Assessment of Phenotypic Variation and Agronomic Type based Distribution of Genotypes Associated with Oil Content in Groundnut (Arachis hypogaea L.)
Author:
U. Nikhil Sagar1*, K. John2, Muga D. Sreevalli3, P. Sandhya Rani4, M. Sudha Rani5 and M. Reddi Sekhar6
Journal Name: Biological Forum, 18(2): 46-53, 2026
Address:
1Ph.D. Scholar, Department of Genetics and Plant Breeding, S.V. Agricultural College, Tirupati-517502 (Andhra Pradesh), India.
2Principal Scientist (Groundnut and Oilseeds), Genetics and Plant Breeding, Institute of Frontier Technology, Regional Agricultural Research Station, Tirupati-517502 (Andhra Pradesh), India.
3Associate Professor, Department of Genetics and Plant Breeding, S.V. Agricultural College, Tirupati-517502 (Andhra Pradesh), India.
4Principal Scientist and Head, Crop Physiology, Agricultural Research Station, Darsi (Andhra Pradesh), India.
5Professor and Head, Department of Genetics and Plant Breeding, S.V. Agricultural College, Tirupati-517502 (Andhra Pradesh), India.
6Associate Dean, S.V. Agricultural College, Tirupati-517502 (Andhra Pradesh), India.
(Corresponding author: U. Nikhil Sagar*)
DOI: https://doi.org/10.65041/BiologicalForum.2026.18.2.7
Groundnut (Arachis hypogaea L.) is a major contributor to global cultivation due to its rich source of good-quality vegetable oil in seeds. In the current investigation, 184 groundnut genotypes were evaluated across two seasons, season-1 (S1) and season-2 (S2) to study the subspecies level variation for oil content in the Arachis subspecies and agronomic types. Significant variability in oil content (39.6%–52.2%) was detected among the groundnut genotypes. The accessions with higher oil content were predominant in the Spanish Bunch/Valencia Bunch types, whereas the accessions with comparatively low oil content were more prevalent in the Virginia Bunch/Virginia Runner types. The genotypes ICG 4746 (S1- 52.2%, S2- 51.7%), ICG 12370 (S1- 51.6%, S2- 48.4%), ICG 4955 (S1- 51.1%, S2- 50.5%) demonstrated higher oil content across both the seasons. These genotypes with high oil content hold potential for their use in breeding programmes to develop groundnut varieties with increased oil quality.
Oil content, Agronomic types, Subspecies level variation, Breeding programmes, Oil quality.
Groundnut, also referred to as the "King of oilseeds" is an essential oilseed legume crop, known for its high oil content and economic significance. It is also referred to as peanut, wonder nut, earthnut, goobernut, and monkey nut (Nwokolo, 1996). It belongs to the family Leguminosae and subfamily Papilionoideae (Stalker and Wilson 2015). The cultivated groundnut is allotetraploid (AABB, 2n = 4x = 40), and includes the two cultivated subspecies, hypogaea and fastigiata, whereas the majority of the 80 species in the genus Arachis are diploid (2n = 2x = 20) (Krapovickas and Gregory 1994). The cultivated groundnut A. hypogaea was initially classified into two large botanical groups, Virginia and Spanish-Valencia on the basis of branching pattern (Krapovickas, 1973; Upadhyaya and Nigam 1999). Early-maturing Spanish-Valencia plants are usually erect, with pods formed around the base (Litzenberger, 1976). The seeds of late-maturity (Virginia-type) plants exhibit alternating branching patterns, and the pods are distributed along secondary and tertiary branches (Stephen, 2009). Early maturity promotes adaptation to a shorter growing season, however late maturation results in prolonged pod-filling and higher yield potential (Kunta et al., 2025). Spanish groundnut cultivars, cultivated mainly in Africa and semi-arid regions of Asia, account for 60% of global groundnut production (Rani et al., 2024).
Globally, it is cultivated in 29.92 million hectares with an annual production of 55.30 million tonnes and a productivity of 1851 Kg ha-1 (Sukrutha et al., 2025). About 80% of total groundnut production in India is crushed for oil extraction, hence improving the oil content and quality, which is of benefit to plant breeders. The most important quality requirements of groundnut as a source of oil are high protein and oil content in the seed. Groundnut seed contains 10–20% carbohydrates, 48–50% oil, and 25–28% protein (Parmar et al., 2023). It also contains antioxidants such as p-coumaric acid and resveratrol, bioactive compounds such as polyphenols, flavonoids, isoflavones, and many B-complex vitamins, as well as monounsaturated and polyunsaturated fatty acids. It is used as animal feed (oil pressings, green and dry haulms) and as raw material in the manufacturing industry (oil cakes and fertilizers). The oilcake contains 7.3% nitrogen, 1.5% P2O5, and 1.3% potassium (Korale et al., 2022).
The nutritional quality of oil is determined by its fatty acid composition. The major fatty acids found in groundnut are palmitic (16:0), stearic (18:0), oleic (18:1), linoleic (18:2), arachidic (20:0), eicosenoic (20:1), behenic (22:0), and lignoseric (24:0) acids. Oleic acid, a monounsaturated fatty acid, and linoleic acid, a polyunsaturated fatty acid, account for 75-80% of total fatty acids in groundnut oil (Sarvamangala et al., 2011). Regular groundnut consumption is linked with a reduced risk of cardiovascular disease, lower blood pressure, cancer, and may benefit those with type II diabetes (Vassiliou et al., 2009). Groundnut consumption has also been associated with the maintenance of body weight.
Groundnut-based ready-to-use therapeutic food products (RUTP) like "Plumpy Nut" and peanut butter have saved the lives of thousands of malnourished children in Africa, where malnutrition is a severe issue. As a result, groundnut becomes increasingly important as a food nutritional source, in addition to an oil source. Because of its great nutritional value, it is often referred to as the "poor man's almond" and hence an important contributor to combat malnutrition (Parmar et al., 2022).
Assessment of phenotypic and genetic diversity is essential before adopting effective and successful crop improvement approaches to ensure ideal selection (Ajmera et al., 2017). The extent and nature of phenotypic and genotypic variability determine the effectiveness of selecting high-potential genotypes in plant breeding programmes (Yami and Abtew 2025). Hence, the present study was carried out to assess the phenotypic variation and distribution of 184 groundnut accessions based on the different agronomic types.
The experimental material used in this study comprised of 184 groundnut mini-core accessions. These mini-core accessions include six botanical varieties: hypogaea, fastigiata, hirsuta, peruviana, vulgaris, and aequatoriana.
A. Phenotyping for oil content
The oil content (%) of the groundnut accessions was estimated using Near-Infrared Spectroscopy (NIRS). The groundnut samples weighing 60 g were examined with a diode array analyzer at wavelengths ranging from 950 to 1650 nm. Each sample was scanned three times and absorbance readings were taken at 5 nm increments to ensure accuracy (Parmar et al., 2023).
B. Phenotypic variability and distribution of the accessions based on agronomic types
The "SRplot" (Scientific and Research plot tool) (Tang et al., 2023) was used to create frequency distribution graphs (violin plots) to study the range and mean of the accessions. The box plots were used to visualise the phenotypic distributions of the groundnut mini-core collection, which included 184 accessions from the two subspecies fastigiata and hypogaea, as well as the agronomic types Valencia bunch/Spanish bunch and Virginia bunch/Virginia runner. These box plots were generated using "ggplot" package in the “R” software (Xia et al., 2018).
A. Phenotypic variability for oil content
The phenotypic variation of 184 accessions for oil content across the two seasons were visualized using violin plots (Fig. 1).
Fig. 1. Phenotypic variation for oil content in the diverse 184 groundnut genotypes. This figure illustrates the violin plots showing the distribution of oil content (%) for various genotypes across S1 and S2, and mean of both the seasons. The box plot represents the average frequency of distribution of genotypes in a particular season.
The mean values of oil content varied among the seasons such as 45.7% in S1 and 46.3% in S2. The oil content ranged from 39.6% to 52.2% in S1, whereas the values ranged from a minimum of 39.6% to a maximum of 51.7% in S2 (Table 1).
Table 1: The oil content (%) of the 184 groundnut accessions across the two seasons and mean performance of the two seasons.
Sr. No. | Accession | Season 1 | Season 2 | MEAN |
1. | ICG 10036 | 45.9 | 46.1 | 46.0 |
2. | ICG 10092 | 45.0 | 46.6 | 45.8 |
3. | ICG 10185 | 44.1 | 44.5 | 44.3 |
4. | ICG 10384 | 47.3 | 47.1 | 47.2 |
5. | ICG 10474 | 47.3 | 46.6 | 46.9 |
6. | ICG 10479 | 50.7 | 48.7 | 49.7 |
7. | ICG 10554 | 45.0 | 43.2 | 44.1 |
8. | ICG 10566 | 46.4 | 47.5 | 46.9 |
9. | ICG 10890 | 47.3 | 48.2 | 47.8 |
10. | ICG 11088 | 43.1 | 43.2 | 43.1 |
11. | ICG 111 | 41.9 | 44.6 | 43.3 |
12. | ICG 11109 | 46.3 | 46.3 | 46.3 |
13. | ICG 11144 | 48.8 | 48.9 | 48.9 |
14. | ICG 11219 | 43.6 | 45.5 | 44.6 |
15. | ICG 11249 | 45.9 | 47.3 | 46.6 |
16. | ICG 11322 | 45.1 | 45.4 | 45.3 |
17. | ICG 1137 | 47.0 | 48.0 | 47.5 |
18. | ICG 1142 | 48.9 | 49.0 | 49.0 |
19. | ICG 11426 | 46.1 | 45.6 | 45.9 |
20. | ICG 11457 | 44.7 | 46.4 | 45.6 |
21. | ICG 115 | 49.4 | 48.8 | 49.1 |
22. | ICG 11515 | 46.4 | 46.3 | 46.4 |
23. | ICG 11651 | 44.2 | 44.0 | 44.1 |
24. | ICG 11687 | 47.1 | 47.2 | 47.1 |
25. | ICG 118 | 46.6 | 47.1 | 46.9 |
26. | ICG 11855 | 45.7 | 45.9 | 45.8 |
27. | ICG 11862 | 44.5 | 45.9 | 45.2 |
28. | ICG 12000 | 47.3 | 49.2 | 48.3 |
29. | ICG 12189 | 42.5 | 45.0 | 43.8 |
30. | ICG 12276 | 46.5 | 47.8 | 47.2 |
31. | ICG 12370 | 51.6 | 48.4 | 50.0 |
32. | ICG 12625 | 45.6 | 45.0 | 45.3 |
33. | ICG 12672 | 46.3 | 45.5 | 45.9 |
34. | ICG 12682 | 44.4 | 47.6 | 46.0 |
35. | ICG 12697 | 43.0 | 45.5 | 44.3 |
36. | ICG 1274 | 43.0 | 43.4 | 43.2 |
37. | ICG 12879 | 44.1 | 46.5 | 45.3 |
38. | ICG 12921 | 47.8 | 47.1 | 47.5 |
39. | ICG 12988 | 47.7 | 47.3 | 47.5 |
40. | ICG 13099 | 45.7 | 45.6 | 45.6 |
41. | ICG 13491 | 47.2 | 47.3 | 47.3 |
42. | ICG 13603 | 48.2 | 48.1 | 48.2 |
43. | ICG 13723 | 41.4 | 43.4 | 42.4 |
44. | ICG 13787 | 44.3 | 45.2 | 44.7 |
45. | ICG 13856 | 50.0 | 49.1 | 49.5 |
46. | ICG 13858 | 49.3 | 49.0 | 49.2 |
47. | ICG 13982 | 44.2 | 45.6 | 44.9 |
48. | ICG 1399 | 47.7 | 48.4 | 48.1 |
49. | ICG 14008 | 46.4 | 45.8 | 46.1 |
50. | ICG 14106 | 48.5 | 47.6 | 48.1 |
51. | ICG 14118 | 46.7 | 47.2 | 46.9 |
52. | ICG 14127 | 45.1 | 45.5 | 45.3 |
53. | ICG 1415 | 49.2 | 48.7 | 49.0 |
54. | ICG 14466 | 46.3 | 45.7 | 46.0 |
55. | ICG 14475 | 44.0 | 43.5 | 43.8 |
56. | ICG 14482 | 46.6 | 45.5 | 46.1 |
57. | ICG 14523 | 49.6 | 48.9 | 49.2 |
58. | ICG 14630 | 47.8 | 47.9 | 47.9 |
59. | ICG 14705 | 47.1 | 46.9 | 47.0 |
60. | ICG 14710 | 49.9 | 49.9 | 49.9 |
61. | ICG 14985 | 48.1 | 46.8 | 47.4 |
62. | ICG 15042 | 46.7 | 46.0 | 46.3 |
63. | ICG 1519 | 47.9 | 48.5 | 48.2 |
64. | ICG 15190 | 44.1 | 45.3 | 44.7 |
65. | ICG 15287 | 45.5 | 45.6 | 45.5 |
66. | ICG 15309 | 46.8 | 46.8 | 46.8 |
67. | ICG 15419 | 44.0 | 44.6 | 44.3 |
68. | ICG 163 | 44.1 | 45.3 | 44.7 |
69. | ICG 1668 | 41.4 | 44.4 | 42.9 |
70. | ICG 1711 | 45.5 | 46.4 | 46.0 |
71. | ICG 188 | 46.2 | 47.6 | 46.9 |
72. | ICG 1973 | 48.0 | 48.7 | 48.4 |
73. | ICG 2019 | 46.6 | 47.7 | 47.1 |
74. | ICG 2106 | 48.1 | 48.3 | 48.2 |
75. | ICG 2381 | 39.6 | 39.6 | 39.6 |
76. | ICG 2511 | 43.6 | 44.2 | 43.9 |
77. | ICG 2772 | 41.1 | 44.3 | 42.7 |
78. | ICG 2773 | 41.4 | 44.6 | 43.0 |
79. | ICG 2777 | 41.6 | 44.5 | 43.1 |
80. | ICG 2857 | 45.5 | 45.7 | 45.6 |
81. | ICG 2925 | 44.0 | 45.5 | 44.8 |
82. | ICG 297 | 47.5 | 48.3 | 47.9 |
83. | ICG 3027 | 43.1 | 44.2 | 43.7 |
84. | ICG 3053 | 43.0 | 44.6 | 43.8 |
85. | ICG 3102 | 46.9 | 47.3 | 47.1 |
86. | ICG 3240 | 47.1 | 47.7 | 47.4 |
87. | ICG 332 | 47.7 | 46.1 | 46.9 |
88. | ICG 334 | 46.4 | 47.5 | 46.9 |
89. | ICG 3343 | 46.7 | 46.7 | 46.7 |
90. | ICG 3421 | 47.3 | 48.1 | 47.7 |
91. | ICG 3584 | 46.3 | 47.6 | 46.9 |
92. | ICG 36 | 49.4 | 49.2 | 49.3 |
93. | ICG 3673 | 49.2 | 49.3 | 49.2 |
94. | ICG 3681 | 48.4 | 48.8 | 48.6 |
95. | ICG 3746 | 46.2 | 47.4 | 46.8 |
96. | ICG 3775 | 44.4 | 47.4 | 45.9 |
97. | ICG 397 | 48.0 | 49.3 | 48.7 |
98. | ICG 3992 | 44.5 | 45.2 | 44.8 |
99. | ICG 4156 | 41.7 | 44.1 | 42.9 |
100. | ICG 434 | 46.1 | 48.0 | 47.1 |
101. | ICG 4343 | 42.7 | 43.6 | 43.2 |
102. | ICG 4389 | 44.5 | 46.5 | 45.5 |
103. | ICG 4412 | 45.0 | 46.0 | 45.5 |
104. | ICG 442 | 49.0 | 50.9 | 50.0 |
105. | ICG 4527 | 40.4 | 43.6 | 42.0 |
106. | ICG 4538 | 46.5 | 47.0 | 46.8 |
107. | ICG 4543 | 46.9 | 47.5 | 47.2 |
108. | ICG 4598 | 43.4 | 45.8 | 44.6 |
109. | ICG 4670 | 44.5 | 45.4 | 45.0 |
110. | ICG 4684 | 46.5 | 47.6 | 47.1 |
111. | ICG 4729 | 48.1 | 47.8 | 48.0 |
112. | ICG 4746 | 52.2 | 51.7 | 51.9 |
113. | ICG 4750 | 46.1 | 46.2 | 46.1 |
114. | ICG 4911 | 46.1 | 46.8 | 46.4 |
115. | ICG 4955 | 51.1 | 50.5 | 50.8 |
116. | ICG 4998 | 50.7 | 49.0 | 49.8 |
117. | ICG 5016 | 43.9 | 43.2 | 43.6 |
118. | ICG 5051 | 45.7 | 45.9 | 45.8 |
119. | ICG 513 | 43.0 | 46.0 | 44.5 |
120. | ICG 5195 | 45.6 | 45.8 | 45.7 |
121. | ICG 5221 | 44.9 | 45.6 | 45.3 |
122. | ICG 5236 | 46.3 | 45.8 | 46.0 |
123. | ICG 5286 | 41.5 | 42.2 | 41.9 |
124. | ICG 532 | 43.9 | 45.5 | 44.7 |
125. | ICG 5327 | 42.4 | 44.2 | 43.3 |
126. | ICG 5475 | 43.6 | 44.1 | 43.8 |
127. | ICG 5494 | 44.6 | 44.5 | 44.5 |
128. | ICG 5609 | 48.7 | 48.7 | 48.7 |
129. | ICG 5662 | 43.1 | 45.2 | 44.1 |
130. | ICG 5663 | 42.4 | 45.3 | 43.8 |
131. | ICG 5745 | 45.3 | 45.1 | 45.2 |
132. | ICG 5779 | 49.3 | 49.9 | 49.6 |
133. | ICG 5827 | 47.9 | 45.9 | 46.9 |
134. | ICG 5891 | 42.4 | 45.6 | 44.0 |
135. | ICG 6022 | 46.0 | 46.4 | 46.2 |
136. | ICG 6057 | 41.6 | 44.6 | 43.1 |
137. | ICG 6201 | 49.2 | 49.5 | 49.3 |
138. | ICG 6263 | 45.4 | 46.9 | 46.1 |
139. | ICG 6375 | 44.5 | 44.9 | 44.7 |
140. | ICG 6402 | 47.5 | 47.8 | 47.6 |
141. | ICG 6407 | 50.3 | 48.9 | 49.6 |
142. | ICG 6646 | 43.8 | 43.8 | 43.8 |
143. | ICG 6654 | 45.3 | 45.9 | 45.6 |
144. | ICG 6667 | 46.0 | 46.7 | 46.3 |
145. | ICG 6703 | 48.1 | 47.4 | 47.8 |
146. | ICG 6766 | 48.6 | 47.2 | 47.9 |
147. | ICG 6813 | 40.9 | 43.4 | 42.2 |
148. | ICG 6888 | 47.7 | 46.3 | 47.0 |
149. | ICG 6892 | 46.0 | 45.9 | 46.0 |
150. | ICG 6913 | 42.0 | 41.6 | 41.8 |
151. | ICG 6993 | 41.5 | 40.8 | 41.1 |
152. | ICG 7000 | 49.1 | 48.7 | 48.9 |
153. | ICG 7153 | 43.5 | 44.0 | 43.8 |
154. | ICG 7181 | 48.8 | 48.6 | 48.7 |
155. | ICG 7190 | 50.2 | 48.2 | 49.2 |
156. | ICG 721 | 43.8 | 45.8 | 44.8 |
157. | ICG 7243 | 44.8 | 46.0 | 45.4 |
158. | ICG 76 | 43.5 | 44.5 | 44.0 |
159. | ICG 7906 | 41.4 | 44.4 | 42.9 |
160. | ICG 7963 | 44.8 | 46.0 | 45.4 |
161. | ICG 7969 | 49.8 | 47.5 | 48.7 |
162. | ICG 8083 | 45.2 | 45.3 | 45.2 |
163. | ICG 81 | 48.1 | 48.1 | 48.1 |
164. | ICG 8106 | 46.5 | 47.0 | 46.7 |
165. | ICG 8285 | 45.1 | 45.8 | 45.4 |
166. | ICG 8490 | 41.2 | 43.8 | 42.5 |
167. | ICG 8517 | 42.1 | 45.0 | 43.6 |
168. | ICG 8567 | 48.4 | 47.6 | 48.0 |
169. | ICG 862 | 40.8 | 43.8 | 42.3 |
170. | ICG 875 | 42.2 | 45.1 | 43.7 |
171. | ICG 8760 | 46.4 | 45.6 | 46.0 |
172. | ICG 9037 | 45.4 | 45.9 | 45.7 |
173. | ICG 9157 | 41.4 | 40.8 | 41.1 |
174. | ICG 9249 | 46.1 | 45.5 | 45.8 |
175. | ICG 928 | 41.6 | 43.7 | 42.7 |
176. | ICG 9315 | 48.2 | 47.9 | 48.1 |
177. | ICG 9418 | 44.3 | 46.4 | 45.3 |
178. | ICG 9507 | 45.4 | 46.0 | 45.7 |
179. | ICG 9666 | 42.1 | 44.3 | 43.2 |
180. | ICG 9777 | 45.2 | 45.6 | 45.4 |
181. | ICG 9809 | 46.7 | 46.8 | 46.8 |
182. | ICG 9842 | 46.2 | 46.6 | 46.4 |
183. | ICG 9905 | 42.8 | 42.9 | 42.8 |
184. | ICG 9961 | 44.2 | 45.9 | 45.0 |
Previous studies also reported the variability for oil content in the range of 45% to 55% (Upadhyaya et al., 2011; Upadhyaya et al., 2003). Across both the seasons, oil content levels were consistently higher in S2 than S1. The genotypes ICG 4746 (52.2%), ICG 12370 (51.6%), ICG 4955 (51.1%), ICG 10479 (50.7%) and ICG 4998 (50.7%) demonstrated higher oil content in S1. Likewise, the genotypes ICG 4746 (51.7%), ICG 442 (50.9%), ICG 4955 (50.5%), ICG 14710 (49.9%) and ICG 5779 (49.9%) showed high oil content in S2. Groundnut varieties with high oil content particularly high oleic acid content are desirable due to their potential health benefits and extended shelf life (Mozingo et al., 2004; Norden et al., 1987).
Similarly, phenotypic variability for oil content was previously reported in groundnut (Shasidhar et al. 2017; Pandey et al., 2014; Sarvamangala et al., 2011). In this study, the genotypes ICG 4746 and ICG 4955 showed high oil content across both the seasons. This stability indicates that these genotypes have developed specific genetic mechanisms that buffer against environmental variations while ensuring consistent oil production (Tang et al., 2022). This inherent stability is beneficial as it reduces the unpredictability of oil content under various cultivation conditions, offering consistent product quality to consumers and processors.
In this study, the identified phenotypic variations and their stability across environments have major implications for breeding strategies. The varieties with stable phenotypic performance, irrespective of environmental changes, can be targeted in breeding programs for large-scale, diverse cultivation. On the other hand, varieties with specific adaptabilities to certain conditions can be utilised for cultivation in specific environments where those conditions prevail (Wang et al., 2023).
B. Phenotypic distribution for oil content based on agronomic types
The box plots were used to demonstrate the phenotypic variations for oil content based on the different agronomic types. The agronomic types differed in their oil content; accessions with high oil content were identified in Spanish Bunch and Valencia Bunch, while the low oil content accessions were found in the Virginia Bunch and Virginia Runner (Fig. 2).
Fig. 2. Phenotypic distribution for oil content based on different agronomic types of the groundnut mini-core accessions across two seasons (A) Season-1, (B) Season-2. The mean values are depicted by the horizontal line in the box.
Previous studies indicated that population genetic and evolutionary factors such as selection, mutation, and recombination rates influence the distribution of genotypes (Joshi et al., 2023; Magwa et al., 2016; Zaitlen et al., 2005). In a study the high blanchability trait was mostly identified in fastigiata subspecies, and the agronomic types like Valencia bunch and Spanish bunch, indicating the effect of genetic background and ecogeographic adaptation. However, low-blanchability was observed mostly in hypogaea subspecies and agronomic type: Virginia runner and bunch types, revealing genetic constraints (Shah et al., 2025). Guo et al. (2026) demonstrated that iron content varied across different botanical types in groundnut and Valencia-type accessions exhibited higher iron concentration than other types.
Similar results were earlier reported in rice (Qian et al., 2017). The accessions from the aus subgroup were found to be a rich source of micronutrients, and several high Zn donor accessions from this subgroup were discovered and are extensively used in the Zn biofortification program in rice (Calayugan et al., 2021; Palanog et al., 2019; Swamy et al., 2016). In another study, the U.S. peanut germplasm including 83 accessions were evaluated for oil content and fatty acid composition (Wang et al., 2010). The preliminary results indicated significant variability in oil content and fatty acid composition across botanical varieties. The subspecies hypogaea var. hypogaea was helpful for identifying accessions with a high oil content and oleic acid. Further screening of U.S. peanut germplasm was focussed on this botanical variety for high oil content and oleic acid.
Yol et al. (2018) carried out a study using 256 groundnut genotypes and the significant variation among the genotypes was identified for oil content with a range of 35.1% to 55.3% in the subspecies fastigiata, and 31.1% to 57% in the subspecies hypogaea. Oil content varied significantly among genotypes, possibly due to genotypic effects and maturation (Sanders et al., 1980). Oil content may vary depending on location, season, temperature, and environmental conditions (Dwivedi et al., 1993). In this study, subspecies fastigiata had slightly higher oil content than subspecies hypogaea, and similar findings were obtained in the groundnut collections (Yol et al., 2018; Wang et al., 2013). Spanish types are more suitable for oil production because of their high oil content, compared to other market types (Liao et al., 2007). This study identified some genotypes from different botanical types that had over 50% oil content, indicating the potential for oil marketing in groundnut. These high-oil content groundnut types can be utilised in various breeding programmes for the development of groundnut varieties with enhanced oil content. Groundnut oil remains less competitive than other crops like rapeseed oil due to its higher market price (Liao, 2014), thus improving oil content in groundnut is crucial. Breeding for high oil content in various market types could increase the marketability of groundnut oil.
This study reports the phenotypic variation associated with oil content among the 184 groundnut accessions. Phenotypic distribution revealed that high oil content genotypes majorly belong to the subspecies fastigiata, and agronomic type, Valencia bunch/Spanish bunch, whereas the genotypes with the low oil content values belong to the subspecies hypogaea, and agronomic type, Virginia bunch/Virginia Runner. The accessions ICG 4746 (S1- 52.2%, S2- 51.7%), ICG 12370 (S1- 51.6%, S2- 48.4%), ICG 4955 (S1- 51.1%, S2- 50.5%) showed higher contents of oil across the two seasons. The superior oil content genotypes identified in the groundnut germplasm collection will serve as a potential resource in various breeding programmes for enhancing oil quality in groundnut.
The genotypes which possess to have higher oil contents could serve as donors in breeding programme for the development for high oil content groundnut cultivars. The subspecies level variation for oil content provides the primary source of genetic variation for groundnut improvement to meet present and future demands.
Ajmera, S., Kumar, S. S. and Ravindrababu, V. (2017). Evaluation of genetic variability, heritability and genetic advance for yield and yield components in rice genotypes. International Journal of Current Microbiology and Applied Sciences, 6(10), 1657-1664.
Calayugan, M. I. C., Swamy, B. P. M., Nha, C. T., Palanog, A. D., Biswas, P. S., Descalsota-Empleo, G. I., Min, Y. M. M. and Inabangan-Asilo, M. A. (2021). Zinc-biofortified rice: A sustainable food-based product for fighting zinc malnutrition. Rice Improvement: Physiological, Molecular Breeding and Genetic Perspectives, Springer, Cham, 449–470.
Dwivedi, S. L., Nigam, S. N., Jambunathan, R., Sahrawat, K. L., Nagbhushanam, G. V. S. and Raghunath, K. (1993). Effect of genotypes and environments on oil content and oil quality parameters and their correlation in peanut (Arachis hypogaea L.). Peanut Science, 20, 84–89.
Guo, J., Huang, L., Luo, H., Chen, W., Yu, B., Zhou, X., Jiang, H., Liao, B., Lei, Y. and Liu, N. (2026). Comprehensive evaluation of seed iron content in peanut germplasm and identification of elite genotypes with high iron. Oil Crop Science, 13(1), 1-9.
Joshi, G., Soe, Y. P., Palanog, A. D., Hore, T. K., Nha, C. T., Calayugan, M. I. C., Inabangan-Asilo, M. A., Amparado, A., Pandey, I. D. and Cruz, P. C. S. (2023). Meta-QTLs and haplotypes for efficient zinc biofortification of rice. The Plant Genome, 16, e20315.
Korale, O.D., Dhuppe, M.V., Patil, S. S., Gite, N. G. and Mirkad, S. B. (2022). Correlation and path analysis for yield and yield contributing characters in groundnut (Arachis hypogaea L.). International Journal of Theoretical & Applied Sciences, 14(1), 22-25.
Krapovickas, A. (1973). Evolution of the genus Arachis. Agricultural Genetics Selection Topics, 135–151.
Krapovickas, A. and Gregory, W. C. (1994). Taxonomía del género Arachis (Leguminosae). Bonplandia, 1, 1–186.
Kunta, S., Gogisetty, N. S., Ben-Israel, G., Levy, Y., Mlelwa, W., Biton, N. Z., Chu, Y., Ozias-Akins, P. and Hovav, R. (2025). Genetic mapping of time-to-maturity trait in hypogaea × fastigiata peanut background reveals a significant effect of pod-related and flowering pattern. BMC Plant Biology, 26(27).
Liao, B. (2014). Peanut breeding. In: Mallikarjuna, N. and Varshney, R.K. (eds.), Genetics, genomics and breeding of peanuts. CRC Press, Boca Raton, 61–78.
Liao, B. and Holbrook, C. C. (2007). Groundnut. In: Singh, R.J. (ed.), Genetic Resources, Chromosome Engineering and Crop Improvement, Vol. 4, Oilseed Crops. CRC Press, Boca Raton, 51–87.
Litzenberger, S. C. (1976). Guide for field crops in the tropics and subtropics. Peace Corps Program Training Journal, 14–20.
Magwa, R. A., Zhao, H. and Xing, Y. (2016). Genome-wide association mapping revealed a diverse genetic basis of seed dormancy across subpopulations in rice (Oryza sativa L.). BMC Genetics, 17, 28.
Mozingo, R. W., O’Keefe, S. F., Sanders, T. H. and Hendrix, K. W. (2004). Improving shelf life of roasted and salted in-shell peanuts using high oleic fatty acid chemistry. Peanut Science, 31(1), 40–45.
Norden, A. J., Gorbet, D. W., Knauft, D. A. and Young, C. T. (1987). Variability in oil quality among peanut genotypes in the Florida breeding program. Peanut Science, 14(1), 7–11.
Nwokolo, E. (1996). Peanut (Arachis hypogaea L.). In: Food and Feed from Legumes and Oilseeds. Springer, 49–63.
Palanog, A. D., Calayugan, M. I. C., Descalsota-Empleo, G. I., Amparado, A., Inabangan-Asilo, M. A., Arocena, E.C., Sta. Cruz, P. C., Borromeo, T. H., Lalusin, A. and Hernandez, J. E. (2019). Zinc and iron nutrition status in the Philippine population and local soils. Frontiers in Nutrition, 6, 81.
Pandey, M. K., Wang, M. L., Qiao, L., Feng, S., Khera, P., Wang, H., Tonnis, B., Barkley, N. A., Wang, J., Holbrook, C. C. and Culbreath, A. K. (2014). Identification of QTLs associated with oil content and mapping FAD2 genes and their relative contribution to oil quality in peanut (Arachis hypogaea L.). BMC Genetics, 15, 133.
Parmar, S., Janila, P., Gangurde, S. S., Variath, M. T., Sharma, V., Bomireddy, D., Manohar, S. S., Varshney, R. K., Singam, P. and Pandey, M. K. (2023). Genetic mapping identified major main-effect and three co-localized quantitative trait loci controlling high iron and zinc content in groundnut. The Plant Genome, 16(4), e20361.
Parmar, S., Sharma, V., Bomireddy, D., Soni, P., Joshi, P., Gangurde, S. S., Wang, J., Bera, S. K., Bhat, R. S., Desmae, H. and Shirasawa, K. (2022). Recent advances in genetics, genomics, and breeding for nutritional quality in groundnut. Accelerated Plant Breeding, 4, 111–137.
Qian, L., Hickey, L. T., Stahl, A., Werner, C. R., Hayes, B., Snowdon, R. J. and Voss-Fels, K. P. (2017). Exploring and harnessing haplotype diversity to improve yield stability in crops. Frontiers in Plant Science, 8, 1534.
Rani, K., Gangadhara, K., Ajay, B. C., Kona, P., Kumar, N. and Bera, S. K. (2024). A comprehensive phenotypic and genotypic evaluation of Spanish groundnuts from diverse crosses to identify superior and stable donors for fresh seed dormancy. Scientific Reports, 14, 14988.
Sanders, T. H. (1980). Fatty acid composition of lipid classes in oils from peanuts differing in variety and maturity. Journal of the American Oil Chemists’ Society, 57, 12–15.
Sarvamangala, C., Gowda, M. V. C. and Varshney, R. K. (2011). Identification of quantitative trait loci for protein content, oil content and oil quality in groundnut (Arachis hypogaea L.). Field Crops Research, 122(1), 49–59.
Shah, P., Gangurde, S. S., Abbai, R., Senthil, R., Mohinuddin, D.K., Sharma, M., Singam, P., Singh, K., Janila, P., Zhao, C. and Bera, S. K. (2025). Genomic analysis uncovers unique haplotype signatures from subspecies and agronomic types associated with blanchability in groundnut. bioRxiv, 2025, 06.
Shasidhar, Y., Vishwakarma, M. K., Pandey, M. K., Janila, P., Variath, M. T., Manohar, S. S., Nigam, S. N., Guo, B. and Varshney, R. K. (2017). Molecular mapping of oil content and fatty acids using dense genetic maps in groundnut (Arachis hypogaea L.). Frontiers in Plant Science, 8, 794.
Stalker, H. T. and Wilson, R. F. (2015). Peanuts: Genetics, Processing, and Utilization. Elsevier.
Stephen, M. (2009). Growth and yield performance of four groundnut varieties in response to seed size. Ph.D. Thesis, Department of Crop and Soil Science, Kumasi, Ghana.
Sukrutha, B., Reddy, C. K. K., Madhuri, K.V.N., Reddy, C. B. R., Kumar, A. R. N., Vemireddy, L. N. and Akkareddy, S. (2025). Principal component analysis and path coefficient analysis for groundnut yield and seed quality attributes (Arachis hypogaea L.). Legum Research- An International Journal, 48(7), 1096-1102.
Swamy, B. P. M., Rahman, M. A., Inabangan-Asilo, M. A., Amparado, A., Manito, C., Chadha-Mohanty, P., Reinke, R. and Slamet-Loedin, I. H. (2016). Advances in breeding for high grain zinc in rice. Rice, 9, 49.
Tang, D., Chen, M., Huang, X., Zhang, G., Zeng, L., Zhang, G., Wu, S. and Wang, Y. (2023). SRplot: A free online platform for data visualization and graphing. PLoS ONE, 18, e0294236.
Tang, Y., Qiu, X., Hu, C., Li, J., Wu, L., Wang, W., Li, X., Li, X., Zhu, H., Sui, J. and Wang, J. (2022). Breeding of a new peanut variety with high oleic acid content and high yield by marker-assisted backcrossing. Molecular Breeding, 42(7), 42.
Upadhyaya, H. D. and Nigam, S. N. (1999). Inheritance of fresh seed dormancy in peanut. Crop Science, 39, 98–101.
Upadhyaya, H. D., Dwivedi, S. L., Nadaf, H. L. and Singh, S. (2011). Phenotypic diversity and identification of wild Arachis accessions with useful agronomic and nutritional traits. Euphytica, 182(1), 103-115.
Upadhyaya, H. D., Ortiz, R., Bramel, P. J. and Singh, S. (2003). Development of a groundnut core collection using taxonomical, geographical and morphological descriptors. Genetic Resources and Crop Evolution, 50(2), 139-148.
Vassiliou, E. K., Gonzalez, A., Garcia, C., Tadros, J. H., Chakraborty, G. and Toney, J. H. (2009). Oleic acid and peanut oil high in oleic acid reverse the inhibitory effect of insulin production of the inflammatory cytokine TNF-α both in vitro and in vivo systems. Lipids in Health and Disease, 8, 25.
Wang, M. L., Chen, C. Y., Davis, J., Guo, B., Stalker, H. T. and Pittman, R. N. (2010). Assessment of oil content and fatty acid composition variability in different peanut subspecies and botanical varieties. Plant Genetic Resources, 8(1), 71–73.
Wang, M. L., Chen, C. Y., Tonnis, B., Barkley, N. A., Pinnow, D. L., Pittman, R. N., Davis, J., Holbrook, C. C., Stalker, H. T. and Pederson, G. A. (2013). Oil, fatty acid, flavonoid, and resveratrol content variability and FAD2A functional SNP genotypes in the U.S. peanut mini-core collection. Journal of Agricultural and Food Chemistry, 61, 2875–2882.
Wang, Z., Zhang, Y., Yan, L., Chen, Y., Kang, Y., Huai, D., Wang, X., Liu, K., Jiang, H., Lei, Y. and Liao, B. (2023). Correlation and variability analysis of yield and quality-related traits in different peanut varieties across various ecological zones of China. Oil Crop Science, 8(4), 236–242.
Xia, Y., Sun, J. and Chen, D. G. (2018). Introduction to R, RStudio and ggplot2. In: Statistical Analysis of Microbiome Data with R. Springer, Singapore, 77–127.
Yami, A. S. and Abtew, W. G. (2025). Assessment of Genetic Variability for Yield and Yield‐Contributing Traits in Groundnut (Arachis hypogaea L.) Genotypes. Journal of Food Quality, 2025(1), 3370389.
Yol, E., Furat, S., Upadhyaya, H. D. and Uzun, B. (2018). Characterization of groundnut (Arachis hypogaea L.) collection using quantitative and qualitative traits in the Mediterranean Basin. Journal of Integrative Agriculture, 17(1), 63-75.
Zaitlen, N. A., Kang, H. M., Feolo, M. L., Sherry, S. T., Halperin, E. and Eskin, E. (2005). Inference and analysis of haplotypes from combined genotyping studies deposited in dbSNP. Genome Research, 15, 1594–1600.
U. Nikhil Sagar, K. John, Muga D. Sreevalli, P. Sandhya Rani, M. Sudha Rani and M. Reddi Sekhar (2026). Assessment of Phenotypic Variation and Agronomic Type based Distribution of Genotypes Associated with Oil Content in Groundnut (Arachis hypogaea L.). Biological Forum, 18(2): 46-53.