**Contents**

## **Beata Medy ´nska-Gulij and Krzysztof Zagata**

**Beata Medy ´nska-Gulij 1 , David Forrest 2 and Paweł Cybulski 1,\***

**Abstract:** This article summarizes the Special Issue of "Multimedia Cartography". We present three main research fields in which multimedia cartography and the study of the effectiveness of multimedia maps are currently taking place. In each of these fields, we describe how published research is embedded in the broader context of map design and user studies. The research refers to contemporary technological trends such as web HTML5 standards, virtual reality, eye tracking, or 3D printing. Efficiency, performance, and usability studies of multimedia maps were also included. The research published in this issue is interdisciplinary. They combine traditional mapping methods with new technologies. They are searching for new places for cartography in, e.g., the environment of computer games. They combine the design of the map with its perception by users.

**Keywords:** multimedia; cartography; animation; spatial visualization; multimedia cartographic product; medium efficiency; medium attractiveness

#### **1. Introduction**

The growing volume of data that can be presented by cartographic visualization requires map makers to use different means of expression. In modern cartographic communication, they are often related to multimedia. They allow integration the map with video, sound, or animation in an interactive environment. Multimedia cartography is a research area in cartography that focuses on the utilization of various multimedia for the effective and efficient visualization and communication of spatiotemporal data. The multidimensionality of geographical data creates certain demands for mapping products, and the answer to these demands is multimedia cartography. Assessing multimedia effectiveness, efficiency, and usability is possible thanks to user studies. In this approach, actual users are involved in the overall process of map design.

#### **2. Dynamic Spatiotemporal Data**

This Special Issue focuses on the crucial features of modern maps, which are effectiveness, efficiency, and attractiveness of information communication. The issues of multimedia cartography include, among others, the study of dynamic maps. Due to its visual attractiveness, animation is eagerly used for data that change over time. It is often presented in global news websites, as well as education, entertainment, weather forecasts on the web or on television. For this reason, it is crucial to conduct research on effectiveness of animated map communication. Popular time-series animation causes some perceptual difficulties for map users. Therefore, Traun et al. [1] conducted empirical research on the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends in spatiotemporal data. Such an approach results from searching for solutions to reduce the cognitive load that animated map users experience. The research experiment with the participation of users presented in this study assumed

**Citation:** Medy ´nska-Gulij, B.; Forrest, D.; Cybulski, P. Modern Cartographic Forms of Expression: The Renaissance of Multimedia Cartography. *ISPRS Int. J. Geo-Inf.* **2021**, *10*, 484. https://doi.org/ 10.3390/ijgi10070484

Received: 6 July 2021 Accepted: 9 July 2021 Published: 14 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the identification of outliers and the recognition of spatial trends. Study results show that, based on the spatial generalization of choropleth map animation, participants had higher outlier detection performance. However, it turned out that there was no significant improvement of participants' ability to correctly recognize the temporal trend. Moreover, the trend detection performance decreased with participants' age.

The effectiveness of presentation of spatiotemporal data was the main issue of the paper from Medy´nska-Gulij et al. [2]. In this study, the main focus was on presentation of moveable and stationary objects in dynamic cartographic presentation. Three types of animated maps were proposed to represent people gathering at mass events. The study adopted experts' opinion and objective effectiveness evaluation. Therefore, various mapping techniques were presented of how to visualize people at a mass event. Methods proposed combined experts subjective opinion and a user study on effectiveness. The results show that the most difficult was to assess the dynamics of spatial relations (e.g., crowdedness, the number of participants). The study results showed the great importance of visually highlighting stationary objects and the need to minimize the number of map elements such as scale bar and north arrow.

Another study in this Special Issue related to cognitive aspects of spatiotemporal data was conducted by Cybulski [3]. This study takes into account two map backgrounds, which are a road map (abstract image) and a satellite map (realistic image). On these two cartographic backgrounds, participants were presented with a set of animated routes of varying difficulty (dependent on length and number of turns). The main goal of the aforementioned study is to assess the effectiveness and efficiency of route memorization based on the cartographic image. Additionally, the study adopts an eye tracking methodology to examine and explain the visual strategy that follows effective route memorization in different map-based tasks conditions. The main result shows that the effectiveness and efficiency of route memorization depends on the route difficulty. The research shows that, in this type of map-based task, the cartographic background has no impact on the memorization process nor visual strategy. The study reveals that, although the starting point of animation was the most fixated location, most errors in route memorization were related to the starting position. Additionally, the study results present the effect of subjective spatial abilities on visual strategy.

The abovementioned studies are in line with the map perception field of cartographic research. They result from the need for an objective assessment of multimedia cartographic products by the users themselves. Such results provide necessary feedback cartographers and modern map makers [4,5].

#### **3. Maps and Interactions**

Interactivity is an crucial feature of modern multimedia cartographic products [6]. Research conducted by Lorek and Horbi´nski [7] analyze old cartographic material in the context of usefulness in designing an interactive multimedia map of spatial development of Gliwice. The spatial development was based on vectorization spatial features from six maps: Urmesstischblätter map, Messtischblätter map, three topographical maps from different time periods, and the OpenStreetMap (OSM) database. A modern spatial database together with archival cartographic material contributed to the design of an interactive map of European freeway junction A1/A4 development. This study presented how to use old cartographic materials with a spatial database and implementation of a JavaScript Library– Leaflet. Research results focus on describing the utility of interactions such as mouse over or mouse click and implementation of W3C standards for new cartographic products.

An important part of interactive multimedia maps is the Graphical User Interface (GUI). The GUI often consists of several interactive buttons which that different functionality of a web mapping service. However, each global mapping service (e.g., Google Maps, Baidu Maps, OSM) has its unique set button graphics and arrangement. Therefore, users experience interaction differently in each of these services. This user experience (UX) while performing interactive map-based tasks was a main focus in the study from Cybulski and

Horbi ´nski [8]. The study examined subjects' experience of interaction through the several different GUI and registered eye movement during the task. The study resulted in some valuable recommendations for map designers. It turned out that it is better for effective interaction to group buttons with similar functionality in the corners of the screen.

Another vital issue related to the interface and interaction is the maps' ability to adapt to the device on which it is displayed. In this context Horbi ´nski et al. [9] performed a study on the responsiveness of GUI and its effectiveness in information communication. The design process of the multimedia map draws from modern JavaScript libraries, external plugins and HTML5 standards. In this study interactions and responsiveness of GUI were based on Google Maps, OpenStreetMap, and additional button arrangement based on the study of Horbi´nski et al. [10]. Analysis of mobile and desktop map-based tasks in a user testing research experiment with recording of eye movements was possible thanks to the use of an online questionnaire and Tobii X2-60 eye tracker. Study participants used three main functions which were: geolocation, spatial search, and route find. The study presented a novel GUI effectiveness index based on three parameters: the time to the first fixation on a target button, time of identification, and the time to the first mouse click. The study results reveal that the most effective way of performing all the interactive tasks was when the GUI was designed first on the desktop application and adapted for the mobile device. This was highly related to participants preferences of using web mapping services on the desktop computer. However, the most effective way of spatial search was when the buttons were in the lower part of the mobile screen, which was closest to the thumb.

The ease of receiving spatial information can prompt users to visit popular sightseeing websites. Therefore, Kato and Yamamoto [11] proposed the development a sightseeing spot recommendation system that could be useful and efficient for tourists. In the study, they an integrated social networking service, a web-geographic information system, and a recommendation system for interactive maps. The research experiment involved users' assessment of the system according to the viewing function, submitting functions, and recommendation functions of sightseeing spots. The study urges designers to take into account users' preferences in the map design process. The significant aspect of this study is that designed system could adopt the knowledge-based recommendations with collaborative recommendations according to users' preferences.

Multimedia presentations can combine text, image, videos, charts and maps, and through the interactive dashboard enables selecting, searching, and filtering the spatial data [12]. Zuo et al. [13] designed a novel map-based interactive dashboard supporting users' spatiotemporal knowledge acquisition and analysis. In the design of the dashboard, they applied interactive maps. Initially, the experiment consisted of free exploration in which participants could interact freely with the dashboard. Subsequently, there was a tasks-solving stage in which participants explored the dashboard to check authenticity of statements being displayed. The experimental study adopted a Gazepoint GP3 eye tracker with 60 Hz frequency. For several tasks, the study revealed the visual strategy that was most common for knowledge acquisition. This resulted in significant suggestions for designers of such dashboards. First of all, the study mentions the important role of the font size and proper labelling. Secondly, the panel arrangement in the interactive dashboard should follow a logical order (e.g., panels with similar content should be placed together).

In summary, interactive maps are the basic element of interest in multimedia cartography [14]. The GUI is especially important because global mapping services such as Google Maps, Bing Maps or Baidu Maps use an interactive interface as well as StreetView photos, videos and images. Studying the user experience during human–map interaction is a source of knowledge for effective and efficient mapping solutions. It also gives you the opportunity to optimize interactions for specific mapping products.

#### **4. New Visualization Methods**

The growing number of spatial and temporal features which can be presented through a cartographic product require searching for new visualization methods. Wielebski et al. [15] proposed a set of complementary visualizations, interactive and static, to examine issues of people's spatial behavior. The aim of this study was to examine the usefulness for data analysis and interpretation of the following track features of human movement in urban space: trajectory, correctness, length and visibility. It also considered additional aspects such as walking time, walking speed, stops, spatial context, and motivation for getting to the finish point. The study involved recording the movement of people with the use of GPS and a questionnaire about pedestrian motivations. A space-time cube (STC) was designed and modified so that trajectories of movement could be presented in pseudo 3D view along with other mentioned features. Visualization was enhanced with buildings in City GML standard. However, the route graph, proposed by Andrienko and Andrienko [16] was assessed as the most useful for differentiation of pedestrian tracks, visualizing track length, walking time, motivation of route choice and pace/stops/tempo. The STC was the most useful for visualizing spatial context, and location in geographic space.

Other hew ways of using multimedia in cartography involves virtual tours and virtual reality (VR). Some of these virtual images uses official data (e.g., topographic databases Halik [17]) and are used by leisure industries to attract tourists [18]. Lai et al. [19] performed a research about processing panoramic images for a VR environment integrated in a multimedia platform. This study contributed especially to improving panoramic photography and image stitching quality, proposed a strategy for producing high quality panoramic images, combination of information technologies, and assessing teaching effectiveness through the multimedia platform. The results show that RMSE of stitched images was below five pixels using a full-frame single-lens reflex camera equipped with an ultra-wide-angle zoom lens on a panoramic instrument (GigaPan EPIC Pro V).

User experience in VR could be different based on previous experience with this technology. Medy´nska-Gulij and Zagata [20] performed an experimental study in which they asked experts and gamers to take an immersive experience in a virtual stronghold of Ostrów Lednicki (Poland). Participants had to take a virtual walk around the stronghold, visit the palatium with a chapel, then go to the viewing point. The VR environment was built in the Unity game engine. The building geometry was based on a point cloud acquired from LiDAR. The gamers group needed less time than the experts group to complete the tour. However, expert users made more comments, and their observations made references to their expertise from academic publications. It turned out that gamers were more homogenous in their comments and opinions. Based on this, the study concludes that cartographic materials could be a fundamental source of information in immersive VR for cultural objects and reconstructions.

Another project related to VR technology and cartographic context is provided by Zagata et al. [21]. They studied the effectiveness of a mini-map for navigation in a reconstructed stronghold. The research adopted eye tracking technology in order to capture the total time users gaze on the mini-map. Participants were asked to find and collect Mieszko I's coins within the stronghold, highlighted on the mini-map. One group of participants used walking, and the second used teleportation for movement in space. Study participants were gamers, playing computer games a minimum 10 h per week. The study was supported by several software packages, e.g., SketchUp, CloudCompare, Photoshop, and Unity game engine. The main hardware was HTC Vive Pro Eye googles with an in-built eye tracking device. The study reveals that participants who examined the mini-map longer needed more time to finish collecting all the coins. There was no significant difference between people walking and teleporting in mini-map examination time. However, participants who used teleportation finished the task faster.

New visualization methods draw from various data gathering methods. Another study analyzed land use mapping techniques according to pedestrian movement registration with unmanned aerial vehicle (UAV) [22]. The research aimed to develop a methodological approach for thematic maps production based on low-level aerial images with recording of pedestrian movement. The study proposed point-to-polygon transformation of pedestrian visualization. The outcome of the research was several thematic maps of

pedestrian density and land use occupied by pedestrians. The results show that formal land use classification could be changed due to the pedestrian movement behavior. The usage of point-to-polygon transformation allowed qualitative data to be obtained, therefore providing complementary information to actual land use visualization.

Visualization of spatial data is not only related to digital cartography. Harding et al. [23] proposed a web application enabling 3D printing of terrain models. This type of terrain visualization is something opposite to the VR environment, and the models that are produced are static in nature. Three-dimensional models provide the aspect of tangibility of cartographic product. Printed physical terrain models are commonly used in geography and geology teaching. The TouchTerrain application uses Google Earth Engine and can process digital 3D terrain models according to given coordinates. Additionally, it provides the possibility to import vector data such as GPS (GPS Exchange Format). In the study, Google Analytics were used to determine TouchTerrain users' characteristics. It turned out that most users are returning users, and only 21% of all users are new users (the results are based on the session between 1 July 2019 and 26 December 2020). The primary location of users, based on IP geolocation, suggests US and Western Europe.

#### **5. Conclusions**

This Special Issue on multimedia cartography highlights detailed research in specific research fields. However, the field of interest in multimedia cartography is much wider. Particularly noteworthy is the importance of the cartographic symbol and the map itself as the core of the multimedia visualization. The leading topic at present is the effectiveness of individual media in conveying information, in particular the effectiveness and efficiency of cartographic communication in multimedia presentation. In the context of changing technology, it is interesting to note the importance of new media such as VR, AR, holography and their significance for cartography. Among the research included in this Special Issue, one can also find questions about the place of maps in new technologies.

As the presented research shows, cartography plays an crucial role in the information communication process. The map is becoming an inseparable element of navigation in space, especially on mobile devices. In addition to the real world, it is part of virtual tours and allows finding specific locations in virtual space. Cartographers search for mapping methods that will be most useful and attractive for users by researching effectiveness, efficiency, and user experience implementing new research methods and technologies. Multimedia maps are used in education, storytelling, navigation, visualization and, above all, in everyday activities.

**Author Contributions:** Conceptualization, Beata Medy ´nska-Gulij, David Forrest, and Paweł Cybulski.; methodology, Beata Medy´nska-Gulij, David Forrest, and Paweł Cybulski; writing—original draft preparation, Beata Medy ´nska-Gulij, David Forrest, and Paweł Cybulski. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**4. Bronchus-associated lymphoid tissue (BALT)**

BALT, an important part of MALT, is classically used to refer to intrapulmonary lymphoid tissue in connection with the pulmonary vessels and adventitia of the bronchi [11, 28]. Macklin [29] named this lymphoid tissue in 1955 as 'sumps' or 'pulmonary tonsils' in which dust and organisms are retained. Subsequently, Bienenstock et al. [28, 30] identified these formations as subepithelial follicular lymphoid aggregates, primarily composed of lymphocytes, organized in the bronchial mucosa in contact with the surface epithelium, and coined the term BALT to describe them.

Although BALT, a secondary lymphoid tissue that plays an important role in the maintenance and regulation of lung mucosal immune homeostasis [8], was initially claimed to resemble Peyer's patches in the small intestine [11]; it was later revealed

that it was quite different from these formations [31]. Compared to GALT where in the founder Peyer's patches are located, it is accepted that BALT is not regularly present during fetal life due to embryonic preprogramming; however, it occurs with antigenic stimulation during the postnatal period [32, 33]. In other words, it is claimed that there is a relatively special lymphoid tissue in the development of BALT. However, studies have shown that BALT exhibits great differences between species [34, 35].

BALT, which was first identified in the bronchial wall of rabbits by Bienenstock et al. [28], is frequently detected in these animals and has the highest number of regions [28, 34]. In terms of the presence and distribution of BALT, rats and guinea pigs [34] follow rabbits, whereas germ-free pigs [28, 34], cats, dogs, and Syrian hamsters [34, 36] do not have this lymphoid tissue. BALT is frequently present in poultry, particularly hens [37]. In mice and humans, the situation with BALT is a little more contradictory [19]. Some scientists suggest that BALT is present in germ-free mice when antigenic stimulation is absent [12], whereas others report that it is not [38, 39]. Besides the differing viewpoints on the presence of BALT in mice, it is assumed that it is only observed infrequently after the neonatal period.

Further, it is claimed that BALT is not present in structurally healthy humans [31] because the features similar to BALT in mice are also found in humans [8]. BALT, in particular, is detectable if it is induced in adults; however, it is only observed in 40% healthy children and adolescents. Factors inducing the presence and distribution of BALT in these adults include infection, pathogen exposure, chronic pulmonary inflammation or autoimmune disease, etc. [32, 33, 40]. Moreover, it is suggested that the formation, size, and amount of BALT depend on the type and duration of exposure [41]. Therefore, it is concluded that BALT varies in different species as well as indifferent physiological states of the same species [8].

#### **4.1 Inducible BALT (**İ**BALT)**

Most of the secondary lymphoid organs found in mice and humans develop embryonically in the absence of microbial stimulation or environmental antigens [42]. Furthermore, the structure and function of several secondary lymphoid organs, particularly those on the mucosal surfaces, are dramatically altered upon exposure to foreign antigens and commensal organisms [43]. Peyer's patches of MALT demonstrate a striking increase in size and complexity following the colonization of commensals [44, 45]. Similarly, in rodents, NALT is not completely developed until the postnatal period; however, microbial exposure accelerates this process [46]. On the other hand, the appendix tissue of rabbits has the characteristics of the primary and secondary lymphoid tissues in terms of being functionally dependent on microbial colonization [47]. However, some lymphoid tissues, known as tertiary lymphoid tissues, develop only after environmental exposure to microbes, pathogens, or inflammatory stimulations. Interestingly, although the lungs of mice and humans normally lack organized lymphoid tissue, tertiary lymphoid structures are frequently observed in lung tissue [38, 48].

BALT is recognized as an inducible tertiary or ectopic lymphoid tissue, unlike the related secondary lymphoid organs. BALT develops during the postnatal period and at anatomically non-lymphoid sites. In terms of disease states characterized by chronic inflammation, infection, or autoimmunity, BALT formation can be induced, and these areas are then known as iBALT [32, 38]. iBALT is a classic example of tertiary lymphoid tissue because it does not develop on a preprogrammed basis; its creation, size, and number in the lungs depend on the type and duration of antigenic *Bronchus-Associated Lymphoid Tissue (BALT) Histology and Its Role in Various Pathologies DOI: http://dx.doi.org/10.5772/intechopen.99366*

exposure [31, 49]. iBALT regions are best characterized in the lungs of rodents and humans. They are observed in the lungs of mammals and birds as well as in possibly all air-breathing vertebrates [41]. The emerging arguments confirm the role of infectious agents, such as isolated lymphoid follicles in the gut, indicating that iBALT may develop in response to microbial exposure [32]. In contrast, BALT is said to have been discovered in germ-free rats [28] and mice [50] as well.

Unlike the classical BALT structure, iBALT does not always have an overlying lymphoepithelium, is not associated with a continuous airway, and can be located adjacent to small pulmonary arteries in the lung parenchyma [32]. However, as both BALT and iBALT have the same function, both tissue types are called BALT [48].

#### **4.2 Microscopic structure of BALT**

Microscopically, BALT is defined as a densely packed cluster of lymphocytes with follicular structures enveloped in a network of reticular stromal cells beneath a specialized airway epithelium devoid of cilium. These structures are claimed to be located along the main bronchial airways embedded in the airway wall with extensive lymphocytic infiltration of the epithelial layer forming a classical dome epithelium (**Figures 3** and **4**) [11].

Further, it is stated that BALT is present in bronchial tree bifurcations to capture respiratory antigens. In species, BALT develops in response to various stimulations rather than being constitutively present in the lung, whereas iBALT does not always have such a defined structure or precise localization in the lung [51].

As a part of the integrated mucosal system including GALT, NALT, and other secondary lymphoid tissue representatives, BALT is known to contain cell types that are responsible for eliciting an appropriate immune response. BALT is mainly defined as an organized structure comprising T- and B-cell domains, dendritic cells (DCs), stromal cells, and high endothelial venules (HEVs) in the T-cell region [38, 52–55].

**Figure 4.** *A higher power light microscopic view of the BALT structure, rat lung (H-E). Red star: BALT formation.*

Furthermore, it is stated that most of its cellular component consists of B cells expressing IgMlo IgDhi; however, depending on the nature of the microbe and/or antigen to which the cells respond, IgG-, IgA-, and even IgE-positive plasma cells may also be present [50, 56–58].

Moreover, in BALT, the most prominent structure is follicular-like lymphocyte accumulation, which is the common microscopic appearance of secondary lymphoid tissues, forming a classical germinal center (active site) [59, 60]. In this structure, surrounded by more mature, small lymphocytes, most of the germinal center comprises antigen-presenting macrophages [58, 61]. Lymphocytes leave the blood and migrate to BALT in the walls of HEVs, which are present at the periphery of the tissue. As there are no afferent lymphatics, these HEVs are thought to be the only entry site where lymphocytes migrate to BALT [59, 60]. In addition, the expression of chemokines in HEVs ensures accurate targeting of lymphocytes to lymphoid tissues [62].

However, in the direction of the bronchial epithelium, a dome-like protrusion similar to Peyer's patches toward the bronchial lumen is sometimes clearly observed [31]. The B-cell follicle, which is the most noticeable characteristic in classic BALT tissues with dome epithelium, is positioned below the epithelium [11]. CD4+ T cells are abundant in B-cell follicles, especially in reactive follicles with germinal centers [63], and CD8+ T cells are uncommon. Moreover, BALT is covered by a lymphoepithelium, which contains M cells that are similar to the M cells present in the dome epithelium of Peyer's patches in some species [31]. M cells are thought to transport antigens from the mucosal lumen to DCs that are in close contact with the dome epithelium [48]. Rabbits, the first and important representative of BALT, have fewer ciliated cells, few goblet cells, and many lymphocytes between epithelial and M cells. Although this basic structure appears to be valid for all species, there are some differences in details [31].

Another cell type that makes up the cellular component of BALT is follicular DCs (FDCs). These cells depend on the lymphotoxin signaling pathway to differentiate

### *Bronchus-Associated Lymphoid Tissue (BALT) Histology and Its Role in Various Pathologies DOI: http://dx.doi.org/10.5772/intechopen.99366*

into conventional lymphoid tissues and BALT [38]. Located at the center of B-cell follicles, these cells present antigen to B cells [64] and provide costimulatory signals that increase B-cell activation and proliferation in germinal centers [65, 66]. FDCs in mice are characterized by their ability to bind to antibodies against CD21/CD35 [38], FDCM1, or FDCM2 [57] and to sequester their immune complexes [67]. In addition, FDCs are responsible for the organization of the follicle and expression of CXCL13, which is responsible for the recruitment of B cells and some T cells in the B-cell area [68]. DCs located at the highest concentration in the T-cell areas of BALT are reportedly capable of preserving the BALT architecture as well as their antigenpresenting ability [48].

BALT is induced to produce IgA<sup>+</sup> cells that secrete polymeric IgA, mainly due to its role in immunity. When polymeric IgA is transported into the lumen, it induces the formation of S-IgA, which has considerable immunological importance [8]. Thus, when BALT is identified as part of the integrated mucosal immune system, the term should be restricted to structures tightly associated with an epithelium infiltrated by lymphocytes. In the integrated mucosal immune system, specific antigen uptake and antigen presentation by M cells occur and immune reactions are initiated, including IgA responses [31].

Immunohistological studies in humans show a preferential central localization of B cells mixed with some CD4+ lymphocytes and macrophages. CD4+ lymphocytes are also present in the area around the HEV, at the edge resembling a crown, and in the epithelium. In addition to the few proliferative cells positive for Ki67 observed in the follicles, many cells positive for the human leukocyte antigen-DR isotype, which is associated with various autoimmune conditions, disease susceptibility, and disease resistance, are evenly distributed in the follicle [69]. This basic structural distribution of lymphoid and non-lymphoid cells has also been noted in BALT in pathological conditions such as rheumatoid arthritis [70], hypersensitivity pneumonia [71], or diffuse panbronchiolitis [72]. Therefore, it is reasonable to conclude that BALT plays an important role in many respiratory system-related pathologies.

### **4.3 Role of BALT in various pathologies**

BALT plays an important role in pulmonary immunity such as regulating microbial homeostasis [73], inducing immune tolerance [74], inhibiting inflammation [75], and supporting immune clearance [76]. Therefore, BALT frequently encounters many pathologies associated with infectious disease agents, allergens, environmental antigens, air-borne particles, autoimmune disease agents, and factors causing malignancy. As these pathological conditions have a broad spectrum, it is not possible to discuss all the roles of BALT; therefore, only a few have been addressed.

### *4.3.1 Role of BALT in resistance to infectious diseases*

The respiratory tract is a typical entry site for viruses. This makes it difficult for the immune system to effectively eliminate viruses and virus-infected cells without causing much damage and inflammation, which jeopardizes the lung's structural and functional integrity. The balance between eliciting an immune response to effectively eliminate viruses and virus-infected cells and to cause less damage and inflammation is maintained by a complex network of innate and adaptive immune mechanisms as well as immunomodulatory and anti-inflammatory mechanisms. Accordingly, BALT could be one of the mechanisms that facilitates viral clearance by eliciting immune

responses and decreasing inflammatory responses [48]. BALT reportedly initiates pulmonary immune responses that are faster and more protective than those initiated at systemic sites. It has been proposed that once generated, BALT could play a key role in combating successive rounds of the same infection as well as assisting in establishing local immunity against unrelated viruses or pathogens [51]. For example, it has been suggested that *Lta*\_/\_ mice without lymph nodes and Peyer's patches are more susceptible to the influenza virus and although they elicit immune responses, both Band T-cell responses are delayed. Based on flow cytometric identification of germinal center B cells in the lung to question where immune responses might be initiated, it was concluded that both B- and T-cell responses are probably produced in the lungs [77]. BALT is suggested to be formed in the lungs of *Lta*\_/\_ mice and locally initiates immune responses against influenza because the germinal center is present only in secondary lymphoid tissues. Another study reported that, in addition to germinal centers, plasma cells specific to influenza nucleoprotein were detected in BALT after influenza infection [58]. However, B-cell responses to influenza are accelerated in mice with pre-existing BALT, and morbidity and mortality rates are markedly reduced in response to a variety of viruses, including influenza, severe acute respiratory syndrome coronavirus, and mouse pneumovirus [78].

*Mycobacterium tuberculosis* (Mtb) infection is one of the serious health threats worldwide and is typically confined to the lungs. Although local immune mechanisms are primarily responsible for keeping Mtb infection under control, once the infection has settled in the lungs, immune mechanisms alone do not appear to be capable of eliminating these bacteria [79]. In humans, Mtb is localized to the granulomas comprising a central nucleus surrounded by macrophages, multinucleated giant cells, and lymphocytes [80]. The lymphocyte clusters surrounding these granulomas are B cells that form structures similar to BALT. These BALT areas associated with granuloma have B-cell follicles, and T-cell areas are present at the outer edge of the follicles [81]. Similar BALT domains, for example, have been discovered in murine models of Mtb infection, where B-cell clusters surrounding the granuloma were observed. Well-defined B-cell domains with FDCs are formed as early as day 42 after pulmonary infection and are protected from infection until at least day 90 [82]. Considering the link between B follicular structures surrounding the granuloma and Mtb uptake, another study showed that B-cell follicles formed around Mtb lesions in mice developed large germinal centers, and the B cells responded to the antigen [83]. Therefore, it is indicated that BALT initiates local pulmonary immune responses against Mtb infection via B cells.

### *4.3.2 Role of BALT in pulmonary responses to allergens and environmental antigens*

Endotoxin, known as lipopolysaccharide (LPS), is a component of the gramnegative bacteria [84, 85] that is commonly present in the environment [86, 87]. The development or exacerbation of asthma [86, 87], bronchitis, and chronic obstructive pulmonary disease [88, 89] is linked to considerable LPS exposure. LPS, a classical T-cell-independent B-cell antigen, and mitogen are thought to bind to TLR4 signaling pathway [84, 85], triggering B-cell activation, proliferation, and differentiation into antibody-secreting cells [90]. TLR4 signaling activates macrophages and DCs, epithelial cells, and even fibroblasts, causing them to produce inflammatory cytokines and chemokines [91, 92]. Experimentally, pulmonary exposure of rats to endotoxin has been found to cause increases in pre-existing BALT and pulmonary plasma cells,

### *Bronchus-Associated Lymphoid Tissue (BALT) Histology and Its Role in Various Pathologies DOI: http://dx.doi.org/10.5772/intechopen.99366*

ultimately leading to the formation of germinal centers [93]. Sustained dosing of LPS prior to pulmonary inflammation in BALT-deficient mice appeared to result in BALT development in the major airways with an accumulation of B cells, T cells, and macrophages in the lungs, and even in BALT-deficient areas [94]. Thus, environmental exposures to LPS, often with additional antigenic or inflammatory components, cause BALT reactivity and pulmonary physiology alterations [95].

Considering the importance of pulmonary inflammation in asthma, a correlation between BALT development and asthma is likely. However, some believe that the presence of BALT is not always associated with asthma [96], but that the reactivity of BALT in patients with asthma is elevated [97]. Further, there is evidence that specific allergens, such as *Aspergillus fumigatus*, might cause pulmonary allergies that are similar to asthma. In allergic bronchopulmonary aspergillosis, large BALT regions characterized by diffuse and IgE-stained germinal centers have been found [98]. Thus, it is suggested that BALT can potentially contribute to allergic reactions by producing IgE locally in response to *A. fumigatus*.

Hypersensitivity pneumonia is defined as an inflammatory disease of the alveoli induced by hypersensitivity to inhaled organic antigens [99]. In contrast to asthma, which affects the airways, this condition affects the alveoli [48]. An occupational exposure often is the cause of hypersensitivity pneumonia; it can occur particularly when farmers are exposed to mold and fungi in barns [100]. Considering that hypersensitivity pneumonia results from chronic pulmonary exposure to the antigen, the emergence of well-developed BALT areas with vast germinal centers and FDC networks is not surprising for researchers [61].


*2.2. Phospho-Mimetic AtGOX1, AtGOX2 and ZmGO1 Exhibit Altered Glycolate Oxidase Activities*

To investigate the potential regulation of *At*GOX1 and *At*GOX2 activity by protein phosphorylation and to see if this was conserved in maize photorespiratory GO1, the kinetic parameters of purified recombinant N-terminal His-tagged GOX proteins (see Figure S3) was undertaken. All selected phosphorylated residues were replaced by an aspartate to mimic a constitutive phosphorylation. To produce phospho-dead GOX proteins, S212/213 and T265/266 (Arabidopsis GOX/*Zm*GO1 numberings) were replaced by an alanine while T4/5 and T158/159 were changed to a valine to also mimic the sequence of *At*HOAX1 and *At*HOAX2 (and *Homo sapiens* HAOX2 for the T4 position) (Figure S2) so as to evaluate the role of these amino acids in substrate specificity. Using glycolate as a substrate, *At*GOX1, *At*GOX2 and *Zm*GO1 presented rather similar KM glycolate (210 μM, 279 μM and 126 μM) and kcat (11.12 s−1, 10.93 s−1 and 14.45 s−1) values (Table 2). The T4/5V mutations did not have any consequences on either KM glycolate or kcat while the T4/5D phospho-mimetic mutations drastically decreased (by 10–20 fold) the kcat of the three recombinant GOX enzymes without significantly altering the Km glycolate (Table 2). Similar results were observed for the T265/266 phospho-site since its mutation to alanine did not have any effect on the calculated kinetic parameters while kcat was strongly decreased for *At*GOX1T265D, *At*GOX2T265D and *Zm*GO1T266D proteins by 99%, 81% and 95% (Table 2). *At*GOX1T158V, *At*GOX2T158V and *Zm*GO1T159V also showed altered kinetic parameters with an improved KM glycolate (2–3 fold lower) but a 4–5 fold decreased kcat compared to their wild-type GOX counterparts (Table 2). When this residue was mutated to mimic a phosphorylated GOX, the resulting recombinant proteins (*At*GOX1T158D, *At*GOX2T158D and *Zm*GO1T159D) were inactive (Table 2). Perhaps surprisingly, the mutated S212/213 phospho-site gave a differential affect amongst the three GOX proteins studied. While *At*GOX1S212A and *At*GOX1S212D did not show any differences in their kinetic parameters compared to *At*GOX1WT, both *At*GOX2S212A and *At*GOX2S212D as well as *Zm*GO1S213A and *Zm*GO1S213D exhibited an approximately 2-fold decrease of their kcat with no change in KM glycolate (Table 2).

**Table 2.** Effect of phospho-site mutations on glycolate-dependent kinetic parameters of recombinant *At*GOX1, *At*GOX2 and *Zm*GO1.

Mean values ± SD from three independent biological replicates. Statistical significance was determined by a Student's *t*-test. Values in bold and marked by an asterisk were significantly different compared to the corresponding WT protein (*p* < 0.05).


**Abstract**

Metabolism of most pharmaceutical drugs occurs in the liver. In drug metabolism, enzymes convert drugs to highly water-soluble metabolites to facilitate excretion from the body. Thus, *in vitro* models for studying drug metabolism usually target hepatocytes or subcellular liver fractions like microsomes, cytosols, or S9 fractions with high concentrations of specific enzymes. The most popular subcellular fraction used during drug discovery tends to be the microsomes, as these are easy to prepare and store, are amenable to high throughput screening, and are a relatively low-cost option. Understanding the metabolic stability and kinetics of glucuronidation of an investigational drug is crucial for predicting the pharmacokinetic parameters that support dosing and dose frequency. This chapter provides detailed information about metabolite profiling, metabolic stability, glucuronidation kinetics, reactive metabolites identification, CYP enzyme inhibition, and general protocols using human liver microsomes.

**Keywords:** metabolism, metabolite profiling, metabolic stability, glucuronidation, reactive metabolites, drug–drug interaction


Preface

The world is changing and so is entrepreneurship. The question is in which ways, how, through which focal directions, and by what means? This is the primary question we explore in *Next Generation Entrepreneurship*.

Turkish philosopher İhsan Fazlıoğlu once said that to further develop and change a discipline or an academic field, one must leave past knowledge about the field behind but also make sure that the history of knowledge is being preserved. This poses a big challenge for entrepreneurship. On one hand, the entrepreneurial activities of human beings are as old as the history of humanity. On the other hand, the character of entrepreneurship is changing and requires new explorations based on what is going on and what the future of entrepreneurial activities will be.

The book starts with the section "Entrepreneurship Education and Young Perspectives". The first chapter in this section, "Entrepreneurship Education for the Next Generation of Higher Education in Taiwan" by Jen Chia-Chang, focuses on the relevance of entrepreneurship education in general and the Taiwan case specifically. The author gives best practices of entrepreneurship education and provides a perspective for the future. The second chapter, "The TIPE Model for Teaching Technology-Based Entrepreneurship" by Hongyi Sun, focuses on different models of technology-based entrepreneurship education to present a conceptual model called Technology, Idea, Product, Enterprise (TIPE) for further usage in entrepreneurship education. The third chapter in this section, "The Antecedents and Determinants of Entrepreneurial Intention among Business Students in Vietnam" by Cuong Nguyen, presents an emerging market case and provides empirical evidence in favor of supporting Vietnam's economic development by means of enhancing potential entrepreneurs.

The second section, "New Challenges for Entrepreneurship," begins with the chapter, "Entrepreneurship in a Different Era" by Li Xiong, which contributes to our understanding of how entrepreneurship differs in the industrial era, the network economy era, and the next generation of intelligent economies. The second chapter in this section, "What's "Next"? On the Future of Digital Entrepreneurship" by Burak Erkut and Vildan Esenyel, deals with the concept of digitalization and digital entrepreneurship and provides next-generation models of digital entrepreneurship in which the three main components are business model, customer base, and social networks.

The book finishes with the section "Shaping the Next Generation of Entrepreneurship," which includes a contribution by Camilo Pena Ramirez and Alberto Levy titled "Network Strategy for Entrepreneurs". The authors provide two case studies from Brasil and Chile to highlight the relevance and pattern of network strategies for next-generation entrepreneurship. The second chapter in this section, "Volunteering as an Explanatory Factor of Social Entrepreneurship: An Analysis of an Educational Context" by Francisco J. Garcia-Rodriguez, et al., highlights that social entrepreneurship should be considered independent of other entrepreneurial activities; volunteering is found to be a key determinant in this setup. The final chapter of this

section (and the book), "Bricolage and Growth-Hacking: Two Smart Concepts of Creating a Business Lacking Resources" by Thomas Baaken, Liguang Liu, and Lea Lapornik, highlights the concepts of bricolage and growth-hacking as being relevant to two different stages of a start-up process.

Overall, this book aims to contribute to our understanding of what next-generation entrepreneurship will be and how we should shape its processes and market.

#### **Burak Erkut and Vildan Esenyel**

Faculty of Economics, Administrative, and Social Sciences, Department of Business, Bahçeşehir Cyprus University, Nicosia, Cyprus Section 1


#### *4.1. Experiment Location and Design*

The experiment was carried out in 2019–2020 at Brody, Poznan University of Life Sciences Experimental Station, in Poland (52◦26 18 N 16◦17 40 E). The following factors were tested: band application of ternary fertilizer mixture (NPS) and different total N rates. Treatments of NP(S) fertilization were as follows: P0—control; P1—8.7; P2—17.4; and P3—26.2 kg ha−1 of P (17%, 33% and 67% of total P uptake for grain yield = 9.0 t ha−1). The N rates were as follows: N0—control; N1—60; N2—120; and N3—180 kg ha−1 of N. A nitrogen rate of 180 kg ha−1 corresponded to 100% of maize requirement for this nutrient in mineral fertilizers. The effect of mineral fertilization with NP(S) was compared to the absolute control (AC), without fertilization with these components. NP(S) were applied to the soil using fertilizer NPS(+Zn) in the proportion 20-20-35+0.3 (calculated for N, P2O5, SO3, Zn). The fertilizer is based on two main chemical compounds: di-ammonium phosphate, DAP [(NH4)2HPO4] and ammonium sulfate [(NH4)2SO4]. Therefore, on plots with localized fertilization P1, P2 and P3, the doses of ammonium nitrogen (NH4-N) were 20, 40 and 60 kg ha−1 of N. A detailed summary of the doses of N, P, S and Zn used in the experiment is given in Table 5. In order to simplify the notation of fertilization treatments, the NPSZn rates were recorded as P rates.

**Table 5.** Rates of nutrients (N, P, S and Zn) depending on the fertilization treatment.

\* Nitrogen band application of di-ammonium phosphate (DAP) and ammonium sulfate.

The NP(S) fertilizer was applied while sowing the seeds. The fertilizer was banded to 5 cm below and away from the seeds. In treatments without NP(S) fertilization or with low total N rates, the maize requirement for N was supplemented with ammonium nitrate (34% N)—broadcast application immediately before sowing. Potassium fertilization was carried out in early spring in the form of potassium salt K(S, Mg) in proportion 41(15, 6.5) at the rate of 66.4 kg ha−1 of K, regardless of the NPS treatment.

A randomized complete block design (RCBD) with four replicates was applied in the experiment. The area of an individual plot was 24.0 m2 (3 × 8 m). The variety *Zea mays* L. "ES Zizou" was used in the experiment (Euralis Semences, Lescar, France), which is a variety of FAO 220 earliness; type of seeds: flint-dent/flint; average early vigor and "stay green" type. Maize was also a forecrop. Sowing was performed on 20 April 2019 and 21 April 2020 with 95,000 seeds per hectare. The row spacing was 0.75 m, while the stem spacing was 0.14 m. Plant protection included the application of herbicide (mixture of terbuthylazine, S-metolachlor and mesotrione) before the emergence of the plants.

#### *4.2. Soil and Meteorological Conditions*

According to the World Reference Base for Soil Resources [73] classification system, the soil in the experiment was classified as Haplic Luvisols. The topsoil was characterized by sandy loam, and the subsoil was characterized by loam texture. The soil organic matter content in the topsoil was around 1.37%. To assess the pH and plant-available nutrient contents, soil samples were collected each year in early spring (March/April), before the application of mineral fertilizers. The soil pH at a depth of 0–0.3 m was acidic in each season. According to the Polish classification [74], the contents of plant-available P in the topsoil were high, irrespective of the year. The content of K in the first year was medium but in the second, very high. The content of Mg was very high; Ca—low; micronutrients—medium; and S—medium. The total content of Nmin (sum of NH4-N and NO3-N in soil depth 0.0–0.9 m) ranged from 33.0 to 93.6 kg ha−1 of N (Table 6).

**Table 6.** Agrochemical properties of soil prior to experiment.

\* soil pH was determined in suspension of 1 M KCl; plant-available nutrients using the Melich 3 method [75] and mineral nitrogen using 0.01 M CaCl2 [76].

The characteristics of climatic conditions were based on data from the meteorological station belonging to the Poznan University of Life Sciences Experimental Station in Brody (west Poland). The long-term (1960–2018) average yearly precipitation and temperature in the study area are about 590 mm and 8.5 ◦C, respectively. In 2019 and 2020, the sum of precipitation was 403 and 496, and the average temperature 12.0 and 11.9 ◦C. In these years, the sum of precipitation during the growing season (April–September) was 253 and 313 mm, respectively. For comparison, the sum of precipitation for the long-term period was 356 mm. Both growing seasons also differed in their rainfall distribution. In the first year, May had better conditions for maize growth than in 2020. In turn, in the second year, the weather conditions were better in June and August. However, rainfall during flowering was greater in 2019 than in 2020 (Figure 5). Mean temperature during the maize growing season was 16.5 ◦C in 2019 and 15.7 ◦C in 2020.

**Figure 5.** Air temperature and precipitation during 2019–2020 growing seasons of maize (in decades of the month). Key growth stages: BBCH 17—seventh leaf growth stage; BBCH 61—onset of flowering. Source: Poznan University of Life Sciences Experimental Station in Brody.

#### *4.3. Plant Sampling and Analysis*

Plant samples were collected at the following growth stages: seventh leaf (BBCH 17—coding system of growth stages, abbreviation (in German): Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie; [77]); beginning of flowering (BBCH 61/62) and technological maturity (BBCH 91/92). Depending on the year (2019 and 2020), the sampling dates were as follows: 8 and 16 June, 19 and 27 July, 27 September and 6 October. In order to obtain the grain yield, maize cobs were hand harvested from an area of 5.25 m2 (2 rows × 3.5 m). Next, the cobs were counted and threshed using the laboratory threshing machine; the moisture of seeds was also determined. The biomass of plant vegetative parts was determined by randomly sampling 5 plants from each plot, regardless of the date of sample collection. The plant samples were dried at 60 ◦C and ground afterward. Nitrogen contents were determined with the Kjeldahl method using a Kjeltec Auto 1031 Analyzer (Foss Tecator AB, Hoganas, Sweden). Total nitrogen uptake (TN) was calculated by summing up the amount of nitrogen accumulated in grains (GN) and in the post-harvest residues (SN).

#### *4.4. Nitrogen Use Efficiency Indices*

In order to assess the effectiveness of nitrogen (N) fertilization, the following standard indices were used [62]:

$$\text{partial factor productivity of N (PFP)} = \text{GY/N}\_{\text{f}} \text{ [kg seeds kg}^{-1} \text{ N}\_{\text{f}}] \tag{6}$$

$$\text{algorithm efficiency of N (AE)} = (\text{GY} - \text{GY}\_0) / \text{N}\_{\text{f}} \text{ [kg seeds kg}^{-1} \text{ N}\_{\text{f}}] \tag{7}$$

$$\text{partial N balance (PNB)} = \text{TN/N}\_{\text{f}} \text{ [kg N kg}^{-1} \text{ N}\_{\text{f}}] \tag{8}$$

apparent N recovery efficiency (RE) = (TN − TN0)/Nf [kg TN kg−1 Nf] (9)

internal N utilization efficiency (IE) = GY/TN [kg seeds kg−1 TN] (10)

physiological N efficiency (PE) = (GY − GY0)/(TN − TN0) [kg seeds kg−1 TN] (11)

where GY—grain yield (kg ha−1); GY0—grain yield in treatment without N application (kg ha−1); Nf—input of N in mineral fertilizers (kg ha−1); TN—total N accumulation in above-ground biomass of maize (kg ha−1); TN0—total N accumulation on control (kg ha−1).

Additionally, the efficiency of N can be determined using the nitrogen gap (Ngap) index based on the concept proposed by Grzebisz and Łukowiak [32]. The following set of equations can be used to calculate Ngap:

maximum attainable yield (GYmax) = cPFP · Nf [kg ha−1] (12)

$$\text{grain yield gap (GY}\_{\text{gap}}) = \text{GY}\_{\text{max}} - \text{GY}\_{\text{a}} \text{[kg} \,\text{ha}^{-1}\text{]} \tag{13}$$

$$\text{nitrogen gap (N}\_{\text{gap}}) = \text{GY}\_{\text{gap}} / \text{cPFP [kg ha}^{-1}] \tag{14}$$

where cPFP is the third quartile (Q3) of the partial factor productivity (PFP) values measured for each plot with N fertilization (kg seeds kg−1 Nf); Nf—input of N in mineral fertilizers (kg ha−1); GYa—grain yield on each plot in a particular growing season (kg ha−1).

#### *4.5. Dry Matter and Nitrogen Management Indices*

The assessment of the impact of fertilization on the method of dry matter (DM) and N management by maize was based on the following indices [68]:

$$\text{dry matter increase (DMI)} = \text{DM}\_{\text{FI}} - \text{DM}\_{\text{A}} \text{ [g plant}^{-1}] \tag{15}$$

dry matter remobilization (DMR) = DMA − DMS [g plant−1] (16)

$$\text{dry matter recombination efficiency (DMRE)} = \text{(DMR/DM}\_{\text{A}}) \times 100 \, [\, \% ] \tag{17}$$

contribution of DMR assimilates to grain (CDMR) = (DMR/DMG) × 100 [%] (18)

$$\text{In nitrogen increase (NI)} = \text{TN}\_{\text{H}} - \text{TN}\_{\text{A}} \text{ [g plant}^{-1}] \tag{19}$$

$$\text{Initrogen remombilization (NR)} = \text{TN}\_{\text{A}} - \text{N}\_{\text{S}} \text{ [g plant}^{-1}] \tag{20}$$

$$\text{nitrogen removal}\\ \text{lithization efficiency (NRE)} = \text{(NR/TN}\_{\text{A}}) \times 100 \text{ [}\%\text{]} \tag{21}$$

contribution of remobilized N to grain (CNR) = (NR/NG) × 100 [%] (22)

where DMH—total above-ground biomass (dry matter) at maturity (g plant−1); DMA—total above-ground biomass at anthesis (g plant−1); DMS—dry matter of crop residues (stems, leaves, husk, corncob) at maturity (g plant−1); DMG—dry matter of grains at maturity (g plant−1); TNH—total N content of above-ground biomass at maturity (g plant−1); TNA—total N content of above-ground biomass at anthesis (g plant−1); NS—N content of crop residues (stems, leaves, husk, corncob) at maturity (g plant−1); NG—N content of grains at maturity (g plant−1).

#### *4.6. Statistical Analysis*

The effects of individual research factors (year, NP treatments) and their interactions were assessed by means of the two-way ANOVA. If the F-ratio was larger than the critical value (*p* < 0.05), the differences between the treatments were evaluated by using the HSD (Tukey's) test (for α = 0.05). The distribution of the data was checked using the Shapiro– Wilk test and the homogeneity of variance with the Bartlett test. Standard error of the mean (SEM) was used to indicate statistical error. Principal component analysis (PCA) was applied for evaluation of the relationships between variables. The Tukey median is surrounded by a bag containing 50% of the data points. The bagplot visualizes the location, spread, correlation, skewness and tails of data. The bagplot cover contains the inliers and outside of the "fence" are outliers [78]. In addition, the relationships between traits were analyzed using Pearson's correlation and linear regression. Statistica 13 software (TIBCO Software Inc., USA) was used for all statistical analyses [79].

#### **5. Conclusions**

The effect of nitrogen application on the maize yield and nitrogen management indices depended on the amount of mineral nitrogen in the soil, nitrogen doses and the method of its application. The most effective use of nitrogen by corn was ensured by the use of ammonium phosphate and ammonium sulphate during the sowing of corn seeds (band application). From the point of view of NUE indices, the optimal dose of N was 60 kg ha−1. With broadcast fertilization and/or a further increase in the N dose, without the simultaneous use of P and S, the values of NUE indices deteriorated, especially in the year with the highest content of Nmin in the soil. Thus, a positive effect of the interaction of N and P(S) was confirmed in the conditions of soil rich in plant-available phosphorus.

Band application had a particularly positive effect on total N accumulation, nitrogen harvest index and internal N utilization efficiency. These parameters were closely related to the grain yield. For diagnostic purposes, the accumulation of N in the above-ground part of maize at the sixth–seventh leaf stage was important.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/plants11131660/s1, Figure S1. Maize grain yield (GY), crop residues biomass (SDM) and total dry matter (TDM) depending on the year and fertilization treatments; Table S1. Maize yield components depending on the year and fertilization treatments; Table S2. Nitrogen concentration in dry matter (DM); accumulation of N in particular plant organs and total N accumulation by maize depending on the year and fertilization treatment; Table S3. Correlation matrix loadings and variance of the significant principal components (PCs) for grain yield of maize and nitrogen use efficiency (NUE) indices (n = 26); Table S4. Pearson correlation matrix between maize grain yield (GY), crop residues dry matter (SDM), total nitrogen accumulation (TN) and nitrogen use indices (n = 26); Table S5. Dry matter and nitrogen management indices depending on fertilization treatment; Table S6. Correlation matrix loadings and variance of the significant principal components (PCs) for grain yield of maize and dry matter and nitrogen management indices (n = 26); Table S7. Pearson correlation matrix between maize grain yield (GY), crop residues dry matter (SDM), total nitrogen accumulation (TN) and nitrogen content at early growth stages and N remobilization and contribution to grain (n = 26).

**Author Contributions:** Conceptualization, P.B.; methodology, P.B., R.Ł. and L.H.; validation, P.B.; formal analysis, P.B. and R.Ł.; investigation, P.B. and R.Ł.; resources, P.B. and R.Ł; data curation, R.Ł.; writing—original draft preparation, P.B., R.Ł. and L.H; writing—review and editing, P.B. and R.Ł.; visualization, R.Ł.; project administration, P.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing not applicable.

**Acknowledgments:** The publication was co-financed within the framework of Ministry of Science and Higher Education programme as "Regional Initiative Excellence" in years 2019-2022, Project No. 005/RID/2018/19. The authors would like to thank Roman Błaszyk from Phosagro Poland for ammonium phosphate supplies and financial support for field research. The authors would like to thank the editor and reviewers for their valuable comments and suggestions, which substantially improved the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

**Fengmin Shen †, Changwei Zhu †, Guiying Jiang \*, Jin Yang, Xuanlin Zhu, Shiji Wang, Renzhuo Wang, Fang Liu, Xiaolei Jie and Shiliang Liu \***

> College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China **\*** Correspondence<EMAIL_ADDRESS>(G.J<EMAIL_ADDRESS>(S.L.); Tel.: +86-0371-68555200 (G.J.);

† These authors contributed equally to this work.

**Abstract:** Nitrogen is a vital element for soil fertility and crop productivity. The transformation of nitrogen is directly affected by tillage practices for the disturbing soil. The characteristics of different nitrogen forms under different tillage modes are still unclear. A 3-year cycle tillage experiment was carried out to assess the combination of rotary tillage (RT), deep tillage (DT), and shallow rotary tillage (SRT) on nitrogen transformation and distribution, wheat yield and nitrogen balance in fluvo-aquic soil from Huang-Huai-Hai Plain in China. The results showed the rotation tillage cycle with deep tillage in the first year increased the total nitrogen (TN), and the main nitrogen form content in 0–30 cm compared with continued rotary tillage (RT-RT-RT). Moreover, the nitrate (NO3 −-N) and ammonium nitrogen (NH4 +-N) content were improved in 20–40 cm by deep tillage practice with the highest value as 39.88 mg kg−1 under DT-SRT-RT. The time, tillage, and depth significantly affected the different nitrogen forms, but there was no effect on dissolved organic carbon (DON) and soil microbial biomass nitrogen (SMBN) by the interaction of time and tillage. Moreover, compared with RT-RT-RT, the rotation tillage promoted the spike number and kernels per spike of wheat, further increasing the wheat yield and nitrogen partial productivity, and with a better effect under DT-SRT-RT. The NO3 −-N and NH4 +-N trended closer and positively correlated with wheat yield in 0–40 cm in 2019. The rotation tillage with deep tillage improved the different forms of nitrogen in 0–30 cm, wheat yield, and nitrogen partial productivity, and decreased the apparent nitrogen loss. It was suggested as the efficiency tillage practice to improve nitrogen use efficiency and crop yield in this area.

**Keywords:** rotation tillage; fluvo-aquic soil; wheat-maize system; nitrogen forms; nitrogen distribution

#### **1. Introduction**

Nitrogen is the necessary element for the plant, which directly decides the crop yield [1]. The nitrogen transformation is affected by various factors, such as tillage, irrigation, fertilization, and so on. Tillage practice is a common agricultural practice to directly disturb and change the soil's physical properties, further affecting soil nutrient conversion and crop productivity [2]. The disturbance degree on soil varies from different tillage methods. Therefore, the effect of different tillage practices on soil physicochemical properties from different depths is different. Moreover, the tillage not only affects the change in soil nitrogen content, but also affects the profile distribution of soil nitrogen due to the downward shift of nitrogen and the effect of crop roots and may therefore influence crop yield and quality [3,4].

Soil nitrogen exists as organic components, its transformation in the soil is essentially associated with the interconversion of inorganic forms such as ammonium (NH4 +-N), nitrate (NO3 −-N), and organic components [5], and is regulated by interactive processes of production and consumption [6]. The transformation process is driven by soil microorganisms. Soil microbial biomass nitrogen (SMBN) and dissolved organic nitrogen (DON)

**Citation:** Shen, F.; Zhu, C.; Jiang, G.; Yang, J.; Zhu, X.; Wang, S.; Wang, R.; Liu, F.; Jie, X.; Liu, S. Differentiation in Nitrogen Transformations and Crop Yield as Affected by Tillage Modes in a Fluvo-Aquic Soil. *Plants* **2023**, *12*, 783. https://doi.org/ 10.3390/plants12040783

Academic Editors: Przemysław Barłóg, Jim Moir, Lukáš Hlisnikovský and Xinhua He

Received: 30 December 2022 Revised: 28 January 2023 Accepted: 7 February 2023 Published: 9 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

+86-0371-68555200 (S.L.)

are important labile soil organic nitrogen (SON) fractions, which are considered actively involved in N mineralization and are potentially more sensitive indicators for agricultural management [7,8]. Although DON only accounts for 0.15–0.19% of soil TN, it is one of the relatively active components of the soil organic nitrogen pool and has important effects on nitrogen transformation and the environment. DON also represents a source of energy and nutrients for microbial growth and activity [9]. Tillage practice directly affects this process by changing the soil microenvironment, such as soil structure, and regulating the soil temperature and moisture, further mediating soil microorganism community and structure, and finally determining the nitrogen transformation. Studies have shown that conservation tillage usually increased soil N content compared to conventional tillage [2,3,10–12], but long-term no-till and reduced tillage produce nutrient accumulation and N mineralization in the soil surface [13], and may also cause topsoil compaction [14], leading to a reduction in the air-filled pore space [15,16], which may decrease the root absorption of soil nutrients and water [17]. Minimum tillage, often in combination with other practices, has been promoted to improve soil health through enhanced microbial activity and increased soil organic matter (SOM) in the surface layer [18–20]. long-term no-till management is a benefit for soil N stocks, N mineralization, and efficient fertilizer N use in corn-based cropping systems on well-drained soils. A meta-analysis by Mahal et al. [21] found that no-till increased potentially mineralizable N relative to moldboard plowing. Tillage can increase rates of soil C and N mineralization by disrupting aggregates and incorporating residue. Yuan et al. [6] found that no-tillage combined with maize straw mulching could simultaneously maintain the retention and availability of soil N to achieve effective N cycling in agroecosystems. Meanwhile, nutrients concentrated near the surface of no-till soils may increase the possibility of loss via erosion, runoff, and volatilization. Nutrient stratification may also reduce the availability and uptake of nutrients by crops.

In contrast, deep tillage can loosen the soil and promote crop roots grow and absorb more soil nutrients, but it may increase the loss of soil nitrogen and other nutrients, and accelerate soil erosion, cause serious environmental pollution and soil degradation thereby affecting ecosystem functions in the field [22,23]. Therefore, rotational tillage with the combination of different tillage practices was suggested to dismiss the disadvantage of long-term mono-conventional tillage [24]. Rotation tillage techniques that reasonably combined different tillage measures can effectively counteract some defects caused by mono-tillage practices [25–27]. In recent years, periodic disturbance of continuous NT systems in the form of occasional tillage, one-time tillage, strategic tillage, targeted tillage, single inversion tillage, one cycle of tillage, etc., has been promoted as a potential strategy to address the challenges of long-term NT management [28–31].

Huang-Huai-Hai Plain is the main agricultural region in China, with mainly wheat and maize double cropping systems. The long-term mono-rotary tillage with soil disturbance depth of around 15 cm in this area leads to a shallow plow layer and thick plow bottom, leading to the uncoordinated supply of soil water, fertilizer, gas, and heat, restricts the extension of crop roots, and impedes the increase in crop yields. Thus, optimum tillage practices are essential for crop production in this area. Therefore, the objectives of this study are (i) to clarify the differentiation in nitrogen transformation and distribution by the different tillage modes (ii) to assess the effect of different tillage modes on crop yield, nitrogen use efficiency, and nitrogen balance (iii) to select the optimum tillage mode according to the above results.

#### **2. Results**

#### *2.1. Distribution of Soil Total Nitrogen under Different Tillage Modes*

The total nitrogen content (TN) decreased with soil depth under all tillage treatments during 2017–2019 (Figure 1). The tillage affects the TN in the 0–40 cm soil layer in 2017 and the effect decreased within 0–30 cm in 2018 and 2019. TN content was no different in 0–10 cm and 40–50 cm in 2017 among the treatments, while it was significantly higher under the treatments with deep tillage combination in 20–40 cm than that under RT-RT-RT. During 2018 and 2019, the deep tillage increased TN content in 0–30 cm soil layer compared with RT-RT-RT, with the higher one under DT-SRT-SRT and DT-SRT-RT as 1.14 g kg−1 and 1.13 g kg−1, respectively, in 2018.

**Figure 1.** Soil total nitrogen content in different soil layers under different treatments. Note: Different lowercase letters indicate significant differences between different treatments at the same soil layer (*p* ≤ 0.05).

#### *2.2. Distribution of Soil Alkaline Nitrogen under Different Tillage Modes*

Similar to TN, the alkaline nitrogen content (AN) under all treatments was decreased with the increase in soil depth during 2017–2019 (Figure 2). Compared with RT-RT-RT, the AN was affected by treatments with deep tillage in 0–40 cm in the first year (2017), and the effect decreased within 0–30 cm during the following two years. During the three-year experiment, the AN content was increased under the treatments with deep tillage compared with that under RT-RT-RT. While among the treatments with deep tillage, AN content did not demonstrate a significantly different or clear trend. It indicated that the effect of deep tillage was bigger than the combined other two tillage modes.

**Figure 2.** Soil alkaline nitrogen content in different soil layers under different treatments. Note: Different lowercase letters indicate significant differences between different treatments at the same soil layer (*p* ≤ 0.05).

#### *2.3. Distribution of Soil Nitrate Nitrogen under Different Tillage Modes*

The nitrate nitrogen (NO3 −-N) content under all treatments was decreased with the increase in the soil depths, and the effect of tillage on NO3 −-N in 0–40 cm during 2017–2019 (Figure 3). In the first year, the NO3 −-N content in 0–20 cm soil layer under RT-RT-RT did not differ from that under the treatments with deep tillage, while, which was lower under RT-RT-RT in 0–40 cm during the following two years. Meanwhile, the NO3 −-N content in 30–40 cm soil layer under treatment with deep tillage increased with time, the NO3 −-N content under DT-SRT-SRT treatments was significantly higher than that under RT-RT-RT treatments, the highest increase was 35.12% in 2018. In 2019, the NO3 −-N content in the 0–40 cm soil layer was significantly increased under DT-SRT-RT with the highest value of 39.88 mg kg−1. This indicated that the deep tillage accelerated the NO3 −-N leaching into the deeper soil layer.

**Figure 3.** Soil nitrate nitrogen content in different soil layers under different treatments. Note: Different lowercase letters indicate significant differences between different treatments at the same soil layer (*p* ≤ 0.05).

#### *2.4. Distribution of Soil Ammonium Nitrogen under Different Tillage Modes*

The ammonium nitrogen (NH4 +-N) content under all treatments decreased with the increasing soil depths, and it slightly increased with time. The effect of tillage on NH4 +-N in 0–40 cm during 2017–2019 (Figure 4). Similar to NO3 −-N, the NH4 +-N content in 0–20 cm soil layer under RT-RT-RT did not differ from that under the treatments with deep tillage, while it was lower under RT-RT-RT in 0–40 cm during the following two years. Meanwhile, the NH4 +-N content in 0–30 cm soil layer under treatment with deep tillage significantly increased with time, the NH4 +-N content under DT-SRT-SRT treatments was significantly higher than that under RT-RT-RT treatments, the highest increase was 35.12% in 2018. In 2019, the NH4 +-N content in the 0–40 cm soil layer was significantly higher under DT-SRT-SRT than that under RT-RT-RT with the highest value of 23.82 mg kg−1 in the 0–10 cm soil layer.

#### *2.5. Distribution of Soil Dissolved Organic Nitrogen under Different Tillage Modes*

The soil dissolved organic nitrogen content (DON) under all treatments decreased first and then slightly increased with time. The major change happened in the 10–30 cm soil layer (Figure 5). The DOC content did not differ from all treatments in 0–10 cm in 2017, but it decreased under RT-RT-RT in the following two years. The DOC was no different in the 40–50 cm soil layer in 2017 and 2018, while it was in the 30–50 cm soil layer in 2019. It indicated that the effect of deep tillage decreased in the third year. Although the DON, generally, was no different among the treatments with deep tillage, the DON under DT-SRT-SRT and DT-SRT-RT was significantly higher than that under RT-RT-RT in 0–30 cm in 2018 and 2019, with the highest value of 23.37 and 25.49 mg kg−1 under DT-SRT-SRT in the 0–10 cm soil layer.

**Figure 4.** Soil ammonium nitrogen content in different soil layers under different treatments. Note: Different lowercase letters indicate significant differences between different treatments at the same soil layer (*p* ≤ 0.05).

**Figure 5.** Soil dissolved organic nitrogen content in different soil layers under different treatments. Note: Different lowercase letters indicate significant differences between different treatments at the same soil layer (*p* ≤ 0.05).

#### *2.6. Distribution of Soil Microbial Biomass Nitrogen under Different Tillage Modes*

The microbial biomass nitrogen content (SMBN) under all treatments decreased with soil depth increasing during 2017–2019 (Figure 6). Although the SMBN slightly fluctuated during the three years, it increased under the treatments with deep tillage compared with DT-SRT-RT in the 0–40 cm soil layer in 2017 and 2019, and in 0–30 cm in 2018. Generally, SMBN did not differ from the treatments with deep tillage in all the depths and years. The SMBN was significantly higher under DT-RT-SRT than under RT-RT-RT in the three-year experiment, with the highest values of 61.06, 63.03, and 63.26 mg kg−1 in 0–10 cm in the three years.

**Figure 6.** Soil microbial biomass nitrogen content in different soil layers under different treatments. Note: Different lowercase letters indicate significant differences between different treatments at the same soil layer (*p* ≤ 0.05).

#### *2.7. The Three-Factor Analysis with Time, Tillage, and Soil Depth on Nitrogen Forms*

The multivariate analysis demonstrated that all nitrogen forms were affected by tillage time (year), tillage modes, and soil sample depth, respectively (Table 1). Therein, the effect of soil depth was the most important factor in the different nitrogen forms. All the nitrogen forms were affected by the interaction of tillage time and sample depth, tillage mode, and sample depth. However, there was no interaction effect on DOC and SMBN by tillage time and tillage mode.

The source of variation: "Time" means the tillage duration (year); "Tillage" means the five tillage modes; "Depth" means the five sample depths. \* represented *p* ≤ 0.05; \*\* represented *p* ≤ 0.01; NS represented *p* > 0.05.

#### *2.8. Wheat Yield, Yield Component, and Fertilizer Partial Productivity*

The wheat yield, yield components, and nitrogen partial productivity changed over different years (Table 2). In the first year (2017), the spike number and thousand kernel weight did not differ from treatments, the difference in yield was driven by kernels per spike. The higher wheat yield was found under RT-RT-RT and DT-RT-RT with 6717 and 6383 kg ha−1, respectively. In 2018 and 2019, generally, the wheat yield and yield component all showed higher under the treatments with deep tillage compared with RT-RT-RT. The highest wheat yield was under DT-SRT-RT in 2018 and 2019 with 6346 and 6557 kg ha−1, respectively. The N partial productivity demonstrated a similar trend with wheat yield, with a higher value of 28.98 and 29.94 kg kg−1 in 2018 and 2019, respectively.

#### *2.9. The Nitrogen Balance under Different Treatments*

The nitrogen balance was calculated in 2019 (Table 3). Although the initial inorganic nitrogen under RT-RT-RT was the lowest one in all the treatments, the nitrogen absorbed by the crop was also the lowest one. The apparent nitrogen loss was the highest one with 46.11 kg ha−1. This indicated that the rotation tillage modes helped to decrease the apparent nitrogen loss, and increase the nitrogen use efficiency.

**Table 2.** Wheat yield, yield components, and fertilizer partial productivity.

Note: Different lowercase letters after the numbers indicate significant differences between different treatments (*p* ≤ 0.05).

**Table 3.** The nitrogen balance under different treatments in 2019.

Note: Different lowercase letters after the numbers indicate significant differences between different treatments (*p* ≤ 0.05).

#### *2.10. The Correlation Analysis between Nitrogen Forms and Wheat Yield during 2017–2019*

The correlation analysis found that the different nitrogen form in different soil layers was negative in the first year of the three-year rotation, while the correlation increased with time (Figure 7). In 2019, except TN and AN, the other nitrogen forms content was all significantly positively correlated with wheat yield in the 0–10 cm soil layer. Additionally, the NO3 −-N and NH4 +-N were positively correlated with wheat yield in the 0–40 cm soil layer.

**Figure 7.** The correlation between nitrogen forms and wheat yield under different soil layers.

#### **3. Discussion**

#### *3.1. The Effect of Tillage Practice on Total Nitrogen*

Soil total nitrogen (TN) is the pool of nitrogen. It is one of the important indicators to assess soil fertility, but the major component of TN is organic nitrogen [32,33], which needs to transform into inorganic nitrogen such as nitrate nitrogen and ammonium nitrogen, to be absorbed by the crop. Soil tillage practices directly change the soil structure to improve the soil microenvironment [34], further mediating the nitrogen transformation process. Although the TN content is not sensitive to agricultural management, the different tillage practices change the TN vertical distribution by the different disturbance degrees of soil [35,36]. The TN content was changed in the 0–40 cm in the first year by different tillage practices, while the effect was decreased in the 0–30 cm during the following two years in this study. In addition, the effect of deep tillage mainly happened in 10–30 cm, and the combination tillage cycle with deep tillage increased TN content compared with RT-RT-RT. This might be because deep tillage helped to mix the surface soil and the deeper soil, the fertilizer, and the nutrient also mixed and provided the source of organic matter, which led to the TN content accumulation in the deeper soil layer [23,37]. The effect of deep tillage on soil nutrients and structure will decline with time [38]. Our results were in accordance with Han et al. [39] and Zhang et al. [23]. While Wang et al. [40] found that subsoiling—no tillage—subsoiling alternately could increase the TN content in the 0–20 cm soil layer, it did not affect the 20–40 cm soil layer. This might be because the soil disturbance by subsoiling was less than that by deep tillage, and the deep tillage takes more source of fertilizer or crop residue from the surface layer into the deeper soil layer [12,41].

#### *3.2. The Effect of Tillage on Nitrogen Components*

The nitrogen components were more sensitive than soil total nitrogen to tillage practices. Nitrate and ammonium nitrogen are the major inorganic nitrogen form in the soil, and they are also the main nitrogen form absorbed by the crop [42]. Their content is determined by the transformation between organic and inorganic nitrogen forms [5] and is regulated by interactive processes of production and consumption [43]. Soil microbial biomass nitrogen (SMBN) reflects the microorganism community, it is used to assess the nitrogen transformation process. Dissolved organic nitrogen (DON) is part of nitrogen that is relatively easy to transform. Tillage regimes impact the depth distribution of soil organic matter and affect the soil pore architecture which in turn influences soil aeration, and further regulates the nitrification, denitrification, and the relevant microorganic community and structure, finally affecting the NO3 −-N and NH4 +-N, DON, and SMBN content [44,45]. Mondal et al. [46] reported that soil nitrogen status can be improved through no-tillage adoption particularly in the surface soil layer in a global meta-analysis. Minimum tillage, often in combination with other practices, has been promoted to improve soil health through enhanced microbial activity and increased soil organic matter (SOM) in the surface layer [18–20]. In contrast, deep tillage or deep subsoiling was conceived to break up the hard pan in farmland, eliminating soil compaction to boost plant root proliferation, penetration, nutrient uptake, and air permeability [38], improving biological health and physical properties of soil [47], facilitating rain infiltration and water retention [48], and hydraulic conductivity [49]. As a result, deep tillage accelerates the nitrogen transformation and distribution in different soil depths, meanwhile allowing the yield of crops to be continuously enhanced. Our study found that deep tillage promoted NO3 −-N and NH4 +-N transportation into the deeper soil layer, especially for NO3 −-N. However, the effect of deep tillage on DON in deeper layers significantly declined with time. Although the deep tillage significantly increased the SMBN compared with RT-RT-RT, there was no significant change in the same soil layers with time. NO3 −-N cannot be fixed by soil colloid particles, and easily leach with soil water. Deep tillage promotes the soil pore and water storage capacity and helps the soil nitrification and NO3 −-N leaching [48,50]. For DON, although it is relatively easy to transform by the microorganism, there is still part of it belongs to organic form, this might the reason for the shorter affected by the deep tillage.

#### *3.3. The Effect of Tillage on Wheat Yield and Nitrogen Balance*

The tillage practices affect the soil structure and nutrient cycle, further regulating crop growth and yield [23]. Previous studies showed that although the no-till or minimal tillage profited to increase the soil nutrient in the surface soil layer [19,20,46], the effect on crop yield was different. Generally, no-till is considered shallow compaction or soil hardening by farm machinery traffic can lead to soil constraints to crop growth [51]. In contrast, most studies reported that deep tillage can increase crop yield by breaking up the hard pan in arable land and eliminating soil compaction to boost plant root proliferation, nutrient uptake, and air permeability [43]. As a result, deep tillage increases the plant availability of subsoil nutrients, which increases crop yield if nutrients are growth-limiting and allows the yield of crops to be continuously enhanced [43]. A similar result was found in this study, the wheat yield was increased under treatment with deep tillage compared with RT-RT-RT. Meanwhile, the nitrogen partial productivity demonstrated a similar trend with wheat yield. This indicated that deep tillage improved the nutrient absorbed by wheat and promoted the yield component and wheat yield. The correlation analysis also supported that it was a closer relationship between the wheat yield and nitrogen forms with time.

#### **4. Materials and Methods**

#### *4.1. Site Description*

The field experiment was carried out in 2016 at Yuanyang, Henan, China (35◦19 N, 113◦50 E). This area is a warm temperate continental monsoon climate. The mean annual air temperature is 14.5 ◦C, the mean annual precipitation is 615 mm, and the annual sunshine hours are 2324 h. The soil type is sandy fluvo-aquic soil developed from Yellow River alluviation, which is Calcaric Cambisol according to WBR [52]. The initial soil properties before the experiment in the 0–20 cm soil layer were: organic matter content 17.3 g kg−1, total nitrogen 1.00 g kg−1, alkaline nitrogen 71.33 mg kg−1, available phosphorus 21.6 mg kg−1, available potassium 108.0 mg kg−1, pH 7.2. The field experiment was a winter wheat (*Triticum aestivum* L. Zhengmai 369)—summer maize (*Zea mays* L. Xundan 29) crop rotation.

#### *4.2. Experimental Design*

The randomized block design with three replicates was carried out. Five treatments with different combinations of tillage modes with three-year cycles were set as (1) continuous rotary tillage (RT-RT-RT); (2) deep tillage–rotary tillage–rotary tillage (DT-RT-RT); (3) deep tillage–rotary tillage–shallow rotary tillage (DT-RT-SRT); (4) deep tillage–shallow rotary tillage–shallow rotary tillage (DT-SRT-SRT); (5) deep tillage–shallow rotary tillage– rotary tillage (DT-SRT-RT). Each plot was 99.2 m2. The information tillage practice before winter wheat seeding during 2016–2018 was demonstrated in Table 4.

**Table 4.** The soil tillage practice before winter wheat seeding during 2016–2018.

The tillage practice is detailed as follows. Summer maize straw was incorporated with all tillage practices. For rotary tillage, a rotary tiller was prepared twice with a depth of 13–15 cm. For deep tillage, first moldboard plows with 28–30 cm, then a rotary tiller was prepared twice with 15–18 cm. For shallow rotary tillage, a rotary tiller was prepared twice with 5–8 cm. The winter wheat was seeded by a seeder machine with a rate of 232.5 kg ha−1. The basal fertilizer (N-P2O5-K2O = 20-16-16) was applied 750 kg ha−1, and then applied 69 kg N ha−1 at the regreening stage in the wheat season. The summer maize and fertilizer were seeded simultaneously with maize density as 67,500 plant ha−1 and 750 ha−1 component fertilizer (N-P2O5-K2O = 28-10-12).

#### *4.3. Soil Sample Collection and Measurement*

The soil was sampled after the wheat harvest during 2017–2019. The 0–50 cm depth soil with 10 cm intervals was sampled by the mixture of 5–10 cores. The sample was divided into two parts, one part was stored at 4 ◦C in the refrigerator to determine soil nitrate nitrogen (NO3 −-N), ammonium nitrogen (NH4 +-N), dissolved organic nitrogen (DON), and microbial biomass nitrogen (SMBN). The other part was air-dried and sieved through 0.85 mm and 0.25 mm to determine the soil alkaline nitrogen (AN) and total nitrogen (TN). The AN was measured by Conway method, and TN was determined by the micro-Kjeldahl method [53]. NO3 −-N and NH4 +-N were extracted from 10 g of fresh soil in 50 mL of 2 mol KCl L−1 (1:10 soil: solution ratio) before filtering [54]. The NO3 −-N and NH4 +-N concentrations in the extract were determined using an automated colorimeter (automatic chemical analyzer, Easychem Plus, Via Fratta Rotonda Vado Largo, Italy, Europe).

The dissolved organic nitrogen (DON) content was extracted using the method presented by Gigliotti et al. [55]. Briefly, 10 g of fresh soil with water at a soil-to-water ratio of 1:2 was shaken for 30 min h at 250 rev/min and 25 ◦C. Next, the supernatant was centrifuged for 10 min at 4000 rev/min before passing through a 0.45 μm membrane filter. The filtrate was measured using a TOC analyzer (Leeman, US17192017, Mason, OH, USA).

The soil microbial biomass nitrogen (SMBN) content was estimated using chloroform fumigation extraction according to the method presented by Vance et al. [56]. Briefly, 20 g of fresh soil was fumigated for 24 h at 25 ◦C with ethanol-free chloroform, the non-fumigated portion was completed simultaneously. Next, the soils were extracted using 60 mL of 0.5 mol K2SO4 L−1, shaken at 200 rev/min for 30 min, and filtered using filter paper (12.5 cm diameter). The organic nitrogen contents in the extracts were determined using a TOC analyzer (Lehman US17192017). In addition, the SMBN content was calculated according to Jenkinson et al. [57]. as follows: microbial biomass nitrogen = *E*N/*k*EN, where EN is the D-value between organic nitrogen extracted from fumigated soils and non-fumigated soils; *k*EN = 0.45.

#### *4.4. Grain Yield, Yield Components, Aboveground Biomass, and Nitrogen Accumulation*

Three replicates of wheat samples (each 1 m2) were randomly selected from each plot to measure yield components (spike number per hectare, grain number per spike, and 1000-grain weight) and nitrogen accumulation at the maturity stage. After threshing, drying, and weighing, wheat grain yield, and straw were calculated according to the national wheat grain and straw warehousing standard (at a moisture content of about 14%). The plant samples were oven dried (80 ◦C) over 48 h and weighed. The grain and straw were divided into two parts, and their nitrogen (N) content was analyzed using the micro-Kjeldahl method (Bao, 2000). Total aboveground nitrogen accumulation was calculated as the grain and straw N content, and the relevant biomass.

We used the certified standard reference materials (bush leaves, GBW07602 (GSV-1); soil, GBW07420), purchased from the National Center of Standard Material in China, to check the measurements.

#### *4.5. Calculation*

The nitrogen absorbed by aboveground biomass was calculated according to Lu et al. [58].

$$\text{GNA} = \text{GB} \times \text{GNC} \tag{1}$$

SNA = SB × SNC (2)

$$\text{ANA} = \text{GNA} + \text{SNA} \tag{3}$$

where GNA was grain nitrogen accumulation (kg·ha−1); GB was grain biomass (kg ha−1); GNC was grain nitrogen content (kg kg−1); SNA was straw nitrogen accumulation (kg ha−1); SB was straw biomass (kg ha−1); SNC was straw nitrogen content (kg kg−1); ANA was aboveground nitrogen accumulation (kg ha−1).

The apparent nitrogen loss (ANL) was calculated based on the ANA according to Xue et al. [59].

ANL = NI − NO (4)

$$\text{NI} = \text{IINS} + \text{NAR} \tag{5}$$

$$\text{NNO} = \text{ANA} + \text{RINS} \tag{6}$$

where ANL was apparent nitrogen loss (kg ha−1); NI was nitrogen input (kg ha−1), it was the sum of IINS and NAR; IINS was initial soil inorganic nitrogen storage (kg ha−1) in the 0–20 cm soil layer; NAR was nitrogen application rate in wheat season (kg ha−1); NO was nitrogen output (kg ha−1), it was the sum of ANA and RINS (kg ha−1); ANA was aboveground nitrogen accumulation (kg ha−1); RINS was residue inorganic nitrogen storage (kg ha−1) in the 0–20 cm soil layer after wheat harvest in 2019.

#### *4.6. Statistical Analysis*

Microsoft Excel 2020 (Microsoft Corp., Redmond, WA, USA) was used to input and organize the data, using SPSS Software (ver. 20.0; SPSS Inc., Chicago, IL, USA) for statistical analysis. The ANOVA analysis was used to compare the difference in different nitrogen forms, grain yield, yield components, nitrogen partial productivity, and nitrogen balance indexes between different tillage modes. The multiple comparisons by the least significant range method (LSD) were to analyze the effect of tillage mode, soil depth, and tillage time on the different nitrogen forms. Origin Pro (ver. 8.5; OriginLab Corporation, Northampton, MA, USA) was used to create the graph. All statistical analyses were performed at a significance level of *p* ≤ 0.05.

#### **5. Conclusions**

The findings carried out from the 3-year cycle tillage experiment showed that the rotation tillage with deep tillage in the first year increased the total nitrogen and the major nitrogen forms content compared with RT-RT-RT. Especially they improved the NO3 −-N and NH4 +-N content in 0–40 cm, with the highest value under DT-SRT-RT. The time, tillage, and depth significantly affected the different nitrogen forms, but there was no effect on DON and SMBN by the interaction of time and tillage. Meanwhile, the rotation tillage promoted the spike number and kernels per spike of wheat, further increasing the wheat yield and nitrogen partial productivity, and with a better effect under DT-SRT-RT. The available nitrogen forms such as NO3 −-N, and NH4 +-N were closely positively correlated with wheat yield in 0–40 cm at with time.

**Author Contributions:** Conceptualization, G.J., F.S. and C.Z.; methodology, F.S. and C.Z.; software, J.Y., X.Z., S.W. and R.W.; validation, X.J. and S.L.; formal analysis, J.Y.; data curation, J.Y., X.Z., S.W., R.W. and F.L.; writing—original draft preparation, F.S. and C.Z.; writing—review and editing, G.J. and S.L.; visualization, J.Y., X.Z., S.W. and R.W.; supervision, G.J. and S.L.; project administration, G.J. and S.L.; funding acquisition, G.J., S.L. and X.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was financially supported by the National Key Research and Development Program of China (Grant No. 2021YFD1700904), the Key Research Project of Henan Colleges and Universities (Grant No. 23A210014), Innovation Training Project of Henan Agricultural University (Grant No. 2022-171), and Undergraduate Laboratory Opening Project of Henan Agricultural University (Grant No. 2022-A161).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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