Data for: Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi

<p>Bremer, L.L. (1,2), McGuire, G. (3,4), DeMaagd, N. (1,5), Trauernicht, C. (5)</p> <p><strong>&nbsp;</strong></p> <p>1 University of Hawaiʻi Economic Research Organization, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>2 Water Resources Research Center, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>3 Department of Geography and Environment, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>4 Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720</p> <p>5 Department of Natural Resources and Environmental Management, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>&nbsp;</p> <p><strong>Bremer, L.L., McGuire, G., Hastings Silao, Z., Kurashima, N., Ticktin, T., Crow, S.E., Giardina, P.C., Winter, K.B., DeMaagd, N., and C. Trauernicht. Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi</strong></p> <p>&nbsp;</p> <p>Multi-strata agroforestry land use scenarios were created using data from the: 2020 State of Hawaiʻi Agricultural Baseline (Perroy and Collier 2020), the Hawaiʻi Carbon Assessment (Jacobi et al. 2017), current (Giambelluca et al. 2013) and RCP 8.5 mid-century projected (Ellison-Timm et al. 2015) rainfall rasters, state land use zoning (SLUC, 2020), slope, elevation, and historical colluvial agroforestry maps (Kurashima et al. 2019). The following rasters display multi-strata agroforestry land-use scenarios.&nbsp;</p> <ul> <li> <p>&ldquo;MS_currentclimate_landuse_scenario&rdquo; represents potential multi-strata agroforestry under current rainfall.</p> </li> <li> <p>&ldquo;MS_RCP85_midcentury_landuse_scenario&rdquo; represents potential multi-strata agroforestry under projected RCP 8.5 mid-century rainfall.&nbsp;</p> </li> </ul> <p>For both scenarios: 1 = dry (550-1500 mm) multistrata agroforestry; 2 = mesic (1500-3000 mm) multi-strata agroforestry; 3 = wet (&gt;3000 mm) multi-strata agroforestry.</p> <p>Estimates of projected changes in above-ground carbon were estimated by comparing modeled AGC in agroforestry scenarios to baseline AGC for current forest (Asner et al. 2016) and estimates of AGC in non-forest vegetation (Selmants et al. 2017). The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid century rainfall.</p> <ul> <li> <p>&ldquo;AGC_change_currentclimate&rdquo;</p> </li> <li> <p>&ldquo;AGC_change_RCP85_midcentury&rdquo;</p> </li> </ul> <p>For both scenarios: -2 = significant decrease (projected maximum &lt; baseline); -1 = trend decrease (projected maximum &gt;&nbsp; baseline &gt; projected mean; 1 = weak increase (projected minimum &lt; baseline &lt; projected mean); 2 = strong increase (projected minimum&nbsp; &gt; baseline).</p> <p>Estimates of projected changes in soil carbon under each scenario were estimated using global meta-analyses (Cardinael et al. 2018; Chaterjee et al. 2018; De Stefano and Jacobson 2017), studies of multi-strata agroforestry transitions in similar climates and soil types, and land-use change studies in Hawaiʻi. The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid-century rainfall.&nbsp;</p> <ul> <li> <p>&ldquo;SoilC_currentclimate&rdquo;</p> </li> <li> <p>&ldquo;SoilC_RCP85_midcentury&rdquo;</p> </li> </ul> <p>For both scenarios, 1 = increase low confidence; 10 = increase medium confidence; 100 = increase high confidence; 2 = no change low confidence; 20 = no change medium confidence; 200 = no change high confidence; 3 =unclear (insufficient data); 4 = unclear (mixed evidence).</p> <p>Projected synergies and tradeoffs in AGC and soil C under each scenario were estimated by combining the soil C and AGC results. The following rasters display projected synergies and tradeoffs in soil C and AGC under current and RCP 8.5 mid-century rainfall.&nbsp;</p> <ul> <li> <p>&ldquo;AGC_Soil_Bivariate_currentclimate&rdquo;</p> </li> <li> <p>&ldquo;AGC_Soil_Bivariate_RCP85_midcentury&rdquo;</p> </li> </ul> <p>For both scenarios: the first value is soil C category: 7 = unknown/uncertain; 8= increase; 9= no change; and the second value is AGC category: 0 = decrease; 1 = no change; 2 = increase.</p> <p><strong>&nbsp;</strong></p> <h2>References:</h2> <p>Asner, G. P., Sousan, S., Knapp, D. E., Selmants, P. C., Martin, R. E., Hughes, R. F., &amp; Giardina, C. P. (2016). Rapid forest carbon assessments of oceanic islands: A case study of the Hawaiian archipelago. 11(1). <a href="https://doi.org/10.1186/s13021-015-0043-4">https://doi.org/10.1186/s13021-015-0043-4</a></p> <p>Cardinael, R., Umulisa, V., Toudert, A., Olivier, A., Bockel, L., &amp; Bernoux, M. (2018). Revisiting IPCC Tier 1 coefficients for soil organic and biomass carbon storage in agroforestry systems. Environmental Research Letters, 13. <a href="https://doi.org/10.1088/1748-9326/aaeb5f/meta">https://doi.org/10.1088/1748-9326/aaeb5f/meta</a></p> <p>Chaterjee, N., Nair, P. K. R., Chakraborty, S., &amp; Nair, V. D. (2018). Changes in soil carbon stocks across the forest-agrofoest-agriculture/pasture continuum in various agroecological regions: A meta-analysis. Agriculture, Ecosystems &amp; Environment, 266, 55&ndash;67. <a href="https://doi.org/10.1016/j.agee.2018.07.014">https://doi.org/10.1016/j.agee.2018.07.014</a></p> <p>De Stefano, A., &amp; Jacobson, M. G. (2017). soil carbon sequestration in agroforestry systems: A meta-analysis. Agroforestry Systems. <a href="https://doi.org/10.1007/s10457-017-0147-9">https://doi.org/10.1007/s10457-017-0147-9</a></p> <p>Elison Timm, O., Giambelluca, T. W., &amp; Diaz, H. F. (2015). Statistical downscaling of rainfall changes in Hawaiʻi based on the CMIP5 global model projections. Journal of Geophysical Research: Atmospheres. <a href="https://doi.org/10.1002/2014JD22059">https://doi.org/10.1002/2014JD22059</a></p> <p>Giambelluca, T. W., Chen, Q., Frazier, A. G., Price, J. P., Chen, Y. L., Chu, P. S., Eischeid, J. K., &amp; Delparte, D. M. (2013). Online Rainfall Atlas of Hawaiʻi. Bulletin Of the American Meteorological Society, 94, 313&ndash;316. <a href="https://doi.org/10.1175/BAMS-D-11-00228.1">https://doi.org/10.1175/BAMS-D-11-00228.1</a></p> <p>Jacobi, J. D., Price, J. P., Fortini, L. B., Gon III, S. M., &amp; Berkowitz, P. (2017). Carbon Assessment of Hawaiʻi Land Cover Map [Map]. USGS.&nbsp;</p> <p><a href="https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b">https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b</a></p> <p>Kurashima, N., Fortini, L., &amp; Ticktin, T. (2019). The potential of indigenous agricultural food production under climate change in Hawaiʻi. Nature Sustainability. <a href="https://doi.org/10.1038/s41892-019-0226-1">https://doi.org/10.1038/s41892-019-0226-1</a></p> <p>Selmants, P. C., Giardina, C. P., Sousan, S., Knapp, D. E., Kimball, H., Hawbaker, T. J., Moreno, A., Seirer, J., Running, S. W., Miura, T., Bergstrom, R., Hughes, R. F., Litton, C. M., &amp; Asner, G. P. (2017). Baseline Carbon Storage and Carbon Fluxes in Terrestrial Ecosystems of Hawaiʻi. USGS.</p> <p>State Land Use Commission. (2020). State Land Use District Boundaries [Map]. Hawaiʻi Statewide GIS Program.</p>