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96 results for “Agroforestry”
Large rainout shelter in a Walnut-Wheat agroforestry system
<p>A large rainout shelter made of foldable tarpaulins attached to cables in an agroforestry system (silvoarable) with hybrid walnut (<em>Juglans regia x nigra</em>) and durum wheat (<span><span><em>Triticum turgidum ssp. durum</em>) in plot A2 of Domaine de Restinclières (coordinates: 43.704274 , 3.860958). Picture taken on 2018-02-06</span></span></p>
Tractor in a grapevine agroforestry system
<p>Small tractor working in grapevine (<em>Vitis vinifera</em>) in an agroforestry system (vitiforestry) with Pine trees (<em>Pinus pinea</em>) in plot B7 of Domaine de Restinclières (France) (coordinates 43.724202 , 3.859748), grapevine and trees were planted in 1996. Picture taken on 2019-09-30</p>
Newly established silvoarable agroforestry system in Dubecno, Central Bohemia, Czechia
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Agroforestry parcel with broadleaved trees in Domaine de Restinclères - France
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Data for Publication - Farmer Preferences and Selection Criteria for Shade Trees in Robusta Coffee Agroforestry Systems in Tshopo Province, DRC
<p>Data used for publication - "Farmer Preferences and Selection Criteria for Shade Trees in Robusta Coffee Agroforestry Systems in Tshopo Province, DRC"</p>
Spatio-temporal change of selected soil physico-chemical properties in grevillea-banana agroforestry systems
<p>This is a data base containin raw data (soil and litter data) as well as the R scripts used for their analyses</p>
Dataset of agroforestry certified by SEMA, Rio Grande do Sul - Brazil.
<p>DESCRIÇÃO:</p> <p>Banco de dados com informações descritivas tabuladas (.xlsx) e polígonos (shapefile) das áreas de manejo de sistemas agroflorestais certificados ambientalmente pela Secretaria Estadual do Meio Ambiente e Infraestrutura do Rio Grande do Sul, Brasil. Os dados foram coletados e sistematizados à partir dos processos de certificação agroflorestal disponíveis publicamente no Sistema Online de Licenciamento Ambiental do Governo do Estado do Rio Grande do Sul (www.sol.rs.gov.br).</p> <p>Obs 1: A matriz de espécies compilada se refere a "espécies mencionadas pelos agricultores certificados como sendo de interesse para cultivo e/ou reconhecidas por eles como presentes nas áreas de manejo". Não são produto de levantamentos em campo. </p> <p>Obs 2: A identidade taxonômica das espécies citadas pode conter imprecisões pois se originam da informação do produtor ou de seu assistente técnico de extensão rural, quando existente, e não passou por qualquer verificação por botânicos especialistas, portanto, se trata de uma lista de espécies de origem etnobotânica. </p> <p>Obs 3: Os nomes vernáculos e nomes científicos foram relacionados utilizando-se como referência as publicações listadas a seguir, considerando a distribuição natural regional das espécies, sempre que a informação original permitiu. </p> <p>DESCRIPTION:</p> <p>Database with tabulated descriptive information (.xlsx) and polygons (shapefile) of the management areas of agroforestry certified environmentally by the State Secretariat for the Environment and Infrastructure of Rio Grande do Sul, Brazil. The data were collected and systematized from the agroforestry certification processes publicly available in the Online Environmental Licensing System of the State Government of Rio Grande do Sul (www.sol.rs.gov.br).</p> <p>Note 1: The species matrix compiled refers to "species mentioned by certified farmers as being of interest for cultivation and/or recognized by them as present in the management areas". They are not the product of field surveys. </p> <p>Note 2: The taxonomic identity of the species cited may contain inaccuracies because they originate from information provided by the farmers or his technical assistant, when available, and have not been verified by expert botanists, therefore, this is a list of species of ethnobotanical origin. </p> <p>Note 3: The vernacular and scientific names were listed using as reference the publications listed below, considering the natural regional distribution of the species, whenever the original information allowed.</p> <p>Backes, A., Nardino, M.1998. Árvores, arbustos e algumas lianas nativas no Rio Grande do Sul. 1ª ed., Editora Unisinos, São Leopoldo, 202 p.</p> <p>Flora e Funga do Brasil. Jardim Botânico do Rio de Janeiro. Disponível em: < <a href="http://floradobrasil.jbrj.gov.br/">http://floradobrasil.jbrj.gov.br/</a> >. Acesso em: 21 jun. 2023</p> <p>Giehl, E.L.H. (coordenador) 2023. Flora digital do Rio Grande do Sul e de Santa Catarina. URL: http://floradigital.ufsc.br</p> <p>Sobral, M., Jarenkow, J. A., Brack, P., Irgang, B., Larocca, J., & Rodrigues, R. S. 2006. Flora Arbórea e Arborescente do Rio Grande do Sul, Brasil. Rima Novo Ambiente, Porto Alegre.</p>
Data from: Appalachian-breeding Vermivora chrysoptera (Golden-winged Warbler) occur at very low densities in mid-elevation forests and agroforestry systems throughout the Andes and isolated massifs of northern Colombia
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Figure 3 in The Chrysidoidea Wasps (Hymenoptera, Aculeata) in Conventional Coffee Crops and Agroforestry Systems in Southeastern Brazil
Figure 3. Venn diagram showing exclusive and shared genera of Chrysidoidea among conventional, agroforestry and transitional systems in the "Pontal do Paranapanema" region, São Paulo, Brazil. Bethylidae in blue, Chrysididae in red and Dryinidae in green color.
Figure 2 in The Chrysidoidea Wasps (Hymenoptera, Aculeata) in Conventional Coffee Crops and Agroforestry Systems in Southeastern Brazil
Figure 2. Malaise trap (Townes model) installed in the conventional system (S.J.F. – Conv.), in the "Pontal do Paranapanema" region, São Paulo, Brazil.
Figure 1 in The Chrysidoidea Wasps (Hymenoptera, Aculeata) in Conventional Coffee Crops and Agroforestry Systems in Southeastern Brazil
Figure 1. Map showing the collection sites of Chrysidoidea wasps in the conventional, agroforestry and transitional systems in the "Pontal do Paranapanema" area, state of São Paulo, Brazil. The different colored squares represent: S.A. - Conv. (red); S.J.F. - Conv. (green); S.F.- SAF (yellow), S.J.M. - SAF (blue); S.S. - SAF (white); S.M.-Trans. (light blue).
Data from: Rubber agroforestry in Thailand provides some biodiversity benefits without reducing yields
<p>Monocultural rubber plantations have replaced tropical forest, causing biodiversity loss. While protecting intact or semi-intact biodiverse forest is paramount, improving biodiversity value within the 11.4 million hectares of existing rubber plantations could offer important conservation benefits, if yields are also maintained. Some farmers practice agroforestry with high-yielding clonal rubber varieties to increase and diversify incomes. Here, we ask whether such rubber agroforestry improves biodiversity value or affects rubber yields relative to monoculture. We surveyed birds, fruit-feeding butterflies and reptiles in 25 monocultural and 39 agroforest smallholder rubber plots in Thailand, the world's biggest rubber producer. Management and vegetation structure data were collected from each plot, and landscape composition around plots was quantified. Rubber yield data were collected for a separate set of 34 monocultural and 47 agroforest rubber plots in the same region. Reported rubber yields did not differ between agroforests and monocultures, meaning adoption of agroforestry in this context should not increase land demand for natural rubber. Butterfly richness was greater in agroforests, where richness increased with greater natural forest extent in the landscape. Bird and reptile richness were similar between agroforests and monocultures, but bird richness increased with the height of herbaceous vegetation inside rubber plots. Species composition of butterflies differed between agroforests and monocultures, and in response to natural forest extent, while bird composition was influenced by herbaceous vegetation height within plots, the density of non-rubber trees within plots (representing agroforestry complexity), and natural forest extent in the landscape. Reptile composition was influenced by canopy cover and open habitat extent in the landscape. Conservation priority and forest-dependent birds were not supported within rubber. Synthesis and applications. Rubber agroforestry using clonal varieties provides modest biodiversity benefits relative to monocultures, without compromising yields. Agroforests may also generate ecosystem service and livelihood benefits. Management of monocultural rubber production to increase inter-row vegetation height and complexity may further benefit biodiversity. However, biodiversity losses from encroachment of rubber onto forests will not be offset by rubber agroforestry or rubber plot management. This evidence is important for developing guidelines around biodiversity-friendly rubber and sustainable supply chains, and for farmers interested in diversifying rubber production.</p>
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> </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> </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> </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. </p> <ul> <li> <p>“MS_currentclimate_landuse_scenario” represents potential multi-strata agroforestry under current rainfall.</p> </li> <li> <p>“MS_RCP85_midcentury_landuse_scenario” represents potential multi-strata agroforestry under projected RCP 8.5 mid-century rainfall. </p> </li> </ul> <p>For both scenarios: 1 = dry (550-1500 mm) multistrata agroforestry; 2 = mesic (1500-3000 mm) multi-strata agroforestry; 3 = wet (>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>“AGC_change_currentclimate”</p> </li> <li> <p>“AGC_change_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios: -2 = significant decrease (projected maximum < baseline); -1 = trend decrease (projected maximum > baseline > projected mean; 1 = weak increase (projected minimum < baseline < projected mean); 2 = strong increase (projected minimum > 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. </p> <ul> <li> <p>“SoilC_currentclimate”</p> </li> <li> <p>“SoilC_RCP85_midcentury”</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. </p> <ul> <li> <p>“AGC_Soil_Bivariate_currentclimate”</p> </li> <li> <p>“AGC_Soil_Bivariate_RCP85_midcentury”</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> </strong></p> <h2>References:</h2> <p>Asner, G. P., Sousan, S., Knapp, D. E., Selmants, P. C., Martin, R. E., Hughes, R. F., & 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., & 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., & 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 & Environment, 266, 55–67. <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., & 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., & 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., & Delparte, D. M. (2013). Online Rainfall Atlas of Hawaiʻi. Bulletin Of the American Meteorological Society, 94, 313–316. <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., & Berkowitz, P. (2017). Carbon Assessment of Hawaiʻi Land Cover Map [Map]. USGS. </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., & 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., & 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>
Agroforestry carbon stocks and greenhouse gas emission rates in central Alberta, Canada
<p>Agroforestry systems (AFS) contribute to carbon (C) sequestration and reduction in greenhouse gas emissions from agricultural lands. However, previously understudied differences among AFS may underestimate their climate change mitigation potential. In this 3-year field study, we assessed various C stocks and greenhouse gas emissions across two common AFS (hedgerows and shelterbelts) and their component land uses: perennial vegetated areas with and without trees (woodland and grassland, respectively), newly planted saplings in grassland, and adjacent annual cropland in central Alberta, Canada. Between 2018 and 2020 (~April–October), nitrous oxide emissions were 89% lower under perennial vegetation relative to the cropland (0.02 and 0.18 g N m−2 year−1, respectively). In 2020, heterotrophic respiration in the woodland was 53% lower in shelterbelts relative to hedgerows (279 and 600 g C m−2 year−1, respectively). Within the woodland, deadwood C stock was particularly important in hedgerows (35 Mg C ha−1 or 7% of ecosystem C) relative to shelterbelts (2 Mg C ha−1 or < 1% of ecosystem C), and likely affected C cycling differences between the woodland types by enhancing soil labile C and microbial biomass in hedgerows. Deadwood C stock was positively correlated with annual heterotrophic respiration and total (to ~100 cm depth) soil organic C, water-soluble organic C, and microbial biomass C. Total ecosystem C was 1.90–2.55 times greater within the woodland than all other land uses, with 176, 234, 237, and 449 Mg C ha−1 found in the cropland, grassland, planted saplings treatment, and woodland, respectively. Shelterbelt and hedgerow woodlands contained 2.09 and 3.03 times more C, respectively, than adjacent cropland. Our findings emphasize the importance of AFS for fostering C sequestration and reducing greenhouse gas emissions and, in particular, retaining hedgerows (legacy woodland) and their associated deadwood across temperate agroecosystems to help mitigate climate change.</p>
Database of Rural Technological Trajectories, their variants and territories featured by peasantries of the Brazilian Northern Region based on Agricultural Censuses and special tabulations for the Economy of Agroforestry Systems (2006 and 2017)
<p><strong>Introduction</strong></p> <p>This database contains the string variables at municipal level that qualifies the techno-productive trajectories (TT) of the Brazilian Northern Region Agrarian Economy, their technological variants (TTV) and territories based on peasantries. The TTs and TTVs were defined and theoretically justified by Costa (2021, p. 217-219).</p> <p><strong>Delimitation of Technological Trajectories, their variants and territories featured by peasantries</strong></p> <p>The TTs are designed by a method that combines <em>differentiation and structural signification</em> of rural production in each territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU).</p> <p><em>Structural differentiation</em> (Phase 1) is necessary because production systems activities play different roles, depending on the systems production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 440 and 441 of Costa (2021).</p> <p>In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These patterns are semantically associated with TTs or TTPs<em> structures</em> that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work. The codes of variable “Technological Trajectories” in this database are “CamponêsT1” for “Peasant Trajectory.T1” in Costa, 2021; “CamponêsT2” for “Peasant Trajectory.T2”; “CamponêsT3” for “Peasant Trajectory.T3”; “PatronalT4” for “Employer.T4”; “PatronalT5” for “Employer.T5”; “PatronalT7” for “Employer.T7”.</p> <p>In turn, the procedures to get the technological variants of TTs (TTVs) for census years 2006 and 2017 are described in Costa, 2021, p. 447-451. The codes of variable “Technological Variants” in this database are “IQ” for “CI = Chemical Intensity” in Costa, 2021; “IM” for “MI = Mechanical Intensity”; “IT” for “LI = Labour Intensity”; “IPst” for “PI = Pasture Improvement”; “IReb” for “HI = Herd improvement”; “Crg” for “LoadC=Load Capacity of Pasture”; “SAF-F” for “AFSs-F = AFSs with the presence of forest management”; “SAF-A” for “AFSs-A = Artificially developed AFSs”; “+” after the attribute for “Attribute clearly verified; “–“ for “Attribute clearly absent”; “0” for “an uncertain attribute”.</p> <p>The Brazilian Northern Region encompasses the municipalities of the federative states Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins. A municipality is codified by the variable “Peasantry” as “ACaboclo_Originário”, meaning a territory of an “original caboclo peasantry (OcP in English or CbO in Portuguese)”, if founded before 1880; “BCaboclo_Forâneo”, meaning a territory of an ”immigrant caboclo peasants (IcP or CbF)”, if founded between 1880 and 1910; “CAgrícola_Forâneo, meaning a territory of a “post-ruber immigrant agricultural peasantry (IpR or FpB)”, if founded between 1910 and 1960; and “DContemporâneo”, meaning a “recent peasantry (ReP or ReC)”, if founded since 1960.</p> <p>The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 2006 and 2017 Agricultural Censuses. The following special cases were handled:</p> <ul> <li>In the Agricultural Census 2017 credit data were not available. However, the Central Bank of Brazil informs for that year total rural credit for family-based and non-family-based agriculture and livestock by municipality.</li> <li>Comparing the production of manioc in the 2006 census with the production of manioc flour in the same year and with the historical production, we arrived at errors in three municipalities in Pará: in Moju a manioc production of 498,907 t is recorded adding the two sets of data (Peasant and Employer), when in fact it is 42,132; in São Miguel do Guamá the figure of 392,784 t is recorded when it actually is 175,941; 204,216 is recorded in Viseu and 106,287 is actual figure. Corrections were made using the proportion manioc/manioc flour prevailing in other municipalities in same microregion.</li> <li>In the census there are the value of the production of manioc and the value of the production of manioc flour. Since we are dealing with the same producer, if we consider in gross value of production or income aggregations both products, we incur double counting. In such cases, the value related to manioc flour was considered.</li> <li>The 2006 census has information on fishing restricted to the monetary income from the sale of fish, as a complementary income variable. The information does not incorporate the value of fish consumed in the establishment. Therefore, it is not a variable equivalent to the GVP of all other products considered. In turn, the 2017 census provides data on fish production as part of livestock (the gross value of fish production in captivity, which makes up the gross value of livestock production) but does not maintain the fish sales variable from the previous census. This income is contained in the variable “other producer income”, in which, none of the other possibilities listed (esgargot, etc.) are adhered to T2 (IBGE, Censo Agropecuário de 2017. Rio de Janeiro, IBGE, 2018). Therefore, two things were done to incorporate fisheries: a) for 2006 the GVP of fishery production was considered the variable "fish sale" under the heading "other producer income" divided by 1 minus the self-consumption rate 31% (Costa et al, 2022); b) for 2017, the GVP of fisheries production resulted from the division of the variable “other producer's incomes” by the same denominator of the operation described in “a”.</li> </ul> <p><strong>The dataset is organized as:</strong></p> <p>1. Data set with variables delimiting TT, TTV and Peasantry</p> <p><em>2006_NorthRegion_TechVariants.csv</em><br> <em>2017_ NorthRegion_TechVariants.csv</em>.</p> <p>In each table the column names are self-explanatory.</p> <p>2. Dataset with special tabulation or the agroforestry systems economy represented by Peasant Trajectory.T2</p> <p><strong> Gross Value of Production</strong></p> <p><em> Table1_NorthRegion_T2_GVP.csv <br> </em>Table 1a – Gross Value of Productios (GVP) by products of AFSs-F and peasantry 2006 and 2017<br> Table 1b – Gross Value of Productios (GVP) by products of AFSs-A and peasantry, 2006 and 201<br> Table 1c – Gross Value of Productios (GVP) by products of T2, technological variant, and peasantry, 2006 and 2017 </p> <p><strong> Real Product</strong></p> <p> Real Product” (RP): For each year (i), the vector of produced quantities (Qi) multiplied by a vector of fixed prices (P1): variation of RP is explained exclusively by the variation of Q. <br> <em>Table2_NorthRegion_T2_RealProduct.csv</em><br> Table 2a – T2 Production by technological variant and peasantry, 2006<br> Table 2b – T2 Production Value (=Real Product) by technological variant, and peasantry, 2006 in R$ 1,000 currents<br> Table 2c – T2 Implicit prices by technological variant, and peasantry, 2006, R$ 1.000 currents (each cel in Table 2b divided by corresponding cel in Table 2a)<br> Table 2d – T2 Production by technological variant, and peasantry, 2017<br> Table 2e – T2 Real Product1 by technological variant, and peasantry, 2017 in R$ 1,000 from 2006 (each cel in Table 2c multiplied by corresponding cel in Table 2d)</p> <p><strong> Key variables</strong><br> <em> Table3_NorthRegion_T2_KeyVariables.csv</em><br> Table 3 – Key variables of T2 economy by peasantry, 2006 and 2017</p> <p><strong>Reference:</strong></p> <p>Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2).</p> <p>COSTA, F. A., FEIJÃO,, L. G., ALMEIDA, I. C., NOGUEIRA, K. N. S., AMERICO, M. C. (2022). Database of a Riverine Economy in Mocajuba, Low Tocantins, Pará, Amazonia, Brazil [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7121336</p>
Linking animal behaviour and tree recruitment: Caching decisions by a scatter hoarder corvid determine seed fate in a Mediterranean agroforestry system
<p><span>1. Seed dispersal by scatter-hoarder corvids is key for the establishment of important tree species from the Holarctic region such as the walnut (<em>Juglans regia</em>). However, the factors that drive animal decisions to cache seeds in specific locations and the consequences of these decisions on seed fate are poorly understood. </span></p> <p><span>2. We experimentally created four distinct, replicated habitat types in a Mediterranean agricultural landscape where the Eurasian magpie (<em>Pica</em> <em>pica</em>) is a common scatter-hoarder: soft bare soil; compacted bare soil; compacted soil with a dense herbaceous cover; and soft linear bare soil made up of the irrigation furrows that separated the rest of the treatments. We also experimentally placed visual landmarks (stones, sticks, and bunches of dry plants) to test if magpies use them to place seed caches. Walnut dispersal from feeders to the habitats was monitored by radio-tracking and camera traps. </span></p> <p><span>3. A sowing experiment simulating natural caches tested the effect of caching type on seed germination and seedling emergence. Seed mass was controlled for the dispersal and sowing experiments.</span></p> <p><span>4. Magpies selected the two habitats with soft soil, and avoided the one with compacted soil, to cache nuts. Seed mass did not affect dispersal distance, germination, or emergence; however, heavier seeds were cached more often under litter and in the habitat with herbaceous cover, whereas lighter seeds were more often buried in the soft bare soil habitat. Seed burial under soil or litter determined seed fate, as there was virtually no emergence from unburied nuts. There was no evidence of any effect of the visual landmarks.</span></p> <p>5. Synthesis. The consequences of seed caching for seedling early establishment are driven by a fine decision-making process of the disperser. Magpies seemed to ponder the characteristics of the habitat and the seed itself to determine where and how to cache each nut. By doing so, magpies reinforced the quality of seed dispersal effectiveness, as they cached walnuts in locations that enhanced both seed survival and seedling emergence.</p>
Figure 4 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 4. Habitus of Chalcidoidea collected in agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum). (A) Haltichella hydara (Walker, 1842) [UNAB 1190], (B) Stypiura sp. [UNAB 1356], (C) Aenasius sp. (Encyrtidae) [UNAB 663], (D) Coelopencyrtus sp. [UNAB 2538], (E) Horismenus striatus Hansson, 2009 (Eulophidae) [UNAB 1196], (F) Brasema sp. (Eupelmidae) [UNAB 1358], (G) Anastatus sp. (Eupelmidae) [UNAB 1192], (H) Neorileya flavipes Ashmead, 1904 (Eurytomidae) [UNAB 1432], (I) Perilampus sp. (Perilampidae) [UNAB 1361], (J) Erotolepsia sp. (Pteromalidae) [UNAB 661], (K) Bubekia tricarinata (Ashmead, 1888) (Pteromalidae) [UNAB 3464], (L) Lelaps affinis Ashmead, 1904 (Pteromalidae) [UNAB 658].
Figure 3 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 3. Habitus of parasitoid wasps collected in agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum). (A) Aclista sp. (Diapriidae) [UNAB 3747], (B) Basalys sp. (Diapriidae) [UNAB 1198], (C) Paramesius sp. (Diapriidae) [UNAB 1194], (D) Trichoplasta sp. (Figitidae) [UNAB 1423], (E) Aganaspis sp. (Figitidae) [UNAB 1464], (F) Tropideucoila sp. (Figitidae) [UNAB 1422].
Figure 2 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 2. Bar charts of parasitoid wasp families and genera of collected individuals inhabiting agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum) from the Colombian Amazon basin. (A) number of individuals per family, (B) number of genera and species per family.
Figure 5 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 5. Habitus of Platygastroidea (Scelionidae) collected in agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum). (A) Apegus sp. [UNAB 1445], (B) Calliscelio sp. [UNAB 1439], (C) Gryon sp. [UNAB 1442], (D) Scelio sp. [UNAB 1438], (E) Xenomerus sp. [UNAB 1443], (F) Thoron sp. [UNAB 3765].
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.