OVIPOSITION AND LARVAL ESTABLISHMENT OF SITODIPLOSIS MOSELLANA (DIPTERA: CECIDOMYIIDAE) ON WHEAT (GRAMINEAE) AT DIFFERENT GROWTH STAGES

1999 ◽  
Vol 131 (4) ◽  
pp. 475-481 ◽  
Author(s):  
H. Ding ◽  
R.J. Lamb
Keyword(s):  
Larval Growth ◽  
Oviposition Preference ◽  
Choice Test ◽  
Growth Stages ◽  
Sitodiplosis Mosellana ◽  
Hatching Time ◽  
Wheat Midge ◽  
Survival And Development ◽  
Different Growth Stages ◽  
Days After Anthesis

AbstractThe wheat midge, Sitodiplosis mosellana (Géhin), infests wheat, Triticum aestivum L., heads only up to anthesis when pollination occurs. The termination of infestation might be due to a deterrence of oviposition or to a suppression of larval growth on developing seeds. These hypotheses were tested in the laboratory by measuring oviposition preference, larval development, and larval preference for plants at different growth stages. Females showed no preference for ovipositing on heads at any stage from the onset of heading up to and including anthesis, and continued to lay eggs at a reduced rate 10 days after anthesis. Survival of newly hatched larvae was reduced on seeds 3–1 days after anthesis and survival and development was greatly reduced on seeds 5 or 6 days after anthesis. Larvae moved away from older seeds and fed on younger seeds in a choice test. Given a hatching time of 5–6 days, a failure to infest wheat after anthesis is not due to oviposition deterrence at anthesis, but to suppression of larval growth and development which begins soon after anthesis.

Factors influencing oviposition by Sitodiplosis mosellana (Diptera: Cecidomyiidae) on wheat spikes (Gramineae)

2001 ◽  
Vol 133 (4) ◽  
pp. 533-548 ◽  
Author(s):  
M.A.H. Smith ◽  
R.J. Lamb
Keyword(s):  
Flag Leaf ◽  
Growth Stages ◽  
Secondary Importance ◽  
Sitodiplosis Mosellana ◽  
Crop Resistance ◽  
Wheat Midge ◽  
Single Spike ◽  
Egg Density ◽  
As Effects ◽  
Different Growth Stages

AbstractFactors that might contribute to variability in the densities of wheat midge eggs, Sitodiplosis mosellana (Géhin), on common and durum wheats, Triticum aestivum L. and Triticum durum Desf., were investigated to improve the quantification of oviposition preferences in relation to crop resistance. Egg densities on wheat spikes were highly variable, with a similar contagious distribution in the laboratory and field, although variance was highest in the laboratory. Females laid eggs in small groups, usually of one to six eggs; most infested spikes had more than one egg group. Females showed no preference for ovipositing on different parts of a spike, although spikelets on one side and at the base often received fewer eggs because these spikelets were covered by the flag leaf and inaccessible for longer than others. Oviposition rates varied from night to night, probably related to the weather. Females showed no preference for spikes at different growth stages, from the time spikes began to emerge until at least flowering. Spike size did not affect egg density, and spike height was a factor only for spikes deep within or protruding above the canopy. Sources of environmental variation such as effects of weather on oviposition rates in the field or spatial phenomena in cages were measurable but of secondary importance. In the field, comparisons among spikes which emerged on the same day could reduce variation in egg density. In the laboratory, variation in egg density could be reduced by using arrays of excised spikes arranged at the same height, leaving the central portion of the array empty. The primary cause of high variability in egg density among spikes was variation in egg-group size and the presence of multiple egg groups on a single spike, factors which cannot be experimentally controlled because they are the result of oviposition behaviour rather than environmental heterogeneity.

Susceptibility of hulled and hulless barley (Gramineae) to Sitodiplosis mosellana (Diptera: Cecidomyiidae)

2002 ◽  
Vol 134 (2) ◽  
pp. 193-203 ◽  
Author(s):  
I.L. Wise ◽  
R.J. Lamb ◽  
M.A.H. Smith
Keyword(s):  
Plant Traits ◽  
Flag Leaf ◽  
Leaf Sheath ◽  
Growth Stages ◽  
Hulless Barley ◽  
Sitodiplosis Mosellana ◽  
Barley Cultivars ◽  
Wheat Midge ◽  
Crop Growth Stages ◽  
Newly Hatched Larvae

AbstractModern hulless wheats, Triticum aestivum L., are more susceptible to the wheat midge, Sitodiplosis mosellana (Géhin), than the hulled, wild, ancestral species. Hulless cultivars of barley, Hordeum vulgare L., are becoming more widely grown in western Canada than in the past. Hulled and hulless cultivars of two-rowed and six-rowed barleys were tested for their susceptibility to wheat midge, to determine if this midge might become a serious pest of barley and to assess which plant traits might affect host suitability. In the field, larval populations on 10 barley cultivars were much lower than on wheat. In the laboratory, when the flag leaf sheath was peeled back to expose preflowering spikes, female midges readily oviposited on spikes of barley, although less so on younger spikes. Few larvae were able to develop on barley when eggs were laid after spikes had flowered. All barleys completed flowering, or nearly so, before spikes emerged from the flag leaf sheath, with two-rowed cultivars flowering earlier than six-rowed barleys. No differences in larval densities were found between hulless and hulled barleys, and therefore, factors other than the hulled trait must account for reduced susceptibility of barley. Because barley flowers within the flag leaf sheath, its period of susceptibility to infestation is much shorter than for wheat, as evidenced by reduced infestation of earlier-flowering two-rowed cultivars compared with later-flowering six-rowed cultivars. Also, the tight closure of the leaf-like glumes that form the florets of barley probably makes access to young seeds more difficult for newly hatched larvae than is the case for wheat. At comparable crop growth stages, larval densities on all the barleys were < 10% of those on spring wheat. The introduction of hulless barley for production in Canada is unlikely to increase wheat midge damage on barley to an economic level.

Cytokinins from terminal buds of Euphoria longana during different growth stages

1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu
Keyword(s):  
Growth Stages ◽  
Terminal Buds ◽  
Different Growth Stages

Effects of Meteorological Factors at Different Growth Stages on Yield Traits of Maize (Zea maysL.) in Heilonggang Basin

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Growth Stages ◽  
Yield Traits ◽  
Different Growth Stages

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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