The central importance of soil for the functioning of terrestrial systems is increasingly recognized. Critically relevant for water quality, climate control, nutrient cycling and biodiversity, soil provides more functions than just the basis for agricultural production. Nowadays, soil is increasingly under pressure as a limited resource for the production of food, energy and raw materials. This has led to an increasing demand for concepts assessing soil functions so that they can be adequately considered in decision-making aimed at sustainable soil management. The various soil science disciplines have progressively developed highly sophisticated methods to explore the multitude of physical, chemical and biological processes in soil. It is not obvious, however, how the steadily improving insight into soil processes may contribute to the evaluation of soil functions. Here, we present to a new systemic modeling framework that allows for a consistent coupling between reductionist yet observable indicators for soil functions with detailed process understanding. It is based on the mechanistic relationships between soil functional attributes, each explained by a network of interacting processes as derived from scientific evidence. The non-linear character of these interactions produces stability and resilience of soil with respect to functional characteristics. We anticipate that this new conceptional framework will integrate the various soil science disciplines and help identify important future research questions at the interface between disciplines. It allows the overwhelming complexity of soil systems to be adequately coped with and paves the way for steadily improving our capability to assess soil functions based on scientific understanding.

Soils play a key role for the functioning of terrestrial ecosystems. Thus, soils are essential for human society not only because they form the basis for the production of food. This has long been recognized, and during the last three decades the need to establish methods to evaluate the ability of soils to provide soil functions has moved toward the top of the agenda in soil science. Quantitative evaluation schemes are indispensable to adequately include soils into strategies to reach sustainable development targets. In this paper we build upon existing approaches and propose a concept to evaluate individual soil functions with respect to the soil's intrinsic potential in contrast to its actual state. This leads to a separation of indicator variables and allows for conclusions on the structure of appropriate models that are required to predict the dynamics of soil functions in response to external perturbation. This concept is demonstrated for the production function, carbon storage and water storage which are evaluated exemplarily for different plots of a long-term field experiment. It is discussed for nutrient cycling and habitat function, where evaluation schemes are still less obvious.

Large weighable lysimeters allow a precise determination of the soil water balance and the quantification of both water exchange at the soil-atmosphere interface and the flux below the root zone toward the groundwater. If well embedded into an equally-vegetated environment, they reach a hitherto unprecedented accuracy in estimating precipitation (P) by rain, dew, fog, rime and snow, and evapotranspiration (ET). Lysimeters largely avoid errors made by traditional measurement systems, such as the wind error of Hellmann rain gauges, the island error of class-A pans, or errors from soil-water measurements that are subject to subsurface heterogeneity. If the amount of seepage water is added to the lysimeter mass, temporal changes of the lysimeter mass can be used to solve the water balance equation for atmospheric fluxes. Increasing mass indicates P, decreasing mass ET. The determination of the net water balance (sum of P and ET) is accurate and robust. A problem arises in the separate estimation of the underlying P and ET fluxes, because weight differences in specified time intervals are affected by stochastic fluctuations due to mechani- cal vibration, which may be caused by wind or other factors. The aim of this study is to evaluate algorithms that aim on eliminating the effects of these fluctuations and to estimate actual fluxes across the soil-atmosphere boundary and the soil water balance from lysimeter measurements. We use synthetic and real measured data from large lysimeters to test which strategies of data evaluation can be applied, and which degree of accuracy can be reached

Measurements of root‐zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground‐based vehicle. Recently, concerns have been raised about a potentially biasing influence of local structures and roads. We employed neutron transport simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that roads introduce a substantial bias in the CRNS estimation of field soil moisture compared to off‐road scenarios. However, this effect becomes insignificant at distances beyond a few meters from the road. Neutron measurements on the road could overestimate the field value by up to 40 % depending on road material, width, and the surrounding field water content. The bias could be largely removed with an analytical correction function that accounts for these parameters. Additionally, an empirical approach is proposed that can be used without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between road‐effect corrected measurements and field soil moisture observations. However, if knowledge about the road characteristics is missing, measurements on the road could substantially reduce the accuracy of this method. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field‐scale soil moisture to support applications in hydrology, remote sensing, and agriculture.