Effects of soil bulk density and strength on seedling growth of annual ryegrass and tall fescue in controlled environment

2010 ◽  
pp. no-no ◽  
Author(s):  
P. W. Bartholomew ◽  
R. D. Williams
1988 ◽  
Vol 18 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Ian G.W. Corns

Soils developed on four parent materials (glaciolacustrine clay, clay loam till, coarse fluvial, and loamy eolian) in west-central Alberta were examined to determine residual effects of logging and use of site-preparation equipment upon soil bulk density. These studies were conducted on sites that were logged during the previous 24 years. Compaction was evident on all soils except those of the Summit association, which were dominantly Brunisolic Gray Luvisols developed on cobbly fluvial deposits of Tertiary age. Compaction was greatest on soils of the Marlboro association, which were dominantly Brunisolic Gray Luvisols developed on clay loam till. Soil bulk density values on the clear-cuts had recovered to those of the controls at comparable depth at ages ranging from 0 (Summit) to 17–21 years (Marlboro). Lodgepole pine and white spruce seedlings were grown on the four soils compacted in the laboratory to three bulk densities approximating the following field conditions: (1) those observed or expected immediately following logging and site preparation; (2) those observed 5–10 years after logging and site preparation; and (3) undisturbed control. In most cases, significant reduction in nine expressions of seedling growth (maximum root depth, maximum root depth in soil core, total weight, shoot weight, root weight, stem diameter, shoot height, seedling survival, and shoot weight: root weight ratio) was observed with increased bulk density.


1991 ◽  
Vol 42 (7) ◽  
pp. 1261 ◽  
Author(s):  
GW Charles ◽  
GJ Blair ◽  
AC Andrews

The effects of temperature (constant 3, 6, 9, 12 and 24�C, and an alternating 6/12�C temperature regime), sowing depth (0, 15, 30 and 45 mm), and soil bulk density (1.1 and 1.3 g cm-3) were examined on the emergence of tall fescue (Festuca arundinacea, cv. Demeter) and white clover (Trifolium repens, cv. Haifa) in a factorial experiment, in controlled temperature cabinets. Mitscherlich curves were fitted to the emergence data and treatment effects on the maximum emergence percentage (A), rate of emergence (K) and time to first emergence (To), were statistically analysed. Temperature was the major factor affecting the fescue A value. The A value was low at 3 and 6�C, but increased as temperature increased to 12�C. It was depressed by the 45 mm sowing depth and by high bulk density at 30 and 45 mm. For white clover, sowing depth had a strong effect on A. Over all temperatures, A was high for surface sowing and low for deeper sowing (30 and 45 mm). For shallow sowing (15 mm), A was intermediate and increased with rising temperature. High bulk density depressed A at 15 mm. For both species, To increased as sowing depth increased, and decreased as temperature increased. The effect of sowing depth was more apparent at low temperatures. The K value for fescue increased gradually as temperature increased, but sowing depth had no effect. For clover, K increased markedly with rises in temperature for surface sowing, with smaller increases for 15, 30 and 45 mm depths. The 6/12�C regime responses for A and To were similar to the constant 12�C, while the K response was similar to the constant 9�C; these trends were similar for fescue and clover. It was concluded that establishment failures from direct drilling tall fescue on the Northern Tablelands can be related to low soil temperatures in winter (below 9�C), and for white clover, to excessive sowing depth (greater than 15 mm), particularly on high bulk density soils.


2010 ◽  
Vol 30 (2) ◽  
pp. 127-132
Author(s):  
Jinbo ZAN ◽  
Shengli YANG ◽  
Xiaomin FANG ◽  
Xiangyu LI ◽  
Yibo YANG ◽  
...  

Crop Science ◽  
1965 ◽  
Vol 5 (5) ◽  
pp. 395-397 ◽  
Author(s):  
R. C. Buckner ◽  
H. D. Hill ◽  
A. W. Hovin ◽  
P. B. Burrus
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.


2021 ◽  
pp. 126389
Author(s):  
Marco Bittelli ◽  
Fausto Tomei ◽  
Anbazhagan P. ◽  
Raghuveer Rao Pallapati ◽  
Puskar Mahajan ◽  
...  

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