基于柑橘叶面积指数和产量的WOFOST模型关键参数校正

为进一步分析不同水肥管理条件对柑橘产量的影响使其更广泛的应用于不同区域,并探明WOFOST模型参数针对柑橘果树生长模拟时的参数敏感性结果和取值情况,开展2021-2022年柑橘不同生育期滴灌水肥控制试验,采用EFAST方法对WOFOST模型模拟叶面积指数(LAI)和产量所需的叶片生长、CO_(2)同化、同化物转换和叶片营养物质等25个参数进行了敏感性分析,使用PEST软件完成高敏感参数的优化,并利用田间试验观测数据对模型模拟的LAI和产量进行验证。结果表明,模型模拟LAI和产量的敏感参数相似,均包括叶面积指数的最大相对增长率、比叶面积、单叶片CO_(2)同化效率,叶片同化物转换效率,初始根长,区别为LAI模拟对茎同化物转换效率,积温等参数敏感,而产量模拟对叶片干物质最大钾浓度敏感;基于2021年数据校正后最优参数模型模拟的不同灌溉管理下LAI最大值(LAI_(m))模拟值与实测值之间R2=0.5800,NRMSE=15.10%,产量模拟值与实测值之间R^(2)=0.6471,NRMSE=6.81%;2022年数据验证表明模拟果实膨大期进行HWMF处理的果树模拟精度最高,果实膨大期各处理LAIm模拟值与实测值之间R^(2)=0.4726~0.8492,NRMSE=13.09%~15.26%,产量模拟值与实测值之间R^(2)=0.4063~0.7022,NRMSE=6.33%~11.33%。研究表明,校正后的WOFOST模型在田间尺度取得了较高的模拟精度,可以开展不同灌溉管理方式对柑橘生长影响的定量化分析。

Effects of water and fertilizer deficit regulation with drip irrigation at different growth stages on fruit quality improvement of kiwifruit in seasonal arid areas of Southwest China

For a long time,seasonal drought occurs frequently in Southwest China,and the management of water and fertilizer in kiwifruit orchards has no quantitative standards,which seriously affects the yield and quality of kiwifruit.Therefore,the effects of water and fertilizer deficit regulation with drip irrigation(WFDRDI)on the quality of kiwifruit at different growth stages were explored to achieve water and fertilizer saving,and green and efficient production of kiwifruit.We select‘Jin Yan'kiwifruit and set two water deficit levels(W_(D20%)and W_(D40%))and three fertilizer deficit levels(F_(D15%),F_(D30%)and F_(D45%))at bud burst to leafing stage(stageⅠ),flowering to fruit set stage(stageⅡ),fruit expansion stage(stageⅢ)and fruit maturation stage(stageⅣ),respectively,with a full irrigation and fertilization as the control treatment(CK)in 2017and 2018.Results showed that the WFDRDI at stageⅡandⅢhad significant effect on fruit physical quality of kiwifruit,specifically,theⅢ-WD40%F_(D30%)andⅢ-W_(D20%)F_(D45%)treatments significantly increased fruit firmness by 13.62 and 15.59%(P<0.05),respectively;theⅡ-W_(D40%)F_(D15%)andⅢ-W_(D40%)F_(D15%)treatments significantly increased dry matter by 8.19 and 6.47%(P<0.05),respectively;theⅢ-W_(D20%)F_(D15%)treatment significantly increased single fruit weight and fruit volume by 9.33 and 12.65%(P<0.05),respectively;theⅡ-W_(D20%)F_(D15%)treatment significantly increased fruit water content by 1.99%(P<0.05).The WFDRDI had an obvious effect on fruit chemical quality of kiwifruit.TheⅢ-W_(D20%)F_(D45%),Ⅳ-W_(D40%)F_(D15%)andⅣ-W_(D20%)F_(D30%)treatments significantly increased vitamin C(Vc)content by 69.96,36.96 and 34.31%(P<0.05),respectively;theⅢ-W_(D40%)F_(D15%)andⅣ-W_(D40%)F_(D15%)treatments significantly increased total soluble solid(TSS)content by 3.79 and 17.05%(P<0.05),respectively,and significantly increased soluble sugar content by 28.61 and 34.79%(P<0.05),respectively;the contents of fructose,glucose and sucrose also had a significantly increasing trend,which was increased significantly by 5.58–19.63%,40.55–60.36%and 54.03–54.92%in theⅢ-WD40%F_(D15%)andⅣ-W_(D40%)F_(D15%)treatments(P<0.05),respectively;sugar–acid ratio was increased significantly in theⅣ-W_(D40%)F_(D15%)treatment by 64.65%(P<0.05).The degree and duration of water and fertilizer deficit had a comprehensive effect on fruit quality of kiwifruit.The WFDRDI at stageⅡandⅢcontribute to improving fruit physical quality,and the threshold of water and fertilizer deficit were 20 and 15%,respectively;stageⅢandⅣare the critical periods for improving fruit chemical quality by water and fertilizer coupling effect,and the threshold of water and fertilizer deficit were 40 and 15%,respectively.Therefore,aiming at precise water and fertilizer saving,theⅠ-W_(D20%)F_(D30%),Ⅱ-W_(D40%)F_(D15%),Ⅲ-W_(D40%)F_(D15%)andⅣ-W_(D40%)F_(D15%)treatments under WFDRDI during the whole growth period of kiwifruit were the best mode to improve quality and production of kiwifruit.

一种融合隐藏语义的人体行为表示方法

人体行为识别是计算机视觉研究的热门领域之一,提出了一种基于隐藏语义的人体行为算法,采取人体骨骼点的三维数据进行处理后构成数据序列,将该数据序列作为分析人体行为的主语义的同时,分析人体行为细节信息在时空特性上的变化作为隐藏语义,然后将二者融合作为人体行为表示.最后使用改进的类均值核主成分分析算法对行为表示数据进行处理,并用支持向量机进行分类.将提出的方法在UTKinect、Florence和MSR Action 3D数据集上进行验证,实验结果证明了所提方法的有效性和普适性.

四川省不同区域地表太阳总辐射模型适用性评价

选用1994-2016年四川省7个辐射站气象数据,在3个辐射区(川西高原Ⅰ区、川东盆地Ⅱ区和川西南山地Ⅲ区)中评价了 6种地表太阳总辐射(R_(s))估算模型在3种天气类型(晴、多云、阴)下的适用性,并分析基于天气类型的组合模型在不同区域的模拟效果,以探寻最适宜全省不同区域的R_(s)估算方法。结果表明:(1)各经验模型在四川省整体表现良好(决定系数R2介于0.554~0.934,P<0.001),Ⅰ区(甘孜和红原站)模拟效果最好的为日照时数模型A-P (平均绝对误差MAE为2.210±0.714MJ·m^(−2)·d^(−1)),Ⅱ区(成都、绵阳和泸州站)、Ⅲ区(峨眉山和攀枝花站)模拟效果最佳的均为混合模型Chen (Ⅱ区MAE为1.510±0.027MJ·m^(−2)·d^(−1),Ⅲ区为1.930±0.006MJ·m^(−2)·d^(−1));(2) 6个模型在四川省3种天气类型下的模拟效果呈晴天>多云>阴天的规律,日照时数模型(A-P和Ba模型)能更好地模拟晴天时的R_(s),混合模型(Chen和Ab模型)则在多云和阴天时模拟效果更佳,Ⅰ区在晴天、多云、阴天3种天气下模拟效果最好的模型分别是A-P (整体评价指标 GPI 为 0.850)、Ab (1.294)、Ba (0.862),Ⅱ区分别为 A-P (0.381)、Chen (1.358)、Chen(1.742),Ⅲ区分别为 Chen (0.204)、Chen (0.857)、Chen (0.526);(3)基于天气类型的组合模型(M_(新))模拟各区R_(s)的效果均比未组合前各模型的效果好(3个区GPI分别为0.558、0.582、0.134)。因此,推荐使用基于天气类型的组合模型来估算四川省Rs。