Search

Your search keyword '"Naresh Kumar, Soora"' showing total 16 results
Start Over Author "Naresh Kumar, Soora"
16 results on '"Naresh Kumar, Soora"'

2. Mathematical vs. machine learning models for particle size distribution in fragile soils of North-Western Himalayas

Author

Bashir, Owais, Bangroo, Shabir Ahmad, Shafai, Shahid Shuja, Shah, Tajamul Islam, Kader, Shuraik, Jaufer, Lizny, Senesi, Nicola, Kuriqi, Alban, Omidvar, Negar, Naresh Kumar, Soora, Arunachalam, Ayyanadar, Michael, Ruby, Ksibi, Mohamed, Spalevic, Velibor, Sestras, Paul, Marković, Slobodan B., Billi, Paolo, Ercişli, Sezai, and Hysa, Artan

Published
2024
Full Text
View/download PDF

3. Long-term adoption of bed planted conservation agriculture based maize/cotton-wheat system enhances soil organic carbon stabilization within aggregates in the indo-gangetic plains

Author

Joseph, Ann Maria, primary, Bhattacharyya, Ranjan, additional, Biswas, D. R., additional, Das, T. K., additional, Bandyopadhyay, K. K., additional, Dey, Abir, additional, Ghosh, Avijit, additional, Roy, Plabani, additional, Naresh Kumar, Soora, additional, Jat, S. L., additional, Casini, Ryan, additional, Elansary, Hosam O., additional, and Bhatia, Arti, additional

Published
2023
Full Text
View/download PDF

5. The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

Author

Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rotter, Reimund P, Kimball, Bruce A, Ottman, Michael J, White, Jeffrey W, Reynolds, Matthew P, Alderman, Phillip D, Aggarwal, Pramod K, Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J, De Sanctis, Giacomo, Doltra, Jordi, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A, Izaurralde, Roberto C, Jabloun, Mohamed, Jones, Curtis D, Kersebaum, Kurt C, Koehler, Ann-Kristin, Liu, Leilei, Muller, Christoph, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry, Oleson, Jorgen E, Palosuo, Tara, Priesack, Eckhart, Eyshi, Rezaei, Ehsan, Ripoche, Dominique, Ruane, Alex C, Semenov, Mikhail A, Scherbak, Lurii, Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold

Subjects
Meteorology And Climatology
Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for is greater than 50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 C to 33 C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Published
2017
Full Text
View/download PDF

6. Global wheat production with 1.5 and 2.0°C above pre-industrial warming

Author

Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andy J., Müller, Christoph, Ruane, Alex C., Waha, Katharina, Thorburn, Peter J., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Eyshi Rezaei, Ehsan, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Naresh Kumar, Soora, Nendel, Claas, O’Leary, Garry J., Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Subjects
1.5°C warming, climate change, extreme low yields, food security, model ensemble, wheat production
Published
2019

7. Coconut, climate change and coastal areas. Chapter 2. Where we are today

Author

Naresh Kumar, Soora, Ollivier, Jean, and Bourdeix, Roland

Subjects
Changement climatique, F40 - Écologie végétale, P40 - Météorologie et climatologie, Zone côtière, Conservation des ressources génétiques, F30 - Génétique et amélioration des plantes, Ressource génétique végétale, P01 - Conservation de la nature et ressources foncières, Cocos nucifera
Published
2018

8. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author

Liu, Bing, primary, Martre, Pierre, additional, Ewert, Frank, additional, Porter, John R., additional, Challinor, Andy J., additional, Müller, Christoph, additional, Ruane, Alex C., additional, Waha, Katharina, additional, Thorburn, Peter J., additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Balkovič, Juraj, additional, Basso, Bruno, additional, Biernath, Christian, additional, Bindi, Marco, additional, Cammarano, Davide, additional, De Sanctis, Giacomo, additional, Dumont, Benjamin, additional, Espadafor, Mónica, additional, Eyshi Rezaei, Ehsan, additional, Ferrise, Roberto, additional, Garcia‐Vila, Margarita, additional, Gayler, Sebastian, additional, Gao, Yujing, additional, Horan, Heidi, additional, Hoogenboom, Gerrit, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt C., additional, Klein, Christian, additional, Koehler, Ann‐Kristin, additional, Maiorano, Andrea, additional, Minoli, Sara, additional, Montesino San Martin, Manuel, additional, Naresh Kumar, Soora, additional, Nendel, Claas, additional, O’Leary, Garry J., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Van der Velde, Marijn, additional, Wallach, Daniel, additional, Wang, Enli, additional, Webber, Heidi, additional, Wolf, Joost, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Asseng, Senthold, additional

Published
2019
Full Text
View/download PDF

9. Climate change impact and adaptation for wheat protein

Author

Asseng, Senthold, primary, Martre, Pierre, additional, Maiorano, Andrea, additional, Rötter, Reimund P., additional, O’Leary, Garry J., additional, Fitzgerald, Glenn J., additional, Girousse, Christine, additional, Motzo, Rosella, additional, Giunta, Francesco, additional, Babar, M. Ali, additional, Reynolds, Matthew P., additional, Kheir, Ahmed M. S., additional, Thorburn, Peter J., additional, Waha, Katharina, additional, Ruane, Alex C., additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Balkovič, Juraj, additional, Basso, Bruno, additional, Biernath, Christian, additional, Bindi, Marco, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, De Sanctis, Giacomo, additional, Dumont, Benjamin, additional, Eyshi Rezaei, Ehsan, additional, Fereres, Elias, additional, Ferrise, Roberto, additional, Garcia‐Vila, Margarita, additional, Gayler, Sebastian, additional, Gao, Yujing, additional, Horan, Heidi, additional, Hoogenboom, Gerrit, additional, Izaurralde, R. César, additional, Jabloun, Mohamed, additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt-Christian, additional, Klein, Christian, additional, Koehler, Ann‐Kristin, additional, Liu, Bing, additional, Minoli, Sara, additional, Montesino San Martin, Manuel, additional, Müller, Christoph, additional, Naresh Kumar, Soora, additional, Nendel, Claas, additional, Olesen, Jørgen Eivind, additional, Palosuo, Taru, additional, Porter, John R., additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Van der Velde, Marijn, additional, Wallach, Daniel, additional, Wang, Enli, additional, Webber, Heidi, additional, Wolf, Joost, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Ewert, Frank, additional

Published
2018
Full Text
View/download PDF

10. Multimodel ensembles improve predictions of crop–environment–management interactions

Author

Wallach, Daniel, primary, Martre, Pierre, additional, Liu, Bing, additional, Asseng, Senthold, additional, Ewert, Frank, additional, Thorburn, Peter J., additional, van Ittersum, Martin, additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Basso, Bruno, additional, Biernath, Christian, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, De Sanctis, Giacomo, additional, Dumont, Benjamin, additional, Eyshi Rezaei, Ehsan, additional, Fereres, Elias, additional, Fitzgerald, Glenn J., additional, Gao, Y., additional, Garcia‐Vila, Margarita, additional, Gayler, Sebastian, additional, Girousse, Christine, additional, Hoogenboom, Gerrit, additional, Horan, Heidi, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt C., additional, Klein, Christian, additional, Koehler, Ann‐Kristin, additional, Maiorano, Andrea, additional, Minoli, Sara, additional, Müller, Christoph, additional, Naresh Kumar, Soora, additional, Nendel, Claas, additional, O'Leary, Garry J., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Wolf, Joost, additional, and Zhang, Zhao, additional

Published
2018
Full Text
View/download PDF

11. High day–night transition temperature alters nocturnal starch metabolism in rice (Oryza sativa L.)

Author

Nitin Sharma, Bhupinder Singh, Anjali Anand, Naresh Kumar Soora, Lekshmy Sathee, Archana Yadav, Ranjeet Kumar, Sangeeta Khetarpal, and Suchitra Pushkar

Subjects
0106 biological sciences, 0301 basic medicine, Maintenance respiration, Oryza sativa, biology, Physiology, Starch, food and beverages, Plant Science, Maltose, 01 natural sciences, 03 medical and health sciences, Horticulture, chemistry.chemical_compound, 030104 developmental biology, Agronomy, chemistry, Anthesis, Respiration, biology.protein, Cultivar, Amylase, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

Transitory starch plays a vital role in maintenance respiration as its degradation products provide substrate for the night respiration. A study was conducted with two contrasting rice cultivars: Vandana (high night temperature susceptible) and Nagina 22 (high night temperature tolerant) by subjecting them to increase in transition temperature from anthesis to physiological maturity. Night respiration on plant area basis increased by 35% in Vandana at 5 days after anthesis but was unaffected in tolerant cultivar. A simultaneous 18% decrease in starch content was observed in the susceptible cultivar. An analysis of the starch-metabolizing enzymes showed that activity of β-amylase increased markedly in Vandana whereas both β and α-amylase decreased in Nagina 22 following high day to night transition temperature exposure. The level of starch breakdown product, maltose, increased in the susceptible cultivar but glucose levels declined in both the cultivars. Concurrently, expression of chloroplastic isoforms α-amylase OsAMY1, OsAMY2 and β-amylase OsBAM2 increased in Vandana. A lower accumulation of dry matter was recorded in the susceptible than the tolerant cultivar. Our study elucidated the regulatory role of transitory starch in supporting the high day to night transition temperature-induced night-time respiration which is mediated by the increased activity of β-amylase through enhanced expression of OsBAM2 in flag leaves of susceptible cultivar.

Published
2017
Full Text
View/download PDF

12. An assessment of regional vulnerability of rice to climate change in India

Author

Rani Saxena, Pramod K. Aggarwal, Surabhi Jain, Nitin Chauhan, Naresh Kumar Soora, and Swaroopa Rani

Subjects
Atmospheric Science, Global and Planetary Change, business.industry, Agroforestry, Crop yield, Vulnerability, Climate change, Animal husbandry, Crop, Agriculture, Environmental science, Climate model, Adaptation, business
Abstract

A simulation analysis was carried out using the InfoCrop-rice model to quantify impacts and adaptation gains, as well as to identify vulnerable regions for irrigated and rain fed rice cultivation in future climates in India. Climates in A1b, A2, B1 and B2 emission scenarios as per a global climate model (MIROC3.2.HI) and a regional climate model (PRECIS) were considered for the study. On an aggregated scale, the mean of all emission scenarios indicate that climate change is likely to reduce irrigated rice yields by ~4 % in 2020 (2010–2039), ~7 % in 2050 (2040–2069), and by ~10 % in 2080 (2070–2099) climate scenarios. On the other hand, rainfed rice yields in India are likely to be reduced by ~6 % in the 2020 scenario, but in the 2050 and 2080 scenarios they are projected to decrease only marginally (

Published
2013
Full Text
View/download PDF

13. A potato model intercomparison across varying climates and productivity levels

Author

Fleisher, David H., primary, Condori, Bruno, additional, Quiroz, Roberto, additional, Alva, Ashok, additional, Asseng, Senthold, additional, Barreda, Carolina, additional, Bindi, Marco, additional, Boote, Kenneth J., additional, Ferrise, Roberto, additional, Franke, Angelinus C., additional, Govindakrishnan, Panamanna M., additional, Harahagazwe, Dieudonne, additional, Hoogenboom, Gerrit, additional, Naresh Kumar, Soora, additional, Merante, Paolo, additional, Nendel, Claas, additional, Olesen, Jorgen E., additional, Parker, Phillip S., additional, Raes, Dirk, additional, Raymundo, Rubi, additional, Ruane, Alex C., additional, Stockle, Claudio, additional, Supit, Iwan, additional, Vanuytrecht, Eline, additional, Wolf, Joost, additional, and Woli, Prem, additional

Published
2016
Full Text
View/download PDF

14. A potato model intercomparison across varying climates and productivity levels.

Author

Fleisher, David H., Condori, Bruno, Quiroz, Roberto, Alva, Ashok, Asseng, Senthold, Barreda, Carolina, Bindi, Marco, Boote, Kenneth J., Ferrise, Roberto, Franke, Angelinus C., Govindakrishnan, Panamanna M., Harahagazwe, Dieudonne, Hoogenboom, Gerrit, Naresh Kumar, Soora, Merante, Paolo, Nendel, Claas, Olesen, Jorgen E., Parker, Phillip S., Raes, Dirk, and Raymundo, Rubi

Subjects
CLIMATE change, POTATOES, CROPS, UNCERTAINTY, AGRONOMY
Abstract

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another ( P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration ( ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant ( P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

16. Climate change impact and adaptation for wheat protein.

Author

Asseng S, Martre P, Maiorano A, Rötter RP, O'Leary GJ, Fitzgerald GJ, Girousse C, Motzo R, Giunta F, Babar MA, Reynolds MP, Kheir AMS, Thorburn PJ, Waha K, Ruane AC, Aggarwal PK, Ahmed M, Balkovič J, Basso B, Biernath C, Bindi M, Cammarano D, Challinor AJ, De Sanctis G, Dumont B, Eyshi Rezaei E, Fereres E, Ferrise R, Garcia-Vila M, Gayler S, Gao Y, Horan H, Hoogenboom G, Izaurralde RC, Jabloun M, Jones CD, Kassie BT, Kersebaum KC, Klein C, Koehler AK, Liu B, Minoli S, Montesino San Martin M, Müller C, Naresh Kumar S, Nendel C, Olesen JE, Palosuo T, Porter JR, Priesack E, Ripoche D, Semenov MA, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Van der Velde M, Wallach D, Wang E, Webber H, Wolf J, Xiao L, Zhang Z, Zhao Z, Zhu Y, and Ewert F

Subjects
Carbon Dioxide metabolism, Droughts, Food Quality, Models, Theoretical, Nitrogen metabolism, Temperature, Adaptation, Physiological, Climate Change, Grain Proteins analysis, Triticum chemistry, Triticum physiology
Abstract

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO 2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO 2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., (© 2018 John Wiley & Sons Ltd.)

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results