Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations

As the function of decomposition fungi has been clearly researched in global carbon cycle, it is obviously value to explore rate fungal populations. This study analyzed relationship between environmental factors and biodiversity step by step. In order interaction with time, model based on Logistic was built Lotka-Volterra employed condition two kinds existing an environment limited resources. The changing trend population number several under different conditions can be predicted through model. To illustrate applicability model, Laetiporus conifericola Hyphoderma setigerum were applied as examples. results showed that higher degree diversity, greater rate, efficiency ecosystem. Its rich species diversity conducive accelerating litter, lignocellulose, circulation entire Based above using data from measuring mycelial elongation each isolate at 10°C, 16°C, 22°C standardized laboratory conditions, growth patterns five combinations simulated. revealed a general increase increasing temperature, which verifies accuracy Moreover, also total after incorporation negatively correlated single action. predictions made for environments, suitable environments determined. future, further optimized, lignin cellulose added fit logs. application scenarios broadened, contribute restoration management ecological environment, well produce good effects fields assisting cycle soil problem restoration. Keywords: equations, decomposition, mathematical DOI: 10.25165/j.ijabe.20231601.7405 Citation: Pan J Y, Le W X, Wang Z A, Chen J. Mathematical predicting rates improved equations. Int Agric & Biol Eng, 2023; 16(1): 60–65.