A new nonlinear method for calculating growing degree days

Zhou, Guanglin; Wang, Quanjiu

doi:10.1038/s41598-018-28392-z

Cited by 53 publications

(38 citation statements)

References 41 publications

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“…Degree-days could be an important component in latent infection simulation models and managerial decisions. Traditional methods for calculating DD assume linear developmental responses to temperature and cannot precisely account for the delay in growth or development at temperatures above the optimal temperature [ 40 ]. Physiologically, we have assumed that below a certain base temperature level (0 °C), Monilinia spp.…”

Section: Discussionmentioning

confidence: 99%

Impact of Postharvest Handling on Preharvest Latent Infections Caused by Monilinia spp. in Nectarines

Garcia-Benitez

Casals

Usall

et al. 2020

JoF

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Latent infections caused by Monilinia spp. in nectarines cause great economic losses since they are not detected and rejected at harvest and can appear at any time post-harvest, even at the consumer’s home. The effect of a pre-cooling chamber, water dump operation, and cold-storage chamber on the activation and/or development of preharvest latent infections caused by Monilinia spp. on nectarines were studied under different postharvest conditions: (a) cold storage for 0, 1, or 3 d at 4 °C at either 75% relative humidity (RH) or 100% RH before water dumping, (b) water dumping for 10 minutes at 15 °C, and (c) cold storage for 0, 3, or 10 d at 4 °C at either 75% RH or 100% RH after water dumping. These storage conditions were transformed to fungal physiological time. For visualization of the latent infections caused by Monilinia spp., the nectarines were placed in sterile paper bags and frozen at −20 °C for 48 h in order to damage the epidermis. To compare different handling scenarios, the incidence of latent infection was modelled for physiological time description by a modified Gompertz model. The activation and/or development of preharvest natural latent infections caused by Monilinia spp. at postharvest was mainly related to temperature and incubation time at postharvest. Storing nectarines with any postharvest handling less than 11 days at 4 °C avoids brown rot symptoms and reduced the activation and/or development of pre-harvest latent infections caused by Monilinia spp., while more cold days caused the exponential phase of latent infection activation and/or development. The Gompertz model employed could be used for predicting the activation and/or development of latent infection caused by Monilinia spp. at postharvest conditions and looks at the postharvest life. To our knowledge, this is the first time that the effects of post-harvest handling on latent infections in fruit have been studied.

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Section: Discussionmentioning

confidence: 99%

Impact of Postharvest Handling on Preharvest Latent Infections Caused by Monilinia spp. in Nectarines

Garcia-Benitez

Casals

Usall

et al. 2020

JoF

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“…Cumulative performance under thermally variable conditions can then be estimated by integrating the TPC function, using experienced temperatures (Casagrande, Logan, & Wallner, 1987; Denny, 2019; Georges, Beggs, Young, & Doody, 2005; Niehaus, Angilletta, Sears, Franklin, & Wilson, 2012; Rollinson et al, 2018; Taylor & Shields, 1990; Worner, 1992). We note here that a recent nonlinear GDD approach has been developed, which closely resembles a TPC approach (Zhou & Wang, 2018). In contrast to traditional GDD approaches, a higher degree of accuracy is expected from using nonlinear approaches because error resulting from Jensen's inequality is minimized.…”

Section: The Fallacy Of the Average: Nonlinearity Of Thermal Performamentioning

confidence: 99%

Thermal variability during ectotherm egg incubation: A synthesis and framework

Massey

Hutchings

2020

J Exp Zool Pt A

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Natural populations of ectothermic oviparous vertebrates typically experience thermal variability in their incubation environment. Yet an overwhelming number of laboratory studies incubate animals under constant thermal conditions that cannot capture natural thermal variability. Here, we systematically searched for studies that incubated eggs of ectothermic vertebrates, including both fishes and herpetofauna, under thermally variable regimes. We ultimately developed a compendium of 66 studies that used thermally variable conditions for egg incubation. In this review, we qualitatively discuss key findings from literature in the compendium, including the phenotypic effects resulting from different patterns of thermally variable incubation, as well as the ontogenetic persistence of these effects. We also describe a physiological framework for contextualizing some of these effects, based on thermal performance theory. Lastly, we highlight key gaps in our understanding of thermally variable incubation and offer suggestions for future studies.

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“…Being non-linearly dependent on temperature and precipitation, the bivariate Gaussian model shares some common features with Cutforth and Shaykewich (1990), Streck et al (2007), Yin et al (1995), Zhou and Wang (2018). However, there are several major differences to previous work: firstly, the model incorporates both temperature and precipitation, allowing for potential interactions between their impacts on growth; secondly, the non-linear function is Gaussian rather than a Beta function (e.g.…”

Section: Two-dimensional Gaussian Yield Response Functionmentioning

confidence: 99%

An empirical, Bayesian approach to modelling crop yield: Maize in USA

Shirley

Pope

Bartlett

et al. 2020

Environ. Res. Commun.

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We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18°C-19°C, corresponding to a daily maximum temperature of 24°C-25°C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of 2°C, relative to 1981-2014, results in the mean yield decreasing by 8%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate.

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A new nonlinear method for calculating growing degree days

Cited by 53 publications

References 41 publications

Impact of Postharvest Handling on Preharvest Latent Infections Caused by Monilinia spp. in Nectarines

Impact of Postharvest Handling on Preharvest Latent Infections Caused by Monilinia spp. in Nectarines

Thermal variability during ectotherm egg incubation: A synthesis and framework

An empirical, Bayesian approach to modelling crop yield: Maize in USA

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