Search Results (32)

Speed breeding technologies offer a promising avenue for accelerating crop improvement, yet their application to biennial crops like sugar beet remains constrained by extended generation cycles. This study examined the effects of supplemental phosphorus-potassium (PK) nutrition on the development of two hybrids under a speed-breeding protocol. Plants received one of four nutritional regimes: PK supplementation, potassium (K) supplementation, standard Knop’s solution (KS), or nutrient deficiency (D). Digital phenotyping confirmed that adequate nutrition maintained photosynthetic health, as deficiency significantly reduced NDVI and increased PSRI by 75 days. The most notable, genotype-specific effects were observed in reproductive architecture. PK nutrition significantly increased the median number of flower stalks by 17% in Smart Iberia KWS (21.0 vs. 18.0) and substantially in Dubravka KWS (33.0 vs. 1.0). PK also supported root development, increasing mini-steckling weight by 45–183% under white light. In the generative phase, plants under PK nutrition consistently showed the highest progression to flowering and capsule formation. A consistent increase in median 1000-seed weight of 24–36% was associated with PK treatment. In conclusion, supplementing standard nutrition with phosphorus and potassium enhances key yield-related architectural traits and supports reproductive development in sugar beet under speed-breeding conditions, with the magnitude of response depending on genotype. This provides a practical basis for optimizing mineral nutrition to improve the efficiency of accelerated breeding protocols. This provides a practical basis for optimizing mineral nutrition to improve the efficiency of speed breeding protocols. Full article

Seed priming is a proven method for enhancing early plant development and stress resilience, yet its field-level effects on sugar beet performance remain underexplored. This study evaluated the impact of seed priming on emergence dynamics, canopy traits, root yield, and sugar productivity over three growing seasons with variable weather conditions in central Poland. We found that primed seeds consistently improved emergence uniformity, plant spacing, and early growth, resulting in a more regular canopy structure and greater biomass accumulation. Sugar beet root yield increased by 6.2–7.7%, primarily due to higher average root mass, while final plant density remained unaffected. Although sucrose content was not significantly altered, sugar beet roots from primed seeds exhibited lower concentrations of molasses-forming substances (Na⁺, K⁺, and α-amino nitrogen). As a result, biological and technological sugar yields increased by 5.9% and 6.1%, respectively. Our results illustrate how seed priming enhances both agronomic performance and processing quality of sugar beet under field conditions, offering a low-cost strategy for stabilizing yield in temperate environments. Full article

Sustainable production also involves analyzing greenhouse gas (GHG) emissions throughout the entire cultivation and processing cycle. The emissions balance for different sugar beet varieties is a key element of environmental assessment in sustainable production systems. It is consistent with the objectives of the European Green Deal and aims to decarbonize agri-food technology. This study aims to assess and compare GHG emissions associated with the cultivation of three sugar beet variants (Viola, Jaromir, and Pulitzer) taking into account their technological and quality characteristics. The varieties were selected based on their registration in the National Register and their importance in agricultural practice in Poland, as well as their contrasting technological profiles, which allow for the assessment of the relationship between raw material quality and GHG balance. The study combines life cycle assessment (LCA) with physiological parameters such as CO₂ assimilation, sugar content, yield, fuel consumption, and fertilizer use. The aim is to identify the correlation between the technological value of a variety and its environmental impact. It has been shown that genotypic characteristics have a significant impact on both yield and emissions. The Viola and Jaromir varieties showed a favorable balance between photosynthetic efficiency and greenhouse gas emissions, while the Pulitzer variety, despite low emissions per kilogram of product, showed poorer yield performance. The importance of using integrated assessment methods combining production efficiency, environmental efficiency, and crop quality was emphasized. Such an approach is essential for the development of sustainable agricultural practices in line with the EU’s climate neutrality goals. Further research is needed to optimize agrotechnical strategies tailored to the requirements of individual varieties, contributing to climate-resilient and environmentally friendly crop production. Full article

Sugar beet (Beta vulgaris L.) is a globally important sugar crop whose hybrid breeding system relies heavily on cytoplasmic male sterility (CMS) lines. Recent advances in sugar beet genomics, particularly the release of high-quality reference genomes and the characterization of organellar genomes, have provided a foundation for elucidating the molecular genetic mechanisms of CMS. Furthermore, innovations in gene editing technologies are enabling transformative functional studies in this field. The precise targeting of CMS-associated mitochondrial genes and nuclear restorer-of-fertility genes not only allows for direct investigation of theoretical models governing fertility regulation through nuclear–cytoplasmic interactions but also holds promise for the targeted development of sterile and restorer lines. This review systematically summarizes progresses in sugar beet genomics, the development of gene editing tools, and the current understanding of the molecular genetics of CMS and fertility restoration in sugar beet. Although challenges remain—such as efficient delivery of editing tools into mitochondria and coordinated editing of multiple genes—the integration of genomic and gene editing technologies is expected to accelerate multi-omics-guided dissection of CMS mechanisms. These advances will facilitate the precise design of high-yield, high-sugar, and stress-resistant sugar beet hybrids, thereby providing core scientific and technological support for the sustainable development of the global sugar industry. Full article

The accumulation of sugar beet (Beta vulgaris L.) root yield across Central and Eastern Europe typically occurs mostly from July to September but can vary substantially depending on precipitation patterns. When summer rainfall is insufficient, the period of intensive yield accumulation may be delayed, often affecting the technological quality of the roots. Conversely, as light and thermal conditions deteriorate in autumn, growth processes slow down, and each cultivar’s response to late-season conditions may vary. To investigate these dynamics, we examined nine sugar beet cultivars (Zeltic, Pacific, Mariza, Everest, BTS 2205N, Jaromir, Jantar, Eliska KWS, and Klara) under three harvest dates (8–10 September—first date; 7–8 October—second date; and 3–5 November—third date) during the 2020–2021 growing seasons. Both cultivar and harvest timing had a significant impact on root yield, sucrose content, and the concentrations of molasses-forming elements (K, Na, and α-amino N), though the magnitude of these effects strongly depended on weather conditions. In 2020, which was characterized by very high precipitation in June and August, harvesting in early September resulted in optimal yield for most cultivars, with no significant benefit from delaying harvest. However, in 2021, when precipitation was moderate and more evenly distributed, later harvest dates enhanced both yield and sucrose content in several cultivars (e.g., Eliska KWS and Jantar). Among all cultivars tested, Eliska KWS consistently demonstrated high root yield and sucrose content. The sucrose content in the roots was strongly influenced by weather conditions in each study year. In 2021, which had average water availability, sucrose content was high, and delaying the harvest led to an increase in sucrose content while reducing molasses-forming elements in the roots. In contrast, in 2020, during summer rainfall, the effect of harvest date on quality traits was significantly weaker and largely dependent on the cultivar. These findings underscore that choosing the optimal harvest date is highly site- and season-dependent, shaped by precipitation distribution, late-season temperatures, and cultivar genotype. In practical terms, these results can help producers and sugar processors align harvest schedules with local conditions to optimize both root yield and technological quality. Full article

The use of plant protection products (PPPs) is the main method of controlling Cercospora leaf spot (CLS), as it constitutes a cheap and effective approach that is easy for farmers to follow. Unfortunately, it is widely recognized that the use of PPPs poses a risk not only to the environment but also to human health. The urgent need for sustainable development, recommended by the European Union and expressed in the “Farm to Fork Strategy”, includes a serious restriction on the use of PPPs. This strategy assumes a 50% reduction in the use of PPPs by 2030. These efforts have driven the exploration of innovative and effective plant protection strategies utilizing new active compounds. The examined substance, N-methyl-N-methoxyamide-7-carboxybenzo(1.2.3)thiadiazole (BTHWA), is a novel amide derivative of benzothiadiazole with the ability to induce systemic acquired resistance (SAR). This work presents a series of experiments conducted in the process of determining the appropriate technology for BTHWA use and proving its effectiveness in controlling CLS in sugar beet cultivation. It has been demonstrated that the application of treatments using BTHWA or BTHWA combined with a fungicide in a reduced number of treatments had the same effect on the reduction of plant infection with C. beticola and obtained root and technological sugar yields the same as those that resulted from the use of a full fungicidal treatment. The results provide grounds for reducing the use of fungicides by showing that the same effects can be attained by combining or replacing them with BTHWA. Full article

Syzygium aromaticum L. Myrtaceae is one of the plants rich in bioactive compounds that have beneficial effects on the skin. Fermented plant extracts (FPEs) obtained from this plant have recently been exploited as new cosmetic ingredients. The concept of our study was related to the use of clove buds in the fermentation process in order to obtain new cosmetic raw materials with high antioxidant potential. The focus was on evaluating antioxidant activity (AA), total polyphenol content (TPC), and lactic acid efficiency (LA_e). For this purpose, the most favourable technological parameters of the fermentation process of clove buds were determined, including the type of microorganisms, initial sugar content, plant raw material content, and fermentation time. The most favourable parameters were correlated with the optimal parameters, which were determined based on response surface methodology (RSM). Based on DPPH and Folin–Ciocalteu assays and GC-MS analysis, optimal points of antioxidant activity against reactive oxygen species (analysed with RSM) were successfully selected, enabling quantitative mathematical representations. The optimisation revealed that using a strain of Lactobacillus rhamnosus MI-0272 in lactic fermentation and plant material (6.40%) and beet molasses (3.20%) results in the highest antioxidant potential of FPE (33.90 mmol Tx/L) and yielding LA with the highest efficiency (96%). The optimised FPE had higher polyphenol content (11.60 mmol GA/L ± 0.14), chelating and antioxidant activity (0.32 mmol Fe²⁺/L ± 0.01 and 11.60 mmol Tx/L ± 0.09), and Fe³⁺ ion reduction (49.09 mmol Fe³⁺/L ± 0.16) than the PE. In addition, the possibility of using the spent plant material remaining after the extraction process to prepare activated carbons capable of treating wastewater was investigated. Full article

The need for sustainable development in the context of pesticide use has been recognized by the European Union. The “Farm to Fork Strategy” indicates a goal of 50% reduction in pesticide use by 2030. To address this challenge, we used the concept of ionic liquids to modify known resistance inducers, i.e., a group of substances whose action is indicated as an alternative to fungicides. A new, patented substance developed by us, which is a choline 3,5-dichlorosalicylate, has been tested in the context of its use in sugar beet cultivation with the aim of controlling Cercospora leaf spot (CLS). The results suggest that the use of this substance in combination with one fungicide treatment reduces disease infection and produces yields very similar to the use of a standard protection program assuming the use of two fungicides. Such results provide the basis for further development of 3,5-dichlorosalicylate in terms of its use in agriculture. Thanks to its use, it was possible to resign from one fungicide treatment, while maintaining protection against CLS and yields at the same level as for the full fungicide protection program. Such an approach is in line with European Union policies. Full article

Precision agriculture is about applying solutions that serve to obtain a high yield from the optimization of resources and the development of technologies based on the collection and use of precise data. Precision agriculture, including camera-guided row detection and hydraulic steering, is often used as an alternative because crop damage can be decreased and driving speed can be increased, comparable to herbicide applications. The effects of different approaches, such as uncontrolled (UC), mechanical weed control (MWC), herbicide weed control (HWC), and mechanical + herbicide control (MWC + HWC), on weed density and yield of sugar beet were tested and evaluated in two trials (2021 and 2022) in South Transylvania Depression at the tested intervals BBCH 19 and 31. Weed control efficacy (WCE) depends on the emergence of the weeds and a good timing of weed controls in all the trials and methods, though the highest yield of sugar beet roots was recorded in the treatment MWC + HWC, with an increase up to 12–15% (56.48 t ha⁻¹) yield from HWC (50.22 t ha⁻¹) and a yield increase of more than 35–40% than MWC (42.34 t ha⁻¹). Our trials show that it is possible to increase yield and have fewer chemical applications with the introduction of new precision technologies in agriculture, including sensor-guided mechanical controls. Full article

The aim of the study was the evaluation of silicon foliar application on sugar beet yield. In the years 2017–2019, the effect of a single foliar application of various forms of silicon (potassium silicate—PS, calcium silicate—CS, sodium metasilicate—SM, and orthosilicic stabilized acid—OSA) applied in the six-leaf phase of sugar beet, 7 and 14 days later on yield and technological quality of sugar beet roots was assessed. It was found that the form of silicon does not have a significant effect on the yield of sugar beet roots, and significantly modifies the biological yield of sugar and the pure sugar yield. The highest biological yield of sugar is achieved by the foliar application of PS, and the pure sugar yield by PS and OSA. The date of foliar application as well as the interaction of the date of application and silicon forms do not have a significant effect on the root yield, biological yield of sugar, and pure sugar yield. The form of silicon has a significant effect on the technological quality of sugar beet roots (sugar, α-amino nitrogen, potassium, and sodium content). The most beneficial effect on the sugar content and reduction of sodium content in sugar beet roots is the foliar application of OSA, and the reduction of α-amino nitrogen and potassium content—PS. The timing of the application of various forms of silicon has a significant effect on the sugar and potassium content in sugar beet roots. The most beneficial effect on the sugar content in the roots is the application carried out 7 days after the six-leaf phase of sugar beet, and the potassium content is most limited by the treatment 14 days after reaching this phase. The interaction of the timing of foliar application and the form of silicon significantly modifies the technological quality features of sugar beet roots: the content of sugar, α-amino nitrogen, potassium, and sodium. The results of the study proved the significant effect of silicon foliar application on the physiological parameters of plants, such as leaf area index (LAI), absorption of photosynthetically active radiation (PAR) and normalized difference vegetation index (NDVI) which are related to yield and sugar beet productivity. Full article

Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorophyll (Chlt), chlorophyll a (Chla), chlorophyll b (Chlb), root yield (RY), sugar yield (SY), and sugar content (SC) under five nitrogen (N) levels (0, 30, 60, 90, and 120 kg N ha⁻¹). This was achieved by using a combination of the gradient boosting regression (GBR) model with published and newly developed two- and three-band spectral indices (2D- and 3D-SRIs). The results showed that the N levels had the highest proportion of variations (80.4–92.9%) for all parameters, except for SC, which had more variation (59.9%) according to year than the N levels (37.2%). All parameters, except SC, showed a significant increase with gradually increasing N levels. Additionally, the N levels displayed linear and strong positive relationships with the chlorophyll parameters, and linear and strong negative relationships with SC, while these relationships were quadratic and strong with RY and SY. Several published and novel 3D-SRIs exhibited moderate to strong relationships (R² = 0.65–0.89) with all parameters. The newly developed 3D-SRIs, which involve wavelengths from the visible, near-infrared, and red-edge regions, such as NDI_{536, 538, 534}, NDI_{738, 750, 542}, and NDI_{448, 734, 398}, were effective in accurately estimating all parameters. Combining 2D-SRIs, 3D-SRIs, and the aggregate of all spectral indices (ASRIs) with GBR models could be a robust strategy for estimating the six observed parameters with reasonable precision. The GBR-ASF-6 SRIs and the GBR-ASF-7 SRIs models performed better in predicting Chl content and SC with R² values of 0.99 and 0.99 (RMSE = 0.073 and 1.568) for the training dataset and R² values of 0.65 and 0.78 (RMSE = 0.354 and 6.294) for the testing datasets, respectively. The obtained results concluded that published and newly developed 3D-SRIs, GBR based on 2D-SRIs or 3D-SRIs, and the aggregate of all ASRIs can be used in practice to accurately estimate the Chl content, production, and quality of sugar beet across a wide range of N levels under semiarid conditions. Full article

Sugar beet is an important root crop with a biennial life cycle. In the first year of its life cycle, it produces huge amounts of leaf and root mass used for the production of sugar and bioethanol, livestock feed, confectionery and pharmaceuticals, fertilizers, and soil restoration. Normally, after exposure to cold temperatures during winter storage, in the second year of its life cycle, it enters its reproductive phase. However, during the first year of growth, sugar beet plants may be susceptible to producing flowering shoots, or “bolting”, due to vernalization and long-day conditions. Bolting reduces both the yield and the sugar content of roots. Here, we review the published research works that study the environmental factors influencing bolting, the genetic (including epigenetic) and physiological mechanisms that regulate the transition to the reproductive phase, and the agrotechnical and breeding practices used to prevent bolting. Models of gene networks that regulate the transition to flowering are presented. Methods for selecting non-bolting plants using conventional, marker-assisted, and genomic breeding are demonstrated. Attention is also paid to the speed breeding technology that stimulates bolting and flowering sugar beet plants in an artificial climate. Growing sugar beet plants “from seed to seed” can potentially accelerate the breeding and seed production of sugar beet. This review compares different conditions for inducing bolting in sugar beet in climatic chambers and greenhouses. It examines parameters such as temperature, duration of light exposure, and light intensity during the pre-vernalization, post-vernalization, and vernalization periods. The present review may be useful for specialists in sugar beet cultivation, breeders working on developing cultivars and hybrids that are resistant to bolting, and molecular biologists studying the genetic and physiological mechanisms underlying the transition of plants into the flowering stage. Full article

Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, $R^2$, while for sugar beet, the Neural Prophet model achieved superior $R^2$ levels of 0.99 and 0.98, respectively. Full article

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area. Full article

New technologies have been developed to maximize agricultural production with rational use of water resources, especially salt water. We conducted field experiments using either the Nano Irrigation system or the drip irrigation system on sandy loam soil to examine the response of sugar beet to different levels of saline irrigation water. Replicates (n = 4) of elementary plots, 4 m wide × 5 m long with two irrigation water salinity treatments (S1 = 1.6 dS m⁻¹, S2 = 6.3 dS m⁻¹), were established in a factorial design under Nano Irrigation. Soil chemical properties and morphological and physiological parameters of sugar beet were measured over two sampling periods. Irrigation with saline water resulted in proline accumulation in leaves and decreased chlorophyll content, leaf area, and root yield. The results suggest that irrigation water of 6 dS m⁻¹ could be used to obtain an acceptable root biomass yield without significant short-term salinity issues in the cultivated soil. Full article