- Email: [email protected]
A model calculation of the carbon footprint of agricultural products: The case of Slovenia
Energy xxx (2016) 1e9
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
A model calculation of the carbon footprint of agricultural products: The case of Slovenia F. Al-Mansour a, *, V. Jejcic b a b
Energy Efficiency Centre, Jozef Stefan Institute, Ljubljana, Slovenia Department of Agricultural Engineering and Energy, Agricultural Institute of Slovenia, Ljubljana, Slovenia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 November 2015 Received in revised form 21 October 2016 Accepted 24 October 2016 Available online xxx
The pursuit of sustainable development entails a strategic policy decision for all modern countries. Greenhouse gas abatement, the utilisation of renewable energy sources, and energy efficiency represent the main pillars of sustainable development. Agriculture contributes a significant share of greenhouse gas emissions and concurrently represents a carbon dioxide (CO2) sink; it thus has twofold opposing impacts on climate change. The carbon footprint of agricultural products is one of main measures for monitoring the efficiency and sustainability of agricultural productivity processes. A model calculation of the carbon footprint in the agricultural sector was developed in order to calculate the carbon footprint of grains, fruit, and other agriculture products based on a calculation of total greenhouse gas emissions resulting from production, from the beginning of the production process to storage and delivery to the final consumer or the food industry. The first obstacles in such a calculation are the availability of input data on energy consumption by unit of land for all forms of agricultural land preparation and other work required for sowing, fertilisation, plant protection, harvesting, internal transportation, and other work. The mineral diesel fuel consumption of tractors with various connected machines and self-propelled work machines (e.g. harvesters or forage harvesters for maize) were measured. In addition, the energy consumption required for harvesting and the internal transport of crops on farms itself was included. The results of the model calculation of the carbon footprint of agricultural products consider the type of farming production for three different sizes of farms and for two scenarios regarding soil tillage and seeding. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Carbon footprint Modelling Life cycle assessment Environmental impact assessment Agricultural farming
1. Introduction The pursuit of sustainable development entails a strategic policy decision of all modern countries. Sustainable development is defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [1]. Research in recent years has been focused on different aspects of sustainable energy and environmental protection [2e5]. One of the environmental objectives of sustainability is the “reduction of negative impacts on the environment and health” [6]. The European Union is firmly committed to sustainable development and in 2001 adopted the first EU “Sustainable Development Strategy (EU SDS)” [7,8] and in 2005 adopted an
* Corresponding author. E-mail addresses: [email protected] (F. Al-Mansour), [email protected] (V. Jejcic).
ambitious and comprehensive renewed SDS for an enlarged EU [9]. The EU SDS identifies 7 key challenges and corresponding targets, operational objectives, and actions. The objective regarding climate change and clean energy is to limit climate change and its costs and the negative effects on society and the environment. Greenhouse abatement, the utilisation of renewable energy sources, and energy efficiency represent the main pillars of sustainability development. Agriculture contributes a significant share of greenhouse gas emissions and concurrently represents a carbon dioxide (CO2) sink; it thus has twofold opposing impacts on climate change . Agriculture is the largest contributor to anthropogenic emissions of greenhouse gases [10]. Agriculture covers approximately 35% of the land area and accounts for nearly 13.5% of the total global anthropogenic GHG emissions, contributing about 25%, 50%, and 70% of CO2, CH4, and N2O, respectively [11], and in the future it will be faced with significant requirements as regards reducing greenhouse gas emissions.
http://dx.doi.org/10.1016/j.energy.2016.10.099 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
2
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Greenhouse gas emissions in the agricultural sector (direct emissions) arise from the use of fossil fuels (for machinery, space and process heating, and other uses), animal breeding (enteric fermentation of domestic animals, storage of livestock manure, etc.), and agricultural land use (fertilisation with mineral and/or organic fertilisers, the rinsing of nitrogen compounds in water, the decomposition of crop residues, processing histosols, etc.). Indirect GHG emissions arise due to the consumption of electricity and district heating in agriculture. The carbon footprint of agricultural products is one of main indicators for monitoring the efficiency and sustainability of agricultural productivity processes. The carbon footprint of a product is of great interest as a central measure of the environmental impact of supply chains [12]. A carbon footprint is a quantitative measurement describing the appropriation of natural resources by humans [13,14] and describes how human activities can impose different types of burdens and impacts on global sustainability [9]. A review of the literature indicates that the major categories of footprints developed to date are carbon, ecological, and water footprints, forming the so-called “footprint family” [15,16]. Determination of the carbon footprint of agricultural products requires a detailed analysis of energy consumption in the various processes used for crop production. Total input energy in agricultural production is the sum of all the components of the energy used in the different processes for the production of outputs (agricultural products e grain, fruit, etc.). Different studies and research have focused on an analysis of the direct energy inputs in grain production and their results are presented in [17e21]. Some studies have addressed individual agricultural processes in the production of one type of grain [22,23] or the energy inputs for the production of one important product (e.g. wheat) [24,25]. A carbon footprint presents the total amount of CO2 and other greenhouse gases (GHGs) emitted over the full life cycle of a process or product [26e28] and has become one of the most important environmental protection indicators [29,30]. A carbon footprint is quantified using such indicators as the GWP (Global Warming Potential) [17], which represents the quantity of GHGs that contribute to global warming and climate change. The carbon footprint of agricultural food products can be compared to the energy labelling of appliances for households. There are different types of farming regarding the preparation of the soil, the use of fertilisers, and other chemical preparations (conventional, organic, integrated, etc.). The calculation of the carbon footprint is carried out for agricultural products using the Life Cycle Assessment (LCA) methodology [30e32].
LCA is one of the most widely recognised approaches to the environmental assessment of products and processes [33]. Several LCA methodologies have been developed thus far, and these have been somewhat improved over the last decades, namely: attributional and consequential LCA, hybrid LCA, process LCA, and input/ output LCA [34]. Current activities regarding database improvement, quality assurance integration, consistency improvement, and the harmonisation of methods are contributing to this improvement process. Mainly, there are two main perspectives on LCA: retrospective and prospective. The selection of the elements of the physical system to be modelled depends on the definition of the goal and the scope of the study [33]. The model calculation of the carbon footprint of agricultural products using LCA requires a large database with data on the energy consumption for each activity in addition to data on the quantity of fertilisers used, annual product yield, plant protection products, etc. This research is the first study on the carbon footprint of agricultural production in Slovenia. The carbon footprint of agricultural products is based on the input of energy for mechanised operations with farm machines and the input of fertilisers for different types of farming (conventional, organic, and integrated) and different farm sizes (small, medium, large). This paper describes the structure of the model calculation of the carbon footprint of agricultural products and the establishment of a database for Slovenia. The results present an assessment of the carbon footprint of grains and fruit by different type and size of farming in Slovenia. 2. Greenhouse gas emissions from agriculture The contribution of direct emissions from agriculture, excluding emissions resulting from fuel use, fertiliser production, and agriculturally-induced land use change, is estimated at 10e12% of global GHG emissions. Total GHG emissions (direct and indirect) rise to 30% of global GHG emissions when additional emissions from fuel use, fertiliser production, and land use change are included (land use change alone accounts for 6e17%) [10,35]. In the Slovenian case, electric irrigation pumps are not used in crop production and direct energy use in Slovenian crop producing farms only stems from diesel fuel (3%e7% biodiesel is mixed with mineral diesel fuel, in accordance with the Slovenian regulation on fuels) [36,37] and natural gas (used in some cases for drying grain). The share of GHG emissions from the agriculture sector in Slovenia is about 10% of the total inventory of greenhouse gas (GHG) emissions (Fig. 1) in Slovenia (without emissions caused by energy consumption in agriculture) [38e40].
25000
GHG Emissions [kt CO2 eq.]
Other 20000
Waste Agriculture
15000
Fuel in households and the commercial sector Fuel in industry
10000
Industrial processes 5000
Energy Transport
0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Fig. 1. Total GHG emissions in Slovenia for the period 2000e2012 by source.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
The GHG emissions decreased by 12.3% in the period from 2000 to 2012, as shown in Fig. 2. The data on GHG emissions was recalculated in 2015 using a methodology based on IPCC 2006 guidelines. The national NH3 inventory was used for Tier 2 estimation of nitrogen volatilisation from manure management systems. The latest data are based on the 2013 EMEP/EEA air pollutant emission inventory guidebook [41], which is compatible with, and complementary to, the 2006 IPCC guidelines . The new GWP for methane (25 vs. 21 CO2 equivalents) and nitrous oxide (298 vs. 310 CO2 equivalents) was used for the recalculation of the effect of individual greenhouse gases in CO2 equivalents [39]. The structure of GHG emissions from the agriculture sector by source is shown in Fig. 3. The largest share of GHG emissions from the agricultural sector in 2013 derives from fermentation in the digestive tract of domesticated animals (53.1%), followed by agriculture soils (24.8%) and manure management (21.4%) [39,42]. 3. The structure of the model calculation of carbon footprints in agriculture The model for carbon footprint calculation is based on the calculation of greenhouse gas emissions (GHG) from fuel consumption, the use of fertilisers, plant protection products, and the
3
transport of agricultural products to the final consumer or the food industry. The model calculation of GHG emissions and energy consumption (hereinafter: the AgrFootprint model) for agricultural products (grains, fruit, vegetables, and other agricultural products) was also established to define the carbon footprint of all the mentioned agricultural products and products from animal breeding (meat, milk, eggs, etc.) [42,43]. The embodied energy stemming from the manufacture of fertilisers, plant protection products, and the machines and other devices used for production were not taken into consideration. An area of one hectare of land used for growing agricultural products (grains, fruit, vegetables) represents the basic unit for the calculation of GHG emissions in the AgrFootprint model (Fig. 4). The basic element of the AgrFootprint model is a process that represents one agricultural operation, device, or other activity (Fig. 5). Each process is described by means of input and output parameters. The AgrFootprint model is presented as a network of serial and parallel processes connected in the same order as the agricultural operations in the production of grain, fruit, or vegetables. The basic concept of the agricultural product model is presented in the form of a block diagram of processes (Fig. 6). The organisation of the AgrFootprint model provides the possibility to review emissions by individual operation (task) and/or set of operations (tasks) (tillage of land, harvesting, etc.). The model provides the possibility to analyse the impact of different variants of agricultural farming, tasks (secondary soil tillage with a rotary tiller or a disk harrow) or different technology (different machines for the same operation). 3.1. The model calculation The model calculation of the carbon footprint of agricultural products (grains, fruit, vegetables) is based on the calculation of the total GHG emissions from the fertilisers used and the fuel used for all production operations on one hectare of agricultural land. The carbon footprint of a product is calculated as the ratio of total GHG emissions to the total product yield of one hectare of agricultural land in kg of product. The total fuel consumption for agricultural production (Eq. (1)) of each crop (Epr,cro) is the sum of all the fuels used for basic and
Fig. 2. The trend of GHG emissions in the agricultural sector in Slovenia (2000e2012).
Fig. 4. Basic unit of agricultural land used for grain, vegetable, or fruit products.
Fig. 3. The structure of GHG emissions from the agriculture sector by source in Slovenia, 2013.
Fig. 5. The basic element of the model for the calculation of the carbon footprint of agricultural products.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
4
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Fig. 6. Schematic block diagram of the agricultural production process.
secondary soil tillage (Ecul), the spreading of artificial fertiliser (Efert), plant protection products (Eprot), harvesting (Eharv), internal transport (Etra), and additional drying (Edry).
Epr;cro ¼ Ecul þEfert þEprot þEharv þEtra þEdry
(1)
Similar equations are used for the calculation of the energy used (fuels and other types of energy) for the production of fruit, grapes, and vegetables. The total energy consumption for the production of agricultural products is equal to the total energy consumption of all production processes (Fig. 6) . The quantity of energy consumed in the production process is input data that represents the energy consumption and number of activities, the overall return period (repetition), and the return period of each activity by year on one hectare of land. The total consumption of fuel, electricity, and district heating for production is shown in Equation (2):
EFCk ¼
m X n X j¼1 i¼1
X EFi i ti
(2)
The total GHG emissions from energy used for one hectare in ton CO2 eq./ha is calculated as the total emissions of each production process, as shown in Equation (3):
EMFk ¼
m X n X j¼1 i¼1
EFi
Xi ÞFfi ti
(3) j
Where: EMFk is GHG emissions from energy consumption for the production of product k; Ffi is the direct or indirect emission factor for fossil fuels, electricity, or district heating. The total GHG emissions from fertilisers used for one hectare in ton CO2 eq./ha are shown in Equation (4):
EMfrk ¼
m X QFrk;h EFrh
(4)
h¼1
j
Where: Where: EFC is energy consumption (fuel and/or other types of energy); EF is the quantity of fuels used or other types of energy; X is the number of activities per year or the return period (X ¼ 0, 1, 2, 3, etc.); T is the return period of the activity (t ¼ 1,2,3 … n); I is the type of fuel or energy used (electricity, district heating); J is the type of agricultural operation (soil tillage, spreading of artificial fertiliser, etc.); K is the type of agricultural product (type of grain, fruit, vegetable, etc.). Parameter X, the “number of activities per year or the return period”, also has the role of a control parameter that defines the relation between the process and the system of production. The value of parameter X 1 is used for processes connected in the model and X > 1 (2,3,4 …) for the repetition of the activity per year. The value X ¼ 0 is for an unused process.
QFr is the quantity of the elements of fertilisers used per hectare (t/ha) for product k; Ffr is the GHG emission factor for the elements of fertilisers in kg CO2 eq./kg; h is the identifying number of a particular element in the fertiliser: nitrogen, phosphorus, potassium, etc. The total GHG emissions (TEMFk) for one hectare are equal to the total GHG emissions from the fertilisers used and from the energy used.
TEMFk ¼ EMFk þ EMfrk
(5)
The carbon footprint of agricultural products (AgCFk) is expressed as the result of the relation between the total GHG emissions and the total product yield, as shown in Equation (6). The value of the carbon footprint is expressed in kg CO2 equivalents per ton of agricultural product [kg CO2 eq./t].
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
AgCFk ¼
TEMFk 1000 YFk
(6)
Where: AgCFk is the carbon footprint of agricultural product k [kg CO2 eq./t];
YFk is the agricultural product yield on one hectare [t/ha]. Carbon dioxide equivalents allow different GHGs to be compared relative to CO2, using their ‘global warming potential’ (GWP), which accounts for their capacity to absorb radiation and their residence time in the atmosphere: 1 kg CH4 ¼ 25 kg CO2
eq.
1 kg N2O ¼ 298 kg CO2
eq.
The calculation of carbon footprints for agricultural products does not include emissions from the production of mineral fertilisers, plant protection products, the manufacture of agricultural machines, and other equipment and devices used. 3.2. The type of farming The type of farming and the capacity of the technologies used have an impact on the carbon footprint of agricultural products. There are different types of farming: conventional, organic, and a third type that is a mixture of the first two, i.e. integrated farming. The main difference between the types of farming is the use of fertilisers: conventional: only mineral fertilisers,; organic: organic fertilisers; and integrated: a mix of mineral and organic fertilisers; a ratio of 80% mineral and 20% organic is typical in Slovenia. Chemical plant protection products are used in conventional and integrated farming (in this paper, only direct energy or the energy to drive machinery for plant protection products is evaluated). For organic production, only the preservatives allowed in organic farming may be utilised. Herbicides are replaced by mechanical methods of weed control (e.g. a tractor with an attached harrow intended for weed control). 3.3. The size of farms The capacity and/or power of the technologies used (tractors and other machinery) for the preparation of the soil depend on the size of the farm or the area of agricultural land. The consumption of energy for land preparation varies according to the power capacity of the tractor, machinery, or appliances used. Farms are divided into three categories regarding the area of land used: small (up to 10 ha); medium (from 10 to 50 ha); and large (over 50 ha).
5
of each process or operation (agricultural operation per hectare), and other energy consumption for transport and additional drying per ton of product [44e51]. The AgrFootprint database also includes data on the specific direct GHG emissions from the use of fertilisers (organic and mineral/artificial) and emission factors for each farming type and fossil fuel used, as well as indirect emissions from the consumption of electricity and district heat. The data in the database are set in line with average production in Slovenia. The input data on the amount of products and fertiliser used per unit of land were defined based on historical or official data [49]. The quantities of fertiliser used are defined for each type of farming. The data on the product yield per hectare are defined in accordance with the available data from CAFS [20] and SORS [49] (the average for the last ten years). Energy consumption is defined for the performance of work operations with tractor-towed machinery (tractor þ machine) which are designed for primary and secondary soil cultivation, sowing, fertilisation, plant care and protection, etc. The fuel consumption (mineral diesel fuel) of tractors was defined by measuring the consumption of a tractor for various operations with or without work machines (e.g. a combine/harvester or forage harvesters for maize). Furthermore, the database includes data on energy consumption stemming from the harvesting and internal transport of crops by tractors (i.e. tractors and trailers). Energy consumption due to the cultivation of land is defined for the conventional cultivation of land with a plough and, as an alternative, direct sowing (no tillage). For sowing, the use of conventional seed drills for row sowing (maize) and compact sowing (wheat, rapeseed, and sunflower) is foreseen. In the case of direct sowing, specific performance planters are foreseen that allow sowing in the stubble. For fertilisation, the use of centrifugal spreaders of mineral fertiliser in granular or manure spreaders (integrated and organic production) is foreseen. Harvesting of silage maize is carried out by self-propelled forage harvesters or tractors with a forage harvester, while for the harvesting of maize a self-propelled combined harvester is used. For transportation, the use of a tractor with a trailer is foreseen. Energy consumption was determined by measuring fuel consumption (by the volumetric method) while carrying out work with connected machines designed for primary and secondary soil tillage, sowing, fertilisation, and plant care and protection. In addition, this also includes energy for harvesting and transporting crops (tractor with trailers) and energy consumption stemming from feeding animals (e.g. machines for transporting silage from bunker silos, fodder mixing trailers, etc.). Energy consumption, i.e. fuel consumption (mineral diesel or gas oil), is expressed in litres per hectare of land. The data on fuel consumption presented in Table 1 is based on the results of fuel measurements taken at different farms in Slovenia [52] The energy consumption data used in AgrFootprint are based on average fuel consumption data for primary and secondary soil tillage, sowing, fertilisation, plant protection products, harvesting, and transportation. In the case of primary soil tillage, the use of a multi-furrow reversible plough is provided. For secondary soil tillage, before sowing either a rotary harrow or a rotating machine (a rotary harrow or tiller) is envisaged.] 5. Results of the calculation of the carbon footprints of agricultural products
4. The model database The AgrFootprint database includes basic information on each agricultural product (grain, fruit, etc.), measures or assumptions regarding the crop yield, the use of fertilisers, the input parameters
The AgrFootprint model calculation was used for the calculation of the carbon footprints of agricultural products (grain, fruit, and vegetables) and animal products such as meat (beef, pork, and poultry), cow milk, and chicken eggs.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
6
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Table 1 The results of fuel consumption measurements taken at six model farms in different locations. Average fuel consumption (l/ha) Primary soil tillage Secondary soil tillage with a cultivator Secondary soil tillage with a rotary harrow Secondary soil tillage with a harrow and simultaneous sowing Soil tillage with a rotary tiller Subsoiling Compact sowing Sowing in rows Spreading of manure Spreading of slurry Spreading of mineral fertilisers Spraying of pesticides
24.98 10.80 16.00 12.69 43.33 13.79 2.89 6.90 15.50 3.38 1.81 0.80
The results of the model calculation of the carbon footprint of five grains and five types of fruit will be presented in the next section. The results presented are defined for the three types of farming and three sizes of farms in terms of the amount of cultivated land. 5.1. The carbon footprints of grains The carbon footprints of the following grains were determined:
maize as a grain; maize for silage; wheat; rapeseed; and sunflower.
5.1.1. Conventional farming The results of the AgrFootprint model calculation of the carbon footprint for grains produced through conventional farming methods are presented in Fig. 7 by farm size and type of sowing. The results of the AgrFootprint model calculation of the carbon footprint of grain show the lowest carbon footprint when conventional farming is used to produce maize for silage production, followed by maize as a grain, wheat, rapeseed, and sunflower. That maize for silage demonstrated the lowest carbon footprint is a result of its high production per unit of land (one hectare) in comparison to maize as a grain and other grains. 5.1.2. Organic farming The results of the AgrFootprint model calculation of the carbon footprint of grains produced by organic farming are presented in Fig. 8 by size of farm. Even in organic production the lowest emissions (footprint) are for maize for silage and the highest emissions per ton of yield are again for oilseeds (rapeseed and sunflower). 5.1.3. Integrated farming The results of the model calculation of the carbon footprint of grains produced by integrated farming (Fig. 9) are similar to those for conventional farming due to the fact that the estimated yield is at the same level as for conventional farming. The carbon footprint of grains produced by integrated farming is slightly lower than for conventional farming. The lowest carbon footprint is for the production of maize and the highest for oilseeds (rapeseed and sunflower). 5.2. The carbon footprint of fruit The carbon footprint was determined for the following fruit:
The carbon footprints of grain are calculated for three types of sowing: - conventional sowing e secondary soil tillage with rotary tillage; - conventional sowing e secondary soil tillage with a disk harrow; and - direct seeding (without any secondary soil tillage).
-
apples; pears; peaches; apricots; olives.
The results of the AgrFootprint model calculation of the carbon footprint of selected fruit in conventional, integrated, and organic
Fig. 7. The carbon footprint of grains produced by conventional farming by farm size and sowing type.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
7
Fig. 8. The carbon footprint of grains produced by organic farming by farm size and sowing type.
Fig. 9. The carbon footprint of grains produced by integrated farming by farm size and sowing type.
farming are presented in Fig. 10 by size of farm. The carbon footprint of fruit produced by means of organic farming is much higher than for production by means of conventional farming, because the yield is lower in organic than in conventional farming. 6. Conclusion The carbon footprint is one of indicators for assessing the sustainability of production. This research is the first on the carbon footprint of agricultural production in Slovenia. The AgrFootprint model is a model for the calculation of the carbon footprint of agricultural products such as grain, fruit, vegetables, meat, milk, and eggs. The structure of the AgrFootprint model is a network of serial and parallel processes connected in the same order as in the process of agricultural production. The input data of the model is defined for the average data of fuel consumption for primary and secondary soil tillage, sowing, fertilisation, plant protection products, harvesting, heating, cooling, additional drying, and internal transportation. The model requires a very large amount of data on
agricultural practices, the types of machines used, energy consumption, the consumption of mineral and organic fertilisers, emission factors, etc. The carbon footprint of agricultural products is very dependent on the use of fertilisers and productivity (the amount of product produced per unit of land area). The GHG emissions from the fertilisers used in all types of farming (conventional, organic, and integrated) have a very high impact on the carbon footprint of grain and fruit. The share of GHG emissions from the fertilisers used represents 42%e76% of the total emissions from crop production [53]. The carbon footprint of grain and fruit products produced by organic farming are higher than those produced by conventional and integrated farming. Block concept modelling enables the calculation of energy use and GHG emissions for each sector of production and the possibility of comparing different technologies and activities. A model calculation of carbon footprints in agriculture is the first step in determining the carbon footprints of agricultural products and the products produced by an individual farm.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
8
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Fig. 10. The carbon footprint of fruit by type of farming and farm size.
An increase in productivity and a decrease in energy consumption in production will have a positive influence on decreasing the carbon footprint of agricultural products [52,54]. The AgrFootprint model for the calculation of carbon footprints enables a comparison of the carbon footprint of agricultural products in Slovenia and other countries. The structure of the model will be used to evaluate improvements in agricultural production technology, farm machines, and the impacts of new types of farming and new fertilisers. The results of the model represent the average carbon footprints of average agricultural products in Slovenia. The future development of the AgrFootprint model will be focused on calculating the carbon footprint of individual agricultural products intended for consumers. It is necessary to adapt the database to calculate the carbon footprint of each farm considering the availability of data on the micro level.
[9] [10]
[11] [12] [13]
[14]
[15]
[16]
Acknowledgements The authors would like to thank the Slovenian Research Agency and the Slovenian Ministry of Agriculture and the Environment for funding the targeted research project “The environmental footprint of agriculture and the agro industry and technological measures for the reduction thereof in the future”. References
[17] [18]
[19]
[20] [21]
[1] Report of the world commission on environment and development: our common future.http://www.un-documents.net/ocf-02.htm [2] Olabi AG. Developments in sustainable energy and environmental protection. Simul Model Pract Theory 2011;19:1139e42. [3] Olabi AG. Developments in sustainable energy and environmental protection. Energy 2012;39:2e5. [4] Olabi AG. State of the art on renewable and sustainable energy. Editorial Energy 2013;61:2e5. [5] Olabi AG. 100% sustainable energy. Energy 2014;77:1e5. [6] Arbon Ian M. Sustainable energy in practice. In: 8th international conference on sustainable energy & environmental protection, key note, SEEP; 2015. 11e14 August 2015, Paisley. [7] Communication from the commission to the European parliament, the council, the European economic and social committee and the committee of the regions, Mainstreaming sustainable development into EU policies: 2009 Review of the European Union Strategy for Sustainable Development, COM. 2009. 400 final, Brussels, 24.7.2009. [8] Communication from the commission. A sustainable Europe for a
[22] [23]
[24] [25]
[26]
[27]
BetterWorld: a European Union strategy for sustainable development, COM. 2001. 264 final, Brussels, 15.5.2001. Council of the European Union. Renewed Eu sustainable development strategy. June 2006. 10917/06, Brussels, 26. D. Pandey, M. Agrawal, Carbon Footprint Estimation in the Agriculture Sector, Book: S. S. Muthu, Assessment of carbon footprint in different industrial sectors, vol. 1, EcoProduction, DOI: 10.1007/978-981-4560-41-2_2. Montzka SA, Dlugokencky EJ, Butler JH. Non-CO2 greenhouse gases and climate change. Nature 2011;476:46e50. Cucek L, Klemes JJ, Kravanja Z. A review of footprint analysis tools for monitoring impacts on sustainability. J Clean Prod October 2012;34(1):9e20. Hoekstra, A.Y., reportWater neutral: reducing and offsetting the impacts of water footprints. UNESCO-IHE, Delft, the Netherlands. Value of Water Research Report Series No. 28. UNEP/SETAC. Life cycle management: how business uses it to decrease footprint, create opportunities and make value chains more sustainable (accessed 17.08.11.), www.unep.fr. Galli, A., Wiedmann, T., Ercin, E., Knoblauch, D., Ewing, B., Giljum, S.., Integrating ecological, carbon and water footprint: defining the “footprint family” and its application in tracking human pressure on the planet. [Technical Document, Surrey, UK]. Galli, A., Wiedmann, T., Ercin, E., Knoblauch, D., Ewing, B., Giljum, S., Integrating ecological, carbon and water footprint into a “Footprint Family” of indicators: definition and role in tracking human pressure on the planet. Ecol Indic 16, 100e112. Alluvione F, Moretti B, Sacco D, Grignani C. EUE (energy use efficiency) of croping systems for a sustainable agriculture. Energy 2011;36:4468e81. Bailey AP, Basford WD, Penlington N, Park JR, Keatinge JDH, Rehman T, et al. A comparison of energy use in conventional and integrated arable farming systems in UK. Agric Ecosyst Environ 2003;97(241 e 253):244e9. Jankowski KJ, Budzynski WS, Kijewski L. An analysis of energy efficiency in the production of oilseed crops of the family Brasicaceae in Poland. Energy 2015;81(674 e 681):676e80. Unakitan G, Hurma H. F.Yilmaz: an analysis of energy use efficiency of canola production in Turkey. Energy 2010;35:3623e7. Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A. Energy flow modeling and sensitivity analysis of inputs for canola production in Iran. J Clean Prod 2011;19:1464e70. Mileusni c ZI, Petrovi c DV, Ðevi c MS. Comparison of tillage systems according to fuel consumption. Energy 2010;35:221e8. Tabatabaeefar A, Emamzadeh H, Ghasemi Varnamkhasti M, Rahimizadeh R, Karimi M. Comparison of energy of tillage systems in wheat production. Energy 2009;34:41e5. Soltani A, Rajabi MH, Zeinali E, Soltani E. Energy inputs and greenhouse gases emissions in wheat production in Gorgan, Iran. Energy 2013;50:54e61. Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H. Applying data envelopment analysis to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production. Energy 2013;58:588e93. EC (European Commission). Carbon Footprint e What It Is and How to Measure It. February 2009. Version 3 of, http://www.envirocentre.ie/includes/ documents/Carbon_Footprint-what_it_is_and_how_to_measure_it-JRC_IESFeb09-b[1].pdf. UK POST (Parliamentary Office of Science and Technology). Carbon footprint of Electricity generation. 2006. No 268.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9 [28] BSI (British Standard Institute). Guide to PAS 2050. How to assess the carbon footprint of goods and services. London, UK.: British Standards; 2008. [29] Wiedmann T, Minx J. A definition of ‘carbon footprint’. In: Pertsova CC, editor. Ecological economics research trends. Hauppauge, NY, US: Nova Science Publisher; 2008. p. 1e11. Ch 1. [30] Jensen JK, Arlbjørn JS. Product carbon footprint of rye bread. J Clean Prod 2014;82. [31] Meier Matthias S, et al. Environmental impacts of organic and conventional agricultural products e Are the differences captured by life cycle assessment? J Environ Management 2015;149:193e208. [32] Eady SJ, et al. Carbon footprint for Australian agricultural products and downstream food products in the supermarket. http://www.researchgate.net/ publication/267799818. [33] Dassisti M, Francesca Intini F, Chimienti M, Starace G. Thermographyenhanced LCA(Life Cycle Assessment) for manufacturing sustainability assessment. The case study of an HDPE (High Density Polyethylene) net company in Italy. Energy 2016;108:7e18. e J, Heijungs R, Hellweg S, et al. [34] Finnveden G, Hauschild M, Ekvall T, Guine Recent developments in life cycle assessment. J Environ Manag 2009;91: 1e21. € m E, Carlsson-Kanyama A, Bo €rjesson P. Environmental impact of di[35] Hallstro etary change: a systematic review. J Clean Prod 2015;91:1e11. [36] Decree on the promotion of the use of biofuels and other renewable fuels for the propulsion of motor vehicles. 2010. p. 11. Official gazette No 92/2010, 19. [37] Decree on the physical and chemical properties of liquid fuels. 2014. p. 8. Official gazette No 64/2014, 29. [38] Slovenian’s environment agency. Slovenia's national inventory report 2013. NIR; 2013. [39] Slovenian’s environment agency. Slovenia's national inventory report. 2013. NIR 2014 v.4. [40] Kyoto protocol to the United nations framework convention on climate change. See also: http://unfccc.int/resource/docs/convkp/kpeng.html. [41] European Environment Agency: EMEP/EEA air pollutant emission inventory guidebook. EEA Technical report No 12/2013. 2013. http://dx.doi.org/10.2800/ 92722 >>>>>>>22. [42] Verbic J. Izpusti toplogrednih plinov v kmetijstvu (Greenhouse gas emissions in agriculture). December 2012. Lombergarjevi dnevi, Ho ce, 5.
9
[43] Jejcic V, Al-Mansour F, et al. Carbon footprint of agriculture and food processing industry and measures for its decreased in the future, Report. Agriculture institute of Slovenia; 2013. [44] Dalgaard T, Halberg N, Porter JR. A model for fossil energy use in Danish agriculture used to compare organic and conventional farming, Agriculture. Ecosyst Environ 2001;87:51e65. Elsevier, str. [45] Handler, F., Nadlinger, M.: Trainer handbook, D 3.8 strategies for saving fuel with tractors, EU projekt intelligent energy Europe, efficient 20, IEE/09/764/ SI2.558250. [46] Kova cev I, Copec K, Fabijani c G, Kosuti c S. Ekonomi cnost proizvodnje soje i je cma nekonvencionalnim sustavima obrade tla. In: Proceedings of the 41th international symposium on agricultural engineering, actual tasks on agricultural engineering; 2013. p. 71e2. [47] Ortiz Canavate J, Hernanz J,L. 2 energy for biological systems, 2.1 energy analysis and saving, 2.1.2 energy saving in crop production, CIGR handboook of agricultural engineering, vol. V. St. Joseph: Energy and Biomass Engineering, ASAE; 1999. p. 13e31. [48] Tabatabaeefar A, Emamzadeh H, Ghasemi Varnamkhasti M, Rahimizadeh R, Karimi M. Comparison of energy of tillage systems in wheat production. Energy 2009;34:41e5. Elsevier, str. [49] Statistical office of the Republic of Slovenia. [50] Chamber of agriculture and forestry of Slovenia. [51] Jokiniemi T, Rossner H, Ahokas J. Simple and cost effective method for fuel consumption measurements of agricultural machinery. Agron Res 2012:97. Biosystem Engineering Special Issue 1. [52] Jecic V, Al-Mansour F, Poje T, Godesa T., Peckaj M, Cesen M, Okoljski odtis kmetijstva in zivilsko predelovalne industrije ter tehnoloski ukrepi za njegovo zni zanje v prihodnosti, Institut “Jo zef Stefan”, Kmetijski institut Slovenije, 10.2011-09.2013.,http://www.dlib.si/?URN¼URN:NBN:SI: DOC-YIJ06MRT. [53] Al-Mansour F, Jejcic V. Carbon footprint of conventional and organic crops production on family farms in Slovenia. In: V: 1st South East European conference on sustainable development of energy, water and environment systems, 29 June; 2014. 3 July 2014, Ohrid. BAN, Marko (ur.), et al. Digital proceedings, (CD Proceedings, ISSN 1847e7186). Ohrid. [54] Garnett Tara. Where are the best opportunities for reducing greenhouse gas emissions in the food system (including the food chain)? Food Policy 2011;36: S23e32.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
A model calculation of the carbon footprint of agricultural products: The case of Slovenia F. Al-Mansour a, *, V. Jejcic b a b
Energy Efficiency Centre, Jozef Stefan Institute, Ljubljana, Slovenia Department of Agricultural Engineering and Energy, Agricultural Institute of Slovenia, Ljubljana, Slovenia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 November 2015 Received in revised form 21 October 2016 Accepted 24 October 2016 Available online xxx
The pursuit of sustainable development entails a strategic policy decision for all modern countries. Greenhouse gas abatement, the utilisation of renewable energy sources, and energy efficiency represent the main pillars of sustainable development. Agriculture contributes a significant share of greenhouse gas emissions and concurrently represents a carbon dioxide (CO2) sink; it thus has twofold opposing impacts on climate change. The carbon footprint of agricultural products is one of main measures for monitoring the efficiency and sustainability of agricultural productivity processes. A model calculation of the carbon footprint in the agricultural sector was developed in order to calculate the carbon footprint of grains, fruit, and other agriculture products based on a calculation of total greenhouse gas emissions resulting from production, from the beginning of the production process to storage and delivery to the final consumer or the food industry. The first obstacles in such a calculation are the availability of input data on energy consumption by unit of land for all forms of agricultural land preparation and other work required for sowing, fertilisation, plant protection, harvesting, internal transportation, and other work. The mineral diesel fuel consumption of tractors with various connected machines and self-propelled work machines (e.g. harvesters or forage harvesters for maize) were measured. In addition, the energy consumption required for harvesting and the internal transport of crops on farms itself was included. The results of the model calculation of the carbon footprint of agricultural products consider the type of farming production for three different sizes of farms and for two scenarios regarding soil tillage and seeding. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Carbon footprint Modelling Life cycle assessment Environmental impact assessment Agricultural farming
1. Introduction The pursuit of sustainable development entails a strategic policy decision of all modern countries. Sustainable development is defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [1]. Research in recent years has been focused on different aspects of sustainable energy and environmental protection [2e5]. One of the environmental objectives of sustainability is the “reduction of negative impacts on the environment and health” [6]. The European Union is firmly committed to sustainable development and in 2001 adopted the first EU “Sustainable Development Strategy (EU SDS)” [7,8] and in 2005 adopted an
* Corresponding author. E-mail addresses: [email protected] (F. Al-Mansour), [email protected] (V. Jejcic).
ambitious and comprehensive renewed SDS for an enlarged EU [9]. The EU SDS identifies 7 key challenges and corresponding targets, operational objectives, and actions. The objective regarding climate change and clean energy is to limit climate change and its costs and the negative effects on society and the environment. Greenhouse abatement, the utilisation of renewable energy sources, and energy efficiency represent the main pillars of sustainability development. Agriculture contributes a significant share of greenhouse gas emissions and concurrently represents a carbon dioxide (CO2) sink; it thus has twofold opposing impacts on climate change . Agriculture is the largest contributor to anthropogenic emissions of greenhouse gases [10]. Agriculture covers approximately 35% of the land area and accounts for nearly 13.5% of the total global anthropogenic GHG emissions, contributing about 25%, 50%, and 70% of CO2, CH4, and N2O, respectively [11], and in the future it will be faced with significant requirements as regards reducing greenhouse gas emissions.
http://dx.doi.org/10.1016/j.energy.2016.10.099 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
2
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Greenhouse gas emissions in the agricultural sector (direct emissions) arise from the use of fossil fuels (for machinery, space and process heating, and other uses), animal breeding (enteric fermentation of domestic animals, storage of livestock manure, etc.), and agricultural land use (fertilisation with mineral and/or organic fertilisers, the rinsing of nitrogen compounds in water, the decomposition of crop residues, processing histosols, etc.). Indirect GHG emissions arise due to the consumption of electricity and district heating in agriculture. The carbon footprint of agricultural products is one of main indicators for monitoring the efficiency and sustainability of agricultural productivity processes. The carbon footprint of a product is of great interest as a central measure of the environmental impact of supply chains [12]. A carbon footprint is a quantitative measurement describing the appropriation of natural resources by humans [13,14] and describes how human activities can impose different types of burdens and impacts on global sustainability [9]. A review of the literature indicates that the major categories of footprints developed to date are carbon, ecological, and water footprints, forming the so-called “footprint family” [15,16]. Determination of the carbon footprint of agricultural products requires a detailed analysis of energy consumption in the various processes used for crop production. Total input energy in agricultural production is the sum of all the components of the energy used in the different processes for the production of outputs (agricultural products e grain, fruit, etc.). Different studies and research have focused on an analysis of the direct energy inputs in grain production and their results are presented in [17e21]. Some studies have addressed individual agricultural processes in the production of one type of grain [22,23] or the energy inputs for the production of one important product (e.g. wheat) [24,25]. A carbon footprint presents the total amount of CO2 and other greenhouse gases (GHGs) emitted over the full life cycle of a process or product [26e28] and has become one of the most important environmental protection indicators [29,30]. A carbon footprint is quantified using such indicators as the GWP (Global Warming Potential) [17], which represents the quantity of GHGs that contribute to global warming and climate change. The carbon footprint of agricultural food products can be compared to the energy labelling of appliances for households. There are different types of farming regarding the preparation of the soil, the use of fertilisers, and other chemical preparations (conventional, organic, integrated, etc.). The calculation of the carbon footprint is carried out for agricultural products using the Life Cycle Assessment (LCA) methodology [30e32].
LCA is one of the most widely recognised approaches to the environmental assessment of products and processes [33]. Several LCA methodologies have been developed thus far, and these have been somewhat improved over the last decades, namely: attributional and consequential LCA, hybrid LCA, process LCA, and input/ output LCA [34]. Current activities regarding database improvement, quality assurance integration, consistency improvement, and the harmonisation of methods are contributing to this improvement process. Mainly, there are two main perspectives on LCA: retrospective and prospective. The selection of the elements of the physical system to be modelled depends on the definition of the goal and the scope of the study [33]. The model calculation of the carbon footprint of agricultural products using LCA requires a large database with data on the energy consumption for each activity in addition to data on the quantity of fertilisers used, annual product yield, plant protection products, etc. This research is the first study on the carbon footprint of agricultural production in Slovenia. The carbon footprint of agricultural products is based on the input of energy for mechanised operations with farm machines and the input of fertilisers for different types of farming (conventional, organic, and integrated) and different farm sizes (small, medium, large). This paper describes the structure of the model calculation of the carbon footprint of agricultural products and the establishment of a database for Slovenia. The results present an assessment of the carbon footprint of grains and fruit by different type and size of farming in Slovenia. 2. Greenhouse gas emissions from agriculture The contribution of direct emissions from agriculture, excluding emissions resulting from fuel use, fertiliser production, and agriculturally-induced land use change, is estimated at 10e12% of global GHG emissions. Total GHG emissions (direct and indirect) rise to 30% of global GHG emissions when additional emissions from fuel use, fertiliser production, and land use change are included (land use change alone accounts for 6e17%) [10,35]. In the Slovenian case, electric irrigation pumps are not used in crop production and direct energy use in Slovenian crop producing farms only stems from diesel fuel (3%e7% biodiesel is mixed with mineral diesel fuel, in accordance with the Slovenian regulation on fuels) [36,37] and natural gas (used in some cases for drying grain). The share of GHG emissions from the agriculture sector in Slovenia is about 10% of the total inventory of greenhouse gas (GHG) emissions (Fig. 1) in Slovenia (without emissions caused by energy consumption in agriculture) [38e40].
25000
GHG Emissions [kt CO2 eq.]
Other 20000
Waste Agriculture
15000
Fuel in households and the commercial sector Fuel in industry
10000
Industrial processes 5000
Energy Transport
0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Fig. 1. Total GHG emissions in Slovenia for the period 2000e2012 by source.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
The GHG emissions decreased by 12.3% in the period from 2000 to 2012, as shown in Fig. 2. The data on GHG emissions was recalculated in 2015 using a methodology based on IPCC 2006 guidelines. The national NH3 inventory was used for Tier 2 estimation of nitrogen volatilisation from manure management systems. The latest data are based on the 2013 EMEP/EEA air pollutant emission inventory guidebook [41], which is compatible with, and complementary to, the 2006 IPCC guidelines . The new GWP for methane (25 vs. 21 CO2 equivalents) and nitrous oxide (298 vs. 310 CO2 equivalents) was used for the recalculation of the effect of individual greenhouse gases in CO2 equivalents [39]. The structure of GHG emissions from the agriculture sector by source is shown in Fig. 3. The largest share of GHG emissions from the agricultural sector in 2013 derives from fermentation in the digestive tract of domesticated animals (53.1%), followed by agriculture soils (24.8%) and manure management (21.4%) [39,42]. 3. The structure of the model calculation of carbon footprints in agriculture The model for carbon footprint calculation is based on the calculation of greenhouse gas emissions (GHG) from fuel consumption, the use of fertilisers, plant protection products, and the
3
transport of agricultural products to the final consumer or the food industry. The model calculation of GHG emissions and energy consumption (hereinafter: the AgrFootprint model) for agricultural products (grains, fruit, vegetables, and other agricultural products) was also established to define the carbon footprint of all the mentioned agricultural products and products from animal breeding (meat, milk, eggs, etc.) [42,43]. The embodied energy stemming from the manufacture of fertilisers, plant protection products, and the machines and other devices used for production were not taken into consideration. An area of one hectare of land used for growing agricultural products (grains, fruit, vegetables) represents the basic unit for the calculation of GHG emissions in the AgrFootprint model (Fig. 4). The basic element of the AgrFootprint model is a process that represents one agricultural operation, device, or other activity (Fig. 5). Each process is described by means of input and output parameters. The AgrFootprint model is presented as a network of serial and parallel processes connected in the same order as the agricultural operations in the production of grain, fruit, or vegetables. The basic concept of the agricultural product model is presented in the form of a block diagram of processes (Fig. 6). The organisation of the AgrFootprint model provides the possibility to review emissions by individual operation (task) and/or set of operations (tasks) (tillage of land, harvesting, etc.). The model provides the possibility to analyse the impact of different variants of agricultural farming, tasks (secondary soil tillage with a rotary tiller or a disk harrow) or different technology (different machines for the same operation). 3.1. The model calculation The model calculation of the carbon footprint of agricultural products (grains, fruit, vegetables) is based on the calculation of the total GHG emissions from the fertilisers used and the fuel used for all production operations on one hectare of agricultural land. The carbon footprint of a product is calculated as the ratio of total GHG emissions to the total product yield of one hectare of agricultural land in kg of product. The total fuel consumption for agricultural production (Eq. (1)) of each crop (Epr,cro) is the sum of all the fuels used for basic and
Fig. 2. The trend of GHG emissions in the agricultural sector in Slovenia (2000e2012).
Fig. 4. Basic unit of agricultural land used for grain, vegetable, or fruit products.
Fig. 3. The structure of GHG emissions from the agriculture sector by source in Slovenia, 2013.
Fig. 5. The basic element of the model for the calculation of the carbon footprint of agricultural products.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
4
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Fig. 6. Schematic block diagram of the agricultural production process.
secondary soil tillage (Ecul), the spreading of artificial fertiliser (Efert), plant protection products (Eprot), harvesting (Eharv), internal transport (Etra), and additional drying (Edry).
Epr;cro ¼ Ecul þEfert þEprot þEharv þEtra þEdry
(1)
Similar equations are used for the calculation of the energy used (fuels and other types of energy) for the production of fruit, grapes, and vegetables. The total energy consumption for the production of agricultural products is equal to the total energy consumption of all production processes (Fig. 6) . The quantity of energy consumed in the production process is input data that represents the energy consumption and number of activities, the overall return period (repetition), and the return period of each activity by year on one hectare of land. The total consumption of fuel, electricity, and district heating for production is shown in Equation (2):
EFCk ¼
m X n X j¼1 i¼1
X EFi i ti
(2)
The total GHG emissions from energy used for one hectare in ton CO2 eq./ha is calculated as the total emissions of each production process, as shown in Equation (3):
EMFk ¼
m X n X j¼1 i¼1
EFi
Xi ÞFfi ti
(3) j
Where: EMFk is GHG emissions from energy consumption for the production of product k; Ffi is the direct or indirect emission factor for fossil fuels, electricity, or district heating. The total GHG emissions from fertilisers used for one hectare in ton CO2 eq./ha are shown in Equation (4):
EMfrk ¼
m X QFrk;h EFrh
(4)
h¼1
j
Where: Where: EFC is energy consumption (fuel and/or other types of energy); EF is the quantity of fuels used or other types of energy; X is the number of activities per year or the return period (X ¼ 0, 1, 2, 3, etc.); T is the return period of the activity (t ¼ 1,2,3 … n); I is the type of fuel or energy used (electricity, district heating); J is the type of agricultural operation (soil tillage, spreading of artificial fertiliser, etc.); K is the type of agricultural product (type of grain, fruit, vegetable, etc.). Parameter X, the “number of activities per year or the return period”, also has the role of a control parameter that defines the relation between the process and the system of production. The value of parameter X 1 is used for processes connected in the model and X > 1 (2,3,4 …) for the repetition of the activity per year. The value X ¼ 0 is for an unused process.
QFr is the quantity of the elements of fertilisers used per hectare (t/ha) for product k; Ffr is the GHG emission factor for the elements of fertilisers in kg CO2 eq./kg; h is the identifying number of a particular element in the fertiliser: nitrogen, phosphorus, potassium, etc. The total GHG emissions (TEMFk) for one hectare are equal to the total GHG emissions from the fertilisers used and from the energy used.
TEMFk ¼ EMFk þ EMfrk
(5)
The carbon footprint of agricultural products (AgCFk) is expressed as the result of the relation between the total GHG emissions and the total product yield, as shown in Equation (6). The value of the carbon footprint is expressed in kg CO2 equivalents per ton of agricultural product [kg CO2 eq./t].
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
AgCFk ¼
TEMFk 1000 YFk
(6)
Where: AgCFk is the carbon footprint of agricultural product k [kg CO2 eq./t];
YFk is the agricultural product yield on one hectare [t/ha]. Carbon dioxide equivalents allow different GHGs to be compared relative to CO2, using their ‘global warming potential’ (GWP), which accounts for their capacity to absorb radiation and their residence time in the atmosphere: 1 kg CH4 ¼ 25 kg CO2
eq.
1 kg N2O ¼ 298 kg CO2
eq.
The calculation of carbon footprints for agricultural products does not include emissions from the production of mineral fertilisers, plant protection products, the manufacture of agricultural machines, and other equipment and devices used. 3.2. The type of farming The type of farming and the capacity of the technologies used have an impact on the carbon footprint of agricultural products. There are different types of farming: conventional, organic, and a third type that is a mixture of the first two, i.e. integrated farming. The main difference between the types of farming is the use of fertilisers: conventional: only mineral fertilisers,; organic: organic fertilisers; and integrated: a mix of mineral and organic fertilisers; a ratio of 80% mineral and 20% organic is typical in Slovenia. Chemical plant protection products are used in conventional and integrated farming (in this paper, only direct energy or the energy to drive machinery for plant protection products is evaluated). For organic production, only the preservatives allowed in organic farming may be utilised. Herbicides are replaced by mechanical methods of weed control (e.g. a tractor with an attached harrow intended for weed control). 3.3. The size of farms The capacity and/or power of the technologies used (tractors and other machinery) for the preparation of the soil depend on the size of the farm or the area of agricultural land. The consumption of energy for land preparation varies according to the power capacity of the tractor, machinery, or appliances used. Farms are divided into three categories regarding the area of land used: small (up to 10 ha); medium (from 10 to 50 ha); and large (over 50 ha).
5
of each process or operation (agricultural operation per hectare), and other energy consumption for transport and additional drying per ton of product [44e51]. The AgrFootprint database also includes data on the specific direct GHG emissions from the use of fertilisers (organic and mineral/artificial) and emission factors for each farming type and fossil fuel used, as well as indirect emissions from the consumption of electricity and district heat. The data in the database are set in line with average production in Slovenia. The input data on the amount of products and fertiliser used per unit of land were defined based on historical or official data [49]. The quantities of fertiliser used are defined for each type of farming. The data on the product yield per hectare are defined in accordance with the available data from CAFS [20] and SORS [49] (the average for the last ten years). Energy consumption is defined for the performance of work operations with tractor-towed machinery (tractor þ machine) which are designed for primary and secondary soil cultivation, sowing, fertilisation, plant care and protection, etc. The fuel consumption (mineral diesel fuel) of tractors was defined by measuring the consumption of a tractor for various operations with or without work machines (e.g. a combine/harvester or forage harvesters for maize). Furthermore, the database includes data on energy consumption stemming from the harvesting and internal transport of crops by tractors (i.e. tractors and trailers). Energy consumption due to the cultivation of land is defined for the conventional cultivation of land with a plough and, as an alternative, direct sowing (no tillage). For sowing, the use of conventional seed drills for row sowing (maize) and compact sowing (wheat, rapeseed, and sunflower) is foreseen. In the case of direct sowing, specific performance planters are foreseen that allow sowing in the stubble. For fertilisation, the use of centrifugal spreaders of mineral fertiliser in granular or manure spreaders (integrated and organic production) is foreseen. Harvesting of silage maize is carried out by self-propelled forage harvesters or tractors with a forage harvester, while for the harvesting of maize a self-propelled combined harvester is used. For transportation, the use of a tractor with a trailer is foreseen. Energy consumption was determined by measuring fuel consumption (by the volumetric method) while carrying out work with connected machines designed for primary and secondary soil tillage, sowing, fertilisation, and plant care and protection. In addition, this also includes energy for harvesting and transporting crops (tractor with trailers) and energy consumption stemming from feeding animals (e.g. machines for transporting silage from bunker silos, fodder mixing trailers, etc.). Energy consumption, i.e. fuel consumption (mineral diesel or gas oil), is expressed in litres per hectare of land. The data on fuel consumption presented in Table 1 is based on the results of fuel measurements taken at different farms in Slovenia [52] The energy consumption data used in AgrFootprint are based on average fuel consumption data for primary and secondary soil tillage, sowing, fertilisation, plant protection products, harvesting, and transportation. In the case of primary soil tillage, the use of a multi-furrow reversible plough is provided. For secondary soil tillage, before sowing either a rotary harrow or a rotating machine (a rotary harrow or tiller) is envisaged.] 5. Results of the calculation of the carbon footprints of agricultural products
4. The model database The AgrFootprint database includes basic information on each agricultural product (grain, fruit, etc.), measures or assumptions regarding the crop yield, the use of fertilisers, the input parameters
The AgrFootprint model calculation was used for the calculation of the carbon footprints of agricultural products (grain, fruit, and vegetables) and animal products such as meat (beef, pork, and poultry), cow milk, and chicken eggs.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
6
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Table 1 The results of fuel consumption measurements taken at six model farms in different locations. Average fuel consumption (l/ha) Primary soil tillage Secondary soil tillage with a cultivator Secondary soil tillage with a rotary harrow Secondary soil tillage with a harrow and simultaneous sowing Soil tillage with a rotary tiller Subsoiling Compact sowing Sowing in rows Spreading of manure Spreading of slurry Spreading of mineral fertilisers Spraying of pesticides
24.98 10.80 16.00 12.69 43.33 13.79 2.89 6.90 15.50 3.38 1.81 0.80
The results of the model calculation of the carbon footprint of five grains and five types of fruit will be presented in the next section. The results presented are defined for the three types of farming and three sizes of farms in terms of the amount of cultivated land. 5.1. The carbon footprints of grains The carbon footprints of the following grains were determined:
maize as a grain; maize for silage; wheat; rapeseed; and sunflower.
5.1.1. Conventional farming The results of the AgrFootprint model calculation of the carbon footprint for grains produced through conventional farming methods are presented in Fig. 7 by farm size and type of sowing. The results of the AgrFootprint model calculation of the carbon footprint of grain show the lowest carbon footprint when conventional farming is used to produce maize for silage production, followed by maize as a grain, wheat, rapeseed, and sunflower. That maize for silage demonstrated the lowest carbon footprint is a result of its high production per unit of land (one hectare) in comparison to maize as a grain and other grains. 5.1.2. Organic farming The results of the AgrFootprint model calculation of the carbon footprint of grains produced by organic farming are presented in Fig. 8 by size of farm. Even in organic production the lowest emissions (footprint) are for maize for silage and the highest emissions per ton of yield are again for oilseeds (rapeseed and sunflower). 5.1.3. Integrated farming The results of the model calculation of the carbon footprint of grains produced by integrated farming (Fig. 9) are similar to those for conventional farming due to the fact that the estimated yield is at the same level as for conventional farming. The carbon footprint of grains produced by integrated farming is slightly lower than for conventional farming. The lowest carbon footprint is for the production of maize and the highest for oilseeds (rapeseed and sunflower). 5.2. The carbon footprint of fruit The carbon footprint was determined for the following fruit:
The carbon footprints of grain are calculated for three types of sowing: - conventional sowing e secondary soil tillage with rotary tillage; - conventional sowing e secondary soil tillage with a disk harrow; and - direct seeding (without any secondary soil tillage).
-
apples; pears; peaches; apricots; olives.
The results of the AgrFootprint model calculation of the carbon footprint of selected fruit in conventional, integrated, and organic
Fig. 7. The carbon footprint of grains produced by conventional farming by farm size and sowing type.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
7
Fig. 8. The carbon footprint of grains produced by organic farming by farm size and sowing type.
Fig. 9. The carbon footprint of grains produced by integrated farming by farm size and sowing type.
farming are presented in Fig. 10 by size of farm. The carbon footprint of fruit produced by means of organic farming is much higher than for production by means of conventional farming, because the yield is lower in organic than in conventional farming. 6. Conclusion The carbon footprint is one of indicators for assessing the sustainability of production. This research is the first on the carbon footprint of agricultural production in Slovenia. The AgrFootprint model is a model for the calculation of the carbon footprint of agricultural products such as grain, fruit, vegetables, meat, milk, and eggs. The structure of the AgrFootprint model is a network of serial and parallel processes connected in the same order as in the process of agricultural production. The input data of the model is defined for the average data of fuel consumption for primary and secondary soil tillage, sowing, fertilisation, plant protection products, harvesting, heating, cooling, additional drying, and internal transportation. The model requires a very large amount of data on
agricultural practices, the types of machines used, energy consumption, the consumption of mineral and organic fertilisers, emission factors, etc. The carbon footprint of agricultural products is very dependent on the use of fertilisers and productivity (the amount of product produced per unit of land area). The GHG emissions from the fertilisers used in all types of farming (conventional, organic, and integrated) have a very high impact on the carbon footprint of grain and fruit. The share of GHG emissions from the fertilisers used represents 42%e76% of the total emissions from crop production [53]. The carbon footprint of grain and fruit products produced by organic farming are higher than those produced by conventional and integrated farming. Block concept modelling enables the calculation of energy use and GHG emissions for each sector of production and the possibility of comparing different technologies and activities. A model calculation of carbon footprints in agriculture is the first step in determining the carbon footprints of agricultural products and the products produced by an individual farm.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
8
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9
Fig. 10. The carbon footprint of fruit by type of farming and farm size.
An increase in productivity and a decrease in energy consumption in production will have a positive influence on decreasing the carbon footprint of agricultural products [52,54]. The AgrFootprint model for the calculation of carbon footprints enables a comparison of the carbon footprint of agricultural products in Slovenia and other countries. The structure of the model will be used to evaluate improvements in agricultural production technology, farm machines, and the impacts of new types of farming and new fertilisers. The results of the model represent the average carbon footprints of average agricultural products in Slovenia. The future development of the AgrFootprint model will be focused on calculating the carbon footprint of individual agricultural products intended for consumers. It is necessary to adapt the database to calculate the carbon footprint of each farm considering the availability of data on the micro level.
[9] [10]
[11] [12] [13]
[14]
[15]
[16]
Acknowledgements The authors would like to thank the Slovenian Research Agency and the Slovenian Ministry of Agriculture and the Environment for funding the targeted research project “The environmental footprint of agriculture and the agro industry and technological measures for the reduction thereof in the future”. References
[17] [18]
[19]
[20] [21]
[1] Report of the world commission on environment and development: our common future.http://www.un-documents.net/ocf-02.htm [2] Olabi AG. Developments in sustainable energy and environmental protection. Simul Model Pract Theory 2011;19:1139e42. [3] Olabi AG. Developments in sustainable energy and environmental protection. Energy 2012;39:2e5. [4] Olabi AG. State of the art on renewable and sustainable energy. Editorial Energy 2013;61:2e5. [5] Olabi AG. 100% sustainable energy. Energy 2014;77:1e5. [6] Arbon Ian M. Sustainable energy in practice. In: 8th international conference on sustainable energy & environmental protection, key note, SEEP; 2015. 11e14 August 2015, Paisley. [7] Communication from the commission to the European parliament, the council, the European economic and social committee and the committee of the regions, Mainstreaming sustainable development into EU policies: 2009 Review of the European Union Strategy for Sustainable Development, COM. 2009. 400 final, Brussels, 24.7.2009. [8] Communication from the commission. A sustainable Europe for a
[22] [23]
[24] [25]
[26]
[27]
BetterWorld: a European Union strategy for sustainable development, COM. 2001. 264 final, Brussels, 15.5.2001. Council of the European Union. Renewed Eu sustainable development strategy. June 2006. 10917/06, Brussels, 26. D. Pandey, M. Agrawal, Carbon Footprint Estimation in the Agriculture Sector, Book: S. S. Muthu, Assessment of carbon footprint in different industrial sectors, vol. 1, EcoProduction, DOI: 10.1007/978-981-4560-41-2_2. Montzka SA, Dlugokencky EJ, Butler JH. Non-CO2 greenhouse gases and climate change. Nature 2011;476:46e50. Cucek L, Klemes JJ, Kravanja Z. A review of footprint analysis tools for monitoring impacts on sustainability. J Clean Prod October 2012;34(1):9e20. Hoekstra, A.Y., reportWater neutral: reducing and offsetting the impacts of water footprints. UNESCO-IHE, Delft, the Netherlands. Value of Water Research Report Series No. 28. UNEP/SETAC. Life cycle management: how business uses it to decrease footprint, create opportunities and make value chains more sustainable (accessed 17.08.11.), www.unep.fr. Galli, A., Wiedmann, T., Ercin, E., Knoblauch, D., Ewing, B., Giljum, S.., Integrating ecological, carbon and water footprint: defining the “footprint family” and its application in tracking human pressure on the planet. [Technical Document, Surrey, UK]. Galli, A., Wiedmann, T., Ercin, E., Knoblauch, D., Ewing, B., Giljum, S., Integrating ecological, carbon and water footprint into a “Footprint Family” of indicators: definition and role in tracking human pressure on the planet. Ecol Indic 16, 100e112. Alluvione F, Moretti B, Sacco D, Grignani C. EUE (energy use efficiency) of croping systems for a sustainable agriculture. Energy 2011;36:4468e81. Bailey AP, Basford WD, Penlington N, Park JR, Keatinge JDH, Rehman T, et al. A comparison of energy use in conventional and integrated arable farming systems in UK. Agric Ecosyst Environ 2003;97(241 e 253):244e9. Jankowski KJ, Budzynski WS, Kijewski L. An analysis of energy efficiency in the production of oilseed crops of the family Brasicaceae in Poland. Energy 2015;81(674 e 681):676e80. Unakitan G, Hurma H. F.Yilmaz: an analysis of energy use efficiency of canola production in Turkey. Energy 2010;35:3623e7. Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A. Energy flow modeling and sensitivity analysis of inputs for canola production in Iran. J Clean Prod 2011;19:1464e70. Mileusni c ZI, Petrovi c DV, Ðevi c MS. Comparison of tillage systems according to fuel consumption. Energy 2010;35:221e8. Tabatabaeefar A, Emamzadeh H, Ghasemi Varnamkhasti M, Rahimizadeh R, Karimi M. Comparison of energy of tillage systems in wheat production. Energy 2009;34:41e5. Soltani A, Rajabi MH, Zeinali E, Soltani E. Energy inputs and greenhouse gases emissions in wheat production in Gorgan, Iran. Energy 2013;50:54e61. Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H. Applying data envelopment analysis to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production. Energy 2013;58:588e93. EC (European Commission). Carbon Footprint e What It Is and How to Measure It. February 2009. Version 3 of, http://www.envirocentre.ie/includes/ documents/Carbon_Footprint-what_it_is_and_how_to_measure_it-JRC_IESFeb09-b[1].pdf. UK POST (Parliamentary Office of Science and Technology). Carbon footprint of Electricity generation. 2006. No 268.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099
F. Al-Mansour, V. Jejcic / Energy xxx (2016) 1e9 [28] BSI (British Standard Institute). Guide to PAS 2050. How to assess the carbon footprint of goods and services. London, UK.: British Standards; 2008. [29] Wiedmann T, Minx J. A definition of ‘carbon footprint’. In: Pertsova CC, editor. Ecological economics research trends. Hauppauge, NY, US: Nova Science Publisher; 2008. p. 1e11. Ch 1. [30] Jensen JK, Arlbjørn JS. Product carbon footprint of rye bread. J Clean Prod 2014;82. [31] Meier Matthias S, et al. Environmental impacts of organic and conventional agricultural products e Are the differences captured by life cycle assessment? J Environ Management 2015;149:193e208. [32] Eady SJ, et al. Carbon footprint for Australian agricultural products and downstream food products in the supermarket. http://www.researchgate.net/ publication/267799818. [33] Dassisti M, Francesca Intini F, Chimienti M, Starace G. Thermographyenhanced LCA(Life Cycle Assessment) for manufacturing sustainability assessment. The case study of an HDPE (High Density Polyethylene) net company in Italy. Energy 2016;108:7e18. e J, Heijungs R, Hellweg S, et al. [34] Finnveden G, Hauschild M, Ekvall T, Guine Recent developments in life cycle assessment. J Environ Manag 2009;91: 1e21. € m E, Carlsson-Kanyama A, Bo €rjesson P. Environmental impact of di[35] Hallstro etary change: a systematic review. J Clean Prod 2015;91:1e11. [36] Decree on the promotion of the use of biofuels and other renewable fuels for the propulsion of motor vehicles. 2010. p. 11. Official gazette No 92/2010, 19. [37] Decree on the physical and chemical properties of liquid fuels. 2014. p. 8. Official gazette No 64/2014, 29. [38] Slovenian’s environment agency. Slovenia's national inventory report 2013. NIR; 2013. [39] Slovenian’s environment agency. Slovenia's national inventory report. 2013. NIR 2014 v.4. [40] Kyoto protocol to the United nations framework convention on climate change. See also: http://unfccc.int/resource/docs/convkp/kpeng.html. [41] European Environment Agency: EMEP/EEA air pollutant emission inventory guidebook. EEA Technical report No 12/2013. 2013. http://dx.doi.org/10.2800/ 92722 >>>>>>>22. [42] Verbic J. Izpusti toplogrednih plinov v kmetijstvu (Greenhouse gas emissions in agriculture). December 2012. Lombergarjevi dnevi, Ho ce, 5.
9
[43] Jejcic V, Al-Mansour F, et al. Carbon footprint of agriculture and food processing industry and measures for its decreased in the future, Report. Agriculture institute of Slovenia; 2013. [44] Dalgaard T, Halberg N, Porter JR. A model for fossil energy use in Danish agriculture used to compare organic and conventional farming, Agriculture. Ecosyst Environ 2001;87:51e65. Elsevier, str. [45] Handler, F., Nadlinger, M.: Trainer handbook, D 3.8 strategies for saving fuel with tractors, EU projekt intelligent energy Europe, efficient 20, IEE/09/764/ SI2.558250. [46] Kova cev I, Copec K, Fabijani c G, Kosuti c S. Ekonomi cnost proizvodnje soje i je cma nekonvencionalnim sustavima obrade tla. In: Proceedings of the 41th international symposium on agricultural engineering, actual tasks on agricultural engineering; 2013. p. 71e2. [47] Ortiz Canavate J, Hernanz J,L. 2 energy for biological systems, 2.1 energy analysis and saving, 2.1.2 energy saving in crop production, CIGR handboook of agricultural engineering, vol. V. St. Joseph: Energy and Biomass Engineering, ASAE; 1999. p. 13e31. [48] Tabatabaeefar A, Emamzadeh H, Ghasemi Varnamkhasti M, Rahimizadeh R, Karimi M. Comparison of energy of tillage systems in wheat production. Energy 2009;34:41e5. Elsevier, str. [49] Statistical office of the Republic of Slovenia. [50] Chamber of agriculture and forestry of Slovenia. [51] Jokiniemi T, Rossner H, Ahokas J. Simple and cost effective method for fuel consumption measurements of agricultural machinery. Agron Res 2012:97. Biosystem Engineering Special Issue 1. [52] Jecic V, Al-Mansour F, Poje T, Godesa T., Peckaj M, Cesen M, Okoljski odtis kmetijstva in zivilsko predelovalne industrije ter tehnoloski ukrepi za njegovo zni zanje v prihodnosti, Institut “Jo zef Stefan”, Kmetijski institut Slovenije, 10.2011-09.2013.,http://www.dlib.si/?URN¼URN:NBN:SI: DOC-YIJ06MRT. [53] Al-Mansour F, Jejcic V. Carbon footprint of conventional and organic crops production on family farms in Slovenia. In: V: 1st South East European conference on sustainable development of energy, water and environment systems, 29 June; 2014. 3 July 2014, Ohrid. BAN, Marko (ur.), et al. Digital proceedings, (CD Proceedings, ISSN 1847e7186). Ohrid. [54] Garnett Tara. Where are the best opportunities for reducing greenhouse gas emissions in the food system (including the food chain)? Food Policy 2011;36: S23e32.
Please cite this article in press as: Al-Mansour F, Jejcic V, A model calculation of the carbon footprint of agricultural products: The case of Slovenia, Energy (2016), http://dx.doi.org/10.1016/j.energy.2016.10.099