Application of Cloud Model to Evaluation of Forest Soil Fertility: A Case in Chinese Fir Plantations in Southern China

Abstract

Soil nutrients are of great significance for maintaining forest growth and ensuring land productivity. A comprehensive scientific evaluation of soil fertility is helpful for sustainable forest management. There are many uncertainties in traditional evaluation methods, that is fuzziness and randomness, which often lead to a large deviation of the evaluation results. In order to comprehensively consider the fuzziness and randomness of soil fertility evaluation, the cloud model was introduced to evaluate the soil fertility of Chinese fir plantations. The cloud model is an uncertain transformation model which can combine a qualitative description with a quantitative calculation as well as reflect the randomness and fuzziness of the principle. It is an effective tool for realizing qualitative and quantitative conversion. The accuracy of the model was evaluated with the Namerow integrated index method, and the results were compared with previous studies. The results showed that the soil fertility was grade II (Ex = 0.653), medium; soil pH was grade II (Ex = 4.59), strongly acidic; soil organic matter (OM) was grade V (Ex = 33.40), rich; total nitrogen (TN) was grade V (Ex = 1.53), rich; total phosphorus (TP) was grade III (Ex = 0.49), poor; total potassium (TK) was grade IV (Ex = 17.10), medium; available nitrogen (AN) was grade V (Ex = 128.30), rich; available phosphorus (AP) was grade II (Ex = 4.10), very poor; and available potassium (AK) was grade III (Ex = 52.73), poor. The degree of artificial influence of soil nutrients in Chinese fir plantations was ranked AN > AK > OM > TK > pH > TN > TP = AP. The soil fertility of the Chinese fir plantation was comprehensively evaluated by the cloud model and the evaluation results were unbiased and reliable. This method makes up for the shortcomings of traditional methods and provides new ideas for soil fertility evaluation. We suggest that this cloud model could be extended to other regions in the future and hope that there will be an article to compare several different statistical techniques.