Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

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.

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

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.

Strategic grazing management and nitrous oxide fluxes from pasture soils in tropical dairy systems.

Greenhouse gases emissions are considered one of the most important environmental issues of dairy farming systems. Nitrous oxide (N2O) has particular importance owing to its global warming potential and stratospheric ozone depletion. The objective of this study was to investigate the influence of two rotational grazing strategies characterized by two pre-grazing targets (95% and maximum canopy light interception; LI95% and LIMax, respectively) on milk production efficiency and N2O fluxes from soil in a tropical dairy farming system based on elephant grass (Pennisetum purpureum Schum. cv. Cameroon). Results indicated that LI95% pre-grazing target provided more frequent defoliations than LIMax. Water-filled pore space, soil and chamber temperatures were affected by sampling periods (P1 and P2). There was a significant pre-grazing target treatment × sampling period interaction effect on soil NH4+ concentration, which was most likely associated with urinary-N discharge. During P1, there was a greater urinary-N discharge for LI95% than LIMax (26.3 vs. 20.9 kg of urinary-N/paddock) caused by higher stocking rate, which resulted in greater N2O fluxes for LI95%. Inversely, during P2, the soil NH4+ and N2O fluxes were greater for LIMax than LI95%. During this period, the greater urinary-N discharge (46.8 vs. 44.8 kg of urinary-N/paddock) was likely associated with longer stocking period for LIMax relative to LI95%, since both treatments had similar stocking rate. Converting hourly N2O fluxes to daily basis and relating to milk production efficiency, LI95% was 40% more efficient than LIMax (0.34 vs. 0.57 g N-N2O/kg milk·ha). In addition, LI95% pre-grazing target decreased urea-N loading per milk production by 34%. Strategic grazing management represented by the LI95% pre-grazing target allows for intensification of tropical pasture-based dairy systems, enhanced milk production efficiency and decreased N-N2O emission intensity.

Strategic grazing management towards sustainable intensification at tropical pasture-based dairy systems.

Agricultural systems are responsible for environmental impacts that can be mitigated through the adoption of more sustainable principles. Our objective was to investigate the influence of two pre-grazing targets (95% and maximum canopy light interception during pasture regrowth; LI95% and LIMax, respectively) on sward structure and herbage nutritive value of elephant grass cv. Cameroon, and dry matter intake (DMI), milk yield, stocking rate, enteric methane (CH4) emissions by Holstein × Jersey dairy cows. We hypothesized that grazing strategies modifying the sward structure of elephant grass (Pennisetum purpureum Schum.) improves nutritive value of herbage, increasing DMI and reducing intensity of enteric CH4 emissions, providing environmental and productivity benefits to tropical pasture-based dairy systems. Results indicated that pre-sward surface height was greater for LIMax (≈135 cm) than LI95% (≈100 cm) and can be used as a reliable field guide for monitoring sward structure. Grazing management based on LI95% criteria improved herbage nutritive value and grazing efficiency, allowing greater DMI, milk yield and stocking rate by dairy cows. Daily enteric CH4 emission was not affected; however, cows grazing elephant grass at LI95% were more efficient and emitted 21% less CH4/kg of milk yield and 18% less CH4/kg of DMI. The 51% increase in milk yield per hectare overcame the 29% increase in enteric CH4 emissions per hectare in LI95% grazing management. Thereby the same resource allocation resulted in a 16% mitigation of the main greenhouse gas from pasture-based dairy systems. Overall, strategic grazing management is an environmental friendly practice that improves use efficiency of allocated resources through optimization of processes evolving plant, ruminant and their interface, and enhances milk production efficiency of tropical pasture-based systems.