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Data synthesis for crop variety evaluation. A review.
Agronomy for Sustainable Development ( IF 7.3 ) Pub Date : 2020-07-09 , DOI: 10.1007/s13593-020-00630-7
David Brown 1, 2 , Inge Van den Bergh 3 , Sytze de Bruin 1 , Lewis Machida 4 , Jacob van Etten 2
Affiliation  

Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.

中文翻译:

用于作物品种评估的数据综合。回顾。

作物品种应满足多种要求,包括农艺性能和产品质量。品种评估取决于田间试验和感官分析产生的数据,这些数据是在农民和消费者参与程度不同的情况下进行的。这种多方面的品种评估既昂贵又费时;因此,应优化使用这些数据。数据综合可以帮助利用现有数据和新数据,将来自不同来源的数据进行合并,并将其与专家知识相结合,以产生新的信息和对决策的支持。作物品种评估的数据综合可以部分基于现有经验和方法,但也需要方法上的创新。我们审查了实现作物品种评估数据综合所需的要素,包括(1)作物品种评估所需的数据类型,(2)数据管理和整合中的主要挑战,(3)旨在解决这些挑战的主要全球举措,(4)当前的统计方法以结合数据进行作物品种评估和( 5)现有的数据合成方法,用于评估品种,以组合来自多个数据源的不同数据集。我们得出的结论是,当前可用的方法有可能克服数据合成中的现有障碍,并且可以推动良性循环,这将鼓励研究人员共享数据并在数据驱动的研究中进行协作。(4)当前的统计方法,用于组合数据以进行作物品种评估;(5)现有的数据综合方法,用于品种评估,以组合来自多个数据源的不同数据集。我们得出的结论是,当前可用的方法有可能克服数据合成中的现有障碍,并且可以推动良性循环,这将鼓励研究人员共享数据并在数据驱动的研究中进行协作。(4)当前的统计方法,用于组合数据以进行作物品种评估;(5)现有的数据综合方法,用于品种评估,以组合来自多个数据源的不同数据集。我们得出的结论是,当前可用的方法有可能克服数据合成中的现有障碍,并且可以启动一个良性循环,这将鼓励研究人员共享数据并开展数据驱动的研究合作。
更新日期:2020-07-09
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