Analysis of Interrelations Structure in Agro-Systems Using the Factor Analysis Technique (FA)

Abstract

A model is not an exact copy of its original, but only its idealised reproduction that is simpler, more understandable, more accessible and easier, safer and more effective to work with. In the presented study, we used the technique of factor analysis (FA). We used 44 parameters to describe an agroecosystem, which proportionally describe the main components of the study agroecosystem. Based on Malinowsky error analysis, we extracted a 6-factor solution. We found out that Factor 1 [Climate factor] had primary factor loads in [average temperatures TIII-TIX (0.99) and [average atmospheric precipitation ZIII-ZIV (0.99)] variables. Factor 2 [Chemical parameters of geological foundation] was mainly saturated by [SiO2-G (0.92), Al2O3-(0.82), (CaO-G (0.83)] variables and secondary loads were observed in soil [SiO2-P (0.61], [CaO-P (0.64], [Al2O3-P (0.32)], [soil skeleton SKEL (0.47)] and [granularity GRN (0.39)] variables. Factor 3 [Phytomass production potential factor] had primary factor loads in [depth of soil profile DSP (0.76)], [quality of organic substances Q4/6 (0.63)], [slopeness SL (0.67)] and [potential phytomass production PROD (0.65)] variables. In factor 4 [Physical-chemical soil properties factor] variables [Al2O3, (0.81)], [granularity GRN (0.69)] and [SiO2 (0.61)] have significant loads. Factor 5 [Erosion by water potential factor] has the highest primary loads in [large-scale arable land ALL (0.70)] and [soil loss as a result of erosion EROS (0.67)] variables, and secondary loads in the [continuous length of plot of land slope LS (0.53)] variable. Factor 6 [Biochemical properties factor] has the highest factor load values in the content of organic substances in soil [content of organic substances in soil H (0.69)]. Secondary loads can be seen in the properties of soil [GRN (0.35)], [SiO2 (0.32)], [Al2O3-P (0.38)] and [depth of groundwater surface GWS (0.39)]. We determined the weight coefficients for the individual factors with the aim of quantifying ecological criteria with the obtained factor structure. The factor score F0 determines the projections of the extracted factors for the individual elements of the selection (it is the value soil-ecological units—VSEU). Row vectors in this matrix represent the distribution of the individual factors for the specific realisation of the selection (spatial distribution). We re-scaled the obtained values of the factor score into seven categories and projected them into VSEU units. We could propose a sustainable agroecosystem management based on quantifying the ecological criteria for each VSEU unit.