Your search found 8 records
1 Casley, D. J.; Kumar, K.. 1987. Project monitoring and evaluation in agriculture. Baltimore, MA, USA: Johns Hopkins University Press. xi, 160p.
(Location: IWMI-HQ Call no: 351.82 G000 CAS Record No: H05433)
A joint study of the World Bank, IFAD, and FAO
2 Casley, D. J.; Kumar, K.. 1987. The collection, analysis and use of monitoring and evaluation data. Baltimore, MA, USA: Johns Hopkins University Press. ix, 174p.
(Location: IWMI-HQ Call no: 001.42 G000 CAS Record No: H05434)
A joint study of the World Bank, IFAD, and FAO
3 Sandhu, B. S.; Bhatnagar, V. K.; Khbra, K. L.; Singh, B.; Kumar, K.. 1990. Canopy temperature as an index of water stress and grain yield in wheat. In Tyagi, N. K.; Joshi, P. K.; Gupta, R. K.; Singh. N. T. (Eds.) Management of irrigation system: Papers from the National Symposium on Management of Irrigation System, Karnal, India February 24-27 1988. Karnal, India: Central Soil Salinity Research Institute. pp.107-122.
(Location: IWMI-HQ Call no: 631.7 G635 TYA Record No: H08201)
4 Kumar, K.. 1990. Conducting mini surveys in developing countries. Washington, DC, USA: USAID. 1 microfiche. (A.I.D. Program design and evaluation methodology report no.15)
(Location: IWMI-HQ Call no: F 092 Record No: H013048)
5 Kadekodi, G. K.; Murthy, K. S. R.; Kumar, K.. (Eds.) 2000. Water in Kumaon: Ecology, value and rights. Nainital, India: Gyanodaya Prakashan. xx, 256p. (Himavikas occasional publication no.13)
Water resources ; Sustainability ; Social aspects ; Land use ; Livestock ; Infrastructure ; Watersheds ; Catchment areas ; Water supply ; Water use ; Drought ; Simulation models ; Villages ; Rural economy ; Households ; Irrigation water ; Women ; Water resource management ; Water policy ; Water rights ; Water law ; Water users / India / Himalayas / Uttar Pradesh / Kumaon / Garhwal / Gaula Watershed / Ramganga watershed / Haigad watershed
(Location: IWMI-HQ Call no: 333.91 G635 KAD Record No: H026970)
6 Kumar, K.; Satyal, G. S. 2000. Physical accounting of water: A micro-watershed analysis. In Kadekodi, G. K.; Murthy, K. S. R.; Kumar, K. (Eds.), Water in Kumaon: Ecology, value and rights. Nainital, India: Gyanodaya Prakashan. pp.127-153.
Watersheds ; Drainage ; Water balance ; Water availability ; Water demand ; Rivers ; Stream flow / India / Ramganga Watershed / Haigad Watershed
(Location: IWMI-HQ Call no: 333.91 G635 KAD Record No: H026975)
7 Vaidya, H.; Tiwari, K.; Rajadhyaksha, N. P.; Shinde, V. R.; Wong, T.; Kulkarni, H.; Dickens, Chris; Tortajada, C.; Bassi, N.; Pandey, V. P.; Jain, A.; Shaw, R.; Anshuman; Mishra, R. R.; Kaur, I.; Bahure, K.; Gupta, T.; Shah, H.; Subramanian, A.; Kumar, K.. 2023. Ensuring water security. White Paper. Ahmedabad, India: Urban20 (U20). 25p.
Water security ; Integrated water resources management ; Urban planning ; Cities ; Infrastructure ; Nature-based solutions ; Sustainable Development Goals ; Social capital ; Human capital ; Financing ; Partnerships ; Networks
(Location: IWMI HQ Call no: e-copy only Record No: H052165)
https://www.u20india.org/Content/WhitePaper/EWS_White%20Paper.pdf
https://vlibrary.iwmi.org/pdf/H052165.pdf
https://vlibrary.iwmi.org/pdf/H052165.pdf
(4.05 MB) (4.05 MB)
8 Kumar, K.; Parihar, C. M.; Nayak, H. S.; Sena, Dipaka R.; Godara, S.; Dhakar, R.; Patra, K.; Sarkar, A.; Bharadwaj, S.; ChandGhasal, P.; 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]
Maize ; Plant growth ; Nitrogen ; Modelling ; Conservation agriculture ; Wheat ; Zero tillage ; Ammonia ; Volatilization / India
(Location: IWMI HQ Call no: e-copy only Record No: PendingH052860)
(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.
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