Neural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo

Bamboo has an important role in international commerce due to its diverse uses, but few studies have been conducted to evaluate its climatic adaptability. Thus, the objective of this study was to construct an agricultural zoning for climate risk (ZARC) for bamboo using meteorological elements spatialized by neural networks. Climate data included air temperature ( T AIR , °C) and rainfall ( P ) from 4947 meteorological stations in Brazil from the years 1950 to 2016. Regions were considered climatically apt for bamboo cultivation when T AIR varied between 18 and 35 °C, and P was between 500 and 2800 mm year −1 , or P WINTER was between 90 and 180 mm year −1 . The remainder of the areas was considered marginal or inapt for bamboo cultivation. A multilayer perceptron (MLP) neural network with a multilayered “backpropagation” training algorithm was used to spatialize the territorial variability of each climatic element for the whole area of Brazil. Using the overlapping of the T AIR , P , and P WINTER maps prepared by MLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variability with T AIR varying between 14 and 30 °C, and P varying between