7/27/2019 Modelling sap flow of young apple rootstocks under various climates

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Agricultural University o Tirana, Faculty o Agriculture & Environment,Department of Horticulture

Address: Koder-Kamez, Tirana, Albania www.ubt.edu.al

Modelling sap ow of young apple rootstocks under variousclimates

E. Kullaj1, L. Lepaja, V. Avdiu2 and F. Thomaj1Dep. Horticulture, Faculty of Agriculture and Environment, Agricultural University of Tirana, Kodr-Kamz, 1010, Tirana, Albania Tel: +355684096186 Email: ekullaj@ubt.edu.al2Department of Fruit Trees and Viticulture, Faculty of Agriculture, University of Pristina, Rr,,Bill Clinton p.n 10000 Prishtine, Kosove

IntroductionTree fruit nursery production is becoming a highly specialised horticultural sector ap-plying a high level of technology, automation and even modelling. Providing adequatewater supply to rootstocks and young plantlets is an essential requisite but often, toavoid the risk of dryness, nurseries apply excess irrigation rates, with both physiologi-cal, quality and ecological costs. The present study uses experimental data to modelthe uctuation in sap ow from apple rootstocks in two different climatic areas, a rathercontinental climate in eastern Albania and a Mediterranean one in western coast of Al-

bania. The results are preliminary and the serve as a basis for the modeling work.

Material and MethodsOne - year old apple rootstocks of Pajam 1 Lancep were used as replicates based onstem diameter and other biometric measurements, vigour, number of lateral shoots, leafarea, etc. Homogeneity of these trees was evaluated under another study (Domi et al.,2013). SFwas measured using sap ow sensors EMS 62 (EMS Brno), based on SHB (stemheat balance) method. Sensors were installed on the stem (12 mm thick) on rootstocksin both regions, Korca (Continental - CONTI.) and Lushnja (Mediterranean - MEDIT). Themeasuring interval was every minute with 1 s warm-up and storing interval every 15minutes during July 2012. Infrared radiometers (Apogee SI 100) continuously measuredTc respectively at these three canopy levels. A portable meteorological station MinikinRTHi (EMS Brno, CZ) measured the Rs, Ta and RH. The closest state meteorological sta-tion measured windspeed, rainfalls and ET

0. VPD was calculated from vapour pressure

and relative humidity.

Experimental design and statistical analysisThe design of the experiment was completely randomized with four replications, eachreplication consisting of three adjacent rows of ve plants. Measurements were taken inthe inner plant of the central row of each replicate, the other plants serving as borders.All the measurements were taken in the same plant in each replicate. Values for eachday and replicate were averaged before the mean and the standard error were calculat-ed. To model the relationship between the series of predictor/explanatory variables (Rs,Ta, Tc, VPD and ET

0) and the response variable (SF), regressions of various orders were

computed using R statistical software (The R Foundation for Statistical Computing). Re-

gression lines were compared using various statistical techniques from R packages.

Results and DiscussionAll the parameters concerning the evaporative demand where relatively constant dur-ing the measurement period with an obvious daily uctuation trend (Figure 1a,b,c,d).Intercorrelations shown in Figure 2 using a correlogram indicate a stronger correlationofSF

CONTIwith ET

0and Rs (0.96 and 0.94 respectively) and less with VPD and Tc (0.89 and

086 respectively) whilst for SFMEDIT a higher correlation with VPD and ET0 (both 0.95) andless with Rs and Tc (both 0.92).

In general, increases in predictor variables were associated with increases in SF. In thecase of Mediterranean climate (Lushnja), climatic parameters did reach threshold values,after which SF levelled off, thus, departing from linearity. Residuals versus Fitted graph(not shown) indicated evidence of curved relationship. Thus, best-t curve using poly-nomial regression, a second-order (quadratic) and third-order (cubic) yielded equationswith a higher determination coefcient. Quite the reverse, in the case of Continentalclimate (Korca), since climatic parameters did not reach threshold values, ANCOVA be-tween the three models did not yielded signicant differences. The study conrmed thatTa (Hateld and Fuchs, 1990; Ortuo et al., 2006) and Tc are not an accurate indicators

of the evaporative demand of the atmosphere.

ConclusionsResults indicate that modelling of sap ow by using best-t regression approach can beused as a gross estimate of daily rates of sap ow, even though there was a certain scat-tering in the relations between the plant-based measurements and the environmentalvariables. The regression analysis indicated that the highest coefcients of determina-

tion were obtained for the regressions of SFCONTI against ET0 and SFMEDIT against VPD.These estimates can be used in automatic irrigation scheduling in apple rootstocks fordifferent climates.

ReferencesDomi et al. (2012) Xylem dynamics of different rootstock/scion combinations of apple under a hot, semi-arid Mediterrane

an climate. Seventh Int. Symp.on Irrigation of Horticulture Crops, Geisenheim, Germany. Acta Horticulturae (in press)Hateld, J.L. and M.F. Fuchs (1990) Evapotranspiration models. In Management of Farm Irrigation Systems. Eds. G.J. Hoff-

man., T A Howell and K H Solomon. pp. 3360. ASAE Monograph, St. Joseph.Ortuo et al. (2006) Relationships between climatic variables and sap ow, stem water potential and maximum daily trunkshrink age in lemon trees. Plant and Soil, 279(1-2), pp. 229-242

Fig. 1. Dynamics of (A) global radiation Rs, (B) air temperature Ta, (C) vapour pressure decitVPD and (D) reference evapotranspiration ET

0during the measurement period (June - July) in

the area of Korca and Lushnja

Fig. 4. Histogram of the studentized residuals of the models in Table 1 for each climaticarea. The graphs show a superimposed normal curve, kernel density curve, and rug plot. Theerrors follow a normal distribution quite well, with the exception of a large outlier.

Poster presented at the9th International Workshop on Sap Flow

Ghent, Belgium 4 - 7 June 2013 www.sapfowworkshop.ino

A B

C D

Fig. 2. Correlogram showing intercorrelations between sap ow (SF) and environmental pa-

rameters (Rs, Tc, VPD, ET0). The lower triangle contains smoothed best t lines and condenceellipses, and the upper triangle contains scatter plots. The diagonal panel contains minimumand maximum values. Rows and columns have been reordered using principal componentsanalysis.

Fig. 3. Histogram of the studentized residuals of the models in Table 1 for each climaticarea. The graphs show a superimposed normal curve, kernel density curve, and rug plot. Theerrors follow a normal distribution quite well, with the exception of a large outlier.