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Real-time monitoring of rhizosphere nitrate fluctuations under crops following defoliation

Abstract

Background

Management regime can hugely influence the efficiency of crop production but measuring real-time below-ground responses is difficult. The combination of fertiliser application and mowing or grazing may have a major impact on roots and on the soil nutrient profile and leaching.

Results

A novel approach was developed using low-cost ion-selective sensors to track nitrate (NO3) movement through soil column profiles sown with the forage crops, Lolium perenne and Medicago sativa. Applications of fertiliser, defoliation of crops and intercropping of the grass and the legume were tested. Sensor measurements were compared with conventional testing of lysimeter and leachate samples. There was little leaching of NO3 through soil profiles with current management practices, as monitored by both methods. After defoliation, the measurements detected a striking increase in soil NO3 in the middle of the column where the greatest density of roots was found. This phenomenon was not detected when no NO3 was applied, and when there was no defoliation, or during intercropping with Medicago.

Conclusion

Mowing or grazing may increase rhizodeposition of carbon that stimulates soil mineralization to release NO3 that is acquired by roots without leaching from the profile. The soil columns and sensors provided a dynamic insight into rhizosphere responses to changes in above-ground management practices.

Background

Grassland comprises nearly 12% of earth’s organic matter, much of which is belowground in roots and soil organic matter [1, 2]. Natural grasslands and forage crops are carbon sinks, important in the context of increasing atmospheric CO2 levels if they are properly managed [3, 4]. Furthermore, the global demand for food protein is increasing each decade and is estimated as 110 ± 7% each year [5], satisfying this need requires increased use of nitrogen (N) fertilisers [6]. The Haber–Bosch process fixes atmospheric N to make fertiliser and uses 1–2% of the world’s energy, and 3–5% of its natural gas expenditure [7]. Decreasing the high rates of N fertiliser use for animal farming and forage crop production is an important target, especially in high-intensity temporary grasslands. Excess N leaches into water supplies causing eutrophication of aquatic environments [8]. For example, in the UK forage grass and legume crops are predicted to have high leaching rates to the environment with this problem exacerbated when crops are cultivated in sandy soils [9]. It is estimated that 60% of applied N fertiliser may be lost through leaching, run-off, denitrification and consumption by microbial populations [10, 11]. N leaching can contaminate human drinking water especially in ground water supplies and may result in decreased life expectancy [12, 13]. In addition, N emissions from grassland and animal production contribute to climate change [14, 15].

Plants take up N from soil in a variety of forms, principally as NO3 and ammonium, but also as amino acids and other organic N compounds [16, 17]. Many forage grasses, including Lolium perenne, preferentially uptake NO3 [18], although the level of NO3 can greatly vary within a field, even for plots metres apart [19, 20]. The ions mobility gives it a higher leaching potential than most other forms of soil N [21]. Repeated cutting and removal of the above ground crop is an essential part of forage management and the impact of defoliation has mostly been studied in hydroponically grown plants. In axenic culture defoliation of grass was shown to stimulate the release of carbon compounds from roots [22]. During the first seven days following defoliation of field grown ryegrass, decreases in biomass and N-content of stubble and roots were observed, concomitant with mobilization of N to shoots [23]. Below ground studies have shown that the C release after defoliation of grass can increase rhizosphere N mineralization rates after just 72 h [24] but decrease the rate organic matter breakdown [3]. In bi-cropping systems with legumes and grasses, de-topping the legume was shown to increase N concentration in the grass with more transfer of 15 N [25].

Soil N measurements are difficult to conduct and they can be unreliable and inconsistent, both across field samples and between testing methods [26]. Soil N estimates have long depended on soil core extracts, or porous ceramic cup or lysimeter samples being sent for later laboratory analysis [27]. Soil and soil water extract analysis is performed using conventional testing methods that include potassium chloride extraction alongside direct analysis of soil water using chromatography and colorimetric tests such as the Griess–Ilosvay reaction [28, 29]. Although these conventional testing methods have been used for decades, there are problems in their standardisation, use, and cost. Samples must be taken routinely and stored for testing, costing time and money. Subsequent N mineralization in soils is known to be affected by abiotic conditions [30], including pH [31], temperature [32], moisture [33], and soil texture and chemistry [31]. Therefore, laboratory analysis can be significantly different from actual real-time soil levels. This is exacerbated when there is high intra- and inter- variation in field soil N levels, as well as variation across seasons and spatially across depths [19, 34]. These conventional testing methods are labour-intensive and expensive and not conducive for addressing the aims of precision agriculture [35].

Ion-selective electrodes can provide an alternative method for measuring in-situ real time soil NO3 levels [36,37,38]. They may be used at different depths in the field to provide soil profile information or used in soil columns. Soil column experiments are used to gain accurate information on the interaction of roots and soil without the complications of field trials. They can be used to screen root rhizosphere systems and how these can interact [39]. Although soil columns are an artificial system, they are superior to media-based or hydroponic systems as their microbiology is more akin to that in nature [40]. Media-based and hydroponic systems lack soil structure and are suboptimal for studying root growth and nutrient uptake for N fertiliser experiments [41]. Soil experiments are a more representative model system for field interactions but studying soil profile behaviour under a crop is difficult. Use of soil columns can be a good compromise system. Figure 1 illustrates how soil columns can be used, particularly for studying aspects of forage crop cultivation. Conventional sampling methods and testing can also be used alongside NO3-selective sensors in column experiments. These experiments can include testing at different depths, with experimental replicates carried out with reasonable practical ease within a convenient timescale, providing meaningful data relating to field systems.

Fig. 1
figure1

Generalised schematic diagram of column for NO3-selective sensor experiments. Soil columns have dimensions of height = 50 cm and inside diameter = 15.4 cm. Possible experimental regime changes are marked in green, with the different assays possible shown in blue. Holes are available at three different levels for placement of the NO3−-selective sensors (shown as tip sensor photo symbol), at the top 1 cm (yellow), the bottom 1 cm (brown), and midway between (orange). Drainage holes were located at the bottom of the column, where soil water can be sampled using a lysimeter indicated with photo symbol of mini suction 10 Rhizon SMS lysimeters (Rhizosphere Research Products B.V., Wageningen, The Netherlands). Note microbial analysis was not performed in this work

Here we describe how soil N changes were measured using NO3-selective sensors in response to cultivation of L. perenne in soil columns. Three depths of the soil NO3 profile were assessed following different management practices. Management practices included varying N availability in the form of NO3 application, defoliation of plant vegetative tissue to simulate crop harvest, and intercropping with the legume Medicago sativa (commonly called lucerne or alfalfa). As NO3-selective sensors respond to changes in soil water nitrate concentration, we tested for their agreement to conventional soil water testing methods of leachate from column drainage holes. Columns were used to simulate the environment in the field and this approach provided data for forage crop agriculture on how management practices can influence soil N levels and the potential for fertiliser leaching.

Results and discussion

Soil column NO 3 profiles vary with management practices

We began by comparing the soil nitrate measurements with the nitrate concentration measurements collected using mini-suction lysimeters (Fig. 2). There was an excellent correlation between data obtained using the two types methods in the same soil (Fig. 2). Soil N changes in columns were measured in response to the cultivation schemes listed in Table 1. These columns were termed ‘Monocrop 1 to 3′, with one column having no plants with a NO3 application at day 0, ‘No crop’. ‘Monocrop 1′, had a dH2O application at day 0, ‘Monocrop 2′ had NO3 application at day 0, and ‘Monocrop 3′ had NO3 application plus vegetative tissue defoliation biomass measurement at day 28, representing the industry standard for regular cuts every 4–8 weeks.

Fig. 2
figure2

NO3-selective sensors readings correlated with conventional soil water NO3 extraction analysis assay results collected using a mini-suction lysimeter (see Materials and Methods for details). The same soil sample was treated with a range of nitrate concentrations each in four different pots and then measured for NO3–N as ppm using both NO3-selective sensors and suction lysimeter water samples assayed using a conventional Griess assay. NO3–N ppm are the units most used by soil scientists; the sensors were calibrated to give outputs in mM too. The graph shows strong agreement between measurements (R2 = 0.99)

Table 1 Column set-up for the Lolium perenne monocropping experiment

The analysed data from four types of column treatment are shown in Fig. 3. ‘No crop’ graph shows NO3 application at day 0–6 with NO3 detected by an increase in top sensor response (yellow plot). From day 2–4, NO3 was detected in the middle portion of the column (middle sensor, orange plot), with a peak detected at days 20–22. This indicated leaching through the soil profile and from day 30 onwards increased NO3 was detected by the bottom sensor (brown plot). The change in NO3 detected at 12 hourly intervals reflects the diurnal changes in columns. In ‘Monocrop 1′ no NO3 application was detected in any level, which is expected with no KNO3 added to this column. By the end of the experiment the above-ground vegetative biomass was low, as shown in Table 2, at only 5.7 ± 0.7 g, showing the L. perenne did not have optimal NO3 supply for growth.

Fig. 3
figure3

Lolium perenne monocrop column experiment NO3-selective sensor data. Mean data (4 columns) are shown for each treatment as indicated in Table 1 as the thickest coloured lines. NO3-selective sensor data are shown for top (yellow), middle (orange), and bottom (brown) levels of the columns. Data is the 12 h average of four experimental replicates in GraphPad Prism 7 (GraphPad Software Inc.), with standard error of the mean indicated with thinner lines of the same colour. Overlayed coloured vertical bars indicate the treatment for the L. perenne crop planted (green), nitrate application at day 0 (blue), or defoliation of total aboveground vegetative biomass at day 28 (purple). Note no nitrate was added to Monocrop 1 columns

Table 2 Total vegetative biomass for Lolium perenne monocropping experiment

Figure 3 shows that for ‘Monocrop 2′ and ‘Monocrop 3′ a NO3 application was detected at days 0–6 by the top sensor response (yellow plot), but both these columns were quickly depleted in NO3 with little increase shown for middle sensor detection. This indicates that the NO3 application was taken up by L. perenne, also inferred from the higher vegetative biomass measurements of 10–11 g compared with ‘Monocrop 1′ (Table 2). ‘Monocrop 2′ and ‘Monocrop 3′ differed in that defoliation occurred in the latter at day 28. An increase or ‘burst’ of detected NO3 was detected by the middle sensor for the defoliation column only (orange plot).

For each plot, the standard error of the mean showed relatively low variation between column experiments. Column experiments allow researchers to carry out multiple repeats of experiments with defined parameters to generate reproducible data relating to plant-soil interactions. Soil columns allow for environmental fluctuations such as diurnal changes and N leaching through the soil profile to be studied more easily when compared to plants in the field. Data from media-based systems are less relevant to agriculture as they are often sterile and are usually in controlled environments for light, temperature and humidity. Hydroponics require regular changing of hydroponic solutions, with nutrients being replenished but soil physical and microbiological interactions are absent. Soil columns allow some control and standardisation of the environment and less heterogeneity when compared to field conditions, including microbial populations, light and temperature changes, and soil moisture levels. Although these environments may have less severe fluctuations than field trials, the columns allow for standardised measurements in laboratory settings which is more relevant than other systems. Soil temperature at depth in columns can vary more than occurs in the field. Standardisation is important for gaining meaningful data in a relatively short time compared to field trials investigating the effect of different management practices on NO3 leaching. This is especially true for high intensity, temporary grasslands like those used by forage growers as there is a lack of evidence to suggest how different practices affect leaching in this environment, especially when compared to permanent pastures. By using soil column systems with in situ monitoring at three depths it is possible to see leaching in real-time in the ‘No crop’ columns. This movement of NO3 through the soil profile was not shown in ‘Monocrop 2′ or ‘Monocrop 3′ suggesting leaching is ameliorated by the uptake of N by roots and can be used to measure the efficiency of root systems to acquire NO3. The data suggests that L. perenne is cultivated efficiently using present forage crop practices with low levels of leaching.

Increased NO3 detected at mid-column depth following defoliation

To assess the detected NO3 changes in more detail, plots showing each column depth separately are presented (see Additional file 1: Figures S2–S6). Additional file 1: Figure S2 shows graphs of independent levels for ‘No crop’ and ‘Monocrop 1′. These graphs show the statistically significant differences in column levels of detected NO3. In addition, conventional soil water testing of drainage hole leachate (black diamonds on brown plot), showed good agreement with bottom sensor NO3 measurements.

Graphs showing separate plots for each level for ‘Monocrop 2′ and ‘Monocrop 3′ are shown in Additional file 1: Figure S3. These show there were no statistically significant different periods in NO3 measurements between sensors in either top or bottom levels throughout the 62 days of the experiment. On the other hand, the middle level transient increase in NO3 following defoliation in ‘Monocrop 3′ is shown to be statistically significant (orange plot), between days 39–48 (p < 0.05). This middle level ‘burst’ reduces from day 48. Conventional soil water testing of drainage leachate, (black diamond plots), show good agreement with bottom sensor data.

By splitting NO3 data for the different depths on columns, it is clear to see the wealth of information which can be provided by soil sensors for cost-effective analysis in columns in real-time (see Figs. 3, 4 and Additional file 1: Figures S2–S6). The NO3-selective sensor data show similar measurements to conventional testing with less effort, which is important for precision agriculture [38]. Detail is also significantly increased, capturing changes in the soil profile and leaching movement; such data definition as 1-min sampling for 12 hourly means over a two-and-a-half-month period is not feasible with current methods. Thus NO3-selective sensor can provide rich data describing rhizosphere processes and which is superior to current conventional testing methods.

Fig. 4
figure4

Lolium perenne and Medicago sativa intercrop column experiment NO3-selective sensor data. Mean data (4 columns) are shown for each treatment as indicated in Table 3 as the thickest coloured lines. NO3-selective sensor data are shown for top (yellow), middle (orange), and bottom (brown) depths in the columns. Data are the 12-hourly average of two experimental replicates in GraphPad Prism 7 (GraphPad Software Inc.), with standard error of the mean indicated with thinner lines of the same colour. Coloured vertical bars indicate the treatments for L. perenne and M. sativa crops (pink), nitrate application at day 0 (blue), or defoliation of total aboveground vegetative biomass at day 28 (purple)

The detected ‘burst’ in NO3 found in the middle level of soil columns following defoliation of L. perenne from NO3-selective sensors is very intriguing. It is possible that L. perenne released NO3 as a stress response after cutting, or because of another process induced by defoliation. It is unlikely that the transient NO3 ‘burst’ is an experimental artefact, as experimental replicates show standardised measurements with similar standard errors of the mean determined (Additional file 1: Figures S2 to S6). It could be argued that a reduction in NO3 uptake was expected due to removal of vegetative tissue and this was observed for L. perenne in hydroponics [42]. In hydroponic systems it has been suggested that N can be released by roots at ~ 5.1–6.1% of total plant N storage under normal conditions [43]; however in solution culture it is difficult to test an individual region of the root.

Following defoliation in hydroponics it is well-documented that the remnant vegetative tissue preferentially takes up more carbon than N [44], in order to restore the C:N ratio of the tissue due to substantially decreased photosynthetic rate [45]. Total N reserves stored as vegetative storage proteins in roots and stem bases have been found to be rapidly degraded after defoliation [46]. Additionally, increasing defoliation of Lolium has led to decreased N uptake and increased plant N remobilization in hydroponic systems [47], and shoot tissue appears to be required for whole plant NO3 reduction in grasses [48]. Moreover, remobilization of plant C-containing compounds in the leaf is shown to be coordinated with N availability to the root [49]. However, as mentioned previously, hydroponic systems cannot be used to assess specific levels of these compounds in roots or soil. It is likely that the detected NO3 increase following defoliation in the column system disappears after 48 days when any available N may be re-taken up by the roots as vegetative growth has re-established photosynthesis in the shoot. Hydroponic systems may not record such changes due to the free diffusion of nutrients like soluble NO3 through solutions. It is possible that other N-containing compounds are released by the roots as a stress response, and changes in amide and amino acid composition have been identified in Lolium xylem sap following defoliation [50], suggesting increased N assimilation [51, 52]. These N-compounds released by roots may be converted to NO3 by rhizosphere microbes and demonstrated by changes in the rhizosphere microbiome before and after defoliation.

It is well-documented that grasses can release carbon exudates from roots in response to defoliation, including Lolium [22, 53, 54]. The complex carbon release profiles change depending on developmental stage or defoliation [55], and it is likely that such carbon exudates are linked to the measured increase in NO3 in the middle level of soil column experiments reported here. This may be mediated by microbial activity which would be absent in media-based or hydroponic systems. Carbon exudates are not only important growth substrates for bacteria, but some may provide host-specific recognition signals promoting nitrifying bacteria [56]. Rhizosphere microbiome analysis after grazing or defoliation of grass has identified more gram-positive bacteria and increased inorganic N pools [3, 24]. We would predict a large transient increase in the population of nitrifiers in the microbiome and this might be driven by an acidification of the rhizosphere [57]. Furthermore, NO3 generated by microbial activity which is usually taken up by leafed plants may not be when defoliation occurs, thus causing an increase in rhizosphere NO3. In addition, root uptake is linked to changes in transpiration rate [58], reducing plant N uptake after defoliation [47]. The NO3-selectivity of the soil sensors may be an important factor to consider for the increase measured after defoliation, as a large transient production of nitrite (NO2) by rhizosphere bacteria may be reported by the sensors. The NO3/NO2 selectivity factor is tenfold greater for NO3 [59]. However, other anions like organic acids, such as malate that might be released by the roots, are unlikely to be a problem [60].

Soil NO3 profiles when Lolium perenne was intercropped with Medicago sativa

Intercropping experiments were carried out with L. perenne and the legume M. sativa, as detailed in Table 3. As before one column had ‘No crop’, and the others were labelled ‘Intercrop 1–3′. ‘Intercrop 1′ had no NO3 application, ‘Intercrop 2′ had a defoliation step at day 28, and ‘Intercrop 3′ had a NO3 application at day 0 and defoliation at day 28. NO3-selective sensor data for each column is found in Fig. 4. ‘No crop’ graph showed a NO3 leaching pattern similar to ‘No crop’ in the monocropping experiments with NO3 detected at day 0–6 at the top sensors of the column (yellow plot), with detection in the middle sensors with a peak at day 18 (orange plot), and a slightly earlier detection by bottom sensors from day 26. This data indicates again that the NO3 application had leached through the soil profile when no crop was present. For ‘Intercrop 1′, NO3 application was not detected in any level, as found in ‘Monocrop 1′. Total vegetative biomass, as shown in Table 4, was however higher for ‘Intercrop 1′ compared to ‘Monocrop 1′ at 8.0 ± 2.0 g, suggesting an N increase in the presence of the legume. ‘Intercrop 2′ graph was like ‘Intercrop 1′ with no significant change in NO3 measured before or after defoliation at any level. Total vegetative biomass for ‘Intercrop 2′ was also similar to ‘Intercrop 1′ at 8.7 ± 1.3 g., see Table 4.

Table 3 Soil column set-up for the Lolium perenne and Medicago sativa intercropping experiment
Table 4 Total vegetative biomass for Lolium perenne and Medicago sativa intercropping experiment

‘Intercrop 3′ in Fig. 4 was most similar in management practice to ‘Monocrop 2′ and ‘Monocrop 3′ treatments and the NO3-selective sensor data is shown in Fig. 3. The NO3 application was detected by the top sensors at the beginning or the experiment and quickly depleted with little detection of NO3 by the middle level sensors from day 6. Total vegetative biomass measurement was also similar for ‘Intercrop 3′ and ‘Monocrop 2–3′, see Tables 2 and 4. However, despite ‘Intercropping 3′ undergoing defoliation at day 28, no NO3 increase was detected by the middle sensors in contrast to the ‘Monocrop 3′ experiments.

Transient NO3 increase was not detected after defoliation in intercropped columns

To assess the NO3 sensor data in more detail, individual sensor data plots were produced for column depth separately. Additional file 1: Figure S4 shows the graphs for ‘No crop’ and ‘Intercrop 1′, with statically significant differences indicated for detected NO3 as described above. Conventional soil water testing of drainage leachate (black diamonds on brown plot), again showed good agreement with lowest depth NO3 sensors measurements.

In monocrop conditions, upon defoliation of L. perenne NO3 sensors detected a transient increase in NO3 in the middle sensor region of soil columns and thus the roots (see Additional file 1: Figure S3). Additional file 1: Figure S5 shows intercropping conditions for separate column levels for defoliated L. perenne grown alongside M. sativa. Here in the intercropping experiment no transient NO3 increase or ‘burst’ is evident at any depth of the column. Data for ‘Intercropping 2′ and ‘Intercropping 3′ suggests little evidence of a NO3 ‘burst’ following defoliation, regardless of a NO3 application being present or absent. Moreover, conventional soil water testing of drainage leachate again showed agreement with bottom NO3-selective sensor data, although ‘Intercrop 3′ was slightly higher.

Furthermore, separate data for ‘Monocrop 3′ with ‘Intercrop 3′ were compared (see Additional file 1: Figure S6). These plots only showed a significant difference in the top and bottom level NO3-selective sensor measurements for a short period at the start of the experiment. This may result from the smaller number of replicates in these intercropping experiments. Most strikingly was the difference between experiments at the middle depth where statistically significant differences were found between plots (orange plot). Despite applying the same management practices, a transient increase in NO3 was not detected following defoliation in ‘Intercrop 3′. This result was statistically significant when compared to ‘Monocrop 3′ between days 34–44 (p < 0.05).

As the middle region NO3 ‘burst’ was not found for ‘Intercrop 3′ with L. perenne grown alongside M. sativa, it is possible that growing the legume has caused a change in the microbial populations that altered N dynamics in the soil. It is known that greater rhizosphere microbiome diversity is found for legumes when compared with grasses [61, 62]. Alternatively, the difference with intercropping may be due to variation in root architecture of legumes. The L. perenne root density is probably decreased in the intercropping column when compared with monocropping and it may be possible that the transient NO3 release is diluted by the mixed root population and can no longer be detected. Nonetheless, the root density is only slightly reduced as the 80:20 plant mix means that only a few M. sativa plants are found in each column. Moreover, this explanation would still require that the legume root responds differently to the grass after defoliation. Defoliation causes changes in transpiration rates in many plants and this effect may be species dependent, so having more species may change how roots interact with the soil [63]. Work comparing grass and legume root responses to defoliation has suggested similar below ground responses [23, 46, 52, 64], but perhaps more investigation in required, although specific species are known to vary in their root branching shapes [65]. Plant root idiotypes having slightly different patterns are important for breeders [66] and, for forage crops, the effect of defoliation on root physiology including the microbiota is likely to be a key trait [67]. The differences found between grasses and legumes, comparing a long tap root in the former to a more branched legume root structure in the latter [68,69,70], may influence the soil N profile changes following defoliation in intercropping systems. Interestingly in mixed clover and grass swards defoliation also caused a change in the composition of microbial populations, although there was no significant effect on microbial activity [71], but our data shows there was no commitment release of NO3 (see Fig. 4).

Conclusions

These NO3-selective sensors can be built in laboratories quickly and cheaply (Additional file 1: Figure S1) and they can measure in real time the available soil water NO3 after calibration with known NO3 concentrations. Furthermore, these measurements that show good agreement with conventional testing methods (Fig. 2, Additional file 1: Figure S2–S6). The sensors offer the advantage of the rapid reporting of soil available nitrate offering a method that is much easier to use when compared with lab chemical assays. However, the sensor measurements may be influenced by chemical interference and this is more likely to be a problem at low concentrations near the detection limit (0.5 mM nitrate [59, 60]). The sensors can be deployed in soil columns to simulate conditions in the field. The transient release of NO3 in the middle soil column region following defoliation of L. perenne was consistent across experiments and was revealed by using the soil NO3 sensors. The rhizosphere transient NO3 release we have observed may not be a problem for forage growers, as plants seem to uptake the available NO3 with little evidence of leaching from the profile. Furthermore, the lack of a NO3 ‘burst’ when L. perenne was intercropped with M. sativa provides evidence for possible advantages for forage crop recovery after cutting although the increase in biomass with intercropping was relatively small (mean values of 11.5 g vs 10.6 g; Tables 2 and 4). Differences between monocropping and intercropping forage systems have already been identified as contributing to above- and below-ground species diversity, significantly affecting soil erosion in studies of permanent pastures [63]. This work used the soil column system to monitor rhizosphere NO3 but other types of nutrient-selective sensors could be built in a similar way by altering the ion-selective membrane used [59, 72]. The use of the soil column and sensor system allows the dynamic monitoring of changes in soil profile nutrient concentrations that can be used to screen crop genotypes with improved rhizosphere traits that reduce leaching losses.

Materials and methods

NO 3 -selective sensor construction

NO3-selective sensors were constructed as described previously [60, 72] using the construction scheme found in Additional file 1: Figure S1. Sensors used 1.25 mL pipette tips (Starlab, Milton Keynes, UK), with tips silanized to a depth of approximately 1 cm with Repelcote™ (Dow Corning, Gillingham, UK). Two membrane solutions were prepared in 2 mL final volume of tetrahydrofuran solvent (Millipore, ≥ 99.9% 1,081,100,500), as follows with chemicals from Sigma-Aldrich unless specified.

  • NO3-selective membrane containing 12 mg tridodecylmethylammonium NO3, 2 mg methyltriphenylphosphonium bromide, 46 mg poly(vinyl chloride) (PVC), 10 mg nitrocellulose (Amersham Hybond ECL, RPN2020D, 0.45 μM, 200 × 200 mm, GE Healthcare), and 130 mg 2-nitrophenyl octyl ether.

  • Reference membrane containing 2 mg potassium tetrakis(4-chlorophenyl)borate, 45 mg polyethylene glycol 3500 and 10 mg nitrocellulose.

Membrane solutions were covered with foil, capped, and sealed with parafilm, then shaken at 150 rpm overnight to ensure reagents were dissolved thoroughly. Silanized tips were then dipped into one of the membrane solutions to a depth of approximately 2 cm. Tips were dried in a fume hood for 48 h to produce a thin (approximately 2–3 mm) membrane. Two backfill solutions were prepared in 200 mL dH2O.

  • Ion-selective backfill containing 2.202 g KNO3 and 1.49 g KCl.

  • Reference backfill containing 3.12 M KCl, 20 mg AgCl2, 1.8 g NaCl, and 0.18 g naphthol green B.

One mL of the backfill solution was loaded into the top of the corresponding membraned tip, air bubbles were displaced, and a sensor of each type paired together. Sensor cables were prepared by stripping ~ 1 cm from each end of 1.5 m lengths of wire (RS Components Ltd.), with one end clamped with ~ 7 cm of Ag wire (99.9%, Palmer Metals, Coventry UK), coated in 50 mM KCl. This end was threaded through an earplug (RS Components Ltd.) and inserted into the backfilled tip using a disposable needle to displace air. Sensors were secured with black cable ties (RS Components Ltd.) and secured in pairs with 2 × 5 cm strips of parafilm (Slaughter Ltd, Basildon, UK). The final sensors and their performance in comparison to a conventional N assay are shown in Fig. 2.

NO 3 -selective sensor calibration

Sensor pairs (NO3-selective and reference) were connected to GP2 loggers (Delta-T Devices Ltd., Cambridge, UK), where ion-selective sensors were (+) channels and paired reference sensors were (−). A calibration programme was installed using DeltaLINK 3.6.2 (Delta-T Devices Ltd.) for ‘Voltage, not powered’ and circuit detection and power channel disabled. A calibration using eight nitrate solutions revealed an identical calibration curve to those previously reported for glass microelectrodes [59, 60]. This calibration could be fitted with a simplified Nicolsky–Eisenman equation to show the same detection limits (0.1 mM) and ion-selectivity coefficients like those reported previously [59, 60]. The calibration was simplified to a linear fitted line for the soil measurements and sensors were placed into solutions of 300, 30, 3 and 0.3 mM KNO3 sequentially for a minimum of 5 min each. The electrical potential of sensors was measured and recorded (mV), and the mean calculated for the final 1 min period in each concentration. A linear regression was fitted for individual sensors alongside the known concentration providing a calibration Eq. (1) for each sensor using Excel® 2016 (Microsoft®).

$$mV=\left(m\times log_{10}N{O}_{3}^{-}mM\right)+c$$
(1)

Sensors with slope factor ‘m’ outside of 45–55 mV were considered not useable and reconstructed. Useable sensors were stored in solutions of 100 mM KNO3 until use. A Delta-T SM300 soil moisture and temperature sensor was also connected into an available channel of one logger.

NO3 -selective sensor measurements and data analysis

Sensors were placed in columns with care to limit disturbance to tip membranes. One to three sensors were placed at each level and outputs (mV) were recorded at 1 min intervals. Sensors ran in columns for a week before experiments began to check all sensors were working and were replaced as needed. Sensors could be conveniently removed from the column, recalibrated and if necessary, easily replaced. Waterproof tape covered the sensor hole for a few hours during this process. Most sensors showed very similar accuracy and calibration and were successfully recalibrated at end of the experiment (62 days).

A laboratory temperature slope coefficient, included in the analysis to compensate for temperature changes in glasshouse between experiments, was derived from the Nicolsky-Eisenmann relationship [59]. Experimental mV outputs were compared to theoretical calculated values across temperatures from 8 to 35 °C. A linear coefficient of compensation was calculated and included in analysis of NO3 mM (2):

$$Temperature\, compensation=\left(0.405\times^\circ{C}\right)+93.6$$
(2)

For each experiment a 6-d resting period (day − 6 to 0) was included with sensor data recorded and watering of soil columns every 2–4 days with dH2O. The experiment then ran from 0 to 56 days. At the end of experiments sensors were recalibrated and, if individual sensors had changed significantly from their first calibration (slope ‘m’), then all their recorded data was removed from subsequent analysis. The arithmetic means for 12 hourly periods between 0 and 12 h were calculated using Excel® 2016. Data were plotted in GraphPad Prism 7 (GraphPad Software Inc.) and analysed in GenStat® 18th Edition (VSN International) to determine statistical significance between columns using student T-tests. A repeated analysis using ANOVA in RStudio (RStudio, Inc.) was used to validate the analysis.

Soil column experiments

Four PVC opaque columns (height = 50 cm, inside diameter = 15.4 cm, wall thicknesses of 0.5 cm) with 5 drainage holes at base (made by John Humble, John Innes Centre Workshop, Norwich, UK) were filled with silty clay soil (sourced from Church farm, John Innes Centre, 52°37′ 59.8836′′ N 1°10′ 46.3440′′ E). Columns included holes for sensors at three levels, top (1 cm depth), middle (25 cm depth) and bottom (49 cm depth), see Fig. 1. Water-holding capacities for soil columns were determined, and columns watered every 2–4 days with dH2O to a similar capacity to promote nutrient flow through the profile. The experiments were conducted in the greenhouse using the locally collected soil and analysis before the experiment revealed NO3–N 101.9 mg kg−1, NH4–N 2.6 mg kg−1, Olsen P 9.55 mg kg−1, pH 8.07, organic matter 2.5 g kg−1. There were four experimental replicate columns for each treatment.

At day 0 the experiment began with the column experimental design summarised in Table 1. KNO3 treatments were 57 kg ha−1, equivalent to the rate of first application for UK forage crop cultivation [73]; this was equivalent to 10.76 g KNO3 in 1 L for each column, or a dH2O control. Seeds of Lolium perenne cv. Aber Magic were kindly provided by the Genebank at The Institute of Biological, Environmental and Rural Sciences (Aberystwyth University, Aberystwyth, Wales, UK). Seeds were surface-sterilised with ethanol 70% (v/v) at a seeding rate of 43.7 kg ha−1 or 0.83 g per column. These were germinated on paper towels for 6 d before transplantation into columns. For defoliation, after 4 weeks the whole vegetative tissue in one column was cut to the soil level to simulate cropping. This tissue was oven dried overnight at 55–60 °C and biomass recorded. For all columns at 8 weeks the total vegetative tissue was cut and again biomass recorded. The experiment was repeated four times. By the end of the experiments the roots of all the forage crops had grown throughout the column.

For one experiment conventional soil water sampling was carried out using soil water samples collected from the base of columns using a mini suction lysimeter (10 Rhizon SMS, Rhizosphere Research Products B.V., Wageningen, The Netherlands). Soil water samples were collected every 1–4 days with analysis at the end of experiment.

Column experiments were repeated as above for intercropping experiments. The experimental design is shown in Table 3 and largely matched that of monocropping experiments. Intercropping columns had a seeding rate mix of 80:20 grass: legume simulating the planting density used by the UK forage industry. This used L. perenne at 0.66 g per column and Medicago sativa cv. Daisy (DLF Forage Seeds, DK, provided by Dengie Crops Ltd., UK), at 0.08 g per column. All other practices matched monocropping experiments, with soil water for conventional soil water analysis taken from drainage holes from one experiment. The whole experiment was repeated twice.

Spectrophotometric Griess determination of NO3 in soil eluates

A reduction-diazotisation reagent was prepared by adding the following solution A to solution B. Solution A was 400 mg VCl3 to 50 mL HCl 1.0 M, with gentle shaking until dissolved. Solution B was 200 mg of sulphanilamide and 10 mg N-(1-naphthyl)ethylenediamine dihydrochloride in 400 mL dH2O [28].

Standard solutions of KNO3 were prepared in 10 mL KCl 2.0 M for NO3 concentrations of 0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0 μg/mL. To 3.5 mL cuvettes (Sarstedt Limited, Nümbrecht, Germany), 1 mL of standard or 1 mL of water sample was added, then 800 μL of the above reduction-diazotisation reagent. Samples were incubated for 20 h at room temperature and their absorbance at 540 nm measured. A calibration regression was calculated using measurements for standards, with R2 of ≥ 0.98. These data were used to give a linear equation for calculating NO3 concentrations in soil water samples, corrected by the dilution factor of the KCl solution.

Availability of data and materials

The images and datasets used and analyzed during the present study are available from the corresponding author on reasonable request.

Abbreviations

ANOVA:

Analysis of variance

PVC:

Poly(vinyl chloride)

References

  1. 1.

    Schlesinger WH. Carbon balance in terrestrial detritus. Annu Rev Ecol Syst. 1977;8(1):51–81.

    CAS  Article  Google Scholar 

  2. 2.

    Parton WJ, Scurlock JMO, Ojima DS, Gilmanov TG, Scholes RJ, Schimel DS, et al. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Glob Biogeochem Cycles. 1993;7(4):785–809.

    CAS  Article  Google Scholar 

  3. 3.

    Shahzad T, Chenu C, Repinçay C, Mougin C, Ollier J-L, Fontaine S. Plant clipping decelerates the mineralization of recalcitrant soil organic matter under multiple grassland species. Soil Biol Biochem. 2012;51:73–80.

    CAS  Article  Google Scholar 

  4. 4.

    Capstaff NM, Miller AJ. Improving the yield and nutritional quality of forage crops. Front Plant Sci. 2018;9:535.

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Tilman D, Balzer C, Hill J, Befort BL. Global food demand and the sustainable intensification of agriculture. Proc Natl Acad Sci USA. 2011;108:20260–4.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Derner JD, Hunt L, Filho KE, Ritten J, Capper J, Han G. Livestock Production Systems. In: Briske DD, editor. Rangeland systems: processes, management and challenges. Cham: Springer International Publishing; 2017. p. 347–72.

    Google Scholar 

  7. 7.

    Nishibayashi Y. Nitrogen fixation. Basel: Springer International Publishing; 2017.

    Google Scholar 

  8. 8.

    Dodds WK, Smith VH. Nitrogen, phosphorus, and eutrophication in streams. Inland Waters. 2016;6:155–64.

    CAS  Article  Google Scholar 

  9. 9.

    DEFRA Report (2009). Protecting our water, soil and air: a code of good agricultural practice for farmers, growers and land managers. In: Department for Environment and Rural Affairs (DEFRA) ed. Norwich, Norfolk: TSO. Crown copyright. ISBN 978 0 11 243284 5

  10. 10.

    Kant S, Bi Y-M, Rothstein SJ. Understanding plant response to nitrogen limitation for the improvement of crop nitrogen use efficiency. J Exp Bot. 2010;62:1499–509.

    PubMed  Article  CAS  Google Scholar 

  11. 11.

    Raun WR, Johnson GV. Improving nitrogen use efficiency for cereal production. Agron J. 1999;91:357–63.

    Article  Google Scholar 

  12. 12.

    Brink C, van Grinsven H, Jacobsen BH, et al. Costs and benefits of nitrogen in the environment. In: Sutton MA, Howard CM, Erisman JW, Billen G, Bleeker A, Grennfelt P, van Grinsven H, Grizzetti B, editors., et al., The European nitrogen assessment. Cambridge: Cambridge University Press; 2011.

    Google Scholar 

  13. 13.

    Sutton MA, Bleeker A, Howard CM, Bekunda M, Grizzetti B, de Vries W et al. (2013) Our Nutrient World: The challenge to produce more food and energy with less pollution. Global Overview of Nutrient Management.: Centre for Ecology and Hydrology (CEH), Edinburgh on behalf of the Global Partnership on Nutrient Management (GPNM) and the International Nitrogen Initiative (INI).

  14. 14.

    Rees RM, Baddeley JA, Bhogal A, Ball BC, Chadwick DR, Macleod M, et al. Nitrous oxide mitigation in UK agriculture. Soil Sci Plant Nutrit. 2013;59:3–15.

    CAS  Article  Google Scholar 

  15. 15.

    Skiba U, Jones SK, Dragosits U, Drewer J, Fowler D, Rees RM, et al. UK emissions of the greenhouse gas nitrous oxide. Phil Trans Roy Soc B Biol Sci. 2012;367:1175–85.

    CAS  Article  Google Scholar 

  16. 16.

    Miller AJ, Cramer MD. Root nitrogen acquisition and assimilation. Plant Soil. 2005;274:1–36.

    CAS  Article  Google Scholar 

  17. 17.

    Xu G, Fan X, Miller AJ. Plant nitrogen assimilation and use efficiency. Annu Rev Plant Biol. 2012;63:153–82.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Weigelt A, Bol R, Bardgett RD. Preferential uptake of soil nitrogen forms by grassland plant species. Oecologia. 2005;142:627–35.

    PubMed  Article  Google Scholar 

  19. 19.

    Bogaert N, Salomez J, Vermoesen A, Hofman G, Van Cleemput O, Van Meirvenne M. Within-field variability of mineral nitrogen in grassland. Biol Fertil Soils. 2000;32:186–93.

    CAS  Article  Google Scholar 

  20. 20.

    Lark RM, Milne AE, Addiscott TM, Goulding KWT, Webster CP, O’Flaherty S. Scale- and location-dependent correlation of nitrous oxide emissions with soil properties: an analysis using wavelets. Eur J Soil Sci. 2004;55:611–27.

    CAS  Article  Google Scholar 

  21. 21.

    Wang H, Gao JE, Li XH, Zhang SL, Wang HJ. Nitrate accumulation and leaching in surface and ground water based on simulated rainfall experiments. PLoS ONE. 2015;10:e0136274.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Paterson E, Sim A. Rhizodeposition and C-partitioning of Lolium perenne in axenic culture affected by nitrogen supply and defoliation. Plant Soil. 1999;216:155–64.

    CAS  Article  Google Scholar 

  23. 23.

    Louahlia S, Macduff JH, Ourry A, Humphreys M, Boucaud J. Nitrogen reserve status affects the dynamics of nitrogen remobilization and mineral nitrogen uptake during recovery of contrasting cultivars of Lolium perenne from defoliation. New Phytol. 1999;142:451–62.

    CAS  Article  Google Scholar 

  24. 24.

    Hamilton EW, Frank DA, Hinchey PM, Murray TR. Defoliation induces root exudation and triggers positive rhizospheric feedbacks in a temperate grassland. Soil Biol Biochem. 2008;40:2865–73.

    CAS  Article  Google Scholar 

  25. 25.

    Rao AV, Giller KE. Nitrogen-fixation and its transfer from Leucaena to grass using N-15. For Ecol Manag. 1993;61:221–7.

    Article  Google Scholar 

  26. 26.

    Knight SM. Soil mineral nitrogen testing: Practice and interpretation. HGCA Res Rev. 2006;58:1–32.

    Google Scholar 

  27. 27.

    Smith KA, Li S. Estimation of potentially mineralisable nitrogen in soil by KCl extraction. Plant Soil. 1993;157:167–74.

    CAS  Article  Google Scholar 

  28. 28.

    Griess P. Bemerkungen zu der Abhandlung der HH. Weselsky und Benedikt “Ueber einige Azoverbindungen.” Ber Dtsch Chem Ges. 1879;12:426–8.

    Article  Google Scholar 

  29. 29.

    Pasquali CEL, Gallego-Picó A, Hernando PF, Velasco M, Alegría JSD. Two rapid and sensitive automated methods for the determination of nitrite and nitrate in soil samples. Microchem J. 2010;94:79–82.

    CAS  Article  Google Scholar 

  30. 30.

    Cameron KC, Di HJ, Moir JL. Nitrogen losses from the soil/plant system: a review. Ann Appl Biol. 2013;162:145–73.

    CAS  Article  Google Scholar 

  31. 31.

    Islam A, Chen D, White RE, Weatherley AJ. Chemical decomposition and fixation of nitrite in acidic pasture soils and implications for measurement of nitrification. Soil Biol Biochem. 2008;40:262–5.

    CAS  Article  Google Scholar 

  32. 32.

    Schmidhalter U. Development of a quick on-farm test to determine nitrate levels in soil. J Plant Nutr Soil Sci. 2005;168:432–8.

    CAS  Article  Google Scholar 

  33. 33.

    Turner DA, Edis RE, Chen D, Freney JR, Denmead OT. Ammonia volatilization from nitrogen fertilizers applied to cereals in two cropping areas of southern Australia. Nutr Cycl Agroecosyst. 2012;93:113–26.

    CAS  Article  Google Scholar 

  34. 34.

    Keil D, Meyer A, Berner D, Poll C, Schutzenmeister A, Piepho HP, et al. Influence of land-use intensity on the spatial distribution of N-cycling microorganisms in grassland soils. FEMS Microbiol Ecol. 2011;77:95–106.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Shaw R, Lark RM, Williams AP, Chadwick DR, Jones DL. Characterising the within-field scale spatial variation of nitrogen in a grassland soil to inform the efficient design of in-situ nitrogen sensor networks for precision agriculture. Agric Ecosys Environ. 2016;230:294–306.

    Article  Google Scholar 

  36. 36.

    Nair PKR, Talibudeen O. Dynamics of K and NO3 concentrations in the root zone of winter wheat at Broadbalk using specific-ion electrodes. J Agric Sci. 1973;81:327–37.

    CAS  Article  Google Scholar 

  37. 37.

    Clark LJ, Gowing DJG, Lark RM, Leed-Harrison PB, Miller AJ, Wells D, et al. Sensing the physical and nutritional status of the root environment in the field: a review of progress and opportunities. J Agric Sci. 2005;143:347–58.

    Article  Google Scholar 

  38. 38.

    Shaw R, Williams A, Miller AJ, Jones D. Assessing the potential for ion selective electrodes and dual wavelength UV spectroscopy as a rapid on-farm measurement of soil nitrate concentration. Agriculture. 2013;3:327.

    Article  Google Scholar 

  39. 39.

    Mairhofer S, Johnson J, Sturrock CJ, Bennett MJ, Mooney SJ, Pridmore TP. Visual tracking for the recovery of multiple interacting plant root systems from X-ray CT images. Mach Vis Appl. 2016;27:721–34.

    Article  Google Scholar 

  40. 40.

    Lewis J, Sjöstrom J. Optimizing the experimental design of soil columns in saturated and unsaturated transport experiments. J Contam Hydrol. 2010;115:1–13.

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Wojciechowski T, Gooding MJ, Ramsay L, Gregory PJ. The effects of dwarfing genes on seedling root growth of wheat. J Exp Bot. 2009;60:2565–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Clement CR, Hopper MJ, Jones LHP, Leafe EL. The uptake of nitrate by Lolium perenne from flowing nutrient solution: II. Effect of light, defoliation, and relationship to CO2 flux. J Exp Bot. 1978;29:1173–83.

    CAS  Article  Google Scholar 

  43. 43.

    Merbach W, Mirus E, Knof G, Remus R, Ruppel S, Russow R, et al. Release of carbon and nitrogen compounds by plant roots and their possible ecological importance. J Plant Nutr Soil Sci. 1999;1999(62):373–83.

    Article  Google Scholar 

  44. 44.

    De Visser R, Vianden H, Schnyder H. Kinetics and relative significance of remobilized and current C and N incorporation in leaf and root growth zones of Lolium perenne after defoliation: assessment by 13C and 15N steady-state labelling. Plant Cell Environ. 1997;20:37–46.

    Article  Google Scholar 

  45. 45.

    Jiang Y, Li Y, Nie G, Liu H. Leaf and root growth, carbon and nitrogen contents, and gene expression of perennial ryegrass to different nitrogen supplies. J Am Soc Hort Sci. 2016;141:555–62.

    CAS  Article  Google Scholar 

  46. 46.

    Volenec JJ, Ourry A, Joern BC. A role for nitrogen reserves in forage regrowth and stress tolerance. Physiol Plant. 1996;97:185–93.

    CAS  Article  Google Scholar 

  47. 47.

    Meuriot F, Morvan-Bertrand A, Noiraud-Romy N, Decau M-L, Escobar-Gutiérrez AJ, et al. Short-term effects of defoliation intensity on sugar remobilization and N fluxes in ryegrass. J Exp Bot. 2018;69:3975–86.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Scheurwater I, Koren M, Lambers H, Atkin OK. The contribution of roots and shoots to whole plant nitrate reduction in fast- and slow-growing grass species. J Exp Bot. 2002;53:1635–42.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Roche J, Turnbull MH, Guo Q, Novák O, Späth J, Gieseg SP, et al. Coordinated nitrogen and carbon remobilization for nitrate assimilation in leaf, sheath and root and associated cytokinin signals during early regrowth of Lolium perenne. Ann Bot. 2017;119:1353–64.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Thornton B, Macduff JH. Short-term changes in xylem N compounds in Lolium perenne following defoliation. Ann Bot. 2002;89:715–22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Bigot J, Lefevre J, Boucaud J. Changes in the amide and amino acid composition of xylem exudate from perennial ryegrass (Lolium perenne L.) during regrowth after defoliation. Plant Soil 1991;136:59–64.

  52. 52.

    Ourry A, Bigot J, Boucaud J. Protein mobilization from stubble and roots, and proteolytic activities during post-clipping re-growth of perennial ryegrass. J Plant Physiol. 1989;134:298–303.

    CAS  Article  Google Scholar 

  53. 53.

    Morvan-Bertrand A, Pavis N, Boucaud J, Prud’Homme MP. Partitioning of reserve and newly assimilated carbon in roots and leaf tissues of Lolium perenne during regrowth after defoliation: assessment by 13C steady-state labelling and carbohydrate analysis. Plant Cell Environ. 1999;22:1097–108.

    CAS  Article  Google Scholar 

  54. 54.

    Thornton B, Paterson E, Midwood AJ, Sim A, Pratt SM. Contribution of current carbon assimilation in supplying root exudates of Lolium perenne measured using steady-state 13C labelling. Physiol Plant. 2004;20:434–41.

    Article  Google Scholar 

  55. 55.

    Clayton SJ, Read DB, Murray PJ, Gregory PJ. Exudation of alcohol and aldehyde sugars from roots of defoliated Lolium perenne L. grown under sterile conditions. J Chem Ecol. 2008;34:1411–21.

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Grayston SJ, Vaughan D, Jones D. Rhizosphere carbon flow in trees, in comparison with annual plants: the importance of root exudation and its impact on microbial activity and nutrient availability. App Soil Ecol. 1997;5:29–56.

    Article  Google Scholar 

  57. 57.

    Li Y, Chapman SJ, Nicol GW, Yao H. Nitrification and nitrifiers in acidic soils. Soil Biol Biochem. 2018;116:290–301.

    CAS  Article  Google Scholar 

  58. 58.

    Jones GB, Alpuerto JB, Tracy BF, Fukao T. Physiological effect of cutting height and high temperature on regrowth vigor in orchardgrass. Front Plant Sci. 2017;8:805–805.

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Miller AJ. Ion-selective microelectrodes for measurement of intracellular ion concentrations. Meth Cell Biol. 1995;49:275–91.

    CAS  Article  Google Scholar 

  60. 60.

    Miller AJ, Zhen R-G. Measurement of intracellular nitrate concentrations in Chara using nitrate-selective microelectrodes. Planta. 1991;184:47–52.

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Chen M, Chen B, Marschner P. Plant growth and soil microbial community structure of legumes and grasses grown in monoculture or mixture. J Environ Sci. 2008;20:1231–7.

    CAS  Article  Google Scholar 

  62. 62.

    Zhou Y, Zhu H, Fu S, Yao Q. Variation in soil microbial community structure associated with different legume species is greater than that associated with different grass species. Front Microbiol. 2017;8:1007.

    PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Bardgett RD, Mommer L, De Vries FT. Going underground: root traits as drivers of ecosystem processes. Trends Ecol Evol. 2014;29:692–9.

    PubMed  Article  Google Scholar 

  64. 64.

    Ourry A, Kim TH, Boucaud J. Nitrogen reserve mobilization during regrowth of Medicago sativa L. (relationships between availability and regrowth yield). Plant Physiol. 1994;105:831–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Pagès L. Branching patterns of root systems: quantitative analysis of the diversity among dicotyledonous species. Ann Bot. 2014;114:591–8.

    PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Del Bianco M, Kepinski S. Building a future with root architecture. J Exp Bot. 2018;69:5319–23.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Macdonald LM, Paterson E, Dawson LA, McDonald AJS. Short-term effects of defoliation on the soil microbial community associated with two contrasting Lolium perenne cultivars. Soil Biol Biochem. 2004;36:489–98.

    CAS  Article  Google Scholar 

  68. 68.

    Marshall AH, Collins RP, Humphreys MW, Scullion J. A new emphasis on root traits for perennial grass and legume varieties with environmental and ecological benefits. Food Energy Secur. 2016;5:26–39.

    PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Paez-Garcia A, Motes C, Scheible W-R, Chen R, Blancaflor E, Monteros M. Root traits and phenotyping strategies for plant improvement. Plants. 2015;4:334.

    PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Helliwell JR, Sturrock CJ, Miller AJ, Whalley WR, Mooney SJ. The role of plant species and soil condition in the physical development of the rhizosphere. Plant Cell Environ. 2019;42:1974–86.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Mawdsley JL, Bardgett RD. Continuous defoliation of perennial ryegrass (Lolium perenne) and white clover (Trifolium repens) and associated changes in the composition and activity of the microbial population of an upland grassland soil. Biol Fert Soils. 1997;1997(24):52–8.

    Article  Google Scholar 

  72. 72.

    Miller AJ, Le Besnerais P-H, Malaurie H. Soil chemistry sensor 2014—Patent PCT/GB2013/053377—WO/2014/096844—G01N 27/403 (2006.01).

  73. 73.

    Robins JG, Lovatt AJ. Cultivar by environment effects of perennial ryegrass cultivars selected for high water soluble carbohydrates managed under differing precipitation levels. Euphytica. 2016;208:571–81.

    Article  Google Scholar 

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Acknowledgements

We thank the British Association of Green Crop Driers for supporting the project and for supplying seed resources, and Marco Fioratti Junod for his help with soil analysis at JIC. We are also grateful to receive seeds from Mr Ianto Thomas and Dr Danny Thorogood from the Genebank at The Institute of Biological, Environmental and Rural Sciences (Aberystwyth University, Aberystwyth, Wales, UK).

Funding

This project was supported by grants BB/P004474/1 (AJM), BB/J00PR9796 and BB/J000PR9799 from the Biotechnology and Biological Sciences Research Council (BBSRC) and the John Innes Foundation. NMC was supported by an ICASE studentship from the BBSRC, grant BB/M015203/1.

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MNC and AJM proposed the idea, MNC conduced the experimental work and created a manuscript draft. All the authors revised the manuscript. All the authors read and approved the manuscript.

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Correspondence to Anthony J. Miller.

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Supplementary Information

Additional file 1: Figure S1.

Schematic diagram of NO3-selective sensor construction. Figure S2. Lolium perenne monocrop column experiment NO3-selective sensor data for ‘No crop’ and ‘Monocrop 1′. Figure S3. Monocrop column experiment NO3-selective sensor data for ‘Monocrop 2′ and ‘Monocrop 3′. Figure S4. Lolium perenne and Medicago sativa intercrop column experiment NO3-selective sensor data for ‘No crop’ and ‘Intercrop 1′. Figure S5. Intercrop column experiment NO3-selective sensor data for ‘Intercrop 2′ and ‘Intercrop 3′. Figure S6 Column experiment NO3-selective sensor data for ‘Monocrop 3′ and ‘Intercrop 3’.

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Capstaff, N.M., Domoney, C. & Miller, A.J. Real-time monitoring of rhizosphere nitrate fluctuations under crops following defoliation. Plant Methods 17, 11 (2021). https://doi.org/10.1186/s13007-021-00713-w

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Keywords

  • Alfalfa
  • Forage crops
  • Grass
  • Leaching
  • Lolium perenne
  • Management practices
  • Medicago sativa
  • Nitrate
  • Rhizosphere
  • Soil profile
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