A Thermal Time Model to Predict Corn Poppy (<i>Papaver rhoeas</i>) Emergence in Cereal Fields

Izquierdo, Jordi; González‐Andújar, José Luis; Bastida, Fernando; Lezáun, Juan Antonio; Arco, María Jesús Sánchez del

doi:10.1614/ws-09-043.1

Cited by 39 publications

(36 citation statements)

References 22 publications

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“…Our results indicated that D. sophia presented low sensitivity to local climate effects, such as precipitation and temperature. These results are surprising because local climate factors are considered to be determinant in weed emergence [22]. This is especially true in Mediterranean climates, where water availability is the most important environmental constraint, due to the combination of high summer temperatures and low rainfall [23].…”

Section: Discussionmentioning

confidence: 99%

Climate Effects and Feedback Structure Determining Weed Population Dynamics in a Long-Term Experiment

Lima

Navarrete²,

González‐Andújar

2012

PLoS ONE

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Pest control is one of the areas in which population dynamic theory has been successfully applied to solve practical problems. However, the links between population dynamic theory and model construction have been less emphasized in the management and control of weed populations. Most management models of weed population dynamics have emphasized the role of the endogenous process, but the role of exogenous variables such as climate have been ignored in the study of weed populations and their management. Here, we use long-term data (22 years) on two annual weed species from a locality in Central Spain to determine the importance of endogenous and exogenous processes (local and large-scale climate factors). Our modeling study determined two different feedback structures and climate effects in the two weed species analyzed. While Descurainia sophia exhibited a second-order feedback and low climate influence, Veronica hederifolia was characterized by a first-order feedback structure and important effects from temperature and rainfall. Our results strongly suggest the importance of theoretical population dynamics in understanding plant population systems. Moreover, the use of this approach, discerning between the effect of exogenous and endogenous factors, can be fundamental to applying weed management practices in agricultural systems and to controlling invasive weedy species. This is a radical change from most approaches currently used to guide weed and invasive weedy species managements.

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Section: Discussionmentioning

confidence: 99%

Climate Effects and Feedback Structure Determining Weed Population Dynamics in a Long-Term Experiment

Lima

Navarrete²,

González‐Andújar

2012

PLoS ONE

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“…Detailed descriptions of these models can be found elsewhere (Grundy, ; Finch‐Savage, ). Models that rely solely on temperature are simpler, data are easily available from meteorological stations and it appears to be accurate enough for prediction purposes (Myers et al ., ; Schwinghamer & Van Acker, ; Izquierdo et al ., ). However, in ecosystems with unpredictable rainfall patterns or water‐limited seasons, which often occurs in Mediterranean dryland areas, temporal patterns of seedling emergence are determined to a large extent by the timing and amount of rainfall.…”

Section: Introductionmentioning

confidence: 97%

Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area

Izquierdo

Bastida

Lezaún³

et al. 2013

Weed Research

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Summary Lolium rigidum is an extremely competitive and prevalent grass weed in cereal fields of Mediterranean areas. The proper timing of control measures is a prerequisite to maximising herbicide efficacy, in terms of both improved control and reduced herbicide inputs. The development of models to predict emergence flushes will contribute to this goal. Pooled cumulative emergence data obtained during three seasons from a cereal field were used to develop a Gompertz model. This explained relative seedling emergence from crop sowing onwards as a function of: (i) standard soil thermal time accumulation (TT) with a base temperature of 1.8°C and (ii) soil thermal time accumulation corrected for soil moisture (cTT). For the latter, no thermal time accumulation was computed for days in which the soil water balance within the upper 10‐cm soil layer indicated no water available for plants, because evapotranspiration was greater than rainfall plus the stored water remaining from the previous day. The model was validated with six datasets from four different sites and seasons. Compared with TT, the model based on cTT showed better performance in predicting L. rigidum emergence, particularly in predicting the end of emergence. Complemented with in‐field observations to minimise deviations, the model may be used as a predictive tool to better control this weed in dryland cereal fields of Mediterranean climate areas.

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“…The ability to predict the emergence pattern of weed species in relation to soil environmental conditions presents a number of practical opportunities to meet these challenges. The development of emergence models could serve as the basis for making decisions on the use of weed management strategies (Forcella et al, 2000;Izquierdo et al, 2009). Parametric nonlinear regression models (PNR) (e.g.…”

Section: Introductionmentioning

confidence: 99%

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

et al. 2016

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Summary Parametric nonlinear regression (PNR) models are used widely to fit weed seedling emergence patterns to soil microclimatic indices. However, such approximation has been questioned, mainly due to several statistical limitations. Alternatively, nonparametric approaches can be used to overcome the problems presented by PNR models. Here, we used an emergence data set of Phalaris paradoxa to compare both approaches. Mean squared error and correlation results indicated higher accuracy for the descriptive ability but similar poor performance for predictive ability of the nonparametric approach in comparison with the PNR approach. These results suggest that our nonparametric cumulative distribution function approach is a valuable alternative to the classical parametric nonlinear regression models to describe complex emergence patterns for P. paradoxa, but not to predict them.

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A Thermal Time Model to Predict Corn Poppy (Papaver rhoeas) Emergence in Cereal Fields

Cited by 39 publications

References 22 publications

Climate Effects and Feedback Structure Determining Weed Population Dynamics in a Long-Term Experiment

Climate Effects and Feedback Structure Determining Weed Population Dynamics in a Long-Term Experiment

Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

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