A Systematic National Stocktake of Crop Models in Morocco

•Crop production at various scales is constrained by climatic and non-climatic variables.•In Morocco wheat stands out as the most studied crop.•AquaCrop process-based crop model and regression-based models are the most frequently calibrated crop models.•Precipitation, temperature, soil properties, irrigation, and fertilization are frequently calibrated indicators.•Empirical models aptly integrate climatic and socioeconomic variables.•Process-based models focus on mostly climatic and biophysical variables. Agriculture is an important sector of the Moroccan economy, employing a huge portion of the Moroccan population and contributing about 14 - 20% to the country's GDP. Unfortunately, agricultural production in Morocco is impacted by climatic, non-climatic, biophysical, and non-biophysical stressors. Researchers have employed various crop models to understand how different crops respond to different environmental conditions such as temperature, precipitation, soil properties, fertilization, and irrigation. Unfortunately, there are no studies that provide a summary and a holistic perspective of the most frequently used models and their calibration inputs in Morocco. This work, therefore, seeks to fill these knowledge gaps by providing a summary of the most calibrated crop models, their calibration input data, the most frequently studied crops, how the studies are published (peer-review or grey literature), and the affiliations of the lead authors. This is achieved through a systematic review of the primary peer review and grey literature. A total of 68 relevant peer review and grey literature papers were considered. The results show that most of the authors are affiliated with Moroccan universities/organizations while wheat is the most studied crop. In addition, the AQUACROP and the regression-based models are the most used crop models. Additionally, most of the models are calibrated in order of importance with variables such as temperature, precipitation, soil properties, irrigation, and fertilizers. On the other hand, there is an observed increase in the use of non-climatic indicators such as poverty, farm income, and literacy levels to fit empirical models. It is still unclear how process-based models will integrate socio-economic indicators. This work has implications for future research as it provides a holistic picture of the key models that are currently used and their calibration. This information can be used by other projects to select methods to use