Greenhouse gas balance and carbon footprint of pasture-based beef cattle production systems in the tropical region (Atlantic Forest biome).

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.

The effect of grazing system intensification on the growth and meat quality of beef cattle in the Brazilian Atlantic Forest biome.

This study was carried out to evaluate the effects of four levels of intensification of grazing systems: 1) degraded pasture - DP; 2) irrigated pasture with high stocking rate - IHS; 3) dryland pasture with high stocking rate - DHS; 4) dryland pasture with moderate stocking rate - DMS; on growth, muscle development and meat quality of Nellore steers (271±2.2kg of live body weight - BW; 15months old) during two consecutive periods (17 and 15months). The final BW, the average daily BW gain, the hot carcass weight and the dress percentage were greater (P<0.0001), and the ribeye area tended to be greater (P=0.085), in the intensified systems compared to the degraded system. Animals in all systems presented similar back fat. Muscle development increased with the intensification of the grazing systems and meat quality was not affected.

Prediction of the chemical body composition of Nellore and crossbreed bulls.

Young Nellore and crossbreed bulls were comparatively slaughtered to generate equation models for predicting the chemical composition of the empty body and carcass from the chemical composition of the Hankins and Howe section (; ). Data were collected from 236 animals from different genetic groups: Nellore, one-half Canchim + one-half Nellore, one-half Angus + one-half Nellore, and one-half Simmental + one-half Nellore, with 48 baseline animals (BW range from 218 to 433 kg) and 188 animals finished in the feedlot (BW range from 356 to 618 kg). The chemical composition prediction equation model was developed for all genetic groups using stepwise regression analysis. Across all animals, the percentages of water and ether extract in the HH section were highly correlated ( < 0.001) with the percentages in the carcass ( = 0.911 and = 0.901, respectively, for water content of the carcass [HOC] and = 0.921 and = 0.921, respectively, for ether extract content of the carcass [EEC]) and empty body ( = 0.937 and = 0.926, respectively, for water content of the empty body [HOEB] and = 0.935 and = 0.939, respectively, for ether extract content of the empty body [EEEB]). The best prediction models were for the traits of empty body weight, HOEB, EEEB, HOC, and EEC. Determination coefficients for predicting the dependent variables obtained from the carcass composition were lower than those obtained from the empty body composition. It was concluded that the chemical composition of the empty body and the carcass can be predicted from the composition of the HH section, using a general equation for different genetic groups.

Prediction of retail beef yield and fat content from live animal and carcass measurements in Nellore cattle.

Data from 156 Nellore males were used to develop equations for the prediction of retail beef yield and carcass fat content, expressed as kilograms and as a percentage, from live animal and carcass measurements. Longissimus muscle area and backfat and rump fat thickness were measured by ultrasound up to 5 d before slaughter and fasted live weight was determined 1 d before slaughter. The same traits were obtained after slaughter. The carcass edible portion (CEP in kg and CEP% in percentage; n = 116) was calculated by the sum of the edible portions of primal cuts: hindquarter, forequarter, and spare ribs. Trimmable fat from the carcass boning process, with the standardization of about 3 mm of fat on retail beef, was considered to be representative of carcass fat content. Most of the variation in CEP was explained by fasted live weight or carcass weight (R(2) of 0.92 and 0.96); the same occurred for CEP% (R(2) of 0.15 and 0.13), and for CEP, the inclusion of LM area and fat thickness reduced the equation bias (lower value of Mallow's Cp statistics). For trimmable fat, most variation could be explained by weight or rump fat thickness. In general, the equations developed from live animal measurements showed a predictive power similar to the equations using carcass measurements. In all cases, the traits expressed as kilograms were better predicted (R(2) of 0.39 to 0.96) than traits expressed as a percentage (R(2) of 0.08 to 0.42).