Home Life Sciences Evolution trend of soil fertility in tobacco-planting area of Chenzhou, Hunan Province, China
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Evolution trend of soil fertility in tobacco-planting area of Chenzhou, Hunan Province, China

  • Yansong Xiao , Yahua Liao , Jianlin Hou , Lijuan Li , Taosha Xu , Fengying Ma , Fahui Yu , Zhipeng Tan , Zhihong He , Hong Jian , Hongguang Li and Bin He EMAIL logo
Published/Copyright: November 28, 2022

Abstract

In this study, the data of fertility indicators of soil samples (0–20 cm) in 1980s, 2000 and 2015 in Chenzhou city were used, and the soil integrated fertility index (IFI) was calculated. The results showed that the soil pH was decreased, total nitrogen (TN), organic matter (OM), available phosphorus (AP) and potassium (AK), exchangeable calcium (Ca2+), magnesium (Mg2+) and available copper (Cu) contents were increased, total phosphorus (TP), available sulfur (S) and water-soluble chlorine (Cl) contents were decreased, total potassium (TK), available boron (B), iron (Fe), manganese (Mn) and zinc (Zn) were decreased first and then increased. In 2015, most of the fields were higher in pH, OM, TN, AN, AK, Ca2+, Mg2+, S, Fe, Mn, Cu and Zn, suitable in B, but lower in TP, AP, TK, available molybdenum (Mo) and Cl. Most of the fields were in the middle grade of IFI in 2000 and 2015, and the mean IFI increased from 0.492 to 0.556 from 2000 to 2015. Thus, for soil improvement, more attention should be paid to adjust soil pH, reduce the application of organic, nitrogen and calcium fertilizers, while increase the fertilizer application of other nutrients.

1 Introduction

Soil fertility influences the growth, yield and quality of tobacco [1,2,3,4,5], which is continuously concerned in China. So far, lots of studies have been conducted with many literature studies published on fertility evaluation of tobacco-planting soil; for example, more than 300 literature studies in Chinese could be retrieved by the title or keywords of “tobacco,” “fertility” and “evaluation” in the China National Knowledge Infrastructure (www.dlib.cnki.net/), which almost covered all the tobacco-planting regions in China and at various scales of province, city and county.

Tobacco usually is planted in the drylands with sandy soil texture, but in many areas of southern China, such as Guangdong, Fujian, Jiangxi, Guangxi and south Hunan and Anhui, it is very common that tobacco is planted in rice fields in the high ridge form (no matter what kind of soil texture) and rotated with late rice. Chenzhou city, with a long history of tobacco-planting as early as in 1,593 and where most paddy fields are under tobacco–rice rotation [6], is the most important and typical planting region of flue-cured tobacco with burnt-pure sweet aroma in China [7]. The area of tobacco-planting in Chenzhou is about 2.67 × 104 hm2 in recent years, which plays an important role in ensuring the high-quality raw materials supply of the tobacco industry and the local social and economic sustainable development.

Some literature studies were published about tobacco-planting soil nutrient status in Guizhou [8,9,10,11], which played an important guiding role in improving the soil fertility and quality of tobacco-planting fields. However, there are limited fertility indicators (e.g., pH, organic matter (OM), total nitrogen (TN) and available boron) were involved in the above-mentioned studies, and so far, there are few reports reflecting the changes in soil fertility [9]. Two questions are still unclear and should be answered which are concerned with the influences of tobacco-planting on soil fertility: (1) does tobacco-planting really can improve soil fertility? if so, by how much? Also, a new round of tobacco-planting soil improvement is underway in China; it is urgent and helpful to know the status and evolution of soil fertility; therefore, this study was conducted in order to quantitatively analyze the soil fertility of tobacco-planting fields in Chenzhou in order to provide further scientific guidance for fertilization and soil improvement for tobacco-planting in Chenzhou.

2 Materials and methods

2.1 Data sources of soil fertility indicators

The data of soil fertility indicators used in this study came from three periods, the 2nd national soil survey conducted in the 1980s [12], and tobacco-planting soil surveys conducted in 2000 and 2015, which included 350, 746 and 1,055 soil samples, respectively. The obtained data of the 1980s were the statistic information of all soil samples, and no data of each sample is available.

According to the historical records, the soil sample of the plough layer in each field was collected randomly at 5–8 points with stainless steel soil drill and then mixed completely. The measured soil properties (soil fertility indicators) included OM, TN, total phosphorous (TP), total potassium (TK), available nitrogen (AN), available phosphorous (AP), available potassium (AK), available boron (B), available iron (Fe), available manganese (Mn), available copper (Cu), available zinc (Zn) and available molybdenum (Mo) for all soil samples in the three periods, and pH (H2O), exchangeable calcium (Ca2+), exchangeable magnesium (Mg2+), available sulfur (S), and water-soluble chlorine (Cl) for the soil samples in 2000 and 2015. The detailed determination methods for soil fertility indicators could be found in related literature studies [13,14].

2.2 Quantitative assessment of soil fertility

There are various methods for the assessment of soil fertility; in this study, soil integrated fertility index (IFI) was used to evaluate soil fertility, and IFI was calculated with the fuzzy mathematics method [15]; first, the membership function types and inflection points of indicators were determined; second, the membership values of the indicators were calculated; third, the weights of the indicators were determined, and finally, IFI was calculated for soil samples according to the following formula:

(1) IFI = ( W i × N i ) ,

where W i stands for the weight of indicator i and N i for the membership value of indicator i. IFI is ranged from 0 to 1. The higher the IFI value, the higher the soil fertility. Generally, IFI is divided into five grades according to the equidistant method [16,17]: ≥0.80 (higher), 0.6–0.8 (high), 0.4–0.6 (Middle), 0.2–0.4 (low) and <0.2 (lower).

2.2.1 Grading standards of fertility indicators

There are many reports available in China on the grading standards of soil fertility indicators for tobacco-planting fields. In this study, the indicators were divided into 4 or 5 grades as in Table 1 based on the actual situation of tobacco-planting soils in Hunan Province [18,19] and the corresponding grading of tobacco-planting soils in neighboring areas of Hunan Province [20,21,22,23,24,25,26].

Table 1

Grading standards of soil fertility indicators for tobacco-planting fields

Fertility indicator Grade
Very low Low Suitable High Very high
pH <5.0 5.0–5.5 5.5–7.0 7.0–7.5 ≥7.5
SOM <10 10–15 15–30 30–40 ≥40
TN <0.5 0.5–1 1–2 2–2.5 ≥2.5
AN <65 65–100 100–180 180–240 ≥240
TP <0.5 0.5–1 1–1.5 ≥1.5
AP <10 10–15 15–30 30–40 ≥40
TK <10 10–15 15–20 20–25 ≥25
AK <80 80–150 150–220 220–350 ≥350
Ca2+ <3 3–6 6–10 10–18 ≥18
Mg2+ <0.5 0.5–1.0 1.0–1.6 1.6–3.2 ≥3.2
S <10 10–16 16–30 30–50 ≥50
B <0.15 0.15–0.3 0.3–0.6 0.6–1.0 ≥1.0
Fe <2.5 2.5–4.5 4.5–10 10–60 ≥60
Mn <5 5–10 10–20 20–40 ≥40
Cu <0.2 0.2–0.5 0.5–1.0 1.0–3.0 ≥3.0
Zn <0.5 0.5–1.0 1.0–2.0 2.0–4.0 ≥4.0
Mo <0.1 0.1–0.15 0.15–0.2 0.2–0.3 ≥0.3
Cl <5 5–10 10–30 30–40 ≥40

Notes: in the first column, SOM, TN, TP, TK, g/kg; AN, AP, AK, S, B, Fe, Mn, Cu, Zn, Mo, Cl, mg/kg; Ca2+, cmol(1/2Ca2+)/kg; Mg2+, cmol(1/2Mg2+)/kg. The same is below.

2.2.2 Calculation of membership value of fertility indicator

The membership function types of the fertility indicators were determined according to their effects on the growth, yield and quality of tobacco, among which, pH, OM, TN, AN and Cl were belonged to the parabolas type, while TP, AP, TK, AK, Ca, Mg, S, B, Fe, Mn, Cu, Zn and Mo belonged to the S type [15,27]. Combining with the actual situation of tobacco growing soils in Chenzhou [8,9,10,11,17], the turning points of membership function of each fertility indicator were determined according to expert experience and related literature studies published [5,16,17,18,19,22,26,28] (Table 2; x 1, lower limiting value; x 2, upper limiting value; x 3, lower optimal value; and x 4 , upper optimal value).

Table 2

Membership function types and turning points of soil fertility indicators

Fertility indicator Membership function type Lower limit, x 1 Lower optimal, x 3 Upper optimal, x 4 Upper limit, x 2
pH Parabolas 5 5.5 7 8
SOM Parabolas 15 20 35 45
TN Parabolas 0.5 1 2 2.5
AN Parabolas 65 100 180 240
Cl Parabolas 5 10 30 40
TP S 0.5 1.5
AP S 10 40
TK S 10 25
AK S 80 350
Ca S 3 20
Mg S 0.5 4
S S 16 30
B S 0.2 1.5
Fe S 4.5 70
Mn S 10 50
Cu S 0.5 4
Zn S 1 5
Mo S 0.15     0.4

The membership functions of S-type and parabolic indicators were calculated as follows:

S type f ( x i ) = 0.1 , x i < x 1 , 0.1 + 0.9 × ( x i x 1 ) / ( x 2 x 1 ) , x 1 x i < x 2 , 1 , x i x 2 ,

Parabolas type f ( x i ) = 0.1 , x i < x 1 , x i x 2 , 0.1 + 0.9 × ( x i x 1 ) / ( x 3 x 1 ) , x 1 x i < x 3 , 1 , x 3 x i < x 4 , 1.0 0.9 × ( x i x 4 ) / ( x 2 x 4 ) x 4 x i < x 2 .

2.2.3 Determination of weights of indicators

The weights of indicators were determined by principal component analysis (PCA), which is commonly used in soil fertility and quality evaluation [21,29,30]. In order to avoid the appearance of a negative value in weight, the measured values of pH, OM, TN, TP, TK, AN, AP, AK, AS, Ca, Mg, S, B, Fe, Cu, Zn, Mo and Cl−1 were standardized using Z-score standardization method, while those of Mn were standardized using the negative range normalization method in SPSS software [31,32]. The KMO test coefficient obtained was 0.727, indicating that the data structure was good and the linear correlation between the data was satisfied, which could be used for principal component analysis, while the p value of Bartlett’s test was less than 0.001, rejecting the null hypothesis, indicating that the data could be extracted by principal components. The obtained weights of indicators are shown in Table 3, and the detailed routine for the acquisition of indicator weights was not listed here.

Table 3

Weight values of soil fertility indicators for tobacco-planting fields in Chenzhou

Indicator pH OM TN TP TK AN AP AK Ca
Weight 0.031 0.075 0.065 0.061 0.026 0.058 0.066 0.065 0.032
Indicator Mg S B Fe Mn Cu Zn Mo Cl
Weight 0.039 0.072 0.067 0.014 0.017 0.115 0.114 0.044 0.039

2.3 Data processing and statistics

Microsoft Excel 2016 and IBM Statistics SPSS 22.0 software were used for the statistical analysis of the data, and Duncan test method (p < 0.05) was used for the analysis of variance and multiple comparisons [31,32].

3 Results

3.1 Statistics and comparison of soil fertility indicators

Table 4 shows the statistical results of the indicators in the three periods. From the average values of the indicators, it is shown in Table 3 that pH was reduced insignificantly from 7.18 in 2000 to 6.99 in 2015 (Sig. = 0.198, two-tailed, the same below). OM showed an increasing tendency between 1980s and 2015, which increased by 18.22% from 1980 to 2000 and significantly by 4.64% from 2000 to 2015 (Sig. = 0.008). TN also showed an increasing tendency between 1980 and 2015, which increased by 44.51% from 1980 to 2000 and insignificantly by 1.14% from 2000 to 2015 (Sig. = 0.680). TP showed a decreasing tendency from 1980 to 2015, decreased by 31.39% from the 1980s to 2000 and significantly by 2.13% from 2000 to 2015 (Sig. = 0.001). TK decreased first then increased within the 1980s–2015, decreased by 53.05% from the 1980s to 2000 and then increased significantly by 16.45% from 2000 to 2015 (Sig. = 0.000). AN increased first then decreased within the 1980s–2015, increased by 51.90% from 1980s to 2000 and then decreased significantly by 9.69% from 2000 to 2015 (Sig. = 0.000). AP and AK both showed an increasing tendency within 1980–and 2015, which increased by 216.85 and 57.64% from the 1980s to 2000 and significantly by 29.36 and 70.36% from 2000 to 2015 (Sig. = 0.000). Ca, Mg and Cu all showed an increasing tendency from 2000 to 2015, which increased significantly by 157.31%, 20.44 and 16.58% (Sigs. of Ca and Mg = 0.000, Sig. Cu = 0.003). S and Cl both showed a decreasing tendency from 2000 to 2015, which decreased significantly by 24.08 and 65.00% (both Sigs. = 0.000). B, Fe, Mn and Zn all decreased first then increased within the 1980s–2015, decreased by 57.50, 17.87, 33.88 and 0.28% from 1980s to 2000 and then increased significantly by 223.53, 85.82, 34.52 and 23.68%, respectively, from 2000 to 2015 (Sigs. of B, Fe and Mn = 0.000, Sig. of Zn = 0.008). Mo showed a decreasing tendency within 1980–2015 and decreased by 16.00% and significantly by 23.81% (Sig. = 0.015).

Table 4

Statistic information of soil fertility indicators in 2000 and 2015 in Chenzhou

Indicators 1980s (n = 350) 2000 (n = 746) 2015 (n = 1,055)
Mean ± S.D. C.V. (%) Grade Mean ± S.D. Grade C.V. (%) Skewness Kurtosis Mean ± S.D. Grade C.V. (%) Skewness Kurtosis
pH / / / 7.18 ± 0.94a High 13.09 −0.98 −0.26 6.99 ± 0.93a Suitable 13.30 −0.97 −0.34
OM 38.8 ± 2.70 6.96 High 45.87 ± 13.98A Very high 30.48 0.03 0.03 48.00 ± 14.37B Very high 29.94 0.46 0.51
TN 1.82 ± 0.15 8.24 Suitable 2.63 ± 0.74a Very high 28.14 0.09 −0.03 2.66 ± 0.71a Very high 26.69 0.89 1.40
TP 1.37 ± 0.17 12.41 Suitable 0.94 ± 0.29A Low 30.85 0.33 1.05 0.92 ± 0.28B Low 30.43 0.68 1.25
TK 23.30 ± 1.61 6.91 High 10.94 ± 2.99A Low 27.33 0.47 0.28 12.74 ± 3.83B Low 30.06 0.41 0.10
AN 147.97 ± 7.87 5.32 Suitable 224.76 ± 68.10A High 30.30 0.37 0.74 202.98 ± 54.04B High 26.62 0.32 1.78
AP 8.90 ± 1.00 11.24 Very low 28.20 ± 12.81A Suitable 45.43 1.73 11.17 36.48 ± 17.74B High 48.63 2.33 10.16
AK 76.60 ± 5.63 7.35 Very low 120.75 ± 60.02A Low 49.71 4.79 46.4 205.71 ± 87.47B Suitable 42.52 0.56 1.14
Ca / / / 12.93 ± 5.77A high 44.62 0.86 1.27 33.27 ± 23.27B very high 69.94 8.26 107.76
Mg / / / 1.37 ± 0.78A Suitable 56.93 0.88 0.89 1.65 ± 1.04B High 63.03 0.62 −1.00
S / / / 51.92 ± 24.48a Very high 47.15 1.57 6.91 39.42 ± 35.81b High 90.84 1.25 2.02
B 0.40 ± / Suitable 0.17 ± 0.10A Low 58.82 1.58 6.75 0.55 ± 0.18B Suitable 32.73 1.23 1.23
Fe 93.61 ± / Very high 76.88 ± 49.20A Very high 64.00 1.30 1.38 142.86 ± 89.55B Very high 62.68 2.94 12.37
Mn 37.37 ± / High 24.71 ± 29.99A high 121.37 5.51 46.82 33.24 ± 31.52B High 94.83 12.66 190.18
Cu 3.74 ± / Very high 4.36 ± 4.28A very high 119.22 13.81 240.83 4.70 ± 5.31B Very high 119.59 15.65 318.23
Zn 3.60 ± / High 3.59 ± 5.37A high 123.17 8.12 85.72 4.44 ± 9.77B Very high 207.87 1.05 1.88
Mo 0.25± / High 0.21 ± 0.28a High 133.33 9.95 156.99 0.16 ± 0.23b Low 143.75 8.18 124.05
Cl / / / 18.49 ± 10.34A Suitable 57.44 1.12 3.89 6.30 ± 9.84B Low 156.19 2.72 11.96

Note: (1) Data defaulted; (2) total sample numbers of Ca2+ and Mg2+ in 2000 is 314; (3) values in the same line followed by different uppercase or lowercase letters are significantly different at the 0.01 or 0.05 level: the same as below.

In the 1980s, OM, TN, TK, AN and AK belonged to the low variation (C.V. < 10%,), while TP and AP belonged to the moderate variation (C.V. = 10–100%). In 2000, pH, OM, TN, TP, TK, AN, AP, AK, Ca, Mg, S, B, Fe and Cl belonged to moderate middle variation, while Mn, Cu, Zn and Mo belonged to the strong variation (C.V. > 100%). In 2015, pH, OM, TN, TP, TK, AN, AP, AK, Ca, Mg, S, B, Fe and Mn remained in the moderate middle variation, Cu, Zn and Mo remained in the strong variation (C.V. > 100%), while Cl changed from the moderate variation to the strong one. It also can be seen from Table 4 that, in the values of C.V.s (%), pH, OM, TN, TP, TK, AN, AP, AK, Mg, Fe, Cu and Mo changed smaller (all lower than 15%), while Ca, S, Zn and Cl increased greatly (56.74, 92.67, 68.78 and 171.90%, respectively), and B and Mn decreased greatly (44.36 and 21.87%, respectively).

Table 5 shows the statistical information on fertility indicators in each grade in 2000 and 2015. It can be seen from tobacco-planting suitability, in 2000 and 2015, 26.81 and 26.73% of the samples were suitable in pH, 65.42 and 62.37% of the samples were higher in pH (including high and very higher grades, the same as below), and 7.77 and 10.90% of the samples were lower in pH (including low and very low grades, the same below). Most soil samples are high in OM, TN, AN, AK, Ca2+, S, B, Fe, Cu and Zn, among which, 86.06 and 90.33% of the samples were higher in OM, 79.36 and 83.13% of the samples were higher in TN, 75.60 and 64.55% of the samples were higher in AN, 82.44 and 27.30% of the samples were lower in AK, 67.52 and 85.12% of the samples were higher in Ca2+, 84.05 and 55.55% of the samples were higher in S, 84.05 and 55.55% of the samples were higher in B, 99.20 and 100% of the samples were higher in Fe, 99.06 and 99.72% of the samples were higher in Cu, and 70.64 and 77.73% of the samples were higher in Zn. Table 5 also shows that obvious proportions of soil samples were lower in TP, AP, Mg2+, Mo and Cl in 2000 and 2015, among which, 90.75 and 79.81% of the samples were lower in TK, 60.05 and 59.24% of the samples were lower in TP, 38.61 and 60.00% of the samples were higher in AP, 40.13 and 32.04% of the samples were lower in Mg2+, 49.20 and 66.54% of the samples were lower in Mo, and 21.18 and 76.30% of the samples were lower in Cl.

Table 5

Grade statistic information of soil fertility indicators in 2000 and 2015 in Chenzhou

Indicators Year Very low Low Suitable High Very high Total
No. % No. % No. % No. % No. % No. %
pH 2000 18 2.41 40 5.36 200 26.81 61 8.18 427 57.24 746 100
2015 45 4.27 70 6.64 282 26.73 134 12.70 524 49.67 1,055 100
SOM 2000 1 0.13 5 0.67 98 13.14 135 18.10 507 67.96 746 100
2015 1 0.09 0 0.00 101 9.57 224 21.23 729 69.10 1,055 100
TN 2000 0 0.00 7 0.94 147 19.71 144 19.30 448 60.05 746 100
2015 0 0.00 0 0.00 178 16.87 278 26.35 599 56.78 1,055 100
AN 2000 3 0.40 16 2.14 163 21.85 278 37.27 286 38.34 746 100
2015 1 0.09 17 1.61 356 33.74 438 41.52 243 23.03 1,055 100
TP 2000 59 7.91 389 52.14 271 36.33 27 3.62 / 0.00 746 100
2015 73 6.92 552 52.32 415 39.34 15 1.42 / 0.00 1,055 100
AP 2000 35 4.69 57 7.64 366 49.06 169 22.65 119 15.95 746 100
2015 30 2.84 73 6.92 319 30.24 231 21.90 402 38.10 1,055 100
TK 2000 290 38.87 387 51.88 63 8.45 6 0.80 0 0.00 746 100
2015 206 19.53 636 60.28 167 15.83 34 3.22 12 1.14 1,055 100
AK 2000 131 17.56 484 64.88 100 13.40 22 2.95 9 1.21 746 100
2015 54 5.12 234 22.18 337 31.94 372 35.26 58 5.50 1,055 100
Ca2+ 2000 2 0.64 29 9.24 71 22.61 160 50.96 52 16.56 314 100
2015 2 0.19 47 4.45 108 10.24 234 22.18 664 62.94 1,055 100
Mg2+ 2000 30 9.55 96 30.57 72 22.93 110 35.03 6 1.91 314 100
2015 72 6.82 266 25.21 248 23.51 375 35.55 94 8.91 1,055 100
S 2000 5 0.67 23 3.08 91 12.20 257 34.45 370 49.60 746 100
2015 10 0.95 81 7.68 378 35.83 365 34.60 221 20.95 1,055 100
B 2000 325 43.57 350 46.92 68 9.12 3 0.40 0 0.00 746 100
2015 0 0.00 39 3.70 656 62.18 329 31.18 31 2.94 1,055 100
Fe 2000 0 0.00 1 0.13 5 0.67 368 49.33 372 49.87 746 100
2015 0 0.00 0 0.00 0 0.00 145 13.74 910 86.26 1,055 100
Mn 2000 36 4.83 148 19.84 282 37.80 170 22.79 110 14.75 746 100
2015 49 4.64 88 8.34 278 26.35 362 34.31 278 26.35 1,055 100
Cu 2000 0 0.00 1 0.13 6 0.80 156 20.91 583 78.15 746 100
2015 0 0.00 1 0.09 2 0.19 223 21.14 829 78.58 1,055 100
Zn 2000 0 0.00 24 3.22 195 26.14 389 52.14 138 18.50 746 100
2015 2 0.19 28 2.65 205 19.43 511 48.44 309 29.29 1,055 100
Mo 2000 223 29.89 144 19.30 114 15.28 126 16.89 139 18.63 746 100
2015 507 48.06 195 18.48 118 11.18 102 9.67 133 12.61 1,055 100
Cl 2000 64 8.58 94 12.60 512 68.63 50 6.70 26 3.49 746 100
2015 683 64.74 122 11.56 218 20.66 13 1.23 19 1.80 1,055 100

3.2 Statistics and comparison of soil IFIs

Tables 6 and 7 present the general and grade statistical results of soil IFIs of tobacco-planting fields, respectively. Tables 6 and 7 show that there was no sample with IFI < 0.2 or ≥0.8 both in 2000 and in 2015, IFI were within 0.230–0.740 with a mean of 0.492 in 2000 and 0.320–0.760 with a mean of 0.556 in 2015, and both covered the lower (<0.2), middle (0.2–0.4) and higher grades (0.4–0.6). However, it also can be seen that the average IFI significantly increased by 13.01% (Sig. = 0.000) from 2000 to 2015. IFI in both 2000 and 2015 belonged to the moderate variation, negative skew distribution in 2000 but near normal distribution in 2015, and flat peak distribution in 2000 and 2015 [31,32]. It is shown in Table 7 that the samples with IFI within 0.4–0.6 were most both in 2000 (77.35%) and 2015 (70.52%), and the sample proportions with IFI within 0.2–0.4 and 0.4–0.6 were decreased from 12.87% in 2000 to 1.61% in 2015 and from 77.35% in 2000 to 70.52% in 2015, respectively, while the sample proportion with IFI within 0.6–0.8 increased from 9.79% in 2000 to 27.87% in 2015.

Table 6

Statistic information of soil IFIs in 2000 and 2015 in Chenzhou

Region Year Sample No. Min Max Mean ± S.D. C.V. (%) Skewness Kurtosis
Guiyang 2000 447 0.254 0.715 0.486 ± 0.077A 15.821 0.230 −0.227
2015 560 0.319 0.756 0.559 ± 0.076B 13.65 −0.014 −0.087
Jiahe 2000 100 0.237 0.736 0.492 ± 0.086A 17.512 0.123 0.353
2015 110 0.408 0.737 0.554 ± 0.073B 13.10 0.174 −0.504
Yongxing 2000 73 0.229 0.735 0.498 ± 0.094A 18.901 −0.121 0.368
2015 115 0.375 0.691 0.547 ± 0.063B 11.57 −0.031 −0.345
Anren 2000 35 0.369 0.702 0.520 ± 0.084A 16.149 0.320 −0.338
2015 100 0.438 0.711 0.563 ± 0.061B 10.76 0.164 −0.334
Yizhang 2000 29 0.371 0.613 0.486 ± 0.069A 14.082 0.236 −0.974
2015 96 0.351 0.725 0.548 ± 0.081B 14.70 −0.007 −0.379
Suxian 2000 20 0.396 0.623 0.508 ± 0.071a 13.959 −0.018 −0.979
2015 45 0.347 0.666 0.546 ± 0.077b 14.10 −0.668 0.141
Beihu 2000 12 0.419 0.608 0.526 ± 0.064a 12.220 −0.533 −1.010
2015 17 0.419 0.674 0.587 ± 0.066a 11.30 −0.740 1.100
Linwu 2000 30 0.350 0.637 0.511 ± 0.087a 17.076 −0.186 −0.957
2015 12 0.349 0.654 0.548 ± 0.085a 15.55 −0.942 1.680
Total 2000 746 0.230 0.740 0.492 ± 0.080A 16.26 0.150 −0.087
2015 1,055 0.320 0.760 0.556 ± 0.074B 13.25 −0.040 −0.101

Lowercase and majuscule indicate significant differences at the 0.05 and 0.01 levels, respectively.

Table 7

Grade statistic information of soil IFIs in 2000 and 2015 in Chenzhou

Region Year IFI
Highest (≥0.8) Higher (0.6–0.8) Middle (0.4–0.6) Lower (0.2–0.4) Lowest ( < 0.2)
No. % No. % No. % No. % No. %
Guiyang 2000 0 0 36 8.05 348 77.85 63 12.00 0 0
2015 0 0 158 28.21 393 70.18 9 1.61 0 0
Jiahe 2000 0 0 9 6.00 79 79.00 12 12.00 0 0
2015 0 0 25 22.73 84 76.36 1 0.91 0 0
Yongxing 2000 0 0 10 13.70 54 73.97 9 12.33 0 0
2015 0 0 33 28.00 82 71.30 0 0 0 0
Anren 2000 0 0 5 14.29 27 77.14 3 8.57 0 0
2015 0 0 28 28.00 72 72.00 0 0 0 0
Yizhang 2000 0 0 2 6.90 24 82.76 3 10.34 0 0
2015 0 0 29 30.21 64 66.67 3 3.13 0 0
Suxian 2000 0 0 2 10.00 17 85.00 1 5.00 0 0
2015 0 0 11 24.44 31 68.89 3 6.67 0 0
Beihu 2000 0 0 2 16.67 10 83.33 0 0 0 0
2015 0 0 7 41.18 10 58.82 0 0 0 0
Linwu 2000 0 0 7 23.33 18 60.00 5 16.67 0 0
2015 0 0 3 25.00 8 66.67 1 8.33 0 0
Total 2000 0 0 73 9.79 577 77.35 96 12.87 0 0
2015 0 0 294 27.87 744 70.52 17 1.61 0 0

From 2000 to 2015, IFI was significantly increased by 13.00% in total (Sig. 000), 14.94% in Guiyang (Sig. = 0.000), 12.70% in Jiahe (Sig. = 0.000), 9.74% in Yongxing (Sigs. = 0.000), 8.24% in Anren (Sig. = 0.001), 12.72% in Yizhang (Sig. = 0.001) and 7.47% in Suxian (Sig. = 0.024), while IFI was increased insignificantly by 11.56 and 7.13% in Beihu (Sig. = 0.147) and in Linwu (Sig. = 0.248). Difference significance test results also showed that in 2000 significant difference in IFI was only founded between Guiyang with Linwu (Sig. = 0.009) and Anren (Sig. = 0.014), and no significant difference among the other regions (Sig. = 0.052–0.977 with a mean of 0.330), while in 2015 significant difference in IFI was only founded between Anren and Suxian (Sig. = 0.047), and no significant difference among the other regions (Sig. = 0.060–0.978 with a mean of 0.442).

Table 7 shows that in 2000 and 2015, the numbers of samples were the most in the middle grade of IFI among the total samples, which were 77.35 and 70.52% in total, 77.85 and 70.18% in Guiyang, 79.00 and 76.36% in Jiahe, 73.97 and 71.30% in Yongxing, 77.14 and 72.00% in Anren, 82.76 and 66.67% in Yizhang, 85.00 and 68.89% in Suxian, 83.33 and 58.82% in Beihu, and 60.00 and 66.67% in Linwu. Table 7 also shows that the proportion of samples in the higher grade of IFI was increased from 9.79% in 2000 to 27.87% in 2015 in total, while that of samples in the lower grade of IFI were decreased from 12.87% in 2000 to 1.61% in 2015 in total.

4 Discussion

Soil fertility assessment is one of the most basic works in soil science research, but it is very important for crop planting; therefore, the latest literature studies could be found even now [33,34,35,36,37]. Meanwhile, soil fertility evaluation of tobacco planting also has been reported more so far; however, the fertility indicators mainly involve pH, OM, TN, AN, TP, AP, TK, AK, Ca2+, Mg2+, trace elements of B, Fe, Mg, Cu and Zn, and Cl; in our study, besides the above indicators, S and Mo were also added; thus, it should be said that the fertility indicators are more complete in our study, which would enable the obtained results are closer to the reality and more feasible in guiding the scientific fertilization and soil improvement. It should be pointed out that CEC is also one of the most important indicators of soil fertility; however, as in many studies of soil fertility assessment conducted in China, the determined indicators of soil fertility usually include soil pH and the contents of OM and main nutrients [1,2,3,4,5], which can not only understand the real state of in each index (whether suitable or not for high-quality tobacco planting) in order to guide the reasonable fertilization and soil improvement, but also can evaluate soil comprehensive fertility based on these indicators. The reason why CEC is rarely used is that CEC is a comprehensive indicator of soil fertility, it can only indicate the general level of soil fertility, but cannot provide the real information on soil pH and the contents of OM and nutrient contents, thus cannot guide the reasonable fertilizer application and soil improvement; therefore, CEC was also not used in our study. Meanwhile, soil pH and OM content are the factors that can influence CEC, so CEC can be omitted when pH and OM are used for soil fertility assessment. We add some content in the second revised manuscript. Meanwhile, the Ca/K and Ca/Mg ratios should be considered in the assessment of soil fertility for tobacco-planting, which is important to instruct the scientific application of potassium by reflecting the nutrient antagonism in soils, but currently, there are no threshold values or grade classifications of Ca/K and Ca/Mg for tobacco, so these two ratios were also not used in this study.

As shown in Table 5, 65.42 and 62.37% of the samples were higher in pH (≥7.0) in 2000 and 2015, respectively. The high value of soil pH of tobacco-planting fields in Chenzhou could be attributed to the application of superphosphate fertilizer and the habit of local farmers using fired soil to improve soil quality [38,39], and it may also be related to that tobacco-planting fields in Chenzhou are mostly located in the limestone hill and mountainous area [10], which also resulted in the increases of Ca2+ and Mg2+ from 2000 to 2015, increased significantly by 157.31 and 20.44%, respectively (Table 4).

OM was increased from 38.8 g/kg in the 1980s to 45.87 g/kg in 2000 and to 48.00 g/kg in 2015, the increase was decided by tobacco-rice rotation, straw returning to the field and organic fertilizer application [11,40,41]. AN increased from 147.97 mg/kg in the 1980s to 224.76 mg/kg in 2000 and then decreased to 202.98 mg/kg in 2015, the decrease in AN from 2000 to 2015 is because the higher content of AN in 2000 is unsuitable (suitable grade is 100–180 mg/kg) for the high-quality tobacco [25], and thus, the applied amount of nitrogen fertilizer was reduced gradually [42]. AP increased from 8.90 mg/kg in the 1980s to 28.20 mg/kg in 2000 and to 36.48 mg/kg in 2015; the remarkable continuous increase could be attributed to the long-term excessive application of phosphatic fertilizer by farmers in China due to the cheap price and yield-increase effect [43,44]. AK increased from 76.6 mg/kg in the 1980s to 120.75 mg/kg in 2000 and to 205.71 mg/kg in 2015, the continuous significant increase was contributed by the large amount application of potassium to guarantee the high-quality tobacco leaves usually with high K content [1,2,3,4,5]. S deceased from 51.92 mg/kg in 2000 to 39.42 mg/kg in 2015, which could be attributed to the reduced use of potassium sulfate for tobacco-planting because it caused soil acidification, and usually high content of S would worsen the quality of tobacco leaves [45,46]. Cl decreased from 18.49 mg/kg in 2000 to 6.30 mg/kg in 2015, which could be attributed to the worry that high Cl content could severely deteriorate the quality of tobacco leaves, so chlorine fertilizer is seldom used for tobacco-planting in many regions [47]. Mo decreased from 0.25 mg/kg in the 1980s to 0.21 mg/kg in 2000 and to 0.16 mg/kg in 2015, which may be related to little concern about Mo and little literature on Mo fertilizer application for tobacco-planting in Hunan [48]. Cu increased from 3.74 mg/kg in the 1980s to 4.36 mg/kg in 2000 and to 4.70 mg/kg in 2015, the increase, on the one hand, may be related to the application of livestock and poultry manure which usually containing Cu [49,50], and on the other hand, may be related to the higher Cu content in paddy soil itself [51,52], as for the changes of B, Zn, Mn and Fe, which decreased from the 1980s to 2010 and then increased from 2000 to 2015, which could be attributed to the gradual application of related trace fertilizers in the farmlands [53].

Li et al. [23] compared soil fertility indicators tobacco-growing areas in Kunming in southwest China in 2010 and 2020 and found that from 2010 to 2020, soil pH decreased by 0.15 units. Soil SOM, N, P and K increased by 1.87 g/kg, 7.21 mg/kg, 5.17 mg/kg and 53.05 mg/kg, respectively; the overall soil fertility increased, and these findings are generally similar to the results in our study.

The change of climate may influence the changes in soil chemical properties, so we analyzed the correlation between the mean annual temperature (T) and precipitation (P) with years from 2000 and 2015, and the results showed that the ranges of the two parameters were 17.9–19.1°C and 1069–1854 mm with the means of 18.6°C and 1450 mm, respectively; but there was no significant correlation between T and P with year. The Pearson coefficients were 0.499 (p = 0.082) and 0.224 (p = 0.462), respectively, which means it is hard to clarify the influence of climate change on the change of soil fertility instructors.

Soil fertility affects or determines the growth, yield and quality of tobacco; it also determines the economic benefits of local tobacco-planting farmers, so more and great concerns have been paid continuously to soil improvement in the tobacco-planting regions with higher input and sufficient guarantee, almost all the tobacco-planting regions in China have formulated and implemented the technical regulation of flue-cured tobacco planting, which enable the same measures adopted for tobacco-planting in ploughing, ridging, fertilization, irrigation, film mulching, etc. It may homogenize the soil fertility of tobacco-planting fields in a large region, so no significant difference in IFI was found in this study between most tobacco-planting regions in Chenzhou, in which significant difference in IFI was only founded between Guiyang with Linwu (Sig. = 0.009) and Anren (Sig. = 0.014) in 2000 and between Anren and Suxian (Sig. = 0.047) in 2015. Our study also showed the increasing tendency of soil IFI of tobacco-planting fields in Chenzhou, it was not only proved by the increases in the contents of OM, AN, AP and AK from 1980 to 2000 (Table 1), meanwhile, but also proved by the proportion of fields with the lower grade of IFI was decreased from 12.87% in 2000 to 1.61% in 2015, the proportion of fields with the higher grade of IFI was increased from 9.79% in 2000 to 27.87% in 2015, and IFI meanly increased from 0.492 in 2000 to 0.556 in 2015, increased by 13.00%. However, it should be pointed out that the soil fertility of tobacco-planting fields in Chenzhou is still in the middle level of IFI (0.4–0.6), the proportion of tobacco-planting fields with the middle grade of IFI was the highest (77.35% in 2000 and 70.52% in 2015), and there was no tobacco-plating field with the highest grade of IFI (≥0.8); therefore, the soil fertility of tobacco-planting fields in Chenzhou still needs to be promoted.

The annual fertilization for tobacco fields in Chenzhou is generally as follows from 2000 to 2015: during tobacco growing season, about 178.5 (N), 139.65 (P2O5) and 420 (K2O) kg/ha are applied in the forms of compound fertilizers, and during rice growing season, about 0–375 kg/ha of the compound fertilizer (N:P2O5:K2O = 15:15:15) and 0–75 kg/ha of urea (N = 46.2%) are applied according to the growing status of rice. Meanwhile, this fertilization pattern is almost the same and seldom changed from 2000 to 2015. From the improvement of tobacco planting soil, it can be seen that in 2015 there were 62.37 and 10.90% of the samples were higher and lower in pH. These tobacco-planting fields should be paid attention to the modification of soil alkalinity or acidity; 90.33, 83.13 and 64.55% of the samples were higher in OM, TN and AN, respectively, and these fields need to reduce the use of organic and nitrogen fertilizers. In Ca2+ and Mg2+, 85.12 and 44.45% of the samples were higher, respectively; these fields should be controlled by the application of alkaline substances or calcium and magnesium fertilizers. 27.61, 32.04, 12.99, 66.54 and 76.30% of the samples were lower in AK, Mg2+, Mn, Mo and Cl, respectively; these fields should be applied with the corresponding fertilizers. Usually, tobacco is a chlorine-free crop, but chlorine is also one of the essential nutrients for tobacco growth [5], and some studies conducted in south China have shown that proper application of chlorine fertilizer to Cl-deficient soils could increase the elasticity, oiliness and yield of tobacco leaves while not reducing the quality of tobacco leaves [47,54]. Nevertheless, for the fields with higher contents of other various nutrients, such as S, Fe, Cu and Mn, there is no need to apply the corresponding fertilizers.

It should be pointed out that there is a certain relationship between soil fertility or IFI with the growth, yield and quality of tobacco [7,55,56,57,58]; it is not evaluated in this study but would be conducted in our further research.

5 Conclusion

This study compared the values of topsoil (0–20 cm) fertility indicators of tobacco planting area in Chenzhou in 1980s, 2000 and 2015 and found that soil pH value was decreased, TN, OM, AP, AK, Ca, Mg and Cu contents were increased, TP, S and Cl contents were decreased, and TK, B, Fe, Mn and Zn were decreased first and then an increased. In 2015, most of the samples (44.45–100%) were higher in pH, OM, TN, AN, AK, Ca2+, Mg2+, S, Fe, Mn, Cu and Zn, most of the samples (62.18%) were suitable in B, while most of the samples (59.24–79.81%) were lower in TK, Mo and Cl. Most of the samples were in the middle grade of IFI (77.35% in 2000 and 70.52% in 2015), and the mean IFI was increased by 13.00% from 0.492 in 2000 to 0.556 in 2015, but both still belonged to the middle grade of IFI. Thus, soil fertility still needs to be promoted, and more attentions should be paid to modify of soil acidity and alkalinity, reduce the application of organic, nitrogen, calcium fertilizers, and increase the application of fertilizers of potassium, magnesium, molybdate and chloride according to the real situation of tobacco-planting fields.

  1. Funding information: Authors state no funding involved.

  2. Conflict of interest: Authors state no conflict of interest.

  3. Data availability statement: Due to confidentiality agreements, supporting data can only be made available to bona fide researchers subject to a non-disclosure agreement. Details of the data and how to request access are available from Mrs. Yansong Xiao (Email: 35149517@qq.com, Mobile No: +86-18975717573) at Chenzhou Tobacco Company of Hunan Province.

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Received: 2022-04-02
Revised: 2022-09-12
Accepted: 2022-09-14
Published Online: 2022-11-28

© 2022 Yansong Xiao et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  50. Effects of the interaction of Notch and TLR4 pathways on inflammation and heart function in septic heart
  51. Monosodium iodoacetate-induced subchondral bone microstructure and inflammatory changes in an animal model of osteoarthritis
  52. A rare presentation of type II Abernethy malformation and nephrotic syndrome: Case report and review
  53. Rapid death due to pulmonary epithelioid haemangioendothelioma in several weeks: A case report
  54. Hepatoprotective role of peroxisome proliferator-activated receptor-α in non-cancerous hepatic tissues following transcatheter arterial embolization
  55. Correlation between peripheral blood lymphocyte subpopulations and primary systemic lupus erythematosus
  56. A novel SLC8A1-ALK fusion in lung adenocarcinoma confers sensitivity to alectinib: A case report
  57. β-Hydroxybutyrate upregulates FGF21 expression through inhibition of histone deacetylases in hepatocytes
  58. Identification of metabolic genes for the prediction of prognosis and tumor microenvironment infiltration in early-stage non-small cell lung cancer
  59. BTBD10 inhibits glioma tumorigenesis by downregulating cyclin D1 and p-Akt
  60. Mucormycosis co-infection in COVID-19 patients: An update
  61. Metagenomic next-generation sequencing in diagnosing Pneumocystis jirovecii pneumonia: A case report
  62. Long non-coding RNA HOXB-AS1 is a prognostic marker and promotes hepatocellular carcinoma cells’ proliferation and invasion
  63. Preparation and evaluation of LA-PEG-SPION, a targeted MRI contrast agent for liver cancer
  64. Proteomic analysis of the liver regulating lipid metabolism in Chaohu ducks using two-dimensional electrophoresis
  65. Nasopharyngeal tuberculosis: A case report
  66. Characterization and evaluation of anti-Salmonella enteritidis activity of indigenous probiotic lactobacilli in mice
  67. Aberrant pulmonary immune response of obese mice to periodontal infection
  68. Bacteriospermia – A formidable player in male subfertility
  69. In silico and in vivo analysis of TIPE1 expression in diffuse large B cell lymphoma
  70. Effects of KCa channels on biological behavior of trophoblasts
  71. Interleukin-17A influences the vulnerability rather than the size of established atherosclerotic plaques in apolipoprotein E-deficient mice
  72. Multiple organ failure and death caused by Staphylococcus aureus hip infection: A case report
  73. Prognostic signature related to the immune environment of oral squamous cell carcinoma
  74. Primary and metastatic squamous cell carcinoma of the thyroid gland: Two case reports
  75. Neuroprotective effects of crocin and crocin-loaded niosomes against the paraquat-induced oxidative brain damage in rats
  76. Role of MMP-2 and CD147 in kidney fibrosis
  77. Geometric basis of action potential of skeletal muscle cells and neurons
  78. Babesia microti-induced fulminant sepsis in an immunocompromised host: A case report and the case-specific literature review
  79. Role of cerebellar cortex in associative learning and memory in guinea pigs
  80. Application of metagenomic next-generation sequencing technique for diagnosing a specific case of necrotizing meningoencephalitis caused by human herpesvirus 2
  81. Case report: Quadruple primary malignant neoplasms including esophageal, ureteral, and lung in an elderly male
  82. Long non-coding RNA NEAT1 promotes angiogenesis in hepatoma carcinoma via the miR-125a-5p/VEGF pathway
  83. Osteogenic differentiation of periodontal membrane stem cells in inflammatory environments
  84. Knockdown of SHMT2 enhances the sensitivity of gastric cancer cells to radiotherapy through the Wnt/β-catenin pathway
  85. Continuous renal replacement therapy combined with double filtration plasmapheresis in the treatment of severe lupus complicated by serious bacterial infections in children: A case report
  86. Simultaneous triple primary malignancies, including bladder cancer, lymphoma, and lung cancer, in an elderly male: A case report
  87. Preclinical immunogenicity assessment of a cell-based inactivated whole-virion H5N1 influenza vaccine
  88. One case of iodine-125 therapy – A new minimally invasive treatment of intrahepatic cholangiocarcinoma
  89. S1P promotes corneal trigeminal neuron differentiation and corneal nerve repair via upregulating nerve growth factor expression in a mouse model
  90. Early cancer detection by a targeted methylation assay of circulating tumor DNA in plasma
  91. Calcifying nanoparticles initiate the calcification process of mesenchymal stem cells in vitro through the activation of the TGF-β1/Smad signaling pathway and promote the decay of echinococcosis
  92. Evaluation of prognostic markers in patients infected with SARS-CoV-2
  93. N6-Methyladenosine-related alternative splicing events play a role in bladder cancer
  94. Characterization of the structural, oxidative, and immunological features of testis tissue from Zucker diabetic fatty rats
  95. Effects of glucose and osmotic pressure on the proliferation and cell cycle of human chorionic trophoblast cells
  96. Investigation of genotype diversity of 7,804 norovirus sequences in humans and animals of China
  97. Characteristics and karyotype analysis of a patient with turner syndrome complicated with multiple-site tumors: A case report
  98. Aggravated renal fibrosis is positively associated with the activation of HMGB1-TLR2/4 signaling in STZ-induced diabetic mice
  99. Distribution characteristics of SARS-CoV-2 IgM/IgG in false-positive results detected by chemiluminescent immunoassay
  100. SRPX2 attenuated oxygen–glucose deprivation and reperfusion-induced injury in cardiomyocytes via alleviating endoplasmic reticulum stress-induced apoptosis through targeting PI3K/Akt/mTOR axis
  101. Aquaporin-8 overexpression is involved in vascular structure and function changes in placentas of gestational diabetes mellitus patients
  102. Relationship between CRP gene polymorphisms and ischemic stroke risk: A systematic review and meta-analysis
  103. Effects of growth hormone on lipid metabolism and sexual development in pubertal obese male rats
  104. Cloning and identification of the CTLA-4IgV gene and functional application of vaccine in Xinjiang sheep
  105. Antitumor activity of RUNX3: Upregulation of E-cadherin and downregulation of the epithelial–mesenchymal transition in clear-cell renal cell carcinoma
  106. PHF8 promotes osteogenic differentiation of BMSCs in old rat with osteoporosis by regulating Wnt/β-catenin pathway
  107. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
  108. Bilateral dacryoadenitis in adult-onset Still’s disease: A case report
  109. A novel association between Bmi-1 protein expression and the SUVmax obtained by 18F-FDG PET/CT in patients with gastric adenocarcinoma
  110. The role of erythrocytes and erythroid progenitor cells in tumors
  111. Relationship between platelet activation markers and spontaneous abortion: A meta-analysis
  112. Abnormal methylation caused by folic acid deficiency in neural tube defects
  113. Silencing TLR4 using an ultrasound-targeted microbubble destruction-based shRNA system reduces ischemia-induced seizures in hyperglycemic rats
  114. Plant Sciences
  115. Seasonal succession of bacterial communities in cultured Caulerpa lentillifera detected by high-throughput sequencing
  116. Cloning and prokaryotic expression of WRKY48 from Caragana intermedia
  117. Novel Brassica hybrids with different resistance to Leptosphaeria maculans reveal unbalanced rDNA signal patterns
  118. Application of exogenous auxin and gibberellin regulates the bolting of lettuce (Lactuca sativa L.)
  119. Phytoremediation of pollutants from wastewater: A concise review
  120. Genome-wide identification and characterization of NBS-encoding genes in the sweet potato wild ancestor Ipomoea trifida (H.B.K.)
  121. Alleviative effects of magnetic Fe3O4 nanoparticles on the physiological toxicity of 3-nitrophenol to rice (Oryza sativa L.) seedlings
  122. Selection and functional identification of Dof genes expressed in response to nitrogen in Populus simonii × Populus nigra
  123. Study on pecan seed germination influenced by seed endocarp
  124. Identification of active compounds in Ophiopogonis Radix from different geographical origins by UPLC-Q/TOF-MS combined with GC-MS approaches
  125. The entire chloroplast genome sequence of Asparagus cochinchinensis and genetic comparison to Asparagus species
  126. Genome-wide identification of MAPK family genes and their response to abiotic stresses in tea plant (Camellia sinensis)
  127. Selection and validation of reference genes for RT-qPCR analysis of different organs at various development stages in Caragana intermedia
  128. Cloning and expression analysis of SERK1 gene in Diospyros lotus
  129. Integrated metabolomic and transcriptomic profiling revealed coping mechanisms of the edible and medicinal homologous plant Plantago asiatica L. cadmium resistance
  130. A missense variant in NCF1 is associated with susceptibility to unexplained recurrent spontaneous abortion
  131. Assessment of drought tolerance indices in faba bean genotypes under different irrigation regimes
  132. The entire chloroplast genome sequence of Asparagus setaceus (Kunth) Jessop: Genome structure, gene composition, and phylogenetic analysis in Asparagaceae
  133. Food Science
  134. Dietary food additive monosodium glutamate with or without high-lipid diet induces spleen anomaly: A mechanistic approach on rat model
  135. Binge eating disorder during COVID-19
  136. Potential of honey against the onset of autoimmune diabetes and its associated nephropathy, pancreatitis, and retinopathy in type 1 diabetic animal model
  137. FTO gene expression in diet-induced obesity is downregulated by Solanum fruit supplementation
  138. Physical activity enhances fecal lactobacilli in rats chronically drinking sweetened cola beverage
  139. Supercritical CO2 extraction, chemical composition, and antioxidant effects of Coreopsis tinctoria Nutt. oleoresin
  140. Functional constituents of plant-based foods boost immunity against acute and chronic disorders
  141. Effect of selenium and methods of protein extraction on the proteomic profile of Saccharomyces yeast
  142. Microbial diversity of milk ghee in southern Gansu and its effect on the formation of ghee flavor compounds
  143. Ecology and Environmental Sciences
  144. Effects of heavy metals on bacterial community surrounding Bijiashan mining area located in northwest China
  145. Microorganism community composition analysis coupling with 15N tracer experiments reveals the nitrification rate and N2O emissions in low pH soils in Southern China
  146. Genetic diversity and population structure of Cinnamomum balansae Lecomte inferred by microsatellites
  147. Preliminary screening of microplastic contamination in different marine fish species of Taif market, Saudi Arabia
  148. Plant volatile organic compounds attractive to Lygus pratensis
  149. Effects of organic materials on soil bacterial community structure in long-term continuous cropping of tomato in greenhouse
  150. Effects of soil treated fungicide fluopimomide on tomato (Solanum lycopersicum L.) disease control and plant growth
  151. Prevalence of Yersinia pestis among rodents captured in a semi-arid tropical ecosystem of south-western Zimbabwe
  152. Effects of irrigation and nitrogen fertilization on mitigating salt-induced Na+ toxicity and sustaining sea rice growth
  153. Bioengineering and Biotechnology
  154. Poly-l-lysine-caused cell adhesion induces pyroptosis in THP-1 monocytes
  155. Development of alkaline phosphatase-scFv and its use for one-step enzyme-linked immunosorbent assay for His-tagged protein detection
  156. Development and validation of a predictive model for immune-related genes in patients with tongue squamous cell carcinoma
  157. Agriculture
  158. Effects of chemical-based fertilizer replacement with biochar-based fertilizer on albic soil nutrient content and maize yield
  159. Genome-wide identification and expression analysis of CPP-like gene family in Triticum aestivum L. under different hormone and stress conditions
  160. Agronomic and economic performance of mung bean (Vigna radiata L.) varieties in response to rates of blended NPS fertilizer in Kindo Koysha district, Southern Ethiopia
  161. Influence of furrow irrigation regime on the yield and water consumption indicators of winter wheat based on a multi-level fuzzy comprehensive evaluation
  162. Discovery of exercise-related genes and pathway analysis based on comparative genomes of Mongolian originated Abaga and Wushen horse
  163. Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review
  164. Evolution trend of soil fertility in tobacco-planting area of Chenzhou, Hunan Province, China
  165. Animal Sciences
  166. Morphological and molecular characterization of Tatera indica Hardwicke 1807 (Rodentia: Muridae) from Pothwar, Pakistan
  167. Research on meat quality of Qianhua Mutton Merino sheep and Small-tail Han sheep
  168. SI: A Scientific Memoir
  169. Suggestions on leading an academic research laboratory group
  170. My scientific genealogy and the Toronto ACDC Laboratory, 1988–2022
  171. Erratum
  172. Erratum to “Changes of immune cells in patients with hepatocellular carcinoma treated by radiofrequency ablation and hepatectomy, a pilot study”
  173. Erratum to “A two-microRNA signature predicts the progression of male thyroid cancer”
  174. Retraction
  175. Retraction of “Lidocaine has antitumor effect on hepatocellular carcinoma via the circ_DYNC1H1/miR-520a-3p/USP14 axis”
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