Soil organic carbon loss in agricultural systems of Mexico due to climate change

Abstract

Soil organic carbon (SOC) plays a key role in ecosystem health, influencing soil’s physical, chemical, and microbiological properties, such as water retention, fertility, and microbiome diversity. SOC modeling, using machine learning and remote sensing, enables the prediction of how agricultural practices and climate change affect its storage. This study aimed to model and project variations in SOC reserves in rainfed and irrigated agricultural soils in Mexico, under current conditions and future climate change scenarios. Therefore, models were developed to relate SOC to variables such as Lang’s index (precipitation and temperature), altitude, slope, bulk density, texture type, and soil depth. These models captured the land relief characteristics and their relationship with agricultural practices and SOC content in soils. The highest SOC levels were observed in irrigated agricultural systems. However, under climate change scenarios, SOC losses of up to 7 % are projected, along with temperature increases of up to 6 °C and precipitation increases of 12%. The reduction in SOC could increase greenhouse gas emissions and diminish the soil’s carbon storage capacity. This study highlights the importance of implementing sustainable management practices and promoting multidisciplinary research to mitigate adverse effects. Additionally, it demonstrates the potential for simulating SOC behavior and generating models useful for evaluating scenarios and supporting decision-making.