The aim of this Research Communication was to apply the data mining technique to classify which environmental factors have the potential to motivate dairy cows to access natural shade. We defined two different areas at the silvopastoral system: shaded and sunny. Environmental factors and the frequency that dairy cows used each area were measured during four days, for 8 h each day. The shaded areas were the most used by dairy cows and presented the lowest mean values of all environmental factors. Solar radiation was the environmental factor with most potential to classify the dairy cow's decision to access shaded areas. Data mining is a machine learning technique with great potential to characterize the influence of the thermal environment in the cows' decision at the pasture.

The life patterns of cassava green spider mite populations were compared using characteristics of spinning behaviour and other associated biological parameters so as to determine the possibility of using these criteria for separating closely related forms or populations of Mononychellus complex of species. These included web structure and density, preferred site for oviposition, feeding, walking, defecation, quiescence, spinning behaviour during walking and type of egg cover. Of these, preferred site for feeding, walking, quiescence and spinning behaviour during walking were found to be consistent and could possibly be used in comparative studies on different mite populations. The surface structure of the leaves i.e. pubescence, and mite population density were found to affect some of the behaviour patterns. Detailed studies on the influence of other factors, such as host plant variety, stage of plant growth, age of leaf colonized, weather, etc. will be necessary to determine the usefulness of these biological criteria in separating sympatric and allopatric populations of the mite.