scholarly journals Predictive Models to Estimate Carbon Stocks in Agroforestry Systems

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1240
Author(s):  
Maria Fernanda Magioni Marçal ◽  
Zigomar Menezes de Souza ◽  
Rose Luiza Moraes Tavares ◽  
Camila Viana Vieira Farhate ◽  
Stanley Robson Medeiros Oliveira ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Carbon Stock ◽  
Agroforestry System ◽  
Agroforestry Systems ◽  
Forest Fragment ◽  
Random Forest Algorithm ◽  
Land Use Systems ◽  
Physical And Chemical Variables ◽  
Physical And Chemical

This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems.

scholarly journals Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm

2020 ◽  
Vol 12 (23) ◽  
pp. 3933
Author(s):  
Anggun Tridawati ◽  
Ketut Wikantika ◽  
Tri Muji Susantoro ◽  
Agung Budi Harto ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Random Forest ◽  
Remote Sensing Data ◽  
Agroforestry System ◽  
Classification Model ◽  
Sensor Data ◽  
Kappa Statistics ◽  
Random Forest Algorithm ◽  
Coffee Plantations ◽  
Multi Temporal ◽  
Sentinel 2

Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

scholarly journals Carbon Stock and Sequestration Potential of an Agroforestry System in Sabah, Malaysia

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 210 ◽  
Author(s):  
Normah Awang Besar ◽  
Herawandi Suardi ◽  
Mui-How Phua ◽  
Daniel James ◽  
Mazlin Bin Mokhtar ◽  
...  
Keyword(s):  
Global Warming ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Oil Palm ◽  
Carbon Stock ◽  
Agroforestry System ◽  
Agroforestry Systems ◽  
Sequestration Potential ◽  
Significant Difference ◽  
The Impact

Total aboveground carbon (TAC) and total soil carbon stock in the agroforestry system at the Balung River Plantation, Sabah, Malaysia were investigated to scientifically support the sustaining of natural forest for mitigating global warming via reducing carbon in the atmosphere. Agroforestry, monoculture, and natural tropical forests were investigated to calculate the carbon stock and sequestration based on three different combinations of oil palm and agarwood in agroforestry systems from 2014 to 2018. These combinations were oil palm (27 years) and agarwood (seven years), oil palm (20 years) and agarwood (seven years), and oil palm (17 years) and agarwood (five years). Monoculture oil palm (16 years), oil palm (six years), and natural tropical forest were set as the control. Three randomly selected plots for agroforestry and monoculture plantation were 0.25 ha (50 × 50 m), respectively, whereas for the natural tropical forest it was 0.09 ha (30 × 30 m). A nondestructive sampling method followed by the allometric equation determined the standing biomass. Organic and shrub layers collected in a square frame (1 × 1 m) were analyzed using the CHN628 series (LECO Corp., MI, USA) for carbon content. Soil bulk density of randomly selected points within the three different layers, that is, 0 to 5, 5 to 10, and 10 to 30 cm were used to determine the total ecosystem carbon (TEC) stock in each agroforestry system which was 79.13, 85.40, and 78.28 Mg C ha−1, respectively. The TEC in the monoculture oil palm was 76.44 and 60.30 Mg C ha−1, whereas natural tropical forest had the highest TEC of 287.29 Mg C ha−1. The forest stand had the highest TEC capacity as compared with the agroforestry and monoculture systems. The impact of planting systems on the TEC showed a statistically significant difference at a 95% confidence interval for the various carbon pools among the agroforestry, monoculture, and natural tropical forests. Therefore, the forest must be sustained because of its higher capacity to store carbon in mitigating global warming.

scholarly journals Ecological services of agroforestry systems in selected upland farming communities in the Philippines

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Romnick Baliton ◽  
LEILA LANDICHO ◽  
Rowena Esperanza Cabahug ◽  
ROSELYN F. PAELMO ◽  
Kenneth Laruan ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Alley Cropping ◽  
Diversity Index ◽  
Agroforestry System ◽  
Agroforestry Systems ◽  
The Philippines ◽  
Ecological Services ◽  
Farming Communities ◽  
Wiener Diversity Index ◽  
Study Sites

Abstract. Baliton RS, Landicho LD, Cabahug RED, Paelmo RF, Laruan KA, Rodriguez RS, Visco RG, Castillo AKA. 2020. Ecological services of agroforestry systems in selected upland farming communities in the Philippines. Biodiversitas 21: 707-717. A study was conducted in three selected upland farming communities in Nueva Vizcaya, Benguet and Quezon, Philippines to assess the ecological services of agroforestry systems. Results showed that alley cropping was the dominant agroforestry system in Nueva Vizcaya, while vegetable-based and coffee-based multistorey systems were found prevailing in Benguet and Quezon provinces. Agrobiodiversity assessment revealed that the values of Shannon-Wiener diversity index of agroforestry systems in the three study sites were considered to be low to moderate, ranging from 2.21 to 2.71. This validates that the number of individuals per species in the agroforestry landscape was not evenly distributed. The means of biomass in the three study sites, ranging from 106.22-127 tons ha-1, were higher than that of agroforestry systems (102.80 tons ha-1) in the Philippines. The agroforestry systems in Nueva Vizcaya had the largest carbon stock of 57.15 ton C ha-1, followed by Quezon 52.96 ton C ha-1 and Benguet 47.80 ton C ha-1. These results are comparable to the overall mean of carbon stock of tree plantations (59.0 ton C ha-1) and higher than that of agroforestry systems in the Philippines, i.e., 45.4 ton C ha-1. Therefore, this article argues that the different agroforestry systems provide ecological services in the upland farming communities in the Philippines.

scholarly journals ORGANIC CARBON AND TOTAL NITROGEN IN THE DENSIMETRIC FRACTIONS OF ORGANIC MATTER UNDER DIFFERENT SOIL MANAGEMENT

2016 ◽  
Vol 29 (2) ◽  
pp. 263-273 ◽  
Author(s):  
MARCELO RIBEIRO VILELA PRADO ◽  
FABRICIO TOMAZ RAMOS ◽  
OSCARLINA LÚCIA DOS SANTOS WEBER ◽  
CAIO BATISTA MÜLLER
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Organic Carbon ◽  
Total Nitrogen ◽  
Agroforestry System ◽  
Native Forest ◽  
Land Use Systems ◽  
System 2 ◽  
Heavy Fractions

ABSTRACT: The evaluation of land use and management by the measurement of soil organic matter and its fractions has gained attention since it helps in the understanding of the dynamics of their contribution to soil productivity, especially in tropical environments. This study was conducted in the municipality of Colorado do Oeste, state of Rondônia, Brazil and its aim was to determinethe quantity of organic carbon and total nitrogen in the light and heavy fractions of organic matter in the surface layers of a typic hapludalf under different land use systems: Native Forest: open evergreen forest, reference environment; Agroforestry System 1: teak (Tectona grandis LF) and kudzu (Pueraria montana); Agroforestry System 2: coffee (Coffea canephora), marandu palisade grass (Brachiaria brizantha cv. Marandu), "pinho cuiabano" (Parkia multijuga), teak and kudzu.; Agroforestry System 3: teak and cocoa (Theobroma cacao); Silvopasture System: teak, cocoa and marandu palisade grass; and Extensive Grazing System: marandu palisade grass. The experimental design was a randomized block in split-split plots (use systems versus soil layers of 0-0.05 and 0.05-0.10 m) with three replications. The results showed that relative to Native Forest, the Agroforestry System 2 had equal- and greater amounts of organic carbon and total nitrogen respectively (light and heavy fractions) in the soil organic matter, with the light fraction being responsible for storage of approximately 45% and 70% of the organic carbon and total nitrogen, respectively. Therefore, the light densimetric fraction proved to be useful in the early identification of the general decline of the soil organic matter in the land use systems evaluated.

scholarly journals Multivariate analysis in the evaluation of soil attributes in areas under different uses in the region of Humaitá, AM

2019 ◽  
Vol 14 (5) ◽  
pp. 1
Author(s):  
José Carlos Marques Pantoja ◽  
Milton César Costa Campos ◽  
Alan Ferreira Leite de Lima ◽  
José Maurício da Cunha ◽  
Emily Lira Simões ◽  
...  
Keyword(s):  
Principal Components ◽  
Management Practices ◽  
Structural Characteristics ◽  
Agroforestry System ◽  
Limiting Factor ◽  
Forest Fragment ◽  
Multivariate Statistical ◽  
Soil Attributes ◽  
Chemical Attributes ◽  
Physical And Chemical

The recognition of the influence of management practices on soil physical and chemical conditions is substantial for sustainable agriculture. For this reason, this study was developed for the purpose of evaluating the behavior of soil attributes under different uses in the region of Humaitá, AM, using multivariate statistical methods. The study was developed in 8 rural properties producing bananas, grassland, maize, coffee, cassava, vegetables, agroforestry system and a forest fragment. Samples of soils with preserved structure in the 0.0 - 0.10 and 0.10 - 0.20 m layers were randomly collected in 5 small trenches per area, totaling 32 samples in the management systems, to determine the physical and chemical attributes. The data were then submitted to univariate and multivariate statistical analysis. Exploratory data analysis (principal components and dendrogram) and frequency of environmental covariates was efficient in distinguishing production environments, so multivariate classification based on physical and chemical attributes of the soil can help in the proper planning of land use. The analysis of the principal components indicates that the BD presents direct dependence with the SPR, signaling the use of the soil with grassland the only one in the process of compaction. Soil acidity is the main limiting factor for crop development, requiring the adoption of pH corrective practices with improvements in nutrient supply. The conversion of the forest to grassland maintained the structural characteristics of the soil, while the other uses increased improvements in physical quality and soil fertility.

Recognition of land use based on random forest algorithm Study area and data

2021 ◽  
Author(s):  
Xiaoyong Zhang ◽  
Jing Tang ◽  
Haoyang Wang ◽  
Rui Xu ◽  
Di Xiao
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Random Forest Algorithm
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