In order to overcome these problems and allow for an efficient and sustainable use of digestates on sandy marginal substrates for plant biomass production, we aim to adapt the digestate fertilization to the Controlled Uptake Long Term Ammonium Nutrition (CULTAN) method proposed by Sommer (2003). According to this method developed for mineral NH4 +-rich fertilizers, fertilizers are not applied broadcast but injected locally directly into the rhizosphere (Sommer, 2005). As the plant roots adjust to the heterogeneous distribution of nutrients in the rhizosphere, they can increase the rooting density and share of fine roots in the fertilized zone (Nkebiwe et al., 2016).

In recent years, the method has been adapted to organic fertilizers that are rich in NH4 + like sludge, manure slurries and digestates (Dell et al., 2000; Pote et al., 2011; Bittman et al., 2012). In various studies on agricultural soils planted with conventional agricultural crops or grasslands, a positive impact of a localized application of organic fertilizers on drought (Garwood and Williams, 1967; Ma et al., 2009), nitrate leaching (Baker, 2001), root growth and nutrient use efficiency (Hodge, 2004; Weligama et al., 2008) as well as NH3 volatilization (Nyord et al., 2008) was shown. All these positive effects of localized organic fertilizer placement sum up in increased biomass yields (Cooke, 1954; Jing et al., 2012). For more information about the underlying processes of the methodology we refer to the review by Nkebiwe et al. (2016), who also focused on localized placement of organic fertilizers into the rhizosphere.

The fertilizer placement technology, already well established on conventional agricultural sites, is however, not yet used for the application of digestates to perennial energy crop cultures grown on marginal sandy field sites. Still, the technology of controlled fertilizer placement tackles major problems of digestate fertilization on sandy marginal sites and is therefore of interest for investigation. In this study, we tested digestate fertilization, applied as localized nutrient depots in the rhizosphere of the perennial energy crop Sida hermaphrodita, grown on a sandy marginal substrate. We compared localized to a broadcast digestate fertilization and mineral fertilization using a conventional NPK-fertilizer and an unfertilized control. S. hermaphrodita is a prairie forb species from North America that grows well on sandy or rocky soils with low organic matter content, producing relatively high biomass yields even with low nutrient levels in the soil (Spooner et al., 1985; Borkowska et al., 2009). In order to follow the structural adaptation of the root system of S. hermaphrodita to the different fertilizer applications a rhizotron experiment was performed under controlled greenhouse conditions. To verify our results under conditions that resemble more the field conditions, we also conducted a 3-year outdoor mesocosm experiment where root proliferation of depot-fertilized plants were followed by using mini-rhizotrons.

The study was designed to answer the following research questions and hypotheses:

Question 1: What causes the reduced root growth on S. hermaphrodita seedlings following digestate application as fertilizer on marginal soil and how does it develop over time?

Hypothesis 1: The root growth is affected by the nitrogen turnover of the fertilizer. High concentrations of ammonium and nitrite following the fertilizer application instantly are toxic to plant roots. The mineralization of ammonium to nitrate will allow enhanced root growth at a later stage.

Question 2: What could be an effective measure to apply digestate fertilization and promote root growth at the same time?

Hypothesis 2: The application of localized patches of digestate (digestate depot) into the rhizosphere will promote root

growth, as the major part of the rhizosphere remains unaffected by the fertilization and therefore by initial adverse effects on root growth.

### MATERIALS AND METHODS

### Study Site and Plant Cultivation

A glasshouse rhizotron study and an outdoor mesocosm experiment were performed at Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, Germany (50◦ 540 3400N 6◦ 240 4700E). For the glasshouse experiment, 80 Sida hermaphrodita seedlings of BBCH stage 12-13 (Jablonowski et al., 2017) were transplanted in February 2016 to a rhizotron (inner dimensions: height: 75 cm; width: 36 cm; depth: 2.6 cm) filled with a sandy substrate (RBS GmbH, Inden, Germany; particle size: ≤1 mm; pH 6.6, no detectable amounts of N, P, K, and C), accounting for 80 rhizotrons in total. Rhizotrons were irrigated manually three times a week to 60–70% of substrate WHC. The detailed climate data for temperature and exposure to light over the 3 months experimental time are presented in **Table 1**.

An outdoor mesocosm experiment was conducted using 21 containers, each filled with 250 L of the same sandy substrate as used in the rhizotrons. Big outdoor containers were chosen to keep growing conditions of S. hermaphrodita as close to field conditions as possible (Poorter et al., 2012a, 2016). Seedlings of S. hermaphrodita of BBCH stage 13–14 were transplanted into the mesocosms in May 2014 (Jablonowski et al., 2017). The detailed establishment of S. hermaphrodita plants into the mesocosms was described earlier (Nabel et al., 2016). The climate data over the 3-year experimental time can be found in **Table 1**.

### Treatments and Fertilization

Rhizotrons and mesocosms received digestate fertilization, applied either homogeneously incorporated (here after referred to as "broadcast fertilization") or as a localized depot (here after referred to as "depot fertilization") in the rhizosphere, broadcast mineral fertilization or no fertilizer supplement. The digestate was obtained from an operating biogas plant using maize silage as feedstock (digestate dry matter content: 7.2%; Ntotal: 0.53%; NH4 +: 0.32%; P: 0.14%; K: 0.68%; Mg 0.037%; Ca: 0.16%; S: 0.03%; organic matter: 5.3%, C:N ratio: 6; pH 8.2; all values referring to fresh weight; ADRW Naturpower GmbH and Co. Kg, Titz-Ameln, Germany). An NPK-fertilizer with an N:P:Kratio similar to the digestate and a high share of ammonium was chosen to allow a comparison between the mineral and the organic digestate fertilization (NPK-fertilizer composition: N: 15% [1% nitrate; 9.5% ammonium; 4.5% isobutylidenediurea]; P: 5%; K: 8%; Mg: 3%; Compo Rasendünger, Compo GmbH, Münster, Germany). Both fertilizers were calculated to simulate a total N application of 40 t digestate ha−1 , which was identified earlier as the optimum dose of digestate fertilization for S. hermaphrodita grown on sandy substrate (Nabel et al., 2014). In mesocosms fertilization treatments were applied in May 2014–2016 to the soil surface and immediately incorporated to minimize possible losses of nitrogen via NH3 (Hayashi et al., 2009). In rhizotrons, broadcast fertilization treatments were applied by homogeneously mixing fertilizers into the top 20 cm of the rhizotrons. For the localized digestate depot fertilization, the same amount of digestate was applied locally as a patch of 4.6 cm diameter. In mesocosms, the digestate depot was injected at a 20 cm distance from the shoot of S. hermaphrodita, at 30 cm depth. In rhizotrons, digestate depots were placed in 15 cm depth and 6 cm distance to the rhizotron wall (**Figure 1**). In rhizotrons each fertilization treatment was applied with 24 replicates (8 replicates × 3 harvests), the unfertilized control treatment was applied in eight replicates (only one harvest). In mesocosms all treatments were applied in six replicates (one harvest after each growing season).

### Measurements

Rhizotrons with S. hermaphrodita plants were placed into a novel phenotyping platform enabling simultaneous and non-destructive measurements of root and shoot growth of rhizotrons-grown plants (GrowScreen-Rhizo2, developed at Forschungszentrum Jülich GmbH, Germany). The design of the new phenotyping system GrowScreen-Rhizo2 is based on the experiences with the GrowScreen-Rhizo1 platform published in 2012 (Nagel et al., 2012). However, the GrowScreen-Rhizo1 concept of having fixed positions for rhizotrons and a moving cabinet for imaging the rhizotrons has been changed to enable higher flexibility and modularity. In the new platform, rhizotrons are moved in trays (five rhizotrons per tray) on a conveyor system and transported to an imaging station which has a fixed position. The inclination angle of the rhizotrons is adjusted always to 45◦ with the transparent plate of the rhizotrons facing downward to force the roots to grow toward the transparent plate. Inside the imaging station individual rhizotrons are taken out of the tray and positioned automatically in front of a high resolution camera (AV GT6600, Allied Vision Technologies, Germany; resolution 142 µm per pixel) which is taking one image of the whole transparent rhizotron surface (**Figure 1C**). During image acquisition, the door of the imaging station is closed automatically with rolling cutter gates to prevent undesired light reflections on the Plexiglas plate of the rhizotrons and the roots are illuminated using two LED panels (30 W 40 mm × 40 mm × 940 mm, color temperature 4000 K, item Industrietechnik GmbH, Germany). After imaging, the gate is opened and the rhizotron is placed back to into the tray completing the routine. Pictures were taken automatically once a week. In addition, plants were harvested at 30, 60, and 90 days after transplanting seedlings to determine leaf, stem, and root biomass. For harvest, rhizotrons were opened and the sandy substrate was carefully washed out with tap water and roots were cleaned thoroughly. Leaves, stems, and roots were cut and separately dried at 70◦C to constant weight to determine the biomass dry weight. The pictures of the roots, acquired by GrowScreen-Rhizo2 during growth were evaluated for total visible root length and root distribution by using the software GROWSCREEN-Root in combination with a graphic tablet with pens (Wacom Cintiq 21UX, 31 CANCOM Deutschland GmbH, Düsseldorf, Germany) (Nagel et al., 2009). In order to visualize the spatial distribution of roots for any given treatment, we wrote an additional software to combine the data from all root images

TABLE 1 | Climate data for the greenhouse rhizotron experiment and the outdoor mesocosm experiment: mean temperature, precipitation and daily light integral (DLI) values during the experimental time from 2014 to 2016 at the Forschungszentrum Jülich GmbH (50◦5304700 north and 6◦2503200 east; 80 m a.s.l.).

of each treatment in one image, representing root structure and distribution for any given treatment. To create these images, the area of a given root picture, as acquired by the GrowScreen-Rhizo2 system, was subdivided into a grid of cells, sized 25 × 25 pixels each. Each cell was initialized with zero. Every time, any part of the root structure was found in a given cell, the value of this particular cell increased by one. Further limiting the maximum increase per pixel per plant to one, allowed us to visualize root distribution in relation (%) to the number of plants per treatment. The generated images were converted from gray scale single channel images to three channel false-color images (Ware, 1988). This conversion was done using Opencv's build-in function applyColorMap using the JET colormap (Bradski, 2000).

In mesocosms, the above-ground biomass was harvested at the end of the growth season in 2014, 2015, and 2016, and dried at 70◦C to constant weight to determine the biomass dry mass. In 2016, the root biomass of four replicates for each treatment was determined by washing out the substrate, cleaning roots thoroughly and drying them at 70◦C to constant weight. Root growth of plants grown in mesocosms was monitored non-invasively by mini-rhizotrons, which are Plexiglas tubes horizontally installed in 30 and 60 cm depth of the mesocosms. Pictures of root systems were taken after 6, 18, and 30 months after planting, using the CI In-Situ Root Imager (CID Bio-Science Inc., Camas, WA, United States). The pictures were than further processed as described above by using the software GROWSCREEN-Root to determine root length and distribution.

Daily light integral values (DLI) were directly measured at place on the rhizotron and mesocosm facility, employing a LI-COR: Li-190 device (LI-COR Environmental – GmbH, Bad Homburg, Germany).

Soil samples were taken at the date of destructive biomass harvests in each rhizotron. In unfertilized-, mineral-fertilized and broadcast digestate-fertilized rhizotrons, one mixed sample was taken in a depth from 0 to 20 cm and a second in the depth of 25–50 cm. In rhizotrons with digestate depot fertilization, one sample was taken in a radius of 3, 6, and 12 cm around the digestate patch, in each rhizotron (**Figure 1**). Soil samples were stored at 4◦C until determination of NH4 +, NO2 −, NO3 −, electric conductivity and pH were performed. NH4 +, NO2 −, and NO3 − were measured via ion chromatography (Dionex DX-500, AS23; eluent: 0.8 mM sodium bicarbonate and 4.5 mM sodium carbonate) in 0.1 M KCl extraction. Soil pH was determined using standard electrodes (Hanna Instruments pH 209 pH-meter, Vöhringen, Germany), using 0.01 M CaCl2 solution at 20◦C. Nitrogen content of S. hermaphrodita leaves was determined by elemental analysis (VarioELcube, Elementar Analysensysteme GmbH, Langenselbold, Germany) after milling dried leaves for 60 sec. in a ball mill at 30 Hz (Retsch Mixer Mill MM 400, Retsch GmbH, Haan, Germany).

### Statistical Analysis

fpls-09-01095 July 26, 2018 Time: 17:6 # 5

The rhizotron experiment consisted of four treatments (digestate broadcast fertilization, localized digestate depot fertilization, mineral broadcast fertilization, and unfertilized control) with three sampling dates (30, 60, and 90 days), with eight repetitions per treatment. The mesocosm experiment had the same treatments in six repetitions of which the above ground biomass was harvested at the end of the vegetation period in 2014, 2015, and 2016. The below-ground biomass of the mesocosms was harvested (n = 4) for each treatment in October 2016. Statistical analysis was performed with analysis of variance (ANOVA) with an a posteriori test in R 3.0.3 (The R Foundation for Statistical Computing, 2014) using the work package "Agricolae" (de Mendiburu, 2014).

# RESULTS

### Soil Analysis in Rhizotrons

Thirty days after transplanting the seedlings, all fertilization treatments showed a rapid conversion from ammonium to nitrate (**Table 2**). However, for the two digestate fertilized variants (broadcast and depot) also the intermediate product of mineralization, nitrite, was found in high concentrations, especially in a 3–6 cm radius around the digestate depot. In digestate broadcast fertilized rhizotrons, 85% of the measured nitrogen was found in the form of nitrate. In digestate depot fertilized rhizotrons, 20% of the measured nitrogen remained as ammonium and 33% was found as nitrite. Sixty days after planting, nitrite was not detected, while nitrate concentrations reached their maximum levels in the digestate-fertilized horizon of broadcast fertilized digestate rhizotrons as well as around the depot fertilized zones. Nitrogen in the form of ammonium was only detected in a 6–12 cm radius around the digestate depots. In digestate broadcast and NPK fertilized rhizotrons, only minor amounts of ammonium were detected in the fertilized horizon. Ninety days after planting, almost all nitrogen in all treatments was present in the form of nitrate, mainly located in the fertilized horizons of digestate broadcast -, NPK fertilization and a 3–6 cm radius around the digestate depots.

After 30 days, nitrogen fertilization, regardless of application technique, lowered the pH to 6.4. Contrastingly, the pH of the sandy substrate outside the 12 cm zone that was affected by depot fertilization had values of pH 7.0. Only within the radius closest to the digestate depots (<3 cm) pH 8.5 was reached. In the following 2 months, pH values generally increased and leveled off between pH 7 and 7.5. Lower pH values (6.6) were only found within a 6 cm radius around the digestate depots and the NPK fertilized horizon of mineral fertilized rhizotrons.

### Root Growth and Root System Architecture Rhizotrons

Thirty and sixty days after planting of S. hermaphrodita into rhizotrons, the form and application technique of the different fertilizers had a strong influence on the distribution of roots (**Figure 2**). However, root mass did not differ significantly between treatments until day 60 but differed strongly in their foraging behavior in the rhizotron experiment (**Figure 3**). Unfertilized control and digestate depot fertilized plants accessed the maximum depth of the rhizotrons within 60 days. Digestate broadcast and NPK fertilized plants only reached a rooting depth of 40 cm with 99% of the measured root length located in the fertilized horizon for digestate broadcast and 60 % for NPK fertilization, respectively. Digestate depot fertilization resulted in a strongly heterogeneous distribution of roots over the rhizotrons. Even though after 30 and 60 days when roots accessed deeper areas of the rhizotrons, no roots were present within a 6 cm radius around the digestate depots. After 60 days, only 1% of the total root length was located within this radius. After 90 days, the root growth pattern changed dramatically and 45% of the root biomass of digestate depot fertilized plants were found within this 6 cm radius around the digestate depots resulting in the formation of a dense root cluster around the digestate depot. Even after 90 days, roots in NPK and digestate broadcast fertilized rhizotrons did not reach the maximum depth of the rhizotrons and major part of their roots remained located in the fertilized horizon (60% for NPK and 80% for digestate broadcast fertilization).

### Mesocosms

After 1 year of growth, S. hermaphrodita in mesocosms located 60% of the measured root length in a depth of 30 cm when fertilized with broadcast digestate or NPK-fertilizer. When fertilized with localized digestate depot, a root cluster was visible at 30 cm depth, accumulating 90% of the measured root length in 30 cm depth, while unfertilized control plants had 70% of the measured root length in the same depth. After 3 years root distribution generally shifted more toward the lower soil levels with 60% of the measured root length at 60 cm depth for digestate broadcast fertilized plants and 55% for NPK fertilized plants. Digestate depot fertilized plants as well as control plants still had the higher share of the measured root length in 30 cm depth with 55 and 60%, respectively.

### Biomass and Mass Fraction Rhizotrons

In rhizotrons biomass increased on average by 40% from 0.7 g after 1 month to 1 g after 2 months, with no significant difference between treatments (**Figure 3**). However, after 90 days the biomass of digestate depot fertilized plants had increased by a factor 5–6.5 g. Broadcast fertilization with digestate or NPK did

### TABLE 2 | Soil and leaf analysis of S. hermaphrodita grown in rhizotrons.

Toxic nitrite concentrations occur especially after 30 days in a close radius to the digestate depot. All fertilization treatments were adjusted to a digestate application of 40 t ha−1 . Digestate broadcast: homogeneously incorporated in the top 20 cm; Digestate depot: localized digestate application; NPK: homogeneously incorporated in the top 20 cm; Control: no fertilization. Rhizotrons n = 8 replicates for each treatment and sampling date; ± indicates the standard error.

not result in a significant increase in biomass compared to the unfertilized control. At that time, unfertilized control plants as well as NPK and digestate depot fertilized plants accumulated approx. 50% of their total biomass in roots. Further, for digestate depot fertilized plants, half of the root biomass was located within a 6 cm radius around the digestate depot (**Figure 4**). Plants that received digestate broadcast fertilization had a 40% smaller root mass fraction than unfertilized control plants while localized digestate depot fertilization and mineral NPK fertilization did not show a significant difference to unfertilized control plants.

### Mesocosms

After three growing seasons in mesocosms, no significant difference between total biomass of plants fertilized with biogas digestate broadcast or depot fertilization was found (**Figure 3**). Depot fertilized plants developed a 25% larger root system than broadcast fertilized plants. NPK fertilized plants produced 20% less biomass than digestate-fertilized plants but six times more than the unfertilized control plants. Digestate depot fertilization resulted in nine times more biomass compared to the unfertilized control and broadcast digestate fertilization seven times, respectively.

# Leaf Nitrogen Content in Rhizotrons

In rhizotrons, the nitrogen concentration of the leaves for digestate broadcast and NPK fertilized plants was between 4 and 5% throughout the experimental time of 3 months (**Table 2**). After 30 days, digestate depot fertilized rhizotron plants only contained 2.9% of nitrogen in their leaves. After 60 days, nitrogen concentration increased to 4.1% and after 90 days it was in the same range as measured for digestate broadcast and NPK fertilized plants. Unfertilized control plants only contained 1.7% of nitrogen.

### DISCUSSION

### Nitrogen Turnover in Rhizotrons

In accordance with the processes described of the CULTAN Method – proposed by Sommer (2003) for mineral ammonium fertilizers, the main nitrogen form of the digestate was

FIGURE 2 | Hit-Map of rhizotrons. In digestate depot fertilized rhizotrons roots of S. hermaphrodita avoid the digestate depot zone for 60 days but form a dense root cluster in the depot zone after 90 days. Colors indicate the number of replicates that grew roots at the specific pixel (black: none; blue: 1–2; green 3–4; orange 5–6; red 7–8 replicates). Orange frames illustrate the fertilized zones of the rhizotrons. All fertilization treatments were adjusted to a digestate application of 40 t ha−1 : Control: no fertilization; Digestate broadcast: homogeneously incorporated in the top 20 cm; Digestate depot: localized digestate application; NPK homogeneously incorporated in the top 20 cm; n = 8 replicates for each treatment and time of harvest.

digestate depot; SMF, stem mass fraction; LMF, leaf mass fraction. All fertilization treatments were adjusted to a digestate application of 40 t ha−1 . Digestate broadcast: homogeneously incorporated in the top 20 cm; Digestate depot: localized digestate application; NPK: homogeneously incorporated in the top 20 cm; Control: no fertilization; n = 8 replicates for each treatment. Values labeled with the same letter are not significantly different at p < 0.05. Error bars indicate the standard error.

mineralized from ammonium to nitrate. For digestate broadcast and NPK fertilization 80–90% of the measured nitrogen was available in the form of nitrate as early as 30 days after fertilization. Nitrate, compared to ammonium, is very mobile in the soil. Accordingly, nitrate was also the form of nitrogen that was found in the non-fertilized lower horizon of the rhizotrons. Since to the lower horizon, no nutrients were added, they must have arrived there by leaching (Baker, 2001). In rhizotrons receiving digestate depot fertilization, conversion from ammonium to nitrite was faster than the conversion from nitrite to nitrate. Accordingly, nitrite accumulated in a radius of 6 cm around the digestate depot. In digestate depot fertilized rhizotrons, digestate was applied locally in very high amounts and thus contained highest NH4 + concentrations. The combination of a regular watering of the rhizotrons with high concentration of organic material with a C/N-ratio of approx. 6 in the depot fertilized zone may have allowed for high microbial activity and created an environment with partially anoxic conditions that favors the formation of nitrite (Nelson and Bremner, 1969; Van Cleemput and Samater, 1995; Möller and Müller, 2012). In addition, the alkaline pH near the digestate fertilized zone favors the formation of nitrite in sandy soils (Greco et al., 2012). Once nitrite starts to accumulate in the soil, it can inhibit microbial activity. In a feedback loop, this may have further favored the formation of even more nitrite as transformation from ammonium to nitrite is not as much effected as the succeeding transformation from nitrite to nitrate (Grant et al., 1979). Nitrate, the mobile form of nitrogen was found from 12 cm distance to the digestate depot (Baker, 2001). Ammonium was only found up to a distance of 6 cm from the digestate depot, as it is not very mobile in soils (Clarke and Barley, 1968; Pang et al., 1973). In our study, 60 days after fertilization, ammonium was almost completely mineralized to nitrate, leaving only traces of ammonium and nitrite in digestate broadcast and NPK fertilized

rhizotrons. In digestate depot fertilized rhizotrons, also after 60 days, high concentrations of ammonium were present, indicating a delayed conversion from ammonium to nitrate compared to broadcast fertilization (Grant et al., 1979; Sommer, 2005). Even though the high pH of >8 in the near depot zone was found to be very favorable for the mineralization of organic N-compounds from corn residues, like digestate (Deng and Tabatabai, 2000). The pH in the depot zone dropped to a range of pH 6.8–7.5 until day 60 after fertilization partially due to diffusion and partially due to the release of protons in the mineralization process (Giusquiani et al., 1995; Barak et al., 1997). Yet, also in digestate depot fertilized rhizotrons almost all ammonium was mineralized to nitrate within 90 days leaving only traces of nitrite and ammonium near the digestate depot.

# Nutrient Status and Root-Growth

### Rhizotrons

The root growth of S. hermaphrodita plants responded to the different fertilizer distributions and consequently different available forms of nitrogen over time. Even though there were no significant differences between fertilizer treatments in terms of biomass, clear differences in root distribution were observed (**Figures 2**, **3**). The broadcast application of digestate and NPK fertilizer, combined with a regular irrigation resulted in optimal supply of nutrients for seedlings of S. hermaphrodita (also indicated by the leaf nitrogen content >4%), reducing the necessity of seedlings to invest into root-growth (Forde and Lorenzo, 2001). Contrastingly, no nutrients were available in unfertilized control plants, while nutrients for digestate depot fertilized plants were not available in the rhizosphere (observable via the low leaf nitrogen content <3%). Consequently, plants invested into a deep and far-reaching root system to gain access to nutrients. These findings match the theory behind the CULTANmethod proposed by Sommer (2003) and also correspond well with results reviewed by Nkebiwe et al. (2016), focusing on controlled placement of fertilizers rich in ammonium. Additionally, the roots of plants growing in digestate depot fertilized rhizotrons, avoided to colonize a zone of approximately 6 cm radius from the digestate depot. This growing pattern was even more pronounced after 60 days. Here, roots in zones of the rhizotrons, not effected by fertilization, reached already the bottom of rhizotron while still very little root growth was observed in the 6 cm radius zone around the digestate depot. Given the high concentrations of ammonium and nitrite found within the 6 cm radius zone around the digestate depot in combination with root damages observed at root tips that contacted the area (**Figure 5**), we conclude that nitrite and ammonium toxicity are the main causes for the avoidance of this zone by the roots within the first 60 days after transplanting (Pan et al., 2016). Again, this temporarily zone of avoidance in a localized depot has been described earlier by Sommer (2003). Oke (1966) found that on sandy soil already nitrite concentrations >40 ppm can be toxic, especially to plant seedlings. In this experiment, we measured nitrite concentrations of <90 ppm in the depot zone. Further, ammonium toxicity was already studied intensively and observed when organic fertilizers, based on biogenic residues after anaerobic digestion were applied (Shaviv, 1988; Salminen and Rintala, 2002; Nkebiwe et al., 2016; Pan et al., 2016). Overall, results show consistently that high ammonium doses from digestate negatively affect seedling growth, mainly due to hampered root development.

After 60 days, the root growth pattern in depot-fertilized rhizotrons changed dramatically and 90 days after planting a dense root cluster of S. hermaphrodita around the nutrient rich digestate depot was observed. Root proliferation in nutrient rich patches was not observed yet for S. hermaphrodita, but for several herbaceous plant species (Rajaniemi and Reynolds, 2004). Plants can increase their rooting density in nutrient rich patches to forage for these nutrients, increasing the nutrient uptake efficiency and thus confer a competitive advantage toward weeds without access to nutrient patches (Robinson et al., 1999; Hodge, 2004; Rajaniemi, 2007). The increased access to nutrients is also shown by a parallel increase in leaf nitrogen content from only 2.9% at 60 days after transplanting to 4.4% measured in leaves of digestate depot fertilized S. hermaphrodita plants 90 days after transplanting. The massive increase of root biomass in the depot zone between day 60 and day 90 after planting, contributing to 50% of the total root mass of digestate depot fertilized S. hermaphrodita plants, resulted in the highest root mass across the four treatments (**Figure 1**). Also in our mesocosm study, the formation of dense root clusters in the digestate depot fertilized zone was observable throughout the 3-year experimental time under outdoor conditions (**Figure 6**).

In rhizotrons, after 90 days, NPK and digestate broadcast fertilization did not result in maximum possible rooting depth (i.e., depth of the rhizotron), and the major share of the root systems were located in the fertilized horizon. As plants were watered throughout the experiment, and roots had already sufficient access to the nutrients (indicated by the leaf nitrogen content >4%) plants did not invest into the formation of a deep reaching root system (Poorter et al., 2012b). Overall, the root systems that developed following digestate broadcast treatment remained smaller than the root systems of NPK fertilized plants. Phytotoxic effects of digestates, particularly affecting root growth of plants at early developmental stages, have been reported earlier and have besides ammonium toxicity also been explained by high concentrations of organic acids in fresh digestates (Salminen and Rintala, 2002; Abdullahi et al., 2008; Drennan and DiStefano, 2014). Control plants that had no access to nitrogen in the sandy substrate, resulting in a low leaf nitrogen content <2% and high investment into a deep reaching root-system, resulted in the highest root mass fraction of all variants (**Figure 4**) and thereby corresponded well to findings of previous studies (Poorter et al., 2012b).

### Mesocosms

In mesocosms, the root mass fraction of S. hermaphrodita was generally higher after three growing seasons than found in rhizotrons. We explain this by the fact, that S. hermaphrodita is a perennial species, of which the shoots die off at the end of each vegetation period, while the major part of the root system stays intact and also serves as reservoir for a successful regrowth

in the following growing period (Borkowska et al., 2009). As we only measured the root mass after three growing periods of mesocosms grown plants, this has a clear effect in the root mass fraction when compared to root mass measured after 90 days in rhizotrons.

# Biomass Yield

### Rhizotrons

In rhizotrons, the above ground biomass increased gradually until day 60 of the experiment as plants invested mainly in root-growth. Accordingly, no significant differences in biomass of S. hermaphrodita were observed at day 60 throughout all treatments. This changed drastically when roots in digestate depot fertilized rhizotrons started to access the nutrient rich depot zone. The intense foraging for nutrients resulted in a faster increase in stem and leaf biomass as observed in all other variants. In his meta-analysis over 39 studies, Nkebiwe et al. (2016) also found a positive effect of localized fertilizer placement on biomass yield, especially for early plant development stages.

Broadcast application of digestate yielded less biomass than NPK fertilization, even though leaf and soil analysis showed an adequate supply with nutrients and water for both variants. However, the root system of digestate broadcast fertilized plants was smaller and less outstretched as the root system of NPK fertilized plants as discussed earlier.

### Mesocosms

After three vegetation periods of S. hermaphrodita in mesocosms under outdoor conditions, the different fertilization treatments showed different responses with respect to biomass than the same treatments in the rhizotrons. No significant biomass yield difference between digestate depot and digestate broadcast fertilization was observed. As discussed earlier, the localized placement of fertilizers has beneficial effects, especially in the early development stages (Nkebiwe et al., 2016). Further, the phytotoxic effects of the digestate, especially observed on the root growth of digestate broadcast fertilized plants in the rhizotrons, also relates to the early development stages of plants (Möller and Müller, 2012). Accordingly, it cannot be expected that effects last over this 3-year experimental time, especially as S. hermaphrodita is a perennial plant that keeps its extensive root system over the years (Borkowska et al., 2009).

The fact that digestate fertilization, independently from the type of application, produced higher biomass yields than mineral NPK fertilization, can be related to an increase of soil fertility over the years and has been discussed in a separate publication (Nabel et al., 2017).

### General Discussion

Localized application of biogas digestate as digestate depot in the rhizosphere of S. hermaphrodita seedlings fostered the successful establishment of S. hermaphrodita on marginal soils. Digestate depot fertilization strongly increased the rooting depth of seedlings, allowing improved access to water which could make them less susceptible to drought stress (Ma et al., 2009;

FIGURE 6 | Root distribution of S. hermaphrodita cultivated in mesocosms measured by mini-rhizotrons after 2 years in 30 cm depth: roots form a dense root cluster in the depot-fertilized zone. Control: no fertilization; Digestate broadcast: homogeneous surface application; Digestate depot: localized digestate application; NPK homogeneous surface application. Pictures show a representative example of n = 4 replicates for each treatment and time of harvest.

Su et al., 2015). Drought can be the main growth-limiting factor, especially on marginal sandy soils with low WHC, making this deep rooting side effect of depot fertilization a positive one for adapting to drought regimes (Borkowska et al., 2009). The rootcluster formation around the digestate depot zone allowed the S. hermaphrodita seedling good access to the nutrients, resulting in rapid increase of biomass. Earlier studies showed that this can be an efficient way to make plants more competitive against weeds and thus lower the need for additional weed control (Blackshaw et al., 2002; Melander et al., 2005). Even though in the presented mesocosms study digestate depot fertilization did not show an increased biomass yield compared to digestate broadcast fertilization after three growing seasons, we still see a high potential in the application of digestate in the form of localized depots in the rhizosphere in marginal soils. As S. hermaphrodita is a perennial crop with an extensive root system, any form of soil cultivation in order to incorporate the digestate after a broad surface application via, e.g., plowing could do severe harm to the plants. In addition, a lack of digestate incorporation could cause high volatile losses of ammonia, with high environmental costs (Rochette et al., 2009; Ma et al., 2010). A controlled placement of digestate depots in the rhizosphere, e.g., via spike-weal injection, would minimize the impact on soil and also minimize volatile losses of ammonia (Kozlovský et al., 2009). As this application technique might also contribute to an increased competitiveness of S. hermaphrodita over weeds and thus reduce the need to weed control, it would further strengthen the concept of extensive biomass production on marginal soils.

### CONCLUSION

The application of digestate as a fertilizer in local depots into the rhizosphere of S. hermaphrodita grown in a marginal sandy substrate in rhizotrons and mesocosms resulted in a dense root formation around the depot-fertilized zone. After 3 months, half of the root biomass of depot-fertilized plants grown in rhizotrons was associated with the depot zone as a dense rootcluster; in contrast, root growth in the digestate broadcast fertilization treatment was strongly reduced. Overall, digestate depot fertilization resulted in a fivefold increase of total biomass compared to digestate broadcast fertilization in rhizotrons. Under outdoor conditions in a 3-year mesocosm experiment the increase in plant biomass was not significant, however. We conclude that digestate depot fertilization can contribute to an

### REFERENCES

improved cultivation of S. hermaphrodita as a perennial energycrop on marginal soils, especially for a successful establishment of seedlings, but its potential growth stimulation effect now needs more research under field conditions.

# AUTHOR CONTRIBUTIONS

MN, SDS, HP, RK, and NDJ conceived the study. MN performed the main experiments and conducted the research under the supervision of SDS, HP, RK, and NDJ. MN wrote the manuscript. KN planned and designed GrowScreen-Rhizo2. CD and CB processed images and analyzed data. VT helped with study design and data evaluation. All authors discussed the results, assisted in the manuscript preparation, and contributed to revisions.

# FUNDING

This study conducted at Forschungszentrum Jülich GmbH, IBG 2: Plant Sciences was institutional funded by the Helmholtz Association (POFIII, Key Technologies for the Bioeconomy). Part of this work was supported by the German Plant-Phenotyping Network (DPPN), which is funded by the German Federal Ministry of Education and Research (BMBF, FKZ: 031A053).

# ACKNOWLEDGMENTS

The digestate and the sand were kindly provided by ADRW Naturpower GmbH and Co. Kg, Ameln, and Rheinische Baustoffwerke, Inden, respectively. The kind provision of the bins by EGN mbH, Viersen, used as mesocosms for plant cultivation is highly appreciated. Many thanks to Lucy Harrison, Marlene Müller, Edelgard Schölgens, Sabine Willbold, and colleagues from ZEA-3 for the maintenance, sampling, and chemical analysis of the plant and soil samples. We thank Andre Schallenberg for the help of setting up and harvesting of the mesocosm experiment. We thank Benedikt Janssen and Tim Schiffer for the technical support with the phenotyping infrastructure and Alexander Putz for providing the drawing of the image station of GrowScreen-Rhizo2. We highly acknowledge the financial support of numerous students' apprentices by the DAAD and IAESTE program, providing great support for this experiment. We thank the reviewers for their valuable comments and suggestions to improve this manuscript.

early phosphorus nutrition and growth in corn (Zea mays L.). J. Environ. Qual. 41, 582–91. doi: 10.2134/jeq2011.0284

incubation in several manitoba soils. Can. J. Soil Sci. 341, 331–341. doi: 10.4141/ cjss73-048

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Nabel, Schrey, Poorter, Koller, Nagel, Temperton, Dietrich, Briese and Jablonowski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Simulation of Soil Organic Carbon Effects on Long-Term Winter Wheat (Triticum aestivum) Production Under Varying Fertilizer Inputs

Bhim B. Ghaley1 \*, Henk Wösten2 , Jørgen E. Olesen3 , Kirsten Schelde3 , Sanmohan Baby3 , Yubaraj K. Karki3 , Christen D. Børgesen3 , Pete Smith4 , Jagadeesh Yeluripati5 , Roberto Ferrise6 , Marco Bindi6 , Peter Kuikman2 , Jan-Peter Lesschen2 and John R. Porter1

1 Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark, 2 Wageningen Environmental Research, Wageningen University and Research, Wageningen, Netherlands, 3 Department of Agroecology, Aarhus University, Tjele, Denmark, 4 Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom, 5 Information and Computational Sciences Group, The James Hutton Institute, Aberdeen, United Kingdom, 6 Department of Agri-food Production and Environmental Sciences, University of Florence, Florence, Italy

### Edited by:

Arne Sæbø, Norwegian Institute of Bioeconomy Research (NIBIO), Norway

### Reviewed by:

Tore Krogstad, Norwegian University of Life Sciences, Norway Peter Thorburn, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia

> \*Correspondence: Bhim B. Ghaley<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

Received: 19 December 2017 Accepted: 23 July 2018 Published: 08 August 2018

### Citation:

Ghaley BB, Wösten H, Olesen JE, Schelde K, Baby S, Karki YK, Børgesen CD, Smith P, Yeluripati J, Ferrise R, Bindi M, Kuikman P, Lesschen J-P and Porter JR (2018) Simulation of Soil Organic Carbon Effects on Long-Term Winter Wheat (Triticum aestivum) Production Under Varying Fertilizer Inputs. Front. Plant Sci. 9:1158. doi: 10.3389/fpls.2018.01158 Soil organic carbon (SOC) has a vital role to enhance agricultural productivity and for mitigation of climate change. To quantify SOC effects on productivity, process models serve as a robust tool to keep track of multiple plant and soil factors and their interactions affecting SOC dynamics. We used soil-plant-atmospheric model viz. DAISY, to assess effects of SOC on nitrogen (N) supply and plant available water (PAW) under varying N fertilizer rates in winter wheat (Triticum aestivum) in Denmark. The study objective was assessment of SOC effects on winter wheat grain and aboveground biomass accumulation at three SOC levels (low: 0.7% SOC; reference: 1.3% SOC; and high: 2% SOC) with five nitrogen rates (0–200 kg N ha−1 ) and PAW at low, reference, and high SOC levels. The three SOC levels had significant effects on grain yields and aboveground biomass accumulation at only 0–100 kg N ha−1 and the SOC effects decreased with increasing N rates until no effects at 150–200 kg N ha−1 . PAW had significant positive correlation with SOC content, with high SOC retaining higher PAW compared to low and reference SOC. The mean PAW and SOC correlation was given by PAW% = 1.0073 × SOC% + 15.641. For the 0.7–2% SOC range, the PAW increase was small with no significant effects on grain yields and aboveground biomass accumulation. The higher winter wheat grain and aboveground biomass was attributed to higher N supply in N deficient wheat production system. Our study suggested that building SOC enhances agronomic productivity at only 0–100 kg N ha−1 . Maintenance of SOC stock will require regular replenishment of SOC, to compensate for the mineralization process degrading SOC over time. Hence, management can maximize realization of SOC benefits by building up SOC and maintaining N rates in the range 0–100 kg N ha−1 , to reduce the off-farm N losses depending on the environmental zones, land use and the production system.

Keywords: grain yield, DAISY model, nitrogen, plant available water, pedotransfer functions, long-term experiment, crop productivity

# INTRODUCTION

fpls-09-01158 August 7, 2018 Time: 16:37 # 2

Soil organic carbon (SOC) supports multiple soil functions determining soil physical, chemical and biological quality parameters (Reeves, 1997; Pan et al., 2009) contributing to the productive capacity of soils for food, fodder, and energy production (Lal, 2004). A number of factors influence SOC stocks and flows, spatially and temporally, in an ecosystem due to climate, land use, soil management, and cropping systems (Canadell et al., 2007). Building up SOC stock through agricultural measures (e.g., cover cropping, residue incorporation, reduced tillage) can affect soil properties, soil water retention and nutrient storage, affecting the productive capacity of soils (Ingram et al., 2016; Paustian et al., 2016). Decomposition of SOC releases mainly N, which can increase crop yields where crop N supply is limited (Palmer et al., 2017). Maintenance or build-up of SOC will require regular inputs of organic matter (OM) into the soil as the mineralization process will continually deplete the SOC over time, especially in environmental zones, where soil moisture and temperature are conducive for the mineralization process. The other effects of increased SOC content are decrease in the bulk density (Chen et al., 2017; Palmer et al., 2017; Minasny and McBratney, 2018) and small increase in volumetric water holding capacity (Rawls et al., 2003). Due to these multiple effects, there is a great interest to quantify SOC effects in agro-ecosystems. SOC increase can have positive and negative effects (Palmer et al., 2017; Minasny and McBratney, 2018). Among the multiple SOC effects, crop productivity and soil water retention are the priorities of the farmers to maintain sustainable agro-ecosystems. As European arable cropping systems are estimated to lose 300 Tg C (1012) year−1 (Janssens et al., 2003), it is necessary to segregate the SOC effects on crop yields and soil water retention and their combined synergistic benefits on crop productivity. Hence, quantification of SOC effects on N supply, soil water retention and crop productivity under varying fertility production system provides a science-based evidence of SOC benefits for making management decisions by farmers.

Winter wheat is one of the most widely cultivated arable crops, and the assessment of SOC-productivity relationship can generate insights into wheat crop management at field scale (Hansen et al., 2000; Christensen et al., 2009). An earlier study assessed SOC effects in winter wheat agro-ecosystem in seven sites representing diverse soil types, SOC content, management and climate including Netherlands (Palmer et al., 2017) found that SOC benefits are tangible in N deficient wheat production systems, whereas the benefits disappear in wheat agro-ecosystems, with surplus N. To add to this body of knowledge, this study provided insights into SOC effects under a context-specific set of soil type, SOC content, management and climate regimes in Denmark. Further, this study provided additional value to the findings of Palmer et al. (2017) because the range of SOC used in our study (0.7–2% SOC) is different than the SOC considered in Netherlands (2.8% and 4.3% SOC). SMARTSOIL1 consortium had access to the SOC and agronomic data on winter wheat from a long-term field trial in Askov from 1929 to 2008 and the field data provided us a unique opportunity to carry out the calibration and validation of DAISY model, to assess the productivity and SOC dynamics under winter wheat cultivation over 80 years. Hence, the study objective was to determine winter wheat productivity at three SOC ranges viz. low: 0.7% SOC; reference: 1.3% SOC; and high: 2% SOC with five nitrogen rates (0–200 kg N ha−1 ) and plant available water (PAW) at the low, reference, and high SOC levels.

# MATERIALS AND METHODS

# Long-Term Field Trial in Askov

The long-term trial site in Askov (LTE-Askov; 55◦ 280N, 09◦ 060E) was established in 1923 and cropping system was 4-year crop rotation cycle of winter wheat, root crop, spring cereal and grass-clover from 1929 to 2008. In the 0–0.20 m plow layer, SOC was 1.3% and sand, silt and clay contents were 76%, 13%, and 11%, respectively, and the bulk density at plow layer was 1.5 g cm−3 (Christensen et al., 2006). LTE-Askov treatments consisted of two treatments, viz: Askov\_0N and Askov\_1.5NPK, implemented in a 4-year crop rotation cycle. Askov\_0N treatment received no input of farmyard manure, nitrogen (N), phosphorus (P), and potassium (K) and crop residues were removed and Askov\_1.5NPK treatment received 150 kg N, 28.5 kg P and 131.4 kg K ha−1 . The measured field data from Askov\_0N and Askov\_1.5NPK winter wheat plots for the period 1929–2008, were split into calibration dataset (1929–1969) and validation dataset (1970–2008).

# DAISY Model Initialization and SOC Simulations

To assess long-term SOC dynamics in arable production systems, process models serve as a robust tool to keep track of multiple plant and soil factors and their interactions affecting SOC dynamics. The soil-plant-atmospheric model, DAISY, was implemented, due to its robustness for simulation of SOC dynamics and crop productivity in diverse climatic and cropping systems (Abrahamsen and Hansen, 2000). DAISY is a dynamic and deterministic soil-plant-atmosphere system model with separate sub-models for crop growth, C and N dynamics, heat, soil water and fate of pesticide use (Abrahamsen and Hansen, 2000). In the model, OM is constituted by added organic matter (AOM), soil microbial biomass (SMB), and soil organic matter (SOM) pool. AOM and SMB constitute relatively fast and slow turnover pools, whereas SOM is split into three pools; inert (SOM3), fast (SOM2), and slow turnover pools (SOM1), characterized by fixed C:N ratios and firstorder decomposition rate coefficients (Hansen et al., 1991). AOM constitutes plant residues, added organic fertilizer or compost, etc.; the SMB pool is driving the biodegradation process and SOM is the recalcitrant humus fraction. Soil C and N dynamics were modeled by assuming constant C:N ratios in each pool (Bruun et al., 2003). The SOM pool, at the start of the simulation period was initialized to a

1http://smartsoil.eu/

steady state by simulating the pre-experimental period for 10 years before the onset of the experiment (Bruun and Jensen, 2002).

Daisy model was implemented in two steps viz. calibration and validation steps. For calibration step, DAISY model inputs were soil, weather, and winter wheat management data from LTE-Askov. The soil data on sand%, silt%, and clay%, bulk density and 1.3% SOC (hereafter called the "reference") was provided to the model. The weather data was retrieved from the weather database, a common database created by the SMARTSOIL project (see footnote 1) to share and store information on long-term trial sites in SMARTSOIL consortium. Where the weather data was missing, the missing data was generated by the LARS-WG 5 weather generator (Semenov and Barrow, 2002; Semenov and Stratonovitch, 2010) based on statistical characteristics of actual sample of available measured weather data from LTE-Askov. The winter wheat management data included land preparation, sowing, fertilization and harvesting dates and application timing of 0, 50, 100, 150, and 200 kg N ha−1 . Every year, the winter wheat was sown on 20th September and harvested on 20th August in Askov\_0N and Askov\_1.5NPK plots. The same planting and harvesting schedule was followed during the simulation period to reduce the yield variability due to these two factors. The N rates of 0, 50, and 100 kg N ha−1 was applied on 15th March and the N rates of 150 and 200 kg N ha−1 was split into two equal doses. The two equal dose consisted of basal dose on 15th March (50% of application rate) and second dose on 25th April (50% of application rate) to coincide with the critical growth stages of winter wheat for maximum uptake of N. In order to accommodate the residual nitrogen effect after the preceding glass-clover in the 4-year crop rotation, the model was provided with nitrogen dose of 40 kg N ha−1 (Høgh-Jensen and Schjoerring, 1996) in Askov\_0N and Askov\_1.5NPK plots.

In order to assess SOC effects on winter wheat productivity, the validated DAISY model was run with 0.7% SOC (low) and 2% SOC (high) in addition to model run with 1.3% SOC during the calibration and validation steps. The low, reference, and high SOC levels reflected the spectrum of SOC levels in Danish soils from sandy to loamy soils and the N rate reflected the standard N application rate in winter wheat production in Denmark. Each SOC level (low, reference, and high) was simulated under five different N rates. Each simulation run sequence consisted of an initial 10 years of the pre-experimental period followed by simulation of low, reference, and high SOC content under five N rates (0–200 kg N ha−1 ) for the period 1929–2008. The initial 10-year run was included in every simulation run to stabilize the treatment effect to a steady state. Each simulation was carried out with low, medium, and high SOC and the same SOC was used for the entire simulation period (1970–2008), which provided the SOC trends over the years during the simulation period. However, after each year of simulation, the same management practice was reset into the model with same dates for land preparation, sowing, fertilization, and harvest dates in each year, during the entire simulation period. Each cycle of wheat production starts with land preparation on 01 September, consisting of plowing the field, followed by seedbed preparation, sowing, fertilizer application, and harvesting.

# Pedotransfer Functions (PTF) for Determination of Plant Available Water (PAW)

To assess the SOC effects on PAW (m3 m−3 ), the correlation between the PAW and SOC was derived by regressing PAW contents at the low, reference, and high SOC contents. LTE-Askov soil data on clay%, silt%, OM%, and bulk density were used in PTF functions to derive saturated moisture content (θs), residual moisture content (θr), van Genuchten curvefitting parameter α (1/cm = α) and van Genuchten curvefitting parameter n and m = 1 – 1/n (Wösten et al., 2001). Four different PTF functions calculated the PAW to compare the differences and improve the reliability in estimation of hydraulic properties. The PTF functions were (a) HYPRES (Wösten et al., 1999), (b) hydraulic properties calculator (HPC) (Saxton and Rawls, 2006), (c) Rosetta model (Schaap et al., 1998), and (d) Danish PTF (Borgesen and Schaap, 2005). HYPRES PTF functions were developed based on 5,521 soil horizon profiles from different countries in Europe (Wösten et al., 1998) whereas HPC was developed with data from 1,722 United States soil samples (Saxton et al., 1986). The Rosetta model was built on the United States soil database whereas the Danish PTF is based on 3,226 soil samples from Denmark (Borgesen and Schaap, 2005). Subsequently, soil water content was calculated at different soil water potentials (kPa) by van Genuchten–Mualem model (VGM) (Vereecken et al., 2010), and PAW was considered as the difference in soil water content between the wilting point and the field capacity. We defined field capacity at 10 kPa and wilting point at 1,500 kPa and the difference of soil water content between the field capacity and the wilting point was taken as PAW.

# Model Calibration, Validation, and Statistics

The model validation was carried out with MODEVAL 2.0 (Smith et al., 1997) by comparative plotting of measured and simulated SOC content in 0–0.20 m soil profile over 1970–2008 in winter wheat plots in Askov\_0N (**Figure 1A**, RMSE = 4.05%) and Askov\_1.5NPK treatments (**Figure 1C**, RMSE = 7.9%) and grain yields in Askov\_0N (**Figure 1B**, RMSE = 5.83%). Measured SOC and grain yields were available every 4 years (4-year crop rotation) and so, 10 measured values were available for 1970–2008 period and the corresponding simulated values from the same 1970–2008 period were used for validation (**Figures 1A–C**). ANOVA tests were run on to assess effect of SOC, N and SOC × N on winter wheat grain and aboveground biomass yields at low, reference, and high SOC under 0–200 kg N ha−1 . The standard error and LSD0.05 of the simulated values were calculated in MS excel using the data analysis tool pack and significant effects are denoted as ∗∗∗P < 0.001, ∗∗P < 0.01, ∗P < 0.05, ns, non-significant.

# RESULTS

### Validation of SOC Dynamics, Winter Wheat Productivity, and PAW

The modeled and the measured values of SOC in 0–0.20 m soil profile in Askov\_0N (**Figure 1A**, R 2 = 0.82∗ and Askov\_1.5NPK treatments (**Figure 1C**, R 2 = 0.77∗ ), had significant positive correlation coefficient. Similarly, significant positive correlation coefficient was obtained for winter wheat grain yields in Askov\_0N (**Figure 1B**, R 2 = 0.76∗ ). The validation on SOC dynamics to 0.20 m soil depth under fertilized (Askov\_1.5NPK, RMSE = 7.9%) and non-fertilized treatments (Askov\_0N, RMSE = 4.05%) demonstrated that DAISY was robust in simulation of winter wheat productivity at the tested SOC range under 0–200 kg N ha−1 treatments. DAISY model has been used in Denmark to quantify soil water balance (Salazar et al., 2013) and SOC (Bruun et al., 2003). This provided the scientific rationale for using DAISY for simulation of grain yields and aboveground biomass (grain + straw) accumulation at low, reference, and high SOC content under 0–200 kg N ha−1 treatments in this study.

The long-term change dynamics of SOC, presents challenges to simulate the SOC dynamics over time due to unavailability of data for calibration and validation of models. In this regard, we had unique access to LTE-Askov data and simulation window of 80 years (1929–2008) to assess the long-term change dynamics

of SOC and triangulate the field data with simulated data and its effects on agronomic productivity. We chose DAISY, due to its robustness to keep track of the SOC flows and stocks in the soil, taking account of the plant and the management factors. We validated the DAISY model SOC and grain yield outputs with measured data from Askov\_0N and Askov\_1.5NPK. The model considers only N as the limiting factor and the grain yields and aboveground biomass are not affected by P and K inputs. In similarity to our study, DAISY model had been used for simulation of crop grain yield and aboveground biomass accumulation in several model comparison exercises (Dewilligen, 1991; Vereecken et al., 1991; Diekkrüger et al., 1995) and validation of crop yield in winter wheat in three sites in the Netherlands (Hansen et al., 1991). In a comparison of nine SOM models to assess management effects (land use, fertilizer, manure, and rotation treatments) on SOC dynamics in seven LTEs in diverse climatic gradients, DAISY outputs were comparable, with a similar margin of error among other models (DNDC, RothC, CENTURY, CANDY, NCSOIL) and even better than the SOMM, ITE, and Verberne models (Smith et al., 1997). This provides a scientific rationale for use of DAISY model to assess SOC dynamics.

# Trend Comparisons of Measured and Modeled SOC Data

The measurement of the SOC at the experimental site started in 1923 in Askov\_0N and in 1929 in Askov\_NPK plot. In 1923, Askov\_0N plot had 1.6% SOC content, which decreased to 1.4% by 1969 and to 1.1% by 2008. Similarly, the Askov\_1.5NPK had 1.8% SOC in 1929 and it reduced to 1.5% by 1969 and to 1.2% by 2008. Bulk density measurements remained the same throughout the measurement period and so the changes in SOC was due to continuous removal of the crop residues and decomposition of the available SOC in the soil. The measured and the modeled SOC values during the calibration period (1969–2008) showed a similar trend for Askov\_0N and Askov\_1.5NPK (**Figure 2**) and the correlation between the measured and modeled SOC and grain yield values are provided in **Figure 1A** (SOC), **Figure 1B** (grain yield), and **Figure 1C** (SOC). The measured SOC value provided from the experimental site showed that the SOC range used for simulation is achievable in the soil and climate conditions at the experimental site.

# SOC and N Effects on Winter Wheat Grain Yield and Aboveground Biomass (Grain + Straw) Accumulation

Soil organic carbon levels and N rates had significant effects (P < 0.001) on winter wheat grain yields and aboveground biomass accumulation. In similarity, SOC × N effects were significant (P < 0.01) for grain yields and aboveground biomass accumulation. The SOC × N interactions implied that the SOC level effects differed at varying N application rates from 0 to 200 kg N ha−1 .

With 0 kg N ha−1 , the winter wheat grain yield increased significantly by 0.28 Mg ha−1 from low to reference SOC content and by 0.30 Mg ha−1 from reference to high SOC content (**Table 1**). Hence, the increase in grain yields from low to high SOC content was 0.58 Mg ha−1 , 31% increase in grain yield, which was a significant improvement in grain yields over the low SOC content. Similarly, at 0 kg N ha−1 , the aboveground biomass (straw + grain) increased significantly by 0.59 and 0.63 Mg ha−1 from low to reference and reference to high SOC levels, respectively.

At 50 kg N ha−1 , the winter wheat grain yield increase was significant, with increase of 0.86 and 0.85 Mg ha−1 from low to reference and reference to high SOC, respectively (**Table 1**), whereas aboveground biomass increase was significant only from low to reference SOC. Similarly, at 100 kg N ha−1 , the grain yield and aboveground biomass increase was significant by 1.17 Mg ha−1 and 1.32 Mg ha−1 , respectively, from low to reference SOC. At 150 kg N ha−1 and 200 kg N ha−1 , there was no significant increase in grain yield and aboveground biomass between low, reference, and high SOC contents.

In summary, there was relatively higher SOC effects on both grain yields and aboveground biomass at 0–100 kg N ha−1 and the effects decreased with increasing N rates until there was no SOC effects at 150–200 kg N ha−1 . With higher SOC content, lower N rate is required to attain a locally relevant yield 'plateau' compared to the soils with lower SOC content and in contrast, higher N rate will be required to attain the same yield 'plateau' with lower SOC content.

# SOC Effects on Plant Available Water (PAW)

A highly significant positive correlation between PAW and SOC was obtained with Danish PTF, given by Y = 1.3094x + 21.319 (R 2 = 0.99∗∗∗) (Y = PAW and X = SOC) (**Figure 3**). The measured PAW at LTE-Askov was 21%, in close proximity to calculated value of 23%, demonstrating the robustness and reliability of the Danish PTF to predict PAW, validating the highly positive correlation between SOC and PAW.

Soil organic carbon and calculated PAW content had significant positive correlation (R 2 = 0.99–1∗∗∗), and higher SOC content retained correspondingly higher PAW in LTE-Askov soils (**Figure 3**). The PAW calculated by VGM, based on generated hydraulic parameters by the four PTFs, demonstrated a similar trend of significant positive correlation between SOC and PAW. PAW based on Danish PTF (Borgesen and Schaap, 2005) resulted in highest calculated PAW content compared to the three other PTFs (HPC, Rosetta, and HYPRES). HYPRES PTF calculated the second highest PAW content followed by HPC and Rosetta PTFs. When the mean of PAW was averaged across the four PTFs at different SOC contents, there was positive correlation between PAW and SOC given by the linear relationship PAW (%) = 1.007 × SOC (%) + 15.641.

The regression relationship showed that the increase in PAW within the tested SOC range (0.7–2% SOC) was only 1.4%, which is not large enough change to affect yields. The effect of such a small change in PAW is difficult to verify in the field and is unlikely to have any significant change in yields and biomass accumulation. Hence, PAW did not have any significant role in yield and aboveground biomass accumulation.

# DISCUSSION

# SOC and Provision of Ecosystem Services

Soil organic carbon affects multiple ecosystem services, and SOC build-up and management can pose different challenges depending on the environmental zones and the context-specific production systems (Palm et al., 2014). SOC maintenance is a challenge, as the mineralization processes continuously degrade SOC over time. Where the arable farming systems are integrated with livestock, the manure from livestock are good sources of OM to build up SOC whereas it can be a challenge in other arable production systems unless dedicated practices like cover crops, no-till or mulch farming are practiced to replenish the SOC (Lehtinen et al., 2014). SOC can have both positive and negative effects, and the management have huge influences on the benefits from SOC. Under N non-limiting wheat production systems, nutrients released through the decomposition of the SOC especially mobile N can leach beyond the root zone and pollute the groundwater, contaminating the water supply for human consumption (Palmer et al., 2017). The losses of N downstream can induce algal bloom and eutrophication, which can have devastating impacts on the aquatic and other fish species. In addition, some of the nitrogen forms can be lost as nitrous oxides, which have global warming potential of 300 times more than the carbon dioxide (Burgin et al., 2013). In contrast, under N deficient wheat production systems, building and maintaining SOC can provide wider benefits with provision of multiple ecosystem services like supply of macro and micronutrients, carbon sequestration, food and fodder production, mitigation of soil erosion and support habitat for biodiversity (Ghaley et al., 2014). Hence, the benefits accrued from SOC increase is evident only in N deficient wheat production system, an important management decision for the wheat producing farmers.

# SOC Effects on Grain Yield and Aboveground Biomass

The range of SOC values used for the simulation at the LTE-Askov is within the ranges reported for the trial site, as evident from the measured SOC values in Askov\_0N and Askov\_1.5NPK plots (LTE-Askov, **Figure 2**). Similar SOC range of 1.2–1.7% was reported from another study at the same trial site (Thomsen and Christensen, 2004). The reduction of SOC content during the experimental period was attributed to decomposition of the SOC releasing N and other macro and micronutrients, and the SOC effects are only transient if efforts are not put into replenishment of OM to maintain the SOC stock.

Our study demonstrated that, at N application rates of 0–100 kg N ha−1 , SOC had benefits in terms of enhancing winter wheat productivity (**Table 1**). Similar positive correlations in grain yield-SOC relationships were reported in several field studies (Thomsen and Christensen, 2004; Persson et al., 2008; Seremesic et al., 2011; Yang et al., 2011; Mikanova et al., 2012) and one simulation study across seven sites and pedo-climatic zones (Palmer et al., 2017). In our simulations, winter wheat grain and straw yields increased with increases in SOC (low, reference, and high), which is supported by findings from another field experiment at LTE-Askov, where increased SOC increased spring barley yields (Christensen et al., 2009). Spring barley grain and straw yields increased with increase in SOC indicating a positive relationship between SOC and yield (Christensen

et al., 2009) in conformity to our study. However, application of more than 90 kg N ha − 1 nullified the SOC effects on grain and straw yield (Christensen et al., 2009) which conforms to our decreasing SOC effects on winter wheat with increasing N fertilizer with significant effect only up to 100 kg N ha − 1 (**Table 1**). The benefits of SOC on spring wheat grain yields was reported from a 24 years trial at Jyndevad in Denmark, where an N substitution rate of 15–27 kg N ha − 1 was attained with long term catch crops building up higher SOC in the soil (Hansen et al., 2000). This increase in yield is similar to the winter wheat yields in our study, where significant increase in grain and biomass yields were obtained with increasing SOC content at 0–100 kg N ha − 1 (**Table 1**), providing evidence of SOC × N effects on winter wheat productivity. Higher SOC content had a significant influence on grain yield and aboveground biomass increase only at 0–100 kg N ha − 1 , which demonstrated the benefits of building up SOC to compensate for the fertilizer N inputs. This indicated that the maximum SOC benefits can be realized only at 0–100 kg N ha − 1 input under the Danish wheat production agro-ecosystems and the benefits were non-existent as the N rates are increased to more than 100 kg N ha − 1 due to N losses into groundwater, eutrophication and algal blooms downstream and nitrous oxide losses as greenhouse gas. Hence, SOC benefits are contextual and multiple benefits are only realized in N deficient wheat production systems.

### SOC Effects on PAW (m 3 m − 3 )

Our study demonstrated that the four PTFs are robust enough to predict PAW based on the minimum soil parameters collected in the field trials, which can provide insights into water availability (m 3 m − 3 ) in the soil. In line with our study, significant positive correlations (**Figure 3**) between SOC and PAW, have been

reported in other studies in volumetric (Rawls et al., 2003) and gravimetric basis (Emerson, 1995) under diverse environments (Bationo et al., 2013) including a study (gravimetric) on 41 Danish soils (Resurreccion et al., 2011). A study in North Dakota in sandy, medium and fine textured soils demonstrated that soils with higher SOC retained more soil water (gravimetric) irrespective of the soil types (Bauer and Black, 1992) supporting the outcome of this study that SOC has positive effects on soil water retention. An exhaustive investigation of soil type-PAW correlation, based on the soil samples collected from across United States (Hudson, 1994), demonstrated a significant positive SOM-PAW correlation (volumetric) across three soil types (sandy, silty clay, and silty loamy clay), in line with our findings. Some recent studies (Palmer et al., 2017; Minasny and McBratney, 2018) also reported SOC positive effects on PAW in line with our study. Hence, our study supports the positive relationship between SOC and PAW. However, the PAW increase was too small to affect the crop yields and aboveground biomass accumulation. Moreover, the underlying mechanisms of SOC-PAW relationship need to be further explored.

### CONCLUSION

The benefits of SOC can be positive and negative and maintenance of SOC will require regular inputs of OM into the soil. The efforts to maintain the SOC and reap the benefits, are contextual depending on the land use, environmental zones, and management practices. In our study, increasing SOC content had significant positive effects on winter wheat grain yield and aboveground biomass at only 0–100 kg N ha−1 and the SOC effects were non-significant with increasing N inputs at 150–200 kg N ha−1 . SOC and PAW were positively correlated but the increase in PAW was minimal with no significant effects on grain yields and aboveground biomass accumulation. Our study findings were similar to other

### REFERENCES

studies (Minasny and McBratney, 2018) carried out in diverse environments (Palmer et al., 2017), which lends credence to this study in confirming that the earlier results from Netherlands and other six sites were equally applicable in Denmark and other relevant environments. In order to improve our analysis, future investigations should include quantification of dis-benefits viz. N leaching, N loss downstream and nitrous oxide loss, to provide additional insights into the extent of dis-benefits with increasing N input. Hence, benefits and dis-benefits parameters need to be measured in future studies in order to generate a complete analysis of SOC effects for improved management decision by farmers, agricultural advisors and policy makers.

### AUTHOR CONTRIBUTIONS

BG wrote the first draft and made the subsequent revisions of the paper with data inputs from HW, JO, JY, and PS. KS, SB, PK, J-PL, and JP reviewed the document and provided inputs to improve the scientific content of the manuscript. RF and MB helped with cleaning the weather data and CB and YK helped with setting up the DAISY model for carrying out simulations.

### ACKNOWLEDGMENTS

We appreciate the financial support from EC SMARTSOIL project (Project number: 289694) for funding the collation of long-term experimental data from the project partners and Mr. Per Abrahamsen for helping with the DAISY model. The support from LANDMARK (Grant Agreement No: 635201), WaterFARMING (Grant Agreement No: 689271), and SustainFARM (Grant Agreement No: 652615) projects are acknowledged to carry out revisions and improvement of the scientific content for resubmission of the manuscript.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Ghaley, Wösten, Olesen, Schelde, Baby, Karki, Børgesen, Smith, Yeluripati, Ferrise, Bindi, Kuikman, Lesschen and Porter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Structural and Functional Features of Chars From Different Biomasses as Potential Plant Amendments

Marta Marmiroli1 , Urbana Bonas1 , Davide Imperiale1,2, Giacomo Lencioni1 , Francesca Mussi3 , Nelson Marmiroli1,2,3 and Elena Maestri1,3 \*

1 Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy, 2 National Interuniversity Consortium for Environmental Sciences (CINSA), Parma, Italy, 3 Interdepartmental Center for Energy and Environment (CIDEA), University of Parma, Parma, Italy

### Edited by:

Peter Schröder, Helmholtz Zentrum München, Helmholtz-Gemeinschaft Deutscher Forschungszentren (HZ), Germany

### Reviewed by:

Tanja Mimmo, Free University of Bozen-Bolzano, Italy Bartosz Adamczyk, University of Helsinki, Finland

> \*Correspondence: Elena Maestri<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

Received: 16 March 2018 Accepted: 11 July 2018 Published: 17 August 2018

### Citation:

Marmiroli M, Bonas U, Imperiale D, Lencioni G, Mussi F, Marmiroli N and Maestri E (2018) Structural and Functional Features of Chars From Different Biomasses as Potential Plant Amendments. Front. Plant Sci. 9:1119. doi: 10.3389/fpls.2018.01119 Biochars result from the pyrolysis of biomass waste of plant and animal origin. The interest in these materials stems from their potential for improving soil quality due to increased microporosity, carbon pool, water retention, and their active capacity for metal adsorption from soil and irrigation water. Applications in agriculture have been studied under different conditions, but the overall results are still unclear. Char structure, which varies widely according to the pyrolysis process and the nature of feedstock, is thought to be a major factor in the interaction of chars with soil and their metal ion adsorption/chelation properties. Furthermore, biochar nutrients and their elemental content can modify soil fertility. Therefore, the use of biochars in agricultural settings should be examined carefully by conducting experimental trials. Three key problems encountered in the use of biochar involve (i) optimizing pyrolysis for biomass conversion into energy and biochar, (ii) physicochemically characterizing biochar, and (iii) identifying the best possible conditions for biochar use in soil improvement. To investigate these issues, two types of wood pellets, plus digestate and poultry litter, were separately converted into biochar using different technologies: pyrolysis/pyrogasification or catalytic (thermo)reforming. The following physicochemical features for the different biochar batches were measured: pH, conductivity, bulk density, humidity and ash content, particle size, total organic substances, and trace element concentrations. Fine porous structure analysis and total elemental analysis were performed using environmental scanning electron microscopy along with energy-dispersive X-ray spectrometry (EDX). Phytotoxicity tests were performed for each biochar. Finally, we were able to (i) differentiate the biochars according to their physicochemical properties, microstructure, elemental contents, and original raw biomass; (ii) correlate the whole biochar features with their respective optimal concentrations when used as plant fertilizers or soil improvers; and (iii) show that biochars from animal origin were phytotoxic at lower concentrations than those from plant feedstock.

Keywords: biochar, low-vacuum SEM/EDX, phytotoxicity, principal component analysis, pyrolysis, soil improvers, trace elements

# INTRODUCTION

fpls-09-01119 August 17, 2018 Time: 12:42 # 2

According to the International Biochar Initiative (IBI) guidelines, "biochar is a solid material obtained from the thermochemical conversion of biomass in an oxygen-limited environment" (International Biochar Initiative [IBI], 2012). Charcoal is a carbon-rich solid product prepared via biomass pyrolysis and is used as a fuel source for producing energy. Charcoal and biochar constitute part of the black carbon by-products resulting from the combustion of organic matter (OM) that includes coal, soot, and graphite (Crombie et al., 2013). Biochar is currently a by-product of the transformation of biomass into bioenergy in thermochemical processes (Qambrani et al., 2017). The physicochemical properties of biochar are mainly connected to its large surface area, ranging from 200 to 400 m2 g −1 . The thermal decomposition of organic material leads to loss of volatile compounds, producing a network of carbon chains present as porous structures with a large inner surface. In turn, this affects the retention of water, the capacity for binding metals and other elements and hosting microbial communities, and changes in soil structure such as porosity and density (Beesley et al., 2013; Joseph et al., 2013; Fellet et al., 2014). Biochar therefore possesses intrinsic capabilities for stimulating plant growth and increasing tolerance to abiotic stresses, by improving the water holding capacity of soils and nutrient retention because of its chemical and electrical properties (Jeffery et al., 2011).

A recent (2015) Italian law allows the use of biochar from plant biomass as a soil amendment and should favor the market introduction of biochars because of their greater efficiency and sustainability when compared to fossil carbon-based solutions. The application of biochar in agriculture would increase carbon storage in soils, provide cobenefits in saving water, avoiding emissions of greenhouse gasses from soils, substituting for fossil fuels used in the production of other soil-improving agents, and providing additional ecosystem services (Woolf et al., 2010). A detailed analysis of biochar benefits is reported by Schröder et al. (2018), in comparison with other types of soil-amending agents.

However, the benefits derived from biochar are strictly dependent on the original biomass (feedstock), on the production process, especially temperature, and on the characteristics affecting its performance as a soil improver (Mimmo et al., 2014; Agegnehu et al., 2017). Converting biomass into biofuels through thermochemical processes is one of the methods more accepted by both legislation and industry to reduce waste and obtain valuable products (Dodds and Gross, 2007). Catalytic (thermo)reforming and pyrolysis processes generate energy, biooil, gas, and biochar. In the former, a thermochemical process, catalyzed by specific elements/compounds transforms organic biomass into energy, gas, bio-oil, and a minimal amount of biochar. In this type of process, biochar can also be used as catalyst (Shen, 2015; Ahmad et al., 2018). Pyrolysis is defined as the thermal biomass decomposition under limited O2 and at an intermediate temperature to obtain energy, gasses, bio-oil, and biochar (Liu W.J. et al., 2017). The quality and elemental content of the biochar obtained during the two processes depends on the operating parameters, temperature, heating rate, type of catalyst, and on the physicochemical features of the feedstock.

New approaches to biochar production are under study in several research projects aiming at developing technologies where small quantities of pyrogases can provide heat to the process, and the remainder is available to supply further renewable heat for agricultural or industrial uses, improving the final energy efficiency and economy.

It has been calculated that globally 140 billion tons of biomass waste from agriculture are produced annually (United Nations Environment Program1 , accessed March, 2018). In the European Union, feedstocks available from renewable sources consist mostly of residues from forestry and agriculture. Forestry residues can be primary, from the field, secondary, from the wood industry, or tertiary, from post-consumer wood. Recent data on biomass availability estimate a total of about 9 and 85 Mt of dry matter from forestry and agriculture respectively, not used for other purposes (Searle and Malins, 2016), that could be available for transformation into biochar applicable to agriculture. According to the EU Waste Framework Directive, the approach to waste management in EU Member States should be based on three main principles, listed in order of priority: (i) preventing waste, (ii) recycling and reusing, and (iii) improving final disposal and monitoring. The use of forestry and other plant maintenance residues to produce biochar is a positive step in activities aiming at producing environmental and economic advantages by upgrading waste material.

In this work, we compare biochars derived from different feedstocks (animal and plant) as sustainable products from waste management, with the aim to establish critical factors that affect the usefulness of biochars as soil improvers and amendments in agriculture, according to international guidelines. Some of the biochars' chemical and physical characteristics were measured; phytotoxicity tests were carried out to establish for the different biochars the germination index using Lepidium sativum L. For two biochars, growth inhibition tests were performed using Hordeum vulgare L. The main findings were that the feedstock type determined the biochar microstructure and elemental composition, which were linked to the toxicity: biochar from animal origin were phytotoxic at lower concentrations than those from plant feedstock.

### MATERIALS AND METHODS

### Biochar Origin and Production

Five different biochars were used in the study. Chars A1, A2, and A3 were obtained as by-products of catalytic (thermo)reforming in a prototype developed in the framework of the research project TERMOREF (Regione Emilia-Romagna); temperature was between 400 and 500◦C for 2–3 h. Char A1 was derived from anaerobic digestate, char A2 from poultry litter, and char A3 from wood pellets. Char A4 was derived from wood

1www.unep.org

pellets in a prototype pyrogasification system <50 kWe in the framework of the research project TERMOREF; temperature was between 500 and 700◦C for 1–2 h. Char E1 was acquired from commercial sources and derived from forest wood and brushwood waste through pyrogasification (Borgo Val di Taro, Italy).

### Physicochemical Analyses

### Sample Pretreatment

fpls-09-01119 August 17, 2018 Time: 12:42 # 3

Biochar samples were sieved and prepared according to the protocol given in EN European Standards (2008).

### pH Evaluation

The pH values of the biochar samples were determined following the guidelines of EN European Standards (1999a) protocol for growing media and soil improvers.

Briefly, samples were passed through a 20 mm sieve and extracted with deionized water in a ratio of 1:5 (v/v). To a sample weight equivalent of 60 mL, 300 mL of deionized water were added in 500 mL glass jars, and samples were shaken for 1 h on an orbital shaker (Model Unimax 2010, Heidolph Instruments, Schwabach, Germany). The pH of each suspension was measured in triplicate using a Model S213 Seven Compact Duo meter (Mettler Toledo, Columbus, OH, United States).

### Electrical Conductivity Evaluation

Electrical conductivity (EC) values of the biochar sample extracts were determined following the EN European Standards (1999b) protocol for growing media and soil improvers.

Briefly, samples were treated as for the pH evaluation; after shaking, suspensions were filtered through Whatman filters (N◦ 1, Whatman, Maidstone, United Kingdom) discarding the first 10 mL. The conductivities of the suspensions were measured, in triplicate, within an hour after extraction, using a Model S213 Seven Compact Duo meter (Mettler Toledo, Columbus, OH, United States), and expressed in mS cm−1 .

### Bulk Density Evaluation

The bulk density of the biochar samples were evaluated following the EN European Standards (2008) protocol, with minor modifications.

A rigid cylinder with a capacity of 1,000 mL and diameter of 100 mm was used to perform the experiment. The cylinder was filled with each sample and a plunger of 650 g with the same diameter of the cylinder was placed on the top for 3 min. After the compaction time, the plunger was removed and the sample weight and volume measured. Results were expressed in g L−1 .

### Particles' Size Distribution Evaluation

The size distribution of the particles of the biochar samples was assessed following the EN European Standards (2007) protocol, with minor modifications.

Briefly, dried samples of biochar were passed through five sieves with different mesh sizes (20, 10, 5, 2, and 1 mm) positioned in tiers (Endecotts, Ltd., London, United Kingdom) from the largest to the smallest. A known mass of sample was placed on the upper sieve and the column was gently shaken by hand for 5 min. The biochar fractions retained on each sieve were then carefully collected and weighed, and the percentage fraction distributions calculated.

### Dry Matter and Moisture Content Measurements

Dry matter and moisture content of the biochar samples were determined following the EN European Standards (2008) protocol.

Briefly, samples of known weight were oven-dried at 105◦C (M710 Thermostatic Oven, F.lli Galli, Milan, Italy) until the difference between two successive weightings did not exceed 0.1 g. The final weight of the samples represents the dry matter content, and the weight loss represents the moisture content; moisture content is expressed as a percentage of the starting weight.

### OM and Ash Content Evaluation

Organic matter and ash content of the biochar samples were determined according to the EN European Standards (2011) protocol.

Fresh samples were oven-dried at 105◦C (M710 Thermostatic Oven, F.lli Galli, Milan, Italy) to a constant weight, and a known weight of the sample was incinerated at 500◦C in a muffle furnace (Model A022, Matest S.p.A., Bergamo, Italy) for 14 h. After incineration, the residues (ash) were weighed and OM was calculated as the difference between the fresh and final incinerated weights. Ash content and OM content were expressed as a percentage of the total initial weight.

### Measurement of Trace Metal Concentrations: Cu, Fe, Ni, Zn, Pb, and Cd

The concentration of six trace metals (Cu, Fe, Zn, Pb, Ni, and Cd) in the biochar samples were determined following the UNI Ente Italiano di Normazione (1998) protocol, with minor modifications.

Samples were oven-dried at 105◦C (M710 Thermostatic Oven, F.lli Galli, Milan, Italy) up to a constant weight, and a known weight of the sample was incinerated at 500◦C in a muffle furnace (Model A022, Matest S.p.A., Bergamo, Italy) inside ceramic crucibles with lids for 14 h. For each biochar, the ash was retrieved from the crucible and solubilized by wet digestion with HNO3 65% (Carlo Erba, Milan, Italy) at 165◦C for 30 min and 230◦C for 30 min in a heated digester thermoblock (DK20, Velp Scientifica, Usmate Velate, MB, Italy). Digested solutions were diluted with deionized water to 30% (v/v) acid concentration. The concentration of each metal was measured using flame atomic absorption spectrometry (AA240FS, Agilent Technologies, Santa Clara, CA, United States) at the following wavelengths: Cu: 324.7 nm; Fe: 248.3 nm; Zn: 213.9 nm; Pb: 217.0 nm; Ni: 232.0 nm; Cd: 228.8 nm. Calibration curves for each metal were prepared using 1,000 ppm certified standard solutions (Agilent Technologies, Santa Clara, CA, United States). Three instrumental replicates were performed on each biological

replicate (three for each sample). The metal concentrations were expressed in mg kg−1 of biochar.

### Low-Vacuum Scanning Electron Microscope With X-Ray Microanalysis (Lv SEM/EDX)

To overcome the problems with high vacuum, an environmental low-vacuum (Lo-vac, 60 Pa) environmental scanning electron microscopy (ESEM/EDX) was used. Finely-powdered biochar (between 10 and 100 µm grain size) was analyzed with no fixation or staining, after a careful positioning and adhesion on 2 cm diameter stainless-steel sample holders covered with adhesive carbon tape. A scanning microscope ESEM FEG2500 FEI (FEI Europe, Eindhoven, Netherlands), operating in lowvacuum (70 Pa) with a large-field detector allowed optimal secondary electron imaging, while the cone pressure-limiting aperture set at 500 µm improved the signal available to the Bruker XFlash R 6 | 30 X-ray detector equipped with a high efficiency 30 mm2 silicon drift detector for nanoanalysis and high-count-rate spectral imaging (Bruker Nano GmbH, Berlin, Germany). Secondary electron imaging was performed at 5 or 10 keV with a beam size of 2.5 µm, and EDX analysis at 20 keV acceleration voltage and beam size of 4 µm. The working distance was approximately 10 mm, and the scanning time 1–3 µs. The xT Microscope Control, xT Microscope Server, and FEI User Management software were used for imaging; the Esprit 1.9 package was used for X-ray spectra acquisition and analysis during acquisition in either point analysis or line-scan mode. X-ray spectra deconvolution and elemental standardless quantification were performed using the P/B-ZAF interactive method [Peak/Background evaluation matrix with atomic number (Z), absorption (A), and secondary fluorescence (F) correction] supported by the "Quantify Method Editor" option of Esprit 1.9 (Goldstein et al., 2003).

### Effects on Growth and Germination Germination Test

The germination index of the biochar matrices was determined following the UNICHIM (2003) protocol.

Biochar samples were sieved to pass a 2 mm mesh. Aliquots of 10, 7.5, 5, 2.5, 1, 0.5, or 0.1 g of dried and sieved biochar were placed in Petri dishes (Ø 90 mm, Sarstedt, Germany) and saturated with deionized water. The suspension was homogenized and covered with a Whatman N◦ 41 filter (Whatman, Maidstone, United Kingdom). Control dishes were prepared containing only deionized water and filter paper. Ten seeds of Lepidium sativum L. (Sementi Dotto Spa, Udine, Italy) were sown in every Petri dish; three biological replicates were performed for each concentration. Sealed dishes were placed in a growth chamber (MIR-554-PE, Panasonic, Osaka, Japan) at 25◦C in the dark. After 72 h, the germinated seeds were counted, and their root lengths were measured to calculate the germination index (GI %).

GI % = (Gt ∗ Lt/Gc ∗ Lc) ∗ 100. where Gc = germinated seeds in the control; Gt = germinated seeds in the treatments; Lc = main root length in the control; Lt = main root length in the treatments.

### Germination Rate and Growth Inhibition Tests

Germination and growth inhibition tests were performed following the EN European Standards (2012) protocol.

The growing substrates were prepared according to the protocol by mixing char at doses of 1, 3, or 5% (w/v) with sphagnum peat (Lithuanian peat, UAB Presto Durpes, Vilnius, Lithuania) limed at pH 5.5–6 with CaCO3 (Sigma-Aldrich, St. Louis, MO, United States), using commercial garden soil as a control (Ecomix, Vialca S.r.l., Pistoia, Italy). Peat had an EC of 400 µS cm−1 and a starting pH of 5.1, and concentration of all metals were below detection limits. Approximately 700 mL of the different mixtures of peat and biochar were distributed in 1,500 mL glass jars and fertilized initially with half-strength Hoagland solution (J.T. Baker, Deventer, Holland) at the rate of 40 mL per kilogram of substrate; each treatment was repeated in triplicate. Control pots were prepared without biochar. Twenty seeds of Hordeum vulgare L. (provided by CIEMAT, Madrid, Spain) were sown in each jar. Jars were kept in a greenhouse under controlled conditions: average temperature of 27◦C during day-time, 16◦C during night-time, with natural light supplemented by metal halide lamps to maintain a minimum light intensity of 300 µmol m−2 s −1 and a photoperiod of 14 h. Jars were watered daily with 10 mL of deionized water. After 5 days, the number of germinated seeds was recorded to calculate the germination rate percentage:

Germination rate % = (number of germinated seeds/number of starting seeds) ∗ 100.

After 11–14 days, when the second true leaf was clearly visible in 50% of the plants in control condition, the above-ground parts of all plants were collected, thoroughly washed, and weighed. The material was then oven-dried at 75◦C to constant weight. The relative growth index (RGI) for each biochar type and growing medium concentration was calculated for both fresh and dry plant biomass:

(RGI) F/D = (Wt/Wc)F/D

where Wt = fresh or dry weight of treated plants; Wc = fresh or dry weight of control plants.

### Statistical Analyses

All statistics was calculated using IBM-SPSS v252 . After verifying the normal distribution and the homogeneity of variance with Levene's test, an analysis of variance (ANOVA) was carried out on the data, followed by Tukey's post hoc test. Dimension reduction calculations were applied to the physicochemical analyses: principal component analysis (PCA) extraction with the Kaiser criterion (λ > 1) and canonical discriminant functions analysis. For testing phytotoxicity, Student's t-test with the Bonferroni correction was used.

2https://www.ibm.com/analytics/data-science/predictive-analytics/spss-statisticalsoftware

TABLE 1 | Physicochemical characterization of chars from thermocatalytic reforming of digestate (A1), poultry litter (A2), and wood pellet (A3) and from pyrogasification of wood pellet (A4) and brushwood (E1).

Means of three biological replicates are reported with standard errors in parentheses. Numbers in bold type represent values that exceed the prescriptions of international guidelines (REFERTIL Consortium, http://www.refertil.info/) on soil improvers. Different lower case letters in rows indicate the significant difference at p < 0.05 (Tukey HSD test).

TABLE 2 | Particle size distribution of chars from thermocatalytic reforming of digestate (A1), poultry litter (A2), and wood pellet (A3) and from pyrogasification of wood pellet (A4) and brushwood (E1).

Data are expressed as the percentage of the total weight of three replicates. Numbers in bold type represent values that exceed the prescriptions of international guidelines (REFERTIL Consortium, http://www.refertil.info/) on soil improvers.

# RESULTS

### Comparison of Physicochemical Features in Different Chars

The applicability in agriculture of chars for soil amendment is strongly affected by their characteristics including structure, chemical composition, and levels of contaminants. Several studies on the standardization of measurements and the choice of relevant parameters are underway (Bachmann et al., 2016). The chars analyzed in the present study share the same production family of origin because they are all surplus agricultural waste; their reuse follows the principles of zero residues within the framework of a circular economy approach (Riding et al., 2015). Three chars (A3, A4, E1) were produced from wood, either pelleted or chipped, whereas two chars (A1, A2) were produced from non-plant sources, digestate and poultry litter respectively. The processes used in producing chars were different, using prototypes for (thermo)reforming and pyrogasification (A1- A2-A3 and A4-E1 respectively). In view of these differences, their physicochemical parameters (**Tables 1–3**) were analyzed to provide information about possible correlations between biomass of origin, production process, and char features (**Figures 1**, **2**).

### pH and Electrical Conductivity

All chars showed high pH values greater than 8 (**Table 1**). There were significant differences, and the highest values were found in the chars of non-plant origin (**Figure 1**). Compared to (thermo)reforming, pyrogasification produced chars with significantly lower pH (**Figure 1**).

Electrical conductivity values were extremely variable (**Table 1**), with highly significant differences linked to the origin of the biomass (**Figure 1**); chars derived from wood showed low values of conductivity compared to chars of non-plant origin, with fourfold differences.

### Density and Particle Size Distribution

Densities of the chars ranged around 0.5 g L−1 (**Table 1**), with the highest values found in chars of non-plant origin produced via (thermo)reforming (**Figure 1**). The distribution of particle size, determined through sequential sieving, did not correlate strictly with density (**Table 2**). Char E1, from chipped brushwood, had the highest proportion of very fine particles of less than 1 mm diameter. A similar size distribution was found in char A2 from poultry litter. Char A1 from digestate had the highest proportion

TABLE 3 | Total concentration of trace elements in chars from thermocatalytic reforming of digestate (A1), poultry litter (A2), and wood pellet (A3) and from pyrogasification of wood pellet (A4) and brushwood (E1).

Means in mg kg−1 dry weight of three biological replicates are reported with standard errors in parentheses. Numbers in bold type represent values that exceed the prescriptions of international guidelines (REFERTIL Consortium, http://www.refertil.info/) on soil improvers. Different lower case letters in rows indicate the significant difference at p < 0.05 (Tukey HSD test).

of large particles, mostly of size greater than 5 mm. In char A3, produced by pyrogasification from wood pellet, most particles were of size between 2 and 5 mm. The size of particles is a critical factor in their distribution and applicability in agricultural conditions, since fine char particles cannot be distributed easily except after transformation into sludge.

### Moisture Content, Organic Matter, and Ash

After the production process, char might contain considerable amounts of water. Moisture content of the original biomass is one of the most critical features for the feed-in process and the efficacy of combustion. The proportion of moisture in fresh chars, which is complementary to the dry weight of the char, is shown in **Table 1**. There is no correlation with the type of biomass or the

type of process (**Figure 1**), and the highest values for moisture are found in poultry litter char A2 and in brushwood char E1.

ANOVA) between chars derived from different feedstocks (P/NP) or from different processes (Th/Py): ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

The dry matter in char can be further divided into OM and residual ash after incineration at 500◦C. **Table 1** reports both values that together equal 100% of dry weight. Significant differences are evident, with chars from wood having significantly higher OM content and less ash (**Figure 1**). No effect can be linked to the production process.

### Content of Trace Metals

One of the potential hazardous features of chars is the possible increase in concentrations of metals and other contaminants due to the reduction of volume and water content from the original biomass. It is expected that all non-volatile elements become more concentrated in char at increasing temperatures. The concentrations on a dry weight basis of six main trace elements were determined and results are reported in **Table 3**.

The elements Cu, Fe, and Zn, which are micronutrients, could originate from the biomass of origin, being essential constituents of cells, whereas Cd, Ni, and Pb should be present only as contaminants.

Iron is present in the chars at the level of g kg−1 dry weight (**Table 3**), with the highest values in chars of non-plant origin (**Figure 2**). Char E1 is an exception since its Fe content is much higher than in chars derived from wood pellets; a possible explanation lies in the origin of the wood as waste in forests and undergrowth, possibly mixed with small amounts of soil and of materials of diverse origin. Chars from pure wood, A3 and A4, have the lowest Fe levels.

Zinc and Cu also reach high levels in chars of non-plant origin (**Table 3**), with highly significant differences (**Figure 2**). Differences in concentrations of Cu and Zn of >100-fold are evident comparing poultry litter char A2 and wood char A4.

Nickel and Pb concentrations do not show any significant differences among chars of different origin or production process (**Figure 2**), but the values were highly variable.

Cadmium was below the detection limit in chars from pure wood pellet, independent of the production process (**Table 3**), while values were significantly higher in chars of non-plant origin (**Figure 2**).

### Classification of Chars Based on Physicochemical Properties

The complete set of data from all replicates and for all parameters was subjected to PCA. In this analysis, OM was retained instead of ash content, and moisture content was retained instead of dry matter; bulk density and particle size distribution were not considered. **Figure 3A** shows the trends of the different parameters as vectors in three dimensions accounting for 93.6% of variance (PC1 = 62.4%, PC2 = 18.6%, PC3 = 12.6%). In the first two dimensions (**Supplementary Figure 1a**), OM content is clearly separated from all other parameters; Cu, Cd, and Zn group together; and pH parallels EC. The second and third dimensions discriminate pH from conductivity and Fe from Ni. Considering the vector lengths, which indicate their relevance in explaining the total variance, and their loading coefficients along the principal components (**Supplementary Figures 1b–d**), we can deduce the best variables for discriminating among chars of different origins and processes. Among the metals the best variables are Cu (PC1), Fe (PC2), and Ni (PC3), and among the other parameters EC (PC2) and OM (PC1 and PC3). Using a 3D representation of the datasets (**Figure 3B**), it can be seen that chars A3, A4, and E1 were grouped together while A1 and A2 were separate but independent of one another.

Through the canonical discriminant function analysis, it was possible to divide the whole variance between only two canonical (orthogonal) functions. This technique, which is a simplified PCA, allowed grouping the different chars in a 2D graph, as shown in **Figure 4**. Chars from wood—A3, A4, and E1—group together and quite distinctly from the chars of non-plant origin—A1 and A2—that do not cluster together. There is no clustering associated with the production process.

### Microstructural Features of Chars

SEM images obtained for chars A1, A2, and E1 demonstrate the presence of plant and non-plant matrices (**Figure 5**). Chars A1 and A2, from digestate and poultry litter respectively, showed mostly animal substrates that appeared as lumps encrusted with fine and coarse powder (**Supplementary Figure 2**). In the case of char E1, we observed a prevalence of highly porous matrix, which was interspersed with lumpy structures covered with micrometer-sized powder (**Figure 5** and **Supplementary Figure 3**). Exclusively porous substrates were seen in chars A3 and A4 (**Figure 6**). In particular, in A3 we observed pores of different sizes, between 5– 20 µm and larger than 20 µm, while in A4 the pores were almost exclusively between 5 and 20 µm. Both the

porous matrices appeared disorganized with pores of different sizes distributed randomly (**Figure 6** and **Supplementary Figure 4**).

Representative examples of electron-induced emitted X-ray spectra for the different char types are reported in **Figures 7**, **8**. On average, 25 X-ray spectra were collected for each biochar to gather a reliable set of measurements of their elemental content. For all chars, the X-ray analysis confirmed the presence of Fe, Cu, Zn, Ni, and Pb, but other metals were also found, for example Ti and Mn. Concentrations of Cd were below the detection limit for all samples. Spectra for chars A1 and E1 confirm the high content of Fe. Chars differed in their contents of light elements as well. Chars A1, A2, and E1 were rich in Si, K, and Ca while in particular the chars of non-plant origin were rich in S, Na, Cl, P, and Mg (**Figures 7**, **8**). Chars A3 and A4 from pure wood pellets showed lower amounts of all light elements and metals in comparison to the other biochars (**Figure 8**). The contents of light elements were comparable in A3 and A4, but K and Ca were lower in A4 than in A3 (**Figure 8**).

### Effect of Chars on Germination and Growth of Plants Germination Index

A basic phytotoxicity test was performed by using germinating seeds of Lepidium sativum on Whatman paper in the presence of increasing quantities of char in water. The effect is measured by taking into account the number of seeds that germinate and the growth of roots after 3 days of incubation, compared to the control with water (**Figure 9** and **Supplementary Figure 5**). Chars A3 and A4 from

pure wood pellets, irrespective of the production process, stimulated plant germination and root growth at the lowest dose, 0.1 g per plate. Char A4 was significantly less toxic than A3 at the higher doses, with a germination index greater than 20%. On the contrary, chars A1, A2, and E1 were strongly inhibitory, and stopped growth altogether at doses between 1 and 3 g per plate. Considering only the germinated seeds, **Figure 7** shows the differences in root growth at different doses, demonstrating the positive effects of chars A3 and A4 and the inhibitory effects of chars A1, A2, and E1.

FIGURE 6 | Environmental scanning electron microscopy images of biochars A3 (2 left) and A4 (2 right) at different magnifications (see figure bottom and white bar bottom right). All images were acquired with electron beam at 5 KV and a working distance between 11.9 and 12 mm. Red rectangles indicate the areas from which the EDX spectra in Figure 8 were acquired.

### Relative Growth Index

Evaluation of the effects of chars on plant growth can also be assessed in pot experiments, by measuring a RGI at different concentrations of char mixed into the growth substrate (soil or peat). The test was performed on Hordeum vulgare, comparing the best- (A4) and worst- (E1) performing chars according to the germination index (**Figure 10**). Char A4 from pyrolysis of pure wood pellets significantly enhanced plant growth at the concentrations of 1, 3, and 5%. Unexpectedly, char E1 also enhanced the growth of plants in comparison to control, with stimulating effects at 3%. In this test on barley, chars E1 and A4 were found to be non-phytotoxic and promising as soil amendments. The dose of 1% in pots corresponds to a distribution of about 45 t ha−1 of char in the field, assuming dispersal in the first 30 cm of topsoil.

### DISCUSSION

The choice of feedstock in biochar production has a significant effect on the properties of the product, its applicability, the benefits in terms of reduction in greenhouse gas emissions, and economic advantages (Galinato et al., 2011; Field et al., 2013). In addition, using biochar as a way of disposing several types of agricultural and forestry wastes complies with the circular economy approach, advocated in several policies and strategies in the European Union and in other countries (Monlau et al., 2016). However, the evaluation of the benefits in adding biochar to agricultural soils has been so far inconclusive (Jeffery et al., 2011;

Agegnehu et al., 2017), even though several studies suggest that biochar addition could provide considerable advantages, but also has a few drawbacks (Tan et al., 2017; Schröder et al., 2018). Characterization of the main properties of chars derived from different feedstocks and different production systems is therefore a necessity to identify the main parameters that can guide the

eV, and the Y axis represents the peak intensity.

decision process before a full-fledged application of biochar to agricultural crops (Tan et al., 2017).

The chars considered in our analysis were obtained from wastes from agriculture and forestry, and they were produced using two different processes at similar temperatures (around 500◦C), (thermo)reforming and pyrolysis/pyrogasification. The properties of the chars were studied to understand the main factors in their application as soil improvers in agriculture.

The distribution of particle size is a relevant factor, since the finest particles can be released to the atmosphere during handling, consequently leading to an undesirable negative impact, thus counteracting the positive effects in terms of climatealtering emissions. In addition, fine particles in the soil decrease the water holding capacity compared to coarser particles (Liu Z. et al., 2017). Pelletizing chars could improve the handling procedures but on the other hand might decrease the large surface and microporosity, which are some of the main positive properties of char (Erlich et al., 2006; Karkania et al., 2012). Wetting biochar is probably a better solution to control small particles (Maienza et al., 2017), as is the case with some of the chars studied here.

FIGURE 9 | Root growth of Lepidium sativum seedlings germinated for 72 h in the presence of chars from different feedstocks and production processes at increasing concentrations (0.1 to 10 g per plate). Means of 30 seeds are reported with standard errors. Growth in distilled water (control) was 34.29 ± 1.31 cm and is shown as a black bar in all graphs. Asterisks indicate significant differences in the growth of treated samples with respect to control: ∗p < 0.05; ∗∗∗p < 0.001. Capital letters indicate significant differences assessed with one-way ANOVA at p < 0.05 between chars tested at the same concentration (in the conditions 0.1, 0.5, and 1 g per plate).

FIGURE 10 | Relative growth index (RGI) of Hordeum vulgare seedlings germinated for 72 h in the presence of chars from pyrolysis of different feedstocks at increasing concentrations (1 to 5%). White bars, char A4; gray bars, char E1. Means of 30 seeds relative to growth of the control in pure soil (black bar) are reported with standard errors. Letters indicate significant differences assessed with one-way ANOVA at p < 0.05.

The highest ash content in our study was found in chars derived from digestate and poultry litter, in line with the data on char from organic waste obtained through slow pyrolysis and other processes (Field et al., 2013; Qambrani et al., 2017). On the contrary, chars from wood have ash content <10%. Products with an ash content >50% are not compatible with the definition of biochar and are classified as pyrogenic carbonaceous material (European Biochar Certificate [EBC], 2015).

Microscopic examination of the different chars provided evidence of how the animal feedstocks, litter, and digestate became biochar matrix devoid of pores; conversely, plant substrates became porous biochar (Beesley and Marmiroli, 2011; Lu et al., 2012; Song et al., 2014). Biochar E1 showed a combination of characteristic wood-derived micro-, meso-, and macro-pores, covered with fine dust and interspersed with lumps of non-plant origin (**Supplementary Figures 3c,d**). In A3 and A4, the porous matrix appeared disorganized with pores of different sizes distributed at random. These structures could be ascribed to the wood pelleting process that brings together wood fragments with diverse structures (Karkania et al., 2012; Xue et al., 2013). The lower level of porosity of animal-derived chars, observed by other authors (Lu et al., 2012; Song et al., 2014), makes them less effective in retaining water in soil (Liu Z. et al., 2017).

Metals and trace elements were analyzed after wet digestion, and with X-ray analysis accompanying SEM. The data from EDX analysis were semi-quantitative, while those from the chemical analysis were quantitative. For many elements, such as Fe, Ni, Zn, and Cu, both techniques confirmed the elemental presence in all chars. EDX identified additional elements such as titanium and manganese, which might be derived from stainless steel components in the processing equipment (Fellet et al., 2014). The presence of heavy metals in high concentrations could be a factor in phytotoxicity (Jones and Quilliam, 2014), particularly evident in chars of non-plant origin analyzed in the present study. The possible release of toxic metals, present in the original feedstock and concentrated into the chars, has been considered a general hazard in the use of chars derived from animal waste, such as sludge and manure (Garlapalli et al., 2016; Wu et al., 2016). However, the high phytotoxicity of chars A1 and A2 could be explained in terms not only of metals, but also of excessive levels of S, Na, and Cl. For example, poultry litter feedstocks are normally rich in S, Na, and Cl and so are the chars obtained from them (Cantrell et al., 2012; Novak et al., 2013). In the case of sewage sludge, there is a wide variation in element content; however, S and Na are abundant and concentrated in derived biochars, as has been reported in other studies (Lu et al., 2012; Song et al., 2014; Mierzwa-Hersztek et al., 2017).

The toxicity of char E1 might be explained partly by its high Ni and Pb content, accompanied by a low pH (**Figure 7** and **Table 1**). A low pH is commonly associated with an increased concentration of aluminum (Al), which is highly toxic to plant roots (Gruba and Mulder, 2008). It is however interesting that while char E1 was toxic in germination tests on Lepidium sativum, it was highly phytostimulating in growth tests with Hordeum vulgare. Similar differences in the response of dicots and monocots have been demonstrated by Knox et al. (2018) using chars from wood pellets. It appears that the phytotoxic or phytostimulating activities of the biochar could be related not only to their metal content, but also to their macro- or micronutrient (K, Ca, S, P, Cl) content because it contributes to the salinity of the growth substrates (Farifteh et al., 2008). It has been shown that the high K content in many chars increases soil K availability and reduces K leaching (Laird et al., 2010). Phosphorus is often tightly bound in soils rich in Fe and Al oxides, but biochar addition increases soil pH, making P more available to plants and microorganisms (Asai et al., 2009; Hale et al., 2013).

It is known that carbonization processes can lead to phytotoxic products, such as PAHs or phenols, and other contaminants might be present depending on the input feedstocks used in the production of the chars (Bachmann et al., 2016). Organic compounds have not been analyzed in this study. Knox et al. (2018) attribute part of the phytotoxic effects to volatile compounds emitted by the biochar, and such emissions could be particularly important when tests are performed in closed containers, such as Petri dishes.

Restoring carbon in soils with biochar fits into the objectives of climate change priorities in Europe and in the world (Barrow, 2012). Biochar has also been proposed as a peat substitute, after the Commission Decisions of 2006 for soil improvers and for growing media specifying that the EU Ecolabel scheme for these products should apply only to those containing neither peat nor OM in their formulations. However, the application of biochar in real agricultural conditions requires simultaneous deployment of other improvers, such as OM, or standard fertilizers (Agegnehu et al., 2017; Bonanomi et al., 2017). It has been demonstrated recently that a coating of OM on the surface of biochar particles after co-composting increases the benefits to soil (Hagemann et al., 2017). In a recent experiment on arid and acidic Nepalese soils, wood biochar addition at 2% (v/v), implemented with NPK fertilization, significantly increased soil water retention capacity, plant-available water, increased soil EC, pH, exchangeable K+, Mg2+ and Ca2+, plant-available P, and organic C, giving rise to significantly improved maize biomass production in comparison to NPK fertilization (Pandit et al., 2018). However, the increase in soil EC corresponds to increase in salinity, which can lead to soil infertility (Farifteh et al., 2008). Therefore, careful land management should be followed according to the type of soil on which the biochars are applied: clay soils have a greater salinity buffer potential than sandy soils, and accurate balance between irrigation and drainage avoids the build-up of ions in the root zone, thus reducing exposition of plants to salt stress (Payen et al., 2016). Another study evaluated the application in a weathered tropical soil of biochar derived from sewage sludge, in combination with mineral fertilizer; after 2 years, only P availability from soil was increased by biochar, but overall the plant production rate increased (Monteiro Faria et al., 2017). These two experiments demonstrate that the direct effects of biochar on soil can be diverse, depending on the feedstock, on the overall biochar features, and on the soil type. However, accompanied by sensible land management, a predominantly stimulating effect on plant growth and yield was observed in both cases.

In this work, we were able to differentiate the biochars according to their physicochemical properties, microstructure, elemental contents, and the raw original biomass and to correlate these features with their respective optimal concentrations when used as plant fertilizers. In particular, biochars of animal origin were more phytotoxic at low concentrations than those of plant origin. In conclusion, it appears that additional experiments are needed to determine other characteristics of biochars to facilitate the use of appropriate feedstock and processing technologies to produce biochars that can be used as safe soil improvers.

### AUTHOR CONTRIBUTIONS

UB, DI, GL, and FM were responsible for chemical analyses and tests with plants. NM, EM, and MM designed the experiments, collected bibliography, and performed the statistical analyses. All authors contributed to data analysis and writing of the paper.

### FUNDING

Funding for the experiments was provided from the FACCE-JPI Program SFS-05-2015: strategies for crop

### REFERENCES

productivity, stability, and quality (Grant No. 652515), project INTENSE, and from Regione Emilia-Romagna POR-FESR 2014–2020 project TERMOREF "Integration of thermochemical and reforming processes on waste biomass and valorization of products in a zero-waste approach."

### ACKNOWLEDGMENTS

The authors acknowledge collaboration from partners of the projects INTENSE and TERMOREF in providing chars and seeds. They appreciate Dr. Laura Paesano's assistance in experimental activities and Dr. Martin Shepherd's helpful suggestions.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.01119/ full#supplementary-material

pyrochar formation. Bioresour. Technol. 220, 168–174. doi: 10.1016/j.biortech. 2016.08.071

inorganic fertilizers for corn production? Evidence from a two-year field experiment. Arch. Agron. Soil Sci. 64, 505–519. doi: 10.1080/03650340.2017. 1360488

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Marmiroli, Bonas, Imperiale, Lencioni, Mussi, Marmiroli and Maestri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Effects of N Fertilizer Sources and Tillage Practices on NH3 Volatilization, Grain Yield, and N Use Efficiency of Rice Fields in Central China

### Tianqi Liu1 , Jinfeng Huang1 , Kaibin Chai1 , Cougui Cao1,2 and Chengfang Li1,2 \*

1 MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China, 2 Hubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China

### Edited by:

Peter Schröder, Helmholtz Zentrum München – Deutsches Forschungszentrum für Gesundheit und Umwelt, Germany

### Reviewed by:

Ilias Travlos, Agricultural University of Athens, Greece Allah Wasaya, Bahauddin Zakariya University, Pakistan

\*Correspondence: Chengfang Li<EMAIL_ADDRESS><EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

Received: 19 September 2017 Accepted: 08 March 2018 Published: 22 March 2018

### Citation:

Liu T, Huang J, Chai K, Cao C and Li C (2018) Effects of N Fertilizer Sources and Tillage Practices on NH3 Volatilization, Grain Yield, and N Use Efficiency of Rice Fields in Central China. Front. Plant Sci. 9:385. doi: 10.3389/fpls.2018.00385 Tillage practices and nitrogen (N) sources are important factors affecting rice production. Few studies, however, have examined the interactions between tillage practices and N fertilizer sources on NH3 volatilization, nitrogen use efficiency (NUE), and rice grain yield. This study aimed to investigate the effects of N fertilizer sources (no N fertilizer, inorganic N fertilizer, organic N fertilizer alone, organic N fertilizer plus inorganic N fertilizer, and slow-release N fertilizer plus inorganic N fertilizer) and tillage practices (no-tillage [NT] and conventional intensive tillage [CT]) on NH3 flux, grain yield, and NUE in the rice field of central China. N sources significantly affected NH3 volatilization, as the cumulative volatilization from the treatments of inorganic N fertilizer, organic N fertilizer, organic N fertilizer plus inorganic N fertilizer, slow-release N fertilizer plus inorganic N fertilizer was 4.19, 2.13, 3.42, and 2.23 folds in 2013, and 2.49, 1.68, 2.08, and 1.85 folds in 2014 compared with that under no N fertilizer treatment, respectively.  Moreover, NT only markedly increased NH3 volatilization from basal fertilizer by 10–14% in average compared with CT, but had no obvious effects on total volatilization during the whole seasons. Tillage practices had no significant effects on grain yield and NUE. Our study suggested that the combination of slow-release N fertilizer plus inorganic N fertilizer and NT might be a sustainable method for mitigating greenhouse gas and NH3 emissions and improving grain yield and NUE in paddy fields of central China.

Keywords: N recovery efficiency, NH3 flux, no-tillage, organic N fertilizer, slow-release N fertilizer

# INTRODUCTION

Nitrogen (N) is one of the most important nutrients for agricultural systems, and thus N fertilizers are frequently used with the aim to achieve high yields of crop. As the most important cereal crop in China, rice accounts for 18.2% of total cultivated land area in China, and inorganic N fertilizers account for 36.2% of the total chemical N fertilizers used in rice production in the world (Department of Rural Social and Economic Investigation of the National Bureau of Statistics, 2017).