Appraisal of arid land status: a holistic assessment pertains to bio-physical indicators and ecosystem values

Abstract

Background: Appraisal of arid land status is very crucial one to know the extent and factors associated with their degradation. Previous studies from arid regions are mostly qualitative in nature (indicator assessment like good, moderate, severe, and very severe) and generally overlooked the significance of temporal fluctuation. Methods: In this study, the temporal status of 12 Indian arid lands was accessed by using a new integrated approach that includes attributes like relative converge score (RCS), herbaceous component score (HCS), soil quality index Modified (SQI), ecosystem monitoring value (EMV), and a modified bare patch index. From each land, data were collected during three seasonal events (pulse, inter-pulse, and non-pulse), and thus, status was evaluated with 36 observations. Data were analyzed by using frequency distribution, principal component analysis (PCA), student t test, and regression technique. Results: RCS and HCS were recorded minimum (0.005, 0.65) during non-pulse event and maximum (0.36, 1.79) during pulse event respectively. With this approach, multi-directional temporal status of lands identified that were grouped into lower (7), moderate (14), high (12), and very high (3) quality lands. Conclusion: This integrated study suggested that in arid regions, although rainfall triggers plant community composition, however, sole utilization of this parameter is unable to portray the true status of lands, and other physical (soil) and biotic (livestock and other anthropogenic) parameters are equally important and influential during other events.

Interactive effects of non-fodder litter and fungal species on soil enzymes: A microcosm temporal assessment from Indian arid zone

“…This cumulative data set was used to create the PCA bi-plot, which graphically depicts the overall scenario for various enzymes that is naturally adjusted by litter-fungalwithdrawal as well as their interactions. Such PCA strategies have been supported by many researchers (Laliberte andLegendre, 2010, Mathur andSundaramoorthy, 2018). The connections between soil enzymes detected during the first and second withdrawal periods, as well as between these two periods, were established using Canonical Correlation Analysis (CCoA).…”

“…PCA was used to quantify correlations among enzymes with weighted scores using the PAST (Hammar et al 2001) and XLSTAT (2017) software. Weights in the soil quality index were determined by dividing the percent of variation in the data set explained by the principal component analysis that contributed the indicated variable by the total percentage of variance explained by all the PCs with more than one eigenvector (Mathur and Sundaramoorthy, 2018).…”