Machine learning reveals drivers of yield sustainability in five decades of continuous rice cropping

Vista sencilla de ítem
dc.creatorYamaguchi, Tomoaki
dc.creatorAngeles, Olivyn
dc.creatorIizumi, Toshichika
dc.creatorDobermann, Achim
dc.creatorKatsura, Keisuke
dc.creatorSaito, Kazuki
dc.date2025-11
dc.date2025-10-07T06:42:02Z
dc.date2025-10-07T06:42:02Z
dc.date.accessioned2026-06-27T04:11:42Z
dc.descriptionThe long-term sustainability of intensive rice systems under climate change is a critical challenge for global food security. Here, we use machine learning techniques to assess the impact of climate change, genotype, and nutrient management on rice yield in the world's longest-running continuous cropping experiment (LTCCE) at the International Rice Research Institute (IRRI) in the Philippines. In the experiment, three to six rice genotypes were cultivated from 1968 to 2017 in three annual cropping seasons—dry, early wet, and late wet seasons—with four nitrogen (N) fertilizer treatments. These genotypes were changed regularly to utilize the best high-yielding, disease- and insect-resistant varieties available at a given time. Our analysis showed that nitrogen application, varietal replacement, solar radiation, and seasonal temperature patterns were major determinants of yield variation. While nitrogen and solar radiation consistently improved yield irrespective of seasons, temperature effects were season-specific. In the dry season, lower temperatures during reproductive and ripening stages were beneficial. In the early wet season, yield gains were observed under higher vegetative-stage temperatures. Enhanced nitrogen mineralization and improved early rice growth may be contributing factors. The late wet season was constrained by low radiation, high disease pressure, and declining N response with prolonged varietal use. These findings demonstrate the value of combining long-term yield data with weather information to assess sustainability in intensive rice systems under increasing climatic and biotic pressures. They also illustrate the need for seasonally tailored and integrated crop, nutrient, and pest management practices, including more frequent variety replacement and rotating varieties between seasons. Breeding dry season varieties with reduced respiration losses and wet season varieties with improved tolerance to humid, low-radiation conditions can play a crucial role in enhancing seasonal adaptation and overall productivity.
dc.identifierhttps://hdl.handle.net/10568/176855
dc.identifier.urihttp://hdl.handle.net/123456789/24668
dc.languageen
dc.publisherElsevier
dc.rightsLimited Access
dc.sourceYamaguchi, Tomoaki, Olivyn Angeles, Toshichika Iizumi, Achim Dobermann, Keisuke Katsura, and Kazuki Saito. "Machine learning reveals drivers of yield sustainability in five decades of continuous rice cropping." Field Crops Research 333 (2025): 110114.
dc.subjectoryza sativa
dc.subjectintensive farming
dc.subjectclimate change
dc.subjectnitrogen fertilizers
dc.subjectnutrient management
dc.subjectgenotype-environment
dc.subjectyields
dc.subjectvarieties
dc.subjectlong-term experiments
dc.titleMachine learning reveals drivers of yield sustainability in five decades of continuous rice cropping
dc.typeJournal Article

Archivos

Colecciones

Instituto Internacional de Investigación del Arroz (IRRI)