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Nitrogen fixation in Australian dairy systems: Review and prospect


Abstract and Figures

Quanstitative measurement of N-2 fixation has rarely been conducted in Australian dairy pastures. The available data indicate that annual N-2 fixation rates in Australian dairy pastures are generally low, due to low pasture legume content. With typical legume contents of grazed pastures less than 30% of total pasture biomass production, annual N-2 fixation in herbage is usually much less than 50 kg ha(-1) year(-1). Other factors which are likely to be able to contribute to increased N-2 fixation input (rhizobia, mineral N management, soil acidity, soil water contents) will have little impact until such time as legume contents are increased. In contrast, for some hay systems, such as those using lucerne, N-2 fixation input is shown to be high (200-300 kg ha(-1) year(-1)). While pasture clover contents remain low there is little value in study or measurement of N-2 fixation, nor in complex modelling, as N-2 fixation will be of little quantitative importance. However, where legume contents, and thus potential N-2 fixation are increased, there is scope for investigation into potential increases in N input from this source, which is invariably linked to fertiliser application, the management of grazing and the N returns in urine and dung. These are the major influences on sward N dynamics and legume N-2 fixation. The inoculant rhizobia used for white clover in Australia (TA1) is likely to be suboptimal. Isolated in Tasmania in 1953 it has been shown to be inferior in N-2 fixation compared with other strains on several occasions. Root pests and diseases are likely to be prevalent and impact directly on clover root growth and perhaps nodulation. Modelling is often used to describe the probable influence of management and/or climate on the operation of agricultural systems. Reliable modelling of N-2 fixation requires capacity to integrate the effects of grazing and pasture composition on soil mineral N dynamics, the influence of this mineral N on nodulation and on suppression of N-2 fixation, and environmental and management influences on soil rhizobial populations. Currently no models have demonstrated this capacity. At present, a suitably calibrated regression model is probably a good option for modelling N-2 fixation in Australian dairy pastures. Environmental benefits ensuing from increasing N-2 fixation and substituting this for fertiliser N are likely to be greater off-farm (reduced GHG emissions at site of fertiliser manufacture) than on, if current fertiliser management is optimal. Nevertheless substituting fixed N for fertiliser N would have modest environmental and feed efficiency benefits.
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Nitrogen xation in Australian dairy systems: review
and prospect
Murray Unkovich
School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.
Abstract. Quantitative measurement of N
xation has rarely been conducted in Australian dairy pastures. The available
data indicate that annual N
xation rates in Australian dairy pastures are generally low, due to low pasture legume content.
With typical legume contents of grazed pastures less than 30% of total pasture biomass production, annual N
xation in
herbage is usually much less than 50 kgha
. Other factors which are likely to be able to contribute to increased N
xation input (rhizobia, mineral N management, soil acidity, soil water contents) will have little impact until such time as
legume contents are increased. In contrast, for some hay systems, such as those using lucerne, N
xation input is shown to be
high (200300 kg ha
While pasture clover contents remain low there is little value in study or measurement of N
xation, nor in complex
modelling, as N
xation will be of little quantitative importance. However, where legume contents, and thus potential N
xation are increased, there is scope for investigation into potential increases in N input from this source, which is invariably
linked to fertiliser application, the management of grazing and the N returns in urine and dung. These are the major inuences
on sward N dynamics and legume N
xation. The inoculant rhizobia used for white clover in Australia (TA1) is likely to be
suboptimal. Isolated in Tasmania in 1953 it has been shown to be inferior in N
xation compared with other strains on several
occasions. Root pests and diseases are likely to be prevalent and impact directly on clover root growth and perhaps nodulation.
Modelling is often used to describe the probable inuence of management and/or climate on the operation of agricultural
systems. Reliable modelling of N
xation requires capacity to integrate the effects of grazing and pasture composition on soil
mineral N dynamics, the inuence of this mineral N on nodulation and on suppression of N
xation, and environmental and
management inuences on soil rhizobial populations. Currently no models have demonstrated this capacity. At present, a
suitably calibrated regression model is probably a good option for modelling N
xation in Australian dairy pastures.
Environmental benets ensuing from increasing N
xation and substituting this for fertiliser N are likely to be greater off-
farm (reduced GHG emissions at site of fertiliser manufacture) than on, if current fertiliser management is optimal.
Nevertheless substituting xed N for fertiliser N would have modest environmental and feed efciency benets.
Received 27 April 2012, accepted 7 August 2012, published online 10 December 2012
Nitrogen (N) in plants is the primary N source for animal and milk
protein production in dairy systems. Biological dinitrogen (N
xation is the process whereby specialised microorganisms are
able to convert N
from the atmosphere into ammonia (NH
) via
the enzyme nitrogenase. This xedN can then be incorporated
into microbial and plant protein. This is a very important process
because, along with fertiliser N (industrial N xation), it provides
the main entry point for N into agricultural systems. There are four
principal forms of N
xation which relate to the type of N
bacteria and to the strength of their relationship with plants.
Some bacteria xN
in a free-living state, while others xN
in association with plants. The associations with plants range from
rather loose associations around plant roots (associative),
endophytic N
-xing bacteria residing in the vascular tissues
of some grasses, and nally, highly-evolved, complex symbioses,
involving morphological changes of both microbe and plant in
specialised root structures (nodules). In legume symbioses the
-xing bacteria pass all of the xed NH
directly on to their
plant hosts which incorporate it into plant protein. The N
symbioses with legume plants (e.g. clovers, medics, peas, beans)
are the most important because they are more highly evolved and
able to x much greater amounts of N than the other associations.
For example, symbiotic N
xation can provide for all of the N
requirements of pasture legumes, while for pasture grasses the N
xing associations are unlikely to be able to provide more than
10% of grass N demand, even under optimal conditions.
The objective of the present review is to document the state of
knowledge of N
xation in dairy pastures in Australia and to
indicate potential areas of research which might increase the
value of N
xation in Australian dairy systems. While the review
is clearly directed at Australian eld studies, the limited
Australian research requires recourse to salient reviews or
critical information from studies elsewhere. This review
focuses primarily on perennial high-rainfall or irrigated legume
pastures where much of the Australian dairy industry is located.
Journal compilation CSIRO 2012 Open Access
Crop & Pasture Science, 2012, 63, 787804 Review
Thorough reviews on N
xation in annual legume pastures can
be found elsewhere (e.g. Unkovich et al.1997; Peoples et al.
1998,2001; Peoples and Baldock 2001) and Unkovich et al.
(1998) provide a detailed study of N dynamics in grazed annual
clover pastures.
Some of the more pertinent reviews on N
xation in grazed
perennial pastures include Haynes and Williams (1993), Jarvis
et al.(1995), Ledgard and Steele (1992), Ledgard (2001),
Menneer et al.(2004), while the reviews of Carlsson and Huss-
Danell (2003) and Cuttle et al.(2003) are also quite useful.
Eckard (1998) provides salient background to the N dynamics of
dairy pastures in Australia and likely responses to N fertiliser
application but does not explicitly deal with N
Operation of the N
-xing legume symbiosis
under eld conditions
Symbiotic N
xation is a complex process involving two
organisms in a dynamic partnership subject to a range of
environmental and management inuences. While the
physiological operation of the symbioses are generally
understood (Schulze 2004; Garg and Geetanjali 2007), an
ability to predict N
xation under eld conditions requires
site-specic knowledge of partner and symbiotic responses to
relevant local environmental and management parameters
(Russelle 2008). Interactions between grazing and competition
for light with grasses exert considerable inuence on the balance
between grasses and legumes in pasture systems but this will not
be considered in detail here. Readers are referred to Schwinning
and Parsons (1996).
Mineral N depresses N
While legumes have the capacity to x atmospheric N
via their
symbioses with rhizobia, they are also able to take up soil mineral
N like non-legume plants. Indeed they have a preference for use of
soil mineral N such that the nodulation and N
xation processes
are downregulated or turned off in the presence of signicant
concentrations of mineral N (see Streeter 1988). The dynamic
relationship between these factors is illustrated in Fig. 1for two
annual pasture legumes grown under controlled (glasshouse)
conditions. The gure highlights that (i) both nodulation and
xation are downregulated by available mineral N (ii) small
amounts of mineral N can stimulate growth, nodulation and N
xation, and (iii) there are signicant differences between
species in the extent of these relationships. Although it is not
illustrated here, the same legume species with different rhizobia
may also vary in their sensitivity to soil mineral N (Unkovich and
Pate 1998).
In the context of grazed dairy pastures, this means that returns
of N in urine and dung will suppress N
xation if most of the
resultant mineral N is not taken up by companion grasses.
Similarly, application of fertiliser N to legume pastures will
suppress clover N
xation (see e.g. Ledgard et al.1996,
2001). Regardless of fertiliser N application, this phenomenon
is most likely to occur under urine patches which may result
in concentrations of readily mineralisable N equivalent to
1000 kg ha
(Haynes and Williams 1993). Such mineral N
concentrations would be expected to suppress N
xation and
nodulation for some months. Soil nitrate concentrations high
enough to suppress nodulation and N
xation may also arise
in rain-fed pastures at the end of summer and into autumn,
particularly in pastures containing annual species (see
Unkovich et al.1998).
In a study in northern Victoria, Mundy (1987) used a
N tracer
to follow fertiliser uptake and N
xation in an irrigated white
clover/ryegrass pasture following the application of 5 or
100 kg N ha
(Fig. 2). Pasture clover content was reduced
from 40% in the 5 kg N ha
treatment to 20% with 100 kg N
fertiliser applied. However, total mineral N uptake by clover
was not reduced but fertiliser N uptake was substituted for N
xation, which was reduced from 74 to 45% of clover herbage
N. The authors indicated that this suppression of N
continued for at least 10 weeks. These data demonstrate the
dynamic interaction between soil mineral N availability, clover
and grass growth, and symbiotic N
xation, even in the absence
of grazing animals. Increased availability of soil mineral N
reduces the competitive advantage of N
-xing legumes under
low soil mineral N supply, switches off legume N
xation and
reduces pasture clover content.
M. truncatula
(barrel medic)
mM NO3-
Total plant N
(mg plant–1)
N fixed
M. littoralis
(strand medic)
1510 01510
Nodule mass
DW plant–1)
Fig. 1. Relationship between mineral N (nitrate) supply and nodulation
(right y-axis) and N
xation for two annual pasture legumes. From Pate and
Unkovich (1999).
5 kg N 100 kg N
N2 fixation
Clover soil mineral N uptake
Clover fertiliser uptake
Grass soil mineral N uptake
Grass fertiliser uptake
Herbage N (kg ha–1)
Fertiliser applied (kg ha
Fig. 2. Sources of herbage N in an irrigated white clover/ryegrass pasture in
northern Victoria 35 days after the application of 5 or 100 kg ha
N fertiliser.
Plotted from the data of Mundy (1987).
788 Crop & Pasture Science M. Unkovich
A second example of the effect of N fertiliser application on
xation in an irrigated white clover/ryegrass dairy pasture
from northern Victoria is shown in Fig. 3. Following application
of 100 kg N ha
xation remained at ~50% of that for
unfertilised pasture over the following 2 weeks.
In a study of a rain-fed white clover pasture in western Victoria
(McKenzie et al.1998), application of 45 kg N ha
had no
measurable impact on N
xation, regardless of fertiliser type
(Table 1). However, in this case, differences between treatments
might not be expected since prior grazing may have provided
much more mineral N than the modest fertiliser application,
and this effect may last many months (Menneer et al.2004)
and, furthermore, very low legume content (9%) and thus low
xation (24kgha
) would mask potential treatment effects
on measured N
In a second experiment McKenzie et al.(1998) applied
060 kg N ha
to the pasture and N
xed ranged from
0.83.7 kg N ha
. While these authors indicated a positive linear
response to increasing N fertiliser N application, this seems an
unlikely conclusion given the difculties of measuring such
small differences in N
xation at the eld level (Unkovich et al.
2008). The results of these two experiments highlight the limited
value in measuring N
xation in low clover content pastures.
Soil water limitations to N
xation activity of legume nodules declines under high soil
water contents associated with ood irrigation (Mundy et al.
1988) or water logging, possibly a consequence of the production
of ethanol in nodules under anoxic conditions (Sprent and
Gallacher 1976). Decreases in soil oxygen availability and
subsequent declines in N
xation may also result from
pugging or increased bulk densities under grazing (Menneer
et al.2001). Nitrogenase activity also declines at low soil
water contents, and probably more so than plant growth
(Davey and Simpson 1990), although it is difcult to separate
these as it is often unclear whether N
xation activity reduction
is due to reduced plant demand for N or a reduced supply of
photosynthate to the nodules. Nodule activity declines with water
stress, but can only recover if the water stress is moderate (Sprent
1971). Recommencement of N
xation after more severe stress
requires regrowth of existing nodules (34 days, Engin and Sprent
1973), but after drought, initiation and growth of completely new
nodules is required, which would take longer (510 days, Davey
and Simpson 1990).
An example of the sensitivity of the N
-xing nodule to soil
water content is given in Fig. 4, which shows nitrogenase activity
for two irrigated white clover pastures in northern Victoria. The
two sites had different soil bulk densities, thus different pore
space, and presumably oxygen availability, but the relative
effects of soil water content were maintained. For irrigated
systems there is thus a challenge to maintain soil water content
within the non-limiting range to maximise N
xation activity.
02 4 6 8 10 12 14 16
Acetylene reduction (mol ha–1 h–1)
0 N
100 kg N
Fig. 3. The sensitivity of N
xation (relative nitrogenase activity) to
applied N in irrigated white clover pasture in northern Victoria. Redrawn
from Mundy et al.(1988).
Table 1. Response of a ryegrass/white clover pasture to fertilisers
measured 37 days after application
All fertiliser treatments were applied at a rate of 45 kg N ha
. From McKenzie
et al.(1998)
Fertiliser %Ndfa N xed (kg ha
None 69 3.6
PKS 60 2.5
Urea 58 1.9
Pastureboosta blend 59 2.0
Ammonium nitrate 65 3.0
Di-ammonium phosphate 65 2.9
Ammonium sulfate 64 2.7
Ammonium nitrate and sulfur 70 4.1
Urea + PKS 69 3.5
Pastureboosta + PKS 66 3.5
Ammonium nitrate + PKS 64 2.6
DAP + PKS 64 2.7
Ammonium sulfate + PKS 61 2.6
Ammonium nitrate and sulfur + PKS 66 3.4
25 30 35 40 45 50 55 60
site 1
site 2
Soil volumetric water content (%)
Acetylene reduction (mol ha
Fig. 4. The sensitivity of symbiotic N xation (relative nitrogenase activity)
to soil water content. Redrawn from Mundy et al.(1988).
xation in Australian dairy systems Crop & Pasture Science 789
White clover may be more susceptible to water stress than
other perennial pasture legume species and lucerne more tolerant
(Kelly et al.1989; Neal et al.2009), Ostrowski (1972) considered
white clover to be more susceptible to water than heat stress.
This is a probable explanation for observed increases in pasture
growth in summer in high-rainfall or irrigated (Kelly and OBrien
1992) environments when clover contents are increased, and
potential increases in N
xation during the warmer months of
the year (see Eckard 1998,2001). The low drought tolerance of
white clover may be a signicant cause of its poor persistence
in many systems, even under irrigated conditions where white
clover may only maintain maximal growth for 45 days after
irrigation (Mason et al.1987). Pasture clover content thus
appears to be higher with more frequent irrigations (Dunbabin
et al.1997). Compared with other perennial legumes, lucerne
may be more tolerant of water stress, producing greater biomass
than ve other perennial legumes when grown under decit
irrigation (Neal et al.2009).
Temperature and N
There is considerable inconsistency in the literature relating
temperature to N
xation in white clover. Whitehead (1995)
suggests that N
xation ceases below a soil temperature of 98C,
but other evidence indicates that it occurs over a wider range
of temperatures (~2408C), and is relatively insensitive to
temperature over quite a wide range (15308C) (Liu et al.
2010). Provided that there is adequate water available, white
clover can maintain a constant N
xation rate over the 20338
temperature range (Ryle et al.1989) and thus the summer
temperatures experienced in the Australian dairy regions
should not be prohibitive to N
xation. Low temperatures
may affect N
xation less than NO
uptake (Hatch and
Macduff 1991). While Bouchart et al.(1998) reported that N
xation in white clover declined more than NH
uptake at low
temperatures (68C), they also showed that this was due to reduced
clover N demand, not to a direct effect of low temperature on N
xation per se. Temperatures as low as 78C were not limiting
to N
xation in white clover (Svenning and MacDuff 1996). In
the study of three white clover pastures in western Victoria
(Riffkin et al.1997), dependence of white clover on N
xation did not decline during the winter months. Dart and
Day (1971) found that most of the legume species studied
(including red clover and lucerne) continued to xN
down to
28C, and N
xation in lucerne was maintained up to 378C (white
clover was not included in the study). Nodulation and N
in lucerne was suggested to cease below 88C (Bordeleau and
Prévost 1994) but this is not consistent with other reports.
Temperature per se is thus unlikely to have any signicant
direct inuence on N
xation at the eld level under
Australian dairy climates, although clearly it will exert indirect
inuence via effects on plant development, plant water relations,
and on the mineralisation of soil N.
Rhizobia and N
The microsymbiont bacteria contained in commercial inoculants
that partner the primary pasture legumes in Australian dairy
systems are given in Table 2. Here it can be seen that while
development of legume inoculants has continued for lucerne
and annual Trifolium species, there has been no development
of rhizobial inoculants for perennial Trifolium species since the
initial release of TA1 in ca. 1954.
Inoculant rhizobia for perennial Trifolium species
The current rhizobia used in the commercial inoculant for
white (Trifolium repens), red (Trifolium pratense) and strawberry
(Trifolium fragiferum) clovers in Australia was isolated in
Tasmania, and rst tested on clovers in 1953 (Paton 1957).
Initially named BA-Tas, it was renamed TA1 (Waters 1957).
In combination with strain NA30 it was recommended for use
as the commercial inoculant for clovers at that time (Waters
1957), primarily because it was effective on a wide range of
annual and perennial Trifolium species (Paton 1957). Although
TA1 was later shown to be poorly competitive with native
rhizobia (Brockwell et al.1972) on alpine soils, it had
appeared to fare better in agricultural soils (Dudman and
Brockwell 1968). Strain NA30 was later annexed from the
culture (Brockwell and Gibson 1968) and TA1 remains the
single strain in the Group B commercial inoculant for white
clover available today (Pulsford and Bullard 1997). Rhizobium
leguminosarum bv. trifolii strain TA1 became a benchmark
organism, and studies deploying this strain of rhizobia
developed into a voluminous literature internationally, but
little of this relates to its eld performance in N
particularly with the varieties of white clover grown in
Australia. As far as I am able to ascertain this has in fact not
been examined, although it has been shown to be less effective
in N
xation on clovers than a range of other eld isolates
on several occasions (see Brockwell and Gibson 1968; Riffkin
et al.1999a). Under laboratory conditions Gibson et al.(1975)
found that very few eld isolates could match its N
effectiveness. Meanwhile, there is a strong tendency for self
selection of suitable rhizobia in the eld (Baird 1955;
Brockwell et al.1972), and this may be reected in the
superior performance of some eld isolates in western Victoria
when compared with TA1 (Riffkin et al.1999b). There is little
doubt that signicant improvements could be made with respect
to the N
xation effectiveness of the microsymbiont used
for white clover in Australia, however, while legume contents
of dairy pastures remain low, there may be little benet realised
from such improvement.
Table 2. Rhizobia used in Australian commercial inoculants for legumes used in dairy systems
Inoculant group Rhizobia Strain Recommended legume hosts
BRhizobium leguminosarum bv. trifolii TA1, (since ca. 1954) Perennial Trifolium spp. (white, red, strawberry clover)
AL Sinorhizobium meliloti RRI128 (since 2001) Lucerne
CRhizobium leguminosarum bv. trifolii WSM1325 (since 2005) Annual Trifolium spp.
790 Crop & Pasture Science M. Unkovich
Inoculant rhizobia for lucerne
Nodulation and rhizobiology of lucerne in Australia has seen
more attention than white clover, probably because lucerne also
has important roles outside of the dairy industry. These studies
have generally shown lucerne to nodulate well and x N with the
range of rhizobia that persist in agricultural soils in Australia
(Bowman et al.1998; Ballard et al.2003), and in this respect
lucerne may be more gregarious than some other Medicago
species (Ballard et al.2003).
xation, soil acidity and salinity
The average soil pH on 44 dairy farms across the country in the
survey of Gourley et al.(2010) was 5.3 (CaCl
), and 4.8 across 71
dairy farms in western Victoria (Riffkin et al.1999a). At such low
soil pH rhizobia (Richardson and Simpson 1989) and nodulation
(Munns 1965b) are likely to be severely compromised. Effects
may be primarily manifest through poor survival of rhizobia at
low pH (Richardson and Simpson 1989; Ballard et al.2003) and/
or inhibition of legume nodulation by toxic aluminium (Unkovich
et al.1996). The white clover inoculant strain (TA1) was shown to
be less persistent in acid soil than ve of six other strains in a eld
comparison on annual clovers (Watkin et al.2000), so this
rhizobia may be relatively sensitive to low soil pH. Consistent
with this, in the survey of Riffkin et al.(1999a) clover dependence
on N
xation was negatively correlated with rhizobial numbers
on light textured soils (mean pH 4.6) but not on medium textured
soils (mean pH 4.9), and the amount of N
xed positively
correlated with soil pH on light but not medium textured soils.
Although lucerne has been shown to selectcompatible, effective
rhizobia under acid soil conditions (Ballard et al.2003), N
xation will most likely be suboptimal under the typical soil
pH of Australian dairy farms. While rhizobial partners more able
to withstand acid soil conditions can be identied (Howieson
et al.1991), these are not a long-term solution to the problem of
acid soil development which requires the addition of lime to
provide improved soil chemical conditions for plant growth,
legume N
xation (Howieson and Ballard 2004) and general
soil health. Lucerne is generally considered more susceptible to
problems of low soil pH than some other legume species and
nodule establishment can be an issue (Munns 1965a).
Irrigation of a white clover/ryegrass pasture with saline water
reduced clover growth but not grass growth (Smith et al.1993),
yet N
xation did not appear to be impaired at the salinities
encountered (5 dS m
). Similarly long-term applications of
sewage sludge to soils under dairy pasture in NSW did not
impair the operation of white clover symbioses or the
effectiveness of the naturalised soil rhizobia (Munn et al.1997).
Pests and diseases
A range of parasitic nematodes are known to infect white clover
across the dairy zone, and to reduce root growth and nodulation
(McLeish et al.1997), with bacterial feeding nematodes being
particularly important as pasture legume content increases
(Yeates and Stirling 2008). Some pests feed directly on clover
nodules (Gerard 2001) and in this case would severely
compromise N
xation capacity. Although specic, direct
effects of pests and diseases on N
xation have not been
studied (quantied) in the eld, the density of some nematode
species was correlated with the amount of N
xed and the
dependence of white clover on N
xation on light textured
soils in a eld survey in western Victoria (Riffkin et al.
1999a). This would imply that nematodes might be reducing
white clover N
xation in this region. Where the white clover
content of pastures are higher this may constitute a signicant
restraint on N
xation potential.
Quantitative estimates of N
xation in Australian
dairy pastures
Interpreting N
xation data in grazed pasture systems
Before examining the available quantitative data on N
in dairy pastures it is worth considering a framework for
interpretation of symbiotic N
xation eld data. From a
systems point of view the key elements are the interactive
effects of soil mineral N, clover : grass ratio, and grazing
pressure, on N
xation as shown in Fig. 5. This highlights
that (1) N
xation generally tops up clover N demand where
it cannot rst be satised by soil mineral N supply, (2) grasses
and other non-legumes are stronger competitors for mineral N
than legumes and thus the mineral N demand of non-legumes
tends to be met rst, (3) the N returns in urine and dung from
grazing animals, and fertiliser N, result in increased mineral N in
the soil which tends to favour growth of grasses over legumes
and to reduce N
xation directly, but contrary to this (4) at the
lower end of the grazing spectrum, increased grazing intensity
may favour the growth of clover over grass due to reduced
shading of the clover, and (5) when clover content is lower it
is forced to depend more on N
xation for its N requirement
because more of the mineral N will be taken by the larger grass
One must be careful when interpreting N
xation data, for
example a clover pasture xing 100% of its N might be considered
excellent, but if the total clover production is only say 500 kg ha
then only the tiny amount of 12 kg N ha
might be xed.
Conversely, if only 20 kg N were xed this might be quite
acceptable for a pasture with a clover yield of 10 t ha
which case %Ndfa would be low but total clover N might be a
respectable 300 kg N ha
. Unambiguous data on N
xation for
pastures thus must include information on clover total N or dry
matter, as well as the amount of N
xed, and the proportional
dependence on N
xation (%Ndfa).
N2 fixation
Mineral N
Fig. 5. Key inuences on N
xation in a grazed clover/grass pasture.
xation in Australian dairy systems Crop & Pasture Science 791
Potential (maximum) N
xation is established by legume
total dry matter production (Fig. 6), with the realisation of this
potential primarily determined by mineral N availability, soil
fertility [primarily phosphorus (P)], and the abundance and
competence of the microsymbiont rhizobia. The gure shows
that a clover production of 14 t ha
could potentially sponsor up
to 700 kg of N
xation annually.
Problems of measurement
Methods for eld measurement of N
xation have been detailed
in Unkovich et al.(2008) and summarised by Peoples et al.
(2009). These reports highlight that there are substantial
obstacles to the reliable quantication of N
xation in the
eld and no available methodology is optimal. Those methods
which use the stable isotope
N are considered the more
reliable and also give time integrated values. The natural
abundance (d
N) methodology is currently the most widely
deployed approach for eld measurement of N
xation in
temperate legumes. Under controlled conditions the relative
activity of the N-xing enzyme, nitrogenase, can be compared
in different treatments using the acetylene reduction assay (e.g.
Mundy et al.1988), and while the assay can be applied to eld
samples it cannot provide reliable quantitative estimates of
symbiotic N
xation at the eld (kg ha
) scale
(Unkovich et al.2008). Other non-isotopic techniques (N
difference, N balance, regression equations) do not measure
xation directly but rely on a suite of assumptions that are
very often invalid, and this reduces their usefulness in many
situations. Regression equations relating clover growth to the
amount of N
xed are becoming popular (e.g. Ledgard et al.
1999; Eckard et al.2001a; Carlsson and Huss-Danell 2003;
Gourley et al.2010) but these may not be as widely applicable
as one might hope. This approach is considered in more detail
in Modelling N
xation in dairy systems, but results of their
application in Australia are not considered to constitute
measurements of N
xation in the present review. Studies
reporting quantitative eld estimates of N
xation in
Australian dairy systems are outlined in Table 3.
kg N ha
Clover shoot DM (t ha–1)
potential N
Fig. 6. Potential N
xation by clover herbage is set by clover total N in
herbage, a function of herbage dry matter and N concentration. The indicated
upper and lower limits around the central line result from the range in N content
(%) observed for white clover across 71 dairy pastures in south-west Victoria
(Riffkin et al.1999a). The slope of the dotted line is 44.8 kgt
(based on a
mean N concentration of 4.48%).
Table 3. Studies quantifying legume N
xation in Australian dairy, high-rainfall or irrigated perennial pastures
Reference Location Notes
White clover
Riffkin et al.(1999a) (see also
Riffkin et al.1997)
South-west Victoria Survey of 71 pastures, qualitative (%Ndfa) rather than quantitative (kg N ha
methodology: d
Riffkin et al.(1999b) (see also
Riffkin et al.1997)
South-west Victoria Three sites, quantitative seasonal and annual estimates, methodology: d
Pakrou and Dillon (2000) South-east South Australia Compared perennial and annual grazed pastures, quantitative annual estimates,
methodology: d
I. R. P. Fillery, pers. comm. 2012 South-west Western Australia Six farms, quantitative annual estimates, methodology: d
McKenzie et al.(1998) South-west Victoria One site, N fertiliser rates, quantitative for 3 months after N applications, only 9%
clover, methodology: d
Mundy et al.(1988) North Victoria One site, varied soil water content and N fertiliser rate, semiquantitative,
measurement period of hours extrapolated to days, methodology: acetylene
Mundy (1987) North Victoria Fertiliser N rates, 70 days, methodology:
N isotope dilution
Smith et al.(1993) North Victoria Irrigation rates with saline water, quantitative seasonal (6 months), methodology:
N isotope dilution
Peoples et al.(1995) New South Wales Irrigation frequency, legume content comparisons, 109 days, methodology: d
Yang et al.(2011) South-east South Australia Surveyed 20 irrigated lucerne hay elds, quantitative (seasonal) estimates,
methodology: d
Gault et al.(1995) Australian Capital Territory Irrigated lucerne, fertiliser and inoculation treatments, quantitative annual
estimates for 3 years, methodology: d
Brockwell et al.(1995) Australian Capital Territory Irrigated lucerne, fertiliser and inoculation treatments, quantitative seasonal
estimate, methodology: d
792 Crop & Pasture Science M. Unkovich
Accounting for whole-plant N
From the point of view of dairy production the N contained in
legume roots that might have been input from N
xation may
not be as important as in cropping systems (see e.g. Khan et al.
2003). However, it represents a N input to the system and as such
can provide for fertility build up and N supply to companion
grasses when roots senesce and the N becomes more readily
available for microorganisms. This may be particularly important
when studying N balances or when modelling mineral N
availability in dairy pasture soils. None of the reports in
Table 3include measurement of the total N in legume roots, a
task which remains an ongoing challenge (McNeill et al.1997).
In the absence of such measurement the pragmatic approach
has been to apply xed ratios of shoot : root N and multiply these
by the amount of shoot N xed to get total N
xation (Unkovich
et al.2010). However, in the absence of the aforementioned root
N measurements (see also Wichern et al.2008) it is difcult to
have condence in the ratios proposed. For white clover a
multiplication factor of 1.7 times herbage N was proposed for
estimating total clover N (herbage + stolons + roots, Jørgensen
and Ledgard 1997) and this has been applied in several studies
(e.g. Peoples et al.2001; Eckard et al.2007), however, most of
the Jorgensen and Ledgard data came from pot studies where
the plants were ungrazed/uncut and only grown for a few weeks.
How such leaf/stolon + root N ratios might relate to eld ratios
for grazed perennial clover is unclear. Although they had one
contrasting data point for a grazed eld experiment this was not
compared with the glasshouse experiments, although they were
plotted on the same graph. In a eld study of subterranean clover
using mowing, McNeill et al.(1997) estimated below-ground
plant N and came up with a similar 1.75 ratio for estimating total
plant N. Unkovich et al.(2010) give a value of 2.0 for lucerne,
based on a pot study. It is not clear how such multiplication factors
might apply across grazing/cutting regimes, soils, water
availabilities, soil fertilities or species, and thus some caution
must be exercised in their use. Nevertheless application of these
approximate ratios might result in a more accurate estimate of
total N
xed than if they were not applied at all and root N was
Grazed white clover pastures
I have only been able to nd 12 reports of eld measurement of N
xation in Australian high-rainfall/irrigated perennial pastures
(Table 3), although there are several other reports on rain-fed,
annual or lower rainfall, perennial pastures (see Peoples and
Baldock 2001).
Annual inputs
For white clover, only four of the datasets in Table 3(Peoples
et al.1995; Riffkin et al.1999b; Pakrou and Dillon 2000;
I. R. P. Fillery, pers. comm. 2012) include annual N
estimates, the remainder of the datasets are for shorter periods of
time. The work of Riffkin et al.(1999b) demonstrated at three
rain-fed sites in south-west Victoria, that N
xation was
primarily limited by the low legume (white clover) content,
averaging only 8% across the three sites. Thus annual N
xation input in herbage was only 1922 kg N ha
, with the
total amount (including roots) being perhaps ~1.7 times this
(Jørgensen and Ledgard 1997)at3237 kg N ha
These values may be slightly under the average for the region,
with an average clover content double these (19%) across 71 dairy
pastures examined (Riffkin et al.1999a), and with values of up to
50% of pasture herbage as clover recorded.
In a recent study in Western Australian dairy pastures
(Table 4), similar low legume contents constrained N
to 287 kg ha
across 2 years and six farmlets
(I. R. P. Fillery, pers. comm. 2012). The higher value in
Farmlet 6 was for a perennial pasture whereas the other
pastures contained annual legumes.
The most comprehensive study of the N stocks and ows in an
Australian dairy pasture comes from the work of Pakrou and
Dillon (2000). This study is invaluable because it used isotopic
measurement of N
xation rather than estimation as has been
used in several other N balance studies (e.g. Eckard et al.2001a,
2007; Gourley et al.2007). The South Australian study by Pakrou
and Dillon (2000) compared a perennial, irrigated white clover/
ryegrass pasture (Fig. 7) with a rain-fed, annual subterranean
clover-based pasture. In contrast to the abovementioned studies,
this involved the sowing of a white clover/ryegrass pasture and
comparing this irrigated pasture with an adjacent, rain-fed,
unrenovated annual Trifolium pasture. In the irrigated white
clover pasture, legume content was just above 50%, and in the
rain-fed annual pasture ~25%. Both pastures were grazed by
cows, with utilisation rates around 70%.
Over the 12-month study period the irrigated white clover
pasture xed 231 kg N ha
in the harvested herbage whereas
the annual subterranean clover based pasture only xed
75 kg N ha
(Fig. 7). The difference between the two pastures
was clearly due to the increased productivity of the white clover
pasture with irrigation, to the longer growing season afforded
by this irrigation, and to the high clover content when compared
with the annual pasture. In the annual pasture, grass N uptake
dominated the accumulation of herbage N whereas in the
perennial pasture clover accounted for 66% of total herbage
N. In the annual pasture, soil mineral N uptake by herbage
totalled 208 kg ha
for the growing season while N
contributed only 75 kg ha
, clearly soil mineral N supply
provided for the bulk of plant N requirements, thus limiting N
xation. The key element of these results is the substantial
xation of N
when pasture productivity (17.2 t ha
) and
clover content (>50%) are high. Interestingly although
productivity of the annual pasture (12.2 t ha
) was 70% of the
irrigated perennial pasture, herbage N accumulation totalled only
47% of that of the perennial pasture. Why the N concentration in
Table 4. N
xation by clover in farmlets in feed allocated to each herd.
Figures in parentheses are shoot N
xation ¾1.75 to account for N
xation above- and belowground, based on the work of McNeill et al.
Year N
xation in clover (kg N ha
) allocated to each farmlet
1 2 345 6
2006 8 (14) 4 (7) 7 (12) 3 (5) 2 (4) 87 (152)
2007 18 (32) 10 (18) 9 (16) 5 (9) 6 (11) 50 (88)
Farmlet 6 is a perennial legume (white clover) pasture, the others contain
annual legumes. Data from Dairy Australia Greener Pastures project, per Ian
Fillery, CSIRO.
xation in Australian dairy systems Crop & Pasture Science 793
herbage was lower in the annual pasture is not clear, but might
relate to differential grazing management (see Unkovich et al.
The nal quantitative estimate of N
xation in a white
clover pasture is that of Peoples et al.(1995), comparing a
clover-dominant (85%) with a grass-dominant (60%) pasture
over 109 days, with low or high irrigation frequency. Few
details of the experiment are given in the Peoples et al.(1995)
review paper. Results are as one might anticipate, with greater
total N accumulation in both pastures under lower soil water
decits, and greater N
xation with higher pasture clover content
and clover N yield (Table 5).
Based on the survey of Riffkin et al.(1999a) white clover
dependence on N
xation in Australian dairy pastures is typically
~65%, indicating reasonable N
-xing capacity, however, the
actual amounts of N
xed are very much limited by low clover
dry matter production as a consequence of low clover content in
most pastures. Much higher rates of N
xation are achievable,
with up to 294 kg N ha
being recorded for a recently
sown, irrigated white clover pasture (Pakrou and Dillon 2000).
In a review of perennial forage legumes in temperate/boreal
environments, Carlsson and Huss-Danell (2003) report N
xation by white clover to be up to 545 kg N ha
, but
this did not include data on white clover from Australia. Mason
et al.(1987) measured irrigated pure white clover pasture annual
dry matter production of almost 23 t ha
in northern Victoria,
which, according to Fig. 6would provide for potential annual
xation of >1000 kg N ha
. This is higher than any value in
the literature for any N
-xing system, but nevertheless shows
that the potential with this species is very high. In current dairy
systems this potential is not being realised due to low pasture
legume contents.
The Achilles heel: low white clover content of pastures
Similar low white clover contents of pastures were reported
earlier in a survey of Australian temperate pastures (Pearson
et al.1997; Hill and Donald 1998), and also earlier in Victoria
(Ward and Quigley 1992). It would thus appear that pasture
clover contents, and potential N
xation in Australian perennial
pastures has probably not improved in almost 20 years, regardless
of the increased application of fertiliser N. Farmers appear
reluctant to resow legumes (Ward and Quigley 1992). It may
well be that for well managed, N fertilised, intensively grazed
perennial ryegrass/white clover pastures, that equilibrium clover
contents are around 20% resulting in the xation of no more than
~100 kg N ha
, similar to that observed in the UK (Parsons
et al.1991; Andrews et al.2007) and NZ (Woodeld and Clark
2009), although Jarvis (1993) suggested that in the UK, dairy
pastures were typically much lower in both clover content
(<10%) and the amount of N
xed (10 kg ha
). These
low clover contents are likely to be suboptimal in terms of dairy
production (Woodeld and Clark 2009) as well as N
and thus efforts to increase N
xation should be rewarded with
increased milk production efciency.
In the absence of cattle grazing and the associated deposition
of high rates of urine and dung, which increase soil mineral N
and most likely depress N
xation (Haynes and Williams 1993;
Ledgard et al.1999), dependence on N
xation may be higher.
For example, in the irrigated pure lucerne systems of south-
eastern Australia (Yang et al.2011) lucerne dependence on N
xation averaged 65% and annual N
xation in herbage
estimated to be >200 kg N ha
Lucerne hay systems
While grazed lucerne pastures are used in Australian dairy
systems they are of relatively minor importance compared
with white clover/ryegrass pastures, but nevertheless important
in the production of hay that feeds directly into the dairy system.
Table 3indicates just three studies quantifying N
xation of
irrigated lucerne in Australia, with the only two of those
(Brockwell et al.1995; Gault et al.1995) providing annual N
xation estimates being experimental sites in the ACT.
Gault et al.(1995) measured N
xation using d
N natural
abundance, in newly established, irrigated lucerne stands cut
for hay, over a 3-year period. Experimental treatments were (1)
no rhizobial inoculation and superphosphate only in the year of
Table 5. N
xation by white clover over 109 days in clover-dominant
(85%) or grass-dominant (60%) pastures irrigated after 60 mm (high) or
120 mm (low) evaporation (from Peoples et al.1995)
Pasture type Irrigation
Clover N
yield (kg ha
(kg ha
Clover dominant Low 108 61 66
High 145 62 90
Grass dominant Low 66 67 44
High 93 71 66
Irrigated white clover
Rainfed annual clover
N2 fixed by clover
soil N uptake by clover
grass N uptake
Total N in herbage (kg ha–1)
Fig. 7. Cumulative plant N acquisition over time in an irrigated white clover
and rain-fed annual clover pasture in the south-east of South Australia. Plotted
from the data of Pakrou and Dillon (2000).
794 Crop & Pasture Science M. Unkovich
sowing (9 kg P ha
), (2) rhizobial inoculation plus annual
applications of superphosphate, and (3) no rhizobial
inoculation, but with annual application of superphosphate and
N fertiliser (33 kg N ha
). Dry matter production and N
increased dramatically after the rst year (Fig. 8), reaching
284 kg N ha year
for the inoculated and P fertilised treatment
in the third year, although this was only marginally more than
for the second year for all treatments (269275 kg N ha
). In the
third year, the uninoculated treatment, which had not received
annual applications of P fertiliser xed much less than the other
treatments. The authors estimated that total N
xation (including
root N) over the 3-year period to exceed 1400 kg N ha
in the
annual P fertilised treatments.
This study shows the potential for N
xation in irrigated
lucerne is very high, provided that attention is paid to crop
nutrition. The removal of 1012 t ha
of hay exports
signicant quantities of nutrients, aside from N, and these
would need to be replaced if growth and N
xation is to
continue uninhibited.
The above treatments were also applied to a 4-year-old lucerne
stand at the same site (Brockwell et al.1995) and N
ranged from 83 to 97 kg N ha
over the 6-month period of study,
giving a nominal annual rate similar to that of Gault et al.(1995).
From the data of Fig. 9it would appear that N
xation continues
unabated at a constant rate over the warmer months where
irrigation water is applied.
The nal example of eld measures of N
xation in lucerne
systems comes from Yang et al.(2011) who surveyed N
in 18 irrigated lucerne hay elds in the south-east of South
Australia. The estimates of N
xation were for standing dry
matter at the time of sampling, in a system which typically has
three hay cuts per year. Mean N
xation in standing biomass
(Table 6) was 73 kg N ha
, or 65% of lucerne herbage N. What
time period these values might represent was not able to be
established, but the authors considered that, on average, annual
values were likely to be 3 times those observed, giving a value
very similar to the annual N
xation indications from the studies
of Brockwell et al.(1995) and Gault et al.(1995). The South
Australian study also indicated that these lucerne stands
continued to xN
many years (>25) after they were established.
Together these data indicate that irrigated lucerne hay crop
systems continue to x considerable amounts of N over time. In
contrast to grazed white clover systems, these hay systems export
substantial quantities of N in herbage. Furthermore, they are often
only grazed lightly such that the build up of soil mineral N does
not occur to the extent that is seen in intensively grazed white
clover pastures. In this case it is not the legume species which are
driving the massive differences in N
xation input between
lucerne and white clover, but rather the presence of the animals,
and the differential management of the systems in which the
legumes are utilised.
Grazing and N
A detailed review of the impacts of grazing animals on legume
xation are given in Menneer et al.(2004). The key element
of grazed dairy systems is the excretion by cattle of at least 75%
Hay N yield and source (kg ha–1)
1988/89 1989/90 1990/91
N fixed
Mineral N uptake
Fig. 8. Sources of N for irrigated lucerne hay in the rst 3 years after
establishment, (1) uninoculated and superphosphate only in the year of sowing
(9 kg P ha
), (2) inoculated plus annual applications of superphosphate, and
(3) uninoculated, annual application of superphosphate and N fertiliser
(33 kg N ha
). Plotted from the data of Gault et al.(1995).
Herbage N (kg ha–1)
Treatment 1 Treatment 2 Treatment 3
Fig. 9. Cumulative seasonal N
xation and mineral N uptake in a 4-year-old irrigated lucerne stand grown
for hay. Treatments same as for Fig. 8. Plotted from the data of Brockwell et al.(1995).
Table 6. Summary of N
xation data from a survey of 18 irrigated
lucerne stands cut for hay in the south-east of South Australia (from Yang
et al.2011)
%Ndfa N xed Mineral N uptake
Mean 65 73 44
Min. 33 33 9
Max. 90 122 90
xation in Australian dairy systems Crop & Pasture Science 795
of the N they ingest from herbage as urine and dung (Whitehead
1995). Maximal N
xation is likely to come from well managed
hay systems rather than grazed systems, this is because optimal
clover content can be more easily managed and the urinary and
dung N returns do not suppress N
xation. However, this does
not mean that ungrazed systems will have greater N
xation than
grazed systems. Ungrazed mixtures of clover and grass are likely
to become grass dominant with shading reducing clover growth
and N
xation (Sanford et al.1995). In a study of an annual
subterranean clover pasture grazed by sheep in Western Australia
(Unkovich et al.1998) a more heavily grazed pasture had lower
grass growth and greater N
xation than a lightly grazed pasture.
While increased grazing pressure can favour clover growth over
grasses, in practise the magnitude of this generally appears quite
small as the effect occurs at the lighter end of grazing intensities
(Doyle et al.2000). Increased grazing pressure usually increases
the N (protein) content of clover (Unkovich et al.1998), and
indeed other pasture species (Kelly et al.2005). The work of
Pakrou and Dillon (2000) highlights the signicance of the
mineral N ux under grazing. Under irrigated, grazed white
clover pasture, the ux of N through the soil mineral N pool
was estimated to be 687 kg N ha
, more than half of which was
derived from animal returns. This study also highlights the
signicant role that N
xation can play when there is a high
clover content, even in the presence of intensive grazing. Under
the annual pasture, mineralisation of soil organic N was driving
the available N pool, being no higher when the animals were on
the pasture than when they there were absent (see Pakrou and
Dillon 2000). Nevertheless excretory N returns from grazing
animals showed up as the key inuence on sward N dynamics and
xation in these two dairy systems.
In terms of N
xation the key elements to note in the perennial
pasture of Pakrou and Dillon (2000) are:
*The legume (white clover) content was high (57%) because
the pasture had been sown only 2 years before, this is atypical
for Australian dairy pastures where legumes contents are
commonly <20%,
*Because the legume content and legume dry matter production
(9.8 t ha
) was high, N
xation was also high (236 kg N ha
excluding an additional 59 kg ha
(25%) estimated for clover
*After mineralisation (687 kg N ha
), cattle intake (419 kg
) and grass mineral N uptake (389 kg N ha
), N
xation was the fourth highest N ux in the system, and
xation was greater than the combined N losses estimated
from leaching, NH
volatilisation and denitrication (209 kg
) and thus the system appeared to be in an approximate N
balance, despite there being no N fertiliser inputs.
The key elements to note for the annual, rain-fed pasture were:
xation was much lower than for the white clover-based
pasture because (a) subterranean clover is an annual and only
grows for part of the year (b) the clover content (25%) was less
than half that of the perennial pasture, and (c) the annual pasture
was rain-fed, not irrigated,
*Most of the clover N was xed (80%),
*The system had a marginally negative N balance overall, and
*The xation rate of 100 kg N ha
in this pasture is higher
than for Australian dairy pastures generally because there were
no fertiliser N inputs.
Differences in N
xation capacity between species
and cultivars
Differences between legume cultivars are unlikely to be of
quantitative importance for N
xation input in Australian dairy
systems. However, where differences in clover productivity are
expressed then those cultivars with greater shoot biomass would
x more N. While this has not been examined specically for
Australian cultivars (N
xation has not been considered in the
Australian white clover breeding program (Carol Harris, NSW
DPI, pers. comm. 2012)), data on nine white clover cultivars from
New Zealand (Ledgard et al.1996) indicated that differences
between cultivars in the amount of N
xed are related to
dry matter production driven differences in clover total N
accumulation, rather than to inherent differences in the N
efciency or shoot N concentration (Fig. 10). While all three of
these variables are used to calculate the amount of N xed, it is
clearly legume dry matter production which is the driving force
in this dataset, and indeed in most others (Unkovich et al.2010).
In an earlier study of differences in N
xation between white
clover cultivars in New Zealand (Ledgard et al.1990), it was
concluded that there were no inherent differences in the capacity
of different cultivars to x N, and thus that N
xation was not
a basis for substituting one for another. Generally speaking, in
breeding for maximum dry matter or total N accumulation, clover
breeding programs might indirectly select for maximal N
xation. However, this does not mean that N
xation is
optimal nor has been selected for, because it may well be that
y = –35.769x + 250.85
R2 = 0.0588
4.4 4.6 4.8 5 5.2
Clover shoot N (%)
y = 2.097x – 21.701
R2 = 0.2162
Clover %Ndfa
y = 0.5047x – 7.0503
R2 = 0.913
100 150 200 250 300
N2 fixed (kg ha–1)
Clover total N (k
Fig. 10. Correlation between clover shoot total N, clover dependence on N
xation (%Ndfa) or clover shoot
N concentration (%,) and the amount of N
xed (kg ha
) for nine white clover cultivars in New Zealand.
Plotted from the data of Ledgard et al.(1996).
796 Crop & Pasture Science M. Unkovich
even with the best available plant material, N
xation could still
be limiting growth, due, for example to poorly effective rhizobia.
With respect to cultivar performance in N
xation, in several
pasture legume species it has been shown that there is a strong
interaction between legume cultivar and rhizobium strain, such
that optimal N
xation potential is achieved with specic
combinations of pasture legume cultivar and rhizobial strain
(see e.g. Ballard et al.2003).
Differences in N
xation between species of legume will be
driven as much by differential management of species/systems
and environment, as by inherent differences between legume
Modelling N
xation in dairy systems
Because eld measurement of biological N
xation is complex
and expensive (Unkovich et al.2008) modelling approaches to
estimate N
xation hold signicant attraction. The basis for
model design can be either empirical (e.g. Høgh-Jensen et al.
2004; Unkovich et al.2010) or dynamic mechanistic (e.g. Boote
et al.2008). Empirical approaches tend to correlate measured N
xation rates with other, more easily measured pasture properties,
t regression equations to the resulting dataset, and then apply
those regressions elsewhere in time or space. Dynamic simulation
models attempt to mimic the primary biological and physical
processes driving plant growth (Sinclair and Seligman 1996),
including N
xation, and usually attempt to be universally
applicable upon local parameterisation. Such so called
mechanistic or dynamic simulation models are usually only
semi-mechanistic as they typically include some empirical
approaches. Liu et al.(2010) reviewed a large number of
approaches to modelling N
xation and the reader is referred
to this thorough exposé of N
xation modelling, the detail of
which is outside the scope of the present review.
Empirical relationships
An example of a typical empirical model for estimating N
xation is given in Fig. 11, which relates legume shoot dry
matter production to the amount of N
xed. This gure is for
herbage N xed, an additional fraction can be added for xed
N possibly contained in roots.
The pros and cons of such approaches are detailed in and Liu
et al.(2010) and Unkovich et al.(2010). The primary limitation
of such approaches are that, aside from the inuence of dry matter
production, they are naive to other possible drivers of N
such as soil fertility, temperature, water availability, grazing
intensity, non-legume pasture content, and microsymbiont
performance. The net effect of such factors are of course
inherent in the observed data and so have been captured for
the data points presented. The problem is that once the regression
is applied in another situation (time or place), these inherent
effects may not apply at the application place/time.
Carlsson and Huss-Danell (2003) found signicantly different
regressions for grazed and mown white clover pastures, and thus
the regressions are not transferable between such management
regimes. Relationships which have been developed elsewhere
(e.g. Ledgard et al.1999) and applied in Australia (e.g. Eckard
et al.2001a,2007) are thus fraught with danger, especially if
applied too specically. Such regressions have no experience
beyond their derivation dataset and thus other regressions might
have equal validity. For example Carlsson and Huss-Danell
(2003) gave linear regressions between white clover dry matter
and N xed accounting for 91% (clover/grass) to 55% (legume
monocultures) of the measured amount of N
xed, without
accounting for N fertiliser application.
Examples such as that in Fig. 11 may approximate behaviour
across regions but are unlikely to be correct at any given point
and should only be applied at the scale at which the regression
is derived. That is, if the data are derived for a range of
treatments within a single eld or farm, they could not be
reliably extrapolated outside of that eld or farm. Conversely,
regression across a range of elds or regions might usefully be
applied across such a scale, but is not likely to apply at sub eld
or region scale. The regressions cannot be reliably used
in situations where they have no previous experience. In this
way they are different to dynamic simulation models which often
respond to local environmental and management inuences.
In the study of Ledgard et al.(2001) the white clover N
concentration did not drop below 4.5%, whereas this was close
to the average for 71 pastures investigated in Victoria (Riffkin
et al.1999a) and in the analysis of broader Australian data by
Unkovich et al.(2010) the mean shoot [N] for white clover was
given as 3.2%, which could account for a signicant difference in
the slope of the regression lines. Indeed Fig. 12 looks much like
y = 35.645x – 40.661
R2 = 0.765
white clover
Shoot DM (t ha–1)
N2 fixed (kg ha–1)
y = 19.551x + 2.0047
R2 = 0.8071
Fig. 11. Correlation between clover shoot dry matter and the amount of
xed, and tted regression equations for white clover and lucerne
grown in Australia. Data from Unkovich et al.(2010).
Ledgard 0N
Unkovich white clove
Ledgard 200N
Carlsson white clover
Unkovich lucerne
ume herba
e dry matter (t ha
fixed in herbage (kg ha
most Australian
data in here
Fig. 12. Comparison of different regression equations used to estimate N
xation in white clover or lucerne from clover shoot dry matter. Details of the
regressions are given in Table 7.
xation in Australian dairy systems Crop & Pasture Science 797
Fig. 6. Furthermore, the clover N
xation in the Ledgard study
did not exceed 94 kg N ha
whereas in the Unkovich dataset
the maximum was 278 kg N ha
and in the Carlsson dataset it
exceeded 400 kg N ha
. As much of the evidence indicates
that clover content and clover dry matter production are low in
Australia (4tha
) the relevant part of Fig. 12 is near the origin.
At 2 t ha
clover dry matter N
xation would range from 30 to
87 kg N ha
depending on which regression equation was
used. Further complications arise because in some instances
signicant N
xation would be indicated with no clover dry
matter (Fig. 12, Carlsson regression). This can occur with
regressions when they are extended beyond their experience,
or where the responses may indeed not be linear, as is likely to be
the case at the lower end of the range when soil mineral N will
become increasingly important.
Given that a value of ~4.5% N for herbage seems typical for
grazed white clover (Fig. 6and Ledgard et al.2001), a shoot : root
N ratio of 1.7 (Jørgensen and Ledgard 1997) and the average
dependence of white clover on N
xation in western Victoria of
65% (Riffkin et al.1999b), this implies a total N
xation for
current systems averaging 50 kg t
clover shoot dry matter, or in
shoots only 29 kg t
herbage. While this might provide a useful
rule of thumb for pastures of low (<25%) legume content from
which the data have been derived, for higher legume content dairy
pastures other factors may play a part in changing %Ndfa or
herbage N concentration and thus alter the relationship between
dry matter and N
Dynamic simulation models
In the review of Liu et al.(2010) of nine mechanistic/process
based models of N
xation, commonalities were the scaling of a
maximum daily N
xation rate as a function of some
combination of temperature, soil water, soil mineral N, plant
carbon availability, and plant development stage. The
implementation of these various factors in a range of models
are shown in Table 8. Eight of the models have been used for
perennial legume pasture species (white clover or lucerne).
When reviewing models the rst consideration is the purpose/
objective of the modelling required. There are many models,
either specically for N
xation, or which have N
xation as a
component, but each has been built with a different specic
purpose in mind. For the present purposes it is assumed that
the modelling objective is to quantify changes in legume N
xation in response to management and climate, rather than
legume physiological responses to climate and management.
Relevant pasture simulation models which have been used in
Australia are given in Table 9, along with their N
simulation capacity.
In GrassGro the potential N
xation rate is calculated as the
total plant N demand less N translocated from belowground
reserves and N recycled from shaded leaves, multiplied by a
factor for the development of nodules in early growth. This
potential rate is then scaled back by low water content and
high mineral N, weighted according to a nodule depth
distribution (Andrew Moore, CSIRO, pers. comm.).
In the DairyMod tool, N
xation is linked directly to
photosynthesis and a value of 6 mg C respired/mg N xed
used as a carbon cost, thus reducing growth of N
clover compared with non-xing clover. An earlier version of
the model constrained N
-dependent clover to 0.6 of the growth
Table 7. Regression equations collated from the literature relating
clover herbage dry matter (kg ha
xation in shoots for a
range of perennial legumes
Such equations have been used to estimate N
xation from legume shoot dry
Reference Legume Regression
Ledgard (2001) White clover = DM * (0.0358 3.59
* N fertiliser
Carlsson and
Huss-Danell (2003)
White clover (generic) = DM * 0.025 +37.2
White clover
= DM * 0.016 + 57.9
White clover
= DM * 0.031 + 23.9
Red clover (generic) = DM * 0.023 + 8.4
Red clover
= DM * 0.016 + 16.5
Red clover (mixtures) = DM * 0.026+ 7.4
Lucerne (generic) = DM * 0.012 + 38.8
= DM * 0.0.013 + 12.3
Lucerne (mixtures) = DM * 0.0.021 + 16.9
Unkovich et al.(2010) White clover = DM * 0.036 40.661
Lucerne = DM *
0.0196 + 2.007
Table 8. Factors used to scale maximum daily N
xation rate in various mechanisticN
xation models
Adapted from Liu et al.(2010) with SGS/DairyMod added and an indication of whether the model has been used for white clover ü(* or lucerne)
Model Temp. Water Mineral N Plant C Growth stage White clover Reference
Sinclair üü––Sinclair (1986)
EPIC ü–– – Cabelguenne et al.(1999)
Hurley üü ü ü üThornley (2001)
Schwinning –– üü üSchwinning and Parsons (1996)
CropGro üü üü Boote et al.(2008)
SOILN üü ü –– üWu and McGechan (1999)
APSIM üü ü* Robertson et al.(2002)
Soussana –– ü–– üSoussana et al.(2002)
STICS üü ü üüBrisson et al.(2009)
GrassGro üü ü* Moore et al.(1997)
SGS/DairyMod ü–– üJohnson et al.(2008)
798 Crop & Pasture Science M. Unkovich
of mineral N-dependent clover, although I think this has now
been removed. A minimum of 20% of legume N comes from N
xation under all conditions. Legumes are not limited for N, with
xation topping herbage N up to the optimal value (Johnson
2005). Graham (2008) provides a thorough review of the
DairyMod tool although does not discuss legume N
In APSIM (Robertson et al.2002)N
xation occurs when
there is insufcient mineral N to meet plant N demand, but the
sensitivity with which N
xation is switched on in the presence
of mineral N, being a cultivar specic parameter. While the
model does not currently have an interaction between soil
mineral N and nodulation, the N
xation routines are currently
being revised and nodule mass will be developed into an integral
part of the N
xation simulation routines.
While DairyMod, APSIM and GrassGro have the capacity to
model N
xation I can nd no published model output showing
xation by pasture or crop legumes, or a comparison of
model output with measured N
xation data. While the
models often show good correlation of model simulated and
measured dry matter production or total N, the validity of these
models nevertheless remains essentially untested in terms of N
xation. The N
xation routines in both APSIM and DairyMod
are currently being revised [M. Robertson (CSIRO) and
I. Johnson (IMJ), pers. comms].
None of these models include any consideration of the
population dynamics, effectiveness or environmental responses
of the microsymbiont and thus will be unable to simulate
responses of the symbiosis to management and environment in
the eld. Those models which ignore the microsymbiont
dynamics will inevitably have limited capacity over time.
There is no physiological process-based model tested for
specic study of N
xation in Australian dairy systems. Until
the N
xation routines in the available models have been tested
against measured data they offer no more in terms of predictive
xation capacity than a suitably calibrated empirical model.
Environmental costs and benets of N
A fair assessment of the environmental costs and benets of
legume N
xation in dairy systems can only be achieved with
consideration of a gamut of factors impinging on the
environmental balance sheet for a dairy farm. While this is
beyond the scope of the present review we can briey consider
some of the issues feeding into and out of legume N in dairy
farming systems. More thorough environmental analyses of
dairy farming systems can be found in a recent volume (de
Klein et al.2008; Kleinman and Soder 2008; Nash and
Barlow 2008) and a range of other relevant articles (Ridley
et al.2004; Andrews et al.2007; Ledgard et al.2009;
Woodeld and Clark 2009).
Urinary N returns from dairy cattle concentrate soluble N at
very high rates and provide the primary point of soluble N excess
and thus the greatest opportunity for environmental impact.
Generally to minimise losses of N via denitrication, leaching
or NH
volatilisation a tightN cycle is required, necessitating
the maintenance of some N limited grass to mop upavailable N
(Parsons et al.1991). However, it is generally thought that a
system with slightly N-decient grass may limit feed quantity
and quality and is not considered optimised in terms of animal
production (Eckard 2001). This is thus not usually recommended
from a milk production perspective but nevertheless could
provide signicant environmental benets.
In a study in the UK, Andrews et al.(2007) considered the
relative merits of (i) an unfertilised perennial ryegrass/white
clover pasture (ii) a perennial ryegrass pasture receiving
200 kg N ha
, and (iii) a perennial ryegrass only pasture
supplied with 350400 kg N ha
. From a N cycling and
leaching perspective, pastures (i) and (ii) were considered
equal as the unfertilised pasture had similar N input from N
xation, and with a similar grazing regime the amount of N
cycling through the animals was about the same. The pasture with
the higher fertiliser N addition rate (iii) was considered to have a
greater N footprint due to increased leaching and nitrous oxide
emissions. Generally it was considered that with similar N inputs,
pasture productivity and grazing intensity the environmental N
footprint would be about the same; that is there may be no inherent
advantage in N
xation per se in terms of N cycling impacts. The
analysis of Andrews et al.(2007) did not include the magnitude
of N
xed in clover roots and thus may have underestimated
the difference in N inputs between treatments.
Generally then, if contrasting systems (grass versus grass/
clover) are equally as productive and have the same stocking rates
or animal products output, they are likely to have very similar
environmental costs/benets. This is because most of the
environmental footprint from dairy systems comes from the
livestock N returns not the N input per se. While substitution
of fertiliser N with clover xed N might improve the
environmental balance sheet on farm, the benet is likely to be
marginal where best practise fertiliser management is already
being used.
Table 9. Legume N
xation simulation capacity of dynamic pasture models used in Australia
Model N
xation functions Reference
GrassGro A fraction of the net remaining demand for N, affected by nodule mass,
developmental stage, soil moisture availability and NO
Moore et al.(1997); A. Moore,
pers. comm.
DairyMod A minimum of 20% of legume N is from xation, where mineral N cannot meet
legume N demand then N
xation tops up herbage N to the optimal shoot [N],
but constrained by plant C availability
Johnson (2005); Johnson et al.
(2008); I. Johnson, pers.
SGS Pasture Model As above Johnson et al.(2003);Johnson
APSIM A function of daily growth rate, up to a maximum daily N
xation rate, with a
legume-specic factor for relative suppression of N
xation by soil mineral N
Robertson et al.(2002)
GRASP Does not incorporate N
xation McKeon et al.(1982)
xation in Australian dairy systems Crop & Pasture Science 799
Excretal N is the primary source of nitrous oxide (N
emissions from dairy systems (de Klein et al.2008; Ledgard
et al.2009). Although legume N
xation was previously thought
to contribute directly to N
O emissions, this has been shown not
to be the case (Rochette and Janzen 2005) and so the direct N
footprint of legume xed N
is minimal. If one were also to
include energy costs of urea fertiliser manufacture (0.732.14 kg
-e kg
Ledgard et al.2011), then substituting xed N
fertiliser N should have some GHG mitigation potential (Ledgard
et al.2009), but not if pasture clover contents are low. Andrews
et al.(2007) considered that savings in CO
-e by substituting
200 kg N for xed N would be negligible on a global scale but
very signicant on a ha
scale. Nitrogen fertiliser manufacture
accounts for ~1% of total global CO
-e emissions. In the future
if legumes with condensed tannins become available (Woodeld
and Clark 2009) additional GHG benets should accrue in terms
of reduced CH
High land-use intensity in the dairy industry is the primary
cause of environmental problems resulting from excess N (de
Klein et al.2008). While similar, well managed clover/grass and
grass only pastures are likely to have the same local
environmental impact, whole system or life cycle analysis
suggests that overall, pastures which contain N
legumes would have a lower net environmental impact than
N-fertilised pastures (Ledgard et al.2009). While ungrazed
legume-dominant hay systems would appear to have a much
lower environmental impact than intensively grazed pastures as
the primary animal driven mineral N uxes would be avoided,
this ignores the fact that the hay will still be fed to animals and
the excretal N returned elsewhere. Although in this case it might
be more effectively managed.
Managing N
xation in Australian dairy
pastures where to from here?
Australian dairy systems have made the inevitable drift from the
exploitation of legume N in extended grazing systems to short
rotational grazing of N fertilised pastures that has characterised
the development of intensive, modern dairy systems elsewhere in
the world. This is due to a perceived increase in system efciency
by increasing the stocking rate to utilise more of the pasture, and
then supplementing the otherwise underfed cows (Lemerle et al.
1992). Such a system increases the return of urinary and dung N to
pastures, further reducing legume content. It is this high intensity
grazing rate that is exerting signicant inuences on N
by clover, through defoliation, treading and returns of urinary N
which cause direct reductions in N
-xing (nitrogenase) activity
and in clover persistence. However, as legumes have several
special benets to dairy cows and to farming systems, they are
likely to have a continuing, perhaps increasing role in dairy
systems in the future, provided that investment is made in the
appropriate areas.
The clover contents of typical dairy pastures are clearly below
the optimum required for effective N
xation input, and perhaps
below what might be optimal in terms of animal nutrition and
milk production (Harris et al.1997). Thus efforts to increase N
xation should be rewarded with both improved animal
production efciency, and environmental benets. Generally
lower rates of N application and moderate intensity grazing
favour white clover persistence and abundance in mixed
pastures (Kelly et al.2005). Legume herbage has distinct
advantages over grasses in terms of animal production and
warrants inclusion in dairy pasture systems. The fact that
clover is able to obtain its own N requirements from the
atmosphere provides an opportunity to reduce input costs and
the environmental impact of dairy agriculture.
In high-rainfall and irrigated pastures, clover contents should
be able to be increased, with multiple benets, including N
xation. However, under rain-fed conditions where summer
droughts occur, perennial legume persistence and N
are likely to be more difcult to maintain, and occasional
resowing could be required. Housed animal systems with cut
and carry forage could be more reliant on legumes and N
whereas intensively grazed pastures will inevitably have lower
clover contents, higher returns of urinary and dung N (intensied
through the addition of supplementary feeding when pasture
supply is limited), increasing the downward pressure on
legumes and N
Eckard et al.(2001b) points out that reduced N fertiliser use
and increased dependence on legumes has now occurred in
Europe, a trend which might follow here, whether this alone
will be sufcient to boost pasture legume contents and N
to the required level is not clear. It is likely to also require lower
stocking rates which is somewhat anachronistic to the current
management paradigm in Australian dairy systems, which
focuses on pasture utilisation efciency rather than N-use
efciency. In any event if pasture legume contents are
increased there will be a requirement for monitoring of legume
growth and N
xation to ascertain whether the other factors
highlighted begin to constrain N
xation (rhizobia effectiveness,
nematodes, grazing intensity and excretal N returns). One
alternative option worth exploring might be the spatial
separation of clover and grass (Chapman et al.2007;
Woodeld and Clark 2009), with potential increases in N
xation input and scope for spatial management of fertiliser,
and improved milk production. Differences in the N
potential (growth) of white clover cultivars are likely, as are
differences in responses to available N (Doyle et al.2000) but
these have not been explicitly explored for Australian clover
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
content (%)
fixed in herbage (kg ha
Pasture herba
e dry matter (t ha
in this
maximum potential N fixation
Fig. 13. Potential N xation by clover assuming herbage N content of 4.48
and 100% dependence on N
xation for a range of pasture clover contents.
Maximum potential N
xation is ~700 kg N ha
depending on clover
N content. Most Australian dairy pastures have a clover content below 25%
and a %Ndfa of ~65%, so actual N
xation in clover herbage must typically be
much less than 80 kg N ha
800 Crop & Pasture Science M. Unkovich
Complex dynamic simulation models are probably not
required to predict the likely outcome of changes in pasture
legume content in terms of N
xation. This should be able to
be modelled relatively simply, or with simple regression
models such as that shown in Fig. 13. The DairyMod, APSIM
and GrassGro models all have some capacity for N
simulation, but this is yet to be exploited. A comparison of
model outputs in terms of N
xation against measured data
are required to ascertain if the current models have anything to
Many thanks to Ian Fillery for the provision of unpubl. data, to Andrew Moore,
Ian Johnson and Michael Robertson for comment on the modelling section,
and to Beverly Henry and anonymous reviewers for comment on an earlier
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... [5], blackgram will be -2.20% [6], and mungbean will be -1.14% [6]. ...
... [6], and mungbean will be -1.14% [6]. ...
... Several methods are used to estimate N 2 fixation in legumes and the choice of the method depends on the legume species, the nature of the experiment, the experimental site [6] and the non -fixing reference plants [4]. Major methods used to measure the amount of N -fixed include: dry matter yield, acetylene reduction assay, xylem urede assay, N difference method and the 15 Groundnut are produced by both smallholder and commercial farmers. ...
... [5], blackgram will be -2.20% [6], and mungbean will be -1.14% [6]. ...
... [6], and mungbean will be -1.14% [6]. ...
... Several methods are used to estimate N 2 fixation in legumes and the choice of the method depends on the legume species, the nature of the experiment, the experimental site [6] and the non -fixing reference plants [4]. Major methods used to measure the amount of N -fixed include: dry matter yield, acetylene reduction assay, xylem urede assay, N difference method and the 15 Groundnut are produced by both smallholder and commercial farmers. ...
... Sin embargo, por su composición química, persistencia debido a su hábito de crecimiento rastrero, adaptabilidad a las zonas templadas, el trébol blanco es la especie de mayor importancia agronómica entre las casi 300 especies del género Trifolium (4) . Aunado a esto, también puede mejorar la fertilidad del suelo por el aporte de nitrógeno de hasta 450 kg N ha -1 mediante fijación simbiótica (5,6,7) . ...
Full-text available
El objetivo del presente estudio fue evaluar un análisis de crecimiento del trébol blanco (Trifolium repens L) y determinar el momento óptimo de cosecha por estación. El experimento se realizó en el Colegio de Postgraduados, Campus Montecillo, Texcoco, México. Se utilizaron 24 parcelas de 3.7 X 1.7 m, distribuidas en un diseño completamente al azar, con ocho tratamientos y tres repeticiones por estación. Los tratamientos consistieron en cortes semanales sucesivos, durante un ciclo de rebrote de ocho semanas, en cada estación del año. Al inicio del estudio se realizó un corte de uniformización y se determinó el forraje residual. Las variables evaluadas fueron: acumulación de materia seca, composición botánica y morfológica e índice de área foliar del trébol blanco. La mayor acumulación de forraje (P<0.05) se presentó en la octava semana en primavera (2,688 kg MS ha-1). La producción de hoja fue mayor (p < 0.05) en primavera, otoño e invierno. El mayor índice de área foliar se alcanzó en la octava semana en primavera (3.0; P< 0.05). Se recomienda aprovechar la pradera de trébol blanco en la sexta semana para primavera-verano y séptima semana en otoño e invierno.
... However, excessive N fertilizer application increases the lignification of alfalfa (Unkovich, 2012), which is supposed to offset the positive effect of N fertilizer application on ADF and NDF concentrations. Our results confirmed that ADF and NDF concentrations did not change when the N application rate was greater than 90 kg ha -1 (Figures 3B, 4B). ...
Full-text available
Introduction In China, alfalfa ( Medicago sativa L.) often grows in marginal land with poor soil fertility and suboptimal climate conditions. Alfalfa production cannot meet demands both in yield and quality. It is necessary to apply fertilizers to achieve high yields and produce high-quality alfalfa in China. However, there is no understanding on the impact of fertilizer application on alfalfa production and the possible optimal application rates across China. Methods We conducted a meta-analysis to explore the contribution of fertilizer application to the yield and quality of alfalfa based on a dataset from 86 studies published between 2004 and 2022. Results and Discussion The results showed that fertilizer application not only increased alfalfa yield by 19.2% but also improved alfalfa quality by increasing crude protein (CP) by 7.7% and decreasing acid detergent fibre by 2.9% and neutral detergent fibre by 1.8% overall compared to the non-fertilizer control levels. The combined nitrogen (N), phosphorus (P) and potassium (K) and combined NP fertilizer applications achieved the greatest yield and CP concentration increases of 27.0% and 13.5%, respectively. Considering both yield and quality, the optimal rate of fertilizer application ranged from 30 to 60 kg ha ⁻¹ for N, 120 to 150 kg ha ⁻¹ for P and less than 120 kg ha ⁻¹ for K. Meta-analysis further showed that the effect of fertilizer application on yield was greater in low soil organic matter (SOM) soils than in high SOM soils. In conclusion, fertilizer application is an effective strategy to improve the yield and quality of alfalfa in China, especially that grown in low SOM soils. This study is helpful for optimizing fertilization schedules of alfalfa in China.
... While legumes have the ability to derive majority of their N requirements from atmospheric N 2 , they are also 89 capable of taking up soil mineral N like non-N 2 -fixing plants (Unkovich 2012). Mapfumo et al (1999) showed that the percentage of N derived from atmospheric N 2 by pigeon pea decreased with increasing soil N availability. ...
... One of the most important assets of legume crops is their symbiosis with rhizobia, owing to which an estimated 40 to 170 Tg of nitrogen enters the atmosphere every year [8][9][10][11][12]. By exploiting the process of biological nitrogen fixation (BNF), or more precisely symbiotic nitrogen fixation (SNF), some crop plants take up from several dozen to even several hundred kg of nitrogen per hectare from the atmosphere per year [13][14][15][16][17][18][19]. In the case of crop plants, some of the nitrogen taken up from the atmosphere is removed with the harvested crops, and the rest, together with the crop residues, enters the soil. ...
Full-text available
Molybdenum (Mo), boron (B), and iron (Fe) play an important role in symbiotic nitrogen fixation by legume plants. The intensity of this process varies in different growth stages of legumes, and the changes are accompanied by changes in the content and translocation of these micronutrients in the plant. A two-year field experiment was conducted to investigate the dynamics of molybdenum, boron, and iron content, translocation, and accumulation in pea plants. Two pea cultivars were studied in six stages of growth, from the four-leaf stage to full maturity. The content of Mo, B, and Fe in the roots of pea was highest from the four-leaf stage to the full flowering stage, i.e., the period of establishment of symbiosis and the most intensive atmospheric nitrogen fixation. The bioaccumulation factors of Mo and Fe were generally highest in the initial stages of pea growth and decreased during generative development, while the reverse pattern was observed for boron. The bioaccumulation factors also indicate high bioaccumulation of Mo and B and low bioaccumulation of Fe in the biomass of pea. The translocation factor indicated a high potential for allocation of Mo from the roots to the aerial parts, increasing during growth; high and stable potential for allocation of boron; and very minor allocation of iron to the aerial parts. The values of all parameters tested were usually dependent on the conditions in which the experiment was conducted (the year), but not on the cultivar of a pea.
... Rhizobium are best known for N 2 -fixation and plant growth promotion (Garrido-Oter et al. 2018). Pea is a legume crop and meets a significant portion of its nitrogen requirement through biological N 2 -fixation (Hossain et al. 2016), and root disease has been shown to negatively impact N 2 -fixation in legumes (Unkovich 2012;Wu et al. 2019). Furthermore, several potential plant pathogenic or immunity-associated bacteria had Plant Soil greater abundance in diseased root and rhizosphere samples in our study. ...