Leaf area, chlorophyll content, and root dry mass in oil palms (Elaeis guineensis Jacq.) affected by the plumero disorder Received for publication: 23 February, 2020. Accepted for publication: 9 November, 2020. Doi: 10.15446/agron.colomb.v38n3.85309 1 Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Bogota (Colombia). 2 Facultad de Ciencias Agropecuarias, Universidad Nacional de Colombia, Palmira (Colombia). 3 Plantaciones Unipalma de los Llanos S.A., Cumaral, Meta (Colombia). * Corresponding author: msespanag@unal.edu.co Agronomía Colombiana 38(3), 335-341, 2020 ABSTRACT RESUMEN The plumero disorder in oil palm is characterized by an abnormality in the development of the leaf area, yellowing of young leaves, and longitudinal chlorotic strips parallel to the central rib. In this research, the leaf area of leaf 17, the specific leaf area, chlorophyll contents, and root dry mass were evaluated in an oil palm (Elaeis guineensis Jacq.) plantation on the northern coast of Colombia to characterize the morphophysiological damage and quantify the sever- ity of the disorder. For the statistical analysis, an ordinal regression model and analysis of variance tests were per- formed. The results indicated that the palm reduces its leaf area before the disorder is visually evident. Leaves became thicker and lower in chlorophyll content. There was also an increase in the tertiary and quaternary root dry mass in the initial grades. This variable decreased in the more severe grades of this disorder. El disturbio del plumero en palma de aceite se caracteriza por una anormalidad en el desarrollo del área foliar, el amarillamiento de las hojas jóvenes y el rayado clorótico longitudinal paralelo a la nervadura central. En esta investigación se determinó el área foliar de la hoja 17, el área foliar específica, los contenidos de clorofilas y la masa seca de raíces en una plantación de palma de aceite (Elaeis guineensis Jacq.) en la costa norte de Colombia con el objetivo de caracterizar los daños morfofisiológicos y cuantificar la severidad del disturbio. Para el análisis estadístico se realizó un modelo de regresión ordinal y pruebas de análisis de varianzas. Los resul- tados indicaron que la palma reduce su área foliar antes de que el disturbio sea evidente a nivel visual. Las hojas se vuelven más gruesas con menor contenido de clorofilas. También se presentó un aumento de la masa seca de raíces terciarias y cuaternarias en los grados iniciales. Esta variable disminuyó en los grados más severos del disturbio. Key words: physiology, damage, disease severity, dry matter. Palabras clave: fisiología, daño, severidad de enfermedad, materia seca. Leaf area, chlorophyll content, and root dry mass in oil palms (Elaeis guineensis Jacq.) affected by the plumero disorder Área foliar, contenido de clorofila, y masa seca de raíces en palmas de aceite (Elaeis guineensis Jacq.) afectadas por el disturbio del plumero Martha Sofía España-Guechá1, Daniel Gerardo Cayón-Salinas2, Iván Ochoa-Cadavid3, and Aquiles Enrique Darghan-Contreras1 Introduction Since 2010, a disorder called plumero has been recognized in the oil palm (Elaeis guineensis Jacq.). This disorder is named after a Spanish word that refers to cleaning dusters because of the similarity between the leaf shape and this object. Symptoms of plumero are characterized by a visual reduction of the leaf area, yellowing of leaves, longitudinal chlorotic strips parallel to the central rib, drying of the tips of the leaf lets in younger trees, and the presence of yellow strips usually located on one side of the central rib. The yellow strip may appear on one or several leaf lets or one or several leaves (Arias et al., 2014). The causal agent or predisposing factors of this disorder are still unknown. Plants show physiological responses to changes in envi- ronmental factors and the incidence of disease-causing organisms. In oil palms, research has been carried out to evaluate the physiological response of the plant to the at- tack of an abiotic agent. For example, the disease known as lethal wilt (Candidatus Phytoplasma asteris) generates increases in the internal temperature, decreasing the rate of photosynthesis and the ability to take nutrients from the oil palm that reduces the ability to produce sugars, the maintenance of leaves, and the production of new roots. During this process, the palm ends up decaying, preventing the leaves from opening their stomata to take up CO2 and transpire (Cayón et al., 2007). The oil palm has a fasciculate radical system composed of primary, secondary, tertiary and quaternary roots (Reyes-Santamaría et al., 2000). Most of the absorption of nutrients is through the quaternary and absorbing tips of primary, secondary and tertiary roots (Tailliez, 1971). Ramírez et al. (2004) indicate that palms with lethal wilt produce very few roots, so they cannot http://dx.doi.org/10.15446/agron.colomb.v38n3.85309 336 Agron. Colomb. 38(3) 2020 take up the water required for nutrition and regulation of leaf temperature. In general, plants are often limited by several stressful factors that occur simultaneously, making it difficult to predict their geographical distribution based on physiological responses of an individual factor (Mittler, 2006; Fischer et al., 2016; Fischer and Melgarejo, 2020). The negative impact of foliar diseases is directly expressed on yield components due to changes that pathogens induce in physiological processes responsible for crop productivity such as the increase in leaf area and the accumulation and distribution of dry mass in plant organs (Schierenbeck et al., 2014). The disease known as bud rot causes a decrease in photosynthesis, stomatal conductance, transpiration, ef- ficient use of water and chlorophyll content, and increases the carotenoid content in oil palms. The detailed description of some morphological and physiological aspects of a diseased plant allows knowing the infection processes, severity and damage of the disease (Rakib et al., 2019). So, the objective of this study was to characterize the morpho-physiological damages and to quantify the severity of the plumero disorder. Materials and methods Location The work was carried out in an oil palm plantation located on the north coast of Colombia (9°56’ N and 73°16’ W, and altitude of 95 m a.s.l.). According to the Holdridge classi- fication system, the plantation is located in the ecological formation tropical dry forest (Espinal and Montenegro, 1963). An average temperature of 27.5°C and average pre- cipitation of 1300 mm were recorded. Diagrammatic scale of the disorder The diagrammatic severity scale proposed by Arias et al. (2014), indicates that palms affected by plumero disorder can be found in five grades: yellow strip, grade 1, grade 2, grade 3 and grade 4. Yellow-strip palms are characterized by the presence of at least one yellow strip in the canopy without visual reduction of the width of the leaf lets, while grades 1 to 4 show a reduction in the width of the leaf lets and sharp insertion of the leaf lets in the rachis. In grade 1, the effect can be seen at the level of the 1st leaf, grade 2 at the level of the 9th leaf, grade 3 at the level of the 17th leaf, and grade 4 when the affected leaves are seen underneath the level of the 17th leaf. To manage a scale with a smaller number of grades to facili- tate the analysis and the diagramming of tables and figures, we used the same scale proposed by Arias et al. (2014), but grades 1 and 2 were combined into grade 1-2 and grades 3 and 4 were combined into grade 3-4. The combination of grades was confirmed by a multiple correspondence analysis of an exploratory nature from which two dimen- sions were extracted from the leaf area (LA) and specific leaf area (SLA) data. The planting year, the number of the lot or farm, the genetic origins, and the degrees of severity were included as factors of plumero, yielding an explained variability of 70.72%. This can be observed in Figure 1; the proximity between the points correspond to grades 1 and 2, as well as grades 3 and 4. FARM.C SEV.YELLOW_STRIP SEV.GR_3 SEV.GR_1 YEAR.2010FARM.A GENTC_ORIG.C_MIXFARM.B YEAR.2008 SEV.HEALTHY FARM.D GENTC_ORIG.BRA GENTC_ORIG.EK_M SEV.GR_2 SEV.GR_4D im en si on 2 Correspondence map columns: main Dimension 1 -1 -0.6 -0.2 0.2 10.6 1.4 -1 -0.6 -0.2 0.2 1 0.6 1.4 FIGURE 1. Multiple correspondence analysis of leaf area (LA) and specific leaf area (SLA). GENTC_ORIG.BRA: genetic origin Braban- ta; GENTC_ORIG.C_MIX: genetic origin Congo Mixed; GENTC_ORIG. EK_M: genetic origin Ekona x Djongo * Mongana; YEAR.2008: plan- ting year 2008; YEAR.2010: planting year 2010; SEV.HEALTHY: severity healthy; SEV.YELLOW STRIP: severity yellow strip; SEV.GR_1: severity grade 1; SEV.GR_2: severity grade 2; SEV.GR_3: severity grade 3; SEV. GR_4: severity grade 4. Palm sampling Samples were collected from 24 healthy palms and 24 palms from each of the severity grades of plumero, according to the multiple correspondence analysis listed in Table 1. The palms were randomly selected. The following variables were determined for each of the palms: leaf area (LA), specific leaf area (SLA), chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl t), primary root dry mass (PRDM), secondary root dry mass (SRDM), tertiary and quaternary root dry mass (TQRDM). Leaf area and specific leaf area The leaf area of leaf 17 (LA17) is an indicator of the total leaf area of the oil palm. LA17 has been used as a model for different measurements in the oil palm due to its average location within the total leaves of the palm and its stable 337España-Guechá, Cayón-Salinas, Ochoa-Cadavid, and Darghan-Contreras: Leaf area, chlorophyll content, and root dry mass in oil palms (Elaeis guineensis Jacq.) affected by the plumero disorder nutrient content (Corley and Tinker, 2003). The formula proposed by Corley et al. (1971) was used to determine the leaf area of leaf 17 (LA) for the oil palms Elaeis guineensis (Eq. 1). LA17 = 0.55 * [(LW) * n] (1) where LA17 is the leaf area of leaf 17(m2), 0.55 is the cor- rection factor for the palms Elaeis guineensis, L is the aver- age length of the four largest central leaf lets (m), W is the average width of the four longest central leaf lets, and n is the total number of leaf lets. To determine the SLA the method of López et al. (2014) was used where a 30 cm long segment was taken from the middle part of two central leaf lets of leaf 17. The width (w) and area of each segment was measured. Segments were then dried in an oven at 75°C for 72 h, and the dry weight was determined. Thus, the SLA (cm2 g-1) was equal to the mean area of the leaf segments divided by their dry weight. SLA = l w / p (2) where, l is the average length of the middle part of the leaf let that in this case takes the value of 30 cm, w is the mean mid-width of the leaf lets, and p is the mean of the dry weight of the middle part of the leaf lets. Chlorophyll The contents of Chl a, Chl b and Chl t were determined using the acetone extraction method (Flórez and Cruz, 2004). This consisted of collecting 10 leaf disks of 5 ml in diameter of non-veined plant tissue that were then preser- ved in Eppendorf tubes with 2 ml of 99% absolute alcohol. The tubes were labelled, covered with aluminum foil, and refrigerated. The leaf disks were macerated with 8 ml of a cold solution of 80% acetone and CaCO3 (0.5 g L-1). Ab- sorbances of 645 nm and 663 nm were determined in the macerate using a spectrophotometer (BioMate 3, Madison, USA). The following equations were used to calculate the content of Chl a (Eq. 3), Chl b (Eq. 4) and Chl t (Eq. 5): Chl a = {[(12.7 × D663) - (2.69 D645)] × V} / (1000 × W) (3) Chl b = {[(22.8 × D645) - (4.48 D663)] × V} / (1000 × W) (4) Chl t = {[(20.2 × D645) + (8.02 D663)] × V} / (1000 × W) (5) where D is the optical density, V the volume of the extract used in the determination of the optical density in ml, and W is the fresh mass in mg of the 10 leaf disks of plant tissue. Root dry mass Soil samples were collected at the four cardinal points of the base of the palm tree using a sharp-edged steel cylinder of known volume. The roots were then washed, separated into primary, secondary, tertiary and quaternary roots that were then dried in an oven at 75°C for 72 h. Since the weight of the roots was in grams and the volume of the soil sample was known, the data was extrapolated to dried roots per cubic meter of soil (kg m-3). Statistical analysis An ordinal regression model with a degree of severity of P was applied as the response variable. The income variables were LA, SLA, Chl a, Chl b, Chl t, PRDM, SRDM, TQRDM in the statistical program Rstudio. Analysis of variance (ANOVA) and the test of minimum significant difference was performed with an alpha calculated using the Bonfe- rroni correction (aB) (Eq. 6). aB = 1 - (1 - a)1/n (6) where n is the number of variables in this research; the number of significant variables in the model was n = 4, so the alpha with the Bonferroni correction used was aB = 0.0127 with a = 0.05. A matrix of correlations between variables was performed to assess the relevance of using univariate analysis instead of multivariate analysis. To facilitate the analysis, grade 1 was collapsed with grade 2 (labelled as grade 1-2) and grade 3 was collapsed with grade 4 (labelled as grade 3-4). This collapse was performed con- sidering an analysis of multiple correspondences that was performed as data exploration. The proximity between the points corresponding to grades 1 and 2, as well as grades 3 and 4 descriptively validated the collapse of the categories. The veracity or falsity of atypical data was also determined using the box-plot graph of the RStudio software. Results All ordinal regression models evaluated reported the vari- ables LA and SLA as significant (P<0.05) and indicated comparisons of degrees of severity: healthy with yellow strip, yellow strip with grade 1-2, and grade 1-2 with grade 3-4. However, the best fit model was model 4, where the variables LA, SLA, Chl t, TQRDM and the severity degree comparisons were reported as significant (healthy with yellow strip, yellow strip with grade 1-2; and grade 1-2 with grade 3-4). This model also reported an Akaike in- formation criterion value of 279.38, the lowest among the 338 Agron. Colomb. 38(3) 2020 various adjusted models (Tab. 1). The parameters of the best fit model are shown in Table 2. This model yielded 53.6% correct classifications; that is 53.6% of the palms observed at a certain degree of severity were correctly classified in that same degree of severity. The correlation matrix (Tab. 3) indicates that the variables LA, SLA, Chl t, and TQRDM were not correlated, so it was not applicable for performing multiple variance analysis. TABLE 1. Adjusted ordinal models. Variable Model 1 Model 2 Model 3 Model 4 Probability LA <0.0001 <0.0001 <0.0001 <0.0001 SLA 0.0069 0.0058 0.0078 0.0063 Chl t 0.0450 0.0464 0.0601 0.0021 Chl b 0.1318 0.1324 0.1741 PRDM 0.6483 SRDM 0.3539 0.4075 TQRDM 0.0010 0.0011 0.0004 0.0010 Healthy | Yellow strip <0.0001 <0.0001 <0.0001 <0.0001 Yellow strip | grade 1-2 <0.0001 <0.0001 <0.0001 <0.0001 Grade 1-2 | grade 3-4 <0.0001 <0.0001 <0.0001 <0.0001 Ordinal statistical models Statistics Model 1 Model 2 Model 3 Model 4 Akaike information criterion 271.17 269.38 268.08 267.94 LA - leaf area, SLA - specific leaf area, Chl b - chlorophyll b, Chl t - total chlorophyll, PRDM - primary root dry mass, SRDM - secondary root dry mass, TQRDM - tertiary and quaternary root dry mass. TABLE 2. Estimated parameters for the reduced model 4. Variable Estimator Standard error t value Probability LA -1.025 0.180 -5.684 <0.0001 SLA -0.055 0.020 -2.730 0.0063 Chl t -0.810 0.264 -3.069 0.0021 TQRDM -0.699 0.213 -3.282 0.0010 Healthy | yellow strip -14.948 2.239 -6.675 <0.0001 Yellow strip | grade 1-2 -13.764 2.180 -6.314 <0.0001 Grade 1-2 | grade 3-4 -11.925 2.087 -5.714 <0.0001 LA - leaf area, SLA - specific leaf area, Chl t - total chlorophyll, TQRDM - tertiary and quaternary root dry mass. TABLE 3. Correlation matrix between physiological variables. SLA Chl t LA TQRDM SLA -0.1113 0.2348 0.1421 Chl t -0.1113 0.0742 -0.0791 LA 0.2348 0.0742 0.0443 TQRDM 0.1421 -0.0791 0.0443 SLA - specific leaf area, Chl t - total chlorophyll, LA - leaf area, TQRDM - tertiary and quaternary root dry mass. Univariate analyses indicated that the variables LA, SLA, Chl t and TQRDM were significant (P<0.05) which means that LA, SLA, Chl t and tertiary and quaternary root dry mass were different in healthy palms from those affected by plumero (Tab. 4). TABLE 4. Anova of the variables leaf area (LA), specific leaf area (SLA), total chlorophyll (Chl t) and tertiary and quaternary root dry mass (TQRDM). LA Source DOF Sum of squares Mean square F value Probability Severity 3 59.3596 19.7865 17.89 <0.0001 Error 120 132.7284 1.106 Total 123 192.088 SLA Source DOF Sum of squares Mean square F value Probability Severity 3 1368.9987 456.3329 4.99 0.0027 Error 120 10978.4834 91.4873 Total 123 12347.4821 Chl t Source DOF Sum of squares Mean square F value Probability Severity 3 4.5253 1.5084 3.04 0.0316 Error 120 59.4762 0.4956 Total 123 64.0016 TQRDM Source DOF Sum of squares Mean square F value Probability Severity 3 14.8904 4.9634 8.58 <0.0001 Error 117 67.666 0.5783 Total 120 82.5565 DOF - Degrees of freedom 339España-Guechá, Cayón-Salinas, Ochoa-Cadavid, and Darghan-Contreras: Leaf area, chlorophyll content, and root dry mass in oil palms (Elaeis guineensis Jacq.) affected by the plumero disorder Leaf area, specific leaf area The leaves of palms affected by plumero (yellow strip, gra- de 1-2, and grade 3-4) had lower LA, SLA and Chl t than healthy palms (Fig. 2A-C). The roots of affected palms in- creased TQRDM at the initial stages (yellow strip and grade 1-2) but then decreased significantly in grade 3-4 (Fig. 2D). Chlorophyll The leaves of palms affected by plumero (yellow strip, grade 1-2, and grade 3-4) showed lower Chl t content than healthy palms (Fig. 2C). Root dry mass The dry matter of tertiary and quaternary roots increased in the initial degrees of severity (yellow strip and grade 1-2) compared to healthy palms, and it was significantly reduced in the last degrees (grade 3-4) (Fig. 2D). Discussion Leaf area and specific leaf area LA progressively decreased as the degree of severity in- creased (Fig. 2A). This occurs long before a reduction in the width of the leaf let, as described by Arias et al. (2014) in the plumero severity scale. The decrease in LA caused by some pathogens drastically affects the interception of solar radiation and reduces the generation and distribution of biomass (Carretero et al., 2009) because they accele- rate the necrosis and senescence of leaves, causing fewer photoassimilates to be used in the synthesis of dry matter (Schierenbeck et al., 2014). The palms affected in grade 3-4 had thicker leaf laminae. The SLA indicated the variation in the relative thickness of the leaves as a consequence of leaf structural alterations, which make the leaf very sensitive to environmental and external factors (Reyes-Santamaría et al., 2000), and a good indicator of crop productivity (Poorter and de Jong, 1999). The reduction of SLA indicated that the leaves have a thicker mesophyll due to the higher number and size of layers of palisade cells (Rodríguez and Cayón, 2008). This suggests that the number of photoassimilates in these leaves is greater (Ayala and Gómez, 2000) or photoassimilates are not efficiently transported from source to sink in response to nutritional deficiencies of elements such as boron (Wimmer and Eichert, 2013), magnesium (Verbrug- gen and Hermans, 2013) or potassium (Gerardeaux et al., a b ab c a ab a b a a a b ab b b a Grade 3-4Grade 1-2Yellow stripHealthy 4 3 2 1 5 D Te rt ia ry a nd q ua te rn ar y ro ot d ry m as s (k g m -3 ) Severity Grade 3-4Grade 1-2Yellow stripHealthy 3 2 1 4 C To ta l c hl or op hy ll (m g g- 1 fr es h w ei gh t) Severity Grade 3-4Grade 1-2Yellow stripHealthy 100 90 80 70 60 110 B S pe ci fic le af a re a (c m 2 g -1 ) Severity Grade 3-4Grade 1-2Yellow stripHealthy 6 4 0.2 8 A Le af a re a of le af 1 7 (m 2 ) Severity FIGURE 2. A) Leaf area of leaf 17 (m2), B) specific leaf area, C) total chlorophyll and D) tertiary and quaternary root dry mass in healthy and affected palms by plumero. Different letters indicate differences in means with P = 0.0127. 340 Agron. Colomb. 38(3) 2020 2010). According to Nenova (2006), SLA is also increased due to iron deficiency and decreases due to excess iron, as observed in plants affected by plumero. However, palms affected by this disorder have a lower SLA because the leaf does not expand as the leaf lets of healthy plants do. The expansion of the cells can be affected by stressful conditions such as temperature, evaporation, or water content of the soil, although the plant recovers quickly once the stress is over (Sadok et al., 2007). However, the elongation and final leaf area are affected in some cases when stress occurs in the last phase of leaf elongation (Granier and Tardieu, 2009). Chlorophyll Palms affected by plumero significantly reduced the chlorophyll content and root growth. A decrease in the chlorophyll content in response to abiotic and biotic stress is manifested by foliar yellowing, followed by wil- ting, affecting photosynthesis (Munné-Bosch, 2008) and consequently, reducing plant biomass (Casierra-Posada and Cutler, 2017; Sánchez-Reinoso et al., 2019). Mandal et al. (2009) stated that the chlorophyll content in diseased leaves of Plantago ovata affected by downy mildew was reduced by 24.39% in slightly chlorotic and 44.90% in severely chlorotic leaves as compared to healthy leaves, which appears to be one of the causes for the reduction of the photosynthesis process. There are several alternatives to evaluate the establish- ment and development of a disease, such as estimating the chlorophyll content in the leaf (Uddling et al., 2007). Such estimation could provide a better alternative to evaluate disease severity in a plant (Chang et al., 2015) and be a good indicator for the degree of disease or infection and changes during pathogenesis (Rakib et al., 2019). The chlorophyll content in Ganoderma-infected oil palm seedlings declines as the infection progresses (Goh et al., 2016). Chang et al. (2015) also report that the chlorophyll content decreases as the disease progresses in different stages of cucumber growth. Chlorophyll content also decreases due to deficien- cies of other nutrients including molybdenum (Agarwala et al., 1978) and boron. The latter generates oxidative damage in chloroplasts (Wimmer and Eichert, 2013). The reduction in chlorophyll content can also be caused by an attack of pathogens. In deciduous leaves attacked by phytoplasmas, chlorosis occurs as a form of cell death at the pathogen’s entry points (Mittelberger et al., 2017). The chlorophyll content is used to determine disease sever- ity in some crops. In the case of fir decline, Oren et al. (1993) propose chlorophyll content and foliar nutrient analysis as a method to represent a range of severity for chlorosis to quickly identify the processes that occur in the soil of some areas where a decline occurs. Root dry mass In palms affected by plumero in the initial stages (yellow strip and grade 1-2) an increase in the development of ter- tiary and quaternary roots has been observed. This type of response could be associated with the response system of the plants to a sulfur deficiency with the slow initial growth of lateral roots and then with the rapid root growth (Hoefgen and Nikiforova, 2008; Gruber et al., 2013). Conclusions The plumero disorder compromises the physiological and productive performance of the affected palms by affect- ing the expansion of the leaf area and the development of roots. This characterization contributes to knowledge of the physiological damages caused by the plumero and is a fundamental tool for managing the disorder in the af- fected plantations. Acknowledgments Thanks to Unipalma de los Llanos S.A. for its support in the development of this research. Literature cited Agarwala, S.C., C.P. Sharma, S. Farooq, and C. Chatterjee. 1978. Effect of molybdenum deficiency on the growth and metabo- lism of corn plants raised in sand culture. Can. J. Bot. 56(16), 1905-1908. Doi: 10.1139/b78-227 Arias, N., D. Ibagué, and A. Ospino. 2014. Guía de bolsillo. Iden- tificación y registro del Plumero en palma de aceite. Centro de Investigación en Palma de Aceite CENIPALMA, Bogota. Ayala, I.M. and P.L. Gómez. 2000. Identificación de variables morfológicas y fisiológicas asociadas con el rendimiento en materiales de palma de aceite (Elaeis guineensis Jacq.). Rev. Palmas 21, 10-21. Carretero, R., R.A. Serrago, M.O. Bancal, A.E. Perelló, and D.J. Mi- ralles. 2009. Absorbed radiation and radiation use efficiency as affected by foliar diseases in relation to their vertical position into the canopy in wheat. Field Crops Res. 116, 184-195. Doi: 10.1016/j.fcr.2009.12.009 Casierra-Posada, C. and J. Cutler. 2017. Photosystem II f luorescence and growth in cabbage plants (Brassica oleracea var. capitata) grown under waterlogging stress. Rev. UDCA Act. Div. Cient. 20(2), 321-328. Doi: 10.31910/rudca.v20.n2.2017.390 Cayón, D.G., C.A. Avellaneda, and F. Rodríguez. 2007. Aspectos fisiológicos asociados a marchitez letal de la palma de aceite. Rev. Palmas 28(1), 373-382. Chang, R.K., Y.H. Wang, X.T. Zhang, G.C. Tang, and Y. Wei. 2015. The research of disease detection method of greenhouse https://doi.org/10.1139/b78-227 341España-Guechá, Cayón-Salinas, Ochoa-Cadavid, and Darghan-Contreras: Leaf area, chlorophyll content, and root dry mass in oil palms (Elaeis guineensis Jacq.) affected by the plumero disorder cucumber leaf based on chlorophyll f luorescence analy- sis. Un ivers. J. Ag r ic. Res. 3(3), 76 -8 0. Doi: 10.13189/ ujar.2015.030302 Corley, R.H.V., J.J. Hardon, and G.Y. Tang. 1971. Analysis of growth of the oil palm (Elaeis guineensis Jacq.) I. Estimation of growth parameters and application in breeding. Euphytica 20, 307-315. Corley, R.H.V. and P.B.H. Tinker. 2003. The oil palm. 4th ed. Black- well Science, Oxford, UK. Espinal, L. and E. Montenegro. 1963. Formaciones vegetales de Co- lombia: memoria explicativa sobre el mapa ecológico. Instituto geográfico Agustín Codazzi IGAC, Bogota. Fischer, G. and L.M. Melgarejo. 2020. The ecophysiology of cape gooseberry (Physalis peruviana L.) - an Andean fruit crop. A review. Rev. Colomb. Cienc. Hortíc. 14(2), 76-89. Doi: 10.17584/ rcch.2020v14i1.10893 Fischer, G., F. Ramírez, and F. Casierra-Posada. 2016. Ecophysi- ological aspects of fruit crops in the era of climate change. A review. Agron. Colomb. 34(2), 190-199. Doi: 10.15446/agron. colomb.v34n2.56799 Flórez, V. and R. Cruz. 2004. Guías de laboratorio de fisiología vegetal. 1st ed. Universidad Nacional de Colombia, Bogota. Gerardeaux, E., L. Jordan-Meille, J. Constantin, S. Pellerin, and M. Dingkuhn. 2010. Changes in plant morphology and dry mat- ter partitioning caused by potassium deficiency in Gossypium hirsutum (L.). Environ. Exp. Bot. 67(3), 451-459. Doi: 10.1016/j. envexpbot.2009.09.008 Goh, K.M., M. Dickinson, P. Alderson, L.V Yap, and C.V. Supra- maniam. 2016. Development of an in planta infection system for the early detection of Ganoderma spp. in oil palm. J. Plant Pathol. 98(2), 255-264. Doi: 10.4454/JPP.V98I2.019 Granier, C. and F. Tardieu. 2009. Multi-scale phenotyping of leaf expansion in response to environmental changes: the whole is more than the sum of parts. Plant Cell Environ. 32, 1175-1184. Doi: 10.1111/j.1365-3040.2009.01955.x Gruber, B.D., R.F.H. Giehl, S. Friedel, and N. von Wirén. 2013. Plasticity of the Arabidopsis root system under nutrient defi- ciencies. Plant Physiol. 163, 161-179. Doi: 10.1104/pp.113.218453 Hoefgen, R . a nd V.J. Ni k iforova. 20 08. Metabolomics inte- grated with transcriptomics: assessing systems response to sulfur-deficiency stress. Physiol. Plant. 132, 190-198. Doi: 10.1111/j.1399-3054.2007.01012.x López, J. 2014. Caracterización fisiológica y morfológica de palmas de aceite Taisha (Elaeis oleífera HBK Cortes) y sus híbridos (Elaeis oleífera HBK Cortes x Elaeis guineensis Jacq.) en la región Amazónica del Ecuador. MSc thesis, Universidad Na- cional de Colombia, Bogota. Mandal, K., R. Saravanan, S. Maiti, and I.L. Kothari. 2009. Effect of downy mildew disease on photosynthesis and chlorophyll f luorescence in Plantago ovata Forsk. J. Plant Dis. Prot. 116(4), 164-168. Doi: 10.1007/BF03356305 Mittelberger, C., H. Yalcinkaya, C. Pichler, J. Gasser, G. Scherzer, T. Erhart, S. Schumacher, B. Holzner, K. Janik, P. Robatscher, T. Müller, B. Kräutler, and M. Oberhuber. 2017. Pathogen-induced leaf chlorosis: products of chlorophyll breakdown found in degreened leaves of phytoplasma-infected apple (Malus x do- mestica Borkh.) and apricot (Prunus armeniaca L.) trees relate to the pheophorbide a oxygenase/phyllobilin pathway. J. Agric. Food Chem. 65(13), 2651-2660. Doi: 10.1021/acs.jafc.6b05501 Mittler, R. 2006. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 11(1), 15-19. Doi: 10.1016/j. tplants.2005.11.002 Munné-Bosch, S. 2008. Do perennials really senesce? Trends Plant Sci. 13(5), 216-220. Doi: 10.1016/j.tplants.2008.02.002 Nenova, V. 2006. Effect of iron supply on growth and photosystem II efficiency of pea plants. Gen. Appl. Plant Physiol. Special issue, 81-90. Oren, R., K.S. Werk, N. Buchmann, and R. Zimmermann. 1993. Chlorophyll-nutrient relationships identif y nutritionally caused decline in Picea abies stands. Can. J. For. Res. 23(6), 1187-1195. Doi: 10.1139/x93-150 Poorter, H. and R. de Jong. 1999. A comparison of specific leaf area, chemical composition and leaf construction costs of field plants from 15 habitats differing in productivity. New Phytol. 143(1), 163-176. Doi: 10.1046/j.1469-8137.1999.00428.x Rakib, M.R.M., A.H. Borhan, and A.N. Jawahir. 2019. The relation- ship between SPAD chlorophyll and disease severity index in Ganoderma-infected oil palm seedlings. J. Bangladesh Agricult. Univ. 17(3), 355-358. Doi: 10.3329/jbau.v17i3.43211 Ramírez, J., R. Bedoya, J. Guerrero, W. Valero, S. Otero, A. Erazo, and R. Bedoya. 2004. Resumen de actividades realizadas sobre la marchitez letal 1994-2004. Palmar del Oriente S.A. Rodríguez, P.A. and D.G. Cayón. 2008. Efecto de Mycosphaerella fijiensis sobre la fisiología de la hoja de banano. Agron. Co- lomb. 26(2), 256-265. Sadok, W., P. Naudin, B. Boussuge, B. Muller, C. Welcker, and F. Tardieu. 2007. Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally f luctuating conditions. Plant Cell Environ. 30, 135-146. Doi: 10.1111/j.1365-3040.2006.01611.x Sánchez-Reinoso, A.D., Y. Jiménez-Pulido, J.P. Martínez-Pérez, C.S. Pinilla, and G. Fischer. 2019. Chlorophyll f luorescence and other physiological parameters as indicators of waterlogging and shadow stress in lulo (Solanum quitoense var. septentrio- nale) seedlings. Rev. Colomb. Cienc. Hortíc. 13(3), 325-335. Doi: 10.17584/rcch.2019v13i3.10017 Schierenbeck, M., M.C. Fleitas, and M.R. Simón. 2014. Componen- tes ecofisiológicos involucrados en la generación de biomasa afectados por enfermedades foliares en trigo. Rev. Agron. Noroeste Argent. 34(2), 247-250. Tailliez, B. 1971. The root system of the oil palm on the San Alberto Plantation in Colombia. Oleagineux 26(7), 435-447. Uddling, J., J. Gelang-Alfredsson, K. Piikki, and H. Pleijel. 2007. Evaluating the relationship between leaf chlorophyll concen- tration and SPAD-502 chlorophyll meter readings. Photosynth. Res. 91, 37-46. Doi: 10.1007/s11120-006-9077-5 Verbruggen, N. and C. Hermans. 2013. Physiological and molecular responses to magnesium nutritional imbalance in plants. Plant Soil 368, 87-99. Doi: 10.1007/s11104-013-1589-0 Wimmer, M.A. and T. Eichert. 2013. Review: mechanisms for boron deficiency-mediated changes in plant water relations. Plant Sci. 2003-2004, 25-32. Doi: 10.1016/j.plantsci.2012.12.012 http://dx.doi.org/10.1016/j.envexpbot.2009.09.008 http://dx.doi.org/10.1016/j.envexpbot.2009.09.008 https://doi:%2010.1007/BF03356305 https://doi.org/10.1139/x93-150 https://doi.org/10.1046/j.1469-8137.1999.00428.x https://doi.org/10.1111/j.1365-3040.2006.01611.x