Models guide policy in a turbulent market

On the day of the Russian invasion, researchers Roel Jongeneel and Siemen van Berkum immediately realised that the impact would be felt worldwide. Jongeneel said: ‘I constantly monitor various markets and regions, including the Black Sea area. Egypt, the world’s largest importer of grain, normally sources around 80% of its grain imports from that region. When that export flow came to a halt, my immediate thought was: what is going to happen there?’

According to Van Berkum, a broad policy question followed almost straight away: ‘What does this mean for the Netherlands, for the EU and for global food security? And what can the ministry do?’

To map the impacts accurately, the researchers looked at three levels: from individual farmers to Europe and the global market.

1. Farm Sustainability Data Network: realtime insight at farm level

With these data, researchers have detailed data on more than a thousand agricultural businesses and could immediately observe what was happening. Jongeneel said: ‘Because of the war, energy and fertiliser prices rose sharply. You might expect farmers to use less fertiliser as a result, but that hardly happened. We saw that many arable farmers had purchased their fertiliser before the price increase.’

The same data added nuance to the energy story, in which the Netherlands is also partly dependent on Russia. Jongeneel: ‘Greenhouse horticulture is not only an energy consumer but, through combined heat and power, sometimes also a producer. That meant there were businesses that benefited from the situation. Insights like these helped the ministry to decide not to start compensating immediately, but first assess what was really going on.’

2. MAGNET: insight into global food security

The MAGNET model shows how trade, production and prices respond worldwide. Van Berkum explains: ‘Everyone sensed that this would drive prices up. With the model, you can quantify how much and in what order of magnitude.’

MAGNET showed where the greatest global vulnerabilities lay (such as Egypt and Turkey) and revealed how markets theoretically adjust when supply falls away: higher prices lead to shifting trade flows and increased production elsewhere. In practice, the researchers observed precisely these mechanisms unfolding. Jongeneel: ‘India, for example, had huge reserves and decided to make them available rather than block their use. That had a stabilising effect, and we saw prices begin to fall sooner than expected.’

3. AGMEMOD: European and Dutch implications

AGMEMOD provides a detailed picture of EU markets and policy. Jongeneel said: ‘Because all Member States and all relevant policy measures are included in the model, you can directly adjust a policy lever and simulate its consequences. One of the questions was what would happen if we temporarily released the 4% ecological focus areas for production. The model calculations immediately showed that the effect on Dutch production and prices would be minimal.’

This knowledge helped the ministry to respond proportionately. Van Berkum said: ‘Our models indicate the order of magnitude of effects, and we can consistently calculate multiple policy options within a short time. We always add sector expertise to this, both qualitative and quantitative. For example, we know that ecological focus areas in the Netherlands are, on average, less productive, which means that temporarily releasing them has only a limited impact. It is the combination of figures and practical knowledge that makes the analysis reliable and useful.’ 

The biggest challenge was formulating realistic scenarios. The researchers did this by combining prior studies, sector expertise, up‑to‑date market information and consultations with the ministry. Van Berkum: ‘We had extensive discussions about assumptions regarding gas and oil prices, fertiliser availability and possible policy responses. We also used our knowledge of markets and crops, and insights from earlier analyses. Only when all that information comes together can you determine which scenarios are genuinely plausible.’

Once several plausible scenarios had been established, the models could get to work quickly. Jongeneel: ‘The policy levers are already in the model. Running the calculations is then quick, sometimes it is literally a matter of flipping a switch. The real work lies in determining which scenarios best approximate reality; running the model is relatively straightforward after that.’ 

In the first weeks after the invasion, there was a widespread fear that the war would lead to prolonged food shortages and extreme price increases. Van Berkum: ‘Those major consequences ultimately did not materialise. Thanks to global responses, the market stabilised faster than many parties, ourselves included, initially thought.’

According to Jongeneel, the crisis chiefly shows how important it is to combine data, models and practical expertise. ‘Models provide direction and an order of magnitude, but without sector knowledge you risk drawing the wrong conclusions. We saw that, for example, with the ecological focus area: the model already showed a small effect, but because we know how those lands are used in practice, we were able to interpret the impact for the Netherlands more accurately.’

The researchers continue to use the models to address new questions. Jongeneel: ‘For instance, together with Ukrainian colleagues we are now looking at the implications for grain prices if Ukraine were to join the EU. That would mean nearly 40% more EU agricultural area, which calls for reliable model calculations.’ 

Researchers at Wageningen Social & Economic Research work closely with the Dutch Ministry of Agriculture, Fisheries, Food Security and Nature, and international research teams contributing to the MAGNET and AGMEMOD models.