Abstract. Healthy soils provide multiple functions that importantly contribute to human wellbeing, including primary production, climate and water regulation, and supporting biodiversity. These functions can partially be combined and some functions also clearly trade-off: this motivates soil multifunctionality research. Society needs scientists to help assess which soils are best for which soil functions and to determine appropriate long-term management of any given soil for optimal function delivery. However, for both tasks science lacks coherent tools and in this paper I propose a way forward. Critically, we lack a common measurement framework that pins soil functioning measurements on a common scale. Currently the field is divided with respect to the methods we use to measure and assess soil functioning and indicators thereof. Only three indicator variables (SOM, acidity, and available P) were commonly measured (>70 % of schemes) across 65 schemes that aim to measure soil health or quality, and no biological measure is implemented in more than 30 % of the 65 schemes. This status quo prevents us from systematically comparing across and within soils; we lack a soil multifunctionality benchmark. We can address this limitations systematically by setting a common measurement system. To do this, I propose to use latent variable modelling based on a common set of functional measurements, to develop a common ‘IQ test for soils’. I treat soil functions as latent variables, because they are complex processes that cannot be measured directly, we can only detect drivers and consequences of these complex processes. Latent variable modelling has a long history in social, economic and psychometric fields, where it is known as factor analysis. Factor analysis aims to derive common descriptors – the factors – of hypothesized constructs by linking measurable response variables together on a common scale. Here, I explain why such a new approach to soil multifunctionality and soil health is needed and how it can be operationalized. The framework developed here is only an initial proposal, the issue of soil multifunctionality is too complex and too important to be addressed in one go. It needs to be resolved iteratively by bands of scientist working intensively together. We need to bring our best science together, in a collaborative effort, to develop progressively more refined ways of sustainably managing one of humanity’s most precious resources: our soils.

中文翻译：

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土壤多功能性基准测试


摘要。健康的土壤具有多种功能，对人类福祉有重要贡献，包括初级生产、气候和水调节以及支持生物多样性。这些功能可以部分组合，有些功能也可以明显权衡：这激发了土壤多功能性研究。社会需要科学家帮助评估哪种土壤最适合哪种土壤功能，并确定对任何特定土壤的适当长期管理，以实现最佳功能交付。然而，对于这两项任务，科学缺乏连贯的工具，在本文中我提出了一种前进的方向。至关重要的是，我们缺乏一个通用的测量框架，可以将土壤功能测量固定在通用的范围内。目前，该领域在我们用来测量和评估土壤功能及其指标的方法方面存在分歧。在旨在测量土壤健康或质量的 65 个计划中，仅测量了三个指标变量（SOM、酸度和有效磷）（占计划的>70%），并且在 65 个计划中超过 30% 的计划中没有实施生物措施。这种现状使我们无法系统地比较土壤之间和土壤内部。我们缺乏土壤多功能性基准。我们可以通过设置通用的测量系统来系统地解决这一局限性。为此，我建议使用基于一组通用功能测量的潜在变量建模，来开发通用的“土壤智商测试”。我将土壤功能视为潜在变量，因为它们是无法直接测量的复杂过程，我们只能检测这些复杂过程的驱动因素和后果。潜变量建模在社会、经济和心理测量领域有着悠久的历史，被称为因子分析。 因素分析的目的是通过在共同尺度上将可测量的响应变量链接在一起，得出假设结构的共同描述符（因素）。在这里，我解释了为什么需要这种土壤多功能性和土壤健康的新方法以及如何实施它。这里开发的框架只是一个初步建议，土壤多功能性问题太复杂、太重要，无法一次性解决。它需要由科学家们密切合作来反复解决。我们需要将最好的科学结合在一起，共同努力，逐步开发出更加完善的方法来可持续地管理人类最宝贵的资源之一：我们的土壤。