Tomato (<i>Solanum lycopersicum</i>) pollinators and their effect on fruit set and quality

Toni, Hermann Cyr; Djossa, Bruno Agossou; Ayenan, Mathieu Anatole Tele; Teka, Oscar

doi:10.1080/14620316.2020.1773937

Cited by 31 publications

(18 citation statements)

References 54 publications

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“…Both Xylocopa spp . and A. mellifera have also been observed on tomato plots in other countries of the region ( Choudourou et al 2012 , Toni et al 2020 ) These studies are concordant with a previous report that acknowledged that tomatoes specifically are likely to benefit from a native assemblage of bees in African countries (2016). As highlighted further in Toni et al (2020) , clearly, more work on tomato pollination in Africa is urgently needed to determine the extent to which tomato production can be improved via supplemental pollination ( Toni et al 2020 ).…”

Section: Resultssupporting

confidence: 91%

“…However, south of the Sahara no bumblebees occur naturally and the importation of B. terrestris is not permitted ( Velthuis and Van Doorn 2006 ). Across the continent managed pollinators are little used ( Toni et al 2020 ), and research has focused instead on encouraging farmers to capitalize on native pollinators. A study in Ghana in 1990 found that fruit set was significantly higher for those plots open to insect visitors compared to plots caged with honey bees alone, although interestingly fruit volume and weight were higher from plants caged with honey bees ( Amoako and Yeboah-Gyan 1990 ).…”

Section: Resultsmentioning

confidence: 99%

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Buzz-Pollinated Crops: A Global Review and Meta-analysis of the Effects of Supplemental Bee Pollination in Tomato

Cooley

Vallejo‐Marín

2021

Journal of Economic Entomology

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Buzz-pollinated plants require visitation from vibration producing bee species to elicit full pollen release. Several important food crops are buzz-pollinated including tomato, eggplant, kiwi, and blueberry. Although more than half of all bee species can buzz pollinate, the most commonly deployed supplemental pollinator, Apis mellifera L. (Hymenoptera: Apidae; honey bees), cannot produce vibrations to remove pollen. Here, we provide a list of buzz-pollinated food crops and discuss the extent to which they rely on pollination by vibration-producing bees. We then use the most commonly cultivated of these crops, the tomato, Solanum lycopersicum L. (Solanales: Solanaceae), as a case study to investigate the effect of different pollination treatments on aspects of fruit quality. Following a systematic review of the literature, we statistically analyzed 71 experiments from 24 studies across different geopolitical regions and conducted a meta-analysis on a subset of 21 of these experiments. Our results show that both supplemental pollination by buzz-pollinating bees and open pollination by assemblages of bees, which include buzz pollinators, significantly increase tomato fruit weight compared to a no-pollination control. In contrast, auxin treatment, artificial mechanical vibrations, or supplemental pollination by non-buzz-pollinating bees (including Apis spp.), do not significantly increase fruit weight. Finally, we compare strategies for providing bee pollination in tomato cultivation around the globe and highlight how using buzz-pollinating bees might improve tomato yield, particularly in some geographic regions. We conclude that employing native, wild buzz pollinators can deliver important economic benefits with reduced environmental risks and increased advantages for both developed and emerging economies.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Buzz-Pollinated Crops: A Global Review and Meta-analysis of the Effects of Supplemental Bee Pollination in Tomato

Cooley

Vallejo‐Marín

2021

Journal of Economic Entomology

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“…UV radiation and blue light increase phenolic compounds, known to enhance colour, taste, nutritional value and shelf life of various crops (Qian et al, 2019; Quintero‐Arias et al, 2021; Robson et al, 2015; Tomás‐Barberán & Espín, 2001). UV radiation, blue and green light may also increase fruit‐set as a consequence of an increased pollination (Chittka & Wells, 2004; Dag, 2008; Toni et al, 2021). Blue and green light regulate water use efficiency (WUE) through stomatal opening (Kotiranta et al, 2015).…”

Section: Photobiological Responses Of Plants Can Be Utilised To Produ...mentioning

confidence: 99%

“…Pollinating insects are used in greenhouses to increase the proportion of fruit set in several crops, tomato being the most prominent (Toni et al, 2021). Bumblebees use UV, blue and green wavelengths to navigate (Chittka & Wells, 2004), so the attenuation of UV radiation by most polycarbonate hinders their navigation.…”

Section: Photobiological Responses Of Plants Can Be Utilised To Produ...mentioning

confidence: 99%

The benefits of informed management of sunlight in production greenhouses and polytunnels

Robson

Pieristè

Durand

et al. 2022

Plants People Planet

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Societal Impact Statement The effective management of light is beneficial for growers of plants in greenhouses, polytunnels and under cloches. The materials and structures used to construct these environments often create light‐limited conditions for crops and change the spectral composition of sunlight they receive. Combining practical measures, drawn from knowledge of plant photobiology, allows growers to monitor, forecast and optimise conditions in their growing environment according to its geographical location and the crop grown. Improved management of light through these measures could be expected to improve food quality and yield, and potentially reduce use of energy, water and pesticides. Summary Horticultural production in greenhouses and in polytunnels expands the viable geographic range of many crop species and extends their productive growing season. These semi‐controlled growing environments buffer natural fluctuations in heat, cold and light and hold potential to improve food security with a low environmental footprint. Over the last decade, technological advances in cladding materials, smart filters, photo‐electric cells for energy production and LED lighting have created opportunities to improve the light environment within these structures. In parallel, there have been large advances in plant photobiology, underpinned by progress in identifying the mechanisms of photomorphogenesis and photoprotection, mediated by plant photoreceptors and their interactions, across regions of the spectrum. However, there remains unexploited potential to synthesise and transfer knowledge from these fields to horticulture, particularly with respect to tailoring the use of sunlight to specific locations and production systems. Here, we systematically explain (1) the value of modelling and monitoring patterns of sunlight to allow for informed design of the growth environment; (2) the means of optimising light conditions through selection of materials and structures; (3) the requirements of different crop plants in terms of the amount and spectral composition of light that will benefit yield and food quality; (4) the potential to combine this knowledge for effective management of the sunlight; and, finally, (5) the additional benefits these actions may bring to growers and society at large, beyond the crops themselves, in terms of water use and energy efficiency.

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“…In this context, the automatic recognition of bees would be especially relevant for the pollination of commercial tomatoes, which need the local native pollinators to enhance the crop productivity [ 4 , 6 – 8 ]. Moreover, the professionals typically involved with the management of tomato crops (e.g.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds

et al. 2021

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Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees–those that are efficient pollinators–is essential to improve the economic returns for farmers. To achieve this, it is important to know the identity of the visiting bees. Nevertheless, the traditional taxonomic identification of bees is not an easy task, requiring the participation of experts and the use of specialized equipment. Due to these limitations, the development and implementation of new technologies for the automatic recognition of bees become relevant. Hence, we aim to verify the capacity of Machine Learning (ML) algorithms in recognizing the taxonomic identity of visiting bees to tomato flowers based on the characteristics of their buzzing sounds. We compared the performance of the ML algorithms combined with the Mel Frequency Cepstral Coefficients (MFCC) and with classifications based solely on the from fundamental frequency, leading to a direct comparison between the two approaches. In fact, some classifiers powered by the MFCC–especially the SVM–achieved better performance compared to the randomized and sound frequency-based trials. Moreover, the buzzing sounds produced during sonication were more relevant for the taxonomic recognition of bee species than analysis based on flight sounds alone. On the other hand, the ML classifiers performed better in recognizing bees genera based on flight sounds. Despite that, the maximum accuracy obtained here (73.39% by SVM) is still low compared to ML standards. Further studies analyzing larger recording samples, and applying unsupervised learning systems may yield better classification performance. Therefore, ML techniques could be used to automate the taxonomic recognition of flower-visiting bees of the cultivated tomato and other buzz-pollinated crops. This would be an interesting option for farmers and other professionals who have no experience in bee taxonomy but are interested in improving crop yields by increasing pollination.

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Tomato (Solanum lycopersicum) pollinators and their effect on fruit set and quality

Cited by 31 publications

References 54 publications

Buzz-Pollinated Crops: A Global Review and Meta-analysis of the Effects of Supplemental Bee Pollination in Tomato

Buzz-Pollinated Crops: A Global Review and Meta-analysis of the Effects of Supplemental Bee Pollination in Tomato

The benefits of informed management of sunlight in production greenhouses and polytunnels

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds

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