Improvement of feed and nutrient efficiency in pig production through precision feeding

- Invited Review - Current status, challenges and prospects for pig production in Asia

Asia is not only the primary region for global pig production but also the largest consumer of pork worldwide. Although the pig production in Asia has made great progress in the past, it still is confronted with numerous challenges. These challenges include: inadequate land and feed resources, a substantial number of small-scale pig farms, escalating pressure to ensure environmental conservation, control of devastating infectious diseases, as well as coping with high temperatures and high humidity. To solve these problems, important investments of human and financial capital are required to promote large-scale production systems, exploit alternative feed resources, implement precision feeding, and focus on preventive medicine and vaccines as alternatives to antibiotics, improve pig breeding, and increase manure recycling. Implementation of these techniques and management practices will facilitate development of more environmentally-friendly and economically sustainable pig production systems in Asia, ultimately providing consumers with healthy pork products around the world.

Characterizing sow feed intake during lactation to explain litter and subsequent farrowing performance

Variation in feed intake results in nearly 20% of sows consuming less than the recommended lysine (Lys) intake for lactating sows. The Lys requirement for lactating sows is based on litter size and piglet average daily gain which influences milk production. Litter size continues to increase every year causing the need for routine reevaluation of nutrient requirements. If dietary inclusion levels are not continuously adjusted this can lead to inadequate daily Lys and energy intake and may negatively impact sow body condition and litter performance. The objective was to characterize the average daily feed intake (ADFI) of sows and define feed intake patterns and their effects on sow body weight, farrowing performance, litter performance, and subsequent farrowing performance. ADFI during lactation was recorded for 4,248 sows from 7 independent research studies. Data collection occurred from November 2021 through November 2023 at a commercial breed-to-wean facility in western Illinois. Each sow was categorized as: consistently low intake (< 5.5 kg/d) throughout the lactation (LLL); low intakes (< 5 kg/d) in the first week, then gradually increased throughout the rest of the lactation period (LHH); gradual increase in intake throughout lactation with no drop and a peak intake after day 10 of lactation (gradual); rapid increase in intake with no drop and the peak intake met before day 10 (rapid); a major drop in feed intake (> 1.6 kg decrease for ≥ 2 d) any time during lactation (MAJOR); minor drop (≤ 1.6 kg for ≥ 2 d; MINOR). Sows were also separated into low (quartile 1; ≤ 25%), average (quartile 2 through 3), or high feed intake (quartile 4; ≥ 75%) by parity (P1, P2, P3+). Sows in the LLL category were younger in parity, had the greatest preweaned mortality, weaned the lightest average pigs, and experienced the greatest loss in body weight percentage compared with sows in all other feed intake categories. Furthermore, sows in the LLL and LHH categories had one fewer subsequent pig born compared with sows in the other four categories. These data support historical findings that feed intake patterns directly contribute to current litter farrowing performance. Lactation intake patterns also influence subsequent farrowing performance. Identifying under-consuming sows that are likely Lys and energy deficient allows producers opportunities to promote consistent, adequate daily intakes to these groups and mitigate negative impacts on sow and litter performance.

Benefits of automated pig feeding system: A simplified cost-benefit analysis in the context of Kazakhstan

Background and Aim

Automated pig feeding system is an emerging technology with the potential to considerably enhance pig farming. This study aimed to explore the benefits of automated pig feeding systems and provide a simplified cost-benefit analysis, which would serve as a valuable decision-making tool for the stakeholders.

Materials and Methods

This study conducted a literature review of automated pig feeding systems and explored recent advancements. We conducted a cost-benefit analysis to assess the economic feasibility of implementing an automated feeding system in pig farming. Finally, the case study site, a pig farm in Kazakhstan, has been introduced to provide key information.

Results

The results described an automated pig feeding system suitable for a farm with 500 pigs in Kazakhstan. The case study was further enhanced using a simplified cost-benefit analysis tailored to the farm's needs and circumstances.

Conclusion

The designed automated pig feeding system is a marked advancement that seamlessly integrates the currently available automation and management technologies. Its distinguishing feature is the inclusion of remote control capabilities and real-time data provision, which utilize modern technology to transform pig farming management.

Digestion and utilization of plant-based diets by transgenic pigs secreting β-glucanase, xylanase, and phytase in their salivary glands

Novel transgenic (TG) pigs co-expressing three microbial enzymes, β-glucanase, xylanase, and phytase, in their salivary glands were previously generated, which exhibited reduced phosphorus and nitrogen emissions and improved growth performances. In the present study, we attempted to explore the age-related change of the TG enzymic activity, the residual activity of the enzymes in the simulated gastrointestinal tract, and the effect of the transgenes on the digestion of nitrogen and phosphorus content in the fiber-rich, plant-based diets. Results showed that all the three enzymes were stably expressed over the growing and finishing periods in the F2 generation TG pigs. In simulated gastric juice, all the three enzymes exhibited excellent gastrointestinal environment adaptability. The apparent total tract digestibility of phosphorus was increased by 69.05% and 499.64%, while fecal phosphate outputs were decreased by 56.66% and 37.32%, in the TG pigs compared with the wild-type littermates fed with low non-starch polysaccharides diets and high fiber diets, respectively. Over half of available phosphorus and water-soluble phosphorus in fecal phosphorus were reduced. We also found the performance of phosphorus, calcium, and nitrogen retention rates were significantly improved, resulting in faster growth performance in TG pigs. The results indicate that TG pigs can effectively digest the high-fiber diets and exhibit good growth performance compared with wild type pigs.

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Precision feeding is a strategy for supplying an amount and composition of feed as close that are as possible to each animal's nutrient requirements, with the aim of reducing feed costs and environmental losses. Usually, the nutrient requirements of gestating sows are provided by a nutrition model that requires input data such as sow and herd characteristics, but also an estimation of future farrowing performances. New sensors and automatons, such as automatic feeders and drinkers, have been developed on pig farms over the last decade, and have produced large amounts of data. This study evaluated machine-learning methods for predicting the daily nutrient requirements of gestating sows, based only on sensor data, according to various configurations of digital farms. The data of 73 gestating sows was recorded using sensors such as electronic feeders and drinker stations, connected weight scales, accelerometers, and cameras. Nine machine-learning algorithms were trained on various dataset scenarios according to different digital farm configurations (one or two sensors), to predict the daily metabolizable energy and standardized ileal digestible lysine requirements for each sow. The prediction results were compared to those predicted by the InraPorc model, a mechanistic model for the precision feeding of gestating sows. The scenario predictions were also evaluated with or without the housing conditions and sow characteristics at artificial insemination usually integrated into the InraPorc model. Adding housing and sow characteristics to sensor data improved the mean average percentage error by 5.58% for lysine and by 2.22% for energy. The higher correlation coefficient values for lysine (0.99) and for energy (0.95) were obtained for scenarios involving an automatic feeder system (daily duration and number of visits with or without consumption) only. The scenarios including an automatic feeder combined with another sensor gave good performance results. For the scenarios using sow and housing characteristics and automatic feeder only, the root mean square error was lower with gradient tree boosting (0.91 MJ/d for energy and 0.08 g/d for lysine) compared with those obtained using linear regression (2.75 MJ/d and 1.07 g/d). The results of this study show that the daily nutrient requirements of gestating sows can be predicted accurately using data provided by sensors and machine-learning methods. It paves the way for simpler solutions for precision feeding.

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods

Recent technological advances make it possible to deliver feeding strategies that can be tailored to the needs of individual pigs in order to optimise the allocation of nutrient resources and contribute toward reducing excess nutrient excretion. However, these efforts are currently hampered by the challenges associated with: (1) estimation of unobserved traits from the available data on bodyweight and feed consumption; and (2) characterisation of the distributions and correlations of these unobserved traits to generate accurate estimates of individual level variation among pigs. Here, alternative quantitative approaches to these challenges, based on the principles of inverse modelling and separately inferring individual level distributions within a Bayesian context were developed and incorporated in a proposed precision feeding modelling framework. The objectives were to: (i) determine the average and distribution of individual traits characterising growth potential and body composition in an empirical population of growing-finishing barrows and gilts; (ii) simulate the growth and excretion of nitrogen and phosphorus of the average pig offered either a commercial two-phase feeding plan, or a precision feeding plan with daily adjustments; and (iii) simulate the growth and excretion of nitrogen and phosphorus across the pig population under two scenarios: a two-phase feeding plan formulated to meet the nutrient requirements of the average pig or a precision feeding plan with daily adjustments for each and every animal in the population. The distributions of mature bodyweight and ratio of lipid to protein weights at maturity had median (IQR) values of 203 (47.8) kg and 2.23 (0.814) kg/kg, respectively; these estimates were obtained without any prior assumptions concerning correlations between the traits. Overall, it was found that a proposed precision feeding strategy could result in considerable reductions in excretion of nitrogen and phosphorus (average pig: 8.07 and 9.17% reduction, respectively; heterogenous pig population: 22.5 and 22.9% reduction, respectively) during the growing-finishing period from 35 to 120 kg bodyweight. This precision feeding modelling framework is anticipated to be a starting point toward more accurate estimation of individual level nutrient requirements, with the general aim of improving the economic and environmental sustainability of future pig production systems.