Crop Yield Estimation and Carrying Capacity in Sar Ali-Abad Summer Rangelands of Golestan Province, Iran

High mountain grassland ecosystems are the best pastures of Golestan province in Iran. These grasslands are an effective means of soil conservation and offer standing green fodder for livestock. Sar Ali-abad summer rangeland with an area of 780 ha is a representative of mountain ecosystems of Golestan province. Estimating forage production to avoid overgrazing is a necessity to ensure the long-term sustainability of these natural ecosystems. To estimate rangeland productivity and carrying capacity was the major objective while, comparing three methods of clipping and weighing (CW), double sampling (DS), and comparative yield (CY) by using 1-m2 plots was the minor objective of this study. There were high correlation coefficients (0.90-0.99) for DS and CY, so the estimated and ranked data were corrected based on the regression equations. There were linear relationships between the corrected and clipped data for the DS and CY methods which confirmed the suitability of both methods against the CW. Currently, the study area was stocked with 1580 AU. Based on the CW method and allowable use (50% of total forage production), potential forage intake of 60 kg sheep (2% body weight), and the area of pasture (700 ha after adjusted for non-usable cliffy area), the total DM of study area was 272825 kg. This amount of forage will support 1516 AUs for grazing season of which this summer range site with limited grazing period was not overgrazed. By using CY and DS methods, we have simulated the regression models for DM estimation. Instead of clipping 180 one square plots, about 30 to 40 plots were clipped which were non-destructive, rapid, and cost effective

Range Condition Classification Based on Quantitative Characteristics of Vegetation

Change in range condition classes over time are usually the basis for monitoring management effectiveness. Several approaches have been proposed to monitor the range condition classes in relation to a bench mark usually called climax stage. There are some types of range condition classification often included in a range inventory. In this paper, six factors of canopy cover, litter frequency, plant vigour, soil protection percentage, plant composition, and present production as a percentage of indicative state were described for determination range conditions. We have determined range condition classes by using R software. This method was developed by FAO projects in Iran. The relationships between different factors and their scores were determined by linear equations. The vegetation data in field were collected in 20 plots of 25x60 cm by established F-shaped layouts. In each plot, species cover percentages, litters, rocks, and bare soils were estimated. Based on our total scores, we got the fair state of range condition. It is possible to create a package in R software to determine condition classes which will be used by range managers and experts

Range Condition Classification Based on Quantitative Characteristics of Vegetation

Change in range condition classes over time are usually the basis for monitoring management effectiveness. Several approaches have been proposed to monitor the range condition classes in relation to a bench mark usually called climax stage. There are some types of range condition classification often included in a range inventory. In this paper, six factors of canopy cover, litter frequency, plant vigour, soil protection percentage, plant composition, and present production as a percentage of indicative state were described for determination range conditions. We have determined range condition classes by using R software. This method was developed by FAO projects in Iran. The relationships between different factors and their scores were determined by linear equations. The vegetation data in field were collected in 20 plots of 25x60 cm by established F-shaped layouts. In each plot, species cover percentages, litters, rocks, and bare soils were estimated. Based on our total scores, we got the fair state of range condition. It is possible to create a package in R software to determine condition classes which will be used by range managers and experts

Enhancing the security of patients’ portals and websites by detecting malicious web crawlers using machine learning techniques

Introduction: There is increasing demand for access to medical information via patients’ portals. However, one of the challenges towards widespread utilisation of such service is maintaining the security of those portals. Recent reports show an alarming increase in cyber-attacks using crawlers. These software programs crawl web pages and are capable of executing various commands such as attacking web servers, cracking passwords, harvesting users’ personal information, and testing the vulnerability of servers. The aim of this research is to develop a new effective model for detecting malicious crawlers based on their navigational behavior using machine-learning techniques. Method: In this research, different methods of crawler detection were investigated. Log files of a sample of compromised web sites were analysed and the best features for the detection of crawlers were extracted. Then after testing and comparing several machine learning algorithms including Support Vector Machine (SVM), Bayesian Network and Decision Tree, the best model was developed using the most appropriate features and its accuracy was evaluated. Results: Our analysis showed the SVM-based models can yield higher accuracy (f-measure = 0.97) comparing to Bayesian Network (f-measure = 0.88) and Decision Tree (f-measure = 0.95) and artificial neural network (ANN) (f-measure = 0.87)for detecting malicious crawlers. However, extracting proper features can increase the performance of the SVM (f-measure = 0.98), the Bayesian network (f-measure = 0.94) and the Decision Tree (f-measure = 0.96) and ANN (f-measure = 0.92). Conclusion: Security concerns are among the potential barriers to widespread utilisation of patient portals. Machine learning algorithms can be accurately used to detect malicious crawlers and enhance the security of sensitive patients’ information. Selecting appropriate features for the development of these algorithms can remarkably increase their accuracy