How can a monocot get so massive?

How can a monocot get so massive?

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Some monocots (such as palms) are impressively thick and massive, yet botanists maintain that they don't have secondary growth. Why do botanists say this? How can it get so big without secondary growth?

The vascular system is different in monocots and dicots. In dicots the vascular tissues are arranged in concentric circles; one of these rings is meristematic cells (undifferentiated cells that can differentiate into any cell type). This ring of meristem tissue is called the vascular cambium and is where secondary growth occurs - xylem grows inwards and phloem grows outwards.


Monocots have a distinctive arrangement of vascular tissue known as an atactostele in which the vascular tissue is scattered rather than arranged in concentric rings. Many monocots are herbaceous and do not have the ability to increase the width of a stem (secondary growth) via the same kind of vascular cambium found in non-monocot woody plants. However, some monocots do have secondary growth, and because it does not arise from a single vascular cambium producing xylem inwards and phloem outwards, it is termed "anomalous secondary growth". (Wikipedia)

For palms specifically:

Palm trees increase their trunk diameter due to division and enlargement of parenchyma cells, which is termed diffuse secondary growth. (Wikipedia)

Parenchyma cells are type of cells found in plant ground tissue, which makes up the bulk of plant mass.

NCERT Class 9 Science Lab Manual – Features of Monocot and Dicot Plants

To study the external features of root, stem, leaf and flower of monocot and dicot plants.

  1. Angiosperms: All flower bearing plants are called angiosperms. It has been divided into two categories, i.e. Monocotyledon and Dicotyledon.
  2. Cotyledon: Seed leaves are called cotyledons.

Diagrammmmatic Representation
Germination. Growth of radicle and plumule from the seed is called germination. The seed swells and the radicle breaks out through the seed coat at the Micropyle.
Micropyle: It is the weak spot in the seed coat where the radicle escapes into the growing medium.
Hypocotyl: The connection between the radicle and the cotyledon, which develops into hook and pushes the growing seed towards the surface.
Epicotyl: It is the mature shoot which begins to grow.

Materials Required
Sample of monocot and dicot plants. 10-20 days prior soaked seeds which have shown the germination, seeds of maize, wheat, green gram, peas. Slide of T.S. of Monocot stem and Dicot stem, microscope.

  1. Take two seeds of maize and peas each and soak them in water. Allow them to become tender. Next day try to split the seed. Observe whether the seeds break into two equal halves.
  2. Take the germinated seeds, bearing root leaves, stems, flowers and have turned into plants.
  3. Observe the root system in both maize and peas, type of venation in leaves. Record your observations.
  4. Now look at the stem of both the plants, woody, fleshy, branched, unbranched etc.
  5. Observe the flowers carefully for both maize and pea plant and carefully see the number of sepals, petals, type of pollens in each flower.
  6. Observe the transverse section of slide showing Monocot and Dicot stem with arrangement of vascular bundles.
  7. Record your observation in the table and draw diagrams of all the parts studied by you.

Observation Table

Monocot and dicot plants have many distinctive features which reveals the difference between them.


  1. Seeds should be soaked for a day or two. (in winters soak the seeds in lukewarm water)
  2. During germination of seed, put the seeds in wet cotton initially.
  3. The colour of sepals and petals in most of the monocots may be same (tepals), do not get confused over this.

Question 1:
Name two divisions of Angiosperms.
Monocots and Dicots.

Question 2:
Name the world’s largest seed and smallest seed.
The largest seed is coco-de-mer and smallest seed is orchid seed both are Monocots.

Question 3:
What are tepals?
In monocot flowers the sepals and petals are mostly of same colours, together they are called tepals.

Question 4:
Which root system contain bulbs?
Fibrous root.

Question 5:
Name the largest family plants of Monocots.

Question 6:
A student observed the slides of T.S. of maize, what type of vascular bundles are seen?
The T.S. of maize shows scattered vascular bundles.

Question 7:
What is the radicle?
The first outgrowth from the germinating seed is called radicle.

Question 8:
What is the function of endosperm?
It provides food for the developing embryo.

Question 9:
An unknown plant contains 6 petals, leaves show parallel venation, is it a monocot or dicot?
It is a monocot plant.


Question 1:
What are cotyledons?
The seed leaves are called cotyledons.

Question 2:
How many petals do monocot and dicot flowers bear?
The monocot flowers bear 3 petals or multiples of 3, the dicot flowers bear 4, 5 or multiples of these numbers of sepals and petals.

Question 3:
What type of stem do we see in monocot plants?
The stem is soft, unbranched and not woody.

Question 4:
What is the function of bulbs present in monocot roots?
The fibrous roots are short, gives less nutrient to the plant, during spring when leaves are shed, these bulbs supply stored food in them to the plant.

Question 5:
When the monocot seed starts germinating initially what type of root system do you observe?
The initial root that comes out of the germinating seed is tap root, it dies and is replaced by adventitious roots.

Question 6:
On soaking the monocot and dicot seeds, which germinates faster?
Monocot seeds germinate faster than the dicot seeds.

Question 7:
Which division of angiosperms provides the food-grain in large quantity?
Monocot seeds are the largest food crop. eg. wheat, maize, rice, barley.

Question 8:
What is common in sugarcane, banana and pineapple?
All of these are Monocots.

Question 9:
What is micropyle?
The weak spot in the seed coat where the radicle escapes out is called micropyle.

Question 10:
Name different parts of seed.
The seed has seed coat, endosperm, embryo.

Question 11:
Identify the seed of the plant.

It is a dicot seed as two cotyledons are seen.


Question 1:
How do we differentiate between fibrous root system and tap-root system?
Tap root: It has a main root with lateral roots growing on the primary root.
Fibrous roots: It has no main root, all roots grow like fibres.

Question 2:
A plant has leaves with reticulate venation and floral parts consisting of 5 sepals, 5 petals, 5 stamens, and 5 carpels. In which group of angiosperms would you place this plant? Give reasons.
The plant is dicot as the flowers are pentamerous i.e. floral parts are five in number.

Question 3:
In a plant, name two features which you would examine to categorise it into a monocot or a dicot.plant.
In any given plant, to classify it as monocot or dicot, we need to see the venation in leaves, reticulate/net like pattern is dicot and parallel venation is monocot.
The flowers are trimerous (three petals) in monocot and pentamerous(five petals) in dicots.

Question 4:
Do all flowers have all the floral parts? Explore.
No, all the flowers may not have all the floral parts.

Multiple Choice Questions (MCQs)

Question 1:
The flowering plant group which is the biggest in the plant kingdom is
(a) fern
(b) gymnosperm
(c) algae
(d) angiosperm.

Question 2:
The main difference in Ferns, Fungi and Angiosperm is that angiosperms have
(a) true leaves, stems, roots
(b) fronds
(c) hyphae
(d) cotyledon

Question 3:
Flowers are mainly herbaceous and includes species that produce bulbs (lilies), grasses, orchids and palms belong to
(a) Dicot
(b) Ferm
(c) Monocot
(d) Moss

Question 4:
Endosperm is present in
(a) monocot seeds
(b) dicot seeds
(c) both (a) and (b)
(d) non of these

Question 5:
Seed leaves are called
(a) veins
(b) stipules
(c) colyledons
(d) radicle

Question 6:
World’s smallest and largest seed is
(a) monocot-smallest, dicot-largest
(b) dicot-smallest, monocot-largest
(c) both dicot
(d) both monocot

Question 7:
The division of angiospenn that provides the largest food crop is
(a) dicot
(b) Monocot
(c) both (a) & (b)
(d) none of these

Questions based on Observational Skills

Question 8:
The given figures of flowers A and B represent

(a) A-Monocot B-Dicot
(b) A-Dicot B-Monocot
(c) Both Monocot
(d) Both Dicot

Question 9:
Vascular tissues diagram is shown below, the correct identification is

(a) A-Monocot B-dicot
(b) A-Dicot B-Monocot
(c) both Monocot
(d) both Dicot.

Question 10:
Seeds were soaked in water, some seeds were soft and were easily broken into two equal halves it is
(a) Monocot seed
(b) Dicot seed
(c) both (a) and (b)
(d) none of these.

Question 11:
The weak spot in the seed from where the germinated part of seeds come out is
(a) radicle
(b) plumule
(c) micropyle
(d) endosperm.

Question 12:
The stem of the monocot plant is
(a) soft
(b) unbranched
(c) not woody
(d) all of these.

Question 13:
A student studied the different parts of seed and labelled it as follows:

The wrong labelling is of
(a) seed-coat
(b) cotyledon
(c) endosperm
(d) both (b) & (c)

Question 14:
Identify the seed of a plant

(a) monocot
(b) dicot
(c) both (a) & (b)
(d) can’t say

Question 15:
Plant A has leaves with parallel venation & Plant B has leaves with reticulate venation. The plants are
(a) A – dicot B – monocot
(b) A – monocot B – dicot
(c) both are monocot
(d) both are dicot

Questions based on Reporting and Interpretation Skills

Question 16:
The monocot among the following is
(a) pine
(b) gram
(c) sugarcane
(d) sunflower.

Question 17:
The number of petals, stamens or other floral parts in monocot flowers are
(a) multiples of five
(b) multiples of four
(c) divisible by three
(d) none of these.

Question 18:
In monocot flower, tepals are
(a) same colour petals & sepals, collectively
(b) petals & sepals are joined together
(c) both (a) & (b)
(d) none of these.

Question 19:
Flower parts are in 3’s or multiples of 3, it is
(a) dicot
(b) monocot
(c) both (a) and (b)
(d) can’t say.

Question 20: .
Which of the following is not the characteristic of dicots?
(a) a tap root system
(b) parallel venation.
(c) two cotyledons.
(d) 5 petals of a flower.

Question 21:
Which of the following is not considered in classification of a plant into monocot or dicot?
(a) number of floral parts
(b) venation in leaves
(c) number of seed leaves
(d) size of maturity.

Question 22:
The radicle and the plumule that grows from the seed on maturity becomes
(a) shoot and root respectively
(b) root and shoot respectively
(c) leaf and stem respectively
(d) stem and leaf respectively.

Question 23:
A student saw a specimen of a plant and described it as having soft stem, which is unbranched and woody, the sepals and petals have same colour and look alike. The given plant is
(a) dicot
(b) monocot
(c) both (a) & (b)
(d) can’t say

Question 24:
Which among these plants is Monocot?
(a) Sugarcane, wheat, rice
(b) Banana, barley, maize
(c) Pineapple, rice & barley
(d) all of these

Question 25:
In the legumes like peas, the food in seed is stored in
(a) Seed coat
(b) Endosperm
(c) Cotyledon
(d) Radicle

Factors That Determine a Tree’s Height

Just like height in humans, the height of a tree is a combination of nature and nurture. No matter how well you care for it, a bristlecone pine will never get as tall as a sequoia.

Trees that have better environments and the right genetics will grow taller than starved trees. Factors that influence an individual tree’s height include:

  • Lifetime. Trees never stop growing, so it’s intuitive that the tallest trees are also the oldest. Redwoods routinely live 500 to 800 years and may live several thousand years. They’re the tallest trees around at nearly 400 feet. But they’re not the longest-lived trees (that’s the bristlecone pine, which can live 5,000+ years but tops out at just 50 feet. Clearly, there’s more to the story than just lifetime.
  • Genetics. It’s obvious at the species level that some species just get taller than others. It’s also likely that certain individual trees have genetics that help them get an edge and grow faster. On the flip side, some trees probably suffer from genetic drawbacks that make soaring heights impossible.
  • Water supply. There’s a reason you don’t see the tallest trees in the desert, or even on mountainsides. The redwoods of northern California and the fig trees of the rainforest have something in common — ample water supply. Without water, trees just can’t put their energy towards height. They’ve got other things to worry about!
  • Sunlight. If a tree is growing in the shade of a taller tree, it’s not getting all of the energy available from the sun. These sunlight-starved trees just can’t grow as tall as their neighbors.
    • Sunlight direction can also influence how tall a tree grows. Trees that have unimpeded sunlight will grow straight up, while a tree that’s searching and reaching for sunlight will grow out first, rather than up.

    Let’s look at how these factors may come together to make the redwoods, sequoias, and tropical fig trees so tall. In each case, these trees live in a climate where they can grow year-round. They get ample water and enough sunlight. There’s plenty of nutrients and they have the genetics to grow tall. Finally, they live long enough to reach incredible height.

    On the flip side, a bristlecone pine lives 5,000 years but spends its life in a harsh environment where it often can’t grow year-round. There’s not much water and the soil is nutrient-poor. While there’s plenty of sunlight and plenty of time in this tree’s life, it just won’t grow above 50 feet or so.

    This all comes together to answer how a tree gets so big. But why is it good to be tall?

    Monocots versus Dicots

    In 1682, John Ray published his Methodus Plantarum Nova, in which Dicotyledones and Monocotyledones were first given formal taxonomic standing. This system was popularized by the French botanist Antoine Laurent de Jussieu in his Genera Plantarum of 1789, a work which improved upon, and gradually replaced, the system of plant classification devised by Linnaeus.

    The fuzzy distinction between the classes.

    Even after the general acceptance of Monocots and Dicots as the primary groups of flowering plants, botanists did not always agree upon the placement of families into one or the other class. Even in this century some plants called paleoherbs have left problems for taxonomy of angiosperms. These plants have a mix of characters which do not occur together in most other flowering plants. For instance, the Nymphaeales, or water lilies, have reticulate venation in their leaves, and what may be a single cotyledon in the embryo. It is not clear whether it is a single lobed cotyledon, or two which have been fused. The water lilies also have a vascular arrangement in their stem similar to that of monocots.

    There are also monocots which posses characters more typical of dicots. The Dioscoreales and Smilacaceae have broad reticulate-veined leaves the Alismataceae have acropetal leaf development and Potamogeton is one of several monocots to have floral parts in multiples of four.

    This "fuzziness" in the definitions of Monocotyledonae and Dicotyledonae is not simply the result of poor botany. Rather, it is a real phenomenon resulting from the shared ancestry of the two groups. It is now believed that some of the dicots are more closely related to monocots than to the other dicots, and that the angiosperms do not all fit neatly into two clades. In other words, the dicots include a basal paraphyletic group from which the monocots evolved. Click here for a cladogram which illustrates our current understanding of basal angiosperm relations.

    The characters which distinguish the classes.

    Despite the problems in recognizing basal angiosperm taxa, the standard distinctions between dicots and monocots are still quite useful. It must be pointed out, however, that there are many exceptions to these characters in both groups, and that no single character in the list below will infallibly identify a flowering plant as a monocot or dicot.

    The table summarizes the major morphological differences between monocots and dicots each character is dicussed in more detail below. For more information, refer to the page on monocot morphology.

    Common questions about the classes.

    Having taught in introductory botany for more than five years, I have fielded many questions from students, and present below some of the more common questions and misconceptions. Thanks go to my students for taking an active role in their own education, and asking these questions

    Q: Are pine trees monocots or dicots?

    A: Pines are conifers, and are neither monocots nor dicots. Only flowering plants are considered to be members of these two classes. This question is similar to asking whether a chicken is a monocot or a dicot it is neither.

    Q: Do all dicots produce flowers?

    A: Yes, sort of. All dicots and monocots are flowering plants, and so are descended from flower-producing plants. However, the flowers are not always large and showy the way we expect flowers to be. Oaks, maples, and sycamore are all dicot trees, but they do not produce obvious flowers. Grasses and cattails are monocots whose flowers are often overlooked because they do not have sepals or petals.

    There are also some flowering plants which flower only rarely. Duckweeds are tiny flowering plants which reproduce and spread primarily by vegetative growth they grow by cellular division, and the resulting cluster will then break apart.

    Q: If monocots don't have wood, then what supports palm trees?

    A: Palms rely on overlapping leaf bases, thickened enlarged cells, and prop roots to stay up. This strategy is also used by cycads and tree ferns. We hope to have a special exhibit soon expanding on the architecture of trees which will explain this in more detail.

    Distribution of eudicots

    Eudicots are hugely abundant in the majority of land-based ecosystems in tropical and temperate environments. They are the dominant plants in rainforests where conditions for tree growth is optimum. They are found on all continents excluding Antarctica in forests, deserts, wetlands, grasslands (although not the dominant plant group), shrublands and herbfields. Eudicot tree species are however not very well adapted to cold environments and they are often replaced by gymnosperms species at higher altitudes and latitudes. In grasslands, the dominant plants are monocots but many eudicot herbs also exist in these ecosystems.

    Plant tissues

    Content below adapted from OpenStax Biology 30.1

    Plant tissue systems fall into one of two general types: meristematic tissue, and permanent (or non-meristematic) tissue. Meristematic tissue is analagous to stem cells in animals: m eristematic cells are undifferentiated continue to divide and contribute to the growth of the plant. In contrast, permanent tissue consists of plant cells that are no longer actively dividing.

    Meristems produce cells that quickly differentiate, or specialize, and become permanent tissue. Such cells take on specific roles and lose their ability to divide further. They differentiate into three main tissue types: dermal, vascular, and ground tissue. Each plant organ (roots, stems, leaves) contains all three tissue types:

    • Dermal tissue covers and protects the plant, and controls gas exchange and water absorption (in roots). Dermal tissue of the stems and leaves is covered by a waxy cuticle that prevents evaporative water loss. Stomata are specialized pores that allow gas exchange through holes in the cuticle. Unlike the stem and leaves, the root epidermis is not covered by a waxy cuticle which would prevent absorption of water. Root hairs, which are extensions of root epidermal cells, increase the surface area of the root, greatly contributing to the absorption of water and minerals. Trichomes, or small hairlike or spikey outgrowths of epidermal tissue, may be present on the stem and leaves, and aid in defense against herbivores.
    • Ground tissue carries out different functions based on the cell type and location in the plant, and includes parenchyma (photosynthesis in the leaves, and storage in the roots), collenchyma (shoot support in areas of active growth), and schlerenchyma (shoot support in areas where growth has ceased)is the site of photosynthesis, provides a supporting matrix for the vascular tissue, provides structural support for the stem, and helps to store water and sugars.
    • Vascular tissue transports water, minerals, and sugars to different parts of the plant. Vascular tissue is made of two specialized conducting tissues: xylem and phloem. Xylem tissue transports water and nutrients from the roots to different parts of the plant, and also plays a role in structural support in the stem. Phloem tissue transports organic compounds from the site of photosynthesis to other parts of the plant. The xylem and phloem always lie adjacent to each other in a vascular bundle (we’ll explore why later).

    Each plant organ contains all three tissue types. Koning, Ross E. 1994. Plant Basics. Plant Physiology Information Website. (6-21-2017). Reprinted with permission.

    Before we get into the details of plant tissues, this video provides an overview of plant organ structure and tissue function:

    Monocots vs. Dicots, With Diagrams

    Flowering plants, also known as angiosperms, can be broken into two groups: monocotyledons (monocots) and dicotyledons (dicots). You’ve probably heard these terms being discussed by gardeners, and if you don’t know the difference it can be intimidating to ask.

    Grasses, onion, monster and palm trees are all examples of monocots and if you look closely you will start to see a lot of things in common. These are in contrast with dicot examples like mint, dandelion, legumes and eucalyptus trees.

    This article will help you understand the difference between these two groups so that you don’t feel left out of the conversation.


    The first way monocots and dicots are different is evident in their names: monocotyledons have one cotyledon, and dicots have two.

    Different embryos between a dicot bean and a monocot grain. The main elements can look vastly different between species. Diagram via Plants Grow Here.

    Inside a seed is a plant embryo, which is the baby plant, a protective barrier, one or two embryonic leaves, and a source of food. These basic elements are expressed differently in different angiosperms.

    Cotyledons are used as a source of food in dicots, as well as the first embryonic leaves, as can be seen in the first 2 leaves of a maple sprout that look nothing like the true leaves that emerge afterwards. Monocots usually have an additional food source in the form of the endosperm.

    See the difference between the cotyledons and the true leaves on this Acer Negundo maple sapling. Image source


    It’s hard to identify a monocot by the roots, but if you see a tap root on the plant, you’ve got a dicot on your hands.

    The embryo contains a baby root called a radicle, and in dicots this embryonic root may emerge as a single tap root, at least at first. A tap root is a thick one that goes straight down, like a carrot or dandelion root.

    This is a tap root that has lateral roots. Image source

    Most dicots outgrow their tap root so that it only plays a special role during germination and early life, and can be said to transition into a fibrous root system.

    Fibrous root systems aren’t based on one main root instead, they have a huge number of roots moving in all directions and branching as they go along. All monocots have a fibrous root system, as seemingly do some dicots.

    The bulb on these spring onions are the stem, and you can see the fibrous roots coming from the base. Image source

    Vascular Systems

    Another way the two types differ is in the way their vascular bundles are ordered.

    Dicots have very orderly bundles forming rings around a centre, whereas monocot bundles are haphazardly arranged throughout the stem.

    If there are tree rings present, it’s definitely a dicot, and if the cut stem looks like a palm tree with lots of undifferentiated fibres and lacking visible xylem and phloem rings, that’s an indicator it’s probably a monocot.

    Dicot vascular bundles of xylem and phloem are arranged in a ring, whereas monocot bundles are sporadic. Diagram via Plants Grow Here.

    Leaf Veins

    Monocots tend to have parallel leaf veins (or venation), where the veins run parallel from the leaf stem (or petiole) to the tip, as seen on a blade of grass.

    Dicots tend to have reticulate leaf venation, where the veins branch off and laterally. There may or may not be one main vein (or midvein) that all others branch from.

    Grasses are an example of parallel leaf venation in a monocot. Image source

    The ficus genus are dicots, and the leaves on this fiddle leaf are reticulate. Photo via Plants Grow Here.

    Leaf venation is not an exact way to make an identification, because there are exceptions and blurring to the rule on both sides.


    One of the best ways to distinguish between the two types of angiosperms is to look at the flowers.

    Flowers are made of individual parts such as stamens, petals, sepals, and more. If these parts number in multiples of three, this indicates a monocot. However, if the parts are in multiples of 4 or 5, you’ve probably got a dicot on your hands.

    Lilly flowers have 3 sepals (that look like petals), 3 petals and 6 stamens. Image source

    Mustard weeds are a dicot that have 4 petals, 4 tall stamina (stamens) and 2 short stamina. Photo via Plants Grow Here.

    Looking at the image of the mustard weed above, we see two flowers that look a bit different. One has 4 leaves more or less displayed in a cross, and the other flower has 3 petals with one hiding behind another so it looks like the flower only has 2 petals.

    If you were to look with a magnifying glass, you’ll see there are 4 tall and 2 short stamens bringing the total to 6 stamens. We might be confused because 6 is a multiple of 3, indicating a monocot yet there are 4 large stamens and 4 petals, indicating dicot(?). Then we see it has a taproot and we know for sure it’s a dicot, because monocots never have a taproot.

    Identifying plants can be very difficult, especially in cases like this where we have one flower missing a petal, which is why it’s always easier to identify a plant where there are many flowers and leaves available so we can get a better picture of what’s normal for the plant

    Secondary Growth

    Some dicots have lateral growth, meaning that they grow thicker each year by growing new tissue between their phloem and xylem in a cambium. Plants that do this create tree rings as more growth is put on in spring and summer than in winter, creating differentiation in the thickness of summer growth and winter growth.

    Dicots start out with many bundles separated in a ring formation, then as secondary growth occurs the secondary xylem and phloem meet and form complete rings. Diagram via Plants Grow Here.

    Some dicots and gymnosperms have secondary growth where meristematic tissue between xylem and phloem produce new tissue which thickens the stem.

    Almost all trees are dicots and get thicker over time, however some monocots show “anomalous secondary growth” where they become thicker without a vascular cambium, sometimes by individual cells thickening over time.

    Palm trees, banana trees and yuccas are all examples of monocots with anomalous secondary growth.

    Are Cone Bearing Plants, Ferns And Mosses Classed As Monocots Or Dicots?

    Trick question! None of these are flowering plants, and are unrelated to monocots and dicots. You can read more about the four groups of plants here.


    As mentioned above, there are exceptions to the rules so if you know the species’ scientific name you’ll be able to do a Google search in order to know for sure.

    Despite the differences, monocots and dicots are still quite closely related. Current thinking is that all angiosperms come from a single line of gymnosperms that developed the flower around 130 million years ago.

    Where To From Here?

    A good place to jump to after monocots and dicots would be learning about the 4 different types of plants, if you haven’t already learnt about them. Monocots and dicots are both part of one group of these main 4: angiosperms, a.k.a. flowering plants.

    Unlike monocots, dicot plants often have secondary growth, which is a method of growing more vascular tissue to grow thicker branches. Learn more about vascular systems of plants via this article.

    Knowing about monocots and dicots can help us choose an appropriate selective herbicide that kills some plants and leaves others. For example, dicamba kills dicots while Fusilade kills grasses. Read all about the different chemicals we can use to control weeds.

    Growth of Plants

    From Seeds

    Plants grow from seeds, because the seeds contain within themselves an embryonic plant and stored food. The seed is a result of pollination, where the ripened ovule is protected by the seed coat, an outer covering of the ovule. Basically, the seed contains the whole immature plant (roots and leaves), the seed’s leaves are called the cotyledons a seed with one leaf is known as monocotyledonous or monocots, while a two-leaved seed is known as dicotyledonous or dicots. For a seed to germinate, right conditions such as quality of the endosperm (food within the seed), moisture, temperature, humidity, light, and quality of the nutrients in the soil, all come in play.

    In the Dark

    To most of us this may seem strange, for we have been schooled in the thought that plants need sunlight to grow. Which is quite right. Some may grow in shade, some indoors, but they all need light, full, partial, or diffused. Plants follow the principle of phototropism, which means that their growth is determined by the direction of the light source. Here, even the concept of gravitropism (gravity), also known as geotropism comes to play, the plant shoots exhibit positive phototropism (growth towards light) and negative gravitropism (defying gravity by shooting upwards), while the roots exhibit exactly the opposite negative phototropism and positive gravitropism.

    When seeds are sown, ‘keep it in a dark place’ is generally a common instruction, but by dark one never means absolute dark, it just refers to keeping the seeds away from direct sunlight. Plants need a source of light to grow, to produce their food through the process of photosynthesis. If plants do not receive light, they will not be able to produce chlorophyll, eventually losing their green color and they will die. This rule also applies to plants that have different colored leaves too. Some plants may grow in the dark, but they are equipped to source their light from the atmosphere. Try a simple experiment at home, take a small plant and keep it in dark, one may have to cover it, and see for yourself, in a few days your plant will wither and die.

    In the Ocean

    For plants to grow in the ocean they do need sunlight, in fact, all marine life is dependent upon the light and the process of photosynthesis. Sunlight penetrates the ocean up to a thousand meters. It is up to 100 meters in depth that receives a good amount of sunlight, this place is called the euphotic zone, while below that till 1000 meters receives diffused sunlight, and is known as disphotic zone. The layer where no light penetrates is called the aphotic zone.

    Basically there are two types of plants found in the ocean ones with roots that grow on the ocean bed, and rootless ones that float about with the water. The ones that float receive all the light they need directly for photosynthesis as they are on the surface of the water, whereas the ones that are rooted are found only in shallow waters in the euphotic zone. No plants are found below this zone as there is not enough sunlight to maintain photosynthesis in deeper waters. Besides sunlight and water (which they have in plenty), marine plants such as phytoplankton, algae, seaweed, kelp, etc., draw carbon dioxide from the atmosphere. Carbon from carbon dioxide is needed for photosynthesis.

    In Water

    Plants have been growing in water and without soil for years now. It is only in the past decade the concept has caught on. Most epiphytic, parasitic, and symbiotic plants do not need soil to grow, but find other mediums to ensure a steady flow of nutrients. The principles of hydroponic gardening are applied to grow plants in water. All nutrients required by a plant are provided through the water, by the way of fertilizer application combined with sunlight they receive, plants have no trouble surviving. Another method is the aeroponic gardening here the plants are suspended in air and their roots are kept moistened with nutrient-rich water. This method is being extensively studied for the purpose of commercial agriculture, especially for places where soil conditions are poor.

    Would you like to write for us? Well, we're looking for good writers who want to spread the word. Get in touch with us and we'll talk.

    Exploration of space brings to mind whether plants will be able to grow in space or not. Plants haven’t been found growing on Mars or Venus, but they are being grown in space shuttles. A specialized chamber called ‘Advanced Astroculture’ a.k.a. ADVASC or a container called Biomass production is used for this process. This chamber is experimentally controlled to provide the plants with water and a supply of carbon dioxide to help them in their own food production through photosynthesis.

    I hope this article has helped you understand the growth mechanism of plants a little better, if you do know more, share it with me.

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    Error bars in experimental biology

    Error bars commonly appear in figures in publications, but experimental biologists are often unsure how they should be used and interpreted. In this article we illustrate some basic features of error bars and explain how they can help communicate data and assist correct interpretation. Error bars may show confidence intervals, standard errors, standard deviations, or other quantities. Different types of error bars give quite different information, and so figure legends must make clear what error bars represent. We suggest eight simple rules to assist with effective use and interpretation of error bars.

    What are error bars for?

    Journals that publish science—knowledge gained through repeated observation or experiment𠅍on't just present new conclusions, they also present evidence so readers can verify that the authors' reasoning is correct. Figures with error bars can, if used properly (1𠄶), give information describing the data (descriptive statistics), or information about what conclusions, or inferences, are justified (inferential statistics). These two basic categories of error bars are depicted in exactly the same way, but are actually fundamentally different. Our aim is to illustrate basic properties of figures with any of the common error bars, as summarized in Table I , and to explain how they should be used.

    Table I.

    Common error bars

    What do error bars tell you?

    Descriptive error bars.

    Range and standard deviation (SD) are used for descriptive error bars because they show how the data are spread ( Fig. 1 ). Range error bars encompass the lowest and highest values. SD is calculated by the formula

    where X refers to the individual data points, M is the mean, and Σ (sigma) means add to find the sum, for all the n data points. SD is, roughly, the average or typical difference between the data points and their mean, M. About two thirds of the data points will lie within the region of mean ± 1 SD, and �% of the data points will be within 2 SD of the mean.

    It is highly desirable to use larger n, to achieve narrower inferential error bars and more precise estimates of true population values.

    Descriptive error bars. Means with error bars for three cases: n = 3, n = 10, and n = 30. The small black dots are data points, and the column denotes the data mean M. The bars on the left of each column show range, and the bars on the right show standard deviation (SD). M and SD are the same for every case, but notice how much the range increases with n. Note also that although the range error bars encompass all of the experimental results, they do not necessarily cover all the results that could possibly occur. SD error bars include about two thirds of the sample, and 2 x SD error bars would encompass roughly 95% of the sample.

    Descriptive error bars can also be used to see whether a single result fits within the normal range. For example, if you wished to see if a red blood cell count was normal, you could see whether it was within 2 SD of the mean of the population as a whole. Less than 5% of all red blood cell counts are more than 2 SD from the mean, so if the count in question is more than 2 SD from the mean, you might consider it to be abnormal.

    As you increase the size of your sample, or repeat the experiment more times, the mean of your results (M) will tend to get closer and closer to the true mean, or the mean of the whole population, μ. We can use M as our best estimate of the unknown μ. Similarly, as you repeat an experiment more and more times, the SD of your results will tend to more and more closely approximate the true standard deviation (σ) that you would get if the experiment was performed an infinite number of times, or on the whole population. However, the SD of the experimental results will approximate to σ, whether n is large or small. Like M, SD does not change systematically as n changes, and we can use SD as our best estimate of the unknown σ, whatever the value of n.

    Inferential error bars.

    In experimental biology it is more common to be interested in comparing samples from two groups, to see if they are different. For example, you might be comparing wild-type mice with mutant mice, or drug with placebo, or experimental results with controls. To make inferences from the data (i.e., to make a judgment whether the groups are significantly different, or whether the differences might just be due to random fluctuation or chance), a different type of error bar can be used. These are standard error (SE) bars and confidence intervals (CIs). The mean of the data, M, with SE or CI error bars, gives an indication of the region where you can expect the mean of the whole possible set of results, or the whole population, μ, to lie ( Fig. 2 ). The interval defines the values that are most plausible for μ.

    Confidence intervals. Means and 95% CIs for 20 independent sets of results, each of size n = 10, from a population with mean μ = 40 (marked by the dotted line). In the long run we expect 95% of such CIs to capture μ here 18 do so (large black dots) and 2 do not (open circles). Successive CIs vary considerably, not only in position relative to μ, but also in length. The variation from CI to CI would be less for larger sets of results, for example n = 30 or more, but variation in position and in CI length would be even greater for smaller samples, for example n = 3.

    Because error bars can be descriptive or inferential, and could be any of the bars listed in Table I or even something else, they are meaningless, or misleading, if the figure legend does not state what kind they are. This leads to the first rule. Rule 1: when showing error bars, always describe in the figure legends what they are.

    Statistical significance tests and P values

    If you carry out a statistical significance test, the result is a P value, where P is the probability that, if there really is no difference, you would get, by chance, a difference as large as the one you observed, or even larger. Other things (e.g., sample size, variation) being equal, a larger difference in results gives a lower P value, which makes you suspect there is a true difference. By convention, if P < 0.05 you say the result is statistically significant, and if P < 0.01 you say the result is highly significant and you can be more confident you have found a true effect. As always with statistical inference, you may be wrong! Perhaps there really is no effect, and you had the bad luck to get one of the 5% (if P < 0.05) or 1% (if P < 0.01) of sets of results that suggests a difference where there is none. Of course, even if results are statistically highly significant, it does not mean they are necessarily biologically important. It is also essential to note that if P > 0.05, and you therefore cannot conclude there is a statistically significant effect, you may not conclude that the effect is zero. There may be a real effect, but it is small, or you may not have repeated your experiment often enough to reveal it. It is a common and serious error to conclude “no effect exists” just because P is greater than 0.05. If you measured the heights of three male and three female Biddelonian basketball players, and did not see a significant difference, you could not conclude that sex has no relationship with height, as a larger sample size might reveal one. A big advantage of inferential error bars is that their length gives a graphic signal of how much uncertainty there is in the data: The true value of the mean μ we are estimating could plausibly be anywhere in the 95% CI. Wide inferential bars indicate large error short inferential bars indicate high precision.

    Replicates or independent samples—what is n?

    Science typically copes with the wide variation that occurs in nature by measuring a number (n) of independently sampled individuals, independently conducted experiments, or independent observations.

    Rule 2: the value of n (i.e., the sample size, or the number of independently performed experiments) must be stated in the figure legend.

    It is essential that n (the number of independent results) is carefully distinguished from the number of replicates, which refers to repetition of measurement on one individual in a single condition, or multiple measurements of the same or identical samples. Consider trying to determine whether deletion of a gene in mice affects tail length. We could choose one mutant mouse and one wild type, and perform 20 replicate measurements of each of their tails. We could calculate the means, SDs, and SEs of the replicate measurements, but these would not permit us to answer the central question of whether gene deletion affects tail length, because n would equal 1 for each genotype, no matter how often each tail was measured. To address the question successfully we must distinguish the possible effect of gene deletion from natural animal-to-animal variation, and to do this we need to measure the tail lengths of a number of mice, including several mutants and several wild types, with n > 1 for each type.

    Similarly, a number of replicate cell cultures can be made by pipetting the same volume of cells from the same stock culture into adjacent wells of a tissue culture plate, and subsequently treating them identically. Although it would be possible to assay the plate and determine the means and errors of the replicate wells, the errors would reflect the accuracy of pipetting, not the reproduciblity of the differences between the experimental cells and the control cells. For replicates, n = 1, and it is therefore inappropriate to show error bars or statistics.

    If an experiment involves triplicate cultures, and is repeated four independent times, then n = 4, not 3 or 12. The variation within each set of triplicates is related to the fidelity with which the replicates were created, and is irrelevant to the hypothesis being tested.

    To identify the appropriate value for n, think of what entire population is being sampled, or what the entire set of experiments would be if all possible ones of that type were performed. Conclusions can be drawn only about that population, so make sure it is appropriate to the question the research is intended to answer.

    In the example of replicate cultures from the one stock of cells, the population being sampled is the stock cell culture. For n to be greater than 1, the experiment would have to be performed using separate stock cultures, or separate cell clones of the same type. Again, consider the population you wish to make inferences about—it is unlikely to be just a single stock culture. Whenever you see a figure with very small error bars (such as Fig. 3 ), you should ask yourself whether the very small variation implied by the error bars is due to analysis of replicates rather than independent samples. If so, the bars are useless for making the inference you are considering.

    Inappropriate use of error bars. Enzyme activity for MEFs showing mean + SD from duplicate samples from one of three representative experiments. Values for wild-type vs. −/− MEFs were significant for enzyme activity at the 3-h timepoint (P < 0.0005). This figure and its legend are typical, but illustrate inappropriate and misleading use of statistics because n = 1. The very low variation of the duplicate samples implies consistency of pipetting, but says nothing about whether the differences between the wild-type and −/− MEFs are reproducible. In this case, the means and errors of the three experiments should have been shown.

    Sometimes a figure shows only the data for a representative experiment, implying that several other similar experiments were also conducted. If a representative experiment is shown, then n = 1, and no error bars or P values should be shown. Instead, the means and errors of all the independent experiments should be given, where n is the number of experiments performed.

    Rule 3: error bars and statistics should only be shown for independently repeated experiments, and never for replicates. If a “representative” experiment is shown, it should not have error bars or P values, because in such an experiment, n = 1 ( Fig. 3 shows what not to do).

    What type of error bar should be used?

    Rule 4: because experimental biologists are usually trying to compare experimental results with controls, it is usually appropriate to show inferential error bars, such as SE or CI, rather than SD. However, if n is very small (for example n = 3), rather than showing error bars and statistics, it is better to simply plot the individual data points.

    What is the difference between SE bars and CIs?

    Standard error (SE).

    Suppose three experiments gave measurements of 28.7, 38.7, and 52.6, which are the data points in the n = 3 case at the left in Fig. 1 . The mean of the data is M = 40.0, and the SD = 12.0, which is the length of each arm of the SD bars. M (in this case 40.0) is the best estimate of the true mean μ that we would like to know. But how accurate an estimate is it? This can be shown by inferential error bars such as standard error (SE, sometimes referred to as the standard error of the mean, SEM) or a confidence interval (CI). SE is defined as SE = SD/√n. In Fig. 4 , the large dots mark the means of the same three samples as in Fig. 1 . For the n = 3 case, SE = 12.0/𢆣 = 6.93, and this is the length of each arm of the SE bars shown.

    Inferential error bars. Means with SE and 95% CI error bars for three cases, ranging in size from n = 3 to n = 30, with descriptive SD bars shown for comparison. The small black dots are data points, and the large dots indicate the data mean M. For each case the error bars on the left show SD, those in the middle show 95% CI, and those on the right show SE. Note that SD does not change, whereas the SE bars and CI both decrease as n gets larger. The ratio of CI to SE is the t statistic for that n, and changes with n. Values of t are shown at the bottom. For each case, we can be 95% confident that the 95% CI includes μ, the true mean. The likelihood that the SE bars capture μ varies depending on n, and is lower for n = 3 (for such low values of n, it is better to simply plot the data points rather than showing error bars, as we have done here for illustrative purposes).

    The SE varies inversely with the square root of n, so the more often an experiment is repeated, or the more samples are measured, the smaller the SE becomes ( Fig. 4 ). This allows more and more accurate estimates of the true mean, μ, by the mean of the experimental results, M.

    We illustrate and give rules for n = 3 not because we recommend using such a small n, but because researchers currently often use such small n values and it is necessary to be able to interpret their papers. It is highly desirable to use larger n, to achieve narrower inferential error bars and more precise estimates of true population values.

    Confidence interval (CI).

    Fig. 2 illustrates what happens if, hypothetically, 20 different labs performed the same experiments, with n = 10 in each case. The 95% CI error bars are approximately M ± 2xSE, and they vary in position because of course M varies from lab to lab, and they also vary in width because SE varies. Such error bars capture the true mean μ on �% of occasions—in Fig. 2 , the results from 18 out of the 20 labs happen to include μ. The trouble is in real life we don't know μ, and we never know if our error bar interval is in the 95% majority and includes μ, or by bad luck is one of the 5% of cases that just misses μ.

    The error bars in Fig. 2 are only approximately M ± 2xSE. They are in fact 95% CIs, which are designed by statisticians so in the long run exactly 95% will capture μ. To achieve this, the interval needs to be M ± t (n𠄱) ×SE, where t (n𠄱) is a critical value from tables of the t statistic. This critical value varies with n. For n = 10 or more it is 𢏂, but for small n it increases, and for n = 3 it is 𢏄. Therefore M ± 2xSE intervals are quite good approximations to 95% CIs when n is 10 or more, but not for small n. CIs can be thought of as SE bars that have been adjusted by a factor (t) so they can be interpreted the same way, regardless of n.

    This relation means you can easily swap in your mind's eye between SE bars and 95% CIs. If a figure shows SE bars you can mentally double them in width, to get approximate 95% CIs, as long as n is 10 or more. However, if n = 3, you need to multiply the SE bars by 4.

    Rule 5: 95% CIs capture μ on 95% of occasions, so you can be 95% confident your interval includes μ. SE bars can be doubled in width to get the approximate 95% CI, provided n is 10 or more. If n = 3, SE bars must be multiplied by 4 to get the approximate 95% CI.

    Determining CIs requires slightly more calculating by the authors of a paper, but for people reading it, CIs make things easier to understand, as they mean the same thing regardless of n. For this reason, in medicine, CIs have been recommended for more than 20 years, and are required by many journals (7).

    Fig. 4 illustrates the relation between SD, SE, and 95% CI. The data points are shown as dots to emphasize the different values of n (from 3 to 30). The leftmost error bars show SD, the same in each case. The middle error bars show 95% CIs, and the bars on the right show SE bars𠅋oth these types of bars vary greatly with n, and are especially wide for small n. The ratio of CI/SE bar width is t (n𠄱) the values are shown at the bottom of the figure. Note also that, whatever error bars are shown, it can be helpful to the reader to show the individual data points, especially for small n, as in Figs. 1 and ​ and4, 4 , and rule 4.

    Using inferential intervals to compare groups

    When comparing two sets of results, e.g., from n knock-out mice and n wild-type mice, you can compare the SE bars or the 95% CIs on the two means (6). The smaller the overlap of bars, or the larger the gap between bars, the smaller the P value and the stronger the evidence for a true difference. As well as noting whether the figure shows SE bars or 95% CIs, it is vital to note n, because the rules giving approximate P are different for n = 3 and for n ≥ 10.

    Fig. 5 illustrates the rules for SE bars. The panels on the right show what is needed when n ≥ 10: a gap equal to SE indicates P ≈ 0.05 and a gap of 2SE indicates P ≈ 0.01. To assess the gap, use the average SE for the two groups, meaning the average of one arm of the group C bars and one arm of the E bars. However, if n = 3 (the number beloved of joke tellers, Snark hunters (8), and experimental biologists), the P value has to be estimated differently. In this case, P ≈ 0.05 if double the SE bars just touch, meaning a gap of 2 SE.

    Estimating statistical significance using the overlap rule for SE bars. Here, SE bars are shown on two separate means, for control results C and experimental results E, when n is 3 (left) or n is 10 or more (right). “Gap” refers to the number of error bar arms that would fit between the bottom of the error bars on the controls and the top of the bars on the experimental results i.e., a gap of 2 means the distance between the C and E error bars is equal to twice the average of the SEs for the two samples. When n = 3, and double the length of the SE error bars just touch (i.e., the gap is 2 SEs), P is 𢏀.05 (we don't recommend using error bars where n = 3 or some other very small value, but we include rules to help the reader interpret such figures, which are common in experimental biology).

    Rule 6: when n = 3, and double the SE bars don't overlap, P < 0.05, and if double the SE bars just touch, P is close to 0.05 ( Fig. 5 , leftmost panel). If n is 10 or more, a gap of SE indicates P ≈ 0.05 and a gap of 2 SE indicates P ≈ 0.01 ( Fig. 5 , right panels).

    Rule 5 states how SE bars relate to 95% CIs. Combining that relation with rule 6 for SE bars gives the rules for 95% CIs, which are illustrated in Fig. 6 . When n ≥ 10 (right panels), overlap of half of one arm indicates P ≈ 0.05, and just touching means P ≈ 0.01. To assess overlap, use the average of one arm of the group C interval and one arm of the E interval. If n = 3 (left panels), P ≈ 0.05 when two arms entirely overlap so each mean is about lined up with the end of the other CI. If the overlap is 0.5, P ≈ 0.01.

    Estimating statistical significance using the overlap rule for 95% CI bars. Here, 95% CI bars are shown on two separate means, for control results C and experimental results E, when n is 3 (left) or n is 10 or more (right). “Overlap” refers to the fraction of the average CI error bar arm, i.e., the average of the control (C) and experimental (E) arms. When n ≥ 10, if CI error bars overlap by half the average arm length, P ≈ 0.05. If the tips of the error bars just touch, P ≈ 0.01.

    Rule 7: with 95% CIs and n = 3, overlap of one full arm indicates P ≈ 0.05, and overlap of half an arm indicates P ≈ 0.01 ( Fig. 6 , left panels).

    Repeated measurements of the same group

    The rules illustrated in Figs. 5 and ​ and6 6 apply when the means are independent. If two measurements are correlated, as for example with tests at different times on the same group of animals, or kinetic measurements of the same cultures or reactions, the CIs (or SEs) do not give the information needed to assess the significance of the differences between means of the same group at different times because they are not sensitive to correlations within the group. Consider the example in Fig. 7 , in which groups of independent experimental and control cell cultures are each measured at four times. Error bars can only be used to compare the experimental to control groups at any one time point. Whether the error bars are 95% CIs or SE bars, they can only be used to assess between group differences (e.g., E1 vs. C1, E3 vs. C3), and may not be used to assess within group differences, such as E1 vs. E2.

    Inferences between and within groups. Means and SE bars are shown for an experiment where the number of cells in three independent clonal experimental cell cultures (E) and three independent clonal control cell cultures (C) was measured over time. Error bars can be used to assess differences between groups at the same time point, for example by using an overlap rule to estimate P for E1 vs. C1, or E3 vs. C3 but the error bars shown here cannot be used to assess within group comparisons, for example the change from E1 to E2.

    Assessing a within group difference, for example E1 vs. E2, requires an analysis that takes account of the within group correlation, for example a Wilcoxon or paired t analysis. A graphical approach would require finding the E1 vs. E2 difference for each culture (or animal) in the group, then graphing the single mean of those differences, with error bars that are the SE or 95% CI calculated from those differences. If that 95% CI does not include 0, there is a statistically significant difference (P < 0.05) between E1 and E2.

    Rule 8: in the case of repeated measurements on the same group (e.g., of animals, individuals, cultures, or reactions), CIs or SE bars are irrelevant to comparisons within the same group ( Fig. 7 ).


    Error bars can be valuable for understanding results in a journal article and deciding whether the authors' conclusions are justified by the data. However, there are pitfalls. When first seeing a figure with error bars, ask yourself, “What is n? Are they independent experiments, or just replicates?” and, “What kind of error bars are they?” If the figure legend gives you satisfactory answers to these questions, you can interpret the data, but remember that error bars and other statistics can only be a guide: you also need to use your biological understanding to appreciate the meaning of the numbers shown in any figure.


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