An Evaluation of Research Interests in Vertical Farming through the Analysis of KPIs Adopted in the Literature
Thus, the main contributions of this work are as follows: providing a functional model of a VFPS, selecting and classifying the KPIs used in those systems, and providing insights about the frequencies of the KPIs used in the literature.
To this aim, the following research questions (RQ) were formulated:
RQ1: What are the main categories in which the VFPS KPIs can be classified based on their objective?
RQ2: What are the most frequently used VFPS KPIs and categories?
RQ3: Do the researchers exhaustively evaluate VFPSs by considering all the KPI categories?
The methodology is divided into four steps. The first three steps are related to the identification of the KPI categories, while the fourth step is devoted to the literature review to identify the KPIs and their frequencies. The four steps are described in the following subsections.
2.1. KPI Dimensions
The productivity dimension is divided into system productivity and crop productivity to separate the KPIs used in order to evaluate how a plant grows in a VFPS (crop productivity) and the ones used to evaluate the productivity of a VFPS viewed as the actual plant production system (system productivity).
2.2. VFPS Elements
Once the KPI dimensions had been defined, we analyzed the elements that compose a VFPS. We recall that the term vertical farming refers to any soilless farming technique that operates in a controlled environment. Moreover, we refer to a production system in a controlled environment that can have from one to several levels of cultivation. Therefore, the model’s boundaries include the growth of the crop from the germination stage to the harvesting stage, omitting all pre-production and post-production stages.
In an IDEF0 model, the primary element is the graphic diagram, which includes boxes, arrows, and interconnections. Boxes represent the major functions of the subject being modeled. These functions can be further detailed in child diagrams until the subject is described at the necessary level for a specific project’s goals. The top-level diagram offers a general and abstract representation of the subject. As you move down the hierarchy, the diagrams become more detailed, providing a comprehensive understanding of how the system functions and interacts.
The sides of the function box have standardized meanings in terms of box/arrow relationships. The role of an arrow is determined by the side of the box with which it interfaces. Arrows entering the left side of the box represent inputs. These inputs are the data or objects that the function consumes or transforms to produce outputs. Arrows entering the box from the top side represent controls. Controls specify the conditions or requirements needed for the function to produce accurate outputs. Arrows leaving the box on the right side are outputs. These outputs are the data or objects that are generated or produced by the function. Arrows connected to the bottom side of the box represent mechanisms. Mechanisms are the means that support the execution of the function, identified by upward-pointing arrows.
The IDEF0 formalism was used to represent VPFSs and identify their elements. The elements that compose a VFPS are all its inputs, outputs, mechanisms, and controls.
We identified three input elements for VFPSs: seeds, environmental inputs provided to the plant to ensure efficient growth (i.e., water, nutrients, CO, temperature, humidity, light intensity, light spectrum, energy, space, and substrate), and packaging material used to prepare the finished product.
The output elements were identified as crops (divided into edible parts and non-edible parts) and environmental outputs (i.e., nutrient surplus, water, and oxygen).
The mechanism elements were divided into the workforce, environmental technologies, and the mechanical structure. Environmental technologies (i.e., sensors, actuators, and microprocessors) enable the system to control and monitor the environmental inputs necessary for crop growth. On the other hand, the workforce includes all the operators who perform non-automated system functions. The level of labor involved in crop growth depends on the system’s automation level. Finally, the mechanical structure supports the crop’s growth, providing the frame of the cultivation system, such as a grow chamber and an irrigation system.
Five control elements were identified: government regulations (i.e., all the regulations that the production system must comply with to conform to the law of a given country), resource constraints (i.e., economic and physical limitations that the cultivation system presents, such as space and energy limitations), crop shape and color (constraints on color or shape concerning market demands), growth constraints (controls based on the physiological parameters of the plant to determine whether the plant is growing healthily or not), and environmental constraints (indicating the ranges of environmental inputs for the plant to grow healthily).
The A-0 diagram was further specified into two functions: growing, and harvesting. Growing refers to the growth cycle of the crop, which varies depending on the crop type being considered. Harvesting, however, means the stage of harvesting, packing, and storing the final product.
2.3. Final KPIs Categories
Regarding productivity, the crop productivity dimension includes the output element of crops in the IDEF0 model. Indeed, this category includes all the KPIs measuring plant physical characteristics. On the other hand, the mechanism elements of environmental technologies and mechanical structure are included in the system productivity dimension because the KPIs of these categories are related to the plant production system. In this sub-dimension, the output element of crops is also included, since the KPIs of this category are related to the crop’s physical characteristics.
Considering sustainability, all the identified elements belong to the environmental sustainability dimension. This is because social and economic sustainability extends beyond the boundaries of the VFPS, and quantifying them only within the system boundaries can lead to a distorted evaluation. Indeed, inside the mechanisms of the IDEF0 model, the workforce element is the only element not described through KPIs because it should fit inside the social sustainability dimension.
The environmental sustainability category includes the environmental inputs and environmental outputs of the VFPS. This category includes all the KPIs that measure the consumption and efficiency of the utilization of the inputs provided to the system.
Regarding quality, all the sub-dimensions include the output element of crops, since the KPIs evaluating the nutritive value, safety, deliciousness, and logistics feasibility are computed by analyzing the grown crops. In addition, the logistic feasibility also includes the input element of packaging materials.
There were eleven identified KPI categories, and they were created through the merging of the identified dimensions and the VFPS elements (fourth column of the table). It can be noticed that the considered VFPS elements were only the inputs, outputs, and mechanisms of the IDEF0 model. Controls were not used to define the categories because they represent the requirements needed for the function to produce accurate outputs and thus impose limits on the production system.
2.4. Paper Selection from the Literature
The initial screening of the abstracts (first examination), where 59 articles were found to be out of scope, was divided between three independent reviewers. Subsequently, the screening process based on the analysis of the full text of the 110 articles was carried out by nine students. To avoid bias caused by the students’ lack of experience, at a later stage, some papers were rescreened by three PhD students. For every paper, we documented the identified key performance indicators (KPIs) in an Excel file. Each column in the file represented a cumulative list of KPIs up to that point, while each row corresponded to the articles screened up to that moment. Consequently, the Excel table was systematically updated for each paper.
After completing the screening process based on the analysis of the full texts of the selected papers, 78 indicators were identified. The KPIs were classified in the categories defined in the previous section. Furthermore, since the KPIs belong to different hierarchical levels, they are presented in a three-level hierarchical way. Level I represents KPIs that give more general information, while in levels II and III, there are KPIs that still assess the same element but give more detailed information.
For each KPI, the theoretical definition is given, together with the frequency of its citations in the analyzed papers. The frequency of each KPI is indicated in brackets in the tables of this section.
3.1. Productivity KPIs
In the productivity dimension, the KPIs with a higher citation frequency were all from the crop productivity category. They were fresh and dry weight, which were cited in 40% and 43% of the articles, respectively, followed by the leaf area, which was cited by 36% of the articles.
3.2. Sustainability KPIs
The categories of environmental input and environmental output in terms of environmental sustainability were merged because some found KPIs need elements of both categories to be computed. In level I, there were the following KPIs:
Resource use efficiency (RUE), i.e., the ratio of the final plant production to the total input ;
Energy consumption, i.e., the decrease in the primary energy consumption required to produce a unit of agricultural product ;
Greenhouse gas (GHG) emissions, i.e., greenhouse gases emitted by agricultural activities that constitute a group of gases contributing to global warming and climate change .
In the sustainability dimension, the KPIs with the highest citation frequency were LUE (light use efficiency) and WUE (water use efficiency), with citations in 19% and 15% of the analyzed papers, respectively.
3.3. Quality KPIs
The KPIs with the highest citation frequency in the quality dimension were all from the safety subcategory. Indeed, natural toxic compounds, nitrate content, and hazardous compounds all had the same citation rate: 11%.
This work reviewed the KPIs of vertical farming with an Internet-of-Things (IoT) architecture. Evaluating and monitoring the performance of a production system is crucial for identifying inefficiencies and shortcomings, allowing for adjustments that optimize resource utilization, reduce waste, and mitigate environmental impact.
To identify relevant KPIs, a research method inspired by literary discovery was employed, emphasizing the development of new knowledge through an extensive literature review on the Scopus database.
Then, the identification and categorization of the KPIs was carried out, laying the basis for addressing RQ2 (What are the most frequently used VFPS KPIs and categories?) and RQ3 (Do the researchers exhaustively evaluate VFPS by considering all the KPI categories?).
Environmental sustainability KPIs were also very prevalent in the literature, since around 46% of the articles used one or more environmental KPIs. These KPIs are being used more and more in articles recently. This can be attributed to the increased interest of the scientific community in the environmental challenges faced by our world today.
The least-used KPIs belonged to the quality dimension, specifically the sub-dimensions of logistical feasibility and deliciousness, where the first one was mentioned in less than 4% of the selected articles, and the latter was mentioned in 16% of the selected articles. In the case of logistical feasibility, this can be because several authors may consider these KPIs as outside of the scope or outside the border of vertical farming systems (shelf life, for example). On the other hand, the low number of citations of the deliciousness category can be attributed to a lack of ability to empirically measure the value of these KPIs, since they are subjective and depend on the user’s perception (such as prestige and ease of use).
The KPIs belonging to nutritive value and safety were slightly more mentioned in the literature (21% and 34% of the articles). A possible reason for this is the ‘ease of measure’ effect. Easily measurable indicators require simple and cheap instrumentation to be measured (biomass, size, etc.), and are thus more frequently used. On the contrary, others require expensive and complex instrumentation, knowledge, and properly qualified staff to be properly assessed (vitamin content, mineral content, etc.). We found that the first ones were more cited than the others. Moreover, to properly evaluate the importance and weight of the selected KPIs, other methods need to be implemented (e.g., a balanced scorecard and analytic hierarchy process (AHP)), because, as we have shown, frequency is not a robust indicator of a KPI’s importance.
Thus, as an answer for RQ2, it was found that dry weight was the most-used KPI in the scientific literature, followed closely by the fresh weight and leaf area indices. Moreover, the six most-cited KPIs all belonged to the crop productivity dimensions, making it the most prevalent dimension in the literature by far.
Moreover, almost all the articles with four or more sub-dimensions always included environmental sustainability and crop productivity. It can be concluded that researchers consider these aspects to be more important and more functional to include in their research regarding the assessment of the level of performance of vertical farming systems.
To the authors’ knowledge, there is a lack of research in the literature on the analysis of KPIs in vertical farming systems. Few works have been published offering a multidimensional classification of KPIs for VFPSs. Notably, most existing studies concentrate on a singular dimension, often emphasizing economic aspects. It is essential to acknowledge a key limitation of this study, which will be addressed in future research. Specifically, this study did not encompass a detailed exploration of the economic and social sustainability sub-dimensions in VFPSs.
In conclusion, this study aimed to address the pressing need for a comprehensive evaluation of vertical farming systems through the multidimensional categorization and selection of KPIs suitable for measuring system performance. Through an extensive literature review that analyzed over 100 scientific articles, we identified and categorized 78 KPIs representing various dimensions of vertical farming evaluation. These KPIs were classified into eleven categories, covering the critical aspects of productivity, sustainability, and quality.
To facilitate this categorization, we constructed a purpose-built model of a vertical farming production system using the IDEF0 methodology, which allowed us to propose a structured KPI classification. After assigning each of the 78 KPIs to the 11 proposed categories, we evaluated the research interest in vertical farming. This evaluation considered the utilization rate of each KPI, and we found out that the highest number of used KPIs belonged to the productivity category (100 articles out of a total of 110), followed by quality KPIs (82 appearances), and, lastly, sustainability KPIs (51 appearances). Among the productivity category, the most-used KPIs were fresh and dry weight, which appeared in almost half of the selected articles. In the sustainability category, LUE (light use efficiency) and WUE (water use efficiency) were the most-cited ones, even though they appeared in less than 20% of the selected papers. KPIs belonging to the quality sub-dimension were less cited, especially those belonging to the logistic feasibility and deliciousness sub-dimensions, whereas the nutritive value and safety sub-categories were more used and were mentioned in over half of the examined papers.
Moreover, we evaluated the number of KPI subcategories that were analyzed in each paper. We found that the majority of the articles focused only on one or two subcategories. A higher number of subcategories being evaluated may better describe a VFPS, but this kind of evaluation (covering three or more subcategories) was found in a relatively small number of articles.
Four main contributions of the present work can be identified concerning the state of the art: (i) providing a functional model of a VFPS to identify its main elements and components, which are needed to compute the KPIs, (ii) providing a taxonomy of categories for the classification of KPIs for a VFPS, (iii) selecting all the KPIs used in the context of vertical farming and classifying them into the identified categories, and (iv) providing insights about the frequencies of the KPIs used in the literature and the coverage of the categories.
The main impact of our analysis is the highlight that the majority of the research in this field tends to emphasize productivity-related KPIs, while those related to sustainability and quality are relatively underrepresented. Moreover, a notable lack of studies addressing the integration of all three dimensions highlights the need for a more balanced and multidimensional approach to assessing vertical farming performance. Thus, presenting a multidimensional categorization, this study can be a reference for subsequent evaluations that include more dimensions of a VFPS.
In our future research, we plan to expand the scope of our model to encompass social and economic sustainability aspects. We also intend to explore dynamic modeling techniques, such as UML diagrams, to capture the evolving behavior of vertical farming production systems (VFPSs). Furthermore, we aim to investigate the possibility of developing a composite indicator that comprehensively represents all the dimensions of VFPS performance, providing a more exhaustive and non-redundant evaluation tool. This research sets the stage for a multidimensional evaluation of vertical farming systems, contributing to their sustainability and success in modern agriculture.
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