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a Dep. of Plant and Soil Sciences, Oklahoma State Univ., 368 Agricultural Hall, Stillwater, OK 74078
b Dep. of Crop, Soil, and Environmental Sciences, Univ. of Arkansas, 1366 W Altheimer Drive, Fayetteville, AR 72704
c Dep. of Horticulture, Univ. of Arkansas, Plant Science Building, Fayetteville, AR 72703
* Corresponding author (lpurcell{at}uark.edu)
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: CIPAR, cumulative intercepted photosynthetically active radiation • CTU, cumulative thermal units after emergence • FLI, fraction of light intercepted • HI, harvest index • MG, maturity group • PAR, photosynthetically active radiation • RUE, radiation use efficiency
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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95%) of the available photosynthetically active radiation (PAR), indicating that complete interception of PAR does not guarantee attainment of maximum yield. Finally, in Phase III, soybean yield per unit ground area is maximized, and there are no further increases in soybean yield for increased plant population.
While Duncan's postulates adequately describe the relationship between soybean yield and plant population, they do not address why the plant population at which different phases occur varies by MG and environmental condition. Since Phase II is dependent on interplant competition, factors such as row spacing, crop maturity, and canopy architecture would likely affect the plant population at which it occurs. Duncan described Phase II simply in terms of complete interception of PAR (i.e., 95%). Describing light interception in these terms, however, neglects to consider both the time required to obtain complete canopy closure and the length of time that the crop intercepts light. The time required for a crop to fully intercept available light, however, may be managed by row spacing and plant population (Ball et al., 2000). The duration that a crop intercepts light during the season may also be managed by selecting desired maturity (Purcell et al., 2002).
Purcell et al. (2002) demonstrated that yield did not continue to increase at high population densities (Phase III) because of decreased radiation use efficiency (RUE, MJ m–2). Furthermore, their data indicated an asymptotic relationship between soybean biomass and CIPAR, and there was little increase in biomass for CIPAR greater than 700 MJ m–2. While their experiments included a range of environmental conditions, only MG IV and earlier soybean cultivars were evaluated.
Previous experiments (Board, 2000; Egli, 1988; Ethredge et al., 1989; Parvez et al., 1989; Shibles and Weber, 1966; Weber et al., 1966; Wiggans, 1939) have emphasized empirical relationships between soybean plant population and soybean yield. Therefore, results have been limited to conditions similar to the experimental conditions, and little progress has been made in developing a mechanistic understanding of how crop maturity and population interact to affect yield. In a companion manuscript (Edwards and Purcell, 2005), we evaluated the response of MG 00 through VI soybean to increasing plant population and identified some of the difficulties that can be faced when trying to describe yield response to plant population using empirical functions. In this manuscript we use light interception as a basis for developing a mechanistic understanding of the relationship between plant population and yield.
We hypothesized that the response of soybean yield to plant population under well-watered conditions was associated with the cumulative amount of light intercepted by a soybean crop from emergence to R6 (Fehr and Caviness, 1977). This relationship was hypothesized to extend across MGs and environments and provide a basis for understanding yield responses to plant population. Given this hypothesis, the objective of this experiment was to develop a mechanistic approach to understanding soybean response to increased population density as a function of light interception, which is affected by both population density and soybean maturity.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Experimental design was a split-plot arrangement of treatments in a randomized complete block with four replications (blocks). In 2001 and 2003 main plots were soybean MG (MG 00, Trail; MG 0, Lambert; MG I, IA 1006; MG II, IA 2008; MG III, Macon; MG IV, Pioneer 94B01; MG V, Hutcheson; and MG VI, NK 6262) and subplots were seeded population (10, 20, 40, 60, and 100 seeds m–2) randomized within MG. In 2002, there were two cultivars for each MG and main plots were soybean MG (00, 0, I, II, III, IV, V, and VI) and subplots were the interaction of seeded population (10, 20, 40, 60, and 100 seeds m–2) and soybean cultivar (MG 00, Jim and Trail; MG 0, AC Comoran and Lambert; MG I, IA 1006 and MN 1801; MG II, Dwight and IA 2008; MG III, Macon and Pana; MG IV, Pioneer 94B01 and MPV 437; MG V, Caviness and Hutcheson; and MG VI, Desha and NK 6262) randomized within MG. These cultivars were selected as well-adapted cultivars for the Midsouth production area based on previous research (Edwards et al., 2003; Ishibashi et al., 2003) and Arkansas Soybean Variety Performance Tests (Dombek et al., 2001).
Plant population for each plot was determined approximately one week after emergence by counting the number of emerged plants in 1 m of row in five different locations within each plot. Actual plant population deviated slightly from seeded population; therefore, plant population, rather than seeded population, was used for all analyses. Soybean developmental stages (Fehr and Caviness, 1977) were determined each year by evaluating soybean from each MG on an approximate 3-d interval.
In 2001 plots were initially seeded May 17, but a 4-cm rainfall immediately after sowing reduced emergence, so the entire plot area was sprayed with 1 kg a.i. ha–1 glyphosate [N-(phosphonomethyl)glycine], tilled, and resown on June 10. Emergence was June 16. In 2002 plots were drill-seeded on May 7, and emergence was May 20. In 2003, plots were initially seeded on May 12, but cool, wet conditions and 7 cm of rainfall in the week following sowing reduced emergence (<70%), and the plot area was sprayed with 1 kg ha–1 glyphosate, tilled, and resown on May 27. Emergence was 3 June 2003.
To remove any edge effects, the two outside rows and 0.6 m from the plot ends were removed immediately before harvest. Harvest index samples were collected immediately before harvest by hand harvesting the aboveground portion of soybean from 1 m2 of plot area. Samples were dried at 50°C for a period of 3 to 5 d, weighed, and threshed. Harvest index was calculated as the ratio of seed mass to total aboveground plant mass. Soybean was harvested using a small-plot combine, and grain weights were corrected to 130 g kg–1 moisture content.
Fraction of light intercepted (FLI) by the soybean canopy was determined approximately every 7 d using a digital imagery technique (Purcell, 2000). In this technique, green pixels are expressed as a fraction of total pixels in the frame, and the fraction of green pixels has a one-to-one relationship with FLI. Software (SigmaScan Pro, V. 5.0, Chicago, IL) was used to quantify green and total pixel numbers, using a macro (Karcher and Richardson, 2005) that automated the analysis process for the large number of images that accumulated during the study.
Leaf expansion and canopy development in soybean are temperature dependent (Sinclair, 1984), and therefore, light interception measurements were evaluated as a function of cumulative thermal units (°C). Thermal units for a given day were calculated as the average daily temperature minus a base temperature of 10°C (Ritchie and NeSmith, 1991), and cumulative thermal units after emergence (CTU) were determined by summing daily thermal units values from emergence. For each cultivar within a given year, FLI was regressed against plant population (plants m–2) and CTU (°C) using the model:
 | [1] |
Total CIPAR was determined by calculating the product of daily FLI and daily incident radiation and summing from soybean emergence to R6. Daily PAR was calculated as 50% of the total solar radiation (Monteith, 1977). Total solar radiation was estimated using the procedure of Hargreaves and Samani (1982) as modified by Annandale et al. (2002). Solar radiation estimates using this procedure agreed closely with observed values across wide geographical and climatological regions without a need for site-specific calibration (Ball et al., 2004).
Yield and end-of-season aboveground biomass data were averaged across replication each year, resulting in one data point for each cultivar x seeding density combination each year (2001, n = 40; 2002, n = 80; and 2003, n = 40). The responses (Y) of end-of-season, aboveground biomass (g m–2) and yield were modeled as an exponential function of CIPAR (MJ m–2, independent variable) using a nonlinear regression model where:
 | [2] |
In Eq. [2], the sum of the intercept and 
is equal to the asymptote, and ß1 represents the responsiveness of Y as CIPAR increased (Fig. 1). In the context of this experiment, a smaller ß1 indicates that greater CIPAR was required to obtain maximum Y. This equation has been successfully used in similar experiments evaluating the response of soybean biomass (Purcell et al., 2002) and maize (Zea mays L.) yield (Edwards et al., 2005) to cumulative light interception.
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Outlier analysis was performed on all regression analyses that used CIPAR as the independent variable. Studentized residual analysis (SAS, V. 9.1, SAS Institute Inc., Cary, NC) was used to identify outliers, and observations having a studentized residual greater than 1.5 were removed from the analysis.
Homogeneity of regression coefficients (C1 and C2) was determined using a Z test, with the null hypothesis being C1 – C2 = 0 (Kanji, 1993). We calculated Z values as:
 | [3] |
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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coefficient for 2003 were significantly different (P < 0.01, data not shown) than those for 2001 and 2003. The ß1 terms, however, which describe the responsiveness of yield and biomass to increasing CIPAR, were not significantly different (P > 0.05) among years of the experiment. Furthermore, ß1 coefficients for biomass in this experiment correspond very closely to those (–0.0032) reported for MG IV and earlier soybean by Purcell et al. (2002).
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0.05) in the intercept (ß0) and coefficients for yield in 2003 compared with 2002 and 2001 (Table 2) asymptotic yield (ßo + ) for 2003 was 1.85-fold greater than in 2001, and 1.91-fold greater than in 2002. In all years, the crop was well watered through the combination of irrigation and rainfall, the soil was fertilized to soil-test specifications, and there were no obvious pests or pathogens that could be associated with yield differences. One environmental factor that was different was cooler temperatures during seed fill in 2003 than in 2001 and 2002 (data not shown). The cooler temperatures of 2003 were associated with a lengthened seed-fill period and greater individual seed mass for all MGs compared with seed-fill period and individual seed mass in 2001 and 2002 (Edwards and Purcell, 2005). The slope of the regression equations presented in Table 2 has units equivalent to measures of RUE (g MJ–1, Sinclair and Muchow, 1999), but data were collected differently and should not be confused with RUE. To measure RUE, sequential plant mass samples are collected from a defined area during a growing season, plant mass (g m–2) is regressed against cumulative intercepted radiation (MJ m–2, Sinclair and Muchow, 1999), and the slope of this regression defines the RUE. In the absence of biotic and abiotic stresses, the relationship between mass accumulation and cumulative intercepted radiation is linear, indicating that RUE is constant over the measurement period (Sinclair and Muchow, 1999). In our measurements, plant mass was collected at crop maturity and was not synchronous with the period of cumulative intercepted radiation (emergence to R6 developmental period).
Approximately twice as much CIPAR in 2001 and 1.5 times as much CIPAR in 2002 and 2003 was required to obtain 95% of asymptotic biomass as was required to obtain 95% of asymptotic yield (Table 2). In the absence of abiotic stress, increases in plant biomass have traditionally been thought to result in increases in grain yield (Spaeth et al., 1984). Our data, however, clearly indicate that HI decreased linearly as CIPAR increased (Fig. 2). While a decline in HI with CIPAR has not previously been reported, higher HI values have been noted for early-maturing cultivars relative to later-maturing cultivars (Johnson and Major, 1979; Schapaugh and Wilcox, 1980). In a companion paper (Edwards and Purcell, 2005) we noted that HI of MG V and VI cultivars had a negative response to increased plant population density. Data presented in Fig. 2 indicate that reduction of HI at higher population densities may be a more general response of HI decreasing with CIPAR.
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coefficients for 2003 were statistically different (P < 0.05) than those for 2001 and 2002, but differences in ß1 coefficients among years were nonsignificant. Nonsignificant differences for ß1 coefficients among years indicates that the responsiveness of relative biomass to increasing CIPAR was not statistically different among years of the experiment. Coefficients for individual years are given in the inset of Fig. 3A. Although ß0 and coefficients differed among years we considered year as a random factor and elected to combine analysis over years, resulting in a highly significant (P < 0.01) model.
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) for relative biomass and indicated that 90 and 95% of asymptotic biomass were attained at 911 and 1142 MJ m–2 of CIPAR, respectively. Similarly, the regression equation in Fig. 3B predicted an asymptote of 0.88 (ß0 + ) for relative yield and indicated that CIPAR values required to obtain 90 and 95% of asymptotic yield were 605 and 731 MJ m–2, respectively. The much lower CIPAR requirement for yield compared to that of biomass is consistent with the reduction in HI associated with increasing CIPAR (Fig. 2).
Since leaf area can be managed by changing plant population and leaf area duration can be managed by MG selection (Purcell et al., 2002), CIPAR can be predicted as a function of plant population and days from emergence to R6. Therefore, we combined data from all years and regressed CIPAR as a function of plant population (PP), days from emergence to R6 (DTR6), their squared terms, and cross products. All terms in the regression were highly significant (P < 0.01) and the predicted equation was:
 | [4] |
Using the relationship in Eq. [4], we calculated CIPAR for soybean of different maturities as a function of plant population (Fig. 4). These predicted isolines illustrate the amount of CIPAR that would be intercepted at given population densities for cultivars that would reach R6 at 80, 90, 100, and 110 d from emergence. The horizontal lines in Fig. 4 represent the amount of CIPAR required to obtain 90 and 95% of the relative asymptotic yield. Figures 3B and 4 also demonstrate the diminishing marginal returns for additional units of CIPAR. For example, to increase yield from 85 to 90% of the asymptote required approximately 70 MJ m–2 of additional CIPAR, but to increase yield from 90 to 95% of the asymptote required approximately 125 MJ m–2 of additional CIPAR. Therefore, while we did observe increases in relative yield for CIPAR greater than 605 MJ m–2, the increases in yield per additional MJ m–2 of CIPAR were smaller than those for each additional MJ m–2 of CIPAR obtained before reaching the 605 MJ m–2 mark.
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Of practical consideration is determining a MG that would give a minimum duration of 80 d from emergence to R6, and hence have the ability to acquire a minimum of 605 MJ m–2. Depending on year (Table 1), either a MG II or III cultivar would have a duration from emergence to R6 80 d. Although it would be possible for a cultivar reaching R6 in 80 d to obtain 605 MJ m–2 of CIPAR, this could only be achieved with high plant populations (>80 plants m–2). This has implications for risk-averse soybean producers, as established plant population is often less than seeded population. For example, a cultivar reaching R6 in 80 d seeded at 80 seeds m–2 and having 100% emergence should intercept roughly 605 MJ m–2 of PAR and obtain at least 90% of asymptotic yield. However, if only 25% of seed emerged due to unfavorable weather conditions, then approximately 498 MJ m–2 of PAR would be intercepted and limit yield potential to approximately 82% of the asymptotic maximum. If the same producer, however, sowed a cultivar reaching R6 in 110 d at 40 seeds m–2 and suffered a 75% stand reduction, crop yield potential would only be reduced from 97 to 95% of the asymptotic maximum.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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90% of maximum soybean yield can be obtained by intercepting 605 MJ m–2 of PAR. An additional 150 to 200 MJ m–2 would be required to reach harvest maturity. Interestingly, the Midsouth receives around 2000 MJ m–2 of PAR during a typical, frost-free growing season (Purcell et al., 2003). The approximately 1200 MJ m–2 of additional unused PAR can, therefore, be thought of as an under-utilized resource that has revenue potential. This idea deserves additional consideration and evaluation to determine if alternative cropping systems could be implemented to harvest this currently unused light energy to increase cash-flow potential. Incident PAR is affected by numerous factors including latitude, day of year, and atmospheric transmisivity (Ball et al., 2004); therefore, soybean sown at locations that have a different incident PAR per day would expectantly accumulate different quantities of CIPAR over specified periods. Because of the direct effect that crop leaf area duration has on CIPAR and relative yield, further research is also needed in predicting how the length of developmental periods changes among MGs for different latitudes and sowing dates. For many locations, duration from emergence to R6 among MGs, will change the MG x population density combinations required to obtain given levels of CIPAR. We hypothesize, however, that critical CIPAR values required to maximize yield under well-watered conditions will have little variance among locations.
While our data indicate a reduction in HI and a plateau in crop mass and yield at higher values of CIPAR, the data presented in this paper do not explain why these phenomena occur. Further research is, therefore, needed to evaluate why additional intercepted PAR is not as efficiently converted to plant biomass and why plant biomass is not as efficiently converted to grain yield at high CIPAR. This information could be useful to plant breeders and physiologists wishing to select for traits that would increase yield of well-watered soybean in southern environments that permit production of longer-season cultivars.
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ACKNOWLEDGMENTS |
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Received for publication September 24, 2004.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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