Farming Practices Research Articles

Assessment of the efficacy of a gonadotropin releasing factor (GnRF) analog to suppress ovarian function in gilts under pre-clinical and clinical conditions.

The administration of a gonadotropin releasing factor (GnRF) analog to pigs has proven to induce antibodies against endogenous GnRF. In gilts (young female pigs), the subsequent blocking of GnRF activity by specific antibodies results in a temporary suppression of ovarian activity and sexual maturation. One pre-clinical and two clinical studies were conducted to assess the ability of the GnRF analog to produce immunologically suppression of the ovarian function, preventing gilts from reaching puberty before harvest, at 27 weeks of age. In the three studies, a significant reduction of size and weight of reproductive organs and gilts in oestrus was demonstrated in vaccinated gilts compared with intact gilts. A significant increase in anti-GnRF antibody levels in sera was observed after the 2nd dose, which lasted until the end of the study in each of the protocols used. Progesterone levels were significantly reduced from 6 to 8 weeks after 2nd vaccination in clinical studies 2 and 1 respectively, and from 6 weeks after 2nd vaccination in the pre-clinical study. Estradiol levels were below the limit of detection for clinical study 2 and significantly reduced in vaccinated gilts at the end of the pre-clinical study and the clinical study 1. Vaccination of gilts with a GnRF analog with two different protocols (1st dose from 10 to 14 weeks of age, and a 2nd dose 8 or 4 weeks later) was effective in reducing the development of puberty for at least 9 weeks post 2nd dose. These results confirm the flexibility of vaccination programs for veterinarians and producers which can be adapted to pig management practices in commercial farms.

Leveraging Cloud and Distributed Computing for Enhanced Precision Agriculture: A Yield Mapping Perspective

Abstract: This article explores the transformative impact of cloud and distributed computing technologies on precision agriculture, with a specific focus on enhancing yield mapping capabilities. Yield mapping, a critical component of modern farming, faces challenges such as data accuracy issues, data loss, edge effects, and temporal variability. The integration of cloud-based solutions and distributed computing addresses these challenges by facilitating the storage, processing, and analysis of large-scale agricultural datasets. Real-time processing and advanced analytics, powered by machine learning algorithms, enable farmers to gain deeper insights into spatial and temporal variations in crop performance. The article discusses the development of user-friendly interfaces and reporting tools that aid in yield map interpretation, as well as the integration of yield data with farm management systems for optimized decision-making. Furthermore, it examines the impact of these technologies on promoting sustainable farming practices, improving crop profitability, and reducing environmental footprints. The article also delves into future directions, including emerging technologies and potential obstacles to widespread adoption. By leveraging cloud and distributed computing, yield mapping evolves from a mere data collection tool to a sophisticated decision support system, paving the way for more efficient, profitable, and sustainable agricultural practices in the face of growing global food demand and environmental challenges.

Transcending Food-Miles: Unpacking the Drivers of In-Store Vertical Farming Adoption Among Food Retailers in France

This paper examines the potential of in-store vertical farming technologies in urban food retail as a solution to the increasing demand for fresh, locally sourced food. By enabling a “sell where you produce” model, vertical farming can reduce food miles, enhance traceability, and lower carbon emissions. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to analyze factors influencing the adoption of these technologies among food retailers in Rouen, France, with a particular focus on the role of risk aversion in shaping attitudes and decisions. The findings highlight that performance expectancy and facilitating conditions are the key drivers of adoption intentions among retail store managers. However, perceived risks, such as high investments and operational costs, pose significant barriers, while social influence appears to have little impact on their decisions. The study underscores the importance of conducting economic evaluations and risk assessments of vertical farming practices to ensure their long-term viability. As vertical farming holds the potential to boost fresh food production in urban areas, reduce food miles, and create sustainable supply chains, it is crucial to carefully weigh its benefits and risks to support its successful adoption in urban food retail.

Narrowband IoT in Livestock Farming: A Technological Innovation for Productivity and Sustainability

The integration of technology in livestock farming is crucial for enhancing production efficiency and animal welfare. This study aimed to develop and evaluate the implementation of a Narrowband IoT (NB-IoT)-based automated monitoring system in poultry farming. Using an experimental design, the research involved 30,000 day-old chicks at PT. Anugerah Teknologi Ternak in Central Java, Indonesia. The NB-IoT system collected real-time data on environmental parameters and poultry activity. Time-series analysis revealed non-stationary data, while correlation analysis showed a strong negative relationship between temperature and humidity (r = -0.8521). Anomaly detection identified 13.33% of observations as anomalous, demonstrating the system's capability for early issue detection. Regression modeling (R-squared = 0.7261) indicated that temperature and humidity significantly influence poultry productivity. The study concludes that NB-IoT implementation in poultry farming has significant potential for enhancing productivity through real-time monitoring and early anomaly detection, supporting more efficient and sustainable precision farming practices. However, limitations in data stationarity and sample generalizability suggest the need for further research to improve long-term predictions and broaden applicability across diverse farming contexts.

Effect of Farmer Group Empowerment and Agribusiness Training Program on Productivity and Income of Coffee Farmers in West Java

This study investigates the impact of Farmer Group Empowerment and Agribusiness Training Programs on the Productivity and Income of coffee farmers in West Java. The research utilizes a quantitative approach with a sample of 200 coffee farmers and significantly positively affect ploys Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to analyze the relationships between the key variables. The results indicate that both Farmer Group Empowerment and Agribusiness Training Programs significantly positively affect Productivity and Income. However, Farmer Group Empowerment shows a stronger impact on productivity compared to training programs, emphasizing the importance of collective action in enhancing farming practices. These findings highlight the need for an integrated approach to agricultural development, combining empowerment with technical training to ensure sustainable improvements in farmers' livelihoods. The study provides valuable insights for policymakers and agricultural development practitioners seeking to promote rural development and increase smallholder farmers' productivity and income.

Comparative analysis of lowland rice (Oryza sativa L. var. PSB Rc18) performance across different farming systems

Organic farming is gaining recognition as a viable alternative to conventional methods, promising soil health preservation and sustained crop productivity with economic benefits. This study evaluated the physiological, growth, and yield responses of the PSB Rc18 rice variety and appraised its economic feasibility under different production systems. The experiment was laid out in Randomized Complete Block Design (RCBD) with four replications and three treatments: T1-best bet organic production system, T2-farmers’ organic production system in Leyte, and T3-farmers’ conventional production system in Leyte. The crop growth rate (CGR) of PSB Rc18 remained consistent across the different systems. However, the Net Assimilation Rate (NAR) peaked significantly between 42-56 days after transplanting (DAT) in the T2. Additionally, the Leaf Area Index (LAI) in T1 was comparable to that of T3. Rice grown under T1 reached heading and maturation earlier than T3. Although T3 produced the highest fresh straw, most productive tillers, and heaviest total biomass, the grain yield was similar across all production systems. Economically, T2 outperformed with a superior benefit-cost ratio of $0.55 and $0.94 per USD invested, considering both regular and premium prices for organic palay. These findings highlight organic farming practices' economic and agronomic viability, suggesting that promoting organic farming can be a beneficial alternative to conventional methods in Leyte. This study underscores the potential for integrating organic practices to enhance sustainability and economic outcomes in rice production, making both T1 and T2 significant options for farmers in Eastern Visayas.

Integrative approaches to improve litchi (Litchi chinensis Sonn.) plant health using bio-transformations and entomopathogenic fungi

Bio-transformations refer to the chemical modifications made by an organism on a chemical compound that often involves the interaction of plants with microbes to alter the chemical composition of soil or plant. Integrating bio-transformations and entomopathogenic fungi into litchi cultivation can enhance symbiotic relationships, microbial enzymatic activity in rhizosphere, disease suppression and promote overall plant health. The integration of biological formulations and entomopathogenic fungi can significantly influence growth, nutrient dynamics, physiology, and rhizosphere microbiome of air-layered litchi (Litchi chinensis Sonn.) saplings. Biological modifications included, K-mobilizers, AM fungi, Pseudomonas florescence and Azotobacter chroococcum along with Metarhizium, entomopathogenic fungi have been used. The treatments included, T1-Litchi orchard soil + sand (1:1); T2-Sand + AM fungi + Azotobacter chroococcum (1:2:1); T3-Sand + Pseudomonas florecence + K-mobilizer (1:1:1); T4- AM fungi + K-mobilizers (1:1); T5, P. Florecence + A. chroococcum + K-mobilizer (1:1:1); T6-Sand + P. florecence (1:2) and T7-Uninoculated control for field performance. Treatments T4-T6 were further uniformly amended with drenching of Metarrhizium in rhizosphere. T2 application significantly increased resident microbe survival, total chlorophyll content and root soil ratio in seedlings. A. chroococcum, Pseudomonas, K-mobilizers and AM fungi increased in microbial biomass of 2.59, 3.39, 2.42 and 2.77 times, respectively. Acidic phosphatases, dehydrogenases and alkaline phosphatases were increased in rhizosphere. Leaf nutrients reflected through DOP were considerably altered by T2 treatment. Based on Eigen value, PCA-induced changes at biological modifications showed maximum total variance. The study inferred that the bio-transformations through microbial inoculants and entomopathogenic fungi could be an encouraging strategy to enhance the growth of plants, health and productivity. Such practices align well with the goals of sustainable agriculture through biological means by reducing dependency on chemical inputs. By delving into these aspects, the research gaps including microbial processes, competitive and symbiotic relationships, resistance in microbes and how complex interactions among bio-transformations, entomopathogenic fungi and microbes can significantly impact the health and productivity of litchi. Understanding and harnessing these interactions can lead to more effective and sustainable farming practices.

SISTEM INFORMASI MEDIA PROMOSI DAN PEMBELAJARAN KELOMPOK TARUNA TANI AULIA

This research focuses on the challenges faced by the Taruna Tani Aulia group in educating farmers about the transition from chemical fertilizers to organic fertilizers. Many farmers find the process of producing organic fertilizers complex and time-consuming, making them reluctant to switch. To address this issue, this study developed an information system that serves as both a promotional tool and a learning platform, facilitating the dissemination of information on the benefits of organic fertilizers and promoting organic fertilizer products sold by Taruna Tani Aulia. The system was developed using the Prototyping Method, allowing for iterative testing and evaluation with direct user feedback. The system was built using the Next.js framework to streamline the development process. The results of testing using the Mean Opinion Score indicated a high level of user acceptance, with a User Acceptance Test score of 87.8%. This suggests that the system is feasible and effective in supporting the promotion and education of organic fertilizers. In conclusion, this information system can help farmers more easily understand and adopt organic farming practices, as well as simplify their access to purchasing organic fertilizers without the need to produce them independently.

Strategies for minimizing carbon footprint in the agricultural supply chain: leveraging sustainable practices and emerging technologies

This article addresses the strategies for minimizing the carbon footprint within the agricultural supply chain, focusing on sustainable practices and emerging technologies. The primary aim is to explore how the adoption of sustainable farming techniques, renewable energy sources, and precision agriculture can significantly reduce carbon emissions and enhance environmental stewardship in agriculture. Key strategies discussed include the implementation of organic farming practices, which avoid synthetic inputs and enhance soil health, and agroforestry, which integrates trees into farming systems to sequester carbon and support biodiversity. The use of renewable energy sources, such as solar, wind, and biogas, is examined for its role in reducing reliance on fossil fuels and lowering greenhouse gas emissions. Precision agriculture technologies, including GPS and IoT sensors, are highlighted for their ability to optimize resource use and reduce waste. Additionally, the article covers advancements in carbon tracking and management technologies, which provide accurate measurement and reporting of emissions, and sustainable logistics and packaging practices aimed at minimizing the environmental impact of transportation and packaging. The findings underscore that integrating these strategies not only contributes to significant reductions in carbon emissions but also presents opportunities for improving efficiency and sustainability across the agricultural supply chain. The article concludes with implications for industry stakeholders, emphasizing the need for continued innovation and policy support to drive widespread adoption of these practices and technologies. By adopting these strategies, the agricultural sector can make substantial progress toward mitigating climate change and fostering a more sustainable future.

Java Development across Industries: Transforming Healthcare, Retail, and Agriculture

Java, a versatile and widely-adopted programming language, is transforming critical industries such as healthcare, retail, and agriculture. This article explores how Java developers leverage the language's robustness, platform independence, and extensive ecosystem to create innovative solutions that address industry-specific challenges. In healthcare, Java's security features and scalability enable the development of secure and interoperable Electronic Health Record systems. Retail benefits from Java's cross-platform compatibility and performance in building omnichannel platforms that integrate online and offline experiences. In agriculture, Java's IoT capabilities and big data integration revolutionize farming practices through precision agriculture and data-driven decision-making. Case studies in each industry demonstrate Java's significant impact on key metrics such as patient data accessibility, online-to-offline conversions, and crop yields. As these sectors continue to evolve, Java's adaptability positions it as a key driver of future advancements.

Typology of Camel Breeding in the Wilaya of El-Oued: Feeding Practices Characterization

Background: Dromedary farming is crucial in El-Oued’s agricultural systems, but challenges such as drought and land expansion are reducing grazing areas and affecting herd management. The research surveyed 102 camel breeders, representing 4592 dromedaries, representing approximately 09% of the total estimated camel herd in this wilaya, regarding herd organization, structure and feeding practices. Three breeder groups were identified: pure pastoral (54.9%), mixed grazing in summer (16.7%) and urban breeders focused on fattening (28.4%). Methods: This study involved a survey of camel breeders from six (06) municipalities in El-Oued to analyze herd organization, structure and feeding practices. Data collection and analysis were aimed at understanding the functioning of camel herders and their integration into the meat market system. Result: The results highlighted significant changes in herd management and operation, showing variable integration of camel production into the meat market system influenced by forage supply variability in Saharan grazing lands. Feeding practices revealed integration with locally produced forage resources, frequent supplementation during grazing and the use of highly energetic concentrated feed as the primary ration for fattening young camels. This study provides valuable insights into camel farming practices in El-Oued, shedding light on the challenges and adaptations of this essential agricultural sector.

Unraveling the Dynamics of Rice Yield in the Zio River Valley, Togo: A thorough analysis of influencing factors

Despite Togo's rice production potential, local production only meets 32% of demand, mainly due to relatively low agricultural productivity. With the aim of improving this situation, this study was undertaken to analyze the factors influencing rice production in the Zio River Valley, which is one of Togo's largest rice-growing areas. Data collected from 413 farmers in the region were analyzed using the Cobb-Douglas production function, incorporating variables such as income, farm size, chemical fertilizer use, organic fertilizer use, pesticide use, group membership, experience, education level, gender, access to credit, access to agricultural advice, age, continuous access to water and marital status. The results indicate that only certain factors, such as farm size, income, amount of chemical fertilizer, use of organic fertilizer and experience, have a significant positive impact on paddy rice production, furthermore the No-formal education farmers show a decrease in production. This study will guide decision-makers and farmers in adopting more effective farming practices to significantly improve rice production in Togo. Emphasis should be placed on aspects such as farm size, income, fertilizer use and experience to boost rice productivity.

The Landscape Ecological Quality of Two Different Farm Management Models: Polyculture Agroforestry vs. Conventional

Low-intensity, diversified agricultural land use is needed to counteract the current decline in agrobiodiversity. Landscape ecology tools can support agrobiodiversity assessment efforts by investigating biodiversity-related ecological functions (pattern–process paradigm). In this study, we test a toolkit of landscape ecology analyses to compare different farm management models: polyculture agroforestry (POLY) vs. conventional monoculture crop management (CV). Farm-scale analyses are applied on temperate alluvial sites (Po Plain, Northern Italy), as part of a broader multi-scale analytical approach. We analyze the landscape ecological quality through landscape matrix composition, patch shape complexity, diversity, metastability, and connectivity indices. We assess farm differences through multivariate analyses and t-tests and test a farm classification tool, namely, a scoring system based on the relative contributions of POLY farms, considering their deviation from a local CV baseline. The results showed a separate ecological behavior of the two models. The POLY model showed better performance, with significant positive contributions to the forest and semi-natural component equipment and diversity; agricultural component diversity, metastability; total farm diversity, metastability, connectivity, and circuitry. A reference matrix for the ecological interpretation of the results is provided. Farm classification provides a quick synthesis of such contributions, facilitating farm comparisons. The methodology has a low cost and quickly provides information on ongoing ecological processes resulting from specific farm management practices; it is intended to complement field-scale assessments and could help to meet the need for a partially outcome-based assessment of good farm practice.

The Impact of Technological Innovations on Agricultural Productivity and Environmental Sustainability in China

Agricultural productivity in China is a fundamental driver of food security and economic growth. Yet, the sector faces profound challenges due to environmental degradation and climate change, which threaten sustainable agricultural practices. This research examines the effects of technological innovations on agricultural Total Factor Productivity and environmental sustainability in China from 2012 to 2022. The study seeks to understand how technological advancements, when considered alongside socioeconomic variables, impact agricultural output while balancing ecological integrity. Employing a comprehensive methodological framework, this research integrates fixed-effects, random-effects, and multilevel mixed-effects models to analyze crucial factors including rural education, technological capability, and environmental conservation initiatives. The study further utilizes structural equation modeling to evaluate both the direct and indirect effects of these determinants on productivity. The results demonstrate that technological innovations substantially enhance agricultural productivity, particularly in provinces with higher socioeconomic development. Additionally, sustainable farming practices and tailored policy interventions are identified as vital in addressing regional productivity imbalances. The research concludes by underscoring the necessity for the continued integration of environmental considerations and emerging technologies to ensure the sustainability of agricultural growth in the long term.

Enabling Intelligence on the Edge: Leveraging Edge Impulse to Deploy Multiple Deep Learning Models on Edge Devices for Tomato Leaf Disease Detection

Tomato diseases, including Leaf blight, Leaf curl, Septoria leaf spot, and Verticillium wilt, are responsible for up to 50% of annual yield loss, significantly impacting global tomato production, valued at approximately USD 87 billion. In Ghana, there is a yield gap of about 50% in tomato production, which requires drastic measures to increase the yield of tomatoes. Conventional diagnostic methods are labor-intensive and impractical for real-time application, highlighting the need for innovative solutions. This study addresses these issues in Ghana by utilizing Edge Impulse to deploy multiple deep-learning models on a single mobile device, facilitating the rapid and precise detection of tomato leaf diseases in the field. This work compiled and rigorously prepared a comprehensive Ghanaian dataset of tomato leaf images, applying advanced preprocessing and augmentation techniques to enhance robustness. Using TensorFlow, we designed and optimized efficient convolutional neural network (CNN) architectures, including MobileNet, Inception, ShuffleNet, Squeezenet, EfficientNet, and a custom Deep Neural Network (DNN). The models were converted to TensorFlow Lite format and quantized to int8, substantially reducing the model size and improving inference speed. Deployment files were generated, and the Edge Impulse platform was configured to enable multiple model deployments on a mobile device. Performance evaluations across edge hardware provided metrics such as inference speed, accuracy, and resource utilization, demonstrating reliable real-time detection. EfficientNet achieved a high training accuracy of 97.12% with a compact 4.60 MB model size, proving its efficacy for mobile device deployment. In contrast, the custom DNN model is optimized for microcontroller unit (MCU) deployment. This edge artificial intelligence (AI) technology integration into agricultural practices offers scalable, cost-effective, and accessible solutions for disease classification, enhancing crop management, and supporting sustainable farming practices.

Enabling Precision Agriculture through a Web-Based Fertilization Management System for Nawungan Selopamioro Fruit Orchards

Precision Agriculture (PA) is an integrated farming system based on information and technology for managing agriculture to identify, analyze, and manage spatial and temporal diversity information in specific locations to obtain optimum and sustainable benefits while minimizing unwanted environmental impacts. Fertilization is one of the crucial phases in agricultural production process considering technical cultivation aspects, costs, and environmental impacts. The current fertilization process at Kebun Buah Nawungan Selopamioro (KBNS) is still conventional, so there is no standard rule in determining the fertilization dose. Therefore, a PA approach is needed to provide suitable fertilizer doses for agricultural production needs. This objective of this study was to develop of a web-based fertilizer management system, integrating with orchard management to enhance accessibility and decision-making. The system calculates fertilizer requirements by analyzing soil nutrient availability (N, P, K), cultivation area, crop type and age, and available fertilizer types. The development followed the waterfall methodology, encompassing stages from requirement analysis to system maintenance. The outcome is a web application that manages land assets, administrative activities, and fertilizer needs tailored to specific land blocks, crop characteristics, and nutrient inventories. Subsequent validation against field conditions ensures the accuracy of its recommendations. Although comprehensive testing confirmed a 100% success rate in functionality, the system currently operates within a limited scope of variables. Future enhancements are planned to incorporate broader agronomic factors, such as soil pH and texture, to augment the system's precision. Despite its limitations, this system represents a significant technological advance in precision agriculture, promising to improve fertilizer application efficiency and support sustainable farming practices.

How do water, food, and energy resources impact environmental conditions in Ghana? A novel dynamic ARDL simulation approach

Rising water, food, and energy demands pose significant resource risks and environmental threats. Despite the critical nature of this issue, there is a notable absence of empirical research linking the consumption of these resources to environmental degradation in Ghana. To fill this gap, this study investigates the short-term and long-term effects of water, food, and energy resource consumption on environmental degradation in Ghana, analyzing data from 1994 to 2020. Ghana's increasing environmental degradation accentuates the urgency of this research, its vulnerability to climate change, and the imperative to sustain human life and protect the environment. Utilizing the novel dynamic ARDL simulation method, this research offers new perspectives that diverge from previous findings. The study's findings demonstrate that water resource consumption positively impacts environmental degradation in the short and long term. However, food and energy resource consumption mitigates environmental degradation in the short and long term. Therefore, the study advocates adopting resource management strategies emphasizing efficiency and sustainability. It calls for policies that improve water efficiency, boost renewable energy uptake, and encourage sustainable farming practices. These measures are intended to lessen the negative impacts of resource exploitation and aid in environmental preservation efforts.

Exploring the combination of agroecological and conventional farming practices through the hybrid farming concept

ABSTRACT Substantial improvements in resource-use efficiency and conservation are required to fulfill the rising and evolving global food demand, while addressing environmental degradation issues. Agroecology has emerged as a promising pathway to tackle these challenges. A binary stance often prevails in traditional discussions on agricultural transitions, where conventional farming is pitted against agroecological cropping. The reality is that farming practices blend conventional and agroecological features, hence are hybrid. Here we propose a conceptual representation of hybrid farming, that combines diverse farming practices from contrasting agricultural models, aiming to meet production goals. A schematic representation is provided and illustrated by a case study located in the Merguellil downstream plain region in central Tunisia. Hybrid farming involves multiple combinations of agroecological and conventional farming practices with a range of different outlooks and objectives. In the case study, farmers implemented these combinations to increase their income and mitigate risks. The blend of practices used in hybrid farming is not static and can be ever-changing. Various factors, such as shifting socioeconomic and environmental conditions, market trends and interactions with upstream and downstream supply chains fuel this dynamism.

Understanding Reasons Farmers to Adopt Mina Padi Farming on Rainfed Swamp Land: A Planned Behavior Theory Approach

This study uses the planned behavior theory to investigate why farmers practice Mina Padi farming (rice-fish integration) on rainfed swamp land. The research will focus on attitudes, subjective norms, perceived behavioral control, and intents. This research was conducted 2023 on 75 farmers who carried out Mina Padi farming (rice-fish integration) on rainfed swamp land in South Konawe Regency, Southeast Sulawesi Province, Indonesia. Descriptive analysis and structural equation modeling (SEM) analysis are the methods of data analysis used, and SmartPLS 3 software is utilized. The study's findings indicate that the intention variable for engaging in Mina Padi farming (rice-fish integration) positively and substantially affects attitudes, social norms, and perceived behavioral control. The most important factor influencing the formation of intentions is the attitude variable. A favorable and substantial influence was also identified for the intention variable on the behavioral variable of engaging in Mina Padi farming (rice-fish integration). Meanwhile, the behavioral control perception variable on the Mina Padi farming behavior variable (rice-fish integration) has a positive but insignificant influence.

Reducing life cycle environmental impacts of milk production through precision livestock farming

In recent decades, the livestock sector has significantly improved its efficiency, productivity, and environmental sustainability. Precision Livestock Farming (PLF) represents a driver in this direction, since it enables to monitor individual animals and herds, and supports the farmer in making better decisions. Although the benefits are clear on a livestock perspective, it is difficult to quantify the environmental benefit of having technology on farm, mostly due to the complexity of collecting data on the same farm before and after a certain solution.In this context, this paper focuses on the assessment of the environmental sustainability of a case-study Italian dairy cattle farm where different technologies were installed one by one: first a mechanical ventilation system (MV) and second an automatic milking system (AMS), without introducing other significant changes to the farm management and practices in the meantime. The environmental impact of milk production on the farm was quantified through the Life Cycle Assessment (LCA) method, and the initial farm configuration was compared with the two scenarios in which each technology was incorporated. Fat and protein corrected milk (FPCM) was used as Functional Unit, and a cradle to farm gate system boundary and biophysical allocation method were selected. This enabled to provide valuable insights for stakeholders about the effect on the environmental sustainability of the use of the two technologies. The results show that for all the evaluated impact categories there is an environmental benefit of the improved scenarios. The biggest benefit can be observed with the installation of mechanical ventilation, to which correspond benefits in terms of animal health, welfare and productivity. Then, also AMS entails sustainability improvements, mainly linked with increased efficiency and productivity. In conclusion, the use of technology on dairy farms improves not only the farm efficiency and the animal management, but also the environmental sustainability. Furthermore, the rapid technological advancements may further enhance this positive trend in reducing the contribution of livestock farming to the environmental impacts provided that farmers adopt them.