RFID技术在家庭农场灌溉中的应用研究

结合加州智能灌溉案例及较成熟的RFID技术,介绍了RFID技术在家庭农场灌溉管理中的应用研究,通过RFID技术与LabVIEW设计的监控界面交互农作物生长环境实时监测,并根据该农作物生长特点,自动控制灌溉时机,完成家庭农场中灌溉自动化管理的典型应用。     

Assessing the propagation of uncertainties in multi-objective optimization for agro-ecosystem adaptation to climate change

It is widely acknowledged that uncertainty needs to be accounted for in climate impact studies, be it in scenario analyses or optimization applications. In this study we investigate how climate and crop model uncertainties affect multi-objective optimization outputs aiming to identify optimum agricultural management adaptations for Western Switzerland. Results are visualized by ternary plots that map optimum management measures, crop yield, erosion and leaching with associated uncertainties for navigating through the optimum adaptation space. We find that the relevance of climate model vs. parameter uncertainty can differ substantially depending on the prioritization of objectives and local conditions. The optimum choice of irrigation level was found to be the decision variable subject to greatest uncertainty particularly on coarser soil. This finding suggests that for the long-term planning of irrigation infrastructure and management, a robust adaptation approach is required for approaching unavoidable uncertainty from a risk management perspective.     

A GIS-based modeling approach for implementation of sustainable farm management practices

Few agricultural producers utilize the true analytical power of GIS and computer simulation models, partly because the loose linkages developed to-date between GIS and most public-domain modeling software are extremely cumbersome to use. The integrated system (EPIC–View) developed in the study allows the integration of a comprehensive hydrologic–crop management model (EPIC) with a desktop GIS to function as a planning tool aimed at implementing sustainable farm management practices. The use of GIS makes possible the integration of diverse spatial data into a comprehensive spatial database. EPIC–View is applied to simulate nitrogen (N) dynamics under conventional and minimum tillage conditions of a field located in Caddo County, Oklahoma. In general, the overall N balance obtained under minimum tillage is better than the balance obtained under conventional tillage over a 5-year model run. Unexplained losses of N averaged 9.55% and 4.2% of the gain in N under conventional and minimum tillage respectively. The integrated modeling system holds immense potential as a farm management tool. Various components of a sustainable agricultural system including irrigation management, crop management, soil management, and pest management, can be efficiently managed. This approach could make farms more economically viable and ecologically sound.     

Water balance study and conjunctive water use planning in an irrigation canal command area: A remote sensing perspective

Water budgeting of the D-36 and D-36 A distributaries confined between Pedda Vagu, Korutla Vagu and Kakatiya main canal of the Sri RamSagar Project (SRSP) Command area was conducted using remote sensing derived crop areas, land cover information, irrigation tank inventory and source-wise distribution of irrigated areas, together with conventional meteorological, canal flows and well inventory data. A semi-empirical water balance model was developed and validated using remote sensing derived objective information of the command area and the validated model used for predicting the groundwater table under normal rainfall conditions. Recharge and water balance in the study area indicated that the net recharge to the aquifer is negative to the tune of 2.54 Mm³ resulting in a fall of the groundwater table by 0.79 m during 1992-93. However, normalized groundwater recharge and water balance estimates indicate an impending waterlogging problem with an annual groundwater table rise of 0.35 m. In view of existing water management practices, a conjunctive water use plan of rotational operation of aquifers and canals is suggested.     

Use of vegetation index and meteorological parameters for the prediction of crop yield in India

Monsoon rainfall distribution over the Indian sub‐continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop yield, normalized difference vegetation index, soil moisture, surface temperature and rainfall data for 16 years (from 1984 to 1999) have been carried out. A non‐linear iterative multivariate optimization approach (quasi‐Newton method with least square loss function) has been used to derive an empirical piecewise linear crop yield prediction equation (with a break point). The derived empirical equation (based on 1984 to 1998 data) has been used to predict 1999 crop yield with R²>0.90. The model has been validated for the three years 1997, 1998 and 1999. A crop yield prediction equation has been obtained for each province in India (for wheat and rice) that accounts for>90% of the variance in the dataset.     

A holistic generic integrated approach for irrigation,crop and field management: the SALTMED model

A successful water management scheme for irrigated crops requires an integrated approach that accounts for water, crop, soil and field management. Most existing models are designed for a specific irrigation system, specific process such as water and solute movement, infiltration, leaching or water uptake by plant roots or a combination of them. There is a shortage in models of a generic nature, models that can be used for a variety of irrigation systems, soil types, soil stratifications, crops and trees, water management strategies (blending or cyclic), leaching requirements and water quality. SALTMED model has been developed for such generic applications. The model employs established water and solute transport, evapotranspiration and crop water uptake equations. In this paper, the model has been run with five examples of applications for one growing season using data from the literature. The model successfully illustrated the effect of the irrigation system, the soil type, the salinity level of irrigation water on soil moisture and salinity distribution, leaching requirements, and crop yield in all cases. Due to the scarcity of data sets that are suitable for model testing over the complete growing season, where different processes are acting simultaneously, a follow up paper will show the results of the model tests using data being collected from two sites in Egypt and in Syria as a part of ongoing SALTMED project.     

IRRIGATE: A dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management

Water management practices in southern France (the Crau plain) need to be modified in order to ensure greater water use efficiency and less environmental damage while maintaining hay production levels. Farmers, water managers and policy makers have expressed the need for new methods to deal with these issues. We developed the biodecisional model IRRIGATE to test new irrigation schedules, new designs for water channels or fields and new distribution planning for a given water resource. IRRIGATE simulates the operation of a hay cropping system irrigated by flood irrigation and includes three main features: (i) border irrigation with various durations of irrigation events and various spatial orders of water distribution, (ii) species-rich grasslands highly sensitive to water deficit, (iii) interactions between irrigation and mowing. It is based on existing knowledge, adapted models and new modules based on experiments and survey data. It includes a rule-based model on the farm scale, simulating dynamically both irrigation and mowing management, and two biophysical models. The two biophysical models are a dynamic crop model on the field scale simulating plant and soil behaviour in relation to water supply, and a flood irrigation model on the border scale simulating an irrigation event according to plant and hydraulic parameters. Model outputs allow environmental (water supply, drainage), social (labour) and agronomic (yields, water productivity and irrigation efficiency) analyses of the performance of the cropping system. IRRIGATE was developed using firstly a conceptual framework describing the system modelled as three sub-systems (biophysical, technical, and decision) interacting within the farm. Then a component-based spatially explicit modelling based on the identification of the interactions between modules, the identification of temporal and spatial scales of modules and the re-use of previous models was used to develop the numerical model. An example of the use of the biodecisional model is presented showing the effects on a real farm of a severe water shortage in 2006.     

A framework to use crop models for multi-objective constrained optimization of irrigation strategies

This paper discusses an innovative framework to use crop models which combines sensitivity analysis, uncertainty analysis and constrained optimisation runs for irrigation optimisation purposes, facing competing constraints on several agricultural variables (e.g. crop yield, total irrigation amount, financial expectations). For simplicity, this ex-post optimisation relies on direct calculations only, exploiting the dispersions on the target variables. The screening of the parameter space for sensitivity analysis yields a reference dispersion which is expectedly reduced by reducing the uncertainties in the sensitive parameters and/or climatic forcings. Additional dispersions are calculated to evaluate if the management controls on irrigation strategies (amounts, triggers, periods) are more influential on model predictions than the remaining uncertainties on the soil, plant, irrigation and climatic inputs, eventually allowing optimisation. As a case study, the Optirrig model is used. A discussion proposes future ways to convert diagnostics into real-time near-optimal decision rules, for example through learning algorithms.     

Analysing complex behaviour of hydrological systems through a system dynamics approach

The interaction among various water cycle components consists of complex, non-linear, and bidirectional (interdependent) biophysical processes which can be interpreted using feedback loops in a system dynamics (SD) environment. This paper demonstrates application of an SD approach with two case studies using a specialised software tool, Vensim. The first case study simulates water balance in a rice field system on a daily basis under aerobic conditions with provision of supplemental irrigation on demand. A physically based conceptual water balance model was developed and then implemented using Vensim to simulate the processes that occur in the field water balance system including percolation, surface runoff, actual evapotranspiration, and capillary rise. The second case study simulates surface–groundwater dynamic interactions in an irrigation area where river water and groundwater are two key sources of irrigation. The modelled system encompasses dynamically linked processes including seepage from the river, evaporation from a shallow watertable, groundwater storage, and lateral flow from upland to lowland areas. The model can be applied to simulate responses of different irrigation management scenarios, to develop strategies to improve water use efficiency and control watertable, to prevent salinization in upland, and to reduce the cost of groundwater abstraction in lowland areas. The discussed applications of the SD approach conclude that it helps to conceptualize and simulate complex and dynamic water system processes deterministically which are otherwise partly simulated by conventional hydrologic and stochastic modelling approaches. It is recognised that conceptualization and implementation phases of this approach are challenging, however, the latter is greatly assisted by modern computer softwares.     

基于人工蜂群算法的灌区水资源优化配置方法

灌区水资源优化配置对于提高灌溉的水资源利用率,增加灌区经济效益,降低地下水的消耗速度,促进水资源可持续发展,建立良好的生态环境具有重要意义。综合考虑灌区有效降雨量与作物需水量,建立以灌区经济效益最大化为目标的水资源优化配置模型,采用人工蜂群算法对水资源优化配置模型进行求解,以江苏省宿迁市皂河灌区为实例,对基于人工蜂群算法的灌区水资源优化配置方法的实际应用效果进行验证。结果表明：相比传统经验式配水方法,所提出的灌区水资源优化配置方法可使灌区经济效益达到 8.33 亿元,总用水量减少约 1 000 万 m³ ,且用水量主要来自地表水,灌区作物水资源分配情况更加合理,同时灌区的作物种植结构也得以改善。     

A Fuzzy Decision Support System for irrigation and water conservation in agriculture

Since agriculture is the major water consumer, web services have been developed to provide the farmers with considerate irrigation suggestions. This study improves an existing irrigation web service, based on the IRRINET model, by describing a protocol for the field implementation of a fully automated irrigation system. We demonstrate a Fuzzy Decision Support System to improve the irrigation, given the information on the crop and site characteristics. It combines a predictive model of soil moisture and an inference system computing the most appropriate irrigation action to keep this above a prescribed “safe” level. Three crops were used for testing the system: corn, kiwi, and potato. This Fuzzy Decision Support System (FDSS) favourably compared with an existing agricultural model and data-base (IRRINET). The sensitivity of the FDSS was tested with random rainfall and also in this extended case the water saving was confirmed.     

Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Although rainfall input uncertainties are widely identified as being a key factor in hydrological models, the rainfall uncertainty is typically not included in the parameter identification and model output uncertainty analysis of complex distributed models such as SWAT and in maritime climate zones. This paper presents a methodology to assess the uncertainty of semi-distributed hydrological models by including, in addition to a list of model parameters, additional unknown factors in the calibration algorithm to account for the rainfall uncertainty (using multiplication factors for each separately identified rainfall event) and for the heteroscedastic nature of the errors of the stream flow. We used the Differential Evolution Adaptive Metropolis algorithm (DREAM_(zs)) to infer the parameter posterior distributions and the output uncertainties of a SWAT model of the River Senne (Belgium). Explicitly considering heteroscedasticity and rainfall uncertainty leads to more realistic parameter values, better representation of water balance components and prediction uncertainty intervals.     

Estimating the uncertainty of modeled carbon sequestration: The GreenCert™ system

The GreenCert? system was developed to help farm and ranch owners to quantify, standardize, pool and market CO₂ emissions offset (sequestration) credits derived from improved rangeland or cropland management. It combines a user-friendly interface with the CENTURY biogeochemical model, a GIS database of soil and climate parameters, and a Monte Carlo-based uncertainty estimation methodology. This paper focuses on uncertainty treatment, discussing sources of error, parameter distributions, and the Monte Carlo randomization approach, culminating in a sensitivity analysis of model parameters.Idealized crop and grazing scenarios were used to evaluate the uncertainty of modeled soil organic carbon stocks and stock changes stemming from variability in site and management parameters. Normalized sensitivity coefficients and an integrated index for relative sensitivity of the model to the ensemble of the tested variables indicate that environmental factors are the most important in determining the actual size of the soil carbon stock, but that management is a much more important determinant of short- to medium-term carbon fluxes. GreenCert? uses the patented C-LOCK^® approach to efficiently limit uncertainty in the most critical phase of the modelling process by maximizing the use of available management information, and quantifies the remaining uncertainty in an unbiased fashion using Monte Carlo parameter randomization.     

Application of ET-NDVI-relationship approach and soil-water-balance modelling for the monitoring of irrigation performance of treed horticulture crops in a key fruit-growing district of Australia

Remotely sensed data from Landsat-8 and Sentinel-2 were used to demonstrate the estimation of irrigation water requirement (ρ) for treed horticulture crops in an important irrigation district of Australia. Crop- and region-specific relationship between satellite-derived evapotranspiration (ET) and normalized difference vegetation index (NDVI) was combined with daily step soil water balance to investigate the performance of horticulture crops for their water use during the peak irrigation demand period (summer) over three years from 2014–15 to 2016–17. Relative irrigation water use (RIWU) as the key irrigation performance indicator was calculated by comparing the irrigation water supply (ψ) records and the ρ estimates. ψ and ρ of the treed horticulture crops showed a strong positive correlation (Coefficient of determination, R² > 0.70; p < 0.001) for each of the three summer seasons investigated, indicating an overall consistency in irrigation pattern. However, the values of both ρ and ψ varied considerably at farm level over the seasons, highlighting the changing demand and supply of crop water over the years. Most farms remained within the optimal irrigation range (0.5–1.5 RIWU) over the seasons – 75% in 2014–15, 68% in 2015–16, and 80% in 2016–17. However, some farms were over-irrigated (>1.5 RIWU) – 12% in 2014–15, 5% in 2015–16, and 8% in 2016–17.     

基于CASA-WOFOST耦合模型的大豆单产遥感估算研究

中国是一个农业大国,在田块甚至是亚田块尺度上进行快速、准确的作物产量估算,不仅可以对农民田间管理进行指导,对于农田生态系统对全球变化的响应评价、制定科学合理的粮食政策、对外粮食贸易和国家粮食安全都具有重要意义。目前主流的估产模型主要有经验统计模型、光能利用率模型、作物生长模型等,每一类模型在各自研究领域相对完整,但是都形成了固定的局限性,为了研究利用遥感技术在小区域范围内田块尺度的作物估产,选取黑龙江省双山农场为研究区,以大豆为研究对象,基于CASA-WOFOST耦合估产模式,利用覆盖作物生长季的时间序列HJ-1A/B遥感影像数据构建高时间分辨率归一化植被指数(Normalized Difference Vegetation Index, NDVI),实现逐日连续监测,分别利用CASA模型和CASA-WOFOST耦合模型对作物进行单产模拟,结果表明：耦合得到的新模型能够具有光能利用率模型较高的运行速度,同时还能发挥作物生长模型模型的机理优势,克服CASA模型在小区域田块尺度上应用的局限性。大豆单产模拟线性回归判定系数(R²)由0.668 53上升到0.844 72,均方根误差(RMSE) 由51.41 kg/hm²下降到29.52 kg/hm²,说明耦合后的模型可以综合考虑光能利用与作物生长生态生理全过程,从而提高作物估产的精度、可靠性和稳定性,为区域田块尺度作物估产提供理论支持,更好地服务于精准农业发展。     

Study on Soybean Yield Estimation Using the CoupledCASA and WOFOST Model

China is an agricultural country. Yield estimating on field scales rapidly and accurately is not only instructional to farmers’ field management, but also important for the response evaluation of farmland ecosystems to climate change, making scientific and rational food policies, external food trade and so on. The current primary estimation models include empirical statistical model, light use efficiency model, and crop growth model. Each type of model is relatively complete in its individual research filed, but all of them have certain amount of limitations. Remote sensing technology was used to estimate crop yield on a field scale within small regional areas. A farm of Heilongjiang Province was selected as the study area, and the soybean was as the research object. Based on the coupled CASA-WOFOST model and time-series HJ-1A/B remotely sensed data which covering the entire growing season of soybean to generate high temporal resolution Normalized Difference Vegetation Index (NDVI), we achieved daily continuous monitoring of crop and simulating crop yield by CASA model and CASA-WOFOST model respectively. The results indicated that the coupled model had a faster running speed of the light use efficiency model, it could also give full play to mechanism advantages of crop growth model and overcome the limitations of the CASA model applied to field scales. The R² of soybean yields increased from 0.668 53 to 0.844 72 and RMSE decreased from 51.41 to 29.52 kg/ha. It is indicated that the coupled mode of light use efficiency model and crop growth model could simultaneously consider the light utilization and the whole physiological and ecological process of crop growth. So that the coupled model could improve the precision, reliability, and stability of crop yield estimation, and provide theoretical support for the estimation of crop yields in regional field scales and better serve the development of precision agriculture.     

Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AQUACROP model

This research proposes A THEORETICAL FRAMEWORK based on model predictive control (MPC) for irrigation control to minimize both root zone soil moisture deficit (RZSMD) and irrigation amount under a limited water supply. We (i) investigate means to incorporate direct measurements to MPC (ii) introduce two Robust MPC techniques – Certainty Equivalence control (CE) and Disturbance Affine Feedback Control (DA) – to mitigate the uncertainty of weather forecasts, and (iii) provide conditions to obtain two important theoretical aspects of MPC – feasibility and stability – in the context of irrigation control. Our results show that system identification enables automation while incorporating direct measurements. Both DA and CE minimize RZSMD and irrigation amount under uncertain weather forecasts and always maintain soil moisture above wilting point subject to water availability. The theoretical results are compared against the model AQUACROP, weather data and forecasts from Shepparton, Australia. We also discuss the performance of Robust MPC under different water availability, soil, crop conditions. In general, MPC shows to be a promising tool for irrigation control.     

CATCHCROP: modeling crop yield and water demand for integrated catchment assessment in Northern Thailand

As part of an Integrated Water Resource Assessment and Management project (IWRAM) in Northern Thailand, a Decision Support System is being constructed in order to provide guidelines for crop diversification and water allocation. The IWRAM software integrates a crop model with hydrological and economic models. Presented here is the integrated crop model, called CATCHCROP, which is capable of simulating yield response to water deficit and fertility depletion. External and internal constraints have largely influenced the model construction. Paucity of observed data and its reliability required the use of conceptual and recognized algorithms. Linkages with the economic and hydrological models led to the choice of a 10-days time step. The stand-alone version of the model has been tested against available data sets coming from two small catchments. First results are fairly satisfactory but it is acknowledged that this kind of integrated model should not be used at the farm plot level to assess cropping practices.     

Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin

The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall–runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash–Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMM data and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km²) to 6.1% (post-calibration at 3125 km²) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km²) was decreased to 15.4% (post-calibration at 3125 km²). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.     

Improved storm water management through irrigation rescheduling for city parks

In this work, a modeling and scheduling approach for an integrated storm water management and irrigation problem is presented. The primary objective is to simultaneously ensure that the green space is irrigated appropriately and the level of the storm water pond is maintained adequately. It is proposed to use closed-loop irrigation scheduling to achieve the objective. A steady-state model is developed to calculate the soil water storage for different irrigation amounts. To handle the uncertainty, real-time feedback from the pond is used to re-evaluate the scheduling optimization problem every week. Simulation results show that the proposed closed-loop scheduling gives much improved control performance.