Article Predictive Models to Estimate Carbon Stocks in Agroforestry Systems Maria Fernanda Magioni Marçal 1,† , Zigomar Menezes de Souza 1 , Rose Luiza Moraes Tavares 2, * , Camila Viana Vieira Farhate 1,3 , Stanley Robson Medeiros Oliveira 1,4 and Fernando Shintate Galindo 1,5   Citation: Marçal, M.F.M.; Souza, Z.M.d.; Tavares, R.L.M.; Farhate, C.V.V.; Oliveira, S.R.M.; Galindo, F.S. Predictive Models to Estimate Carbon Stocks in Agroforestry Systems. Forests 2021, 12, 1240. https:// doi.org/10.3390/f12091240 Academic Editor: Michael P. Strager Received: 3 August 2021 Accepted: 20 August 2021 Published: 14 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 School of Agricultural Engineering (Feagri), University of Campinas (Unicamp), Campinas 13083-970, Brazil; femagioni@gmail.com (M.F.M.M.); zigomarms@feagri.unicamp.br (Z.M.d.S.); camilavianav@hotmail.com (C.V.V.F.); stanley.oliveira@embrapa.br (S.R.M.O.); fs.galindo@yahoo.com.br (F.S.G.) 2 School of Agronomy, University of Rio Verde (UniRV), Rio Verde 75901-970, Brazil 3 School of Agricultural and Veterinarian Sciences, University State of São Paulo (Unesp), Jaboticabal 14884-900, Brazil 4 Brazilian Agricultural Research Corporation (Embrapa), Campinas 13083-970, Brazil 5 School of Agronomy, University State of São Paulo (Unesp), Ilha Solteira 15385-000, Brazil * Correspondence: roseluiza@unirv.edu.br † This manuscript is part of the Master thesis of the first author, available online at http://repositorio.unicamp.br/jspui/handle/REPOSIP/344442 (accessed on 15 August 2021). 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. Keywords: organic matter; carbon sequestration; land use systems; data mining technique; ran- dom forest 1. Introduction The use of agroforestry systems to achieve optimum agronomic benefits through the efficient use of resources (nutrients, light, water collection, and utilization) has received great attention for its contribution to mitigating climate change through organic carbon sequestration [1]. In this context, understanding the dynamics and storage of soil carbon, especially in agroforestry systems, is essential for informing public policies focused on disseminating these agricultural practices [2]. Forests 2021, 12, 1240. https://doi.org/10.3390/f12091240 https://www.mdpi.com/journal/forests