Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, which are harvested several times annually, is very complicated and has received less attention. Therefore, in this paper, the most important vegetation indices developed to estimate alfalfa yield are using Sentinel-2 time series images. In this research, 144 alfalfa samples were collected periodically in a destructive way from alfalfa farms of MAGSAL Agricultural and Production Company (Qazvin) near the time of satellite pass, and then the efficiency of 10 of the most famous vegetation indices to estimate alfalfa yield was evaluated based on Sentinel-2 images. The results of this research showed that the estimated alfalfa yield using the index had the highest correlation ( ) and the lowest root-mean-square-error (RMSE = 0. 316 ) compared to the field data collected in the middle of August. In addition, the results showed that the red edge indices did not solve the saturation problem of vegetation indices and that the green vegetation indices were more capable of estimating alfalfa yield than the red edge indices.

The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in MAGSAL, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2117 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that 𝐶 𝐼 𝑟 𝑒 𝑑 𝑒 𝑑 𝑔 𝑒 index with 𝑅 2 = 1. 55 and the lowest root mean square error (𝑅 𝑀 𝑆 𝐸 = 2. 874 𝑘 𝑔 𝑚 2 ⁄ ) was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses.

Estimating dry biomass is one of the important parts of production estimation. Among vegetation indices, leaf area index (LAI) is the most common used index to estimate water demand and yield. In this study, attempts have made to estimate LAI without destroying plant and by using the AccuPAR-LP80 crop scanner device. The case study performed in MAGSAL Agro-Industrial Company Qazvin, Iran with the aim of introducing relations to estimate the amount of dry biomass via LAI for three plant- maize, sugar beet and alfalfa. LAI values of above mentioned plants measured through nondestructive method by calibrated AccuPAR-LP80 crop scanner. Statistical evaluation showed that the highest correlation was for maize with R2=0.96 and the lowest was for alfalfa with R2=0.87. In addition, measured dry biomass was a linear function of fraction of photosynthesis active radation (fPAR). Statistical evaluation showed that correlation coefficient varies from 0.94 to 0.90 and PMSE from 2.85 to 3.3 kg ha-1 for maize and alfalfa, respectively.

Portfolio definition is the most important decision for individuals and legal persons that invest in stock. The main objective of this paper is study and determination of optimal portfolio for stock of active food industrial company in Tehran stock based on value at risk (VaR) index. For this purpose, we used weekly static of stock of active food industrial company in Tehran from Bahman 1387 - Tir 1389. Also for analysis of static and data, we used mathematical programming with integral number. The results show that the stock of Salemin and MAGSAL farming and animal husbandry are exist in all optimal portfolio that with increasing VaR, the stock of Mino industrial company is also introduces in optimal portfolio. Other results of this paper show that there is a direct relation between VaR and return expected of investors and also there isn’t a specified relation between VaR and kind of optimal portfolio (number of stocks in portfolio).

Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize.  For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in MAGSAL, Qazvin.  Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with  outperformed other VIs to estimate LAI of silage maize. Moreover, the  values of non-linear regression models were higher that the liner ones.

Evaluating the performance of irrigation systems by providing the possibility of increasing irrigation efficiency in those systems is an important factor in the field of water resources management. In this study, the performance of three systems were evaluated, including linear move sprinkler irrigation systems in maize, sugar beet and alfalfa fields of MAGSAL Agro-industrial Company which is located in Qazvin plain, in 2013. In order to assess the performance of the cited irrigation systems, the coefficients of uniformity were determined by creating a water collection network of cans to gather the irrigation water in different points of the net. The results indicate that the average values of the coefficient of uniformity and the coefficient of distribution uniformity are 73.3 and 61.9, respectively which express low amounts of the coefficients in the systems of interest. On the other hand, the potential efficiency of water use (PELQ) and actual efficiency (AELQ) in the lower quarter were calculated as 68.21 and 50.6, respectively. These results depict bad management and exploitation. Likewise three-dimensional model of the spatial distribution of the output water of the sprinklers at the field level was drawn by GS + software. Overall, results reveal that the evaluated linear move sprinkler irrigation systems in this experiment were not good in performance which the main reasons can be suggested as weaknesses in management of them.