The cash crop integration component aimed to incentivize tree management by linking reforestation efforts with short-term income generation. Top-performing farmers, assessed based on tree survival rates and GAP training participation, were awarded cash crop inputs such as soya beans and groundnuts. These crops were selected for their adaptability to local soils, market demand, and ability to complement agroforestry systems. Farmers achieved an average 12% increase in soya bean yields (350 kg/acre) and 10% increase in groundnut yields (240 kg/acre), with incomes averaging UGX 1,050,000 ($285) for soya beans and UGX 900,000 ($244) for groundnuts. The inclusion of cash crops encouraged farmers to maintain their agroforestry systems, reducing tree felling for short-term needs.

The primary purpose of tree planting at community level is to achieve large-scale ecosystem restoration while enhancing local livelihoods through agroforestry. The project partnered with four communities to mobilize 425 farmers for tree planting, distributing 73,867 seedlings. Farmers were trained in Good Agroforestry Practices (GAP), including tree planting techniques, mulching, pest and disease management, and soil fertility enhancement. Tree species like Grevillea robusta and Agrocarpus were selected for their fast growth, timber production potential, and ability to improve microclimates and soil structure. Tree planting activities focused on degraded lands prone to erosion and drought, effectively addressing flood control, biodiversity restoration, and ecosystem loss.

The purpose of community-based permanent nursery beds is to ensure the production of high-quality, resilient seedlings for reforestation efforts while building local capacity. Each of the four project districts (Luwero, Mbale, Busia, and Kapchorwa) established one centralized nursery bed per location, equipped with essential tools, irrigation facilities, and trained nursery operators. Seeds were delivered early (December 2023–January 2024) to allow for the full growth and hardening process, ensuring seedlings met survival standards. The nurseries produced 96,423 seedlings of multi-purpose tree species, including Grevillea and Agrocarpus, which were selected for their adaptability to local climatic conditions, drought resistance, and soil stabilization properties. Nurseries also served as training hubs, where farmers learned good agroforestry techniques, seed propagation, pest control, and seedling management techniques.

This building block is to explain how I developped an App that allow fishers to contribute to marine science knowledge in Africa. 

Initially we gave fishers a pre-printed form to report opportunistic sightings they encountered. However, the form was getting lost most of the time. 

We decided to move to a digital solution. The existing App by then required internet to work and was just too complicated for fishers. So we thought we shoud develop an App that will be more userfriendly for fishers. 

We wrote the  algorithm (workflow) of the App and then contracted an Indian development company to write the code. 

Later we had to bring the development of SIREN back to Cameroon to reduce the cost of developement. 

We work with volunteer around the world that will continuously support with the development of the SIREN

This building block focuses on the establishment of strong partnerships between academic institutions (Universidad Politécnica de Yucatán), local governments (Secretaría de Desarrollo de Sustentable del Estado de Yucatán), and conservation organizations (International Union for Conservation of Nature and Natural Resources), private sector (Huawei), and local communities (Dzilam de Bravo) to enhance the collection and analysis of biodiversity data, access to technological infrastructure, government program instrumentation and application, and local ownership and execution.

This building block is centered on the use of Convolutional Neural Networks (CNNs), including Siamese and Autoencoder architectures, to detect and identify individual jaguars based on unique features such as rosette patterns and morphology. These algorithms process camera-trap data efficiently, reducing the time required for analysis and providing critical insights for decision-making in conservation.

The system combines data from drones, satellites, camera traps, and geospatial tools to create a comprehensive monitoring framework. This approach can be adapted for other environmental challenges, such as flood monitoring, by integrating relevant data sources specific to those contexts.

Relevant ecological processes and incidents that are of interest in environmental change research typically occur in remote areas beyond the reach of terrestrial communication infrastructures. Data generated in the field using animal tags in these regions can often only be transmitted with a delay of days or even weeks. To overcome this delay and ensure no delay in the early-warning system, GAIA develops a satellite communication module for the tags as well as a nanosatellite operating in low earth orbit (LEO): In order to be able to transmit collected data and information directly from the transmitting node to the LEO satellite (Low Earth Orbit), a high-performance satellite IoT radio module will be integrated into the new tags. This guarantees immediate, secure and energy-efficient transmission of the extracted data. The communication system is based on the terrestrial mioty® technology and will be adapted to satellite-typical frequency bands such as L- and S-band for the project. Typical communication protocols, which are sometimes used in the IoT sector, are usually designed for small packet sizes. Further development of the mioty® system will therefore also aim to increase the data rate and message size to enable application scenarios such as image transmissions.

To meet the goal of the GAIA Initiative to develop and put into practice a high-tech early-warning system for environmental changes, a new generation of animal tags is a key component. GAIA teams are working on the hardware and software development of miniaturized animal tags with lowest-power sensor technology with camera and image processing. The tags will be energy-autonomous, optimally adapted to the anatomy of vultures and are the basis for further technological features under development such as on-board artificial intelligences for behaviour detection and image recognition as well as a satellite-based IoT communication system.

Additionally, GAIA is developing concepts of distributed artificial intelligence and networks of micro-processors – animal tags that act just like a swarm. Analogous to natural swarm intelligence, the GAIA initiative is mapping digital swarm intelligence in an ad hoc network of microprocessors. These spontaneously forming networks are the foundation for distributed and sensor-based analysis of large amounts of data. Following this path will make it possible for vulture tags, for example, that are present at the same location during feeding events, to link and share tasks such as artificial intelligence analyses and data transmission.

Having data collected and analysed through a digital tool is an innovation, although to foster changes and ensure local communities and Indigenous people's protection and sustainable lifestyle the data is used for law enforcement and advocacy purposes. To this end, strong local, national and international advocacy networks are needed to take action against reported abuses and to achieve policy changes. By enabling information to be shared between users and opening up data to the general public, the tool facilitates collaborative working.