Supply Chains and Consumers

*Current Issues and Challenges in the Dairy Industry*

Van Camp J. Nutritional and technological aspects of milk fat globule membrane material. International Dairy

[27] Rombaut R, Camp JV, Dewettinck K. Phospho- and sphingolipid distribution during processing of milk, butter and whey. International Journal of Food Science and Technology. 2006;**41**:

[28] Tamime AY, Robinson RK. Yoghurt: Science and Technology. Cambridge, UK: Woodhead Publishing Ltd.; 1999

[29] Sayed MME, Askar AA, Hamzawi LF, Fatma FA, Mohamed AG, El Sayed SM, et al. Utilization of buttermilk concentrate in the manufacture of functional processed cheese spread. Journal of American Science. 2010;**6**(9)

[30] Govindasamy-Lucey S, Lin T, Jaeggi JJ, Johnson ME, Lucey JA. Influence of condensed sweet cream buttermilk on the manufacture, yield, and

functionality of pizza cheese. Journal of

Dairy Science. 2006;**89**:454-467

[32] Bahrami M, Ahmadi D,

2015;**54**:73-78

2014;**1**(4):1-14

[31] Kumari J, Kumar S, Gupta VK, Kumar B. The influence of mixing sweet cream buttermilk to buffalo milk on quality of Chhana production. Milchwissenschaft. 2012;**67**:57-60

Beigmohammadi F, Hosseini F. Mixing sweet cream buttermilk with whole milk to produce cream cheese. Irish Journal of Agricultural and Food Research.

[33] Suneeta P, Bhatt JD, Prajapati JP. Evaluation of selected emulsifiers and buttermilk in the manufacture of reduced-fat Paneer. Basic Research Journal of Food Science and Technology.

Journal. 2008;**18**:436-457

435-443

Research & Reviews: Journal of Dairy Science and Technology. 2013;**2**(1):1-11

[17] Dairy India. Milk movements, utilization and trade. 1985;**1981**(82):

[18] Santha IM, Narayanan KM. Composition of ghee-residue. Journal of Food Science and Technology.

[19] Santha IM, Narayanan KM. Composition of ghee-residue lipids. Indian Journal of Dairy Science.

[20] Tamine AY. Dairy Fats and related Products. Blackwell Publishing Ltd;

[21] Verma BB, De S. Preparation of chocsi due burfi from ghee residue. Indian Journal of Dairy Science.

[22] Galhotra KK, Wadhwa BK. Chemistry of ghee-residue, its

[24] Vanderghem C, Bodson P, Danthine S, Paquot M, Deroanne C, Blecker C. Milk fat globule membrane and buttermilks: From composition to valorization. Biotechnology, Agronomy, Society and Environment.

significance and utilization—A review. Indian Journal of Dairy Science.

[23] Pruthi TD, Narayanan KM, Bhalerao VR. Indian Journal of Dairy Science.

[25] Sodini I, Morin P, Olabi A, Jime'nezflores R. Compositional and functional properties of buttermilk: A comparison

between sweet, sour, and whey buttermilk. Journal of Dairy Science.

[26] Dewettinck K, Rombaut R, Thienpont N, Le TT, Messens K,

20-24

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1978:370-374

1993:142-146

1971;**24**:185

2010;**14**:485-500

2006;**89**:525-536

2009

**38**

**41**

**Chapter 4**

**Abstract**

chain.

value chain

**1. Introduction**

A Synthesis of Risks in Dairy Value

Chains in Southern Africa: Cases

An increase in frequency and intensity of slow- and fast-onset disasters in Southern Africa has crippled milk producers' value chain with catastrophic effects to consumers. Milk production is vulnerable to disruptions from natural disasters, poor transport and infrastructure. The chapter considers the cases of South Africa and Zimbabwe, two countries that have organized dairy production. Against this bleak backdrop, this chapter explores the contribution of the milk industry to the economy and the benefits to consumers of milk and dairy products. The chapter also identifies the key players in the dairy supply chain in Southern Africa. It explores different types of disaster risks prevalent in Southern Africa and how they affect the production of raw and processed milk along dairy supply chains. It further interrogates risk management strategies employed by the key players to mitigate these risks to make dairy supply chains sustainable. This chapter reviewed literature and analyzed governments, nongovernmental organizations, and industries' documents with the aim to present value chain resilience strategies. This chapter also presents an insight into the policymakers and milk industries on the risk reduction strategies that are employed to mitigate the effects of risks on the milk and dairy products' value

**Keywords:** dairy industry, drought, milk production, risk reduction strategies,

The Southern African Development Community (SADC) has experienced the growing demand for dairy products, increased the milk returns, employee productivity, quality milk yields, and demand, as well as the application of world-class technology. Mlambo and Zitsanza [1] contemplate this growing demand which has led to the dairy industry's contribution to the economic development of both South Africa and Zimbabwe. Furthermore, the price fluctuations in the SADC region have led to an increase in the milk demand. The milk production, favorable trade, and job creation can be utilized as criterions to determine the economic benefits of the dairy industry. The agricultural sector contributes to the gross domestic product (GDP) of the emerging and developing countries including South Africa and Zimbabwe. Hence, the dairy demand is expected to grow by 2.3% a year over the next decade.

of South Africa and Zimbabwe

*Chari Felix and Ngcamu Bethuel Sibongiseni*

#### **Chapter 4**

## A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa and Zimbabwe

*Chari Felix and Ngcamu Bethuel Sibongiseni*

#### **Abstract**

An increase in frequency and intensity of slow- and fast-onset disasters in Southern Africa has crippled milk producers' value chain with catastrophic effects to consumers. Milk production is vulnerable to disruptions from natural disasters, poor transport and infrastructure. The chapter considers the cases of South Africa and Zimbabwe, two countries that have organized dairy production. Against this bleak backdrop, this chapter explores the contribution of the milk industry to the economy and the benefits to consumers of milk and dairy products. The chapter also identifies the key players in the dairy supply chain in Southern Africa. It explores different types of disaster risks prevalent in Southern Africa and how they affect the production of raw and processed milk along dairy supply chains. It further interrogates risk management strategies employed by the key players to mitigate these risks to make dairy supply chains sustainable. This chapter reviewed literature and analyzed governments, nongovernmental organizations, and industries' documents with the aim to present value chain resilience strategies. This chapter also presents an insight into the policymakers and milk industries on the risk reduction strategies that are employed to mitigate the effects of risks on the milk and dairy products' value chain.

**Keywords:** dairy industry, drought, milk production, risk reduction strategies, value chain

#### **1. Introduction**

The Southern African Development Community (SADC) has experienced the growing demand for dairy products, increased the milk returns, employee productivity, quality milk yields, and demand, as well as the application of world-class technology. Mlambo and Zitsanza [1] contemplate this growing demand which has led to the dairy industry's contribution to the economic development of both South Africa and Zimbabwe. Furthermore, the price fluctuations in the SADC region have led to an increase in the milk demand. The milk production, favorable trade, and job creation can be utilized as criterions to determine the economic benefits of the dairy industry.

The agricultural sector contributes to the gross domestic product (GDP) of the emerging and developing countries including South Africa and Zimbabwe. Hence, the dairy demand is expected to grow by 2.3% a year over the next decade.

**Figure 1.** *Effects on the demands of the dairy products. Source: authors.*

The primary drivers of growth in demand remain population growth and growth in the per capita consumption of dairy products [2]. There is a plethora of benefits in improving the levels of milk production and profitability of dairy farmers. These include the following (**Figure 1**):


**43**

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa…*

In Zimbabwe, the agricultural sector provides employment and livelihood to 70% of the population, contributing between 40 and 60% of exports and 15–25% of gross domestic product (GDP) [3]. The dairy sector is equally critical for the success of rural communities as it reduces poverty and ensure food and nutrition security. At the height of production in 1990, milk production reached an all-time annual high of 262 million liters [4]. However, the estimated demand for milk and milk products of 180 million liters in Zimbabwe presents a supply gap of 129 million liters, implying that there is an opportunity for import substitution through improved competitiveness and increased production, especially from local smallholder dairy farmers [4]. On the contrary, the World Wildlife Fund South Africa (WWF [5]) suggests that the South African dairy products import percentage has superseded the export percentage since 2010, although the South African milk production has been changing relatively. *Agriculture Statistics* [6] posits that over the last 20 years, the milk production has remained constantly due to the substantial decrease of the dairy of the national herd. The sudden change in production occurred even though the number of farmers has declined since 1993 with the dairy sectors being detrimentally affected [7], whereas international dairy product prices dropped by 61% from February 2014 to May 2016. The decrease in prices was caused by higher production, fueled by higher producer prices, and a decrease in demand, especially from China [2]. Furthermore, the milk consumption in South Africa has declined, and South African farmers are unable to compete against farmers from the first world countries who receive state funding from their countries and export their products to South Africa. Hence, this slow-onset disaster (drought) had a multiplicity of repercussions including the severe depletion of the natural grazing with livestock slaughter, reduction of summer crop plantations, extreme temperatures in summer months, and grain deficits with an increase in importations. Furthermore, the inability of the agricultural sector to attract clients with purchasing power, a depreciating currency, and an increase in food prices were the effects of drought in southern Africa. It is imperative to investigate the risk issues in the dairy value chain due to the importance of the dairy industry in regional economic development,

*DOI: http://dx.doi.org/10.5772/intechopen.84573*

contribution to the GDP, and poverty alleviation.

The scarcity of farmland and water has limited the growth of the dairy industry. The key players in the value chain are input suppliers, dairy farmers and milk processors, middlemen, government, financial institutions, nongovernmental organizations, buyers in the markets, and value chain supporters. Both large-scale and many smallholder dairy farmers in the country need several inputs from input suppliers to raise the cows and produce raw milk. South African and Zimbabwean milk production is dominated by large-scale farmers who own fairly large farms with high producing pure exotic cows. The other players in the dairy value chain in Zimbabwe are middlemen (wholesalers and retailers) who buy milk produce from farmers and processors in bulk in order to retail to the consumers. The sale of milk and milk products is through supermarkets and shops around the country. The processing companies also sell milk products directly to final consumers through their salesmen who patrol streets in towns and residential areas with refrigerated push and bicycle carts. The other key players are the consumers of the milk products themselves. Without the consumers in the value chain, there is no business; hence, milk products' consumers are important in the milk value chain. Dairy value chain supporters provide support to the main actors to guarantee that dairy products get to the final consumer. The supporters in the dairy value chain in Zimbabwe include:

**2. Milk production value chain**


#### *A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa… DOI: http://dx.doi.org/10.5772/intechopen.84573*

In Zimbabwe, the agricultural sector provides employment and livelihood to 70% of the population, contributing between 40 and 60% of exports and 15–25% of gross domestic product (GDP) [3]. The dairy sector is equally critical for the success of rural communities as it reduces poverty and ensure food and nutrition security. At the height of production in 1990, milk production reached an all-time annual high of 262 million liters [4]. However, the estimated demand for milk and milk products of 180 million liters in Zimbabwe presents a supply gap of 129 million liters, implying that there is an opportunity for import substitution through improved competitiveness and increased production, especially from local smallholder dairy farmers [4].

On the contrary, the World Wildlife Fund South Africa (WWF [5]) suggests that the South African dairy products import percentage has superseded the export percentage since 2010, although the South African milk production has been changing relatively. *Agriculture Statistics* [6] posits that over the last 20 years, the milk production has remained constantly due to the substantial decrease of the dairy of the national herd. The sudden change in production occurred even though the number of farmers has declined since 1993 with the dairy sectors being detrimentally affected [7], whereas international dairy product prices dropped by 61% from February 2014 to May 2016. The decrease in prices was caused by higher production, fueled by higher producer prices, and a decrease in demand, especially from China [2]. Furthermore, the milk consumption in South Africa has declined, and South African farmers are unable to compete against farmers from the first world countries who receive state funding from their countries and export their products to South Africa. Hence, this slow-onset disaster (drought) had a multiplicity of repercussions including the severe depletion of the natural grazing with livestock slaughter, reduction of summer crop plantations, extreme temperatures in summer months, and grain deficits with an increase in importations. Furthermore, the inability of the agricultural sector to attract clients with purchasing power, a depreciating currency, and an increase in food prices were the effects of drought in southern Africa. It is imperative to investigate the risk issues in the dairy value chain due to the importance of the dairy industry in regional economic development, contribution to the GDP, and poverty alleviation.

#### **2. Milk production value chain**

The scarcity of farmland and water has limited the growth of the dairy industry. The key players in the value chain are input suppliers, dairy farmers and milk processors, middlemen, government, financial institutions, nongovernmental organizations, buyers in the markets, and value chain supporters. Both large-scale and many smallholder dairy farmers in the country need several inputs from input suppliers to raise the cows and produce raw milk. South African and Zimbabwean milk production is dominated by large-scale farmers who own fairly large farms with high producing pure exotic cows. The other players in the dairy value chain in Zimbabwe are middlemen (wholesalers and retailers) who buy milk produce from farmers and processors in bulk in order to retail to the consumers. The sale of milk and milk products is through supermarkets and shops around the country. The processing companies also sell milk products directly to final consumers through their salesmen who patrol streets in towns and residential areas with refrigerated push and bicycle carts. The other key players are the consumers of the milk products themselves. Without the consumers in the value chain, there is no business; hence, milk products' consumers are important in the milk value chain. Dairy value chain supporters provide support to the main actors to guarantee that dairy products get to the final consumer. The supporters in the dairy value chain in Zimbabwe include:

*Current Issues and Challenges in the Dairy Industry*

include the following (**Figure 1**):

**Figure 1.**

tries are no exception.

• An increase in milk returns.

• An increase in milk yields.

• An increase in labor productivity.

*Effects on the demands of the dairy products. Source: authors.*

• The creation of job opportunities.

• Acceleration of women empowerment.

• Development of farmers' cooperatives.

• Improved demand for quality of milk and its price.

The primary drivers of growth in demand remain population growth and growth in the per capita consumption of dairy products [2]. There is a plethora of benefits in improving the levels of milk production and profitability of dairy farmers. These

• A growing demand for dairy products in developing countries and SADC coun-

• Improved supply of milk yields by the utilization of production in technology.

**42**

dairy services, the Department of Veterinary Services, Livestock Research Institute, extension services, farmers' unions, and nongovernmental organizations.

There are a multiplicity and diverse actors in the dairy supply value chain who perform various pivotal roles that service dairy industries including educators from agricultural schools, universities and technical colleges, farmers and stock people, farm advisors, local agribusiness, policymakers, and research scientists. There are also pivotal key stakeholders in the dairy value chain in South Africa which include the Department of Agriculture, Forestry and Fisheries, National Disaster Management Centres, Industrial Development Corporation, Land Bank, Banking Association of South Africa, South African National Consumer Union, and National Chamber of Milling. The stakeholders aim to improve the productive performance of the training and development programs and training on the foundations of dairy production technology. Midgley [8] mentions that consumers, dairy processors, informal traders, retailers, bulk milk collectors, transport operators, importers and exporters, and large commercial and medium and small dairy producers can be considered as the dairy supply value chain. The author argues that the dairy industry has noticed the number of producers declining with the cattle sizes increasing and the milk production efficiencies improving.

#### **3. Drought effects in the dairy industry**

The SADC region has been prone to drought, which is associated with the climatic phenomena called EL NINO. This phenomenon occurred when sea temperatures surpassed the Western coast of South America affecting global weather patterns. The effects of EL NINO in South Africa has resulted in seven out of nine provinces being declared disaster zones which had catastrophic effects on the dairy supply including the milk. The severe impact of drought in the SADC region with South Africa as no exception has drastically paralyzed the milk supply value chain. This became conspicuous as most dairy farmers were unable to produce and supply sufficient milk due to the impact of drought which has increased the cost of milk drastically. Consequently, this led to the majority of dairy farmers in South Africa to experience a reduction in the milk production, which led to an increase in prices by retailers which had an adverse effect on consumers. The local supply situation remains uncertain as the final effect of the 2016 and 2017 drought remains to be seen. Lower grain prices will probably have a beneficial effect on production but the scarcity of roughage, higher beef prices, and the weaker condition of herds after the drought impacted negatively on production. Milk production growth remained slow during the rest of 2017 [2]. There are a number of factors that have necessitated some dairy processors to pay commercial farmers an exorbitant amount of money per liter per average for milk to ensure a consistent supply which includes, *inter alia*:


**45**

**Figure 2.**

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa…*

The above factors have influenced retailers to increase operating costs for the entire milk value chain, which necessitated retailers to increase the dairy milk and other dairy products which consumers purchase at a hefty price. Lakew [9] opines that the dairy farmer's profitability on their products are negatively affected; a reduction in the production of milk and unfavorable balance of trade can be origi-

Transporters collect and transport bulk raw milk from farms to processing plants, usually situated in towns. In Zimbabwe, the transport system is dominated by the National Dairy Co-operative (NDC), an organized farmers' co-operative transport organization. The transporters use refrigerated bulk tanks to ensure that the quality of milk is maintained. The major processing companies are Dairibord Zimbabwe Private Limited (DZPL), Dendairy (Pvt) company, and Nestle Zimbabwe, which add value to raw milk by being processed into various milk products such as yogurt, cheese, pasteurized milk, ice cream, and butter.

The complex dairy value chain comprises dairy farmers, transporters, processors,

wholesalers, retailers, and consumers who use milk products created in the value chain [10]. The dairy supply chain is vulnerable to disruptions from numerous risks as it involves many stakeholders. Risks may arise from any component within its supply chain. According to Gertenbach [11], environmental factors which include temperature, rainfall (quantity and distribution), sun hours, and soil types contribute significantly to livestock production. Climate change has negatively affected the SADC region's dairy farmers and industry in particular. For instance, the increased temperatures have decreased the dry matter intake for animals, reproductive performance declined, and the overall productivity declined. Heat stress impairs milk

nated on the decline in the production of milk (**Figure 2**).

*Adverse effects of droughts in the dairy supply. Source: authors.*

**4. Risks in dairy value chains**

*DOI: http://dx.doi.org/10.5772/intechopen.84573*


*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa… DOI: http://dx.doi.org/10.5772/intechopen.84573*

**Figure 2.** *Adverse effects of droughts in the dairy supply. Source: authors.*

The above factors have influenced retailers to increase operating costs for the entire milk value chain, which necessitated retailers to increase the dairy milk and other dairy products which consumers purchase at a hefty price. Lakew [9] opines that the dairy farmer's profitability on their products are negatively affected; a reduction in the production of milk and unfavorable balance of trade can be originated on the decline in the production of milk (**Figure 2**).

Transporters collect and transport bulk raw milk from farms to processing plants, usually situated in towns. In Zimbabwe, the transport system is dominated by the National Dairy Co-operative (NDC), an organized farmers' co-operative transport organization. The transporters use refrigerated bulk tanks to ensure that the quality of milk is maintained. The major processing companies are Dairibord Zimbabwe Private Limited (DZPL), Dendairy (Pvt) company, and Nestle Zimbabwe, which add value to raw milk by being processed into various milk products such as yogurt, cheese, pasteurized milk, ice cream, and butter.

#### **4. Risks in dairy value chains**

The complex dairy value chain comprises dairy farmers, transporters, processors, wholesalers, retailers, and consumers who use milk products created in the value chain [10]. The dairy supply chain is vulnerable to disruptions from numerous risks as it involves many stakeholders. Risks may arise from any component within its supply chain. According to Gertenbach [11], environmental factors which include temperature, rainfall (quantity and distribution), sun hours, and soil types contribute significantly to livestock production. Climate change has negatively affected the SADC region's dairy farmers and industry in particular. For instance, the increased temperatures have decreased the dry matter intake for animals, reproductive performance declined, and the overall productivity declined. Heat stress impairs milk

*Current Issues and Challenges in the Dairy Industry*

and the milk production efficiencies improving.

**3. Drought effects in the dairy industry**

dairy services, the Department of Veterinary Services, Livestock Research Institute,

There are a multiplicity and diverse actors in the dairy supply value chain who perform various pivotal roles that service dairy industries including educators from agricultural schools, universities and technical colleges, farmers and stock people, farm advisors, local agribusiness, policymakers, and research scientists. There are also pivotal key stakeholders in the dairy value chain in South Africa which include the Department of Agriculture, Forestry and Fisheries, National Disaster Management Centres, Industrial Development Corporation, Land Bank, Banking Association of South Africa, South African National Consumer Union, and National Chamber of Milling. The stakeholders aim to improve the productive performance of the training and development programs and training on the foundations of dairy production technology. Midgley [8] mentions that consumers, dairy processors, informal traders, retailers, bulk milk collectors, transport operators, importers and exporters, and large commercial and medium and small dairy producers can be considered as the dairy supply value chain. The author argues that the dairy industry has noticed the number of producers declining with the cattle sizes increasing

The SADC region has been prone to drought, which is associated with the climatic phenomena called EL NINO. This phenomenon occurred when sea temperatures surpassed the Western coast of South America affecting global weather patterns. The effects of EL NINO in South Africa has resulted in seven out of nine provinces being declared disaster zones which had catastrophic effects on the dairy supply including the milk. The severe impact of drought in the SADC region with South Africa as no exception has drastically paralyzed the milk supply value chain. This became conspicuous as most dairy farmers were unable to produce and supply sufficient milk due to the impact of drought which has increased the cost of milk drastically. Consequently, this led to the majority of dairy farmers in South Africa to experience a reduction in the milk production, which led to an increase in prices by retailers which had an adverse effect on consumers. The local supply situation remains uncertain as the final effect of the 2016 and 2017 drought remains to be seen. Lower grain prices will probably have a beneficial effect on production but the scarcity of roughage, higher beef prices, and the weaker condition of herds after the drought impacted negatively on production. Milk production growth remained slow during the rest of 2017 [2]. There are a number of factors that have necessitated some dairy processors to pay commercial farmers an exorbitant amount of money per liter

per average for milk to ensure a consistent supply which includes, *inter alia*:

• Importation of dairy products (milk) from other countries was very expensive

• The effects of drought leading to poor pasture conditions.

as the Rand was very weak despite lower international prices.

• Increase in electricity tariffs increased input costs for farmers and milk

• The volatile exchange rate made imports expensive.

• Increase in grain prices.

processors.

extension services, farmers' unions, and nongovernmental organizations.

**44**

production, reproductive performance, metabolic and health status, and immune response. The dairy cows are less productive in the event of increased temperature levels. Hence, cows that are experiencing extreme heat are identified by the signs of the reduced feed intake, which directly contributes to the decreased milk yield. The extreme climatic variations which are prevalent in the SADC region have both direct and indirect impacts on the dairy cattle where the following have been identified:


The main risks associated with Zimbabwe dairy include financial, technology, political unrest, policy barrier, and natural disasters.

#### **4.1 Financial risks**

To purchase the required infrastructure in the dairy industry requires large sums of money [12]. The high perishability of milk requires dairy farmers to make substantial capital investments right from production up to sale. The procedure to secure finance in Zimbabwe is burdensome and highly bureaucratic and complex [12]. Credit providers have become more risk-averse and are equally reluctant to offer loans to farmers producing on land that lacks collateral value. Women entrepreneurs are adversely affected where banks demand collateral security in the form of property in urban areas for them to access business loans. Fewer women than men own fixed assets [13]. High lending rates of up to 14% [14] make the cost of capital expensive. Available financing is more suitable for short-run farming projects, while there is limited availability of medium to long-term finance for the broader agricultural sector. Resultantly, farmers are unwilling to make long-term investments in dairy farming leaving Zimbabwe food insecure [15].

#### **4.2 Input risks**

The most important dairy component is the livestock itself—the heifers. Building the dairy herd takes long gestation periods of up to 9 months. The long gestation makes it difficult to grow the herd much faster to boost milk output. In like manner, dairy farmers incur high costs to breed or purchase heifers which become a production constraint [12]. An equally important input is electricity provided by a stateowned monopoly, the Zimbabwe Electricity Supply Authority (ZESA). The frequent disruptions in power supplies have seen a decrease in capacity utilization in the agricultural sector which, in turn, affects capacity utilization simultaneously fuelling input costs in the dairy industry as the dairy processors have to consider other sources of power like generators to prevent disruptions in their production lines [16]. The high-input costs push the price of the final milk products up.

Furthermore, the Zimbabwe dairy industry has very high labor costs negatively affecting viability. An increase in labor costs reduces returns, and income earned may not be adequate to cover costs [17]. Zimbabweans are among the heavily taxed in the world. Currently, above paying taxes to the Zimbabwe Revenue Authority,

**47**

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa…*

dairy producers pay levies to the Dairy Services Department, Environmental Management Authority, Agriculture Marketing Authority, Local Authorities, and

The poor performance in the agricultural sector is also as a result of poor government policies. During the period from 1998 to 2000, Zimbabwe experienced negative economic growth. There was political instability in Zimbabwe following the fast track land reform program. The political instability negatively affected milk production as large-scale commercial dairy farmers were among those who lost their farms land invaders [19]. Following the implementation of the fast track land reform, cattle population declined. It is estimated that the dairy herd was reduced by 50% from what it was before the land reform program in year 2000. Land tenure security is threatened by lack of title deeds, and therefore, dairy farmers are not prepared to make long-term investments, negatively affecting milk quantity and quality [20]. Political commitment to creating an enabling environment for invest-

The other challenge that local farmers face is their limited capacity to influence policy outcomes. Intervention by NGOs is heavily restricted by the restrictive political environment. Governance concerns continue to block any progressive success made toward foreign interventions in the form of assistance from emergency

The high costs of breeding dairy cattle translate to very uncompetitive raw milk which costs US \$0.62 per liter compared to neighboring South Africa and Kenya, which costs US \$0.40 and US \$0.30, respectively [22]. There is fierce competition emanating from the influx of foreign milk and milk products from plants in Europe and South America, which is choking the dairy industry to date [23]. Most dairy products from South Africa are threatening the agricultural sector, thereby prompting dairy farmers and processors to come up with initiatives that promote the buying and consumption of locally produced dairy products to mitigate the unfair

The disastrous drought which affected the SADC region between the period of 2015 and 2017 has negatively affected the already ailing agricultural sector (commercial, small holding, and subsistence) which left farmers financially distressed. The intensity and magnitude of drought which struck South African farmers including dairy farmers were beyond their world-class disaster contingency plans. Even though South African farmers are recognized as the best in the world in terms of their planning and production and risk assessment and planning, they did not cope with the disaster. The extreme risk to the dairy production and its value chain is associated with the climatic variations with mostly the variable weather conditions more especially droughts. The recent slow-onset disaster (drought) directly affects both rain-fed and irrigated pastures, as well as prices of purchased feeds. The climatic risks also encapsulate erratic rainfall patterns, heavy rainfall and floods, and heat waves. Extreme weather conditions have negative repercussions which include damage to water and energy infrastructure; outbreaks of pests and diseases; high costs of energy for cooling under hot conditions; and disruption of

*DOI: http://dx.doi.org/10.5772/intechopen.84573*

**4.3 Political risks**

**4.4 Competition risks**

**4.5 Natural disaster risks**

Zimbabwe National Water Authority, among others [18].

ment growth in Zimbabwe is questionable and uninspiring [21].

interventions to long-term development support.

competition from foreign dairy products [24, 25].

transport of perishable milk due to road and bridge destruction.

dairy producers pay levies to the Dairy Services Department, Environmental Management Authority, Agriculture Marketing Authority, Local Authorities, and Zimbabwe National Water Authority, among others [18].

### **4.3 Political risks**

*Current Issues and Challenges in the Dairy Industry*

• Fodder and pasture yields decreased,

• Increased susceptibility to diseases,

• Shortage and increased feed costs.

• Infrastructural destruction, and

**4.1 Financial risks**

**4.2 Input risks**

• Cost increase due to overutilization of energy.

political unrest, policy barrier, and natural disasters.

production, reproductive performance, metabolic and health status, and immune response. The dairy cows are less productive in the event of increased temperature levels. Hence, cows that are experiencing extreme heat are identified by the signs of the reduced feed intake, which directly contributes to the decreased milk yield. The extreme climatic variations which are prevalent in the SADC region have both direct and indirect impacts on the dairy cattle where the following have been identified:

The main risks associated with Zimbabwe dairy include financial, technology,

To purchase the required infrastructure in the dairy industry requires large sums of money [12]. The high perishability of milk requires dairy farmers to make substantial capital investments right from production up to sale. The procedure to secure finance in Zimbabwe is burdensome and highly bureaucratic and complex [12]. Credit providers have become more risk-averse and are equally reluctant to offer loans to farmers producing on land that lacks collateral value. Women entrepreneurs are adversely affected where banks demand collateral security in the form of property in urban areas for them to access business loans. Fewer women than men own fixed assets [13]. High lending rates of up to 14% [14] make the cost of capital expensive. Available financing is more suitable for short-run farming projects, while there is limited availability of medium to long-term finance for the broader agricultural sector. Resultantly, farmers are unwilling to make long-term

The most important dairy component is the livestock itself—the heifers. Building

Furthermore, the Zimbabwe dairy industry has very high labor costs negatively affecting viability. An increase in labor costs reduces returns, and income earned may not be adequate to cover costs [17]. Zimbabweans are among the heavily taxed in the world. Currently, above paying taxes to the Zimbabwe Revenue Authority,

the dairy herd takes long gestation periods of up to 9 months. The long gestation makes it difficult to grow the herd much faster to boost milk output. In like manner, dairy farmers incur high costs to breed or purchase heifers which become a production constraint [12]. An equally important input is electricity provided by a stateowned monopoly, the Zimbabwe Electricity Supply Authority (ZESA). The frequent disruptions in power supplies have seen a decrease in capacity utilization in the agricultural sector which, in turn, affects capacity utilization simultaneously fuelling input costs in the dairy industry as the dairy processors have to consider other sources of power like generators to prevent disruptions in their production lines [16]. The

investments in dairy farming leaving Zimbabwe food insecure [15].

high-input costs push the price of the final milk products up.

**46**

The poor performance in the agricultural sector is also as a result of poor government policies. During the period from 1998 to 2000, Zimbabwe experienced negative economic growth. There was political instability in Zimbabwe following the fast track land reform program. The political instability negatively affected milk production as large-scale commercial dairy farmers were among those who lost their farms land invaders [19]. Following the implementation of the fast track land reform, cattle population declined. It is estimated that the dairy herd was reduced by 50% from what it was before the land reform program in year 2000. Land tenure security is threatened by lack of title deeds, and therefore, dairy farmers are not prepared to make long-term investments, negatively affecting milk quantity and quality [20]. Political commitment to creating an enabling environment for investment growth in Zimbabwe is questionable and uninspiring [21].

The other challenge that local farmers face is their limited capacity to influence policy outcomes. Intervention by NGOs is heavily restricted by the restrictive political environment. Governance concerns continue to block any progressive success made toward foreign interventions in the form of assistance from emergency interventions to long-term development support.

#### **4.4 Competition risks**

The high costs of breeding dairy cattle translate to very uncompetitive raw milk which costs US \$0.62 per liter compared to neighboring South Africa and Kenya, which costs US \$0.40 and US \$0.30, respectively [22]. There is fierce competition emanating from the influx of foreign milk and milk products from plants in Europe and South America, which is choking the dairy industry to date [23]. Most dairy products from South Africa are threatening the agricultural sector, thereby prompting dairy farmers and processors to come up with initiatives that promote the buying and consumption of locally produced dairy products to mitigate the unfair competition from foreign dairy products [24, 25].

#### **4.5 Natural disaster risks**

The disastrous drought which affected the SADC region between the period of 2015 and 2017 has negatively affected the already ailing agricultural sector (commercial, small holding, and subsistence) which left farmers financially distressed. The intensity and magnitude of drought which struck South African farmers including dairy farmers were beyond their world-class disaster contingency plans. Even though South African farmers are recognized as the best in the world in terms of their planning and production and risk assessment and planning, they did not cope with the disaster. The extreme risk to the dairy production and its value chain is associated with the climatic variations with mostly the variable weather conditions more especially droughts. The recent slow-onset disaster (drought) directly affects both rain-fed and irrigated pastures, as well as prices of purchased feeds. The climatic risks also encapsulate erratic rainfall patterns, heavy rainfall and floods, and heat waves. Extreme weather conditions have negative repercussions which include damage to water and energy infrastructure; outbreaks of pests and diseases; high costs of energy for cooling under hot conditions; and disruption of transport of perishable milk due to road and bridge destruction.

While all countries suffer from disasters, low-income countries are more susceptible to the impact of disaster risks. The natural and manmade disaster risks have severely disrupted dairy production, thereby leading to increased prices of dairy produce, decreased sales, and created perpetual vulnerability. Unexpected climate change affecting Zimbabwe and other southern African countries are exposing dairy farmers to both production and marketing risks. They tend to affect many farms and dairy processing firms. Secondary data available on climatology such as rainfall pattern erraticism and extreme weather events in Zimbabwe show that the country is already experiencing the effects of climate change [26].

The unbearably high temperatures extended Zimbabwe's dry regions that are less productive, thereby shrinking the main farming regions. These human-induced climate changes are caused by the greenhouse effect [27] and mostly affect African countries like Zimbabwe resulting in food insecurity. The challenges posed by unforeseen climate changes are depleting the most essential natural resource, water. It is increasingly becoming difficult to sustain viable agriculture given such harsh, unpredictable weather conditions for many agro-based economies like Zimbabwe. Rain-fed agriculture is becoming less reliable to maximize agricultural productivity.

Zimbabwe, being an agro-based economy, faces severe threats from these climatic changes. Dairy farming, in particular, thrives well in regions which record high rainfall. Zimbabwe, in particular, is at risk and is vulnerable to these new climatic conditions because it heavily relies on rain for its agricultural activities [28]. These erratic rainfall patterns and dry spells are impacting negatively on the productivity of dairy farms. The low rainfall experienced in Zimbabwe country makes dairy cows breeding more difficult by the day as there are changes in feed resources [29].

Over a million cattle starved to death as a result of the 1991/1992 drought [30, 31]. The impact of the drought was felt by individual farmers, as well as all the industries dependent on agricultural raw materials such as milk and beef processing [31]. The 2015/2016 drought threatened food security in Zimbabwe as thousands of cattle starved to death due the drought [32]. Grazing conditions remained poor in most of the southern half of the region [32]. The foot-and-mouth disease (FMD) of year 2015 also contributed to the calamity as it resulted in a decline in the national herd [33].

#### **4.6 Technology risks**

Poor technology in Zimbabwe, among other factors, has adversely affected capacity utilization in the milk processing industry [34]. Dairy farmers face technological risks as they have problems cooling milk in areas without electricity, adversely affecting the quality of milk. Consequently, some farmers use manual milking which is quite difficult for large herds. Low agricultural output is, therefore, attributed to the low capital endowment (Zimbabwe Vulnerability Assessment Committee [35]).

#### **5. Risk management strategies employed by stakeholders**

Various strategies which can be harnessed in order to increase domestic milk production and yield a positive contribution to the economy include, *inter alia:*


**49**

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa…*

• Increasing the total number of the milking herd (cows) without changing

• Reforming small holding dairy farms to larger farms and "mega farms."

• To avoid the high mortality rates of young stock.

• The development of a national breeding center.

fodder crops, and the utilization of crop residences.

support and assistance to the dairy farming sector [21].

• The importation of breeding heifers.

• To have a skilled labor force.

tional assistance [15]:

**5.1 Collaboration**

• To eliminate wastage at the production plant (farm) and by the consumer.

There is no straight solution to manage risks. Each value chain possesses its uniqueness; so, the criterion for management differs from others. Various risk mitigation strategies to mitigate the risks associated with the dairy value chain are explored in this section. The dairy farmers have utilized various mitigation strategies. These strategies include the use of smaller dairy breeds like Jersey, growing

Furthermore, the low-cost to high-cost adaption strategies have been utilized to counter heat stress on dairy cattle productivity and reproductive performance. The low-cost measures employed by farmers include reducing overcrowding, maximizing shade, improving ventilation, and high-cost measures included the designing and installation of thermos air conditioning. Both adaptation and mitigation strategies were utilized by dairy farmers to ensure that production and productivity inputs are at an optimal level. Sprinkler fans, changing the feeding periods to coincide with the cooler times of the day and reducing the exertion required by animals to gain access to food, minerals, and water are the mitigation strategies that were being employed by farmers. To fully implement the above strategies dairy farmers relied on collaboration, legislation and policy, education and training, insurance, technology, and interna-

A plethora of commentators [36–38] opine that a key strategy to effectively mitigate risk on dairy supply is through collaboration among key stakeholders. Such key stakeholders from diverse sectors and disciplines including leaders of government ministries, NGOs, and private sector organizations play a pivotal role in risk reduction. The collaboration and partnership of stakeholders yields positive results as partner organizations share skills, technical knowledge, information and resources, experiences, and best practices resulting in saving money due to elimination of duplications and wastage. Collaboration is also evident in Zimbabwe's dairy sector. The Zimbabwe farming community has formed collaborations with NGOs to try and mitigate exposure to risk. There are many NGOs providing assistance in the agrarian sector in Zimbabwe of which Technoserve, Land O'Lakes, European Union, United States Agency for International Development (USAID), and Zimbabwe Agricultural Competitive Program (ZimACP) are active in providing

• Increasing the number of dairy farms, the size of the milking cows, and per cow

*DOI: http://dx.doi.org/10.5772/intechopen.84573*

dairy farms.

production combined.

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa… DOI: http://dx.doi.org/10.5772/intechopen.84573*


*Current Issues and Challenges in the Dairy Industry*

decline in the national herd [33].

tion of dairy farmers.

**4.6 Technology risks**

While all countries suffer from disasters, low-income countries are more susceptible to the impact of disaster risks. The natural and manmade disaster risks have severely disrupted dairy production, thereby leading to increased prices of dairy produce, decreased sales, and created perpetual vulnerability. Unexpected climate change affecting Zimbabwe and other southern African countries are exposing dairy farmers to both production and marketing risks. They tend to affect many farms and dairy processing firms. Secondary data available on climatology such as rainfall pattern erraticism and extreme weather events in Zimbabwe show that the

The unbearably high temperatures extended Zimbabwe's dry regions that are less productive, thereby shrinking the main farming regions. These human-induced climate changes are caused by the greenhouse effect [27] and mostly affect African countries like Zimbabwe resulting in food insecurity. The challenges posed by unforeseen climate changes are depleting the most essential natural resource, water. It is increasingly becoming difficult to sustain viable agriculture given such harsh, unpredictable weather conditions for many agro-based economies like Zimbabwe. Rain-fed agriculture is becoming less reliable to maximize agricultural productivity. Zimbabwe, being an agro-based economy, faces severe threats from these climatic

changes. Dairy farming, in particular, thrives well in regions which record high rainfall. Zimbabwe, in particular, is at risk and is vulnerable to these new climatic conditions because it heavily relies on rain for its agricultural activities [28]. These erratic rainfall patterns and dry spells are impacting negatively on the productivity of dairy farms. The low rainfall experienced in Zimbabwe country makes dairy cows

breeding more difficult by the day as there are changes in feed resources [29]. Over a million cattle starved to death as a result of the 1991/1992 drought [30, 31]. The impact of the drought was felt by individual farmers, as well as all the industries dependent on agricultural raw materials such as milk and beef processing [31]. The 2015/2016 drought threatened food security in Zimbabwe as thousands of cattle starved to death due the drought [32]. Grazing conditions remained poor in most of the southern half of the region [32]. The foot-and-mouth disease (FMD) of year 2015 also contributed to the calamity as it resulted in a

Poor technology in Zimbabwe, among other factors, has adversely affected capacity utilization in the milk processing industry [34]. Dairy farmers face technological risks as they have problems cooling milk in areas without electricity, adversely affecting the quality of milk. Consequently, some farmers use manual milking which is quite difficult for large herds. Low agricultural output is, therefore, attributed to the low capital endowment (Zimbabwe Vulnerability Assessment Committee [35]).

Various strategies which can be harnessed in order to increase domestic milk production and yield a positive contribution to the economy include, *inter alia:*

• Prioritizing increasing the number of dairy farmers without emphasizing chang-

• Emphasizing the increasing yields per cow milk rather than expanding the popula-

**5. Risk management strategies employed by stakeholders**

ing the average milk production per cow or farm.

country is already experiencing the effects of climate change [26].

**48**

There is no straight solution to manage risks. Each value chain possesses its uniqueness; so, the criterion for management differs from others. Various risk mitigation strategies to mitigate the risks associated with the dairy value chain are explored in this section. The dairy farmers have utilized various mitigation strategies. These strategies include the use of smaller dairy breeds like Jersey, growing fodder crops, and the utilization of crop residences.

Furthermore, the low-cost to high-cost adaption strategies have been utilized to counter heat stress on dairy cattle productivity and reproductive performance. The low-cost measures employed by farmers include reducing overcrowding, maximizing shade, improving ventilation, and high-cost measures included the designing and installation of thermos air conditioning. Both adaptation and mitigation strategies were utilized by dairy farmers to ensure that production and productivity inputs are at an optimal level. Sprinkler fans, changing the feeding periods to coincide with the cooler times of the day and reducing the exertion required by animals to gain access to food, minerals, and water are the mitigation strategies that were being employed by farmers.

To fully implement the above strategies dairy farmers relied on collaboration, legislation and policy, education and training, insurance, technology, and international assistance [15]:

#### **5.1 Collaboration**

A plethora of commentators [36–38] opine that a key strategy to effectively mitigate risk on dairy supply is through collaboration among key stakeholders. Such key stakeholders from diverse sectors and disciplines including leaders of government ministries, NGOs, and private sector organizations play a pivotal role in risk reduction. The collaboration and partnership of stakeholders yields positive results as partner organizations share skills, technical knowledge, information and resources, experiences, and best practices resulting in saving money due to elimination of duplications and wastage. Collaboration is also evident in Zimbabwe's dairy sector. The Zimbabwe farming community has formed collaborations with NGOs to try and mitigate exposure to risk. There are many NGOs providing assistance in the agrarian sector in Zimbabwe of which Technoserve, Land O'Lakes, European Union, United States Agency for International Development (USAID), and Zimbabwe Agricultural Competitive Program (ZimACP) are active in providing support and assistance to the dairy farming sector [21].

NGOs such as Land O'Lakes partner National Association of Dairy Farmers (NADF) train community livestock workers in dairy management [39]. Likewise, milk processing companies, Dairibord Zimbabwe Holdings, Nestle Zimbabwe, and Dendairy develop small, medium, and large-scale farmers across the country through heifer programs to boost milk production [40]. The livestock was distributed to farmers in an effort to ensure continuity of supply across the supply chains. According to the Dairibord Holdings Annual [40], this milk supply intervention has realized benefits as it has contributed 8% to the milk supplies for Dairibord Zimbabwe.

#### **5.2 Legislation**

Disaster legislation is one of the instruments that can highlight the efforts and commitment a country has in disaster reduction and management practices. This section highlights the legal and institutional framework that deals with risk reduction and management in Zimbabwe. The Civil Protection Department is tasked with the mandate of preparing for and providing for prevention where possible, as well as mitigating the effects of disaster whenever it occurs, through the Civil Protection Act of 2001 [41]. This was a reflection of the government's commitment to disaster management [42]. The Civil Protection Act of 2001 resulted in the setting up of a Civil Protection Department under the flagship of the Ministry of Local Government, Rural and Urban Development [43]. Besides the Zimbabwe Civil Protection Unit efforts, there has been an increased focus on disaster risk reduction (DRR) by other sectors of government. The Zimbabwean Civil Protection Act is complimented by other acts: Environmental Management Act (20:27), the Rural District Councils Act (29:12), the Urban Councils Act (29:14), the Water Act No. 31 of 1998, the Defence Act (11:02), the Police Act (11:10), and the Public Health Act (15:09) [44].

#### **5.3 International assistance**

Zimbabwe is among the top 40 recipients of disaster risk reduction (DRR) financing from humanitarian organizations. However, there is still a concentration of DRR financing by these humanitarian organizations within the top four recipients (Pakistan, India, Indonesia, and Bangladesh) [45]. The farming community has formed collaborations with international NGOs to try to mitigate exposure to risk. There are many NGOs providing assistance in the agrarian sector in Zimbabwe of which Technoserve, Land O'Lakes, European Union, USAID, and Zimbabwe Agricultural Competitive Program (ZimACP) are active in providing support and assistance to the dairy farming sector [21]. The activities of these organizations are coordinated by the Food and Agriculture Organization.

#### **5.4 Policy**

In a plight to increase agricultural activity and curb the risks posed by natural hazards in Zimbabwe, various stakeholders have formulated the Comprehensive Agricultural Policy Framework (2012–2032) [46]. Due to changes in the socioeconomic environment, such as the land reform program, there has been a need to review the national agricultural policy. The policy is aimed at, among other issues, increasing production and productivity of livestock and improved animal health and welfare in the country [46]. The Comprehensive Agricultural Policy Framework also recommended agricultural subsidies so that local farmers will be able to compete with imports. Despite these noble efforts, a gap still exists concerning agricultural policy formulation and implementation which will guide

**51**

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa…*

any programs directed toward mitigation of natural hazards and meteorological disasters like drought. Zimbabwe has to date made many attempts to create a comprehensive agricultural policy, which have remained in draft form to date [21].

Education and training strengthen all aspects of risk management at all the stages in the risk management cycle. Risk management (RM) education can be introduced in school curricula. Zimbabwe has successfully integrated DRR and emergency preparedness into its education system. Education would be a handy strategy with most dairy farmer's literate (96%) and are able to interact with providers of farmer training courses [47]. Similarly, conferences compliment formal

Zimbabwe has a total of 25 registered insurance companies and 15 insurers, representing about 60%, which currently provide agricultural insurance [49]. However, there is a low penetration of agricultural insurance products in the country. Furthermore, insurers do not provide specialized agricultural insurance packages. Insurance enables the farmers to transfer risks to insurance companies [50]. Insurance reduces individual loss exposure, thus spreading risks by collecting premiums from many individuals and paying for damage caused by natural disasters that are very large for individual households and companies. Agricultural insurance policies cover against a many risks including drought, floods, heat waves, and other natural disasters. One such insurance by Zimnat Lion Insurance, Zimnat Livestock Insurance, insures farmers against fire, theft, lightning, explosion, and

Most dairy farms in the developed world make use of emerging technologies to improve efficiency and profitability in dairy enterprises. In particular, automation technology is used to improve profitability, milk quality, reduce costs of production, and improved animal welfare. These new automated technologies have incorporated computers and cellphones application to manage milk production and animal health. Various technologies recommended to dairy farmers in Zimbabwe were first tested on demonstration plots before they were adopted across the country. However, adoption of these technologies was a hurdle to poor farmers because of

The dawn of the agricultural revolution which is engrained on technology has increased efficiency and profitability in the South African and Zimbabwean dairy industry. The introduction of technology has boosted milk yields, enhanced milk quality, and reduced the costs associated with producing white stuff. **Table 1**

The abovementioned technologies assist the dairy industry production as there is a scarcity of committed labor in both the developing and developed countries. Furthermore, such new technologies save time and reduce labor expenses, thus

*DOI: http://dx.doi.org/10.5772/intechopen.84573*

education and workshop training [48].

**5.5 Education and training**

**5.6 Insurance**

death of livestock [51].

resource unavailability [52].

*5.7.1 Emerging technologies and benefits in the dairy industry*

increasing efficiency, productivity, and profits.

depicts emerging technologies and benefits in the dairy industry.

**5.7 Technology**

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa… DOI: http://dx.doi.org/10.5772/intechopen.84573*

any programs directed toward mitigation of natural hazards and meteorological disasters like drought. Zimbabwe has to date made many attempts to create a comprehensive agricultural policy, which have remained in draft form to date [21].

#### **5.5 Education and training**

Education and training strengthen all aspects of risk management at all the stages in the risk management cycle. Risk management (RM) education can be introduced in school curricula. Zimbabwe has successfully integrated DRR and emergency preparedness into its education system. Education would be a handy strategy with most dairy farmer's literate (96%) and are able to interact with providers of farmer training courses [47]. Similarly, conferences compliment formal education and workshop training [48].

#### **5.6 Insurance**

*Current Issues and Challenges in the Dairy Industry*

**5.2 Legislation**

**5.3 International assistance**

NGOs such as Land O'Lakes partner National Association of Dairy Farmers (NADF) train community livestock workers in dairy management [39]. Likewise, milk processing companies, Dairibord Zimbabwe Holdings, Nestle Zimbabwe, and Dendairy develop small, medium, and large-scale farmers across the country through heifer programs to boost milk production [40]. The livestock was distributed to farmers in an effort to ensure continuity of supply across the supply chains. According to the Dairibord Holdings Annual [40], this milk supply intervention has realized benefits as it has contributed 8% to the milk supplies for Dairibord Zimbabwe.

Disaster legislation is one of the instruments that can highlight the efforts and commitment a country has in disaster reduction and management practices. This section highlights the legal and institutional framework that deals with risk reduction and management in Zimbabwe. The Civil Protection Department is tasked with the mandate of preparing for and providing for prevention where possible, as well as mitigating the effects of disaster whenever it occurs, through the Civil Protection Act of 2001 [41]. This was a reflection of the government's commitment to disaster management [42]. The Civil Protection Act of 2001 resulted in the setting up of a Civil Protection Department under the flagship of the Ministry of Local Government, Rural and Urban Development [43]. Besides the Zimbabwe Civil Protection Unit efforts, there has been an increased focus on disaster risk reduction (DRR) by other sectors of government. The Zimbabwean Civil Protection Act is complimented by other acts: Environmental Management Act (20:27), the Rural District Councils Act (29:12), the Urban Councils Act (29:14), the Water Act No. 31 of 1998, the Defence

Act (11:02), the Police Act (11:10), and the Public Health Act (15:09) [44].

coordinated by the Food and Agriculture Organization.

Zimbabwe is among the top 40 recipients of disaster risk reduction (DRR) financing from humanitarian organizations. However, there is still a concentration of DRR financing by these humanitarian organizations within the top four recipients (Pakistan, India, Indonesia, and Bangladesh) [45]. The farming community has formed collaborations with international NGOs to try to mitigate exposure to risk. There are many NGOs providing assistance in the agrarian sector in Zimbabwe of which Technoserve, Land O'Lakes, European Union, USAID, and Zimbabwe Agricultural Competitive Program (ZimACP) are active in providing support and assistance to the dairy farming sector [21]. The activities of these organizations are

In a plight to increase agricultural activity and curb the risks posed by natural hazards in Zimbabwe, various stakeholders have formulated the Comprehensive Agricultural Policy Framework (2012–2032) [46]. Due to changes in the socioeconomic environment, such as the land reform program, there has been a need to review the national agricultural policy. The policy is aimed at, among other issues, increasing production and productivity of livestock and improved animal health and welfare in the country [46]. The Comprehensive Agricultural Policy Framework also recommended agricultural subsidies so that local farmers will be able to compete with imports. Despite these noble efforts, a gap still exists concerning agricultural policy formulation and implementation which will guide

**50**

**5.4 Policy**

Zimbabwe has a total of 25 registered insurance companies and 15 insurers, representing about 60%, which currently provide agricultural insurance [49]. However, there is a low penetration of agricultural insurance products in the country. Furthermore, insurers do not provide specialized agricultural insurance packages. Insurance enables the farmers to transfer risks to insurance companies [50]. Insurance reduces individual loss exposure, thus spreading risks by collecting premiums from many individuals and paying for damage caused by natural disasters that are very large for individual households and companies. Agricultural insurance policies cover against a many risks including drought, floods, heat waves, and other natural disasters. One such insurance by Zimnat Lion Insurance, Zimnat Livestock Insurance, insures farmers against fire, theft, lightning, explosion, and death of livestock [51].

#### **5.7 Technology**

Most dairy farms in the developed world make use of emerging technologies to improve efficiency and profitability in dairy enterprises. In particular, automation technology is used to improve profitability, milk quality, reduce costs of production, and improved animal welfare. These new automated technologies have incorporated computers and cellphones application to manage milk production and animal health. Various technologies recommended to dairy farmers in Zimbabwe were first tested on demonstration plots before they were adopted across the country. However, adoption of these technologies was a hurdle to poor farmers because of resource unavailability [52].

#### *5.7.1 Emerging technologies and benefits in the dairy industry*

The dawn of the agricultural revolution which is engrained on technology has increased efficiency and profitability in the South African and Zimbabwean dairy industry. The introduction of technology has boosted milk yields, enhanced milk quality, and reduced the costs associated with producing white stuff. **Table 1** depicts emerging technologies and benefits in the dairy industry.

The abovementioned technologies assist the dairy industry production as there is a scarcity of committed labor in both the developing and developed countries. Furthermore, such new technologies save time and reduce labor expenses, thus increasing efficiency, productivity, and profits.


**Table 1.**

*Emerging technologies and benefits in the dairy industry.*

#### **6. Conclusion**

This chapter espoused the high level of preparedness and resilience by dairy farmers during and in the aftermath of droughts to selected countries. It is observed in this chapter that while drought effects have paralyzed the dairy industry, the demand of dairy products has remained constant. The increase of the demand of the dairy industry has improved the quality of life of people as it provided formal and seasonal employment. Moreover, it also increased competition among the dairy farmers coupled with profits gained. Consumers also benefited as they have purchased quality dairy products which were influenced by the competition among dairy industries. This chapter has depicted the adverse effects of drought which have affected the dairy supply value chain from the grazing fields, herd health and productivity, infrastructure, economy, and resource availability. Various technological inventions and applications have been seen as beneficial to dairy farmers which has increased the health and productivity of cows, monitoring of the entire business and detection strategies which have increased cow milk yields. The technological, financial, political, and natural disasters and input risks have been the dominant risks in the dairy supply chain and have had catastrophic effects on

**53**

**Author details**

Chari Felix1

provided the original work is properly cited.

\* and Ngcamu Bethuel Sibongiseni2

2 Walter Sisulu University, East London, South Africa

\*Address all correspondence to: charifelix93@gmail.com

1 Bindura University of Science Education, Bindura, Zimbabwe

© 2019 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,

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa…*

consumers. Various risk mitigation strategies have been implemented to mitigate the risks associated with the dairy supply chain that includes collaboration, legislation, policy, education and training, technology insurance, and international assistance. However, most strategies failed because of unavailability of resources to

A major limitation in this study is methodological in nature as this chapter only

employed a document analysis. This research method makes it difficult to test the reliability and validity of the findings as inferences cannot be used to other countries. It is advisable for future researchers to employ various methodologies and approaches in both countries where reliability and validity testing will be

*DOI: http://dx.doi.org/10.5772/intechopen.84573*

fully implement them.

conducted.

#### *A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa… DOI: http://dx.doi.org/10.5772/intechopen.84573*

consumers. Various risk mitigation strategies have been implemented to mitigate the risks associated with the dairy supply chain that includes collaboration, legislation, policy, education and training, technology insurance, and international assistance. However, most strategies failed because of unavailability of resources to fully implement them.

A major limitation in this study is methodological in nature as this chapter only employed a document analysis. This research method makes it difficult to test the reliability and validity of the findings as inferences cannot be used to other countries. It is advisable for future researchers to employ various methodologies and approaches in both countries where reliability and validity testing will be conducted.

### **Author details**

*Current Issues and Challenges in the Dairy Industry*

**Technology Benefits**

Cow collars • Track and collect data on the health, habits, and happiness of the herd.

production.

○ Intruders. ○ Stock thieves. ○ Illegal invaders.

○ Pelt patterning.

production.

Drone technology • Monitor the location of the herd.

Facial recognition technology • Using details such as:

Robotic milking technology • Enhanced milk yields.

*Emerging technologies and benefits in the dairy industry.*

including laptop or smartphones. • Share abnormal information with a vet. • Detect illness and respond early. • Detect when the cow is in heat.

○ Identify perimeters that need repair.

○ Monitor the entire farming business.

○ Identify areas of dry land that require irrigation.

○ Distance between the ages and length of face. • Detect each cow in a dairy farmer's herd. • Send alerts when a cow behaves erratically: ○ Walking irregularly or missing feeds.

○ Track the link between each cow's food intake and their milk

• Data can be accessed anywhere by using modern devices

• Boost chances of healthy pregnancies which enhance milk

• Monitor the entire farm and identify early risks including:

**52**

**6. Conclusion**

**Table 1.**

This chapter espoused the high level of preparedness and resilience by dairy farmers during and in the aftermath of droughts to selected countries. It is observed in this chapter that while drought effects have paralyzed the dairy industry, the demand of dairy products has remained constant. The increase of the demand of the dairy industry has improved the quality of life of people as it provided formal and seasonal employment. Moreover, it also increased competition among the dairy farmers coupled with profits gained. Consumers also benefited as they have purchased quality dairy products which were influenced by the competition among dairy industries. This chapter has depicted the adverse effects of drought which have affected the dairy supply value chain from the grazing fields, herd health and productivity, infrastructure, economy, and resource availability. Various technological inventions and applications have been seen as beneficial to dairy farmers which has increased the health and productivity of cows, monitoring of the entire business and detection strategies which have increased cow milk yields. The technological, financial, political, and natural disasters and input risks have been the dominant risks in the dairy supply chain and have had catastrophic effects on

Chari Felix1 \* and Ngcamu Bethuel Sibongiseni2

1 Bindura University of Science Education, Bindura, Zimbabwe

2 Walter Sisulu University, East London, South Africa

\*Address all correspondence to: charifelix93@gmail.com

© 2019 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.

### **References**

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[32] SADC Agromet. Food Security Early Warning Systems Update Agromet 2015/2016, Agriculture Season-January-February Update; 19 February 2016. 2016;**6**. Available from: www.sadc.int/fanr

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of climate change on livestock

*DOI: http://dx.doi.org/10.5772/intechopen.84573*

[18] Mutono S. Milk production declines.

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reconstruction: Present state, on-going projects and prospects for reinvestment. Development Planning Division, Development Bank of Southern Africa. Working Paper Series No: 32; 2012

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[24] Mataranyika M. SA Imports Pose Threat. News 24 (Online). 2015. Available from: http://allafrica.com [Accessed: 05 February 2016]

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The Patriot. 2014

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa… DOI: http://dx.doi.org/10.5772/intechopen.84573*

[18] Mutono S. Milk production declines. The Patriot. 2014

[19] Mzumara M. An overview of Zimbabwe's macroeconomic environment. International Journal of Economics and Research. 2012;**v3i1**: 33-69. ISSN: 2229-6158. Available from: online@www.ijeronline.com

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[21] Anseeuw W, Kapuya T, Saruchera D. Zimbabwe's agricultural reconstruction: Present state, on-going projects and prospects for reinvestment. Development Planning Division, Development Bank of Southern Africa. Working Paper Series No: 32; 2012

[22] Kawambwa P, Hendriksen G, Zandonda E, Wanga L. Business Viability Assessment Study of Small Holder Dairy Farming in Zambia. Wageningen: Alterra; 2014

[23] Gadzikwa EC. The future of the manufacturing sector in Zimbabwe. Institute of Chartered Accountants of Zimbabwe Congress, 18-20 July 2013, Victoria Falls. Available from: https://www.icaz.org.zw/iMISDocs/ manufacture.pdf. [Accessed: 21 May 2017]

[24] Mataranyika M. SA Imports Pose Threat. News 24 (Online). 2015. Available from: http://allafrica.com [Accessed: 05 February 2016]

[25] Mpofu B. Dairy Sector Pushes for Protectionism. News Day (Online). 2013. Available from: http://www.newsday. co.zw [Accessed: 16 January 2016]

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et al. Climate Change Impacts, Vulnerability and Adaptation in Zimbabwe. Working Paper No. 3; 2012

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[28] Chagutah T. Towards improved public awareness for climate related disaster risk reduction in South Africa: A participatory development communication perspective. JÀMBÁ. Journal of Disaster Risk Studies. 2009;**1**:2

[29] Masama E. Research note: Impact of climate change on livestock production in Zimbabwe. International Open and Distance Learning Journal. 2013;**2**(1):47-53. Available from: http:// www.iodlj.zou.ac.zw/ejournal/index. php/journal/article/viewFile/74/77

[30] Gumbo, D. Zimbabwe: Country Case Study on Domestic Policy Frameworks for Adaptation in the Water Sector: OECH Global Forum on Sustainable Development. 2006. Retrieved from: http://www.oecd. org/environment/cc/36318866.pdf [Accessed: 26 July 2015]

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[32] SADC Agromet. Food Security Early Warning Systems Update Agromet 2015/2016, Agriculture Season-January-February Update; 19 February 2016. 2016;**6**. Available from: www.sadc.int/fanr

[33] Zimbabwe Vulnerability Assessment Committee (ZimVAC). 2016 Rural Livelihoods Assessment. Harare: Zimbabwe Vulnerability

**54**

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[1] Mlambo K, Zitsanza N. Economies of scale, capacity utilization and productivity measurement in

[11] Gertenbach W. Dairy farming in South Africa: Where to now? Institute for Animal Production, Western Cape Department of Agriculture; 2006. Available from: www.fao.org

[12] Hahlani CD, Garwi J. Operational Challenges to Small Dairy Farming: The Case of Maryfield Dairy Settlement Scheme in Chipinge District of Zimbabwe. 2014;**19**:87-94. Available

from: www.iosrjournals.org

10 October 2016]

[13] Zimbabwe National Chamber Of Commerce (ZNCC). Women Agribusiness Entrepreneurs in

[14] Commercial Farmers Union. Zimbabwean agriculture within an African global context. In: 71st Annual Congress Report, 28 October, 2014. 2014. Retrieved from: http://www.cfuzim. org/~cfuzimb/images/brochure2014.pdf

[Accessed: 12 December 2015]

[15] Chari F, Ngcamu BS. An assessment of the impact of disaster risks on dairy supply chain performance in Zimbabwe. Cogent Engineering. 2017;**4**:1409389

[16] Nyakazeya P. Power cuts costing Zimbabwe millions. 2016. Retrieved from: http://www.financialgazette. co.zw/power-cuts-costing-zimmillions/ [Accessed: 06 April 2016]

[17] Zvinorova PI, Halimani TE, Mano RT, Ngongoni NT. Viability of smallholder dairying in Wedza, Zimbabwe. Tropical Animal Health Production. 2013;**45**:1007. DOI: 10.1007/

s11250-012-0325-8

Zimbabwe: Evaluating Access to Capital and Markets. Nathan Associates. 2016. Reviewed by the United States Agency for International Development. Retrieved from: http://www.zncc. co.zw/docs/Zimbabwe-Women-EntrepreneursFinal.pdf [Accessed:

Zimbabwean commercial agriculture. African Development Bank Reviews. 2001;**9**(2):15-32. DOI: 10.1111/j.1467- 8268.1997.tb00153.x. Available from: http://www.afdb.org/knowledge/ reviews/reviews\_vol9\_n2.htm

[2] Coetzee K. Global dairy crisis is over.

[3] Food and Agriculture Organisation

[4] Stichting Nederlandse Vrijwilligers. Evaluation of Small Holder Dairy Programmes in Zimbabwe. SNV Report;

[5] WWF SA. Agriculture: Facts and

[6] Agriculture Statistics. Directorate: Agricultural Statistics of the National Department of Agriculture, Pretoria;

[7] Census of Commercial Agriculture. Agricultural Statistics South Africa. Statistics South Africa; 2008. Available from: http://www.statssa.gov.za

[8] Midgley SJE. Commodity Value Chain Analysis for Dairy. South Africa:

[9] Lakew 2017. 2015/2016 Agriculture season-January-February Update; 19 February 2016. Issue no. 6. Available

[10] Curtis M. Milking the poor: How EU subsidies hurt dairy producers in

Trends. South Africa. 2015

(FAO). Crop and Food Supply Assessment Mission to Zimbabwe: Special Report. 2003. Available from: ftp://ftp.fao.org/docrep/fao/005/

The Dairy Mail. 2017

y9730e/y9730e00.pdf

2012

2008

WWF-SA; 2016

from: www.sadc.int/fanr

Bangladesh. Dhaka. 2011

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Assessment Committee (ZimVAC); 2016. Available from: http://reliefweb. int/sites/reliefweb.int/files/resources/ zimvac\_2016\_rural\_livelihoods\_ assessment.pdf

[34] Nyamwanza T, Mavhiki S, Nyamwanza L, Chagwesha M. Capacity utilisation strategies in the milk processing industry in Zimbabwe. The Journal of Management and Marketing Research. 2015;**19**:1-9

[35] Zimbabwe Vulnerability Assessment Committee (ZimVAC). Security Assessment. Harare; 2009

[36] Chen J, Sohal AS, Prajogo DI. Supply chain operational risk mitigation: A collaborative approach. International Journal of Production Research. 2013;**51**(7):2186-2199

[37] Guzman M. Total disaster risk management approach: Towards effective policy in disaster risk reduction and response. In: Regional Workshop on Total Disaster Risk Management 7-9 August; Philippines; 2002

[38] Murigi JM. Strategies of minimizing the effects of supply chain disruption caused by natural disasters in Kenya: A case study of Brookside dairy limited. Prime Journal of Business Administration and Management. 2013;**3**(4):971-978

[39] Land O'Lakes. Zimbabwe: Restoring Confidence in Milk Collection. Land O'Lakes: International Development; 2014

[40] Dairibord Holdings Limited. Annual Report 2015. 2015

[41] Government of Zimbabwe. Civil Protection Act. Harare: Government Printers; 2001

[42] Betera L. Overview of Disaster Risk Management and Vulnerability. Zimbabwe: Civil Protection Unit; 2011 [43] Bongo P, Chipangura P, Sithole M, Moyo F. A rights-based analysis of disaster risk reduction framework in Zimbabwe and its implications for policy and practice, Jamba. Journal of Disaster Risk Studies. 2013;**5**(2):2-11

[44] Shamano N. An investigation into the disaster risk reduction (DRR) efforts in Gutu District (Zimbabwe): A focus on drought early warning systems. In: Partial Fulfilment of the Requirements f or the Degree, Masters in Disaster Management in the Disaster Management Training and Education Center for Africa. University of Free State, Free State; 2010

[45] Kellett J, Spark D. Disaster risk reduction: Spending where it should count. 2012. Available from: http:// www.globalhumanitarianassistance. org/wp-content/uploads/2012/03/GHA-Disaster-Risk-Report.pdf

[46] Government of Zimbabwe. Comprehensive Agricultural Policy Framework (2012-2032) Executive Summary, 2012

[47] Stichting Nederlandse Vrijwilligers (SNV). Rural Agriculture Revitalisation Programme Dairy breeding study report. 2013. Retrieved from: http:// www.snv.org/public/cms/sites/default/ files/explore/download/zimb abwe\_ smallholder\_dairy\_breeding\_study\_ report.pdf [Accessed: 13 November 2015]

[48] Nhlapho B. Zimbabwe grappling with devastating El Nino as world leaders discuss climate change. 2015. Online. Available from: www.voa. zimbabwe.com

[49] Tsikirayi CMR, Makoni E, Matiza J. Analysis of the uptake of agricultural insurance services by the agricultural sector in Zimbabwe. Journal of International Business and Cultural Studies. 2013;1-14

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*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa…*

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[50] Shield. Shield Health Financing Reform: Who is Covered by Health Insurance Schemes and Which Services

[51] Zimnat Lion Insurance. Farming Insurance. 2016. Available from: http:// www.zimnatlion.co.zw/businessinsurance/farming-insurance/ [Accessed: 16 November 2016]

Smallholder development programme in resettled and communal areas in Zimbabwe. In: Proceedings of a Workshop on the Future of Livestock Industries in East and Southern Africa Held from 20-23 July in Kadoma, Zimbabwe. Addis Ababa, Ethiopia: ILCA; 1992. pp. 165-172. Available from: http://www.fao.org/Wairdocs/ILRI/

Are Used in Tanzania? 2011

[52] Mupunga EG, Dube DMJ.

x5485E/x5485e0q.htm

*A Synthesis of Risks in Dairy Value Chains in Southern Africa: Cases of South Africa… DOI: http://dx.doi.org/10.5772/intechopen.84573*

[50] Shield. Shield Health Financing Reform: Who is Covered by Health Insurance Schemes and Which Services Are Used in Tanzania? 2011

*Current Issues and Challenges in the Dairy Industry*

[43] Bongo P, Chipangura P, Sithole M, Moyo F. A rights-based analysis of disaster risk reduction framework in Zimbabwe and its implications for policy and practice, Jamba. Journal of Disaster Risk Studies. 2013;**5**(2):2-11

[44] Shamano N. An investigation into the disaster risk reduction (DRR) efforts in Gutu District (Zimbabwe): A focus on drought early warning systems. In: Partial Fulfilment of the Requirements f or the Degree, Masters in Disaster Management in the Disaster Management Training and Education Center for Africa. University of Free

[45] Kellett J, Spark D. Disaster risk reduction: Spending where it should count. 2012. Available from: http:// www.globalhumanitarianassistance. org/wp-content/uploads/2012/03/GHA-

State, Free State; 2010

Disaster-Risk-Report.pdf

Summary, 2012

2015]

zimbabwe.com

Studies. 2013;1-14

[46] Government of Zimbabwe. Comprehensive Agricultural Policy Framework (2012-2032) Executive

[47] Stichting Nederlandse Vrijwilligers (SNV). Rural Agriculture Revitalisation Programme Dairy breeding study report. 2013. Retrieved from: http:// www.snv.org/public/cms/sites/default/ files/explore/download/zimb abwe\_ smallholder\_dairy\_breeding\_study\_ report.pdf [Accessed: 13 November

[48] Nhlapho B. Zimbabwe grappling with devastating El Nino as world leaders discuss climate change. 2015. Online. Available from: www.voa.

[49] Tsikirayi CMR, Makoni E, Matiza J. Analysis of the uptake of agricultural insurance services by the agricultural sector in Zimbabwe. Journal of International Business and Cultural

Assessment Committee (ZimVAC); 2016. Available from: http://reliefweb. int/sites/reliefweb.int/files/resources/ zimvac\_2016\_rural\_livelihoods\_

[34] Nyamwanza T, Mavhiki S,

utilisation strategies in the milk processing industry in Zimbabwe. The Journal of Management and Marketing

Committee (ZimVAC). Security Assessment. Harare; 2009

[36] Chen J, Sohal AS, Prajogo DI. Supply chain operational risk mitigation: A collaborative approach. International Journal of Production Research. 2013;**51**(7):2186-2199

[37] Guzman M. Total disaster risk management approach: Towards

August; Philippines; 2002

2013;**3**(4):971-978

effective policy in disaster risk reduction and response. In: Regional Workshop on Total Disaster Risk Management 7-9

[38] Murigi JM. Strategies of minimizing the effects of supply chain disruption caused by natural disasters in Kenya: A case study of Brookside dairy limited. Prime Journal of Business Administration and Management.

[39] Land O'Lakes. Zimbabwe: Restoring Confidence in Milk Collection. Land O'Lakes: International Development;

[40] Dairibord Holdings Limited.

[41] Government of Zimbabwe. Civil Protection Act. Harare: Government

[42] Betera L. Overview of Disaster Risk Management and Vulnerability. Zimbabwe: Civil Protection Unit; 2011

Annual Report 2015. 2015

Printers; 2001

Research. 2015;**19**:1-9

Nyamwanza L, Chagwesha M. Capacity

[35] Zimbabwe Vulnerability Assessment

assessment.pdf

**56**

2014

[51] Zimnat Lion Insurance. Farming Insurance. 2016. Available from: http:// www.zimnatlion.co.zw/businessinsurance/farming-insurance/ [Accessed: 16 November 2016]

[52] Mupunga EG, Dube DMJ. Smallholder development programme in resettled and communal areas in Zimbabwe. In: Proceedings of a Workshop on the Future of Livestock Industries in East and Southern Africa Held from 20-23 July in Kadoma, Zimbabwe. Addis Ababa, Ethiopia: ILCA; 1992. pp. 165-172. Available from: http://www.fao.org/Wairdocs/ILRI/ x5485E/x5485e0q.htm

**59**

**Chapter 5**

**Abstract**

Mind, Consumers, and Dairy:

Applying Artificial Intelligence,

Mind Genomics, and Predictive

*Ryan Zemel, Somsubhra Gan Choudhuri, Attila Gere,* 

We present a new approach to more deeply understand the mind of consumers with respect to food products, using a combination of artificial intelligence to provide the ideas, Mind Genomics to understand how consumers respond to these ideas, uncovering Mind-Sets, and statistical assignment of new people to these newly uncovered Mind-Sets using the PVI, personal viewpoint identifier. We illustrate the approach in detail with yogurt, and then present data from other studies about yogurt, milk, and cheese, to reveal new knowledge about emergent Mind-Sets for conventional dairy products. The key benefits of the approach are the scope (many ideas from artificial intelligence), discipline (experimental design to uncover what is important), actionability findings, mind-types, speed (within 2 or

**Keywords:** consumer, conjoint analysis, e-commerce, product development

Our twenty-first century world is awash with information. One need only look at the amount of information on the Internet about any topic, and the likelihood is that the number of sites is in the tens of thousands, if not more, at least for topics which are popular. It is not the lack of information which is the bane of our century,

Our thinking to deal with such an abundance of information is either to shut out most of it, or do some type of directed search for the topic. One cannot absorb the totality of information in a popular subject, nor perhaps even form a reasonable opinion based upon deep knowledge, unless perhaps one has specialized in the topic and has amassed a great deal of information after years of practice. There are of course tools which sift ideas, such as Google® for conventional websites, and Google Scholar® for academic papers. These sifting tools aggregate data "on the fly," presenting the raw material as different sites to explore. One can then use the

**1. Introduction: the Big Data or fire hose of information**

but the plethora, metaphorically the fire hose of information.

*Himanshu Upreti, Yehoshua Deite, Petraq Papajorgji* 

Viewpoint Typing

3 days), and cost (low because of automation).

*and Howard Moskowitz*

### **Chapter 5**

## Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing

*Ryan Zemel, Somsubhra Gan Choudhuri, Attila Gere, Himanshu Upreti, Yehoshua Deite, Petraq Papajorgji and Howard Moskowitz*

### **Abstract**

We present a new approach to more deeply understand the mind of consumers with respect to food products, using a combination of artificial intelligence to provide the ideas, Mind Genomics to understand how consumers respond to these ideas, uncovering Mind-Sets, and statistical assignment of new people to these newly uncovered Mind-Sets using the PVI, personal viewpoint identifier. We illustrate the approach in detail with yogurt, and then present data from other studies about yogurt, milk, and cheese, to reveal new knowledge about emergent Mind-Sets for conventional dairy products. The key benefits of the approach are the scope (many ideas from artificial intelligence), discipline (experimental design to uncover what is important), actionability findings, mind-types, speed (within 2 or 3 days), and cost (low because of automation).

**Keywords:** consumer, conjoint analysis, e-commerce, product development

#### **1. Introduction: the Big Data or fire hose of information**

Our twenty-first century world is awash with information. One need only look at the amount of information on the Internet about any topic, and the likelihood is that the number of sites is in the tens of thousands, if not more, at least for topics which are popular. It is not the lack of information which is the bane of our century, but the plethora, metaphorically the fire hose of information.

Our thinking to deal with such an abundance of information is either to shut out most of it, or do some type of directed search for the topic. One cannot absorb the totality of information in a popular subject, nor perhaps even form a reasonable opinion based upon deep knowledge, unless perhaps one has specialized in the topic and has amassed a great deal of information after years of practice. There are of course tools which sift ideas, such as Google® for conventional websites, and Google Scholar® for academic papers. These sifting tools aggregate data "on the fly," presenting the raw material as different sites to explore. One can then use the

Google® tools to get a sense of what is "au courant," although the effort to do so may be more daunting in the execution than in the expectations before the effort is made.

With the foregoing introduction, the next question is how does the novice, whether scientist or simply interested layman, learn about the mind of a consumer toward a specific product? For instance, let the product be "milk." A Google® search of consumer + milk shows a mere 130 million sites. Refining the focus for Consumer Attitudes Regarding Milk, again on Google® revealed 6,280,000 hits. A more focused search, this time for academic papers, on Google Scholar®, for Consumer Attitudes Towards Milk, revealed 90,700 hits. Certainly, enough for a number of PhDs, and for a lifetime of reading, but what about the practical problem of the small, even a start-up company, wanting to develop a new product? The "plethora of choice" in the world of information is simply paralyzing, so that the expeditious answer is to guess, to solicit the advice of an expert, to buy a book of trends in food, to run a focus group, or perhaps to spend a great deal of money developing products and concepts with the full confidence that it MUST BE GOOD [1, 2].

Whether the foregoing picture presents a positive development, a negative development, or perhaps just a development without valence is not the issue. The issue for this chapter is whether one can use the mass of information to understand issues, say in dairy, with these issues relating to the attitudes of consumers. Simply stated, can we create a system to rapidly and profoundly understand the mind of the consumer regarding a specific topic, and, where possible, incorporate the contribution of the "Big Data of Relevant Information?"

#### **2. Surveys, observations, and their limits**

For a century now, the norm for understanding subjective reactions to products has been to ask people to talk about these products in focus groups or other qualitative methods [3], and for those who are quantitatively oriented, to ask questions of people in a survey. Often surveys begin with topics about what one does in general, such as food preferences and food habits [4], now evolving down to a momentary survey after a relevant experience to ask 'How did we do?, or 'Would you recommend us to someone with whom you do business?' the now-ubiquitous NPS, (Net Promoter Score), analyzed by [5].

As the amount of information increases, and as companies run surveys about the attitudes and usages of product, whether dairy or other food products, it is becoming increasingly obvious that data are cheap to obtain, but true knowledge of the so-called actionable nature is expensive. By actionable, we mean the use of the data to effect some change, whether that be convincing someone to try or buy a product, or learning how to change the ingredients of a product to increase acceptance. Surveys are limited to the respondent's conscious efforts to answer the interviewer's questions. Often, they require knowledge to which the respondent may not be privy, or may require "politically correct" answers. An example of the former, information to which the respondent is not privy, is what to do to change the fat content of milk, or to make the milk taste like it is full fat. The latter, "politically correct" answers come from the desire to give the correct or socially approved answer. For example, a person who loves whipped cream in great amount on cake as a delicious dessert may simply not describe dessert preferences, or when doing so may consciously or perhaps even unconsciously forget one's lifelong obsession with mountains of whipped cream when allowed to consume it.

**61**

rate success.

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

The sheer abundance of data, this so-called "hydrant effect" may seduce one into thinking that the "answers are there" but the reality is that one learns far more from simple experiments. In recent years, author HRM has introduced the new, now more rapidly emergent science of Mind Genomics [6, 7]. The name Mind Genomics is metaphorical. It posits that knowledge about decision-making comes from presenting people with combinations of ideas of different types, measuring their responses, and determining which ideas or sets of ideas (mind-genomes) drive the decision-making. To further the metaphor, each topic area of experience comprises a variety of aspects. The aspects of a topic to which a person attends while making a decision are the so-called "mind genomes." Furthermore, each topic area has a

Mind Genomics has already been applied to the dairy world in a number of different, easy-to-do experiments. For example, one study looked at the different ways of making a decision about what a dairy product (yogurt) is worth. Through the Mind Genomics method, it became possible to extract various mind-genomes about yogurt, with each person embodying one of a set of mutually exclusive genomes. The objective of that study was to identify a group of individuals who valued

Other studies of dairy have involved products such as milk, yogurt, cheese and

We live in an age of instant gratification, of superficial thinking, of information abundance, and most sadly, a belief that whatever we do has to be made simple, dumbed down. When our focus is to understand the mind of the consumer toward a dairy product, this might mean running a few focus groups to get a "sense" of today's customer, or doing a general survey about dairy using any of the widely available survey platforms like Survey Monkey® [9]. One could also mine the Web for information, and produce a summarized report of trends. The aforementioned approach provides a great deal of information, often delightfully presented in newsletters, at conferences, at webinars. Yet, there is something missing, the translation

One of the most common, traditional methods of using the data is to present the information from these surveys, focus groups, and so forth to the agency and marketing professionals, often called "creatives." It becomes the job of the creative to synthesize the information, and with her or his skill, experience, and insight, to emerge with the final "idea," whether the idea be fully formed or even modestly sketched out. We are accustomed to experiments in the world of physical features. These experiments may range from a simple change in a product, and the measurement of the consumer response to the product (so-called "cook and look"), all the way up to DOE, Design of Experiment [10]. DOE specifies different combinations of ingredients, and then measures the response to the combination in order to identify what each product ingredient contributes, and how a specific combination performs in a consumer test. DOE is usually in the purview of R&D, and represents a dramatic investment of time and money, but also an increase in the opportunity for a corpo-

**4. Positives and negatives of experiments to understand the consumer** 

**3. Changing the paradigm from Big Data and surveys to small** 

limited set of these mind-genomes, almost mind-alleles, in some sense.

texture or mouthfeel as the basic criterion for decision-making [8].

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

**experiments**

so forth.

**mind toward dairy**

of the information into product concepts.

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*

#### **3. Changing the paradigm from Big Data and surveys to small experiments**

The sheer abundance of data, this so-called "hydrant effect" may seduce one into thinking that the "answers are there" but the reality is that one learns far more from simple experiments. In recent years, author HRM has introduced the new, now more rapidly emergent science of Mind Genomics [6, 7]. The name Mind Genomics is metaphorical. It posits that knowledge about decision-making comes from presenting people with combinations of ideas of different types, measuring their responses, and determining which ideas or sets of ideas (mind-genomes) drive the decision-making. To further the metaphor, each topic area of experience comprises a variety of aspects. The aspects of a topic to which a person attends while making a decision are the so-called "mind genomes." Furthermore, each topic area has a limited set of these mind-genomes, almost mind-alleles, in some sense.

Mind Genomics has already been applied to the dairy world in a number of different, easy-to-do experiments. For example, one study looked at the different ways of making a decision about what a dairy product (yogurt) is worth. Through the Mind Genomics method, it became possible to extract various mind-genomes about yogurt, with each person embodying one of a set of mutually exclusive genomes. The objective of that study was to identify a group of individuals who valued texture or mouthfeel as the basic criterion for decision-making [8].

Other studies of dairy have involved products such as milk, yogurt, cheese and so forth.

#### **4. Positives and negatives of experiments to understand the consumer mind toward dairy**

We live in an age of instant gratification, of superficial thinking, of information abundance, and most sadly, a belief that whatever we do has to be made simple, dumbed down. When our focus is to understand the mind of the consumer toward a dairy product, this might mean running a few focus groups to get a "sense" of today's customer, or doing a general survey about dairy using any of the widely available survey platforms like Survey Monkey® [9]. One could also mine the Web for information, and produce a summarized report of trends. The aforementioned approach provides a great deal of information, often delightfully presented in newsletters, at conferences, at webinars. Yet, there is something missing, the translation of the information into product concepts.

One of the most common, traditional methods of using the data is to present the information from these surveys, focus groups, and so forth to the agency and marketing professionals, often called "creatives." It becomes the job of the creative to synthesize the information, and with her or his skill, experience, and insight, to emerge with the final "idea," whether the idea be fully formed or even modestly sketched out.

We are accustomed to experiments in the world of physical features. These experiments may range from a simple change in a product, and the measurement of the consumer response to the product (so-called "cook and look"), all the way up to DOE, Design of Experiment [10]. DOE specifies different combinations of ingredients, and then measures the response to the combination in order to identify what each product ingredient contributes, and how a specific combination performs in a consumer test. DOE is usually in the purview of R&D, and represents a dramatic investment of time and money, but also an increase in the opportunity for a corporate success.

*Current Issues and Challenges in the Dairy Industry*

that it MUST BE GOOD [1, 2].

tion of the "Big Data of Relevant Information?"

**2. Surveys, observations, and their limits**

Promoter Score), analyzed by [5].

whipped cream when allowed to consume it.

is made.

Google® tools to get a sense of what is "au courant," although the effort to do so may be more daunting in the execution than in the expectations before the effort

With the foregoing introduction, the next question is how does the novice, whether scientist or simply interested layman, learn about the mind of a consumer toward a specific product? For instance, let the product be "milk." A Google® search of consumer + milk shows a mere 130 million sites. Refining the focus for Consumer Attitudes Regarding Milk, again on Google® revealed 6,280,000 hits. A more focused search, this time for academic papers, on Google Scholar®, for Consumer Attitudes Towards Milk, revealed 90,700 hits. Certainly, enough for a number of PhDs, and for a lifetime of reading, but what about the practical problem of the small, even a start-up company, wanting to develop a new product? The "plethora of choice" in the world of information is simply paralyzing, so that the expeditious answer is to guess, to solicit the advice of an expert, to buy a book of trends in food, to run a focus group, or perhaps to spend a great deal of money developing products and concepts with the full confidence

Whether the foregoing picture presents a positive development, a negative development, or perhaps just a development without valence is not the issue. The issue for this chapter is whether one can use the mass of information to understand issues, say in dairy, with these issues relating to the attitudes of consumers. Simply stated, can we create a system to rapidly and profoundly understand the mind of the consumer regarding a specific topic, and, where possible, incorporate the contribu-

For a century now, the norm for understanding subjective reactions to products has been to ask people to talk about these products in focus groups or other qualitative methods [3], and for those who are quantitatively oriented, to ask questions of people in a survey. Often surveys begin with topics about what one does in general, such as food preferences and food habits [4], now evolving down to a momentary survey after a relevant experience to ask 'How did we do?, or 'Would you recommend us to someone with whom you do business?' the now-ubiquitous NPS, (Net

As the amount of information increases, and as companies run surveys about the attitudes and usages of product, whether dairy or other food products, it is becoming increasingly obvious that data are cheap to obtain, but true knowledge of the so-called actionable nature is expensive. By actionable, we mean the use of the data to effect some change, whether that be convincing someone to try or buy a product, or learning how to change the ingredients of a product to increase acceptance. Surveys are limited to the respondent's conscious efforts to answer the interviewer's questions. Often, they require knowledge to which the respondent may not be privy, or may require "politically correct" answers. An example of the former, information to which the respondent is not privy, is what to do to change the fat content of milk, or to make the milk taste like it is full fat. The latter, "politically correct" answers come from the desire to give the correct or socially approved answer. For example, a person who loves whipped cream in great amount on cake as a delicious dessert may simply not describe dessert preferences, or when doing so may consciously or perhaps even unconsciously forget one's lifelong obsession with mountains of

**60**

We deal in this chapter with consumer knowledge, ideas. How does one experiment with ideas about dairy? The answer to this question is quite simple. One can present ideas, simple or compound, about a dairy product, and obtain ratings about the ideas. **Figure 1** shows an example of three advertisements about yogurt from Chobani®, presented in the original language, and deconstructed as a preparation for analysis by experimental design:

The choice of concepts in **Figure 1** is simply that. The reasons behind the choice must be left to probing questions asked of those who evaluated the concepts, and/or left to the talented researcher who can "connect the dots" and tell an engaging, and possibly insightful story.

A better way to understand the world of dairy from the mind of the consumer involves experimentation, preferably easy, fast, and inexpensive experimentation that anyone can do [11]. We illustrate the strategy with data from a study

#### **Figure 1.**

*Comparison of three text advertisements for Chobani® yogurt taken from the Web (December, 2018), and their deconstruction for study by experimental design (Mind Genomics).*

**63**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

1 Identify the topic The topic may be product, service, or literally anything where human experience and judgment play a key role.

structured reservoir of raw material.

the product, for this study of yogurt.

used simply to promote creative thought.

each of four questions, or sixteen answers in total.

7 Create a rating scale The scale comprises a question and an anchored scale (lowest and highest scale points each have a defining phrase).

8 Select respondents The entire objective of the exercise is to have respondents judge these

*product?*

company.

feelings.

The Web and social media present an almost inexhaustible number of ideas. Mine the Web and social media to extract "ideas" in rough form, simple if possible. Consider these to be "nuggets of ideas," a semi-

The objective is to find four aspects of the topic that can be put as questions which together, and in sequence, *tell a story*. The topic is the description of

The questions require the user to "think" about the story of what the product is. The questions will never be shown to the respondent. The questions will be

Return now to the information extracted by artificial intelligence. With the four questions as a guide, and with the semi-structured reservoir of raw material, provide four answers or phrases for each question. One's mind, one's creative intuitions from the semi-structured reservoir of ideas, and one's ability to craft a sentence allow one to generate the necessary four answers to

Make the introduction simple, with little information other than what the study is about, and what the respondent should do. The information will come from the answers to the questions (the messages, the elements).

What do you want the respondent to consider when making a judgment? The easiest is an evaluative attribute, such as: *How interested are you in this* 

ideas. The respondents who participate may come from the corporation's customers, or from a commercial panel. It is always easier to work with panelists who are compensated for their participation. The fastest, easiest, and often the most productive way is to work with a commercial

Find out age, and gender. Put in a third classification question dealing with the topic. The APP used here is limited to three classification questions to make the system quick and inexpensive to execute.

Each respondent evaluates a UNIQUE SET OF 24 VIGNETTES. This unique set is important. It means that increasing the number of respondents allows the researcher to test more of the "space of the combinations" rather than

The response time is the time from the presentation of the vignette on the screen to the response. The time is measured in seconds.

Optional, to obtain more information from the respondent about her or his

For the typical, most-used, 9-point scale, convert the rating of 1–6 to 0. Convert the rating of 7–9 to 100. Then add a very small random number to

The reason for the transformation is that although the rating scale is easy to use, it is not clear what a scale value means. It is a lot easier to use a binary scale. The key is how to bisect the 9-point rating scale. We are somewhat stringent, with "no" corresponding to the bottom 2/3 of the scale, and "yes"

simply testing the same combinations again and again.

the now-converted value of 0 or 100, respectively.

corresponding to the top 1/3 of the scale.

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

**Step Activity Rationale**

and social media using artificial intelligence

aside, and concentrate on the topic, by formulating four questions

2 Interrogate the Web

3 Put the Web output

4 Edit the four questions, and set them up to be

5 Answer each question with four answers

6 Create an introduction for the respondents

to read

9 Get classification information

10 Present the respondent with 24 vignettes, one after another

11 Collect the rating from each vignette and measure response time

12 Ask the respondent

13 Transform the 9-point ratings to binary

another question, openended, about a relevant aspect of the topic

answered

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*


*Current Issues and Challenges in the Dairy Industry*

for analysis by experimental design:

possibly insightful story.

We deal in this chapter with consumer knowledge, ideas. How does one experiment with ideas about dairy? The answer to this question is quite simple. One can present ideas, simple or compound, about a dairy product, and obtain ratings about the ideas. **Figure 1** shows an example of three advertisements about yogurt from Chobani®, presented in the original language, and deconstructed as a preparation

The choice of concepts in **Figure 1** is simply that. The reasons behind the choice must be left to probing questions asked of those who evaluated the concepts, and/or left to the talented researcher who can "connect the dots" and tell an engaging, and

A better way to understand the world of dairy from the mind of the consumer involves experimentation, preferably easy, fast, and inexpensive experimentation that anyone can do [11]. We illustrate the strategy with data from a study

*Comparison of three text advertisements for Chobani® yogurt taken from the Web (December, 2018), and their* 

*deconstruction for study by experimental design (Mind Genomics).*

**62**

**Figure 1.**


**65**

lated in **Table 1**.

**Table 1.**

**with a dairy product, yogurt**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

respondents).

statements.

These might be considered the basic mental alleles of judgment of a topic. They are not exhaustive, but suggest groups with different thoughts about what is important and relevant (positive or negative) versus irrelevant.

To find Mind-Sets, we array the coefficients from our respondents (but not the additive constant), creating a matrix. The columns are the elements (our 16 elements). The rows are the respondents (our 50

We compute a measure of distance between each pair of respondents, using an accepted distance measure. In our case, we use the value (1-Pearson R). The Pearson R, or correlation varies from a high of +1 (perfect linear relation, meaning a distance of 0 between the two respondents), down to a low of −1 (perfect inverse relation, meaning a

Clustering then reveals non-overlapping groups of meaningfully different

We choose the fewest number of clusters or Mind-Sets (parsimony), such

The set of answers in the study (the original set of 16) now are filtered to identify which answers most efficiently differentiate among Mind-Sets. The PVI, personal viewpoint identifier, emerging from the experiment typically comprises 3–7 such answers from the original 16, now recast as

The different answers (aforementioned 3–7) are presented in random order for each person to be mind-typed and assigned to one of the Mind-Sets. The person to be assigned either agrees with the statement or disagrees with the statement (or feels the statement is important or unimportant). Thus, the response is binary, no/yes, unimportant/

The pattern of responses assigns the person to one of the Mind-Sets, the

that these Mind-Sets tell a meaningful story (interpretability).

distance of 2 between the two respondents).

respondents, showing different Mind-Sets.

that required a total of 6 hours, done at a very low cost, dealing with yogurt. The emphasis on speed, cost, and simplicity is important for the tenor of the chapter. Our goal is to present a new paradigm, more powerful than other previous approaches, as well as far faster, and significantly more economical, all leitmotifs for today, as of this writing (December, 2018). The strategy is very simple, encapsu-

important, disagree/agree.

"best guess" assignment.

**5. Toward a new paradigm: front to back Mind Genomics experiment** 

A good way to understand the features of the paradigm and what it delivers to the user comes through the demonstration with a common product that can be moderately modified, with that innovation driven by the consumer requirements. This is the typical situation, wherein there is no major technical innovation, but there is the corporate need to offer something new and attractive. The ingoing assumption is that the "new product" is somewhere "out in the ether." The features of the new product must be discovered, and not slogans, but real ideas. The effort may be too slow or cumbersome when fighting against other internal priorities,

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

**Step Activity Rationale**

statistics (clustering) to uncover Mind-Sets, but judgment to name them

20 Within any topic, Mind Genomics allows us to uncover basic groups of responses, so-called

Mind-Sets

21 Use conventional

22 Create a set of questions from the experiment, the pattern of answers to which assigns a new person to one of the Mind-Sets uncovered in the experiment

*The paradigm explicated using yogurt.*

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*


#### **Table 1.**

*Current Issues and Challenges in the Dairy Industry*

The vignettes were constructed according to a basic experimental design. The design was permuted for each respondent. The experimental design allows us

For other, bigger designs, also created in this fashion, using a permuted individual-level design, there may be more questions and more answers per question. The mathematics is precisely the same. The only difference is the number of coefficients. There is one coefficient for each answer.

Using the same mathematics, create another model for each respondent, this time using response time as the dependent variable. Prepare the data by recoding any response time of 30 seconds or over as 30 seconds because the longer time probably represents an interruption in the experiment. The equation does not have the additive constant, k0. We write it as

By applying OLS regression to the data from each respondent, we ensure that each respondent generates an individual set of 16 coefficients, and for

We use that coefficient as the basis of understanding the pattern of responses for each participant, as well as averaging the coefficient across subgroups to understand the average of the subgroup, and thus the pattern

We can combine the additive constant with up to four answers or elements and add their values to estimate the performance of the

Recall from above (#16) that the additive model is written as: Binary

would be achieved if the vignette had no elements, no answers.

increases the proportion of respondents who rate the concept 7–9. A negative coefficient means that adding the answer to a combination or a concept decreases the proportion of respondents who rate the concepts 7–9. It is not that they do not like the idea. It is just that the element is not a

base for a subgroup, the average coefficient is not stable.

When the coefficient is ... here is how to interpret

0 to +5 does not hurt, but not important 0 to −5 negative, only slightly damaging −5 to −10 negative, could be damaging −10 or lower strong negative

The additive constant, k0, tells us the likely rating on the scale of 0–100 that

The coefficient tells us the contribution of each element or answer. Each of

Positive coefficients mean that adding the answer to a combination or vignette

We consider results from base sizes as few as 8–10 respondents. Below that

To find subgroups, we simply combine the coefficients from the people who fall into the group. This could be gender, age, pattern of usage, etc. Then, across the respondents selected for the subgroup, average their respective additive constants, and corresponding coefficients, to estimate

Alternatively, we can simply put raw data together for all the relevant respondents in a subgroup, and run one OLS regression. This is called the Grand Model. The parameters of the Grand Model typically correlate highly with the corresponding average parameters estimated by averaging

to estimate the coefficients of the model for each respondent.

Binary Rating = k0 + k1(A1) + k2(A2) ... k16(D4).

Time (seconds) = k1(A1) + k2(A2) ... k16(D4).

interest, an additive constant as well.

of their thinking about the topic.

Rating = k0 + k1(A1) + k2(A2) … k16(D4).

the 16 answers has an average coefficient.

particularly strong positive.

group performance.

the individual models.

+15 or higher major positive +10 to +15 strong positive +5 to +10 positive

The equation created is of the form:

follows:

combination.

**Step Activity Rationale**

individual respondent, use OLS (ordinary least-squares) regression to relate the presence/absence of the 16 answers to the binary ratings (0/100)

presence/absence of the

14 At the level of the

15 OLS relates the

16 answers to the response time

16 The unit of analysis is the individual coefficient

17 The coefficients tell us about how the respondents react to individual elements

18 Combine groups of respondents based on any criteria

19 Previous studies suggest norms

**64**

*The paradigm explicated using yogurt.*

that required a total of 6 hours, done at a very low cost, dealing with yogurt. The emphasis on speed, cost, and simplicity is important for the tenor of the chapter. Our goal is to present a new paradigm, more powerful than other previous approaches, as well as far faster, and significantly more economical, all leitmotifs for today, as of this writing (December, 2018). The strategy is very simple, encapsulated in **Table 1**.

#### **5. Toward a new paradigm: front to back Mind Genomics experiment with a dairy product, yogurt**

A good way to understand the features of the paradigm and what it delivers to the user comes through the demonstration with a common product that can be moderately modified, with that innovation driven by the consumer requirements. This is the typical situation, wherein there is no major technical innovation, but there is the corporate need to offer something new and attractive. The ingoing assumption is that the "new product" is somewhere "out in the ether." The features of the new product must be discovered, and not slogans, but real ideas. The effort may be too slow or cumbersome when fighting against other internal priorities,

or when the assignment must be to an outside, not-necessarily quickly responsive organization. Nor, in fact, is there the desire to wait until some start-up corporation develops a product, and then "snatch up" the corporation, making up by acquisition what one lacks in creation and innovation.

Our case history here is yogurt, although the precise steps can be used for virtually any dairy product, any food product, and indeed any product or service about which people write and talk. The specifics for yogurt are thus meant only as didactic examples.

The specific study on which we elaborate began on December 19, 2018, and finished on December 23, 2018. Of that time, the first 2 days were devoted to refining an existing software which scoured the Internet, discovering and reporting on trends, with these trends specified to be in the food industry. On December 23, we ran the study, and emerged with the results. After the holiday period, on January 4, 2019, we developed the PVI, the personal viewpoint identifier. Altogether, the paradigm, from knowledge development to testing to the personal viewpoint identifier can be said to have required approximately 48 hours of real time, taking into account the development time, as well as the disrupting time respectively. The objective is to show how to "do it" by actually doing it. In the elaboration, we present the different steps following the outline in **Table 1**.

#### **6. The raw material**

**Figure 2** shows an example of the summary information for "Yogurt" yielded by the artificial intelligence system created by authors Choudhuri and Upreti and named "SamanthaSM" for this early stage. **Figures 3** and **4** show examples of the output of Samantha, using the artificial intelligence system.

#### **Figure 2.**

*The matrix of information about the products. The matrix emerges from the artificial intelligence platform, "SamanthaSM," previously designed to deal with the entire vertical of food and beverage.*

**67**

**Figure 3.**

**Figure 4.**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

**Figure 2**, shows a matrix of information about the products. This matrix, with circles and a short phrase, gives a sense of the different ideas. It is important to note that the effort from artificial intelligence is not to create the final questions and answers, but rather to provide hints, suggestions, from which the questions and

Mind Genomics works by presenting the respondents with combinations of ideas, messages, or in our case, combinations of answers to four questions created from the raw material shown in part in **Figures 1**–**3**. As we noted earlier, the role of artificial

answers are crafted. We will see the nature of the crafting later.

*A health-oriented yogurt deconstructed by SamanthaSM.*

**7. Moving from raw material to questions and answers**

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

*Korean smoothie deconstructed by SamanthaSM.*

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*

**Figure 3.** *Korean smoothie deconstructed by SamanthaSM.*

*Current Issues and Challenges in the Dairy Industry*

what one lacks in creation and innovation.

ent the different steps following the outline in **Table 1**.

output of Samantha, using the artificial intelligence system.

**6. The raw material**

or when the assignment must be to an outside, not-necessarily quickly responsive organization. Nor, in fact, is there the desire to wait until some start-up corporation develops a product, and then "snatch up" the corporation, making up by acquisition

Our case history here is yogurt, although the precise steps can be used for virtually any dairy product, any food product, and indeed any product or service about which people write and talk. The specifics for yogurt are thus meant only as didactic examples. The specific study on which we elaborate began on December 19, 2018, and finished on December 23, 2018. Of that time, the first 2 days were devoted to refining an existing software which scoured the Internet, discovering and reporting on trends, with these trends specified to be in the food industry. On December 23, we ran the study, and emerged with the results. After the holiday period, on January 4, 2019, we developed the PVI, the personal viewpoint identifier. Altogether, the paradigm, from knowledge development to testing to the personal viewpoint identifier can be said to have required approximately 48 hours of real time, taking into account the development time, as well as the disrupting time respectively. The objective is to show how to "do it" by actually doing it. In the elaboration, we pres-

**Figure 2** shows an example of the summary information for "Yogurt" yielded by the artificial intelligence system created by authors Choudhuri and Upreti and named "SamanthaSM" for this early stage. **Figures 3** and **4** show examples of the

*The matrix of information about the products. The matrix emerges from the artificial intelligence platform,* 

*"SamanthaSM," previously designed to deal with the entire vertical of food and beverage.*

**66**

**Figure 2.**

**Figure 4.** *A health-oriented yogurt deconstructed by SamanthaSM.*

**Figure 2**, shows a matrix of information about the products. This matrix, with circles and a short phrase, gives a sense of the different ideas. It is important to note that the effort from artificial intelligence is not to create the final questions and answers, but rather to provide hints, suggestions, from which the questions and answers are crafted. We will see the nature of the crafting later.

#### **7. Moving from raw material to questions and answers**

Mind Genomics works by presenting the respondents with combinations of ideas, messages, or in our case, combinations of answers to four questions created from the raw material shown in part in **Figures 1**–**3**. As we noted earlier, the role of artificial


#### **Table 2.**

*The four questions and four answers from each question, created by inspecting the information provided by the artificial intelligence platform, and generating the relevant statements to be used in the actual field execution.*

intelligence, and particularly the SamanthaSM platform, is to present suggestions that the researcher can use to elaborate. The output from the artificial intelligence system comprises both a set of words in **Figure 2** to "jog the mind," as well as links to deeper information (**Figures 3** and **4**). Thus, the Mind Genomics system gives room for suggested topics, as well as for the human elaboration of those topics.

**Table 2** presents the set of four questions, extracted from the information provided by the artificial intelligence platform, and then elaborated and edited to move from information to questions. Each question, in turn, generates four answers, or more correctly, the researcher provides four answers to each question. The answers may be taken directly from the information provided by the artificial intelligence platform, or the answers may be polished and edited information, or perhaps even new ideas sparked by the information provided, by not actually part of the information provided. The reality is that it does not really matter where the information comes from. The Mind Genomics effort is attempting to discover "what works." The information provided to it is the raw material. The goal is to get the best information and identify "what works."

#### **8. Knowledge from responses to mixtures of answers—the contribution of experimental design**

One could take the 16 answers in **Table 2** and rate each of the ideas on a scale of interest. Presenting the answers one at a time and obtaining an answer is the survey

**69**

Kahneman [16].

personal computer or a tablet.

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

method, widely used, but unable to spark the creation of a new product idea in the way it is structured. By presenting the answers one at a time, and then requiring the respondent to rate each idea alone, we are left with ratings of single ideas, but no idea of how ideas interact with and compete with each other, as they drive interest. The respondent may also change the criterion of judgment, judging healthful ingredients more leniently, and the more indulgent features more stringently. A potentially more productive way mixes and matches the answers, creating vignettes. The answers become the building blocks. Rather than building one answer at a time, starting with the most popular, we create combinations of answers using a recipe book (experimental design). The responses to the mixtures of answers help us understand the performance of the single elements. We do that by deconstructing the response to a blend, our mixture of answers, to the part-worth contribution of each answer. This notion was developed extensively by Norman Anderson [12], formalized as the method of conjoint measurement [13], popularized in business and academic circles by Professor Paul Green of The Wharton School of Business of the University of Pennsylvania [14, 15], and finally expanded, and made available

worldwide as a method of knowledge building by author HRM [7].

the method of OLS (ordinary least-square regression), discussed later.

systematically varies the pairs of elements which appear together.

along with the 9-point rating, the transformed value for the rating, and the response time for that vignette (test combination). Each respondent evaluates a totally different set of vignettes. The underlying experimental design is the same in a mathematical sense, but the actual vignettes differ, because a permutation scheme

**10. The study setup by the researcher and the respondent experience**

At its basic level, the Mind Genomics study is an experiment, albeit couched in the form of a survey. The researcher systematically varies the stimulus inputs, the answers, according to the experiment design (**Table 3**), records the respondent's rating as well as time of response, and then analyzes the results. **Figure 5** shows what the respondent sees (test vignette) when using a smartphone. The same vignette can be presented in a slightly different configuration to fit the screen of a

The typical Mind Genomics experiment with BimiLeap® takes approximately 4–5 minutes from start to finish. Many respondents begin with the typical strategy of trying to be "correct." The respondent may spend more time at the start than at the end, reading the vignettes, in order to make sure that they have gotten all the relevant information. By the time the respondent reaches second, and certainly the third vignette, however, this effort begins to subside, and the respondent answers, almost automatically, at an intuitive level, the System 1 of Nobel Laureate Daniel

Mind Genomics works with various experimental designs. For these studies, we work with the so-called 4 × 4 design, namely four questions, each question requiring four answers. **Table 2** showed the raw materials, the answers or features (elements, ideas, messages) for this study. The experimental design for the 4 × 4 design comprises 24 different combinations. Each of the 16 answers or elements is statistically independent of every other answer, allowing us to analyze the data by

**Table 3** shows the first six vignettes or test combinations for one respondent,

**9. The 4 × 4 design used in mind genomics**

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*

method, widely used, but unable to spark the creation of a new product idea in the way it is structured. By presenting the answers one at a time, and then requiring the respondent to rate each idea alone, we are left with ratings of single ideas, but no idea of how ideas interact with and compete with each other, as they drive interest. The respondent may also change the criterion of judgment, judging healthful ingredients more leniently, and the more indulgent features more stringently.

A potentially more productive way mixes and matches the answers, creating vignettes. The answers become the building blocks. Rather than building one answer at a time, starting with the most popular, we create combinations of answers using a recipe book (experimental design). The responses to the mixtures of answers help us understand the performance of the single elements. We do that by deconstructing the response to a blend, our mixture of answers, to the part-worth contribution of each answer. This notion was developed extensively by Norman Anderson [12], formalized as the method of conjoint measurement [13], popularized in business and academic circles by Professor Paul Green of The Wharton School of Business of the University of Pennsylvania [14, 15], and finally expanded, and made available worldwide as a method of knowledge building by author HRM [7].

#### **9. The 4 × 4 design used in mind genomics**

Mind Genomics works with various experimental designs. For these studies, we work with the so-called 4 × 4 design, namely four questions, each question requiring four answers. **Table 2** showed the raw materials, the answers or features (elements, ideas, messages) for this study. The experimental design for the 4 × 4 design comprises 24 different combinations. Each of the 16 answers or elements is statistically independent of every other answer, allowing us to analyze the data by the method of OLS (ordinary least-square regression), discussed later.

**Table 3** shows the first six vignettes or test combinations for one respondent, along with the 9-point rating, the transformed value for the rating, and the response time for that vignette (test combination). Each respondent evaluates a totally different set of vignettes. The underlying experimental design is the same in a mathematical sense, but the actual vignettes differ, because a permutation scheme systematically varies the pairs of elements which appear together.

#### **10. The study setup by the researcher and the respondent experience**

At its basic level, the Mind Genomics study is an experiment, albeit couched in the form of a survey. The researcher systematically varies the stimulus inputs, the answers, according to the experiment design (**Table 3**), records the respondent's rating as well as time of response, and then analyzes the results. **Figure 5** shows what the respondent sees (test vignette) when using a smartphone. The same vignette can be presented in a slightly different configuration to fit the screen of a personal computer or a tablet.

The typical Mind Genomics experiment with BimiLeap® takes approximately 4–5 minutes from start to finish. Many respondents begin with the typical strategy of trying to be "correct." The respondent may spend more time at the start than at the end, reading the vignettes, in order to make sure that they have gotten all the relevant information. By the time the respondent reaches second, and certainly the third vignette, however, this effort begins to subside, and the respondent answers, almost automatically, at an intuitive level, the System 1 of Nobel Laureate Daniel Kahneman [16].

*Current Issues and Challenges in the Dairy Industry*

B1 Flavorful fruit enhances the yogurt

C2 Complements a meal as the perfect side C3 Perfect as a natural energy boost C4 Improves recovery after daily exercise

D1 Provides your body with the protein it craves D2 Low sugar... without sacrificing great taste D3 Probiotic-rich and immune system boosting D4 Only the most natural and organic ingredients

B2 The yogurt has a colorful, picturesque appearance B3 The texture of the yogurt is creamy and delicate B4 The rich taste compliments the appetizing aroma

C1 For those on the move and in need of a quick breakfast

Question C—When would you eat this yogurt?

Question D—What are the health benefits of this yogurt?

A1 A frozen yogurt A2 A Greek yogurt A3 A yogurt smoothie A4 A plain yogurt

**Question A—What type of product is this?**

Question B—What does this product deliver in terms of sensates?

intelligence, and particularly the SamanthaSM platform, is to present suggestions that the researcher can use to elaborate. The output from the artificial intelligence system comprises both a set of words in **Figure 2** to "jog the mind," as well as links to deeper information (**Figures 3** and **4**). Thus, the Mind Genomics system gives room

*The four questions and four answers from each question, created by inspecting the information provided by the artificial intelligence platform, and generating the relevant statements to be used in the actual field execution.*

**Table 2** presents the set of four questions, extracted from the information provided by the artificial intelligence platform, and then elaborated and edited to move from information to questions. Each question, in turn, generates four answers, or more correctly, the researcher provides four answers to each question. The answers may be taken directly from the information provided by the artificial intelligence platform, or the answers may be polished and edited information, or perhaps even new ideas sparked by the information provided, by not actually part of the information provided. The reality is that it does not really matter where the information comes from. The Mind Genomics effort is attempting to discover "what works." The information provided to it is the raw material. The goal is to get the best information

**8. Knowledge from responses to mixtures of answers—the contribution** 

One could take the 16 answers in **Table 2** and rate each of the ideas on a scale of interest. Presenting the answers one at a time and obtaining an answer is the survey

for suggested topics, as well as for the human elaboration of those topics.

**68**

**Table 2.**

and identify "what works."

**of experimental design**


#### **Table 3.**

*The first six vignettes for one respondent. The 4 × 4 design prescribes 24 vignettes of precise design in terms of the elements which each vignette comprises.*

**71**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

**Figure 6** shows the external dynamics of the experiment. The top set of figures shows the average response time in seconds, by position of the vignette. We see that whether we deal with the Total Panel, with males, or with females, the pattern is virtually the same. The average response time after the first vignette tested drops to a constant level. Despite the long time and the extensive number of vignettes,

*The average time in seconds needed for a respondent to read a vignette and assign a rating (top row of graphs),* 

Up to now, we have focused on the setup and execution of the study. The more interesting part of the study comes from the discovery of just how the answers, the stimulus inputs under the researcher's control, "drive" the response, in this case interest. In this section, we look at the results from our experiment with 50 respondents. We will look at the additive constant to get a sense of baseline interest, then at the coefficients to see which elements or answers drive interest, and then search for Mind-Sets, groups of ideas which "move together." Each of our 50 respondents will be assigned to

a.Total Panel shows an additive constant of 56, meaning that in the absence of any elements in the vignette, we expect 56% of the answers to be ratings of 7–9. Basically, yogurt is liked. It will be up to the elements to drive liking much higher.

b.The "Total Panel," with all 50 respondents, shows NO very strong elements. This means that if we continue to try these types of ideas, it is likely that for the general population nothing will work, or when some element works, it will be

c.The answer is dividing the respondents into Mind-Sets. The Mind-Sets are selected from the mathematical clustering to "make sense." The computer only divides the respondents by the pattern of coefficients. It is the researcher and

d.Mind-Set MS1: Modestly interested in yogurt (additive constant 37), but interested in the type of yogurt, especially high protein and convenient. They may like yogurt for its probiotic qualities. We could call these the health-through-a

the marketer who must make sense of the Mind-Sets.

a Mind-Set based upon the pattern of coefficients. **Table 4** shows the results.

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

respondents still seem to vary their ratings.

**Figure 6.**

probably by accident.

good-tasting-food.

**11. What drives interest in yogurt: results from our study**

*and the average rating assigned to the test vignette (bottom row of graphs.).*

#### **Figure 5.**

*The respondent experience when using a smartphone with a small screen.*

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*

#### **Figure 6.**

*Current Issues and Challenges in the Dairy Industry*

**Order 1 2 3 4 5 6** A1 0 0 0 1 0 1 A2 0 0 0 0 0 0 A3 0 0 1 0 1 0 A4 0 0 0 0 0 0 B1 0 0 0 0 1 1 B2 0 0 0 0 0 0 B3 1 1 0 0 0 0 B4 0 0 1 1 0 0 C1 1 0 1 0 1 0 C2 0 1 0 1 0 0 C3 0 0 0 0 0 0 C4 0 0 0 0 0 1 D1 1 0 0 0 0 0 D2 0 0 0 1 1 0 D3 0 0 1 0 0 0 D4 0 1 0 0 0 1 Rating 7 7 8 8 8 5 Binary 101 100 100 101 101 0 Res Time 13 10 6 5 5 3

*The first six vignettes for one respondent. The 4 × 4 design prescribes 24 vignettes of precise design in terms of* 

**70**

**Figure 5.**

**Table 3.**

*the elements which each vignette comprises.*

*The respondent experience when using a smartphone with a small screen.*

*The average time in seconds needed for a respondent to read a vignette and assign a rating (top row of graphs), and the average rating assigned to the test vignette (bottom row of graphs.).*

**Figure 6** shows the external dynamics of the experiment. The top set of figures shows the average response time in seconds, by position of the vignette. We see that whether we deal with the Total Panel, with males, or with females, the pattern is virtually the same. The average response time after the first vignette tested drops to a constant level. Despite the long time and the extensive number of vignettes, respondents still seem to vary their ratings.

#### **11. What drives interest in yogurt: results from our study**

Up to now, we have focused on the setup and execution of the study. The more interesting part of the study comes from the discovery of just how the answers, the stimulus inputs under the researcher's control, "drive" the response, in this case interest. In this section, we look at the results from our experiment with 50 respondents. We will look at the additive constant to get a sense of baseline interest, then at the coefficients to see which elements or answers drive interest, and then search for Mind-Sets, groups of ideas which "move together." Each of our 50 respondents will be assigned to a Mind-Set based upon the pattern of coefficients. **Table 4** shows the results.


The prudent developer might well repeat this step 3–4 times, with different sampling of ideas from SamanthaSM, and with new populations of respondents, perhaps retaining the strong performing ideas, for a final test (e.g., step #5) comprising only strong performing answers or elements which have proved themselves.


#### **Table 4.**

*The results from the study, showing the coefficients for interest (binary transform) both from the Total Panel (ToT), and from the three complementary Mind-Sets (MS1, MS2, MS3).*

**73**

**Table 5.**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

We now turn to the second important variable, response time. The BimiLeap® APP from Mind Genomics measured the number of seconds from the presentation of the vignette on the screen to the response. The analysis deconstructs the response time in seconds into the part-worth contribution of each element in the vignette. The model does not have an additive constant, so that the response time is "0" in the absence of any elements. Furthermore, **Figure 6** (top panels) suggests that the response time to the first vignette should be discarded. That response time is longer than the other response times, probably because when making that first rating, the respondent is not accustomed to the procedure, and there may be some issues both with eye-hand coordination, and with using the scale. By the second vignette,

**Response times from vignettes 2–24 TOT MS1 MS2 MS3**

A2 A Greek yogurt... high in protein 0.7 1.1 0.9 **0.1** A3 A yogurt smoothie... no spoon required 1.0 1.1 0.9 0.9 A4 A plain yogurt... versatile, customizable 1.0 1.2 0.8 1.0

B2 The yogurt has a colorful appearance 1.2 1.8 **0.1** 1.0

C2 A healthy meal and snack alternative 0.8 **0.4** 0.7 1.5 C4 Improves recovery after daily exercise 1.0 0.5 1.3 1.5 C3 Perfect as a natural energy boost 1.1 0.7 1.6 1.0 C1 For those in need of a quick breakfast 1.2 0.9 1.6 1.5

*Coefficients for response time both from the Total Panel (TOT), and from the three complementary Mind-Sets* 

Health and good taste

1.0 1.3 0.8 0.9

0.9 1.0 0.9 0.7

1.0 1.5 **0.5** 0.7

1.0 1.8 **0.2** 0.6

**0.3 −0.2 −0.3** 1.4

0.5 **0.1 0.4** 1.4

0.7 0.9 **0.5** 0.7

1.3 1.4 0.7 1.6

Multisensory Low-

calorie snack

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

however, the response time is quite stable.

Question A: Type

A1 A frozen yogurt... a guilt-free indulgence

Question B: Traits

taste and health

alternative

B1 Flavorful fruit enhances the yogurt...

B3 Nutrient-rich nuts improve the texture and flavor-profile of the yogurt

B4 The yogurt is plant-based... a better

Question C: Situation

Question D: Benefit D3 Probiotic-rich... immune system

D1 Provides your body with the protein it craves... essential for keto diets

D2 Low sugar... without sacrificing great

D4 Only the most natural and organic

*(MS1, MS2, MS3) emerging from the rating question.*

boosting

taste

ingredients

**12. Response times and their relation to Mind-Sets**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*

### **12. Response times and their relation to Mind-Sets**

*Current Issues and Challenges in the Dairy Industry*

multisensory appeal of yogurt.

Question A: Type

Question B: Traits

taste and health

alternative

B1 Flavorful fruit enhances the yogurt...

B3 Nutrient-rich nuts improve the texture and flavor-profile of the yogurt

B4 The yogurt is plant-based... a better

Question C: Situation

Question D: Benefit D3 Probiotic-rich... immune system

D2 Low sugar... without sacrificing great

D4 Only the most natural and organic

D1 Provides your body with the protein it craves... essential for keto diets

*(ToT), and from the three complementary Mind-Sets (MS1, MS2, MS3).*

boosting

ingredients

taste

themselves.

ably looking for a low-calorie snack.

e.Mind-Set MS2: A yogurt aficionado (higher additive constant of 58), likes the

f. Mind-Set MS3: A yogurt aficionado (higher additive constant of 48), but prob-

The prudent developer might well repeat this step 3–4 times, with different sampling of ideas from SamanthaSM, and with new populations of respondents, perhaps retaining the strong performing ideas, for a final test (e.g., step #5) comprising only strong performing answers or elements which have proved

Tentative name Health &

**Group TOT MS1 MS2 MS3**

Base size 50 22 13 15 Additive constant 56 37 58 58

A1 A frozen yogurt... a guilt-free indulgence -3 9 8 −30 A2 A Greek yogurt... high in protein −3 **14** 4 −34 A3 A yogurt smoothie... no spoon required −4 **17** −1 −37 A4 A plain yogurt... versatile, customizable −7 8 1 −37

B2 The yogurt has a colorful appearance 1 −5 **22** −8

C3 Perfect as a natural energy boost 0 5 −3 −5 C2 A healthy meal and snack alternative −1 −2 −6 4 C4 Improves recovery after daily exercise −1 2 −9 1 C1 For those in need of a quick breakfast −6 −8 −12 0

*The results from the study, showing the coefficients for interest (binary transform) both from the Total Panel* 

good taste

4 −3 **25** −4

1 −3 **11** −3

−2 −6 **22** −16

2 **15** −14 −3

0 9 −28 **10**

−1 9 −15 −5

−4 8 −21 −6

Multisensory Low-

calorie snack

**72**

**Table 4.**

We now turn to the second important variable, response time. The BimiLeap® APP from Mind Genomics measured the number of seconds from the presentation of the vignette on the screen to the response. The analysis deconstructs the response time in seconds into the part-worth contribution of each element in the vignette. The model does not have an additive constant, so that the response time is "0" in the absence of any elements. Furthermore, **Figure 6** (top panels) suggests that the response time to the first vignette should be discarded. That response time is longer than the other response times, probably because when making that first rating, the respondent is not accustomed to the procedure, and there may be some issues both with eye-hand coordination, and with using the scale. By the second vignette, however, the response time is quite stable.


#### **Table 5.**

*Coefficients for response time both from the Total Panel (TOT), and from the three complementary Mind-Sets (MS1, MS2, MS3) emerging from the rating question.*

**Figure 7.**

*Scatterplot showing the relation between the coefficient for response time (ordinate) and the coefficient for interest (abscissa). The patterns are shown for the Total Panel, and for the three Mind-Sets, respectively.*

Our objective here is to discover whether the 16 answers each generate the same response time. The way to do that is again by OLS regression. This time, however, we put all the relevant data into one set, and estimate one "grand" regression model for that relevant data. By "relevant," we mean first eliminating ALL data from the first vignette (order #1), no matter who the respondent happens to be. We then either divide the data into three groups, depending upon the Mind-Set of the respondent, allowing us to estimate the response time per element for each Mind-Set, or we look at all the data in one group for Total Panel. We run the Grand Models for this analysis, rather than running the individual-level models.

**Table 5** shows the coefficients for response time estimated for each of the 16 elements, both for the Total Panel and, respectively, for the three Mind-Sets generated from the ratings assigned to the vignettes. **Table 5** shows clear differences in estimated response time (RT) across the elements, and across the Mind-Sets.

When we plot response time against interest, with the points corresponding to the 16 coefficients for the 16 elements, **Figure 7** suggests differences in response time may not strongly co-vary with the interest in the message estimated from the rating. That is, more interesting messages or elements are not necessarily responded to more quickly. *This lack of strong co-variation between interest and response time differs from what has been recently uncovered by author HRM in a study of the same type, dealing with a political issue, the Russian-Ukrainian conflict of 2018, rather than yogurt*. It may well be that the studies of RT require topics which are involving. Yogurt simply may be not particularly involving even though the data may make sense.

#### **13. Finding these respondents in the population**

Our efforts to create a new yogurt concept through experimental design (BimiLeap®), powered by access to trends through artificial intelligence (SamanthaSM) have uncovered a new way to understand a product category and prepare to create new concepts. We see clearly from the data in **Table 4** as well

**75**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

as from the array of previous studies on dairy that people perceive the features of a dairy product in different ways, at least in terms of what they consider to be interesting and important. Our identification of the mental genomes, these alleles of preference, pertains only to the respondents whom we tested, generally small groups of consumers from easy, affordable panels. How do we generalize our findings, either to discovering the distribution of these basic Mind-Sets in the population or, more importantly, discovering individuals who are members of these Mind-Sets, and who can be further studied? The further studies may be as simple as their preferences for concepts created for the product (e.g., yogurt products), on to purchase and consumption patterns, and even beyond to possible health and genetic correlates of segment membership? One approach to predicting Mind-Set membership looks at the pattern of coefficients for the Mind-Sets (**Table 4**), and selects elements showing the greatest differentiating power, that is, the biggest difference for the average panelist. Each selected element is then edited to become a question, to be answered NO or YES, or some other appropriate pair of responses for the same type of binary decision. The questions are incorporated into a short questionnaire. The pattern of responses shows the Mind-Set to which the respondent probably belongs. The feedback to the respondent or to a marketing company using the data appears in **Figure 6**, in the three right panels. The personal viewpoint identifier is easy to create using summary data, is quick to administer, and can be configured as either a "fun" tool to engage customers, or as a more serious tool to understand the mind of the consumer. From one study, one can proceed to type up to the millions of respondents, should one wish to study entire populations. For this study on yogurt, the personal viewpoint identifier is shown in demonstration form

**14. Five-year prospects: trend definition, product design, mass mind-**

data analysis and experimental science, moving on to new vistas. These vistas include a new way of exploring ideas, uncovering possibly new-to-the-world mindgenomes, and finally, understanding how neurophysiological processes indicated by response time co-vary with interest in the product. We now move beyond the data to suggest opportunities and applications, some of which are already in their nascent stages, and some of which are easily done, but simply have not been

As presented here, the approach we present begins with a combination of social

*Trend definition*: The objective of trend spotting is to identify general patterns of what is happening, usually from an exploration of websites and conversations, and their distillation into general patterns. The patterns provide broad patterns, not specifics. Thus, for dairy, we might find a trend emerging for cultured milk products like kefir, combined with new flavors and interesting incorporations, such as chia seeds. The trend spotter may guess about the nature of this trend. What would happen if the new ideas could be incorporated in a Mind Genomics study, with the respondents asked to rate the likelihood of each vignette as an emerging trend? The answers would range from absolutely never to current to approximately a year or a two in the future. In this case, the trend is defined not so much by what one observes as by a combination of that which is observed, with some conscious

*Product design*: This chapter presents the Mind Genomics as an effort to deconstruct the response to individual features of dairy products based upon the response to vignettes. One can also look at the Mind Genomics as a "Mixmaster" of ideas,

*DOI: http://dx.doi.org/10.5772/intechopen.85532*

in this link: http://162.243.165.37:3838/TT04.

**typing, personalization**

elaborations of what might be.

implemented.

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive… DOI: http://dx.doi.org/10.5772/intechopen.85532*

as from the array of previous studies on dairy that people perceive the features of a dairy product in different ways, at least in terms of what they consider to be interesting and important. Our identification of the mental genomes, these alleles of preference, pertains only to the respondents whom we tested, generally small groups of consumers from easy, affordable panels. How do we generalize our findings, either to discovering the distribution of these basic Mind-Sets in the population or, more importantly, discovering individuals who are members of these Mind-Sets, and who can be further studied? The further studies may be as simple as their preferences for concepts created for the product (e.g., yogurt products), on to purchase and consumption patterns, and even beyond to possible health and genetic correlates of segment membership? One approach to predicting Mind-Set membership looks at the pattern of coefficients for the Mind-Sets (**Table 4**), and selects elements showing the greatest differentiating power, that is, the biggest difference for the average panelist. Each selected element is then edited to become a question, to be answered NO or YES, or some other appropriate pair of responses for the same type of binary decision. The questions are incorporated into a short questionnaire. The pattern of responses shows the Mind-Set to which the respondent probably belongs. The feedback to the respondent or to a marketing company using the data appears in **Figure 6**, in the three right panels. The personal viewpoint identifier is easy to create using summary data, is quick to administer, and can be configured as either a "fun" tool to engage customers, or as a more serious tool to understand the mind of the consumer. From one study, one can proceed to type up to the millions of respondents, should one wish to study entire populations. For this study on yogurt, the personal viewpoint identifier is shown in demonstration form in this link: http://162.243.165.37:3838/TT04.

#### **14. Five-year prospects: trend definition, product design, mass mindtyping, personalization**

As presented here, the approach we present begins with a combination of social data analysis and experimental science, moving on to new vistas. These vistas include a new way of exploring ideas, uncovering possibly new-to-the-world mindgenomes, and finally, understanding how neurophysiological processes indicated by response time co-vary with interest in the product. We now move beyond the data to suggest opportunities and applications, some of which are already in their nascent stages, and some of which are easily done, but simply have not been implemented.

*Trend definition*: The objective of trend spotting is to identify general patterns of what is happening, usually from an exploration of websites and conversations, and their distillation into general patterns. The patterns provide broad patterns, not specifics. Thus, for dairy, we might find a trend emerging for cultured milk products like kefir, combined with new flavors and interesting incorporations, such as chia seeds. The trend spotter may guess about the nature of this trend. What would happen if the new ideas could be incorporated in a Mind Genomics study, with the respondents asked to rate the likelihood of each vignette as an emerging trend? The answers would range from absolutely never to current to approximately a year or a two in the future. In this case, the trend is defined not so much by what one observes as by a combination of that which is observed, with some conscious elaborations of what might be.

*Product design*: This chapter presents the Mind Genomics as an effort to deconstruct the response to individual features of dairy products based upon the response to vignettes. One can also look at the Mind Genomics as a "Mixmaster" of ideas,

*Current Issues and Challenges in the Dairy Industry*

*Scatterplot showing the relation between the coefficient for response time (ordinate) and the coefficient for interest (abscissa). The patterns are shown for the Total Panel, and for the three Mind-Sets, respectively.*

for this analysis, rather than running the individual-level models.

**13. Finding these respondents in the population**

Our objective here is to discover whether the 16 answers each generate the same response time. The way to do that is again by OLS regression. This time, however, we put all the relevant data into one set, and estimate one "grand" regression model for that relevant data. By "relevant," we mean first eliminating ALL data from the first vignette (order #1), no matter who the respondent happens to be. We then either divide the data into three groups, depending upon the Mind-Set of the respondent, allowing us to estimate the response time per element for each Mind-Set, or we look at all the data in one group for Total Panel. We run the Grand Models

**Table 5** shows the coefficients for response time estimated for each of the 16 elements, both for the Total Panel and, respectively, for the three Mind-Sets generated from the ratings assigned to the vignettes. **Table 5** shows clear differences in estimated response time (RT) across the elements, and across the Mind-Sets.

When we plot response time against interest, with the points corresponding to the 16 coefficients for the 16 elements, **Figure 7** suggests differences in response time may not strongly co-vary with the interest in the message estimated from the rating. That is, more interesting messages or elements are not necessarily responded to more quickly. *This lack of strong co-variation between interest and response time differs from what has been recently uncovered by author HRM in a study of the same type, dealing with a political issue, the Russian-Ukrainian conflict of 2018, rather than yogurt*. It may well be that the studies of RT require topics which are involving. Yogurt simply may be not particularly involving even though the data may make sense.

Our efforts to create a new yogurt concept through experimental design (BimiLeap®), powered by access to trends through artificial intelligence

(SamanthaSM) have uncovered a new way to understand a product category and prepare to create new concepts. We see clearly from the data in **Table 4** as well

**74**

**Figure 7.**

whether these ideas or elements be based upon yogurt, upon dairy in general, or even other foods and situations. When these elements from disparate sources are combined, elements not only for yogurt, for example, the outcome is a new set of possible products. The promising ideas can be combined. When, for the most part, the ideas from different areas really do not work together, the ratings for the combinations will be low, and there will not be any strong performing elements, suggesting that the raw materials simply do not work together.

Perhaps the most important contribution of Mind Genomics is to combine profound knowledge of a person's interest in dairy products with both the ability to guide the person to eat better, and to understand how preferences for dairy co-vary with behavior. The full elaboration of the social use of Mind Genomics for health issues and dairy awaits the new generation of researchers, interest in dairy, in health, and in commerce, respectively. We have presented early indications and of these new developments.

#### **Acknowledgements**

Author AG thanks the support of Premium Postdoctoral Research Program of the Hungarian Academy of Sciences.

### **Author details**

Ryan Zemel1 , Somsubhra Gan Choudhuri<sup>2</sup> , Attila Gere3 , Himanshu Upreti<sup>2</sup> , Yehoshua Deite4 , Petraq Papajorgji<sup>5</sup> and Howard Moskowitz6 \*


3 Faculty of Food Science, Department of Postharvest Science and Sensory Evaluation, Szent István University, Budapest, Hungary


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

© 2019 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.

**77**

*Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive…*

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*DOI: http://dx.doi.org/10.5772/intechopen.85532*

[1] Govindarajan V, Trimble C. Strategic innovation and the science of learning. MIT Sloan Management Review.

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### **References**

*Current Issues and Challenges in the Dairy Industry*

whether these ideas or elements be based upon yogurt, upon dairy in general, or even other foods and situations. When these elements from disparate sources are combined, elements not only for yogurt, for example, the outcome is a new set of possible products. The promising ideas can be combined. When, for the most part, the ideas from different areas really do not work together, the ratings for the combinations will be low, and there will not be any strong performing elements,

Perhaps the most important contribution of Mind Genomics is to combine profound knowledge of a person's interest in dairy products with both the ability to guide the person to eat better, and to understand how preferences for dairy co-vary with behavior. The full elaboration of the social use of Mind Genomics for health issues and dairy awaits the new generation of researchers, interest in dairy, in health, and in commerce, respectively. We have presented early indications and of

Author AG thanks the support of Premium Postdoctoral Research Program of

suggesting that the raw materials simply do not work together.

**76**

**Author details**

these new developments.

**Acknowledgements**

Yehoshua Deite4

Ryan Zemel1

provided the original work is properly cited.

, Somsubhra Gan Choudhuri<sup>2</sup>

, Petraq Papajorgji<sup>5</sup>

2 AI Palette, Inc., Singapore, Republic of Singapore

5 Universiteti Europian i Tiranes, Tirane, Albania

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

Evaluation, Szent István University, Budapest, Hungary

6 Mind Genomics Associates, Inc., White Plains, NY, USA

1 Limbic Reviews, Chicago, IL USA

the Hungarian Academy of Sciences.

4 Sifra Digital Inc., Jerusalem, Israel

© 2019 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,

3 Faculty of Food Science, Department of Postharvest Science and Sensory

, Attila Gere3

and Howard Moskowitz6

, Himanshu Upreti<sup>2</sup>

\*

,

[1] Govindarajan V, Trimble C. Strategic innovation and the science of learning. MIT Sloan Management Review. 2004;**45**:67

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[3] Esmerino EA, Ferraz JP, Filho ERT, Pinto LPF, Freitas MQ, Cruz AG, et al. Consumers' perceptions toward 3 different fermented dairy products: Insights from focus groups, word association, and projective mapping. Journal of Dairy Science. 2017;**100**(11):8849-8860. Available from: http://www.sciencedirect.com/ science/article/pii/S0022030217308159

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Section 4

Functional Dairy Food

Products

Section 4
