On the public citation graph

TabPFN correction of climate-zoned crop-model residuals improves regional winter wheat yield estimation

https://doi.org/10.2139/ssrn.7451081
0 signed · 45 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/csc2.20016openalex-snapshot
A new approach to crop model calibration: Phenotyping plus post‐processing
inferredcites →https://doi.org/10.1007/bf00994018openalex-snapshot
Support-Vector Networks
inferredcites →https://doi.org/10.1007/s11119-024-10184-3openalex-snapshot
Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea
inferredcites →https://doi.org/10.1016/j.agrformet.2012.11.017openalex-snapshot
Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe
inferredcites →https://doi.org/10.1016/j.agrformet.2019.05.018openalex-snapshot
Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia
inferredcites →https://doi.org/10.1016/j.agrformet.2023.109477openalex-snapshot
Climate warming extends the effective growth period of winter wheat and increases grain protein content
inferredcites →https://doi.org/10.1016/j.agrformet.2024.109909openalex-snapshot
Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain
inferredcites →https://doi.org/10.1016/j.agrformet.2025.110941openalex-snapshot
Impact of vertical and seasonal variation in leaf traits on simulating soybean canopy photosynthesis via 1D and 3D modeling
inferredcites →https://doi.org/10.1016/j.agsy.2018.03.002openalex-snapshot
Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe
inferredcites →https://doi.org/10.1016/j.agsy.2018.06.018openalex-snapshot
25 years of the WOFOST cropping systems model
inferredcites →https://doi.org/10.1016/j.cliser.2025.100605openalex-snapshot
Crop choice advisory for the West African Sudan Savanna based on soil type and presowing rainfall forecasts: A machine learning residual model approach
inferredcites →https://doi.org/10.1016/j.compag.2020.105554openalex-snapshot
Practices for upscaling crop simulation models from field scale to large regions
inferredcites →https://doi.org/10.1016/j.compag.2023.107663openalex-snapshot
Interpretability of deep learning models for crop yield forecasting
inferredcites →https://doi.org/10.1016/j.compag.2025.110336openalex-snapshot
Development of an ecohydrological model for coupled simulation of water and carbon fluxes, crop growth, and canopy spectra over croplands
inferredcites →https://doi.org/10.1016/j.ecolind.2025.114460openalex-snapshot
Evaluating the predictability of SIF at multiple temporal scales for winter wheat yield in Huang-Huai-Hai plain
inferredcites →https://doi.org/10.1016/j.fcr.2015.03.005openalex-snapshot
From field to atlas: Upscaling of location-specific yield gap estimates
inferredcites →https://doi.org/10.1016/j.fcr.2025.110089openalex-snapshot
Overcoming wheat yield stagnation in China depends more on cultivar improvements than water and fertilizer management
inferredcites →https://doi.org/10.1016/j.jclepro.2025.145570openalex-snapshot
Projected changes in future wheat yield across China under CMIP6 climate simulations and agricultural management scenarios
inferredcites →https://doi.org/10.1016/j.jhydrol.2025.134849openalex-snapshot
Dynamic assessment of ecological quality in China from 2000 to 2024 based on artificial intelligence
inferredcites →https://doi.org/10.1016/j.ophoto.2024.100064openalex-snapshot
Improving spatial transferability of deep learning models for small-field crop yield prediction
inferredcites →https://doi.org/10.1016/j.rse.2004.03.014openalex-snapshot
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter
inferredcites →https://doi.org/10.1016/j.rse.2005.08.014openalex-snapshot
Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data
inferredcites →https://doi.org/10.1016/j.scitotenv.2017.03.208openalex-snapshot
Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes
inferredcites →https://doi.org/10.1023/a:1008202821328openalex-snapshot
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1029/2000jd900468openalex-snapshot
Modeling CO 2 and water vapor turbulent flux distributions within a forest canopy
inferredcites →https://doi.org/10.1029/2003gl019385openalex-snapshot
Comparison of MODIS and AVHRR 16‐day normalized difference vegetation index composite data
inferredcites →https://doi.org/10.1038/nclimate1916openalex-snapshot
Uncertainty in simulating wheat yields under climate change
inferredcites →https://doi.org/10.1038/s41586-024-08328-6openalex-snapshot
Accurate predictions on small data with a tabular foundation model
inferredcites →https://doi.org/10.1038/s43016-021-00341-6openalex-snapshot
Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields
inferredcites →https://doi.org/10.1038/s43017-023-00491-0openalex-snapshot
Climate change impacts on crop yields
inferredcites →https://doi.org/10.1080/01431160801927194openalex-snapshot
Agreement evaluation of AVHRR and MODIS 16‐day composite NDVI data sets
inferredcites →https://doi.org/10.1088/1748-9326/ac0f26openalex-snapshot
A comprehensive uncertainty quantification of large-scale process-based crop modeling frameworks
inferredcites →https://doi.org/10.1109/34.868688openalex-snapshot
Normalized cuts and image segmentation
inferredcites →https://doi.org/10.1111/gcb.17188openalex-snapshot
Land surface conductance linked to precipitation: Co‐evolution of vegetation and climate in Earth system models
inferredcites →https://doi.org/10.1111/gcb.70936openalex-snapshot
Observation‐Constrained Agroecosystem Model Inversion Reveals Continental‐Scale Variation of Winter Wheat Traits
inferredcites →https://doi.org/10.1111/j.1365-3040.2010.02193.xopenalex-snapshot
Leaf physiological responses to extreme droughts in Mediterranean Quercus ilex forest
inferredcites →https://doi.org/10.1111/pce.12871openalex-snapshot
Estimating the sensitivity of stomatal conductance to photosynthesis: a review
inferredcites →https://doi.org/10.1111/pce.12990openalex-snapshot
Seasonal variability of the parameters of the Ball–Berry model of stomatal conductance in maize ( Zea mays L.) and sunflower ( Helianthus annuus L.) under well‐watered and water‐stressed conditions
inferredcites →https://doi.org/10.1371/journal.pone.0221862openalex-snapshot
Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble
inferredcites →https://doi.org/10.2135/cropsci2009.10.0564openalex-snapshot
Breeding and Cereal Yield Progress
inferredcites →https://doi.org/10.3389/fpls.2022.1000224openalex-snapshot
County-scale crop yield prediction by integrating crop simulation with machine learning models
inferredcites →https://doi.org/10.5194/bg-11-7159-2014openalex-snapshot
Drought impact on carbon and water cycling in a Mediterranean Quercus suber L. woodland during the extreme drought event in 2012
inferredcites →https://doi.org/10.5194/gmd-14-1379-2021openalex-snapshot
Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum (STEMMUS–SCOPE v1.0.0)
inferredcites →https://doi.org/10.5194/gmd-18-8379-2025openalex-snapshot
Modeling wheat development under extreme weather with WOFOST-EW v1
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.