Search

This paper studies the dynamic relationships between per-capita combustible renewables and waste (CRW) consumption, agricultural value added (AVA), carbon dioxide (CO2) emissions, and real gross domestic product (GDP) for the case of Brazil, spanning the period 1980–2013. There is long-run cointegration between the considered variables. Short-run empirical findings reveal that there is a unidirectional causality running from agriculture to CO2 emissions and to GDP. However, there is long-run bidirectional causality between all considered variables. The long-run estimates show that both CRW consumption and AVA contribute to increase economic growth and to decrease CO2 emissions. Agricultural production and CRW consumption appear to play substitutable roles in the Brazilian economy as increasing CRW consumption reduces AVA in the long run, and vice versa. In addition, economic growth increases agricultural production and CRW production. Brazil is advised to continue encouraging agriculture and biofuels productions. The present substitutability between agriculture and biofuels productions could be reduced or even stopped by encouraging second-generation biofuels and discouraging first-generation biofuels. This may be done by policies of subsidization or of taxation, encouraging R&D and giving advantageous credits. [ABSTRACT FROM AUTHOR]

Brazil has emerged as an agro-export powerhouse: from being a net-agricultural importer and food aid recipient as recently as the 1960s and 1970s, it has now become the world’s third largest agricultural exporter, after the US and EU. What is more, Brazil’s new role as a major agricultural trader has provided an important foundation for its enhanced status and influence in global economic governance, as an emerging power and one of the ‘BRICS’. This paper analyses how such a remarkable transformation was brought about. I argue that Brazil’s emergence as an agricultural powerhouse was the result not of its natural factor endowments, but extensive intervention on the part of the Brazilian state that had the effect of constructing a new comparative advantage. This transformation was propelled by state-driven innovation and related policies that opened up massive new areas of the country to agriculture, enabled it to shift to producing goods in direct competition with the world’s dominant agricultural exporters, and generated significant gains in productivity and competitiveness. The irony is that the intention of these policies, initiated in the 1970s, was to foster industrial development in Brazil as part of its import-substitution industrialisation programme, yet they wound up having precisely the opposite effect – transforming Brazil into one of the world’s dominant agricultural powers. [ABSTRACT FROM AUTHOR]

This paper proposes a method to support sugarcane yield forecast using vegetation spectral indices, principal component analysis and historic yield data. The study area is located in the State of São Paulo, Brazil, and is divided into 11 production plots (108.75 ha), where sugarcane of the RB85 5536 variety is cultivated on red latossol (oxissol‐type) soil and flat topography. The data employed in the study include radiometrically and geometrically corrected enhanced thermatic mapper Plus (ETM+)/Landsat‐7 and ASTER/Terra images, acquired in June and April 2001, respectively, and historic harvest data measured in 2000 and 2001. The method comprises several steps: (a) enhancement of specific spectral responses of vegetation constituents; (b) reduction of spectral dimensions with prioritization of information and weighing of parameters related to foliar area; the data processed through these steps are reduced to a single image (the synthesis image), from which the mean DN (digital number) per cultivated area is calculated; (c) the image DNs are subsequently transformed into ton of stalk per hectare (t ha-1) through normalization, which requires knowledge of the previous year's yield for the cultivated production plots under analysis. Yield estimates using the method showed greater precision in comparison to the ubiquitous visual methods employed by the sugarcane agro‐industry in Brazil. Using factual productivity data of the year 2000 harvest only, the method achieved estimate errors varying between 2.57% and 5.65%, compared with 9.06% expected by the sugar factory; whereas using data from the year 2001 harvest, error margins were remarkably lower, around 1%. [ABSTRACT FROM AUTHOR]