Grasslands Development Initiatives

### **Chapter 5**

## Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation in Developing Countries

*Rahmathulla Mohamed Nikzaad and Noordeen Nusrathali*

#### **Abstract**

Many conventional farming approaches in developing nations segregate livestock and crop production, ignoring the synergistic advantages and sustainable land management possibilities that may be gained by combining the two. In order to increase agricultural output and foster grassland conservation, this chapter investigates the idea of merging livestock and crop systems. By highlighting the advantages and challenges of the approach, this chapter draws attention to the potential benefits of integration, including enhanced soil fertility, efficient resource use, increased productivity, and better protection of grassland ecosystems. The importance and viability of a variety of integrated agricultural methods, including agro-pastoral, mixed, and silvopastoral systems, in a variety of geographical settings, are explored. The purpose of this chapter is to educate policymakers, academics, and practitioners on the need of integrating livestock and crop production for achieving long-term agricultural sustainability in low-income nations.

**Keywords:** grass land, conservation, climate change, livestock crop, developing countries

#### **1. Introduction**

#### **1.1 Background**

Agriculture is the lifeline of many developing nations, sustaining millions of people by providing them with employment and an essential income [1]. However, poor production, soil erosion, and inefficient use of resources are common problems for conventional farming in these areas. The need for sustainable agriculture techniques is becoming more urgent as the world's population is expected to rise to roughly 9.7 billion by 2050 and climate change offers greater dangers [2]. Innovative solutions are needed to meet the challenge of increasing food production while reducing environmental consequences and protecting scarce resources. Among them, integrating livestock and agricultural systems stands out as a method with the potential to help

developing nations overcome obstacles and advance sustainable land management [3, 4]. This progressive kind of farming takes into account all aspects of food production, rather than just the crops and cattle [5]. Instead, it promotes cooperation between these two agricultural pillars in order to maximize gains in production, grassland ecosystem preservation, and rural community prosperity [4].

Instead of keeping crop farming and animal farming separate, as is common in traditional farming approaches, integrated farming takes a more holistic and creative approach [6]. To solve the problems of contemporary agriculture, it takes into account the symbiotic relationship between crop production and animal husbandry [6]. The goal of this method is to increase agricultural output while also protecting grassland ecosystems and improving the quality of life in rural areas [4]. Traditional and indigenous agricultural traditions have long acknowledged the advantages of reciprocal interactions between crops and animals, therefore the idea of integrating livestock and crop systems is not new [7]. These time-tested methods have always known the need of coordinating the ecological roles of crops and livestock within a single system [8]. Industrialization and the Green Revolution, which prioritized specialized and intensive farming, led to the widespread replacement of these integrated techniques with more standardized and simpler ways [3, 4].

Recent years, however, have seen a resurgence in interest in these integrated systems and their promotion as a long-term answer to the problems plaguing contemporary agriculture [9–11]. Integrating livestock and agricultural systems has the potential to overcome many of the drawbacks of conventional farming [6]. New methods that improve resource use and nitrogen cycling might slow the decline of agricultural productivity and soil quality [12]. Including livestock in agricultural systems has the potential to help farmers due to the nutrient-rich manure created by animals [13]. Cattle may be given crop scraps in return, which is good for both parties [14]. Furthermore, this approach offers the possibility of safeguarding grassland ecosystems and preserving biodiversity [7]. Protecting grassland biodiversity, halting soil erosion, and improving ecosystem health may all be possible via the use of sustainable grazing practices within integrated systems [15].

Integrating livestock and crop production allows for more diversity and stability. Having many streams of income may help farmers weather economic storms and improve their financial stability [16]. Having additional food alternatives is one way in which a diverse agricultural system contributes to food security [17]. Although there is much to be gained by implementing integrated agricultural systems, there are a number of challenges that must be overcome before they can be implemented on a large scale. Raising awareness and educating the public may help increase the adoption of integrated systems [18]. It is critical to give farmers with knowledge, training, and extension services to aid in their transition to more sustainable and integrated practices [18].

Agricultural techniques may also be affected by sociocultural issues. Encouraging farmers to adopt new methods requires an appreciation for, and an understanding of, traditional knowledge and beliefs [19]. Creating an enabling environment for the broad adoption of integrated agricultural methods requires supportive policies, incentives, and institutional structures. The promise of integrated systems must be acknowledged by policymakers, who must then enact enabling legislation to encourage farmers to embrace this sustainable method [20]. Technology and infrastructure play critical roles in making integrated systems more practical. Integrated agricultural approaches are more likely to be adopted successfully when farmers have access to better seed varieties, animal types, and market connections [21].

*Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation… DOI: http://dx.doi.org/10.5772/intechopen.113109*

Consequently, the issues experienced by conventional agricultural methods in poor countries may be mitigated by the integration of livestock and crop systems [22]. Farmers may increase output, protect ecosystems, and better the lives of people in rural areas by understanding the interdependence of crops and animals and using this knowledge to their advantage [3, 4]. Integrated livestock and crop systems may lead the way towards a more resilient, productive, and environmentally aware agricultural future in developing nations via joint efforts and a commitment to sustainable practices [10]. Food security, economic prosperity, and ecological preservation may all be ensured for future generations by adopting and promoting integrated farming techniques in developing nations [23].

The primary objectives of this chapter are to perform a thorough research and analysis of traditional agricultural methods in developing countries and to emphasize the limitations and hazards that these practices provide to sustainable agriculture and food security. In order to stress the need of combining livestock and agricultural systems as a sustainable and successful approach to land management in developing countries, this chapter highlights potential benefits such as higher productivity, improved soil health, and grassland conservation. Different types of integrated farming will be examined, including agro-pastoral systems, mixed farming, and silvopastoral systems, and their relative relevance and feasibility in different types of environments. This chapter will also cover the issues and methods necessary to implement integrated livestock and agricultural systems, such as land management practices, animal waste management, and the incorporation of technology. Integration's social and economic implications, such as its impact on rural residents' incomes, food security, and climate change adaptation preparedness, will also be examined. The purpose of this chapter is to contribute to the existing body of knowledge on sustainable agricultural approaches in developing countries by assisting policymakers, researchers, and farmers in supporting integrated livestock and crop systems for greater productivity and grassland conservation.

#### **2. Advantages and challenges of integrating livestock and crop systems**

#### **2.1 Advantages of integrating livestock and crop systems**

#### *2.1.1 Enhanced nutrient cycling*

One of the primary advantages of integrating livestock and crop systems is the efficient nutrient cycling it facilitates [12]. Livestock produces organic matter in the form of manure, which can be utilized as natural fertilizers for crops [24]. This reduces the dependence on synthetic chemical fertilizers, closing nutrient loops, and promoting sustainable soil health and fertility [24]. In return, crop residues and byproducts can serve as feed and forage for livestock, minimizing waste and maximizing resource utilization [25] (**Figure 1**).

#### *2.1.2 Improved soil health*

The integration of livestock and crop systems contributes to improved soil health [27]. Livestock grazing can help break up compacted soils, improve soil aeration, and stimulate biological activity [28]. Additionally, the organic matter from livestock manure enriches the soil, enhancing its water-holding capacity and nutrient content. Healthy soils support better crop growth and resilience to environmental stresses [27].

**Figure 1.** *Nutrient cycle in crop-livestock systems [26].*

#### *2.1.3 Grassland conservation*

Sustainable grazing practices within integrated systems can play a vital role in grassland conservation [15]. Properly managed rotational grazing allows for rest and recovery periods for pastures, preventing overgrazing and maintaining biodiversity [29]. By preserving natural habitats and avoiding grassland conversion, integrated systems contribute to the conservation of grassland ecosystems and the wildlife they support [30].

#### *2.1.4 Diversification of income streams*

Integrating livestock and crop systems offers farmers the opportunity to diversify their income streams. Relying on both crops and livestock provides multiple sources of revenue, reducing the vulnerability to market fluctuations and ensuring economic stability for rural communities [4].

#### *2.1.5 Resilience to climate change*

Integrated systems can enhance resilience to climate change impacts. Diversification of production and income sources, as well as improved soil health, can make farms more adaptable to changing climatic conditions, such as altered rainfall patterns and extreme weather events [31].

*Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation… DOI: http://dx.doi.org/10.5772/intechopen.113109*

#### *2.1.6 Efficient resource utilization*

By combining crops and livestock, farmers can optimize resource utilization. For instance, livestock can graze on crop residues and cover crops, reducing the need for feed supplements. In turn, crop residues and by-products can be used as supplementary feed for livestock [32] (**Figure 2**).

#### **2.2 Challenges of integrating livestock and crop systems**

#### *2.2.1 Knowledge and awareness*

The requirement for knowledge and awareness among farmers is a major obstacle to integrating livestock and agricultural systems. Adopting new integrated techniques in farming involves access to knowledge, training, and extension services [33, 34]. Many traditional agricultural practices have been handed down from generation to generation.

#### *2.2.2 Socio-cultural factors*

Agricultural methods may be affected by cultural and social norms. Engaging with local people and respecting their traditional knowledge is vital since introducing new techniques may be met with hostility if they disagree with deeply ingrained practices or beliefs [6].

**Figure 2.** *Advantages of crop live integration.*

#### *2.2.3 Policy and institutional support*

The adoption of integrated agricultural methods relies heavily on enabling policies and institutional structures. To fully realize integration's potential, governments must acknowledge its value and reward it, empowering farmers with the means they need to implement and maintain such systems [4].

#### *2.2.4 Technology and infrastructure*

It might be difficult to develop integrated livestock and agricultural systems in areas with limited access to necessary technologies and infrastructure. Facilitating the adoption of integrated techniques requires better access to improved crops, animal breeds, and market links [35].

#### *2.2.5 Land management and space constraints*

In order to meet the demands of both crops and cattle, land must be managed carefully in an integrated system. Proper rotational grazing may be difficult in areas with limited acreage, necessitating creative land use planning to maximize output [36].

#### *2.2.6 Animal waste management*

Animal waste must be properly managed to avoid pollution and protect public health. When incorporating animals into agricultural systems, it is crucial to develop effective and sustainable waste management procedures [37].

#### **3. Integrated farming approaches**

Integrated farming approaches encompass various systems that combine crop cultivation and livestock rearing in a synergistic manner [38]. These approaches aim to optimize resource utilization, enhance productivity, and promote sustainable land management [38]. Each integrated farming system adapts to the specific ecological, social, and economic context of a region, offering flexibility in implementation [39]. Some of the key integrated farming approaches include:

#### **4. Agro-pastoral systems**

Agro-pastoral systems integrate crop production and livestock grazing in the same area [40]. Livestock, such as cattle, sheep, or goats, are allowed to graze on fallow or harvested crop fields, consuming crop residues and weeds [40]. The manure from livestock returns nutrients to the soil, benefiting subsequent crop growth. This system fosters a balanced nutrient cycle, conserves grassland ecosystems, and promotes biodiversity by alternating between grazing and crop production [12].

#### **4.1 Mixed farming**

Mixed farming involves combining both crops and livestock on the same farm, but in distinct areas [41]. Farmers allocate specific plots for crop cultivation and maintain

*Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation… DOI: http://dx.doi.org/10.5772/intechopen.113109*

separate areas for livestock grazing or raising [41]. The integration occurs at the farm level, with complementary interactions between crops and livestock. For example, crop residues and by-products can be used as animal feed, while manure enriches the soil for crop cultivation [42].

#### **4.2 Silvopastoral systems**

Silvopastoral systems combine trees or agroforestry components with livestock grazing. In these systems, farmers plant trees or shrubs on grazing lands, providing shade and shelter for livestock [43]. The trees also contribute to soil improvement through their root systems and enhance biodiversity. Livestock, in turn, contribute to tree management by pruning and seed dispersal, aiding in natural regeneration [44]. Silvopastoral systems promote carbon sequestration, mitigate climate change impacts, and improve the overall ecological resilience of the farming landscape [45].

#### **4.3 Integrated aquaculture-agriculture systems**

This approach combines fish or aquatic organism farming with crop production. Farmers integrate fish ponds or aquaculture tanks within their agricultural fields or in proximity to them [46]. Fish wastes serve as natural fertilizers for crops, while the crops provide shade and natural food sources for the fish. This system enhances overall farm productivity and diversifies income streams [47].

#### **4.4 Integrated crop-livestock-forest systems**

This system integrates crop cultivation, livestock rearing, and forest components within the same agricultural landscape. Farmers cultivate crops, raise livestock, and maintain forests or agroforestry areas. The system maximizes resource utilization, conserves biodiversity, and offers economic and environmental benefits [48].

Integrated farming approaches offer numerous advantages for enhancing productivity and promoting sustainable land management in developing countries. By optimizing resource utilization, conserving biodiversity, and promoting resilience to climate change, these systems pave the way towards a more sustainable and prosperous agricultural future [4]. Addressing challenges through knowledge dissemination, institutional support, and socio-cultural awareness will be critical in facilitating the widespread adoption of integrated farming approaches [49].

#### **5. Socio-economic implications**

#### **5.1 Rural livelihoods**

Communities who depend substantially on agriculture as a source of income may feel the greatest effects of integrated agricultural systems [16]. Increasing economic stability and decreasing reliance on changes in any one sector [16] is possible for rural communities by combining several agricultural activities, such as crop agriculture, livestock husbandry, aquaculture, and agroforestry.

#### *5.1.1 Diversification of income streams and poverty alleviation*

Monoculture, in which farmers only engage in one kind of farming, is common in conventional agriculture. This may be dangerous if the crop fails because of things like pests, illnesses, or bad weather [50]. However, farmers that use integrated systems might increase their revenue by diversifying their sources of income. Farmers may multitask by tending to many fields, herds, and tanks at once [4]. By reducing the possibility of total income loss and spreading the risk, diversification may help relieve poverty in rural areas [51].

#### *5.1.2 Enhancing livelihood resilience through integrated practices*

The resilience of rural lifestyles is boosted by integrated systems as well. Farmers who engage in a variety of endeavors are better able to respond to unexpected changes. They have many revenue streams to fall back on in the event of crop failure [52]. Furthermore, sustainable and regenerative methods are commonly emphasized in integrated systems, which assist conserve natural resources and sustain land productivity over the long term, ensuring future generations' ability to make a living [52].

#### **5.2 Food security**

#### *5.2.1 Contributing to food production and availability*

Integrated systems contribute significantly to food production and availability. By combining different agricultural practices, farmers can optimize land use and resource allocation [33]. For instance, they can use animal manure as fertilizers for crops, and crop residues can be fed to livestock, creating a closed-loop system that maximizes productivity [33]. This efficient use of resources helps increase food production and availability, reducing the risk of food shortages in rural areas.

#### *5.2.2 Strengthening food security in vulnerable regions*

Integrated systems can play a crucial role in enhancing food security in vulnerable regions, particularly those prone to environmental and climate-related challenges [53]. By adopting climate-resilient practices, farmers can mitigate the impact of adverse conditions like droughts, floods, or extreme temperatures on their crops and livestock. Moreover, diversification of crops and livestock provides a buffer against food crises caused by localized crop failures or diseases [54].

#### *5.2.3 Promoting dietary diversity and nutrition*

Through integrated systems, farmers can grow a diverse range of crops and rear different types of livestock. This diversity extends to the local food supply and promotes dietary diversity among rural communities [55]. Consuming a varied diet with a mix of fruits, vegetables, grains, and animal products can significantly improve nutrition and overall health, reducing the prevalence of malnutrition and diet-related health issues [55].

*Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation… DOI: http://dx.doi.org/10.5772/intechopen.113109*

#### **5.3 Climate change resilience**

#### *5.3.1 How integrated systems can enhance resilience to climate change?*

Climate change poses a significant threat to agricultural productivity, with increased occurrences of extreme weather events and shifting climatic patterns [56]. Integrated systems are more resilient to these changes because they offer flexibility and adaptability. By combining various farming practices, farmers can adjust their operations in response to changing conditions. For instance, during droughts, they can focus on drought-resistant crops and reduce water-intensive practices [57].

#### *5.3.2 Mitigating climate-related risks through diversified production*

Integrated systems also mitigate climate-related risks by reducing dependence on a single crop or livestock species. If one element of the system is affected by a climaterelated hazard, other components can still provide income and sustenance [58]. This reduces the vulnerability of farmers to the adverse effects of climate change, such as crop failures or livestock losses [33].

#### *5.3.3 Carbon sequestration potential of integrated farming*

One important aspect of climate change resilience is the potential to sequester carbon dioxide from the atmosphere [59]. Integrated farming practices, especially agroforestry and cover cropping, can enhance carbon sequestration in the soil and vegetation. Trees and cover crops capture carbon, mitigating greenhouse gas emissions and contributing to climate change mitigation efforts [60].

#### **6. Case studies**

#### **6.1 Case studies from Africa**

Examples of Successful Integration in African Countries:

Case Study 1: In Zambiya, a farming community successfully implemented an integrated farming system that combined crop cultivation with fish farming (aquaponics). They used the nutrient-rich water from fish ponds to fertilize the crops, while the crops filtered the water for the fish. This integration led to increased crop yields and fish production, improving food security and providing additional income streams for the community [61].

Case Study 2: In Ethiopia, a project focused on integrating agroforestry with traditional crop cultivation. Farmers planted trees alongside their crops, providing shade and windbreaks, which reduced soil erosion and water loss. The trees also contributed fruits, nuts, and timber, adding to the farmers' income. This sustainable approach improved soil fertility, enhanced climate resilience, and empowered the local communities economically [62].

#### **6.2 Case studies from Asia**

Integrating Livestock and Crop Systems in Asian Nations:

Case Study 1: In India, a dairy cooperative introduced an integrated model where crop residues and by-products were utilized as animal feed. This reduced the pressure on natural resources and minimized waste while increasing milk productivity. The cooperative also promoted organic farming practices, enhancing the overall sustainability of the system [63].

Case Study 2: In Vietnam, the combination of rice farming with duck rearing has proven successful. Ducks forage in rice paddies, consuming insects and weeds, which reduces the need for chemical pesticides and manual weeding. The ducks also provide an additional income source through egg and meat production [64].

#### **6.3 Case studies from Latin America**

Experiences of Integrating Systems in Latin American Countries:

Case Study 1: In Brazil, a cooperative of small-scale farmers adopted an integrated system that combined cattle ranching with silvopastoral practices. They planted trees and forage crops in pasture areas, providing shade and nutritious fodder for the cattle. This approach reduced deforestation, improved livestock health, and increased the overall productivity of the land [65].

Case Study 2: In Colombia, coffee farmers integrated beekeeping into their coffee plantations. Bees enhanced coffee pollination, leading to higher coffee yields. Additionally, the farmers could sell honey and beeswax, diversifying their income sources and promoting biodiversity [66].

#### **6.4 Challenges unique to the region and potential solutions**

Land Fragmentation: Many Asian countries face land fragmentation due to inheritance laws. Integrated systems might require larger land areas, which can be challenging to assemble. Land consolidation programs or community-based land-sharing arrangements could be potential solutions [67].

Water Management: In regions with water scarcity, managing water resources becomes critical for integrated systems. Implementing water-saving irrigation techniques, rainwater harvesting, and efficient water use can address this challenge [68].

Traditional Practices: Convincing farmers to shift from traditional practices to integrated systems can be challenging. Demonstrating the economic and environmental benefits through pilot projects and on-farm trials can help build confidence and encourage adoption [69].

#### **7. Policy and institutional support**

#### **7.1 Policy frameworks**

Analyzing Existing Agricultural Policies and their Impact on Integration: Before promoting integrated farming, it is crucial to assess the existing agricultural policies to understand their impact on integration. Some policies may inadvertently hinder the adoption of integrated systems due to their focus on monoculture or specific agricultural sectors [70]. Additionally, subsidies and incentives may favor conventional farming practices, making it challenging for farmers to transition to integrated approaches [71]. An in-depth analysis can help identify gaps and areas where policy adjustments are needed to encourage integration.

*Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation… DOI: http://dx.doi.org/10.5772/intechopen.113109*

#### **7.2 Policy recommendations to support integrated farming**

Incentives for Diversification: Governments can introduce financial incentives, such as subsidies or tax breaks, to encourage farmers to adopt integrated farming practices. These incentives could target crop-livestock integration, agroforestry, aquaponics, or other sustainable practices that enhance resilience and productivity [72].

Research and Development Funding: Governments should allocate funds for research and development specific to integrated systems. This investment can support the creation of best practices, improved technologies, and knowledge dissemination to farmers through extension services [73].

Land Tenure Security: Secure land tenure is essential for farmers to invest in integrated systems, as many practices involve long-term planning. Governments can provide land tenure guarantees and clarify land rights to instill confidence in farmers to adopt integrated approaches [74].

Market Access and Value Chains: Strengthening market linkages for integrated farming products can incentivize farmers to adopt such practices. Governments can facilitate access to markets, promote fair trade practices, and support value addition initiatives to increase the profitability of integrated systems [75].

Sustainable Agriculture Standards: Integrate sustainable agriculture standards into policy frameworks to encourage the adoption of ecologically friendly and socially responsible practices. These standards can guide farmers towards integrated approaches and promote environmentally friendly production [76].

#### **7.3 The way forward for policymakers, researchers, and farmers**

The role of policymakers in facilitating integrated farming is vital [77]. They need to give sustainable farming more weight in legislative frameworks, incentivize the use of integrated systems, fund studies on their efficacy, and bolster extension services for farmers [77].

Scientists should keep looking for new ways to improve the efficiency and scalability of integrated agricultural methods. Context-specific and implementable solutions may be found via collaborative research initiatives including stakeholders from academics, governments, and agricultural communities [73].

The use of integrated systems by farmers should be promoted and supported. Farmers may make a smooth shift to sustainable methods with the support of training programs, extension services, and access to capital and markets [78].

Food security, climate change, and poverty may all be combated via sustainable agriculture if livestock and agricultural systems are integrated. Agriculture and rural communities may have a more secure and sustainable future if governments, academics, and farmers all adopt this strategy.

#### **7.4 Institutional capacity building**

Strengthening Institutions for Promoting Integrated Systems:

Promoting integrated farming in developing countries requires a tailored approach that addresses the unique challenges and opportunities in these regions. Several studies and reports highlight the importance of strengthening institutions, providing training and extension services, and creating supportive policy frameworks to foster integrated farming practices.


By implementing these strategies in a developing country context, governments and relevant stakeholders can foster the adoption of integrated farming practices, leading to increased agricultural productivity, improved environmental sustainability, and enhanced livelihoods for smallholder farmers.

### **8. Future perspectives and conclusions**

#### **8.1 Future prospects of integration**

#### *8.1.1 Potential for scaling up integrated systems in developing countries*

There is great potential for expanding integrated agricultural systems in underdeveloped regions. The strain on agricultural systems to increase food production while being environmentally friendly will only increase as the world's population rises. Meeting this need while also resolving environmental and social issues is possible via the use of integrated techniques. Food security, poverty reduction, and rural

*Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation… DOI: http://dx.doi.org/10.5772/intechopen.113109*

development are all aided by integrated systems because they encourage diversity, resource efficiency, and resilience [4].

Governments, international organizations, and the corporate sector are more likely to support sustainable agriculture if public opinion shifts in its favor. Knowledge sharing and the use of lessons gained from regional case studies may also help with the scaling up of integrated systems [84].

#### *8.1.2 Integrating new technologies and ideas into agriculture*

Recent technological developments have been essential in spreading the word about integrated farming. Farms may be monitored and managed more effectively with the use of precision agricultural technology including remote sensing, drones, and sensor-based systems. These advancements allow for more precise input distribution, which in turn leads to less waste and higher output [85].

In addition, best practices, weather predictions, market pricing, and financial services are all more easily accessible to farmers thanks to digital platforms and mobile apps. With this knowledge, smallholder farmers will be better able to manage their resources and make educated agricultural decisions [86].

#### **8.2 Conclusion**

Integrating livestock and crop systems holds significant promise for advancing grassland conservation and environmental sustainability in developing nations. This approach offers multifaceted benefits, including heightened resource efficiency, amplified production, resilience to climate change, and preservation of biodiversity. By capitalizing on the synergies between livestock and crop production, farmers can optimize nutrient recycling, minimize waste, and reduce reliance on external inputs, resulting in increased yields, efficient land use, and sustainable resource management. This integrated model not only bolsters income stability for farmers by diversifying revenue sources but also fortifies resilience against climatic fluctuations and fosters adaptable production strategies. Overcoming hurdles like limited knowledge and institutional support remains essential for widespread adoption. Empowering farmers through educational initiatives, knowledge-sharing platforms, and tailored policies can bolster their confidence and competence in implementing integrated methods. Policymakers must champion sustainable practices, foster supportive frameworks, and enhance institutions to facilitate this transition and ensure equitable access to capital, inputs, and markets. In this collaborative endeavor, integrated livestock and agricultural systems emerge as a transformative solution to address pressing challenges, steering developing nations towards a sustainable and ecologically harmonious agricultural future.

*Grasslands – Conservation and Development*

#### **Author details**

Rahmathulla Mohamed Nikzaad\* and Noordeen Nusrathali Faculty of Technology, Department of Biosystems Technology, South Eastern University of Sri Lanka, Sri Lanka

\*Address all correspondence to: mnikzaad@seu.ac.lk

© 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.

*Integrating Livestock and Crop Systems for Enhanced Productivity and Grassland Conservation… DOI: http://dx.doi.org/10.5772/intechopen.113109*

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#### **Chapter 6**

## Determination of Grass Quality Using Spectroscopy: Advances and Perspectives

*Manuela Ortega Monsalve,Tatiana Rodríguez Monroy, Luis Fernando Galeano-Vasco, Marisol Medina-Sierra and Mario Fernando Ceron-Munoz*

#### **Abstract**

Spectroscopy is a promising technique for determining nutrients in grasses and may be a valuable tool for future research. This chapter reviews research carried out in recent years, focusing on determining the quality of grasses using spectroscopy techniques, specifically, spectrophotometry. The chemical methods used to determine the nutritional quality of grasses produce chemical residues, are time-consuming, and are costly to use when analyzing large crop extensions. Spectroscopy is a non-destructive technique that can establish the nutritional quality of grass easily and accurately. This chapter aims to describe the techniques focused on the use of spectroscopy and machine learning models to predict and determine the quality of grasses. A bibliographic review was conducted and recent research articles were selected that showed spectroscopic techniques applied to grasses. Different methods and results focusing on the quality of the grasses were compiled. In general, this review showed that the most commonly used spectroscopic method is near-infrared analysis. Spectroscopy is a very effective tool that opens the way to new types of technologies that can be applied to obtain results in determining the quality of pastures, leaving behind the use of traditional methods that represent higher costs and disadvantages compared to traditional methods based on precision agriculture.

**Keywords:** electromagnetic spectrum, nutrient prediction, precision agriculture, sustainable production, vegetation index

#### **1. Introduction**

Grasses and forages are essential for feeding herbivorous animals, especially ruminants that produce milk and meat. In recent years, the growth of the world's population and the resulting need to increase animal food production, have forced grass production to generate a greater amounts of biomass and to seek new technologies that maximize production efficiency. Therefore, it's important to remember that for optimal grass crop production, the soil on which these crops are grown must have

good nutrient levels and stable physical characteristics. Therefore, soil characteristics can directly affect the quality and productivity of grass crops. For example, soils that are deficient in one or two nutrients will experience a decrease in grass production [1].

The most important step in determining soil or grass quality is to know the amount of nutrients present. The best way to obtain this information is through a chemical analysis, which indicates nutrient excesses or deficiencies and allows for the development of appropriate fertilization programs [2]. However, the methods used to determine the chemical properties require time, skilled labor, and the use of chemical reagents that are contaminated and dangerous [3]. In addition, according to [4], laboratory analysis presents a number of challenges, such as the analysis of a reduced number of samples and that it can only be performed in the laboratory by destroying the original samples. It also requires a specialized laboratory, a demanding task that is not practical for people working in the field [5]. For this reason, technological alternatives are being considered that are capable of predicting grass properties without generating negative impacts on the environment, and that can be used to control the growth and development of grasses [6]. New calibration techniques and laboratory and remote sensing (RS) belonging to the spectroscopy can predict grasses components without chemical analysis [7].

This chapter aims to describe the techniques focused on the use of spectroscopy and machine learning models that allow the prediction and determination of the quality of grasses. The collection of information on investigative articles was carried out from January 2022 to March 2023 in the databases Google Scholar, ScienceDirect, and SpringerLink. The search terms used were: grasses, soil, spectroscopy, nutrient prediction, agriculture, vegetation index, and machine learning models. Research articles that had a clear methodology and results of using the technique were selected, collecting important information from each of them.

#### **2. Use of spectroscopy in grasses**

Spectroscopy captures different portions of the electromagnetic spectrum (ES). The light variations in the ES regions cover the visible spectrum (VIS) consisting of bands like red (R), green (G), and blue (B) and the infrared (IR) region which is divided into the near-infrared (NIR), short wave infrared (SWIR), medium wave infrared (MWIR) and long wave infrared (LWIR) spectra as shown in **Figure 1**. The VIS and IR region of the ES are the most used equipment that detects pasture quality by spectroscopy.

**Figure 1.**

*Visible and infrared regions of the electromagnetic spectrum. Adapted to [8].*

#### *Determination of Grass Quality Using Spectroscopy: Advances and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112990*

Among the spectroscopic techniques is spectrophotometry, a branch based on the use of different parts of the electromagnetic spectrum and the presence of light to detect different elements according to their chemical composition. Among the methods using spectrophotometry we can find cameras with NIR, RGB, multispectral (MSI), hyperspectral (HSI) and RS bands. NIR works with vibrations [9, 10]; and according to [11], this technique has gained attention because it's widely used in different areas of science, proving to be successful in obtaining information that is difficult to obtain with competing techniques. Now, spectroscopy uses the MWIR zone, which is known as a vibrational method that uses the light emitted in a sample to measure the transmission, absorption and reflection of light. This technique detects the structures of the sample as the molecules have chemical bonds that generate vibrational energy [12, 13]. In the same way, HSI detects signals or contiguous bands that contain a very narrow spectral bandwidth [14], greatly facilitate the detection of sample elements [15]. However, the use of HSI has not been sufficiently explored due to it's high acquisition cost and the large amount of spectral data that must be processed [16, 17]. The difference between these methods is the range of the spectrum they use to determine different types of features related to the spectral bands.

Cameras that record the spectrum's bands can be mounted on unmanned aerial vehicles (UAVs) or placed permanently in a laboratory to record data from samples. UAVs are widely used to collect data in the field due to their simplicity of operation and on-site data collection. For this reason, [7] used UAVs with HSI cameras to evaluate quality characteristics in grasses and demonstrated that this method can predict forage quality parameters.

**Figure 2** shows two ways to analyze the quality of grasses through different forms that use spectrophotometry.

The components of a sample can be analyzed from great distances using satellites; this is known as RS. RS has been used to study the spectral, spatial, and temporal variations of electromagnetic waves, and revealed the correlations between them and the proprieties of different terrestrial materials [18]. In other words, it focuses on identifying the materials present on the earth's surface and on understanding the

#### **Figure 2.**

*Different forms to apply spectroscopy to determine the quality of grasses: (a) hyperspectral images in the laboratory and (b) UAV drone with RGB, multispectral or hyperspectral moving cameras.*

phenomena that occur on it through their spectral signature, that is, it seeks to establish a relationship between the properties of the light reflected or emitted by terrestrial objects and the intrinsic properties of these objects [19]. To perform this type of data analysis, RS analysis and processing techniques are used, using satellites, to capture electromagnetic radiation at different wavelengths [20, 21]. All of this provides valuable information about the composition and dynamics of the land surface allowing producers to detect crop variations and facilitate a more precise application of fertilizers [22] nutritional composition and general condition of plants.

It's important to note that plants exhibit dynamic behavior in ES due to phenological changes in the plant, lighting conditions associated with topography (slope and orientation), sun position over the year, and soil moisture conditions, which can result in significant variations in the spectral response pattern [23].

The spectral signature of plants is characterized by how they reflect light in the visible spectrum (400–700 nm). Chlorophyll absorbs strongly in the visible spectrum around the B and R bands, resulting in a low reflectance, and the G spectral band, the reflectance is higher. It's in this region of the spectrum that photosynthesis occurs, a process in which energy is absorbed [24]. On the other hand, in the NIR region, the vegetation shows its highest reflectance in the NIR between 700 and 1300 nm, around 45–50% (**Figure 3**). This phenomenon is the result of diffusion caused by the refractive indices of the intracellular fluid and the intercellular spaces present in the plant mesophyll, especially in the spongy mesophyll. In contrast, at wavelengths between 1300 and 2500 nm, the reflectivity of the sheet it's controlled by water absorption, resulting in reflectance values of 10–20% [18]. When the vegetation is mature or under stress due to disease, insect attack, or low humidity, changes in the spectral characteristics of the leaves occur [25]. In general, these changes occur simultaneously in the VIS and IR regions, but there are greater alterations in IR. This behavior explains the great utility of ES for the study of vegetation.

**Figure 3** shows the spectral signature of the vegetation under different phenological conditions. It can be observed that the reflectance is influenced by the

#### **Figure 3.**

*Changes in the spectral signature of healthy and diseased vegetation in the visible and infrared portions of the electromagnetic spectrum. Adapted to [19].*

concentration of pigments in the leaves, mainly chlorophyll and carotenoids. In the VIS region of the spectrum, reflectance and transmittance are low due to strong absorption by leaf pigments.

#### **2.1 Vegetation indices in grasses**

From the study of spectroscopy, numerous vegetation indices (VI) have been developed. These indices are defined as parameters calculated using reflectance values at different wavelengths, to extract information related to vegetation [26]. In this context, VI is generated by mathematical calculations involving the different spectral bands of the images. These calculations produce a new image that highlights specific characteristics related to the physiological functioning of the plants [27]. The VI defined so far have in common the use of reflectance values in the R and NIR spectral bands, which are dedicated to the spectral behavior in this region of the ES. The reflectance of the vegetation goes from a relative minimum in the R corresponding to the absorption band of chlorophyll to an absolute maximum in the NIR, which is a consequence of the multiple scattering of the radiation within the cellular structure [25].

One of the most commonly used indices in vegetation assessment is the normalized difference vegetation index (NDVI), which quantifies the relationship between the energy absorbed and emitted by terrestrial objects [28]. In addition to NDVI, other indices relate to biomass and leaf area index. These include the green normalized difference vegetation index (GNDVI), which uses the G band instead of the R-band of the spectrum. There is also the RATIO index, which relates the high NIR reflectance of vegetation to low R reflectance. The enhanced vegetation index (EVI) is an enhancement of NDVI designed to perform better in areas of dense vegetation and to reduce atmospheric effects by being sensitive to variations in vegetation cover and leaf area. Similarly, the adjusted ground vegetation index (SAVI) is an enhancement of NDVI that attempts to compensate for the effects of ground brightness by relating the nutrients and R bands [29, 30]. Another index used in grasses is the normalized pigmented chlorophyll ratio index (NPCI) [31]. The VI is useful in determining productive characteristics in grasses, such as height, presence of disease, or available quantity.

**Table 1** shows the most commonly used vegetation indices used to evaluate biomass production and nutritional content of grasses, and the results obtained when applying these indices. They are mainly used to determine the productive properties that can directly influence the quality of the plants and their use in animal feed.

Among the results found to evaluate the quality and nutritional content of forages by spectroscopy, the work of [40] stands out. The reason is that this study has found a significant correlation between forage yield and RGB bands based on VI. This positive relationship demonstrates the usefulness of VI derived from color imagery for estimating forage quality and nutrient content. Soil chemical characteristics were also evaluated as an indirect measure of grass quality. One study found that soil nutrient content, especially potassium and phosphorus, was strongly related to electrical conductivity and NDVI [41]. These results demonstrate the relationships between soil and crop characteristics and allow the optimization of variable rate fertilization.

Likewise, Fava et al. [33] evaluated biomass and nitrogen status in grasses at three different growth stages and in grazed and ungrazed plots, finding good results for assessing nitrogen content, green biomass, and leaf area index using the NIR (775– 820 nm) and longer wavelengths of the red edge (740–770 nm). Another report shows the feasibility of using NDVI to assess moisture content in forages.


*Vegetation index: NDVI, normalized difference; RATIO, simple ratio indices; GNDVI, green normalized difference; NDRE, normalized difference red edge; SAVI, soil adjusted; OSAVI, optimized soil adjusted; EVI, enhanced; R, red; G, green; B, blue; NIR, near infrared; RE, red edge.*

**Table 1.**

*Vegetation index, formulas, constants and characteristics were evaluated to predict the quality of pastures through spectroscopic techniques.*

#### **2.2 Quality characteristics in grasses determined through spectroscopic techniques**

In general, the quality of grasses is measured using various characteristics such as dry matter, digestibility, energy, organic matter, protein, and carbohydrates [42, 43]. Also, by many VI, factors affecting the quality of grasses, such as plant diseases, amount of biomass, or type of fertilization, are directly related to the nutritional characteristics (**Figure 4**). In addition, the quality of the grasses can also be measured indirectly, through the content of soil nutrients. In the soil matrix, the content of sand, silt, and clay can be monitored by spectroscopy [44, 45], the content of organic carbon and organic matter, which are synonymous with soil fertility and therefore, of plant growth [46, 47]. However, several studies have managed to determine using spectroscopy many characteristics focused on the productivity of grasses.

Results of previous studies agree that the use of spectroscopy in forages can be useful to determine factors such as mass and growth, light interception, turf heterogeneity, nitrogen deficiency, and drought stress, using non-destructive methods [48]. The determination of the concentration of nutrients such as phosphorus, potassium,

**Figure 4.**

*Main nutritional and production quality characteristics evaluated in grasses.*

*Determination of Grass Quality Using Spectroscopy: Advances and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112990*

sulfur, calcium, and magnesium, some minor elements. and the sugar content of grasses has also been studied using spectroscopic techniques [49]. In addition, spectroscopy can work in other areas indirectly related to the quality of grasses and plants, for example, [50] they worked on forest dynamics and land use; in addition [51] applied spectroscopic techniques to determine the quality of the milk from cattle fed with fresh grasses and [52] evaluated carcasses of lambs that were fed with fresh grasses. This is due to the fact that there are many characteristics, both direct and indirect, that can be evaluated in grasses to determine their quality. This can open new avenues for future research and can clarify that these techniques, belonging to the new PA, are widely used in the investigative field of vegetation.

#### **2.3 Investigations carried out in grasses using spectroscopic techniques**

A study conducted by [53] demonstrates the implementation of portable spectroscopic sensors to determine the nutrient content in grasses, finding good results in determining height and protein content, which can lead to improved productivity for farmers through access to this type of technology. In this review, it has been shown that NIR instruments are the most widely used in spectroscopy and many of the studies carried out in grasses focus on the use of this method, with good results. For example, Restaino et al. [54] used 120 grass samples analyzed by NIR to determine nutrients in grasses, demonstrating great potential in this technique; Danelli et al. [55] predicted grass quality parameters using NIR, using 1615 samples, managed to find optimal settings, and considered this technique as an aid in grass management; Catunda et al. [56] developed calibration strategies in grasses using NIR to predict their nutritional composition using 2622 samples, they found that characteristics such as ash, protein, and fiber can be easily determined using this methodology; Parrini et al. [57] evaluated the quality of fresh grasses using NIR, finding the estimation of some chemical parameters of grasses associated with quality feasible.

The NIR methodology has the advantage that it can be applied in the laboratory and in the field, for this reason, Serrano et al. [5] used grasses for continuous grazing to test the technique in the field, demonstrating that the combination of spectroscopic and ultrasonic methods to determine pasture quality factors can be accurate even in very heterogeneous grasses due to grazing animal. In this review, 95% of the studies carried out have included NIR analysis in the laboratory or through UAVs, where methodologies that include the use of HSI and multispectral images can also be observed, while the other percentage have used techniques that include only RGB bands. This leads to the idea that studies of grasses have evolved to include not only the visible part of the spectrum but also using the NIR region, which provides more information due to its greater number of spectral bands.

**Table 2** summarizes the information found by different authors on the use of spectroscopy in grasses to determine their quality, including parameters such as the method used, the spectral characteristics of the method, and the results obtained in this review.

#### **2.4 Machine learning models and predictive metrics applied to grass quality**

In the area of measuring the intensities of reflected light in narrow spectral bands, particularly in the NIR range, advances in optical methods have enabled the identification of chemical bonds between hydrogen and carbon, hydrogen and nitrogen, and hydrogen and oxygen. These chemical bonds have proven to be key indicators for


*SR, spectral range; HSI, hyperspectral; CP, crude protein; DM, dry matter; DL, deep learning; MSI, multispectral; UAV, unmanned aerial vehicles.*

#### **Table 2.**

*Compilation of different spectral methodologies and characteristics of the equipment used in spectroscopy applied to grasses.*

determining the nutritional characteristics of grass, including the presence of crude protein, fibers, and other plant constituents [57, 59, 62]. These new technological alternatives, along with the use of machine learning statistical models, can produce good results in predicting various nutrient variables in soils and grasses. According to [63], machine learning algorithms have great potential for analyzing spectral data and analyzing attributes in grasses. Statistical models perform the function of developing, evaluating, and improving the equations that calibrate and predict the presence of elements or nutrients from reflectance spectra, replacing traditional chemical techniques [64].

Knowing the relationship between the spectral bands and the grass variables requires the use of multivariate statistics [65], so several statistical models have been developed. The most commonly used is the partial least squares regression (PLSR), which explores the relationships that may exist between all the variables [66]. The cubist model (CUB) is also used to predict nutrients using spectroscopy. This algorithm performs the construction of an unconventional regression tree [67], the prediction is made based on intermediate linear models step by step, and it creates

#### *Determination of Grass Quality Using Spectroscopy: Advances and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112990*

subsets of data and rules to select only some predictor variables [68]. On the other hand, the random forest (RF) model works with many decision trees that are known as classifiers; these use representative variables of the sample, forming classifier nodes for the set of variables that have multivariate characteristics, in the case of the spectral bands of each sample [69].

In addition, the support vector machine (SVM) algorithm, using the hyperplane, recognizing different categories, and maximum margins, manages to separate the data into different categories by selecting the most appropriate vectors for the prediction [70]. However, there are many algorithms that can be applied to spectroscopic data. Others widely used are principal component regression (PCA), multiple linear regression (MLR), and artificial neural networks (ANN) [71, 72]. To evaluate the predictive performance of the above machine learning models, [73] considers that some of the most commonly used metrics in these models are the coefficient of determination (*R*<sup>2</sup> ), the ratio of performance to deviation (RPD), and the root-meansquare error (RMSEP). It's important to obtain models that have *R*<sup>2</sup> values close to 1, low RMSEP values since the difference between the predicted and observed values of the model should be small, and RPD values greater than 2 [74]. These model selection parameters will indicate that the selected model can efficiently predict grass quality properties using spectroscopic methods.

In reviewing the selected articles, it was found that 84% of the articles belonging to this review used the PLSR machine learning model for data analysis. The remaining percentage includes PCA, SVM, and CUB models. The widespread use of the PLSR model for predicting quality variables using spectroscopy may be due to its ease of execution in various statistical software, in addition to the fact that it is a model that reduces the dimensionality of the data, and can reduce memory requirements.

For this reason, the PLSR models used in the various studies collected in this review presented optimal fits in predicting different characteristics in the grasses, providing high reliability for use in future studies. For example, [75] used this model to predict crude protein (CP) in grasses and found an *R*<sup>2</sup> of 0.98 and an RPD of 4.12, indicating a high fit of the model found. Results of a *R*<sup>2</sup> were also found to predict the CP in the grass of 0.69 and 0.73 respectively, but the RPD found was 1.95, which means that the performance of the model for this feature is regular [76, 77]. Another similar result was found by [78], where the RPD found was 1.84, the *R*<sup>2</sup> was 0.77 and the RMSEP was 2.05. Finally, for the CP variable, [5] found an *R*<sup>2</sup> of 0.84 and an RPD of 4. According to all the results related to this nutritional variable of the grass, the CP can be predicted with PLSR models with a high fit, but many samples must be evaluated to make the model more and more fit. The fibers variable was also been predicted using PLSR models, [75] finding an *R*<sup>2</sup> of 0.94, an error of 2.94, and an RPD of 2.83. For the same variable, *R*<sup>2</sup> of 0.67 and 0.76 and RMSEP of 5.77 and 4 were obtained [7, 56]. This variable was predicted using RS and found a fit of 0.66 and an RPD of 1.71 [79]. This variable resulted in minor adjustments to the CP variable but has great potential to be predicted using spectroscopy and machine learning models.

Regarding other variables that have been predicted in grasses, [55] managed to find an *R*<sup>2</sup> for the ash characteristic in the grass of 0.75 and an error of 1.01, which means that this technique allows one to know the amount of minerals in the grass. Also, [80] found a good fit for the nutrient variable EE in the grass, finding an *R*<sup>2</sup> of 0.73, an RDP of 1.69, and an RMSEP of 0.23. The CP, fibers, and dry matter variables are the most analyzed by researchers and the CP variable is the one that shows the highest adjustment and prediction results in the different evaluation criteria of the models. However, the other nutritional variables also present good adjustment results.

Regarding the productive variables of grasses, VI has also been widely used in spectroscopy to determine nutritional and productive variables, mainly in remote sensing and moving cameras. In general, the variables related to the quality of the pasture, evaluated using spectroscopy and statistical analysis, have generated optimal results in terms of their adjustment, which indicates that the use of these techniques is a great tool for knowing the quality of the grass, before being offered as food to cattle. Regarding the statistical models, although SVM and CUB have been used in this type


*PLSR, partial least squares; PCA, principal component analysis; RF, random rorest;* R*<sup>2</sup> , determination coefficient; RMSEP, root-mean-square error; RPD, ratio of performance to deviation.*

#### **Table 3.**

*Nutritional characteristics evaluated in grasses using spectroscopy.*

*Determination of Grass Quality Using Spectroscopy: Advances and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112990*

of study, they are not preferred by researchers, and it is then, the PLSR model is the most used. The NIR methodology is the most used to carry out research on grass quality due to its wide range in the electromagnetic spectrum, NIR the interaction between plant compounds can be observed much easier, while equipment that only RGB cameras or multispectral may be limited in predicting plant properties due to few bands and little information available. This is due to the great simplicity of programming and interpretation of the results, as it reduces the dimensionality of the spectral data. **Table 3** shows a summary of the studies carried out on the grasses, the statistical models applied and the fitting values found in each one of them.

#### **3. Conclusions**

Spectroscopy has been widely used in recent years to evaluate the quality of grasses. This is confirmed by the large amount of research supporting its use and demonstrating its effectiveness in predicting productive and nutritional variables in grasses. Both laboratory and field methods can be easily applied, and the use of machine learning statistical models to predict variables related to quality has promoted its use in agricultural areas. In line with the rapid advances that have occurred in the field of precision agriculture, it is expected that the development and use of spectroscopic techniques to determine grass quality will increase in the coming years due to the need to limit the use of chemical analysis and promote non-destructive methods in the field. The development and widespread use of more accessible machine learning statistical models and portable equipment is expected to enable the collection and analysis of real-time information in the field to facilitate the work of graziers.

#### **Acknowledgements**

This publication and the financial support of the MSc students was possible thanks to the project "Design and validation of predictive models to determine Cation Exchange Capacity (CEC), Organic Matter (OM) and Nitrogen (N) in soils from hyperspectral images" through the agreement 2022-7204, financed by the University of Antioquia Foundation.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **List of abbreviations**



### **Author details**

Manuela Ortega Monsalve<sup>1</sup> \*, Tatiana Rodríguez Monroy<sup>1</sup> , Luis Fernando Galeano-Vasco<sup>2</sup> , Marisol Medina-Sierra<sup>2</sup> and Mario Fernando Ceron-Munoz<sup>2</sup>

1 Animal Sciences, GAMMA Research Group, Faculty of Agricultural Sciences, Antioquia University, Medellin, Antioquia, Colombia

2 GAMMA Research Group, Faculty of Agricultural Sciences, Antioquia University, Medellin, Antioquia, Colombia

\*Address all correspondence to: manuela.ortegam@udea.edu.co

© 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.

*Determination of Grass Quality Using Spectroscopy: Advances and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.112990*

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### **Chapter 7**

## Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

*Tatiana Rodríguez Monroy, Manuela Ortega Monsalve, Luis Fernando Galeano-Vasco, Marisol Medina-Sierra and Mario Fernando Ceron-Munoz*

#### **Abstract**

This chapter provides an overview of cation exchange capacity (CEC) and its importance as an indicator of soil fertility, particularly in the assessment of grassland quality. The limitations of traditional methods are highlighted, and the need to explore more agile approaches to grassland quality assessment is emphasized. The increasing use of hyperspectral information (HSI) as an accurate tool for measuring soil properties, which promotes more effective and sustainable rangeland management, is further explored. This provides data on soil fertility and forage quality, enabling more accurate decisions. The benefits and challenges of using HSI data to estimate CEC and its potential to improve pasture and forage production will also be examined. HSI technology allows information to be collected and analyzed from reflected light at different wavelengths, providing a clear understanding of soil physical and chemical properties. In addition, a case study illustrating the estimation of CIC using hyperspectral cameras in the department of Antioquia, Colombia, is presented. The chapter emphasizes the relevance of this topic in the rangeland context and concludes with a future outlook that anticipates a change in the management and understanding of grazing systems.

**Keywords:** agriculture, fertility, soil, spectroscopy, supervised algorithms, remote sensing

#### **1. Introduction**

The soil is a nonrenewable natural resource that provides essential ecosystem and environmental services. It acts as a support system and nutrient provider for plant growth, supporting food production, and supplying raw materials for a wide range of human activities [1]. To assess the condition and sustainability of the soil, it is necessary to consider its quality, taking into account the integration of chemical, physical, and biological factors [2].

The physical properties of soil are fundamental to understanding its ability to store and release nutrients and to facilitate plant root development. These properties include texture (sand, silt, and clay content), structure (aggregates), porosity (spaces between particles), bulk density, and water holding capacity [3]. Soil respiration, microbial biomass, nitrogen mineralization, and earthworm density are also considered to assess the biological aspects of the soil [4]. On the other hand, soil chemical properties also influence nutrient availability and are essential for plant growth. Among the most important are soil pH, which controls nutrient availability, CEC, which indicates the soil's ability to retain nutrients, and the concentration of primary, secondary, and micronutrients [5].

In this context, soil quality serves as a tool to examine changes caused by soil management practices, such as excessive fertilization, inadequate irrigation, uncontrolled grazing, nutrient depletion through erosion, physical decomposition of aggregates due to over-tillage, loss of organic matter, salinization, and alkalinization [6]. Soil fertility management is one of the most critical decisions as nutrients often limit plant growth and animal performance [5]. Nutrient balances in the grass are crucial in defining fertilization needs when the nutrient balance is negative and in reducing the purchase of off-farm inputs [7]. Therefore, the objective of this chapter is to analyze CEC in grass production and to present a case study for its determination using HSI technology.

#### **2. CEC in grass production and the use of spectra in its estimation**

#### **2.1 CEC as an indicator of soil fertility**

CEC is a soil property that describes its ability to provide nutrients in the soil solution, making them available for plant uptake. It is a key indicator for assessing soil fertility [8] as it determines the soil's capacity to retain and supply cations essential for healthy plant growth and development. CEC is defined as the sum of exchangeable cations in the soil, including calcium (Ca2+), magnesium (Mg2+), potassium (K+ ), sodium (Na<sup>+</sup> ), hydrogen (H+ ), aluminum (Al3+), iron (Fe2+), manganese (Mn2+), zinc (Zn2+), and copper (Cu2+), expressed in cmol<sup>þ</sup>kg�<sup>1</sup> or meq/100 g (milliequivalents per 100 grams of soil) [9].

The term "cation exchange" refers to the process by which cations are exchanged in the soil solution, and subsequently absorbed by plant roots. The presence of clay minerals and organic matter (OM) in the soil enhances the amount of cations in the soil solution due to their negative charge, which attracts positively charged ions (cations) to their surfaces through electrostatic forces. As a result, the cations remain within the root zone of the soil (**Figure 1**).

In soil, OM can exhibit CEC by weight that is 4–50 times higher than that of clay. Unlike clay minerals, OM has a distinct negative charge source. This negative charge results from the dissociation of organic acids within OM, resulting in a net negative charge that is balanced by the presence of cations in the soil. This negative charge on OM is referred to as pH-dependent CEC as the dissociation of organic acids is affected by soil pH. Consequently, the actual soil CEC varies as a function of soil pH. For example, a neutral soil with pH 7 containing the same amount and type of OM will have a higher CEC than a soil with a lower pH, such as pH 5 [9]. On the other hand, clay has a high capacity to attract and retain cations due to its chemical structure. The *Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification… DOI: http://dx.doi.org/10.5772/intechopen.112991*

#### **Figure 1.** *Cation exchange in the root zone of a grass. Source: Own elaboration.*

ratio of CEC to clay content in weight percent can vary significantly because clay minerals exhibit different CECs due to variations in their structure and chemical composition. The proportion of exchangeable cations also varies among different clay minerals. For instance, montmorillonite clay has a higher CEC, heavily weathered kaolinite clay has a lower CEC, and slightly higher CEC is found in the less weathered illite clay [10]. In this context, the estimation of CEC in livestock production systems becomes crucial as it identifies the soil's ability to retain nutrients and provide a favorable environment for pasture growth. By utilizing soil testing techniques, producers can determine the CEC and take appropriate measures to enhance it. This involves the proper application of amendments and fertilizers, adjusting soil pH, and adopting grazing management practices that prevent soil compaction and promote MO incorporation [11]. Improving the soil's CEC ensures the adequate availability of essential nutrients for pasture growth, leading to higher livestock productivity. Moreover, enhancing soil conditions contributes to the conservation of ecosystem structure and health, promoting long-term sustainable management.

#### **2.2 Methods used for the determination of CEC**

To quantify CEC and evaluate soil fertility, various direct or conventional methods, as well as indirect or addition methods, have been established. Direct methods involve determining CEC as a single measure by saturating the soil exchange sites with a solution of specific cations, allowing for unique and quantitative measurements. These methods enable CEC determination at different soil pH levels using unbuffered reagents such as KCl, NH4Cl, and organometallic cations, as well as in a buffered medium to eliminate pH variation in measurements and express all results on the same basis [12]. On the other hand, indirect methods use related parameters, such as exchangeable bases and exchangeable acidity and employ equations to determine CEC. This involves summing up cations and displacing exchangeable cations in the soil using a saturating salt solution, such as ammonium acetate [13].

Currently, the standard reference method most commonly used by laboratories to determine CIC is to saturate the exchange complex with ammonium acetate (1 N at

pH 7) for both acidic and alkaline soils. This method uses an ammonium acetate solution to displace the exchangeable cations present in the soil and measures the concentration in the solution to determine the CEC. Another method is the calculation with the sum of exchange bases (Ca+2, Mg+2, K+1, and Na+1). For this process, extraction of 30 to 60 ml of ammonium acetate and 50 ml of 96% ethyl alcohol is required, and for quantification, 50 ml of 10% sodium chloride, 20 ml of formaldehyde, phenolphthalein, and sodium hydroxide are used [14].

The determination of CEC in the laboratory, through direct and indirect methods, presents certain limitations. The preparation of samples, chemical analysis, and interpretation of results requires considerable time, qualified personnel, and specialized equipment. However, a promising alternative for CEC determination is HSI reflectance spectroscopy. This nondestructive technique enables a rapid and efficient analysis of soil, providing detailed information about its properties. By utilizing HSI images, soil reflectance is captured across multiple spectral bands, allowing for the detection of interactions among soil chemical elements. This novel approach has the potential to overcome some of the limitations associated with traditional laboratory methods, facilitating a more precise and detailed assessment of CEC in different soil areas [15].

#### **2.3 Spectroscopy, an alternative to assess soil fertility**

The need for faster and more cost-effective analysis has led to the widespread use of infrared spectroscopy. Spectroscopy uses the interaction of light with soil components to provide valuable information about soil properties. The technique is based on measuring the reflection, absorption, and emission of light at different wavelengths to characterize the chemical composition and physical structure of the soil [16]. By analyzing the amount of reflected, absorbed, and transmitted light in each spectral band, detailed data on the different elements and compounds present in the soil can be obtained. This allows a rapid and nondestructive assessment of the chemical and mineralogical composition of the soil, including an estimate of the CEC. This approach reduces the need for traditional laboratory analysis and provides faster results, which can be particularly useful in situations where frequent monitoring of CEC is required over large areas or in resource-limited conditions [17]. It is an emerging technology that successfully predicts the physicochemical properties of soils [15], including CEC, pH, clay content, carbon content, total nitrogen content, and other elements, by correlating spectral data extracted from images with their chemical and physical concentrations [18–20].

This technique is based on the fact that materials reflect electromagnetic energy in the form of different patterns and wavelengths due to their chemical composition, physical structure, and surface properties. HSI captures the radiation reflected from objects in many very narrow spectral bands, creating large data cubes per pixel [21]. The data cubes contain the radiance received by the sensor in a particular band of the spectrum, corresponding to the size of the pixel, which is the smallest visual unit that appears in the image. Each pixel is defined by an integer number known as the digital level (ND). In this sense, the information of an image can be represented as a threedimensional numerical matrix since it has spatial information on its X and Y axes, corresponding to the geographical coordinates of the image, and spectral information on the Z axis. Considering that HSI images contain a large amount of data, handling, storage, and processing is challenging due to the high spectral variability and correlation in the data. The analysis of large amounts of data involves the following phases:

*Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification… DOI: http://dx.doi.org/10.5772/intechopen.112991*

**Figure 2.** *Hyperspectral information from a hyperspectral image. Adapted from [23].*

obtaining the data to be processed, transforming the data so that it can be used, applying the data exploration technique, and evaluating the results obtained [22], as shown in **Figure 2**.

Data transformations are applied to HSI data to reduce noise, improve quality, and enable modeling. Some transformations used for noise removal are the Savitzky-Golay (SG) filter, also known as the digital smoothing polynomial, which reduces the effect of noise without causing much distortion in the spectrum, especially in the width and height of the bands. It is also simple and efficient and involves only a linear convolution with a set of filter coefficients [24]. Another technique used is standard normal variation (SNV) [25], which highlights important patterns and relationships between different bands. On the other hand, the detrending technique (DT) corrects the trend of the data [26].

Similarly, several data exploration techniques have been used to relate the spectra to soil properties and have been used to relate the spectra to soil properties. Among the most widely used are partial least squares regression (PLSR), which can handle large and noisy data sets, and the support vector machine (SVM) method, which is characterized by its ability to generate robust models with few training samples. Other methods, such as artificial neural networks (ANN), are also used in HSI analysis. ANNs consist of an input layer, one or more hidden neuron layers, and an output layer. Decision trees and random forests (RF), which are built from rules, have bifurcations or branches that depend on a condition based on linear regression. These algorithms have been widely used in the scientific literature for the prediction of various soil characteristics using spectral or multidimensional data [27]. Therefore, the aim of this paper is to evaluate supervised learning algorithms in soil CIC estimation from HSI images.

#### **3. Case study**

#### **3.1 Generalities**

Livestock production is a rapidly growing sector in Colombia. At present, the country's cattle population is spread over 620.807 farms, with a total of 29.642.539 animals, an increase of 1.2% compared to 2022 [28]. This economic activity covers 34 million hectares of land [29], divided into three production systems: dairy, beef, and dual purpose, of which 70% is managed under extensive production systems, with an average stocking density of 0.9 animals per hectare [30].

Livestock diets are based on pasture and forage, with occasional use of concentrates [29]. Although precise data on the range of forage species used in livestock production are not available, it is widely recognized that kikuyu grass (*Cenchrus clandestinus* (Hochst. ex Chiov.) Morrone) plays a crucial role as a forage base in specialized dairy production in high tropical climates [31, 32]. Kikuyu grass is particularly dominant in these areas and provides a constant and abundant source of forage for cattle [33]. In the case of cattle in the mid and low tropics, it has been observed that the most commonly used forage species are star grass (*Cynodon nlemfuensis*), various species of *Brachiaria* (such as *B. decumbens*, *B. humidicola* and *B. brizantha*) and some native species [34].

Livestock productivity is closely linked to the ability of producers to manage their pastures effectively. Proper management involves the accurate and timely application of nutrients necessary for plant growth, which promotes early pasture recovery and maintains sustainable forage production throughout the year [35]. In this sense, a fundamental strategy is to ensure that the soil has the optimum physical and chemical characteristics for adequate plant development. This ensures an environment conducive to the growth of healthy and high-quality grasses.

In the search for faster and more cost-effective alternatives for estimating CEC and assessing soil quality, infrared spectroscopy is emerging as a viable option. This technique provides rapid and nondestructive measurements of soil chemical and mineralogical composition, including CEC estimation, by analyzing the reflection, absorption, and transmission of light at different wavelengths. By using spectroscopy, producers can obtain detailed information on nutrient availability and soil characteristics, enabling them to make informed decisions on nutrient application and pasture management to maximize livestock productivity.

The study area for the estimation of CEC by infrared spectroscopy was located in the department of Antioquia, Colombia, covering the nine subregions and 96 municipalities of the department, as shown in **Figure 3**. The information was obtained from 1997 soil samples collected between November 2020 and November 2022 in pastoral and cocoa production systems. The altitudes of the sampled areas ranged from 0 to 2900 masl and were characterized by a high spatial and temporal variability.

#### **3.2 Methodology**

To determine CEC in soil, 1997 samples were analyzed according to the Colombian technical standards NTC 5667:2017 for soil sampling in the field [36] and NTC 5805:2003 for sample preparation for chemical analysis [37]. Laboratory analysis of CEC was carried out at the Colombian Agricultural Research Corporation AGROSAVIA using the indirect method of sum of bases [38].

To record and extract HSI information, HSI images were taken by drying the soil samples at 40°C for 48 hours in a forced-air oven and sieving them at 2 mm. The images were taken using two cameras, the Baldur S-384 N or SWIR (short wave infrared) and the Baldur V-1024 N or VNIR (visible and near infrared). The SWIR camera had a spectral range of 951.61–2517.86 nm, 288 spectral bands, 384 spatial pixels, and a 30 cm lens, while the VNIR camera had a spectral range of 485.14– 955.65 nm, 88 spectral bands, 1024 spatial pixels, and a 30 cm lens. Reflectance (R)

*Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification… DOI: http://dx.doi.org/10.5772/intechopen.112991*

**Figure 3.** *Location of soil samples collected in the 96 municipalities of the nine subregions of the department of Antioquia.*

values were then extracted from the SWIR and VNIR cameras in a region of interest located in the center of the image to avoid pixel errors. Digital masking was applied to pixels with R values greater than 0.2 and less than 0.9 to remove saturated and shaded pixels. This process was performed iteratively using the SpectralPy [39], Spectral [40], and NumPy [41] libraries of the Python 3.8.2 programming language [42].

The overlapping bands detected in the transition region between 950 and 960 nm were removed using the Spectrolab library created by [43] in the R-Project programming language [44]. Band 955 was removed so that the spectra recorded with the VNIR camera ended at band 951, while the SWIR camera started at band 957. Finally, the hyperspectral band database was obtained.

To build the models, the CEC variable was used in its original form and transformed using the Box-Cox method with a value of *λ* ¼ �0*:*1547 and the square root transformation. In addition, the reflectance values were converted to absorbance (ABS) using (Eq. (1)).

$$\text{ABS} = \log\_{10} \frac{1}{R} \tag{1}$$

In addition, SG, SNV, and DT transformations were applied using the prospectr library [45].

PLSR algorithms were applied to these combinations using 5, 10, 15, 20, 25, and 28 components. Each component was constructed by linearly combining the original variables with the intention of maximizing the covariance between the predictor variables and the response variable. SVM models with linear and polynomial kernels were used, with model building costs of 500 and 100, respectively. The data set was divided into training data (train) with 75% of the data and test data (test) with 25% of the data.

To assess the performance and fit of the models, the coefficient of determination (*R*<sup>2</sup> ) was used for both the training and test data. In addition, the residual prediction variance (RPD) was calculated. *R*<sup>2</sup> is obtained by squaring the correlation between the predicted and actual values (Eq. (2)) and provides a measure of how well the model fits the data.

$$R^2 = \mathbf{1} - \frac{\sum\_{i=1}^{n} (y\_i - \hat{y}\_i)^2}{\sum\_{i=1}^{n} (y\_i - \overline{y})^2} \tag{2}$$

where:

*n* : number of observations

*yi* : real values

^*yi* : values predicted by the model *y*

^*y* : mean of the real values

Eq. (3) defines the ratio of performance to deviation (RPD), which is the ratio of the standard deviation of the reference values (observations) to the standard deviation of the differences between the observations and the model predictions. The RPD is a measure used to assess the performance of a predictive model by comparing the accuracy of the reference values with the variability of the model's predictions [46].

$$RPD = \frac{\sqrt{\frac{1}{n-1}\sum\_{i=1}^{n}\left(\boldsymbol{y}\_{i} - \overline{\boldsymbol{y}}\right)^{2}}}{\sqrt{\frac{1}{n}\sum\_{i=1}^{n}\left(\boldsymbol{y}\_{i} - \boldsymbol{\hat{y}}\_{i}\right)^{2}}}\tag{3}$$

#### **3.3 Results and discussion**

The CEC values the soils in the department of Antioquia show a wide variability, ranging from a minimum of 0.21 to a maximum of 61.8 cmol (+)/kg. The mean CEC obtained was 8*:*62 � 3*:*33 cmol (+)/kg, with a coefficient of variation of 123.86 cmol (+)/kg. The mode was 0.62 and the median was 33.3 cmol (+)/kg. These results indicate significant variation in CEC across the department, with most samples having low levels of CEC. These results are consistent with previous studies carried out in the northern, northeastern, and Urabá regions of Antioquia [47, 48].

The CEC variable was transformed using the Box-Cox method, resulting in transformed data with a minimum dimension of �1.76 and a maximum dimension of 3.04. The mean obtained after transformation was 1*:*09 � 0*:*99 cmol (+)/kg, with a mode of �0.49 and a median of 1.09 cmol (+)/kg. We also performed the transformation using the square root of CEC, which gave values ranging from a minimum of 0.45 to a maximum of 7.86. The resulting mean was 1*:*82 � 1*:*41 cmol (+)/kg, with a mode of 0.78 and a median of 1.09 cmol (+)/kg. The transformation successfully reduced the scale of the data, as shown in **Figure 4**.

The spectral reflectance of the soil increased as the wavelength increased and remained consistent across different soil samples. Figure illustrates the pattern of the average curves from various samples, displaying several absorption valleys. A reflection peak was observed between 950 and 957 nm, caused by the overlapping noise from VNIR and SWIR sensors, which aligns with the findings described by [49].

The spectral reflectance of the soil increased with wavelength in the visible, nearinfrared (NIR) and mid-infrared (MIR) regions, and this behavior was consistent across the different soil samples. A prominent reflection peak was observed between 950 and 957 nm, attributed to the overlapping noise of the VNIR and SWIR sensors,

*Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification… DOI: http://dx.doi.org/10.5772/intechopen.112991*

**Figure 4.** *Diagram of soil CEC distribution, square root, and Box-Cox.*

which is consistent with the results described by [49]. In addition, the **Figure 5** illustrates the average curve patterns of the analyzed samples, showing three absorption valleys in the NIR and MIR regions. These observations are consistent with those reported by [50], suggesting that these valleys correspond to the hydroxyl and clay absorption bands of the soils.

A total of 144 PLSR models and 97 SVM models were evaluated for CEC prediction. The PLSR models showed promising results, achieving a *RPD*train greater than 2, indicating their ability to explain data variability. However, the SVM models outperformed the PLSR models, showing even better performance in explaining data

**Figure 5.** *The spectral signature of the soil.*

variability, as evidenced by *RPD*train and *RPD*test values greater than 3 and *R*<sup>2</sup> train and *R*2 test values greater than 0.70. On the other hand, the Kolmogorov-Smirnov (KS) test to assess the goodness of fit test shows that the results obtained for the three best models indicate that the probability distributions of the predicted and observed data adequately fit **Table 1**. These results further support the robustness and quality of the selected SVM models for CEC prediction.

The results obtained in this study are consistent with the findings of [51], who evaluated the predictive ability of the MIR region for CIC (Cation Exchange Capacity). In their research, promising results were obtained using PLSR models, with a coefficient of determination (*R*<sup>2</sup> ) of 0.92 and an RPD value of 3.5.

The results reported by [52] agree that SVM models outperformed PLSR models for all soil properties evaluated, including clay, sand, pH, total organic carbon, and permanganate oxidizable carbon, in both training and validation data. These results confirm the relevance and potential of SVM models as a promising option for predicting soil properties from spectral data. Furthermore, the results obtained are in line with the previous study by [53], where they determined the CEC of 142 samples using a spectral range from 350 to 2500 nm. They found a PLSR model with *<sup>R</sup>*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*<sup>76</sup> in the training data and *<sup>R</sup>*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*72 in the test data.

On the other hand, it is observed that the models perform better when working with the CEC variable transformed with *sqrtCEC*. As for the HSI data, high performance is obtained with the SNV and DT transformations applied. These results are in line with the results of a study carried out by [41], which showed that preprocessing methods for hyperspectral data improve the accuracy of the evaluated models.

**Figure 6** shows a scatter plot comparing the predicted and observed values of the SVM model. The blue dots represent the observed values, while the red dots represent the predicted values. It can be seen that the red dots follow a trend close to the diagonal line, suggesting that the SVM model has effectively captured the variability in the data. It is also important to note that applying the KS test (p-value) to the data yielded values greater than 0.05, indicating that there is no significant difference between the observed and predicted values.

#### **4. Conclusions**


In this book chapter, we present promising results in the prediction of the CEC index as an indicator of soil fertility using HSI in the context of pasture production.

*Alg:, Algorithm used for modeling; Variable, conversion method used in the CIC; Trans, data transformation method; train, training data; test, testing data; R*<sup>2</sup> *, coefficient of determination; RPD, ratio of performance to deviation; TestKS p*ð Þ � *value , p-value of the Kolmogorov–Smirnov test; SVM, support vector machines; CEC, cationic interaction capacity; ABS, absorbance; R, reflectance; SNV, standard normal variation; DT, detrending technique.*

**Table 1.**

*Results of the five best performing models for predicting cation exchange capacity in the soil.*

*Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification… DOI: http://dx.doi.org/10.5772/intechopen.112991*

**Figure 6.** *Observed and predicted values from the SVM model.*

The application of HSI technology has shown significant potential for assessing optimal soil conditions and improving management strategies in pasture and forage production. However, it is essential to recognize and address the challenges associated with the use of HSI data in the analysis of soil physicochemical parameters. HSI techniques represent an interdisciplinary field that incorporates and adapts different concepts, approaches, and algorithms. Several signals underline the growing importance of HSI technology in remote sensing. One such indicator is the continuous increase in the number of HSI sensors, supported by the many applications that rely on this technology. In addition, the increase in the number of scientific publications also supports this growth. It is important to note that the methods used to analyze remotely sensed HSI data are not always straightforward. Key challenges include the processing of large amounts of information, high dimensionality, and the need for specialized techniques to extract meaningful patterns and insights. In addition, rigorous data pre-processing and calibration processes are essential to ensure the accuracy and reliability of predictions. One of the most obvious barriers is the management of large data archives, which is hampered by the lack of specialized hardware [27]. As the field of HSI analysis continues to develop, it is imperative that researchers and practitioners develop creative strategies to overcome these barriers and maximize the benefits of HSI technology in soil fertility improvement and pasture production management. Overcoming these challenges will unlock the true potential of HSI technology and usher in a transformative era in soil-related agricultural research and applications. By fully embracing HSI technology and actively addressing its challenges, there is an opportunity to unlock the full potential of hyperspectral data and stimulate positive changes in soil fertility, rangeland productivity, and sustainable agricultural practices.

#### **Acknowledgements**

This publication and the financial support of the MSc students was possible thanks to the project "design and validation of predictive models to determine cation exchange capacity (CEC), organic matter (OM), and nitrogen (N) in soils from hyperspectral images" through the agreement 2022-7204, financed by the University of Antioquia Foundation.

#### **Author details**

Tatiana Rodríguez Monroy<sup>1</sup> \*, Manuela Ortega Monsalve<sup>1</sup> , Luis Fernando Galeano-Vasco<sup>2</sup> , Marisol Medina-Sierra<sup>2</sup> and Mario Fernando Ceron-Munoz<sup>2</sup>

1 Animal Sciences, GAMMA Research Group, Faculty of Agricultural Sciences, Antioquia University, Medellin, Antioquia, Colombia

2 GAMMA Research Group, Faculty of Agricultural Sciences, Antioquia University, Medellin, Antioquia, Colombia

\*Address all correspondence to: tatiana.rodriguezm@udea.edu.co

© 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.

*Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification… DOI: http://dx.doi.org/10.5772/intechopen.112991*

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### **Chapter 8**

## Impact of Agro-Pastoralism on Grasslands in Serengeti and Ugalla Ecosystems, Tanzania

*Pius Yoram Kavana, Bukombe John Kija, Emmanuel Pagiti Reuben, Ally Kiyenze Nkwabi, Baraka Naftal Mbwambo, Simula Peres Maijo, Selemani Rehani Moshi, Shabani Matwili, Victor Alexander Kakengi and Stephen Justice Nindi*

#### **Abstract**

This chapter delves into the intricate relationship between agro-pastoralism and grassland ecosystems in Tanzania's Western Serengeti and Ugalla Ecosystems. Despite the acknowledged contribution of agro-pastoralism to rural well-being and economic development, its impact on the delicate balance of grassland ecosystems remains unclear in these crucial Tanzanian landscapes. The chapter aims to illuminate agro-pastoralism's environmental, social, and economic dimensions in these regions. Guided by research questions exploring current conditions, potential solutions, and the path toward sustainable grassland resource utilization, the study employed a systematic literature review and data analysis using R software. Key findings highlight challenges from the progressive expansion of agro-pastoral activities, leading to trade-offs between ecosystem services and productivity. The study identifies agro-pastoral clusters across the area, revealing variations in economic activities and their impact on grassland utilization. Impacts on natural resources, such as soil pH changes, reduced herbaceous biomass, and shifts in plant composition, are discussed. The legal framework related to natural resource conservation in grasslands emphasizes the need for a balanced, ecologically sustainable approach. Efforts to alleviate agro-pastoral impacts, including introducing climate-smart agriculture, are explored. The chapter concludes by emphasizing the importance of integrated, participatory methods for sustainable management in the Serengeti and Ugalla ecosystems. Recommendations include promoting sustainable land use practices, implementing rotational grazing, and enhancing community involvement in decision-making.

**Keywords:** agro-pastoral activities, grassland ecosystems, grassland utilization, grazing land, herbaceous plants

#### **1. Introduction**

Agro-pastoralism has been extensively studied at regional and global scales in relation to environmental processes [1]. However, the effect of agro-pastoralism on grassland ecosystems and the livelihood of people in Tanzania has remained unknown. It is widely acknowledged that agro-pastoralism contributes to the wellbeing of people in rural areas and the country's economy. On the other hand, little has been done to quantify the contribution of agro-pastoralism to the livelihood of people in Serengeti and Ugalla grassland ecosystems. The present study investigates the impact of agro-pastoralism on herbaceous plants and soil properties in Serengeti and Ugalla grasslands, as well as its contribution to livelihoods.

Grassland covers more than 40% of the Earth's surface [2] and about 6.8% of the land surface of Tanzania [3]. Grassland plays an essential role in ecosystem productivity and biogeochemical processes [4]. Grassland ecosystems have local importance for maintaining biodiversity and food production, influencing ecological processes, water, and climate regulation [5]. Approximately 49.3% of grassland in the world encounters degradation problems [6], and the grasslands of Tanzania are not exceptional in degradation problems. There is a progressive growth in human population and conversion of grasslands to agro-pastoral land [7]. This affects grassland as there can be conflicting goals between agro-pastoralism and the maintenance of grassland ecosystem services. Maximizing the provisioning of services from agro-pastoralism tends to result in a trade-off with plant composition and other ecosystem services. As a result of the non-linear relationships between ecosystem services, such as grassland composition and agro-pastoral productivity, managing them poses challenges [8]. When one service is optimized, other services are reduced or lost (a situation known as 'trade-offs') [8]. As communities continue to transform grassland ecosystems to obtain greater provision of specific services, the demand for more agro-pastoral land may lead to diminishing some grassland services.

#### **1.1 Research questions**


#### **1.2 The objective of the chapter**

To depict the current situation of grasslands resulting from anthropogenic activities and its future scenario.

### **2. Methodology**

#### **2.1 Study area**

The study was conducted in Western Serengeti and the Ugalla ecosystem in Western Tanzania (**Figure 1**). Western Serengeti is home to agri-pastoralists, and the population *Impact of Agro-Pastoralism on Grasslands in Serengeti and Ugalla Ecosystems, Tanzania DOI: http://dx.doi.org/10.5772/intechopen.113800*

**Figure 1.** *Location of study sites in Serengeti and Ugalla ecosystems.*

growth rate there is higher than in the north, east, and south of the National Park [9]. It is considered to have low suitability for arable agriculture. Although its subsistence economy is primarily based on agro-pastoralism [10], poor delivery of agricultural extension services, and insufficient input supplies limit this activity, which leads to the practice of extensive cropping and livestock keeping in villages that encroach on protected areas [11]. The study was conducted in four districts: Serengeti, Bunda, Meatu, and Bariadi.

Ugalla ecosystem comprises the Tanzania Ramsar site with many important socioeconomic and cultural values. Some of the most important include harvesting wetland-related products, including fish, forest products, medicinal plants, honey, and wildlife. Other values of importance to the local communities include flood control, water supply, and dry-season grazing. The human population's subsistence economy in and around the proposed site depends mainly on farming, fishing, hunting, and honey gathering. Although honey gathering and fishing are not normally permitted in game reserves in Tanzania, it has been the practice to permit such activities in

the game reserves in the Ramsar site as these activities predate the establishment of the reserves. Large numbers of fishing and beekeeping camps operate throughout the Ramsar site during the dry season (July to December). Permanent fishing villages are present around some of the lakes, such as Lake Sagara.

The estimated number of cattle is 15–20,000 in the central portion of the wetland and more cattle in the southern parts of the Ramsar site. Groups of nomadic pastoralists also move into the area during the dry season. The traditions of the local people on this site do not allow hunting or capturing of some birds like ground hornbill and animals like bushbuck. The majority of the Ramsar Site is under the direct jurisdiction of government agencies, comprising game reserves of 2.45 million ha, forest reserves of about 650,000 ha, and the balance being district or village land amounting to about 150,000 ha. The Ramsar site is surrounded by forest reserves in certain areas, notably in the northeast and south. In the central and northwest portions, the site is bounded by open or public land or agricultural areas primarily controlled by villages or district authorities. The study was conducted in five districts, including Urambo, Uyui, Kaliua, and Sikonge in the Tabora region and two districts in the Katavi region that include Mpanda (Nsimbo district council) and Mlele (Mlele and Mpindwe district councils).

#### **2.2 Data collection and analysis**

A systematic review of the scientific literature on agro-pastoral impact in grasslands was conducted using guidelines outlined by Pullin and Stewart [12] and Inskip and Inskip & Zimmermann [13]. Google Scholar, as well as other search engines, was used to determine the body of knowledge on the subject. To determine relevance and applicability, the search protocol was preceded by predefined filters for keywords [12, 13]. The relationship between agro-pastoral activities and edaphic factors was analyzed using R software version 3.5.0 [14].

#### **2.3 Agro-pastoralism in Tanzania**

Agro-pastoralism refers to a livelihood strategy that involves growing of crops and keeping of livestock by the local communities. This kind of livelihood significantly relies on rainfall patterns and the availability of natural pastures. Depending on cattle management types, there are three types of agro-pastoralism practiced in Tanzania. Brandström et al. [15] identified unilocal agro-pastoralism where livestock herds graze in the neighborhood of the homesteads and are taken back to craals every night. Another form of agro-pastrolism is bilocal, where herds graze near homesteads during certain months, but move far away for pasture and water during others. In addition, Brandström et al. [15] identified multilocal agro-pastoralism, in which a small number of cattle is permanently kept at the homesteads while the main herd is far from it throughout the year. In Tanzania, livestock density is highest in agropastoral areas around Lake Victoria [15]. This agro-pastoral area uses most of its labour in cultivation, where surplus is used to rear livestock by grazing crop residues. The same strategy is also applied in the agro-pastoral regions of central Tanzania [16]. Data from the 2014–2015 and 2016–2017 annual agriculture censuses [17, 18] show that agro-pastoralism is practiced throughout Tanzania's mainland (**Figure 2**).

Based on the data, the Mwanza region had the highest number of agro-pastoral operators whereas the Dar-es-Salaam region had considerably fewer operators. Further, cluster analysis revealed three main clusters of the regions practicing agropastoralism in Tanzania's mainland (**Figure 3**).

*Impact of Agro-Pastoralism on Grasslands in Serengeti and Ugalla Ecosystems, Tanzania DOI: http://dx.doi.org/10.5772/intechopen.113800*

#### **Figure 2.**

*Average number of agro-pastoral operators in different regions of Tanzania mainland. Source: Authors' synthesis from NBS [17, 18] data.*

**Figure 3.** *Regional clusters of agro-pastoral operators in Tanzania's mainland. Source: Authors' analysis from NBS [17, 18] data.*

The Dar-es-Salaam region was a distinctive cluster. The other cluster included the regions of Dodoma, Mara, Singida, Mwanza, Kagera, Manyara, Simiyu, Geita, Tabora, Mbeya, and Shinyanga. The final cluster included the regions of Tanga, Morogoro, Lindi, Njombe, Pwani, Kilimanjaro, Arusha, Rukwa, Ruvuma, Mtwara, Kigoma, Iringa, and Katavi. Additional research into the various clusters revealed that clusters 1 and 3 were dominated by crop farming (**Figure 4**). However, in cluster 1 operators were very few compared to other clusters. The reason for the low involvement of operators in cluster 1 can be associated with its city dwellers who are mainly engaged in salary jobs. Pastoralism activity was lacking in cluster 1, a circumstance implying the absence of land for roaming animals in the city.

In cluster 2, crop farming and agro-pastoralism businesses were fairly evenly distributed. In cluster 2, agro-pastoralism appeared to be a clear and unchangeable decision regarding social and economic life. As a result, the agro-pastoralists in cluster 2 likely descended from a pastoral society that turned to farming as a means of subsistence after suffering cattle losses due to natural disasters. Crop farming alone was the primary source of income in cluster 3. Cluster 3 agro-pastoralists likely arose from farming groups that decided to start raising cattle. Climate change and the requirement to use animals to boost agricultural output to obtain surplus crops in favorable years may drive the shift to livestock husbandry.

Compared to the Agrarian households in clusters 2 and 3, which engaged in crop farming and agro-pastoralism, fewer practiced pastoralism. This suggests that practicing pastoralism in Tanzania is challenging due to the effects of population expansion, climate change, and variability. Climate stress alters the amount, patterns, and distribution of rainfall and lengthens dry spells and droughts that reduce grazing land and water resources, resulting in decreased cattle output [19]. The number of households engaged in pastoralism in clusters 2 and 3 may decline if the country's climate stress persists.

Agro-pastoralism, in general, entails using tools, methods, and knowledge to alter the natural environment to create a variety of goods. Specific agro-pastoral clusters are shaped by the integration of economic activities used in agro-pastoral systems, and variances between these economic activities cause inconsistencies between clusters. Agro-pastoralism has criticism, and there are many diverse varieties that, depending on the economic circumstances of the local community, may place more or less emphasis on agriculture. The social, political, ecological, and geographic context in which these groups are found determines how often there are continual changes and transitions between more farming and more herding across time [20].

#### **Figure 4.**

*Distribution of households involved in different agricultural activities among clusters in Tanzania's mainland. Source: Authors' synthesis from NBS [17] data.*

*Impact of Agro-Pastoralism on Grasslands in Serengeti and Ugalla Ecosystems, Tanzania DOI: http://dx.doi.org/10.5772/intechopen.113800*

#### **2.4 Natural resources available in grasslands**

Grasslands in Tanzania are dominated by native plants that include grasses, sedges, herbs, and scattered trees. The dominant plants in western Serengeti include *Themeda triandra* in wildlife grazing areas, *Cynodon dactylon* in livestock grazing areas, and *Chloris pycnothrix* in areas with mixed grazing of livestock and wildlife [21]. Grasslands in the Ugalla ecosystem are dominated by plant species that include *Digitaria macroblephara*, *Setaria pumila,* and *Echnocloa pyramidalis* in protected areas. In contrast, *Pennisetum polystachyon* commonly dominates cultivated land in villages (**Figure 5**).

Common trees in western Serengeti include *Kigelia africana* found along seasonal river banks; *Phoenix reclinate* found along rivers and swamps; *Commiphora Africana* scattered in various places; *Vachellia xanthophloea* found along rivers, swamps, and floodplains; *Vachellia tortilis* found in regular plains and *Vachellia drepanolobium* found in seasonally water-logged soils. Thornless deciduous trees adapted to uni-modal rainfall patterns are common in the Ugalla ecosystem. The common tree species include *Brachystegia augustistipulata*, *Brachystegia boehmii*, *Brachystegia glaberrima*, *Brachystegia glaucescens*, *Brachystegia longifolia*, *Brachystegia spiciformis*, *Brachystegia taxifolia*, *Julbernardia globiflora*, *Julbernardia paniculate*, *Isoberlinia angolensis,* and *Isoberlinia tomentosa*. The dominant genera in this ecosystem include Brachystegia, Julbernadia, Isorberlinia, Markhamia, Grewia, Terminalia, Syzygium, Vachellia, and Combretum [22]. Vegetations available in the grassland ecosystems of Ugalla and Serengeti provide non-timber forest products such as wild fruits, mushrooms, ropes, honey, bamboo, fodder, and brooms. Gathering of non-timber forest products helps local communities to boost their livelihood and households' upkeep. These products are used as food, medicine, and some of them are used for decorations.

#### **2.5 Effect of agro-pastoralism on natural resources**

Agro-pastoralism is a predominant livelihood in Serengeti and Ugalla ecosystems, where land and natural pasture are primary natural resources. Livestock moves in the vicinity of the villages and grazes on communal grazing lands, fallow lands, and crop fields after harvest. Studies showed that there is growth in human population and conversion of land to agriculture in agro-pastoral areas of Tanzania [7]. The increase in human population triggered the need for food sufficiency that stimulated cultivation and livestock keeping (**Figure 6**) based on land availability. Competition for

**Figure 5.** *Plants field assessment in Ugalla and Serengeti ecosystems.*

#### **Figure 6.**

*Relationship between livestock population and land cultivated for crop production. TLU = Tropical Livestock Unit. Source: Authors' synthesis from NBS [17, 18] data.*

natural resources, especially land, has become an issue of major concern and cause of conflict worldwide [23].

The change in land use in the upper catchments within ecosystems resulted in higher peak water flow during the rains and lower peak water flow during river drought [24]. Water abstraction levels for consumptive uses are normally highest during the dry season, imparting additional strain on the rivers within ecosystems during critical low water flow. This situation compelled the development of the Tanzania National Water Policy, which requires the protection of reserve flows to enable water availability for basic human needs and sustain ecosystems [25].

Soil is an important component of any ecosystem. It supports life systems by delivering ecosystem goods and services. For instance, the soil is one of the major carbon sinks, and plays roles in water regulation, soil fertility, and food production, which have effects on human well-being [26]. In any biological ecosystem, soil pH greatly influences biogeochemical processes [27]. Soil pH influences varieties of soil biological, chemical, and physical properties and processes that affect plant growth and biomass yield [28]. Despite the roles played by soil pH in ecosystems, keeping large numbers of livestock in communal grazing land tends to lower it [29]. For example, Tamartash et al. [30], demonstrated soil acidity to increase with increasing grazing intensity. Nevertheless, low soil pH affects soil microbial activities of decomposing organic carbon, shown by an increase in C:N ratio as the stocking rate increases (**Figure 7B**). Soil pH controlled microbial activity that consequently influenced microbial decomposition of carbon and nitrogen [27]. Therefore, agro-pastoral activities are envisaged to lead to acidification of soil.

Low soil pH (<6) in western Serengeti caused by agro-pastoral activities resulted in low herbaceous standing biomass (< 2000 kgDM/ha) and low herbaceous plant species richness (<5 species/quadrat), as shown in **Figure 6C** and **D**, respectively. Results obtained in western Serengeti concur showed that a soil pH of 6.3 was optimum for standing biomass and species richness of herbaceous vascular plants in miombo woodlands [31].

*Impact of Agro-Pastoralism on Grasslands in Serengeti and Ugalla Ecosystems, Tanzania DOI: http://dx.doi.org/10.5772/intechopen.113800*

**Figure 7.** *Effect of animal density on soil properties and consequent effect on herbaceous plants attributes.*

#### **2.6 Legal framework on natural resources conservation in relation to grasslands**

*Wildlife Conservation Act No. 5 of 2009:* The Act provides for the conservation of wildlife and ensures protection, management, and sustainable utilization of wildlife resources, habitats, ecosystems, and the non-living environment supporting such resources, habitats, or ecosystems with actual or potential use or value. The Act is important in grassland conservation as wildlife habitats in the sense that grasses form the main basal diet for wild herbivores in Tanzania.

*Plant Protection Act No. 13 of 1997:* The Act provides for the prevention of the introduction and spread of harmful organisms, to ensure sustainable plant and environmental protection, to control the importation and use of plant protection substances, and to regulate the export and import of plants and plant products. The Act is important in grassland conservation as it inhibits the introduction and spread of organisms that may alter grassland species' composition and ecosystem functions.

*Land Act No. 4 of 1999 and Village Land Act No. 5 of 1999:* The Land Act and Village Land Act have provisions that are important for environmental management. The fundamental principle of the Land Act is to ensure that land is used productively and that any such use complies with the principles of sustainable development. Among others, the Act prohibits any development activities in environmentally sensitive areas such as wetlands and swamps and 60 m from the shoreline and riverbanks. The Village Land Act also empowers the village government to have legal control of village land and its uses. This also includes prohibiting or minimizing land problems

like bushfires as well as land use-related conflicts between farmers and livestock keepers/pastoralists. This Act limits human activities that may be detrimental to grassland ecosystems; for example, most of the grasslands in the Ugalla ecosystem are found in seasonally flooded lands. These areas are regarded by the Land Act as sensitive areas that require users to comply with sustainable development principles.

*Water Resource Management Act No. 11 of 2009 and Water Supply and Sanitation Act No. 12 of 2009:* The Water Resources Management Act (WRMA) provides the legal framework for the management of water resources within the integrated water resources management (IWRM) framework. The Act provides for pollution control and issues discharge permits for effluents to water bodies, including the underground strata. The Act also provides flood mitigation and control measures to inhibit or lessen the risk of flooding, flood damage, and water pollution. The Water Supply and Sanitation Act provides a legal framework to ensure water quality by protecting water works and storage facilities against pollution. The Act further gives a mandate to the Local Government Authorities to enact by-laws concerning water supply and sanitation for efficient and sustainable provision of these services in their areas. Grasslands are considered to regulate water above groundwater quantity and flow and limit soil erosion that contributes to water quality by minimizing sedimentation in rivers and lakes. This conquers the Act that emphasizes flood mitigation and pollution control.

#### **2.7 The Grazing-Land and Animal Feed Resources Act (Act No. 13 of 2010)**

This law sets the necessities to manage and control grazing lands, animal feed resources, and trade. The law establishes the National Grazing-land and Animal Feed Resources Advisory Council, which is responsible for promoting participatory, equitable use and management of grazing-land resources. The council works to foster collaboration with public and private institutions or authorities in issues related to the use and management of grazing land. Since most of the grazing lands are grasslands, this Act is concerned with the sustainable use of grasslands in a broad sense. However, it is bound to grazing lands that leave out grasslands with no grazing value but are important for the provisioning of ecosystem services.

#### **2.8 Impacts of agro-pastoralism on grassland natural resources**

Sustainable standing plant biomass production to support livestock and wildlife in grasslands requires understanding critical variables in soil, plant, and grazing effects imparted by grazers. A study conducted in western Serengeti showed that agro-pastoral activities resulted in low residue standing biomass availability in communal grazing and cultivated lands [3]. High livestock grazing pressure was the leading cause of low-standing biomass in communal grazing lands. It was shown that an increase in the stocking rate of grazing animals caused a decline in plant biomass. Heavy grazing due to a high stocking rate removes the growing points of grazed plants [32], thereby reducing plants' growth potential and, thus, a decline in plant biomass. Overgrazing and continuous cultivation in grasslands contribute to the deterioration of soil properties [29]. Agro-pastoral systems in the Western Serengeti and Ugalla ecosystems are mainly rain-fed.

The productivity of grasslands depends on the availability of water, either for plant growth or for livestock and wildlife production. Water for crop production and pasture growth comes from rainfall, while livestock and wildlife drink water from rain-fed water sources. It has been observed that streams and natural water

#### *Impact of Agro-Pastoralism on Grasslands in Serengeti and Ugalla Ecosystems, Tanzania DOI: http://dx.doi.org/10.5772/intechopen.113800*

sources have been shrinking due to rainfall variability and dry spells in ecosystems. Agricultural expansion, human population growth, and bushfires are considered to be salient ecological drivers of changes in the abundance of plants [33]. Agricultural expansion and human population growth are common phenomena in Western Serengeti and Ugalla ecosystems [3, 34, 35]. This situation leads to the shrinkage of grassland due to land clearance for cultivation. Therefore, the shrinkage of grassland and its product results in a consequential effect that devastated the sustainability of agro-pastoralism. The sudden effect of overgrazing and cultivation on the remaining part of grassland results in soil erosion, which affects the re-growth of plant and grass species. The pressurized grassland then starts to offer ecosystem services at a declining rate which results in a reduction in productivity.

#### **2.9 Efforts to alleviate impacts of agro-pastoralism in Serengeti and Ugalla ecosystems**

The performance of agro-pastoral systems is determined by the availability of land, water, and energy, which hit hard by climatic perturbation. In an endeavor to ameliorate the impact of agro-pastoralism and climate change in grasslands, the government, in collaboration with NGOs tried to introduce climate-smart agriculture. Climate-smart agriculture intends to increase productivity and income, the ability to adapt and build community resilience to climate change, and enhance food and nutrition security while achieving mitigation co-benefit in line with national economic development priorities. However, the adoption rate ranges from 4–30% [36, 37]. It was observed that climate-smart agriculture is site-specific that needs to consider local factors and co-design solutions with the communities that make use of grasslands.

#### **3. Conclusion**

Community livelihoods within the Serengeti and Ugalla grassland ecosystems are influenced by agriculture and livestock keeping, known as agro-pastoralist farming. Despite this, the study showed that agro-pastoralism adversely affects grasslands' natural resources. Plant composition, soil properties, and grassland ecosystem services are all affected by the continuous expansion of cultivated land and the keeping of large herds of grazing animals within limited grazing areas. Due to overgrazing and cultivation, soil fertility and plant biomass have been reduced, resulting in insufficient feed for grazing animals. As grasslands and natural resources are under pressure, sustainable grassland use is at risk.

#### **3.1 Way forward**

Integrated and participatory approaches are needed to ensure the sustainability of the Serengeti and Ugalla ecosystems. In order to preserve grasslands, agro-pastoralism policies, and management strategies should balance economic benefits and grassland ecology conservation. Emphasis should be placed on promoting sustainable land use practices that consider the land's carrying capacity and grasslands' ecological needs. Using rotational grazing, climate-smart agriculture, and soil conservation strategies can mitigate the adverse effects of agro-pastoral practices. Furthermore, involving local communities in decision-making will enhance a sense of ownership

and stewardship, resulting in better community involvement in the management of natural resources. These interventions' success depends on the cooperation of government agencies, NGOs, and local communities. These holistic approaches can help to maintain the delicate balance between human livelihoods and preserving grasslands' biodiversity and ecosystem services.

### **Declaration**

Some parts of this chapter are based on an unpublished part of the main author's Ph.D. thesis on 'influence of agro-pastoralism on herbaceous plants diversity and livelihood of communities in western Serengeti' submitted to the Senate of Sokoine University of Agriculture in Tanzania.

### **Author details**

Pius Yoram Kavana1 \*, Bukombe John Kija2 , Emmanuel Pagiti Reuben1 , Ally Kiyenze Nkwabi1 , Baraka Naftal Mbwambo1 , Simula Peres Maijo1 , Selemani Rehani Moshi1 , Shabani Matwili1 , Victor Alexander Kakengi2 and Stephen Justice Nindi2

1 Tanzania Wildlife Research Institute, Western Wildlife Research Centre, Kigoma, Tanzania

2 Tanzania Wildlife Research Institute, Arusha, Tanzania

\*Address all correspondence to: pykavana@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.

*Impact of Agro-Pastoralism on Grasslands in Serengeti and Ugalla Ecosystems, Tanzania DOI: http://dx.doi.org/10.5772/intechopen.113800*

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### *Edited by Muhammad Aamir Iqbal*

Welcome to the edited volume *Grasslands - Conservation and Development*, a unique and interesting exploration unveiling the delicate interplay of conservation and development of grasslands, which are the Earth's most strategic ecosystem. The inspiration behind compiling this edited volume lies in the recognition that grasslands are beyond open fields on the Earth's surface, encompassing intricate systems that bridge the functions of human societies and natural ecosystems. From the iconic and diverse prairies of North America to the vast savannas of Africa to the diversified steppes of Eurasia, grasslands tend to be shaped by both human endeavor and the forces of nature. This book presents research works about imparting resilience to grasslands through effective conservation and novel development initiatives. This edited volume showcases the efficacy and dynamic feasibilities that emerge when grassland conservation and development strategies work hand in hand for the greater benefit of ecosystems, grass species, and human beings. This book is not just an exploration but it is also a call to action. After examining the challenges and eco-biologically viable strategies documented in this book, each and every person must consider their role in the journey towards embracing grassland conservation and development. This book is an invitation to all stakeholders, including researchers, environmentalists, policymakers, students, and ranchers, who have a passion for shaping the sustainable conservation, restoration, and development of grasslands.

### *W. James Grichar, Agricultural Sciences Series Editor*

Published in London, UK © 2024 IntechOpen © Irena Carpaccio / unsplash

Grasslands - Conservation and Development

IntechOpen Series

Agricultural Sciences, Volume 8

Grasslands

Conservation and Development

*Edited by Muhammad Aamir Iqbal*