# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 5 Conclusions  
The integration of the AquaCrop model with satellite data proves to be a useful tool for forecasting crop yields in the JECAM fields of Wielkopolska from 2018 to 2023. This model's strength lies in its ability to finely adjust predictions to local soil conditions and management factors, significantly contributing to the optimization of agricultural processes.  
The utilization of satellite data, particularly NDVI, facilitates the monitoring of vegetation changes, providing valuable insights into plant health and biomass quantity. AquaCrop, calibrated based on Leaf Area Index (LAI) data, consistently generates coherent yield predictions, offering a foundation for more precise crop planning and effective resource management.  
Results from extensive statistical analyses indicate a decreasing trend in error indicators across the four crop types, suggesting a gradual improvement in the predictive accuracy of the model over the studied six-year period. However, it is essential to note that this conclusion is based on a relatively limited dataset. Further studies covering additional years are necessary in order to validate and maintain conclusions regarding the observed trend.  
Furthermore, the findings of this study contribute significantly to the existing body of knowledge in agricultural management by demonstrating the effectiveness of integrating advanced modeling techniques with satellite data for crop yield forecasting. By leveraging high-resolution satellite imagery and ground-based measurements, this research enhances our understanding of crop growth dynamics and provides valuable insights into optimizing agricultural practices. This improved predictive accuracy of the AquaCrop model, coupled with satellite data, not only aids in better crop planning and resource allocation, but also lays the groundwork for implementing precision agriculture strategies. Thus, this study not only addresses the immediate needs of agricultural management in the Wielkopolska region, but also serves as a valuable reference for similar agricultural systems globally.  
Conceptualization, K.D.-Z.; methodology, E.P.-C.; software, E.P.-C. and C.N.O.; validation, E.P.-C. and C.N.O.; formal analysis, E.P.-C.; investigation, E.P.-C.; resources, E.P.-C.; data curation, E.P.-C., C.N.O. and R.G.; writing--original draft, E.P.-C. and C.N.O.; writing--review and editing, E.P.-C. and K.D.-Z.; visualization, C.N.O.; supervision, K.D.-Z.; project administration, K.D.-Z.; funding acquisition, K.D.-Z. All authors have read and agreed to the published version of the manuscript.  
The research work was conducted within the project financed by the National Centre for Research and Development under Contract No. PLRPA/02/SAPOL4 crop/43/2018, titled \"SA Polish collaborative crop growth monitoring and yield assessment system for early warning utilizing new satellite Earth Observations data from Copernicus Programme\". This work was supported by The National Centre for Research and Development within the program \"POLNOR 2019 call\"Project: \"Tools for information to farmers on grasslands yields under stressed conditions to support management practices\" (2020-2023).  
**Data Availability Statement:** Data will be made available upon request.  
**Acknowledgments:** We extend our heartfelt thanks to the entire team at the Remote Sensing Centre for their invaluable ground measurements conducted from 2018 to 2023. Additionally, we appreciate the significant contributions of our colleagues at the Agricultural Research Council from South Africa for their expertise in knowledge exchange related to crop assessment.  
**Conflicts of Interest:** The authors declare no conflicts of interest.