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This study aims to investigate the effects of different selenium (Se) sources on the growth performance of Chinese broilers and provide a scientific rationale for adding Se additives to broiler feed. Relevant studies that meet standard inclusion criteria were identified and extracted from China National Knowledge Infrastructure and Wanfang and Chinese Scientific Journal (VIP) databases. A total of 9 studies with 539 subjects were included. A meta-analysis was performed with STATA15.0 to estimate the combined standardized mean difference (SMD) with a 95% confidence interval (95% CI). Heterogeneity test of articles was examined by Q-test, and the results showed that P values of feed conversion ratio, average daily gain (ADG), and average daily intake were all less than 0.05, suggesting a strong heterogeneity among the selected literature. Therefore, the random effect model is selected to calculate the SMD of the three indexes. The combined SMDs (95% CI) of feed:gain, ADG, and average daily feed intake (ADFI) were −0.39 (−1.03, 0.25), 0.26 (−0.29, 0.81), and −1.45 (−3.09, 0.20), respectively, and the P values were all less than 0.05. This study shows that the absolute differences in the growth performance (feed:gain, ADG, and ADFI) of broilers fed with either organic or inorganic Se supplements at the same dose were quite small. The P values of Egger’s test were 0.770, 0.089, and 0.426, respectively, for the above indexes, showing no significant publication bias. Sensitivity analysis ensured the stability and reliability of the results. In summary, the effects of organic and inorganic Se in feed on the growth performance of broilers are statistically equal.

The rice‐fish system has gained significant interest in recent years because of its effective usage of limited land and freshwater resources. To scientifically guide the improvement of rice field fishery production, the data in this study were selected from the latest China Fishery Statistical Yearbook, and therefore the development of paddy aquaculture was investigated. In order to more precisely predict the production of rice‐fish in China, this paper introduces an artificial neural network with the SSA‐BP model, which solves the drawbacks of the BP neural network such as easy to fall into local optimum and slow convergence speed when it is used for prediction. Firstly, the SSA‐BP model incorporates the aquaculture area (split by water area), the national freshwater fish seedling output, the national end‐of‐year ownership of inland fishing vessels, the number of freshwater fisheries practitioners as input variables, and the production of rice‐fish as an output variable; secondly, the SSA optimization algorithm was used to find the optimal initial thresholds and weights for the BP neural network, and finally the SSA‐BP prediction model was constructed. The results revealed that the overall expansion of the rice field fishery was swift in the last five years, and the output of cultivated fish in China's rice fields rose by nearly 20% yearly in the past five years. Compared with the BP neural network and GA‐BP models, the accuracy of the SSA‐BP prediction model was enhanced by 61.01% and 16.15%, respectively, which was more suited for predicting the production of rice‐fish. [ABSTRACT FROM AUTHOR]