A new soil quality index based on morpho-pedological indicators as a site-specific web service applied to olive groves in the Province of Jaen (South Spain)

• Published in: Computers and electronics in agriculture 2018-03, Vol.146, p.66-76
• Main Authors: Calero, J., Aranda, V., Montejo-Ráez, A., Martín-García, J.M.
• Format: Article
• Language: English
• Subjects: Agricultural management; Agriculture; Categorical principal component analysis; Decision making; Field soil properties; Indicators; Land use; Nonlinear analysis; Nonlinear principal component analysis; Optimal scaling; Principal components analysis; Qualitative analysis; Quality assessment; Scoring functions; Soil erosion; Soil sciences; Soils; Webs
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2018.01.016

•Morphological soil attributes can't be suitably used in accurate soil quality assessments.•We convert these attributes into a numerical scale by an optimal scaling procedure.•Once transformed, they can be included in a numerical index of soil quality (FSQI).•FSQI may contribute to sustainable soil management in poorly surveyed zones. Soil quality has become a fundamental concept in soil science and agriculture, but it can be difficult to apply its theoretical and experimental approaches to poorly surveyed zones where precision techniques are far from being applied. In this paper, we propose a new technique that enables little-used qualitative morpho-pedological data to be managed and integrated into a single Field Soil Quality Index (FSQI). Nonlinear Principal Component Analysis (NLPCA), a technique able to handle categorical data, is applied here to deal with morpho-pedological indicators. When categorical values are transformed, they can be properly analyzed and interpreted. This procedure requires less expert knowledge, so it can help soil quality assessments by non-experts. We applied the FSQI protocol to soils in the most important olive-growing area in the world, Jaen Province (Southern Spain), which has serious problems with soil degradation and erosion. First, a soil database for the study area was compiled, including 18 morphological attributes for 131 surface horizons belonging to eight Land Use Types. Secondly, the NLPCA provides optimal scalings and attribute weights that transform and integrate morphological indicators into a simple weighted additive index (FSQI). Thirdly, the scaling functions and weights found were applied to the same attributes of an evaluation set comparing two soil management types (conventional vs. organic) in olive groves. The FSQI means for the first (conventional) were significantly lower than in the organic groves (0.278 vs. 0.463, P