RESUMO -Avaliaram-se os efeitos de quatro níveis de concentrado -NC (0, 1, 3 e 5 kg/vaca/dia) e dois de proteína bruta -PB (11 e 13% na matéria seca total) sobre o consumo, a digestibilidade e o desempenho de vacas leiteiras. Utilizaramse oito vacas mestiças Holandês-Zebu com 520 ± 40 kg, distribuídas em um quadrado latino 8 × 8 em oito períodos de 10 dias para avaliação dos efeitos de tratamento, animal e período. O experimento foi conduzido em pastagem de capim-elefante (Pennisetum purpureum, Schum) na estação chuvosa e os concentrados foram constituídos de fubá de milho, farelo de soja, ureia e mistura mineral. O consumo de matéria seca (MS) não diferiu entre os tratamentos e o consumo do pasto tendeu a diminuir com o aumento de NC. Os consumos de PB e carboidratos não-fibrosos (CNF) aumentaram de acordo com os NC e PB na dieta, enquanto os consumos de nutrientes digestíveis totais, CNF, fibra em detergente neutro (

Economic development, international food and feed demand, and government policies have converted Brazil’s natural ecosystems into agricultural land. The Integrated Farm System Model (IFSM) was evaluated using production, economic, and weather data collected on two cooperating farms in the Legal Amazon and Cerrado biomes in the Midwest state of Mato Grosso, Brazil. Three sustainable agricultural intensification strategies, namely grain supplementation, pasture re-seeding, and pasture fertilization were simulated in IFSM with double the beef cattle stocking density compared to extensive grazing. Livestock dry matter consumption simulated in IFSM was similar for pasture grazing estimates and actual feed consumed by beef cattle on the two collaborating farms. Grain supplementation best balanced beef production and profitability with lower carbon footprint compared to extensive grazing, followed by pasture fertilization and pasture re-seeding. However, pasture re-seeding and fertilization had greater use of water and energy and more nitrogen losses. Human edible livestock feed use was greatest for grain supplementation compared to other modeled systems. While grain supplementation appears more favorable economically and environmentally, greater use of human edible livestock feed may compete with future human food needs. Pasture intensification had greater human edible feed conversion efficiency, but its greater natural resource use may be challenging.

The intensification of Brazil's beef cattle production system can involve different strategies to increase beef production while reducing deforestation in the Amazon biome and mitigating climate change. This study economically evaluates a cooperating beef farm in the state of Mato Grosso, Brazil's Amazon biome over three crop years (2015–16 to 2017–18), transitioning from an extensive grazing system to a semi-intensive system using five sustainable agricultural intensification (SAI) practices. These five practices include (1) grain supplementation for cattle, (2) pasture fertilization, (3) pasture re-seeding, (4) crop–livestock integration (CLI) and (5) irrigated and fertilized pasture that is rotationally grazed. The relative costs of these five SAI strategies used on this cooperating farm are compared. The adoption of SAI strategies increased beef productivity 5.7% (228–241 kg live-weight sold per hectare) and gradually improved net farm income by ~130% over the 3 years of transition (−US$94.79 to $29.80 ha−1). Grain supplementation (US$188 ha−1) had the cheapest cost per hectare, followed by pasture fertilization (US$477 ha−1) and pasture reseeding (US$650 ha−1). The most costly practice was in-ground irrigation of fenced rotationally grazed pasture (US$1600 ha−1) with the second most costly being CLI (US$672 ha−1). Despite adoption challenges of these SAI practices, past research confirm these five practices can increase beef productivity and profitability while reducing carbon footprint. Regardless of the cost per hectare of each practice, farmer adoption can be improved through education, support and incentives from both the public and private sectors.

Equations to predict body weight (BW) of crossbred Holstein-Zebu dairy heifers were developed and compared with current models (Heinrichs et al. for Holsteins, United States; Reis et al. for crossbred Holstein-Zebu, Brazil). The data set was constructed from 150 measurements of BW (320 ± 107 kg) and biometric measurements such as heart girth (HG, 161 ± 19.5 cm), withers height (WH, 126 ± 11.0 cm), and hip height (HH, 132 ± 11.3 cm) of heifers from 5 commercial dairy producers in the southern Amazon region in Brazil. The data were evaluated using mixed nonlinear models with herd as a random effect. Three nonlinear equations were fitted: BW (kg)=0.00058·HG (cm)(2.6135); BW (kg)=0.000618·HG (cm)(2.7362); and BW (kg)=0.000196·HH (2.8793). An independent database was constructed to evaluate the models from 38 treatment means of 4 feeding trials: BW 258 ± 54.3 kg, HG 142.5 ± 11.8 cm, WH 113.2 ± 6.0 cm, and HH 118.7 ± 9.1 cm (mean ± SD). The evaluations were based on the relationship between observed and predicted values of BW by linear regression, root mean square prediction error (RMSPE), and concordance correlation coefficient analysis. Only the proposed model using HG accurately predicted observed BW, with bias (observed - predicted) of 4.83 kg and RMSPE of 5.41% of observed BW (87.7% of random error). The models using WH and HH failed to accurately predict observed BW, with a bias of -3.06 and 72.02 kg, and RMSPE of 9.40% of observed BW (75.2% of random error and 23.1% of systematic error) and 30.81% of observed BW (81.2% of mean bias). Additionally, the models of Heinrichs and Reis used for comparison did not predict BW accurately, with a bias of 19.32 and 29.37 kg and RMSPE of 9.08% of observed BW (68.4% of mean bias and 31.4% of random error) and 12.58% of observed BW (81.9% of mean bias). The largest concordance correlation coefficient of the proposed HG-nonlinear model (0.930), compared with the models of Heinrichs and Reis of 0.845 and 0.708, confirmed the greater accuracy and precision of the new equation to predict BW in crossbred Holstein-Zebu dairy heifers.

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The phosphorus (P) chemistry of biochar (BC)-amended soils is poorly understood. This statement is based on the lack of published research attempting a comprehensive characterization of biochar’s influence on P sorption. Therefore, this study addressed the kinetic limitations of these processes. This was accomplished using a fast pyrolysis biochar made from a mix of waste materials applied to a highly weathered Latossolo Vermelho distrofico (Oxisol) from São Paulo, Brazil. Standard method (batch method) was used. The sorption kinetic studies indicated that P sorption in both cases, soil (S) and soil-biochar (SBC), had a relatively fast initial reaction between 0 to 5 min. This may have happened because adding biochar to the soil decreased P sorption capacity compared to the mineral soil alone. Presumably, this is a result of: (i) Inorganic phosphorus desorbed from biochar was resorbed onto the mineral soil; (ii) charcoal particles physically covered P sorption locations on soil; or (iii) the pH increased when BC was added SBC and the soil surface became more negatively charged, thus increasing anion repulsion and decreasing P sorption.

This study was conducted to assess the body and empty body fat physical and chemical composition through biometric measurements (BM) as well as postmortem measurements taken in 40 F Angus × Nellore bulls and steers. The animals used were 12.5 ± 0.51 mo of age, with an average shrunk BW of 233 ± 23.5 and 238 ± 24.6 kg for bulls and steers, respectively. Animals were fed 60:40 ratio of corn silage to concentrate diets. Eight animals (4 bulls and 4 steers) were slaughtered at the beginning of the trial, and the remaining animals were randomly assigned to a 1 + 2 × 3 factorial arrangement (1 reference group, 2 sexes, and 3 slaughter weights). The remaining animals were slaughtered when the average BW of the group reached 380 ± 19.5 (6 bulls and 5 steers), 440 ± 19.2 (6 bulls and 5 steers), and 500 ± 19.5 kg (5 bulls and 5 steers). Before the slaughter, the animals were led through a squeeze chute in which BM were taken, including hook bone width (HBW), pin bone width, abdomen width (AW), body length (BL), rump height, height at the withers, pelvic girdle length (PGL), rib depth (RD), girth circumference (GC), rump depth, body diagonal length (BDL), and thorax width. Additionally, the following postmortem measurements were obtained: total body surface (TBS), body volume (BV), subcutaneous fat (SF), internal physical fat (InF), intermuscular fat, carcass physical fat (CF), empty body physically separable fat (EBF), carcass chemical fat (CFch), empty body chemical fat (EBFch), fat thickness in the 12th rib, and 9th to 11th rib section fat. The equations were developed using a stepwise procedure to select the variables that should enter into the model. The and root mean square error (RMSE) were used to account for precision and accuracy. The ranges for and RMSE were 0.852 to 0.946 and 0.0625 to 0.103 m, respectively for TBS; 0.942 to 0.998 and 0.004 to 0.022 m, respectively, for BV; 0.767 to 0.967 and 2.70 to 3.24 kg, respectively, for SF; 0.816 to 0.900 and 3.04 to 4.12 kg, respectively, for InF; 0.830 to 0.988 and 3.44 to 8.39 kg, respectively, for CF; 0.861 to 0.998 and 1.51 to 10.98 kg, respectively, for EBF; 0.825 to 0.985 and 5.96 to 8.46 kg, respectively, for CFch; and 0.862 to 0.992 and 5.54 to 12.19 kg, respectively, for EBFch. Our results indicated that BM that could accurately and precisely be used as alternatives to predict different fat depots of F Angus × Nellore bulls and steers are AW, GC, or PGL for CF estimation; HBW and RD for CFch estimation; and body lengths such as BL and BDL for InF and SF estimation, respectively.

We evaluated and compared empirical equations used for assessing beef cattle body composition, developed in 2010 (M10), 2012 (M12), 2006 (V06) and 1946 (HH46). Forty-eight F1 Nellore × Angus bulls and steers, aged 12.5 ± 0.51 months old, with initial shrunk bodyweight of 233 ± 23.5 kg and 238 ± 24.6 kg, respectively, were used in this experiment. The trial was a randomised factorial arrangement of treatments (two genders and five slaughter weights). The animals were randomly assigned to five slaughter-weight-based groups: baseline, maintenance, and 380, 440 and 500 kg. The diet comprised maize silage and concentrate (60 : 40). After slaughter, the 9th–11th rib section cut was dissected into muscle, fat and bone. The remaining carcass was similarly dissected. Other variables evaluated as partial predictors of body composition included empty bodyweight, dressing percentage, visceral fat percentage, and organ and viscera percentage. The values estimated with predictive equations were compared with observed values. For the physically separable carcass composition, only the M12 equation estimated precisely and accurately the amount of muscle (r2 = 0.98, root-mean-square error (RMSE) = 5.64 kg, concordance correlation coefficient (CCC) = 0.96) and fat (r2 = 0.94, RMSE = 4.91 kg, CCC = 0.96) tissue present in the carcass. The V06 and M10 equations estimated precisely and accurately the amount of carcass chemical components; HH46 could explain only the amount of crude protein (r2 = 0.84, RMSE = 4.71 kg, CCC = 0.90) content in the carcass. The equations used to predict empty body chemical composition failed to estimate correctly the amount of chemical contents present in the empty bodyweight. However, V06 can be used to estimate the crude protein (r2 = 0.91, RMSE = 5.97 kg, CCC = 0.93) content in the empty bodyweight. Furthermore, M10 could be used to estimate ether extract (r2 = 0.94, RMSE = 8.13 kg, CCC = 0.84) content, although this had to be analysed by gender, because such variables (i.e. ether extract) presented a pronounced effect, especially for steers, on total chemical fat.