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Sources of Growth and Spatial Concentration of Coconut Crop in the State of Pará, Brazilian Amazon

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  • Paulo Silvano Magno Fróes Júnior
  • William Lee Carrera de Aviz
  • Fabrício Khoury Rebello
  • Marcos Antônio Souza dos Santos

Abstract

The State of Pará contributes to approximately 10.10% of the Brazilian production of coconut (Cocos nucifera L.). It is an important center of production for the crop, mainly due to some factors such as its edaphoclimatic conditions that are favorable for the plant development, the availability of rural credit and the presence of business groups with expertise on the activity and agro industrial processing. This survey used data from Instituto Brasileiro de Geografia e Estatística (IBGE) (2018) to make an analysis of the activity in the state between 1974 and 2016, evaluating by the Shift-Share Analysis the sources of production growth, harvested area and productivity. Furthermore, the study also analyses the evolution of coconut prices, concentration and specialization of some micro regions of the State in coconut crop production using the Locational Gini Coefficient and Location Quotient. The main results show an expressive increase in coconut production in the state of Pará economy since the 1980s, showing that between 1974 and 2016 the production increased by 9.41% per year, the harvested area 7.88% p.a. and productivity 1.42% p.a. It is also possible to observe an expressive concentration and specialization of the activity in the Micro region of Tomé-Açu, responsible for 57.40% of the state production.

Suggested Citation

  • Paulo Silvano Magno Fróes Júnior & William Lee Carrera de Aviz & Fabrício Khoury Rebello & Marcos Antônio Souza dos Santos, 2024. "Sources of Growth and Spatial Concentration of Coconut Crop in the State of Pará, Brazilian Amazon," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 11(2), pages 159-159, April.
  • Handle: RePEc:ibn:jasjnl:v:11:y:2024:i:2:p:159
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    References listed on IDEAS

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    1. Lidia Ceriani & Paolo Verme, 2012. "The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(3), pages 421-443, September.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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