A importância do gerenciamento dos recursos hídricos para a produção dos agricultores familiares : o caso do Projeto Público de Irrigação Nilo Coelho

Dissertação (mestrado)—Universidade de Brasília, Departamento de Geografia, 2013.O presente trabalho acadêmico é resultado de uma pesquisa no Projeto Público de Irrigação Nilo Coelho (PPINC), nos municípios de Petrolina (PE) e Casa Nova (BA), junto aos agricultores irrigantes familiares com o objetivo de investigar o manejo dos recursos hídricos. No contexto brasileiro e consequentemente no semiárido nordestino, é sabida a importância da agricultura irrigada. Sem dúvida os projetos de irrigação auxiliam na diminuição da pobreza, na geração de empregos e na melhoria da renda. Porém, para o sucesso da atividade, algumas variáveis devem ser satisfeitas. Nesse trabalho, o enfoque se dará sobre o manejo da água no lote do agricultor irrigante familiar. Este grupo merece atenção especial por geralmente ser mais vulnerável no manejo da água. Para investigar a variável citada, foi utilizada a pesquisa qualitativa baseada em entrevistas semi-estruturadas, narrativas, vivências dos envolvidos e observação de campo. Como resultado foram identificados problemas relacionados ao manejo da água nos lotes dos agricultores irrigantes familiares. Desse modo, foram sugeridas ações no sentido de contribuir à realidade do irrigante. O trabalho concluiu que existem problemas no manejo de água dos agricultores irrigantes familiares entrevistados como desperdício de água, sistemas de irrigação ineficientes, falta de conhecimento a respeito do negócio e visão ultrapassada, entre outros. Com isso, exige-se um esforço de todos os envolvidos na pesquisa para inserir o agricultor irrigante familiar no contexto que o cerca. __________________________________________________________________________ ABSTRACTThis dissertation is the result of a research in the irrigated perimeter of Nilo Coelho (PPINC) in the municipalities of Petrolina (PE) and Casa Nova (BA), which involves the small producers with the aim to investigate the water resources management. In Brazilian context and consequently in the Northeast semi-arid, it is known the importance of irrigated agriculture. Undoubtedly, irrigation projects helps in poverty reduction, employment generation and enhancement of the small producers’ income. However, to achieve success in irrigating activity some variables must be fulfilled. This dissertation will focus on the water management in family irrigating producer areas. This group deserves special attention because generally they are more vulnerable in water management To investigate the mention variable, was used the qualitative research which was based on semi- structured interviews, narratives and experiences of the involved and field observation. As result it was identified problems related to water management on small producers areas. Thereby, actions were suggested towards to contribute on reality of the irrigate. The research concluded that there are problems on the water management by the family irrigating producer interviewed like water waste, inefficient irrigated systems, lack of knowledge regarding the business and outdated perspective. Therefore, it requires an effort from all involved in the research to insert the small producer in the context that surrounds them

Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation

Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach