Multidimensional analysis of environmental impacts from potato agricultural production in the Peruvian Central Andes.

Rain-fed potato systems, being the most important cash crop in the Peruvian Central Andes, play a key role in food security. Quantifying the environmental impacts and understanding their complex interactions is an important step towards an improvement of the technical sustainability of these systems. From 2005 until 2015, 58 potato field plots located on a transect of Mantaro Valley, Junín, Peru were investigated at field level during the rainy cropping seasons. All external inputs used for crop production were measured and registered on fortnightly basis. A life cycle assessment (LCA) was performed (per ton yield fresh weight) to assess the most important potential environmental impact categories (EICs). Due to the intrinsic variability of the production systems, a cluster analysis (k-means algorithm) and linear discriminant analysis (LDA) were implemented to group and evaluate the classification based on the EICs values. Furthermore, latent variables were obtained using exploratory factor analysis (EFA) to investigate the correlational structure of main biophysical inputs (kg ha) and EICs values (kg unit-eq. t). Similarly, data envelopment analysis (DEA) was used to quantify the relative environmental efficiency based on the EICs values (unit-eq. t, input) and the productivity level (kg ha, output). Overall LCA results showed considerable EICs values for acidification and eutrophication due to the inappropriate or sub-optimal use of fertilizer sources. Restricted use of machinery and low technology level caused low global warming potential and cumulative energy demand. Based on the cluster analysis, three groups were found mainly defined by the nature of the inputs and EICs values: inorganic, organic and mixed systems. LDA showed a good overall classification accuracy for the groups (98.3%), being cumulative energy demand the most important discriminant variable due to scarce machinery use. In addition, EFA proved that the first and second latent variables are correlated with an inorganic- and organic-oriented agriculture respectively, being the inorganic more associated with the EICs values. Environmental efficiency (from 0.04 to 0.61 on average) was linked to the quantity and source of the inputs, showing that potential environmental savings can be reached if more balanced input sources are used.