Assessment of climate change impacts on soybean and sugar beet production in relation to uncertainty of general circulation models in Iran

Earth is faced with dramatic changes in the weather systems due to global warming, which leads to climate change. Climate change affects water resources and crop production especially soybean and sugar beet yield which are major industrial crops in Iran. This study aims to assess the impact of projected climate change on soybean and sugar beet production considering the uncertainty of General Circulation Models (GCMs). Soybean data were collected from four different varieties treated under three irrigation treatments in the field experiments carried out at Karaj Seed and Plant Improvement Institute in two successive years (2010 and 2011). Sugar beet data were also collected from three different genotypes and irrigation treatments in the field experiments carried out at Karaj Sugar Beet Seed Institute in two successive years (2002 and 2003). These data used for calibration and validation of AquaCrop model to simulate yield and biomass of soybean and sugar beet. On the other hand, five and seven GCMs respectively collected from the Fourth and the Fifth Assessment Reports existed in data distribution centre of IPCC. Emission scenarios including B1, A1B, and A2 for AR4, RCP2.6, and RCP8.5 for AR5 were applied to predict future climate change. LARS-WG was downscaled by observed data then the weighting method of Mean Observed Temperature-Precipitation (MOTP) has been used to determine the uncertainty between climate models. Weighted multi model ensemble means for climate change scenarios related to temperature (ΔT) and precipitation (ΔP) applied to LARS-WG to generate ensemble means of temperature and precipitation for the period of 2020-2039 centered on 2030s. These ensemble means were incorporated into the calibrated AquaCrop model to predict final yield and biomass in the future 2030s. The results of statistical analysis between simulated and observed values of yield and biomass for all soybean varieties and sugar beet genotypes at different irrigation levels did not indicate any significant differences between the observed and simulated values. It has been suggested that AquaCrop is a valid model to predict yield and biomass for the study area in the future. The results of Mann-Kendall trend test for the mean of annual minimum temperature (T- min), maximum temperature (T-max), and precipitation (Pre) during 1985-2014 showed that there is an increasing trend in T-min and T-max, while Pre did not have a significant trend. Furthermore, comparison between historical period (1985-2010) and future climatic variables during soybean growing months (July–October) and sugar beet growing months (May-November) indicated that climatic variables increased by the 2030s. The soybean and sugar beet yield, biomass, water productivity based on evapotranspiration (WPET) and water productivity based on irrigation (WPIR) increased for all treatments in the 2030s. Qualitative yield of soybean and sugar beet was also predicted for 2030s. The result showed that oil content of soybean increased similarly as yield increased in the future period while protein content decreased inversely with yield. It was also predicted that sugar yield and white sugar yield of sugar beet increased similarly as yield increased in the future. The correlation between climatic variables and soybean averaged yield and biomass of four varieties in three irrigation levels showed that correlation coefficients had positive values. Soybean yield and biomass had most significant correlation with T-max at the 99% confidence level in treatments of without water stress and mild water stress whereas in severe water stress soybean yield and biomass had most significant correlation respectively with Pre and T-max at the 95% confidence level. The correlation between climatic variables and sugar beet averaged yield and biomass of three genotypes in three irrigation levels showed that correlation coefficients had positive values. Sugar beet yield and biomass had most significant correlation respectively with T-max and CO2 at the 99% confidence level in all irrigation treatments. The findings showed that crops could reach an optimal threshold temperature and take advantage of elevated CO2 rate, which led to increasing of crop production in the future. This research can contribute to the science of impact assessment of climate change on crops, which is significantly important for irrigation water management, agricultural decision-making, and implementing adaptation approaches in the future.

Text FK 2019 111 - ir.pdf Download (1MB)

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