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1
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The reasons of the regional differences of agricultural land prices - Hungarian case study from Hungary

100%
Fekete-Farkas M., Toth-Naar Z., Baranyai Z., Szucs I., Vinogradov S. A.
Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu
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2013
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tom 15
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nr 1
Agricultural land market in Hungary is in process of development, the prices of land is times lower compared to land prices in old member states of the EU. Because of their lower income Hungarian nationals do not have substantial possibilities to acquire ownership over land like nationals of the old member states of the EU. In order to preserve the agricultural sector from shocks that might arise from the differences in land prices and income with the rest of EU, Hungary as the others Central and Eastern European new member states countries (the Czech Republic, Estonia, Latvia, Lithuania, Poland and Slovakia) during the accession negotiations in 2003 was granted the possibility to maintain existing national provisions restricting the acquisition of agricultural land or forests. Based on the data provided by the Hungarian Farm Accountancy Data Network and by the Hungarian Central Statistic Office the authors examined the specific impacts of factors influencing on arable land prices.
2
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Predicting the properties of corrugated base papers using multiple linear regression and artificial neural networks

100%
Adamopoulos S., Karageorgos A., Rapti E., Birbilis D.
Drewno. Prace Naukowe. Doniesienia. Komunikaty
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2016
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tom 59
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nr 198
3
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Effect of elicitors as stimulating substances on sensory quality traits in color sweet bell pepper (Capsicum annuum L. cv. Fascinato and Orangela) grown under greenhouse conditions

67%
Jimenez-Garcia S. N., Vazquez-Cruz M. A., Miranda-Lopez R., Garcia-Mier L., Guevara-Gonzalez R. G., Feregrino-Perez A. A.
Polish Journal of Food and Nutrition Sciences
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2018
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tom 68
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nr 4
The effect of the application of salicylic acid (SA) and hydrogen peroxide (H2 O2 ) was evaluated on sensory quality traits of two commercial sweet bell pepper varieties, Fascinato and Orangela, by descriptive sensory evaluation, principal component analysis (PCA), and Multivariate Analysis of Variance (MANOVA). A sensory descriptive lexicon was established for the sweet pepper and an intensity score was assigned for 25 sensory attributes in all the samples. Such intensity score was performed by twelve trained panelists. Among the results it was shown that foliar application of elicitors to pepper crop produced signifi cant differences in sensory traits of the harvested fruits. MANOVA allowed detecting differences in aroma profi le among treatments and varieties, showing important changes depending on the combination of elicitors applied on each variety. The principal component analysis (PCA), showed that the PC1 explained 81.02%, while PC2 explained 9.24% of the variance in the results. A strong correlation between varieties and treatments 2 (36 mM H2 O2 ) and 4 (0.1 mM SA and 36 mM H2 O2 ) with PC1 was observed. Treatments 2 and 4 were related to changes in aroma and flavor traits. Fascinato was more susceptible to the effect of elicitor than Orangela. Therefore, elicitor combinations used in this experiment were capable to improve sensory quality of peppers, and not only promote an increase in shelf life as previous studies have shown.
4

Comparative prediction of stream water total nitrogen from land cover using artificial neural network and multiple linear regression approaches

67%
Amiri B. J., Nakane K.
Polish Journal of Environmental Studies
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2009
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tom 18
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nr 2
151-160
Performance of two data-driven models that were developed using Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) approaches were investigated in prediction of Total Nitrogen (TN) concentration in twenty-one river basins in Chugoku district of Japan. Comparison of TN concentration predictions, which were carried out using an ANN-based model and MLR-based model indicated that prediction of the former model (r²=0.94, p<0.01) was more accurate than that of the latter model (r²=0.85, p<0.01). Lack of a sufficient data set that might be considered an obstacle for cross-validating models that are developed was dealt with using a Monte Carlo-based sensitivity analysis of the developed models. This initiative could provide reliable information for judging predictive capacity of the developed models stochastically. Result of sensitivity analysis revealed that predictive capacity of the ANN-based model varied between 0-2 mg/L. Moreover, prediction of the negative outputs was not observed. using the ANN-based model for TN concentration in stream water.
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