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    Covid-19 and Food Security: Impact on Wheat
    (2022) Özden, Cevher; Bulut, Mutlu; Şen, Burak
    The new coronavirus, emerged in Wuhan, China in December 2019, turned into a major global\rpandemic and has caused many deaths around the world. Covid-19 pandemic has adversely affected\revery aspect from economy, education to health system. During Covid-19 pandemic, access to\rfoodstuffs has become even more important, and some countries have imposed restrictions on\rexports of basic food items for fear of food shortages. These restrictions and quotas are feared to\rdisrupt the flows of trade for staple foods such as wheat, corn and rice, which has deepened the\rconcerns for food security. This study was conducted to examine the effects of the Covid-19\rpandemic on wheat price, production and trade and to review the policies of wheat exporter\rcountries. According to the results of the study, Covid-19 did not cause fear in wheat markets, and\rno shortages of wheat are expected in the short term. Although countries have reduced the measures\rthey have taken as of May, uncertainties regarding food safety still persist for the coming years.\rWorld economies have shrunk significantly as a result of the drastic measures they have taken\ragainst covid-19, which could worsen the situation for low income households
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    Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures
    (Mdpi, 2023) Ozden, Cevher; Bulut, Mutlu; Boga, Demet Canga; Boga, Mustafa
    Simple Summary This study employs Fully Convolutional Regression Networks (FCRN) and U-Shaped Convolutional Network for Image Segmentation (U-Net) architectures tailored to the dataset containing dropping images of dairy cows collected from three different private dairy farms in Nigde. The main purpose of this study is to detect the number of undigested grains in dropping images in order to give some useful feedback to raiser. It is a novel study that uses two different regression neural networks on object counting in dropping images. To our knowledge, it is the first study that counts objects in dropping images and provides information of how effectively dairy cows digest their daily rations. 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.
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    Forecasting Seasonal Milk Production Using MARS Algorithm for Multiple Continuous Responses in Holstein Dairy Cattles
    (2024) Boğa, Demet Çanga; Boğa, Mustafa; Bulut, Mutlu
    In this study, seasonal milk yield estimation will be made using multivariate adaptive regression spline (MARS) algorithm for multiple continuous responses in dairy cattle (Holstein hybrid). For the research, milking records for the years 2020-2021 were collected from 157 dairy animals using Holstein hybrid dairy cattle from a research farm in Konya, Türkiye. The amount of feed given in this experiment was not changed and the effect of the season on the estimation of milk yield was investigated in the study. The analyzed independent variables used in the study were pregnancy status (PS), number of days milked (MDN), Lactation Number (LN), age of cows (months), average seven-day milk yield (7-Day Average Milk-SDMY), last lactation milk yield (last_MY), number of inseminations (IN), peak yield (Pik_Yield) and target variables were calculated as (YieldAutumn/winter/spring/summer (kg) = Mean milk mean of season. In this context, the ehaGoF package was used to measure the prediction performance of the simultaneous MARS model established with the earth package for MARS analysis. MARS estimation equations obtained simultaneously for four dependent variables (multiple responses) are given. By looking at the MARS equation, the MARS model estimation equation was determined for the optimum milk yield, the threshold values, the three threshold values determined in the model were determined as MDN, Age, Peak_Yield, and the corresponding values were respectively; 159 days, 39.6 (months) and 37.1 kg/day. Considering the estimation equation, it is seen that the independent variables MDN, SDMY and LN are the most important variables in determining the estimation equation. It is seen that the best fitting value for the estimation equation of the dependent variables is the YieldWinter variable.