In a global context climate change is among the most extensive challenges facing human societies and ecosystems in the decades to come and some of the most substantial impacts of climate change and global population growth will be on agriculture and food systems. A 70% increase of food production by 2050 is required (United Nations 2013) to keep up with global population growth. At the same time, food production is strongly interconnected with and dependent on weather-factors in an increasingly variable climate and hence vulnerable to climate induced risks. In the World Economic Forum 2019 Global risks report, two risks are singled out as having both a high likelihood and dire consequences: extreme weather events and the failure of climate-change mitigation and adaptation (World Economic Forum 2019).
The project aims at investigating how seasonal forecasts can be co-produced and introduced to the Norwegian agriculture sector to reduce climate risk exposure.
Sub-seasonal to seasonal (SDS) forecasting attempts to provide climate information, such as temperature and precipitation data, for the time range of 10 days to three months ahead in time. Unlike deterministic weather forecast, that can provide expected specific values for certain parameters on a certain day, SDS-forecasts give a probabilistic outlook on whether the values will be in the above normal, near normal or below normal range. What is “normal” is based on the historic average weather data in a certain reference period. Sitting in between weather forecasts and long term climate projections, seasonal forecasts serve as a promising tool to reduce climate risks by introducing reliable weather data into the pre-seasonal and seasonal decision-making processes. What kind of crop to cultivate? When and how much to sow? Which pests to prepare for? How much fertilizer to apply? When to harvest? Whether to invest in irrigation systems? These are just some of the fundamental questions that are highly impacted by weather conditions and need to be addressed under great uncertainty.
The project is based on the co-production of knowledge, which is a collaborative model that evolved as a response to the frequent apparent gap in scientific supply and user demand within the conventional linear approach of research. It can be referred to as a deliberate interdisciplinary cooperation between various stakeholders to achieve a common goal and investigate enablers and barriers for success. Within the “Climate Futures” Centre for Research based Innovation we are therefore cooperating with various stakeholders from all levels of the agricultural value chain covering private, public and educational institutions and organizations. We intend to identify to which degree SDS forecasting can serve as a climate adaptation tool, and to analyze how their consequent application in practical use can impact the structurally unique agricultural system in Norway.
By grounding the work in an ontology of socio-ecological resilience theory, we argue that long term forecasting can serve as an important tool to increase climate resilience in agriculture both on farm- and food-system level.
Based on the within-method triangulation strategy, it is intended to apply three types of qualitative data collection methods: case-based focus groups, interviews and group model building. The data is partly generated on field trips, visiting relevant stake holders and practitioners throughout the agriculture system.