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Get Free AccessAbstract Ecuador has committed to climate change mitigation by reducing greenhouse gas emissions from anthropogenic activities, including livestock production. This sector can also contribute to mitigation by increasing carbon sequestration and storage capacity through increasing tree coverage. Although feasible, tree cover expansion would require agricultural land, creating a conflict with food production, thus appropriate areas need to be selected, and the impacts on livestock production quantified. Following water protection legislation and soil protection guidelines, we use a combination of measurements on 100 farms with land cover datasets to identify suitable areas for increasing tree coverage in livestock landscapes. Land cover classification was performed in 2 km areas around 100 livestock farms in the coastal and Amazon regions in Ecuador. Riparian zones were identified following legislation and steepland pastures identified from a slope map. Pasture in riparian areas or steeplands (slope > 15%) were considered suitable for increasing tree coverage by restoration or silvopastoral systems (SPS). The impact of forest/silvopastoral on biomass production was quantified. Overall, 855 km of rivers were studied, between 1–18% of riparian areas were classified as pastures, whilst from the 85992 ha included in the land cover classification, 41668 ha were identified as steepland, 1–28% of steepland was covered by pastures. We estimate that steepland pasture conversion into forest could offset 1.8–10% of Ecuador’s annual GHG emissions from the agricultural sector, but its impacts could represent a decrease > 50% of pasture biomass production at landscape level. Transformation of riparian and steepland pastures into SPS, could deliver important carbon sequestration, with a reduction in pasture biomass production of less than 20%.
Juan Pablo Iñamagua-Uyaguari, Nuala Fitton, Pete Smith (2023). Planting trees in livestock landscapes to protect soil and water also delivers carbon sequestration. , 97(6), DOI: https://doi.org/10.1007/s10457-023-00857-9.
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Type
Article
Year
2023
Authors
3
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1007/s10457-023-00857-9
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