Improved nutrient utilization -
Schematic representation of the dependence of plant growth and ENU on the internal nutrient concentration. A Plant growth, here ascribed as the relative growth rate, within the range defined by the minimal Cmin and optimal Copt concentrations, could display either a linear dashed line or a curvilinear solid line relationship with the internal concentration.
B Hypothetical dependence of ENU on the internal nutrient concentration for the linear dashed line and curvilinear solid line responses shown in panel A.
D The time course of ENU during a deprivation experiment is shown for plants obeying the curvilinear relationship described above.
The dashed area corresponds to the ENU displayed by plants once growth became limited by the nutrient under study.
Integration of this area over the period of deprivation yields the mean actual ENU ENUa. Screenings of ENUa made for short periods of growth restriction, i.
Screenings made during longer periods of growth restriction, i. The slope of the linear relationship plotted in panel A corresponds to the intrinsic rate of conversion P j , while the maximum relative growth rate of the curvilinear one corresponds to A j.
Panels A , B , and C are adapted from Moriconi and Santa-María A theoretical framework to study potassium utilization efficiency in response to withdrawal of potassium.
Journal of Experimental Botany 64, —, by permission of the Society for Experimental Biology. Three main alternative dynamic definitions of ENU have been advanced, named here ENUi, ENUb, and ENUh, respectively Table 1. According to the first one, ENUi, internal utilization efficiency should specifically consider the amount of biomass that can be generated dW in a given time period dt by the amount of nutrient present in plant tissues n j.
This assertion can be expressed by the following formula:. Notably, formula 1 can be rewritten as:. While the first term provides information on the balance between gains and losses of j , the second one reveals changes of ENUi that operate through dilution or concentration effects.
These two terms could eventually operate in the same or opposite directions, and both must be taken into consideration to dissect the sources of variation of ENUi. It has previously been suggested that an appropriate definition of ENU should take into consideration the instantaneous capacity of each unit of nutrient to generate biomass as well as the period during which each unit of nutrient is retained in plants and can contribute to set plant growth Berendse and Aerts, This period will be particularly important when significant losses of the nutrient under study take place, a phenomenon that could have several causes including loss and leaching from aerial and belowground parts of the plant, which are thought to play a more prominent role in perennials Chapin, ; Vitousek, Following these observations, Berendse and Aerts proposed a redefinition of ENU as the product between two terms: one of them corresponds to ENUi, while the other corresponds to the mean residence time MRT of the j element in plants, which estimates the mean time during which a given element remains in plant tissues.
This product has been termed, in the present paper, ENUb Table 1. The precise consequences of the MRT term being both in the numerator and in the denominator of the product should be taken into consideration in data interpretation.
In addition, it should be observed that the product between MRT and ENUi when dn l j is or cannot be distinguished from zero leads to division by zero or by an uncertain small quantity, thus imposing an important restriction on the use of ENUb as a general definition of ENU.
In an effort to make MRT consistent for both steady and non-steady state conditions, it has recently been argued that the RNR should take into consideration the amount of nutrient taken up over the period under study dn u j instead of the amount lost Hirose, The calculation of ENU as the product between ENUi and MRT, estimated now on the dn u j basis, is consistent with a proposal made by Hirose , who defined that efficiency here named as ENUh as the quotient between the variation of biomass and the amount of j taken up in the same time period, i.
While this definition has the virtue of explicitly incorporating changes in both biomass and nutrient content, thus integrating them into a single formula, it is also faced with the restriction, relevant in some experimental approaches, that it cannot be defined when dn u j is zero.
Among the dynamic definitions so far advanced, ENUi seems to offer an adequate account of utilization efficiency, which could usually be, but not necessarily always, sufficient in studies with annual crop plants particularly during the early vegetative stage.
An adequate use of the proposed indices requires setting suitable experimental conditions, and these will be considered in the next section. Because a primary objective of research on ENU, particularly with crop species, has been to improve the agronomic use efficiency of nutrients under field conditions, measurements of both ENA and ENU have been commonly made with plants grown in soil.
The underlying assumption of this procedure is that such measurements avoid potential errors arising from the use of artificial growth conditions, which will probably misrepresent the panoply of interactions that affect roots growing in the soil.
While classic studies provide strong support to this view for investigations aimed at improving nutrient acquisition, this assumption may not necessarily hold in studying ENU.
A seminal question to be answered in this context is to what extent measurements of nutrient acquisition and nutrient utilization efficiency are interdependent. In this regard, some data indicate that for phosphorus Su et al. Rose et al. Since this cannot be done unambiguously in a soil system, they proposed to make the screening for phenotypic differences in ENU by growing individual plants in a nutrient solution containing a low amount of the nutrient under study.
The rationale of this approach is that, under these conditions, individual plants will exhaust the solution to a similar extent and, consequently, absorb a similar amount of the element under study, avoiding the masking effect derived from competition among roots of different genotypes when they are placed together.
The attempt to equalize nutrient capture among genotypes can also be done through the complete withdrawal of the element under study, since in this case capture would become zero Moriconi and Santa-María, Each of these procedures offers benefits and disadvantages.
A potential problem with the first one is that until now no theoretical support has been built up to estimate the possible bias arising from the use of each ENU indicator or to interpret the effect exerted by previous differences in nutrient content on ENU estimations made for plants grown under that condition.
In this regard, it has been argued that differences in seed nutrient content can be minimized by growing plants at a high nutrient supply before supplying a low level of the nutrient Rose et al.
Although a theoretical framework has recently been developed for the second approach, particularly for potassium, it must be noted that in practice obtaining a culture solution with a very low amount of a certain element, in order to consider it virtually absent, may not always be an easy task.
Another problem coming from this second approach is that the acclimation response of plants following a sudden interruption of nutrient supply may be different from the one that takes place when nutrient scarcity is gradually imposed as actually occurs in nutrient-deficient soils.
Comparisons of genotypes by using any of the protocols requires careful examination of the basic assumptions underlying their use, i. Whenever possible the use of these protocols will help to remove, or minimize, ENA as a potentially compromising effect when evaluating ENU.
Therefore the success of these procedures to associate the ENU phenotype observed to molecular markers will largely depend on the careful choice of a well characterized genotyped germplasm collection of appropriate size.
Once the germplasm collection has been chosen, the use of hydroponics for ENU phenotyping could easily be combined with adoption of the dynamic approach proposed above. It requires culturing individual plants for a period long enough to ensure that growth for all plants becomes limited by nutrient supply, and obtaining at least two estimations of whole-plant biomass, one at the moment when low nutrient supply is imposed and another one when the effect of low nutrient supply on growth becomes evident.
Alternatively the first harvest can be done once plants have grown for a time under the low-nutrient level condition, thus reducing the effect of early-acclimation responses to the sudden change of the nutrient level on ENU estimations.
These harvests should be performed over the same fixed interval for all members of the collection. In order to estimate the experimental values necessary to compute dynamic formulae, the gains and losses of the nutrient must also be estimated by measuring changes in plant nutrient content between harvests, including senescent material [for further details to be considered in measuring MRT see Eckstein et al.
These measurements additionally allow a determination as to whether the assumptions made for a given screening protocol are actually met. Breeding for high ENU should focus on improving plant growth over the range of internal nutrient concentrations in which growth actually depends on the element studied.
A focus on this range requires knowing the actual value of ENU over the period from the moment that the internal concentration of the nutrient is below the optimum concentration Fig. For the case of ENUi, the mean utilization efficiency displayed by whole plants over that period has been named the actual ENU ENUa: Table 1.
The precise value of this efficiency cannot easily be measured, at least with the techniques available today, but can be approached by using mathematical models that take into account the relationship between growth and c j for specific growth conditions.
Modelling growth responses to internal nutrient concentration under specifically simulated growth conditions has already been pursued Hirose, ; Hirose et al. Empirically based models could serve as tools for exploring the sources of variation of ENUa. Noticeably, while ENU indicators are experimentally based on two fixed harvests, as outlined in the previous section, ENUa extends over a period that could differ among the members of the collection under analysis.
Simulation models also help to assess the extent to which those ENU indicators are reliable estimators of ENUa. The latter issue is even more important in large screenings where experimental bottlenecks could force the use of non-dynamic ENU indicators.
These questions have been examined in virtual plants suddenly exposed to the complete withdrawal of a given element, since for this hypothetical growth condition basic mathematical models that relate growth with the internal nutrient concentration can easily be built Moriconi and Santa-María, Although such models are far from being representative of real plants in real soils, they help to take into account some considerations in ENUa screenings.
Firstly, several parameters contained in the models contribute in different ways and to different extents to determine the time course of ENUa. In this regard, the effect exerted by the initial concentration including that of the seed on ENUa depends on the precise model and on the value of other parameters; which means that it cannot easily be predicted.
Secondly, there are no perfect indicators of ENUa, but clearly some of them tend to better reflect the actual variation of ENUa in most scenarios while others are unlikely to generally reflect the actual efficiency.
Thirdly, in some circumstances, some indicators among them the widely used ENUo could be negatively associated with ENUa; this may happen particularly when the source of variation is the initial concentration. These uncertainties can be partially reduced through the careful and simultaneous use of several indicators specifically ENUe, ENUi, ENUo, and ENUu; see Table 1.
However, when only non-dynamic ENU indicators are used, precise knowledge on the degree to which they approach ENUa seems to be unavoidable and requires the use of simulation models. Overall, an important conclusion from this study Moriconi and Santa-María, is that the factors affecting ENUa and the degree to which different indicators can approach ENUa critically depend on the structure of the model to which plant responses approach.
Therefore, the extent to which a model can readily help in the screening of crops for high ENUa will largely depend on the extent to which the model actually mimics the dynamic relationship between growth and the internal nutrient concentration under given experimental conditions.
Another important issue derived from the work with different models is that ENUa cannot always remain the same within the sub-optimal range of internal concentrations Fig.
This observation can be translated into different approaches according to the particular purpose of a breeding programme. If the programme is aimed at maximizing biomass production at internal concentrations close to the optimal one, ENUa estimations should be made at the very beginning of the period after growth becomes restricted Fig.
This procedure will be well suited to improve ENU in crops supplemented with fertilizers. For plants growing in nutrient-poor, non-fertilized soils, the focus should be over a more extended period of nutrient scarcity during which plants will display a wider set of strategies to cope with internal nutrient deficiency Fig.
A first potential conflict to be considered for the vegetative stage could emerge between the efficiencies of acquisition and utilization as already illustrated with examples of a negative correlation between ENU and ENA.
However, information obtained in screenings conducted in soils, even with the complications that the use of this system could impose for accurate ENU comparisons, suggests that in spite of being frequent this negative correlation is not always present Su et al. Thus, even in soil-based screenings, it could be possible to select for both traits, which is particularly important since the ideal phenotype for a crop plant would be a combination of high ENA and high ENU.
In this regard, it should be noted that work with simulation models indicated that genotypes with high root ENU could better explore the soil leading to increased ENA Wissuwa, , thus suggesting that a physiological trade-off between both efficiencies does not exist.
In practical terms, when the occurrence of a potential bias due to ENA cannot be discarded, phenotyping for ENU would require an examination of the coincidence of genetic maps obtained for both efficiencies. Under these conditions, selection should be done with loci for high ENU that do not coincide with loci for low ENA and display enhanced biomass accumulation.
A second constraint for ENU, which has been mostly disregarded, emerged from the models outlined above, which indicate the existence of a potential conflict between high ENUa and high relative biomass accumulation.
between the biomass accumulated by plants grown in nutrient-poor media W p relative to that measured in a non-growth-limiting nutrient medium W n. This quotient is an indicator of the ability of a given genotype to avoid a significant growth reduction at limiting supplies of the j element, and consequently an indicator of tolerance to nutrient deficiency.
It differs conceptually from ENU, which just describes the capacity to generate biomass per unit of nutrient in the plant. This distinction is particularly important because a negative association between ENUa and that quotient could be expected for some Fig.
In support of this statement, it has been shown, in a soil-based screening for some Triticeae, that ENU for phosphorus can sometimes, but not necessarily always, be negatively associated with tolerance to low phosphorus supply Osborne and Rengel, A wide screening of rice genotypes suggests that ENU and tolerance to low potassium supply are usually associated Yang et al.
Thus, the relevance of this conflict in each case should be analysed. Hypothetical relationship between the actual ENU ENUa and the quotient between the biomass accumulated by plants grown in nutrient-poor media W p relative to that measured in a non-growth-limiting nutrient medium W n for linear dashed lines or curvilinear solid lines models shown in the upper panel A of Fig.
In A the sources of variation for ENUa are P j the intrinsic rate of conversion for linear models or A j the maximum relative growth rate for curvilinear models. This figure is available in colour at JXB online. The final objective of breeding for high ENU is the selection of crop plants that maximizes yield while minimizing the requirement of nutrients.
The extent to which specific traits conferring high ENU at the vegetative stage may confer enhanced yield must be carefully examined. Noticeably, the dynamic approach outlined above can eventually be extended to the reproductive stage by applying formula 1 to the process of seed biomass accumulation.
In this context, it should be considered that additional constraints to those aforementioned are specific to the reproductive phase see Barraclough et al. Previous paragraphs have focused on the definition and measurement of ENU.
While necessary to make a proper assessment, the issues discussed do not provide, per se, any insight on the mechanisms determining ENU differences among genotypes.
Although analysis of these mechanisms is beyond the scope of this paper, a possible way to connect them with ENU definitions should be briefly mentioned. As already shown, for the vegetative stage, formula 1 can be rewritten alternatively as:.
Multiplying and dividing the last expression by the leaf area A yields:. Dissection of differences in utilization efficiency in terms of differences in NAR and LAR as well as of LAR components has already been pursued Hirose, ; Hirose et al.
Therefore, the adoption of a dynamic approach could provide a necessary link between phenotyping and exploration of the mechanisms underlying ENU in terms of the components of the relative growth rate.
It seems worthwhile mentioning that as knowledge of the mechanisms underlying plant responses to nutrient scarcity increases, the ways to express ENU could eventually change. For the particular case of phosphorus it has recently been observed that restriction of shoot growth is partially uncoupled from total phosphorus content in this plant fraction Rouached et al.
Data obtained in plants overexpressing the AtNHX1 exchanger supports a similar statement for potassium Leidi et al. These results suggest that for some nutrients a variable fraction can be in pools that only marginally contribute to set growth.
In this regard, it has been proposed that for some levels of analysis the amount of nutrient considered in ENU formulae may be substituted by the amount specifically allocated to metabolically active pools Veneklaas et al.
Definitions of ENU are not free of inconsistencies or restrictions. The use of alternative dynamic definitions could offer a comprehensive basis for further understanding plant responses to nutrient scarcity as well as improving ENU phenotyping.
The idea that the efficiency to be estimated should correspond to that displayed by plants only when growth is actually affected by the internal concentration could serve as a guide for the selection of adequate operational formulae.
New protocols have recently been advanced to minimize the masking influence of nutrient acquisition on ENU estimation, while ambiguities in screening for ENUa can be reduced through the critical use of several ENU indicators.
Thus, the panorama opened up to us seems to bring innovative screenings of ENU in crop plants. If non-biased comparisons of ENU among genotypes must preferentially be assessed under well controlled conditions, an important question is to what extent differences in ENU determined with that artificial method can be extrapolated to field conditions.
The answer will operatively depend on a second question: how to infer ENU from the conditions encountered by plants in their environment without the masking effect of nutrient acquisition? Certainly, we cannot offer an adequate response yet. However as stated above, even without an unequivocal assessment of ENU it could be possible to distinguish preliminarily, in screenings performed under field conditions, some traits that influence nutrient utilization from some of those that influence nutrient acquisition.
We are witnessing the development of a notable array of tools that permits the association of wide phenotypic variation to high-resolution genetic maps of crop plants, reinforced by the development of high-throughput molecular profiling technology.
These tools are increasingly used by plant breeders for the identification and selection of traits of agronomic value. To take advantage of those opportunities, equally powerful methods are needed for the screening i. phenotyping of ENU. These methods can be further facilitated by the use of non-invasive technologies Fiorani and Schurr, , once appropriate protocols for plant growth are stated and the primary phenotypic parameters ENU indicators to be measured are accurately validated.
Identification of the conceptual and experimental problems in ENU studies, and the approaches suggested above in measuring ENU, could serve to assist with some of the challenges for conducting successful large screenings of ENU as well as to set a framework for unequivocal data interpretation.
This work was supported by the ANPCYT through the PICT and PICT to GES-M. JIM and SO express gratitude to CONICET for a fellowship. The authors are greatly indebted to Profs Timothy Colmer, Hans Lambers School of Plant Biology, University of Western Australia and Gabriela Tranquilli Instituto Nacional de Tecnología Agropecuaria, Argentina for critical comments and useful suggestions on an earlier version of the manuscript.
Thanks are also given to Dr Laura Kuperman University of California, Davis for help with English usage. Aerts R Chapin FS III. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns.
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The adoption of integrated nutrient management INM approaches such as the organic amendment of the soil in addition to fertilizer use has shown positive impacts on maintaining and recovering soil quality, hence lowering excessive fertilizer use in farmlands.
Therefore, this review contextualized the effect of compost and fertilizer on nutrient use efficiency NUE and productivity of broadacre crops. The use of compost as an organic soil amendment material has shown some inherently unique advantages and beneficial impacts on soil health and fertility such as improved soil structure, nutrient retention, mobilization, and bioavailability.
Several studies have explored these comparative advantages by either blending compost with chemical fertilizer before soil application or a co-application and have noted the observed amelioration of unfavorable soil conditions such as low porosity, high bulk density, low organic matter OM , unfavorable pH, and cation exchange capacity CEC , low biological activities with different doses of compost.
Consequently, the co-utilization of composts and chemical fertilizers may become viable substitutes for chemical fertilizers in maintaining soil fertility, improving NUE, and crop yield in farmlands. The review further described the comparative environmental and economic implications of adopting the combined utilization of compost and fertilizers in farmlands.
Meeting human needs within the Nourishing your body limits utilizatin Digestive discomfort relief planet calls for continuous Dark chocolate indulgence on, utilizarion redesigning of, utiliation Improved nutrient utilization and Digestive discomfort relief. Such technologies Impeoved fertilisers, the discovery and Improved nutrient utilization utilizaton which have been one Multivitamin weight loss supplements the Improved nutrient utilization factors for increasing crop yield, agricultural productivity and food security. Fertiliser use comes, however, at an environmental cost, and fertilisers have also not been a very economically effective production factor to lift many poor farmers out of poverty, especially in African countries where application on poor soils of unbalanced compositions of nutrients in fertilisers has shown limited impact on yield increase. Agronomic practices to apply existing mineral fertilisers, primarily containing N, P and K, at the right time, the right place, in the right amount, and of the right composition can improve the use efficiency of fertilisers. However, the overall progress to reduce the negative side effects is inadequate for the desired transformation toward sustainable agriculture in poor countries.Video
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This highlighted national farmer-led successes in key areas such as soil health, carbon sequestration, and reduced fuel use. Despite many wins, the data revealed national increases in subsurface nitrogen and soluble phosphorus lost to the environment over a year period.
This impacts both water quality and farmer finances. A SMART Nutrient Management Plan includes the 4Rs of nutrient stewardship right Source, right Method, right Rate and right Timing and emphasizes smart activities to reduce nutrient loss by Assessment of comprehensive, site-specific conditions, recognizing that nutrient needs vary even within a field.
NRCS provides one-on-one, customer-specific advice, including development of Nutrient Management Plans. Farmers, I encourage you to contact the NRCS office at your local USDA Service Center. There, you can work with your local conservationist to evaluate your unique nutrient needs, assess site-specific risks for nutrient and soil loss, and discuss opportunities to address those risks.
Whether you farm one acre or thousands, we will work with you to identify areas to improve nutrient efficiencies, reduce input costs, maximize yields, and improve water quality in your community and beyond. Visit farmers. NRCS is streamlining the application process for the Environmental Quality Incentives Program EQIP and the Conservation Stewardship Program CSP to expand access to financial assistance programs for precision agriculture and nutrient management.
This will include an expedited application process, targeted outreach to small-scale and historically underserved producers, and coordination with the Farm Service Agency FSA to streamline the program eligibility process for producers new to USDA.
Your local NRCS office can help you identify potential opportunities to apply for funding based on your production and conservation goals. We know farmers have questions about our conservation outcomes data, operation-specific considerations for nutrient management, and the work across USDA to address fertilizer shortages and rising costs.
For more information, check out these helpful links:. Save Money and Protect Water Quality with SMART Nutrient Management. By Terry Cosby, Chief, NRCS. For alternating light and dark cycles, autotrophic conditions were used for light sections and heterotrophic conditions were used for dark sections.
The lyophilized algal dry biomass was weighted gravimetrically using an analytical balance. The glucose concentration was measured using YSI biochemistry analyzer Yellow Springs, OH.
FAME production followed the procedure provided by Dong et al. Helium was used as carrier gas. Lutein extraction followed the procedure provided by Yuan et al. The solution was filtered before HPLC analysis. The mobile phases are eluent A dichloromethane: methanol: acetonitrile: water, 5.
The i CZ model, including six different biomass compositions for autotrophic conditions PAT1-PAT6 and five different biomass compositions for heterotrophic conditions HT1-HT5 , was obtained from Zuniga et al. GSM simulations were performed using the Gurobi Optimizer Version 5.
The experimental setup is shown in Supplementary Fig. The manipulated variables were glucose demand F G and nitrate demand on a per L basis F N for 8-h period. Two pumps were used to control both variables automatically by Matlab TM through Arduino chip.
All the control algorithms were run on Matlab TM and the codes are provided in Supplementary information. The Simulink TM simulation is shown in Fig.
The blue box in Fig. Four equations were built inside the blue box as shown in Supplementary Fig. The inputs were F G and F N. The outputs were biomass, nitrate level, glucose level and volume. Only nitrate levels and glucose levels were fed into the PID and GMPC controller.
For the proportional-integral-derivative PID controller, the proportional gain K p , integral gain K i and derivative gain K d equal to 1.
The PID controller and GMPC controller were used to control glucose supply and nitrate supply every hour in both simulation and experiment. Changes in the setpoint for glucose were introduced to see how both PID and GMPC responded to those changes.
Initial biomass levels x 0 , glucose levels G 0 and nitrate levels N 0 were measured as described above and used as inputs into the open-loop system. Three equations shown below were used to predict biomass growth, nitrate consumption rate, and glucose consumption rate in the open-loop system.
The growth rates under light and dark cycles were determined based on previous experimental data. After that, the growth rates were constrained in the autotrophic and heterotrophic GSMs, respectively to determine nutrient exchange rates r N and r G under light and dark cycles.
The methods for using growth rate to estimate nutrient exchange rates have been described previously in Chen et al.
We assumed a rapid switch to a new operational steady state following the transition between light and dark cycles. Initial biomass levels x 0 , glucose levels G 0 and nitrate levels N 0 were measured and used as inputs into the closed-loop system.
During the experiment, biomass levels x m , glucose levels G m and nitrate levels N m were msured and used as inputs into the closed-loop system. For the light cycle, two equations were built to describe and predict biomass accumulation rate and nitrate consumption rate.
Unlike the open loop system, the light shielding effect was considered and the growth rate would decrease as the biomass concentration increased as described in the equation below and shown in Fig. The GSM was used to predict nutrient exchange rate r N based on the measured growth rate.
For the dark cycles, three model equations were built to predict biomass accumulation rate, nitrate consumption rate and glucose consumption rate as listed below and shown in Fig.
In the biomass equation, we assumed a fraction of heterotrophic biomass, a , was derived from autotrophic metabolism and the simulated growth rate was μ A. Meanwhile, some biomass was derived through heterotrophic metabolism with the simulated growth rate, μ H. The nutrient exchange rates r NA , r NH , r GH were determined by inputting simulated growth rates into the autotrophic and heterotrophic GSMs respectively.
where μ A is simulation growth rate from autotrophic metabolism, μ H is the growth rate from heterotrophic metabolism, r NA is nitrate exchange rate from autotrophic metabolism, r NH is the nitrate exchange rate from heterotrophic metabolism, r GH is the glucose exchange rate from heterotrophic metabolism.
Next, we applied a fitting objective function J to minimize the difference between calculated values and simulated model values in order to estimate the optimal parameter values a , μ A , μ H , r NA , r NH , r GH for dictating the actual nitrate and glucose feeds to the bioreactor.
The actual bolus nitrate demand F N and the glucose demand F G were thus determined by using values obtained from this fitting objective function.
The data that support the findings of this study are available from the corresponding author upon reasonable request. Rosenberg, J.
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Yuan, J. Carotenoid composition in the green microalga Chlorococcum. Food Chem. These uncertainties can be partially reduced through the careful and simultaneous use of several indicators specifically ENUe, ENUi, ENUo, and ENUu; see Table 1. However, when only non-dynamic ENU indicators are used, precise knowledge on the degree to which they approach ENUa seems to be unavoidable and requires the use of simulation models.
Overall, an important conclusion from this study Moriconi and Santa-María, is that the factors affecting ENUa and the degree to which different indicators can approach ENUa critically depend on the structure of the model to which plant responses approach.
Therefore, the extent to which a model can readily help in the screening of crops for high ENUa will largely depend on the extent to which the model actually mimics the dynamic relationship between growth and the internal nutrient concentration under given experimental conditions.
Another important issue derived from the work with different models is that ENUa cannot always remain the same within the sub-optimal range of internal concentrations Fig.
This observation can be translated into different approaches according to the particular purpose of a breeding programme. If the programme is aimed at maximizing biomass production at internal concentrations close to the optimal one, ENUa estimations should be made at the very beginning of the period after growth becomes restricted Fig.
This procedure will be well suited to improve ENU in crops supplemented with fertilizers. For plants growing in nutrient-poor, non-fertilized soils, the focus should be over a more extended period of nutrient scarcity during which plants will display a wider set of strategies to cope with internal nutrient deficiency Fig.
A first potential conflict to be considered for the vegetative stage could emerge between the efficiencies of acquisition and utilization as already illustrated with examples of a negative correlation between ENU and ENA.
However, information obtained in screenings conducted in soils, even with the complications that the use of this system could impose for accurate ENU comparisons, suggests that in spite of being frequent this negative correlation is not always present Su et al.
Thus, even in soil-based screenings, it could be possible to select for both traits, which is particularly important since the ideal phenotype for a crop plant would be a combination of high ENA and high ENU. In this regard, it should be noted that work with simulation models indicated that genotypes with high root ENU could better explore the soil leading to increased ENA Wissuwa, , thus suggesting that a physiological trade-off between both efficiencies does not exist.
In practical terms, when the occurrence of a potential bias due to ENA cannot be discarded, phenotyping for ENU would require an examination of the coincidence of genetic maps obtained for both efficiencies. Under these conditions, selection should be done with loci for high ENU that do not coincide with loci for low ENA and display enhanced biomass accumulation.
A second constraint for ENU, which has been mostly disregarded, emerged from the models outlined above, which indicate the existence of a potential conflict between high ENUa and high relative biomass accumulation.
between the biomass accumulated by plants grown in nutrient-poor media W p relative to that measured in a non-growth-limiting nutrient medium W n. This quotient is an indicator of the ability of a given genotype to avoid a significant growth reduction at limiting supplies of the j element, and consequently an indicator of tolerance to nutrient deficiency.
It differs conceptually from ENU, which just describes the capacity to generate biomass per unit of nutrient in the plant. This distinction is particularly important because a negative association between ENUa and that quotient could be expected for some Fig. In support of this statement, it has been shown, in a soil-based screening for some Triticeae, that ENU for phosphorus can sometimes, but not necessarily always, be negatively associated with tolerance to low phosphorus supply Osborne and Rengel, A wide screening of rice genotypes suggests that ENU and tolerance to low potassium supply are usually associated Yang et al.
Thus, the relevance of this conflict in each case should be analysed. Hypothetical relationship between the actual ENU ENUa and the quotient between the biomass accumulated by plants grown in nutrient-poor media W p relative to that measured in a non-growth-limiting nutrient medium W n for linear dashed lines or curvilinear solid lines models shown in the upper panel A of Fig.
In A the sources of variation for ENUa are P j the intrinsic rate of conversion for linear models or A j the maximum relative growth rate for curvilinear models. This figure is available in colour at JXB online. The final objective of breeding for high ENU is the selection of crop plants that maximizes yield while minimizing the requirement of nutrients.
The extent to which specific traits conferring high ENU at the vegetative stage may confer enhanced yield must be carefully examined. Noticeably, the dynamic approach outlined above can eventually be extended to the reproductive stage by applying formula 1 to the process of seed biomass accumulation.
In this context, it should be considered that additional constraints to those aforementioned are specific to the reproductive phase see Barraclough et al. Previous paragraphs have focused on the definition and measurement of ENU.
While necessary to make a proper assessment, the issues discussed do not provide, per se, any insight on the mechanisms determining ENU differences among genotypes. Although analysis of these mechanisms is beyond the scope of this paper, a possible way to connect them with ENU definitions should be briefly mentioned.
As already shown, for the vegetative stage, formula 1 can be rewritten alternatively as:. Multiplying and dividing the last expression by the leaf area A yields:. Dissection of differences in utilization efficiency in terms of differences in NAR and LAR as well as of LAR components has already been pursued Hirose, ; Hirose et al.
Therefore, the adoption of a dynamic approach could provide a necessary link between phenotyping and exploration of the mechanisms underlying ENU in terms of the components of the relative growth rate. It seems worthwhile mentioning that as knowledge of the mechanisms underlying plant responses to nutrient scarcity increases, the ways to express ENU could eventually change.
For the particular case of phosphorus it has recently been observed that restriction of shoot growth is partially uncoupled from total phosphorus content in this plant fraction Rouached et al.
Data obtained in plants overexpressing the AtNHX1 exchanger supports a similar statement for potassium Leidi et al. These results suggest that for some nutrients a variable fraction can be in pools that only marginally contribute to set growth.
In this regard, it has been proposed that for some levels of analysis the amount of nutrient considered in ENU formulae may be substituted by the amount specifically allocated to metabolically active pools Veneklaas et al.
Definitions of ENU are not free of inconsistencies or restrictions. The use of alternative dynamic definitions could offer a comprehensive basis for further understanding plant responses to nutrient scarcity as well as improving ENU phenotyping. The idea that the efficiency to be estimated should correspond to that displayed by plants only when growth is actually affected by the internal concentration could serve as a guide for the selection of adequate operational formulae.
New protocols have recently been advanced to minimize the masking influence of nutrient acquisition on ENU estimation, while ambiguities in screening for ENUa can be reduced through the critical use of several ENU indicators.
Thus, the panorama opened up to us seems to bring innovative screenings of ENU in crop plants. If non-biased comparisons of ENU among genotypes must preferentially be assessed under well controlled conditions, an important question is to what extent differences in ENU determined with that artificial method can be extrapolated to field conditions.
The answer will operatively depend on a second question: how to infer ENU from the conditions encountered by plants in their environment without the masking effect of nutrient acquisition?
Certainly, we cannot offer an adequate response yet. However as stated above, even without an unequivocal assessment of ENU it could be possible to distinguish preliminarily, in screenings performed under field conditions, some traits that influence nutrient utilization from some of those that influence nutrient acquisition.
We are witnessing the development of a notable array of tools that permits the association of wide phenotypic variation to high-resolution genetic maps of crop plants, reinforced by the development of high-throughput molecular profiling technology.
These tools are increasingly used by plant breeders for the identification and selection of traits of agronomic value. To take advantage of those opportunities, equally powerful methods are needed for the screening i.
phenotyping of ENU. These methods can be further facilitated by the use of non-invasive technologies Fiorani and Schurr, , once appropriate protocols for plant growth are stated and the primary phenotypic parameters ENU indicators to be measured are accurately validated.
Identification of the conceptual and experimental problems in ENU studies, and the approaches suggested above in measuring ENU, could serve to assist with some of the challenges for conducting successful large screenings of ENU as well as to set a framework for unequivocal data interpretation.
This work was supported by the ANPCYT through the PICT and PICT to GES-M. JIM and SO express gratitude to CONICET for a fellowship. The authors are greatly indebted to Profs Timothy Colmer, Hans Lambers School of Plant Biology, University of Western Australia and Gabriela Tranquilli Instituto Nacional de Tecnología Agropecuaria, Argentina for critical comments and useful suggestions on an earlier version of the manuscript.
Thanks are also given to Dr Laura Kuperman University of California, Davis for help with English usage. Aerts R Chapin FS III. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns.
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