While planning for an artificial hybridization programme involving dioecious plants, which of the following st

NCERT Exemplar · Q16

Q. While planning for an artificial hybridization programme involving dioecious plants, which of the following steps would not be relevant:

(a) Bagging of female flower

(b) Dusting of pollen on stigma

(d) Collection of pollen

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Concept understanding — Bagging Technique Utility

Let’s begin with something you already know from everyday life.

Imagine you have to make a very important decision — say, which college course to pick. You could ask one friend for advice. But that one friend might be biased (they love their own subject) or misinformed (they heard something wrong). So instead, you ask ten different people — teachers, seniors, parents, counsellors — and then take a majority vote or an average of their opinions. That collective decision is almost always more reliable than any single person’s advice.

That is the core intuition behind Bagging.

What Bagging Does

In data analysis and machine learning, Bagging stands for Bootstrap Aggregating. It is a technique used to make a prediction model more stable and accurate.

Here is the precise meaning: instead of building one single model (like one decision tree or one rule-based classifier) on the entire dataset, you create many slightly different versions of the same model, each trained on a different random sample of the original data. Then you combine all their outputs — usually by taking a majority vote (for classification) or an average (for prediction).

The key word is Bootstrap. A bootstrap sample is created by randomly picking data points from the original dataset with replacement — meaning the same data point can be picked more than once in a single sample, and some points may be left out. Each sample is different, so each model sees a slightly different view of the world.

Why It Matters

The main utility of Bagging is reducing variance — that is, reducing the model’s tendency to change wildly if you give it a slightly different set of data.

Think of it this way: a single model can be “nervous” — it might overreact to a few unusual data points (outliers) or to random noise in the data. But when you average the opinions of many such models, their individual nervousness cancels out. The combined model becomes calm, stable, and more accurate.

Important

Bagging works best when the individual models are unstable — meaning they change a lot with small changes in the training data. Decision trees are a classic example. Bagging turns a shaky tree into a robust forest.

Key Points to Remember (for exams)

Purpose: To improve accuracy and stability by combining multiple models.
How it works: Create many random samples (with replacement) from the original data. Train a separate model on each sample. Combine their predictions.
Why it helps: Reduces overfitting (the model memorising noise) and lowers variance.
Trade-off: It does not reduce bias much — if the individual model is fundamentally weak, bagging won’t fix that. It only makes a good model more reliable. …

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In dioecious plants, emasculation is not needed because male and female flowers occur on separate plants, so there is no risk of self-pollination.

The question asks you to identify which step is irrelevant when planning artificial hybridization for dioecious plants. To answer this, you first need to recall what dioecious means. Dioecious plants have male flowers and female flowers on different individuals — think of papaya or date palm. This is the opposite of monoecious plants, where both flower types occur on the same plant (like maize or cucumber).

Now, artificial hybridization is a controlled breeding technique where the breeder deliberately crosses two selected parent plants. The standard procedure involves three main steps: emasculation (removing the male reproductive parts from the female parent to prevent self-pollination), bagging (covering the emasculated flower to protect it from unwanted pollen), and then dusting the stigma with pollen collected from the desired male parent.

Note

Emasculation is only necessary when the flower you are using as the female parent is bisexual (has both stamens and pistil) or when male and female flowers are on the same plant. In such cases, you must remove the stamens before they shed pollen, otherwise the flower might self-pollinate and ruin your cross.

Here is the key point: in a dioecious plant, the female plant never produces pollen. Its flowers are purely pistillate. There is no risk of self-pollination because the female flower has no stamens to remove. Therefore, the step of emasculation becomes completely unnecessary. You can directly bag the female flower (to keep stray pollen from other male plants away), collect pollen from a separate male plant, and dust it on the stigma. …

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Q.While planning for an artificial hybridization programme involving dioecious plants, which of the following steps would not be relevant:

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