Abstract
Data of the physical and chemical properties of soils from three vineyards located in Vale dos Vinhedos, Bento Gonçalves, Rio Grande do Sul state, in southern Brazil, were processed. Soil mapping was performed by means of four profiles and the digital elevation model in detailed scale. Then, superficial soils (0–20 cm) were sampled according to a grid pattern. Analysis of variance (ANOVA), kriging, and unsupervised classification methods were applied on physical and chemical data of superficial soils sampled according to grid pattern. This study aimed to compare both methods, the conventional soil mapping and the map produced with superficial soil sampling, about their potential for definition of the management zones, as an approach for precision agriculture. Maps elaborated by conventional soil mapping overlapped partially with the maps derived from superficial sampling, probably due to the specific methodological differences of each case. Anyway, both methods are complementary because of the focus on vertical variability and horizontal variability, respectively. In that sense, slope appears as significant edaphic parameter, due to its control on water circulation in the profile of soil.
Highlights
Notable advances in pedological research were reached in the 1990s, after a period of stagnancy, when a significant search for a rational use of natural resources and the equilibrium of biogeochemical cycles took place
In this way, monitoring and evaluation of soil resources began a new age, due to the quality of information derived from new technologies as geographical information systems (GIS) and remote sensing
Tayari et al [1] discussed the relation among GPS, GIS, and precision agriculture (PA)
Summary
Notable advances in pedological research were reached in the 1990s, after a period of stagnancy, when a significant search for a rational use of natural resources and the equilibrium of biogeochemical cycles took place In this way, monitoring and evaluation of soil resources began a new age, due to the quality of information derived from new technologies as geographical information systems (GIS) and remote sensing. In a more contemporaneous definition, “PA is a whole-farm management approach using information technology, satellite positioning data, remote sensing and proximal data gathering. These technologies seek improving returns on inputs while potentially reducing environmental impacts. The state-of-the-art of PA on arable land, permanent crops and within dairy farming are reviewed, mainly in the European context, altogether with some economic aspects of the adoption of PA” [4]
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