Promoting Sustainable Agriculture: Impacts of Innovative Soil Management Approaches on Human Health and Ecosystems ^†

Abstract

Soil use and its proper management are key elements of sustainable development. However, given the complexity of the issue, it is necessary to address it using an interdisciplinary approach. The proposed work aims to analyze the consequences, in terms of damage assessment, of two different soil management systems of a cereal crop through the use of Life Cycle Assessment (LCA) methodology. One system follows a traditional approach and the other utilizes a Decision Support System (DSS). The long-term impacts on human health, ecosystems, and resource availability are calculated by employing the ReCiPe 2016 endpoint method. The results show notable reductions in resource use and environmental impacts with DSS, with a 41% decrease in damage to human health, a 24% reduction in ecosystem damage, and a 23% reduction in resource use. Hence, implementing new technologies and new management strategies in agriculture can lead to more sustainable management choices and can avoid long-term burdens compared to a traditional approach.

1. Introduction

Real sustainable development can be attained if food security, water security, energy security, climate change abatement, biodiversity protection, and ecosystem service delivery are achieved [1]. Also considering that, according to the latest FAO estimates, the world population will increase to 9 billion by 2050, the necessity of addressing all these challenges through an interdisciplinary approach, given their extreme complexity and linkage, has emerged [2,3]. Soil security is recognized to be central to the sustainable development of humanity and the planet [4,5]. The European Union recognizes how soil protection and soil safety are a priority and intrinsically linked to the other challenges that humanity has to face [6]. Soil insecurity, in fact, has consequences for agricultural productivity, the provision of water, increased greenhouse gasses, and the loss of biodiversity [7]. In this context, proper soil management plays a key role in restoring the fertilizing capacity of the soil, regulating the carbon and nitrogen cycle, serving as a basin for biodiversity, and preserving its cultural significance and connection to the land [5]. Achieving this requires agronomic practices commonly used in agriculture to be implemented with tools and systems that allow for better optimization in the use of resources, with positive consequences for humans and the environment. From this perspective, Agriculture 4.0 and the technologies used to support it are key elements to consider in order to ensure greater soil security and a development that is truly sustainable [8]. Among the various tools that can be used to optimize agronomic practices, one that is increasingly gaining popularity is the Decision Support System (DSS). It is a human–computer system that integrates data from diverse sources to give farmers recommendations to support their decisions. Hence, the DSS does not aim to give direct instructions, but to make the farmers free to make the final decision [8]. It suggests the best time for irrigation, fertilization, and harvesting by monitoring the surrounding environment using an array of advanced sensors and connections. These sensors send the collected data to a central control unit, which processes it in combination with information from local weather stations. This integration allows the system to accurately determine, for example, the optimal times for fertilization, making the best use of soil and atmospheric conditions to enhance the effectiveness of nutrient application. Using optical sensors, farmers can also be notified when crops are infected by any diseases. However, although such a system is supportive in soil management, there are limitations that prevent its wide diffusion, such as skepticism from farmers themselves, incomplete functionality, bad GUI, and inadequate requirement analysis [9]. For these reasons, research is increasingly advancing the development of DSSs that can integrate the most information and data possible to offer a truly advantageous service for optimizing agronomic practices [10].

However, although from a technological development point of view, the scientific community and industries are striving for more and more state-of-the-art DSSs [11], to the best of our knowledge, the study of the actual sustainability associated with the use of these systems is still in its early stages. The main hypothesis of this study is that DSSs in agronomic practices can significantly improve resource optimization and sustainability compared to traditional methods. Specifically, the research aims to analyze and compare the long-term impacts on human health, ecosystems, and resource use between traditional agronomic practices and those guided by DSSs. This comparison is conducted using the Life Cycle Assessment (LCA) methodology to quantify the benefits of DSSs in terms of reduced inputs such as fertilizers and pesticides, improved timing and application efficiency, and overall environmental and human health outcomes. By focusing on a cereal crop in central Italy, this study seeks to provide empirical evidence on the effectiveness of DSSs in promoting sustainable agricultural practices and enhancing soil security, ultimately contributing to broader sustainable development goals.

2. Materials and Methods

To compare the impacts of the two different soil management systems in a cereal crop, the Life Cycle Assessment (LCA) methodology was used. This is a widely adopted methodology whose validity is widely recognized on a scientific level for calculating the impacts of a product, process, or service. It is regulated by ISO (14040:2006, 14044:2006) [12,13], and in agreement with these standards, the current LCA study has been conducted in four steps: (1) goal and scope definition, (2) life cycle inventory, (3) life cycle impact assessment, and (4) LCA result interpretation.

2.1. Goal and Scope

The aim of this research is to analyze the long-term impacts on humans and the environment of two different land management systems. These two systems are referred to here as “traditional agronomic practices” and “DSS agronomic practices”. It is important to note that the key difference between these systems lies in the varying amounts of inputs used by farmers. In the first case, the quantity of products such as fertilizers and pesticides is determined by the farmer’s expertise. In the second case, the DSS advises farmers on the actions to take, including the optimal timing for applying fertilizers and pesticides, as well as the ideal amounts. The functional unit chosen for this analysis is 1 hectare (ha), and the inputs considered include diesel consumption, fertilization, pesticide use, and seeding activities. The calculation method used to conduct the analyses is the ReCiPe 2016 endpoint method, which is a damage assessment method that allows, through appropriate conversion factors, to group the impact categories of the midpoint method into 3 damage categories: “human health,” “ecosystems,” and “resources.” This method was chosen because it is robust and one of the most widely used in Life Cycle Analysis for agri-food products [14].

2.2. Life Cycle Inventory

The data used for analysis were collected in the 2020–2021 season from farms located in central Italy. The selection of these two agricultural farms was based on their representativeness of cereal cultivation practices in central Italy. Both farms employ typical agronomic practices common in the region, making them ideal case studies for this research. One farm utilizes traditional methods, while the other incorporates a DSS, allowing for a comprehensive comparison of the two approaches. Their geographical location, farm size, and crop management techniques reflect the standard practices and challenges faced by cereal farmers in central Italy, ensuring that the findings of this study are relevant and applicable to the broader agricultural community in this area.

Table 1. Life cycle inventory of the two soil management practices: traditional and DSS.

	Traditional Agronomic Practices	DSS Agronomic Practices
Input
Diesel (L/ha)	96.20	87.60
Total N units (kg/ha)	103.78	86.36
Phosphorous (kg/ha)	34.82	n.g. 1
Pesticide (kg/ha)	2.88	2.07
Seeds (kg/ha)	190	202
Output
Yield (t/ha)	5.76	5.48

¹ not given.

lists the set of inputs and outputs collected for the two different management systems. However, it is important to acknowledge some limitations related to the agronomic inputs. For instance, the use of agricultural machinery for field operations was not included in the analysis due to insufficient data. This omission could impact the overall assessment of resource use and environmental impacts, as machinery usage plays a significant role in modern agricultural practices.

2.3. Life Cycle Impact Assessment

The method used for the damage assessment analysis was the ReCiPe endpoint 2016 method. The choice of this method lies in the fact that it allows long-term impacts to be assessed by linking the 18 midpoint categories with the 3 endpoint areas of protection through specific “Damage Pathways”. The software used for calculations is SimaPro 9.4.0.3 (PRé Sustainability, Amersfoort, The Netherlands).

The 3 categories with their respective units of measurement are as follows:

• Damage to human health (HH): For this category, the unit of measurement used is disability-adjusted loss of life years (DALYs), which is an estimate of years of life lost due to the increase in respiratory diseases, chronic degenerative diseases, cancer, and malnutrition.
• Damage to ecosystems (ED): For this category, the unit of measurement used refers to the number of species lost per year (species per year), considering species found in freshwater, sea, and terrestrial species.
• Damage to resource availability (RA): The unit of measure for this category is cost increases (dollars), due to the increased cost of extraction and energy costs.

3. Results and Discussion

The characterized results of the performed analyses are shown in

Table 2. Characterized results of the ReCiPe 2016 endpoint method.

Impact Categories	Unit	Traditional Agronomic Practices	DSS Agronomic Practices
Global warming, human health	DALY	1.02·10−3	7.31·10−4
Global warming, terrestrial ecosystems	species.yr	3.08·10−6	2.21·10−6
Global warming, freshwater ecosystems	species.yr	8.41·10−11	6.03·10−11
Stratospheric ozone depletion	DALY	1.30·10−6	1.08·10−6
Ionizing radiation	DALY	3.84·10−7	1.92·10−7
Ozone formation, human health	DALY	4.40·10−6	3.71·10−6
Fine particulate matter formation	DALY	2.12·10−3	9.81·10−4
Ozone formation, terrestrial ecosystems	species.yr	6.35·10−7	5.36·10−7
Terrestrial acidification	species.yr	2.10·10−6	8.15·10−7
Freshwater eutrophication	species.yr	2.67·10−7	1.84·10−7
Marine eutrophication	species.yr	1.01·10−9	9.26·10−10
Terrestrial ecotoxicity	species.yr	7.10·10−8	3.82·10−8
Freshwater ecotoxicity	species.yr	4.47·10−8	2.55·10−8
Marine ecotoxicity	species.yr	8.75·10−9	5.05·10−9
Human carcinogenic toxicity	DALY	2.47·10−4	1.67·10−4
Human non-carcinogenic toxicity	DALY	5.71·10−4	4.31·10−4
Land use	species.yr	3.73·10−6	3.63·10−6
Mineral resource scarcity	USD2013	2.85	1.12
Fossil resource scarcity	USD2013	1.30·102	1.02·102
Water consumption, human health	DALY	6.23·10−5	5.63·10−5
Water consumption, terrestrial ecosystem	species.yr	4.24·10−7	3.88·10−7
Water consumption, aquatic ecosystems	species.yr	6.95·10−11	7.07·10−11

. The “impact categories” column shows the linkage between midpoint categories and endpoint categories. The results of the analyses are summarized in Table 2. The “impact categories” column illustrates the connection between midpoint and endpoint categories. For instance, the midpoint category “global warming” can affect multiple endpoint categories (e.g., HH and ED) because GHG emissions negatively impact both ecosystems and human health. Rising temperatures contribute to biodiversity loss, ecosystem service disruption, extreme weather events, and increased air pollution, leading to respiratory and cardiovascular diseases [15]. Other midpoint categories, such as “terrestrial acidification” and “freshwater eutrophication”, harm the environment and living species, mainly due to fertilizer and pesticide use [15].

The results of the damage assessment are also shown in Figure 1 as percentage values, in order to graphically understand the difference between the two management systems. By adding the values together with the same unit of measurement (DALY, species.yr, USD2013), we obtain the result expressed in terms of damage assessment for “traditional agronomic practices” and for “DSS agronomic practices.” Specifically for the former, the following values are obtained: human health 4.02·10⁻³ DALY; ecosystems 1.04·10⁻⁵ species.yr; resources 1.33·10² USD2013. For “DSS agronomic practices,” on the other hand, the following values are obtained: human health 2.37·10⁻³ DALY; ecosystems: 7.83·10⁻⁶ species.yr; resources 1.03·10² USD2013.

The analysis demonstrates that the use of DSSs in agronomic practices significantly reduces human and ecosystem damage, as well as resource use. Specifically, these reductions amount to 41% for human health damage, 24% for ecosystem damage, and 23% for resource use. This improvement is primarily due to more accurate fertilizer application with DSSs compared to traditional methods, with nitrogen-based fertilizer use decreasing from 103.78 kg/ha to 86.36 kg/ha and the elimination of phosphorus fertilizer (previously 34.82 kg/ha). These findings underscore the potential of DSSs to enhance resource optimization and environmental sustainability in agriculture. For policymakers, the study highlights the role of DSSs in promoting sustainable agricultural practices by reducing chemical inputs, improving soil health, and aligning with broader goals such as climate change mitigation and biodiversity conservation. For farmers, DSSs offer practical benefits including cost savings and potentially higher crop yields due to better-timed applications of fertilizers and pesticides, thereby improving the economic viability of farming operations.

However, the study has limitations. It focuses on two farms in central Italy, limiting the generalizability of the findings to other regions with different conditions. The study also lacks comprehensive socio-economic data such as cost savings, profitability, labor requirements, and farmer perceptions, which are essential for a full sustainability assessment. Additionally, the research only covers the 2020–2021 season, necessitating longer-term studies to understand the enduring impacts of DSSs on soil health and crop yields. Moreover, the functionality and user-friendliness of DSSs, along with adoption barriers such as initial investment costs, technical complexity, and the need for substantial training and support, were not deeply explored. Addressing these challenges is crucial for maximizing the benefits of DSSs in agriculture. Future research should focus on these aspects to provide more practical recommendations for DSS adoption. Despite its limitations, the study underscores the importance of DSSs in input management and resource optimization. Once refined, DSSs will be essential in Agriculture 4.0, aiding in resource allocation, climate adaptation, and sustainable management transitions.

4. Conclusions

This research highlights the significant advantages of employing Decision Support Systems (DSSs) in agronomic practices over traditional methods. Using Life Cycle Assessment (LCA) methodology, we compared the long-term impacts on humans and the environment of traditional and DSS-based management in cereal crop cultivation. The results showed notable reductions in resource use and environmental impacts with DSS, including a 41% decrease in damage to human health, a 24% reduction in ecosystem damage, and a 23% reduction in resource use, primarily due to more precise fertilizer application and the elimination of phosphorus fertilizer use. While this preliminary study emphasizes DSSs’ potential in mitigating negative impacts, further validation with diverse crops and over extended periods is needed. Despite their limitations, the study illustrates DSSs’ crucial role in optimizing input management and resource use, making them essential for Agriculture 4.0. As these systems are refined, they will become increasingly vital for resource allocation, climate adaptation, and sustainable agricultural management. In conclusion, integrating advanced technologies like DSSs in agronomic practices offers a promising pathway to achieving sustainable development goals. By enhancing resource use efficiency and effectiveness, these technologies contribute significantly to the long-term sustainability of agriculture and the preservation of vital ecosystem services.