Using Artificial Intelligence in Agroforestry as an Economic Solution for Carbon Recycling in Tanzania

Using Artificial Intelligence in Agroforestry as an Economic Solution for Carbon Recycling in Tanzania

Proscovia Paschal Kamugisha, Sebastian Faustin Mhanga
DOI: 10.4018/978-1-6684-4649-2.ch001
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Abstract

Anthropogenic activities intensify greenhouse gases (GHG) emission. The emissions lead to air pollution, public health risks, and climate change vagaries. Global deaths due to air pollution amounted to 15 deaths/minute in 2016. Moreover, floods, storms, and droughts accounted for 59%, 26%, and 15% respectively of crop losses between 2003 and 2013. Carbon recycling is among efforts to curb GHG which form 75% of GHG. The recycling methods include carbon capture and storage (CCS), carbon capture and utilization (CCU) and carbon capture, storage, and utilization (CCUS). However, these methods are too expensive for developing countries like Tanzania. Agroforestry is a cost-effective carbon recycler compared to other solutions. Besides, the Neem tree has a higher capacity of sequestering carbon at an average of 161% compared to other tree species in the tropics. Application of artificial intelligence can intensify Neem tree-based farming to hasten carbon sequestration.
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Introduction

Background

The sustainable development goal 13 of the United Nations intend to achieve net-zero greenhouse gas (GHG) emissions by 2050. To attain this objective, transformation of value chains across the economy is the prerequisite (Rizos et al, 2018). Transformation encompasses new products and business models to look for means of converting what a normal linear economy considered as wastes into a new product circle. Energy-intensive industries normally emit Carbon dioxide (CO2) and hydrocarbons as wastes that in turn pollute the environment, impair food security and pose health risks to the public. Carbon dioxide (CO2) and methane (CH4) accounted for 73% and 19% of greenhouse gases (GHGs) emitted in 2019 (Olivier & Peters, 2020). The concentration of CO2 in the atmosphere has increased by 31% since the commencement of second half of nineteenth century during the industrial era (UNCTAD, 2019).

This situation has resulted into increased global air temperatures, melting of ice and snow that in turn lead to increased risen sea levels (UNCTAD, 2019). The rise of air temperature is supported by evidence from the five warmest years ever occurred since 1880 as from 2015 that reached the maximum in 2019 with global land and ocean temperature of 0.950C above the average (Olivier & Peters, 2020). Public health concerns and substantial agricultural losses have also been documented. According to (Landrigan et al., 2018) deaths attributable to air pollution accounted for 16% leading to the tune of 9 million premature deaths in 2015 worldwide. Likewise, WHO (2016) revealed that in 2016, almost a quarter (23%) of all global deaths were attributable to air pollution, amounting to 8.2 million deaths annually; which is equivalent to 936 deaths per minute. The ambient air pollution was responsible for 4.2 million premature deaths in 2016; whose 7.11% (300,000) cases were children under the age of 5 years (WHO, 2018). The global welfare losses associated with air pollution stood at US$ 4.6 trillion which is equivalent to 6.2% of the world economic output, 1.7% losses of GDP in low & middle income countries and 2% losses of GDP in high income countries per annum (Landrigan et al., 2018). However, the burden of disease attributable to air pollution is heaviest in low and middle-income countries (LMICs) (Figure 1) (WHO, 2018; WHO, 2016). It is estimated that deaths associated with outdoor air pollution in Africa has increased from 164,000 cases in 1990 to 258,000 cases in 2017 (Rees et al., 2019) that is equivalent to a growth of 60% in a 17 years period.

Figure 1.
978-1-6684-4649-2.ch001.f01

In Tanzania, almost 19% of annual deaths is associated with impacts of air pollution related diseases that amounted to 74,170 cases in 2016 (URT, 2019b). ; At the same time, the proportion of Death and Disability Adjusted Life Years (DALYs) attributable to communicable and non-communicable diseases accounted for 62% and 30.8% respectively in 2016 (URT, 2019a). Moreover, the report estimated that diseases attributable to air pollution have costed the country up to US$ 540 and US$ 665 million in 2015 due to lost productivity and welfare damages (URT, 2019a).

Episodes of adverse weather conditions rank the second negative impact of air pollution. The climate change accounted for 65% of vagaries of weather conditions in more than half of last decade worldwide (Herring et al., 2018). Floods, storms and droughts accounted for 59%, 26% and 15% respectively of global crop losses and damages to agricultural infrastructure between 2003 and 2013 (FAO, 2018). In monetary terms, natural disasters were responsible for $96 billion worth of agricultural produce loss in developing countries between 2005 and 2015 (FAO, 2018). Meanwhile the increased temperature of 10C in developing countries is associated with a decline of 2.66% agricultural output; and decreased export growth by 2.0–5.6% (Jones & Olken, 2010).

Key Terms in this Chapter

Internet of Things: Refers to a network of computer associated devices connected to the internet to facilitate communication between the devices through the internet for performing various specific functions.

Artificial Intelligence: Is the application of programmed machines to perform tasks or processes which are normally performed by human beings who can normally reason, learn and solve problems.

Carbon Recycling: Is the process of capturing carbon from the atmosphere through mechanical or biological processes and use it for socio-economic purposes without allowing it to go back to the atmosphere.

Carbon Sequestration: Is the process of capturing carbon from the atmosphere and store it in the form of biomass or gas deposits underground.

Agroforestry: Is a farming system in which woody perennials are deliberately planted and managed on the same land unit with herbaceous crops and/or animals or aquatic life forms or insects arranged in a spatial or temporal sequence.

Machine Learning: Is a process of programming a machine with application of computer science and data to imitate the way human beings learn through trial and error in order to improve the accuracy of machine performance.

Bioeconomic Solution: Refers to a solution of an economic problem whose building blocks are derived from biological resources and processes of a renewable nature.

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