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Soybean Yield Formation: What Controls It and How It Can Be Improved

Authors:
1
Soybean Yield Formation: What Controls
It and How It Can Be Improved
James E. Board and Charanjit S. Kahlon
School of Plant, Environmental, and Soil Sciences
Louisiana State University Agricultural Center
US
1. Introduction
Soybean [Glycine max (L.) Merr.; family leguminosae, sub family Papilionoideae; tribe
Phaseoleae] is the most important oilseed crop grown in the world (56% of world oil seed
production) (US Soybean Export Council, 2008). Major producers are the US (33% of
world production), followed closely by Brazil (28%) and Argentina (21%). Remaining
producers are China, India, and a few other countries. Currently, soybean is grown on
about 90.5 million hectares throughout the world with total production of nearly 220
million metric tons (US Soybean Export Council, 2008). At current prices, total value of
the world’s soybean crop is about $100 billion. Soybean is used as human food in East
Asia, but is predominately crushed into meal and oil in the US, Argentina, and Brazil; and
then used for human food (as cooking oil, margarine, etc.) or livestock feed (Wilcox, 2004).
These uses are derived from the crop’s high oil (18%) and protein (38%) content. Soybean
meal is a preferred livestock feed because of its high protein content (50%) and low fiber
content. Soybean oil is mainly used by food processors in baked and fried food products
or bottled into cooking oil. Other uses are biodiesel products and industrial uses. Global
demand for soybean has been increasing over the last several years because of rapid
economic growth in the developing world and depreciation of the US dollar (US Soybean
Export Council, 2008).
In response to this demand, world production has been increasing through a combination of
increased production area and greater yield. Among major producers, most of this increase
in Argentina and Brazil has come from increased production area, whereas in the US it has
come from increased yield (US Soybean Export Council, 2008). However, over the last 10
years US soybean yields have been increasing by only 66 kg ha-1 yr-1 compared to 396 kg ha-1
yr-1 for corn (USDA, 2007). An even greater problem is the disparity in yield between the
three main producing countries [US, Argentina, and Brazil (2,800 kg ha-1)] and that in the
remainder of the world (1,510 kg ha-1) (US Soybean Export Council, 2008). Because of the
limited potential for increasing production area, it is very important that yield be
accelerated in order to meet increasing global demand. Our objective is to describe the basic
processes affecting yield formation in soybean and to apply this information to development
of management and genetic strategies for increasing soybean yield. First, we will outline
potential yield gains possible with management modifications in soybean. Secondly, the
main abiotic and biotic stresses will be detailed describing their modes of action on yield.
Soybean Physiology and Biochemistry
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This will be followed by development of a paradigm integrating how these stresses act on
crop growth dynamics and yield component formation to affect final yield. This paradigm
will be applied to examples of everyday problems faced by soybean farmers in coping with
environmental stresses such as determination of stress-prone developmental periods,
identification of stress problems affecting yield, determining the efficacy for modified
management practices, and predicting yield potential of a field. Once environmental
parameters have been discussed, a similar analysis will be applied to genetic strategies for
yield improvement. Our objective here is to identify which plant factors explain yield
improvement during cultivar development. Such factors may serve as indirect selection
criteria for increasing the efficiency of cultivar development breeding programs.
2. Enviromental stress and soybean yield
Recent yield increases for soybean production in the US (66 kg ha-1 yr-1) can be attributed to
both a genetic and environmental component (USDA, 2007). Comparison of old and new US
soybean cultivars have shown a range of genetic gain from cultivar development of 10 to 30
kg ha-1 yr-1 (Boerma, 1979; Specht and William, 1984; Specht et al., 1999; Wilcox, 2001). More
recent research has indicated gains towards the higher end of this range (Kahlon et al.,
2011). Thus, it can be approximated that recent yield gains within the US are about 50% due
to cultivar genetic improvement and 50% to improved cultural practices. Potential gains
from improved cultural practices for any given locale are usually determined by comparing
farmer yields with those done using recommended practices (Foulkes et al., 2009). In the US,
many states conduct these studies within farmer fields in which one area of a field receives
typical practices and an adjacent area receives recommended practices (Louisiana Agric. Ext.
Serv., 2009). In Louisiana, the typical soybean farmer produces an average yield 70% of that
expected if recommended production practices were followed. Similar yield potential
studies in other parts of the world show yields ranging from 60 to 80% of the optimal level
(Foulkes et al., 2009). This yield gap is attributed to a suboptimal physical environment (i.e.
inadequate solar radiation, temperature, photoperiod, water, soil factors) coupled with
inadequate application of fertilizer and pest control. Thus, improvement of cultural
practices can be expected to increase yield anywhere from 25 to 66%. Yield increases for
countries outside the US, Brazil, and Argentina would be even greater, since their yield
levels are substantially below those of the major producers (1510 vs. 2800 kg ha-1 , US
Soybean Export Council, 2008).
The inability of a soybean farmer to achieve optimal yield, when adapted cultivars are
grown, is caused by environmental stress. We define environmental stress as a deficiency
or excess of some factor large enough to significantly reduce yield and/or impair crop
quality. Environmental stresses are divided into two kinds, abiotic and biotic. Abiotic
stresses are non-living stresses which can be divided into atmospheric factors (e.g. solar
radiation, air temperature, humidity, and rainfall) and soil factors (eg. fertility, pH,
compaction, waterlogging, soil structure, saline intrusion). Biotic stresses are living factors
which are generally referred to as pests (weeds, insects, diseases, and nematodes).
Although environmental stresses can initially affect crops by several physiological
mechanisms, in most cases the final effect on yield occurs by reducing the canopy
photosynthetic rate [uptake of CO2 m
-2 (land area) d-1] (Fageria et al., 2006). Canopy
photosynthesis combines the plant’s basic genetic photosynthetic capacity per unit leaf
Soybean Yield Formation: What Controls It and How It Can Be Improved
3
area (leaf photosynthesis) with leaf area index (LAI, leaf area/ground area ratio) and
canopy architecture to give a comprehensive picture of the crop’s ability to obtain CO2
from the atmosphere. The importance of the photosynthetic reactions in crop growth and
yield formation cannot be overestimated. It is estimated that 75 to 95% of crop dry weight
is derived from CO2 fixed through photosynthesis (Imsande, 1989; Fageria et al., 2006).
Photosynthesis produces the basic carbohydrates used for producing more complex
carbohydrates, proteins, and lipids, all of which contribute to dry matter (Loomis and
Connor, 1992a). It also supplies the chemical energy for metabolism. Because of this close
linkage between canopy photosynthesis and dry matter accumulation, seasonal crop
patterns of canopy photosynthetic activity and crop growth rate [CGR, dry matter
accumulation per day per m2 [g m-2 (land area) d-1] parallel one another (Imsande, 1989).
For the remainder of the chapter, CGR will be used synonymously with canopy
photosynthetic rate.
Both parameters increase slowly after emergence and then increase exponentially until early
reproductive development (Fig. 1) [R1-R3, stages according to Fehr and Caviness (1977) (see
Table 1 for definitions and descriptions)] (Imsande, 1989). Plateau rates are maintained until
R5 and then fall as the seed filling period progresses. Seasonal total dry matter (TDM)
curves reflect these patterns for CGR and canopy photosynthetic rate (Fig. 2, Carpenter and
Board, 1997). The first period of seasonal dry matter accumulation is called the exponential
phase. Growth is initially slow, but increases exponentially with plant size until maximal
light interception is achieved. At this point, maximal CGR is achieved and the crop enters
the linear growth phase where CGR is relatively constant (subject to stress-induced
decreases). As senescence nears and leaf fall commences, the CGR slows until reaching zero.
This last period is called the senescent phase. Crop growth rate is an example of a growth
dynamic parameter. Growth dynamic parameters are rates and levels of total dry matter
(TDM), dry matter partitioning (e.g. harvest index), leaf area index (LAI), light interception
(LI), and radiation use efficiency that characterize soybean’s seasonal growing pattern
(Loomis and Connor, 1992a). Canopy photosynthetic rate and CGR are important to study
because they directly control TDM production. Final yield is a function of TDM produced
and the percentage of dry matter transferred into the seed (i.e. harvest index) (Loomis and
Connor, 1992a). Crop growth rate, in turn, is regulated by the level of ambient light and the
percentage of this light intercepted by the crop [the two terms combined will be called
light interception (LI)]. The importance of LI in controlling CGR is derived from its use as
an energy source to produce ATP and NADPH for fixation of CO2 into carbohydrates. The
effect of LI on CGR and TDM is measured by radiation use efficiency (dry
matter/intercepted light; g MJ-1). Optimal radiation use efficiency depends on the absence
of any stress reducing the effect of LI on TDM. Light interception and radiation use
efficiency are controlled by LAI and net assimilation rate [dry matter produced per unit
leaf area; g m-2(leaf area) d-1]. Crop growth rate is maximized when LAI is large enough to
intercept 95% of the sun’s light [3-4 for narrow rows; 5-6 for wide rows (Board et al.,
1990a)], sunlight is not blocked by clouds, and no stress factors are present to interfere
with the ability of intercepted light to stimulate net assimilation rate and CGR (as
measured by radiation use efficiency). For example, a crop can be maximizing LI, but if
drought stress is present and the stomata are closed so CO2 cannot enter the leaf, net
assimilation would fall, reducing CGR and TDM. This effect would be reflected in
reduced radiation use efficiency.
Soybean Physiology and Biochemistry
4
Developmental Stages Descriptions of Developmental Stages
Vegetative Stages
VE
Emergence - cotyledons have been pulled
through the soil surface.
V1 Completely unrolled leaf at the unifoliate
node.
V2 Completely unrolled leaf at the first node
above the unifoliate leaf.
V5 Completely unrolled leaf at the fifth node
on the main stem beginning with the
unifoliate node.
Reproductive stages
R1 First flower: One flower at any node on
the plant.
R3 Pod initiation: Pod 0.5 cm (1/4”) long at
one of the four uppermost nodes on the
main stem with a fully developed leaf.
R4 Pod elongation: Pod 2 cm (3/4”) long at
one of the four uppermost main stem
nodes with a fully developed leaf.
R5 Seed Initiation: Seed within one of the
pods at the four uppermost main stem
nodes having a fully developed leaf that
is 0.3 cm long (1/8”).
R6 Full seed stage: Pod at one of the four
uppermost main stem nodes having a
fully developed leaf that has at least one
seed that has extended to the length and
width of the pod locule.
R7 Physiological maturity: Presence of one
pod anywhere on the plant having the
mature brown color. 50% or more of
leaves are yellow.
Table 1. Descriptions of the vegetative and reproductive developmental stages of soybean
during the typical growing season.
Dry matter accumulation is important in yield formation because yield components
recognized as important in controlling yield on the environmental level [node m-2,
reproductive node m-2 (node bearing a viable pod), pod m-2, and seed m-2] are responsive
to TDM accumulation (Egli and Yu, 1991; Board and Modali, 2005). Yield components
are morphological characteristics whose formation is critical to yield. For soybean, yield
Soybean Yield Formation: What Controls It and How It Can Be Improved
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components which have potential to influence yield are seed number per area (seed m-2),
seed size (g per seed), seed per pod (no.), pod number per area (pod m-2), pod
per reproductive node (no.), reproductive node number per area (reproductive node m-2),
percent reproductive nodes (%; percentage of nodes becoming reproductive), and node
number per area (node m-2). Yield components in soybean can be organized into
a sequential series of causative relationships where: yield is controlled by primary yield
components seed size and seed m-2; seed m-2 is controlled by secondary yield components
seed per pod and pod m-2; pod m-2 is controlled by tertiary yield components pod
per reproductive node and reproductive node m-2; and reproductive node m-2
is controlled by quaternary yield components node m-2 and percent reproductive nodes.
Thus, yield components are the vehicle through which canopy photosynthetic rate and
CGR affect yield.
Fig. 1. Temporal profiles of the relative daily rates of plant growth and canopy CO2
exchange. Profiles for dry matter accumulation and canopy CO2 exchange were derived by
curve fitting. For each of these two parameters several sets of published data, obtained with
field grown plants, were plotted and the best-fit curves were generated. Curve presented in
Imsande (1989).
Development and growth of soybean during the growing season are summarized in Fig. 3.
Soybean development is separated into the vegetative development period (emergence to
R1) and reproductive development period (R1 to R7). However, vegetative growth (leaves,
stems, and nodes) extends from emergence to R5 (Egli and Leggett, 1973). The reproductive
development period is separated into the flowering/pod formation period (R1 to R6) and
the seed filling period (R5 to R7). The seed filling period, in turn, is divided into the initial
lag period of slow seed filling (R5-R6) and the rapid seed filling period (R6-R7) when seed
growth rate is maximal (Egli and Crafts-Brandner, 1996). Pod and seed numbers are
determined by R6 (Board and Tan, 1995), before rapid seed filling starts. The linkage of
Soybean Physiology and Biochemistry
6
environmental stress with canopy photosynthetic activity, CGR, yield component formation,
and yield can be illustrated by examining the effects of the three most common abiotic
stresses for soybean production: temperature extremes, drought, and canopy light
interception (Hollinger and Angel, 2009).
Fig. 2. Seasonal growth curve for a typical soybean crop showing the progression of total
dry matter (TDM) accumulation across the exponential, linear, and senescent growth
phases. Data adapted from Carpenter and Board (1997).
2.1 Temperature extremes and soybean yield
Temperature stress in soybean is manifested through effects on photosynthesis and CGR
(Paulsen, 1994), reproductive abnormalities (Salem et al., 2007), and phenological events
(Huxley and Summerfield, 1974). Among these factors, the effect on canopy
photosynthesis and CGR has the greatest effect on yield. Temperatures above 350 C can
inhibit pollen germination and pollen tube growth (Salem et al., 2007; Koti et al., 2004).
However, since anther dehiscence occurs at 8 to 10 A.M., temperatures in most soybean
growing areas would not be above the critical level during these events. The effect of
warmer temperature interacting with shorter photoperiod to hasten phenological
development (Hadley et al., 1984) can result in small plants having insufficient light
interception for optimal canopy photosynthesis and crop growth rate (Board et al., 1996a).
Thus, temperature effects on phenology indirectly affect yield through the same processes
as direct temperature effects on canopy photosynthesis and CGR. Determination of heat
units for soybean developmental timing uses a base temperature of 70 C, minimum
optimum temperature of 300 C, maximum optimum temperature of 350 C, and an upper
limit of 450 C (Boote et al., 1998).
Soybean Yield Formation: What Controls It and How It Can Be Improved
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Fig. 3. Progression of vegetative organs and yield components across the developmental and growth periods of Soybean.
Definitions and description of stages are in Table 1. Stages according to Fehr & Caviness (1977).
Soybean Physiology and Biochemistry
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Effects of temperature on canopy photosynthesis and CGR are characterized by an
optimal temperature response range falling between minimal and maximal optimal
temperatures, and suboptimal and supraoptimal temperatures falling below and above
the optimal range, respectively (Hollinger and Angel, 2009). The most sensitive part of the
photosynthetic apparatus to heat stress is photosystem II. Specifically, the splitting of
water to provide electrons to the light reactions is inhibited (Paulsen, 1994). Temperatures
falling below the minimal optimal level reduce canopy photosynthesis and CGR through
reduced reaction rates and/or enzyme inactivation. Studies conducted under constant
day time temperatures (12-16 hours per day) across an extended period generally have
reported an optimal temperature range for photosynthesis of 25-350 C (Jeffers and Shibles,
1969; Campbell et al., 1990; Jones et al., 1985; Gesch et al., 2001; Vu et al., 1997). However,
under natural growing conditions, maximal daily temperature usually occurs for only 1-2
hours (Louisiana Agric. Exp. Stn., 2010). When heat stress studies are conducted under
more realistic conditions of short-term stress, temperature had to be raised to 42-430 C to
have a deleterious effect on soybean photosynthesis (Ferris et al., 1998). These results are
corroborated by Fitter and Hay (1987) who stated that for plants from most climatic
regions, temperatures of 45-550 C for 30 minutes were sufficient to cause irreversible
damage to the photosynthetic apparatus. In conclusion, under typical growing conditions,
the optimal temperature range for soybean canopy photosynthetic rate appears to be 25-
400 C. A similar optimal temperature range of 26 to slightly above 360 C for crop growth
rate has also been reported (Sato and Ikeda, 1979; Raper and Kramer, 1987; Sionit et al.,
1987; Baker et al., 1989, Hofstra, 1972). Adverse effects on yield were entirely due to high
day time temperatures rather than night time temperatures (Hewitt et al., 1985; Raper and
Kramer, 1987; Gibson and Mullen, 1996).
At the crop level, heat-stress induced reductions in canopy photosynthesis affect yield
components being formed at the time of the stress. Stresses occurring during flowering and
pod formation (R1-R5) affect seed number, whereas stress during seed filling (R5-R7)
reduces seed size (Gibson and Mullen, 1996). Both reductions were linked with lower
photosynthetic rates. Concomitant with these reductions in canopy photosynthesis and
yield components are decreased TDM and plant size. Soybean yield was as sensitive to heat
stress during flowering/pod formation (R1-R5) as during seed filling (R5-R7). A summary
for heat stress effects on yield formation is shown in Table 2.
Similar to heat stress, cold stress also adversely affects canopy photosynthesis when
temperatures fall below 250 C. This results in less LAI, TDM, seed production, and yield
(Baker et al., 1989). However, when research is conducted under cold temperature
regimes similar to field conditions (intermittent nightly cold temperature or short-term
cold treatments) adverse effects do not occur until temperature drops to 100 C (Seddigh
and Jolliff, 1984 a,b; Musser et al., 1986). Seddigh and Jolliff (1984 a,b) showed that nightly
cold temperature of 100 C vs. 160 C or 240 C slowed CGR during the vegetative and early
reproductive periods. However, pod and seed numbers were not reduced, because the
cooler temperatures extended the period to R5, thus allowing vegetative TDM
accumulation to equilibrate across nightly temperature treatments. The 24% yield loss
caused by reducing nightly temperature from 160 C to 100 C was entirely due to reduced
seed size. Musser et al. (1986) reported that 1-wk chilling treatments (100 C) during
the late vegetative and early reproductive period did not reduce early pod production.
Chilling stress is a unique cold effect to plants where temperature at 10-120 C or below
causes a cell membrance phase transition from liquid-crystalline to solid-gel form
(Bramlage et al., 1978). Consequently, cell metabolism is disrupted resulting in potential
Soybean Yield Formation: What Controls It and How It Can Be Improved
9
adverse effects on yield. In the case of the Bramlage et al. (1978) study, pod numbers
equilibrated after return to normal conditions resulting in no effect on yield. Thus, under
natural growing conditions, soybean yield is resilient to cold temperatures that fall to
as low as 150 C. However, temperatures below this level pose a significant risk
for reducing yield, especially when they fall to 100 C. Yield loss is assured with even short
term exposure to freezing temperatures [2 hr a night for 1 wk (Saliba et al., 1982)]. Effects
of freezing injury are irreversible. Thus, freezing temperatures during flowering/pod
formation (R1-R5) cause much greater yield losses (70% loss) compared with freezing at
R6 (25% loss). A summary of cold stress effects on yield formation is shown in Table 3.
Physiological
Disruptions
Affected Canopy Level
Growth Processes
Affected Yield
Components
Temperature Parameters
Impairment of
photosystem II
Reduced canopy
photosynthesis and
CGR
Reduction of seed
number or seed
size depending on
timing of stress.
Short-term exposure to
temp.>400C
Enzyme
denaturation and
deactivation
Reduced canopy
photosynthesis and
CGR
Reduction of seed
number or seed
size depending on
timing of stress.
Short-term exposure to temp
>400C
Increased
development rate
Reduced canopy
photosynthesis and
CGR by shortening
emergence-R5 period
Reduction of seed
number.
Under short days development
rate increases with degree days
[Base temp=70C
Min. optimum temp=300C
Max. optimum temp= 350C
Upper limit=450C].
Developmental stage sensitivity
to heat stress not clearl
y
defined.
Table 2. Summary of heat stress effects on soybean physiology, growth, and yield
components.
2.2 Drought stress and yield
Drought stress (i.e. soil water too low for optimal yield) is recognized as the most
damaging abiotic stress for soybean production in the US (Heatherly, 2009). However,
only about 8% of the entire hectarage is irrigated. In the main part of the Midwestern US
soybean region east of the Mississippi River, little irrigation is done. For example, in
Illinois, the nation’s largest soybean producing state, most areas receive sufficient rainfall
for optimal yield (Cooke, 2009). Soybean water relations are aided by the state’s deep soils
that allow greater water extraction relative to shallow claypan soils in the Southeastern
US. Irrigated areas are concentrated in the drier parts of the soybean growing region
(western Midwest or Great Plains states) such as Nebraska where 46% of soybean
hectarage is irrigated (Pore, 2009). Irrigation is also common in some Southeastern states
where shallow-rooted soils combined with erratic rainfall make drought stress a threat.
Currently, about 75% of soybean hectarage in Arkansas is irrigated and the figure for
Mississippi is 25-30%. Increased irrigation in the Great Plains and Southeastern states has
been stimulated by research showing large yield increases of over 1,000 kg ha-1 under
irrigated vs. nonirrigated conditions (Specht et al., 1999; Heatherly and Elmore, 1986).
Drought stress is a complicated agronomic problem that is conditioned not only by lack of
rain but by evapotranspiration from the soil/plant system, rooting depth and proliferation,
Soybean Physiology and Biochemistry
10
Physiological
Disruption
Affected Canopy
Level Growth
Processes
Affected Yield
Components
Temperature
Parameters
Reduced metabolic
reaction rates
Reduced canopy
photosynthesis and
CGR
Reduced seed
number or seed size
depending on timing
of stress.
Although 250C
required for optimal
canopy photo. and
CGR, cold effects
under natural growing
conditions usually
affect yield only <150
C.
Chilling stress
(Membrane
malfunctioning,
enzyme inactivation,
ion leakage)
Reduced canopy
photosynthesis and
CGR
Reduced seed
number or seed size
depending on timing
of stress.
0-10/12 0C; Yield more
affected by chilling
stress during seed
filling than
flowering/pod
formation period.
Freezing (cell death)
tissue damage
Reduced canopy
photosynthesis and
CGR
Reduced seed
number or seed size
depending on timing
of stress.
00C Yield more
affected by freezing
during flowering/pod
formation than seed
fillin
g
.
Table 3. Summary of cold stress effects on soybean physiology, growth, and yield
components.
and how much rainfall gets into and stays in the rooting zone (Loomis and Connor, 1992b).
Thus, in addition to rainfall, other factors that influence occurrence of drought stress are:
tillage systems (conservation vs. conventional tillage), plant genetics (rooting characteristics,
stomatal control, leaf reflectance, osmotic adjustments, leaf orientation and size, etc.),
climatic factors (relative humidity, temperature, and wind), and soil factors (soil texture and
structure, compaction, hardpans, pH, and slope).
Drought stress occurs when loss of water from leaves exceeds that supplied from the roots
to such a degree that water potential in those leaves falls to levels resulting in physiological
disruptions that eventually reduce CGR and yield (Loomis and Connor, 1992b). Another
aspect of drought stress is low water potential in root nodules which reduces nitrogen
fixation. Consequently, the crop may become N deficient which can also contribute to
reduced CGR and yield (Purcell and Specht, 2004). Although there are many physiological
processes potentially affected by drought stress, the main factors which are most important
in yield loss are seed germination and seedling establishment, cell expansion,
photosynthesis, and nitrogen fixation (Raper and Kramer, 1987). Water entrance and loss
from a crop is controlled by water potential, the energy of water measured as a force in bars
or pascals (1 bar=0.1 MPa) (Loomis and Connor, 1992b). Water potential differences between
components of a system describe the direction of water flow, since water will always flow
from a greater to a lesser water potential. Pure water has the highest water potential (0 MPa)
and water potential of natural systems will have negative values below that for pure water.
In plants, water potential is mainly controlled by solute potential (increased concentration
makes water potential lower or more negative) and turgor pressure (positive hydrostatic
pressure against the cell wall makes water potential greater or less negative). In soil, solute
concentration also affects water potential. However, matric potential (adhesion of water
Soybean Yield Formation: What Controls It and How It Can Be Improved
11
onto soil particles) is also an important component of soil water potential. Water is lost from
the leaves by transpiration to the atmosphere. For this water to be replaced, root water
potential must be lower than soil water potential to create water inflow from soil to root.
When a soil is initially at field capacity (maximal water a soil will hold after natural
drainage), soil water potential is at about -0.02 MPa (Loomis and Connor, 1992b). This
corresponds to volumetric water contents (volume of water per volume of soil) of 0.6 and
0.35 for clay and sand soils, respectively. At night, water potentials for soil, roots, and leaves
are in equilibrium. During the day, water loss from the leaves depresses leaf water potential
below root water potential resulting in movement of water from root to leaves in the xylem.
Consequently, root water potential falls below soil water potential resulting in water
flowing into the root. As water is withheld from the crop for successive days, the water
potential for soil, roots, and leaves steadily drops. When midday leaf water potential falls to
-1.5 MPa, stomata will close to conserve water. Meanwhile, as the soil dries the conductance
of water from soil to root drops making it difficult to resupply the plant with water.
Continued drought past this point will cause leaf water potential to fall below -1.5 MPa
resulting in possible death. Eventually, soil water potential may fall to -1.5 MPa at which
point water no longer enters the root from the soil (wilting point). Plant available water is
defined as the soil water content between field capacity and the wilting point. Irrigation to
avoid drought stress is usually recommended when plant available water falls to 50%, a
level indicated by a soil water potential of -0.05 to -0.06 MPa for a silt loam or clay soil and -
0.04 to -0.05 for a sandy soil. (Univ. of Arkansas Coop. Ext., 2006). This corresponds to a
volumetric water content of 0.4 and 0.23 for clay and sand soils, respectively.
Once injurious soil water potential levels are reached, physiological disruptions occur
which adversely affect CGR, yield component formation, and yield. Because of the large
amount of water the soybean seed must imbibe for successful germination (50% of fresh
weight), adequate moisture at planting is an important agronomic problem. Helms et al.
(1996) cautioned that stand establishment could be difficult when soil water is sufficient
to cause seed imbibition but not germination. Seed planted into a soil having a
gravimetric water content (water wgt./soil wgt.) of 0.07 kg kg-1 was great enough for
imbibition, but too low for root emergence. Increasing water content to 0.09 kg kg-1
allowed successful germination and emergence. Drought stress during the seedling
emergence and stand establishment period can result in a suboptimal plant population for
optimal yield. Because of low plant population, LAI and LI are inadequate to create a
CGR that optimizes yield.
Once successful stand establishment is achieved, one of the most sensitive physiological
processes to drought stress is reduced cell expansion resulting from decreased turgor
pressure (Raper and Kramer, 1987). As leaf water potential falls, cell and leaf expansion are
affected before photosynthesis. Bunce (1977) reported a linear relationship between soybean
leaf elongation rate and turgor pressure. Decreasing leaf water potential to -0.80 MPa
reduced leaf elongation rate by 40% relative to greater values. Consequently, leaf area and
plant dry matter were reduced 60% and 65%, respectively. These results were subsequently
confirmed in field experiments (Muchow et al., 1986). Thus, occurrence of drought stress
during vegetative growth (emergence to R5) can reduce LAI and LI to levels insufficient for
optimal CGR and yield. Decreased photosynthetic rate is not initiated until leaf water
potential falls into the range of -1.0 to -1.2 MPa (Raper and Kramer, 1987). The rate starts
declining more rapidly as water potential falls below -1.2 MPa. Plants suffering this level
of drought would have greater reductions of CGR and yield because not only would LAI
Soybean Physiology and Biochemistry
12
be reduced, but the net assimilation rate (photosynthetic rate per unit LAI) would also be
reduced. Drought stress effects on photosynthesis become irreversible once water
potential falls below -1.6 MPa.
Another physiological process sensitive to drought stress is nitrogen fixation (Purcell and
Specht, 2004). Decreased nitrogen fixation starts when water potential of root nodules starts
falling below -0.2 to -0.4 MPa (Pankhurst and Sprent, 1975). Because of the high protein
content of its seed, soybean has a greater demand for nitrogen compared with other crops
(Sinclair and de Wit, 1976). Soybean obtains nitrogen from fixation and directly from the
soil. During seed filling, much of seed nitrogen demand is met by remobilization from the
leaves. The contribution of nitrogen fixation to the plant’s nitrogen supply varies inversely
with soil nitrogen availability (Harper, 1987). In the Midwestern US which has soils of
relatively high residual NO3, about 25-50% of total plant nitrogen comes from fixation. In
contrast, in soils having low nitrogen, fixation can contribute up to 80-94% of the plant’s
nitrogen. Thus, any stress (drought or other) that restricts nitrogen fixation can result in a
nitrogen deficiency (leaf nitrogen falling below 4%, Jones, 1998) which can reduce net
assimilation rate and CGR. Ample evidence indicates that nitrogen fixation is more sensitive
to drought than photosynthesis, TDM accumulation, transpiration, or soil nitrogen uptake
(Purcell and Specht, 2004).
Because of its effects on CGR, drought creates changes in certain growth dynamic and yield
component parameters. In general, drought stress during the vegetative growth period
(emergence to R5) has adverse effects on LAI, TDM, CGR, and plant height (Scott and
Batchelor, 1979; Taylor et al., 1982; Muchow, 1985; Meckel et al., 1984; Desclaux et al., 2000;
Pandey et al., 1984; Ramseur et al., 1985; Cox and Jolliff, 1986; Constable and Hearn, 1980;
Hoogenboom et al., 1987; Cox and Jolliff, 1987). In a dry growing season, nonirrigated vs.
irrigated soybean will begin showing diminished TDM accumulation by the late vegetative
or early reproductive period (Scott and Batchelor, 1979). By R3, LAI differences between
irrigated vs. drought-stressed soybeans will be obvious (Cox and Jolliff, 1987), with
concomitant effects on LI and CGR (Muchow, 1985; Taylor et al., 1982; Ramseur et al., 1985;
Pandey et al., 1984). Among vegetative growth indicators of drought stress, reduced
internode length and plant height are the most sensitive (Desclaux et al., 2000). The effect of
drought stress on plant height is reflected in rooting depth (Mayaki et al., 1976b). During the
emergence-R5.5 period, rooting depth is twice the plant height. Thus, occurrence of early-
season drought impairs the plant’s future potential for obtaining water. If a fortuitous
rainfall interrupts this impaired growth dynamic process, TDM levels may return to normal
without yield being affected (Hoogenboom et al., 1987). However, continuation of drought
will accentuate TDM differences between irrigated and nonirrigated soybean. Decreased
TDM and yield are closely correlated in such a condition (Cox and Jolliff, 1986; Meckel et al.,
1984). In cases where drought stress occurs during the seed filling period, growth
characteristics are of course different. Since plant height and vegetative TDM have already
been determined, no effect on these parameters is seen. Drought during seed filling
accelerates the senescence process by increasing the rate of chlorophyll and protein
degradation. This shortens the seed filling period causing reduced seed size and yield (De
Souza et al., 1997).
When soybean faces seasonal drought or drought initiated by R1, yield loss results
predominately from reduced pod and seed numbers and seed size is relatively unaffected
(Sionit and Kramer, 1977; Ramseur et al., 1984; Pandey et al., 1984; Meckel et al., 1984; Cox
and Jolliff, 1986; Constable and Hearn, 1980; Lawn, 1982; Ball et al., 2000). Thus, when
confronted with drought stress, soybean reduces seed m-2 so that normal seed size can be
Soybean Yield Formation: What Controls It and How It Can Be Improved
13
maintained. Although some have reported mild adverse effects of drought on seed per pod
(Ramseur et al., 1984; Pandey et al., 1984), others have shown no effect (Lawn, 1982; Elmore
et al., 1988). In contrast, consistent reports have shown pod m-2 is reduced by drought
during the R1-R6 seed formation period (Sionit and Kramer, 1977; Ramseur et al., 1984;
Pandey et al., 1984; Snyder et al., 1982; Neyshabouri and Hatfield, 1986; Cox and Jolliff, 1986;
Ball et al., 2000). Based on these results, we conclude that reduced seed m-2 from drought
stress is derived predominately from reduced pod m-2 rather than seed per pod. Because
pod per node is not severely affected by drought (Elmore et al., 1988), reduced pod and seed
m-2 caused by drought results mainly from decreased node m-2, mainly resulting from
reduced branch development (Taylor et al., 1982; Snyder et al., 1982; Frederick et al., 2001).
In addition to reduced node m-2, drought stress during the flowering period retards early
ovary expansion because of reduced photosynthetic supply (Westgate and Peterson, 1993;
Liu et al., 2004; Kokubun et al., 2001). The period from 10 days before R1 to 10 days after R1
is the critical period.
Drought stress occurring at the start of linear seed filling (R6) can also reduce seed number,
but the main effect of drought initiated at this time or later is on reduced seed size (Sionit
and Kramer, 1977; De Souza et al., 1997; Brevedan and Egli, 2003; Doss and Thurlow, 1974).
In cases where drought stress is similar at different developmental periods, yield loss is
generally twice as great for the R1-R6 vs. R6-R7 periods (Kadhem et al., 1985; Korte et al.,
1983b; Shaw and Laing, 1966; Eck et al., 1987; Brown et al., 1985; Hoogenboom et al., 1987;
Korte et al., 1983b). Some studies show that within the R1-R6 period, the most drought
sensitive phase is R3-R5 (Kadhem et al., 1985; Korte et al., 1983a). This explains why most
irrigation studies have identified parts or all of the seed formation period as the most
drought prone period (Heatherly and Spurlock, 1993; Elmore et al., 1988; Kadhem et al.,
1985; Hoogenboom et al., 1987; Eck et al., 1987; Korte et al., 1983a; Korte et al., 1983b; Brown
et al., 1985; Morrison et al., 2006). These studies far outweigh early studies indicating that
seed filling had the same or greater sensitivity to drought as the seed formation period
(Shaw and Laing, 1966; Snyder et al., 1982; Sionit and Kramer, 1977). Irrigation during the
vegetative period has consistently proven unnecessary for alleviating drought stress
(Heatherly and Spurlock, 1993; Neyshabouri and Hatfield, 1986). Lack of irrigation response
during the vegetative period is likely due to the limited water use during that period
(Reicosky and Heatherly, 1990). In conclusion, based on yield component responses, the
most drought prone period during soybean development is R1-R6. Drought effects on
soybean yield formation are summarized in Table 4.
2.3 Light interception and yield
Because of the importance of canopy photosynthesis and CGR in affecting yield, the level of
intercepted photosynthetically active radiation (commonly referred to as light) is one of the
most important stresses affecting soybean yield (Loomis and Connor, 1992a). Although a
very complicated process, photosynthesis can be simplified by viewing it as three basic
parts: 1) Movement of CO
2 from the atmosphere to the chloroplasts; 2) Light reactions in
which absorption of specific wavelengths of radiation (red and blue light) cause ionization
(photoelectric effect) and result in production of the high-energy compounds ATP and
NADPH; and 3) Carbon fixation reactions in which the ATP and NADPH produced in the
light reactions is used to fix CO2 into organic compounds (Fageria et al., 2006). The major
environmental factors affecting canopy photosynthetic rate and CGR are atmospheric [CO2],
Soybean Physiology and Biochemistry
14
temperature, water availability, and light level absorbed by the canopy. An understanding
of how light affects canopy photosynthesis is critical for analyzing the effect of
environmental stress on yield.
Physiological
Disruptions
Affected Canopy
Level Growth
Processes
Affected Yield
Components
Drought Parameters
Reduced cell
expansion
Reduced LAI
and LI.
Reduced canopy
photosynthesis and
CGR.
Reduced seed m-2 or
seed size depending
on timing of stress
Decrease of leaf water
potential to -0.80 MPa or
less reduces turgor
pressure and cell
expansion.
Reduced nitrogen
fixation
Reduced canopy
photosynthesis and
CGR.
Reduced seed m-2 or
seed size depending
on timing of stress
Decline starts at -0.2 to -
0.4 MPa.
Reduced net
assimilation rate
Reduced CGR
Reduced seed m-2 or
seed size depending
on timing of stress.
Seed m-2 reduction
mainly due to
reduced node m-2
and pod m-2.
Reduced seed size
due to reduced
effective filling
period.
Water potential below -
1.2 MPa
Most drought prone
period is the R1-R6 seed
formation period.
Irrigation recommended
when soil at 50%
available water. Drought
sensitivity of rapid seed
filling (R6-R7) is less
than half that for R1-R6
period.
Table 4. Summary of drought stress effects on soybean physiology, growth, and yield
components.
For soybean, as well as other C3 crop species, photosynthetic rates of individual leaves
increase asymptotically to a light intensity of 500 micro moles m-2 s-1 (or 100 W m-2) (Hay
and Porter, 2006); an intensity equivalent to about 25% of full sun in many soybean-growing
regions. However, this relationship does not transfer to the canopy level; largely because of
uneven shading for leaves in the mid and lower canopy levels which do not receive
saturating light intensities. Although top leaves do not increase their photosynthetic rates as
light intensity increases above 25% of full sun, mid and lower canopy leaves would receive
increased light within the responsive range; thus resulting in an overall increase in canopy
photosynthetic rate (Hay and Porter, 2006). In cases of crops having erect leaves with low
canopy light extinction coefficients such as ryegrass, canopy photosynthetic rate increases
linearly with increasing intensity to the full-sun level (Hay and Porter, 2006). Although
soybean canopies having LAI<4.0 [canopy cover (95%) (Shibles and Weber, 1965)] saturate
the canopy photosynthetic rate at intensity levels less than full sun, those having LAI >4.0
show continual increase up to full-sun conditions (Shibles et al., 1987). The increased canopy
photosynthesis responds to increased light intensity in an asymptotic rather than linear
fashion (Jeffers and Shibles, 1969). At any given time, light intercepted by the canopy
Soybean Yield Formation: What Controls It and How It Can Be Improved
15
depends on LAI and the intensity of ambient light. Prior to canopy closure (LAI of 3.0 to 5.0
depending on row spacing), CGR primarily is influenced by LAI (Shibles and Weber, 1965),
whereas ambient light level mainly affects CGR after canopy closure. Major research aims
have been to determine yield response to reduced LI across different developmental
periods; to assess yield losses related to specific reductions in LI; and to determine if
different stresses reducing LI (e.g. shade, nonoptimal row spacing, subnormal plant
population, and defoliation) affect yield by similar mechanisms. In the current discussion,
we will examine the effects of shade, row spacing, plant population, and defoliation on
yield.
2.3.1 Light interception and yield: Shade stress
Studies with heavy shade treatment (63%) demonstrated that the flowering/pod
formation period (R1 to R6) was more sensitive to reduced LI than the period of linear
seed filling (R6 to R7; rapid seed filling period) (Jiang and Egli, 1995; Egli, 1997).
Application of shade during the seed determination period reduced yield by 52% (Jiang
and Egli, 1995), whereas the same light interception reduction during rapid seed filling
reduced yield by only 24% (Egli, 1997). Thus, within the reproductive period, the
flowering/pod formation period was twice as sensitive to reduced LI as compared with
the rapid seed filling period. Within the flowering (R1-R4) and pod formation (R4-R6)
periods, yield responses to shade were similar (Jiang and Egli, 1993, 1995). Yield loss can
occur with as few as 9 continuous days of heavy shade (80%) at any time during the
flowering/pod formation period (Egli, 2010).
When shade stress is applied continuously across the reproductive period, yield losses occur
with as little as 30% shade (22-31% yield loss) (Egli and Yu, 1991). Increasing shade stress to
50% resulted in a 55% yield loss. Yield losses were entirely due to reduced seed number
rather than seed size. When faced with a reduced crop growth rate induced by shade stress
starting at first flowering, soybean reduces its seed number so that when seed filling
commences, seed size is unaffected. In such cases, yield is said to be “source restricted”
during flowering and pod formation (i.e. yield reduction occurred due to lower CGR);
whereas during seed filling yield was “sink restricted” (i.e. yield reduction occurred due to
reduced seed number and was unaffected by changes in CGR). A summary of shade effects
on yield is shown in Table 5.
2.3.2 Light interception and yield: Row spacing and plant population
Early studies which altered LI through row spacing and plant population demonstrated
that optimizing light during the reproductive period (R1 to R7) was more important than
during the vegetative period (emergence to R1) (Brun, 1978; Christy and Porter, 1982;
Johnson, 1987; Tanner and Hume, 1978; Shibles and Weber, 1965). More recent studies
suggest that reduced LI during the vegetative period can reduce yield if it results in a
suboptimal CGR during the subsequent flowering/pod formation period (Board et al.,
1992; Board and Harville, 1996). Row spacing and plant population have similar effects on
LI, CGR, TDM, and yield component formation as do the aforementioned shade studies.
Reducing row spacing from 100 to 50 cm increases LI and accelerates CGR during the
vegetative, flowering/pod formation, and seed filling periods (Board et al., 1990). Greater
CGR in narrow vs. wide rows was evident as early as 16 days after emergence (Board and
Harville, 1996). During most of the vegetative and flowering/pod formation periods,
accelerated CGR was due more to increased LAI than to net assimilation rate (Board et al.,
Soybean Physiology and Biochemistry
16
1990b). However, initial increases in CGR in narrow vs. wide rows during the vegetative
period were influenced as much by increased net assimilation rate as increased LAI. This
probably occurred due to greater interception of light per unit LAI in narrow vs. wide
rows at this time (Board and Harville, 1992). Increased yield in narrow vs. wide rows is
more evident in short-season soybean production, such as in late vs. normal planting
dates or growing early vs. late maturing cultivars (Board et al., 1990a; Boerma and Ashley,
1982; Carter and Boerma, 1979).
Physiological
Disruption
from Shade Stress
Affected Canopy
Level Growth
Processes
Affected Yield
Components
Shade Parameters
Reduced
photosynthetic light
reactions
Reduced canopy
photosynthesis and
CGR
Reduced seed
number if shade
applied during R1-
R6 period.
Reduced seed size
if shade applied
during R6-R7
period.
Most sensitive stress
period is R1-R6. Reduced
yield occurs (24% yield
loss) with as few as 9 d of
heavy shade (83%). Shade
decreases yield as it
decreases CGR < 16 gm-2d-1
during R1-R5 period.
Moderate shade (30%)
during R1-R6 period
reduces yield 22-31%.
Shade stress during linear
seed filling period (R6-R7)
has half the effect on yield
vs. the R1-R6 period.
Table 5. Summary of shade effects on soybean physiology, growth, yield components, and
yield.
Although narrow vs. wide culture enhances CGR at all three developmental periods,
yield increases result entirely from increased pod and seed production (Egli and Yu,
1991; Board et al., 1990b, 1992). Seed per pod and seed size, yield components formed
during the seed filling period (R5 to R7) were not affected by reduced row spacing. The
dominant yield components controlling pod and seed production were node m-2 and
reproductive node m-2, which are formed during the vegetative period and part of the
flowering/pod formation periods (emergence to R5) (Board et al., 1990b; 1992). Thus,
greater LI and CGR in narrow vs. wide rows has its beneficial effect on yield between
emergence and R6, with the main effect occurring from emergence to R5. In cases where
wide rows achieve 95% light interception by first flowering, no yield loss occurs (Board
et al., 1990a). Reduced yield in wide vs. narrow rows starts occurring when average
LI across the R1 to R5 period is reduced by 14% (Board et al., 1992). In summary, changes
in row spacing affected yield by a mechanism very similar to that reported for shade
treatments applied throughout the reproductive period (Egli and Yu, 1991); i.e. pod and
seed numbers produced during the emergence to R6 period were reduced by the lower
CGR so that seed size (produced during the R5 to R7 seed filling period) could remain
constant.
Plant population studies conducted under short-season conditions also have outlined a
yield-control mechanism very similar to those described for narrow vs. wide row spacing
and shade (Ball et al., 2000, 2001; Purcell, 2002). Increasing plant population above the
Soybean Yield Formation: What Controls It and How It Can Be Improved
17
normal recommendation of 25-35 plant m-2 increased LI early in the vegetative period
[similar to the findings for narrow vs. wide row spacing (Board and Harville, 1996)]
resulting in an accelerated CGR during the R1 to R5 period, greater dry matter
accumulation, and yield (Ball et al., 2000). Purcell et al. (2002) determined that increased
yield responded linearly to increased photosynthetically active radiation accumulated
across the emergence to R5 period. Thus, the period during which increased LI benefitted
yield in high vs. normal plant population was the same as that described for narrow vs.
wide rows (Board et al., 1990a; Board et al., 1992). Yield increases were shown to be caused
by increased node m-2 and pod m-2 (Ball et al., 2001), similar to findings by Board et al.
(1990b, 1992) for narrow vs. wide row spacing. Data indicate that subnormal plant
populations can achieve yields similar to those of normal populations if average light
interception across the R1 to R5 period is 90% (Carpenter and Board, 1997). Yield losses started
occurring when average light interception across this period falls 14% below that for full-
coverage canopies. This yield response to reduced light interception corresponds very closely
to that shown by Board et al. (1992) for wide vs. narrow row spacing. Row spacing and plant
population effects on yield, growth and yield components are summarized in Table 6.
2.3.3 Light interception and yield: Defoliation
Several biotic and abiotic stresses such as hail, insect leaf feeders, and diseases affect yield
through defoliation. Potential physiological responses to defoliation include effects on
canopy photosynthesis, TDM, altered partitioning of TDM to plant parts, leaf abscission,
delayed leaf senescence, delayed crop maturity, changes in leaf specific weight, and reduced
nitrogen fixation, as well as others (Welter, 1993). Convincing evidence has shown that
insect defoliation reduces yield through LI effects on canopy photosynthetic activity and/or
CGR. Ingram et al. (1981) infested soybean with velvetbean caterpillar during the
reproductive period to study effects on physiological processes and yield. The treatments
resulted in a 50% reduction in LAI resulting in LI falling to 83% of the control level during
the seed filling period. Corresponding to reduced LI, canopy photosynthetic activity
declined to 85% of control and yield was reduced to 86% of control. Yield loss occurred
through reduced seed size caused by reduced seed growth rate which was entirely
attributed to decreased photosynthetic supply. Similar results were found by Board and
Harville (1993) for partial defoliation treatments made to create a LI gradient during the
reproductive period. Yield loss occurred only when defoliation was severe enough to reduce
LAI below 3.0 and light interception below 95% for extended periods. Although these
studies involved manual defoliation, rather than insect defoliation, research has shown that
yield responses from either manual or insect defoliation are similar if applied during the
same growth period and if leaf removal rates are similar (Higgins, et al., 1983; Turnipseed
and Kogan, 1987). The connection between LI and soybean yield response to defoliation has
been reinforced by research showing that photosynthetic rates in leaves damaged by
defoliation are similar to undamaged controls (Peterson and Higley, 1996). Thus, leaves
remaining after defoliation cannot compensate photosynthetically for lost leaf material and
the reduced LI directly decreases the photosynthetic rate and yield. Browde et al. (1994)
using a combination of defoliating insects, nematodes, and herbicide damage, concluded
that light interception was the “unifying explanation for yield losses”. Similar conclusions
were reached by Board et al. (1997) who reported a linear relationship between yield and LI
at the temporal midpoint of the seed filling period.
Soybean Physiology and Biochemistry
18
Previous defoliation studies have indicated that yield response is affected not only by the
severity of insect infestation, but also the timing of the attacks. Defoliation during the
vegetative period (emergence to first flowering) usually has shown little effect on yield,
largely due to leaf regrowth potential at this time. Since defoliation during the vegetative
period usually does not have a long-term depressing effect on LI and CGR, little effect on
yield has been reported (Haile et al., 1998a,b; Weber, 1955). These results are similar to those
of Jiang and Egli (1995) where shade during the vegetative period did not reduce yield if
crop growth rate during the R1 to R5 period was unaffected. Fifty percent defoliation
between appearance of the first trifoliate leaf and full flowering had little effect on yield
(Weber, 1955). Significant yield losses (20%) occurred in this study only when 100%
defoliation was applied during this period. Pickle and Caviness (1984) reported no yield loss
when soybean received 100% defoliation at the fifth leaf stage.
Greater yield responses to defoliation have been reported during the reproductive period
with greatest effect near the start of seed filling (R5). Yield losses from 100% defoliation at
R2 were only 25%, but rose sharply as defoliation was delayed to R3, R4, and R5 (see Table 1
for definitions of R stages) (Fehr et al., 1977). Increased defoliation tolerance at early
reproductive stages (near first flower) were later determined to be caused by rapid leaf
regrowth (Haile et al., 1998a,b). Greatest yield loss (75-88%) occurred at R5 (Fehr et al.,
1977), a finding substantiated by later studies (Fehr et al., 1981; Gazzoni and Moscardi, 1998;
Goli and Weaver, 1986). Delay of total defoliation to R6.6 resulted in only a 20% yield loss
(Board et al., 1994), supporting the view of greater tolerance to defoliation as seed filling
progresses.
Partial defoliation treatments initiated at R1 and terminated at R3, R4, R5, and R6.5 resulted
in significant yield loss (approximately 15%) when average LI during the R1 to R5 period
was reduced at least by 17-20% (Board and Harville, 1993; Board and Tan, 1995). Although
these partial defoliations resulted in decreased LI during seed filling, yield losses were
almost entirely due to reduced pod m-2 and seed m-2 rather than seed size. These results are
similar to shade responses shown by Egli and Yu (1991). In summary, shade, defoliation,
wide row spacing, and subnormal plant population affect yield through reduced CGR
during all or part of the period between emergence and R6. Stresses that operate during the
entire emergence to R6 period (wide row spacing, subnormal plant population), cause these
reductions to pod and seed numbers through lower production of node m-2 and
reproductive node m-2. However, in cases where CGR is reduced only during the
flowering/pod formation period (e.g. defoliation stress initiated at R1), lower pod and seed
numbers can also be affected by decreased pod per reproductive node.
As defoliation is delayed past the start of initial seed filling (R5), yield losses attenuate and
yield components causing the yield loss change. By the time seed number is determined and
soybean starts rapid seed filling (R6), yield losses from 100% defoliation are half that
compared with 100% defoliation at R5 (Goli and Weaver, 1986). Thus, similar to findings
with shade stress (Egli and Yu, 1991), yield was twice as sensitive to defoliation stress
during the flowering/pod formation period compared with the rapid seed filling period.
Defoliation during seed filling affects yield mainly through reduced seed size, although seed
number is also affected if defoliation occurred at or before R6 (Board et al., 2010). Every 0.1
unit delay in developmental stage from R5 to R7 (e.g. 5.4 to 5.5 or 6.2 to 6.3) resulted in a 5%
reduction in yield loss caused by 100% defoliation. Throughout early and mid seed filling
(R5 to R6.2), defoliation had to be sufficient to reduce light interception by about 20% to
decrease yield (Board et al., 2010; Ingram et al., 1981; Board et al., 1997). Once soybean
Soybean Yield Formation: What Controls It and How It Can Be Improved
19
Physiological
Disruptions from Wide
vs. Narrow Row
Spacin
g
Affected Canopy Level
Growth Processes
Affected Yield
Components
Row Spacing
Parameters
Reduced LAI and LI
efficiency results in
lower canopy LI.
Growing at nonoptimal
wide row spacing
reduces canopy
photosynthesis and CGR
during emergence-R6
period.
Reduced node and
reproductive node
numbers, pods and
seeds.
Sensitive stress period
is emergence to R6.
Wide vs. narrow rows
reduces yield
whenever LI falls
enough to reduce
average CGR (R1-R5)
below 15 gm-2 d-1. Seed
filling period is
unaffected by LI in
wide vs narrow rows .
Physiological
Disruption
From Subnormal Plant
Population
Affected Canopy Level
Growth Processes
Affected Yield
Components
Plant Population
Parameters
Reduced LAI results in
lower canopy LI
Reduced canopy
photosynthesis and CGR
during emergence-R6
Period
Reduced node and
reproductive node
numbers, pods and
seeds.
Sensitive period is
emergence-R6. Yield
losses occur when
average CGR (R1-R5)
falls below 15 g m2 d-1.
Seed filling period is
unaffected.
Table 6. Summary of row spacing and plant population effects on soybean physiology,
growth, yield components, and yield.
passes into the last half of the seed filling period, defoliation must be at or close to 100%
(resulting in a 50% relative LI reduction) to cause yield loss (Board et al., 2010; Board et al.,
1997). The effects of defoliation stress on yield formation are summarized in Table 7.
Physiolo
g
ical
Disruption from
Defoliation
Affected Canopy
Level Growth
Processes
Affected Yield
Components
Defoliation Parameters
Reduced LAI and
canopy LI
Reduced canopy
photosynthesis
and CGR.
Defolilation during
R1-R6 period
reduces node and
reproductive node
numbers, pod per
reproductive node,
pods and seed.
Defoliation during
R6-R7 reduces seed
size.
Vegetative period is not sensitive
to defoliation stress unless at 100%
level. Period most sensitive to
defoliation stress is R1-R6.2.
Significant yield losses start
occurring when light interception
across this period falls 17-20% and
CGR falls below 15 g m-2 d-1 .
During R6.2-R7 period must have
total defoliation to get significant
yield loss; i.e. 50% reduction in
relative LI. Thus, yield is half as
sensitive to defoliation during
R6.2- R7 as durin
g
R1-R6.2.
Table 7. Summary of defoliation effects on soybean physiology, growth, yield components,
and yield.
Soybean Physiology and Biochemistry
20
3. A general mechanism for explaining stress effects on yield
Our discussion on temperature, drought, and light interception has outlined a paradigm of
how these factors cause yield loss (Fig. 4). Despite differences in initial physiological
disruptions, environmental stress first affected canopy photosynthesis and CGR. Coupled
with length of the emergence to R5 period (related to maturity group), these growth
dynamic rates influence TDM(R5), the dry matter level at which vegetative TDM, node m-2,
reproductive node m-2, and pod per reproductive node are maximized (Board and Harville,
1993; Board and Tan, 1995) (Fig. 3). These yield components, in turn, regulate pod m-2 and
seed m-2 which mediate stress effects on yield. Thus, TDM(R5) serves as a benchmark
indicator for yield potential. Because yield component production per unit dry matter (yield
component production efficiency) differs with environmental (Board and Maricherla, 2008)
and genotypic factors (Kahlon and Board, 2011), final yield component number is also
affected by this factor (Fig.4). Seed size usually plays a much smaller role in explaining
environmental influences on yield. Support for this paradigm can be seen in data for a single
cultivar grown across a wide environmental range (Fig. 5). Yield is highly correlated with
seed m-2 (R2=0.83), but shows no relationship with seed size. Because of the paramount
importance of nodes, pods, and seeds in regulating environmental effects on yield, the most
stress-prone period is between emergence and R5, the predominant period in which these
yield components are formed.
Fig. 4. Paradigm for explaining how environmental stresses affect growth, yield
components, and yield. MG=Maturity Group.
We acknowledge that some abiotic and biotic stresses do not follow the paradigm outlined
above. Stresses that directly impair reproductive structures (i.e. flowers, pods, and seeds)
without acting through CGR fall into this category. Examples are the southern green stink
bug [Nezara viridula (L.)] which punctures the soybean seed; temperatures that are
sufficiently hot or cold during or near to fertilization to disrupt pod development (Salem et
al., 2007; Koti et al., 2004); and diseases such as pod and stem blight [Diaporthe phaeseolorum
(var. sojae)] which enter pods through abrasions, cracks, or other injuries (Athow and
Laviolette, 1973). Although these exceptions exist, analyses of many environmental stresses
indicates that the mode of action for yield reduction at the canopy level is similar to that
described for temperature extremes, drought, and reduced light interception; and that such
stresses affect yield through the paradigm explained in Fig. 4. Although it is impossible to
cover all the possible abiotic and biotic environmental stresses affecting soybean in a single
chapter, a few of them will be described.
Nitrogen deficiency is a common limiting factor for soybean yield (Tolley-Henry and Raper,
1986). Optimal growth and yield of soybeans, as well as other crops, requires a greater input
of N than any other nutrient. Soybean obtains its N either directly from the soil or from
symbiotic N2 fixation by the bacteria Bradyrhizobium japonicum. Deficiency symptoms
Soybean Yield Formation: What Controls It and How It Can Be Improved
21
Fig. 5. The relationship of soybean yield with seed m-2 and seed size for cultivar Iroquois
grown across 21 locations in the Midwestern US, 1996 (USDA, Unpublished data).
are manifested as decreased photosynthetic rate, reduced initiation and expansion of leaves,
and lower growth rates for stems and roots (Tolley-Henry and Raper, 1986). Approximately
50% of soybean’s leaf N is in rubisco, the enzyme involved in CO2 carboxylation onto
Ribulose di Phosphate (Sinclair, 2004). This enzyme is recognized as the rate-limiting step
for photosynthesis. Thus, when N becomes deficient, the entire photosynthetic cycle
declines, as evidenced by high correlation of leaf photosynthetic rate with [N] (Tolley-Henry
et al., 1992) and soluble protein (Ford and Shibles, 1988; Sung and Chen, 1989). Thus, any
stress which adversely affects N2 fixation (e.g. inadequate inoculation, low pH soils,
drought, etc.) can create N deficiency and yield loss in soybean. When a N deficiency results
in leaf [N] falling below 5%, photosynthetic rate starts declining (Tolley-Henry et al., 1992).
Soybean Physiology and Biochemistry
22
Unavailability of N for 10 or more d results in cessation of dry matter accumulation (Tolley-
Henry and Raper, 1986). Associated with this, leaf initiation and expansion stops.
Consequently, LAI, LI, and CGR are greatly reduced during the emergence-R5 period,
resulting in decreased seed m-2 and yield (Koutroubas et al., 1998). Thus, on the canopy
level, N deficiency affects yield in a manner similar to that shown for temperature extremes,
drought, and deficient light interception.
Several biotic stresses of soybean show a similar mechanism of yield loss. Among biotic
stresses, farmers in the Southeastern US spend the greatest amount of money for weed
control. Weeds reduce yield through competition with soybeans for water, light, and
nutrients (Hoeft et al., 2000). Depending on weed species, weed population, and
environmental conditions, there is a “critical period” early in soybean development when
weeds must be controlled to maintain yield (Hoeft et al., 2000). Failure to control weeds in
the critical period results in reduced soybean vegetative TDM(R5) and yield (Hagood et al.,
1980, 1981). As with drought, reduced light interception and N deficiency, yield loss
occurred through reduced pod and seed numbers.
4. Development of yield-loss prediction tools for diagnosing environmental stress
problems
A major barrier to improved yield is correct identification of environmental stresses causing
yield losses. During any given growing season, a soybean crop can be faced with a series of
potential yield-limiting stresses. For example, an early-season drought stress may have
slowed CGR during the vegetative period. This might be followed by a waterlogging stress
during the flowering/pod formation period (R1-R6) which left standing water on the field
for 2-3 d (sufficient to slow CGR, Scott et al., 1989). Finally, a late-season attack of defoliating
insects during rapid seed filling (R6-R7) may have decreased LAI enough to cause
significant yield loss. Correct identification of which factor(s) caused the yield loss aids in
devising remedial strategies to improve yield. If the entire yield loss was due to early-season
drought stress, then the farmer may consider irrigation when a similar future stress occurs.
On the other hand, if the early-season drought stress was shown not to play a role in yield
loss, the farmer would know that his crop could tolerate such drought periods without
suffering yield loss. If waterlogging was identified as the causative factor of yield loss, then
the farmer may wish to consider planting on raised beds or sloping the field in a given
direction so that water runs off the field rather than ponding. If the yield loss was caused by
the late-season insect defoliation, the farmer should consider more vigilant monitoring and
control of whatever pest was infesting the field.
Using the paradigm outlined in Fig. 4 for explaining environmental stress effects on yield,
yield-loss prediction tools can be identified which aid farmers in making decisions such as
those described above. Because CGR during the emergence to R5 period plays a critical role
in stress effects, TDM levels at developmental stages that are easily identifiable could be
used as putative yield-loss prediction tools. Since vegetative growth ends near R5 (Egli and
Leggett, 1973), TDM(R5) serves as an integrative measure of growing conditions during the
emergence to R5 period. Total dry matter (R5) also has value in predicting yield (Fig. 6). The
R5 stage is easy to identify by the appearance of fully-elongated pods at the top four main
stem nodes. Total dry matter at R1 (also an easily identifiable developmental stage) could be
used to indicate growing conditions at an intermediate stage of the vegetative growth
period. Based on an analyses of studies conducted across 1987-1996 near Baton Rouge, LA
Soybean Yield Formation: What Controls It and How It Can Be Improved
23
involving a wide range of environmental conditions (years, planting dates, row spacings,
plant populations, and waterlogging stress) achievement of optimal yield was shown to be
associated with a TDM(R1) level of 200 g m-2 and a TDM(R5) level of 600 g m-2 (Fig. 6)
(Board and Modali, 2005). Dry matter levels below these resulted in a curvilinear decline in
yield, while increases above this level gave only small insignificant yield increases. Yield
components identified as important for yield formation (seed m-2, pod m-2, reproductive
node m-2, and node m-2) demonstrated similar curvilinear responses to TDM(R1) and
TDM(R5) as did yield.
Fig. 6. Yield response to total dry matter at R1 [TDM (R1)] and total dry matter at R5 [TDM
(R5)] for soybean grown across a range of environment conditions near Baton Rouge, LA,
1987 through 1996.
Use of TDM(R1) and TDM(R5) as yield-loss prediction tools can be illustrated by analyzing the
aforementioned case of decreased yield resulting from three possible stresses across the
growing season: drought during the vegetative period, waterlogging during the
flowering/pod formation period, and insect defoliation during rapid seed filling.
Soybean Physiology and Biochemistry
24
Determination that TDM(R1) was optimal (200 g m-2 or greater), but TDM(R5) was suboptimal
(<600 g m-2) would indicate that the waterlogging stress contributed to the yield loss, but the
drought stress did not. Such a result would be manifested in a reduction in seed m-2. If TDM
levels at R1 and R5 were both optimal, then the yield loss probably resulted from the insect
defoliation. This would be reflected by a reduction in seed size, but no reduction in seed m-2.
Seed size can be determined from a field by random sampling of 100-seed samples. Seed m-2
can then be easily calculated by dividing seed size into seed yield (as dry matter). Thus, a
knowledge of when stresses occur, developmental stage timing, TDM(R1) and TDM(R5), and
seed size and seed m-2 data, greatly aid in diagnosing yield-limiting stresses.
Because of the large size of many commercial soybean farms, it is not practical to determine
TDM(R1) and TDM(R5) by conventional sampling methods. However, simple regression
methods have been developed that allow easy, rapid, and accurate determination of these
parameters. Total dry matter (R5) can be predicted from a multiple regression equation using
canopy closure (CC) date (achievement of 95% light interception) and days to R5 (R5days)
[TDM(R5)= -20.1-(5.9 x CC)+(13.7 x R5days)] (R2=0.81). The regression model was verified
using independent data (R2=0.90). Both canopy closure date and days to R5 are parameters
that can easily, rapidly, and accurately be determined in commercial soybean fields.
Because of the relationship between TDM, LAI, and LI (Loomis and Connor, 1992a),
TDM(R1) can be predicted from LI. Determination of light interception under field
conditions can now be done rapidly and accurately for commercial soybean farms using
digital photographic methods developed by Purcell (2000). When grown in narrow-row
culture (50 cm or less), a light interception of 92% at R1 is associated with a dry matter level
of 200 g m-2 (Board et al., 1992; Board and Harville, 1996). In the case of wide rows (75-100
cm), light interception of 68% at R1 is associated with a dry matter of 200 g m-2. The greater
light interception value for narrow rows occurs because LAI in narrow rows intercepts more
light per unit LAI (Board and Harville, 1992). In conclusion, TDM(R1) and TDM(R5) are
robust yield-loss prediction tools that can be used in conjunction with seed m-2 and seed size
data to efficiently analyze environmental stress problems in soybean.
5. Genetic strategies for yield improvement
Across a 60-year period, cultivar development efforts by soybean breeders have resulted in
a 21-31 kg ha-1yr-1 increase in soybean yield (Wilcox, 2001). Selection for yield during this
process has been done through empirical yield trials across a range of different
environments (Fehr, 1987; Frederick and Hesketh, 1994). Desirable lines are selected as
future cultivars based on high and stable yields across years and locations. Thus, factors
responsible for this yield improvement have not been clearly identified. In an effort to
identify indirect yield criteria for streamlining cultivar development, scientists have
endeavored to determine the pertinent factors related to genetically-induced yield
enhancement in the cultivar development process.
Several studies have sought to explain yield improvement in the cultivar development
process through greater production of specific yield components. However, results have
been mixed. Boerma (1979) reported that yield improvement was attributed to greater pod
production, although this was apparent only in maturity group VIII cultivars, and not in
maturity group VI and VII. Frederick et al. (1991) also demonstrated that increased yield in
new compared with old cultivars was related to increased pod number. In contrast, Specht
and Williams (1984) demonstrated a small increase in seed size averaging 0.1 g/year. Other
Soybean Yield Formation: What Controls It and How It Can Be Improved
25
research indicated that the relative importance of seed number and seed size in explaining
greater yield in the cultivar development process may depend on cultivar comparisons
being made. Gay et al. (1980) demonstrated that within indeterminate maturity group III
cultivars, the newer cultivar Williams yielded more than the older cultivar Lincoln because
of greater seed size. On the other hand, in comparing determinate maturity group V
cultivars, the newer cultivar Essex yielded more than the older cultivar Dorman because of
greater seed number. More recent studies comparing old and new Midwestern cultivars
clearly indicated that yield improvement was more strongly related to seed m-2 than seed
size (De Bruin and Pedersen, 2009). The authors also stated that greater seed m-2 appeared to
be related to greater seed per pod, although other yield components were not examined.
Comprehensive research from China involving determinate and indeterminate soybeans in
four areas of the country showed that greater yield occurred through differential increases
of pods per plant, seed per pod, and seed size (Cui and Yu, 2005). Based on the diversity of
results from different researchers, countries, and germplasms, it appears that yield
improvement with cultivar development can occur through different yield component
mechanisms. However, for the Southeastern and Midwestern US soybean-growing regions,
most studies conclude that cultivar yield improvement in new vs. old cultivars has been
more controlled by changes in seed m-2 than seed size. Recent studies involving
Southeastern US cultivars indicated that genetic differences in new vs. old cultivars were
sequentially controlled by node m-2, reproductive node m-2, pod m-2, and seed m-2 (Kahlon
et al., 2011).
Because of its importance in crop production, researchers have also tried to determine if leaf
photosynthetic rate plays a role in explaining yield improvement during cultivar
development. This objective has been studied by comparing carbon exchange rates (CER)
per unit leaf area in new vs. old cultivars and also between parents and progeny in a
breeding program. Results have been mixed. Early studies by Larson et al. (1981) involving
cultivars released between 1927 to 1973 found no correlation between yield and leaf
photosynthetic rate. Gay et al. (1980) also found little change in CER between two new and
two old cultivars. Similar results were reported by Frederick et al. (1989). In contrast,
Dornhoff and Shibles (1970) compared 20 cultivars released across time and demonstrated a
general trend between CER and yield, although exceptions occurred. More recent studies by
Morrison et al. (2000) with new and old Canadian cultivars did report a 0.52 % per yr
increase in the photosynthetic rate, a level very similar to the annual yield increase shown
by these cultivars. However, an inverse relation of photosynthetic rate per leaf with LAI
may have negated some of the positive effect of increased photosynthetic rate. The increase
in photosynthetic rate was related to an increase in stomatal conductance.
Results of studies looking at CER in progeny of a breeding program have also been mixed.
Buttery and Buzzell (1972) determined that over 60% of cultivars developed from breeding
programs had CER greater than their parent cultivars. Ojima (1972) also was successful in
demonstrating increased CER in early progeny lines vs. parental cultivars. However, other
research has not demonstrated positive results. Wiebold et al. (1981) crossed two parental
cultivars with contrasting high and low CER and could not find improved CER by the F3
and F4 generations. Ford et al. (1983) found similar disappointing results. The current
general consensus is that using CER as an indirect selection criterion in a breeding program
has limited value (Frederick and Hesketh, 1994).
Measurement of photosynthesis on the canopy level (canopy apparent photosynthesis, CAP)
has shown greater association with final yield compared with CER (Harrison et al., 1981;
Soybean Physiology and Biochemistry
26
Wells et al., 1982). However, the degree of correlation was not high (r=0.5). Using cultivars
and plant introductions differing in CAP and seed filling period, Boerma and Ashley (1988)
showed positive partial correlations of yield with CAP (averaged during the reproductive
period) (r=0.63) and seed filling period (r=0.54). The product of CAP x seed filling period
was even more closely related to yield (r=0.78). However, the inherent difficulties involved
in measuring CAP (variable light and temperature conditions; tedious equipment set-up)
preclude its use as an indirect selection tool in a breeding program.
The roles of TDM accumulation and harvest index in explaining yield improvement during
cultivar development have also shown mixed results. Salado-Navarro et al. (1993) examined
18 Southeastern cultivars released from 1945 to 1982, but found no relationships between
improved yield with either TDM or harvest index. Gay et al. (1980) explained yield
differences between new and old cultivars as governed more by increased harvest index
rather than TDM accumulation. More recent studies involving new vs. old cultivars in
Canada (Morrison et al., 1999) and Japan (Shiraiwa and Hashikawa, 1995) have also
supported the importance of harvest index for explaining greater yield. In the case of the
Canadian study, no differences in TDM were shown between new and old cultivars. These
results are supported by Chinese studies which reported a greater role for harvest index vs.
TDM accumulation for explaining yield improvement in cultivar development programs
(Cui and Yu, 2005).
In contrast, Frederick et al. (1991) (US cultivars) reported little role for harvest index in
explaining genetic improvement in soybean and attributed greater importance to TDM
accumulation. Cregan and Yaklich (1986) reported similar findings. These results were
supported by Kumudini et al. (2001) who showed that TDM accumulation contributed 78%
to greater yield in new vs. old cultivars, whereas harvest index contributed only 22%.
Greater TDM accumulation occurred entirely during the seed filling period and was
supported by the longer leaf area duration (leaf area index integrated over time) for the new
cultivars. De Bruin and Pedersen (2009) supported Kumudini’s findings and attributed yield
enhancement in new vs. old Midwestern cultivars as entirely due to dry matter and not
harvest index. However, this more recent study differed from Kumudini in concluding that
the greater dry matter accumulation was partly due to greater crop growth rate (R1-R5.5)
prior to seed filling.
6. Summary and conclusion
Because of soybean’s importance in meeting world food needs, increased demand for
agricultural commodities fueled by global economic development, and the limited potential
for expansion of arable land, it is imperative that strategies be developed for coping with the
effects of environmental stress on crop yields. Accurate identification and correction for
environmental stress problems potentially can increase yield from 25-66%, with increases
being greater in the developing compared with developed world. Environmental stresses
can be divided into either abiotic stresses (atmospheric and soil factors) or biotic stresses
(pest problems). Because such a high proportion of crop dry matter is derived from either
current or previous photosynthesis, the vast majority of environmental stresses affect yield
through the canopy photosynthetic rate and CGR. The majority of soybean research has
conclusively demonstrated that environmental stress affects yield through control of seed m-
2, which, in turn, is controlled by sequential formation and growth of node m-2, reproductive
node m-2, and pod m-2. Since formation of these yield components occurs across the
Soybean Yield Formation: What Controls It and How It Can Be Improved
27
emergence to R6 period, this is the period where stresses depressing crop growth rate have
their greatest effect on yield. Although yield is less sensitive to stress during the rapid seed
filling period (R6-R7), stresses during this period can also reduce yield if sufficiently severe.
Correct advice to soybean farmers concerning correction of environmental stresses depends
on accurate identification of which potentially damaging biotic and abiotic factors occurring
in any growing season significantly reduce yield (i.e. act as stresses). Development of TDM
levels at R1 and R5 as yield-loss prediction tools facilitates this process. Both developmental
stages are easy to identify and yield has shown robust asymptotic relationships of TDM(R1)
and TDM(R5) with yield reaching plateau levels at 200 g m-2 TDM(R1) and 600 g m-2
TDM(R5). Accurate and rapid regression methods were outlined for indirect calculation of
these parameters. Thus a farmer having knowledge of TDM(R1) and TDM(R5), the timing of
potential stress events, and knowledge of seed m-2 and seed size, would be able to identify
which potential stresses actually cause yield loss.
On the genetic level, the majority of yield formation studies indicate that seed m-2 plays a
larger role in yield improvement than seed size. However, exceptions to this exist and it
must be realized that alternative mechanisms of yield improvement are possible between
different germplasm pools and geographic regions. Although little research has been done
beyond the primary yield component level, studies that have been conducted indicate that
genetic influences on seed m-2 are mediated by node m-2, reproductive node m-2, and pod
m-2. Genetic studies involving old and new soybean cultivars indicate that both TDM
accumulation and harvest index play roles in explaining yield improvement. However, the
evidence is so conflicting at this point in time that definitive statements are not possible.
Although much research has been done on the subject, there is little evidence to suggest that
improved yield has resulted from improved photosynthetic rate per unit leaf area, canopy
photosynthesis, or CGR.
7. Acknowledgements
The authors wish to acknowledge financial support from the Louisiana Soybean Promotion
Board, the USDA Risk Management Agency, the southern Soybean Reseach Program, and
the Kentucky Soybean Board.
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