@InProceedings{AguiarSiFeGoRiRu:2007:InPr,
               author = "Aguiar, Daniel Alves de and Silva, Wagner Fernando da and Feitosa, 
                         Fl{\'a}via de Fonseca and Gon{\c{c}}alves, F{\'a}bio 
                         Guimar{\~a}es and Rizzi, Rodrigo and Rudorff, Bernardo Friedrich 
                         Theodor",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Center for 
                         Development Research - ZEF} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Federal de Pelotas – UFPel} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise espacial da colheita da cana-de-a{\c{c}}{\'u}car no 
                         Estado de S{\~a}o Paulo: a influ{\^e}ncia da 
                         precipita{\c{c}}{\~a}o",
            booktitle = "Anais...",
                 year = "2007",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares and Fonseca, Leila Maria Garcia",
                pages = "2231--2238",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 13. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "GEOPROCESSAMENTO, Aplica{\c{c}}{\~o}es e Modelagem Ambiental, 
                         simple linear regression, spatial regression, geoprocessing, 
                         agricultural monitoring.",
             abstract = "Geographic information about the sugarcane harvest development is 
                         an essential tool for crop estimation systems. In Brazil the 
                         sugarcanes harvest occurs between April and November and the 
                         long-term rains prevent the usual progress of harvest activities. 
                         As a result, the raw material availability for sugar and alcohol 
                         production becomes affected almost every crop. In this work we 
                         evaluated three regression models to identify the correlation 
                         degree between harvest and rain. We applied the classical linear 
                         model and the models with global and local spatial effects in two 
                         periods between September and October 2004s crop (PI and PII). 
                         Results from statistical and spatial analyzes (Moran, LISA map, 
                         and clustering maps) suggested the model with local spatial 
                         effects as the best alternative.",
  conference-location = "Florian{\'o}polis",
      conference-year = "21-26 abr. 2007",
           copyholder = "SID/SCD",
                 isbn = "978-85-17-00031-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2006/11.15.15.48",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2006/11.15.15.48",
           targetfile = "2231-2238.pdf",
                 type = "Geoprocessamento: Aplica{\c{c}}{\~o}es e Modelagem Ambiental",
        urlaccessdate = "15 jun. 2024"
}