11 research outputs found

    The use of citrus pulp silage in Diplodus puntazzo nutrition

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    [EN] Purpose Studying the efect of citrus pulp silage in the growth performance, amino acid retention of sharpsnout sea bream juveniles. Methods Citrus pulp is an energy and protein source that has been used to replace wheat meal in Diplodus puntazzo fngerlings; 96 fsh of 21.5±0.67 g were nourished with two experimental diets for 105 days. The control diet (CON) contained 421.6 g/kg of protein and 22.6 MJ/kg of energy. The citrus pulp diet (CPD) included 442.7 g/kg of protein and 22.02 MJ/ kg of energy. Results At the end of this trial, the CPD did not afect the growth parameters, but the feed efciency values exhibited statistically signifcant diferences, with those fsh fed the CPD having higher values, possibly due to the presence of limonene in the diet. The CPD also resulted in a reduction in amino acid (AA) content due to the protein being bound to fbre; this consequence did not afect the fnal fsh AA profle. For AA retention, only arginine displayed signifcant diferences, although the rest of the AAs also showed efects of AA reduction from the CPD. Conclusion Citrus pulp silage can be used in D. puntazzo feeding, without adverse in the growth performance and the amino acid profle, although it did cause a reduction in feed efciencyNogales Mérida, S.; Tomas-Vidal, A.; Jover Cerda, M.; Sánchez Lozano, NB.; Velazco Vargas, JL.; Martínez-Llorens, S. (2018). The use of citrus pulp silage in Diplodus puntazzo nutrition. International Journal Of Recycling of Organic Waste in Agriculture (Online). 2018:1-8. https://doi.org/10.1007/s40093-018-0235-5S182018Alnaimy A, Gad AE, Mustafa MM, Atta MAA, Basuony HAM (2017) Using of citrus by-products in farm animals feeding. 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Small Rumin Res 83:14–21. https://doi.org/10.1016/j.smallrumres.2008.11.009Rodriguez APO, Gominho-Rosa MDC, Cargnin-Ferreira E, De Francisco A, Fracalossi DM (2012) Different utilization of plant sources by the omnivores jundiá catfish (Rhamdia quelen) and Nile tilapia (Oreochromis niloticus). Aquacult Nutr 18:65–72. https://doi.org/10.1111/j.1365-2095.2011.00877.xSaleh HM, El-Mor M (2015) Feeding habits of the sharp snout sea bream, Diplodus puntazzo (Cetti, 1777) (Teleostei: Sparidae) from Benghazi Coast, Eastern Libya. Int J Bioassays 4:3860–3865Shan Y (2017) Comprehensive utilization of citrus by-products. Academic Press, AmsterdamShimada T, Shindu M, Miyazawa M (2002) Species differences in the metabolism of (+)− and (−)− Limonenes and their metabolites, carveols and carvones, by cytochrome P450 enzymes in liver microsomes of mice, rats, guinea pigs, rabbits, dogs monkeys, and humans. 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    Long-Term Impact of Wastewater Irrigation on Soil Pollution and Degradation: A Case Study from Egypt

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    There is consensus on the impact of wastewater irrigation on soil properties and heavy metal accumulation. The studies that show the impact of temporal changes as a result of different long-term additions of wastewater on the heavy metal accumulation and degradation of soil are extremely limited. This study was carried out to assess heavy metal contamination in soils irrigated with wastewater for more than 30 years in Egypt. A total number of 12 irrigation water samples and 12 soil profiles were collected during 2020 and were chemically characterized. The results showed that soils irrigated with wastewater over the long term contained significantly higher concentrations of heavy metals compared to fields irrigated with fresh water. Heavy metal levels in water and soil samples were within the permissible limits, with the exception of Cd concentration in water (0.03 mg L−1). Continuous cultivation for a long period of time (30 years) using raw urban wastewater application has led to the adverse effect of increasingly available Pb concentration (5.44 mg kg−1). Similar temporal behavior was seen for Cd and Fe, which increased by 0.98 and 11.2 mg kg−1, respectively, after 30 years. The heavy metals in wastewater-irrigated soils significantly increased in clayey soils, as compared to sandy soils irrigated from the same source. Our findings provide important information for decision makers in Egypt and similar countries for the development of a strategy for the use of wastewater in irrigation for sustainable agricultural management

    Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique

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    To meet the needs of Egypt’s rising population, more land must be cultivated. Land evaluation is vital to achieving sustainable agricultural production. To determine the soil capability in the northeast Nile Delta region of Egypt, the present study introduces a new form of integration between the Agriculture Land Evaluation System (ALES Arid) model and the machine learning (ML) approach. The soil capability indicators required for the ALES Arid model were determined for the 47 collected soil profiles covering the study area. These indicators include soil pH, soil salinity, the sodium adsorption ratio (SAR), the exchangeable sodium percentage (ESP), the organic matter (OM) content, the calcium carbonate (CaCO3) content, the gypsum content, the clay percentage, and the slope. The ALES Arid model was run using these indicators, and soil capability indexes were obtained. Using GIS, these indexes helped to classify the study area into four capability classes, ranging from good to very poor soils. To predict the soil capability, three machine learning algorithms named traditional RVFL, sine cosine algorithm (SCA), and AFO were also applied to the same soil criteria. The developed ML method aims to enhance the prediction of soil capability. This method depends on improving the performance of Random Vector Functional Link (RVFL) using an optimization technique named Aptenodytes Forsteri Optimization (AFO). The operators of AFO were used to determine the best parameters of RVFL since traditional RVFL is sensitive to parameters. To assess the performance of the developed AFO-RVFL method, a set of real collected data was used. The experimental results illustrate the high efficacy of AFO-RVFL in the spatial prediction of soil capability. The correlations found in this study are critical for understanding the overall techniques for predicting soil capability

    Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique

    No full text
    To meet the needs of Egypt’s rising population, more land must be cultivated. Land evaluation is vital to achieving sustainable agricultural production. To determine the soil capability in the northeast Nile Delta region of Egypt, the present study introduces a new form of integration between the Agriculture Land Evaluation System (ALES Arid) model and the machine learning (ML) approach. The soil capability indicators required for the ALES Arid model were determined for the 47 collected soil profiles covering the study area. These indicators include soil pH, soil salinity, the sodium adsorption ratio (SAR), the exchangeable sodium percentage (ESP), the organic matter (OM) content, the calcium carbonate (CaCO3) content, the gypsum content, the clay percentage, and the slope. The ALES Arid model was run using these indicators, and soil capability indexes were obtained. Using GIS, these indexes helped to classify the study area into four capability classes, ranging from good to very poor soils. To predict the soil capability, three machine learning algorithms named traditional RVFL, sine cosine algorithm (SCA), and AFO were also applied to the same soil criteria. The developed ML method aims to enhance the prediction of soil capability. This method depends on improving the performance of Random Vector Functional Link (RVFL) using an optimization technique named Aptenodytes Forsteri Optimization (AFO). The operators of AFO were used to determine the best parameters of RVFL since traditional RVFL is sensitive to parameters. To assess the performance of the developed AFO-RVFL method, a set of real collected data was used. The experimental results illustrate the high efficacy of AFO-RVFL in the spatial prediction of soil capability. The correlations found in this study are critical for understanding the overall techniques for predicting soil capability

    Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

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    Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI
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