Climate change-related lessons learned from a long-term field experiment with maize

Maize is the second most important cereal crop in European agriculture and a widely used raw material for feed, food, and energy production. Climate change studies over Europe predict a significant negative change in maize production. Finding appropriate and feasible adaptation strategies is a top priority for agriculture in the twenty-first century. Long-term agricultural experiments provide a useful resource for evaluating biological, biogeochemical, and environmental aspects of agricultural sustainability and for predicting future global changes. For the first time, we have been able to formulate a response to the question of which sowing date or hybrid choice strategies will prove beneficial in the future for the Pannonian region, based on sufficiently long experimental data. The objective of the study was to analyze a 30-year period of a multi-factorial long-term experiment at Martonvásár (Hungary) searching for traces of climate change as well as for favorable combinations of agro-management factors that can be used as adaptation options in the future. To analyze and extrapolate the data both in space and time, a multivariate statistical (response surface) model and a process-based crop simulation model were used. The results of the study yielded the following conclusions: (1) intensification of fertilization would not promote sustainable development in the region, (2) late hybrids have no perspective in the Pannonian climatic zone, and (3) earlier planting may become an effective adaptation option in the future. Our comprehensive methodology combines long-term historical weather and climate projection data with statistical and simulation models for the first time to provide agricultural stakeholders with more reliable adaptation strategies. It is essential to facilitate effective knowledge transfer to encourage farmers to adopt the proposed new practices. The collection of more detailed data for the entire Carpathian Basin will allow for the improvement of the models and projections.