Agricultural Energy Demands

Green urbanism, as described by Lehmann (2010), is holistically defined as a balanced relationship between urban and rural areas. Eco-city modeling depends on urban developments, energy-efficient practices and general support for sustainability, but it also depends on interactions with the agricultural sector on numerous facets. Farming and the livestock sector are significant contributors to global anthropogenic-induced GHG emissions. According to the IPCC, 14.5% of all human-induced GHG emissions are emitted as predominantly CH4, N2O and CO2 by livestock supply chains. Based on statistics provided by the Food and Agriculture Organization (FAO) of the United Nations, CH4 and N2O emissions from agriculture are dependent on a combination of the development status of countries and the physical size of a country (or continent). According to the FAO statistics division (FAOSTAT) model, the average emissions by continent between 1990 and 2014 were:

  • • Asia (the largest continent) generated 42.3% of the total CH4 and N2O produced from agricultural activities; China (mainland) was the largest contributor in Asia;
  • • the Americas made the second-largest contribution at approximately 25.3% of the total GHG contribution from the agricultural sector, Brazil contributing more CO2-equivalent emissions than the United States of America,
  • • Africa was in third place with its contribution between 1990 and 2014 estimated at 14.4%, just more than
  • • Europe with its 13.8% contribution to the GHG emissions average and
  • • Oceania having the lowest average contribution at approximately 4.1%.

The sources of CH4 and N2O (and CO2) in the agricultural sector in each country or continent show a relatively comparable trend. There are typically four primary sources of emissions from livestock supply chains that contribute to the buildup of CH4 and other gases in the atmosphere. The four primary sources as described by the FAO (2016) are

  • • the digestive process of ruminants and/or non-ruminants in a process called enteric fermentation,
  • • anaerobic decomposition of organic manure,
  • • aspects concerning livestock feed that include expansion of feeding crops and pastures through removal of natural land, the manufacturing of fertilizers and feed transport and processing, and
  • • energy consumption in all sectors of the supply chain.

Modeling GHG emissions in the agricultural sector requires gathering, analyzing and updating data in its sub-domains. The agricultural sub-domains supported by dataset models such as the FAOSTAT model are, for example

  • • enteric fermentation—(40.2% contribution to the total emissions from the agricultural sector, the largest contributor by a fair margin)—primary emissions: CH4,
  • • manure management—(6.9% contribution to the total emissions from the agricultural sector)—primary emissions: CH4 and N2O,
  • • rice cultivation—(10.2% contribution to the total emissions from the agricultural sector)—primary emissions: CH4,
  • • synthetic fertilizers—(11.5% contribution to the total emissions from the agricultural sector)—primary emissions: N2O,
  • • manure applied to soils—(3.7% contribution to the total emissions from the agricultural sector)—primary emissions: N2O,
  • • manure applied to pastures—(15.5% contribution to the total emissions from the agricultural sector, the second-largest contributor)—primary emissions: N2O,
  • • crop residues—(3.6% contribution to the total emissions from the agricultural sector)—primary emissions: N2O,
  • • cultivation of organic soils—(2.8% contribution to the total emissions from the agricultural sector)—primary emissions: N2O,
  • • burning crop residues—(0.5% contribution to the total emissions from the agricultural sector, the lowest estimated contributor)—primary emissions: CH4 and N2O, and
  • • burning savannas—(5.2% contribution to the total emissions from the agricultural sector)—primary emissions: CH4 and N2O (FAO 2016).

In preparing national GHG estimates for the agricultural sector, national inventory compilers experience distinctive challenges in gathering and regularly updating national statistics for agriculture, forestry and other land use

(cropland, grassland, wetlands and settlements) (Tubiello et al. 2015). This difficulty is especially prominent in developing countries (but also evident in developed nations) with already limited capacity to identify and collect reliable activity data and quantify emissions by sources and removals by sinks (Tubiello et al. 2015). In the agricultural sector, anthropogenic GHG emissions and removals of managed land are defined where human interventions and practices have been applied to perform production, ecological or social functions (IPCC 2006). Emission and removal processes are organized by ecosystem components of biomass, dead organic matter, soils and livestock. Agricultural emissions account for emissions produced in all the agricultural sub-domains, whereas emissions of non-CO2 gases such as CH4 and N2O generated through crop and livestock production and management activities are among the predominant emissions modeled in agricultural practices.

Agricultural modeling and estimates of food production and consumption can be achieved with relatively good accuracy based on several assumptions, but determining the world average per capita availability of food for direct human consumption allows for good estimates of supply and demand, translating to a fair assumption with relatively low uncertainty on the GHG emissions associated with food production. The primary drivers of increased food production leading to higher GHG emissions from the agricultural sector are growing populations and varying income. The demand for agricultural products such as livestock (meat and milk) also increases with increases in population growth, rises in population wealth, increases in per capita consumption and changes in diets. The environmental implications associated with the expansion of livestock are deforestation, overgrazing and ground erosion and increases in CH4 and N2O emissions. In countries where intensive livestock operations on industrial scale are practiced, point-source pollution, such as effluents, is also evident in the agricultural and livestock sector (Alexandratos and Bruinsma 2012).

Ground water is the primary source of water for agriculture and industry; water scarcity or the depletion of underground reservoirs and environmental degradation effects in the agricultural sector are also felt in the urban areas for which it provides, which is all the more reason to include the agricultural sector in eco-city planning. In Beijing in China, for example, sustainable development among cross-disciplines is achieved by the development of urban agriculture (Wong and Yuen 2011 ).2 Sustainability projects also include practices of eco-sanitation, where organic wastewater and compost are used for urban agriculture, urban farming or urban gardening. Urban agriculture also includes management and care of farm animals through the practice of animal husbandry, cultivating freshwater and saltwater aquatic populations under controlled conditions known as aquaculture, growing trees or shrubs among crops or pastureland in a practice known as agroforestry, urban beekeeping and the cultivation of crops with additional purposes related to 2Urban agriculture concerns the cultivation, processing and distribution of edible vegetation or livestock in an urbanized area.

art, science, technology and business, as well as in space-limited urban areas using vertical gardens for example.

Eco-city planning and sustainable city development should therefore also account for practices that remove (sink) carbon emissions from the atmosphere through urban agriculture, additionally contributing to the sustainability of these eco-cities. Watershed modeling of sustainable developments, which is also applied to forestry, is presented by Das (2008) and provides valuable insight into enhancing watershed retention to be used in agriculture and within eco-city urbanized environments. Das (2008) proposes a model for improving the hydrologic status or presence of water in the watershed through a package of interventions. The research presented by Das (2008) is another indication of the interdependence of agriculture, forestry and urbanization, which inevitably translates to eco-city modeling. The work shows how to prioritize integrated watershed development in order to attain overall sustainability for current and future populations.

The Global Livestock Environmental Assessment Model (GLEAM) developed by the FAO of the United Nations is a modeling framework that simulates the environmental impacts of the livestock sector. It represents the bio-physical processes and activities along livestock production chains under a life cycle assessment approach (FAO 2016). GLEAM is developed and designed to identify and quantify several environmental impact classifications in the agricultural sector and includes analysis of GHG emissions, land use and land degradation, nutrient and water use, as well as interaction with biodiversity. Among the described outputs of GLEAM, which include animal spatial distribution, production of manure, feed rations and livestock commodities production, the output of emissions from different stages of production (farming) provides valuable insight into the agricultural carbon footprint on micro- and macro-scale. The sources of GHG emissions in the GLEAM model are divided into three primary categories:

  • • upstream emissions—related to feed production, processing and transportation,
  • • animal production emissions from enteric fermentation, manure management

and energy use on farms, and

• downstream emissions by processing and transport of livestock commodities (FAO 2016).

The GLEAM model therefore provides an evidence-based diagnostic of anthropogenic-induced effects on the environment and can be applied to create a sustainable livestock and agricultural sector. Although GHG and abundant concentrations of CO2 present some advantages in agriculture, since a higher concentration of CO2 would increase the yield for many crops, apart from maize, millet and sorghum, the adverse effects of loss of soil organic matter, leaching of soil nutrients, salinization and erosion, increased infestation of weeds, insects and diseases and a higher probability of extreme environmental conditions, such as droughts and floods, overshadow the miniscule benefits it may hold. Effective and holistic eco-city planning must take agricultural carbon-emission effects into account to produce sustainable growth. The following section summarizes the parameters required for carbon-emission modeling in the sectors discussed: residential households, industry, transport and agriculture.

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