Читать книгу Agricultural Informatics - Группа авторов - Страница 22

Markov chain model is mathematical model in a probabilistic manner. It uses a stochastic process in which Markov chain of output of an experiment depends only on the results of the initial experiments. Alternately, present state determines next state. Markov chains derived the name from the mathematician who belonged to Russian origin (1856–1922). He initiated the theory of stochastic processes. Markov chain approach was used for prediction of cotton yield from pre-harvest data of crops [32]. The application of the Markov chain approach in predicting crop yields was investigated along with the analysis of data for yield of cotton crop for two leading states for cotton crop production. California and Texas were the states of study. Data was taken for the four-year period from 1981 to 1984. Probability distribution was estimated using Markov chain. Selection of key variables for the key within each period for the baseline data was done using multiple linear regressions and multiple rank regressions. Means of these predicted yield distributions was used for yield forecast. Sugarcane yield forecast was obtained from the model that implemented second order Markov chain. Results concluded that the second order Markov chain model can be preferred over other models of regression and first order Markov chain model for crop yield forecasting [33].