Nondestructive Leaf Area Estimation for Chia.

Publikations-Art

Zeitschriftenbeitrag

Autoren

Mack, L., Capezzone, F., Munz, S., Piepho, H.P., Claupein, W., Phillips, T., Graeff-Hönninger, S.

Erscheinungsjahr

2017

Veröffentlicht in

Agronomy Journal

Band/Volume

109/5

Seite (von - bis)

1-10

Leaf area (LA) is an important agronomic trait but is diffi cult
to measure directly. It is therefore of interest to estimate LA
indirectly using easily measured correlated traits. Th e most
commonly used approach to predict LA uses the product of leaf
width (LW) and leaf length (LL) as single predictor variable.
However, this approach is insuffi cient to estimate LA of chia
(Salvia hispanica L.) because the leaves are diff erently shaped
depending on leaf size. Th e objectives of this study were to
develop a nondestructive LA estimation model for chia using
LW and LL measurements that can take diff erences in leaf
shape into account and to determine whether population and
nitrogen fertilizer level have an eff ect on the accuracy of a LA
estimation model. A total of 840 leaves were collected from
fi ve diff erent fi eld experiments in 2015 and 2016 conducted
in southwestern Germany. Th e experiments comprised eight
populations of black- and white-seeded chia (07015 ARG, 06815
BOL, 06915 ARG, G8, G7, G3, W13.1, and Sahi Alba 914)
and three nitrogen fertilizer levels (0, 20, and 40 kg N ha-1).
We used meta-regression to integrate the data accounting for
heterogeneity between experiments, populations, and nitrogen
levels. Th e proposed LA estimation model with two measured
predictor variables (LL and LW) was LA = 0.740 × LL0.866 ×
LW1.075 and provided the highest accuracy across populations
and nitrogen levels [r = 0.989; mean squared deviation (MSD) =
2.944 cm4]. Th e model LA = 1.396 × LW1.806 with only LW
was almost as accurate (r = 0.977; MSD = 5.831 cm4).