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Article

Research on the Cattle Farm Endowments from the Climate Change Adapting Perspective

by
Steliana Rodino
,
Rodica Chetroiu
*,
Diana Maria Ilie
,
Ancuța Marin
,
Vili Dragomir
,
Alexandra Marina Manolache
and
Petruța Antoneta Turek-Rahoveanu
Research Institute for Agriculture Economy and Rural Development, 011464 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(13), 1339; https://doi.org/10.3390/agriculture15131339
Submission received: 7 May 2025 / Revised: 16 June 2025 / Accepted: 18 June 2025 / Published: 22 June 2025
(This article belongs to the Special Issue Strategies for Resilient and Sustainable Agri-Food Systems)

Abstract

All agricultural sectors are under the influence of environmental factors, which act alongside the flow of activities. In the context of efforts to adapt to the effects of climate change, the purpose of this work is to evaluate the level of endowment of cattle farms with equipment and facilities involved in ensuring an adequate microclimate, in the efficient management and administration of feed and water for animals. This research is based on the processing of data from 83 cattle farms in Romania, of different sizes and located in different landforms, collected through a quantitative survey, through a questionnaire. This paper indicates that the existing level of these types of facilities is insufficient and highlights the importance of investments in equipment necessary to adapt to the effects of climate change, especially for smaller farms, but also for large farms. These types of investment refer to technologies for air cooling, microclimate control, feed management, and automation. This paper highlights the need to increase the technological level in Romanian cattle farms, to adapt to climate change challenges. The promotion of appropriate technologies must be included in an integrated strategy for the equipping and modernization of cattle farms, for an effective diminution of climate risks. This means adopting a systemic approach that includes investments in infrastructure, innovation, and support for farmers.

1. Introduction

The effects of climate change are a key aspect impacting agricultural production, agricultural practices, and technologies [1,2]. In Romania, the effects of climate change are manifested by increasing average annual temperatures, reducing water resources, and intensifying extreme weather events. In this context, a long-term temperature increase of 2–3 °C is expected, accompanied by frequent and severe droughts, especially in lowland areas. Torrential rains, hail, storms, and unusual frosts will also become more frequent, with agriculture being one of the most vulnerable activities, with reduced crop and animal production, increased disease frequency, and increased production costs.
In recent years, the energy required to maintain a suitable microclimate for animal husbandry has been increasing [3]. Recent advances in digital agriculture, with various tools and techniques, aim to mitigate climate risks and food insecurity, and the adoption of control and automation platforms aims primarily to increase resource productivity [4,5]. Innovative technologies are expected to present several challenges for conventional agriculture [6]. Modern technologies are increasingly being implemented in agricultural activities, with solutions based on information and communication technology [7]. The integration of climate change adaptation technologies allows for the provision of useful information and warnings to farmers regarding environmental conditions that are not suitable for animal welfare [8].
Farmers’ adaptation strategies seem to not always succeed in mitigating the impact of climate change, as evidenced by crop and livestock losses [9]. The magnitude of climate change events requires strategies for farmers to adapt and manage their impacts [10,11,12]. The economic, social, natural, physical, and human capital are amplified by the occurrence of repeated droughts, and these challenges, associated with low levels of technical endowment, can undermine the adaptive capacity and amplify associated economic losses [13]. The use of new technologies can ensure better food traceability and more efficient use of natural resources and inputs [14]. Smart farming technologies, as essential solutions, also address the labor shortage [15].
Due to the growth of the world population, for which it is necessary to ensure food resources, positive financial results from agriculture are gaining particular importance [16]. Financial and profitability concerns, as well as farmers’ perceptions of climate risks, influence their attitudes and intentions to change current systems and adopt sustainable opportunities [17]. The adoption of advanced technologies in livestock production can contribute to increasing its productivity and sustainability [18,19]. Modern technologies and automation can allow farmers to optimize technological processes, reduce feed waste, improve growth rates and production levels, increase animal welfare, and intervene quickly, reducing losses. These technologies also contribute to more environmentally friendly animal husbandry. These technologies address optimizing resource management and increasing productivity and environmental sustainability, thus allowing farmers to use inputs based on real-time data and specific activity requirements [20]. The implementation and integration of smart technologies in animal husbandry allow farmers to maximize the cost–benefit ratio and permanently monitor the main animal performance indicators [21]. The modern technologies, including precision agriculture, penetrate agriculture domains, where they can influence the quantity and quality of agricultural production [22].
Dairy cows are sensitive to heat stress, due to their high metabolic rate for milk production, so heat stress can be avoided by using microclimate control systems. As a result, a whole series of environmental control systems have been developed for dairy farms [23]. Implementations of intelligent digital microclimate control systems should be viewed in the light of ensuring the quality of the microclimate, imposed by the requirements of modern livestock production technologies. The quality of the microclimate is improved by changing the power of heating or cooling and ventilation equipment, based on measurements of air temperature, humidity, and carbon dioxide content.
Farm microclimate systems include a computer, control and measurement devices, an interface, and executive devices. Tasks are transmitted from the computer to the controllers; for values of temperature, humidity, and indoor air pollution, the controllers are in communication with devices measuring ventilation, heating, or cooling air [24]. These systems ensure that environmental parameters are maintained within optimal limits, supporting both productivity and animal welfare. In this context, it is essential to recognize the ability of animals to interact with the environment and to make choices that reflect their needs and preferences. Previous studies have demonstrated the ability of animals to consciously self-select their behaviors when interacting with the characteristics of their environment. When they achieve their selected goals, animals have positive reactions, which increase their level of well-being. If external characteristics are not favorable, animals can experience negative, unpleasant, and demotivating effects, which compromise well-being [25,26]. Therefore, maintaining a stable and comfortable microclimate is not only a technical requirement, but also a behavioral and welfare imperative.
This concern becomes more relevant in the context of climate change. To reduce thermal stress in animal housing, adaptation measures such as intake air cooling systems, insulation, spatial orientation of housing and modification of the indoor climate are used. Devices that cool the air have shown the highest performance. Selecting appropriate adaptation measures, in addition to animal welfare, can be seen as a contribution to strengthening the economic resilience of farmers [27].
Due to the need for efficiency and sustainability in the livestock sector, research has been carried out to manage and quantify the progress achievable when introducing innovative monitoring in cattle breeding by assessing microclimate, air quality, energy, and water use on farms. These aspects aim to improve resource efficiency, competitiveness, digitalization, and sustainable production [28].
In the current climate context, key changes in nutritional approaches for livestock farms are needed, which can contribute to the formation of more sustainable farming systems [29]. Animal welfare policies could be a useful tool in the climate transition, and from this perspective, the application of welfare measures could lead to a deepening of the issues related to the sustainability of agriculture [30,31].
The purpose of this work is to evaluate the level of equipment and investments in cattle farms which can be involved in ensuring a technological environment conducive to reducing the effects of climate change on production activities, from the perspective of ensuring an appropriate microclimate for animals, adequate management of feed stocks, automatic distribution of feed and water, etc. The significance of this approach lies in the fact that it provides an overview of the degree of technological equipment in cattle farms related to technical and economic adaptation measures to new environmental and climate conditions.
The cognitive (scientific) objective of this research is to analyze the level of technological equipment of cattle farms in Romania, in the context of adapting to climate change and generating new knowledge, with the aim of highlighting the role of these equipment in ensuring microclimate control for animals, managing food and water, and streamlining production activities.
The utilitarian (practical) objective is to substantiate the need for investments in modern equipment and technologies for cattle farms, in order to effectively adapt to climate change, increase animal welfare, improve production sustainability, and use research results in the development of support strategies and policies for farms.
The research problem is the discrepancy between the need to adapt to new climatic conditions and the current state of technological equipment of cattle farms in Romania, which limits their ability to adapt effectively to the effects of climate change on production activities, especially considering that this issue has not been sufficiently studied up to now.
The research questions that formed the basis of the study are as follows:
RQ1: What are the effects of climate change that cattle farms have faced and at what level did they occur?
RQ2: What facilities, related to ensuring a technological environment capable of reducing the effects of climate change on production activities, are available on farms?
RQ3: How does the level of endowment with different types of equipment differ depending on criteria such as the size of the farm, or the landform in which it is located?
The research hypotheses on which this study started were the following:
H1. 
Environmental and climate changes have affected both the level of the most important input in cattle breeding, namely, feed, as well as the level of production obtained;
H2. 
In their vast majority, cattle farms do not have a technological level corresponding to ensuring microclimate and technological parameters that would allow easier adaptation to climate changes.
This paper highlights the need for investments in equipment involved in ensuring adaptation to the effects of climate change, to a greater extent in medium-sized farms, compared to large ones. This research shows that integration of advanced technologies in the livestock sector not just as a trend, but as long-term strategic necessity, can allow maximizing yields, protecting the environment, increasing animal welfare, and contributing to a more sustainable and efficient production in the future.
The general outline of the action plan for this research study undertaken was as follows:
-
Establishing the objectives and research issues;
-
Developing the questions in a questionnaire;
-
Sending the questionnaire to the farmers;
-
Processing the data and information received from the farmers;
-
Conducting data analyses and formulating the results and discussions;
-
Developing research conclusions.

2. Materials and Methods

2.1. Technical Design

The research in this paper is based on data from 83 cattle farms across Romania. The data were obtained at the beginning of the year 2025, by applying a method of collecting information through a quantitative survey. This study used a structured research method, based on a self-administered questionnaire with open-ended questions, completed independently by respondents, without the intervention of an interviewer. For the application of the questionnaires, the support of the county departments for agriculture was called upon to send them to cattle farms. It should be noted that the expectations regarding the number of farms that could be the subject of this study were higher than the number of farms that have answered. Thus, this was seen as a limitation of the present study. However, considering the number of respondents and the responses received, it was appreciated that this study could be carried out with the data available.
By development region, 19 farms (22.89%) were from the North-West Region, 1 (1.20%) was from the West Region, 24 farms (28.92%) were from the Central Region, 2 farms (2.41%) were from the North-East Region, 33 farms (39.76%) were from the South-East Region, and 4 farms (4.82%) were from the South Region. The farms that participated in this study are spread throughout Romania, thus ensuring a wide variety and, at the same time, a specificity of the conditions of the areas they are located in. These may also refer to varied technological conditions, different breeds, different feed structures, etc. (Figure 1).
Through the questionnaire, 22 questions were addressed to cattle farmers. So, in addition to data on the administrative and geographical location of the farms, the questions referred to the following aspects:
-
If the farms have been affected by climate change, what effects have been manifested in the farm’s production activity and to what extent;
-
If the farm shelters provide ventilation, whether they have air cooling systems;
-
If they are equipped with equipment for controlling microclimate parameters in the shelters;
-
If they have means of production and storage for animal feed;
-
If they have automatic feeding and watering for animals.
The survey was pre-tested on a small sample of respondents to assure that the questions are understandable to respondents and whether any changes need to be made to their content so that there are no problems with farmers completing the survey. The questions in the questionnaire were formulated in a clear manner, with as simple a language as possible, aiming for a complete understanding by the farmers, regardless of their level of professional training. The questions were open-ended, without closed answer options, such as “yes”, “no”, or “I don’t know”, but each respondent had the freedom to express themselves openly, in their own words, describing the actual situation, considering that every piece of information or detail is important for a better understanding of the answers received.”

2.2. Data Analysis Methods

The degree of endowment of farms with different types of equipment was calculated as the share of farmers’ affirmative answers to the question regarding the existence of that equipment on the farm, out of the total number of respective answers.
The data were interpreted using SPSS quantitative analysis software (SPSS Statistics 20), and the chi-square test was used to capture and present the main characteristics of the farms, as well as the technological level corresponding to ensuring microclimate in shelters and technological parameters.
The variables used in the calculations were the following:
-
Independent variables (predictors): location of the farms according to the landforms, size of the farms;
-
Dependent variables (outcomes): different farm equipment.
The chi-square test was used to determine whether there was a significant association between these variables. The calculated chi-square was compared with a critical value, obtained depending on the degrees of freedom and the level of significance (usually 0.05). For example, it was analyzed whether the farm size influenced the farm facilities.

3. Results and Discussion

3.1. Distribution of Farms

The number of cattle on the farms studied amounted to 10,236 heads, which are exploited in 83 cattle farms. Analyzing the distribution of the farms participating in the study according to the landforms in which they were located, it was found that the majority were located in hilly areas, representing 59.04% of the total, while farms in plain areas represent 40.96%, but the number of animals was higher in farms in plain areas, where 5872 cattle (57.37%) of the total animals were found, compared to 4364 cattle (42.63%) in hilly and plateau areas. This denotes a larger size of farms in the plain, probably due to the more favorable infrastructure, land characteristics, and more intensive systems. In comparison, in hilly and plateau areas, there may be environmental and technical constraints (Table 1).
The climate in the hilly areas of Romania is of the temperate-continental type, with average annual temperatures between 7 °C and 10 °C, with higher precipitation than in the plains, even over 700 mm/year, with higher relative humidity, with summers being hot, but less hot than in the lowlands. In comparison, the climate in the plains is of the transitional continental type, with thermal contrast and humidity deficit, with average annual temperatures between 10 °C and 11.5 °C, low precipitation, between 400 and 600 mm/year, with frequent periods of drought, very hot and often dry summers, with temperatures frequently above 35 °C, and frequent and intense winds, accentuating evapotranspiration. These significantly influence agricultural activities and the need for microclimate technologies, especially in animal husbandry.
By farm size, determined using the standard output coefficients SO used to calculate the economic size of an agricultural holding, 84.34% were medium farms, under 200 heads, and 15.66% were large farms, over 200 heads. In the case of medium farms, 61.43% were in the hilly area and 38.57% in the plain area. Large farms represented 15.66% of the total farms and were better represented in the plain area, where they were in proportion to 53.85%, the remaining 46.15% being in the hilly and plateau areas. To determine the landform in which the farms were located, the statement that the farmers gave in the questionnaire was taken into account, where a direct question was asked regarding this criterion.
In Figure 2, the distribution of farms within this study is graphically illustrated.
Descriptive statistical indicators of the sample of farms in this study indicated an average farm size of 123.3 heads, with a minimum of 10 heads and a maximum of 1225 heads. The median was 59, and the standard deviation was 202.3 heads, indicating that there were several size classes, a fact illustrated by the clustering of farm size in this study (Figure 3).
Thus, 47% of farms were in the 10–50 head class, 26.5% in the 51–100 head class, 12.1% in the 101–200 head class, 9.6% in the 201–499 head class, and only 4.8% in the over 500 head class.

3.2. Data Analysis

3.2.1. Reported Effects of Climate Changes

Following the questions addressed to farmers regarding their knowledge on the effects of climate change on their activity, it was found that most respondents (95.18%) were aware of these effects, while only a small number—4.82% (four respondents)—stated that they did not know them. This result indicated that, for the most part, farmers are informed about the impact of climate change. The negative responses were attributed more to the terminology associated with climate change phenomena than to their lack of knowledge of the phenomena being manifested.
The farmers surveyed confirmed the significant impact of climate change on their activity, all reporting at least one climatic phenomenon felt in the area where they carry out their activity. The most frequently reported phenomenon was drought, felt in several forms, with severe consequences on agricultural production and animal husbandry. According to the respondents’ reports, this was accompanied by “extreme temperatures”, “scorching and heatwaves”, “hail and uneven precipitation”, “reduced pasture quality”, and “reduced water quantity”. “Strong winds”, “soil erosion”, and “pest infestations”, which were exacerbated by drought, were also reported. All of these constitute answers to research question RQ1.
Most farmers reported a significant drop in milk production, with fluctuations ranging between 15% and 50%. This was mainly driven by drought and high temperatures, which affected both the quantity and quality of feed and the welfare of the animals. The decline in pastures and the lack of feed had a direct impact on milk production, necessitating the supplementation of concentrated feed, which, according to the farmers’ responses, generated an increase in expenses by 35–40% in some farms. The problems were not only at the production level, but also from the affected animal reproduction. Farmers reported a decrease in birth rates and behavioral changes caused by heat stress and insufficient feed, which led to a reduction in herds in some farms by up to 40% (also, these were answers for RQ1).
There were concerns in previous research related to ensuring an appropriate microclimate in bovine shelters, as they confirmed that the share of the impact of microclimate on animal productivity is 25–30% [32,33]. Also, a stable microclimate is an important factor in reducing calf mortality by 20%, which influences the level of profitability [34]. Other studies showed that animal productivity is determined 10–30% by microclimate, and failure to meet optimal limits can lead to a reduction in milk production by 10–20% and an increase in mortality in young animals by 5–40% [35].
Another major problem was the decrease in feed production, with maize, hay, and alfalfa crops being severely affected, with losses up to 80% in some areas (RQ1 answer). To compensate for the deficit, many farms have had to purchase additional feed, which has led to high costs.
All these problems reported by farmers confirm the statements in the research hypothesis H1 of this paper, that climate changes have affected both the level of the most important input in cattle breeding, which is feed, as well as the level of production obtained.
In addition to production problems, farmers have reported a deterioration in animal health, manifested by the appearance of diseases, an increase in the number of parasites, and a reduced growth rate in calves and bulls. Heat stress and insufficient feed have also led to higher mortality, especially during the calving period.
Another challenge has been the decrease in water resources, which has generated additional costs to ensure the consumption needs of animals. All these effects have put significant financial pressure on farmers, reducing incomes and increasing operating expenses (RQ1 answer).
All of these demonstrate that climate change is directly affecting the farms, which require the adaptation of resource management methods and the implementation of solutions to reduce the impact of climate change on agricultural production. Therefore, the consequences of climate change have been felt in a complex way, influencing production, animal health and costs. The exponential increase in costs has endangered the sustainability of many farms, forcing farmers to adopt urgent measures to maintain their activity in the face of extreme conditions.

3.2.2. Technological Equipment

The analysis of technological equipment to ensure the microclimate and the technological parameters necessary to adapt to climate change showed that there was almost equality between farmers who have a ventilation system in animal shelters (50.6%) and those who do not benefit from them (49.4%) (Table 2).
However, air cooling systems were in very few farms (12.05%), which indicates a vulnerability to extreme temperatures. Insulation of shelters is ensured in 46.99% of farms, while 53.01% do not have such an improvement, which can affect the comfort and well-being of animals. Regarding the monitoring of microclimate parameters, only 30.12% of farms were equipped with a control system, while 69.98% operate without such technology. This situation shows that most farms depend on traditional methods to maintain shelter conditions.
Automation of feeding and watering processes is still low. Although 62.24% of farms have feed production lines, only 42.17% have spaces for their storage, these being mainly halls and warehouses. Automatic feeding is implemented in only 28.92% of farms, and automatic watering is used in 44.58%, indicating a significant dependence on manual methods. Thus, it results that although some farms have started to adopt modern technologies, the majority still operate with traditional systems. The lack of facilities for air conditioning, feeding, and watering can affect both productivity and animal welfare, which suggests the need for investments in farm modernization.
Ventilation Systems
Adequate ventilation is essential to guarantee animal welfare and ensure efficient and sustainable production [36,37]. The main advantage of naturally ventilated shelters is energy savings, but this system is particularly vulnerable to climate change [38,39]. Ventilation can significantly reduce the concentration of harmful gases in animal shelters [40]. The analysis of the presence of ventilation systems in animal shelters highlights that large farms are considerably better equipped in this regard. Thus, 83.33% of large farms have ventilation provided, compared to 45.07% of medium farms, where the lack of an adequate system can affect animal health and productivity (Figure 4).
As for the landform criteria, the differences in this are minor, approximately half of the farms located in the plains, as well as those in hilly areas, have ventilation provided.
Of the farms that do not benefit from a ventilation system, the majority mention that they provide natural ventilation for the animals by opening windows and doors.
In calculating the Chi-square test for the correlation of ventilation with different analyzed variables, it can be observed that there are significant differences between the calculated Chi values and the theoretical ones (Table 3).
In calculating the Chi-square test for the correlation of ventilation with different analyzed variables, it is observed that there are significant differences between the calculated and theoretic Chi-square values. The test analysis shows that only the size of the farm significantly influences the existence of the ventilation system in farms. The value of 0.014 confirms the existence of a significant correlation, and the Chi-square calculated value (6.01) is higher than the theoretic Chi-square value (3.84) for the significance threshold of 0.05.
In contrast, the landform (Chi-square calculated 0.01) does not show significant correlations, with the significance value exceeding the threshold of 0.05. This suggests that ventilation depends more on economic and management resources than on the landform of the area where the farm is located, because the technologies and climatic conditions are relatively similar, and the farm infrastructure plays an important role.
Air Cooling Systems
The analysis of the equipment of cattle shelters with air cooling systems highlights the fact that large farms are better equipped in this respect, 38.46% of them were equipped with air cooling systems, compared to only 7.14% of medium-sized farms. The analysis of the distribution according to the landforms of the area shows that 17.65% of farms located in plain regions were equipped with cooling systems, compared to only 8.16% of those located in hilly areas (Figure 5).
This difference can be explained by the more moderate temperatures in hilly regions, where additional cooling is not always necessary, compared to natural cooling, because by opening windows and doors a more favorable climate can be created for the animals.
The Chi-square test analysis shows that farm size significantly influences the existence of a cooling system in the shelters. The value of 0.001 confirms the existence of a significant correlation, and the calculated Chi value (11.61) is higher than the theoretical value (10.83) (Table 3).
The results obtained regarding the influence that farm size has on the equipping of farms with ventilation and cooling systems are also supported by studies [36,41] that emphasize the importance of adequate ventilation and cooling systems in animal shelters and their effectiveness in reducing heat stress for cows [42]; so, efforts are needed from farmers to limit temperature increases [43].
Shelter Insulation
Raising cattle in temperate and warm areas represents a challenge for farmers, from the perspective of designing shelters that are adapted to current environmental conditions, especially with high temperatures in summer and low temperatures in winter [44]. The design and construction of shelters must consider the microclimate and implement management perspectives to reduce animal stress, minimize labor, maximize work efficiency, and optimize production performance [45,46].
The insulation of shelters is an essential factor in optimizing the microclimate, having a direct impact on reducing thermal stress, making energy consumption more efficient, and improving cattle productivity. Previous research has highlighted concerns about preventing moisture permeability of building elements through thermal renovation [47,48]. According to the study conducted by Borshch et al. (2021) [49], the use of thermal insulation materials, such as polycarbonate, contributes to maintaining an adequate thermal environment, reducing energy losses, and optimizing feed conversion. Thus, insulation not only protects animals during cold periods, but also supports efficient farm management, ensuring optimal use of resources and increasing milk production efficiency.
The data collected in this study highlight the fact that only 46.99% of the farms participating in the questionnaire have complete insulation of shelters, specifying the use of specific solutions, such as sandwich panels or shade tarpaulins, while a significant percentage of respondents do not have insulation, considering it non-essential in the specific context of their farms (Figure 6).
Analyzing the distribution of insulation according to farm size, it was observed that large farms registered a higher percentage of insulation of shelters, compared to medium-sized farms. This trend can be explained by the easier access of large farms to financial resources, as well as an increased awareness of the impact of microclimate on livestock production.
Regarding the location of farms, no significant differences were observed between the plain regions (46.94%) and the hill regions (46.99%) in terms of ensuring insulation of shelters.
The results of the Chi-square test confirm that there is no significant correlation between farm size or landform and the presence of insulation in shelters. Therefore, it can be deduced that other factors, such as the availability of financial resources and the degree of awareness of the importance of an optimal microclimate, may influence the decision to insulate shelters to a greater extent (Table 3).
Microclimate Control Systems
In the current climatic conditions, controlling the microclimate parameters in which cattle are raised is one of the challenges of the sector, with regard to the digital automation of production processes [34]. Microclimate measurement and control systems in shelters ensure the monitoring of minimum microclimate standards for animal welfare in farms, automatically measuring CO2, ammonia concentrations, temperature, and humidity in the animal housing space.
Managing and maintaining adequate environmental conditions in animal shelters is necessary to create optimal thermal comfort and to protect them from climatic stress. Maintaining an adequate temperature is necessary for the health and productivity of animals. Controlling humidity levels is necessary to prevent respiratory problems, and ventilation ensures adequate air flow to eliminate excess heat, humidity, and harmful gases (ammonia, etc.).
Of all participants in this study, 32.12% considered that they ensure control of these parameters, but the vast majority do not. Depending on the size of the farm, it is observed that larger farms are better equipped (61.54% of them control it), and in terms of landforms, it is found that farms in plain areas have slightly better control of the microclimate than those located in hilly areas. From this, the need for investments and strategies to improve microclimate conditions can be seen, especially in medium-sized farms (Figure 7).
Analyzing the influence of farm size or landform on the probability of equipping with microclimate control systems, by applying the chi-square test, it was found that there is a significant association only between variables of size and control systems. Since the calculated Chi-square (5.30) is higher than the theoretical Chi-square (3.84), for a significance threshold of 0.05 (degrees of freedom = 1), the result is statistically significant (Table 3).
Therefore, farm size is an important factor in ensuring microclimate control mechanisms, suggesting that larger farms have more advanced resources and technologies for their management. In response to the demand for dairy products, the size of milk production has changed in recent years, with a trend towards larger companies, which has led to an increase in interest in agricultural automation [50]. The landform where the farm was located (plain or hill) did not significantly influence this variable, with no major differences between farms located in these areas from this point of view.
It is estimated that increased heat stress in farm animals can affect food security, and from this perspective, climate change must be a concern for livestock production. Developing appropriate strategies to support livestock production in this context gains importance. Adaptation of shelters is a cost-effective approach to reducing the magnitude of climate change. Adaptation to climate change can also be achieved by appropriately designing shelters, oriented east–west, as this direction minimizes the possibility of direct light entering the interior, the use of fans and other cooling devices, as well as the use of appropriate roofing materials. A properly designed roof reduces direct and indirect sunlight, as well as rainwater. The design of the shelter must also meet the needs of the respective agro-ecological areas [51].
Feed Production and Storing Facilities
The analysis of data on the endowment of feed production means on the farm highlights the fact that most farms have such facilities. Large farms have the highest percentage of production line provision, 84.62%, compared to medium-sized farms (55.71%) (Figure 8).
These data suggest that large farms are better equipped, due to financial resources and higher production volume. Previous research has been conducted comparing the low intensity of digitalization for small farms with the higher intensity of digitalization for large farms [52]. From landform point of view, farms located in the plain have a percentage of 61.76% with feed production lines, and those in the hills, 59.18%, there being a small difference in this regard.
Regarding the existence of silos for storing feed, only 42.17% of farms have such facilities, which highlights a significant deficit in feed storage infrastructure. Large farms have a significantly higher percentage of silos (76.92%), compared to medium-sized farms, which have only 35.71%. Significant differences depending on the landforms are evident, since in the plains only 26.47% of farms have silos, while in the hilly areas, this percentage is much higher (53.06%) (Figure 9).
These results indicate a greater concern for feed storage in hilly areas, where seasonal feed variability may require better resource management. Conversely, in the plains, where feed production is relatively constant, the lack of silos may create risks in the adequate management of feed in the long term.
The Chi-square test was applied to analyze the statistical significance of the relationships between the variables farm size, landform, and the existence of feed production lines (Table 3).
For the presence of feed production lines on the farm, the results were marginally significant (p = 0.051), suggesting a tendency for the size of the farm to influence their existence. Analyzing these values, the calculated Chi-square value (3.82) is very close to the theoretical value (3.84), and the significance value (0.051) is slightly higher than the 0.05 threshold.
In the case of the landform, the calculated Chi-square value (0.06) is much lower than the theoretical value (3.84), and the significance value (p = 0.813) is much higher than the 0.05 threshold. This suggests that the landform does not have a significant impact on the presence of feed production lines. Therefore, we can conclude that there is no significant association between the landform and the existence of feed production lines.
There is evidence that the impacts of climate change will be felt throughout the supply chain, from agricultural production, to processing, storage, transport, and consumption [53]. The livestock sector requires a significant number of natural resources [54]; therefore, the existence of feed storage facilities at the livestock farm level is necessary. Based on the data from the questionnaire, applying the Chi-square test, an analysis was made of the level of equipment with silos for storing feed, considering the variables farm size and landforms (Table 3).
In the case of farm size, the Chi-square test indicated a Pearson Chi-square value of 7.63, which is higher than the tabular value of 3.84, for the significance threshold of 0.05 (degrees of freedom = 1). This suggests that there is a significant relationship between farm size and the existence of silos for storing feed. Also, the sig value (0.006), which is lower than the threshold of 0.05, confirms the existence of a statistically significant relationship between these two variables. Basically, large farms are more likely to have silos for storing feed, which can be attributed to greater financial resources and the need to manage larger quantities of feed.
The analysis of the landform revealed a Pearson chi-square value of 5.82, which exceeds the tabular value of 3.84 (for the significance threshold of 0.05 and degrees of freedom = 1). This suggests a significant relationship between the landform in the area where the farm is located and the existence of silos. The significance value (0.016) is also lower than the 0.05 threshold, confirming that landform significantly influences the existence of silos for storing feed on the farm.
Therefore, the results indicate that both the size of the farm and the landform have a significant influence on the existence of silos for storing feed. The sig value is lower than the 0.05 threshold in both cases, thus concluding that both variables are statistically significant. Previous research highlighted statistically significant associations between farmers’ perceived vulnerabilities and some variables, such as landscape characteristics [55].
Automatic Feeding and Watering
Data on the use of automatic feeding and watering systems indicate a low adoption of these technologies. Only 30.12% of farms use them, while 54.22% do not have such automation at all, and 15% use them partially (providing only one of them). The differences are evident between large and medium-sized farms: only 21.43% of medium-sized farms have automatic feeding and watering, compared to 76.92% of large farms, which are more technologically advanced (Figure 10).
Also, notable differences appear between the landforms. In the plains, only 20.59% of farms have both automatic feeding and watering, and a higher percentage, 76.47%, do not benefit from these systems at all. In the hilly area, the situation is more balanced, with 36.73% of farms having both systems, and 24.49% using them partially. This distribution suggests that farms in hilly areas have a higher degree of modernization than those in the plains, where dependence on traditional methods is greater.
The analysis of the influence of variables regarding farm size and landform on the provision of automatic feeding and watering indicates a statistically significant relationship between these variables and the use of automation technologies on farms (Table 3).
The results obtained from the Chi-square analysis suggested the existence of significant differences between farms, depending on their size, in terms of the existence of automatic feeding and watering systems. The calculated Chi-square value (Pearson chi-square) is 16.38, which exceeds the tabular value of 13.82 (for the significance threshold of 0.001 and 2 degrees of freedom). This suggests that there is a significant association between farm size and the existence of automatic systems. The main reason may be that large farms have greater financial resources, which allow them to invest in automated systems, but also a larger number of animals, which become difficult to manage manually. Technology is designed to make agricultural activities more efficient, through the minimum input of material and human resources, while protecting the environment [56]. Previous studies have shown that the application of new technologies is conducive to optimizing agricultural production factors, improving their allocation efficiency, and constitutes important ways of qualitative development of agriculture [57]. Some studies have even proposed wind power systems for farms, as an investment aimed at reducing energy costs [58].
Regarding the landform, the analysis indicated major differences between those on the plains and those located in hilly areas. The calculated Chi-square value (Pearson chi-square) is 12.95, which is much higher than the tabulated value of 5.99 calculated for the significance level of 0.05 (df = 2). In addition, the p-value = 0.0015 is much lower than the significance level of 0.05, which confirms a significant relationship between the landforms and the use of automatic feeding and watering systems. Farms located in hilly areas are more likely to adopt such technologies, because the terrain is more difficult to access or more variable in terms of climatic conditions, which may determine the need for more efficient resource management, and automation becomes a more viable solution in these conditions.
Thus, both the size of the farm and the landform are significant factors that influence the use of automatic feeding and watering systems. Larger farms and those located in hilly areas tend to adopt these technologies more frequently, suggesting a link between resource availability, applied technology, and geographic location.
Comparing the level of equipment between medium-sized and large farms, it was found that there are notable differences between them. Thus, large farms invest more in ventilation, cooling systems, microclimate control, and insulation to maintain optimal environmental conditions, ensuring animal health and productivity. Large farms are also better equipped to produce and store their own feed, thus streamlining costs, and use automated feeding and watering systems on a larger scale (Figure 11).
In terms of the landforms in which the farms were located, it was found that the situation was somewhat balanced regarding ventilation and insulation, but farms in the plains use a higher percentage of air cooling and microclimate control systems. This suggests the existence of a higher risk of overheating in the shelters. There are also small differences in terms of feed production facilities, but their storage was better developed in the hilly area, as is the automation of feeding and watering, possibly in a more favorable economic and technological context (Figure 12).
The data in Figure 9 and Figure 10 constitute answers to research question RQ3 from the beginning of this study. Also, they confirm the research hypothesis H2, that in their vast majority, cattle farms do not have a technological level corresponding to ensuring microclimate and technological parameters that can allow easier adaptation to climate changes.

4. Conclusions

The paper highlighted the existing level of equipment of cattle farms from the perspective of adapting to the effects of climate change. Thus, ventilation systems in animal shelters, as well as air cooling systems, need to be improved and expanded, especially in medium-sized farms. Also, only half of the farms have complete insulation of the shelters and only a third stated that they ensure control of microclimate parameters. Regarding automatic feed storage and administration, the data showed a low adoption of these technologies, especially in medium-sized farms. Given the existing level, it can be concluded that there is a great need for equipment with such systems and facilities to counteract the effects of environmental and climate change. The research results showed that large farms benefit from a more developed infrastructure and technology, which allows them to better manage environmental conditions, thus being able to optimize production performance, compared to medium-sized farms, which have more limited resources. Differences in landforms may indicate the influence of determinants such as economic conditions, climatic conditions, and access to technology.
Promoting technologies applicable on farms should be only a first step in a broad strategy aimed at improving agricultural equipment options to minimize climate risks. Research efforts should be expanded in a targeted, applied manner to more cattle farms, with more active participation of farmers and researchers, towards finding the most viable solutions. Farmers should become aware of the implications of having a higher technological level, aimed towards adapting to the effects of climate change and increasing production efficiency, emphasizing on-farm investments in this direction. Policy makers can play an important role in supporting farmers in accessing investment funds to put their technological development intentions into practice.

Author Contributions

Conceptualization, S.R., R.C. and D.M.I.; methodology, R.C. and D.M.I.; software, D.M.I.; validation, S.R. and V.D.; formal analysis, R.C., D.M.I., A.M. and P.A.T.-R.; investigation, A.M.M.; resources, A.M., P.A.T.-R. and A.M.M.; data curation, D.M.I., R.C. and A.M.M.; writing—original draft preparation, R.C. and D.M.I.; writing—review and editing, S.R. and V.D.; visualization, R.C.; supervision, S.R.; project administration, R.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Agriculture and Rural Development, ADER 22.1.2. project.

Institutional Review Board Statement

This study did not require ethical approval.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Gomez-Zavaglia, A.; Mejuto, J.C.; Simal-Gandara, J. Mitigation of emerging implications of climate change on food production systems. Food Res. Int. 2020, 134, 109256. [Google Scholar] [CrossRef] [PubMed]
  2. Arulmozhi, E.; Basak, J.K.; Sihalath, T.; Park, J.; Kim, H.T.; Moon, B.E. Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building. Animals 2021, 11, 222. [Google Scholar] [CrossRef]
  3. Firfiris, V.K.; Martzopoulou, A.G.; Kotsopoulos, T.A. Advanced energy conservation practices in livestock buildings. In Engineering Applications in Livestock Production; Academic Press: Cambridge, MA, USA, 2024; pp. 265–294. [Google Scholar]
  4. Raihan, A. A review of digital agriculture toward food security and environmental sustainability. J. Food Agric. Res. 2024, 4, 19–54. [Google Scholar]
  5. Mihai, R.; Mãrginean, G.E.; Marin, M.P.; Hassan, A.A.M.; Marin, I.; Fîntîneru, G.; Vidu, L. Impact of precision livestock farming on welfare and milk production in Montbeliarde dairy cows. Sci. Papers. Ser. D. Anim. Sci. 2020, 63, 308–313. [Google Scholar]
  6. Dhanaraju, M.; Chenniappan, P.; Ramalingam, K.; Pazhanivelan, S.; Kaliaperumal, R. Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture 2022, 12, 1745. [Google Scholar] [CrossRef]
  7. Žuraulis, V.; Pečeliūnas, R. The Architecture of an Agricultural Data Aggregation and Conversion Model for Smart Farming. Appl. Sci. 2023, 13, 11216. [Google Scholar] [CrossRef]
  8. Provolo, G.; Brandolese, C.; Grotto, M.; Marinucci, A.; Fossati, N.; Ferrari, O.; Beretta, E.; Riva, E. An Internet of Things Framework for Monitoring Environmental Conditions in Livestock Housing to Improve Animal Welfare and Assess Environmental Impact. Animals 2025, 15, 644. [Google Scholar] [CrossRef]
  9. Ndlovu, E.; Prinsloo, B.; Le Roux, T. Impact of climate change and variability on traditional farming systems: Farmers’ perceptions from south-west, semi-arid Zimbabwe. Jàmbá J. Disaster Risk Stud. 2020, 12, a742. [Google Scholar] [CrossRef]
  10. Kalele, D.N.; Ogara, W.O.; Oludhe, C.; Onono, J.O. Climate change impacts and relevance of smallholder farmers’ response in arid and semi-arid lands in Kenya. Sci. Afr. 2021, 12, e00814. [Google Scholar] [CrossRef]
  11. Dutta, S.; Maiti, S.; Garai, S.; Abrar, F.; Jha, S.K.; Bhakat, M.; Mandal, S.; Kadian, K.S. Analyzing adaptation strategies to climate change followed by the farming community of the Indian Sunderbans using Analytical Hierarchy Process. J. Coast. Conserv. 2020, 24, 1–14. [Google Scholar] [CrossRef]
  12. Cheng, M.; McCarl, B.; Fei, C. Climate change and livestock production: A literature review. Atmosphere 2022, 13, 140. [Google Scholar] [CrossRef]
  13. Ahmad, M.M.; Yaseen, M.; Saqib, S.E. Climate change impacts of drought on the livelihood of dryland smallholders: Implications of adaptation challenges. Int. J. Disaster Risk Reduct. 2022, 80, 103210. [Google Scholar] [CrossRef]
  14. Strateanu, A.G.; Udrea, L.; Sandu, M. Analysis of the microclimate conditions and parameters in the shelters for dairy cows and their influence on the animal’s welfare. Ann. Valahia Univ. Targoviste. Agric. 2022, 14, 23–26. [Google Scholar]
  15. Seo, H.-J.; Oh, B.-W.; Seo, I.-H. Environmental Monitoring and Thermal Data Analysis Related to Mortality Rates in a Commercial Pig House. Agriculture 2025, 15, 635. [Google Scholar] [CrossRef]
  16. Ouma, S. Farming as Financial Asset: Global Finance and the Making of Institutional Landscapes; Agenda Publishing Limited: Newcastle upon Tyne, UK, 2020; p. 220. [Google Scholar]
  17. Pallauf, M.; von Cramon-Taubadel, S.; Hüttel, S. Cattle grazing under trees: German farmers’ intentions to adopt modern silvopastoral systems under climate change risk perceptions. In Sustainable Food Systems Discussion Papers; Georg-August University of Göttingen: Göttingen, Germany, 2025; pp. 18–20. [Google Scholar]
  18. Ali, W.; Ali, M.; Ahmad, M.; Dilawar, S.; Firdous, A.; Afzal, A. Application of modern techniques in animal production sector for human and animal welfare. Turk. J. Agric. Food Sci. Technol. 2020, 8, 457–463. [Google Scholar] [CrossRef]
  19. Vaintrub, M.O.; Levit, H.; Chincarini, M.; Fusaro, I.; Giammarco, M.; Vignola, G. Precision livestock farming, automats, and new technologies: Possible applications in extensive dairy sheep farming. Animal 2021, 15, 100143. [Google Scholar] [CrossRef]
  20. Getahun, S.; Kefale, H.; Gelaye, Y. Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic re-view. Sci. World J. 2024, 2024, 2126734. [Google Scholar] [CrossRef] [PubMed]
  21. Monteiro, A.; Santos, S.; Gonçalves, P. Precision agriculture for crop and livestock farming—Brief review. Animals 2021, 11, 2345. [Google Scholar] [CrossRef]
  22. Vrchota, J.; Pech, M.; Švepešová, I. Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic. Agriculture 2022, 12, 1080. [Google Scholar] [CrossRef]
  23. Neves, S.F.; Silva, M.C.; Miranda, J.M.; Stilwell, G.; Cortez, P.P. Predictive models of dairy cow thermal state: A review from a technological perspective. Vet. Sci. 2022, 9, 416. [Google Scholar] [CrossRef]
  24. Ivanov, Y.; Novikov, N. Digital intelligent microclimate control of livestock farms. E3S Web Conf. EDP Sci. 2020, 175, 11012. [Google Scholar] [CrossRef]
  25. Mellor, D.J.; Beausoleil, N.J.; Littlewood, K.E.; McLean, A.N.; McGreevy, P.D.; Jones, B.; Wilkins, C. The 2020 Five Domains Model: Including Human–Animal Interactions in Assessments of Animal Welfare. Animals 2020, 10, 1870. [Google Scholar] [CrossRef] [PubMed]
  26. Ivanov, Y.; Novikov, N. Dual-channel digital control of energy consumption and air supply in microclimate systems of livestock premises. IOP Conf. Ser. Earth Environ. Sci. 2021, 937, 032002. [Google Scholar] [CrossRef]
  27. Schauberger, G.; Hennig-Pauka, I.; Zollitsch, W.; Hörtenhuber, S.J.; Baumgartner, J.; Niebuhr, K.; Piringer, M.; Knauder, W.; Anders, I.; Andre, K.; et al. Efficacy of adaptation measures to alleviate heat stress in confined livestock buildings in temperate climate zones. Biosyst. Eng. 2020, 200, 157–175. [Google Scholar] [CrossRef]
  28. Lovarelli, D.; Bambi, G.; Barbari, M.; Becciolini, V.; Bonfanti, M.; Bovo, M.; Porto, S.M.C.; Tassinari, P.; Guarino, M. The attended outcomes of the project Instructions from PLF Data Analysis to improve the Cattle farming (INDACAT). In Proceedings of the 11th European Conference on Precision Livestock Farming, Bologna, Italy, 9–12 September 2024. [Google Scholar]
  29. Rivero, M.J.; Lopez-Villalobos, N.; Evans, A.; Berndt, A.; Cartmill, A.; Neal, A.L.; McLaren, A.; Farruggia, A.; Mignolet, C.; Chadwick, D.; et al. Key traits for ruminant livestock across diverse production systems in the context of climate change: Perspectives from a global platform of research farms. Reprod. Fertil. Dev. 2021, 33, 1–19. [Google Scholar] [CrossRef]
  30. Bozzo, G.; Corrente, M.; Testa, G.; Casalino, G.; Dimuccio, M.M.; Circella, E.; Brescia, N.; Barrasso, R.; Celentano, F.E. Animal Welfare, Health and the Fight against Climate Change: One Solution for Global Objectives. Agriculture 2021, 11, 1248. [Google Scholar] [CrossRef]
  31. de Sousa, K.T.; Deniz, M.; do Vale, M.M.; Dittrich, J.R.; Hötzel, M.J. Influence of microclimate on dairy cows’ behavior in three pasture systems during the winter in south Brazil. J. Therm. Biol. 2021, 97, 102873. [Google Scholar] [CrossRef]
  32. Tikhomirov, D.; Vasilyev, A.N.; Budnikov, D.; Vasilyev, A.A. Energy-saving automated system for microclimate in agricultural premises with utilization of ventilation air. Wirel. Netw. 2020, 26, 4921–4928. [Google Scholar] [CrossRef]
  33. Kuzmichev, A.; Tikhomirov, D.; Trunov, S.; Kuzmichev, I.; Lamonov, N. Modular Thermal Air Curtain for Protection of Gate Openings with Variable Vector of Air Jet Direction and Adjustable Width of Slot. Invention Patent RU 2716299 C1; Application No. 2019125012, 7 August 2019. [Google Scholar]
  34. Havelka, Z.; Kunes, R.; Kononets, Y.; Stokes, J.E.; Smutny, L.; Olsan, P.; Kresan, J.; Stehlik, R.; Bartos, P.; Xiao, M.; et al. Technology of microclimate regulation in organic and energy-sustainable livestock production. Agriculture 2022, 12, 1563. [Google Scholar] [CrossRef]
  35. Shablia, V.; Kunets, V.; Danilova, T.; Shablia, P. Influence of weather conditions in the cold period of year on the microclimate in cowsheds and milk productivity of cows. Sci. Papers. Ser. D. Anim. Sci. 2024, 67, 177–184. [Google Scholar]
  36. Santolini, E.; Bovo, M.; Barbaresi, A.; Torreggiani, D.; Tassinari, P. Evaluation of microclimate in dairy farms using different model typologies in computational fluid dynamics analyses. J. Agric. Eng. 2024, 55. [Google Scholar] [CrossRef]
  37. Andrade, R.R.; de Fátima Ferreira Tinôco, I.; Ferraz, G.A.E.S.; Becciolini, V.; Rossi, G.; Barbari, M. Spatial Variability of Microclimatic Parameters in a Closed Compost-Bedded Pack Barn for Dairy Cows with Tunnel Ventilation. In Conference of the Italian Society of Agricultural Engineering; Springer International Publishing: Cham, Switzerland, 2022; pp. 1029–1037. [Google Scholar]
  38. Mylostyvyi, R.; Izhboldina, O.; Chernenko, O.; Khramkova, O.; Kapshuk, N.; Hoffmann, G. Microclimate modeling in naturally ventilated dairy barns during the hot season: Checking the accuracy of forecasts. J. Therm. Biol. 2020, 93, 102720. [Google Scholar] [CrossRef] [PubMed]
  39. Dovlatov, I.M.; Yurochka, S.S.; Pavkin, D.Y.; Polikanova, A.A. Technology of Forced Ventilation of Livestock Premises Based on Flexible PVC Ducts. In International Conference on Intelligent Computing & Optimization; Springer Nature: Cham, Switzerland, 2023; pp. 353–360. [Google Scholar]
  40. Gao, L.; Er, M.; Li, L.; Wen, P.; Jia, Y.; Huo, L. Microclimate environment model construction and control strategy of enclosed laying brooder house. Poult. Sci. 2022, 101, 101843. [Google Scholar] [CrossRef] [PubMed]
  41. Staicu, E.; Drăghici, M.; Bădic, L.; Ivanciu, C.; Mitrănescu, E. Research on monitoring microclimate chemical factors and their influence on the welfare of intensive swine rearing. Bull. UASVM Vet. Med. 2008, 65, 222–224. [Google Scholar]
  42. D’Emilio, A. Mitigating heat stress of dairy cows bred in a free-stall barn by sprinkler systems coupled with forced ventilation. J. Agric. Eng. 2017, 48, 190–195. [Google Scholar] [CrossRef]
  43. Holka, M.; Kowalska, J.; Jakubowska, M. Reducing Carbon Footprint of Agriculture—Can Organic Farming Help to Mitigate Climate Change? Agriculture 2022, 12, 1383. [Google Scholar] [CrossRef]
  44. Perez Garcia, C.A.; Bovo, M.; Torreggiani, D.; Tassinari, P.; Benni, S. Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach. Agriculture 2024, 14, 316. [Google Scholar] [CrossRef]
  45. Singh, V.K.; Pandey, H.O.; Biswal, P.; Nayak, D.N. Basic considerations for engineered livestock housing. In Engineering Applications in Livestock Production; Academic Press: Cambridge, MA, USA, 2024; pp. 15–36. [Google Scholar]
  46. Angrecka, S.; Herbut, P.; Godyń, D.; Vieira, F.M.C.; Zwolenik, M. Dynamics of Microclimate Conditions in Freestall Barns During Winter—A Case Study from Poland. J. Ecol. Eng. 2020, 21, 129–136. [Google Scholar] [CrossRef]
  47. Mazurchuk, S.; Marchenko, N.; Tsapko, Y.; Bondarenko, O.; Buyskikh, N.; Andor, T.; Forosz, V. Ways to increase the production efficiency of hardwood blanks. E3S Web Conf. 2021, 280, 07010. [Google Scholar] [CrossRef]
  48. Tsapko, Y.; Horbachova, O.; Mazurchuk, S.; Bondarenko, O. Study of resistance of thermomodified wood to the influence of natural conditions. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1164, 012080. [Google Scholar] [CrossRef]
  49. Borshch, O.O.; Borshch, O.V.; Mashkin, Y.; Malina, V.; Fedorchenko, M. Behavior and energy losses of cows during the period of low temperatures. Sci. Horiz. 2021, 24, 46–53. [Google Scholar] [CrossRef]
  50. Mishra, S.; Sharma, S.K. Advanced contribution of IoT in agricultural production for the development of smart livestock environments. Internet Things 2023, 22, 100724. [Google Scholar] [CrossRef]
  51. Kumar, R.; Thakur, A.; Thakur, R.; Dogra, P.K. Livestock Shelter Management: Climate Change Perspective. In Climate Change and Livestock Production: Recent Advances and Future Perspectives; Sejian, V., Chauhan, S.S., Devaraj, C., Malik, P.K., Bhatta, R., Eds.; Springer: Singapore, 2021. [Google Scholar]
  52. Wilmes, R.; Waldhof, G.; Breunig, P. Can digital farming technologies enhance the willingness to buy products from current farming systems? PLoS ONE 2022, 17, e0277731. [Google Scholar] [CrossRef]
  53. Godde, C.M.; Mason-D’Croz, D.; Mayberry, D.E.; Thornton, P.K.; Herrero, M. Impacts of climate change on the livestock food supply chain; a review of the evidence. Glob. Food Secur. 2021, 28, 100488. [Google Scholar] [CrossRef] [PubMed]
  54. Grossi, G.; Goglio, P.; Vitali, A.; Williams, A.G. Livestock and climate change: Impact of livestock on climate and mitigation strategies. Anim. Front. 2019, 9, 69–76. [Google Scholar] [CrossRef]
  55. Morales-Reyes, Z.; Barbosa, J.M.; Sánchez-Zapata, J.A.; Pérez-Ibarra, I. Farmer perceptions of the vulnerabilities of traditional livestock farming systems under global change. Ambio 2025, 1–19. [Google Scholar] [CrossRef]
  56. Hrynevych, O.; Canto, M.B.; García, M.J. Tendencies of Precision Agriculture in Ukraine: Disruptive Smart Farming Tools as Cooperation Drivers. Agriculture 2022, 12, 698. [Google Scholar] [CrossRef]
  57. Han, M.; Liu, R.; Ma, H.; Zhong, K.; Wang, J.; Xu, Y. The Impact of Social Capital on Farmers’ Willingness to Adopt New Agricultural Technologies: Empirical Evidence from China. Agriculture 2022, 12, 1368. [Google Scholar] [CrossRef]
  58. Tryhuba, A.; Bashynsky, O.; Hutsol, T.; Rozkosz, A.; Prokopova, O. Justification of Parameters of the Energy Supply System of Agricultural Enterprises with Using Wind Power Installations. E3S Web Conf. 2020, 154, 06001. [Google Scholar] [CrossRef]
Figure 1. In green—the counties participating in the study, with the number of cattle farms included in the research. In yellow—counties not participating in the research.
Figure 1. In green—the counties participating in the study, with the number of cattle farms included in the research. In yellow—counties not participating in the research.
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Figure 2. Farm size. Source: authors’ elaboration.
Figure 2. Farm size. Source: authors’ elaboration.
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Figure 3. Clustering the farm size. Source: authors’ elaboration.
Figure 3. Clustering the farm size. Source: authors’ elaboration.
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Figure 4. Degree of farms equipped with ventilation systems. Source: authors elaboration.
Figure 4. Degree of farms equipped with ventilation systems. Source: authors elaboration.
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Figure 5. Degree of farms equipped with air cooling systems. Source: authors’ elaboration.
Figure 5. Degree of farms equipped with air cooling systems. Source: authors’ elaboration.
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Figure 6. Presence of insulation of shelters. Source: authors’ elaboration.
Figure 6. Presence of insulation of shelters. Source: authors’ elaboration.
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Figure 7. Degree of farms equipped with microclimate control systems. Source: authors’ elaboration.
Figure 7. Degree of farms equipped with microclimate control systems. Source: authors’ elaboration.
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Figure 8. Degree of farms equipped with feed production facilities. Source: authors’ elaboration.
Figure 8. Degree of farms equipped with feed production facilities. Source: authors’ elaboration.
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Figure 9. Degree of farms equipped with silos for storing feed. Source: authors’ elaboration.
Figure 9. Degree of farms equipped with silos for storing feed. Source: authors’ elaboration.
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Figure 10. Degree of farms equipped with automatic feeding and watering systems. Source: authors’ elaboration.
Figure 10. Degree of farms equipped with automatic feeding and watering systems. Source: authors’ elaboration.
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Figure 11. Comparative degree of equipping in medium and large farms. Source: authors’ elaboration.
Figure 11. Comparative degree of equipping in medium and large farms. Source: authors’ elaboration.
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Figure 12. Comparative degree of farm equipping in plain and hilly areas. Source: authors’ elaboration.
Figure 12. Comparative degree of farm equipping in plain and hilly areas. Source: authors’ elaboration.
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Table 1. Multicriterial distribution of farms and heads.
Table 1. Multicriterial distribution of farms and heads.
IndicatorsMeasure UnitLandformTotal
PlainHill and Plateau
Number of farmsno.344983
%40.9659.04100.00
Headsno.5872436410,236
%57.3742.63100.00
Farm sizeMediumno.274370
%38.5761.4384.34
Largeno.7613
%53.8546.1515.66
Source: authors’ calculations.
Table 2. Specific technological equipment in cattle farms (answers for RQ2).
Table 2. Specific technological equipment in cattle farms (answers for RQ2).
Facility TypesMeasure UnitYESNO
Ventilation in animal sheltersno.4241
%50.6049.40
Air cooling systems in sheltersno.1073
%12.0587.95
Shed insulationno.3944
%46.9953.01
Control of optimal microclimate parameters in sheltersno.2558
%30.1269.88
Feed production linesno.5033
%60.2439.76
Feed storage silosno.3548
%42.1757.83
Automatic feedingno.2459
%28.9271.08
Automatic wateringno.3746
%44.5855.42
Source: authors’ calculations.
Table 3. The results of the Chi-square test.
Table 3. The results of the Chi-square test.
CriteriaPearson
Chi-Square
Critical
Chi Value
Significance
Level
df Asymp. Sig.
(2-Sided)
Existence of ventilation systems
Farm size6.013.840.0510.014
Landform0.013.840.050.927
Air cooling systems
Farm size11.6110.830.00110.001
Landform1.703.840.050.192
Insulation of shelters
Farm size0.723.840.0510.395
Landform0.003.840.050.991
Microclimate control systems
Farm size5.303.840.0510.021
Landform0.143.840.050.712
Feed production lines in farms
Farm size3.823.840.0510.051
Landform0.063.840.050.813
Equipment for storing forages
Farm size7.633.840.0510.006
Landform5.823.840.050.016
Automatic feeding and watering
Farm size16.3813.820.00120.0003
Landform12.955.990.050.0015
Source: authors’ calculations; df—degree of freedom.
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Rodino, S.; Chetroiu, R.; Ilie, D.M.; Marin, A.; Dragomir, V.; Manolache, A.M.; Turek-Rahoveanu, P.A. Research on the Cattle Farm Endowments from the Climate Change Adapting Perspective. Agriculture 2025, 15, 1339. https://doi.org/10.3390/agriculture15131339

AMA Style

Rodino S, Chetroiu R, Ilie DM, Marin A, Dragomir V, Manolache AM, Turek-Rahoveanu PA. Research on the Cattle Farm Endowments from the Climate Change Adapting Perspective. Agriculture. 2025; 15(13):1339. https://doi.org/10.3390/agriculture15131339

Chicago/Turabian Style

Rodino, Steliana, Rodica Chetroiu, Diana Maria Ilie, Ancuța Marin, Vili Dragomir, Alexandra Marina Manolache, and Petruța Antoneta Turek-Rahoveanu. 2025. "Research on the Cattle Farm Endowments from the Climate Change Adapting Perspective" Agriculture 15, no. 13: 1339. https://doi.org/10.3390/agriculture15131339

APA Style

Rodino, S., Chetroiu, R., Ilie, D. M., Marin, A., Dragomir, V., Manolache, A. M., & Turek-Rahoveanu, P. A. (2025). Research on the Cattle Farm Endowments from the Climate Change Adapting Perspective. Agriculture, 15(13), 1339. https://doi.org/10.3390/agriculture15131339

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