In-field heterogeneity of apple replant disease: Relations to abiotic soil properties

Scientia Horticulturae 259 (2020) 108809

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In-field heterogeneity of apple replant disease: Relations to abiotic soil properties

T

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Margaux Simona, , Eva Lehndorffa,b, Andreas Wredec, Wulf Amelunga Institute of Crop Science and Resource Conservation – Soil Science and Soil Ecology, University of Bonn, Nussallee 13, 53115, Bonn, Germany Soil Ecology, University of Bayreuth, Dr.-Hans-Frisch-Str. 1-3, 95448, Bayreuth, Germany c Department of Horticulture, Landwirtschaftskammer Schleswig-Holstein, Thiensen 16, D25373, Ellerhoop, Germany a

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Apple replant disease Soil aggregates In-Field variability Rhizosphere Macronutrients Micronutrients

Orchards affected by apple replant disease may show a distinct in-field variability of the severity of apple replant disease (ARD) symptoms. As abiotic soil properties can affect pathogen survival but show considerable variability at the scale of a few meters, we hypothesized that the variability of abiotic soil properties may be used to explain parts of the in-field variability of ARD. We sampled soil and rhizosphere of 32 apple trees with different degree of ARD symptoms after two years of growth at the horticultural research station Heidgraben, Germany. The sandy soils correspond to Entic Podzols. Soil analyses comprised soil aggregate-size fractionations, the assessment of the stocks of nitrogen and essential macro- and micronutrients, as well as a tracing of stable δ15N natural abundance both in rhizosphere and non-rhizosphere soil. The results showed that there was indeed a huge in-field variability of tree growth, following a Gaussian distribution, but classified here in shoot heights of < 40, 40–80, 80–120, and > 120 cm, respectively. An effect of soil aggregation on ARD or vice versa was not detected. Elevated tree growth, however, went along with a consumption of soil macronutrients (R2: = 0.3 – 0.8 for the n = 4 ARD classes; significant only for K on individual plant basis). Intriguingly, opposite correlations were observed for micronutrients, where reduced tree growth occurred at subsites short in micronutrient supply (R2 = 0.9 for Mn and Co, significant for Mn and Co for ARD infected plants up to 100 cm shoot length on individual plant basis). Moreover, the plants showing most stunted shoot growth revealed lowest δ15N values in the rhizosphere, as typical for reduced N cycling. Our data therefore confirm that at least parts of the in-field variability of ARD severity is correlated to reduced micronutrient supply and N cycling, possibly via effects on habitat properties of pathogenic soil microbiota.

1. Introduction

severity of ARD but cannot be seen as the primary cause. Also, Mazzola and Manici (2012) confirmed this assumption, as they suggested that abiotic factors may intensify symptoms of ARD but not work as a causal trigger factor. Nevertheless, heterogeneous prevalence of ARD at a given site has often been observed, suggesting that there may also be infield controls of abiotic soil properties on the ecological nishes of antagonists and pathogens and thus on ARD severity, particularly at agricultural soils, which have often been shown to have variable, patchy abiotic soil properties (Pätzold et al., 2008; Gebbers and Adamchuk, 2010; Winkelmann et al., 2019). Attempts to unravel and fight the complex etiology of ARD should thus include the correlative assessment to abiotic soil properties. In general, the heterogeneity of physical and chemical soil properties at agricultural soils modulates growth and yield of crops, but is also involved in the incidence of soil diseases and pests (Dordas, 2008;

Apple replant disease (ARD) affects apple production worldwide, occurring in symptoms like reduced plant growth, stunted trees and lower fruit yields (Mazzola and Manici, 2012; Spath et al., 2015; Winkelmann et al., 2019). The ARD arises by repeatedly replanting young apple trees at the same site, and as the disease can persist for more than 30 years, it exhibits high economic pressure in perennial cropping systems (Klaus, 1939; Winkelmann et al., 2019). Previous studies proved an influence of biotic factors on ARD. Particularly soilborne organisms like the fungi Fusarium and Rhizoctonia, the oomycetes Phytophthora and Pythium as well as nematodes like Pratylenchus have been identified as causal agents (Mai and Abawi, 1978; Mazzola, 1998; Manici et al., 2013). Regarding abiotic soil properties, already Bronsart (1949) assumed that they may be involved as a regulating factor of the

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Corresponding author. E-mail address: [email protected] (M. Simon).

https://doi.org/10.1016/j.scienta.2019.108809 Received 29 May 2019; Received in revised form 22 July 2019; Accepted 23 August 2019 Available online 17 September 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Heterogeneity of apple plant growth at the study site Heidgraben. (A) = plot 8c; (B) = plot 8d; (C) = harvested apple plants from plot 8d.

One reason might be that many of the above-cited correlations refer to ARD symptoms between sites, which makes it difficult to separate these effects from confounding and partly unknown co-variables, such as management history, soil mineralogy, or climatic and weather indices. To circumvent these problems, we here took advantage of heterogeneous prevalence of ARD symptoms within a given field, which occurred despite identical management, parent material and climatic conditions. As plant health is mainly affected by processes in the rhizosphere, it will likely not be sufficient to correlate ARD symptoms to bulk soil properties, but analyses should specifically also consider the rhizosphere, i.e., the soil which is directly surrounded by the root and affected by it (Darrah, 1993; Gregory, 2006). This region acts as hotspot of microbial activity, metabolization, nutrient turnover and as habitat for most of the pathogen- root interactions (Hartmann et al., 2008; Berg and Smalla, 2009; Mommer et al., 2016). All these processes transform N with heavy N remaining in case of non-efficient N use, due to discrimination against the heavier isotope 15N, reflected after all in an increase of δ15 N-values (Watzka et al., 2006; Kriszan et al., 2014). Thus we finally used the natural abundances of the stable isotope of nitrogen (δ15 N) as indicator for nitrogen cycling processes in ecosystems (Robinson, 2001), and thus also in the rhizosphere of ARD soil. The main objective of this work was to examine in-field relationships between the severity of ARD and physical (soil texture, bulk density, aggregates) as well as chemical (pH, macro- and micronutrient concentrations, δ15N) soil and rhizosphere properties. For this purpose, we sampled horticultural soil and rhizosphere from a research station in Heidgraben in Northern Germany, which shows severe but heterogeneous ARD occurrence within given climate and sandy soil order (Entic Podzol; IUSS, 2015). The apple trees were classified by shoot length into so-called bonitur-classes of different ARD severity; the assessment of physical and chemical soil properties followed conventional approaches, though separated into rhizosphere and bulk soil.

Pätzold et al., 2008). Notably, soil heterogeneity is primary defined by parent material, climate, relief, vegetation, human impact and the kind and intensity of soil forming processes (Pätzold et al., 2008). These sitespecific soil parameters in turn characterize, besides chemical soil properties, the physical soil status. For example, grain-size distribution (texture), organic matter content, and bulk density are mainly responsible for the arrangement of soil pores and aggregates and therewith influence moisture and oxygen supply (Bronsart, 1949; Weil and Brady, 2016). Thus, also ARD has been found to correlate with physical soil properties such as bulk density, the loss of specific pore sizes, waterlogging and related shifts of the pH-value (Bronsart, 1949; Fazio et al., 2012). Furthermore, different soils, especially sandy and light soils are more vulnerable towards ARD than loamy soils (Mahnkopp et al., 2018; Winkelmann et al., 2019). Finally, apple replant disease seems to be a result of several biotic and abiotic effects. If abiotic soil properties contribute to the ARD effect, this might not only help explaining why ARD persists for several years and even decades, it might help in particular to identify and forecast systematic patterns of ARD disease symptoms at field scale. While soil heterogeneity is frequently reflected in the heterogeneous distribution of soil texture, the primary particles agglomerate with several binding agents (e.g., cations, oxides, carbonates, organic matter) to aggregates of various size classes in the field (Tisdall and Oades, 1982; Bronick and Lal, 2005; Totsche et al., 2018). Hence, aggregate-size distribution might serve as an integrated proxy for different soil functions involved in the modulation of the severity of ARD. Even microbial biodiversity and thus likely also that of pathogens can be linked to aggregate size (Kihara et al., 2012). Other important soil parameters to be considered when explaining ARD comprise the macro- and micro-nutrient availability as well as the pH-value, which affects nutrient availability (Willett et al., 1994; Jonkers et al., 1980; Utkhede, 2006) and thus nutrient cycling in ARD soil (Fazio et al., 2012). In soils with low pH values of 4–4.5, ARD symptoms have been less frequently observed than at elevated pH (Willet et al., 1994). Furthermore, it has been suggested that a lack of a specific trace elements might be involved in the induction of replant disease, however, scientific evidence is still sparce (Bronsart, 1949). 2

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rates are based on practical fertilizer quantities in the tree nursery sector for reaching a mineral nitrogen amount of 70 kg N ha−1 a−1 and an extractable level of 124–166 mg kg−1 soil (Averdieck, 2006). The δ15N of the fertilizer used here were suggested to be around zero (Kriszan et al., 2009). A comprehensive description about the field design and management techniques can be found in Mahnkopp et al. (2018).

2.2. Determination of plant shoot length and soil sampling At 7th of November in 2017, soil sampling as well as determination of shoot length were conducted. The shoot length of eight plants per plot, 32 plants in total, were determined. For covering the heterogeneity of apple plant growth, the plants under study covered the full ranges of shoot lengths per plot. As ARD symptoms, however, may be very variable also in time, while we sampled one time-point only, we clustered the data into four bonitur-classes with shoot lengths of < 40, 40–80, 80–120, and > 120 cm, respectively (Fig. 2). These clustered data were then also the basis for subsequent analyses; for transparency, individual data are additionally shown in the Appendix. To correlate these shoot length groups with soil properties, we sampled for every determined plant corresponding rhizosphere soil and non-rhizosphere soil, resulting in 64 soil samples in total. In doing so, rhizosphere soils were collected after the method of Wang and Zabowski (1998): carefully taking out the whole seedling, mild shaking of roots until soil not tightly adhering was removed and then collecting the rhizosphere soil by putting the roots in a plastic bag and roughly shaking the roots. Non-rhizosphere soil was taken from soil surrounding the rooting area in a soil depth of 25–30 cm. First preparations of soil samples for further analyses comprised sieving to 2 mm grain size with a subsequent air-drying.

Fig. 2. Boxplots indicate four shoot length bonitur-classes of ARD affected trees divided in 0–40 (n = 3); 40–80 (n = 7); 80–120 (n = 12) and > 120 cm (n = 10) at site Heidgraben, N-Germany.

2. Material and methods 2.1. Apple-Replant-Disease and field design High ARD incidence was observed in the sandy Entic Podzols near Heidgraben, N-Germany (Mahnkopp et al., 2018), making this site ideal for investigating relationships between ARD and field-scale variation in soil properties. Furthermore, a significant heterogeneity of apple plant growth could be observed at this site (Figs. 1–3), despite of homogenous field management. Here, we focused on the four randomly arranged ARD-plots at Heidgraben with a size of 10 m x 10.05 m per plot. The first 880 rootstock seedlings of the Malus domestica selection ‘Bittenfelder Sämling’ were planted in 2009, with a distance of 25 cm within rows and 45 cm between rows per plot. The repeated replanting of apple seedlings followed in a two-year-cycle, resulting in the 5th replanting generation at Heidgraben in the year 2017, when samples were taken. The annual fertilization during the year 2017 consisted of nitrogen and potassium applications at a rate of 200 kg ha−1 kalimagnesia (30% K2O) and 100 kg ha−1 kalkammonsalpeter (27% N, divided in 13.5% NO3 and 13.5% NH4), twice a year. These application

2.3. Site characterization and physical soil analyses To exclude that correlations between the heterogeneity of apple plant growth and soil properties was influenced by heterogeneity in parent material and related geogenic properties similarity in soil horizon designation and major soil reference group was confirmed by triplicate augering in each of the field plots. The final soil properties are shown in Table 1. Soil texture (grain size distribution) was analyzed by wet sieving (sand fraction) and sedimentation (silt and clay fraction) after Köhn (ISO 11277, 2002). Soil bulk density (g cm−3) was estimated in soil cores of 100 cm³ after drying.

Table 1 Major site-specific properties of the study site Heidgraben (WRB = World Reference Base for soil resources;* according to WRB 2015). Annual temperature and precipitation referred to the sampling year 2017.

Fig. 3. Heterogeneity of apple plant growth shown in shoot length [cm] at the study site Heidgraben. Counts (n = 130) representing the amount of measured shoot length and reflected the heterogeneity of ARD at this study site, respectively. Here, 32 plants were randomly selected for sampling, though with the rule that the full range of observed shoot lengths being covered for analyses. Shoot length values represent a normal distribution (p > 0.05). 3

Study site

Heidgraben

Latitude (WGS84) Longitude (WGS84) Annual temperature (°C) Annual precipitation (mm) Reference soil group*

53°41'57.116” 9°40'59.415” 10 1142.4 Entic Podzol

Texture

Sand [g kg−1] Silt [g kg−1] Clay [g kg−1] WRB Bulk density [g cm−3]

925 ± 3 27 ± 2 33 ± 9 Sand (Medium sand) 1.37 ± 0.07

Depth [cm] & Horizons (WRB)

0-27 27-42 42-65 65-100

Ap EB Bsh C

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t-tests for each bonitur-class separately (p < 0.05). The strength of

2.4. Aggregate analyses

effect (f) according to Cohen (1992) was calculated as = The method of aggregate-size separation was performed as outlined by Lobe et al. (2011), which is basically an adaption from Elliott (1986) and the aggregate hierarchy concept of Tisdall and Oades (1982), focusing on the aggregate size fractions of: 2000 – 250 μm; 250 – 53 μm; 53 – 20 μm and < 20 μm. Briefly, 10 g of soil was carefully wetted and slaked for 5 min on the first sieve of 250 μm. The electrically driven sieve-tower, including the 250 μm and 53 μm sieve, moved 300 times within 10 min with a vertical movement of 3 cm up and down. Corrections for sand content were conducted after Elliott et al. (1991) for the fractions 2000 – 250 μm and 250 – 53 μm, respectively.

R2 1 − R2

. For

further interpretation of linear regressions of plant available micro- and macronutrients to shoot length of apple plants, the f-value calculated on Rsqr was treated as trend and strength of effect calculated on Adj Rsqr was treated as significant effect during discussion. According the evaluation criteria of Cohen (1992), effect strength is classified as follows: (f) = 0.10 corresponds to a weak effect, (f) = 0.25 to a moderate effect and (f) = 0.40 to a strong effect. 3. Results 3.1. Heterogeneity of apple replant disease

2.5. Chemical soil analyses The spatial distribution of variation in shoot length (Fig. 3) followed a Gaussian distribution (Shapiro-Wilk test; p > 0.05), i.e., it followed a random pattern, not biased by patchy occurrence at the study site. Shoot length of the plants (n = 130 out of 880 plants sampled) varied from 12 to 163 cm (Table 3). Out of this range, plants with minimum and maximum height (n = 2) as well as additional other 30 plants were selected for sampling (total n = 32). We used the established boniturclasses (Fig. 2) to divide these plants into four groups of equal range, thus increasing overall sample n per class but leaving a skewed distribution of sample size per shoot length interval. Yet, this procedure allowed us to reduce unexplained variability in the field and to better illustrate related effects when relating gradients of ARD severity to differences in soil properties. The individual correlations based on all sampling points are presented in the Supplementary Materials and discussed accordingly.

Soil pH was measured in 0.01 M CaCl2 with a glass electrode. Total carbon (C) and nitrogen (N) contents were measured after dry combustion (ISO 10694, 1995; ISO 13878, 1998) with an elemental analyzer (vario MICRO cube, Elementar, Hanau, Germany), after soil materials were milled (Retsch, Germany). Milled soil material was also used for 15N/14N isotope analysis with an isotope ratio mass spectrometry (IRMS). Plant-available P and K contents were determined after calcium-acetate calcium-lactate extraction (CAL Pi; after Schüller, 1969) with subsequent quantification using the molybdenum blue method (Murphy and Riley, 1962) and a spectrophotometer (SPECORD 205, Analytik Jena,Germany; for P) or an atomic absorption spectroscopy (AAS; for K), respectively. Plant-available magnesium was extracted in 0.0125 M CaCl2 (after VDLUFA, 1991) and also analyzed by AAS. Plant-available micronutrients like Fe, Co, Cu, Ni and Zn were extracted with DTPA (diethylenetriaminepentaacetic acid; after VDLUFA, 2002) and determined by inductively coupled plasma – optical emission spectroscopy (Ultima 2 ICP-OES, HORIBA Jobin Yvon, Longjumeau, France). Measured macro- (K, P and Mg) and micronutrients (Fe, Co, Cu, Ni and Zn) were calculated as stocks.

3.2. Physical soil properties as related to bonitur-classes Soil properties represented little if any variations in soil texture and bulk density at each plot and over the entire study site (Table 1). Soil texture and bulk density were thus not related to the heterogeneity of apple plant growth, as formerly observed for in-between site comparisons (Mahnkopp et al., 2018). Nevertheless, aggregate-size distributions also showed considerable variations: the content of macroaggregates (2000 – 250 μm) ranged from 42.3 - 52.3 g kg−1, large microaggregates (250 – 53 μm) ranged from 30.1 to 39.0 3 g kg−1 soil and the small ones (53-20 μm) from 9.6 – 11.0 g kg−1 (Table 2). Variations in aggregate distribution were larger between rhizosphere and non-rhizosphere soil than among different bonitur-classes; yet differences between these soil compartments were not significant (Table 2), except for group 40–80 cm. In any case, differences in aggregate distribution was not related to ARD severity but rather reflected general rhizosphere effects on soil aggregation processes (Gregory, 2006). The sand content of aggregate fractions in rhizosphere and nonrhizosphere soil ranged between 870–903 g kg−1. The total amount of

2.6. Statistical analyses Statistical analyses were performed in SigmaPlot 13.0, (Systat Software Ins., USA). Normal distribution was tested by Shapiro-Wilk test (p > 0.05) and for homogeneity of variances by Brown-Forsythe test (p < 0.05). Rhizosphere and non-rhizosphere samples within each shoot length group were treated as dependent variables within respective shoot length groups (representing ARD severity). To identify statistical differences among bonitur-classes, we performed separate One-Way Repeated Measured ANOVA, where bonitur-classes (shoot length) either in rhizosphere or non-rhizosphere soil were treated as repeated measures. If significant differences occurred, we used the Tukey HSD test for post-hoc separation of means (p < 0.05). To test for the effect of rhizosphere vs. non-rhizosphere, we performed paired

Table 2 Weight distribution of aggregate fractions indicate measured values after aggregate-size separation with a subsequently sand correction (see M&M 2.4.) Presented aggregate fractions [g kg−1] of soil samples given as mean and standard error [SE]. (1n = 3; 2n = 7; 3n = 12; 4n = 10). Different lowercase letters indicate significant differences of rhizosphere and non-rhizosphere soil within each shoot length group (p < 0.05). Shoot length group [cm]

Weight distribution of aggregate fractions [g kg−1]

rhizosphere

0 – 401 40 – 802 80 -1203 > 1204

Aggregate size [μm] 2000 - 250 250 - 53 46.5 ± 2.5 35.5 ± 3.5 45.7 ± 3.7 30.1 ± 1.8 42.3 ± 2.1 32.8 ± 1.0 46.3 ± 3.0 33.1 ± 1.2

53 - 20 11.0 ± 10.7 ± 10.1 ± 10.2 ±

3.0 1.7 0.6 0.6

< 20 19.5 ± 18.1 ± 20.3 ± 21.0 ±

0.5 1.1a 1.3 1.1

878 884 891 903

non- rhizosphere

0 – 401 40 – 802 80 -1203 > 1204

52.3 47.1 44.8 49.7

10.0 9.60 10.5 10.0

0.6 1.2 0.4 0.5

23.7 27.3 21.8 19.5

4.1 3.5b 2.2 1.9

879 880 870 897

Soil compartment

± ± ± ±

9.4 4.3 1.7 5.0

39.0 30.6 31.9 32.1

± ± ± ±

0.6 2.5 1.5 2.5

4

± ± ± ±

sand content in aggregate fractions [g kg−1]

± ± ± ±

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Table 3 Chemical soil parameters given as mean and standard deviation (SD). Same letters indicate that these values have been tested and capital letters indicate that there is a significant difference within the respective shoot length between rhizosphere and non-rhizosphere soil (p < 0.05). (BD = Bulk density; SOC = soil organic carbon; Quantity = refers to identified amount of apple plants). Soil compartment

rhizosphere

non- rhizosphere

Shoot length of apple plants [cm]

Quantity

Rhizosphere [g]

pH [CaCl2]

SOC [g kg−1]

Ntot [g kg−1]

Stot [g kg−1]

C/N ratio

groups 0 - 40 40 - 80 80 -120 > 120

Min. 12 43 81 122

Max. 33 79 118 163

n 3 7 12 10

Mean 116 557 735 791

SD 43.3 294 198 230

Mean 5.45 5.30 5.14 4.98

SD 0.48 0.50 0.36 0.46

Mean 30.5 27.8 26.8 28.0

SD 2.97 3.58 2.80 2.97

Mean 1.64 1.49 1.40 1.49

SD 0.22 0.22 0.19 0.19

Mean 0.31 0.28 0.26 0.28

SD 0.04 0.04 0.04 0.03

Mean 18.6 18.7a 19.2b 18.9c

SD 0.70 0.49 0.69 0.57

0 - 40 40 - 80 80 -120 > 120

– – – –

– – – –

– – – –

– – – –

– – – –

5.27 5.14 5.14 5.15

0.30 0.26 0.31 0.43

32.9 28.5 28.0 30.2

3.75 4.77 4.39 4.65

1.69 1.45 1.41 1.53

0.24 0.28 0.26 0.28

0.30 0.28 0.27 0.28

0.04 0.04 0.04 0.04

19.5 19.8A 20.0B 19.9C

0.81 0.60 0.69 0.74

larger trees. Intriguingly, contrasting results were found for the plant-available micronutrients manganese, iron and cobalt. At this, with increasing micronutrient stocks an increasing shoot length of apple plants could be observed, or, in other words, ARD was more pronounced when micronutrients were increasingly lacking (Fig. 4). The effect was significant when data were clustered according to bonitur-class. Even when looking on single point correlations in the pH-class of 4.9–5.6 we found a significant correlation for Mn and a very significant correlation for Co up to a shoot length of 100 cm, reflecting the range of most ARD infected plants by their stunted growth (Supplementary Figure A2). This effect could also be observed when evaluating our data after strength of effect according to Cohen. Here, in particular Fe and Co showed a moderate to strong effect between nutrients and shoot length (Supplementary Table A2). These single-point correlations, however, clustered according to pH ranges. The higher the pH, the lower was overall nutrient availability (Supplementary Figure A2), as also commonly known for soil systems (Weil and Brady, 2016). The positive correlations between available micronutrient stocks and shoot length (i.e., the negative correlation between available micronutrient stocks and severity of ARD) showed regression lines that almost ran in parallel for acidic soils with pH < 4.9 and for the less acidic ones with pH > 4.9 values. No such relationships were found for the micronutrient elements copper, nickel and zinc (Supplementary Table A3). Furthermore, differences in δ15N natural abundance did not linearly relate to boniturclass, although rhizosphere soil tended to have lower δ15N values than the non-rhizosphere soil, with minimum δ15N values found for the rhizosphere of the most affected ARD plants (Fig. 5).

truly formed aggregates were thus smaller or equal than 13%, displaying a small degree of aggregation at this sandy study site (Table 2). The majority of particles within a given aggregate-size did thus not exhibit a hierarchical order as suggested for other soils by Tisdall and Oades (1982) or Oades and Waters (1991), but largely only sand grains, merely covered with some organic or inorganic materials at the surface. When correcting aggregate-size distribution for sand content (Supplementary Table A1), we found some larger overall variation at the study site but clearly again no relationship to shoot length groups, neither for the rhizosphere nor for the non-rhizosphere soil. We therefore discount the possibility that physical factors of topsoil aggregation accounted for the variation in ARD severity at our study site. 3.3. Chemical soil properties as related to bonitur-classes The mass of sampled rhizosphere [g] indicates greater weights with increasing shoot length of apple plants, due to larger root growth (Supplementary Figure A1). The rhizosphere showed consistently larger soil organic carbon (SOC) concentrations than the non-rhizosphere soil, but differences were not significant (Table 3). There was also no significant difference in pH value, total N or S content between rhizosphere and non-rhizosphere soil, nor between the different boniturclasses. However, there were significant differences in the C/N ratio of bonitur-classes 40–80, 80–120 and > 120 between rhizosphere and non-rhizosphere soil, indicating a rhizosphere effect on ARD severity, as suggested, for instance, also for other cultures when decomposition of organic plant residues may lead to the release of toxic compounds (Gur and Cohen, 1989; Benizri et al., 2005; Yang et al., 2012). Nevertheless, the highest pH values were found for the smallest shoot length group of 0–40 cm and the lowest value for the highest shoot length; similarly, we observed that lower shoot length of apple plants was linked to larger SOC and Stot concentrations (Table 3), likely reflecting the smaller size of the rhizosphere soil and thus closer vicinity to the roots at subsites with small but most ARD affected plants. To circumvent such problems, we thus mathematically pooled the element contents in the rhizosphere with that of the non-rhizosphere soil, which provided an estimate of total nutrient stocks at a given subsite (here referenced to a cylinder of 25 cm in diameter). In contrast to the general soil properties mentioned above, the stocks of plant-available macronutrients phosphate, potassium and magnesium showed a negative relationship to ARD symptoms: the larger the plant, the less plant-available nutrients remained (Fig. 4). Data variation was still high and not clearly displayed when expressing the data on a single plant basis (data not shown). Only potassium showed a significant correlation to shoot length of apple plants, which was also detectable by the evaluation concept of Cohen (1992), indicating a strong strength of effect (Supplementary Table A2). The relationships to bonitur-classes are plausible, also for the other available macronutrients that only showed a trend effect of declining stocks related to better root growth, most likely representing nutrient uptake by

4. Discussion 4.1. Heterogeneity of Apple Replant Disease as related to physical soil properties Although the major reference soil group did not vary at our study site Heidgraben, the occurrence of Apple Replant Disease was significantly heterogeneous (Fig. 1; 3; Table 1). Earlier studies suggest that particularly sandy soils are vulnerable to ARD (Winkelmann et al., 2019; Mahnkopp et al., 2018) and many studies (Mai and Abawi, 1981; Mazzola and Manici, 2012; Mahnkopp et al., 2018) indicated a stunted growth as one symptom of ARD, thus supporting our observations of a wide range of shoot length reflecting different severity of ARD (Fig. 2; 3). Soil physical properties showed little variation and as such did not modulate ARD severity at the study site (Table 1,2). Vice versa we may argue that our data clearly show that different severity of ARD symptoms may develop independently from soil physical properties at this sandy site. Also, other physical soil properties like bulk density and specific larger pore sizes known to affect ARD due to incremental soil compaction by agricultural machinery (Bronsart, 1949) have not been 5

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Fig. 4. The left side of the bar charts show the stocks of plant-available macronutrients (P, K, Mg) and the right side of micronutrients (Mn, Fe, Co) in relation to the shoot length as inverse proxy for ARD severity.

et al. (2011), for instance, found for sandy soils in subtropical Africa that aggregates break down significantly during 2-years of tillage, while Kösters et al. (2013) concluded that it may take one to two decades until soil structure restores, a time-span, much larger than allowed here for the ARD soil to rest.

reported a major promoting factor for the prevalence of ARD. As ARD is known to occur immediately after replanting young apple plants (Yim et al., 2013; Grunewaldt-Stöcker et al., 2019; Winkelmann et al., 2019), slow changes in physical soil properties in the future will hardly affect these findings. In this regard, also heterogeneous soil management is unlikely to be able to explain differences in ARD as found here across all bonitur classes. The elevated sand contents are responsible for the low degree of aggregation (Table 1; 2; Supplementary Table A1), and rhizosphere effects on aggregation were minor to absent. At least for sandy soils, we have to refute the hypothesis that aggregation is a suitable integrative parameter of soil functions related to ARD. Yet, we have to keep in mind that every second year the apple plants were cut off and ploughed under, i.e., mechanical impacts on soil structure might be severe. Lobe

4.2. Heterogeneity of Apple Replant Disease as related to chemical soil properties Spath et al. (2015) had already concluded that abiotic factors of the soil did not significantly influence the etiology of the ARD in Southern Tyrol. We can confirm these findings for the in-field variations of pH and major macronutrients in this sandy site. Differences in macronutrient supply were the result of plant uptake rather than reflecting 6

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however, we do not have a clear explanation. 4.3. Rhizosphere as a hot-spot for soil-plant interactions as related to ARD Our study has shown only weak and usually non-significant differences of soil properties between rhizosphere and non-rhizosphere soil. One reason may be the short growth duration of two years, which leads to a less pronounced rhizosphere than in very old apple orchards. Another reason, however, may be related to insecurities in the sampling of rhizosphere soil (Winkelmann et al., 2019). Particularly the small, most-infected apple plants evolved the least quantity of rhizosphere soil compared to the better developed apple plants (Supplementary Figure A1). As a result, increasing amounts of former non-rhizosphere soil dilutes the properties of the rhizosphere when larger amounts are sampled with improved plant growth. This probably also influenced the significance of C/N ratios (cf. Table 3) as no effect was found for the smallest shoot length group. The significant difference in C/N ratios generally indicated a clear difference in nutrient uptake between rhizosphere and surrounding soil. First assumptions reflect a depletion of NH4 in rhizosphere soil (Yanai et al., 2003) and a differentiated carbon cycle (Sokolova, 2015) between the two compartments as causal factor behind this. Likely, causal investigations on ARD interactions in the rhizosphere should be restricted to defined gradients if not even to the rhizoplane.

Fig. 5. Presented stable isotope values of nitrogen (δ15 N) given as mean and error bars indicate the standard error [SE]. Shoot length ranges [cm] indicate the bonitur-classes as described in 2.2. (Fig. 2). No significant differences at p = < 0.05.

preference of ARD-inducing pathogens to specific patches rich in macronutrient supply. Intriguingly, this conclusion does not hold true for some micronutrients. They rather accumulated in subsites where plants were largest, i.e., despite potential plant uptake (Fig. 4; Supplementary Figure A2). Especially for small plants that were most affected by ARD, growth was most stunted at reduced availability of micronutrients (Co, Mn; see also Supplementary Figure A2; Table A2). Our data therewith give some support to very early suggestions of Bronsart (1949) that lacking supply of some micronutrients may promote ARD severity. Likewise, this does not directly translate into plantuptake, because none of the plants showed visible symptoms of micronutrient deficiency (Fig. 1). We rather assume that certain functions of the antagonistic (or pathogenic) microbial community may be linked to micronutrient supply and competition, here particularly for Mn and Co, known, e.g., to be major element of enzyme systems (Nihorimbere et al., 2011; Hartikainen et al., 2013). If true, also metabolic rates of certain microbial species will be altered. That this can happen, is shown in the stable N isotope composition of the soil (Fig. 5). At least the plants showing most stunted shoot growth altered the soil δ15N value less than did the larger plants that exhibited less ARD symptoms. (Fig. 5). Likely this reduced alteration of soil δ15N values is related to lower intensity of N cycling (Högberg, 1997; Kriszan et al., 2014), although we cannot exclude that also changes in soil δ15N values also reflect changes in N uptake mechanisms and related 15N discrimination processes in the plant (Evans, 2001; Kalcsits et al., 2014). Deciphering these processes is difficult, as also the size of rhizosphere soil depends on root biomass and thus ARD symptoms; besides, we did not find any relation between Mn and Co and δ15N (R2 < 0.1; n.s., data not shown) Nevertheless, even if biological factors remain the main cause of ARD, balancing the micronutrient management of ARD sites should thus not be ignored. An unknown effect of pH on the growth of fruit trees growing on an infected soil was already also observed by Mai and Abawi (1978). Follow-up studies discussed the effect of pH on ARD severity differentially (e.g. Willett et al., 1994; Utkhede, 2006). However, the pH grouping of micronutrient data as done here has also previously been reported as indirectly influencing ARD expression (Fan et al., 2010; Fazio et al., 2012). Generally, the availability of these cationic micronutrients is lower at elevated pH (Amelung et al., 2018), and indeed, at higher pH the regression line for the decrease in plant growth as related to micronutrient supply had a steeper slope than at more acidic pH conditions (Supplementary Figure A2). The data additional grouped at a threshold pH value around 4.9 (Supplementary Figure A2), for which,

5. Conclusions Our study showed that large in-field variability of ARD symptoms occurred, which, however, was not related to variations in most basic soil properties, neither in bulk soil, nor in the rhizosphere. While in the latter compensation effects from increased rhizosphere mass with improved woody plant growth may convolute the findings, we have to concur that biotic factors rather than abiotic ones are main causes of ARD. Intriguingly, however, there was a negative though pH-dependent interaction of ARD severity and initial micronutrient supply, evident here particularly for Co and Mn. These findings give rise to the assumption that there might be specific types of ARD pathogens that are better adapted to the deficiency of certain micronutrients than other, e.g., antagonistic microorganisms. Declaration of Competing Interest The authors declare no competing interests Acknowledgements The German Federal Ministry of Research and Education funded the project BonaRes ORDIAmur (FKZ 031B0025) within the framework of the BonaRes program. We thank Stefan Pätzold for his support in this study. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.scienta.2019.108809. References Amelung, W., Blume, H.-P., Fleige, H., Horn, R., Kandeler, E., Kögel-Knabner, I., Kretzschmar, R., Stahr, K., Wilke, B.-M., 2018. Scheffer/Schachtschabel: Lehrbuch Der Bodenkunde, 17th edition. Springer-Verlag, Berlin, pp. 750 (ISBN: 978-3-66255870-6). Averdieck, H., 2006. Düngung Von Baumschulkulturen Im Freiland. Meyer-Taschenbuch, Aktuelles Baumschulwissen, Hermann Meyer, Rellingen, pp. 161–175. Benizri, E., Piutti, S., Verger, S., Page, L., Vercambre, G., Poessel, J.L., Michelot, P., 2005. Replant diseases: bacterial community structure and diversity in peach rhizosphere as determined by metabolic and genetic fingerprinting. Soil Biol. Biochem. 37, 1738–1746. https://doi.org/10.1016/j.soilbio.2005.02.009.

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