Simple models for predicting leaf area of mango (Mangifera indica L.)

ISSN: 2084-3577Journal of Biologyand Earth SciencesBIOLOGYORIGINAL ARTICLESimple models for predicting leaf area of mango(Mangifera indica L.)Maryam Ghoreishi 1 ,2 , Yaghoob Hossini 2 , Manochehr Maftoon 11Department of Agriculture, Fars Science and Research Branch, Islamic Azad University, Shiraz, Iran2Department of Soil and Water, Agricultural and Natural Resources Research Center of Hormozgan, Bandar Abass, IranABSTRACTMango (Mangifera indica L.), one of the most popular tropical fruits, is cultivated in a considerable part ofsouthern Iran. Leaf area is a valuable parameter in mango research, especially plant physiological andnutrition field. Most of available methods for estimating plant leaf area are difficult to apply, expensiveand destructive which could in turn destroy the canopy and consequently make it difficult to performfurther tests on the same plant. Therefore, a non-destructive method which is simple, inexpensive, andcould yield an accurate estimation of leaf area will be a great benefit to researchers. A regressionanalysis was performed in order to determine the relationship between the leaf area and leaf width, leaflength, dry and fresh weight. For this purpose 50 mango seedlings of local selections were randomlytook from a nursery in the Hormozgan province, and different parts of plants were separated inlaboratory. Leaf area was measured by different method included leaf area meter, planimeter, ruler(length and width) and the fresh and dry weight of leaves were also measured. The best regressionmodels were statistically selected using Determination Coefficient, Maximum Error, Model Efficiency,Root Mean Square Error and Coefficient of Residual Mass. Overall, based on regression equation, asatisfactory estimation of leaf area was obtained by measuring the non-destructive parameters, i.e.number of leaf per seedling, length of the longest and width of widest leaf (R 2 = 0.88) and alsodestructive parameters, i.e. dry weight (R 2 = 0.94) and fresh weight (R 2 = 0.94) of leaves.Key words: destructive; leaf area; mango; non-destructive; regression linear models.J Biol Earth Sci 201 2; 2(2): B45-B53Corresponding author:Maryam GhoreishiDepartment of Agriculture, Fars Science and ResearchBranch, Islamic Azad University, Shiraz, Iranand Department of Soil and Water, Agricultural andNatural Resources Research Center of Hormozgan,791 5847669, Tooloo str., Golshahr, Bandar Abass, Irane-mail: maryamgh1 967@yahoo.comOriginal Submission: 07 May 201 2; Revised Submission: 24 June 201 2; Accepted: 1 4 July 201 2Copyright © 201 2 Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.http://www.journals.tmkarpinski.com/index.php/jbes or http://jbes.strefa.ple-mail: jbes@interia.euJournal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B45

Ghoreishi et al.Simple models for predicting leaf area of mangoINTRODUCTIONThe leaf area measurement is one of the mostimportant parameter in agricultural researchespecially in plant physiology and nutrition. Thisparameter is a representative of plant growth anddevelopment. Also its relationship with theabsorption of light, respiration and photosynthesis isimportant. The leaf area index is a key structure forforest ecosystems properties and the reason is theroles that green leaves have on controlling physicaland biological processes on vegetation cover.Therefore, accurate estimation of leaf area index isnecessary for studying ecophysiology, interaction ofthe atmosphere and ecosystems and global climatechange [1 ]. Leaf area index widely used to describethe vegetation photosynthesis and respirationlevels. This index is also used extensively inecophysiology, and water balance modeling andcharacterization of vegetation-atmosphere interactions[2].Determination of the leaf area is necessary forknowing how the energy transfers and dry matteraccumulation processes in the vegetation. Leafarea is very important in the analysis of vegetationand also as a factor that makes possible todeterminate the light interception, plant growth [3].Many methods of measuring plants leaf area havebeen presented but most of them are mix of severalmeasurement models with complex and difficultmathematical equations. For example measuringleaf area by optical methods and imagespectroscopy [4] cannot be carried out everywhere.Even in methods such as using digital cameras andcalculating the surface by computer programs;although taking photos is fast and very accurateanalysis, but because of vast number of leaves thisprocess takes a long time and often equipments arevery expensive [5, 6]. Other methods are include,blue printing, photographic and planimeter that theyall need to be separated leaves from the plantswhich cause the destruction of vegetation in orderto create special problem in some studies thoseinclude plots with limited number of plants to beable to continue alternatively different tests on them[6, 7, 8].Mango (Mangifera indica L.) is one of the worldsoldest and the most popular tropical fruit, becauseof its wonderful fragrance, flavor, high nutritionalvalue and the beauty of its color variety [9, 1 0].Mango as one of the well adapted tropical fruit insouth of Iran has been introduced to this region byimporting mango seed from India to south of Iranmore than 300 years ago. Mango cultivation hasbeen gradually spread to a very narrow costalregion of Persian Gulf (geographic coordinates,from 25°, 24´ up to 28°, 57´ in North latitude andfrom 53°, 41 ´ up to 59°, 1 5´ in Eastern longitudeswith average rainfall of about 1 50 to 200 mm/year).At present, mango under grown in Iran is about4400 hectare and in Hormozgan about 2700hectare which consists of some old and traditionalorchards derived from seedlings and some newlyestablished orchard with grafted new cultivars.Mango cultivation has been considered as one ofthe most desirable potentials for renovation of theold orchard and development of rural areas insouthern provinces of Iran specially Hormozgan,Sistan and Balochestan.Mango fruit flavor depends on the balancebetween organic acids (citric acid, malice acid andascorbic acid) and sugar level solutions (sucrose,fructose and glucose) [11 ]. Reduction ratio of leavesto fruit could increase the amount of fructose inmango fruit. High value of this ratio increased therate of sucrose in the fresh and dry weight of thefruit significantly. In general, carbohydratesconstitute 60% of fruit dry weight which the mainingredients are sugar and acids. The amount ofcarbohydrates depends on the rate of the leavesphotosynthesis which it also depends on the leafarea and the leaf photosynthetic capacity [1 2].According to a recent article we are able to changethe mango flavor and the popularity of the fruit bychanging or even increasing the number of leaf tofruit ratio.Estimating the leaf area without separating theleaves of the plant provides a simple, accurate anddetailed cost method that can solve many problemsof measuring the leaf area, especially in developingcountries. Researches in this field results that areusing simple regression equations provide a modelthat includes estimations of the leaf area bymeasuring the leaf length and the width individuallyor in combination. In a research [1 3] to estimate thelevel of banana leaf area, a model containing bothparameters was considered. In both length andwidth of leaves were shown high correlationcoefficient with a banana leaf area (r = 0.98). In astudy on a cucumber plant [1 4], it was observed asignificant correlation between the length and thewidth of cucumber leaves, plant fresh weight andJournal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B46

Ghoreishi et al.Simple models for predicting leaf area of mangodry weight with leaves area. In another research[1 5] in order to measure the leaf area of 1 4 types offruit trees such as almond, citrus, olives, walnut,pistachio, etc. They reported that regressionequations can be used for estimation a branch leafarea with measuring the length of the longest leaf ofthat branch without separating it and the leavenumbers. In other studies [1 6, 1 7] observed thatusing an experimental model could help to estimatethe leaf area in the main stem of grapevine basedon measuring the number of the leaf in the mainstem and the leaf area of the tallest and thesmallest leaves. They found that the method is avalid and reliable way to estimate leaf area in thevineyards of Spain (R 2 = 0.94). Other researcherswere applied a simple regression model forestimating the leaf area in chestnut successfully[1 8]. In the present study is followed an attempt toprovide a simple model, cheap, fast and without anydestroying the leaves (separating leaves from theplant) to measure leaf area in seedling of mango.leaf as a leaf width were measured). Leaves, stemsand roots of each seedling separately weighed andthen dried at a temperature between 60 to 70°C for72 hours and at the next stage dry weight of leaves,stems and roots were measured. In this study bymeasuring some of the parameters of leaves andusing conventional regression equations, the leafarea and also the growth in other parts of the plantwere estimated. Then the estimated leaf areaplotted against the measured values and themodels were compared. Also, comparison ofquantitative models to calculate the statisticDetermination Coefficient (R 2 ), Root Mean SquareError (RMSE), Maximum Error (ME), EfficiencyFactor (EF), Coefficient of Residual Mass (CRM)and the index Mean Square Error (MSE) for each ofthe models was performed. The calculations ofeach statistic are listed as follows:MATERIALS AND METHODSThis research was done on the production ofmango seedlings in the nursery of Hormozganprovince. The first, 50 trees of mango seedlingsfrom nursery of Minab and Roudan city (the twoimportant regions of cultivate mangoes in theHormozgan province in southern Iran) wereselected. Seedlings randomly selected from localvarieties (Abassi, Shanai, Klaksorkh, Khooshei,Mikhaki, Sabzanbeh, Halili), those were grown infield conditions. Leaves were sampled during thegrowing season of plants from February to June2011 (seedlings age was between 8 to 1 2months).Then cut off the seedlings from crown andwas transferred to the laboratory. The roots of eachplant were carefully brought out of the soil. Inlaboratory different parts of plants, were separatedfrom each other and the leaves of each plant werecounted individually. Fresh weight of leaves perplant was measured. Next, the surface of eachseparated leaves were measured by the leaf areameter (Li-31 00). Also, all figures of the leaves werecopied on a paper and the leaf surface wasdetermined with Planimeter device (KP-90 N). Thenthe length and the width of every seedlings leaveswas measured consequently with a ruler (the leaflength, from leaf tip to the junction of the petioles asa leaf length and the width from the widest part of aWhich they show Ê estimated values, E imeasured values, Ē average measured values andn is the number of samples. CRM model tended toestimate higher or lower than the measured valuesindicate. If CRM value is a positive, indicates anunderestimation of the actual amount of leaf areaand if CRM is negative, model over estimates leafarea. EF parameter has been used to determine theaccuracy of data modeling. The maximum amountis the one that can be achieved when the estimateddata and the observed data both shows sameamount. The determination coefficient is theindicative extent of the relationship betweenmeasured and estimated values. The high and theless amount of R 2 showed the closer or furtherrelationship between estimated and measuredvalues of the leaf area. ME shows the maximumerror. RMSE can measure accuracy and validity oftraining and test data sets. Smaller obtained valuethe proximity of predicted data with the valuemeasured. If all the estimated and the measureddata are the same, the statistics result would beME = 0, CD = 1 , EF = 1 and the CRM = 0 [1 9, 20,21 ].Journal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B47

Ghoreishi et al.Simple models for predicting leaf area of mangoRESULTSThis study is proposing a simple model toprediction leaf area of various local mangoseedlings by measuring of length, width andnumber of leaves. The regression analysis showedthat most of the variation in leaf area values wasexplained by length and width. Then regressionmodel can be a good alternative method fordetermining leaf area instead of leaf area meterdevice (Table 1 ).In this experiment the possibility of measuringdry and fresh weight of leaf, stem and also root inmango seedlings by measuring the length and widthof leaves were studied. The equations associatedwith a statistically relevant statistics are given inTable 2.In some experience that we have to cut the plantfor some examination in lab, we can use somesimple devices that working with them are simplerthan using leaf area meter. Based on, thesedifferent regression equations were obtained byusing Planimeter and Oven devices. Some of theproposed models with statistically relevant statisticsare given in Table 3.DISCUSSIONMany researchers have been carried out toestimate leaf area through measuring leafdimensions. In general, leaf length, leaf width, orcombinations of these variables have been used asTable 1. Model of leaf area estimation of mango seedlings in field conditions without destroying the plant, along withrelevant a statistically statistics.LA: leaf area measured by leaf area meter; SL: leaf area of longest leaf; SW: leaf area of widest leaf; L: length of longestleaf; W: width of widest leaf; N: number of leaf per seedling. Note: Sequence of the models in the table above isaccording to the preference (P < 0.01 ).Table 2. Comparison of different models to estimate of leaves dry and fresh weight, fresh weight of stem and root dryweight of mango seedlings in field conditions without destroying the plant.DW: dry weight; FW: fresh weight, L: length of longest leaf; W: width of widest leaf; N: number of leaf per seedling(P < 0.01 ).Journal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B48

Ghoreishi et al.Simple models for predicting leaf area of mangoTable 3. Comparison of quantitative different models to estimate leaf area of mango seedlings in field conditions.LA: leaf area measured by leaf area meter; P: leaf area measured by planimeter; R: leaf area measured by ruler; DW:dry weight; FW: fresh weight (P < 0.01 ).parameters of leaf area models. In someresearches it is necessary to measure the leaf areaof leaves, without damaging the plant and continuetesting while the plant is performing its functions.The non-destruction of the plant (leaves) andconsecutive measurements at a desired time periodis especially important. In this direction manyresearches do modeling for estimating the leaf areaa garden and crop plants has been done without thedestruction of vegetation [7, 8, 1 3, 1 4, 1 5, 22]. Inthis part of the experiment, in order to minimize thetime, facilitate the determination and also to obtainan accurate estimation of the leaf area, wedeveloped simple methods, involving themeasurement of some parameters like the length,width and the number of leaves per plant. Based onstatistically statics, the reliable models werecompared and the best models according to tableone was found. All of the length-width models canprovide accurate estimations of mango seedlingsleaf area, but comparison between models showsthat models 1 &2 from Table 1 are preferred due tohigher R 2 , lower RMSE and higher EF, in spite ofusing more leaf dimension characteristics thanother models. Following the discussion withequations 3 and 4 were observed despite the use ofFig. 1. Relationship between actual leaf area (measured by leaf area meter devices) with leaf area model estimating (byruler) from table one (parameter from 1 to 8 corresponding models M1 to M8).Journal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B49

Ghoreishi et al.Simple models for predicting leaf area of mangoa variable measuring (only leaf length) due to lowerR 2 and higher RMSE the previous equations incomparison with these equations were preferred.After that sequence of the models (from 5 to 8) inthe Table 1 is according to statistically staticspreference. The different non-distractive model (thelinear and exponential regression) and relationshipbetween actual leaf area and leaf area measuredwith dimensions of leaves were showed in Figure 1 .Results from the present study were inaccordance with some of the previous studies onestablishing reliable equations for predicting leafarea through measuring leaf dimensions. Leaf areaestimation models in some species of fruit trees andsome other plants such as chestnut [1 8],grapevines [23], peach [24], pecan [25], bergeniapurpurascens [26], cabbage and broccoli [27],grapevine [28] and basil [29], were developed usingleaf length, width and number of leaf per shoot asperformed in this study.Further research for estimating, the fresh anddry weight of leaves by measuring the length andwidth of leaf without separating the leaf from theplant, was obtained by several equations that thebest of them was selected in based on thecomparison statistically statics between theequations. Figure 2 shows the linear relationshipbetween leaf dry and fresh weight, with the width ofthe widest leaf times the length of the longest leafleaves times the number of leaves with relativelyhigh determination coefficient (R 2 =0.86, R 2 =0.84).According to these results, leaf length, width andnumber of leaf contribute to accurately determinefresh and dry weight of mango. Result of this studyis approved by some of the previous studies oncucumber [1 4], on pistachio [30], and on cotton [31 ].To validate the developed models for theestimation of stem fresh weight and root dry weightof seedlings without damaging the plants, measuredand estimated data were compared.The freshweight of stem, with the sum of the width of thewidest leaf and the length of the longest leaf timesFig. 2. Relationship between leaf fresh weight (a) and leaf dry weights (b) with leaf area model estimating from table two(parameters 11 and 1 2).Fig. 3. Relationship between stem fresh weight (a), root dry weight (b) with leaf area model estimating from table two(parameters 9 and 1 0).Journal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B50

Ghoreishi et al.Simple models for predicting leaf area of mangothe number of leaves show a significant linearregression relationship (R 2 = 0.72). Root dry weightwith the square of the width of the widest times thenumber of leaves show relatively a gooddetermination coefficient (R 2 = 0.65). Linearrelationship can be seen in Figure 3.However high determination coefficient forestimating the root dry weight was not obtained inthis model, but due to the importance of knowingthe amount of root growth in physiological andnutritional studies, and considering the difficulties indirectly measuring the root, this model can be agood guide to solve this problem. Results obtainedfrom this research are consistent with the researchresults conducted on cucumber [1 4].As can be seen in rows 1 3 and 1 4 in Table 3,measuring the leaf area with ruler and planimeterare beneficial methods for determining the leaf areaof mango leaves, according to the determinationcoefficient of regression models (R 2 = 0.99). Otherstatistics in rows 1 3 and 1 4 in Table 3 are alsoconfirmed this. Linear relationship with the leaf areaand two methods (planimeter and ruler) is shown inFigure 4.Each of the recommended methods has someadvantages compare to the method using a leafarea meter. Planimeter device, is used in theplanimeter method, in comparison with the leaf areameter is much cheaper and more available, due toits light weight, and being small and portable todifferent locations. Since the leaf area measured bya planimeter had an extremely high correlation withthe measurement using a leaf area meter it wasconsidered a suitable alternative device. Anotherway is measuring the length and the width of theseedling leaves with a ruler. Although it takes a lotof time to measure leaf area but the simplicity andextremely low cost compared to the other methodsand considering that the amount of leaf areameasured with this method strongly agreed with theactual amount of the leaf area, makes it a good wayto determine the leaf area. In particular, it allowsmeasuring without separating the leaves of theseedlings and therefore not destroying it during themeasuring procedure.Other possible proposed methods of predictingthe leaf area of mango leaves are measurements offresh and dry weight of leaves and then using themFig. 4. Relationship between leaf area with leaf area meter and digital planimeter (a) and ruler (b).Fig. 5. Relationship between leaf area with leaf area meter with fresh weight (a) and dry weight (b) of mango leaves.Journal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B51

Ghoreishi et al.Simple models for predicting leaf area of mangoin the regression models, respectively, in rows 1 5and 1 6 are given in Table 3, could be obtainedreasonably estimated from the leaf area due to highR 2 and low RMSE, these models were validatedwith other research on basil [29], and on peanut[32]. Linear relationship between the leaf area andeach of the methods of fresh weight, dry weight isshown in Figure 5.These two methods are much simpler and fasterthan methods of planimeter and ruler, while theother two methods require measurement of all theleaves separately. However the disadvantage isthat, unlike the method of measuring with a ruler(measuring without separating), the leaves of theplant could be separated. As mentioned, thecomparison between the two methods of freshweight and dry weight shows despite thedetermination coefficient is almost the same, usingthe fresh weight method is preferred, first, becauseit is time saving (no need for spending time in theoven for drying leaves) and secondly, as a fresh leaftissue is required in many experiments (includingdetermination of chlorophyll, sugars, hormones,antioxidants, etc). In general, these methods areusually used in situations where the researchershave to do chemical tests on the elements of plantand the destruction of vegetation is not consideredin research.CONCLUSIONIn this case study, the simple regression modelswere obtained to estimate the leaf area of mangothat can be used with high percentage confidence inthe physiological and nutritional studies. The resultsindicate that the leaf area of a mango seedling withhigh speed and accuracy can be achieved bymeasuring the length of the longest leaf, width ofthe widest leaf and also number of leaf withoutusing expensive equipments. Models were timesaving and easily predicted in the field conditions. Apoint to note is the estimation method of the leafarea without destruction; it makes possible tomeasure of leaf area in reload period of the plantgrowth. Thus this model can be convenient andquick alternative, especially at places where there isno access to modern equipment or other devices formeasuring the leaf area.ACKNOWLEDGEMENTSGratitude is expressed to Ms. Ghanizadeh, Ms.Armat, and Mr. Nezhadi for technical assistance.TRANSPARENCY DECLARATIONThe authors declare no conflicts of interest.REFERENCES1 .2.3.4.5.6.7.8.9.1 0.11 .Chen JM, Rich PM, Gower ST, Norman JM, PlummerS. Leaf area index of boreal forests: theory,techniques, and measurements. J Geophys Res.1 997; 1 02(D24): 29429-29443.Rich PM, Chen J, Sulatycki SJ, Vashisht R,Wachspress WS. 1 995. Calculation of leaf area indexand other canopy indices from gap fraction: a manualfor the LAICALC software. Kansas Applied RemoteSensing Program Open File Report.Bhatt M, Chanda SV. Prediction of leaf area inPhaseolus vulgaris by non-destructive method. BulgJ Plant Physiol. 2003; 29(1 -2): 96-1 00.Gosa AG, Schaepman-Strub G, Kooistra L,Schaepman M, Pellikka P. 2007. Estimation of leafarea index using optical field intriments and imagingspectroscopy. Proceeding 5th Earsel Workshop onImaging Spectroscopy. Bruges, Belgium.Bignami C, Rossini F. Image analysis estimation ofleaf area index and plant size of young hazelnutplants. J Horticult Sci. 1 996; 71 : 11 3-1 21 .Lu HY, Lu CT, Wei ML, Chan LF. Comparison ofdifferent models for nondestructive leaf areaestimation in taro. Agron J. 2004; 96: 448-453.Cristofori V, Rouphael Y, Gyves EM, Bignami C. Asimple model for estimating leaf area of hazelnutfrom linear measurements. Scient Horticult. 2007;11 3: 221 -225.Ugese FD, Baiyeri KP, Mbah BN. Leaf areadetermination of shea butter tree (Vitellaria paradoxaC. F. Gaerts). Int Agrophys. 2008; 22: 1 67-1 70.Tahir FM, Ibrahim M, Hamid K. Seasonal variation innutrient concentration of bearing and non-bearingterminals in mango (Mangifera indica L.). Asian JPlant Sci. 2003; 2(1 ): 11 3-11 5.Shafique MZ, Ibrahim M, Helali MOH, Biswas SK.Studies on the physiological composition of differentmango cultivars at various maturity levels.Bangladesh J Sci Ind Res. 2006; 41 (1 -2): 1 01 -1 08.Medlicott AP, Thompson AK. Analysis of sugars andorganic acids in ripening mango fruits (Mangiferaindica L. var Keitt) by high performance liquidchromatography. J Sci Food Agricult. 1 985; 36: 561 -566.Journal of Biology and Earth Sciences, 201 2, Vol 2, Issue 2, B45-B53B52

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