A simulation model of climate effects on plant productivity and variability in cauliflower (Brassica oleracea L. botrytis)

Olesen, Jørgen E.; Grevsen, Kai

doi:10.1016/s0304-4238(99)00068-0

Cited by 26 publications

(19 citation statements)

References 43 publications

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“…Model parameterisation was carried out in growth chambers with suboptimal light quality and intensity (83 W·m −2 ), which could have hampered growth. However, it has been shown that cauliflower development can be modelled from temperature data alone (Olesen & Grevsen 2000).…”

Section: Discussionmentioning

confidence: 99%

Prediction of flowering time in Brassica oleracea using a quantitative trait loci‐based phenology model

Uptmoor

Schrag

et al. 2011

Plant Biology

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Uniformly developing plants with a predictable time to harvest or flowering under unfavourable climate conditions are a major breeding goal in crop species. The main flowering regulators and their response to environmental signals have been identified in Arabidopsis thaliana and homologues of flowering genes have been mapped in many crop species. However, it remains unclear which genes determine within and across genotype flowering time variability in Brassica oleracea and how genetic flowering time regulation is influenced by environmental factors. The goal of this study is model-based prediction of flowering time in a B. oleracea DH-line population using genotype-specific and quantitative trait loci (QTL) model input parameters. A QTL-based phenology model accounting for genotypic differences in temperature responses during vernalisation and non-temperature-sensitive durations from floral transition to flowering was evaluated in two field trials. The model was parameterised using original genotype-specific model input parameters and QTL effects. The genotype-specific model parameterisation showed accurate predictability of flowering time if floral induction was promoted by low temperature (R(2) = 0.81); unfavourably high temperatures reduced predictability (R(2) = 0.65). Replacing original model input parameters by QTL effects reduced the capability of the model to describe across-genotype variability (R(2) = 0.59 and 0.50). Flowering time was highly correlated with a model parameter accounting for vernalisation effects. Within-genotype variability was significantly correlated with the same parameter if temperature during the inductive phase was high. We conclude that flowering time variability across genotypes was largely due to differences in vernalisation response, although it has been shown elsewhere that the candidate FLOWERING LOCUS C (FLC) did not co-segregate with flowering time in the same population. FLC independent vernalisation pathways have been described for several species, but not yet for B. oleracea.

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Section: Discussionmentioning

confidence: 99%

Prediction of flowering time in Brassica oleracea using a quantitative trait loci‐based phenology model

Uptmoor

Schrag

et al. 2011

Plant Biology

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“…65 used in Kage et al (2000). More recently, Olesen & Grevsen (2000) published a LUE approach for calculating dry matter production also assuming a linear decrease of LUE with increasing levels of radiation intensity. Their value of LUE 0 is much lower (5 .…”

Section: H K a G E C A L T A N D H S T ü T Z E Lmentioning

confidence: 99%

Aspects of nitrogen use efficiency of cauliflower II. Productivity and nitrogen partitioning as influenced by N supply

2003

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SU MMARYBased on studies concerning dry matter (DM) partitioning, DM production, root growth, nitrogen (N) contents of cauliflower organs and soil nitrate availability (first part of the paper Kage et al. 2003 b), an integrated simulation model for the cauliflower/soil system is constructed, parameterized and evaluated.Dry matter production of cauliflower is described and predicted using a simple light use efficiency (LUE) based approach assuming a linear decrease of light use efficiency with increasing differences between actual, NCA Prot , and ' optimal ', NCAopt Prot area based leaf protein concentrations. For 2 experimental years the decline of LUE with decreasing nitrogen concentration was at 0 . 82 and 0 . 75 (g DMrm 2 /(MJrg N)). Using the parameters obtained from the first experimental year shoot DM production data of cauliflower from five independent experiments with varied N supply containing intermediate harvests could be predicted with a residual mean square error (RMSE ) of 72 g/m 2 for intermediate harvest DM values ranging from about 50 to 900 g/m 2 . Nitrogen uptake and partitioning of cauliflower was simulated using functions describing an organ size dependent decline of N content. Leaf nitrate was considered explicitly as a radiation intensity dependent pool, mobilized first under N deficiency. The curd was assumed to have a sink priority for nitrogen. The model predicted shoot N uptake including data of intermediate harvest with a RMSE of 2 . 4 g/m 2 for intermediate harvest N values ranging from about 3 to 30 g/m 2 . Nitrogen uptake of cauliflower at final harvest was correlated to final leaf number.A scenario simulation was carried out to quantify seasonal variation in N uptake of cauliflower cultivars under unrestricted N availability. Due to variations in the length of the vernalization phase, simulated shoot N uptake ranged from about 260 kg N/ha for spring planted crops to about 290 kg N/ha for summer planted crops of the cultivar 'Fremont '. The cultivar ' Linday ', which shows a more severe delay of vernalization under high temperatures, shows on average a larger shoot N uptake for summer planted crops of about 320 kg N/ha and a much larger variation of shoot N uptake.

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“…We also expect the differences between broccoli and cauliflower crops in response to stress, which should be helpful to estimate their sensitivity to low temperature. Results published by Długosz-Grochowska et al (2012) and Grabowska et al (2014) suggest that broccoli is relatively tolerant to chilling temperatures, while the response of cauliflower plants to environmental conditions is stronger in comparison to broccoli, and the species seems to be more affected by extreme thermal factors (Diputado, 1989;Olesen and Grevsen, 2000). However, direct comparative studies of broccoli and cauliflower responses to low temperature treatment are scarce and very difficult to achieve.…”

Section: Introductionmentioning

confidence: 99%

Alterations in Chlorophyll a Fluorescence and Pigments Concentration in the Leaves of Cauliflower and Broccoli Transplants Subjected to Chilling

Wojciechowska

Kalisz

Sękara

et al. 2016

Not Bot Horti Agrobo

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Chlorophyll a fluorescence parameters and photosynthetic pigments content in leaves of broccoli (Brassica oleracea L. var. italica) cv. 'Monaco' F 1 and cauliflower (Brassica oleracea L. var. botrytis) cv. 'Bruce' F 1 transplants were analyzed to investigate the effects of chilling temperatures (6 and 10 °C) and the exposure length (1 or 2 weeks) on the photosynthetic apparatus condition in 3-year experiment. Data were compared to control plants exposed to 14 °C for 1 and 2 weeks. The lowest values of maximum quantum efficiency of photosystem II (F v /F m ) and the performance index (PI) were observed in cauliflower chilled at 6 °C for 2 weeks. This was not accompanied by any substantial reduction in chlorophylls concentration, however, significant correlations between F v /F m or PI and chlorophylls content in cauliflower leaves were found. There was no negative effects of chilling on photosynthetic activity and chlorophyll content in broccoli leaves, the amount of carotenoids significantly increased in the broccoli chilled for 2 weeks in 6 °C. The chlorophyll a fluorescence indices and accelerated carotenoids synthesis showed that broccoli had less vulnerable photosynthetic apparatus to stress-induced temperatures and more efficient protection mechanisms in comparison to cauliflower transplants.

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A simulation model of climate effects on plant productivity and variability in cauliflower (Brassica oleracea L. botrytis)

Cited by 26 publications

References 43 publications

Prediction of flowering time in Brassica oleracea using a quantitative trait loci‐based phenology model

Prediction of flowering time in Brassica oleracea using a quantitative trait loci‐based phenology model

Aspects of nitrogen use efficiency of cauliflower II. Productivity and nitrogen partitioning as influenced by N supply

Alterations in Chlorophyll a Fluorescence and Pigments Concentration in the Leaves of Cauliflower and Broccoli Transplants Subjected to Chilling

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