Bio
José de Jesus Sousa Lemos is a Full Professor at the Universidade Federal do Ceará, where he is affiliated with the Graduate courses in Agricultural Economics and the Development and Environment Program (PRODEMA). He is also a Researcher and Productivity Fellow at the Brazilian National Council for Scientific and Technological Development (CNPq) and serves as the Coordinator of the Laboratory of the Semi-arid (LabSar).
Experience
Full Professor
0 - Present • Graduate courses in Agricultural Economics, Development and Environment Program (PRODEMA)Brazilian National Council for Scientific and Technological Development (CNPq)
Researcher and Productivity Fellow
0 - PresentLaboratório do Semiárido
Coordenador
0 - PresentResearch
Impacts of Prices on Brazil’s Natural Rubber Exports in Four Historical Periods over 198 Years
The objectives of the research are: a – to create a model that describes the trajectory of natural rubber exports from 1827 to 2024, with its price as an exogenous variable; b – to assess the impact of prices on the model that describes the trajectory of exports; c – to estimate the heterogeneities/homogeneities of […]
ARIMAX Model to Forecast Grain Production Under Rainfall Instabilities in Brazilian Semi-arid Region
The state of Ceará has most of its area in Brazil's semi-arid region. Initially, the research segmented Ceará's annual rainfall into 6 periods: very rainy, rainy, normal-humid, normal-dry, drought and very drought. This segmentation was based on the annual rainfall in the state between 1901 and 2020. The research estimated the average rainfall and instability of both the annual rainfall in the state during the period and those estimated for the periods in which the rainfall was segmented. The research then developed forecast models for harvested areas, yields, production values and average annual grain prices between 1947 and 2020, the years in which this information is available. To make these forecasts, the research used the ARIMAX model, which is an extension of the Box-Jenkins model, with the addition of an exogenous variable. The exogenous variable included in the model was the annual rainfall observed between 1947 and 2020, assuming that this variable influences these forecasts. The results showed that the state's rainfall has a high level of instability and that the adjusted models proved to be parsimonious and robust from a statistical point of view.
Hybrid Model of Artificial Neural Networks and Principal Component Decomposition for Predicting Greenhouse Gas Emissions in the Brazilian MATOPIBA Region
Greenhouse gas (GHG) emissions in agricultural production represent a global environmental challenge, and it is necessary to understand the factors that influence them to develop sustainable practices. The general objective of this research is to investigate some of the factors that probably influence GHG emissions and reductions in agricultural production in the MATOPIBA region of Brazil between 2006 and 2017. A hybrid methodology was used, and the first stage used linear models (decomposition into principal components) and non-linear models (artificial neural networks) to determine the relationships that should exist between the dependent variable (GHG emissions) and 11 variables. The data was obtained from the 2006 and 2017 Brazilian Agricultural Census, MapBiomas, SEEG, and NOAA. The results showed that of the 373 municipalities that make up MATOPIBA, only 100 did not see an increase in GHG emissions between 2006 and 2017. The principal component decomposition method reduced the 11 initial variables into 3 orthogonal and unobserved variables. In one of the unobserved variables, 4 of the five variables that are supposed to cause a reduction in GHG emissions were brought together. The 5 variables thought to have caused an increase in GHG emissions were condensed into 5.
