Agricultural production is intimately linked with meteorological conditions, and therefore with the state of the climate system. Growth of plants depends on temperature, radiation, water availability (precipitation), and evapotranspiration (humidity, wind speed, temperature, and radiation).
Agricultural production is intimately linked with meteorological conditions, and therefore with the state of the climate system. Growth of plants depends on temperature, radiation, water availability (precipitation), and evapotranspiration (humidity, wind speed, temperature, and radiation). Climate change has demonstratively affected crop production in several regions of the world over the past decades. Also, non-physical elements of agricultural production are affected, including price stability and food access.
Climate change will impact food production directly through, for instance, rising temperatures and increased atmospheric moisture demand, changes in precipitation, increasing atmospheric carbon dioxide concentration, and flooding of coastal plains. Different measures of temperature have been linked to crop yields and quality, including high daytime temperatures, unusually hot nights, and frost occurrence. Also, climate change is expected to increase the interannual variability of crop yields through the interannual variability in precipitation, extreme temperatures, and other extreme meteorological events that may harm the yields (e.g. hail or excessive rainfall) in many regions. Furthermore, the costs and benefits of climate change on food production are strongly latitude dependent. Low-latitude countries will likely experience a negative impact on crop production, while in some northerly countries the climate change impact on crop production might be positive due to the lengthening of the growing season.
Crop growth models can be used to monitor the production during the season, but also to estimate the effect of climate change on crop production. Crop growth models are, however, not capable of simulating certain (future) climate extremes, but rather use weather/climate information as input. Therefore climate models are required to project what regions will be exposed to climate change, and to what extent. These boundary conditions can be used as input for crop growth models although a bias correction to the model output might be necessary before application. Typical meteorological parameters that are used as input for crop growth models include daily values of the maximum and minimum temperature, global radiation, wind speed, vapor pressure, evapotranspiration, and rainfall. Next to exposure to climate change, the sensitivity of crops and the adaptive capacity of those regions need to be assessed to obtain the extent to which food systems are vulnerable to climate change. For instance, food prices are influenced by changes in agricultural production, but also other factors such as transport availability might be affected by climate change.
Interannual variability is of major importance for agricultural production. The effect of three successive years with poor crop yields may be that a farmer goes out of business due to financial problems. Also, the occurrence of extremes is crucial for the agricultural sector, especially at certain moments during the year. For instance, extreme rainfall during the harvest period may make it difficult to harvest the crop, or the quality of the product may be much lower. High temperatures during flowering may cause poor development of grains and fruits; freezing during flowering or germination of crops may damage the crops, and therefore result in lower production. It is thus vital to know during what moment of the harvest period the last spring frost occurs. Furthermore, the spatial extent of extremes is important. When a large area is affected by extremes, production may be lower over a large region, which, in turn, may affect food prices. Regions that are not or less affected may profit substantially from this, and may experience higher financial gains than in a year with a high physical production.
All these climate aspects, including interannual variablity and the occurrence, intensity, duration, and spatial extent of extreme events, are potentially better simulated in high-resolution climate models because future changes in the atmospheric circulation patterns are better resolved. Furthermore, the representation of the interactions between climate variables is likely improved in higher resolution models.
Policy makers at the national, European or international level may be interested in the relation between climate change and agriculture. They could ask questions such as: Which regions are affected more or less by climate change? What is the potential impact of climate change on production of specific crops? What are possible directions of adaptation?
Specific companies (agricultural production, financing, etc.) may be interested more specifically in questions such as: When turn conditions for a specific crop/cultivar unsuitable and when should other crops/cultivars (or even regions) be considered? Is it profitable to move production to another region, and for how long? What are the risks of extremes and will they change substantially in the near future? Which regions will become suitable for a certain crop/cultivar with climatic change and what is the vulnerability related to the year-to-year climatic variability in the expected climate of 2050?
Within PRIMAVERA, we aim to reach smaller and medium enterprises, such as local farms, by collaborating with research groups that work on the impacts of climate change on agriculture and food security or by engaging with interest groups of e.g. farmers. Research groups typically have experience with the type of information that is of interest to users. Also, the spatial scales that are used in those studies are comparable to the output of the PRIMAVERA high-resolution simulations. In this way, we expect that our understanding of the impacts of future climate change on agriculture and food security can be improved, which would make the agricultural sector more resilient to climate change.