Intellectual Capital in Agribusiness: Integrating Digital Solutions for Sustainable Development

Vardan Aleksanyan; Karlen Khachatryan

SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 2, 2024

Research article

DOI: https://doi.org/10.48554/SDEE.2024.2.3

Intellectual Capital in Agribusiness: Integrating Digital Solutions for Sustainable

Development

Vardan Aleksanyan*

, Karlen Khachatryan

Yerevan State University, Yerevan, Republic of Armenia, [email protected],

[email protected]

*

Corresponding author: [email protected]

T

Abstract

his article explores the integration of digital solutions to enhance the sustainable development

of agribusiness through the activation of the introduction of intellectual capital. The analysis is

carried out taking into account various factors affecting yields, such as soil type, fertilizer use,

market prices, employee education level, product demand, and automation level. The level of automation,

the use of geographic information systems, access to big data, and hours of employee training were

chosen as factors of intellectualization. Random forest, ARIMA, SARIMA, and LSTM models were

used to predict yields. The data were taken from the statistical portals of Armenia and Georgia (137

observations). The results of the study show that the LSTM model demonstrated the best prediction

accuracy with an average absolute error of 8.30 and a standard error of 102.47. The random forest

model showed an average absolute error of 24.87 and a standard error of 828.23, while the ARIMA and

SARIMA models did not show significant results. The study revealed significant correlations between

digital solutions characterizing the level of intellectual capital in agricultural enterprises and agricultural

land productivity, including the level of automation and access to big data. Analysis was also conducted

on the impact of intellectual capital on the sustainability of agribusiness, including the impact of the

level of education and training hours of employees. It is concluded that the integration of innovative

technologies, such as big data and automation, contributes to improving the efficiency of agricultural

production.

Keywords: intellectual capital, agribusiness, sustainable development, digital solutions, yield forecasting, random forest, ARIMA, SARIMA, LSTM, big data, automation, agriculture

Citation: Aleksanyan, V., Khachatryan, K., 2024. Intellectual Capital in Agribusiness: Integrating Digital

Solutions for Sustainable Development. Sustainable Development and Engineering Economics 2, 3.

https://doi.org/10.48554/SDEE.2024.2.3

Aleksanyan, V., Khachatryan, K., 2024. Published by Peter the Great St. Petersburg Polytechnic University

44

Enterprises and sustainable development of regions

SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 2, 2024

Научная статья

УДК 330.14

DOI: https://doi.org/10.48554/SDEE.2024.2.3

Интеллектуальный Капитал в Агробизнесе: Интеграция Цифровых Решений

для Устойчивого Развития

Вардан Алексанян*

, Карлен Хачатрян

Ереванский Государственный университет, [email protected], [email protected]

Автор, ответственный за переписку: [email protected]

1

*

Д

Аннотация

анная статья исследует интеграцию цифровых решений для повышения устойчивого

развития агробизнеса через активизацию внедрения интеллектуального капитала.

Анализ проводится с учетом различных факторов, влияющих на урожайность, таких

как тип почвы, использование удобрений, рыночные цены, уровень образования работников,

спрос на продукцию и уровень автоматизации. В качестве факторов интеллектуализации

выбраны уровень автоматизации, использование геоинформационных систем, доступ к

большим данным и часы обучения работников. Применены модели Random Forest, ARIMA,

SARIMA и LSTM для прогнозирования урожайности. Данные взяты со статистических порталов

Армении и Грузии (137 наблюдений). Результаты исследования показывают, что модель LSTM

продемонстрировала наилучшую точность предсказаний со средней абсолютной ошибкой 8.30

и среднеквадратичной ошибкой 102.47. Модель Random Forest показала среднюю абсолютную

ошибку 24.87 и среднеквадратичную ошибку 828.23. В то время как модели ARIMA и SARIMA

не показали значимые результаты. В процессе исследования были выявлены значимые

корреляции между цифровыми решениями, характеризующими уровень интеллектуального

капитала на агропредприятиях, и урожайностью сельскохозяйственных угодий, включая уровень

автоматизации и доступ к большим данным. Также проводится анализ влияния интеллектуального

капитала на устойчивость агробизнеса, включая влияние уровня образования и часов обучения

работников. Сделаны выводы о том, что интеграция инновационных технологий, таких как

большие данные и автоматизация, способствует повышению эффективности агропроизводства.

Ключевые слова: интеллектуальный капитал, агробизнес, устойчивое развитие, цифровые решения,

прогнозирование урожайности, Random Forest, ARIMA, SARIMA, LSTM, большие данные, автоматизация,

сельское хозяйство

Цитирование: Алексанян, В., Хачатрян, К., 2024. Интеллектуальный Капитал в Агробизнесе: Интеграция

Цифровых Решений для Устойчивого Развития. Sustainable Development and Engineering Economics 2, 3.

https://doi.org/10.48554/SDEE.2024.2.3

Эта работа распространяется под лицензией CC BY-NC 4.0

Петра Великого

Предприятия и устойчивое развитие регионов

45

Iintellectual capital in agribusiness: integrating digital solutions for sustainable development

1. Introduction

Research on intellectual capital in agribusiness is aimed at analysing the importance of digital

technologies and intangible assets in creating efficiency. In terms of economic aspects, we can highlight

the importance of knowledge, skills, and innovation in improving efficiency and productivity. The human capital of skilled workers, the structural capital of processes in the organization, and the relational

capital obtained from networks and partnerships contribute to the formation of intellectual potential

among agricultural producers (Scafarto et al., 2016; Zaytsev et al., 2020).

Automation, big data analysis, geographic information systems (GIS), and other digital solutions

help transform traditional farming practices into new forms of management while increasing the efficiency of business operations. Digital technologies make it possible to increase the return on control and

management of agribusiness, creating conditions for increasing yields, reducing losses, and increasing

the quality of resource use. Integration of digital solutions is necessary to solve problems related to resource reduction and the need to adapt new agricultural practices (Balaji and Mamilla, 2023; Shirokov

et al., 2023; Zaytsev et al., 2024).

The purpose of this article is to investigate the integration of digital solutions to increase the sustainable development of agribusiness through the introduction of intellectual capital. This study aims to

analyse the impact of various factors on yield, including soil type, fertilizer use, market prices, employee

education, product demand, and automation levels. The following methods are used to achieve these

goals:

- collection and analysis of statistical data from the statistical portals of Armenia and Georgia

- application of predictive models for yield analysis and forecasting

- correlation analysis to identify significant relationships between digital solutions and productivity

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- analysis of the significance of the impact of intellectual capital on the sustainability of agribusiness

The object of this research is the agricultural enterprises of Armenia and Georgia that use digital

solutions and intellectual capital in their activities to support strategies aimed at achieving sustainable

development. The subject of this study is the factors influencing crop yields, their relationship with intellectual capital, and digital solutions in agribusiness. The research uses predictive models such as random

forest, ARIMA, SARIMA, and LSTM.

2. Literature Review

Intellectual capital is the basis for the development of many economic sectors, including agro-industrial production, where the integration of intellectual achievements, primarily digital solutions, is the

basis for increasing the sustainability of agribusiness. In the context of agribusiness, intellectual capital

includes components that affect the yield and overall development of agricultural enterprises. Table 1

presents the main components of intellectual capital, as well as highlighting aspects that can affect the

modelling of intellectual capital in agribusiness (Edvinsson and Malone, 1997; Sveiby, 1997).

Table 1. Components of intellectual capital in agribusiness

Example in the context of a

business model

Knowledge, skills, and experience Level of education of employof employees

ees, hours of training

Organizational processes and

Level of automation

innovations

Component of intellectual capital Definition

Human capital

Structural capital

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Aleksanyan, V., Khachatryan, K.

Relational capital

Networks and communications,

access to information and technology

Access to big data

The efficiency of using intellectual capital and its impact on the sustainable growth of agribusiness

can be investigated with a focus on optimizing processes and using human capital to increase productivity. Digital transformation to improve the sustainable value of products and services of agri-food systems

can significantly improve the efficiency of supply chains, reducing gaps in access to information and

resources, especially for small producers (Balaji and Mamilla, 2023; Silva et al., 2022). The impact of

intellectual capital on the profitability of agribusiness companies has shown that structural capital and

human capital have the main impacts (Ovechkin et al., 2021).

By analysing the various components of intellectual capital, including human, structural, and relational capital, it is possible to identify their impact on companies’ financial performance. In practice,

researchers note that the impact of management measures on changing the structure of intellectual capital contributes to the growth of productivity and competitiveness of companies. In this context, it is

necessary to ensure the development of new approaches for assessing and managing intellectual capital

in various sectors of the national economy, including the agro-industrial sector (Pedro et al., 2018; Xu

and Liu, 2020). It is proposed to identify the contribution of intellectual capital to several key indicators

of agribusiness (Table 2).

Table 2. Impact of intellectual capital on key agribusiness indicators

Key indicator

Human capital

Structural capital

Relational capital

Productivity

High level of

knowledge and

skills

Increased productivity

Efficient use of

resources

Process optimization, use

of new technologies

Access to advanced data

and information

Reduced costs through

automation

Introduction of environmentally friendly technologies

Product quality improvement

Improved market positions

Strengthening ties with

environmental organizations

Expanding market relations

Financial profitability

Sustainability and environmental friendliness

Competitiveness

Innovative management methods

The focus on the components of intellectual capital in the context of their role in ensuring sustainable development makes it possible to form models for managing the processes of intellectualization.

A particularly clear manifestation of intellectual capital is positively noted for small and medium-sized

enterprises, where investment in human and structural capital contributes to improving the competitiveness of companies. At the same time, depending on the size of the business and the scope of economic

relations, it is possible to adapt various methods for evaluating intangible assets and intellectual capital

(Gołacka et al., 2020; Osinski et al., 2017). Consequently, it is possible to develop and apply methods

for evaluating and managing intellectual assets, including analysing digital solutions that affect the performance of agribusiness entities.

Digital solutions are being actively implemented in the economic management of agribusiness,

ensuring the rationalization of management processes at different levels. To achieve these goals, many

enterprises attract financing, which makes it possible to activate innovative processes in agro-industrial

production. At the state level, the issues of financing innovative processes in the agricultural sector are

strategically important (Dumanska, 2018a, 2018b). These aspects define the role of intellectual capital

in the strategy of ensuring economic security, emphasizing the formation of potential for managing

intellectual resources in the context of achieving sustainable economic development and national security. To a large extent, it is necessary to use methods and tools that can be used to analyse and improve

Sustain. Dev. Eng. Econ. 2024, 2, 3. https://doi.org/10.48554/SDEE.2024.2.3

47

Iintellectual capital in agribusiness: integrating digital solutions for sustainable development

socioeconomic indicators (Rodionov et al., 2020; Zhogova et al., 2020). This study analyses the digital

solutions presented in Table 3.

Table 3. Digital solutions in agribusiness

Digital technology

Example in the context of a data

model of a mapping model

Description

Analysis of large volumes of data for

making informed decisions

Use of automated systems for process

Automation

management

Geographic information Spatial data collection, analysis, and

systems (GIS)

visualization

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Field condition monitoring and yield

Drones

assessment

A network of interconnected devices

Internet of Things (IoT)

for data collection and exchange

Artificial intelligence

Using machine learning algorithms for

(AI)

data analysis and forecasting

Using robots to perform agricultural

Robotics

tasks

Applications for farmers that provide

Mobile applications

access to information and tools

Online platforms for selling agriculturE-commerce platforms

al products

Distributed ledger technology for transChain

parency and traceability

Big data

Access to big data

Automation level

Crop area optimization

Precise crop control and management

Sensors for monitoring soil conditions and growth

Yield forecasting and risk management

Automated harvesters

Weather forecasting, inventory

management, and task planning

Direct sales to consumers, supply

chain management

Traceability of product provenance

and anti-counterfeiting

The researchers propose methods for improving the innovation management systems in the enterprises of the agro-industrial complex. In order to increase the efficiency of innovation implementation

and improve management processes, one should turn not only to financing digital solutions but also to

creating conditions for managing digitalization processes (Zinina and Tezina, 2016). The use of digital

solutions can significantly improve the efficiency of agro-industrial processes, reduce gaps in access to

information and resources, and improve interaction between participants in the agri-food chain. It is proposed to highlight the impact of digital solutions on intellectual capital in agribusiness (Table 4).

Table 4. Impact of digital solutions on intellectual capital in agribusiness

Digital technology

Structural capital

Improving data-driven

Employee development

decision-making

Reducing physical

Improving process

workload

efficiency

Relational capital

Strengthening partnerships

through data exchange

Geographic information systems (GIS)

Technology training

Optimizing land use

Access to spatial data

Drones

Operator training

Internet of Things

(IoT) service providers

Artificial intelligence

(AI)

Improving technical

literacy

Training in new methods of analysis

Training in working

with robotic systems

Field monitoring and

management

Monitoring real-time

conditions

Forecasting and optimization

Automating routine

tasks

Improving communication

with service providers

Exchanging data between

devices

Improving customer interaction

Big data

Automation

Robotics

48

Human capital

Increasing productivity

Improving logistics links

Sustain. Dev. Eng. Econ. 2024, 2, 3. https://doi.org/10.48554/SDEE.2024.2.3

Aleksanyan, V., Khachatryan, K.

These digital solutions and components of intellectual capital form the basis for improving the

efficiency and sustainability of agricultural enterprises. At the beginning of the 21st century, it was noted

that digital solutions and other intelligent aspects of farm management should be integrated in agriculture. In practice, this contributes to the development of small food enterprises. It is noted that various

aspects of management, including organizational culture and access to technology, directly affect the

implementation of innovations in small firms (Avermaete et al., 2003). To analyse the effectiveness of

intellectual capital in agribusiness, it is acceptable to use econometric methods that take into account

economic and technological factors that affect the productivity and sustainability of agribusiness entities. Based on digital solutions, it becomes possible to form networks of interaction between various

participants in the agro-industrial sector to assess their impact on the operational, financial, and social

indicators of enterprises (Asatryan et al., 2022; Rey et al., 2023).

3. Materials and Methods

Data from the statistical portals of Armenia and Georgia were used for the study. A total of 137

observations were collected, including a number of variables that affect crop yields (Table 5).

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Table 5. Selected indicators for modelling

Variable

Months

Crop_Yield

Precipitation

Soil_Type

Fertilizer_Use

Seed_Fertilizer_Cost

Market_Prices

Education_Level

Demand

Competition

Farm_Workers

Automation_Level

GIS_Usage

Big_Data_Access

Training_Hours

Description

Observation period

Yield

Precipitation

Soil type

Fertilizer usage

Cost of seeds and fertilizers

Market prices for products

Level of education of employees

Demand for products

Market competition

Number of agricultural workers

Automation level

Usage of geographic information

systems

Access to big data

Employee training hours

Unit of measurement

Months

Currency/hectare

Millimetres

1, 2, 3

Fraction (0–1)

Currency

1, 2, 3

Index

Share (0–1)

People

Share (0–1)

Share Data (0–1)

Hours

The selected variables allow us to assess the impact of various factors on crop yields and analyse

the relationship between digital solutions, intellectual capital, and agribusiness sustainability.

3.1 Modelling

To achieve the goal of the study, predictive models were used that have unique characteristics and

methods of data analysis (Table 6).

3.1.1 Random Forest

The random forest model is an ensemble machine learning method that uses multiple decision trees

for predictions. Each tree is trained on a random subsample of data, and the final result is obtained by

averaging the predictions of all trees.

Advantages:

- Resistance to overfitting

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Iintellectual capital in agribusiness: integrating digital solutions for sustainable development

- Ability to work with a large number of attributes

- High accuracy of predictions

3.1.2 ARIMA

The ARIMA model is used for time series analysis and forecasting. It combines autoregression,

integration, and moving average, which allows one to model data based on seasonal and time dependencies.

Advantages:

- Designed for time series analysis

- Takes into account seasonal fluctuations

3.1.3 SARIMA

The SARIMA model is an extension of the ARIMA model and includes additional parameters for

analysing time series with a particularly pronounced seasonal component.

Advantages:

- Accounts for seasonal changes

- Suitable for data with strong seasonality

3.1.4 LSTM

The LSTM model is a type of recurrent neural network designed to work with sequential data and

time series. LSTM is able to store long-term dependencies in data due to its memory cell architecture.

Advantages:

- Accounts for long-term dependencies

- High accuracy of predictions for time series

- Resistance to the problem of vanishing gradients

Table 6. Comparison of predictive models

Application examples

Model

Advantages

Disadvantages

Random

Forest

High accuracy, resistance to

overfitting

Suitable for time series, seasonality

Accounting for seasonal

changes

Accounting for long-term dependencies, high accuracy

Lots of computing resources,

Yield factor analysis

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complexity of interpretation

Limited application with non-linDemand forecasting

ear dependencies

Yield forecasting

Difficulty in setting parameters

with seasonality

Long learning time, the need for Time series forecastbig data

ing

ARIMA

SARIMA

LSTM

The models selected for data analysis allow us to take into account and model complex relationships between variables that affect the yield of agribusiness. Their use allows us to make predictions with

increased accuracy, which is the basis for making informed decisions.

4. Results and Discussion

4.1 Results of Predictive Models

4.1.1. Random Forest

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Aleksanyan, V., Khachatryan, K.

The random forest model showed good results:

- Root mean square error (RMSE): 828.23

- Mean absolute error (MAE): 24.87

This model effectively takes into account many factors (Figure 1) that affect yield and can be useful for analysing the relationships between variables. The constructed model takes into account parameters for hyperparametric modelling, which allows initializing the random forest model and searching for

the best parameters using GridSearchCV.

Figure 1. Random forest results

4.1.2. ARIMA

The ARIMA model did not show significant results:

- Root mean square error (MSE): 7810.76

- Mean absolute error (MAE): 80.94

This model did not allow us to identify significant results, which may be due to the high complexity and non-linearity of factors affecting yield (Figure 2).

Figure 2. ARIMA results

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Iintellectual capital in agribusiness: integrating digital solutions for sustainable development

4.1.3. SARIMA

The SARIMA model also did not show strong results:

- Root mean square error (MSE): 3126.34

- Mean absolute error (MAE): 46.19

This model, despite taking into account seasonal fluctuations, could not take into account all the

factors affecting the yield (Figure 3).

Figure 3. SARIMA results

4.1.4. LSTM

The LSTM model demonstrated high accuracy:

- Root mean square error (MSE): 102.47

- Mean absolute error (MAE): 8.30

This model showed the best results among all the models considered, as it was able to take into

account long-term dependencies and nonlinear relationships between variables, taking into account the

scaling inversion for the predicted values (Figure 4).

Figure 4. LSTM results

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Aleksanyan, V., Khachatryan, K.

5. Discussion

5.1. Model Comparison

The results show that the LSTM model is the most effective for predicting yield revenues of agribusiness enterprises. The ability of the model to process sequential data and take into account complex

time dependencies makes it important for conducting research in this area.

Mean absolute error (MAE) and mean square error (MSE) metrics were used to evaluate the predictions of various models. The results showed that the models have different degrees of accuracy in

predicting yield (Table 7).

Table 7. Estimation of model accuracy

Model

Random forest

ARIMA

SARIMA

LSTM

MAE

24.87

80.94

46.19

8.30

MSE

828.23

7810.76

3126.34

102.47

The LSTM model showed the best results in comparison with other models, as it has low average

absolute error and root mean square error, which indicates the reliability of this model in predicting the

yield of agribusiness enterprises—that is, the efficiency of companies’ activities. The random forest

model showed good results but lost out to LSTM in terms of accuracy. ARIMA and SARIMA were not

able to adequately cope with the task of predicting yield in this study.

5.2 Impact of Factors on Yield

Analysis of the significance of various factors affecting yield showed that the following factors

have the most significant impact:

- A high level of automation leads to an increase in the efficiency of operations at agribusiness

enterprises, which has a positive effect on yields

- Using big data for analysis and decision-making allows us to more accurately predict and optimize processes

- High-quality training of employees contributes to improving their skills, which, in turn, improves

the results of their work

Figure 5 shows a graph of Shapley Additive Explanations values that reflects the impact of factors

on the yield prediction model. SHAP values show how much each feature affects the model output.

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Iintellectual capital in agribusiness: integrating digital solutions for sustainable development

Figure 5. SHapley Additive exPlanations (SHAP) results

Key observations:

- Market_Prices – the impact of market prices on the yield prediction model is very positive, especially for high values of market prices (red dots).

- Big_Data_Access – Access to big data has a positive impact on model prediction, which highlights the importance of information and data in crop management.

- Competition and Demand factors have different effects on the model, which indicates a complex

interaction between market conditions and the performance of agricultural enterprises.

- Training_Hours – Employee training hours have a positive impact on model prediction, which

confirms the importance of human capital.

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- Automation_Level – A high level of automation has a positive effect on the model’s predictions,

indicating the importance of technological equipment.

- Precipitation – precipitation show mixed effects, which may depend on specific climatic conditions and their impact on the crop.

- Education_Level – This has the least impact on the model’s predictions.

- The SHAP value graph allows us to quantify the impact of various factors on the yield prediction

model. Figure 6 shows a graph of the significance of traits that reflects the influence of the various factors on crop prediction. The graph shows the relative significance of each factor in the model used for

analysis.

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Aleksanyan, V., Khachatryan, K.

Figure 6. Feature significance graph

Key observations:

- Market_Prices has the highest significance (about 0.35).

- Big_Data_Access is the second most important factor (about 0.2020).

- Competition and Demand factors affect the model with high significance (about 0.15 and 0.12,

respectively).

- Months and Automation_Level have a moderate impact, emphasizing the importance of seasonal

changes and the level of automation in the production process.

- Training_Hours has a noticeable impact.

- Anticipation has some influence on the prediction of the model.

- The Seed_Fertilizer_Cost, Farm_Workers, IoT_Usage, Fertilizer_Use, Soil_Type, Education_

Level factors are less significant than other factors but still contribute to the model.

6. Conclusion

The results of the study highlight the importance of intellectual capital in agribusiness. The level

of education of employees and their training have a direct impact on the efficiency of using digital technologies and, consequently, on productivity. The integration of innovative technologies, such as big data

and automation, helps to increase crop yields and improve the sustainability of agricultural production.

Digital technologies contribute to the sustainable development of agribusiness. The use of big data allows agribusinesses to analyse information for decision-making, which helps optimize processes and

reduce costs. Automation allows us to increase the efficiency of agricultural operations, reducing the

impact of the human factor and increasing productivity.

The study showed that the use of LSTM models for predicting yield gains is the most appropriate

(compared to the random forest, ARIMA, and SARIMA models). Significant correlations were found

between digital solutions (level of automation, access to big data) and productivity. Thus, it is possible

to develop some recommendations for agribusiness enterprises:

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Iintellectual capital in agribusiness: integrating digital solutions for sustainable development

1. Strengthen training and professional development of agricultural workers to improve the efficiency of digital technologies use.

2. Implement and use big data to analyse and make informed decisions in agricultural production.

3. Increase the level of process automation to improve operational efficiency.

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of the network on operating, financial and social performance. Journal of Intellectual Capital 24(6), 1447–1463. https://doi.

org/10.1108/jic-04-2023-0087