<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"><identifier identifierType="DOI">10.7910/DVN/LXBZMU</identifier><creators><creator><creatorName nameType="Personal">Castro, Fabio</creatorName><givenName>Fabio</givenName><familyName>Castro</familyName><nameIdentifier SchemeURI="https://orcid.org/" nameIdentifierScheme="ORCID">0000-0001-5908-7225</nameIdentifier><affiliation>International Center for Tropical Agriculture (CIAT)</affiliation></creator><creator><creatorName nameType="Personal">Bunn, Christian</creatorName><givenName>Christian</givenName><familyName>Bunn</familyName><nameIdentifier SchemeURI="https://orcid.org/" nameIdentifierScheme="ORCID">0000-0003-2175-8745</nameIdentifier><affiliation>International Center for Tropical Agriculture (CIAT)</affiliation></creator></creators><titles><title>The impact of Climate change in cocoa production in Colombia, Ecuador and Peru</title></titles><publisher>Harvard Dataverse</publisher><publicationYear>2022</publicationYear><subjects><subject>Agricultural Sciences</subject><subject>Earth and Environmental Sciences</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_1666" subjectScheme="AGROVOC">climate change</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_9000024" subjectScheme="AGROVOC">crop modelling</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_49834" subjectScheme="AGROVOC">machine learning</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_5083" subjectScheme="AGROVOC">geographical distribution</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_8679" subjectScheme="AGROVOC">agricultural research</subject><subject>random forest</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_7713" subjectScheme="AGROVOC">cacao</subject><subject subjectScheme="Research Region">Latin America and the Caribbean</subject><subject subjectScheme="Research Lever">Multifunctional Landscapes</subject><subject subjectScheme="Research Lever">Climate Action</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_1666" subjectScheme="AGROVOC">climate change</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_7713" subjectScheme="AGROVOC">cocoa</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_9000024" subjectScheme="AGROVOC">crop modelling</subject><subject schemeURI="http://aims.fao.org/aos/agrovoc/c_5083" subjectScheme="AGROVOC">geographical distribution</subject></subjects><contributors><contributor contributorType="ContactPerson"><contributorName nameType="Organizational">Alliance Data Management</contributorName><affiliation>The Alliance of Bioversity International and CIAT</affiliation></contributor><contributor contributorType="ContactPerson"><contributorName nameType="Personal">Castro, Fabio</contributorName><givenName>Fabio</givenName><familyName>Castro</familyName><affiliation>International Center for Tropical Agriculture (CIAT)</affiliation></contributor><contributor contributorType="ContactPerson"><contributorName nameType="Personal">Bunn, Christian</contributorName><givenName>Christian</givenName><familyName>Bunn</familyName><affiliation>International Center for Tropical Agriculture (CIAT)</affiliation></contributor><contributor contributorType="Producer"><contributorName nameType="Organizational">Bioversity International and the International Center for Tropical Agriculture</contributorName></contributor><contributor contributorType="Distributor"><contributorName nameType="Organizational">Bioversity International and the International Center for Tropical Agriculture</contributorName></contributor></contributors><dates><date dateType="Issued">2022-11-01</date><date dateType="Submitted">2022-11-08</date><date dateType="Updated">2024-01-31</date><date dateType="Collected">2020-01/2021-12</date></dates><resourceType resourceTypeGeneral="Dataset">Geospatial Data</resourceType><sizes><size>144368</size><size>535142</size><size>513964</size><size>343133</size><size>349917</size><size>156432</size><size>47040</size><size>48273</size><size>118205</size><size>117668</size><size>152162</size><size>162317</size><size>155380</size><size>315582</size><size>319620</size></sizes><formats><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format><format>application/zip</format></formats><version>1.1</version><rightsList><rights rightsURI="info:eu-repo/semantics/openAccess"/><rights/></rightsList><descriptions><description descriptionType="Abstract">&lt;P>These data contain spatial information on  agroclimatic zones for cocoa farming in Colombia, Ecuador and Peru, as well as impact gradient layers that illustrate the effect that climate change might have on cocoa crops in the same three countries.
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View list and description of files:&lt;br>
01	RF_current_col.tif	Colombia suitability raster – baseline &lt;br>
02	RF_2060s_col.tif	Colombia suitability raster – 2040 - 2060 &lt;br>
03	RF_2080s_col.tif	Colombia suitability raster – 2060 - 2080 &lt;br>
04	IG_2060s_col.tif	Colombia impact gradient – baseline vs 2040 - 2060 &lt;br>
05	IG_2080s_col.tif	Colombia impact gradient - baseline vs 2060 - 2080 &lt;br>
06	RF_current_ecu.tif	Ecuador suitability raster – baseline &lt;br>
07	RF_2060s_ecu.tif	Ecuador suitability raster – 2040 - 2060 &lt;br>
08	RF_2080s_ecu.tif	Ecuador suitability raster – 2060 - 2080 &lt;br>
09	IG_2060s_ecu.tif	Ecuador impact gradient – baseline vs 2040 - 2060 &lt;br>
10	IG_2080s_ecu.tif	Ecuador impact gradient - baseline vs 2060 - 2080 &lt;br>
11	RF_current_per.tif	Peru suitability raster – baseline &lt;br>
12	RF_2060s_per.tif	Peru suitability raster – 2040 - 2060 &lt;br>
13	RF_2080s_per.tif	Peru suitability raster – 2060 - 2080 &lt;br>
14	IG_2060s_per.tif	Peru impact gradient – baseline vs 2040 - 2060 &lt;br>
15	IG_2080s_per.tif	Peru impact gradient - baseline vs 2060 - 2080&lt;br>
&lt;br>
Estos datos contienen información espacial de las zonas agroclimaticas para el cultivo de cacao en Colombia, Ecuador y Perú, así como también las capas de gradiente de impacto que ilustran el efecto que tendrá el cambio climático sobre el cultivo de cacao para los mismos tres países..&lt;/P></description><description descriptionType="Other">Methodology:
Random forest analysis.</description></descriptions><geoLocations><geoLocation><geoLocationBox><eastBoundLongitude>-66.8501</eastBoundLongitude><southBoundLatitude>-4.23347</southBoundLatitude><northBoundLatitude>13.39152</northBoundLatitude><westBoundLongitude>-81.7334</westBoundLongitude></geoLocationBox></geoLocation><geoLocation><geoLocationBox><eastBoundLongitude>-75.2001</eastBoundLongitude><southBoundLatitude>-5.0168</southBoundLatitude><northBoundLatitude>1.4581</northBoundLatitude><westBoundLongitude>-81.3334</westBoundLongitude></geoLocationBox></geoLocation><geoLocation><geoLocationBox><northBoundLatitude>-0.0334</northBoundLatitude><southBoundLatitude>-18.3501</southBoundLatitude><eastBoundLongitude>-68.6501</eastBoundLongitude><westBoundLongitude>-81.3334</westBoundLongitude></geoLocationBox></geoLocation></geoLocations><fundingReferences><fundingReference><funderName>DESIRA program of the European Commission</funderName></fundingReference></fundingReferences></resource>