The new coronavirus, emerged in Wuhan, China in December 2019, turned into a major global pandemic and has caused many deaths around the world. Covid-19 pandemic has adversely affected every aspect from economy, education to health system. During Covid-19 pandemic, access to foodstuffs has become even more important, and some countries have imposed restrictions on exports of basic food items for fear of food shortages. These restrictions and quotas are feared to disrupt the flows of trade for staple foods such as wheat, corn and rice, which has deepened the concerns for food security. This study was conducted to examine the effects of the Covid-19 pandemic on wheat price, production and trade and to review the policies of wheat exporter countries. According to the results of the study, Covid-19 did not cause fear in wheat markets, and no shortages of wheat are expected in the short term. Although countries have reduced the measures they have taken as of May, uncertainties regarding food safety still persist for the coming years. World economies have shrunk significantly as a result of the drastic measures they have taken against covid-19, which could worsen the situation for low income households.

PurposeThis study aims to reveal consumers' internal and external motivations to prefer online food shopping. The paper proposes an integrated model including aspirations, capabilities, subjective norms (divided into online resources and offline resources), perceived value and traditionalism to examine their effects on consumers' intention to do online food shopping.Design/methodology/approachA cross–sectional design was used to understand which factors affect consumers' intention to do online food shopping. The data were collected from a total of 400 people via an online survey. The conceptual model was tested using structural equational modeling to understand the relationships between the factors.FindingsThe results suggest that the conceptual framework can be used to have a better understanding of consumers' internal and external motivations to do online food shopping. The study proves that aspirations have a considerable direct effect on and a mediating role between capabilities, subjective norms from online resources, traditionalism and the effect of COVID-19 pandemics and the intention. Also, traditionalism was found to be an antecedent for consumers to prefer online food shopping.Practical implicationsThis study reveals better insights for the sellers, marketers and system providers dealing with supplying food products through online channels. The findings suggest that the stakeholders take into consideration aspirations, capabilities, subjective norms, perceived value and traditionalism to organize their activities in food marketing in the online area.Originality/valueIn this study, aspirations–capabilities framework was adopted and confirmed within consumers' online food shopping domain. Also, it was proved that traditionalism was a driver of individuals' intention to do online shopping for food products.

Deep learning algorithms can now be used to identify, locate, and count items in an image thanks to advancements in image processing technology. The successful application of image processing technology in different fields has attracted much attention in the field of agriculture in recent years. This research was done to ascertain the number of indigestible cereal grains in animal feces using an image processing method. In this study, a regression-based way of object counting was used to predict the number of cereal grains in the feces. For this purpose, we have developed two different neural network architectures based upon Fully Convolutional Regression Networks (FCRN) and U-Net. The images used in the study were obtained from three different dairy cows enterprises operating in Nigde Province. The dataset consists of the 277 distinct dropping images of dairy cows in the farm. According to findings of the study, both models yielded quite acceptable prediction accuracy with U-Net providing slightly better prediction with a MAE value of 16.69 in the best case, compared to 23.65 MAE value of FCRN with the same batch.

Green fodder plants have an important place in animal nutrition in terms of meeting the nutritional needs of animals and increasing appetite. Especially in dairy cattle breeding, green feeds are needed for milk yield and quality. In order to meet the green feed needs of ruminant animals, the scarcity of agricultural areas, water use, environmental and climate factors can cause negative effects. The increase in the prices of green feeds, which cannot be sustained throughout the year, increases the tendency to soilless agriculture. The increase in feed prices due to many reasons in soil-dependent agriculture may cause the breeder to ignore the nutritional needs of the animal and cause the feeding not to be done correctly. For this purpose, it is thought that with hydroponic production, which is one of the soilless farming systems, the negative conditions related to the environment and soil can be eliminated and the feed costs can be reduced by ensuring the continuity of green feed throughout the year. Although there is a disease-free growing environment with hydroponic production, the digestibility of the products to be obtained will increase and feed efficiency will increase. In addition, with hydroponic production, the digestibility, crude protein values, vitamin and mineral contents of feeds with high cellulose content (such as barley, wheat, maize) increase. Meat, milk yield and quality, animal performance and health will also be positively affected by the increase in feed utilization. In addition to all these, it also allows the plants with a long growing period to benefit in a short time. Especially the feeds obtained by hydroponic production are used to obtain green feed of 18-20 cm in 6-7 days, and to feed animals with the obtained feed root.

Multivariate time series forecasting has an important role in many real-world domains. Especially, price prediction has always been on the focus of researchers. Yet, it is a challenging task that requires the capturing of intra-series and inter-series correlations. Most of the models in literature focus only on the correlation in temporal domain. In this paper, we have curated a new dataset from the official website of Turkish Ministry of Commerce. The dataset consists of daily prices and trade volume of vegetables and covers 1791 days between January 1, 2018 and November 26, 2022. A Spectral Temporal Graph Neural Network (StemGNN) is employed on the curated dataset and the results are given in comparison to Convolutional neural networks (CNN), Long short-term memory (LSTM) and Random Forest models. GNN architecture achieved a state-of-the-art result such as mean absolute error (MAE): 1,37 and root mean squared error (RMSE): 1.94). To our knowledge, this is one of the few studies that investigates GNN for time series analysis and the first study in architecture field.

Bu kitapta pazarlama alanında yaşanan dijital dönüşüm ve dijital pazarlama stratejileri ağırlıklı olmak üzere, butik pastacılık, sigorta, tarım ve sağlık hizmetlerinde dijital pazarlama, kuşakların dijital pazarlamaya bakış açıları, oyunlaştırma, yerel ve küresel boyutları ile hizmet kalitesi, bütünleşik pazarlama iletişimi ve marka bilinirliği ilişkisi, kompulsif satın alma, paylaşım ekonomisi, pazarlama sosyolojisi ve pazarlamada tüketim ritüelleri ve organik ürünlerin pazarlanması konuları ele alınmış ve tartışılmıştır. Pazarlama alanında birçok konunun farklı yaklaşımlar ile ele alındığı bu kitap alanında yetkin 27 yazarın katkıları ile hazırlanmıştır.

In Turkey, bottled water market has shown a rapid development since the 1990s. This study is aim to reveal demographic and economic factors affecting on bottled water consumption in Adana. A logistic regression model was used to achieve research goal. The results of the study showed that women consumers, with undergraduate and higher education consumers and individuals with monthly income of 2500 TL are more likely to consume bottled water. However, there is no relationship between age, marital status and household size variables and bottled water consumption. Bottled water companies should take into account to gender, education level and income level in their marketing strategies.

Climate has a significant impact on agricultural production. According to scientific data, climate change is having a growing impact on the planet's life, and different regions of the world are experiencing this impact in different ways. The greatest challenge to achieving sustainable development is climate change, which also poses a serious risk to the survival of humanity. It will have far-reaching consequences within the context of animal production, and particularly in regions of vital importance to the world's nutrition and livelihoods. All species have ideal climatic parameters for survival in animal husbandry, and changes to these variables have a negative impact on the quality and quantity of farm animals and animal products. There are four key areas where the effects of climate change on animal productivity can be studied. These include effects on pasture quality, effects on animal diseases and pests, effects on animal health, growth, and reproduction. They also include implications on the availability, quality, and price of feed crops. Planning for the sector's future is crucial in order to meet the population's food needs, lessen the impact of climate change on livestock output, and reduce the sector's contribution to global warming. Adapting animal husbandry to climate change is required to reduce all these harmful effects.