Next Article in Journal
Economic and Environmental Assessment of Conventional Lemon Cultivation: The Case of Southeastern Spain
Previous Article in Journal
Soil Erosion Characteristics of the Agricultural Terrace Induced by Heavy Rainfalls on Chinese Loess Plateau: A Case Study
Previous Article in Special Issue
Intercontinental Migration Facilitates Continuous Occurrence of the Desert Locust Schistocerca gregaria (Forsk., 1775) in Africa and Asia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 8
10.3390/agronomy14081841
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Different Tillage Years on Soil Composition and Ground-Dwelling Arthropod Diversity in Gravel-Sand Mulching Watermelon Fields

by
Haixiang Zhang
1,2,
Ziyu Cao
1,3,
Yifan Cui
1,2,
Changyu Xiong
1,3,
Wei Sun
1,
Ying Wang
1,
Liping Ban
2,
Rong Zhang
1 and
Shuhua Wei
1,*
1
Institute of Plant Protection, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750002, China
2
College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China
3
College of Biological Science & Engineering, North Minzu University, Yinchuan 750002, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(8), 1841; https://doi.org/10.3390/agronomy14081841
Submission received: 30 June 2024 / Revised: 16 August 2024 / Accepted: 17 August 2024 / Published: 20 August 2024
(This article belongs to the Special Issue Sustainable Pest Management under Climate Change)
Browse Figures
Review Reports Versions Notes

Abstract

:
Arthropods play a crucial role in ecological processes and agricultural productivity. Soil physicochemical properties, indicators of soil health, are closely linked to arthropod communities. Gravel-sand mulching, commonly employed in arid farming, initially enhances water retention and temperature regulation but may contribute to land degradation with prolonged use. This study investigated how varying tillage durations affected soil properties and arthropod diversity under gravel-sand mulching. The analysis employed multiple comparison methods, covariance analysis (ANCOVA), non-metric multidimensional scaling (NMDS), and redundancy analysis (RDA). The results indicated that while soil fertility was better preserved in cultivated fields compared to in the desert grassland, arthropod diversity significantly decreased with longer cultivation periods. A total of 1099 arthropods from 79 species were sampled, by Barber trap. The highest diversity was observed in native grassland (NG), with 305 arthropods from 39 species, while tillage 21 years (GPS-21Y) exhibited the lowest diversity, with only 103 arthropods from 6 species. Dominant species included the carnivores Labidura japonica and Cataglyphis aenes. The analysis revealed low similarity in arthropod communities between GPS-21Y and other fields and high similarity in soil physicochemical properties between NG and the transition zone (STZ). RDA showed available potassium (APP) was negatively correlated with arthropod species diversity and concentration, total Nitrogen (TN) was positively correlated with arthropod species diversity but negatively correlated with species concentration, total phosphorus (TP) was negatively correlated with arthropod species diversity and concentration. This study provides insights into the relationship between maintaining soil fertility and supporting arthropod diversity in grassland agriculture. While soil fertility and arthropod diversity were correlated, continuous cropping practices negatively impacted arthropod diversity, offering valuable information for pest management and sustainable agricultural practices.

1. Introduction

Arthropods are crucial components of ecosystems, providing valuable services while also causing damage to agricultural systems [1]. In agroecosystems, herbivorous arthropods contribute 18–20% of global crop yield losses [2]. Conversely, predators and parasitoids targeting herbivores offer significant benefits for pest regulation [3,4], while wild bees play an essential role as pollinators, enhancing crop yields [5,6,7]. Additionally, arthropods are closely linked to soil health, serving as soil quality indicators [8].
Watermelon, an economically significant fruit of the Cucurbitaceae family [9], is cultivated extensively worldwide, with China being the largest producer [10,11,12]. Successful watermelon cultivation requires adequate water, nutrients, and optimal temperatures. In arid and semi-arid regions, limited water resources often constrain production [13]. Gravel-sand mulching, a traditional agricultural practice in such areas, involves covering the soil with sand, gravel, or mixtures (Figure 1) to enhance water retention and regulate temperature [14,15]. However, long-term use of gravel-sand mulching can lead to soil degradation and reduced soil fertility, affecting soil microbial structure and increasing plant disease susceptibility [13,16]. This practice is widely employed in regions with low precipitation, including Colorado, Texas, and Montana in the USA [17], South Africa [18], Montpellier, France [19], Chamoson, Switzerland [20], and Lanzhou in Gansu Province, China [21]. The cascade effect describes how changes in one ecosystem subsystem can impact others [22]. For example, interactions between plants and herbivorous insects and the relationship between plant traits and soil microbial communities exemplify this concept [23]. Nitrogen, a limiting factor for insect growth, can influence plant community composition and insect populations [24,25,26]. Research on cascade effects has demonstrated varied impacts, such as increased populations of invasive ants in calcium-fertilized lands [27]. Additionally, studies have shown that low nitrogen conditions can benefit certain insect species, while excessive nitrogen can negatively impact the parasitism rates of wheat aphids [28,29].
Long-term agricultural practices can lead to soil degradation and the loss of ecological balance, resulting in reduced biodiversity, particularly in arid and semi-arid regions [30,31]. Despite this, there is limited research on how changes in arthropod diversity interact with soil physicochemical properties in desert-grassland areas. This study addressed this gap by examining soil physicochemical properties and arthropod diversity in gravel-sand-mulched watermelon fields in Zhongwei City, Ningxia, China. Specifically, the study aimed to answer the following questions: (1) How do soil physicochemical properties and arthropod diversity change over different watermelon-planting years?; (2) How does arthropod diversity respond to variations in soil physicochemical properties?. By addressing these questions, the study sought to provide insights into the development of dryland agriculture and biodiversity conservation in arid and semi-arid regions.

2. Materials and Methods

2.1. Site Details and Field Experimental Design

The study was conducted in the gravel-sand-mulched planting area of Xiangshan Township, Zhongwei City, Ningxia, China (104°50′0″ E−105°30′0″ E, 37°0′0″ N−37°25′0″ N). This region, located in the desert grassland of Northwest China, is characterized by water scarcity, a fragile ecological environment, and low precipitation. The area experiences high evaporation, with approximately 50% vegetation coverage, predominantly herbaceous plants with about 15 cm in height. Annual rainfall is <200 mm, while evaporation is >2000 mm. Gravel-sand mulching is a traditional dry farming practice in the region. As a significant local industry, watermelon cultivation is crucial for boosting farmers’ income and supporting socio-economic development. However, the lack of unified planning and management has led to extensive desertification and sand planting, particularly in the desert grassland of Zhongwei City, the primary production area for Ningxia’s gravel-sand-mulched watermelon cultivation [32,33,34]. To investigate the effects of varying durations of gravel-sand mulching on the local environment, areas with 1, 5, 10, and 21 years of watermelon cultivation were designated as treatment areas, labeled GPS-1Y, GPS-5Y, GPS-10Y, and GPS-21Y. Native desert grassland and its transition zone adjacent to the compacted sand planting area were used as control areas, labeled NG and STZ. All research fields were previously natural grasslands with no history of crop cultivation. Watermelons were planted directly without any preceding crop rotation. Each research area was uniformly sized, with each field measuring approximately 1 km × 1 km, and the research areas were located approximately 3 km apart.

2.2. Soil Collection and Physicochemical Properties Measurements

A random five-point sampling method was adopted in the monitoring area, with a minimum spacing of 150 m between sampling points. Mixed soil samples were collected from the 0 to 20 cm surface layers, ensuring the removal of sand and gravel mixtures before sampling. The samples were then analyzed for various properties, including total nitrogen (g/kg), total phosphorus (mg/kg), total potassium (mg/kg), alkali hydrolyzable nitrogen (mg/kg), available phosphorus (mg/kg), available potassium (mg/kg), organic matter (g/kg), and electrical conductivity (EC) as 10 indices of leaching fluid conductivity us/cm, pH, and water content. Each index was measured in 10 replicates and categorized into soil nutrient indices (total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali hydrolyzable nitrogen (AHN), available potassium (APP), available phosphorus (APK), and organic matter (OM)) and physical properties indices (EC, pH, and water content (W)). Fresh soil samples were stored in aluminium boxes for moisture content determination. The soil samples were cleared of impurities, naturally air-dried and passed through a 2 mm sieve before measuring the remaining soil indices (Table 1) [35,36].

2.3. Collection of Arthropods

In May, July, and September 2023, arthropod samples were collected during the beginning, blooming, and declining periods, corresponding to peak arthropod activity. A random 5-point sampling method was used for field investigation and sampling [32].
Barber trap: Disposable plastic cups (diameter 7.5 cm; height 9.0 cm) were used as traps. Five sampling points were established in each monitoring area, serving as five replicates. Each replicate consisted of five traps spaced >5 m apart in a random 5-point arrangement. Thus, a total of 25 traps were set in each monitoring area. The cups were buried in the soil, flushed with the ground surface and filled with approximately 60 mL of an attractant solution of 33.33% ethylene glycol (ethylene glycol/water = 1:2) with a 3% detergent. Arthropods trapped in the cups were collected every 10 days, and the traps were replaced simultaneously.

2.4. Statistical Analysis of Data

One-way analysis of variance (ANOVA) was conducted to investigate differences in soil physicochemical properties and arthropod diversity among GPS-1Y, GPS-5Y, GPS-10Y, GPS-21Y, STZ, and NG across the six study areas. Arthropod species were classified into three categories based on their abundance: dominant species (proportion: ≥10%), common species (10% ≤ proportion < 1%), and rare species (0% ≤ proportion < 1%) [37,38]. Analysis of covariance (ANCOVA) was employed to examine the effects of planting years and soil physicochemical properties on arthropod diversity, with tillage years as the fixed factor and soil physicochemical properties as the covariate [39]. We used non-metric multidimensional scaling (NMDS) based on the Bray−Curtis distance matrix to separately explore differences in arthropod communities and soil physicochemical properties under different land use types. Redundancy analysis (RDA) was performed to identify patterns in arthropod beta diversity and their correlation factors.
For statistical analysis, the aov() function from the stats package and the HSD.test() function from the agricolae package were used for one-way ANOVA and Duncan’s multiple comparisons of the differences in soil physicochemical properties and diversity indices in R language, respectively [40]. The metaMDS function in the vegan [41] package was utilized for NMDS analysis, while the rda function was used for RDA. Graphical visualization was performed using the ggplot2 package, with data analysis and graphical output completed in R version 4.3.1 [42].

3. Results

3.1. Changes in Soil Physicochemical Properties

Generally, the changes in soil physicochemical properties of NG and STZ were consistent, with soil nutrients (TP, TK, TN, APP, AHN, APK, and OM) being significantly lower than those in the gravel-sand-mulched melon fields with varying tillage years. Gravel-sand mulching plots with different tillage years maintained higher water content and lower salinization compared to the control groups—NG and STZ. However, soil nutrients gradually decreased with increased years of planting.
In the control areas (NG and STZ), TK was significantly lower than in the gravel-sand mulching plots with different years of treatment (p < 0.05). TP in NG and STZ was significantly lower than in the gravel-sand mulching plots, and TP in GPS-1Y was significantly lower than in the other GPS treatments (p < 0.05). TN in GPS-1Y was significantly higher than in GPS-10Y and GPS-21Y, and TN in GPS-5Y was significantly higher than in GPS-21Y (p < 0.05). APP in NG was significantly higher than in GPS-1Y, GPS-5Y, and GPS-21Y, whereas APP in GPS-21Y was significantly lower than in STZ, GPS-1Y, and GPS-10Y (p < 0.05). AHN in GPS-1Y was significantly higher than in all other monitored areas, and AHN in STZ was significantly lower than in GPS-1Y and GPS-5Y (p < 0.05). APK in GPS-21Y was significantly the lowest among all monitored areas (p < 0.05). The pH in NG and GPS-1Y was significantly higher than in STZ, GPS-5Y, and GPS-10Y (p < 0.05). EC in NG was significantly higher than in GPS-1Y, GPS-5Y, and GPS-10Y, and EC in STZ and GPS-21Y was significantly higher than in GPS-10Y (p < 0.05). OM in GPS-1Y was significantly the highest among all monitored areas, and OM in NG was significantly higher than in STZ (p < 0.05). W in NG was the lowest among all monitored areas and was significantly lower than in GPS-1Y (p < 0.05) (Figure 2).

3.2. Changes in Arthropod Taxa Composition under Different Tillage Years

A total of 1099 arthropods representing 79 species were sampled and categorized into the following ecological function groups: carnivores, omnivores, herbivores, detritivores, and pollinators (Table A1). NG exhibited the highest number of arthropods across all study areas, with 305 animals from 39 species. Conversely, GPS-21Y had the fewest arthropods, with 103 animals from 6 species. Among all sampled arthropods, Labidura japonica (Laja) and Cataglyphis aenes (Caae) were the most numerous, with individual numbers accounting for 19.29% and 15.92% (proportion: >10%) of the total, respectively, and were classified as dominant species within the carnivore group (Table A1). Additionally, carnivores were the most abundant group overall, while pollinators were the least numerous. GPS-5Y had the highest relative abundance of carnivores, while GPS-1Y and GPS-5Y did not have any pollinators (Figure 3; Table 2).

3.3. Effects of Different Tillage Years and Soil Physicochemical Properties on Species Diversity

After controlling soil physical and chemical properties, the analysis showed that tillage years significantly affected various arthropod diversity indices, taking soil physicochemical properties as covariates (Table 3).

3.4. Changes in Arthropod Diversity

Analysis of areas with varying planting years revealed that the species number, the Margalef richness index, the Shannon diversity index, and the Pielou evenness index in GPS-21Y were significantly lower than those in the other three treatment groups (p < 0.05). These indices for all four treated groups were also lower than those observed in the native grassland (NG) area. Notably, the number of individuals in GPS-5Y was significantly higher than in the other three treatment groups (p < 0.05), but still lower than in the NG area. The Simpson dominance index in GPS-21Y was higher than in the other three treatment groups and the NG area (Figure 4).

3.5. Similarity of Arthropod Composition and Soil Properties and Correlation between the Two

NMDS analysis indicated that the stress levels for arthropod community composition and soil properties across different tillage years did not exceed 0.2 (Figure 5A,B), suggesting reliable results. There was low similarity in arthropod communities between GPS-21Y and the other treatment groups (Figure 5A). In contrast, there was a high similarity in soil physicochemical properties between NG and STZ (Figure 5B). RDA identified three soil factors significantly affecting arthropod diversity in the following order of influence: available potassium (APP) > total phosphorus (TP) > total nitrogen (TN). APP was positively correlated with the arthropod individual number (abundance) and the species number (richness) but negatively correlated with the abundance of dominant species Laja and Caae, as well as with the Margalef, Shannon, Simpson, and Pielou diversity indices. TN was positively correlated with arthropod abundance, species richness, the Margalef, Shannon, and Pielou diversity indices, and Caae but negatively correlated with Simpson and Laja. TP was positively correlated with arthropod abundance and Laja but negatively correlated with species richness, Margalef, Shannon, Simpson, Pielou, and Caae.

4. Discussion

N, P, and K are commonly used in evaluating soil nutrients, reflecting changes in soil nutrients from different perspectives. TP indicates the total storage of soil phosphorus and helps evaluate soil nitrogen availability. TN helps assess soil fertility and nitrogen supply capacity, while TK indicates soil potassium supply potential and reflects soil weathering [36,43]. TK and TP were significantly higher in different years of cultivation than in NG and STZ, suggesting that long-term planting and continuous fertilizer application increase soil potassium and phosphorus content [44]. TN was significantly higher in GPS-1Y than in GPS-10Y and GPS-21Y, and TN in GPS-5Y was significantly higher than in GPS-21Y. This may be due to faster decomposition of organic matter and fertilizers in the early planting stages, releasing more nitrogen, or it may indicate that perennial planting rapidly depletes TN, making it difficult to replenish soil fertility through fertilization alone [45,46].
AHN was significantly higher in GPS-1Y than in all other areas, indicating higher initial fertility in newly planted areas [47]. AHN in STZ was significantly lower than in GPS-1Y and GPS-5Y, showing that agricultural practices can significantly improve soil fertility [48]. In long-term cropping areas, APK was significantly lower than in all other regions, suggesting that crop consumption exceeds the natural replenishment rate of phosphorus in the soil. This is consistent with research showing that long-term agricultural activities can deplete certain key soil nutrients [49]. pH was significantly higher in NG and GPS-1Y than in STZ, GPS-5Y, and GPS-10Y, possibly due to soil acidification from agricultural activities and fertilizer use [50]. EC in NG was significantly higher than in GPS-1Y, GPS-5Y, and GPS-10Y, while EC in STZ and GPS-21Y was significantly higher than in GPS-10Y. The high EC value in NG may result from natural salt accumulation, climate, geology, and other conditions [51]. High EC values in STZ and GPS-21Y might be due to salt accumulation from long-term agricultural irrigation and fertilization [52,53]. OM in GPS-1Y was significantly higher than in other regions, possibly due to the initial application of large amounts of organic fertilizer or cover crop residues, increasing soil organic matter content [54]. OM in NG was significantly higher than in STZ, reflecting organic matter accumulation in natural grassland soils due to natural vegetation decomposition and less human disturbance [55,56]. The water content in NG was significantly lower than in GPS-1Y, possibly due to the primary grassland’s soil structure and vegetation cover, which allowed water to evaporate quickly. In contrast, GPS-1Y effectively maintained higher soil moisture due to gravel cover and water retention measures [57].
The native grassland (NG) area supported the highest number of arthropods in terms of individual count and species richness, highlighting the importance of undisturbed habitats in maintaining high biodiversity [58,59]. This is consistent with previous research, indicating that natural habitats typically support more diverse and stable arthropod communities due to the availability of various niches and less anthropogenic disturbance [60,61]. In contrast, the area with the longest cultivation period (GPS-21Y) had the lowest arthropod abundance and species richness. This decline in biodiversity is likely due to the cumulative effects of long-term agricultural practices, which can degrade soil quality, reduce habitat complexity and limit resource availability for various species [62,63]. Carnivores were the most abundant ecological group, suggesting that agricultural practices might favor predators due to altered prey dynamics or habitat conditions that support their survival and reproduction [64]. The absence of pollinators in GPS-1Y and GPS-5Y is particularly concerning, as these areas may lack the necessary floral resources or suitable habitats required for pollinator species, which are crucial for ecosystem services and agricultural productivity [65].
After controlling for these properties, the analysis demonstrated that tillage years significantly impacted various arthropod diversity indices, taking soil physicochemical properties as covariates. This finding indicates that changes in cultivation periods notably alter arthropod diversity, independent of differences in soil physicochemical properties [66,67]. Compared with the maintenance of soil physical and chemical properties in pressed sand melon fields, watermelon cultivation that destroys the original grassland habitat significantly reduces arthropod diversity by planting a single species and increases the vulnerability of the local desert grassland habitat.
Regarding the species number, the Margalef index, the Shannon diversity index, and the Pielou index, GPS-21Y showed significantly lower values than other planting areas of different years and the NG areas. This suggests that biodiversity and ecosystem structure can be negatively affected over time and with prolonged planting, possibly due to soil erosion, habitat destruction, or other factors leading to ecosystem degradation [68,69]. On the other hand, the number of arthropod individuals in GPS-5Y was significantly higher than in other years’ planting areas. However, the number of individuals was still lower than the NG, possibly due to ecosystem changes caused by human intervention [70,71]. The Simpson index of GPS-21Y was significantly higher than in other planting areas of different years and primary grassland (NG) areas. This suggests that after 21 years of watermelon cultivation, arthropod communities tend to congregate in a few species, possibly due to ecosystem degradation [72]. These results support that pressed sand cultivation impacts biodiversity and ecosystem structure. Long-term cultivation can lead to reduced biodiversity and ecosystem degradation [73]. Medium-term (5 years) planting may promote an increase in the number of individuals but may still not return to the level of the original ecosystem. These findings highlight the importance of preserving primary grasslands and avoiding over-cultivation to maintain biodiversity and ecosystem health.
It is undeniable that farmers who have practiced gravel-sand mulching for watermelon cultivation over extended periods have effectively maintained soil nutrients through field operations. The nutrient levels in these fields can be significantly higher than those in uncultivated grasslands [74,75]. Gravel-sand mulching fields can also maintain higher water content and lower salinization, likely due to fertilization and proper field management during long-term planting [76]. The NMDS analysis revealed that the low similarity in arthropod communities between GPS-21Y and the other groups suggests significant community shifts with prolonged cultivation, likely due to habitat alteration and resource depletion [60]. The RDA analysis identified APP, TP, TN as key soil factors influencing arthropod diversity. APP’s positive correlations with arthropod abundance and richness and its negative correlation with diversity indices imply that higher phosphorus levels may result in dominance by fewer species, thereby reducing the overall diversity [77,78]. Conversely, TN supported diverse arthropod communities, as indicated by its positive correlation with most diversity metrics. TP showed a more complex relationship: while it promoted abundance, it negatively impacted diversity indices, likely due to competitive exclusion and habitat homogenization [79].

5. Conclusions

This study found that gravel-sand mulching areas for watermelon cultivation maintained soil physicochemical properties comparable to those of intact desert grasslands. A total of 1099 arthropods, representing 79 species, were sampled. NG had the highest number of arthropods, whereas GPS-21Y had the lowest. Dominant species included the carnivores Labidura japonica and Cataglyphis aenes. Tillage years, they significantly affected arthropod diversity indices, even after accounting for soil physicochemical properties. Arthropod diversity in gravel-sand mulching fields was notably lower than in the intact desert grassland. Additionally, there was low similarity in arthropod communities between GPS-21Y and the other groups, while soil physicochemical properties were highly similar between NG and STZ. RDA indicated that three soil factors significantly influenced arthropod diversity, with the order of influence being APP > TP > TN. These findings are expected to provide valuable insights for pest management and sustainable grassland agriculture.

Author Contributions

Data curation, S.W., L.B., H.Z., Z.C., W.S., Y.C., Y.W. and R.Z.; formal analysis, Y.C., C.X., H.Z. and Z.C.; funding acquisition, S.W., L.B. and R.Z.; investigation, S.W., L.B. and H.Z.; methodology, S.W., L.B., W.S. and H.Z.; project administration, S.W., L.B., W.S. and Y.W.; resources, S.W., L.B. and Z.C.; software, H.Z., C.X. and Z.C.; supervision, S.W., L.B., W.S., Y.W. and R.Z.; validation, H.Z., Y.C., Z.C., W.S., C.X. and Y.W.; visualization, H.Z., Z.C. and R.Z.; writing—original draft, Y.C., H.Z. and S.W; writing—review and editing, S.W., L.B., C.X. and Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundations of China (32160344), the National Key R & D Program of China (2022YFD1401104), the Ningxia Province Sci-Tech Innovation Demonstration Program of High-Quality Agricultural Development and Ecological Conservation (NGSB-2021-14-05), and the National Science & Technology Fundamental Resources Investigation Program of China (2019FY100403).

Data Availability Statement

Data will be made available on request.

Acknowledgments

Thanks to the local farmers for allowing our team to do research in their fields. We sincerely thank anonymous reviewers for helpful suggestions on an earlier draft of this manuscript. We also express gratitude to the editors for their guidance and support throughout the publication process.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Appendix A

Table A1. Number of individuals of arthropods in different study areas.
Table A1. Number of individuals of arthropods in different study areas.
TaxaNameNGSTZGPS-1YGPS-5YGPS-10YGPS-21YProportion (%)
Dominant taxa
CarnivoreLabidura japonica14011638819.29%
CarnivoreCataglyphis aenes5931282829015.92%
Common taxa
CarnivoreSpider 114211214008.01%
OmnivoreBlaps Fabricius344300107.10%
CarnivoreSpider 74214141002.73%
CarnivorePseudotaphoxenus brevipennis151713303.55%
CarnivoreIndia red feather Fantail121271503.37%
OmnivoreTrigonocnera pseudopimelia33400003.37%
Carnivorescutiger coleoptrata142510203.00%
CarnivoreButhus martensii23530102.91%
HerbivoreAmpedus nigrinus02231902.37%
CarnivoreSpider1311409102.27%
HerbivoreMantitheus pekinensis131000002.09%
HerbivoreTeleogryllus infernalis00020101.09%
OmnivorePenthicus alashanicus18010001.73%
HerbivoreNemobius caudatus0005020.64%
DetritivoreTermites00134001.55%
Rare taxa
HerbivoreGlyptobothrus albonemus01600001.46%
OmnivoreMicrodera kraatzi alashanica8210001.00%
OmnivoreBlaps opaca10100101.09%
CarnivoreSpider 150050601.00%
CarnivoreHarpalus sinicus1710100.91%
OmnivoreTachinidae0081000.82%
CarnivoreSpider 22001200.45%
DetritivoreLethrus potanini5200010.73%
DetritivoreFlax fly1240100.73%
HerbivoreEodorcadion virgatum0600000.55%
OmnivorePterocoma reitteri5100000.55%
CarnivoreAdonia variegata0041000.45%
Carnivoreharlequin ladybird0020300.45%
HerbivoreSphingonotus ningsianus4100000.45%
CarnivorePseudotaphoxenus mongolicus1020100.36%
HerbivoreDorysthcnes paradoxus0110110.36%
OmnivoreBlaps variolosa3100000.36%
PollinatorBee 44000000.36%
Detritivorelepismatidae0004000.36%
CarnivorePterostichus gebleri0300000.27%
CarnivoreSpider 81200000.27%
CarnivoreEpicauta sibirica0201000.27%
CarnivoreSpider 52000100.27%
CarnivoreSpider 61000200.27%
CarnivoreCamponotus0030000.27%
CarnivoreSpider 102010000.27%
HerbivoreZichya piechockii3000000.27%
DetritivorePorcellio0030000.27%
HerbivoreCrambus pinellus0210000.27%
HerbivoreDeracanthus potanini2010000.27%
Omnivoreichneumon0001100.18%
CarnivoreFormica candida0020000.18%
CarnivoreVespidae1010000.18%
OmnivoreCyphogenia chinensis2000000.18%
HerbivoreChromonotus bipunctatus1100000.18%
HerbivoreChloebius aksuanus0101000.18%
HerbivoreAmrasca biguttula0020000.18%
CarnivoreScolopendridae0100000.09%
CarnivoreCophinopoda chinensis1000000.09%
CarnivoreCarabus brandti Faldermann0010000.09%
CarnivoreCicindela elisae0100000.09%
CarnivorePheropsophus jessoensis0001000.09%
CarnivoreSympetrum imitans0000100.09%
HerbivoreConocephalidae0010000.09%
HerbivoreAngaracris rhodopa0100000.09%
HerbivoreLygaeus murinus0001000.09%
OmnivorePterocoma vittata0100000.09%
DetritivoreGymnopleurus mopsus1000000.09%
OmnivoreAnatolica nureti0000100.09%
PollinatorBee 10000100.09%
DetritivoreNicrophorus concolor0000100.09%
HerbivoreConorrhynchus conirostris0100000.09%
PollinatorBee 70100000.09%
PollinatorBombus richardsi0000010.09%
OmnivoreSphecidae0010000.09%
PollinatorBee 51000000.09%
OmnivoreSternoplax szechenyii1000000.09%
HerbivoreLoxostege verticalis1000000.09%
OmnivoreProsodes kreitneri1000000.09%
HerbivoreLixus divaricatus0100000.09%
HerbivoreAgrotis segetum0100000.09%
HerbivoreBothynoderes Punctiventris0100000.09%
Total 305188121234148103100%
Note: proportion (%), rare taxa (<1%); common taxa (1–10%); dominant taxa (>10%)

References

  1. Flores-Gutierrez, A.M.; Mora, F.; Avila-Cabadilla, L.D.; Boege, K.; del-Val, E. Assessing the Cascading Effects of Management and Landscape on the Arthropod Guilds Occurring in Papaya Plantations. Agric. Ecosyst. Environ. 2020, 293, 106836. [Google Scholar] [CrossRef]
  2. Oerke, E.-C. Crop Losses to Pests. J. Agric. Sci. 2006, 144, 31–43. [Google Scholar] [CrossRef]
  3. Isaacs, R.; Tuell, J.; Fiedler, A.; Gardiner, M.; Landis, D. Maximizing Arthropod-Mediated Ecosystem Services in Agricultural Landscapes: The Role of Native Plants. Front. Ecol. Environ. 2009, 7, 196–203. [Google Scholar] [CrossRef]
  4. Zhang, W.; Ricketts, T.H.; Kremen, C.; Carney, K.; Swinton, S.M. Ecosystem Services and Dis-Services to Agriculture. Ecol. Econ. 2007, 64, 253–260. [Google Scholar] [CrossRef]
  5. Shi, X.; Ma, C.; Gustave, W.; Orr, M.; Sritongchuay, T.; Yuan, Z.; Wang, M.; Zhang, X.; Zhou, Q.; Huang, Y.; et al. Effects of Arsenic and Selenium Pollution on Wild Bee Communities in the Agricultural Landscapes. Sci. Total Environ. 2024, 907, 168052. [Google Scholar] [CrossRef] [PubMed]
  6. Pirk, C.W.W.; Crewe, R.M.; Moritz, R.F.A. Risks and Benefits of the Biological Interface between Managed and Wild Bee Pollinators. Funct. Ecol. 2017, 31, 47–55. [Google Scholar] [CrossRef]
  7. Liu, R.; Chen, D.; Luo, S.; Xu, S.; Xu, H.; Shi, X.; Zou, Y. Quantifying Pollination Efficiency of Flower-Visiting Insects and Its Application in Estimating Pollination Services for Common Buckwheat. Agric. Ecosyst. Environ. 2020, 301, 107011. [Google Scholar] [CrossRef]
  8. Menta, C.; Remelli, S. Soil Health and Arthropods: From Complex System to Worthwhile Investigation. Insects 2020, 11, 54. [Google Scholar] [CrossRef] [PubMed]
  9. Baloglu, M.C.; Ulu, F.; Altunoglu, Y.C.; Pekol, S.; Alagoz, G.; Ese, O. Identification, Molecular Characterization and Expression Analysis of RPL24 Genes in Three Cucurbitaceae Family Members: Cucumber, Melon and Watermelon. Biotechnol. Biotechnol. Equip. 2015, 29, 1024–1034. [Google Scholar] [CrossRef]
  10. Wijesinghe, S.A.E.C.; Evans, L.J.; Kirkland, L.; Rader, R. A Global Review of Watermelon Pollination Biology and Ecology: The Increasing Importance of Seedless Cultivars. Sci. Hortic. 2020, 271, 109493. [Google Scholar] [CrossRef]
  11. Fehér, T. 21—Watermelon: Citrullus lanatus (Thunb.) Matsum. & Nakai. In Genetic Improvement of Vegetable Crops; Kalloo, G., Bergh, B.O., Eds.; Pergamon: Amsterdam, The Netherlands, 1993; pp. 295–311. ISBN 978-0-08-040826-2. [Google Scholar]
  12. Adeoye, I.B.; Olajide-Taiwo, F.B.; Adebisi-Adelani, O.; Usman, J.M.; Badmus, M.A. Economic Analysis of Watermelon Based Production System in Oyo State Nigeria. J. Agric. Biol. Sci. 2011, 6, 53–59. [Google Scholar]
  13. Zhou, W.; Zhou, X.; Cai, L.; Jiang, Q.; Zhang, R. Temporal and Habitat Dynamics of Soil Fungal Diversity in Gravel-Sand Mulching Watermelon Fields in the Semi-Arid Loess Plateau of China. Microbiol. Spectr. 2023, 11, e03150-22. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, Y.; Xie, Z.; Malhi, S.S.; Vera, C.L.; Zhang, Y.; Guo, Z. Effects of Gravel–Sand Mulch, Plastic Mulch and Ridge and Furrow Rainfall Harvesting System Combinations on Water Use Efficiency, Soil Temperature and Watermelon Yield in a Semi-Arid Loess Plateau of Northwestern China. Agric. Water Manag. 2011, 101, 88–92. [Google Scholar] [CrossRef]
  15. Hao, H.; Zhao, X.; Wang, Y.; Zhang, Y.; Xie, Z.; Guo, Z.; Wang, R. Effects of Gravel-Sand Mulching on Soil Bacterial Community and Metabolic Capability in the Semi-Arid Loess Plateau, China. World J. Microbiol. Biotechnol. 2017, 33, 209. [Google Scholar] [CrossRef] [PubMed]
  16. Qiu, Y.; Wang, Y.; Xie, Z. Long-Term Gravel–Sand Mulch Affects Soil Physicochemical Properties, Microbial Biomass and Enzyme Activities in the Semi-Arid Loess Plateau of North-Western China. Acta Agric. Scand. Sect. B—Soil Plant Sci. 2014, 64, 294–303. [Google Scholar] [CrossRef]
  17. Li, X.-Y. Effects of Gravel and Sand Mulches on Dew Deposition in the Semiarid Region of China. J. Hydrol. 2002, 260, 151–160. [Google Scholar] [CrossRef]
  18. Adams, J.E. Influence of Mulches on Runoff, Erosion, and Soil Moisture Depletion. Soil Sci. Soc. Am. J. 1966, 30, 110–114. [Google Scholar] [CrossRef]
  19. Lamb, J.; Chapman, J.E. Effect of Surface Stones on Erosion, Evaporation, Soil Temperature, and Soil Moisture. J. Am. Soc. Agron. 1943, 35, 567–578. [Google Scholar] [CrossRef]
  20. Nachtergaele, J.; Poesen, J.; van Wesemael, B. Gravel Mulching in Vineyards of Southern Switzerland. Soil Tillage Res. 1998, 46, 51–59. [Google Scholar] [CrossRef]
  21. Li, X.-Y.; Gong, J.-D.; Wei, X.-H. In-Situ Rainwater Harvesting and Gravel Mulch Combination for Corn Production in the Dry Semi-Arid Region of China. J. Arid Environ. 2000, 46, 371–382. [Google Scholar] [CrossRef]
  22. Wookey, P.A.; Aerts, R.; Bardgett, R.D.; Baptist, F.; Bråthen, K.A.; Cornelissen, J.H.C.; Gough, L.; Hartley, I.P.; Hopkins, D.W.; Lavorel, S.; et al. Ecosystem Feedbacks and Cascade Processes: Understanding Their Role in the Responses of Arctic and Alpine Ecosystems to Environmental Change. Glob. Chang. Biol. 2009, 15, 1153–1172. [Google Scholar] [CrossRef]
  23. Wardle, D.A.; Bardgett, R.D.; Klironomos, J.N.; Setälä, H.; Van Der Putten, W.H.; Wall, D.H. Ecological Linkages Between Aboveground and Belowground Biota. Science 2004, 304, 1629–1633. [Google Scholar] [CrossRef] [PubMed]
  24. Clark, C.M.; Tilman, D. Loss of Plant Species after Chronic Low-Level Nitrogen Deposition to Prairie Grasslands. Nature 2008, 451, 712–715. [Google Scholar] [CrossRef] [PubMed]
  25. Shen, H.; Dong, S.; DiTommaso, A.; Xiao, J.; Lu, W.; Zhi, Y. Nitrogen Deposition Shifts Grassland Communities through Directly Increasing Dominance of Graminoids: A 3-Year Case Study from the Qinghai-Tibetan Plateau. Front. Plant Sci. 2022, 13, 811970. [Google Scholar] [CrossRef] [PubMed]
  26. Song, M.-H.; Zheng, L.-L.; Yin, T.-F.; Zhang, X.-Z.; Yu, F.-H.; Cornelissen, J.H.C. Changes in Quantity Rather than Palatability of Alpine Meadow Species Induce Cascading Effects of Long-Term Nitrogen Fertilization on Phytophagous Insect Abundance. J. Veg. Sci. 2018, 29, 867–876. [Google Scholar] [CrossRef]
  27. Reihart, R.W.; Angelos, K.P.; Gawkins, K.M.; Hurst, S.E.; Montelongo, D.C.; Laws, A.N.; Pennings, S.C.; Prather, C.M. Crazy Ants Craving Calcium: Macronutrients and Micronutrients Can Limit and Stress an Invaded Grassland Brown Food Web. Ecology 2021, 102, e03263. [Google Scholar] [CrossRef] [PubMed]
  28. Joern, A.; Behmer, S.T. Impact of Diet Quality on Demographic Attributes in Adult Grasshoppers and the Nitrogen Limitation Hypothesis. Ecol. Entomol. 1998, 23, 174–184. [Google Scholar] [CrossRef]
  29. Wang, L.; Cui, H.; Chang, X.; Zhu, M.; Zhao, Z. Increased Nitrogen Fertilization Inhibits the Biocontrol Activity Promoted by the Intercropping Partner Plant. Insect Sci. 2021, 28, 1179–1190. [Google Scholar] [CrossRef] [PubMed]
  30. Fengrui, L.; Songling, Z.; Geballe, G.T. Water Use Patterns and Agronomic Performance for Some Cropping Systems with and without Fallow Crops in a Semi-Arid Environment of Northwest China. Agric. Ecosyst. Environ. 2000, 79, 129–142. [Google Scholar] [CrossRef]
  31. Stanturf, J.A. Chapter 5—Landscape Degradation and Restoration. In Soils and Landscape Restoration; Stanturf, J.A., Callaham, M.A., Eds.; Academic Press: Cambridge, MA, USA, 2021; pp. 125–159. ISBN 978-0-12-813193-0. [Google Scholar]
  32. Wei, S.; Huang, W.; Zhu, M.; Gao, L.; Wang, Y.; Zhang, R.; Li, Z.; Zhao, Z. The Asymmetric Responses of Carabid Beetles to Steppe Fragmentation in Northwest China. Glob. Ecol. Conserv. 2020, 23, e01058. [Google Scholar] [CrossRef]
  33. Wang, D.; Hao, H.; Liu, H.; Sun, L.; Li, Y. Spatial–Temporal Changes of Landscape and Habitat Quality in Typical Ecologically Fragile Areas of Western China over the Past 40 Years: A Case Study of the Ningxia Hui Autonomous Region. Ecol. Evol. 2024, 14, e10847. [Google Scholar] [CrossRef] [PubMed]
  34. Guan, Q.; Guan, W.; Yang, J.; Zhao, S.; Pan, B.; Wang, L.; Song, N.; Lu, M.; Li, F. Spatial and Temporal Changes in Desertification in the Southern Region of the Tengger Desert from 1973 to 2009. Theor. Appl. Clim. 2017, 129, 487–502. [Google Scholar] [CrossRef]
  35. Wang, T.; Ren, W.; Yang, F.; Niu, L.; Li, Z.; Zhang, M. Effects of Different Tillage and Residue Retention Measures on Silage Maize Yield and Quality and Soil Phosphorus in Karst Areas. Agronomy 2023, 13, 2306. [Google Scholar] [CrossRef]
  36. Wu, Y.; Yu, Y. Soil Carbon, Nitrogen and Phosphorus Fractions and Response to Microorganisms and Mineral Elements in Zanthoxylum Planispinum ‘Dintanensis’ Plantations at Different Altitudes. Agronomy 2023, 13, 558. [Google Scholar] [CrossRef]
  37. Hubbell, S.P. Commonness and Rarity in a Neotropical Forest: Implications for Tropical Tree Conservation. Conserv. Biol. Sci. Scarcity Divers. 1986, 205–231. [Google Scholar]
  38. Jorgensen, S.E.; Fath, B.D. Encyclopedia of Ecology; Elsevier B.V.: Amsterdam, The Netherlands, 2008; ISBN 978-0-444-52033-3. [Google Scholar]
  39. Heil, K.; Schmidhalter, U. Improved Evaluation of Field Experiments by Accounting for Inherent Soil Variability. Eur. J. Agron. 2017, 89, 1–15. [Google Scholar] [CrossRef]
  40. Fromont, C.; Riegler, M.; Cook, J.M. Relative Abundance and Strain Diversity in the Bacterial Endosymbiont Community of a Sap-Feeding Insect Across Its Native and Introduced Geographic Range. Microb. Ecol. 2017, 74, 722–734. [Google Scholar] [CrossRef] [PubMed]
  41. Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R.; Legendre, P.; Mc Glinn, D.; Minchin, P.R.; O’Hara, R.B.; Simpson, G.L.; Solymos, P.; et al. Vegan: Community Ecology Package. R Packages Version 2.5–7. Available online: https://CRAN.R-project.org/package=vegan/ (accessed on 28 November 2020).
  42. Grunsky, E.C. R: A Data Analysis and Statistical Programming Environment–an Emerging Tool for the Geosciences. Comput. Geosci. 2002, 28, 1219–1222. [Google Scholar] [CrossRef]
  43. Rodríguez-Berbel, N.; Soria, R.; Villafuerte, A.B.; Ortega, R.; Miralles, I. Short-Term Dynamics of Bacterial Community Structure in Restored Abandoned Agricultural Soils under Semi-Arid Conditions. Agronomy 2023, 13, 86. [Google Scholar] [CrossRef]
  44. Belay, A.; Claassens, A.; Wehner, F. Effect of Direct Nitrogen and Potassium and Residual Phosphorus Fertilizers on Soil Chemical Properties, Microbial Components and Maize Yield under Long-Term Crop Rotation. Biol. Fertil. Soils 2002, 35, 420–427. [Google Scholar] [CrossRef]
  45. Riggs, C.E.; Hobbie, S.E. Mechanisms Driving the Soil Organic Matter Decomposition Response to Nitrogen Enrichment in Grassland Soils. Soil Biol. Biochem. 2016, 99, 54–65. [Google Scholar] [CrossRef]
  46. Colla, G.; Rouphael, Y.; Mirabelli, C.; Cardarelli, M. Nitrogen-use Efficiency Traits of Mini-watermelon in Response to Grafting and Nitrogen-fertilization Doses. Z. Pflanzenernähr. Bodenk. 2011, 174, 933–941. [Google Scholar] [CrossRef]
  47. Xu, S.; Li, P.; Sayer, E.J.; Zhang, B.; Wang, J.; Qiao, C.; Peng, Z.; Diao, L.; Chi, Y.; Liu, W.; et al. Initial Soil Organic Matter Content Influences the Storage and Turnover of Litter, Root and Soil Carbon in Grasslands. Ecosystems 2018, 21, 1377–1389. [Google Scholar] [CrossRef]
  48. Chen, B.; Yang, H.; Song, W.; Liu, C.; Xu, J.; Zhao, W.; Zhou, Z. Effect of N Fertilization Rate on Soil Alkali-Hydrolyzable N, Subtending Leaf N Concentration, Fiber Yield, and Quality of Cotton. Crop J. 2016, 4, 323–330. [Google Scholar] [CrossRef]
  49. Xu, C.; Chen, Y.; Zang, Q.; Li, Y.; Zhao, J.; Lu, X.; Jiang, M.; Zhuang, H.; Huang, L. The Effects of Cultivation Patterns and Nitrogen Levels on Fertility and Bacterial Community Characteristics of Surface and Subsurface Soil. Front. Microbiol. 2023, 14, 1072228. [Google Scholar] [CrossRef] [PubMed]
  50. Bai, Z.; Caspari, T.; Gonzalez, M.R.; Batjes, N.H.; Mäder, P.; Bünemann, E.K.; de Goede, R.; Brussaard, L.; Xu, M.; Ferreira, C.S.S.; et al. Effects of Agricultural Management Practices on Soil Quality: A Review of Long-Term Experiments for Europe and China. Agric. Ecosyst. Environ. 2018, 265, 1–7. [Google Scholar] [CrossRef]
  51. Kudrevatykh, I.Y.; Kalinin, P.I.; Mitenko, G.V. The Effect of Changes Vegetation Cover on the Chemical Properties of Steppe Soils during Climate Aridization. Plant Soil 2023, 491, 265–284. [Google Scholar] [CrossRef]
  52. Yan, F.; Zhang, F.; Fan, J.; Hou, X.; Bai, W.; Liu, X.; Wang, Y.; Pan, X. Optimization of Irrigation and Nitrogen Fertilization Increases Ash Salt Accumulation and Ions Absorption of Drip-Fertigated Sugar Beet in Saline-Alkali Soils. Field Crops Res. 2021, 271, 108247. [Google Scholar] [CrossRef]
  53. Chen, H.; Ren, A.; Hu, Z.; Jia, C.; Wang, J. Effects of Irrigation and Fertilization on Soil Salt Migration, Yield, and Water Use Efficiency of Winter Wheat in the Yellow River Delta. Crop Sci. 2022, 62, 1584–1602. [Google Scholar] [CrossRef]
  54. van Wesemael, B.; Chartin, C.; Wiesmeier, M.; von Lützow, M.; Hobley, E.; Carnol, M.; Krüger, I.; Campion, M.; Roisin, C.; Hennart, S.; et al. An Indicator for Organic Matter Dynamics in Temperate Agricultural Soils. Agric. Ecosyst. Environ. 2019, 274, 62–75. [Google Scholar] [CrossRef]
  55. Peñuelas, J.; Poulter, B.; Sardans, J.; Ciais, P.; van der Velde, M.; Bopp, L.; Boucher, O.; Godderis, Y.; Hinsinger, P.; Llusia, J.; et al. Human-Induced Nitrogen–Phosphorus Imbalances Alter Natural and Managed Ecosystems across the Globe. Nat. Commun. 2013, 4, 2934. [Google Scholar] [CrossRef] [PubMed]
  56. Simpson, M.; McLenaghen, R.D.; Chirino-Valle, I.; Condron, L.M. Effects of Long-Term Grassland Management on the Chemical Nature and Bioavailability of Soil Phosphorus. Biol. Fertil. Soils 2012, 48, 607–611. [Google Scholar] [CrossRef]
  57. Han, J.; Xie, J.; Zhang, Y. Potential Role of Feldspathic Sandstone as a Natural Water Retaining Agent in Mu Us Sandy Land, Northwest China. Chin. Geogr. Sci. 2012, 22, 550–555. [Google Scholar] [CrossRef]
  58. Morris, M.G. The Effects of Structure and Its Dynamics on the Ecology and Conservation of Arthropods in British Grasslands. Biol. Conserv. 2000, 95, 129–142. [Google Scholar] [CrossRef]
  59. Kaiser, W.; Avenant, N.L.; Haddad, C.R. Assessing the Ecological Integrity of a Grassland Ecosystem: The Applicability and Rapidity of the SAGraSS Method. Afr. J. Ecol. 2009, 47, 308–317. [Google Scholar] [CrossRef]
  60. Tsiafouli, M.A.; Thébault, E.; Sgardelis, S.P.; de Ruiter, P.C.; van der Putten, W.H.; Birkhofer, K.; Hemerik, L.; de Vries, F.T.; Bardgett, R.D.; Brady, M.V.; et al. Intensive Agriculture Reduces Soil Biodiversity across Europe. Glob. Chang. Biol. 2015, 21, 973–985. [Google Scholar] [CrossRef] [PubMed]
  61. Dornelas, M. Disturbance and Change in Biodiversity. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 3719–3727. [Google Scholar] [CrossRef] [PubMed]
  62. Bardgett, R.D.; Wardle, D.A. Aboveground-Belowground Linkages: Biotic Interactions, Ecosystem Processes, and Global Change; Oxford University Press: Oxford, UK, 2010; ISBN 978-0-19-954687-9. [Google Scholar]
  63. Bengtsson, J. Disturbance and Resilience in Soil Animal Communities. Eur. J. Soil Biol. 2002, 38, 119–125. [Google Scholar] [CrossRef]
  64. Schneider, M.K.; Lüscher, G.; Jeanneret, P.; Arndorfer, M.; Ammari, Y.; Bailey, D.; Balázs, K.; Báldi, A.; Choisis, J.-P.; Dennis, P.; et al. Gains to Species Diversity in Organically Farmed Fields Are Not Propagated at the Farm Level. Nat. Commun. 2014, 5, 4151. [Google Scholar] [CrossRef] [PubMed]
  65. Potts, S.G.; Biesmeijer, J.C.; Kremen, C.; Neumann, P.; Schweiger, O.; Kunin, W.E. Global Pollinator Declines: Trends, Impacts and Drivers. Trends Ecol. Evol. 2010, 25, 345–353. [Google Scholar] [CrossRef] [PubMed]
  66. Sapkota, T.B.; Mazzoncini, M.; Bàrberi, P.; Antichi, D.; Silvestri, N. Fifteen Years of No till Increase Soil Organic Matter, Microbial Biomass and Arthropod Diversity in Cover Crop-Based Arable Cropping Systems. Agron. Sustain. Dev. 2012, 32, 853–863. [Google Scholar] [CrossRef]
  67. Gkisakis, V.; Volakakis, N.; Kollaros, D.; Bàrberi, P.; Kabourakis, E.M. Soil Arthropod Community in the Olive Agroecosystem: Determined by Environment and Farming Practices in Different Management Systems and Agroecological Zones. Agric. Ecosyst. Environ. 2016, 218, 178–189. [Google Scholar] [CrossRef]
  68. Uchida, K.; Takahashi, S.; Shinohara, T.; Ushimaru, A. Threatened Herbivorous Insects Maintained by Long-Term Traditional Management Practices in Semi-Natural Grasslands. Agric. Ecosyst. Environ. 2016, 221, 156–162. [Google Scholar] [CrossRef]
  69. Parker, M.; Mac Nally, R. Habitat Loss and the Habitat Fragmentation Threshold: An Experimental Evaluation of Impacts on Richness and Total Abundances Using Grassland Invertebrates. Biol. Conserv. 2002, 105, 217–229. [Google Scholar] [CrossRef]
  70. Cloudsley-Thompson, J. Thermal and Water Relations of Desert Beetles. Naturwissenschaften 2001, 88, 447–460. [Google Scholar] [CrossRef]
  71. Rivnay, E. The Influence of Man on Insect Ecology in Arid Zones. Annu. Rev. Entomol. 1964, 9, 41–62. [Google Scholar] [CrossRef]
  72. Collinge, S.K. Effects of Grassland Fragmentation on Insect Species Loss, Colonization, and Movement Patterns. Ecology 2000, 81, 2211–2226. [Google Scholar] [CrossRef]
  73. Moebius-Clune, B.N.; van Es, H.M.; Idowu, O.J.; Schindelbeck, R.R.; Kimetu, J.M.; Ngoze, S.; Lehmann, J.; Kinyangi, J.M. Long-Term Soil Quality Degradation along a Cultivation Chronosequence in Western Kenya. Agric. Ecosyst. Environ. 2011, 141, 86–99. [Google Scholar] [CrossRef]
  74. Cui, X.; Wang, J.; Zhang, L.; Chen, T.; Zhuge, Y.; Dong, Y. Soil Nutrient Retention and Yield Effect of Nitrogen, Phosphorus Synergists on Wheat/Maize Rotation in Brown Soil. Agronomy 2022, 12, 2445. [Google Scholar] [CrossRef]
  75. Zheng, X.; Wei, L.; Lv, W.; Zhang, H.; Zhang, Y.; Zhang, H.; Zhang, H.; Zhu, Z.; Ge, T.; Zhang, W. Long-Term Bioorganic and Organic Fertilization Improved Soil Quality and Multifunctionality under Continuous Cropping in Watermelon. Agric. Ecosyst. Environ. 2024, 359, 108721. [Google Scholar] [CrossRef]
  76. Weyers, S.L.; Archer, D.W.; Johnson, J.M.F.; Wilts, A.R. Management Drives Differences in Nutrient Dynamics in Conventional and Organic Four-Year Crop Rotation Systems. Agronomy 2020, 10, 764. [Google Scholar] [CrossRef]
  77. Tilman, D. Niche Tradeoffs, Neutrality, and Community Structure: A Stochastic Theory of Resource Competition, Invasion, and Community Assembly. Proc. Natl. Acad. Sci. USA 2004, 101, 10854–10861. [Google Scholar] [CrossRef] [PubMed]
  78. Hassell, M.P.; Tilman, D. Resource Competition and Community Structure. J. Anim. Ecol. 1984, 53, 697. [Google Scholar] [CrossRef]
  79. de Vries, F.T.; Hoffland, E.; van Eekeren, N.; Brussaard, L.; Bloem, J. Fungal/Bacterial Ratios in Grasslands with Contrasting Nitrogen Management. Soil Biol. Biochem. 2006, 38, 2092–2103. [Google Scholar] [CrossRef]
Agronomy 14 01841 g001
Figure 1. (a) Gravel-sand-mulched watermelon fields; (b) watermelons.
Figure 1. (a) Gravel-sand-mulched watermelon fields; (b) watermelons.
Agronomy 14 01841 g001
Agronomy 14 01841 g002
Figure 2. Changes in soil physicochemical properties at different tillage years. Notes: APP is available potassium (mg/kg), TK is total potassium (mg/kg), TP is total phosphorus (mg/kg), AHN is alkali hydrolyzable nitrogen (mg/kg), PH is pH, EC is the conductivity of the leaching solution (us/cm), TN is total nitrogen (g/kg), OM is organic matter (g/kg), APK is available phosphorus (mg/kg), W is water content; areas with 1, 5, 10, and 21 years of watermelon cultivation were designated as treatment areas and labeled GPS-1Y, GPS-5Y, GPS-10Y, and GPS-21Y, respectively. Native desert grassland and its transition zone adjacent to the compacted sand planting area were used as control areas, labeled NG and STZ. Lowercase letters indicate significant differences (p < 0.05) between soil factors.
Figure 2. Changes in soil physicochemical properties at different tillage years. Notes: APP is available potassium (mg/kg), TK is total potassium (mg/kg), TP is total phosphorus (mg/kg), AHN is alkali hydrolyzable nitrogen (mg/kg), PH is pH, EC is the conductivity of the leaching solution (us/cm), TN is total nitrogen (g/kg), OM is organic matter (g/kg), APK is available phosphorus (mg/kg), W is water content; areas with 1, 5, 10, and 21 years of watermelon cultivation were designated as treatment areas and labeled GPS-1Y, GPS-5Y, GPS-10Y, and GPS-21Y, respectively. Native desert grassland and its transition zone adjacent to the compacted sand planting area were used as control areas, labeled NG and STZ. Lowercase letters indicate significant differences (p < 0.05) between soil factors.
Agronomy 14 01841 g002
Agronomy 14 01841 g003
Figure 3. Relative abundances of ecological function arthropod groups.
Figure 3. Relative abundances of ecological function arthropod groups.
Agronomy 14 01841 g003
Agronomy 14 01841 g004
Figure 4. Changes in diversity of arthropods at different tillage years. Notes: Lowercase letters indicate significant differences (p < 0.05) between arthropod Diversity.
Figure 4. Changes in diversity of arthropods at different tillage years. Notes: Lowercase letters indicate significant differences (p < 0.05) between arthropod Diversity.
Agronomy 14 01841 g004
Agronomy 14 01841 g005
Figure 5. NMDS ordination map based on species abundance (A) and soil physicochemical properties (B). RDA for arthropod community, dominant species, and soil physicochemical properties in four types of grasslands (C). Note: Laja is Labidura japonica number of individuals; Caae is Cataglyphis aenes number of individuals. Note: ***, p < 0.001; **, p < 0.01.
Figure 5. NMDS ordination map based on species abundance (A) and soil physicochemical properties (B). RDA for arthropod community, dominant species, and soil physicochemical properties in four types of grasslands (C). Note: Laja is Labidura japonica number of individuals; Caae is Cataglyphis aenes number of individuals. Note: ***, p < 0.001; **, p < 0.01.
Agronomy 14 01841 g005
Table 1. Methods for determinations of soil physicochemical properties.
Table 1. Methods for determinations of soil physicochemical properties.
IndicatorsAbbreviationUnitsMethodsInstrument Model
Total nitrogenTNmg/kgK2SO4, CuSO4, Se boiling + automatic interrupted chemical analyzerSMARTCHEN 450 Chemistry analyzers, KPM Analysis Group, Rome, Italy
Total phosphorusTPmg/kgAmmonium phosphomolybdate + automatic chemical analyzer colorimetryAgilent ICP-OES 5110, Agilent Technologies, Inc., Kuala Lumpur, Malaysia
Total potassiumTKmg/kgHydrofluoric acid cooking-ICP-0ES detectionAgilent ICP-OES 5110, Agilent Technologies, Inc., Kuala Lumpur, Malaysia
Alkali hydrolyzable nitrogenAHNmg/kgAlkaline diffusion method——
Available phosphorusAPKmg/kgSodium bicarbonate extraction + automatic interrupted chemical analyzerSMARTCHEN 450 Chemistry analyzers, KPM Analysis Group, Rome, Italy
Available potassiumAPPmg/kgAmmonium acetate extraction-ICP-0ES detectionAgilent ICP-OES 5110, Agilent Technologies, Inc., Kuala Lumpur, Malaysia
Organic matterOMg/kgTOC analyzer, ElimontaElimonta VARIO TOC, Elementar trading Co., Ltd, Frankfurt, Germany
Conductivity of the leaching solutionECus/cmConductivity meterDDS-307A, Shanghai Yi Electrical Scientific Instruments Co., LTD, Shanghai, China
pHPH pH meterPHS-2F, Shanghai Yi Electrical Scientific Instruments Co., LTD, Shanghai, China
Water contentW%Gravimetric methodDHG-9075A, Shanghai Yiheng Scientific Instrument Co., LTD, Shanghai, China
Table 2. Analysis of abundance differences of functional arthropod taxa in different tillage years.
Table 2. Analysis of abundance differences of functional arthropod taxa in different tillage years.
Abundance
PollinatorDetritivoreHerbivoreOmnivoreCarnivore
STZ0.60 ± 0.40 a 1.00 ± 0.77 b 9.80 ± 4.33 a 10.40 ± 5.45 b 17.20 ± 5.44 c
NG1.00 ± 0.77 a 1.00 ± 0.77 b 5.00 ± 1.14 ab 23.20 ± 5.36 a 30.80 ± 3.02 bc
GPS-1Y0.00 ± 0.00 a 1.60 ± 0.98 ab 2.40 ± 1.44 b 0.40 ± 0.40 b 42.40 ± 8.38 b
GPS-5Y0.00 ± 0.00 a 4.00 ± 1.34 a 1.60 ± 0.68 b 2.20 ± 0.66 b 19.20 ± 7.12 c
GPS-10Y0.20 ± 0.20 a 0.40 ± 0.24 b 4.00 ± 2.55 ab 0.80 ± 0.37 b 24.20 ± 2.71 c
GPS-21Y0.00 ± 0.00 a 0.60 ± 0.60 b 3.60 ± 1.21 ab 0.60 ± 0.40 b 60.00 ± 5.21 a
Notes: Lowercase letters indicate significant differences (p < 0.05) between abundance of different functional taxa of arthropods.
Table 3. Results of ANCOVA models for arthropod diversity.
Table 3. Results of ANCOVA models for arthropod diversity.
Tillage Year
Sum SqdfFp
Arthropod richness475.1196.8051.15 × 10−8 ***
Arthropod abundance196218.7398.45× 10−3 **
Arthropod Margalef29.4881129.4621.18 × 10−9 ***
Arthropod Simpson1.1281246.4386.01 × 10−12 ***
Arthropod Shannon9.2281209.9112.3 × 10−11 ***
Arthropod Pielou0.4283168.271.54 × 10−7 ***
Note: ***, p < 0.001; **, p < 0.01.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, H.; Cao, Z.; Cui, Y.; Xiong, C.; Sun, W.; Wang, Y.; Ban, L.; Zhang, R.; Wei, S. Effects of Different Tillage Years on Soil Composition and Ground-Dwelling Arthropod Diversity in Gravel-Sand Mulching Watermelon Fields. Agronomy 2024, 14, 1841. https://doi.org/10.3390/agronomy14081841

AMA Style

Zhang H, Cao Z, Cui Y, Xiong C, Sun W, Wang Y, Ban L, Zhang R, Wei S. Effects of Different Tillage Years on Soil Composition and Ground-Dwelling Arthropod Diversity in Gravel-Sand Mulching Watermelon Fields. Agronomy. 2024; 14(8):1841. https://doi.org/10.3390/agronomy14081841

Chicago/Turabian Style

Zhang, Haixiang, Ziyu Cao, Yifan Cui, Changyu Xiong, Wei Sun, Ying Wang, Liping Ban, Rong Zhang, and Shuhua Wei. 2024. "Effects of Different Tillage Years on Soil Composition and Ground-Dwelling Arthropod Diversity in Gravel-Sand Mulching Watermelon Fields" Agronomy 14, no. 8: 1841. https://doi.org/10.3390/agronomy14081841

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop