Comparative study regarding the influence of phytobiotcs as feed additives on biochemical composition of Oreochromis Niloticus meat

The aim of this research was to evaluate the biochemical composition of the Nile tilapia meat in conditions of five phytobiotics administrated in feed. This experiment was performed in duplicate. The experimental variants were: V1 – control variant, V2 – 1% chilli pepper (Capsicum annuum) / kg feed, V3 – 1% black pepper (Pipper nigrum) / kg feed, V4 – 1% onion (Allium cepa) / kg feed, V5 – 1% goji fruits (Lycium barbarum) / kg feed and V6 – 1% basil (Ocimum basilicum) / kg feed. For biochemical analysis from muscle tissue, the sampling was performed at the beginning and at the end of the experiment from fresh meat. The results showed a significant differences (p<0.05), between variants in which were administered phytobiotics compared with the control variant, in case of the protein content (%), moisture content (%) and dry matter (%). Also, in this paper are presented the dynamics of the nutrient retention efficiency (PER-g/g, PUE-%, RP-g/fish and RLg/ fish) in the muscle tissue of the Nile tilapia. The best results were obtained in the case of the V3 variant regarding to retained protein (RP), PER and PUE and in V5 variant regarding to retained lipid (RL). In conclusion, the use of these phytobiotics in a concentration of 1% / kg feed in the Nile tilapia diet, led to significant changes (p <0.05) in the positive sense of the percentage moisture, protein and dry matter from muscle tissue and has contributed to the increasing the nutritional value of fish

Water quality evaluation of cyprinid pond based production system effluent

Aquaculture is the fastest growing food production sector, according to FAO report. Romanian aquaculture has an estimated production of 10000 tones per year, most of it being provided by cyprinids pond aquaculture production systems. However, pond aquaculture rises certain problems regarding the sustainability, as large concentrations of nitrogen and phosphorus may be recorded in effluents. Therefore, the aim of our study is to evaluate the sustainability of a single pond based cyprinids production system, situated in Galaţi - Romania, by analysing the water quality of the effluent. Water samples were collected from the pond effluent and the following analysis were determined: temperature (T°C), dissolved oxygen (DO), pH, nitrites (NO2), ammonia (NH3),orthophosphates (PO4),total zinc (Zn) chlorides (Cl-), bicarbonates (HCO3-), electro conductivity (EC) and total dissolved solids (TDS).The results were compared to the present national legislation (HG no. 202/2002) regarding the water quality in cyprinid farming and also to water quality criteria for aquaculture. Statistical analysis included normality test Kolmogorov Smirnov in order to determine the distribution of the registered data and Pearson correlation coefficient was applied for the analyzed parameters. The main conclusion of this research was that the technological water of the studied fish pond is suitable for fish rearing and sustainable for the environment, in terms of temperature, DO, pH, Cl-, HCO3-, Zn, EC and TDS. However, NO2, NH3 and PO4 concentrations were above the admissible limit imposed by the romanian legislation. Therefore, in order to improve sustainabily it is recommended that various modern multi-trophic technics should be applied, so that phosphorus and nitrogen compounds are valorized at maximum capacity

Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework

This research provides an analytical and predictive framework, based on state-of-the-art machine-learning (ML) algorithms (random forest (RF) and generalized additive models (GAM)), that can be used to assess and improve the Common Agricultural Policy (CAP) impact/performance over the agricultural and rural environments, easing the identification of proper instruments that can be used by EU policy makers in CAP’s financial management. The applied methodology consists of elaborating a custom-developed analytical framework based on a dataset containing 22 relevant indicators, considering four main dimensions that describe the intricacies of the EU agricultural and rural environment, in the CAP context: rural, emissions, macroeconomic, and financial. The results highlight that an increase of the agricultural research and development funding, as well as the agriculture employment rate, negatively influence the degree of rural poverty. The rural GDP per capita is influenced by the size of the employment rate in agriculture. It seems that environmental sustainability, identified by both fertilizers used and emissions from agriculture parameters, significantly influences the GDP per capita. In predicting emissions in agriculture, the direct payment, degree of rural poverty, fertilizer use, employment in agriculture, and agriculture labor productivity are the main independent parameters with the highest future importance. It was found that when predicting direct payments, the rural employment rate, employment in agriculture, and gross value added must be considered the most. The agricultural, entrepreneurial income prediction is mainly influenced by the total factor productivity, while agricultural research and development investments depend on gross value added, direct payments, and gross value added in the agricultural sector. Future research, related to prediction models based on CAP indicators, should also consider the marketing dimension. It is recommended for direct payments to be used to invest in upgrading the fertilizers technologies, since environmental sustainability will influence economic growth

Predictive Innovative Methods for Aquatic Heavy Metals Pollution Based on Bioindicators in Support of Blue Economy in the Danube River Basin

Heavy metal pollution is still present in the Danube River basin, due to intensive naval and agricultural activities conducted in the area. Therefore, continuous monitoring of this pivotal aquatic macro-system is necessary, through the development and optimization of monitoring methodologies. The main objective of the present study was to develop a prediction model for heavy metals accumulation in biological tissues, based on field gathered data which uses bioindicators (fish) and oxidative stress (OS) biomarkers. Samples of water and fish were collected from the lower sector of Danube River (DR), Danube Delta (DD) and Black Sea (BS). The following indicators were analyzed in samples: cadmium (Cd), lead (Pb), iron (Fe), zinc (Zn), copper (Cu) (in water and fish tissues), respectively, catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), malondialdehyde (MDA) (in fish tissues). The pollution index (PI) was calculated to identify the most polluted studied ecosystem, which revealed that Danube River is seriously affected by the presence of Fe (IP = 4887) and strongly affected by the presence of Zn (IP = 4.49). The concentration of Cd in fish muscle tissue was above the maximum permitted level (0.05 µg/g) by the EU regulation. From all analyzed OS biomarkers, MDA registered the highest median values in fish muscle (145.7 nmol/mg protein in DR, 201.03 nmol/mg protein in DD, 148.58 nmol/mg protein in BS) and fish liver (200.28 nmol/mg protein in DR, 163.67 nmol/mg protein, 158.51 nmol/mg protein), compared to CAT, SOD and GPx. The prediction of Cd, Pb, Zn, Fe and Cu in fish hepatic and muscle tissue was determined based on CAT, SOD, GPx and MDA, by using non-linear tree-based RF prediction models. The analysis emphasizes that MDA in hepatic tissue is the most important independent variable for predicting heavy metals in fish muscle and tissues at BS coast, followed by GPx in both hepatic and muscle tissues. The RF analytical framework revealed that CAT in muscle tissue, respectively, MDA and GPx in hepatic tissues are most common predictors for determining the heavy metals concentration in both muscle and hepatic tissues in DD area. For DR, the MDA in muscle, followed by MDA in hepatic tissue are the main predictors in RF analysis