On-farm experimentation: assessing the effect of combine ground speed on grain yield monitor data estimates
On-farm experiments (OFE) typically do not account for limitations of grain yield monitors such as the dynamics of grain flow through a large combine. A common question asked within OFE is how ground speed impacts yield estimates from grain yield monitors. Therefore, the objective of this study was...
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description | On-farm experiments (OFE) typically do not account for limitations of grain yield monitors such as the dynamics of grain flow through a large combine. A common question asked within OFE is how ground speed impacts yield estimates from grain yield monitors. Therefore, the objective of this study was to determine if combine ground speed influences the ability of grain yield monitors to report yield differences for OFE. Six sub-plot treatment resolutions that differed in length (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) of imposed yield variation were harvested at combine ground speeds of 3.2 and 6.4 km h
−1
. Treatments were replicated 3 times. The intentional yield variability in maize (
Zea mays L.
) was created by alternating nitrogen application (0–202 kg N ha
−1
) across the treatment lengths. A factory installed yield monitor (YM3) and a third-party platform (P1) using the controller area network (CAN) bus data were used to collect yield data and compared to plot combine data collected from adjacent rows for each treatment length along a pass. Comparisons were made between each YM and plot combine yield estimates for each low and high yield treatment lengths. Combine ground speed did not significantly impact yield estimates (
p
≥ 0.31 for all speed interactions) except speed * method due to lack of calibration. There were no significant differences the computed yield differences (all speed interactions
p
≥ 0.40). Combine ground speed did not significantly influence the ability of yield monitoring technologies (i.e. mass flow sensor) to estimate the average low and high yields (
p
≥ 0.31 for all speed interactions for individual plot lengths except when operating outside the calibrated flow range of the mass flow sensor. Operating outside the calibrated flow range of the mass flow sensor resulted in mass flow rate being overestimated by an average of 23% for both yield monitors (YM3 and P1). |
doi_str_mv | 10.1007/s11119-024-10210-4 |
format | Article |
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−1
. Treatments were replicated 3 times. The intentional yield variability in maize (
Zea mays L.
) was created by alternating nitrogen application (0–202 kg N ha
−1
) across the treatment lengths. A factory installed yield monitor (YM3) and a third-party platform (P1) using the controller area network (CAN) bus data were used to collect yield data and compared to plot combine data collected from adjacent rows for each treatment length along a pass. Comparisons were made between each YM and plot combine yield estimates for each low and high yield treatment lengths. Combine ground speed did not significantly impact yield estimates (
p
≥ 0.31 for all speed interactions) except speed * method due to lack of calibration. There were no significant differences the computed yield differences (all speed interactions
p
≥ 0.40). Combine ground speed did not significantly influence the ability of yield monitoring technologies (i.e. mass flow sensor) to estimate the average low and high yields (
p
≥ 0.31 for all speed interactions for individual plot lengths except when operating outside the calibrated flow range of the mass flow sensor. Operating outside the calibrated flow range of the mass flow sensor resulted in mass flow rate being overestimated by an average of 23% for both yield monitors (YM3 and P1).</description><identifier>ISSN: 1385-2256</identifier><identifier>EISSN: 1573-1618</identifier><identifier>DOI: 10.1007/s11119-024-10210-4</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Agriculture ; Atmospheric Sciences ; Biomedical and Life Sciences ; Calibration ; Chemistry and Earth Sciences ; Computer Science ; Controller area network ; corn ; Crop yield ; Estimates ; Flow rates ; Grain ; grain yield ; Ground speed ; Life Sciences ; mass flow ; Mass flow rate ; Monitors ; nitrogen ; Physics ; precision ; Remote Sensing/Photogrammetry ; Sensors ; Soil Science & Conservation ; Statistics for Engineering ; Zea mays</subject><ispartof>Precision agriculture, 2025-02, Vol.26 (1), p.11-11, Article 11</ispartof><rights>The Author(s) 2024</rights><rights>Copyright Springer Nature B.V. Feb 2025</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c277t-f30f0a45c4b8d79fb77c7695437d31ba180821eb5f69beeca9f8a1b36c0401d03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11119-024-10210-4$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11119-024-10210-4$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Gauci, A. A.</creatorcontrib><creatorcontrib>Lindsey, A.</creatorcontrib><creatorcontrib>Shearer, S. A.</creatorcontrib><creatorcontrib>Barker, D.</creatorcontrib><creatorcontrib>Hawkins, E. M.</creatorcontrib><creatorcontrib>Fulton, John P.</creatorcontrib><title>On-farm experimentation: assessing the effect of combine ground speed on grain yield monitor data estimates</title><title>Precision agriculture</title><addtitle>Precision Agric</addtitle><description>On-farm experiments (OFE) typically do not account for limitations of grain yield monitors such as the dynamics of grain flow through a large combine. A common question asked within OFE is how ground speed impacts yield estimates from grain yield monitors. Therefore, the objective of this study was to determine if combine ground speed influences the ability of grain yield monitors to report yield differences for OFE. Six sub-plot treatment resolutions that differed in length (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) of imposed yield variation were harvested at combine ground speeds of 3.2 and 6.4 km h
−1
. Treatments were replicated 3 times. The intentional yield variability in maize (
Zea mays L.
) was created by alternating nitrogen application (0–202 kg N ha
−1
) across the treatment lengths. A factory installed yield monitor (YM3) and a third-party platform (P1) using the controller area network (CAN) bus data were used to collect yield data and compared to plot combine data collected from adjacent rows for each treatment length along a pass. Comparisons were made between each YM and plot combine yield estimates for each low and high yield treatment lengths. Combine ground speed did not significantly impact yield estimates (
p
≥ 0.31 for all speed interactions) except speed * method due to lack of calibration. There were no significant differences the computed yield differences (all speed interactions
p
≥ 0.40). Combine ground speed did not significantly influence the ability of yield monitoring technologies (i.e. mass flow sensor) to estimate the average low and high yields (
p
≥ 0.31 for all speed interactions for individual plot lengths except when operating outside the calibrated flow range of the mass flow sensor. Operating outside the calibrated flow range of the mass flow sensor resulted in mass flow rate being overestimated by an average of 23% for both yield monitors (YM3 and P1).</description><subject>Agriculture</subject><subject>Atmospheric Sciences</subject><subject>Biomedical and Life Sciences</subject><subject>Calibration</subject><subject>Chemistry and Earth Sciences</subject><subject>Computer Science</subject><subject>Controller area network</subject><subject>corn</subject><subject>Crop yield</subject><subject>Estimates</subject><subject>Flow rates</subject><subject>Grain</subject><subject>grain yield</subject><subject>Ground speed</subject><subject>Life Sciences</subject><subject>mass flow</subject><subject>Mass flow rate</subject><subject>Monitors</subject><subject>nitrogen</subject><subject>Physics</subject><subject>precision</subject><subject>Remote Sensing/Photogrammetry</subject><subject>Sensors</subject><subject>Soil Science & Conservation</subject><subject>Statistics for Engineering</subject><subject>Zea mays</subject><issn>1385-2256</issn><issn>1573-1618</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kE1LAzEURQdRsFb_gKuAGzfRfM1kxp0Uv6DQja5DJvNSUztJTVLQf29qBcGF2SSBc-97nKo6p-SKEiKvEy2nw4QJTAmjBIuDakJryTFtaHtY3rytMWN1c1ydpLQipMQEm1RvC4-tjiOCjw1EN4LPOrvgb5BOCVJyfonyKyCwFkxGwSITxt55QMsYtn5AaQMwoODLXzuPPh2sBzQG73KIaNBZI0jZjTpDOq2OrF4nOPu5p9XL_d3z7BHPFw9Ps9s5NkzKjC0nlmhRG9G3g-xsL6WRTVcLLgdOe01b0jIKfW2brgcwurOtpj1vDBGEDoRPq8t97yaG920Zr0aXDKzX2kPYJsVpLRjvOioKevEHXYVt9GW7QgkheN3wHcX2lIkhpQhWbYoqHT8VJWrnX-39q-JffftXuxDfh1KB_RLib_U_qS-CQojZ</recordid><startdate>20250201</startdate><enddate>20250201</enddate><creator>Gauci, A. A.</creator><creator>Lindsey, A.</creator><creator>Shearer, S. A.</creator><creator>Barker, D.</creator><creator>Hawkins, E. M.</creator><creator>Fulton, John P.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20250201</creationdate><title>On-farm experimentation: assessing the effect of combine ground speed on grain yield monitor data estimates</title><author>Gauci, A. A. ; Lindsey, A. ; Shearer, S. A. ; Barker, D. ; Hawkins, E. 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A.</creatorcontrib><creatorcontrib>Lindsey, A.</creatorcontrib><creatorcontrib>Shearer, S. A.</creatorcontrib><creatorcontrib>Barker, D.</creatorcontrib><creatorcontrib>Hawkins, E. M.</creatorcontrib><creatorcontrib>Fulton, John P.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Precision agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gauci, A. A.</au><au>Lindsey, A.</au><au>Shearer, S. A.</au><au>Barker, D.</au><au>Hawkins, E. M.</au><au>Fulton, John P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On-farm experimentation: assessing the effect of combine ground speed on grain yield monitor data estimates</atitle><jtitle>Precision agriculture</jtitle><stitle>Precision Agric</stitle><date>2025-02-01</date><risdate>2025</risdate><volume>26</volume><issue>1</issue><spage>11</spage><epage>11</epage><pages>11-11</pages><artnum>11</artnum><issn>1385-2256</issn><eissn>1573-1618</eissn><abstract>On-farm experiments (OFE) typically do not account for limitations of grain yield monitors such as the dynamics of grain flow through a large combine. A common question asked within OFE is how ground speed impacts yield estimates from grain yield monitors. Therefore, the objective of this study was to determine if combine ground speed influences the ability of grain yield monitors to report yield differences for OFE. Six sub-plot treatment resolutions that differed in length (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) of imposed yield variation were harvested at combine ground speeds of 3.2 and 6.4 km h
−1
. Treatments were replicated 3 times. The intentional yield variability in maize (
Zea mays L.
) was created by alternating nitrogen application (0–202 kg N ha
−1
) across the treatment lengths. A factory installed yield monitor (YM3) and a third-party platform (P1) using the controller area network (CAN) bus data were used to collect yield data and compared to plot combine data collected from adjacent rows for each treatment length along a pass. Comparisons were made between each YM and plot combine yield estimates for each low and high yield treatment lengths. Combine ground speed did not significantly impact yield estimates (
p
≥ 0.31 for all speed interactions) except speed * method due to lack of calibration. There were no significant differences the computed yield differences (all speed interactions
p
≥ 0.40). Combine ground speed did not significantly influence the ability of yield monitoring technologies (i.e. mass flow sensor) to estimate the average low and high yields (
p
≥ 0.31 for all speed interactions for individual plot lengths except when operating outside the calibrated flow range of the mass flow sensor. Operating outside the calibrated flow range of the mass flow sensor resulted in mass flow rate being overestimated by an average of 23% for both yield monitors (YM3 and P1).</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11119-024-10210-4</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Agriculture Atmospheric Sciences Biomedical and Life Sciences Calibration Chemistry and Earth Sciences Computer Science Controller area network corn Crop yield Estimates Flow rates Grain grain yield Ground speed Life Sciences mass flow Mass flow rate Monitors nitrogen Physics precision Remote Sensing/Photogrammetry Sensors Soil Science & Conservation Statistics for Engineering Zea mays |
title | On-farm experimentation: assessing the effect of combine ground speed on grain yield monitor data estimates |
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