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Article

Manure Application Is the Key to Improving Soil Quality of New Terraces

by
Xiaopeng Shi
1,
Xin Song
1,
Guibin Zhao
2,
Qifeng Yang
3,
Lynette K. Abbott
4 and
Fengmin Li
1,*
1
State Key Laboratory of Grassland Agroecosystem, Institute of Arid Agroecology, College of Ecology, Lanzhou University, Lanzhou 730000, China
2
Gansu Provincial Agricultural Technology Extension Station, Lanzhou 730000, China
3
Gansu Provincial Department of Agriculture and Rural Affairs, Lanzhou 730000, China
4
UWA Institute of Agriculture, UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA 6000, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 15166; https://doi.org/10.3390/su142215166
Submission received: 28 September 2022 / Revised: 27 October 2022 / Accepted: 11 November 2022 / Published: 16 November 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
Building level terraces is a crucial strategy for agriculture development in mountainous areas. There have been many studies on improving the soil quality of terraces, but the main factors involved are still unclear. We conducted an 18-year long-term experiment on a newly built terrace with four fertilization treatments: applied mineral nitrogen and phosphorus fertilizer (NP), applied sheep manure (M), applied sheep manure combined with mineral nitrogen and phosphorus fertilizer (MNP), and an unfertilized control (CK). A soil quality index (SQI) was used to evaluate the dynamic evolution of soil quality in the terrace for these fertilization treatments, and the relationship between soil quality and crop yield was investigated. A total data set (TDS) and a minimum data set (MDS) were used to calculate the SQIs according to the linear scoring method and the nonlinear scoring method of soil indicators, respectively. The results showed that the SQI for all treatments increased over time, and both the SQI and crop yield were significantly increased by fertilization treatments. The SQI of all three fertilized treatments in the sixth rotation cycle increased by 38–313% compared to the control in the first rotation cycle (3 years). There was no significant difference in the SQI between the M and MNP, but it was significantly higher than for both the NP and CK. During the 18 year experimental period, the SQI for the M and MNP treatments showed an upward trend, while it tended to be stable after initially increasing for the NP and CK treatments. For each treatment, the SQI calculated by the linear and nonlinear scoring methods using the MDS and TDS were all significantly positively correlated, and were also significantly positively correlated with crop yield. Overall, the soil quality in the terrace was increased by fertilization; however, the application of manure was the key to a rapid increase in soil quality, and the SQI measurements demonstrated a clear link between the soil quality of the terrace and crop yield.

1. Introduction

Building level terraces is a crucial strategy to improve agricultural development in mountainous areas, which is of great significance for global sustainable development. However, the topsoil of newly built terraces is severely disturbed, and the nutrient content in the topsoil is usually very low, making it difficult to obtain a similar yield level to conventional farmland in the short term [1,2,3,4]. Improving the soil quality of newly built terraces has always been a major problem for mountain agriculture.
Soil is the life support system on Earth, and improving soil quality is vital for developing terraced agroecosystems. The application of mineral fertilizer or manure, or a combination of both, can increase crop yield and soil nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) [5,6,7,8,9]. Manure application can also improve soil organic matter (SOM) and soil physical properties [10,11,12,13]. Therefore, fertilization measures can improve soil quality and crop yield of newly built terraces. However, the long-term application of mineral fertilizer alone can lead to soil degradation [14,15,16], and imbalanced fertilization measures may lead to a deficit of other nutrients in the soil, which will seriously threaten the sustainability of agroecosystems. While the application of mineral fertilizer or manure or both is a common management practice for improving the soil quality of terraces [17,18,19,20], most studies have only lasted for a few years. It is still unclear how soil quality evolves in terraces following their establishment under different fertilization managements on long-term time scales. Therefore, it is difficult to obtain sufficient evidence to support the analysis of soil quality-related factors.
Soil quality refers to the ability of soil to play a role in the ecosystem, maintain productivity and environmental quality, and promote plant growth and animal health [21]. It has been widely used to evaluate the sustainable management of agroecosystems [22]. Soil quality can be dynamically assessed by comparing the trend of soil quality over time at the same study site [23,24]. Individual soil attributes cannot fully reflect soil quality, and soil quality requires comprehensive and systematic evaluation based on soil properties [25]. Currently, many methods and tools are used in soil quality assessment, such as Test Kit cards, soil management assessment frameworks, and the SQI. Due to the complexity and variability of the soil system, there is no unified soil quality assessment method [26,27,28]. Among them, the SQI is the most widely used method because it can quantitatively analyze soil quality flexibly and integrate the soil’s physical, chemical, and biological properties [29]. Based on a comprehensive assessment of indicators of soil quality and their weightings, the SQI integrates soil indicators into a simplified format that can improve understanding of soil processes and inform appropriate management and policy interventions [30]. Therefore, the SQI can be used to monitor the temporal dynamics of soil properties and functions and quantify the long-term impact of land-use change and soil management strategies [31].
Through an 18 year long-term cropping rotation field experiment (three years for each crop rotation cycle, with a total of six rotation cycles), this study assessed the dynamics of soil quality from the establishment of a newly built terrace and the relationship between SQI and crop yield. The main purpose was to provide a scientific basis for effectively improving the soil quality following the establishment of newly built terraces under different fertilizer regimes, and for promoting sustainable production in mountainous regions.

2. Materials and Methods

2.1. Study Site

This study was conducted in Zhonglianchuan (36°02′ N, 104°24′ E, 2400 m asl.), Yuzhong County, Gansu Province, China. The local area, which is a typical dryland agricultural area, belongs to the semi-arid region in the Loess Plateau of China. The annual average temperature in this area is 6.5 °C, and the monthly average maximum temperature and minimum temperatures are 19 °C and −8.0 °C, respectively [32]. During the experimental period from 2004 to 2021, the average annual precipitation was 331 mm. The precipitation data comes from the meteorological station data at the study site. Low mountains and hills dominate this area, and terraces are central to the establishment of dryland farming.
A sloping cropland was built into a level terrace in 2002 and was fallowed for a year. The experiment commenced in 2004. According to the FAO taxonomy, the soil is classified as Heima (Calcic Kastanozems) and is rich in potassium, and the average available potassium content was 183 mg kg−1 [33]. Before the experiment, the soil was barren, with a 2.25 g kg−1 soil organic carbon (SOC), a 0.21 g kg−1 soil total nitrogen (TN), a 2.30 mg kg−1 inorganic nitrogen (IN), a 0.57 mg kg−1 available phosphorus (AP), and a pH of 9.0 (1:2.5 soil to water).

2.2. Experimental Design

Four fertilization treatments were designed: unfertilized control, CK; applied mineral nitrogen (N) and phosphorus (P) fertilizer alone, NP; applied sheep manure alone, M; and applied the same amount of manure as the M treatment plus the same amount of the mineral N and P fertilizer as the NP treatment; and the MNP. Each treatment had three replicates with an area of 29.25 m2 (6.5 m × 4.5 m). During the 18 years from 2004 to 2021, 70 kg N ha−1 yr−1 and 15.7 kg P ha−1 yr−1 were applied to the NP treatment. For the M treatment, from 2004 to 2006, 20 t ha−1 yr−1 sheep manure was applied, and from 2006 to 2021 10 t ha−1 yr−1 sheep manure was applied except in 2010 (where 5 t ha−1 was applied) to avoid overuse and waste. In the MNP treatment, the manure applied each year was equal to that applied to the M treatment, and the mineral N and P fertilizers applied were equal to that applied to the NP treatment. The average nutrient content of the sheep manure applied was: organic carbon (C) 150 g kg−1, total N 10 g kg−1, and total P 0.84 g kg−1; pH 8.1, electrical conductivity 4.87 dS m−1 [34]; total solids 38.5%, volatile solids 30.1% [35]. The contents of Cr, Cd, Pb, As, and Hg ranged from 3.2 to 40.1, 0.5 to 4.7, 2.4 to 70.8, 0.5 to 4.9, and 0.02 to 2.39 mg kg−1 [36]. Firmicutes, Proteobacteria, and Bacteroidetes were the dominant phyla of bacterial communities in sheep manure, while Ascomycota was the dominant phyla of fungal communities [37]. From 2004 onwards, field pea (P. sativum L.), spring wheat (T. aestivuml.), and potato (S. tuberosum L.) were planted in a rotation sequence, and the crops were harvested in sequence annually. And crop was ripe once a year in this study area.

2.3. Sampling and Measurement

Grain yields of peas and wheat and potato tuber yields were measured at crop maturity. Peas and wheat were harvested at ground level. After each harvest, the aboveground crop parts were removed from the plot. The yield was determined after drying at 70 °C to constant weight.
From 2004 to 2021, soil samples (0–20 cm) were taken annually after the crop was harvested. Five soil samples were taken randomly and mixed into one soil sample for each plot. The soil samples were then passed through a 2 mm mesh sieve. Part of the sieved fresh soil was used to determine the IN and soil microbial biomass carbon (MBC). The remaining sieved soil was air-dried. A part of the dried soil was used to determine soil pH and AP. Another part was used to determine the SOC, TN, and soil total phosphorus (TP) after passing through a 0.15 mm sieve. Soil samples were taken at 20 cm intervals and dried at 105 °C to determine soil water content at a 0 to 2 m depth. Soil water storage (SWS, mm) was calculated as SWC × BD × h × 10, where SWC is soil gravity water content (g g−1), BD is soil bulk density (g cm−3), and h is soil depth (cm).
We used a 2 M KCl to extract the soil samples and then measured the IN in the filtrate with an auto-flow injection system (SKALAR, Tinstraat, The Netherlands). The soil’s MBC was determined by the chloroform fumigation-extraction method [38]. The Olsen-P method was used to measure AP [39]. The Kjeldahl method was used to measure TN [40]. The molybdate colorimetric method was used to measure TP [41]. The SOC was measured by the Walkley and Black dichromate oxidation method [42]. A glass electrode measured soil pH in a 1:2.5 soil: water suspension.

2.4. Soil Quality Assessment

This study used the linear scoring method and the nonlinear scoring method [28,43] for both the total data set (TDS) and the minimum data set (MDS) to calculate the SQI to evaluate soil quality [44]. We first performed a Pearson correlation analysis on the relationship between soil attributes and crop yield (Table 1). Soil attributes that significantly correlated with crop yield were selected for the TDS. A standardized principal component analysis (PCA) was then performed on the soil properties selected in the TDS to identify the potential soil indicators representing the MDS [26,29]. In this process, only the principal components with eigenvalue ≥1 and can explain at least 5% of the total variation of the dataset are selected for MDS identification. Highly weighted variables were defined as those with absolute values within 10% of the highest factor loading. If there was only one highly weighted indicator in the principal component, it was directly selected as the MDS. If there was more than one indicator, the redundancy of indicators was determined according to the correlation between indicators. If the indicators were not related, each indicator was retained in the MDS. Otherwise, only the indicator with the highest load was selected for the MDS.
After the MDS was determined, the soil indicators were transformed into dimensionless indicators by using linear and nonlinear equation functions [28]. In this study, the pH was greater than 7.5 and was significantly negatively correlated with yield, so the scoring equation of ‘less is better’ was used. Other indicators were significantly positively correlated with yield, so the ‘more is better’ scoring equation was used.
For linear scoring, ‘more is better’ (Equation (1)) or ‘less is better’ (Equation (2)) functions were used as follows [45]:
S L = X i − X m i n X m a x − X m i n
S L = X m a x − X i X m a x − X m i n
where SL is the linear score of the soil indicator, ranging from 0 to 1, Xi is the soil indicator value, and Xmax and Xmin are the maximum and minimum values of each soil indicator value, respectively.
For non-linear scoring, the following sigmoidal function (Equation (3)) was used as follows [26]:
S N L = 1 1 + X i X m b
where SNL is the nonlinear score of the soil indicator, Xi is the selected soil variable value, and Xm is the mean value of each soil variable. b is the slope of the equation, which is −2.5 for ‘more is better’ indicators and 2.5 for ‘less is better’ indicators.
After the score conversion of each soil indicator was completed, the SQI was calculated according to the following equation (Equation (4)) [26,29]:
SQI = ∑ i = 1 n W i × S i
where Wi is the weight of each soil indicator and is the ratio of the communality of a variable to the total communality of all variables in the PCA. Si is the score of each soil indicator. n is the number of soil indicators in the TDS or MDS.

2.5. Statistical Analyses

A one-way analysis of variance (ANOVA) was performed to test the effect of the fertilization treatments on SQI in the sixth crop rotation cycle. A Pearson correlation analysis was used to analyze the relationship between the soil attributes and crop yield. The software used was an SPSS 17.0 version (SPSS Inc., Chicago, IL, USA). An Origin 9.2 (Originlab originpro 2015, Northampton, MA, USA) was used for linear fitting between the SQI and crop yield, and for creating graphs.

3. Results

3.1. Effects of Fertilization on Soil Properties

After 18 years in this long-term experiment, fertilization significantly increased the TP, IN, and AP, and significantly decreased the soil’s pH (p < 0.05) (Table 2). The SOC and TN increased in all treatments. The SOC, TN, and MBC in the M and MNP treatments were significantly higher than those in NP and CK (p < 0.05). The SOC, TN, and MBC in the NP treatment were higher than in CK, but the difference was insignificant. Compared with the initial soil pH, it decreased in the CK treatment after 18 years of continuous crop production. Soil water storage at a 0 to 2 m soil depth decreased in all treatments relative to the initial value, but it was higher in soil from the M and MNP treatments than in soils from NP and CK.

3.2. Development of the TDS and MDS

This study considered a total of 11 soil indicators, including SOC, TN, TP, C:N, C:P, N:P, IN, AP, MBC, pH, and soil water storage at a 0–2 m depth (SWS). A Pearson correlation analysis showed no significant correlation between C:N and crop yield (Table 1). There was a significant negative correlation between soil pH and crop yield. Other soil indicators were significantly positively correlated with crop yield. Therefore, the TDS excluded the C:N ratio, but 10 soil indicators were included.
The PCA analysis showed that the eigenvalues of the first three principal components (PCs) were greater than one. The first three PCs coordinated 78.15% of the total variations among 10 soil attributes: PC1, PC2, and PC3 accounted for 57.21%, 10.88%, and 10.06% of total variations, respectively (Table 3). In PC1, the factor loading of the SOC was the largest, and TN, C:P, and N:P were high-load variables. However, the TN, C:P, and N:P were significantly positively correlated with SOC (Table 1). Thus, only the SOC in PC1 was selected for the MDS. There was only one high load variable in PC2 and PC3 (the TP and SWS, respectively), so both were selected for the MDS. Therefore, the MDS included three variables in total: the SOC, TP, and SWS.

3.3. Comparison and Dynamics of the SQI

The SQI obtained by linear and nonlinear scoring methods using the MDS were SQI (ls-MDS) and SQI (nls-MDS), respectively (Figure 1a,c), and the SQI obtained by linear and nonlinear scoring methods using the TDS were SQI (ls-TDS) and SQI (nls-TDS), respectively (Figure 1b,d). There was a significantly positive correlation between the SQI (ls-MDS), SQI (nls-MDS), SQI (ls-TDS), and SQI (nls-TDS), and the correlation coefficient was greater than 0.97 (Table 4). Except for the first rotation cycle (RC1), the SQI in the CK treatment was the lowest in the second to sixth crop rotation cycles (Figure 1). From the second rotation cycle, the SQI in the NP treatment was higher than that in the CK, and the difference increased with the time extension and, from the fifth rotation cycle, the SQI in the NP treatment tended to be stable. During the six rotation cycles, the SQI in the M and MNP treatments were significantly greater than that of the CK and NP treatments and showed an overall upward trend. The SQI in MNP was slightly higher than that of the M treatment, but the difference decreased with time.
The SQI (ls-MDS), SQI (nls-MDS), SQI (ls-TDS), and SQI (nls-TDS) in CK treatment increased by 38%, 18%, 25%, and 27%, respectively, in the sixth rotation cycle (RC6) compared RC1. Compared with the SQI of CK in RC1, the SQI (ls-MDS) of NP, M, and MNP in RC6 increased by 119%, 281%, and 312%, respectively; the SQI (nls-MDS) increased by 38%, 94%, and 94%, respectively; the SQI (ls-TDS) increased by 113%, 281%, and 294%, respectively; and the SQI (nls-TDS) increased by 88%, 173%, and 173%, respectively. In RC6, the SQI (ls-MDS, nls-MDS, ls-TDS, nls-TDS) of the MNP and M treatments were significantly higher than that of the NP and CK treatments (p < 0.05) (Figure 2), and the SQI of the NP treatment was significantly higher than that of the CK treatment (p < 0.05). Although the SQI in MNP was slightly higher than M, there was no significant difference between the M and MNP treatments (p > 0.05). During the experiment, the SQI in each rotation cycle was significantly positively correlated with the total crop yield of peas, wheat, and potatoes in the corresponding rotation cycle (Figure 3). The correlation coefficients between the SQI (ls-MDS, nls-MDS, ls-TDS, and nls-TDS) and crop yield were about 0.69–0.70.

4. Discussion

Fertilization increased the TP, IN, and AP in the soil, which can be attributed to the input of the N and P from fertilizers. Compared with the initial value, the soil pH decreased and AP increased in the unfertilized soil after 18 years which could be attributed to the organic acids secreted by crop roots alleviating soil alkalinity and releasing soil P [46]. The SOC increased in all treatments, which can be attributed to the higher input of organic carbon into the soil compared to loss through microbial decomposition. Other long-term experiments have found similar changes in soil C [7,47,48]. Soil C and N cycles are strongly coupled, and the effect of long-term fertilization on the TN was similar to that on SOC. The increase in the TN may be partially related to the biological N2 fixation of the legume crops (pea). In addition, the SOC, TN, and MBC could be significantly increased by applying manure compared with applying no fertilizer or mineral fertilizer alone [5,49]. The application of manure affects the structure and composition of the microbial community, such as bacterial and fungal abundances [50,51]. Additionally, soil microorganisms had a vital effect on the SOC [52], thus the change in microbial community structure may further affect SOC and agroecosystem functioning. Due to low precipitation and high evapotranspiration in our study area, the SWS decreased in all treatments. The SWS in the M and MNP treatments were higher than in the NP and CK. This was likely to be because the increase in the SOC contributed to soil water retention [53].
The SOC reflects the SOM which affects soil physical, chemical, and biological properties [53,54], such as aggregate stability, soil compactness, nutrient availability, nutrient sources required for microbial growth, and water retention. Therefore, SOC is the most crucial soil indicator. In this study, the TDS was constructed based on eleven soil attribute indicators (Table 1) determined during the 18-year field experimental period. Among these indicators, the SOC and SWS reflected soil physical properties, the MBC reflected soil biological properties, and other indicators reflected soil chemical properties. Thus, although other soil physical properties, such as soil bulk density, porosity, and soil water holding capacity were not measured in this study, the TDS would have reflected the physical, chemical, and biological properties of the soil. The MDS that was developed included the three soil attribute indicators: SOC, TP, and SWS. In most studies, the SOC has been included in the MDS when evaluating the SQI, while other properties vary greatly [25,29,55]. This is because soil quality is impacted by specific soil types, research sites, and climatic conditions.
In this study, the SQI (ls-TDS) was better for distinguishing among differences in soil quality at different stages of crop rotation compared with the SQI (ls-MDS) and SQI (nls-MDS), and SQI (nls-TDS), especially regarding the M and MNP treatments (Figure 1). This is because the TDS includes more comprehensive soil attribute indicators than the MDS. Qi, Darilek [56] also found that the TDS was more accurate and comprehensive than the MDS, but the MDS still fully represented the TDS in evaluating the SQI and could save time and cost. In soil quality assessments, the non-linear scoring method is more sensitive than the linear scoring method [28,43]. However, the difference between the SQI (ls-MDS, ls-TDS) and SQI (nls-MDS, nls-TDS) was not significant in our study. The SQI (nls-TDS, nls-MDS) was higher than the SQI (ls-TDS, ls-MDS), which may be attributed to the different scoring methods. There was a significant correlation between the SQI (Table 4). Therefore, for either the MDS or TDS, irrespective of whether linear or nonlinear score methods were used to calculate the SQI, it can be used to evaluate soil quality in terraced fields.
In the second and third rotation cycles, the SQI of the M and MNP treatments did not increase, which was related to the reduction of the applied amount of manure, because the application of manure would substantially affect the SOC and TP content [5,7]. The application rate of manure was 20 t ha−1yr−1 in RC1, and 10 t ha−1yr−1 in all years thereafter, except 2010, when the application rate was 5 t ha−1. In RC6, the SQI of the M and MNP treatments was significantly higher than that of the NP treatment (Figure 2). This indicates that the application of manure could significantly increase soil quality [14]. Moreover, the SOC weights were relatively higher in the MDS and TDS (0.487 and 0.121, respectively). So, the SQI is sensitive to the application of manure. The SQI for MNP was the highest, which was related to the highest fertilizer input. Although no fertilizer was applied to the control during the experiment, its SQI also increased slightly. This demonstrated the positive ecological functions of terraces in improving soil quality [1,2,3,4]. On the other hand, our study was a field pea-wheat-potato rotation system, and crop rotation could also improve soil quality [57]. Therefore, the increase in soil quality in the unfertilized soil may also be related to crop rotation in this study.
The SQI (ls-MDS), SQI (nls-MDS), SQI (ls-TDS), and SQI (nls-TDS) were significantly positively correlated to crop yields (Figure 3). Studies have shown that the SQI was significantly positively correlated with wheat yield [29], maize yield [28], and rice yield [45]. These results were highly similar to our study. This is because the soil attribute indicators in the TDS and MDS are related to crop productivity. In our study, the soil attributes in the MDS and TDS were significantly correlated with crop yield (Table 1). These results indicate that the SQI can link the terrace soil quality and productivity and effectively reflect the terrace productivity in the study area. Therefore, soil attribute indicators in the TDS and MDS in this study can represent the key soil attributes that affect crop yield. Fertilization significantly increased crop yield, and crop yield in the NP was significantly higher in the first three rotation cycles (the first 9 years) than in the M (p < 0.05), but the opposite in the last three rotation cycles (the last 9 years) (Figure 4). This indicates that the long-term impact of manure on crop yields will increase [58,59]. There was no significant difference in the SQI between the M and MNP. Still, the yield of the MNP was significantly higher than that of the M (Figure 4), which may be attributed to the slowly released nutrients from the slow mineralization of manure may not meet nutrient demand for the crop’s rapid growth period [60,61]. Moreover, the SQI of the M was higher than that of the NP, but the crop yield of the NP in the first three rotation cycles was significantly higher than that of the M. Therefore, the SQI cannot fully reflect the productivity of the terrace agroecosystem in short term. However, in the long run, the SQI can reflect the productivity of terraces under long-term fertilization measures.
Many studies have shown that the long-term application of mineral fertilizer alone will lead to soil degradation [14,15,16]. In this study, the yield for the NP treatment in the last three rotation cycles decreased by 18% compared with the first three rotation cycles (Figure 4), and the SQI (ls-MDS) in RC6 in the NP treatment decreased by 8% compared to that in RC5. This indicates that the long-term application of mineral fertilizer alone is not conducive to sustainable yield increase.
Building sloping land into terraced fields has the positive ecological function of improving soil quality and crop yield. However, the soil is deeply disturbed, the topsoil is buried, and the raw soil is exposed during the construction of the terraces, which makes the soil quality of the newly constructed terraces poor and hinders the ecological functions of the terraces [1,2,3,4]. At the beginning of the experiment, the SOC was only 2.25 g kg−1, the TN was 0.21 g kg−1, and the AP was 0.57 mg kg−1. However, fertilization measures effectively improved the soil quality, and the application of manure significantly increased the soil quality. The nutrient management strategy of combined application of manure and mineral fertilizer had a better effect on improving the soil quality of terraces and it also had the highest crop yield. This indicates that adopting an appropriate fertilization strategy can significantly improve soil quality and crop productivity for built terraces, which will improve the ecological function of the terraces. Compared with RC1, crop yield in the CK treatment decreased by 30% in the last three rotation cycles (Figure 4), and the SQI (ls-MDS) of the CK in RC6 decreased by 8% compared with that in RC5. These results indicate that although the terraces had positive ecological functions, long-term continuous crop production without fertilization could cause soil quality decline in terraced fields. Therefore, fertilization measures, especially in combination with the application of manure, are required to improve the soil quality of barren terraces following their establishment to obtain a high crop yield of terraces.

5. Conclusions

We assessed soil quality evolution using the SQI for an 18-year long-term field experiment on a newly built terrace. Both the TDS and MDS were used to calculate the SQI with linear and nonlinear scoring methods, respectively. Fertilization significantly increased crop yield, and the TP, AP, and IN and decreased soil alkalinity. Meanwhile, the SOC, TN, and MBC were increased significantly by manure application. There were significant positive correlations between the SQI (ls-TDS), SQI (ls-MDS), SQI (nls-TDS), and SQI (nls-MDS), so all of them could be used to evaluate the soil quality of the terrace. Additionally, crop yield is closely related to the soil quality index. Over the years, the soil quality for all treatments increased, and the fertilization effect was significant. The SQI of the MNP and M treatments tended to increase, while the SQI of the NP and CK increased first and then stabilized. Manure application significantly increased the SQI, indicating the crucial role of applying manure in improving soil quality. Therefore, in the long run, the newly built terraces have the potential to increase ecological function associated with improved soil quality regardless of the fertilization measures. Long-term application of mineral fertilizers can also improve soil quality, but the effect is limited. Thus, the application of manure can improve soil quality more quickly in the short term and continuously improve soil quality in the long term. This study provided a scientific basis for rapidly and continuously improving the soil quality of newly built terraces.

Author Contributions

Conceptualization, F.L., X.S. (Xiaopeng Shi) and X.S. (Xin Song); investigation, X.S. (Xiaopeng Shi) and X.S. (Xin Song); formal Analysis, X.S. (Xiaopeng Shi); Writing—original draft, X.S. (Xiaopeng Shi); writing—review and editing, F.L., L.K.A., X.S. (Xiaopeng Shi), X.S. (Xin Song), G.Z. and Q.Y.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (Grant No. 32071550, 31770480), the Gansu Provincial Funds of International Cooperation Center, the ‘111’ program (BP0719040).

Data Availability Statement

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Stavi, I.; Fizik, E.; Argaman, E. Contour bench terrace (shich/shikim) forestry systems in the semi-arid Israeli Negev: Effects on soil quality, geodiversity, and herbaceous vegetation. Geomorphology 2015, 231, 376–382. [Google Scholar] [CrossRef]
  2. Deng, C.; Zhang, G.; Liu, Y.; Nie, X.; Li, Z.; Liu, J.; Zhu, D. Advantages and disadvantages of terracing: A comprehensive review. Int. Soil Water Conserv. Res. 2021, 9, 344–359. [Google Scholar] [CrossRef]
  3. Branca, G.; Lipper, L.; McCarthy, N.; Jolejole, M.C. Food security, climate change, and sustainable land management. A review. Agron. Sustain. Dev. 2013, 33, 635–650. [Google Scholar] [CrossRef] [Green Version]
  4. Ramos, M.C.; Cots-Folch, R.; Martínez-Casasnovas, J.A. Effects of land terracing on soil properties in the Priorat region in Northeastern Spain: A multivariate analysis. Geoderma 2007, 142, 251–261. [Google Scholar] [CrossRef]
  5. Liu, E.; Yan, C.; Mei, X.; He, W.; Bing, S.H.; Ding, L.; Liu, Q.; Liu, S.; Fan, T. Long-term effect of chemical fertilizer, straw, and manure on soil chemical and biological properties in northwest China. Geoderma 2010, 158, 173–180. [Google Scholar] [CrossRef]
  6. Frederik, V.D.B.; Jakob, M.; Jensen, L.S. Long-term fertilisation strategies and form affect nutrient budgets and soil test values, soil carbon retention and crop yield resilience. Plant Soil 2018, 434, 47–64. [Google Scholar]
  7. Qaswar, M.; Jing, H.; Ahmed, W.; Dongchu, L.; Shujun, L.; Lu, Z.; Cai, A.; Lisheng, L.; Yongmei, X.; Jusheng, G.; et al. Yield sustainability, soil organic carbon sequestration and nutrients balance under long-term combined application of manure and inorganic fertilizers in acidic paddy soil. Soil Tillage Res. 2020, 198, 104569. [Google Scholar] [CrossRef]
  8. Xie, J.; Shi, X.; Zhang, Y.; Wan, Y.; Hu, Q.; Zhang, Y.; Wang, J.; He, X.; Evgenia, B. Improved nitrogen use efficiency, carbon sequestration and reduced environmental contamination under a gradient of manure application. Soil Tillage Res. 2022, 220, 105386. [Google Scholar] [CrossRef]
  9. Githongo, M.; Kiboi, M.; Ngetich, F.; Musafiri, C.; Muriuki, A.; Fliessbach, A. The effect of minimum tillage and animal manure on maize yields and soil organic carbon in sub-Saharan Africa: A meta-analysis. Environ. Chall. 2021, 5, 100340. [Google Scholar] [CrossRef]
  10. Du, Y.; Cui, B.; Zhang, Q.; Wang, Z.; Sun, J.; Niu, W. Effects of manure fertilizer on crop yield and soil properties in China: A meta-analysis. Catena 2020, 193, 104617. [Google Scholar] [CrossRef]
  11. Zeng, X.; Xiao, Z.; Zhang, G.; Wang, A.; Li, Z.; Liu, Y.; Wang, H.; Zeng, Q.; Liang, Y.; Zou, D. Speciation and bioavailability of heavy metals in pyrolytic biochar of swine and goat manures. J. Anal. Appl. Pyrolysis 2018, 132, 82–93. [Google Scholar] [CrossRef]
  12. Blanchet, G.; Gavazov, K.; Bragazza, L.; Sinaj, S. Responses of soil properties and crop yields to different inorganic and organic amendments in a Swiss conventional farming system. Agric. Ecosyst. Environ. 2016, 230, 116–126. [Google Scholar] [CrossRef] [Green Version]
  13. Fu, Y.; de Jonge, L.W.; Moldrup, P.; Paradelo, M.; Arthur, E. Improvements in soil physical properties after long-term manure addition depend on soil and crop type. Geoderma 2022, 425, 116062. [Google Scholar] [CrossRef]
  14. Li, P.; Li, Y.; Xu, L.; Zhang, H.; Shen, X.; Xu, H.; Jiao, J.; Li, H.; Hu, F. Crop yield-soil quality balance in double cropping in China’s upland by organic amendments: A meta-analysis. Geoderma 2021, 403, 115197. [Google Scholar] [CrossRef]
  15. Robertson, G.P.; Vitousek, P.M. Nitrogen in Agriculture: Balancing the Cost of an Essential Resource. Annu. Rev. Environ. Resour. 2009, 34, 97–125. [Google Scholar] [CrossRef] [Green Version]
  16. Raza, S.; Miao, N.; Wang, P.; Ju, X.; Chen, Z.; Zhou, J.; Kuzyakov, Y.; Na, M. Dramatic loss of inorganic carbon by nitrogen-induced soil acidification in Chinese croplands. Glob. Change Biol. 2020, 26, 3738–3751. [Google Scholar] [CrossRef]
  17. Liu, C.A.; Li, F.R.; Zhou, L.M.; Zhang, R.H.; Jia, Y.; Lin, S.L.; Wang, L.J.; Siddique, K.H. Effect of organic manure and fertilizer on soil water and crop yields in newly-built terraces with loess soils in a semi-arid environment. Agric. Water Manag. 2013, 117, 123–132. [Google Scholar] [CrossRef]
  18. Yue, Z.; Liu, Y. Effect of different fertilizer cultivation methods on new terraced fields in southern mountainous area of Ningnan. Soil Water Conserv. China 2015, 9, 52–55. [Google Scholar]
  19. Rashid, M.; Rehman, O.U.; Alvi, S.; Kausar, R.; Akram, M.I. The Effectiveness of Soil and Water Conservation Terrace Structures for Improvement of Crops and Soil Productivity in Rainfed Terraced System. Pak. J. Agric. Sci. 2016, 53, 241–248. [Google Scholar]
  20. Zhao, Y.; Li, F.-M. Effects of Tillage and Fertilization on Soil Water, Quality and Crop Yield in Newly-Built Terraces. Master’s Thesis, Lanzhou Univertisy, Lanzhou, China, 2018; p. 54. [Google Scholar]
  21. Doran, J.W.; Parkin, T.B. Defining and assessing soil quality. In Defining Soil Quality for a Sustainable Environment; Doran, J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A., Eds.; Soil Science Society of America and American Society of Agronomy: Madison, WI, USA, 1994; pp. 1–21. [Google Scholar]
  22. Carter, M.R. Soil quality for sustainable land management: Organic matter and aggregation interactions that maintains soil functions. Agron. J. 2002, 94, 38–47. [Google Scholar] [CrossRef]
  23. Karlen, D.L.; Stott, D.E. A framework for evaluating physical and chemical indicators of soil quality. In Defining Soil Quality for a Sustainable Environment; Doran, J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A., Eds.; Soil Science Society of America: Madison, WI, USA, 1994; pp. 53–72. [Google Scholar]
  24. Sainju, U.M.; Mukherjee, A.; Lal, R. Comparison of Soil Quality Index Using Three Methods. PLoS ONE 2014, 9, e105981. [Google Scholar]
  25. Raiesi, F. A minimum data set and soil quality index to quantify the effect of land use conversion on soil quality and degradation in native rangelands of upland arid and semiarid regions. Ecol. Indic. 2017, 75, 307–320. [Google Scholar] [CrossRef]
  26. Yu, P.; Liu, S.; Zhang, L.; Li, Q.; Zhou, D. Selecting the minimum data set and quantitative soil quality indexing of alkaline soils under different land uses in northeastern China. Sci. Total Environ. 2017, 616–617, 564–571. [Google Scholar] [CrossRef]
  27. Nakajima, T.; Lal, R.; Jiang, S. Soil quality index of a crosby silt loam in central Ohio. Soil Tillage Res. 2015, 146, 323–328. [Google Scholar] [CrossRef]
  28. Mamehpour, N.; Rezapour, S.; Ghaemian, N. Quantitative assessment of soil quality indices for urban croplands in a calcareous semi-arid ecosystem. Geoderma 2021, 382, 114781. [Google Scholar] [CrossRef]
  29. Li, P.; Shi, K.; Wang, Y.; Kong, D.; Liu, T.; Jiao, J.; Liu, M.; Li, H.; Hu, F. Soil quality assessment of wheat-maize cropping system with different productivities in China: Establishing a minimum data set. Soil Tillage Res. 2019, 190, 31–40. [Google Scholar] [CrossRef]
  30. Obade, V.P.; Lal, R. A standardized soil quality index for diverse field conditions. Sci. Total Environ. 2016, 541, 424–434. [Google Scholar] [CrossRef]
  31. Karlen, D.L.; Ditzler, C.A.; Andrews, S.S. Soil quality: Why and how? Geoderma 2003, 114, 145–156. [Google Scholar] [CrossRef]
  32. Kong, M.; Jia, Y.; Gu, Y.-J.; Han, C.-L.; Song, X.; Shi, X.-Y.; Siddique, K.H.; Zdruli, P.; Zhang, F.; Li, F.-M. How Film Mulch Increases the Corn Yield by Improving the Soil Moisture and Temperature in the Early Growing Period in a Cool, Semi-Arid Area. Agronomy 2020, 10, 1195. [Google Scholar] [CrossRef]
  33. Su, Y. Potassium balance and potash application effect in cultivated lands of Gansu Province. Soil 2001, 2, 73–76. (In Chinese) [Google Scholar]
  34. Qin, A.; Fang, Y.; Ning, D.; Liu, Z.; Zhao, B.; Xiao, J.; Duan, A.; Yong, B. Incorporation of Manure into Ridge and Furrow Planting System Boosts Yields of Maize by Optimizing Soil Moisture and Improving Photosynthesis. Agronomy 2019, 9, 865. [Google Scholar] [CrossRef] [Green Version]
  35. Liu, L.; Xiong, R.; Li, Y.; Chen, L.; Han, R. Anaerobic digestion characteristics and key microorganisms associated with low-temperature rapeseed cake and sheep manure fermentation. Arch. Microbiol. 2022, 204, 188. [Google Scholar] [CrossRef] [PubMed]
  36. Ma, J.; Chen, Y.; Antoniadis, V.; Wang, K.; Huang, Y.; Tian, H. Assessment of heavy metal(loid)s contamination risk and grain nutritional quality in organic waste-amended soil. J. Hazard. Mater. 2020, 399, 123095. [Google Scholar] [CrossRef] [PubMed]
  37. Wan, J.; Wang, X.; Yang, T.; Wei, Z.; Banerjee, S.; Friman, V.-P.; Mei, X.; Xu, Y.; Shen, Q. Livestock Manure Type Affects Microbial Community Composition and Assembly During Composting. Front. Microbiol. 2021, 12, 621126. [Google Scholar] [CrossRef] [PubMed]
  38. Voroney, R.P.; Winter, J.P.; Beyaert, R.P. Soil microbial biomass C and N. In Soil Sampling and Methods of Analysis; Carter, M.R., Gregorich, E.G., Eds.; Lewis Publishers, Division of CRC Press: Boca Taton, FL, USA, 1993. [Google Scholar]
  39. Olsen, S.R. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate; United States Department of Agriculture: Washington, DC, USA, 1954; Volume 939, p. 19. [Google Scholar]
  40. Bremner, J.M.; Mulvaney, C. Nitrogen-Total. In Methods of Soil Analysis; Page, A.L., Miller, R.H., Keeney, D.R., Eds.; American Society of Agronomy, Soil Science Society of America: Madison, WI, USA, 1982; pp. 595–624. [Google Scholar]
  41. O’Halloran, I.P.; Cade-Menun, B.J. Total and organic phosphorus. In Soil Sampling and Methods of Analysis; Carter, M.R., Gregorich, E.G., Eds.; Canadian Society of Soil Science/CRC Press: Boca Raton, FL, USA, 2006. [Google Scholar]
  42. Nelson, D.W.; Sommers, L.E. Total Carbon, Organic Carbon and Organic Matter. In Methods of Soil Analysis; Page, A.L., Miller, R.H., Keeney, D.R., Eds.; American Society of Agronomy: Madison, WI, USA, 1982; pp. 539–579. [Google Scholar]
  43. Askari, M.S.; Holden, N.M. Indices for quantitative evaluation of soil quality under grassland management. Geoderma 2014, 230–231, 131–142. [Google Scholar] [CrossRef]
  44. Andrews, S.S.; Karlen, D.L.; Cambardella, C.A. The Soil Management Assessment Framework: A Quantitative Soil Quality Evaluation Method. Soil Sci. Soc. Am. J. 2004, 68, 1945–1962. [Google Scholar] [CrossRef]
  45. Biswas, S.; Hazra, G.C.; Purakayastha, T.J.; Saha, N.; Mitran, T.; Roy, S.S.; Basak, N.; Mandal, B. Establishment of critical limits of indicators and indices of soil quality in rice-rice cropping systems under different soil orders. Geoderma 2017, 292, 34–48. [Google Scholar] [CrossRef]
  46. Wang, Y.; Huang, Q.; Gao, H.; Zhang, R.; Yang, L.; Guo, Y.; Li, H.; Awasthi, M.K.; Li, G. Long-term cover crops improved soil phosphorus availability in a rain-fed apple orchard. Chemosphere 2021, 275, 130093. [Google Scholar] [CrossRef]
  47. Yadav, R.; Purakayastha, T.; Khan, M.; Kaushik, S. Long-term impact of manuring and fertilization on enrichment, stability and quality of organic carbon in Inceptisol under two potato-based cropping systems. Sci. Total Environ. 2017, 609, 1535–1543. [Google Scholar] [CrossRef]
  48. Ren, F.; Misselbrook, T.H.; Sun, N.; Zhang, X.; Zhang, S.; Jiao, J.; Xu, M.; Wu, L. Spatial changes and driving variables of topsoil organic carbon stocks in Chinese croplands under different fertilization strategies. Sci. Total Environ. 2021, 767, 144350. [Google Scholar] [CrossRef]
  49. Li, B.; Song, H.; Cao, W.; Wang, Y.; Chen, J.; Guo, J. Responses of soil organic carbon stock to animal manure application: A new global synthesis integrating the impacts of agricultural managements and environmental conditions. Glob. Change Biol. 2021, 27, 5356–5367. [Google Scholar] [CrossRef] [PubMed]
  50. Jin, H.; Zhang, D.; Yan, Y.; Yang, C.; Fang, B.; Li, X.; Shao, Y.; Wang, H.; Yue, J.; Wang, Y.; et al. Short-term application of chicken manure under different nitrogen rates alters structure and co-occurrence pattern but not diversity of soil microbial community in wheat field. Front. Microbiol. 2022, 13, 975571. [Google Scholar] [CrossRef]
  51. Guo, Z.; Wan, S.; Hua, K.; Yin, Y.; Chu, H.; Wang, D.; Guo, X. Fertilization regime has a greater effect on soil microbial community structure than crop rotation and growth stage in an agroecosystem. Appl. Soil Ecol. 2020, 149, 103510. [Google Scholar] [CrossRef]
  52. Liang, C.; Schimel, J.P.; Jastrow, J.D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2017, 2, 17105. [Google Scholar] [CrossRef] [PubMed]
  53. Murphy, B. Key soil functional properties affected by soil organic matter-evidence from published literature. IOP Conf. Ser. Earth Environ. Sci. 2015, 25, 012008. [Google Scholar] [CrossRef]
  54. Cotching, W.E. Organic matter in the agricultural soils of Tasmania, Australia-A review. Geoderma 2018, 312, 170–182. [Google Scholar] [CrossRef]
  55. Mendes, I.C.; Sousa, D.M.G.; Dantas, O.D.; Lopes, A.A.C.; Junior, F.B.R.; Oliveira, M.I.; Chaer, G.M. Soil quality and grain yield: A win–win combination in clayey tropical oxisols. Geoderma 2021, 388, 114880. [Google Scholar] [CrossRef]
  56. Qi, Y.; Darilek, J.L.; Huang, B.; Zhao, Y.; Sun, W.; Gu, Z. Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. Geoderma 2009, 149, 325–334. [Google Scholar] [CrossRef]
  57. Karlen, D.L.; Hurley, E.G.; Andrews, S.S.; Cambardella, C.A.; Meek, D.W.; Duffy, M.D.; Mallarino, A.P. Crop Rotation Effects on Soil Quality at Three Northern Corn/Soybean Belt Locations. Agron. J. 2006, 98, 484–495. [Google Scholar] [CrossRef] [Green Version]
  58. Han, X.; Hu, C.; Chen, Y.; Qiao, Y.; Liu, D.; Fan, J.; Li, S.; Zhang, Z. Crop yield stability and sustainability in a rice-wheat cropping system based on 34-year field experiment. Eur. J. Agron. 2020, 113, 125965. [Google Scholar] [CrossRef]
  59. Gai, X.; Liu, H.; Liu, J.; Zhai, L.; Yang, B.; Wu, S.; Ren, T.; Lei, Q.; Wang, H. Long-term benefits of combining chemical fertilizer and manure applications on crop yields and soil carbon and nitrogen stocks in North China Plain. Agric. Water Manag. 2018, 208, 384–392. [Google Scholar] [CrossRef]
  60. Seufert, V.; Ramankutty, N.; Foley, J.A. Comparing the yields of organic and conventional agriculture. Nature 2012, 485, 229–232. [Google Scholar] [CrossRef] [PubMed]
  61. Berry, P.M.; Sylvester-Bradley, R.; Philipps, L.; Hatch, D.J.; Cuttle, S.P.; Rayns, F.W.; Gosling, P. Is the productivity of organic farms restricted by the supply of available nitrogen? Soil Use Manag. 2002, 18, 248–255. [Google Scholar] [CrossRef]
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Figure 1. Dynamics of soil quality index (SQI) during crop rotation. The SQI was calculated by the linear scoring (ls) method using minimum data set (MDS) and total data set (TDS), respectively (a,b), and the SQI was calculated by the non-linear scoring (nls) method using MDS and TDS, respectively (c,d). CK represents unfertilized control; NP represents applied mineral N and P fertilizer; M represents applied sheep manure; MNP represents the combined application of mineral N and P fertilizer and sheep manure (the same below). The vertical bar in the figure represents the mean ± standard deviation.
Figure 1. Dynamics of soil quality index (SQI) during crop rotation. The SQI was calculated by the linear scoring (ls) method using minimum data set (MDS) and total data set (TDS), respectively (a,b), and the SQI was calculated by the non-linear scoring (nls) method using MDS and TDS, respectively (c,d). CK represents unfertilized control; NP represents applied mineral N and P fertilizer; M represents applied sheep manure; MNP represents the combined application of mineral N and P fertilizer and sheep manure (the same below). The vertical bar in the figure represents the mean ± standard deviation.
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Figure 2. SQI in the sixth rotation cycle. SQI was calculated by the linear scoring (ls) method using the MDS and TDS (a,b), and SQI was calculated by the non-linear scoring (nls) method using the MDS and TDS (c,d). The red vertical bar in the figure represents the mean ± standard deviation. CK, NP, M, and MNP represent four treatments. Different lowercase letters among treatments indicate that the difference in SQI was significant (p < 0.05), otherwise, there was no significant difference (p > 0.05).
Figure 2. SQI in the sixth rotation cycle. SQI was calculated by the linear scoring (ls) method using the MDS and TDS (a,b), and SQI was calculated by the non-linear scoring (nls) method using the MDS and TDS (c,d). The red vertical bar in the figure represents the mean ± standard deviation. CK, NP, M, and MNP represent four treatments. Different lowercase letters among treatments indicate that the difference in SQI was significant (p < 0.05), otherwise, there was no significant difference (p > 0.05).
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Figure 3. The relationship between SQI and crop yield. SQI was calculated by the linear scoring (ls) method using the MDS and TDS (a,b), and SQI was calculated by the non-linear scoring (nls) method using the MDS and TDS (c,d). *** represents p < 0.001.
Figure 3. The relationship between SQI and crop yield. SQI was calculated by the linear scoring (ls) method using the MDS and TDS (a,b), and SQI was calculated by the non-linear scoring (nls) method using the MDS and TDS (c,d). *** represents p < 0.001.
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Figure 4. Average total yield in rotation cycle during the 18-year experiment. Different lowercase letters indicated significant differences between treatments (p < 0.05), otherwise, there was no significant difference (p > 0.05). The total yield in each rotation cycle was calculated by the sum of the yield of peas, wheat, and potatoes. RC1-3 refers to crop rotation cycles 1 to 3; RC4-6 refers to crop rotation cycles 4 to 6. CK represents unfertilized control; NP represents applied mineral N and P fertilizer; M represents applied sheep manure; MNP represents the combined application of mineral N and P fertilizer and sheep manure.
Figure 4. Average total yield in rotation cycle during the 18-year experiment. Different lowercase letters indicated significant differences between treatments (p < 0.05), otherwise, there was no significant difference (p > 0.05). The total yield in each rotation cycle was calculated by the sum of the yield of peas, wheat, and potatoes. RC1-3 refers to crop rotation cycles 1 to 3; RC4-6 refers to crop rotation cycles 4 to 6. CK represents unfertilized control; NP represents applied mineral N and P fertilizer; M represents applied sheep manure; MNP represents the combined application of mineral N and P fertilizer and sheep manure.
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Table
Table 1. The Pearson correlation analysis between soil indicators and crop yield.
Table 1. The Pearson correlation analysis between soil indicators and crop yield.
IndicatorsSOCTNTPC:NC:PN:PINAPpHMBCSWSYield
SOC1
TN0.898 **1
TP0.494 **0.471 **1
C:N0.248 **−0.143 *0.0381
C:P0.990 **0.882 **0.400 **0.265 **1
N:P0.877 **0.970 **0.392 **−0.175 **0.880 **1
IN0.568 **0.541 **0.548 **0.0930.517 **0.479 **1
AP0.610 **0.636 **0.677 **−0.0070.536 **0.557 **0.635 **1
pH−0.617 **−0.548 **−0.412 **−0.189 **−0.606 **−0.513 **−0.357 **−0.402 **1
MBC0.560 **0.505 **0.334 **0.0990.541 **0.495 **0.528 **0.283 **−0.361 **1
SWS0.184 **0.0560.0390.280 **0.178 **0.0420.0750.111−0.0380.0911
Yield0.411 **0.371 **0.299 **0.0830.381 **0.332 **0.499 **0.318 **−0.365 **0.580 **0.148 *1
where *, ** indicates a significant correlation and p-values are less than 0.05 and 0.01, respectively. SOC, TN, and TP represent soil organic carbon, total nitrogen, and total phosphorus, respectively; C:N, C:P, and N:P represent the ratio of SOC to TN, SOC to TP, and TN to TP, respectively; IN and AP represent soil inorganic nitrogen and available phosphorus, respectively; MBC represents microbial biomass carbon; and SWS represents soil water storage at a 0 to 2 m soil depth (the same below).
Table
Table 2. The 18-year long-term fertilization effects on soil properties.
Table 2. The 18-year long-term fertilization effects on soil properties.
Soil IndicatorsSOCTNC:NTPC:PN:PINAPMBCpHSWS
Unitsg kg−1g kg−1 g kg−1 mg kg−1mg kg−1mg kg−1 mm
Initial Value2.250.2110.710.593.810.362.240.5861.839.06330
End Value
CK2.92 b 0.36 b 8.150.60 c 4.850.591.12 d 3.35 c 52.27 b 8.73 b 192 b
NP3.47 b 0.46 b 7.490.66 b 5.30.713.30 c12.48 b 78.37 b 8.51 a 189 b
M7.51 a 0.87 a 8.620.65 b 11.481.335.94 b 14.24 b 160.26 a 8.53 a 254 a
MNP7.08 a 0.82 a 8.580.70 a 10.151.187.23 a 24.35 a 156.74 a8.48 a 225 ab
Different lowercase letters in the same column indicate that the difference between treatments is significant (p < 0.05), otherwise, there is no significant difference.
Table
Table 3. The results of principal component analysis and the commonality and weight of each soil indicator in the total data set (TDS) and the minimum data set (MDS).
Table 3. The results of principal component analysis and the commonality and weight of each soil indicator in the total data set (TDS) and the minimum data set (MDS).
Soil
Indicators		TDSMDS
PC1PC2PC3CommunalityWeightCommunalityWeight
SOC0.950−0.214 0.010 0.948 0.121 0.750 0.487
TN0.927−0.180 −0.135 0.909 0.116
TP0.637 0.6180.073 0.792 0.101 0.666 0.432
C:P0.918−0.314 −0.014 0.941 0.120
N:P0.891−0.273 −0.167 0.896 0.115
IN0.708 0.418 0.106 0.688 0.088
AP0.742 0.464 0.112 0.778 0.100
pH−0.668 0.077 0.125 0.468 0.060
MBC0.633 −0.075 0.033 0.408 0.052
SWS0.142 −0.231 0.9560.987 0.126 0.125 0.081
Eigenvalue5.721 1.088 1.006
Percent (%)57.208 10.876 10.063
Cumulative Percent (%)57.20868.08478.147
The bold type indicates that the corresponding soil indicator is a highly weighted variable. Slanted bold indicates the soil indicators were selected into the MDS.
Table
Table 4. Correlations among soil quality indexes (SQI) calculated using total or minimum data sets and linear or non-linear scoring methods.
Table 4. Correlations among soil quality indexes (SQI) calculated using total or minimum data sets and linear or non-linear scoring methods.
SQISQI (ls-MDS)SQI (ls-TDS)SQI (nls-MDS)SQI (nls-TDS)
SQI (ls-MDS)1
SQI (ls-TDS)0.984 **1
SQI (nls-MDS)0.975 **0.975 **1
SQI (nls-TDS)0.984 **0.988 **0.972 **1
where ** represents p < 0.01. MDS represents the minimum data set. TDS represents the total data set. SQI (ls-MDS) and SQI (ls-TDS) represent the SQI calculated by linear scoring method (Equations (1) or (2)) using MDS and TDS. Respectively. SQI (nls-MDS) and SQI (nls-TDS) represent the SQI calculated by non-linear scoring methods (Equation (3)) using MDS and TDS, respectively.
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Shi, X.; Song, X.; Zhao, G.; Yang, Q.; Abbott, L.K.; Li, F. Manure Application Is the Key to Improving Soil Quality of New Terraces. Sustainability 2022, 14, 15166. https://doi.org/10.3390/su142215166

AMA Style

Shi X, Song X, Zhao G, Yang Q, Abbott LK, Li F. Manure Application Is the Key to Improving Soil Quality of New Terraces. Sustainability. 2022; 14(22):15166. https://doi.org/10.3390/su142215166

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Shi, Xiaopeng, Xin Song, Guibin Zhao, Qifeng Yang, Lynette K. Abbott, and Fengmin Li. 2022. "Manure Application Is the Key to Improving Soil Quality of New Terraces" Sustainability 14, no. 22: 15166. https://doi.org/10.3390/su142215166

APA Style

Shi, X., Song, X., Zhao, G., Yang, Q., Abbott, L. K., & Li, F. (2022). Manure Application Is the Key to Improving Soil Quality of New Terraces. Sustainability, 14(22), 15166. https://doi.org/10.3390/su142215166

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