جستجوی مقالات مرتبط با کلیدواژه « نیشابور » در نشریات گروه « اقتصاد کشاورزی »

Today the incomes of the rural non-farm sector (ie the income from wage and self-employment activities in trade, production and services) are an important source of income for farmers and other rural households, including those without land. This has led policymakers to develop these activities in rural areas. For this purpose, the present study has considered the income structure of rural households in Neyshabur county and its related factors. The data is gathered through a questionnaire survey carried out on 380 rural households from four districts (Zabarkhan, Markazi, Sarvelayat and Mianjolgeh) in the Neyshabour county. The survey is conducted between September 2017 to February 2018. The household was chosen through a multi-stage stratified random sampling. In this study, according to various studies such as Davis et al (2017), rural household income is divided into two groups: farm income (crop, orchard and livestock) and non-farm income (non-farm self-employment, wage employment, pension- profit -rent and cash subsidy) have been divided and multilevel Tobit models have been used to investigate the determinants of rural household income in each of these groups. The results of the questionnaire show that 51.16% of the average income (21.19 million Tomans) of a rural household in Neyshabur in 2016-2017 from farm and 48.83% from the non-farm sector .The results showed that household head characteristics such as age, sex and education did not affect the farm income and the income of this sector was dependent on variables such as farmland size, farmland ownership, number of livestock and number of employees in the sector. In addition, the results revealed that higher education of the household head only affected non-farm wage income and access to credit only on non-farm self-employment income. It is noteworthy that the increase in the number of households employed if they were engaged in non-farm self-employment has generated more annual household income than all five groups of rural households.

Inefficiency of marketing networks of agricultural products and the price differences between sale price by farmers and purchase price by final consumers are the main issues in the marketing of agricultural products. This study aimed mainly at investigating the factors affecting the marketing margins in Neyshabour plain of Iran. For this purpose, within the geographically weighted regression with adaptive cores (GWR-A), the upgraded version of ‘Mark Up Model’ was used for all crops. Statistics and information were obtained through interviews and questionnaires completed by 366 farmers in 2016. Moran test results showed that there was a positive spatial autocorrelation on the marketing margin of crops. The results of GWR model indicated a positive impact of marketing costs and retail prices in most areas is positive and a negative impact of cultivated areas on the marketing margins. Also, the coefficients of the Mark Up Model were estimated regionally and with respect to farmer’s location. Finally, a number of suggestions were made, including: reforming the marketing network in order to reduce the margin of the market, using statistical methods based on the location of data in similar studies, reforming the market system and the sale of products based on the development of professional commerce companies, implementing the special information plans related to the market information on agricultural products as well as the implementation of specific cultural and social programs.

Several techniques are used to evaluate decision making units in DMUs with a restricted multiplier. DEA is recognized as a methodology widely used to evaluate the relative efficiency of a set of decision-making units (DMUs) involved in a production process. This approach assumes that the input and output data of the different decision making units (DMUs) are measured with precision. Although DEA is a powerful tool to use measure efficiency, there are some restrictions that need to be considered. One important restriction involves the sensitivity of DEA to the specific data under analysis. In this paper, the linear robust optimization framework of Bertsimas and Sim is used to concentrate on the DEA with uncertain data to determine the efficiency of irrigated wheat farms in Neyshabur County.
This paper proposes a linear robust data envelopment analysis (LRDEA) model using imprecise data represented by an uncertainty set. The method is based on the robust optimization approach of Bertsimas and Sim to seek maximization of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between a constraint’s protection and its efficiency. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. The most significant uncertainties for a DEA model are input and output data that arise from errors. Application of the proposed model (LRDEA) to the case study (Neishabour district irrigated wheat farms) demonstrates the reliability and flexibility of the model. Monte Carlo simulation was implemented to examine the quality of the LRDEA model 100 random numbers were generated for each input and output of DMUs.
In this section, a case study of Neishabour county irrigated wheat farms is presented to illustrate the use of the methodology in this proposal, which consists of 95 DMUs, one input and five outputs. For the input and output data uncertainty, ten given maximums of a constraint’s violation probability were considered with respect to nominal values: 10%, 20%, up to 100% (i.e. we used Γ = 0.10, 0.20, up to1.00). The results show that the Gamma value decreases as the probability of constraint violation increases. The LRDEA model result shows how efficiency declines as the level of conservatism of the solution increases, that is, as the constraint violation probability decreases. According to the method, if all Gammas equal 0, then robust and original DEA models are the same. The most of the difference between the mean of optimal and actual amount of inputs is related to the two inputs of pesticide and cultivation land in both of the DEA and RDEA models. Accordingly, holding participatory extension classes to train farmers to increase yield and optimal use of existing agricultural land with a cooperative of efficient farmers is recommended. Also, the extinction of integrated pest management (IPM) to increasing non-optimal use of pesticide in the study area is proposed. Monte Carlo simulation was implemented to examine the quality of the LRDEA model 100 random numbers were generated for each input and output of DMUs. In the simulation violation probabilities ranging from 0.1 to 1.0 (at a constant the level of ε), percentages of average conformity are quite high. . However, it declines very rapidly as P approaches 0.7.
Evaluating the performance of many activities by a traditional DEA approach requires a precise input and output data. However, input and output data in real-world problems are often imprecise or vague. To deal with imprecise data, this study uses a robust optimization approach as a way to quantify vague data in DEA models. It is shown that the Bertsimas and Sim approach can be a useful tool in DEA models without introducing additional complexity into the problem (we called linear robust data envelopment analysis (LRDEA)). A case study of Neishabour county irrigated wheat farms is presented to illustrate the reliability and flexibility of the proposed model. The problem was solved for a range of given uncertainty and constraint violation probability levels using the GAMS software. This example suggests that our approach identifies the tradeoff between levels of conservatism and efficiency. As a result, efficiency decreases as the constraint violation probability increased. Additionally the LRDEA approach provides both a deterministic guarantee about the efficiency level of the model, as well as a probabilistic guarantee that is valid for all symmetric distributions.

In the recent years، increasing demand for products such as protein-rich meals and oilseeds has caused the food basket to shift towards the proteins and fats consumption. This change has been happening in Iran too. Shortage in oilseed productions has been a serious problem in Iran. To overcome the problem، the government planed some programs to encourage oilseed productions. Beside government’s programs to encourage oilseed productions، educational programs are required. The educational programs should be organized based on the farmers’ educational needs. To achieve the goal، an accurate educational model is required. This study attempted at assessing Canola growers’ educational needs in Zebarkhan county of Neyshabour، using Ortiz''s model. The survey was based on a structured questionnaire. To select the study interviewees، simple classified random sampling was used and eventually 60 farmers completed the study questionnaires. The results showed that canola grower''s education needs differ between two canola growers groups. Those farmers who ceased canola cultivation needed 50% more training than those who continued canola production in the study year.. The practical programs were not effective and appropriate for farmers. As a result of that، farmers who ceased canola cultivation were not interested in re-cultivating the plant. Due that the canola cultivation is manageable، continuous studies on scientific and indigenous knowledge integration is suggested.