The effects of 100 nm pure boron nanoparticles on the primary antioxidant system of Nile tilapia (Oreochromisniloticus).

In this study, the effects of 100 nm boron nanoparticles (B-NPs) on the primary antioxidant status of Nile tilapia were researched via analysis of enzyme activities and related gene expressions. This is a new study which focuses on the relationship between B-NPs and oxidative stress that contribute to the literature in terms of its scope. Fish (n = 15) for each group were exposed to three different concentrations as 5, 25 (n2) and 125 (n3) mg/L during 96 h to see the response of the primary antioxidant system. According to the results, SOD expressions differed in all treatment groups compared to the control group (P < 0.05). CAT expressions were different in 5 and 125 mg/L groups compared to control and 25 mg/L groups (P < 0.05). GPX expressions were only different in 125 mg/L group (P < 0.05). The changes in enzyme activities of SOD and CAT were significantly different in 25 mg/L groups. GPX enzyme activities were not significant (P > 0.05). TBARS concentrations in 25 mg/L group were significantly different from those in the control and 125 mg/L groups (P < 0.05).

The Mediator Role of Attitudes in Fish Choice Behavior: A Turkish Market Survey.

Due to the dynamic nature of demand, it is becoming increasingly important for the fish industry to investigate the changing choice behaviors of consumers in the face of increasing demand. This research investigated the role of attitudes and socio-demographic characteristics, which are the main factors in the fish choice behavior of consumers and in fish consumption behavior. In this context, an ordered probit model was constructed to analyze the effect of attitudes and socio-demographic characteristics as independent variables on fish consumption and purchase intention as the dependent variables. In addition, descriptive statistics were also used to reveal the current preferences related to fish. The data required for the model and descriptive statistics were obtained from 421 participants using a cross-sectional consumer survey covering the main cities of the seven regions of Turkey. The results show that while consumers prefer fish more than red meat and less than poultry, they mostly buy fresh fish from fish markets. Moreover, taste, physical appearance, convenience, wild fish, and seller trust attitudes have a significant and positive relationship with the dependent variable (the frequency of fish purchase and consumption) and price has a negative and significant relationship. Moreover, an increase in education level has a positive and significant relationship with the frequency of fish consumption. The research results provide important suggestions and information for decision-makers in the fish industry to implement effective policies and meet the consumer expectations of producers and distributors in the fish industry. In addition, the current study provides guidance for future research.

Measurement of resource use efficiency in corn production: a two-stage data envelopment analysis approach in Turkey / Medição da eficiência do uso de recursos na produção de milho: uma abordagem de análise de dados em duas fases na Turquia

This research analyzed the efficiency situation of corn farms operating in the Adana province of Turkey. In this context, required farm management data were collected from 111 corn farmers by using face to face survey method during the 2019-2020 cultivation season. To determine the technical efficiency (TE) levels of corn farms, Data Envelopment Analysis (DEA) was applied. Furthermore, factors that cause the inefficiency in corn farms were detected by using the Tobit regression model. According to research results, the average TE levels of corn farms in the research area under the variable return to scale conditions are reported as 0.887 (111 farms). These results suggested that if farms reduced their input use by 11.3% on average, they can achieve the same output level and be able to reach full technical efficiency. The most ineffective source in terms of farms performance is machine expenditures with 68.2% of excessive use followed by labor use. In this regard, mechanization modernization, education and training of the labor force and more sensitive fertilizers and pesticide use can increase the efficiency of corn farms. Results of the Tobit regression model indicated that factors such as experience, education, number of tractors and size of the irrigated area positively influenced the TE, whereas family size in corn farming has a negative effect.

Esta pesquisa tem como objetivo analisar a situação de eficiência das fazendas de milho operando na província de Adana, na Turquia. Neste contexto, os dados necessários de gestão da fazenda foram coletados de 111 produtores de milho usando o método de pesquisa frente a frente durante a temporada de cultivo de 2019-2020. Para determinar os níveis de eficiência técnica (TE) das fazendas de milho, foi aplicada a Análise Envoltória de Dados (DEA). Além disso, os fatores que causam a ineficiência nas fazendas de milho foram detectados por meio do modelo de regressão Tobit. De acordo com os resultados da pesquisa, os níveis médios de TE das fazendas de milho na área de pesquisa sob as condições de retorno variável à escala são encontrados em 0,887 (111 fazendas). Esses resultados sugerem que, se as fazendas reduzirem o uso de insumos em 11,3% em média, podem atingir o mesmo nível de produção e alcançar eficiência técnica plena. A fonte mais ineficaz em termos de desempenho das fazendas são os gastos com máquinas, com 68,2% do uso excedente continuado com o uso de mão de obra. Nesse sentido, a mecanização, a modernização, a educação e o treinamento da força de trabalho e o uso de fertilizantes e pesticidas mais sensíveis podem ser sugeridos para aumentar a eficiência das fazendas de milho. Os resultados do modelo de regressão Tobit indicam que fatores como experiência, escolaridade, número de tratores e tamanho da área irrigada influenciaram positivamente no TE, enquanto o tamanho da família na cultura do milho tem efeito negativo.

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models.

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the "Enter" method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685-3.65% and 0.9988-0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.