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Yield Monitor Data Cleaning is Essential for Accurate Corn Grain and Silage Yield Determination
Author(s) -
Kharel T.P.,
Swink S.N.,
Maresma A.,
Youngerman C.,
Kharel D.,
Czymmek K.J.,
Ketterings Q.M.
Publication year - 2019
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2018.05.0317
Subject(s) - silage , yield (engineering) , agricultural engineering , raw data , data quality , agronomy , environmental science , software , automation , engineering , mathematics , computer science , statistics , operations management , biology , metric (unit) , mechanical engineering , materials science , metallurgy , programming language
Core Ideas Corn silage and grain yield monitors collect yield data of relevance to farmers. Evaluation of quality of yield monitor data is essential, especially for silage. A data cleaning protocol, consistent across fields, farms, and years, is needed. Semi‐automation is needed for quick and consistent processing of whole‐farm data.Yield monitor data are being used for a variety of purposes including conducting on‐farm studies, assessing nutrient balances, determining yield potential, and creating management zones. However, standardization of raw data processing is needed to obtain comparable data across fields, farms, and years. Our objective was to evaluate the impact of data cleaning protocols on corn ( Zea mays L.) grain and silage yield data at the whole field (with and without headlands) and within field (soil map unit) scales. Corn silage data from 145 fields (three farms) and grain data from 88 fields (three farms) were processed. Comparisons were made to evaluate yields among three levels of cleaning: (i) none; (ii) automated cleaning (“Auto”) with filter settings derived for 10 fields per farm; and (iii) automated cleaning with manual inspection for unrepresentative patterns, after the automated cleaning step was completed (“Auto+”). The Auto+ cleaning process was conducted separately by three individuals to evaluate person‐to‐person differences. Spatial Management System software was used to read raw data and transfer to Ag Leader format. Yield Editor software was used to clean data (Auto and Auto+). Results showed the necessity of data cleaning, especially for corn silage. However, considering less than 5% deviation between methods at three spatial scales, the Auto and Auto+ cleaning resulted in similar output, as long as (i) each field or subfield included at least 100 harvester measurement points, and (ii) a moisture filter was applied for corn silage data.