Modelling the course of frost tolerance in winter wheat

Bergjord, Anne Kari; Bonesmo, Helge; Skjelvåg, Arne Oddvar

doi:10.1016/j.eja.2007.10.002

Cited by 55 publications

(73 citation statements)

References 41 publications

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“…Snow cover is usually considered as beneficial, or even necessary, for winter survival, as it protects plants from being exposed to lethally low temperatures (Belanger et al 2002). Results from Bergjord et al (2008) do, however, indicate that a long-lasting snow cover might also have an exhausting effect, causing a reduction of the plants' frost tolerance, especially if the soil is unfrozen.…”

Section: Resultsmentioning

confidence: 99%

“…A cross validation of the model indicated that its parameters were satisfactorily insensitive to variation in winter weather (Bergjord et al 2008). However, the model has not yet been tested by the use of independent data derived from common field management practices.…”

Section: Introductionmentioning

confidence: 99%

“…In FROSTOL this is actuated by terminating the ability of the plants to further hardening, and lowering the temperature threshold for dehardening, after vernalization saturation. A more complete description of the model is given by Bergjord et al (2008).…”

Section: Introductionmentioning

confidence: 99%

“…Semenov and Porter 1995, Rickman et al 1996, Jamieson et al 1998, the recently published FROSTOL (Bergjord et al 2008) is one of very few models that specifically aims at predicting winter survival of the crop. FROSTOL simulates course of frost tolerance, expressed as LT 50 (the temperature at which 50% of the plant populations are killed), on a daily (t) time step from sowing onwards as:…”

Section: Introductionmentioning

confidence: 99%

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Model prediction of frost tolerance as related to winter survival of wheat in Finnish field trials

Bergjord¹,

Bonesmo²,

Skjelvåg³

2008

AFSci

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The model FROSTOL simulates course of frost tolerance in winter wheat on a daily basis from sowing on as affected by soil temperature (2 cm), snow cover, phenological development, and a genotypic maximum level of frost tolerance (LT 50 ). A series of cultivar trials in Finland was used to evaluate the model's ability to estimate plant survival in natural field environments during winters with differing weather conditions. Recorded survival was compared with number of intersections between the curves of simulated LT50 and the soil temperature curve for each field. A cumulative stress level (CSL) was calculated based both on number of intersections and FROSTOL simulated stress levels. The correlation between CSL and field recordings was quite low. While the field trials characterize a general ability to stand various types of winter stress, FROSTOL estimates damage caused by the soil temperature regime only. However, FROSTOL simulations seemed to correspond reasonably well to field observations when low temperature was the eventual cause of damage.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model prediction of frost tolerance as related to winter survival of wheat in Finnish field trials

Bergjord¹,

Bonesmo²,

Skjelvåg³

2008

AFSci

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“…This result might be explained by the lack of considered overwintering processes in the model. The use of frost tolerance models (e.g., [61]) might improve the simulations. Surprisingly, however, for Uppsala, which is located farther north than Lund, the deviations could not be related to winter temperatures, although they are known to influence yield even more than in southern Sweden [57].…”

Section: Discussionmentioning

confidence: 99%

Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

et al. 2014

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Abstract:The impact of adverse weather conditions (AWCs) on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under-or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI) related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously OPEN ACCESSAtmosphere 2014, 5 1021 associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical ("event scenario") approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.

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The role of genotypic and climatic variation at the range edge: A case study in winegrapes

Jones,

Bogdanoff,

Wolkovich

2023

American J of Botany

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PremiseChanges in habitat suitability due to climate change are causing range shifts, with new habitat potentially available at cold range edges. We must predict these range shifts, but forecasters have limited knowledge of how genetic differences in plant physiological tolerances influence range shifts. Here, we focus on a major determinant of species ranges—physiological tolerance to extreme cold—to ask how warming over recent decades and genetic variation shape expansion across complex landscapes.MethodsWe examined how genotypes vary in maximum cold tolerance from 9 years of cold hardiness data across 18 genotypes from 13 sites, using winegrapes (Vitis vinifera subsp. vinifera) as a case study. Combining a Bayesian hierarchical dose‐response model with gridded climate data, we then project changes in climatic suitability near winegrapes’ current cold range‐edge between 1949 and 2016.ResultsPlants increased maximum cold hardiness non‐linearly with decreasing air temperature (maximum cold hardiness: –23.6°C), but with substantial (by 2°C) variation across genotypes. Our results suggest, since the 1980s, decreasing freeze injury risk has made conditions more favorable for all genotypes at the cold range‐edge, but conditions remained more favorable for more cold hardy genotypes and in warmer areas. There was substantial spatial variation in habitat suitability, with the majority of suitably warm habitat located in a narrow north‐south oriented strip.ConclusionsWe highlight the importance of genotypic differences in physiological tolerances when assessing range shift potential with climate change. Habitat improvements were unevenly distributed over the spatially complex landscape, though, emphasizing the importance of dispersal in range expansion.

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Modelling the course of frost tolerance in winter wheat

Cited by 55 publications

References 41 publications

Model prediction of frost tolerance as related to winter survival of wheat in Finnish field trials

Model prediction of frost tolerance as related to winter survival of wheat in Finnish field trials

Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

The role of genotypic and climatic variation at the range edge: A case study in winegrapes

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