USDA LTAR Common Experiment measurement: Post-processing of spatial grain yield data

Authors: OWEN, OLIVIA - University Of Missouri; Sudduth, Kenneth - Ken; Abendroth, Lori

Submitted to: Protocols.io
Publication Type: Research Notes
Publication Acceptance Date: 8/27/2024
Publication Date: 8/27/2024
Citation: Owen, O.K., Sudduth, K.A., Abendroth, L.J. 2024. USDA LTAR Common Experiment measurement: Post-processing of spatial grain yield data. Protocols.io. https://doi.org/10.17504/protocols.io.4r3l2qmy3l1y/v1
DOI: https://doi.org/10.17504/protocols.io.4r3l2qmy3l1y/v1

Interpretive Summary: This protocol is part of a set of protocols for the LTAR Cropland Common Experiment, and provides guidance for post-processing of combine yield monitor data. Grain yield data collected with a combine yield monitor facilitates research on cropping system productivity with respect to soil and landscape variability. However, the raw data obtained from the yield monitor must be edited or “cleaned” to provide more accurate yield values. Clean yield data provide researchers and farmers with information that reveals spatial patterns due to soil type, water availability, and field traffic patterns while enabling the alignment of grain yields with in-season point measurements. Following the steps outlined in this protocol will insure combine yield monitor data is of sufficient accuracy to be used in research investigations.

Technical Abstract: This protocol provides general recommendations for in-field data collection and detailed methods for post-processing grain yield data. Grain yield is an important measurement for evaluating and comparing the overall economic productivity of cropping systems. Collecting yield data using manual methods of point samples or obtaining average yields over an experimental area (e.g., with a weigh wagon) provides basic information regarding the system. However, additional information about cropping system productivity with respect to soil and landscape variability can be obtained if spatial yield data are collected. This process is facilitated by equipping the combine with a grain yield monitor that allows for the collection of continuous yield data. Maps are then produced from yield monitor data; however, errors will arise from combine operation, yield monitor performance, and/or crop conditions. The raw data must be edited or “cleaned” to provide more accurate yield values. Clean yield data provide researchers and farmers with information that reveals spatial patterns due to soil type, water availability, and field traffic patterns while enabling the alignment of grain yields with in-season point measurements. The process for obtaining high-quality yield monitor data consists of two steps: (1) in-field data collection and (2) post-processing for cleaning, formatting, and assessing yield data. Monitors in the combine collect a wide range of data such as location coordinates, machine speed, grain fl ow rate, and grain moisture. Ag Leader Integra and John Deere Greenstar are two common brands of yield monitors used within the LTAR network. For post-processing, data are either transferred wirelessly or exported via a USB drive and then imported into a program such as Ag Leader SMS or USDA-ARS Yield Editor. Using the software, data are reviewed, removed, or edited as a part of the cleaning process to provide accurate data. A survey of the LTAR sites in 2022 showed four sites using Ag Leader SMS software for post-processing, three using John Deere software, and three using ARS Yield Editor software. This protocol applies to combine yield data for grain crops, including corn, soybean, wheat, and oat. Procedures for non-grain crops such as cotton, peanut, and forages are expected to vary slightly.