Illustrative Case Studies

#### **Chapter 5**

## Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis

*Tanya Zerbian, Mags Adams and Neil Wilson*

#### **Abstract**

The Covid-19 pandemic has presented new challenges for food production, distribution, and consumption and has exacerbated existing inequalities in access to food. However, it has also provided new opportunities for local communities to work differently, to increase collaboration, and to improve outcomes for those most in need. This chapter focuses on how various local food initiatives within a specific UK city, Preston in NW England, interact, cooperate and collaborate, and the changes to these interactions during a crisis. The findings derive from a social network analysis (SNA) conducted during summer 2020 examining how relationships changed during the crisis, and online semi-structured interviews. Using resilience as a framework to understand these dynamics, the chapter argues that social preconditions, such as a previously organised local food network in partnership with local authorities, have helped communities to self-organise and respond to difficult circumstances. Moreover, it also highlights the ways in which responses to major disruption (Covid-19) can bring about the collective questioning of current models of emergency food provisioning and create stronger collaborative bonds within already organised networks. We demonstrate that such processes could potentially improve food insecurity outcomes by combining locally grown food and dignified food access options.

**Keywords:** food resilience, social capital, food security, local food systems, Covid-19, local food initiatives

#### **1. Introduction**

The global Covid-19 situation has presented new food production, distribution, and consumption challenges and has potentially exacerbated existing inequalities for those in deprived areas. Significantly, the implications of the Covid-19 pandemic on global food supply chains and food systems' resilience have aggravated food insecurity indicators. As defined by the Food and Agriculture Organisation (FAO), food security is a condition that "exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life" ([1], p. 49). The FAO estimates that up to 811 million people worldwide faced hunger

in 2020 – up to 161 million more than in 2019 – as conflict, climate extremes, and economic slowdowns, aggravated by the Covid-19 pandemic, continued to increase in frequency and intensity [2]. The World Food Program (WFP) calculated that the number of acutely food insecure people in the countries where it operates reached more than 271 million people directly due to the aggravating impact of the Covid-19 pandemic. In the UK, it is estimated that the number of people experiencing food insecurity quadrupled due to lack of food in shops, economic impacts, and isolation brought about by the pandemic [3].

As well as these challenges, the Covid-19 situation presents new opportunities for local food initiatives to work differently, increase collaboration, and improve outcomes for those most in need. Local food initiatives usually refer to social innovations that aim to address environmental and social issues derived from current food system structures, reconfiguring food supply chains and relations within a locality [4]. The collective responses of local food initiatives to the disruption caused by Covid-19 provide the perfect space to increase knowledge about how local food systems – collaborative networks that integrate individual local food initiatives efforts [5] – and could potentially lead to better food security outcomes. Case studies have increasingly documented how networked responses in diverse local communities during the Covid-19 crisis managed to respond to rising food insecurity needs and the opportunities this might provide for food systems change [3, 6]. Our research aimed to expand this body of literature by providing knowledge about how various local food initiatives interact, cooperate, and collaborate, how these changed during the Covid-19 pandemic and what this means for a local food system. To date, there are few studies that have investigated the changing structure of local food systems using a comparative research design before and during a disruption. Lessons learned from this examination might help local responses to future crises such as the climate crisis and other external stresses that affect food systems and society.

We focus on the local food system of the Local Authority Area of Preston in the Lancashire region of the UK. In the first section of the chapter, food security resilience is introduced. By providing an overview of the concepts of resilience and social capital, a theoretical framework is presented that is used to unpack the dynamics of Preston's local food system. The following section outline the methodology used to study Preston's local food system – namely, a social network analysis (SNA) conducted during 2020, examining collaborative relationships before and during the crisis, and online semi-structured interviews with a subset of local food initiatives. Next, the results from the research are presented in order to illuminate the changing characteristics of the local food system and its potential outcomes. The final section returns to the concept of food security resilience, using social capital as a proxy, to highlight important lessons learned from the case study presented, namely the relevance of previous social preconditions to ensure adaptation and response.

#### **2. Social resilience, a key factor in addressing food security needs during a crisis**

#### **2.1 Food security resilience; beyond ecology**

Resilience is a concept that holds different meanings depending on the various situations in which it is being used [7]. Ecology literature usually frames resilience as a technical concept that refers to the "capacity of a system to withstand shocks and

#### *Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis DOI: http://dx.doi.org/10.5772/intechopen.101998*

external pressures while maintaining its basic structure, processes, and functions" ([8], p. 601) In this context, resilience was perceived as an isolated 'outcome' rather than connected to specific abilities, as many academics and practitioners now recognise [9]. Resilience thinking has expanded from this initial narrow definition by integrating adaptability and transformability as crucial ingredients [10, 11]. Social theory has contributed to this reconceptualisation adding essential dimensions, such as agency and collective action, to the concept [12]. As such, resilience is defined at the communal rather than individual level, focusing on coordinated efforts and cooperative adaptation [13]. Here, resilience refers to the ability of a given community or group to cope with external shocks and disturbances to its infrastructure and functioning [10]. It involves both the capacity to learn and adapt to ongoing pressures using existing economic, social, and environmental resources while also developing new strategies and capabilities [11].

Both literature and practice have increasingly acknowledged the potential of resilience thinking to contribute to food security. Tendall et al. [14] develop the notion of food security resilience at the system level by breaking it down into four components: robustness (the capacity to withstand the disturbance in the first place before any food security is lost); redundancy (the extent to which elements of the system are replaceable, affecting the capacity to absorb the perturbing effect of the disturbance and avoid as much food insecurity as possible); flexibility and thus rapidity (or the speed with which the food system can recover any lost food security); and finally, resourcefulness and adaptability (how much of the lost food security is recovered). More broadly, it has been argued that food security resilience is "about the capacities of households and communities, to deal with adverse events in a way that does not affect negatively their long-term wellbeing and/or functioning" ([12], p. 806). Although Tendall et al.'s [14] definition offers a strong starting point to understand how particular local food systems have been able to respond to the Covid-19 pandemic, resilience variables such as those proposed are difficult to observe and measure, and there is no current consensus on how to do so [7].

Therefore, to understand how local food systems can contribute to food security and what is needed to address external stresses, this study assessed the changes in *resilience capacities* (the inputs required to achieve resilience) of Preston's local food system. Although these capacities cannot be regarded as a proxy for the *actual* resilience of a system, there is a direct linkage between them and the potential of a system to be resilient [7]. Thus, they are helpful variables for understanding why a particular system might successfully respond to a specific crisis. Building on literature that integrates social theory into resilience thinking, this study concentrated on social resilience capacities of local food systems using social capital as an analytical tool, which other scholars have regarded as a key feature of community and social resilience [10, 12, 15].

Overall, there is not a universal definition of social capital [16]. Adler and Known [17] categorised definitions of social capital depending on whether their focus was on an individual or a collective group, and divided the definitions into three categories. The first refers to social capital as a resource that an individual has as a result of their external linkages with other actors [13]. The second category focuses on the structure of relations of multiple actors that give the collectivity cohesiveness, which facilitate common goals. In this category, social capital is defined as "the features of social organisation, such as trust, norms, and networks, that can improve the efficiency of society by facilitating coordinated actions" ([18], p. 167). It is thus defined by its function to facilitate certain action within a social structure [19]. The third category

of social capital refers to both external linkages and internal linkages of a social grouping. The current study adopts the second view of social capital, as it allows the analysing of local food systems' structure and the collective characteristics that facilitate action in times of crisis. In this regard, it moves away from focussing on an individual resource pool to address adversity towards the social resilience capacities of local food systems as a whole.

To aid the analysis of social capital influence upon the response of local food systems to emergencies, two forms of social capital are examined: bonding and bridging social capital. Bonding usually refers to strong and emotional connections, such as friends or family, among individuals that commonly share similar characteristics in class, race, attitudes, and available information and resources [17, 18, 20]. Bridging describes loose relationships that enables information to be exchanged across diverse groups [16]. Bridging social capital, in contrast to bonding social capital, usually appears in more open networks, increasing chances to expand and access new relationships, information, resources, and opportunities [21].

#### **2.2 The changing relationships of local food initiatives pre- and during Covid-19**

The methodology used in this study involved a three-phase process. Phase I consisted of an initial internet search to identify a preliminary list of local food initiatives supporting one or more areas that contribute to the sustainability and food security of the Preston, Lancashire area. Local food initiatives in Preston were identified based on their nature as a component of a local food system as characterised by Clément [22]. Clément identifies local food initiatives as those that focus on direct local food marketing, local food procurement, food access programmes, and food education and policy [21]. We added an overarching criteria of having a specific focus on improving food security and sustainability at the local level and follow ethical principles to differentiate them from the conventional food system [23]. We initially identified 44 organisations in Preston that could be considered local food initiatives working within the local food system.

Phase II involved gathering survey data from key personnel working in these organisations to establish which local food initiatives have active relationships and collaborations and which are more marginal within Preston's local food system. The survey identified how these connections have changed since the Covid-19 crisis developed and enabled comparison with pre-Covid-19 relationships. To do this, we asked questions relating to the scale of interactions between organisations before and during the crisis. To answer these questions participants had to indicate which option best described their relationship with other organisations in the local food system. The scale used in the study was derived from the four Cs of interorganisational partnering to respond to a disaster and Himmelman's collaboration continuum [24, 25]. Reflecting increasing degrees of interaction and integration with other organisations, the options provided were 'communicating' (exchange of ideas and information), 'sharing' (communicating and sharing of resources for mutual benefit), and 'collaborating' (communicating, sharing and working together to create something new). Based on the definitions of bonding and bridging social capital, collaborating refers to the former, while communicating and sharing to the latter.

The survey analysis was coupled with SNA to measure the social capital features of the local food system, following a network approach to social capital, which focuses on the patterns and collection of relationships within a group [26]. SNA has been identified as beneficial for demonstrating the relationships among food systems'

#### *Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis DOI: http://dx.doi.org/10.5772/intechopen.101998*

actors both visually and numerically [27]. Gephi, an open-source platform for visualising and analysing network graph data, was used to analyse network-based questions to assess the overall characteristics of the local food system and identify central actors within it. Of the 44 identified organisations, 21 local food initiatives completed the survey. Although there are various methods available to impute the missing data of non-respondents, doing so can create biased network measures and metrics [28]. Missing data in this context is missing at random and the probability of it being missing is unrelated to the value of the missing connections and observed organisational attributes [29]. Therefore, the analysis was based on the 21 responses from local food initiatives that we received. Phase III included semi-structured interviews with key stakeholders in the local food system and will be discussed further in Section 2.3.

#### *2.2.1 The social network of Preston's local food system*

Data about social networks is depicted as sociograms. Sociograms are graphs showing network actors (in our case these are local food initiatives which are represented as 'nodes' in the network) and their relationships (these are the connections between the local food initiatives and are represented as 'edges') [30]. Relationships (edges) can be directed (having a certain quality that can be different in both directions) or undirected (where the type of relationship is not specified). We gathered information about both, as knowing the direction of the edges can provide information about reciprocal relationships. Reciprocal relationships denote the level of trust between organisations because it reflects the cultivation and utilisation of tangible and intangible resources by network members for the common interest [16, 21]. **Figures 1** and **2** illustrate the sociograms of the relationships among organisations before Covid-19 and during Covid-19. For the SNA, we concentrated on measures of connectivity and centrality1 , as they represent some of the fundamental structural properties of importance to any network and have been used to clarify the vulnerability of networks [30].

**Table 1** shows the local food system's connectivity network measures, comparing pre-and during Covid-19. Network diameter is the longest distance between any two nodes (i.e., how many edges are between the two most distant nodes). A short network diameter means it is possible to move through the network in a very few steps through a small number of nodes and implies that an idea or resource will spread quickly across the network, signalling integration to the system [31]. The average path length is the mean distance between all possible pairs of nodes in the network; the closer to 1, the more connected the network [32]. In the case of Preston, with a diameter of 2 and an average path length of approx. 1.5 even before Covid-19, the local food system was already 'compact' [33].

Similarly, network density – the number of identified links divided by the maximum possible number of links [32] – remains between 0.44 and 0.45. This measure captures the bonding social capital within the local food system, reflecting sociological ideas like cohesion, solidarity, and membership, by calculating how many edges exist between actors compared to how many edges between actors are possible; the closer to 1, the more connected the network is [34]. In terms of resilience, having a medium network density, low diameter, and average path length means that resources

<sup>1</sup> Connectivity is an aggregate metric that gives information about the cohesiveness of the network as a whole; the interconnectedness of actors. Centrality is a measure relating to individual nodes. It indicates which nodes possesses critical positions in the network [27].

**Figure 1.** *Sociogram pre-Covid-19 - Preston's local food system.*

#### **Figure 2.**

*Sociogram during Covid-19 - Preston's local food system.*


#### **Table 1.**

*Connectivity measures in Preston's local food system.*

#### *Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis DOI: http://dx.doi.org/10.5772/intechopen.101998*

can spread quickly between organisations. In times of crisis, such connectivity can facilitate rapid social action and setting up new processes and activities without the potential for duplication of activity and attendant waste of resources, making it easier to respond to changing situations such as Covid-19. This could explain the successful response to food insecurity described by participants (see Section 2.3.). Based on these measures, it could be argued that Preston's local food system already possessed a strong level of bonding social capital, as it demonstrates collective cohesiveness. However, as will be seen next, this changes when looking at the *types* of relationships present.

**Figure 1** illustrates the overarching interconnectivity between organisations of Preston's local food system before Covid-19. The size of the nodes in the sociograms indicates the importance of an organisation within the network. The edges (connections) are coloured based on the type of relationship: blue: communicating, red: sharing, green: collaborating. The local food system before Covid-19 already shows a high number of edges between the many organisations within it. Approximately half of edges were collaborative relationships, and the other half were communicating and sharing connections (see **Figure 1**). Notably, the sociogram pre-Covid-19 presents a small network of organisations, which share collaborative ties with the same initiatives. In this regard, there was a strong presence of bridging social capital exemplified through weaker ties such as communicating or resource sharing, with a sub-group of organisations with an enhanced bonding social capital reflected through collaborative relationships.

Comparing the sociograms before and during Covid-19, it can be identified that the pandemic has affected the associations between local food systems' members, although the overall features of the local food system remain the same. Significantly, it has increased the quality of interactions. **Figure 2** illustrates a higher number of green coloured, collaborative relationships across the local food system, accounting for 60% of the edges. In this regard, many weaker connections in the form of sharing and communicating pre-Covid-19 were replaced by collaborations during-Covid-19, signalling the creation of bonding social capital from previous connections based on bridging social capital.

Despite the overarching interconnectivity between organisations within Preston's local food system, it can be identified that a small number of organisations have particularly central roles in the network, which has been strengthened during Covid-19. To understand the role of specific organisations within the network, we used centrality measures to identify the most connected actors in the network that hold a significantly higher than average number of links [31]. In-degree centrality is the number of edges pointing towards a node, i.e., how popular or sought-after a given organisation is. Outdegree centrality denotes the outgoing connections of a node with other organisations, which refers to the sociability or outreach of an organisation [31]. This is important to understand the social resilience capacities of a local food system, as it points to particularly influential and prominent actors that could facilitate rapid response, network organisation, or those holding the resources needed to adapt. **Table 2** presents the degree centrality per organisation. The nodes in **Figures 1** and **2** are sized according to their in-degree centrality score, which indicates the number of incoming links a local food initiative possesses. From this, four organisations, the local authority, the food redistributor, CGA (a community housing association), and Let us Grow Preston (LGP - a network of community gardens), can be identified as having high levels of in-degree and out-degree centrality. As such, they hold an advantageous position concerning their roles and leadership within the local food system. This has remained during Covid-19, albeit with the scores increasing for each organisation, indicating an increased number of connections.


**Table 2.** *Centrality measures per node.*

#### *Food Systems Resilience*

**108**

#### *Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis DOI: http://dx.doi.org/10.5772/intechopen.101998*

Betweenness centrality measures how often a node lies on the shortest path between two other notes. This helps to identify the brokers or gatekeepers, those with links that stretch well beyond their local network neighbours, as these nodes are the critical actors on the path for routes of exchange. Eigenvector centrality measures the influence of a node in a network concerning the importance or connectedness of its neighbours [35]. Both betweenness and eigenvector centrality refers to the effect that an organisation may have within a network. Based on their eigenvector and betweenness scores (see **Table 2**), the local authority and LGP are also the most strategically located overall to create links with other local food initiatives and share information and resources [31, 36]. The position of these organisations has been strengthened during Covid-19, indicating their potential role in structuring an organised response to the crisis, act as a bridge to facilitate information exchange and new information flows (bridging social capital), and increasing trustful connections (bonding social capital).

The following section uses data from semi-structured interviews to build on these findings and provide explanations for why Preston's local food system has remained relatively unchanged in terms of overall characteristics, but more significantly changes in relation to the strength of ties. It explains how the previous structure of the local food system helped a coordinated response to the crisis, and the role of LGP and the local authority in facilitating coordination.

#### **2.3 The importance of previous connections for self-organisation and adaption**

In addition to the survey and SNA, we conducted semi-structured, in-depth interviews with a purposively selected subset of survey respondents. Of the 21 respondents to the survey, nine participated in this Phase. Additionally, to gain a deeper insight into Preston's local food system, two local food researchers who had been involved in collaborative work within the local food system before Covid-19 were interviewed. Interviews lasted between 45 and 90 minutes, were conducted online following Covid-19 restrictions, and were recorded with the participant's consent. Interviews were transcribed, and analysis was supported by NVivo software, following Stake's [37] guidelines to qualitative case study analysis, which focuses on pattern recognition across the collected data. The use of case study analysis was intended to gather further explanatory details about the local food system and its changes.

As the SNA has shown, Preston's local food system already had a high degree of connections before Covid-19, including both bonding and bridging social capital. This is mainly because Preston's local authority had created a space in 2019 where local food initiatives within Preston could share their approach to food insecurity, could discuss various models of food aid provision, and foster mutual learning. According to participants, this initiative was taken up very positively by local food initiatives:

*"My feeling is that they definitely, the meeting I went to, there was an enthusiasm around sharing and working together. There was a collective kind of wanting to do that […]" (local food expert).*

This demonstrates the potential for developing bonding social capital was present before COVID-19, fostering stronger collective sharing and mutual learning. With the facilitation of the local authority, this embryonic food poverty alliance was working closely with LGP, a community gardens network initiated by the local authority,

to grow and collect surplus food from allotments and gardens to use the produce in food insecurity schemes and nutrition education. These events prior to Covid-19 further suggest the centrality of local authorities in fostering coordinated approaches towards food-related issues and increasing social capital within local food systems. In addition, while the local food system was not necessarily demonstrating strong *collaborative* ties pre-Covid-19, as seen in the previous section, it reveals that providing opportunities to share information (bridging) is important in facilitating coherence between organisations and that can lead to increased bonding social capital in local food systems.

Interview findings corroborated the centrality of the local authority and the importance of previous relationships, as found through the SNA, to respond to the Covid-19 food insecurity crisis in the city. Covid-19 acted as a catalyst for the food poverty alliance by strengthening ties that pre-existed the pandemic. Pre-existing relationships that previously simply shared information, extended to collectively working towards a common purpose. In March 2020, the local authority called for a joint meeting of the food poverty alliance and other local food initiatives working on food access and LGP, leading to the creation of a WhatsApp group for coordination. Multiple interviewees reinforced the importance of the council's leadership in ensuring the successful organisation of networked responses:

*"And that I think, really, it's just having that permanency, 'cause a lot of the organisations involved in the community food hub and the network are charity-based. So, they can't necessarily focus on that side of um, sort of leading on the project, so what [the local authority] have been doing is they've taken that kind of lead to coordinate things, and I think it definitely needs somebody like that to focus on it, 'cause we are all funding dependent, we might not be here tomorrow, but it still needs somebody to carry on and push that forward" (community food hub).*

The importance of the local authority role in coordinating the food poverty alliance is not only because many local food initiatives are reliant on external funding. Participants, including the local authority, perceived that the alliance was moderated and formed in an inclusive and accepting manner, leading to a feeling of building collective realities and a shared mission under a notion of diversity:

*"And I think that is partly because from the onset I think we've all recognised that each of the groups are unique and offer their own individual services and I think that has been key. We are not, certainly the network isn't trying to mould everybody to deliver one certain service. It's actually recognising that everybody is […] unique and special in their own rights" (local authority).*

This signals a high level of respect among the participants of the food poverty alliance, acknowledging the uniqueness of each. Significantly, this indicates that bonding social capital and cohesiveness can still be present in non-homogenous groups, leading to a closely connected network, yet open enough to accept new entries. This acknowledgment of diversity within the alliance has led to the development of new connections. Interviewees agreed that Covid-19 prompted new relations between organisations, which might not have been considered previously. Covid-19 prompted a closer collaboration between food banks organised by diverse faith and ethnic groups and community gardens and sustainable food initiatives. This lead to a cross-fertilisation of beliefs, demographics, and purposes. In terms of social resilience capacity,

#### *Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis DOI: http://dx.doi.org/10.5772/intechopen.101998*

this meant that bridging social capital was invigorated, promoting channels for the food poverty alliance to expand and potentially build stronger links with heterogenous groups. Indeed, the ability to respond quickly to Covid-19 in terms of food access was attributed to the strengthening of the relationships among these diverse groups:

*"I know from an organisational point of view, how much I became under pressure at end of March to about July and then is continuing and couldn't have done it without my partners and then having those conversations. And as you know, everybody was learning, we went from face-to-face meetings to learning a technology that nobody was au fait with […]. Even though, there were difficult times, we got to do it all." (community centre).*

This experience emphasises the importance of developing trust and mutual support in collaborative relationships. In Preston's case, Covid-19 acted as a catalyst to reach higher levels of these attributes, helping member organisations to collectively overcome the challenges imposed by COVID-19 due to the increased strength of their connections. This increased coherence and thus new-found bonding capital among local food initiatives also meant a better response to food access concerns that might have been overlooked otherwise. Notably, this was related to the increased information sharing among organisations and the exchange of food and resources. While talking about the benefits of joint coordination, one participant explained how, with the help of various providers, they were able to respond to a gap in food access for students in the city:

*"It came to light through one of the other organisations… There is about three or four hundred students from South India who are in Preston and… The university were just, just 'go away and leave us alone'. So, between us, between the various food providers we got on to the Vice Chancellor and said, 'What are you doing? You should be helping these people'. And… The university said, 'Oh, well we are shut down and we can't do this, and we can't do that…' And we said, 'Yes you can get a key and open the door to one of your big rooms and between us we will find food and the students can come to this one spot'" (community food market).*

This communication between the food initiatives and the university ultimately led to a process being put in place to support these students. The university was not one of the organisations identified for the SNA as they are not a significant part of the local food system in the city, but this example illustrates how a local food system with strong bridging and bonding capital can swiftly identify and support other organisations outside of already established platforms. Furthermore, the ability to feed back to the food poverty alliance was highlighted as important for making sure that those in vulnerable positions were receiving food according to their needs, culture, and eating habits. Significantly, these examples elicited reflection across the local food initiatives, and led to discussions that questioned the adequacy of some of the models and food currently being used:

*"So, in a crisis situation sometimes you have to do things because if it's a matter of you know somebody going hungry […] But I said that this is a plan strategy we need […] be supporting our local small local businesses who are struggling, who may go out of business, who may be forced into poverty if we don't support them. So, you just perpetuate in that cycle and he, he's, I think he's going to get it now" (food hub).*

*"I was having a meeting the other day and saying 'yes, we are giving food parcels out, but what else goes with giving a food parcel, how are we making a difference other than putting that food on that table, but what else has that family learned? […] What else is happening in the house? Is there other issues? Who is actually talking?'" (community centre).*

The above statements illustrate ways in which having spaces for discussion and knowledge exchange helps initiatives to move beyond a model of emergency food aid that mainly uses surplus food. Indeed, the prominent participation of LGP, which during the pandemic decided to grow as much food as possible and collect as much fresh local food from allotments and community gardens for the food poverty alliance, has signalled a possible mechanism for introducing other local and sustainable food to address food insecurity needs. The local authority reflects this sentiment:

*"And then of course, LGP have been key to this, because LGP work with all the local allotments […], so LGP have been providing all the food hubs with fresh produce and continue to do so. I know in some of the areas they've been talking about more community allotments, growing spaces, having gardens where they can grow their own produce and that will definitely without a doubt will be on the agenda going forward." (local authority).*

Although 'it is by no means perfect' and 'there is still a lot to do', as participants mentioned, the development of the local food system in Preston suggests the importance of developing both bridging and bonding social capital through strong collaborative links and information exchange across the diversity of organisations in the local food system to be able to respond better to future crises. Notably, the role of local authorities has been identified as key in such a process. More importantly, the Covid-19 pandemic has fostered the creation of spaces of mutual reflection, whereby the purpose and avenues of emergency food aid are reconsidered, and more sustainable and structural strategies are considered.

#### **3. Social resilience capabilities for improving food security outcomes during crises**

This analysis of how the relationships between Preston's local food initiatives changed because of the Covid-19 pandemic reveals the importance of how social resilience capacities can help communities better respond to shocks and disturbances. Within this local food system strong communicative, sharing, and collaborative relationships and connections were already present before the pandemic hit, with engagement occurring across an already highly connected network. Collaboration, mutual sharing, and communication between different types of local food initiative indicate the presence of both bonding (strong collaborative connections) and bridging (loose relations through sharing and communicating) social capital before Covid-19. In particular, the prior formation of a food poverty alliance by the local authority provided the opportunity to construct a relatively cohesive response to food insecurity. Findings highlight that the critical component of these ties is the quick mobilisation of resources (e.g., food and information). This provided the capacity during Covid-19 to ensure food access across multiple communities during this major disruption to food systems and society's structures. Reflecting on these features of

#### *Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis DOI: http://dx.doi.org/10.5772/intechopen.101998*

local food systems in relation to the literature on resilience and social capital, can help us better understand the role of networks of local food initiatives in adaptation, crisis mitigation and collective reflection and what these dynamics could mean for future successful food security responses.

Returning to the two types of social capital used to analyse the food security resilience capacity of Preston's local food system, it can be argued that bonding and bridging social capital worked in complementary but distinct ways before and during the crises [21]. Bonding social capital, due to preparatory work of the food poverty alliance, helped the local food system adapt quickly to new ways of delivering food, whilst bridging capital helped integrate a more diverse set of local food initiatives. As explained by Putnam, bonding social capital fosters mobilising solidarity, allowing communities to 'get by', as in the case of increasing exchange of food and resources in Preston. On the other hand, bridging social capital is essential to 'get ahead', broadening identities and reciprocity across diverse groups [21]. In this regard, despite the presence of a relatively collaborative network before Covid-19, which others have argued can limit possibilities for expansion and inclusion [13], the presence of bridging social capital before Covid-19 might have helped the 'openness' of the alliance to create bridges across local food initiatives in terms of religion, type and beneficiaries. In addition, results show how a particular emergency can increase the level and type of social capital within local food systems, from loose connections based on information sharing to collaborative ties, leading to greater bonding social capital. Increased bonding social capital has been related to trust and a sense of unity within communities [38]. Indeed, interviews highlight new levels of trust and respect among the food poverty alliance and across the local food system, working towards a common aim in a recognition of diversity as a result of newer collaborative relationships.

The literature on local food systems and local food initiatives has increasingly identified the potential benefits of increased collaboration between different types of organisations working on food-related concerns [39–41]. Our findings show that providing the space for local food initiatives to meet helps shape and develop relationships. This has enabled discussions within the local food system about some of the disadvantages of food aid and the potential to develop avenues of support that can bring about better food insecurity solutions. In particular, this has demonstrated the possibility of creating a bridge between organisations working with vulnerable communities and those focusing on local food, spaces which have previously been heavily criticised for being exclusionary and 'elitist' [42]. Moreover, food aid organisations have frequently been presented as supporting short-term strategies that concentrate on emergency patch work and sacrificing long-term solutions, thereby creating dependant and passive recipients of charity whilst also benefiting big corporations along the way [43, 44]. Providing spaces of deliberation for initiatives within the local food system to develop collective responses to food insecurity is shown to increase the possibility of questioning current models of food provisioning and to develop more imaginative structural solutions.

In addition, this study highlights the importance of a neutral organisation, with resources and strategically located in the local food system, to bridge ties between diverse organisations. Preston's case showcases the role of city councils in developing social capital within local food systems [16]. This means that urban food governance – the modes of interaction within local food systems and the operational and decisionmaking mechanisms that steer changes in it – have the potential to create synergies within local food systems [45]. Notably, given that local food initiatives often have limited capacities to manage collaborative spaces [46, 47], local authorities have the

advantaged position to adopt a leading role in forming partnerships and strategies within the local food system and more so in times of crisis. Moreover, the above findings lend support to acknowledging the need for a coordinated response to emergency situations and crisis. However, this does not mean that, after crisis mitigation, no contingency plans should be adopted in these new collaborative spaces. Previous studies have highlighted the lack of consideration of vulnerabilities of food supply structures and crisis management plans in local food strategies and partnerships [48]. In this sense, local authorities should also take advantage of the collectivisation of food security responses to learn from the experience of the Covid-19 pandemic and ensure that structures, in combination with social resilience capacities, are in place to respond effectively to emerging risks.

Although the lessons learnt from Preston's case reveal the importance of social resilience capacities and urban food governance in being able to respond and adapt to sudden emergencies to ensure food security, the long-term impacts of the changes Covid-19 has had on the dynamics of local food systems remain to be seen. Bonding capital could lead to a close network of those already established initiatives, with less opportunity for others to join. Higher levels of trust among the food poverty alliance might also act as a barrier [36]. In particular, there is a risk of stagnation if the considerations resulting from the reflexive discussions and dialogue among local food initiatives does not lead to a broader focus beyond food poverty. Scholars indicate the deficiencies and challenges of a siloed focus of urban food governance spaces, such as diminishing its potential to create more transformative interventions [48, 49].

#### **4. Conclusions**

This article has sought to draw attention to the role of social resilience capacities in helping communities to self-organise and respond to difficult circumstances, especially during times of crises and disruption. This study is primarily aimed at revealing the structures needed to ensure that food access is guaranteed across diverse communities in all circumstances. Using SNA and semi-structured interviews with key actors within Preston's local food system, this research has helped shed some light on the relevance of social capital, both bridging and bonding, in developing collective food security responses in times of crises. Although it is essential to ensure physical infrastructures such as food supply chains and storage are in place to support food security, building social infrastructures like cohesion and trust across local food systems should also become a priority in cities to support populations, particularly those most vulnerable, in disaster. A key actor in Preston in developing these processes has been the local authority. As such, the research finds evidence good urban food governance is important for leveraging the collectivisation of food insecurity initiatives. Given that social capital can be fostered or deteriorated [16], a key focus in the future of local food systems, and urban food governance, should be on harnessing the new found bonding social capital to increase cohesiveness, but also seek to build up connections across diverse communities and local food initiatives.

While we acknowledge that our case may not be representative of all local food systems, it provides a place to begin unpacking the relevance of local food initiatives' relations in addressing food security challenges. The inclusion of diversity within already established networks and alliances within local food systems can lead to collective reflexive processes and questioning of current approaches to food system deficiencies. Future research should examine how the increased collaborative ties

#### *Social Resilience in Local Food Systems: A Foundation for Food Security during a Crisis DOI: http://dx.doi.org/10.5772/intechopen.101998*

developed by the Covid-19 pandemic are affecting local food systems' dynamics in the long-term and if these help move those systems beyond charity-based approaches to food insecurity. A particular focus should be if the increased connectedness of communities and local food initiatives due to solidarity remains even when external shocks are no longer a threat, working towards a collective effort to ensure food for all. With increased research in these areas and others, we will begin to better understand the nuanced nature of social capital and local food initiatives relations for food security resilience and creation of long-term solutions to food insecurity within local food systems.

#### **Author details**

Tanya Zerbian\*, Mags Adams and Neil Wilson University of Central Lancashire, Preston, UK

\*Address all correspondence to: tzerbian1@uclan.ac.uk

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 6**

## Self-Sustained Communities: Food Security in Times of Crisis

*Kriengsak Chareonwongsak*

#### **Abstract**

The COVID-19 pandemic has caused an increase in the number of poor people around the world and led to the risk of food insecurity on a global scale. Even in Thailand, a country where food production exceeds domestic demand, the COVID-19 pandemic affects food security. The increased unemployment and the consequent loss of income resulting from the pandemics undermine food accessibility and affordability for many people. This chapter addresses the problem of food insecurity in Thailand during and after the COVID-19 crisis. It provides an analysis of the current status of food insecurity and food system resilience in Thailand and suggests solutions. It also proposes the adoption of a "Food Self-Sustained Community (FSSC)" model, which refers to the concept of building food security in a community. By planning and designing in advance, a community can switch its normal form of production seamlessly to a self-sufficiency model that prepares it for future crises, so that the community can produce enough food for all members without relying on sources outside the community.

**Keywords:** Food self-sustained communities, food security, food system resilience, crisis, COVID-19

#### **1. Introduction**

The spread of COVID-19 has severely affected the well-being of many people. It is not only the health effects but also the containment measures related to the pandemic that affects the economy. FAO estimated that 720–811 million people suffered from famine worldwide in 2020, a 9.9% increase from the previous year [1]. Even in Thailand, which can produce more food than its domestic demand, and by 2020 was the 13th largest food exporter in the world [2], in the face of the COVID-19 pandemic, it was reported that people consume less food or face starvation [3] disclosing a concern about access that surpasses availability.

In every crisis, food security awareness is raised and suggestions are made on how to solve the problems and develop food systems to ensure survival for countries' populations. Many different proposals for food security have been advocated, ranging from global, country, community, household, to individual levels [4–8]. There are seemingly opposite methods, such as market dependence or self-sufficiency [9], protection of domestic markets, and the liberalization of food trade [10, 11]. Players in the food

system may be centralized or decentralized, and large or small entities [12, 13]. Food production knowledge and technology may be modern or indigenous [14, 15].

The objective of this chapter is to review and analyze the impact of the COVID-19 pandemic on food security in Thailand and review and analyze food system resilience and the challenges of building such resilience in a Thai context. Then, the Food Self-Sustained Community (FSSC) model will be discussed as an innovative approach to create community food system resilience and make communities competitive in normal times and self-reliant in food in times of crisis.

#### **2. Analytical framework**

The conceptual framework developed for considering the impact of the COVID-19 crisis on food security in Thailand will be based on the relationship between food systems and food security. Food systems have the following elements and activities throughout the food supply chain:


These elements and activities are linked by food transportation, logistics, and finance [6, 16–18]. The four pillars of food security are food availability, food access, food utilization, and food stability [19].

Based on literature reviews [19–22], this conceptual framework assumes that the elements and activities of food systems and food security are related as follows (**Table 1**)—factors of food production, food production, food processing, and food stock are related to food availability and stability, as they are related to the supply of food products. Food consumption is related to food utilization. Food stocks, markets, trade, logistics, and finance are correlated with food availability, access, and stability because they are activities that relate to food distribution. In addition, the four pillars of food security are also interrelated, for example, food production and food stock affect food availability and food price stability, which affects food accessibility.

For the analysis of the impact of COVID-19 on the Thailand food system, the shocks on the food system are divided into four components—health crisis (the situation due to the outbreak), containment measures (pandemic control measures such as lockdown and the closing of borders), economic crisis (economic depression due to the effects of the outbreak and the containment measures), and the international situation and the response of foreign countries (**Figure 1**).

#### *Self-Sustained Communities: Food Security in Times of Crisis DOI: http://dx.doi.org/10.5772/intechopen.104425*


*Note: Definitions of the four pillars of food security are based on FAO's definition in "An Introduction to the Basic Concepts of Food Security," 2008. Availability refers to the availability of a sufficient supply of food. Access refers to the ability of individuals to acquire sufficient food. Utilization refers to the ability of individuals to utilize food to achieve a state of nutritional well-being. Stability refers to the stability of the other three dimensions of food security over time.*

#### **Table 1.**

*Relationship between the food system and food security.*

#### **Figure 1.**

*Framework for analysis—The impact of COVID-19 on food security.*

An analysis of food system resilience will also follow the elements and activities of food systems, classified into three periods—pre-crisis, during the crisis, and postcrisis. The term "crisis" means situations where the food system malfunctions and poses a risk of food insecurity due to COVID-19 outbreaks and the responses from governments and other sectors. The pre-crisis food system resilience consists of the ability to prevent crises (prevention), preparedness to deal with the crises (preparation), and the pre-warning system. Food system resilience during a crisis consists of protection from the impact of the crisis (protection), mitigating the effects of the crisis (mitigation), adaptation to cope with the crisis (adaptation), and recovery.

Post-crisis resilience analysis is unrealized. Therefore, the analysis is based on what has been learned (learning) by the authors to provide suggestions for improvement (transformation) of food system resilience in Thailand [23–25]. Other challenges affecting food system design are then analyzed, in particular the trade-off between the system goals and future risks for food security.

### **3. Impact of COVID-19 on the food system in Thailand**

The COVID-19 outbreak in Thailand commenced in January 2020 and the government announced a nationwide lockdown and closed borders for the first time in late March 2020 (these measures were relaxed 3–4 months later). In the first wave of the outbreak, 4237 people were reported as infected. A second wave of the pandemic occurred from late 2020 to March 2021, affecting some areas of the country. As a result, lockdowns were announced for five provinces that had experienced outbreaks, with a total of 24,626 people reported to be infected. Later, a third wave occurred, in April 2021, resulting in the infection of more than 2 million people, as of December 2021 [26], and prompting the government to close down establishments, department stores, restaurants and announce the imposition of a curfew until the end of August 2021. The medical care and state quarantine systems were unable to cope with the situation, therefore, it was necessary to switch to home isolation by allowing those without severe symptoms to be treated at home [27]. The Omicron variant has caused a 4th wave of the Covid-19 outbreak in Thailand, with more infections after the new year 2022. However, the number of infections in the 4th wave was not as high as expected and the symptoms of those infected are less severe, and therefore, the government relaxed closures and containment measures. **Figure 2** shows the level of measures taken by the Thai government to control COVID-19 in line with the

#### **Figure 2.**

*The Thai government's responses to COVID-19 and daily new cases.*

#### *Self-Sustained Communities: Food Security in Times of Crisis DOI: http://dx.doi.org/10.5772/intechopen.104425*

severity of the outbreak [28, 29]. The pandemic and the government control measures have resulted in a generalized economic recession. These factors and situations have affected the food system and caused food insecurity in Thailand.

Note: The Containment and Health Index is a composite index that is calculated from 14 component indicators include eight indicators related to closures and containment measures (namely school closures, workplace closures, cancelation of public events, restrictions on gatherings, reductions in public transport, stay at home requirements, restrictions on internal movement, and International travel controls) and six indicators related to health measures (namely public information campaigns, testing policy, contact tracing, facial coverings, vaccination policy, and the protection of elderly people). The Economic Support Index is a composite index that is calculated from two component indicators related to economic measures namely, Income support and Debt/contract relief for households.

Data source: Hale, Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government. Data use policy: Creative Commons Attribution CC BY standard.

#### **3.1 Impact on factors of production**

The COVID-19 outbreak has caused an increase in the prices of imported production factors because of the imposition of restrictions to contain outbreaks of the virus. This has been especially the case in chemical fertilizers, which have seen large price increases since the middle of 2020, due to the reduced production of raw materials for fertilizer production and the increase in shipping costs due to container shortages for international shipping [30]. For example, the Urea price increased (in USD per metric ton) from \$216 in May 2020 to \$418 in September 2021.

Thailand is heavily dependent on imports of chemical fertilizers, which comprise almost all of the country's total use [31]. This means that the country's food system will be unable to avoid the impact of COVID-19.

Thailand's agricultural sector faces a problem of labor shortage because most of the country's farms are small and labor-intensive. They also employ a large number of foreign workers, often seasonal migrant labor [32]. The closure of the borders to contain COVID-19 caused foreign workers to panic and many left the country and were then unable to return to Thailand [33]. In the first 6 months of 2020, there was a reduction of around 545,000 foreign workers in Thailand or 18.2% of the total usual number of migrant workers in Thailand [34].

The agricultural sector is also at risk of a shortage of funding for the production of the next cultivation due to losses and lower household income. The increased cost of inputs, with a decrease in revenue due to reduced demand for food (because the lockdown measures have caused the economic recession and have limited the travel of foreign tourists), will cause food producers to suffer losses [35]. In addition, 76% of Thai agricultural households rely on nonagricultural income and 75% of the households have members working outside the agricultural sector [36]. Owing to the recession, nonagricultural workers now have lower incomes and there is increased unemployment. This will cause the total income of agricultural households to decrease as well.

#### **3.2 Impact on food production and processing**

The first wave of the COVID-19 outbreak caused the GDP of Thailand's agricultural sector in 2020 to contract by 3.3% compared to the previous year [37]. Factors that contributed to the decline in agricultural GDP were border closure and lockdown measures [38]. However, effective control measures implemented in response to the first wave of outbreak increased the export of some food products, because Thai food products were trusted to be disease-free, while other food-producing countries had more severe outbreaks [39].

However, the second wave of the pandemic, which occurred at the end of 2020, centered on the fishing industry workforce cluster and the country's large seafood wholesale market, severely affected seafood production and caused some countries to ban the import of seafood from Thailand [40]. Similarly, during the third wave of the pandemic outbreaks occurred in factories, including a large food-processing factory. As a result, the factories were shut down to disinfect and control the outbreak among workers, resulting in some food products being in shortage of supply for a period of time [41].

#### **3.3 Impact on food stocks, market, and trade**

In the macro view (national scale), food production in Thailand is sufficient to meet the needs of the country's people. But in the micro view (household and individual scales), some people face the problem of not having access to food. The risk of spreading disease in restaurants, wholesale and retail markets of agricultural products caused the government to announce the closure of these places from time to time to limit the spread of the pandemic, resulting in the blockage of the usual food distribution channels [42]. Although the government allowed restaurants and food shops to offer take-home and home delivery meals, home dining behavior resulted in lower consumption than eating at restaurants and food shops. In addition, at certain times COVID-19 also affected food price stability in Thailand. For example, the lockdown during the first wave of the outbreak resulted in soaring rice prices [43] and public anxiety led to food hoarding, resulting in short-term food shortages [42].

#### **3.4 Impact on food consumption**

The border closure and lockdown measures greatly reduced food demand due to the disappearance of about 40 million foreign tourists and exports. The economic recession caused by the pandemic control measures resulted in many workers suffering a reduced income and unemployment. It is estimated that up to 6 million workers experienced a reduced income or unemployment [44], especially workers in the tourism sector. Affected people, especially the poor, unemployed workers, and vulnerable groups, have a reduced ability to buy food. A survey conducted by the International Health Policy Program found that as many as 85.4% of low-income residents in urban slums experienced food insecurity due to declining incomes, higher food prices, and difficulty in purchasing food [45]. Similarly, rural smallholder farmers engaged in monocultural agriculture were affected by the lack of channels to sell their produce. Reverse immigration of household members from the city to rural areas increased the pressure on rural households, due to increased household food needs [46]. These people experiencing economic hardships had to adjust their dietary habits by reducing their food consumption and switching to cheaper and less nutritious foods [45].

#### **3.5 Impact on food logistics and finance**

The COVID-19 crisis has affected the distribution of food by reducing the flow of food products and finance in the food system. Concerns about the spread of

#### *Self-Sustained Communities: Food Security in Times of Crisis DOI: http://dx.doi.org/10.5772/intechopen.104425*

pathogens through food transport have increased costs in food safety control processes. The closure of food retail and wholesale markets has resulted in higher food transportation and distribution costs due to a lack of distribution centers [47]. Higher food logistics costs hinder access to food for people with lower incomes and lower their purchasing power.

The lockdown has also prevented some groups of people from accessing adequate and quality food because alternative food distribution channels have not been developed to replace the old channels that have been closed. For example, patients or people who are quarantined under the home isolation system have difficulty going out to buy food because the authorities require that they must be detained at home. However, no alternative food supply system was provided for this group of people [48]. Closing schools and replacing them with online learning means that schoolchildren in poor families are not able to enjoy quality school lunches. People suffering from malnutrition have been unable to receive nutrients from medical services in hospitals because doctors and nurses have heavy workloads from caring for COVID-19 patients and also due to the cutting of the public health budget allocated for other diseases [3].

#### **4. Food system resilience in Thailand**

The COVID-19 crisis has prompted a response from various sectors to intervene in the food system to address food insecurity and improve the adaptation of players and elements in the system. This section comprises a review and analysis of the status of food system resilience in Thailand, both before and during the crisis. Lessons obtained are then used to suggest changes to Thailand's food system during the postcrisis period.

#### **4.1 Factors of production resilience**

#### *4.1.1 Pre-Crisis*

Thailand's food system is at risk of uncertainty. The agricultural sector has the highest number of poor people compared to other sectors. In addition, in this sector, the elderly account for 46% of the total workers and this percentage is likely to increase [49]. Half of the country's farmers do not own their land and 56% of farmers owning land possess less than 10 rai (4 acres) of land [50]. Land use for energy crops and nonagricultural activities is also increasing, and only 22% of agricultural land is irrigated [51]. Moreover, most agricultural activities are dependent on inputs from foreign producers and large domestic companies, such as producers of chemical fertilizers, pesticides, plant breeding, animal breeding, and animal feed [52].

In the past, the government has continuously issued various policies and measures to solve these problems, for example, taxation of land and buildings to reduce the problem of landholding without use; provision of the Sor Por Kor 4-01 agricultural land title deeds to the poor; re-zoning of agricultural land use and zoning of food crops and energy crops; and development of water management systems and expansion of irrigated areas [53, 54]. However, the solutions to the problems are still difficult to implement. As a result, alternative economy groups have offered food sovereignty as a solution as part of a campaign to enable small farmers to own food inputs independently of the monopoly of big business [55].

#### *4.1.2 During the Crisis*

To cope with the COVID crisis, the government has taken measures to alleviate short-term shortages of production factors, such as a project to support subsidies for farmers, a moratorium on debt, a reduction of debt burdens, and extending the loan repayment period for Bank for Agriculture and Agricultural Cooperatives customers [56]. The border closure measure was relaxed temporarily to allow the importation of workers to work in the agricultural sector [34].

#### *4.1.3 Post-crisis*

The lesson is that Thailand is at risk of facing food insecurity due to its high dependence on imports of food production factors from abroad, especially chemical fertilizers. At the national level, the development of the production capacity of agricultural inputs is therefore an answer to prevent shocks to the food system, such as the development of the domestic fertilizer industry or promoting organic agriculture to reduce the use of agrochemicals. At the base level, it is difficult for small-scale farmers to be self-reliant on all inputs. But if farmers cannot control or rely on themselves in terms of all the factors of production, there is a risk that food production will be disrupted in times of crisis.

#### **4.2 Food-production and food-processing resilience**

#### *4.2.1 Pre-Crisis*

Although Thailand can produce more food than the demand, the risk is that the agricultural sector has the lowest productivity compared to other sectors. The agriculture sector accounts for 30% of the workforce, but only 10% of GDP [57]. Most farmers are smallholders, resulting in low productivity because they cannot use high-priced machinery and have to rely on foreign unskilled workers. Most agriculture production is monoculture, resulting in low food diversity. Agricultural products in Thailand are concentrated on just 5 or 6 crops, some of which are non-food crops or those which are low in nutritional value. Vegetable farming occupies 0.9% of the total agricultural land use and concentrates on only eight types of vegetables [58]. In response, the government has promoted large farms to improve productivity and the use of agricultural technology. The Young Smart Farmer project was established to promote the new generation of farmers in the adoption of precision agriculture. On the other hand, some NGOs are trying to promote agroecological sustainable intensification [59].

#### *4.2.2 During the Crisis*

Rural areas with diverse food production or a food security system that had been set up in advance were less affected by the crisis. Meanwhile, urban slums offer less food security than rural communities and rural smallholders who cultivate monocultures, and consequently, are affected to a greater extent. Some communities (such as the Karen community, Ban Pa Tung Ngam, Chiang Mai Province) were not seriously affected by the outbreak and lockdown measures because they had a self-sufficient production system and there was a system in place for those affected to receive assistance. For example, highland hill tribe communities consist of largely self-sufficient

#### *Self-Sustained Communities: Food Security in Times of Crisis DOI: http://dx.doi.org/10.5772/intechopen.104425*

villages and have a culture of sharing food with the poor. These communities, in addition to producing enough food to consume in the community, can also share food with the people of other communities [60].

The outbreak also led to more qualitative improvements in food production, in particular a focus on the development of food safety standards [61]. Food production for export was also forced to develop safety and sanitation standards, especially fruit exports to China. In addition, the government requires large industrial plants to use "Bubble and Seal" measures to control the spread of the disease in factories [62]. This allows better control and limits the spread of the outbreak, but creates higher costs for entrepreneurs as well.

#### *4.2.3 Post-crisis*

The lesson is that the economy of scale is important to the competitiveness of food production, but the economy of scope is essential to food availability and utilization. A community that can produce its own food will be less affected by unexpected shocks than communities that are unable to produce food at all. And communities that are prepared in advance are better able to cope with crises than communities that are not ready. Development of the resilience of the food system must be done before the crisis.

#### **4.3 Food stock, market, and trade resilience**

#### *4.3.1 Pre-Crisis*

Under normal circumstances, the market mechanism plays a role in ensuring food availability, stability, physical access to food through the reserve, distribution, and trade of food. Food access channels for consumers in Thailand are diverse, ranging from modern trade, e-commerce, community markets, and hawker stalls to mobile grocery stores. However, the channels through which farmers can sell their food products directly to customers and retailers are still limited. The controversy about the market system of agricultural products in Thailand concerns oligopoly or exploitation by middlemen or large businesses. Big agribusinesses will purchase food products only on a contract farming basis with the condition that the farmers must purchase all their inputs from those businesses. On the other hand, the big agribusinesses argue that the mechanism is like a service and a marketing guarantee to farmers, most of whom lack marketing capabilities [63, 64].

#### *4.3.2 During the Crisis*

The COVID crisis has led to community adaptation. Community markets have been established on a local level by members of local communities for farmers to bring their products to sell locally, while some farmers have adapted to selling food products directly to consumers through networks of relatives and friends in cities and online systems or online marketplaces [65]. Meanwhile, some communities (such as Ban Pa Pae, Mae Hong Son Province) had already prepared food reserve systems to ensure that community members do not have shortages of the food products they need in times of crisis. For example, community food banks or rice banks, where people in the community stored rice in a collective barn for members to borrow for consumption, on the condition that it must be returned in kind, or as money, with interest in the following year [66].

#### *4.3.3 Post-crisis*

According to economic theory, fully competitive markets make food allocation and distribution more efficient. However, the agricultural markets in Thailand are not truly competitive [67]. Moreover, crises tend to affect food markets, to a greater or lesser extent. Therefore, having a food reserve system is essential for maintaining food security at all levels. In addition, the development of marketability, alternative channels, and reserve channels in selling the products of farmers and food producers are important steps, to create continuity in food production for smallholders and reduce food waste caused by unsold products.

#### **4.4 Food consumption resilience**

#### *4.4.1 Pre-Crisis*

The food access situation in Thailand is determined by economic factors rather than social factors. Thailand has reduced the number and proportion of the poor continuously. The number of people living below the poverty line has continued to decline from 34 million, or 65.17% of the country's total population in 1988, to 4.3 million, or 6.24% in 2019, but there are still 5.4 million near-poor people or 7.79% of the country's population. The Thai government has provided income benefits that are quite inclusive for nearly all groups, from child support subsidies up to the age of 6, school lunch subsidies, a pension for the elderly and the disabled, to a living allowance for the 14.5 million people who hold state welfare cards. Still, these programs provide a relatively limited amount of funding. Moreover, the identification of the poor is not entirely accurate, with inclusion and exclusion errors [68].

#### *4.4.2 During the Crisis*

The response to the impact of COVID-19 on food security in Thailand has emphasized the role of the government sector and demand-side interventions. For affected workers who are in the formal economy, unemployment compensation and cash transfer from the Social Security Fund will be provided. But Thailand also has a large number of informal workers, comprising around 54% of the labor force [69]. The government therefore issued economic remedial measures to address the impact of the pandemic and lockdown measures, including cash transfers, conditional cash transfers, reductions in public utility costs, a debt moratorium, and expansion of soft loans for businesses to maintain employment and maintain people's ability to access food.

However, the government aid measures are not enough. Most of them are shortterm measures, lasting only 2–3 months during the lockdown. But the economic recession has caused a large number of people to be unemployed and revenues have declined for a longer duration than just during the lockdown period. During the first wave of the pandemic, 30.5 million people, or 40% of the country's population, received cash transfers. However, even though the government's cash transfer measures have covered a large number of people, as many as 3 million people are still missing out on the state aid measures. These include marginalized people, bedridden patients, and those who cannot register for assistance [70].

#### *4.4.3 Post-crisis*

The lesson is that tackling poverty and inequality including income insurance (unemployment insurance) is an important factor in reducing the impact of the crisis and maintaining people's ability to access food. But a large number of informal workers creates asymmetric information problems, which prevents governments from helping people affected by food shortages. It also forces governments to take universal measures, which is ineffective in budgeting. The question is, for a developing country like Thailand with a large informal economy, how can the lack of information and income insurance for the poor, marginalized, and other vulnerable groups be solved?

#### **4.5 Food logistics resilience**

#### *4.5.1 Pre-Crisis*

Thailand lacks planning or preparation of systems for dealing with different types of crises, in particular, a system for allocation of aid and distribution of food and necessities to those affected by crises sufficiently and thoroughly. In several past crises, government measures to address food insecurity have been often ad hoc and failed to provide food for all of these vulnerable groups. Businesses and civil societies, therefore, had to come in and fill the gaps in food systems. However, it was often scattered, redundant, lacking in continuity and organization [71].

#### *4.5.2 During the Crisis*

The cooperation of government, business, and civil society has a role to play in closing the gaps in state measures that are inaccessible to some vulnerable groups. Civil society organizations that were taking care of vulnerable groups before the crisis play an important role in providing food through community kitchens and food banks to groups that often do not have access to government aid measures [72]. Networks of civil society organizations also play a role in matching food supply and demand, by purchasing food from smallholder farmers who are unable to sell their products for sale or distribution to people who need food [73]. Likewise, the armed services, including the air force and army, help facilitate food exchanges between far-flung communities, for example, using planes to transport rice products from hill tribe communities in the north in exchange for dried fish, which is a food product of maritime communities in the south [74].

Business organizations' Corporate Social Responsibility activities include the distribution of supplementary food to different groups of people, as well as encouraging people to participate in food donation campaigns. One form of food donation that was very popular in the first wave of the outbreak was "Happiness-sharing Pantries", placing cupboards in public places for people to donate or pick up food to consume [75]. However, the assistance was done by various groups of people in an *ad hoc* way, and there was no central cooperation and organization of assistance systems so that they were comprehensive, adequate, and continuous. One problem with the Happinesssharing Pantries projects is that some people took all the food from cupboards until there was nothing left to share with others. This problem caused donors to become discouraged and eventually ended the project [76].

#### *4.5.3 Post-crisis*

The lesson is that cooperation between government, business, and people sectors is essential to building food security, especially the provision and delivery of food to vulnerable groups. A civil society organization that works closely with a particular community on an ongoing basis will access information on vulnerable groups and will serve as a mechanism that allows food to be delivered to those people who are in real need. But in the macro view, information systems about vulnerable groups and food aid delivery system design are required to make assistance available to everyone. Moreover, ensuring people's food security should not be merely seen as a relief, but should also develop food self-reliance.

#### **5. Challenges of building food system resilience in Thailand**

Building food system resilience for food security in Thailand also faces challenges due to a trade-off, or conflict, between several issues, described in the following section.

#### **5.1 Market vs. self-sufficiency**

Controversies about food systems inevitably emerge during every crisis, when difficulties are created and many people are exposed to food insecurity risks. Proposals on the food system in Thailand vary between the two extremes of a continuum, self-sufficiency and free trade. The main controversy focuses on whether the Thai agricultural system should be one of market agriculture, which focuses on production for sale in response to market demand, or self-sufficiency agriculture, which focuses on production for one's own consumption. If there is any leftover produce, then this can be sold [4].

The supporting rationale for the market-based production system is to create wealth through specialized production, which enables efficient use of economic resources. Market-based production provides food security because food production increases and prices are lower while consumers still have access to a variety of quality food through market mechanisms [77]. The potential negative aspect of this is that farmers who do not improve productivity could suffer lower incomes, putting them at risk of food insecurity.

However, it is argued that, under normal circumstances, the system of global food trade is not fully free and competition is not fair due to the implementation of measures to protect domestic agricultural markets and subsidize farmers within developed countries. In times of crisis, market mechanisms may fail, to the extent that farmers cannot rely on outside markets. Market-based production also makes the structure of food production homogenous. This makes it more dependent on food imports from foreign countries or from outside the area, which then increases the risk of food insecurity [78].

On the other hand, self-sufficiency production focuses on producing more diverse foods, which reduces the risk of food insecurity [79, 80]. The self-sufficiency production system also focuses on mixed farming and animal husbandry by imitating nature, resulting in high quality and safe food production. It also creates food sovereignty by reducing dependency on imports and inputs from large companies and maintaining the fertility of the soil, as well as water and ecosystems. However, the efficiency,

competitiveness [81], and producer motivation of self-sufficiency production have been questioned, because it is seen as requiring farmers to adopt a plain lifestyle without many amenities.

#### **5.2 Macro versus Micro**

There is a question about what level the unit of analysis on food security should be: individual, household, community, national or global. In the past, the food security concept emphasized a unit of analysis at the macro level, considering global or national food security. This can be observed from definitions, debates, policies recommendations, and the design of food security indicators, which generally focus on the national or international context, for example, the debates about whether to liberalize food trade or not and the development of international comparative food security indicators. Subsequently, there has been an increase in interest in food security at the micro level, that is at the community, household, and individual scales [14, 82].

Macro-level food security will ensure everyone in the world or an individual country has the opportunity for food security, but that does not mean it will always lead to micro-level food security, especially in times of crisis where food transport is limited or market systems have failed. Emphasis on achieving food self-sufficiency at the national level may distract governments from addressing food security at the household level [83]. Ensuring macro-level food security is often the role of the state, but, in practice, governments are often unable to ensure food security for all citizens because too large a unit creates asymmetric information problems. On the other hand, micro-level food security practices will help fill gaps that the government has failed to cover and alleviate the burden on the government [84]. There is still an argument that it is not possible, even at a national level, to be self-sufficient in all types of food [85]. The question is what is the optimal size of the analytical unit? Is it small enough to ensure that everyone is cared for and large enough to provide adequate food in terms of quality and quantity? In fact, food security at the household and individual levels cannot be guaranteed without national food security. Therefore, building food security may need to be undertaken at all levels but the question is how each level of food security should be organized.

#### **5.3 Efficiency versus stability**

A common phenomenon in Thailand is that the countryside serves as a social cushion in times of crisis. Under normal circumstances, many rural people migrate to cities in search of the better economic opportunities that they offer in comparison to rural areas. But every time there is a severe crisis, to survive, people migrate back to their rural homelands [86, 87]. This can be seen in the COVID-19 crisis, where, in the first wave of the outbreak in February–April 2020, it is estimated that 2 million people migrated back to the countryside, and, in the second half of 2020, a monthly average of 200,000 migrated back to the countryside [46]. However, this does not mean that everyone in the city has a country house to migrate back to. Consequently, many people in crisis-affected cities are still at high risk of food insecurity.

At present, the idea of urban farming is gaining more and more attention. But there is a question regarding whether it is necessary for households or urban communities to produce their own food. The price of land in the city is high, therefore, urban food production has a very high opportunity cost compared to rural food production.

However, urban food production has advantages in terms of transportation and logistics costs. Would using urban land to produce food be more cost-effective than buying food from the countryside? On the contrary, if there is no preparation for hedging at all, urban communities will also suffer a lot of damage when a severe crisis occurs.

An interesting question is what should be the cost of hedging for food insecurity risks? The risk management principle states that the cost of hedging is equal to the likelihood of a crisis multiplied by the impact of the crisis. In history, severe crises are likely to occur only occasionally, or infrequently, but if they happen, the impact is so severe that there are many deaths. However, the changes in today's world may be a catalyst for more frequent crises and increase the need for hedging.

Chareonwongsak [88] states that the world has entered the "Pandemic New Normal" era, where pandemics will become more frequent so that it becomes a new normal. The world is more connected and more people live in cities, making pandemics easier to occur and spread faster. This is consistent with the "IPBES Workshop Report on Biodiversity and Pandemics," which indicates that future pandemics will occur more frequently, spread faster, and inflict more damage [89]. There is the possibility of a black swan or an unprecedented crisis because there are new predisposing factors, such as severe climate change and cyber-attacks on countries' financial systems or food chains [90].

#### **6. Suggestions on building food system resilience in Thailand: the FSSC model**

The fact that Thailand is a food producer and net exporter makes food security issues seem less of a concern. But the spread and impact of COVID-19 have helped to reveal the fact that the food system in Thailand is still vulnerable to food insecurity for many people. It also reveals the country's under-preparedness to deal with crises. The weakness in the Thai food system is that the Thai government lacks information about people at risk of food inaccessibility due to the large proportion of informal workers while most of the workers in developed countries are formal workers. The government mainly uses macro-level measures, namely cash transfer, to address food inaccessibility. But there is a lack of an alternative system to distribute food to people who have not received help. In a world where crises are more frequent, food system resilience needs to be built to face crises of all forms and levels of severity as well as maintain food security for everyone, therefore, an innovative food system model is required. The food system must be developed at both the macro and micro levels and have the ability to maintain food security in both normal and critical times without exorbitant cost.

The FSSC model presented in this chapter is a proposal for developing food system resilience to protect food security in Thailand. This concept developed from a stream of several concepts—the Mid-stream economy [91], Self-sustained communities [92], and the Linked self-sustained communities [92], applying these concepts in the context of building food security.

This concept stream consists of four main components. First, strength-based production and liberalization of food trade to create wealth during normal times. Second, self-sufficiency in food in times of crisis and at all levels. Third, preparation of a switching mechanism/policy design for readiness in changing the mode, between liberalization in normal situations and self-sufficiency in times of crisis. And fourth, the interconnection of food systems between communities and between all levels to ensure food security at both micro and macro levels.

The development of the FSSC aims to make area-based communities self-sufficient in food in times of crisis for a number of reasons.

First of all, future crises could limit domestic and international food trade and transport. For example, a hyper-inflation crisis or a cyber-attack on the financial system of the country or the world could make it impossible to use the money to buy food. Future pandemic crises could also force governments to use lockdown measures and close borders.

Secondly, the food system at the household level is usually too small to be selfsufficient in food. Meanwhile, countries are too large to be aware of all information and to allocate timely assistance to all people during crises. Therefore, a community that is not so small that it cannot be self-sufficient, or so large that members are not related to each other, is the right unit to maintain food security in times of crisis.

Thirdly, building food security in communities in times of severe crises (which lead to food system failures through wars, disasters, hyperinflation, and similar events) must temporarily integrate all food system activities in the community, to shorten the food supply chain and to build the ability to supply enough food to the people in the community for a given period of time.

Fourth, communities should be self-sufficient in food only in times of crisis in order not to lose the opportunity to create wealth from carrying out economic activities according to the strength of the community during normal times.

The creation of the FSSC has the following strategic proposals:

#### **6.1 Promoting integration into FSSC**

FSSCs may be built on the base of existing area-based communities or create new ones by bringing together groups of people who are related and share the common intent to create an FSSC. FFSCs may develop on the concept of Work-Life Integration [93], by creating communities that facilitate people working and living in the same area, as well as the benefit of preventing the effects of epidemics that may occur in the future.

#### **6.2 Designing food systems in the community**

Ensuring that communities have enough food in times of crisis must come from setting goals. How many members does the community have? How much food, and how many different types are needed? How long should a community supply food to its members during a crisis? Communities must design and plan in advance where, in times of crisis, they will get their food from, what to produce, how to produce, how much, how to stock input and food products, and how to allocate food products to community members. However, the design of a community food system requires consideration of the conditions, constraints, and context of each community.

#### **6.3 Joint production planning in the community**

FSSC may be the solution to the problems in the Thai agricultural sector with many small farmers and elderly workers. FSSC promotes the integration of agricultural farms for joint production planning, procuring, and sharing inputs and resources, including the use of technology and agricultural machinery together which will create an economy of scale. At the same time, farmers in the FSSC may plan to produce a variety of yields to distribute products together and share revenues together. This will allow the community to produce a variety of food products. It also creates an economy of scope and diversification of risks.

#### **6.4 Developing the FSSC system and infrastructure**

Developing FSSCs to be able to switch to self-sufficiency, community systems, and infrastructures needs to be done in advance, such as community water storage, community seed banks, community gardens, community alternative energy generation systems, community food banks, community markets and food allocation systems, community data management and information systems (such as projections for production, stock, and community food needs), and community savings promotion and welfare systems.

#### **6.5 Promoting education and R&D on FSSC**

FSSC's food production may be unique and differ according to the context and limitations of each community. In times of crisis, where communities cannot rely on sources or agents outside the community, the FSSC food production system tends to be a closed-loop food system, where the outputs and waste from one activity are inputs to other activities until it becomes a cycle or ecosystem. Food production in urban communities with limited space, technology, and methods needs to be developed to optimize the use of space. Also, training for members of the FSSC and the promotion of food system-related R&D in the FSSC needs to be supported.

#### **6.6 Community development based on the strength of the community**

In normal times, each FSSC should have a development and production approach that matches the strengths of the community. Each FSSC development should not have the same pattern or produce the same goods and services over and over. But each community should be developed according to its strength, ideology, wisdom, identity, value, image, and uniqueness. Thus, each FSSC will have a unique selling point that will enable it to create more added value for its products and services. Then, a strong economy in a community can also be a better shield against the impact of a crisis.

#### **6.7 Design and preparation of switching mechanisms**

The FSSC food system should be developed to be as competitive as possible under normal conditions to enable the FSSC to be able to produce and sell food continuously, without much subsidization or intervention. However, during normal times, it is not necessary for every FSSC to produce all its own food requirements. But a switching mechanism must be designed and prepared to be able to supply food to the entire community in the event of a crisis, such as preparation of a community food reserve system, transformation of vacant spaces in communities and individual households into food production areas, changing the type of food produced to be more versatile, faster yielding, changing cultivation methods for higher yields (despite the fact that the product characteristics may not be as beautiful as before, such as smaller fruits, thinner vegetables), etc. The switching mechanism encompasses the development of leadership, management, morals, and community systems such as structure, processes, rules, and culture that encourage community members to be willing to switch to a self-sufficiency mode.

#### **6.8 Connecting FSSC networks**

In fact, it is unlikely that each community will be able to produce food for its own consumption forever without having to rely on the world outside the community at all. Therefore, FSSCs should establish a network to link with other FSSCs and to enable the trading, exchange, and sharing of knowledge, resources, products, and risks. For example, food production planning between communities, the development of food supply chains between communities, the development of food logistics, information and finance between communities, the organization of knowledge sharing and resources among the communities, and the development of food exchange and sharing systems among communities in times of crisis. The link between FSSCs will help support the development of communities in normal times and increase the ability to self-sufficiency and restoration of the community's food system in times of crisis.

#### **6.9 Developing FSSC promotion policy**

Governments should develop national policies to promote FSSC, including academic and financial support for FSSC transformation, developing prototypes and learning centers for FSSC in both urban and rural areas, designing urban development and building a community that integrates both workplaces and living facilities in the same area, land use planning and zoning of food production, developing information systems for food system management at the national level, developing early warning systems, developing public-private cooperation systems for food production and distribution in a systematic, thorough and continuous manner, developing international food security cooperation, and the development of food diplomacy.

#### **7. Conclusion**

The COVID-19 crisis has affected food security and revealed the shortcomings of the food system in Thailand. The FSSC is an innovative idea resulting from the synthesis of the good points of various food economy systems, with the aim of ensuring food security in both normal and critical times. The development of FSSCs also emphasizes preparation to prevent the impact of crises on food insecurity in communities without creating excessive expenses or opportunity costs. In normal times, FSSCs can also connect to the global market to produce goods and services according to their strengths to create wealth. But communities are designed to be ready to adapt to self-reliance in times of crisis.

However, the FSSC model is still just a concept and it has never been implemented in practice. In addition, the concept development took place from the consideration of Thailand's context, which is a country capable of producing enough food to meet overall domestic demand. Therefore, in applying this concept to other countries with different contexts, it is necessary to adapt it appropriately to the local context. Developing FSSCs involves not just the design of food systems, but the design of communities, which is more complicated because it has to take into account the economic, societal, and political dimensions in each community and also the motivational dimensions, relationships, and other dimensions of human beings. Finally, the FSSC model also needs studies, research, and experimental development of the prototype to improve the model for practical application.

The FSSC model and its associated thoughts have overlays and differentiated parts from City Region Food Systems (CRFS) supported by RUAF [94]. Both concepts have the same goals, namely food security, sustainable development, economic development, and social inclusion and equity. FSSC has a focus on improving area-based community food security and extending communities' connectivity. CRFS focuses on improving the food security of the city-center food system that is linked to the surrounding area. By successfully pushing the FSSC model, it is possible to learn from the CRFS, for example, building cooperation and inclusive participation, formulating an academic-based development strategy and taking into account the context of the food system in each area, developing the capacity of individuals and organizations involved, and building effective systems to drive the development.

### **Acknowledgements**

The author acknowledges the support of the Nation-Building Institute and Institute of Future Studies for Development.

### **Author details**

Kriengsak Chareonwongsak Nation-Building Institute, Bangkok, Thailand

\*Address all correspondence to: kriengsak@kriengsak.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### **Chapter 7**

## Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks: The Case of Rural Households in Boricha *Woreda*, Sidama National Regional State, Ethiopia

*Adane Atara Debessa, Degefa Tolossa and Berhanu Denu*

#### **Abstract**

This chapter reports on the coping strategies employed by households in the event of food insecurity shocks and the nexus between the types of coping strategies and resilience to food insecurity in one of the food-stressed woreda from Sidama National Regional State, Ethiopia. The households use various consumption-based coping strategies that run from compromising the quality of food-to-food rationing. Repeatedly occurring food shortage has also forced some households to employ resilience erosive coping mechanisms such as selling reproductive assets. Such coping strategies have an important implication on the household's capacity to cope with the future food insecurity-related shocks, with a statistically significant relationship between the nature of coping strategies utilized in response to previous food insecurity-related shocks and the household's resilience to upcoming shocks. Coordinating crises management based on humanitarian intervention with households' livelihood assets protection and resilience strengthening is the major policy implication of this study.

**Keywords:** households, coping strategy, resilience

#### **1. Introduction**

Food is the most basic need for survival, growth, and good health of human beings. Freedom from hunger is the most fundamental human right that can be attained if an individual is food secure [1]. However, a significant proportion of the world's population still lives under the situation of food insecurity. As it is clear from the FAO et al. [2] report on the state of food and nutrition in the world, even the prospect itself is not sufficiently bright to the extent expected. Five years after the world committed to ending hunger, it has been learned that, the world is still off track to achieve this objective by 2030. Given the current pace, the world is making headway neither towards Sustainable

Development Goal target 2.1, of ensuring access to safe, nutritious and sufficient food for all people all year round, nor towards target 2.2, of ending all forms of malnutrition [2].

Looking at the trends and projections of the state of global food insecurity may help to understand this claim. According to the same report, the number of undernourished people was 690 million in 2019 (60 million more than in 2014), and is expected to exceed 840 million in 2030. When it comes to Africa, the continent's share of undernourishment prevalence for 2019 exceeds one-third of global undernourishment with about 250 million undernourished people. This figure represents about 19% of its overall population and is projected to be about26% in 2030 [2].

Various reports show that Ethiopia hosts a handful proportion of food insecure people. For instance, WFP and CSA [3] report the persistence of poverty and food insecurity despite the country's efforts to counteract the situation. MOFED [4] reported a level of food poverty prevalence of 33.6% in 2014 *versus* 31.8% in 2012/13. However, Ethiopia is moving in a good direction to improve the situation. A joint report of WFP and CSA [5], showed that the country has made tremendous socio-economic progress that resulted in the reduction of the prevalence of hunger and undernourishment to 25.5%. Nevertheless, the country still embraces a noticeable level of food-insecure people.

Response mechanisms to food insecurity shocks varies based on the objectives of the agents responding to it as well as the level at which they are targeted. As active actors/ agents/of their own, households employ various coping strategies (response mechanisms) in the event of shocks that challenge their food security. According to Maxwell and Caldwell [6], USAID [7], and Degefa [8], such strategies are not uniform and may also not be equally sustainable, as in some cases they may erode household's capacity to withstand future food insecurity shocks. Although, effects of households' coping mechanisms and resilience to future shocks have been widely discussed, mainly at the conceptual level, empirical statistical evidences on the nexus are quite limited.

For instance, though Carter et al. [9] provide elegant theoretical explanation on the linkage between shock-initiated coping mechanisms and a household's resilience, the unavailability of data on coping strategies constrains them from including this variable in their estimation model. The study of Tran [10] fails to make the distinction between positive and negative coping at the empirical level and focuses only on the immediate positive effects to recover from shocks. However, a particular coping strategy, though resilience erosive, can contribute to smooth current consumption and/or recovery from shocks. Moreover, capturing resilience only through the recovery speed proxy is also too simplistic. Thus, there is an increasing understanding of resilience as an *ex ante* capacity of households to withstand the effect of shocks [11–16]. This way of conceptualizing enables to better capture the essence of resilience as absorptive (buffering), adaptive, as well as transformative capacity in addition to recognizing a futuristic nature. Considering this scenario, this chapter brings forward the linkage between resilience and coping mechanisms, focusing on Boricha woreda as a case study. For that, the following interrelated questions are discussed: (1) how do the study area's households respond to food insecurity shocks? (2) does the resilience level of households vary based on the nature of previously employed coping mechanisms?

#### **2. Linkage between household's resilience to food insecurity and coping mechanisms**

Maxwell and Caldwell [6] identify four coping strategies that households employ when they face food shortages or do not have the resources to purchase food. They

#### *Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks… DOI: http://dx.doi.org/10.5772/intechopen.102613*

include taking action on the quality of food to eat, looking for options that help increase food supply, reducing the number of household members that they have to feed through such mechanisms like sending some of them to neighbors' houses, and managing the deficit through mechanisms such as food rationing. Conceptually, these strategies are consumption-based ones having a lesser impact on the households' capacity to cope with future food insecurity shocks.

Carter et al. [9] put the households' actions to cope with shock-induced food security challenges in a certain rational decision-based logical order. As per this source, initially households choose to depend on the markets and other institutions that they have access to. To maintain their consumption standard without further asset depletion, households with financial market access or access to informal finance might borrow against future earnings. Resorting to insurance arrangements, seeking for and receiving disaster aid as well as working for long hours are also coping options that they can exercise before taking action against their productive assets. Households without access to such options may opt to sustain their consumption by drawing down on their assets: the decision which they argue can further increase the sensitivity of assets and weaken the future. Finally, households may cope by reducing consumption. This coping strategy can be the last option for those lacking other assets or options and may also be pursued by households who are reluctant to increase their future vulnerability due to depletion of the stock of assets. However, coping by reducing consumption is regarded unfavorably as it does have multiple costs, i.e., immediate hunger as well as the long-term effect on children's growth and development [17].

To the linkage between coping mechanisms and shocks, it is postulated that adverse events (shocks) may cause a decline in assets and incomes in the short-run and might have negative effects on household livelihoods in the longer-run [10]. However, the extent of the effects, depends on the nature of the shocks, the asset dynamics, as well as on the coping strategies employed. Carter et al. [9] opine that when a given shock happens, it will have both direct and indirect impact on households' resilience to future shocks. Firstly, the shock itself brings direct harm to the quality of households' asset. As households' respond to shocks using their assets and resources, the indirect impact comes via such responses to a particular shock. The whole idea here is that the coping mechanisms used in response to food insecurityrelated shocks at a given point can cause a decline in the household's ability to cope with future shocks depending on the strategies employed in between two time periods.

The origin of the concept of resilience is linked to the field of ecology. According to Holling [18], in ecology, the term resilience is used as a measure of systems persistence and capacity to absorb changes and disturbances and still retain the same relationship with state variables. To a household's food security, resilience has been conceptualized as the ability of the household to maintain its food security withstanding shocks and stresses, depending on the options available and its ability to handle risks [11]. Accordingly, resilience is a multifaceted capacity: absorptive, adaptive, and transformative. While explaining the linkage between the nature of coping strategies and resilience, Frankenberger et al. [19] sustain those certain strategies may have negative and permanent consequences to resilience. Positive coping strategies are those based on available skills and resources, to face, manage and recover from shocks and that do not compromise resilience. On the other hand, negative coping strategies, if employed, undermine future options making it more difficult to cope with the next shock or stress [20]. Hence, it can be argued that the resilience status of a household at

**Figure 1.** *Conceptual representation of food insecurity shocks-coping strategies-resilience nexus.*

a particular time point (resilience to future food insecurity shocks) is partly a reflection of the type of coping strategies previously employed. **Figure 1** represents this conceptualization.

### **3. Illustrative case**

#### **3.1 Description of the study area**

The illustrative case is based on the data collected from one of the food-stressed *woredas from* the Sidama National Regional State called Boricha *woreda*. As per the CSA [21] report, Boricha *woreda* has a total population of 250,260 inhabitants, of whom 125,524 are men and 124,736 women. Yirba is the administrative capital. The area has two rain periods a year: the short rainy months (the *belg rain-from March to May*) and the long rainy months (the *kiremnt* rain from June to October). The remaining months constitute the dry season when both humans and animals face water shortages. Besides that, Boricha w*oreda* is known for unreliable rainfall patterns (both in amount and periodicity) for a couple of years and associated food stresses. Mixed subsistence agriculture supports the livelihood of the population. Enset and maize are the two dominant food crops grown at the household level. Khat, coffee, and livestock are also part of the household's economy in the area through their concentration is not uniform across all *kebeles.* Complete dependence on rain-fed farming for subsistence together with rainfall variability exposes people to high risks of harvest loss that easily translates into food insecurity [22]. There are 39 *kebeles (the lowest administrative unit)* in Boricha *woreda*. Of these, three are urban and 36 are rural. According to SNNPR [23] livelihood profile report, these *Kebeles* are classified into three livelihood zones: Sidama Coffee Livelihood, Sidama Maiz Belt Livelihood, and Agro-pastoralist Livelihood.

#### **3.2 Methodological briefing**

Based on insights from literature and the resulting framework presented in **Figure 1**, it was assumed that the coping strategies employed by households in response to food

*Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks… DOI: http://dx.doi.org/10.5772/intechopen.102613*

insecurity shocks that happened at time (T0), can have an influence on the resilience level at a time (T1) in a way that households with negative coping strategies at T0scoreless on resilience at T1. As the households' coping mechanisms are the response actions to shocks, data can be captured usually *ex post* (or retroactively). Accordingly, the linkage between the level of resilience and household coping mechanisms was examined based on surveys before time T1 in response to various stressors/shocks challenging their food security situation. Conceptually, the study examined the relationship between the nature of coping mechanisms employed at time (T0) and the resilience status of households at the time (T1), the proxy of households' capacity to effectively respond to future food insecurity shocks.

The selection of the illustrative study was based on a cross-sectional survey conducted by using structured questionnaires and key informants' interviews. It involved 420 randomly selected households from three randomly selected *kebeles* (one *kebele* from each livelihood zone). As resilience is a multi-dimensional concept that is not directly observable, it has to be measured through a proxy. To this end, the study adopted the FAO's Resilience Index Measurement Analysis Model (RIMA) originally proposed and used by [11, 12]. The model quantitatively assesses household resilience through latent variable modeling. Accordingly, in the study, resilience was treated as a latent variable to be estimated by using seven indicators (dimensions): agricultural assets, agricultural technology adoption, access to basic services, social capital, social safety nets, adaptive capacity, income and food access. Each of these seven indicators of resilience is a latent variable to be estimated using observable household-level variables. Using the Principal Component Analysis (PCA), the estimation of resilience score (index) was done hierarchically. First, an index for each of the above dimensions of resilience was done separately using observable variables. Then, the resilience score for each household was estimated with PCA based on the indices of those resilience dimensions (indicators) (see **Figure 2**). All the seven indicator variables were strongly loaded on the first component and the component scores were used as resilience index for each household. The following path diagram (**Figure 2**) has been adapted from [12], in order to visually depict this estimation procedure.

At the household level, the resilience index was estimated using the Eq. (1) below, which was further transformed using the weighting mechanisms and applying the Bartlett method of component scoring. The Bartlett method was selected as it generally produces latent variable scores that are unbiased and univocal [24].

**Figure 2.** *Household's resilience estimation procedure.*

$$\mathbf{R\_i} = \mathbf{w\_{AA}AA\_i} + \mathbf{w\_{ATA}ATA\_i} + \mathbf{w\_{ABS}ABS\_i} + \mathbf{w\_{SC}SC\_i} + \mathbf{w\_{SS}SS\_i} + \mathbf{w\_{AC}AC\_i} + \mathbf{w\_{IFA}IFA\_i} \tag{1}$$

where:

*Ri* = resilience of household i, *AAi* = agricultural assets, *ATAi* = agricultural technology adoption, *ABSi* =access to basic services, *SCi* = social capital, *SSi* = social safety nets, *ACi* = adaptive capacity, *IFAi* = income and food access, *w* ¼ Weight for each indicator of resilience.

The surveys to analyze the coping mechanisms measurements included two sets of questions: consumption-based (strategies employed in the last 7 days before the date of the survey) and non-consumption based (strategies used in the last 2 years preceding the survey date). The analysis of data on coping strategies was done descriptively using percentages. The linkage between households' resilience and the previously employed coping mechanisms was examined using contingency table and chi-square tests as well as using the odds ratio. In the analysis, households were categorized into two groups: those who previously employed negative (resilience erosive) coping mechanisms and those who did not employ such coping strategies over the past 2 years. In the current study, such categorization was done based on insights from conceptual literature such as [7, 19]. Hence, based on these conceptual works, coping strategies such as selling of reproductive animals, oxen used for farming, and land, land rental, withdrawal of children from school, borrowing money at the high interest rate, and diversion of loans from MFIs were treated as resilience erosive or negative strategies. Accordingly, households who did use any of these coping strategies over the past 2 years were classified under the negative coping category. Based on Guyu and Muluneh [15] and considering the relative location of the surveyed households on the latent variables (resilience scores,) the study households were categorized into resilient and none- resilient groups.

#### **4. Findings and discussion**

#### **4.1 Coping strategies adapted**

Literature indicates that the response of households to food insecurity challenges include different coping strategies. These may involve the modification of consumption habits (consumption-based coping strategies) and/or use of the available resources (non-consumption-based strategies). For instance, Christiaensen and Boisvert [25] contend that when they anticipate food shortage people start to consider changing their consumption habits rather than waiting until food is completely exhausted. Though such change in the consumption habits is generally believed to be a short-term adjustments, it could go long as a normal habit even in the situation where non-consumptionbased strategies too are activated. This is mainly true in the situation where a given community lives under long standing food stress in terms of availability and/or access. The point here is that though non-consumption-based strategies such as selling key productive assets are used, foods obtained through such actions could still be subject to consumption-based coping such as rationing. This can lead us to safely argue that the two sets of coping strategies, consumption and non-consumption based, should not be seen as completely isolated and mutually exclusive as they appear in the literature. Notwithstanding the complexity here, the analysis of the household's coping strategies was done in light of the general assumption that households are rational

*Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks… DOI: http://dx.doi.org/10.5772/intechopen.102613*

decision-makers and thus, the first options are those with the least impact on livelihood or future food security.

Consumption-based coping strategies constitute short-term alteration of consumption patterns. Writers like Watts [26], Corbett [27], and Devereux [28] consider them as easily reversible strategies that do not jeopardize long-term prospects as they mostly do not require a commitment of domestic resources. The households' responses summary (**Table 1**) indicates that 60.2% (253) of the households rely on less preferred foods at least once in a week. 45.5% (191) reported that the consumption of adults was restricted in favor of children. According to one of the elderly key informants "during food shortage, usually mothers take the burden of not having to eat giving priority to children and father*"*. Similarly, a total of 181 (43%) and 141 (33.6%) households limited portion sizes and reduced the number of meals. The proportion of households who reported that they borrowed food or relied on the help from a friend/relative and purchased food on credit was 39.5% (166) and 32.4% (136), respectively. All the remaining coping strategies summarized in **Table 1** were utilized by a small proportion of the households. Only 7.6% (32) of the surveyed households indicated that they relied on wild foods and/or immature crops. Probably, this could be due to the timing of the survey, as it was conducted just after the harvesting period (dry season). Similarly, only a small number of households, 17.9% (75), gave priority to working members at the expense of non-working members, and only 1.7% (7) fastens the entire day. Again, a relatively small proportion of total households, 13.6% (57), consumed seed stocks held for the next season at least once a week. The proportion of households who engaged in the coping behavior of sending family members to eat elsewhere and begging was12.1% (51) and 2.6% (11), respectively. Such findings could be because the experienced level of food insecurity might not be of the extent that forces households to engage in such behaviors or due to the strong local culture that discourages such practices.


#### **Table 1.**

*Consumption based coping strategies.*

Complementary and non-consumption-based coping strategies (**Table 2**) included, selling reproductive animals at least once within the last 2 years period (42.6%), and renting (10%) or selling (2.1%) their lands (10%). About 20.7% (87) and 21% (88) of the households had removed their children from school and borrowed


#### **Table 2.**

*Non-consumption based coping strategies used by households.*

*Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks… DOI: http://dx.doi.org/10.5772/intechopen.102613*

money at high-interest rates respectively. A total of 37.6% (158) households reported that they coped by selling small animals and about 19% (80) migrated to nearer areas in search of wage labor. Almost none, 1.9% (8), of the households had engaged in the coping behavior of diverting loans from Monetary Financial Institutions (MFIs) to consumption and only 4.8% (20) households had drawn on financial savings to respond to the food insecurity problem. This could be due to a lack of cash savings to draw from and/or limited access to MFIs both of which are common in the rural context. Nearly half, 51.7% (217), reported that they have appealed for food aid to overcome food insecurity within the last 2 years. One-third of the households, 33.3% (140), reported that they used selling firewood as a coping mechanism (see **Figure 3**).

According to the key informants, they collect fire wood from the forest around Bilate River towards the border of Loka Abaya *woreda* and supply to Dila Anole and Balela towns. From our discussions, we further learned that due to persistent food stress, poor people have made collecting and selling fire wood as a regular source of income for food purchase. However, the issue of concern exists. That is, if left unchecked, such a heavily reliance on forests could wipe out the only left over of the ancient forests in the area. Almost all elderly key informants stressed that in the past most of the *woreda* had been covered by dense forests that hosted many wild animals until the downfall of the emperor regime. But, the increasingly growing demand for farm land since then has resulted in the clearance of forests to its demise.

#### **4.2 Relationship between previously employed coping mechanisms and resilience status (level) of the households**

As referred above, several authors such as Frankenberger et al. [19], Carter et al. [9], Tran [10], and USAID [7], pinpoint that the types of coping mechanisms employed by households in response to previously happened shocks can affect their resilience to future shocks.

Based on these conceptual backdrops, we have endeavored to understand how the previously used coping strategies of households relate to their resilience status. To this end, households were asked if they experienced one or more shocks challenging their food security situation in the last 2 years preceding the survey and the responses are summarized in **Table 3**. Most of the surveyed households, 79.3% (333), experienced one or more types of shocks that they believe affected their food security situation. Households have also identified a set of coping strategies employed in the past 2 years to cope with food insecurity problems/shocks (**Table 2**).

When it comes to identifying negative coping strategies (erosive resilience), it seems that literatures lack perfect unanimity. With the argument that they undermine future options making it more difficult to cope with next shocks, Pasteur [20]


#### **Table 3.**

*Previously experienced food security situation threatening shocks.*

considers strategies such as delaying medical treatment, exploiting natural resources, taking children out of school, eating less, eating less nutritious food, and eroding productive assets as resilience erosive coping strategies. However, some of the strategies considered as negative coping here are consumption-based (temporary adjustments on eating) that are considered by others as easily reversible. Specially, stage 2 and stage 3 coping strategies from the list identified by Watts [26] and Frankenberger [29] are generally treated as erosive coping mechanisms. Based on the literature and on study area's context, selling reproductive animals, oxen, and land, or renting land, taking children from school, borrowing money at high-interest rates, and diversion of loans from MFIs to consumption were considered as negative (resilience erosive) coping in this illustrative case. Accordingly, households were classified into two coping categories (**Table 4**): those who used negative coping in the past 2 years and those who did not. As indicated in the table, 59.5% (250) of the households employed one or more negative (erosive) coping strategies in the last 2 years preceding the date of the survey.

The households' resilience position (status) was determined based on their relative resilience scores and using the criteria of [15]. Based on relative resilience score (index) achieved by households, Guyu and Muluneh [15] classify four resilience categories: Vulnerable (resilience index (RI) < 0.100). Moderately Resilient (0.100 ≤ RI < 0.250), Resilient (0.250 ≤ RI < 0.500) and Highly Resilient (RI ≥ 0.500). Using the resilience scores estimated through the Bartlett method in PCA and applying these cutoff schemes, households are categorized into four categories (**Table 5**). A very significant proportion of the surveyed households (61%) was not resilient (or vulnerable to food insecurity shocks) and only 39% was resilient at different levels. With these pieces of information on the nature of previously employed coping and resilience status, now the discussion turns to examine the relationship between the nature of coping mechanisms and the relative resilience position (status) of the households. Our analysis proceeds with the proposition that the nature of previously used coping strategies can affect the predictive resilience of households (estimated at time T1) in the form that those with prior negative coping strategies scoreless on resilience. Contingency Table and chi-square test statistic, and


#### **Table 4.**

*Households by coping type.*


#### **Table 5.**

*Distribution of household resilience status.*

*Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks… DOI: http://dx.doi.org/10.5772/intechopen.102613*

the odds ratio were employed to analyze and test this proposed relationship of the two variables. **Table 6** presents cross-tabulation of previously employed coping types and households' resilience levels. About 59.5% (250) of the households used one or more types of erosive resilience (negative) coping strategies within the last 2 years. From this group, only 19.6% (49) was found to be resilient (scoring relatively high on resilience index) at time T1 (time of the survey). Most households, 80.4% (201), that adapted one or more negative coping strategies were found to be non-resilient. On the other hand, out of the total households who did not previously use negative coping strategies, 67.6% (115) was found to be resilient at time T1 (scoring relatively high on resilience index) against 32.4% (55) scoring relatively low on resilience (nonresilient).

The Chi-Square test was run as a way of checking if the observed frequency (or percentage) differences in the contingency table (**Table 6**) were statistically significant. In statistical terms, it tests the implicit null hypothesis that there is no relationship between types/nature of previously employed coping strategies and the resilience status of the households. That is, it tests the hypothesis that the household's resilience score (status) at time T1 is independent of types of coping methods employed by a household in response to shocks that occurred before time T1. The result of the Chi-Square test (**Table 7**) revealed high significance for *χ*2 (1) = 98.149, P < 0.001 indicating an association between household's resilience status and types of previously employed coping strategies. Besides the association between these two variables, it does not show the strength of the relationship that has been detected. Therefore, the Phi test for 2 by 2 contingency table, was also performed [30] giving a noticeable level of association between the household's resilience level and types of coping strategies previously employed (**Table 8**). The sign of the relationship is also as expected as the two variables were coded similarly.


**Table 6.**

*Cross-tabulation of households' resilience level and previously used coping strategy.*


#### **Table 7.**

*Tests of association between resilience status and coping type.*


#### **Table 8.**

*Test of the strength of association (resilience level and coping type).*

Both association (Chi-Square) and strength of association (Phi test) tests highlighted the existence of meaningful relationships between the two variables under consideration. To further check the strength of association between the two variables the odds ratio was used as a supplement to the Phi test. The odds ratio here refers to the ratio of the odds that a household will be resilient to future shocks with no prior use of negative coping strategies to the odds that a household will be resilient through it previously used some kind of negative coping strategies. Based on frequencies in **Table 6**, the odds ratio was computed as:

Oddsratio ¼ Odds of being resilient with no prior use of negative coping ÷Odds of being resilient with prior use of negative coping (2)

Odds of being resilient with no prior use of negative coping

¼ Number of resilient households who didn't use negative coping

÷Number of nonresilient households who did not use negative coping ¼ 115÷55 ¼ 2*:*0909

(3)

(4)

This ratio shows that the number of households who are resilient with no prior use of negative coping is as twice as those who are non-resilient though they did not employ negative (erosive) coping before. It is also possible to be resilient or nonresilient to future shocks without prior negative (erosive) coping. However, it is more likely to be resilient than non-resilient given the initial state (previous experience in terms of coping type) is that of no negative (erosive) coping strategy.

Odds of being resilient with prior use of negative coping

¼ Number of resilient households who did use negative coping

÷Number of nonresilient households who did use negative coping

¼ 49*=*201 ¼ 0*:*24378

*Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks… DOI: http://dx.doi.org/10.5772/intechopen.102613*

The ratio here shows that the number of resilient households experiencing previous negative coping is about four times less than the number of non-resilient households.

Given the two pieces of information (odds ratios presented above) and referring to the first equation, the odds ratio of interest here (the odds that a household will be resilient to future shocks with no prior use of negative coping strategies to the odds that a household will be resilient through it previously used some kind of negative coping strategies) can be computed as follows:

$$\text{Odds ratio} = 2.09090 \div 0.24378 = 8.57\tag{5}$$

The odds ratio indicates that households who did not previously use negative coping strategies were 8.57 times more likely to be resilient to future shocks. So, the clear implication of this finding is that the type of coping mechanisms used in response to given food insecurity-related shocks at a particular point in time can have an impact on households' ability to respond to the upcoming shocks. This finding is in line with the Chi-Square test result above and the extant theoretical literature discussed in the chapter.

#### **5. Conclusion**

Depending on the initial state of the households, some of the coping strategies can lead to the poverty trap and erode the ability to cope with similar problems in the future. If left uncontrolled, even the coping mechanisms with no immediate individual impact, like selling firewood, may not be environmentally sustainable. This is especially true in the case of the study area as the source of firewood collection is, mostly, the single leftover of the ancient forest, which is confined to marginal areas around the Billate River. Additionally, the coping mechanisms utilized currently by the households can have important implications on their capacity to cope with future shocks, depending on their resource base. Hence, well-targeted interventions that go beyond saving lives (humanitarian emergency) and focusing on livelihood assets protection and capacity building to future shocks is the recommended policy option.

#### **Author details**

Adane Atara Debessa<sup>1</sup> \*, Degefa Tolossa<sup>2</sup> and Berhanu Denu<sup>3</sup>

1 Addis Ababa University College of Business and Economics, Ethiopia

2 Geography and Development Studies, Addis Ababa University College of Development Studies, Ethiopia

3 Addis Ababa University College of Business and Economics, Ethiopia

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

© 2022 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.

*Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks… DOI: http://dx.doi.org/10.5772/intechopen.102613*

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#### **Chapter 8**

## How to Build Food Safety Resilience in Commercial Restaurants?

*Rayane Stephanie Gomes De Freitas and Elke Stedefeldt*

#### **Abstract**

In this chapter, food safety is portrayed as an intrinsic component of food security and food systems. The objective is to discuss the 'commercial restaurant' system and the 'kitchen worker' subsystem from the perspective of building resilience in food safety. Relationship maps built for the system and subsystem guide the presentation and discussion of structural, organisational, social and symbolic aspects and elements. Resilience investigation is based on the references of the International Risk Governance Centre Resource Guide on Resilience and current and emerging topics related to food safety, such as risk perception of foodborne diseases, cognitive illusions, sociological aspects, social dimension of taste, humanisation and working conditions and precariousness of work in kitchens. In the final section, a list of recommendations for building resilience in commercial restaurants is presented to help researchers, decision-makers and practice agents apply this concept in their fields of expertise.

**Keywords:** food safety, food systems, restaurants, food handlers, foodborne disease

#### **1. Introduction**

There is an urgent need for food safety to be critically rethought in the twenty-first century, considering the breadth of systemic interconnections that predispose food, the environment, animals and humans to known and unknown hazards. These hazards may be present in activities related to food production, processing, distribution, preparation and consumption. One of the barriers to the scientific advancement of food safety is that it is often not treated as an essential and indispensable component of food security in food systems.

However, these three components are inextricably linked. According to the report *The State of Food Security and Nutrition in the World 2021*, food security and nutrition embrace the right of everyone to access quality food based on practices that promote health and are environmentally, culturally, economically and socially sustainable, considering the lenses of food systems as essential to address recent issues [1]. Unsafe food exposes people to several diseases and malnutrition, and there is a greater probability of these conditions worsening among the most vulnerable [2]. Quality food, on the other hand, corresponds to harmless food produced in a way that respects the interaction between man, animal welfare and environmental conservation, provides healthy food choices and encompasses the dimensions of food preference, food preparation, feeding practices, food storage and water access [3, 4]. Food safety should be repositioned, because it is a component that undoubtedly makes up the triad, which includes food security and food systems, guaranteeing the human right to adequate food and health.

The crisis triggered by the COVID-19 pandemic exposed the fragility and unpreparedness of health services and the vulnerability of humans to the deficiencies caused by the current food system in several areas, making the words 'foresight, preparedness, and resilience' the new directive for leaders of global food systems [5]. Therefore, food safety needs to expand its scope of action, i.e. extend beyond the regulations that ensure the prevention of foodborne diseases (FBD) and also cover the long-term threats arising from risks associated with food, which affect the population and ecosystem at a global level [6].

Nowadays, people face an extremely complex paradigm, which will be difficult to understand and solve if it is only comfortably based on digital modelling, artificial intelligence, Big Data, large economic resources and food surpluses [5, 7]. This paradigm is imbricated by social and political aspects, which are erased by the dehumanisation of the people making up the systems due to the use of digital and technological resources in an issue that requires a broad approach on human values [7]. The systems' resilience approach allows for incursion on aspects and elements that permeate multiple domains, such as social, psychological, physical and information [8]. Nonetheless, the structural, organisational, social and symbolic domains that permeate commercial restaurants and kitchen workers, as a system and subsystem respectively, with focus on the issues of humanisation and the precariousness of work in the industry, have been scarcely investigated.

The theoretical references regarding resilience and aspects in relation to social and symbolic dimensions, respectively, which underpin the analyses presented here, are the two volumes of the International Risk Governance Center (IRGC) Resource Guide on Resilience [9, 10] and the social theory of the French philosopher and sociologist Pierre Bourdieu [11, 12]. In light of Pierre Bourdieu's social theory, which describes the constant dialectics between the individual and the social world as modulators of actions, thoughts and judgements [11], the social and symbolic aspects present in the system (i.e. commercial restaurants) and subsystems (i.e. consumers, managers and kitchen workers) are presented and discussed in this chapter.

The National Academy of Sciences defines resilience as 'the ability to prepare and plan for, absorb, recover from, and more successfully adapt to adverse events' [13]. Food safety resilience in commercial restaurants was conceptualised based on this definition and following the proposition by Linkov et al. [8], which states that to operationalise the concept of resilience, it is necessary to describe the resilience of what, for what and for whom. We propose that the concept of 'food safety resilience in commercial restaurants' is the ability of a system to prepare proactively for an adverse event, whether of immediate scope (e.g. FBD, notifications, complaints or fines) or related to globally imminent crises in health and, in its occurrence, have the knowledge, skill and ability to absorb it, recover and adapt to the new state, ensuring the humanisation of individuals at all stages of the process.

Meal expenses outside home favourably influence the economy of a country and represent a significant part of family spending; however, eating out can present

risks to the consumers' health [14, 15]. The commercial restaurants of interest in the present discussion comprise establishments outside the institutional scope (e.g. companies, schools and hospitals), focusing on self-service, à la carte, fast food and similar modalities.

We understand the need to view commercial restaurants as a large system to characterise their particularities and interconnections with other systems and subsystems. This broad and detailed knowledge has the potential to provide decisionmakers with information capable of minimising the vulnerability of places to external and internal shocks. The reference of resilience fits perfectly into this issue, since it seeks to investigate and manage systemic risks that are not easily detected using traditional risk analysis or that have low probability of occurrence but have serious consequences [16].

The objective of this chapter is to present and discuss the commercial restaurant system and the kitchen worker subsystem (i.e. professionals directly involved in meal production) to provide the means for food safety to be humanised, critically rethought, repositioned in the face of the current interconnected scenario of food systems and resilient in the face of imminent disruptive events.

#### **2. Commercial restaurants as a system**

The commercial restaurant system anchors three fundamental subsystems: consumers, managers and employees (i.e. professionals directly and indirectly linked with meal production). The system shown in **Figures 1** and **2** summarises the relations established between the system and subsystems. The construction of this system was based on the current scenario of restaurants in the city of São Paulo, SP, Brazil. São Paulo is recognised as the largest Brazilian metropolis with the largest number of inhabitants in the country, and although it is the economic heart of South America's largest economy, holding the largest stock market and sheltering the headquarters for many companies overall Latin American, it has intense socio-economic and sociospatial inequalities [17, 18].

In its current conformation, this system is governed by competitiveness, in that each restaurant seeks to maintain its reputation and attract more customers than its competitors. To this end, the order of priorities for commercial restaurants is to guarantee tasty meals, cost-effectiveness in the production of each meal, rapid delivery, quality service, an environment that provides a pleasant experience to the consumer and finally, the safety of the food offered. However, the lack of food safety can ruin the image of a restaurant, causing layoffs, fines, notifications or even the closure.

#### **2.1 Consumer subsystem**

The consumer subsystem has an extremely relevant role, as consumers' individual or collective decisions regarding food consumption and production have the potential to impact and even drive new practices towards food systems that provide healthy and sustainable meals [3]. However, consumers often do not recognise their role as protagonists within the system. Their order of priorities for choosing the restaurant is tasty meals, cost-effectiveness, service agility, helpful service, pleasant environment and food safety. Consumers have gaps in the knowledge that they can be sources of external contamination of food in restaurants through practices such as coughing,

#### **Figure 1.**

*Commercial restaurant system map—part 1. For a complete overview, see also part 2 (Figure 2).*

sneezing, touching food with dirty hands, among other similar actions, and regarding a broad notion of risky situations and conditions for food contamination presented in sanitary laws. However, in case consumers experience an FBD or witness something that is inconsistent with food safety, they stop going to the place. Although food safety is least prioritised, it is relevant in the determination of the choice of restaurant.

*How to Build Food Safety Resilience in Commercial Restaurants? DOI: http://dx.doi.org/10.5772/intechopen.101481*

#### **Figure 2.**

*Commercial restaurant system map—part 2. For a complete overview, see also part 1 (Figure 1).*

We state the need for public policies on food safety to empower consumers as agents of safe practices and advocates for change, through actions that generate knowledge about the impacts of unsafe food on food systems and human health.

Other external sources of contamination, such as the origin of the food, urban pests and the presence of domestic animals in the meal preparation environment, are likely to affect the systems. It is possible to deal with these sources of contamination that threaten food safety, as the infrastructure and economic resources of restaurants are available for such purposes.

#### **2.2 Manager subsystem**

Managers make up the most influential subsystem within the system, as they are responsible for organising and planning daily work, physical structure and human resources. For managers, the order of service priorities is established in the following sequence: profit, restaurant reputation, improving their competitiveness, consumer satisfaction, alignment with modern industry trends, food safety and employee welfare. The leadership style is crucial in building and maintaining resilient systems. Horizontal leadership organises the environment in a collaborative manner, provides improvements based on the opinion of all employees, shares food safety values with the whole team and ensures decent working conditions. This leadership model contributes to building resilient systems, as it recognises that food safety requires investing in employee welfare and workplace harmony.

Educational gaps (e.g. difficulties in interpreting texts, concepts and technical language in their daily application) in this subsystem can negatively influence business management and the work environment, decreasing the incentive to follow food safety practices. It is noteworthy that the education of leaders is a step to be promoted constantly in a way that it covers contents beyond food safety. Themes that can be included to build resilient systems are meal production sustainability, water use awareness in the stages of food preparation, management of food quantities to avoid waste through disposal, use of sustainable packaging, reduction of ultra-processed foods in recipes, full use of food, waste management, conscious use of cleaning materials, food purchase from small producers and local traders, combating precariousness of work in kitchens and humanisation of labour relations.

The social world, governed by visible and invisible structures, permeates the sphere of work with the particularities of family, friend and social class experiences and permanence in several areas. Bourdieu [11] proposes that human beings act, think, appreciate and notice the world through a lens called *habitus*, forged through their life experiences and the characteristics of the social class to which they belong. The social world is full of disputes for power positions, which establish the dominant and the dominated agents. Dominant agents with the largest amount of capital, i.e. concrete or abstract assets that are rare, scarce or valuable in their field (work industry), whether economic, social or cultural, govern the rules of the social space analysed [11]. The leadership is the dominant group, and through the recognition of their capital by the dominated group, they hold the symbolic power in restaurants.

However, the symbolic power relegated to dominant agents in this work industry often reverberates in dehumanising practices for the dominated, i.e. kitchen workers. These dehumanising practices, in terms of treatment, social interaction, guarantee of rights, valuation or recognition of work, undermine any possibility of building resilient systems. Resilience requires initiative and proactivity, as they are needed to develop adaptive systems that can respond to unavoidable events [19], and these elements are not likely to be developed in environments that dehumanise work teams. The question 'is it possible to deal or not?' found in the system map (**Figure 1**), was proposed to raise the problem of the secular social paradigm established between managers and employees (dominant and dominated, respectively) on power issues, with the intention of overcoming it and subsequently achieving a desirable level of system resilience.

#### **2.3 Kitchen worker subsystem**

This subsystem comprises highly complex relationships and singularities shaped by social, symbolic, educational, generational, cognitive and motivational aspects that are influenced by social incorporations in previous work, the dimensions of the act of cooking and food safety as millennial practices.

For better understanding, this subsystem has been subdivided into employees who have direct contact with food, i.e. who produce the meals, and employees who do not prepare food, but have indirect contact with it, such as cleaning staff, waiters, motorcycle couriers and cashiers. Both groups have common characteristics concerning the high probability of having educational gaps that hinder the monitoring of food safety practices and the motivation to participate in training in the area and having limited right to speak in their workplaces. Emphasis should be given to the fact that food safety can only be implemented in the foreground when all professionals can collaborate with the construction of food safety values and decisions appropriate to their own social contexts, regardless of their professional position at the restaurant [20]. Resilience must be the base of the pillars of a collective construction that does not scold or punish those who speak out and collaborate with their own work and life experiences. It is understood that on a micro scale (i.e. individual), resilience must operate considering human experiences, rights and well-being [21].

The service priorities of the group of employees who produce the meals are arranged in the following order: taste and seasoning of the meals served, agility to deliver the meals within the predetermined time and finally, food safety. In the Brazilian context, it has been noted that knowledge of food safety, having not been stimulated, presented and reiterated throughout the years of basic education, is outdated, creating a gap for its practical application and the recognition of its relevance.

There are two segments within the aforementioned group: kitchen workers who have never participated in food safety training and those who have already participated. Regarding the former, studies show that their level of knowledge about food safety and hygiene and their perception of FBD risk are low [22, 23]. Risk perception refers to the way people understand the likelihood of adverse events [24]. Safe food handling by the workers of this group is mostly supported by their perception of cleanliness of the premises and food instead of the perception of FBD risk. As a result, there is a greater likelihood of not identifying the hazards that cause FBD, whether chemical, physical or biological, and consequently, a greater risk of FBD.

At this point, we would like to conceptualise and characterise a variant of resilience for the commercial restaurant system, the 'non-resilient'. Non-resilient systems are inflexible and disharmonious environments, which undergo major infrastructural, economic, organisational and social impacts in the occurrence of an adverse event, as they lack the technological, human and financial conditions to improve the aspects that make up their systems. They may find themselves in a scenario of food production within the stipulated schedule, but in conditions wherein food safety is at high risk and working conditions can be precarious and dehumanised. The presence of researchers in the area (e.g. Nutrition, Veterinary Medicine, Biomedicine and Food Engineering) is considered a threat to these systems, which do not seek to improve the quality of meals offered to consumers and fear sanitary inspection acts, as they are aware of their non-compliance with food safety practices. Consumers are the main subsystem that can improve these systems through complaints; however, most are not likely to be addressed because of general system disorganisation, lack of resources and lack of food safety education by leaders and employees.

Systems with kitchen workers who never participated in food safety training do not possess the desired characteristics for building and maintaining resilience.

It is essential to note the complex and interconnected web of relationships between elements and aspects belonging to the segments within this subsystem. Kitchen workers who have participated in food safety training tend to present characteristics consistent with the type of training they have received. Effective training seeks, among its specificities, to be continuous, long-term and appropriate in method and content, and it aims to suppress practices that represent an FBD risk arising from family *habitus*, cognitive illusions and common sense regarding food safety, thus stimulating the autonomy of kitchen workers.

Cognitive illusions lead people to have judgements, perceptions or memories that differ from objective reality and occur involuntarily, being difficult to prevent [25]. Optimistic bias is the manifestation of a positive perspective regarding future events, and with it, a person feels protected from negative events or less susceptible to them [26, 27]. The illusion of control causes people to present an illusory perspective of control over situations that is incompatible with reality [28]. Both illusions have been documented in research with food handlers [29, 30]. Internal locus of control reveals whether a person notices that their actions stem from their own behaviours and not from external agents (e.g. luck, chance, fate, powerful people and superior beings) [31]. Research has shown that the internal locus of control is the most appropriate for kitchen workers, as they can take responsibility for the food safety practices adopted in the preparation of meals, which does not occur when they present an external locus of control [22, 32].

Fair and horizontal power relations between the dominant and the dominated created by the stimulus generated in the work team cause a multiplicity of actions and behaviours that positively influence the incorporation of knowledge regarding food safety practices. Harmonious environments that collectively encourage food safety can present resilience in the face of adverse events.

Symbolic gains have an indispensable role in the spheres of individual and collective behaviour. The recognition given by managers, co-workers and consumers, understood here as capitals of this social space, legitimates the value of the work done. Therefore, the amount of capital possessed by each worker determines the positions in which they are distributed, and it may influence the group regarding leadership in food safety and social support. Humanisation permeates symbolic gains, since recognition is inherent to human identity, and its absence can translate into a form of oppression, self-image depreciation and a reductive way of life [33].

Kitchen workers who receive effective food safety training and apply the knowledge in their daily practice tend to have a long-term impact on safe food production, decreasing the risk of FBD. However, some gaps can still occur in the follow-up of safe practices because of both factors internal to the kitchen worker and factors external to them, which are inherent to the systems. Regarding internal factors, we understand that there are action thresholds, such as personal problems, lack of identification with the restaurant sector, tiredness, laziness and desire to leave early. Uncertainty is one of the crucial elements to understand, study and manage risks [34]. Uncertainty associated with the reference of resilience, especially regarding the flexibility of systems, helps understand that it is not possible to have total control of all risks and that adaptations are necessary [8]. Acknowledging the existence of these factors strengthens the means for decision-makers to adjust their actions, practices and training modes to anticipate adverse events that may arise from human limitations relevant to the area.

External factors are correlated to critical functions adjusted to the reality of each place that can result in shocks to commercial restaurant systems. The critical functions identified so far are deficient infrastructure, failure to follow the rules in the sanitary legislations, lack of frequent training for all workers in the system, leadership that is not the example to be followed in food safety practices, top-down relationship of nutritionists with employees, lack of understanding and use of current food safety concepts, lack of conditions conducive to dignity at work, disharmonious interpersonal relationships, lack of response to consumers' suggestions, lack of openness towards scientific research in the place and lack of planning and preparation for resilience.

While recognising the existence of critical functions of structural, organisational, social and symbolic orders, which hinder the construction of resilience, it is also realised that systems need to adapt because of their own characteristics, aiming at better preparation and planning for adverse situations.

Given this fact, two models of action for resilience can be implemented, the passive or the active. Martin [35] conceptualises two types of resilience in view of the referential of safety and risk. Passive resilience is established in the absorption of adverse events, rapid recovery and return to the state of normality or usual functioning, while active resilience, as an improvement, seeks to become stronger with the learning provoked by adversity, generating greater capacity to deal with future disruptive events [35]. Based on this reflection, we developed conceptualisations applied to food safety in commercial restaurants, which are as follows:

Passive resilience: Passive resilience is present in commercial restaurants in which no adaptations to improve the elements and practices are implemented after the adverse event, even though recovery occurs. Meal preparation happens within the stipulated period, but safe practices in food safety are not applied in most of them. A certain accommodation of the individuals of these systems is identified since the meals are delivered without major procedural difficulties, and there is no charge by formal agencies regarding full compliance with safe practices. In these environments, social relations between kitchen workers and managers are often conflicting, and there is no openness to conduct research because of the insecurity generated by the environment. In these restaurants, consumers act as the main agents capable of promoting changes related to food safety.

Active resilience: Commercial restaurants that are active resilient systems have high capacity to recover from and adapt to adverse events. They become consolidated in systems that are more flexible and open to changes that result in food safety and workers' well-being. As a result, there is greater work organisation and higher level of alignment in structural, formative and interactional issues. Active resilience represents the ideal conditions of this type of system. In the occurrence of an adverse event, the restaurants that present this variant recover more quickly, which demonstrates that they have learned and are in better conditions to respond to new adverse events.

In the restaurant context, passive resilience is preferred over non-resilience. However, when active resilience is experienced, restaurants tend to be less vulnerable to internal and external shocks that can disrupt normal functioning generating negative effects on the economy of the place, on workers and on the consumers' health. Hence, it is recommended to manage systems with the construction of active resilience as an objective.

Studying kitchen workers who have participated in ineffective training has shown that it is, among several characteristics, unable to suppress negative influences on

food safety arising from family *habitus* and common sense, not periodic and constant, focuses only on passing microbiological scientific information and legislation, reaches a superficial level of knowledge and does not stimulate the autonomy of kitchen workers. They present medium to low-risk perception, absence of prospective risk thinking and greater influence of actions inconsistent with safe practices found in common sense and family *habitus*, such as defrosting at room temperature, reusing leftovers of ready-to-eat food, prolonged exposure of food to room temperature and not disposing of possibly contaminated food.

Moreover, restaurants wherein this scenario is a reality are highly likely to present a 'non-resilient' system, with characteristics of conservatism and inflexibility. These social spaces indirectly cause the suppression of kitchen workers' right to speak about their working conditions and food safety, for fear of losing their job, reprisals or generating a bad reputation for their workplace.

A disharmonic or non-motivating work environment, an aspect that can be changed during the preparation stage for the construction of active resilience, combines inappropriate and unfair conditions between leaders and employees, conflicts, swearing and disrespect among the team and the lack of shared values, practices and concepts in food safety from all those present in the workplace. Disharmonic or non-motivating environments dehumanise workers and cause precarious working conditions since job satisfaction is insufficient, and they have poor infrastructure (i.e. lack of equipment, utensils, space to work, thermal comfort, insufficient number of employees and high noise levels), which can lead to pain and occupational diseases [36, 37]. Furthermore, it is possible to find workers hurrying to meet the schedule for finishing the meals because of the lack of structure, which makes them more susceptible to errors, work accidents and the non-performance of steps essential to food safety.

Throughout the text, and also indicated on the system map (**Figures 1** and **2**), situations in which changes can be made and situations that are difficult to access because of their individual and particular character are highlighted. This holistic and integrated view of elements, factors and aspects enables decision-makers, policy makers and leaders of each system to identify the vulnerabilities present either on a micro (e.g. subsystems) or macro scale (economic sector of out-of-home meals and public health), contributing to food safety and food security in food systems.

#### **3. Social and subjective aspects of the kitchen worker subsystem**

Considering its high complexity and multiple singularities, a subsystem map (**Figure 3**) was developed to facilitate the visualisation of the elements pertinent to this subsystem. Only the aspects that have not yet been presented in the system will be depicted.

Meal taste and seasoning have been established as a priority of effort and commitment from the perspective of the kitchen worker. Culinary knowledge comes from the culture of each nation and region passed on from generation to generation and transposed to the *habitus*. The social dimension of taste incorporates the *habitus* with food-related family practices and taste elements characteristic of each social class, reflected in lifestyle and preferences regarding product and food use and consumption [12]. Knowledge exchange between individuals in the restaurants they work or have worked for enables cultural exchange, enriching the result of the meals and the learning of practices that can help or hinder food safety.

**Figure 3.** *Kitchen worker subsystem map.*

The perception of cleanliness in the work environment and of oneself also acts as a guide for these practices, being shaped in the aforementioned basis and in the referential of dirt and cleanliness (i.e. purification) ancestrally brought by diverse cultures to culminate in what is now understood as hygiene [38, 39].

Self-efficacy, the foundation of human action, refers to how much a person believes in their own ability to control to some extent their functioning and that of the environment, reaching spheres of motivation self-regulation through result expectations [40, 41]. It is believed that self-efficacy can modulate kitchen workers' food safety practices as they envision benefits to consumer health, reducing multiple harms in their workplace and maintaining their jobs. Self-efficacy, when developed favourably, tends to reduce vulnerability to stress and depression and strengthen aspects of resilience in the face of future adversity [41]. In a personal scope, resilience is defined by the Oxford Advanced American Dictionary as 'the ability of people or things to feel better quickly after something unpleasant, such as shock, injury, etc.' [42]. In the context of commercial restaurants, personal resilience is built owing to life and work experiences that enable kitchen workers to better withstand and recover to respond satisfactorily to the occurrence of an FBD, shocks of any order and stressful situations. In a systemic

way, micro (individual) and macro (systems) scale resilience are interrelated, and it is not possible to dissociate or compartmentalise them, since one affects the other.

Considering the precariousness present in the meals production sector, it is common to observe kitchen workers having double or triple shifts in order to ensure the livelihood of their families. These shifts can be composed of another work shift, temporary activities related to food production or in another sector and household and family care activities. It is necessary to emphasise that the political and employment scenarios affect the workload and quality of life of these workers. In addition, in the Brazilian context, this group is composed of people from low-income social classes, who often face the lack of adequate housing conditions, urban transport problems and difficulties in health care, among others. Such facts constitute the sphere of concerns that inhabit their daily lives and influence the structure that would be suitable for their full development and performance as workers. In food safety, resilience is also interconnected with broader national scenarios.

The life history of kitchen workers can also influence food safety decisions. Living in situations with food insecurity tends to generate resistance towards discarding food that is not in proper condition for consumption. Making an analysis based on the studies of the anthropologist and sociologist Goffman [43], kitchen workers often dislike interaction with the public, maintain a distance and are shy, which reflects in their preference to work in the back region (i.e. the kitchen) rather than expose themselves to judgements or false performances in the front region (i.e. the dining area with consumers), a fact which is also a product of their social position.

Finally, gender and age issues regarding kitchen workers are relevant in identifying obstacles to food safety practices. Older workers in the sector show an inclination to maintain the *status quo* of their practices, i.e. they are more resistant to changes proposed in view of food safety updates. This tends to occur because of the consolidation of a professional *habitus* throughout the years of their professional experience. Furthermore, because of their social position, they report that they consider themselves incapable of adapting to other jobs, performing functions that are not related to meal production [44]. Male and young workers are usually less resistant to changing their practices, both for being less influenced by the matriarchal reference to meal preparation and for having little or no previous experience with cooking. Knowing these facts enables the designing of strategies aligned to the needs of each profile, aiming to overcome socially constructed barriers and foster new practices for the construction of active resilience.

#### **4. Recommendations for building food safety resilience in commercial restaurants**

**Table 1** lists recommendations that can improve the development of public policies, legislation and guidelines for the meal production sector to contribute to the construction of active food safety resilience.

The recommendations to build food safety resilience in commercial restaurants are intended to promote the absorption, recovery and adaptation capacity of the systems in the occurrence of adverse events through preparation and planning at multiple levels of dimensions involving people, structure and organisation and by considering the interconnections with sustainability needed in the area. The steps of absorption, recovery and adaptation tend to occur in a more agile and collaborative manner when the people involved in the systems understand the scope of action required to build active resilience and put efforts to achieve it in their daily work practice.

#### **Recommendations for building food safety resilience in commercial restaurants**

To provide all workers who make up the system with continuous education that is appropriate to their educational, management and food safety needs

To enable a work environment in which workers can exercise the right to speak without reprimands

To listen to all work team for collective decision-making on food safety organisation, planning and preparation

To share food safety and sustainability values with the entire team, aligning concepts on these topics

To stimulate means to make leaders, in micro or macro scale, a food safety example to be followed to motivate similar behaviour in the team

To provide structural and organisational means to implement food safety practices in the daily working routine

To make efforts to kitchen environments maintain horizontal relations between all positions, based on dialogue and qualitative listening regarding multiple needs and experiences

To combat the precariousness of the meal production sector through decent working conditions

To humanise relations between professionals in all positions based on respect for individuality, appreciation of their work and recognition of the importance of everyone's voice in collective decision-making in food safety

To periodically investigate the system and subsystems for possible vulnerabilities concerning critical functions and new situations that may emerge

To have work plans for resilience preparedness and FBD prevention adapted to the reality of the systems and updated face of relevant changes, without being bound by time frames

To have a vision of the interconnection of systems (field production, food service production, storage, transport, distribution, water resources, environmental preservation, etc.), implementing actions that ensure sustainability at all stages

To become aware that the use of financial resources in measures or infrastructure to ensure food safety is proactive action to prevent financial and other losses to the systems

To develop guidelines and training in food safety that include not only microbiological aspects and sanitary legislation, but also cultural practices and experiences in preparing safe meals in the context of social interaction (i.e. with family, friends, celebrations, common sense, etc.) to contextualise these guidelines

To encourage interdisciplinary research allied to human sciences, which will focus on understanding the factors identified in **Figure 1** as 'is not possible to deal'

To encourage research on resilience in interconnected systems: food purchase, transport, distribution and other systems

#### **Table 1.**

*Recommendations to build food safety resilience in commercial restaurants.*

#### **5. Conclusions**

The concepts, elements, factors and knowledge that make up food safety resilience in commercial restaurants point to the fact that its construction needs to be based on a strong foundation to guarantee fair and appropriate conditions for working, learning about food safety and sustainability, humanising interpersonal relationships between professionals and providing an environment that facilitates collective decisionmaking regarding food safety and its daily application.

As subsystems, consumers, managers and kitchen workers contribute according to their dispositions, capacities and perceptions to mitigate or intensify FBD risks and to create decent working conditions. One of the central characteristics of risk is uncertainty, which permeates the decisions of these three subsystems that can engage in building active resilience through their choices.

When considering resilient food systems that are capable of withstanding adversities, the interconnected systemic vision is the most capable of promoting preparation and planning, as it ensures food safety, food security and sustainability in its broad aspects and particularities.

#### **Acknowledgements**

This chapter was funded partly by the Brazilian Funding Agency Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Fund Code 001.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Thanks**

We thank Angelo Bonito for the improvements made in the graphic design of all figures presented in this chapter.

### **Author details**

Rayane Stephanie Gomes De Freitas1 and Elke Stedefeldt<sup>2</sup> \*

1 Postgraduate Program in Nutrition, Universidade Federal de São Paulo, São Paulo, Brazil

2 Department of Preventive Medicine, Universidade Federal de São Paulo, São Paulo, Brazil

\*Address all correspondence to: elke.stedefeldt@unifesp.br

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*How to Build Food Safety Resilience in Commercial Restaurants? DOI: http://dx.doi.org/10.5772/intechopen.101481*

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[24] Boholm A. Risk perception and social anthropology: Critique of cultural theory. Ethnos. 1996;**61**(1-2):64-84. DOI: 10.1080/00141844.1996.9981528

[25] Pohl RF. Introduction: cognitive illusions. In: Pohl RF, editor. Cognitive Illusions: A Handbook on Fallacies and Biases in Thinking, Judgment and Memory. Hove and New York: Psychology Press—Taylor and Francis Group; 2004. pp. 1-20

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[29] Da Cunha DT, Braga ARC, Passos EDC, Stedefeldt E, De Rosso VV. The existence of optimistic bias about foodborne disease by food handlers and its association with training participation and food safety performance. Food Research International. 2015;**75**:27-33. DOI: 10.1016/j.foodres.2015.05.035

[30] Rossi MSC, Stedefeldt E, Da Cunha DT, De Rosso VV. Food safety knowledge, optimistic bias and risk perception among food handlers in institutional food services. Food Control. 2017;**73**:681-688. DOI: 10.1016/ j.foodcont.2016.09.016

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[38] Douglas M. Purity and Danger: An Analysis of Concepts of Pollution and Taboo. New York: Routledge; 1966. p. 193

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#### **Chapter 9**

## Legumes Cropping and Nitrogen Fixation under Mediterranean Climate: The Case of Montado/Dehesa System

*Fernando Teixeira*

#### **Abstract**

Climate change contributes to the environmental pressures that the Montado/Dehesa systems are experiencing, leading to an impoverishment of the floristic composition of the understorey. The strongly acidic soils of these systems are associated with nutrient deficiencies, nutritional disorders and the toxicity of metals, especially Mn and Al; these problems are discussed with emphasis on the antagonism between Fe and Mn and the relationship between K concentration and Mg uptake and concentration. The potential for the use of the legume-rhizobia symbiosis to increase biological nitrogen fixation and avenues for research are discussed. The co-colonization of the roots of legumes with arbuscular mycorrhizal (AM) fungi and the effects on P and Mn uptake are discussed. A better understanding of the relationships between soil pH, organic matter content (SOM), microbial community, soil P content and the plant strategies to mobilize it, as well as plant effects on the soil solution concentrations of Mn, is important for the management of these systems. The increase of biological nitrogen fixation in these systems, through the breeding of tolerant cultivars to acidic soils and a stepwise legumes enrichment, alongside soil fertility management, may contribute to increasing biomass production, SOM content and overall ecological plasticity.

**Keywords:** sustainable agriculture, Montado/Dehesa, legume, biological nitrogen fixation, acid soil, Mediterranean climate

#### **1. Introduction**

Plant biomass production is strongly correlated with nitrogen (N) availability which, in most farming systems, is dependent on the use of N-fertilizers. These N-fertilizers are obtained, with few exceptions, from the Haber-Bosch industrial process of atmospheric N2 fixation which is energy demanding and responsible for 1.44% of the global emissions of carbon dioxide (CO2) [1]. Contrastingly, most plants of the family Fabaceae (legumes), which comprises 751 genera and 19,500 species [2], can establish symbiotic relationships with rhizobia bacteria capable of fixing atmospheric N2 into ammonia (NH3), through the development of root nodules that host the bacteria (bacteroids). This symbiosis has been explored by humankind since the early beginning of agriculture and it still is an essential part of many traditional agriculture farming systems (e.g., see [3]). In the Mediterranean basin and Europe at large, the rise of modern agriculture, which cannot be decoupled from relatively cheap N-fertilizers, has driven the abandonment of legumes in the farming systems. Still, legume usage in the frame of mixed pastures, and forages, did not decline over time as steeply as grain legumes did [4].

The Montado (in Portugal) or Dehesa (in Spain), is an agro-silvopastoral system, typical of the Southwestern part of the Iberian Peninsula, characterized by a savannah-like landscape, where the main tree species are cork and holm oak (*Quercus suber* and *Quercus ilex*, respectively), where it occupies an area of ca. 3.5 Mha [5]. The Montado/Dehesa is the result of the interaction of humans with the land, and it would not exist without it; cork and firewood harvesting, livestock, farming, pastures and cereal crops, among others, are activities that help to maintain the landscape features [5] and contribute to the rich biodiversity [6]. These ecosystems are presently under significant environmental pressures. Projections of the climate change in the Mediterranean basin show that in the decades to come the Iberian Peninsula will experience a reduction in precipitation and higher temperatures throughout the year (e.g., see [7]). Models suggest that these climatic changes will affect the distribution of the cork and holm oak, with an important reduction in the presence of these trees in the regions where they are presently found (e.g., see [8]). Other important environmental pressures on these ecosystems arise from the soil properties, affecting their resilience, namely, the strongly acidic reaction (pH < 5.5). In these soils, manganese (Mn) toxicity is often pointed out as the main cause of the low biomass productivity of the pastures (e.g., see [9]). Legumes may help to improve N content and P availability (organic P) through their rich underground biomass and surface plant residues and, thus, increase SOM content and counteract soil acidification. This chapter focuses on the legume-rhizobia symbiosis under rainfed farming in the acidic soils of the Montado/Dehesa systems, conditioned by the Mediterranean climate. The legume-rhizobia and tripartite symbiosis with AM fungi and a set of factors that have been identified as particularly challenging for legumes production are briefly reviewed. Possible avenues of research are identified that may allow, in the future, to enhance biological N-fixation and biomass production in these systems through a stepwise, low-input, legumes enrichment strategy.

#### **2. Root-nodule symbiosis as mitigation of environmental pressures**

The biological N-fixation produced by the legume-rhizobia symbiosis may have a profound effect on the Montado/Dehesa ecosystem by increasing the N content of the system and its availability to grasses and other forbs, increasing the overall biomass production and the soil organic matter (SOM) content. The term rhizobia designate diazotrophic bacteria of two different classes of Proteobacteria, encompassing species and strains well beyond those of the genus *Rhizobium*. Rhizobia N2-fixation only occurs in the frame of the symbiotic relationship with legumes [10]. Legumerhizobia symbiosis is energy demanding for the plants, and thus, it only happens if there's not enough nitrogen available (nitrate and ammonium) in the soil to meet the plants' needs (e.g., see [11]). The bacteria in the symbiosis receive in exchange

*Legumes Cropping and Nitrogen Fixation under Mediterranean Climate: The Case... DOI: http://dx.doi.org/10.5772/intechopen.104473*

photosynthates as a carbon source. The plants control the symbioses, and nodule formation, through regulatory mechanisms, such as the "autoregulation of nodulation" (AON), carbon and nitrogen regulation of nodulation, among others (e.g., [11]). For the symbiosis to occur, both the legume host and the microsymbiont must be compatible [12]. The soil and climate conditions found in the Montado/Dehesa will dictate if legumes sowed, even when inoculated with compatible rhizobia, will produce functional nodules, as the survival and thriving of both symbionts in the following years will only occur if both can cope with those conditions. In the next paragraphs, these environmental pressures are discussed along with the contribution of successful legume-rhizobia symbioses to mitigate them.

#### **2.1 Soil reaction and toxicity of metals**

Increasing SOM content may help to counteract soil acidification, to the extent that SOM constitutes an important proton buffer, and SOM depletion and low calcium (Ca2+) saturation of the cation-exchange capacity (CEC) of the soil [13] may constitute one of the main reasons for soil acidification in the Montado/Dehesa system. The high concentration of protons in the soil solution leads to the solubilization of heavy metals that may become toxic to the plants, namely, aluminum (Al3+) and Mn2+ (e.g. [14, 15]). The concentration of these toxic elements that plants may endure will vary with species and cultivars but often they have much lower thresholds than their wild counterparts (e.g., [16]). In low pH soils, nodule formation and nodule weight can be reduced by percentages above 90% and 50%, respectively [17]. Rhizobia bacteria can be found in a wide range of proton concentrations, with species (strains) surviving at pH values as low as 4 [18]. Nonetheless, soil acidification might have a profound effect on the survival of the bacterial strains present and thus on the occurrence of matching symbionts [19]. *Bradyrhizobium* spp. are, generally, more pH-resistant (tolerant) than *Rhizobium* spp. [17].

Proton [H<sup>+</sup> ] concentration in soil solution and the interaction with other elements, namely Al3+ and Mn2+, affect plant growth. Aluminum [Al3+] has no known biological function (e.g., [20]) but it can impair plant growth when in relatively high concentrations in the soil solution. The major factor affecting Al3+ concentration in soil solution is proton concentration and the presence of other ions that react with the dissolving/ precipitating surfaces [15], namely, SOM (e.g., [21]). pH values above 4.5–5.5 are considered as leading to the precipitation of Al3+which in relatively high concentrations affects root elongation and root hair formation likely due to the binding to the pectic matrix of the cell walls, substituting Ca, and hence cell wall thickening and rigidity (e.g., [22, 23]). The aerial part of the plant is also affected by Al3+ via induced nutrient deficiencies of magnesium (Mg), Ca and P, phytohormones imbalances and drought stress [22], but transport to the shoots, with some exceptions, is usually limited [24]. Plant Al-tolerance is characterized by the production of root exudates, organic acids and mucilage capable to chelate Al3+, and by a lower CEC of the surface cell walls [22]. Pasture/forage legumes have different tolerance to different Al3+ concentrations. For example, the genus *Trifolium* has a higher tolerance than species of the genus *Medicago* (e.g., [25]), and very tolerant species, like *Lupinus luteus* (e.g., [26]), are capable of coping with Al3+ concentrations more than 20-fold than the most sensitive legumes. Wood et al. [18], working with *Trifolium repens* (white clover), observed an inhibitory effect of Al3+ on root hairs formation and root elongation, at concentrations of 50 μM and at pH 4.3 and 4.7, and no multiplication of *Rhizobium trifolii* and reduced nodulation for Al3+ concentrations of 50 μM at pH 5.5. Different

rhizobia strains have been shown to grow at much higher Al3+ concentrations than the host [27]. Manganese [Mn2+] plays an important role in plant growth, as a cofactor in many processes, from photosynthesis to the control of oxidative stresses (e.g., [28]); plant requirements of Mn are very low and a concentration of 50 μg Mn. g−1 shoot DM is considered sufficient for normal plant growth [29]. Mn2+ concentration in soil solution is pH related, with concentrations reducing sharply above pH values of ca. 5–5.5 (e.g., [30]), but it is also dependent on the oxidation-reduction conditions of the soil (e.g. [14]), plant characteristics, namely, carboxylate exsudation behavior [30], and the microbiological activity (e.g. [31]). In studies with nutrient solutions, with similar ranges of pH and Mn2+ concentrations, it has been reported the inhibitory effect of Mn on the formation of root hairs of important commercial crops, such as soybean (e.g., [32]). Other studies, with similar Mn2+ concentrations, did not find any effect of Mn on root hairs formation or root elongation, e.g. in *T. repens* (white clover) [18]. Chen et al. [32] suggest that the soybean responses to Mn toxic concentrations, leading to the inhibition of root elongation, may be due to root cell wall modification and lignification. Many transporters can transport excessive amounts of Mn into the root cells, such as the iron-regulated transporters (IRT1), the "natural resistance-associated macrophage protein" (NRAMP), and many others [28]. The mechanisms of plant Mn-tolerance involve both, the ability to excrete and to store Mn in the cells. Nazeri et al. [33] observed a sharp decrease of Mn concentration in the roots of non-mycorrhizal *Trifolium subterraneum* after the supply of P, consistent with the excretion of Mn as no change in concentration of Mn in the shoots was observed. Although the mechanisms for Mn storage in the shoots are not known for most species, the ability to increase the concentration of carboxylate anions in the cells to chelate Mn is a possible explanation at least for some species [29]. Wood et al. [18] did not detect any effect of Mn at 200 μM on nodule formation in *T. repens*, for a pH range from 4.3 to 5.5. On the other hand, Izaguirre-Mayoral and Sinclair [34] observed that Mn at concentrations of 70 and 90 μM inhibited growth and nodulation of a soybean Mn-sensitive genotype but not on a tolerant genotype. Critical toxicity concentrations for Mn in the above-ground biomass range from 200 to 3500 μg.g−1 dry weight [35]. Some legume species are exceptionally tolerant to high leaf concentrations of Mn, above 7000 μg.g−1 dry weight (e.g., *Lupinus albus*) [29]. Keyser et al. [36] found no effect of Mn2+ (200 μM solution) in the growth of 23 strains of cowpea rhizobia and 10 *Rhizobium japonicum* (*Bradyrhizobium japonicum*), although a slowed growth was observed when Ca2+ concentrations were also low. Wood et al. [18] did not observe any effect of Mn2+ (200 μM solution) on the numbers of *R. trifolii*, and no interaction with Ca.

#### **2.2 Soil reaction, nutrient deficiencies and nodulation**

Phosphorus [P] is an important element in molecules participating in the intracellular buffering system (the conjugate acid-base pair H2PO4 − –HPO4 2−), in the energy metabolism of the cells (e.g., ATP, adenosine triphosphate), in the formation of nucleic acids, among others. In acidic soils, low available P in soil solution is mainly due to its retention as adsorbed P on the surface of soil particles of Al- and Fe oxides [37]. Some plant species can exudate to the rhizosphere important amounts of carboxylates that are capable to mobilize Al- and Fe-oxide-sorbed P and also organic P. The organic P is then hydrolyzed by phosphatases, which are exudate to the rhizosphere. The inorganic P uptake by the plant occurs through a high-affinity inorganic

#### *Legumes Cropping and Nitrogen Fixation under Mediterranean Climate: The Case... DOI: http://dx.doi.org/10.5772/intechopen.104473*

P transporter in the plasma membrane of the root cells, belonging to the PHT1 gene family [38]. This strategy of P-mobilization is accompanied by the mobilization of other nutrients such as Mn [29]. Another strategy most plants follow is the promotion of symbiosis with arbuscular mycorrhizal (AM) fungi capable of scavenging phosphorus (available P) [39]; this strategy will be discussed further ahead. The relative importance of each of these strategies of P uptake, for each plant species/cultivar, and the interactions with the environment, may have an impact on the availability of other nutrients, namely, Mn and their uptake. Plants must possess adequate levels of phosphorus (P) otherwise the N-fixation rate by the microsymbiont will be conditioned by P-availability. For example, the molybdenum-dependent nitrogenase requires for each mol of N2 reduction, 16 mol of ATP [40]. Nodulating plants allocate a substantial part of the P uptake to the nodules in soils with low available P [41] and P fertilization may have an important effect on biologically-fixated N (e.g. [42]).

Iron [Fe2+] is essential for biological N-fixation, for example, due to its role in the FeMo cofactor of nitrogenase [43] and the prosthetic group of the leghemoglobin. Fe content and availability to plants in acidic soils are usually high, but plant Fe-deficiency can occur in sandy soils with high concentrations of Mn2+ in soil solution [44]. Legumes, like all dicots, mobilize Fe through the acidification of the rhizosphere; the mobilized Fe3+is then reduced to Fe2+ by plasma membrane reductases and the uptake happens through plasma membrane iron-regulated transporters (IRT1), in what is known as the strategy I of iron uptake [45]. Mn and Fe antagonistic relationship has been observed in many studies with legumes and non-legumes (e.g. [46]). Izaguirre-Mayoral and Sinclair [34] observed that: (i) a higher Mn concentration in the leaves of two soybean cultivars when in the presence of low Fe and high Mn concentrations in the culture solution and; (ii) a lower concentration of Fe in the leaves with increasing Mn concentrations in the culture solutions with high Fe concentration. In acidic soils, the Mn-induced accumulation of Fe in the roots may affect nodulation and nitrogenase activity.

Calcium [Ca2+] is an essential nutrient in plant cells, namely, by its structural role in the cell walls and membranes, and the signaling role in the cytosol [47]. Calcium also plays many roles in the nodulation process of legumes, viz., in the root hair deformation and entrapment of rhizobia soon after nod factor release by the rhizobia [48]. The uptake of Ca2+ is mediated by plasma membrane transporters, the Ca channels [47]. These Ca channels may be permeable to Mn [28]. Nitrogenase activity can be reduced in acidic soils, particularly, if Ca concentration is low and at the early stages of plant development in common bean (*Phaseolus vulgaris* L. Dobruganca) [49]. Liming, to increase soil pH from 5.2 to 7.3, was shown to increase nodulation, root and shoot weight in 14 lucerne cultivars (*Medicago sativa*) [50]. Muofhe and Dakora [42], working with rooibos (*Aspalathus linearis*), observed a 27.2% increase in biologicallyfixed N in response to Ca supply.

Magnesium [Mg2+], besides its role in the chlorophyll molecule, and in a multitude of enzymes, also plays an essential role in ATP; ATP, to become biologically active requires binding with Mg (e.g., [51]). Several studies show a negative effect of K on Mg concentration in the shoot tissues (for reviews see, e.g., [52, 53]. This interaction of K x Mg may be of significance because, in the acidic soils of the Montado/Dehesa, K availability might be high, and low Mg concentration in the plant shoots may have a significant effect on plant growth and nutritional value as feed. The Mg2+ transporter(s) responsible for uptake into the root cells is(are) poorly known (e.g., [52]), although there is evidence of Mg2+ transport through

Ca-channels [47]. Reduced translocation of Mg from the roots to the shoots, in presence of high K<sup>+</sup> concentration, might be the cause [53]. According to an analysis performed by Rietra et al. [52] on 94 peer-reviewed papers and 117 interactions (synergistic, antagonistic or zero-interactions) on crop yields, no interactions were found between Mg and Mn.

Molybdenum [Mo] is essential for some enzymes found in plants, involved in nitrogen metabolism and phytohormones synthesis [54]. Mo, as seen for Fe, is essential for biological N-fixation due to its role in the FeMo cofactor of nitrogenase [43]. A molybdate transporter type 1 (MTR1), that is a molybdate-specific transporter, has been identified in *Medicago truncatula*, and their expression in the nodules was determined [55]. Mo availability to plants in the soil solution correlates positively with decreasing proton concentration, being highest for soils with pH > 6.6, and with the percentage of soil particles with diameters smaller than 20 μm [56]. Adhikari and Missaoui [50], working with 14 Lucerne cultivars (*M. sativa*), a species particularly sensitive to low pH, observed that plants grown in soils with a pH of 5.2 and Mo supplementation, had a statistically significantly higher number of nodules (53% more nodules) than the control.

#### **2.3 Temperature**

In the Montado/Dehesa, biomass accretion happens from fall through winter and spring. The length of the growing period will vary as there is no consistent rainfall pattern from year to year. The daily minimum soil temperatures in the Winter months are often well below 5°C at 2 cm depth (e.g., [57]). In mid-Winter, as the growth rate of legumes increases in responding to favorable temperature and water availability so increases the potential for biological N-fixation. Biomass accretion of the annual species of the understorey ends in late May or early June after soil-available water has been used and the air temperatures are still relatively mild.

The tolerance of rhizobia to low temperatures varies, with different minimum temperatures for growth as low as 5°C, and survival −10°C [58]. Gibson [59] studied the effect of time and temperature in nodule formation of four subterranean clovers (*T. subterraneum*) cultivars and three *R. trifolii* strains, and observed inhibition of nodule formation below root temperature of 7°C, and an increased time to nodule formation as temperatures decreased below 22°C (from 4.1 to 5.7 days at 22°C to 20.2 to 24.2 days at 7°C); the author also observed that for plants with roots at 12°C, time to detect leghaemoglobin in nodules varied between 5 and 8 days (2–4 days for plants with root temperature of 22°C). Peltzer et al. [60], in a study with *Lupinus angustifolius* cv. Yandee, observed that nodule initiation at temperatures between 7 and 12°C failed due to insufficient exudation of flavonoids from the legume to activate nod factors of *Bradyrhizobium*. However, nitrogenase activity in nodules formed at adequate temperatures may occur at a much wider range of temperatures. Dart and Day [61] observed that nitrogenase activity, of nine different species, had a maximum for root temperatures of around 20 to 30°C, and that some species sustained nitrogenase activity for temperatures from 2 to 40°C; these authors also observed that at the temperature range of 2 to 10°C, this activity was only slightly reduced for *Vicia sativa* and *T. subterraneum*. In the winter months, low temperatures and relatively low light exposure of the understorey, as encountered in the Montado/Dehesa, is likely to affect the photosynthetic activity of legumes, and the carbohydrate content in the nodules (e.g., see [11]), affecting plant growth and nitrogenase activity.

*Legumes Cropping and Nitrogen Fixation under Mediterranean Climate: The Case... DOI: http://dx.doi.org/10.5772/intechopen.104473*

#### **2.4 Water stress**

Extended periods of low or no precipitation during the growing season are very common in the Montado/Dehesa region and can affect symbiosis. Unsaturated soil conditions, and soil texture (especially in clayey soils), conditioning the diameter and continuity of saturated soil pores, affect rhizobia motility [62]. Thus, in the presence of a low concentration of rhizobia per gram of soil, the initiation of symbiosis may be dependent on transient saturated conditions after rainfall. N-fixation of nodulated legumes may be severely impaired by drought, well before photosynthesis is reduced, and the mechanisms for this response are species-specific and not fully understood; O2 limitation, C availability and N feedback mechanisms have been proposed as playing an important role in the regulation of nitrogenase activity during drought periods [63]. A better understanding of these mechanisms would allow faster and smarter breeding for drought-tolerant legume species. On the other hand, the Montado/Dehesa systems are located in peneplains, and waterlogging is a common problem in some areas. Waterlogging has a profound effect on aeration and the redox conditions of the soil that can impose high Mn2+ availability over time [14]. The nodules, in saturated soils, will be deprived of free O2, essential for the oxidation of the carbohydrates to produce the energy needed for the nitrogenase activity; also the diffusion of CO2 and H2, gases that can inhibit nitrogenase activity, will be hindered [61]. Roberts et al. [64] discuss the model/role of a gas diffusion barrier in the nodules, capable to maintain a microaerobic state, ca. 20 nM O2, under normal atmospheric conditions, that assure nitrogenase activity at suboptimal rates; changes of the O2 partial pressure of the atmosphere lead to short term changes of the gas diffusion barrier permeability and the rapid inhibition of the nitrogenase activity (transient and fully recoverable), or long term changes, leading to changes in the cellular and subcellular morphology, including the formation of lenticels and secondary aerenchyma on the surface of the nodules. Depending on the severity of the hypoxic conditions and the exposure time, the adaptation of the legume, regarding the number of nodules and nitrogenase activity, may not be sufficient and, depending on the species/cultivars, the recovery and survival might be compromised. Pampana et al. [65] observed that 5 days of waterlogging during the flowering period were sufficient to reduce the number of pods and seeds of white lupin plants almost three-fold, as well as seed weight and shoot and root dry matter. On the other range of the spectrum, Pugh et al. [66] observed that white clover (*T. repens*) grown under saturated conditions from germination had, after 9 weeks, higher shoot dry matter than normally watered plants; the authors also observed that the plants normally watered had a substantial reduction of the acetylene reduction activity (an indicator of nitrogenase activity) when waterlogged (a reduction to 4%, when compared to previous activity) and that the acetylene reduction activity increased when permanently waterlogged plants were suddenly drained (a 250% increase). Both drought and waterlogging in the Montado/Dehesa are likely to affect the biological N-fixation although the effect of N-fixation on biomass yield requires further experiments allowing the separation of other effects on biomass yield (photosynthetic activity, nutrient uptake and translocation, root anoxia/hypoxia, and so on).

#### **2.5 The importance of tripartite symbiosis**

Legumes, besides symbioses with rhizobia bacteria, can establish symbioses with AM fungi in mutualistic relationships where the fungi increase the plant uptake

of water and nutrients, in particular phosphorus, and receive photosynthates in exchange [39]. Most plants are co-colonized by multiple AM fungi species and endemic AM fungi, well adapted to the soil conditions, will compete with inoculated AM fungi for mycorrhization of the roots [39]. These symbioses may be important for N-fixation if in the presence of low concentrations of plant-available P. The mycorrhizal component may account for much of the P uptake of legumes and the direct uptake can be residual. Nazeri et al. [33] showed that mycorrhizal plants of *T. subterraneum*, grown under low P-available conditions, had higher P concentration in the roots and shoots, and lower Mn concentrations, when compared with non-inoculated plants, indicating alternative strategies to acquire P. Alho et al. [67], studying the effect of intact extraradical AM propagules, in undisturbed soils, on the infection of *T. subterraneum* by the fungi, observed that plants infected with intact propagules had statistically significant higher P and N concentrations in the shoots (214 to 515% and 203 to 479%, respectively), higher shoots and nodules dry weight (274 to 618% and 398 to 640%, respectively), and much lower concentration of Mn in the roots (34 to 56%) when compared to control (disturbed soil) 42 days after growth started; these authors observed also that the preceding plants, i.e. the plants grown to establish the mycorrhiza, being more or less mycoptrophic, affected the infection of *T. subterraneum*, with non-mycorrhizal species producing statistically significantly lower values for all those variables when compared with plants infected with intact propagules produced by mycotrophic species.

#### **3. Gaps in current research**

To increase the soil productivity in the Montado/Dehesa ecosystem, the correction of the soil reaction by liming is expensive but, where economically viable, it is effective, either with calcitic or dolomitic limes (e.g., [13]). However, the economic and social benefits of liming must be balanced with the ecological impact of this practice. From an ecological point of view, liming contributes to the emissions of greenhouse gases (GHG) from mining, transporting and incorporating the lime into the soil. Additionally, liming causes a marked stratification of the soil profile pH and the effects on the forest stand and acidophilic endemic species, in the long term, are unknown. On the other hand, liming potentially yield higher carbon sequestration (SOM), the improvement of several topsoil properties, higher feed production and quality (protein content), just to name a few. Unfortunately, although there are many metric approaches to quantify these variables there is no reliable model to assist in the decision to correct the soil reaction through liming in the Montado/Dehesa.

Alternatively, and although in a wider time frame, the benefits of liming can be achieved through higher SOM content (increasing CEC and the soil buffering capacity) and the management of soil fertility and plant nutritional deficiencies. Endemic legumes species, with cultivars selected for the traits of interest, can increase the N content of the system and N availability to other forbs and grasses, and, along with the correction of plant nutrient deficiencies, enhance biomass production and SOM content. Seeding with no-till systems would allow the preservation of the SOM content, without the exacerbation of microbial activity. It would also allow a sequential introduction of the cultivars of interest, beginning with those species/cultivars that can tolerate the soil conditions and boost soil organic matter (cultivars selected aiming acid soils reclamation and tolerant to the low light conditions of the understorey), creating favorable conditions for the survival of the rhizobia of interest (already

#### *Legumes Cropping and Nitrogen Fixation under Mediterranean Climate: The Case... DOI: http://dx.doi.org/10.5772/intechopen.104473*

present or inoculated) and the preservation of AM fungi, in what can be defined as the first step in a **stepwise legume-enrichment**. These first introduced species/ cultivars would be kept through self-seeding by allowing narrow bands to grow to maturity (seed formation) when cutting the pasture for fodder (hay or silage), or by grazing the legume-improved pastures only in the Summer. After achieving a design threshold of SOM content, correlated with higher nutrient availability and soil buffering capacity, pH-sensitive cultivars, capable of higher biomass accretion and adapted for the multi-diverse environments of the Montado/Dehesa, namely, the light/shade exposure, could be sowed. This low-input strategy for legume-rich forage in the Montado/Dehesa would require multidisciplinary research. The next paragraphs will discuss avenues of research readily identifiable: (i) legume species and phenotypic traits; (ii) microsymbionts and symbioses; (iii) soil fertility and nutritional problems.

**Legume species and phenotypic traits**. In the Montado/Dehesa system, and conditioned by the spatial variability (environmental variability) caused by the forest stand, the best approach to improve biological N-fixation is through the use of mixtures of legumes with different phenotypical traits, capable of occupying these different environments. The plants' genera and species that should be the subject of plant breeding, are not dissimilar from those in the mixtures of the *Sown Biodiverse Permanent Pasture Rich in Legumes* system (see [68]), namely, the genus *Trifolium*, which has many species that are, at least, naturalized in the Iberian Peninsula, and several other endemic genera, including *Ornithopus*, *Lotus* or *Lupinus*; however, breeding for the acidic conditions of the Montado/Dehesa should include traits such as low pH tolerance, Al tolerance, Mn tolerance (the storage capacity or exclusion of Mn), shade tolerance (photosynthetic efficiency), drought tolerance, waterlogging tolerance, high nutrient use efficiency, diseases and pests tolerance, matching rhizobia (the persistence in the soil) and the potential to mycorrhizal symbiosis.

Pastures sowed with mixtures of legumes in the Montado/Dehesa, in soils with pH in water between 4.9 and 5.94, increased the biomass production by more than three-fold, as well as the SOM content, and the protein content of grasses and nonlegume forbs [69]. However, the positive effects observed in this study decreased continually from the first year onwards, suggesting the inadequacy of the cultivars sowed. From a stepwise legume-enrichment perspective, lupins may play an important role in the first steps of legume enrichment. The Mediterranean basin is the place of origin of important annual lupin species, with an important genetic pool for plant breeders. For example, in 2009, the number of accessions (landrace and wild types) distributed among different institutions totalled 1804 in Portugal and 5057 in Spain [70]. Lupins are tolerant to acidic soils, with low available P, and can cope with very high concentrations of Mn in the shoot tissues (e.g. [71]). Thus, at least conceptually, well-adapted cultivars of lupins, with good biomass accretion, mixed with other highly tolerant hardy cultivars of other genera could be sowed, increasing SOM and nutrient availability, and establishing/increasing the microsymbionts population, and their ability to survive. In this respect, lupins do not possess very high specificity to their rhizobia microsymbiont, being able to establish symbiosis with several species of *Bradyrhizobium* [72].

**Microsymbionts and symbioses**. Through screening of acid-tolerant rhizobia strains present in these soils, their matching legume hosts and N-fixation efficiency may lead to the expansion of the area of legume-ameliorated pastures in the Montado/ Dehesa systems. In this respect, *Bradyrhizobium* species (and their hosts) may be of particular interest due to their higher tolerance to low pH soils [17]. Concerning AM

fungi, when breeding legumes for improved biomass yield, the best cultivars are likely to be deprived of the genetic apparatus that favors symbiosis or alter the regulatory mechanism (the thresholds), increasing the specificity or decreasing susceptibility with the microsymbionts (e.g., [73]). Thus, at least conceptually, breeding new cultivars of legumes from endemic wild types may preserve the ability of these cultivars to establish symbiotic relationships with the different AM fungi present in these soils. In mycorrhizal legumes, the symbiosis may have a profound effect on P and Mn uptake and concentration (e.g. [33, 67]). The work of Alho et al. [67], studying plants and their mycotrophic character, and the highly positive effect of intact mycorrhizal on the infection of *T. subterraneum*, supports the concept of a **stepwise enrichment of legumes in the Montado/Dehesa**, based on the plant species present at the beginning of the process, and by the effect of no-till direct seeding of new cultivars to maximize mycorrhizal symbiosis. For annual legume crops, and especially under the Mediterranean climate and acidic soils of the Montado/Dehesa, the benefits from a tripartite symbiosis may be synergic, with an effect on biomass accretion caused by improved P uptake and N-fixation, much higher than the simple addition of the isolated effect of the microsymbionts, but this is yet to be demonstrated.

**Soil fertility and plants' nutritional problems**. The management of soil fertility is paramount for increasing the productivity and sustainability of these systems. Where total P is extremely low, P fertilization is needed and may induce higher N-fixation. Nevertheless, as observed by Hernández-Esteban et al. [69], P-fertilization has only a limited effect on pasture productivity, and produced a higher effect when applied to sown legume pastures; the reasons for the low effect of P on natural pastures may have to do with the phenotypical traits of the wild flora which have evolved adaptation mechanisms to thrive in these poor and very dynamic environments. Even in strongly acidic soils, where the P-fertilizers are quickly adsorbed/precipitated in relatively insoluble forms, they will enter the soil's P-pool and will be made available by the plants and microbes in the future. The P-mobilizing strategies of legumes, non-legumes and the microbial community (e.g. [74]), and their effects on Mn availability and uptake of the different groups (legumes, other forbs and grasses), justify a comprehensive study of the relationships between these and other variables. In this respect, the P distribution within the plant (P allocation to the shoots, roots and nodules) can become, as suggested by [37], a tool for the determination of the symbiotic efficiency and/or the adaptation of the legumes (hostbacteria symbiosis) to the environmental conditions. Other plant nutritional disorders that can be detrimental to plant growth and biological N-fixation, such as the Fe and Mn antagonism, or the inhibitory effect of high K<sup>+</sup> uptake on Mg2+ uptake and concentration in the shoot tissues, should be further researched, as they can define new approaches to nutrient management, floristic composition of pastures, plant breeding, and others. The complexity of the relationships between different nutrient uptake and the concentrations of these elements in the plant tissues poses many challenges, namely, for screening candidate cultivars. A high-throughput ionomic approach, and the correlations between these elements in the plant tissues, which are highly species- and environmental-specific, can be a very useful tool (e.g. [75]).

#### **4. Concluding remarks**

The potential for biological N-fixation with legumes in the Montado/Dehesa systems is lower than in more northern regions in Europe due to the erratic rainfall *Legumes Cropping and Nitrogen Fixation under Mediterranean Climate: The Case... DOI: http://dx.doi.org/10.5772/intechopen.104473*

patterns and the relatively low temperature during part of the growing season, and the poor and strongly acid soils. Increasing the potential N-fixation through liming is expensive and, in these sensitive biodiverse systems, with unknown consequences in the long term.

Legumes bred for tolerance to acid soils and associated metal toxicity, for drought and waterlogging, and for the low light conditions in Winter, could provide biodiversity and the potential to increase N-fixation in the multi-diverse environment, both spatial and temporal, of the Montado/Dehesa. A **stepwise approach**, through the use of no-till direct seeding, starting with the introduction of mixtures of hardy tolerant legume species/cultivars, and adequate soil and plant nutrient management can potentially create the soil conditions necessary for a second phase introduction of more sensitive legumes, but with higher biomass and N-fixation potential. Such a low-input strategy for legume-rich forage has the potential to increase the sustainability and productivity of these systems, by increasing the contents of N, C and organic P.

The avenues of research that are needed may prove beneficial beyond the natural borders of the Montado/Dehesa, by identifying legume cultivars and rhizobia strains tolerant to strongly acidic soil conditions useful in other regions of the world.

#### **Funding**

This work is funded by National Funds through FCT - Foundation for Science and Technology under the Project UIDB/05183/2020.

#### **Conflict of interest**

The author declares no conflict of interest.

#### **Author details**

Fernando Teixeira MED – Mediterranean Institute for Agriculture, Environment and Development, Institute for Advanced Studies and Research, Universidade de Évora, Polo da Mitra, Évora, Portugal

\*Address all correspondence to: fteixeir@uevora.pt

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[74] Tian J, Ge F, Zhang D, Deng S, Liu X. Roles of phosphate solubilizing microorganisms from managing soil phosphorus deficiency to mediating biogeochemical P cycle. Biology. 2021;**10**(2):158. DOI: 10.3390/biology 10020158

[75] Pii Y, Cesco S, Mimmo T. Shoot ionome to predict the synergism and antagonism between nutrients as affected by substrate and physiological status. Plant Physiology and Biochemistry. 2015;**94**:48-56. DOI: 10.1016/j. plaphy.2015.05.002

#### **Chapter 10**

## Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat

*Shivani Sood, Harjeet Singh and Suruchi Jindal*

#### **Abstract**

Rusts are plant diseases caused by obligate fungi parasites. They are usually host-specific and cause greater losses of yields in crops, trees, and ornamental plants. Wheat is a staple food crop bearing losses specifically due to three species of rust fungi namely leaf rust (*Puccinia triticina*), stem rust (*Puccinia graminis*), and yellow rust (*Puccinia striiformis*). These diseases are usually inspected manually by a human being but at a large scale, this process is labor-intensive, time-consuming, and prone to human errors. Therefore, there is a need for an effective and efficient system that helps in the identification and classification of these diseases at early stages. In the present study, a deep learning-based CNN (i.e., VGG16) transfer learning model has been utilized for wheat disease classification on the CGIAR image dataset, containing two classes of wheat rust disease (leaf rust and stem rust), and one class of healthy wheat images. The deep learning models produced the best results by tuning the various hyper-parameters such as batch size, number of epochs, and learning rate. The proposed model has reported the best classification accuracy rate of 99.54% on 80 epochs using an initial learning rate from 0.01 and decayed to 0.0001.

**Keywords:** food security, plant disease detection, wheat rust disease, deep learning, convolutional neural networks

#### **1. Introduction**

Rust diseases are the fungal diseases of plants, mainly grasses, caused by fungi. They affect the aerial plant parts especially leaves but can also attack stems and even flowers and fruits. They bear complex life cycles that require two alternative unrelated hosts. Rusts produce spore pustules which vary in color according to the rust species. About 7000 rust species are known to affect a variety of host plants globally. They can cause a wide range of symptoms depending upon the host species like the formation of Galls or swellings on the branches, formation of Canker on the trunks, and formation of Spores on the surface of the leaf. Leaf rust is also known as bown rust due to the brown color of circular urediniospores on the surfaces of the leaf of the crop. Yellow rust or stripe rust is characterized by the yellow color of stripes on the surfaces of the leaf. Stem rust is also brown and characterized by the patches of brown color

on the surface of stems. Many approaches are being deployed to combat the problem of these diseases which involves accurate phenotyping which means characterization of the diseases at field level followed by genotyping to find out the genes responsible for its cause. Many germplasm resources are being explored and screened by scientists worldwide to find new sources of resistance. Precision phenotyping is the key requirement to achieve the goals. So far there are manual interventions involved to screen these diseases. But manual scoring of these diseases is a cumbersome job in large pre-breeding and breeding programs. Therefore, there is a strong need for high precision phenomics which involves imaging using high-quality cameras or equipment followed by image analysis using newly developed software and tools. In today's era of artificial intelligence, it is possible to explore high-end phenomics to achieve better yields of important crops like wheat. Many machine learning and deep learning models have been tested and tried to analyze and characterize wheat fungal diseases [1–4]. One of the main reasons for the popularity of these techniques is the use of GPUs (graphics processing units). The classification tools, computer vision, and GPUs are combined in a single framework called deep learning [5]. Deep learningbased models have been used in the various applications of agriculture for end-to-end learning. With the use of GPUs, deep learning can give a better solution to the given problem in a shorter time [6]. The process of building such models is computationally challenging but using GPU power becomes very easy [7, 8]. Fungal diseases have been identified using image processing techniques on different horticulture and agriculture crops. Various feature extraction and classification algorithm have been used to detect the different types of fruits, vegetables, and cereal crops.

Among the various rust diseases, soybean-, coffee-, and wheat-rusts are the most damaging diseases. Therefore, the constant efforts are being done worldwide, to combat this problem. Wheat is one of the staple food crops in addition to rice and maize. The total area under wheat in the world is around 220 million hectares with a production of 772.64 million metric tons (2020–2021). Wheat rusts especially leaf rust, stem rust, and yellow rusts are major fungal diseases that affect the production of the wheat crop throughout the world particularly in South Asian countries [9]. As per the prediction of the Food and Agricultural Organization (FAO) of the United Nations, wheat production might not be fulfilled the requirement in near future due to rapid population growth [10, 11]. In this chapter we discuss the usefulness of deep learning-based algorithms to identify rust using wheat as a case study.

#### **2. Computer vision approaches for plant disease identification**

Human perception is based on the interaction between the brain and the eye. On the other hand, computer vision system (CVS) is used to emulate human vision for gathering information without physical interaction [12, 13].

It is also defined as the process of automatic acquisition, and analysis from image data. CVS emulates the dynamic vision system whose operation is very transparent and natural. The data is processed in various stages such as capturing, processing, and analysis of images. **Figure 1** depicts the steps involved during image processing. In the first stage, image acquisition and pre-processing are involved. The images can be acquired using high-resolution cameras and sensors. Further, the images are preprocessed through data cleaning, background removing, adding/removing noise, and also enhancing the quality of images. In the second stage, the images are segmented. The segmentation process involves extracting only important and useful information

*Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat DOI: http://dx.doi.org/10.5772/intechopen.104426*

 **Figure 1.**  *Steps of image processing techniques.* 

from the whole image that further helps in the discrimination of classes. In the third stage, the high-level analysis is performed in which direct emphasis is done on the recognition (objects) and interpretation (making results). In a CVS, the following attributes contribute to decision-making: shape, color, texture, and also size. **Figure 2**  depicts the utilization of various artificial intelligence algorithms in plant disease detection. These algorithms are further divided into machine learning and deep learning-based classifiers. The description of these algorithms is illustrated in the coming subsections.

#### **2.1 Machine learning based approaches**

 Classification is the process of dividing the dataset into different categories or groups by adding labels. Nowadays, the machine learning and deep learning approaches are performing well for classifying the algorithm images based on their category. Following are the machine learning algorithms which are used to classify plant disease and are based on supervised learning. Supervised learning is a type of learning where labels (category of images) are given along with input images.

#### *2.1.1 k -Nearest neighbor*

 It is the machine learning algorithm used for classification and calculated by *k* -neighbors. It is mostly used in image processing, machine learning, and also for statistical estimation. This algorithm worked on the principle of calculating the distance between different data points using Euclidean distance and Manhattan distance [ 14 , 15 ]. It works with the following steps: (a) getting data, (b) define *k* neighbors, (c) calculate the neighbor distance using Euclidean distance or Manhattan distance and (d) assign new instances to the majority of the neighbors.

 **Figure 2** *. Description of machine learning and deep learning algorithm used for plant disease detection.* 

#### *2.1.2 Decision tree*

 It is the algorithm of machine learning which comes under supervised learning to solve regression and classification-based problems. The decision tree is the graphical representation of pre-defined rules along with the solution. The graph of the decision tree has two types of nodes: one is decision nodes and another is leaf nodes. Additionally, the edges store the information of the answers to the questions, and leaf nodes store the actual output. In Sabrol and Kumar [ 16 ], Chopda et al. [ 17 ] and Rajesh et al. [ 18 ], the authors reported appreciable results in plant disease classification and recognition.

#### *2.1.3 Support vector machine*

 Support vector machine (SVM) is a very popular classifier used in statistical learning. The classifier aims to discriminate the classes from each other. In SVM, a hyperplane is used to discriminate one class from another. Those points which are close to the hyperplane are referred to as support vectors. The task of the SVM is to classify the different categories based on some features. Additionally, this algorithm performs well in extreme classes. Let us consider, color, texture, shape are some

#### *Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat DOI: http://dx.doi.org/10.5772/intechopen.104426*

features of a particular plant. If we consider two features such as color and texture to classify diseased and healthy leaves. To classify them, the optimal decision boundary is required. Optimal decision boundaries could result in greater misclassification for the new instance. Therefore, the boundary support vectors are very important than all the training examples. This algorithm works well for linearly separating data points whereas in some cases if the data points are not linearly separable then 2-dimensional (2D) feature spaces are converted into 3-dimensional feature spaces. But the only problem is that it is computationally very expensive. In addition to that, it provides kernel function which can reduce the computational cost to convert 2D feature space to 3-dimensional feature space. Using kernel function the dot product is performed between two vectors. Especially, this is used to transform non-linear to linear transformation space. Various popular kernel functions are polynomial, radial basis, sigmoid kernels used to change 2D data to high dimensional feature space. Choosing the best kernel is a non-trivial task and is a hyper-parameter that can be selected by performing various experiments on the data. The main benefit of using SVM is that it is memory efficient and effective for high-dimensional feature space data.

#### *2.1.4 Artificial neural networks*

It is the special type of machine learning algorithm used for classification. The researchers have been working on artificial neural networks (ANNs) since the beginning of the 1980s [19]. ANNs are a special type of classification algorithm and their structure is inspired by the human brain. ANNs takes input from the external world in the form of feature vector or patterns. Each input value is multiplied by their corresponding weights that are summing with the bias value. Further, the result is mapped to the activation function (binary, sigmoid) and produced the output. Other than these algorithms, there are various algorithms available that reported appreciable results in image recognition such as Random Forest, Naive Bayes, many more. Initially, we started with the study of traditional computer vision approaches used for plant disease detection. Plant disease can be caused by fungi, bacteria, and viruses from which fungi are the common disease organism. It is the type of disease that can be formed by taking energy from plants. The fungal disease has been identified using image processing techniques on different horticulture/agriculture crops [20]. To detect the different types of fruits, vegetable, commercial, and cereal crops that have been utilized using various feature extraction and classification algorithms. They achieved appreciable classification accuracy to identify the disease from horticulture/ agriculture crops. Han et al. proposed a novel technique for feature extraction using super-pixel and marker-controlled segmentation methods for the classification of yellow rust and septoria diseases. They have used SVM and ANN for these disease classifications. Their experimentation concludes that SVM classifiers outperformed well than ANN classifiers for the classification of disease [21]. Su et al. experimented with the detection of fungal yellow rust disease on wheat crops. The author collected RGB images with a high-resolution camera and there are a total of three different classes present in region of interest (RoI) as rust, healthy, and background. To monitor the yellow rust, they used the U-Net deep learning architecture and the results were compared with the Random Forest algorithm. They found that U-Net-based segmentation outperformed spectral images. In their work, the average precision of 81.06%, recall of 90.10%, and F1-score of 84.00% have been achieved to segment the disease from spectral images [22]. An application of Fuzzy C-Means clustering has been proposed as the model to identify the wheat leaf disease [23]. In their work, they

extracted inter- and intra-class features and further combined them to build a model for identifying the different wheat plant diseases. Although the traditional machine learning-based techniques are performing well for image classification, still there are certain limitations such as it requires manual feature extraction and is only suitable for small datasets, which may lead to the over-fitting problem [ 23 , 24 ].

#### **2.2 Deep learning-based approaches**

 Convolutional neural network (CNN) is a popular neural network, designed for solving computer vision problems. The architecture of CNNs is shown in **Figure 3** . The images are represented in the form of pixel values. In the convolution layers, the operation of convolution is performed i.e., the kernel is slide over the input image after choosing the padding and stride values at each layer. Thereafter, the power of non-linearity is to give the non-linear mapping with the input images in such a way that after the non-linear mapping it becomes linearly separable. ReLU activation function is used to change all the negative values to positive values. With this, the pooling layer is used to down sample the different feature maps for getting the most prominent features i.e., the convolution layer performs these triplet operations like convolution followed by ReLU and ReLU followed by pooling one after another. These triplets operations are typically stacked one after another and also based on these triplets, the depth of the neural network has been defined. After these layers, the network is followed by one or more fully connected layers which are responsible for classification.

 To build the CNN model, all the above-mentioned parameters play a very important role. To build the custom CNN model, the numbers of convolution layers, max-pool layer, number of filter values, filter size, stride, padding, number of fully connected layers need to be specified. Increasing the number of convolution layers will produce different feature maps and also increasing the fully-connected layers increase the training time of the model. Although, the custom CNN model reported appreciable accuracy. The process of creating a custom CNN model takes more time. Therefore, the concept of transfer learning comes into the picture. Transfer learning is a concept of deep learning where the weights of pre-trained models are reused for a new problem. Every year, there is a competition held on the ImageNet dataset. Many researchers developed new models to classify the different objects of the ImageNet dataset and reported good classification accuracy and reduced error rate. There are variants of transfer learning models such as ResNet, GoogleNet, and EfficientNet varied in terms of the number of layers, filter size, number of filters used, stride, padding, and so on. Some of the few models are elaborated as given below:

**AlexNet** : AlexNet model is a transfer learning model which is based on CNN's and is proposed by Alex Krizhevsky for classifying the different objects of ImageNet Large Scale Visual Recognition Challenge (ILSVRV). Training can be performed on hundreds of epochs. GPUs are the game-changer in deep learning. Using GPUs, the model

 **Figure 3.**  *The basic architecture of CNNs.* 

#### *Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat DOI: http://dx.doi.org/10.5772/intechopen.104426*

will train in very little time and with less effort. AlexNet is the eight-layer network that has a further five convolution layers, and three fully-connected layers including the output layer. It used the ReLU activation function instead of the sigmoid function. In this model, the initial layers used variant sizes of kernels i.e., 11 × 11, 5 × 5, and 3 × 3 to get different features maps as an output. Thereafter, fully-connected layers are used to train the model based on the extracted features.

**VGG16**: Visual geometry group (VGG) model is the first runner-up of the ImageNet dataset in 2014. It has 13 convolution layers, 5 max-pool layers, and 3 FC layers. The output layer used the softmax activation to classify the 1000 different objects. VGG16 model is different from the AlexNet model in terms of kernel size and the number of layers. VGG16 model used the same kernel size whereas the AlexNet model used the different kernel size. Additionally, the VGG16 model is 16-layered but the AlexNet model is 8-layered architecture. In the present study, the VGG16 model has been utilized to classify the wheat rust diseases and the elaboration is given in Section 3.2.

Modern deep learning architectures are significantly popular to solve agriculturerelated problems. Sladojevic et al. developed a CNNs based model for plant disease classification. The model recognized 13 different types of plants. In their work, they used 30,880 images in the training and 2589 images for validation and reported a classification accuracy of 96.30% [25]. Zhang et al. proposed a deep learning model for the detection of rust disease of wheat crop from hyperspectral images. In their work, they automate the process of detecting yellow rust-captured images from unmanned aerial vehicle (UAV). Yellow rust is a fungal disease that can cause 100% loss for the wheat crop. The author used the Inception-ResNet model for feature extraction and reported the highest accuracy of 85.00% when compared with the random forest that was 77.00% [26]. A deep learning model has been built for grading wheat stripe rust disease [27]. In their work, they used different mobile devices to capture images and build their dataset, referred WSRgrading. It contained 5242 wheat leaf images at six different levels. They build and proposed the model by adding an attention layer in the pre-trained DenseNet model and build a new model named as C-DenseNet which has been reported a good classification accuracy of 97.99%. Genaev et al. classify the rust disease from the wheat crop. In their work, they used the CGIAR dataset, containing three classes (healthy wheat, leaf rust, and stem rust). They implemented the DenseNet transfer learning model and reported the F1-score and AUC of 0.90 and 0.98, respectively [28]. Jia et al. in proposed the model for detection and segmentation of fruit features for optimal harvesting of apples using Mask R-CNN. ResNet model was used as the backbone of this network. The model was tested on 120 images and reported precision and recall rates of 97.31% and 95.70%, respectively [29]. The shortage of the wheat disease dataset motivated the researchers to create the dataset which should be publicly available for all [30]. They are motivated to collect more data that will help the research community for conducting the research competitions on wheat diseases classification. Finally, they attempted to prepare their WFD2020 dataset which contains 2414 images. They performed their experiments using the EfficientNet CNN-based model and reported 94.20% classification accuracy.

In the recent decade, deep learning techniques are highly utilized for image processing. Deep learning models are producing appreciable results than machine learning methods [31]. **Figure 4** depicts the utilization of computer vision approaches (i.e., old machine learning methods and modern deep learning approaches) for the wheat crop. These statistics have been built based on work done from the period (2015 to July 2021) for classifying most of the wheat crop diseases. Deep learning approaches include CNN-based architecture such as VGG16, ResNet, Faster R-CNN, and so on. In

 **Figure 4.**  *Year-wise statistics publication of wheat disease detection.* 

different circumstances, the traditional machine learning approaches include SVM, Random Forest, and so on. The analysis concludes that the modern deep learning architectures have been utilized more for classifying most of the wheat crops diseases as compared to traditional machine learning approaches.

### **3. Classification of wheat rust disease**

#### **3.1 Dataset description**

 There are standard datasets that are publicly available for research experimentation in the computer vision and image processing domain, such as PASCAL VOC [ 32 ], ImageNet [ 33 ], IMDB-Wiki [ 34 ], CIFAR [ 35 ], and PlantVillage [ 36 ]. CGIAR dataset is one of the dataset publicly available on https://www.kaggle.com/shadabhussain/ cgiar-computer-vision-for-crop-disease [ 37 ]. This dataset was further distributed in three different classes of wheat rust i.e., healthy wheat, leaf rust, and stem rust. A sample of each class is shown in **Figure 5** . Most of the images in this dataset were collected by CIMMYT and its partners from Ethiopia and Tanzania. Additionally, a few images were sourced from the Google image database. The images in this dataset have the specific characteristics like (i) all are colored (ii) mixed format, (iii) different orientation, (iv) variable quality, and captured with different resolutions. The datasets are already classified into two categories i.e., 876 images and 610 images for training and testing, respectively. From the training dataset (i.e., 876 images) a total of 863 images have been filtered and considered for training the model. In the present study, the 863 images dataset was further split for training and validation in the ratio of 3:1 (i.e., 75% data in training and 25% into validation). **Table 1** describes the classwise distribution of this dataset. It is a challenging task to build an efficient model that is capable to classify all three classes of images accurately.

*Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat DOI: http://dx.doi.org/10.5772/intechopen.104426*

 **Figure 5.**

 *Sampled images of (a) healthy wheat plant, (b) leaf rust, and (c) stem rust.* 


 **Table 1.**

 *Class-wise distribution of image dataset.* 

#### **3.2 Methodologies used for training the model**

 Deep learning is a popular methodology used for image processing. In deep learning models, features are extracted automatically and little human intervention is required to train the model. Deep learning models are quite efficient to discover the internal structure or patterns of high-dimensional data. However, directly processing the original images leads to inappropriate recognition results, therefore, it is necessary to pre-process the images before feeding them to the model. Pre-processing involves e.g. resizing, enhancing, or removing noise of the input images. It is worth mentioning that CNNs perform better for image recognition and classification. There are various transfer learning models which are based on CNNs like AlexNet, VGG16, GoogleNet, and Inception V3, that are pre-trained on the ImageNet dataset. ImageNet is the standard dataset that contains 1000 different categories of objects. CNN's based transfer learning models reported appreciable results to classify 1000 different objects present in the ImageNet dataset. In the present study, the VGG16 model has been utilized and the architecture is depicted in **Figure 6** . This model is the composition of 16-layers (13 convolution layers, and 3 fully connected layers). In this model, the images are processed in standard size i.e., 224 × 224. The reason for resizing the fixed image size is to extract the uniform or equal feature maps at the end of the convolution process. This model used a fixed size of kernel i.e., 3 × 3. Sometimes, the kernel is referred to as a filter that is responsible for extracting features from the given images. These extracted patterns or features might be horizontal edges, vertical edges, and a combination of both. Initially, a convolution process has been performed to extract the features, and thereafter the classification is done. In the convolution operation, the kernel/filter is sliding over the image starting from the top left to the bottom right corner to extract the features.

 The movement of the kernel is either pixel-wise or by skipping some pixels using stride values. If the stride value is 1 then the movement of the kernel is shifted by one pixel after another and if the stride value is 2, then the movement of the kernel

 **Figure 6.**

 *The architecture of VGG16 for wheat rust disease detection.* 

is shifted by two-pixel values during the operation of convolution. The convolution layers are used to identify the pattern or features from the images which further help in discriminating the classes. The initial layers extract the general features like edges and the subsequent layers extract the domain-specific features. Each convolution block is followed by the max-pool layer which is used for down-sampling the feature maps. In this process, the dimensionality of the image is reduced by retaining the most prominent feature. At the end of the convolution layers, different feature maps are generated as an output. These feature maps are further flattened and mapped with a fully connected layer in the classification module. Here, the model has a feature vector of size 4096 neurons also referred to as dense layer. This feature vector is further passed to the next dense layer of the same size. Finally, the last layer neurons are fully connected to the output neurons by using the soft-max activation function. However, in the current study, we considered the three classes classification problem. Therefore, the output layer changed to three classes using the soft-max probability function. The actual learning starts from data using forward and backward passes. In the forward pass, input neurons are multiplied with the weight values and also apply the activation function as ReLU. ReLU activation function adds non-linearity to the model i.e., all the negative pixel-values become positive after passing through it. On the other hand, in backward pass back-propagation is used to minimize the loss value. In this process, weights and biases are getting updated from the last to the initial layer by calculating the gradients at each layer using a convolution operator.

 To summarize this model, the important and noticeable point is that this model has a total of 14,789,955 parameters but 75,267 are trainable parameters and the rest are non-trainable, the reason is that using transfer learning, the already trained weights have been used during building the model. Therefore, the model is trained in less time with fewer number parameters.

#### **3.3 Hyper-parameter tuning**

 Hyper-parameter tuning is the backbone of any deep learning model. Finding the best parameters is a very tedious task, it needs many experiments to be performed while building the model. Hyper-parameters include learning rate, batch size, loss function, number of epochs, and optimizer is usually considered for tuning the model. To build the classification model for three classes each hyper-parameter is considered within a specific range. In this way, several experiments have been performed to build an efficient model. After performing some experiments with the variation in

*Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat DOI: http://dx.doi.org/10.5772/intechopen.104426*

the given hyper-parameters, it was concluded that model accuracy is highly dependent on the batch size, learning rate, number of epochs, and size of the dataset. In the present study, the following hyper-parameters has been utilized: *batch size = 10*, *optimizer = Adam*, *loss function = categorical cross-entropy*, *initial learning rate = 0.01*, *decay learning rate = 0.0001*, *epochs = 80*. Using these parameters, the model produced good classification accuracy.

#### **4. Experimental results**

#### **4.1 Accuracy and loss results**

As discussed in Section 3.1 image dataset of wheat disease classification has been utilized to train the model. We used the online google colab platform with GPU support. Among the performed experiments, we discuss the best one, which produces the highest training accuracy. **Table 2** illustrates the training and validation accuracies obtained at different epochs (varied from 10 to 90) along with their loss values. Here, the training accuracy starts with 81.42% on 10 epochs and ends up with 99.54% on 80 epochs. We continued to compute the accuracy for the 90 epochs also but did not get any significant improvement in training accuracy. Although more experiments could be performed by increasing the number of epochs, the accuracy obtained at epoch 80 was quite promising. On the other hand, the validation accuracy fluctuating between 74.76% and 79.05% at different epochs, as shown in **Figure 7**. Similarly, it was observed that the training loss decreases at every increasing step of the epoch (from 10 to 80). Beyond that, the loss has started to increase. In contrast, the validation loss is fluctuating between 0.60 and 0.65 up to 40 epochs. Then, after 70 epochs it starts increasing rapidly (**Figure 8**).

#### **4.2 Model evaluation**

To test the performance of the trained model, we performed the test experiments on the validation data (i.e., 25% of the total dataset). In this way, a total of 36 sample images of healthy leaf, 87 sample images of leaf rust, and 94 sample images of stem


#### **Table 2.**

*Comparison of training accuracy, validation accuracy, and training loss, and validation loss at different epochs.*

 **Figure 7.**  *Representation of the comparison of training and validation accuracy.* 

 **Figure 8.**

 *Representation of the comparison of training and validation loss.* 

rust have been considered. The evaluation of the testing results was done using a confusion matrix. **Figure 9** illustrates the accuracy and confusion with other intraclasses, wherein, it is shown that leaf rust class samples are confused with stem rust class samples due to less variation between classes.

*Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat DOI: http://dx.doi.org/10.5772/intechopen.104426*

 **Figure 9.**  *Confusion matrix at epoch = 80.* 

#### **5. Conclusions**

 To summarize this book chapter, different machine learning and deep learningbased models have been discussed to solve plant disease classification and detection problems. Considering a case study of wheat rust diseases, a deep learning-based model is proposed to classify the different wheat rust diseases using a pre-trained VGG16 model. Based on the CGIAR dataset with three classes (stem rust, leaf rust, and healthy wheat), the proposed model has been optimized and produced the classification accuracy of 99.54%, and when evaluated on unseen data it gave a validation accuracy of 77.14%. This model will further help farmers or experts to diagnose disease in the early stages. Although these models give good training accuracy, they were not appropriate to classify stem- and leaf rust when result plot on confusion metrics. This is due to the fact that some images in this dataset contained multiple diseases, meaning that one image contained the features of both leaf- and stem rust. Detection and classification of the wheat rust disease in the early stages lead to high yield at the production level [ 38 ]. In the future, we will extend this work by collecting real-time images of wheat rust disease and also incorporating object detection-based algorithms such as Yolov3, Faster R-CNN, and Mask R-CNN [ 39 ] to exactly localize the location of the disease in the image.

*Food Systems Resilience*

### **Author details**

Shivani Sood1 , Harjeet Singh1 \* and Suruchi Jindal<sup>2</sup>

1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

2 School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, Punjab, India

\*Address all correspondence to: harjeet.singh@chitkara.edu.in

© 2022 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.

*Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat DOI: http://dx.doi.org/10.5772/intechopen.104426*

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### *Edited by Ana I. Ribeiro-Barros, Daniel S. Tevera, Luís F. Goulao and Lucas D. Tivana*

This book addresses some of the major challenges of food systems associated with a diversity of agricultural contexts and priorities. It contributes to the conversation on global food and nutrition security by unpacking the intertwined connections between food system resilience, food policies, and global food markets. The contributing authors provide careful analyses of how shocks to food systems (e.g., COVID-19 pandemic lockdowns) and crises to global food systems (e.g., the global food price crisis of 2008) have disrupted the food value chains in ways that undermine global initiatives to achieve food and nutrition security for all. The book is divided into two sections. Section 1 focuses on global food systems transformation with the goal of moving towards resilience. Two chapters in this section employ a global context approach to address the key factors undermining food systems' resilience and sustainability. Section 2 presents case studies drawn from Africa, Asia, and Europe with different pathways for the transition to food systems resilience, highlighting the importance of policy approaches as well as smart and innovative strategies to ensure the production of nutritious foods at affordable costs, the reduction of food wastage, and the valorization of sub-products.

> *Usha Iyer-Raniga, Sustainable Development Series Editor*

Published in London, UK © 2022 IntechOpen © Andre2013 / iStock

Food Systems Resilience

IntechOpen Series

Sustainable Development, Volume 1

Food Systems Resilience

*Edited by Ana I. Ribeiro-Barros, Daniel S. Tevera,* 

*Luís F. Goulao and Lucas D. Tivana*