Change of soil hardness and soil properties due to tile drainage in the Red River Valley of the North

2008 ◽  
Author(s):  
Xinhua Jia ◽  
Thomas F Scherer ◽  
Thomas M DeSutter ◽  
Dean D Steele
Keyword(s):  
Soil Properties ◽  
River Valley ◽  
Tile Drainage ◽  
Red River ◽  
Red River Valley ◽  
The North ◽  
Soil Hardness

Methods, techniques, and considerations for subirrigation practices in the Red River Valley of the North

2013 ◽  
Author(s):  
Kyle L Horntvedt ◽  
Xinhua Jia ◽  
Thomas F Scherer ◽  
Dean D Steele ◽  
Thomas M DeSutter
Keyword(s):  
River Valley ◽  
Red River ◽  
Red River Valley ◽  
The North

Effect of Subsurface Drainage on Water Availability in the Red River Valley of the North

2010 ◽  
Author(s):  
Xiao Pang ◽  
Xinhua Jia ◽  
Thomas M DeSutter ◽  
Thomas F Scherer ◽  
Dean D Steele ◽  
...  
Keyword(s):  
Water Availability ◽  
Subsurface Drainage ◽  
River Valley ◽  
Red River ◽  
Red River Valley ◽  
The North

scholarly journals Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North

Soil Systems ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Umesh Acharya ◽  
Aaron L. M. Daigh ◽  
Peter G. Oduor
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regression Trees ◽  
River Valley ◽  
Red River ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Red River Valley ◽  
The North ◽  
Learning Techniques

Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of <0.040 m3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat.

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