By the year 2050, experts believe that the demand for food globally will rise by a staggering 110%. Unfortunately, this increased demand comes at a time when approximately 40% of croplands and pastures are facing threats due to rising global temperatures, high concentrations of greenhouse gases, and various other environmental factors.
A team of researchers from Skoltech, the Institute of Geography of the Russian Academy of Sciences, and other esteemed institutions have utilized open data and artificial intelligence to project how agricultural land suitability may evolve over the next 25 years. Their study, which is documented in IEEE Access, suggests that there will likely be an expansion of croplands in the northern regions.
The researchers employed a three-stage methodology for their study. This included gathering and preparing data, training a machine learning model, and assessing the outcomes by forecasting cropland distribution based on a variety of climate models and socioeconomic pathways scenarios. The focus of their investigation was on Eastern Europe and Northern Asia.
Valery Shevchenko, the lead author of the study and a research engineer at Skoltech’s Applied AI Center, highlighted the significance of utilizing open data sources such as ERA5 and CMIP models. These sources provide valuable insights into climate analysis and predictions of climate change up to the year 2100.
The researchers considered three distinct climate change scenarios based on the CMIP models to assess how different climatic parameters could impact agricultural land use. These scenarios included a sustainable, low-emission future, a ‘business-as-usual’ trajectory with moderate emissions, and a high fossil fuel dependency scenario with increased greenhouse gas emissions.
The study’s findings suggest that by 2050, there will be an increase in arable land, however, this expansion will predominantly occur in northern regions. The researchers caution that certain agricultural areas may require additional irrigation to sustain productivity, thereby posing potential risks to food production in those regions.
Valery Shevchenko emphasizes the importance of utilizing predictive models to raise awareness about the evolving landscape of agriculture. By considering various climate scenarios, researchers can inform policymakers and stakeholders about potential challenges and opportunities for sustainable food production in the future.
The research underscores the need for proactive measures to address the changing dynamics of agricultural land use. By leveraging technology, open data, and artificial intelligence, stakeholders can better prepare for the challenges posed by climate change and ensure food security for future generations.
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