Method for preparing vegetable culture medium from garden waste

Abstract

The invention discloses a method for preparing a vegetable culture medium by utilizing garden waste. The method comprises the following steps of S1, data preparation, S2, model establishment, S3, system classification, S4, data acquisition, S5, waste preparation and S6, productivity simulation. The step S1 of data preparation comprises the following steps: S11, site selection; s12, arranging detection points; s13, crop selection; step S2, establishing a model, including the following steps: S21, establishing a climate model; s22, constructing parameters; s23, establishing a productivity model; s24, determining data; s25, data acquisition is carried out; the step S3 of classification system comprises the following steps: S31, crop classification; S41, climate data; s42, data interference; s43: a future factor; s5, productivity simulation: S51, selecting a field test area in the area; according to the method for preparing the vegetable culture medium from the garden waste, appropriate crop growth of general horticultural crops under different climates can be calculated through the simulation method, and then the purpose of improving productivity is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of waste preparation, specifically a method for preparing vegetable cultivation substrates by utilizing garden waste. Background Art

[0002] With the rapid development of facility agriculture in my country, seedling substrates have become increasingly important and have become an important part of vegetable cultivation. The role of vegetable seedling cultivation is to promote crop growth and development and harvest early; reduce and avoid the impact of adversity on crop growth and development; facilitate intensive management, achieve labor saving, water saving and effective control of the occurrence of pests and diseases; ensure uniform growth in the field, improve the quality of vegetable products, and facilitate mechanized one-time harvesting. Vegetable seedling substrate is a high-quality soil made of organic, inorganic and other materials based on the nutrients required for seedling growth as seedling soil. The traditional seedling cultivation method of vegetable farmers is to directly use a mixture of organic fertilizer and garden soil as seedling soil, but the effect is not very ideal. In particular, some areas are facing the problem of reduced productivity of crops in the normal growth cycle due to climate interference and changes. This change may have a significant impact on the productivity of horticultural crops. Therefore, a method for preparing a vegetable cultivation substrate using garden waste is provided to solve the above problems. Summary of the Invention

[0003] The object of the present invention is to provide a method for preparing vegetable cultivation substrate using garden waste, so as to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for preparing a vegetable cultivation substrate using garden waste, comprising the following steps: S1 data preparation, S2 model establishment, S3 classification system, S4 data acquisition, S5 waste preparation, and S6 productivity simulation; Step S1 data preparation includes the following steps: S11: Site selection: select the horticultural experimental area based on the conditions required to simulate crop productivity: S12: Lay out detection points, typical monitoring points and collect data from each measuring point; S13: Crop selection: select a variety of common horticultural crops and prepare for planting in the corresponding test areas; Step S2 establishes a model, including the following steps: S21: Climate model: A climate model suitable for horticultural crop growth was established in each experimental area and coupled with the EPIC crop growth model to form a growth model that grows with climate change; S22: Construct parameters to build a general crop growth model with fewer parameters and higher accuracy for multiple crops, which simulates the crop phenological development process based on climate change; S23: Productivity model, which integrates crop leaf area index, root depth and soil moisture into crop interaction interfaces, and forms a crop productivity model based on climate change; S24: Data determination: using productivity model data to perform model calibration and verification, and determining crop parameter values and soil parameter values of the crop productivity model; S25: Data collection, collecting and recording data used in determining the crop productivity model; Step S3 classifies the system, including the following steps: S31: Crop classification, establish a crop classification system, and establish a crop classification system for remote sensing based on the information identifiability of electronic information technology images; Step S4 data acquisition includes the following steps: S41: Climate data: Based on the meteorological data of the crop growth environment in the experimental area, data on crop growth under different climatic conditions are collected; S42: Disturbance data: Based on the meteorological data of the crop growth environment in the experimental area, the climate is adjusted, the crop growth environment is disturbed, and data under each climate is collected; S43: Future factors, using the high-resolution climate model ClimateAP to generate future climate data for the experimental area, forming a high-precision climate dataset; Step S5, waste preparation, comprises the following steps: S51: Garden waste is crushed using a crusher; S52: dehydrating the crushed waste; S53: mixing the dehydrated waste with a fertilizer additive; S54: Place the mixed waste and scraps in the experimental field; Step S6 productivity simulation includes the following steps: S51: Select a field trial area within the region and deploy multiple typical monitoring points within the trial area. The monitoring points should be representative of the region and cover the main soil types, crop types, and climate changes in the region. S52: Collect soil samples from different depths at each monitoring point, measure the bulk density of the soil samples using a drying method, and obtain the particle size data of the soil samples using a particle analyzer; S53: Monitor and record the crop growth data at each monitoring point during each climate change period; S54: Regularly monitor and record crop growth indicators and climate data. Aboveground dry matter mass is measured using the 75°C constant temperature drying method.

[0005] Preferably, step S6 data verification is also included, in which the model is parameterized and verified based on the measured data from the experiment, and finally the data of the above-mentioned sample plots are used as the input data of the model, and the model is run with the simulated climate change scenario as the driving variable, and the obtained results are analyzed and studied. The simulation results can reveal the changes in the productivity of horticultural crops during climate change, and then analyze the impact of climate change on horticultural crops.

[0006] Preferably, in step S6 data verification, the model is parameterized and verified based on the measured photosynthetic data of the test plot and the survey data, and finally the data of the above plot is used as the input data of the model.

[0007] Preferably, the step S1 of data preparation further includes the following steps: S14: Select field test areas within the region and set up typical monitoring points within the test areas to collect soil particle size and bulk density data, crop phenological development data, soil moisture monitoring data, and irrigation data at each monitoring point.

[0008] Preferably, the data collection step S25 further includes the following steps: S251: Meteorological data collection: establish the element observation values in the meteorological data of each test area and the meteorological element values of all grids to obtain the spatialized meteorological factor dataset; S252: Soil collection, using a weighted synthesis method to construct soil quality parameters representing the overall soil conditions for crop growth in the experimental area.

[0009] Preferably, the step S11: studying the main factors and mechanisms of the gradual broad-leaved transformation of coniferous forests in the horticultural crop classification system in site selection, and establishing experimental plots according to site conditions; the step S23: obtaining environmental data in the productivity model and predicting future environmental factors, obtaining climate factor data from meteorological stations related to the experimental base, and establishing a data model.

[0010] Preferably, the productivity model in step S5 mainly includes a climate model and a crop model. Since climate variables are the main driving variables of the crop model, the crop model can be directly used to collect data on the impact of climate change on crops.

[0011] Preferably, in the model establishment step S2, a climate model suitable for the Asia-Pacific region is established, and the productivity model adopts bilinear interpolation and dynamic local regression methods to downscale the climate data into scale-free point data.

[0012] Preferably, the productivity model generates 9 environmental factor data: average annual temperature, average temperature of the hottest month, average temperature of the coldest month, annual accumulated temperature greater than 5°C, average annual precipitation, wet season precipitation, dry season precipitation, precipitation pH value, and annual accumulated temperature less than 0°C.

[0013] Preferably, the productivity model generates 9 environmental factor data: average annual temperature, average temperature of the hottest month, average temperature of the coldest month, annual accumulated temperature greater than 20°C, average annual precipitation, wet season precipitation, dry season precipitation, precipitation pH value, and annual accumulated temperature less than 5°C.

[0014] Compared with the existing technology, this solution designs a method for preparing vegetable cultivation substrate using garden waste, which has the following beneficial effects: (1) The program can use this simulation method to calculate the appropriate crop growth for common horticultural crops under different climates, thereby achieving the goal of improving productivity.

[0015] (2) The scheme can effectively improve the accuracy of model predictions by integrating the most important physiological process of horticultural crops, photosynthesis and climate change, into the productivity model. Secondly, it can effectively improve the scientific nature of crop parameters and the accuracy of data, realize the simulation data obtained for climate interference changes, and then obtain the growth conditions of different crops under different climatic environments for general horticultural crops, so as to improve productivity. DETAILED DESCRIPTION

[0016] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] The first technical solution provided by the present invention is a method for preparing a vegetable cultivation substrate using garden waste, comprising the following steps: S1 data preparation, S2 model establishment, S3 classification system, S4 data acquisition, S5 waste preparation, and S6 productivity simulation. Step S1 data preparation includes the following steps: S11: Site selection: select the horticultural experimental area based on the conditions required to simulate crop productivity: S12: Lay out detection points, typical monitoring points and collect data from each measuring point; S13: Crop selection: select a variety of common horticultural crops and prepare for planting in the corresponding test areas; Step S2 establishes a model, including the following steps: S21: Climate model: A climate model suitable for horticultural crop growth was established in each experimental area and coupled with the EPIC crop growth model to form a growth model that grows with climate change; S22: Construct parameters to build a general crop growth model with fewer parameters and higher accuracy for multiple crops, which simulates the crop phenological development process based on climate change; S23: Productivity model, which integrates crop leaf area index, root depth and soil moisture into crop interaction interfaces, and forms a crop productivity model based on climate change; S24: Data determination: using productivity model data to perform model calibration and verification, and determining crop parameter values and soil parameter values of the crop productivity model; S25: Data collection, collecting and recording data used in determining the crop productivity model; Step S3 classifies the system, including the following steps: S31: Crop classification, establish a crop classification system, and establish a crop classification system for remote sensing based on the information identifiability of electronic information technology images; Step S4 data acquisition includes the following steps: S41: Climate data: Based on the meteorological data of the crop growth environment in the experimental area, data on crop growth under different climatic conditions are collected; S42: Disturbance data: Based on the meteorological data of the crop growth environment in the experimental area, the climate is adjusted, the crop growth environment is disturbed, and data under each climate is collected; S43: Future factors, using the high-resolution climate model ClimateAP to generate future climate data for the experimental area, forming a high-precision climate dataset; Step S5, waste preparation, comprises the following steps: S51: Garden waste is crushed using a crusher; S52: dehydrating the crushed waste; S53: mixing the dehydrated waste with a fertilizer additive; S54: Place the mixed waste and scraps in the experimental field; Step S6 productivity simulation includes the following steps: S51: Select a field trial area within the region and deploy multiple typical monitoring points within the trial area. The monitoring points should be representative of the region and cover the main soil types, crop types, and climate changes in the region. S52: Collect soil samples from different depths at each monitoring point, measure the bulk density of the soil samples using a drying method, and obtain the particle size data of the soil samples using a particle analyzer; S53: Monitor and record the crop growth data at each monitoring point during each climate change period; S54: Regularly monitor and record crop growth indicators and climate data. Aboveground dry matter mass is measured using the 75°C constant temperature drying method.

[0018] It also includes step S6 data verification, parameterizing and verifying the model based on the measured data from the experiment, and finally using the data from the above-mentioned sample plots as the input data of the model, and running the model with the simulated climate change scenario as the driving variable, and analyzing the results obtained. The simulation results can reveal the changes in the productivity of horticultural crops during climate change, and then analyze the impact of climate change on horticultural crops.

[0019] In the data verification step S6, the model is parameterized and verified based on the measured photosynthetic data of the test plot and the survey data, and finally the data of the above plot is used as the input data of the model.

[0020] The step S1 data preparation further includes the following steps: S14: Select field test areas within the region and set up typical monitoring points within the test areas to collect soil particle size and bulk density data, crop phenological development data, soil moisture monitoring data, and irrigation data at each monitoring point.

[0021] The data collection step S25 further includes the following steps: S251: Meteorological data collection: establish the element observation values in the meteorological data of each test area and the meteorological element values of all grids to obtain the spatialized meteorological factor dataset; S252: Soil collection: Use a weighted comprehensive method to construct soil quality parameters that represent the overall soil conditions for crop growth in the experimental area. Specifically, this includes selecting corresponding soil properties as soil quality evaluation indicators based on different soil functions and preset purposes.

[0022] Among them, the step S11: studying the main factors and mechanisms of the gradual broad-leaved transformation of coniferous forests in the horticultural crop classification system in site selection, and establishing experimental plots based on site conditions; the step S23: obtaining environmental data in the productivity model and predicting future environmental factors, obtaining climate factor data from meteorological stations related to the experimental base, and establishing a data model.

[0023] The productivity model in step S5 mainly includes a climate model and a crop model. Since climate variables are the main driving variables of the crop model, the crop model can be directly used to collect data on the impact of climate change on crops.

[0024] In the step S2 of establishing the model, a climate model suitable for the Asia-Pacific region is established, and the productivity model adopts bilinear interpolation and dynamic local regression methods to downscale the climate data into scale-free point data.

[0025] Among them, the productivity model generates 9 environmental factor data: average annual temperature, average temperature of the hottest month, average temperature of the coldest month, annual accumulated temperature greater than 5°C, average annual precipitation, wet season precipitation, dry season precipitation, precipitation pH value, and annual accumulated temperature less than 0°C.

[0026] The first technical solution provided by the present invention: the productivity model generates 9 environmental factor data: average annual temperature, average temperature of the hottest month, average temperature of the coldest month, annual accumulated temperature greater than 20°C, average annual precipitation, wet season precipitation, dry season precipitation, precipitation pH value, and annual accumulated temperature less than 5°C.

[0027] The difference from the first technical solution is that it uses different temperature bases to record data on the impact of climate change on crop productivity.

[0028] The other steps in the second technical solution are the same as those in the first technical solution.

[0029] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.

[0030] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for preparing a vegetable cultivation substrate using garden waste, characterized in that: The following steps are involved: S1 data preparation, S2 model building, S3 classification system, S4 data acquisition, S5 waste preparation and S6 productivity simulation; Step S1 data preparation includes the following steps: S11: Site selection: select the horticultural experimental area based on the conditions required to simulate crop productivity: S12: Lay out detection points, typical monitoring points and collect data from each measuring point; S13: Crop selection: select a variety of common horticultural crops and prepare for planting in the corresponding test areas; Step S2 establishes a model, including the following steps: S21: Climate model: A climate model suitable for horticultural crop growth was established in each experimental area and coupled with the EPIC crop growth model to form a growth model that grows with climate change; S22: Construct parameters to build a general crop growth model with fewer parameters and higher accuracy for multiple crops, which simulates the crop phenological development process based on climate change; S23: Productivity model, which integrates crop leaf area index, root depth and soil moisture into crop interaction interfaces, and forms a crop productivity model based on climate change; S24: Data determination: using productivity model data to perform model calibration and verification, and determining crop parameter values and soil parameter values of the crop productivity model; S25: Data collection, collecting and recording data used in determining the crop productivity model; Step S3 classifies the system, including the following steps: S31: Crop classification, establish a crop classification system, and establish a crop classification system for remote sensing based on the information identifiability of electronic information technology images; Step S4 data acquisition includes the following steps: S41: Climate data: Based on the meteorological data of the crop growth environment in the experimental area, data on crop growth under different climatic conditions are collected; S42: Disturbance data: Based on the meteorological data of the crop growth environment in the experimental area, the climate is adjusted, the crop growth environment is disturbed, and data under each climate is collected; S43: Future factors, using the high-resolution climate model ClimateAP to generate future climate data for the experimental area, forming a high-precision climate dataset; Step S5, waste preparation, comprises the following steps: S51: Garden waste is crushed using a crusher; S52: dehydrating the crushed waste; S53: mixing the dehydrated waste with a fertilizer additive; S54: Place the mixed waste and scraps in the experimental field; Step S6 productivity simulation includes the following steps: S51: Select a field trial area within the region and deploy multiple typical monitoring points within the trial area. The monitoring points should be representative of the region and cover the main soil types, crop types, and climate changes in the region. S52: Collect soil samples from different depths at each monitoring point, measure the bulk density of the soil samples using a drying method, and obtain the particle size data of the soil samples using a particle analyzer; S53: Monitor and record the crop growth data at each monitoring point during each climate change period; S54: Regularly monitor and record crop growth indicators and climate data. Aboveground dry matter mass is measured using the 75°C constant temperature drying method.

2. The method for preparing a vegetable cultivation substrate using garden waste according to claim 1, wherein: It also includes step S6 data verification, parameterizing and verifying the model based on the measured data from the experiment, and finally using the data from the above-mentioned sample plots as the input data of the model, and running the model with the simulated climate change scenario as the driving variable, and analyzing the results obtained. The simulation results can reveal the changes in the productivity of horticultural crops during climate change, and then analyze the impact of climate change on horticultural crops.

3. The method for preparing a vegetable cultivation substrate using garden waste according to claim 2, wherein: In step S6, data verification, the model is parameterized and verified based on the measured photosynthetic data of the test plot and the survey data, and finally the data of the above plot is used as the input data of the model.

4. The method for preparing a vegetable cultivation substrate using garden waste according to claim 1, wherein: The step S1 data preparation further includes the following steps: S14: Select field test areas within the region and set up typical monitoring points within the test areas to collect soil particle size and bulk density data, crop phenological development data, soil moisture monitoring data, and irrigation data at each monitoring point.

5. The method for preparing a vegetable cultivation substrate using garden waste according to claim 1, wherein: The step S25 data collection further includes the following steps: S251: Meteorological data collection: establish the element observation values in the meteorological data of each test area and the meteorological element values of all grids to obtain the spatialized meteorological factor dataset; S252: Soil collection, using a weighted synthesis method to construct soil quality parameters representing the overall soil conditions for crop growth in the experimental area.

6. The method for preparing a vegetable cultivation substrate using garden waste according to claim 1, wherein: The step S11: studying the main factors and mechanisms of the gradual broad-leaved transformation of coniferous forests in the horticultural crop classification system during site selection, and establishing experimental plots based on site conditions; the step S23: obtaining environmental data in the productivity model and predicting future environmental factors, obtaining climate factor data from meteorological stations related to the experimental base, and establishing a data model.

7. The method for preparing vegetable cultivation substrate using garden waste according to claim 1, wherein: The productivity model in step S5 mainly includes a climate model and a crop model. Since climate variables are the main driving variables of the crop model, the crop model can be directly used to collect data on the impact of climate change on crops.

8. The method for preparing vegetable cultivation substrate using garden waste according to claim 1, wherein: In the step S2 of establishing the model, a climate model suitable for the Asia-Pacific region is established, and the productivity model adopts bilinear interpolation and dynamic local regression methods to downscale the climate data into scale-free point data.

9. The method for preparing a vegetable cultivation substrate using garden waste according to claim 8, characterized in that: The productivity model generates data on nine environmental factors: average annual temperature, average temperature of the hottest month, average temperature of the coldest month, annual accumulated temperature greater than 5°C, average annual precipitation, wet season precipitation, dry season precipitation, precipitation pH, and annual accumulated temperature less than 0°C.

10. The method for preparing vegetable cultivation substrate using garden waste according to claim 8, characterized in that: The productivity model generates data on nine environmental factors: average annual temperature, average temperature of the hottest month, average temperature of the coldest month, annual accumulated temperature greater than 20°C, average annual precipitation, wet season precipitation, dry season precipitation, precipitation pH, and annual accumulated temperature less than 5°C.