A Multidisciplinary Approach Integrating Emergy Analysis and Process Modeling for Agricultural Systems Sustainable Management—Coffee Farm Validation

Abstract

Agricultural production operates via the existing relationships between nature and economy. Its sustainable development requires tools that provide a broad vision of the energy flows that intervene in these systems, to support the decision-making process in energy management. To quantify the environmental resources and economic inputs involved, emergy analysis has been used, as well as process modeling, to make a detailed record of the system inputs. The research aim is to propose a multidisciplinary approach that integrates emergy analysis and process modeling in agricultural systems, promoting their sustainable management. This approach was validated in the Los Naranjos coffee farm in Popayán, Colombia, during the years 2018–2020. The results show that the farm achieved its best energy performance and was more sustainable in 2020, producing 1693 kg/ha of green coffee, with the lowest Unit Emergy Value (UEV; 1.12 × 10⁶ seJ/J) and the highest Emergy Sustainability Index (0.24), for the three years analyzed. In addition, natural inputs contribute approximately 27% of the total emergy, and those from the economy contribute 73%. In conclusion, this approach allows a precise and complete analysis of the system’s energy flows, significant energy uses, and energy sources at each production process stage, helping to establish the basis for an energy management system. We consider that the proposed multidisciplinary approach is a tool that would help in the sustainable management of any agricultural system, and its implementation and comparison in various contexts would be important.

1. Introduction

Energy is essential for human development; however, the economic growth model that predominates in the world is based on the consumption of fossil fuels, which is the main cause of climate change [1,2]. Beyond different technological solutions that have been proposed to improve energy efficiency, decrease greenhouse gas (GHG) emissions, and contribute to climate change mitigation [3,4], the reduction in the consumption of energy resources and the direct use of renewable energies are fundamental for true sustainable development [5]

One of the main challenges facing humanity is to ensure food security and equity in the context of the growth of its demography, with serious emerging environmental issues, decreasing energy supplies, and finite natural resources [6,7]. The progressive global demand for food has caused agricultural intensification, generating a significant increase in the use of inputs such as fertilizers, pesticides, herbicides, machines, and mechanization of processes, many of which are derived from non-renewable natural resources (i.e., fossil energy) [8]. This type of intensive production causes environmental problems such as erosion and reductions in soil fertility, the contamination of water resources, and the loss of biodiversity. All of the above can negatively influence the future of agricultural production [9,10].

To face these challenges, it is important to evaluate the sustainability and energy footprint of agricultural production processes and to identify energy flows, as well as the use and percentage of renewable and non-renewable sources within the systems [11,12,13]. Agriculture operates via the interaction of natural resources and economic inputs. For this reason, it is necessary to have tools that provide a valuation in equivalent terms when comparing the use of resources (environmental and economic) [14]. The above facilitates the finding of structural and functional failures in these systems, helping to guide decision-making and to seek adequate accounting for environmental inputs in relation to economic ones, thus achieving an optimal use of resources and a complete and integral management of the agricultural productive system [15,16,17].

In recent years, different environmental assessment tools have been developed and applied in agricultural contexts, such as ecological footprint [18], material flow analysis [19], ecological network analysis [20], life cycle analysis [21], exergy [22] and emergy [23]. Rodríguez et al. (2019) [13] identified that the emergy method stands out for the analysis of efficiency and sustainability in agricultural systems from a holistic perspective.

In the literature, there are several works that have applied the emergy technique in agricultural contexts. For example, [24] determined the best model of green coffee production in a farm located in the Cerrado region (Brazil), through the evaluation of different emergy flows, with the objective of contributing to sustainable development (environmental–economic). [25] performed a comparative analysis of emergy in five corn field systems in Guyana, which applied different by-products to generate benefit to soil properties. In Iran, 193 wheat and maize farmers were chosen, and the sustainability of their production systems were evaluated using emergy, analyzing the environmental and ecological performance of these two systems [26]. Cuadra and Rydberg (2006) [27] conducted an emergy analysis for the production, processing and export of coffee in Nicaragua, identifying that sales of roasted coffee and instant coffee generate greater benefits for Nicaragua in relation to fair trade, compared to sales of green coffee.

The emergy method is based on concepts from thermodynamics and ecology, providing a comprehensive view of the measurement of environmental resources and economic inputs [28,29]. However, for the case where a suitable recording of the inputs and outputs of the agricultural system is not carried out, or the processes, stages, components, variables, flows and equipment, involved in production are not clear, it is essential to articulate tools such as emergy with process modeling techniques. It is also necessary to be able to relate the indicators calculated in emergy with energy and environmental management tools to improve decision-making processes. Precisely, this research aims to propose a multidisciplinary approach that integrates emergy analysis, process modeling (using the ANSI/ISA-88 Standard) [30], and the basis for proposing an energy management system considering the ISO 50001—Energy Management Systems standard. The proposed approach is validated in a coffee production system, at the Experimental Farm (Los Naranjos) in Supracafé (Supracafé, 2021), Cajibío, Cauca, Colombia (21°35′08″ N, 76°32′53″ W) [31]. The data taken for the analysis correspond to the years 2018, 2019, and 2020.

2. Methodology

2.1. Methodological Approach

From the relationship between physics and ecology, an agricultural production system, whose function is to provide food, can be studied as an open and dissipative system; in other words, it consumes energy to grow and maintain itself, establishing a flow of matter and energy with its environment [15,32,33]. Energy flows are unidirectional and dissipative (lossy flow), while matter flows can be circular and conservative (Figure 1). In an agricultural system, the basic energy supply comes from environmental resources (especially solar energy), and in its unidirectional flow, it is partially converted into matter (shrubs, leaves, fruits, etc.). However, the system’s input is not only provided by nature’s contributions, but also depends to a large extent on resources from the economic system (fertilizers, insecticides, fungicides, machinery, infrastructure, labor, etc.). This combination of natural and economic inputs must be properly accounted for, in order to achieve an optimal use of resources and to manage agricultural systems based on complete and integral analyses [17,34].

The primary productivity of an ecosystem is given by the quantity of organic substances synthesized by autotrophic organisms (e.g., plants), through the process of photosynthesis, making use of solar energy, carbon dioxide, and water. Two essential variables are known in this process; the first, gross production (GP), which is defined as the rate of synthesized organic substances (fixed energy) resulting in translocatable and storable products, and the second, net production (NP), which is the difference between gross production and the respiration process. The latter can be accumulated as biomass and measured in a given time, but it can also be transformed into dead organic matter (dry fallen leaves) that can be transformed by detritivores and decomposers, used by herbivores, or transported to other ecosystems. These processes in complex natural systems (e.g., forest) ensure the balance and maintenance of ecosystems by generating high GP and intense respiration, which results in low NP, thus being valued as highly efficient systems. However, in productive ecosystems such as coffee, efficiency is understood as yield, whereby one is trying to increase net production without increasing respiration significantly, which is why energy subsidies from fertilizers, fungicides, insecticides, tillage (imported), etc., are required. Considering the above, agricultural coffee production process also involve the generation of wastes such as mucilage, cisco, wastewater, and also soil and water pollution due to agrochemical use.

When evaluating the transfer of energy between the elements of a system, for example, the path of solar energy in an agricultural production process, there is a loss of exergy (or useful energy) in each of the conversion processes. Losses occur because the fraction of energy that is not converted into matter is simply transformed into heat. Thus, although matter can be considered as a form of energy storage, and can be expressed in the same units as energy (Joules), the cost of producing matter from energy varies greatly with the level of complexity of each system. Therefore, the concept of transformity arises [35,36], which allows us to quantify the energy required to produce a unit of mass. By convention, transformity is linked to solar radiation (TrSolar = 1 seJ/J). This alternative makes it possible to measure the environmental resources and economic inputs involved in an agricultural system, for which it is important to carry out a correct recording of inputs and outputs, allowing the estimation of efficiency and sustainability indicators. The above helps us to form a baseline for energy resources management in these systems. In this article, a multidisciplinary approach is proposed that considers the integration of three stages (Figure 2): (i) process modeling, (ii) emergy analysis, and the (iii) basis for an energy management system.

2.1.1. Phase 1—ANSI/ISA-88 Standard

ANSI/ISA-88 is a standard addressing batch process control. This tool allows for modeling and designing industrial processes, by describing equipment and procedures [37]. To date, the ISA-88 committee has published five parts of the standard. For this research, Part 1: Models and Terminology, which seeks to model the production process in relation to the chronological procedures and associated resources, is very useful. For this purpose, three models are defined: (i) procedural control model—directs equipment-oriented actions to take place in an ordered sequence in order to carry out a process-oriented task; (ii) physical model—can be used to describe the physical assets of an enterprises (sites, areas, process cells, units, equipment modules, and control modules) and (iii) process model—describes a sequence of chemical, physical or biological activities for the conversion, transport or storage of material or energy (Figure 2) [30].

Phase 1 is applied at each stage of an agricultural production system. Furthermore, this process modeling using ISA-88 helps to characterize reliably the energy flows (considering convention proposed by emergy analysis) present in the agricultural production system.

In this way, the results of phase 1 are the necessary inputs for phase 2 (energy flows of environmental and economic resources), where emergy analysis is carried out. In other words, process modeling helps us to identify accurately all the inputs and outputs of the system, e.g., inputs from nature, fertilizers, equipment, machinery, human labor, etc.

2.1.2. Phase 2—Emergy Analysis

In the 1970s, Odum employed the concept of “embodied energy” to refer to differences in energy quality in terms of its generation costs and used the “quality factor” relationship to indicate how many Joules of one type of energy were required to produce another type of energy [38]. In 1983, Odum abandoned the concept of “embodied energy” in favor of the concept of “emergy” [35].

Emergy is defined as the available energy previously required, directly and indirectly, through input pathways to make a product or service (Y = R + N + F, Figure 2, Phase 2). The emergy unit is solar emergy joules (seJ). The emergy flows represent three resources categories: (i) R as renewable resources, (ii) N as non-renewable resources (R and N flows are provided by the environment and are economically free) and (iii) F the inputs from the economy (provided by the market and related to fluxes by the economy).

Emergy is a quantitative tool, based on the law of conservation of energy, thermodynamics, systems theory [39], and systems ecology [40,41]. This tool, by using the sun as a common unit, accounts for the flows of environmental services, which support the processes that occur in the society–nature relationship [42], and integrates inputs from the economy, achieving a holistic analysis of the systems [35].

The quotient of a product’s emergy divided by its energy is defined as its transformity (seJ/J) [43], and refers to the emergy needed to obtain 1 J of a product or service, directly or indirectly. Unit Emergy Values (UEVs) (such as transformity and specific emergy—total emergy input to energy output of a system) are calculated based on the emergy required to produce one unit of output as a joule or gram [44,45]. With modeling processes, each flow is expressed in grams (g or kg) for mass, joules (J) for energy, or money ($), according to its case. The next step is multiplying the amount of input (mass, energy, or money) by its corresponding unit emergy value (UEV), transforming the flow into solar emergy per energy (seJ/J, transformity).

The UEV indicates the hierarchical position on a thermodynamic scale, i.e., the higher its value, the more processing, transformations and emergy creation associated with the product or service, and a low UEV means that less emergy inputs are needed to produce a given amount of output [46]. The UEVs values used in this text are extracted from the literature (they have been used in the context of the tropics and agricultural coffee production) and are relative to a baseline of 15.83 × 10²⁴ seJ/year [33]. The use of a given baseline allows results to be compared with transparent calculation procedures.

The emergy methodology follows three main steps: (i) representation of the system under study on an energy diagram, identifying inputs, outputs and system boundaries (symbols proposed by [43] are used); (ii) elaboration of an emergy accounting table that quantifies all system input flows (in units of energy, mass or money) where each flow will be multiplied by its respective UEV (seJ/J); (iii) calculation of the emergy indices to help discussions of the system’s emergy performance. A brief definition of emergy indices used in this work is presented in

Table 1. Specifications and formula of emergy-based indices.

Indices	Formula	Specifications
Total Emergy (seJ)	$Y_{coffee}=R+N+F$	Total emergy resources required to support the production system
UEV (seJ/J)	$T=\frac{Y_{coffee}}{E}=\frac{Total emergy}{Product energy}$	System efficiency—amount of emergy required to produce an output unit in joules. A high UEV indicates that fewer products are obtained with the same amount of energy invested, or that more resources are needed to obtain the same level of production [47].
% Emergy Renewability	$\%R=\frac{\left(R+F_R\right)}{Y_{coffee}}\ast 100$	Percentage of the renewable energy used by the system. Systems with a high R percentage are more environmentally sustainable and can react more quickly to economic stress [48].
Emergy Yield Ratio	$EYR=\frac{Y_{coffee}}{F}$	Ratio between the outflow emergy and the inflow emergy, and the emergy coming from inflows or economic resources. It is a measure of the system’s ability to exploit the local natural resources by means of an external resource investment from the outside economic system, and reflects the potential contribution of the process to the main economy [48].
Environmental Loading Ratio	$ELR=\frac{N+F_N}{R+F_R}$ $F_N=M_N+S_N \mathrm{y} F_R=M_R+S_R$	Ratio of non-renewable and imported emergy use to renewable emergy use. It indicates the pressure produced by the system on the environment and can be considered as a measure of ecosystem stress.
Emergy Investment Ratio	$EIR=\frac{F}{N+R }$	Yield of the emergy derived from economic resources, over the emergy derived from local resources. Indicates the effectiveness of an investment to drive a local development process.
Emergy Sustainability Index	$ESI=\frac{EYR}{ELR}$	Aggregated indicator, which links the characteristics of EYR to those of the ELR. The goal for sustainability is to obtain the highest yield ratio at the lowest environmental load. A system with a higher ESI is more sustainable.

.

The indexes calculated in phase 2 provide important information to define and structure the basis of an energy management system (substantiated, e.g., on ISO 50001). The used energy sources, the significant consumptions per stages, the % of system renewability, system emergy, and the UEVs are key data that allow decision-makers to start structuring an EMS.

2.1.3. Phase 3—Basis for the Energy Management Systems—EMS (ISO 50001)

Energy management is a process of continuous culture improvement adopted by an organization in relation to its energy performance [49]. An energy management system allows for adopting best practices in all areas and organization levels, considering the PDCA (Plan–Do–Check–Act) approach. The current version of the ISO 50001 standard includes a High-Level Structure (HLS), which helps to integrate with other standards such as ISO 9001 (quality) and 14001 (environmental).

Phase 2 contributes to phase 3, the different indices related to the energy performance and sustainability of the analyzed system. This information is key to decision-making and to beginning structuring the basis of an energy management system supported by the continuous improvement cycle.

This continuous improvement of energy performance corresponds to quantitative results that the organization can achieve in terms of energy use and consumption. In the first stage of planning, it is important to conduct an energy review to know and understand the current state of the organization, identifying the energy matrix, significant energy uses, savings potentials, and opportunities for improvement. This is fundamental information for establishing objectives, energy goals, energy performance indicators, energy baselines, and action plans for continuous improvement (Figure 2, Phase 3). In this way, the planning of the energy management system represents the basis for compliance with the energy policy defined by the organization.

2.2. Coffee Production System Description of the “Los Naranjos” Farm

The “Los Naranjos” experimental farm is located in Cajibío municipality, Cauca department, Colombia (21°35′08″ N, 76°32′53″ W) [31], at an average altitude of 1800 MASL, producing exclusively green coffee for export (Figure 3). The farm’s area is about 37.5 ha coffee planted, of which 27 ha are the Arabica (caturra) variety [50], and the crop density is 5000 trees/ha. There are four stages in the production process: (i) planting, where the coffee bushes are grown in alleys, and a shaded coffee plantation system is used, producing high-quality and traceable micro-lots; (ii) harvesting, which is carried out by the Women’s Coffee Growers Association of Cauca (AMUCC), and between the months of April and July the largest quantity of coffee cherries are harvested; (iii) wet processing, where the coffee cherries are cleaned, selected (maturity and size), pulped, fermented, washed, and finally dried, resulting in parchment coffee (dry); and (iv) the pre-processing or threshing of the coffee, in which their husks are removed, obtaining green coffee as the final product, which is packaged in 70 kg bags (“excelso café de Colombia”) and stored for future trade (export). Throughout the production process, there is a commitment to social and environmental sustainability and the promotion of innovation, highlighting for the last two stages a technical and industrialized infrastructure (machinery, equipment and buildings). Coffee cultivation began in 2009 (plantation), and the first harvest took place in 2012.

3. Results

3.1. Phase 1: Production System Modeling of the “Los Naranjos” Farm (Planting–Harvesting Stage)

Figure 4 shows the operations and flows of the plantation and harvesting stages by-products, starting with the coffee seed, up to its transformation into coffee cherries. The activities carried out in these stages are undertaken manually (human labor). A process flow diagram (PFD) is developed for the stages of the wet processing of coffee and the pre-processing or threshing of coffee, where there are several equipment modules, and it is necessary to register them in order for them to be included in the system’s energy flows analysis.

The energy flows identified in the plantation stage start from items 1 to 16, and for the harvesting stage are between items 17 and 20 (Table 3). This modeling process allows for a correct inventory of the different energy flows that intervene in the coffee-producing farm, and thus, to carry out an emergy analysis that reflects the system’s reality. In addition, for these first two stages it is necessary to define the procedural control model, which helps to determine the system’s structure and behavior in relation to the actions executed in chronological order. This model is composed of a five-unit procedure and 17 operations (Figure 4 and

Table 2. Procedural control model in plantation and harvesting stages.

Procedure	Unit Procedure	Operation	Phase
Plantation and harvesting coffee	Seed germination	Construction	Building the germinator
		Planting 1	Sowing seeds in germinator
		Shade	Protecting seeds from direct sunlight
		Irrigation 1	Keeping the germinator moist
	Coffee nursery	Preparation	Preparing substrate (organic fertilizer)
		Fill	Filling coffee bags
		Selection 1	Selecting seedbeds
		Transplant	Transplanting seedbeds to bags
		Irrigation 2	Keeping bag soil moist
	Plantation	Plot-spacing	Establish planting density
		Tillage	Digging holes for each seedling
		Selection 2	Selecting seedlings
		Planting 2	Sowing the seedlings in the pits
	Fertilization and integrating pests/weeds Management	Fertilize for growth	Fertilizing coffee in growth
		Trimming	Pruning coffee plants
		Fertilize for production	Fertilizing coffee in production
	Harvesting	Harvesting	Harvesting coffee beans

).

3.2. Phase 2: Energy Description of the Agricultural System Using Emergy Analysis

Figure 5 presents the coffee production model of the “Los Naranjos” farm using the energy systems symbology. The diagram shows the system boundaries and the energy sources that drive the processes. On the left side of the diagram are energies from nature (I = R + N, where R—renewables, and N—nonrenewables); on the top are resources from the economy, such as fuels, chemicals, electricity, labor, machinery, etc. (F = Materials + Services), and on the right side is the yield or total emergy (Y = R+ N + F). The farm’s production system modeling, performed in phase 1, provides a complete inventory of energy flows involved in each system’s stage. The energy flow accounting and analysis involved in the production system of the “Los Naranjos” farm was performed for the years 2018, 2019, and 2020.

Table 3. Emergy for the coffee production in the “Los Naranjos” farm, 2018.

Item	Description	Class	Annual Flow (Unit/Year ha)			Emergy per Unit (seJ/Unit)	Emergy (seJ/Year ha)	Ref.
Plantation *			2 First Years/30 Years	Year of 2018	Total
1	Solar insolation (J)	R	2.72 × 1012	4.07 × 1013	4.34 × 1013	1.00	4.34 × 1013	[43]
2	Wind, kinetic energy (J)	R	3.54 × 106	5.31 × 107	5.67 × 107	2.52 × 103	1.43 × 1011	[43]
3	Rain, chemical energy (J)	R	4.01 × 109	6.01 × 1010	6.41 × 1010	3.06 × 104	1.96 × 1015	[43]
4	Rain, geopotential energy (J)	R	8.65 × 107	1.30 × 109	1.38 × 109	1.76 × 104	2.44 × 1013	[43]
5	Evapotranspiration (J)	R	4.52 × 109	6.78 × 1010	7.23 × 1010	3.98 × 104	2.88 × 1015	[51]
6	Soil erosion (J)	N	6.46 × 109	9.68 × 1010	1.03 × 1011	7.40 × 104	7.64 × 1015	[43]
7	Nitrogen (g)	F	1.67 × 104	2.50 × 105	2.67 × 105	6.62 × 109	1.77 × 1015	[27]
8	Phosphate (g)	F	3.33 × 103	5.00 × 104	5.33 × 104	9.35 × 109	4.99 × 1014	[27]
9	Potassium (g)	F	1.48 × 104	2.22 × 105	2.37 × 105	9.32 × 108	2.21 × 1014	[27]
10	Urea (g)	F	1.80 × 104	2.70 × 105	2.88 × 105	6.62 × 109	1.91 × 1015	[27]
11	Cal (g)	F	2.87 × 103	4.30 × 104	4.59 × 104	1.68 × 109	7.71 × 1013	[24]
12	Organic fertilizer (J)	80% R	1.43 × 103	0	1.43 × 103	3.87 × 109	5.55 × 1012	[52]
13	Seeds (J)	F	3.77 × 105	0	3.77 × 105	5.85 × 104	2.20 × 1010	[27]
14	Machinery and equipment (g)	F	6.07 × 102	9.10 × 103	9.71 × 103	6.70 × 109	6.50 × 1013	[53]
15	Human labor (USD)	10% FR	3.06 × 101	1.89 × 102	2.19 × 102	2.25 × 1013	4.94 × 1015	[27,52]
16	Pesticides and fungicides (g)	F	1.87 × 102	2.80 × 103	2.99 × 103	1.48 × 1010	4.42 × 1013	[54]
	Total for plantation						2.21 × 1016
Harvesting			Annual Flow (Unit/Year ha)—2018
17	Machinery and equipment (J)	F	1.60 × 104			6.70 × 1009	1.07 × 1014	[53]
18	Fuel and lubricants (J)	F	1.15 × 1010			1.11 × 105	1.27 × 1015	[24]
19	Harvest transport—vehicle (USD)	F	3.00 × 101			2.25 × 1013	6.75 × 1014	[27]
20	Human labor (USD)	10% FR	5.48 × 102			2.25 × 1013	1.23 × 1016	[27,52]
	Total for Harvesting						1.44 × 1016
Wet processing of coffee
21	Solar insolation (J)	R	1.12 × 1012			1.00	1.12 × 1012	[43]
22	Wind, kinetic energy (J)	R	5.31 × 107			2.52 × 103	1.34 × 1011	[43]
23	Evaporation (g)	R	1.86 × 106			1.45 × 105	2.69 × 1011	[55]
24	Water (J)	R	1.78 × 107			8.60 × 104	1.53 × 1012	[43]
25	Drying yard or “paseras” (g)	F	6.50 × 104			2.42 × 109	1.57 × 1014	[27]
26	Machinery and equipment (USD) **	F	7.77 × 101			2.65 × 1013	2.06 × 1015	[27]
27	Human Labor (USD)	10% FR	1.41 × 102			2.25 × 1013	3.18 × 1015	[27,52]
28	Fuel (J)	F	3.87 × 109			1.11 × 105	4.30 × 1014	[24]
29	Buildings (USD)	F	5.08 × 101			2.65 × 1013	1.35 × 1015	[27]
30	Electricity (USD)	F	3.44 × 101			2.65 × 1013	9.12 × 1014	[27]
	Total for Drying						8.09 × 1015
Pre-processing or threshing coffee
31	Machinery and equipment (USD) **	F	4.94 × 101			2.65 × 1013	1.31 × 1015	[27]
32	Human Labor (USD)	10% FR	1.21 × 102			2.25 × 1013	2.72 × 1015	[27,52]
33	Buildings (USD)	F	3.39 × 101			2.65 × 1013	8.98 × 1014	[27]
34	Electricity (USD)	F	1.72 × 101			2.65 × 1013	4.56 × 1014	[27]
35	Jute bags (g)	F	1.00 × 102			2.31 × 1010	2.31 × 1012	[56]
	Total for pre-processing						5.38 × 1015
	(Y) Total Emergy		3.87 × 1010			1.29 × 106	4.99 × 1016	Calculated in this study

* The planting stage was carried out in 2009, where the energy flows are applied (items 1–16); however, this is an investment that is being made in the long term, in this study, and according to the indications of experts in coffee production, it is estimated that the crop can have a productive life of 30 years [57]. For this reason, it is necessary to divide the energy applied in the first 2 years (where the crop had no production) between the 30 years of useful life. Similarly, each year (year analyzed in the 2018 table), it is necessary to perform the maintenance of the crop where energy flows must be added (items 1–16, except 13 seeds). This energy applied during the first 2 years, which is a long-term investment, is only done for the plantation stage. ** The machinery and equipment inventory is made considering the process flow diagram (PFD)—equipment modules available upon request.

shows the emergy analysis for the year 2018, when the age of the crop was 9 years (other evaluation tables are available Appendix A).

According to previous studies [48], 90% of human labor is produced by nonrenewable resources (product of F), while 10% is obtained by renewable natural resources and is counted as an FR input, and 80% of organic fertilizer is produced by renewable natural resources.

For the green coffee production system (Table 3), inputs from the economy (F) contribute 75% of the total emergy, of which 70% are energies of non-renewable origin (FN), and 5% are renewable energies (FR). Energy from nature (I) represents 25% of the total emergy needed to produce green coffee, of which 15% is from non-renewable sources (IN) and 10% from renewable sources (IR). The production of coffee cherries for 2018 was 8765 kg/ha; applying a ratio of 0.18 [57] to calculate the amount of green coffee, 1578 kg/ha are obtained. The energy equivalence of 1 kg of green coffee is 2.45 × 10⁷ Joules [24]); thus, the calculated UEV for the system is 1.29 × 10⁶ seJ/J.

Two previous studies have performed emergy analysis in a coffee productive farm, one in Brazil [24] and the other in Nicaragua [27]. Regarding these investigations, the green coffee production of the “Los Naranjos” farm, Cauca, Colombia is 37% more energy efficient than that reported in Nicaragua, which was 1.77 × 10⁶ seJ/J [27]. On the other hand, the Brazilian system [24], whose transformity was 4.25 × 10⁵ seJ/J, is approximately 3 times more efficient than the Colombian system, and 4 times more than Nicaragua’s.

The system studied has standardized processes; as a result, there are no considerable variations in the percentages of energy contributions for the three years analyzed (

Table 4. Energy contribution, production, emergy and UEV in the “Los Naranjos” farm.

Year	Energy Contribution (%)						Production—Coffee Cherries (kg/ha)	Production—Green Coffee (kg/ha)	Emergy (seJ/Year ha)	UEV (seJ/J)
	I	IR	IN	F	FR	FN
2018	25	10	15	75	5	70	8765	1578	4.99 × 1016	1.29 × 106
2019	27	11	16	73	4	69	8532	1536	4.74 × 1016	1.26 × 106
2020	27	11	16	73	4	69	9407	1693	4.66 × 1016	1.12 × 106

). For the years 2019 and 2020, inputs from nature (I) contributed 27%, and inputs from the economy (F) contributed 73%. The value of emergy, i.e., the useful energy used directly or indirectly to generate a product (green coffee), is gradually decreasing each year. Therefore, it is possible to indicate that the system was 15% more energy efficient in 2020 compared to 2018, with a UEV of 1.12 × 10⁶ seJ/J, due to the reduction in the system’s emergy and the increase in production.

Analysis of the input and output energy flows of the system allows the determination of the driving factors that can impact the agricultural production process sustainability. The contribution of renewable and non-renewable sources for each stage of the coffee production process studied must be known (see

Table 5. %R Contribution of coffee production per stage in the “Los Naranjos” farm.

Year	Plantation			Harvesting
	%R	%N	Total	%R	%N	Total
2018	10.83	33.3	44.2	2.47	26.34	28.81
2019	11.53	36.96	48.49	2.24	24.27	26.51
2020	11.45	34.30	45.76	2.48	26.99	29.47
	Wet processing				Pre-processing
	%R	%N	Total	%R	%N	Total
2018	0.64	15.56	16.20	0.54	10.24	10.78
2019	0.52	14.81	15.33	0.41	9.26	9.67
2020	0.49	14.80	15.29	0.38	9.10	9.48

), in order to propose strategies that lead to the use of renewable and environmentally friendly energy.

Referring to the emergy invested in each stage of the production process (see Figure 6), the planting stage is the one with the highest emergy demand, with an average of 46.1% for the 3 years analyzed of the total emergy of the system, wherein soil erosion and the human labor are the highest contribution flows. The harvesting stage contributes an average of 28.3% of energy demand, and human labor is the largest flow. The wet processing of coffee contributes 15.6%, and the pre-processing stage 10%. In these last two stages, the highest contribution flows are human labor, machinery and equipment.

The above is explained by the fact that the “Los Naranjos” farm’s objective is to produce high-quality export-type coffee, requiring, mainly in the two first stages, human labor to carry out the planting, fertilization, selection and harvesting of coffee cherries in optimal conditions, and in the final stages, the use of special equipment in different operations to guarantee the final product quality. Figure 6 also shows that there is an emergy reduction of 0.328 × 10¹⁶ seJ/year ha in 2020 compared to 2018. Contrary to this, the production of green coffee was higher in 2020 (115 kg/ha) than in 2018.

Figure 7 shows the main emergy flows in production stages of the “Los Naranjos” farm in 2018, as follows: (i) During plantation the emergy’s value was 2.21 × 10¹⁶ (seJ/year ha), which corresponds to 44.2% of total emergy, of which 15% is from soil erosion, 9.9% from human labor, 9.8% from renewable natural resources (solar insolation, wind, rain, evapotranspiration), and 9% from fertilizers (nitrogen, phosphate, potassium, urea, lime). (ii) The emergy during the harvesting stage was 1.44 × 10¹⁶ (seJ/year ha), which is equivalent to 28.8% of total emergy, of which human labor contributed 24.7%, fuel and lubricants 2.5%, and harvest transportation vehicle 1.4%. (iii) For the wet processing stage of coffee, the emergy was 8.09 × 10¹⁵ (seJ/year ha), corresponding to 16.2% of the system’s emergy. Here, human labor contributed 6.4%, the machinery and equipment 4.1%, the buildings and drying yard 3%, and electricity and fuel 2.7%. (iv) The emergy during the pre-processing stage was 5.38 × 10¹⁵ (seJ/year ha), equivalent to 10.8% of the total emergy, of which the human labor contributed 5.4%, the machinery and equipment 2.6%, the buildings 1.8%, and the electricity 0.9%. In all green coffee production, human labor is the activity that contributes the most emergy, 2.32 × 10¹⁶ (seJ/year ha), which is equivalent to 46.4% of the required system’s emergy.

3.3. Phase 3: Basis for the Energy Management Systems—EMS (ISO 50001)

In addition to the modeling and energy performance of the systems, it is important to obtain the environmental performance indicators, which offer key information for the decision-making process and are the basis for an energy management system (EMS). An emergy index summary for green coffee production for the 3 years analyzed is presented in Figure 8.

The Emergy Yield Ratio (EYR) does not vary greatly in the three years studied, with values of 1.34 for 2018 and 1.35 in 2020, indicating little capacity to exploit local resources, and that the emergy of the system is largely based on economic resources. However, it should be noted that the system’s productivity increased considerably, from 1578 kg/ha of green coffee in 2018 to 1693 kg/ha in 2020, showing that the productivity increase does not require a higher percentage of energy purchased in the system balance.

The Environmental Loading Ratio (ELR) in 2018 was 5.9, and it was 5.7 in 2020, indicating a reduction in the pressure exerted by the system on the environment. The ELR is closely linked to the “% Emergy Renewability” used by the system, since a low value of renewable emergy generates greater environmental pressure. At “Los Naranjos” farm, the % Emergy Renewability was 14.4% in 2018, and 14.8% in 2020. Thus, the ELR values indicate that the system generates a moderate environmental impact, depending to a high degree on the emergy provided by external resources (purchased). Finally, it is interesting to highlight the reduction in the ELR (lower environmental impact) in these 2 years, in relation to the increase in productivity, showing that the system is able to produce more without increasing the negative impacts on the environment when processes are well adapted.

The Emergy Investment Ratio (EIR) in 2018 was 2.98, and was 2.71 in 2020. These values indicate that the emergy derived from local resources is low, compared to the emergy derived from economic resources. Despite this, it is important to consider that in addition to the increase in productivity, there was an improvement in the EIR, which means an increase in the efficiency in the use of local resources for the year 2020.

Finally, the Emergy Sustainability Index (ESI) had a slight increase over the 3 years studied, going from 0.22 in 2018 to 0.24 in 2020, indicating that the system improved in relation to its sustainability. This indicator is composite and relates the EYR and ELR. For a system to be sustainable, it must obtain the highest ratio of yield and the lowest environmental load. The above is explained by the small increase in EYR from 2018 to 2020, and by the decrease in ELR in this time period. Comparing this indicator with the result obtained in Brazil [24], which was 0.14, and in Nicaragua [27], which was 0.13, it is possible to say that the farm studied in Colombia is a more sustainable system.

According to [15], in a natural system, transformities reach a theoretical lower limit, i.e., the highest possible energy efficiency. On the other hand, coffee production is an intervening, man-made system, which is driven by energy resources from nature and the economy, depending on market demands, and the socio-ecological conditions surrounding the production process. Relating ESI, the UEV and production (Figure 9) provides an idea about the “optimal” production system of the “Los Naranjos” farm, which allows for obtaining a low UEV (efficient) and a high ESI (sustainable).

Comparing the three years analyzed, the “Los Naranjos” farm had its best environmental and energy performance in 2020, with a production of 1693 kg/ha of green coffee, where the UEV was the lowest, 1.12 × 10⁶ seJ/J, and the Emergy Sustainability Index (ESI) increased. This indicates that the decisions taken in recent years have led to a notable improvement in the efficiency and sustainability of the system.

The emergy analysis here conducted (2018–2020) offers a report on agricultural production practices commonly adopted by the Los Naranjos farm. The most representative emergy flow in the three years analyzed is the “human labor” flow; in 2018 it contributed 46.49% of the total emergy of the system, in 2019 it was 43.35%, and in 2020 it was 42.63%. In recent years, according to information from the Los Naranjos farm managers, educational programs have been implemented for employees, and time and movement studies have been carried out to optimize the practices of employees in the different stages of the production process. These programs have contributed to improinge the system’s sustainability and efficiency, mainly in the harvesting and wet processing stages.

This system’s improvement in relation to energy performance is the basis for structuring an energy management system (EMS) in an organization. According to ISO 50001, in the first stage (Plan) of the PDCA continuous improvement cycle, an energy review should be performed to understand the current state of the organization, identifying energy flows and significant energy uses. In this way, the process and energy modeling performed in this study generate a baseline of energy and environmental performance indicators, which could be very useful in the decision-making process, and in the definition of objectives, goals, action plans, and policies, which guide the organization to continuously improve.

4. Conclusions

Global population growth has necessitated more food production, generating agricultural intensification, which results in the greater use of agricultural supplies as well as causing environmental problems. Agriculture combines natural resources and economic inputs to produce food. This socio-ecological interaction requires a comprehensive assessment that contributes to the sustainable development and energy efficiency of these systems.

Through this research, a multidisciplinary assessment approach is proposed that integrates process modeling, emergy analysis, and the basis of an energy management system (EMS). This approach was validated in a coffee production system, in “Los Naranjos” farm, Cajibío, in the department of Cauca, Colombia, during the time period from 2018 to 2022. This proposal makes it possible to: (i) have a comprehensive inventory of energy flows from nature and the economy, (ii) identify the significant uses of energy in the different production system stages, and (iii) obtain environmental sustainability indicators that help in the decision-making process and establish an EMS basis.

In the validation of the coffee production system, it was observed that the highest Emergy Renewability percentage was achieved in the year 2020, with a value of 14.8%. The EYR did not change considerably during the period studied, with values between 1.34 and 1.35, indicating that most of the system’s output came from economic resources. The EIR performed better in 2020 with a 2.71 value, indicating a slight improvement over emergy from local resources. The ESI increased in the analyzed period, from 0.22 to 0.24, improving the sustainability of the farm. Along with these indicators, the increase in the system’s productivity should be highlighted, as it went from 1578 kg/ha of green coffee in 2018 to 1693 kg/ha in 2020. The emergy value gradually decreased each year, indicating a 15% more energy efficient system in 2020 compared to 2018. Finally, it can be inferred that the farm achieved its best energy performance and was more sustainable in 2020, producing 1693 kg/ha of green coffee, with the lowest UEV (1.12 × 10⁶ seJ/J) and the highest Emergy Sustainability Index (ESI) (0.24).

On average for the three years analyzed, the stage with the highest emergy demand was the plantation stage with a 46.1% share of total emergy. The harvesting stage contributed 28.3%, the wet coffee processing stage 15.6%, and the pre-processing stage 10%. Throughout the entire production process, the activity that contributed the most emergy was human labor, with approximately 46% of the total emergy. The system has a large part of its processes standardized in order to guarantee the quality of its export product. In addition, according to information reported on the farm, there has been continuity in the employee training processes, generating an important installed human capacity that fulfills operational and administrative functions, but which also generates added value in the products through research, innovation, and entrepreneurship processes, which are related to the regional coffee sector.

Emergy flows such as solar insolation, wind, rain, and evapotranspiration, which depend on local climatic variations that influence the production process, are incorporated in the analysis of emergy carried out from 2018 to 2020. However, the climatic conditions of the area during the three years analyzed did not show significant variations that would considerably affect the farm’s productivity. Rainfall (m) was the variable showing the greatest variation, with values of 0.159 in 2018, 0.142 in 2019, and 0.165 in 2020. Notably, the seasonal regime is bimodal, and there are two rainy seasons (from April to June and from August to November, and two summer periods) in most of the country. Climate data were obtained from national databases (http://www.ideam.gov.co (accessed on 25 May 2022), and from https://agroclima.com (accessed on 25 May 2022)). Furthermore, due to the standardized production of the farm under study, there is no evidence of serious biological effects such as rust and fungi.

The annual emergy analysis helps to diagnose significant energy consumption at each stage of the agricultural production process. From this perspective, sustainable alternatives can be analyzed, e.g., shading, renewable energy sources, agro-ecological systems, recycling, optimization of human labor, etc., to help mitigate possible scenarios of change (climatic, economic, market and political). This systemic approach enables decision-makers to find more efficient and sustainable options for each particular system.

To improve the studied system, it is recommended that the farm make use of alternative sources of electrical energy, such as photovoltaic. It would be useful to implement this type of technology mainly in the wet processing and pre-processing stages. In addition, we suggest looking for alternatives that help to optimize the harvesting stage, and it would also be helpful to consider the loss of coffee beans in the transportation from the crop to the collection center.

The proposed multidisciplinary approach allows for an integral analysis of the energy flows involved at the coffee production farm, determining the significant energy uses and the energy sources at each process stage. This information is essential to decision-making in any agricultural system, and for establishing the basis for an Energy Management System (EMS). Ultimately, it would be beneficial if the multidisciplinary approach could be implemented in other agricultural production systems, which would help to enrich the discussion on this topic.