Sugar Tech最新文献

As a major sugar crop, sugar beet plays a crucial role in the global sugar industry, alongside the dominant sugarcane. Despite its advantages in mechanization and adaptability to temperate climates, the sugar beet industry faces significant challenges, including pest and disease pressures, environmental stressors, and intense competition from cane sugar and artificial sweeteners. This review systematically examines the global sugar beet industry, focusing on production trends, technological advancements, and market dynamics. We highlight the growing role of technological innovations in improving yields and combating biotic and abiotic stresses, as well as the regulatory changes shaping pest management practices. The paper also discusses the competitive disadvantages of sugar beet, including higher production costs and limited pricing competitiveness compared to cane sugar. Future directions for the industry emphasize the need for a comprehensive strategy that includes technological adoption, sustainable farming practices, and market diversification to ensure continued relevance in the evolving global sugar market. This review provides valuable insights for stakeholders aiming to enhance the international competitiveness of sugar beet production.

When plant growth and development are affected by the environmental stresses, heat shock transcription factors (HSFs) are activated and respond to stresses in many plants. However, the function of the HSF genes family in sugar crop is still unknown. In this study, a total of 16 BvHSF genes in sugar beet (Beta vulgaris L.) were firstly identified by bioinformatics techniques, and they were unevenly distributed on seven of the nine chromosomes. The BvHSF genes were divided into Group A, Group B, and Group C by phylogenetic analysis, and members of the same family were found to have similar protein motifs and gene structures. Moreover, the expression levels of BvHSFs under salt stress were analyzed by qRT-PCR. The results indicated that most of the BvHSF genes were upregulated to varying degrees, which once again verified that BvHSFs were involved in the response of sugar beet to salt stress. This is inextricably related to the composition of cis-acting regulatory elements of BvHSFs. The results from current study implied that BvHSFs play important roles in the response to salt stress and provide a valuable resource for further study of salt tolerance genes in sugar beet.

The purpose of this research is to investigate the impact of different irrigation and fertilization levels on the yield and quality of wet sugar beet pulp used as animal feed in the 2019–2020 period. While the yield and quality analyses of sugar beet and the wet sugar beet pulp obtained from it were carried out in Yenişehir College Laboratories and Food and Feed Control Center Research Institute, Bursa, Turkey, BUÜ Yenişehir College Agricultural Production Area was chosen as the research area. In order to determine the effect of the irrigation-fertilization interaction on sugar beet and the wet sugar beet pulp obtained from it, four different irrigation and three fertilization treatments were determined and a total of twelve study treatments were created. In the research, drip irrigation systems were preferred in order to carry out irrigation and fertilization applications in a controlled manner. The amount of plant water consumption (ET) was calculated with the water balance equation (I + P − Dp ± ΔSW). Irrigation water amounts were determined as 800.0–200.0 mm and 820.0–205.0 mm, respectively. ET values were determined as 530.0–228.5 mm and 552.0–340.8 mm, respectively. While sugar beet root yield values in the 2019–2020 research years were determined as 112.0–110.0 t ha⁻¹ and 40.7–40.5 t ha⁻¹, respectively, wet sugar beet pulp by-product yield rates varied between 49.8–50.3% and 39.6–39.4%. When the dual interaction of irrigation-fertilization factors and the yield and quality losses of sugar beet roots and fresh pulp as a by-product are evaluated together, the I₇₅F₇₅ issue should be preferred. As a result, the importance of obtaining sugar from sugar beet roots and using the chemical content of the pulp obtained as a by-product as animal feed has been tried to be revealed.

Global warming is contributing to an increase in the frequency of extreme climate events, leading to more frequent droughts that pose significant abiotic stressors affecting the growth and yield of sugar beet. To address the detrimental effects of drought stress on sugar beet seedlings, this study simulated a drought environment and examined the impact of arbuscular mycorrhizal fungi (AMF) symbiosis on seedling growth. The findings revealed that AMF inoculation under drought conditions enhanced the photosynthesis rate and increased the content of photosynthetic pigments in the leaves of sugar beet. Additionally, it effectively mitigated cell membrane damage in the seedlings, elevated the levels of osmoregulatory substances, and enhanced antioxidant enzyme activities in both leaves and roots. The inoculation of AMF regulates the physiological processes associated with sugar beet growth, alleviates the adverse effects of drought stress, and promotes seedling development. Consequently, AMF can be regarded as a valuable bioregulator in sugar beet cultivation under drought conditions, providing significant practical benefits for improving sugar beet yield.

This study introduces a novel approach utilizing a convolutional neural network (CNN) architecture for the detection and classification of Cercospora beticola and Erysiphe betae diseases, aiming to enhance both the quantity and quality of sugar beet yield, a pivotal commodity in agriculture. The research focuses on disease identification and plant categorization, leveraging deep learning (DL) techniques for sustainable agricultural practices. Delayed detection and treatment of these diseases pose significant threats to harvest productivity, emphasizing the importance of timely intervention. Timely and accurate disease detection is crucial for improving sugar beet yield and quality for agricultural production. This study employed DL methods to classify sugar beet leaf images into healthy or diseased categories, followed by sub-classification into Cercospora beticola or Erysiphe betae. The proposed model's efficacy was evaluated through comparative analysis with established models such as the Visual Geometry Group networks (VGG16 and VGG19), InceptionV3, AlexNet, and ResNet50, renowned for their robust performance in image classification tasks. The dataset consisted of 4128 samples covering healthy and diseased sugar beet leaves, further classified as Cercospora beticola and Erysiphe betae. Additionally, the performance of the proposed model was compared with other models in terms of train time. Remarkably, although transfer learning is not implemented in the proposed model, it achieves 98% accuracy, 96% precision, 100% recall, and 98% F1-score, exceeding transfer learning models. This study advocates adopting a CNN model with a light-weight structure, facilitates rapid assembly, and has high recognition sensitivity of disease classification.

The objectives of this study are to investigate the possibility of utilizing sugar beet for biogas production with a high methane content. For the last three hundred years, it has been an important source of sugar, particularly in Europe and other temperate regions of the world, but changes in modern agriculture, world trade and economics have led to a decline in the use of sugar beet as a raw material for sugar factories. As sugar is an important product and an important ingredient for many industries, sugar beet will continue to be grown in many countries for strategic reasons. Nevertheless, this plant has become an interesting source for many new byproducts and technologies. The sugar beet root not only has a sugar content of about 20%, but also contains an abundance of pectin, cellulose, hemicellulose and other materials that are used for the production of textiles and biodegradable materials such as bioplastics. Due to global warming and the rise in average temperatures in many regions of the world, the energy sector will rely on biofuels such as bioethanol and biogas. Many countries are acquiring automotive technology based on the use of ethanol. Biogas with a high methane content can be produced through the use of sugar beet fermentation technology. This is also an acceptable alternative and a way to move to more environmentally friendly energy sources. Many regions of the world have problems with saline soils. Since sugar beets has a high tolerance to salt, they can be grown on these soils to improve fertility and other soil properties and create a more suitable environment for plant and human life. The sugar beet grown on these soils can be used as animal feed or as a raw material for various industries to produce paper, bioplastics or biogas and ethanol. Byproducts of the sugar industry such as molasses and beet pulp can be used for several purposes. Molasses is an environmentally friendly product derived from sugar manufacturing process from beat and are being utilized for several byproducts. Intercropping sugar beet with other crops has many advantages. Sugar beet products as feed for dairy cows has increased the quantity and quality of milk. Sugar beet has found its place in the circular economy and in many new technological byproducts. Many countries have launched programs to breed and develop new products of using sugar beet.

In plants, leaves are the primary source of sunlight, and the production of photosynthetic materials. Estimating the rate of yield reduction due to leaf loss plays an important role in farm management. This research aimed to achieve the highest possible crop yield and quality while also utilizing pruned leaves for animal feed. To investigate the optimal timing for leaf pruning at different weeks of vegetative growth on the morphological, and physiological characteristics of sugar beet over the years 2022, and 2023 growing seasons in Eskişehir, Türkiye. This study included treatment of ten pruning times (PT): July 20, and 27; August 3, 10, 17, 24, and 31; September 7, and 14; and October 25 (control treatment and no pruning). The results showed the pruning treatments had a significant effect on morphological, and physiological characteristics in both years. The highest root yields were observed at the later pruning times, particularly at PT10 in both the years, with yields of 104.31 tons ha⁻¹ in 2022 and 136.74 tons ha⁻¹ in 2023. Other notable pruning times included PT6 and PT5, which also showed substantial root yields. Sugar content was higher during the earlier pruning times. The peak sugar contents were found at PT1 in both years, with 17.17% in 2022 and 13.79% in 2023. For leaves, the dry matter content was highest at late pruning times (PT8–PT10). PT10 showed the highest dry matter contents. Crude protein and crude ash contents remained relatively constant at different pruning times, but considerable values were observed from PT1 to PT8, contributing to the overall nutritional value of the leaves. The highest NDF and DMD contents were noted at late pruning times (PT7–PT10) in both years. Combining these findings, it was concluded that PT6 to PT8 were the most favorable pruning times, balancing high root yield, medium to high sugar content, and high nutritional values of leaves.