A web‐based decision support system to enhance IPM programs in Washington tree fruit

Jones, Vincent P.; Brunner, Jay F.; Grove, Gary G.; Petit, Brad; Tangren, G.V.; Jones, W. E.

doi:10.1002/ps.1913

Cited by 77 publications

(62 citation statements)

References 27 publications

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“…Decision support systems (DSS) are becoming a vehicle of choice to provide information in complex situations (Magarey et al, 2002;Samietz et al, 2007;Jones et al, 2010). Many existing agricultural decision support systems are aimed at dealing with time-sensitive information-such as forecasting when pests and diseases require various management interventions to prevent crop loss-and are often paired with short-range weather forecasts to enable users to respond.…”

Section: Decision Support Systems Existing Use Of Decision Support Symentioning

confidence: 99%

“…Therefore, from the user perspective, it is important for the models included in the DSS to interact in some fashion. Experience has shown that for a DSS to be deemed usable and adopted by decision-makers, it must incorporate a significant number of models so that users come to the DSS over a significant fraction of the growing season (Jones et al, 2010). This sort of DSS essentially opens a new communication channel that allows a more efficient transfer of general (e.g., pest management guidance) as well as specific (model-based) information.…”

Section: Decision Support Systems Existing Use Of Decision Support Symentioning

confidence: 99%

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Northwest U.S. Agriculture in a Changing Climate: Collaboratively Defined Research and Extension Priorities

Yorgey

Hall

Allen

et al. 2017

Front. Environ. Sci.

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In order for agricultural systems to successfully mitigate and adapt to climate change there is a need to coordinate and prioritize next steps for research and extension. This includes focusing on "win-win" management practices that simultaneously provide short-term benefits to farmers and improve the sustainability and resiliency of agricultural systems with respect to climate change. In the Northwest U.S., a collaborative process has been used to engage individuals spanning the research-practice continuum. This collaborative approach was utilized at a 2016 workshop titled "Agriculture in a Changing Climate," that included a broad range of participants including university faculty and students, crop and livestock producers, and individuals representing state, tribal and federal government agencies, industry, nonprofit organizations, and conservation districts. The Northwest U.S. encompasses a range of agro-ecological systems and diverse geographic and climatic contexts. Regional research and science communication efforts for climate change and agriculture have a strong history of engaging diverse stakeholders. These features of the Northwest U.S. provide a foundation for the collaborative research and extension prioritization presented here. We focus on identifying research and extension actions that can be taken over the next 5 years in four areas identified as important areas by conference organizers and participants: (1) cropping systems, (2) livestock systems, (3) decision support systems to support consideration of climate change in agricultural management decisions; and (4) partnerships among researchers and stakeholders. We couple insights from the workshop and a review of current literature to articulate current scientific understanding, and priorities recommended by workshop participants that target existing knowledge Yorgey et al.Priorities for U.S. Northwest Agriculture in a Changing Climate gaps, challenges, and opportunities. Priorities defined at the Agriculture in a Changing Climate workshop highlight the need for ongoing investment in interdisciplinary research integrating social, economic, and biophysical sciences, strategic collaborations, and knowledge sharing to develop actionable science that can support informed decision-making in the agriculture sector as the climate changes.

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Section: Decision Support Systems Existing Use Of Decision Support Symentioning

confidence: 99%

Section: Decision Support Systems Existing Use Of Decision Support Symentioning

confidence: 99%

Northwest U.S. Agriculture in a Changing Climate: Collaboratively Defined Research and Extension Priorities

Yorgey

Hall

Allen

et al. 2017

Front. Environ. Sci.

View full text Add to dashboard Cite

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“…Actually, regardless of the available amount of the novel chemical compounds, their choices in IMP are evaluated based on more than one criteria in which application time is indispensable for their success. Therefore, the design and implementation of web-based decision support systems, to enhance IPM programs, makes possible the application of information intensive decision making (Jonew et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Modular structure of web-based decision support systems for integrated pest management. A review

Damos

2015

Agron. Sustain. Dev.

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Sustainable pest management implies less pesticide use and replacement by safe control alternatives. This requires decision support for rational pest management. However, in practice, successful decision making is dependent upon the availability of integrated, high-quality information. Computer-aided forecasting and related decision support systems make pest control more sustainable by avoiding unwanted consequences of pesticide applications. Here, I review integrated pest management for web-based decision support systems. The major points are the following: (1) Principles of integrated pest management are compatible with sustainable agriculture. (2) Pest models serve as basis of decision making because they offer means to predict the exact time of pest phenological development and initiate management actions. Most models are climate driven. (3) New hardware technology has permitted the registration of automatically recorded climatic data. This data can be combined with pest models through logical operations and forecasting algorithms to develop a software of pest management. (4) Dynamic web interfaces can serve as decision support systems providing the user with real-time pest warnings and recommendations for management actions. (5) Ontology web programing and semantic knowledge representations provide a way to classify and describe agrodata to facilitate information sharing and data exploitation over distributed systems. (6) Most available pest management data is published on static web pages and, thus, cannot be classified as decision support systems. Some web-based decision support systems provide user-interactive content and real-time pest forecasts and management support.

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“…Phenological models have been important tools used to improve the management of codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae) in many apple producing areas worldwide (Tomkins et al, 1987;Knight and Croft, 1991;Blago, 1992;Ahmad et al, 1995;Boivin et al, 2005;Samietz et al, 2007;Knight, 2007;Jones et al, 2008;2010;. The Predictive Extension Timing Estimator (PETE) model was the first widely-adopted phenological model developed for codling moth in the USA, and is based on a time-varying distributed delay algorithm (Welch et al, 1978).…”

Section: Introductionmentioning

confidence: 99%

“…The potential impact of this difference between models on the effectiveness of insecticide programs targeting eggs and neonate larvae of the codling moth was shown conceptually to be an important concern for effective insecticide-based management of this pest. This concern is heightened today as growers must rely on the use of selective insecticides which often have shorter residual activity than the previous broad spectrum insecticides (Brunner et al, 2005;Jones et al, 2010). Therefore, phenology models available to aid management decisions need to be accurate at a temporal scale consistent with spray timing decisions to reduce the probability of control failures (Knight, 2007).…”

Section: Introductionmentioning

confidence: 99%

Modeling codling moth (Lepidoptera: Tortricidae) phenology and predicting egg hatch in apple orchards of the Maule Region, Chile

Barros-Parada

Knight

Contreras

2015

Chilean J. Agric. Res.

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Studies were conducted in the Maule Region to characterize the phenology of the codling moth, Cydia pomonella (L.) The Predictive Extension Timing Estimator (PETE) and a logistic phenological model were validated with eight data sets of cumulative moth catches in sex pheromone (PH) and kairomone-baited traps and the cumulative occurrence of fruit injuries from apple (Malus domestica Borkh.) orchards during the 2009-2011 seasons. Second, the start of egg hatch was predicted from the first sustained male and female moth catches (biofix) in traps baited with pear ester (PE), PE+acetic acid (AA), PE+PH, and PH alone. Both phenological models fit data well except that the logistic provided a better fit than the PETE model of the phenology of egg hatch of the codling moth in the first generation, with a difference of 11 d between models in the prediction of 50% egg hatch. No significant difference was found between biofix dates established for males using either PH or PE+PH lures or for the biofix date based on female catches with PE+AA or PH+PE. The biofix established with the sustained female catch occurred nearly 11 d later than the male-based biofix. The use of a female biofix provided on average a 4-d improvement in the prediction of first egg hatch compared with the traditional use of a male biofix, but this difference was not significant. The use of PE+AA lures increased the proportion of cases when a female-based biofix could be established compared with the use of the PH+PE lure.

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A web‐based decision support system to enhance IPM programs in Washington tree fruit

Abstract: The design of the system facilitates education on a range of time-sensitive topics and will make it possible easily to incorporate other models, new management recommendations or information from new sensors as they are developed.

Cited by 77 publications

References 27 publications

Northwest U.S. Agriculture in a Changing Climate: Collaboratively Defined Research and Extension Priorities

Northwest U.S. Agriculture in a Changing Climate: Collaboratively Defined Research and Extension Priorities

Modular structure of web-based decision support systems for integrated pest management. A review

Modeling codling moth (Lepidoptera: Tortricidae) phenology and predicting egg hatch in apple orchards of the Maule Region, Chile

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