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• Sobol' method is used to evaluate the sensitivity of APSIM-Wheat yield predictions. • Yield is most sensitive to initial nitrogen amount, soil parameters, precipitation. • Estimated yield is influenced most by the limiting resource under given conditions. • Scenarios for high fertilization level or dry seasons have lower yield variability. The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol' method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs' impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool. [Display omitted] [ABSTRACT FROM AUTHOR]

Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops. [Display omitted] • Crop model is a tool to simulate crop growth, seek solutions for mitigating negative environmental impacts on production. • APSIM Classic has been used as an example to explore wheat yield prediction performance and the influential factors. • Reviewed and established a meta-database from 76 published studies. • Overall: the model predicts wheat yield with RMSE=1 t/ha. Fully calibrated and under stress-free condition: RMSE=0.64 t/ha. • Calibration method, heat, frost, water, and nitrogen stresses were identified as factors causing the model to mis-simulate. [ABSTRACT FROM AUTHOR]

Background: Over 100 mutations in the SRD5A2 gene have been identified in subjects with 46,XY disorder of sex development (DSD). Exploration of SRD5A2 mutations and elucidation of the molecular mechanisms behind their effects should reveal the functions of the domains of the 5α-reductase 2 enzyme and identify the cause of 46,XY DSD. Previously, we reported a novel compound heterozygous p.Q6X/p.H232R mutation of the SRD5A2 gene in a case with 46,XY DSD. Whether the compound heterozygous p.Q6X/p.H232R mutation in this gene causes 46,XY DSD requires further exploration. Methods: The two 46,XY DSD cases were identified and sequenced. In order to identify the source of the compound heterozygous p.Q6X/p.H232R mutation, the parents, maternal grandparents, and maternal uncle were sequenced. Since p.Q6X mutation is a nonsense mutation, p.H232R mutation was transfected into HEK293 cells and dihydrotestosterone (DHT) production were analyzed by liquid chromatography–mass spectrometry (LC–MS) for 5α-reductase 2 enzyme activities test. Apparent michaelis constant (Km) were measured of p.H232R mutation to analyze the binding ability change of 5α-reductase 2 enzyme with testosterone (T) or NADPH. Results: The sequence results showed that the two 46,XY DSD cases were the compound heterozygous p.Q6X/p.H232R mutation, of which the heterozygous p.Q6X mutation originating from maternal family and heterozygous p.H232R mutation originating from the paternal family. The function analysis confirmed that p.H232R variant decreased the DHT production by LC–MS test. The Km analysis demonstrated that p.H232R mutation affected the binding of SRD5A2 with T or NADPH. Conclusions: Our findings confirmed that the compound heterozygous p.Q6X/p.H232R mutation in the SRD5A2 gene is the cause of 46,XY DSD. p.H232R mutation reduced DHT production while attenuating the catalytic efficiency of the 5α-reductase 2 enzyme. [ABSTRACT FROM AUTHOR]

Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody’s algorithm, the South China algorithm, Kelly’s algorithm, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody’s algorithm, the South China algorithm and Kelly’s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody’s algorithm, the South China algorithm and Kelly’s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD < 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms. [ABSTRACT FROM AUTHOR]