Journal of the Austrian Society of Agricultural Economics (JASAE) (ISSN:18158129, E-ISSN:18151027)

Aim and Scope

Aim-

Journal of the Austrian Society of Agricultural Economics (JASAE) is an Open Access International Journal Which Aims to Publish High-quality Scientific Articles in the Field of Horticulture, Agriculture and Soil Science, Agronomy; Biology; Economics Academic Field: Mathematical and Statistical Methods in Economics; Agriculture and Animal Husbandry; Forestry and Many More. Our Aim is to Give an Open Space to Scientists Who Can Publish and Deliver Scientific Knowledge About the Relevant Field for the People in the Society. Gongcheng Kexue Yu Jishu/Advanced Engineering Science

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
Scope-

Journal of the Austrian Society of Agricultural Economics (JASAE) is a peer-reviewed journal. The journal seeks to publish original research articles that are hypothetical and theoretical in its nature and that provide exploratory insights in the following fields but not limited to:

Horticulture Agriculture Soil Science Agronomy
Biology Economics Biotechnology Agricultural chemistry
Soil development in plants aromatic plants subtropical fruits
Green house construction Growth Horticultural therapy Entomology
Medicinal Weed management in horticultural crops plant Analysis Tropical

See More Scopes

Latest Journals
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-01-11-2022-185
Total View : 1

Abstract : Kalamansi orange is the main and superior commodity from Bengkulu Province, Indonesia. This orange has been used by the community as a source of beverages managed by local industries. However, there is no data reporting the chemical compounds in kalamansi oranges from Bengkulu Province. The purpose of this study was to analyze the chemical compounds detected in the extract of kalamansi orange from Bengkulu Province using LC-QTOF-MS/MS. Measurements with LC-QTOF-MS/MS carried out in the Laboratory of PT. Saraswanti Indo Genetech (SIG), Bogor, Indonesia. The results of this study show that the extract of kalamansi orange contains alkaloid compounds in the form of nortopane and cadambine (2α, 3β, 6exo-trihydroxy nortropane, 2α, 3β, dihydroxy nortropane and 3α-dihydrocadambine). In addition, the kalamansi orange extract also has 19 bioactive compounds from the flavonoid group. The results of the analysis of vitamin C levels in the orange extract using High Performance Liquid Chromatography (HPLC) showed that the vitamin C content in the extract was 0.04 ± 0.0002%. Information about the bioactive compounds in orange extract is expected to be the first step for greater utilization for this typical citrus commodity of Bengkulu Province..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-20-10-2022-181
Total View : 1

Title : Quantitative Analysis of the Breast Mass characteristics and Image Textures on Ultrasound Images
by Le Thanh Luc, Le Nhat Tan, Huynh Thi Khanh Nhi, Pham Tan Thi,
Abstract : Analysis of breast mass characteristics via ultrasound images plays an important role in cancer screening and treatment planning. Currently, Radiomics is utilized as a useful quantitative tool for medical image analysis. In this work, there are a total of 96 Radiomics features of biological characteristics and image textures from 3 main groups were extracted to analyze. The values and importance values of features and feature groups were quantified by statistical methods. Based on these results, the differences in tumor characteristics and image textures between benign and malignant breast masses were analyzed in depth, which can reveal the biological properties discrimination among groups. Moreover, several classification models, such as the Support Vector Machine and Ensemble learning model, and an oversampling method were utilized to confirm the discriminant performance via Radiomics-based features in the limited-amount dataset. The statistical feature analysis results extensively reveal the biological characteristics and texture differences between benign and malignant tumors. This result orient the further feature extraction process, in order to improve the classification and biological analysis of tumors. In addition, the classification results of the ensemble learning approaches with oversampling technique presented the highest performance, with the F1 score of 87% for benign tumors and 65% for malignant tumors, although training in a small number of images..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-20-10-2022-180
Total View : 0

Abstract : Early detection and diagnosis of breast cancer is the key to controlling, curing this disease as well as reducing costs for the patient. AI-based computer-aided diagnosis (CAD) systems designed to help physicians make faster and more accurate decisions, convolutional neural network (CNN) has earned many achievements in the medical field. However, the performance of the CNN model is highly dependent on the quantity and quality of the input data sets, which is a big challenge for medical imaging because the image collection is very limited. To solve this problem, Deep Convolutional Generative Adversarial Network (DCGAN) is proposed to generate breast tumor images from the original breast ultrasound image BUSI dataset consisting of 437 benign masses and 210 malignant tumors. Then, in order to determine the performance of the proposed model, the newly created images combined with the images in the original data will be evaluated qualitatively, visually and quantitatively through the classification problem by feeding to 2 classification models: simple self-designed and Densenet to evaluate their quality and clinical value. The classification performance using only the original data yielded a sensitivity of 37% and an F1-score of 51% then increased to a sensitivity of 81% and an F1-score of 70%. The results show that breast tumor images generated from the DCGAN network can be used to significantly increase the efficiency thus has the potential to assist physicians in medical image reading task..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-13-10-2022-179
Total View : 437

Title : The Impact of Food Sovereignty of Cereal Crops on Water Consumption in the Agricultural Sector in the Kingdom of Saudi Arabia
by Adel M. Ghanem, Khalid N. Alrwis, Othman S. Alnashwan, Suliman A. Almojel, Sattam F. Almodarra, Sharaf Aldin B. Alaagib, Nageeb M. Aldawdahi,
Abstract : The research aimed to measure the impact of food sovereignty of grain crops on water consumption in the agricultural sector during the period 1990-2020. This study resulted in a set of results, the most important of which is, the total amount of water used in the production of grains amounted to 136.32 billion m3, representing 27.0% of the total amount of water used in the agricultural sector during the period 1990- 2020. Increases of 10% in the ratio of grain area to area yield and the summer grain area to its winter counterpart resulted in increases of 10.7% and 3.66 % in the amount of water utilized in grain production, respectively. Furthermore, a 10% increase in the expected amount of water required in grain production results in a 2.8 % increase in the water used in agriculture. In light of the scarcity of water resources, expanding the cultivation of the most significant winter cereal crops (wheat) while reducing the area of summer cereal crops with high water requirements, the most important of which is sorghum, is required to reduce water use..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-21-09-2022-174
Total View : 5

Title : Perceived Price Analysis on Selected Processed Livestock Meat Product Brands
by Agung Triatmojo, Mujtahidah Anggriani Ummul Muzayyanah, Nguyen Hoang Qui,
Abstract : Consumers have several considerations before choosing a particular product brand. Earlier studies showed that consumers no longer consider price when selecting a brand of processed meat products. However, factors influencing product brand selection decisions are price and differences in consumer characteristics. The study utilized a cross-sectional design with a survey method to analyse the effects of consumers' sociodemographic characteristics on perceived price in choosing a brand of processed meat products. Primary data were collected using a convenience sampling technique and a self-administered questionnaire. A total of 456 respondents were consumers of processed meat products, including sausages, nuggets, corned meat, meatballs, and meat floss. The results showed that farmers 18-42 years old accounted for 68.64% with single status (56.80%). A total of 60% of respondents were non-tertiary (69.74%), and 64.91% of farmers had fewer than 4 members in their houses and mostly stayed in urban areas (65.13%). The differences in income, location, gender, and recent education significantly influence perceived price in product brand selection decisions (P<0.001). This study concluded that sociodemographic characteristics impact consumers’ perceived prices, yet income is the dominant factor in determining selected processed livestock meat product brands..
Full article
SJR
International Collaboration

Our Certificates
Certificates
Certificates
Certificates
Certificates
Certificates
Certificates
Certificates
Certificates
Certificates
Certificates
Certificates
//