This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The potential factors are categorized into four subgroups: governmental effects, agricultural inputs, macroeconomic indicators, and climatic conditions. The selected agricultural goods for price change measurement include wheat and maize representing subsistence goods, and olive oil and cotton as marketing goods. The autoregressive distributed lag (ARDL) model is applied to observe both the short- and long-term impacts of the variables on price developments. The results suggest that government effectiveness, regulatory quality, nitrogen use, water price, money supply, exchange rate, and GDP under the related categories are the most effective factors in price changes. Among the variables under the category of climatic conditions, significant values are obtained only in the analysis of the temperature impact on olive oil. The analysis also reveals the variable impact of crises on the prices of the chosen products, depending on the goods involved. The maize and wheat analyses yield particularly noteworthy results. In the long run, nitrogen use demonstrates a substantial positive impact, registering at 29% for wheat and 19.47% for maize, respectively. Conversely, GDP exhibits a significant negative impact, with 26.15% and 20.08%. Short-term observations reveal that a unit increase in the governmental effect leads to a reduction in inflation for these products by 17.01% and 21.42%. However, changes in regulatory quality result in an increase in inflation by 25.45% and 20.77% for these products, respectively.

Accurate forecasting of tourism demand and income holds paramount importance for both the tourism industry and the national economy. This study aims to address several objectives: (1) specify the best forecasting model in the prediction of tourist arrival volumes and tourism income for Turkey; (2) assess the degree of impact exerted by various determinants on the tourism forecasts; (3) generate forecasts for tourist arrival volumes and tourism income using the most suitable models; and (4) examine potential scenarios illustrating the ramifications of the Russia-Ukraine war on tourist arrival volumes and tourism income. The forecasting models employed in this study encompass a comprehensive set of statistical methods, including ETS, ARIMA, TRAMO-SEATS, X13, X11, STL, Grey, and their combinations with ANN. In the ANN models, exogenous variables such as the global financial crisis, the Turkey-Russia warplane crash crisis, the COVID-19 pandemic, and USD/TRY exchange rates are incorporated. The results unveil the identification of five superior models: ETS, Grey, hybrid ETS-ANN, hybrid Grey-ANN, and hybrid ARIMA-ANN models, which exhibit the lowest MAPE and sMAPE values. Forecasts for the forthcoming quarters are examined under two scenarios: assuming the continuity or cessation of the Russia-Ukraine war. Comparative analysis of the relative effects of exogenous variables indicates that COVID-19 has the most substantial impact on tourist arrival volumes, and tourism income is primarily influenced by the USD/TRY exchange rate.

Tr en Bu çalışmanın amacı, Borsa İstanbul (BİST) Şehir Endeksinde yer alan nüfusu en yoğun beş şehirde (İstanbul, Ankara, İzmir, Bursa, Antalya) 01.08.2010 – 01.08.2019 döneminde ay içi ve ay dönümü anomalilerin varlığının incelenmesidir. Çalışma kapsamında, getirilerin hesaplanması için söz konusu şehirlerin borsa endekslerinin (XSIST, XSANK, XSIZM, XSBUR, XSANT) günlük kapanış fiyatlarından yararlanılmıştır. Analiz döneminde en yüksek ortalama getirinin İzmir Şehir Endeksinde, en düşük ortalama getirinin ise Antalya Şehir Endeksinde olduğu görülmüştür. Anomalilerin incelenmesinde, değişen varyans (heteroskedastisite) sorununun bulunması nedeniyle kukla değişkenlerin yer aldığı GARCH (1,1) modeli kullanılmıştır. Analizlerin sonucunda, İstanbul dışındaki dört şehir endeksinde ay içi anomalisi, nüfus açısından en büyük üç şehir olan İstanbul, Ankara ve İzmir Endekslerinde ise ay dönümü anomalisi tespit edilmiştir. This study aims to analyze the presence of intra-month and turn-of-the-month anomalies in the five most crowded cities (Istanbul, Ankara, Izmir, Bursa, Antalya) in Borsa Istanbul (BIST) City Indices, for the period of 08.01.2010 - 08.01.2019. Within the context of the study, daily closing prices of stock market indices (XSIST, XSANK, XSIZM, XSBUR, XSANT) of these cities are benefitted to calculate the returns. In the analysis period, it is seen that the highest average return is in Izmir City Index, whereas the lowest one is in Antalya City Index. In the analysis of anomalies, the GARCH (1,1) model including dummy variables is used because of the heteroscedasticity problem. As a result of the analyses, an intra-month anomaly is determined in four city indices except Istanbul and a turn-of-the-month anomaly is found in Istanbul, Ankara and Izmir Indices, which are the three most crowded cities in terms of population.

Promoting sustainable agricultural production requires farmers to adopt new technologies such as organic farming to increase the agricultural productivity, while conserving the environment. Adoption and diffusion of new technologies need a long process, as experienced in the past. There are social and economic factors, identified in the literature, and those could cause delays in farmers’ use of new technologies. Hence, technology adoption and diffusion are important policy issues in agriculture. For that reason, this paper provides a literature review including factors influencing the adoption and diffusion of technology in agriculture and aims to contribute to the future studies and policies, especially focusing on the social capital or the social aspects, which are proven not to be analyzed by the previous studies comprehensively. The results show that interaction with neighbors and relatives, and membership in a group or organization, which represent the social aspects, has a positive influence on adoption and diffusion of new technologies. Hence, policy-makers should incorporate the social aspects when designing the policies, such as cost sharing programmes, to promote adoption and diffusion of new technologies.

Purpose: This study aims to explain the relationship between agricultural mechanization and production by using annual data between the 2006-2019 period at the regional level of Turkey. Design/Methodology/Approach: 12 regions under NUTS 1 and ARDL test are used for analysis. As variables, the number of machines used, labor, income and land are used. Findings: The empirical results reveal that agricultural production has a positive relationship with income, land and machine usage, but negative with labor in the long term. Although the negative impact of labor is an unexpected result, it is asserted that the machines substitute the labor in some studies. Hence, the increase in machines leads to fall in the number of labor and it increases the agricultural production. Also, results show that the rise in income causes the more agricultural production, labor and machine usage because it creates a resource for the more inputs, investment and new machines. It also makes being agricultural labor attractive. On the other hand, the agricultural production is also a reason of income because the more agricultural production increases, the more profit and income increases. Another causality result shows that there is an important impact of the rise in the agricultural land on the machine usage. Originality/Value: When looking into the literature, it is seen that there is scarcely any study handling the mechanization impact on agricultural production in Turkey especially empirically. Hence, this study aims to fill the gap in this field and explain the relationship between agricultural mechanization and agricultural production clearly.