水稻群体分蘖动态模型构建与应用  

Construction and application of dynamic tillering model for rice population

作　　者：王小卉[1,2] 李绪孟 唐启源[2] 邹丹[3] 罗友谊 李可夫 彭剑 李灿[6] 曹乐平 WANG Xiaohui;LI Xumeng;TANG Qiyuan;ZOU Dan;LUO Youyi;LI Kefu;PENG Jian;LI Can;CAO Leping(College of Information and Intelligence,Hunan Agricultural University,Changsha 410128,China;College of Agronomy,Hunan Agricultural University,Changsha 410128,China;Hengyang Academy of Agricultural Sciences,Hengyang 421200,China;Agriculture and Rural Bureau of Hengshan County,Hengyang 421399,China;Guohao Academy,Tongji University,Shanghai 200092,China;Ningyuan County Bureau of Agriculture and Rural Affairs,Ningyuan 425600,China;Hunan Biological And Electromechanical Polytechnic,Changsha,410127,China)

机构地区：[1]湖南农业大学信息与智能科学技术学院,长沙410128 [2]湖南农业大学农学院,长沙410128 [3]衡阳市农业科学院,衡阳421200 [4]衡山县农业农村局,衡阳421399 [5]同济大学国豪书院,上海200092 [6]宁远县农业农村局,宁远425600 [7]湖南生物机电职业技术学院,长沙410127

出　　处：《农业工程学报》2024年第10期213-221,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：湖南省重点研发计划项目(2022NK2047);现代农业产业技术体系岗位专家项目(CARS-01-26);湖南农业大学双一流建设项目(SYL2019001);创新平台与人才计划-农村科技特派员(2022NK4167)。

摘　　要：为定量分析水稻群体茎蘖数量动态变化过程及分蘖动态特征,该研究使用双Logistic模型分别描述分蘖发生与死亡过程,建立水稻群体分蘖动态模型;根据水稻分蘖过程的时序特征定义描述分蘖过程的特征指标,并推导出分蘖特征指标的计算式;基于不同基因型品种的种植方式、种植时期、种植密度下水稻分蘖动态数据集检验模型优度和适应性;并应用分蘖动态模型和指标探索分蘖动态对种植密度的响应规律。结果表明,所建模型对不同基因型水稻品种在不同种植方式、种植时期和种植密度下的分蘖动态数据拟合优度较好,标准均方根误差S_(RMSE)服从均值小于5%的Gamma分布,并且99%的S_(RMSE)小于10%。基于所建模型计算的分蘖特征指标(包括模型参数)对种植密度有很好的响应;留一法检验表明模型的预测性较好,观测值与模拟值的R^(2)=0.96。所建模型能够精确描述水稻茎蘖数量演变过程,具有很好的拟合优度、适应性和可解释性,可用于分析基因、环境、农艺措施对分蘖动态的影响,分蘖特征指标可望成为分析基因与环境互作的重要表型参数,对指导水稻精准栽培也有重要理论价值和实际意义。Here a dynamic model was developed on the tiller number of the rice population.The double Logistic model was also used to quantitatively analyze the dynamic process of the tiller occurrence and the extinction.A set of indicators was defined to describe the tillering dynamics,including the total number of growing tillers(Ng),the total number of dead tillers(Nd),the number of retained tillers(Nr),the start time of tillering(Tst),the peak time of tillering(Tpt),the end time of tillering(Tet),the start time of tillers death(Tsd),the peak time of tillers death(Tpd),the end time of tillers death(Ted),the duration of tillering(Dt),the duration of tillers death(Dd),the inherent rate of tillering(Rit),the inherent rate of tillers death(Rid),the maximum tillering rate(Rmt),the maximum tillers death rate(Rmd).According to the temporal characteristics of the rice tillering,the formula was derived to calculate the indicators of the tillering dynamics.The goodness and adaptability of the model were tested with the dynamic datasets of the rice tillers under different genotypes,transplanting,sowing time,and transplanting density.The model and the indicators were used to explore the dynamic tillering response to the cultivation density.The results were as follows.(1)The model shared the better fitting for the dynamic dataset of rice tillers under different genotypes,transplanting,planting time,and transplanting density.The standard root mean square error(S_(RMSE))was followed by the Gamma distribution with the mean was less than 5% and 99% S_(RMSE) less than 10%.(2)The dynamic indicators and model parameters of tillers after calculation showed a better response to the cultivation density.Taking the planting density test of Huiliangyou 898 as an example,the number of tillers per unit area was accelerated and then slowed down after transplanting at 15 to 33.75 hills/m^(2).The number of tillers per unit area reached the peak at 45-50 d after transplanting.After the peak,the number of tillers per unit area decreased rapidly and then

关 键 词：水稻 模型 分蘖动态 双Logistic模型 种植密度 参数拟合 

分 类 号：S112[农业科学—农业基础科学]