A. A. Gauci, A. Lindsey, S. A. Shearer, D. Barker, E. M. Hawkins, John P. Fulton
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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). 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引用次数: 0
摘要
农场试验(OFE)通常没有考虑到粮食产量监测的局限性,例如大型联合收割机中粮食流动的动态。OFE内部经常被问到的一个问题是,地面速度如何影响谷物产量监测器对产量的估计。因此,本研究的目的是确定联合地面速度是否影响粮食产量监测仪报告OFE产量差异的能力。在3.2和6.4 km h−1的联合地面速度下,收获了6个不同长度的子地块处理分辨率(7.6、15.2、30.5、61.0、121.9和243.8 m)的强制产量变化。处理重复3次。玉米(Zea mays L.)在不同处理期间交替施氮(0 ~ 202 kg N ha−1),造成有意产量变异。使用工厂安装的产量监控器(YM3)和第三方平台(P1)使用控制器局域网(CAN)总线数据收集产量数据,并与相邻行收集的沿着通道每个处理长度的组合数据进行比较。在每个低产量和高产量处理长度下,对每个YM和小区组合的产量估计值进行了比较。由于缺乏校准,除速度*法外,联合地面速度对产量估计没有显著影响(所有速度相互作用的p≥0.31)。计算产率差异无显著性差异(所有速度相互作用p≥0.40)。联合地面速度对产量监测技术(即质量流量传感器)估计平均低产量和高产量的能力没有显著影响(p≥0.31),所有速度相互作用对单个地块长度的影响,除非在质量流量传感器的校准流量范围之外运行。在质量流量传感器的校准流量范围之外工作,导致两个产量监测器(YM3和P1)的质量流量平均高估了23%。
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 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.