Your search found 14 records
(Location: IWMI-HQ Call no: P 4348 Record No: H019278)
2 Sharma, D. K.; Qadar, A. 1996. Effect of scheduling irrigation with poor quality water on growth, yield and water use efficiency of wheat. ICID Journal, 45(2):67-80.
(Location: IWMI-HQ Call no: PER Record No: H020046)
3 Tyagi, N. K.; Sharma, D. K.; Luthra, S. K. 2000. Determination of evapotranspiration and crop coefficients of rice and sunflower with lysimeter. Agricultural Water Management, 45(1):41-54.
(Location: IWMI-HQ Call no: PER Record No: H026108)
(Location: IWMI-HQ Call no: PER Record No: H026518)
(Location: IWMI-HQ Call no: PER Record No: H031255)
(Location: IWMI-HQ Call no: 631.7.3 G000 TYA Record No: H032201)
7 Tyagi, N. K.; Sharma, D. K.. 2003. Improving wheat productivity in Indo-Gangetic Plains: Constraints and technological options. Unpublished report, Central Soil Salinity Research Institute, Karnal, India. 41p.
(Location: IWMI-HQ Call no: P 6481 Record No: H032787)
(Location: IWMI-HQ Call no: P 6946 Record No: H035137)
(Location: IWMI-HQ Call no: P 6967 Record No: H035158)
10 Singh, R. K.; Redona, E.; Gregorio, G. B.; Salam, M. A.; Islam, M. R.; Singh, D. P.; Sen, P.; Saha, S.; Mahata, K. R.; Sharma, S. G.; Pandey, M. P.; Sajise, A. G.; Mendoza, R. D.; Toledo, M. C.; Dante, A.; Ismail, A. M.; Paris, T. R.; Haefele, S. M.; Thomson, M. J.; Zolvinski, S.; Singh, Y. P.; Nayak, A. K.; Singh, R. B.; Mishra, V. K.; Sharma, D. K.; Gautam, R. K.; Ram, P. C.; Singh, P. N.; Verma, O. P.; Singh, A.; Lang, N. T. 2010. The right rice in the right place: systematic exchange and farmer-based evaluation of rice germplasm for salt-affected areas. In Hoanh, Chu Thai; Szuster, B. W.; Kam, S. P.; Ismail, A. M; Noble, Andrew D. (Eds.). Tropical deltas and coastal zones: food production, communities and environment at the land-water interface. Wallingford, UK: CABI; Colombo, Sri Lanka: International Water Management Institute (IWMI); Penang, Malaysia: WorldFish Center; Los Banos, Philippines: International Rice Research Institute (IRRI); Bangkok, Thailand: FAO Regional Office for Asia and the Pacific; Colombo, Sri Lanka: CGIAR Challenge Program on Water and Food (CPWF). pp.166-182.
(Location: IWMI HQ Call no: IWMI 551.457 G000 HOA Record No: H043056)
(5.08 MB)
11 Sharma, D. K.; Purohit, G. 2014. Improving the liveability of cities: the role of solar energy in urban and peri-urban areas. In Maheshwari, B.; Purohit, R.; Malano, H.; Singh, V. P.; Amerasinghe, Priyanie. (Eds.). The security of water, food, energy and liveability of cities: challenges and opportunities for peri-urban futures. Dordrecht, Netherlands: Springer. pp.151-162. (Water Science and Technology Library Volume 71)
(Location: IWMI HQ Call no: IWMI Record No: H047026)
Solar energy utilisation is the most important energy resource for bridging the gap between demand and supply of various energy needs in urban and peri-urban areas. The energy consumption is basically in terms of electricity for many appliances and equipment in homes, thermal energy for heating and cooling in homes and passive solar architecture for energy efficient buildings. On the other hand, the conventional energy consumption also induces the ecological imbalance such as the generation of greenhouse gases. Therefore solar energy may be considered an environmentally friendly alternative energy source for sustainable development. In this chapter, different active and passive solar energy harnessing techniques have been discussed, analysed and recommended leading to zero energy buildings (ZEBs) in urban and peri-urban areas. Here the study of solar energy applications for all types of energy needs in a residential building for advanced, ecological and smart liveability is presented. In this Chapter, we suggest some effective ways to harvest solar energy in urban and peri-urban areas using active and passive solar techniques.
12 Burman, D.; Mahanta, K. K.; Sarangi, S. K.; Mandal, S.; Maji, B.; Mandal, U. K.; Bandyopadhyay, B. K.; Humphreys, E.; Sharma, D. K.. 2015. Effect of groundwater use on groundwater salinity, piezometric level and boro rice yield in the Sundarbans of West Bengal. In Humphreys, E.; Tuong, T. P.; Buisson, Marie-Charlotte; Pukinskis, I.; Phillips, M. (Eds.). Proceedings of the CPWF, GBDC, WLE Conference on Revitalizing the Ganges Coastal Zone: Turning Science into Policy and Practices, Dhaka, Bangladesh, 21-23 October 2014. Colombo, Sri Lanka: CGIAR Challenge Program on Water and Food (CPWF). pp.61-67.
(Location: IWMI HQ Call no: IWMI Record No: H047194)
(0.31 MB) (11.9 MB)
13 Sarangi, S. K.; Burman, D.; Mandal, S.; Maji, B.; Tuong, T. P.; Humphreys, E.; Bandyopadhyay, B. K.; Sharma, D. K.. 2015. Reducing irrigation water requirement of dry season rice (boro) in coastal areas using timely seeding and short duration varieties. In Humphreys, E.; Tuong, T. P.; Buisson, Marie-Charlotte; Pukinskis, I.; Phillips, M. (Eds.). Proceedings of the CPWF, GBDC, WLE Conference on Revitalizing the Ganges Coastal Zone: Turning Science into Policy and Practices, Dhaka, Bangladesh, 21-23 October 2014. Colombo, Sri Lanka: CGIAR Challenge Program on Water and Food (CPWF). pp.68-79.
(Location: IWMI HQ Call no: IWMI Record No: H047195)
(0.29 MB) (11.9 MB)
14 Kumar, K.; Parihar, C. M.; Nayak, H. S.; Sena, Dipaka R.; Godara, S.; Dhakar, R.; Patra, K.; Sarkar, A.; Bharadwaj, S.; Ghasal, P. C.; Meena, A. L.; Reddy, K. S.; Das, T. K.; Jat, S. L.; Sharma, D. K.; Saharawat, Y. S.; Singh, U.; Jat, M. L.; Gathala, M. K. 2024. Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system. Scientific Reports, 14:11743. [doi: https://doi.org/10.1038/s41598-024-61976-6]
(Location: IWMI HQ Call no: e-copy only Record No: H052860)
(2.40 MB) (2.40 MB)
Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar’s anthesis and physiological maturity, with observed value falling within 5% of the model’s predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9–16%. The study also demonstrated the model's ability to accurately capture soil nitrate–N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop’s growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.
Powered by DB/Text
WebPublisher, from