Precision Agriculture and Soil and Water Management in Cranberry Production — Special Issue

doi:10.1139/cjss-2016-0143

Can. J. Soil. Sci.

2017

DOI: 10.1139/cjss-2016-0143

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Precision Agriculture and Soil and Water Management in Cranberry Production — Special Issue

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Cited by 2 publications

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Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China

Li¹,

Shao

Zhao

et al. 2019

Can. J. Soil. Sci.

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Soil moisture is a key element of the hydrological cycle, and it significantly impacts the surface water and energy fluxes. However, a knowledge gap exists on the spatial variability of root-zone soil moisture at the regional scale in arid and hyperarid regions. Thus, soil moisture measurements at 142 sites were taken in Xinjiang (northwest China), and the relationships between soil moisture and 19 environmental factors were analyzed. The results showed that both absolute gravitational soil water content (SWC) and relative extractable water (REW) increased with increasing soil depth in the 0–100 cm soil profile. It generally decreased in the order of cropland > forestland > grassland > shrubland > bare land. Semivariograms suggested that SWC had moderate spatial dependence over a large range of 473–558 km, and REW was more randomly distributed at the regional scale in Xinjiang. Redundancy analysis suggested that environmental factors could explain 47.5%–50.9% of the variability of soil moisture, which was more strongly driven by land surface factors (p < 0.01) than by climatic factors (p > 0.05). Soil properties and other local variables explained, respectively, 40.7% and 32.3% of the variability of soil moisture in the 0–100 cm soil profile. Soil properties independently accounted for 12.8% and 28.1% of the variability in soil moisture in the 0–50 and 50–100 cm soil layers, respectively. Soil texture, field capacity, wilting point, organic carbon, bulk density, land use, and normalized difference vegetation index were the dominant factors influencing soil moisture variations.

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Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China

Li¹,

Shao

Zhao

et al. 2019

Can. J. Soil. Sci.

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Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

et al. 2020

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Real-time monitoring of soil matric potential has now become a common practice for precision irrigation management. Some crops, such as cranberries, are susceptible to both water and anoxic stresses. Excessive variations in soil matric potential in the root zone may reduce plant transpiration, due to either saturated or dry soil conditions, thereby reducing productivity. A timely supply of the right amount of water is, therefore, fundamental for efficient irrigation management. In this paper, we compare the capabilities of a machine learning-based model and a physics-based model to predict soil matric potential in the root zone. The machine learning model is a random forest algorithm, while the physics-based model is a two-dimensional solver of Richards equation (HYDRUS 2D). After training and calibration on a dataset collected in a cranberry field located in Québec (Canada), the performance of the two models is evaluated for 30 different time frames of 72-h soil matric potential forecasts. The results highlight that both models can accurately forecast the soil matric potential in the root zone. The machine learning-based model can achieve better performance when compared to the physics-based model, but forecasting accuracy decreases rapidly toward the end of the 72-h lead time, while the error for the Richards equation-based model does not increase with time and remain small compared to the typical measurement error.

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Precision Agriculture and Soil and Water Management in Cranberry Production — Special Issue

Cited by 2 publications

References 13 publications

Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China

Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

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