Novel Grasslands Conservation Approaches

#### **Chapter 1**

## Introductory Chapter: Present and Future of Artificial Intelligence in Grasslands Conservation

*Muhammad Aamir Iqbal*

#### **1. Introduction**

The grasslands entail any ground cover encompassing grasses as a predominant vegetation, whereas shrubs and tree cover generally tend to prevail on less than 10% of that area [1, 2]. Recently, grasslands conservation and development have attained the attention of researchers and policy makers owing to their strategic pertinence in terms of carbon sink, watershed areas, feed source for livestock etc. Fundamentally, the concept of grasslands conservation involves those initiatives which ensure protection of grassland ecosystems and sustainable provision of ecosystem services through scientific maintenance of their ecological integrity and preventing the loss of biodiversity. Besides this, there is another concept of grasslands development which encompass different anthropogenic interferences (e.g., construction of roads, dams, wild-life sanctuaries, amusement parks etc.) intended for diversifying and multiplying the ecosystems services offered by grassland areas primarily through enhancement of their agricultural usage [3]. Traditional grassland ecosystem monitoring has mainly relied on field surveys, however these are being increasingly enriched and developed towards a large range, high spatio-temporal resolution and high-precision directions. The conservationists and researchers call for innovative solutions to effectively manage a variety of anthropogenic interferences and handle environmental problems that have recently seriously threatened the ecosystem functioning of grasslands.

Grasslands conservation requisites the exploration of their productive potential in order to plan grazing and reseeding activities [1, 2]. Currently, approaches employed for grasslands monitoring (e.g., visual assessments) are labour demanding, costly and, therefore have remained inadequate in terms of practical use. Additionally, nutritional value of native grass species has remained neglected and un-monitored since their quality assessment requires intricate laboratory analyses, involving huge expenditures and technical expertise [4]. Alternatively, artificial intelligence (AI) based tools might be developed and optimized as viable, reliable and cost-effective methods to monitor and assess grassland production potential under changing pedoclimatic scenarios [5]. Moreover, digitalized monitoring using automated tools might assist in recognizing native grass species, invasive species and analysing soil fertility status for formulating effective management and conservation options. For instance, unmanned aircraft systems (UAS) integrating numerous types of camera and sensors hold potential to precisely collect scattered spatial and temporal information having remarkably high-resolution in the visible and infrared spectrum [6]. Likewise, AI,

deep learning (DL) and machine learning (ML) could aid to construct 2D and 3D models [7] for establishing grass species composition, total vegetation cover, barren patches, and nutritional quality of grass species. Furthermore, ML and AI algorithms may be employed for localizing the grass species from the images and sensors supplied data. Using these approaches, grass species can be localized down to centimetre scale and allows maps construction to illustrate grass cover distribution on large swathes of grasslands.

This chapter briefly introduces fundamentals and application types of artificial intelligence with special emphasis on deep learning, drone's types and their utilization potential for grasslands monitoring and conservation. Moreover, different challenges that could emerge while using artificial intelligence tools for grasslands conservation and development have also been objectively highlighted.

#### **2. Artificial intelligence (AI)**

The AI entails an amalgamation of technologies having human-like cognitive capacities which enable them to precisely learn, accurately perform, and effectively make decisions. It is fundamentally a software which might initiate logical reasoning, learn from patterns, and resultantly can solve intricate problems in a highly rationale manner [8, 9]. Interestingly, AI is typically defined in a specific computer science method's context that it uses such as machine learning (ML), reinforcement learning (RL), and deep learning (DL). The AI software can also be physically implemented in the form of humanoid robots, autonomous cars and robotic hands. The presence of AI is felt wherever any type of machine is performing a wide range of tasks by utilizing reasonably high level of independent intelligence that is typically practiced by humans [8, 10]. In comparison to natural intelligence of humans and other biological organisms, the machine intelligence is different in the sense that it is artificially created and digital in nature and functioning. In recent generation AI has emerged as one of the most exciting research area owing to its unprecedented potential to solve the intricate problems particularly to reduce labour use and ensure efficient resource utilization [8, 9, 11]. In agriculture, a wide range of AI approaches have been suggested including robotics, DL, ML, neural network (NN), support vector machine (SVM), random forest etc. **Figure 1** illustrates AI applications especially cognitive related applications (data fusion and mining, artificial neural networks etc.), robotics applications (unmanned aerial vehicles or drones, swarm robots etc.) and natural interface concerning applications (virtual and augmented reality, computer vision etc.).

Broadly, intelligent systems are classified into four categories including (і) systems having humans like cognitive potential, (іі) systems capable to act like humans, (ііі) systems that rationally think to solve any problem, and (іν) systems which act rationally to solve intricate challenges [9, 12]. This classification of intelligent systems is based on their potential to think and act while their success is measured in comparison to human rationality and performance. Thus, AI systems hold potential to assist in grasslands conservation as these can be effectively used for manipulation and long-term storage of data regarding grass cover and composition along with soil condition and pest record. Data manipulation entails potential to deduce and infer novel knowledge from existing information and data set. Additionally, these AI systems can precisely record data through vigorous acquisition of evaluation parameters at inaccessible altitudes of grasslands along with data analyses and representation through interpretation of the acquired knowledge. Among AI techniques, artificial neural

*Introductory Chapter: Present and Future of Artificial Intelligence in Grasslands Conservation DOI: http://dx.doi.org/10.5772/intechopen.114190*

**Figure 1.**

*Different types of artificial intelligence applications especially cognitive applications (data fusion and mining, artificial neural networks etc.), robotics applications (unmanned aerial vehicles or drones, swarm robots etc.) and natural interface applications (virtual and augmented reality, computer vision etc.).*

networks (ANN) has emerged as one the most strategic technique that is developed by interconnecting nodes having potential to perform intricate functions as human brain [10, 13, 14]. This technique has been successfully employed in agriculture for estimating and forecasting methane emission from fields and soil moisture contents. Besides, scaled conjugate gradient and Quasi-Newton based neural network algorithms have been employed to estimate soil traits and environmental variables impact and predict dynamics of soil moisture content on hourly basis [12, 14]. However, focus of this study is robotics, deep learning and drones for their potential utilization in grasslands conservation and development.

#### **3. Robotics**

Robotics a branch of technology aims to design and construct robots for different applications and operations. Robots are basically machine resembling humans having potential to replicate actions typically performed by humans [8, 9]. There have been industrial robots and mobile robots during last many decades and now service robots have been focussed in order to develop their closer interaction with humans to fulfil their emerging needs. Few pertinent examples of service robots include medical robots, underwater robots, field robots, rehabilitation robots, construction robots and humanoid robots [9]. Among AI approaches, robots are currently most widely used in agriculture after drones to assist in many operations including visual detection, weeding, spray application, and harvesting having flexibility and adjustment options to match the requirement of various tasks. Such robots are typically two-wheeled

entailing a mobile base to execute a spewing mechanism to control the movements of the robot through a wireless tool. Additionally, robots might be fitted with high resolution cameras to monitor the growth conditions of native grass species and monitor the effects of abiotic stresses (drought, heat, chilling, salinity, heavy metal toxicity, soil compaction, water logging etc.). Moreover, this monitoring can effectively reveal and detect the presence and extent of pest attack in different areas of grasslands. Similarly, robots can be equipped to collect soil samples for determining the physicochemical characteristics of the grasslands soils at varying altitudes which are typically inaccessible for humans. Moreover, robots can perform the function of surveillance for reporting over-grazing and invasion of exotic species in the grasslands which in the longer run results in the deterioration of ecosystem functioning of the grasslands. However, research and practical implementation of robots based surveillance programs are still awaited for grasslands monitoring and conservation.

#### **4. Deep learning (DL)**

The DL technology is relevant to AI, ML and data science (DS) with advanced analytics and recently it has emerged as a focussed research area in computer science and intelligent computing. Generally, AI is the name of incorporating human intelligence and behaviour to different machines, set of machines or complex machine systems [13], while ML represents learning method from data set or experience, which automates the building of analytical models. Likewise, DL also entails learning from data set whereby computation is processed through multi-layer neural networks [7, 13, 15]. In DL, the term deep encompasses the concept of multiple stages or levels through which processing of data are performed in order to build a data-driven model. Therefore, DL is a technique that trains multiple computers enabling them to process information in such a way which mimics human neural processes. It is fundamentally a ML technique that instructs computers to learn by example from large data sets and architectures of neural networks. The DL technique has been successfully employed to teach computers that imitates the way humans gain and process intricate information and knowledge [13].

The original idea of DL was envisaged in 1943 [12] and it was meant for creating a model entailing a single biological neuron and its progressive development to link numerous individual neurons together to build an ANN. Recently, deep neural networks (DNN) have superseded the ANN by virtue of comparatively larger number of layers and multiple data sets. It has been enabled to separate signal of interest from noise in data set by utilizing algorithms including backpropagation in order to optimize parameters of interest in a single layer from the previous layer [13].

Recently, DL based methods have been developed to process the data set of ultra-high-resolution images which might be utilized for monitoring the grasslands features like vegetation cover and extent of exotic species invasion. Particularly, deep CNNs (convolutional neural networks) have recently revolutionized the image processing and their interpretation through remarkable improvement in specified object detection and precise classification of assigned tasks [13, 15]. Interestingly, deep CNN is basically a network which is comprised of many layers to take image's pixels as input data to predict interpretation as output. The underlying mechanism is CNNs primarily apply different learned filters (thousands in number) to the image's regions internally and finally combines those to find out targeted information [15]. Importantly, DL uses end-to-end approach which allows targeted features automatic

*Introductory Chapter: Present and Future of Artificial Intelligence in Grasslands Conservation DOI: http://dx.doi.org/10.5772/intechopen.114190*

detection without human interaction owing to its capability to learn and detect the desired features. Recently, such methods have been successfully introduced for the detection and counting of plants and animals in varying ecological context and this feature might be utilized to recognize native grass species cover in grasslands in order to formulate the organize conservation initiatives.

#### **5. Drones**

Drones are basically airborne self-propelled devices having a programmable regulator for controlling and regulating their movement without any on-board pilot. Since last decade, drones also known as unmanned aerial systems (UAS), remotely piloted aircraft systems (RPAS) or unmanned aerial vehicles (UAV) have attracted the attention of researchers in civilian and scientific spheres, because of their potential to usher a new era of precision agriculture, remote sensing and environment studies [16, 17]. The UAV can provide images of high resolution of natural phenomena at a relatively low-cost, risk-free, rapid and systematic manner [18, 19]. For these reasons, UAV hold potential to become a major trend in grasslands and wildlife related research. Drones are classified in various ways-by size, range, endurance and carrying capacity. **Figure 2** portraits the classification of different types of drones based on their size, range, take-off weight, wing type, power source, assembling requirement and nature of use or applications (monitoring or logistic). However, wing based categorization is the simplest classification especially fixed wing drones are capable

#### **Figure 2.**

*Classification of different types of drones based on their size, range, take-off weight, wing type, power source, assembling requirement and nature of use or applications (monitoring or logistic).*

to carry heavy loads over comparatively longer distances while working just like airplanes [16, 19–22].

Likewise, ready to fly drones have higher take-off weight potential and thus, these hold bright perspectives to perform over-seeding operation on vast swathes of grasslands. Contrarily, small sized (fixed-wing and rotary-wing) drones might find their use in generating high resolution videos and still photography of grasslands situated on varying altitudes. Such types of UAVs equipped with multispectral sensors and lightweight cameras can potentially deliver real-time professional mapping at a fraction of the cost involved in previously human-employed photogrammetric techniques. Additionally, compact thermal vision cameras might also be installed on medium size UAV along with hyperspectral sensors and laser scanners [19, 21] for vegetation studies in grasslands and forests. Moreover, drones can be equipped with sensing devices [18, 21] for recording distinct environmental attributes (temperature, relative humidity, precipitation or air pollution) of far-flung grasslands. Moreover, large size aerial platforms having capability to lift heavier payloads might represent one of the most economical solution for sampling of grasslands soils or deliver grass seeds in degraded patches.

The UAV's success in performing grassland conservation operations can be partially predicted by keeping in view their great flexibility to carry different types of cameras, sensors, lasers and other devices. The scope of operations (visual observance, species recognition, grass composition assessment, over-seeding, nutrients application, invasive species intrusion assessment etc.) might determine the optimum combination of airborne devices and payload. These UAVs can assist in accurate retrieval of grassland traits to formulate conservation plans by adjusting grazing frequency and intensity. Optical imaging systems might be carried by airborne devices as payload which might serve as an alternate of non-destructive methods for nutritional quality assessment of grass species. Similarly, vegetation spectral response by employing visible and near-infrared wavelengths can generate useful information on vegetation composition in grasslands. Moreover, by combining multivariate modelling with spectral measurements can reveal complex relationship between grass traits and canopy reflectance. The grasses canopy's spectral response in the visible and near-infrared (NIR) regions (between 450 and 900 nm wavelength) can unveil biophysical and biochemical traits such as canopy cover, chlorophyll content, primary nutrients (nitrogen, phosphorous and potassium) contents of grass species. Furthermore, UAV borne imaging systems entail capability to provide spatially continuous information, which is much more advantageous in comparison to point-based measurements for exploring spatial patterns of grasslands monitoring parameters. Drones can be equipped to estimate grasses traits based on canopy spectral measurements in the optical domain that is generally classified as empirical and physically-based approaches. Empirical approaches rely on large data set to fit prediction models, whereas physical approaches need adequate parametrization of a Radiative Transfer Model (RTM) in order to translate spectral information into grass traits. The RTM-based frameworks have greater potential for generalization however performance could be suboptimal particularly in grasslands having multiple grass species. Contrastingly, empirical models have better adaptability for complex datasets however, these have limited applications. Thus, quick and risk-free evaluation favours deployment of UAV borne optical imagery, while empirical models might be utilized as an initial option to characterize grasslands.

Traditional assessment methods (survey expeditions by technical staff or researchers) generally focus on recording data of biomass cover seasonally or

#### *Introductory Chapter: Present and Future of Artificial Intelligence in Grasslands Conservation DOI: http://dx.doi.org/10.5772/intechopen.114190*

occasionally which limit drawing accurate conclusions for grasslands conservation and development owing to small data sets. However, grass species in grasslands need frequent monitoring owing to changing climate scenarios, wild fires, uncontrolled intensity of grazing by non-concerned ranchers, deteriorated soil conditions, invasive plant species etc. There is dire need to develop a perennial monitoring systems for grasslands that has potential to cope with rapidly varying climatic conditions because such frequent data acquisition is vital for making informed decisions. Therefore, prediction models relying on limited spectral datasets are always prone to errors while such uncertainties can be effectively dealt with the use of aerial borne platforms equipped with high resolution cameras and state-of-the-art sensor systems.

#### **6. Challenges and future perspectives**

The AI based models hold advantages of being flexible and adaptive, however their application for monitoring grasslands can be intricate and opaque. Nonetheless, these opaque models may become computationally expensive (exponentially) with higher levels of data complexity. Despite all advantages, the ANNs are extremely data intensive and requisite time-consuming parameter tuning approaches to process information [9, 10]. These limitations of ANNs have led to the development of easierto-train algorithms including decision trees (DTs), support vector machines (SVMs), and random forests (RFs), however their application for grasslands monitoring still awaits target-specific research. In addition, there is need to create trust on AI based forecasts along with developing ethical and moral standards of their application in grasslands conservation and development. To achieve these goals, detailed explanations of AI decisions will be necessary in order to provide insights pertaining to the rationale the AI model use to draw conclusions and make forecasts. Additionally, frequent trainings and expertise would be needed to increase the interpretability (the propensity of humans to interpret and understand AI algorithms results). The trust of stakeholders on AI based forecast can be increased through robust explainability entailing strong, easy to comprehend and user-friendly justifications on all the decisions and predictions produced by the AI systems. For utilization in grasslands conservation and development, future research need to assess explainable AI types such as (і) opaque systems revealing no reasoning about their algorithmic architecture, (іі) interpretable systems offering mathematical analyses of their algorithmic mechanisms, (ііі) systems relying on symbols such as text and visualizations to allow users to understand how algorithm perform conclusions, and (іν) explainable systems that perform automated reasoning to craft concise explanations of their algorithmic mechanisms with minimum human interferences [14].

The assigning of clear responsibility and subsequent accountability of AI based forecasts and decisions continues to remain one of the biggest challenge and same has been realized for their utilization in grasslands conservation. There must be concise responsibility assignment mechanism before operationalization of AI techniques in grasslands in order to avoid potential accidents, and misleading predictions. In addition, there is need to overcome moral proxy problem associated with AI system because these have been designed by humans for serving human ends and to make decisions on their behalf, but these are not advanced enough so far to differentiate morally right or wrong decisions and processes.

Moreover, future research needs to address challenges like AI based model's biasing of results owing to non-representative data, narrow applicability due to non-inclusive usage contexts and their potential failure to differentiate among grass species. In grasslands conservation and development, preventing bias (tendency to learn from a preferred data pattern instead of actual data distribution) and pursuing inclusiveness and fairness in AI based approaches are bound to determine the human-machine partnerships in future. Interestingly, an AI system is generally useful as per assumptions of algorithm, however it can only be expected to precisely forecast decisions based on recorded observations, whereas it make inaccurate predictions in unprecedented, novel and unexpected situations. Likewise, rethinking regarding who will own, store, process and control grasslands data is also a challenge which needs redressal to prevent disputes of ownership, privacy as well as security risks.

#### **7. Conclusions**

Owing to climate change, human interferences and ecological pressures, it is high time to implement novel solutions for conserving the natural and semi-natural grasslands. These goals could be effectively achieved through deployment of AI based solution. For instance, use of drones and robots merit to perform conservation actions and reinforce effective management, but multidisciplinary research must resolve the operational and analytical shortcomings that undermine the prospects for their integration in grasslands conservation and development strategies. Future usefulness of AI based solutions will depend on accuracy and reliability of recommendations forecasts made by AI systems. This requires research based development of designs and usability of AI systems involving greater transparency and explainability. Additionally, stakeholders need to focus on governance and outcome based AI approaches involving formal sectors (government or corporate oversight) as well as informal sector (ranchers, tourists etc.). Cantering ranchers and other stakeholders during AI model designing and affixing clear responsibilities along with establishment of a chain of accountability regarding AI decisions may serve as a starting point of AI based solutions for conserving grasslands. Moreover, digitalization of grasslands monitoring operation might increase algorithmic bias risks which necessitate algorithmic objectivity on which AI models thrive and interpret the recorded information. Finally, initially soft compliance must be progressively proceeding to stringent laws for preventing abuse of data and AI systems. However, creative and critical thinking are required to create incentives and establish institutions for ranchers and other human population residing in close vicinity of grasslands to engage them in AI based grassland conservation solutions.

*Introductory Chapter: Present and Future of Artificial Intelligence in Grasslands Conservation DOI: http://dx.doi.org/10.5772/intechopen.114190*

#### **Author details**

Muhammad Aamir Iqbal Faculty of Agriculture, Department of Agronomy, University of Poonch Rawalakot, Azad Jammu & Kashmir, Pakistan

\*Address all correspondence to: aamir1801@yahoo.com

© 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### **Chapter 2**

## Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles in Sino-Pakistan's Grasslands Perspectives

*Chunjia Li, Saima Iqbal, Serap Kizil Aydemir, Xiuqin Lin and Muhammad Aamir Iqbal*

#### **Abstract**

In China and Pakistan, grasslands serve as carbon sink, ecological barriers, watershed for low riparian regions, feedstock, and minerals extraction sites for drilling and mining and offer numerous associated benefits like wool, herbs for traditional medicines, tourism and leisure, and so forth. However, grassland ecosystems have been persistently degraded by anthropogenic disturbances (land use changes, tourism, intensive grazing, uncontrolled fire, vegetation clearance, invasive weeds, and climate change drivers (heat, drought, chilling, salinity, and shifting of rainfall patterns). To conserve and develop grasslands, soil nitrogen (N) and carbon (C) hold pertinence for maintaining the primary productivity of grass species. Hence, estimating the extent of numerous interventions on N and C cycling along with grass-microbe interactions has become imperative from socioeconomic and environmental perspectives. Thus, to achieve this goal, this chapter has been tailored to compile recent knowledge on the productivity status and persistent degradation of grasslands in China and Pakistan. Additionally, invasive weeds' prevalence in grasslands, grass–microbe interactions and their influence on the growth of plant species, microclimate, and availability of nutrients have been objectively analyzed along with synthesizing the recent advances on C and N dynamics in grasslands ecosystems.

**Keywords:** grassland conservation, ecosystem development, intensive grazing, parasitic microbes, nitrification, denitrification

#### **1. Introduction**

Globally, grasslands (GL) prevail in the regions that generally receive sufficient precipitation to support robust growth of grass species; however, climatic and anthropogenic factors hamper large perennial trees' emergence in these areas. Therefore,

GL occurrence is directly correlated with rainfall (generally in-between of precipitation occurring in deserts and forests) and normally undergoes intensive grazing and wildfire to produce a plagioclimax in previously forested areas. In a narrow sense, GL entail any ground cover having grasses as a predominant vegetation, with meager or no tree cover. Likewise, UNESCO (United Nations Educational, Scientific and Cultural Organization) defines GL as those lands that are dominantly covered by herbaceous plants having tree and shrubs cover of fewer than 10% of the total land area. Interestingly, GL have been classified among world's largest ecosystems by encompassing over 52.5 million km2 area that accounts for 40% of earth's land surface (excluding Greenland and Antarctica). In contrast, woody savannah occupies 13.8% of the global land area, while shrubs prevail on 12.7% area. Moreover, nonwoody grassland and tundra are situated on 8.3 and 5.7% of earth's land area, respectively [1–3]. The GL offer numerous ecosystem services including habitat provision for thriving flora and fauna biodiversity and contribution to food production systems, along with delivering a wide range of cultural services. The GL store over 34% of carbon stock in the terrestrial ecosystems, of which 90% gets stored belowground as soil organic carbon (SOC), and thus, GL constitute a vital factor in carbon (C) sequestration phenomenon globally. However, GL have remained quite vulnerable to invasive weeds species, anthropogenic disturbances (e.g., land conversion for carrying out modern input-intensive farming, uncontrolled grazing by livestock, etc.), and climate change (CC) [2, 4–6].

In northern China, over 45% of the total GL area is occupied by restored GL, of which over 25% area has undergone serious degradation during last decade. It was estimated that actual net primary productivity (NPP) and human-induced NPP have witnessed a pronounced decrease (0.60 and 5.62 gC m<sup>2</sup> per year, respectively). On the other hand, potential NPP has experienced an increment of 2.27 gC m<sup>2</sup> per year. The CC-associated drivers have been attributed as the prime reasons requiring the restoration of GL situated in the regions of Qinghai, Yunnan, Xinjiang, and Inner Mongolia. Contrarily, anthropogenic interferences were the dominant catalysts for GL degradation, resulting in a reduction of 51932.3 Gg C in their NPP. The human-induced degradation has remained ubiquitous over time in the GL of northeast and northwest China. Except sloppy GL (where climatic change drivers serve as drastic factors), human activities have remained the primary cause of degradation for all types of GL [3, 4, 7–11].

In Pakistan, the predominant GL areas are situated on large swathes of lower Chitral, Waziristan (KPK province), Azad Jammu and Kashmir (AJK), and Gilgit-Baltistan (GB). These GL are mostly found at an altitude of 1400–3600 m, and most of them receive sufficient rainfall (250–750 mm) for robust growth of a wide range of grasses. Like China, GL in Pakistan are also under increasing pressure of anthropogenic perturbations that have noticeably decreased their productivity and ecosystem services. **Figure 1** demonstrates the prominent drivers associated with CC, anthropogenic interferences, and grazing livestock that have induced and triggered land degradation in GL, necessitating their conservation. Fundamentally, the concept of GL conservation entails such practices that are adopted for the protection and sustainable management of GL ecosystems in order to maintain biodiversity and ecological integrity for persistent provision of ecosystem services. In the modern era, GL conservation has become associated with the monitoring of anthropogenic interferences and grass–microbe interactions along with carbon and nitrogen (N) cycles [12–16]. The study of these interactions and nutrient cycles might unveil feasible solution for imparting sustainability to traditional and modern ecosystem services offered *Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*

#### **Figure 1.**

 *Pronounced drivers related to anthropogenic interferences (unplanned land use changes, tourism resulting in overutilization of grasslands, and increase in human population directly dependent on grasslands), climate change (salinity, drought, chilling stress, and unpredictable precipitation regimes), and grazing livestock (causing overgrazing without any regard to local vegetation, soil compaction by large hordes, and burning of animal feces) that induce and trigger land degradation in grasslands.* 

by GL. Moreover, considering the rapidly changing land uses, varying physiographic landscapes, and explicitly diverse ecosystems in the GL of China and Pakistan, it has become increasingly vital to investigate C and N cycles. Furthermore, to impart sustainability to ecosystem services offered by GL biome, it has become crucial to study the C and N alterations and especially the rates of nitrification and denitrification across GL. For instance, studying the implications associated with C and N cycles holds potential to increase our understanding about ecological factors, environmental variables, and human footprint in these vital ecosystems [ 17 – 21 ].

 This chapter has been tailored to enhance the existing understanding on the current scenarios of grasslands in China and Pakistan along with synthesizing the recent advances on invasive weeds prevalence (particularly knapweed species, parthenium and Johnsongrass) along with C and N dynamics in grasslands. Three vital questions have been addressed including (i) how do grass–microbe interactions function in GL ecosystems and in what way they effect the growth of plant species, microclimate, and availability of nutrients in the soil solution? (ii) how do different factors regulate C deposition, turnover, and stability in GL ecosystems? and (iii) how do N mineralization, deposition, utilization and losses (as leaching and volatilization) get affected in GL soils? It has been foreseen that assessing the microbe– grass species interaction along with C and N cycling from such perspectives might be crucial to comprehend the impacts of climatic variables and anthropogenic perturbations on GL ecosystems and their implications for the environment in the neighboring countries of Pakistan and China.

#### **2. Current scenario of Sino-Pakistan grasslands**

In China and Pakistan, many regions are predominantly natural or improved GL; however, in stricter terms, no GL in these countries can be declared entirely natural. The underlying reason is the involvement of numerous interferences such as humantriggered fires that tend to influence flora and fauna of GL. Another instance of human-associated interference in GL is livestock grazing. Contrastingly, more invasive types of interventions include vegetation clearance to introduce crops farming in the areas of GL and subdivision of GL area with or without temporary or permanent fencing. Moreover, other examples of interferences include water points, provision of grazing area, or season extension along with implementation of numerous improvement techniques (overseeding of leguminous and pasture grasses, application of organic manures and mineral fertilizers, and so on). The GL are expanded on over 40% of China's territory and serve as crucial ecological barrier primarily in northern parts of China. However, these have experienced climate-induced degradation over time, particularly owing to global warming and drought. To make the matter even worse, anthropogenic interferences in the form of land misuse, population growth, and unplanned overgrazing by livestock have further triggered the land degradation in these crucial GL. The net results of these interferences include the emergence of a range of environmental and ecological issues of serious nature over the past couple of decades. Few instances are air and water pollution, natural habitat's unbalancing, and destruction and vegetation degradation. Consequently, land degradation and other disruptions have multiplied economic losses [22–27].

In southwest China, Qinghai–Tibet Plateau (QTP) expands on Tibet and Qinghai province along with large swathes of Sichuan, Yunnan, and Gansu provinces, accounting for around 25% of China's territory. As per estimate, QTP grasslands area represent 30% of China's total GL. The QTP is considered as the highest plateau in the world, with an average elevation of 4000 m [15, 16]. This region is commonly referred to as third pole, roof of the world, hot island, and formation center of numerous species (both flora and fauna). Recently, land degradation progressing to desertification, severe deforestation, and noticeable decline in permafrost have been recorded as the most pronounced cover changes in entire QTP [26–29]. Different types of GL prevalent in QTP include temperate meadow steppe and Alpine meadow steppe, which have been presented in **Figure 2**. Keeping in view the pertinence of GL in northern China in terms of serving as an ecological barrier coupled with a feeding base for livestock production, the initiative involving the installation of enclosures by local governments has emerged as an effective GL restoration measure [30, 31]. However, the impacts of such measures on ecological functioning and production potential of GL still need to be quantified.

Like China, GL in Pakistan are considered very productive in terms of providing numerous ecosystem services. The pastoralist families living around GL get feed for their livestock, and a variety of by-products are prepared from their wool that provide a decent source of income. In true sense, GL-supported animal husbandry holds significant economic pertinence for hundreds of thousands families in Pakistan. The GL biome is situated on large swathes of lower Chitral, Waziristan (KPK province), AJK, and GB, mostly at an altitude of 1400–3600 m with average annual rainfall of 250–750 mm. Most of these GL receive rainfall during the monsoon season. In some regions, most part of precipitation is received in the form of heavy snow especially at higher altitudes of AJK. Overall, dry temperate type of climate having subtropical and subhumid conditions prevails in these GL. Generally, the growing seasons remain *Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*

#### **Figure 2.**

 *Different types of grasslands prevalent in QTP regions of China that have been classified on the basis of prevailing climatic conditions, altitude, and dominant type of vegetation.* 

quite short and cool for most part of the year. The mean temperature ranges from 5–15°C. Among these GL, the Himalaya mountain ranges comprise a wide variety of terrestrial ecosystems having diverse land uses that support human population of over 200 million. The lower Himalaya within Pakistan's geographical boundaries encompass GL, arable, and forest ecosystems that have been subjected to various human perturbations during last couple of decades. Notably, natural forests and GL are being converted into arable areas for shifting subsistence farming to modern high-input commercial farming systems [ 13 – 17 ]. In both Pakistan and China, the grazing systems (GS) in all types of GL are roughly divided into traditional (TGS) and commercial grazing (CGS) systems [ 32 , 33 ].

#### **3. Invasive weeds in grasslands**

 Weeds are the unwanted plants that tend to invade, capture the growth resources (moisture, nutrients, solar radiation, space, etc.), and suppress the growth of desired plants. In GL, invasive weeds (IW) reduce biodiversity and degrade their ecosystem functioning to a great extent [ 34 , 35 ]. Previously, most of the research efforts have been focused on short-term weeds management options, whereas developing means to introduce long-term resistance of GL against IW has been neglected so far. Recently, CC has intensified the problem of IW in grasslands globally and especially in Pakistan and China. These IW reduce the quality of GL along with imparting

negative ecological impacts such as biodiversity loss and nutrient cycling alterations [36–38]. A number of factors tend to influence the dynamics of IW invasion such as agro-botanical traits of native grass species, physicochemical characteristics of the soil, and local climate factors. Still, very limited understanding has been unveiled pertaining to the relative importance of environmental conditions on IW invasion dynamics [34, 37].

Recently, in GL of Pakistan, China, USA, and Canada, knapweed species (at least 15 species particularly *Centaurea solstitialis*, *Centaurea diffusa*, *Centaurea maculos*a, *Acroptilon repens,* etc.) primarily originating from Eurasia have emerged as one of the most prime challenges. These species seriously deteriorate the nutritional value of forage grasses, trigger soil erosion, and reduce microbial population in the soil, leading to reduced availability of essential plant nutrients. Besides knapweed species, Johnsongrass (*Sorghum halepense*) has also got the attention of researchers for being one of the most destructive IW in all types of GL [36, 38, 39]. This IW has become prevalent in the GL of Pakistan and China beyond its native origin of Eurasia. Its prevalence is persistently expanding owing to superior botanical traits especially selfpollination-based reproduction, robust growth, wider root networks, and superior adaptability to CC. Resultantly, this IW has seriously reduced native plant diversity in GL on all continents of the globe. Its prevalence has been reported only in tropical and subtropical GL, but it has demonstrated resilience in colder GL of China and Canada as well. Moreover, *Parthenium* (originating from Americas) has now infested GL in over 40 countries of the world [40]. It has invaded GL, and it is causing serious losses to biodiversity, microbial biomass, and deleterious effects for grazing animals and herders. Furthermore, it is increasingly becoming an uncontrolled IW owing to having unique morphological, physiological, and superior ecological adaptive features [40]. Future research is awaited to sort out biologically viable, economically feasible, and environment-friendly solutions to control the population of IW in the GL.

#### **4. Plant–microbe interaction in grassland ecosystem**

In all types of GL, soil microbiome–grasses interactions occur in multiple ways that can either boost or hamper the survival and development of one or both of them. Interestingly, numerous biotic and abiotic characteristics of GL's soil are primarily driven by different species of soil microorganisms. Especially, beneficial microbes (commonly referred to as mutualists) and harmful microbes (generally termed as pathogens) can significantly influence the growth and coexistence of grass species with subsequent reciprocal interactions. Recently, this phenomenon has been termed as plant–soil-feedback (PSF). The nature and degree of interaction among individual plant–microbe species has been extensively studied; however, significant gaps exist in our understanding pertaining to the interactive competition and interspecific mutual association involving multiple species of grasses and soil microbes. Soil fertility status imparts strong influence on the type and population growth of different microbes that in result determine the nature of their association with grass species. For instance, pronounced shifts in mycorrhizal colonies occur with soil nutrients availability, while the loads of pathogens tend to multiply with increasing N availability. Alternatively, on a nutrient abundant soil, grass species overcome the negative effects of pathogens by activating their defense mechanisms and strengthening the tolerance level. Thereby, potential adverse effects of pathogens get neutralized and grass plants emerge victorious in their interaction with harmful microbes [41–48].

#### *Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*

Another fundamental aspect of grass-microbes' interaction is pertaining to soil organic carbon (SOC) whereby grass species are facilitated by soil microbes to acquire the requisite nutrients. The grass diversity serves as a founding driver for SOC formation and storage in GL soils by elevating belowground C inputs through the addition of root biomass and exudates. Additionally, it also promotes microbial diversity and growth, their turnover, along with necromass entombment. In order to enhance SOC storage in GL's soils, grass biodiversity and C inputs by grass roots are crucial. Moreover, different types of fungi and bacteria tend to impart strong influence on the accumulation, stabilization, and turnover of SOC in GL. Likewise, microbial necromass also triggers SOC accumulation and stabilization with total share of 23–47% in total SOC of GL. Previously, grass–microbial feedbacks have been studied in the competition theory context which suggest that soil microbes tend to shape grass community structure by altering root growth and canopy development. Contrastingly, it has also been indicated that intra- and inter-grass competitions tend to alter the microbial types, their population structure, and growth dynamics of both grass species and soil microbes. However, these findings have limited validity owing to studying grass–microbe interactions on either arable lands or under controlled environments. Overall, interspecific competition among grass species and their interactions with microbial communities simultaneously determine and strongly influence the structure and composition of plant communities in GL. However, accumulating evidences have recently indicated that plant functional groups tend to shape soil and grass roots-linked microbial communities by modifying the soil conditions and quality traits over multiple plant generations [49–54], and this aspect needs further research to explore the dynamics of plant functional groups in GL soils.

The arbuscular mycorrhizal fungi (AMF) residing in grasses' roots tend to derive their C requirement directly from host grass species and also regulate soil C sequestration capacity (CSC). In GL and forests soil, the CSC per unit N may be 1.7-fold greater owing to the domination of ectomycorrhizal fungi–grass species association. The underlying reason is AMF (especially ectomycorrhizal fungi) hold potential to produce enzymes that are vital for degradation of organic N present in grass litter. Contrastingly, many fractions of organic C are present in relatively higher concentrations in ecosystems dominated by AMF especially in GL. It is worth mentioning that climate variables especially temperature and precipitation regulate microbe's metabolic activity and patterns of SOC storage through microbial necromass. In temperate GL, cold and moist soils serve as platform to promote microbial necromass C (MNC) accumulation. The maximum MNC accumulation occurs in GL receiving annual precipitation of over 1000 mm with <0°C mean annual temperature. Thus, microbial diversity present in the root zone determines the SOC storage by regulating MNC and the production of organo-mineral compounds. Furthermore, microbial diversity promotes the grass litter-derived OM stabilization efficiency, which in turn promotes the growth and health of grass species in GL [50, 55–59]. Future research is needed to unveil the impact of climatic factors on mutualistic grass–microbe interactions and decomposition of organic litter for releasing nutrients.

#### **5. Carbon cycle and C-sequestration in grasslands**

Globally, GL store over one-third of the terrestrial C stocks and might act as crucial sink of soil C as well. The diversity of grass species increases SOC storage by increasing C inputs to belowground soil horizons and promotes the microbial necromass. The CC has altered the storage capacity of SOC of GL by modifying C inputs processes along with influencing the microbial anabolism and catabolism. The well-planned grazing keeping in view the grass cover of GL and restoration of grass biodiversity may serve as low-cost and effective soil C accumulation to halt the phenomenon of CC globally. It is interesting to note that achievable potentials for SOC sequestration in grasslands for biodiversity restoration and improved grazing management are 2.3–7.3 billion tons of CO2 equivalents annually and 148–699 megatons of CO2 year−1, respectively. However, SOC sequestration potential for global GL can be progressively enhanced to 147 megatons of CO2 year−1 if legumes plant population is increased in pasturelands [60–65]. It is high time to initiate overseeding programs for increasing the native legumes percentage in all types of GL.

#### **5.1 Net primary productivity (NPP), actual NPP, and potential NPP**

The GL's net primary productivity (NPP) refers to the C quantity fixed by grass species through its utilization in the photosynthesis process within a specific time and particular GL area. The NPP serves as a vital indicator to study C cycle and to assess the growth of diversified vegetation in GL [30]. The NPP of all types of GL in Pakistan and China has remained vulnerable to anthropogenic disturbances and CC; however, it may serve as a valuable tool to enhance our understanding regarding GL response to these stresses. Contrastingly, actual NPP (ANPP) presents insights regarding grass species real conditions and could also be influenced by both meteorological factors as well as anthropogenic drivers. However, potential NPP (PNPP) is solely driven by CC factors and tends to represent the hypothetical growth conditions by excluding the anthropogenic disturbances [11, 56].

#### **5.2 Organic carbon (OC) cycle in grasslands soil**

Only 1–2% of OC is present as dissolved organic matter (OM) globally. The OC in the soils of GL is generally distributed into two fractions including particulate organic matter (POM) and mineral-associated organic matter (MAOM). The POM and MAOM are differentiated on the basis of their physicochemical characteristics, mean residing time in soil, and way of formation. The POM formation is primarily regulated by CC drivers especially precipitation and temperature, whereas MAOM formation depends on soil properties (cation exchange capacity, silt and clay content, and availability of microbial released N). Interestingly, the fragmentation of plant and microbial residues results in POM formation; thus, it is generally composed of fragments having lightweight and large polymers. On the other hand, small molecules exuded from the roots of grasses or leaching from grass residues form the MAOM. After microbial assimilation (necromass), these are associated to minerals directly but tend to have significantly lower C:N ratio. The underlying reasons include proportionally greater microbial origin and extended mean residence time in GL soils ranging from decades to centuries. In comparison, POM has residence time of less than 10 years to few decades. It is worth mentioning that 46, 9, and 7% of root exudates, root tissues, and aboveground C residues, respectively, get transformed into MAOM, whereas only a small percentage of root litter (19%) is transformed into POM. Thus, grass species that allocate greater C to roots tend to contribute much higher toward MAOM formation and soil C sequestration in GL. In the top layers of GL soils, 50–75% of SOC is found as MAOM. The average C:N

*Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*

ratios for MAOM and POM vary from 10 to 12 and 16–18, respectively. Therefore, SOC accrual in MAOM requires substantially greater N compared to the equivalent accrual of SOC in POM [22, 56, 57].

#### **5.3 Climate-related drivers and C sequestration in grasslands**

Majority (67%) of world's GL are present in arid, semiarid, and cold climates, whereas rest expand into the humid climates. Thus, accrual and stability of C sequestration (CS) in GL is prone to be influenced by CC variables, which tend to exert diverse impacts primarily through grass–microbes mediated mechanisms. These impacts often vary in degree and extent with type of GL and soil along with extent of variation in climate drivers. For instance, higher temperature increases root-derived C input but inhibits the MAOM decomposition through suppression of soil respiration and fungal growth in semiarid steppe. Contrastingly, elevated temperature enhances the vegetation cover of C4 grasses along with triggering the decay rate of SOC fractions in humid tall grass prairies. Likewise, higher temperature in alpine GL causes permafrost degradation, which results in pronounced reduction of SOC storage owing to decreased microbial networks and accelerated decay of SOC. In contrast to MAOM, the POM has remained more sensitive (3-fold higher) to climate variations in different types of GL. It might be inferred that GL having higher MAOM proportion tend to meagerly contribute to carbon-climate feedbacks in GL soil [22, 56].

#### **5.4 Grazing pressure and SOC**

Globally, livestock grazing constitutes the most common use of GL. The grazing intensity and regrowth periods often determine the productivity and sustainability of GL under varying temperatures and other climatic variables. There are several drawbacks associated with uncontrolled grazing such as reduction in grasses diversity, cover, and root-microbe-mediated transformation of SOC formation along with increased erosion due to empty patches. Grazing significantly (4–23%) decreases SOC stock as recorded in five continents, whereas 23% reduction in SOC stock was recorded in the tropics, and the corresponding value for temperate GL was 4.5%. Another perspective is natural GL subjected to random grazing tends to have greater SOC storage owing to better grass diversity and cover, mixing of soil layers by trampling of livestock, seed dispersal of deep-rooted grass species, and greater diversity of soil microbes that trigger the formation of OC [64–71].

Additionally, large grazing ruminants work to loosen up soil layers and expose OM's large aggregates to organo-mineral interaction through the promotion of vertical soil mixing. Nevertheless, the extent of impacts imparted by livestock grazing on soil CS is never uniform rather context-dependent in terms of climate variables, soil type, grass species, types of livestock, herbivore diversity, and grazing strategies in terms of grazing intensity and duration. For temperate GL receiving sufficient precipitation, the negative impacts caused by greater grazing intensity are ameliorated, whereas elevated temperatures coupled with extended grazing duration result in significant reduction of SOC. Interestingly, GL entailing C4 grass species record greater SOC in comparison to GL having C3 grasses as dominant vegetation. Moreover, compared to cattle grazing, sheep grazing generally imparts more pronounced negative impacts on SOC especially in the top layers of GL soil. Furthermore, a recent investigation has inferred that soil CS get modulated by soil nutrients availability and grazing intensity of livestock especially in temperate GL [58, 67–69].

#### **5.5 Management options for SOC storage in grasslands**

The GL management in a scientific manner holds potential to increase their storage capacity for SOC through mitigation of C losses by climate variables, overgrazing, and other degradation factors [21, 70–74].


### **6. Nitrogen cycle in grasslands**

Among essential nutrients, N is a vital nutrient for robust growth of grasses, and it is one of the primary limiting nutrients in GL ecosystems [66–70]. Different types of GL produce huge quantities of plant biomass owing to regrowth potential of grass species. The grazing livestock retain only a meager proportion of N contained in forage while its major chunk gets lost through their dung and urine. These grazing animals' excreta add N to the soil as organic N. Thereafter, it undergoes transformation in the soil through the processes of denitrification, leaching, and ammonia volatilization. These transformations result in N losses and ultimately contribute to environmental pollution. During the last couple of decades, significant increment in atmospheric N deposition caused by anthropogenic activities (particularly combustion of fossil fuels) has become the root cause of many ecological disruptions in GL. The atmospheric reactive N gets deposited through wet and dry pathways such as oxidized NOy and reduced NHx. Additionally, N deposition is generally accompanied with the deposition of SO2 − that depends on different N compounds [26, 59, 61]. **Figure 3** depicts the major components of N along with different sources of N addition and losses from the soil.

#### **6.1 N-uptake, resorption efficiency, and proficiency**

1.The N uptake is the process of transporting roots-absorbed N toward the canopy of grasses through xylem. The rate of N uptake depends on external environmen*Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*

 **Figure 3.**

 *The nitrogen cycle encompassing vital components or forms of nitrogen in atmosphere and belowground spheres along with processes and sources of nitrogen addition and depletion in a typical grassland ecosystem.* 

tal drivers, genetic potential of grass species, N concentration in soil solution, and growth stage of grasses.


#### **6.2 N dynamics in temperate grasslands of China**

 In China, temperate GL are expanded on large swathes of land area and offer numerous economic, cultural, and social ecosystem services along with serving as ecological barrier particularly in northern China. Therefore, it is of prime pertinence to investigate the dynamics of N deposition in these GL ecosystems. For instance, in Inner Mongolia's temperate meadow steppe, the rate of N deposition was low; however, it has been advocated as conducive to N addition over time. Additionally, non-Nfixing grass species recorded greater NRE and lower phosphorus resorption efficiency (PRE) compared to N-fixing species. Moreover, N availability in soil solution tends to impart a strong influence on symbiotic N fixation by altering the performance of

rhizobacteria in the root zone of plants. Therefore, future research needs to explore the impact of N manures on nutrient resorption and N cycling in GL having sufficient legume cover. The fresh soil collected from GL exhibited greater concentration of ammonia compared to nitrate form of N. However, both were present in noticeably low concentration. Likewise, the concentration of mineralizable N remained in opposition to annual temperature rhythm as winter season recorded the maximum values, whereas summer and early autumn exhibited the minimum values. This might be attributed to addition and/or decay of OM in the soil. The concentration of ammonia and nitrate forms of N was also associated with soil pH as acid soils having pH less than 6 recorded higher concentration of ammonia, whereas less acidic soils chiefly produced nitrate [14, 75–78].

The microbial communities in temperate GL ecosystems chiefly drive and regulate N cycle (addition, losses, and transformation into different N types), whereas inherent edaphic drivers such as inorganic N composition and climate variables influence the quantity of N that escapes into the environment. The N cycling in a GL ecosystem entails a series of processes that work in cohesion to release fixed N into the environment. For instance, nitrification and denitrification may be regarded as the model processes that determine N uptake and losses. It is worth mentioning that nitrification process involves ammonium conversion into nitrates, whereas denitrification refers to the microbial process involving the release of substantial quantities of gaseous N as N2 and N2O. These processes are primarily governed by soil microenvironment, whereas microbes play crucial role in N2O (a potent greenhouse gas) emission. The final products due to incomplete denitrification are N2 and N2O, which impart pronounced repercussions on the gaseous chemistry of atmosphere as 16% higher N2O concentrations have been deposited in the atmosphere compared to pre-industrial era. Overall, both of these processes contribute over 70% of the total N2O emissions from terrestrial ecosystems [14, 72, 79–81]. At local and global levels, mountainous GL serve as a strategic terrestrial ecosystem by providing diverse ecosystem services. However, these vital ecosystems have been persistently under mounting pressure of anthropogenic interferences resulting in distorted N cycling, fixation, and release into the soil and atmosphere. For instance, natural inaccessible forests generally experience meager human interferences and thus record noticeably higher SOM in comparison to heavily grazed GL. Therefore, such scenarios urge future research aimed at monitoring and subsequently formulating effective management plans to ensure sustainable N fixation and lower N emissions from GL ecosystems.

#### **7. Future perspectives of grasslands in Sino-Pakistan context**

It has been established beyond any shadow of doubt that GL hold strategic pertinence for both Pakistan and China by offering a range of ecosystem services [1, 3, 12, 13].

1.However, future research especially in temperate GL of Pakistan must tease apart the relative association of grass plant–microbial interactions in terms of mutualism and IW species especially *Parthenium*, knapweeds, Johnsongrass, and so forth. For instance, plant functional groups and allelochemicals secreted by different IW might influence grasses botanical traits particularly root length, which must be studied in conjunction with C and N acquisition strategies as well as microbial taxa present in the root zone of grasses.

*Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*


SOC accumulation (POM and MAOM fraction) with GL types, soil characteristics, and climatic drivers.

12. Keeping in view the pertinence of the subject matter, future research must focus to explore different aspects of N transformation in GL system, N uptake dynamics in grasses and its impact on grasses diversity, N budgeting, and environmental impacts on N manures application in GL.

There are a few other research aspects that need the attention of researchers such as possibility of overseeding C4 grass species along with legumes for improving the N fixation and forage quality of GL [48, 51]. Likewise, future research may unearth the dynamics of negative grass–microbial associations in fertile soils and potential means to shift this interaction into mutualistic association. Similarly, the impacts of climatic variables in conjunction with anthropogenic disturbances on C and N dynamics of different types of GL must be studied. Additionally, key threats such as IW dominating the native grass species, drilling and mining activities, environmental pollution caused by greenhouse gaseous emissions from GL, land degradation, clearing of grasses for converting GL into agricultural lands, overgrazing, bushfires, and so on must be studied to quantify their impact on soil fertility, vegetation productivity, and environmental concerns. Besides, plant–soil feedback has emerged as a vital framework for analyzing multi-trophic interactions among livestock, grass species, and soil microbes residing in the rhizosphere; however, concrete research-based findings are still lacking pertaining to such feedback under CC scenarios. In most of GL, herbivory patterns extensively get affected by CC drivers that further modify the resulting grass–soil feedbacks. Learning from China, there is dire need to develop infrastructure including roads and dams' construction near GL areas in Pakistan keeping in view the geographical dynamics of GL and its ecological consequences.

The GL present in the Hindu-Kush and Himalayan region within Pakistan's geographical boundaries provide a rich resource base for nomadic grazing along with numerous associated ecosystem services. However, GL's quality is seriously decreasing in this region primarily owing to anthropogenic interferences. Therefore, future research planning must focus GL present in high-altitude mountainous areas that are highly susceptible to CC drivers (especially global warming and shift of precipitation patterns). Particularly, the upper Indus basin, a complex area of the Himalayas, has recently witnessed pronounced effects of climatic variability. There is a dire need to formulate policy guidelines with strict implementation plans for the pastoralists in order to ensure sustainable grazing intensity. However, effective monitoring mechanism is a prerequisite to attain reliable information regarding vegetation cover in mountainous GL and impact of CC on soil and vegetation cover [16, 18].

Based on initiatives taken in China regarding the conservation of GL such as construction of fences and boundaries around rapidly degrading GL, it has become evident that until and unless the local dynamics are not appropriately studied and understood, the development of policies and implementation of interventions are less likely to yield desired results. Previously, very scant studies (that too involving only focus group discussions and interviews) have been executed in this region to quantify the impact of CC on vegetation cover in the Himalayas. Therefore, future of these GL lies in conducting a series of systematic assessments to understand GL dynamics in the region, which should formulate the basis for devising future management policies and plans of action. Tackling GL's degradation has remained a daunting challenge in China as well, especially when taking CC into the equation. Future perspectives of

*Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*

northern China's GL lie in expanding the understanding about the drivers that trigger GL degradation especially in Qinghai–Tibet Plateau. Thereafter, bio-economically feasible mitigation measures can be taken to combat this drastic situation for future sustainability. Moreover, such an information might serve as a critical factor for devising conservation and restoration initiatives in different types of GL [31]. The scant understanding of degradation processes and socioeconomic and ecological drivers and absence of long-term monitoring are the real issues that must be redressed for imparting sustainability to ecosystem services offered by GL, and this knowledge gap still needs to be filled within this scope.

#### **8. Conclusion**

Grasslands (temperate, alpine, meadow, and steppe) have been confronted with serious challenges by anthropogenic interferences, invasive weed species, environmental variables, and ecological alterations. Future studies need to address emerging challenges associated with vegetation cover contamination by toxic weeds (e.g., *Parthenium*) and anthropogenic-climatic drivers to preserve nitrogen and carbon in GL's soils. Additionally, devising knowledge-based strategies to restore grass species diversity to restrict the invading weeds, preserve SOC and organic N stocks, and promote additional C sequestration for mitigating the climate change phenomenon is the need of the hour. Moreover, future research advances need to highlight the strategic role of soil microbes in regulating the microbial necromass carbon, N transformations, and degradation of organic litter along with C and N storage capacity of GL soils in China and Pakistan. Likewise, the impacts of climate drivers, intensive grazing, wild or well-planned fire, and so forth must be taken into account to devise policies intended for preservation, restoration, and development of GL. Last but not least, future research must be targeted to address the context dependency and uncertainty about the proposed mitigation solutions by considering their trade-offs and possible synergies through collaborative research efforts.

### **Author details**

Chunjia Li1,2, Saima Iqbal3 , Serap Kizil Aydemir4 , Xiuqin Lin1,2 and Muhammad Aamir Iqbal5 \*

1 National Key Laboratory for Biological Breeding of Tropical Crops, Yunnan, China

2 Sugarcane Research Institute, Yunnan Academy of Agricultural Sciences / Yunnan Key Laboratory of Sugarcane Genetic Improvement, Yunnan, China

3 Institute of Microbiology, Government College University, Faisalabad, Pakistan

4 Faculty of Agriculture and Natural Sciences, Department of Field Crops, Bilecik Şeyh Edebali University, Bilecik, Turkey

5 Faculty of Agriculture, Department of Agronomy, University of Poonch Rawalakot, Azad Jammu & Kashmir, Pakistan

\*Address all correspondence to: aamir1801@yahoo.com

© 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Invasive Weeds Dynamics, Plant-Microbes Interactions, and Carbon-Nitrogen Cycles… DOI: http://dx.doi.org/10.5772/intechopen.114381*

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#### **Chapter 3**

## Grasslands Development for Ecotourism: Aesthetic Perspectives

*Raina Ijaz, Nidaa Harun and Muhammad Aamir Iqbal*

#### **Abstract**

Grasslands (also known as savanna, prairie, steppe, and pampas) are natural or seminatural areas encompassing vegetation belonging to the family Poaceae as the most dominant vegetation, while, sedges and rushes may also constitute a minor proportion. These provide numerous natural products such as food feed medicinal raw material, and honey along with nonproduct-based ecosystem services. Grasslands in lowlands and mountains either in natural form or developed landscape can provide an added value in terms of ecotourism opportunities owing to having huge esthetic and recreational potential compared to uniform agricultural areas. Grasslands characterized by high species and habitat diversity-based ecotourism are nature-based tourism whereby people visit natural or developed areas for recreation, sight-seeing, permitted and controlled hunting, on-site purchase of organic products, etc., and are usually managed by adopting sustainable practices. Ecotourism generates multifaceted economic advantages for local communities such as direct sale of products to tourists. However, ecotourism may also have a variety of negative impacts when the tourists' number multiplies which leads to overuse of resources. The most pronounced challenges confronted to the development of grasslands for ecotourism include lack of community cooperation, careless herders, need of hefty investment, and absence of trained human capital along with climate change and loss of biodiversity.

**Keywords:** agronomy and horticulture, prairie, pampas, herders, rangelands

#### **1. Introduction**

Grasslands (natural or developed areas entailing predominantly grass species with meager shrubs along with other vegetation like trees) are one of the vital elements of terrestrial ecosystem for having economic, social, ecological, and cultural roles [1, 2]. The raising of dairy animals in a grassland-supported feeding system provides farmers with decent revenues. These not only constitute as the vital source of feed for milch and draught animals but also their importance in terms of forage quality association with animal-origin products is beyond any shadow of doubt [3, 4]. Grasslands hold potential to not only provide hefty quantities of food and fiber but also play a pertinent role in imparting sustainability to the ecosystem. It deserves mentioning that grassland ecosystems represent a vital sustainer of biodiversity by offering optimal conditions pertaining to diversified species and habitats for birds, animals, and invertebrates. However, in order to halt biodiversity loses, there is dire need to

strive for conserving the grasslands through wise and sustainable uses. Along with habitat protection and species conservation, grasslands contribute to enhancing the quantity and value of traditional products (medicinal raw material, forage, grasses for beverage, honey, etc.) along with non-commodity outputs in the form of balanced ecosystem functioning and resilience to environmental paradigms [5, 6]. Different types of grasslands (natural, man-made, developed, seminatural, tropical, temperate, etc.) tend to produce different quantities and qualities of ecosystem services (ES), while a variety of factors such as vegetation cover, climate, and geography are taken into account for classifying the grasslands as depicted in **Figure 1**.

Globally, the pertinence of grasslands becomes even more important as over one-quarter of the earth's land surface is covered with grassland ecosystems [1, 2]. There exist vast grassland systems in the planes of North America, sub-Saharan Africa, South America, Australia, and Asia. These are the chief source of dairy products and meat, along with accounting for about one-third of the total carbon of all terrestrial ecosystems. These provide numerous ES such as night cooling, soil erosion control, and flood mitigation [7, 8]. As per agricultural perspectives, there are three major types of grasslands including natural, seminatural, and improved grasslands [9, 10]. Natural grasslands basically form the grassland biomes and these were created by practices related to climate, fire, and wildlife grazing [11], but are also used by livestock. In comparison to natural, seminatural grasslands are formed through human management, and for their maintenance, it needs livestock grazing or hay-cutting [12]. Whereas, improved grasslands are pastures that are formed by plowing and sowing agricultural varieties or non-native grasses having high production potential. Artificial fertilizers and intensive management are required for maintenance [13, 14]. The former two grassland types need conservation attention because of their biodiversity significance, reduction in area, and the fact that their full capacity to deliver multiple types of ES. **Figure 2** presents different ecosystems services offered by the grasslands to improve ecosystem functioning and diversified utilities for humans.

#### **Figure 1.**

*Different factors for classifying the grasslands such as climate, geography, and socio-humanistic.*

*Grasslands Development for Ecotourism: Aesthetic Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112588*

**Figure 2.**

*Different ecosystem services offered by the grasslands to improve the ecosystem functioning and other diversified utilities for humans.*

#### **2. Esthetic value of grasslands**

One of the primary reasons grasslands hold such vibrant appeal is their inherent esthetic beauty. The visual appeal of grassland landscapes lies in their simplicity and vastness. The open expanse of grasses swaying gently in the wind, the vibrant colors of wildflowers scattered across the fields, and the panoramic views stretching to the horizon create a sense of tranquility and awe. The changing colors of greenery, diversity of wildlife in natural or developed grasslands, and chirping of different species of birds create amazing scenes and enhance the esthetic value of grasslands. Likewise, the esthetic perspective of grasslands becomes undeniable owing to unique beauty of this type of ecosystems that appeals to tourist's senses. Additionally, the visual attraction of grasslands lies in their open spaces and attention-grabbing colors of different types of vegetation. Moreover, the tourists are attracted to the calmness of environment in grasslands whereby grasses movement with parcel of wind creates a unique landscape.

Grasslands also possess unique features and natural attractions that contribute to and enhance their unmatched ornamental value. These include striking geological formations, such as limestone outcrops or rocky ridges, which add texture and character to the landscape. The presence of rivers, wetlands, and lakes within grassland ecosystems adds further diversity and visual interest. These features provide opportunities for eco-tourists to engage in activities like bird watching, hiking, or wildlife spotting, enhancing the overall experience of the grassland environment. Esthetic experience reflects the intimate relation of people with their ecological system [15]. This experience fluctuates as per levels of environmental organization and level of

**Figure 3.** *The levels of esthetic perception in the perspectives of ecosystem ecology.*

human perception (**Figure 3**). Esthetic value strongly influences people's inspiration for biodiversity conservation at the landscape as well as species levels [15–17]. Esthetic perception of humans varies according to their level of organization in ecosystem and scale at which humans integrate this information. Functional ecology, community and ecosystem ecology, and landscape ecology are important to study different organization levels of transmitter. Neuroaesthetics and psychology help to study the cognitive processes correlating visual information to emotion from the receiver point of view. Influence of landscape perception on human behavior or mental health can be studied through social science and psychology. The relationship between culture and nature can be studied via fields of philosophy, art, and humanities (e.g., symbolic values and sense of place) [18, 19].

#### **3. Threats to grasslands ecosystem**

From past century, grassland ecosystems are facing declining around the world and this decline is still continuing. Both natural disturbances (biotic and abiotic stresses) and anthropogenic influences are the causes for this grassland degeneration [20, 21]. Agricultural activity can be considered as the one of major threat to these systems as occurring on 7.1 million km2 of the earth's total grassland biome. Reduction in species diversity is also consequence of loss of grassland habitats because of habitat fragmentation, over exploitation by local grazers, amplified nitrogen deposition from the atmosphere, and alterations in fire frequency. Impacts of human activities not only resulted in biodiversity loss at local but also at global level [22, 23]. **Figure 4** presents pronounced threats confronting to the ecosystems of the grasslands.

The survival of remaining biodiversity will only be possible if humans willing to allocate their services and funds for its conservation. Hence, it can be said

#### **Figure 4.**

*Pronounced threats confronting to the ecosystems of the grasslands.*

that biodiversity conservation critically depends on the basic human concerns for ecological, economic, esthetic, cultural, ethical, and spiritual. These above-stated values motivate communities to support conservation. Nevertheless, human biodiversity preferences are poorly understood by conservationists. Interpretations about public preferences might allow even better prediction of the acceptance of biodiversity management actions, and facilitate the development of appropriate ways of collaborating these, hence, increasing the probability of biodiversity management success [3, 24].

These above-stated ecological issues with the emerging trends of tourism reinforced the development of ecotourism in natural territories. The development of ecotourism activities will inevitably impart positive impacts on the living environment of wild flora and fauna in protected areas. Ecotourism holds the potential to be developed as a promising strategy to channelize tourism-based revenues for promoting environmental conservation and contributing to poverty alleviation. However, vulnerable social-ecological conditions may limit the effects of ecotourism in dry rangelands around the world. However, detailed and target-oriented research must be conducted to promote ecotourism construction by maintaining a balance between conservation and livelihood of local communities [25]. To ensure the conservation and development of grasslands ecosystem having robust ecological diversity, over-seeding of ornamental grasses in natural grasslands might be developed as a critical strategy to promote ecotourism. Supplementation of ornamental grasses with lawn plantation has valuable impact in terms of esthetic, recreational, and health-hygiene.

#### **4. What is ecotourism?**

In terms of defining ecotourism, it must be stated that it continues to remain a debatable topic so far among researchers and policy makers. The International Union for Conservation of Nature elaborates ecotourism as an environmentally responsible traveling to an undisturbed natural area with an aim to study, enjoy, and/or appreciate nature which leads to the promotion of conservation of that

natural area along with having meager negative visitor's impact, and offers active venues of socioeconomic involvement for the local community. Another definition of ecotourism entails that it is a business that organizes holidays to different places of natural beauty in such a manner that assists local people economically without damaging the environment. Likewise, Global Ecotourism Network defines ecotourism as the responsible travel to natural areas in such a way that conserves the environment, sustains the local people socially and economically along with creating the knowledge and understanding by educating all stakeholders. Another definition of ecotourism encompasses it as an activity for experiencing and learning about natural areas, their landscape (flora, fauna, and their habitats) and cultural artifacts from the surrounding locality. It further illustrates that a symbiotic relationship between the tourist activities and environment is developed when ecotourism theories are translated into appropriate policy initiatives, carefully articulated planning, and tactful practicum. Ecotourism has been regarded as a specific form of nature-based tourism or a nature-based traveling in the field of tourism. The most pronounced characteristic of ecotourism is that it is naturebased tour or travel for recreation without damaging the environment. Another definition of ecotourism regards it as a form of tourism that is inspired primarily by the history of a natural area and its indigenous cultural and traditional values. The ecotourists usually visit relatively undeveloped regions in the spirit of participation, appreciation, and sharing knowledge and cultural exchange with natives of the area. The ecotourists practice a nonconsumptive use of natural resources and indigenous wildlife which contributes to the site's conservation and economic upliftment of the local communities [26–28].

Theoretically, ecotourism has emerged as one of the most sustainable, economically viable, pro-environment, and promising solutions to attain the goals of zero hunger, poverty alleviation, conservation, and local development. The underlying factor is ecotourism can effectively channelize revenues generated from tourism towards the support services aimed at conserving the environment. In addition, it holds potential to serve as an alternative income source for local people and resultantly they are enabled to reduce their dependency exploitation grassland's ecosystems and local wildlife including highly endangered species that are at the brink of complete extinction. Moreover, it assists to transmit traditional ecological knowledge in a systematic way along with improving public awareness regarding environmental crises, especially in far-flung and marginal regions of the globe. Therefore, ecotourism especially community-based ecotourism needs to be developed as a critical path for attaining the UN Sustainable Development Goals [29–35].

Most prominently, community-based grasslands ecotourism can effectively help to achieve SDG-1 (No poverty), SDG-8 (decent work and economic growth), SDG-14 (Life below water), and SDG-15 (Life on land). However, this potential of grasslands ecotourism has still not been realized even partially owing to a variety of hurdles and challenges. The most pronounced limiting factors include the shortages of requisite financial resources, trained human capital, and social capital having low level of awareness pertaining to the potential of grasslands ecotourism. These limiting factors are further worsened by the imperfection of tourism market, especially the conspicuous consumption and green washing business. Another vital aspect as in many cases, grassland ecotourism fails to aid community development and grasslands conservation, which has raised serious questions on the idealistic claims made by

#### *Grasslands Development for Ecotourism: Aesthetic Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112588*

many sociologists, economists, researchers, and scholars regarding the potential of grassland ecotourism [36–39].

Interestingly, policy makers are confronted to a bigger challenge pertaining to the development of ecotourism in dry land's grasslands, which occupy over 28% of the globe's land area and support around 2 billion people in direct or indirect way. These grasslands effective management, conservation, and development have become critical in order to achieve global sustainability. **Figure 5** presents different vital elements of an effective ecotourism policy. Grasslands and community-based ecotourism have successfully played a strategically vital role in the local economies of many developed countries of North America, Australia, and Europe by achieving a sustainable balance of trade-offs between grasslands conservation and development and means of livelihoods. However, it deserves mentioning that in developing countries of South Asia, especially the Indo-Pak subcontinent, most of the herders have meager access to the markets, public services, technical knowledge, infrastructure, etc., which has prevented the development of grassland ecotourism [10, 40]. Likewise, a wide range of grasslands ecotourism-associated undesirable outcomes have emerged in China, including fraud, exorbitant charges, severe damage by off-road vehicles, and more pronouncedly, eruption of conflicts between local herders and tourists. These undesirable results indicate serious knowledge gaps in applying ecotourism theories without giving due consideration to local economic needs and cultural values.

#### **Figure 5.**

*Elements of an effective ecotourism policy covering aspects related to local community, grassland conservation, and tourists' perspectives.*

#### **5. Case studies**

#### **5.1 Ecotourism in Ergun grassland**

Socioecological effects of ecotourism were surveyed in one of China's mostadmired ecotourism regions, that is, Ergun grassland. In comparison to livestock feeding, ecotourism at local helped to attain multiple sustainable goals, like source of income for natives, development of community cooperation, and awareness about conservation of local natural resources. Nevertheless, this affects the diversity of forb species and succeeding reduction in ES. Hence, it is considered that ecotourism in the particular region, still requires improvement and only extensive research which offers economically and biologically viable solutions to local challenges can promote ecotourism in natural, seminatural, or improved grasslands [41].

#### **5.2 Kalahari's landscape**

Botswana has various kinds of ecosystems which are enriched with diversified wild species. Kalahari is one the important semidesert of the country that covers the 84% area of whole country. The landscape of Kalahari is dominant with grasslands, scattered trees, and xerophytic vegetation. The Okavango Delta, the Savuti, and the Chobe are at the northeastern sides of Kalahari. These are much wetter areas and rich with diverse wildlife species which are supported by grassland ecosystem. Botswana is unique in that most of its biodiversity is conserved, with a higher percentage of its total landmass conserved than any other country. This conservation level is chiefly achieved through ecotourism. Government policy, high-income, and low-volume tourism support ecosystem conservation in Botswana [42–44].

#### **6. Advantages of grasslands development for ecotourism**

Globally, intensively managed grasslands have long been recognized for being the site of conservation for plant and animal biodiversity along with their huge potential for social and cultural utilizations. For the time being, natural and seminatural grassland's capacity to deliver a variety of ES as a part of modern and commercially oriented agricultural systems has surprisingly been understudied compared to other natural resources. Theoretically, it may be perceived that in case of income generation from ecotourism in grasslands, locals might be pursued to exploit lesser biomass from the grasslands. In this way, the conserved biomass may be allocated to support the regulating and other cultural services providers. Thus, development of grasslands for ecotourism holds potential to serve as an alternative and sustainable income source for locals. In addition, it can suppress the chances of conflicts eruption between conservation efforts and livelihood. However, it must be kept in mind that grasslands ecotourism can be promoted at much faster pace by following the community and culturally based principles and traditions. The underlying reason is community-based ecotourism tends to fairly share the revenues with local people, which in turn might assists in developing local institutions for sustainable management of grasslands and wildlife in accordance with devised policies and practices. **Figure 6** illustrates numerous pronounced advantages that can be extracted by promoting grasslands as an avenue of ecotourism. To sum up, grasslands ecotourism must be considered as for, by, and with local community, which in long run, can assist to achieve the sustainable

**Figure 6.**

*A variety of advantages that can be achieved by promoting grasslands ecotourism.*

development goals of zero hunger, poverty alleviation, food security, and environment protection through community engagement, economic efficiency, social justice, and environment conservation [45]. **Figure 7** summarizes different enterprises that can be involved in producing quality services to tourists for developing grassland ecotourism in the greater benefit of native communities, tourists, grassland resources, and the environment.

#### **7. Future challenges and perspectives**

Despite huge potential of grasslands to serve as new centers of ecotourism, this task cannot be without multiple challenges and hurdles. The development of grasslands for ecotourism loses its charms keeping in view the dryness of regions which entails only dry rangelands. Higher temperatures coupled with lesser precipitation as characterized by dry grasslands. These suboptimal climatic conditions seriously compromise the concept of developing grasslands as an alternative income source for local communities and local governments. Besides suboptimal agroclimatic conditions, another pronounced challenge posed to the development of grasslands for ecotourism is the herder's low socioeconomic status. The poor economic conditions of local herders serve as one of the biggest challenges and distort human acts of goodwill. Thus, climate change, environmental issues, and poor economic condition of local herders have significantly counteracted the benefits associated with ecotourism especially related to biomass conservation. As experienced in the dry grasslands of China, rural community explicitly declined to abide by the set of regulation and policies for grasslands development. Likewise, lack of reforms in the grassland's tenure system has caused the division of public grasslands into fragmented, herders-owned plots

#### *Grasslands Development for Ecotourism: Aesthetic Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112588*

which get subject to severe overuse without any management plan. The net result is those herders are never interested and inclined to community cooperation while facing numerous external challenges alone [45–47]. **Figure 8** shows numerous but the most serious challenges confronted to the development of grasslands for ecotourism.

Moreover, other pronounced challenge in the way of developing grasslands for ecotourism is consistent drainage of human capital from rural communities of regions in the vicinity of grasslands. Over time, this situation has led to communities having higher number of aging people having little desire and motivation to strive for developing grasslands. Interestingly, such demographics also result in weak public services and deteriorated infrastructure [48]. Thus, living in socioeconomically declining community, local herders are not able to realize the potential advantages of community-based ecotourism. Hence, without addressing these socioeconomic hurdles, it may be overoptimistic theory to endorse positive social-ecological outcomes of ecotourism.

Future perspective of developing grasslands for ecotourism depends on extensive research and policy initiatives pertaining to ES and food security as grasslands can be utilized for attaining green forage and preserved fodder. This can lead to boost the meat production alongside many other ES. By integrating grasslands into farming systems and land-use decisions locally and regionally, their potential to contribute to functional landscapes and to food security and sustainable livelihoods can be greatly enhanced through research and facilitating different stakeholders. Semi-natural grassland is a product of human management and requires planned livestock grazing or hay cutting in order to appropriately maintain them, and might be encroached by invasive shrubs and trees if not properly cared of which is bound to decrease grasslands value for ecotourism. Last but not least, future research needs to focus on improved grasslands which are the pastures developed from plowing and sowing of non-native grasses with high production potential. Agronomists and horticulturists must redirect their research for formulating optimized practices for ensuring robust growth of native vegetation in grasslands through artificial fertilization and thereafter suggesting biologically viable management package for improved grasslands in order to obtain diversified ES.

#### **8. Conclusions**

Grasslands (both natural and developed) are one of the most essential elements of sustainable ecosystem for having multifunctional roles in the perspectives of economics, ecology, sociology, and cultural ethics. Grasslands need to be appropriately managed in order to sustain the grass-based ecosystems and surrounding communities that rely on them for their livelihood. It has been inferred that grasslands development can assist in raising of milk and draught animals paves the way to provide farmers with sufficient income and that too with relatively meager capital investment. In contrast, regions where grasslands are not productive enough to provide sufficient food products on competitive basis owing to adverse environmental conditions, the only way is to create a grassland tourism activity. The development of grasslands for ecotourism offers valuable recreation and generates tourism-associated business activities. The development of grasslands for ecotourism must be done by keeping in view that one of the main purposes of tourists is to spend a holiday and enjoy from a range of site seeing, services, and amenities in a natural environment free from pollution and hustle bustle of cities life. Ecotourism also holds potential to attract

typical tourist who are looking for organic products (wine, fruits, olive oil, honey, cheese, meat, milk, etc.). Grasslands must be managed by using sustainable practices that are bound to ensure high diversity of species in multifaceted habitats that attract many species of birds, vertebrates, and invertebrates. The local communities can earn higher profits by selling quality products as consumers perceive that food products coming from organic grasslands are of higher quality. Moreover, the development of grasslands for promoting ecotourism can put a halt to migration of local communities to nearby cities. However, there are numerous challenges in the way of grassland ecotourism such as hefty investment requirement, need of trained human capital, and establishment of a paradigm between grasslands conservation and development activities for tourism. Furthermore, side effects of grasslands ecotourism can only be effectively addressed by systematic evaluation and adopting the appropriate measures. Finally, there are emerging concerns that presence of tourists in large number deteriorate ecosystem functioning of the grasslands. However, a variety of benefits including sociocultural values promotion, exchange of knowledge and ethics, monetary benefits, and conservation of environment advocate development of grasslands for ecotourism.

### **Author details**

Raina Ijaz1 , Nidaa Harun<sup>2</sup> and Muhammad Aamir Iqbal3 \*

1 Faculty of Agriculture, Department of Horticulture, University of Poonch Rawalakot, Pakistan

2 Faculty of Life Sciences, Department of Botany, University of Okara, Pakistan

3 Faculty of Agriculture, Department of Agronomy, University of Poonch Rawalakot, Azad Jammu & Kashmir, Pakistan

\*Address all correspondence to: muhammadaamir@upr.edu.pk

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 4**

## Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan

*Satoshi Irei, Satoshi Kameyama, Hiroto Shimazaki, Asahi Sakuma and Seiichiro Yonemura*

#### **Abstract**

In every Spring, prescribed grassland burning, so-called Noyaki in Japanese, has been conducted for over a 1000 years by local residents in the Aso region, Japan, for the purpose of grassland conservation because Noyaki prevents invasion of woody plants in the grassland and helps the growth of grasses, which were an important resource of primary industry for roofing materials of houses and livestock feed. Meanwhile, biomass burning is known to be one of the most significant sources of airborne substances including mercury. Taking advantage of the characteristics and resources of the place we live in, we here describe our on-going study for the emission of gaseous mercury from the traditional Noyaki in the Aso region and other grasslands of western Japan. During Noyaki, we sampled and measured gaseous mercury from the Noyaki plumes to better understand mercury emissions and cycles in the local environment. Results showed, on average, 3.8 times higher atmospheric mercury concentrations, demonstrating the emission of gaseous mercury from the Noyaki. The possible origins, novel information the results inferred, and future research direction are discussed in this chapter.

**Keywords:** Minamata convention on mercury, gaseous mercury, biomass burning, plant uptake, dry deposition, wet deposition

#### **1. Introduction**

#### **1.1 Prescribed open grassland burning in the Aso region for the conservation of grassland**

The country of Japan consists of 14,125 islands, is located at the longitude between 153°59<sup>0</sup> <sup>12</sup>″ and 122°55<sup>0</sup> <sup>57</sup>″ and the latitude between 20°25<sup>0</sup> <sup>31</sup>″ and 45°33<sup>0</sup> <sup>26</sup>″, and has the total area of 377, 974.17 km<sup>2</sup> (**Figure 1a**) [1]. Approximately 126 million people reside in this country [2]. Depending on the region, the climate is divided into subtropical, temperate, or boreal and the temperate predominantly covers most of the land. Japan is a green country; forests and grassland account for 66 and 5% of the whole land [3, 4]. The largest grassland in Japan is located in the Aso region, Kumamoto Prefecture.

**Figure 1.**

*(a) Map of Japan, (b) Kyushu Mainland, and (c) Aso Region. Map images are from the Elevation World Hillshade (courtesy to the ESRI base map, https://doc.arcgis.com/en/data-appliance/2022/maps/world-hillshade.htm).*

Aso is a regional name and it is located nearly in the center of Kyushu Island in western Japan (**Figure 1b**). Geographically, this region consists of a large caldera (463 km<sup>2</sup> ) with the surrounding area (**Figure 1c**). The altitude is between 400 and 800 m, and the average temperature throughout the year is 13°C approximately [5]. Active volcanoes located in the center of the caldera, "the Aso five mountains", are the landmark of this region. With respect to local governments, the Aso region consists of seven municipalities: Aso city, Oguni town, Minami Oguni town, Takamori town, Ubuyama village, Minami Aso village, Nishihara village. Agriculture, pasture, and tourism are the major industries in these municipals, and the latter two use grasslands. The landscape of the grassland contributes to the local economy. Approximately 1,850,000 tourists in 2018 and 829,000 tourists visited and stayed in the Aso region [6, 7]. This accounts for approximately 23% of tourist visits of Kumamoto Prefecture. Highland breeze in the grassland attracts visitors from all over Japan and overseas.

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*

The grasslands in the Aso region and other local areas such as Hirado in Nagasaki Prefecture and the Akiyoshi-dai plateau in Yamaguchi Prefecture are burnt every Spring (*e.g.*, **Figure 2a** and **b**) after the annual grass dies and dried during the late Fall (**Figure 3**).

**Figure 2.** *(a and b) Photograph of Noyaki in Aso.*

**Figure 3.** *Photograph of the Aso grassland before Noyaki.*

The grassland burning is called "Noyaki" or "Yamayaki" in Japanese (referred to as Noyaki hereafter). According to a scientific study, Noyaki in Aso has been conducted since 10,000 years ago [8]. The purpose of Noyaki in the ancient era was to have open sight for hunting wild animals, but now it has shifted to the prevention of wild forestation of the unused lands, pest control, and reset of annual habitat plants [8, 9]. With this background, there is no doubt that people love Noyaki and it is a symbol of Spring's coming.

#### **1.2 Plant communities in the Aso grassland**

The grasslands of the Aso region in Kyushu, Japan, exist both inside and outside of the Aso-Kuju National Park, which covers an area of 72,680 ha [10]. The area of grassland dominated by native species such as *Miscanthus* (*Miscanthus sinensis*) and *Nezasa* (*Pleioblastus chino var. viridis*) within the national park, covering approximately 15,000 ha, approximately 43% of whole grassland in the Aso region [11]. In the grassland, controlled or prescribed burning is conducted as a traditional event every March. This artificial burning event has a significant impact on the regeneration of grassland vegetation and the dominance of certain species. The dominant plant species in the burned grassland include the following [12, 13]: Herbaceous plants of Miscanthus (*Miscanthus sinensis*), bamboo grass (*Pleioblastus chino var. viridis*), Eulalia grass (*Miscanthus transmorrisonensis*), silver grass (*Miscanthus sacchariflorus*), pampas grass (*Miscanthus floridulus*), Japanese yamayuzu (*Fallopia japonica*), Japanese ladybell (*Adenophora triphylla*), bracken fern (*Pteridium aquilinum*); Shrubs of willows (*Salix spp.*), oaks (*Quercus spp.*), sourberries (*Viburnum spp.*), mountain azaleas (*Rhododendron brachycarpum*), painted ferns (*Athyrium niponicum*),

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*

hydrangeas (*Hydrangea spp.*), honeysuckles (*Lonicera spp.*), and others. Furthermore, the following plants, which invaded during the time when the mainlands of Japan, Kyushu, and Shikoku islands were connected to the Asian continent, remain as endemic species: "Continental relics": field chickweed (*Stellaria matsudae*), false hellebore (*Veratrum nigrum*), daylily (*Hemerocallis citrina*); "Boreal plants": herbaceous saxifrage (*Saxifraga stolonifera*), golden-rayed lily (*Lilium auratum*), Bistort (*Bistorta officinalis Delarbre subsp. japonica*), lilies (*Lilium spp.*).

#### **1.3 Mercury emission from grassland burning**

Crutzen et al*.* [14] reported the significance of biomass burning as a source of airborne substances in the global atmosphere. Since then extensive emission studies have been done on this subject in fields (*e.g.*, [15–31]), in laboratories (*e.g.*, [32–40]), by satellites (*e.g.*, [40]), and by modeling [41–45]. Published results have also been reviewed (*e.g.*, [46–49]).

Veiga et al. [50] reported indirect observations of gaseous mercury emissions from biomass burning for the first time. Since then gaseous mercury from biomass burning has also been studied in North America [37, 51–53], Africa [54], Europe and Russian Federation [55], South America [56], long-range transport [57–59], and laboratory [38, 40]. To date, it has been reported that biomass burning accounts for ~8% of global mercury emissions to the atmosphere [60]. To the best of our knowledge, the emission of mercury from Noyaki in Japan was reported for the first time by Irei [61, 62]. Its stable mercury isotopic compositions were also reported for the first time worldwide. Even though the emission size may not be as large as wildfires overseas, there is no information available for Noyaki to that date.

Plants are known to uptake mercury from the ground and ambient air (*e.g.*, [63–82]). Sawgrass, a dominant plant species found in the Aso region, is also not an exception. Sawgrass in Florida, relative plant species of Japanese sawgrass, inhales gaseous mercury from the air and fixes it in its own body [83]. Thus, there is no surprise even if Japanese Noyaki emits gaseous mercury to the atmosphere.

The Japanese Ministry of Environment recently updated the domestic emission inventory of mercury for the fiscal year of 2020 (**Figure 4**) [84], showing that ~87% of mercury, out of 10.7 tons of annual mercury emissions, originates from anthropogenic sources. Only volcanic emissions, accounting for ~13%, are the source other than anthropogenic sources. In order to better the mercury cycle in the natural environment, we should not miss any significant source, and Noyaki is one of the veiled domestic emission sources. How much of gaseous mercury was emitted during Noyaki? From where did the emitted mercury come from? Finding answers to these open questions will make our mercury cycle studies progress.

In general, the total emission (TE, g) of chemical species of interest from biomass burning can be estimated using a common mass balance approach.

$$\text{TE} = \text{FL} \times \text{BA} \times \text{CE} \times \text{EF} \tag{1}$$

where a fuel load or FL, also used to be referred to as a "biomass density", is a dried mass of biomass per unit area (typically in kg m�<sup>2</sup> ), burned area or BA is the total area burnt (m2 ), combustion efficiency or CE is the fraction of converted biomass carbon to airborne carbonaceous products, such as carbon dioxide (CO2), and an emission factor or EF is the quantity of substance of interest emitted per kg of biomass burnt (g kg�<sup>1</sup> ) [18, 47, 51, 60, 85]. FL can be estimated according to the information on the biomass

#### **Figure 4.**

*Domestic Hg emission inventory. The chart was produced according to the information that the Japanese Ministry of Environment has published for the year of 2020 (website). The values are relative to the total domestic Hg emission, 10.7 tons y*�*<sup>1</sup> .*

density map [41], but it can also be measured directly (*e.g.*, [24–26, 60]). For a large area, FL is highly uncertain [42]. BA is primarily achieved using remote sensing technology through two methods [86]: The approach involving calculation of the extent of the burned area through the comparison of two images taken before and after the fire [87, 88] and the approach involving time-series analysis of regularly captured satellite images from the real-time monitoring of the fire [89] using the MODIS satellite (https:// modis.gsfc.nasa.gov/data/dataprod/mod14.php). A CE is hard to know in a real fire, but a modified CE (MCE), the excess concentration (denoted as Δ hereafter, which is the concentration during Noyaki subtracted by the background concentration) ratio of CO2 to the sum of excess concentrations of carbon monoxide (CO) and CO2 (ΔCO + ΔCO2), has been used as an alternate index of CE [18, 90]. An EF for a substance of interest is a key parameter depending on the types and moisture content of the fuel and burning conditions (smoldering and flaming fires). Of the parameters and variable in Eq. (1), EFs have been most intensively discussed in biomass-burning emission studies (*e.g.*, [46]). An EF can be further broken down to as follows [18, 46]:

$$\text{EF} = \text{ER} \times \frac{\text{MW}\_i}{\text{12}} \times \text{CC} \times \text{1000} \tag{2}$$

ER is an excess mole concentration ratio of the targeted substance to the sum of the excess carbon mole concentrations of carbonaceous (reference) substances, which are measured in field studies, MW*<sup>i</sup>* is the molecular weight of substance of interest (200.59 g mol�<sup>1</sup> in the case of mercury), 12 is the atomic mass of carbon, and CC is the carbon content of fuel, which is often assumed as 0.5 [18, 36], and 1000 is a conversion factor for the product in g base to kg base. It should be noted that the sum of the excess concentrations of carbonaceous species is often approximated as the sum of ΔCO and ΔCO2 or the excess mole concentration of CO2 divided by MCE.

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*

#### **1.4 Sampling and measurement**

In the Aso region, there are a number of stock farms and they possess own grasslands. In every Spring they burn the grasslands to restore the perennial plants. Thus, occurrences of prescribed fires are spotted over the region. Approximately 160 km<sup>2</sup> of the Aso grassland is burnt in total every Spring [91].

Field sampling and measurements for chemical substances emitted from Noyaki can be conducted by either airborne or on the ground. The former method (*i.e.*, use of aircraft) is often chosen in biomass burning studies overseas because open biomass burnings, mostly wildfires, are large and data gained during flights over plumes can represent the emission [46]. It is also because airborne sampling and measurements are more safely conducted. However, the high cost, limited accessibility, fast moving speed, etc. of manned aircraft sampling and measurements are overpowered in the Japanese Noyaki studies where only one or few km<sup>2</sup> of grassland is burnt at each stock farm. Meanwhile, ground-based sampling and measurements can be done at fixed measurement stations and/or on ground vehicles. The advantages of ground-based measurements are that instruments and measuring devices can be set more easily and quickly, compared to aircraft measurements. Relatively low cost of use of cars is another advantage. However, the success of ground-based measurements strongly relies on the weather conditions, such as wind speed and directions, proximity to the fires and plumes, landscapes of grasslands, paved roads one can drive vehicles on, etc. Depending on how quickly fire spreads, safety is another factor that one must consider. In our field studies, we have chosen ground-based sampling and measurements using vehicles.

In biomass burning studies, measurements of CO and CO2 are the base because the major products of biomass burning are CO2 and CO, accounting for more than 95% of products in general, and their ratio often indicates burning conditions, such as smoldering and flaming. CO2 and CO are also contained in background air, therefore, their excess concentrations from their background level need to be checked for the evaluation of burning conditions.

Gaseous mercury concentrations can be measured either by automated mercury analyzers or the combination of conventional amalgam trap sampling and its offline analysis using a cold-vapor atomic fluorescent or absorbance spectrometer.

For stable mercury isotope analysis, nano gram order of total gaseous mercury (TGM) needs to be collected. This collection can be done through either chemical traps [92], multiple commercially available gold amalgam sampling tubes [80, 93], or large volume gold amalgam sampling tubes, namely BAuTs [62]. Collected TGM was then converted to gaseous elemental mercury (GEM) by heating, and the converted GEM is oxidized to Hg2+ and captured in sulfuric acid/permanganate mixture or reversed aqua resia mixture. Prepared sample solutions were then analyzed by a multi-collector inductively coupled plasma mass spectrometer or MC-ICP-MS.

#### **1.5 Potential of Noyaki studies in western Japan**

Since the start of our Noyaki emission studies in 2019, solid evidence of mercury emission has been confirmed [61, 62]. The papers also report the similarly fractionated δ<sup>x</sup> Hg values of the excess TGM to the fractionated δ<sup>x</sup> Hg values of mercury found in plant species reported by others, implying that the excess TGM was likely supplied from the grassland plant. The pilot studies above gave us an overview of what was happening during the Noyaki events and raised some intriguing open questions: How

much was TGM produced and emitted to the atmosphere from Noyaki in this region? What was the origin of TGM emitted from Noyaki?

The first question is the primary one that anyone will have. This estimation has not ever been done, thus, the source has not been included yet in the domestic mercury emission inventories of gaseous mercury that the Ministry of Environment Japan has ever reported. Under the current circumstance that a number of nations implement the regulation on man-made mercury use, namely the Minamata Convention on Mercury, this evaluation is worthwhile and contributes to update the mercury budget. It is also interesting how variable "the annual routine emissions" are. Such information is new and can be gained only from the prescribed burning conducted routinely. The information can be extended to the fate of atmospheric mercury, which can be applicable not only to the local environment but global one as well.

The second question delves into the mercury cycle in detail. Identifying the origin (s) will help us to better understand phenomena occurring on mercury in the natural environment, which contributes to the intellectual and novel input into our current understanding of mercury cycle.

#### **2. Methodology**

#### **2.1 Estimation of burnt area**

In the following manner, we conducted the process of satellite image analysis. First, we downloaded Landsat-8 surface reflectance and surface temperature products (path = 112, row = 037) observed on April 9, 2023, from the United States Geological Survey Earth Explorer website (https://earthexplorer.usgs.gov/). For the purpose of creation of a spatial distribution map of prescribed burns, we adopted a supervised classification approach. In this classification method, we curated ground reference data for two classification categories: "burned" and "unburned." In the next step, we selected three vegetation groups: "pastureland," "*Miscanthus sinensis* (including *Pleioblastus chino var. viridis*) stand", and "*Miscanthus sinensis* (excluding *Pleioblastus chino var. viridis*) stand" using a 1:25,000 scale existing vegetation map. To reduce spatial bias in the ground reference data, we determined 130 reference points from each group. For the three selected vegetation groups (130 ground reference data points each 3), we randomly allocated "training data (100)" and "validation data (30)" for each vegetation group. We adopted the Random Forest algorithm, a type of machine learning algorithm with a proven track record in land use and land cover classification using satellite imagery. This algorithm was executed using the random Forest package (version 4.7.1.1) in R (version 4.2.1). For the classification results of the controlled burns'spatial distribution map, we evaluated the accuracy with overall accuracy and a Kappa coefficient.

#### **2.2 Fuel loads and emission factor**

For wildfire studies, fuel load information can be gained from estimation based on CO and CO2 measurements under the assumption that the fuel burned contains 50% of carbon content in weight (*e.g.,* [18, 36]) or from the actual analysis of habitat plants in the field [73]. The former is a top-down method and practical for cases where sampling biomass representing the habitat plants in the burned area is difficult (*e.g.*, forests with a large diversity of plant species). In contrast, the latter, a bottom-up method of actual

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*

#### **Figure 5.**

*Photograph of example of sampling aboveground grass in a 1 1 m quadrat: (a) before the grass was reaped, and (b) after the grass was reaped.*

measurements of biomass, is ideal. For the Aso grassland, this ideal case can be applied because the composition of plant species is simple; only two plant species, *Miscanthus sinensis* and *Pleioblastus chino var. viridis*, predominantly occupy the grassland burned. This makes the retrieval of aboveground fuel loads and chemical contents of the biomass pool feasible.

Fuel load, the mass of biomass per unit area, was obtained experimentally in the Aso grassland study. In the grassland, a 1 � 1 m square quadrat was defined using plastic rods, then the plants and litters within the quadrat were sampled (**Figure 5**). The plant samples were then brought to the laboratory, and dried in the air-conditioned room (24°C and 24% for temperature and humidity, respectively) until the masses were stabilized. Their masses and chemical contents were then measured. If combustion completeness, CE = 1, is given, an EF for a substance of interest will be equivalent to the content in the biomass, thus, the EF will be easily gained from this bottom-up approach.

#### **2.3 Sampling and measurement of airborne chemical species**

In our emission study in Aso, sampling and measurements for airborne chemical species were conducted in a vehicle. In-situ measurements for CO and CO2 were made by a CO analyzer (Model 48C, Thermo Fisher Scientific.) and a CO2 analyzer (LI-810, LI-COR Corp., Lincoln, NB, U.S.A.). For TGM measurements, plume gas was drawn through a gold-coated sand trap (N-160, Nippon Instruments Corp., Osaka, Japan) at the rate of 0.5 L min�<sup>1</sup> using a mini-air pump (MP-W5P, Shibata Scientific Technology Ltd., Souka, Japan), and the tube samples were brought to the laboratory and analyzed for TGM by cold-vapor atomic fluorescent spectroscopy (CV-AFS, Nippon Instruments Corp.). TGM for stable mercury isotope analysis was captured through a BAuT sampling tube [61, 62] at the flow rate of 80 L min�<sup>1</sup> . After the sampling, the BAuT tube was brought to the laboratory, then the TGM captured was converted to oxidized mercury (II) in 40% reversed aqua resia solution. The prepared solution samples were then analyzed by an MC-ICP-MS (Neptune Plus, Thermo Fisher Scientific GmbH, Bremen, Germany) for the isotopic composition.

<sup>δ</sup>xHgð Þ¼ ‰ xHg 198Hg � � sample xHg 198Hg � � 3133 � 1 2 6 4 3 7 5 � 1000 (3)

where x stands for the stable mercury isotope with mass x, and the bracketed isotope ratios with subscripts "sample" and "3133" indicate the stable mercury isotope ratios of mass x relative to the mass 198 for the sample and SRM 3133 (NIST), respectively.

Either a Teflon-coated glass fiber filter (Pallflex, Emfab, Pall Corp., Port Washington, NY, U.S.A.) or quartz fiber filter (Pallflex, Tissuequartz, Pall Corp.) installed in filter holder (Innovation NILU AS, Kjeller, Norway) or capsule filter (Balston, Parker Hannifin Corp., Cleaveland, IL, U.S.A.) was attached to all the inlets for sampling and measurements described above.

Instruments and devices referred above were loaded to a vehicle, together with batteries for their power supplies, then plumes from Noyaki were chased. When the car was drove into plumes the car was stopped in the plumes and the engine was stopped until the plume is gone. The sampling and measurements were continued until the Noyaki of the day there ended.

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*

#### **3. Results and discussion**

#### **3.1 Burned area**

There have been previous efforts that estimate the extent of BA in the Aso region using satellite imagery [94]. However, the estimates were based on the conventional maximum likelihood method and the reliability of the estimates was not evaluated. In our study, we employed the Random Forest algorithm, which is a machine learning method with better classification performance than the maximum likelihood method, and then evaluated the reliability of classification results in terms of overall accuracy and Kappa coefficient. The image analysis results (spatial distribution map of prescribed burns) were very accurate, with an overall accuracy of 0.972 and a Kappa coefficient of 0.944. This analysis demonstrated the potential and practical utility of high-precision spatial distribution estimation for prescribed burn monitoring using satellite imagery. Within the boundaries of individual pasturelands, areas that have undergone prescribed burns and those that have not were intermixed. The obtained data were used for more precise determination of the burnt area on more fine scale, and currently, boundaries are under the cross check. For future work, estimating burned biomass in prescribed burn monitoring, the capability of satellite image analysis covering extensive areas (enabling spatial distribution understanding) will be highly effective and essential (**Figure 6**).

#### **3.2 Measurements**

#### *3.2.1 Aboveground fuel loads*

Aboveground fuel loads we found in the Aso region are listed in **Table 1**. The table also shows fractions of two major habitat plant species, *Miscanthus sinensis* and *Pleioblastus chino var. viridis*. Results showed that the fuel loads were highly variable, from 0.6 to 1.58 kg m<sup>2</sup> . The compositions at most of the locations, except Minamioguni 1, were split by the two plant species. Minamioguni 1 was surrounded by trees, and, therefore, the most contributing, biofuel there was litter, which was included as other. Compared to the fuel loads reported to date, such as for African savanna (0.36– 0.48 kg m<sup>2</sup> [25], 0.20–2.5 kg m<sup>2</sup> [95]), Austrarian savanna (0.56 kg m<sup>2</sup> [24], 0.11– 0.74 kg m<sup>2</sup> [95]), North American meadow (0.4 kg m<sup>2</sup> , [52]), and central and south American savanna (0.30–1.38 kg m<sup>2</sup> [95]), our observations were in compatible level.

#### *3.2.2 TGM concentration*

Observed atmospheric concentrations of TGM in the background air and Noyaki plumes were on average ( SD) 1.4 0.5 and 5.2 3.6 ng m<sup>3</sup> , respectively. TGM concentrations varied substantially (**Figure 7**) and the variation was attributed to the proximity to the fire. With consideration of this atmospheric dilution, we are now confident that Noyaki indeed emits TGM into the atmosphere. As stated earlier, this emission has not been included in the Japanese domestic emission inventory that the Ministry of Environment Japan published, therefore, the emission source needs to be evaluated for a better understanding of the mercury cycle in the natural environment.

#### *3.2.3 CO and CO2 measurements*

We defined the background concentrations of CO and CO2 as 0 ppmv and 422 ppmv, respectively. Therefore, for our data, the subtraction of these concentrations

#### **Figure 6.**

*Overlaid images of a landscape map and burned (red) and unburned (light green) grasslands of the Aso region identified from the satellite images. Sources of the landscape map: World Hillshade, Esri, Airbus DS, USGS, NGA, NASA, CGIAR, N Robinson, NCEAS, NLS, OS, NMA, Geodatastyrelsen, Rijkswaterstaat, GSA, Geoland, FEMA, Intermap, and the GIS user community.*

was applied due to their mixing ratios observed in the background air. Using the results obtained from the field study at the Akiyoshidai National Park, we show the overview of this research project.

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*


#### **Table 1.**

*Fuel loads in the grassland of Aso and fractions of the two major habitat plant species,* Miscanthus sinensis *and* Pleioblastus chino var. viridis.

#### **Figure 7.**

*Average atmospheric concentrations of TGM observed during the Noyaki (red bar) and ordinary days (blue bar) in the period of 2019–2023.*

The ground-based measurements of CO and CO2 showed the complexity of the combustion state, flaming and smoldering. For example, observations for CO and CO2 at Akiyoshidai in the time period between 10 AM and 12 PM demonstrated their unsynchronized variations in detail, but roughly speaking, their trends were similar (**Figure 8a**). MCEs in this time period varied from 86 to 104% and on average SD, 97.2 1.3% (**Figure 8b**). The fractions of CO relative to the sum of CO and CO2 were on average SD, 2.9 4.0%. The scatter plot of ΔCO against ΔCO2 showed a high correlation (r2 = 0.768) with a slope of 0.02, indicating a proportional relationship and nearly complete combustion (**Figure 9**).

#### **Figure 8.**

*Time series plot of (a) volume-based CO2 (blue) and CO (red) mixing ratios, and (b) modified combustion efficiency (*i.e.*, ΔCO2/(ΔCO + ΔCO2)) observed during the prescribed grassland burning at the Akiyoshidai National Park.*

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*

**Figure 9.**

*Scatter plot of excess CO and CO2 mixing ratios (ΔCO and ΔCO2) observed during the prescribed grassland burning at the Akiyoshidai National Park.*

#### *3.2.4 Excess concentration ratio, emission factor, and Total mercury emission*

ERs for <sup>Δ</sup>TGM/(ΔCO2 <sup>+</sup> <sup>Δ</sup>CO) and <sup>Δ</sup>TGM/ΔCO are approximately 1.6 � <sup>10</sup>�<sup>9</sup> and 0.56 � <sup>10</sup>�<sup>7</sup> , respectively. According to Eq. (2) with the assumptions that the carbon content of fuel was 50%, the majority of this carbon was converted to CO2 and CO, and CO2:CO was 0.97:0.03 (discussed in subsection 3.2.3 for MCE), the corresponding EFs estimated from the ERs for ΔTGM/(ΔCO2 + ΔCO) and ΔTGM/ΔCO shown above are 13.2 � <sup>10</sup>�<sup>6</sup> and 14.0 � <sup>10</sup>�<sup>6</sup> g Hg kg�<sup>1</sup> , respectively. The values are significantly low, compared to other EFs reported for the grassland fires ranging from 38 to 510 � <sup>10</sup>�<sup>6</sup> <sup>g</sup> Hg kg�<sup>1</sup> ([51], references therein). The results were obtained during the emission study in Akiyoshidai, therefore, we applied these EFs to the prescribed fire there. Given the FL of 1.0 kg m�<sup>2</sup> with the 11.4 km2 of burned area reported [96]. The estimated Hg emission from there is 150.5 and 159.6 g y�<sup>1</sup> , which is approximately 0.02% of the annual Japanese domestic emission of TGM that the Ministry of Environment Japan reported.

#### *3.2.5 Stable mercury isotope ratios*

A limited number of TGM samples collected using BAuTs were analyzed for stable mercury isotope ratios. In order to characterize the isotopic compositions we evaluated if the observed isotopic compositions were reflected by mass dependent fractionation (MDF) or mass independent fractionation (MIF) using the Eq. (4) [97].

$$
\Delta^{\mathbf{x}} \mathbf{H} \mathbf{g} (\mathfrak{H} \mathbf{o}) \approx \mathfrak{G}^{\mathbf{x}} \mathbf{H} \mathbf{g} - \left( \mathfrak{G}^{202} \mathbf{H} \mathbf{g} \times \mathfrak{J}\_{\mathbf{x}} \right) \tag{4}
$$

**Figure 10.**

*Stable isotope ratios of TGM collected from the Noyaki plume and background air. The figure was reproduced from the previously published data [60]. See the text and Eqs. (3) and (4) for the definitions of these isotope ratios.*

where β<sup>x</sup> is the mass-dependent fractionation factor for mass x (0.2520, 0.5024, 0.7520, and 1.4930 for mass 199, 200, 201, and 204, respectively).

Results showed that Δ199Hg values for TGM in the Noyaki plumes were on average ( SD), 0.23 0.05‰, while those for TGM in the background air were on average, 0.13 0.12‰, indicating TGM emitted from the Noyaki underwent small MIF and TGM in the background air did not. Furthermore, δ202Hg of TGM from the background air and Noyaki plumes showed discrete compositions (**Figure 10**). It is interesting that our observations shown in **Figure 10** resembled the isotopic compositions found in plants, such as rice straw [75], foliage [79–81], and in soil [82]. Was atmospheric GEM aspirated from the air together with CO2, then oxidized and fixed in the plant body? Or was the deposited mercury on the ground taken up through the roots of plants? Answers to those open questions will be found by continuing overall grassland studies, including prescribed biomass burning and wet and dry deposition of mercury as well as mercury content in plant sections and soil.

#### **4. Conclusion**

Our study concretely confirmed that the prescribed biomass burning of the grasslands in Aso and other grasslands in western Japan emitted TGM into the atmosphere. The estimation of the emitted TGM during the Noyaki in the Aso region is currently under evaluation. Preliminary estimation of the TGM emission from Noyaki in the Akiyoshidai National Park resulted in 150–160 g TGM emission, which accounts for 0.02% of the whole domestic emission of mercury that the Japanese Ministry of Environment reported. Due to more than 10 times larger area of the Aso grassland than the Akiyoshi-dai grassland, it is possible that the emission of mercury from Noyaki in Aso can rise to 1% or similar, and for whole Noyaki in Japan may rise to a few %. The investigation is currently on-going.

*Mercury Emission from Prescribed Open Grassland Burning in the Aso Region, Japan DOI: http://dx.doi.org/10.5772/intechopen.113293*

δx Hg values of TGM showed similar values to the reported δ<sup>x</sup> Hg of the plant uptake TGM, implying the atmospheric mercury can be the origin. However, our current dataset could not identify whether or not the plant uptake of TGM from the air or wet and/or dry depositions contributed to the grassland mercury. Further detailed study combined with atmospheric monitoring in the grassland may help us to find the answer.

#### **Acknowledgements**

The authors thank The Sumitomo Foundation (Grant number 2230266) and the National Institute for Minamata Disease (NIMD) for the financial support of the research on mercury emissions from the grassland burning. The authors also thank the assistant Nanami Yamamoto from the NIMD for assisting our laboratory work in this project.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

Satoshi Irei1 \*, Satoshi Kameyama<sup>2</sup> , Hiroto Shimazaki<sup>3</sup> , Asahi Sakuma<sup>3</sup> and Seiichiro Yonemura<sup>4</sup>

1 Department of Environment and Public Health, National Institute for Minamata Disease, Kumamoto, Japan

2 Biodiversity Division, National Institute for Environmental Studies, Ibaraki, Japan

3 Department of Civil Engineering, National Institute of Technology, Kisarazu College, Chiba, Japan

4 Faculty of Bioresource Sciences, Prefectural University of Hiroshima, Hiroshima, Japan

\*Address all correspondence to: satoshireinimd@gmail.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section 2
