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Tradeoffs in the quest for climate smart agricultural intensification in Mato Grosso, Brazil. Repositório Alice
GIL, J. D. B.; GARRETT, R. D.; ROTZ, A.; DAIOGLOU, V.; VALENTIM, J. F.; PIRES, G. F.; COSTA, M. H.; LOPES, L. B.; REIS, J. C. dos.
Low productivity cattle ranching, with its linkages to rural poverty, deforestation and greenhouse gas (GHG) emissions, remains one of the largest sustainability challenges in Brazil and has impacts worldwide. There is a nearly universal call to intensify extensive beef cattle production systems to spare land for crop production and nature and to meet Brazil's Intended Nationally Determined Contribution to reducing global climate change. However, different interventions aimed at the intensification of livestock systems in Brazil may involve substantial social and environmental tradeoffs. Here we examine these tradeoffs using a whole-farm model calibrated for the Brazilian agricultural frontier state of Mato Grosso, one of the largest soybean and beef...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Integrated crop management; Climate; Climate models; Pastures; Environmental sustainability; Greenhouse gas emissions; Sustainable agricultural intensification; Livestock; Deforestation; Amazonia; Agricultural management models; Land.
Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1105798
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Variáveis e modelos para estimativa da produtividade do cafeeiro a partir de índices de vegetação derivados de imagens Landsat. Repositório Alice
BERNARDES, T.; MOREIRA, M. A.; VERONA, J. D.; SHIMABUKURO, Y. E.; LUIZ, A. J. B..
Abstract. Coffee fields present a specific pattern of productivity resulting in high and low production in alternated years. Branches grown the first phenological year will produce coffee beans the second phenological year. In high-production years a plant works mostly to grain-filling to the detriment of new branches which will be responsible for production the following year. In low-production years the plant works rather to grow new branches which will produce beans the subsequent year. This feature can be related to the foliar biomass, which can be estimated through remote sensing derived vegetation indices. Several studies report this feature must be incorporated in modeling coffee yield coupled with agrometeorogical models. In this paper we derived...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Coffee yield; Biophysics variables; Vegetation indices; Radiometric correction; Stepwise regression; Café; Produtividade; Sensoriamento remoto; Coffee; Grain yield; Agricultural management models; Statistical models.
Ano: 2013 URL: http://www.alice.cnptia.embrapa.br/handle/doc/963264
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