The impacts of no-tillage (NT) and moldboard plough (MP) managements on infiltration rate and preferential flow were characterized using a combined technique of double-ring device and dye tracer on a black soil (Molli...The impacts of no-tillage (NT) and moldboard plough (MP) managements on infiltration rate and preferential flow were characterized using a combined technique of double-ring device and dye tracer on a black soil (Mollisols) in Northeast China. The objective of this study is to evaluate how tillage practices enhance soil water infiltration and preferential flow in favor of soil erosion control in the study area. The steady infiltration rates under NT management are 1.6 and 2.1 times as high as those under MP management in the 6th and 8th years of the tillage management in place, while the infiltrated water amounts under NT management are 1.4 and 2.0 times as high as those under MP management, respectively. The depth of methylene blue penetrated into NT soil increases from 43cm in the 6th year to 57cm in the 8th year, which are 16cm and 19cm deeper than those in MP soil, respectively. The results of morphologic image show that more biological macro-pores occur in NT soil than in MP soil. These macro-pores play a key role in enhancing preferential flow in NT soil, which in turn promotes water infiltration through preferential pathways in NT soil. The results are helpful to policy-making in popularizing NT and have the implications for tillage management in regard to soil erosion control in black soil region of China.展开更多
【目的】利用土壤近表面空气温湿度与土壤内部参数的关联关系对耕作层土壤水分、温度进行精准预测,为实现精细化农业种植管理提供服务。【方法】针对土壤耕作层水分、温度预测在训练集获取与模型验证等方面的实际需求,设计了基于嵌入式...【目的】利用土壤近表面空气温湿度与土壤内部参数的关联关系对耕作层土壤水分、温度进行精准预测,为实现精细化农业种植管理提供服务。【方法】针对土壤耕作层水分、温度预测在训练集获取与模型验证等方面的实际需求,设计了基于嵌入式系统及窄带物联网(Narrow band internet of things,NB-IoT)无线通信技术的物联网数据采集系统。在此基础上基于深度Q学习(Deep Q network,DQN)算法探索了一种模型组合策略,以长短期记忆(Long short-term memory,LSTM)、门限循环单元(Gated recurrent unit,GRU)与双向长短期记忆网络(Bidirectional long short-term memory,Bi-LSTM)为基础模型进行加权组合,获得了DQN-L-G-B组合预测模型。【结果】数据采集系统实现了对等间隔时间序列环境数据的长时间稳定可靠采集,可以为基于深度学习的土壤水分、温度时间序列预测工作提供准确的训练集与验证集数据。相对于LSTM、Bi-LSTM、GRU、L-G-B等模型,DQN-L-G-B组合模型在2种土壤类型(壤土、砂土)耕作层上水分与温度预测中的均方根误差(Root mean square error,RMSE)、平均绝对误差(Mean absolute error,MAE)、平均百分比误差(Mean absolute percentage error,MAPE)都有一定程度的降低,R2提高了约0.1%。【结论】通过该物联网数据采集系统与DNQ-L-G-B组合模型,可以有效地完成基于土壤近表面空气温、湿度对耕作层土壤中水分、温度的精准预测。展开更多
基金Under the auspices of National Natural Science Foundation of China(No.31170483)Key Direction in Knowledge Innovation Programs of Chinese Academy of Sciences(No.KZCX2-EW-QN307)Foundation of Excellent Young Talents in Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(No.C08Y13)
文摘The impacts of no-tillage (NT) and moldboard plough (MP) managements on infiltration rate and preferential flow were characterized using a combined technique of double-ring device and dye tracer on a black soil (Mollisols) in Northeast China. The objective of this study is to evaluate how tillage practices enhance soil water infiltration and preferential flow in favor of soil erosion control in the study area. The steady infiltration rates under NT management are 1.6 and 2.1 times as high as those under MP management in the 6th and 8th years of the tillage management in place, while the infiltrated water amounts under NT management are 1.4 and 2.0 times as high as those under MP management, respectively. The depth of methylene blue penetrated into NT soil increases from 43cm in the 6th year to 57cm in the 8th year, which are 16cm and 19cm deeper than those in MP soil, respectively. The results of morphologic image show that more biological macro-pores occur in NT soil than in MP soil. These macro-pores play a key role in enhancing preferential flow in NT soil, which in turn promotes water infiltration through preferential pathways in NT soil. The results are helpful to policy-making in popularizing NT and have the implications for tillage management in regard to soil erosion control in black soil region of China.
文摘【目的】利用土壤近表面空气温湿度与土壤内部参数的关联关系对耕作层土壤水分、温度进行精准预测,为实现精细化农业种植管理提供服务。【方法】针对土壤耕作层水分、温度预测在训练集获取与模型验证等方面的实际需求,设计了基于嵌入式系统及窄带物联网(Narrow band internet of things,NB-IoT)无线通信技术的物联网数据采集系统。在此基础上基于深度Q学习(Deep Q network,DQN)算法探索了一种模型组合策略,以长短期记忆(Long short-term memory,LSTM)、门限循环单元(Gated recurrent unit,GRU)与双向长短期记忆网络(Bidirectional long short-term memory,Bi-LSTM)为基础模型进行加权组合,获得了DQN-L-G-B组合预测模型。【结果】数据采集系统实现了对等间隔时间序列环境数据的长时间稳定可靠采集,可以为基于深度学习的土壤水分、温度时间序列预测工作提供准确的训练集与验证集数据。相对于LSTM、Bi-LSTM、GRU、L-G-B等模型,DQN-L-G-B组合模型在2种土壤类型(壤土、砂土)耕作层上水分与温度预测中的均方根误差(Root mean square error,RMSE)、平均绝对误差(Mean absolute error,MAE)、平均百分比误差(Mean absolute percentage error,MAPE)都有一定程度的降低,R2提高了约0.1%。【结论】通过该物联网数据采集系统与DNQ-L-G-B组合模型,可以有效地完成基于土壤近表面空气温、湿度对耕作层土壤中水分、温度的精准预测。
