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Reference evapotranspiration (ET0) is an important variable to estimate all the forms of evaporation and transpiration from crops and vegetation. An accurate estimation of ET0 plays a critical role in crop water management. The Food and Agriculture Organization of the United Nations (FAO) has recommended the Penman Monteith (PM) model as the standard to estimate the ET0. However, the PM model requires a large number of input parameters, including the solar radiation (Rs) or sunshine hours (n), the maximum and minimum air temperatures (Tmax and Tmin), Relative Humidity (RH), and wind speed at 2m high (u2). In addition to temperature data, most datasets are often unavailable, incomplete, or of uncertain quality in practice. Thus, the ET0 estimation using temperature data has drawn much attention in recent years. In this study, the recent estimation models were compared to improve the high accuracy of ET0 using the temperature data. The traditional Hargreves-Samani (HS) model (HS1), the improved HS model (HS2), and the PM temperature approach (PMTPP) were recently proposed by Paredes and Pereira. Some latest models were also introduced, including the ea estimation model proposed by Qiu et al., and the temperature-based Rs model (Rs-K) proposed by Korachagaon to improve the PM temperature approach (PMTNew). All models were calibrated and tested using the dataset collected from 51 radiation stations in China during the period 1967-2016. The results showed that the Rs-K model performed a better accuracy for the ET0 estimation, compared with the Rs model proposed by Paredes and Pereira (Rs-HS). For instance, the values of modelling efficiency (EF) were much closer to 1.00, the correlation coefficient (r) increased from 0.92-0.99 to 0.94-0.99, the values of Root Mean Square Error (RMSE), and the Relative Root Mean Square Error (RRMSE) were reduced by 0.08-0.25 mm/d and 0.03-0.09, respectively, at different climate zones. Except for the arid and hyper-arid climate zones, the average values of RMSE and RRMSE in the HS2 model decreased by 0.05-0.09 mm/d and 0.02-0.03, respectively, and the average values of EF increased by 0.03-0.10, compared with the HS1 model. Although the PMTPP model produced a similar r, the mean values of RMSE and RRMSE from the dry sub-humid and humid climate zones were reduced by 0.01-0.15 mm/d and 0-0.05, respectively, while the EF values increased by 0.01-0.06, compared with the HS2 model. Therefore, the PMTPP model presented a better accuracy than the HS model in estimating the ET0 at various climate zones. Except for the arid and semi-arid climate zones, the PMTNew model further reduced the RMSE and RRMSE by 0.04-0.12 mm/d and 0-0.04, respectively, indicating the higher EF and r values, compared with the PMTPP. In addition, the HS2 model presented a higher Global Performance Index (GPI) value than the HS1 model, indicating a better GPI value in the humid zone. Except for the humid climate zones, the PMTPP model presented a higher GPI value at various climate zones, the mean value of GPI range -0.06-0.42, compared with the HS2 model. The PMTNew model also presented a better GPI of each station, 90% of which ranked the first. Therefore, it is strongly recommended to use the PMTNew model to estimate the ET0 of each station, when the limited data is available. [ABSTRACT FROM AUTHOR]

China has the largest waterfowl industry, accounting for almost 75% of the waterfowl production in the world. The air temperature in the lion-head goose breeding environment is crucial to the survival of lion-head goose. The air temperature is susceptible to many factors such as relative humidity, illumination intensity, total suspended particulates, etc. So, it is significant to understand timely and accurately the change of the air temperature which can prevent environment deterioration, disease outbreaks, and optimize breeding management. Grasping the trend of the air temperature value timely and accurately is the key to the high-density healthy lion-head goose culture. The traditional prediction methods in temperature have problems such as low prediction accuracy, poor robustness, and poor generalization ability, etc. To improve the prediction accuracy of the air temperature of the lion-head goose breeding environment, a predicting model of air temperature fusing Principal Component Analysis (PCA), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed for lion-head goose breeding environment. First, the key impact factors of air temperature in lion-head goose breeding were extracted by principal component analysis, which eliminated redundant information between variables and reduced the complexity of the prediction model structure. Therefore, the key impact factors were selected air temperature, relative humidity, illumination intensity, total suspended particulates, respectively. Then, SVR-ARMA model was used to build the nonlinear prediction model between the temperature and these key impact factors for lion-head goose breeding, in which, first, the air temperature was predicted by SVR, then the air temperature prediction values were corrected by the residual prediction value of ARMA model. Because the kernel parameter and the regularization parameter in the SVR training procedure significantly influence forecasting accuracy, a leave-one-out cross-validation method was utilized to optimize the SVR model parameters and biases under a parameter space design. With this model, the air temperature change had been predicted for the lion-head goose breeding environment from July 21st, 2018 to July 30th, 2018 in Shanwei city, Guangdong province. The experimental results showed that the proposed combination prediction model of PCA-SVR-ARMA had a better prediction effect than the standard BP neural network, standard support vector regression machine, PCA-BPNN, PCA-SVR, and PCA-BPNN-ARMA method. And the relative Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for the temperature between the PCA-SVR-ARMA and PCA-SVR models were 29.59%, 40.37%, and 60.75% respectively under the same experimental conditions. The relative MAPE, RMSE, and MAE for the temperature between the PCA-SVR and standard SVR models were 31.78%, 15.89%, and 29.45% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-SVR-ARMA and PCA-BPNN-ARMA models were 55.64%, 35.66%, and 55.26% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-BPNN-ARMA and PCA-BPNN models were 43.16%, 30.63%, and 44.16% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-BPNN and BPNN models were 10.34%, 4.80%, and 7.98% respectively. To sum up, the SVR model had a better prediction performance under the condition of small samples, while the residual error correction method based on the ARMA model effectively improved prediction performance. The temperature prediction model based on PCA-SVR-ARMA exhibited best prediction accuracy and generalization performance when compared with other traditional forecasting models. Therefore, the presented model based on PCA-SVR-ARMA could meet the actual demand for accurate forecasting of temperature and provide a reference for environment control in healthy breeding and seedling breeding of lion-head goose. As well as it also could help farmers make decisions and reduce farming risks. [ABSTRACT FROM AUTHOR]