Modeling individual nutrient uptake by plants: Relating demand to microclimate

Aericultural

ELSEVIER

Svstems 50 (1996) 101-I 14 Elsevier k&&e Limited Printed in Great Britain. 030%521X/96/$15.CKl

0308-521X(94)00054-9

Modeling Individual Nutrient Uptake by Plants: Relating Demand to Microclimate’ K. R. Mankin & R. P. Fynn Department

of Agricultural Engineering, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, Ohio 44691, USA

l(Received 15 January

1994; revised and accepted 23 September 1994)

ABSTRACT This paper proposes an approach for modeling plant uptake of individual nutrients based on incident microclimatic variables and independent of water uptake. Current nutrient uptake models, though quite complex, have not been developed in a form directly applicable to nutrient management systems for greenhouse crop production. This paper proposes an approach for predicting plant nutrient use based on the assumption that plant nutrient demand, as determined by photosynthetic rate, determines the required rate of nutrient uptake for optimal growth. This general model allows spectfic models to be developed with direct functional relationships between individual nutrient uptake rates and incident microclimatic variables. A simple model is presented which applies nutrient demand directly to photosynthetic photon flux density.

INTRODUCTION Intelligent management of applied nutrients to plants could improve crop production by increasing growth rates, reducing the amount (and cost) of applied nutrients, and reducing the amounts of unused nutrients which accumulate in soil, ground water, and surface water. Whereas water use ‘Salaries and research support were provided by State and Federal funds appropriated to the Ohio Agricultural Research and Development Center and The Ohio State University. The first author was also funded through an OARDC Director’s Fellowship and the Charles E. Thorne Memorial Assistantship. Additional funding was provided by The Bedding Plants Foundation. OARDC Manuscript Number 157-93. Reference to commercial products or trade names is made with the understanding that no discrimination and/or endorsement is intended or implied. 101

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and transpiration can be modeled and water delivered to plants with relative accuracy, nutrient uptake requirements have been less well quantified. Because knowledge of plant nutrient use has not kept pace with knowledge of water use, development of nutrient application models and control technologies has been impeded. Manually adjusted individual nutrient injectors are already in widespread use by commercial greenhouse growers, and computer-controlled injectors are under development (Fynn & Roberts, 1992; Papadopoulos & Liburdi, 1989; Bauerle et al., 1988). Though this technology allows nutrient applications to meet exact plant nutrient requirements, these needs are not well quantified. Models which predict near-future, individual nutrient requirements of plants are needed to make intelligent use of injector technology. Fynn et al. (1994, 1989) address this need using an expert systems approach, applying the experience of an expert greenhouse operator in selecting optimal nutrient recipes. This paper, instead, presents a more mechanistic approach, applying the existing literature on nutrient uptake and plant responses to environment toward the development of a demand-based nutrient uptake model. The need for reduced nutrient bpplication Growers of greenhouse crops commonly add nutrients to plant root zones at levels 2 to 10 times higher than those required for optimal growth (Drees et al., 1990; Nelson, 1990). For example, poinsettia or chrysanthemum production using 600 ml of solution with 300 mg 1-l nitrogen per 15 cm pot every 3 days with 10 pots m-2 results in fertilizer use of 2190 kg ha-’ yr-’ compared with 100-400 kg ha-’ yr’ for agronomic and vegetable crops. This practice is intended to ensure that the available nutrient supply meets or exceeds the plants’ needs. However, unless the leachate and runoff are physically captured, the load from unused nutrients strains the subsurface ecosystem and can lead to hazardous soil, groundwater and surface water contamination. Morisot et al. (1978) found nitrate soil solution contents averaging 140 mg 1-l and as high as 500 mg 1-l from 1.O to 1.6 m beneath 19 floriculture greenhouses in France. Clearly, there is a need to reduce nutrient effluent. Because systems which capture and treat leachate and runoff may be cost-prohibitive, researchers look toward source reductions in water and nutrient use by intelligent management as the most promising method of reducing greenhouse effluent (Drees et al., 1990; Nelson, 1990; Fynn et al., 1989). Knowledge of actual plant nutrient requirements is essential for reducing the adverse environmental impacts of agricultural management systems. Early plant growth simulation models often assumed that all

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nutrients which reached the root were absorbed (Scaife, 1974). Many current crop growth models predict nitrogen uptake using an estimate of plant N demand. SOYMOD (Meyer et al., 1979) and TOMMOD (Elwell & Bauerle, 1990) used a demand function, a ratio of carbon to nitrogen related to an ideal C:N range, to direct N translocation to specific plant parts, while supply was considered to be non-limiting. Nitrate uptake in GLEAMS and EPIC was related to the incremental difference between plant N concentration and an assumed optimal N concentration based on phenological age (Ramanarayanan et al., 1994). The following discussion aims to improve upon these models by: (1) more completely describing the basis for using a demand approach in nutrient uptake models; (2) presenting a refined demand model which allows the direct incorporation of microclimatic parameters in nutrient demand prediction.

MODELING

NUTRIENT

UPTAKE

Nutrient uptake versus ‘Supply’

The relationship between root zone nutrient concentration, or nutrient supply, and nutrient uptake was an area of active research as early as 1952, when Epstein and Hagen began using the Michaelis-Menten concept to relate nutrient absorption by excised roots to external nutrient concentration. They borrowed the enzyme kinetics concept of a carrier-ion complex to explain the unidirectional transport of ions through the root membrane. Michaelis-Menten kinetics accurately describe uptake in dilute nutrient solutions of less than 1 mM (Epstein, 1973). However, for higher nutrient concentrations the data may be more accurately described by a multiphasic mechanism (Nissen, 1991), graphically represented by a series of Michaelis-Menten hyperbolas with successively increasing plateaus. Multiphasic kinetics suggests that one transport structure displays both carrier- and channel-like properties depending on conformational changes in response to external solute concentrations (Nissen, 199 1). Nutrient supply models have experimental and practical limitations. The use of nutrient-starved, excised root tissue immersed in a labelled nutrient solution to determine uptake rate may not reflect nutrient uptake in vivo. Characterization of the relationship between uptake and concentration is confounded further by nutrient depletions in the immediate root zone caused by the time-dependency of both nutrient uptake and replenishment. However, when external concentration and temperature

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change gradually, as is common in natural ecosystems, it has been suggested that a nearly constant supply of nutrients is maintained over a wide range of temperatures and external nutrient concentrations (Glass & Siddiqi, 1985). Several studies have demonstrated that for wide ranges of nutrient concentrations common in natural soil solutions, uptake is independent of concentration for example, nitrate in dwarf bean in the range of 0.08 to 0.48 mM (l-7 mg 1-l N) (Breteler & Nissen, 1982); K’, Cl-, and other ions in a variety of plants over the same range (e.g. 3-19 mg 1-l K) (Nissen, 1974); and K+, Rb+, Cl*-, and many other ions between 0.2 and 1.0 mM (e.g. 8-39 mg 1-l K) (Epstein, 1973). Other researchers have demonstrated that the limiting nutrient in the external solution may be used almost entirely to depletion (Ingestad & Lund, 1986; Ingestad, 1982), and that dilute nutrient solutions, near the K, value (the concentration at half-maximal uptake) found by traditional uptake studies, can sustain maximal growth (references in Clarkson, 1985; Ingestad, 1982; Clement et al., 1978). In any case, plants may naturally seek an equilibrium between feeding mechanism, nutrient uptake, and photosynthesis (Ingestad & Agren, 1988). The conclusion to be drawn from this large body of work is that near equilibrium or within wide bands of nutrient concentrations, nutrient supply may be less important than nutrient demand. Nutrient uptake versus ‘Demand’ The ‘demand’ approach to nutrient uptake modeling is based on the assumption that actual plant nutrient uptake (U,, mg mm2hr-‘) is ultimately driven by nutrient demand (D,). U, = D, + X,,

(1)

The additional term, X,, is positive for luxury consumption of nutrients, and negative when plants draw on stored nutrients to help meet a current demand. X, may vary in magnitude and sign among nutrients, plants types, growth stages and growth conditions, though it is generally assumed to be small relative to D, as long as external supply is relatively constant and non-limiting (Bloom et al., 1985). Once at equilibrium (i.e. small or constant X,), a decreased nutrient status relative to plant relative growth rate increases demand and signals for increased uptake. The signal may elicit root growth to increase the overall uptake area, or modify the transport system to increase uptake rate (Ingestad & Agren, 1988; Glass & Siddiqi, 1985; Clement et al., 1978). Nye and Tinker (1969) suggest that root demand, determined by plant growth and external concentration (diffusion gradient), must be the

Modeling individual nutrient uptake by plants

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driving force for nutrient uptake. Scaife and Smith (1973) counter that since plants maintain nutrient status within tight tolerances regardless of external concentration, nutrient concentration at the root surface only indirectly controls uptake. They suggest that plant nutrient status provides a more reliable indicator of how well nutrient supply is keeping pace with demand. A split-root study by Claassen and Barber (1977) demonstrated that maximal growth rate could be maintained with only 50% of the roots supplied with potassium, suggesting that plant demand has some self-regulating role in overcoming nutrient supply deficiencies. This demonstrates that root uptake mechanisms must rely on complex feedback signals from other parts of the plant, and consequently that whole, intact plants must be used in nutrient uptake studies. The proposed demand model approach remains applicable as long as the mechanism for nutrient uptake is ‘facilitated’ and not merely unrestricted passive influx. All forms of facilitated transport, whether ‘passive’ facilitated diffusion or active transport, selectively transport ions across the root cellular membrane, which affords the plant a level of specificity in uptake of nutrients. As more work is done to elucidate the precise physiological mechanism(s) used to control assimilation of individual nutrients, truly mechanistic models may be attainable.

DEVELOPMENT

OF A NUTRIENT

DEMAND

MODEL

Relating growth and photosynthesis to nutrient uptake Plant growth, in the most general sense, is a result of transforming raw materials (CO,, Hz0 and mineral nutrients) into plant materials. Ingestad and Lund (1986) studied the effects of mineral nutrition on growth under steady-state internal nutrient concentrations (i.e. where X, is constant and U,, = D,) and growth. Under these conditions, uptake rate of nutrients (per unit of nutrient in the plant) is directly related to relative growth rate (see also Salisbury & Ross, 1992, Ch. 7). This can be written as: u* ----_=a_ u?l

G,

(2)

mP

or, U, = aC,,G P where, a = proportionality constant; m, = mass of nutrient in plant (mg m-‘);

(3)

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= mass of plant (g mm2); C, = plant nutrient concentration (m, m,-‘, mg g-‘); and Gp = plant growth rate (g me2 hr’)

mP

Many researchers have shown a correlation between nutrient uptake and plant productivity (Landi & Fagioli, 1983; Motto & Salamini, 1981; Cacco et al., 1980; Frick & Bauman, 1979; Cacco et al., 1978). Raper et al. (1977a,b) found nitrate uptake was directly related to the photosynthate supply from the leaves of tobacco, cotton, and soybean, and concluded that there is a balance among nitrate uptake, photosynthate supply and plant growth functions. Plant growth rate is commonly measured by the rate of increase in dry weight, which essentially reflects the rate of net photosynthetic uptake of C02. McCree (1967) suggests that the relationship between net photosynthetic rate (P,, g mm2hr-‘) and growth is linear. This has been a common assumption in dynamic crop modeling. For instance, Curry (1971) used this assumption in a simulation model of corn growth, and found a good correlation with field data for the first 45 days of a 60-day simulation (Curry & Chen, 1971). Charles-Edwards and Acock (1977) also used the assumption that growth is a linear function of photosynthesis in their model of chrysanthemum growth, and found good correlations with actual growth data for the entire 35day simulation period. The same mechanistic assumptions, when applied to Eqn (3) yield: U, = bC,,P,, where b is a proportionality constant. This general model for nutrient uptake could be used with available models of photosynthesis (e.g. Gary, 1988; Curry, 1971; Idso & Baker, 1967). The following section focusses on one factor, PPFD, and demonstrates how the demand model can be used to relate nutrient uptake directly to a measurable microclimate parameter. Relating PPFD to nutrient uptake Many studies have shown a strong relationship between uptake of various nutrients and incident irradiance levels. The earliest work in this area studied the role of light in ion absorption using green algae, in which the same cells performed the functions of both photosynthesis and the primary acquisition of mineral nutrients (Epstein, 1972). Epstein indicated that green algae’s accumulation of both nitrate and phosphate depended upon light as a source of energy. Epstein also reported that the leaf tissue of corn accumulated potassium in the light at twice the rate it did in the

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dark. He concluded that the transport of inorganic ions by plant cells was affected by mechanisms dependant upon energy from cellular metabolism. Magalhaes and Wilcox (1983aJ) demonstrated that total uptake of N, P, K, Ca and Mg increased with increasing irradiance for plants supplied with NO,--N, but surprisingly plants supplied with NH,‘-N showed either no response or decreased uptake with irradiance. Other studies also showed a correlation between uptake of various nutrients and irradiante: K, Ca and Mg by Chu and Toop (1975) N and K by Adams (1980) and Adams and Massey (1984). Tremblay et al. (1988) offer a word of caution which is particularly pertinent for studies in which both solar and artificial lighting are used: equal photosynthetic irradiance does not necessarily result in equivalent nutrient uptake since spectral quality influences several physiological processes related to uptake. Diurnal variations were found to affect uptake of NH,+ and K’ (Hatch et al., 1986). Statistical analysis of these data revealed that 5 and 6 hr lag times (respectively for NH4+ and K’) gave the best correlations between uptake and irradiance. Similar results were obtained by Clement et al. (1978) who recorded peak rates of N03- uptake with a 5-6 hr lag behind solar radiation. This lag time was not reported by Gislerod and Adams (1983) who found that uptake of both water and K’ increased in response to solar radiation, with the highest nutrient uptake rates occurring during the brightest part of the day. Nonetheless, these data suggest that diurnal variations in the uptake rate of NH,‘, NO,- and K’ may result from ‘feedback mechanisms which regulate the demand for mineral nutrients through the photoperiodic supply of photosynthate’ (Hatch et al., 1986). Photosynthesis generally increases proportional to the PPFD intercepted by leaves up to a level where light saturates the mechanism for a given plant-environment system. McCree (1972a,b) found the relationship between photosynthetic rate and irradiance (I) to be described by the rectangular hyperbola: P,

=

PnlaJ Km+ I

(5)

using established Values of P,,, and K,,, can be determined Michaelis--Menten curve-fitting methods (Lineweaver & Burke, 1934; Persoff & Thomas, 1988). A nutrient uptake demand model

The proposed model relates individual nutrient uptake to intercepted PPFD by the Michaelis-Menten form of the rectangular hyperbola equation (Mankin & Fynn, 1992):

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U, = bC,

f’m,xPPFDl = KJ’PFDI K, + [PPFD] Km+ [PPFD]

(6)

where P max = maximum rate of net photosynthesis

(g mm2hi’) u max = maximum rate of nutrient uptake (mg m-* hi’) Knl = Michaelis-Menten constant (here, the PPFD at %Pmax or %U,,,) PPFD= intercepted photosynthetic photon flux density (pm01 m-* ss’) Pang (1992) presents data for uptake of N, P, K and Ca by New Guinea Impatiens which provide general support for this model (Fig. 1).

The demand concept may be expanded to include the influences of other microclimatic parameters to provide an interactive prediction of nutrient uptake. For instance, U, can be calculated using eqn (4), above, with P,, 600

(a)

a 1

500

‘M

400

d

300

4 't D

200 100

0

200

400

600

800

1000

800

1000

PPFD (umol/m2.s) 600

(b)

3 2.

500

‘M

400

9 i 't

300 200 100 0 0

200

400

600

PPFD (umollm2.s) Fig. 1. Empirical data demonstrating

the general applicability of the model relationship (Eqn (6)), between PPFD and individual nutrient uptake rate of (a) nitrogen and (b) potassium by New Guinea Zmpatiens (Pang, 1992). Note that N uptake rate saturated at relatively low light levels while K uptake rate did not approach saturation.

Modeling individual nutrient uptake by plants

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from the model in Gary (1988) for vegetative tomato plants, and assumed values of C,, = 3 mg g-’ and b = 1. Three-dimensional response surfaces show the combined effects of these parameters on nutrient uptake (Fig. 2). These models are based on the assumption that individual nutrients are taken up independently within a plant’s tolerance (or luxury consumption) range. This simplifies the system by discounting interactions, but interactions do exist between nutrients (Sutherland, 1988). More work needs to be done to quantify nutrient supply interactions with regard to uptake.

Fig. 2. Response of nutrient uptake rate to (a) incident PPFD and day temperature (CO, = 350 ppm); and (b) incident PPFD and canopy COz level (T = 20°C). Based on the model described by Eqn (4) with P. from a model of vegetative tomato plants (Gary, 1988) C, = 3 mg g-’ and b = 1. Units: CJ, nutrient uptake rate (mg me2 hr-‘); I, PPFD (W mm’); T, day temperature (“C); and C, CO2 concentration (ppm).

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The nutrient demand concept suggests that plant demand determines both the amounts and types of each individual nutrient required. Wareing et al. (1968) demonstrated that demand for photosynthates has a positive effect on rate of photosynthesis, and that demand may come in the form of higher light levels, higher levels of C02, or even partial defoliation, which affects the photosynthate source/sink relationship. The strength and proximity of plant resource sinks have been found to be major determining factors for photosynthetic rate of photosynthetic tissue (Evans, 1991; Hilliard & West, 1970; Neales & Ingoll, 1968; Kazaryan et al., 1965). It follows that nutrient requirements must be dependent on the specific plant materials being produced at any given time and stage of growth. Sutherland (1988) demonstrated a distinct change in nutrient uptake as cucumber development moved between growth stages (vegetative + flowering + fruiting). In contrast, Willits et al. (1992) found that there was a gradual change from week to week in the relationship between relative assimilation rate and relative growth rate for chrysanthemum. This may indicate that the plant’s nutrient demand characteristics change continually as the plant grows, and may not be assumed to remain constant throughout each stage of growth. To the extent that the models of plant photosynthesis do not account for the changing relationship between environment and nutrient uptake as the plant develops, the demand model constants can be readily modified to account for such changes.

CONCLUSIONS A ‘demand-based’ model for nutrient uptake is proposed based on the influence of a plant’s microclimate on uptake through a physiological response, notably photosynthesis. The rate of photosynthesis is assumed to establish a level of demand for many individual nutrients. A logical extension of this and other potential demand models is their use within predictive fertigation control systems. Combination of a nutrient demand model with a water use model would allow construction of a model which could adjust fertilizer and water applications according to realtime changes in plant nutrient and transpiration demands.

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systems.

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Adams, P. & Massey, D. M. (1984). Nutrient uptake by tomatoes from recirculating solutions. Proc Znt. Sot. Soilless Culture, 71-9. Bauerle, W., Short, T., Mora, E., Hoffman, S. & Nantais, T. (1988). Computerized individual-nutrient fertilizer injector: the system. HortScience, 23(5), 910. Bloom, A. J., Chapin, F. S., & Mooney, H. A. (1985). Resource limitation in plants - An economic analogy. A. Rev. Ecol. Systemat., 16, 363-92. Breteler, H. & Nissen, P. (1982). Effect of exogenous and endogenous nitrate concentration on nitrate utilization by dwarf bean. Plant Phys., 70, 754-9. Cacco, G., Ferrari, G. & Saccomani, M. (1978). Pattern of sulfate uptake during root elongation in maize: Its correlation with productivity. Physiologia Plantarum, 48, 375-8. Cacco, G., Ferrari, G. & Saccomani, M. (1980). Variability and inheritance of sulfate uptake efficiency and ATP-sulfurylase activity in maize. Crop Sci., 18, 503-5. Charles-Edwards, D. A. & Acock, B. (1977). Growth response of a Chrysanthemum crop to the environment. II. A mathematical analysis relating photosynthesis and growth. Ann. Bot., 41, 49-58. Chu, C. B. & Toop, E. W. (1975). Effects of substrate potassium, substrate temperature and light intensity on growth and uptake of major cations by greenhouse tomato plants. Can. J. Plant. Sci., 55, 121-6. Claassen, N. & Barber, S. A. (1977). Potassium influx characteristics of corn roots and interaction with N, P, Ca, and Mg influx. Agron. .I., 69, 8604. Clarkson, D. T. (1985). Factors affecting mineral nutrient acquisition by plants. A. Rev. Plant Phys., 36, 77-l 15. Clement, C. R., Hooper, M. J., Jones, L. H. P. & Leaf, E. L. (1978). The uptake of nitrate by Lolium perenne from flowing nutrient solution: II. Effect of light, defoliation, and relationship to CO, flux. J. Exp. Bot., 29(112), 1173-83. Curry, R. B. (1971). Dynamic simulation of plant growth - Part I. Development of a model. Transactions of the ASAE, 14(5), 946-59. Curry, R. B. & Chen, L. H. (1971). Dynamic simulation of plant growth - Part II. Incorporation of actual daily weather and partitioning of net photosynthate. Trans. ASAE, 14(6), 11704. Drees, B. M., McWilliams, D., Sweeten, J. M. & Wilkerson, D. C. (1990). Water management guidelines for the Texas greenhouse industry. Texas Agricultural Extension Service, HORT 4-5. Elwell, D. L. & Bauerle, W. L. (1990). TOMMOD: a new model of greenhouse tomato development. Presented at Columbus, Ohio, June. ASAE Paper No. 90-7029, American Society of Agricultural Engineers, St. Joseph, Michigan. Epstein, EL. (1972). Mineral nutrition of plants: principles and perspectives. Wiley, New York. Epstein, E. (1973). Mechanisms of ion transport through plant cell membranes. . Znt. Rev. Cytol., 34, 123-68. Epstein, E. & Hagen, C. E. (1952). A kinetic study of the absorption of alkali cations by barley roots. Plant Phys., 27, 457-74. Evans, A. S. (1991). Whole-plant responses of Brassica campestris (Cruciferae) to altered source-sink relation. Am. .Z. Bot., 78, 394-400. Frick, H. & Bauman, L. F. (1979). Heterosis in maize as measured by K uptake properties on seedling roots: pedigree analysis of inbred with high or low augmentation potential. Crop Sci., 19, 707-10.

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