RECOGNITION AND PRIORITY OF KEY SUCCESS FACTORS (KSF) INCUSTOMERS CLUBS AND CUSTOMERS LOYALTY PROGRAMS

The present study aims to identify and prioritize the Key Success Factors (KSFs) of Customer clubs and Customer Loyalty programs, in Bank Mellat Iran. The different models of Key Success Factors from previous researchers have been studied, and according to 12 experts of bank Mellat Iran, a model of KSFs of Customer clubs and Customer Loyalty programs in banking industry, has been presented. It’s a combination of previous researches models, including 20 factors affecting the success of Bank Customer clubs and Customer Loyalty program. A questionnaire of 20 success factors have been designed for determining the effect of each factor on the other 19 factors. It has been filled by 12 experts with over 10 years of experience in banking industry. Then, it has been analyzed by Fuzzy DEMATEL method, and the research results has been extracted. This research concluded 20 main key success factors of Customer clubs and Customer Loyalty programs, in Bank Mellat Iran; the 5 fist most important success factors are in order as: 1. the seller's contact assets, 2. price, discounts and free products, 3. Lack of attention to monitoring system and continuous supervision, 4. Quality of customer services, 5. Creating value for customers

Simulation of Rice Yield and its Components Using SWAP Model and Remote Sensing Technology for Optimal Use of Water and Soil

Given the importance of soil and water resources in the development of sustainable agriculture, increasing world population and the growing need for crop production, predicting crop yields using plant simulation models and remote sensing technology is very crucial. The aim of this study was to estimate the yield of rice components including straw, paddy and biomass of Hashemi cultivar during different growth stages with SWAP model and to provide regression equations by extracting NDVI and SAVI plant indices from Sentinel-2 and Landsat-7 and 8 satellite images. It was done in the National Rice Research Institute. Comparison of statistical variables indicated that the mean values of coefficient of determination (R2) and model efficiency factor (EF) in estimating the yield of rice components in different stages of growth with SWAP model were more than 0.70 and 0.90, respectively, and with an error of 1.93 to 6.54% was equivalent to 134.21 to 470.43 kg/ha. The slight difference between the measured and simulated values showed that the SWAP model estimates the rice yield in the study area with appropriate accuracy. The results also showed that the extracted NDVI and SAVI indices with very good accuracy estimate the yield of rice components at different stages of growth. However, the highest amount of correlation was related to the reproductive development stage. Finally, R2 for NDVI at different growth stages as well as  the entire growth period for straw, paddy, and biomass were higher than the SAVI index, revealing more accuracy of NDVI than SAVI

SALTMED model as an integrated management tool for water, crop, soil and N-fertilizer water management strategies and productivity: field and simulation study

This paper is a follow-up from a paper which described the SALTMED model. In this paper the focus is on the model application,using data of tomato and potato from field experiments in Italy, Greece (Crete) and Serbia. Drip full irrigation, drip deficit irrigation, drip as partial root drying (PRD), sprinkler and furrow irrigation were used in the 3-yr experiment between 2006 and 2008. In drip-irrigated experiments, the drip line was 10–12 cm below the surface. Dry matter, final yield, soil moisture and soil nitrogen were successfully simulated. The study showed that there is a great potential for saving water when using subsurface drip, PRD or drip deficit irrigation compared with sprinkler and furrow irrigation. Depending on the crop and irrigation system, the amount of fresh water that can be saved could vary between 14 and 44%. PRD and deficit drip irrigation have proved to be the most efficient water application strategies with the highest water productivity

Development of an agricultural drought assessment system : integration of agrohydrological modelling, remote sensing and geographical information

Iran faces widespread droughts regularly, causing large economical and social damages. The agricultural sector is with 80-90 % by far the largest user of water in Iran and is often the first sector to be affected by drought. Unfortunately, water management in agriculture is also rather poor and hence water productivity of crops WP is far below potential. The growing water scarcity due to drought and the increasing water demands of industries, households and environment, are major threats to sustainable agricultural development in Iran. Therefore, the development of a reliable agricultural drought assessment system would be very beneficial for proper operational decision making on farms, for early warning, for identification of potential vulnerability of areas and for mitigation of drought impacts. Given the current water scarcity, the limited available amount of water should be used as efficient as possible. To explore on-farm strategies which result in higher WP-values and thus economic gains, the physically based agrohydrological model Soil Water Atmosphere Plant (SWAP), was calibrated and validated using measured data at 8 selected farmer’s fields (wheat, fodder maize, sunflower and sugar beet) in the Borkhar irrigation district in Iran during the agricultural year 2004-05. Using the calibrated SWAP model, on-farm strategies i.e. deficit irrigation scheduling, optimal irrigation intervals and extent of cultivated area, were analyzed based on relations between WP- indicators and water consumption. The results showed a large potential of the improvement of water productivity under limited water supply in the Borkhar irrigation district. Although agrohydrological models like SWAP offer the possibilities for predicting crop yield, such models may become inaccurate because of uncertainty of input parameters like irrigation scheduling, soil hydraulic parameters and planting dates. This holds especially true when applying distributed modelling at regional scale. Hence to reduce the uncertainty in application of SWAP at regional scales, remotely sensed data of leaf area index and evapotranspiration were used in combination with a geographical information system. The remotely sensed data were inserted into the distributed SWAP model using data assimilation techniques i.e. sequential updating. Data of LAI were derived from Visible and Near Infrared (VNIR) spectral bands of remote sensing data with moderate to high spatial resolution. However, due to resolution limitations of existing remotely sensed data i.e. thermal bands, these data could not be used directly for routine ET estimation of individual fields. Therefore, a new disaggregation method based on linear disaggregation of ET components within each MODIS pixel, was developed and applied to the simulated MODIS data. The results of the proposed approach were further compared with two other disaggregation approaches being based on weighted ratios, as derived from dividing ET maps of high and low spatial resolution data. The biggest advantage of the proposed linear disaggregation approach was that the number of high spatial resolution images needed in this method is low, i.e. the approach can even be applied using one land cover map only. As in many regions access to high spatial resolution thermal images is currently not possible, the linear disaggregation method can still be used to assess drought impacts far in advance. Water balance components as computed by SWAP are quite sensitive to the upper boundary conditions, and hence to irrigation times and application depths. In order to know how much water has been applied, the cumulative actual ET data were therefore used in an automatic calibration mode, i.e. inverse modelling of irrigation scheduling. The ability of inverse modelling to reproduce the initial irrigation times and depth, was first investigated using forward cumulative SWAP simulated ET data based on 5, 15 and 30 days. Thereafter, the cumulative disaggregated remotely sensed ET data based on 5 days were used in the inverse modelling process. The results showed that the performance of inverse modelling is promising in identifying the irrigation time and depth of irrigation using 5 days based cumulative ET data. However, irrigation amounts, which rewet the soil profile beyond field capacity and thus cause excessive percolation, could not be detected by the applied inverse modelling approach. Also, assimilation of remotely sensed data into a distributed SWAP by automatic calibration needed a large amount of computation time, especially at regional scale. Hence, to insert the valuable information from remotely sensed land surface data into the SWAP model at regional scale, a simple updating assimilation technique was used. The SWAP model was implemented in a distributed way using the spatial distributed information of soil types, land use and water supply on a raster basis with a grid size of 250 m. In order to link spatial information data with SWAP, a coupling program was written by the author in MATLAB. This program took care of the transfer of in- and output data from one system to the other, as well as to run the model for each pixel. To have a prediction of crop yield far in advance, the sequential updating process of remotely sensed based data (LAI and/or relative evapotranspiration ET/ETp) was halted at one respectively two months before the end of the wheat growing season. During the sequential updating process known weather data were used, while for the remaining part of the growing season different scenarios were considered based on weather data of a dry, wet and normal year. A value for the optimum gain factor Kg, that performed best with respect to the observations, was selected Simulation with assimilation of both LAI and ET/ETp -data at both the regional and field scale (bias about %) was very promising in forecasting crop production one month in advance. However, longer term predictions i.e. two months in advance, resulted in a higher bias between the simulated and statistical data. It appeared that in the assimilation process LAI data have a dominant influence. Because of this dominant influence, it is suggested to repeat the assimilation process using the LAI data of the most advanced satellite i.e. IRS-P6 (ResourceSAT1&2) with higher spatial and temporal resolution. The surface water in the Borkhar irrigation canal network is provided by diversion of the water from the Zayande Rud river. Since this river is mainly fed by the snow melt from January to April, a comprehensive drought assessment system on seasonal basis can be developed by integration of the developed agricultural drought assessment system with the estimates of available surface water being derived from snow pack and snow cover

MONITORING OF SNOW COVER VARIATION USING MODIS SNOW PRODUCT

Snow is one of the integral components of hydrological and climatic systems. Needless to say, snow cover areas (SCA) are considered as indispensable input of hydrological and general circulation models. Studying the spatial and temporal variability of SCA is of the paramount importance for tremendous variety of research such as climate change, water supply and properly managing water resources. In this study by means of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product, the variation of snow cover extent (SCE) in Karoun basin located in western part of Iran is evaluated for twelve years' duration; since 2000 to 2012. The results show that the paramount occurrence of SCE is observed during February months of 2003, 2010 and 2011 as well as during December months of 2006 and 2009.The utmost occurrence of SCE is considered during January months of the other remaining years. Annual average shows that SCE varies from 11% in 2011 to 22% in 2006. According to Mann-Kendal trend test, throughout twelve years; 2000 to 2012, a majority of the pixels in the study area have no considerable trend although there is a decreasing trend on a small portion of the pixels located in the eastern part the study domain

Assimilation of satellite data into agrohydrological models to improve crop yield forecasts

This paper addresses the question of whether data assimilation of remotely sensed leaf area index and/or relative evapotranspiration estimates can be used to forecast total wheat production as an indicator of agricultural drought. A series of low to moderate resolution MODIS satellite data of the Borkhar district, Isfahan (Iran) was converted into both leaf area index and relative evapotranspiration using a land surface energy algorithm for the year 2005. An agrohydrological model was then implemented in a distributed manner using spatial information of soil types, land use, groundwater and irrigation on a raster basis with a grid size of 250 m, i.e. moderate resolution. A constant gain Kalman filter data assimilation algorithm was used for each data series to correct the internal variables of the distributed model whenever remotely sensed data were available. Predictions for 1 month in advance using simulations with assimilation at a regional scale were very promising with respect to the statistical data (bias = ±10%). However, longer-term predictions, i.e. 2 months in advance, resulted in a higher bias between the simulated and statistical data. The introduced methodology can be used as a reliable tool for assessing the impacts of droughts in semi-arid regions

Increasing water productivity of irrigated crops under limited water supply at field scale

Borkhar district is located in an and to semi-arid region in Iran and regularly faces widespread drought. Given current water scarcity, the limited available water should be used as efficient and productive as possible. To explore on-farm strategies which result in higher economic gains and water productivity RP), a physically based agrohydrological model, Soil Water Atmosphere Plant (SWAP), was calibrated and validated using intensive measured data at eight selected farmer fields (wheat, fodder maize, sunflower and sugar beet) in the Borkhar district, Iran during the agricultural year 2004 - 2005. The WP values for the main crops were computed using the SWAP simulated water balance components, i.e. transpiration T, evapotranspiration ET, irrigation 1, and the marketable yield Y-M in terms in terms of YMT-1, YMET-1 and YMI-1. The average WP, expressed as $T-1 (US$ m(-3)) was 0.19 for wheat, 0.5 for fodder maize, 0.06 for sunflower and 0.38 for sugar beet. This indicated that fodder maize provides the highest economic benefit in the Borkhar irrigation district. Soil evaporation caused the average WP values, expressed as Y-M ET-1 (kg m(-3)), to be significantly lower than the average WP, expressed as Y-M T-1, i.e. about 27% for wheat, 11% for fodder maize, 12% for sunflower and 0.18 for sugar beet. Furthermore, due to percolation from root zone and stored moisture content in the root zone, the average WP values, expressed as Y-M I-1 (kg m(-3)), had a 24 - 42% reduction as compared with VIP, expressed as Y-M ET-1. The results indicated that during the limited water supply period, on-farm strategies like deficit irrigation scheduling and reduction of the cultivated area can result in higher economic gains. Improved irrigation practices in terms of irrigation timing and amount, increased WP in terms of Y-M I-1 (kg m(-3)) by a factor of 1.5 for wheat and maize, 1.3 for sunflower and 1.1 for sugar beet. Under water shortage conditions, reduction of the cultivated area yielded higher water productivity values as compared to deficit irrigation. (c) 2007 Elsevier B.V. All rights reserved