Effects of temperature on spikelet number of wheat

Halse, N J; Weir, R N

doi:10.1071/ar9740687

Cited by 42 publications

(20 citation statements)

References 10 publications

(19 reference statements)

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“…FLUID & HALLORAN (1984) proposed that wheat genotypes have a basic development rate, irrespective of their vernalization and photoperiod requirements, and that this basic rate may be used in breeding for a wider environmental adaptation. On the basis of the study reported here, as well as on other studies (HALSE &WEIR, 1974;BAGGA & RAWSON, 1977) it may be concluded that the basic development rate is highly affected by temperature and that breeding for wider adaptation also involves reduced temperature sensitivity of the basic developmental stages, most notably of GS2. As a result of reduced thermo-sensitivity of GS2 there are wheat genotypes better able to sustain grain number per spike at a high temperature, as exemplified by Kalyansona (BAGGA & RAWSON, 1977) and some of the cultivars tested in this experiment.…”

Section: Discussionmentioning

confidence: 83%

“…Several investigators found that when the vernalization and photoperiod requirements are fully satisfied, wheat varieties differ in the extent of heat effect on spikelet number per spike (HALSE & WEIR, 1974;HALLORAN, 1977). A most remarkable example is given in the work of BAGGA & RAW~ON (1977), where Kalyansona, compared with Condor showed excellent stability in grain number per spike at increasing temperatures.…”

Section: Introductionmentioning

confidence: 92%

“…Reduction of the duration of GS2 under the influence of high temperature resulted in a reduction in the numbers of spikes per plant (FISCHER & MAURER, 1976) and of spikelets and/or grains per spike (HALSE&WEIR, 1970;WARRINGTON etal., 1977;JOHNSON&KANEMASU, 1983). High temperature reduces spikelet number through its effect on both the duration and rate of spikelet initiation (HALSE & WEIR, 1974). Temperature during GSl does not affect spike size but higher temperature at this phase decreases tillering and the number of spikes per plant (WARRINGTON et al, 1977).…”

Section: Introductionmentioning

confidence: 99%

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Differential reaction of wheat cultivars to hot environments

Shpiler

Blum

1986

Euphytica

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Ten to 20 spring wheat (Triticum uestivum L.) cultivars of Israeli origin were grown in three winter (normal) and two summer (abnormal) growing seasons. During the period of emergence to anthesis mean daily temperature was on the average 12°C higher and photoperiod was about 3 h longer in the summer than in the winter. Data was collected on the durations of the periods from emergence to double-ridge (GSI), double ridge to anthesis (GS2) and anthesis to grain maturation (GS3), as well as on yield and yield components.The duration of all developmental stages was reduced by high temperature. While the duration of GS2 was the most thermo-sensitive, it may also have been reduced by the longer summer photoperiod. The effect of photoperiod on GS2 could not be isolated, but the results were interpreted to show that the effect of photoperiod on the duration of GS2 was relatively small.The most heat-affected yield component was number of grains per spikelet and the least affected component was the number of spikes per plant. High temperature reduced grain weight via reduced grain growth duration and not grain growth rate. A general linear regression model of yield on its components revealed that while variation for number of spikes per plant had the greatest effect on yield variation among cultivars in the winter, variation for number of grains per spikelet and spikelets per spike were by far the most important in the summer. Grain weight was the least important component, in this respect, in all seasons. Varieties which sustained the highest yield in hot environments were able to maintain the longest duration of GS2 and the highest number of grain per spike.

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Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Differential reaction of wheat cultivars to hot environments

Shpiler

Blum

1986

Euphytica

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“…evaluate the effect of environmental factors on the initiaThe spike development is divided into three phases; tion of the spikelet primordium (Halse and Weir, 1974). the leaf primordium, spikelet primordium, and floret However, such studies are rather scarce.…”

Section: Initiation and Development Of Spikelets And Florets In Wheatmentioning

confidence: 99%

Initiation and Development of Spikelets and Florets in Wheat as Influenced by Shading and Nitrogen Supply at the Spikelet Phase

Toyota¹,

Tsutsui²,

Kusutani³

et al. 2001

Plant Production Science

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“…Biologically, the ability to phenotype spike traits is of great importance, with uses in several different research areas. For example, spikelet development and thus yield can be affected by abiotic stresses such as high temperature and drought [12,26] or changes in sowing date [1]. Other traits such as the presence of awns, a bristle or hair like structure extending from the end of each floret (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Multi-task Plant Phenotyping

Pound

Atkinson

Wells

et al. 2017

Preprint

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Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation. We then present a deep learning approach capable of accurately localising wheat spikes and spikelets, despite the varied nature of this dataset. As well as locating features, our network offers near perfect counting accuracy for spikes (95.91%) and spikelets (99.66%). We also extend the network to perform simultaneous classification of images, demonstrating the power of multi-task deep architectures for plant phenotyping. We hope that our dataset will be useful to researchers in continued improvement of plant and crop phenotyping. With this in mind, alongside the dataset we will make all code and trained models available online.

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Effects of temperature on spikelet number of wheat

Cited by 42 publications

References 10 publications

Differential reaction of wheat cultivars to hot environments

Differential reaction of wheat cultivars to hot environments

Initiation and Development of Spikelets and Florets in Wheat as Influenced by Shading and Nitrogen Supply at the Spikelet Phase

Deep Learning for Multi-task Plant Phenotyping

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