Impact of axial root growth angles on nitrogen acquisition in maize depends on environmental conditions

Abstract

Backgrounds and Aims Crops with reduced requirement for nitrogen (N) fertilizer would have substantial benefits in developed nations, while improving food security in developing nations. This study employs the functional structural plant model SimRoot to test the hypothesis that variation in the growth angles of axial roots of maize (Zea mays L.) is an important determinant of N capture.

Methods Six phenotypes contrasting in axial root growth angles were modelled for 42 d at seven soil nitrate levels from 10 to 250 kg ha⁻¹ in a sand and a silt loam, and five precipitation regimes ranging from 0·5× to 1·5× of an ambient rainfall pattern. Model results were compared with soil N measurements of field sites with silt loam and loamy sand textures.

Key Results For optimal nitrate uptake, root foraging must coincide with nitrate availability in the soil profile, which depends on soil type and precipitation regime. The benefit of specific root architectures for efficient N uptake increases with decreasing soil N content, while the effect of soil type increases with increasing soil N level. Extreme root architectures are beneficial under extreme environmental conditions. Extremely shallow root systems perform well under reduced precipitation, but perform poorly with ambient and greater precipitation. Dimorphic phenotypes with normal or shallow seminal and very steep nodal roots performed well in all scenarios, and consistently outperformed the steep phenotypes. Nitrate uptake increased under reduced leaching conditions in the silt loam and with low precipitation.

Conclusions Results support the hypothesis that root growth angles are primary determinants of N acquisition in maize. With decreasing soil N status, optimal angles resulted in 15–50 % greater N acquisition over 42 d. Optimal root phenotypes for N capture varied with soil and precipitation regimes, suggesting that genetic selection for root phenotypes could be tailored to specific environments.

INTRODUCTION

Intensive nitrogen (N) fertilization is a major economic, energy and environmental cost of crop production in developed nations (Boyer et al., 2002; Ladha et al., 2005; Sutton et al., 2011). In developing nations, low soil N availability and limited access to N fertilizer limit crop yields and food security (Azeez et al., 2006; Heffer and Prud’homme, 2009). The development of crop genotypes with enhanced ability to acquire soil N is therefore an important goal for global agriculture.

In the majority of high-input agroecosystems, the dominant form of bioavailable N is nitrate (Miller and Cramer, 2005). Because nitrate is highly soluble in water, it is subject to leaching, which can result in loss of N from the root zone especially of fertilized crops. Water and solute transport through soils depend highly on the soil type and precipitation regime, which determine the velocity of dissolved nitrate moving downwards and consequently its position in the soil profile at a specific time. Therefore, soil type and precipitation have to be taken into account when investigating the benefit of specific root systems for taking up nitrate efficiently. In commercial maize production, N is typically applied early in the season as fertilizers containing nitrate or N forms (ammonium, urea) that rapidly convert to nitrate (Frink et al., 1999). When the rate of descent of nitrate in the soil profile via leaching exceeds the rate of descent of root foraging, nitrate can leach below the root zone, which is a significant cause of the low recovery of N fertilizer (Cassman et al., 2002; Sheldrick et al., 2002; Janzen et al., 2003).

Root phenotypes that rapidly descend into the soil may be able to capture some of this nitrate before it is lost (Lynch, 2013, and references therein). This potential benefit will depend on the co-localization of soil nitrate with root foraging activity in space and time. A number of factors affect the rate of nitrate leaching, including the amount and form of fertilizer applied, rate of conversion of soil N pools to nitrate, acquisition of nitrate by soil organisms, soil texture and hydraulic properties, and water availability (Sogbedji et al., 2000; Di and Cameron, 2002). In addition to fertilizer, mineralization of soil organic matter in the epipedon is a source of N, and is often the major source of N in low-input systems (Poudel et al., 2001). In such environments the epipedon may release nitrate over the growing season. A number of plant attributes will also affect nitrate acquisition, including several physiological, anatomical and architectural phenes (phene is to phenotype as gene is to genotype, sensu Lynch, 2011), which are also non-uniform in space and time, and are affected by plant growth, which is in turn affected by soil properties. The hypothesis that deeper root phenotypes will enhance N acquisition in the majority of agricultural systems, despite the fact that N availability may be greater in surface soils in some situations, is consistent with available evidence (Lynch, 2013).

A primary determinant of the depth of soil exploration by root systems is the growth angle of axial roots. In several crop species, genetic variation in axial root growth angles is associated with rooting depth. In common bean and maize, shallow growth angles enhance topsoil foraging and acquisition of topsoil resources such as phosphorus (Lynch and Brown, 2001; Zhu et al., 2005; Lynch, 2011; Richardson et al., 2011). In common bean, wheat, sorghum and rice, steep growth angles enhance subsoil foraging and water acquisition under terminal drought (Ho et al., 2005; Manschadi et al., 2008; Uga et al., 2011; Mace et al., 2012). Substantial genetic variation for axial root growth angles is present in maize germplasm (Hochholdinger et al., 2004; Zhu et al., 2005; Lynch, 2007; Burton et al., 2014). In this study we address the hypothesis that the growth angle of axial roots, by determining the speed of root descent into the subsoil, is important in crop N capture. We approach this problem using a functional–structural plant model, SimRoot (Lynch et al., 1997; Postma and Lynch, 2011a, b), in order to assess the potential utility of phenotypic variation in axial root growth angles on N capture in a range of phenotypes and environments. To account for the position and movement of nitrate in the soil, SimRoot was coupled to a water and solute transport model similar to the work of Clausnitzer and Hopmans (1994), Somma et al. (1998), Dunbabin et al. (2002), Javaux et al. (2008), and Šimůnek and Hopmans (2009). Simulations encompassed contrasting root architectures and soil types across varying precipitation scenarios and soil nitrate concentrations. Validation of the utility of root growth angles for N capture would be useful in guiding crop breeding programmes.

MATERIAL AND METHODS

Simulations were conducted with SimRoot (Lynch et al., 1997; Postma and Lynch, 2011a, b), a functional structural plant model, linked to SWMS_3D (Šimůnek et al., 1995), a three-dimensional water and solute transport model. Water transport through the soil was estimated numerically using Richards’ equation (Richards, 1931) and solute transport by the dispersion–convection equation. Partial differential equations are solved for respective boundary conditions on a finite element grid. We simulated the growth of six different maize root architectures under varying environmental conditions. The maize root architectures varied in branching angles following genotypic variation for branching angles observed in field-grown maize (Trachsel et al., 2011, 2013). The varying environmental conditions included two soil types for which we varied the nitrate availability, and precipitation. Maize growth was simulated to 42 d after germination.

Model description

SimRoot and the link between SimRoot and SWMS_3D were described in detail by Postma and Lynch (2011b). Here we give a short description of the model as for this study the same code was used as in Postma and Lynch (2011b). SimRoot simulates a single maize plant which is an individual of a monoculture maize crop. Mirrored boundary conditions were used as vertical boundaries for root growth. SimRoot simulates maize root architecture in three dimensions in vector space and represents the root system by connected root nodes, spaced 0·5–1 cm apart. Connected root nodes form roots of specific root classes which form the whole root system. Each root has a tip for which growth rate and direction are computed. Root angles are determined by a lateral and a radial branching angle and a root class-specific gravitropism factor (Lynch et al., 1997; Pagès et al., 1989; Ge et al., 2000). A set of branching rules determines the generation of new primordia behind the root tip. These primordia will grow into new roots of different preassigned root classes. Growth rates of the root tips are a function of carbon availability. SimRoot explicitly estimates carbon costs for growth, nutrient uptake and respiration and allocates carbon based on predefined potential growth rates and a binary priority scheme (Postma and Lynch, 2011a). Carbon sources are the seed reserve and photosynthesis. Carbon is stored when source strength exceeds sink strength. Shoot growth in SimRoot is a function of nutrient status of the crop. Nutrient status is determined by total nutrient uptake relative to the optimal and minimal nutrient content in the plant. Uptake is simulated using a Michaelis Menten kinetics formulation (Kochian and Lucas, 1982; Barber, 1995) where the concentration at the root surface is an, by inverse distance weighted, average of the concentration of nearby finite element nodes in the SWMS_3D code (Postma and Lynch, 2011b). Water uptake by the roots is estimated as the length of respective root segments multiplied by potential transpiration (Somma et al., 1998). Water and solute transport in the soil are simulated by SWMS_3D (Šimůnek et al., 1995), which is a three-dimensional (3-D) finite element model solving the Richards equation for saturated–unsaturated water transport and the convection–dispersion equation for nitrate transport in the soil. Water and nitrate uptake by roots is lumped into respective sink terms. We used in SWMS_3D a cubic finite element grid with a resolution of 1 cm, which currently provides the best balance between speed and numerical accuracy (Postma et al., 2014). We also included a one-pool nitrate mineralization model as described by Yang and Janssen (2000), and ran this model for every finite element node independently.

Input parameters

Values for carbon allocation to leaves, stem and roots were taken from figure 4 of Drouet and Pagès (2007). Plant density was 6·25 plants m⁻². Values for root growth rates were taken from phenotyping measurements performed at the Lynch lab in the greenhouse at Pennsylvania State University (Jaramillo-Velastegui, 2011) and in the field (Trachsel et al., 2011). Different root classes and development stages were taken into account. Growth rates decreased over time. The tap root and seminal roots emerged first from the seed. Nodal and brace roots emerged later from the mesocotyl. All roots could have laterals of first and second order, and fine laterals emerging from second-order laterals. Nodal roots (including brace roots) which emerged late during the simulation time would not develop all three stages of lateral roots. Different root classes had different diameters according to observations made by Silberbush and Lynch (2009) in nutrient solution studies; diameters of nodal roots were thicker towards the maize stem. In the beginning of all simulations the same mass of nitrate was available in the silt loam and in the sandy soil for every specific nitrate level. Nitrate concentrations in the soil water were set at higher initial nitrate concentrations for the sandy soil, where water content was lower for the same soil water potential. Water potential, assuming a groundwater table at 3 m depth, was taken as the lower boundary condition for the simulation of water movement. Atmospheric conditions such as precipitation and potential evaporation read from an input file were employed as the upper boundary condition.

Simulated variation in root angles

In SimRoot two parameters determine the root angles for each root class: the lateral branching angle and a gravitropism parameter. Both parameters influence the direction of root growth in the vertical plane. The branching angle describes the initial direction with regard to the parent root. After emergence of the root, gravitropism starts to slowly direct the growth direction downwards. We varied the branching angles for the different root classes: seminal, nodal and brace roots, and angles of the respective lateral roots of first and second order were kept constant. The primary root was set to grow vertically. Values for rates of gravitropism were drawn from uniform distributions with maximum and minimum values, in order to have some variation between roots of the same root class. Branching angles of seminal, nodal and brace roots were varied such that we could simulate six different root architectures (Fig. 1), which represented the genetic variation in root steepness that was observed in the field by Trachsel et al. (2011). We simulated (1) very steep (80° with respective to the horizontal), (2) steep (65°), (3) normal (50°) and (4) shallow (25°) root systems and two dimorphic root systems which had (5) normal seminal roots and very steep nodal roots (50 and 80°) and (6) shallow seminal roots and very steep nodal roots (25 and 80°).

Fig. 1.

Simulated maize root system with different branching angles after 42 d. Top row: very steep, steep, normal; bottom row: normal–very steep, shallow–very steep, shallow.

Simulated variation in soil type, precipitation and nitrate availability

For optimal nitrate uptake, root placement needs to be coincident with the nitrate profile. Initially nitrate availability is greater towards the soil surface, although this changes with time as nitrate may leach to deeper layers. We varied precipitation and soil type to determine what root system architecture would be optimal for different environments. A fine-textured silt loam and a coarse-textured sand (Table 1) were simulated. The sand was divided into two (ploughing zone and below) and the silt loam was divided into three (ploughing zone, B and C horizon) soil layers. Parameters in between the depths given in Table 1 were interpolated by SimRoot, thus avoiding an unrealistic jump of water contents along the border between two layers. The soil bulk density d_b (first column in Table 1) is needed for quantification of nitrate transport. The next four parameters are the so-called van Genuchten parameters (van Genuchten, 1980) which describe the relationship between soil water content and water retention. This soil water retention curve, together with the saturated hydraulic conductivity K_s, is essential to model water transport through a soil. van Genuchten parameters for the silt loam were estimated from the particle size distribution obtained for a soil characterization in immediate proximity (S89-PA-014-088, Soil Characterization Laboratory, Pennsylvania State University) using Rosetta (Schaap et al., 2001). Parameters for the sandy soil were taken from Wösten et al. (1999). The molecular diffusion coefficient for nitrate was set to 0·216 cm² d⁻¹, the saturated diffusion coefficient was set to 1·6416 cm² d⁻¹, and the longitudinal and transversal dispersivities to 1·0 and 0·5 cm, respectively. Precipitation data for 42 d, starting on 1 June 2009, were taken from the USDA National Resource Conservation Service weather station at Rock Springs, PA (Supplementary Data Fig. S1). Total precipitation was 12·4 cm. Five precipitation scenarios were simulated, with factors of 0·50, 0·75, 1·00, 1·25 and 1·50 of the daily values. Evaporation in the model was adjusted accordingly.

Table 1.

Bulk density d_b and the van Genuchten parameters θ_r (residual water content), θ_s (water content at saturation), α, n and the saturated hydraulic conductivity K_s for the silt loam, and the sand; α and n describe the shape of the soil–water retention curve

Soil type	Depth (cm)	db (g cm−3)	θr (cm3 cm−3)	θs (cm3 cm−3)	α (hPa−1)	n	Ks (cm d−1)
Silt loam	0–21	1·27	0·0529	0·4448	0·0049	1·5728	34·6921
	25–38	1·42	0·0782	0·4498	0·0129	1·2867	11·4057
	42–140	1·50	0·0634	0·4169	0·0069	1·6192	7·5737
Sand	0–28	1·50	0·0250	0·4030	0·0383	1·3774	60·00
	32–140	1·60	0·0250	0·3660	0·0430	1·5206	70·00

We varied the initial nitrate availability in the soil using seven levels: 10, 25, 50, 100, 150, 200 and 250 kg ha⁻¹. The initial relative distribution of nitrate in the soil was kept the same in all treatments and was based on soil measurements taken at Rock Springs Experimental Farm in 2009 (see model verification for detailed description). Mineralization rates were scaled proportional to the nitrate levels. If the mineralization rate was not scaled, the low nitrate simulations would be more strongly influenced by mineralization, which was assumed to be greater in the topsoil.

System description

We simulated 42 d of growth of a single maize plant which is a representative individual of a monoculture maize crop. Row spacing was set at 60 cm, spacing between plants in the row at 28 cm and depth of the soil profile at 140 cm. The simulation started with seed germination in the middle of a 60-cm-wide × 28-cm-long × 140-cm-deep column. Realistic root density was maintained by mirroring the roots at the boundary back into the column (Postma and Lynch, 2011b). Root architectures are shown in Fig. 1 without boundary effects.

Runs

Simulation runs were performed in a factorial design of six root architectures, seven nitrate levels and five precipitation levels. These 210 combinations were performed for two different soils in four repetitions each, leading to a total of 1680 simulations. All computations were performed on the lionxi cluster of the Pennsylvania State University, http://rcc.its.psu.edu/hpc/systems/lionxi/#specs. Repetitions were necessary to reduce noise introduced by the random number generator. Variation between repetitions was caused by stochasticity in growth rates, gravitropism and branching frequency among roots of the same class.

Visualization and statistical analysis of results

The results were visualized for the mean of four replicated simulations if not stated otherwise. Nitrate uptake, the main criterion on how beneficial the different root architectures performed, is shown as heat maps for sand and silt loam separately, with nitrate levels on the abscissa and precipitation factors on the ordinate. To quantify the effect of root architecture, soil type and precipitation we fitted an analysis of variance model for the seven N levels separately, including all possible second-order interaction and the residual (third-) order interaction terms. We estimated D₉₀ values of nitrate and roots for every day of the simulation. The D₉₀ value is the soil depth above which 90 % of nitrate or root mass are situated. We also estimated root length below D₉₀ nitrate.

Nitrate concentrations in the soil profile over time

To give an estimate of further development of nitrate leaching in the soil column and root system distribution after 42 d, two field sites with different soil types were selected to monitor nitrate concentrations in the soil profile during the growing season: a loamy sand at the experimental field ‘De Bovenbuurt’, Wageningen University, the Netherlands, and a silt loam at the Rock Springs Experimental Farm, Penn State University, Pennsylvania, USA. No field data were available for the sandy soil. At the experimental field ‘De Bovenbuurt’ two fields were selected located close to each other, with one field under long-term arable management (since 1980), whereas the other field was converted from grassland to arable land in April 2000, two years before the start of the nitrate concentration measurements in 2002. Mineral fertilizer N was applied to each field at 125 kg ha⁻¹ on 16 May. Additionally each field received an application of cow manure at 785 kg ha⁻¹. Both fields were sown to maize (‘Crescendo’) on 13 May. Each field plot was sampled biweekly starting at 24 April. The soil profile was sampled to a depth of 60 cm. The layers 0–10 and 10–40 cm were sampled using a 3·0-cm-diameter soil probe, and the layer 40–60 cm was sampled using a 2·5-cm-diameter soil probe for the first three measurements, and using a 2·0-cm-diameter soil probe further on. Soil samples were dried at 40 °C, extracted with 0·01 m NaCl and nitrate concentrations were measured using continuous flow analysis (Skalar, Breda, the Netherlands). The data of three replicate measurements in each field plot were averaged. The D₅₀ of each field plot at each point in time was calculated indicating the depth above which 50 % of the total amount of nitrate in the soil profile (0–60 cm) is located. Averages of the D₅₀ of two fields plots were plotted against time (days after germination, d.a.g.). At the Rock Springs Experimental Farm, two fields were selected located close to each other, with one field receiving conventional N fertilization (103 kg ha⁻¹) applied in the maize rows on 19 May, whereas the other field received no N fertilization. On 2 June 2009, two genotypes (OHW48 and NYH277) were sown in adjacent plots within each field, resulting in four experimental plots (Trachsel et al., 2013). We collected weekly soil samples at each plot, starting on 4 June and biweekly towards the end of the growing season. The soil profile was sampled with depth increments of 10 cm, to a depth of 60 cm using a 7/8-inch-diameter soil probe. Samples of three replicate soil cores were merged for each depth. Nitrate was extracted from the soil with 1 m KCl solution and concentrations were analysed colorimetrically (Multiscan Ex Primary EIA V. 2.3). For the fertilized and unfertilized fields averages of the D₅₀ of the two genotypes plots were plotted against time (d.a.g.).

RESULTS

The influence of root architecture on nitrate uptake was strongly influenced by the initial nitrate status of the soil, soil type and precipitation. The finer textured silt loam had greater water-holding capacity and therefore greater nitrate retention than the sand. This led to greater nitrate availability for the plant in the silt loam (Fig. 2, Table 2). Nitrate acquisition by a specific root architecture was closely related to the rate of nitrate leaching as influenced by soil type and precipitation. The shallow rooting architectures, including the normal, shallow–steep and shallow phenotypes, increased nitrate uptake in soils with low initial nitrate content and low precipitation rate (Figs. 2 and 4). The very steep root architecture had poor nitrate acquisition in all environments except for the extremely high leaching scenario. Root architectures with intermediate root growth angles had good nitrate uptake under a range of environments (Fig. 2).

Fig. 2.

Simulated nitrate uptake after 42 d shown in single plots for respective root systems growing in a silt loam (A) and, on next page, in a sandy soil (B). Results are given in mmol per plant with colours from light grey to dark green for increasing nitrate uptake. The soil nitrate level is shown on the x-axis and the precipitation factor on the y-axis.

Table 2.

Analysis of variance for N uptake differentiated for the seven simulated N levels (N010 to N250); the influence of the single factors root architecture (root arc.), soil type and precipitation (prec.) as well as the second-order interaction terms are shown as percentages of the variance explained

	d.f.	N010 var (%)	N025 var (%)	N050 var (%)	N100 var (%)	N150 var (%)	N200 var (%)	N250 var (%)
Root architecture	5	28·40	40·74	38·79	18·82	10·94	7·24	5·33
Soil type	1	55·77	39·10	44·98	67·56	77·35	82·45	86·14
Precipitation	4	1·14	0·86	0·81	0·15	0·36	0·53	0·63
Root arc. × soil type	5	1·98	0·74	0·85	2·01	1·63	1·07	0·35
Root arc. × prec.	20	10·42	15·97	13·55	10·40	9·01	7·67	6·41
Soil type × prec.	4	1·56	1·41	0·64	0·62	0·58	0·84	0·94
Residuals	20	0·73	1·17	0·37	0·43	0·14	0·19	0·20
Sum		100·00	100·00	100·00	100·00	100·00	100·00	100·00

Fig. 4.

Root profiles after 42 d growing under low (factor 0·5, left two panels) and high precipitation (factor 1·5, right two panels), and soil water nitrate concentration (blue line) and nitrate uptake by the roots (red line). Results are shown for the shallow root system in sand and an initial N concentration of 50 kg ha⁻¹. It is clearly visible how nitrate uptake coincides with nitrate concentration in the soil profile, and how precipitation influences the position and width of the nitrate peak.

All values presented in this and the following paragraphs are the means of four replications after 42 d.a.g. As expected, initial soil N content had the largest effect on nitrate uptake. Reduced soil nitrate availability (<100 kg ha⁻¹ at the beginning of the simulation) decreased root and shoot growth (Supplementary Data Fig. S2). The simulated plants experienced stress when nitrate uptake was less than needed to obtain the optimal N content of roots, stem and leaves specified in the input module (Postma and Lynch, 2011a; Dathe et al., 2013). The model reduced leaf area expansion and photosynthesis rates under N limitation, and as a result carbon assimilation decreased. Seed N reserves were depleted after 12 d, and plants started experiencing N stress about 20 d.a.g. Plants grown on soils with 10 kg N ha⁻¹ remained very small, with the lowest shoot dry weights of 2·81 and 3·93 g as means over five precipitation levels for the very steep root architecture grown in sand and silt loam, respectively. The required initial soil nitrate content for reaching maximum shoot growth of 35 g d. wt after 42 d.a.g. was 100 kg ha⁻¹ for the silt loam for all precipitation regimes and root phenotypes except for the normal–very steep root architecture when precipitation was reduced by half (data not shown). In the sand, maximum shoot growth for all precipitations was only reached when the initial nitrate content was 250 kg ha⁻¹. The average root weight for an initial 250 kg N ha⁻¹ and precipitation of 1·0 was 8·4 g for both soil types while the shallow root architectures in sand had slightly greater root dry weights when simulated with 50–200 kg N ha⁻¹. Under low initial nitrate availability, shoots and roots of the very steep and steep root architectures remained smaller than the other architectures (Fig. S2). In high N soils uptake reached an optimum where plants did not grow further but acquired more N. The root to shoot ratios reflected the level of stress the plants experienced, with proportionately more carbon allocated to roots when plants experienced nutrient stress (Postma and Lynch, 2011a; Dathe et al., 2013). Root/shoot ratios of 1·0 were reached for the most stressed plants (10 kg N ha⁻¹ level, shallow and the shallow–very steep root architectures, sand, precipitation levels of 1·50 and 1·25). The lowest root/shoot ratios of 0·24 were attained by non-stressed plants with maximum growth.

Soil type greatly affected nitrate uptake by plants with different root architectures. The effect of soil type increased with increasing soil N level with the exception of plants simulated for 10 kg N ha⁻¹ (Table 2). After 42 d more N was taken up by plants in silt loam (Fig. 2A) than in sand (Fig. 2B). The silt loam retained more water and more dissolved nitrate was available for uptake by the plant, because silt loam had a greater porosity than sand (θ_s in Table 1) and the pores were smaller. Nitrate in the sand leached deeper and its pulse was wider (Fig. 3) because the sand had a smaller water retention (high value for alpha, which is correlated to an early air-entry point, see Table 1) and greater hydraulic conductivity (high value for K_s, Table 1). As a result, total nitrate uptake by roots was 45 % greater in the silt loam than in the sand (1081 mg per plant in the sand and 1560 mg in the silt loam) when averaged for all six root phenotypes, at an initial 250 kg N ha⁻¹ and a precipitation level of 1·0. As nitrate availability was greater in the silt loam, the effects of root architecture in unstressed plants were less pronounced in the silt loam than in the sand (Supplementary Data Fig. S3).

Fig. 3.

Initial nitrate concentration and after 21 and 42 d for the sandy soil (solid line) and the silt loam (dashed line). Simulations were run with 200 kg N ha⁻¹ and a precipitation factor of 1·0 for both soils.

Specific root architectures could enhance N uptake especially for soils with low N content (Table 2). For the normal root architecture nitrate uptake was more influenced by N than by precipitation (Fig. 2B), whereas nitrate uptake for the shallow and the shallow–very steep root architectures was influenced by both N and precipitation (uptake varies following a diagonal pattern in Fig. 2B). This effect was not as pronounced in the silt loam where nitrate uptake for the normal architecture also increased with increasing precipitation. Under high precipitation the very steep and steep root architectures enabled the plant to take up more nitrate, especially in the silt loam (two upper panels in Fig. 2A). In all cases, the very steep phenotype acquired less nitrate than the steep phenotype. The shallow root architecture took up more nitrate when precipitation was low. The dimorphic root architecture shallow–very steep had a similar uptake pattern for both soils, with N uptake increasing diagonally towards higher initial N content and low precipitation, while the normal–very steep architecture had a diagonal pattern with N uptake increasing towards higher initial N content and high precipitation in the silt loam (Fig. 2A) and a more parallel pattern with higher initial N content levels in the sand. Under normal precipitation the normal phenotype performed better than the steep phenotype.

Root architecture combined with precipitation had a large influence on nitrate uptake, especially for plants simulated with low initial N content (Table 2). The influence of precipitation on the co-localization of nitrate and roots in an extreme environment is shown in Fig. 4 for the shallow root system. Under low precipitation, nitrate concentration and uptake have their peak values at a depth of 24 cm (left two panels), whereas under high precipitation nitrate concentrations peaked at a lower level than the roots. After 42 d, the respective root systems had dry weights of 7·95 and 8·58 g and took up 553 and 379 mg N per plant under the low and high precipitation regimes, respectively. In general, nitrate uptake corresponds more with nitrate concentration than with root length.

The relationship between D₉₀ of root length and nitrate in the soil column provides insight into the co-localization of root growth and nitrate movement. This is shown for the shallow root architecture under low and high precipitation scenarios (Fig. 5A, B), and for shallow–very steep and very steep root architectures (Fig. 5C, D). The graphs for the shallow root system show that D₉₀ of roots moved below the D₉₀ of nitrate at about day 23 when precipitation was low (Fig. 5A), while the D₉₀ of roots always stayed above the D₉₀ of nitrate when precipitation was high (Fig. 5B). This result explains why the shallow root system is beneficial under the low precipitation regime, as the roots grow where most of the nitrate is located. In addition, more roots remained below the D₉₀ of nitrate under low precipitation, where they can capture the nutrient as it moves down the soil profile.

Fig. 5.

D₉₀ is the depth where 90 % of a specific property is reached or accumulated on top of that depth. Shown are the D₉₀ values of soil nitrate and roots and the cumulated length of roots below D₉₀ nitrate for the sandy soil and (A) the shallow root system growing in low fertile soil with 25 kg N ha⁻¹ and a precipitation factor of 0·5 and (B) a precipitation factor of 1·5. Bottom row: simulations run with 200 kg N ha⁻¹ and a precipitation factor of 1·0, and (C) for the shallow–very steep root architecture and (D) for the very steep root architecture. The scale for root length below D₉₀ nitrate (secondary vertical axis) was adjusted to the respective maximum depth of D₉₀ roots (primary vertical axis).

The root architectures shown in Fig. 5C, D were simulated for sand, an initial N content of 200 kg ha⁻¹ and the precipitation scenario of 1·0. Under these conditions, the shallow–very steep root architecture took up 22 % more nitrate than the very steep root architecture (1132 and 929 mg, respectively). The D₉₀ values of roots and nitrate are almost parallel for the shallow–very steep root architecture, with the D₉₀ of roots moving below the D₉₀ of nitrate at day 32, indicating that this dimorphic root system is optimal for nitrate uptake. D₉₀ of the very steep growing roots moved below the D₉₀ of nitrate already at day 13 and reached a depth of 100 cm towards the end of the simulation, indicating suboptimal conditions for nitrate uptake. Figure 5D shows a situation where the roots grew deeper than the nitrate available in the soil profile, whereas Fig. 5B shows a situation where the roots stayed above the nitrate. Both situations led to decreased nitrate uptake.

Nitrate concentrations in the soil profile were monitored over time in the field. During the first 42 d.a.g., the observed D₅₀ for nitrate remained at a constant level in both the sand and the silt loam soils (Fig. 6A). From 50 d.a.g. onwards the D₅₀ for nitrate started to move downwards. In the sand it declined from 10 cm shortly after fertilization to 31 cm depth at 110 d.a.g., and 45 cm at 200 d.a.g., indicating that nitrate leached down 21 cm during the growing season and 15 cm more during the autumn season. In the fertilized silt loam the D₅₀ for nitrate declined from 8 to 25 cm during the growing season. In the unfertilized silt loam the D₅₀ for nitrate was on average 15 cm deeper than in the fertilized soil, but followed the same leaching pattern. The total nitrate content in the soil profile started to decline after 50 d.a.g. (Fig. 6B), and decreased from 363 to 25 kg ha⁻¹ in the loamy sand soil, from 412 to 58 kg ha⁻¹ in the fertilised silt loam soil and from 60 to 20 kg ha⁻¹ in the unfertilized silt loam soil. In contrast to the field studies, the D₅₀ for nitrate in the simulation studies moved downward from the start of the simulation period. In the low precipitation scenarios it declined from 10 to 22 cm, whereas in the high precipitation scenarios it decreased from 10 to 30 cm in the silt loam and from 10 to 40 cm in the sand.

Fig. 6.

(A) D₅₀ for soil N-${\text{NO}}_{\text{3}}^{\text{−}}$ and (B) total N-${\text{NO}}_{\text{3}}^{\text{−}}$ (kg ha⁻¹) in the soil profile over time (days after germination). The D₅₀ for soil N-${\text{NO}}_{\text{3}}^{\text{−}}$ indicates the depth at which 50 % of the total amount of nitrate in the soil profile (0–60 cm) is reached. Soil types are fertilized silt loam (mineral fertilizer N 146 kg ha⁻¹), unfertilized silt loam and fertilized loamy sand (mineral fertilizer N 125 kg ha⁻¹, organic manure 785 kg ha⁻¹). All fields were sown to maize. Red lines indicate results from simulations with fertilized silt loam and sandy soil under maize with precipitation factor 1, which means that simulated precipitation was equal to measured precipitation.

DISCUSSION

This study investigates the influence of phenotypic variation in the growth angles of axial roots of maize plants on nitrate capture in contrasting leaching environments created by varying precipitation, soil texture and N availability. Our results support the hypothesis that axial root growth angles are important determinants for nitrate acquisition in maize. Root growth angle influenced plant N capture by affecting the co-localization of root foraging activity with nitrate availability in time and space (Fig. 5), and also by affecting the dispersion of roots and thereby the extent of competition among roots of the same plant. The effect of specific root phenotypes on N acquisition depended on the soil and precipitation environment. Shallow rooting, as occurred in the normal, dimorphic and shallow rooting phenotypes, increased nitrate uptake in environments with low nitrate mobility due to greater soil water retention or low precipitation (Fig. 4). Shallow rooting also increased nitrate uptake in soils with low nitrate availability, where establishment of the plant early in the season, when nitrate availability is still greater in the topsoil, is critical for further growth of the plant. Dimorphic root phenotypes, with shallow seminal roots during seedling growth, and deep nodal roots during later growth stages, performed well in most scenarios (Fig. 2). These results should be useful in guiding the selection of maize genotypes with superior N acquisition.

Extreme root architectures may be beneficial in specific environments

Root architectures with extremely steep or shallow angles performed well in extreme environments that created either very shallow or very deep N localization profiles. When nitrate was leaching rapidly with greater precipitation, steep root growth angles were beneficial for nitrate uptake. Steep growth angles result in deeper root foraging, which can acquire nitrate from deeper domains of the soil profile. In phenotypes with very steep growth angles, roots grow too close together, creating counterproductive competition for nitrate among roots of the same plant, decreasing overall uptake, as observed previously for P acquisition in common bean (Rubio et al., 2001). Shallow roots are beneficial when nitrate stays in the topsoil, caused by low precipitation and high evaporation rates. In contrast, root architectures with intermediate growth angles performed well over a wide range of environments. Plants will perform best when they allocate root foraging to soil domains where the nitrate in the soil water is situated, or where it will be localized at a later stage of plant growth (Dunbabin et al., 2003; Lynch, 2013). Root architectures with intermediate root growth angles such as the ‘normal’, ‘normal–very steep’ and ‘shallow–very steep’ root systems in these simulations fulfil these requirements over a wide range of precipitation regimes and associated soil nitrate distributions. These root phenotypes have more uniform spatial distributions of soil exploration than extremely steep or extremely shallow architectures.

Influence of soil type on nitrate retention and availability

We observed significant effects of soil texture on nitrate leaching and nitrate capture by roots. The significant increase in N uptake for plants simulated in the silt loam is partly due to the following effects: initial nitrate in the model was specified in the input file as concentrations in soil water. Simulations were started at day zero with a gradient of soil water potential, assuming a groundwater table at 300 cm depth. At comparable soil water potentials, sand contains less water than silt loam. To obtain the same amount of nitrate in the sand and in the silt loam (e.g. 250 kg N ha⁻¹) nitrate concentrations in the water phase of the sand were set higher. Water transport was simulated according to a diffusion equation, i.e. water and dissolved nitrate moves down the soil profile homogeneously (Fig. 3). This situation is different from a real world scenario, where water and nitrate could, for example, remain in small pockets of stagnant water and thus would not leach out from the soil column as fast as in the simulations. Because of better water-holding capacity, nitrate availability will always be enhanced in a finer textured soil, but the effect would not be as pronounced as in the present simulations.

Nitrate uptake after 42 d of simulation

Simulations were run for 42 d and maize plants and their root systems were still young compared with about 120 d at maturity. We assumed that only one fertilization at the beginning of the growing season took place. Under humid conditions nitrate will continue to leach further down the soil profile during the growing season (Fig. 6A). The roots will grow and increase their surface area and potential nitrate uptake, but we could not parameterize the model for older root systems because we did not have empirical data for older plants and only very few are available in the literature. We assume that the effects of root growth angle on N capture would extend to later stages of plant development, but this remains to be validated. Other factors such as increasing soil hardness, acidity, reduced oxygen and suboptimal temperature may limit root extension into deep soil domains regardless of root growth angle (Lynch and Wojciechowski, 2015). Nitrogen supply regimes resulting from varying N sources and application regimes, including the important case of the dominance of N mineralization from soil organic matter and crop residues as occurs in natural ecosystems and many low-input agroecosystems, were not modelled in this study. We propose that the main concepts emerging from our results, of the importance of spatiotemporal synchrony of N availability and root foraging, and the fitness advantage of intermediate phenotypes given the stochasticity of precipitation regimes, would apply to other N regimes as well, but this requires additional research.

Considering water limitation

The version of SimRoot employed here mechanistically simulates the effects of reduced N status on photosynthesis and leaf area expansion, but does not simulate heterogeneous water uptake patterns from partially dry soil. Root growth angle is also likely to affect water acquisition. Therefore, the effects of root growth angle on plant growth under N limitation may be different under conditions of water limitation, which is common in maize production. For simplicity, we may consider water limitation to consist of a terminal drought scenario in which water availability increases with soil depth, or intermittent drought scenarios in which water is sporadically available in surface soil strata. In the case of terminal drought it has been proposed that root phenotypes optimizing nitrate capture through greater rooting depth may also be optimal for water capture (Lynch, 2013). In the case of intermittent drought dimorphic root systems permitting vigorous exploration of both shallow and deep soil domains may be preferable. Active root foraging in the topsoil would also improve the capture of recently mineralized N in the form of ammonium. The availability of water in the topsoil is uncertain because of the stochastic nature of precipitation patterns. Therefore, dimorphic root systems have several merits when more than one soil resource has marginal availability, as is commonly the case in developing nations, and under drought.

Measured and modelled nitrate leaching

In agreement with the simulation studies, empirical results showed nitrate leaching during the growing season. However, the field results showed downward movement of nitrate only after 50 d.a.g., whereas the simulation studies indicated downward movement of nitrate from the start of the simulation period. This discrepancy may be caused by characteristics of the field soil that were not included in the simulation study. Preferential flow of water through cracks and earthworm channels in the silt loam soil and finger flow in the sandy loam soil may have caused a fast downward transport of precipitation and greater retention of nitrate in shallow soil domains. Also, shortly after fertilization, nitrate may have been immobilized by a growing soil microbial biomass (Smith and Davis, 1974). The second increase in nitrate content in the loamy sand soil after 28 d.a.g. (Fig. 6B) probably resulted from mineralization of the organic fertilizer. The median nitrate concentrations in the fertilized field were located at shallower depth than in the unfertilized field (Fig. 6A), which can be explained by shallower localization of soil organic matter mineralization. This result is comparable to that of Thorup-Kristensen et al. (2009) who also found that inorganic N was located at shallower depth in the fields that received green manure than in fields that did not. Our results agree with other studies that showed slow if any downward movement of nitrate during the growing season when recommended fertilizer N levels were applied (Peterson et al., 1970; Commoner et al., 1974; Jackson and Williams, 1979; Westerman et al., 1994; Thorup-Kristensen et al., 2009). The finding that nitrate concentrations remain greatest in the topsoil during the growing season does not necessarily mean that no leaching occurs during the growing season. Nitrate leaching during the growing season under fertilized conditions was observed by Postma-Blaauw (2008). Ongoing mineralization in the topsoil may, however, have replenished nitrate and therefore masked any losses due to leaching (Cameron et al. 1978).

Trade-offs between shallow and deep root foraging

Trade-offs exist between shallow and deep root foraging, as plants have limited internal resources to maintain large root systems capable of adequately exploiting the entire soil profile. This is especially true under edaphic stress, in which root metabolic costs are a significant constraint on overall plant growth (Lynch, 2015). Several primary soil resources are generally concentrated in the topsoil, including P, K and ammonium, while in many soils water and nitrate may eventually be concentrated in deeper soil domains (Lynch and Wojciechowski, 2015). Root architectural phenotypes that allocate root foraging primarily to one soil domain may therefore be suboptimal for resource capture from the entire profile and therefore for plant growth. A clear example of this is the trade-off for water vs. phosphorus acquisition observed in shallow- or deep-rooted common bean genotypes (Ho et al., 2005). One avenue to co-optimize crop acquisition of deep and shallow soil resources at the stand level is the use of genetic mixtures or multilines composed of closely related cultivars with complementary root architectures (Henry et al., 2010). Such niche complementarity for soil resource acquisition is one reason that traditional crop polycultures overyield their component monocultures (Zhang et al., 2014). Another avenue to optimize the capture of deep and shallow resources is dimorphic architectural phenotypes (Dunbabin et al., 2002). Examples of this in common bean are architectures combining shallow adventitious roots with deep basal roots (Walk et al., 2006) and multiple whorls of basal roots that expand the vertical range of soil exploration (Miguel et al., 2013). A fundamental difference between the root architectures of monocot and dicot species is that in monocots, nodal roots continually emerge from shoot nodes near or above the soil surface over time, whereas in dicots the majority of roots emerge from existing roots that may already be in deeper soil domains. Monocot root systems are therefore temporally dimorphic because their young roots continually explore shallow domains while older roots may explore deeper domains. In this context it is noteworthy that the dimorphic phenotypes evaluated in this study, the ‘normal–very steep’ and ‘shallow–very steep’ phenotypes, performed well in terms of N capture in all scenarios except the scenario of fine soil with above normal precipitation, and consistently outperformed the ‘steep’ phenotype, showing that this concept is applicable to maize as well.

Root plasticity

These simulations generated architectural phenotypes that were affected by environmental conditions through plant growth and development but were not plastic, i.e. they did not fundamentally change in response to local nitrate availability. Nodal root growth angles have been reported to become steeper under N stress (Trachsel et al., 2013). In this study, which included both sandy and silt loam soil textures, out of a set of 108 maize inbred lines, the 29 lines best adapted to N stress included 11 steep phenotypes, 11 plastic phenotypes that became steep under N stress and seven shallow phenotypes. Statistical analysis showed this number of plastic phenotypes to be disproportionately represented among lines adapted to N stress. Plasticity attained by acceleration of the normal steepening of growth angles with younger nodes (York and Lynch, 2015) could result in dimorphic root architectures as modelled in the current study. The effects of variation of nodal root growth angles among nodal positions, and its response to N availability, on nitrate capture deserve further investigation.

The ‘steep, cheap and deep’ root ideotype

The ‘steep, cheap and deep’ (SCD) ideotype proposes that steep axial root growth angles should increase the capture of N under leaching conditions, as well as water capture under terminal drought (Lynch, 2013). The results of this study indicate that the effects of axial root growth angle on nitrate capture by maize roots depends strongly on N availability, soil texture and the leaching regime. The moderately shallow root phenotypes performed well in many scenarios, especially with limited N availability and limited precipitation. These scenarios are relevant to many maize production systems, including low-input systems in developing nations, as well as high-input systems, in which high planting density increases resource competition among neighbouring plants. Indeed, US commercial maize lines have developed shallower axial root growth angles over the past century (York et al., 2016). Other aspects of the SCD ideotype appear to have a simpler relationship with N capture, including a reduced number of axial roots (Saengwilai et al., 2014b), reduced lateral root branching (Postma et al., 2014; Zhan and Lynch, 2015) and increased formation of root cortical aerenchyma (Saengwilai et al., 2014a), all of which act to decrease the metabolic cost of soil exploration (Lynch, 2015). The effects of root phenes that reduce the metabolic cost of soil exploration may be less sensitive to environmental variables than phenes such as axial root growth angle which have no direct effect on such costs, and therefore may be more useful targets of genetic selection in crop breeding programmes. Nevertheless, the significant effects of axial root growth angles on nitrate capture by maize root systems indicates that this phene could be a useful selection criterion in maize breeding programmes targeting specific production environments. Recent advances in phenotyping roots of mature field-grown maize plants will be useful tools in this process (Trachsel et al., 2011; Bucksch et al., 2014; Colombi et al., 2015).

CONCLUSIONS

Our results support the hypothesis that the growth angles of axial roots are important determinants for nitrate acquisition in maize. Root growth angle influenced plant N capture by affecting the co-localization of root foraging activity with nitrate availability in time and space, and also by affecting the clustering of roots and thereby counterproductive competition among roots of the same plant for N capture. The effect of specific root phenotypes on N acquisition depended on the soil and precipitation environment. Shallow rooting increased nitrate uptake in environments with low nitrate mobility due to soil water retention or low precipitation. Shallow rooting also increased nitrate uptake in soils with low nitrate availability, where establishment of the plant early in the season when nitrate availability is still greater in the topsoil is critical for further development of the plant. Dimorphic root phenotypes, with shallow seminal roots during seedling growth, and deep nodal roots during later growth stages, performed well in most scenarios. These results should be useful in guiding the selection of maize genotypes with superior N acquisition.

SUPPLEMENTARY DATA

Supplementary data are available online at www.aob.oxfordjournals.org and consist of the following. Figure S1: precipitation data from 1 June to 12 July 2009 from the USDA-NRCS weather station Rock Springs, Pennsylvania. Figure S2: simulated root and shoot dry weights over time for maize grown under different soil nitrogen levels in a silt loam and sandy soil. Figure S3: nitrate uptake after 42 d for all roots systems.