Assessing the greenhouse gas emissions of Brazilian soybean biodiesel production.

Soybean biodiesel (B100) has been playing an important role in Brazilian energy matrix towards the national bio-based economy. Greenhouse gas (GHG) emissions is the most widely used indicator for assessing the environmental sustainability of biodiesels and received particular attention among decision makers in business and politics, as well as consumers. Former studies have been mainly focused on the GHG emissions from the soybean cultivation, excluding other stages of the biodiesel production. Here, we present a holistic view of the total GHG emissions in four life cycle stages for soybean biodiesel. The aim of this study was to assess the GHG emissions of Brazilian soybean biodiesel production system with an integrated life cycle approach of four stages: agriculture, extraction, production and distribution. Allocation of mass and energy was applied and special attention was paid to the integrated and non-integrated industrial production chain. The results indicated that the largest source of GHG emissions, among four life cycle stages, is the agricultural stage (42-51%) for B100 produced in integrated systems and the production stage (46-52%) for B100 produced in non-integrated systems. Integration of industrial units resulted in significant reduction in life cycle GHG emissions. Without the consideration of LUC and assuming biogenic CO2 emissions is carbon neutral in our study, the calculated life cycle GHG emissions for domestic soybean biodiesel varied from 23.1 to 25.8 gCO2eq. MJ-1 B100 and those for soybean biodiesel exported to EU ranged from 26.5 to 29.2 gCO2eq. MJ-1 B100, which represent reductions by 65% up to 72% (depending on the delivery route) of GHG emissions compared with the EU benchmark for diesel fuel. Our findings from a life cycle perspective contributed to identify the major GHG sources in Brazilian soybean biodiesel production system and they can be used to guide mitigation priority for policy and decision-making. Projected scenarios in this study would be taken as references for accounting the environmental sustainability of soybean biodiesel within a domestic and global level.

Análise dos atributos do solo e da produtividade da cultura de cana-de-açúcar com o uso da geoestatística e árvore de decisão / Analyze the soil attributes and sugarcane yield culture with the use of geostatistics and decision trees

Um dos desafios da agricultura de precisão é oferecer subsídios para a definição de unidades de manejo para posteriores intervenções. Portanto, o objetivo deste trabalho foi avaliar os atributos químicos do solo e a produtividade da cultura de cana-de-açúcar por meio da geoestatística e mineração de dados pela indução da árvore de decisão. A produtividade da cana-de-açúcar foi mapeada em uma área de aproximadamente 23ha, utilizando-se o critério de célula, por meio de um monitor de produtividade que permitiu a elaboração de um mapa digital que representa a superfície de produção para a área em estudo. Para determinar os atributos de um Argissolo Vermelho-Amarelo, foram coletadas as amostras no início da safra 2006/2007, utilizando-se uma grade regular de 50 x 50m, nas profundidades de 0,0-0,2m e 0,2-0,4m. Os dados dos atributos do solo e da produtividade foram analisados por meio da técnica de goestatística e classificados em três níveis de produção para indução de árvore de decisão. A árvore de decisão foi induzida no programa SAS Enterprise Miner, sendo utilizado algoritmo baseado na redução de entropia. As variáveis altitude e potássio apresentaram os maiores valores de correlação com a produtividade de cana-de-açúcar. A indução de árvores de decisão permitiu verificar que a altitude é a variável com maior potencial para interpretar os mapas de produtividade de cana-de-açúcar, auxiliando na agricultura de precisão e mostrando-se uma ferramenta adequada para o estudo de definição de zonas de manejo em área cultivada com essa cultura.

One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane.