Developing scoring functions to assess soil quality at a regional scale in rangelands of SW Spain

Abstract

The drawing of maps of soil quality at a large scale is increasingly being more useful to land planners and stakeholders. Nevertheless, it involves different methodological steps from the description of soil profiles in the field until the regional mapping of integrative soil quality index (IQI) values. The development of proper scoring functions is a paramount task for the calculation of these IQI values since every parameter needs to be standardized accordingly and weighting factors are usually estimated by multivariate techniques. The main goal of this study was to map soil quality in the Spanish region of Extremadura (commonly known by its rangelands called dehesas). To do that, i) we gathered information from 194 soil profiles described throughout the region, ii) we calculated the weighting factors of ten meaningful parameters used as indicators by using multivariate techniques (Principal Component Analysis, PCA; and Analytic Hierarchy Process, AHP), and iii) we developed standard scoring functions (SSFs) that represent the singularity of every variable (less is better, more is better). We established upper and lower limits for standardizing the values of each indicator properly. Regarding weighting factors, soil texture was highlighted by the PCA and nutrients by the AHP. Once IQI values were calculated, two regional maps of soil quality were drawn by using interpolation methods (ordinary kriging). The IQI maps showed remarkable spatial differences in soil quality presumably induced by land management. We conclude this methodology could be useful and we encourage other colleagues to test its effectiveness in places where soil data are available.

Evaluating soil quality status of fluvisols at the regional scale: A multidisciplinary approach crossing multiple variables

“…The values of the whole dataset considering the selected soil quality indicators (SQI) were analyzed using the software SPSS 23.0 (IBM, USA). In order to obtain the minimum feasible dataset to calculate a soil quality index, specific parameters based on the main parent materials (limestones and marls), land uses (grazing, rainfed agriculture, fruits and vegetables, forestry, riparian vegetation) (Fernández et al, 2020; Keshavarzi et al, 2020; Pulido, Barrena‐González, Badgery, Rodrigo‐Comino, & Cerdà, 2018; Rodrigo‐Comino, Keshavarzi, Bagherzadeh, & Brevik, 2019) and from the literature (Armenise, Redmile‐Gordon, Stellacci, Ciccarese, & Rubino, 2013; Guo, Sun, Ouyang, Han, & Li, 2017; Sun, Zhou, & Zhao, 2003; Triantafyllidis, Kosma, & Patakas, 2018; Yu, Liu, Zhang, Li, & Zhou, 2018) such as soil texture (sand and clay), OM, CaCO 3 , total N, pH, available water at 33 (field capacity) and 1,500 KPa (wilting point) were selected. After this, a universal soil quality index was designed to establish representative indicators by assigning weights for the selected SQI to allow estimating and interpreting a unique index.…”

“…Different indicator units were needed to develop a standard scoring function (Andrews et al, 2002; Karlen & Stott, 1994; Mukherjee & Lal, 2014). Since there is no information about the upper and lower thresholds in the specific Fluvisols of Málaga province, the minimum and maximum observational values of the variables in the region were considered as lower and upper thresholds, respectively, as Pulido et al (2018) carried out for rangelands in SW Spain (Fernández et al, 2020). Weight for each indicator method was assigned by the commonality of each indicator calculated by mathematical statistics of standardized factor analysis (FA) (Shukla, Lal, & Ebinger, 2006; Sun et al, 2003).…”

“…Finally, the analytic hierarchy process (AHP) (Saaty, 1990) was needed to establish the hierarchy to those variables. The AHP method was conducted using expert choice software (https://journals.sagepub.com/doi/abs/10.1177/089443939701500209?journalCode=ssce) following the necessary steps highlighted by previous authors (Bagherzadeh, Gholizadeh, & Keshavarzi, 2018; Fernández et al, 2020; Keshavarzi et al, 2020).…”

Understanding the effect of shifting cultivation practice (slash-burn-cultivation-abandonment) on soil physicochemical properties in the North-eastern Himalayan region

“…The variation observed in clay, SOC, Exch. cations and CEC under the studied land uses may be attributed to a combination of intrinsic factors such as weathering, erosion, deposition, and soil-forming processes and extrinsic ones such as management practices (Pulido et al, 2020;Vasu et al, 2016).…”

Soil Quality Index for the Two Agricultural Areas along the Naboc River in Davao de Oro, Philippines Contaminated with Mercury

“…The soil quality index (SQI) was used to describe the soil quality of both agricultural areas, which was used extensively by authors, especially when anthropogenic influences were involved (Fernández et al 2020;Klimkowicz-Pawlas et al 2019;Li et al 2018;Masto et al 2015;Vasu et al 2016). The principal component analysis (PCA) was used as the method for soil indicator selection or the minimum data set (MDS) to reduce the number of indicators or parameters into significant or highly correlated to the principal components avoiding redundancy (Stefanoski et al 2016).…”