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In this present study, Soil and Water Assessment Tool (SWAT) embedded with ArcGIS interface has been used to simulate the surface runoff from the un-gauged sub-catchments in the upper catchment of Subarnarekha basin. Model calibration and validation were performed with the help of Sequential Uncertainty Fitting (SUFI-2) in-built in the SWAT-CUP package (SWAT Calibration Uncertainty Programs). The model was calibrated for a period from 1996 to 2008 with 3 years warm up period (1996-1998) and validated for a period of 5 years from 2009 to 2013. The model evaluation was performed by Nash - Sutcliffe coefficient (NSE), Coefficient of determination (R2) and Percentage Bias (PBIAS). The degree of uncertainty was evaluated by P and R factors. Basing upon the R2, NSE and PBIAS values respectively, of the order of 0.90, 0.90 and -12%, during calibration and 0.85, 0.83 and -15% during validation, substantiate performance of the model. All uncertainties of model parameters have been well taken by the P and R factors respectively, of the order of 0.95 and 0.77 during calibration and 0.82 and 0.87 during validation. The runoff generation from 19 sub-catchments of Adityapur catchment varies from 29.2-44.1% of the annual rainfall and average surface runoff simulated for the entire catchment is 545 mm. As the surface runoff generated in most of the sub-catchments amounts to above 30% of rainfall, it is recommended for adequate number of structural interventions at appropriate locations in the catchment to store the rainfall excess for providing irrigation, recharging groundwater and restricting the sediment and nutrient loss.

The water requirement of the crop is an important phenomenon to estimate the crop yield and understanding the crop water necessity at different crop phenological stages as soil and plant water deficits cause yield reduction. The most crucial crop stages that dictate crop yield are at the vegetative and reproductive stages, reducing the crop yield by more than 35% and 50%, respectively. Our study is to determine the crop water stress using the Crop Environmental Resource Synthesis (CERES)-Sorghum model, which is a component of the Decision Support System for Agrotechnology Transfer (DSSAT)—crop simulation model (CSM). Crop water stress is simulated spatially for Rainfed Kharif Sorghum (Sorghum bicolor (L.) Moench) in 10 districts of Maharashtra state, India, from 2000 to 2018 using DSSAT-CSM. Besides other factors as well that impact crop yield, rainfall also has an impact on crop growth, development, and managing water efficiency for the crops. Simulated crop water stress above a specific threshold value of ≥ 0.5 (50%) impacts crop growth and development process. Considering the drought year 2015 with sowing dates June (15, 22, 29) and July (6, 15); it shows that late sowing of kharif sorghum for 2015 minimal crop water stress can be seen. CERES-Sorghum model can efficiently determine the crop water stress at different crop phenological stages with different dates of sowing.

With the changing climate, reliable climate projections are essential for agriculture risk management. The present study aims to explore the output of a regional climate model (RCM) at different climatic regimes and its applications in crop simulation models. Here, a comparative study of the cotton crop growth and yield response for Akola in the central and Hisar northern agroclimatic zone of India represents rainfed and irrigated growing regions of cotton, respectively. The RegCM4 projections and its bias-corrected values of temperature and precipitation data for the period 1971–2005 are compared with the observations to assess its reliability with the crop simulation models as weather inputs. The results signify that the RCM model is wet, which implies that, it shows high rainfall intensity in terms of frequency as a number of rainy days and amount. The model also shows night warming as there is a significant decline in maximum temperature and minimal decline in minimum temperature, thus there is a reduced diurnal temperature difference. Overall model highly underestimates temperature and overestimates rainfall. Strikingly reduced numbers of intense warm and cold events are simulated. Model is highly biased for rainfall events >0 mm/day and 5mm/day, and moderately biased for rainfall >5 mm/day. Precipitation bias-correction, using quantile mapping approach, shows excellent agreement at an annual scale. But precipitation variability could not be captured that well as it is a ‘distribution-based method'. However, it worked well in the irrigated Hisar region than the rainfed Akola region. The bias-corrected RegCM4 climate inputs are utilized in Decision Support System for Agro-technology Transfer (DSSAT) simulations for cotton yields, Leaf Area Index (LAI) and ball number at maturity/m2 (NM) for both regions. Bias-corrected outputs are in better agreement with corresponding observations than non-bias-corrected outputs in both regions. Future research could apply these simulated model data complemented with reliable bias correction techniques to explicitly study climate change's impact on crop productivity.

CANEGRO-Sugarcane model was used to assess the impact of climate change on sugarcane in different combinations of elevated temperature and CO2 concentrations. Additionally, we used dynamically downscaled bias-corrected regional climate model (RCM) data using RegCM4 under RCP4.5 scenario (2040–2060) to project the future change in sugarcane stalk fresh mass (SFM) and sucrose mass (SM). The results showed an increase in temperature, rainfall and solar radiation in the future projections at the study site. The SFM and SM were found to be vulnerable (3–25% decrease) by increasing temperature (1–4 °C), however, a higher concentration (2–14% increase) was observed for both SFM and SM under elevated CO2 levels (450–850 ppm). The combined effect of increased temperature and elevated CO2 had a beneficial effect on SFM but negative on SM (more for rainfed condition). Overall, SFM was projected to increase by 3–39% (rainfed) and 7–47% (irrigated) in 2040–2060 relative to 1971–2000 in diverse agro-climatic zones of the region. Similarly, SM was projected to decrease by 9–69% (rainfed) and 6–37% (irrigated). In general, water stress conditions combined with the projected increase in temperature adversely affected the sugarcane. The findings suggest the development of a efficient water use, heat-tolerant cane variety and improved farm management strategies in the near future to assist the sugar industry and to adapt to the changing climate in northern India. This is required in the greater perspective of decrease in sucrose mass in spite of double-fold increase in CO2.

In this study, effort is made to estimate weekly rainfall over India at 36 meteorological subdivisions during monsoon 2016 (June to September) by using several precipitation products from different satellites which include Indian National Satellite System (INSAT) INSAT-3D derived Hydro Estimate (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) rainfall products, Integrated Multi-satellite Retrievals for GPM (IMERG) derived from JPL, NASA, and National Center for Medium Range Weather Forecasting (NCMRWF) Merged Satellite Gauge (NMSG) of IMD. Different satellite estimates, and rain gauge satellite merged products have been compared with gridded rainfall products prepared by India Meteorological Department (IMD). HE at 4 km pixel resolution and IMSRA at 0.100 × 0.100 resolution are derived rainfall products from Indian satellite INSAT-3D.GPM (IMERG) product is derived at 0.100 × 0.100 resolution from Global Precipitation Mission (GPM) satellite jointly operated by JAXA (Japan) and NASA (USA). NCMRWF Merged Satellite-Gauge (NMSG) rainfall data has been generated at a 0.25° × 0.25° grid resolution by IMD & NCMRWF. The data is analyzed preliminarily for the southwest monsoon season of 2016 at weekly temporal resolution. The overall comparison shows that GPM (IMERG) estimates better rainfall over the coastal and plain region, however, HE estimates better rainfall over the hilly region, in general, and for Kerala region, particularly. NMSG product needs improvement over the hilly region and considerable improvement is required in IMSRA product. For all 36 meteorological subdivisions, NMSG product has 0.56 average Nash-Sutcliffe efficiencies (NSE) for 17 weeks with IMD Gridded rainfall data. However, for satellite-derived rainfall by GPM (IMERG), HE and IMSRA have 0.29, 0.1 and −0.8 average NSE at 17 weeks, respectively. Based on this study, it is concluded that NMSG daily rainfall product is best, however, amongst satellite-derived rainfall, overall GPM gives a better result with a few exceptions and IMSRA need more modifications.

Rice is an important cereal crop and part of daily diet not only in India but also throughout Asia. Agriculture is highly dependent on the variations in temperature, precipitation and solar radiation and long-term climate patterns. The pattern of changing climate in last 30 years indicate that predictable and possible changes in rainfall and temperature can reduce agriculture outputs and yields particularly for rice and wheat significantly. The main aim of this research paper is to study the Kharif Rice Productivity in Hisar, Haryana under changing climate. The study focuses on the impacts of climate change namely intensity, timings and spatial distribution of rainfall. Kharif rice is more vulnerable to meteorological drought due to growing uncertainties in monsoon rains under the changing climate patterns. The study of seasonal changes in precipitation at decadal scale for observed and CORDEX projected precipitation for the selected region was done to understand the impacts of climate change. Along with this, DSSAT Crop Simulation Model was run to quantify the water stress in the years with more negative rainfall departure and to identify the reasons for variability in yield. DSSAT model was able to simulate phenological events and final grain yield at maturity stage with reasonable accuracy under varied weather conditions. The analysis of the simulated results indicates the association between yield and rainfall amount and its distribution during the season and different phenological growth stages of rice. The results further indicate that water stress at important stages such as Booting, Heading and Flowering majorly impact the final yield.

The water resource management is a present need for sustaining the living environment on a watershed. The anthropological activities on any watershed cause resources imbalance that eventually affects the watershed performance. Remote sensing and geographical information system are the powerful tools to identify such changes in the watershed either by natural causes and/or by human intervention. Such knowledge is useful for scientific planning and management of watershed. The present study has been highlighted the morphometric analysis with land use and land cover (LULC) changes and hydrologic performance during 2001 and 2011 over the Champua watershed of Upper Baitarani river basin, Odisha, India. The various parameters of morphometry which cause soil erosion have been evaluated and are used to prioritize the sub-watersheds. Also, LULC changes from two satellite imaginaries of the years 2001 and 2011 have been classified and percentage area changes of the respective classes for the sub-watersheds over one decade have been estimated. Then, various land features which would increase runoff and cause more soil erosion have been evaluated to prioritize the sub-watershed. Finally, prioritization is assigned for sub-watersheds, according to severity effects due to morphometric parameter and LULC changes. To archive for management point of view, the conservation practices or measures are recommended as per their final priority. These results obtained from prioritization of sub-watershed would be useful for hydraulic engineers for planning and management.

Odisha State is located in eastern part of India which is blessed with sufficient amount of rainfall. Major part of the state is covered by Mahanadi river basin; Mahanadi river basin originated from Sihawa town and disposed out to Bay of Bengal near Sambalpur, Odisha State. The distribution pattern is erratic and uneven due to climate, topography, hydrology and geomorphology of the state. Catchment area of the Mahanadi is vast; therefore, drainage order is also high (about six); due to these features large amount of water has generated, and it will be appeared as flood at downstream through deltaic region to Bay of Bengal. In this connection, water resources planning and management are one of the crucial issues. For any hydrological study, it is necessary to identify the characteristics and climatic variability of the catchment. In the present study, long-term (1901–2013) rainfall time series has been utilized for rainfall variability analysis using nonparametric tests, viz. Mann–Kendall (MK), modified Mann–Kendall (MMK) and Theil–Sen’s slope. The average annual rainfall of the Odisha State is to be found as about 1438 mm; out of them, about 78% occurs in the monsoon season. In the present study, it was found that the maximum and minimum rainfalls occurred at Bhadrak and Puri districts, respectively. As per change point detection, it was found to be as 1945 is shift change year. Further, it was noticed that most of the districts are showing positive trends before change point and vice versa. Similar results are estimated in the monsoon season because most of the rainfall has occurred in the monsoon season. Both positive and negative trends have been found in Odisha State; detailed study has been carried out for rainfall behavior identification of the state. The present study is useful for agricultural and water resources planning and mitigation.

The rainfall forecast of global forecasting system (GFS) model running at the India Meteorological Department (IMD), i.e., GFS T574 (25 km) and GFS T1534 (12.5 km) is verified at its natural resolution. A verification study is conducted for the rainfall forecast over Indian window during the summer monsoon season (June–September) 2016. Regional verification of rainfall forecast was also carried out over five homogeneous regions of India, i.e., north India, west coast of India, northeast India, central India and peninsular India. The skill of the model forecast is also evaluated for yes/no, categorical and few heavy rainfall cases over India. Results show that both T1534 and T574 models are able to provide more realistic spatial distribution of seasonal mean rainfall over India. This study also shows that both the model forecasts are skillful in capturing the 24 h accumulated rainfall over climatologically heavy rainfall regions, but the magnitude and location fluctuated. The rainfall forecast for different rainfall categories in terms of different statistical skill scores, i.e., ratio score, HK, bias, hit rate and HKQ showed a relatively high skill for T1534 compared to T574 over most parts of the country from the day 1 to day 5 forecasts. It is also found from this study that the skill in predicting day 1 to day 3 forecasts of both the models was found to be good and usable for all parts of the country except for high terrain regions in India. The results documented here are expected to be useful to the operational forecasters in day-to-day weather forecasting over Indian monsoon regions.

Since 2002, Gravity Recovery and Climate Experiment (GRACE) satellite has provided monthly estimates of land water storage variations by monitoring time-variable components of earth's gravity field. We present GRACE satellite RL05 gravity data at 1° × 1° resolution to estimate terrestrial water storage change (surface water + groundwater) over India during Indian Summer Monsoon (ISM) and Northeast (NE) monsoon periods for the year 2015. As the NE monsoon was above normal in 2015, groundwater storage has increased significantly. By using GLDAS2.1 (1° × 1° resolution data) land surface state variables, we deduced as to how Terrestrial Water Storage (TWS) is partitioned into canopy water and soil moisture components. By computing storage changes in GRACE TWS, Global Land Data Assimilation System (GLDAS) land surface state variables and by using a terrestrial-based water balance approach, we calculated changes in the groundwater storage. The GRACE data indicate the variation of 15 cm in the TWS during the ISM period over India and a gain of 2.35 cm NE season over Peninsular India. After the ISM season, the groundwater storage was increased by 13 cm over India and 6.45 cm during the NE monsoon over Peninsular India. We also observed a high run-off (sub-surface + surface) rate in both the monsoon periods. The accumulated rainfall over India was estimated as 76.58 cm out of which 33.8 cm was computed as run-off (ISM season). Similarly, the mean rainfall in Peninsu lar India was calculated as 8 cm out of which 3.86 cm was approximated as run-off (NE monsoon season).

One-dimensional hydrodynamic models overestimate river cross-section derived from freely available SRTM DEMs. The present study aims to minimize the overestimation of river flow. DEM-extracted cross-sections obtained from 30 m and 90 m resolutions show higher elevation values than the actual river cross sections of Krishna and Bhima rivers, India. To minimize the overestimation of the river flow, DEM-extracted cross-sections are modified using known cross-section of the river. The corrections for cross sections extracted from DEM, are obtained by subtracting the DEM-derived cross-sections from a known cross-section of the river. Monsoons flows that occurred in years 2006 and 2009 in Krishna and Bhimariver have been used for modeling. The MIKE HYDRO River model performance with modified DEM-extracted cross-sections of river improves as the correlation coefficient, root mean square error, index of agreement, Nash Sutcliffe efficiency & Percentage deviation in peak (%) values are improved.

Sampling of groundwater and surface water at 33 sites near the Malanjhkhand copper mines in India indicated that elevated concentrations of Cu (15.01 mg/l) and Mn (9.040 mg/l) were locally present in water bodies near the mine sites. Multivariate statistical techniques including principal component analysis, cluster analysis and a Pearson correlation matrix were used to determine the relationships between different water quality parameters in water samples. Mining and associated activities were responsible for the different physico-chemical properties of ground and surface water. An assessment of the heavy metal pollution index (HPI) indicated that four surface water sampling points HPI exceeded the critical index value of 100, indicating that water had been contaminated due to discharges from the mines and that groundwater had been locally contaminated due to the infiltration of contaminated leachate from waste dumps.

The paper aims to explore the biasness in the RegCM climate model outputs for diverse agro-climatic zones of Uttar Pradesh, India, with emphasis on wheat (Rabi growing season) and rice (Kharif growing season) yields with and without bias correction using quantile mapping approach for the baseline period of 1971–2000. The result shows that RCM highly underestimated the maximum and minimum temperature. There exists a bias towards lower precipitation in annual and Kharif and higher in Rabi along with strikingly low intense warm (maximum temperature > 45 °C and 40 °C) and high cold events (maximum temperature 50 mm/day. Bias correction through quantile mapping approach, however, showed excellent agreement for annual and seasonal maximum and minimum temperature and satisfactory for extreme temperatures but drastically failed to correct rainfall. The study also quantified the biasness in the simulated potential, irrigated, and rainfed wheat and rice yield using DSSAT (Decision Support System for Agro-technology Transfer) crop model by employing observed, RCM baseline, and RCM baseline bias-corrected weather data. The grain yields of RCM-simulated wheat and rice were high while the bias-corrected yield has shown good agreement with corresponding observed yield. Future research must account for the development of more reliable RCM models and explicitly bias correction method in specific to complement future analysis.

The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982–2008) as well as independent predictions for the period 2009–2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions as well as India as a whole during all the four seasons.

Steel slag, a by-product generated during steel production poses a solid waste disposal challenge. The same slag contains valuables such as titanium, nickel, vanadium, iron, aluminum, silica among others. The extracted components can cause productive value addition in diverse realms like wastewater treatment to semiconductor devices. A paradigm shift is required towards waste valorization for developing profitable extraction methods for these constituent elements. In this review, an attempt was made to assimilate such technologies into one concrete and concise compendium. The pros and cons of each method were weighed comparatively with respect to applicability and innovation. An effort was attempted for assessing key design aspects and scale-up potential vis-a-vis the economic analysis for the valorization techniques. It was seen that the leaching technique was the most widely used in the resource recovery domain followed by other techniques like fusion, hydrothermal treatment. Techniques that dominate the exploration of some of the metals from their lean ores or respective mineral slags might also be explored for their recovery from steel slag. Some technologies like slag filter development for phosphate removal has already been tested in real-life conditions while others are in the nascent stages of development. Special focus can be directed towards the simultaneous extraction of multiple components or direct in-situ product formation and its subsequent separation. This paper reviews various technologies for steel slag valorization to guide researchers working in the field of slag resource recovery and sustainable waste utilization.

The Extended Range Forecasts System (ERFS) has been generating monthly and seasonal forecasts on real-time basis throughout the year over India since 2009. India is one of the major rice producer and consumer in South Asia; more than 50% of the Indian population depends on rice as staple food. Rice is mainly grown in kharif season, which contributed 84% of the total annual rice production of the country. Rice cultivation in India is rainfed, which depends largely on rains, so reliability of the rainfall forecast plays a crucial role for planning the kharif rice crop. In the present study, an attempt has been made to test the reliability of seasonal and sub-seasonal ERFS summer monsoon rainfall forecasts for kharif rice yield predictions at Kharagpur, West Bengal by using CERES-Rice (DSSATv4.5) model. These ERFS forecasts are produced as monthly and seasonal mean values and are converted into daily sequences with stochastic weather generators for use with crop growth models. The daily sequences are generated from ERFS seasonal (June–September) and sub-seasonal (July–September, August–September, and September) summer monsoon (June to September) rainfall forecasts which are considered as input in CERES-rice crop simulation model for the crop yield prediction for hindcast (1985–2008) and real-time mode (2009–2015). The yield simulated using India Meteorological Department (IMD) observed daily rainfall data is considered as baseline yield for evaluating the performance of predicted yields using the ERFS forecasts. The findings revealed that the stochastic disaggregation can be used to disaggregate the monthly/seasonal ERFS forecasts into daily sequences. The year to year variability in rice yield at Kharagpur is efficiently predicted by using the ERFS forecast products in hindcast as well as real time, and significant enhancement in the prediction skill is noticed with advancement in the season due to incorporation of observed weather data which reduces uncertainty of yield prediction. The findings also recommend that the normal and above normal yields are predicted well in advance using the ERFS forecasts. The outcomes of this study are useful to farmers for taking appropriate decisions well in advance for climate risk management in rice production during different stages of the crop growing season at Kharagpur.

Worldwide waste valorization and recovery of non-utilized components/unreacted compounds/ side streams/ byproducts are being researched upon. These are indispensable for environment protection, process economics and emerging technology development concepts like zero liquid discharge. Existing techniques suffer from problems like secondary contamination, complex design, costly raw materials, and extreme process conditions. Membrane crystallization offers a robust and simple solution for sustainable recovery of valuables from effluent streams. This review was undertaken to critically assess efficacy of membrane crystallization for value-added product recovery integrating the basic principles of process intensification: economics, efficiency, and sustainability. Processes like membrane distillation integrated crystallization, osmotic membrane crystallization, concentrative pressure-driven crystallization were analysed with respect to selective separation, byproduct recovery percentage, and viability. Several approaches on process intensification and comparative performance evaluation were discussed. Integrative technology options combining conventional methods like precipitation, evaporative crystallization as well as standalone versions were explored in detail to ascertain techno-commercial viability. It was concluded that membrane crystallizers provides an efficient and eco-friendly means for value added product recovery from diverse waste streams. This critical review on insights aims to formulate concrete guidelines towards applicability of membrane based crystallization for value added product recovery.

Extreme rainfall events are a significant cause of loss of life and livelihoods in Odisha. Objectives of the present study are to determine the trend of the extreme rainfall events during 1991-2014 and to compare the events between two periods before and after 1991. Block level daily rainfall data were used in identifying the extreme rainfall events, while district level aggregation was used in analysing the trend in three categories, viz., heavy, very heavy and extremely heavy rainfall as per criteria given by India Meteorological Department (IMD). The state as a whole received one extremely heavy, nine very heavy, and forty heavy rainfall events in a year. When percentage of occurrence of each category out of the total extreme events over different districts was considered, maximum % of extremely heavy rainfall occurred in Kalahandi (5.8%), very heavy rainfall in Bolangir (23.8%) and heavy rainfall in Keonjhargarh (85.4%). Trend analysis showed that number of extreme rainfall events increased in a few districts, namely, Bolangir, Nuapada, Keonjhargarh, Koraput, Malkangiri, and Nawarangapur and did not change in other districts. In Puri district, extremely heavy rainfall frequency decreased. New all-time record high one-day rainfall events were observed in twenty districts during 1992 to 2014, surpassing the earlier records, which could be attributed to climate change induced by global warming. Interior south Odisha was found as the hot spot for extreme rainfalls.

IMD started issuing quantitative district level weather forecast upto 5 days on operational basis from 1st June, 2008. The products comprise of quantitative forecasts for seven weather parameters, viz., rainfall, maximum and minimum temperatures, wind speed and direction, relative humidity and cloudiness. The rainfall forecast is generated based on multi model-ensemble techniques (MME). For other parameters, ECMWF forecasts (presently IMDGFS) are used. These forecast products are further value added, by the respective MCs/RMCs and forwarded to 130 Agrometeorological Field Units (AMFUs) for preparation of weather based District Agromet Advisory Service bulletin. This Meteorological Monograph describes the performance skill of the operational district level weather forecasts over different parts of India rainfall during monsoon and temperature during winter and summer from 2012-14. The Monograph also highlights limitations and future scope for further improvement of the MME models. The verification results show weather forecasts are reasonably accurate and value addition has improved the accuracy of model forecast. Though the MME model could predict the weather in hill regions in the North but in other regions having some hilly areas, the same could not come true in respect of temperature. North East region of the country shows very less accuracy due to its predominantly humid sub-tropical climate with hot, humid summers, severe monsoons and mild winter.

Pulses have very good opportunity to find place in different agro-ecosystems. Nowadays, soil organic carbon and crop productivity have become major concerns in sustainable agriculture. Since last six decades in our country, improvement in productivity of most of the field crops has been obtained but much success could not be achieved in increasing area and productivity of pulse crops. From 1980–1981 to 2013–2014, the total cropped area under pulses in India increased from 22.46 to 25.21 m ha with the peak of 26.40 m ha in 2010–2011. Due to stagnant production and an increase in population, the per capita availability of pulses has been declined in the range of 35.4 g to 47.2 g/capita/day during 2009–2014. Significant increment in area and productivity of pulse crops is possible by intercropping of these crops with sugarcane which is grown in more than 5.15 million ha area in India. Thus, in irrigated agro-ecosystem, there is a great scope of increasing area under pulse crops through crop diversification/intensification besides improving their productivity. Adoption of pulse crops with autumn (October)-planted cane in furrow-irrigated raised-bed system where sugarcane is planted in furrows and component crop is planted on raised beds also provides great scope for increasing crop productivity and optimizing input use efficiencies in the system. Crop residues recycling of pulses addresses the key issue of decreasing soil organic carbon created due to indiscriminate/imbalanced use of fertilizers and lower use/unavailability of organic manures. Mechanization of diversified cropping systems will certainly boost the adoption of recommended technologies by the farmers. Various tractor-drawn implements for planting of sugarcane and component pulse crops have been developed. Mechanical harvesters for pulse crops have already been developed and are being used by the farmers. Thus, cost of production could be reduced and profitability may be increased with introduction of these crops in irrigated agro-ecosystem besides improving soil fertility and sustainability for longer period. In present paper, authors presented the opportunities of agronomic interventions for pulse crops in sugarcane-based system to improve the crop productivity, profitability and sustainability.

Crop growth simulation models, properly validated against experimental data have the potential for facilitating strategic decision making in agriculture. Such validated models can also make use of the information generated for site specific experiments and trials to other sites and for different time durations. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, data from seven field experiments conducted at Jorhat (26° 47' N, 94°12' E; 87 m amsl) during 1998-2005 for long duration rice cultivar Ranjit grown under rainfed conditions were collected. Genetic coefficients required for running the CERES-Rice v4.5 model were derived and the performance of the model under the climate of upper Brahmaputra valley was evaluated. These results indicate that the CERES Rice v4.5 model is capable of estimating growth stages and grain yield of rice cultivar Ranjit in the climatic conditions of upper Brahmaputra valley with reasonable accuracy. Hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

Rain gardens or bioretention systems are the best storm water management practices, which use natural processes of the hydrological cycle such as infiltration and evapotranspiration. Rain gardens were first coined for residential use in 1990 in Prince George’s County, Maryland which was an alternative to the conventional system of sidewalks and gutters. However, countries like Japan, China, Australia and U.S.A. are encouraging the use of rain garden these days for the sustainable development of the country. From the last few decades, the world is witnessing harmful results of urbanization. This has led to a rapid increase in impervious land cover and deterioration of the quality of the ecosystem. The impervious surface of concrete and asphalt seal the soil layers and causes excessive surface runoff, which leads to the problem of urban flooding. Also, chemical and oils falling from vehicles on roads get washed away with storm water and enters the natural water bodies leading to their pollution. Rain garden reduces and delays the flood peaks as well as helps in groundwater recharge and enhances the biodiversity. Moreover, its vegetation works as a filter media for storm water treatment. Rain garden is a low impact development (LID) technique having a long-term performance and is aesthetically pleasing. This paper quotes the benefits and chronological order of implementation of bioretention systems in urban cities having the problem of storm water management with an aim to create awareness among scientific communities.

Wide range of inter-annual climatic variability and frequent occurrence of extreme climatic events in Indian context is a great concern. There is a need to assess the impact of these events on agriculture production as well suggest the agri-management options for sustenance. The appropriate region specific agro-advisory needs to be established for the farmers and other stake holders. Crop simulation models are effective tools for assessing the crops’ response to these climate related events and for suggesting suitable adaptation procedures for ensuring higher agricultural production. Remote sensing and GIS are effective tools in this regard to prepare the regional based agro-advisories, by linking with the crop simulation models and relational database layers of bio-physical and socio-economic aspects. For effective agro-advisory services, there is a need to link the other biotic and abiotic stresses for accurate estimates and generating window of suitable agri-management options. Crop simulation models can effectively integrate these stresses for crop and soil processes understanding and ultimate yield formation. In this review article, we have discussed about the inter-annual/ seasonal climatic variability and occurrence of extreme climatic events in India and demonstrated the potential of crop models viz., INFOCROP, WTGROWS, DSSAT to assess the impact of these events (also including climate change) on growth and yield of crops and cropping systems and thereby suggesting appropriate adaptation strategies for sustenance. The potential of remote sensing for crop condition assessment and regional/national yield forecast has been demonstrated. Crop simulation tools coupled with remote sensing inputs through GIS can play an important role in evolving this unique operational platform of designing weather based agro-advisory services for India.

Climate change and agriculture are interrelated processes both of which take place on global and regional scales with varying magnitude. Agriculture is most vulnerable to global warming as compared to other enterprises. The studies conducted in mountains of Himachal Pradesh showed increase in both maximum and minimum temperature range from 0.4 to 2.4 °C, decrease of annual rainfall amount from 10 to 40 per cent and change of weather parameters. The investigations with respect to climate, the impact of increasing temperature and climate variability showed shift of apple crop to higher elevations in H.P. The simulated studies on elevated carbon dioxide levels of 50 to 100 ppm as extreme climatic events from existing 370 ppm showed increase in yield of maize (3.0 to 5.4%), wheat (3.6 to 7.3%), rice (7.6 to 20.8%), soybean (1.3 to 3.5) and mustard (4.0 to 6.0 %) both in rainfed and irrigated conditions under different agro-climatic conditions of Himachal Pradesh. The simulated studies on extreme events of 1 and 2 °C rise showed decrease of maize and soybean yield whereas rice, wheat and mustard showed increase in yield due to rise in temperature. The 10 to 20 per cent decrease in rainfall showed less reduction in the yield of maize and soybean under sub-humid and sub-temperate agro climatic region. The delay of planting windows according to rainfall pattern in the region under present conditions proved beneficial for managing impacts of extreme weather events. The 20 years simulated results showed that delay of sowing by 10 days in both agro climatic zone of H.P. offsets 1 and 2oC rise in temperature on crop productivity of maize, soybean and wheat. Similarly, 20 years simulation indicated benefit in yield of wheat and mustard where sowing was delayed upto first fortnight of November.

Sugarcane is a cash crop in Uttar Pradesh; economic condition of the farmers is highly dependent on sugarcane production. However, average yield of the state has gone up from 39.5 t/ha (1950-51) to 59.2 t/ha (2009-10), was observed associated with fluctuating weather conditions, whereas other major sugar producing area in India have average yield of 70 t/ha. The result of the above study showed that there was an average rising trend in the annual minimum temperature (0.03° Cyr-1) over all the agro-climatic zones of the state. Out of nine agro-climatic zones, four zones namely South Western Zone, Central Plain Zone, Western Plain Zone and Eastern Plain zone, which were marked by decreasing annual rainfall trend. However, Vindhyan Zone, Mid Western Zone and Bhabhar and Tarai Zone show rising trend. To explain better relation between cane yield and weather parameters this study also show that maximum, minimum temperature and moisture plays the most important role during germination, tillering, grand growth and ripening phases of the sugarcane. Considering extreme weather, we found that temperature below 25 °C, above 35 °C and 40 °C are slowing down the growth and finally reducing the final yield. It is also noticed that temperature and rainfall extremes had high possibility of governing sugarcane yields but there were also quite a number of instances wherein the extremes couldn’t be reasoned directly for the yield fluctuations. Therefore, to sustain the productivity, this study recommends the improvements of the adoptive responses of varieties, management of the risk associated with extreme weather events by providing weather linked value-added advisory services to the farmers and crop insurance agencies.

Ammonia discharged in industrial effluents bears deleterious effects and necessitates remediation. Integrated systems devoted to recovery of ammonia in a useful form and remediation of the same addresses the challenges of waste management and its utilization. A comparative performance evaluation study was undertaken to access the suitability of different zeolite based systems (commercial zeolites and zeolites synthesized from fly ash) for removal of ammonia followed by its subsequent release. Four main parameters which were studied to evaluate the applicability of such systems for large scale usage are cost-effectiveness, ammonia removal efficiency, performance on regeneration, and ammonia release percentage. The results indicated that synthetic zeolites outperformed zeolites synthesized from fly ash, although the later proved to be more efficient in terms of total cost incurred. Process technology development in this direction will be a trade-of between cost and ammonia removal and release efficiencies.

Use of seasonal and sub-seasonal forecast products of experimental extended range forecast system (ERFS) in crop models is investigated for improving the rice grain yield prediction skill for the ensuing monsoon season in the experimental station at Bhubaneswar, India. A stochastic disaggregation is used to downscale seasonal and monthly forecast products in daily weather sequences. These weather series are taken as input in Crop Estimation through Resource and Environment Synthesis (CERES)-rice crop simulation model for the crop yield prediction at different stages of forecast: June–September (4 month forecast), July–September (3 month forecast), August–September (2 month forecast) and monthly forecast for September (1 month forecast). To avoid a technological trend in historical yield data, yields simulated with observed weather data have been used as the benchmark (observed rice yield) to evaluate the yields simulated using experimental ERFS forecasts. The findings recommend the efficiency of forecast products to capture year-to-year variability in observed rice yield at experimental stations. A significant enhancement in the prediction skill is noticed as the season advances due to incorporation of observed weather data, reducing uncertainty of yield prediction. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

A statistical study of pulsing hiss emissions observed during daytime at low latitude Indian ground stations Jammu (geomag. lat., 22°26′N; L = 1.17) and Srinagar (geomag. lat., 24°10′N; L = 1.28) is presented. From the detailed statistical analysis of the observed data it is found that the reported day time pulsing hiss emissions is a rare phenomena at low latitudes. The spectrograms of pulsing hiss emissions observed during daytime clearly show band limited spectrums regularly pulsing with almost equal period of the order of few seconds in the frequency range of 300 Hz–8 kHz. The intensity and bandwidth of daytime pulsing hiss remain almost constant in the frequency range of 300 Hz–8 kHz in clear contrast to the pulsing hiss emission observed during nighttime at low latitudes. The daytime pulsing hiss time period is about 0.5 s which is small in comparison to time period (~1.43 s) of the low latitude pulsing hiss observed during nighttime. The generation and propagation mechanisms of pulsing hiss emissions are discussed. The micropulsations propagating along the geomagnetic field lines modulate the growth rate of the wave resulting in hiss intensity.

Production of crops is greatly influenced by weather phenomena and therefore any change in climate will have major effects on crop yield and productivity. Using NYD analysis for prediction of crop yield on seasonal basis, it has been observed that maximum temperature may cause the reduction in yield of rice in Eastern Uttar Pradesh by 1.0 to 1.1% per ha by 2020. Similarly, minimum temperature may decrease the yield of rice by 1.5 to 1.9% per ha in Eastern Uttar Pradesh. From future scenario of rainfall it was observed that south-west monsoonal rainfall would be the major factor for controlling the yield of rice. The role of maximum temperature for wheat production in Bihar state is more significant as compared to Eastern Uttar Pradesh. The model predicts that wheat yield may decrease by 5-6% in Bihar state due to increase in maximum temperature by the end of 2080 whereas this decrement in Eastern Uttar Pradesh may be 1.5-2.0%.

Characteristics of nighttime VLF/ELF emissions are examined on the basis of the data obtained at our low latitude ground station Jammu (geomagnetic latitude, 22°26′ N; L = 1.17), India during our VLF/ELF campaign. From the detailed analysis of a huge amount of acquired VLF/ELF data at Jammu we have found three remarkable events which clearly exhibit a rise in their frequency in pre and past midnight sectors during magnetically quiet and substorm periods. Our analysis shows that the frequency drift in VLF/ELF emissions seems to be a rare phenomenon at low latitudes during magnetically quiet and substorm periods in pre and post midnight sectors. This property of temporal frequency drift (regular frequency increases with time) in VLF/ELF emissions observed at our station Jammu are interpreted in terms of a quasi-linear electron cyclotron instability model for wave excitation. The initial frequency increase is believed to be due to a combind effect of L-shell drift of energetic electrons. Further, the frequency drifts in VLF/ELF emissions observed at Jammu have been used to estimate the large scale electric field during quiet and substorm periods in pre and post midnight sectors. This investigation would be most useful for the study of the wave-particle interaction processes, magnetospheric plasma structure and particle dynamics, especially during quiet periods in premidnight sector at low latitudes.

The discrete VLF chorus emissions recorded at low latitude ground station Jammu (geomag. lat. = 22°26′N, L = 1.17) are reported and their characteristics based on spectral analysis have been carried out. These discrete chorus emissions are falling tone emissions which are rarely observed at low latitude ground stations. These discrete chorus emissions are generated during a strong geomagnetic storm period of 2–7 May, 1998. We have computed the frequency sweep rate, repetition period and source region of the individual chorus elements. It is observed that the sweep rate increases with time.

This paper reports quasi-periodic pulsing hiss emissions recorded during daytime in the frequency range of 50 Hz–15 kHz at low latitude station Jammu (geomag. lat. = 22 ◦ 26 � N; L =1 .17). It is noted that pulsing VLF emissions are a rare phenomena at low latitudes. The various spectrograms of pulsing VLF hiss emissions presented in this paper clearly show band limited spectrums regularly pulsing with almost equal period of the order of few seconds in the frequency range of ∼ 3–8 kHz. Generation and propagation mechanism of these emissions are briefly discussed.

In this study, we have attempted to enhance the utility of soil–plant–atmosphere–water (SPAW) model that has been used successfully by various workers in different countries for soil moisture prediction under different cropping conditions. One of the major climatic inputs for SPAW model is pan evaporation, which in many places is not readily available. To address the above, and to get the benefit of this model in regions characterized by limited weather data availability, this study was undertaken using computed ET0 from air temperature by the 1985 Hargreaves equation, as one of the inputs in place of pan evaporation. For the purpose, actual air temperature collected from experimental farm area, as well as forecast air temperature collected from National Centre for Medium Range Weather Forecasting, Government of India, were used. First, the SPAW model was calibrated and its performance was evaluated under wheat, taking layerwise and profile soil moisture as the variables for comparison between the predict...

CROPGRO-Soybean model calibrated for local conditions of Raipur has been used to evaluate the relevance of medium range weather forecast relative to the soybean crop growth period. A procedure that makes use of historical weather data, medium range weather forecast (mrwf) and current weather data in conjunction with the CROPGRO-Soybean model was developed to arrive at a probable distribution of predicted yield. A series of perfect mrwf for 5 days were assumed for assessing the sensitivity of the crop management system to forecast information. The relative importance by time of year was taken as a reduction in variance due to a perfect 5-day mrwf. The results of the study, conducted for two reference years 1986 (low-production year) and 1993 (high-production year) at Raipur, showed that the yield estimation can be done 20 days in advance before the physiological maturity for low-production year (1986) and 15 days in advance before physiological maturity for high-production year (1993). For both the years mrwf during reproductive phases are more valuable. It has also been concluded that the longer forecast periods are responding earlier in the growing season with higher values too.

Available sulphur (S) extracted in five extractants, crop response to S application and use efficiency of S by mungbean (Vigna radiata L.) and urdbean (Vigna mungo L.) were studied on different soil types of pulse growing regions of India. Among extractants tested, 1 N NH4OAc extracted higher S followed by Morgan's reagent, 0.001 N HCl, 0.15% CaCl2, and 1% NaCl. Response of urdbean was in higher magnitude as compared to mungbean to 20 mg S kg−1. In both the crops, larger response was obtained in Inceptisols followed by Vertisols and lower response was found in Alfisols. Larger utilization of native soil S in case of Alfisols by both the crops resulted in lower levels of response to added fertilizer S. Owing to high degree of correlation with plant response, 0.15% CaCl2 and Morgan's reagent can be used as soil test methods for assessing the available S supply and predicting the crop response to applied S on these soils.

A weather generator based on short methods of WGEN in which the weather parameters are estimated from the monthly weather summaries instead from daily values has been calibrated and evaluated in this study. The model parameters were calibrated using daily observed weather data from 40 locations in India for 20 years (15 years for some of the locations). These parameters are calculated in terms of monthly weather statistics of rainfall were directly used to generate the daily weather at selected locations of India. Result indicate that there is several deviations in summery of generated weather statistics as compared to observed statistics, but over all nature of generated rainfall is fairly good replications of observed rainfall. Generated rainfall can be helpful in rice crop planning in North India because of better agreement between generated and observed rainfall during monsoon months in which rice crop is grown in North India. However it can not be useful in winter months planning of wheat because of lesser agreement between generated and observed rainfall during winter months. In addition due to several deviations in generated temperature (both maximum and minimum temperature) the use of the generated weather in crop simulation and hydro- logical models will not yield realistic results. To use this for agricultural and hydrological impact studies it need to be calibrated for maximum and minimum temperature generation as well.

The CERES-Rice crop simulation model, calibrated and validated for the varieties PR106 in NW India. IR36 in central India and Jaya in south India, is used for nalysing the effect of climate change on rice productivity in the country. Plausible climate change scenario for the Indian subcontinent as expected by the middle of the next century taking into account the projected emissions of greenhouse gases and sulphate aerosols, in a coupled atmosphere-ocean model experiment performed at Deutsches Klimarechenzentrum, Germany, is adopted for the study. The adopted scenario represented an increase in monsoon seasonal mean surface temperature of the order of about 1.5° C over the south India and 1°C over northwest and central India in the decade 2040-49 with respect to the 1980s and an increase in rainfall of the order of 2 mm per day over south India while the simulated decrease of the order about -1 mm and -1.5 mm over northwest and central India respectively. The IPCC Business-as-usual scenario projection of plant usable concentration of CO2 about 460 PPM by the middle fo the next century are also used in the crop model simulation (CERES - Rice V3 Model). Simulation studies carried out with the climate change scenarios over different parts of the country are analysed and interpreted.

The CERES-Rice v3. crop simulation model, calibrated and validated for its suitability to simulate rice production in the tropical humid climate Kerala State of India, is used for analysing the effect of climate change on rice productivity in the state. The plausible climate change scenario for the Indian subcontinent as expected by the middle of the next century, taking into account the projected emissions of greenhouse gases and sulphate aerosols, in a coupled atmosphere-ocean model experiment performed at Deutsches Klimarechenzentrum, Germany, is adopted for the study. The adopted scenario represented an increase in monsoon seasonal mean surface temperature of the order of about 1.5°C, and an increase in rainfall of the order of 2 mm per day, over the state of Kerala in the decade 2040–2049 with respect to the 1980s. The IPCC Business-as-usual scenario projection of plant usable concentration of CO2 about 460 PPM by the middle of the next century are also used in the crop model simulation. On an average over the state with the climate change scenario studied, the rice maturity period is projected to shorten by 8% and yield increase by 12%. When temperature elevations only are taken into consideration, the crop simulations show a decrease of 8% in crop maturity period and 6% in yield. This shows that the increase in yield due to fertilisation effect of elevated CO2 and increased rainfall over the state as projected in the climate change scenario nearly makes up for the negative impact on rice yield due to temperature rise. The sensitivity experiments of the rice model to CO2 concentration changes indicated that over the state, an increase in CO2 concentration leads to yield increase due to its fertilisation effect and also enhance the water use efficiency of the paddy. The temperature sensitivity experiments have shown that for a positive change in temperature up to 5°C, there is a continuous decline in the yield. For every one degree increment the decline in yield is about 6%. Also, in another experiment it is observed that the physiological effect of ambient CO2 at 425 ppm concentration compensated for the yield losses due to increase in temperature up to 2°C. Rainfall sensitivity experiments have shown that increase in rice yield due to increase in rainfall above the observed values is near exponential. But decrease in rainfall results in yield loss at a constant rate of about 8% per 2 mm/day, up to about 16 mm/day.

This study is aimed at assessing the impact of thermal and moisture stresses associated with observed intraseasonal and interannual variability in key climatic elements on the nature and extent of losses in growth and yield of soybean crop in central India through the use of CROPGRO model. The crops are found to be more sensitive to higher cumulative heat units during cropping season. The yields respond substantially to temporal variations in rainfall (associated with observed swings in the continuity of monsoon). Prolonged dry spells at critical life stages of the soybean crop are found to adversely affect crop development and growth and hence the yields at selected sites. We have also examined the plausible effects of future climate change on soybean yields in the selected region based on simulations carried out for doubled atmospheric CO2 level and with modified weather variables using the available seasonal projections for the future. Our findings on the response of elevated CO2 concentrations in the atmosphere suggest higher yields (50% increase) for soybean crop for a doubling of CO2. However, a 3°C rise in surface air temperature almost cancels out the positive effects of elevated CO2 on the yield. Soybean crops at selected site are more vulnerable to increases in maximum temperature than in minimum temperature. The combined effect of doubled CO2 and anticipated thermal stress (likely by middle of the next century) on soybean crop is about 36% increase in yield at the selected sites. A decline in daily rainfall amount by 10% restricts this yield gain to about 32%. Deficient rainfall with uneven distribution during the monsoon season could be a critical factor for the soybean productivity even under the positive effects of elevated CO2 in the future.

Agricultural sector is one of the sensitive areas which would be influenced by the projected global warming and associated climate change. In spite of the uncertainties about the precise magnitude of climate change on regional scales, an assessment of the possible impacts of changes in key climatic elements on our agricultural resources is important for formulating response strategies. In this study, vulnerability of wheat and rice crops in northwest India to the projected climate change is examined. CERES wheat and rice models adopted for the study were validated for their ability to reproduce yields at the selected NW Indian stations. The sensitivity experiments with these models showed higher yields for both wheat and rice (28% and 15% respectively for a doubling of CO2) under elevated CO2 levels. A 3°C (2°C) rise in air temperature nearly cancels out the positive effect of elevated CO2 on the wheat (rice) yields. While the wheat crops are found to be sensitive to increase in maximum temperature, the rice crops are vulnerable to increase in minimum temperature. The combined effect of enhanced CO2 and imposed thermal stress on the wheat (rice) crop is 21% (4%) increase in yield for the irrigation schedule presently practised in the region. While the adverse impacts of likely water shortage on wheat crops would be minimised to a certain extent under elevated CO2 levels, they would largely be maintained for the rice crops resulting in about 20% net decline in rice yields. In general, acute water shortage conditions combined with the thermal stress should adversely affect both the wheat and more severely the rice productivity in NW India even under the positive effects of elevated CO2 in the future.

Central Mining Research Institute (CMRI) in Dhanbad is a research laboratory under the Council of Scientific and Industrial Research in New Delhi, which is part of the Government of India. This institute has been established to provide scientific and technological inputs to the Indian mining industries for optimization of technologies with respect to safety, economy, conservation, and environmental management in mining sectors. Acute water shortages have developed in recent years in the CMRI campus. Only two tube wells have been drilled at the campus, and these are incapable of providing adequate water supply for research activities and campus residents. Hence, an additional source of groundwater might be needed here to solve the water scarcity problem. To study the groundwater condition in detail and to recommend a new deep tube well, two resistivity imaging surveys were carried out in the CMRI campus along the traverses D1 and D2 as shown in Figure 1.

Medium range forecast, a recently introduced service in India, has not been evaluated satisfactorily in terms of agricultural applications. In this article an attempt has been made to evaluate the performance of medium range weather forecast for Delhi and Ludhiana environment in the rabi (winter) season of 1994 - 95 by comparing the forecasted with actual weather components, mainly maximum and minimum temperature, wind speed, sunshine hours, and rainfall. Furthermore, yield and growth response of wheat simulated under adequate, moderate, and limited irrigated conditions were compared for forecasted and actual weathers employing wheat growth simulator (WTGROWS). Medium-range forecast performance for predicting maximum and minimum temperatures for both the locations had predictability lying in the range of 70 to 85 percent. The precision was somewhat better in case of maximum temperature assessment. Wind speed for both locations was poorly assessed. Forecasted rainfall matched actual rainfall on some occasions only at Ludhiana but the performance, in terms of seventy-two hours around the forecasted time, gave better results. The observed bright sunshine hours showed poor predictability (less than 30%). Forecasted grain yields for all levels of soil moisture availability treatments at both the locations were.

Tilth is a qualitative term that describes the physical state of soil. There is need for a quantitative understanding of soil tilth to help scientists, engineers, and farmers better understand how to manage soil. A “tilth index” based upon five soil physical properties was developed to quantify tilth. Values of bulk density, cone index, aggregate uniformity coefficient, organic matter content, and plasticity index were used to calculate an index, that ranges from zero for conditions unusable by the plant to one for a soil that is nonlimiting for plant growth.

This article 'Effect of silicon carriers and time of application on rice productivity in a rice-wheat cropping sequence' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain details on the research reported.

At present, various methods are available to analyze storm runoff data. Among these, application of the Z-transform is comparatively new, and the technique has been used to identify rainfall from analysis of a single-storm runoff. The technique works under the premise that the rainfall-runoff process behaves as a linear system for which the Z-transform of the direct runoff equals the product of the Z-transforms of the transfer function and the rainfall. In the hydrologic literature, application aspects of this method to the rainfall-runoff process are lacking. As a result, some of the conclusions reported in the literature are questionable. The present study provides a rational basis for the application of the Z-transform technique to the analysis of storm-runoff data. Using data from a previously reported study, the study also explains the selection aspects of the unit hydrograph and the rainfall and lays the necessary background for the correct use of this method by hydrologists.

Gasification of biomass, which includes a series of interlinked thermochemical processes like pyrolysis and gasoline refining, offers a promising scope to meet energy needs both for stationary and mobile purposes. The present communication is aimed at discussing the gasification programme in India with particular reference to the following five main areas: R& D, pilot-testing and evaluation including prototype production, demonstrative application and evaluation of biomass gasification, training and manpower development, commercialisation and manufacture development. Emphasis is laid upon the need to integrate energy plantation on sub-standard soils with biomass gasification.