AI Summary & Key Takeaways
AI InsightWater and light use efficiencies werequantified and predicted through the best-fit (non-) linear models.
📌 3 Key Takeaways
Abstract
This experimental study was carried out using the 'Camarosa' cultivar strawberry plants grown in pots in greenhouse conditions. One control andtwo drought levels were created by bringing the existing soil water content ofthe pot to the field capacity ($I_100$-control) and using its 66% ($I_66$-mild droughtstress) and 33% ($I_33$-severe drought stress) in irrigation. The experimentaldesign of the randomized complete blocks design was applied in fourreplicates with 10 pots per replicate amounting to a total of 120 pots. In orderto determine the plant response to the generated stress levels, stomatalconductivity (Sc, mmol $m^2$ $s^{-1}$ ), total chlorophyll content (SPAD, μmol $m^{-2}$ $s^{-1}$ ),chlorophyll concentration (CC, mg $g^{-1}$ ), leaf surface temperature (LST, °C),photosynthetic quantum yield (Qy, %), photosynthetically active radiation(PAR, W $m^{-2}$ ), leaf water content (LWC, %), yield (g $pot^{-1}$ ), leaf area (LA, $cm^2$ ),leaf number (LN), and crop water use (ET) were measured in three plants pereach replicate. 1.89, 3.62, and 5.82 L $pot^{-1}$ were applied to $I_33$, $I_66$, and $I_100$ asirrigation water, while 2.59, 3.92, and 5.59 L $pot^{-1}$ were crop water used fromthem, respectively. Average strawberry yield varied between 80 and400 g $pot^{-1}$ . The increased drought stress decreased Sc, SPAD, CC, Qy, PAR,LWC, LA, and LN but increased LST. All the measured variables hadsignificant relationships with irrigation water and crop water use. Yield had alinear relationship with LST and LN and a polynomial relationship with Sc,SPAD, CC, Qy, PAR, LWC, and LA. Water and light use efficiencies werequantified and predicted through the best-fit (non-) linear models.