# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 4 Discussion  
Wielkopolska stands out as one of the most significant agricultural regions in Poland. The region's importance in agriculture underscores the crucial need for monitoring and evaluating crop growth to ensure sustainability, especially in light of dynamic environmental conditions. This study provides a tool for understanding and predicting crop performance in Wielkopolska's agricultural region through a monitoring system. The  
Figure 7: In-situ LAI and NDVI-based on observed and projected yields for the sugar beet crop type from 2018 to 2023.  
study demonstrates that by utilizing not only on-site collected data but also high-resolution satellite data, the process can be made more efficient in the territory of Poland. This, in turn, shows that agricultural practices can be effectively planned and managed. For winter wheat, the LAI-based RMSE value is determined as 0.46 t\(\cdot\)ha\({}^{-1}\), while the NDVI-based RMSE value is 0.40 t\(\cdot\)ha\({}^{-1}\). Additionally, two other studies in the literature have conducted similar measurements for winter wheat yield predictions. In [PERSON] and [PERSON]'s 2009-2010 study in Turkey, conducted due to the impact of varying drought events on rain-dependent winter wheat farming, the RMSE value was determined to be 0.32 t\(\cdot\)ha\({}^{-1}\)[27]. [PERSON] et al., in their study in Lebanon between 2017 and 2019, integrated the canopy cover fraction derived from Copernicus-Sentinel 2 satellite data into the AquaCrop model to simulate the yield of winter wheat. The results show that the RMSE for yield ranged between 0.34 and 0.42 t\(\cdot\)ha\({}^{-1}\) after integration [28]. According to the findings obtained for the maize crop, the LAI-based RMSE value is determined as 0.01 t\(\cdot\)ha\({}^{-1}\) while the NDVI-based RMSE value is 0.06 t\(\cdot\)ha\({}^{-1}\). In a study conducted by [PERSON] et al. in Telangana, India, for the years 2013-2014, which investigated the impact of climate change on maize agriculture and examined the effects of additional irrigation, an RMSE value ranging from 0.06 to 0.12 t\(\cdot\)ha\({}^{-1}\) was identified [29]. In the study conducted by [PERSON] and [PERSON] in Uganda for the years 2014-2015, aiming to increase production in rainffed agricultural systems, predictions made using the AquaCrop model revealed an RMSE value of 0.11 t\(\cdot\)ha\({}^{-1}\). These findings indicate that studies conducted under different agricultural conditions encounter similar levels of prediction errors [30]. When examining the sample studies, a slight difference is observed. However, upon reviewing studies conducted by [PERSON] et al., the presence of various applications such as irrigation treatments, sub-treatments (no mulching and mulching), and two sub-sub-treatments (namely, normal fertilizer and high fertilizer) for maize might suggest that the differences in outcomes could be attributed to these divergent approaches. Upon reviewing the studies of [PERSON] and [PERSON], the observed difference is believed to stem from various factors within their studies, including pests, fertility, and management. These factors have been overlooked in our study. For sugar beet cultivation, LAI-based RMSE was determined to be 0.95 t\(\cdot\)ha\({}^{-1}\), while the NDVI-based RMSE was found to be 0.87 t\(\cdot\)ha\({}^{-1}\) for the year 2018, representing the highest values achieved. [PERSON] et al. conducted a study in 2012 and 2014 to calibrate and validate the AquaCrop model for sugar beet. They aimed to assess crop responses to varying irrigation levels and evaluate yield and water efficiency across different irrigation water allocations in two main sugar beet production areas in Spain, covering both spring and autumn cultivation seasons. Based on the performance results, AquaCrop demonstrated a high level of accuracy, with an RMSE of 0.85 t\(\cdot\)ha\({}^{-1}\) when simulating the final yield [31]. Similarly, [PERSON] et al.