Weed Emergence Models

Royo‐Esnal, Aritz; Torra, Joel; Chantre, Guillermo R.

doi:10.1007/978-3-030-44402-0_5

Cited by 10 publications

(20 citation statements)

References 115 publications

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“…Barga et al (2017) working with arid land species showed that a higher variation in the annual precipitation regime turns into a bet‐hedging germination strategy, thus seeds are capable of germinating in response to low‐amount precipitation events rather than waiting for optimal thermal and soil moisture conditions to occur. From a practical viewpoint, a wide emergence window would complicate the definition of the optimal intervention tactic, such as delayed crop sowing, tillage or herbicide application time (Cirujeda & Taberner, 2009; Royo‐Esnal et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…The impact of climate events on weed population dynamics and specifically on field emergence is of utmost importance from both an ecophysiological knowledge‐based perspective as well as from an optimal weed management strategic point of view. As indicated by Royo‐Esnal, Torra, and Chantre (2020), a precise characterisation of weed species field emergence dynamics has two main objectives: (1) understanding seedling emergence allows us to better comprehend the weed population dynamics, and the capacity for competition with the crop; (2) optimising control—as early growth stages show the highest susceptibility to control interventions.…”

Section: Introductionmentioning

confidence: 99%

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Climate effect on Avena fatua field emergence dynamics: A 38‐year experiment in the semiarid Pampean region of Argentina

Renzi

Traversa

Vigna

et al. 2022

Annals of Applied Biology

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Climate events modulate weed population dynamics mainly by influencing field seedling emergence patterns. Field experiments aiming to study the influence of climate on the early‐stage establishment of weeds are of utmost importance from a practical and strategic decision‐making management point of view. The objective of this study was to analyse the effect of climate variations on the field emergence dynamics of Avena fatua in the southwest area of the semiarid Pampean region of Argentina. Field emergence patterns of A. fatua were monitored from 1977 to 2015. Seedling counts were destructively sampled at weekly intervals. Three quadrats (1 m2 each) were randomly distributed on a 5 ha experimental field with a high natural population density of A. fatua in the absence of a crop. Results show that the emergence strategy of this species is highly plastic with a striking variation in response to year‐to‐year climate signals. Avena fatua field emergence strategies were classified as staggered, early, medium and late based on both chronological and hydrothermal‐time parameters. In the short‐term, precipitation regimes during both the after‐ripening and emergence phases largely explained the resulting emergence strategy. In the long‐term, the combined effect of a reduction in both the precipitation frequency and the mean minimum temperature correlated with an increase in the staggered emergence pattern. Results also point out the adaptability of A. fatua in the area under study, further suggesting a bet‐hedging fitness strategy that could diminish the risk of population decline under changing climate scenarios. From an agronomic perspective, the occurrence of staggered emergence patterns with an extended emergence window would complicate the definition of the optimal time for weed control. Thus, tailoring decisions based on the Southern Oscillation Index (SOI) episodes (neutral, negative or positive) forecast plus the implementation of weed emergence models could lead to more accurate and sustainable weed management decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Climate effect on Avena fatua field emergence dynamics: A 38‐year experiment in the semiarid Pampean region of Argentina

Renzi

Traversa

Vigna

et al. 2022

Annals of Applied Biology

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“…For the aforementioned reasons, many researchers have developed weed emergence models with two key purposes: to understand the factors that influence weed emergence and to predict weed emergence patterns 4,8 . With respect to the latter, over 95 weed models have been developed worldwide, comprising 58 and 37 models for dicotyledonous and monocotyledonous species, respectively 9 . These models typically predict weed emergence according to a thermal time (TT) or hydrothermal time (HTT), which are obtained with the accumulation of daily thermal degree (DTD) or daily hydrothermal degree (DHD).…”

Section: Introductionmentioning

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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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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“…Generally, those biological times are related to the accumulation of emergence or biomass using sigmoidal regression models (SRM) such as Logistic and Gompertz. 19 However, in recent years, artificial neural networks (ANNs) have emerged as an alternative predictive method that can overcome limitations in the accuracy and robustness of SRMs. The main limitations of SRMs include their reliance on accurate initial parameter values to achieve optimal results.…”

Section: Introductionmentioning

confidence: 99%

“…Weed phenological models are based on growth responses to environmental factors such as temperature [thermal time (TT)] and more complex parameters that integrate several environmental factors such as moisture [hydrothermal (HTT)] and daylength [photothermal (PhTT)]. Generally, those biological times are related to the accumulation of emergence or biomass using sigmoidal regression models (SRM) such as Logistic and Gompertz 19 . However, in recent years, artificial neural networks (ANNs) have emerged as an alternative predictive method that can overcome limitations in the accuracy and robustness of SRMs.…”

Section: Introductionmentioning

confidence: 99%

The impact of burial depth on Centaurea diluta emergence and modelling of its growth using a nonlinear regression and artificial neural network

Sousa‐Ortega,

Alcántara,

Leon

et al. 2023

Pest Management Science

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BackgroundCentaurea diluta Aiton (North African knapweed) is a major weed concern in Spain due to limited herbicides capable of controlling it, and the limited knowledge of its biology hinders the development of integrated weed management strategies.ResultsThe current study presents results from two experiments that aimed to a) determine the effect of seed burial on seedling emergence; and b) model its phenology progression using sigmoidal (SRM) and artificial neural network models (ANN) based on different cohort emergence times. In first experiment, burial at 2 cm and 5 cm decreased C. diluta emergence by 54% and 90%, respectively compared to the emergence at 0 cm. In the second experiment, under non crop‐weed competition conditions, the emergence delay led to reductions in leaf number, rosette diameter, plant height and dry biomass by 63%, 50%, 59% and 93%, respectively. Seed production per plant exceeded 21,469. According to the growth model, leaf number was the most consistent morphological trait and critical for timing weed control actions, so it was used to compare SRMs and ANNs. On average, ANNs increased the precision in 5.72% (± 2.4 leaves) compared to SRMs. This slight performance of ANNs may be valuable for controlling C. diluta since control methods must be applied at the 4‐leaf stage to achieve good efficacy.ConclusionSeed burial at 5 cm depth is an effective method reducing C. diluta emergence. ANNs accurately predicted the leaf number employing environmental variables can help increase the efficiency of C. diluta control actions and reduce the risk of escapes.This article is protected by copyright. All rights reserved.

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Weed Emergence Models

Cited by 10 publications

References 115 publications

Climate effect on Avena fatua field emergence dynamics: A 38‐year experiment in the semiarid Pampean region of Argentina

Climate effect on Avena fatua field emergence dynamics: A 38‐year experiment in the semiarid Pampean region of Argentina

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

The impact of burial depth on Centaurea diluta emergence and modelling of its growth using a nonlinear regression and artificial neural network

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