针对西北绿洲灌区小麦连作普遍、化肥施用量较大及氮素利用率低等问题,探究麦后复种绿肥对减量施氮小麦籽粒产量和氮素利用的补偿效应,以期为构建减氮小麦高效生产技术提供理论依据。本研究依托始于2018年的定位试验进行,2020-2022年期...针对西北绿洲灌区小麦连作普遍、化肥施用量较大及氮素利用率低等问题,探究麦后复种绿肥对减量施氮小麦籽粒产量和氮素利用的补偿效应,以期为构建减氮小麦高效生产技术提供理论依据。本研究依托始于2018年的定位试验进行,2020-2022年期间采集数据。试验采用裂区设计,主区设4种绿肥种植模式,即麦后分别复种毛叶苕子混播箭筈豌豆(HCV)、箭筈豌豆(CV)、油菜(R)和麦后休闲(F);副区为3种施氮水平:试区习惯施氮量(N3,180 kg hm^(–2))、习惯施氮减量20%(N2,144 kg hm^(-2))、习惯施氮减量40%(N1,108 kg hm^(-2))。研究表明,习惯施氮减量20%和40%显著降低了小麦籽粒产量和氮素吸收,但麦后复种毛叶苕子混播箭筈豌豆可补偿因减量施氮40%造成的籽粒产量和氮素吸收损失,且麦后复种毛叶苕子混播箭筈豌豆结合减量施氮20%提高小麦籽粒产量21.4%和氮素吸收6.9%(P<0.05)。麦后复种毛叶苕子混播箭筈豌豆可补偿因减量施氮40%造成的氮素利用率损失,且其结合减量施氮20%氮素利用率提高13.4%(P<0.05)。其补偿机制归因于:(1)麦后复种毛叶苕子混播箭筈豌豆在减量施氮40%条件下可补偿小麦氮素吸收速率,提高氮素净同化速率34.3%(P<0.05),维持穗部氮素分配,增加茎氮素转运率6.6%(P<0.05)。(2)与麦后休闲传统施氮量相比,麦后复种毛叶苕子混播箭筈豌豆结合减量施氮20%提高氮素平均吸收速率和氮素净同化速率7.2%和34.1%(P<0.05),增加灌浆初期至成熟期穗氮素分配6.7%(P<0.05),提高叶、茎氮素对穗的转运贡献率17.8%、8.9%(P<0.05)。因此,在干旱绿洲灌区,麦后复种毛叶苕子混播箭筈豌豆是实现小麦减氮40%的可行措施,麦后复种毛叶苕子混播箭筈豌豆结合减氮20%可通过提高小麦氮素吸收速率和氮素净同化率,提高叶、茎对穗的转运贡献率从而促进穗部氮素分配,实现小麦产量和氮素利用率双提升。展开更多
Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a BP neural network （call...Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a BP neural network （called the H-BP algorithm） for compensating function model errors is put forward. The function model is assumed as y =f（x1, x2,… ,xn）, and the special structure of the H-BP algorithm is determined as （ n ＋ 1） ×p × 1, where （n ＋ 1） is the element number of the input layer, and the elements are xl, x2,…, xn and y＇ （ y＇ is the value calculated by the function model）; p is the element number of the hidden layer, and it is usually determined after many tests; 1 is the dement number of the output layer, and the element is △y = y0-y＇（y0 is the known value of the sample）. The calculation steps of the H-BP algorithm are introduced in detail. And then, the results of three methods for compensating function model errors from one engineering project are compared with each other. After being compensated, the accuracy of the traditional methods is about ± 19 mm, and the accuracy of the H-BP algorithm is ± 4. 3 mm. It shows that the proposed method based on a neural network is more effective than traditional methods for compensating function model errors.展开更多
文摘针对西北绿洲灌区小麦连作普遍、化肥施用量较大及氮素利用率低等问题,探究麦后复种绿肥对减量施氮小麦籽粒产量和氮素利用的补偿效应,以期为构建减氮小麦高效生产技术提供理论依据。本研究依托始于2018年的定位试验进行,2020-2022年期间采集数据。试验采用裂区设计,主区设4种绿肥种植模式,即麦后分别复种毛叶苕子混播箭筈豌豆(HCV)、箭筈豌豆(CV)、油菜(R)和麦后休闲(F);副区为3种施氮水平:试区习惯施氮量(N3,180 kg hm^(–2))、习惯施氮减量20%(N2,144 kg hm^(-2))、习惯施氮减量40%(N1,108 kg hm^(-2))。研究表明,习惯施氮减量20%和40%显著降低了小麦籽粒产量和氮素吸收,但麦后复种毛叶苕子混播箭筈豌豆可补偿因减量施氮40%造成的籽粒产量和氮素吸收损失,且麦后复种毛叶苕子混播箭筈豌豆结合减量施氮20%提高小麦籽粒产量21.4%和氮素吸收6.9%(P<0.05)。麦后复种毛叶苕子混播箭筈豌豆可补偿因减量施氮40%造成的氮素利用率损失,且其结合减量施氮20%氮素利用率提高13.4%(P<0.05)。其补偿机制归因于:(1)麦后复种毛叶苕子混播箭筈豌豆在减量施氮40%条件下可补偿小麦氮素吸收速率,提高氮素净同化速率34.3%(P<0.05),维持穗部氮素分配,增加茎氮素转运率6.6%(P<0.05)。(2)与麦后休闲传统施氮量相比,麦后复种毛叶苕子混播箭筈豌豆结合减量施氮20%提高氮素平均吸收速率和氮素净同化速率7.2%和34.1%(P<0.05),增加灌浆初期至成熟期穗氮素分配6.7%(P<0.05),提高叶、茎氮素对穗的转运贡献率17.8%、8.9%(P<0.05)。因此,在干旱绿洲灌区,麦后复种毛叶苕子混播箭筈豌豆是实现小麦减氮40%的可行措施,麦后复种毛叶苕子混播箭筈豌豆结合减氮20%可通过提高小麦氮素吸收速率和氮素净同化率,提高叶、茎对穗的转运贡献率从而促进穗部氮素分配,实现小麦产量和氮素利用率双提升。
基金The National Basic Research Program of China(973 Program)(No.2006CB705501)the National High Technology Research and Development Program of China (863 Program)(No.2007AA12Z228)
文摘Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a BP neural network （called the H-BP algorithm） for compensating function model errors is put forward. The function model is assumed as y =f（x1, x2,… ,xn）, and the special structure of the H-BP algorithm is determined as （ n ＋ 1） ×p × 1, where （n ＋ 1） is the element number of the input layer, and the elements are xl, x2,…, xn and y＇ （ y＇ is the value calculated by the function model）; p is the element number of the hidden layer, and it is usually determined after many tests; 1 is the dement number of the output layer, and the element is △y = y0-y＇（y0 is the known value of the sample）. The calculation steps of the H-BP algorithm are introduced in detail. And then, the results of three methods for compensating function model errors from one engineering project are compared with each other. After being compensated, the accuracy of the traditional methods is about ± 19 mm, and the accuracy of the H-BP algorithm is ± 4. 3 mm. It shows that the proposed method based on a neural network is more effective than traditional methods for compensating function model errors.
