Models of the Response of Foliar Parasites to the Combined Effects of Temperature and Duration of Wetness

Duthie, J.

doi:10.1094/phyto.1997.87.11.1088

Cited by 96 publications

(54 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…survival, dispersal, infection, latency, sporulation) or combined epidemic components (e.g. disease foliar pathogens to the combined effects of temperature and duration of leaf wetness (Duthie, 1997). These models have commonly been used in disease forecasting systems, with the reliability of model predictions heavily dependent on the quality of the dataset used for model development (Krause , 1975).…”

Section: Asr Model Types and Applicationsmentioning

confidence: 99%

Models and applications for risk assessment and prediction of Asian soybean rust epidemics

et al. 2006

View full text Add to dashboard Cite

Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed.

show abstract

Section: Asr Model Types and Applicationsmentioning

confidence: 99%

Models and applications for risk assessment and prediction of Asian soybean rust epidemics

et al. 2006

View full text Add to dashboard Cite

show abstract

“…(See reference [14] for a more detailed description.) The function α represents the inhibition pressure, which depends on climatic and environmental conditions [10], [11], [13]. It is appropriate to model α as an almost-periodic function, taking into account yearly seasonal changes.…”

Section: System Modelmentioning

confidence: 99%

“…This function is shown in Figure 1; it reflects the seasonality of empirically-based severity index models found in the literature [10], [11], [13]. Parameters used in the simulation are summarized in Table I.…”

Section: A Model Discretizationmentioning

confidence: 99%

“…The literature on Anthracnose pathosystem is extensive [4], [5], [7], [16], [18], [23], [28]. There have been several attempts to model the spread of CBD and to identify efficient control strategies [10], [11], [13], [17], [18], [19], [20], [21], [28]. Possible control methods include genetic methods [3], [4], [5], [15], [27], biological control [12], chemical control [5], [22], [24] and cultivational practices [5], [19], [20], [21], [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal control of anthracnose using mixed strategies

Fotsa-Mbogne

Thron

2015

Mathematical Biosciences

View full text Add to dashboard Cite

Abstract-In this paper we propose and study a spatial diffusion model for the control of anthracnose disease in a bounded domain. The model is a generalization of the one previously developed in [14]. We use the model to simulate two different types of control strategies against anthracnose disease. Strategies that employ chemical fungicides are modeled using a continuous control function; while strategies that rely on cultivational practices (such as pruning and removal of mummified fruits) are modeled with a control function which is discrete in time (though not in space). Under weak smoothness conditions on parameters we demonstrate the well-posedness of the model by verifying existence and uniqueness of the solution for given initial conditions. We also show that the set [0, 1] is positively invariant. We first study control by pulse strategy only, then analyze the simultaneous use of continuous and pulse strategies. In each case we specify a cost functional to be minimized, and we demonstrate the existence of optimal control strategies that can be evaluated numerically using the gradient method presented in [1]. We discuss the results of numerical simulations both for a spatiallyaveraged version of the model and for the full model.

show abstract

“…In our previous study, we developed a sensor network for agricultural use called a Field Server (Fukatsu & Hirafuji, 2005, Fukatsu et al, 2006, Fukatsu et al, 2009a) that enables effective crop and environment monitoring by equipped sensors and autonomous management. Monitoring with Field Servers facilitates growth diagnosis and risk aversion by cooperating with some agricultural applications such as crop growing simulations, maturity evaluations, and pest occurrence predictions (Duthie, 1997;Iwaya & Yamamoto, 2005;Sugiura & Honjo, 1997;Zhang, et al, 2002). However, it is insufficient for obtaining detailed information about farming operations, because these operations are performed flexibly in every nook and cranny depending on crop and environment conditions.…”

Section: Introductionmentioning

confidence: 99%