A. A. Gauci, A. Lindsey, S. A. Shearer, D. Barker, E. M. Hawkins, John P. Fulton
{"title":"On-farm experimentation: assessing the effect of combine ground speed on grain yield monitor data estimates","authors":"A. A. Gauci, A. Lindsey, S. A. Shearer, D. Barker, E. M. Hawkins, John P. Fulton","doi":"10.1007/s11119-024-10210-4","DOIUrl":null,"url":null,"abstract":"<p>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<sup>−1</sup>. Treatments were replicated 3 times. The intentional yield variability in maize (<i>Zea mays L.</i>) was created by alternating nitrogen application (0–202 kg N ha<sup>−1</sup>) 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 (<i>p</i> ≥ 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 <i>p</i> ≥ 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 (<i>p</i> ≥ 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).</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"14 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10210-4","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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).
期刊介绍:
Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.
There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:
Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.
Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.
Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.
Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.
Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.
Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.