李广1,,,
刘强2
1.甘肃农业大学林学院 兰州 730070
2.甘肃农业大学信息科学技术学院 兰州 730070
基金项目: 甘肃农业大学学科建设基金GAU-XKJS2018258
甘肃农业大学学科建设基金GAU-XKJS2018254
甘肃农业大学学科建设基金GAU-XKJS2018255
国家自然科学基金项目31560343
国家自然科学基金项目31560378
甘肃农业大学青年导师基金项目GAU-QNDS-201702
详细信息
作者简介:王钧, 研究方向为水土保持与荒漠化防治。E-mail:julianwong82@163.com
通讯作者:李广, 主要研究方向为水土保持与荒漠化研究。E-mail:lig@gsau.edu.cn
中图分类号:S157.4+2计量
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被引次数:0
出版历程
收稿日期:2018-10-26
录用日期:2019-03-25
刊出日期:2019-08-01
Soil water storage under conservation tillage based on LSTM neural network simulation in the Loess Plateau Gully Region of central Gansu
WANG Jun1, 2,,LI Guang1,,,
LIU Qiang2
1. College of Forestry, Gansu Agricultural University, Lanzhou 730070, China
2. College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
Funds: the Subject Construction Fund of Gansu Agricultural UniversityGAU-XKJS2018258
the Subject Construction Fund of Gansu Agricultural UniversityGAU-XKJS2018254
the Subject Construction Fund of Gansu Agricultural UniversityGAU-XKJS2018255
the National Natural Science Foundation of China31560343
the National Natural Science Foundation of China31560378
the Youth Tutor Fund Project of Gansu Agricultural UniversityGAU-QNDS-201702
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Corresponding author:LI Guang, E-mail:lig@gsau.edu.cn
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摘要
摘要:为分析陇中黄土高原沟壑区不同保护性耕作措施的贮水效果，本研究利用春小麦/豌豆（W/P）、豌豆/春小麦（P/W）轮作的长期定位试验，分别设置传统耕作（T）、免耕（NT）、传统耕作秸秆覆盖（TS）和免耕覆盖（NTS）4种耕作措施，以当地月平均气温、月降水量、月平均辐射量、月平均蒸发量、月作物耗水量为输入因子，以月土壤贮水量为输出，建立基于长短期记忆（Long Short-Term Memory，LSTM）神经网络的土壤贮水量预测模型，并对该模型的有效性进行评估。研究结果表明：1）基于LSTM神经网络建立的土壤贮水量模型对陇中黄土高原沟壑区保护性耕作下土壤贮水量预测具有较好的适用性，模型模拟结果的平均均方根误差为7.76 mm，平均绝对误差为6.95 mm，相对误差控制在-5%~+5%的范围内。2）P/W轮作序列中各处理的土壤贮水量均比W/P轮作序列增加1.09%~1.43%。3）不同轮作序列，NTS处理的贮水效果均优于其他3种耕作措施，在W/P轮作序列中，NTS处理的年均土壤贮水量比T、NT和TS分别增加2.89%、1.70%和2.46%；在P/W轮作序列中，NTS处理的年均土壤贮水量比T、NT和TS分别增加3.03%、1.91%和2.57%。4）不同降水年型，NTS处理的土壤贮水量最高，且干旱年效果更加显著，其中丰水年NTS处理的土壤贮水量比T、NT和TS平均增加2.71%、1.48%和2.19%，而干旱年平均增加3.97%、2.54%和3.64%。5）保护性耕作措施的贮水效果随季节发生变化，作物生长前期（3-5月）保护性耕作措施的贮水优势较为明显，进入作物生长旺盛期（5-6月）保护性耕作措施与传统耕作的贮水效果差异不显著，而作物生长后期（7月）保护性耕作措施较传统耕作土壤贮水量明显增加。基于LSTM神经网络模拟环境下免耕覆盖的贮水保墒效果最好，为陇中黄土高原沟壑区最适宜的保护性耕作措施。
关键词:保护性耕作/
春小麦/豌豆轮作/
土壤贮水量/
LSTM神经网络/
预测模型/
陇中黄土高原沟壑区
Abstract:In order to analyze the effects of soil water storage for four different tillage measures[traditional tillage (T), no-tillage with no straw cover (NT), traditional tillage with straw incorporation (TS) and no-tillage with straw cover (NTS)], the field research was conducted in the Loess Plateau Gully Region of central Gansu. The objectives of the study were to establish a prediction model for soil water storage based on Long Short-Term Memory (LSTM), and to evaluate the model's effectiveness using the long-term positioning experiment of the rotation sequence for spring wheat/pea (W/P) and pea/spring wheat (P/W) crops. In the experiment, monthly average temperature, monthly precipitation, monthly average radiation, monthly average evaporation and monthly crop water consumption constituted input factors, and value for soil water storage constituted the output factor in the prediction model. The results of the present study showed that:1) The water storage model based on LSTM neural network showed good applicability for predicting soil water storage, particularly in conservation tillage practice, in the Loess Plateau Gully Region of central Gansu. The average root mean square error and mean absolute error of the model simulation were 7.76 mm and 6.95 mm, respectively; moreover, the relative error was controlled between -5% and +5%. 2) In P/W rotation sequence, the soil water storage of various treatments increased by 1.09%-1.43% as compared to W/P. 3) Within different rotation sequences, water storage effect of NTS treatment turned out to be better than those for other three tillage measures. In W/P rotation sequence, the annual average soil water storage of NTS treatment was 2.89%, 1.70% and 2.46% higher than that of T, NT, and TS, respectively. In P/W rotation sequence, the average annual soil water storage of NTS treatment increased by 3.03%, 1.91% and 2.57%, respectively, compared to that of T, NT, and TS. 4) In the years with different precipitation, the soil water storage of NTS treatment was the highest, and this effect was markedly more significant in the dry year. The soil water storage of NTS treatment increased by 2.71%, 1.48% and 2.19%, on average, in the wet year, and it increased by 3.97%, 2.54% and 3.64%, on average, in the dry year as compared to the values recorded for T, NT and TS. 5) The water storage effect for conservation tillage measures varied with the season. There was obvious water storage advantage of conservation tillage measures during the early stages of crop growth (March-May). However, there was not significant between the results of conservation tillage measures and traditional tillage during the full growth stage (May-June). Nevertheless, the soil water storages for conservation tillage increased significantly during the late stage of crop growth (July). Going by the effect on soil water storage based on LSTM neural network, no-tillage mulching remains the best practice as well as the most suitable measure of protective tillage in the Loess Plateau Gully Region of central Gansu.
Key words:Conservation tillage/
Spring wheat/pea rotation/
Soil water storage/
LSTM neural network/
Prediction model/
Loess Plateau Gully Region of central Gansu
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图1基于LSTM神经网络的土壤贮水量模型示意图
Figure1.The diagram of soil water storage model based on LSTM neural network
下载: 全尺寸图片幻灯片
图2LSTM神经网络细胞结构图
Figure2.Cell structure of LSTM neural network
下载: 全尺寸图片幻灯片
图3不同春小麦(W)/豌豆(P)轮作序列不同耕作措施的土壤贮水量模拟值与观测值相关分析
T:传统耕作; NT:免耕; TS:传统耕作秸秆覆盖; NTS:免耕秸秆覆盖。
Figure3.Correlation analysis of LSTM neural network simulated and observed values of soil water storage under different tillage measures and different sequences of spring wheat (W) and pea (P) rotation
T: traditional tillage; NT: no-tillage with no straw cover; TS: traditional tillage with straw incorporation; NTS: no-tillage with straw cover.
下载: 全尺寸图片幻灯片
图42002—2017年春小麦(W)/豌豆(P)轮作序列不同耕作措施土壤贮水量与年降水量的关系
T:传统耕作; NT:免耕; TS:传统耕作秸秆覆盖; NTS:免耕秸秆覆盖; P:降水量。
Figure4.Relationship between soil water storage and annual precipitation of different tillage measures under different sequences of spring wheat (W) and pea (P) rotation from 2002 to 2017
T: traditional tillage; NT: no-tillage with no straw cover; TS: traditional tillage with straw incorporation; NTS: no-tillage with straw cover; P: precipitation.
下载: 全尺寸图片幻灯片
图52002—2017年春小麦(W)/豌豆(P)轮作序列不同耕作措施下各月土壤贮水量的变化
T:传统耕作; NT:免耕; TS:传统耕作秸秆覆盖; NTS:免耕秸秆覆盖; P:降水量。
Figure5.Monthly changes of soil water storage of different tillage measures under different sequences of spring wheat (W) and pea (P) rotation from 2002 to 2017
T: traditional tillage; NT: no-tillage with no straw cover; TS: traditional tillage with straw incorporation; NTS: no-tillage with straw cover; P: precipitation.
下载: 全尺寸图片幻灯片
图6不同耕作措施下春小麦/豌豆轮作系统(W/P)春小麦农田月平均土壤贮水量在丰水年和干旱年的变化特征
T:传统耕作; NT:免耕; TS:传统耕作秸秆覆盖; NTS:免耕秸秆覆盖; P:降水量。
Figure6.Monthly changes of soil water storage of spring wheat farmland of spring wheat/pea rotation sequence (W/P) in wet and dry years under different tillage measures
T: traditional tillage; NT: no-tillage with no straw cover; TS: traditional tillage with straw incorporation; NTS: no-tillage with straw cover; P: precipitation.
下载: 全尺寸图片幻灯片
图7不同耕作措施下豌豆/春小麦轮作系统(P/W)豌豆农田月平均土壤贮水量在丰水年和干旱年的特征
T:传统耕作; NT:免耕; TS:传统耕作秸秆覆盖; NTS:免耕秸秆覆盖; P:降水量。
Figure7.Monthly changes of soil water storage of pea farmland of pea/spring wheat rotation sequence (P/W) in wet and dry years under different tillage measures
T: traditional tillage; NT: no-tillage with no straw cover; TS: traditional tillage with straw incorporation; NTS: no-tillage with straw cover; P: precipitation.
下载: 全尺寸图片幻灯片表1试验田0~200 cm土层土壤容重
Table1.Soil bulk density in 0-200 cm soil layers of the experimental field
| 土层Soil layer (cm) | 0~5 | 5~10 | 10~30 | 30~50 | 50~80 | 80~110 | 110~140 | 140~170 | 170~200 |
| 容重Soil bulk density (g?cm-3) | 1.29 | 1.23 | 1.32 | 1.20 | 1.14 | 1.14 | 1.13 | 1.12 | 1.11 |
下载: 导出CSV表2基于LSTM神经网络的土壤贮水量模型参数
Table2.Parameters of LSTM neural network model for soil water storage
| 参数Parameter | 值Value |
| 隐藏层数Hidden layer | 20 |
| 最大迭代次数Maximum iterations | 50 |
| 批尺寸Batch size | 5 |
| 损失函数Loss function | mse |
| 下降方式Descent method | Adadelta optimization algorithm |
| 学习速率Learning rate | 0.001 |
下载: 导出CSV表3不同春小麦(W)/豌豆(P)轮作序列不同耕作措施下土壤贮水量LSTM神经网络模型模拟值与实测值的统计指标
Table3.Statistical indices of LSTM neural network simulated and observed values of soil water storage under different tillage measures and different sequences of spring wheat (W) and pea (P) rotation
| 序列 Sequence | 处理 Treatment | 模拟平均值 Simulated average (mm) | 实测平均值 Measured average (mm) | 相关系数 Correlation coefficient | 均方根误差 Root mean square error (mm) | 平均绝对误差 Mean absolute error (mm) |
| W/P | T | 322.90 | 322.17 | 0.99 | 7.02 | 6.02 |
| NT | 327.57 | 326.85 | 0.99 | 7.26 | 6.23 | |
| TS | 324.59 | 323.15 | 0.99 | 7.19 | 6.16 | |
| NTS | 332.01 | 330.79 | 0.99 | 7.52 | 6.76 | |
| P/W | T | 326.40 | 325.95 | 0.99 | 8.09 | 7.33 |
| NT | 329.60 | 328.81 | 0.99 | 8.24 | 7.68 | |
| TS | 327.63 | 326.28 | 0.99 | 8.22 | 7.67 | |
| NTS | 334.77 | 333.40 | 0.99 | 8.56 | 7.78 | |
| T:传统耕作; NT:免耕; TS:传统耕作秸秆覆盖; NTS:免耕秸秆覆盖。T: traditional tillage; NT: no-tillage with no straw cover; TS: traditional tillage with straw incorporation; NTS: no-tillage with straw cover. | ||||||
下载: 导出CSV参考文献
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