# Non-Linear Model-Based Sequential Decision-Making in Agriculture

arXiv: https://arxiv.org/abs/2509.01924 · HTML: https://mlsanity.com/paper/2509.01924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Model-based Reinforcement Learning and the Eluder Dimension](https://mlsanity.com/paper/1406.1853/) (2014)
- [Practical Contextual Bandits with Regression Oracles](https://mlsanity.com/paper/1803.01088/) (2018)
- [Mostly Exploration-Free Algorithms for Contextual Bandits](https://mlsanity.com/paper/1704.09011/) (2017)
- [Adapting to Misspecification in Contextual Bandits](https://mlsanity.com/paper/2107.05745/) (2021)
- [Misspecified Linear Bandits](https://mlsanity.com/paper/1704.06880/) (2017)
- [A CNN-RNN Framework for Crop Yield Prediction](https://mlsanity.com/paper/1911.09045/) (2019)
- [Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature](https://mlsanity.com/paper/2102.04168/) (2021)
- [Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR](https://mlsanity.com/paper/2302.03201/) (2023)
- [Optimal Thompson Sampling strategies for support-aware CVaR bandits](https://mlsanity.com/paper/2012.05754/) (2020)
- [The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates](https://mlsanity.com/paper/1803.00316/) (2018)
- [Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards](https://mlsanity.com/paper/1902.00819/) (2019)
- [Bandit Algorithms for Precision Medicine](https://mlsanity.com/paper/2108.04782/) (2021)
- [Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms](https://mlsanity.com/paper/1908.06746/) (2019)

## Abstract (arXiv)

Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides. Nitrogen inputs are central to this tension. They are indispensable for crop growth yet major drivers of greenhouse gas emissions, nutrient runoff, and escalating production costs. Addressing these intertwined pressures requires adaptive decision-support tools that are statistically principled, economically sustainable and interpretable for practitioners. We develop nonlinear model-based bandit algorithms as a framework for adaptive fertilizer management under uncertainty. Building on classical mechanistic yield-response models, our approach links algorithmic exploration-exploitation strategies directly to interpretable biological processes such as maximum yield and nutrient efficiency. This grounding makes recommendations transparent for practitioners while supporting cost-effective and sustainable input use. Methodologically, we establish regret and sample complexity results for the well-specified nonlinear case, examine robustness under misspecification, and evaluate the proposed methods through profit-oriented simulations and an offline replay case study on publicly available multi-site corn nitrogen field trials from the U.S. Midwest. The results show that incorporating biologically meaningful mechanistic structure enables faster learning and higher profit as evidence accumulates, with flexible nonparametric baselines providing a competitive alternative in pooled and heterogeneous settings. Our findings illustrate how interpretable, uncertainty-aware sequential decision rules can support economically sustainable fertilizer recommendations and contribute to more efficient agricultural input use.