Machine learning-based prediction of solar PV power using experimental data - N. Kumar; P. S. Paliyal; A. Yadav; A. Aggarwal; R. K. Pachuari (International Journal of Energy and Water Resources, 2026)

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Exploring the feasibility and challenges of AI-based rainfall-induced landslides prediction - Dibyajyoti Chutia; Ranjan Kumar Deka; Rahul Kumar; Praveen Kumar; Avinash Chauhan; Puyam S. Singh; Nilay Nishant; Prakash Biswakarma; K. K. Sarma; S. P. Aggarwal (Natural Hazards, 2026)

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

Optimizing Flood Inundation Scenarios: Perspective of Flood Damage and Relief Economics - Susanta Das; B. M. Arjun; Diganta Barman; K. K. Sarma; S. P. Aggarwal (Journal of the Indian Society of Remote Sensing, 2025)

Enhancing resilience with operational hydrometeorological-based flood early warning system in northeast India - Diganta Barman; B M Arjun; Shyam Sundar Kundu; Rekha Bharali Gogoi; Shanbor Kurbah; Anupal Baruah; Aniket Chakravorty; S P Aggarwal (Journal of Earth System Science, 2025)

Geospatial Insights for Assessing Agricultural Drought Hazards of Rabi Season in Assam - V. Senpakapriya; Jonali Goswami; Debashree Battacharjee; Ranjan Das; K. K. Sarma; S. P. Aggarwal (Journal of the Indian Society of Remote Sensing, 2025)

P30-66 EPAA review of regulatory requirements for a second (non-rodent) species in repeated-dose toxicity studies and potential 3Rs opportunities - N. Printemps; M. Aggarwal; M. Anderton; P. Bishop; F. Chanut; K. Clark; M. Corvaro; P. Deford; O. Doehr; J. Fretton; J.-C. Garcin; A. Kampkoetter; et al. (Toxicology Letters, 2025)

Advanced geospatial synthesis of urban heat Island dynamics: interlinking climate change, land use/land cover, vegetative indices, and thermal variability in Guwahati metropolitan area, assam, India - Toushif Jaman; Suraj Kumar Swain; Jenita Mary Nongkynrih; Kasturi Chakraborty; Rekha Bharali Gogoi; K. K. Sarma; S. P. Aggarwal (Theoretical and Applied Climatology, 2025)

Integrating hydrospatial Variables with Machine Learning for Groundwater Potential Zonation in Dhalai District, Tripura - Toushif Jaman; Shashank Bhaskar; Suraj Kumar Swain; Victor Saikhom; Rekha Bharali Gogoi; K. K. Sarma; S. P. Aggarwal (Water Conservation Science and Engineering, 2025)

Influence of Multi-Hazard on Socio-Economic Vulnerability of Tura Town Area of Meghalaya, India: An Urban Modeling in Respect of Disaster Risk Reduction Using Geospatial Technologies - Toushif Jaman; Jenita Nongkynrih; B.C. Sumanth; Nikita Lahiri; Bandarilang Kshiar; K. K. Sarma; S. P. Aggarwal (Journal of the Indian Society of Remote Sensing, 2025)

GeoAI-based soil erosion risk assessment in the Brahmaputra River Basin: a synergistic approach using RUSLE and advanced machine learning - Toushif Jaman; Shashank Bhaskar; Victor Saikhom; Rekha Bharali Gogoi; K. K. Sarma; S. P. Aggarwal (Environmental Monitoring and Assessment, 2025)

Impact of Terrestrial Weather on the Space Weather of the Ionosphere-Thermosphere: Initial Results from a NASA Living with a Star Focused Science Topic - J. Oberheide; D. Aggarwal; B. Bergsson; S. Chakraborty; S. Debchoudhury; M. Dhadly; F. Gasperini; L. Goncharenko; V. L. Harvey; C. Heale; P. Inchin; J. Li; et al. (Surveys in Geophysics, 2025)
The ionosphere-thermosphere (IT) is a convergence point of energy and processes that interconnect Earth’s atmosphere with space. Processes generated by terrestrial weather in the lower atmosphere (i.e., troposphere and stratosphere, altitudes less than ~ 50 km) are recognized by the scientific community as sources of variability in both the structure and composition of the IT. Exposed to persistent wave forcing from terrestrial weather sources and solar and magnetic forcing, the IT is a domain of compelling scientific inquiry that connects thermodynamics, fluid dynamics, electrodynamics and plasma physics. Predicting its space weather is of significant national interest for space situation awareness including the very low earth orbit as the new frontier of space operations. Advancing the understanding of whole atmosphere interconnections between terrestrial and space weather requires coordinated modeling and observational efforts across different spatial and temporal scales. Toward this goal, the National Aeronautics and Space Administration (NASA), through the living with a star (LWS) program, established in 2022 a focused science topic (FST) to study the problem from various angles. In this manuscript we report on the vision, goals and status of the ongoing FST “Impact of Terrestrial Weather on the Ionosphere—Thermosphere”. Initial results show bigger impacts on the IT than hitherto thought and help to more clearly define the state-of-the-art in the context of future NASA missions such as EZIE, DYNAMIC and GDC.

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Investigation of the atmospheric boundary layer characteristics over a high altitude station in North East India using measurements and reanalysis datasets - Manasi Gogoi; Arup Borgohain; Shyam S. Kundu; Som Sharma; Arundhati Kundu; Pradip K. Bhuyan; N. V. P. Kiran Kumar; Nilamoni Barman; S. P. Aggarwal (Theoretical and Applied Climatology, 2024)

Low Ionosphere Density Above the Earthquake Epicentre Region of Mw 7.2, El Mayor–Cucapah Earthquake Evident from Dense CORS Data - Gopal Sharma; Karan Nayak; Rosendo Romero-Andrade; M. A. Mohammed Aslam; K. K. Sarma; S. P. Aggarwal (Journal of the Indian Society of Remote Sensing, 2024)

Comparative evaluation of management modules against Maydis leaf blight disease in maize (Zea mays) - Sumit Kumar Aggarwal; K. S. Hooda; Harleen Kaur; Robin Gogoi; Prashant Chauhan; P. K. Bagaria; Pardeep Kumar; Mukesh Choudhary; Rahul Kumar Tiwari; Milan Kumar Lal (European Journal of Plant Pathology, 2024)

Reconnaissance to characterisation of land subsidence due to groundwater overdraft and oil extraction in and around Mehsana City, Gujarat, India by long-term hybrid differential interferometric SAR technique - R.S. Chatterjee; Shravanee Singha; Ashish Aggarwal; Vimanyu Sharma; Pranshu Pranjal; Anuradha Karunakalage; P.K. Jain; A. Nagar; D.S. Mitra; D. Kumar; N.R. Patel; P. Chauhan (Journal of Hydrology, 2023)

A combined approach using b-value and ionospheric GPS-TEC for large earthquake precursor detection: a case study for the Colima earthquake of 7.7 Mw, Mexico - Karan Nayak; Rosendo Romero-Andrade; Gopal Sharma; Juan Luis Cabanillas Zavala; Charbeth López Urias; Manuel E. Trejo Soto; S. P. Aggarwal (Acta Geodaetica et Geophysica, 2023)

Hydrological-hydrodynamic nexus for predicting flood plain inundation: a case study in Pagladiya-Putimari catchment, India - Diganta Barman; Anupal Baruah; Arjun B. M; Shiv P. Aggarwal (Arabian Journal of Geosciences, 2023)

Mutation spectrum and enzyme profiling of G6PD deficiency in neonates of north India: a prospective study - Upasana Bhattacharyya; Preeti Deswal; Sunil Kumar Polipalli; Diksha Sharma; Manpreet Kaur; Madhulika Kabra; Neerja Gupta; Ramesh Agarwal; A. K. Deorari; V. K. Paul; Shevendru Roy; R. K. Sanjeev; et al. (Journal of Genetics, 2023)

Impact of Biomass Burning on Black Carbon and NO2 Over North Eastern Region of India Using Multi-satellite Observations - Arup Borgohain; Manasi Gogoi; Nilamoni Barman; Arundhati Kundu; Trisanu Banik; Shyam S. Kundu; Pradip K. Bhuyan; S. P. Aggarwal (Journal of the Indian Society of Remote Sensing, 2023)

Holistic framework for flood hazard assessment in a trans-boundary basin - Anupal Baruah; Diganta Barman; Balakrishna Madayala Arjun; Bajitborlang L. Chyne; Shiv P Aggarwal (Acta Geophysica, 2023)

Monkeypox outbreak: Wastewater and environmental surveillance perspective - Ananda Tiwari; Sangeet Adhikari; Devrim Kaya; Md. Aminul Islam; Bikash Malla; Samendra P. Sherchan; Ahmad I. Al-Mustapha; Manish Kumar; Srijan Aggarwal; Prosun Bhattacharya; Kyle Bibby; Rolf U. Halden; et al. (Science of The Total Environment, 2023)

Performance evaluation of a novel dustable powder formulation of Trichoderma viride for seed treatment against Fusarium wilt in chickpea - P. C. Pradhan; A. Mukhopadhyay; R. Kumar; N. Patanjali; A. Kundu; D. Kamil; T. K. Bag; R. Aggarwal; C. Bharadwaj; A. Singh (Indian Phytopathology, 2022)

Multiple modules for the management of banded leaf and sheath blight of maize in India - S. K. Aggarwal; K. S. Hooda; P. K. Bagaria; Harleen Kaur; Robin Gogoi; Prashant Chauhan; R. P. Singh (Indian Phytopathology, 2022)

Investigation of basin characteristics: Implications for sub-basin-level vulnerability to flood peak generation - Rajeev Ranjan; Pankaj R. Dhote; Praveen K. Thakur; Shiv P. Aggarwal (Natural Hazards, 2022)

First record of sulfate-driven anaerobic methane oxidation and associated dolomite precipitation in Kachchh Basin, western India - V. K. Srivastava; B. P. Singh; Som Dutt; A. Aggarwal (Carbonates and Evaporites, 2022)

Estimation of forest aboveground biomass using combination of Landsat 8 and Sentinel-1A data with random forest regression algorithm in Himalayan Foothills - Saurabh Purohit; S. P. Aggarwal; N. R. Patel (Tropical Ecology, 2021)

Coupling Remote Sensing and GIS with KINEROS2 Model for Spatially Distributed Runoff Modeling in a Himalayan Watershed - Sameer Saran; Geert Sterk; S. P. Aggarwal; V. K. Dadhwal (Journal of the Indian Society of Remote Sensing, 2021)

Identification of flash-floods-prone river reaches in Beas river basin using GIS-based multi-criteria technique: validation using field and satellite observations - Sachchidanand Singh; Pankaj R. Dhote; Praveen K. Thakur; Arpit Chouksey; S. P. Aggarwal (Natural Hazards, 2021)

Retraction Note: Engineered Polyallylamine Nanoparticles for Efficient In Vitro Transfection - Atul Pathak; Anita Aggarwal; Raj K. Kurupati; Soma Patnaik; Archana Swami; Yogendra Singh; Pradeep Kumar; Suresh P. Vyas; Kailash C. Gupta (Pharmaceutical Research, 2020)

Fusarium graminearum microRNA-like RNAs and their interactions with wheat genome: a much-needed study - Lalit L. Kharbikar; Arti S. Shanware; M. S. Saharan; Shweta K. Nandanwar; P. N. Sivalingam; Malkhan S. Gurjar; Rashmi Aggarwal (Indian Phytopathology, 2019)

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming - Bing Liu; Pierre Martre; Frank Ewert; John R. Porter; Andy J. Challinor; Christoph Müller; Alex C. Ruane; Katharina Waha; Peter J. Thorburn; Pramod K. Aggarwal; Mukhtar Ahmed; Juraj Balkovič; et al. (Global Change Biology, 2019)
Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO 2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming

Climate change impact and adaptation for wheat protein - Senthold Asseng; Pierre Martre; Andrea Maiorano; Reimund P. Rötter; Garry J. O’Leary; Glenn J. Fitzgerald; Christine Girousse; Rosella Motzo; Francesco Giunta; M. Ali Babar; Matthew P. Reynolds; Ahmed M. S. Kheir; et al. (Global Change Biology, 2019)
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.

Multimodel ensembles improve predictions of crop–environment–management interactions - Daniel Wallach; Pierre Martre; Bing Liu; Senthold Asseng; Frank Ewert; Peter J. Thorburn; Martin van Ittersum; Pramod K. Aggarwal; Mukhtar Ahmed; Bruno Basso; Christian Biernath; Davide Cammarano; et al. (Global Change Biology, 2018)
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles ( MME s). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction ( MSEP ) of e‐mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Vertical drains to minimize duration of seasonal waterlogging in Eastern Ganges Basin flood plains: a field experiment - S. A. Prathapar; N. Rajmohan; B. R. Sharma; P. K. Aggarwal (Natural Hazards, 2018)

Effect of elevated temperature on soil hydrothermal regimes and growth of wheat crop - P. Pramanik; Bidisha Chakrabarti; Arti Bhatia; S. D. Singh; A. Maity; P. Aggarwal; P. Krishnan (Environmental Monitoring and Assessment, 2018)

Global sensitivity and uncertainty analyses of a web based crop simulation model (web InfoCrop wheat) for soil parameters - P. Krishnan; Pramila Aggarwal (Plant and Soil, 2018)

Aegle marmelos differentially affects hepatic markers of glycolysis, insulin signalling pathway, hypoxia, and inflammation in HepG2 cells grown in fructose versus glucose-rich environment - H. Aggarwal; J. Nair; P. Sharma; R. Sehgal; U. Naeem; P. Rajora; R. Mathur (Molecular and Cellular Biochemistry, 2018)

Estimation of Snow Cover Area, Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data - Praveen K. Thakur; S. P. Aggarwal; G. Arun; Sahil Sood; A. Senthil Kumar; Sneh Mani; D. P. Dobhal (Journal of the Indian Society of Remote Sensing, 2017)

One-dimensional hydrodynamic modeling of GLOF and impact on hydropower projects in Dhauliganga River using remote sensing and GIS applications - Praveen K. Thakur; Suruchi Aggarwal; S. P. Aggarwal; S. K. Jain (Natural Hazards, 2016)

Visibility Graph Analysis of the 2003–2012 Earthquake Sequence in the Kachchh Region of Western India - Luciano Telesca; Michele Lovallo; S. K. Aggarwal; P. K. Khan; B. K. Rastogi (Pure and Applied Geophysics, 2016)

Dynamics of ultrafine particles inside a roadway tunnel - V. K. Mishra; M. L. Aggarwal; P. Berghmans; E. Frijns; L. Int Panis; K. M. Chacko (Environmental Monitoring and Assessment, 2015)

Flood monitoring using microwave remote sensing in a part of Nuna river basin, Odisha, India - Sananda Kundu; S. P. Aggarwal; Nanette Kingma; Arun Mondal; Deepak Khare (Natural Hazards, 2015)

Multimodel ensembles of wheat growth: many models are better than one - Pierre Martre; Daniel Wallach; Senthold Asseng; Frank Ewert; James W. Jones; Reimund P. Rötter; Kenneth J. Boote; Alex C. Ruane; Peter J. Thorburn; Davide Cammarano; Jerry L. Hatfield; Cynthia Rosenzweig; et al. (Global Change Biology, 2015)
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield ( GY ) and grain protein concentration ( GPC ). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC . The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Purification and characterization of a novel toxin from Bipolaris sorokiniana , causing spot blotch of wheat and analysis of variability in the pathogen - M. Jahani; R. Aggarwal; S. Gupta; S. Sharma; P. Dureja (Cereal Research Communications, 2014)

Biopharmaceutical considerations and characterizations in development of colon targeted dosage forms for inflammatory bowel disease - Rajkumar Malayandi; Phani Krishna Kondamudi; P. K. Ruby; Deepika Aggarwal (Drug Delivery and Translational Research, 2014)

Snow Cover Area Mapping Using Synthetic Aperture Radar in Manali Watershed of Beas River in the Northwest Himalayas - Praveen K. Thakur; Pradeep K. Garg; Shiv P. Aggarwal; Rahul D. Garg; Sneh Mani (Journal of the Indian Society of Remote Sensing, 2013)

Mass-size distribution of PM10 and its characterization of ionic species in fine (PM2.5) and coarse (PM10−2.5) mode, New Delhi, India - Khem Singh; S. Tiwari; A. K. Jha; Shankar G. Aggarwal; D. S. Bisht; B. P. Murty; Zahid H. Khan; Prabhat K. Gupta (Natural Hazards, 2013)

An assessment of regional vulnerability of rice to climate change in India - Naresh Kumar Soora; P. K. Aggarwal; Rani Saxena; Swaroopa Rani; Surabhi Jain; Nitin Chauhan (Climatic Change, 2013)

Simulating the impacts of climate change on cotton production in India - K. B. Hebbar; M. V. Venugopalan; A. H. Prakash; P. K. Aggarwal (Climatic Change, 2013)

Stage and Discharge Forecasting by SVM and ANN Techniques - S. K. Aggarwal; Arun Goel; Vijay P. Singh (Water Resources Management, 2012)

Synthesis and biological evaluation of naphthalene, furan and pyrrole based chalcones as cytotoxic and antimicrobial agents - Abhishek Budhiraja; Kanika Kadian; Mandeep Kaur; Vikas Aggarwal; Atul Garg; Sameer Sapra; Kunal Nepali; O. P. Suri; K. L. Dhar (Medicinal Chemistry Research, 2012)

River bank erosion hazard study of river Ganga, upstream of Farakka barrage using remote sensing and GIS - Praveen K. Thakur; Chalantika Laha; S. P. Aggarwal (Natural Hazards, 2012)

Estimation of structural vulnerability for flooding using geospatial tools in the rural area of Orissa, India - Praveen K. Thakur; Sreyasi Maiti; Nanette C. Kingma; V. Hari Prasad; S. P. Aggarwal; Ashutosh Bhardwaj (Natural Hazards, 2012)

Trade-off between productivity enhancement and global warming potential of rice and wheat in India - A. Bhatia; H. Pathak; P. K. Aggarwal; N. Jain (Nutrient Cycling in Agroecosystems, 2010)

Endophytic Bacteria from Ocimum sanctum and Their Yield Enhancing Capabilities - Rashmi Tiwari; Alok Kalra; M. P. Darokar; Mahesh Chandra; Nitin Aggarwal; A. K. Singh; S. P. S. Khanuja (Current Microbiology, 2010)

Rhinosporidiosis in Delhi, North India: Case Series from a Non-endemic Area and Mini-review - Malini R. Capoor; Geetika Khanna; Rajni; Kadambari Batra; Deepthi Nair; V. P. Venkatchalam; Pushpa Aggarwal (Mycopathologia, 2009)

Economic liabilities of environmental pollution by coal mining: Indian scenario - Y. C. Sharma; P. Aggarwal; T. N. Singh (Environment, Development and Sustainability, 2009)

Comparative genomic study of spo0E family genes and elucidation of the role of Spo0E in Bacillus anthracis - Gyanendra P. Dubey; Azeet Narayan; Abid R. Mattoo; Gajendra P. Singh; Raj K. Kurupati; Mohd. S. Zaman; Anita Aggarwal; Renu B. Baweja; Sharmila Basu-Modak; Yogendra Singh (Archives of Microbiology, 2009)

Development and characterization of ten novel microsatellite markers from olive ridley sea turtle (Lepidochelys olivacea) - Ramesh K. Aggarwal; Albert Lalremruata; Thirumalaisamy P. Velavan; Ayyadevara Pavani Sowjanya; Lalji Singh (Conservation Genetics, 2008)

Blood glutathione levels in head and neck malignancies - Y. P. Singh; O. P. Sachdeva; S. K. Aggarwal; K. Chugh; Harbans Lal (Indian Journal of Clinical Biochemistry, 2008)

Properties of Bacillus anthracis spores prepared under various environmental conditions - Renu B. Baweja; Mohd S. Zaman; Abid R. Mattoo; Kirti Sharma; Vishwas Tripathi; Anita Aggarwal; Gyanendra P. Dubey; Raj K. Kurupati; Munia Ganguli; N. K. Chaudhury; Somdutta Sen; Taposh K. Das; et al. (Archives of Microbiology, 2007)

RETRACTED ARTICLE: Engineered Polyallylamine Nanoparticles for Efficient In Vitro Transfection - Atul Pathak; Anita Aggarwal; Raj K. Kurupati; Soma Patnaik; Archana Swami; Yogendra Singh; Pradeep Kumar; Suresh P. Vyas; Kailash C. Gupta (Pharmaceutical Research, 2007)

PTEN down regulates AP-1 and targets c-fos in human glioma cells Via PI3-kinase/Akt pathway - Dimpy Koul; Ruijun Shen; Shishir Shishodia; Yasanuri Takada; Krishna P. Bhat; Shrikanth A. G. Reddy; Bharat B. Aggarwal; W. K. Alfred Yung (Molecular and Cellular Biochemistry, 2007)

Age-related decrease in rod bipolar cell density of the human retina: an immunohistochemical study - P Aggarwal; T C Nag; S Wadhwa (Journal of Biosciences, 2007)

Dynamics of Organic Carbon and Microbial Biomass in Alluvial Soil With Tillage and Amendments in Rice-wheat Systems - B. Banerjee; P. K. Aggarwal; H. Pathak; A. K. Singh; A. Chaudhary (Environmental Monitoring and Assessment, 2006)

Effect of low-dose omega-3 fatty acids substitution on blood pressure, hyperinsulinemia and dyslipidemia in Indians with essential hypertension: A pilot study - A. Bhise; P. V. Krishnan; R. Aggarwal; M. Gaiha; J. Bhattacharjee (Indian Journal of Clinical Biochemistry, 2005)

Increasing the efficiency of agronomy experiments in potato using INFOCROP-POTATO model - J. P. Singh; P. M. Govindakrishnan; S. S. Lal; P. K. Aggarwal (Potato Research, 2005)

Lipid composition and amino acid uptake during rotavirus infection and protection with trypsin inhibitor in malnourished infant mice - R. Katyal; S V Rana; S. Ojha; V. Singh; R. P. Aggarwal (Indian Journal of Clinical Biochemistry, 2005)

Lipid peroxide levels and antioxidant status in alcoholic liver disease - Seema Gupta; Rajesh Pandey; Ranjan Katyal; H. K. Aggarwal; R. P. Aggarwal; S. K. Aggarwal (Indian Journal of Clinical Biochemistry, 2005)

Molecular Mapping of Loci Affecting the Contents of Three Major Fatty Acids in Indian Mustard (Brassica juncea L) - R. A. K. Aggarwal; R. Sharma; R. Kumar; T. Mohapatra; R. P. Sharma (Journal of Plant Biochemistry and Biotechnology, 2003)

Effects of dicyandiamide, farmyard manure and irrigation on crop yields and ammonia volatilization from an alluvial soil under a rice ( Oryza sativa L.)-wheat ( Triticum aestivum L.) cropping system - Banerjee B.; Pathak H.; Aggarwal P. (Biology and Fertility of Soils, 2002)

Rainfall-runoff and soil erosion modeling using Remote Sensing and GIS technique — a case study of tons watershed - A S Jasrotia; S D Dhiman; S P Aggarwal (Journal of the Indian Society of Remote Sensing, 2002)

Climate Change and Rice Yields in Diverse Agro-Environments of India. II. Effect of Uncertainties in Scenarios and Crop Models on Impact Assessment - P. K. Aggarwal; R. K. Mall (Climatic Change, 2002)

Climate Change and Rice Yields in Diverse Agro-Environments of India. I. Evaluation of Impact Assessment Models - R. K. Mall; P. K. Aggarwal (Climatic Change, 2002)

Process optimization for the production of sugar for the bioethanol industry from Tapioca, a non-conventional source of starch - N.K. Aggarwal; P. Niga; D. Singh; B.S. Yadav (World Journal of Microbiology and Biotechnology, 2001)

Process optimization for the production of sugar for the bioethanol industry from sorghum, a non-conventional source of starch - N.K. Aggarwal; P. Nigam; D. Singh; B.S. Yadav (World Journal of Microbiology and Biotechnology, 2001)

Performance and persistence of phosphate solubilizingAzotobacter chroococcum in wheat rhizosphere - V. Kumar; N. Kumar Aggarwal; B. P. Singh (Folia Microbiologica, 2000)

Influence of analogue-resistant mutants ofAzotobacter chroococcum solubilizing phosphate on yield and quality of sunflower (Helianthus annuus) - V. Kumar; N. Kumar Aggarwal; B. P. Singh (Folia Microbiologica, 2000)

Genetic diversity and phylogenetic relationship as revealed by inter simple sequence repeat (ISSR) polymorphism in the genus Oryza - S. P. Joshi; V. S. Gupta; R. K. Aggarwal; P. K. Ranjekar; D. S. Brar (Theoretical and Applied Genetics, 2000)

Cellular resistance to vincristine suppresses NF-κB activation and apoptosis but enhances c-Jun-NH2-terminal protein kinase activation by tumor necrosis factor - D. K. Giri; P. Pantazis; B. B. Aggarwal (Apoptosis, 1999)