Determining the performance of the CROPGRO-quinoa model under different irrigation levels and evaluating the effect of climate change on quinoa yield and yield components under Mediterranean conditions

This thesis study aimed to evaluate the potential of the DSSAT-CROPGRO Quinoa V.8.2 crop simulation model in predicting the timing of key phenological stages (flowering and maturity), yield, and yield components of quinoa under different irrigation levels. The analysis utilized data from field experiments conducted in 2014 and 2015 in Tarsus, Turkey. Additionally, the study investigated the impacts of current and projected climate scenarios on quinoa performance. Sensitivity analysis was performed by altering meteorological parameters (temperature, precipitation, and CO₂) within possible ranges, and the effects on grain yield and phenological stages were assessed using the coefficient of variation (CV%). A line-source sprinkler irrigation system was employed to create varying irrigation levels. The plot closest to the lateral represented full irrigation (I100), while the farthest plot represented rainfed conditions (I0). Intermediate plots represented deficit irrigation levels (I75, I50, and I25). Irrigation was scheduled during the early vegetative, late vegetative, flowering, and grain filling stages by replenishing depleted moisture in the 60 cm soil profile to field capacity in the I100 treatment. Statistical analysis revealed that the effects of different irrigation levels on quinoa yield and its components were significant at the 1% error level. The highest yields were recorded under I100 (4700–4230 kg/ha in 2014 and 2015, respectively), while the lowest yields were observed under I0 (2500 and 1930 kg/ha, respectively). The model calibration was performed using data from 2014, and validation was conducted using data from 2015. To assess the potential impacts of climate change on quinoa yield, RCP4.5 and RCP8.5 climate scenarios were applied using global climate models (HadGEM2-ES, MPI-ESM-MR, GFDL-ESM2M). Future periods were categorized as near-term (2016–2045), mid-term (2046–2075), and long-term (2076–2098). The CROPGRO-Quinoa model demonstrated good performance in simulating phenological stages (time to flowering and maturity), seasonal variations in soil water content, and leaf area index (LAI). Moreover, differences between observed and simulated values for quinoa yield and maximum LAI were within acceptable limits, indicating that the CROPGRO-Quinoa V4.8.2 model can reliably predict quinoa growth, yield, and the potential impacts of climate change under Eastern Mediterranean conditions. Under full irrigation (I100) and the RCP4.5 scenario, quinoa yield is expected to increase by 3.9% and 8.6% in the near and mid-term periods, respectively, but decrease by -4.6% in the long-term. Under the RCP8.5 scenario, yield is projected to increase by 3.1% in the near-term, but decrease by -7.0% and -13.7% in the mid- and long-term periods, respectively. Under deficit irrigation (I50), the RCP4.5 scenario indicates yield increases of 2.6% and 6.0% in the near and mid-term, respectively, but a decrease of -7.5% in the long-term. Under RCP8.5, yield is projected to increase by 1.7% in the near-term, but decrease by -8.0% and -20.0% in the mid- and long-term, respectively. Under rainfed conditions (I0), the RCP4.5 scenario projects yield increases of 0.8% and 4.3% in the near and mid-term, respectively, but a decrease of -9.1% in the long-term. Similarly, under RCP8.5, yield is expected to increase by 1.0% in the near-term, but decrease by -9.6% and -27.8% in the mid and long-term, respectively. Keywords: Quinoa, Irrigation Program, CROPGRO model, sensitivity analysis, climate scenarios