@Article{MartinsTRAGGSPP:2018:ImDrMa,
               author = "Martins, Minella Alves and Tomasella, Javier and Rodriguez, Daniel 
                         Andres and Alval{\'a}, Regina C{\'e}lia S. and Giarolla, 
                         Ang{\'e}lica and Garofolo, Lucas Lopes and Siqueira J{\'u}nior, 
                         Jos{\'e} L{\'a}zaro and Paolicchi, Luiz Thiago Lucci Corr{\^e}a 
                         and Pinto, Gustavo L. N.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Centro 
                         Nacional de Monitoramento e Alertas de Desastres Naturais 
                         (CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Centro Nacional de Monitoramento e Alertas de Desastres 
                         Naturais (CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Improving drought management in the Brazilian semiarid through 
                         crop forecasting",
              journal = "Agricultural Systems",
                 year = "2018",
               volume = "160",
                pages = "21--30",
                month = "Feb.",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura 
                         sustent{\'a}vel} and {Pr{\^e}mio CAPES Elsevier 2023 - ODS 8: 
                         Trabalho decente e crescimento econ{\^o}mico}",
             keywords = "Maize, Crop forecast, AquaCrop, Eta RCM.",
             abstract = "In this paper, we evaluated the performance of the model AquaCrop 
                         for crop yield forecasting in the Brazilian semiarid (BSA) using 
                         meteorological observation and Eta model seasonal climate 
                         forecasts as input data. The study area is characterized by low 
                         rainfall that is poorly distributed throughout the rainy season; 
                         thus, the region's agricultural productivity is vulnerable to 
                         climate conditions. AquaCrop was first calibrated using field 
                         experiments and subsequently applied to simulate an operational 
                         crop yield forecast system for maize under rainfed conditions. 
                         Simulations were performed with daily data for 37 growing seasons 
                         for the period 20012010. The seasonal climate forecast was used in 
                         combination with observed meteorological data to anticipate the 
                         crop forecast. Soil characteristics were derived from pedotransfer 
                         functions (PTFs). We were able to demonstrate the ability of the 
                         seasonal crop yield forecast system to provide timely and accurate 
                         information about maize yield at least 30 days in advance of the 
                         harvest. The development of improved crop yield forecasting system 
                         is crucial for implementing drought-preparedness measures in the 
                         BSA region.",
                  doi = "10.1016/j.agsy.2017.11.002",
                  url = "http://dx.doi.org/10.1016/j.agsy.2017.11.002",
                 issn = "0308-521X",
             language = "en",
           targetfile = "martins_improving.pdf",
        urlaccessdate = "27 abr. 2024"
}