Experimental Designs and Estimation Methods for On-Farm Research: A Simulation Study of Corn Yields at Field Scale
Experimental Designs and Estimation Methods for On-Farm Research: A Simulation Study of Corn Yields at Field Scale
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DOI：
10.2134/agronj2019.03.0142
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发表时间：
2019-11-01
期刊：
影响因子：
2.1
通讯作者：
Federico Martin, Nicolas
中科院分区：
文献类型：
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作者：
Agustin Alesso, Carlos;Ariel Cipriotti, Pablo;Federico Martin, Nicolas
使用精准农业技术的农场试验使农民能够根据他们的田地数据做出决定。农场试验的结果取决于试验设计和进行的统计分析。为了改进农场研究实验，需要提供关于处理效果估计的准确性以及通过替代试验设计和分析获得的不同空间结构情景下假设检验的I型错误率的详细信息。用无条件高斯地统计模拟技术模拟了15个随机场的3000个产量数据集，并采用10个试验设计和3种估计方法进行了模拟，试验单位为138~9969m(2)。空间结构、试验设计和估计方法对总体平均产量和处理偏差没有影响。在第I类错误率高于名义错误率的情况下，块金/窗槛比和变异函数范围的未处理变化对估计器的效率和精度有显著影响，且随着空间自相关程度的提高而增加。无论设计如何，空间方法对空间结构的变化都是稳健的。治疗的随机化增加了模型估计者的不确定性。一般而言，治疗效果估计的准确性随着较小重复次数的增加而增加。在这些估计和地块的大小之间观察到了相反的趋势。分析表明，在两处理试验中，由于试验单元的大小和数量，检验总体处理效果的最佳设计是分耕机、带状小区和棋盘。
On-farm experimentation using Precision Agriculture technology enables farmers to make decisions based on data from their fields. Results from on-farm experiments depend on the experimental design and statistical analyses performed. Detailed information about the accuracy of the treatment effect estimates, and Type I error rates of hypothesis testing under different spatial structure scenarios attained by alternative experimental designs and analysis is required to improve on-farm research experiments. Three thousand yield data sets were drawn from 15 random fields simulated by unconditional Gaussian geostatistical simulation technique and were modeled by applying 10 experimental designs and three estimation methods with experimental units ranging from 138 to 9969 m(2). No effect of spatial structure, experimental design, and estimation methods was observed on overall mean yield and treatment bias. Unaddressed changes of nugget/sill ratio and range of variogram had a significant effect on estimator efficiency and accuracy with Type I error rates above the nominal rate, which increased with higher spatial autocorrelation. Spatial methods were robust to changes in spatial structure regardless of the design. Randomization of treatment increased the uncertainty of model estimators. In general, the accuracy of treatment effect estimates increased with the number of replications of smaller size. The opposite trend was observed between those estimates and the size of the plots. Analyses showed that the best designs for testing the overall treatment effect in two-treatment experiments would be split-planter, strip-plots, and chessboard because of their size and number of experimental units.