4.4 Article

Carbon and nitrogen storage in agroforests, tree plantations, and pastures in western Oregon, USA

Journal

AGROFORESTRY SYSTEMS
Volume 60, Issue 2, Pages 123-130

Publisher

KLUWER ACADEMIC PUBL
DOI: 10.1023/B:AGFO.0000013267.87896.41

Keywords

afforestation; carbon sequestration; Douglas-fir; silvopasture

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Pastures store over 90% of their carbon and nitrogen below-ground as soil organic matter. In contrast, temperate conifer forests often store large amounts of organic matter above-ground in woody plant tissue and fibrous litter. Silvopastures, which combine managed pastures with forest trees, should accrete more carbon and nitrogen than pastures or timber plantations because they may produce more total annual biomass and have both forest and grassland nutrient cycling patterns active. This hypothesis was investigated by conducting carbon and nitrogen inventories on three replications of 11 year-old Douglas-fir (Pseudotsuga menziesii)/perennial ryegrass (Lolium perenne)/subclover (Trifolium subterraneum) agroforests, ryegrasss/subclover pastures, and Douglas-fir timber plantations near Corvallis, Oregon in August 2000. Over the 11 years since planting, agroforests accumulated approximately 740 kg ha(-1) year(-1) more C than forests and 520 kg ha(-1) year(-1) more C than pastures. Agroforests stored approximately 12% of C and 2% of N aboveground compared to 9% of C and 1% of N above ground in plantations and less than 1% of N and C aboveground in pastures. Total N content of agroforests and pastures, both of which included a nitrogen-fixing legume, were approximately 530 and 1200 kg ha(-1) greater than plantations, respectively. These results support the proposition that agroforests, such as silvopastures, may be more efficient at accreting C than plantations or pasture monocultures. However, pastures may accrete more N than agroforests or plantations. This apparent separation of response in obviously interrelated agroecosystem processes, points out the difficulty in using forest plantation or pasture research results to predict outcomes for mixed systems such as agroforests.

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