Partition of photosynthates between shoot and root in spring wheat (Triticum aestivum L.) as a function of soil water potential and root temperature

1994 ◽  
Vol 164 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Xiaomeili Li ◽  
Yongsheng Feng ◽  
Larry Boersma
Keyword(s):  
Triticum Aestivum ◽  
Water Potential ◽  
Soil Water ◽  
Spring Wheat ◽  
Soil Water Potential ◽  
Triticum Aestivum L ◽  
Root Temperature

scholarly journals WHEAT CULTIVARS: RESPONSE TO IRRIGATION AND SOWING DATES

1996 ◽  
Vol 53 (2-3) ◽  
pp. 217-222
Author(s):  
A.E. Klar ◽  
T. Hossokawa
Keyword(s):  
Triticum Aestivum ◽  
Water Potential ◽  
Soil Water ◽  
Aluminium Toxicity ◽  
Triticum Aestivum L ◽  
Wheat Cultivars ◽  
Growing Seasons ◽  
Yield Level ◽  
Sowing Dates ◽  
Similar Yield

This study was carried out in an Alfisol-Oxisol transition sandy-clay texture, using six wheat cultivars (Triticum aestivum, L.): two tall and tolerant to soil aluminium toxicity (BH-1146, and IAC-18), and four semi-dwarf cultivars - Anahuac, IAC-162, IAC-24, and IAC-60 - of which only the first two are sensitive to soil aluminium toxicity. Two minimum soil water potentials (ys) levels were used: 1. watered, when Ys reached about -0.05 MPa; 2. dry, when the water potential reached around -1.5 MPa. Two sowing dates, 05/22/92 and 06/11/92, were used. The results showed that Anahuac and IAC-60 are the most indicated cultivars for the studied region; when irrigated all cultivars presented similar yield level under no irrigation conditions; the irrigation was not sufficient to avoid yield differences between the two growing seasons; differences in rainfall were important for the crop in the dry treatment for both seasons.

THE EFFECT OF SOIL WATER POTENTIAL, TEMPERATURE AND SEEDING DEPTH ON SEEDLING EMERGENCE OF WHEAT

1979 ◽  
Vol 59 (3) ◽  
pp. 259-264 ◽  
Author(s):  
R. DE JONG ◽  
K. F. BEST
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Minimum Temperature ◽  
Seedling Emergence ◽  
Potential Temperature ◽  
Soil Water Potential ◽  
Triticum Aestivum L ◽  
Planting Dates ◽  
Water Potentials ◽  
Soil Temperatures

Daily emergence counts were made on Canthatch wheat (Triticum aestivum L.) grown in five soil types, at four soil temperatures and three water potentials and planted at five different depths. Regardless of soil type, soil water potential or depth of planting, 50% emergence generally occurred within a week at 19.4 and 26.7 °C, and within 2 wk at 12.2 °C, but it took up to 6 wk at 5 °C. The heat sum required to attain 50% seedling emergence did not increase significantly with decreasing soil water potentials, but the minimum temperature for emergence dropped from 1.3 to 0.2 °C as the water potential decreased from −⅓ to −10 bar. It was suggested that the seedlings compensated for the increased water stress by lowering their minimum temperature requirements. Increasing the planting depth not only increased the heat requirement for emergence, but it also increased the variability of emergence, especially at low temperatures. Practical aspects concerning planting dates and depths were considered.

scholarly journals Interactive effects of elevated CO2, O3, and soil water deficit on spring wheat (Triticum aestivum L. cv. Nandu)

Agronomie ◽  
1999 ◽  
Vol 19 (8) ◽  
pp. 677-687 ◽  
Author(s):  
Colleen Hudak ◽  
Jürgen Bender ◽  
Hans-Joachim Weigel ◽  
Joseph Miller
Keyword(s):  
Triticum Aestivum ◽  
Soil Water ◽  
Water Deficit ◽  
Elevated Co2 ◽  
Spring Wheat ◽  
Triticum Aestivum L ◽  
Interactive Effects ◽  
Soil Water Deficit

Soil Water Potential and Bromacil Uptake by Wheat

Weed Science ◽  
1975 ◽  
Vol 23 (2) ◽  
pp. 127-130 ◽  
Author(s):  
J. D. Schreiber ◽  
V. V. Volk ◽  
L. Boersma
Keyword(s):  
Triticum Aestivum ◽  
Water Potential ◽  
Soil Water ◽  
Transpiration Rate ◽  
Soil Water Potential ◽  
Dry Weight ◽  
Silt Loam ◽  
High Concentration ◽  
Water Potentials ◽  
Application Rates

The uptake of14C labeled bromacil [5-bromo-3-sec-butyl-6-methyluracil] by wheat plants (Triticum aestivumL. ‘Gaines’) grown in a Woodburn silt loam was studied at soil water potentials of −0.35 and −2.50 bars, and in solutions containing 2.0 and 4.5μg/ml bromacil. Transpiration rate, shoot and root dry weight, and bromacil content were measured as a function of time. Bromacil uptake into the root and foliar portions of the wheat plants increased with time. At the low bromacil concentration, 70%, and at the high concentration, 42%, more bromacil was taken up by the plant at the higher soil water potential. Uptake of bromacil increased concurrently with increased transpiration of water. The bromacil concentration in the transpiration stream was greater at the −0.35 bar than at the −2.50 bar soil water potential at both bromacil application rates. Transpiration rates of the plants treated with bromacil were nearly the same after a 40-hr exposure at both soil water potentials. The rate of bromacil uptake and accumulation may be influenced by the effect of soil water potential on the apoplastic movement of water and solutes in the roots.

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 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.

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