The aim of this research was to characterize cropping systems integrated with cattle raising in the Guayas River basin, Ecuador. Samples from 50 farms included as study cases, and 19 variables (16 input and 3 output variables) were studied. All cases were classified by factorial and cluster analysis during the first stage, based on the principal component and non-hierarchical cluster analysis (k-mean) methods. Four clusters (I, II, III, IV) were defined and codified according to the mean values of the related variables. Finally, the productive response was evaluated by one-way analysis of variance, considering each particular codification as study factors depending on the clusters; the output variables were regarded as the productive response. The results showed the priority of the components, which expressed more variability in the systems studied, and depended on input use, residues introduced, and food produced. The classification made according to the variables included in the input component comprised half the samples in clusters III and IV with the highest values. The cropping productive response was dependent on the amount of inputs utilized, whereas, the response of cattle raising was highest in the categories with the lowest input utilization levels.The aim of this research was to characterize cropping systems integrated with cattle raising in the Guayas River basin, Ecuador. Samples from 50 farms included as study cases, and 19 variables (16 input and 3 output variables) were studied. All cases were classified by factorial and cluster analysis during the first stage, based on the principal component and non-hierarchical cluster analysis (k-mean) methods. Four clusters (I, II, III, IV) were defined and codified according to the mean values of the related variables. Finally, the productive response was evaluated by one-way analysis of variance, considering each particular codification as study factors depending on the clusters; the output variables were regarded as the productive response. The results showed the priority of the components, which expressed more variability in the systems studied, and depended on input use, residues introduced, and food produced. The classification made according to the variables included in the input component comprised half the samples in clusters III and IV with the highest values. The cropping productive response was dependent on the amount of inputs utilized, whereas, the response of cattle raising was highest in the categories with the lowest input utilization levels
