The effect of soil physical conditions produced by various cultivation systems on the root development of winter wheat

1973 ◽  
Vol 80 (3) ◽  
pp. 435-443 ◽  
Author(s):  
J. R. Finney ◽  
B. A. G. Knight
Keyword(s):  
Winter Wheat ◽  
Root Development ◽  
Soil Physical Properties ◽  
Seminal Root ◽  
Cereal Crops ◽  
Extension Rate ◽  
Cultivation Systems ◽  
Physical Conditions ◽  
Cultivated Soil ◽  
Lower Bulk Density

SummaryThe results of a field experiment on winter wheat are presented in which the effects of the soil physical properties produced by different cultivation systems were studied in relation to root development.The extension rate of the seminal root axes was slower in undisturbed or shallow (5 cm) cultivated soil than in ploughed or deep (20 cm) cultivated soil leading to earlier lateral branching and the production of an effectively shallower seminal root system which persisted throughout the season. During the first four weeks after drilling the soil in the ploughed and the deeper cultivated plots was of lower bulk density and there were more pores of a diameter suitable for unimpeded root extension. It is suggested that seminal root development in uncultivated soils, which are less readily deformed than cultivated ones, is related to the probability of the extending seminal axes contacting pores of suitable dimensions.The implications of shallower rooting are discussed and related to field experience with cereal crops drilled directly into uncultivated soils where success has consistently been achieved on free-draining, open-textured soils.

scholarly journals Effects of Wheat streak mosaic virus on Root Development and Water-Use Efficiency of Hard Red Winter Wheat

Plant Disease ◽  
2010 ◽  
Vol 94 (6) ◽  
pp. 766-770 ◽  
Author(s):  
J. A. Price ◽  
F. Workneh ◽  
S. R. Evett ◽  
D. C. Jones ◽  
J. Arthur ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Water Use Efficiency ◽  
Mosaic Virus ◽  
Water Use ◽  
Root Development ◽  
Field Studies ◽  
Wheat Streak Mosaic Virus ◽  
Hard Red Winter Wheat ◽  
Use Efficiency ◽  
Red Winter Wheat

Greenhouse and field studies were conducted to determine the effects of Wheat streak mosaic virus (WSMV), a member of the family Potyviridae, on root development and water-use efficiency (WUE) of two hard red winter wheat (Triticum aestivum) cultivars, one susceptible and one resistant to WSMV. In the greenhouse studies, wheat cultivars were grown under three water regimes of 30, 60, and 80% soil saturation capacity. After inoculation with WSMV, plants were grown for approximately 4 weeks and then harvested. Root and shoot weights were measured to determine the effect of the disease on biomass. In all water treatments, root biomass and WUE of inoculated susceptible plants were significantly less (P < 0.05) than those of the noninoculated control plants. However, in the resistant cultivar, significance was only found in the 30 and 60% treatments for root weight and WUE, respectively. Field studies were also conducted under three water regimes based on reference evapotranspiration rates. Significant reductions in forage, grain yield, and crop WUE were observed in the inoculated susceptible plots compared with the noninoculated plots. Both studies demonstrated that wheat streak mosaic reduces WUE, which is a major concern in the Texas Panhandle because of limited availability of water.

RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network

2017 ◽  
Vol 8 (2) ◽  
pp. 842-847 ◽  
Author(s):  
M. Dyrmann ◽  
R. N. Jørgensen ◽  
H. S. Midtiby
Keyword(s):  
Neural Network ◽  
Winter Wheat ◽  
Convolutional Neural Network ◽  
Cereal Crops ◽  
Weed Detection ◽  
Cereal Fields

This paper presents a method for automating weed detection in colour images despite heavy leaf occlusion. A fully convolutional neural network is used to detect the weeds. The network is trained and validated on a total of more than 17,000 annotations of weeds in images from winter wheat fields, which have been collected using a camera mounted on an all-terrain vehicle. Hereby, the network is able to automatically detect single weed instances in cereal fields despite heavy leaf occlusion.

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