Investigation and identification of helminth parasites in five fish species of Anzali Wetland

This study aimed to identify of helminth parasites in some native and economic fish of Anzali wetland was performed. 314 fish specimens, including: Alburnus hohenackeri (60 Specimen), Blicca bjoerkna (75 Specimen), Hemiculter leucisculus (75 Specimen), Pseudorasbora parva (52 pcs) Rhodeus amarus (52 Specimen), from Cyprinidae (Cyprinidae), during 1 year (2012-2013) and seasonally, from three different stations (Shijan, Pirbazar and Pasikhan) via gill net (50-70 mm) and fyke net were sampled. Samples were transported to the Inlandwater Aquaculture Institute in Bandar Anzali. In the laboratory, After biometry and determine the age and sex of fish, different body parts for searching of parasites were checked and parasites by using identification keys were identified. As a result of this study, 9 species of helminth parasites of fishes were identified include: Gyrodactylus sp., Dactylogyrus sphyrna , Dactylogyrus sp., Ligula intestinalis, Diplostomum paraspathaceum, Diplostomum spathaceum, Diplostomum sp., Posthodiplostomum cuticola and Rhabdochona denudate. The highest frequency of parasites in trematodes (in 5 fish species studied) and the minimum frequency in cestodes (each at 1 fish species studied) were seen. According to statistical analysis using Chi-Square test and KruskalWallis, the pollution of the sex, weight, length and sampling stations, there is no statistically significant difference (P> 0.05)

Energy Consumption, Carbon Emissions and Global Warming Potential of Wolfberry Production in Jingtai Oasis, Gansu Province, China

During the last decade, China's agro-food production has increased rapidly and been accompanied by the challenge of increasing greenhouse gas (GHG) emissions and other environmental pollutants from fertilizers, pesticides, and intensive energy use. Understanding the energy use and environmental impacts of crop production will help identify environmentally damaging hotspots of agro-production, allowing environmental impacts to be assessed and crop management strategies optimized. Conventional farming has been widely employed in wolfberry (Lycium barbarum) cultivation in China, which is an important cash tree crop not only for the rural economy but also from an ecological standpoint. Energy use and global warming potential (GWP) were investigated in a wolfberry production system in the Yellow River irrigated Jingtai region of Gansu. In total, 52 household farms were randomly selected to conduct the investigation using questionnaires. Total energy input and output were 321,800.73 and 166,888.80 MJ ha−1, respectively, in the production system. The highest share of energy inputs was found to be electricity consumption for lifting irrigation water, accounting for 68.52%, followed by chemical fertilizer application (11.37%). Energy use efficiency was 0.52 when considering both fruit and pruned wood. Nonrenewable energy use (88.52%) was far larger than the renewable energy input. The share of GWP of different inputs were 64.52% electricity, 27.72% nitrogen (N) fertilizer, 5.07% phosphate, 2.32% diesel, and 0.37% potassium, respectively. The highest share was related to electricity consumption for irrigation, followed by N fertilizer use. Total GWP in the wolfberry planting system was 26,018.64 kg CO2 eq ha−1 and the share of CO2, N2O, and CH4 were 99.47%, 0.48%, and negligible respectively with CO2 being dominant. Pathways for reducing energy use and GHG emission mitigation include: conversion to low carbon farming to establish a sustainable and cleaner production system with options of raising water use efficiency by adopting a seasonal gradient water pricing system and advanced irrigation techniques; reducing synthetic fertilizer use; and policy support: smallholder farmland transfer (concentration) for scale production, credit (small- and low-interest credit) and tax breaks

Developing GHG mitigation strategies for agro-sectors : Feasibility study for the dairy sector

The financial sector can and wants to play an important role in contributing to the reduction of GHG emissions in agro-sectors. Rabobank commissioned a feasibility study to assess the possibilities and challenges for what is seen as three key-elements of a joint approach for the international financial sector: (1) methodologies for setting GHG emission targets; (2) estimation of current emissions for monitoring purposes; and (3) identification of mitigation options. This report gives an overview of available literature on these elements. A main conclusion of the report is to align where possible with sectoral or national GHG targets and tools of programmes.---De financiële sector kan en wil een rol spelen in het verlagen van de emissie van broeikasgassen in de agro-sectoren. Rabobank heeft opdracht gegeven voor een haalbaarheidsstudie naar drie kernpunten van een gezamenlijke benadering voor de internationale financiële sector: (1) methodologie voor vaststellen van doelen voor reductie van broeikasgasemissies; (2) het schatten van huidige emissies om monitoring mogelijk te maken en (3) het identificeren van opties om emissies broeikasgassen te reduceren. Dit rapport geeft een overzicht van de beschikbare literatuur rond deze punten. Een hoofdconclusie van het rapport is om waar mogelijk af te stemmen met sectorale of nationale doelen en programma’s

Imputing missing value through ensemble concept based on statistical measures

Many datasets include missing values in their attributes. Data mining techniques are not applicable in the presence of missing values. So an important step in preprocessing of a data mining task is missing value management. One of the most important categories in missing value management techniques is missing value imputation. This paper presents a new imputation technique. The proposed imputation technique is based on statistical measurements. The suggested imputation technique employs an ensemble of the estimators built to estimate the missing values based on positive and negative correlated observed attributes separately. Each estimator guesses a value for a missed value based on the average and variance of that feature. The average and variance of the feature are estimated from the non-missed values of that feature. The final consensus value for a missed value is the weighted aggregation of the values estimated by different estimators. The chief weight is attribute correlation, and the slight weight is dependent to kernel function such as kurtosis, skewness, number of involved samples and composition of them. The missing values are deliberately produced randomly at different levels. The experimentations indicate that the suggested technique has a good accuracy in comparison with the classical methods