During the competition, we will provide you with an analysis of the ideas of the competition questions. For more ideas, see the business card at the bottom of the article.
Question E
Question 1. From the perspective of mechanism analysis, establish a mathematical model of the impact of different grazing strategies (grazing methods and grazing intensity) on the soil physical properties (mainly soil moisture) and vegetation biomass of the Xilin Gol Grassland.
Question 1 can be simplified as the effects of grazing methods and grazing intensity on soil moisture and vegetation biomass.
The independent variables of the model we want to build are:
1. Grazing methods: According to the question, there are five types: continuous grazing throughout the year, grazing prohibition, selective grazing rotation, light grazing, and rest grazing during the growing season. Among them, the two meanings of grazing prohibition and light grazing overlap with grazing intensity. Therefore, no additional consideration is required; in addition, the impact of the strategy on the grassland emphasizes the impact on time, and the impact on space is reflected in the inherent nature of the grassland . Therefore, in fact, only two grazing methods need to be considered as independent variables: all Annual continuous grazing and time-sharing grazing. In order to simplify the model, this variable can be reflected in the model as a proportional coefficient.
2. Grazing intensity: This variable can be expressed directly by a constant S , and S is the number of sheep per square meter. Note that the "sheep" here is just a livestock unit. Cows, horses, and camels = 6 sheep, cubs of cows, horses, and camels = 3 sheep, and lambs = 0.5 sheep.
The dependent variable of the model is:
1. The change of soil humidity dh/dt: where h represents humidity, and what we output as a model is the change of h with time, so it is necessary to differentiate with time, and the amount of vegetation is the same.
2. Change of vegetation biomass dw/dt: where w represents vegetation biomass. Note: Vegetation biomass data are dry weights in Annex 15, not to be confused with data in Annexes 5, 6, and 10.
For the impact of grazing intensity on vegetation change, the formula in the extended reading can be directly applied:
For the impact of grazing intensity on soil moisture, it can be combined with the soil moisture in Annex 3, the soil evaporation data in Annex 4, the precipitation in Annex 8, and the data in Annex. Since the only factors affecting soil moisture are soil evaporation, precipitation, and grazing conditions, after removing the effects of soil evaporation and precipitation (these two items are known from the appendix), what remains is the impact of grazing conditions on soil moisture.
From the vegetation biomass data in Appendix 15 combined with the relationship between the aspects just mentioned and plant growth, the grazing situation can be inversely deduced. From this, we can get the impact of grazing situation on the allowable humidity.
Considering time-sharing grazing, we can finally add a proportional coefficient to the model output.
Question 2. Based on the soil moisture data in Annex 3, the soil evaporation data in Annex 4, and the precipitation data in Annex 8, please establish a model to predict the soil moisture at different depths in 2022 and 2023 while keeping the current grazing strategy unchanged, and complete The following table.
It can be known from Question 1 that the current grazing strategy can be deduced from the vegetation biomass, and the impact of grazing strategy on soil moisture has been obtained in Question 1, so the model in Question 1 can be directly applied.
Be careful not to be affected by the complicated formulas in the extended reading when doing this question:
We only need to take E(α) as a whole as a known quantity.
Question 3. From the perspective of mechanism analysis, establish a mathematical model of the influence of different grazing strategies (grazing methods and grazing intensity) on the soil chemical properties of Xilin Gol Grassland. Please combine the data in Appendix 14 to predict the values of soil organic carbon, inorganic carbon, total N, and soil C/N ratio in the Xilin Gol Grassland monitoring plots (12 grazing plots) under different grazing intensities in the same period in 2022, and complete the table below.
This small question is relatively simple, just use the data in Appendix 14 to apply the regression model. A separate regression model was designed for each chemical property, and the parameters were adjusted separately. Due to the limited amount of data and features, it is recommended to use the decision tree model here.
Question 4. Use the desertification degree index prediction model and the data provided by the attachment (including the data collected by yourself) to determine the desertification degree index value of the monitoring points under different grazing intensities. And please try to give a quantitative definition of soil compaction. On the basis of establishing a reasonable soil compaction model, combined with question 3, give a grazing strategy model to minimize the desertification degree index and compaction degree.
Question 4 is divided into two sub-questions. The desertification prediction model for the first sub-question has been given in detail in Article 3 of the extended reading and will not be analyzed further. Now look at question 2.
The known compaction formula is:
B = f（W,C,O）
The lower the soil moisture w, the larger the bulk density c, the lower the organic matter content O, and the more serious the degree of soil compaction B. Among them, the soil moisture data is in Appendix 3, the organic matter content is in Appendix 14, and the bulk density is in Appendix 7 (it is a constant). The model can be specifically expressed as:
Here, the ratio between the three coefficients is mainly determined according to the degree of influence of the three factors. The data given in the question does not measure the obvious characteristics of this indicator, so the determination of the coefficient is justified.
Combined with the impact of grazing strategy on O in question 3, the influence of grazing strategy on B can be further obtained, and combined with the desertification degree prediction model, the optimal grazing strategy can be obtained.