Feasibility of Machine Learning-Based Rice Yield Prediction in India at the District Level Using Climate Reanalysis Data

Aug 5, 2026

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Yield forecasting is the practice of estimating agricultural productivity before harvest, supporting planning decisions for farmers, governments, and insurers. This study evaluates whether machine learning models can predict Kharif season rice yields at India’s district level months ahead. Nineteen models were trained using 20 years of climate, satellite, and yield data across 247 rice districts, and a dynamic dashboard was built to assess district-level reliability. Results indicate strong predictive performance and SHAP-based interpretation of key climate and remote-sensing drivers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

The societal implications of accurate crop yield forecasting in India

Overview of approaches and variables used to model crop yields

arXiv :2403 .07967v1 [ cs .LG] 12 Mar 2024 Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data Djavan De Clercq, Adam Mahdi University of Oxford Abstract Yield forecasting, the science of predicting agricultural productivity before the crop harvest occurs, helps a wide range of stakeholders make better decisions around agricultural planning. This study aims to investigate whether machine learning-based yield prediction models can capably predict Kharif season rice yields at the district level in India several months before the rice harvest takes place. The methodology involved training 19 machine learning models such as CatBoost, LightGBM, Orthogonal Matching Pursuit, and Extremely Randomized Trees on 20 years of climate, satellite, and rice yield data across 247 of India’s rice-producing districts. In addition to model-building, a dynamic dashboard was built understand how the reliability of rice yield predictions varies across districts. The results of the proof-of-concept machine learning pipeline demonstrated that rice yields can be predicted with a reasonable degree of accuracy, with outof-sample R2, MAE, and MAPE performance of up to 0.82 , 0.29, and 0.16 respectively. These results outperformed test set performance reported in related literature on rice yield modeling in other contexts and countries. In addition, SHAP value analysis was conducted to infer both the importance and directional impact of the climate and remote sensing variables included in the model. Important features driving rice yields included temperature, soil water volume, and leaf area index. In particular, higher temperatures in August correlate with increased rice yields, particularly when the leaf area index in August is also high. Building on the results, a proof-ofconcept dashboard was developed to allow users to easily explore which districts may experience arise or fall in yield relative to the previous year. The dashboard show that the model may perform better in some regions than in others. For instance, the absolute percentage error for predicted versus actual yields ranged from an average of 7. 1% in districts in Uttarakhand to an average of 14.7% in Uttar Pradesh. This study underscores the potential for policymakers to consider scaling and operationalizing machine learning approaches to rice yield prediction in the context of agricultural early warning systems to deliver timely crop yield forecasts on a rolling basis throughout the season, thereby equipping agricultural decision-makers with the ability to make informed choiceson irrigation scheduling, fertilizer application, and harvest planning to optimize crop output and resource use. 1 Introduction 1.1 The societal implications of accurate crop yield forecasting in India Yield forecasting is the science of predicting agricultural productivity as measured by crop yield – the ratio of the total mass of the harvested product (such as rice) to the area used to cultivate the crop – before the harvest takes place, typically a few months in advance [1] . Pre-harvest prediction of crop yields is important in helping a wide range of stakeholders make better decisions around agricultural planning. For farmers, accurate crop yield forecasts can facilitate decision-making around what to grow and when to grow it [2] . In addition, near real-time monitoring of crop growth can inform the use of preventive measures such as irrigation and fertilization to boost agricultural productivity where needed [3] . For governments, yield prediction is relevant to the formulation of policies related to national food security, such as pricing policies for domestic markets, and policy decisions on the import and export of different crops [4] . Accurate crop yield forecasting may also enable better design of insurance products that mitigate climate risks and stabilize farmer incomes [5] . Weather-based crop insurance, for insta

FAQs

What is the goal of this research on rice yield forecasting?

The research investigates whether machine learning-based models can predict Kharif season rice yields at the district level in India several months before harvest.

How were the machine learning models built and trained?

Nineteen machine learning models, including CatBoost and LightGBM, were trained on 20 years of climate, satellite, and rice yield data covering 247 rice-producing districts.

Which factors were identified as important drivers of rice yields?

SHAP analysis highlighted the importance and directional impact of climate and remote sensing variables, with key features including temperature, soil water volume, and leaf area index.

Aug 5, 2026

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