Sugar Beet Farms Research Articles

German sugar beet farmers’ intention to use autonomous field robots for seeding and weeding

Robotic weed control is not yet widely adopted, despite its technological availability and proven economics and sustainability in crop cultivation by replacing seasonal labor and synthetic pesticides. This impedes technologically enabled changes toward more sustainable agricultural systems. Given that adopting robotics for the weeding process requires changing existing systems, farmers' appraisals for the new and the current weeding technology may constitute barriers. However, this dualism has been largely ignored by previous studies. Based on a duality approach, we investigate farmers’ beliefs, and adaptive and maladaptive appraisals of current and new robotic weeding in sugar beets. The main variable of interest is their behavioral intention to adopt weeding robots. For our sample of German farmers, we identify the main enablers perceived efficacy of the robots and social norms. The main barrier are maladaptive rewards from traditional weeding. We recommend policy incentives to promote large-scale uptake of new and more sustainable robotic technologies. To improve efficacy perceptions of such robotic systems public demonstrations/talks are mostly relevant. Maladaptive rewards can be reduced, for instance, by notifying about the dependency of the current practices on future availability of synthetic inputs or seasonal workers.

Field observation and verbal exchange as different peer effects in farmers’ technology adoption decisions

AbstractFarmers’ adoption of novel technologies is influenced by other farmers’ behavior, a phenomenon known as peer effects. Although such effects have been studied intensively, the literature does not clearly distinguish between those that result (1) from verbal exchanges with other farmers and (2) from field observations, including the application of technology, its outcomes, and field conditions. We extend existing theoretical concepts and hypothesize that verbal information exchanges and field observations are two types of peer effects. Using data from an online survey of German sugar beet farmers’ application of mechanical weeding from early 2022, we find that the likelihood of adopting mechanical weeding increases across all model specifications by around 26%–28% if at least one adopter is known and by approximately 30%–32% if at least one field is observed. The two types of peer effects complement and reinforce each other in explaining adoption decisions. The effects increase with the number of adopters known and fields observed but decrease with larger distances to the observed fields. The findings can support designing extension services and future peer effects research that should consider the distinction between peer effects arising from verbal exchanges and field observations.

Unveiling constraints and cultivating potential: Optimizing spring sugar beet yield through boundary line analysis

To enhance crop yields, narrowing the gap between actual and achievable yields is crucial. One approach involves identifying yield-limiting factors and their optimal levels. Boundary line analysis (BLA), a statistical method, quantifies yield response to environmental or managerial factors while accounting for variable factors. In 2020, a study surveyed 180 spring sugar beet farms in Iran. The findings revealed a 50.7 % yield gap (11.13 t ha−1) between farmers’ average sugar yield (10.84 t ha−1) and potential yield (21.97 t ha−1). BLA implicated low plant density, soil organic content, lack of potassium (K)-containing fertilizers, and non-adherence to crop rotation principles in 81.3 %, 75.5 %, 53.4 %, and 51.1 % of farms, respectively. Optimal levels were estimated at 9.5 plants m−1, 1.15 % soil organic content, 100 kg ha−1 K fertilization rate, and 5–9.8 as the rotation numerical value. Other limiting factors included time from cultivation to thinning and sowing date. The optimal minimum time between planting and thinning stood at 38 days, with the latest sowing date estimated at 94 Julian days (May 5). Achieving attainable yield necessitates limiting root exposure to 4.9 days, keeping disease damage below 15.3 %, and increasing irrigation frequency to over 10 times. In the short term, increasing plant density to optimal levels, promoting K fertilizer use, and adjusting thinning and sowing dates can significantly boost sugar yield per unit area.

Stereo Vision for Plant Detection in Dense Scenes.

Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.

Sugar beet farming goes high-tech: a method for automated weed detection using machine learning and deep learning in precision agriculture

Sugar beet farming goes high-tech: a method for automated weed detection using machine learning and deep learning in precision agriculture

First surveillance of pesticides in soils of the perimeter of Tadla, a Moroccan sugar beet intensive area.

With the long-term application of pesticides on sugar beet farms in the irrigated perimeter of Tadla in Morocco for over 50years, pesticide monitoring is necessary to assess soil health. The objective of our study was to monitor multiple pesticide residues in topsoil samples collected from post-harvest sugar beet fields and verify their migration to deep soil layers. Topsoil and deep soil samples were collected from arbitrarily selected sugar beet fields in the IPT. In this study, a target-screening method was applied. All target pesticides were detected in soil samples, with tefluthrin being the most frequently detected pesticide. The residue with the highest concentration in soil samples was DDE. All the soil samples contained a mixture of pesticide residues, with a maximum of 13 residues per sample. The total pesticide content decreased toward more profound layers of soil, except in one field where it reached a concentration of 348µg/kg at the deeper soil layer. For pesticides detected at the three soil depths, only tefluthrin concentration increased in the deep soil layer. The results provide comprehensive and precise information on the pesticide residue status in sugar beet soils warning against the multiple risks that this contamination can cause. This study indicates the need of regular monitoring of pesticides over a large area of the perimeter to enable decision-makers to pronounce the impacts of the extension and intensification of sugar beet cultivation at the irrigated perimeter of Tadla.

Fusing vegetation index and ridge segmentation for robust vision based autonomous navigation of agricultural robots in vegetable farms

Vision based autonomous navigation is widely used for agricultural robots. However, factors such as large area of weed, discontinuous crop rows, and differences in ambient lighting condition during different plant growth stages have brought challenges to autonomous robotic navigation in farms. This paper presents a vision based method of fusing vegetation index and ridge segmentation for robust and precise extraction of navigation lines in lettuce farms. Firstly, vegetation index from the captured image is computed, and farm ridges are extracted using a semantic segmentation net. Then, vegetation index and ridge segmentation result are fused to obtain plant segmentation result. Since the method only needs to segment ridges, it does not need tedious manual labeling of vegetable plants in pixels to train plant segmentation net, yet provides accurate and reliable plant segmentation. Secondly, a modified Progressive Sample Consensus (PROSAC) algorithm and a distance filtering are proposed to fit line using the center points of plants, which effectively eliminates outliers and extract reliable and accurate center line of the current lane for autonomous navigation. Comprehensive experiments are carried out to validate the effectiveness of the method. The results show that the proposed method outperforms the conventional methods based on only vegetation index or ridge segmentation, by effectively reducing the interference caused by weeds, irregular branches and leaves, and missing rows. The proposed method runs at 10 Frames Per Second (FPS), thus satisfies real-time navigation of robots. Although the proposed method is only demonstrated in lettuce farms, it can be naturally applied to other vegetable farms, e.g. broccoli and early stage sugar beet farms.

Characterization and pathogenicity of Fusarium species causing sugar beet root rot in Morocco

AbstractThe sugar beet crop ranks second globally among the plant species grown mainly for sugar production. In Morocco, the area under sugar beet farming occupies approximately 57,000 ha yielding more than 3 million tons of roots. However, sugar beet root‐tip rot (RTR) caused by Fusarium spp. dramatically reduces the anticipated yields, the purity of the resulting juice, and the sugar concentration. The current study aimed at identifying and characterizing the Fusarium species responsible for the root rot in sugar beet grown in the Khenifra‐Beni Mellal region of Morocco. In this survey, 69 isolates of Fusarium were sampled from sugar beet roots showing typical symptoms of root rot from 2019 to 2021. After screening based on the pathogenicity test, 28 isolates were selected and identified based on morphological features and sequence analyses of the ribosomal internal transcribed spacer (ITS) region and translation elongation factor 1 α 34 (TEF‐1 α). Fusarium oxysporum was the most frequently identified species, followed closely by F. solani, F. equiseti, F. nygmai, F. brachygibbosum, F. proliferatum, F. culmorum, and F. falciforme. Six weeks after inoculations under greenhouse conditions, the studied isolates caused internal vascular discoloration and tip rot of sugar beet roots, with disease incidences ranging from 37.5% to 100.0% and a disease index between 30.3% and 70.5%. Isolates belonging to F. solani were the most aggressive. Moreover, the majority of isolates significantly reduced plant growth. To our knowledge, this research article is the first report of Fusarium species inducing RTR in sugar beet in Morocco.

Fungicidal Protection as Part of the Integrated Cultivation of Sugar Beet: An Assessment of the Influence on Root Yield in a Long-Term Study

Despite the major role of non-chemical treatments in integrated plant protection, fungicides often need to be applied as a crop protection treatment in sugar beet farming. They should be used based on a good understanding of the requirements and effectiveness of the active ingredients. In 11-year field experiments, the effect that one and three foliar applications of fungicides containing various active ingredients (triazoles, benzimidazoles, strobilurines) had on sugar beet root yields was assessed, depending on various thermal and rainfall conditions. It was found that in eight of the 11 years, foliar application of fungicides increased yields compared to unprotected plants, and three foliar treatments during the growing season were more effective than a single application. The negative correlation of the root yield of fungicidally protected plants with total June rainfall was weaker than the same relationship for unprotected plants. At the same time, the positive correlation between the yield of fungicidally protected sugar beets and average June air temperature was stronger than the same relationship for unprotected plants. The research results indicate the need to conduct long-term field experiments and to continuously improve integrated production principles for sugar beet, especially regarding the rational use of pesticides.

Evaluation of the effectiveness of the functioning of the sugar beet food subcomplex of the Grodno region

The article provides information on the current state of the sugar beet food subcomplex in Belarus and, in particular, in the Grodno region. The necessity of introducing the main effective innovative projects in the conditions of our country is substantiated, which can help increase the profitability of the work of the main sugar beet farms in the Grodno region.

Understanding the sugar beet holobiont for sustainable agriculture.

The importance of crop-associated microbiomes for the health and field performance of plants has been demonstrated in the last decades. Sugar beet is the most important source of sucrose in temperate climates, and-as a root crop-yield heavily depends on genetics as well as on the soil and rhizosphere microbiomes. Bacteria, fungi, and archaea are found in all organs and life stages of the plant, and research on sugar beet microbiomes contributed to our understanding of the plant microbiome in general, especially of microbiome-based control strategies against phytopathogens. Attempts to make sugar beet cultivation more sustainable are increasing, raising the interest in biocontrol of plant pathogens and pests, biofertilization and -stimulation as well as microbiome-assisted breeding. This review first summarizes already achieved results on sugar beet-associated microbiomes and their unique traits, correlating to their physical, chemical, and biological peculiarities. Temporal and spatial microbiome dynamics during sugar beet ontogenesis are discussed, emphasizing the rhizosphere formation and highlighting knowledge gaps. Secondly, potential or already tested biocontrol agents and application strategies are discussed, providing an overview of how microbiome-based sugar beet farming could be performed in the future. Thus, this review is intended as a reference and baseline for further sugar beet-microbiome research, aiming to promote investigations in rhizosphere modulation-based biocontrol options.

How much can farmers pay for weeding robots? A Monte Carlo simulation study

This paper investigates the Maximum Acquisition Values (MAVs) of weeding robots and their determinants in both organic and conventional sugar beet farming in Germany. The MAV is defined in this paper as the price of the weeding robot that renders the same net profit as the current weeding methods. For our analysis, a Monte Carlo simulation approach is used, combined with empirical data and data collected from weeding robot companies. The results show that the MAVs of mechanical weeding robots for organic farming are substantially higher than that of spot spraying robots for conventional farming. Technology attributes are more influential than labour cost in determining the MAVs of weeding robots: in organic farming, technology attributes such as area capacity and weeding efficiency impact the MAVs of mechanical weeding robots the most; in conventional farming, supervision intensity and the robot’s ability to save herbicides are the most influential factors. The wage rate of unskilled labour, relevant for manual weeding, plays a more important role in determining the MAVs than that of skilled labour, relevant for supervision of the robot. This implies that a shortage of seasonal workers and hence increases in the wage of low-skilled labour could be important drivers of the adoption of mechanical weeding robots. Plot characteristics such as plot size and mechanisation level only have limited impacts on the MAVs.

Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model

Sugar beet is an essential crop for the sugar industry that have a very crucial role in agro-industry of Türkiye and Konya ranks first in terms of total sugar beet production and harvested area. The predictions, that the world's human population will reach 9 billion by the end of the current century and that demand for food will increase, are forcing farmers for the decision to search for new areas for agriculture or choose the crops that will be most productive in already cultivated lands. The aim of this study was to apply the LINTUL-MULTICROP Model for investigating the adaptation of sugar beet for the current climatic conditions and for climate change scenarios to show the response of sugar beet to an increase level of carbon dioxide and temperature. Four different scenarios were compared to check the effects of the climate change on sugar beet farming in the semi-arid Konya Region as followings: i) scenario (a) is the current climate conditions; ii) scenario (b) is the average temperatures increased 2 °C, iii) scenario (c) is 200 ppm increasing atmospheric CO2; iv) scenario (d) new optimum sowing and harvest dates in sugar beet farming and increased temperatures and atmospheric CO2 amount were simulated together. The optimum sowing and harvesting dates of sugar beet were moved 13 days back for sowing, and 8 days forward for harvesting. The highest yield was estimated under conditions of 2 °C and 200 ppm increased atmosphere temperature and CO2 levels with new sowing and harvest dates. The yields under irrigated conditions varied between 74.4 t ha-1 and 111.2 t ha-1. The irrigation water requirements of sugar beet were ranged from 618.8 mm to 688.5 mm for different scenarios. In conclusion, the cultivation of sugar beet tends to alter in semi-arid Konya environment.

B. H. Roberts: A Life in the Public ArenaFrank J. Cannon: Saint, Senator, Scoundrel

B. H. Roberts: A Life in the Public ArenaFrank J. Cannon: Saint, Senator, Scoundrel

Optimisation of irrigation strategy in sugar beet farming based on yield, quality and water productivity

Present experiments were conducted to determine the effects of different irrigation levels on yield, yield components and quality of drip-irrigated sugar beet under sub-humid conditions. Field experiments were conducted in the 2019 and 2021 growing seasons in the Bursa province of Turkey. Experiments were carried out in completely randomised blocks design with three replications. Irrigations were scheduled based on the replenishment of 100 (S1), 66 (S2), 33 (S3), and 0% (S4) of soil water depletion within the soil profile of 90 cm using 7-day irrigation intervals. In 2019, root yields varied between 29.32 (S4)–86.31 (S1) t/ha and sugar yields between 6.33–13.57 t/ha. In 2021, root yields varied between 26.18 (S4)–74.56 (S1) t/ha and sugar yields between 6.56–12.53 t/ha. Effects of different irrigation levels on investigated parameters were found to be significant (P < 0.01). The crop water consumption values were significantly related to root and sugar yields (P < 0.01). Based on present findings, S1 treatment was recommended to get the highest root and sugar yields. In case of limited water resources, S2 (33% water shortage) treatment with the highest water productivity and irrigation water productivity values could be recommended to ensure maximum efficiency from the applied irrigation water quantity.

Chlorophyll Response to Water Stress and the Potential of Using Crop Water Stress Index in Sugar Beet Farming.

Field experiments were conducted in 2019 and 2021 growing seasons to evaluate the chlorophyll readings and crop water stress index (CWSI) response to full and deficit irrigation for drip-irrigated sugar beet (Beta vulgaris L.) under sub-humid climate of Bursa, Turkey. In addition, the changes of soil water content under different irrigation treatments and statistical relationships between chlorophyll and CWSI values and ETc, root yield and sugar yield were investigated. Experiments were carried out in a completely randomized blocks design with three replications. Irrigations were scheduled based on the replenishment of 100 (S1), 66 (S2), 33 (S3), and 0% (S4) of soil water depletion within the soil profile of 0–90 cm using 7 day irrigation intervals. Lower and upper baselines obtained by measurements based on the canopy temperature from the treatments full irrigated and non-irrigated were used to calculate CWSI. The variations in CWSI values were consistent with the variations of seasonal soil water contents induced by the different irrigation practices. CWSI values generally varied between 0 and 1 throughout the experimental periods. In 2019, seasonal mean chlorophyll readings varied between 203.3 and 249.1, and mean CWSI values varied between 0.12 and 0.85. In 2021, seasonal mean chlorophyll readings varied between 232.7 and 259.3 and mean CWSI values between 0.19 and 0.89. Unlike chlorophyll values, CWSI decreased with increased irrigation water amount. In both years, statistically significant relationships were determined between chlorophyll readings and CWSI and ETc, root yield and sugar yield. The greatest root yield was achieved with a seasonal mean CWSI value of 0.12. An exponential equation determined as “Root Yield = 10.804e−1,55CWSI” between seasonal average CWSI values and root yield can be used for estimation of root yield in sugar beet farming. The mean CWSI values determined by infrared thermometer technique can be used in determination of crop water stress and irrigation scheduling of sugar beet cultivation under sub-humid climatic conditions.

A Study on the Evaluation of the Socioeconomic Effects of Sugar Factories: A Case of Kastamonu

Since the first periods of the Republic, great importance has been attached to the industry in Turkey. One of the most important of these is sugar factories. The sugar adventure, which started with the first opened Uşak and Alpullu Sugar Factory, continued with the Kastamonu Sugar Factory opened in 1963. In this study, it was aimed to examine the socioeconomic effects of sugar beet agriculture and sugar production on Kastamonu. For this purpose, the survey method, one of the quantitative research methods, was used in addition to the field studies carried out in the factory and district centers (Center, Devrekani, Tosya, Taşköprü and Seyçiler). With the questionnaire applied to 127 randomly selected farmers, it was aimed to measure the parameters such as the path followed by the local farmer in sugar beet production, the problems experienced, earnings, sowing and harvesting. Within the framework of the meeting held with the factory representative, information was provided about the works at the factory, privatization and the general situation of the farmers interested in sugar beet farming. According to the findings, sugar production in Kastamonu province is important in terms of creating a livelihood for the farmer and employment. In addition, the migration of the young population negatively affects the number of farmers in the region. The increase in fixed costs in production affects the farmer negatively. In particular, the regulation of input costs can be suggested as the main factor that will positively affect the yield in sugar beet.

Increasing the return on equity by financial leverage in sugar beet farms in Slovakia

The paper evaluates and analyses selected financial ratios of a set of farms in Slovakia in 2017. The farms are divided into two groups (sugar beet producers and sugar beet non-producers). The data used for the analysis were obtained from the National Bank of Slovakia (NBS) over the period 2009 – 2017 and the database of the Ministry of Agriculture and Rural Development of the Slovak Republic (MARD SR). Farmers generally achieve a low level of return on equity. In the paper, we analyze the development of interest rates and measure the potential effect of financial leverage, which can increase the profitability (return on equity) of the farms. Moreover, we test the statistically significant difference of selected ratios between the group of producers and non-producers of sugar beet in Slovakia. In 2017, the return on equity of sugar beet producers in Slovakia reached lower results in comparison to sugar beet non-producers. The differences in return on equity are explained by the lower level of equity per ha and thus higher indebtedness of non-producers of sugar beet.

Strategic planning approach to the development of contract farming in the Egyptian sugar industry

The strategies required to develop contract farming in the Egyptian sugar industry were examined by applying the Strength-Weakness-OpportunitiesThreats (SWOT) approach in combination with an analytic hierarchy process. Data were collected from sugar beet farmers, extension workers, experts, and employees of the Dakhalia Sugar and Refining Company in Egypt. Market risk has the highest overall priority score among the strengths. Noncommitment of farmers to harvest time received the highest overall priority score among the weaknesses, while valorization of sugar industry wastes had the highest overall priority score among the opportunities, and accumulation of large reserves of sugar at the factory was perceived as the most important threat to the sugar industry. These findings can be used to formulate strategies based on current situation analysis and create suitable conditions for successful contract farming in the sugar sector.
 Keywords: strategies; SWOT-AHP; contract farming; sugar industry; Egypt

Profitability Development and Resource Reallocation: The Case of Sugar Beet Farming in Germany

AbstractFollowing the 2006 reform of the European Union sugar market, and in anticipation of the quota abolition, a reallocation of sugar production has occurred. Using a Lowe quantity index, we evaluate the productivity and profitability of sugar beet farming in Germany from 2004 to 2013. The results show that an increase in total factor productivity partly compensated for losses in terms of trade. Moreover, the contribution of production reallocation to sector productivity growth varied across regions with distinct ownership structures of sugar processing companies. These findings have implications for policy and industry, as it transitions to a liberalised market.