The sustainability of a sugarcane plantation in Brazil assessed by the eddy covariance fluxes of greenhouse gases

Cabral, O. M. R.; Freitas, Helber C.; Cuadra, Santiago Viana; Andrade, Cristiano Alberto de; Ramos, N. P.; Grutzmacher, Priscila; Galdos, Marcelo Valadares; Packer, Ana Paula; Rocha, Humberto Ribeiro da; Rossi, Paulo

doi:10.1016/j.agrformet.2019.107864

Cited by 19 publications

(15 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Afterwards, EdiRe software was used to process the high-frequency data, averaging every 30 min. Detailed information on data processing, quality control, and post-processing can be found in Campos et al (2019) and Cabral et al (2020). In addition, data for any day with rain greater than 0.5 mm was removed.…”

Section: Study Areas and Respective Datamentioning

confidence: 99%

STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Bezerra¹,

Cunha²,

Falente³

et al. 2023

Preprint

View full text Add to dashboard Cite

Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications of the aerodynamic resistances. The use of these SEB models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET is mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called STEEP. Our model uses the Plant Area Index to represent the woody structure of the plants in calculating the moment roughness length. In the aerodynamic resistance for heat transfer, the parameter kB-1 was included, correcting it with RS soil moisture. Besides, the remaining λET in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the STEEP algorithm on the Google Earth Engine platform, using worldwide free data. Four sites with eddy covariance data located in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region, were used to evaluate the STEEP model. Our results show that STEEP based on the specific characteristics of the SDTF increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which in general, ET for these SDTF is overestimated by traditional SEB models, as happened in our research with the SEBAL. The STEEP model had similar or superior behaviour and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.

show abstract

Section: Study Areas and Respective Datamentioning

confidence: 99%

STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Bezerra¹,

Cunha²,

Falente³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Agricultural soil management is the main N 2 O emitter into the atmosphere, accounting for 85% of the country's total due to waste deposition in the soil and application of nitrogen fertilizers, with a direct influence on increasing the productivity of national livestock and agriculture production [9] . Additionally, direct manure deposition on the soil by animals in pastures contributes to 34% of emissions from managed pasture soils, followed by the application of organic fertilizers (30%), synthetic fertilizers (24%), agricultural residues incorporated into the soil (9%), and management of organic soils (3%) [10] .…”

Section: Greenhouse Gas Emissionsmentioning

confidence: 99%

Perspectives on carbon footprint of agricultural land-use in Brazil

Gama-Rodrigues¹,

Gama-Rodrigues²,

Vicente³

et al. 2022

Carbon Footprints

View full text Add to dashboard Cite

Brazil is one of the main producers in the agricultural and forestry sector worldwide, with production systems based on high consumption of inputs that contribute to high levels of greenhouse gas (GHG) emissions. This paper presents an analysis of the scenario of national GHG emissions and carbon footprints in the major production systems of agriculture, including livestock production and forestry, and the potential for soil carbon storage as a mitigation strategy under these systems. The main sources of national GHG emissions are beef cattle due to enteric fermentation and the management of agricultural soils through the use of nitrogen fertilizers. The increasing adoption of low-carbon agriculture has led to a reduction in the carbon footprint through no-till technologies, agrosilvopastoral systems, N2 fixation, and tree plantations. These technologies deserve to be increasingly disseminated to generate economic opportunities leading to financial gains from the commercialization of carbon credits and payment for environmental services.

show abstract

“…Developing renewable energy is one of the leading global interests in promoting sustainability and environmental quality, including modern electricity grids worldwide, which have begun to rely more heavily on renewable energy sources [1][2][3]. Ethanol is a renewable fuel produced by the fermentation of sugarcane extract and molasses.…”

Section: Introductionmentioning

confidence: 99%

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

et al. 2020

View full text Add to dashboard Cite

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.The production of biofuel from sugarcane is supported by the National Agency of Petroleum, Natural Gas and Biofuels' (ANP) sustainable development programs to achieve goals to reduce greenhouse gas emissions. Therefore, annual national targets were developed by the Brazilian National Biofuel Policy (RenovaBio) [12,15]. These goals are established in units of Decarbonization Credits (CBios) calculated from annual mandatory targets defined individually for each producer and distributor according to their participation in the fossil fuel market [12,16,[18][19][20][21].The certification of biofuel production assigns different marks to each biofuel producer and importer, in a value inversely related to the carbon amount of biofuel produced. The note reflects the individual contribution of each producing agent to mitigate a specific amount of GHG concerning its fossil substitute (in tons of CO 2 equivalent). The total emission is compared with that of the equivalent fossil fuel (gasoline, for ethanol), resulting in a final score (Energy-Environmental Efficiency Rating), characterizing the mitigation of emissions. This note generates CBios for biofuel producers and importers; with the decarbonization of the Brazilian energy matrix, there is a mechanism for the commercialization of these CBios linked to the carbon intensity of biofuels [2,12,16,18].The incentive to reduce pollutant emissions in the biofuel chain goes far beyond the use o...

show abstract

The sustainability of a sugarcane plantation in Brazil assessed by the eddy covariance fluxes of greenhouse gases

Cited by 19 publications

References 76 publications

STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Perspectives on carbon footprint of agricultural land-use in Brazil

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Contact Info

Product

Resources

About