|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|84007||158857||2016||9 صفحه PDF||سفارش دهید||دانلود رایگان|
این مقاله ISI می تواند منبع ارزشمندی برای تولید محتوا باشد.
- تولید محتوا برای سایت و وبلاگ
- تولید محتوا برای کتاب
- تولید محتوا برای نشریات و روزنامه ها
پایگاه «دانشیاری» آمادگی دارد با همکاری مجموعه «شهر محتوا» با استفاده از این مقاله علمی، برای شما به زبان فارسی، تولید محتوا نماید.
• Addressing learning behaviour: experimentation vs. optimisation.
• Re-asserting objectives: enhancing accessibility and renouncing added complexity.
• Anticipating future requirements: maintenance and distribution.
• Delivering the DSS: engaging users through facilitated workshops.
• Building confidence: credibility to evaluation.
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 re-development 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 re-asserting 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, particularly the need for qualified workshop facilitators, as well as transparency and evaluation to build user confidence.
Journal: Computers and Electronics in Agriculture - Volume 121, February 2016, Pages 260–268