空间、时间和时空作物产量及相关数据的变异性和不确定性。

A B McBratney, B M Whelan, T M Shatar
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引用次数: 31

摘要

精准农业理论在大面积农业实践中的应用依赖于对信息不确定性后果的成功处理,即与决定产量组成部分和/或环境损失的那些因素的时空变化有关的信息。本文讨论了产量及其相关变量的时空变化的不确定性。这两个组成部分的大小为适当的管理规模提供了建议。同时报告空间和时间变化是罕见的,这些类型的过程的理论仍处于起步阶段。本文简要介绍了一些理论,然后列举了几个例子,包括洛桑研究所的经典实验、澳大利亚的产量监测实验、丹麦的长期大麦试验和土壤湿度监测网络。在单场范围内,年际变化明显大于空间变化。由此得出的结论是,如果精准农业要有坚实的科学基础,并最终取得实际成果,那么仍有待认真研究的零假设是:“考虑到相对于单个领域规模的产量的巨大时间变化,那么最佳的风险规避策略是统一管理。”
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variability and uncertainty in spatial, temporal and spatiotemporal crop-yield and related data.

Application of the theories of precision agriculture to the practicalities of broad-acre farming relies on successful handling of the ramifications of uncertainty in information, i.e. information pertaining to the spatial and temporal variation of those factors which determine yield components and/or environmental losses. This paper discusses the uncertainty of yield and related variables as measured by their spatial and temporal variance. The magnitude of these two components gives a suggestion as to the appropriate scale of management. Simultaneous reporting on spatial and temporal variation is rare and the theory of these types of process is still in its infancy. Some brief theory is presented, followed by several examples from the Rothamsted classic experiments, yield-monitoring experiments in Australia, a long-term barley trial in Denmark, and a soil moisture monitoring network. It is clear that annual temporal variation is much larger than the spatial variation within single fields. This leads to the conclusion that if precision agriculture is to have a sound scientific basis and ultimately a practical outcome then the null hypothesis that still remains to be seriously researched is: 'given the large temporal variation in yields relative to the scale of a single field, then the optimal risk aversion strategy is uniform management.'

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