Hydraulic conductance and soil water potential at the soil-root interface of Pinus pinaster seedlings inoculated with different dikaryons of Pisolithus sp.

1992 ◽  
Vol 10 (3) ◽  
pp. 231-244 ◽  
Author(s):  
M. S. Lamhamedi ◽  
P. Y. Bernier ◽  
J. Andre-Fortin
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Pinus Pinaster ◽  
Soil Water Potential ◽  
Hydraulic Conductance

Effect of Soil Water Potential of Two Soils on Soybean Emergence 1

1979 ◽  
Vol 71 (6) ◽  
pp. 980-982 ◽  
Author(s):  
L. G. Heatherly ◽  
W. J. Russell
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Soil Water Potential

scholarly journals Simulating sunflower canopy temperatures to infer root-zone soil water potential

1984 ◽  
Vol 31 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Bhaskar J. Choudhury ◽  
Sherwood B. Idso
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Root Zone ◽  
Soil Water Potential

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

NITROGEN TRANSFORMATIONS NEAR UREA IN SOIL WITH DIFFERENT WATER POTENTIALS

1988 ◽  
Vol 68 (3) ◽  
pp. 569-576 ◽  
Author(s):  
YADVINDER SINGH ◽  
E. G. BEAUCHAMP
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Incubation Period ◽  
Relative Rate ◽  
Soil Water Potential ◽  
Nitrification Inhibitor ◽  
Silt Loam ◽  
Nitrogen Transformations ◽  
Water Potentials ◽  
Initial Soil

Two laboratory incubation experiments were conducted to determine the effect of initial soil water potential on the transformation of urea in large granules to nitrite and nitrate. In the first experiment two soils varying in initial soil water potentials (− 70 and − 140 kPa) were incubated with 2 g urea granules with and without a nitrification inhibitor (dicyandiamide) at 15 °C for 35 d. Only a trace of [Formula: see text] accumulated in a Brookston clay (pH 6.0) during the transformation of urea in 2 g granules. Accumulation of [Formula: see text] was also small (4–6 μg N g−1) in Conestogo silt loam (pH 7.6). Incorporation of dicyandiamide (DCD) into the urea granule at 50 g kg−1 urea significantly reduced the accumulation of [Formula: see text] in this soil. The relative rate of nitrification in the absence of DCD at −140 kPa water potential was 63.5% of that at −70 kPa (average of two soils). DCD reduced the nitrification of urea in 2 g granules by 85% during the 35-d period. In the second experiment a uniform layer of 2 g urea was placed in the center of 20-cm-long cores of Conestogo silt loam with three initial water potentials (−35, −60 and −120 kPa) and the soil was incubated at 15 °C for 45 d. The rate of urea hydrolysis was lowest at −120 kPa and greatest at −35 kPa. Soil pH in the vicinity of the urea layer increased from 7.6 to 9.1 and [Formula: see text] concentration was greater than 3000 μg g−1 soil. There were no significant differences in pH or [Formula: see text] concentration with the three soil water potential treatments at the 10th day of the incubation period. But, in the latter part of the incubation period, pH and [Formula: see text] concentration decreased with increasing soil water potential due to a higher rate of nitrification. Diffusion of various N species including [Formula: see text] was probably greater with the highest water potential treatment. Only small quantities of [Formula: see text] accumulated during nitrification of urea – N. Nitrification of urea increased with increasing water potential. After 35 d of incubation, 19.3, 15.4 and 8.9% of the applied urea had apparently nitrified at −35, −60 and −120 kPa, respectively. Nitrifier activity was completely inhibited in the 0- to 2-cm zone near the urea layer for 35 days. Nitrifier activity increased from an initial level of 8.5 to 73 μg [Formula: see text] in the 3- to 7-cm zone over the 35-d period. Nitrifier activity also increased with increasing soil water potential. Key words: Urea transformation, nitrification, water potential, large granules, nitrifier activity, [Formula: see text] production

OSMOTIC CONTROL OF THE MATRIC SOIL-WATER POTENTIAL

Soil Science ◽  
1966 ◽  
Vol 102 (6) ◽  
pp. 394-398 ◽  
Author(s):  
B. ZUR
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Soil Water Potential

scholarly journals Growth responses to soil water potential indirectly shape local species distributions of tropical forest seedlings

2018 ◽  
Author(s):  
Stefan J. Kupers ◽  
Bettina M. J. Engelbrecht ◽  
Andrés Hernández ◽  
S. Joseph Wright ◽  
Christian Wirth ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Water Potential ◽  
Soil Water ◽  
Soil Water Potential ◽  
Species Distributions ◽  
Growth Responses ◽  
Local Species
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