Development of a computational platform for forest productivity management

• Grant number: 17/00788-8
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: November 01, 2017
• End date: October 31, 2018
• Field of knowledge: Agronomical Sciences - Forestry Resources and Forestry Engineering - Forest Management
• Principal Investigator: Eduardo Moré de Mattos
• Grantee: Eduardo Moré de Mattos
• Company: Geplant Tecnologia Florestal Ltda
• City: Piracicaba
• Pesquisadores principais: Marina Shinkai Gentil Otto ; Rodrigo Eiji Hakamada
• Associated research grant(s): 19/00712-7 - Development of an integrated platform for forest productivity management, AP.PIPE
• Associated scholarship(s): 18/08142-2 - Development of a computational platform for forest productivity management, BP.TT; 17/21239-2 - Development of a computational platform for forest productivity management, BP.PIPE

Abstract

With the growing demand from the forestry industry for optimization of the planting costs of eucalypt and pine plantations, gains and maintenance of productivity levels have been decisive factors in maintaining the sector's competitiveness in Brazil and in the world. Currently, Brazil's planted forest areas have advanced significantly on the so-called new forest frontiers, such as Maranhão, Tocantins, Piauí and Mato Grosso do Sul. While in traditional plantation areas there is greater security regarding the investment, at this new fronties the risk of productivity losses are higher due to lack of adaptation of genetic material and silvicultural practices. However, even in traditional planting areas, it has been evident that the climatic oscillations have brought difficulties for the planning and performance of forest production. Thus, the need for a planning, research and decision support tool has become increasingly evident. capable of (i) predicting the productivity of planted forests in already managed areas and over regions of forest expansion, for the actual and future climatic conditions (ii) to simulate and propose management practices that increase forest productivity within sustainable bases and (iii) to identify which factors cause productivity constraints, as well as to discriminate the magnitude of these impacts. Considering this current need of the forest market, this project proposes to evaluate the technical viability of a computational platform for productivity management in forest plantations, capable of describing current and future forest production, based on spatialized ecophysiological processes, representing the relations with the physical (soil and climate) and biological (genetic materials and physiology) environment. The platform will be based on the integration of Big Data sources (climatic data, soils, forest inventory, physiology, and remote sensing vegetation indices), within an accessible and easy-to-use interface platform that enables rapid analysis of maps and graphs. (AU)