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Variation Characteristic of Soil Temperature at Deep Layers in Kashi City Abudoukerimu ABASI , Maihebureti MAIMAITIYIMING,Nu'erpatiman MAIMAITIREYIMU,Gulimiri Chinese Journal of Agrometeorology    2014, 35 (03): 237-242.   DOI: 103969/jissn1000-6362201403001 Abstract （16304）      PDF（pc） （2307KB）（23195）       Knowledge map    Save The variation of monthly average soil temperature at 0.8m,1.6m and 3.2m deep layers in Kashi from 1981 to 2010 was investigated by using linear trend analysis and accumulated variance methods The results showed that annual average soil temperature at deep layers of 0.8m, 1.6m and 3.2m decreased from 1984 to 1992 significantly(P<0.01) and increased significantly from 1996 to 2004 (P<0.01),but the liner trend of soil temperature was not significant during the whole period Soil temperature at different deep layers increases significantly in winter and spring but decreased significantly in summer and autumn,of which at 0.8m layer in summer(P<0.01),1.6m layer in autumn(P<0.05) was significant respectively From decadal variation, average soil temperature at different deep layer in 1980s was higher than that in 1990s and first 10 years of 21st century Air temperature variation was one of the main factors affecting deep layer soil temperature and there was positive correlation between them Precipitation also had a certain impact on the deep soil temperature,and average soil deeper temperature was related to precipitation increasing Annual average soil temperature at 0.8m and 1.6m depth had abrupt changes in 1985 and 2009,and 3.2m depth was in 1985 and 2008 by the Mann Kendall test The results could provide scientific reference to adapt to climate change for Kashi Reference | Related Articles | Metrics | Comments（0）

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Simulation of Daily Air Temperature Inside Plastic Greenhouse Based on Harmonic Method LI Qian,SHEN Shuang he,TAO Su lin,ZOU Xue zhi Chinese Journal of Agrometeorology    2014, 35 (01): 33-41.   DOI: 10.3969/j.issn.1000-6362.2014.01.005 Abstract （16062）      PDF（pc） （2473KB）（11768）       Knowledge map    Save Micro-climate data inside plastic greenhouse in Cixi,Zhejiang province,during the period from 2006 to 2009 was analyzed based on three kinds of weather conditions in winter and spring seasons.Taking the weather elements outside plastic greenhouse as independent variables,the second-order harmonic model parameters were got by through the stepwise regression and a harmonic prediction model for hourly air temperature inside greenhouse was established and validated with three kinds of weather, ie,sunny day, partly cloudy day and overcast day in winter and spring respectively.The results showed that the coefficient of determination (R2)between the predicted and the measured value was more than 0.92 both in sunny day and partly cloudy day,and root mean square error (RMSE) and absolute error (AE) were less than 3.0℃ and 2.4℃ respectively R2 between the predicted air temperature and the measured value in overcast day was approximately 0.79,and RMSE was less than 3.0℃ and RE was approximately 2.0℃,which was lower than that of in partly cloudy day but higher than that of in sunny day.Under the same weather condition, predicted air temperature in winter was higher than that of in spring.Air temperature phase inside plastic greenhouse was a little ahead of outside, especially in sunny and partly cloudy day,and it was higher in winter than that of in spring.Daily minimum air temperature inside plastic greenhouse was lower than that of outside greenhouse,especially in spring.Application of the harmonic analysis to predict of air temperature inside plastic greenhouses under specific weather conditions was studied,and the research results could certainly provide scientific guidance for micro-scale cultivation management in plastic greenhouse. Reference | Related Articles | Metrics | Comments（0） Cited: Baidu(1)

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Research Progress in Application of Crop Growth Models SUN Yang-yue, SHEN Shuang-he Chinese Journal of Agrometeorology    2019, 40 (07): 444-459.   DOI: 10.3969/j.issn.1000-6362.2019.07.004 Abstract （2321）      PDF（pc） （792KB）（7544）       Knowledge map    Save The crop growth model can not only simulate the dynamic growth of crops on a single point scale, but also evaluate the relationship between crop growth status and environmental factors from a systematic perspective. This paper reviews latest works related to crop growth model, with particular focuses on the research of climate change to agricultural production and application of crop growth model at regional scale. In addition, this paper summarizes the current research on the development of agricultural decision support systems(DSS) with crop growth models as the core. The research is intended to promote crop growth models to be more widely used in researches on ecology, agriculture, regional climate resources and climate change filed. Research results show that the crop growth model is widely and deeply used in China and abroad. Under the background of climate change, the application research of crop growth model to the impact of historical period climatic conditions and agrometeorological disasters on crop production status and yield has been extensive and relatively mature. Using global climate models (GCMs) or regional climate models (RCMs) to construct future climate change scenarios, coupled with crop growth models, has evolved into an important tool for assessing the impact of future climate change on agricultural production. By integrating and consolidating multi-crop growth model, multi-climate model ensemble simulation and optimizing climate simulation data correction methods, the uncertainty of climate change impact assessment on agricultural production can be effectively reduced. The remote sensing data assimilation technology can apply the site model to the regional scale to evaluate the impact of different meteorological factors on agricultural production, broaden the application scale range of the crop growth model and effectively improve the accuracy of crop yield estimation. The research and application of agricultural decision support system with crop growth model as the core is more and more diversified, and it is an important tool to assist agricultural production management and decision-making. However, due to the complexity of crop ecosystems, there are still great uncertainties in crop growth model simulation results. In the future, the exploration of crop growth and process coupling mechanism needs to be strengthened in order to improve the model and promote it more widely used. Reference | Related Articles | Metrics | Comments（0）

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Progress of DSSAT-CSM Model Application in Maize Planting Research WANG Yu-ling, XU Chun-xia, BI Ya-qi, FAN Jun, GUO Rui-jia, WANG Jing, FAN Xing-ming Chinese Journal of Agrometeorology    2023, 44 (06): 492-501.   DOI: 10.3969/j.issn.1000-6362.2023.06.004 Abstract （643）      PDF（pc） （448KB）（6114）       Knowledge map    Save Crop models play an important role in the simulation, evaluation and prediction of maize production. Through literature review, the authors systematically summarized the development and application of DSSAT-CSM model in China; the composition, development and shortcomings of DSSAT-CSM model; and the process and results of using crop model to simulate the key factors affecting maize growth. It provided reference and technical support for crop model to optimize maize growth and yield by adjusting crop variety parameters, temperature variation, nitrogen fertilizer measures, irrigation system and key soil factors. Uncertainty and deficiencies of current crop models were the key factors that limited simulation accuracy and efficiency. Therefore, standardizing data collection, coupling multiple types of crop models, optimizing dynamic management processes, and modifying and optimizing models are the future trends of crop models. Related Articles | Metrics | Comments（0）

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