Janssen Sander J C, Porter Cheryl H, Moore Andrew D, Athanasiadis Ioannis N, Foster Ian, Jones James W, Antle John M
Wageningen University and Research Centre, Postbus 47, 6700AA Wageningen, Netherlands.
Agricultural & Biological Engineering, University of Florida, PO Box 110570, Gainesville, FL 32611, USA.
Agric Syst. 2017 Jul;155:200-212. doi: 10.1016/j.agsy.2016.09.017.
Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner. Our qualitative evaluation suggests that as an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This will enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. This adoption requires the widespread uptake of a set of best practices as standard operating procedures.
长期以来,农业建模在模型实施方面一直存在碎片化问题。开发了许多模型,存在大量冗余,模型之间往往耦合不佳,模型组件很少被重复使用,并且常常难以应用模型来为农业部门生成实际解决方案。为改善这种情况,我们认为需要一个开放、自我维持且坚定的社区来共同开发农业模型以及相关数据和工具,将其作为一种公共资源。这样的社区可以从信息和通信技术(ICT)的最新发展中受益。我们研究如何利用这些发展来设计和实施面向农业生产系统的下一代数据、模型和决策支持工具。我们的目标是评估相关技术的成熟度、预期发展以及造福农业建模社区的潜力。所考虑的技术包括协作开发方法以及以跨学科方式让利益相关者和用户参与开发的方法。我们的定性评估表明,作为一项总体研究挑战,需要解决数据源的互操作性、模块化粒度开放模型、应用的参考数据集以及特定用户需求分析方法,以使农业建模进入大数据时代。这将带来更高的分析能力以及新数据源的综合利用。总体而言,农业系统建模需要迅速采用和吸收最先进的数据和ICT技术,重点关注受益者的需求以及为那些开发模型应用的人员提供便利。这种采用需要广泛采用一套最佳实践作为标准操作程序。