Introduction Among the many strategies adopted to mitigate climate change is the reduction of atmospheric carbon in a process called carbon sequestration, which consists of the transfer of carbon dioxide from the atmosphere to other global pools, such as the soil [1]. Therefore, quantifying the soil carbon is of great importance for successfully measuring the efficiency of carbon sequestration practices and providing accurate reports [2]. The correct measurement of soil carbon is a costly and cumbersome process requiring shipping of samples from the field to laboratories, greatly limiting its applicability [3]. In order to reduce the cost and time required for analyses, several techniques have been developed, such as laser-induced breakdown spectroscopy [4] and online visible and near-infrared spectroscopy with random forests [5]. Even though these new techniques are faster and less expensive, samples still are required to be collected in the field. Developing a method that could provide estimates of the carbon content of farms, using easily obtained variables such as soil texture and practices, would contribute to understanding the relationship between these variables and soil carbon, facilitating carbon sequestration initiatives [6]. Thus, the aim of this project was to train a model on the data available and verify its validity
