Application of Multivariate Analysis Techniques for Selecting Soil Physical Quality Indicators: A Case Study in Long‐Term Field Experiments in Apulia (Southern Italy). Issue 3 (1st May 2019)

• Record Type: Journal Article
• Authors: Castellini, Mirko; Stellacci, Anna Maria; Barca, Emanuele; Iovino, Massimo
• Is Part Of: Soil Science Society of America Journal. Volume 83:Issue 3(2019)
• Page Start: 707
• Page End: 720
• Publication Date: 2019-05-01
• Subjects: Soils -- United States -- Periodicals; Soil science -- Periodicals; Periodicals; 631.4973
• DOI: 10.2136/sssaj2018.06.0223
• Languages: English
• ISSNs: 0361-5995

Abstract

Abstract : Core Ideas: Soil physical quality (SPQ) on two long‐term experiments was evaluated. Relationships among five SPQ indicators (BD, PMAC, AC, PAWC and RFC) were evaluated. Two multivariate analysis techniques (PCA, SDA) were applied to select key indicators. PCA and SDA generally identified RFC as a key soil physical indicator. An optimal AC range was suggested to assess the air capacity of agricultural soils Long‐term field experiments and multivariate analysis techniques represent research tools that may improve our knowledge on soil physical quality (SPQ) assessment. These techniques allow us to measure relatively stable soil conditions and to improve soil quality judgment, thereby reducing uncertainties. A monitoring of SPQ under long‐term experiments, aimed at comparing crop residue management strategies (burning vs. incorporation of straw, FE1) and soil management (minimum tillage vs. no tillage, FE2), was established during the crop growing season of durum wheat. The relationships between five SPQ indicators (bulk density [BD], macroporosity [PMAC ], air capacity [AC], plant available water capacity [PAWC], and relative field capacity [RFC]) were evaluated, and two techniques of multivariate analysis (principal component analysis and stepwise discriminant analysis) were applied to select key indicators for SPQ assessment. According to the used indicators, an SPQ from optimal to intermediate (i.e., not definitely poor) was detected in 65% of the observations in FE1 and in 54% in FE2. The main results showed a significant negative relationship between RFC and AC, and multivariate analysis identified RFC as a key SPQ indicator, mainly in FE2. Plant available water capacity and BD showed the highest discriminating capability in the FE1 dataset. The highest scores of RFC assessment were highlighted for burning and minimum tillage treatments (+1 and +2). An optimal AC range, derived from optimal RFC limits, was obtained and was suggested to better assess the AC of agricultural soils (0.10 ≤ AC ≤ 0.26 cm 3 cm –3 ).