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Data Collection

This document discusses the existing agricultural data collection efforts, focusing on crops such as wheat, corn, soybean, and others. It addresses key aspects of data standardization and sharing, with insights from experts like Mark Schwartz and Alex Pavlista. Emphasis is placed on phenology data and its importance in predicting plant responses to climate change. The collaborative nature of data sharing is also highlighted, discussing the challenges of co-authorship in shared datasets. This synthesis aims to enhance research accuracy and improve decision-making processes in agriculture.

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Data Collection

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  1. Data Collection

  2. Existing Data • Lilac • Mark Schwartz • Grasshopper data • Stephen Johnson • European Corn Bore Flights • Web site • Dept of Ag statistics proportion of wheat headed, jointed, turned, harvested • Corn • Soybean • Sorghum • Potato • Alex Pavlista • Grape data

  3. Ancillary Data • Precision • Numbers • Accuracy • Training • Daily

  4. Collection Network • What • Smooth Brome • How • Variety trials on annual crops • Is it freely shared? • Issue of how to share data – do they need to be co-authors

  5. Data Formats • Standardization • Clones • Annuals • Planting date • Emergence date (leaf) • Bud/anthesis

  6. Phenology to quantify and predict • Absolutely a part of quantifying the impact and providing information to provide information to help us adjust to the change • How many years? • More is better, but even some over time will be of help • What kind of data? • NAS (National Ag Statistics Service) is perhaps useful, but needs standardization and well defined data • Need indicator standardization • Could correlative work be done to standardize a given crop to lilac data? • Plant response to increase heat signals • Focus on winter annuals and perennials, but information on spring crops could also be valuable given emergence and planting date, data to go with it. • What quality needs? • Tight definition of what is measured.

  7. Temperature and Photoperiod

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