Visible and near infrared reflectance spectroscopy is an effective method for the quantitative estimation of soil available phosphorus content.However

when the spectral data collected from the soil of one region are used for those of other regions

problems such as low prediction accuracy and model failure may occur.In order to solve these problems

a prediction model through transfer learning method was established in this paper

with soil samples from southern Anhui province as the source domain and soil samples from northern Anhui province as the target domain to improve the accuracy of soil available phosphorus model prediction.By comparing the prediction accuracy of the model before and after the transfer

it was found that the model for southern Anhui region could not be directly used for Northern Anhui region

in that case

model failure may occur.The coefficient of determination(R2) and the ratio of prediction to deviation(RPD) of the model were-0.19 and 0.92

respectively

and the root mean square error of prediction(RMSEP) of the model was 1.04.The predictive accuracy of modeling for the North Anhui region with a small sample size was low

with R2 and RPD of 0.61 and 1.60

respectively

and RMSEP of 0.60.Based on the transfer component analysis(TCA) and adding some samples from northern Anhui to build the model

the prediction accuracy of the model for available phosphorus in samples from northern Anhui was significantly improved

with the R2 and RPD improved to 0.79 and 2.18

respectively

and the RMSEP reduced to 0.44.Results showed that the prediction model based on TCA method for soil available phosphorus in southern Anhui region could be applied to that in northern Anhui region

improving the prediction accuracy and reducing the modeling cost

and it provides a new idea for the wide application of prediction model for soil available phosphorus.