CN110826797B - Method for determining optimal agricultural planting system based on multi-target comprehensive evaluation system - Google Patents
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Abstract
The invention discloses a method for determining an optimal agricultural planting system in a research area based on a multi-target comprehensive evaluation system, which comprises the following steps of S1: localization of the agricultural system model; s2: preliminarily setting a plurality of alternative agricultural planting systems; s3, acquiring climate, soil and agricultural management data information of a research area, and simulating and calculating target parameters of each alternative agricultural planting system in a research period by using the agricultural system model, wherein the target parameters comprise crop yield, nitrous oxide emission and soil organic carbon change; s4: calculating the Carbon Footprint (CF) of agricultural production of each alternative agricultural planting system in the research period according to nitrous oxide emission, soil organic carbon change and crop yield; s5, comparing the Carbon Footprints (CF) of agricultural production in the research period for each alternative agricultural planting system, and selecting the planting system with the minimum carbon footprint as the optimal agricultural planting system in the research period in the research area.
Description
Technical Field
The invention relates to the technical field of agricultural information, in particular to a method for determining an optimal agricultural planting system in a research area based on a multi-target comprehensive evaluation system.
Background
A regional farming system requires scientific planning and selection based on regional climate, resources and production environment conditions. The development of a proper planting system in a proper area can give consideration to production, economic and environmental benefits at the same time, and the sustainable development of agriculture is realized. However, in practice, the determination of a regional farming system mainly continues historical traditions, pursuit of high yield on a single occasion, pursuit of economic benefits on a single occasion, or following a mass flow or following administrative directives; the agricultural planting system in the region lacks scientific, comprehensive and long-term planning; the existing evaluation technology of agricultural planting systems is mainly used for evaluating the yield or the obtained economic benefit data obtained by the field contrast test in the past years.
Therefore, the existing agricultural planting system evaluation technology is time-consuming, labor-consuming, high in cost, one-sided in evaluation and time lag, and cannot be used for carrying out quantitative evaluation on comprehensive benefits and long-term scientific planning of future agricultural planting systems due to unpredictability. Due to the lack of long-term scientific planning, in recent years, the agriculture in China has structural contradictions, and the main contradiction is on the supply side. Agricultural planting structure adjustment and optimization need to be developed, corresponding scientific theory guidance and technical support are needed, but the existing technology cannot meet the requirement.
Accordingly, new techniques are needed to at least partially address the above-described limitations of the prior art.
Disclosure of Invention
Aiming at the defects of the prior art, the method adopts an advanced agricultural system model simulation technology, carries out model parameter optimization and verification based on farmland experiments and recorded data, sets a planting system which can be selected in a region, carries out comprehensive simulation of the agricultural system, establishes a multi-target comprehensive evaluation system, evaluates the performance of each planting system and determines the optimal planting system.
According to one aspect of the invention, a method for determining an optimal agricultural planting system based on a multi-target comprehensive evaluation system is provided, which comprises the following steps:
s1: according to the experimental observation data of crops in the research area, parameter optimization and verification are carried out on the agricultural system model, and localization of the model is realized;
s2: preliminarily setting a plurality of alternative agricultural planting systems, for example, for model simulation, test and comparison selection;
s3, acquiring climate, soil and agricultural management data information of a research area, and simulating and calculating target parameters of each alternative agricultural planting system in a research period by using the agricultural system model, wherein the target parameters comprise crop yield, nitrous oxide emission and soil organic carbon change;
nitrous oxide emissions are represented by the following formula (1):
wherein E isN20Global warming potential in kgCO as a cumulative annual amount of nitrous oxide emitted from soil2-eq ha-1；N2O is the annual cumulative amount of nitrous oxide (kg CO) emitted from the soil2-eq ha-1) α is a Global Warming Potential (GWP) factor of 298.
The soil organic carbon change is represented by the following formula (2):
wherein Δ SCS is the annual change in kg CO of Soil Organic Carbon (SOC) in the 0-20cm soil surface layer over the study period2-eq ha-1；SCSendSCS for Soil Organic Carbon (SOC) storage in the 0-20cm soil surface layer at the last year of the study periodbeginSoil Organic Carbon (SOC) reserves in the 0-20cm soil surface layer for the first year of the study period; m is the period of investigationThe number of years;
wherein the research period comprises a past period referred to as a reference climate period and a future period referred to as a future climate period;
s4: according to nitrous oxide (N)2O) discharge (E)N20) And soil organic carbon change (Δ SCS) and crop yield, calculating the Carbon Footprint (CF) of agricultural production over the study period for each alternative agricultural planting system, as represented by formula (3) below:
wherein the CF unit is kg CO2-eq kg-1(ii) a Yield is the annual crop Yield of each agricultural planting system, and the unit is kgha-1；
S5, comparing the Carbon Footprints (CF) of agricultural production in the research period for each alternative agricultural planting system, and selecting the planting system with the minimum carbon footprint as the optimal agricultural planting system in the research period in the research area.
According to an embodiment of the present invention, where m is 10, for example, the base climate time period may be the past 2000-year 2009, the future climate time period may be the future 2050-year 2059, and so on.
According to the embodiment of the present invention, the study period is plural, for example, the past period referred to as the reference climate period and the future period referred to as the future climate period may be included, such as the year 2009 2000 and 2050 and 2059 described above.
According to an embodiment of the present invention, wherein the target parameters further include Nitrogen Use Efficiency (NUE) and Water Use Efficiency (WUE), as shown in the following formulas (4) and (5):
wherein ET is the total annual evapotranspiration of the agricultural planting system, irrigation is the annual irrigation quantity, and the Napplication rate is the annual nitrogen application quantity;
said method comprises the determination of the optimal farming system using the integrated index of development (IntInd) instead of said Carbon Footprint (CF) as evaluation index, i.e. the above step 5) is replaced by:
calculating the integrated indicator of development (IntInd) as shown in the following formulas (6) and (7):
IntInd＝CF/(βNUE+γWUE) (6)
+β+γ＝100％ (7)
wherein β and gamma are respectively weight coefficients of Carbon Footprint (CF), Nitrogen Utilization Efficiency (NUE) and Water Utilization Efficiency (WUE) of agricultural production, the value ranges of the weight coefficients are respectively 35-45%, 26-34% and 26-34%, and the agricultural planting system with the minimum IntInd value is the optimal agricultural planting system in the research area.
According to an embodiment of the invention, wherein the agricultural system model is an APSIM or CERES series crop model.
According to an embodiment of the invention, wherein the crop is selected from wheat, corn, soybean and rice.
According to an embodiment of the invention, wherein the agricultural planting system is selected from the group consisting of wheat crop rotation, corn crop rotation, soybean crop rotation, wheat-soybean crop rotation, corn-soybean crop rotation, wheat-corn crop rotation without irrigation and wheat-corn crop rotation optimization systems.
According to the embodiment of the invention, the localization in the step S1 comprises the steps of collecting soil parameters, meteorological parameters and field management parameters of the site scale of the research area, and gradually calibrating the variety parameters based on the G L UE method, wherein (i) the variety parameters influencing flowering and maturation are optimized according to the accumulated temperature required by the variety, and (ii) the variety parameters determining yield are calibrated.
According to an embodiment of the invention, wherein said stepwise calibration of the breed parameters comprises estimation of deviations from measured and simulated values of fertility and yield using Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE) variables, for example, as shown in equations (8) and (9) below:
wherein, OiAnd SiObserved and simulated values of variables (e.g. growth period or yield, etc.), OavgThe average value of the observed values; n is the sample size.
Compared with the prior art, the invention has the following advantages:
1) the advanced agricultural system simulation model is adopted to carry out comprehensive simulation of the agricultural system, field tests are replaced, and the defects of time consumption, labor consumption, manpower, material resources, financial resources and the like in the prior art that the cost is high and the like are overcome.
2) The advanced agricultural system simulation model can be used for simultaneously testing and evaluating a plurality of planting systems, a plurality of production, environment and economic indexes, and the evaluation is convenient, flexible and comprehensive. The defects of inflexible original technology, few monitoring indexes, one-sided evaluation and the like are overcome.
3) The advanced agricultural system simulation model can be used for simulating the performances of the planting system in any time period in the past, the present and the future, the evolution of the climate environment and the like is considered, and timeliness and predictability are achieved. The defects of time lag, no advance predictability and the like in the prior art are overcome.
4) The advanced agricultural system simulation model can be flexibly applied to various regions, multiple points and various planting systems, and the restriction of the application of the prior art on space is solved.
In a word, the technology can comprehensively evaluate and predict the performance of various optional planting systems in a region in the aspects of production, environment, economy and the like, compares the advantages and disadvantages of various planting systems and determines the optimum planting system.
Drawings
The same reference numbers in the drawings identify the same or similar elements or components. The objects and features of the present invention will become more apparent in view of the following description taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic flow diagram of a method for determining an optimal agricultural planting system based on a multi-objective comprehensive evaluation system according to one embodiment of the present invention.
FIG. 2 is a graphical representation of the results of a method according to one embodiment of the present invention applied to a simulated target parameter in a region of interest in North China plain;
FIG. 3 is a schematic diagram of simulated changes of target parameters under two climate change scenarios (RCP2.6, RCP8.5) of current benchmark (Baseline, 2009 2000) and future (2050), and two cultivation managements (traditional cultivation and protective cultivation) when the method according to one embodiment of the invention is applied to eight agricultural planting systems in a certain research area of North China plain;
fig. 4 is a schematic diagram of simulation results of Carbon Footprint (CF) of eight agricultural planting systems applied to four research areas in north china plain under two climate change scenarios and two farming managements in 2000-2009 and 2050-2059 in the future by using the method according to one embodiment of the invention.
Fig. 5 is a schematic diagram of simulation results of the comprehensive index of development (IntInd) of eight agricultural planting systems applied to four research areas in north china plain in two climate change scenarios and two farming managements in 2000-2009 and 2050-2059 in the future by using the method according to one embodiment of the invention.
Detailed Description
For the purpose of clearly illustrating the aspects of the present invention, preferred embodiments are given below in conjunction with the accompanying drawings. The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses.
It should be understood that the agricultural system model (crop model) referenced in the present invention is known per se, such as the various sub-modules of the model, various parameters, operating mechanisms, simulation processes such as localization, yield simulation, etc., and therefore the present invention focuses on the process of determining the optimal agricultural planting system in the area of investigation using the crop model.
FIG. 1 is a schematic flow diagram of a method for determining an optimal agricultural planting system based on a multi-objective comprehensive evaluation system according to one embodiment of the present invention. As shown in fig. 1, the invention provides a method for determining an optimal agricultural planting system based on a multi-objective comprehensive evaluation system, which specifically comprises the following steps of S1-S5:
and S1, according to the crop experiment observation data, performing parameter optimization and verification on the agricultural system model to realize the localization of the model and ensure that the model has reliable simulation capability. In the present invention, the agricultural system model (crop model) may adopt apsim (agricultural production system simulator) model or ceres (crop evaluation throughput and environmental synthesis) series crop model, and of course, other suitable crop models may also be adopted. These models are known per se in the art, and before they are applied to a specific region, they should generally be optimized, i.e. localized, to suit the local region, using local specific observations (experimental data), for example, data recorded by a local agricultural observation station.
The processes and principles of model parameter optimization (localization) and simulation prediction described above are well known to those skilled in the art, and thus the present invention is only exemplary and briefly described.
For example, for the APSIM model, the crop model parameter optimization may include collecting site-scale soil parameters, meteorological parameters, and field management parameters for the study area, and performing stepwise calibration of the variety parameters (i.e., genetic parameters) based on the G L UE method, (i) optimizing genetic parameters that affect flowering and maturation according to the required temperature of the variety, and (ii) calibrating genetic parameters that determine yield, wherein the G L UE method is well known in the art, such as some crop models with their own G L UE tool, which may be used to perform optimal calibration.
Wherein the soil parameters may include soil type, color, grade, permeability, reflectivity, soil layer thickness, soil moisture evaporation limit (mm), runoff curve number, and soil drainage rate (fraction day), among others-1) 0-1% of photosynthesis factor, lower limit of soil water or 28109and water content of pinch point (cm)3cm-3) Water capacity (cm) in field3cm-3) Saturated water content (cm)3cm-3) Soil volume (g cm)-3) Soil organic carbon, nitrogen (wt.%), soil PH, clay content (wt. -%)<0.002mm particle size) and powder content (wt.% 0.002-0.05mm particle size). The meteorological parameters may include solar radiation on day (MJ m)-2) The highest daily temperature (DEG C), the lowest daily temperature (DEG C), the daily rainfall (mm), and the like. The field management parameters can include site-scale irrigation, variety, fertilization, planting density, seeding mode, and the like.
Genetic parameters may also be referred to as variety parameters, with different parameters for different crops. In the invention, 5-7 relatively key genetic parameters can be selected according to requirements. For example, wheat may include vernalization sensitivity (vern _ sens), photoperiod sensitivity (photop _ sens), temperature of flower bud differentiation to flowering (tt _ floral _ initiation (° Cd)), temperature of emergence to jointing (tt _ end _ of _ jravenile), temperature of filling to maturity (tt _ startgf _ to _ mat), number of seeds per gram of stalk weight (grain _ per _ gram _ stem), and potential filling rate (potential _ grain _ filling _ rate) (gkerrel)-1day-1) Seven genetic parameters). Maize may include 6 genetic parameters: accumulated temperature from emergence to larva (tt _ emerg _ to _ endjuv (° Cd)), accumulated temperature from flowering to the start of filling (tt _ flower _ to _ start _ grain (° Cd)), accumulated temperature from flowering to maturity (tt _ flower _ to _ uniformity (° Cd)), and heat demand change required for flower bud differentiation caused by increase of photoperiod per hour (photoperiod _ slope (° Ch))-1) Maximum number of seeds per ear (Head _ grain _ no _ max), and grain fill rate (mg grain _ gth _ rate)-1day-1)). Other genetic parameters which may also correspond, for example, to rice, may include, for example, the minimum number of days required to reach the heading stage, the minimum number of days required to reach the filling stage, the sensitivity of the development rate to the photoperiod, the maximum number of grains per ear, the grain weight per ear, and the like.
The step-wise calibration of the breed parameters may include estimating deviations from simulated and measured values of fertility and yield using Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE), as shown in equations (8) and (9) above.
After localization of the model at step S1, step S2 may be implemented. More specifically, according to the climate of a research area, the experiment and experience of the past planting system and the like, factors such as regional production, environment, economy, culture and the like are considered, and a plurality of alternative planting systems are preliminarily set for model simulation, test and comparative selection. For example, for northern central plains, the following agricultural planting systems may be considered: CW, wheat single cropping; CM, corn single crop; CS, soybean simple crop; WS, wheat-soybean crop rotation; MS, corn-soybean crop rotation; WM, wheat-corn rotation; WM-NI, wheat-corn crop rotation without irrigation; WM-OPT, wheat-corn crop rotation optimizing system, etc; these planting systems are all present in the area of investigation.
Then, step S3 is performed: and acquiring data information such as climate, soil, agricultural management and the like of a research area, and simulating and calculating target parameters of each alternative agricultural planting system in a research period by using the agricultural system model. More specifically, the target parameters can include production, environment and other indexes, and the parameters can be used for establishing a multi-target (production, environmental economy) comprehensive evaluation index system of the agricultural planting system. For example, the production-related indicators may include crop yield; environmental indicators may include Nitrogen Use Efficiency (NUE), Water Use Efficiency (WUE), evapotranspiration volume (ET), Ground Water Replenishment (GWR), nitrogen leaching, nitrous oxide emission (E)N20) And soil organic carbon changes, etc.
For example, nitrous oxide emissions are represented by the following formula (1):
wherein E isN20Global warming potential in kgCO as a cumulative annual amount of nitrous oxide emitted from soil2-eq ha-1；N2O is the annual cumulative amount of nitrous oxide (kg CO) emitted from the soil2-eq ha-1) α is a Global Warming Potential (GWP) factor of 298.
The soil organic carbon change is represented by the following formula (2):
wherein Δ SCS is the annual change in kg CO of Soil Organic Carbon (SOC) in the 0-20cm soil surface layer over the study period2-eq ha-1；SCSendSCS for Soil Organic Carbon (SOC) storage in the 0-20cm soil surface layer at the last year of the study periodbeginSoil Organic Carbon (SOC) reserves in the 0-20cm soil surface layer for the first year of the study period; m is the number of years of the study period, e.g. m is 10;
the Nitrogen Use Efficiency (NUE) and Water Use Efficiency (WUE) are shown by the following formulas (4) and (5):
wherein ET is the total annual evapotranspiration of the agricultural planting system, irrigation is the annual irrigation volume, and the Napplication rate is the annual nitrogen application volume.
Other target parameters such as Ground Water Replenishment (GWR), nitrogen leaching, etc. may also be derived from model simulations, and are not described herein.
Next, step S4 may be implemented to calculate the Carbon Footprint (CF) of each alternative agricultural planting system for agricultural production at different periods based on nitrous oxide emissions (EN20), soil organic carbon change (Δ SCS), and crop yield, as represented by the following formula (3):
wherein the CF unit is kg CO2-eq kg-1(ii) a Yield is the annual crop Yield of each agricultural planting system, and the unit is kgha-1；
Then, the Carbon Footprints (CF) of agricultural production of each alternative agricultural planting system in the research period are compared, and the planting system with the minimum footprints is selected to be the optimal agricultural planting system in the corresponding period of the research area. Wherein the research period may comprise a plurality of, for example, may comprise a past period referred to as a reference climate period, and a future period referred to as a future climate period; for example, the reference climate time interval can be the past year 2009-2000-.
Preferably, the optimal agricultural planting system may be determined using the integrated index of development (IntInd) instead of the Carbon Footprint (CF) as an evaluation index. The integrated indicator of development (IntInd) is shown in the following formulas (6) and (7):
IntInd＝CF/(βNUE+γWUE) (6)
+β+γ＝100％ (7)
wherein β and gamma are respectively weight coefficients of Carbon Footprint (CF), Nitrogen Utilization Efficiency (NUE) and Water Utilization Efficiency (WUE) of agricultural production, the value ranges of the weight coefficients are respectively 35-45%, 26-34% and 26-34%, and the agricultural planting system with the minimum IntInd value is the optimal agricultural planting system in the research area.
Examples
By taking four places (south-yang, new county, thaan and goldenrain city) in North China plain as a research area and taking wheat and corn as examples, the technical method disclosed by the invention is used for carrying out prediction research on a future optimal planting system in the research area, wherein the research time periods are (Baseline, 2000-shaped and 2009) and future (2050-shaped and 2059), and the weight coefficients of a Carbon Footprint (CF), Nitrogen Utilization Efficiency (NUE) and Water Utilization Efficiency (WUE) are respectively 40%, 30% and 30%, wherein an APSIM model is used for observing data information by adopting climate, soil and site history experiments for many years.
Wherein, the variety parameters and the initial specific values of the wheat and the corn are shown in the following table 1.
The planting period interval of the corn is 5-25-7-1 days and 5-25-6-25 days, and the wheat is sown within 15 days after the corn is harvested. The interval of wheat planting density is set to be 150-800 plants per square meter, and the corn is set to be 4-9 plants per square meter. Fertilizing twice in the sowing period and the jointing period, setting the total nitrogen fertilizing amount at 11 levels from 0 to 300kg/ha, and increasing by taking 30kg/ha as a step length. Corn was not irrigated, wheat was irrigated to 6 levels: 1) no irrigation, 2) 80mm during sowing, 3) 80mm during sowing + 80mm before winter, 4) 80mm during sowing + 80mm before winter + 80mm during jointing, 5) 80mm during sowing + 80mm before winter + 80mm during jointing, 6) 80mm during sowing + 80mm before winter + 80mm during jointing.
Table 1: genetic parameter values adopted by wheat and corn models at four representative sites
The results of the simulation calculations are shown in fig. 2-5.
FIG. 2 is a graph of multi-objective simulation results including Yield, of eight farming systems (CW, wheat crop rotation; CM, corn crop rotation; CS, soybean crop rotation; WS, wheat-soybean crop rotation; MS, corn-soybean crop rotation; WM, wheat-corn crop rotation; WM-NI, wheat-corn crop rotation without irrigation; WM-OPT, wheat-corn crop rotation optimization system) in the New countryside area of North China plain; SOC, soil organic carbon; N2O, nitrous oxide; n, leaching by nitrogen; GWR, groundwater recharge; ET, evapotranspiration; NUE, nitrogen utilization efficiency; WUE, water use efficiency.
FIG. 3 is a schematic diagram of simulated changes of target parameters of eight agricultural planting systems (CW, wheat single crop; CM, corn single crop; CS, soybean single crop; WS, wheat-soybean crop rotation; MS, corn-soybean crop rotation; WM, wheat-corn crop rotation; WM-NI, wheat-corn crop rotation without irrigation; WM-OPT, wheat-corn crop rotation optimization system) in the goldenrain area of North China plain under two climate change scenarios (RCP2.6, RCP8.5) of the current benchmark (Baseline, 2000-.
FIG. 4 is a schematic diagram of simulation results of four places (Koelreuteria, Nanyang, Taian and New county) of eight agricultural planting systems (CW, wheat single crop; CM, corn single crop; CS, soybean single crop; WS, wheat-soybean crop rotation; MS, corn-soybean crop rotation; WM, wheat-corn crop rotation; WM-NI, wheat-corn crop rotation without irrigation; WM-OPT, wheat-corn crop rotation optimization system) Carbon Footprint (CF) under two climate change scenarios (RCP2.6, RCP8.5) at present (Baseline, 2000-containing 2009) and future (2050-containing 2059) and two cultivation managements (traditional cultivation; Conservation containing) in North China plain.
FIG. 5 is a schematic diagram of simulation results of the development of comprehensive indexes (IntInd) of eight agricultural planting systems (CW, wheat single crop; CM, corn single crop; CS, soybean single crop; WS, wheat-soybean crop rotation; MS, corn-soybean crop rotation; WM, wheat-corn crop rotation; WM-NI, wheat-corn crop rotation without irrigation; WM-OPT, wheat-corn crop rotation optimization system) in the current (Baseline, 2000-containing 2009) and future (2050-containing 2059) (RCP2.6, RCP8.5) climate change scenarios and two cultivation management (Conventional cultivation; Conservation cultivation) in four places (Koelken, south-Yang, Taan and New county) in North China.
As shown, the simulation results show that each planting system varied under different time period climate change scenarios, with WM-OPT (wheat-corn rotation optimization system) overall performance being optimal, Carbon Footprint (CF) and development integration index (IntInd) values being small in each time period, and CS (soybean crop rotation) overall performance being worst and large in each time period.
Compared with the prior art, the agricultural planting system model is adopted, multi-source data and information are integrated, evaluation and optimization of the agricultural planting system are carried out, and multiple targets such as production, environment and economic benefit of the agricultural production system are considered. The method has the advantages that 1) an appropriate agricultural planting system is scientifically selected according to the climate and environmental conditions of a certain region, and the utilization efficiency of agricultural resources is improved. 2) Promotes agricultural production, gives consideration to production, environment and economic benefits, and promotes agricultural sustainable development. 3) Reduce the agricultural production input and cost.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the specific embodiments in the present specification should not be construed as a limitation to the present invention.
Claims (9)
1. A method for determining an optimal agricultural planting system based on a multi-target comprehensive evaluation system comprises the following steps:
s1: according to the experimental observation data of crops in the research area, parameter optimization and verification are carried out on the agricultural system model, and localization of the model is realized;
s2: preliminarily setting a plurality of alternative agricultural planting systems;
s3, acquiring climate, soil and agricultural management data information of a research area, and simulating and calculating target parameters of each alternative agricultural planting system in a research period by using the agricultural system model, wherein the target parameters comprise crop yield, nitrous oxide emission and soil organic carbon change;
nitrous oxide emissions are represented by the following formula (1):
wherein E isN20Global warming potential in kg CO as a cumulative annual amount of nitrous oxide emitted from the soil2-eqha-1；N2O is the annual cumulative amount of nitrous oxide discharged from the soil in kg of CO2-eq ha-1α is a Global Warming Potential (GWP) factor of 298, the organic carbon change of soil is represented by the following formula (2):
wherein Δ SCS is the annual change in kg CO of Soil Organic Carbon (SOC) in the 0-20cm soil surface layer over the study period2-eq ha-1；SCSendSCS for Soil Organic Carbon (SOC) storage in the 0-20cm soil surface layer at the last year of the study periodbeginSoil Organic Carbon (SOC) reserves in the 0-20cm soil surface layer for the first year of the study period; m is the number of years in the study period;
s4: according to nitrous oxide emission (E)N20) Soil organic carbon change (Δ SCS) and crop yield, calculating the Carbon Footprint (CF) of agricultural production over the study period for each alternative agricultural planting system, as represented by the following formula (3):
wherein the CF unit is kg CO2-eq kg-1(ii) a Yield is the annual crop Yield of each agricultural planting system in kg ha-1；
S5, comparing the Carbon Footprints (CF) of agricultural production in the research period of each alternative agricultural planting system, and selecting the planting system with the minimum carbon footprint as the optimal agricultural planting system in the research period in the research area;
wherein the target parameters further include Nitrogen Use Efficiency (NUE) and Water Use Efficiency (WUE) as shown in the following formulas (4) and (5):
wherein ET is the total annual evapotranspiration of the agricultural planting system, irrigation is the annual irrigation quantity, and N application rate is the annual nitrogen application quantity;
the method includes using an integrated index of development (IntInd) instead of the Carbon Footprint (CF) as an evaluation index to determine an optimal farming system,
wherein the Integrated indicators of development (IntInd) are represented by the following formulas (6) and (7):
IntInd＝CF/(βNUE+γWUE) (6)
+β+γ＝100％ (7)
wherein β and gamma are respectively weight coefficients of Carbon Footprint (CF), Nitrogen Utilization Efficiency (NUE) and Water Utilization Efficiency (WUE) of agricultural production, the value ranges of the weight coefficients are respectively 35-45%, 26-34% and 26-34%, and the agricultural planting system with the minimum IntInd value is the optimal agricultural planting system in the research area.
2. The method of claim 1, wherein m is 10.
3. The method of claim 1, wherein the study period is plural, including a past period referred to as a reference climate period and a future period referred to as a future climate period.
4. The method of claim 1, wherein the agricultural system model is an APSIM or CERES series crop model.
5. The method of claim 1, wherein the crop is selected from the group consisting of wheat, corn, soybean, and rice.
6. The method of claim 1, wherein the agricultural growing system is selected from the group consisting of wheat crop rotation, corn crop rotation, soybean crop rotation, wheat-soybean crop rotation, corn-soybean crop rotation, wheat-corn crop rotation without irrigation, and wheat-corn crop rotation optimization systems.
7. The method of claim 1, wherein the localization at S1 comprises collecting site-scale soil parameters, weather parameters and field management parameters, and performing gradual calibration of variety parameters based on the G L UE method (i) optimizing variety parameters that affect flowering and maturity according to the accumulated temperature required by the variety, and (ii) calibrating variety parameters that determine yield.
8. The method of claim 7, wherein the step-by-step calibration of the breed parameters comprises estimating deviations of the simulated and measured values of the variables using Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE), as shown in equations (8) and (9):
Oiand SiRespectively an observed value and an analog value, O, of said variableavgThe average value of the observed values; n is the sample size.
9. The method of claim 8, wherein the variables are fertility and yield.
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