This study compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from Landsat 8 satellite data as explanatory variables . The area falls in the Shivpuri Tehsil of Madhya Pradesh, India, which is predominantly a semi-arid tract of the Bundelkhand region. The Enhanced Vegetation Index-1 (EVI-1) was identified as the most suitable input variable for the regression models, although the collective effect of a number of the vegetation indices was evident. The EVI-1 was also the most suitable input variable to SVM, due to its capacity to distinctly differentiate diverse vegetation classes. The performance of SVM was better over regression models for estimation of the AGB. Based on the SVM-derived and the ground observations, the AGB of the area was precisely mapped for croplands, grassland and rangelands over the entire region.

The effect of different salinity levels on germination, seedling growth, seedling vigour and chlorophyll contentsof five mungbean [Vigna radiata (L.) Wilczek] cultivarsviz. Pusa Baisakhi, PS7, B-105, Pusa-105 and Suniana was studied. The percentage of germination, seedling growth parameters and seedling vigour index were found to decrease under salinity stress. Salinity caused reduction in chlorophyll contents, chlorophyll a/b ratio and chlorophyll stability index. Among the five cultivars studied Pusa Baisakhi showed better growth performance and comparatively higher tolerance to salinity.