Digital ecosystem in Guatemala

This technical brief provides an overview of the digital development gaps and challenges in Guatemala's agrifood systems. Based on the USAID Digital Ecosystem Framework, 323 actors across 14 types of organizations were identified as digital agrifood innovators in the country. In-depth assessments on challenges and opportunities were conducted on 50 select actors using a new survey instrument, Rapid Screening Tool. We find that Guatemala has a good mix of founders, technical resources, an educated workforce, and a growing tech industry. The government's substantial efforts toward digital society, rights, and data governance were remarkable. However, major challenges constraining digital ecosystems were weak digital literacy, inadequate infrastructure, and low affordability of digital technologies and solutions for both users and service providers in rural areas. We recommend specific actions for CGIAR to support partners in realizing the transformative potential of digital innovations

Enabling farming data traceability in Mexico

CGIAR Research Initiative on Digital Innovation collaborated with CIMMYT and Bluenumber to deploy a pilot project in Mexico to evaluate a scalable data traceability approach that can incentivize farmers' sharing data and sustainable production using open data sharing protocol with the self-sovereign identity (SSI) technology. This progress report from Bluenumber provides an overview of the project activities in 2022

Ecosistema de plataformas y modelos que soportan el desarrollo de servicios digitales para toma de decisiones en el sector agropecuario en Latinoamérica

Centroamérica es una de las regiones más vulnerables a los efectos de la variabilidad climática, eventos extremos, y el cambio climático. Los servicios de información y sistemas de apoyo a la toma de decisiones son herramientas claves para adaptarse a la variabilidad climática, y anticipar y responder a eventos extremos. Esta nota describe de manera general el ecosistema de plataformas y modelos que soportan el desarrollo de servicios digitales para la toma de decisiones en el sector agropecuario de Honduras y Guatemala. Los hallazgos que se resumen aquí hacen parte de la iniciativa AgriLAC Resiliente del CGIAR. Como primera medida, se realizó un diagnóstico general de herramientas digitales y sistemas de apoyo a la toma de decisiones, que brinda un panorama general del ecosistema. Para cada herramienta, se realizó una evaluación de su proceso de desarrollo, ciclos de aprendizaje, y proceso de diseño centrado en el usuario. De igual manera, como parte de la fase de evaluación llevada a cabo en 2022, se llevaron a cabo reuniones y entrevistas con con la partes interesadas con el fin de recopilar información, identificar fortalezas y desafíos actuales, lo cual permitió identificar algunas de las principales necesidades existentes en los servicios meteorológicos en cuanto a la generación de información e implementación de plataformas de difusión de servicios de información que permitan proveer a los usuarios de servicios de monitoreo meteorológico y climático regional y de pronóstico a diferentes escalas

Using explainable machine learning techniques to unpack farm-level management x climate interactions

Optimizing the management of maize production systems, including the milpa (intercropping of maize with beans and other species), is crucial for improving on-farm productivity and ultimately reducing food insecurity. This presentation showcases the results of a study aimed at identifying determinants of maize yield in Guatemala using agronomic and climate data. The study employs interpretability techniques in machine learning to explain the interactions between climatic factors and crop management in productivity. The study follows a three-step approach: (1) an Extract, Transform, Load (ETL) process of data, involving feature engineering and data standardization and cleaning; (2) identification of algorithms, metrics, and algorithmic tuning; and (3) delving into interpretability using techniques such as SHAP (SHapley Additive exPlanations), partial dependence plots (PDP), accumulated local effects (ALE) plots, and Friedman's H-statistic to evaluate interactions between feature

Multiyear Maize management dataset collected in Chiapas, Mexico

For several decades, maize (Zea mays L.) management decisions in smallholder farming in tropical regions have been a puzzle. To best balance alternative management practices' environmental and economic outcomes, an extensive dataset was gathered through CIMMYT's knowledge hub in Chiapas, a state in southern Mexico. In a knowledge hub, farmers, with the support of farm advisors, compare conventional and improved agronomic practices side-by-side and install demonstration fields where they implement improved practices. In all these fields data on on-farm operations and results is collected. The dataset was assembled using field variables (yield, cultivars, fertilization and tillage practice), as well as environment variables from soil mapping (slope, elevation, soil texture, pH and organic matter concentration) and gridded weather datasets (precipitation, temperature, radiation and evapotranspiration). The dataset contains observations from 4585 fields and comprises a period of 7 years between 2012 and 2018. This dataset will facilitate analytical approaches to represent spatial and temporal variability of alternative crop management decisions based on observational data and explain model-generated predictions for maize in Chiapas, Mexico. In addition, this data can serve as an example for similar efforts in Big Data in Agriculture

Promoting sustainable agricultural intensification and crowdsourcing plot information

Provision of adequate and timely information to farmers on the ground means optimizing crop production decisions, reducing costs and eliminating adverse effects of overuse of agricultural inputs, e.g. fertilizer. AgroTutor aims to support farmers across Mexico with benchmarking information, including historical and potential yield on the area where the plot is located, historical costs, income and profit as well as agronomical recommendations. Location and limits of parcels can be saved, and agronomical activities including costs, pictures and videos can be then added to document the cropping system

Leisure-time physical activity, sedentary behavior, and risk of breast cancer: Results from the SUN (‘Seguimiento Universidad De Navarra’) project

Evidence is still limited on the influence of sedentary lifestyles on breast cancer (BC) risk. Also, prospective information on the combined effects of both sedentariness and leisure-time physical activity (LTPA) is scarce. We aimed to assess the association of higher sedentary behavior and LTPA (separately and in combination) with the risk of BC in a middle-aged cohort of university graduates. The SUN Project is a follow-up study initiated in 1999 with recruitment permanently open. Baseline assessments included a validated questionnaire on LTPA and sedentary habits. Subsequently, participants completed biennial follow-up questionnaires. Multivariable adjusted Cox models were used to estimate the hazard ratios (HR) for incident BC according to LTPA, TV-watching, the joint classification of both, and a combined 8-item multidimensional active lifestyle score. We included 10,812 women, with 11.8 years of median follow-up of. Among 115,802 women-years of follow-up, we confirmed 101 incident cases of BC. Women in the highest category of LTPA (>16.5 MET-h/week) showed a significantly lower risk of BC (HR = 0.55; 95% CI: 0.34–0.90) compared to women in the lowest category (≤6 MET/h-week). Women watching >2 h/d of TV sh owed a higher risk (HR = 1.67; 95% CI:1.03–2.72) than those who watched TV 2 h/d may substantially increase BC risk, independently of each other