Prediction of the livestock carrying capacity using neural network in the meadow steppe
T. S. Wu
A B C , H. P. Fu A B G , G. Jin D , H. F. Wu E and H. M. Bai A B F
+ Author Affiliations
- Author Affiliations
A School of Microelectronics, Tianjin University, Tianjin 300072, China.
B Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin University, Tianjin 300072, China.
C School of Physics and Electronic Information, Hulunbuir College, Hulunbuir 021008, China.
D Hulunbuir City Hailar Meteorological Bureau, Hulunbuir 021000, China.
E Chengdu Ganide Technology, Chengdu 610073, China.
F School of Mathematics and Statistics, Hulunbuir College, Hulunbuir 021008, China.
G Corresponding author. Email: hpfu@tju.edu.cn
The Rangeland Journal 41(1) 65-72 https://doi.org/10.1071/RJ18058
Submitted: 15 May 2018 Accepted: 21 October 2018 Published: 3 January 2019
Abstract
In order to predict the livestock carrying capacity in meadow steppe, a method using back propagation neural network is proposed based on the meteorological data and the remote-sensing data of Normalised Difference Vegetation Index. In the proposed method, back propagation neural network was first utilised to build a behavioural model to forecast precipitation during the grass-growing season (June–July–August) from 1961 to 2015. Second, the relationship between precipitation and Normalised Difference Vegetation Index during the grass-growing season from 2000 to 2015 was modelled with the help of back propagation neural network. The prediction results demonstrate that the proposed back propagation neural network-based model is effective in the forecast of precipitation and Normalised Difference Vegetation Index. Thus, an accurate prediction of livestock carrying capacity is achieved based on the proposed back propagation neural network-based model. In short, this work can be used to improve the utilisation of grassland and prevent the occurrence of vegetation degradation by overgrazing in drought years for arid and semiarid grasslands.
Additional keywords: BPNN, NDVI, precipitation, prediction.
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