1
|
Parihar AK, Gupta S, Hazra KK, Lamichaney A, Sen Gupta D, Singh D, Kumar R, Singh AK, Vaishnavi R, Jaberson MS, Das SP, Dev J, Yadav RK, Jamwal BS, Choudhary BR, Khedar OP, Prakash V, Dikshit HK, Panwar RK, Katiyar M, Kumar P, Mahto CS, Borah HK, Singh MN, Das A, Patil AN, Nanda HC, Kumar V, Rajput SD, Chauhan DA, Patel MH, Kanwar RR, Kumar J, Mishra SP, Kumar H, Swarup I, Mogali S, Kumaresan D, Manivannan N, Gowda MB, Pandiyan M, Rao PJ, Shivani D, Prusti AM, Mahadevu P, Iyanar K, Das S. Multi-location evaluation of mungbean ( Vigna radiata L.) in Indian climates: Ecophenological dynamics, yield relation, and characterization of locations. Front Plant Sci 2022; 13:984912. [PMID: 36204050 PMCID: PMC9530336 DOI: 10.3389/fpls.2022.984912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 08/18/2022] [Indexed: 06/01/2023]
Abstract
Crop yield varies considerably within agroecology depending on the genetic potential of crop cultivars and various edaphic and climatic variables. Understanding site-specific changes in crop yield and genotype × environment interaction are crucial and needs exceptional consideration in strategic breeding programs. Further, genotypic response to diverse agro-ecologies offers identification of strategic locations for evaluating traits of interest to strengthen and accelerate the national variety release program. In this study, multi-location field trial data have been used to investigate the impact of environmental conditions on crop phenological dynamics and their influence on the yield of mungbean in different agroecological regions of the Indian subcontinent. The present attempt is also intended to identify the strategic location(s) favoring higher yield and distinctiveness within mungbean genotypes. In the field trial, a total of 34 different mungbean genotypes were grown in 39 locations covering the north hill zone (n = 4), northeastern plain zone (n = 6), northwestern plain zone (n = 7), central zone (n = 11) and south zone (n = 11). The results revealed that the effect of the environment was prominent on both the phenological dynamics and productivity of the mungbean. Noticeable variations (expressed as coefficient of variation) were observed for the parameters of days to 50% flowering (13%), days to maturity (12%), reproductive period (21%), grain yield (33%), and 1000-grain weight (14%) across the environments. The genotype, environment, and genotype × environment accounted for 3.0, 54.2, and 29.7% of the total variation in mungbean yield, respectively (p < 0.001), suggesting an oversized significance of site-specific responses of the genotypes. Results demonstrated that a lower ambient temperature extended both flowering time and the crop period. Linear mixed model results revealed that the changes in phenological events (days to 50 % flowering, days to maturity, and reproductive period) with response to contrasting environments had no direct influence on crop yields (p > 0.05) for all the genotypes except PM 14-11. Results revealed that the south zone environment initiated early flowering and an extended reproductive period, thus sustaining yield with good seed size. While in low rainfall areas viz., Sriganganagar, New Delhi, Durgapura, and Sagar, the yield was comparatively low irrespective of genotypes. Correlation results and PCA indicated that rainfall during the crop season and relative humidity significantly and positively influenced grain yield. Hence, the present study suggests that the yield potential of mungbean is independent of crop phenological dynamics; rather, climatic variables like rainfall and relative humidity have considerable influence on yield. Further, HA-GGE biplot analysis identified Sagar, New Delhi, Sriganganagar, Durgapura, Warangal, Srinagar, Kanpur, and Mohanpur as the ideal testing environments, which demonstrated high efficiency in the selection of new genotypes with wider adaptability.
Collapse
Affiliation(s)
| | - Sanjeev Gupta
- Indian Council of Agricultural Research, Krishi Bhawan, New Delhi, India
| | - Kali K. Hazra
- ICAR-Indian Institute of Pulses Research, Kanpur, India
| | | | | | - Deepak Singh
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Raju Kumar
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anil K. Singh
- ICAR-Indian Institute of Pulses Research, Kanpur, India
| | - Rakesh Vaishnavi
- Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST), Srinagar, India
| | | | - Sankar P. Das
- ICAR Research Complex for North Eastern Hilly Region, Agartala, India
| | - Jai Dev
- Chaudhary Sarwan Kumar Himachal Pradesh Krishi Vishvavidyalaya, Palampur, India
| | - Rajesh K. Yadav
- Chaudhary Charan Singh Haryana Agricultural University, Hisar, India
| | - B. S. Jamwal
- Pulses Research Sub-Station, SKUAST-Jammu, Srinagar, India
| | | | - O. P. Khedar
- Rajasthan Agricultural Research Institute, Jaipur, India
| | | | | | - R. K. Panwar
- Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India
| | - Manoj Katiyar
- Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, India
| | - Pankaj Kumar
- Acharya Narendra Deva University of Agriculture and Technology, Ayodhya, India
| | - C. S. Mahto
- Birsa Agricultural University, Ranchi, India
| | - H. K. Borah
- Regional Agricultural Research Station, Shillongani, India
| | - M. N. Singh
- Institute of Agricultural Science, BHU, Varanasi, India
| | - Arpita Das
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, India
| | - A. N. Patil
- Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Pulses Research Unit, Akola, India
| | - H. C. Nanda
- Indira Gandhi Krishi Vishwavidyalaya, Raipur, India
| | - Vinod Kumar
- Jawaharlal Nehru Krishi Vishwa Vidyalaya, Regional Agricultural Research Station, Sagar, India
| | | | | | - M. H. Patel
- Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushi Nagar, India
| | - Raja R. Kanwar
- S.G. College of Agriculture and Research Station, Jagdalpur, India
| | - Jitendra Kumar
- Rajmohni Devi College of Agriculture and Research Station, Ambikapur, India
| | - S. P. Mishra
- Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, Chitrakoot, India
| | - Hitesh Kumar
- Banda University of Agriculture and Technology, Banda, India
| | - Indu Swarup
- Regional Research Centre on Pulses, College of Agriculture, Indore, India
| | - Suma Mogali
- University of Agricultural Sciences (UAS), Dharwad, India
| | - D. Kumaresan
- Tamil Nadu Agricultural University (TNAU), Coimbatore, India
| | | | - M. Byre Gowda
- University of Agricultural Sciences, Gandhi Krishi Vigyana Kendra (GKVK), Bangalore, India
| | | | - Polneni J. Rao
- Regional Agricultural Research Station (PJTSAU), Warangal, India
| | - D. Shivani
- PJTSA-Agricultural Research Station, Madhira, India
| | - A. M. Prusti
- Odisha University of Agriculture and Technology, Bhubaneswar, India
| | - P. Mahadevu
- College of Agriculture, UAS, GKVK, Mandya, India
| | - K. Iyanar
- Tamil Nadu Agricultural University (TNAU), Coimbatore, India
| | - Sujata Das
- Odisha University of Agriculture and Technology, Bhubaneswar, India
| |
Collapse
|
3
|
Rao PJ, Jyoti R, Mews PJ, Desmond P, Khurana VG. Preoperative magnetic resonance spectroscopy improves diagnostic accuracy in a series of neurosurgical dilemmas. Br J Neurosurg 2013; 27:646-53. [PMID: 23461752 DOI: 10.3109/02688697.2013.771724] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECT The purpose of this study was to evaluate the usefulness of preoperative magnetic resonance spectroscopy (MRS) in neurosurgical patients with diagnostically challenging intracranial lesions. METHODS Included in this study are twenty-three consecutive patients presenting to the neurosurgery service with diagnostically challenging intracranial lesions and who were investigated by conventional MR imaging and proton ((1)H) MRS, followed by surgery with subsequent histopathological diagnosis. An experienced neuroradiologist (RJ) blinded to the final histopathology evaluated the imaging studies retrospectively. Provisional diagnoses based on preoperative clinical and conventional MR data versus preoperative MRS data were compared with definitive histopathological diagnoses. RESULTS Compared with preoperative clinical and conventional MR data, (1)H MRS improved the accuracy of MR imaging from 60.9% to 83%. We found (1)H MRS reliably distinguished between abscess and high-grade tumour, and between high-grade glioma and low-grade glioma, but was not able to reliably distinguish between recurrent glioma and radiation necrosis. In 12/23 cases (52%) the (1)H MRS findings positively altered our clinical management. Two representative cases are presented. CONCLUSIONS Our study supports a beneficial role for (1)H MRS in certain diagnostic intracranial dilemmas presenting to neurosurgeons. The information gleaned from preoperative (1)H MRS can be a useful adjunct to clinical and conventional MR imaging data in guiding the management of patients with intracranial pathologies, particularly high-grade tumour versus abscess, and high-grade versus low-grade glioma. Further larger prospective studies are needed to clearly define the utility of (1)H MRS in diagnostically challenging intracranial lesions in neurosurgery.
Collapse
Affiliation(s)
- P J Rao
- Department of Neurosurgery, The Canberra Hospital, Garran, ACT, Australia
| | | | | | | | | |
Collapse
|
6
|
Murty JS, Muralidhar B, Goud JD, Rao PJ, Babu BR, Rao VS. Hierarchical gene diversity and genetic structure of tribal populations of Andhra Pradesh, India. Am J Phys Anthropol 1993; 90:169-83. [PMID: 8280194 DOI: 10.1002/ajpa.1330900204] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Gene diversity and genetic structure of tribal populations of Andhra Pradesh, India, have been analyzed under a hierarchical model consisting of five regions of the state, tribes within the regions, and local subpopulations within the tribes. Average gene diversity has been estimated from gene frequency data for 15 polymorphic loci by using nested gene diversity analysis of GST. The intralocation coefficient of gene diversity was estimated at 96% of the total, whereas the intertribal, within--and between--regional gene diversities were found to be only 1.90, 0.95, and 1.43%, respectively. The estimate of gene diversity was higher for loci with higher degrees of polymorphism such as ABO, MN, ESD, and PTC and lower for loci with low-level polymorphism and extreme gene frequencies such as Hb, Tf, PHI, 6PGD, and Hp. The nature of selective preference or neutrality at the loci seems to be important in this respect. Tribes of the plains exhibit the least gene diversity, apparently because of higher gene flow among them. The contribution of loci with intermediate gene frequencies in intertribal and regional gene diversity was found to be higher than for loci with extreme allelic frequencies. These results suggest that the most significant component of variation is between individuals within locations and that variation between local subpopulations is negligible in the genetic structure of a population. Forces like selection, gene flow and drift also influence the diversity depending upon the nature of the locus.
Collapse
Affiliation(s)
- J S Murty
- Department of Genetics, Osmania University, Hyderabad, India
| | | | | | | | | | | |
Collapse
|