Modeling the Carbon Sequestration Potential of Multifunctional Agroforestry-Based Phytoremediation (MAP) Systems in Chinandega, Nicaragua

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Abstract

Global sustainability challenges associated with increasing resource demands from a growing population call for resource-efficient land-use strategies that address multiple sustainability issues. Multifunctional agroforestry-based phytoremediation (MAP) is one such strategy that can simultaneously capture carbon, decontaminate soils, and provide diverse incomes for local farmers. Chinandega, Nicaragua, is a densely populated agricultural region with heavily polluted soils. Four different MAP systems scenarios relevant to Chinandega were created and carbon sequestration potentials were calculated using CO2FIX. All scenarios showed the potential to store significantly more carbon than conventional farming practices, ranging from 2.5 to 8.0 Mg CO2eq ha−1 yr−1. Overall, carbon sequestration in crops is relatively small, but results in increased soil organic carbon (SOC), especially in perennials, and the combination of crops and trees provide higher carbon sequestration rates than monoculture. Changes in SOC are crucial for long-term carbon sequestration, here ranging between 0.4 and 0.9 Mg C ha−1 yr−1, with the most given in scenario 4, an alley cropping system with pollarded trees with prunings used as green mulch. The adoption rate of multifunctional strategies providing both commodity and non-commodity outputs, such as carbon sequestration, would likely increase if phytoremediation is included. Well-designed MAP systems could help reduce land-use conflicts, provide healthier soil, act as climate change mitigation, and have positive impacts on local health and economies.

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APA

Kåresdotter, E., Bergqvist, L., Flores-Carmenate, G., Haller, H., & Jonsson, A. (2022). Modeling the Carbon Sequestration Potential of Multifunctional Agroforestry-Based Phytoremediation (MAP) Systems in Chinandega, Nicaragua. Sustainability (Switzerland), 14(9). https://doi.org/10.3390/su14094932

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