resulting pool of models demonstrated high accuracy on the dataset. Day length, solar radiation and temperature had a large impact on the model accuracy, while the impact of precipitation was comparatively small and the impact of maximal temperature has the maximal variation. The model pool was used to investigate the behavior of accessions from the dataset in case of temperature increase by 0.05–6.00°. The time to flowering changed differently for different accessions. The Pearson correlation coefficient between the SNP value and the change in time to flowering revealed weak but significant association of SNP7 with behavior of the accessions in warming climate conditions. The same SNP was found to have a considerable influence on model prediction with a permutation test. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.

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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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Predicting Seedling Emergence of Three Canarygrass (Phalaris) Species under Semi-Arid Conditions Using Parametric and Non-Parametric Models

Cited by 5 publications

References 31 publications

Validation of a new tool to predict the weed emergence in Spain

Validation of a new tool to predict the weed emergence in Spain

Modeling of Flowering Time in Vigna radiata with Approximate Bayesian Computation

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

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