Agronomy Journal 93:1235-1242 (2001)
© 2001 American Society of Agronomy
CORN
Season-Long Characterization of Vertical Distribution of Leaf Area in Corn
Nandkishor Boedhram*,a,
Timothy J. Arkebauerb and
William D. Batchelorc
a Dep. of Agric. and Biosyst. Eng., 124 Davidson Hall, Iowa State Univ., Ames, IA 50011
b Dep. of Agron., Univ. of Nebraska, Lincoln, NE 68588
c Dep. of Agric. and Biosyst. Eng., 219B Davidson Hall, Iowa State Univ., Ames, IA 50011
* Corresponding author (boedhram{at}iastate.edu)
Received for publication April 3, 2000.
 |
ABSTRACT
|
|---|
Amount and vertical distribution of leaf area are essential for estimating radiation interception for canopy photosynthesis modeling. The objective of this study was to quantify vertical distribution of leaf area in corn (Zea mays L.) during the entire growing season. Field experiments were conducted in 1994 and 1995 near Mead, NE. Treatments included irrigation vs. no irrigation and three N rates (0, 68, and 135 kg ha-1). A leaf-dissecting device was built for accurate clipping and collection of leaves at 0.10-m height intervals. A normal curve gave an excellent fit to measured vertical distributions of leaf area index (LAI) at 0.10-m height intervals (r2 > 0.94), regardless of applied N, soil moisture, crop developmental stage, or year. It was concluded that corn LAI was distributed symmetrically in the vertical from crop emergence to maturity. Plots of LAI vertical distributions indicated not only the onset and magnitude of differences among treatments, but also how stress was shared among all canopy layers. Of the three fitted curve parameters, height of symmetry and measure of green-canopy spread were good indicators of plant growth, but LAI in interval containing height of the symmetry axis was not. The existence of a symmetrical vertical distribution of LAI fulfilled the requirement for applying the three-point Gaussian method of integration in corn.
Abbreviations: GS1, Gaussian height above which 11% of total leaf area index is present • GS2, Gaussian height above which 50% of total leaf area index is present • GS3, Gaussian height above which 89% of leaf area index is present • LAI, leaf area index • LAItot, total leaf area index • LAI0.10m, leaf area index per 0.10-m height interval • RMSE, root mean square error • Xopt, midpoint of interval containing Ymax • Ymax, maximum leaf area index per 0.10-m height interval • 
2, measure of green-canopy spread
|
INTRODUCTION
|
|---|
AMOUNT AND VERTICAL DISTRIBUTION of leaf area are essential for estimating attenuation of photosynthetic photon flux density within a canopy (Sivakumar and Virmani, 1984). Leaf area and light distribution are important input parameters in canopy photosynthesis modeling. Vertical distribution of leaf area has often been constructed from leaf areas per horizontal layers, based on height (Acock et al., 1978), cumulative leaf area index (LAI) (Norman, 1978; Goudriaan, 1986; Pattey et al., 1991), and leaf number (Leuning et al., 1991; Connor et al., 1995).
Dwyer et al. (1992) fitted a third-order polynomial to the downward cumulative LAI in six corn hybrids around silking (R1; Ritchie et al., 1986) when all leaves were fully expanded. Tollenaar and Aguilera (1992) constructed vertical distributions of leaf area in three corn hybrids using leaf areas per 0.50-m height intervals at 2 wk after the R1 stage. No analytical description was given for the vertical distribution of leaf area. Biscoe et al. (1975) found a near-symmetrical vertical distribution of leaf area in barley (Hordeum vulgare L.) at two developmental stages. Ross (1975) reported that the height interval with highest leaf foliage density increased from 0.5 of total canopy height at V5 to 0.8 of total canopy height at the R1 stage. That pattern was true for densely planted corn in Estonia and sparsely planted corn in Tajikistan. Amount and distribution of LAI are major factors determining light interception by the canopy, which is essential in modeling crop growth and yield and in C balance studies. Yet, there is a lack of published information on leaf area distribution in corn over an entire growing season. The objective of this study was to quantify the vertical distribution of leaf area over the entire growing season in field-grown corn subjected to irrigation vs. no irrigation and three N application rates.
|
MATERIALS AND METHODS
|
|---|
The field experiments were conducted in 1994 and 1995 near Mead, NE. The experimental design was a randomized complete block design with a split-plot treatment arrangement. The experiment was replicated four times in 1994 and twice in 1995. The commercial corn hybrid, Pioneer 3394, was planted on 17 May 1994 and 18 May 1995. Final plant population density was 6.1 and 5.8 plants m-2 in 1994 and 1995, respectively. Irrigation, through overhead sprinkler, vs. no irrigation were main plots, and N application rates (0, 68, and 135 kg ha-1; hereafter called 0N, 68N, and 135N, respectively) were subplots. Nitrogen fertilizer was applied as urea [(NH2)2CO], 46–0–0, on 16 June 1994 (V6) and 15 June 1995 (V4). Each subplot was 7.6 (10 rows) by 25 m. Two outer rows on each side and 2-m buffer at each end of the subplots were kept as border zones. Phenology (Ritchie et al., 1986) was determined once or twice per week. The soil was a Sharpsburg silty clay loam (fine, montmorillonitic, mesic, Typic Argiudoll) with a water-holding capacity of 0.40 cm3 cm-3. Water content of the top 0.30 m of soil was determined gravimetrically at weekly intervals. Weeds were controlled with preplant herbicides, a mechanical cultivation at the V8 stage, and hand hoeing. Weather data were collected with an automated station located next to the experimental site.
An apparatus was built and used for accurate dissection and collection of leaf segments per 0.10-m height intervals (Fig. 1) . It consisted of a plywood base (1.22 by 2.44 by 0.02 m) with L-shaped galvanized steel strips (legs were 0.08 and 0.02 m) attached to it at 0.10-m intervals, thus creating 24 catchment areas. The device was extended 0.50 m later in the growing season to accommodate the tallest corn plants. The apparatus was housed in an air-conditioned laboratory next to the experimental site; it was placed on a 1-m high table for work convenience. Harvested plants, four per plot per sampling date, were rushed to the laboratory for quick dissection. Plants were momentarily held in an upright position to get the leaves reoriented in their field position before placing them onto the apparatus. Leaves were cut at their intersections with the vertical legs of the metal sheets with a pair of scissors. Leaf cutting always proceeded from the top to the bottom of the plants. After dissecting, leaf segments per each catchment area were collected and run through an area meter (model LI-3100, LI-COR. Lincoln, NE) to determine leaf area, which was subsequently converted to LAI per 0.10-m height interval (LAI0.10m). Plots of LAI vertical distributions were constructed using the LAI0.10m values.
View larger version (41K):
[in this window]
[in a new window]
|
Fig. 1. Schematic diagram of the leaf-dissecting device used to accurately clip and collect corn leaves at 0.10-m height intervals.
|
|
Two equations were tested for fit to the vertical distributions of LAI0.10m. The first was a three-parameter normal curve (Landsberg, 1977):
 | [1] |
where Y is LAI0.10m, Ymax is the maximum LAI0.10m, X is the midpoint of a 0.10-m height interval, Xopt is the midpoint of the interval containing Ymax, and 2 is the dispersion coefficient that controls the spread of the curve about Xopt. The larger the value of 2, the broader is the distribution of leaf area about Ymax. The second equation was a four-parameter, skewed normal curve:
 | [2] |
where b is an empirical skewness parameter, and all other factors are as defined in Eq. [1]. Notice that when b = 0, Eq. [2] is identical to Eq. [1].
Equations were fit using the curve-fitting technique of DeltaGraph (DeltaPoint, Monterey, CA). The program first linearizes the equation and then uses the chi-square method iteratively to find the best curve. The program requires initial parameter values to execute. Therefore, a good set of initial values is essential for reducing execution time and increasing accuracy of the final solution. Accuracy of the model fits was tested using the root mean square error (RMSE) technique. Both models gave excellent, comparable fits (r2 > 0.94) to the data sets (not shown). For simplicity, Eq. [1] was chosen to analytically describe the vertical distributions of LAI0.10m. In accordance with the objective, limited attempts were made to compare observed or predicted data sets between main plots (irrigation vs. no irrigation) or among subplot (N) treatments. Variability of measured data was evaluated through standard errors. In 1994, data from the two irrigation treatments were pooled for analysis purposes because of insignificant differences (not shown).
Gaussian Integration Method in Corn
Integration in time or space is usually performed with numerical methods such as the Eulerian, Simpson, or Runge-Kutta (Goudriaan, 1986). These methods are applicable because they allow feedback of the integrated value or state variable. When there is no such feedback, but rates in the total integration interval are known, the Gaussian integration method can be very efficient and accurate. An example of no feedback is canopy photosynthesis, which can be calculated from a known light regime in the canopy. The basic idea of the Gaussian integration method is to measure rate at representative positions in the total integration interval. This integration method can be simple (one point) to sophisticated (multipoint) and requires a symmetrical distribution of the variable. The three-point Gaussian integration method was introduced after it was shown that leaf area was distributed symmetrically in the vertical.
|
RESULTS
|
|---|
Weather conditions were markedly different between the two experimental years (Table 1), which resulted in diverse growing conditions. Rainfall from May through September was adequate in 1994 (515 mm compared with normal of 560 mm), except for a short period around the R1 stage, which was compensated with two irrigations totaling 54 mm. The growing season of 1995 was extremely dry, with only 278 mm of rain, about half of which was recorded before plant emergence at the end of May. Irrigation was applied regularly at 4- to 5-d intervals between 22 June (V5) and 30 August (R3) at a rate of about 30 mm wk-1. June and July of 1995 were particularly dry, with only 50 mm of precipitation compared with 345 mm in 1994 and a normal of 210 mm. In 1995, soil moisture was adequate in the irrigated plots, except for a short period before the R1 stage when it came close to the permanent wilting point of 0.24 cm3 cm-3. Soil water content in the nonirrigated plots was at permanent wilting point from mid-July till the end of the growing season. Early season (May) temperatures were above normal in 1994 and below normal in 1995 (Table 1). Midseason (July and August) temperatures were close to normal in 1994 and 4 to 5°C above normal in 1995. During the period from June to August of 1994, there were 30 d with maximum temperature of 30°C or above compared with 62 d in 1995.
View this table:
[in this window]
[in a new window]
|
Table 1. Weather and irrigation information during the growing seasons of 1994 and 1995 at Mead, NE. Normal maximum temperatures are for the period of 1961 to 1990.
|
|
Seasonal Course of Total Leaf Area Index
The diverse weather conditions between the two experimental years (Table 1) were clearly reflected in total LAI (LAItot) (Fig. 2a and 2b)
. In 1994, LAItot was pooled across irrigated and nonirrigated treatments because of insignificant differences (not shown). Excellent growing conditions early in the growing season of 1994 resulted in a very steep linear phase development of LAItot between the V8 and V13 stage, which was reached in as little as 40 d after plant emergence (Fig. 2a). Consequently, there was little difference in LAItot among the three N treatments during that period. After the V13 stage, LAItot increased slowly in the 68N and 135N treatments but started to decrease in the 0N treatment from its peak value of 3.0 at the V13 stage mainly because of senescence of lower leaves. The greatest LAItot value, attained at the R1 stage, was 3.6 in the 68N and 4.0 in the 135N treatment. After the R1 stage, LAItot stayed constant for about 3 wk before decreasing linearly till the end of the season.
View larger version (36K):
[in this window]
[in a new window]
|
Fig. 2. Seasonal course of leaf area index (LAI) in corn fertilized with 0, 68, or 135 kg N ha-1 in (a) 1994 (data combined for irrigated and nonirrigated treatments) and (b) 1995 (data separate for irrigated and nonirrigated treatments).
|
|
Total LAI in the irrigated plots of 1995 was similar among the N treatments only up to the V7 stage (Fig. 2b). Thereafter, it proceeded in accordance with amount of applied N. In contrast to 1994, all N treatments attained the greatest LAItot, ranging from 2.2 to 3.8, at the R1 stage. Thereafter, LAItot of all N treatments decreased linearly until 22 September when leaves were killed by severe frost. In contrast, the seasonal pattern of LAItot was similar among the three nonirrigated N treatments (Fig. 2b), possibly because of lack of N uptake by the drought-stricken plants. The greatest LAItot was about 1.2 for all N treatments and was recorded at the V14 stage. At that time, plants were heavily stunted, with maximum heights of only 1.4 m, and showed no developmental growth beyond the V14 stage. Thus, under very good growing conditions (1994), growth rate of LAItot was rapid and similar among N treatments for a longer period of time: about 40 d from plant emergence or until the V13 stage. Under moderately good growing conditions (irrigated plots of 1995), growth rate of LAItot was moderate and similar for a shorter period of time (until the V7 stage). In both cases, after the period of similar LAItot growth crop, development proceeded in accordance with amount of applied N fertilizer. Under severe drought stress conditions (nonirrigated plots of 1995), plant development was poor but similar among the N treatments during the life span of the plants.
Seasonal Trend of Vertical Distribution of Leaf Area Index
The three-parameter normal curve (Eq. [1]) gave excellent fits to all vertical distributions of LAI0.10m (r2 > 0.94), i.e., regardless of N and irrigation treatments, crop growth stage, or experimental year. Observed data sets and best-fitted curves for six selected days in 1994 are shown in Fig. 3
. As with LAItot, there were minimal differences in the vertical distribution of LAI0.10m among the N treatments up to the V13 stage (Fig. 3a and 3b). After the V13 stage, the magnitude of LAI0.10m at corresponding heights varied in accordance with amount of applied N (Fig. 3d, 3e, and 3f). Also, the lowest height interval with green leaves was higher in the 0N treatment than in the 68N or 135N treatments because of early leaf senescence. The three fitted curves, one for each N treatment per sampling date, ran quasi-parallel after the V13 stage, meaning that the difference in LAItot was shared about evenly by all canopy levels. The largest Ymax in all treatments was observed between the V11 and V16 stage (Fig. 3b and 3c), which coincided with the period of most-rapid leaf appearance and elongation.
View larger version (34K):
[in this window]
[in a new window]
|
Fig. 3. Observed leaf area index LAI at 0.10-m height intervals (LAI0.10m), and fitted normal curves on six selected days in 1994 in corn fertilized with 0, 68, or 135 kg N ha-1. Data were combined for irrigated and nonirrigated treatments.
|
|
Figure 4
depicts the observed LAI0.10m distributions and fitted best curves at six growth stages in the irrigated experiment of 1995. As with LAItot, distribution of LAI0.10m and fitted curves were similar among N treatments only until the V7 stage (Fig. 4a). The advantage of added N on leaf area development was quite evident after the V7 stage (Fig. 4b, 4c, 4d, 4e, and 4f). However, unlike in 1994, the advantage was noticeable only in the middle and upper parts of the canopy. Figure 5
shows the vertical distribution of LAI0.10m and fitted curves on six dates in the nonirrigated experiment of 1995. Unlike the 1994 experiments and irrigated experiments in 1995, the vertical distribution of LAI0.10m and fitted best curves for the three N treatments were similar throughout most of the growing season.
View larger version (33K):
[in this window]
[in a new window]
|
Fig. 4. Observed leaf area index at 0.10-m height intervals (LAI0.10m), and fitted normal curves on six selected days in 1995 in irrigated corn fertilized with 0, 68, or 135 kg N ha-1.
|
|
View larger version (28K):
[in this window]
[in a new window]
|
Fig. 5. Observed leaf area index at 0.10-m height intervals (LAI0.10m), and fitted normal curves on six selected days in 1995 in nonirrigated corn fertilized with 0, 68, or 135 kg N ha-1.  Crop did not develop beyond the V14 stage because of severe drought.
|
|
Seasonal Pattern of the Fitted Curve Parameters
The seasonal course of the fitted curve parameters is depicted in Fig. 6
. The seasonal pattern of Ymax (Fig. 6a, 6d, and 6g) mirrored that of LAItot (Fig. 2). However, the magnitude of Ymax was not sufficient to describe crop health. For example, the seasonal trend of Ymax of the nonirrigated experiment in 1995 (Fig. 6g) closely resembled that of 1994 (Fig. 6a). Yet, the former experiment produced no kernel yield because of severe drought stress while the latter had very good yield, ranging from 7500 to 12500 kg ha-1 (Table 2). Seasonal trend of Xopt (Fig. 6b, 6e, and 6h) closely followed that of total canopy height (r2 = 0.97, n = 113; data not shown). Thus, if Xopt was a good measure of crop performance, then total canopy height could be used to quantify it. Season maximum of Xopt was about 1.6, 1.4, and 0.7 m in 1994 plots, irrigated 1995 plots, and nonirrigated 1995 plots, respectively. Seasonal pattern of 2, a dispersion coefficient that controls the spread of the curve (Fig. 6c, 6f, and 6i), mirrored the vertical distribution of LAItot about Xopt. Thus, if 2 was a good measure of canopy performance, then the vertical distribution of LAItot could be used to quantify it.
View this table:
[in this window]
[in a new window]
|
Table 2. Kernel yield at harvest of corn grown under rain-fed and irrigated conditions and fertilized with 0, 68, and 135 kg N ha-1.
|
|
Gaussian Integration Method Applied to Corn
We showed above that the vertical distribution of LAI was symmetrical and, thus, the Gaussian three-point integration method was applicable on a corn canopy. When this method is applied, for example, to estimate total canopy photosynthesis, then the integration interval must be taken as LAItot. Rate of photosynthesis must then be measured at three representative depths: in the middle of the green canopy (at Xopt) and at relative distances of (0.15)0.5 LAItot on either side of Xopt. These heights are thus at canopy levels above which 11, 50, and 89% of LAItot is present (hereafter referred to as GS1, GS2, and GS3, respectively). Also, the rate at GS2 (Xopt) must be taken as 1.6 of the observed rate (Goudriaan, 1986). Several researchers (e.g., Pattey et al., 1991; Goudriaan, 1986) have used this integration method to calculate canopy photosynthesis in corn without explicitly showing or referring to the existence of a symmetrical vertical distribution of LAI.
Seasonal Trend of the Gaussian Heights
Plots of the three Gaussian heights vs. canopy height are depicted in Fig. 7
. There was an excellent linear relationship between each of the Gaussian heights and canopy height in both years (r2 > 0.94, n = 75). The slope of GS2 (Xopt) was 0.60 in 1994 and 0.71 in 1995. The higher value in 1995 was a reflection of more senesced lower leaves because of drought stress. Similar trends were also noted for GS1 and GS2. Furthermore, a slope larger than 0.5 for GS2 implied that its relative position was higher than 0.5 canopy height. It was concluded that Xopt was always at exactly the middle of the green canopy (because of the symmetry) but higher than 0.5 canopy height whenever there was leaf senescence in the lowest part of the canopy. Also, a linear relationship between the Gaussian levels and canopy height also implied that two points (samplings) would suffice to regress the relationship for the entire growing season.
View larger version (33K):
[in this window]
[in a new window]
|
Fig. 7. Linear relationship between the three Gaussian heights (GS1, GS2, and GS3) and canopy height in corn fertilized with 0, 68, or 135 kg ha-1 in (a) 1994 and (b) 1995. GS1, GS2, and GS3 correspond to canopy levels above which 11, 50, and 89% of total LAI, respectively, is present.
|
|
|
DISCUSSION
|
|---|
The combined effects of N fertilizer, irrigated vs. nonirrigated treatments, and weather in the two experimental years created a wide range of growing conditions. Thus, conclusions on the vertical distribution of leaf area drawn from this study should be regarded robust. The 1994 growing season was marked by adequate rainfall and above-average temperatures, especially early in the season, which promoted rigorous plant growth and very good yield. The 1995 growing season was extremely dry. In fact, it was so dry that the rain-fed plants did not develop beyond the V14 stage (zero yield). The irrigated plants of 1995 yielded less than in 1994.
The leaf-dissecting device was extremely helpful in dissecting and collecting leaves per 0.10-m height intervals. Plants placed on the device had almost all leaves resting on the vertical bars of the device, mainly because of a phyllotaxis of 180 degrees, and warranted accurate leaf cutting at 0.10-m intervals. A similar procedure for measuring the vertical distribution of leaf area in corn was employed by Dwyer et al. (1992). They held the sampled plants against a backdrop with horizontal lines marked every 0.10 m and measured leaf area within each 0.10-m vertical increment. A third-order polynomial accurately described the relationship between cumulative LAI and plant height, starting at the plant top.
In this study, a normal curve gave an excellent fit to all data sets of vertical distributions of LAI0.10m, regardless of crop stage, treatments employed, or year of study. No published information was found to explicitly relate our results to. However, symmetrical or near-symmetrical distributions could be deduced from work by Norman (1978) and Ross (1975) for corn and Biscoe (1975) for barley grain. Comparison of the vertical distributions of LAI0.10m among the N treatments over time clearly identified onset and magnitude of leaf area differences due to stress.
The fitted normal curve was empirical, but its parameters could be ascribed some biological meaning. For example, Xopt and 2 were good measures of crop performance. Thus, higher values of Xopt and 2 were indicative of tall plants with a large dispersion of green leaves. The Ymax was a good measure of foliage density but not good for describing crop performance. For example, Ymax of stressed plants in 1995 (nonirrigated) was comparable to that of the unstressed plants in 1994, yet the stressed plants produced zero seed yield.
The excellent linear relationship between Xopt and canopy height implied that two samplings would suffice to draw this relationship for the entire growing season. Also, the relative position of Xopt with respect to canopy height could be indicative of crop health. When plants were young and healthy, Xopt was usually at 0.5 canopy height, but under stress, it acquired a relatively high position in the canopy because of senesced lower leaves. In 1994, a good year for plant growth and development, the slope of the regression of GS2 (Xopt) on canopy height was 0.60 compared with 0.71 in 1995, which was a very dry year. Similar results were reported by Ross (1975), who found that GS2 increased from 0.5 canopy height at the V5 stage to 0.8 at the R1 stage in dense corn in Estonia.
This study has clearly demonstrated the presence of a symmetrical vertical distribution of LAI in corn from plant emergence to physiological maturity. The symmetry fulfilled the requirement for applying the three-point Gaussian method of integration on corn canopies. The basic idea of this method is to quickly and accurately estimate canopy-level processes (e.g., photosynthesis) from weighted rate measurements at three representative canopy levels. This numerical method is particularly useful in studies that scale-up plant physiological processes from the leaf to canopy level.
A rationale for future research would be further generalization of the vertical distribution of leaf area through inclusion of genotypes from different maturity classes and with different morphological traits. Also, the study should be coupled with plant physiological and light interception measurements to understand their interrelationships. Sampling per 0.10-m height intervals was perhaps too detailed, but it very well served its purpose. Dwyer et al. (1992) and Biscoe (1975) also used 0.10-m height intervals in their studies. Details on the vertical distribution of LAI could become obscure when the height interval is too large (e.g., Tollenaar and Aguilera, 1992; they used 0.50-m height intervals). Height intervals of 0.20 to 0.30 m should be ideal to preserve accuracy and drastically reduce workload.
|
NOTES
|
|---|
This research was supported by funds from the USDA Regional Research Project no. NE-175.
|
REFERENCES
|
|---|
- Acock, B., D.A. Charles-Edwards, D.J. Fitter, D.W. Hand, L.J. Ludwig, J.W. Wilson, and A.C. Withers. 1978. The contribution of leaves from different levels within a tomato crop to canopy net photosynthesis: An experimental examination of two canopy models. J. Exp. Bot. 29:815–827.[Abstract/Free Full Text]
- Biscoe, P.V., R.K. Scott, and J.L. Monteith. 1975. Barley and its environment: III. Carbon budget of the stand. J. Appl. Ecol. 12:269– 293.
- Connor, D.J., V.O. Sadras, and A.J. Hall. 1995. Canopy nitrogen distribution and the photosynthetic performance of sunflower crops during grain filling—a quantitative analysis. Oecologia 101:274– 281.
- Dwyer, L.M., D.W. Stewart, R.I. Hamilton, and L. Houwing. 1992. Ear position and vertical distribution of leaf area in corn. Agron. J. 84:430–438.[Abstract/Free Full Text]
- Goudriaan, J. 1986. A simple and fast numerical method for the computation of daily totals of crop photosynthesis. Agric. For. Meteorol. 38:249–254.
- Landsberg, J.J. 1977. Some useful equations for biological studies. Exp. Agric. 13:273–286.
- Leuning, R., R.N. Cromer, and S. Rance. 1991. Spatial distribution of foliar nitrogen and phosphorus in crowns of Eucalyptus grandis. Oecologia 88:504–510.
- Norman, J.M. 1978. Modeling the complete crop canopy. p. 248–277. In B.J. Barfield and J.F. Gerber (ed.) Modification of the aerial environment of crops. ASAE, St. Joseph, MI.
- Pattey, E.P., P. Rochette, R.L. Desjardins, and P.A. Dube. 1991. Estimation of the net CO2 assimilation rate of a maize (Zea mays L.) canopy from leaf chamber measurements. Agric. For. Meteorol. 55:37–57.
- Ritchie, S.W., J.J. Hanway, and G.O. Benson. 1986. How a corn plant develops. Spec. Rep. 48. Coop. Ext. Serv., Iowa State Univ. of Sci. and Technol., Ames.
- Ross, J. 1975. Radiative transfer in plant communities. p. 13–55. In J.L. Monteith (ed.) Vegetation and the atmosphere. Academic Press, New York.
- Sivakumar, M.V.K., and S.M. Virmani. 1984. Crop productivity in relation to interception of photosynthetically active radiation. Agric. For. Meteorol. 31:131–241.
- Tollenaar, M., and A. Aguilera. 1992. Radiation use efficiency of an old and new maize hybrid. Agron. J. 84:536–541.[Abstract/Free Full Text]
This article has been cited by other articles:
|
|
|
|
 
V. Ciganda, A. Gitelson, and J. Schepers
Vertical Profile and Temporal Variation of Chlorophyll in Maize Canopy: Quantitative "Crop Vigor" Indicator by Means of Reflectance-Based Techniques
Agron. J.,
September 8, 2008;
100(5):
1409 - 1417.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
F. Solari, J. Shanahan, R. Ferguson, J. Schepers, and A. Gitelson
Active Sensor Reflectance Measurements of Corn Nitrogen Status and Yield Potential
Agron. J.,
May 7, 2008;
100(3):
571 - 579.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. D. Subedi and B. L. Ma
Ear Position, Leaf Area, and Contribution of Individual Leaves to Grain Yield in Conventional and Leafy Maize Hybrids
Crop Sci.,
September 23, 2005;
45(6):
2246 - 2257.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. I. Lizaso, W. D. Batchelor, K. J. Boote, M. E. Westgate, P. Rochette, and A. Moreno-Sotomayor
Evaluating a Leaf-Level Canopy Assimilation Model Linked to CERES-Maize
Agron. J.,
April 27, 2005;
97(3):
734 - 740.
[Abstract]
[Full Text]
[PDF]
|
|
|
TOP
ABSTRACT
INTRODUCTION
