Work

Stage‑dependent bias and practical leaf area index estimations derived from the LAI‑2200C plant canopy analyzer in soybean canopies

Year: 2026

Type: article

Abstract: Leaf area index (LAI) is fundamental for estimating canopy radiation interception and yield. Although the LAI-2200C Plant Canopy Analyzer enables non‑destructive measurements, its default assumptions are difficult to satisfy in row‑planted soybean, where canopy structure and leaf‑angle distribution change with growth stage and can introduce bias. We measured LAI at V, R2, R5, and R6 using both the LAI‑2200C and destructive sampling. Bias sources under the default setting and mitigation using Campbell’s ellipsoidal model and the clumping‑free effective LAI (Le) approach were evaluated. The ellipsoidal-model correction reduced bias during canopy formation (V–R2), whereas the Le approach offset overcorrections under strong directional heterogeneity and lowered root mean square error. Segmented regression showed that Le increased the saturation threshold, expanding the applicable LAI range. In contrast, under strong canopy aggregation (notably at R6), all corrections became unstable, indicating limits of gap‑fraction‑based estimation.

Source: Plant Production Science

DOI: 10.1080/1343943x.2026.2722022

Authors: Hiroko Yamaura, Hiroyuki Kato, Nguyen Cong Thinh, Erina Fushimi, Kensuke Kimura, Etsushi Kumagai

Institutions: National Agriculture and Food Research Organization, Western Regional Research Center, Western Region Agricultural Research Center

Language: English

Topic: Leaf Properties and Growth Measurement

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,017