| Abstract | 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 [P], air capacity [AC], plant available water capacity [P], 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 cm). |
| Text | 424058 2019 10.2136/sssaj2018.06.0223 Scopus 2 s2.0 85064954439 AC air capacity; B burning; BD bulk density; I incorporation; MT minimum tillage; NT no tillage; PAWC plant available water capacity; PCA principal component analysis; P MAC macroporosity; RFC relative eld capacity; SDA stepwise discriminant analysis; SPQ soil physical quality; ST sampling time Application of multivariate analysis techniques for selecting soil physical quality indicators A case study in long term field experiments in Apulia Southern Italy Castellini M.; Stellacci A.M.; Barca E.; Iovino M. Council for Agricultural Research, Economics Research Center for Agriculture and Environment CREA AA , Via C. Ulpiani 5, Bari, 70125, Council for Agricultural Research and Economics Research Center for Agriculture and Environment CREA AA , Via C. Ulpiani 5, 70125, Bari, Italy, , Italy; Dep. of Soil, Plant and Food Sciences, Univ. of Bari Aldo Moro , Via G. Amendola 165/a, Bari, 70126, Dep. of Soil, Plant and Food Sciences, Univ. of Bari Aldo Moro , Via G. Amendola 165/a, 70126, Bari, Italy, , Italy; Water Research Institute IRSA , National Research Council CNR , Bari, Water Research Institute IRSA , National Research Council CNR , Bari, Italy, , , Italy; Water Research Institute IRSA , National Research Council CNR , Bari, Water Research Institute IRSA , National Research Council CNR , Bari, Italy, , , Italy; Dep. of Agricultural, Food and Forest Sciences, Univ. of Palermo, Viale delle Scienze, Palermo, 90128, Dep. of Agricultural, Food and Forest Sciences, Univ. of Palermo, Viale delle Scienze, 90128, Palermo, Italy, , Italy 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 P , air capacity AC , plant available water capacity P , 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 cm . 83 Published version http //www.scopus.com/record/display.url eid=2 s2.0 85064954439 origin=inward Articolo in rivista Soil Science Society of America, 0361 5995 Soil Science Society of America journal Soil Science Society of America journal Soil Sci. Soc. Am. j. Soil Science Society of America journal. SSSA journal SSSAJ emanuele.barca BARCA EMANUELE |
