Rabi Narayan Sahoo

Bio: Rabi Narayan Sahoo is an academic researcher from Indian Agricultural Research Institute. The author has contributed to research in topics: Soil water & Leaf area index. The author has an hindex of 21, co-authored 108 publications receiving 1799 citations. Previous affiliations of Rabi Narayan Sahoo include Indian Council of Agricultural Research & Council of Scientific and Industrial Research.

Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)

Abstract: In situ, non-destructive and real time mineral nutrient stress monitoring is an important aspect of precision farming for rational use of fertilizers. Studies have demonstrated the ability of remote sensing to monitor nitrogen (N) in many crops, phosphorus (P) and potassium (K) in very few crops and none so far to monitor sulphur (S). Specially designed (1) fertility gradient experiment and (2) test crop experiments were used to check the possibility of mineral N–P–S–K stress detection using airborne hyperspectral remote sensing. Leaf and canopy hyperspectral reflectance data and nutrient status at booting stage of the wheat crop were recorded. N–P–S–K sensitive wavelengths were identified using linear correlation analysis. Eight traditional vegetation indices (VIs) and three proposed (one for P and two for S) were evaluated for plant N–P–S–K predictability. A proposed VI (P_1080_1460) predicted P content with high and significant accuracy (correlation coefficient (r) 0.42 and root means square error (RMSE) 0.180 g m−2). Performance of the proposed S VI (S_660_1080) for S concentration and content retrieval was similar whereas prediction accuracies were higher than traditional VIs. Prediction accuracy of linear regressive models improved when biomass-based nutrient contents were considered rather than concentrations. Reflectance in the SWIR region was found to monitor N–P–S–K status in plants in combination with reflectance at either visible (VIS) or near infrared (NIR) region. Newly developed and validated spectral algorithms specific to N, P, S and K can further be used for monitoring in a wheat crop in order to undertake site-specific management.

Land Suitability Analysis for Different Crops: A Multi Criteria Decision Making Approach using Remote Sensing and GIS

Abstract: Land evaluation procedure given by FAO for soil site suitability for various land utilization types has been used to assess the land suitability for different crops and for generating cropping pattern for kharif (summer) and rabi (winter) seasons in Kheragarah tehsil of Agra. Kheragarah tehsil suffers from many types of land degradation such as such as salinity, watelogging, ravines, degraded hills and rock quarries (AIS & LUS, 2000). The database on soil, land use/land cover was generated from data derived from IRS -P6 remote sensing satellite and soil survey to perform an integrated analysis in the geographic information system environment. Agricultural and non-agricultural lands were delineated using the Decision Tree Classifier (DTC) and non-agricultural areas were masked for removal from future analysis. Different soil chemical parameters and physical parameters were evaluated for different crops. Subsequently all of them were integrated using a multi criteria decision making and GIS to generate the land suitability maps for various crops. Kharif and rabi season cropping patterns maps were developed by integrating crop suitability maps for the winter and summer seasons separately. Results indicated that about 55 % is highly suitable (S1) for sugarcane and 60%, 54% and 48 % of the area are moderately suitable (S2) for cultivation pearl millet, mustard and rice respectively. 50 % of the area is found to be marginally suitable (S3) for growing maize. It was also found that better land use options could be implemented in different land units as the conventional land evaluation methods suffer from limitation of spatial analysis for the suitability of various crops.

Soil enzymes: Kuprevich, V. F. & Shcherbakova, T. A.: Translated from the Russian edition (1966), published by the Indian National Scientific Documentation Centre, New Delhi 1971. 392 pp., offset printing from type script, paper cover. Obtainable from U.S. Department of Commerce, National Technical Information Service, Springfield, Va. 22151

Abstract: B6nassy, C., 1955. R6marques sur deux Aphelinid6s: Aphelinus mytilaspidis Le Baron et Aphytis proclia Walker. Annls l~piphyt. 6: 11-17. Lord, F. T. & MacPhee, A. W., 1953. The influence of spray programs on the fauna of apple orchards in Nova Scotia II. Oyster shell scale. Can. Ent. 79: 196-209. Pickett, A. D., 1946. A progress report on long term spray programs. Rep. Nova Scotia Fruit Grow. Ass. 83 : 27-31. Pickett, A. D., 1967. The influence of spray programs on the fauna of apple orchards in Nova Scotia XIV. Can. Ent. 97: 816-821. Tothill, J. D., 1918. The predacious mite Hemisarcoptes malus Shimer and its relation to the natural control of the oyster shell scale Lepidosaphes ulmi L. Agric. Gaz. Can. 5 : 234-239.

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 M I C H A E L A. W H I T E*, K I R S T E N M. DE BEURS w , K A M E L D I D A Nz, D AV I D W. I N O U Y E § ,

Abstract: Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by ! 60 days and in standard deviation by ! 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

Abstract: The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

Direct Seeding of Rice: Recent Developments and Future Research Needs

Abstract: Rice (Oryza sativa L.), a staple food for more than half of the world population, is commonly grown by transplanting seedlings into puddled soil (wet tillage) in Asia. This production system is labor-, water-, and energy-intensive and is becoming less profitable as these resources are becoming increasingly scarce. It also deteriorates the physical properties of soil, adversely affects the performance of succeeding upland crops, and contributes to methane emissions. These factors demand a major shift from puddled transplanting to direct seeding of rice (DSR) in irrigated rice ecosystems. Direct seeding (especially wet seeding) is widely adopted in some and is spreading to other Asian countries. However, combining dry seeding (Dry-DSR) with zero/reduced tillage (e.g., conservation agriculture (CA)) is gaining momentum as a pathway to address rising water and labor scarcity, and to enhance system sustainability. Published studies show various benefits from direct seeding compared with puddled transplanting, which typically include (1) similar yields; (2) savings in irrigation water, labor, and production costs; (3) higher net economic returns; and (4) a reduction in methane emissions. Despite these benefits, the yields have been variable in some regions, especially with dry seeding combined with reduced/zero tillage due to (1) uneven and poor crop stand, (2) poor weed control, (3) higher spikelet sterility, (4) crop lodging, and (5) poor knowledge of water and nutrient management. In addition, rice varieties currently used for DSR are primarily selected and bred for puddled transplanted rice. Risks associated with a shift from puddled transplanting to DSR include (1) a shift toward hard-to-control weed flora, (2) development of herbicide resistance in weeds, (3) evolution of weedy rice, (4) increases in soil-borne pathogens such as nematodes, (5) higher emissions of nitrous oxide—a potent greenhouse gas , and (6) nutrient disorders, especially N and micronutrients. The objectives of this chapter are to review (1) drivers of the shift from puddled transplanting to DSR; (2) overall crop performance, including resource-use efficiencies of DSR; and (3) lessons from countries where DSR has already been widely adopted. Based on the existing evidence, we present an integrated package of technologies for Dry-DSR, including the identification of rice traits associated with the attainment of optimum grain yield with Dry-DSR.