Impacts of agritech on sustainable agriculture in Sub-Saharan Africa: a quantile regression approach towards SDG 2.4

Abstract

Agricultural greenhouse gas emissions on the planet threaten both food security and climate change. The United Nations is calling for food security and sustainable agriculture to end hunger by 2030. Sustainable Development Goal 2.4 addresses resilient agricultural practices to combat climate change and produce sustainable food. Resilient agricultural practices are only possible with agricultural technologies (AgriTech) that will create a digital transformation in agriculture. AgriTech can meet the increasing food demand by increasing production efficiency while increasing resource efficiency by combating problems such as climate change and water scarcity. The aim of this study is to examine the impacts of AgriTech usage on sustainable agriculture in Sub-Saharan African (SSA) countries. The analyses were conducted using panel data from 20 SSA countries between 2000 and 2022. In this study, MMQR (Method of Moments Quantile Regression) provided consistent results across quantiles in variable interactions, while GMM (Generalized Method of Moments) and KRLS (Kernel Regularized Least Squares Method) approaches were used to ensure consistency of results. The findings confirm that AgriTech (ATECH) and agricultural value added (AGRW) contribute significantly to sustainable agriculture in SSA countries. The coefficients of ATECH and AGRW variables are negative and statistically significant in all quantiles. This shows that when AgriTech use and agricultural value added increase in SSA, emissions from agriculture decrease and the environment improves. However, agricultural credits (ACRD) are insufficient to reduce agricultural emissions. Furthermore, agricultural workers (AEMP) and internet use (INT) help reduce agricultural emissions up to the 60th and 50th quantiles, while this effect disappears at higher quantile levels. These results emphasize the importance of integrating green procurement and green production technologies supported by green credits into agricultural production in order to achieve sustainable agricultural development goals in SSA. Policies that facilitate farmers’ access to agricultural green credits should be adopted in SSA societies. Infrastructure works that will increase farmers’ access to the internet should be increased. Awareness of agricultural workers on green production and sustainability should be provided to agricultural workers.

Highlights.

• The results show that agricultural technologies, agricultural growth, agricultural labor, and internet use reduce agricultural emissions in SSAcountries, while credit use increases agricultural emissions.

• AgriTech use (ATECH) and agricultural value-added (AGRW) have statistically significant negative coefficients in all quantiles, indicating that increasing AgriTech and value-added reduce agricultural greenhouse gas emissions.

• The potential of AgriTech to reduce emissions is higher in low-emission quantiles (10–30%), while the effect is relatively weaker in high-emission quantiles.

• Agricultural credits (ACRD) only provide environmental improvements in the low-emission quantile (25%) and are insufficient to reduce emissions in high quantiles.

• Agricultural labor (AEMP) and internet use (INT) significantly reduced emissions at 10–50% quantiles, while this effect disappeared at higher quantiles. Farmers’ success in reducing emissions is directly dependent on their internet access.

• Panel instantaneous momentum quantile regression (MMQR) was preferred to capture heterogeneous interactions, and the robustness of the results was confirmed with the GMM and KRLS approaches.

Highlights

• The results show that agricultural technologies, agricultural growth, agricultural labor, and internet use reduce agricultural emissions in SSAcountries, while credit use increases agricultural emissions.

• AgriTech use (ATECH) and agricultural value-added (AGRW) have statistically significant negative coefficients in all quantiles, indicating that increasing AgriTech and value-added reduce agricultural greenhouse gas emissions.

• The potential of AgriTech to reduce emissions is higher in low-emission quantiles (10–30%), while the effect is relatively weaker in high-emission quantiles.

• Agricultural credits (ACRD) only provide environmental improvements in the low-emission quantile (25%) and are insufficient to reduce emissions in high quantiles.

• Agricultural labor (AEMP) and internet use (INT) significantly reduced emissions at 10–50% quantiles, while this effect disappeared at higher quantiles. Farmers’ success in reducing emissions is directly dependent on their internet access.

• Panel instantaneous momentum quantile regression (MMQR) was preferred to capture heterogeneous interactions, and the robustness of the results was confirmed with the GMM and KRLS approaches.

Introduction

With the increase in the global population and economic growth, production activities and energy usage in production are also on the rise [27, 51]. Problems such as environmental pollution, climate change, and resource scarcity, driven by energy intensity in production, are experienced not only in the production and service sectors but also in agriculture [17, 74, 93]. Globally, economic development is supported by sustainability-focused approaches across many sectors [13, 30]. In agricultural activities that meet food needs, the greenhouse gas emissions, material, water, and environmental impacts caused by energy intensity are higher than in other industries [49]. There is a need for eco-friendly agricultural sustainability models that ensure food security while enhancing agricultural productivity and economic development, alongside reducing environmental damages. Disruptions in agricultural production and supply chains, as well as the reduction of arable lands, pose threats to food security. To overcome these challenges, the development of technological and sustainable farming approaches is crucial. Agricultural technology (AgriTech) has the ability to develop sustainable agriculture through processes such as adaptation to technology, flexibility of transformation, the development of technological infrastructure, and standardization in tools, equipment, and processes [18]. In low-income countries like Sub-Saharan Africa (SSA), persistent issues with food security and agricultural productivity have heightened interest in transitioning to technological agriculture and research in this area. Environmental agricultural models, such as green purchasing and green production, are also becoming more widespread in sustainable agriculture. Practices like the European Green Deal and direct farm-to-consumer food supply chains improve the performance of food supply chains while also reducing migration from agricultural regions to urban areas, thus helping preserve agricultural lands [71]. Green practices in agriculture are also being employed in high-income countries in Africa, and these practices are known to have a positive impact on productivity [62].

Agricultural sustainability is an approach that enhances the performance of the agricultural supply chain, including harvesting, packaging, and delivery stages, through digital technologies such as precision agriculture, artificial intelligence, cloud computing, the internet of things, machine learning, big data analytics, and drones [11, 74]. The use of these tools significantly contributes to preventing the deterioration of food security by increasing agricultural outputs. Genetic technologies like CRISPR-Cas9, which manage crop diseases, sustainable agricultural production, crop management, and quality control processes in an integrated manner, help reduce environmental damage while increasing soil fertility, crop yields, and food safety [47].

In order to meet sustainable development goals worldwide, there is a transition process from agriculture to manufacturing and service sectors. In African countries, it is seen that production and employment are largely based on agriculture, and approximately 20% of the gross domestic product (GDP) is obtained from the agricultural sector [81]. Despite agriculture being the most important productive sector, employing 50% of the labor force in Africa, it does not represent the same proportion of the gross national product, indicating that agricultural activities are not sufficiently productive [87]. Gains in productivity and economic sustainability in agriculture involve decision-making processes such as expanding farmland, developing technological agriculture, planting new crops, and increasing material and human resources [57, 86]. One of the factors positively influencing sustainable agriculture is the use of information technologies, including cloud computing, the internet of things, artificial intelligence, robotics, and big data technologies [7]. With the widespread adoption of these tools in agriculture, costs associated with activities like planting, monitoring, and irrigation decrease, while productivity increases. Additionally, technological agriculture creates safer and more efficient working environments for stakeholders in the agricultural sector [28, 82].

The United Nations (UN) reached a consensus on a total of 17 Sustainable Development Goals (SDGs) from 2015 to 2030 to ensure sustainable development, alleviate climate change, and combat inequality and injustice [16, 29]). Of these goals, SDG-2 is one of the most fundamental sustainable development goals with its social, economic, and environmental dimensions. It aims to eliminate hunger, improve nutrition, and promote agricultural sustainability by ensuring food security for societies, with a focus on agricultural activities [34]. To achieve this goal, it is essential to support sustainable agriculture and adopt practices that reduce harmful emissions affecting the environment [70]. As agricultural production intensifies, energy consumption increases, leading to higher greenhouse gas emissions, which pose global environmental threats [23]. Overcoming environmental damage, resource limitations, and global warming challenges makes it difficult to ensure food security for the growing population using traditional agricultural methods [40]. In overcoming these challenges, the impact of AgriTech, which consists of smart agriculture, precision farming, information processing technologies, and biotechnology, plays a significant role. One way to increase efficiency in agricultural production is to develop plans and policies at both strategic and operational levels that support sustainable development by using environmentally friendly technologies that will reduce emissions and promote green purchasing and green production.

AgriTech, comprising smart agriculture, precision farming, computing technologies, and biotechnology, plays a crucial role in overcoming these challenges. One way to enhance productivity in agriculture is by developing plans and policies at strategic and operational levels that promote sustainable development and employ eco-friendly technologies to reduce emissions, support green purchasing, and encourage green production. AgriTech integrates technology into agriculture, addressing issues like increasing food demand due to population growth, the impact of climate change on crop productivity, and water scarcity by improving resource efficiency and decision-making processes. For many countries, adapting to technology in agriculture is critical to boosting food security and crop productivity. This is especially true for developing countries and low-income regions like SSA, where technological agriculture is gaining greater importance. SSA government institutions are supporting the adaptation of technology to agricultural activities to make agricultural productivity sustainable. Field studies have shown that technology-driven agricultural production approaches lead to sustainable productivity [83]. One such approach is the use of green technology, specifically mineral fertilizers, to increase crop productivity while reducing inputs. In SSA countries, fertilizer production based on green technology has been adopted as a tool to reduce costs by lowering inputs and increasing productivity [52, 78]. Research on technologies that increase agricultural productivity provides a valuable perspective for all countries, assisting them in their agricultural endeavors. It is especially crucial for SSA countries, where more than 50% of economic income is derived from agriculture, to ensure food security and drive economic development. However, even when fertilizer subsidies are applied, reducing input use does not always guarantee an increase in productivity. Nevertheless, these measures are preferred by farmers as they help lower costs and reduce debt. Subsidized inputs often enhance agricultural production, but the extent to which they meet profitability and sustainability goals is still debated. Due to the uncertainty of profitability increases, it is known that only about half of farmers in SSA countries demand unsubsidized fertilizer purchases [52].

As in other sectors, it is important for countries to develop common decision-making and implementation mechanisms in agricultural activities to achieve the SDGs. As in other sectors, it is important for countries to develop shared decision-making and implementation mechanisms to achieve the Sustainable Development Goals in agriculture. With increasing demand for agricultural products, more agricultural activities are being undertaken, and the waste generated from these processes harms the environment. To meet growing demand, the variety and quantity of supplied products are also increasing. Ensuring that these products are produced through environmentally friendly green production, green supply, and green purchasing processes, and preferring recyclable products to reduce waste and return it to nature, has become an obligation for countries today. The use of new technology products that enhance environmental quality not only increases agricultural productivity and employment but also contributes to the environmental dimension of sustainable agriculture. Agricultural sustainability is at risk due to environmental damage and threats to food security. Climate change, caused by global warming, and the high energy consumption associated with agricultural production result in emissions and waste that cause environmental harm. Moreover, the "Zero Hunger" target (SDG-2), which focuses on ensuring food security within the Sustainable Development Goals, is being jeopardized, making it more challenging to achieve agricultural development and sustainable agriculture. In the literature, solutions to these problems focus on developing technology-based agriculture, including green production techniques, drones in planting, irrigation, and monitoring phases, and integrated information systems and mobile interaction tools that enable fast and reliable communication within the agricultural supply chain. These studies mostly involve analyzing the technological aspects used in agricultural production and field applications aimed at increasing productivity and identifying environmental damage. Recent literature shows that studies within the scope of research have typically focused on the technological features used in agricultural production [3, 33, 63, 68], while field studies have focused on the organization and supply of agricultural technology in production [3, 36, 37] and the smart agricultural food system [55]. Data-driven studies have included empirical analyses measuring the adoption of technologies such as artificial intelligence, mobile technologies, and drones in agriculture [35, 42, 48]. Studies examining the relationship between sustainable agriculture and AgriTech have increasingly used quantitative methods, rather than statistical approaches, as the agricultural industry requires larger data sets and technologies like the Internet of Things [46].

The agricultural sector is under environmental pressure due to global production and energy demands. Due to these pressures, some problems may arise in achieving sustainable development goals. Agricultural technologies (AgriTech) can play an important role in eliminating these difficulties, especially in low-income regions. Considering the importance of agriculture in the economic structure and the problems related to the sustainability of this sector in SSA countries, the importance of addressing the environmental impacts of AgriTech becomes evident. In light of the above explanations, the original contributions of this article will answer the following questions: i) How can agricultural technologies, agricultural developments, internet usage, the increase in agricultural value added, and the number of employment in agriculture promote sustainable agriculture? ii) How do the independent variables in the study contribute to achieving the SDG-2 target? iii) How do agricultural technologies, agricultural developments, the increase in agricultural value added, internet usage, and the number of employess in agriculture support or hinder the agricultural sector, considering the social, environmental, and economic balances?

This study analyzes the relationship between the variables of agricultural technologies (ATECH), agricultural development (ACRD), individuals using the internet (INT), agricultural value added (AGRW), and number of employment in agriculture (AEMP) in achieving sustainable agriculture (ACO₂) for 20 SSA economies. The aim of this study is to reveal the determinants of sustainable agriculture in SSA countries by examining the effects of determinants such as AgriTech use, agricultural development, internet use, agricultural value added, and agricultural employment on agricultural emissions. Agricultural emission intensity (ACO₂) was used as the dependent variable of the study to represent sustainable agriculture. AgriTech (ATECH), agricultural credits (ACRD), agricultural value-added (AGRW), agricultural employment (AEMP), and internet use (INT) are the independent variables. The study conducted with these variables will contribute to revealing the effects of technology and knowledge-based applications in agriculture on environmental sustainability and closing the gap in the literature.

The main objective of the study is to explore the relationship between the independent variables that constrain or facilitate the achievement of sustainable agriculture in SSA economies. (i) This paper is structured by covering the case studies and theoretical parts of multiple studies on the factors of sustainable agriculture. (i) This framework provides important and diverse insights into how ATECH, ACRD, AGRW, AEMP, and INT contribute to or constrain the achievement of sustainable agriculture. (ii) This study focuses on SSA countries rather than other economies. This is because sustainable agriculture is not at the desired level in these countries. Finding the factors that affect and contribute to the formation of sustainable agriculture in SSA countries can guide the dissemination of agricultural technologies, provision of agricultural development, and increase of agricultural added value. In this context, the study aims to reveal the basic policy implications to achieve the SDG-2 target. It is important to know the factors that support sustainable agriculture in order to achieve this target. (iii) The results obtained from the article will contribute to the formation of policies for the provision of ACO₂, dissemination of agricultural technologies, increase in agricultural added value and provision of agricultural development in SSAeconomies. Therefore, understanding the factors that affect ACO₂ is very important for SSAeconomies. (iv) In order to obtain robust results from the impact of ATECH, ACRD, AGRW, AEMP, and INT on ACO₂ in SSAeconomies, the MMQR (Method of Moments Quantile Regression) approach was used. A Consistency check of the results was provided by dynamic regression estimator GMM (Generalized Method of Moments) and KRLS (Kernel Regularized Least Squares Method) quantile regression estimator. (v) In order to obtain consistent results in the interaction between variables and quantiles in the article, we used data from 20 SSAeconomies between 2000 and 2022. (vi) In the literature, no integrated comprehensive study analyzing the relationship between agricultural sustainability and AgriTech has been found. It is seen that the literature focuses on studies on the adoption of AgriTech. In SSAeconomies, difficulties such as internet inadequacy, low numbers of digital literacy, and inadequate infrastructure in information technologies make it difficult for agricultural technologies to be widely adopted. However, agricultural technologies provide opportunities such as market access, benefit to decision-making process in agricultural management, and increase income sources for agricultural stakeholders. For this reason, it is important to adopt agricultural technologies by developing policies that will overcome these difficulties. Thus, productivity and agricultural development can be achieved in SSAeconomies, especially those with low income levels and whose livelihoods are largely based on agriculture. vii) In addition, the study differs from previous studies because it uses the independent variables (ATECH, ACRD, AGRW, AEMP, and INT) on ACO₂ in the same model. viii) In studies conducted on AgriTech and sustainability, analyses based on expert opinions using quantitative methods and Multi-Criteria Decision Making Methods (MCDM) were conducted. No study using an integrated statistical method to determine the impact of AgriTech on sustainable agriculture was found in the recent literature reviewed by the authors. ix) In the study, reaching the effects of variables determined from the literature and considered important for sustainable agriculture for low-income SSAeconomies, where 50% of the economy is dependent on agriculture, at different quantile levels constitutes the original aspect of the study. As a result, this study will both fill the gap in the literature by revealing the impacts of AgiTech and agricultural development on sustainable agriculture with the help of strong econometric analyses conducted for SSAeconomies and will provide a roadmap with a broad perspective for agricultural stakeholders carrying out agricultural activities in these countries.

The remainder of this paper is structured as follows: Sect. "Literature Review" provides a comprehensive review of the AgriTech literature, examining its characteristics, components, and studies focused on enhancing agricultural practices, as well as research exploring the relationship between agricultural sustainability and SDG-2, including their methods and findings. Sect. "Data and Methodology" introduces the dataset employed in the analysis and details the econometric methodology used. Sect. "Empirical Results and Discussion" presents the empirical results, offering a comparative discussion with existing literature to identify convergences and divergences. Finally, Sect. "Conclusion and Policy Recommendations" concludes the study by summarizing the main findings, outlining limitations, and offering policy recommendations.

Literature review

The literature review presented in this section consists of three subsections. The first subsection examines studies on sustainable agricultural development. The second subsection discusses the determinants of sustainable agriculture, and the final subsection presents the methods used in studies on sustainable agriculture.

Advances in sustainable agricultural development research

The United Nations Open Working Group proposed the Global Sustainable Development Goals (SDGs), consisting of 17 targets. All of these SDGs are approaches focused, directly or indirectly, on ensuring the sustainability of human life. According to 2024 data, slightly more than 10% of the world's approximately 8.2 billion population is hungry, and nearly 30% of people are estimated to lack access to sufficient quantity and quality of food. These figures show that 890 million people are hungry, while 2.3 billion people cannot access enough food, indicating a significant number of people globally are facing hunger. Sustainable Development Goal 2 (SDG2), introduced with the target of reducing hunger to zero, aims to eradicate global hunger by 2030 [58]. Hunger was developed to address this hunger problem and is the agricultural Sustainable Development Goal aimed at ensuring sustainable access to food, enabling human survival, and improving quality of life [92].

Today, sustainable agricultural development, which has become a necessity, aims to reduce environmental damage in agriculture, improve technological efficiency, and enrich society [43, 72]. Due to the increasing difficulty of accessing essential daily food in low-income societies, such as those in African countries, the importance of resources and investments allocated to SDG-2 has grown. Technological and infrastructure investments in sustainable agriculture not only enhance agricultural productivity but also offer approaches to mitigate global climate change. While the risk of hunger could be reduced to 5% by 2030 in North, West, and Southern Africa, in Central and Eastern Africa, where SSA countries are more prevalent, the rate is expected to exceed 10% (D'Croz et al., 2019).

Successful efforts to increase agricultural productivity play a crucial role in reducing global hunger and poverty and improving societal welfare. In SSA countries, where the majority of livelihoods rely on agriculture, this sector is the key driver of economic development. Consequently, adopting sustainable agricultural practices is vital for boosting productivity and reducing hunger and poverty in these regions [2]. However, SSA countries have yet to reach the desired levels of agricultural productivity compared to the global average [54]. In these countries, agricultural productivity remains low because stakeholders in the agricultural sector have not transitioned to technological farming. Due to the agricultural dependence of their economies, the low productivity makes it extremely difficult for people to escape hunger and poverty. To improve this low agricultural productivity, merely producing enough food to meet the growing demand is not sufficient in SSA countries, where there has been no shift to technological agriculture. Achieving the Agricultural Sustainable Development Goal requires increased investment in agricultural technology, with a greater emphasis on agriculture in these countries [61]. Transitioning to agricultural technology should involve practices such as prioritizing crops based on the geographical features of Africa, developing irrigation technologies suited to the climate, and utilizing environmentally friendly production and monitoring tools. Additionally, green supply chains and production processes reduce waste and emissions from production and energy consumption, thereby protecting land from potential damage and increasing productivity.

Determinants of sustainable agriculture

In the literature, sustainable agricultural driving forces are usually divided into two broad categories. These are those who are connected to direct productivity increases and support long-term environmental, social, and economic sustainability. Firstly, it is known that the adoption of eco-virtuous and sensitive technologies such as drops and sensor-based irrigation, soil and product health, and genetic tools such as CRISPR/CAS9 increases the use of resources [85]. Most importantly, Norton and Algwan [59] show that classical extension and training programs are vital. For example, the innovations and the possibility of adopting modern machines are significantly higher for farmers exposed to structured education. Pannell and Claassen [64], who completed the technological purchase, have determined that in-farm welding protection measures (minimizing water attraction, preventing soil erosion and limiting agricultural chemical flow) both directly improve the ecosystem health and increase the elucidation of "green" subsidies and credit plans of policy makers.

Beyond the agronomic inputs, production economy factors play a central role. Employment costs, variable and fixed input expenses and land rent rates are the main determinants of farm profitability and therefore affect the capacity of farmers to invest in sustainable applications [84]. At the same time, environmental parameters such as temperature, humidity, precipitation schemes strongly regulate land suitability and yield risk [22, 26]. In addition, household revenues have a significant effect on non-agricultural activities, demographic profiles of farmers (age, education, risk avoidance), market access, trade tariffs and investments made to information technology infrastructure (eg farm management software, mobile consultancy services).

These insights are synthesized into four intertwined dimensions. These include socio-demographic (eg education, gender, risk preferences), corporate support (land ownership security, credit access, cooperatives), resource donation (farm size, climate exposure, information flow) and socio-economic context (emphasis of technology (Junpeng vd, 2023). Agricultural greenhouse gas emissions caused by synthetic fertilizers, pesticides, plastic, and diesel fuel machines constitute a permanent difficulty [24, 90]. Searchinger et al. [75] has achieved evidence that targeted public investments for low emission technologies such as biogubons, solar-powered pumps and precision application equipment can significantly reduce the carbon footprint of the sector. Moreover, Xu et al. (2021) emphasizes that detailed analysis of emission sources should give priority to the most effective interventions of policymakers (eg replacing gasoline pumps with electric alternatives), while real -time monitoring data leads to adaptable management and continuous improvement.

Finally, the new generation of vehicles (the Internet of Objects (IoT) sensors, drones for air surveillance and artificial intelligence-supported decision support systems) transforms traditional farming by optimizing precise irrigation and efficiency estimation [46]. However, in Sub-Sahara Africa, it continues to be restricted by a large-scale adoption, low digital literacy, and weak bit infrastructure [25]. FinTech solutions that expand micro loans, crop insurance products, and mobile payment platforms can close these gaps by improving the access of farmers to capital and risk management tools [53]. As Bashiru [14] emphasizes, integration of intelligent agriculture (CSA) approaches in terms of climate-product diversification, agricultural forestry, and water harvest structures will be essential to create durability against the stress of temperature and drought in the region. These related factors show a comprehensive route towards sustainable, productive and climate resistant agriculture.

A view of methods used in researching sustainable agriculture

Within the scope of sustainable agriculture, research has focused on approaches that aim to increase product quality and productivity of agricultural lands while meeting food security. In this subsection of our study, some studies in the literature that investigate sustainable agriculture dimensions with different methods have been examined.

Kaur et al. [45] used a hybrid method including analytical hierarchy process (AHP) and Agro-Eco-Resource Zoning (AERZ) methods to solve the problem that agricultural producers in the Punjab state of India have difficulty in deciding on the best yield and income despite being productive due to the change in ecological resources. In the study in question, first the weights of the factors consisting of ecological resources were determined by the AHP and transferred to geographic information systems (GIS). Thematic maps were created with the AERZ method and divided into four state classes where the products are suitable for planting.

Mushtaq et al. [56] evaluated the suitability of land for mulberry cultivation in the Baramulla region of India with the AHP method in GIS. The results obtained from the study revealed the rates of very suitable, moderately suitable, suitable, and unsuitable lands by determining the suitability map in mulberry cultivation site selection. Hugagg et al. (2022), in the study where they used AHP, remote sensing (RS), and GIS approaches, developed a model that rates sustainable agriculture, land productivity, drought sensitivity index, irrigation water quality, and renewable factors that are effective in land management plans and aims to decide on the most appropriate region selection.

In the study developed in the study aiming to determine the sustainable agriculture criteria in Iran, Veisi et al. [88] analyzed the views of agricultural stakeholders using AHP. In the said study, it was concluded that the most important criteria of sustainable agriculture in Iran are the resilience of agricultural systems, support, self-confidence, and equal opportunities. Yaqoob et al. [91], in their study investigating agricultural sustainability, food security, climate change, and financial difficulties with panel analysis, collected demographic and agricultural data between 1973 and 2020 in India, Pakistan, and Bangladesh. Data analysis results showed that low crop productivity and land density are the result of demographic density. In addition, the study emphasized that technological adaptation is necessary to meet food demand.

In their study, Auci and Pronti [8] developed a panel regression model that shows the extent to which decisions of agricultural producers in Italy to adopt sustainable irrigation systems such as water conservation and saving technologies will affect land productivity and economic income. The results of the study reveal that adopting smart irrigation technologies determines land value. However, producers who adopt these technologies are more productive than those who do not. Wang and Zhang [89] developed a hybrid model that determines green supply chain factors in sustainable agriculture and integrates the factors associated with adopting a green supply chain with fuzzy logic and the delphi method. Zuo and Hong [96] collected agricultural data in China in agricultural technology and conducted panel data analysis, revealing that technological agricultural tools ensure land integration and prevent abandonment of agricultural land.

Research gap

Although there are studies examining the factors affecting sustainable agriculture in SSA countries, these studies mostly use multi-criteria decision making methods (MCDM). In addition, agricultural sustainability is generally associated with food security and production efficiency. Studies aiming to control agricultural emissions are quite few. The explanatory variables ATECH, ACRED, AEMP, AGRW, and INT, which are thought to affect agricultural emissions, have not been analyzed together before. The study is unique in that it is designed for SSA countries, whose production and employment are largely based on agriculture but still suffer from hunger, and the econometric model is created with a wide regressor group. In addition, there is no study in this field where the MMQR quantile approach, which we can evaluate the results according to different agricultural emission intensities, and the KRLS approach with a machine learning algorithm, which we check for robustness, are used together.

Especially in the context of SSA, no study has been found that comprehensively addresses the impact of AgriTech use, digital access, and financial resources on sustainable agricultural practices and agricultural emissions. Moreover, not only the direct impact of agricultural credits but also their mediating role in access to technology and information has not been sufficiently addressed in the literature. This study fills the gap in the literature by evaluating the interaction between AgriTech, internet use and agricultural credits through environmental outcomes, providing more evidence and insights for researchers and policymakers for sustainable agriculture in SSA.

Data and methodology

Data

In the study, the impact of agricultural technologies (ATECH) and agricultural development (ACRD) on sustainable agriculture (ACO₂) in 20 SSA countries1 was analyzed econometrically. With the established regression model, strong results were obtained regarding the relationships between agricultural value added (AGRW), agricultural workers (AEMP), and internet usage (INT) and sustainable agriculture. Other SSA countries were excluded from the model according to the accessible data of the selected variables. Our data set consists of natural logarithms of annual data between 2000 and 2022. We used logarithmic forms of the data to counteract the heteroskedasticity problem. Detailed information about the data is shown in Table 1.

Table 1.

Description of variables

Variable	Description of Variables	Definitions	Source
ACO2	Agricultural emissions	Emissions per value of agricultural production on the agricultural region (kg/USD)	FAOSTAT
ATECH	Agricultural technology (AgriTech)	Number of agricultural technology startups	Tracxn.com
ACRD	Credit to agriculture	Credit to agriculture, forestry and fishing	FAOSTAT
AGRW	Agricultural growth	Value added in agriculture, forestry, and fishing ($ million)	FAOSTAT
AEMP	Employment in agriculture	Agriculture, forestry, and fishing workers (% of total employment)	WDI
INT	Internet usage	Individuals using the Internet (% of population)	WDI

Model specification

The equation for the econometric analysis to be applied to reveal the relationships between AgriTech, agricultural development, agricultural added value, agricultural workers, and internet usage with sustainable agriculture is shown in Eq. 1.

$$lnACO2it=f(lnATECHit,lnACRDit,lnAGRWit,lnAEMPit,lnINTit)$$ 1

The function form of the equation is shown in Eq. 2.

$$lnACO2it=\beta 0+\beta 1lnATECHit+\beta 2lnACRDit+\beta 3lnAGRWit+\beta 4lnAEMPit+\beta 5lnINTit+\epsilon it$$ 2

The prefix ln in Eq. 2 represents the natural logarithm of the variables, i represents the country-specific cross-sections, and t represents time. This study focused on reducing emissions from agriculture in order to ensure sustainable agriculture. Therefore, the dependent variable of the econometric model is represented by ACO₂, which represents the emissions per value of production in the agricultural region. CO₂ emissions and greenhouse gas emissions emitted into the atmosphere are not limited to human activities, and agriculture and animal husbandry also contribute significantly to this pollution [12, 97]. In agricultural activities, the use of nitrogenous fertilizers, burning of biomass and agricultural wastes in open areas, methane gas released in paddy fields, and ammonia and methane gas released from farm animals release significant amounts of emissions into the air, causing climate change [15, 80]. Reducing greenhouse gas emissions is critical for sustainable agricultural activities [39]. Agricultural emissions for the 20 SSA countries within the scope of the analysis are shown in Fig. 1. The country with the highest emission level is Botswana, while the lowest emission value is observed in Nigeria.

Fig. 1.

Agricultural emissions in SSA Countries

The main independent variable of the study was determined to be agricultural technologies (ATEK). Agricultural technologies (AgriTech) adopted in order to achieve “Climate-Smart Agriculture” (CSA) as defined by the Food and Agriculture Organization of the United Nations (FAO) make significant contributions to agricultural productivity [20]. AgriTechs support agriculture in many areas such as combating pests and diseases, controlled fertilizer use, combating drought, and increasing agricultural efficiency and productivity by combating the climate crisis [10]. Figure 2a and b show the number of agricultural technology initiatives in SSA countries. Since the number of countries is large, it is presented in two separate graphs to avoid confusion. The countries with the highest agricultural technology initiatives are observed as Nigeria, Kenya, and Ghana, respectively, while the lowest agricultural technology data are observed in Gabon, Gambia, and Burundi.

Fig. 2.

a Agricultural technologies in SSAcountries. b Agricultural technologies in SSA countries

Agricultural credits (ACRD) were included in the model as the other independent variable of the study. Financial institutions with weak assets and unstable income have low agricultural credit [38] which restricts farmers’ access to green agricultural technology [44, 76, 77] and therefore causes high agricultural emissions as well as low agricultural productivity [94]. Agricultural value added (AGRW), agricultural workers (AEMP), and internet usage (INT) in SSA countries were determined as the control variables of the study. Although agricultural production can cause environmental pollution, it can support oxygen production by increasing biodiversity and contribute to a clean environment [32]. According to Ma et al. [50], increasing agricultural mechanization significantly reduces carbon emission intensity through the scale advantage in land management. Considering that the agricultural sector accounts for more than half of the employment in SSA countries, the inclusion of agricultural workers in the analysis was inevitable. Following Rehman et al. [70], we included agricultural employment as a regressor of sustainable agriculture in the econometric model. The INT variable was included in the model as a sustainable agriculture regressor following Abdullah et al. [1]. Internet use can increase agricultural productivity by providing farmers with easy and fast access to information and provide them with ease of marketing their products.

Figure 3 shows the agricultural credit rates of SSAcountries. Nigeria, Kenya and Ethiopia are the countries with the highest agricultural development in the analysis, while Gabon, Guinea and Togo have the lowest agricultural credit rates. Table 2 shows the descriptive statistics of the variables.

Fig. 3.

Agricultural development in SSAcountries

Table 2.

Descriptive statistics

	ACO2	ATECH	ACRD	AGRW	AEMP	INT
Mean	12.080	35.275	237.595	7925.108	53.278	11.261
Median	5.635	17.745	79.963	2593.065	50.620	4.795
Maximum	133.200	331.500	4175.044	113,644.4	91.864	80.400
Minimum	0.760	0.527	0.023	127.220	14.888	0.015
Std. Dev	23.127	55.302	460.978	17,633.452	18.428	15.076
Observation	460	460	460	460	460	460

When we examine Table 2, it is observed that the agricultural emissions in Sub-Saharan African countries are 12.08 kg/USD on average, and the highest agricultural emission is observed in Botswana with 133.2 kg/USD, and the lowest agricultural emission is observed in Ghana with 0.76 kg/USD. The country with the highest AgriTech is Nigeria (331.5), while the lowest is Gabon, and the AgriTech average in SSA is 35.27. The highest agricultural credit value is $4,175,044,000 (Nigeria) and the lowest is $22,750 (Guinea), while the agricultural credits in 20 SSA countries are recorded as an average of $237,595,200. The highest agricultural value added is Nigeria with $113,644,400,000 and the lowest is Botswana with $127,220,800, with an average agricultural value added of $7,925,108,000. Agricultural employment is highest in Burundi (91.86%) and lowest in Botswana (14.88%), with an average value of 53.27% across 20 SSA countries. The country with the highest individual internet usage is Botswana (80.4) and the country with the lowest is Ethiopia (4.79), with an average value of 11.26 across the panel. In the next stage, normality tests were performed for the variables both individually and on a model basis, and the results are presented in Table 3.

Table 3.

Normality tests

	ACO2	ATECH	ACRD	AGRW	AEMP	INT
Skewness	3.831487	3.034156	4.389591	4.177158	0.106815	2.048602
Kurtosis	16.72561	12.50385	28.15509	20.87106	1.981041	7.326479
Jarque–Bera	4736.341***	2436.996***	13,605.51***	7459.077***	20.77502***	680.5222***
Shapiro-Wilks	0.40293***	0.58560***	0.52441***	0.42557***	0.96551***	0.73327***
lnACO2 = ƒ(lnATECH, lnACRD, lnAGRW, lnAEMP, lnINT)				Statistic
Mardia Skewness				12.11572***
Mardia Kurtosis				51.53838***
Henze-Zirkler				6.36242***
Doornik-Hansen				9.10926***

*** p < 0.01, ** p < 0.05, * p < 0.10

Skewness, kurtosis and Jarque–Bera tests as well as Shapiro–Wilk test show non-normal distribution of all variables. We also tested the normality of our regression model with Mardia, Henze-Zirkler and Doornik Hansen tests. Just as the variables do not have normal distribution individually, the model also exhibits non-normal distribution. Therefore, our findings show that it would be more appropriate to use panel quantile regression estimators instead of OLS in regression estimations. The strength of this method, compared to linear estimation methods, is its ability to capture heterogeneous effects of the dependent variable in different quantiles.

Table 4 shows whether our independent variables have a multicollinearity problem. The fact that all of the multicollinearity test results we tested with the variance inflation factor (VIF) are less than 5 and the average VIF is 3.13 indicates that there is no multicollinearity problem. Figure 4 shows the dynamics of the analyzed variables over time.

Table 4.

Variance inflation factor

	VIF	1/VIF
lnATECH	4.69	0.213209
lnACRD	3.39	0.294605
lnAGRW	3.59	0.278203
lnAEMP	2.00	0.499290
lnINT	1.95	0.511648
Mean VIF	3.13

Fig. 4.

Evolution of variables

Methodology

Before starting the analyses, the characteristics of the variables were determined with four different cross-sectional dependency tests, namely Breusch and Pagan [21] LM, Pesaran [65] CDLM and CD, and Pesaran [67] LMAdj, and two different homogeneity tests developed by Pesaran and Yamagata [67]. Cross-sectional dependency test statistics are given below.

$$LM=T{\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\tilde{\rho }}_{\mathit{ij}}^2$$ 3

$${\mathit{CD}}_{\mathit{LM}}={(\frac{1}{N\left(N,-,1\right)})}^{\frac{1}{2}}{\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N\left(T,{\tilde{\rho }}_{\mathit{ij}}^2,-,1\right)$$ 4

$$CD={[\frac{2T}{N\left(N,-,1\right)}]}^{\frac{1}{2}}{\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\tilde{\rho }}_{\mathit{ij}}\left(T,-,k\right){\tilde{\rho }}_{\mathit{ij}}^2$$ 5

$$L{M}_{\mathit{Adj}}={\left[\frac{2T}{N\left(N-1\right)}\right]}^{\frac{1}{2}}\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\tilde{\rho }}_{\mathit{ij}}\left(T-k\right){\tilde{\rho }}_{\mathit{ij}}^2-\mu T_{\mathit{ij}}\left./\phantom{\mu T_{\mathit{ij}}{(v{T}_{\mathit{ij}}^2)}^{\frac{1}{2}}}\right){(v{T}_{\mathit{ij}}^2)}^{\frac{1}{2}}$$ 6

Homogeneity tests are calculated with the help of the following statistics.

$$\tilde{\mathrm{\Delta }}=\sqrt{N}\frac{N^{-1}\tilde{S}-k}{\sqrt{2k}}$$ 7

$${\tilde{\mathrm{\Delta }}}_{\mathit{Adj}}=\sqrt{N}\frac{N^{-1}\tilde{S}-k}{v_{\mathit{ar}}\left(t,,,k\right)}$$ 8

The fact that the variables show cross-sectional dependence and their slope coefficients are not homogeneous implies that they have complex properties. Descriptive statistics on variables also confirm that the data distribution is not homogeneous. Whether the series contains a unit root or not was tested with CADF and CIPS tests developed by Pesaran [66]. In the study, quantile regression analysis was used to obtain inferences about the relationships of variables at different percentiles depending on heterogeneous characteristics. The MMQR (Panel moments quantile regression) approach is independent of conditional mean effects and does not seek normal distribution [5, 95]. The MMQR estimator is accompanied by location and scale, and strong results are achieved [9, 93]. In order to check the robustness of the regression estimates obtained in the study, GMM (generalized model of moments dynamic regression estimator and machine learning (ML-based KRLS (kernel regularized least squares method were used.

The GMM estimator is an estimator with effective results that can solve problems such as endogeneity, autocorrelation and heteroskedasticity and estimate unknown parameters. The estimator can eliminate the endogeneity problem arising from the relationship between the dependent variable and the error terms with a lag value of 1 of the dependent variable [6].

The KRLS regression test proposed by Hainmueller and Hazlett [41] is a powerful test that can easily calculate the relationships between variables with complex properties with the ML technique. The sample points measured with the target values of the regression model are connected with the LS (least squares) method. The complex relationship between variables can be understood by means of a kernel. KRLS is a reliable and flexible regression procedure.

Empirical results and discussion

Before the econometric results, the correlation between the variables was examined, and the results are presented in Table 5. It was observed that the variables lnATECH, lnACRD, lnAGRW, lnAEMP, and lnINT were negatively correlated with the lnACO₂ variable. It is also noteworthy that agricultural credits (ACRD) and agricultural added value (AGRW) were positively correlated with AgriTech (ATECH).

Table 5.

Correlations

	lnACO2	lnATECH	lnACRD	lnAGRW	lnAEMP	lnINT
lnACO2	1
lnATECH	−0.320107	1
lnACRD	−0.152972	0.828584	1
lnAGRW	−0.521159	0.776755	0.708084	1
lnAEMP	−0.301613	−0.020220	−0.034247	−0.178395	1
lnINT	−0.010773	0.145973	0.227965	0.229903	−0.564454	1

The econometric flowchart of the study is shown in Fig. 5. The results of the cross-sectional dependency test applied to determine the qualities of the variables are presented in Table 6.

Fig. 5.

Flowchart of the analysis

Table 6.

Cross-sectional dependency test results

Test	lnACO2	lnATECH	lnACRD	lnAGRW	lnAEMP	lnINT
LM	1733.838*** (0.0000)	923.953*** (0.0000)	1368.207*** (0.0000)	2909.097*** (0.0000)	3203.135*** (0.0000)	3859.999*** (0.0000)
CDLM	78.171*** (0.0000)	36.625*** (0.0000)	59.414*** (0.0000)	138.460*** (0.0000)	153.544*** (0.0000)	187.241*** (0.0000)
CD	77.716*** (0.0000)	36.170*** (0.0000)	58.960*** (0.0000)	138.006*** (0.0000)	153.090*** (0.0000)	186.786*** (0.0000)
LMAdj	18.068*** (0.0000)	17.912*** (0.0000)	29.149*** (0.0000)	50.234*** (0.0000)	52.028*** (0.0000)	62.047*** (0.0000)

P-Values in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10

In Table 6, it is seen that all the results of the cross-sectional dependency tests applied to the variables are significant at the 0.01 significance level. This situation rejects the null hypothesis that there is no cross-sectional dependency of the variables. The SSA countries included in the analysis are cross-sectionally interconnected. Economic shocks that occur in countries also affect other countries. The homogeneity test was tested with the procedure of Pesaran and Yamagata [67] and the results are presented in Table 7.

Table 7.

Homogeneity test results

	Δ̃	Δ̃Adj
Test Statistic	11.781***	14.125***
P-Value	0.0000	0.0000

Notes: *** p < 0.01, ** p < 0.05, * p < 0.10

Table 7. The fact that the statistics of the Delta and Corrected Delta tests are significant at the 0.01 significance level rejects the homogeneity of the slope coefficients. The unit root properties of the variables were tested with Pesaran’s [66] CADF and CIPS tests and are presented in Table 8. CADF and CIPS panel unit root test results show that all variables contain unit roots at the level and the difference variables are stationary. The fact that the variables are cross-sectionally dependent and heterogeneous indicates that they are complex. Standard regression tests are not sufficient to understand the complex relationships between variables. Therefore, the regression estimates of the model were made with MMQR, and the numerical results of the interactions of the variables at different quantile levels are presented in Table 9.

Table 8.

Panel unit root test results

	CADF		CIPS
	I(0)	I(1)	I(0)	I(1)
lnACO2	−1.564	−3.786***	−1.406	−4.750***
lnATECH	−2.139	−3.420***	−2.215	−4.662***
lnACRD	−1.877	−3.116***	−1.014	−4.657***
lnAGRW	−0.968	−3.465***	−2.228	−7.596***
lnAEMP	−2.258	−2.554***	−1.438	−3.706***
lnINT	−2.295	−2.375***	−2.312	−3.354***

*** p < 0.01, ** p < 0.05, * p < 0.10

Table 9.

MMQR quantile regression results

	Location	Scale	10%	20%	30%	40%
lnATECH	−0.1542** [0.065]	−0.0767** [0.039]	−0.2667*** [0.070]	−0.2310*** [0.063]	−0.2079*** [0.061]	−0.1858*** [0.062]
lnACRD	0.1886*** [0.027]	0.0368*** [0.016]	0.1345*** [0.029]	0.1517*** [0.026]	0.1628*** [0.025]	0.1734*** [0.025]
lnAGRW	−0.4287*** [0.059]	−0.2367*** [0.035]	−0.0817*** [0.064]	−0.1921*** [0.057]	−0.2632*** [0.056]	−0.3313*** [0.056]
lnAEMP	−0.5074*** [0.154]	−0.3065*** [0.091]	−0.9568*** [0.166]	−0.8139*** [0.149]	−0.7218*** [0.144]	−0.6335*** [0.146]
lnINT	−0.0320*** [0.030]	−0.0838*** [0.018]	−0.1549*** [0.033]	−0.1158*** [0.029]	−0.0906*** [0.028]	−0.0665** [0.029]
	50%	60%	70%	80%	90%
lnATECH	−0.1633*** [0.064]	−0.1309** [0.071]	−0.0891 [0.083]	−0.0585 [0.094]	−0.0235 [0.108]
lnACRD	0.1842*** [0.026]	0.1998*** [0.029]	0.2198*** [0.034]	0.2346*** [0.038]	0.2514*** [0.044]
lnAGRW	−0.4009*** [0.059]	−0.5007*** [0.068]	−0.6295*** [0.077]	−0.7241*** [0.083]	−0.8321*** [0.097]
lnAEMP	−0.5434*** [0.152]	−0.4142** [0.169]	−0.2473 [0.197]	−0.1249 [0.221]	0.0149 [0.255]
lnINT	−0.0418** [0.030]	−0.0065 [0.034]	0.0391 [0.039]	0.0725* [0.043]	0.1108** [0.050]

^*** p < 0.01, ^** p < 0.05, ^* p < 0.10, [] contains bootstrapped standard errors

present the quantile plot of the analyzed variables for the model. When Table 9 is examined; It is estimated that the effect of AgriTech on agricultural emissions is negative in all quantiles and statistically significant at the 0.01 (p < 0.01) significance level. This shows us that agricultural technologies support sustainable agriculture by reducing greenhouse gas emissions from agriculture in SSA countries in all quantiles. The result obtained is found to be consistent with Zhang et al. [94]. The power of agritech to reduce agricultural greenhouse gas emissions is higher at lower quantile levels, while this effect decreases at higher quantile levels. In other words, agricultural technology improves the environment more effectively in SSA countries with low agricultural greenhouse gas emissions. In countries where agricultural greenhouse gas emissions are high, the environmental improvement effect of AgriTech decreases. We can explain this by the fact that AgriTech initiatives have not reached sufficient levels in countries with high quantile levels of intensive agricultural greenhouse gas emissions. Conventional farming activities that do not integrate agricultural technologies can cause various agricultural difficulties. For example, excessive tillage and improper soil management can reduce soil quality and cause erosion. Continuously producing the same product can increase the need for fertilizer use in the soil and cause the soil to lose fertility. Mismanagement of irrigation practices can pollute or deplete water resources. Mismanagement of fertiliser, pesticide and chemical use can increase agricultural greenhouse gas emissions, causing climate change and reducing biodiversity. Agricultural technologies have the ability to combat all these challenges. They can reduce greenhouse gas emissions from agricultural activities, prevent water waste and excess energy consumption, and ensure resource efficiency. They can ensure soil fertility by properly managing waste from activities [69, 73] (Fig. 6).

Fig. 6.

Quantile distribution of different independent variables on lnACO₂

The coefficients of the lnACRD variable were found to be positive and statistically significant (p < 0.01) at all quantile levels. Agricultural credits cause an increase in agricultural emissions in SSA countries with an increasing trend at each quantile level. Financial institutions with weak assets and unstable income have low agricultural credit [38], which limits farmers' access to green agricultural technology [44]. Thus, agricultural productivity decreases while at the same time causing high agricultural emissions [94]. The fact that agricultural credits increase agricultural emissions in SSA can be explained by the lack of access to sufficient agricultural credits in these countries. Adjognonet et al. [4] in their study investigating agricultural credit in SSA found that very few farmers used credit from formal sources. It was also found that these farmers did not spend the credit they used on agricultural inputs.

The coefficients of the lnGRW variable, which are negative and statistically significant (p < 0.01) across all quantiles, explain that agricultural value added supports sustainable agriculture. A 1% increase in lnGRW reduces agricultural emissions by 0.08% at the lowest quantile (10%) and by 0.83% at the highest quantile (90%) in the long term. The contribution of agricultural value added to the improvement of agricultural emissions has increased by approximately 100% across all quantiles. Doğan [31] and Zwane et al. [97] confirm that agricultural growth reduces emissions in their studies.

When we look at the coefficients of the lnAEMP variable; we see that it is negative and statistically significant (p < 0.01) at the lower and middle quantile levels (10%−60%). In this range, it is considered significant only at the 60% quantile level at the 0.05 significance level. In these quantiles, the ability of agricultural workers to reduce agricultural emissions has gradually decreased and continued to decrease between 70% and 80% quantiles and has become insignificant. At the highest quantile level (90%), the lnAEMP coefficient turned positive but was statistically insignificant. In their study on Bhutan, Rehman et al. [70] concluded that agricultural workers significantly reduced carbon emissions in parallel with our study. Although farmworkers in SSA countries can reduce agricultural emissions at the lower and middle quantile levels, they fall short at the higher quantile levels. This can be explained by the fact that the agricultural workforce in SSA countries with high agricultural emissions is not innovative and compatible with environmental sustainability content. When SSA economies integrate environmental sustainability principles into their education programs, farmers will be able to improve environmental quality through green investment and green production choices. The inadequacy of agricultural workers in improving environmental quality in high quantiles is paralleled by the inadequacy of internet use in the same quantiles. Inadequate internet access can be shown as a factor preventing agricultural workers from making green decisions for the environment.

Figure 7 shows the summary of the effects of variables on agricultural emissions (ACO₂). The coefficient of internet usage in SSA countries is found to be negative and significant at low and middle quantile levels (10%–50%). In these low and middle agricultural emission intensive countries, internet usage significantly reduces agricultural emissions and supports sustainable agriculture. It is estimated as negative and insignificant at the 60% quantile level and positive and insignificant at the 70% quantile level. At high quantile levels (80%–90%), it is seen as positive and statistically significant. In countries with high agricultural emissions, internet use is insufficient to improve the environment. Inadequate internet use also limits access to agricultural technology use. It may also cause transaction costs and information asymmetry to increase while hindering the development of the financial system. This situation can also be explained by the fact that the inadequate flow of information regarding investment opportunities makes financial integration difficult. In addition, inadequate internet use may prevent agricultural stakeholders from reaching larger market shares, resulting in demand and production losses [60].

Fig. 7.

Effects on agricultural emissions (ACO₂)

Robustness analysis and discussion

The GMM dynamic regression estimator and the KRLS quantile regression estimator were used in the robustness checks of the results obtained from the MMQR regression estimator, and the findings are presented in Table 10.

Table 10.

Robustness analysis results

Dependent Variable: lnACO2	GMM	AVG	25th	50th	75th
lnATECH	−0.023007***	−0.217234***	−0.368035	−0.258786	−0.083083
lnACRD	−0.012414	0.12518***	−0.018947	0.142317	0.251641
lnAGRW	−0.104455***	−0.138457***	−0.468393	−0.099972	0.240943
lnAEMY	0.312216***	−0.307273***	−1.04867	−0.244122	0.518199
lnINT	−0.028314**	−0.079554***	−0.220178	−0.10775	0.000193
Sargan Hansen test	χ2 = 18.21308 Prob > χ2 = 0.9988
Arellano-Bond test	AR(1): −0.87295 Prob >|z|= 0.3827 AR(2): −0.52525 Prob >|z|= 0.5994

The Sargan-Hansen test result in Table 10 shows that the instrumental variables are valid, and the results of the Arellano-Bond test show that there is no autocorrelation problem in the model. Therefore, the assumptions of the GMM regression estimator are met.When the GMM dynamic regression estimates are examined, it is seen that the coefficients of the lnATECH, lnAGRW and lnINT variables are negative and statistically significant. These findings indicate that the increase in the use of agricultural technology and internet use in SSA countries provides environmentally friendly agricultural added value. We can say that the use of agricultural technology and internet use both directly reduces agricultural emissions in these economies and indirectly supports sustainable agriculture by increasing agricultural added value. The lnAEMP variable is estimated as positive and significant. This estimated value can be explained by the insufficient awareness of farmworkers to reduce agricultural emissions across the panel. No significant effect was detected for the lnCRD variable. According to estimates from the KRLS estimator, AgriTech supports sustainable agriculture by reducing agricultural emissions at all quantile levels. Agricultural credits reduce agricultural emissions at low quantile levels (25th) and increase them at medium and high quantile levels (50th and 75th). In countries with high emissions, agricultural credits are insufficient to improve the environment. Agricultural value added, agricultural workers, and internet use reduce agricultural emissions at low and medium quantile levels and increase them at high quantile levels. When agricultural production increases, environmental quality increases at low and medium quantile levels (25th and 50th), but quality decreases at high quantile levels (75th). This can be explained by the increase in production along with the increase in fertilizers and pesticides used in agriculture [79]. In countries with high agricultural emissions, internet access of agricultural workers may not be sufficient to reduce emissions. The GMM and KRLS estimates are approximately consistent with the MMQR estimates.

Conclusion and policy recommendations

SDG-2 focuses on a global vision to combat hunger, improve access to safe food, and promote agricultural sustainability. Within this framework, integrating sustainable food production systems and environmentally friendly, resilient agricultural practices that protect soil and land into production are key goals. Increasing agricultural productivity significantly contributes to social well-being by reducing hunger and poverty worldwide. In the context of sustainable agriculture, reducing greenhouse gas emissions while increasing agricultural productivity is the most critical challenge agricultural societies must address. Combating ecological damage, managing scarce resources, and confronting the climate crisis while enhancing food security and crop productivity is a challenge that extends beyond traditional agricultural practices. In this regard, AgriTech, encompassing smart agriculture, precision agriculture, information technologies, and biotechnology, has begun to replace traditional agricultural practices, and its effects on sustainable agriculture have become a topic of interest. Understanding the long-term interactions of factors affecting sustainable agriculture in agricultural societies, such as Sub-Saharan African (SSA) countries where 50% of the economy is based on agriculture, is vital for both contributing to the literature and guiding decision-makers in these countries.

This study analyzed the relationship between AgriTech and sustainable agriculture econometrically from an agricultural development perspective, producing strong empirical results. In addition to AgriTech and agricultural development, the long-term interactions of internet usage, agricultural value added, and agricultural workers with sustainable agriculture were also examined. Due to data limitations, only 20 SSA countries were included in the study, with analyses conducted using annual data from 2000 to 2022. The characteristics of the variables were tested with various cross-sectional dependence, homogeneity, normality, and unit root tests. Since the cross-sectionally dependent and heterogeneous series do not show normal distribution, traditional regression estimators cannot produce robust results. In this case, the marginal interactions between the variables along the quantiles were estimated using the MMQR approach, which provides up-to-date and effective results. The robustness of the results was tested using the GMM approach, which captures dynamic interactions between variables, and the KRLS quantile regression approach based on machine learning. The GMM and KRLS estimates confirm the MMQR estimates. According to empirical findings, AgriTech in SSA countries significantly reduces agricultural emissions at all quantiles. The potential of AgriTech to improve agricultural emissions is higher when agricultural emissions are low. The results suggest that agricultural value added reduces agricultural emissions in parallel with AgriTech. This shows that technology integrated into agriculture increases agricultural value added on the one hand and reduces emissions on the other. Similarly, agricultural workers and internet use have parallel results. Although both variables reduce agricultural emissions at low and medium quantile levels (10–50%), they are insufficient at high quantiles. The ability of workers to reduce agricultural emissions is directly proportional to their access to the internet. Access to information and communication technologies in SSA countries both directly supports sustainable agriculture and indirectly contributes by increasing agricultural workers' awareness and access to information. Agricultural credits improve the environment at the 25th quantile level, where agricultural emissions are low, according to the KRLS robustness test findings in Table 10, but they are insufficient at high levels.

Based on the empirical findings from econometric analysis, this study provides some policy recommendations for reducing emissions in SSA countries to achieve sustainable agriculture in the long term. SSA governments and all agricultural stakeholders should take measures to increase agricultural productivity and reduce emissions to support sustainable agriculture. First, green procurement and green production processes should be expanded by moving away from traditional agricultural activities and integrating green technologies into agricultural production. Second, access to the internet should be improved, IT infrastructure should be strengthened, and training programs should be implemented to increase the number of digitally literate individuals. Third, agricultural policies should be adopted that make financing more accessible and subsidized for agricultural stakeholders to access AgriTech. Green financing sources should be increased and their use should be expanded. Private sector investments should be encouraged to support this. Fourth, agricultural workers should be trained in environmentally friendly production practices and agricultural technologies.

A significant limitation of the study is the lack of consistent agricultural data for low-income SSA countries post-2022. Furthermore, data was only available for 20 SSA countries, allowing the creation of a panel dataset for analysis.

This study provides in-depth insights into the interactions between AgriTech, agricultural development, agricultural value-added, internet usage, and agricultural workers and their impacts on sustainable agriculture, using panel data for SSA countries. Sustainable agriculture was represented by agricultural emissions in SSA. Future studies could represent sustainable agriculture using different indicators, allowing for more nuanced analyses of the factors affecting sustainable agriculture. While this study draws conclusions for SSA as a whole, future research could apply different analytical methods to explore individual country characteristics. Moreover, the econometric approach applied in this study could be extended to other countries or regions in future studies.

Abbreviations

AHP

Analytical Hierarchy Process

AERZ

Agro-Eco-Resource Zoning

AgriTech

Agricultural Technology

AHP

Analytical Hierarchy Process

CO₂

Carbon dioxide

CSA

Climate-smart agriculture

CADF

Cross-sectional Augmented Dickey-Fuller

CIPS

Cross-Sectional Im-Pesaran-Shin

FAO

Food and Agriculture Organization

FinTech

Financial Technology

GMM

Generalized Method of Moments

KRLS

Kernel Regularized Least Squares Method

MCDM

Multi-Criteria Decision-Making Methods

MMQR

Method of Moments Quantile Regression

SDGs

Sustainable development goals

SSA

Sub-Saharan Africa

VIF

Variance inflation factor

WDI

World Bank Development Indicators

Author contributions

BK Conceptualization, Methodology, Formal Analysis, Writing—Original Draft. M Ç: Literature Review, Data Collection, Writing—Review & Editing. A E: Data Curation, Visualization, Writing—Review & Editing. AP: Supervision, Funding Acquisition, Writing—Review & Editing. AB: Methodology, Validation, Writing—Original Draft, Supervision. M.R.Making all revisions.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Availability of data and materials

Data would be made available upon reasonable request from the corresponding author.

Declarations

Ethics approval and consent to participate

This study does not involve human or animal participants. Therefore, ethical approval is not required. All data used in the study were obtained from open sources, and the research process was conducted in accordance with scientific ethical principles.

Competing interests

The authors declare no competing interests.