Yield Forecasting Research Articles

Analyzing the impact of climatic conditions on rainfed wheat yield in northwest Iran: a parametric and nonparametric approach

ABSTRACT It is essential to comprehend the relationship between agricultural yields and climatic conditions, especially concerning food security and the possible threats to crop output. Wheat is a crucial agricultural crop that covers a significant amount of rainfed production regions in Iran. This study utilized parametric and nonparametric approaches to assess rainfed wheat yield. The study centered on the Tabriz area in northwest Iran, examining precipitation patterns concerning rainfed wheat cultivation. The study focused on analyzing seasonal and distinct rainfall patterns during the cultivation period, utilizing widely recognized drought metrics such as the standardized precipitation index and the standardized precipitation–evaporation index. The study findings indicate a notable upward trend in rainfed wheat output over the analyzed period. The Mann–Kendall test resulted in a p-value of 0.031, indicating statistical significance for the observed rising trend. We conducted trend removal and normalized rainfed wheat yield figures based on seasonal precipitation to study the data more thoroughly. The second phase of the growing season was particularly notable, spanning from the completion of germination to the beginning of blooming. Instead of analyzing precipitation for the full growing season, concentrating on autumn precipitation or the time from germination to blooming might improve yield forecasts and determinations.

Capsular attention Conv-LSTM network (CACN): A deep learning structure for crop yield estimation based on multispectral imagery

Precise prediction of agricultural production output is crucial for farmers, policymakers, and the Farming-related industry. This article introduces a novel methodology to crop yield forecasting using a capsular neural network equipped with Conv-LSTM and attention mechanism. Our model combines the strengths of 3DCNN, and Conv-LSTM, which can capture the temporal dependencies and 3D features of crop yield data, and attention mechanism, which Can prioritize the most significant characteristics for making predictions. We evaluated CACN on a sizable collection of data of soybean crop yield in the United States from 2003 to 2019 and evaluated against various cutting-edge deep learning models. The outcomes indicate that our suggested approach surpasses other models in performance in terms of RMSE, correlation coefficient, and prediction error map. Specifically, our model achieved approximately 14 % improvement in terms of RMSE, compared to the state-of-the-art model Deep-Yield. Our model also demonstrated the ability to extract more meaningful features and capture the complex relationships between crop yield data and meteorological variables. Overall, our proposed method shows great potential for accurate and efficient crop yield forecasting and can be applied to other crops and regions.

AI in Soil Health Monitoring: A Data-Driven Approach

Abstract: The sustainability of ecosystems and agricultural productivity depend on healthy soil. However, conventional techniques for evaluating soil health are sometimes time-consuming, labor-intensive, and restricted in their geographic reach. New opportunities for scalable, real-time soil health monitoring have been made possible by recent developments in artificial intelligence (AI) and data-driven methodologies. This study examines a thorough AI-powered system for tracking soil health, emphasizing methods for data collection, processing, and predictive modeling. AI models can provide precise forecasts of soil characteristics, health indicators, and possible crop yields by combining data from multiple sources, such as remote sensing, soil sensors, and historical data. This study offers a comprehensive analysis of recent AI applications in soil health monitoring and suggests a reliable, scalable approach intended to incorporate diverse data sources, guaranteeing precise and effective soil health assessment.

Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield

Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.

Integrated Analysis of Solar-Induced Chlorophyll Fluorescence, Normalized Difference Vegetation Index, and Column-Average CO2 Concentration in South-Central Brazilian Sugarcane Regions

Remote sensing has proven to be a vital tool for monitoring and forecasting the quality and yield of crops. The utilization of innovative technologies such as Solar-Induced Fluorescence (SIF) and satellite measurements of column-averaged CO2 (xCO2) can enhance these estimations. SIF is a signal emitted by crops during photosynthesis, thus indicating photosynthetic activities. The concentration of atmospheric CO2 is a critical factor in determining the efficiency of photosynthesis. The aim of this study was to investigate the correlation between satellite-derived Solar-Induced Chlorophyll Fluorescence (SIF), column-averaged CO2 (xCO2), and Normalized Difference Vegetation Index (NDVI) and their association with sugarcane yield and sugar content in the field. This study was carried out in south-central Brazil. We used four localities to represent the region: Pradópolis, Araraquara, Iracemápolis, and Quirinópolis. Data were collected from orbital systems during the period spanning from 2015 to 2016. Concurrently, monthly data regarding tons of sugarcane per hectare (TCH) and total recoverable sugars (TRS) were gathered from 24 harvest locations within the studied plots. It was observed that TRS decreased when SIF values ranged between 0.4 W m−2 sr−1 μm−1 and 0.8 W m−2 sr−1 μm−1, particularly in conjunction with NDVI values below 0.5. TRS values peaked at 15 kg t−1 with low NDVI and xCO2 values, alongside SIF values lower than 0.4 W m−2 sr−1 μm−1 and greater than 1 W m−2 sr−1 μm−1. These findings underscore the potential of integrating SIF, xCO2, and NDVI measurements in the monitoring and forecasting of yield and sugar content in sugarcane crops.

Automation and AI in Precision Agriculture: Innovations for Enhanced Crop Management and Sustainability

Precision agriculture is one of the ways to achieve food security and sustainability through better resource-use optimization and crop productivity dealing with the challenges posed by the growing population and addressing environmental concerns. The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture (PA), with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management. The accuracy of crop monitoring and health assessments has increased by 30–50 percent as a result of AI-powered solutions, which have improved resource-based decision-making. Systems for precision irrigation and fertilization have increased crop yields by 5–15 percent when using 25–40 percent less water and 30-40 percent less fertilizer, respectively. Robotic harvesters and sprayers are examples of automation technologies that have reduced labor expenses by 20–40 percent and increased operational efficiency by 35 percent. Additionally, AI-based prediction models have reduced pest damage by 20–25 percent and reached an accuracy of 85–90 percent for crop yield forecasts and pest control. Despite these developments, issues of scalability, affordability for small farms, and data privacy still exist, which can hinder technology adoption among farmers. The evaluation follows by outlining ideas for future research, such as 5G, blockchain, and AI integration with cloud and edge computing. These technologies could improve decision-making and transparency in precision agriculture by enabling real-time data transmission, secure data management, and enhanced traceability, thus addressing current limitations and fostering trust among stakeholders.

Modelling and Performance Evaluation of Machine Learning Techniques in Forecasting Cereal Yield for Shri Ganganagar Region, Rajasthan, India

One of agriculture's most difficult issues is predicting crop yield. Crop yield forecasting enables necessary decisions to be made to guarantee food security. The current study looks into the use of statistical and machine learning approaches for wheat and rice yield prediction using long-term weather and yield data of ShriGanganagar, Rajasthan, India. Weather-based models may give accurate crop production estimates, but choosing the right model for agricultural output projections can be difficult. As a result, different models were compared in this study to determine the best model for rice and wheat yield prediction including Multiple linear regression (SMLR), Artificial neural network (ANN), Least absolute shrinkage and selection operator (LASSO), Elastic net (ELNET), Ridge regression, and K-nearest neighbor (K-NN). K-NN outperformed ANN in rice crops and fared better in wheat crops based on the lowest nRMSE value. Ridge regression based on nRMSE was the next best model for Rice yield prediction in the examined region, and KNN was the second best model for Wheat crop.

Transforming Crop Management Through Advanced AI and Machine Learning: Insights into Innovative Strategies for Sustainable Agriculture

The integration of artificial intelligence (AI) and machine learning (ML) into crop management is transforming modern agriculture by enhancing efficiency, sustainability, and resilience. This review explores the multifaceted applications of AI and ML in key areas such as precision farming, pest and disease management, and harvest optimization. The use of AI-driven predictive analytics allows for more accurate forecasting of crop yields, pest outbreaks, and weather conditions, enabling farmers to make data-driven decisions that optimize resource use and reduce environmental impacts. A significant advancement is the integration of AI and ML with the Internet of Things (IoT) and autonomous farming equipment. These technologies enable real-time monitoring and precise interventions, enhancing productivity and minimizing labor costs. In crop breeding and genomics, AI accelerates the development of resilient crop varieties, which is crucial for adapting to climate change and increasing food demands. Despite the many benefits, challenges such as data quality, infrastructure limitations, and high implementation costs remain. The adoption of AI technologies is uneven, with small-scale farmers in developing regions facing barriers due to limited access to data and resources. Ethical concerns related to data privacy and the digital divide must also be addressed to ensure equitable access to these technologies. The future of AI and ML in agriculture lies in the development of more advanced predictive models, enhanced integration with the IoT, and the widespread use of autonomous farming systems.

Leveraging crop yield forecasts using satellite information for early warning in Senegal

Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20% decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (R-squared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.

Predictive Cultivation: Integrating Meteorological Data and Machine Learning for Enhanced Crop Yield Forecast

Agriculture is a key component of Telangana’s economy, and greater performance in this sector is crucial for inclusive growth. A central challenge is yielding estimation to predict crop yields before harvesting. This paper addresses this challenge with machine learning approaches includes Naive Bayes, KNN and Random Forest. The parameters considered for model testing are crop, season, rainfall and location. This paper includes a case study of Telangana with the help of Telangana weather data set to provide analysis on the key factors like overall rainfall recorded with respect to each Mandal, overall seasonal yield in selected years, seasonal yield of major crops like Bengal gram, groundnut and maize, and overall yield in two different agricultural seasons: rabi and kharif. Random forest machine learning model produces highest accuracy of 99.32% when compared with other process models.

METHODS OF FORECASTING GRAIN CROP YIELD INDICATORS TAKING INTO ACCOUNT THE INFLUENCE OF METEOROLOGICAL CONDITIONS IN THE INFORMATION-ANALYTICAL SUBSYSTEM

Forecasting crop yields is one of the key challenges for the agricultural sector, especially in the context of a changing climate and unstable weather conditions. Kazakhstan, possessing significant territories suitable for growing grain crops, faces many challenges related to the effective management of agricultural activities. In this regard, yield forecasting becomes an integral part of planning and decision-making processes in agriculture. Information and analytical subsystems that integrate yield forecasting methods allow agribusinesses to estimate future production more accurately, minimise risks associated with climate change and optimise resource use. An important component of such systems is the consideration of weather conditions, as weather factors have a direct impact on crop growth and development. The purpose of this article is to develop and evaluate modern methods of forecasting grain yields taking into account the influence of weather conditions, as well as their integration into information-analytical subsystems to improve the accuracy of agricultural forecasting. To achieve this goal, the article addresses the following tasks: to analyse existing methods of yield forecasting and identify their advantages and disadvantages, to develop forecasting models, including machine learning methods such as gradient bousting and recurrent neural networks, to validate the developed models on the basis of historical data using cross-validation methods, to evaluate the effectiveness of the proposed methods and compare them with basic models such as linear regression and simple average, to evaluate the effectiveness of the proposed methods and to compare them with the basic models such as linear regression and simple average. This article reviews modern methods of forecasting grain crop yields in Kazakhstan, as well as technologies used in information-analytical subsystems. Particular attention is paid to the analysis of the influence of meteorological conditions on yields and the development of models that take this factor into account. The presented review and research results are aimed at improving the existing approaches to the management of agricultural processes under conditions of growing uncertainty caused by climate change. The article explores an important scientific task related to the development of methods for step-by-step forecasting of agrometeorological factors and grain yields, relying on the principle of analogy.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations

Various yield forecasting methods have been reported in literature, but the benefits of assimilating seasonal climate forecasts and satellite observations for in-season yield forecasting during different growth stages have rarely been examined using machine learning. By synthesizing census yields, seasonal climate forecasts (SCF) and satellite-based gross primary production (GPP), this study develops a machine learning (i.e., Random Forest)–based in-season maize yield forecasting model for Northeast China, which produces over 40 % of China's total maize production. Based on the dynamically trained model, 11 numerical experiments are conducted to investigate the benefits of assimilating SCF and GPP relative to the experiments without using SCF and GPP for yield prediction in four forecast phases (planting–three leaves, three leaves–jointing, jointing–tasseling, tasseling–milk). Generally, our yield forecasting model exhibits a promising skill with a low bias of 4.11 %−4.97 %, when the observed climate and GPP are used as inputs. When climate forecasts are assimilated into the model by sampling historical climate, satisfactory yield forecasting can be achieved one month before harvest with a bias of 5.50 %−5.63 %. Bias-correcting climate forecast data from dynamical weather forecast models has a larger benefit for yield prediction with a lower bias of 4.77 %−5.06 %. Furthermore, we found a better benefit of assimilating SCF when compared with GPP during the first three forecast phases, although its relative importance decreases substantially towards harvest. Finally, phase-dependent maps indicating the best model are produced for each county, with historical resampling methods performing best in 40 %, 32 %, 27 %, and 21 % of counties from Phase 1 to Phase 4 and dynamical weather forecast models showing greater predictability in 54 %, 58 %, 58 %, and 52 % of counties during Phases 1–4, respectively. This study provides a useful yield forecasting framework for the breadbasket of China, which can be extended to other crops.

Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey

This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models.

Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing Data

Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing Data

Comparative analysis of leaf area index and maize yield estimation assimilating remote sensing and DSSAT crop simulation model

Maize is a global staple crop, impacting food security, economic development, and agricultural sustainability. This study investigates the integration of Sentinel-1A Synthetic Aperture Radar (SAR) data with the DSSAT CERES-Maize crop simulation model to estimate Leaf Area Index (LAI) and rabi maize yield in Belagavi district, Karnataka. Field data, including LAI, days to anthesis, silking, grain filling, and farmers' field practices, were collected for model calibration and validation, supplemented by crop-cutting experiments (CCE) to determine actual yields. The study revealed strong correlations between LAI values obtained from remote sensing (RS) and field observations, with RS-derived LAI showing an average agreement of approximately 96.07% compared to field measurements. The DSSAT model exhibited slightly better performance, averaging 97.09%. Statistical analysis for LAI showed an R² value of 0.853 for RS and 0.864 for DSSAT, indicating strong correlations with observed LAI values. For maize yield estimation, the DSSAT model demonstrated higher accuracy with an average yield of 8129 kg/ha, compared to RS-derived yield averages of 7533.9 kg/ha and CCE yield averages of 8096.6 kg/ha. The average concordance between DSSAT and CCE yields was 94.19%, while RS and CCE yields had an average concordance of 92.29%. Statistical analyses revealed coefficients of determination of 0.854 for DSSAT-CCE and 0.867 for RS-CCE comparisons. The study underscores the value of combining RS data with DSSAT for comprehensive and accurate crop yield forecasting, highlighting the potential for improved agricultural assessments and decision-making.

Crop yield estimation uncertainties at the regional scale for Saxony, Germany

AbstractIn times of climate change and global population growth, agricultural yield forecasts play an increasingly important role. For example, predicting yields as early as possible in the event of a drought is crucial for decision‐makers in politics, government, and business. The aim of this study was to provide precise yield predictions at agricultural regions as early as possible with a minimum amount of weather data. Random forest models were used for this purpose. Although more than 290,000 datasets were available for analysis, all models tended to be heavily overfitting, which can be explained by the strong fragmentation of the input data by crop, region, and prediction time. The models reacted very differently to unknown datasets. It was found that the regionally trained models achieved lower (≥10%) relative root mean square errors (RRMSEs) than the supra‐regionally trained models. Rapeseed and barley achieved good predictions. Wheat had good potential, too. Corn, potatoes, and sugar beet achieved often too high RRMSEs. The results showed that targeted model selection for each region and an extension of the training time series could enable very good regional yield forecasts for rapeseed and cereals in the future.

Field scale wheat yield prediction using ensemble machine learning techniques

Wheat is crucial for global food security and plays significant role in achieving United Nations Sustainable Development Goal 2 (Zero Hunger). In India, wheat accounts for 33.5 % of total cereal production. Accurate and cost effective yield predictions are essential for maintaining food security. Wheat yield forecasting is influenced by various factors, such as genotype and environmental conditions. Among these, the effect of morpho-physiological traits in the field is important for predicting yield but hasn't been studied much using ensemble machine learning methods. This study aims to bridge this gap by evaluating 29 morpho-physiological traits to predict site specific wheat yield. Big data framework was used to develop and refine several ensemble machine learning models based on field trial datasets. The developed models are optimized to reduce errors and prevent overfitting and underfitting, boosting their predictive precision. Each model's efficiency was evaluated using performance metrics such as mean absolute error, mean absolute deviation, root mean square error, R2, and overall accuracy. The ensemble model, which combines random forest (RF) and artificial neural networks (ANN), demonstrated better performance by achieving a mean absolute percentage error of 4.65 %, and R2 value of 98.48 % and 98.18 % accuracy on test data.Our results demonstrate that ensemble models combining RF with support vector regression (SVR) outperformed individual models such as RF, SVR, ANN, multivariate adaptive regression splines (MARS). These findings are a promising step towards future research focused on creating more advanced ensemble methods with finely tuned hyperparameters for improving the accuracy of large-scale wheat yield prediction.

Multiple yield curve modeling and forecasting using deep learning

AbstractThis manuscript introduces deep learning models that simultaneously describe the dynamics of several yield curves. We aim to learn the dependence structure among the different yield curves induced by the globalization of financial markets and exploit it to produce more accurate forecasts. By combining the self-attention mechanism and nonparametric quantile regression, our model generates both point and interval forecasts of future yields. The architecture is designed to avoid quantile crossing issues affecting multiple quantile regression models. Numerical experiments conducted on two different datasets confirm the effectiveness of our approach. Finally, we explore potential extensions and enhancements by incorporating deep ensemble methods and transfer learning mechanisms.

Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data

CONTEXTYield forecasting, the science of predicting agricultural productivity before the crop harvest occurs, helps a wide range of stakeholders make better decisions around agricultural planning. OBJECTIVEThis 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. METHODOLOGYThe 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 district. RESULTS AND CONCLUSIONSThe results of the proof-of-concept machine learning pipeline demonstrated that rice yields can be predicted with a reasonable degree of accuracy, with out-of-sample R2, MAE, and MAPE performance of up to 0.82, 0.29, and 0.16 respectively. This performance outperformed test set performance reported in related literature on rice yield modelling 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-of-concept dashboard was developed to allow users to easily explore which districts may experience a rise 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. SIGNIFICANCEThis 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 choices on irrigation scheduling, fertilizer application, and harvest planning to optimize crop output and resource use.