ABSTRCT Earth is faced with dramatic changes in the weather systems, which leads to climate change. Climate change affects water resources and crop production. In this study, five and seven general circulation models (GCMs) were respectively collected via the IPCC Fourth and Fifth Assessment Reports. Emission scenarios including B1, A1B, and A2 for AR4 and RCP2.6 and RCP8.5 for AR5 were applied to predict future climate change. The weighting method of mean observed temperature-precipitation (MOTP) was utilized to compute uncertainty related to different climate models. The scenario files made by ΔT and ΔP were applied to the downscaled model of LARS-WG to generate weighted multi-model ensemble means of temperature and precipitation for the period 2020-2039 centered on 2030s. These ensemble means were incorporated into the calibrated AquaCrop model to predict final yield and biomass. In this study, soybean data were applied for four different varieties under three irrigation treatments in field experiments carried out at Karaj Seed and Plant Improvement Institute in two successive years. However, the results of statistical analysis between the model output and observed data for all varieties and irrigation treatments in the calibration year (2010) and validation year (2011) were the same at the 95% confidence level. It is suggested that AquaCrop is a valid model to predict yield and biomass for the study area in the future. Furthermore, comparing future climatic variables to the historical period during the soybean growing season showed enhancement of these variables by the 2030s. The amplitude change of temperature was larger in AR5, whereas the amplitude change of precipitation and CO2 were larger in AR4. The soybean yield and biomass increased for all treatments in the 2030 s with positive correlation with the climatic variables. The maximum temperature represented the most significant correlation with yield and biomass for almost all treatments. Finally, soybeans might achieve an optimal threshold temperature in the future, leading to yield increases in the 2030s.

Abstract. The AquaCrop model, calibrated for 2002 and validated for 2003, is used to simulate sugar beet root dry yield, dry biomass, water productivity based on irrigation (WPi), and water productivity based on total water input (WPi+p) in an experimental field of the Karaj Sugar Beet Seed Institute (Karaj, Iran). Three irrigation treatments including full irrigation, 75% deficit irrigation, and 50% deficit irrigation were carried out in the main plots. The results of statistical comparison between the model output and observed data in the calibration (2002) and validation (2003) years showed that the AquaCrop model reliably simulated sugar beet yield and the biomass under different genotypes and irrigation levels. AquaCrop did a better job of simulating dry biomass than root dry yield. The findings show that by decreasing water input, including irrigation and precipitation, WPi, and WPi+p will increase. In total, statistical indicators and scatter plots indicated that the AquaCrop model had enough fitness to predict yield, biomass, and water productivity for the future. Keywords: AquaCrop, Yield; Biomass, WPi, WPi+p.

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Abstract. Oil palm performs a considerable role in Malaysia’s economic system as Malaysia is the second-biggest palm oil manufacturer in the world. In oil palm plantations. Basal stem rot (BSR) is a disease caused by Ganoderma boninense that is responsible for a considerable annual losses, particularly in South East Asia. The disease remains an unresolved problem in most production areas due to lack of disease management strategy to detect the infected palms at their early stage. In recent years, advancement in remote sensing platforms and image processing methods have produced remarkable results for the detection of diseases at early stage. In this study, support vector machine (SVM) classifier was performed on UAV and Pleiades imagery to determine the ideal classification model for the early diagnosis of BSR disease in oil palms. The investigation's results showed that UAV provided the most accurate prediction, with a total accuracy of 68.28%, while 64.52% of the early Ganoderma infections could be identified with accuracy levels of 64.07% and 64.49%, respectively. The early Ganoderma infection could be recognized with an overall accuracy of 64.07% and 64.49%, respectively, while the Pleiades had an overall accuracy of 68.28% and 64.52%. Although the categorization accuracy appeared to be only modest at first glance, the quantity of detail offered by the imageries suggested that the accuracies were acceptable.