Soil quality: assessment, indicators & management
Good soil quality is of fundamental importance to both local and global food production and to ecosystem resilience, but how do we define and measure soil quality and the impact that management practices have on it?
In this section of iSQAPERiS we integrate soil science and agricultural management practices. We review concepts of soil quality and measured or visually assessed soil properties (such as organic matter content or earthworm density) that can be used as indicators of quality. Using data from long-term field experiments, both in the iSQAPER study sites and elsewhere, we identify those soil quality indicators that respond to changes in management practice. We describe the ways in which many soil quality indicators can be readily assessed in the field making them useful for monitoring the impact of changes in practice.
Note: There is currently Restricted access to some of these sections. This is to allow the authors time to publish their results.
Soil quality - a critical review
Sampling and analysis or visual examination of soil to assess its quality and potential for use is widely practiced from plot to national scales. Here we review how soil quality has been defined and how the concept has been used to broaden the understanding of the ecosystem services that soil provides. The choice of soil quality indicators is discussed in-depth with respect to requirements of indicators and methods to select a minimum dataset. We propose the crucial steps to be taken for successful soil quality assessment and analyze to what extent these have been implemented so far. [D3.1]
Management practices and soil quality
Understanding interacting effects of agricultural management practices on soil quality indicators is a key part of iSQAPER research programme, essential for the development of »SQAPP: the soil quality app and for »Upscaling from local to regional the effects of widespread implementation of practices. Such effects can be best analysed from data of agricultural long-term experiments (LTEs), where soils are experimentally manipulated to identify the key drivers of soil change. These trials allow the study of changes over time in soil properties under various types of treatment (e.g. plough/no-tillage) and their respective intensities (e.g. ploughing frequency). In this section of iSQAPERiS we analyse and summarise data from 30 European and 40 Chinese LTEs in order to have a basis for making generic recommendations for agricultural practices. [D3.2]
Soil quality indicators from long-term experiments
Using results from »Soil quality - a critical review and »Management practices and soil quality and data collected in 10 European and 5 Chinese LTEs, we investigate which commonly used soil quality indicators are most senstive to two groups of management practices (tillage and nutrient management) across a range of locations and soil types. Nine indicators (including total organic carbon, particulate organic matter, microbial nitrogen and penetration resistance) are shown to be sensitive. [D3.3]
Novel soil quality indicators
Novel techniques for measuring certain soil parameters, especially in the fields of biochemistry and biology, represent a unique opportunity to better understand the effects of soil management on soil functions. Here we assess the suitability of four novel soil quality parameters (labile carbon fractions, soil suppressiveness to Pythium ultimum, nematode DNA extraction, and community level physiological profiling with MicroResp®) for use as soil quality indicators. [D3.4]
Visual soil and plant quality assessment
Visual assessment of soil and plant quality is an important part of evaluating the effects of different agricultural management practices. Methods for visually assessing 14 different parameters linked to soil quality and 6 for assessing plant quality are described.
Using data from the iSQAPER study sites the associations between measured soil properties, visual soil assessment score values and climate variables have been tested and modelled. Logistic regression models based on a few measured soil properties (organic matter content, labile organic carbon, pH and sand/silt/clay) and climate variables and indices (mean temperature, mean rainfall, aridity index and net primary productivity) a number of visual soil assessment score values (soil structure and consistency, porosity, stability, subsoil compaction, colour, earthworm density, surface ponding and susceptibility to erosion) can be predicted with good accuracy. [D6.2]