首页|Hydraulic traits predict stem growth across Hevea brasiliensis clones in a Malaysian climatically marginal area
Hydraulic traits predict stem growth across Hevea brasiliensis clones in a Malaysian climatically marginal area
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NSTL
Elsevier
? 2021Competition for land resources is forcing rubber (Hevea brasiliensis Müll. Arg.) production into more agroclimatically vulnerable zones, which are more likely to be affected by drought. It is therefore of interest to determine whether there are particularly drought resistant rubber genotypes. Established plant drought-resistance indicators include xylem resistance to embolism under water stress (P50, water potential at which 50% xylem conductivity is lost), and hydraulic safety margins (HSM50), defined as the difference between P50 and minimum leaf water potential (Pmin) under driest conditions in the year. We report here on measurements of in-situ growth performance of nine mature rubber clones at an agro-climatically marginal site in North-Western Malaysia and their hydraulic, leaf and stem traits to investigate inter-clonal variation in drought resistance and growth rate. We find that P50 varies substantially across clones, between ?3.05 and ?1.37 MPa, while HSM50 varied within the range of ?0.11 MPa to 1.37 MPa. Similar to what has been reported across species, we find a growth-hydraulic safety trade-off between rubber clones with faster growth rates associated with a lower HSM50 and less negative P50, with one exception. Based on hierarchic linear regression we find that almost all of the best growth models include hydraulic traits, besides morphological traits, indicating that hydraulic traits are important to predict growth accurately. Furthermore, rubber genotypes with high growth rate and low hydraulic safety margin (HSM50) were associated with lower wood density, higher leaf to sapwood area and larger leaf area. Overall while there are clones that are more drought resistant and are thus suited for plantation in marginal areas, they tend to be less productive.
NSTL
Elsevier
