**2. Risk and risk management in agriculture**

Landowners make decisions every day concerning farming operations that affect their income. Several factors may affect the performance of the activities, some of them are deterministic while others are rather stochastic [22]. Many of the factors that affect farmers' decisions cannot be predicted such as weather conditions, market price volatility, labour availability, machinery, equipment failure and government policy change. The most common sources of risk in farming can be divided into five areas (**Figure 1**) [23].

Farming has become increasingly riskier over the years, due to market liberalization and globalization. This situation affects smallholder farmers severely, they need to improve skills for both production and business management. Changes in prices are beyond the control of any individual farmer. Price movements follow supply and demand trends, which change unexpectedly affecting the market price [24].

Financial risk occurs when money is borrowed to finance farm businesses [23]. Risks are associated with future interest rates, lender's willingness to provide funds and the ability of the farmer accomplish loan repayment. Smallholder farmers who borrow money at high-interest rates are prone to fail debt repayments, especially in

**Figure 1.** *Sources of risk in farming.* situations of lower than expected prices, combined with low yields, which can even lead to the sale of the farm. Another important source of risk comes from government policy affecting farming, such as price support and subsidies. Subsidies and regulations are examples of decisions taken by the government that can have a major impact on the farm business [24]. Finally, it is important to mention human-related risks because, in many regions, labour migration from rural areas is a common factor. Migration can cause labour shortages, farmers often face uncertainty concerning labour availability to meet demand during a farming season.

Risk management involves anticipating potential problems and planning to reduce their detrimental effects [23]. There are several strategies that farmers apply in order to cope with risks related to land use. Farmers can reduce risk by applying new technologies and practices designed to address specific risks, common to their area of production. Risk-reducing inputs (e.g. fertilizers and integrated pest management) might reduce the risk of low yields and crop damage. Similarly, irrigation reduces the risk of low rainfall. New seed varieties are being developed with certain characteristics to be resistant to drought, disease and pests. Nevertheless, not all inputs might reduce risk, even if fertilizer is used, the crop still depends on water availability, which may or may not be favourable. When soil moisture levels are low, using a fertilizer can still result in low yields. Understanding how farmers make land management decisions is critical to designing strategies to reduce risk exposure. Profitability of a particular land use obviously encourages farmers to allocate land to it; nevertheless, motivations behind decisions are often more complex than simple profit maximization [25].

#### **2.1 Land-use diversification**

Farmers' decisions about how best to use resources are driven by the goal of improving their well-being. Well-being is defined across many dimensions, including income, security of livelihood and health. Decisions about land use are influenced by the potential benefit of each activity, which, in turn, depends on the available technology, market and environmental conditions [26].

A key factor to consider is farmers' preferences towards risks. People generally do not become involved in risky situations unless there is a chance of making high profits. Many farmers around the world integrate crops and livestock to reduce risk and improve efficiency and sustainability. The risk involved with the farming options can be an essential factor in assessing preferences, because risk-averse farmers tend to choose the option with the lowest uncertainty, despite the fact that the potential reward may be lower as well [22]. Farmers are often regarded as risk-aversive, which means they give up on profits provided that a certain income is guaranteed.

For risk-averse farmers, land-use diversification improves the overall performance of the farm because it spreads risk among several crops, which should have a low market correlation [27]. Risk-averse farmers may achieve high levels of risk reduction by mixing two or more land-use options whose financial yields fluctuate independently from one another (low or negative correlations). In other words, in periods when returns from one asset drop, another one may generate unexpectedly high returns, thus moderating the effects of economic booms and busts [25].

#### **2.2 The safety-net use of non-timber forest products**

Evidence suggests the potential of NTFP to contribute to conservation of forest and to improve livelihood of rural landowners. Nevertheless, the exploitation of NTFP *Non-Timber Forest Products as an Alternative to Reduce Income Uncertainty in Rural… DOI: http://dx.doi.org/10.5772/intechopen.102970*

has often been neglected both in policy and research, because they are often regarded as secondary forest products. Recent research suggests that NTFPs are an important source of cash income for communities living in remote areas and safety net for diversifying income along the year [6]. Rural households, which have limited credit and insurance options, apply diversification strategies in order to reduce aggregate risk. The safety-net use of NTFP extraction may take the diversification strategy equivalent to a portfolio analysis, because the households use NTFP extraction as a risk-free asset [28]. Even though NTFP extraction might have a low annual value, it can provide insurance in the case of unexpected losses.

NTFP extraction appears to be efficient for poor rural households. Many NTFPs do not have strong positive correlation among themselves or with agricultural output [7] so they can be efficient risk-management instruments. Two characteristics of NTFP are important to note. First, there are low capital and skills requirements to NTFP extraction as well as open or semi-open access to the resource, so poor households can easily extract the resource. The poorest people are those who are the most engaged in NTFP extraction. Second, NTFPs habitually have low return to labour, so they have poor potential to alleviate poverty [28, 29].

### **3. Methods**

The study area is located in mountain dry forest areas in southern Ecuador, where tara populations naturally occur, and the population is currently participating in the market of tara (see **Figure 2**), covering an approximate extension of 2396 km2 . The area is characterized by a temperate and dry climate, with two well-defined seasons: dry and rainy, the latter from December to April. The average altitude is 1385 metres above sea level. The annual rainfall has a mean value of 953 mm, and the annual temperature mean value is 18°C.

**Figure 2.** *Location of the project.*

The estimated population in 2020 was about 34,424 people, with approximately 8200 households. More than 70% of the population live in the countryside, scattered across numerous small villages and communities. This region is characterized by subsistence agriculture and livestock production, with a poverty rate of 29%. It is estimated that 30% of households live below the country's poverty line [20].

#### **3.1 Data collection**

This study is based on primary data collection through a field survey to 125 rural households between January and July 2019. The questionnaire was focused on the socio-economic characteristics of the households, the assets and the main sources of income. Other questions include the participation of the different members of the household in the different income-generating activities, such as agriculture, livestock and the collection and sale of NTFPs.

Data were analysed both qualitatively and quantitatively to provide a deeper understanding of household production, management and use of NTFPS. The quantitative data were analysed using the Statistical Package for Social Science (SPSS) software and Microsoft Excel to obtain descriptive statistics such as percentages of responses, frequencies, means and standard deviations.

To calculate family income, volumes of production were multiplied by the current price at farm gate. Net household income was calculated as the difference between household income and production cost, main costs included labour and inputs (e.g. fertilizers, manure, seeds and pesticides).

#### **3.2 Risks assessment and diversification**

In order to assess risk, information on prices of the most valuable agricultural options for the study area was collected from Faostat for the period 1999–2009 [30]. As information of price for tara for the Ecuadorian market is scarce, we used data obtained from Peruvian institutions as a proxy, since the market for tara is better developed there.

Price values were simulated in an excel sheet based on their mean value and standard deviation using Eq. (1). This simulation generated normal random variables for price for each year of time horizon.

$$\mathbf{Y} = \text{NORMINV}(\mathbf{RAND}(), \overline{P}, \text{STDP}) \tag{1}$$

where Y is normally distributed random variable, NORMINV is excel-based assumed normal distribution, RAND () is the probability, *P* is average of price, and STDP stands for its standard deviation. We later performed a Monte Carlo Simulation to model the probability distribution of returns. Based on this information, we analysed the level of dependency of the markets through correlation and covariance analysis.

Diversification was performed through the application of the portfolio analysis [22]. Our work model farmers' options for balancing economic return and risk. This approach attempts, by means of the allocation of land to various land-use practices, to maximize the expected economic return, for a given level of accepted risk, which is represented by the standard deviation (SD) of economic return, through careful selection of the proportions of agricultural options. Those portfolios that provide the

*Non-Timber Forest Products as an Alternative to Reduce Income Uncertainty in Rural… DOI: http://dx.doi.org/10.5772/intechopen.102970*

largest economic return for a given SD are termed efficient portfolios. All others are considered inefficient.

The expected economic return of a portfolio with two or more assets, Rp, is obtained by adding the expected economic returns, ri, weighted by their proportions, fi, of the single land-use options.

$$R\_p = \sum\_i f\_i \cdot r\_i \tag{2}$$

The SD of economic returns for the portfolio, σp, is quantified as follows:

$$
\sigma\_p = \sqrt{\sum\_i \sum\_j f\_i \cdot f\_j \cdot \text{cov}\_{ij}} \tag{3}
$$

with:

$$\sum\_{i} f\_i = \mathbf{1} \quad f\_{i,j} \ge \mathbf{0} \quad \mathbf{var}\_i := \mathbf{cov}\_{i,i} \quad \mathbf{cov}\_{i,j} = \rho\_{i,j} \cdot \mathbf{s}\_i \cdot \mathbf{s}\_j$$

where i and j are the indices for the specific land-use options; fi is the proportion of a specific agricultural land-use practice in the portfolio; si is the SD of returns for land-use practice i; ρi,j is the coefficient of correlation between the returns for options i and j; vari is the variance and covi,j is the covariance between the economic returns for options i and j. Using this method, the effects of diversification can be identified for different combinations of land-use options, provided that the variability of their financial return is not perfectly positively correlated (ρ 6¼ 1).

The selection of the optimal portfolio can be made based on the reward-to-variability ratio (Eq. (4)) [31], where the portfolio return, Rp, minus the return of a riskless benchmark investment, Rri, is divided by the portfolio standard deviation σp.

$$\max R\_p = \frac{\mathbf{R}\mathbf{p} - R\_{ri}}{\sigma\_p} \tag{4}$$

The riskless benchmark yield, Rri, is assumed as the interest yield that famers could obtain when investing in a safe financial asset. We assume that farmers can sell their land and invest that money in the capital market at an interest equal to 5%. Value of land has been set at a conservative value of US\$2000 per ha, for which a riskless yield equal to US\$100 can be obtained yearly.

### **4. Results and discussion**

#### **4.1 Livelihood strategies**

Subsistence farming is the main economic activity in the region. Rain-fed subsistence farming relies on traditional crops such as corn and beans. Other representative crops are peas, coffee and peanuts on a smaller scale. Only 25% of the land is used for productive activities, due to the low soil quality and lack of labour, irrigation and financial resources. Regarding livestock production, landowners are engaged with

cattle, pig and poultry production (**Table 1**). We observed high variation among the returns of households along with all the activities.

Concerning the two main farming options, a meaningful number of households depend on family labour (**Table 2**), nevertheless, hired labour is also required, especially during seasonal migration periods from June to December. Improved seeds are seldom used so far in the region, as most households admit to using their seeds. Technification of farming is also low, fertilizers either organic or conventional are occasionally used at the farms, the use of pesticides is more habitual though. The number of households that are reported to have loans and debt is also limited, most of them work with local banks and financial cooperatives.

#### **4.2 Income from tara and other NTFPs**

The data revealed that about 21% of the people collect fuel wood (e.g. *Eucalyptus globulus* and *Acacia macracantha*). An even smaller proportion collect NTFP other than tara, mainly *Marsdenia condurango* Rchb.f., locally known as condurango. The total amount of condurango harvested is later sold at the local market. There is an increasing demand for the product, but the collection occurs over a brief period, mostly by women. Tara, on the other hand, has a more established local market (**Table 3**). It is collected once a year in natural forests, mainly public areas. The

#### **Table 1.**

*Annual returns of the main products in the study area.*

**Table 2.** *Forms of production.*

*Non-Timber Forest Products as an Alternative to Reduce Income Uncertainty in Rural… DOI: http://dx.doi.org/10.5772/intechopen.102970*

**Table 3.**

*Local NTFPs harvested in the study area.*

collection period ranges from May to December. Nevertheless, there is a waste of around half of the harvest caused by inadequate storage which produces losses due to fungus, under moist warm conditions. Training programmes and simple equipment can boost production, speed up processing times and reduce losses, they are essential tools for local farmers to enhance harvesting and post-harvesting.

In recent years, research on the role of forest-related income in rural livelihoods has been gaining momentum. Case studies around the world study the interactions between forests and livelihoods, and the contribution of NTFPs range from 6 to 45% [32]. In the case of tara, it currently represents less than 5% of household income. This activity generates an annual average of USD 100, which complements the income from agriculture. The potential of NTFP to improve farmers' livelihoods should not be exaggerated as poorer communities are the main actors in NTFP extraction [6].

#### **4.3 Risk assessment and diversification**

Farmers often choose productive activities that maximize their well-being, given the resources and opportunities available to them. To assess the risk involved in farming activities, five productive options were selected: maize, beans, cattle ranching, pig and poultry together with tara. **Figure 3** displays the prices of the selected products over the period 1999–2019.

Based on this information, we calculated the correlation matrix to observe the degree of dependency between the selected options. We observe a considerable dependency among the markets of beans and animal breeding as shown in the correlation matrix (**Table 4**). Tara on the other hand has a medium positive correlation

**Figure 3.** *Price volatility of farming options.*

#### **Table 4.**

*Correlation matrix.*

with all the agricultural options, except maize. NTFP collection is positively correlated with an agricultural shortfall and expected risk [33].

To achieve income diversification, combinations of five agricultural products and tara were tested by applying the portfolio theory. We calculated portfolios for given amounts of return subject to a minimum standard deviation, as a measure of risk. The resulting shares were highly dependent on the performance of each asset in terms of return and risks. Tara, for instance, was part of the portfolios with the lowest returns (**Figure 4**). It only was part of the portfolios with returns below 300 USD. As the amount of portfolio income rises, the share of tara drops to zero. This situation is a result of the low returns that tara harvesting delivers so far. Nevertheless, improved management of the species might enhance the overall financial performance of tara as an income generator option for rural households. So far, we have measured the risk of tara based on the volatility of the price, but little is known about how natural risks affect the supply of pods.

**Figure 4.** *Share of agricultural options in optimized land-use portfolios based on expected return.*

*Non-Timber Forest Products as an Alternative to Reduce Income Uncertainty in Rural… DOI: http://dx.doi.org/10.5772/intechopen.102970*

#### **Table 5.**

*Land-use portfolios for southern Ecuador.*

Land-use portfolios were more diverse under 500 USD, above that income, portfolios were dominated by maize and cattle ranching. This behaviour is explained due to two main reasons, the financial performance of options and safety-net effects. First, cattle ranching delivers higher profits than any other option, despite the risk involved with the activity. Cattle also serve as a saving strategy used by farmers under distress. Maize, on the other hand, has a low risk compared with the other options and serves food security. Farmers who operate under subsistence conditions tend to be the most risk-averse. The provision of food for their dependants is an overriding priority for many of them. Activities with a monetary reward are frequently forgone in favour of meeting the objective of producing their own food.

We determined the optimal land-use portfolio by applying the Sharpe ratio, which is constructed based on the performance of the reward-to-variability ratio (**Table 5**). According to this method, the portfolio with the best performance was the one that achieved 300 USD. Above this bar all the portfolios deliver a riskier outcome, they are thus, inefficient.

### **5. Discussion and conclusion**

Rural households relying upon subsistence agriculture face multiple uncertainties that so far are poorly understood. Moreover, they usually have poorer risk management strategies due to lack of training, limited access to credits and long distance to local and regional markets. This study, based on rain-fed subsistence agriculture, explores income diversification based on farm options and NTFPs, as a strategy to cope with risks.

Even though a body of literature has documented the potential of NTFPs' on poverty reduction [1, 5] livelihoods improvement and environmental sustainability [34], evidence suggests that the impact that NTFPs might have on household income should not be overestimated [6]. This is particularly important because landowners engaged in the collection of NTFPs generally live in poor conditions where even the most basic healthcare and educational services are lacking [35].

Even though the collection of tara is a commercial activity with potential for the economy of the study area and a source of income for women, we observed that its extraction has low poverty alleviation effects, due to its low return. Nonetheless, it can become a viable tool to compensate for the agricultural risk, if properly managed during harvest and post-harvest campaigns to reduce losses. The assimilation of tara as a viable business among farmers can boost the achievement of SDGs related to economic growth, decent work and gender equality [3, 4, 21].

Tara is being harvested from wild populations, a common pattern by poorer rural households in developing countries. As generally, harvest leaves the forest structure intact, it can also be promoted as an alternative for forest conservation. Sustainable commercial exploitation of NTFP could serve as a stimulus to sound forest management [13].

NTFPs are among the forest products that better serve as livelihood resources in the face of climate stresses [36], since NTFPs can provide income opportunities [37]. The use of NTFPs has also been recognized as important for the climate resilience of small production systems since natural forests are more resistant to climate change than monocultures [38, 39]. Migration is one adaptation strategy practised by farmers due to climate change as it provides off-farm income [40]. Nonetheless, we observed in the study area that it affects household performance because it reduces labour availability during the cropping season.

NTFP trade also face risks and challenges, which can lead to only short-term returns, rather than sustainable businesses [41]. They recommend to paying attention to the governance of resources, organizations and gender. All of these factors are extremely relevant for most developing countries, and certainly for our case study because collectors of tara are mostly women. The authors recommend considering on how men and women participate in the collection, who benefits and controls NTFP harvest and trade and how the benefits are shared within the household. Lack of knowledge can also affect the performance of NTFP, we found a waste of about half of the harvest due to fungus. Limited knowledge about storing technologies, processing opportunities, market information and how to domesticate NTFPs constrains its overall outcome as a generator of income.

Moreover, the contribution of NTFPs to improved livelihoods can be assured through a process of gradual domestication in man-made vegetation types such as forest gardens and plantations. There is often a gradual transition from the collection of wild products in natural forests to enrichment planting in secondary forests and managed home gardens. Tara is tolerated in the farms, where it provides shade and serves as a reference in the division of farms [13]. Future studies should assess the potential of tara as a commercial plantation to reduce the impact on wild areas and reduce the time spent by farmers for collection, as natural areas are located remotely.


**1. Introduction**

328 Non-Flavivirus Encephalitis

Turner NA, Harris J, Russell AD & Lloyd D (2000) Microbial differentiation and changes in

Uschuplich V, Mileusnic D & Johnson M (2004) Pathologic quiz case. Progressive fatal

Waldner H, Collins M & Kuchroo VK (2004) Activation of antigen-presenting cells by

Walochnik J, Aichelburg A, Assadian O, Steuer A, Visvesvara G, Vetter N & Aspock H

Willaert E & Stevens AR (1976) Indirect immunofluorescent identification of Acanthamoeba

encephalopathy in an immunosuppressed patient with a history of discoid lupus erythematosus. Subacute granulomatous meningoencephalitis (Acanthamoeba

microbial products breaks self tolerance and induces autoimmune disease. *J Clin*

(2008) Granulomatous amoebic encephalitis caused by Acanthamoeba amoebae of genotype T2 in a human immunodeficiency virus-negative patient. J Clin Microbiol

susceptibility to antimicrobial agents. *J Appl Microbiol* 89: 751-759.

culbertsoni). *Arch Pathol Lab Med* 128: e109-111.

causing meningoencephalitis. Pathol Biol (Paris) 24

*Invest* 113: 990-997.

46: 338-340.

Free-living amoebae (FLA) belonging to *Acanthamoeba* and *Sappinia* genera as well as *Balamuthia mandrillaris* and *Naegleria fowleri* species are aerobic, mitochondriate, eukaryotic protists that occur worldwide and can potentially cause infections in humans and other animals (Visvesvara and Maguire, 2006; Visvesvara et al., 2007). Due to the fact that these amoebae have the ability to exist as free-living organisms in nature and only occasionally invade a host and live as parasites within host tissue, they have also been called amphizoic amoebae (Page, 1988).

All four amoebae are known so far to cause infections of the central nervous system (CNS). Several species of *Acanthamoeba* (i.e. *A. castellanii*, *A. culbertsoni*, *A. hatchetti*, *A. polyphaga*, *A. rhysodes*), the only known species of *Balamuthia*, *B. mandrillaris*, two species of *Sappinia* genus, *S. diploidea* and *S.pedata*, and only one species of *Naegleria*, *N. fowleri*, are known to cause disease in humans and other animals (Khan, 2006; Visvesvara et al., 2007).

Fig. 1. Trophozoite (right) and Cyst stages (left) in *Acanthamoeba* sp.

Encephalitis Due to Free Living Amoebae: An Emerging Issue in Human Health 331

individuals undergoing organ transplants or cancer chemotherapy, and drug abusers (Khan,

Therefore, the onset of AGE is slow and subtle and develops as a chronic disease from several weeks to months (Visvesvara & Maguire, 2006, Visvesvara et al. 2007). The usual features of AGE consist of headache, stiff neck, and mental-state abnormalities, as well as nausea, vomiting, low-grade fever, lethargy, cerebellar ataxia, visual disturbances, hemiparesis, seizures and coma. Facial palsy with numbness resulting in facial asymmetry is often seen. Cerebral hemispheres are usually the most heavily affected CNS tissue. They are often edematous, with extensive hemorrhagic necrosis involving the temporal, parietal, and occipital lobes. Computerized tomography (CT) scans of the brain show large, low-density abnormalities mimicking a single or multiple space-occupying mass. Magnetic resonance imaging (MRI) with enhancements shows multiple, ring-enhancing lesions in the brain

Acanthamoebae infecting the CNS are not readily found in the cerebrospinal fluid (CSF), although they have been isolated from the CSF in a few cases. *Acanthamoeba* that had apparently entered from the nasopharynx through a fistula have been detected in the CSF of a patient without CNS disease (Petry et al., 2006). CSF examination in general reveals lymphocytic pleocytosis with mild elevation of protein and normal or slightly depressed glucose. Examination of the autopsied brain reveals cerebral edema, areas of cortical and basal ganglia softening, and multiple necrotic and hemorrhagic areas of CNS tissues. The brainstem, cerebral hemispheres and cerebellum may show areas of hemorrhagic infarcts.  Necrotic tissue with lipid-containing macrophages and neovascularization suggesting a tumour is often seen. Trophozoites and cysts of acanthamoebae are usually spread all over the infected tissue. Many blood vessels are thrombotic with fibrinoid necrosis and cuffed by polymorphonuclear leukocytes, amoebic trophozoites, and cysts. Multinucleated giant cells forming granulomas may be seen in immunocompetent patients but less often in

Some infected individuals, mostly with HIV/AIDS, develop chronic ulcerative skin lesions, abscesses, or erythematous nodules (Seijo-Martínez et al., 2000; Visvesvara & Maguire, 2006, Visvesvara et al., 2007), especially of the chest and limbs. These nodules are usually solid but sometimes they become ulcerated and purulent. The prodromal period is unknown and several weeks or months may elapse following infection before the disease becomes apparent. Because of the time delay, the precise portal of entry is not clearly known, but the wide dissemination of these amoebae in the environment allows for many possible contacts and modes of infection. Trophic amoebae and/or cysts of *Acanthamoeba* have been isolated from the nasal mucosa of healthy individuals, suggesting a nasopharyngeal route as one means of invasion. Amoebae may also enter the body through ulcers in the skin, resulting in hematogenous dissemination to the lungs and brain, or by inhalation of amoebic cysts

In addition to causing CNS infections, *Acanthamoeba* also causes a vision-threatening disease, *Acanthamoeba* **keratitis** (AK) which mostly affects contact lens wearers although many cases have been reported worldwide in non contact lens users mostly related to a previous corneal trauma [6, 7, 8]. The number of affected individuals is increasing worldwide. Morever, recent outbreaks of *Acanthamoeba* keratitis have been recently reported in the United States and Australia (Verani et al., 2009; Tu and Joslin, 2010; Patel et al., 2010;

2006; Visvesvara et al., 2007; da Rocha-Azevedo et al., 2009).

immunocompromised patients.

(Visvesvara & Maguire, 2006; Visvesvara et al 2007).

(Seijo-Martínez et al., 2000; Shirwadkar et al. 2006; Visvesvara et al., 2007).


**Preface to "Bioinformatics and Machine Learning for Cancer Biology"**

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of "omics" technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.

This book presents some of the latest progresses on leveraging bioinformatics and machine learning for cancer biology, which is particularly useful and attractive for cancer biologists, bioinformaticians, machine learning experts, computational biologists and other scientists or researchers in life sciences and biology. We would like to thank all the authors contributing to this book who made significant contributions to the better understanding of various cancers as well as cancer-related analysis method improvements. We are eager to see more exciting discoveries in cancer biology with the help of bioinformatics analysis and machine learning in the near future.

> **Shibiao Wan, Yiping Fan, Chunjie Jiang, and Shengli Li** *Editors*


**2. Texture formation by intense rolling**

Intense rolling with heat treatment has been utilized to fabricate the finegrained stainless steel plates and sheets [4, 7, 17]. In the following, AISI304 sheet was employed as a work to reduce its thickness from 10 mm down to 1 mm by intense rolling. EBSD analysis was used to describe the crystallographic change in the rolled AISI304 sheet.

#### **2.1 Rolling procedure**

*Electron Crystallography*

shearing process in metal forming.

Angle Misorientation) mapping.

Let us consider how to control the crystallographic structure of metals and metallic alloys by the technology of plasticity in metal forming and surface treatment. **Figure 1** depicts three case studies on the crystallographic structure change by rolling, shearing, and nitrogen supersaturation. Even when the initial grains are equiaxial, some of them are forced to partially align along the rolling direction. The skew distortion by intense rolling drives to shear the grains with spin-rotation as shown in **Figure 1a**. In case of embossing and piercing the sheet metals, the grains are distorted to plastically flow along the shearing plane with grain size refinement as depicted in **Figure 1b**. In the low temperature plasma nitriding, the plastic straining is induced by the nitrogen supersaturation into metallic lattices to form the slip-line system as shown in **Figure 1c**. Without externally applied stresses, the slip-line network is formed from the surface to the depth together with the nitrogen interstitial diffusion. In particular, the low temperature plasma nitriding process [10–15] works as a powerful means to demonstrate that austenitic and martensitic stainless steel substrates are hardened and modified to have two-phase structure with the average grain size of 0.1 μm. These previous studies proved that grain size as well as crystallographic structure should be significantly controlled by the materials processing other than the

In the present chapter, the crystallographic structure evolution of stainless steels during the rolling, the piercing, and the plasma nitriding at 623 K for 14.4 ks is first described to deduce the mechanism of microstructure evolution during metal forming and materials processing. Next, the uniaxial loading test of plasma nitrided work at 623 K is performed to investigate the possibility of further microstructure evolution during posterior metal forming. Through these experiments, the effect of the interstitial element concentration as well as the plastic straining on the crystallographic evolution is discussed to search for the materials science model to describe the interaction between the interstitial mobility and the plastic straining. In the following, EBSD (Electron Back Scattering Diffraction) is employed to make crystallographic analyses. This technique is based on the automatic analysis of the Kikuchi pattern by the excitation of the electron beam on the surface of the sample in SEM (Scanning Electron Microscope) [16]. Among several analytical tools in EBSD, the crystallographic orientation for each grain is described by IPF (Inverse Pole Figure) and the strain induced phase transformation is also analyzed by phase mapping. In addition, the equivalent plastic strain distribution is estimated by KAM (Kernel

*Three types of crystallographic evolution during metal forming and plasma nitriding. (a) Intense rolling,*

*(b) embossing and piercing, and (c) low temperature plasma nitriding.*

**56**

**Figure 1.**

A typical rolling system was illustrated in **Figure 2a**. AISI304 sheet with the initial thickness of 10 mm was compressed and sheared between two work rolls in a single reduction. Since the reduction of thickness was 10% in this single rolling, nine steps were utilized to reduce the thickness down to 1 mm through this intense rolling. **Figure 2b** shows the rolled sheet by 90% reduction in thickness. This rolling is effective to reduce the average grain size of stainless steel works for embossing and piercing to be discussed in later.

#### **2.2 Microstructure evolution by intense rolling**

The original AISI304 sheet was characterized by three high intensity peaks in XRD analysis. As depicted in **Figure 3**, three peaks were detected in correspondence to γ (111), γ (200), and γ (220) planes in the austenitic phase. This microstructure changes to nearly full-martensitic phase; as also depicted in **Figure 3**, three martensitic peaks were detected as α' (211), α' (200), and α' (110) besides for γ (220) and γ (111). This proves that original austenitic grains massively transform to martensitic ones in AISI304 sheets during the intense rolling. These martensitic grains can be inversely transformed back to austenite by heat treatment. This technique is useful to reduce the average grain sizes to be stated later.

#### **2.3 Controlled crystallographic structure by intense rolling**

In addition to the strain-induced phase transformation in **Figure 3**, the intense rolling has much influence on the crystallographic structure in AISI304. **Figure 4** depicts the inverse pole figure mapping, the KAM distribution, and the phase mapping on the cross-section of rolled AISI304 sheet, analyzed by EBSD. As stated before, the average grain size is reduced down to 1 μm in most of AISI304, since the plastic strains are applied to these regions as shown in **Figure 4a** and **b**. Low plastic strained regions in **Figure 4b** corresponds to assembly of larger grains laterally aligned in the rolling direction. Through comparison between **Figure 4b** and **c**, these

#### **Figure 2.**

*Intense rolling process with high reduction in thickness. (a) Illustration on the rolling process in multi-steps for reduction of thickness by 90% and (b) rolled AISI304 sheet.*

textures are classified into two zones. One is thin and long full-martensitic textures. The shorter and dotted textures are corresponding to the retained austenitic textures.

These textured crystallographic structures are further controlled by the low temperature plasma nitriding as well as the heat treatment to be discussed in later.

#### **Figure 3.**

*Variation of XRD diagrams from the original AISI304 sheet before rolling to AISI304 after intense rolling and furthermore to rolled AISI304 sheet after low temperature plasma nitriding.*

#### **Figure 4.**

*EBSD analysis on the cross-section of rolled AISI304 sheet. (a) IPF mapping, (b) KAM distribution, and (c) phase mapping.*

**59**

**Figure 5.**

*Micro-/Nano-Structuring in Stainless Steels by Metal Forming and Materials Processing*

inversely transformed together with reduction of grain sizes [4, 7].

1.5 μm became nearly the same as D = 7.5 μm as depicted in **Figure 5b** and **c**.

Fine piercing is an essential process in metal forming for accurate drilling of holes and for fine blanking. In the last decade, metastable austenitic stainless steel type AISI304 with fine grains has been developed by [18–20]; the effects of fine crystallographic structure on the elasto-plastic deformation have been closely studied in [21–23]. **Figure 5** shows three metastable austenitic stainless steel AISI304 sheets with the thickness of 100 μm, where the grain size was reduced by rolling process from the normal-grained sheet with the average grain size (D) of 7.5 μm. Two fine-grained AISI304 sheets were yielded to have D = 3.0 and 1.5 μm, respectively, by reverse transformation of the strain-induced martensitic phase. In this thermo-mechanical treatment, near-fully martensitic grains in **Figure 4c** are

Their IPF maps analyzed by EBSD were shown on the cross section in the sheet width direction. When D = 7.5 μm, most of grains have a preferred orientation to [111] direction, as shown in **Figure 5a**. The crystallographic structure for D = 3 and

These AISI304 sheets with different grain sizes were pierced by CNC stamper under the same conditions; e.g., the punch diameter was 100 μm, the die diameter, 110 μm, and the clearance, 5% of sheet thickness. **Figure 6** compares IPF mapping as well as phase mapping on the cross-section of punched hole among three AISI304 sheets with the different grain size by D = 1.5, 3.0, and 7.5 μm. As commonly seen in **Figure 6a**–**c**, most of grains along the side surface of hole are distorted and refined in size. The shearing of AISI304 sheet by piercing leaves the process-affected zones along the shearing plane. The phase mapping in **Figure 5d**–**f** reveals that these process-affected zones are just corresponding to the martensitic phase [24]. That is, the original austenitic matrix to AISI304 sheets is forced to transform to martensite by the shearing strain during the piercing process. This strain-induced martensitic transformation is a non-diffusive shear transformation; each grain in the affected-

*IPF map and intensity map in the ND direction analyzed by EBSD. (a) Normal-grained AISI304 sheet with F = 7.5 μm, (b) fine-grained AISI304 sheet with D = 3.0 μm, and (c) fine-grained sheet with D = 1.5 μm.*

*DOI: http://dx.doi.org/10.5772/intechopen.91281*

**3. Crystallographic change by piercing**

**3.1 Microstructure evolution by piercing**

process zone massively makes transformation [25, 26].

*Micro-/Nano-Structuring in Stainless Steels by Metal Forming and Materials Processing DOI: http://dx.doi.org/10.5772/intechopen.91281*