# A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction

arXiv: https://arxiv.org/abs/2111.08900 · HTML: https://mlsanity.com/paper/2111.08900/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 59 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [A CNN-RNN Framework for Crop Yield Prediction](https://mlsanity.com/paper/1911.09045/) (2019)
- [Crop Yield Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1902.02860/) (2019)
- [Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt](https://mlsanity.com/paper/2008.04060/) (2020)
- [Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach](https://mlsanity.com/paper/2001.09902/) (2020)

## Built on by (most cited first, 28 of 28)

- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer](https://mlsanity.com/paper/2309.09067/) (2023)
- [Dynamic Graph Representation Learning with Neural Networks: A Survey](https://mlsanity.com/paper/2304.05729/) (2023)
- [WildSAT: Learning Satellite Image Representations from Wildlife Observations](https://mlsanity.com/paper/2412.14428/) (2024)
- [Neural networks for geospatial data](https://mlsanity.com/paper/2304.09157/) (2023)
- [FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems](https://mlsanity.com/paper/2311.10255/) (2023)
- [Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs](https://mlsanity.com/paper/2210.13648/) (2022)
- [On the Generalizability of Foundation Models for Crop Type Mapping](https://mlsanity.com/paper/2409.09451/) (2024)
- [Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data](https://mlsanity.com/paper/2212.11367/) (2022)
- [Agri-GNN: A Novel Genotypic-Topological Graph Neural Network Framework Built on GraphSAGE for Optimized Yield Prediction](https://mlsanity.com/paper/2310.13037/) (2023)
- [Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation](https://mlsanity.com/paper/2411.03223/) (2024)
- [A weakly supervised framework for high-resolution crop yield forecasts](https://mlsanity.com/paper/2205.09016/) (2022)
- [A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs](https://mlsanity.com/paper/2407.05197/) (2024)
- [TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting](https://mlsanity.com/paper/2410.20439/) (2024)
- [California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops](https://mlsanity.com/paper/2506.10228/) (2025)
- [VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting](https://mlsanity.com/paper/2508.03589/) (2025)
- [A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction](https://mlsanity.com/paper/2509.15966/) (2025)
- [FARM: Fine-Tuning Geospatial Foundation Models for Intra-Field Crop Yield Regression](https://mlsanity.com/paper/2510.26609/) (2025)
- [SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes](https://mlsanity.com/paper/2606.19623/) (2026)
- [An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction](https://mlsanity.com/paper/2304.09761/) (2023)
- [Generative Probabilistic Planning for Optimizing Supply Chain Networks](https://mlsanity.com/paper/2404.07511/) (2024)
- [Temporal receptive field in dynamic graph learning: A comprehensive analysis](https://mlsanity.com/paper/2407.12370/) (2024)
- [KG-FGNN: Knowledge-guided GNN Foundation Model for Fertilisation-oriented Soil GHG Flux Prediction](https://mlsanity.com/paper/2506.15896/) (2025)
- [DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction](https://mlsanity.com/paper/2507.05849/) (2025)
- [Out-of-Distribution Generalization in Climate-Aware Yield Prediction with Earth Observation Data](https://mlsanity.com/paper/2510.07350/) (2025)
- [PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction](https://mlsanity.com/paper/2605.23478/) (2026)
- [STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling](https://mlsanity.com/paper/2606.08484/) (2026)
- [Evaluating and improving crop-yield forecasting methods during extreme drought](https://mlsanity.com/paper/2608.17971/) (2026)

## Abstract (arXiv)

Climate change is posing new challenges to crop-related concerns including food insecurity, supply stability and economic planning. As one of the central challenges, crop yield prediction has become a pressing task in the machine learning field. Despite its importance, the prediction task is exceptionally complicated since crop yields depend on various factors such as weather, land surface, soil quality as well as their interactions. In recent years, machine learning models have been successfully applied in this domain. However, these models either restrict their tasks to a relatively small region, or only study over a single or few years, which makes them hard to generalize spatially and temporally. In this paper, we introduce a novel graph-based recurrent neural network for crop yield prediction, to incorporate both geographical and temporal knowledge in the model, and further boost predictive power. Our method is trained, validated, and tested on over 2000 counties from 41 states in the US mainland, covering years from 1981 to 2019. As far as we know, this is the first machine learning method that embeds geographical knowledge in crop yield prediction and predicts the crop yields at county level nationwide. We also laid a solid foundation for the comparison with other machine learning baselines by applying well-known linear models, tree-based models, deep learning methods and comparing their performance. Experiments show that our proposed method consistently outperforms the existing state-of-the-art methods on various metrics, validating the effectiveness of geospatial and temporal information.