Lentil and chickpea crop growth and yield prediction across Europe

Lentil ( Lens culinaris M. ) and chickpea ( Cicer arietinum L. ) are valuable grain legumes that reduce dependency on synthetic fertilizers, enhance soil health, and diversify crop rotations, improving resilience to climate variability. However, their adoption in Europe remains limited due to irregular yields caused by biotic and abiotic stresses. Process-based soil-crop models can provide insights into the soil-plant-atmosphere dynamics of these legumes and support the development of innovative cropping systems. This study presents the first species-specific parameterization of the agro-ecosystem model MONICA for lentil and chickpea in Europe addressing a critical gap in simulating the growth of minor crops. A recently proposed generic calibration protocol was adapted and applied to real multi-environment field data. Following this structured protocol, 27 crop-related parameters were calibrated using literature-derived values, direct measurements, and mathematical optimization. Calibration utilized a unique multi-variable data from 28 site-year-management units (SYMU) for lentil and 24 for chickpea across diverse European climates along a latitudinal gradient. Evaluation utilized an independent dataset of grain yields encompassing numerous SYMU. After calibration, MONICA successfully simulated phenology, aboveground biomass, plant nitrogen concentration, grain yield, plant height, leaf area, and soil moisture, achieving acceptable model efficiency (0.32 to 0.98) and low relative bias (−7% to 3%) for most variables. However, it underpredicted yield in high-performing SYMU and showed greater errors for dynamic variables. Improvements in simulating biomass partitioning, physiological maturity, and detailed soil data are needed to simulate grain yield more accurately for future climate change adaption studies. This study provides a robust framework for parameterizing new crops and evaluating the agronomic performance of lentil and chickpea under diverse climatic scenarios, contributing to design sustainable cropping systems. • An agro-ecosystem model was calibrated for lentil and chickpea via a structured workflow. • Calibration: IA 0.32–0.98; relative bias − 34% to 28% across variables. • Protocol enables parameterizing new crops in generic crop models. • Yield underestimated in high-yielding site-years. • Correct maturity timing is crucial for end-season yield and other outputs.