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[Excerpt] Introduction Skin wounds are susceptible to infection by multiple types of microorganisms, such as bacteria, fungi, and virus., This research was funded by FEDER funds through the Operational Competitiveness Program–COMPETE, under the project POCI-01-0247-FEDER-047124, and by National Funds through Fundação para a Ciência e Tecnologia (FCT), under the project UID/CTM/00264/2020. ANID N°21190396 Chilean Doctoral Scholarship. Doctorado en Ciencias de Recursos Naturales, Universidad de La Frontera, Temuco, Chile

Poster presented at the 2nd International Agrobiodiversity Congress An Information System for Mexican Agrobiodiversity: Open Data and Tools to Support Agrobiodiversity Conservation. Agrobiodiversity conservation demands multi-sectoral collaboration supported by evidence. In this session, we will share the process of developing an Agrobiodiversity Information System (SIAgroBD) at the National Commission for the Knowledge and Use of Biodiversity (CONABIO), a governmental agency that coordinates data collection and analysis in support of the sustainable management of natural resources in Mexico. SIAgroBD aims to generate knowledge that enables the conservation of traditional agriculture and native crops of global importance. It is a comprehensive project at the national level that is part of the Mexican Agrobiodiversity GEF project. Research teams from different universities are collecting new data for SIAgroBD on native crops and their wild relatives in collaboration with local communities. Additionally, we are integrating agronomic data, qualitative assessments of local use of agrobiodiversity, and nutritional data from partner institutions. These data recognize the ecological, human and cultural dimensions of agroecosystems, in contrast with the current trends in big data for agriculture, which focus on conventional breeding and production. The need for recognizing and conserving the diversity of native crops and traditional practices drives the design and development of SIAgroBD. Our information system is intended for a wide audience, from academic researchers to policymakers and the general public. As part of developing SIAgroBD, we are promoting the adoption of digital field data collection tools, vocabulary standards, and open data training. We expect that the lessons we have learned in this project are valuable for practitioners and decision makers seeking examples of knowledge infrastructures centered on agrobiodiversity.&nbsp

Crop wild relatives (CWR) are wild plants that are the ancestors of important crops for human well-being. CWR hold genetic diversity that can be vital for plant breeding programs and the sustainability of agriculture, particularly given global change. Conservation of CWR genetic diversity thus has become a global food security issue, and several countries are actively developing conservation strategies including the generation of a national checklist and inventory of CWR, the assessment of current threat status, the identification of knowledge and conservation, and the establishment of genetic reserves. In this context, Mexico, Guatemala, and El Salvador, in collaboration with experts abroad (University of Birmingham, UK, and IUCN), are working together in a project to contribute towards safeguarding Mesoamerican CWR (http://www.psmesoamerica.org/en/). One important step is to identify CWR conservation area networks framed within the systematic conservation planning approach. However, genetic diversity is generally not addressed during the planning process. As it is unfeasible to sample and perform genetic analyses of hundreds of species due to limited timeframes and conservation budgets, we propose a novel approach to overcome the lack of genetic data. We used two criteria to develop proxies for genetic diversity (PGD): environmental variability, as given by climate, soil and topographic spatially-defined variables; and historic differentiation, as shown by phylogeographic patterns found in other species of the same habitat and region. We tested our approach by using genomic data from an empirical study of maize wild relatives distributed in Mexico. By combining species distribution models of 120 Mesoamerican CWR taxa and 102 PGD, we delimited areas of potential population differentiation. Furthermore, we considered each taxon's IUCN Red List category and habitat preference, assessed by experts during the project, to determine areas for CWR conservation in Mexico, using the Zonation conservation planning tool. Areas identified as important for CWR in situ conservation are located within sites of high cultural diversity and in areas where agriculture originated and traditional agriculture is ongoing. Also, our study design maximizes the representation of CWR throughout its distribution, thus highlighting the need for comprehensive analysis to encompass the genetic variability of taxa. The results of this work represent a first national and regional guide to promote CWR in situ conservation and sustainable management that contributes towards achievement of the CBD Global Strategy for Plant Conservation, Sustainable Development Goals and Aichi Targets.

Fil: Pengue, Walter. Universidad Nacional de General Sarmiento. Instituto del Conurbano. Área de Ecología; Argentina. Fil: Gemmill Herren, Barbara. World Agroforestry Centre; Kenya. Fil: Balázs, Bálint. Environmental Social Science Research Group; Hungría. Fil: Ortega, Enrique. University of Campinas. Faculty of Food Technology; Brasil. Fil: Viglizzo, Ernesto. Universidad Nacional de La Pampa. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina. Fil: Viglizzo, Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina. Fil: Acevedo, Francisca. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. Coordinación General de Agrobiodiversidad y Recursos Biológicos; México. Fil: Diaz, Daniel N. Instituto Nacional de Tecnología Agropecuaria; Argentina. Fil: Díaz de Astarloa, Diego. Universidad Nacional de General Sarmiento. Instituto del Conurbano; Argentina. Fil: Fernandez, Rosa. Instituto Nacional de Tecnología Agropecuaria; Argentina. Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Fil: Giampietro, Mario. Autonomous University of Barcelona. Institute of Environmental Sciences and Technologies; España. Fil: Goldberg, Andrea. Instituto Nacional de Tecnología Agropecuaria; Argentina. Fil: Khosla, Ashok. Development Alternatives; India. Fil: Westhoek, Henk. PBL Netherlands Environmental Assessment Agency. Water, Agriculture and Food; Países Bajos. Chapter 3 provides an overview of the diversity of agriculture and food systems, each with different contributions to global food security, impacts on the natural resource base and ways of working through food system supply chains. We describe “eco-agri-food systems” and further identify their many manifestations through a review of typologies. We identify challenges ahead with existing systems due to prevailing economic and political pressures resulting in patterns of invisible flows and impacts across global food systems. We describe pathways to ensure sustainability by securing the benefits from working with, rather than against, natural systems and ecosystem processes and the challenges for farmers, communities and societies to reorient food value chains and build resilience in eco-agri-food systems.

Supplementary figures, tables and information on methods

Provides an overview of the diversity of agriculture and food systems, each with different contributions to global food security, impacts on the natural resource base and ways of working through food system supply chains. We describe “eco-agri-food systems” and further identify their many manifestations through a review of typologies. We identify challenges ahead with existing systems due to prevailing economic and political pressures resulting in patterns of invisible flows and impacts across global food systems. We describe pathways to ensure sustainability by securing the benefits from working with, rather than against, natural systems and ecosystem processes and the challenges for farmers, communities and societies to reorient food value chains and build resilience in eco-agri-food systems Fil: Pengue, Walter Alberto. Universidad Nacional de General Sarmiento; Argentina. Universidad de Buenos Aires; Argentina Fil: Gemmill Herren, Barbara. World Agroforestry Centre; Kenia Fil: Balázs, Bálint. Environmental Social Science Research Group; Hungría Fil: Ortega, Enrique. Universidade Estadual de Campinas; Brasil Fil: Acevedo, Francisca. National Commission for the Knowledge and Use of Biodiversity,; México Fil: Diaz, Daniel N,. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Díaz de Astarloa, Diego. Universidad Nacional de General Sarmiento; Argentina Fil: Fernandez, Rosa. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones En Recursos Naturales, Agroecología y Desarrollo Rural. - Universidad Nacional de Rio Negro. Instituto de Investigaciones En Recursos Naturales, Agroecología y Desarrollo Rural; Argentina Fil: Giampietro, Mario. Universidad de Barcelona; España Fil: Goldberg, Andrea. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Khosla, Ashok. Development Alternatives; India Fil: Westhoek, Henk. PBL Netherlands Environmental Assessment Agency. Water, Agriculture and Food; Países Bajos

International audience

The increasing consumption worldwide of tortilla chips make it relevant to design and optimize their industrial quality analysis. Surface, structural, and total oil content during frying of tortilla chips fried at 160, 175, and 190°C for different times were analyzed. The aim was to obtain a relationship between oil content and features from their digital images. The results showed a high linear correlation (R > 0.90) between oil content with image features at each frying temperature, indicating that trustable models can be developed, allowing the prediction of oil content of tortilla chips by using selected features extracted from their digital images, without the necessity of measuring them. Cross-validation technique demonstrated the repeatability of each model and their good performance (>90%). © 2014 Copyright Taylor and Francis Group, LLC.