Phosphorus (P) fertilizers are widely applied to the paddy wetland ecosystems in subtropical China (Ma et al. 2011) to promote high crop yields. However, P fertilizer use efficiency levels ABSTRACT The effects of phosphorus (P) fertilizer on P loss potential, soil Olsen-P and neutral phosphatase activities in paddy soils fertilized with superphosphate or pig manure (PM) were evaluated in this paper. Data were collected from a field experiment in the Tai Lake Basin, China. Superphosphate rates were 0, 17.5, 26.7, and 35.0 kg P/ha, and PM rates were 0, 1.4, 2.1, and 2.8 t/ha for each crop, respectively. Soil Olsen-P in the plow layer increased to a greater extent with PM than with superphosphate. Pig manure increased neutral phosphatase activities in the plow layer compared with PM-free treatment. In contrast, superphosphate inhibited neutral phosphatase activities compared with superphosphate-free treatment. Spring application of P fertilizer markedly increased the total P of surface water in November (< 0.01 vs. 0.10 mg/L) compared with P-free treatment. The total P of shallow groundwater at a 75 cm depth was ~0.01 mg/L. Phosphorus fertilizer did not influence Olsen-P or neutral phosphatase activities under the plow layer. Downward movement of P did not occur. Appropriate rate of P application of 26.2 kg P/ha for each crop in this soil reduced the risk of P loss in the paddy wetland ecosystem.

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Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.