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Source: http://feeds.feedburner.com/AobBlog?format=xml

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<title>Open-Source Dendrometers for Real-Time Plant Water Monitoring</title>
<link>https://botany.one/2024/05/open-source-dendrometers-for-real-time-plant-water-monitoring/</link>
<comments>https://botany.one/2024/05/open-source-dendrometers-for-real-time-plant-water-monitoring/#respond</comments>
<dc:creator><![CDATA[botanyone]]></dc:creator>
<pubDate>Fri, 03 May 2024 15:00:38 +0000</pubDate>
<category><![CDATA[Growth & Development]]></category>
<category><![CDATA[Arduino]]></category>
<category><![CDATA[dendrometer]]></category>
<category><![CDATA[dicotyledon]]></category>
<category><![CDATA[featured]]></category>
<category><![CDATA[micro-controller]]></category>
<category><![CDATA[monocotyledon]]></category>
<category><![CDATA[water potential]]></category>
<guid isPermaLink="false">https://botany.one/?p=80704</guid>
<description><![CDATA[Scientists have developed an open-source instrument that can measure how much water plants have without hurting them, helping us understand plants better and use water for farming more wisely.
The post <a href="https://botany.one/2024/05/open-source-dendrometers-for-real-time-plant-water-monitoring/">Open-Source Dendrometers for Real-Time Plant Water Monitoring</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
Scientists have long sought new ways to continuously monitor how changing water availability affects plants. Traditional methods of measuring water potential, a key indicator of plant water stress, require invasive sampling that damages tissue. Now researchers have developed <a href="https://academic.oup.com/aobpla/article/16/2/plae009/7615917">entirely open-source instruments capable of tracking plant water status</a> with unprecedented high frequency and non-destructive precision. The development, carried out in the USDA, was published in AoB PLANTS.
Dendrometers are sensors that measure radial stem changes driven by plant water dynamics. Most existing designs have limitations for long-term continuous monitoring. However, a team led by Sean Gleason has created a novel contact dendrometer that overcome these issues. By combining dendrometer data with periodic conventional water potential measurements, their device can estimate water potential every minute or less with minimal disturbance on plant tissues exhibiting little or no secondary growth (petioles, monocotyledon stems).
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
Our focus with this study was not to confirm the already reported relationship between diameter variation and water potential, but rather, to describe the development of an inexpensive sensor that would make high-resolution diameter measurement devices available to anyone with a need for diameter data, as well as traits that can be derived from these data. As such, the sensor, software and analysis tools required to collect and make sense of these measurements needed to be entirely open-source, free or inexpensive and straightforward to build, modify and share with others.
<cite>Gleason et al. 2024</cite></blockquote>
In laboratory trials, the researchers tested both dendrometers on sunflower and corn during simulated drying and watering cycles. The contact sensor is affixed directly to plant stems, while the optical version employs a non-touching technique. Remarkably, the two methods showed very close agreement with each other and with direct water potential readings. They detected subtle 50 kPa differences corresponding to slight changes in transpiration, vapor pressure and soil moisture levels.
<figure class="wp-block-image size-large"><img fetchpriority="high" decoding="async" width="770" height="702" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=770%2C702&ssl=1" alt="Dendrometer in use." class="wp-image-80706" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=1080%2C985&ssl=1 1080w, https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=300%2C274&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=768%2C700&ssl=1 768w, https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=370%2C337&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=270%2C246&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=570%2C520&ssl=1 570w, https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?resize=740%2C675&ssl=1 740w, https://i0.wp.com/botany.one/wp-content/uploads/2024/05/plae009_fig1.jpg?w=1200&ssl=1 1200w" sizes="(max-width: 770px) 100vw, 770px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Contact dendrometer design and use. Adjustable PLA filament version of contact dendrometer requiring elastic bands (A), non-adjustable PLA filament version of contact dendrometer (B) and resin-printed compression spring design (C). Contact dendrometer placed on Helianthus petiole (D), and Zea stem (E). Source: Gleason et al. 2024</figcaption></figure>
To demonstrate real world use, the team deployed the contact dendrometer on sumac (Rhus typhina) branches in an outdoor setting for a week. It continuously recorded natural fluctuations in branch diameter driven by the plant’s water status.  
Two key advantages to using this new dendrometer for quantifying water potential are its high temporal sampling frequency, measuring in seconds, and the ability to sample at multiple points across the same plant or organ. Additionally, all hardware designs, software and instructions are published openly for anyone to replicate and improve upon the design. 
Being able to monitor plant water relations so frequently and non-invasively opens new opportunities for research. Scientists can gain deeper insights into physiological responses under stressful conditions, and growers may find ways to manage irrigation more sustainably by precisely tracking crop water needs. With open sourcing ensuring accessibility, these novel dendrometers have the potential to advance our understanding and management of plant water relations worldwide, offering a game-changing tool for both research and sustainable agriculture.
READ THE ARTICLE 
Gleason S., Stewart J., Allen B., Polutchko K., McMahon J., Spitzer D. and Barnard D. (2024). “<a href="https://academic.oup.com/aobpla/article/16/2/plae009/7615917">Development and application of an inexpensive opensource dendrometer for detecting xylem water potential and radial stem growth at high spatial and temporal resolution</a>” AoB PLANTS. Available at: <a href="https://doi.org/10.1093/aobpla/plae009">https://doi.org/10.1093/aobpla/plae009</a>
The post <a href="https://botany.one/2024/05/open-source-dendrometers-for-real-time-plant-water-monitoring/">Open-Source Dendrometers for Real-Time Plant Water Monitoring</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></content:encoded>
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<slash:comments>0</slash:comments>
<post-id xmlns="com-wordpress:feed-additions:1">80704</post-id> </item>
<item>
<title>A Universal Theory of Sex</title>
<link>https://botany.one/2024/04/a-universal-theory-of-sex/</link>
<comments>https://botany.one/2024/04/a-universal-theory-of-sex/#respond</comments>
<dc:creator><![CDATA[Sarah Covshoff]]></dc:creator>
<pubDate>Fri, 26 Apr 2024 19:24:18 +0000</pubDate>
<category><![CDATA[Taxonomy & Evolution]]></category>
<category><![CDATA[apogamy]]></category>
<category><![CDATA[apomixis]]></category>
<category><![CDATA[featured]]></category>
<category><![CDATA[haploid selection]]></category>
<category><![CDATA[meiosis]]></category>
<category><![CDATA[Muller’s ratchet]]></category>
<category><![CDATA[polyploidy]]></category>
<category><![CDATA[stress response]]></category>
<category><![CDATA[terrestrialization]]></category>
<guid isPermaLink="false">https://botany.one/?p=80696</guid>
<description><![CDATA[Plants and animals overwhelmingly reproduce sexually, but why gamble with the DNA like that?
The post <a href="https://botany.one/2024/04/a-universal-theory-of-sex/">A Universal Theory of Sex</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
Sexual reproduction in plants and animals presents an evolutionary enigma. It is genetically risky, time-consuming, and error-prone – possibly leading to reduced fitness. And yet, at least 99% of all plants and animals reproduce sexually. This commonality raises the question of whether universal evolutionary pressures exist to select for and maintain sexual reproduction. 
Elvira Hörandl argues in a review paper in Annals of Botany that <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcae044/7630939">the need for DNA repair may be the basis for the near universally of sex in the plant and animal kingdoms</a>. And she uses land plants as a tool to make her case.  
“Land plants are of special interest for this question because, on the one hand, sexual reproduction is predominant, similar to animals; on the other hand, some theories developed for animals are not readily applicable to plants because autotrophic organisms have different physiological constraints.” 
The key process in sexual reproduction is meiosis, defined as the pairing and recombination of corresponding parental chromosomes. During this process, parental DNA is fused to create genetic variation in offspring. The new genetic combinations can be advantageous, neutral, or even detrimental to fitness if well-adapted genes are broken up.  
Hörandl argues that plants don’t need meiosis to achieve advantageous genetic combinations — polyploidy can be used instead. In plants, many species are polyploid, i.e., have multiple genomes, and individual genetic variation is correspondingly high. This built-in genetic diversity confers the necessary ‘phenotypic plasticity’ for plants to respond to environmental stressors such as light, heat, drought, and salt. In this context, asexual reproduction, where plants reproduce without meiosis, can make sense as a reproductive strategy.
However, while many plants do reproduce asexually, Hörandl shows that many of those species run both sexual and asexual reproductive pathways in parallel in the same plant in a flexible manner and do not rely solely on asexual forms of reproduction. 
And so, the question becomes, if polyploidy and asexual reproduction are enough to create the necessary genetic diversity for fitness, why then do plants bother with sexual reproduction?  
Hörandl suggests that ‘DNA restoration theory’ provides the answer.  DNA restoration theory posits that the major function of meiosis is to repair damaged DNA and remove negative mutations in each generation. In that light, genetic recombination is not the end goal of sexual reproduction but is instead a by-product of a DNA repair mechanism.  
DNA needs repair because it is continuously damaged by mitochondrial respiration as well as by photosynthesis in plants. Hörandl writes that: 
“In the long term, asexual reproduction without any recombination would result in genomic decay owing to the accumulation of deleterious mutations, specifically in small populations, finally leading to extinction of the asexual lineage.” 
Indeed, sexual reproduction, and consequently meiosis, is triggered in plants grown in DNA damage-inducing, stressful environments. Hörandl notes that only 
“…a little bit of sex (a mean of ~6 % recombined offspring in three progenies) is sufficient to avoid accumulation of mutations over generations.” 
Consequently, species may have evolved the ability to reproduce sexually in order to repair and maintain their DNA.
READ THE ARTICLE
Hörandl, E. (2024) “<a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcae044/7630939">Apomixis and the paradox of sex in plants</a>,” Annals of Botany, p. mcae044. Available at: <a href="https://doi.org/10.1093/aob/mcae044">https://doi.org/10.1093/aob/mcae044</a>.
The post <a href="https://botany.one/2024/04/a-universal-theory-of-sex/">A Universal Theory of Sex</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></content:encoded>
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<slash:comments>0</slash:comments>
<post-id xmlns="com-wordpress:feed-additions:1">80696</post-id> </item>
<item>
<title>Forest Loss Leads to Less Diverse Diets for Orchid Bees</title>
<link>https://botany.one/2024/04/forest-loss-leads-to-less-diverse-diets-for-orchid-bees/</link>
<comments>https://botany.one/2024/04/forest-loss-leads-to-less-diverse-diets-for-orchid-bees/#respond</comments>
<dc:creator><![CDATA[Alun Salt]]></dc:creator>
<pubDate>Thu, 25 Apr 2024 16:32:47 +0000</pubDate>
<category><![CDATA[Close Encounters]]></category>
<category><![CDATA[Bee-plant interactions]]></category>
<category><![CDATA[Brazil]]></category>
<category><![CDATA[Euglossa]]></category>
<category><![CDATA[Pollen diet]]></category>
<guid isPermaLink="false">https://botany.one/?p=80689</guid>
<description><![CDATA[Orchid Bees are thought to have generalist diets, but take forest plants from the menu, and they become a lot less successful.
The post <a href="https://botany.one/2024/04/forest-loss-leads-to-less-diverse-diets-for-orchid-bees/">Forest Loss Leads to Less Diverse Diets for Orchid Bees</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
In forests as far north as Mexico and as far south as Argentina Orchid Bees fly. These insects play an important role in pollination in the tropics. Male orchid bees are known for their elaborate perfume-collecting behaviours, while females are considered generalists when it comes to gathering pollen to feed their offspring. However, a new study by Samuel Boff and colleagues, published in the Journal of Insect Conservation suggests that <a href="https://link.springer.com/article/10.1007/s10841-024-00575-2">the pollen diets of these bees may not be as diverse as once thought</a>, with deforestation leading to a narrowing of the floral resources available to them.
The study, conducted by researchers from Brazil, Germany and Mexico, analysed pollen from excreted waste in nests of the orchid bee <a href="https://www.inaturalist.org/taxa/308898-Euglossa-cordata">Euglossa cordata</a> across different vegetation types and levels of forest cover in Brazil. The study examined how bees foraged in the Atlantic forest, the grasslands of the Cerrado and the zone where the two ecosystems meet. The ecologists found that pollen diet composition differed significantly between vegetation types, with diet specialisation being lowest in forested areas and highest in disturbed habitats.
<figure class="wp-block-image size-full"><img decoding="async" width="770" height="530" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=770%2C530&ssl=1" alt="" class="wp-image-80691" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?w=967&ssl=1 967w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=300%2C207&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=768%2C529&ssl=1 768w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=370%2C255&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=270%2C186&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=435%2C300&ssl=1 435w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=570%2C393&ssl=1 570w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Euglossa-cordata.jpg?resize=740%2C510&ssl=1 740w" sizes="(max-width: 770px) 100vw, 770px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Euglossa cordata female foraging on the flower of Dichorisandra thyrsiflora, Itu (Brazil). Photo: Samuel Boff.</figcaption></figure>
The team found a strong correlation between pollen richness, the number of brood cells, and the amount of forest cover. A search through other published results revealed this pattern was consistent across multiple Euglossa species, suggesting that the effects of habitat loss on pollen diversity are widespread among Orchid Bees. A closer study of the pollen shows that the bees were using various plants they were fond of foraging in above ground-level. Boff and colleagues write:
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
Although Euglossa collect resources at herbs and shrubs, most of plant genus display the habit of forest elements (e.g. trees, epiphytes and lianas). Our findings may help to explain the use of connected forest fragments by bees to enable them to inhabit forested sites (<a href="https://doi.org/10.1111/j.1365-294X.2004.02374.x">Dick et al. 2004</a>) and their lower population sizes in open areas (<a href="https://doi.org/10.1007/s13592-014-0314-1">Silveira et al. 2015</a>) and urbanized areas (<a href="https://doi.org/10.1007/s10841-018-0075-7">Cândido et al. 2018</a>).
<cite>Boff et al. 2024</cite></blockquote>
The findings suggest that even generalist species like Euglossa cordata, which are adapted to forage on a wide variety of plants, are still heavily dependent on the rich floral resources found in forests. The loss of these habitats, particularly the trees and lianas that provide essential pollen sources, could have cascading effects on orchid bee populations and, in turn, on the numerous plant species they pollinate.
READ THE ARTICLE
Boff, S., da Luz, C.F.P., Raizer, J., Pinto, G.S., Santos, L.F., Freitas, B.M., da Silva, C.I., Quezada-Euàn, J.J.G., Soro, A. and Alves-dos-Santos, I. (2024) “<a href="https://link.springer.com/article/10.1007/s10841-024-00575-2">Deforestation narrows pollen diet diversity of generalist orchid bees,</a>” Journal of Insect Conservation. Available at: <a href="https://doi.org/10.1007/s10841-024-00575-2">https://doi.org/10.1007/s10841-024-00575-2</a>.
The post <a href="https://botany.one/2024/04/forest-loss-leads-to-less-diverse-diets-for-orchid-bees/">Forest Loss Leads to Less Diverse Diets for Orchid Bees</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></content:encoded>
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<slash:comments>0</slash:comments>
<post-id xmlns="com-wordpress:feed-additions:1">80689</post-id> </item>
<item>
<title>Symphony of Insights: Transforming Crop Simulation with Sound and Music</title>
<link>https://botany.one/2024/04/symphony-of-insights-transforming-crop-simulation-with-sound-and-music/</link>
<comments>https://botany.one/2024/04/symphony-of-insights-transforming-crop-simulation-with-sound-and-music/#respond</comments>
<dc:creator><![CDATA[Rachel Shekar]]></dc:creator>
<pubDate>Wed, 24 Apr 2024 08:00:00 +0000</pubDate>
<category><![CDATA[Computational Models]]></category>
<category><![CDATA[Growth & Development]]></category>
<category><![CDATA[3-D canopy architecture modelling]]></category>
<category><![CDATA[Abiotic stresses]]></category>
<category><![CDATA[accessibility]]></category>
<category><![CDATA[Art]]></category>
<category><![CDATA[crop simulation model]]></category>
<category><![CDATA[featured]]></category>
<category><![CDATA[music]]></category>
<category><![CDATA[science communication]]></category>
<category><![CDATA[simulation]]></category>
<category><![CDATA[sound]]></category>
<category><![CDATA[sound art]]></category>
<category><![CDATA[visual art]]></category>
<category><![CDATA[visual programming]]></category>
<guid isPermaLink="false">https://botany.one/?p=80640</guid>
<description><![CDATA[Revolutionizing scientific communication and insight through auditory and visual representations.
The post <a href="https://botany.one/2024/04/symphony-of-insights-transforming-crop-simulation-with-sound-and-music/">Symphony of Insights: Transforming Crop Simulation with Sound and Music</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
<figure class="wp-block-audio"><audio controls src="https://anchor.fm/s/49a2320/podcast/play/85341978/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2024-3-12%2F374118802-44100-2-30d6ce6803c77.m4a"></audio><figcaption class="wp-element-caption">You can listen to this page as an audio file (English only)</figcaption></figure>
In the realm of scientific research and exploration, the presentation and interpretation of complex data has traditionally relied heavily on visual representations. However, with the advent of innovative techniques, the use of sound and music to depict scientific data and biological processes has emerged as a powerful tool.
By leveraging the pivotal roles that sight and hearing play in human communication and our understanding of the world around us, the utilization of sound and music not only offers a fresh perspective to scientists but also provides a new way to communicate scientific findings with students and the general public. Moreover, it serves as a valuable tool for those who are visually impaired.
From comprehending <a href="https://app.aavso.org/jaavso/article/3417/">star brightness</a> and <a href="https://pubs.acs.org/doi/full/10.1021/acs.jchemed.1c00857">protein folding</a> to deciphering <a href="https://www.whoi.edu/oceanus/feature/a-new-ocean-soundscape/">ocean water chemistry</a>, this tool has been instrumental in unraveling a wide range of scientific phenomena. And now, its applications have extended to effectively communicating and sharing valuable insights derived from crop models.
Crop models are sophisticated computer-based tools that simulate and predict the growth, development, and yield of crops under different environmental conditions. They integrate various parameters such as climate data, soil properties, crop characteristics, and management practices to provide valuable insights into how crops respond to different stressors.
A new paper published in in silico Plants present <a href="https://doi.org/10.1093/insilicoplants/diad023">new software that visually and audibly represents simulations of crops in real-time</a>.
Researcher Simone Bregaglio and colleagues at the Council for Agricultural Research and Economics developed mandala, which is able to reproduce plant physiological processes to understand the effects of cold, heat, and drought stresses on crop growth and development.
<figure class="wp-block-video wp-block-embed is-type-video is-provider-videopress"><div class="wp-block-embed__wrapper">
<iframe title="VideoPress Video Player" aria-label='VideoPress Video Player' width='770' height='399' src='https://videopress.com/embed/q0RMZlaS?cover=1&preloadContent=metadata&useAverageColor=1&hd=0' frameborder='0' allowfullscreen data-resize-to-parent="true" allow='clipboard-write'></iframe><script src='https://v0.wordpress.com/js/next/videopress-iframe.js?m=1674852142'></script>
</div><figcaption>In this simulation for maize growing in Rome drought stress can be heard as acute violin vibrato notes and seen as a distortion of the perimeter of the yellow circles. Reduced biomass and yield from drought stress can be seen at the end of the growing season.</figcaption></figure>
Mandala features classical string instruments playing in unison to convey essential information such as production level, stress functions, and flowering while a drum beats to denote the passing of time. “The music was developed according to the movement of the seed circles assigning a different starting note and a different musical instrument for each production level (potential = C2, double bass; cold-limited = E2, light blue; drought-limited = G2, viola; heat-limited = C3, violin). At the beginning of the growing season the four seeds start playing a triads chords made by three notes plus unison (a root, a third and fifth). When they start growing the ten external circles enlarge and depart from the central circle in an opening movement and start the chords scale progression in a pentatonic scale (from C2 + to C4+). We decided to use the pentatonic scale as the notes of the pentatonic scale are harmonious in whichever order they are played. It occurs in many musical traditions and in addition many past and contemporary musicians from different cultures have recognized pentatonic scales’ emotive and therapeutic power, emphasizing their suitability to convey the narrative of plant stress. Also, the use of classical string instruments playing in unison aligns with the crop cycle’s symbolic representation and has the intention to present the mandala as a chord quartet,” explains Carriero.
<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="520" height="310" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig4.jpeg?resize=520%2C310&ssl=1" alt="" class="wp-image-80651" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig4.jpeg?w=520&ssl=1 520w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig4.jpeg?resize=300%2C179&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig4.jpeg?resize=370%2C221&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig4.jpeg?resize=270%2C161&ssl=1 270w" sizes="(max-width: 520px) 100vw, 520px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Sounds used to abstract crop phenology and growth. The colored notes on the musical staff are related to the four production levels (green = potential; light blue = cold-limited; yellow = drought-limited; pink = heat-limited) and their progression is linked to different phenological stages (from seed to maturity).</figcaption></figure></div>
It also includes visual art to depict data. Four colors are used to represent four plants: an unstressed plant and plants subjected to cold, heat, or drought stress. Visuals depict phenology and physiological functions such as stress, photosynthesis, and yield formation.
<div class="wp-block-image">
<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="520" height="289" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig3.jpeg?resize=520%2C289&ssl=1" alt="" class="wp-image-80650" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig3.jpeg?w=520&ssl=1 520w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig3.jpeg?resize=300%2C167&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig3.jpeg?resize=370%2C206&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad023_fig3.jpeg?resize=270%2C150&ssl=1 270w" sizes="(max-width: 520px) 100vw, 520px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Symbols to render crop phenology and growth, and exemplification of the effect of stress (red) on flowering (left) and yield and biomass (right).</figcaption></figure></div>
Mandala’s crop simulation model incorporates standard plant functions such as the utilization of growing degree days to forecast developmental rate, efficiency of light interception and utilization, and sensitivity to temperature and drought. The simulations also rely on environmental inputs like light, temperature, and humidity.
Using this information, the model simulates physiological processes, encompassing phenology, photosynthesis, and yield formation. It particularly highlights the impact of cold, heat, and drought stress on crop growth and development. Using the model, the authors were able to replicate existing yield data for maize and wheat across multiple locations spanning a period of three years.
The mandala software is freely downloadable from <a href="https://zenodo.org/records/10210150">Zenodo</a>. To use the software, modify the patch and editing data, users must first download the free <a href="https://visualprogramming.net/">vvvv software</a>. Customizable resources include the vvvv patch, the audio files, the source code of the crop model with a sample console application and an R file to analyze and visualize model results.
Users can utilize the graphical interface to assess how altering the duration of crop cycles and other crucial factors affects the overall yield. The source code itself can also be customized. For instance, users can add additional crops and alter visual and auditory elements.  
By combining auditory cues with visual representations, mandala provides a unique and immersive experience for understanding the growth and development of crops. This innovative approach enhances scientific communication and offers valuable insights into the effects of environmental factors on crop yields.
READ THE ARTICLE:
Simone Bregaglio, Giulia Carriero, Roberta Calone, Maddalena Romano, Sofia Bajocco, Playing a crop simulation model using symbols and sounds: the ‘mandala’, in silico Plants, Volume 6, Issue 1, 2024, diad023, <a href="https://doi.org/10.1093/insilicoplants/diad023">https://doi.org/10.1093/insilicoplants/diad023</a>
<hr class="wp-block-separator has-alpha-channel-opacity"/>
The <a href="https://zenodo.org/records/10210150">mandala</a> software is freely downloadable at <a href="https://zenodo.org/records/10210150">https://zenodo.org/records/10210150</a>.
The post <a href="https://botany.one/2024/04/symphony-of-insights-transforming-crop-simulation-with-sound-and-music/">Symphony of Insights: Transforming Crop Simulation with Sound and Music</a> appeared first on <a href="https://botany.one">Botany One</a>.
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<post-id xmlns="com-wordpress:feed-additions:1">80640</post-id> </item>
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<title>How an Italian butterfly is navigating the Anthropocene</title>
<link>https://botany.one/2024/04/how-an-italian-butterfly-is-navigating-the-anthropocene/</link>
<comments>https://botany.one/2024/04/how-an-italian-butterfly-is-navigating-the-anthropocene/#respond</comments>
<dc:creator><![CDATA[Alun Salt]]></dc:creator>
<pubDate>Tue, 23 Apr 2024 08:00:00 +0000</pubDate>
<category><![CDATA[Close Encounters]]></category>
<category><![CDATA[butterfly]]></category>
<category><![CDATA[Conservation]]></category>
<category><![CDATA[Ecological connectivity]]></category>
<category><![CDATA[italy]]></category>
<category><![CDATA[urban ecology]]></category>
<guid isPermaLink="false">https://botany.one/?p=80679</guid>
<description><![CDATA[A new study reveals that endangered butterflies can find unlikely refuge in cities, if urban greenspaces are managed with connectivity in mind.
The post <a href="https://botany.one/2024/04/how-an-italian-butterfly-is-navigating-the-anthropocene/">How an Italian butterfly is navigating the Anthropocene</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
When you think of conservation, a city might seem like an unlikely helper. Across the world, urbanisation is leading to habitat loss, pushing species to extinction. Yet a new study by Leonardo Ancillotto and colleagues in the journal Urban Ecosystems shows how urban parks and gardens are providing <a href="https://link.springer.com/article/10.1007/s11252-024-01542-0">a lifeline for threatened species</a> like the Italian Festoon butterfly, Zerynthia cassandra.
The study of the Italian cities of Rome and Bari found that the butterfly occupied between a quarter and a third of the potential usable habitat available. Ancillotto and colleagues argue that a little more thought about connectivity in urban environments could help secure a future for species in cities.
<h2 class="wp-block-heading">The secret of success in the concrete jungle</h2>
<figure class="wp-block-image size-full is-resized"><img loading="lazy" decoding="async" width="770" height="585" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=770%2C585&ssl=1" alt="" class="wp-image-80682" style="width:650px;height:auto" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?w=1011&ssl=1 1011w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=300%2C228&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=768%2C583&ssl=1 768w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=370%2C281&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=270%2C205&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=570%2C433&ssl=1 570w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=740%2C562&ssl=1 740w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Zerynthia_cassandra.jpg?resize=80%2C60&ssl=1 80w" sizes="(max-width: 770px) 100vw, 770px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Zerynthia cassandra. Photo: <a href="https://commons.wikimedia.org/wiki/File:Zerynthia_cassandra.jpg">Lucarelli / Wikimedia Commons</a>.</figcaption></figure>
The <a href="https://www.inaturalist.org/taxa/121825-Zerynthia-cassandra">Italian Festoon butterfly</a> is a black and orange (or possibly yellow) butterfly, with red highlights. It grows to about 5cm or 2 inches across and is found on the Italian mainland, south of Turin and Milan, and across a fair amount of Sicily. The exact place to find them is around Aristolochia plants, in particular <a href="https://www.inaturalist.org/taxa/333672-Aristolochia-rotunda">Aristolochia rotunda</a> in Rome and <a href="https://www.inaturalist.org/taxa/358157-Aristolochia-clusii">Aristolochia clusii</a> in Bari. The butterflies value the plants because they need a host for their caterpillars. The leaves of Aristolochia plants offer a location to lay eggs and a source of food for the hatched caterpillars.
If you’re familiar with Aristolochia then you probably find this unlikely. The leaves contain a lot of food for hungry belly. This would not be good for the plant, and so the plants protect the leaves with aristolochic acid. This chemical attacks the kidneys and, if you can eat it fior long enough, it can also cause cancer. Normally, being poisoned from birth would be bad news, but <a href="https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1570-7458.2001.00881.x">it seems to be the opposite for some caterpillars</a>, including the Italian Festoon. Instead, the caterpillars are able to store the poisons and so become poisonous themselves to their predators. So, in addition to food and shelter, the Aristolochia plants give the insects a defence against their enemies.
The Italian Festoon’s ability to specialise on certain Aristolochia plants means that when they find them, they can exploit them – even in the middle of a city. If there are convenient plants forming green corridors between clusters then they can also disperse between these sites.
<h2 class="wp-block-heading">Connecting the dots for conservation</h2>
A home that others cannot exploit might seem perfect for an insect, but the specialisation has also caused the Italian Festoon some problems. The caterpillars don’t travel a lot, but you wouldn’t expect them too – they’re caterpillars. Unfortunately, the adult butterflies don’t seem to travel much either. Ancillotto and colleagues refer to earlier research showing that Italian Festoons don’t fly as well as other butterflies, and <a href="https://link.springer.com/article/10.1007/s10841-014-9662-4">rarely travel more than 200 metres from their home plant</a>.
This distance isn’t a hard limit, so more remote sites can be colonised, given enough time. Ancillotto and colleagues say their results show that sites even up to a kilometre away could be considered connected.
Not surprisingly, the team also found that plant abundance was a strong predictor of butterfly occurrence. Aristolochia rotunda and Aristolochia clusii like specific ecological traits. They prefer to be at ecotones, the places where habitats transition, in this case the edge of woodlands. It also likes disturbed soil patches. Here, the butterfly may be receiving some unexpected help. Ancillotto and colleagues say that the sort of patches Aristolochia likes are the sort of patches that wild boars create, and Italy is starting to <a href="https://www.euronews.com/green/2022/05/13/wild-boars-are-ravaging-rome-here-s-how-locals-are-fighting-back">experience wild boars settling in urban sites</a>.
Something Aristolochia doesn’t like is synanthropic vegetation. These are the kinds of plants that can colonise fresh soils as opportunists, weeds being an obvious example. Instead, Italian Festoons prefer to forage among early-flowering herbaceous plants. This would tie in with the period they’re on the wing, <a href="https://uk.inaturalist.org/taxa/121825-Zerynthia-cassandra">mainly around April and May</a>. The connections therefore aren’t just needed in the right places, but also need to be hospitable at the right time.
<h2 class="wp-block-heading">Butterfly-friendly management</h2>
All these requirements create difficult demands for conservation scientists. Ancillotto and colleagues write:
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
In our case, the ecological needs of Aristolochia plants, preferring ecotones and relatively disturbed soils, as well as the need of adult Zerynthia for early-flowering plant species in the immediate surroundings, make habitat conservation particularly challenging. The current lack of specific protection of the species’ favored habitats (despite that the European Habitats Directive indirectly protects the locations where listed species actually occur), and the obvious difficulties in mapping and quantifying ecotonal habitats and their peculiar conditions (<a href="https://link.springer.com/article/10.1007/s10841-012-9492-1">Nowicki et al. 2013</a>), make in fact ecotonal-specialized species highly challenging to conserve.
</blockquote>
The possible benefit of an urban environment is that some of these demands can be met in the course of usual urban management. The authors highlight partial mowing and low intensity grazing as actions that can help create the habitats necessary for the butterflies to thrive.
They also highlight that, given the information, the public could also wish to help by planting suitable flowers to aid endangered butterfly colonisation. This has a two-fold action helping conservation. Not only are there more places to host the butterflies, there’s also less likelihood of illegal collection.
READ THE ARTICLE
Ancillotto, L., Mosconi, F. and Labadessa, R. (2024) “<a href="https://botany.fyi/jBYrom">A matter of connection: the importance of habitat networks for endangered butterflies in anthropogenic landscapes</a>,” Urban Ecosystems. Available at: <a href="https://doi.org/10.1007/s11252-024-01542-0">https://doi.org/10.1007/s11252-024-01542-0</a>.
Cover image: <a href="https://www.canva.com/">Canva</a>.
The post <a href="https://botany.one/2024/04/how-an-italian-butterfly-is-navigating-the-anthropocene/">How an Italian butterfly is navigating the Anthropocene</a> appeared first on <a href="https://botany.one">Botany One</a>.
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<title>Researchers shine a light on iridescent leaves </title>
<link>https://botany.one/2024/04/researchers-shine-a-light-on-iridescent-leaves/</link>
<comments>https://botany.one/2024/04/researchers-shine-a-light-on-iridescent-leaves/#respond</comments>
<dc:creator><![CDATA[Sarah Covshoff]]></dc:creator>
<pubDate>Mon, 22 Apr 2024 09:26:59 +0000</pubDate>
<category><![CDATA[Growth & Development]]></category>
<category><![CDATA[blue leaves]]></category>
<category><![CDATA[cell walls]]></category>
<category><![CDATA[cellulose]]></category>
<category><![CDATA[chiral thin films]]></category>
<category><![CDATA[Cyperaceae]]></category>
<category><![CDATA[Eriocaulaceae]]></category>
<category><![CDATA[featured]]></category>
<category><![CDATA[Ferns]]></category>
<category><![CDATA[iridescence]]></category>
<category><![CDATA[Orchidaceae]]></category>
<category><![CDATA[Rapateaceae]]></category>
<category><![CDATA[structural colour]]></category>
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<description><![CDATA[Some plant leaves are blue, but have no blue pigments. How does that work?
The post <a href="https://botany.one/2024/04/researchers-shine-a-light-on-iridescent-leaves/">Researchers shine a light on iridescent leaves </a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
All leaves are green. Or are they? Lundquist et al recently surveyed a diversity of plant species and found <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcae045/7637436">at least 130 have iridescent blue leaves</a>, making them more common than you might think. These ‘living jewels’, widely distributed in the dark understories of tropical rainforests, have photonic crystals in their leaves that produce intense, vivid metallic blue hues. 
Most of the visual world is coloured by pigments – reds, yellows, blues and greens — that are produced by molecules reflecting back colour. For example, chlorophyll is the pigment that makes leaves appear green. But, not all biological colours are pigment-based.
Some colours, called <a href="https://botany.one/2012/07/my-favourite-colour-is-structural-colour/">structural colours</a>, arise when the shape of an animal or plant cell can bend incident light to create iridescence. The dazzling colours we see on bird feathers and on the backs of beetles and butterflies are examples of structural colours in animals. In plants, structural colours are found in flowers, fruits and even leaves, but until now it has been difficult to study its prevalence across plant genera. As Lundquist et al report:
“Most species are not in cultivation and are widely distributed across the tropics, often as narrow endemics restricted to inaccessible locations such as the more remote Venezuelan tepuis.” 
<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="770" height="530" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=770%2C530&ssl=1" alt="" class="wp-image-80672" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=1080%2C743&ssl=1 1080w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=300%2C206&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=768%2C528&ssl=1 768w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=370%2C254&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=270%2C186&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=435%2C300&ssl=1 435w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=570%2C392&ssl=1 570w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?resize=740%2C509&ssl=1 740w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Blue-Leaves.jpg?w=1200&ssl=1 1200w" sizes="(max-width: 770px) 100vw, 770px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Examples of leaves that appear blue thanks to structural colour. Lundquist et al. 2024.</figcaption></figure>
To overcome this obstacle, and perform the first broad investigation of photonic structure in leaves, Lundquist et al gathered living specimens from Brunei as well as dried material from the herbarium at Royal Botanic Gardens, Kew in the United Kingdom. They specifically hunted for nanostructures called helicoidal cell wall layers that are made up of cellulose molecules arranged in a helical screw shape. These helicoidal structures are common in leaf epidermis and can act as photonic crystals to produce structural colour.
<a href="https://botany.one/2023/05/a-new-life-for-centuries-old-herbaria/">Herbariums</a>, with their brown, dry leaf samples, may not seem like the most logical place to study plant colour, but helicoidal layers, if present, stay intact during the drying process and their reflectance can be studied after re-hydration with water overnight. Lundquist et al. measured whether the samples transmit either left- or right-handed circularly polarized light and observed helicoidal structures using electron microscopy.
To their surprise, they could detect the helicoidal structures in 20 genera and they could recover the colour recorded when the herbarium sample was first collected:
“The ability of herbarium specimens to recover structural colour upon hydration was not anticipated prior to this study … yet most of the leaf samples investigated here regained the approximate leaf colours stated on the herbarium labels.” 
Most of the species reflected blue light, but green and/or violet light was also common.
Lundquist et al. predominantly found the helicoidal structures in ferns and monocots (Orchidaceae family and three Poales families, Cyperaceae, Eriocaulaceae and Rapateaceae).
Additionally, based on the phylogeny of these species, Lundquist et al. determined that helicoidal structures have evolved at least 38 times, in a remarkable example of convergent evolution. Lundquist et al. speculate that these structures may have an adaptive benefit for photoprotection and/or minimizing herbivory by confusing insect predators. 
READ THE ARTICLE
Lundquist, C.R., Rudall, P.J., Sukri, R.S., Conejero, M., Smith, A., Lopez-Garcia, M., Vignolini, S., Metali, F. and Whitney, H.M. (2024) “<a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcae045/7637436">Living jewels: iterative evolution of iridescent blue leaves from helicoidal cell wall</a>s,” Annals of Botany, p. mcae045. Available at: <a href="https://doi.org/10.1093/aob/mcae045">https://doi.org/10.1093/aob/mcae045</a>.
The post <a href="https://botany.one/2024/04/researchers-shine-a-light-on-iridescent-leaves/">Researchers shine a light on iridescent leaves </a> appeared first on <a href="https://botany.one">Botany One</a>.
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<post-id xmlns="com-wordpress:feed-additions:1">80627</post-id> </item>
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<title>Harnessing the Power of Synthetic Data for Deep Learning Image Analysis</title>
<link>https://botany.one/2024/04/harnessing-the-power-of-synthetic-data-for-deep-learning-image-analysis/</link>
<comments>https://botany.one/2024/04/harnessing-the-power-of-synthetic-data-for-deep-learning-image-analysis/#respond</comments>
<dc:creator><![CDATA[Rachel Shekar]]></dc:creator>
<pubDate>Fri, 12 Apr 2024 17:17:08 +0000</pubDate>
<category><![CDATA[Computational Models]]></category>
<category><![CDATA[Growth & Development]]></category>
<category><![CDATA[computer vision]]></category>
<category><![CDATA[CPlantBox]]></category>
<category><![CDATA[deep learning]]></category>
<category><![CDATA[FSPM]]></category>
<category><![CDATA[HPC]]></category>
<category><![CDATA[visualization]]></category>
<guid isPermaLink="false">https://botany.one/?p=80585</guid>
<description><![CDATA[The Synavis framework provides a solution to overcome data scarcity for training deep learning biological image analysis models.
The post <a href="https://botany.one/2024/04/harnessing-the-power-of-synthetic-data-for-deep-learning-image-analysis/">Harnessing the Power of Synthetic Data for Deep Learning Image Analysis</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
<figure class="wp-block-audio"><audio controls src="https://anchor.fm/s/49a2320/podcast/play/84871080/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2024-3-2%2F372989104-44100-2-924e3078fa94.mp3"></audio><figcaption class="wp-element-caption">You can listen to this page as an audio file (English only).</figcaption></figure>
Deep learning models have revolutionized plant modeling by automating the extraction of plant features and characteristics from images. This high-throughput data enables researchers to analyze complex plant traits, such as growth patterns and disease susceptibility, more efficiently.
Deep learning models must be trained using diverse images to develop robust and generalized representations. However, obtaining this type of data is a time-consuming and resource-intensive process. Apart from conducting experiments, it involves the meticulous collection of substantial volumes of high-quality images, which then need to be segmented and stored appropriately. Additionally, the images must be annotated, where specific information about the objects, regions, or attributes depicted within them is added to each file. This step is crucial in enabling the algorithms to comprehend and learn from the data effectively.
To overcome the scarcity of training data, researchers have explored the use of synthetic data generation, which involves creating artificial plant images that mimic real-world data. Synthetic data can help in training deep learning models more effectively by providing large and diverse datasets.
A new article published in in silico Plants by Dirk Helmrich, PhD student at Forschungszentrum Jülich and the University of Iceland, and colleagues <a href="https://academic.oup.com/insilicoplants/article/6/1/diad022/7462759">introduces a framework called Synavis which generates synthetic plant data and connects and directly communicates with deep learning training frameworks</a>.
<figure class="wp-block-image aligncenter size-full"><img loading="lazy" decoding="async" width="575" height="432" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=575%2C432&ssl=1" alt="A figure with an explanation of how plants are simulated in CPlantBox at the top. First, model parametrization uses parameters from direct measurements. Then, the model simulates a 2d image of a plant. Last, the plant is reconstructed with geometry to create a 3d image.
At the bottom are examples of photorealistic environments rendered using Unreal Engine. These are images of a field with a rainy, morning, foggy or sunny environment." class="wp-image-80588" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?w=575&ssl=1 575w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=300%2C225&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=400%2C300&ssl=1 400w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=200%2C150&ssl=1 200w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=370%2C278&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=270%2C203&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=570%2C428&ssl=1 570w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/photo-rendering-3.jpg?resize=80%2C60&ssl=1 80w" sizes="(max-width: 575px) 100vw, 575px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Individual plants are simulated in CPlantBox using measured data. Their architecture is defined by topological and geometric information in CPlantBox. Unreal Engine uses this data to produce photorealistic renderings of the plants within a virtual environment and is capable of augmenting scene data.</figcaption></figure>
Synavis is composed of two components: a Functional–Structural Plant Model (FSPM) and Unreal Engine.
FSPMs simulate realistic plant morphology, mimicking various plant development dynamics under specific environmental conditions. The FSPM <a href="https://doi.org/10.1093/insilicoplants/diad009">CPlantBox</a> is used to generate graph-like plant structural data using algorithms. A visualization module is then used to produce 3D plants from the CPlantBox data.
Then, <a href="https://unrealengine.com/">Unreal Engine</a>, a graphics engine capable of photorealistic rendering, is used to generate visual representations of the plants within a virtual environment. Unreal Engine possesses the capability to augment scene data, including plant position, density, age, and lighting, thereby generating a variety of image variations.
<figure class="wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe loading="lazy" title="Synavis: Coupling FSPM Visualization to Graphics Engines" width="770" height="433" src="https://www.youtube.com/embed/H9cw_aE-l3A?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div><figcaption class="wp-element-caption">A video overview of Synavis created by Dirk Helmrich.</figcaption></figure>
The authors tested the validity of the data rendered using Synavis by comparing it with real-world data from an experiment that was conducted previously. To create simulated data, CPlantBox was configured to virtually replicate the experiment. Images from the experiment were input into CPlantBox and the simulated individual plant geometries were inserted into UE and scaled up to field scale. They then compared the measurements of leaf blade length from the actual plants in the experiment and the simulated plants. The measurements obtained from the synthetic images were closely related to those from the actual experiment.
The resulting images can be directly integrated with a deep learning model for training purposes using Synavis. During training, the model learns to recognize patterns, features, and relationships within the images. By exposing the model to a wide range of image variations it becomes capable of generalizing and understanding the underlying structures and characteristics of the visual data.
“We believe that synthetic data can be extremely helpful to combat data scarcity. With Synavis, we have developed a toolset that connects individually very powerful frameworks. Most importantly, we wanted to devise a way to check how well we can actually replicate the data, in a way that is more practical – by subjecting our virtual images to a typical data analysis pipelines and checking if we successfully end up where we started,” explained Helmrich.
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<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="520" height="220" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad022_fig7.jpeg?resize=520%2C220&ssl=1" alt="A figure with three panels. On the right is a 3D image of a synthetic plant. In the center is a similar image of a real-world image of a plant. On the right is a comparison of leaf lengths between the synthetic images and the experimental data. The figure shows that the data follows the same trends, but that the synthetic data values are a bit lower." class="wp-image-80591" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad022_fig7.jpeg?w=520&ssl=1 520w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad022_fig7.jpeg?resize=300%2C127&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad022_fig7.jpeg?resize=370%2C157&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/m_diad022_fig7.jpeg?resize=270%2C114&ssl=1 270w" sizes="(max-width: 520px) 100vw, 520px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Comparison of the parameter extraction pipeline between synthetic and real-world data and resulting data.</figcaption></figure></div>
This is not the first such framework, but it has several advantages over other approaches. “Synavis connects frameworks by providing a platform to talk to each other. The coupling is very straight forward, standardized and also does not require storing of data. The simulation, the virtual world being rendered, and the deep learning toolsets exist concurrently. If the model predicts correctly once and gets it wrong another time, you can interpolate between those states, always with a cohesive virtual environment in between,” Helmrich concluded.
READ THE ARTICLE:
Dirk Norbert Helmrich, Felix Maximilian Bauer, Mona Giraud, Andrea Schnepf, Jens Henrik Göbbert, Hanno Scharr, Ebba Þora Hvannberg, Morris Riedel, <a href="https://academic.oup.com/insilicoplants/article/6/1/diad022/7462759">A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning</a>, in silico Plants, Volume 6, Issue 1, 2024, diad022, <a href="https://doi.org/10.1093/insilicoplants/diad022">https://doi.org/10.1093/insilicoplants/diad022</a>
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The code used in Helmrich et. al. (2023) is open source and available under the <a href="https://github.com/dhelmrich/Synavis" target="_blank" rel="noreferrer noopener">Synavis</a> and <a href="https://github.com/dhelmrich/SynavisUE" target="_blank" rel="noreferrer noopener">SynavisUE</a> repositories with an example available under <a href="https://github.com/dhelmrich/SynavisUEexample" target="_blank" rel="noreferrer noopener">SynavisUEexample</a>. The CPlantBox official code can be found at <a href="https://github.com/Plant-Root-Soil-Interactions-Modelling/CPlantBox" target="_blank" rel="noreferrer noopener">on the institute’s GitHub page</a>. The branch associated with this article has been forked to <a href="https://github.com/dhelmrich/CPlantBox" target="_blank" rel="noreferrer noopener">this page</a>.
German translation by Dirk Helmrich.
The post <a href="https://botany.one/2024/04/harnessing-the-power-of-synthetic-data-for-deep-learning-image-analysis/">Harnessing the Power of Synthetic Data for Deep Learning Image Analysis</a> appeared first on <a href="https://botany.one">Botany One</a>.
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<title>From the Seed Bank to the Field: What Storage Does to the Seeds of an Alpine Grass?</title>
<link>https://botany.one/2024/04/from-the-seed-bank-to-the-field-what-storage-does-to-the-seeds-of-an-alpine-grass/</link>
<comments>https://botany.one/2024/04/from-the-seed-bank-to-the-field-what-storage-does-to-the-seeds-of-an-alpine-grass/#respond</comments>
<dc:creator><![CDATA[Carlos Andres Ordonez Parra]]></dc:creator>
<pubDate>Tue, 09 Apr 2024 19:00:00 +0000</pubDate>
<category><![CDATA[Growth & Development]]></category>
<category><![CDATA[Seed Science Research]]></category>
<guid isPermaLink="false">https://botany.one/?p=80615</guid>
<description><![CDATA[Seeds are usually stored before they are sown, but does this induce any kind of change in them? Chen and colleagues bring us a fascinating study of a Tibetan grass species to answer this question.
The post <a href="https://botany.one/2024/04/from-the-seed-bank-to-the-field-what-storage-does-to-the-seeds-of-an-alpine-grass/">From the Seed Bank to the Field: What Storage Does to the Seeds of an Alpine Grass?</a> appeared first on <a href="https://botany.one">Botany One</a>.
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Many people may think that studying seed germination is quite a straightforward process, where scientists put their seeds under various different conditions until they eventually discover the ones where seeds germinate the most and more rapidly. Moreover, people expect that the germination results found in the lab will somehow reflect how well seeds will perform in the field. Unfortunately, especially for seed scientists like me, these things rarely occur or are as simple as that, mostly because seed germination is controlled by a myriad of factors both before, during, and after seed development. Among all these factors, perhaps one of the most striking for researchers are all the conditions that the seed faces once it has reached maturity, mainly because nowadays we subject seeds to conditions that they would never face in the wild: the conditions of a <a href="https://www.youtube.com/watch?v=gAP_JKKg2kE&t=3s" target="_blank" rel="noreferrer noopener">seed bank</a>.
Seed banks are facilities dedicated to the storage of seeds with the aim of conserving them under controlled conditions. Such conditions include low temperatures and low humidity, which aim to extend their shelf life for several decades. Seed banks dedicated to conserving our planet’s plant biodiversity have been <a href="https://www.theguardian.com/environment/2022/apr/15/seed-banks-the-last-line-of-defense-against-a-threatening-global-food-crisis" target="_blank" rel="noreferrer noopener">growing and increasing</a> so that these seeds can be used in the future, for example, to reintroduce species that have become extinct in the wild or to restore sites that have been degraded in some way. Given the practical importance of seed banks and the potential effect of storage conditions, it is increasingly necessary to undertake studies to understand how seed banking affects germination.
In a fascinating study recently published in <a href="https://doi.org/10.1017/S0960258523000223" target="_blank" rel="noreferrer noopener">Seed Science Research</a>, researchers from the State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems of Lanzhou University evaluated how storage affected the germination of Elymus nutans –a perennial grass from the alpine meadows of the eastern <a href="https://en.wikipedia.org/wiki/Tibetan_Plateau" target="_blank" rel="noreferrer noopener">Qinghai-Tibet Plateau</a>. Specifically, they aimed to evaluate whether storage affected germination responses to temperature and seedling emergence in the field and if these were somehow related.
<figure class="wp-block-image size-full is-resized"><img loading="lazy" decoding="async" width="468" height="263" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau1.png?resize=468%2C263&ssl=1" alt="" class="wp-image-80618" style="width:650px" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau1.png?w=468&ssl=1 468w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau1.png?resize=300%2C169&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau1.png?resize=370%2C208&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau1.png?resize=270%2C152&ssl=1 270w" sizes="(max-width: 468px) 100vw, 468px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Alpine meadows landscape in the Qinghai-Tibet Plateau.</figcaption></figure>
The researchers collected seeds in ten populations along an elevation gradient across the eastern Qinghai-Tibet Plateau in sites ranging from 2800 to 3600 m above sea level. These seeds were germinated under different temperature conditions in the laboratory, either immediately after collection or after six months in storage at room temperature. A similar experiment was conducted by planting the fresh and stored seeds of the ten populations in three different sites across the same elevation gradient. Altogether, these two experiments allowed the researchers to evaluate whether storage alters the temperature conditions required for germination and if this translates to changes in seedling establishment in the field.
Interestingly, seeds stored for six months germinated more quickly than fresh seeds, regardless of the population they originally came from. Stored seeds also required higher temperatures to germinate but require less exposure time to achieve it. More notably, the variation in germination responses was reduced in stored seeds, suggesting that storage made the responses of the different populations of this species more homogeneous. Therefore, these results imply that storage altered the physiology of Elymus nutans seeds by increasing their germination capacity and shifting their temperature requirements.
<figure class="wp-block-image size-full is-resized"><img loading="lazy" decoding="async" width="468" height="351" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?resize=468%2C351&ssl=1" alt="" class="wp-image-80620" style="width:650px" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?w=468&ssl=1 468w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?resize=300%2C225&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?resize=400%2C300&ssl=1 400w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?resize=200%2C150&ssl=1 200w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?resize=370%2C278&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?resize=270%2C203&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/Tibet_Plateau2.png?resize=80%2C60&ssl=1 80w" sizes="(max-width: 468px) 100vw, 468px" data-recalc-dims="1" /><figcaption class="wp-element-caption">Alpine Meadows in the Gansu Province, the region where the experiment was developed. Photo by <a href="https://commons.wikimedia.org/wiki/File:Southeast_Tibet_meadows.jpg">NoGhost, Wikicommons</a>.</figcaption></figure>
Such a positive effect was also observed in the field experiment, where the crested seeds had a higher establishment success and even higher survival rates in some populations. Storage did not have the same homogenising effect observed in the laboratory experiments, suggesting these physiological effects are not translated to the field. 
Still, one thing is clear: seed storage seems to be a very promising tool for ecological restoration. Instead of using the seeds immediately after they are collected, they can be stored for more effective sowing. Storage has already been reported as a <a href="https://doi.org/10.3390/plants9050636" target="_blank" rel="noreferrer noopener">useful technique for breaking shallow </a>non-deep physiological dormancy –the same dormancy class present in Elymus nutans– but this study is the first of its kind to demonstrate its practical applicability with a large number of populations and over a considerable environmental gradient. Previous studies have shown that storage can positively or negatively affect seedling establishment in the fill. Still, these promising results underscore the importance of assessing such effects, as they might hold the key to more successful restoration projects.
READ THE PAPER:
Chen, D., Yuan, H., Bao, J., Zhao, X., Fu, X., & Hu, X. (2024). Dry storage alters intraspecific variation in phenotypic traits at early life stages: evidence from a dominant alpine meadow species. Seed Science Research, <a href="https://doi.org/10.1017/S0960258523000223">https://doi.org/10.1017/S0960258523000223</a>
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<figure class="alignleft size-large is-resized"><img loading="lazy" decoding="async" width="770" height="1027" src="https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=770%2C1027&ssl=1" alt="" class="wp-image-79549" style="width:350px" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=810%2C1080&ssl=1 810w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=225%2C300&ssl=1 225w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=768%2C1024&ssl=1 768w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=1152%2C1536&ssl=1 1152w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=1536%2C2048&ssl=1 1536w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=900%2C1200&ssl=1 900w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=600%2C800&ssl=1 600w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=450%2C600&ssl=1 450w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=300%2C400&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=150%2C200&ssl=1 150w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=1200%2C1600&ssl=1 1200w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=370%2C493&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=270%2C360&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=570%2C760&ssl=1 570w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?resize=740%2C987&ssl=1 740w, https://i0.wp.com/botany.one/wp-content/uploads/2023/10/Profile-Picture1.jpg?w=2310&ssl=1 2310w" sizes="(max-width: 770px) 100vw, 770px" data-recalc-dims="1" /></figure></div>
Carlos A. Ordóñez-Parra
<a href="https://caordonezparra.github.io/">Carlos</a> (he/him) is a Colombian seed ecologist currently doing his PhD at Universidade Federal de Minas Gerais (Belo Horizonte, Brazil) and working as a Science Editor at Botany One and a Social Media Editor at Seed Science Research. You can follow him on X and BlueSky at @caordonezparra.
Spanish and Portuguese Translation by Carlos A. Ordóñez-Parra. (in progress)
The post <a href="https://botany.one/2024/04/from-the-seed-bank-to-the-field-what-storage-does-to-the-seeds-of-an-alpine-grass/">From the Seed Bank to the Field: What Storage Does to the Seeds of an Alpine Grass?</a> appeared first on <a href="https://botany.one">Botany One</a>.
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<post-id xmlns="com-wordpress:feed-additions:1">80615</post-id> </item>
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<title>A Comparative Analysis for Predicting Wheat Yield in Rainfed Areas of Morocco</title>
<link>https://botany.one/2024/04/a-comparative-analysis-for-predicting-wheat-yield-in-rainfed-areas-of-morocco/</link>
<comments>https://botany.one/2024/04/a-comparative-analysis-for-predicting-wheat-yield-in-rainfed-areas-of-morocco/#respond</comments>
<dc:creator><![CDATA[Rachel Shekar]]></dc:creator>
<pubDate>Mon, 08 Apr 2024 19:50:30 +0000</pubDate>
<category><![CDATA[Computational Models]]></category>
<category><![CDATA[Growth & Development]]></category>
<category><![CDATA[APSIM]]></category>
<category><![CDATA[empirical model]]></category>
<category><![CDATA[featured]]></category>
<category><![CDATA[machine learning]]></category>
<category><![CDATA[Morocco]]></category>
<category><![CDATA[wheat]]></category>
<category><![CDATA[yield]]></category>
<guid isPermaLink="false">https://botany.one/?p=80600</guid>
<description><![CDATA[A new study comparing crop yield prediction models reveals insights into the effectiveness of different modeling in the face of climate change. 
The post <a href="https://botany.one/2024/04/a-comparative-analysis-for-predicting-wheat-yield-in-rainfed-areas-of-morocco/">A Comparative Analysis for Predicting Wheat Yield in Rainfed Areas of Morocco</a> appeared first on <a href="https://botany.one">Botany One</a>.
]]></description>
<content:encoded><![CDATA[
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Model-assisted decision-making can minimize farmers’ vulnerability to climate change by predicting crop yield and the variables that influence it. With this information farmers can anticipate changes in crop yields, adjust their farming practices, explore crop diversification options, and plan for adaptation strategies to reduce vulnerability to climate-related risks.
<a href="https://doi.org/10.1093/insilicoplants/diad020">A new study published in in silico Plants compares the yield predictive capacity of multiple modeling approaches.</a>
Achraf Mamassi, a former PhD student at University of Liège in Belgium and Mohammed VI Polytechnic University in Morocco (now a Postdoctoral fellow at INRAE in France) led a study that compared the ability of process-based mechanistic models and empirical models to predict wheat yield across the rainfed areas of Morocco.
Morocco has experienced considerable warming, increasingly erratic rainfall, and an overall decline in precipitation over the last few decades, resulting in highly variable crop yields and large yield gaps. An <a href="https://climateknowledgeportal.worldbank.org/sites/default/files/2021-09/15725-WB_Morocco%20Country%20Profile-WEB.pdf">increase in the frequency and intensity</a> of extreme events is expected under climate change, putting the food and economic balances of the country at risk.
Rainfed parts of Morocco are responsible for 80% of the total cereal production of the country. These areas are particularly vulnerable to climate change.
The research focused on two distinct types of models: mechanistic and empirical models. These models diverge in their fundamental principles and levels of complexity.
Mechanistic (or process-based) models are based on a deep understanding of the biological and physiological processes underlying crop growth. They are developed using scientific principles and mathematical equations that represent the physical, chemical, and biological processes occurring in crops. Mechanistic models simulate the interactions between environmental factors (such as temperature, rainfall, and radiation), soil conditions, and crop characteristics to predict crop responses and yield. The <a href="https://www.apsim.info/documentation/model-documentation/crop-module-documentation/wheat/">APSIM-Wheat</a> model was chosen to represent this model type.
Empirical (or statistical) models, on the other hand, are based on observed relationships between input variables (such as weather conditions, soil properties, and management practices) and crop yield. These models do not explicitly incorporate the underlying biological mechanisms of crop growth. Instead, they rely on statistical methods and historical data to establish patterns and correlations between inputs and outputs.  Two empirical models, multiple regression (MR) and random forest (RF) models, were considered in this study. MR are based on simple linear relationships between predictors and response variables while RF is based on complex statistical algorithms.
The study incorporated a dataset spanning three years, encompassing data from 125 wheat fields located in the rainfed regions of Morocco. The dataset comprised various parameters such as phenology, temperature, rainfall, soil chemistry, and crop management practices. The fields were assigned to regions based on annual rainfall (i.e. favorable, intermediate, and unfavorable).
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<figure class="aligncenter size-large"><img loading="lazy" decoding="async" width="770" height="421" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=770%2C421&ssl=1" alt="" class="wp-image-80604" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=1080%2C590&ssl=1 1080w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=300%2C164&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=768%2C420&ssl=1 768w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=1536%2C839&ssl=1 1536w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=2048%2C1119&ssl=1 2048w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=1200%2C656&ssl=1 1200w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=370%2C202&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=270%2C147&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=570%2C311&ssl=1 570w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?resize=740%2C404&ssl=1 740w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig1.jpeg?w=2310&ssl=1 2310w" sizes="(max-width: 770px) 100vw, 770px" data-recalc-dims="1" /></figure></div>
<a href="https://academic.oup.com/view-large/figure/433818334/diad020_fig1.jpg"></a>
The performance of the three models was evaluated by assessing the precision and accuracy of the simulated yields in comparison to the measured yield.
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<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="640" height="257" src="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig2-new-sm.jpg?resize=640%2C257&ssl=1" alt="" class="wp-image-80602" srcset="https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig2-new-sm.jpg?w=640&ssl=1 640w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig2-new-sm.jpg?resize=300%2C120&ssl=1 300w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig2-new-sm.jpg?resize=370%2C149&ssl=1 370w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig2-new-sm.jpg?resize=270%2C108&ssl=1 270w, https://i0.wp.com/botany.one/wp-content/uploads/2024/04/diad020_fig2-new-sm.jpg?resize=570%2C229&ssl=1 570w" sizes="(max-width: 640px) 100vw, 640px" data-recalc-dims="1" /></figure></div>
The results showed that APSIM-wheat’s predictive capacity was higher than the empirical models. Both empirical models were able to make accurate, but not necessarily precise, predictions. However, the random forest approach was unable to fit a model and identify yield predictors in unfavorable regions. The APSIM model was consistently more precise than the empirical models although it was less accurate for intermediate and unfavorable regions.
Nonetheless, mechanistic models are useful because they can identify the critical variables that contribute to predicting wheat yield. This is because they are built based on the biological and physiological processes that drive crop growth. These models revealed several significant variables that affect yield, including leaf density and distribution during the heading stage, climate factors such as maximum temperatures at emergence and tillering, and the amount of fertilizer applied during the heading stage.
Rather than clearly supporting the superiority of one type of model over another, the authors advocated for complementary use of all approaches depending on data availability and targeted time horizon for yield simulations (one-year vs. decades) and the modelling objectives.
Empirical models are preferred for long-term predictions due to their precision and ability to capture the effects of climate variability. In contrast, Mechanistic models like APSIM are more suitable for making agronomic recommendations that save time and resources by focusing on those with the most significant impact. They have the advantage of being able to predict yield as soon as essential parameter values become available.
Mamassi explained the impact of this work on Moroccan farmers.
“The adoption of a fundamental pillar of Precision Agriculture, represented by mechanistic and empirical modeling, in Moroccan agriculture could empower farmers in rainfed regions. Firstly, it contributes to assessing the potential yield of these regions and highlighting factors behind crop yield gaps. Secondly, it offers precise long-term predictions and efficient recommendations customized to specific agro-pedo-climatic conditions, respectively. This could lead to better resilience against climate variability, optimized resource allocation, and improved yields, ultimately enhancing the sustainability and productivity of Moroccan farming practices.”
READ THE ARTICLE:
Achraf Mamassi, Marie Lang, Bernard Tychon, Mouanis Lahlou, Joost Wellens, Mohamed El Gharous, Hélène Marrou, A comparison of empirical and mechanistic models for wheat yield prediction at field level in Moroccan rainfed areas, in silico Plants, Volume 6, Issue 1, 2024, diad020, <a href="https://doi.org/10.1093/insilicoplants/diad020">https://doi.org/10.1093/insilicoplants/diad020</a>
The post <a href="https://botany.one/2024/04/a-comparative-analysis-for-predicting-wheat-yield-in-rainfed-areas-of-morocco/">A Comparative Analysis for Predicting Wheat Yield in Rainfed Areas of Morocco</a> appeared first on <a href="https://botany.one">Botany One</a>.
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<title>Scientists Discover How New Caledonia Birthed Over 40 Unique Palm Species </title>
<link>https://botany.one/2024/04/scientists-discover-how-new-caledonia-birthed-over-40-unique-palm-species/</link>
<comments>https://botany.one/2024/04/scientists-discover-how-new-caledonia-birthed-over-40-unique-palm-species/#respond</comments>
<dc:creator><![CDATA[botanyone]]></dc:creator>
<pubDate>Sun, 07 Apr 2024 15:53:12 +0000</pubDate>
<category><![CDATA[Taxonomy & Evolution]]></category>
<category><![CDATA[Arecaceae]]></category>
<category><![CDATA[Areceae]]></category>
<category><![CDATA[biogeography]]></category>
<category><![CDATA[molecular dating]]></category>
<category><![CDATA[New Caledonia]]></category>
<category><![CDATA[phylogeny]]></category>
<category><![CDATA[speciation]]></category>
<category><![CDATA[target sequence capture]]></category>
<category><![CDATA[ultramafic]]></category>
<guid isPermaLink="false">https://botany.one/?p=80632</guid>
<description><![CDATA[Scientists used DNA to create a family tree of New Caledonia's unique palm trees and discover how they arrived on the islands over many millions of years through long distance travel and evolving to different environments. 
The post <a href="https://botany.one/2024/04/scientists-discover-how-new-caledonia-birthed-over-40-unique-palm-species/">Scientists Discover How New Caledonia Birthed Over 40 Unique Palm Species </a> appeared first on <a href="https://botany.one">Botany One</a>.
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The unique palm flora of New Caledonia contains over 40 species found nowhere else on Earth. However, the complex geological history of the island and lack of detailed phylogenetic studies have left major questions around how this diversity arose. A new multi-year study led by Victor Pérez-Calle recently published in Annals of Botany has constructed <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcae043/7634765">the most comprehensive phylogenetic tree of New Caledonian palms</a> to date, tracing the origins and evolution of these iconic island endemic plants over millions of years.
The team sequenced 151 nuclear genes from 37 endemic palm species alongside 77 close relatives from across the wider palm family. This generated a well-resolved family tree allowing divergence times to be estimated using fossil calibration points. Ancestral habitat preferences for factors like elevation, rainfall and soil type were also inferred and compared with present-day sister species to explore possible drivers of speciation.
Four major clades of New Caledonian palms were revealed that diverged from Australian and New Guinean ancestors between 35-15 million years ago during the Eocene and Miocene. This suggests palms reached the islands via at least three long-distance dispersal events from neighbouring regions when land bridges were submerged. Once established, the palms underwent further in-situ diversification, with habitat transitions frequently coinciding with the emergence of new species over time.
Notably, changes in soil substrate type seem to have played a greater role in older divergences compared to more recent splits, indicating substrate specialization may have been an initial driver of diversification replaced by other factors like climate. The team also found evidence palms colonized out of New Caledonia on at least five occasions, mainly towards nearby Pacific islands.
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Our estimate of phylogenetic relationships among New Caledonian palms species, the most robust yet available, provides important support for the prevailing classification while pinpointing issues, such as the resurrection of Campecarpus and Veillonia and the need for further study of Rhopalostylidinae relative to Basseliniinae. Our biogeographical analyses reveal the prominent role played by New Guinea lineages in shaping New Caledonian palm diversity (via Australia in the case of Archontophoenicinae) and highlight New Caledonia as a source of palm diversity for the neighbouring regions, especially the Pacific Islands.
<cite>Lundquist et al. 2024</cite></blockquote>
The results support reclassifying two endemic genera as distinct from relatives but clarify an ongoing debate around the basal relationships within one subfamily. Looking ahead, follow up population studies using this robust phylogenetic framework promise new insights into mechanisms like ecological speciation that generated New Caledonia’s extraordinary palm biodiversity within a compact land area. More broadly, the island’s endemic flora reflects its history acting as both a recipient and donor of diversity across the vast Pacific region over tens of millions of years.
The research demonstrates how subtle geological and climatic changes shape biodiversity patterns throughout time by unfolding the complicated edges of palm development in this biodiversity hotspot. It also emphasizes islands as melting pots which can incubate and export new species via long distance dispersal processes fundamental to understanding biology on a global scale.
READ THE ARTICLE
Pérez-Calle V., Bellot S., Kuhnhäuser B. G., Pillon Y., Forest F., Leitch I. J. and Baker W. J. (2024) “<a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcae043/7634765">Phylogeny, biogeography and ecological diversification of New Caledonian palms (Arecaceae)</a>” Annals of Botany. Available at: <a href="https://doi.org/10.1093/aob/mcae043">https://doi.org/10.1093/aob/mcae043</a>
The post <a href="https://botany.one/2024/04/scientists-discover-how-new-caledonia-birthed-over-40-unique-palm-species/">Scientists Discover How New Caledonia Birthed Over 40 Unique Palm Species </a> appeared first on <a href="https://botany.one">Botany One</a>.
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