Biological and Practical Importance of Light Microenvironments in a Tree: Participatory Research to Match Teaching and Learning Styles G

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Biological and Practical Importance of Light Microenvironments in a Tree: Participatory Research to Match Teaching and Learning Styles G. A. Picchioni,* S. A. Weinbaum, D. L. Daniel, and H. Karaca

ABSTRACT usual profit-generating portion of the tree, are driven by the photosynthetic activity of nearby leaves and the importation Productivity and crop quality vary strongly among the diverse light microenvironments within a tree’s canopy. An effective of carbohydrates (Marschner, 1995, p. 131). The level of nat- teaching exercise to convey this biologically and agriculturally ural irradiance within tree canopies is spatially linked to the important principle to plant science students is lacking. We ap- allocation of metabolic resources such as reduced carbon (C) plied a simple participatory learning approach to teach the im- and nitrogen (N) (Flore and Lakso, 1989; Weinbaum et al., portance of light microenvironment in the dense canopy of a ma- 1989). Accordingly, the light microclimate conditions the lo- ture pecan tree [Carya illinoinensis (Wangenh.) K. Koch ‘Sch- calized availability of C and N and determines in large mea- ley’]. Leaves and nuts were sampled along a steep light flux gra- sure the productivity and quality of the tree’s crop within that dient through the canopy, and specific leaf weight (leaf weight per portion of the canopy. Intracanopy light distribution is thus unit leaf area), was used as a simple, proven, and integrative mea- both physiologically and economically important. sure to quantify the incident light gradient. Simple regression At a given level of management, a tree’s quantity and qual- analysis revealed the importance of specific leaf weight (light ex- ity of yield involve a complex interaction between light in- posure) as a predisposing factor controlling nut quality and ni- tensity, relative light distribution, time, space, and the translo- trogen (N) allocation. Multiple regression analysis further aided cation of high-energy products from leaves to fruit. In addi- systematic learning in that it exposed students to statistical com- tion, the availability of light and number of fruit on a branch plexities in determining major sources of variation in nut at- during 1 yr can have a pronounced impact on the production tributes (“masking” of one independent variable by another). and quality of that branch during the succeeding year, which This exercise enabled participatory learning in the scientific describes a localized carry-over effect (Klein et al, 1991b; method, including collection and processing of field samples, statistical analysis, data interpretation, and hypothesis testing. Tustin et al., 1992). The self-generated findings induced a sense of ownership among As shown by the literature, the study of tree canopy–light students, resulting in positive learning outcomes and strength- relationships combines practical and scientific learning ele- ening the match between teaching and learning styles beyond the ments along with challenges to the teacher, all of which are traditional classroom lecture approach. The work also provided consistent with the needs of a plant science curriculum. Our an experiential, discovery-based setting that addressed a deficit own observations on teaching at the undergraduate level in- in the scientific pecan literature. The exercise is simple, inex- dicate that (i) it is difficult to adequately convey this type of pensive, fits nicely within a semester time-frame, and broadly ap- subject matter through traditional lectures and readings, and plicable. It encourages active student participation in an in- (ii) undergraduate students generally prefer a hands-on labo- quiry-driven environment. ratory setting to enliven their initial exposure to new and technical material. Despite student preferences for active learning, it has recently been stated that overhead trans- OR NEARLY TWO CENTURIES, sunlight has been recognized parencies and chalk boards remain the primary teaching tools Fas an indispensable natural resource for plant growth and in today’s agricultural classroom (Whittington, 1997a). Thus, survival (Epstein, 1972, p. 8), and interest in its role in the pro- we either lack or underutilize creative, experiential teaching ductivity and quality of tree crops has increased. The contin- techniques. Increased use of hands-on, participatory learning ued relevance of this topic is apparent in trade journals devoted strategies to teach technical subjects would undoubtedly im- to tree crop production (e.g., Green, 1997; Phillips, 1997; prove students’ critical thinking skills, understanding and use McEachern, 1999), as well as in scientific journals (e.g., of the scientific method, spirit of inquiry, ability to interpret Kuden and Son, 1997; Fallahi and Kilby, 1997; Worley and experimental data, practical experience, and employability Mullinix, 1997; Grossman and DeJong, 1998; Guimond et al., upon graduation (Salvador et al., 1995; Stearns, 1995; Riley, 1998; Genard et al., 1998). 1997; Cantliffe and Kostewicz, 1998; Corak and Grabau, The biological and agricultural significance of light (pho- 1988). tosynthetic photon flux) gradients within a tree’s canopy is im- Although technical accounts of the influence of light on tree portant to convey to plant science students. Dry matter accu- productivity and fruit quality are numerous, we could find no mulation (growth) and chemical composition of fruit, the published material that utilized this concept for teaching. Thus, an experientially driven method to demonstrate phe- G.A. Picchioni, Dep. of Agronomy and Horticulture, New Mexico State notypic plasticity and the importance of light and light distri- Univ., Las Cruces, NM 88003; S.A. Weinbaum, Dep. of Pomology, Univ. of California, Davis, CA 95616; D.L. Daniel, Dep. of Economics/International bution on tree productivity and fruit quality appeared war- Business and Univ. Statistics Center, New Mexico State Univ., Las Cruces, ranted. Such a method would ideally be simple, require min- NM 88003; and H. Karaca, Soil Science Dep., Mustafa Kemal Univ., imal expense and equipment, fit within the limited time frame Serinyol-Hatay 31000, Turkey. Received 14 Sept. 1999. Corresponding au- of the syllabus, and require active participation by the students. thor ([email protected]). Specific leaf weight (leaf mass per unit leaf area) varies Published in J. Nat. Resour. Life Sci. Educ. 29:78–87 (2000). with light intensity (Tustin et al., 1992), and represents an in- http://www.JNRLSE.org tegration of incident light upon a given position inside the tree

78 • J. Nat. Resour. Life Sci. Educ., Vol. 29, 2000 canopy (Weinbaum et al., 1989; Klein et al., 1991a). Com- Sprugel et al., 1991; Weinbaum et al., 1994). Thus, we relied pared with other techniques of light assessment, using specific on simple and multiple regression analyses to provide a sys- leaf weight as a bioassay to quantify specific light microen- tematic linkage between observational data and positive in- vironments within tree canopies is easy (Marini and Barden, terpretive outcomes. We anticipated that this approach would 1981). In addition to its strong dependence on natural irradi- help plant science students develop a combined awareness of ance, specific leaf weight has also been shown to be positively the biological and agricultural significance of light distribu- correlated with the allocation of N to leaves, leaf photosyn- tion in the tree canopy. thetic capacity, and the number, growth, and quality of fruit at discrete canopy positions. Such findings have been ob- MATERIALS AND METHODS tained for peach [Prunus persica (L.) Batsch] (DeJong and Suggested Reading Doyle, 1985), prune (Prunus domestica L.) (Weinbaum et al., 1989; Southwick et al., 1990), apple (Malus × domestica Ideally, students should be given preparatory time in the Borkh.) (Barritt et al., 1987 and 1991), and walnut (Juglans form of literature review,orareading primer. The contents regia L.) (Klein et al., 1991a, 1991b). extracted from that preparation could be summarized in writ- Pecan [Carya illinoinensis (Wangenh.) K. Koch] is the ing and evaluated for grading purposes, if so desired by the only U.S. commercial tree fruit and nut crop indigenous to instructor.We focused on important light-dependent tree phys- North America. Production statistics illustrate the global im- iological parameters and included pecan-specific literature portance of U.S. pecan cultivation (National Agricultural Sta- when appropriate. Selections (below) are only suggested and tistics Service, 1998) and the expanding land areas in the could no doubt be expanded with more thorough review of lit- semiarid southwestern U.S., including far west Texas, south- erature. ern New Mexico, much of Arizona, and central California (Peña, 1995). There are virtually no published data on the re- Measurement, significance, and biochemical/structural de- lationship between irradiance, specific leaf weight, and pecan terminants of specific leaf weight (Marini and Barden, 1981; Weinbaum et al., 1989) nut production and quality. Related research on pecan has been The relationship between specific leaf weight, light, and limited to pruning and tree thinning studies to improve light crop quality (Barritt et al., 1987; Southwick et al., 1990; penetration and maintain production and quality (Worley, Corelli-Grapadelli and Coston, 1991; Klein et al., 1985, 1993; Amling et al., 1985; Worley and Mullinix, 1997). 1991b; Tustin et al., 1992; Campbell and Marini, 1992) Those studies were based on entire, single-tree observations, The relationship between specific leaf weight, photosyn- or on replicated, multiple-tree plots and did not consider thesis, and N allocation (DeJong and Doyle, 1985; within-canopy variation in light intensity or crop characteris- Campbell et al., 1992) tics. The relationship between leaf/fruit ratio and crop quality The lack of data on intracanopy light microenvironments and productivity (Hansen et al., 1982; Hansen, 1992; in pecan trees is somewhat surprising considering the vigor- Roper and Loescher, 1987) ous growth characteristics of pecan, its commercial impor- The relationship between fruit number and yield (Forshey tance, and the ease of specific leaf weight measurement. Pecan and Elfving, 1977) trees are fast-growing and attain canopy closure and a height Light and foliar traits (Ryugo et al., 1980; Dale, 1988; of about 10 m in as few as 10 yr (Malstrom et al., 1982). Pecan Barritt et al., 1991) nut production and quality decrease as trees crowd each other, Light, canopy management, and orchard systems (Jackson, and fruiting becomes restricted to the uppermost sunlit 1980; Witt et al., 1989; Ramos et al., 1990; Worley, branches (Witt et al., 1989). Pecan trees represent excellent 1993; Wünsche et al., 1996; Grossman and DeJong, subjects for studying the effects of intracanopy light gradients, 1998) not only because of the lack of aforementioned data, but also The concept of a tree branch or individual shoot as a mod- because the mature pecan canopy is a very large and dense ular, readily observable, and biologically meaningful structure that would be expected to possess a steep gradient subunit of the canopy (Watson and Casper, 1984; of light intensity. Sprugel et al., 1991) Our aim was to assess the functional relationship between the natural irradiance gradient within a large pecan tree canopy Experimental Approach (Tree Selection, and pecan nut quality, nut yield, and N allocation. Wehy- Data Collection, and Sample Analyses) pothesized that reproductive performance within the tree canopy would vary in response to light exposure. The project We designed the study to serve as a laboratory exercise for also emphasized hands-on motivational learning. While the an upper division pomology course. All of the procedures (de- primary purpose of the project was to serve as a teaching ex- scribed below) were required for the student to receive full ercise, the authors were also interested in the impact of light credit for the exercise. The duration of the project was 14 wk. gradients on pecan nut quality characteristics, since this had Two field laboratories, two indoor laboratories, and indepen- not yet been quantitatively addressed in the literature. Hence, dent study were required during this time to complete the in addition to developing a useful teaching exercise, we have work. There were 15 enrollees and the course was taught dur- developed and present herein some initial, small-scale results ing the 15-wk fall 1997 semester. on this subject, which need to be researched further. A single, 81-yr-old ‘Schley’ pecan tree was identified for Mature trees consist of a large number of semi-autonomous study at the New Mexico State University (NMSU) Fabian units, each composed of current-year shoots, leaves, and de- Garcia Agricultural Science Center in Las Cruces veloping fruit (Sparks, 1977; Watson and Casper, 1984; (32°16¢43.0² N, 106°46¢15.6² W). The tree had a ground-line

J. Nat. Resour. Life Sci. Educ., Vol. 29, 2000 • 79 circumference of 1.9 m and was located near the center of a specific leaf weight of five replicate leaflet samples from 2-ha experimental pecan orchard, surrounded by similar trees fruiting shoots of the inner, most shaded portion of the canopy at an average spacing of 18 by 18 m. Throughout the orchard, (south side) averaged 5.9 ± 0.2 mg cm-2 (unpublished data). tallest branches had reached heights of 15 m or more at the This result was anticipated, based on the limited transmission time of our study. The site was established in 1916 to evalu- of light to the extreme inner canopy of mature pecan trees cul- ate the adaptability of pecan cultivars to New Mexico (Gar- tivated under our conditions (Malstrom et al., 1982), and cia and Fite, 1925), and over the course of 81 yr, the trees had based on a similar, highly positive relationship between light developed into very large grafted trees. Detailed historical exposure and specific leaf weight in other tree crops as dis- records (pre-1950) are unavailable, but the orchard is one of cussed above. the oldest known pecan plantings in existence in the western Specific leaf weight was determined on each of the leaflet USA (J. Fowler and D. Sullivan, 1999, personal communi- samples described above (for all points) by pooling the ob- cation). Since about 1970, the trees are known to have been servations (shoot identification number, leaflet area, and leaflet irrigated, fertilized, and treated for pest infestations as outlined dry weight) and then transcribing the 15-observation dataset by the New Mexico Cooperative Extension Service (Herrera, onto a Microsoft Excel 97 spreadsheet (Microsoft Corp., Red- 1994). mond, WA). Specific leaf weight was calculated as the ratio Tree crowding and shading within and among canopies had of leaflet dry weight (mg) to leaflet area (cm2). Calculation of clearly limited pecan yield and reduced nut quality (Witt et al., leaflet N per unit leaflet area (N in µg cm-2) was made as %N 1989). Severe pruning (pollarding) was initiated in January in dry matter × Specific leaf weight (mg cm-2) × 10. 1998 to increase light penetration but maintain the existing By 5 November, shucks (involucres) of mature nuts had trees in the planting. Thus, the study during the 1997 grow- split (dehisced), indicating the time of nut maturity (Herrera, ing season represented an ideal learning opportunity in that a 1997). On this date, students returned to their branches and significant light flux gradient existed across the canopy. counted the number of compound leaves per shoot and num- Single, current-year, fruiting shoots were monitored from ber of mature nuts per shoot. All fruits per shoot were har- 20 August until the time of nut maturity on 5 November.On vested, their shucks were removed and discarded, and the 20 August, 15 shoots were tagged on the south side of the nuts were then placed in prelabeled paper bags to air dry at canopy along a gradient from the most exposed region of the room temperature for 7 d. Total nut dry weight per shoot was canopy (extreme periphery) to the least exposed region (within measured to assess total yield per shoot. Average mass per nut 1 m of the trunk). The number of developing nuts per shoot was calculated as total nut weight per shoot divided by num- ranged between 3 to 5 and averaged 3.9 ± 0.2 for all 15 ob- ber of nuts per shoot, then the nuts were shelled. The total ker- servations. nel weight for all nuts per shoot was obtained, and percent- Each of the 15 students selected one shoot and the results age kernel was expressed as the weight percent of total ker- were later combined to provide a 15-observation dataset. nel to total nut on the shoot. After tagging, students removed the two opposing leaflets Students also evaluated nut quality by critical and subjec- from the middle, pinnately compound leaf of their shoot by tive, group-wide (consensus) analysis on a scale of 0 to 100 the standardized method outlined by Sparks (1978), and then (quality rating) based on the following weightings: shell ap- placed them in a prelabeled, sealable plastic bag on ice. Im- pearance (5%); nut size (15%); percentage kernel (40%); and mediately thereafter, leaflets were taken to the laboratory and the appearance, color, shelling characteristics, texture, and fla- their areas were determined using a portable area meter (LI- vor of kernel (40%). Before the nut harvesting date, students COR 3000; LI-COR, Lincoln, NE). Leaflets were then washed were asked to review work by McEachern (1992) and Grauke in 1% liquinox (phosphate-free soap), followed by a wash in and Thompson (1996), which clearly explains that percentage 0.1 M HCl, and then rinsed three times with deionized water kernel and mass per nut (expressed in the industry as number (Smith and Storey, 1976). of nuts per unit mass) are major nut quality characteristics in After washing, leaflets were placed in small, prelabeled the pecan industry, and as such, are important determinants of paper bags, which were stapled on their tops and taken to a economic return to the pecan grower. drying oven at 60°C for 24 h. The dry weight of each sample was recorded using an analytical balance, and the tissues were Regression Analysis ground using a Wiley mill to pass through a 40-mesh screen. The ground tissue was forwarded to the NMSU Soil, Water, After obtaining and entering all data into the Excel pro- and Air Testing Laboratory for the acid block digestion program, students were taught how to perform regression analy- cedure and determination of total Kjeldahl N concentration sis in Excel, which includes elementary curve fitting options (Parkinson and Allen, 1975) using an autoanalyzer (Techni- and regression analysis. Results were initially presented and con AAII, Technicon Instruments, Tarrytown, NY). Total discussed as a 15-point dataset with specific leaf weight, leaflet N was expressed as percent N in the dry matter and as number of nuts per shoot, and number of compound leaves per mass of N per unit of leaflet area. shoot as independent variables, and percentage kernel, mass Using a 20 August sampling date (as described above), spe- per nut, total nut weight per shoot, nut quality rating, and cific leaf weight (dry leaflet matter per unit leaflet area, mg leaflet N as dependent variables. Both linear and quadratic cm-2) varied positively with light exposure within the canopy terms for number of nuts per shoot and number of compound of this tree based on comparisons of discrete canopy zones. leaves per shoot were studied in the analysis. To facilitate ex- Specific leaf weight of five replicate leaflet samples from amination of residuals and probability (P) values derived fruiting shoots of the outer, most exposed periphery of the from t-tests of significance, further regression analysis was southern side of the tree averaged 9.2 ± 0.3 mg cm-2, while performed using JMP Version 3 (SAS Inst., Cary, NC).

80 • J. Nat. Resour. Life Sci. Educ., Vol. 29, 2000 Periodic discussion sessions, supported by earlier assigned ermost, well-exposed shoots (9.4 mg cm-2; Fig. 1). This in- reading, were generated and focused on brainstorming the pos- crease is indicative of progressively increasing light exposure sible influences of more than one independent variable. Mul- (photosynthetic photon flux) from poorly exposed to highly tiple regression was enlisted after group discussion led to the exposed canopy positions. conclusion that measured factors other than specific leaf The percentage of nut weight occupied by kernel (per- weight might also be expected to contribute to the variation centage kernel) was correlated positively with exposure (as in an observed leaf or nut characteristic. Only when a second conveyed by specific leaf weight), and a quadratic equation independent variable was suspected by the group as a bio- was developed by the students to best describe this relation- logically relevant, causal factor was it tested as a component ship (Fig. 1). The leaflet sampling date used for specific leaf in the regression model. While the coefficient of determina- weight (20 August) occurred just after the time that kernels 2 tion (R ) indicates the amount of response variability ac- had begun to fill, while the percentage kernel measurement (5 counted for by a particular model, finalized regression mod- November plus 7 d air drying) was made at the end of kernel els were chosen on the basis of significant t-tests on the in- filling corresponding to the time of nut maturation (Herrera, dependent variable coefficients using a significance criterion 1997). Thus, shoot exposure to sunlight (as measured by spe- of 5%. cific leaf weight) was a major predisposing factor in deter- Evaluation of Student Performance mining the capacity for kernel filling and accounted for 80% of the variation in this process. The increasing light exposure In addition to instructor-documented active participation probably resulted in increased local availability of C assimi- throughout this semester-long laboratory project, the graded lates to neighboring fruit during kernel filling, which repre- portion of the exercise included an independent, take-home sents a period of high metabolic resource demand in pecan composition writing assignment geared toward interpretation (Wood, 1995). Such a localized effect of light on fruit growth, of the regression analyses and plots. The instructor posed the development, import of carbohydrates, and quality has been following issues/questions related to light distribution in noted in other tree crops (Ryugo et al., 1980; Barritt et al., pecan: How does light exposure affect specific leaf weight? 1987; Southwick et al., 1990; Corelli-Grapadelli and Coston, What are the biological components of specific leaf weight? 1991; Tustin et al., 1992), but we are unaware of a similar re- How important is specific leaf weight in determining the total ported observation in pecan. yield potential on a shoot? How is specific leaf weight asso- In contrast to percentage kernel, specific leaf weight (as a ciated with pecan nut quality? Students then used their data sole independent variable) accounted for only 31% of the to address these questions by composing a one-page narrative variation in mass per nut across the canopy positions. The best- summary. fit equation was quadratic and similar in form to that of Fig. List of Materials Needed 1, but there was more scattering of the points (data not shown). The number of nuts per shoot appeared to be a dominating fac- A summary of materials needed for this exercise is listed tor in controlling mass per nut (Fig 2A). Seventy-two percent below, which does not include the cost for leaflet N analysis. of the within-canopy variation in mass per nut was accounted We processed the dried leaflet tissue through the grinding for by number of nuts per shoot alone, irrespective of expo- stage, and then forwarded the 15 ground samples to the NMSU sure (specific leaf weight), following a quadratic decrease with Soil, Water, and Air Testing Laboratory employees. They increase in number of nuts per shoot. Mass per nut decreased then conducted the digestion and analyses, by the method cited sharply when the number of nuts per shoot increased from 3 above, for a total cost of $150 ($10 per sample). Thus, the en- to 4, from an average of 6.1 ± 0.4 to 3.5 ± 0.3, but mass per tire expendable materials cost of this exercise was due to nut did not decrease further when number of nuts per shoot leaflet chemical analysis, estimated at $150. All other mate- increased to 5. rials are common in any plant science laboratory, and included the following: • mature tree with dense canopy • paper bags (lunch size or smaller) • sealable plastic bags (quart size) • permanent markers • wire tags • leaf area meter • drying oven • analytical balance (accurate to 1 mg, preferably to 0.1 mg) • Wiley mill (or equivalent) with a 40-mesh screen • computer plus software (laboratory network system, if possible, with electronic spreadsheet, plotting, and regression analysis)

RESULTS AND DISCUSSION Fig. 1. Percentage kernel as a function of specific leaf weight. Regression Canopy Observations and Regression Analyses equation: Percentage kernel = 25.77(Specific leaf weight) - 1.49(Spe- cific leaf weight)2 - 59.33, R2 = 0.80. The P values for linear and qua- Specific leaf weight nearly doubled from the extremely dratic specific leaf weight terms were 0.0078 and 0.0167, respec- -2 shaded, interior portion of the canopy (5.6 mg cm ) to the out- tively.

J. Nat. Resour. Life Sci. Educ., Vol. 29, 2000 • 81 After discussion of this observation, students were asked Table 1. Probability (P) values derived from t-tests in regression analy- to review previously assigned literature (e.g., Hansen, 1992; ses, rounded to four decimal places, for evaluating multiple factors af- fecting mass per nut and total nut weight per shoot. The P values cor- Roper and Loescher, 1987), which explains the general inverse respond to the coefficients for the listed independent variables in the relationship between tree crop quality and yield, and between multiple regression models. Absence of a P value for a particular co- quality and increasing fruit/leaf (sink/source) ratio, as is gen- efficient denotes lack of statistical significance. The multiple regression equations, derived from results obtained in Fig. 2 and 3, were: erally analogous to the relationship shown in Fig. 2A. The in- Mass per nut = -12.72(No. of nuts per shoot) + 1.46(No. of nuts per tuitive relationship involving number of nuts per shoot and shoot)2 + 0.42(Specific leaf wt.) + 27.76, R2 = 0.83; Total nut wt. per mass per nut arises from the fact that an increasing number of shoot = -37.73(No. of nuts per shoot) + 4.83(No. of nuts per shoot)2 + 2 nuts per shoot represented an increasing number of sinks 1.49(Specific leaf wt.) + 76.09, R = 0.73. competing for the finite photosynthetic resources available for Dependent variable the C gain and dry matter accumulation (growth) of each de- Independent variable Mass per nut Total nut wt. per shoot veloping nut on a shoot. An increase in number of nuts per P value shoot from 4 to 5 did not result in a further carbohydrate No. of nuts per shoot 0.0010 0.0015 source limitation, at least under these conditions. (No. of nuts per shoot)2 0.0018 0.0013 Students were advised to plot the residual prediction error Specific leaf wt. 0.0180 0.0093 (actual minus predicted mass per nut) from the model shown (Specific leaf wt.)2 -- -- No. of compound leaves per shoot -- -- in Fig. 2A (based only on linear and quadratic terms for num- (No. of compound leaves per shoot)2 -- -- ber of nuts per shoot as independent variables, and mass per nut as dependent variable) against specific leaf weight to fur- mass per nut was (at least visually) heavily masked by a vari- ther observe a possible influence of light exposure (specific able crop load (number of nuts) on the shoot. The finalized leaf weight) on mass per nut (Fig. 3A). A plot of the mass per multiple regression model (Table 1) provided an R2 of 0.83, nut values against specific leaf weight (not shown) did not re- and thus accounted for 83% of across-canopy variation in mass veal much relationship between these two variables (see above per nut. Taken together, the results showed that both light ex- discussion). However, the residual plot showed an increasing posure (specific leaf weight), through its direct impact on residual with increasing specific leaf weight, and this rela- photosynthesis and localized carbohydrate availability, and tionship was confirmed by a significant P value for slope. This sink competition (number of nuts per shoot), through its in- indicated that specific leaf weight was, in fact, an important fluence on carbohydrate consumption, affected the final mass variable in determining mass per nut, but that its influence on of each nut on a shoot.

Fig. 2. Relationships (A) between mass per nut and number of nuts per Fig. 3. Contribution of specific leaf weight to residual error (y)ofpre- shoot, and (B) between total nut weight per shoot and number of nuts dicting (A) mass per nut and (B) total nut weight per shoot with num- per shoot. Regression equations: Mass per nut = -12.98(No. of nuts ber of nuts per shoot as independent variable. Residuals obtained as per shoot) + 1.49(No. of nuts per shoot)2 + 31.57, R2 =0.72; Total nut actual minus predicted values from data in Fig. 2. Regression equa- weight per shoot = -38.66(No. of nuts per shoot) + 4.94(No. of nuts tions: Mass per nut residual error = 0.41(Specific leaf weight) - 3.24, per shoot)2 + 89.70, R2 = 0.48. The P values for linear and quadratic R2 = 0.41; Total nut weight per shoot residual error = 1.48(Specific number of nuts per shoot terms were(A) 0.0034 and 0.0060, respec- leaf weight) - 11.57, R2 = 0.47. The P values for the linear specific leaf tively, and (B) 0.0069 and 0.0063, respectively. weight term were(A) 0.0101, and (B) 0.0047.

82 • J. Nat. Resour. Life Sci. Educ., Vol. 29, 2000 Percentage kernel and mass per nut are critical nut quality of nuts per shoot (Fig. 2B). The sharp decrease in mass per nut indices in the pecan industry and play a major role in deter- on the 4-nut shoots below that of the 3-nut shoots resulted in mining the price that the grower receives for a crop (McEach- a measurably lower total nut weight per shoot at a number of ern, 1992). In this exercise, the maximum values obtained for nuts per shoot value of 4. However, with the 5-nut shoots, hav- percentage kernel were about 50 to 55% (Fig. 1), and the max- ing similar mass per nut as the 4-nut shoots, the total shoot nut imum value for mass per nut was about 7 g (Fig. 2A), which weight increased. In apple, Forshey and Elfving (1977) re- corresponds to approximately 143 nuts per kg (65 nuts per ported a positive, linear correlation between total yield per tree pound). These values are not considered to be of highest qual- and total fruit number per tree. In our example involving total ity for ‘Schley’ pecan based on industry standards, since pecan nut yield per shoot, linear and quadratic terms of num- Childers et al. (1995, p. 321) reported ‘Schley’ percentage ker- ber of nuts per shoot produced a model that accounted for nel and mass per nut standards of 60% and 54 nuts per pound nearly 50% of the variation in total shoot nut weight (Fig. 2B), (e.g., larger nut size), respectively. Our selected tree, although and the relationship was not linear. well maintained over its near century of life, had reached full Using the same procedure as described above for mass per canopy closure and had been shaded by adjacent trees at the nut, the students plotted the residuals of the regression model time of the study. Therefore, the light was probably inadequate from Fig. 2B, involving the linear and quadratic terms for to attain highest percentage kernel and mass per nut. However, number of nuts per shoot (residual as dependent variable) the light gradient within the canopy provided an ideal in- against specific leaf weight (independent variable). The resid- quiry-based learning opportunity for the students. Moreover, ual vs. specific leaf weight plot (Fig. 3B) provided a signifi- the large range in nut quality (percentage kernel and mass per cant slope. Thus, the multiple regression model (Table 1), ac- nut) implicated the important roles of exposure (specific leaf counting for 73% of the variation in total nut weight per weight) and, in the case of mass per nut, both shoot repro- shoot, again indicated that specific leaf weight, while an im- ductive sink strength (number of nuts per shoot) and exposure, portant factor, was not the sole cause of total shoot nut weight as indicated in Fig. 1, 2A, and 3A. variation across the canopy exposures. Total nut weight per shoot, an indicator of marketable Students recorded overall nut quality characteristics (qual- yield per shoot, tended to increase as light exposure (specific ity rating), a subjective but relevant rating scheme for the end- leaf weight) increased, but specific leaf weight accounted for user processor and consumer. Quality rating was based on a only 36% of the variation. A quadratic equation was fit to the value from 0 to 100 and weighted on (in increasing impor- data and again, the line form resembled that of Fig. 1 with pro- tance) shell appearance, nut size, percentage kernel, and ker- nounced scattering (data not presented). Specific leaf weight nel appearance, color, shelling characteristics, texture, and fla- is a reflection of light exposure and thus is believed to be a strong determinant of current-year photosynthetic capacity (DeJong and Doyle, 1985). Some portion of the total nut weight per shoot (shell and nonkernel tissue) had accrued early in the season (April–June) and was dependent on stored carbohydrate reserves and not current photosynthates. This may have, at least in part, explained the relatively low correlation between total nut weight per shoot and specific leaf weight. Total nut weight of any given shoot was equivalent to the product of mass per nut and number of nuts per shoot. In our relatively small dataset, total nut weight per shoot showed a more complicated but apparent trend with increasing number

Fig. 5. The correlation between (A) leaflet N concentration as percentage of N in leaflet dry matter or (B) leaflet N content as N mass per leaflet area and specific leaf weight. Regression equations: Leaflet N Fig. 4. Pecan nut quality rating as a function of specific leaf weight. Re- concentration = 0.06(Specific leaf weight) + 2.06, R2 = 0.29; Leaflet gression equation: Quality rating = 6.15(Specific leaf weight) + 9.16, N content per leaflet area = 29.77(Specific leaf weight) -33.30, R2 = R2 = 0.40. The P value for the linear specific leaf weight term was 0.94. The P values for the linear specific leaf weight term were(A) 0.0110. 0.0369 and (B) 0.0001.

J. Nat. Resour. Life Sci. Educ., Vol. 29, 2000 • 83 vor. The quality rating increased with increasing exposure Table 2. Cognitive outcomes expressed as percentage of total class en- (specific leaf weight) in a significant, linear fashion (Fig. 4). rollment. Specific leaf weight accounted for 40% of the cross-canopy Question % of enrollment variation in quality rating, and 76% with exclusion of the two 1. Specific leaf weight increased with increasing light exposure 91 high points, which appeared to deviate from the average line 2. Specific leaf weight is a function of leaf thickness, number of palisade cells or cell layers, or accumulation of solutes 27 (data shown without exclusion of those two points). 3. Specific leaf weight was not the only factor controlling total Only a marginally positive linear correlation was derived nut weight per shoot 82 when leaflet N concentration expressed as a percentage of the 4. Percentage kernel increased with increasing exposure (specific leaf weight) 82 leaflet dry matter was plotted against specific leaf weight 5. Mass per nut increased with increasing exposure (Fig. 5A). This relationship, almost a scattering of points, (specific leaf weight) 55 provided only a slightly significant slope. A lack of strong correlation between specific leaf weight and leaf N concentration variables. Nearly half of the class suggested that a greater num- (as a percentage of dry matter) has also been reported in wal- ber of data points might have improved the correlation be- nut and prune canopies (Weinbaum et al., 1989; Klein et al., tween specific leaf weight and total nut weight per shoot (re- 1991a). sponse not shown). Thus, a majority of the students realized Leaflet N content expressed as N mass per unit leaflet area that total shoot yield potential was not dependent on a single eliminates the contribution of dry matter to leaflet N content external factor, and that the experimental design (more ob- and more accurately reflects leaflet N allocation patterns servations) could be improved. among canopy units (Weinbaum et al., 1989). There was a The class was evenly divided in its interpretation of light strongly positive linear correlation and highly significant exposure (specific leaf weight) and its effect on mass per nut slope for the relationship between specific leaf weight and (Question 5). The lack of consistent response favoring an ef- leaflet N content per unit leaflet area (Fig. 5B). The approx- fect of specific leaf weight on mass per nut may have been be- imate twofold range in specific leaf weight, a reflection of the cause not all students were able to visualize the strong influ- steep photosynthetic photon flux gradient throughout the ence of a second variable (number of nuts per shoot) and its canopy, was associated with a parallel, twofold difference in pronounced masking of the effect of specific leaf weight. leaflet N content per leaflet area. A nearly identical correla- Since the results in Table 2 lack a control group of re- tion was observed in an earlier study involving pecan’s Jug- spondents due to small class size, we have no objective means landaceae relative walnut (Klein et al., 1991a). of comparing the cognitive effectiveness of this exercise, as These observations support the hypothesis that a tree pref- compared with other instructional techniques. Therefore, fur- erentially allocates its N to the most exposed canopy regions. ther and controlled classroom assessment is needed beyond In the well-exposed canopy positions, relatively high avail- this investigational study. This project, however, provided a ability of sunlight and high concentrations of N-containing readily observable sense of ownership and motivation for photosynthetic proteins combine to create localized vegetative discovery for the students through an alternative question–an- zones of high photosynthetic capacity, high levels of pho- swer linkage to traditional teaching methods. The postlabo- toassimilatory products, and high growth and quality poten- ratory writing assignment enabled timely, creative use of cog- tial of nearby reproductive sinks (DeJong and Doyle, 1985; nitive and interpretive skills for the relationship between light Flore and Lakso, 1989; Weinbaum et al., 1989). and tree performance. Through personal observation by the instructor, all students provided positive feedback and recog- Student Responses nized this combined laboratory and field activity as “a unique Following the several laboratory meetings to pool, enter, experience,” “fun,” “challenging,” and a positive step toward plot, and analyze class regression data (discussed above), stu- addressing the need for more “hands-on” and “real-world” in- dents were assigned an independent, take-home writing (com- structional training in plant science courses. This demon- position) assignment to assess their interpretation of the in- strates constructive matching between learning and teaching fluence of light exposure on pecan leaf and nut characteris- styles that is known to increase student achievement level and tics. The instructor provided guidance by first prompting the renew satisfaction in teaching (Whittington and Raven, 1995). students to consider key concepts demonstrated by the exercise. Selected and abbreviated responses are summarized in TAKE-HOME LESSONS FOR STUDENTS Table 2. AND INSTRUCTOR Most of the students wrote that specific leaf weight and per- In this experiential setting, we applied simple techniques centage kernel were positively related to light exposure within to study a complex biological process involving the interac- the canopy (Questions 1 and 4). Only a minor proportion of tion of light, space, time, and the distribution of a tree’s meta- students discussed the physiological components of specific bolic resources. We learned that a mature, dense tree canopy leaf weight (Question 2). This question extended beyond the consists of multiple, semi-autonomous behavioral units scope of the course content and the low response may indi- among the different light microenvironments, and that varia- cate differential retention of material taught in plant physiol- tion in the performance of these units is of practical and eco- ogy courses in the early to midportion of the undergraduate nomic importance. The study demonstrates that a tree dis- curriculum. Most students wrote that specific leaf weight tributes a greater proportion of its resources to the more ex- alone was not the only factor contributing to variation in total posed canopy positions, and that this resource allocation strat- nut weight per shoot (Question 3), probably because of the rel- egy is accompanied by greater productivity than occurs in less atively weak correlation the class obtained between these exposed canopy positions.

84 • J. Nat. Resour. Life Sci. Educ., Vol. 29, 2000 This exercise was used to convey practical lessons on the this exercise, has also been suggested (Berghage and Lownds, biology and agriculture management of the case study. Canopy 1991). Answers to not-so-easy questions on how light affects management is a phrase widely used by the grower/practi- the tree became more revealing through regression analyses. tioner and tree physiologist, but one that must be taken only For example, variation in mass per nut and total nut weight at face value by undergraduate students through reading as- per shoot were not simple functions of light exposure (specific signments and lectures. Students cannot experience shading, leaf weight). Rather, they were more complex phenomena that light exposure, and canopy management in an artificial (indoor varied largely because of combined, localized influences of classroom) setting. The case study tree had a dense canopy exposure and the collective demand for C by varying numbers with diverse light microenvironments, and the potential clearly of fruiting sinks (Table 1; Fig. 2 and 3). Related study on tree existed to increase its productivity and crop quality through crops with fleshy fruits (e.g., prune, apple, and peach) tends corrective procedures (canopy management). Thus, this ex- to be dominated by the simple cause-and-effect relationship ercise represented a microcosm of a mature pecan orchard, and between specific leaf weight (or photosynthetic photon flux) as such, was analogous to a larger scale agroecosystem as it and crop quality characteristics, but this relationship has ex- grows into maturity over the long-term, and then begins to explained only a minor percentage of the fruit quality variation perience uneven light distribution, reduced nut quality, and de- (e.g., < 50%) within the tree canopies (Southwick et al., 1990; clining yields, due to intracanopy and mutual limb shading Campbell and Marini, 1992; Barritt et al., 1987; Corelli-Gra- (e.g., crowding) by adjacent trees (Witt et al., 1989; Worley, padelli and Coston, 1991). Thus, students were exposed to not 1993). only the practical usefulness of statistics, but also to statisti- The exercise also illustrates and provides an experiential cal complexities (e.g., masking of specific leaf weight effect foundation for the much broader concept of phenotypic plas- by number of nuts per shoot) in a field of interest to them. This ticity. Phenotypic plasticity is the adaptive ability (plasticity) should elevate student interest in understanding statistical of an individual plant to alter its physiology/morphology in methods. response to temporal and spatial environmental variability (Schlichting, 1986). Thus, leaf thickness, leaf weight per unit APPLICABILITY AND CONCLUSION area (specific leaf weight), and leaf N content per leaf area within the tree were conditioned by intracanopy light gradi- The exercise can be adapted to other tree species, as evi- ents and were associated positively with productivity indices denced by the scientific literature (discussed above), but as we in pecan. The fact that a tree is comprised of repetitive, semi- have shown, it is particularly lesson-oriented if it combines autonomous, and highly adaptive modules (Sprugel et al., participatory learning with a commodity-specific research 1991) is a fundamental distinction between higher plants need. The class assumed an active role in conducting original (modular organisms) and animals, and this is an important bi- research and in making a scientific contribution, which departs ological lesson for students. from the typical demonstration trial method for teaching the It is relevant to note that pecan quality grading in the in- scientific method (Corak and Grabau, 1988). Unlike a demon- dustry, as succinctly described by McEachern (1992), in- stration, our exercise did not have a predetermined outcome. volves random nut samplings originating from undefined por- The uncharted territory contributed to the uniqueness of the tions of multiple trees of an orchard block. This sampling pro- project and it augmented our central theme of motivational tocol masks the within-canopy variability in nut quality and learning. shoot productivity, and while it is a necessary practice for the Courses on forest ecology, silviculture, pomology, arbori- pecan industry, it prevents us from visualizing the tree through culture, and urban forestry could potentially incorporate such the forest, that is, the spatial biology of an individual tree’sre- a laboratory project. The exercise is ideally suited to upper source allocation strategy as it responds to light. Statistical level undergraduate students, and the breadth of exposure analysis elucidated this dilemma and provided the vehicle for and analyses can be expanded for graduate students. The ex- students to practice interpretation of results without extensive ercise can be completed in one, 15-wk fall semester to en- knowledge of statistical theory. An incoming knowledge of re- compass both a midsummer leaf analysis and crop maturation. gression analysis would be helpful for this exercise but was A 10-wk syllabus could include a tree species with an earlier not a prerequisite for the course. This did not seem to hamper crop maturation date, such as apple or pear. Our small dataset students’ motivation and willingness to perform, provided can be increased (and preferably should be) to include more that an occasional walking through a thinking process was pro- observations and to accommodate a larger class enrollment. vided in class. They learned what basic regression analysis is, This activity is a thought and analysis-driven approach to how to generate regression output with a computer, the dif- provide a hands-on, tangible linkage between the biological ference between independent and dependent variables, and, and practical meanings of an increasingly important subject. most importantly, how regression analysis can be used in the Data collection is straightforward and the methods can be read- systematic and methodical interpretation of data, such as in the ily taught to students having little or no research experience, identification of major factors that cause spatial variation in and with only routine equipment and very minimal facilities. tree crop yield and quality. In recent surveys of agriculture faculty and employers, ACKNOWLEDGMENT problem solving ability, critical thinking skills, and the practice of structured methods for interpretation of data were rated This work was supported by a grant from the Oak Ridge as important developmental characteristics of undergraduate Associated Universities Junior Faculty Enhancement Awards education (Andelt et al., 1997; Whittington, 1997b). Increased Program to G.A. Picchioni (award 97-321), and by the New opportunity for writing as a learning medium, as provided in Mexico Agricultural Experiment Station.. Appreciation is ex-

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