Rice feeds more than half of humanity, yet the world’s paddies face a tightening squeeze: demand is projected to reach 650 million tonnes by 2050 while global rice yields are climbing at only about 1.0 percent per year, far short of the 2.4 percent annual growth needed to double production by mid-century. A new two-year field study from the ICAR-Indian Agricultural Research Institute in New Delhi now offers a data-driven way forward, combining machine learning with detailed agronomic measurements to predict rice grain yield with remarkable accuracy and to reveal a surprising sweet spot where cutting nitrogen fertilizer by a quarter does not cost a single tonne of grain.
The research, published in the Journal of Agriculture and Food Research, grew the semi-dwarf Basmati variety PB 1692 during the kharif seasons of 2022 and 2023 in a split-plot experiment. Five nitrogen regimes were tested against four zinc fertilization strategies, from conventional prilled urea at the full recommended dose of 130 kilograms of nitrogen per hectare to reduced doses supplemented with foliar sprays of liquid nano-urea, nano-zinc oxide, and even a cyanobacterial formulation based on Anabaena torulosa. From 300 plot-level observations, the team compiled 22 variables spanning plant height, tiller density, dry matter production, panicle characteristics, and nitrogen and zinc concentrations and uptake in straw, hull, bran, and white rice kernels.
Those variables became the training ground for five machine learning algorithms: multiple linear regression, support vector regression, k-nearest neighbors, gradient boosting, and random forest. The researchers applied recursive feature elimination with 10-fold cross-validation to select the most informative predictors and used exhaustive grid-search optimization to tune the random forest’s hyperparameters, fixing the number of trees at 500 after confirming error convergence. A strict bias-variance criterion, requiring the difference in R-squared between training and cross-validation to stay below 10 percent, guarded against overfitting, and final performance was confirmed on a held-out test set using Lin’s concordance correlation coefficient.
The random forest emerged as the clear champion. It achieved R-squared values of 0.97 in 2022 and 0.96 in 2023, with root mean square errors of just 0.08 tonnes per hectare and mean absolute errors of 0.063, outperforming support vector regression, which averaged an R-squared of 0.92, and leaving k-nearest neighbors, the weakest performer at R-squared values between 0.73 and 0.80, far behind. Taylor diagrams, which simultaneously display correlation, standard deviation, and error, visually confirmed the random forest’s dominance in both training and prediction stages across both seasons.
More revealing than raw accuracy was what the model considered important. Using two feature-importance metrics, the percentage increase in mean squared error and the increase in node purity, the random forest ranked total nitrogen uptake, grain nitrogen concentration, dry matter production, and total zinc uptake as the dominant drivers of yield. Plant height and tiller counts, traits farmers and agronomists often watch closely, carried relatively little predictive weight. The message is biologically coherent: fertilizer application alone guarantees nothing unless the crop actually captures, assimilates, and remobilizes nitrogen into grain, processes that depend on root architecture, photosynthetic capacity, and source-sink coordination during grain filling.
The field results reinforced that insight. The full recommended dose of 130 kilograms of nitrogen per hectare produced the highest grain yields, 4.79 and 4.52 tonnes per hectare in 2022 and 2023, gains of 25.4 and 28.0 percent over the unfertilized control, along with nitrogen uptake of roughly 120 kilograms per hectare and zinc uptake about 35 percent higher than in unfertilized plots. But the treatment combining 97.5 kilograms of soil nitrogen with two foliar sprays of nano-urea produced statistically equivalent yields, demonstrating that a 25 percent reduction in mineral nitrogen input could maintain productivity when paired with the nanoscale foliar supplement.
Zinc fertilization delivered its own measurable benefits. Foliar sprays of 0.1 percent nano-zinc oxide produced the strongest yield attributes, raising panicle weight by 9.6 percent and grains per panicle by 4.1 percent on a two-year mean basis compared with no zinc, and adding roughly 0.2 tonnes of grain per hectare. Zinc uptake reached nearly 600 grams per hectare under the nano-zinc treatment, a 10.7 percent increase over the control. Because zinc deficiency remains a widespread human nutritional problem, agronomic biofortification of rice grain through such foliar strategies carries significance well beyond the yield column.
The climate accounting added a compelling twist. Using the CCAFS Mitigation Option Tool, the researchers estimated greenhouse gas emissions in carbon dioxide equivalents, incorporating both fertilizer production and field-induced emissions. The 97.5-kilogram nitrogen plus nano-urea treatment cut absolute emissions by 188.9 kilograms of carbon dioxide equivalent per hectare, a 6.55 percent reduction, and lowered emission intensity by 3.22 to 4.62 percent relative to the full recommended dose, all without a yield penalty. Deeper cuts to 65 kilograms of nitrogen, whether with nano-urea or conventional urea, saved more emissions, 377.2 and 323.5 kilograms per hectare respectively, but at a significant cost in grain, defining the agronomic boundary of an acceptable trade-off. Strikingly, the unfertilized control had the worst emission intensity per kilogram of grain, showing that yield and climate goals are inseparable in irrigated rice.
The study’s authors are candid about limitations. Weather variables were excluded because the two experimental seasons were climatically stable, yet weather remains a major determinant of crop performance, and future models should incorporate it. The work spanned only two years and a single rice variety, and several of the most powerful predictors, including total nutrient uptake and biomass, require destructive sampling and laboratory analysis that are impractical for routine farm use. The researchers argue that the next step is to build simplified, transferable frameworks using readily available inputs such as weather, soil data, fertilizer records, and remotely sensed vegetation indices, potentially augmented by drone observations and process-based crop models.
Even with those caveats, the study marks a meaningful advance in precision agriculture. Most machine learning yield studies lean on satellite or meteorological data; this one integrated field-measured agronomic, physiological, and nutrient variables within a single interpretable framework, and the model’s importance rankings aligned with established crop physiology rather than statistical artifacts. For a crop on which billions of people depend, the combination of a random forest that explains 96 to 97 percent of yield variability and a fertilizer strategy that trims nitrogen and emissions without sacrificing harvest offers a rare, quantified win for both food security and the climate.
Subject of Research: Machine learning prediction of rice grain yield and yield-emission trade-offs under nitrogen and zinc fertilization in a two-year field experiment
Article Title: Rice yield prediction using machine learning models and yield-emission trade-offs from field experiment
Article References: Reddy, K. S., Shivay, Y. S., Prasanna, R., Kumar, D., Peramaiyan, P., Mandi, S., Nayak, S., Baral, K., Alekhya, G., Reddy, K. S., & Borate, R. B. (2026). Rice yield prediction using machine learning models and yield-emission trade-offs from field experiment. Journal of Agriculture and Food Research, 31, Article 103309. https://doi.org/10.1016/j.jafr.2026.103309
Image Credits: AI Generated
DOI: 10.1016/j.jafr.2026.103309
Keywords: rice, machine learning, random forest, nitrogen uptake, nano-urea, zinc biofortification, greenhouse gas emissions, yield prediction, precision agriculture, nutrient use efficiency, Basmati rice, sustainable farming
Cite Scienmag News
APA
MLA
Chicago
Alan Morgan. (September 30, 2026). Machine Learning Pinpoints Nitrogen Uptake as Key to Predicting Rice Yields and Cutting Emissions. Scienmag. https://scienmag.com/machine-learning-pinpoints-nitrogen-uptake-as-key-to-predicting-rice-yields-and-cutting-emissions/
Alan Morgan. “Machine Learning Pinpoints Nitrogen Uptake as Key to Predicting Rice Yields and Cutting Emissions.” Scienmag, 30 September 2026, https://scienmag.com/machine-learning-pinpoints-nitrogen-uptake-as-key-to-predicting-rice-yields-and-cutting-emissions/. Accessed 30 September 2026.
Alan Morgan. “Machine Learning Pinpoints Nitrogen Uptake as Key to Predicting Rice Yields and Cutting Emissions.” Scienmag. September 30, 2026. https://scienmag.com/machine-learning-pinpoints-nitrogen-uptake-as-key-to-predicting-rice-yields-and-cutting-emissions/
Copy citation
Download RIS
Tags: agronomic data analysisBasmati ricecrop yield modelingfield experiment for rice cultivationgreenhouse gas emissionsimpact of micro-nutrients on rice yieldsMachine learningmachine learning in agriculturenano-fertilizer applicationsnano-ureanitrogen fertilizer optimizationnitrogen management strategiesnitrogen uptakenutrient use efficiencyprecision agricultureprecision agriculture in rice farmingRandom Forestreducing rice emissionsricerice yield predictionsustainable farmingsustainable rice productionyield predictionzinc biofortification
