Evaluation of productivity, welfare, and economic gains through precision livestock farming

Introduction

Livestock production systems worldwide face mounting challenges arising from climate variability, rising input costs, labour shortages, animal health risks, and increasing societal concerns regarding animal welfare and environmental impact (FAO, 2022; Jiang et al., 2023) [4, 6] . With global demand for animal-derived protein projected to rise steadily, the livestock sector is under pressure to achieve higher efficiency while simultaneously reducing its ecological footprint (FAO, 2022) [4] . Traditional livestock management practices, which rely heavily on manual observation and periodic monitoring, often fail to detect health, welfare, and environmental problems at an early stage, leading to productivity losses and inefficient resource use. Precision Livestock Farming (PLF) has emerged as a transformative approach that applies sensor technologies, automation, artificial intelligence, and big data analytics to continuously monitor animals, their environment, and management processes in real time (Reed et al., 2021; Papakonstantinou et al., 2024) [11,10] . Technologies such as wearable sensors, automated milking systems, machine vision, precision feeding units, thermal imaging, sound analysis, and environmental sensors enable farmers to make timely and informed decisions at both individual-animal and herd levels. Previous studies have demonstrated that PLF can significantly enhance production efficiency, improve disease detection, support animal welfare, and reduce environmental emissions (Caja et al., 2016; Gómez et al., 2021; Trapanese et al., 2025) [3,5,14] . (Caja et al., 2016) [3] . Despite its demonstrated potential, PLF adoption remains uneven across species, farm sizes, and regions. Large commercial dairy and swine farms have been early adopters, while small and medium-scale producers-particularly in developing countries-face barriers related to capital investment, technical skills, and access to advisory services.

In the Indian context, where livestock plays a critical role in rural livelihoods, employment, and nutritional security, empirical evidence on the quantified benefits of PLF remains limited. The present study addresses this gap by providing a statistically robust, multi-sectoral assessment of PLF adoption across dairy, poultry, swine, and beef systems. The objectives are to (i) quantify productivity, health, welfare, environmental, and economic impacts of PLF; (ii) compare outcomes between PLF and non-PLF systems; and (iii) identify socio-economic and technological factors influencing adoption success. By integrating technical performance indicators with socio-economic analysis, the study contributes valuable insights for researchers, policymakers, and practitioners aiming to promote sustainable livestock intensification.

Materials and Methods a. Study Area and Farm Selection

The study was conducted across multiple livestock production regions representing diverse agro-climatic and management conditions. A total of 90 livestock farms were selected using stratified sampling, comprising 30 dairy farms, 30 poultry farms, and 30 swine farms. Farms were further categorized into small, medium, and large-scale operations based on herd or flock size and annual turnover. Both PLF-adopting and non-adopting (control) farms were included to facilitate comparative analysis.

b. PLF Technologies Assessed

The study evaluated a range of PLF technologies relevant to each livestock sector. In dairy systems, automatic milking systems, rumination and activity sensors, heat detection collars, body weight sensors, and precision feeding units were assessed. Poultry systems included automated climate controllers, precision feeders and drinkers, machine vision systems, and sound-based disease detection tools. Swine systems were evaluated using RFID-based identification, automated weighing platforms, precision feeding systems, thermal imaging cameras, and environmental sensors for ammonia and temperature monitoring. Beef systems primarily employed GPS collars, activity sensors, and pasture monitoring tools.

c. Data Collection and Indicators

Data were collected over a three-year period (2022-2024) using a combination of automated sensor records, farm management logs, veterinary reports, and structured farmer surveys. Key indicators were grouped into five domains: productivity (milk yield, average daily gain, feed conversion ratio), health (disease incidence, morbidity rates), welfare (lameness scores, behavioral indices, mortality), environment (methane, ammonia, nitrogen and phosphorus emissions), and economics (cost-benefit ratio, net returns, labor savings).

d. Statistical Analysis

Descriptive statistics were computed to summarize central tendencies and variability. Inferential analyses included independent and paired t-tests to compare PLF and control farms, and one-way ANOVA to assess differences across species and adoption levels. Repeated measures ANOVA was applied for time-series disease data. Multiple linear regression and stepwise regression models were used to identify determinants of PLF adoption and performance outcomes. Effect sizes were estimated using Cohen's d and eta-squared statistics, while Pearson correlation coefficients were calculated to examine associations among key variables. All analyses were conducted at a 5 percent level of significance.

Results and Discussion a. PLF Adoption Patterns Across Sectors: PLF adoption patterns revealed uneven sectoral penetration, with dairy and swine systems accounting for the largest share of deployments. Dairy farms represented approximately 38 percent of total PLF installations, followed by swine (30 percent), poultry (22 percent), and beef systems (10 percent). Higher adoption in confined production systems reflects the suitability of sensor-based monitoring and automation in controlled environments. Limited adoption in beef systems is primarily attributable to extensive management practices and higher infrastructure costs (Fig 1). [8,7] . Dairy farms recorded an average increase of 13.5 percent in milk yield following PLF integration, driven by improved heat detection, optimized feeding, and early disease identification. Swine farms experienced a 15 percent improvement in average daily gain due to precision feeding and growth monitoring, while poultry farms achieved a 9 percent improvement in feed conversion efficiency through automated climate and feeding systems. One-way ANOVA confirmed significant differences across species (F = 9.46, p<0.001), indicating species-specific responses to PLF technologies (Fig 2). [1,5] . Dairy herds showed a decline in mastitis and lameness prevalence from 18 percent to 10 percent, while swine farms recorded reductions in respiratory and gastrointestinal disorders from 21 percent to 12 percent. Poultry farms also experienced lower mortality and reduced incidence of respiratory stress due to automated climate control and early warning systems. These improvements reflect the effectiveness of real-time monitoring in enabling early diagnosis and timely intervention, thereby reducing disease severity and treatment costs. Paired t-tests confirmed that disease reductions were statistically significant across species (p<0.01), with large effect sizes (Cohen's d ranging from 0.75 to 0.91). The findings corroborate earlier evidence that sensor-based surveillance enhances preventive health management and improves herd-level resilience (Fig3). [3,15] . due to continuous monitoring of behavior, activity, and physiological indicators. Automated tracking of rumination time, lying behavior, locomotion, and social interaction allowed farmers to identify stress, discomfort, and early signs of lameness more effectively. Welfare scores increased by 16-27 percent across species, with the highest gains observed in dairy and swine systems (Table 1 andFig 4). Correlation analysis revealed strong positive relationships between rumination time and welfare score (r = 0.71), and a negative association between lying abnormalities and lameness incidence (r = -0.63). These results demonstrate that PLF technologies support proactive welfare management and align livestock production with ethical and societal expectations. . Poultry and beef systems also exhibited efficiency gains due to optimized feeding schedules and environmental control. Multiple regression analysis identified precision feeding as the strongest predictor of feed efficiency improvement (β = -0.67, p < 0.001). Enhanced feed efficiency translated into significant economic benefits, with farm profits increasing by 18-26 percent. Reduced labor requirements, lower veterinary expenses, and higher output contributed to faster return on investment, particularly in dairy and swine farms. f. Environmental Sustainability Environmental assessment indicated consistent reductions in methane, nitrogen, and phosphorus emissions in PLFmanaged farms, supporting earlier evidence on emission mitigation through precision feeding and housing management (Nair et al., 2023; Bernabucci, 2025) [9,2] . Dairy systems achieved the highest methane reduction (14 percent), while swine farms recorded the largest reductions in nitrogen (11 percent) and phosphorus (8 percent) (Table 2 and Fig 6). Poultry systems demonstrated moderate but consistent improvements, whereas beef systems showed comparatively lower reductions due to extensive management practices. One-way ANOVA confirmed significant reductions in methane and nitrogen emissions in PLF farms (p < 0.05). These improvements are attributed to precision feeding, improved manure management, and optimized housing conditions, highlighting PLF's contribution to climate-smart livestock production.

g. Socio-Economic Impacts

Socio-economic analysis revealed substantially higher farmer satisfaction, training uptake, and technology retention in PLF farms compared to control farms, consistent with adoption and diffusion studies in digital agriculture (Singh & Meena, 2021) [13] . Farmer satisfaction reached 88 percent in PLF farms versus 72 percent in non-PLF farms, while training uptake and technology retention were nearly double. Multiple regression analysis showed that farm size, access to training, and system complexity significantly influenced adoption success (R² = 0.62). These findings underscore the importance of extension services, capacity building, and financial support mechanisms in scaling PLF adoption, particularly among small and medium-scale producers (Tabl3 3 and Fig 7). Economic analysis showed average two-year return-oninvestment (ROI) periods for large dairy and swine farms, propelled by reductions in labor costs, medication expenses, and increased output. Smallholder beef and poultry operations, however, faced variable payback timeframes, often requiring grant or cooperative models to offset upfront investment. Labor savings for PLF farms averaged 18% across all sectors, as automation replaced repetitive manual tasks (Table 4).

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