Assessment of a decision support system for chemical control of annual ryegrass (<i>Lolium rigidum</i>) in winter cereals

González‐Andújar, José Luis; Fernández‐Quintanilla, César; Bastida, Fernando; Calvo, Rosa; Izquierdo, Jordi; Lezáun, Juan Antonio

doi:10.1111/j.1365-3180.2011.00842.x

Cited by 17 publications

(16 citation statements)

References 30 publications

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“…Statistical coefficients of model performance showed that by using thermal time accumulation corrected for soil moisture, a reasonable predictive ability was achieved for a variety of geographic locations and soil types. Integrated into a multi‐year demographic model based on the complete annual life cycle of L. rigidum or into a decision support system for integrated management of this weed (González‐Andújar et al ., ), the model may contribute to help farmers decide on herbicide application time.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

Self Cite

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“…The models can easily be extended to allow the input of parameters related to the cost of each management, as well as potential grain yield losses due to the presence of Lolium plants. Another possibility is the incorporation of the likelihood of herbicide resistance into different management strategies, allowing future predictions of resistance evolution in the area [24] or be included in a decision support system to develop decision-making tools [25].…”

Section: Discussionmentioning

confidence: 99%

Modeling the Population Dynamics and Management of Italian Ryegrass under Two Climatic Scenarios in Brazil

Pagnoncelli

Trezzi

González-Andújar

2020

Plants

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Italian ryegrass (Lolium multiflorum L.) is an annual grass widely distributed in cultivated crops around the world. This weed causes significant yield reduction in many crops and has developed herbicide resistance. The aim of this study was to develop a cohort-based stochastic population dynamics model that integrates both emergence (thermal time) and dynamic population models as a tool to simulate the population dynamics of susceptible and resistant populations of L. multiflorum under the effects of climate change. The current climate scenario and the increase in the average air temperature by 2.5 °C were considered. Chemical and cultural management strategies commonly used in the South Region of Brazil during the winter and summer seasons were incorporated into the model. In the absence of control and under the current climate conditions, the seed bank population grew until reaching an equilibrium density of 19,121 ± 371 seeds m−2 for the susceptible and 20463 ± 363 seeds m−2 for the resistant populations. Considering the second climate scenario, the seed bank reaches an equilibrium density of 24,182 ± 253 seeds m−2 (+26% in relation to the current scenario) for the susceptible population and 24,299 ± 254 seeds m−2 (+18% in relation to the current scenario) for the resistant one. The results showed that the effect of the rise in temperature implies an increase in population in all the management strategies in relation to the current climate scenario. In both climate scenarios, the strategies based on herbicides application controlling cohorts 1 and 2 were the most efficient, and cropping systems including winter oat-soybeans rotation had a smaller impact on the L. multiflorum seed bank than crop rotations including winter wheat or summer corn. Crop rotations including wheat and corn for L. multiflorum management as an adaptive strategy under the future climate change are suggested.

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“…Although the underlying extension paradigm is not new, this choice warrants explanations given the popularity of optimisation methods in agricultural DSS, especially for weed management (e.g. Neeser et al, 2004;Parsons et al, 2009;Gonzalez-Andujar et al, 2011).…”

Section: Addressing Learning Behaviour: Experimentation Vs Optimisationmentioning

confidence: 99%

Beyond modelling: considering user-centred and post-development aspects to ensure the success of a decision support system

Lacoste

Powles

2016

Computers and Electronics in Agriculture

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RIM, or 'Ryegrass Integrated Management', is a model-based decision support system (DSS) for weed management in broadacre cropping systems that was updated to continue aid the delivery of key recommendations to manage herbicide resistance. This article complements earlier publications by documenting the rationales that underpinned the redevelopment efforts. The objectives are to inform the next development cycle of RIM and its delivery, as well as its adaptation to other situations. Specifically, the article aims at providing developers and project managers with key aspects to be considered before and after (re-)developing this type of model-based agricultural DSS. Reviewers report a lack of similar efforts, with modelling aspects generally better documented than underpinning rationales, including those related to implementation. Yet, this type of initiative is necessary considering that agricultural DSS can become expensive projects, and that uptake by target audiences is typically low in spite of known pitfalls and limitations. The key elements that contributed to the thought process behind upgrade choices are thus provided, as well as practical consequences for modelling. Clearly reasserting cost-effectiveness objectives and favouring human aspects led to: retaining the 'what-if' learning strategy rather than developing optimisation features; renouncing added modelling intricacies; enhancing the software accessibility; and anticipating future maintenance and distribution requirements. Strategies to maximise the impact of RIM are also discussed,

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Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals

Cited by 17 publications

References 30 publications

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

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

Modeling the Population Dynamics and Management of Italian Ryegrass under Two Climatic Scenarios in Brazil

Beyond modelling: considering user-centred and post-development aspects to ensure the success of a decision support system

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