A Platform for GHG Emissions Management in Mixed Farms

Abstract

This research introduces an innovative platform designed to manage greenhouse gas (GHG) emissions in mixed farms. Emphasizing the urgent need to address GHG emissions, particularly methane (CH₄) and nitrous oxide (N₂O), the platform targets mixed farming systems where the interplay of livestock and crop production significantly contributes to environmental impacts. Our methodology is grounded in comprehensive data collection, encompassing soil data, energy consumption, and detailed livestock information. Utilizing the Agricultural Internet of Things (AIoT), it facilitates real-time data acquisition and analysis, providing insights into various farm activities’ GHG emissions. This approach allows for precise monitoring and management of emissions from different sources, including enteric fermentation in livestock and fertilizer use in crop production. Results from the application show its effectiveness in offering a clear and interactive visualization of GHG emissions, aiding farmers in making informed decisions for sustainable farm management. The platform’s user management system, coupled with advanced data processing and visualization capabilities, underscores its potential as a vital tool for sustainable farming. Conclusively, Solution4Farming represents a significant advancement in digital agriculture, combining IoT technology with sustainable practices. Though initially designed for Romanian cattle farming, Solution4Farming’s anticipated expansion to various farming environments suggests a broader impact and relevance in sustainable agriculture.

1. Introduction

Greenhouse gases (GHGs) are currently one of the leading environmental issues threatening the planet and challenging hundreds of researchers around the world. Greenhouse gases are the collective name for several (transparent in visible light) gases that can “trap” the thermal radiation of the planet; the atmosphere can be compared to the “blanket”, which does not pass this radiation, causing consequent heating of the Earth [1]. The Intergovernmental Panel on Climate Change (IPCC) [2], an organization that conducts systematic research on tracing climate change due to anthropogenic factors, distinguishes the following GHGs: carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and fluorinated gases (HFC, PFC, SF6, and NF3). In 2022, the IPCC released its 6th report, which presents the trend in the contribution of each gas to total human-caused greenhouse gas emissions over the period 1990–2019 (File S1, Figure 1).

According to the IPCC, the primary drivers of global GHG emissions are five major sectors: energy systems, industry, buildings, transport, and AFOLU (agriculture, forestry, and other land use). The authors in [3] analyzed the EDGAR v5.0 database and calculated total global trends of GHG emissions (File S1, Figure 2).

Methane occupies the biggest share of overall GHG gases emitted from plant fields and livestock. The 6th ICPP report pointed out CH₄ as one of the key warmers and drew attention to the problem of the need for its immediate reduction [4]. According to the Global Monitoring Laboratory, between June 2021 and 2022, methane levels increased by 17.64 parts per billion (ppb), which is the highest since the beginning of the registration (1984) [5]. In 2021, the Climate and Clean Air Coalition published the Global Methane Assessment Report, which stated that reducing methane emissions by 45% will entail not only a 0.3 °C reduction in temperature rise by 2030 but also a reduction in deaths, asthma, and crop loss [6]. Cumulatively, animal and plant agriculture represents the second (30%) and the third (18%), respectively, among the main human-related sources of methane emissions, right after fossil fuel use (33%). Moreover, more specifically, enteric fermentation, the process of food digestion in ruminants, takes the leading position over all the anthropogenic reasons for high CH₄ levels (27%) [7].

The second place after methane in agriculture is nitrous oxide, N₂O. According to the report, there is a worrying trend affecting climate change: N₂O levels have increased by 20% in 2018, with emissions from human activity being the main cause of this rapid expansion [8]. When talking about agriculture, N₂O emissions are primarily conditioned by manure left on the fields, synthetic fertilizers, and crop residues.

The problem of high levels of methane and nitrous oxide emitted from the agricultural cluster brings into the spotlight the need for new ecological and cost-effective crop and livestock management solutions using the principles of circular economy, which emphasize the importance of reuse, sharing, zero waste, and utilization of the full potential of resources. The concept of mixed farms (i.e., farms targeted at utilizing symbiotic connections between animal and plant agriculture) represents one of the main directions of GHG emissions mitigation strategies [9]. For example, the combination of horticulture and bovine livestock seems to be quite promising: sheep can be fed the sections of various horticultural plants that are not edible, such as lettuce, broccoli, artichokes, or broad beans, and the manure can act as a fertilizer for these plants.

It is hard to overestimate the influence of the Fourth Technological Revolution on modern society; it brought a significant change to all branches of human life. The concept of the Internet of Things (IoT) emerged under the framework of Industry 4.0, allowing the connection of dozens and hundreds of small portable devices to collect, store, transmit, analyze, and reuse large amounts of heterogeneous information. IoT leads the population to better process management, decision-making procedures, resource optimization, and business deals in the areas of healthcare (IoHT—Internet of Health Things) [10,11,12], industry (IIoT—Internet of Industrial Things) [13,14], military (MIoT—Military Internet of Things) [15], and particularly in agriculture (AIoT—Agricultural Internet of Things) [16].

The IoT itself refers to the system of interrelated devices that collect, transmit, store, process, or analyze different data flows collected from the environment. The “things” could be whatever has a connection to the other “thing” and the Internet; talking in a mixed farm paradigm, it could be anything from a light sensor to a tractor. The branch of IoT dealing with farming specifics is called Agricultural IoT, and here and after will be referred to as AIoT.

AIoT allows farmers to collect in real-time all the relevant information across fields and livestock (weather conditions, size of livestock, air pollution, soil moisture, etc.), helping them to analyze the state of the farm and find optimal management decisions to increase productivity and avoid harming the environment.

AIoT goes hand in hand with Precision Agriculture (PA) and Precision Livestock Farming (PLF) concepts, which, in turn, are inextricably linked with the concept of Decision Support System (DSS). The first concept, PA, according to the International Society of Precision Agriculture (ISPA), refers to “a management strategy that gathers, processes, and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability, and sustainability of agricultural production” [17]. Further, PLF is targeted specifically at in-the-moment animal monitoring. PLF assures the best possible use of farm resources, which in turn regulates the health of the animals and helps to control GHG emissions [18]. Thus, both PA and PLF comprise the same principle but operate in different areas. The structure of the concepts considers four main parts (Figure 1) [16].

WSN: a group of sensors distributed over the farm collects data related to the livestock (size, food intake, sounds, etc.), crop (soil moisture, water supply, fertilizer intake, etc.), and environmental parameters (GHG emissions, temperature, location of the workers and agricultural equipment, etc.). The relevant metrics and ways to measure them are discussed in detail in the next subsection.

Data processing module: the raw data collected by portable devices are transmitted to the data processing module, which performs specific manipulations with it, such as sorting, structuring, processing, storage, further transmission, etc.

DSS: the processed flows of information from the data module are being forwarded to the Decision Support module. DSS utilizes modeling and simulation to assist customers in finding the optimal solution.

Action: the target customer (farmer, administrator, and manager) considers the suggestions from DSS and decides.

The AFOLU provides around a quarter of general human-caused GHG emissions, with methane and nitrous oxide topping the list. The development of the AIoT concept gave a wide range of opportunities to track, systematize, and analyze farming-related information, helping to optimize management not just from the standpoint of direct margin but also the release of gases into the atmosphere. The key sources of agriculture’s GHG emissions and the corresponding main directions for mitigation strategies are analyzed in Table 1.

Table 1. Key sources of GHG emissions in the agriculture sector.

Category	Gas	Problem Description	Mitigation Strategies	Ref.
Enteric fermentation	CH4	In the process of digestion of a ruminant—enteric fermentation—methane is released in its stomach, which is either burped or excreted from the body through flatulence. Currently, multiple directions are being developed to overcome the CH4 emissions caused by enteric fermentation. In [6], the authors gathered and systematized the leading mitigation strategies.	Dietary manipulations, breeding management, and rumen modification.	[2,9]
Rice cultivation	CH4	Rice fields are covered with water; in these conditions, there is a lack of oxygen in the soil. The lack of oxygen, in turn, leads to the activity of methanogens (bacteria) that feed on carbon and produce methane. In this way, methane is synthesized by bacteria found in the roots of rice. Bacteria use acetate and other carbon-containing molecules as raw materials for the synthesis of methane. There are many factors catalyzing methane emissions from rice fields (type of soil, rice, fertilizer, water management, etc.) and particularly high temperatures. Thus, we can observe a closed loop: rice fields emit methane that increases the temperature of the planet, which increases emissions of methane from rice fields.	New varieties of rice with a lower carbohydrate content.	[19]
Synthetic fertilizers	CH4 N2O	Synthetic fertilizers are currently irreplaceable in extensive cropping systems organized to fulfill a growing demand in society; compared to organic fertilizers, they significantly accelerate the cultivation of plants. However, the manufacture of synthetic nitrogen fertilizers is a significant source of GHG, while synthetic nitrogen fertilizers themselves are acknowledged as the most significant cause of direct N2O emissions from agricultural soils. Currently, there is no relevant substitute for synthetic fertilizers; however, researchers are seeking new possibilities; e.g., the authors of [12] proved that despite the partial replacement of synthetic fertilizer with manure having promising effects (e.g., a significant reduction in N2O), it increases CH4 emissions by 2–3 times, especially in paddy rice fields.	Replacement/partial substitution of synthetic fertilizers with other types of nitrogen fertilizers; low-carbon fertilizers; and N management techniques.	[20,21]
Manure management	CH4 N2O	Animal slurry and manure contain inorganic nitrogen, carbon, and water; they provide the main substrates required for the microbial production of N2O and CH4. The difference between solid manure and slurry is depicted in Figure 3. In general, methane emissions are higher when manure is stored and handled in liquid form.	Production of biogas from manure for heating (anaerobic digestion), cooling of the slurry, dietary manipulation, and composting.	[22]
Crop residues	CH4 N2O	Under certain conditions, the organic mass of plant residues undergoes decomposition under the influence of the vital activity of microorganisms, resulting in the formation of carbon dioxide and methane. The authors of [16] determined that leaving crop residues on the soil surface generates fewer methane emissions than their incorporation into the soil. Another way to dispose of crop residues—burning—causes the immediate release of significant amounts of GHGs.	Production of biogas from crop residues for heating.	[23,24]
Biomass burning
Degraded organic soils	N2O CO2	Soil and land degradation is a combination of natural and anthropogenic processes that lead to changes in soil functions, quantitative and qualitative deterioration of their composition and properties, and a decrease in the natural and economic significance of lands.	Land reclamation.	[25]

Table 1. Key sources of GHG emissions in the agriculture sector.

Category	Gas	Problem Description	Mitigation Strategies	Ref.
Enteric fermentation	CH4	In the process of digestion of a ruminant—enteric fermentation—methane is released in its stomach, which is either burped or excreted from the body through flatulence. Currently, multiple directions are being developed to overcome the CH4 emissions caused by enteric fermentation. In [6], the authors gathered and systematized the leading mitigation strategies.	Dietary manipulations, breeding management, and rumen modification.	[2,9]
Rice cultivation	CH4	Rice fields are covered with water; in these conditions, there is a lack of oxygen in the soil. The lack of oxygen, in turn, leads to the activity of methanogens (bacteria) that feed on carbon and produce methane. In this way, methane is synthesized by bacteria found in the roots of rice. Bacteria use acetate and other carbon-containing molecules as raw materials for the synthesis of methane. There are many factors catalyzing methane emissions from rice fields (type of soil, rice, fertilizer, water management, etc.) and particularly high temperatures. Thus, we can observe a closed loop: rice fields emit methane that increases the temperature of the planet, which increases emissions of methane from rice fields.	New varieties of rice with a lower carbohydrate content.	[19]
Synthetic fertilizers	CH4 N2O	Synthetic fertilizers are currently irreplaceable in extensive cropping systems organized to fulfill a growing demand in society; compared to organic fertilizers, they significantly accelerate the cultivation of plants. However, the manufacture of synthetic nitrogen fertilizers is a significant source of GHG, while synthetic nitrogen fertilizers themselves are acknowledged as the most significant cause of direct N2O emissions from agricultural soils. Currently, there is no relevant substitute for synthetic fertilizers; however, researchers are seeking new possibilities; e.g., the authors of [12] proved that despite the partial replacement of synthetic fertilizer with manure having promising effects (e.g., a significant reduction in N2O), it increases CH4 emissions by 2–3 times, especially in paddy rice fields.	Replacement/partial substitution of synthetic fertilizers with other types of nitrogen fertilizers; low-carbon fertilizers; and N management techniques.	[20,21]
Manure management	CH4 N2O	Animal slurry and manure contain inorganic nitrogen, carbon, and water; they provide the main substrates required for the microbial production of N2O and CH4. The difference between solid manure and slurry is depicted in Figure 3. In general, methane emissions are higher when manure is stored and handled in liquid form.	Production of biogas from manure for heating (anaerobic digestion), cooling of the slurry, dietary manipulation, and composting.	[22]
Crop residues	CH4 N2O	Under certain conditions, the organic mass of plant residues undergoes decomposition under the influence of the vital activity of microorganisms, resulting in the formation of carbon dioxide and methane. The authors of [16] determined that leaving crop residues on the soil surface generates fewer methane emissions than their incorporation into the soil. Another way to dispose of crop residues—burning—causes the immediate release of significant amounts of GHGs.	Production of biogas from crop residues for heating.	[23,24]
Biomass burning
Degraded organic soils	N2O CO2	Soil and land degradation is a combination of natural and anthropogenic processes that lead to changes in soil functions, quantitative and qualitative deterioration of their composition and properties, and a decrease in the natural and economic significance of lands.	Land reclamation.	[25]

The Food and Agriculture Organization of the United Nations [26] collects and systematizes world statistics on GHG emissions originating from the agriculture sector. Investigating the latest available 5 year time span (2016–2020) data on methane and nitrous oxide emissions for some European countries, we can indeed notice an increasing overall trend (Figure 4 and Figure 5). Further, despite environmental, agricultural, ecological, and other conditions that vary from country to country, it is possible to identify the leading sources of CH₄ and N₂O emissions: enteric fermentation and synthetic fertilizers, respectively.

In this paper, we propose to present an innovative solution aimed primarily at farmers, which refers to a complex tool that is a support system for decisions associated with reducing the impact of activities on mixed farms on the environment.

Following the structure of the IoT concept when applying it to the agriculture sector, we should decide which parameters we need to monitor to have a sufficient information base for optimal management. There are no specific classifications of the agriculture-relevant metrics available in the literature so far. Thus, considering the GHG sources listed in Table 1, we summarize them in

Table 2. Key metrics in the area of mixed farms.

№	Metric		Sensor Type/Measurement Technique	Group C—Crop, L—Livestock, and E—Environment	Ref.
1	Size of the animal			L
2	Temperature of the animal			L
3	Sounds			L
4	Movements (patterns, anomalies)		Accelerometers, pedometers	L	[27]
5	Food intake/quality of food		Registration for EMG data on masticatory muscles	L	[28]
6	Portion size			L
7	Chewing patterns		Fiber Bragg grating sensors (FBG)	L	[29]
8	Soil moisture			C
9	Fertilizer intake			C
10	Temperature		Temperature/humidity sensors	E
11	Humidity		Temperature/humidity sensors	E
12	Air Quality Index (AQI)	CH4	Gas sensors	E	[10]
		N2O
		CO2
		CO
		NH3
		PM 1
		PM 2.5
		PM 10
13	Light	Ambient light sensors	E
14	Rainfall		E
15	Wind speed		E

, combining them into three groups: livestock-related, crop-related, and environment-related.

The more metrics, the better; however, one should consider the complexity of the outcome system and correlate it with the opportunities and budget. Thus, the initial step in the development of the DSS is the general purpose, output and input of the system. Consequently, the main output of the planned DSS is the Air Quality Index (AQI), while all the other parameters might act as inputs.

The Air Quality Index is a complex parameter showing the qualitative composition of the air and is calculated based on the measured pollution concentrations [10,30]. It was created primarily to regulate air quality in large cities and guide people in emergencies when concentrations of pollutants begin to exceed values that are safe for human health. There is a common set of pollutants that is usually tracked in most countries; however, practically, the AQI set, as well as the methods of its calculation and result assessment, can vary from place to place depending on the environmental conditions and policies of the countries. For example,

Table 3. AQI in Romania, Finland, Poland, and Spain. Key: ● indicates presence of pollutant.

Country	Set of Pollutant Gases for Index Calculation
	SO2	NO2	O3	CO	PM 10	PM 2.5	BC
Poland [31]	●	●	●	●	●	●	●
Romania [30]	●	●	●	●	●
Spain	●	●	●	●	●	●
Finland [32]	●	●	●	●	●	●

presents the pollutants for AQI of some European countries (Romania, Finland, Poland, and Spain), where ‘BC’ is ‘black carbon’. In this report, we do not imply any specific AQI but refer to it as a measure for assessing air quality. The pollutants included in the calculation and the calculation method itself are subject to further determination.

The modern GHG-oriented DSS tools, as an output, calculate the so-called carbon footprint that is being measured in CO₂ equivalents per kg of product, e.g., milk. This parameter considers not just carbon emissions but also methane and nitrous oxide.

Analyzing the latest IPCC report, we concluded that the leading GHGs from livestock and crop fields are methane (CH₄) and nitrous oxide (N₂O) while processing the statistics over the past 5 years provided by FAOSTAT for the countries of interest (Romania, Poland, Finland, and Spain) confirms that the main sources of those gases in the considered areas are enteric fermentation and synthetic fertilizers, respectively.

Next, this work gathers a list of metrics relevant to monitoring mixed farms that need to be considered to make economically and environmentally wise management decisions.

Table 4. Key challenges in the area of mixed farms related to the implementation of a DSS.

Challenge	Description	Ref.
Large amount of data	An increase in the amount of data for analysis because of population growth, which entailed higher supply and demand for both livestock and agriculture products.	[33]
Heterogeneity of data	An increase in the number and types of parameters to be tracked: livestock, agricultural, environmental, economic-related metrics, etc.	[33]
Separation of emission footprints of different products in mixed farms	The ability to reliably segregate footprints for mixed enterprises is crucial since eco-friendly production goals are focused on a separated emission source’s impact rather than a farm-level footprint.	[34]
Environmental pollution by anthropogenic methane (CH4)	In the process of digestion of a ruminant (enteric fermentation), methane is released in its stomach, which is either burped or excreted from the body through flatulence.	[2]
Resistance to adoption innovations		[35]

presents the state-of-the-art core challenges in mixed farms related to DSS creation and use.

Accurate measurement of greenhouse gas (GHG) emissions at the livestock farm-level presents a significant challenge for farmers, as it is difficult to precisely quantify emissions from all possible sources on the farm. This difficulty underlines the growing importance of software-based decision support systems (DSSs) that focus on GHG emissions. These advanced tools are designed to offer clear and specific guidance to users, helping them to effectively reduce GHG emissions in their farming operations [36].

Alexandropoulos, E. et al. [37] classify mixed farm DSS tools into three groups based on their scope of assessment. The first group consists of emission calculators that estimate various emissions at the farm-level. The second group includes tools that evaluate two pillars of sustainability, combining emission calculations with economic factors like costs and profits. The third group encompasses tools that provide a comprehensive assessment of farm sustainability across all three pillars: environmental, social, and economic.

1.1. Methodological Foundations

The majority of tools for assessing GHG emission indicators in agriculture employ elements of the IPCC 2006 methodology, using either Tier 1 or Tier 2 methodologies for farm-level emission calculations. Tier 2 is usually employed for the calculation of methane emissions from enteric fermentation and other country-specific emission factors [37].

The IPCC’s GHG emissions guidelines feature a detailed three-tier system for estimation. Tier 1 is the simplest, using basic livestock data like species and population. Tier 2 adds depth with details such as livestock subcategories and specific feed intake. The most complex, Tier 3, incorporates sophisticated parameters including detailed feed intake, regional and seasonal variations in livestock, and the quality of animal food and supplements. While Tier 1 is easier to implement, its lack of detailed geographical data can lead to inaccuracies, making it better suited for minor emission sources such as manure management. In contrast, Tiers 2 and 3, with their greater complexity, offer more precise and region-specific emission estimations [38].

1.2. Importance of GHG Calculators in the Broader Context of Life Cycle Assessment Methodologies

The IPCC’s GHG calculation methodologies, while crucial, form just one part of the broader, more comprehensive Life Cycle Assessment (LCA). LCAs encompass a wide range of environmental impacts beyond GHG emissions, including resource depletion, ecosystem damage, and pollution, thus offering a more holistic evaluation of a product or system’s overall environmental footprint throughout its entire life cycle.

Another study [39] exemplifies how IPCC methodologies are used in conjunction with LCA. These studies highlight the differences in results when employing IPCC methodologies versus a full LCA approach, demonstrating the complementary nature of these methods in environmental assessment.

1.3. Inputs

Alexandropoulos, E. et al. [37] detail that Decision Support Systems (DSS) require varying levels of input data. More comprehensive data input leads to a more accurate and holistic assessment of farm sustainability. However, this comes with a trade-off: increased complexity and a higher likelihood that farm personnel may discontinue using the tool due to its complexity.

1.4. Outputs

DSS focuses on either GHG emission calculations or broader sustainability assessments.

Apart from the raw amounts of individual gases emitted (NO₂, CH₄, and CO₂), the outputs can be divided into three types, according to [37]: (i) aggregated results, which could be a total of various indicators (like total annual methane emissions from a farm) or a single indicator estimate (like kg CO₂ equivalent/year for all farm emissions), (ii) scores indicating the performance level of sustainability indicators for a specific farming system, and (iii) graphical representations.

Although sustainability scores provide insight, e.g., a qualitative indicator of the achieved emissions reduction, and DSS tools like SAFA, DLG, RISE, and KSNL provide users with such a sustainability score, these tools generally lack automated suggestions for altering practices to boost sustainability. However, some, including KSNL and RISE, offer consultation services through the preparation of expert reports [37].

The decision support approach for these tools includes several methods like contribution analysis, scenario analysis, and progress monitoring. Most tools apply scenario analysis, allowing users to test different farm practices and modify inputs to see various outcomes. Contribution analysis helps in understanding each source’s contribution to overall emissions. Additionally, many tools enable progress monitoring, letting users repeatedly apply the tool to the same project and save their inputs for continuous assessment [40].

Other less common methods include benchmarking, action plans, comparative assessment, and knowledge transfer. Comparative tools, such as Cool Farm Tool and Overseer, provide automatic comparisons between multiple outputs (like graphs or scores) within the same interface (e.g., side by side). Non-comparative tools, like FarmAC and BEK, lack this automatic feature but can still be used for manual comparative assessments. Additionally, tools that include guidebooks and a user-friendly interface facilitate knowledge transfer [37].

1.5. General Stakeholder Requirements

In the realm of agricultural Decision Support Systems (DSS), stakeholders prioritize systems that are user-friendly and accessible. Essential features include farm-level inputs that are easily understandable and manageable, reducing the likelihood of users abandoning the tool. A user-friendly interface, coupled with a comprehensive PDF manual, provides necessary guidance on input handling [41]. Additionally, aligning sustainability practices with the economic aspects of farming is crucial to fostering adoption. The output from these systems should be clear and actionable, ideally summarized in a detailed report that pinpoints issues, such as high GHG emissions, and proposes effective mitigation strategies [42].

For GHG emission calculators without a comprehensive report or consulting services, users must have the necessary knowledge to experiment with various mitigation methods. To address this, DSS tools should include user manuals that offer guidance in understanding and testing different approaches and interpreting these results [37].

1.6. Challenges, Limitations, Future Directions, and Improvements

A methodological limitation lies in the inconsistencies in results among different DSS, even those using IPCC methodologies, which defines a need for more research regarding the accuracy of methodologies.

Another limitation is the decreased automation of GHG mitigation consultations from DSS. The primary objective of an effective Decision Support System (DSS) is to offer practical, immediate advice to users. Instead of overwhelming them with numerous complex outputs like extensive arithmetic reports, the tool should focus on delivering clear, specific guidance. This approach helps ensure that the information provided by the DSS is easily comprehensible and actionable for the end-users, facilitating better decision-making in their operations [43]. While at present, this is achieved mostly through manually-created expert reports, the next milestone in the field lies in the automation of the creation of such GHG mitigation strategy reports. Some potential methods to achieve this would be the application of modeling techniques such as multi-criteria optimization or continuous simulations that model the dynamics of agricultural systems in real-time, which would both require detailed data and robust computational models to accurately capture the complexity of agricultural systems [38].

Multi-criteria optimization strategies could involve balancing various sustainability objectives, such as maximizing yield while minimizing emissions and resource use. Tools like genetic algorithms or Pareto front analysis can help identify optimal trade-offs between competing goals. Continuous simulation could take into account the complex interactions between soil, climate, crop growth, and farming practices. It can be used to simulate the impact of various management decisions on GHG emissions over time, helping to devise strategies that reduce emissions while maintaining farm productivity.

Another improvement would be marked by continuous communication between tool developers and data providers, which would aid in the streamlining of updates and upgrades. Datasets also need to be continuously updated with the latest emission factors and diversified to incorporate underrepresented geographical regions, such as the tropics, for example [44], to increase the accuracy of results. Moreover, the incorporation of updated emission estimation methodologies (like the 2019 refinement of the IPCC 2006 guidelines) and Tier 2 approaches will lead to increased accuracy [37].

Some models also use machine learning to model livestock GHG emissions [45,46]. Machine learning models are adept at detecting intricate and nonlinear relationships between variables in datasets. These models, along with statistical and mechanistic models, are classified as Tier 3 approaches by the IPCC. They are typically more complex, require substantial data input, and exhibit a lower degree of uncertainty in their predictions [38].

2. Materials and Methods

2.1. Platform Rationale and Target Audience

Emerging from an ecological scope, our innovative solution targets a specific segment, primarily farmers, who emphasize direct profit from such tools over the environmental impact mitigation potential of their agricultural activities. Therefore, the Decision Support Systems (DSS) for mixed farms must be intricate. They should holistically encompass ecological, technological (such as speed, multifunctionality, and handling vast heterogeneous data), economic, social (user friendliness and adoption resistance), and governmental (policies and standards) facets.

2.2. Preliminary Architecture

Central to our methodology is a strategic approach to developing decision support systems. As illustrated in Figure 2, the high level system architecture encompasses input data, models/measurement modules, and resulting indicators. This design ensures an in-depth evaluation of a mixed farm’s environmental impact, delivering crucial insights for more sustainable farming practices.

The input data is comprehensive, covering all GHG emission sources, including enteric fermentation from livestock, manure management, plant cultivation, energy, and fuel consumption. Furthermore, data regarding air pollutants (e.g., particulate matter and ammonia) and other variables like water consumption and waste quantities are included. Details regarding the data flow for GHG emissions, air pollutants, and other potential parameters are elaborated in the following sections.

2.3. GHG Emissions Data Flow—From User Input to Output

The Solution4Farming web application adheres to methodologies consistent with international protocols, more specifically, the 2006 IPCC guidelines [47] for the comprehensive calculation of GHG emissions.

Data pertinent to direct GHG emissions assessment is split into two primary modules (A and B) rooted in core activities: livestock (Chapter 10, Volume 4, 2006 IPCC guidelines for National Greenhouse Gas Inventories) [47] and the corresponding plant cultivation (Chapter 11, 2006 IPCC guidelines for National Greenhouse Gas Inventories) [47]—as referenced in Figure 3. The livestock-induced GHG emissions result from enteric fermentation and manure management processes and are composed mainly of CH₄ and N₂O. Conversely, GHG emissions from plant cultivation are predominantly N₂O, linked with fertilizer usage, leaching, volatilization, or shifts in land management strategies.

For each subcategory of modules A and B, in the following sections, the input data, including its name, symbol, measurement unit, and value type (whether user-defined, a default value, or calculated), are presented in the diagrams found in File S2. Moreover, the data flow, IPCC equations used, and output data are described.

The user inputs from the Emissions Calculator Form on the Solution4Farming application are marked in the diagrams in blue boxes, whereas the implicit constants from the IPCC 2006 guidelines are marked by green boxes, which are values that are extracted from the database. The intermediary calculations and final computed emissions for each step are enclosed in orange boxes (File S2—describe the module’s architecture).

2.4. Air Pollutants

The air pollution data collection is represented in the data flow architecture (in Figure 4), which interlinks the sensing system components. The data acquisition modules (S1, S2, and S3), embedded within the Data Acquisition Level, are equipped with an array of sensors capturing an extensive range of parameters from the environment. The captured data is then relayed to the Multiprotocol Gateway (2.1), which subsequently transmits this information to the cloud. The Middleware (3.1) in the Data Persistence Level serves as the intermediary between the Gateway and the data persistence component (4.1), ensuring seamless data flow. The raw sensor data, once stored in the dedicated database, is subjected to rigorous analysis by the Decision Support Component (5.1) at the Logical Level, transforming this data into meaningful insights.

These insights are then relayed to the Data Presentation Level (6.1), where users can interact with and visualize them.

2.5. Waste and Water Environmental Indicators

Following user input of yearly waste production and water consumption in metric cubes, the Waste/Livestock Capita and Water/Livestock Capita indicators are computed, which provide a metric that is indirectly scaled by the farm’s production capacity. The average value of the indicator in the farm population is also computed for comparison with the individual.

3. Results

The main goal of the Solution4Farming platform is to enable farmers and agricultural entities to understand and manage their greenhouse gas emissions effectively. By digitizing this process, the platform aims to streamline data collection, analysis, and visualization, fostering informed decision-making and promoting sustainable practices.

3.1. User Flow and Application Logic

The user flow and application logic are described in Figure 5. Starting with the user or client side, there are functionalities for login/signup, saving new farm data, and selecting parameters for data visualization. This interaction is facilitated through requests to a virtual machine running a Streamlit version 1.24.1application, ensuring data protection with SSL/TLS encryption. The Solution4Farming application manages both the user interface and the GHG calculation functions and communicates directly with the database. The user interface includes fields for user input, data visualization logic, and Dashboards for GHG emissions and pollution. Upon user data input, processing, and storage in the database, results in the form of data visualizations (e.g., pollution and GHG charts) can be sent back to the user or client as a response, completing the interaction cycle. The core of the application involves GHG calculation functions that take the user’s input to compute relevant metrics. These metrics, along with other pertinent data, are stored in a database containing tables for submissions, entries, users, and sensor time-series data. The platform backend runs a script periodically to fetch sensor data from the CSV file endpoint generated through the Environmental Data API and save the measured values into the database. The next subsections will be dedicated to explanations of the major components of the Solution4Farming web platform.

3.2. Prerequisite Software Components for the Solution4Farming Web App

3.2.1. Application Web Framework

Streamlit is an open-source Python library designed originally for data scientists to rapidly create interactive web apps. Streamlit’s framework is based on a server–client model, with the app running as a local server and a web client displaying the app. It uses Flask for handling HTTP requests and Tornado for WebSockets and asynchronous operations.

Customization is limited; e.g., for entirely custom widgets or complex interaction patterns, developers may find Streamlit restrictive and may require advanced web development skills.

Performance can also be an issue for computation-heavy apps unless carefully optimized, typically using Streamlit’s caching mechanisms. This issue is because Streamlit employs a unique reactive programming model that deviates from conventional event-driven paradigms by executing scripts from the beginning upon each user interaction, such as button clicks or input submissions. Nevertheless, its efficiency lies in only refreshing the UI components directly influenced by such interactions. This is achieved by a sophisticated caching mechanism and a differential update strategy, which collectively ensure that only the altered segments are updated on the front end, thus preventing full re-renders. Consequently, Streamlit allows the construction of code as if it were a sequential narrative.

When it comes to deployment and scalability, Streamlit apps are easy to set up on a local machine or a server, but scaling for a wider audience necessitates using cloud services, containers, and load balancers to handle more users and maintain performance. Scaling databases and other services is also essential for supporting larger user bases.

3.2.2. Deployment and Security

The Solution4Farming application was deployed on a virtual machine (VM) running Ubuntu 20.04 LTS, a popular and robust operating system preferred for enterprise solutions. The VM was provisioned with sufficient computing and memory resources to ensure optimal performance of the application. The deployment process began with the setup of a virtual environment to manage dependencies, using the respective ‘python3-venv’ library that is included in python 3.10.6. Within this environment, necessary Python libraries were installed, including Streamlit and other project-specific dependencies, ensuring isolation from system-wide Python packages and versions.

To ensure that the application was accessible and secure over the local corporate network, Secure Socket Layer (SSL) was implemented. For this purpose, self-signed SSL certificates were generated, providing encrypted HTTPS connections for the app. With the assistance of Nginx, incoming HTTP requests were directed to the Streamlit app while also handling the SSL termination. This deployment strategy not only provided an encrypted connection but also maintained the integrity and confidentiality of the data being transferred over the local network.

3.3. User Management

The Solution4Farming web app presents a robust user management system incorporating multiple user management modules, leveraging the capabilities of the “streamlit-authenticator” library, which offers users the ability to: create new accounts by providing a username, email, and password; log in; change their password; and reset their password via email. The “streamlit-authenticator” library automatically hashes the user passwords, providing an added layer of security: a password that is stored in a dedicated ‘users’ table within the app’s encrypted database.

Each farm is uniquely associated with one user. This user has access to the entirety of the Solution4Farming web functionalities. This design ensures a one-to-one relationship between farms and users, simplifying data management and accountability. Future iterations of the app will likely incorporate a more intricate identity access management system, as a need for a differentiated access system arises—a system that grants varying degrees of access based on employee roles and responsibilities.

3.4. Saving New Farm Data

3.4.1. User Input Form

The app allows the input of agricultural data by offering users a structured, three-part form consisting of (i) Soil Data such as quantity of organic and inorganic fertilizers; (ii) Energy Consumption, such as fuel and electricity usage; and (iii) Livestock Data. One of the app’s distinct features is its ability to gather data per livestock category. Users can select from a drop-down menu, which lists three cattle categories: dairy, heifers, and primiparous and young. This differentiation not only allows for a more granular analysis of livestock-related emissions but is necessary for the GHG calculation, as each species has parameter values unique to them. For each selected category, a dynamic new five step input form appears.

The meaning of the input, output, and intermediary data for the above forms has been described extensively in the methodology section.

3.4.2. Session State Management

To preserve the saved values between step completions and eventual page refreshes, data entered by the user is temporarily stored in the “session_state”, making it retrievable for later processing. This stateful behavior ensures that data remains accessible even if a user navigates away from the current page.

The “session_state” variable (Figure 6) is not just a flat structure but is hierarchically organized as a dictionary. The primary keys of this dictionary align with the three aforementioned segments of the form. Delving deeper into the livestock segment, a secondary layer of keys emerges, mirroring the livestock categories. For each livestock category, a sequence of five steps ensues, each outlining a process that contributes to either CH₄ or NO₂ emissions, represented by a third layer of keys (1–5). The fourth layer of keys consists of the values pertinent to each of these processes as detailed in the livestock section of the methodology in this article and comprises both the raw inputs from the user, such as quantity and type of feed, and the final and intermediary GHG calculated values. This hierarchical nature ensures data is not only stored but also organized in a manner conducive to later analysis by the GHG calculation functions.

3.4.3. Final Submission

After successfully filling in all required data, users are prompted to click on the “final submission” button. This action triggers the app to run the GHG calculation functions, which also save the results to the “session_state” and subsequently transfer the data saved in the “session_state”, both raw and processed, to the appropriate tables and columns of the database by employing the SQLite3 Python library methods, ensuring its permanence and readiness for subsequent computations and data visualization.

3.5. Greenhouse Gas Calculation Functions

The Solution4Farming web app employs a series of comprehensive functions to calculate GHG emissions, following the guidelines that have been extensively described in the methodology section of this article, for the three main emission sources: energy consumption, soil emissions, and livestock emissions.

Regarding livestock emissions, for each user input livestock category from the session state variable, the values from each step are retrieved and processed by a function catered to each step that represents one of the five GHG emitting processes previously detailed in the livestock section of File S2 (CH₄ from enteric fermentation, CH₄ from manure management, N₂O from manure management, N₂O lost through volatilization, and N₂O lost through leaching). The output is also saved in the session state variable under the appropriate key name in the hierarchical structure previously described.

3.6. Database Architecture

The Solution4Farming web app employs an SQLite database, renowned for its lightweight nature, and is fortified with encryption measures to ensure data privacy. This is achieved using the “SQLCipher” library, an open-source extension to SQLite that offers transparent 256 bit AES encryption of database files and ensures data cannot be accessed without proper authorization.

3.6.1. Tables

The database is comprised of the following four tables:

• Users: this table is dedicated to storing user-related information, primarily their login credentials and other relevant data;
• Submissions: Each submission captures a snapshot of user input at a specific time and date. It stores raw data and calculates emissions for soil and energy; it also holds references to the corresponding livestock emissions data entries in the entries table;
• Entries: acts as a repository for livestock related data, capturing raw user inputs, intermediary calculations, and final GHG emissions values for each livestock category;
• Sensors: contains the readings for the sensors at the specified intervals (timestamp column) for the individual users (user column).

3.6.2. Relational Structure

The strength of this database lies in its relational design. Each submission can have multiple associated entries, with a maximum of three, corresponding to the distinct livestock categories, as illustrated in Figure 6. This relationship is established using the foreign key in the entries table, named “submission_id”, which links to the primary key (id) in the submissions table. The design choice of separating livestock emissions data (entries table) from soil and energy data (submissions table) allows for efficient aggregation and filtering during data visualization. Analysts or users can discern emission patterns across livestock categories, enabling targeted interventions or changes in livestock management.

The primary key “usernames” from the Users table also represents a foreign key for both the submissions and sensors tables.

3.7. Data Processing and Visualization

3.7.1. Data Retrieval

Leveraging the SQLite3 Python library, the app fetches data through precise SQL (Structured Query Language) queries and stores it in Pandas DataFrames. These matrices simplify data manipulation, offering flexibility in handling and transforming diverse data structures.

3.7.2. Data Transformation and Comprehensive Emission Profiling

The real challenge lies in summing up these various emissions to represent a farm’s total GHG impact coherently and to identify the main contributing sources. The process involves converting all emissions data to a common unit of measurement and, further, to CO₂ equivalents. The conversion to CO₂ equivalents is achieved by multiplying the quantity of each type of gas (CO₂, N₂O, and CH₄) with its specific Global Warming Potential (GWP), a standard practice in GHG accounting, to provide a unified view of their global warming impact.

The aforementioned Pandas DataFrames can be processed in different ways, unlike ones employing SQL, such that values in columns can be aggregated (e.g., summed) or filtered by certain criteria (e.g., values from an adjacent column).

3.7.3. Plotly Visualization

The Plotly library works in tandem with Pandas, taking these DataFrames and converting them into a plethora of visual formats. The synergy between Pandas and Plotly empowers the app to offer dynamic, interactive visualizations based on real-time data. The two main Dashboards of the Solution4Farming application are the GHG Emissions Dashboard and the Pollution Dashboard, outlined in the following sections:

I.

GHG Emissions Dashboard

The data can be viewed in a simple tabular format or by using visualizations that provide interactivity and insight. The tabular data (Figure 7) is organized in the following manner: For each historical submission by the user, the data is divided as follows: (i) raw user inputs and intermediary metrics; and (ii) calculated emission outputs. Both sections are further divided into individual tables for livestock, energy, and soil emissions. Moreover, the calculated output (emissions) can be displayed as either the quantity of individual gases emitted in tones or can be converted to CO₂ equivalents using a toggle button.

On the other hand, the Dashboard provides a granular view of GHG emissions, allowing users to toggle between different levels of data aggregation, filtering, and detailed breakdowns. For example, on most graphs, the livestock emissions can be broken down in two ways: either by indicating the individual livestock category that produced them or by indicating the chemical process through which they were generated. The user can select this breakdown via a toggle button. All the graphs have keys, and each component can be hovered over with the mouse to show additional information such as source labels, numeric values in CO₂ equivalents, etc.

The GHG Dashboard is divided into two main parts: (a) Comparison with own historical data or (b) Comparison with the average values of other farms. The visualizations are detailed below:

(a)

Comparison with own historical data (Figure 8): the first visualization aids in comparing emissions over time, essentially comparing past submissions in between them, each one representing a parallel bar. These bars appear chronological and are each composed of colored “stacks” representing the individual aggregated sources. The user can select which historical submissions to show (multiple selections). This essentially shows the evolution of farm emissions;

(b)

Comparison with the average values of other farms (Figure 9): the second part of the GHG Dashboard aids in the positioning of the selected individual farm submission among the average in terms of GHG performance and is formed by two visualizations and two metrics:

• User’s position on the GHG/Capita distribution: The first visualization is performed using the GHG/Capita of livestock species metric, which is an indirect indicator of how much a farm emits, scaled by its production capacity. The user can select one individual submission and view its placement in comparison with the average to see whether he emits more or less than the average. This visualization is performed by depicting a histogram of the distribution of the “GHG/capita” metric across the farm population. The average value (mean) is provided by a vertical blue line, and the value of the individual farm is provided by either a green or red vertical line, depending on whether it is higher or lower than average. This visualization’s interactivity is provided by the ability of the user to select or deselect various emission sources to see which sectors are the highest contributors;
• Composition comparison with average: The composition of emissions shows the proportions of the individual emission contributors and is represented by two side by side pie charts for both the average farm population and selected individual submissions, respectively. This graph is also interactive, so the user can include or exclude the distinct emission sources;
• Waste and water metrics: the average waste production and water consumption values are also figured next to the selected individual submission values for comparison (Waste/Capita and Water/Capita).

II.

Air Pollutant Dashboard

The Air Pollutants Dashboard (Figure 10) presents near real-time sensor readings through line charts. Data is fetched from the database and stored in Pandas DataFrames. Based on the user’s selected timeframe, the data undergoes aggregation at various granularities, computing hourly, every 4 h, daily, or weekly averages. Such flexibility allows users to observe trends, anomalies, or patterns with precision. The pollutant levels are also color-coded on the line charts as a gradient from green to yellow to red, with bright red indicating threshold levels obtained from the EEA (European Environment Agency).

4. Discussion

The integration of technology in agriculture, particularly for managing greenhouse gas (GHG) emissions, has been a focal point of contemporary research, drawing attention to the transformative impact of technological advancements. A recent article issued by the University of California’s “Global Perspective” online journal [48] delves into the broader implications of technology on society and underscores the pivotal role of technology policy in guiding these changes. It emphasizes the dynamic relationship between technological evolution and societal needs, highlighting the need for a forward-thinking approach to technology development and implementation.

Related Works

In the realm of sustainable agriculture, the authors of [49] provide a comprehensive bibliometric analysis of the literature from 2000 to 2021. It focuses on the utilization of AI in sustainable agriculture, offering insights into the current state and future directions of AI applications in this field. This systematic study, visualized using advanced tools like VOSviewer and Biblioshiny, underscores the growing significance of AI in enhancing agricultural practices.

The “Technology and Innovation Report 2021” [50] from UNCTAD (United Nations Conference on Technology and Development) further emphasizes the urgency for developing nations to adapt to the rapid technological changes shaping markets and societies. It advocates for equitable use and adaptation to the ongoing technological revolution, particularly in the context of developing economies and their integration into global technological advancements.

The authors of [51] review various whole-farm modeling approaches for quantifying and mitigating greenhouse gas emissions in livestock systems, evaluating their strengths, limitations, and potential for integrating climate change impact simulations and adaptation measures. Moreover, Refs. [52,53] discusses sustainable agricultural practices like crop rotation, cover crops, and reduced tillage as strategies to mitigate GHG emissions, thereby contributing to the broader discourse on climate-smart agriculture.

This study [54] addresses measurement protocols, modeling for upscaling field measurements, and the impacts of agricultural practices on GHG emissions. It delves into potential changes in land use, livestock management, and manure management, also providing a comprehensive view of available mitigation options.

Drawing parallels with these studies, Solution4Farming emerges as a comprehensive platform that not only tracks and analyzes GHG emissions but also offers actionable insights for sustainable farm management. This approach is critical to addressing the environmental challenges in mixed farming, where the interplay of crop cultivation and livestock rearing presents unique challenges and opportunities for technological intervention.

The Solution4Farming platform, as demonstrated in this paper, serves as an innovative approach to managing greenhouse gas (GHG) emissions in mixed farming environments. It harnesses the capabilities of advanced technologies like AIoT and precise data analysis tools to provide real-time insights into GHG emissions, directly contributing to more sustainable agricultural practices.

The platform’s strength lies in its comprehensive data collection and processing capabilities, which cover a wide range of parameters, from soil data and energy consumption to livestock details. By integrating these diverse data sources, Solution4Farming enables farmers to gain a holistic understanding of their farms’ GHG emissions. This holistic approach is crucial in pinpointing key emission sources and identifying potential areas for improvement.

Moreover, the visualization tools provided by the platform play a pivotal role in translating complex data into actionable insights. The GHG Emissions and Pollution Dashboards, with their interactive and user-friendly interfaces, empower farmers to make informed decisions, potentially leading to optimized resource utilization and reduced environmental impact.

The Solution4Farming applications fulfill most of the requirements of modern day agricultural GHG mitigation DSS and are comparable to many existing DSS in terms of user friendliness and ease of interpretation of results. The strength of the calculator components lies in the use of the more accurate Tier 2 methodology, mostly as opposed to Tier 1. The Solution4Farming application also uses state-of-the-art decision support methods: (i) contribution analysis through graphical representations (pie and bar charts), and (ii) scenario analysis, progress monitoring, and comparative assessment through the ability to introduce multiple submissions and analyze them in parallel in the same window.

Some limitations may include the somewhat reduced number of inputs in comparison to other DSSs and the lack of inclusion of other sustainability pillars such as the economic and social. Future improvements to the application will include the enhancement of scoring methods for more sustainability indicators and the automation of user consultation for decision-making.

Moreover, the current scope of the application, focused on Romanian climate conditions and cattle as the primary livestock, highlights the need for expansion and customization to cater to diverse geographical regions and farm configurations. Future enhancements, including a broader range of livestock categories and a comprehensive database of IPCC emission factors, are essential for the platform to become a universally applicable tool in sustainable agriculture.

The platform’s integration of technological innovations with ecological principles showcases a significant stride toward more environmentally responsible farming. By leveraging IoT and data analytics, Solution4Farming not only guides operational decisions but also aligns them with sustainability goals, setting a precedent for future agricultural management systems.

The initial version of the Solution4Farming web app has established itself as a specialized tool adeptly tailored to the unique climatic and agricultural landscape of Romania. Its focused design and functionality have provided valuable insights specific to the region’s farming practices. However, this specificity also underscores its current limitations in scope and adaptability. As we acknowledge these constraints, it becomes evident that the platform’s future evolution must aim toward accommodating a more diverse array of farm structures and environmental conditions. Such an expansion, while introducing greater complexity, is essential for transforming the app into a versatile and indispensable tool for global sustainable agriculture. This vision for expansion and refinement sets the context for a deeper exploration into the technical architecture of the Solution4Farming platform, its current capabilities, and the envisioned technological enhancements that could redefine its utility and impact in the agricultural sector.

The Solution4Farming platform exemplifies a significant step in agricultural technology, particularly in GHG emissions management. Its implementation using the Streamlit framework and SQLite database demonstrates a judicious selection of technologies, aiming to balance functionality and user accessibility. Streamlit, with its ease of use for developing interactive web applications, allows for the rapid deployment of data-driven interfaces. This choice aligns with the current trend towards democratizing data analytics, making complex data more accessible and actionable for end-users, in this case, farmers and agricultural stakeholders.

SQLite, chosen for its lightweight and efficient nature, is apt for managing the type of data typically generated in agricultural settings. The database’s encryption via the ‘SQLCipher’ library is a crucial feature, ensuring data security and privacy—a growing concern in the age of digital agriculture. This approach to data management not only safeguards sensitive information but also aligns with global data protection regulations, which are increasingly important in technology applications.

The platform’s user management system, facilitated by the Streamlit authenticator library, adds a layer of security and personalized experience. This feature is vital in ensuring that the data input and resulting analytics are specific to individual farms, allowing for tailored recommendations and insights.

Looking forward, the Solution4Farming platform has substantial room for growth and improvement. One potential area is the expansion of the platform’s capabilities to include a broader range of farm types and environmental conditions. This could involve integrating more diverse datasets, encompassing various climatic zones and farming practices, thereby increasing the platform’s applicability globally.

Another avenue for enhancement is the incorporation of more advanced AI and machine learning algorithms. These could be utilized for predictive analytics, offering farmers foresight into potential environmental impacts based on current practices. This predictive capability would be a significant leap forward, enabling proactive rather than reactive management of GHG emissions.

The use of IoT in agriculture, as highlighted in contemporary research, presents another opportunity for the platform. Integrating IoT devices for real-time data collection could provide more granular and immediate insights into farm operations. This would not only enhance the accuracy of GHG emissions tracking but also open avenues for optimizing other aspects of farm management, such as resource allocation and yield prediction.

Furthermore, the platform could benefit from enhanced scalability and interoperability. As farms vary greatly in size and complexity, the platform must be able to scale accordingly to accommodate different data volumes and complexities. Interoperability with other agricultural technology systems could also be a focus, ensuring that the Solution4Farming platform can seamlessly integrate into the existing technological ecosystem of modern farms.

Lastly, the platform’s user interface and experience could be continually refined to cater to a broad spectrum of users with varying levels of technical expertise. This includes making the platform more intuitive and providing more educational resources to help users understand and interpret the data and recommendations provided.

In summary, while the Solution4Farming platform is a robust tool in its current state, its evolution in line with the latest trends and advancements in agricultural technology will be crucial. As it adapts and grows, it has the potential to become an even more valuable asset in the pursuit of sustainable and efficient agriculture.