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Evaluation of Spent Grain Biochar Impact on Hop (Humulus lupulus L.) Growth by Multivariate Image Analysis

Evaluation of Spent Grain Biochar Impact on Hop (Humulus lupulus L.) Growth by Multivariate Image... applied sciences Article Evaluation of Spent Grain Biochar Impact on Hop (Humulus lupulus L.) Growth by Multivariate Image Analysis 1 , 2 2 3 , Tiziana Amoriello * , Simona Fiorentino , Valerio Vecchiarelli and Mauro Pagano * CREA Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Roma, Italy Centro Appenninico del Terminillo Carlo Jucci, Università degli Studi di Perugia, Via Comunali 43, 02100 Rieti, Italy; simona.fiorentino@unipg.it (S.F.); valerio.vecchiarelli@unipg.it (V.V.) CREA Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo (Roma), Italy * Correspondence: tiziana.amoriello@crea.gov.it (T.A.); mauro.pagano@crea.gov.it (M.P.); Tel.: +39-06-514941 (T.A.) Received: 20 November 2019; Accepted: 8 January 2020; Published: 10 January 2020 Abstract: Biochar is generally considered as an e ective soil amendment, which can improve soil organic matter and nutrients content and enhance crop productivity. In this study, biochar derived from brewers’ spent grain (BSG) was used in a pot and field experiment to assess whether its addition to soil could a ect hop plant growth. The experiment was conducted in Central Italy during the period March–August 2017. Three di erent German cultivars of hop plant (Hallertau Magnum, Perle, Spalter spalt) were considered. Biochar was added to the pot soil at 20% level. Its e ect on the roots was evaluated using multivariate image analysis (MIA) and the statistical technique of general linear models (GLM), whereas the shoots, bines length and yield using GLM. Results showed that biochar significantly improved root growth (p < 0.0001). Regarding shoots, no variability for the genotypes was observed during the vegetative period, whereas slight di erences resulted before plant dormancy, especially for the Hallertau Magnum cultivar. No di erences in the number of leaves or bines length were observed between the two treatments for all cultivars. The addition of biochar to the soil significantly improved yield (number of cones). These results highlighted that BSG-derived biochar can be useful to improve hop plant growth and cones production. Keywords: amendment; biochar; brewers’ spent grain; hop; image analysis; plant growth 1. Introduction Beer is one of most consumed beverages in the world, and plays an important role in the global economy. In 2018, the overall beer production amounted to about 1.94 billion hectolitres. However, beer processing produces a huge amount of waste: for every hectolitre of beer produced, about 20 kg of spent grain, 0.2–0.4 kg of spent yeast and 0.3 kg of spent hops/hot trub are generated. In particular, the 85% of total solid waste is represented by brewers’ spent grain (BSG), the residue left after barley malting and separation of the wort during the brewing process, and it contains the husk and the outer layer of barley kernel [1]. BSG is mainly composed of protein (more than 20%) and fibers, represented by a lignino-cellulosic material, whose main constituents are hemicellulose (28–35%), cellulose (17–25%) and lignin (7–27%) [1–3]. Appropriate management of these waste streams has become a challenging issue. The solid by-products from the brewing process are mainly disposed of as waste or sold as animal feed, due to their considerable amount of valuable compounds (proteins, lipids, carbohydrates, polyphenols, and minerals) and nitrogen-containing nutrients. However, brewing industries are interested in new Appl. Sci. 2020, 10, 533; doi:10.3390/app10020533 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 533 2 of 11 solutions to reduce the amount of waste produced and transform by-products in added value products. Therefore, in a perspective of a zero waste approach and in order to reduce waste storage and logistics costs, alternatives to valorize these residues and to recover and reuse them in a sustainable and profitable way are continuously proposed and developed. Moreover, by-products can be transformed in combustible gas, to be reused in the beer production cycle, or in soil amendments for agricultural applications [4,5]. A valid alternative can be the transformation of BSG into biochar, which can be used as an e ective soil amendment for the production of vegetables. Biochar is a carbon-rich, fine-grained and porous material with stable physical and chemical properties, produced from waste biomass through pyrolysis, e.g., a thermal decomposition of lignocellulose biomass by heating at elevated temperatures (generally between 350 and 700 C) under limited oxygen conditions [6–8]. It has stable aromatic C structures, low O and H to C ratios, low bulk density, moderate cation exchange capacity (CEC), and high ash content, pH and surface area [9,10]. Due to these properties, it is recognized as a multifunctional material which can be applied for long-term C sequestration and climate change mitigation [11,12]. It can be used successfully in agriculture to increase the organic C content of soil and reduce the leaching of nutrients; it could have positive e ects on soils contaminated with heavy metals and organic pollutants [6,12–15]. Furthermore, biochar can be useful as soil amendment, due to its high porosity and sorption capacity and large surface area, reducing soil bulk density, improving soil structure, and increasing soil water holding capacity [7]. Biochar application can increase plant root growth, root penetration, and nutrient and water uptake [16]. In fact, N concentration in soil is increased by biochar during critical stages of plant growth, and N uptake and fertilizer recovery from roots are mostly guaranteed [7,17]. Biochar amendment also significantly enhances microbial activity and abundance, probably due to a greater adsorption of various nutrients [18,19]. Such positive influence could have e ects on soil structure and indirectly on plant growth and rooting patterns [20]. Numerous studies have been conducted in controlled environments to evaluate the impact of biochar on soil properties and plant growth. However, the e ects are not always positive: benefits on soil and plant growth strongly depend on the feedstock sources used to produce the biochar, production conditions (pyrolysis temperature, heating rate and residence time), di erences in soil properties and specie-specific root growth patterns [15,21–23]. Therefore, this study aims to assess the potential ability of BSG-derived biochar for soil amendment and its influence on hop plant growth. 2. Materials and Methods 2.1. Biochar Production and Characterization A fifty kilograms sample of wet BSG was chosen randomly from a big container at a craft brewery. Subsequently, three replicates of about 1 kg each were selected randomly from this sample, oven-dried at 105  1 C for 78 h and successively subjected to elemental analysis for the determination of the CHN content, according to UNI EN 15104: 2011. Ash content was analyzed by heating ground dried BSG samples (maximum grain size 2 mm) at 550 C  1 C for 8 h in a mue furnace, according to UNI EN 14775: 2010. Three analytical replicates were performed for all parameters analyzed and for each sample. Biochar was prepared from BSG using the pyrolytic reactor Elsa D17 (pat. BLUECOMB) and through a thermochemical process that can reach carbonization temperatures of 400–500 C [24,25]. Biochar was chemically characterized by Fourier transformed infrared spectroscopy analysis (FT-IR), according to the methodology described by Amoriello et al. [26]. Spectra were collected at room temperature with a FT-IR spectrometer (iS 50 FT-IR Nicolet Thermo Fisher Scientific Inc., Waltham, MA, USA) equipped with a single-reflection horizontal ATR cell with a diamond crystal [26]. Appl. Sci. 2020, 10, 533 3 of 11 2.2. Botanical and Agronomic Characteristics of Hop Plant Hop (Humulus lupulus L.) is a dioecious perennial plant, belonging to the Cannabaceae family. It reaches maturity after the first three years and remains productive for over 20–25 years [27]. The hop plant consists of a perennial rootstock (“crown”) of rhizomes below ground, annual climbing bines above ground, which provide the canopy and photosynthetic capacity to support flowering, and flowers that develop at the terminal buds of lateral branches and are harvested as green cones. The perennial root system can grow more than 4 m deep and up to 5 m laterally. Rhizomes feed the growth of the productive canopy and ensure the survival of the plant from successive seasons. The growing season spans from March to August–September in the northern hemisphere. Hops emerge in early spring and grow up to a height of 5–7 m on poles or under a trellis system. Flowering starts in late June or early July in the northern hemisphere. The cones mature for picking between August and September, depending on climate conditions and genotype. The female strobiles (cones) represent the most interesting parts of the plant from a technological point of view, as they are one of the essential ingredients of the brewing industry, providing aroma, bitterness, flavor, and antimicrobial properties to beer [28]. 2.3. Experimental Site and Design 0  0 The study was carried out in Rieti, Italy (latitude 42 24 29”52 N, longitude 12 51 36”36 E) during the period March–August 2017. The sampling site is characterized by relatively cold and rainy winters and hot and dry summers. The long-term average annual temperature recorded between 1980 and 2009 was 12.1 C; the mean temperature during the growing season was 16.0 C; the average maximum temperature for the summer months was 30.1 C; the average minimum temperature for the coldest months was 1.6 C. In summer, maximum temperatures were often over 30 C. The annual mean precipitation was equal to 1021 mm and it fell mainly from October to December. Summer rainfall was irregularly distributed and the total mean amount over June–August was 136 mm. In 2017, the annual average temperature was 12.7 C; the average temperature in the growing season was 17.4 C; the average maximum temperature for the hottest months (July to August) was 33.2 C. The annual precipitation, precipitation for the growing season, and precipitation over the hottest months was 721 mm, 186 mm, and 27 mm, respectively. The annual relative humidity was 68%. A completely randomized experimental design was conducted during the hop growing season in a climate uncontrolled greenhouse environment from March to 5 May and outside from 6 May to 28 August. Three di erent cultivars of German hop plant (Hallertau Magnum, Perle, Spalter spalt) were considered for the study due to their economic importance and their sensitivity to environmental conditions [27]. These three varieties grow well in most climates, but best in warm, dry and sunny regions. Hallertau Magnum produces very good yields (1700–2300 kg ha ); the cone’s structure is very large and longish size. Perle also produces good yields (1600–2100 kg ha ); cone size is small to medium. Spalter spalt yield amounts 1750–2000 kg ha ; the cone size is small to medium. In March, hop rhizomes were placed in plastic pots (15 cm  14 cm) with soil containing acid peat, expanded perlite and clay. Each pot contained 8 L of soil. The packed pot soil had a pH of 6.0, 3 1 a dry bulk density of 1.1 g cm , an electric conductivity of 0.45 dS m and a porosity of 90% v/v. Hop rhizomes for each variety were randomly assigned between pots with soil added with 20% of biochar (1.6 L of biochar and 6.4 L of soil containing acid peat, expanded perlite and clay) and pots without biochar (control). Trials with biochar were replicated seven times, whereas controls (without biochar) were run in triplicate. We chose an unbalanced sampling to better evaluate the variability of the biochar e ect on hop plants. Young plants were transferred from pots to the field at the beginning of May, corresponding to the moment of bines elongation. No biochar was added in field. Until June, the plants were irrigated with 0.5 L of water every two days; in the two warmest months (July and August), the same amount of water was given every day. Plant development was monitored through the percentage of roots in pot (as described in the Section 2.4) and the number of leaves at the beginning of May; shoot diameter was measured by caliper Appl. Sci. 2020, 10, 533 4 of 11 at five di erent times (T1: 30 June, T2: 18 July, T3: 27 July, T4: 3 November, T5: 21 November), i.e., before flowering, near cones maturity, before plant dormancy; measurement of climbing bines length and number of cones at maturity. 2.4. Statistical Analysis and Multivariate Image Analysis In order to quantify the e ect of biochar on root growth for each genotype, a non-destructive technique, e.g., a multivariate image analysis, was carried out according to Fongaro et al. [29]. At the beginning of May, plants were extracted from pots and the images of roots and soil for each plant were acquired twice using a digital camera Nikon D750 at a high resolution and a color depth of 16 bits, saving the captured images in uncompressed RAW format. The images were acquired by photographing the basis and four external opposite sides. To create the final data set, a region of interest (ROI) of 472 472 pixels, representative of the whole sample surface, was extracted from each image using the Lightroom Classic CC 2018. The images were processed by PLS Toolbox 8.5 (Eigenvectors Research, Inc., Manson, WA, USA) for MATLAB R2016b (The MathWorks Inc., Natick, MA, USA). Red (R), green (G), blue (B) values, measures in the RGB space color, were calculated. A principal component analysis (PCA) on the RGB values was carried out, providing the pixel distribution in the score space (score plot). The first principal component (PC1) contained most of the original information, with a decreasing amount in the remaining score images. PC1 score image. PC1 score image was converted from the RGB space color into the HSI (hue, saturation, intensity) space color. Then, the Hue channel was extracted to obtain the hue image. A pixel segmentation in di erent intensity ranges, corresponding on di erent material (soil and root), was carried out to select roots. In this way, it was possible to quantify the percentage of roots present in each pot and for each cultivar. The influence of genotype, treatment and the two-way interaction on roots percentage or number of leaves or bines length or number of cones was tested using the technique of General Linear Models (GLM). The statistical model was: Y = +  +  +   + " (1) ij 0 1 i 2 j 3 i j ij where Y = roots percentage or number of leaves or bines length or number of cones, = mean e ect ij 0 common to all observations; = unknown regression parameters;  = treatment (i = 1, 2; 1 = with 1,2,3 i biochar, 2 = without biochar);  = cultivar (j = 1, 2, 3; 1 = Hallertau Magnum, 2 = Perle, 3 = Spalter spalt); " = error term. ij GLM was also applied to monitor the plant development in time. The statistical model was: Y = +  +  + +   +   +   +    + " (2) 0 2 3 5 6 7 ijk 1 i j k 4 i j i k j k i j k ijk where Y = diameter of shoots, = mean e ect common to all observations; = unknown ijk 0 1–7 regression parameters;  = treatment (i = 1, 2; 1 = with biochar, 2 = without biochar);  = cultivar i j (j = 1, 2, 3; 1 = Hallertau Magnum, 2 = Perle, 3 = Spalter spalt); = time (k = 1, ::: ,5; 1 = 30 June, 2 = 18 July, 3 = 27 July, 4 = 3 November, 5 = 21 November); " = error term. ijk Post hoc Tamhane test (p < 0.05) was carried out in order to evaluate di erences between groups (with or without biochar) for each considered variable. Statistical analysis was performed with SPSS 20.0 software (SPSS, Inc., Chicago, IL, USA). 3. Results 3.1. Brewers’ Spent Grain and Biochar Characterization BSG analysis showed an average moisture content of 8.8%, and a content of ashes, C, H, N contents of 5.3%, 45.7%, 9.0% and 4.2%, respectively, on the dry weight basis. Brewers’ spent grain (BSG) and biochar were characterized by FT-IR spectra (Figure 1). Appl. Appl. Sci. Sci. 2020 2020,, 10 10,, x FO 533 R PEER REVIEW 5 of 5 of 11 11 Figure 1. FT-IR spectra of brewers’ spent grain (BSG) and biochar. Figure 1. FT-IR spectra of brewers’ spent grain (BSG) and biochar. The two spectra showed di erent profiles and changes of functional groups, demonstrating the influence The two spec of the pyr tra show olytic tred eatment differe on nt profile the chemical s and ch stran uctur ges of e offunction the rawal groups, demonstrating th material. In fact, the organic e matter influence pyr oolysis f the pyro caused lytic water treatment on loss and tvariation he chemical in concentration structure of the ra of mineral w materi components al. In fact, the due orga to the nic heat-induced matter pyrolym sis c ass aused loss [ water loss an 30]. BSG profile d vshowed ariation a inpeak concentration o at 3274 cm f miner corresponding al components due to the to a vibrational −1 band heat-in consistent duced mass with loss hydr [30oxyl ]. BSG grprofi oupsle (O-H); showed two a peak a peaks tat 327 2926 4 cm and corresp 2853 cm ondi,ng t corr oesponding a vibrationa tol −1 aliphatic band consi C-H stent wi stretch; th hydroxyl a peak at groups (O- 1742 cm , H associated ); two pea with ks at t he 2926 pr esence and 28of 53aldehydes, cm , correspond ketones ing to and −1 carboxyl aliphatic C gr-oups H stret (Cc= h; O); a p aeband ak at with 1742 c am peak , assoc at 1633 iated cm with , assigned the presence to ar omatic of aldehydes, ketones an lignin components d −1 (C carb =C); oxyl a gro peak up at s (C 1030 =Ocm ); a b,acorr nd w esponding ith a peakto atC-O-C 1633 cm stretch. , assiThe gned thermal to arom decomposition atic lignin comstr ponent ongly s −1 influenced (C=C); a peak the a intensity t 1030 cm of , corre biochar spon bands dingfr to C-O om 4000 -C stretch. to 1600 cm The thermal d (carboxylic ecomposition bonds, amides strongly and −1 aliphatic influencehydr d thocarbon) e intensit[y o 30].f bioch In particular ar ban ,d the s from disappearance 4000 to 16 of00 the cm peaks (carb at 2926 oxylic and bo2nds, am 853 cm idmay es and be −1 due aliphat to the ic hyd fact rocarb that methyl on) [30gr ]. In oups, part which icularar , te he the dis weakest appearance o functional f the gr peoups, aks atas 29 well 26 and as the 285OH 3 cm groups, may br be due to the f eak during pyr act tha olysis. t methyl The CH groups, peaks shift whi fr com h are being the wea morekaliphatic est functi to ona mor l grou e aromati ps, as c w (and ell a eventually s the OH disappear groups, break altogether) during pyrolysis. The CH [31]. The cleavage peaks of these shift gr froups om being more al contributes iphat to mass ic toloss more aromat during thermal ic (and decomposition eventually disappe andar toaltog the pr ether) oduction [31]. T ofh “non-condensable” e cleavage of these group gas. Moderate s contribdi utes to erences mass l between oss duri BSG ng and thermal d biochar eco prmposition ofiles froman 16d to the pro 00 to 1500 cmduction o may befattributed “non-condensable to the formation ” gas. M ofo carbonate-carboxyl derate differences −1 gr beoup tween BSG during pyr and b olysis. ioch At ar p last, rofil biochar es from showed 1600 t similar o 1500 cm functional may gr be a oups, ttri between buted to the f 600 and orma 1500 tio cm n of . These carbonate-carboxyl group results were in accor dance during with pyrol pry evious sis. Atstudies last, bi on ocha BSG-derived r showed si biochar milar fu [32 nc ,33 tion ]. al groups, The FT-IR −1 analysis between 60 highlighted 0 and 1500 cm the presence . These results were in acc of functional groups such ordance w as hydr ith previous oxyl and carboxyl studies on BSG groups, necessary -derived to biocha consider r [32,33 biochar ]. The FT- a soilIR a amendment nalysis hifor ghlimpr ighted the oving of prthe esence o cation f functional gro exchange capacity ups such and as as h a potential ydroxyl adsorbent and carboxyl groups, nec [34]. essary to consider biochar a soil amendment for improving of the cation exchange capacity and as a potential adsorbent [34]. 3.2. Multivariate Image Analysis 3.2. Multivariate Image Analysis Multivariate image analysis was applied to extract roots region and to quantify di erences in percentage for the two treatments, with and without biochar. Representative images from roots and Multivariate image analysis was applied to extract roots region and to quantify differences in soil samples for each cultivar and treatment after RGB processing and PCA score images are shown in percentage for the two treatments, with and without biochar. Representative images from roots and Figure 2. soil samples for each cultivar and treatment after RGB processing and PCA score images are shown in Figure 2. Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 11 Appl. Sci. 2020, 10, 533 6 of 11 Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 11 Figure 2. RGB and PCA score images of roots and soil samples representative of different cultivars Figure 2. RGB and PCA score images of roots and soil samples representative of di erent cultivars and treatments, obtained from image analysis process. (a) RGB image of Hallertau Magnum without Figure 2. RGB and PCA score images of roots and soil samples representative of different cultivars and treatments, obtained from image analysis process. (a) RGB image of Hallertau Magnum without biochar; (b) RGB image of Hallertau Magnum with biochar; (c) RGB image of Perle without biochar; and treatments, obtained from image analysis process. (a) RGB image of Hallertau Magnum without biochar; (b) RGB image of Hallertau Magnum with biochar; (c) RGB image of Perle without biochar; (d) RGB image of Perle with biochar; (e) RGB image of Spalter Spalt without biochar; (f) RGB image biochar; (b) RGB image of Hallertau Magnum with biochar; (c) RGB image of Perle without biochar; (d) RGB image of Perle with biochar; (e) RGB image of Spalter Spalt without biochar; (f) RGB image of of Spalter Spalt with biochar; (g) PCA score image of Hallertau Magnum without biochar; (h) PCA (d) RGB image of Perle with biochar; (e) RGB image of Spalter Spalt without biochar; (f) RGB image Spalter Spalt with biochar; (g) PCA score image of Hallertau Magnum without biochar; (h) PCA score score image of Hallertau Magnum with biochar; (i) PCA score image of Perle without biochar; (j) PCA of Spalter Spalt with biochar; (g) PCA score image of Hallertau Magnum without biochar; (h) PCA image of Hallertau Magnum with biochar; (i) PCA score image of Perle without biochar; (j) PCA score score image of Perle with biochar; (k) PCA score image of Spalter Spalt without biochar; (l) PCA score score image of Hallertau Magnum with biochar; (i) PCA score image of Perle without biochar; (j) PCA image of Perle with biochar; (k) PCA score image of Spalter Spalt without biochar; (l) PCA score image image of Spalter Spalt with biochar. score image of Perle with biochar; (k) PCA score image of Spalter Spalt without biochar; (l) PCA score of Spalter Spalt with biochar. image of Spalter Spalt with biochar. For all the images, the PC1 score image explained all the variance, being related to pixels of both For all the images, the PC1 score image explained all the variance, being related to pixels of both the root and soil. Comparing the PC1 score images and the relative RGB images, it was possible to For all the images, the PC1 score image explained all the variance, being related to pixels of both the root and soil. Comparing the PC1 score images and the relative RGB images, it was possible to distinguish areas with different color intensity, indicated with blue (soil) and yellow (root). Thus, the the root and soil. Comparing the PC1 score images and the relative RGB images, it was possible to distinguish areas with di erent color intensity, indicated with blue (soil) and yellow (root). Thus, the same false color was assigned to the pixels having the same characteristics, well distinguishing the distinguish areas with different color intensity, indicated with blue (soil) and yellow (root). Thus, the same false color was assigned to the pixels having the same characteristics, well distinguishing the two regions in the PC1 score images. The pixel segmentation allowed the extraction of the root areas same false color was assigned to the pixels having the same characteristics, well distinguishing the two regions in the PC1 score images. The pixel segmentation allowed the extraction of the root areas (in black) and the quantification of the roots percentages. Results obtained were plotted in Figure 3. two regions in the PC1 score images. The pixel segmentation allowed the extraction of the root areas (in black) and the quantification of the roots percentages. Results obtained were plotted in Figure 3. Box plots showed a strong influence on root growth present in the pots with biochar in comparison (in black) and the quantification of the roots percentages. Results obtained were plotted in Figure 3. Box plots showed a strong influence on root growth present in the pots with biochar in comparison to to those without biochar. Box plots showed a strong influence on root growth present in the pots with biochar in comparison those without biochar. to those without biochar. Figure 3. Box plots of root percentages of three di erent hop cultivars (Hallertau Magnum, Perle and Figure 3. Box plots of root percentages of three different hop cultivars (Hallertau Magnum, Perle and Spalter spalt) and two treatments (presence or absence of biochar). Outliers are displayed as filled stars. Spalter spalt) and two treatments (presence or absence of biochar). Outliers are displayed as filled Figure 3. Box plots of root percentages of three different hop cultivars (Hallertau Magnum, Perle and stars. Spalter spalt) and two treatments (presence or absence of biochar). Outliers are displayed as filled stars. Appl. Sci. 2020, 10, 533 7 of 11 3.3. Assessment of Hop Plant Response to Biochar Amendment The evaluation of e ect of BSG-derived biochar was carried out using the technique of General Linear Models (GLM). The GLM analysis of roots percentage showed that the two single factors (genotype and treatment) were statistically significant, unlike the two-way interactions between cultivar and treatment. The treatment was the main contributing factor to the percentage of roots (p < 0.0001), followed by cultivar (p = 0.0330). In fact, as clearly shown in Figure 3, the addition of biochar in the soil produces a greater development of the roots. This phenomenon is even more evident for the Perle cultivar. Regarding shoot development, specifically the shoot diameter, no variability ascribable to the genotypes was observed for the first three times (Table 1). Table 1. Measurements of diameter of shoots (mean standard deviation) at five di erent times (T1: 30 June, T2: 18 July, T3: 27 July, T4: 3 November, T5: 21 November). Diameters within columns of each cultivar and treatment followed by di erent letters (a or b) are significantly di erent. Hallertau Magnum Perle Spalter Spalt Time No Biochar Biochar No Biochar Biochar No Biochar Biochar T1 7 2 a 8 2 a 8 3 a 6 1 a 8 3 a 5 1 a T2 8 3 a 9 2 a 9 1 a 7 1 a 8 3 a 7 2 a T3 9 2 a 10 2 a 10 1 a 8 1 a 9 2 a 8 2 a T4 15 3 b 15 3 b 11 1 a 12 2 b 11 1 a 12 3 ab T5 16 2 b 16 4 b 11 1 a 13 4 b 11 1 a 13 3 b On the contrary, significant but slight di erences resulted among diameters measured before plant dormancy, especially for the Hallertau Magnum cultivar. It is interesting to note that the Perle and Spalter Spalt cultivars tended to be more a ected by the presence of biochar during the last phase of plant development, although these di erences are not statistically significant. This trend was confirmed by the GLM analysis: only the single genotype and time factors were highly significant (p < 0.0001), whereas the single treatment factor, all the two-way interactions and the three-way interaction were not statistically significant. The GLM analysis on number of leaves at the beginning of May and climbing bines length at harvest highlighted no significant di erences between cultivar and treatment and their two-way interaction, as shown in Table 2. Table 2. Measurements of number of leaves (mean standard deviation) at the beginning of May, and climbing bines length and number of cones at maturity. Statistical di erences between treatments (with biochar or without biochar) for each cultivar and parameter are displayed with di erent letters (a or b). Hallertau Magnum Perle Spalter Spalt No Biochar Biochar No Biochar Biochar No Biochar Biochar Number of leaves 20 8 a 18 6 a 19 4 a 15 3 a 26 14 a 25 14 a Bines length (cm) 547 55 a 570 30 a 552 27 a 532 61 a 555 94 a 550 75 a Number of cones 97 17 a 144 20 b 84 14 a 123 18 b 75 12 a 106 16 b As regards the number of harvested cones, statistical di erences (p < 0.0001) were observed between treatments for each cultivar: the cones of the plants treated with biochar resulted more numerous compared to those of plants without biochar (Table 2). Instead, the genotype factor and the two-way cultivar-treatment interaction resulted not significant by the GLM analysis. 4. Discussion Despite the high adaptability to di erent environmental conditions, hop cultivars of European origin are more susceptible to changes in pedo-climatic conditions compared to the American Appl. Sci. 2020, 10, 533 8 of 11 genotypes [27]. Hops require a large amount of water during the growing seasons to optimize yield and quality. Furthermore, soil has to be nutrient-rich, light, well drained, well supplied with moisture, but free from waterlogging, with an optimal pH range between 6.0 and 6.5., although hops also grow on soils with pH from 4.8 to 8.0 [27]. Our study wanted to verify if biochar can positively a ect hop growth. As mentioned above, biochar can improve soil water retention, cation exchange capacity, the content of di erent nutrients, and reduce soil bulk density and N leaching [7]. Moreover, biochar can enhance the uptake of N and other nutrients by improving the root development and the whole plant physiological status [17]. Then, biochar may reduce plant uptake of potentially toxic elements (such as Pb, Cd and As) from soils, due to the adsorption of metals to the negative charges on the surface of biochar particles [35]. However, the ecacy of biochar application depends on various factors concerning both soil and plant. Understanding these factors is essential for maximizing soil productivity and minimizing potential deleterious environmental e ects. For example, the positive e ect of biochar amendment is more remarkable in a coarse-textured than in a fine-textured soil, and sandy soils are more responsive than clay-rich soils; this positive e ect could be also maximized reducing biochar particle sizes in the range of 0.5–1.0 mm [19]. The biomass feedstock could influence the hydraulic properties of biochar: biochar with high porosity and surface area can improve water adsorption [36]. Previous studies have shown discordant experimental evidences about the supposed beneficial e ects of biochar on root growth. Numerous papers reported positive responses on root growth parameters [37–41]. On the contrary, other authors [39,42,43] showed negligible benefits on root growth. These discrepancies could depend on di erent factors: plant species, growing environment, soil properties, feedstock, biochar properties, pyrolysis conditions [15,21–23]. Furthermore, improved concentrations of dissolved organic C after biochar addition and the adsorption of phenolic compounds by biochar may positively a ect root growth [38,43]. Moreover, biochar may stimulate a faster root turnover and changes in root morphology [43]. Although there are few detailed studies on the influence of biochar on the root system, it is generally recognized that plants with a longer root length, a larger surface area and more root tips may be able to get more nutrients and grow better [44–46]. In the present study, the addition of biochar amendment positively a ected hop root growth, increasing root length and mass density, as well represented in Figure 2. Moreover, a higher presence of roots could be positively correlated with nutrient uptake, and induce potential benefits in plant growth and development, including aboveground biomass. Benefits and limitations of biochar on plant growth could depend on the types of biochar and the incorporation rates [47–49]. In our study, no statistical di erences were observed between number of leaves at the moment of bines elongation and climbing bines length at harvest. Regarding hop yield, biochar seemed to have positively a ected cones production. This production varies greatly during the first years of the plant vegetation. In fact, the hop plant reaches productive and qualitative stability only after the third year. Nevertheless, biochar could have helped the plant when the water requirement was greater. In fact, the summer period, characterized by dry weather conditions, caused long periods of water stress, compensated only by emergency irrigation. Hops require a high water availability for successful plant growth and cones production [27], and changes in soil moisture retention may have been one of the most important factor in explaining positive biochar e ects on crop yield. Due to its high porosity and specific surface area, biochar increases soil porosity, and, consequently, the soil hydraulic properties, such as the water storage capacity and the absorption capacity [42]. Furthermore, the induced macro-porosity due to the larger particle size of biochar positively a ects soil water permeability [50]. Based on our findings, the application of biochar could be a promising strategy to enhance hop plant growth and cones production. However, further investigation on plant physiological mechanisms and hop cones quality is required to identify optimal levels of biochar application and to better understand the e ect of biochar on hop plant growth. Appl. Sci. 2020, 10, 533 9 of 11 5. Conclusions The present study was conducted to assess the e ect of BSG-derived biochar on the hop plant development. The results reinforce the hypothesis that biochar can be a promising and e ective soil amendment for hop crops, supplying key nutrients for the plant growth and improving soil properties. The recovery and reuse of the brewing industry by-products is a good approach of circularity in this industrial sector from the environment protection and waste management standpoint. In fact, the reuse of brewers’ spent grain, as feedstock to obtain biochar, will contribute to the adoption of environmentally sustainable practices and a more ecient use of resources, also reducing the use of chemical input. Moreover, it can contribute to decrease the negative economic and environmental impact of the disposal of by-products, decreasing the CO production, and increasing the sustainability of the farming sector. Author Contributions: All authors conceptualized and designed the experiments. M.P., V.V. and S.F. collected the data. T.A. performed the statistical analysis and image analysis. T.A. wrote the manuscript in consultation with all other authors. M.P., V.V. and S.F. revised and improved the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The valuable technical assistance of Marco Castellani (Department of Agricultural and Forestry Sciences at the University of Tuscia) is gratefully acknowledge. The authors also thank Paola Manzi (CREA Research Centre for Food and Nutrition) for providing the FT-IR instrument. Conflicts of Interest: The authors declare no conflict of interest. References 1. Mussatto, S.I.; Dragone, G.; Roberto, I.C. Brewers’spent grain: Generation, characteristics and potential applications. J. Cereal Sci. 2006, 43, 1–14. [CrossRef] 2. Lynch, K.M.; Ste en, E.J.; Arendt, E.K. Brewers’ spent grain: A review with emphasis on food and health. 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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute

Evaluation of Spent Grain Biochar Impact on Hop (Humulus lupulus L.) Growth by Multivariate Image Analysis

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applied sciences Article Evaluation of Spent Grain Biochar Impact on Hop (Humulus lupulus L.) Growth by Multivariate Image Analysis 1 , 2 2 3 , Tiziana Amoriello * , Simona Fiorentino , Valerio Vecchiarelli and Mauro Pagano * CREA Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Roma, Italy Centro Appenninico del Terminillo Carlo Jucci, Università degli Studi di Perugia, Via Comunali 43, 02100 Rieti, Italy; simona.fiorentino@unipg.it (S.F.); valerio.vecchiarelli@unipg.it (V.V.) CREA Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo (Roma), Italy * Correspondence: tiziana.amoriello@crea.gov.it (T.A.); mauro.pagano@crea.gov.it (M.P.); Tel.: +39-06-514941 (T.A.) Received: 20 November 2019; Accepted: 8 January 2020; Published: 10 January 2020 Abstract: Biochar is generally considered as an e ective soil amendment, which can improve soil organic matter and nutrients content and enhance crop productivity. In this study, biochar derived from brewers’ spent grain (BSG) was used in a pot and field experiment to assess whether its addition to soil could a ect hop plant growth. The experiment was conducted in Central Italy during the period March–August 2017. Three di erent German cultivars of hop plant (Hallertau Magnum, Perle, Spalter spalt) were considered. Biochar was added to the pot soil at 20% level. Its e ect on the roots was evaluated using multivariate image analysis (MIA) and the statistical technique of general linear models (GLM), whereas the shoots, bines length and yield using GLM. Results showed that biochar significantly improved root growth (p < 0.0001). Regarding shoots, no variability for the genotypes was observed during the vegetative period, whereas slight di erences resulted before plant dormancy, especially for the Hallertau Magnum cultivar. No di erences in the number of leaves or bines length were observed between the two treatments for all cultivars. The addition of biochar to the soil significantly improved yield (number of cones). These results highlighted that BSG-derived biochar can be useful to improve hop plant growth and cones production. Keywords: amendment; biochar; brewers’ spent grain; hop; image analysis; plant growth 1. Introduction Beer is one of most consumed beverages in the world, and plays an important role in the global economy. In 2018, the overall beer production amounted to about 1.94 billion hectolitres. However, beer processing produces a huge amount of waste: for every hectolitre of beer produced, about 20 kg of spent grain, 0.2–0.4 kg of spent yeast and 0.3 kg of spent hops/hot trub are generated. In particular, the 85% of total solid waste is represented by brewers’ spent grain (BSG), the residue left after barley malting and separation of the wort during the brewing process, and it contains the husk and the outer layer of barley kernel [1]. BSG is mainly composed of protein (more than 20%) and fibers, represented by a lignino-cellulosic material, whose main constituents are hemicellulose (28–35%), cellulose (17–25%) and lignin (7–27%) [1–3]. Appropriate management of these waste streams has become a challenging issue. The solid by-products from the brewing process are mainly disposed of as waste or sold as animal feed, due to their considerable amount of valuable compounds (proteins, lipids, carbohydrates, polyphenols, and minerals) and nitrogen-containing nutrients. However, brewing industries are interested in new Appl. Sci. 2020, 10, 533; doi:10.3390/app10020533 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 533 2 of 11 solutions to reduce the amount of waste produced and transform by-products in added value products. Therefore, in a perspective of a zero waste approach and in order to reduce waste storage and logistics costs, alternatives to valorize these residues and to recover and reuse them in a sustainable and profitable way are continuously proposed and developed. Moreover, by-products can be transformed in combustible gas, to be reused in the beer production cycle, or in soil amendments for agricultural applications [4,5]. A valid alternative can be the transformation of BSG into biochar, which can be used as an e ective soil amendment for the production of vegetables. Biochar is a carbon-rich, fine-grained and porous material with stable physical and chemical properties, produced from waste biomass through pyrolysis, e.g., a thermal decomposition of lignocellulose biomass by heating at elevated temperatures (generally between 350 and 700 C) under limited oxygen conditions [6–8]. It has stable aromatic C structures, low O and H to C ratios, low bulk density, moderate cation exchange capacity (CEC), and high ash content, pH and surface area [9,10]. Due to these properties, it is recognized as a multifunctional material which can be applied for long-term C sequestration and climate change mitigation [11,12]. It can be used successfully in agriculture to increase the organic C content of soil and reduce the leaching of nutrients; it could have positive e ects on soils contaminated with heavy metals and organic pollutants [6,12–15]. Furthermore, biochar can be useful as soil amendment, due to its high porosity and sorption capacity and large surface area, reducing soil bulk density, improving soil structure, and increasing soil water holding capacity [7]. Biochar application can increase plant root growth, root penetration, and nutrient and water uptake [16]. In fact, N concentration in soil is increased by biochar during critical stages of plant growth, and N uptake and fertilizer recovery from roots are mostly guaranteed [7,17]. Biochar amendment also significantly enhances microbial activity and abundance, probably due to a greater adsorption of various nutrients [18,19]. Such positive influence could have e ects on soil structure and indirectly on plant growth and rooting patterns [20]. Numerous studies have been conducted in controlled environments to evaluate the impact of biochar on soil properties and plant growth. However, the e ects are not always positive: benefits on soil and plant growth strongly depend on the feedstock sources used to produce the biochar, production conditions (pyrolysis temperature, heating rate and residence time), di erences in soil properties and specie-specific root growth patterns [15,21–23]. Therefore, this study aims to assess the potential ability of BSG-derived biochar for soil amendment and its influence on hop plant growth. 2. Materials and Methods 2.1. Biochar Production and Characterization A fifty kilograms sample of wet BSG was chosen randomly from a big container at a craft brewery. Subsequently, three replicates of about 1 kg each were selected randomly from this sample, oven-dried at 105  1 C for 78 h and successively subjected to elemental analysis for the determination of the CHN content, according to UNI EN 15104: 2011. Ash content was analyzed by heating ground dried BSG samples (maximum grain size 2 mm) at 550 C  1 C for 8 h in a mue furnace, according to UNI EN 14775: 2010. Three analytical replicates were performed for all parameters analyzed and for each sample. Biochar was prepared from BSG using the pyrolytic reactor Elsa D17 (pat. BLUECOMB) and through a thermochemical process that can reach carbonization temperatures of 400–500 C [24,25]. Biochar was chemically characterized by Fourier transformed infrared spectroscopy analysis (FT-IR), according to the methodology described by Amoriello et al. [26]. Spectra were collected at room temperature with a FT-IR spectrometer (iS 50 FT-IR Nicolet Thermo Fisher Scientific Inc., Waltham, MA, USA) equipped with a single-reflection horizontal ATR cell with a diamond crystal [26]. Appl. Sci. 2020, 10, 533 3 of 11 2.2. Botanical and Agronomic Characteristics of Hop Plant Hop (Humulus lupulus L.) is a dioecious perennial plant, belonging to the Cannabaceae family. It reaches maturity after the first three years and remains productive for over 20–25 years [27]. The hop plant consists of a perennial rootstock (“crown”) of rhizomes below ground, annual climbing bines above ground, which provide the canopy and photosynthetic capacity to support flowering, and flowers that develop at the terminal buds of lateral branches and are harvested as green cones. The perennial root system can grow more than 4 m deep and up to 5 m laterally. Rhizomes feed the growth of the productive canopy and ensure the survival of the plant from successive seasons. The growing season spans from March to August–September in the northern hemisphere. Hops emerge in early spring and grow up to a height of 5–7 m on poles or under a trellis system. Flowering starts in late June or early July in the northern hemisphere. The cones mature for picking between August and September, depending on climate conditions and genotype. The female strobiles (cones) represent the most interesting parts of the plant from a technological point of view, as they are one of the essential ingredients of the brewing industry, providing aroma, bitterness, flavor, and antimicrobial properties to beer [28]. 2.3. Experimental Site and Design 0  0 The study was carried out in Rieti, Italy (latitude 42 24 29”52 N, longitude 12 51 36”36 E) during the period March–August 2017. The sampling site is characterized by relatively cold and rainy winters and hot and dry summers. The long-term average annual temperature recorded between 1980 and 2009 was 12.1 C; the mean temperature during the growing season was 16.0 C; the average maximum temperature for the summer months was 30.1 C; the average minimum temperature for the coldest months was 1.6 C. In summer, maximum temperatures were often over 30 C. The annual mean precipitation was equal to 1021 mm and it fell mainly from October to December. Summer rainfall was irregularly distributed and the total mean amount over June–August was 136 mm. In 2017, the annual average temperature was 12.7 C; the average temperature in the growing season was 17.4 C; the average maximum temperature for the hottest months (July to August) was 33.2 C. The annual precipitation, precipitation for the growing season, and precipitation over the hottest months was 721 mm, 186 mm, and 27 mm, respectively. The annual relative humidity was 68%. A completely randomized experimental design was conducted during the hop growing season in a climate uncontrolled greenhouse environment from March to 5 May and outside from 6 May to 28 August. Three di erent cultivars of German hop plant (Hallertau Magnum, Perle, Spalter spalt) were considered for the study due to their economic importance and their sensitivity to environmental conditions [27]. These three varieties grow well in most climates, but best in warm, dry and sunny regions. Hallertau Magnum produces very good yields (1700–2300 kg ha ); the cone’s structure is very large and longish size. Perle also produces good yields (1600–2100 kg ha ); cone size is small to medium. Spalter spalt yield amounts 1750–2000 kg ha ; the cone size is small to medium. In March, hop rhizomes were placed in plastic pots (15 cm  14 cm) with soil containing acid peat, expanded perlite and clay. Each pot contained 8 L of soil. The packed pot soil had a pH of 6.0, 3 1 a dry bulk density of 1.1 g cm , an electric conductivity of 0.45 dS m and a porosity of 90% v/v. Hop rhizomes for each variety were randomly assigned between pots with soil added with 20% of biochar (1.6 L of biochar and 6.4 L of soil containing acid peat, expanded perlite and clay) and pots without biochar (control). Trials with biochar were replicated seven times, whereas controls (without biochar) were run in triplicate. We chose an unbalanced sampling to better evaluate the variability of the biochar e ect on hop plants. Young plants were transferred from pots to the field at the beginning of May, corresponding to the moment of bines elongation. No biochar was added in field. Until June, the plants were irrigated with 0.5 L of water every two days; in the two warmest months (July and August), the same amount of water was given every day. Plant development was monitored through the percentage of roots in pot (as described in the Section 2.4) and the number of leaves at the beginning of May; shoot diameter was measured by caliper Appl. Sci. 2020, 10, 533 4 of 11 at five di erent times (T1: 30 June, T2: 18 July, T3: 27 July, T4: 3 November, T5: 21 November), i.e., before flowering, near cones maturity, before plant dormancy; measurement of climbing bines length and number of cones at maturity. 2.4. Statistical Analysis and Multivariate Image Analysis In order to quantify the e ect of biochar on root growth for each genotype, a non-destructive technique, e.g., a multivariate image analysis, was carried out according to Fongaro et al. [29]. At the beginning of May, plants were extracted from pots and the images of roots and soil for each plant were acquired twice using a digital camera Nikon D750 at a high resolution and a color depth of 16 bits, saving the captured images in uncompressed RAW format. The images were acquired by photographing the basis and four external opposite sides. To create the final data set, a region of interest (ROI) of 472 472 pixels, representative of the whole sample surface, was extracted from each image using the Lightroom Classic CC 2018. The images were processed by PLS Toolbox 8.5 (Eigenvectors Research, Inc., Manson, WA, USA) for MATLAB R2016b (The MathWorks Inc., Natick, MA, USA). Red (R), green (G), blue (B) values, measures in the RGB space color, were calculated. A principal component analysis (PCA) on the RGB values was carried out, providing the pixel distribution in the score space (score plot). The first principal component (PC1) contained most of the original information, with a decreasing amount in the remaining score images. PC1 score image. PC1 score image was converted from the RGB space color into the HSI (hue, saturation, intensity) space color. Then, the Hue channel was extracted to obtain the hue image. A pixel segmentation in di erent intensity ranges, corresponding on di erent material (soil and root), was carried out to select roots. In this way, it was possible to quantify the percentage of roots present in each pot and for each cultivar. The influence of genotype, treatment and the two-way interaction on roots percentage or number of leaves or bines length or number of cones was tested using the technique of General Linear Models (GLM). The statistical model was: Y = +  +  +   + " (1) ij 0 1 i 2 j 3 i j ij where Y = roots percentage or number of leaves or bines length or number of cones, = mean e ect ij 0 common to all observations; = unknown regression parameters;  = treatment (i = 1, 2; 1 = with 1,2,3 i biochar, 2 = without biochar);  = cultivar (j = 1, 2, 3; 1 = Hallertau Magnum, 2 = Perle, 3 = Spalter spalt); " = error term. ij GLM was also applied to monitor the plant development in time. The statistical model was: Y = +  +  + +   +   +   +    + " (2) 0 2 3 5 6 7 ijk 1 i j k 4 i j i k j k i j k ijk where Y = diameter of shoots, = mean e ect common to all observations; = unknown ijk 0 1–7 regression parameters;  = treatment (i = 1, 2; 1 = with biochar, 2 = without biochar);  = cultivar i j (j = 1, 2, 3; 1 = Hallertau Magnum, 2 = Perle, 3 = Spalter spalt); = time (k = 1, ::: ,5; 1 = 30 June, 2 = 18 July, 3 = 27 July, 4 = 3 November, 5 = 21 November); " = error term. ijk Post hoc Tamhane test (p < 0.05) was carried out in order to evaluate di erences between groups (with or without biochar) for each considered variable. Statistical analysis was performed with SPSS 20.0 software (SPSS, Inc., Chicago, IL, USA). 3. Results 3.1. Brewers’ Spent Grain and Biochar Characterization BSG analysis showed an average moisture content of 8.8%, and a content of ashes, C, H, N contents of 5.3%, 45.7%, 9.0% and 4.2%, respectively, on the dry weight basis. Brewers’ spent grain (BSG) and biochar were characterized by FT-IR spectra (Figure 1). Appl. Appl. Sci. Sci. 2020 2020,, 10 10,, x FO 533 R PEER REVIEW 5 of 5 of 11 11 Figure 1. FT-IR spectra of brewers’ spent grain (BSG) and biochar. Figure 1. FT-IR spectra of brewers’ spent grain (BSG) and biochar. The two spectra showed di erent profiles and changes of functional groups, demonstrating the influence The two spec of the pyr tra show olytic tred eatment differe on nt profile the chemical s and ch stran uctur ges of e offunction the rawal groups, demonstrating th material. In fact, the organic e matter influence pyr oolysis f the pyro caused lytic water treatment on loss and tvariation he chemical in concentration structure of the ra of mineral w materi components al. In fact, the due orga to the nic heat-induced matter pyrolym sis c ass aused loss [ water loss an 30]. BSG profile d vshowed ariation a inpeak concentration o at 3274 cm f miner corresponding al components due to the to a vibrational −1 band heat-in consistent duced mass with loss hydr [30oxyl ]. BSG grprofi oupsle (O-H); showed two a peak a peaks tat 327 2926 4 cm and corresp 2853 cm ondi,ng t corr oesponding a vibrationa tol −1 aliphatic band consi C-H stent wi stretch; th hydroxyl a peak at groups (O- 1742 cm , H associated ); two pea with ks at t he 2926 pr esence and 28of 53aldehydes, cm , correspond ketones ing to and −1 carboxyl aliphatic C gr-oups H stret (Cc= h; O); a p aeband ak at with 1742 c am peak , assoc at 1633 iated cm with , assigned the presence to ar omatic of aldehydes, ketones an lignin components d −1 (C carb =C); oxyl a gro peak up at s (C 1030 =Ocm ); a b,acorr nd w esponding ith a peakto atC-O-C 1633 cm stretch. , assiThe gned thermal to arom decomposition atic lignin comstr ponent ongly s −1 influenced (C=C); a peak the a intensity t 1030 cm of , corre biochar spon bands dingfr to C-O om 4000 -C stretch. to 1600 cm The thermal d (carboxylic ecomposition bonds, amides strongly and −1 aliphatic influencehydr d thocarbon) e intensit[y o 30].f bioch In particular ar ban ,d the s from disappearance 4000 to 16 of00 the cm peaks (carb at 2926 oxylic and bo2nds, am 853 cm idmay es and be −1 due aliphat to the ic hyd fact rocarb that methyl on) [30gr ]. In oups, part which icularar , te he the dis weakest appearance o functional f the gr peoups, aks atas 29 well 26 and as the 285OH 3 cm groups, may br be due to the f eak during pyr act tha olysis. t methyl The CH groups, peaks shift whi fr com h are being the wea morekaliphatic est functi to ona mor l grou e aromati ps, as c w (and ell a eventually s the OH disappear groups, break altogether) during pyrolysis. The CH [31]. The cleavage peaks of these shift gr froups om being more al contributes iphat to mass ic toloss more aromat during thermal ic (and decomposition eventually disappe andar toaltog the pr ether) oduction [31]. T ofh “non-condensable” e cleavage of these group gas. Moderate s contribdi utes to erences mass l between oss duri BSG ng and thermal d biochar eco prmposition ofiles froman 16d to the pro 00 to 1500 cmduction o may befattributed “non-condensable to the formation ” gas. M ofo carbonate-carboxyl derate differences −1 gr beoup tween BSG during pyr and b olysis. ioch At ar p last, rofil biochar es from showed 1600 t similar o 1500 cm functional may gr be a oups, ttri between buted to the f 600 and orma 1500 tio cm n of . These carbonate-carboxyl group results were in accor dance during with pyrol pry evious sis. Atstudies last, bi on ocha BSG-derived r showed si biochar milar fu [32 nc ,33 tion ]. al groups, The FT-IR −1 analysis between 60 highlighted 0 and 1500 cm the presence . These results were in acc of functional groups such ordance w as hydr ith previous oxyl and carboxyl studies on BSG groups, necessary -derived to biocha consider r [32,33 biochar ]. The FT- a soilIR a amendment nalysis hifor ghlimpr ighted the oving of prthe esence o cation f functional gro exchange capacity ups such and as as h a potential ydroxyl adsorbent and carboxyl groups, nec [34]. essary to consider biochar a soil amendment for improving of the cation exchange capacity and as a potential adsorbent [34]. 3.2. Multivariate Image Analysis 3.2. Multivariate Image Analysis Multivariate image analysis was applied to extract roots region and to quantify di erences in percentage for the two treatments, with and without biochar. Representative images from roots and Multivariate image analysis was applied to extract roots region and to quantify differences in soil samples for each cultivar and treatment after RGB processing and PCA score images are shown in percentage for the two treatments, with and without biochar. Representative images from roots and Figure 2. soil samples for each cultivar and treatment after RGB processing and PCA score images are shown in Figure 2. Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 11 Appl. Sci. 2020, 10, 533 6 of 11 Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 11 Figure 2. RGB and PCA score images of roots and soil samples representative of different cultivars Figure 2. RGB and PCA score images of roots and soil samples representative of di erent cultivars and treatments, obtained from image analysis process. (a) RGB image of Hallertau Magnum without Figure 2. RGB and PCA score images of roots and soil samples representative of different cultivars and treatments, obtained from image analysis process. (a) RGB image of Hallertau Magnum without biochar; (b) RGB image of Hallertau Magnum with biochar; (c) RGB image of Perle without biochar; and treatments, obtained from image analysis process. (a) RGB image of Hallertau Magnum without biochar; (b) RGB image of Hallertau Magnum with biochar; (c) RGB image of Perle without biochar; (d) RGB image of Perle with biochar; (e) RGB image of Spalter Spalt without biochar; (f) RGB image biochar; (b) RGB image of Hallertau Magnum with biochar; (c) RGB image of Perle without biochar; (d) RGB image of Perle with biochar; (e) RGB image of Spalter Spalt without biochar; (f) RGB image of of Spalter Spalt with biochar; (g) PCA score image of Hallertau Magnum without biochar; (h) PCA (d) RGB image of Perle with biochar; (e) RGB image of Spalter Spalt without biochar; (f) RGB image Spalter Spalt with biochar; (g) PCA score image of Hallertau Magnum without biochar; (h) PCA score score image of Hallertau Magnum with biochar; (i) PCA score image of Perle without biochar; (j) PCA of Spalter Spalt with biochar; (g) PCA score image of Hallertau Magnum without biochar; (h) PCA image of Hallertau Magnum with biochar; (i) PCA score image of Perle without biochar; (j) PCA score score image of Perle with biochar; (k) PCA score image of Spalter Spalt without biochar; (l) PCA score score image of Hallertau Magnum with biochar; (i) PCA score image of Perle without biochar; (j) PCA image of Perle with biochar; (k) PCA score image of Spalter Spalt without biochar; (l) PCA score image image of Spalter Spalt with biochar. score image of Perle with biochar; (k) PCA score image of Spalter Spalt without biochar; (l) PCA score of Spalter Spalt with biochar. image of Spalter Spalt with biochar. For all the images, the PC1 score image explained all the variance, being related to pixels of both For all the images, the PC1 score image explained all the variance, being related to pixels of both the root and soil. Comparing the PC1 score images and the relative RGB images, it was possible to For all the images, the PC1 score image explained all the variance, being related to pixels of both the root and soil. Comparing the PC1 score images and the relative RGB images, it was possible to distinguish areas with different color intensity, indicated with blue (soil) and yellow (root). Thus, the the root and soil. Comparing the PC1 score images and the relative RGB images, it was possible to distinguish areas with di erent color intensity, indicated with blue (soil) and yellow (root). Thus, the same false color was assigned to the pixels having the same characteristics, well distinguishing the distinguish areas with different color intensity, indicated with blue (soil) and yellow (root). Thus, the same false color was assigned to the pixels having the same characteristics, well distinguishing the two regions in the PC1 score images. The pixel segmentation allowed the extraction of the root areas same false color was assigned to the pixels having the same characteristics, well distinguishing the two regions in the PC1 score images. The pixel segmentation allowed the extraction of the root areas (in black) and the quantification of the roots percentages. Results obtained were plotted in Figure 3. two regions in the PC1 score images. The pixel segmentation allowed the extraction of the root areas (in black) and the quantification of the roots percentages. Results obtained were plotted in Figure 3. Box plots showed a strong influence on root growth present in the pots with biochar in comparison (in black) and the quantification of the roots percentages. Results obtained were plotted in Figure 3. Box plots showed a strong influence on root growth present in the pots with biochar in comparison to to those without biochar. Box plots showed a strong influence on root growth present in the pots with biochar in comparison those without biochar. to those without biochar. Figure 3. Box plots of root percentages of three di erent hop cultivars (Hallertau Magnum, Perle and Figure 3. Box plots of root percentages of three different hop cultivars (Hallertau Magnum, Perle and Spalter spalt) and two treatments (presence or absence of biochar). Outliers are displayed as filled stars. Spalter spalt) and two treatments (presence or absence of biochar). Outliers are displayed as filled Figure 3. Box plots of root percentages of three different hop cultivars (Hallertau Magnum, Perle and stars. Spalter spalt) and two treatments (presence or absence of biochar). Outliers are displayed as filled stars. Appl. Sci. 2020, 10, 533 7 of 11 3.3. Assessment of Hop Plant Response to Biochar Amendment The evaluation of e ect of BSG-derived biochar was carried out using the technique of General Linear Models (GLM). The GLM analysis of roots percentage showed that the two single factors (genotype and treatment) were statistically significant, unlike the two-way interactions between cultivar and treatment. The treatment was the main contributing factor to the percentage of roots (p < 0.0001), followed by cultivar (p = 0.0330). In fact, as clearly shown in Figure 3, the addition of biochar in the soil produces a greater development of the roots. This phenomenon is even more evident for the Perle cultivar. Regarding shoot development, specifically the shoot diameter, no variability ascribable to the genotypes was observed for the first three times (Table 1). Table 1. Measurements of diameter of shoots (mean standard deviation) at five di erent times (T1: 30 June, T2: 18 July, T3: 27 July, T4: 3 November, T5: 21 November). Diameters within columns of each cultivar and treatment followed by di erent letters (a or b) are significantly di erent. Hallertau Magnum Perle Spalter Spalt Time No Biochar Biochar No Biochar Biochar No Biochar Biochar T1 7 2 a 8 2 a 8 3 a 6 1 a 8 3 a 5 1 a T2 8 3 a 9 2 a 9 1 a 7 1 a 8 3 a 7 2 a T3 9 2 a 10 2 a 10 1 a 8 1 a 9 2 a 8 2 a T4 15 3 b 15 3 b 11 1 a 12 2 b 11 1 a 12 3 ab T5 16 2 b 16 4 b 11 1 a 13 4 b 11 1 a 13 3 b On the contrary, significant but slight di erences resulted among diameters measured before plant dormancy, especially for the Hallertau Magnum cultivar. It is interesting to note that the Perle and Spalter Spalt cultivars tended to be more a ected by the presence of biochar during the last phase of plant development, although these di erences are not statistically significant. This trend was confirmed by the GLM analysis: only the single genotype and time factors were highly significant (p < 0.0001), whereas the single treatment factor, all the two-way interactions and the three-way interaction were not statistically significant. The GLM analysis on number of leaves at the beginning of May and climbing bines length at harvest highlighted no significant di erences between cultivar and treatment and their two-way interaction, as shown in Table 2. Table 2. Measurements of number of leaves (mean standard deviation) at the beginning of May, and climbing bines length and number of cones at maturity. Statistical di erences between treatments (with biochar or without biochar) for each cultivar and parameter are displayed with di erent letters (a or b). Hallertau Magnum Perle Spalter Spalt No Biochar Biochar No Biochar Biochar No Biochar Biochar Number of leaves 20 8 a 18 6 a 19 4 a 15 3 a 26 14 a 25 14 a Bines length (cm) 547 55 a 570 30 a 552 27 a 532 61 a 555 94 a 550 75 a Number of cones 97 17 a 144 20 b 84 14 a 123 18 b 75 12 a 106 16 b As regards the number of harvested cones, statistical di erences (p < 0.0001) were observed between treatments for each cultivar: the cones of the plants treated with biochar resulted more numerous compared to those of plants without biochar (Table 2). Instead, the genotype factor and the two-way cultivar-treatment interaction resulted not significant by the GLM analysis. 4. Discussion Despite the high adaptability to di erent environmental conditions, hop cultivars of European origin are more susceptible to changes in pedo-climatic conditions compared to the American Appl. Sci. 2020, 10, 533 8 of 11 genotypes [27]. Hops require a large amount of water during the growing seasons to optimize yield and quality. Furthermore, soil has to be nutrient-rich, light, well drained, well supplied with moisture, but free from waterlogging, with an optimal pH range between 6.0 and 6.5., although hops also grow on soils with pH from 4.8 to 8.0 [27]. Our study wanted to verify if biochar can positively a ect hop growth. As mentioned above, biochar can improve soil water retention, cation exchange capacity, the content of di erent nutrients, and reduce soil bulk density and N leaching [7]. Moreover, biochar can enhance the uptake of N and other nutrients by improving the root development and the whole plant physiological status [17]. Then, biochar may reduce plant uptake of potentially toxic elements (such as Pb, Cd and As) from soils, due to the adsorption of metals to the negative charges on the surface of biochar particles [35]. However, the ecacy of biochar application depends on various factors concerning both soil and plant. Understanding these factors is essential for maximizing soil productivity and minimizing potential deleterious environmental e ects. For example, the positive e ect of biochar amendment is more remarkable in a coarse-textured than in a fine-textured soil, and sandy soils are more responsive than clay-rich soils; this positive e ect could be also maximized reducing biochar particle sizes in the range of 0.5–1.0 mm [19]. The biomass feedstock could influence the hydraulic properties of biochar: biochar with high porosity and surface area can improve water adsorption [36]. Previous studies have shown discordant experimental evidences about the supposed beneficial e ects of biochar on root growth. Numerous papers reported positive responses on root growth parameters [37–41]. On the contrary, other authors [39,42,43] showed negligible benefits on root growth. These discrepancies could depend on di erent factors: plant species, growing environment, soil properties, feedstock, biochar properties, pyrolysis conditions [15,21–23]. Furthermore, improved concentrations of dissolved organic C after biochar addition and the adsorption of phenolic compounds by biochar may positively a ect root growth [38,43]. Moreover, biochar may stimulate a faster root turnover and changes in root morphology [43]. Although there are few detailed studies on the influence of biochar on the root system, it is generally recognized that plants with a longer root length, a larger surface area and more root tips may be able to get more nutrients and grow better [44–46]. In the present study, the addition of biochar amendment positively a ected hop root growth, increasing root length and mass density, as well represented in Figure 2. Moreover, a higher presence of roots could be positively correlated with nutrient uptake, and induce potential benefits in plant growth and development, including aboveground biomass. Benefits and limitations of biochar on plant growth could depend on the types of biochar and the incorporation rates [47–49]. In our study, no statistical di erences were observed between number of leaves at the moment of bines elongation and climbing bines length at harvest. Regarding hop yield, biochar seemed to have positively a ected cones production. This production varies greatly during the first years of the plant vegetation. In fact, the hop plant reaches productive and qualitative stability only after the third year. Nevertheless, biochar could have helped the plant when the water requirement was greater. In fact, the summer period, characterized by dry weather conditions, caused long periods of water stress, compensated only by emergency irrigation. Hops require a high water availability for successful plant growth and cones production [27], and changes in soil moisture retention may have been one of the most important factor in explaining positive biochar e ects on crop yield. Due to its high porosity and specific surface area, biochar increases soil porosity, and, consequently, the soil hydraulic properties, such as the water storage capacity and the absorption capacity [42]. Furthermore, the induced macro-porosity due to the larger particle size of biochar positively a ects soil water permeability [50]. Based on our findings, the application of biochar could be a promising strategy to enhance hop plant growth and cones production. However, further investigation on plant physiological mechanisms and hop cones quality is required to identify optimal levels of biochar application and to better understand the e ect of biochar on hop plant growth. Appl. Sci. 2020, 10, 533 9 of 11 5. Conclusions The present study was conducted to assess the e ect of BSG-derived biochar on the hop plant development. The results reinforce the hypothesis that biochar can be a promising and e ective soil amendment for hop crops, supplying key nutrients for the plant growth and improving soil properties. The recovery and reuse of the brewing industry by-products is a good approach of circularity in this industrial sector from the environment protection and waste management standpoint. In fact, the reuse of brewers’ spent grain, as feedstock to obtain biochar, will contribute to the adoption of environmentally sustainable practices and a more ecient use of resources, also reducing the use of chemical input. Moreover, it can contribute to decrease the negative economic and environmental impact of the disposal of by-products, decreasing the CO production, and increasing the sustainability of the farming sector. Author Contributions: All authors conceptualized and designed the experiments. M.P., V.V. and S.F. collected the data. T.A. performed the statistical analysis and image analysis. T.A. wrote the manuscript in consultation with all other authors. M.P., V.V. and S.F. revised and improved the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The valuable technical assistance of Marco Castellani (Department of Agricultural and Forestry Sciences at the University of Tuscia) is gratefully acknowledge. The authors also thank Paola Manzi (CREA Research Centre for Food and Nutrition) for providing the FT-IR instrument. Conflicts of Interest: The authors declare no conflict of interest. References 1. Mussatto, S.I.; Dragone, G.; Roberto, I.C. Brewers’spent grain: Generation, characteristics and potential applications. J. Cereal Sci. 2006, 43, 1–14. [CrossRef] 2. Lynch, K.M.; Ste en, E.J.; Arendt, E.K. Brewers’ spent grain: A review with emphasis on food and health. 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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Journal

Applied Sciences – Multidisciplinary Digital Publishing Institute

Published: Jan 10, 2020

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