Capturing geospatial data on farm management practices in vulnerable farmer-water systems: Lessons from the Sahel

 Emphasis has been placed worldwide recently on the need to view social sensing and geospatial big data as an analogue of remote sensing data. The attempt to establishing a firm footing of this kind of data is essential, for example, to: 1) understand the complex coupling of human and natural systems and 2) make useful policy interventions related to sustainable land and water management. However, most vulnerable communities to natural disasters, whose livelihood and economies are dependent on farming, lack such data. Without suitable socio-economic and farm management data, agricultural governance becomes less responsive or even fails, particularly when the agricultural systems are affected by natural disasters. In this study, we highlight nine lessons learned from our first experience during extensive and comprehensive household surveys of farm management practices recently conducted in the arid and semi-arid zones of Sudan. The aim here is to offer guidelines for researchers and practitioners to carry out successful campaigns in similar settings. These campaigns were implemented as part of the DFG funded SHADRESS project, “Sociohydrological analysis of drought resilience in Sahelian Sudan farming systems”. The surveys were conducted by means of Information and Communication Technology (ICT) via smartphone app and traditional paper-based approach. Two hypotheses were assessed: First, the two survey methods can be integrated and utilized to generate direct and ongoing communication between farming stakeholders. Second, this stakeholder network and dialogue can help acquire big social datasets to fill the data gap within the agriculture sector and, subsequently, address water and food security. We categorize the lessons and guidelines as logistics, technology, culture and behavior related. More than 70 questions related to the socio-hydrological farming system were addressed. The surveys resulted in capturing responses from ~1640 households distributed over three farming systems, namely traditional rainfed, mechanized rainfed and irrigated systems. This dataset contains rich information to enable detailed spatial analyses of farm management strategies and understanding of generic concepts of farmer-water interactions in a drought-vulnerable region.