Precision of grain yield monitors for use in on-farm research strip trials

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2023-12-11 DOI:10.1007/s11119-023-10092-y

A. A. Gauci, J. P. Fulton, A. Lindsey, S. A. Shearer, D. Barker, E. M. Hawkins

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Abstract

On-farm research (OFR) has become popular as a result of precision agriculture technology simplifying the process and farm software capabilities to summarize results collected through the technology. Different OFR designs exists with strip-trials being a simple approach to evaluate different treatments. Common in OFR is the use of yield monitors to collect crop performance data since yield represents a primary response variable in these type studies. The objective was to investigate the ability of grain yield monitoring technologies to accurately inform strip trials when frequent yield variability exists within an experimental unit. A combination of six sub-plot treatment resolutions (TR) that differed in length of imposed yield variation (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were harvested at combine ground speeds of 3.2, 6.4, 7.2, and 8.1 kph, depending on study site (three study sites total). Intentional yield differences in maize (Zea mays L.) were created for each sub-plot by alternating the amount nitrogen (N) applied: 0 or 202 kg N/ha. Yield was measured by four commercially available yield monitoring (YM) technologies and a weigh wagon. Comparisons were made between the accumulated mass of the YM technology and weigh wagon through percent differences along with testing the significance of the plotted relationship between YM and weigh wagon. Results indicated that yield monitoring technology can be used to evaluate strip trial performance regardless of yield frequency and variability (error < 3%) within an experimental unit when operating within the calibrated range of the mass flow sensor. Operating outside of the calibrated range of the mass flow sensor resulted in > 15% error in estimating accumulated weight and overestimation of yield by 23%. Finally, no significant differences existed in estimating accumulated weight values between grain yield monitor technologies (all p-values ≥ 0.54).

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用于农场研究带状试验的谷物产量监测器的精度

精准农业技术简化了研究过程，农场软件也能总结通过该技术收集到的结果，因此农场研究（OFR）变得越来越流行。农场研究有不同的设计，条带试验是评估不同处理的一种简单方法。由于产量是此类研究的主要反应变量，因此在 OFR 中常见的是使用产量监测器收集作物表现数据。这项研究的目的是调查谷物产量监测技术在试验单元内产量变化频繁的情况下为条带试验提供准确信息的能力。根据研究地点的不同，在联合收割机地面速度为 3.2、6.4、7.2 和 8.1 千米/小时（共三个研究地点）的条件下，收割了六个子地块处理决议 (TR)，其施加的产量变化长度各不相同（7.6、15.2、30.5、61.0、121.9 和 243.8 米）。通过交替施用氮（N）量，在每个子地块中形成玉米（Zea mays L.）的有意产量差异：0 或 202 千克氮/公顷。产量通过四种市场上可买到的产量监测（YM）技术和称重车进行测量。通过百分比差异对产量监测技术和称重车的累积质量进行比较，并测试产量监测技术和称重车之间绘图关系的显著性。结果表明，当在质量流量传感器的校准范围内操作时，无论试验单位内的产量频率和变异性（误差< 3%）如何，产量监测技术都可用于评估带状试验的性能。在质量流量传感器校准范围之外操作时，累计重量估计误差为 15%，产量估计过高 23%。最后，不同谷物产量监测技术在估算累计重量值方面没有明显差异（所有 p 值均≥ 0.54）。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

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.