Drought reduces the quantity and quality of water resources and ultimately increases the risk of agricultural activities at the farm level. Therefore, by developing risk-based economic models, the effects of changes i...
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Drought reduces the quantity and quality of water resources and ultimately increases the risk of agricultural activities at the farm level. Therefore, by developing risk-based economic models, the effects of changes in the quantity and quality of water resources on farmers' decisions should be examined. In this regard, the economic effects of drought on farmers in the Qazvin Plain Irrigation Network have been evaluated. For this purpose, firstly, the river flow quality was simulated using the Qual2k model in 1998-2016 and then using the biophysical model, the effects of water quality on crop yield were estimated. Finally, the risk effects of changes in water quantity and quality were calibrated using a Positive risk Mathematical Programing. The risky effects of drought are estimated in three scenarios, including increasing salinity of water, reducing the amount of water and a combination of these two. The results showed that farmers' response to changes in water quantity is much greater than its qualitative changes, because the change in water quantity occurs in a shorter time and the cropping pattern leads to the products with less water requirement such as irrigated barley. Also, profit (Gross Margin) and farmer's income risk declined in all scenarios. Although the negative effects due to reducing the amount of water are more than the effects of increasing the salinity of water, but reducing water quality as a result of drought will have significant effects on the sustainability of agricultural products.
CONTEXT: The planned interventions to enhance adaptation and build resilience of predominantly rainfed and vulnerable smallholder production systems to climate change in African drylands include small-scale irrigation...
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CONTEXT: The planned interventions to enhance adaptation and build resilience of predominantly rainfed and vulnerable smallholder production systems to climate change in African drylands include small-scale irrigation using rainwater harvesting reservoirs. However, the required technological improvement, participatory breakthroughs, institutional settings, farm risk management, and investment justification are rarely established comprehensively and integrated into adaptive planning. OBJECTIVES: To develop and assess new reservoir-based climate-resilient and risk-efficient irrigation cropping strategies synthesizing technological, participatory, institutional, managerial, and investment capabilities. METHODS: We crafted an innovative rice and vegetable pond irrigation system together with governance arrangements and cropping schedule adjustments tailored to the system. The system was validated through a fiveyear participatory on-farm experiment in northern Ghana. Using agronomic, hydrological, and socioeconomic data obtained from the experiments and surveys, we constructed and extended empirical bioeconomic models through simple risk programming to identify irrigation cropping strategies that are the most efficient in securing smallholders' food and income and resilient to interannual climate fluctuations. The net present values were computed to determine the financial effects of the identified strategies on the investment payback of the pond system. RESULTS AND CONCLUSIONS: Shifting sowing dates allowed smallholders to irrigate timely, albeit sparingly, without compromising their autonomous adaptations in rainfed systems. Supplementary irrigation and improved weed control increased the average rice yield by 23%, more than doubled the profitability, and lowered the coefficients of its variation compared to that of rainfed rice (from 48% to 38%). Vegetable irrigation in the dry season was even more profitable. The risk-efficient cropping strategies identified by empir
Selected risk programming solutions (i.e., profit maximization, Target-MOTAD, and MOTAD) are tested in an economic environment outside the data set from which they were developed. Specifically, solutions are derived f...
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Selected risk programming solutions (i.e., profit maximization, Target-MOTAD, and MOTAD) are tested in an economic environment outside the data set from which they were developed. Specifically, solutions are derived from either a longer 10-year (1965-74) or shorter 6-year estimation period (1969-74), and then, they are tested for consistent risk-income characteristics over a later 10-year period (1975-84). risk solutions estimated from earlier periods perform well in the later test period in spite of different economic conditions between time periods. However, favorable performance may be related to the specific example used in this analysis. Further testing for other farm situations is needed before general conclusions can be reached.
Virtual enterprise is becoming a production form of project organization. Minimizing risk in VE and ensuring success are the key problems to overcome. In this paper, in view of different risk effects from stochastic c...
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ISBN:
(纸本)9781424467129
Virtual enterprise is becoming a production form of project organization. Minimizing risk in VE and ensuring success are the key problems to overcome. In this paper, in view of different risk effects from stochastic completion time choices of sub-projects, a risk programming model based on Markov process is presented. A genetic algorithm (GA) for this problem with embedded program evaluation and review technique (PERT) is developed to solve this problem. The performance of this algorithm is demonstrated by a case and the experimental problems of different sizes. The results of this trial demonstrate the real life capability of the algorithm.
This paper designes a Monte Carlo Simulation combined Particle Swarm Optimization (MCS-PSO) for the stochastic risk programming model of virtual enterprise (VE). The stochastic characters of the risk in VE are conside...
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ISBN:
(纸本)9781424464968
This paper designes a Monte Carlo Simulation combined Particle Swarm Optimization (MCS-PSO) for the stochastic risk programming model of virtual enterprise (VE). The stochastic characters of the risk in VE are considered, which are described by random variables. So a stochastic programming model is proposed for risk management of VE. In detail, this is a chance constraint programming model, One of the great advantages of this class of model is that it can actually describe the risk preference of the manager. When the number of risk factors and the number of actions increase, the size of the problem will be huge. Therefore Particle Swarm Optimization (PSO) is employed to sovle the problem. On the other hand, to deal with the random variables, Monte Carlo Simulation is combined with PSO (MCS-PSO). Finally, numerical examples are given to illustrate the effectiveness of the MCS-PSO and the result shows that the risk programming model is very useful for VE.
The effects of cropland slope, distance to surface water, farmers' risk attitudes, and farmers' nitrogen (N) fertilizer applications on potential N delivery to streams and costs of reducing N delivery were eva...
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The effects of cropland slope, distance to surface water, farmers' risk attitudes, and farmers' nitrogen (N) fertilizer applications on potential N delivery to streams and costs of reducing N delivery were evaluated for a representative Virginia peanut-cotton farm. Target MOTAD and generalized stochastic dominance were used to select preferred plans for different levels of risk aversion. Costs of reducing N delivery were lower on farms where fields were located close to surface water, where N was overapplied relative to extension fertilizer recommendations, and where the operator was risk averse. Cropland slope had less effect on cost of reducing N delivery relative to other factors.
Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations of uncertain crop and livestock returns. We show the instability of model solutions with few obs...
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Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations of uncertain crop and livestock returns. We show the instability of model solutions with few observations and discuss how to use available information to derive an appropriate multivariate distribution function that can be sampled for a more complete representation of the possible risks in risk-based models. For the particular example of a Norwegian mixed livestock and crop farm, the solution is shown to be unstable with few states of nature producing a risky solution that may be appreciably suboptimal. However, the risk of picking a sub-optimal plan declines with increases in number of states of nature generated by Latin hypercube sampling. (c) 2009 Elsevier Ltd. All rights reserved.
Virtual enterprises (VEs) are essential components of global manufacturing. Minimizing risk is the key problems to overcome in order to ensure the success of a VE. The operation of VE is always organized by project mo...
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Virtual enterprises (VEs) are essential components of global manufacturing. Minimizing risk is the key problems to overcome in order to ensure the success of a VE. The operation of VE is always organized by project mode and there are many uncertain factors that are fuzzy in VE. This paper focuses on these two main features of VE. It establishes the fuzzy synthetic evaluation embedded nonlinear integer programming model of risk programming for virtual enterprises and presents a tabu search algorithm with an embedded fuzzy synthetic evaluation for the model. A simulation study suggests that the method is effective. (c) 2008 Elsevier B.V. All rights reserved.
With the development of word economy and IT, virtual enterprise is considered as one of the most promising paradigms for future enterprises. As Virtual Enterprise's agility and diversity of its members, which make...
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ISBN:
(纸本)9781424407361
With the development of word economy and IT, virtual enterprise is considered as one of the most promising paradigms for future enterprises. As Virtual Enterprise's agility and diversity of its members, which make its internal and external risk with great character of stochastic. The multi actions single choices model of risk programming is proposed in this paper, which combines with the probability measure risk evaluation method. It's a stochastic programming model with stochastic variables. In this model the total risk of Virtual Enterprise and the total cost using to reduce the risk must be held with a confidence level alpha,beta, respectively. To solve the model, a genetic algorithm is designed, which combines with the Monte Carlo simulation Finally, a numerical example is given to illustrate the effective of this algorithm.
The risks of virtual enterprises exist almost everywhere for they are dynamic, temporary and multi-partners. In order to restrict the risk to the acceptable level, the Multi Strategies Multi Choices (MSMC) risk progra...
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ISBN:
(纸本)9781424415281
The risks of virtual enterprises exist almost everywhere for they are dynamic, temporary and multi-partners. In order to restrict the risk to the acceptable level, the Multi Strategies Multi Choices (MSMC) risk programming model is proposed in this paper considering the fuzzy characteristics and project organization mode of virtual enterprises. There are many control strategies can be selected for each risk to reduce the global risk level of project in this model. In addition, the multi-layer strategies process mechanism and two properties, are presented in this paper. To solve this model, the ant colony algorithm is designed, which combines with the Fuzzy Synthetic Evaluation (FSE) embed HFMFs (Hyper-trapezoidal Fuzzy Membership Functions) evaluation. Simulation has shown that this method is both effective and efficient in achieving satisfied solutions for risk programming of virtual enterprises.
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