Effects of drip irrigation and nitrogen fertigation on stand growth and biomass allocation in young triploid Populus tomentosa plantations

Forest Ecology and Management 461 (2020) 117937

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Effects of drip irrigation and nitrogen fertigation on stand growth and biomass allocation in young triploid Populus tomentosa plantations

T

Yuelin Hea, Benye Xia, Mark Bloombergb, Liming Jiaa, , Dehai Zhaoc, ⁎

⁎

a

Ministry of Education Key Laboratory of Silviculture and Conservation, Beijing Forestry University, Beijing 100083, China School of Forestry, University of Canterbury, Christchurch, New Zealand c Warnell School of Forestry and Natural Resources, University of Georgia, GA 30602, USA b

ARTICLE INFO

ABSTRACT

Keywords: Biomass allocation Drip irrigation Fertigation Soil water potential Augmented factorial Dirichlet distribution

A field experiment was conducted in 2017–2018 to investigate the effects of drip irrigation and N fertigation (DIF) on stand growth and biomass allocation in a triploid Populus tomentosa plantation in the North China Plain. The experiment, which included a 3 × 4 factorial structure (four levels of additional N fertigation in each of three levels of irrigation) and an untreated control, was laid out as a complete randomized block design with 5 replicates. Based on the proper model for the augmented factorial design, the results of ANOVA for stand basal area increment and stand total biomass in 2017 and 2018 showed that high irrigation (irrigation when soil water potential at soil 20 cm depth reached −20 kPa) significantly enhanced early stand growth, but additional N fertigation did not further improve early stand growth. The Dirichlet regression model was directly fitted biomass proportion data from destructively sampled trees to differentiate between developmental and treatment effects on biomass allocation. After correction for the tree-size effect, the high irrigation or the high-fertigation associated DIF regimes altered biomass allocation to some extent. Additional N fertigation did not affect early stand growth but affected biomass allocation, suggesting the monitoring of longer-term responses is necessary.

1. Introduction

et al., 2001; Glynn et al., 2003; Wang et al., 2015). Few studies, however, have investigated the interactive effect of water and nitrogen availability, or tried to develop site-specific water and nutrient management strategies (except Ibrahim et al., 1998; Coyle and Coleman, 2005; Dong et al., 2011; Wang et al., 2015; Yan et al., 2018). Poplars, the fast-growing tree species (Perry et al., 2001; Dickmann, 2006), have been widely planted on the North China Plain. This region is characterized by a semi-humid temperate monsoon climate, and the relatively low rainfall levels make irrigation necessary for supplement soil water during the spring to early summer growing season (Kang and Eltahir, 2018). Poplar plantations including Populus tomentosa plantations are the most dominant broadleaf forests in the region. As the most important commercial species, P. tomentosa is planted for pulpwood and sawtimber production (Zhu et al., 1995, 1998), and is also used to plant cropland shelterbelts (Di et al., 2019). The potential productivity of poplar plantations is highly dependent on the soil moisture and nitrogen resource, especially in semi-humid areas (Rennenberg et al., 2010; Wang et al., 2015; Xi et al., 2016, 2017). The productivity of poplar plantations in China is less than the average level of productivity over the world (Kang and Zhu, 2002; Xi et al., 2013; Yan et al., 2018), due to less intensive silvicultural practices. Poplars have higher demand

Woody biomass is an important biomass feedstock in China. The demand for forest products including woody-based products and energy continues to increase, as human population and the standard of living increase (Zhou et al., 2011). Short-rotation forestry (SRF) with intensive silvicultural management is a good way to meet the increasing demand for wood (Schulze et al., 2017). SRF is a common practice of cultivating fast-growing trees such as poplars, aspens, willows and sycamore, in which irrigation and nitrogen (N) fertilization have been widely applied (Cobb et al., 2008; Samuelson et al., 2008; Tullus et al., 2009; Brinks et al., 2011). Water and N status of the soil are highly related to biomass production, and several studies reported that irrigation and N fertilization are effective ways to enhance stand productivity (Albaugh et al., 2004; Karačić and Weih, 2006; Campoe et al., 2013; Coyle et al., 2016). Fertigation, application of water with an appropriate concentration of nutrient elements to the plant root zone, is an efficient method in terms of economic benefits and environmental protection (Yosef, 1977; Feigin et al., 1982). There is increasing research on the application of irrigation and fertigation in short rotation poplar plantations (Stanturf

⁎

Corresponding authors. E-mail addresses: [email protected] (L. Jia), [email protected] (D. Zhao).

https://doi.org/10.1016/j.foreco.2020.117937 Received 2 December 2019; Received in revised form 23 January 2020; Accepted 25 January 2020 0378-1127/ © 2020 Elsevier B.V. All rights reserved.

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for nutrients especially for nitrogen than many tree species and have been found to respond well to fertilization (Rennenberg et al. 2010; Wang et al., 2015; O’Neill et al. 2014). Thus, addition of nitrogen has been used as a critical silvicultural practice in poplar plantations (Stanturf et al. 2001; Coleman et al. 2004a; Yan et al., 2018). In general, more intensive management including irrigation and fertilization can enhance the productivity and may also alter biomass allocation among different components (Osório et al., 1998; Giardina et al., 2003; Novaes et al., 2009; Eziz et al., 2017; Hayes et al., 2017). More research is needed to develop site- and species-specific silvicultural regimes for enhancing stand productivity and stem wood production (Subedi et al., 2012; Wang et al., 2015). To analyze biomass allocation patterns, the allocation approach and allometric equation approach are commonly used (Poorter and Sack, 2012). The allocation approach employs biomass ratios or fractions of different components. The allometric equation approach is based on allometric analysis of component biomass data using a power law. Besides the disadvantages of each of these two methods discussed in the review article of Poorter and Sack (2012), there are other pitfalls of these methods. For example, applying transformations for biomass ratios or allometric equations might distort the relationships among biomass components, and separately analyzing biomass ratios or allometric equations ignores the additivity constraint that all biomass fractions remain non-negative and sum to 1 or all biomass components sum to the total biomass (Zhao et al., 2016). The effect of a given treatment on biomass allocation usually depends on the plant size (Poorter et al, 2012). However, most allometric analyses ignore the size-dependent treatment effect (e.g., Harmens et al., 2000). The Dirichlet regression model (DRM) (Hijazi and Jernigan, 2009), in which the fractional components are assumed to follow the Dirichlet distribution, could be used for biomass allocation analysis. The DRM approach ensures that proportions of all components sum to 1 and avoids the transformation of biomass proportions. By assigning different sets of predictors including plant size to the Dirichlet distribution parameters, we can directly test the effects of treatments on biomass allocation (Zhao et al., 2016). The objective of this study was to evaluate the effects of drip irrigation, with and without additional N fertigation, on biomass production and allocation in young triploid P. tomentosa plantations on sandy loam soil. To do this, we tested the following hypotheses:

2.2. Experiment design A triploid P. tomentosa clonal plantation (Clone S86: (P. tomentosa × P. bolleana) × (P. alba × P. glandulosa)) was established in April 2016 with 2-year-old bare-root stem cuttings. The diameter at breast height of the cuttings ranged from 2.12 to 3.51 cm with an average of 2.68 cm, and height was from 2.60 to 3.62 m with an average of 3.30 m. The trees were planted 3 m apart in rows 2 m apart, and the planting density was 1666 trees ha−1. For each tree, 70 g compound fertilizer with N + P2O5 + K2O ≥ 31% (14-12-5) and organic matter (OM) ≥ 15% was applied as base fertilizer. A surface drip irrigation and fertigation (DIF) system was installed in the plantation in October 2016, and irrigation and fertigation were applied from April 2017 onwards. Each tree row was installed with two drip laterals, each sitting on one side of the tree line and about 30 cm away from the trunk. The laterals (Dripnet PC 16250, Netafim Ltd, Tel Aviv, Israel) had an inside diameter of 1.55 cm and had in-line pressure-compensating drippers at a spacing of 50 cm. The dripper flow was 1.6 L h−1 at a pressure of 100 kPa. The experiment was an augmented factorial design (Piepho et al., 2006), comprising of an untreated (non-irrigation, non-fertigation) control (CK) and twelve DIF treatments (total thirteen treatments) (Table 2). The DIF treatments were a factorial combination of three levels of irrigation and four levels of N fertigation. The levels of irrigation were selected according to the previous study of the quantitative relationship between poplar growth and soil water availability in the growing season (Xi et al., 2016) and soil characteristics of the experimental site (Table 1). The trees were irrigated when the tensiometermeasured soil water potential (SWP) at 20 cm soil depth under the dripper reached −20, −33, and −45 kPa (denoted as I20, I33, and I45, respectively), the corresponding soil water content was 79%, 67% and 60% of field capacity, and 73%, 57% and 48% of soil water availability, respectively. Irrigation stopped when the average soil water content in the soil wetting volume (having a semi-spheroid shape with fieldmeasured radius of 24–25 cm and the depth of 50–70 cm in 2017 and 2018) (Li et al., 2018) reached field capacity. The amount of irrigation to be applied was determined based on the corresponding soil water content before and after irrigation and the soil wetting volume. The actual irrigation amount was also recorded by reading the flow meter each time for each plot. Combined with each irrigation level, N fertilizer was additionally applied through drip irrigation system at four N application rates (0 and 0, 80 and 120, 150 and 190, 220 and 260 kg N ha−1 year−1 used in 2017 and 2018; denoted as F0+0, F80+120, F150+190, and F220+260, respectively). More details of drip irrigation and nitrogen fertigation treatments are shown in Table 2. The trial was laid out as a complete randomized block design with 5 replicates. Each block contained thirteen plots corresponding to the thirteen experimental treatments. Each treatment plot included eight tree belts (each belt including 4 trees), with the middle four tree belts (16 trees) used for tree growth measurement, and the other two belts for a buffer between plots. Additionally, plastic film was buried to 40 cm depth of soil between plots. At the beginning of the growing season of each year, the same amount of irrigation was applied to both the control and treatment plots to promote leaf expansion, and herbicide was used to control weeds during the experimental period.

(1) drip irrigation or drip irrigation plus N fertigation enhanced stand growth; (2) there were interaction effects of drip irrigation and N fertigation on stand growth; (3) irrigation and fertigation treatments altered biomass allocation patterns. Another purpose of this work was to show how to properly analyze data from an augmented factorial design and how to use the DRM approach for biomass allocation analysis. 2. Materials and methods 2.1. Site description The experiment was conducted at a research field near Qingping town, Gaotang County, Shandong Province, China (36°48′46″ N, 116°05′24″ E). This area has a warm temperate monsoon climate, with mean annual temperature, free water-surface evaporation, and forestfree period of 13.2 °C, 1880 mm, and 204 days, respectively. Mean annual precipitation is 544.7 mm, and mainly in July and August. The soil is a sandy loam and its physical and chemical properties are shown in Table 1. The regional climate and the relatively low nitrogen content in the soil make irrigation and nitrogen fertilization necessary for cultivating poplar plantations.

2.3. Growth and biomass determination Every tree in the measurement plots was tagged and measured for the diameter at breast height (DBH) at the beginning of the experiment (March 28, 2017) and in each dormant season (October) in 2017 and 2018. These data were then used to determine the annual increment in the stand basal area (BA, m2 ha−1). In late October 2017, four trees per plot (two below average DBH and two above average DBH) were selected for destructive biomass sampling from the I20F80+120, I20F150+190, I20F220+260, and CK plots in 2

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Table 1 Physical and chemical characteristics of the soil at the experimental site. Soil depth (cm)

Particle size distribution (%)

Saturated water content (cm3 cm−3)

Field capacity (cm3 cm−3)

pH

Clay

Bulk density (g cm−3)

Total nitrogen (g·kg−1)

Mineral nitrogen (mg·kg−1)

Available phosphorus (mg·kg−1)

Available potassium (mg·kg−1)

Organic matter (g·kg−1)

Sand

Silt

0–20 20–40 40–60 60–80

62.23 63.37 58.35 62.35

35.08 34.08 38.76 34.89

2.69 2.55 2.89 2.76

1.43 1.39 1.41 1.42

0.43 0.44 0.44 0.44

0.35 0.33 0.34 0.34

8.18 8.46 8.54 8.49

0.31 0.34 0.13 0.14

3.17 2.62 2.57 2.11

13.96 3.61 1.05 1.06

89.49 70.05 65.15 49.92

5.77 3.73 2.29 1.56

four blocks. A total of 16 trees were harvested from the corresponding buffer rows. In late October 2018, a total of 21 average trees (DBH close to mean DBH for the plot) were harvested from the I20F0+0, I20F80+120, I20F150+190, I20F220+260, I33F220+260, I45F220+260 and CK plots in three blocks. The root systems of the sample trees were excavated manually. A 2 m × 3 m area (6 m2) centered on the stump was excavated to a depth of 100 cm to collect the root component. Each sample tree was separated into leaf, branch, stem, stump, coarse roots (> 2 mm), and fine roots (< 2 mm) components. The fresh weight of each component and its representative subsample was determined in the field. The subsamples were taken to the laboratory and oven-dried to a constant weight at 65 °C for determining dry weight. The total dry biomass of each component was calculated by fresh to oven-dry weight ratio of the subsample and total fresh weight of that component. Above-ground biomass was the sum of leaf, branch and stem biomass, and belowground biomass was the sum of stump, coarse-root and fine-root biomass. Tree total biomass (TM) was the sum of above- and below-ground biomass. A tree total biomass equation TM = b0 DBH b1 was fitted to the biomass data from the sampled trees (Figure S1). Stand-level total biomass (t ha−1) was estimated from the plot inventory data and the fitted biomass equation (b0 = 0.063 and b1 = 2.678). Five leaf litter traps (1 m × 1 m square m per trap) were randomly distributed throughout each of the plots to estimate stand-level leaf litter biomass. Stand leaf dry biomass was the sum of leaf litter biomass and total leaf biomass remaining on the trees.

treatments (DIF) with the control (CK). Secondly, it allowed us to test whether there were significant effects of irrigation, additional fertigation, and irrigation and fertigation interactions in the factorial structure. Therefore, the analysis of variance (ANOVA) was carried out separately for stand BA increment and stand total biomass in 2017 and 2018, using the following model:

yikl (j) = µ +

i

+

j

+

k (j )

+

l (j )

+ ( )kl (j) +

(1)

ikl (j)

where, yikl (j) is the value of response variable for the kth irrigation and the lth fertigation in the ith block; µ is the overall mean; i is the effect of the ith block (i = 1, 2, …, 5); j is the mean effect of the treatments ( j = 1, 2 ); k (j) is the effect of the kth irrigation (k = 1, 2, 3); l (j) is the effect of the lth fertigation (l = 1, 2, 3, 4) ; ( ) kl (j) is the interaction effect of the kth irrigation and lth fertigation; and ikl (j) is the random effect for the kth irrigation and lth fertigation in the ith block. The biomass component proportions were directly modeled using the Dirichlet regression model (DRM). Specifically, let {y1 , …, yM } represent the M individual biomass components, total biomass M yT = m = 1 ym , and the component biomass proportions pm = ym yT . Thus, the vector of component proportions is p = (p1 , …, pM ) with M constraints pm (0, 1) and m = 1 pm = 1. Assume the fractional components p follow the Dirichlet distribution with parameters 1, …, M > 0 :

Dir(M , ) = f (p ) = where,

2.4. Statistical analysis

B( ) =

The study used an augmented factorial experiment design. This allowed comparison of the irrigation or irrigation plus fertigation

M m =1 ( m ) M ( m=1 m )

1 B( )

=

M m=1

pmm 1 ,

M m=1 ( m )

( 0)

,

= ( 1, …,

(2) M ),

and

0

=

M m=1

m.

The expected values, the variances, and covariances of the component proportions are

Table 2 Experimental design and implantation overview in this study. Treatment

T1: I20F80+120 T2: I20F150+190 T3: I20F220+260 T4: I20F0+0 T5: I33F80+120 T6: I33F150+190 T7: I33F220+260 T8: I33F0+0 T9: I45F80+120 T10: I45F150+190 T11: I45F220+260 T12: I45F0+0 Control

Irrigation when SWP at 20 cm depth (kPa)

−20 −20 −20 −20 −33 −33 −33 −33 −45 −45 −45 −45 Non-irrigation

Irrigation amount (m3 ha−1 year−1)

Irrigation times (times year−1)

Fertigation amount (kg ha−1 year−1N)

2017

2018

2017

2018

2017

2018

3474 3474 3474 3474 151 151 151 151 167 167 167 167 0

4557 4557 4557 4557 1146 1146 1146 1146 0 0 0 0 0

29 29 29 29 1 1 1 1 1 1 1 1 0

25 25 25 25 5 5 5 5 0 0 0 0 0

80 150 220 0 80 150 220 0 80 150 220 0 0

120 190 260 0 120 190 260 0 120 190 260 0 0

Fertigation times (times year−1)

6 6 6 0 6 6 6 0 6 6 6 0 0

SWP is soil water potential. The irrigation amount was calculated based on the corresponding soil water content and the soil wetting volume, and the actual irrigation amount was also recorded by reading the flowing meter each irrigation time for each plot. The six N fertigation times were April 25, May 15. June 5, June 25, July 18, and August 7 in 2017; April 26, May 18. June 4, June 24, July 16, and August 3 in 2018. In each year, 3/5 of N fertigation amount was applied evenly in the first 3 times, and other 2/5 evenly applied in the last 3 times. N fertilizer – urea nitrate solution (233.5 g N L−1) was injected directly into water with 4% additive rate by hydraulic driven injector (MixRite Model 2504, Tefen, Israel). 3

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E[pm ] =

m 0

, Var[pm ] =

m( 0 m) , 2 0 ( 0 + 1)

and

Cov[pm , pm ]=

Table 3 Analysis of variance for stand basal area (BA) increment and stand total biomass in 2017 and 2018, respectively.

m m 2 0 ( 0 + 1)

m ) , respectively. Assuming tree biomass allocation is tree-size-dependent and affected by treatments, the log link-functions for the shape parameter of each component can be related to tree size variables and treatment dummy variables as

(m

log(

m ) = gm (X m,

(3)

m )(m = 1, …, M ).

The maximum likelihood estimates of parameters are obtained with the full log-likelihood of the Dirichlet distribution defined in Eq. (4):

l (p ) = log

(

M m=1

)

M

m

m=1

log (

m)

+

M m =1

(

m

1) log(pm ). (4)

Then m = exp{gm (Xm, m)} , and pm = m 0 . The biomass ratio between two components is m m (m m ) . In this study, biomass data of the average trees sampled in 2018 from the I20F0+0, I20F80+120, I20F150+190, I20F220+260, I33F220+260 I45F220+260, and CK plots were used to fit the DRM. Tree size was represented by tree total biomass (TM, kg) and the following dummy variables were set to distinguish these treatments:

T1 =

1, if I20 F80 + 120 treatment 1, if I20 F150 + 190 treatment , T2 = , 0, otherwise 0, otherwise

T3 =

1, if I20 F220 + 260 treatment 1, if I20 F0 + 0 treatment , T4 = , 0, otherwise 0, otherwise

T7 =

1, if I33 F220 + 260 treatment 1, if I 45 F220 + 260 treatment , and T11 = . 0, otherwise 0, otherwise

m)

=

00m

+ i=1

0im Ti

+

10m

+ i=1

1im Ti

(i = 1, 2, 3, 4, 7, 11; m = 1, …, M )

df

MS

F

p value

BA Increment in 2017 Block Control vs. DIF Irrigation (DIF) Fertigation (DIF) Irrigation × Fertigation (DIF) Error

4 1 2 3 6 48

3.962 2.223 6.613 0.296 0.648 0.464

8.544 4.794 14.261 0.638 1.397

< 0.001 0.034 < 0.001 0.594 0.235

BA Increment in 2018 Block Control vs. DIF Irrigation (DIF) Fertigation (DIF) Irrigation × Fertigation (DIF) Error

4 1 2 3 6 48

2.869 1.827 2.194 0.088 0.396 0.263

10.915 6.952 8.350 0.334 1.506

< 0.001 0.011 < 0.001 0.801 0.197

Stand Total Biomass in 2017 Block Control vs. DIF Irrigation (DIF) Fertigation (DIF) Irrigation × Fertigation (DIF) Error

4 1 2 3 6 48

80.408 50.191 93.857 9.554 15.023 10.357

7.764 4.846 9.063 0.922 1.451

< 0.001 0.033 < 0.001 0.437 0.215

Stand Total Biomass in 2018 Block Control vs. DIF Irrigation (DIF) Fertigation (DIF) Irrigation × Fertigation (DIF) Error

4 1 2 3 6 48

325.120 238.210 390.242 25.529 55.113 38.748

8.391 6.148 10.071 0.659 1.422

< 0.001 0.017 < 0.001 0.581 0.225

fitted to the DRM, which is simplified to a beta regression since there were two components in this case. The parameter estimates are shown in Table 4. For any specific treatment, tree biomass allocation parameters were significantly dependent on the tree size (TM), I20 plus all fertigation treatments (T1, T2 and T3) and the I45F220+260 treatment (T11). There were no significant effects among the I20F0+0 (T4), I33F220+260 (T7), and the CK treatments on biomass allocation parameters. The above- and below-ground proportions in total tree biomass can be estimated by the following model (6):

The following log link-functions were used for biomass component proportions:

log(

Source

log(TM ) (5)

The DRMs for the proportions of tree total biomass in aboveground and belowground components, proportions of aboveground biomass in leaves, branches and stem, and proportions of belowground biomass in the stump, coarse roots and fine roots were separately fitted using R Package DirichletReg (Maier 2014). The parameters whose estimates were not statistically significantly different from zero (p > 0.05) were excluded from the models.

= TM 2.5250 0.7103(T1+ T2+ T3) + 0.9006T11 = 0.5189TM 2.2216 0.6990(T1+ T2+ T3) + 0.8603T11 Pabove = 1 ( 1 + 2 ) Pbelow = 2 ( 1 + 2) = 1 Pabove 1

2

3. Results

(6)

The predicted above- and below-ground biomass proportions based on Eq. (6) are shown in Fig. 2. The I45F220+260 treatment (T11) had the highest aboveground biomass proportion and lowest belowground biomass proportion, with all other treatments being quite similar. Above-ground biomass allocation parameters for leaf, branch and stem components were highly dependent on the tree size (TM) and the DIF treatments (Table 4). The leaf, branch and stem proportions in above-ground biomass can be estimated by the following model (7):

3.1. Stand growth The resulting ANOVAs for stand basal area increments and stand total biomass in the second and third years (i.e., 2017 and 2018) are presented in Table 3. There were significant differences in both BA increments and stand total biomass in both 2017 and 2018 between control (CK) and the mean of 12 DIF treatments. Interactions of drip irrigation and fertigation were not significant. The effect of drip irrigation was significant, while the effect of additional fertigation was not significant. Multiple comparisons among drip irrigation treatments showed that the irrigation treatment I20 significantly enhanced stand growth in both 2017 and 2018, compared with other irrigation levels (I33, I45 and CK) among which there were no significant differences (Fig. 1).

2

36.6020(T1+ T2 + T3) + 69.5920(T 7 + T 11 ) TM1.5000 11.3750(T 1+ T2 + T3) 21.5170(T 7 + T 11 ) 1= e = 0.0611e 38.6579(T1+ T2+ T3)+ 69.5533(T 7+ T11) TM 2.4956 12.0056(T1+ T2+ T3) 21.5316(T7+ T11 ) 3

= e 40.1623(T1+ T2+ T3) + 67.8398(T7+ T11) TM1.9276 12.3804(T1+ T2+ T3) Pleaf = 1 ( 1 + 2 + 3 ) Pbranch = Pstem =

(

2 3

(

+ +

1 1

20.9645(T 7+ T11 )

+ 3) + 3)

2 2

(7)

3.2. Biomass allocation

Based on the similarity of parameter estimates for a specific treatment across leaf, branch and stem components, the treatments could be classified into three groups: (T1, T2, T3), (T7, T11), and (T4, CK). In

Above- and below-ground proportions in total tree biomass were 4

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Table 4 Parameter estimates and their standard errors (SE) and p values for biomass components (aboveground-belowground biomass allocation of tree total biomass; leaf, branch, and stem biomass allocation of aboveground biomass; stump, coarse- and fine-root biomass allocation of belowground biomass) fitted using Dirichlet regression model. Component

Parameter

Aboveground-belowground biomass allocation Aboveground: log log(TM) (α1) (T1 + T2 + T3) log (TM) T11 log(TM) Belowground: log Intercept (α2) log(TM) (T1 + T2 + T3) log (TM) T11 log(TM)

Estimate

SE

of total biomass 2.5250 0.1513 −0.7103 0.2064 0.2907 0.1020 0.1549 0.2061

0.0019 < 0.0001 < 0.0001 0.0007

0.8603

0.2907

0.0031

general, the first group (T1, T2 and T3) resulted in the highest stem biomass proportion and the lowest branch and leaf biomass proportion, and the third group (T4, CK) had the smallest stem proportion and highest branch proportion (Fig. 3). The second and third groups of treatments had big differences in stem and branch proportions and did not have differences in leaf proportion. Compared with other treatments, the treatments with the highest level of fertigation (T3, T7 and T11) had significantly different effects on below-ground biomass allocation parameters. Because there was no significant tree-size effect on below-ground biomass allocation (Table 4), the stump, coarse-root and fine-root proportions in belowground biomass had constant values and can be estimated by the following model (8):

2

( 1+ ( 1+

2 2

+ 3) + 3)

0.0162 < 0.0001 0.0039 0.0165 < 0.0001 0.0025 0.0166 0.0021 0.0194 < 0.0001 0.0014 0.0198

4. Discussion and conclusions 4.1. Effects of irrigation and fertigation on stand production The experimental design in this study is an augmented factorial design. Differences between the control and DIF treatments and differences among the DIF treatments should be analyzed in one model using all data. The augmented factorial design has been used in several studies, but the methods of data analysis might be inappropriate (e.g., Wang et al., 2015). As an additional purpose of this work is to show how to do the ANOVA for augmented factorial design. The ANOVA results (Table 3) supported the first hypothesis and rejected the second one. That is, compared with the control, drip irrigation or irrigation plus additional fertigation significantly enhanced stand growth in terms of stand basal area increment and stand total biomass. However, there were no significant interactions between irrigation and N fertigation, and no significant effects of additional N fertigation on early stand growth. These results demonstrated that drip irrigation treatments improved, but additional N fertigation did not further improve early stand growth. Detailed multiple comparisons among drip irrigation and the control treatments showed that the high irrigation (I20) treatments had a

3.3663T3+ 1.2357T11 1 = 55.9244e = 47.9808e3.3140T3 0.3814T7+ 1.4909T11 3.1776T3+ 1.4232T11 3 = 4.4938e Pstump = 1 ( 1 + 2 + 3 )

3

0.0052 0.0160 < 0.0001 0.0034

Belowground biomass allocation to stump, coarse-root, fine-root components Stump: log(α1) Intercept 4.0240 0.2612 < 0.0001 3.3663 0.6339 < 0.0001 T3 T11 1.2357 0.6361 0.0517 Coarse root: log(α2) Intercept 3.8708 0.2623 < 0.0001 3.3140 0.6344 < 0.0001 T3 T7 −0.3814 0.1358 0.0050 1.4909 0.6355 0.0190 T11 Fine root: log(α3) Intercept 1.5027 0.2602 < 0.0001 T3 3.1776 0.6335 < 0.0001 T11 1.4232 0.6345 0.0249

Fig. 1. Least square means for stand basal area (BA) increment (A) and stand total biomass (B) under 3 irrigation levels and control in 2017 and 2018, respectively. The same letter among irrigation treatment and control in the same year indicate no significant difference (Tukey’s HSD, = 0.05).

Pcoarseroot = Pfineroot =

< 0.0001 0.0006

0.9006 −0.6560 2.2216 −0.6990

Aboveground biomass allocation to leaf, branch and stem components T1 + T2 + T3 36.6020 13.0970 Leaf: log(α1) T7 + T11 69.5920 28.8990 log(TM) 1.5000 0.1310 (T1 + T2 + T3) log −11.3750 3.8860 (TM) (T7 + T11) log(TM) −21.5170 8.9520 Branch: log(α2) Intercept −2.7953 0.4678 T1 + T2 + T3 38.6579 13.3765 69.5533 28.9960 T7 + T11 log(TM) 2.4956 0.1940 (T1 + T2 + T3) log −12.0056 3.9716 (TM) (T7 + T11) log(TM) −21.5316 8.9855 Stem: log(α3) T1 + T2 + T3 40.1623 13.0796 T7 + T11 67.8398 29.0184 log(TM) 1.9276 0.1311 (T1 + T2 + T3) log −12.3804 3.8805 (TM) (T7 + T11) log(TM) −20.9645 8.9935

2

p value

(8)

Although the difference in fine-root biomass proportion was very small among the treatments, the T7 treatment had a markedly larger stump proportion and the smaller coarse-root proportion, while the T11 treatment had the smaller stump proportion and the higher coarse-root proportion than all other treatments (Fig. 4). 5

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Fig. 2. Relationships between proportions of aboveground and belowground in tree total biomass and cultural treatments and tree size (tree total biomass). Treatments include I20F80+120 (T1), I20F150+190 (T2), I20F220+260 (T3), I45F220+260 (T11); Other: I20F0+0 (T4), I33F220+260 (T7), CK.

Fig. 4. Relationships between proportions of stump, coarse-root, and fine-root components in tree total belowground biomass and cultural treatments, and tree size (tree total biomass). Treatments include I20F220+260 (T3), I33F220+260 (T7), I45F220+260 (T11); Other: I20F80+120 (T1), I20F150+190 (T2), I20F0+0 (T4), CK.

eastern US. A positive effect of N fertigation on tree growth was also found in 3–5-year old P. tomentosa plantations on silty soil (Wang et al., 2015) and 2–4-year old poplar plantations on sandy loam and silt loam soil (Yan et al., 2018) on the North China Plain. In arid regions, however, the positive response to nutrient amendment may not be achieved without irrigation (Linder, 1989). The lack of early growth response to additional N fertigation in this study, however, may suggest that N did not appear to limit the early growth of the plantations. We monitored the foliar N concentrations from April to August in 2017 and 2018. The foliar N levels (> 1.8%) were adequate for growth across all treatments, even without fertigation. The site appeared to have adequate N even without fertilizer. Compared with about 30 kg ha−1 available N in 0–80 cm soil layer at the start of the experiment and 200–480 kg ha−1N applied with the fertigation, the base slow-release fertilizer (N + P2O5 + K2O ≥ 31% (14-12-5) per tree or equivalent to 5.06 kg ha−1N) applied at time of planting might be just a starter for the young rooted cuttings and could not explain enough this lack of growth response. With lower field capacity and poorer fertilizer retention performance on sandy loam soils, more nitrogen appeared to leach or leak into deeper soil under rainfall and irrigation management (Li and Liu, 2011; Dai et al., 2015; He et al., 2018). Fertigation is a very precise way to apply N that results in minimum leaching. Many studies have tried to optimize the amount of fertilizer used in poplar plantations. Coleman et al. (2004b) demonstrated that additional 50 kg N ha−1 year−1 fertilizer could meet the N requirements for young poplar stands on loamy-sand soils. Based on growth response from an experiment on a different site on the North China Plain, Wang et al. (2015) recommended 115 kg N ha−1 year−1 and Xi et al. (2017) recommended 192 kg N ha−1 year−1 as the N fertigation regime for triploid P. tomentosa plantations under drip irrigation. In other studies, 180 kg N ha−1 year−1 under −50 kPa drip irrigation was recommended for 2–4-year old poplar plantations (Yan et al., 2018), 350 kg N ha−1 year−1 for non-irrigated triploid P. tomentosa plantation, and 380–500 kg N ha−1year−1 for flood irrigated triploid P. tomentosa plantations (Ren et al., 2012). Fertigation rates were set up according to the findings of these previous studies on similar sites and the nutrient demand characteristics of fast-growing species (Dickmann et al., 2002). However, our results clearly demonstrated that additional N fertigation

Fig. 3. Relationships between proportions of stem, branch, and leaf components in tree total aboveground biomass and cultural treatments, and tree size (tree total biomass). Treatments include I20F80+120 (T1), I20F150+190 (T2), I20F220+260 (T3), I33F220+260 (T7), I45F220+260 (T11); Other: I20F0+0 (T4), CK.

positive and largest effect on production in our study (Fig. 1). This suggested that water availability was the most important factor limiting growth of P. tomentosa stands on the sandy loam soils found on the trial site. The type of the soil was well-drained and had a low water storage capacity (Table 1). The importance of irrigation for tree growth on similar soils has been reported in many studies e.g. for sycamore (Acre pseudoplantanus) in southeastern US (Lockaby and Baker, 1997). Although some adverse fertilization effects in the absence of irrigation have been observed in aspen seedlings due to additional moisture stress caused by fertilizer (van den Driessche et al., 2003), most studies have demonstrated that fertilization increased the productivity of fast-growing species. Coyle and Coleman (2005) and Coyle et al. (2008) found both the irrigation and fertilization increased aboveground biomass production of cottonwood and sycamore seedling in the 6

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did not have effects on early growth.

above- and below-ground biomass allocation. Trees in the highest irrigation plus additional fertigation treatments (I20F80+120, I20F150+190 and I20F220+260) allocated more biomass to stem and less to branches and leaves (Fig. 3), suggesting trees might have higher photosynthetic efficiency (Poorter and Nagel, 2000; Poorter et al, 2012). The high irrigation only (I20F0+0) and CK treatments generally resulted in higher branch and leaf, and lower stem biomass proportions. Trees under lower irrigation plus highest fertigation (I33F220+260 and I45F220+260) treatments also allocated more biomass to branches and leaves compared with the high irrigation treatments. The treatments with high irrigation would benefit stem wood production, since they resulted in higher tree size TM and a proportionally higher allocation of TM to stem wood. Although the effects of additional N fertigation on early growth were not detectable, the highest fertigation associated treatments (I20F220+260, I33F220+260, I45F220+260) significantly changed belowground biomass allocation pattern (Table 4 and Fig. 4). In order of the I20F220+260, I33F220+260, I45F220+260, and other treatments, fine-root proportions were 0.035, 0.048, 0.044, and 0.041; coarse-root proportions were 0.433, 0.352, 0.502, and 0.443; stump proportions were 0.532, 0.600, 0.454, and 0.516. Slight differences in fine-root proportions and big differences in coarse-root proportions found in this study were consistent with the findings of Coleman et al. (2004b). The difference in treatment effects mainly comes from the difference between stump and coarse-root proportions. The stump/coarse-root biomass ratios (i.e., biomass fraction ratios) are 1.228, 1.707, 0.903, and 1.166 for I20F220+260, I33F220+260, I45F220+260, and others, respectively. In summary, high irrigation appears to have a positive effect, but additional N fertigation has no effect on stand growth of 2- to 3-year old triploid P. tomentosa plantation on sandy loam soils on the North China Plain. Biomass allocation of triploid P. tomentosa plantation was altered by high irrigation or high-fertigation DIF regimes to some extent, but responses generally did not vary markedly or consistently with irrigation or fertigation. We are continuing to monitor growth response to irrigation and fertigation since these may change as the stands age.

4.2. Effects of irrigation and fertigation on biomass allocation In the analysis of biomass allocation among more than two components, the commonly used methods such as biomass ratio or allometric equation approaches require data transformation (e.g., arcsine square-rooted transformation or log-transformation) and then separately fitting multiple equations. With these traditional methods, it would be difficult to give the whole picture of biomass allocation and to rigorously test statistical hypotheses. In this study, however, we used the Dirichlet regression model (DRM) approach. We assumed biomass proportions follow the Dirichlet distribution (a generalization of the beta distribution in two-component case) and directly fitted biomass component proportions without any data transformation. The effects of variables such as tree size or treatments on the biomass allocation can be statistically tested in the DRM approach (Zhao et al. 2016). Our results showed that tree size significantly influenced total biomass allocation between above- and below-ground components, with an increase in biomass allocated to aboveground component with increasing tree size. Tree size also significantly influenced above-ground biomass allocation among stem, branches and leaves components, but did not affect below-ground biomass allocation among stump, coarse-root and fine-root components (Table 4). After correction for the differences in tree size, effects of treatments on biomass allocation were still detectable and quantifiable. Therefore, our comprehensive analysis using the DRM approach supported our third hypothesis that irrigation and fertigation treatments altered biomass allocation pattern of young P. tomentosa on sandy loam soils. However, effects of treatments were not consistent across components, and there seemed to be an interaction between the effects of tree size and treatments. While the I20F0+0 treatment did not affect any biomass allocation patterns, the high irrigation plus additional fertigation treatments (T1, T2, T3 or I20F80+120, I20F150+190 and I20F220+260) changed some biomass allocation patterns (Figs. 2–4). Compared with other treatments, the proportion of stem wood biomass declined more sharply with increasing tree size for the high irrigation plus additional fertigation (I20F80+120, I20F150+190 and I20F220+260), and the proportion of leaf biomass increased with increasing tree size whereas it declined for all other treatments. Based on the growth response to irrigation, it seems likely that water availability was a limiting factor to tree growth in this experiment. Trees under adequate irrigation treatment should allocate less biomass to below-ground, according to the functional equilibrium hypothesis (Brouwer, 1983; Poorter and Nagel, 2000). Our results disagree with this point. Irrigation only influenced the wetting zone. We know that P. tomentosa is getting a lot water from deep in the soil and beyond the irrigation zone. In this study, we excavated roots in a 2 m × 3 m area. The drip irrigated trees may reduce fine roots in the wetting zone (a semi-spheroid shape with the radius of 24–25 cm and the depth of 50–70 cm) but will still need to have good root development outside and below the wetting zone. Functional equilibrium hypothesis works when all the root zone is wetter. The fast-growing species also need more stable below-ground structure to maintain growth at young stage (Bardgett et al., 2014). Coleman et al. (2004b) observed a fertilizer-induced shift in biomass allocation from below-ground to above-ground. In our study, the highfertigation with low-irrigation (T11: I45F220+260) treatment decreased below-ground biomass proportion when compared with all other treatments. There was an effect from T1, T2 or T3 (I20F80+120, I20F150+190 or I20F220+260) but this effect was not particularly strong (Fig. 2). There was no N fertigation only treatment in our experiment, so we were unable to differentiate between N fertilization and irrigation effects on biomass allocation. In general, our results indicated that water and nitrogen resource availability did not strongly influence

CRediT authorship contribution statement Yuelin He: Conceptualization, Investigation, Formal analysis, Writing - original draft. Benye Xi: Investigation. Mark Bloomberg: Writing - review & editing. Liming Jia: Supervision, Funding acquisition. Dehai Zhao: Supervision, Methodology, Formal analysis, Writing original draft. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research was jointly supported by the National Key Research and Development Program of China (2016YFD0600403), the Key Technologies R&D Program of China (2015BAD09B02), the National Natural Science Foundation of China (31670625), and Short-term International Student Program for Postgraduates of Forestry First-Class Discipline (2019XKJS0501). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2020.117937. 7

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