Short communication. Integration of emergence and population dynamic models for long term weed management using wild oat (Avena fatua L.) as an example

Abstract

Weed emergence models and weed population models have shown to be important tools for decision making. However, there have been no attempts to integrate a weed emergence model with a population dynamics model to build an improved model with increased predictive capacity. In this paper, a method of integrating both types of model is presented and an application building a mathematical model based on previously reported seedling emergence and population dynamics data to simulate cohort-dependent population dynamics of wild oat is given. Three management scenarios (S 1 , S 2 , S 3 ) were considered. In S 1 , farmers are not aware of the time of weed emergence make control decisions as a stochastic process. Under S 2 , farmers are aware of the time of weed emergence and make decisions considering the time of emergence. The effect of 100% control when 80, 90, 95 and 100% of wild oats had emerged was examined. In S 3 there was «no control». In the absence of control the wild oat population grew in a sigmoid manner and reached an equilibrium density at about 16,000 seeds m -2 in the soil seed bank. In S 1 , simulation resulted in an average population equilibrium at about 13,000 seeds m -2 . This equilibrium position represented only a 19% reduction of the carrying capacity of the system. In S 2 , the 95% and 100% emerged weeds, produced population extinction after 16 and 6 years, respectively. In S 2 with 90% and 80% of emerged weeds the carrying capacity of the system was reduced by 95% and 28%, respectively. Scenario S 2 with minimum uncertainty always gave better results than S 1 . Integrating simple population models with emergence models would help farmers in long-term decision making for weed management.Additional key words: degree-days, Gompertz model, life-cycle model, logistic model, simulation, weed control. ResumenComunicación corta. Integración de modelos de dinámica de poblaciones y de emergencia para el manejo a largo plazo de malas hierbas: la avena loca (Avena fatua L.) como ejemplo Se construyó un modelo matemático basado en datos de emergencia y demografía para simular la dinámica de poblaciones de la avena loca (Avena fatua L.). Tres escenarios de manejo fueron considerados (S 1 , S 2 y S 3 ). En S 1 , los agricultores carecen de conocimiento sobre el proceso temporal de la emergencia de la avena loca y toman sus decisiones de manera aleatoria. En S 2 , los agricultores tienen conocimiento del proceso temporal de la emergencia de la mala hierba. Consideramos el efecto sobre la dinámica de poblaciones de un control del 100% cuando han emergido el 80, 90, 95 y 100% de las plantas. El S 3 fue el correspondiente a la no aplicación de medidas de control. En S 3 la población creció sigmoidalmente, alcanzando una densidad de equilibrio de aproximadamente 16.000 semillas m -2 en el suelo. La simulación correspondiente a S 1 dio lugar a un equilibrio de la población de 13.000 semillas m -2 . Este equilibrio supone solamente una reducción del 19% de la capacidad de carga del sistema. En S 2 , con las ...

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

“…Furthermore, the model can give information about the predicted density of seedlings, if there are data on the actual non‐dormant seed bank densities that can be obtained from soil seed bank analyses. Further information about weed density dynamics can also be obtained by integrating the model with population dynamic models (González‐Díaz et al ., )…”

Short communication: Evaluation of a model for predicting Avena fatua and Descurainia sophia seed emergence in winter rapeseed

“…Chantre et al (2012) reported that the Weibull model was deemed to provide a better fit than the other models for A. fatua seedling emergence in Argentina. However, Gonzalez-Diaz et al (2007) reported that the Logistic model was deemed to provide a better fit than the other models for A. fatua seedling emergence in Spain. The differences between A. fatua and D. sophia emergence patterns in semiarid conditions might be attributed mainly to a highly unpredictable precipitation regime, fuctuating thermal environment and seed dormancy level variations (Chantre et al, 2012).…”

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

“…Several studies have already been carried out to assess the long-term population behavior of different weed species, including Lolium rigidum [5][6][7]. To the best of our knowledge, there are no studies of this kind on L. multiflorum.…”