A Bayesian network approach to model farmers' crop choice using socio-psychological measurements of expected benefits of ecosystem services

Models of ecosystem management typically measure the benefits of ecosystem services in terms of ecological or biophysical variables, which are influenced by management decisions and biophysical/ecological conditions. This study uses farmers' expected benefits of ecosystem services as input variables to model their decision between planting rice, annual crops or perennial crops. Based on the theory of planned behavior, a Bayesian network is constructed to model crop choice depending on attitudes toward the ecosystem services of biomass production, reduction of soil erosion, and water quality improvement. The relative importance of these decision-making criteria is quantified using the Analytical Hierarchy Process. Results indicate that Bayesian networks can use socio-psychological measurements to model decision-making. Especially as an extension to biophysical or economic models, they can serve as a powerful tool for grasping the more abstract socio-psychological dimensions of benefits of ecosystem services, and how they translate into the decisions of ecosystem managers.     

Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture

Seasonal climate forecasts (SCFs) have received a lot of attention for climate risk management in agriculture. The question is, how can we use SCFs for informing decisions in agriculture? SCFs are provided in formats not so conducive for decision-making. The commonly issued tercile probabilities of most likely rainfall categories i.e., below normal (BN), near normal (NN) and above normal (AN), are not easy to translate into metrics useful for decision support. Linking SCF with crop models is one way that can produce useful information for supporting strategic and tactical decisions in crop production e.g., crop choices, management practices, insurance, etc. Here, we developed a decision support system (DSS) tool, Climate-Agriculture-Modeling and Decision Tool (CAMDT), that aims to facilitate translations of probabilistic SCFs to crop responses that can help decision makers adjust crop and water management practices that may improve outcomes given the expected climatic condition of the growing season.     

Searching for simplified farmers' crop choice models for integrated watershed management in Thailand: A data mining approach

This study used the C4.5 data mining algorithm to model farmers' crop choice in two watersheds in Thailand. Previous attempts in the Integrated Water Resource Assessment and Management Project to model farmers' crop choice produced large sets of decision rules. In order to produce simplified models of farmers' crop choice, data mining operations were applied for each soil series in the study areas. The resulting decision trees were much smaller in size. Land type, water availability, tenure, capital, labor availability as well as non-farm and livestock income were found to be important considerations in farmers' decision models. Profitability was also found important although it was represented in approximate ranges. Unlike the general wisdom on farmers' crop choice, these decision trees came with threshold values and sequential order of the important variables. The decision trees were validated using the remaining unused set of data, and their accuracy in predicting farmers' decisions was around 84%. Because of their simple structure, the decision trees produced in this study could be useful to analysts of water resource management as they can be integrated with biophysical models for sustainable watershed management.     

基于物联网技术的农田节水灌溉系统的研究

干旱缺水是制约我国农业生产发展的主要因素之一,随着现代化大农业方针的推进、水资源日趋紧张,为有效的解决农业用水问题,本文介绍一种基于物联网技术的农田节水灌溉系统,本系统通过实时采集土壤水分数据与农作物的需水量进行并联分析,制定最优灌溉方案以实现按需灌溉,从而合理的利用水资源,同时也最大限度的降低电能的消耗。     

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.     

Optimum control of irrigation water application

The problem of controlling soil water within the root zone of irrigated crops to minimize the expected loss is examined. Control is accomplished by scheduling the amount of and timing irrigations to replenish the soil water reservoir depleted by the crop's water consumption. Actual evapo-transpiration rates are a function of the prevailing soil water level and the evaporative demand, which may be considered to be either deterministic or probabilistic. For crops grown on a particular soil, an optimum soil water level is defined as the lowest soil water level above which crops are not stressed. The reduced yield of a crop is related to its growth stage and to the amount and duration that the soil water content is below this optimum value.Existing inventory models are adapted for the purpose of determining the optimal irrigation policy, that is, the timing and amount of water application that result in the minimum irrigation cost to the farmer. Solutions to complex decision-making problems are currently available for a variety of irrigation situations.     

基于物联网云平台的遂溪县灌区管理系统构建研究

2019年以来,遂溪县连续实施农业水价综合改革,建设了多处用水计量自动监测站点,需要对不同厂商、不同类型用水计量设施进行集中管理。结合遂溪县灌区管理实际,基于物联网云平台构建遂溪县灌区管理系统,包括灌区概况、灌区一张图、实时监测、运行管理、工程管护、智能巡检、水价改革、用水统计、管理APP等模块。该系统的构建有利于全面提高用水计量自动监测站点管理水平,为持续发挥遂溪县农业水价综合改革效能及灌区现代化管理提供技术支撑,为相关地区灌区信息化建设起到示范作用。     

A chance-constrained multi-period model for a special multi-reservoir system

We develop a model for optimal multi-period operation of a multi-reservoir system for a basin operating under a conjunctive use of ground and surface water. The inflows to the main reservoir as well as the irrigation demands are stochastic. The ground stock suffers from severe overdrafts increasing the risk of the total depletion of the aquifer in addition to the quality degradation and the threat of seawater intrusion. We treat the uncertainties in the inflows through chance constraints and penalties of failure to release the planned amounts of surface water from the main reservoir. However, we reflect uncertainties in irrigation demands by opting for deficit irrigation and using adequate production functions to estimate the expected crop yields. We attempt in the model to avoid large deficits except perhaps for periods where crop yields are relatively insensitive to water shortage. The objective is to maximize the total expected profit of the entire region. We illustrate the model through an example partially based on some hypothetical data.     

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.     

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.     

Stochastic models for the coordinated production and shipment problem in a supply chain

In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a stochastic environment. The supplier controls the production, holds inventory and ships the products to the retailer to satisfy the external demand. We model the system as a Markov decision process, and show that the optimal production and transportation decisions are complex and non-monotonic. Therefore, we analyze two widely-used shipment policies in the industry as well, namely time-based and quantity-based shipment policies in addition to a hybrid time-and-quantity based shipment policy. We numerically compare the performances of these policies with respect to the optimal policy and analyze the effects of the parameters in the system.     

Expanding risk consideration in integrated models – The role of downside risk aversion in irrigation decisions

We present a bio-economic model that accounts for the effects of water and nitrogen use on the first three moments of profit margin distributions in Swiss maize production. We thus also account for downside risks in farmers' decision making processes, which extents currently used bio-economic modeling approaches that address agricultural water use. We find that because irrigation reduces the negative skewness of profit margin distributions, i.e. downside risk, farmers have an additional incentive to use irrigation more intensively. Not considering downside risks may thus imply an underestimation of agricultural water use.     

The implementation of a wateruse model to calculate radar backscatter with application to irrigation monitoring

This letter describes a coupled water use and radar backscatter model designed to assist irrigation monitoring and scheduling. The three components of the model (soil, plant, radar backscatter) are presented and simulations with the model explore its effectiveness in estimating soil and crop canopy moisture for potato crops by comparison with measurements obtained for test fields in Cambridgeshire, England, UK.     

An operational policy for an integrated inventory system under consignment stock policy with controllable lead time and buyers’ space limitation

This paper aims to enable the decision maker of an integrated vendor–buyer system, under Consignment Stock (CS) policy, to make the optimal/sub-optimal production/replenishment decisions where the buyer places a space limitation to the vendor and the lead-time is controllable with an extra investment. Within any production cycle, the vendor produces at a finite rate and ships the outputs to the buyer with a number of equal-sized lots. With a long-term consignment stock agreement, the vendor takes responsibility to maintain a certain inventory level in the buyer's warehouse. Some of the shipments are delayed so that the buyer's inventory does not go beyond the capacity limitation. The buyer compensates the vendor after the complete consumption of the products. The holding cost consists of a storage component and a financial component. Two constraint four-variable non-linear integer optimization models are established wherein the buyer space limitation is considered. Because the developed models are mathematically very difficult to solve, three doubly hybrid meta-heuristic algorithms are employed to solve the models. The computational results show that one of these three algorithms works very well both in the sense of the success rate and the mean CPU time. The analysis of the computational example also reveals the quantitative effects the buyer space limitation may have to the annual joint total expected cost (JTEC) of the integrated system.     

Social learning with heterogeneous agents and sequential decision making

In this paper, we consider the problem of social learning in a network of agents where the agents make decisions sequentially by choosing one of two hypotheses on the state of nature. Each agent observes a signal generated according to one of the hypotheses and knows the decisions of all the previous agents in the network. The network contains two types of agents: rational and irrational. A rational agent makes a decision by not only using its private observation but also the decisions of each of the agents which already made decisions. To that end, the agent employs Bayesian theory. An irrational agent makes a decision by ignoring the available information and by randomly choosing the hypothesis. We analyze the asymptotic performance of a system with rational and irrational agents where we study rational agents that use either a deterministic or random decision making policies. We propose a specific random decision making policy that is based on the social belief and the private signals of the agents. We prove that under mild conditions the expected social belief in the true state of nature tends to one if the rational agents use the proposed random policy. In a network with rational agents that use deterministic policy, the conditions for convergence are stricter. We provide simulation results on the studied systems and compare their performance.     

公平关切下考虑企业社会责任的物流服务供应链协调

针对由单个物流服务集成商(LSI)和单个功能型物流服务提供商(FLSP)组成的物流服务供应链(LSSC),考虑LSI的企业社会责任(CSR)和FLSP的物流服务水平,研究LSI和FLSP的公平关切偏好在成本分担契约和成本分担-两部关税的组合契约下对LSSC最优决策、各成员利润和整体利润以及供应链协调性的影响.研究表明:公平关切偏好不影响LSSC在不同契约下的协调状态,但影响LSSC成员的利润;成本分担-两部关税的组合契约能实现LSI和FLSP的双赢.LSI在成本分担契约下应降低自己的公平关切偏好并与公平关切偏好程度较低的FLSP合作,在成本分担-两部关税的组合契约下应增加自身的公平关切偏好并与公平关切偏好较低的FLSP合作;FLSP在两种契约下都应该增加自身的公平关切偏好并与公平关切偏好较低的LSI合作.     

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.     

考虑公平参照点差异的建设项目绿色合作努力行为决策

建设活动对环境影响显著,绿色合作是促进建设项目绿色绩效提升的重要途径.对此,建立绿色合作中主导方与成员方间的Stackelberg博弈模型,分析绝对公平与相对公平两类公平参照点下公平偏好对绿色合作努力行为决策的影响.研究发现,公平偏好将影响建设项目绿色合作参与方的两类努力行为决策,且影响方向与公平参照点类型及收益分配水平有关.在不同公平参照点下,努力行为决策将受到收益分配临界点的影响,在设计收益分配方案时,应关注所选取的公平参照点及相关收益分配临界点,以实现绿色合作参与方努力行为的有效诱导.     

基于信息间隙决策理论的发电商电量分配策略

在电力市场环境下,发电商需要在现货市场、双边合同和期权等交易选择中合理分配交易电量.针对现货市场电价的严重不确定性,采用信息间隙决策理论,提出风险回避发电商在多种交易选择中的电量分配鲁棒决策模型.算例仿真验证了模型方法的合理性和有效性,并表明,所提出方法提供了发电商不同预期收益目标下的电量分配策略可抵抗的现货价格波动幅度,风险回避发电商可由此评价不同的电量分配策略,并采用相应的策略来保证预期收益目标.