This document explores the differences in total liveweight (LW) that can be expected from:
In the brazil_cattle_population.Rmd, we offers three options to infer annual LW per municipality:
LW is also determine per region following MCTI (2020) and compared to values from Erasmus listed below:
First, we take the total municipal cattle population per age group and derive the state total cattle LW. We use the MCTI LW for this exercise before comparing difference with LW estimates (further below).
The different estimates provide large differences in total LW in Brazil (for 2016):
The largest estimate is the standard estimate (current approach) and the lowest is the SIGSIF approach. There is a 500 Mtonne differences between the lowest and highest estimates. The Census and Anualpect approach lead to roughly the same LW estimate.
There are large differences with the SIGSIF approach, which makes me think that it is too low.
We can compare the head population only with the MCTI (2020) estimates
Compared the values of MCTI for 2016 which was 217492377
So the current methods is close to the MCTI total heads, but the combination of approaches (e.g. Census vs. Anualpec) can get the range of heads. We then need to compare the estimate of enteric methane emissions.
We then attribute the emissions factors to the cattle population age groups to derive the total state enteric methane emissions (focusing on 2016).
The lower enteric methane emission estimates were derived using lower emission factors that were calculated assuming that the animals do not need to travel far to get food (Ca = 0.17).
Compared the values of MCTI for 2016 which was 11350.5 Gg CH4.
We then recalculate enteric methane emissions using the high emission factors with (Ca = 0.36). It seems that MCTI was using either or.
Compared the values of MCTI for 2016 which was 11350.5 Gg CH4.
We compare our results for category-specific emissions with MCTI using “high” and “low” emission factors
## [,1]
## YEAR "2016"
## CALF_1_BEEF_CENSUS_CH4 "1494902780"
## CALF_1_2_BEEF_CENSUS_CH4 "2547031368"
## ADULT_2_BEEF_CENSUS_CONFINED_CH4 "260950308"
## VACAS_2_BEEF_CENSUS_UNCONFINED_CH4 "2895271228"
## BOIS_2_BEEF_CENSUS_UNCONFINED_CH4 "2817436624"
## TOUROS_2_BEEF_CENSUS_CH4 "166959420"
## CALF_1_BEEF_DAIRY_CENSUS_CH4 "1647519755"
## CALF_1_2_BEEF_DAIRY_CENSUS_CH4 "2807104793"
## ADULT_2_BEEF_DAIRY_CENSUS_CONFINED_CH4 "274628580"
## VACAS_2_BEEF_DAIRY_CENSUS_UNCONFINED_CH4 "3105170116"
## BOIS_2_BEEF_DAIRY_CENSUS_UNCONFINED_CH4 "3165891708"
## TOUROS_2_BEEF_DAIRY_CENSUS_CH4 "182549220"
## CALF_1_BEEF_DAIRY_ANUALPEC_CH4 "1958111120"
## CALF_1_2_BEEF_DAIRY_ANUALPEC_CH4 "2207884360"
## ADULT_2_BEEF_DAIRY_ANUALPEC_CONFINED_CH4 "293893473"
## VACAS_2_BEEF_DAIRY_ANUALPEC_UNCONFINED_CH4 "3181880258"
## BOIS_2_BEEF_DAIRY_ANUALPEC_UNCONFINED_CH4 "3567013631"
## TOUROS_2_BEEF_DAIRY_ANUALPEC_CH4 "101443740"
## CALF_1_BEEF_SIGSIF_CH4 "219940"
## CALF_1_2_BEEF_SIGSIF_CH4 "6196626085"
## VACAS_2_BEEF_SIGSIF_CONFINED_CH4 "15829953"
## BOIS_2_BEEF_SIGSIF_CONFINED_CH4 "40394070"
## VACAS_2_BEEF_SIGSIF_UNCONFINED_CH4 "606695589"
## BOIS_2_BEEF_SIGSIF_UNCONFINED_CH4 "1231244506"
We compare the above results with those published for 2016 by MCTI(2016)
Bulls (> 2 y):
Males (> 2 y, unconfined):
Females (> 2 y, unconfined):
Adults (>2 y, confined):
Calves (< 1 y)
Calves (1-2 y):
So this shows that the MCTI procedure has been well applied and our numbers are robust at the country and category level (right range). We should ditch the SIGSIF approach as the results are too low compared to MCTI, a mix of Census (without dairy) and Anualpec (with dairy).
Link to the MCTI report: MCTI report