Analysis of vegetation and climate change during Late Pleistocene from Ziro Valley, Arunachal Pradesh, Eastern Himalaya region

2014 ◽  
Vol 101 ◽  
pp. 111-123 ◽  
Author(s):  
Amalava Bhattacharyya ◽  
Nivedita Mehrotra ◽  
Santosh K. Shah ◽  
Nathani Basavaiah ◽  
Vandana Chaudhary ◽  
...  
2017 ◽  
Vol 171 ◽  
pp. 199-215 ◽  
Author(s):  
Carlos E. Cordova ◽  
Louis Scott ◽  
Brian M. Chase ◽  
Manuel Chevalier

2018 ◽  
Vol 10 (11) ◽  
pp. 12552-12560
Author(s):  
Bikramjit Sinha

This paper gives a brief review of the studies on zooplankton fauna of Arunachal Pradesh, the major shareholder of the eastern Himalaya biodiversity hotspot.  Altogether, 66 species of zooplankton (45 Rotifera, 20 Cladocera, & one Copepoda) have been recorded along with their distribution in the state, wherever available.  It is apparent that there is a lack of serious taxonomic studies on all three major groups of zooplankton from this Himalayan state.  The urgency and importance of documenting the zooplankton fauna of this biogeographically unique and biodiversity-rich state is highlighted in view of the fragility of the ecosystem as well as the effect of climate change. 


2006 ◽  
Vol 49 (2) ◽  
pp. 154-162 ◽  
Author(s):  
Chunhai Li ◽  
Lingyu Tang ◽  
Zhaodong Feng ◽  
Hucai Zhang ◽  
Weiguo Wang ◽  
...  

2016 ◽  
Vol 450 ◽  
pp. 306-316 ◽  
Author(s):  
Isla S. Castañeda ◽  
Thibaut Caley ◽  
Lydie Dupont ◽  
Jung-Hyun Kim ◽  
Bruno Malaizé ◽  
...  

2021 ◽  
Author(s):  
Nadine McQuarrie ◽  
Mary Braza

<div> <p>One of the first order questions regarding a cross-section representation through a fold-thrust belt (FTB) is usually “how unique is this geometrical interpretation of the subsurface?”  The proposed geometry influences perceptions of inherited structures, decollement horizons, and both rheological and kinematic behavior.  Balanced cross sections were developed as a tool to produce more accurate and thus more predictive geological cross sections.  While balanced cross sections provide models of subsurface geometry that can reproduce the mapped surface geology, they are non-unique, opening the possibility that different geometries and kinematics may be able to satisfy the same set of observations. The most non-unique aspects of cross sections are: (1) the geometry of structures that is not seen at the surface, and (2) the sequence of thrust faulting.  We posit that integrating sequentially restored cross sections with thermokinematic models that calculate the resulting subsurface thermal field and predicted cooling ages of rocks at the surface provides a valuable means to assess the viability of proposed geometry and kinematics.  Mineral cooling ages in compressional settings are the outcome of surface uplift and the resulting focused erosion.  As such they are most sensitive to the vertical component of the kinematic field imparted by ramps and surface breaking faults in sequential reconstructions of FTB.  Because balanced cross sections require that the lengths and locations of hanging-wall and footwall ramps match, they provide a template of the ways in which the location and magnitude of ramps in the basal décollement have evolved with time.  Arunachal Pradesh in the eastern Himalayas is an ideal place to look at the sensitivity of cooling ages to different cross section geometries and kinematic models. Recent studies from this portion of the Himalayan FTB include both a suite of different cross section geometries and a robust bedrock thermochronology dataset. The multiple published cross-sections differ in the details of geometry, implied amounts of shortening, kinematic history, and thus exhumation pathways. Published cooling ages data show older ages (6-10 Ma AFT, 12-14 Ma ZFT) in the frontal portions of the FTB and significantly younger ages (2-5 Ma AFT, 6-8 Ma ZFT) in the hinterland. These ages are best reproduced with kinematic sequence that involves early forward propagation of the FTB from 14-10 Ma.  The early propagation combined with young hinterland cooling ages require several periods of out-of-sequence faulting. Out-of-sequence faults are concentrated in two windows of time (10-8 Ma and 7-5 Ma) that show systematic northward reactivation of faults.  Quantitative integration of cross section geometry, kinematics and cooling ages require notably more complicated kinematic and exhumation pathways than are typically assumed with a simple in-sequence model of cross section deformation.  While also non-unique, the updated cross section geometry and kinematics highlight components of geometry, deformation and exhumation that must be included in any valid cross section model for this portion of the eastern Himalaya.</p> </div>


2015 ◽  
Vol 12 (2) ◽  
pp. 52-62 ◽  
Author(s):  
VK Choudhary

Maize (Zea mays L.) being a widely space crop were tried with different combinations of legumes cowpea (Vigna unguiculata L. Walp), frenchbean (Phaseolus vulgaris L.) and blackgram (Vigna mungo L.) as intercrops at different planting geometry to find out their suitability during 2009, 2010 and 2011 at eastern Himalayan, Arunachal Pradesh, India. Three experiments were carried out in sequence to identify suitable planting geometry to accommodate intercrops, screening best legume crops and subsequently best performed row ratio of maize and legume crops were intercropped in third experiment with 1:1, 1:2 and 1:5 row proportions. Sole maize gave the maximum grain yield with 4571.1 kg ha-1, whereas, stover yield was highest with maize-cowpea intercrop at 1:2 row ratios (8013.4 kg ha-1) and 57.1 kg ha-1 day-1 production efficiency followed by frenchbean and least with blackgram. Competition indices like land equivalent ratio (LER) was highest with 1:2 row ratio of maize-frenchbean (1.66), land equivalent coefficient (0.67). But, highest area time equivalent ratio (ATER) noticed with 1:2 row ratio of maizeblackgram (1.47). Relative crowding coefficient (K) and competition ratio were noticed higher with 1:2 row ratio of maize-cowpea, whereas, cowpea combinations has better crowding coefficient and blackgram combinations registered better competitiveness. Monetary advantage index (MAI) was 6433.2 with 1:2 row ratio of maize-blackgram followed by maize-cowpea and lowest with maize-frenchbean with the trend of 1:2>1:5>1:1 row ratios. DOI: http://dx.doi.org/10.3329/sja.v12i2.21916 SAARC J. Agri., 12(2): 52-62 (2014)


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