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金虫 (小有名气)

应助: 10 (幼儿园)
金币: 1061.7
散金: 350
红花: 10
帖子: 152
在线: 136.5小时
虫号: 1257069
注册: 2011-04-06
性别: GG
专业: 作物栽培与耕作学

[求助] 摘要翻译求润色

Abstract：Processing tomato in Xinjiang arid area characteristics and advantages of industry, after years of development, production and processing base acreage account for more than 90% capacities. Xinjiang is in the arid and semi-arid rocky, with abundant light and heat resource, low rainfall, evaporation, temperature difference between day and night, effective accumulative temperature high , is especially conducive to processing tomato dry matter accumulation, increasing the content of soluble solids and lycopene formation. Drip irrigation technology has water, and fertilizer, pollution benefits of drip irrigation technology not only improves the processing tomato yield and relatively furrow irrigation reduces water loss, effectual control of the furrow during planting tomato plants leggy. Crop simulation models have become under different environmental conditions in the management and optimization of an essential means of crop growth, which has been a crop dry matter partitioning of the essential contents of the simulation model. Predecessors for dry matter partitioning and yield prediction of tomato simulation model studies are focused on greenhouse tomatoes, but the drip processing tomato shoot dry matter partitioning and yield prediction simulation model has been reported rarely. Therefore, it is necessary to explore the processing tomato under drip irrigation aboveground dry matter partitioning and yield formation of dynamic changes in order to optimize the layout of drip irrigation and cultivation processing tomato crop management theory.
In this study, quantitative analysis of the growth of processing tomato drip feature, set the different varieties of sowing date, Based on partitioning index (PI) and harvest index (HI) of drip processing tomato dry matter partitioning and yield prediction simulation model was established. Partitioning index (PI) and harvest index (HI) were physiological development time (PDT) function, the model fully considered the relative thermal effectiveness(RTE), relative photoperiod Effectiveness(RPE), intrinsic development factor (IDF) on different sowing dates, varieties of processing tomato development progress influence, with independent experimental data were used to validate the model, the results show that the model for processing tomato each growth stage (emergence to flowering, flowering to fruit-setting, fruit-setting to maturing, maturing to ending date) of dry weight, total dry weight of the whole growth period, shoot dry weight of stem, leaf, fruit dry weight predicted and 1:1 straight line between R2 respectively 0.9642, 0.9469, 0.9227, 0.9602, 0.9940, 0.9629, 0.9828, 0.9585; RMSE and RE were 27.85kg/hm2, 9.61%, 179.83kg/hm2, 10.99%, 292.77kg/hm2, 8.15% , 114.16kg/hm2, 8.99%; 361.5kg/hm2, 7.00%; 241.15kg/hm2, 9.86%; 203.9kg/hm2, 9.81%; 665.8kg/hm2, 11.77%. Model for processing tomato yield predictions with the 1:1 line between R2 is 0.9689; RMSE and RE were 6985.6kg/hm2, 7.64%. The model for different sowing dates; processing tomato varieties dry matter partitioning and yield the predicted value and a higher degree of fit between the simulated values, indicating that the model has good predictive and applicability.

我的金币比较少，如果哪位大侠可以帮忙，我都给你了！

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