Sustainability of the Sugar Beet Crop

Stevanato, Piergiorgio; Chiodi, Claudia; Broccanello, Chiara; Concheri, Giuseppe; Biancardi, Enrico; Pavli, Ourania I.; Skaracis, George N.

doi:10.1007/s12355-019-00734-9

Cited by 64 publications

(43 citation statements)

References 61 publications

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“…As is known, this criterion is a technique for fitting the "best" curve to the sample , observations. It involves minimizing the sum of the squared (vertical) deviations of points from the curve: (2) where:…”

Section: Discussionmentioning

confidence: 99%

“…whose solution allows the assessment of the coefficients a 0 , a 1 and a 2 and then attainment of the 2nd order least-squares polynomial (2). At the end of the calculations, for each performance parameter, the residuals average e was evaluated through the following equation:…”

Section: Discussionmentioning

confidence: 99%

“…Referring instead to Ukraine, the previously referred ratios are 5.1% and 5.7%, respectively [1]. However, it should be mentioned that beet and sugar production regulation within the EU is based on the Common Market Organization (CMO) and that, in 2006, the CMO was completely amended, leading to a strong reduction in EU sugar production [2]. Some countries such as France and Germany, considered more suited to beet production and more efficient from an industrial point of view, have been little affected by the changes, whereas others such as Italy have suffered significant consequences.…”

Section: Introductionmentioning

confidence: 99%

“…Some countries such as France and Germany, considered more suited to beet production and more efficient from an industrial point of view, have been little affected by the changes, whereas others such as Italy have suffered significant consequences. Italy, at the time active in the sector with 19 sugar industries and approximately 1.4•10 9 m 2 of beet-cultivated area, after the reform gradually reduced their industrial plants from 19 to 4 and their overall beet-cultivated area decreased considerably [2]. Sugar beet is an extensive crop of great agronomic value with significant productive and economic returns; moreover, it has always been considered an "improvement crop" from which all crops in succession benefit [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Performance Assessment of Front-Mounted Beet Topper Machine for Biomass Harvesting

et al. 2020

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Sugar beet is an extensive crop of great agronomic value with significant productive and economic returns and Ukraine’s sugar beet accounts for about 5.1% of the overall world production. Sugar beets and the by-products resulting from its manufacturing transformation are a significant renewable energy resource. A new high-quality performance prototype of a sugar beet top harvester, front mounted on a tractor, was built by the authors in Ukraine. The aim of this study is to evaluate the main performance parameters related to the operation of this new machine. Field tests were carried out linking the prototype to a wheel tractor, whilst suitable sensors measured the significant kinematic and dynamic parameters, allowing experimental data collection to assess the machine’s performance parameters. The entire technological process of harvesting and transporting the beet tops to the beet top storage unit required power ranging from 6.42 to 17.65 kW. At the topmost tested forward speed, the required tractor traction force was less than 1.9 kN with the power required by the shaft that drives the screw conveyor ranging from 3.1 to 4.6 kW. This value was the lowest for a speed of the tractor–beet top harvesting machine aggregate ranging from 0.9 to 1.2 m · s − 1 .

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Assessment of Front-Mounted Beet Topper Machine for Biomass Harvesting

et al. 2020

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“…It is also the source for bioethanol and animal feed [1,2]. According to the prediction of per capita sugar consumption and population growth, the world sugar production is about 175 metric tons at present, and needs to be increased by one million tons every year to meet the demand of 230 million tons in 2050 [3,4]. To improve global sugar production, plant breeding programs need to find the desired sugar beet genotypes to meet the requirements [2,5].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

et al. 2020

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Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.

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Quantifying inherent predictability and spatial synchrony in the aphid vector Myzus persicae: field‐scale patterns of abundance and regional forecasting error in the UK

Bell

Clark

Stevens

et al. 2022

Pest Management Science

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BACKGROUNDSugar beet is threatened by virus yellows, a disease complex vectored by aphids that reduces sugar content. We present an analysis of Myzus persicae population dynamics with and without neonicotinoid seed treatment. We use six years' yellow water trap and field-collected aphid data and two decades of 12.2 m suction-trap aphid migration data. We investigate both spatial synchrony and forecasting error to understand the structure and spatial scale of field counts and why forecasting aphid migrants lacks accuracy. Our aim is to derive statistical parameters to inform regionwide pest management strategies. RESULTSSpatial synchrony, indicating the coincident change in counts across the region over time, is rarely present and is best described as stochastic. Uniquely, early season field populations in 2019 did show spatial synchrony to 90 km compared to the overall average weekly correlation length of 23 km. However, 70% of the time series were spatially heterogenous, indicating patchy between-field dynamics. Field counts lacked the same seasonal trend and did not peak in the same week. Forecasts tended to under-predict mid-season log 10 counts. A strongly negative correlation between forecasting error and the proportion of zeros was shown. CONCLUSIONField populations are unpredictable and stochastic, regardless of neonicotinoid seed treatment. This outcome presents a problem for decision-support that cannot usefully provide a single regionwide solution. Weighted permutation entropy inferred that M. persicae 12.2 m suction-trap time series had moderate to low intrinsic predictability. Early warning using a migration model tended to predict counts at lower levels than observed.

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Sustainability of the Sugar Beet Crop

Cited by 64 publications

References 61 publications

Performance Assessment of Front-Mounted Beet Topper Machine for Biomass Harvesting

Performance Assessment of Front-Mounted Beet Topper Machine for Biomass Harvesting

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Quantifying inherent predictability and spatial synchrony in the aphid vector Myzus persicae: field‐scale patterns of abundance and regional forecasting error in the UK

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