Simulating the effect of climate change on sugar beet yield, water productivity and growing period in Qazvin using the AquaCrop model

Extended Abstract
Introduction
Global warming has accelerated since the industrial revolution, but due to human activities and environmental degradation, this phenomenon has received increased attention in recent years. Future trends in global warming are expected to be influenced by scenarios projecting continued increases in greenhouse gas emission, particularly carbon dioxide. These scenarios predict varying levels of carbon dioxide production worldwide, which in turn affect climate factors, crop growth, and yield. Changes in climate can significantly impact agricultural production environments. To assess and predict the extent of these impacts on crop growth and yield, crop simulation models are essential tools.  Among these, the AquaCrop model is widely used due to its user-friendliness, high accuracy and reliability. This model is capable of simulating variouse field conditions and is applicable to a wide range of agricultural crops, including corn, canola, wheat, saffron, and quinoa.. Sugar beet is considered a strategic crop in Iran, with its yield highly dependent on irrigation water and fertilizer application.
Materials and Methods
All data were collected from a research field located at the Faizabad research station in Qazvin, Iran, over three agricultural years. The experimental factors included irrigation management at four intervals (I1: 6, I2: 9, I3: 12, and I4: 15 days) and fertilizer application at three levels (F0: 21, F1: 30, and F2: 39 kh ha-1). The LARS-WG model was used to simulate meteorological data for Qazvin under two climate change scenarios: an optimistic scenario (SSP2-4.5) and a pessimistic scenario (SSP5-8.5). These simulations were performed using two atmospheric general circulation models, GFDL-ESM4 (G model) and MRI_ESM2-0 (M model), across two future periods: 2026-2045 (near-future) and 2046-2099 (-uture). The AquaCrop model was then used to simulate sugar beet yield and water productivity based on these climate projections. Some statistical criteria were applied to evaluate the performance and accuracy of both the LARS-WG and AquaCrop models..
Results and Discussion
During the calibration stage, the RMSE statistics for the AquaCrop model showed a yield prediction error of 3511 kg ha-1,  placing its accuracy in the excellent category (NRMSE<0.1). For water productivity, the model’s error was 1.2 kg m-3, with accuracy evaluated as good (0.1<NRMSE<0.2). In the validation stage, the AquaCrop model showed some errors and underestimations in simulating yield and water productivity, as indicated by the mean bias error (MBE) exceeding acceptable thresholds. The RMSE for yield during validation was 2797 kg ha-1. The LARS-WG model was evaluated using observed and simulated climate data for the baseline period (1950-2014). While slight underestimation of temperature and overestimation of some parameters were observed, the RMSE values were small enough to be considered negligible. The NRMSE values for all three variables studied were below 0.1, indicating high model accuracy. Furthermore, the efficiency coefficient (EF) and the index of agreement (d) were both greater than 0.8, demonstrating strong model performance. The coefficient of determination (R²) exceeded 0.9, indicating that the LARS-WG model explained over 90% of the variance in the observed data. Comparing rainfall between models, the baseline rainfall predicted by model G was lower than that of model M. Future projections revealed that under the optimistic scenario, rainfall is expected to increase compared with the current conditions, while the pessimistic scenario predicts a slight decrease. Minimum temperature changes were less consistent; the pessimistic scenario showed minimal increases, whereas the optimistic scenario exhibited more variability. Model M predicted the lowest minimum temperature, while model G predicted an increase. These climatic changes imply an increase in evapotranspiration, which, along with effects on crop growth duration, can alter the length of growing period. Since increases in evapotranspiration surpass increases in minimum temperature, the overall length of the growing period is expected to decrease. Rainfall is projected to increase by approximately 6 mm in the near-future and 8 mm in the far-future. Correspondingly, the sugar beet growing period (time from planting to harvest) is predicted to shorten comapred with the baseline period. The average growth period was 160 days during the baseline period, decreasing to 154 days in the near-future  and 153 days in the far-future. Simulations using models G and M for the near future under optimistic  and pessimistic scenarios yielded growth periods of 156 and 152 days, respectively. For the far future, these values were 155 and 150 days, respectively. These findings indicate that, under climate change, the growth and development phases of many crops will shorten, with the magnitude depending on crop type and climatic severity. Yield changes under water and fertilizer stress mirrored observed treatments, suggesting similar trends will continue in coming decades. In model G, the average sugar beet yield for the near future under optimistic and pessimistic scenarios were 40746 and 38046 kg.m-3, respectively representing decreases of 40 and 44% compared with current conditions. Furthermore, yield reductions are projected to be more severe in the far-future than in the near-future.
Conclusion
Based on climate change scenarios and growth simulations using the Aquacrop model, the results showed a yield gap of approximately 40 and 70% between potential and actual sugar beet yield. To address this issue, it is recommended to increase the planting density compared with current practices. By reducing the difference between potential and actual evapotranspiration, this strategy may help narrow the yield gap and improve overall productivity under changing climatic conditions.