Abstract
This article analyzes the replacement of Sugarcane by the adequate crop considering the producer's choices according to the dimensions of soil, relief, climate, and precipitation in Sao Paulo state, Brazil. The panorama is the hypothetical end of the combustion engine in the Euro 7 emissions standard context. The analysis is done in two sequences: the first is with the Artificial Intelligence methodology that searched, by Machine Learning, all the Sugarcane geographic coordinates in the Sao Paulo state and, through Deep Learning, applied, in the places found, the producers' knowledge, concerning climate, environmental and physical dimensions. The second sequence is an econometric analysis, where the authors estimate the price elasticity of demand for 25 crops from the AIDS demand system approach. Artificial Intelligence results coincide with the total area of Sugarcane in Sao Paulo observed in the PAM (agricultural statistics for the municipality produced annually by the Brazilian Institute of Geography and Statistics) and show that 48 crops are suitable for its geographic coordinates. With the results of both sequences in hand, the authors point to 17 competitive crops in the Interest Area, but only six highlighted crops are suitable for sowing in all the 13 Central Supply Zones (known in Brazil as Ceasa) of state.
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