This article presents a systematic review of the ecophysiological mechanisms underpinning the essential role of plant-growth-promoting microorganisms (PGPMs) in improving rice yield and quality. The scientific literature is thoroughly reviewed, highlighting how PGPMs positively influence the growth, development, and health of rice plants. Key aspects, such as nitrogen fixation, nutrient solubilization, hormone production, and disease resistance induction, are emphasized. Additionally, technological advancements related to PGPM use are analyzed, including the identification of effective strains, the formulation of enhanced biofertilizers, and genetic engineering. The article concludes that PGPMs represent a promising tool with which to boost the sustainability and productivity of rice cultivation, providing a robust foundation for future research and practical applications in a field crucial to global food security.

Climate change is projected to increase global temperatures and alter rainfall patterns. In Peru, these changes could adversely affect the central basin of the Huallaga River by increasing pest and disease incidence, evapotranspiration, and water consumption. This basin is one of the country’s main rice-producing regions, where the crop is traditionally cultivated using inefficient practices, such as continuous flood irrigation. This study evaluated the effects of different irrigation management strategies on the growth and yield of rice (Oryza sativa var. INIA 516 LM1-La Unión 23), the water footprint as an indicator of water use efficiency, and the incidence of pests and diseases associated with irrigation regimes. Three irrigation treatments were implemented: Traditional flooding T1 (maintenance of a 0.15 m water layer with replenishment every 4 days), Optimized flooding T2 (replenishment every 7 days), and Intermittent rainfed irrigation T3 (replenishment every 14 days). Although no significant differences were observed in biometric parameters, yield, or pest and disease incidence, a trend of decreasing yield with longer irrigation intervals was noted: traditional flooding (7.91 t∙ha⁻¹) > reduced flooding (7.82 t∙ha⁻¹) > intermittent rainfed (7.14 t∙ha⁻¹). The incidence of white leaf virus and Burkholderia glumae was highest in the intermittent rainfed treatment, followed by optimized flooding, with the lowest incidence in traditional flooding. Yield reduction and the use of rainwater to cover water requirements resulted in a lower total water footprint for traditional flooding (834.0 m³∙t⁻¹), followed by optimized flooding (843.6 m³∙t⁻¹) and intermittent rainfed (923.9 m³∙t⁻¹). This reflects an improvement in rainwater use efficiency. The findings suggest intermittent rainfed irrigation enhances water use efficiency without significantly compromising rice yield or increasing disease incidence in rice var. INIA 516 LM1-La Unión 23 in the central basin of the Huallaga River.

Rice is a globally important crop and a staple in the diet of a large part of the world’s population. This underscores the need for hybridization and improvement of rice genotypes to meet food demand in an environmentally sustainable manner. Geographic Information Systems (GIS) have proven to be valuable tools for the morphometric phenotyping of different genotypes. In this study, seven different rice genotypes were evaluated with the objective of selecting those with high yield. Multispectral imagery was used to develop prediction models based on supervised learning algorithms, including Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Elastic Net (EN), and Neural Networks (NN). The variables studied were plant height, number of panicles, number of tillers, and yield. The results showed the following performances: R² = 0.44 for plant height using Random Forest, R² = 0.92 for number of panicles with Neural Networks, R² = 0.44 for number of tillers with SVM, and R² = 0.31 for yield with SVM. This technology significantly supports traditional selection methodologies for hybridization and improvement by providing a spatial approach that enhances and facilitates selection criteria