7 resultados para sugarbeet
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The main objective of this research was the study of the soil nematode community, and in particular plant parasitic nematodes (PPN), from a field located in Portugal’s southern region, used for sugarbeet production. The study was performed from February to July 2003, covering part of the fallow period previous to tomato cultivation, the alternative crop in the rotation. The end of the fallow period in March and the soil preparation period in May were marked by a significant reduction in the numbers of PPN, whereas their numbers increased on the following tomato crop. The genus Helicotylenchus stood out as the most representative group, forming 90% of all PPN counted each month. The genus Heterodera was relatively abundant in the months following the previous sugarbeet crop, and numbers of the genus Meloidogyne increased during the tomato crop. The correlations between these group and environmental parameters show that, apart from the direct influence of the host, pH, organic matter, temperature and soil moisture significantly influenced nematode abundance and community composition.
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This extension circular is a slide rule used to help a producer calculate the row spacing, seed population, and estimated percentage of emergence of sugarbeet. A producer can also use this slide rule to find the plant population from plants/100 feet of row at 22" and 30" row spacings.
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Abstract: The potential variance in feedstock costs can have signifi cant implications for the cost of a biofuel and the fi nancial viability of a biofuel facility. This paper employs the Grange Feed Costing Model to assess the cost of on-farm biomethane production using grass silages produced under a range of management scenarios. These costs were compared with the cost of wheat grain and sugarbeet roots for ethanol production at an industrial scale. Of the three feedstocks examined, grass silage represents the cheapest feedstock per GJ of biofuel produced. At a production cost of €27/tonne (t) feedstock (or €150/t volatile solids (VS)), the feedstock production cost of grass silage per gigajoule (GJ) of biofuel (€12.27) is lower than that of sugarbeet (€16.82) and wheat grain (€18.61). Grass biomethane is also the cheapest biofuel when grass silage is costed at the bottom quartile purchase price of silage of €19/t (€93/t VS). However, when considering the production costs (full-costing) of the three feedstocks, the total cost of grass biomethane (€32.37/GJ of biofuel; intensive 2-cut system) from a small on-farm facility ranks between that of sugarbeet (€29.62) and wheat grain ethanol (€34.31) produced in large industrial facilities. The feedstock costs for the above three biofuels represent 0.38, 0.57, and 0.54 of the total biofuel cost. The importance of feedstock cost on biofuel cost is further highlighted by the 0.43 increase in the cost of biomethane when grass silage is priced at the top quartile (€46/t or €232/t VS) compared to the bottom quartile purchase price.
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A study was conducted to estimate variation among laboratories and between manual and automated techniques of measuring pressure on the resulting gas production profiles (GPP). Eight feeds (molassed sugarbeet feed, grass silage, maize silage, soyabean hulls, maize gluten feed, whole crop wheat silage, wheat, glucose) were milled to pass a I mm screen and sent to three laboratories (ADAS Nutritional Sciences Research Unit, UK; Institute of Grassland and Environmental Research (IGER), UK; Wageningen University, The Netherlands). Each laboratory measured GPP over 144 h using standardised procedures with manual pressure transducers (MPT) and automated pressure systems (APS). The APS at ADAS used a pressure transducer and bottles in a shaking water bath, while the APS at Wageningen and IGER used a pressure sensor and bottles held in a stationary rack. Apparent dry matter degradability (ADDM) was estimated at the end of the incubation. GPP were fitted to a modified Michaelis-Menten model assuming a single phase of gas production, and GPP were described in terms of the asymptotic volume of gas produced (A), the time to half A (B), the time of maximum gas production rate (t(RM) (gas)) and maximum gas production rate (R-M (gas)). There were effects (P<0.001) of substrate on all parameters. However, MPT produced more (P<0.001) gas, but with longer (P<0.001) B and t(RM gas) (P<0.05) and lower (P<0.001) R-M gas compared to APS. There was no difference between apparatus in ADDM estimates. Interactions occurred between substrate and apparatus, substrate and laboratory, and laboratory and apparatus. However, when mean values for MPT were regressed from the individual laboratories, relationships were good (i.e., adjusted R-2 = 0.827 or higher). Good relationships were also observed with APS, although they were weaker than for MPT (i.e., adjusted R-2 = 0.723 or higher). The relationships between mean MPT and mean APS data were also good (i.e., adjusted R 2 = 0. 844 or higher). Data suggest that, although laboratory and method of measuring pressure are sources of variation in GPP estimation, it should be possible using appropriate mathematical models to standardise data among laboratories so that data from one laboratory could be extrapolated to others. This would allow development of a database of GPP data from many diverse feeds. (c) 2005 Published by Elsevier B.V.
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This article forecasts the extent to which the potential benefits of adopting transgenic crops may be reduced by costs of compliance with coexistence regulations applicable in various member states of the EU. A dynamic economic model is described and used to calculate the potential yield and gross margin of a set of crops grown in a selection of typical rotation scenarios. The model simulates varying levels of pest, weed, and drought pressures, with associated management strategies regarding pesticide and herbicide application, and irrigation. We report on the initial use of the model to calculate the net reduction in gross margin attributable to coexistence costs for insect-resistant (IR) and herbicide-tolerant (HT) maize grown continuously or in a rotation, HT soya grown in a rotation, HT oilseed rape grown in a rotation, and HT sugarbeet grown in a rotation. Conclusions are drawn about conditions favoring inclusion of a transgenic crop in a crop rotation, having regard to farmers’ attitude toward risk.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)