Management of raspberry and strawberry grey mould in open field and under protection. A review

Xu, Xiangming; Wedgwood, Erika; Berrie, A. M.; Allen, J. J.; O'Neill, T. M.

doi:10.1007/s13593-011-0032-2

Cited by 25 publications

(28 citation statements)

References 27 publications

(38 reference statements)

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“…The transplant quality has a marked impact on the yield and quality of the strawberry fruits after transplanting [1]. The strawberry propagation efficiency and the transplant quality are both deeply influenced by various diseases and variable environmental condition in the field [2,3]. In past decade, runner plant propagation in plant factory with artificial lighting has attracted attention among researchers for its advantages of virus-free year-round production [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Effect of Light Intensity on Rooting and Growth of Hydroponic Strawberry Runner Plants in a LED Plant Factory

Zheng

et al. 2019

Agronomy

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To rapidly produce strawberry (Fragaria × ananassa Duch. cv. Benihoppe) transplants from cuttings, suitable light intensities for unrooted runner plants at the rooting stage and rooted runner plants at the seedling stage were determined in a plant factory under LED lighting. At the rooting stage, unrooted runner plants at the 3-leaf stage were hydroponically rooted for 6 days under light intensity of 30, 90, 150, and 210 µmol m −2 s −1 , respectively. At the seedling stage, rooted runner plants were hydroponically grown for 18 days under light intensity of 90, 180, 270, and 360 µmol m −2 s −1 , respectively. The tube LED lights consisting of white and red LED chips were used as sole light source, and photoperiod was controlled as 16 h d −1 . The results showed that the maximum root number (7.7) and longest root length (14.8 cm) of the runner plants were found under 90 µmol m −2 s −1 at the rooting stage. Photosynthetic activity in runner plant leaves under 90 µmol m −2 s −1 were higher than that under 30, 150, and 210 µmol m −2 s −1 . Higher light intensity at the range of 90-270 µmol m −2 s −1 increased the stomatal conductance of newly formed leaves of rooted runner plants, thus improving the net photosynthetic rate and growth of rooted runner plants at the seedling stage. The crown diameter, shoot and root dry weights, and root to shoot ratio of rooted runner plants increased by 9.7%, 38.8%, 106.1%, and 48.7%, respectively, when the light intensity increased from 90 to 270 µmol m −2 s −1 . However, there was no further improvement of runner plant growth under 360 µmol m −2 s −1 . Furthermore, no significant difference of increased dry biomass per mole of photons delivered was found between 180 and 270 µmol m −2 s −1 . In consideration of transplant quality and economic balance, light intensity of 90 µmol m −2 s −1 at the rooting stage and 270 µmol m −2 s −1 at the seedling stage were suggested for rapidly producing hydroponic strawberry transplants based on unrooted runner plants in the LED plant factory.

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Section: Introductionmentioning

confidence: 99%

Effect of Light Intensity on Rooting and Growth of Hydroponic Strawberry Runner Plants in a LED Plant Factory

Zheng

et al. 2019

Agronomy

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“…Raspberries are grown increasingly under protection, usually in plastic or ‘Spanish’ tunnels, to extend the production season (Xu et al , ). Glen Ample is a popular Scottish spine‐free raspberry cultivar that is grown widely in Europe and appreciated for the big size and high quality of berries.…”

Section: Introductionmentioning

confidence: 99%

Occurrence and genetic diversity ofRaspberry leaf blotch virus(RLBV) infecting cultivated and wildRubusspecies in Finland

et al. 2015

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Raspberry leaf blotch virus (RLBV) is a recently described (−)ssRNA virus (genus Emaravirus) associated with the long-known, severe raspberry leaf blotch disorder (RLBD). The virus is presumably transmitted by the raspberry leaf and bud mite (Phyllocoptes gracilis; Eriophyidae). Cultivated and wild raspberries (Rubus idaeus) displaying RLBD or yellowing symptoms were sampled in 14 districts in Finland and tested for RNA3 and RNA5 of RLBV by reverse transcription PCR (RT-PCR) and dot blot hybridization. A total of 59 samples were tested for RLBV, including 36 plants of cultivated raspberry, 20 wild raspberry plants, one ornamental purple flowering raspberry plant (Rubus odoratus), and two samples of P. gracilis (20 mites pooled per sample) collected from RLBD-affected Glen Ample. Fifty-three (93%) of the 57 plants tested were RLBV-positive, including seven raspberry cultivars (Balder, Glen Ample, Jenkka, Malling Minerva, Maurin Makea, Muskoka, Ottawa) and purple flowering raspberry, which is a new host for RLBV. RLBV was also detected by RT-PCR in mites (P. gracilis) collected from buds of RLBV-positive raspberry plants. The partial nucleotide (nt) sequence of the RLBV NP gene (nt 835-1284 of RNA3) was determined for 21 isolates obtained from 10 districts in Finland. Identical isolates were detected in distant districts, and distinctly different isolates were found in the same raspberry cultivation. Furthermore, eight different NP sequence variants were detected in the nine plants of a single raspberry cultivar (Glen Ample) tested from two districts. The nt and deduced amino acid sequences of the 21 isolates were 92.0-100% and 89.3-100% identical, respectively. Phylogenetic analysis revealed two main clusters. One cluster included three isolates, of which two isolates were from one farm in Finland and one isolate was previously characterised in Scotland. The other cluster contained the remaining 19 isolates characterised in this study. The results indicate that RLBV is widely distributed in cultivated and wild raspberries in Finland and shows considerable genetic variability.

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“…Wide adoption of autonomous sensor technology in the future will yield secondary science benefits, generating rich time-series data to mine for correlates of climate change, leading to enhanced predictions of climate change susceptibility to inform climate resilience and adaptation measures. These datasets can also feed back into an enhanced understanding of modified varieties' real-world performance in relation to microclimates, mycorrhizal fungi, pests and diseases (Camprubi et al 2007;Xu et al 2012). Powdery mildew infection risk, for example, is likely to be predictable using environmental data (Carisse et al 2013).…”

Section: 5 Agronomic Implicationsmentioning

confidence: 99%

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

Lee

Monteiro²,

Barclay

et al. 2019

Preprint

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Predicting harvest timing is a key challenge to sustainably develop soft fruit farming and reduce food waste. Soft fruits are perishable, high-value and seasonal, and sales prices are typically time-sensitive.In addition, fruit harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. A novel approach for predicting soft fruit phenology and yields was developed and tested, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR). Fleeces were added to a subset of the plants to generate additional withinpolytunnel variation. Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic growing conditions. After 10,000 iterations, machine learning revealed that RH was the optimal factor informing the coefficients of these curves, perhaps because it is an integrative metric of air temperature and water status. Trigonometric models transformed weather forecasts, which showed a relatively low agreement with polytunnel air temperature (R 2 = 0.6) and RH (R 2 = 0.5) measurements, into more accurate polytunnel-specific predictions for temperature and RH (both R 2 = 0.8). We present a framework for using machinelearning techniques to calculate curve coefficients and parametrise coupled weather models which can predict fruit yields and timing to a greater degree of accuracy that previously possible. Dataloggers measuring environmental and yield data could infer model parameters using iterative training for novel fruit varieties or crop types growing in different locations without a-priori phenological information. At this stage in the development of artificial intelligence and networked microsensors, this is a step forward in generating bespoke phenological prediction models to inform and support growers.

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Management of raspberry and strawberry grey mould in open field and under protection. A review

Cited by 25 publications

References 27 publications

Effect of Light Intensity on Rooting and Growth of Hydroponic Strawberry Runner Plants in a LED Plant Factory

Effect of Light Intensity on Rooting and Growth of Hydroponic Strawberry Runner Plants in a LED Plant Factory

Occurrence and genetic diversity ofRaspberry leaf blotch virus(RLBV) infecting cultivated and wildRubusspecies in Finland

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

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