Soil nutrients are a very important factor in the growth of crops. Previously, our research on soil nutrients was mainly based on static. Soil nutrients at the time were reflected by data detected by soil nutrient tests, and it was not possible to judge a time. Soil nutrients within the section. So how to accurately determine the changes in soil nutrients within a time period?
Soil is the foundation of agricultural production and an important part of the natural ecosystem. Under the current background of increasing concern for sustainable development, accurate assessment of soil nutrient changes has become an important part of sustainable development. In the past, the evaluation of soil nutrients was usually based on static evaluation. The results can only reflect the current status of soil nutrients, and soil nutrients are a system that is in a dynamic change. Therefore, it is necessary to make nutrient status in different periods of time. The dynamic evaluation reflects the laws and trends of its changes and provides a reference for the sustainable development of agriculture.
The Markov process is a stochastic process that studies the state of an event and the transition between states. It uses the initial probability of different states of an event at time t0 and the transition relationship between states to study the state of change at time t0+t. The Markov process has no post-effects, that is, the state transition probability is only related to the transition starting state, the number of transition steps, the post-transition state, and has nothing to do with the initial time before the transition. Markov process also has time homogeneity, namely: a random process, from t0 to t0+t, the probability of the state transition from i to state j pij(t0,t0+t), this probability and the The time t0 is irrelevant and only relates to the state i, j and the time interval t. The Markov process has been widely used in industrial control, economic management, and other fields, and has achieved very good results, but its application in soil science is rarely reported because soil nutrient changes are affected by a variety of uncertainties. The strong randomness is a stochastic process with Markovian characteristics. Therefore, we can use Markov chains to simulate the process of soil nutrient changes, and use dynamic transfer methods to determine the dynamics of changes in soil nutrients. evaluation of.
This paper uses 11 counties in Chengdu Plain as the evaluation area to study the variation trends of three typical nutrient indexes of organic matter, total N, and quick-acting K in recent 20 years to verify the feasibility and advantages of Markov's application in dynamic evaluation of soil nutrients. .
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