Journal of the Austrian Society of Agricultural Economics (JASAE)

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..
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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..
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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..
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Abstract : Most neglected, forgotten and under-exploited vegetables may go into extinction except their food, nutritional and nutraceutical potentials are discovered and harnessed. The fruit of Corchorus olitorius is one of its edible portions currently being under-exploited for dietary purposes due to poor awareness of their nutritional and anti-nutritional profiles. In order to unravel the potentials in Corchorus fruit for human nutrition and promote its utilization, organically produced Corchorus fruits were analyzed for nutrients and anti-nutrients, their correlations and mineral ratios using standard analytical procedures. Results showed that the fruits of Corchorus accessions varied significantly (p<0.05) in the nutritional and anti-nutritional parameters investigated. The fruits were rich in calcium, potassium, magnesium and phosphorus had appreciable iron and zinc but low in sodium and copper. The Ca/P ratios ranged from 11.04 to 25.53 while the Na/K was in the range of 0.0184-0.0202. Zinc showed a significantly negative relationship with phytate (r= -0.80**). Magnesium had significantly negative relationship with saponin (r= -0.69*). Crude fibre was significantly and negatively correlated with phytate (r=-0.79**). Dry matter had significantly negative relationship with saponin (-0.67*). The results indicated that the organically produced fruits were nutrient-rich with excellent mineral ratios suggesting their nutritional and nutraceutical potential benefits..
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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..
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