The potential for acid-detergent insoluble ash (ADIA), alkaline-peroxide lignin (APL), and acid-detergent lignin (ADL) to predict fecal output (FO) and dry matter digestibility (DMD) by cattle offered bermudagrass [Cynodon dactylon (L.) Pers.] hays of different qualities was evaluated. Eight ruminally cannulated cows (594 ± 35.5 kg) were allocated randomly to 4 hay diets: low (L), medium low (ML), medium high (MH), and high (H) crude protein (CP) concentration (79, 111, 131, and 164 g CP/kg on a DM basis, respectively). Diets were offered in 3 periods with 2 diet replicates per period and were rotated across cows between periods. Cows were individually fed 20 g DM/kg of body weight in equal feedings at 08:00 and 16:00 h for a 10-d adaptation followed by a 5-d total fecal collection. Actual DM intake (DMI), DMD, and FO were determined based on hay offered, ort, and feces excreted. These components were then analyzed for ADL, APL, and ADIA concentration to determine marker recovery and marker-based estimates of FO and DMD.

Forage management and environmental conditions affect water soluble carbohydrate (WSC) storage, and, in turn, influence ruminant forage utilization in silvopastoral systems. The objective was to determine effects of four dependent variables: forage species [(non-native, C₃ (orchardgrass (Dactylis glomerata L.)) and native C₄ mix (8:1:1 big bluestem (Andropogon gerardii Vitman), little bluestem (Schizachyrium scoparium Michx. Nash) and indiangrass (Sorghastrum nutans L.))]; fertility (poultry litter and an unfertilized control); forage sampling date (mid-May, late-May, early-June, mid-June, and late-June); and hour of day (0800, 1100, 1400, and 1700 h) on WSC accumulation in a silvopasture. Concentrations of WSC (g kg DM⁻¹) were greater (p ≤ 0.05) for C₃ forages, with poultry litter not impacting WSC accumulation. Overall, WSC was greatest in mid-June, with the lowest WSC concentration observed at 0800 compared to 1100, 1400, and 1700 h (p ≤ 0.05). Therefore, harvesting forages later in the day resulted in greater WSC. A stepwise regression model indicated acid detergent fiber, ash, and forage P concentration were the best predictors (R₂ = 0.85, p ≤ 0.05) of forage WSC. These results may be useful in future studies aimed at explaining diurnal cattle grazing preference and optimum forage harvest timing in silvopastoral systems.

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha⁻¹ AU⁻¹) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg⁻¹ (126% greater grazing hours per AU), soil Cr <0.098 mg kg⁻¹ (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

The effect of time of fecal sampling on the accuracy of acid-detergent insoluble ash (ADIA) and alkaline-peroxide lignin (APL) for the prediction of fecal output (FO) in cattle was evaluated. Eight ruminally cannulated cows (594 ± 35.5 kg) were allocated randomly to 4 bermudagrass [Cynodon dactylon (L.) Pers.] hay diets markedly different in crude protein concentration (79–164 g/kg) with 2 replicates per diet for 3 periods. Cows were offered hay individually at 20 g DM/kg of body weight daily in equal feedings at 08:00 and 16:00 h for a 10-d adaptation period followed by 5-d of total fecal collection. Fecal grab samples also were taken each day during the fecal collection period at 06:00, 12:00, 18:00, and 24:00 h either directly from the rectum or from freshly voided feces. Samples were composited within cow and time across the 5 d total fecal collection period. Additionally, forage, ort, and fecal samples were analyzed for concentrations of APL and ADIA. Fecal concentrations of ADIA and APL were not affected by sampling time (P ≥ 0.22), even though diet affected (P < 0.01) fecal ADIA and APL concentrations. There were no diet × sampling time interactions (P ≥ 0.60). Estimates of FO and dry matter digestibility (DMD) from ADIA and APL were not affected (P ≥ 0.16) by sampling time or the diet × sampling time interaction (P ≥ 0.74). Estimates of FO and DMD from markers from different sampling times or all different combinations of sampling time were not different (P ≥ 0.72) from those of total collection among internal markers. Little variation in concentrations of ADIA and APL in daily fecal excretion across time increases flexibility in fecal grab sampling schedules for predicting FO and DMD.

Livestock production is increasingly subjected to environmental and economic challenges related to water quantities being utilized, expressed as green (evapotranspiration from rainwater), blue (surface and groundwater), or grey (waste) water footprints at each stage of the product life cycle. Published data indicated that the largest share of water being used for producing beef in the U.S. can be traced back to growing forage and feed (> 90%), whereby the green water footprint was substantially greater (12,933 L water/kg of product) than the blue water footprint, as only a small amount of pasture- and cropland is irrigated (525 L/kg). Based on prevailing quantification methods, feed conversion ratios, and grazing land required, water footprints for beef produced through grazing alone can be relatively high. Green water footprints can easily reach more than 19,000 L/kg for beef from grazing compared with a maximum of 1,731 L/kg for chicken under a typical scenario. However, much of the existing grazing land cannot or should not be converted to cropland for various ecological reasons, and large water footprints would remain for the vegetation even if cattle were removed. Life cycle assessments (LCA) were historically developed to provide a framework for evaluation of the full life cycle of a product or service and to ultimately model environmental impacts through life cycle impact assessments methods. Life cycle assessments grew more refined during past years and efforts are being made to reflect the environmental and economic consequences of different livestock and crop production systems more accurately than in the past. Typical beef production systems on natural and naturalized grasslands in North America generate environmental, economic, and societal benefits that should be reflected in future LCA, farm policies, and regulations. To increase the water use efficiency of each segment of the beef supply chain and thereby to reduce water footprints, grazing systems and methods as well as external inputs should be further optimized and integrated towards enhanced ecosystem services, thereby lowering the overall environmental impact of livestock production.

Calcium addition is necessary to balance the high phosphorus concentrations in high-concentrate diets. However, calcium sources differ in their bioavailability. The objective of this study was to determine apparent Ca and Mg absorption and retention in goats offered diets containing different sources of Ca. Spanish-Boar goats (n = 16; 19.6 ± 1.88 kg) were stratified by weight and randomized to dietary treatments consisting of Purina Antlermax^® 16 containing either calcium carbonate (CC), Calmin^® (CM) or Milk Cal^® (MC). Goats were offered their respective diets at a total of 2% of BW in equal feedings at 0830 and 1800 h while housed in 1.0 × 1.5 m pens with plastic-coated expanded metal floors. Goats were offered increasing amounts of a control corn-based high-concentrate diet on mixed bermudagrass (Cynodon dactylon) pasture until they were consuming the diet at 2% of BW. They were then moved to individual pens and adjusted to their respective diets along with removal of hay from the diet over a 7-d period. Goats were then offered their respective diets for an additional 14-d adaption period to diet and facilities. Total urine and feces were then collected for 7 d and analyzed for Ca and Mg by inductively coupled plasma emission spectroscopy. Data were analyzed using PROC MIXED of SAS. Calcium and Mg intake were not different (P ≥ 0.12) among diets. Calcium apparent absorption and retention (g/d and % of Ca intake) were greatest (P < 0.05) in goats offered CC and did not differ (P ≥ 0.20) between goats offered the CM and MC diets. Magnesium apparent absorption and retention (g/d and % of Mg intake) were greatest (P < 0.05) in goats offered CC and did not differ (P ≥ 0.69) between goats offered the CM and MC diets. Therefore, calcium and magnesium were more available for goats from the diet containing calcium carbonate compared with diets containing Calmin and Milk Cal. Copyright © 2016. American Society of Animal Science .