Banca de QUALIFICAÇÃO: MARYELLE KLEYCE MACHADO NERY

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : MARYELLE KLEYCE MACHADO NERY
DATE: 23/08/2024
TIME: 08:00
LOCAL: sala virtual
TITLE:

ESTIMATION OF PRODUCTIVITY AND GROWTH OF GREEN DWARF COCONUT FRUITS USING AGROMETEOROLOGICAL MODELS IN THE EASTERN AMAZON

KEY WORDS:

Growth analysis; climate; Cocos nucifera L.; fruit development; artificial intelligence.

PAGES: 65
BIG AREA: Ciências Agrárias
AREA: Agronomia
SUBÁREA: Agrometeorologia
SUMMARY:

The coconut palm (Cocos nucifera L.) is a perennial crop of great importance in humid tropical regions, playing a crucial role in the economy and food security of these areas. In Brazil, coconut production has significantly increased over the past five years, with growth exceeding 11%, resulting in a current output of over two million tons, spread across approximately 190,000 hectares. The states of Ceará, Bahia, and Pará stand out as the main producers. Due to its perennial nature, the coconut palm is particularly sensitive to climatic and seasonal variations, which affect all stages of its development cycle, especially after the inflorescence opens. Under adverse conditions, it is common to observe the abortion of female flowers and premature fruit drop, factors that compromise productivity. In this context, knowledge and application of effective techniques for predicting agricultural productivity and studying fruit growth patterns are essential for the sustainable management of coconut cultivation, especially in tropical regions. The development of agrometeorological models to estimate fruit productivity and growth is fundamental, as climatic variations directly impact the different phenological stages of the coconut palm, significantly influencing the productivity and final quality of the coconut. This study aims to develop a predictive productivity model and evaluate the fit of nonlinear models in describing coconut fruit growth, using variables such as transverse and longitudinal diameters, fresh mass, and volume. The research will be conducted in a commercial coconut cultivation area located at Fazenda Reunidas - SOCOCO, in northeastern Pará. The methodology employs a combination of traditional statistical methods and advanced machine learning techniques to explore productivity and growth estimation, addressing the complex interaction between climate and coconut phenology, from inflorescence opening to fruit maturation. This innovative approach seeks to develop a robust model for estimating the productivity and growth of the green dwarf coconut, with the aim of optimizing planning and promoting sustainable crop management.

COMMITTEE MEMBERS:
Presidente - 1356961 - PAULO JORGE DE OLIVEIRA PONTE DE SOUZA
Externo à Instituição - JOAQUIM CARLOS BARBOSA QUEIROZ
Externo à Instituição - GLAUCO DE SOUZA ROLIM - UNESP
Externo à Instituição - MARCUS DE BARROS BRAGA