The Mixteca Datascape
(The Data Pillar of the Mixteca Learning Landscape)
A shared platform for data, information, and knowledge exchange as part of the Mixteca Learning Landscape
The Mixteca Learning Landscape: Towards Sustainable Agrifood Systems
The Mixteca Learning Landscape (MLL) is a transdisciplinary initiative designed to catalyze sustainable transformation in the agrifood systems of the Mixteca Region of Oaxaca, Mexico. This region faces acute environmental degradation, extreme poverty, and outmigration—challenges best understood as wicked problems requiring systemic and inclusive solutions. The MLL proposes a dynamic learning and collaboration platform that integrates scientific knowledge, local practices, and innovation networks to co-develop and scale socio-technical solutions from the field to the landscape level.
Structured around three interconnected “landscapes”—Actors, Partnerships, and Data—the MLL seeks to: (1) empower local stakeholders to define and co-create solutions; (2) build robust science–practice–policy coalitions for sustainable innovation; and (3) establish an open data ecosystem to support analysis, learning, and impact tracking. The ultimate goal is to generate coherent, context-specific pathways to resilient and equitable agrifood systems while serving as a replicable model for other regions facing similar complex challenges.
DATOS DISPONIBLES
Soil Data
Data from XXX soil samples taken by XX institutions between 2010 and 2025.
Farm household data
XX farm household surveys covering between XX and XX farm households in XXX, XXX, XXX years.
Agronomic Data
More than XX plots with management and performance data recorded between XXX and XXX
Maps
All layers of fisiographic and climatic information available as well as statistics per municipality and locality
Agronomic Trials Data
XX agronomic experiments testing a diversity of cropping systems and technologies
Remote Sensing Data
High resolution images satelite images and UAV flights euiped with multispectral camera available
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