Rice Yield Drivers Shift Under Organic and Conventional Management
The yield gap in rice does not behave as a single problem with a single fix. In this two-season field study, 80 genetically diverse genotypes — 62 landraces and 18 modern varieties — were grown under both organic and conventional production systems, and the traits linked to grain yield shifted depending on the management regime. The work was designed to do more than rank cultivars. It aimed to identify which plant features mattered most when nutrient supply, soil biology, and crop management changed at the field level.
Field testing took place during the Kharif seasons of 2023 and 2024 at two nearby sites in Vellore, India, one maintained under certified organic management for eight consecutive years and the other under conventional production. The two fields were close enough to share the same agro-climatic region, which strengthened the comparison by reducing confounding from location. Researchers used an alpha lattice design with three replications and then applied pooled mixed-model ANOVA, genetic variability analyses, correlation and path coefficient analyses, and principal component analysis. The design created a broad picture of how genotype, environment, and management interacted across traits tied to yield.
A consistent pattern emerged: genotype mattered, but so did the production system.
Trait Associations Were Not the Same in Organic and Conventional Plots
The study found significant genotypic variation and clear differences in trait expression between the two systems. Under conventional management, flag leaf breadth, number of productive tillers per plant, hundred seed weight, and flag leaf length stood out as the most prominent traits associated with grain yield. These variables also showed strong positive direct effects in path analysis, suggesting that they are not merely correlated with yield but help shape it directly in high-input conditions.
The organic system told a different story. There, leaf area, number of productive tillers per plant, hundred seed weight, plant height, and days to 50% flowering emerged as the key traits with positive direct effects. The shift matters because it suggests that organic rice selection cannot rely on the same trait priorities used in conventional breeding programs. Nutrient release from organic amendments occurs more gradually, and plants appear to respond through a different balance of canopy development, developmental timing, and tiller productivity.
The data also reinforce a familiar field truth: the same genotype can perform well in one system and lose ground in another.
Landraces Kept Pace With Modern Varieties in the Organic System
Genotypic best linear unbiased prediction, or BLUP, provided the ranking framework for yield performance across both systems. Under organic management, Ponmani samba (G40), Varakkal (G60), and Sengalapattu sirumani (G49) produced the highest yield BLUP estimates. Under conventional management, Mani samba (G29), TKM1 (G80), and Bavani (G6) ranked highest.
A separate group of genotypes showed consistency across both environments. Milagu samba (G31), Polinel (G39), and Ponmani samba (G40) remained among the strongest performers under both production systems, marking them as especially useful breeding materials. In practical terms, that kind of stability is valuable because it identifies genetic resources that do not depend on a single management package to express yield potential. For organic breeding, especially, stable performance across contrasting systems is often more informative than peak performance in one environment alone.
Why the Same Yield Trait Does Not Mean the Same Biology
Grain yield is a composite endpoint, and the study makes clear that its component traits do not contribute equally in every setting. In conventional systems, the dominance of leaf size traits and productive tiller number points to a plant architecture that capitalizes on readily available inputs. In organic systems, the stronger role of leaf area, plant height, and flowering timing suggests a different physiological balance, one that may be linked to nutrient uptake efficiency, prolonged resource capture, and developmental coordination under slower nutrient release.
Correlation and path analysis were especially useful here because they separated simple association from direct contribution. A trait can correlate with yield without driving it, but path analysis helps identify which features exert the strongest direct effects. In this dataset, the production system itself altered those relationships. The result is not just an inventory of traits; it is a reminder that selection criteria should follow the farming context, not the other way around.
Breeding for Organic Rice Cannot Borrow Uncritically From Conventional Selection
The practical implication is straightforward. Rice breeding for organic production needs its own selection logic. Modern high-yielding varieties have dominated much of the cultivated area under organic conditions, yet they were usually bred under conventional, high-input environments. That mismatch can leave yield gains on the table when nutrients are released more slowly or when plant performance depends on traits that conventional selection has not prioritized.
The landrace material in this study is particularly important because traditional rice populations often carry local adaptation, stress tolerance, and nutritional traits that are not captured by standard yield pipelines. The top-performing landraces in this study — especially Ponmani samba, Varakkal, Sengalapattu sirumani, Milagu samba, and Polinel — provide concrete candidates for further evaluation. Their performance under organic conditions gives breeders a short list of genotypes worth advancing into more detailed multi-location and multi-season testing.
The multivariate analyses also support that next step. Principal component analysis helped separate major sources of variation among genotypes, while the BLUP rankings identified stable yield performers rather than one-season standouts. Together, these methods point toward selection strategies that are more likely to hold up across management systems. For organic rice improvement, the message is less about finding a universal winner and more about matching genotype to production context with greater precision.
The study leaves one central task for follow-up work: verifying whether these trait associations and genotype rankings persist across more environments, more seasons, and broader organic management conditions before they are used more widely in breeding decisions.