• Crop Suitability: Identifying crops that thrive in local conditions while supporting agroforestry practices.
• Farmer Training: GAP for cash crops, focusing on planting density, pest management, and post-harvest handling to improve yields.
• Market Access: Establishing links with traders and milling companies to secure 15% higher prices and reduce reliance on middlemen.
• Monitoring and Evaluation:  Digital monitoring and evaluation, regular farm visits to assess crop performance and address challenges promptly.

• Farmer Training: Comprehensive GAP training to equip farmers with technical skills in tree care, pruning, and pest management.
• Species Suitability: Selecting trees adapted to regional environmental conditions to maximize survival and growth rates including soils, weather, culture and .
• Monitoring Systems: Continuous farmer field visits to monitor growth, survival rates, and emerging challenges.
• Community Ownership: Collaborating with SEs and local leaders ensured trust, commitment, and adoption of sustainable tree management practices.

• Technical Knowledge: Trained operators with skills in seed management, seedling management, farmer training, community mobilisation and engagement, root pruning, and hardening-off processes.
• Access to Inputs: Reliable supply of quality seeds, potting materials, and pest control inputs.
• Water Availability: Sustainable irrigation systems to overcome drought periods and maintain seedling health.
• Community Engagement: Active participation from farmers and local leaders to monitor and support nursery operations.

• passion and determination
• availability of seed fund to develop an initial version of the SIREN App
• Collaboration with local App developpers
• Extending the collaboration to international volunteers 
• understand

The purpose of this building block is to provide an accessible and user-friendly platform that acts as a central hub for biodiversity data. It allows users to contribute data (e.g., camera-trap images or additional species records), visualize trends, and understand key insights about jaguar populations. The platform is designed to democratize access to AI-driven conservation tools and foster collaboration across stakeholders, improving conservation outcomes.

Enabling factors:

• Development of a secure, scalable, and user-friendly web application to handle large datasets.
• Collaboration with technical experts in AI, web development, and conservation biology to design functionalities that meet user needs.
• Accessibility features to ensure the platform can be used by decision-makers, academic researchers, and local conservation practitioners alike.
• A robust data governance framework to protect sensitive data while promoting transparency and sharing

The purpose of this building block is to foster cooperation among diverse stakeholders to ensure the effective implementation of conservation technologies. These partnerships enable the sharing of resources and expertise, empowering local actors to participate in conservation projects and creating a framework for sustainability.

Enabling factors:

• Strong engagement and alignment between stakeholders, including academic institutions, government agencies, conservation organizations, private sector and local communities.
• Signed agreements that define clear roles, responsibilities, and benefits for all parties involved.
• Access to local knowledge and expertise to ensure the relevance and effectiveness of conservation actions.

The purpose of this building block is to enhance the monitoring and understanding of jaguar populations by automating the identification process. The algorithms detect jaguars in camera-trap images and classify individuals, contributing to understanding population size, distribution patterns, and behaviors. This facilitates conservation planning and policy-making by decision-makers. Additionally, the models are scalable and can be adapted to other species and ecosystems, expanding their applicability beyond the Yucatán Peninsula.

Enabling factors:

• Availability of high-quality camera-trap data for training and validating the algorithms.
• Technical expertise in AI and machine learning for developing and fine-tuning models.
• Collaborative partnerships with local institutions for field data collection and algorithm design, development and testing.
• Access to sufficient computational resources to train and deploy the algorithms effectively.

Embedding Fisher Friend within the Fish for All Centre Programme:
MSSRF integrated Fisher Friend into its Fish for All Centre Programme, focusing on sustainable fisheries development. This alignment leveraged existing resources, expertise, and networks, providing a strong foundation for promoting Fisher Friend.

Engagement with INCOIS:
Collaborating with the Indian National Centre for Ocean Information Services (INCOIS), MSSRF ensured the provision of critical oceanographic data and advisories. This partnership enhanced the app’s accuracy and relevance for fishers.

Engagement with Departments of Fisheries and the Indian Coast Guard:
MSSRF worked closely with government departments to align Fisher Friend’s services with government priorities. These partnerships also facilitated policy advocacy and integration with existing fisheries initiatives.

Partnership with Fisher Associations and Local NGOs:
By partnering with fisher associations and local NGOs, MSSRF leveraged local networks and expertise, promoting Fisher Friend through trusted channels and building credibility among fishers.

• Regularly refreshed data on terrain, vegetation, and weather is crucial for accuracy.
• Trained personnel must operate geospatial tools and interpret risk maps.
• Risk maps should inform planning and resource allocation at local and regional levels.

• Reliable access to real-time data from sensors, satellites, drones, and cameras is critical.
• High-quality sensors and data processing systems must be available to collect and analyze diverse data types.
• Systems must use compatible formats to integrate data seamlessly.