The production of beef cattle in the Atlantic Forest biome mostly takes place in pastoral production systems. There are millions of hectares covered with pastures in this biome, including degraded pasture (DP), and only small area of the original Atlantic Forest has been preserved in tropics, implying that actions must be taken by the livestock sector to improve sustainability. Intensification makes it possible to produce the same amount, or more beef, in a smaller area; however, the environmental impacts must be assessed. Regarding climate change, the C dynamics is essential to define which beef cattle systems are sustainable. The objectives of this study were to investigate the C balance (t CO2e./ha per year), the intensity of C emission (kg CO2e./kg BW or carcass) and the C footprint (t CO2e./ha per year) of pasture-based beef cattle production systems, inside the farm gate and considering the inputs. The results were used to calculate the number of trees to be planted in beef cattle production systems to mitigate greenhouse gas (GHG) emissions. The GHG emission and C balance, for 2 years, were calculated based on the global warming potential (GWP) of AR4 and GWP of AR5. Forty-eight steers were allotted to four grazing systems: DP, irrigated high stocking rate pasture (IHS), rainfed high stocking rate pasture (RHS) and rainfed medium stocking rate pasture (RMS). The rainfed systems (RHS and RMS) presented the lowest C footprints (−1.22 and 0.45 t CO2e./ha per year, respectively), with C credits to RMS when using the GWP of AR4. The IHS system showed less favorable results for C footprint (−15.71 t CO2e./ha per year), but results were better when emissions were expressed in relation to the annual BW gain (−10.21 kg CO2e./kg BW) because of its higher yield. Although the DP system had an intermediate result for C footprint (−6.23 t CO2e./ha per year), the result was the worst (−30.21 CO2e./kg BW) when the index was expressed in relation to the annual BW gain, because in addition to GHG emissions from the animals in the system there were also losses in the annual rate of C sequestration. Notably, the intensification in pasture management had a land-saving effect (3.63 ha for IHS, 1.90 for RHS and 1.19 for RMS), contributing to the preservation of the tropical forest.

Despite the importance of the role of Climate Finance to comply with the United Nations Framework Convention on Climate Change 1.5°C objective, there is no consensus on the definition of Climate Finance and the estimated assessment of its aggregated flows and effects remains challenging. Despite being a major emitter and having a significant and cost-effective mitigation potential, the livestock sector has so far only received a marginal share of Climate Finance. As demand for animal protein products continues to increase (68% between 2010 and 2050), there is a compelling case for channeling more Climate Finance investments into the sector to incentivize greenhouse gas emissions reduction at scale. Bottlenecks in linking the livestock sector to Climate Finance include the insufficient capacity to assess the cost-benefit of projects, high upfront cost and risk perception of investors, the informality of the sector, non-existence of Climate Finance instruments dedicated to the livestock sector and lack of cost-efficient Monitoring, Reporting and Verification systems. Nevertheless, recent developments provide avenues to increase the access of the animal protein sector to Climate Finance.

Methane (CH4) is a greenhouse gas (GHG) produced and released by eructation to the atmosphere in large volumes by ruminants. Enteric CH4 contributes significantly to global GHG emissions arising from animal agriculture. It has been contended that tropical grasses produce higher emissions of enteric CH4 than temperate grasses, when they are fed to ruminants. A number of experiments have been performed in respiration chambers and head-boxes to assess the enteric CH4 mitigation potential of foliage and pods of tropical plants, as well as nitrates (NO3−) and vegetable oils in practical rations for cattle. On the basis of individual determinations of enteric CH4 carried out in respiration chambers, the average CH4 yield for cattle fed low-quality tropical grasses (>70% ration DM) was 17.0 g CH4/kg DM intake. Results showed that when foliage and ground pods of tropical trees and shrubs were incorporated in cattle rations, methane yield (g CH4/kg DM intake) was decreased by 10% to 25%, depending on plant species and level of intake of the ration. Incorporation of nitrates and vegetable oils in the ration decreased enteric CH4 yield by ∼6% to ∼20%, respectively. Condensed tannins, saponins and starch contained in foliages, pods and seeds of tropical trees and shrubs, as well as nitrates and vegetable oils, can be fed to cattle to mitigate enteric CH4 emissions under smallholder conditions. Strategies for enteric CH4 mitigation in cattle grazing low-quality tropical forages can effectively increase productivity while decreasing enteric CH4 emissions in absolute terms and per unit of product (e.g. meat, milk), thus reducing the contribution of ruminants to GHG emissions and therefore to climate change.

Accurate estimates of methane (CH4) production by cattle in different contexts are essential to developing mitigation strategies in different regions. We aimed to: (i) compile a database of CH4 emissions from Brazilian cattle studies, (ii) evaluate prediction precision and accuracy of extant proposed equations for cattle and (iii) develop specialized equations for predicting CH4 emissions from cattle in tropical conditions. Data of nutrient intake, diet composition and CH4 emissions were compiled from in vivo studies using open-circuit respiratory chambers, SF6 technique or the GreenFeed® system. A final dataset containing intake, diet composition, digestibility and CH4 emissions (677 individual animal observations, 40 treatment means) obtained from 38 studies conducted in Brazil was used. The dataset was divided into three groups: all animals (GEN), lactating dairy cows (LAC) and growing cattle and non-lactating dairy cows (GCNL). A total of 54 prediction equations available in the literature were evaluated. A total of 96 multiple linear models were developed for predicting CH4 production (MJ/day). The predictor variables were DM intake (DMI), gross energy (GE) intake, BW, DMI as proportion of BW, NDF concentration, ether extract (EE) concentration, dietary proportion of concentrate and GE digestibility. Model selection criteria were significance (P < 0.05) and variance inflation factor lower than three for all predictors. Each model performance was evaluated by leave-one-out cross-validation. The Intergovernmental Panel on Climate Change (2006) Tier 2 method performed better for GEN and GCNL than LAC and overpredicted CH4 production for all datasets. Increasing complexity of the newly developed models resulted in greater performance. The GCNL had a greater number of equations with expanded possibilities to correct for diet characteristics such as EE and NDF concentrations and dietary proportion of concentrate. For the LAC dataset, equations based on intake and animal characteristics were developed. The equations developed in the present study can be useful for accurate and precise estimation of CH4 emissions from cattle in tropical conditions. These equations could improve accuracy of greenhouse gas inventories for tropical countries. The results provide a better understanding of the dietary and animal characteristics that influence the production of enteric CH4 in tropical production systems.

The relationship between DM intake (DMI) and enteric methane emission is well established in ruminant animals but may depend on measurement technique (e.g. spot v. continuous gas sampling) and rumen environment (e.g. use of fermentation modifiers). A previous meta-analysis has shown a poor overall (i.e. 24 h) relationship of DMI with enteric methane emission in lactating dairy cows when measured using the GreenFeed system (GF; Symposium review: uncertainties in enteric methane inventories, measurement techniques, and prediction models. Journal of Dairy Science 101, 6655 to 6674). Therefore, we examined this relationship in a 15-week experiment with lactating dairy cows receiving a control diet or a diet containing the investigational product 3-nitrooxypropanol (3-NOP), an enteric methane inhibitor, applied at 60 mg/kg feed DM. Daily methane emission, measured using GF, and DMI were clustered into 12 feed-intake timeslots of 2 h each. Methane emission and DMI were the lowest 2 h before feeding and the highest within 6 h after feed provision. The overall (24 h) relationship between methane emission and DMI was poor (R2 = 0.01). The relationship for the control (but not 3-NOP) cows was improved (R2 = 0.31; P < 0.001) when DMI was allocated to timeslots and was strongest (R2 = 0.51; P < 0.001) 8 to 10 h after feed provision. Analysis of the 3-NOP emission data showed marked differences in the mitigation effect over time. There was a lack of effect in the 2-h timeslot before feeding, the mitigation effect was highest (45%) immediately after feed provision, persisted at around 32% to 39% within 10 h after feed provision, and decreased to 13%, 4 h before feeding. These trends were clearly related to DMI (i.e. 3-NOP intake) by the cows. The current analysis showed that the relationship of enteric methane emission, as measured using GF, and DMI in dairy cows depends on the time of measurement relative to time of feeding. The implication of this finding is that a sufficient number of observations, covering the entire 24-h feeding cycle, have to be collected to have representative emission estimates using the GF system. This analysis also revealed that the methane mitigation effect of 3-NOP is highest immediately after feed provision and lowest before feeding.

There is a trend to reduce the space allowance per animal in cattle feedlot, despite its potential negative impact on animal welfare. Aiming to evaluate the effects of space allowance per animal in outdoor feedlots on beef cattle welfare, a total of 1350 Nellore bulls (450 pure and 900 crossbred) were confined for 12 weeks using three space allowances: 6 (SA6), 12 (SA12) and 24 (SA24) m2/animal (n = 450 per treatment). Bulls were housed in three pens per treatment (n = 150 per pen). The first 6 weeks in the feedlot were defined as ‘dry’ and the last as ‘rainy’ period, according to the accumulated precipitation. Animal-based (body cleanliness, health indicators and maintenance behaviour) and environmental-based indicators (mud depth and air dust concentration) were assessed weekly during the feedlot period. Most of the health indicators (nasal and ocular discharge, hoof and locomotion alterations, diarrhoea, bloated rumen and breathing difficulty) were assessed in a subset of 15 animals randomly selected from each pen. Coughs and sneezes were counted in each pen. Maintenance behaviours (number of animals lying and attending the feed bunk) were recorded with scan sampling and instantaneous recording at 20-min intervals. Postmortem assessments were carried out in all animals by recording the frequencies of macroscopic signs of bronchitis, pulmonary emphysema, nephritis and urinary cyst and by measuring the weight and cortical and medullar areas of adrenal glands (n = 30 per pen). Compared with SA12 and SA24, SA6 showed a greater number of sneezes per minute during the dry period and a greater percentage of animals with locomotion alterations during the rainy period. Coughing, diarrhoea and nasal discharge affected a larger number of animals in the SA6 relative to the other two groups. During the rainy period, there was a lower percentage of animals with nasal and ocular discharge, and a greater percentage of animals with abnormal hoof and lying. A lower percentage of animals in SA6 and SA12 (but not SA24) attended the feed bunk during the rainy relative to the dry period. A mud depth score of 0 (no mud) was most frequent in SA24 pens, followed by SA12 and then SA6. Adrenal gland weight and cortical area were lower in SA24 animals compared with those in SA6 and SA12. The results show that decreasing the space allowance for beef cattle in outdoor feedlots degrades the feedlot environment and impoverishes animal welfare.

Manually counting hens in battery cages on large commercial poultry farms is a challenging task: time-consuming and often inaccurate. Therefore, the aim of this study was to develop a machine vision system that automatically counts the number of hens in battery cages. Automatically counting hens can help a regulatory agency or inspecting officer to estimate the number of living birds in a cage and, thus animal density, to ensure that they conform to government regulations or quality certification requirements. The test hen house was 87 m long, containing 37 battery cages stacked in 6-story high rows on both sides of the structure. Each cage housed 18 to 30 hens, for a total of approximately 11 000 laying hens. A feeder moves along the cages. A camera was installed on an arm connected to the feeder, which was specifically developed for this purpose. A wide-angle lens was used in order to frame an entire cage in the field of view. Detection and tracking algorithms were designed to detect hens in cages; the recorded videos were first processed using a convolutional neural network (CNN) object detection algorithm called Faster R-CNN, with an input of multi-angular view shifted images. After the initial detection, the hens’ relative location along the feeder was tracked and saved using a tracking algorithm. Information was added with every additional frame, as the camera arm moved along the cages. The algorithm count was compared with that made by a human observer (the ‘gold standard’). A validation dataset of about 2000 images achieved 89.6% accuracy at cage level, with a mean absolute error of 2.5 hens per cage. These results indicate that the model developed in this study is practicable for obtaining fairly good estimates of the number of laying hens in battery cages.

In animal sciences, the number of published meta-analyses is increasing at a rate of 15% per year. This current review focuses on the good practices and the potential pitfalls in the conduct of meta-analyses in animal sciences, nutrition in particular. Once the study objectives have been defined, several key phases must be considered when doing a meta-analysis. First, as a principle of traceability, criteria used to select or discard publications should be clearly stated in a way that one could reproduce the final selection of data. Then, the coding phase, aiming to isolate specific experimental factors for an accurate graphical and statistical interpretation of the database, is discussed. Following this step, the study of the levels of independence of factors and of the degree of data balance of the meta-design represents an essential phase to ensure the validity of statistical processing. The consideration of the study effect as fixed or random must next be considered. It appears based on several examples that this choice does not generally have any influence on the conclusions of a meta-analysis when the number of experiments is sufficient.