Seed Sector in Turkey and Analysis of Sunflower Seed Purchasing Behaviour of Farmers： Example of Thrace Region

Selection of suitable seed in agriculture has great importance in terms of scarce resources and efficient use of facilities. Efficient use of inputs has been one of the key objectives of agricultural policies in all over the world. Therefore, studies continue about production of new seeds with high yield and resist to diseases. The 66.7% of the total seed production in Turkey are met by the private sector （hybrid sunflower, corn, potato, soybean, cotton and vegetable seed）. And 53.4% of the total sunflowers in Turkey are grown in Thrace region. It is therefore appropriate to be selected as the research area. In this regard, the aim of this study was to examine the dynamics in seed sector and particularly investigate the sunflower seed purchasing behavior of farmers with special attention to Thrace region. As a methodology, 198 questionnaires which pursued sunflower farm activities in the region were utilized. As a result of the descriptive statistics, it could be concluded that almost 20% of seed supply in Turkey is imported and that 70%-80% of it is directly used as an “input” for growing vegetative products and the remaining is used as “intermediate goods” for growing “certified seeds” for domestic or international markets.

Network Diffusion Framework to Simulate Spreading Processes in Complex Networks

With the advancement of computational network science,its research scope has significantly expanded beyond static graphs to encompass more complex structures.The introduction of streaming,temporal,multilayer,and hypernetwork approaches has brought new possibilities and imposed additional requirements.For instance,by utilising these advancements,one can model structures such as social networks in a much more refined manner,which is particularly relevant in simulations of the spreading processes.Unfortunately,the pace of advancement is often too rapid for existing computational packages to keep up with the functionality updates.This results in a significant proliferation of tools used by researchers and,consequently,a lack of a universally accepted technological stack that would standardise experimental methods(as seen,e.g.,in machine learning).This article addresses that issue by presenting an extended version of the Network Diffusion library.First,a survey of the existing approaches and toolkits for simulating spreading phenomena is shown,and then,an overview of the framework functionalities.Finally,we report four case studies conducted with the package to demonstrate its usefulness:the impact of sanitary measures on the spread of COVID-19,the comparison of information diffusion on two temporal network models,and the effectiveness of seed selection methods in the task of influence maximisation in multilayer networks.We conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science.