ABSTRACT In family farming, mechanisation, when appropriate to the task and to the size of the property, can be one way of easing tiring and repetitive work, such as garlic harvesting. The present study is based on a model of harvester currently on the market, and proposes several improvements for mechanising the harvest on family farms, with an emphasis on sloping terrain, to reduce costs and increase productivity. Since the equipment currently on the market reduces the operational performance of these producers resulting in the high prices of commercial garlic, the aim of this study was to evaluate, using the Finite Element Method, the von Mises stresses and the displacement of a garlic harvester in different topographies. A structural analysis was carried out, and the vibration modules in the three main parts that make up the structure of the garlic harvester were checked: the structure supporting the scarifier point, the structure of the plant transport system, and the structure at the rear of the harvester. The structural modifications that were implemented corrected the initial high-stress conditions, showing improvements in stress concentration and areas of displacement. As such, the entire structure of the garlic harvester tends to withstand the proposed loads. From the results of the simulations, we conclude that the connecting support may undergo greater response from structural dynamics, and that frequencies greater than 45.7 Hz should be avoided since there are several natural frequencies above this range that might cause interference to the structure.

ABSTRACT Estimation of soil loss susceptibility is of great importance for the management of watersheds. Thus, several models for soil loss prediction have been proposed. This study estimated the total annual soil loss for the Santa Rita watershed, located in southern Brazil, using the Revised Universal Soil Loss Equation. In addition, a classification to soil loss index (ISL) was proposed to identify regions with critical soil loss values. Altitude, slope, land use, and soil class data were applied to the model, in addition to spatial information for 78 soil samples collected within the study area. It was found that there is an average annual loss of 35.94 Mg ha-1 year-1, with the most substantial loss occurring in areas with predominantly exposed soil and annual cropping. Furthermore, the ISL values indicated that approximately 50% of the study area is experiencing erosion estimates above the tolerable limit. Results emphasize the need for changes in conservation and application practices present in the watershed, considering land use and soil bearing capacity.

ABSTRACT Erosion process occurs naturally, shaping the Earth’s surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and cover (C), and conservation practices (P) to estimate average annual soil losses. Images derived from remote sensing techniques are generally used to quantify the spatialization of C factor; however, the variation in land use throughout the year is not usually considered. This study aimed to estimate soil losses in an important subwatershed of Candiota river watershed (CRWsub) by using RUSLE, considering land use and rainfall erosivity in different periods of the year. The periods considered were P1 (January, February and March), P2 (April, May and June), P3 (July, August and September) and P4 (October, November and December). Based on the results, the lowest soil losses occurred in P1. Probably, the high vegetation cover in the soil increases its protection against rainfall erosivity. In P3, the heavy rainfall events are predominantly frontal, occurring in the same months as those when the preparation of the soil for later planting takes place; that is, there is no vegetation cover in this period, thus making the soil more prone to erosion. The use of different images to classify and identify land uses is the best way to understand soil losses throughout the year in the study area. It was possible to observe that agricultural areas are generally associated with greater soil losses in the subwatershed. In addition, the land uses were considered to vary quarterly, thereby making it possible to identify the periods most prone to erosion processes throughout the year. Finally, the erosion percentages in the subwatershed can be linked to the tolerance index for different land-uses, soil classes, and slope categories.