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Practical Manual: Growth Analysis Using Choy Sum Vegetable

23 hours ago
3 min read

This is the lab manual for the first practical of AGR3301 Crop Physiology at the Faculty of Agriculture, Universiti Putra Malaysia. It follows on from Lecture 1A (Introduction to Crop Physiology) and the self-guided Lesson 1B on the mathematics of growth analysis. Those lessons teach the growth parameters through worked examples. In this practical, you produce the numbers yourself, from a crop you grow and harvest.


This manual goes hand in hand with the group Excel workbook, AGR3301_Practical1_Group_Datasheet.xlsx. The manual tells you what to do. The workbook is where every measurement from the experiment goes, and where all seven growth parameters, the statistics and the report figures are calculated. You can't complete the practical with one and not the other.


At each step, the manual tells you which sheet of the workbook to use:

  • Setup: group details, sowing date, tray size and the random pot allocation

  • Tray Log: solution depth, EC readings, top-ups and observations

  • Monitor Pots: plant height and leaf number every three days

  • Harvest 14, 21 and 28 DAS: leaf area on harvest day, then dry weights a week later

  • Growth Parameters: LAI, AGR, CGR, RGR, NAR, SLA and SLW, calculated automatically from your harvest data

  • Charts: the five report figures, built automatically

  • How to Make a Graph: a step-by-step practice sheet for means, standard errors and error bars

Enter your data on the same day you measure, and keep one master copy of the workbook per group. The completed workbook is submitted with the report as the raw-data appendix.


What the practical is about

Choy sum (Brassica rapa var. parachinensis, sawi batang putih, variety 881A) is grown over four weeks. It grows fast, and its leaves are the harvested product, so leaf area and leaf traits link directly to yield.

The plants grow in sand culture. Sand supplies no nutrients, so every mineral comes from a nutrient solution that rises into each pot by capillary action from a shallow reservoir in the tray. Growth takes place under LED lighting with a 13-hour photoperiod.

Each group grows one tray of 24 pots. Plants are harvested destructively at 14, 21 and 28 days after sowing, six pots at a time, with six backup pots. A set of monitor pots is also measured for height and leaf number every three days.


What you measure and calculate

From leaf area and dry mass, you calculate seven classic growth-analysis parameters:

  • Leaf area index (LAI)

  • Absolute growth rate (AGR)

  • Crop growth rate (CGR)

  • Relative growth rate (RGR)

  • Net assimilation rate (NAR)

  • Specific leaf area (SLA)

  • Specific leaf weight (SLW)


You then describe and explain how the crop's size, growth rate, efficiency and leaf construction change from 14 to 28 days after sowing.


What's inside the manual

  • Set-up: how to build the capillary sub-irrigation tray, with a cross-section diagram, and how to dilute the nutrient stock to an EC of 1.0 mS/cm, with the full calculation shown.

  • Methods: day-0 set-up, plant care every three days, thinning, and the step-by-step harvest procedure. This includes washing roots, separating organs, measuring leaf area with the Easy Leaf Area smartphone app, and oven-drying.

  • Calculations: how to work out ground area per plant from the tray size, plus a formula table for all seven parameters with units and example checks.

  • Statistics made simple: a fully worked example of mean, standard deviation and standard error, with every step shown in numbers, and when to use SD versus SE.

  • Graphing guide: how to lay out growth graphs properly, add custom error bars in Excel, and read them, with the five required figures listed.

  • Prediction table: predict each trend before you measure it, then compare with what you observe.

  • Report guide: the expected report structure and seven discussion questions linking the numbers back to plant physiology.


Marks go to correct calculations, clear figures and discussion that explains the numbers with physiology, not to "perfect" data. Messy real data, honestly reported and explained, is good science.

 
 
 

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