dc.contributorDurigon, Angelica
dc.contributorhttp://lattes.cnpq.br/8404003252073790
dc.contributorFerraz, Simone Erotildes Teleginski
dc.contributorCuadra, Santiago Vianna
dc.contributorCera, Jossana Ceolin
dc.contributorUhlmann, Lilian Osmari
dc.contributorSantos, Daniel Caetano
dc.creatorPeralta, Diego Enrique Portalanza
dc.date.accessioned2022-11-04T17:42:49Z
dc.date.accessioned2023-09-04T19:40:11Z
dc.date.available2022-11-04T17:42:49Z
dc.date.available2023-09-04T19:40:11Z
dc.date.created2022-11-04T17:42:49Z
dc.date.issued2022-01-28
dc.identifierhttp://repositorio.ufsm.br/handle/1/26760
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8627226
dc.description.abstractEcuador is one of the most vulnerable countries to climate change and climate variability phenomena that could significantly affect various sectors and aspects of human life, such as economy and agriculture. Rice (Oryza sativa L.) is one of the most fundamental staple crops and feeds more than 50% of the world’s population. Its production needs to increase by 70% by 2050 to meet the growing requirement for food associated with a growing population and economic expansion. General circulation models (GCMs) suggest that increasing greenhouse gas intensities may affect the global climate. The future main challenges to food security presented by climate change call for extraordinary efforts and skill to simulate and foresee the relations concerning crop growth dynamics, the environment, and crop management. Here we used dynamically downscaled Regional Climate Model (RCM) data for the Ecuadorian domain to generate precipitation and air temperature climate projections (2070 – 2099) under three different climate change scenarios and a Crop Model to quantify climate change impact on rice crop. For this, the total Ecuadorian area was divided into three regions: Coastal (CO), Highlands (HL), and Amazon (AM), following political boundaries, and after that two main CO producer regions were analyzed. For the Representative Concentration Pathway (RPC) 2.6 temperature will have a mean change of 1.35, 1.55, and 1.21 ºC for CO, HL, and AM, respectively. Seasonally, June, July, and August (JJA) presented the largest shift with a positive change of 1.45 °C. A less significant mean shift is observed in December, January, and February (DJF) (Ecuadorian rainy season) presenting the lowest alteration with a mean delta change of 1.30 °C. The spatial change in productivity presented different patterns for Guayas and Los Rios. Our results showed that the RegCM4-VSM simulations for Guayas will have a negative impact on production under all climate scenarios (RCP 2.6, 4.5 and 8.5). The analysis is not only the first assessment of the impact of climate change on rice productivity using crop modelling, but it also provides solutions to projected deficiencies in the rice crop that alert Ecuador’s food security. Like this, climatic policies should be strengthened particularly in adaptation measures.
dc.publisherUniversidade Federal de Santa Maria
dc.publisherBrasil
dc.publisherMeteorologia
dc.publisherUFSM
dc.publisherPrograma de Pós-Graduação em Meteorologia
dc.publisherCentro de Ciências Naturais e Exatas
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.subjectGCM
dc.subjectRCM
dc.subjectDownscaling
dc.subjectRCPs
dc.subjectMudanças climáticas
dc.subjectModelagem de culturas
dc.subjectVery simple model
dc.subjectClimate change
dc.subjectCrop modeling
dc.titleProjeções climáticas e seu impacto na produtividade potencial da cultura do arroz no Equador utilizando o modelo Simple
dc.typeTese


Este ítem pertenece a la siguiente institución