O Comitê Executivo da Rede Brasileira de Pesquisa em Soberania e Segurança Alimentar e Nutricional tem a satisfação de confirmar a realização do III Encontro Nacional de Pesquisa em SSAN nos dias 08, 09 e 10 de Novembro de 2017, na cidade de Curitiba (PR), em local ainda a ser escolhido entre as opções existentes.

There are high hopes that the process of drawing up an international human rights instrument for the promotion and protection of peasant rights will soon be crowned with success: In September, the Human Rights Council decides whether the newly negotiated UN Declaration on the rights of peasants and other people working in rural areas will be sent to the United Nations’ General Assembly in New Y

The General Assembly of the United Nations adopted the Decade of Family Farming 2019-2028 on 20 December 2017 in order to improve the position of family farmers in all five (5) continents.

NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new online advanced webinar series: Crop Mapping using Synthetic Aperture Radar (SAR) and Optical Remote Sensing. This three-part training, presented in English and Spanish, is open to the public and builds on previous ARSET agricultural trainings. Here we present more advanced radar remote sensing techniques using polarimetry and a canopy structure dynamic model to monitor crop growth. The training will also cover methods that use machine learning methods to classify crop type using a time series of Sentinel-1 & Sentinel-2 imagery. This series will include practical exercises using the Sentinel Application Platform (SNAP) and Python code written in Python Jupyter Notebooks, a web-based interactive development environment for scientific computing and machine learning.

NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Large Scale Applications of Machine Learning using Remote Sensing for Building Agriculture Solutions. Remote sensing data is becoming crucial to solve some of the most important environmental problems, especially pertaining to agricultural applications and food security. Participants will become familiar with data format and quality considerations, tools, and techniques to process remote sensing imagery at large scale from publicly available satellite sources, using cloud tools such as AWS S3, Databricks, and Parquet. Additionally, participants will learn how to analyze and train machine learning models for classification using this large source of data to solve environmental problems with a focus on agriculture.