15 results about "Water productivity" patented technology

The invention relates to the technical field of agricultural information, in particular to a crop yield prediction method, a verification method, a computer system and a readable storage medium. Standardized moisture productivity, a crop coefficient before canopy aging, a harvest index, a maximum canopy coverage degree, a canopy growth coefficient, a canopy attenuation coefficient, a minimum rooting length and a maximum rooting length are selected as analysis parameters, and the main effect of crop model parameters is quantified through a first-order sensitivity index. The interaction effect of crop model parameters is quantified through the global sensitivity index, and the complexity of a parameter system is reduced; a differentiation calibration strategy is constructed based on sensitivity grading, a traditional all-parameter parameter adjustment mode is replaced, and the complexity of a parameter system is remarkably reduced. The objective of the invention is to solve the problem of how to improve the crop yield prediction precision of a crop model in a climate-variable region.

The invention discloses a CatBoost-based irrigation area moisture productivity prediction method, and the method employs a CatBoost machine learning model to construct an irrigation area moisture productivity prediction model, can more effectively process the complex nonlinear relation and high-dimensional data among climate, soil, underground water and management measures, remarkably improves the precision and adaptability of irrigation moisture productivity prediction, and improves the prediction precision and adaptability of the irrigation moisture productivity. And the limitation of the traditional model in the aspects of dynamic nonlinear environment and data demand is overcome. Meanwhile, the Bayesian optimization technology is innovatively combined with the CatBoost model, automatic and intelligent optimization of model hyper-parameters is achieved, blindness of manual parameter adjustment is effectively avoided, the prediction precision and generalization ability of the model are further improved, the problem that a machine learning model is prone to overfitting is solved, and the method is suitable for large-scale popularization and application. And the robustness of the model in a complex environment is ensured. According to the method, quantifiable and generalizable decision support is provided for precise water saving, salt control and crop layout optimization.

The invention discloses a crop water distribution method and device, a medium and equipment, and relates to the field of water resource management, and the method comprises the steps: constructing a multi-target simulation-optimization model; the multi-objective simulation-optimization model comprises an EPI crop growth model and an optimization model which takes maximized economic water productivity, maximized irrigation water productivity and maximized nutritive water productivity as objective functions and takes irrigation water quantity and salt accumulation in a crop growth period as constraints; inputting the acquired meteorological data, crop data, soil data, channel data and groundwater data into a simulation model of a multi-target simulation-optimization model, determining simulation results of actual crop evapotranspiration, final crop yield and straw quantity, taking the simulation results as input of an optimization model, and determining an irrigation water distribution scheme; constructing a water-salt migration model; and updating the simulation result through the water-salt migration model so as to optimize the irrigation water distribution scheme until a preset optimization target is achieved.

The invention discloses a precise irrigation decision-making method based on soil-crop-environment information, and relates to the technical field of agricultural irrigation, and the method comprises the following steps: calculating the irrigation quota accommodated by a planned wetting layer, calculating the accumulated water consumption of crops, and deciding the irrigation date; according to the method, multi-source information, dynamic threshold optimization and an intelligent feedback mechanism are deeply fused, the planned wetting layer depth is dynamically adjusted according to crop root system distribution to change the irrigation quota, the actual water consumption of crops is calculated through crop-environment data, future weather forecast is considered, and the irrigation efficiency is improved. Soil moisture deep seepage and nitrate nitrogen leaching loss caused by rainfall after irrigation can be effectively avoided, the crop yield and the water and fertilizer utilization efficiency are remarkably improved, and meanwhile a dynamic irrigation system capable of changing according to the environment and crop requirements is formulated by fully considering soil characteristics, crop water consumption characteristics and environment information. The crop yield and the water productivity can be obviously improved.

This application discloses a method, apparatus, equipment, medium, and product for predicting regional crop water productivity, relating to the field of smart agriculture. The method includes inputting historical and future climate data into an ecosystem process model to obtain simulated net primary productivity and simulated evapotranspiration for historical and future times; using a bias mapping method based on the simulated evapotranspiration, actual evapotranspiration, and simulated evapotranspiration for future times to obtain predicted evapotranspiration for future times; training multiple machine learning models based on the simulated net primary productivity, agricultural management data, and grain yield per unit area for historical times to obtain a crop yield prediction model; obtaining the predicted grain yield per unit area for future times based on the crop yield prediction model; and obtaining the regional crop water productivity based on the predicted evapotranspiration and the predicted grain yield per unit area for future times. This application can achieve high-precision, high spatiotemporal resolution, and high-reliability prediction of regional crop water productivity.

The present application relates to a kind of water culture crop biomass prediction methods based on stage-specific feedback evapotranspiration calculation, comprising the following steps: according to growth day or accumulated temperature, water culture crop growth is divided into three stages;For current growth stage, calculate stress factor and regulate the weight corresponding to each stage stress factor;Multiply basic crop coefficient, canopy coverage, reference evapotranspiration and stress factor and corresponding weight, calculate stage-specific evapotranspiration;Multiply water productivity adjustment value, the ratio of stage-specific evapotranspiration and reference evapotranspiration and environmental stress factor and corresponding weight, predict biomass increment, obtain water culture crop biomass prediction value;Using ensemble Kalman filter, integrated assimilation canopy coverage and biomass prediction value, carry out sustained biomass prediction in the whole growth cycle of water culture crop.Compared with prior art, the present application has the advantages of biomass prediction accuracy.

The invention provides a straw returning effect evaluation model construction method, system and device and a medium, and belongs to the field of intelligent agriculture, and the method comprises the steps: obtaining meteorological data, soil data, field management data, crop yield data and crop moisture productivity of a training region; effect quantity indexes of the crop yield and the crop moisture productivity are calculated based on Meta analysis, and response variables of the crop yield and the crop moisture productivity of the to-be-evaluated region are obtained; performing data enhancement on the response variables based on an SMOTE method to obtain a synthetic sample of the crop yield and the crop moisture productivity; the meteorological data, the soil data and the field management data serve as input features of a machine learning model, the response variables and the synthetic samples serve as target variables of the machine learning model to optimize hyper-parameters, and a trained straw returning effect evaluation model is obtained. According to the method, the complex interaction effect of climate-soil-management measures can be accurately described, so that the accuracy and generalization ability of an evaluation result are improved.

The invention discloses a crop water utilization efficiency paradox evaluation method and device and a computer program product, and belongs to the technical field of agricultural irrigation. The method comprises the following steps: acquiring grid scale resource environment data of a target region; processing the grid scale resource environment data based on the trained machine learning model to obtain a grid scale crop blue water demand; calculating the crop water productivity according to the grid scale crop blue water demand; obtaining a crop water utilization efficiency paradox index according to the grid scale crop blue water demand and the crop water productivity; and analyzing the crop water utilization efficiency paradox condition of the target region according to the crop water utilization efficiency paradox index. The device comprises a data acquisition module, a grid scale data obtaining module, a first calculation module, a second calculation module and a judgment module. The grid-scale crop water utilization efficiency paradox index can be obtained, water resource management is performed on a target area in a targeted manner, and waste of environmental resources and manpower and financial resources is reduced.

The present invention discloses a crop water use efficiency paradox evaluation method, device, and computer program product, belonging to the field of agricultural irrigation technology. The method comprises: acquiring grid-scale resource and environmental data for a target region; processing the grid-scale resource and environmental data based on a trained machine learning model to obtain grid-scale crop blue water demand; calculating crop water productivity based on the grid-scale crop blue water demand; obtaining a crop water use efficiency paradox index based on the grid-scale crop blue water demand and crop water productivity; and analyzing the crop water use efficiency paradox status in the target region based on the crop water use efficiency paradox index. The device comprises a data acquisition module, a grid-scale data acquisition module, a first calculation module, a second calculation module, and a judgment module. The grid-scale crop water use efficiency paradox index can be obtained, enabling targeted water resource management in the target region and reducing waste of environmental resources, manpower, and financial resources.

The present invention refers to a composition based on algal and / or plant extracts and its use in agriculture to improve water use efficiency and / or water productivity in plants and / or agricultural water management thus resulting in increased yield of crop plants per unit water used.

The present invention discloses a method for optimizing crop planting structure based on multi-scale and all-round evaluation. The SWAT-WF distributed hydrological model is used to simulate the regional crop water productivity and production water footprint, and a multi-objective spatial crop planting structure optimization model with the goals of maximizing regional economic water productivity, minimizing blue water dependence, and minimizing grey water footprint is established. Combining multi-level benefit indicators, a comprehensive evaluation index system based on regional, provincial, and national scales is constructed. Through the entropy weight-TOPSIS comprehensive evaluation model, the optimization of multiple crop planting structure schemes is realized. The method of the present invention considers the comprehensive benefits within the region and the impact on the grain trade pattern outside the region, provides reasonable suggestions for the adjustment of regional planting layout, and has important significance for the sustainable development of multi-scale agricultural systems.

The application belongs to the field of dryland agricultural water-saving technology, and provides a dryland agricultural water-saving equivalent calculation method, which comprises the following steps WP ave S1, calculating the average water productivity of a research area under the condition of the same type of soil and traditional management Y a S2, obtaining the actual crop yield of the research area after the target water-saving technology is used ET a S3, calculating the theoretical evapotranspiration of the crop yield according to the average water productivity and the actual crop yield WP ave S4, calculating the dryland agricultural water-saving equivalent after the water-saving technology is used by subtracting the actual crop evapotranspiration from the theoretical evapotranspiration and performing unit conversion Y a Y a ET Theo ET Theo ET ET a W sav The dryland agricultural water-saving equivalent calculation method provided by the application breaks the technical deficiency that the dryland rain-fed agriculture cannot calculate the physical water-saving amount for the first time, so that the water-saving technology can be converted into an intuitive and physical water-saving equivalent. The application fills the long-term blank of lacking direct water amount index in the field.​​​​​

The application discloses a kind of precision irrigation decision-making methods based on soil-crop-environment information, it is related to agricultural irrigation technical field, including the following steps: calculating the irrigation quota that plan wet layer contains, calculating crop cumulative water consumption and deciding irrigation date;The application is through depth fusion multi-source information, dynamic threshold optimization and intelligent feedback mechanism, according to crop root system distribution dynamic adjustment plan wet layer depth changes irrigation quota, through crop-environment data calculation crop actual water consumption, and consider future weather forecast, can effectively avoid the soil moisture deep seepage and nitrate nitrogen leaching loss after irrigation occurs rainfall, significantly improve crop yield and water and fertilizer utilization efficiency, simultaneously by fully considering soil characteristics, crop water consumption characteristics and environmental information formulate can according to environmental and crop demand change dynamic irrigation system, can significantly improve crop yield and water productivity.

The invention relates to a hydroponic crop biomass prediction method based on stage specificity feedback evapotranspiration calculation. The hydroponic crop biomass prediction method comprises the following steps that hydroponic crop growth is divided into three stages according to the number of growth days or accumulated temperature; stress factors are calculated for the current growth stage, and weights corresponding to the stress factors in all the stages are regulated and controlled; multiplying the basic crop coefficient, the canopy coverage rate, the reference evapotranspiration, the stress factor and the corresponding weight, and calculating stage-specific evapotranspiration; multiplying the moisture productivity adjustment value, the ratio of the stage specific evapotranspiration to the reference evapotranspiration, the environmental stress factor and the corresponding weight, and predicting the biomass increment to obtain a hydroponic crop biomass prediction value; and performing continuous biomass prediction in the whole growth cycle of the hydroponic crops by adopting ensemble Kalman filtering and assimilating the canopy coverage rate and the biomass prediction value in a combined manner. Compared with the prior art, the method has the advantages of accurate biomass prediction and the like.

The invention provides a water resource optimization decision-making method for multi-objective two-stage opportunity constrained programming, and belongs to the field of agricultural water resource optimization configuration, and the method comprises the steps: collecting basic data; selecting a typical hydrological year; generating a localized AquaCrop model, and simulating the growth process of crops in different hydrological scenes; on the basis of a localized AquaCrop model, constructing a rice secondary moisture production function under different hydrological year scenes; constructing a random multi-target two-stage opportunity constrained programming water distribution model; setting the boundary of the water distribution model; and carrying out deterministic equivalent transformation and model solution on the chance constraint. According to the method, rainfall randomness and water supply and demand uncertainty can be handled under the condition of future climate change, and multi-target conflicts such as net economic benefits, crop water productivity and water supply and demand matching satisfaction are coordinated; the irrigation strategy generated through optimization reduces the underground water exploitation amount on the premise of controlling the water supply risk, and the water utilization efficiency of an irrigation area is improved.