Method for evaluating phosphorus loss risk of different crop systems in plateau basin

Abstract

The invention discloses a method for evaluating the phosphorus loss risk of different crop systems in a plateau basin, and the method comprises the following steps: S1, obtaining source factor evaluation indexes of a target region: phosphorus surplus and soil total phosphorus, and migration factor evaluation indexes: surface runoff depth, soil erosion modulus, distance from the target region to a river, and distance from the target region to a target water body; s2, evaluating a phosphorus loss risk through the phosphorus index; the calculation formula of the phosphorus index is shown in the specification; pI represents a phosphorus loss risk index, Si and Wi are respectively a risk value and a weight corresponding to the source factor evaluation index, and Tj and Wj are respectively a risk value and a weight corresponding to the migration factor evaluation index. According to the method, phosphorus surplus in crop production is used as a source factor instead of phosphorus input commonly used in previous research, the method is better used for drainage basin non-point source phosphorus loss risk evaluation, and a phosphorus index evaluation system for the southern plateau agricultural drainage basin in China is established.

Description

Technical Field

[0001] This invention belongs to the field of environmental agriculture technology, specifically, it relates to a method for assessing the risk of phosphorus loss in different crop systems in plateau watersheds. Background Technology

[0002] Phosphorus (P) is a major limiting nutrient for crop growth, and large amounts of phosphate fertilizers are applied in agricultural production. Under the influence of precipitation and soil erosion, phosphorus in farmland soil enters receiving water bodies through runoff, interflow, and adsorption on the surface of sediment.

[0003] Currently, methods for assessing phosphorus loss from non-point source pollution typically include empirical models, such as the output coefficient method and the general soil loss equation method (Gao et al., 2024; Noor et al., 2010); mechanistic models, such as the AnnAGNPS model and the SWAT model (Yuan et al., 2011; Nasr et al., 2007); and pollution index methods, such as the phosphorus index method and the agricultural pollution potential index (APPI) (Li et al., 2019; Guo et al., 2014). Among these, the Phosphorus Index Model (PIM) has advantages such as simplicity, efficiency, small and easily obtainable data, and high accuracy (Drewry et al., 2011), and is one of the classic models for identifying key source areas and assessing pollution risks (Gburek et al., 2000; Sharpley et al., 2003). As a tool for integrated phosphorus management, it has been widely used by scholars at home and abroad (Li et al., 2019; Buchanan et al., 2013). This method was first proposed by foreign scholars Lemunyon and Gilbert (Lemunyon and Gilbert, 1993), and was later modified by GBUREK and SHARPLEY to incorporate the distance from farmland to water bodies into the index system. By assessing the interaction between source factors and migration factors affecting phosphorus loss, the risk of phosphorus loss is characterized, and then the key source areas of phosphorus loss are determined based on the level of risk (Gburek and Sharpley, 2000).

[0004] However, current research on the phosphorus index method mainly focuses on farmland or small watershed scales, which is limited by their small scale and fails to consider the temporal and spatial differences in crop planting and the differences in planting structure within regions (Zhang et al., 2015; MA et al., 2024). Furthermore, the assessment of loss risk is mainly based on single factors, such as fertilization status and soil phosphorus content (Habibiandehkordi et al., 2020; MA et al., 2024). Phosphorus surplus represents the amount remaining after external supply and absorption by crops, more accurately reflecting soil nutrients (Guejjoud et al., 2023). Currently, there are few studies on phosphorus loss risk assessment using phosphorus surplus as an indicator, especially in plateau lake basins.

[0005] Therefore, this invention aims to construct a phosphorus loss risk assessment system for typical planting structures in plateau lake watersheds, with phosphorus surplus as the primary source factor. Based on the Iowa phosphorus index model, this system is modified according to the characteristics of Chinese plateau lakes and with reference to other phosphorus index models. Phosphorus surplus and total soil phosphorus are used as source factors, while surface runoff depth, soil erosion modulus, distance from rivers, and distance from target water bodies are used as migration factors. This system assesses the risk of phosphorus loss from surface water by different crop systems in the watershed and identifies key phosphorus loss source areas. Through this invention, we aim to provide a scientific basis and technical support for the rational management of phosphorus resources and the effective control of agricultural non-point source pollution, and to contribute to the innovation of research methods for phosphorus loss from crop farming at the watershed scale. Summary of the Invention

[0006] To overcome the problems existing in the prior art, this invention proposes a method for assessing the risk of phosphorus loss in different crop systems in plateau watersheds.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0008] A method for assessing the risk of phosphorus loss in different crop systems in a plateau watershed includes the following steps:

[0009] S1, obtain the source factor evaluation indicators of the target area: phosphorus surplus and total phosphorus in the soil, as well as the migration factor evaluation indicators: surface runoff depth, soil erosion modulus, distance of the target area from the river and distance of the target area from the target water body;

[0010] S2, assesses phosphorus loss risk using the phosphorus index; the formula for calculating the phosphorus index is: ;

[0011] In the formula, PI represents the phosphorus loss risk index, and S i and W i These are the risk values ​​and weights corresponding to the source factor evaluation indicators, T. j and W jThese are the risk values ​​and weights corresponding to the migration factor evaluation indicators.

[0012] Furthermore, the formula for calculating phosphorus surplus is:

[0013] Soil phosphorus surplus = Soil phosphorus income - Soil phosphorus expenditure;

[0014] Soil phosphorus income = phosphorus brought in by organic fertilizer + phosphorus brought in by chemical fertilizer + phosphorus brought in by precipitation + phosphorus brought in by irrigation water + phosphorus brought in by straw returning to the field;

[0015] Soil phosphorus expenditure = phosphorus carried away by harvest + phosphorus carried away by waste + phosphorus carried away by surface runoff.

[0016] Furthermore, the total phosphorus data of the soil was obtained by sampling and testing typical plots in various townships and administrative villages in the watershed. Soil from the topsoil layer of 0-20 cm was collected from each plot, and after mixing, 1-1.5 kg was taken by quartering. After air drying, the sample was passed through a 60-mesh sieve for later use and tested according to the NaOH fusion-molybdenum antimony colorimetric method in "Soil Agrochemical Analysis".

[0017] Furthermore, the surface runoff depth (mm) is the product of rainfall (mm) and runoff coefficient, and the soil erosion modulus is calculated using the modified general soil loss equation (RUSLE).

[0018] Through the above technical solution, the present invention can achieve at least the following beneficial effects:

[0019] This invention is the first to use the phosphorus surplus in crop production as the source factor, rather than the phosphorus input commonly used in previous studies, which is better suited for assessing the risk of non-point source phosphorus loss in watersheds and has established a phosphorus index evaluation system for agricultural watersheds in the southern plateau of China. Attached Figure Description

[0020] Figure 1 This is a heatmap showing the correlation between the phosphorus loss risk index in crop farming and various influencing factors in this invention.

[0021] Figure 2 This is a ranking of the importance of the influencing factors of the phosphorus loss risk index in crop farming in this invention;

[0022] Figure 3 This is a distribution map of vegetable continuous cropping and rice-rapeseed rotation planting in the Erhai Lake basin in this invention;

[0023] Figure 4 This is a schematic diagram illustrating the phosphorus surplus in continuous cropping of vegetables in this invention;

[0024] Figure 5 This is a schematic diagram of the phosphorus surplus in the rice-rapeseed rice-rice hybrid rice model in this invention;

[0025] Figure 6 This is a spatial distribution map of phosphorus loss risk in the rice-rapeseed rotation system of this invention;

[0026] Figure 7 This is a spatial distribution map of phosphorus loss risk in the vegetable continuous cropping system of this invention. Detailed Implementation

[0027] Example 1

[0028] A method for assessing the risk of phosphorus loss in different crop systems in a plateau watershed includes the following steps:

[0029] S1, obtain the source factor evaluation indicators of the target area: phosphorus surplus and total phosphorus in the soil, as well as the migration factor evaluation indicators: surface runoff depth, soil erosion modulus, distance of the target area from the river and distance of the target area from the target water body;

[0030] S2, assesses phosphorus loss risk using the phosphorus index; the formula for calculating the phosphorus index is: ;

[0031] In the formula, PI represents the phosphorus loss risk index, and S i and W i These are the risk values ​​and weights corresponding to the source factor evaluation indicators, T. j and W j These are the risk values ​​and weights corresponding to the migration factor evaluation indicators. The weights and risk level values ​​of the source factor and migration factor are shown in Table 1 below.

[0032] Table 1 Evaluation Index System for Phosphorus Loss

[0033]

[0034] The formula for calculating phosphorus surplus is as follows:

[0035] Soil phosphorus surplus = Soil phosphorus income - Soil phosphorus expenditure

[0036] Soil phosphorus intake = phosphorus brought in by organic fertilizer + phosphorus brought in by chemical fertilizer + phosphorus brought in by precipitation + phosphorus brought in by irrigation water + phosphorus brought in by straw returning to the field

[0037] Soil phosphorus expenditure = Phosphorus carried away by harvest + Phosphorus carried away by waste + Phosphorus carried away by surface runoff

[0038] The phosphorus revenue and expenditure data parameters used to calculate the phosphorus surplus are derived from the in-situ monitoring experiment of nutrient loss in runoff plots established in this invention.

[0039] Soil total phosphorus data were obtained through sampling and testing of typical plots in various townships and administrative villages within the watershed. Soil samples from the 0–20 cm topsoil layer were collected from each plot, mixed, and then quartered to obtain 1–1.5 kg samples. These samples were air-dried and passed through a 60-mesh sieve for later use. The total phosphorus content of the soil samples was determined according to the methods described in "Soil Agrochemical Analysis," specifically using the NaOH fusion-molybdenum antimony colorimetric method.

[0040] Migration factor calculation

[0041] Surface runoff and soil erosion are important transport factors for phosphorus loss. This invention uses annual runoff depth to measure the impact of surface runoff on phosphorus loss risk. Surface runoff depth (mm) is the product of rainfall (mm) and runoff coefficient. A spatial distribution map of annual runoff depth in the watershed is drawn using rainfall data from rain gauges distributed throughout the watershed and runoff coefficients obtained from actual monitoring in the runoff plots set up in this invention. The modified Universal Soil Loss Equation (RUSLE) is used to calculate the soil erosion modulus. Considering the distance attenuation effect of the distance between the pollution source and the water body on the phosphorus loss risk level, and given that rivers eventually flow into receiving water bodies and affect them, the distances of the pollution source from the river and the receiving water body are selected as migration factors.

[0042] Statistical analysis

[0043] Using ArcGIS, a 30m×30m fishing net was created for each factor raster, and the values ​​were extracted to points. First, based on the Spearman correlation coefficient as an evaluation index, correlation analysis was performed on the phosphorus index and each factor to determine whether relationships existed between variables. Then, the Boruta Random Forest algorithm was used to rank the importance of source factors, migration factors, and factors influencing the phosphorus index, identifying key factors affecting phosphorus loss from farmland in the Erhai Lake basin. Random forest, a classic machine learning model, is an ensemble learning method proposed by Leo Breiman in 2001. It improves prediction accuracy by constructing multiple decision trees and integrating the results. In assessing variable importance, the main basis is to observe the degree of decrease in model accuracy by shuffling the values ​​of a certain variable.

[0044] Correlation analysis showed that the phosphorus index was significantly negatively correlated with the distance from the source to the river (p<0.001) and positively correlated to some extent with the soil erosion modulus (p<0.05). Figure 1 ).

[0045] Random forest-based ranking of the importance of phosphorus index influencing factors shows that the distance from the source to the river is the most significant factor affecting the phosphorus index of farmland in the Erhai Lake basin, with an importance eigenvalue of 31.4, followed by soil erosion modulus, with an importance eigenvalue of 3.1. Figure 2 ).

[0046] Example 2

[0047] Scenario simulation

[0048] Scenario analysis combines qualitative and quantitative methods to describe and analyze future development paths. This invention takes the Erhai Lake basin as the research object and, based on the "Regulations on the Protection and Management of Erhai Lake in Dali Bai Autonomous Prefecture, Yunnan Province" and related regulations, conducts a scenario simulation study on the reduction of phosphorus surplus in the basin and the prohibition of fertilizer use in the surrounding areas of rivers and lakes.

[0049] Scenario-based measures were implemented to reduce fertilizer application, controlling the phosphorus surplus in both vegetable continuous cropping and rice-rapeseed rotation systems to less than 100 kg / hm². 2 200kg / hm 2 300kg / hm 2 (corresponding to low, medium, and high risk levels of the source factors respectively), and a fertilizer-free zone is set up within 500m around the river (Table 2).

[0050] To further explore specific strategies for reducing phosphorus surplus, an in-situ observation experiment was conducted in a field runoff plot, setting up different fertilization scenarios to study their phosphorus reduction effects. Based on conventional fertilization (the fertilization amount that local farmers are accustomed to), the fertilization method was changed, setting up two scenarios: fertilization with all-organic fertilizer and optimized fertilization (with conventional fertilizer nitrogen and phosphorus fertilizers reduced by 25%, and potassium fertilizer increased by 25%).

[0051]

[0052] Data source

[0053] In this invention, the spatiotemporal distribution of the two planting patterns was obtained through data from the National Land Survey, high-resolution imagery, and on-site verification of crop planting types in 80 plots. Fertilizer application rates (organic and chemical) and application methods were obtained by surveying typical plots from 3-5 households in each administrative village. Soil total phosphorus data was obtained by sampling 311 samples from typical plots in 16 townships and administrative villages in the Erhai Lake basin. Parameters used to calculate phosphorus surplus included: phosphorus content brought in by organic fertilizer, phosphorus content brought in by chemical fertilizer, phosphorus content brought in by precipitation, phosphorus content brought in by irrigation water, phosphorus content brought in by straw return to the field, phosphorus content carried away by harvested materials, phosphorus content carried away by waste, and phosphorus content carried away by surface runoff. These parameters were obtained from the in-situ nutrient loss monitoring experiment established in this study. Rainfall data used to calculate the migration factor came from data from 90 rain gauge stations already deployed within the basin. Digital elevation model (DEM) data with a resolution of 30m was obtained from the geospatial data cloud platform (https: / / www.gscloud.cn / ).

[0054] result

[0055] Using data from the Third National Land Survey and high-resolution imagery, and through on-site verification of crop planting types in various plots, a spatial distribution map of vegetable rotation and rice-rapeseed cropping within the watershed was obtained (see...). Figure 3-4The Erhai Lake basin has a total vegetable rotation area of ​​1,667 hectares, mainly distributed in Dali Town and Yinqiao Town in the west of Erhai Lake and Wase Town in the east. There is also a rice-rapeseed rotation area of ​​1,030 hectares, mainly distributed in Xizhou Town in the west of Erhai Lake and Haidong Town in the east. Overall, both planting patterns are primarily concentrated in the lakeside areas of the western region.

[0056] The results of routine fertilization phosphorus surplus monitoring in runoff plots showed that ( Figure 5 The soil phosphorus surplus in the vegetable-continuous cropping plot was 545.05 kg / hm2, which is classified as extremely high risk according to the source factor risk level. The soil phosphorus surplus in the rice-rapeseed plot was 297.25 kg / hm2, which is classified as high risk according to the source factor risk level.

[0057] Based on the actual conditions in this region and referring to other research results, the risk of phosphorus loss in crop production in the Erhai Lake Basin is divided into five levels: extremely low (phosphorus index < 6), low (phosphorus index range 6-10), medium (phosphorus index range 10-14), high (phosphorus index range 14-17), and extremely high (phosphorus index > 17). In the rice-rapeseed rotation system, the proportions of areas with extremely low risk, low risk, medium risk, high risk, and extremely high risk are 43.2%, 0.0%, 42.2%, 14.6%, and 0.0%, respectively. Figure 6 High-risk and extremely high-risk areas accounted for 14.6% of the total area, mainly distributed in Xizhou Town in the west and Haidong Town in the east, and concentrated along the river. The proportions of vegetable rotation areas at extremely low risk, low risk, medium risk, high risk, and extremely high risk were 25.5%, 0.0%, 1.5%, 36.8%, and 36.2%, respectively. Figure 7 High-risk and extremely high-risk areas accounted for 73% of the total area, mainly distributed in Dali Town and Yinqiao Town in the west, and primarily in areas near rivers. Compared with the rice-rapeseed rotation system, the total area of ​​areas with high and extremely high risk of phosphorus loss from perennial terrestrial vegetables increased by 709.2%.

[0058] Table 3 shows the simulation results of phosphorus loss risk scenario analysis in crop production. Different scenarios have different impacts on phosphorus loss risk. In single-measure scenarios, the loss risk gradually decreases as the phosphorus surplus decreases. Scenario S2 represents the most cost-effective strategy among the single-measure scenarios, namely, controlling the phosphorus surplus in both vegetable continuous cropping and rice-rapeseed rotation systems to below 200 kg / hm². 2 .

[0059] Compared to phosphorus surplus reduction scenarios S2 and S3, scenarios considering transportation factors show a slightly lower reduction rate of phosphorus loss risk, but still significantly higher than that of S1. Combined scenarios (S5, S6, S7) that consider both phosphorus surplus reduction and transportation factors show higher reduction efficiency than the individual scenarios. However, compared to scenario S4 which only considers transportation factors, the combined scenarios only increase the reduction rate of phosphorus loss risk by 0-1.37%. Therefore, scenario S4 offers significantly better value than the combined scenarios (S5, S6, S7).

[0060] In summary, scenarios S2 and S4 represent two cost-effective strategies for controlling phosphorus loss risk. To further investigate the impact of fertilization methods on phosphorus loss risk in crop production, this study modified fertilization methods based on conventional fertilization (the local farmers' habitual application rate), setting up two scenarios: full organic fertilizer substitution and optimized fertilization (reducing nitrogen and phosphorus fertilizer application by 25% and increasing potassium fertilizer application by 25%). In-situ monitoring experiments of nutrient loss in runoff plots were conducted to obtain the phosphorus surplus content under both fertilization methods. The monitoring results showed that the phosphorus surplus under the full organic fertilizer substitution fertilization method in the perennial vegetable planting pattern was 464.16 kg / hm². 2 The optimized fertilization resulted in a phosphorus surplus of 465.04 kg / hm². 2 The phosphorus surplus in the rice-rapeseed rotation system under the conditions of full organic fertilizer substitution and optimized fertilization was 251.94 hm². 2 160.06hm 2 Under both fertilization adjustment methods, the risk level of phosphorus surplus for perennial upland vegetables remained at an extremely high level, while the risk level of phosphorus surplus in the rice-rapeseed rotation model decreased from high to medium risk through optimized fertilization. This indicates that adjusting fertilization methods has a weak effect on reducing the risk of phosphorus loss from continuous vegetable cropping, and prohibiting fertilization within 500 meters of riverbanks should be a primary consideration. Under optimized fertilization, the phosphorus surplus in the rice-rapeseed rotation model was less than 200 kg / hm². 2 This falls within the phosphorus surplus range defined in scenario S2. Therefore, when formulating strategies to reduce the risk of phosphorus loss in rice-rapeseed rotation systems, it is important to focus on optimizing fertilization to control the phosphorus surplus.

[0061] Table 3 Scenarios for reducing phosphorus loss risk in medium-risk, high-risk, and extremely high-risk areas.

[0062]

[0063] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for assessing the risk of phosphorus loss in different crop systems in a plateau watershed, characterized in that: Includes the following steps: S1, obtain the source factor evaluation indicators of the target area: phosphorus surplus and total phosphorus in the soil, as well as the migration factor evaluation indicators: surface runoff depth, soil erosion modulus, distance of the target area from the river and distance of the target area from the target water body; S2, assesses phosphorus loss risk using the phosphorus index; the formula for calculating the phosphorus index is: ; In the formula, PI represents the phosphorus loss risk index, and S i and W i These are the risk values ​​and weights corresponding to the source factor evaluation indicators, T. j and W j These are the risk values ​​and weights corresponding to the migration factor evaluation indicators; The formula for calculating phosphorus surplus is: Soil phosphorus surplus = Soil phosphorus income - Soil phosphorus expenditure; Soil phosphorus income = phosphorus brought in by organic fertilizer + phosphorus brought in by chemical fertilizer + phosphorus brought in by precipitation + phosphorus brought in by irrigation water + phosphorus brought in by straw returning to the field; Soil phosphorus expenditure = phosphorus carried away by harvest + phosphorus carried away by waste + phosphorus carried away by surface runoff.

2. The method for assessing the risk of phosphorus loss in different crop systems in a plateau watershed according to claim 1, characterized in that: The total phosphorus content in the soil was obtained by sampling and testing typical plots in various townships and administrative villages in the watershed. Soil from the topsoil layer of 0-20 cm was collected from each plot, and after mixing, 1-1.5 kg was taken by quartering. After air drying, the sample was passed through a 60-mesh sieve for later use and tested according to the NaOH fusion-molybdenum antimony colorimetric method in "Soil Agrochemical Analysis".

3. The method for assessing the risk of phosphorus loss in different crop systems in a plateau watershed according to claim 1, characterized in that: The surface runoff depth is the product of rainfall and runoff coefficient, and the soil erosion modulus is calculated using the modified general soil loss equation (RUSLE).

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