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The evaluation of the physical attributes of the soil is of fundamental importance for the understanding of the impacts caused by the different uses in the agricultural systems. In this sense, the objective of this work was to evaluate the changes in physical attributes of the soil in an area with different uses located in the north of Espírito Santo. The experiment followed a randomized block design (DBC), in a 4 × 2 factorial scheme, represented by 4 areas (coffee, fruit, pasture and native forest) and 2 depth classes (0-10 and 10-20 cm), resulting in a total of 8 treatments with 5 replications. The physical attributes evaluated were: texture, Ds (soil density); Dp (particle density); Ma (macroporosity); Mi (microporosity) and Pt (total porosity). The data obtained were submitted to analysis of variance and the comparison of means was performed using the Tukey test at 5%, using the statistical program R© 4.2. Then, the physical attributes data were grouped into a similarity dendrogram, using the Euclidean distance method. The area with native forest presented the best physical attributes of the soil, followed by: coffee, fruit and pasture, not differing in depth. As for the analysis by grouping, native forest was similar to coffee growing and fruitful showed the greatest dissimilarity between land uses, especially in relation to forest.

The present study had as objective to determine mathematical equations to estimate the leaf area of pear cv. ‘Triunfo’ using linear dimensions of the leaves. For that, 300 healthy leaves of different sizes from each quadrant of plants from the small farm of Boa Vista located in the city of Montanha, at the northern side of the State of Espírito Santo, Brazil were used. The length (L) along the main vein was measured, along with the maximum width (W) of the leaf blade and observed leaf area (OLA), in addition to the product of the length and width (LW) of each leaf. From these measurements models of linear equations of first degree, quadratic and power were adjusted and their respective R2, using OLA as dependent variable and L, W and LW as independent variable. Based on the proposed equations, the data were validated obtaining the estimated leaf area (ELA). The mean of the ELA and OLA were compared by Student t test 5% probability. The mean error (E), the mean absolute error (MAE) and the root mean squared error (RMSE) was also used as validation criterion. The best equation model was defined based on the non-significant values from the comparison of means of ELA and OLA, E, MAE and RMSE values closer to zero and highest R2. The leaf area of pear cv. ‘Triunfo’ can be estimated by the equation ELA = -0.432338 + 0.712862(LW) non-destructively and with a high degree of precision.