Data describing the eco-physiological responses of Elaeagnus angustifolia grown under contrasting regime of water and fertilizer in coal-mined spoils.

To improve our understanding of how coal mining areas can be re-vegetated and ecosystem function restored, we examined the potential effects of five water (W) regimes (40, 50, 60, 70 and 80% of field capacity), five nitrogen (N) (0, 24, 60, 96 and 120 mg kg‒1 soil) and five phosphorus (P) fertilizer doses (0, 36, 90, 144 and 180 mg kg‒1 soil), which control the growth and development of Elaeagnus angustifolia under adverse environmental conditions. To optimize the W-N-P application rate, three factors and five levels of central composite design along with an optimization technique named response surface methodology were utilized. Here we provide data on root-shoot biomass ratio, leaf dry matter content, stomatal conductance, chlorophyll (Chl) a, Chl b, membrane stability index and soluble protein content of E. angustifolia. The data described in this article are available in Mendeley Data, DOI: 10.17632/2vfbrdxyf2.2[1]. These data could be used to evaluate the improvement in growth performance of E. angustifolia subjected to various regimes of W, N and P. This dataset showed that E. angustifolia grew optimally in coal-mine spoils when irrigated at 66% of field capacity and supplemented with 74.0 mg N and 36.0 mg P kg‒1 soil. This could considerably help the success of revegetation in coal-mined degraded arid areas where W is scarce. This article contains data complementary to the main research entitled "Fine-tuning of soil water and nutrient fertilizer levels for the ecological restoration of coal-mined spoils using Elaeagnus angustifolia" in the Journal of Environmental Management (Roy et al., 2020).

Specifications Table

Value of the Data

These data will be useful for researchers interested in revegetation in coal-mined degraded arid areas, worldwide.

This data will be useful to understand the potential impact of W, N and P on the revegetation of E. angustifolia grown in drought-prone coal mine spoils.

These data can be used by researchers to implement more efficient and effective field research after reviewing how different combinations of W, N and P interact on physiological parameters of E. angustifolia and how optimum W-N-P doses facilitate revegetation intervention programs.

Data Description

This dataset contains two tables and four figures. Data provided in Table 1 shows the interaction effect of W, N and P on root-shoot (R/S) biomass ratio, leaf dry matter content (LDMC), stomatal conductance (Gs), chlorophyll (Chl) a, Chl b, membrane stability index (MSI) and soluble protein (SP) content of E. angustifolia. The amount of W, N and P application is designated as a subscript in the treatment column (such as W70 = 70% field capacity, N96 = 96 mg kg‒1 and P144 = 144 mg kg‒1). Table 2 shows the desirability specifications of numerical optimization for central composite design. Fig. 1 shows representative individual leaves of E. angustifolia. Fig. 2 shows the interaction effect of W, N and P on (a–c) root-shoot (R/S) biomass ratio, (d–f) leaf dry matter content (LDMC), (g–i) stomatal conductance (Gs), (j–l) chlorophyll a, and (m–o) chlorophyll b in the leaves of E. angustifolia. Fig. 3 shows the interaction effect of W, N and P on (a–c) membrane stability index (MSI) and (d–f) soluble protein (SP) content in the leaves of E. angustifolia. Fig. 4 Pearson's correlation coefficient shows the effects of various regimes of W, N and P on the various growth responses of E. angustifolia. Raw and analysis of variance data of different responses associated with the figures are available at https://data.mendeley.com/datasets/2vfbrdxyf2/2.

Table 1

Interaction effect of water (W), nitrogen (N) and phosphorus (P) on root-shoot (R/S) biomass ratio, leaf dry matter content (LDMC), stomatal conductance (Gs), chlorophyll (Chl) a, Chl b, membrane stability index (MSI) and soluble protein (SP) content of E. angustifolia.

			Gs	Chl a	Chl b		SP
Treatments	R/S	LDMC g g‒1	mol m‒2 s‒1	mg g‒1 FW	mg g‒1 FW	MSI %	mg g‒1 FW
W70N96P144	0.74 ± 0.01cde	0.229 ± 0.01b	0.263 ± 0.02abcd	1.95 ± 0.13ab	0.99 ± 0.13a	55.17 ± 0.957de	56.78 ± 2.5ab
W70N96P36	0.76 ± 0.03bc	0.235 ± 0.03b	0.287 ± 0.02a	2.07 ± 0.14a	0.88 ± 0.04abcde	54.45 ± 0.427def	57.26 ± 1.1a
W70N24P144	0.68 ± 0.03f	0.236 ± 0.02b	0.284 ± 0.01ab	1.99 ± 0.08ab	0.92 ± 0.06abc	51.22 ± 3.664g	31.59 ± 3.5fgh
W70N24P36	0.7 ± 0.02def	0.241 ± 0.03b	0.239 ± 0.03abcde	1.74 ± 0.15abcde	0.84 ± 0.05abcdef	53.22 ± 0.489efg	25.07 ± 2hi
W50N96P144	0.8 ± 0.02ab	0.252 ± 0.01b	0.212 ± 0.02cde	1.45 ± 0.13e	0.66 ± 0.04fgh	45.03 ± 0.361hi	50.29 ± 5.4bc
W50N96P36	0.84 ± 0.02a	0.262 ± 0.02b	0.282 ± 0.01ab	1.75 ± 0.18abcde	0.72 ± 0.06defgh	47.04 ± 0.879h	51.77 ± 1.4abc
W50N24P144	0.71 ± 0.03cdef	0.267 ± 0.02ab	0.252 ± 0.01abcde	1.75 ± 0.15abcde	0.77 ± 0.06bcdefg	43.13 ± 0.368i	26.1 ± 2.1ghi
W50N24P36	0.74 ± 0.02cde	0.265 ± 0.02b	0.245 ± 0.01abcde	1.67 ± 0.04bcde	0.75 ± 0.02cdefgh	43.99 ± 0.85i	22.58 ± 1.6i
W80N60P90	0.71 ± 0.02cdef	0.231 ± 0.02b	0.265 ± 0.02abcd	1.88 ± 0.15abcd	0.9 ± 0.03abcd	55.48 ± 0.323cde	45.48 ± 3.6cd
W40N60P90	0.81 ± 0.02ab	0.32 ± 0.03a	0.235 ± 0.01abcde	1.48 ± 0.09e	0.58 ± 0.04h	42.28 ± 0.339i	27.88 ± 3.5ghi
W60N120P90	0.8 ± 0.02ab	0.242 ± 0.03b	0.291 ± 0.02a	1.66 ± 0.11bcde	0.73 ± 0.04defgh	52.12 ± 0.974fg	56.36 ± 1.1ab
W60N0P90	0.69 ± 0.02ef	0.258 ± 0.02b	0.268 ± 0.01abc	1.58 ± 0.08cde	0.69 ± 0.08fgh	50.45 ± 0.431g	24.27 ± 0.1i
W60N60P180	0.72 ± 0.02cdef	0.247 ± 0.01b	0.196 ± 0.01e	1.88 ± 0.06abcd	0.94 ± 0.09ab	52.08 ± 1.007fg	39.3 ± 0.4de
W60N60P0	0.76 ± 0.02bc	0.257 ± 0.01b	0.209 ± 0.02cde	1.91 ± 0.15abc	0.96 ± 0.08a	47.4 ± 0.276h	36.06 ± 0.5ef
W60N60P90	0.74 ± 0.01cde	0.255 ± 0.01b	0.214 ± 0.01cde	1.54 ± 0.12e	0.67 ± 0.07fgh	59.06 ± 0.538ab	38.05 ± 0.8ef
W60N60P90	0.73 ± 0.03cdef	0.243 ± 0.01b	0.226 ± 0.01bcde	1.51 ± 0.1e	0.64 ± 0.05gh	57.16 ± 1.105bcd	35.67 ± 0.8ef
W60N60P90	0.72 ± 0.02cdef	0.236 ± 0.02b	0.208 ± 0.01cde	1.55 ± 0.04de	0.7 ± 0.07efgh	58.35 ± 0.34abc	32.57 ± 1.2efg
W60N60P90	0.75 ± 0.03bcd	0.259 ± 0.02b	0.218 ± 0.01cde	1.52 ± 0.06e	0.66 ± 0.04fgh	60.04 ± 0.547ab	34.72 ± 2.3ef
W60N60P90	0.73 ± 0.03cdef	0.261 ± 0.02b	0.205 ± 0.01de	1.56 ± 0.06de	0.69 ± 0.04fgh	55.42 ± 0.151cde	35.45 ± 2.6ef
W60N60P90	0.74 ± 0.01cde	0.256 ± 0.03b	0.211 ± 0.01cde	1.57 ± 0.11de	0.65 ± 0.03gh	61.22 ± 1.345a	38.77 ± 3.1de

Table 2

Specifications of desirability for numerical optimization in central composite design.

Name	Goal	Lower limit	Upper limit	Importance
Water (% FC)	minimize	40	80	3
Nitrogen (mg kg‒1)	minimize	0	120	3
Phosphorus (mg kg‒1)	minimize	0	180	3
Leaf dry matter content	is in range	0.68	0.84	3
Root-shoot ratio	is in range	0.229	0.32	3
Stomatal conductance	is in range	0.196	0.291	3
Chlorophyll a	is in range	1.45	2.07	3
Chlorophyll b	is in range	0.58	0.99	3
Membrane stability index	maximize	42.28	61.22	3
Soluble protein	maximize	22.58	57.26	3

Fig. 1

Representative individual leaves of E. angustifolia.

Fig. 2

The effects of various regimes of W and N-P doses on (a–c) root-shoot (R/S) biomass ratio, (d–f) leaf dry matter content (LDMC), (g–i) stomatal conductance (Gs), (j–l) chlorophyll a, and (m–o) chlorophyll b in the leaves of E. angustifolia.

Fig. 3

The effects of various regimes of W and N-P doses on (a–c) membrane stability index (MSI) and (d–f) soluble protein (SP) content in the leaves of E. angustifolia.

Fig. 4

Pearson's correlation coefficient shows the effects of various regimes of W and N-P doses on the various growth responses of E. angustifolia.

Experimental Design, Materials and Methods

Pot Experiment and Design

The pot experiment was conducted at Northwest Agriculture and Forestry University in Yangling, China, in a plastic shed. Coal-mined soil was taken from the Yangchangwan area of Lingwu, Ningxia, China. We used shovels to collect spoil materials at a depth of 50 cm, followed by air drying, bulking, hand crushing, and sifting through a 2 mm mesh. Exactly 14 kg of coal spoil were placed in plastic pots, and each pot was planted with a one-year-old identical E. angustifolia seedling at March 2018. Seedlings were watered daily for the first month to guarantee proper establishment in pots, after which water-stress treatments began and continued until October 2018. The quantity of moisture lost from each pot by transpiration and evaporation was determined using a weighing technique, and daily watering was performed throughout the study period [2]. The N-fertilizer (in the form of urea) was applied at 4 times (¼ N at every one-month interval) and P was given (in the form of triple superphosphate) in two halves.

The experiment was designed using the central composite design (CCD) technique. CCD was used to produce 5 levels of each of three factor, and response surface methodology (RSM) was used to optimize the W–N–P rates. The following equation was used to code the independent variables in this study:

Here, represents the coded value of the different independent variables; indicates the actual value of the independent variable; denotes the actual value of at the center point; and indicates the step change value. In our study, we used round values for N and P doses and nearest 10 for W doses.

The CCD consists of a set of 23 factorial runs, six axial points and six replicates at the center points. In total, 20 treatments were produced and each treatment was repeated 3 times (3 × 20 = 60).

A second-order polynomial model was used to fit the experimental data as below:

Here, response variables are denoted by Y, and the constant coefficient is β0. Interpreted linear coefficients are represented by β1, β2 and β3, and interactivity coefficients by β12, β13 and β23. Quadratic coefficients are denoted by β11, β22 and β33. Coded values of W, N, and P are denoted by A, B, and C.

Assessment of Morphological Parameters

The root-shoot (R/S) biomass ratio (dry weight basis) was measured by dividing below-ground biomass with above-ground biomass. The leaf dry matter content (LDMC) was measured for each leaf (ten leaves chosen from each treatment) as the ratio of the leaf dry weight to the leaf saturated fresh weight [6].

Determination of Stomatal Conductance and Photosynthesis Pigment Contents of E. angustifolia Leaves

Stomatal conductance of E. angustifolia leaves was measured using CIRAS-3 (Portable Photosynthesis System, Amesbury, MA, USA). Measurements were conducted on a sunny day during the hours of 8:30 am to 11:30 am. Chlorophyll (Chl) a and Chl b were extracted from fully expanded leaves as described by Roy et al. [2]. In brief, 100 mg of fresh leaves were placed in test tube and combined with 10 mL of ethanol, acetone and distilled water mixture (4.5:4.5:1) and kept overnight at 4 °C in a dark condition. The absorbance was taken at 645, 663 and 470 nm using a Pharmacia Ultra Spec Pro UV/VIS spectrophotometer (Pharmacia, Cambridge, England), and the contents of Chl a and Chl b were calculated using the following formula of Arnon [3].where

A = Absorbance at specific wavelengths.

V = final volume of chlorophyll extract.

W = fresh weight of tissue extracted.

12.7, 2.69, 22.9 and 4.68 are the constants.

Estimation of Membrane Stability Index and Soluble Proteins

The membrane stability index of E. angustifolia leaves was measured using a conductivity meter. Ten leaf pieces (2 cm in diameter) were cut from fresh leaves and properly cleansed with double distilled water before being put in a test tube containing 15 ml of distilled water. The tubes were kept at room temperature overnight. Water conductance (T1) was measured using an electrical conductivity meter after incubation. The conductivity was measured (T2) again after autoclaving the test tubes for 10 min at 120 °C. The membrane stability index (MSI) was calculated as follows:

The soluble protein was extracted from fresh leaf samples in a solution containing 50 mM sodium phosphate buffer (pH 7.8), followed by centrifugation at 10,000 rpm for 20 min at 4 °C. The supernatant was used to evaluate the soluble protein concentration using the Bradford technique [4] using bovine serum albumin as a standard.

Statistical Analysis and Optimization

The analysis of variance was performed to assess the individual and interaction effects of 3 independent variables (W, N, and P) on a variety of response variables. The numerical data in the tables and figures represent the means and standard errors (SEs) of 3 replicates for each treatment. The optimal W-N-P rate was determined using Derringer's desired function technique and Design Expert statistical software (version 11.0, Stat-Ease, Inc., Minneapolis, MN, USA). Pearson's correlation coefficients were shown as a matrix of correlations.

Ethics Statements

Not applicable.

CRediT authorship contribution statement

Rana Roy: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Jinxin Wang: Conceptualization, Resources, Project administration, Funding acquisition. Tanwne Sarker: Investigation, Visualization. Abdul Kader: Writing – review & editing. Ahmed Khairul Hasan: Writing – review & editing. Emre Babur: Writing – review & editing.

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.

Subject	Agricultural and Biological Science
Specific subject area	Plant Physiology, Plant nutrition
Type of data	Table, Figure
How the data were acquired	The CIRAS-3 (Portable Photosynthesis System, Amesbury, MA, USA) was used to assess the stomatal conductance of Elaeagnus angustifolia seedlings. Chlorophyll (Chl) a and Chl b were extracted from fully expanded leaves as described by Roy et al. [2]. In brief, 100 mg of fresh leaves were placed in a test tube and combined with 10 mL mixture of acetone, ethanol and water (4.5:4.5:1) and kept overnight at 4 °C in a dark condition. The absorbance of the extracts was taken at 645, 663 and 470 nm using a Pharmacia Ultra Spec Pro UV/VIS spectrophotometer (Cambridge, England), and the contents of Chl a and Chl b were calculated according to the formula of Arnon [3]. The membrane stability index of E. angustifolia leaves was assessed with the help of a conductivity meter. Soluble protein concentration was measured in the crude extract by the method of Bradford [4] using bovine serum albumin as a standard. The response surface methodology was used for analyzing the effects of three independent variables [like water (W), nitrogen (N), phosphorus (P)] on the response variables.
Data format	RawAnalyzed
Experimental factors	The W, N, and P were the study's independent variables, while integrated growth performance was the response variable.
Parameters for data collection	Coal-mined spoil was taken from the Yangchangwan coal mining site in Lingwu, Ningxia, China. Shovels were used to gather spoil samples from the surface at a depth of 50 cm, which were then consolidated, dried in the open air, crushed by hand, and sieved through a mesh of 2 mm. One-year-old similiar E. angustifolia seedlings were planted into 14 kg of coal spoils in plastic pots (320 mm upper diameter, 270 mm bottom diameter) in early March 2018. Seedlings were watered daily for the first month to guarantee proper establishment in pots, after which water-stress treatments began and followed for five months. Pots were watered
	daily to keep water content at field capacity for different water treatments, according to the weight method described by Roy et al. [2]. The N-fertilizer (in the form of urea) was applied at 4 times (¼ N at every one month interval) and P was given (in the form of triple superphosphate) in two halves.
Description of data collection	Plant growth attributes were measured at the end of the experimental period. Gas-exchange parameters were taken on a sunny day during the hours of 8:30 am to 11:30 am [5].
Data source location	Northwest A&F University, Yangling (N 34°16′, E 108°4′), Shaanxi, 712100, China.
Data accessibility	Repository name: MendelyDataData identification number: 10.17632/2vfbrdxyf2.2Direct URL to data: https://data.mendeley.com/datasets/2vfbrdxyf2/2
Related research article	R. Roy, J. Wang, M.G. Mostafa, D. Fornara, A. Sikdar, T. Sarker, X. Wang, M. Shah, Fine-tuning of soil water and nutrient fertilizer levels for the ecological restoration of coal-mined spoils using Elaeagnus angustifolia. J. Environ. Manage. 270 (2020) 110,855. https://doi.org/10.1016/j.jenvman.2020.110855