Anomaly-Based Crop Yield Forecasting in Poland Using Satellite and Reanalysis Meteorological Data

CONTEXT Crop yield forecasting is essential for agricultural planning, food security assessment, and climate change adaptation. In Poland and other regions of Central Europe, interannual variability in early-season meteorological conditions is a key driver of yield variability, yet its systematic use in regional-scale forecasting models remains limited. OBJECTIVE This study aims to quantify the influence of early-season meteorological anomalies on yields of nine key field crops in Poland and to assess the potential for generating yield forecasts several months before harvest using satellite and reanalysis data. METHODS Linear regression models were developed using anomalies of 2-metre temperature, precipitation, reference evapotranspiration and root-zone soil moisture derived from ERA5 reanalysis and EUMETSAT H-SAF and LSA-SAF satellite products. Models were trained separately for each crop and each of the 16 Polish voivodeships using data from 2004 to 2022. A rolling time-based cross-validation scheme was applied, complemented by an independent hold-out validation for 2019–2022. RESULTS AND CONCLUSIONS Positive temperature anomalies during January–April were consistently associated with higher yields across all winter cereals, while soil moisture deficits in the upper root zone (SM1 and SM2 layers) represented the primary limiting factor. Reference evapotranspiration anomalies in May–June provided a proxy signal for radiation and atmospheric demand conditions. Preliminary forecasts were feasible up to four months before harvest for winter cereals and up to three months for spring cereals. Independent validation for the Opolskie voivodeship yielded Pearson correlation coefficients of 0.6–0.8 for most crops, with maize showing lower agreement (r ≈ 0.45). SIGNIFICANCE The proposed anomaly-based framework provides interpretable, timely and operationally relevant yield forecasts for regional agricultural planning under increasing climate variability. Its transparency and reliance on freely available satellite and reanalysis products make it suitable for implementation in early-warning systems across Central Europe.