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Agronomy Journal 95:430-435 (2003)
© 2003 American Society of Agronomy

SOYBEAN

Carbon-13 Discrimination Can be Used to Evaluate Soybean Yield Variability

D. E. Clay*, S. A. Clay, J. Jackson, K. Dalsted, C. Reese, Z. Liu, D. D. Malo and C. G. Carlson

Eng. Resour. Cent., South Dakota State Univ., Brookings SD 57007

* Corresponding author (david_clay{at}sdstate.edu)

Received for publication May 13, 2002.

   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY
 REFERENCES
 
Diagnostic tools for assessing the cause of soybean [Glycine max (L.) Merr.] yield variability in whole fields are needed. The objective of this study was to determine if 13C discrimination ({Delta}) can be used to assess the factors responsible for soybean yield variability. Research was conducted in five eastern South Dakota fields between 1999 and 2001. Yields in the summit–shoulder areas of Brookings, Moody, South Dakota State University (SDSU), and Lovjoy were 20 to 60% less than the rest of the field. Adding water to plants growing in the summit–shoulder areas in Moody and SDSU increased yield and {Delta}. However, in the foot-slope position, adding water did not impact yield or {Delta}. Based on the spatial relationships among protein content, yields, chlorophyll meter readings, and {Delta} at Moody and SDSU, (i) the reduced yields in the summit–shoulder areas most likely resulted from reduced plant vigor resulting from water stress; (ii) lower protein concentrations in summit–shoulder areas in Moody had a limited impact on {Delta}; and (iii) interactions among water availability, protein content, and yield can occur. In a combined analysis in the four fields where grain samples were collected at harvest (Moody, SDSU, Lovjoy, and TE80), {Delta} explained 62% of the total yield variability. Results from this experiment suggest that {Delta} can be used to help assess water stress, provided that N stress is absent. By understanding the causes of yield variability, producers will be able to make better management decisions.

Abbreviations: DGPS, differentially corrected global positioning system • SDSU, South Dakota State University • {Delta}, 13C discrimination


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY
 REFERENCES
 
MOST C3 PLANTS, including soybean, respond to water stress by closing their stomata, thus reducing photosynthesis and ultimately biomass production (Souza et al., 1997). In legumes such as soybean, water stress can also reduce N2 fixation (Serraj and Sinclair, 1996; Serraj et al., 1998). Therefore, in landscapes with variable levels of available water, it is possible that water stress can reduce yields by at least three mechanisms (N stress caused by reduced N2 fixation, reduced plant vigor caused by water stress, and a combination of reduced N fixation and reduced plant vigor).

In semiarid environments, evapotranspiration is often estimated using a mass balance approach (Hatfield et al., 2001). However, unless runoff and runin are considered in fields containing topographic relief, these estimates may not be accurate. To solve this problem, water flow models are used to estimate runoff and runin. Water flow models, linked to crop growth models such as CROPGRO-soybean, then can be used to determine yield losses due to water stress (Batchelor and Paz, 1999; Basso, 2000). However, to calibrate a model such as CROPGRO-soybean, yield information from a number of different sites and years are needed. This information is not available for most production fields; therefore, alternative techniques for evaluating water stress in soybean fields are needed.

Stable isotopic {Delta} may provide information that can be used to evaluate the factors responsible for yield variability (Clay et al., 2001a, 2001b). In wheat (Triticum aestivum L.) and corn (Zea mays L.), water and N stress have opposite effects on isotopic {Delta} (Clay et al., 2001a, 2001b). Before discussing why N and water stress have opposite effects on {Delta}, a few definitions are needed.

The ratio between 13C and 12C is the R value (O'Leary, 1993). The R value is used to calculate {delta}13C using the equation:

where R(sample) is the 13C/12C ratio of the sample and R(standard) is the 13C/12C ratio of PDB, a limestone from the Pee Dee formation in South Carolina (O'Leary, 1993; Farquhar and Lloyd, 1993). Typically, {delta}13C values for air, C3, and C4 plants are -8, -27, and -13{per thousand}, respectively. A negative sign indicates that the sample has a lower 13C/12C ratio than PDB. In many cases, it is convenient to report {Delta}, which is calculated using the equation:

[2]
where {delta}13Ca is the {delta}13C value of air and {delta}13Cp is the measured value of the plant.

Isotopic {Delta} can be used to evaluate water stress in C3 plants because ribulose bisphosphate carboxylase (RuBisCO), which catalyzes the combination of CO2 with ribulose diphosphate to form two molecules of 3-phosphoglyceric acid, discriminates against 13CO2 (O'Leary, 1993; Farquhar and Lloyd, 1993). If the plants are not water stressed, then stomata are open and discrimination is high. However, if plants are water stressed, then the stomata are partially closed, which in turn reduces {Delta}. In C3 plants, photosynthesis-induced {Delta} has been described by the equation:

[3]
where a is the {Delta} due to CO2 diffusion in air (4.4{per thousand}), b is {Delta} caused by carboxylation (30{per thousand} when corrected for the equilibrium effect of CO2 dissolution), Ci is the intercellular partial pressure of CO2, and Ca is atmospheric CO2 partial pressure (O'Leary, 1993; Farquhar and Lloyd, 1993). Equation [3] predicts that {Delta}C3 is directly related to the Ci/Ca ratio. A similar discussion is available for C4 plants (Clay et al., 2001a).

Nitrogen stress can impact {Delta} in both C3 and C4 plants (Clay et al., 2001a; Smeltekop et al., 2002). Many plants respond to N deficient conditions by producing less chlorophyll and biomass and may use less water. If N stress reduces the plants photosynthetic capacity, then it is likely that the CO2 demand will decrease and the Ci/Ca ratio will increase. Under these conditions, Eq. [3] predicts that N stress will increase {Delta} in C3 plants. The hypothetical impact of water and N stress on {Delta} in C3 and C4 plants was confirmed in field studies conducted by Clay et al. (2001a)(2001b) and Smeltekop et al. (2002). In a Montana study, Clay et al. (2001b) reported that for wheat, a yield loss of 1 Mg ha-1 due to water stress resulted in a 1.13{per thousand} decrease in grain {Delta}. Conversely, N stress increased grain {Delta} by 0.01{per thousand} for each kilogram per hectare of N deficiency in the plants. Smeltekop et al. (2002) and Clay et al. (2001a) developed an approach that combined {Delta} and the plants' N percentage to separate total yield loss into yield losses due to N and water stress in corn and wheat. Similar experimental approaches are needed for determining the factors responsible for soybean yield variability. The objective of this study was to determine if {Delta} can be used to assess the factors responsible for soybean yield variability.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 SUMMARY
 REFERENCES
 
Research was conducted in five fields (Brookings in 1999, Moody in 2000, SDSU in 2001, Lovjoy in 2000, and TE80 in 2000) located in eastern South Dakota. The field designations, soil types, latitude and longitude coordinates, crop cultivars, tillage, rotations, and plant populations of each field are reported in Table 1. In Brookings and Moody, soil samples from each soil horizon in the dominant soil series were collected in 2001. Soil bulk density and the water content at the permanent wilting point (1.5 MPa) were determined on these samples (Klute, 1986). Elevation at all sampling sites was measured with a carrier-phase differentially corrected global positioning system (DGPS). A weather station located within 15 km of each site was used to measure precipitation and air temperature. Growing degree days were calculated using a base 10°C. In the year before planting soybean, soil samples (minimum eight cores per composite sample) were collected from the 0- to 15-cm depth from at least a 1-ha grid. These samples were analyzed for Olsen P and K (Frank et al., 1998; Warncke and Brown, 1998). Based on the laboratory analysis, P and K fertilizers were applied for the 2-yr rotation. Weeds were controlled with appropriate herbicides. Yields were measured with a calibrated yield monitor (Lems et al., 2001).


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  		Table 1. Field locations, dominant soil types, and landscape type of the different study sites.

 
At Brookings in 1999, gravimetric water contents at two depths (0–15 and 15–60 cm) were determined on soil samples collected from 50 points located on four transects on 13 July, 27 July, 4 August, 17 August, and 26 August. The sampling points were located every 30 m along four transects. Whole-plant samples harvested from 1-m2 areas located near the sampling points were collected on 13 July, 27 July, 10 August, and 26 August. Dried and ground plant samples were analyzed for total N and {Delta}.

At Moody, gravimetric soil water contents were measured on soil samples (0–15 and 15–60 cm) collected from 50 sampling points on 7 June, 28 June, 18 July, 5 Sept., and 27 Sept. 2000. The sampling points were located every 30 m along four transects. Soil samples from individual horizons at sampling points in summit–shoulder and footslope areas were analyzed for plant available water. Grain samples were collected on 27 September from 1-m2 areas at the sampling points.

In a field located near SDSU, gravimetric water contents (0–15 and 15–60 cm depths) were measured on 21 June, 12 July, 26 July, and 6 Aug. 2001 at 62 points located every 15 m on six transects. A chlorophyll meter (Spad 502, Minolta Corp., Ramsey, NJ) was used to measure relative greenness of 10 plants at each sampling location on 19 July. At maturity, a plot combine was used to measure soybean yields in a 1.52- by 6.1-m area for each sampling point.

At Lovjoy in 2000, grain samples were hand-harvested at maturity from forty-five 1-m2 sampling points located every 30 m on three transects. A chlorophyll meter was used to measure relative greenness on 25 July and 14 August. At TE80, grain samples were collected after maturity from 18 points in 2000. These sampling points were located at shoulder, backslope, and footslope positions.

A randomized block experiment was conducted at Moody and SDSU to determine the impact of water and landscape position on yield and {Delta}. The experiment, conducted in summit–shoulder and footslope positions, contained two treatments (plants that were and were not watered weekly with 3.81 cm of water between 7 July and 15 August). At Moody, the experiment had eight blocks at each landscape position while at SDSU, the experiment had four blocks at each landscape position. In each watered plot, water was applied to a single 15-cm plastic ring pounded 7.5 cm into the ground. The ring was used to ensure that water infiltrated into the soil. Soybean plants located within 15 cm of the ring were hand-harvested at maturity for the watered treatments. For the nonwatered treatments, plants from nonwatered adjacent areas were hand-harvested at maturity. Analysis-of-variance analysis was used to determine treatment differences.

All grain and whole-plant samples were analyzed for total N and {Delta} on a 20-20 Europa ratio mass spectrometer (Europa Sci., Cheshire, England). Grain samples from Moody, SDSU, and Lovjoy were analyzed for oil and protein on a NIR S5000 (Foss Tech, Silver Spring, MD).


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
SUMMARY
 REFERENCES
 
Yield Spatial Variability
Site Characteristics
During the growing season (May through October), precipitation ranged from 35.7 cm at Lovjoy to 42.6 cm at SDSU. Growing degree days ranged from 1300 at Moody to 1365 at Brookings (Table 1). Each site contained both well-drained (Brookings, Vienna, and Moody) and poorly drained soils (McIntosh, Cubden, Hamerly, and Lamo) with rolling topographies. Parent materials were loess over glacial till or alluvium. The pH values (0.01 M CaCl2) in summit–shoulder soils ranged from 6 to 7, and the pH values in footslope soils ranged from 7 to 8.

Spatial Yield Variability
Grain yields were greatest at TE80 and least at Lovjoy (Table 2). The field with the lowest standard deviation had the highest yield, and the field with the highest standard deviation had the lowest yield. At Brookings, yields in the high-elevation areas were 30 to 40% less than yields in the low-elevation areas (Fig. 1) . At Moody, corn yields in high-elevation (summit–shoulder) areas were 20 to 40% less than yields in low-elevation areas (Fig. 2) . The spatial relationship between yield and elevation was demonstrated in Fig. 2. The other fields had similar spatial relationships.


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  		Table 2. The mean, standard deviation, median, and skewness for yield and 13C discrimination {Delta} at the five study sites.

 


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  		Fig. 1. The relationships between yield and elevation at Brookings, Moody, South Dakota State University (SDSU), and Lovjoy.

 


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  		Fig. 2. The Moody yield map superimposed on the field elevation map.

 
Spatial and temporal variability in {Delta} was observed in all fields. At Brookings, {Delta} in whole-plant samples collected on 27 July at Brookings ranged from 20 to 21{per thousand}. As the season progressed, {Delta} decreased faster for plants located in high-elevation than low-elevation areas (data not shown). Smedley et al. (1991) had a similar temporal responses and attributed decreasing {Delta} to increasing water stress.

Landscape differences in yield and {Delta} could have resulted from many factors, including water stress–induced reduction in N2 fixation, stomatal closure, diseases, insect damage, or nutrient deficiencies. In this paper, three different mechanisms for causing yield and {Delta} spatial variability were investigated. The first mechanism was that water stress reduced N2 fixation by the nodules, which in turn, increased N stress in the plant. Under these conditions, N deficient plants should have relatively low N percentage, yields, and chlorophyll content and high {Delta}. The second mechanism was that water stress reduced plant vigor and CO2 conductance through the stomata. These plants should have relatively high N percentages, low yields, and low {Delta}. The third mechanism was that water stress reduced both N2 fixation and stomatal CO2 conductance. These plants most likely would have relatively low N percentages, yields, and {Delta}.

Water Stress Impact on Yield
At Moody and SDSU, watered soybean plants located in summit–shoulder areas had higher yields and {Delta} than nonwatered plants. In footslope areas, watering did not influence yield or {Delta} (Table 3). This experiment confirmed that water stress reduced yields. However, it did not confirm the mechanism responsible for the yield reduction.


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  		Table 3. The influence of watering soybean plants located in the summit–shoulder and footslope areas on yield and 13C discrimination ({Delta}) at Moody in 2000 and SDSU in 2001.

 
Associated with lower yields in summit–shoulder areas was less available water. For example, at Brookings, gravimetric soil water contents ranged from 0.15 g g-1 soil in the divergent summit areas to more than 0.30 g g-1 soil in the footslope areas on 13 July. As the season progressed, soils in the summit–shoulder area dried out faster than soils in the footslope position. For example, at Moody in summit–shoulder area, gravimetric water contents on 15 August approached 0.15 g g-1 soil while in footslope areas, water contents ranged from 0.20 to 0.27 g g-1 soil (data not shown). Based on gravimetric water contents and the permanent wilting point (1.5 MPa) in summit–shoulder (0.13 g g-1 soil) and footslope (0.15 g g-1 soil) areas, the plant available water on 15 August in summit–shoulder and footslope areas (surface 60 cm of soil) was 1.6 and 7.8 cm, respectively. Similar spatial and temporal changes in soil water at SDSU were observed (data not shown). Landscape differences in plant available water may have resulted from several factors. First, runoff from summit–shoulder areas with subsequent runon into footslope areas influenced the total available water. Second, capillary movement of water from ground water to the root zone in footslope areas increased available water. Third, lateral movement of water from the summit to the footslope area increased available water. These data show that in summit–shoulder areas, water stress reduced soybean yields.

Separating Nitrogen and Water Impacts on Yield and Carbon-13 Discrimination
Data collected at Moody and SDSU were used to determine if factors responsible for producing yield variability could be identified. A transect at Moody had a 15-m elevation change (Fig. 3) . Soil water contents in the highest elevation areas were less than those in low-elevation areas. Soybean plants growing at Sampling Points 12 and 14 (summit–shoulder area) had lower yields, protein, and {Delta} values than plants at Sampling Points 10 and 16. Visual N deficiency symptoms (chlorotic leaves) were not evident. The lower grain protein contents at Sampling Points 12 and 14 could have been attributed to several factors, including that N2 fixation was reduced by water stress (Serraj and Sinclair, 1996; Serraj et al., 1998). If lower protein content in the summit–shoulder area influenced {Delta}, then the protein reduction from Sampling Point 10 to 12 or from 16 to 14 should have resulted in a corresponding increased {Delta}. These increases were not observed.



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  		Fig. 3. Elevation, soil water (0–60 cm), yield, 13C discrimination ({Delta}), and protein at 15 sampling points along a transect in Moody.

 
At SDSU, similar results were observed. Average soil water contents (0–60 cm) in the most and moderately water-stressed areas on 6 Aug. 2001 were 0.134 g g-1 (±0.02 g g-1) and 0.156 g g-1 (±0.011 g g-1), respectively. Average yield (1780 kg ha-1 ± 200 kg ha-1) and {Delta} (17.3{per thousand} ± 0.22{per thousand}) was lower in the most water-stressed area than the moderately water-stressed area (yield = 2630 kg ha-1 ± 208 kg ha-1 and {Delta} = 19.6{per thousand} ± 0.14{per thousand}). In the most water-stressed area, chlorophyll meter readings on 19 July were higher (41.8 ± 1.2) than the moderately water-stressed area (38.9 ± 1.3), and the average protein contents of plants harvested from these two areas were similar (0.376 g g-1). Results from Moody and SDSU indicate that (i) reduced yields in the summit–shoulder areas most likely resulted from reduced plant vigor resulting from water stress; (ii) lower protein concentrations in summit–shoulder areas at Moody had a limited impact on {Delta}; and (iii) interactions among water availability, protein content, and yield can occur. Interactions among these variables may partially explain the results of Wesley et al. (1998) and Purcell and King (1996).

Relationship between Yield and Carbon-13 Discrimination
At Moody, SDSU, and Lovjoy, {Delta} was lower in grain samples collected at harvest from summit–shoulder than footslope areas. Grain samples collected from TE80 did not follow this pattern. The percentage of yield variability explained by {Delta} ranged from 0.01% in TE80 to 80% in Brookings. In a combined analysis of the four fields where plant samples were collected at harvest (Moody, SDSU, Lovjoy, and TE80), {Delta} explained 62% of the total yield variability (Fig. 4) . Results from this analysis indicate that {Delta} can be used to assess water stress in soybean, provided that N stress is absent, and that water stress was a primary factor responsible for reduced soybean yields in summit–shoulder areas at Brookings, Moody, SDSU, and Lovjoy.



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  		Fig. 4. The combined relationship between yield and 13C discrimination ({Delta}) at Brookings, Moody, Lovjoy, and South Dakota State University (SDSU).

 
Similar relationships between {Delta} and yield have been observed for other plants. For example, Clay et al. (2001b) reported that for wheat grown under non-N-limiting conditions, 84% of the yield variability over a 3-yr period was explained by the equation yield (kg ha-1) = -11 000 + 884{Delta}. Based on this equation, water stress reduced {Delta}1.13{per thousand} for every megagram-per-hectare loss in wheat yield. For soybean, water stress reduced {Delta} 2.6{per thousand} for every megagram-per-hectare loss in yield. The {Delta} values where yield loss occurred were different for wheat and soybean. In wheat, relative to a yield of 4900 kg ha-1 with a {Delta} value of 18 {per thousand}, a 40% yield loss due to water stress occurred at a {Delta} value of 15.8{per thousand}, whereas in soybean, relative to 2730 kg ha-1 at a {Delta} value of 20{per thousand}, a 40% yield loss occurred at 17.3{per thousand}.

Crop breeders have had mixed results in using {Delta} as a tool for evaluating water use efficiency of different varieties (Hall et al., 1994). Mixed results could be a consequence of different plant attributes designed to increase water use efficiency having opposite effects on {Delta}. For example, water use efficiency can be improved by increasing the root depth or early stomatal closure (Ludlow and Muchow, 1990; Muchow and Sinclair, 1991; Sinclair and Muchow, 2001). Increasing the rooting depth should increase available water and {Delta} in C3 plants (Eq. [3]) while early stomatal closure should reduce {Delta} (Eq. [3]). Clearly, the potential influence of the individual attributes on {Delta}, yield, and drought tolerance must be understood to use {Delta} as a diagnostic tool.


   SUMMARY
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY
REFERENCES
 
Yields in the summit–shoulder areas of Brookings, Moody, SDSU, and Lovjoy were 20 to 60% less than the rest of the field. Adding water to plants growing in the summit–shoulder areas at Moody and SDSU increased yield and {Delta}. However, in the footslope position, adding water did not impact yield or {Delta}. Based on the spatial relationships among protein content, yields, chlorophyll meter readings, and {Delta} at Moody and SDSU, (i) the reduced yields in the summit–shoulder areas most likely resulted from reduced plant vigor resulting from water stress; (ii) lower protein concentrations in summit–shoulder areas at Moody had a limited impact on {Delta}; and (iii) interactions among water availability, protein content, and yield can occur. Interactions among these variables may partially explain the results of Wesley et al. (1998) and Purcell and King (1996). In a combined analysis in the four fields where grain samples were collected at harvest (Moody, SDSU, Lovjoy, and TE80), {Delta} explained 62% of the total yield variability.

This analysis suggests that at Moody, SDSU, and Lovjoy, water stress reduced summit-area soybean yields between 25 and 60%, and at TE80, water stress had a limited impact on yield. Results from this experiment suggest that {Delta} can be used to help assess water stress, provided that N stress is absent. By understanding the causes of yield variability, producers will be able to make better management decisions.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY
 REFERENCES
 
Support provided by NASA, South Dakota Soybean Research and Promotion Council, North Central Soybean and United Soybean Boards, and USDA-CSREES-NRI. Experiment station no. 3309.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY
 REFERENCES
 




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