The present study has illustrated the estimation of the soil organic carbon (SOC) distribution from point survey data (prepared after laboratory test) by a hybrid interpolation method, viz. regression kriging (RK) in a part of the Narmada river basin in the central India. In this study, eight selected predictor variables are used such as, brightness index (BI), greenness index (GI), wetness index (WI), normalized difference vegetation index (NDVI), vegetation temperature condition index (VTCI), digital elevation model (DEM), and slope and compound topographic index (CTI). The RK method has given satisfactory results as observed from the level of accuracy. Finally, the amount of SOC content in varied slope, soil and landuse categories has been analysed. Concentration of SOC has been observed to be more in low elevated areas in clay soil with mainly agricultural and vegetated lands

• Conservation agriculture (CA) practice meaningfully stabilized organic carbon (OC) upto lower depth of soil. • Crop intensification with nutrient exhaustive crops lowers OC stabilization in soil even after adoption of CA practice. • Farmers practice of growing rice and wheat produce less yield with less storage of carbon in soil. • Rice-wheat-legume crop with CA proved as most efficient in terms of crop yield, soil health and carbon sequestration.

• Soil physicochemical and biological properties were used for SQI development. • Major parameters studied were FDA, DHA, MBC, SOC, Av. NPK, AWC, MAS and SPR. • Highest SQI value of 0.90 was found in ZTDSR-ZTW at 0-10 cm soil depth.

Digital Elevation Model (DEM) is one of the important parameters for soil erosion assessment. Notable uncertainties are observed in this study while using three high resolution open source DEMs. The Revised Universal Soil Loss Equation (RUSLE) model has been applied to analysis the assessment of soil erosion uncertainty using open source DEMs (SRTM, ASTER and CARTOSAT) and their increasing grid space (pixel size) from the actual. The study area is a part of the Narmada river basin in Madhya Pradesh state, which is located in the central part of India and the area covered 20,558 km2. The actual resolution of DEMs is 30 m and their increasing grid spaces are taken as 90, 150, 210, 270 and 330 m for this study. Vertical accuracy of DEMs has been assessed using actual heights of the sample points that have been taken considering planimetric survey based map (toposheet). Elevations of DEMs are converted to the same vertical datum from WGS 84 to MSL (Mean Sea Level), before the accuracy assessment and modelling. Results indicate that the accuracy of the SRTM DEM with the RMSE of 13.31, 14.51, and 18.19 m in 30, 150 and 330 m resolution respectively, is better than the ASTER and the CARTOSAT DEMs. When the grid space of the DEMs increases, the accuracy of the elevation and calculated soil erosion decreases. This study presents a potential uncertainty introduced by open source high resolution DEMs in the accuracy of the soil erosion assessment models. The research provides an analysis of errors in selecting DEMs using the original and increased grid space for soil erosion modelling

Presently, rice-fallows are targeted for cropping intensification in South Asia. Rice-fallows a rainfed mono-cropping system remain fallow after rice due to lack of irrigation facilities and poor socio-economic condition of the farmers. Nevertheless, there is the scope of including ecologically adaptable winter crops in water-limited rice-fallow conditions with effective moisture conservation practices. The study aimed to identify the winter-crops that are adaptable and productive in rice-fallow conditions and to evaluate the different tillage-based crop establishment practices for soil moisture conservation, grain yield, economics, and sustainability parameters. Six different crop establishment and residue management (CERM) practices viz.,zero-tillage direct seeded rice (ZTDSR), zero-tillage transplanted rice (ZTTPR), puddled transplanted rice (PTR), ZTDSR with rice residue retention (ZTDSR_R+), ZTTPR with rice residue retention (ZTTPR_R+), PTR with rice residue retention (PTR_R+) as main-plot treatment and five winter crops (chickpea, lentil, safflower, linseed, and mustard) as sub-plot treatment were evaluated in a split-plot design. The productivity of grain legumes (chickpea and lentil) was higher over oilseed crops in rice-fallow conditions with an order of chickpea > lentil > safflower > mustard > linseed. Among the CERM practices, ZTDSR_R+and ZTDSR treatments increased the grain yield of all the winter crops over PTR treatment, which was primarily attributed to higher soil moisture retention for an extended period. Grain yield increment with conservation tillage practices was highly prominent in safflower (190%) followed by lentil (93%) and chickpea (70%). Rice grain yield was higher (7–35%) under PTR treatment followed by ZTDSR treatment. Conservation tillage practices (ZTDSR, ZTTPR) reduced energy use (11–20%) and increased the energy ratio over conventional tillage practice (PTR), higher in rice-safflower, rice-lentil and rice-chickpea rotations. Higher net return was attained in rice-safflower and rice-chickpea rotations with ZTDSR_R+treatment. Predicted emission of greenhouse gases was markedly reduced in ZTDSR treatment (30%) compared to ZTTPR and PTR treatments. Hence, the study suggests that cropping intensification of rice-fallows with the inclusion of winter crops like chickpea, lentil, and safflower following conservation tillage practices (ZTDSR_R+in particular) could be the strategic options for achieving the higher system productivity, economic returns, and energy use efficiency with the reduced emission of greenhouse gases.