Avances de investigación en producción animal en Iberoamérica

Publishe

Use of Near-Infrared Spectroscopy to Estimate Fiber and Crude Protein Content in Fodders

Objective: Demonstrate the need to use locally generated data in the calibration of a near-infrared spectrometer (NIRS) in order to predict the chemical characteristics of fodder; instead of using data bases from other geographic regions, as is commonly done in Mexico. Design/Methodology/Approach: Two groups of samples collected in prairies of the central highlands of Mexico, the first group was used to calibrate the equipment; the equations generated were validated with a second group, collected in prairies that were different from the ones of the calibration group, but in the same geographic zone. Results: The best regression coefficients of the NIRS predictions, compared to traditional laboratory analyses were for crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), dry matter (DM) and organic matter (OM) (0.93, 0.87, 0.87, 0.56, 0.72 y 0.68 respectively). The lowest predictive value was observed in ashes (0.27). Limitations of the study/implications: The results show the need to use local materials in the calibration process. Conclusions: NIRS will make predictions of their chemical composition, since this is influenced by geographic origin of the sample and its botanical compositionObjective: Demonstrate the need to use locally generated data in the calibration of a near-infrared spectrometer (NIRS) to predict the chemical characteristics of fodder; instead of using data bases from other geographic regions, as is commonly done in Mexico. Design/Methodology/Approach: Two groups of samples collected in prairies of the central highlands of Mexico, the first group was used to calibrate the equipment; the equations generated were validated with a second group, collected in prairies that were different from the ones of the calibration group, but in the same geographic zone. Results: The best regression coefficients of the NIRS predictions, compared to traditional laboratory analyses were for crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), dry matter (DM) and organic matter (OM) (0.93, 0.87, 0.87, 0.56, 0.72 y 0.68 respectively). The lowest predictive value was observed in ashes (0.27). Limitations of the study/implications: The results show the need to use local materials in the calibration process. Conclusions: NIRS will make predictions of their chemical composition, since this is influenced by geographic origin of the sample and its botanical compositio

TIPIFICACIÓN DE LOS SISTEMAS CAMPESINOS DE PRODUCCIÓN DE LECHE DEL SUR DEL ESTADO DE MÉXICO

"La pro ducción de leche en México se desarrolla en condiciones heterogéneas, desde el punto de vista tecnológico, socio económico, y de las explotaciones. El objetivo de este estudio fue tipi?car los sistemas campesinos de producción de leche (SCPL) de

Effect of live weight pre- and post-lambing on milk production of East Friesian sheep

The study was conducted to analyse the effect of sheep body weight (BW) at mating, mid-pregnancy, lambing, early lactation, mid-lactation and late lactation on milk yield and patterns of milk production. Also, the effects of environmental factors such as number of lambing (NL) and type of lambing (TL) on BW and milk production were analysed. A total of 52 multiparous East Friesian ewes from an experimental flock were used. Ewes were assigned to three different groups according to their BW at each productive stage: low (LBW), moderate (MBW) and high BW (HBW). Lactations were fitted using the mechanistic model described by Pollott. Total milk yield (TMY), peak yield (PY) and time at peak yield (TPY) were also calculated. HBW ewes had consistently higher TMY (p < .001) and PY (p < .05) values, than LBW and MBW in most of productive stage measured. There was a positive linear relationship (p < .05) between TMY and BW in all-productive stage, except at mid-gestation where the relationship was quadratic. HBW ewes weighted at mid-pregnancy showed the highest values of maximum secretion parameter (p = .04) of Pollott model, which could partially explain the better milk yield of HBW ewes. A significant effect of NL on BW (p = .007) and TMY (p = .007) was observed. The BW ewe’s in pregnancy and early lactation is a useful indicator at farm level to improve the milk yield performance in dairy sheep

Predicted milk production per hectare based on yield and chemical composition of native and hybrid maize silage varieties on temperate and tropical regions

"The objective of the present study was to characterize maize silage according to chemical composition, maize silage yield, as well as their predicted milk production. A search was made on studies related to maize silage yield, density, chemical composit

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database

Enteric methane (CH4) from ruminants is the major driver of global warming and climate change. Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (~ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data