Landraces are very useful to create genetically desirable crops which are resistant to biotic and abiotic stresses

1. Outline of Research Work
(ORW)
Asif Ahmad Theakery
M.Sc. (Ag). Genetics & Plant breeding Ist year
Department of Genetics & Plant breeding
College of Agriculture,
Rani Lakshmi Bai Central Agricultural University
Jhansi (UP) - 284003

2. Outline of Research Work (ORW)
Name of student : Asif Ahmad Theakery
ID Number : AG/PG/090/22
Degree : M.Sc.(Ag) Genetics & Plant breeding
Chairman : Dr. Brijesh Kumar Mehta
Members : Dr. Anshuman Singh
Dr. Shashikumara P.
Dr. K. K. Dwivedi

3. PROPOSED TITLE OF THESIS
Morphological Characterization and Microsatellite Marker Based
Diversity Analysis of Forage Type Maize Landraces of India

4. Introduction
• Maize (Zea mays L.) is one of the principal cereal crop grown in as many as 94 countries and
provides food for more than 4.5 billion peoples around the globe (Shiferaw et al., 2011).
• Globally, maize is cultivated in 200 million ha area with an annual production of 1162 million
tonnes (FAOSTAT 2022).
• In India, maize is grown over 9.57 million ha area with a production of 28.77 million tonnes
(FAOSTAT 2022).
• and bio-fuel.
Fig. 1: Maize utilization pattern in India
• Maize in India, contributes nearly 10% in the national
food basket and adds more than 100 billion to the
agricultural GDP along with employment generation to
over 100 million man-days at the farm and downstream
agricultural and industrial sectors (Chaudhary et al.
2012).
• It is utilized in diverse forms like food, feed, industrial
products, fodder

5. • Apart from food and feed value, maize is equally important for providing
green- and dry- fodder along with silage making for lean period.
• It is grown in both kharif and summer season in India.
• Its fodder is considered superior over other cereal fodders due to its high
biomass, fast growth, high palatability and high nutritional quality.
• Maize fodder is also free from anti-nutritional factors, unlike presence of HCN
in sorghum fodder.
• Maize is a popular crop for silage making due to presence of sufficient
quantities of soluble sugars required for proper ensiling.
• It can be intercropped with legume fodder crops such as cowpea, thus
providing balanced fodder to livestock.
• Further, the cultivation of specialty corn such as sweet corn and baby corn can
provides large quantities of green fodder, apart from sweet and baby corn in a
short period of time.
Maize as fodder

6. Land races
• Landrace are the primitive varieties which have evolved over centuries through both
artificial and natural selection but without a systematic and sustained plant breeding
efforts.
• These are being maintained by the local farming communities since ancient times for
their subsistence agriculture.
• Adapted to various soil type and climatic conditions.
• Store houses of genetic variability and useful traits for crop improvement.

7. REVIEW OF WORK DONE

8. Reference No. of
landraces
used
Origin Traits used for diversity
analysis
Results
Choudhary
et al.
(2023)
47 Punjab, Maharashtra,
NEH
Morpho-physiological
traits: 10
SSRs: 40
Based on morpho-physiological
traits: 3 clusters
Based on SSRs: 5 clusters
Sharma et
al.
(2010)
48 HP, UK, J&K, Manipur,
Nagaland, AP,
Meghalaya, Jharkhand,
Bihar, WB, MP,
Rajasthan, Sikkim
primitive (19)
Agro-Morphological traits:
8
SSRs: 42
1. Four major clusters
2. Sikkim Primitive accessions fall
in one cluster, distinctly separated
from the rest of the accessions.
Yousif et al.
(2021)
47 J&K SSRs: 25 Two major clusters
Rathod et
al. (2020)
24 Mizoram SSRs: 93 Three major clusters
Diversity analysis in Indian maize landraces

9. Reference No. of
landraces
used
Origin Traits used for
diversity analysis
Results
Kumar et
al. (2015)
41 Himachal Pradesh
and Jammu and
Kashmir
Phenotypic traits: 11
Grain quality traits: 5
1. Differences were significant among
the landraces.
2. Four clusters.
Saiyad &
Kumar
2018
75 Mexico, Andhra
Pradesh, Gujarat and
MP
Morphological: 8
Fodder quality: 7
1. Differences were significant among
the landraces.
2. Five clusters.
Wasala &
Prasanna
2013
48 Haryana, UP,
Gujarat, Rajasthan,
HP, Bihar, WB, MH,
UK, KN, AP,
Jharkhand, J&K,
Orissa
SSRs: 42 Seven major clusters with significant
genetic diversity were observed.
Goenka et
al. (2021)
99 HP, J&K, UK,
Assam, Sikkim,
Meghalaya, Manipur,
Mizoram, Arunachal
Pradesh
Agro-morphological: 13 Eight clusters

10. Research Gap
• Most of the previous studies were conducted on morphological or molecular or
biochemical traits (grain) of maize landraces, but the study combining the
morphological and molecular traits are limited.
• Previous researchers characterized the Indian maize landraces for grain -yield and -
attributing traits, and grain quality traits, and diversity analysis based on SSR markers
was performed on the maize landraces which possessed grain yield and grain quality
attributes.
• Previous researchers included fewer number of morphological traits characterization
studies.
• However, the forage type landraces of India have not been characterized for fodder
attributing traits.
• Further, molecular characterization of selected forage type landraces of India has not
been done.

11. Objectives
1. To characterize the forage type maize landraces of India for morphological traits
and forage attributes
2. To assess the diversity among forage type land races using micro satellite
marker

12. Materials and Methodology
Item Details
Location Central Research Farm, IGFRI, Jhansi
Experimental design Augmented design
Number of blocks 6
Plot size 3.0 m × 1.0 m (Paired rows)
Number of test treatments 96 landraces of maize
Number of check treatments 4 (AT, J-1006, J-1007, KDFM-1)
Total Number of treatments 100
Number of Traits
22 (13 morphological, 5 forage quality and 4
micronutrients

13. Observations to be recorded

14. 14/28
Objective 1: To characterize the forage type maize landraces of India for morphological traits and forage
attributes
The landraces will be characterized for 31 morphological traits as per DUS characteristics of PPVFRA, 2001
S. No. Trait S. No. Trait
1. Leaf: angle between blade and stem (on leaf just above upper ear) 17. Leaf: width of blade
2. Leaf: attitude of blade 18. Ear: length without husk
3. Stem: anthocyanin colouration of brace root 19. Ear: diameter
4. Tassel: time of anthesis 20. Ear: shape
5. Tassel: anthocyanin colouration at base of glume 21. Ear: number of rows of grains
6. Tassel: anthocyanin colouration of glume excluding base 22. Ear: type of grains
7. Tassel: anthocyanin colouration of anthers 23. Ear: colour of top of grain
8. Tassel: density of spikelets 24. Ear: colouration of glume of cob
9. Tassel: angle between main axis and lateral branches 25. Kernel: row arrangement
10. Tassel: attitude of lateral branches 26. Kernel: poppiness
11. Ear: time of silk emergence 27. Kernal: sweetness
12. Ear: anthocyanin colouration of silks 28. Kernel: waxiness
13. Leaf: anthocyanin colouration of sheath below ear 29. Kernel: opaqueness
14. Tassel: length of main axis above lowest side branch 30. Kernel: shape
15. Plant length up to flag leaf 31 Kelnel: 1000 kernel weight
16. Plant: ear placement

15. 1. Leaf length
2. Leaf weight
3. Stem weight
4. Leaf : stem ratio
5. Stem diameter
6. Number of leaves
7. Green fodder yield
8. Dry matter yield
9. Per day productivity
Characterization for forage attributes

16. Objective 2: To assess the diversity among forage type land races using micro satellite markers
96 maize landraces
Leaf samples of 5 randomly selected
plants from 3 week old seedlings
Visualization of PCR product using Gel Documentation System
DNA isolation and quantification
PCR amplification using SSR markers distributed throughout the genome
Manual gel scoring
Establishment of genetic relationship among the landraces

17. Statistical analysis
Descriptive statistics including mean, standard error, range, coefficient of variance (CV), skewness, genotypic coefficient of
variance (GCV), phenotypic coefficient of variance (PCV), environmental coefficient of variance (ECV), broad-sense heritability
(hBS), genetic advance over mean (GAM) will be computed through Rstudio using augmented RCBD R package version 0.1.7
ANOVA will be done with Rstudio using augmented RCBD R package version 0.1.7
Principal component analysis (PCA) based clustering will be done with factoextra and FactoMineR packages in Rstudio
Analysis of molecular variance (AMOVA) and diversity indices calculation will be done using GenAlEx software
The number of alleles per locus, major allele frequency, gene diversity, heterozygosity, polymorphism information content (PIC)
calculation and neighbour- joining (NJ) dendrogram construction will be done using PowerMarkerV.3.25
Mantel test will be performed using GenAlEx software to estimate the correlation between the distance matrix of morphological
traits and genetic distance matrix of SSR markers.
Genetic relationships among individual landraces will be analyzed with a model-based clustering approach using STRUCTURE
software.