Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach

Tavares, Rose Luiza Moraes; Oliveira, Stanley Robson de Medeiros; Barros, Flávio Margarito Martins de; Farhate, Camila Viana Vieira; Souza, Zigomar Menezes de; Scala, Newton La

doi:10.1590/1678-992x-2017-0095

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…For the 0.05‐ to 0.10‐m and 0.10‐ to 0.20‐m depths, we observed that the sand feature was selected as the most important to generate the model for prediction of soil C stock. The relationship between clay and C stock is already well established in the literature (Carbonell‐Bojollo, Torres, Rodriguez‐Lizana, & Ordónez‐Fernández, 2012; Razafimbelo et al., 2008; Six, Elliott, & Paustian, 2000; Six et al., 2002), the former being a key feature in the process of stabilization of the C in the soil due to its ability to promote the aggregation of particles and grant physical protection to the organic C stock (Tavares et al., 2018). In contrast, our results pointed to sand as an important feature in the storage of C in the soil.…”

Section: Discussionmentioning

confidence: 91%

Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon

et al. 2020

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In the Amazon, there are soils associated with continued human occupation known as "archeological black earth" (ABE). Due to its physical and chemical properties, ABE is more productive than other typical soils in the same region. Therefore, its carbon (C) sequestration mechanism has been a major topic of discussion by the scientific community, aiming to replicate similar characteristics in other soils. Thus, the objective of this study was to develop a predictive model using feature selection and decision tree induction methods for predicting soil C stock in ABE under different land use scenarios. The experiment was carried out in agricultural (coffee, cacao, and beans), pasture, and forest areas. Four feature selection approaches were used to identify the most relevant variables for the proposed model: (i) correlation-based feature selection, (ii) the χ 2 test, (iii) the Wrapper method, and (iv) no feature selection. The decision tree induction technique available in the Weka software was selected for data classification. Soils under cacao and coffee cultivation tend to accumulate more C when compared with soils located at bean crops, pasture, or forest land use systems. Land use and sand content were among the most important variables for the prediction of soil C stock in ABE. Furthermore, the use of a decision tree was effective at predicting soil C stocks for these soils because it enables the creation of models with high accuracy rates of 83, 74, and 81% (using seven, seven, and four rules at depths of 0.00-0.05, 0.05-0.10, and 0.10-0.20 m, respectively). 1 INTRODUCTION In the Amazon, soils with anthropogenic A horizon are associated with long-term human occupation by pre-Columbian indigenous societies (Santos et al., 2017), which formed extensive deposits of organic residues, significantly

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Section: Discussionmentioning

confidence: 91%

Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon

et al. 2020

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“…RF application is beneficial to large-scale data, provides resistance to overfitting, and has recently been used to study the connection between various factors and carbon emissions [ 18 ]. RF has also been used to investigate carbon flux emissions from soils and forests [ 19 , 20 ]. Mascaro et al.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor

Heltzel¹,

Johnson²,

Zaki³

et al. 2022

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“…Random Forest (RF) classification is a widely used machine learning algorithm and features advantages of strong resistance to overfitting, good adaption to large-scale data, and variable selection that is not restricted by collinearity. RF algorithms have been previously used to investigate carbon sinks in soils and forests [ 28 , 29 ]. However, little research has investigated the application of RF to influencing factors involved in national and regional carbon emissions (e.g., [ 30 ]).…”

Section: Introductionmentioning

confidence: 99%

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

2021

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China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

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Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach

Cited by 15 publications

References 33 publications

Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon

Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon

Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

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