Precision Feeding and Management of Farm Animals, 2nd Edition

Two randomized complete block design experiments evaluated the influence that varying degrees of ingredient inclusion accuracy in a finishing diet have on growth performance and carcass traits. Treatments included (1) normal inclusion tolerance with a 0.454 kg tolerance for all ingredients (CON) or (2) variable inclusion tolerance where each ingredient was randomly increased or decreased but the targeted as-fed quantity for the daily delivery was met (VAR). In Experiment. 1, black Angus heifers (n = 60; initial shrunk BW = 460 ± 26.2 kg) were used in a 112 d experiment. Ten pens in total (5 pens/treatment, 6 heifers/pen) were used. The targeted diet (DM basis) consisted of high-moisture ear corn (75%), dried distiller’s grains (20%), and a liquid supplement (5%). As-fed inclusion rates for DDGS and LS varied from formulated targets by −20, −15, −10, −5, 0, +5, +10, +15 or +20%. The HMEC inclusion was adjusted so that the targeted as-fed amount of the diet was delivered daily. Treatment did not alter ADG, DMI, G:F, HCW, dressing percentage, rib-eye area, rib fat, USDA marbling score, KPH, yield grade, retail yield, empty body fat, or body weight at 28% estimated EBF, nor liver abscess prevalence or severity (p ≥ 0.15). In Exp. 2, Charolais–Angus cross steers (n = 128; initial shrunk BW = 505 ± 32.1 kg) were used in a 94 d experiment. Steers were assigned to pens (8 pens/treatment; 8 steers/pen) and one of the two management strategies used in Exp. 1 was employed. Ractopamine HCl was fed (300 mg per head daily) during the final 28 d. Diets consisted of (DM basis) dry-rolled corn (63%), dried distiller’s grains plus solubles (15%), liquid supplement (5%), grass hay (7%), and corn silage (10%). Ingredient inclusions were randomized in the same manner as Exp. 1, except LS inclusion was held constant. Corn silage inclusion was adjusted so that the targeted as-fed amount of the diet was delivered each day. Steers from VAR had increased (p = 0.01) DMI, but similar (p = 0.75) ADG resulting in reduced (p ≤ 0.02) G:F and growth-performance-predicted Net Energy for maintenance and gain. Treatment did not influence (p ≥ 0.38) HCW, dressing percentage, rib-eye area, rib fat, KPH, yield grade, retail yield, empty body fat, or body weight at 28% estimated EBF. A tendency for an increased USDA marbling score (p = 0.08) was noted in VAR. Under the conditions of this experiment, randomly altering ingredient proportions can impact growth performance and efficiency measures depending upon the type of finishing diet fed. Full article

Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement. Full article

Automatic Milking System (AMS) installations are increasing in the USA despite the higher investment cost than conventional systems. Surveys on AMSs conducted outside the USA focused on small–medium herds, specific regions, or aspects of AMS milking. This study described farmers’ perceptions about the decision-making process of adopting an AMS in the USA’s large dairies (≥7 AMS boxes) regarding changes in technology, housing, management practices, labor, herd performance, and health. After being contacted, 27 of 55 farmers from large AMS herds completed the survey. The main reasons for adopting an AMS were labor costs, cows’ welfare, and herd performance. Most farms constructed new barns, used a free-flow traffic system, and changed their feed management. Increases in water and energy use were perceived by 42% and 62% of farmers, respectively. Farmers estimated decreases in labor costs of over 21%, and AMS employees worked 40–60 h/week. Milk production increases were reported by 58%, with 32% observing higher milk fat and protein content. Easier sick cow detection, better mastitis management, and improvements in pregnancy rates were reported. Thus, farmers transitioning to AMSs perceived altered resource utilization, labor cost savings, and improvements in employee quality of life, animal welfare, and farm management. While 54% of respondents would recommend an AMS to other farms, 38% suggested considering additional aspects prior to adoption. Full article