DSpace Coleção: Artigo em periódico indexado (CPPSE)

Water performance indicators and benchmarks for dairy production systems.

2024-01-01T00:00:00Z

Título: Water performance indicators and benchmarks for dairy production systems. Autoria: PALHARES, J. C. P.; MATARIM, D. L.; SOUSA, R. V. de; MARTELLO, L. S. Conteúdo: The aim of the study is to discern benchmarks for the indicators L water cow−1 day−1 and L water kg milk−1 day−1 per type of production system and season. A total of 876 commercial dairy farms underwent comprehensive water consumption monitoring from January 2021 to December 2022. The monitored water consumptions were animal drinking water and water usage for cleaning. Confined systems exhibited the highest average for animal drinking and cleaning, 87.5 L water cow−1 day−1 and 84.4 L water cow−1 day−1, respectively. Semi-confined systems presented the lowest average for animal drinking, 54.4 L water cow−1 day−1. Pasture systems showed the lowest average for cleaning, 45.2 L water cow−1 day−1. The benchmarks proposed in this study can serve as the first references for animal drinking and milking parlor washing consumption for production systems in tropical conditions.

Derivation of prediction error variance for non-genotyped individuals in genomic selection.

2026-01-01T00:00:00Z

Título: Derivation of prediction error variance for non-genotyped individuals in genomic selection. Autoria: JUNQUEIRA, V. S.; YOKOO, M. J. I.; CARDOSO, F. F. Conteúdo: Genomic selection has transformed plant and animal breeding by enabling accurate prediction of genetic merit using DNA markers; however, comprehensive genotyping of all selection candidates remains economically prohibitive for most breeding programs. While breeding programs must decide which subset of individuals to genotype within budget constraints, current approaches rely primarily on experience-based decisions rather than quantitative frameworks. We present explicit mathematical derivations for prediction error variance (PEV) in non-genotyped individuals under mixed model equations, providing a theoretical foundation for evaluating genotyping strategies prospectively. The approach derives PEV expressions for non- genotyped selection candidates under different relationship matrix structures, including pedigree-based, genomic, and hybrid single-step methodologies that combine both information sources. The derivations accommodate complex breeding program structures with historical training populations containing both genotypes and phenotypes alongside contemporary selection candidates with only pedigree information. Using Schur complement methods applied to partitioned mixed model equations, the framework enables calculation of prediction uncertainty without requiring actual phenotypic data from selection candidates. The expressions simplify under different information scenarios, from cases with complete phenotypic data to situations where only relationship information is available. The method was validated through simulations across six scenarios with populations ranging from 180 to 15,500 individuals, confirming numerical equivalence with direct matrix inversion while demonstrating computational and memory advantages that increase with population size. Although genomic relationship matrix operations dominate the complexity, matrix decomposition techniques, including Cholesky factorization and APY methodology, can improve efficiency. The mathematical framework provides quantitative tools for transitioning from experience-based to mathematically- informed genotyping decisions, with applications extending to any field requiring prospective quantification of prediction uncertainty under resource constraints.

Effect of two-doses of 3-nitrooxypropanol (3-NOP) on methane emissions, performance, rumen microbiome, and metabolomics in Nellore cattle.

2026-01-01T00:00:00Z

Título: Effect of two-doses of 3-nitrooxypropanol (3-NOP) on methane emissions, performance, rumen microbiome, and metabolomics in Nellore cattle. Autoria: AMÂNCIO, B. R.; MAGNANI, E.; NUNES, A. T.; SILVA, T. H.; CORTINHAS, C. S.; CARVALHO, V. V. de; TAMASSIA, L. F. M.; ZIHLMANN, R.; BERNDT, A.; SANTOS, J. de O.; CÔNSOLO, N. R. B.; BENEDETI, P. D. B.; ARNANDES, R. H. B.; PAULA, E. M. Conteúdo: This study evaluated the effects of two doses of 3-nitrooxypropanol (3-NOP) on methane (CH4) emissions, performance, dry matter (DM) intake, apparent digestibility, rumen microbiome and metabolomic profile of Nellore cattle fed a high-concentrate finishing finishing diet. Seventy-five 20-month-old Nellore bulls, 361.6 ± 30.08 kg body weight (BW), were individually housed with ad libitum access to feed and water. Animals were distributed in a completely randomized study design, with three treatments and 25 animals per treatment, which were: 1) CON, control (basal diet + mineral premix without 3-NOP), 2) 3-NOP65 (Basal diet + mineral premix + 65 mg 3-NOP/kg of DM), 3) 3-NOP85 (Basal diet + mineral premix + 85 mg 3-NOP/kg of DM). The 115-d trial included a 3-wk adaptation period with increasing dietary concentrate levels from 50% to 88%. Enteric CH4 emissions were measured using the sulfur hexafluoride (SF6) tracer gas technique. Supplementation with 3-NOP had no detrimental effect on final BW (P = 0.89) and average daily gain (ADG; P = 0.94), but DM intake increased linearly with 3-NOP inclusion (P = 0.05). Methane emissions (g/d) were reduced by 13.2% and 26.7% in the 3-NOP65 and 3-NOP85 groups, respectively (P < 0.05), without adverse effects on animal health. Rumen microbiome analysis revealed a quadratic response in the relative abundance of the phylum Euryarchaeota (P = 0.01). Metabolomic analysis indicated significant changes in amino acid and energy metabolism, with proline, arginine, and threonine identified as key discriminant metabolites (VIP > 1) in the 3-NOP85 group. These findings demonstrate that 3-NOP supplementation effectively reduces CH4 emissions in a dose-dependent manner while maintaining animal performance and health.

Direct determination of soil carbon stocks by NIRS: an advanced approach.

2026-01-01T00:00:00Z

Título: Direct determination of soil carbon stocks by NIRS: an advanced approach. Autoria: FREITAS, V. S.; BENTO, L. R.; NAPOLITANO, V.; OLIVEIRA, P. P. A.; PEZZOPANE, J. R. M.; BERNARDI, A. C. de C.; MILORI, D. M. B. P.; MARTIN NETO, L. Conteúdo: Soil carbon stocks are obtained as the product of carbon content, soil bulk density, and specific layer thickness. The determination of soil bulk density requires field sampling procedures that can be labor-intensive and time- consuming, particularly when sampling deep soil layers or large areas. In this study, near-infrared spectroscopy (NIRS) was used to directly estimate volumetric soil carbon (Ps, kg C m 3 ), allowing easier estimation of soil carbon stocks. This approach aims to reduce the difficulties and high cost of traditional soil sampling, which involves opening trenches and collecting undisturbed soil samples using a volumetric ring for soil bulk density determination, as well as using conventional techniques as CHN elemental analyzer. A dataset of 576 soil samples (480 for calibration and 96 for validation) was measured by NIRS, collected from a long-term field experiment encompassing different tropical pasture management systems and a reference native vegetation area: recovered pasture (RP), intercropped pasture (CON), extensive pasture (EX), and native forest (FO). The best model, employing the spectral first derivative as a preprocessing step and Partial Least Squares (PLS) regression as the multivariate algorithm, achieved performance of R 2 2.85 in calibration, and R 2 = 0.79, RMSEC = 4.5 kg C m =0.85, RMSEP = 4.1 kg C m 3 3 , RPD = 2.16 and RPIQ = , RPD = 2.53 and RPIQ = 3.63 in validation. Carbon stocks calculated from NIRS-predicted values in the validation dataset showed no significant differences from reference measurements. These findings demonstrate that NIRS can provide reliable, rapid, and cost-effective estimates of soil carbon stocks, highlighting its potential application in carbon credit assessments.