Frost damage on grain number in wheat at different spike developmental stages and its modelling

Martino, Diana L.; Abbate, Pablo Eduardo

doi:10.1016/j.eja.2018.10.010

Cited by 32 publications

(15 citation statements)

References 48 publications

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“…The severity of reproductive frost stress was estimated by randomly selecting 10 spikes per plot and frost-induced sterility (FIS) was assessed on the outside florets of each spikelet excluding the terminal, basal, and supernumerary spikelets by the method proposed by Martino and Abbate (2019) . FIS is the number of sterile florets per spike expressed as a percentage of the total number of possible grains that could have formed in the outside florets.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of G × E × M Interactions to Increase Harvest Index and Yield of Early Sown Wheat

Porker

Straight²

2020

Front. Plant Sci.

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Harvest index (HI) is the ratio of grain to total shoot dry matter and is as a measure of reproductive efficiency. HI is determined by interactions between genotypes (G), environment (E), and crop management (M). Historic genetic yield gains due to breeding in wheat have largely been achieved by increasing HI. Environmental factors are important for HI and include seasonal pattern of water supply and extreme temperatures during crop reproductive development. Wheat production in Australia has been dominated by fast-developing spring cultivars that when sown in late-autumn will flower at an optimal time in early spring. Water limited potential yield can be increased by sowing slower developing wheats with a vernalization requirement (winter wheat) earlier than currently practiced such that their development is matched to environment and they flower at the optimal time. This means a longer vegetative phase which increases rooting depth, proportion of water-use transpired, and transpiration efficiency by allowing more growth during winter when vapour pressure deficit is low. All these factors can increase biomass accumulation, grain number and thus grain yield potential. However higher yields are not always realized due to a lower HI of early sown slow developing wheats compared to fast developing wheats sown later. Here, we evaluate genotype × management practices to improve HI and yield in early sown slow developing wheat crops using 6 field experiments conducted across south eastern Australia from 2014 to 2018 in yield environments ranging from ~1 to ~4.7 t/ha. Practices included low plant densities (30–50 plants/m²), mechanical defoliation, and deferred application of nitrogen fertilizer. Lower plant densities had similar yield and HI to higher plant densities. Defoliation tended to increase HI but reduce yield except when there was severe stem frost damage. Deferring nitrogen had a variable effect depending on starting soil N and in crop rainfall. All management strategies evaluated gave variable HI and yield responses with small effect sizes, and we conclude that none of them can reliably increase HI in early sown wheat. We propose that genetic improvement is the most promising avenue for increasing HI and yield in early sown wheat, and postulate that this could be achieved more rapidly through early generation screening for HI in slow developing genotypes than by crop management.

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Section: Methodsmentioning

confidence: 99%

Evaluation of G × E × M Interactions to Increase Harvest Index and Yield of Early Sown Wheat

Porker

Straight²

2020

Front. Plant Sci.

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“…To account for the damage caused by successive frost, Martino et al [53] recently proposed a model using heat transfer in the wheat head, this plot heater method could be used to test this model. Being able to confirm and identify damage from a single event would allow a better understanding of temperature-damage-yield relationship in that the reduction in grain number per event could be determined.…”

Section: Advantages Of a Plot Heater That Is Automated And Uses Diesementioning

confidence: 99%

An Automated Plot Heater for Field Frost Research in Cereals

et al. 2019

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Frost research to improve genetics or management solutions requires a robust experimental design that minimizes the effects of all other variables that can cause plant damage except for the treatment (frost). Controlled environment facilities cannot faithfully replicate field radiative frost processes, but field studies are limited by the reliability of field methods to exclude frost. An effective field frost exclusion method needs to prevent frost damage while not impacting growing microclimate or yield, and be automatic, modular, mobile, and affordable. In this study, we designed an effective prototype treatment with these features for field frost research that uses diesel heating. The effectiveness of the plot heater to provide an unfrosted control is evaluated by monitoring canopy temperature (CT) and air temperature during frost events, showing that these remain above zero in the heated plots when ambient temperature drops below zero. We find that the plot heater method can prevent potential frost damage at the plot-scale, while not appearing to have an impact on either plant development or yield components. This offers a potential new tool for frost field crop researchers to incorporate a plot-scale control into their experimental design.

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“…In this type of case, there is a small amount of data to build a predictive model with high accuracy. Due to heat shocks, there were physiological impacts on wheat production that has to be modeled to reduce its loss [8], [9]. The prediction of frost events needs some complex decision analysis that uses probabilities and economics.…”

Section: Introductionmentioning

confidence: 99%

Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

et al. 2020

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The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are accurate, then the damage caused by frost can be reduced. In this paper, an ensemble learning approach is used to detect frost events with Convolutional Neural Network (CNN). We have used this to get more efficient and accurate results. Frost events need to be predicted earlier so that the farmer can take on-time precautionary measures. So, for measurement and analysis of Google Play, we have scrapped a dataset of the Agricultural category from different genres and collected the top 550 application of each category of Agricultural applications with 70 attributes for each category. The prediction of frost events prior few days of an actual frost event with an accuracy of 98.86%.

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Frost damage on grain number in wheat at different spike developmental stages and its modelling

Cited by 32 publications

References 48 publications

Evaluation of G × E × M Interactions to Increase Harvest Index and Yield of Early Sown Wheat

Evaluation of G × E × M Interactions to Increase Harvest Index and Yield of Early Sown Wheat

An Automated Plot Heater for Field Frost Research in Cereals

Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

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