Brucellosis is an important zoonotic disease of animals and humans caused by bacteria of the genus Brucella. Brucellae are Gram-negative intracellular bacteria which infect a wide variety of animals including goats, sheep, buffaloes, cows, pigs, and wildlife. The objectives of this study were to determine the seroprevalence and spatial distribution of brucellosis in Central Punjab, Pakistan. A total of 1083 blood samples of goats, sheep, buffaloes, and cows were collected from 38 villages of four districts (Kasur, Faisalabad, Lahore, and Okara) of Punjab, Pakistan, and screened for brucellosis by Rose Bengal Plate test (RBPT) and PCR confirmed. Epidemiological, demographic data and GPS coordinates for every sample were collected. By using interpolation of the Aeronautical Reconnaissance Coverage Geographic Information System (Arc GIS), a surface plot was generated applying inverse distance weight (IDW). It was found that 35 (3.23%) serum samples were positive for brucellosis. In eight (61.5%), six (75%), seven (87.5%), and eight (89%) villages, positive goats, sheep, buffaloes, and cattle were detected, respectively. In general, older animals are more often positive for brucellosis. In goats bucks were more often RBPT positive than females while in sheep, buffaloes, and cattle more females were positive. The spatial distribution of brucellosis shows that it is widely distributed in the western region of the study area in goats and in the South-West region in sheep. Similarly, for buffaloes it is restricted to the south-east and north-west regions, and in cattle brucellosis is present in western region of study area only. Reflected by this study, brucellosis poses a risk for livestock in developing countries due to lack of awareness by officials, owners, and consumers, and control measures are missing. A risk map of brucellosis was generated to develop effective strategies for awareness rising and to improve the quality of control programs in Pakistan.

Journal of Coastal Zone Management AbstractTo investigate coastline changes, more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures. This research focused on the integration of remote sensing, Geographic Information Systems (GIS) and modelling techniques to provide meaningful interrelationship on the spatial and temporal dynamics of coastline changes along Keti-Bunder and Kharo-Chann in Indus Delta. Data were extracted from Multi Spectral Scanner (MSS), Thematic Mapper (TM) and (Advanced Space borne Thermal Emission and Reflection Radiometer) ASTER images having the duration of thirty eight years starting from 1973 to 2011. Object based image analysis technique was used to extract the water on the criterion of (Normalized Difference Water Index) NDWI, (Modified Normalized Difference Water Index) MNDWI and threshold level slicing. Topographic maps were also used to extract the topographic information.Digital Shoreline Analysis System (DSAS) was used to analyse and predict rates of coastline change. The correlation between the calculated values of shoreline using linear regression equation 2011 and the values which was extracted from the remote sensing data of the same year having the average percentage accuracy is almost 92%. It means linear regression can be used to model the coastal erosion and accretion. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Results showed that erosion rate of KetiBunder and Kharo-Chann is 16.54 and 63.79 meter per year along the coastline respectively. The areas which can be eroded and accreted in year 2020 will be almost 28.6 square kilometres and 3.6 square kilometres respectively.

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Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network.