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优化测定饲料作物干物质和养分产量的方案的准确性。

Optimizing accuracy of protocols for measuring dry matter and nutrient yield of forage crops.

机构信息

Department of Animal Science, One Shields Avenue, University of California, Davis, CA 95616, USA.

University of California Cooperative Extension, 3800 Cornucopia Way, Suite A, Modesto, CA 95358, USA.

出版信息

Sci Total Environ. 2018 May 15;624:180-188. doi: 10.1016/j.scitotenv.2017.11.203. Epub 2017 Dec 14.

Abstract

Farmers around the world must precisely manage nutrients applied to and removed from crop fields to maintain production and without causing nutrient pollution. This study is the first to quantify the baseline accuracy of current industry measurement protocols and achievable accuracy from intensifying protocols for measuring dry matter (DM), nitrogen (N), potassium (K), and phosphorus (P) yields from forage crops harvested for silage. The 'true' DM and nutrient yields of three fields each of corn, sorghum, and small grain were intensively measured by weighing and sampling every truckload of harvested forage. Simulations quantified the accuracy of practical sampling protocols by repeatedly subsampling the complete dataset for each field to measure average truckload weight and average DM and nutrient concentrations. Then uncertainty was propagated to DM, N, P, and K yield calculations using standard error equations. Yields measured using current industry protocols diverged from the true yields of some fields by more than ±40%, emphasizing the need for improved protocols. This study shows that improving average DM and nutrient concentration measurements is unlikely to improve accuracy of yield measurements if average load weight is not precisely measured. Accuracy did not come within 27% of true yields without weighing all truckloads on some fields even when DM and nutrient concentration measurements were perfectly accurate. Once all truckloads were weighed, the timing of forage sample collection to measure average DM concentration had the greatest impact on accuracy; precision improved by an average of 6.2% when >3 samples were evenly spaced throughout the harvest compared to the same number of consecutive samples. All crop fields are affected by within field variation in growing conditions that results in heterogeneity in DM and nutrient yield. Globally, this study provides foundational methodology to quantitatively evaluate and improve yield measurement protocols that ultimately support sustainable crop production.

摘要

世界各地的农民必须精确管理施加于和从农田中去除的养分,以维持产量,同时避免养分污染。本研究首次定量评估了当前行业测量规程的基准精度以及通过强化规程来提高青贮饲料收获的饲料作物干物质 (DM)、氮 (N)、钾 (K) 和磷 (P) 产量测量精度的实际可达程度。通过对每车收获的饲料进行称重和采样,对三个玉米、高粱和小粒谷物田的“真实”DM 和养分产量进行了密集测量。模拟通过对每个田块的完整数据集进行重复子采样来测量平均车装载重量和平均 DM 和养分浓度,从而量化了实际采样规程的精度。然后,使用标准误差方程将不确定性传播到 DM、N、P 和 K 产量计算中。使用当前行业规程测量的产量与一些田块的真实产量相差超过±40%,这强调了需要改进规程。本研究表明,如果不能精确测量平均装载重量,提高平均 DM 和养分浓度测量精度不太可能提高产量测量的精度。在某些田块上,如果不称所有车装载重量,即使 DM 和养分浓度测量完全准确,产量的精度也无法达到真实产量的 27%以内。一旦所有车装载重量都被称重,收集饲料样本以测量平均 DM 浓度的时间就成为了影响精度的最大因素;与相同数量的连续样本相比,当 >3 个样本均匀分布在整个收获期时,精度平均提高了 6.2%。所有作物田都受到田间生长条件变化的影响,这导致 DM 和养分产量存在异质性。在全球范围内,本研究为定量评估和改进最终支持可持续作物生产的产量测量规程提供了基础方法。

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