The Role of Precision Agriculture Technologies in Enhancing Sustainable Agriculture

Abstract

Despite the known benefits of precision agriculture, the adoption is challenging due to the cost of investment and the farm sizes. Therefore, profitability is an important aspect to consider. This study aimed to evaluate the net returns, profitability, and investment efficiencies of PA by different economic farm sizes. The study was based on data retrieved from the Farm Accountancy Data Network (FADN) and Eurostat (year 2021). The study examined four countries (Poland, Germany, France, and Romania) under field crop farming using an investment cost of EUR 35,941–EUR 71,883 and a 20% and 15% reduction in the cost of crop protection and fertilizer usage, respectively, without compromising productivity. There is a positive relationship between the adoption of PA and farm returns for larger-scale farms. The result of the profitability and analysis of investment efficiency using Net Present Value (NPV) showed a positive value for economic farm sizes of EUR 100,000 and above. Hence, it is not economically advisable that all farmers use PA technologies with the hope that they will be profitable but with public support (subsidies) more farms will be able to use PA and be profitable. This is also an opportunity to meet the goals of the European Union Green Deal of minimizing emissions that cause climate change.

1. Introduction

Agriculture is vital in the global economy, as it supplies food and raw materials to support the increasing worldwide population. It is also crucial for sustaining human existence, and to increase food availability and facilitate food security, the agricultural sector is of utmost significance [1,2]. However, the current state of agriculture faces substantial problems that need to be tackled to ensure food security and sustainability. One of the primary issues is the need to feed a rapidly growing global population while at the same time reducing the environmental impact and preserving natural resources for future generations.

This puts significant pressure on agriculture to produce more food, often leading to the intensification of production systems, which can have negative environmental consequences. The rivalry for the already limited resources of land and water will intensify quickly as a result of this growth [3]. In addition to the need to produce more food, agriculture also faces other challenges related to the sustainability of its practices.

The interdependence between food production and environmental protection is a crucial issue that must be addressed. Several agricultural practices impede long-term sustainability. Traditional farming applies inputs uniformly, regardless of potential variability or heterogeneity, throughout the field, which damages the environment [4]. Conventional farming methods often result in excessive use of water, fertilizers, and pesticides, leading to soil degradation, water pollution, loss of biodiversity, and climate change [5,6].

Therefore, it is increasingly important that we discover innovative methods to develop and manage our agricultural systems. In response to these challenges, precision agriculture (PA) has emerged as a transformative and innovative approach that leverages advanced technologies to optimize crop yields while minimizing inputs, reducing environmental impact, and boosting agricultural profitability [7,8]. Unlike traditional farming, precision farming focuses on managing and maintaining individual fields rather than the entire acreage, utilizing a system-based methodology to optimize both economic and environmental benefits.

Precision agriculture, according to Nabi et al. [9], is defined as a highly effective farming system that utilizes the latest technology and management techniques to achieve maximum agricultural productivity. By adopting best practices in crop planning, tillage, planting, chemical application, harvesting, and post-harvest processing, farmers can significantly enhance productivity, improve product quality, and minimize the impact of their activities on the environment. Such a strategy involves careful consideration of various factors, including optimal crop rotation, judicious use of fertilizers and pesticides, and the use of energy-efficient technologies, among others [9].

By leveraging advanced technologies such as remote sensing, satellite navigation systems, and geographic information systems, farmers can now track and analyze various factors such as soil quality, crop health, pest infestations, humidity, and temperature levels. This data-driven approach empowers farmers to make informed decisions about planting, fertilizing, harvesting, and achieving their goals, leading to a more sustainable and profitable farming enterprise [10].

Furthermore, precision agriculture is a vital approach that can significantly help to improve agricultural production, enable farmers to understand crop conditions, prevent damages, and optimize resource usage by applying just the right quantity of pesticide, nutrients, and irrigation water [11,12]. This comprehensive method of precision agriculture involves various components, such as data analysis, decision-making processes, background data, record-keeping systems, specialized equipment, and evaluation and revision. Precision agriculture is also particularly crucial in light of climate change and the increasing world population, as it helps reduce resource consumption and achieve maximum harvest potential. The system also relies on sensors to measure environmental parameters and control climate conditions for each crop, making it a highly effective and efficient approach to farming [13]. Precision agriculture has grown to encompass various fields, such as precision horticulture, precision viticulture, and precision livestock farming.

One of the most compelling advantages of precision agriculture lies in its capacity to lessen the environmental implications of farming. In addition, Liu et al. [14] discovered that precision agriculture technologies promote productivity, raise net return, and increase input utilization efficiency while minimizing the producer’s increased risk associated with net return variability. Producers can precisely calculate the necessary quantity of fertilizer and select the most efficacious varieties for a specific region. Moreover, indirect benefits, such as minimizing environmental impact by decreasing pesticide usage on crops, are associated with several PA technologies. It also helps in the reduction of groundwater depletion [15]. PA enhances fertilizer usage, reduces nitrogen losses, reduces cost, and promotes farm profitability by matching input needs with crop requirements. Along with cost-effective plant pest and disease management that enhances production and supports environmental sustainability, precision agricultural technology also gives farmers access to accurate pest monitoring and forecasts [16].

However, despite these advantages, small farmers have obstacles in using PA technologies, especially in emerging and under-developed nations. Mizik [17] defined small farms utilizing various scholarly sources, some of which are international organizations. The FAO [18] highlighted the importance of family-oriented farming and noted that 10 ha is the cutoff threshold for small farms. They also stressed the role of labor and personal consumption. There is no agreed-upon definition of size in the European Union [19] because it depends on both economic and physical aspects. A farm is deemed small in the United States if its annual income is below USD 250,000 [20].

For instance, Onyango et al. [21] discovered that limited access to Western PA technology, limited capabilities, and budgetary constraints result in little to no use of the technology in developing countries like those in Sub-Saharan Africa (SSA). Also, larger producers are more likely to use PA technologies because they can afford the costs and their demand for efficiency over wider areas. Nevertheless, due to its high implementation costs, lack of training and technical support, farmers are hesitant to adopt precision agriculture technologies on their farms [22].

In addition, the primary hurdles of deploying PA technologies are high initial costs, extended payback times, and uncertainty about the economic benefits, making it particularly difficult for lower-income farmers to finance the equipment. Moreso, farm size is a fundamental hindrance to utilizing PA technologies. Larger farms are more likely to use PA due to their capacity to pursue economies of scale, larger output potential, and better resource control (e.g., labor and land). Additionally, larger farms generally have higher variability in field features, making the economic benefits of PA adoption more substantial compared to farms with smaller field heterogeneity [23].

It is also important to note that many areas do not have sufficient digital infrastructure, such as electricity, internet, etc., which are imperative to the technology’s success. Even if they were available, some farmers may not have the financial means to purchase the required equipment. For instance, several PA applications require smartphones with an internet connection and plan, but not all farmers may possess the financial resources to afford it [11]. According to Krell et al. [24], only about 25% of Kenyan farmers who possessed mobile phones utilized their devices for farming-related activities, such as checking production-related data or utilizing farming-related apps. To promote PA technology, a mix of financial support as well as expanding networking and knowledge exchange among farmers is important [25].

In Germany, 30% of farmers utilize precision agriculture technology, whereas in Hungary, it is only 9.9% of farmers [26]. Research reveals that the United States, Canada, Australia, and France are the primary countries exploring AI systems for agriculture. Artificial intelligence (AI) is seen as a significant force driving digitalization in Germany, where the government budgeted EUR 3 billion until 2025 for AI-related scientific and business research [27]. Precision agriculture is still in its early stages in Romania [28]. The most prevalent precision agriculture technologies in Poland are Global Navigation Satellite Systems (GNSS), which have improved to include variable rate nitrogen application and satellite image processing for biomass monitoring. Some companies offer soil conductivity mapping, and larger farms use GNSS-based software for machinery efficiency [12]. The level of adoption of PA technologies in Poland and Romania may be similar, as they are both characterized by a fragmented agrarian structure.

Precision agriculture has many established advantages and has gained significant interest as a potential solution for the challenges confronting modern agriculture, but implementing these technologies is not without difficulties, especially in places with little infrastructure and resources. Hence, it is important to thoroughly analyze the cost and profitability associated with these technologies to make an informed conclusion regarding the implementation of PA technologies. Understanding the economic implication and investment efficiency of precision agriculture adoption is also crucial for both farmers and policymakers, as it can impact investment decisions, technology acceptance, and, ultimately, the nation’s capacity to sustain agricultural output.

This research is of a microeconomic nature and uses data from the Farm Accountancy Data Network (FADN) agricultural accounting. However, they cover large aggregates, such as economic size classes in individual countries; hence, the level of analysis can be considered quite general. The FADN database includes farms representative of the country, type of farming, and economic size classes, and financial data is collected according to a unified methodology. Therefore, despite their generality, the results obtained clearly indicate the differences in the profitability of using precision agriculture.

The aim of the study is to determine the economic viability of precision farming techniques, depending on the economic size of the farm, in selected countries of the European Union. An additional objective is to indicate the scale of public support for the development of this system.

2. Materials and Methods

2.1. Materials

Four countries were chosen (Poland, Germany, France, and Romania). These countries represent different regions in Europe, each of which has a unique mix of soil types, farming practices, and agricultural landscapes. Poland and Romania represent the new Member States, where the socialist system prevailed until the end of the 1980s. Germany and France (both in Western Europe) are the biggest European Union (EU) agriculture producers. They have similar agriculture and the same climatic zone. Poland, Germany, and France have similar crops e.g., cereals, sugar beets, potatoes, etc. Romania is different; it has specific agriculture and dual agriculture. In every country, there is a dominant presence of private farms.

In each country, farms representing the field crop type were studied. The study area was the entire country, with no division by region. Data from 2021 have been used.

2.2. Methodology

This study was primarily based on variables retrieved from the Farm Accountancy Data Network (FADN) database and Eurostat. The FADN database provided detailed information on farm economic parameters, such as family farm income, production costs, agricultural output, cost of crop protection and fertilizer used, and the relation between chemical cost and crop production.

The following FADN variables have been used:

• SE295—cost of fertilizers;
• SE300—cost of crop protection.

The statistical data from the Eurostat database were used in the research to provide data on the number of holdings and their areas according to economic size classes.

The main aim of applying precision agriculture on farms in this research is to reduce the use of chemical input. It was thought that there would be no detrimental effects on yield levels from a 20% reduction in plant protection products and a 15% reduction in mineral fertilization [29]. Therefore, for this research, a 20% reduction in plant protection and a 15% reduction in fertilizer usage were assumed after the implementation and use of precision techniques. Precision farming necessitates an additional expenditure for each agricultural farm, ranging from EUR 35,941 to 71,883 [30].

The net present value (NPV) and internal rate of return (IRR) methods were crucial factors to evaluate while analyzing investment efficiency from the standpoint of our study’s objectives.

The present value of cash inflows less the present value of cash outflows over time is known as net present value, or NPV [31]. In the present study, cash flows are indicated as the amount saved by the reduction in the cost of fertilizer and crop protection and are used in making investment decisions on the profitability of using precision techniques by the farmers. To obtain NPV, there is a need to estimate the time that the investment generates cash flows and the amount of future cash flows generated, which is the extra income generated from the reduction in the use of fertilizers and crop protection, as well as a discount rate (inflation rate). The inflation rate of 2021 for each country was used, which was the last year before the war on Ukraine. The formula of the NPV method is defined by:

$$\mathrm{NPV}={\sum }_{\mathrm{t}=0}^{\mathrm{n}}{\displaystyle \frac{\mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h} \mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}}{{(1+i)}^{\mathrm{t}}}}-\mathrm{I}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l} \mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}$$

NPV = net present value;

$i$ = discount rate (inflation rate, 2021);

t = time;

Cash flow = sum of the 20% reduction in the cost of crop protection and the 15% reduction in the cost of fertilizer for year t.

Initial investment = from the literature, the implementation of PA technologies requires an additional investment cost ranging from EUR 35,941 to EUR 71,883 [30]. The initial investment is a critical factor in determining the investment efficiency of PA technologies. Additionally, it serves as a fundamental value utilized in the calculation of the net present value (NPV) approach.

A positive NPV means the investment is profitable, and a negative NPV indicates a potential loss.

Agricultural tractors are classed as type 746 “Tractors” under the Classification of Fixed Assets (KŚT). The List of Fixed Assets specifies that they are subject to an annual depreciation rate of 14% [32]. This rate of depreciation is equivalent to 7 years of use (100/14) = 7.14.

Considering the objectives of the analysis, it was important to determine the value of public support to expand the land area under PA usage. The difference between the real value of the investment and the value of the investment at zero profitability is the necessary value of public support for single farms. The total value of public support was calculated by multiplying the total number of farms in each economic farm size by the value of public support needed per farm. These variables for this study were estimated based on this methodology using the formula:

VPS = R − V

where

VPS = value of Public Support (EUR/farm);

R = real value of investment (EUR/farm);

V = value of the investment at zero profitability, calculated as:

$$\mathrm{V}={\sum }_{\mathrm{t}=0}^{\mathrm{n}}{\displaystyle \frac{\mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h} \mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}}{{(1+i)}^{\mathrm{t}}}}$$

where

$i$ = discount rate (inflation rate, 2021);

t = time;

Cash flow = sum of the 20% reduction in the cost of crop protection and the 15% reduction in the cost of fertilizer.

The research scheme is presented in Figure 1.

3. Results and Discussion

3.1. Analysis of the Cost and Net Farm Returns

Table 1. Costs of chemical means of crop production and potential savings due to the use of precision agriculture for field crop type of farming in 2021.

Economic Size	Metrics	Poland	Germany	France	Romania
2000–<8000 EUR	Cost of fertilizers (EUR)	1022	-	-	657
	Cost of crop protection (EUR)	237	-	-	465
	20% reduction in crop protection (EUR)	47	-	-	93
	15% reduction in fertilizer (EUR)	153	-	-	99
	Cash flow (EUR)	201	-	-	192
8000–<25,000 EUR	Cost of fertilizers (EUR)	2327	-	2982	1838
	Cost of crop protection (EUR)	812	-	1192	1221
	20% reduction in crop protection (EUR)	162	-	239	244
	15% reduction in fertilizer (EUR)	349	-	447	276
	Cash flow (EUR)	512	-	686	520
25,000–<50,000 EUR	Cost of fertilizers (EUR)	6416	7418	6938	4828
	Cost of crop protection (EUR)	2656	3722	4468	3190
	20% reduction in crop protection (EUR)	531	744	894	638
	15% reduction in fertilizer (EUR)	962	1113	1041	724
	Cash flow (EUR)	1494	1857	1934	1362
50,000–<100,000 EUR	Cost of fertilizers (EUR)	13,168	14,755	13,152	10,530
	Cost of crop protection (EUR)	6156	8112	9092	6524
	20% reduction in crop protection (EUR)	1231	1622	1818	1305
	15% reduction in fertilizer (EUR)	1975	2213	1973	1580
	Cash flow (EUR)	3206	3836	3791	2884
100,000–<500,000 EUR	Cost of fertilizers (EUR)	36,977	36,597	30,731	35,640
	Cost of crop protection (EUR)	19,386	23,663	25,055	23,467
	20% reduction in crop protection (EUR)	3877	4733	5011	4693
	15% reduction in fertilizer (EUR)	5547	5490	4610	5346
	Cash flow (EUR)	9424	10,222	9621	1003
≥500,000 EUR	Cost of fertilizers (EUR)	252,845	129,043	62,585	148,899
	Cost of crop protection (EUR)	154,059	97,919	65,736	98,653
	20% reduction in crop protection (EUR)	30,812	19,584	13,147	19,731
	15% reduction in fertilizer (EUR)	37,927	19,357	9388	22,335
	Cash flow (EUR)	68,739	38,940	22,535	42,066

Source: own calculation based on FADN Database.

shows the relationship between chemical costs and crop production, the reduction in fertilizer and crop protection usage, and the cash flow in Poland, Germany, France, and Romania for the year 2021, segmented into different economic farm sizes for field crop types of farming. The table presents several important findings about how crop production is affected by chemical costs, as well as the possible advantages of cutting back on fertilizer and crop protection costs. This shows how much money farmers spend on chemical inputs to support crop development and protection. In all the economic size groups, implementing a 15% reduction in fertilizer usage and a 20% reduction in crop protection owing to PA results in significant savings irrespective of the country. The results indicate that the volume of chemical input cost savings depends on the economic size and associated scale of production rather than on the country. The volume of savings in each country increases with successive economic size classes.

The results of the research are in line with other studies that found that the use of PA causes a reduction in chemical usage. Colaço and Molin [33] studied the effects of variable application (VA) of inputs on orange plants vs. conventional uniform application (UA) fertilizations. Indicators of soil fertility, leaf nutritional status, and surface production maps were used by the researchers to determine the VA requirements (P and K). Their results indicated that an average of 39% less input was required per ha and per year.

Likewise, Aggelopoulou et al. [34] used a plantation’s production map to measure how much nitrogen each plant and orchard area absorbed from the soil in their investigation of variable nitrogen application for apple trees. The research showed that the VA of N saved up to 38% of N for comparable crop production. Compared to uniform fertilizer treatments, variable-rate fertilizer applications reduced the amount of nitrogen fertilizer used by 56% and 50% in 2012 and 2013, respectively [35]. Furthermore, up to 38% of expenditures may be avoided with precision agriculture; the majority of these savings will come from using less fertilizer. Farmers that adopt variable application techniques can save as much as 51% on phytosanitary pesticides and 46% on fertilizers [36]. In the study conducted by Tozer [37], it was found that there were lower variable costs as a result of the precision system’s reduced input utilization and reduced overlap of input, which resulted in a higher level of profitability compared to a more traditional method. In addition, Bucci et al. [38], in their study on measuring farm profitability after adopting precision agriculture technologies, stated that the entire net savings amount to 4.3% per hectare, with a capital cost of EUR 44.08/ha derived by taking into account the full investment in PA technologies and a net savings of EUR 33.47 per hectare. Their research revealed that the adoption of PA technologies led to a small but noticeable drop in the cost of production. They were able to conclude that even with the relatively small net savings of 4.3% per hectare, PA technologies’ cost-effectiveness increased farming enterprises’ overall profitability and sustainability. According to a study carried out by Bora et al. [39] in North Dakota, United States, it was discovered that 34% of farms utilizing GPS guidance systems reduced fuel consumption by 6.32% and machine time by 6.04%, saving 1647 L of gasoline (USD 1305) per farm. Additionally, 27% of farms utilizing auto steering systems lowered fuel consumption by 5.33% and machine time by 5.75%, saving 1866 L of gasoline (USD 1479) per farm. Both technologies indicated a good return on investment. Robertson et al. [40] conducted research on six grain farms in Australia. The farmers were able to recover their initial investment of USD 55,000 to USD 189,000 within a period of two to five years using PA technologies. The projected annual revenues range from USD 14 to USD 30 per hectare, whereas the capital investment per hectare ranges from USD 14 to USD 44. In addition, due to reduced overlap, a portion equivalent to 10% of the spraying budget was saved.

3.2. Analysis of the Investment Efficiency of Precision Agriculture (PA) with the Use of the Net Present Value (NPV) Method

The NPV method was used to evaluate the economic implications of adopting PA technologies. The table below (

Table 2. Discounted * cash flow in EUR.

Investment Cost		EUR 35,941				EUR 71,883
Metrics		Poland	Germany	France	Romania	Poland	Germany	France	Romania
2000–<8000 EUR	Year 0	35.9	-	-	35.9	71.9	-	-	71.9
	Year 1	0.2	-	-	0.2	0.2	-	-	0.2
	Year 2	0.2	-	-	0.2	0.2	-	-	0.2
	Year 3	0.2	-	-	0.2	0.2	-	-	0.2
	Year 4	0.2	-	-	0.2	0.2	-	-	0.2
	Year 5	0.2	-	-	0.1	0.2	-	-	0.1
	Year 6	0.1	-	-	0.1	0.1	-	-	0.1
	Year 7	0.1	-	-	0.1	0.1	-	-	0.1
	Sum	1.2	-	-	1.1	1.2	-	-	1.1
	IRR	−34.8	-	-	−34.8	−70.7	-	-	−70.8
8000–<25,000 EUR	Year 0	35.9	-	35.9	35.9	71.9	-	71.9	71.9
	Year 1	0.5	-	0.7	0.5	0.5	-	0.7	0.5
	Year 2	0.5	-	0.7	0.5	0.5	-	0.7	0.5
	Year 3	0.4	-	0.7	0.4	0.4	-	0.7	0.4
	Year 4	0.4	-	0.6	0.4	0.4	-	0.6	0.4
	Year 5	0.4	-	0.6	0.4	0.4	-	0.6	0.4
	Year 6	0.4	-	0.6	0.4	0.4	-	0.6	0.4
	Year 7	0.4	-	0.6	0.4	0.4	-	0.6	0.4
	Sum	3.0	-	4.5	3.0	3.0	-	4.5	3.0
	IRR	−33.0	-	−31.4	−32.9	−68.9	-	−67.4	−68.9
25,000–<50,000 EUR	Year 0	35.9	35.9	35.9	0.0	71.9	71.9	71.9	71.9
	Year 1	1.5	1.9	1.9	1.4	1.5	1.9	1.9	1.4
	Year 2	1.4	1.7	1.9	1.2	1.4	1.7	1.9	1.2
	Year 3	1.3	1.7	1.8	1.2	1.3	1.7	1.8	1.2
	Year 4	1.2	1.6	1.8	1.1	1.2	1.6	1.8	1.1
	Year 5	1.2	1.6	1.8	1.1	1.2	1.6	1.8	1.1
	Year 6	1.1	1.5	1.8	1.0	1.1	1.5	1.8	1.0
	Year 7	1.1	1.5	1.7	1.0	1.1	1.5	1.7	1.0
	Sum	8.7	11.6	12.7	7.9	8.7	11.6	12.7	7.9
	IRR	−27.3	−24.4	−23.2	−28.0	−63.2	−60.3	−59.1	−64.0
50,000–<100,000 EUR	Year 0	35.9	35.9	35.9	35.9	71.9	71.9	71.9	71.9
	Year 1	3.2	3.8	3.8	2.9	3.2	3.8	3.8	2.9
	Year 2	2.9	3.6	3.7	2.6	2.9	3.6	3.7	2.6
	Year 3	2.8	3.5	3.6	2.5	2.8	3.5	0.0	2.5
	Year 4	2.6	3.4	3.6	2.4	2.6	3.4	3.6	2.4
	Year 5	2.5	3.3	3.5	2.2	2.5	3.3	3.5	2.2
	Year 6	2.4	3.2	3.4	2.1	2.4	3.2	3.4	2.1
	Year 7	2.3	3.1	3.4	2.0	2.3	3.1	3.4	0.0
	Sum	18.6	23.9	25.0	16.8	18.6	23.9	25.0	16.8
	IRR	−17.3	−12.0	−11.0	−19.2	−53.2	−48.0	−46.9	−55.1
100,000–<500,000 EUR	Year 0	35.9	35.9	35.9	35.9	0.1	71.9	71.9	71.9
	Year 1	9.4	10.2	9.6	10.0	9.4	10.2	9.6	10.0
	Year 2	8.5	9.6	0.0	9.1	8.5	9.6	9.3	9.1
	Year 3	8.1	9.3	9.2	8.6	8.1	9.3	9.2	8.6
	Year 4	7.7	9.0	9.0	8.2	7.7	9.0	9.0	8.2
	Year 5	7.3	8.8	8.9	7.8	7.3	8.8	8.9	7.8
	Year 6	7.0	8.5	8.7	7.4	7.0	8.5	0.0	7.4
	Year 7	6.7	8.3	8.6	7.1	6.7	8.3	8.6	7.1
	Sum	54.8	63.8	63.4	58.4	54.8	63.8	63.4	58.4
	IRR	18.8	27.8	27.4	22.4	−17.1	−8.1	−8.5	0.0
≥500,000 EUR	Year 0	35.9	35.9	35.9	35.9	71.9	71.9	71.9	71.9
	Year 1	68.7	38.9	22.5	42.1	68.7	38.9	22.5	42.1
	Year 2	62.2	36.6	21.8	38.1	62.2	36.6	21.8	38.1
	Year 3	59.2	35.5	21.5	36.2	59.2	35.5	21.5	36.2
	Year 4	56.3	34.5	21.1	34.5	56.3	34.5	21.1	34.5
	Year 5	53.6	33.4	20.8	32.8	53.6	33.4	20.8	32.8
	Year 6	51.0	32.4	20.5	31.2	51.0	32.4	20.5	31.2
	Year 7	48.5	31.4	20.2	29.7	48.5	31.4	20.2	29.7
	Sum	399.6	242.9	148.5	244.6	399.6	242.9	148.5	244.6
	IRR	363.7	206.9	112.5	208.6	327.8	171.0	76.6	172.7

* Discounted cash flow is the equivalent benefit expected yearly from the use of PATs considering the inflation rate. Source: own calculation based on FADN Database.

) shows a comprehensive result of the analysis of investment efficiency for each country studied with the use of NPV. It was assumed that the additional cost of investment of about EUR 35,941–EUR 71,883 is needed to implement PA technologies. The calculation was performed for both the minimum investment cost of EUR 35,941 and the maximum of EUR 71,883 for each country analyzed. The initial investment cost was not discounted because it is in today’s money (year 0) at its present value.

For Table 2, the cash flow was discounted and deducted from the initial investment of EUR 35,941 and EUR 71,883 to obtain the NPV. The discounting was at an annual depreciation rate of 14%, as stated in the methodology, and this is equivalent to 7 years of use. A 5% discount rate (inflation rate for Poland) was used [41]. For Germany, a 3% discount rate was used [42]. The inflation rate of 1.6% was applied as the discount rate [43] for France and the inflation rate of 5% for Romania [44].

From Table 2, with an investment of EUR 35,941 only farms with an economic farm size of EUR 100,000 and above gave a positive return on investment for each country.

However, with a higher investment cost of EUR 71,883 in the use of precision technologies, only the economic farm size of EUR ≥500,000 was positive and profitable, as shown in Table 2. Therefore, with lower investment, more farms can use precision tools and be profitable. As evident from Table 2, the lower the economic farm sizes, the greater the loss (negative return on investment). It is difficult for smaller farms to use PA technologies because of the large investment cost. Therefore, before the adoption and implementation of precision technologies, some important factors to take into consideration are the farm sizes and the cost of investment. This correlates with the research conducted by Vecchio et al. [45], which examined the complex aspects influencing the likelihood of an Italian farmer’s adoption of new PA technologies. The authors discovered that the most technologically adept farmers shared comparable characteristics, such as having extensive agricultural fields that usually covered an area of 143 hectares. Also, numerous factors such as farm size, as well as others such as technologies utilized and the regional heterogeneity of soil conditions and yield response, impact how profitable PA can be. Another study also highlighted how crucial farm scale is for PA technologies’ acceptability and profitability. Large farms can earn greatly from the scale-related gains that come with implementing precision agriculture technologies (PATs), but smaller farms might meet problems because of the high costs and significant investment necessary. Therefore, when examining the viability and effectiveness of applying PA technologies, farm scale becomes a crucial element [38]. A study was also conducted evaluating the financial effects of utilizing precision agricultural technologies on cotton crops, and the research was conducted in a 3500 ha overall agricultural area. The result showed that farmers’ profitability was boosted by the PA system, which enhanced the operational income and profitability index. The positive net present value (NPV) was an improvement over the conventional method, according to the investment feasibility indicators [46]. Moreover, Shruthi et al. [47] discovered that the net present value (NPV) at a 12% discount rate after ten years was positive. This demonstrated that the technology investment was both financially and practically possible.

3.3. Share of Farms in Total Numbers of Holdings and Agricultural Areas in the Examined Countries That Can Implement Precision Agriculture

Table 3. Farm structure by economic size.

Economic Size	Total Holdings = 100				Total Agricultural Area of Farms = 100
	Poland	Germany	France	Romania	Poland	Germany	France	Romania
<2000 EUR	26.2	0.5	5.5	72.2	4.8	0.1	1.1	13.7
2000–<8000 EUR	37.5	15.2	8.5	21.4	16.4	1.9	1.4	16.4
8000–<25,000 EUR	20.3	22.7	13.7	4.5	20.3	5.6	3.2	11.1
25,000–<50,000 EUR	8.0	12.4	11.4	1.0	14.8	5.9	5.6	8.1
50,000–<100,000 EUR	4.8	12.5	15.4	0.5	15.2	8.9	13.3	8.9
100,000–<500,000 EUR	2.9	28.9	39.3	0.4	20.0	40.1	63.2	22.8
≥500,000 EUR	0.3	7.7	6.2	0.1	8.6	37.6	12.3	19.0

Source: own calculation based on Eurostat database.

reflects the number of farms in holdings and total agricultural areas in each economic farm size that can implement precision agriculture and be profitable in the examined counties. This table shows the share of farms in the total number of holdings and agricultural areas by economic class.

The result from Table 3 shows that with an investment of EUR 35,941, only 3.2% of farms and 28.6% of land in Poland can implement PA. However, only 0.3% of farms and 8.6% of land in Poland can implement precision agriculture with an investment of EUR 71,883. For Germany, with an investment of EUR 71,883, PA can be implemented successfully on only 7.7% of farms and 37.6% of the land, whereas with an investment of EUR 35,941, 36.6% of farms and 77.7% of the land in Germany can implement PA. Only 6.2% of farms and 12.3% of land in France can implement PA when EUR 71,883 is invested, but with an investment of EUR 35,941, 45.5% of total agricultural land owned by a farmer or farming entity managing 75.5% of all agricultural land in France can effectively implement PA. This means more farmers can use precision agriculture and be profitable in France compared to the other countries. In Romania, only very few farmers manage larger farm sizes, and over 70% of farmers manage small farm sizes. Therefore, with an investment of EUR 71,883, only 0.1% of agricultural entities managing 19% of land can implement PA. An investment of EUR 35,941 allows only 0.5% of holdings and 41.8% of land. Farmers should not jump to the conclusion that using precision technologies will lead to an increase in their profitability. This research has shown the total number of farms in each country studied that can implement precision agriculture and be profitable based on the calculation of the NPV and IRR. A study was also conducted by Dhoubhadel [48], who discovered that farmers should not adopt PA technologies in the hopes of enhancing farm profitability, even though they might be cost-effective investments and increase some operational efficiency. Lencsés et al. [49] found that younger farm owners and those with a farm size larger than 300 hectares were the two criteria that affected and determined Hungarian farmers’ usage of PA technology. Another research conducted in Germany showed that among German crop producers, farm size had a strong link with PA adoption; more notably, if farmers possess a substantial amount of arable land, then they are more inclined to adopt precision agricultural technologies, as indicated by Paustian and Theuvsen’s [50] study.

3.4. Need for Support for Precision Agriculture (PA) from Public Funds

Only for the largest farms (Table 3) is the use of precision farming technologies profitable. Nevertheless, public support in the form of investment subsidies can significantly increase the number of farms and the area where it can be applied. The required value of investment at zero profitability for every economic size that was not profitable was calculated. This was performed to determine how much is needed to support the farmers in various farm sizes to be able to use PA with the investment cost of EUR 35,941 and be profitable. Public support is not necessary for economic farm sizes of EUR 100,000 and above, with a minimum investment cost of EUR 35,941. Table 2 shows that the value of the discounted flows increases in successive economic size classes. According to the methodology used, this means that the gap between investment at zero profitability and the real value of investment is decreasing. It was, therefore, assumed that public support should only apply to farm sizes of EUR 50,000 to less than 100,000. In practice, it is more realistic and necessary to offer support only to this economic farm size. In this class, in every country value of support per farm is the smallest, so taking into account only these holdings is in line with the principle of rationality of public spending.

The value of investment at zero profitability in each economic farm size by the farmers, the value of support per farm (additional amount of money needed to reach the minimum required investment cost for PA usage), and the total value of public support needed to enable all farms in each economic size to implement PA are shown in

Table 4. Gap in the value of investment costs and the general value of public support needed to use precision agriculture (Economic size EUR 50,000–<100,000).

Specification		Poland	Germany	France	Romania
The real value of investment (EUR/farm)		35,941	35,941	35,941	35,941
The value of the investment at 0 profitability (EUR/farm)		18,642	23,923	24,978	16,769
Value of public support (EUR/farm)—difference between the gap real investment costs and 0 profitability		17,299	12,019	10,963	19,172
No of farms (EUR 50,000–<100,000)		62,610	32,860	60,340	12,910
Area of agricultural land	ha	2,237,890	1,480,940	3,637,530	1,126,230
	Total area of agricultural land = 100	15.2	8.9	13.3	8.9
The total value of public support (EUR)		1,083,112,135	394,931,390	661,525,072	247,510,940

Source: own calculation based on Eurostat and FADN database.

. The amount farmers can invest to use precision agriculture and be at zero profitability decreases as the economic farm size increases, meaning more support is needed for smaller farm sizes for the four countries, as shown in Table 4.

The result from Table 4 shows that 62,610 farms in Poland are classified as having an economic size between EUR 50,000 and <100,000. This equates to a national investment of about EUR 1 billion. Therefore, 15% more agricultural land (2,237,890 hectares) in Poland will be able to implement PA with public support, meaning close to half of the total agricultural area (43.8%) will be covered by precision agriculture. In Germany, there are 32,860 farms with an economic size of EUR 50,000–<100,000, which translates into an investment of about EUR 395 million on a national scale for all farms in this economic size. This farm size covers 8.9% (1,480,940 ha) of the total agricultural area. This indicates that with support, 86.6% of the total agricultural area will be covered by precision agriculture, which is a significant portion. Similarly, in France, farmers in the economic size range of EUR 50,000–<100,000 require total support of EUR 10,963 to make use of PA, as they may only invest EUR 24,978 without incurring a loss per farm. France has a total of 60,340 farms in this particular economic farm size, which amounts to a total investment of EUR 662 million on a national scale for all farms in this category. This farm size also covers 13.3% (3,637,530 ha) of the total agricultural area. Therefore, if all farms of at least EUR 50,000 implemented precision agriculture, it would account for more than 88% of France’s total agricultural land. Furthermore, to facilitate the use of PA by farmers in Romania with economic sizes ranging from EUR 50,000 to <100,000, public support of EUR 19,172 per farm is necessary. This amounts to a total national investment of EUR 248 million for all farms falling within these economic sizes. Also, 8.9% (1,126,230) of the entire agricultural area corresponds to this economic size. This means that with the subsidies, precision agriculture will cover over half (50.7%) of Romania’s agricultural land.

When the EU helps and supports these farms by subsidizing PATs, then more than half of the total agricultural area can be covered by PATs, which serves as an opportunity to meet the goal of the European Union Green Deal of minimizing emissions into the environment, which causes climate change.

According to World Economic Forum [51] predictions, precision agriculture could cut greenhouse gas emissions by 5–10% by 2030 if the technology is installed on 15–25% of farms. In addition, a report by Soto et al. (2019) [23] states that precision agriculture lowers agricultural greenhouse gas emissions in Europe by 1.5% to 2%. The key method used for this is variable rate application, which helps to decrease N₂O emissions by providing plants with the exact amount of fertilizer they need [52].

Therefore, there is a need for support for precision agriculture from public funds. With investment support (subsidies) from public funds, many more farms will be able to use PATs, even in Poland and Romania.

In the years 2023–2027, the Common Agriculture Policy of the EU is focused on achieving environmental and climate goals and digitizing agriculture. Therefore, to achieve goals such as reducing the use of fertilizers and pesticides, support for precision agriculture was also introduced. Among the surveyed countries, Germany has introduced an eco-scheme dedicated to digital farming technology [53].

Figure 2 below shows the share of farms that can use PA and be profitable with and without public support for the minimum value of investment of EUR 35,941. The figure indicates clearly that more farms (total holdings and agricultural areas) in Poland, Germany, France, and Romania will be covered by PA with subsidies. The importance of public support cannot be over-emphasized because there is a significant increase in the share of farms in the total number of farms that can implement PA with public support (subsidies).

4. Conclusions

This study was primarily based on variables retrieved from the Farm Accountancy Data Network (FADN) database and Eurostat. The FADN database provided detailed information on farm economic parameters, such as family farm income, production costs, agricultural output, cost of crop protection and fertilizer used, and the relation between chemical cost and crop production. The type of farming used is field crops only for the year 2021. The statistical data from the Eurostat database were used in the research to provide data on the number of holdings and their areas according to economic size classes.

The main aim of applying precision agriculture on farms in this research is to reduce the use of chemical inputs. Reducing the costs of fertilization and plant protection was the basis for examining the profitability of precision farming investments using the NPV method. Due to the high value of precision agriculture equipment investment, larger farms profit more from the reduction in chemical usage in crop production than smaller farms. The amount saved rises with the economic farm size. Even with variances in total output and chemical cost across all economic size groups, the savings from reductions were significant.

From the results of the profitability analysis of investment efficiency, it can be concluded that regardless of the country, larger farms had a positive net present value (NPV). On the other hand, smaller farms with an economic size of less than EUR 100,000 demonstrated a negative NPV. Consequently, farm size is a key aspect when considering the profitability and cost efficiency of adopting PA technologies. The scale-related gains that come with deploying PA technologies can be particularly beneficial for large farms, but the significant investment expenditures may cause issues for smaller farms.

The profitability of using precision agriculture depends on the economic size class and is independent of the country. The difference is that in some countries, there are more farms capable of implementing precision agriculture, and in others, there are fewer.

Moreover, based on the information gathered from the literature, it is evident that PA contributes to the optimization of chemical use, thereby minimizing the impact on the environment. As an indirect benefit of sustainable agriculture, PA has enormous potential to reduce environmental effects by minimizing chemical usage without compromising productivity. However, the decision to use this system depends on the farm’s profitability. This study shows that the cost and profitability of employing PA technologies make them more viable for larger farm sizes. Therefore, it does not follow that all farms should adopt these technologies in the hopes of making a profit. Of course, smaller farms can also benefit from precision farming technology, but only on the condition of cooperation, for example, by joint purchase of equipment [17].

Finally, to avoid financial losses as well as minimize environmental damage, farmers should have organizational support and public support in the form of subsidies to enable more agricultural areas to use PA. The NPV was equated to zero to obtain the amount of investment needed for farmers in each economic size to break even (zero profitability). From this, the additional amount needed to reach the minimum threshold for PA technology usage was calculated, which is the value of public support (subsidy) to be provided to the farmers. It is also more realistic and necessary to offer support only to the economic farm size of EUR 50,000–<100,000, as others are too small for public support to be justified. For all farms in this economic size, the total value of the public subsidy needed is EUR 1,083,112,135 in Poland, EUR 394,931,390 in Germany, EUR 661,525,072 in France, and EUR 247,510,940 in Romania. Additionally, there is a chance to achieve the European Union Green Deal’s target of reducing emissions into the atmosphere that contribute to climate change. Therefore, public support is essential to assist precision agriculture usage.