DecLaRe

Worldwide, sustainable land management is increasingly important to maintaining the fragile balance between human demands and ecosystem services of available natural resources, to enhance food security, to reduce the risk of conflicts and to foster adaptation to climate change. In this context, DecLaRe aims to identify recommendation domains for scalable innovations towards sustainable crop production and animal husbandry in West Africa. Focusing on northern Benin and northern Ghana, DecLaRe builds on available local scientific knowledge and databases. It enhances their use by fundamental research at the field level and modelling at the national to regional level, aiming to construct a decision support system (DSS) that can be used for land use and land management as well as for policy development. The collaborative setup of this open-source DSS with local and regional stakeholders in a series of workshops will take into account land tenure, crop suitability, soil characteristics, livestock management, and regional food availability. Digital information technology will be used to project the effects of innovations on food availability, farmer income, and system resilience to climate change. Next to guiding local, regional, and international stakeholders towards sustainable land management options, a close connection between science, policy, and the private sector will facilitate effective use of the DSS by decision makers in the partner countries and beyond. The project will also engage with existing MSc and PhD programmes on sustainable land management in the African partner countries and will train 12 additional PhD students in this context.

PIK assumes tasks in two main areas: First, PIK is leading the design and implementation of the panel household survey in northern Ghana. The dataset will contain extensive household data representative of northern Ghana, which will provide the basis for various analyses by the partners involved. In particular, it will allow analysis of existing land tenure systems and their incentives for climate-adapted agriculture and livestock production. Furthermore, PIK will contribute to the analysis of participatory data to complement the DSS with socio-economic data regarding land rights and land use scenarios. The second focus of the PIK subproject includes the modeling of climate impacts and adaptation options in agriculture. This includes the provision of climate data and the evaluation of projections regarding temperature, precipitation and other variables under different emission scenarios. Process-based yield models and machine learning approaches are used to simulate future yields and land suitability for cropping practices under climate change impacts. The same models will also be used to model adaptation options and test their biophysical suitability.