Data/hora: 27/07/2024 07:11:21

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	14/09/2021	
Data da última atualização: 	14/09/2021	
Autoria: 	MARÇAL, M. F. M.; SOUZA, Z. M. de; TAVARES, R. L. M.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M.; GALINDO, F. S.	
Afiliação: 	MARIA FERNANDA MAGIONI MARÇAL, FEAGRI/UNICAMP; ZIGOMAR MENEZES DE SOUZA, FEAGRI/UNICAMP; ROSE LUIZA MORAES TAVARES, UNIVERSITY OF RIO VERDE; CAMILA VIANA VIEIRA FARHATE, FEAGRI/UNICAMP, UNESP; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; FERNANDO SHINTATE GALINDO, FEAGRI/UNICAMP, UNESP.	
Título: 	Predictive models to estimate carbon stocks in agroforestry systems.	
Ano de publicação: 	2021	
Fonte/Imprenta: 	Forests, v. 12, n. 9, p. 1-15, Sept. 2021.	
DOI: 	https://doi.org/10.3390/f12091240	
Idioma: 	Inglês	
Notas: 	Article 1240. Na publicação: Stanley Robson Medeiros Oliveira.	
Conteúdo: 	Abstract: 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.	
Palavras-Chave: 	Agroforestry systems; Data mining technique; Floresta aleatória; Land use systems; Mineração de dados; Modelo preditivo; Predictive models; Random forest; Sequestro de carbono; Sistemas agroflorestais; Sistemas de uso da terra.	
Thesagro: 	Matéria Orgânica; Uso da Terra.	
Thesaurus Nal: 	Agroforestry; Carbon sequestration; Land use; Organic matter.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/225942/1/AP-Predictive-models-Forests-2021.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (20946-1) - UPC - AP - DD -  -  -  - DIS