Observation-based Agriculture Support for Improved Sustainability

Cooperating countries: Nigeria and Austria

Coordinating institution: Technische Universität Wien (Vienna University of Technology), Alexander Gruber alexander.gruber@geo.tuwien.ac.at 

Partner institution: Federal University Dutse

Project duration: 

Budget: EUR 29.120

Abstract: 

Nigerian agricultural systems operate under harsh climatic conditions where food security is often threatened by frequent drought and limited water resources. The risks and impacts of yield loss could be mitigated substantially by providing farmers with information about crop states and growing conditions, which could help them inform agricultural management decisions. However, obtaining such information in an accurate and timely manner is difficult and expensive with traditional methods.

The Observation-based Agriculture Support for Improved Sustainability (OASIS) project is a transdisciplinary effort between Earth observation experts, farmers, and agricultural decision makers to improve agricultural management practices in Nigeria using satellite data. Satellites can provide frequent estimates of crop-relevant parameters such as soil moisture, vegetation health, or land surface temperature, which could help inform decisions such as when to plant, irrigate, or harvest.

In OASIS, we will confront the information needs for agricultural management with the potential and limitations of satellite products. In a co-creation process, we will identify what information is most relevant to farmers, what can be gleaned from satellites, and in which way information should be presented. Based on this, prototype decision support tools will be developed, taking also into consideration data uncertainties and how they affect optimal management recommendations. The developed tools will be tested in controlled field experiments to assess their added value in a realworld environment using rigorous statistical methods to estimate and compare the quality of decisions made by farmers using traditional methods, and those informed by the developed tools.