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Keywords: ,Data, Data collection, Data processing, GNSS, Harvesting, Mapping. Metadata, Semantics, Spatial variability, Yield monitor.

1 Purpose and Scope

1.1  Purpose

1.1.1    The purpose of this standard is to improve the collection, processing, and visualization of data files containing geographic harvest data such as yield, moisture-content (MC), and other spatially variable properties such as grain protein content, cotton fiber maturity, etc. with the goals of preserving information content, enabling interoperability among different software products and measurement systems, and accurately conveying this information to users.

1.1.2    There is an existing standard in the domain of machine-generated field operations data: ISO 11783-10. That standard presents a generalized data model for agricultural machines and implements (including harvesters), and a mechanism for serializing data (i.e., a file format). This National Standard does neither; its intent with respect to ISO 11783 is to complement that standard by providing context regarding how geographic harvest data is used in practice. This is necessary because the ISO 11783-10 standard presents geographic harvest data in a generic way.

1.1.3    The recommendations contained herein are not specific to ISO-11783-based systems, however, although the ISO 11783-10 XML (4.100) format is used when providing examples. Non-ISO systems can benefit from this National Standard, especially in terms of maximizing interoperability with Farm Management Information Systems (FMIS).

1.1.4    This standard is not meant to be prescriptive of the manner in which field equipment records these data; rather, it is meant to highlight the importance of accurately preserving the meaning of the data, and thus enable processing of harvest data into a form that is fit for use within FMIS software and easily serializable for data exchange.

1.2  Scope

1.2.1    The scope of this standard comprises the processes of collection, processing, and visualization of geographic harvest data. To the effect of enabling users to be successful in these processes, this standard recommends minimum data requirements for geographic harvest data.

1.2.2    Given that growers have an increasing burden to capture field operations data due to ever-growing requirements for traceability (4.91) in the supply chain, this National Standard also seeks to simplify harvest record-keeping, and therefore enable better traceability through standardization of the meaning of the properties of geographic harvest data.

1.2.3    The minimum data requirements established here can be applied to data files collected with yield monitors (4.102) and associated sensors on a harvesting machine and to files associated with software products used for processing these files and developing maps and other forms of visualization.

1.2.4    Furthermore, this standard defines the metadata (4.68) needed to describe resources (4.81) in geographic harvest data files so that adequate data processing and visualization (e.g., map development) can occur.

1.2.5    This standard also provides recommendations for maximizing consistency and clarity in the visualization and interpretation of geographic harvest data (e.g., yield maps).

1.2.6    Note that the scope of geographic harvest data is not restricted to numeric data. For example, yield and moisture content are numeric, but cotton lint color grade is categorical.

1.2.7    A dataset may be produced for a specific application or for a set of presupposed applications, the requirements of which are described by means of a document that the ISO 19157 International Standard on data quality (4.26) in geographic data calls the data product specification. The quality of a dataset can only be assessed by knowledge about its data quality elements (ISO 19157:2013, clause B.1); i.e., the data elements that convey the value of data quality measures. In some cases, a dataset's data quality can be assessed indirectly by its non-quantitative quality information usage, lineage, and purpose (ISO 19115-1:2013). Data quality elements evaluate the difference between the dataset and a perfect dataset corresponding to the data product specification. This is another way of saying that the quality of a dataset is contingent on the purpose it is being used for, and the requirements thereof.

1.2.8    This standard defines a set of realistically available data quality measures (e.g., HDOP in clause 5.5.1.4, and number of satellites in clause 5.5.1.5) that can be used to determine whether a dataset meets the specific needs of a specific application of a user or organization. These measures are considerably fewer than the standardized data quality measures presented in the ISO 19157 standard for data quality in geographic information. Factors contributing to this gap in the availability of harvest data quality measures include a lack of demand and technological limitations of some of the hardware involved. The increasing regulatory pressure faced by growers in some jurisdictions may drive advancement in this space.

1.2.9    A final scope note: This standard addresses geographic harvest data collected from the harvester during the harvest process itself. Data collected after the operation, such as grading information or other observations or measurements performed on the crop once harvested, are out of scope.