SPATIAL DISTRIBUTION OF PRODUCTIVE, ENVIRONMENTAL, AND SOCIOECONOMIC FACTORS TO DISCRIMINATE DAIRY CATTLE PRODUCTION IN THE SOUTH OF BRAZIL

Cervo, Heitor José; Peripolli, Vanessa; Bremm, Bárbara; Barcellos, Julio Otávio Jardim; Borges, João Batista Souza; McManus, Concepta

doi:10.1590/1809-6891v19e-33194

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ZOOTECNIA • Ciênc. anim. bras. 19 • 2018 • https://doi.org/10.1590/1809-6891v19e-33194 copy

SPATIAL DISTRIBUTION OF PRODUCTIVE, ENVIRONMENTAL, AND SOCIOECONOMIC FACTORS TO DISCRIMINATE DAIRY CATTLE PRODUCTION IN THE SOUTH OF BRAZIL

ESPACIALIZAÇÃO DOS FATORES PRODUTIVOS, AMBIENTAIS E SOCIOECONÔMICOS PARA DISCRIMINAR A PRODUÇÃO DE BOVINOS LEITEIROS NO SUL DO BRASIL

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The South region of Brazil differs from the others due to its high milk production rates. Production heterogeneity, climate and soil diversity, and environmental and socioeconomic circumstances contribute to differentiate political-administrative regions. In this study, we aimed to spatialize the production, environmental, and socioeconomic factors that best discriminate bovine milk production in the states of the South of Brazil. Multivariate analyses were performed to discriminate both the studied variables and the mesoregion and cities of these states. The Western Catarinense and Northwestern Rio-grandense mesoregions showed the highest production indices, but they were discriminated at a low level (35.76 %). The formation of clusters showed that Casca, Marau, and Santo Cristo from the Northwestern Rio-grandense and Concordia, Coronel Freitas, Palmitos, and São Lourenço do Oeste from the Western Catarinense mesoregion had higher production indices. The municipal clusters were discriminated at a high level by production (98.24%) and by environmental and socioeconomic (72.75%) factors. Production variables were the most important for local clusters discrimination. The specificities and peculiarities to each region were evidenced through the heterogeneity of production and environmental and socioeconomic factors. Therefore, it is necessary to design and implement specific technological innovations to each region to maximize productive efficiency and minimize adverse environmental effects in dairy herds.

Keywords:
cluster; multivariate analysis; principal components; spatialization

Mesoregion	Milked Cows (head)	Milk (1,000 liters)	Productivity (L/cow/year)	Milked Cows/ Effective Herd(%)
W_SC	4762.4^a	13720^a	2859.0^a	26.19
SE_RS	3991.9^ab	6043^bcd	1512.1^bcde	21.24
NW_RS	3921.5^ab	10275^ab	2604.8^a	23.65
SW_RS	3807.5^ab	7000^bc	1726.9^bcd	31.79
NE_RS	2953.2^abc	6925^bc	2791.1^a	21.52
ME_RS	2502.0^bcd	5511^bcd	1993.4^b	21.70
MW_RS	2382.3^bcd	2943^cd	1173.5^e	24.57
Highlands_SC	2375.4^bcd	3348^cd	1295.0^de	22.10
S_SC	2269.4^bcd	4069^cd	1637.7^bcdc	21.27
Itajaí Valley_SC	2254.7^bcd	3807^cd	1727.1^bcd	24.02
Greater Flor_SC	1851.7^cd	2486^cd	1390.9^cde	24.52
N_SC:	1535.3^cd	3138^cd	1801.5^bc	26.58
POA Met_RS	755.1^d	1453^d	1736.8^bcd	23.59
CV (%)	84.42	94.45	31.34	76.84
Mean	3033.45	7154.81	2173.35	23.89

Mesoregion	MC_area (head/km²)	MC_GDP (head/RS 1,000)	MC_Pop (head/hab)	AM_area(1,000L/km²)	AM_GDP (1,000L/RS 1,000)	AM_pop (1,000L/ inhabitants)	Produc_area (L/cow/km²)	Produc_GDP ((L/cow/RS 1,000)	Produc_pop (L/cow/inhabitants)	MCEH_area(%/km²)	MCEH_gdp (%/RS 1,000)	MCEH_pop(%/inhabitants)
MW_RS	9.40^bcd	0.0278^bcde	0.4223^abcde	10.75^d	0.0288^cde	0.4475^cd	5.168^cd	0.0141^cd	0.2111^cd	0.1359^ab	0.000349^ab	0.00525^abc
ME_RS	11.76^bcd	0.0346^abcde	0.5122^abcde	28.93^bcd	0.0831^abcde	1.2970^abc	10.854^bcd	0.0308^abc	0.4724^abc	0.1032^ab	0.000316^ab	0.00499^abc
Greater Flor_SC	5.08^cd	0.0252^bcde	0.2823^cde	7.05^d	0.0308^cde	0.3487^cd	5.402^cd	0.0164^cd	0.1816^d	0.0833^ab	0.000324^ab	0.00373^abc
POA Met_RS	3.76^cd	0.0105^de	0.1501^e	7.20^d	0.0203^de	0.2895^cd	10.533^bcd	0.0260^bcd	0.3671^bcd	0.1470^ab	0.000391^ab	0.00523^abc
NE_RS	19.31^ab	0.0462^abcd	0.6395^abcd	53.05^abc	0.1147^abc	1.6499^ab	19.808^a	0.0484^a	0.6793^a	0.1566^ab	0.000376^ab	0.00512^abc
NW_RS	21.74^ab	0.0570^bcd	0.7896^ab	56.23^ab	0.1476^ab	2.0519^ab	15.033^ab	0.0394^ab	0.5642^ab	0.1344^ab	0.000385^ab	0.00544^abc
N_SC	2.48^d	0.0053^e	0.0735^e	4.74^d	0.0097^e	0.1357^d	3.845^cd	0.0081^d	0.1102^d	0.0612^ab	0.000122^b	0.00161^c
W_SC	26.56^a	0.0584^ab	0.8525^ab	76.19^a	0.1624^a	2.4176^a	20.291^a	0.0486^a	0.6966^a	0.1821^a	0.000471^a	0.00649^ab
SE_RS	21.89^ab	0.0711^a	0.8705^a	34.26^bcd	0.1077^abcd	1.3172^abc	6.655^cd	0.0216^bcd	0.2801^bcd	0.0882^ab	0.000289^ab	0.0036 ^abc
Highlands_SC	3.60^cd	0.0331^bcde	0.4022^bcde	4.72^d	0.0417^cde	0.5064^cd	2.933^d	0.0236^bcd	0.2841^bcd	0.0412^b	0.000359^ab	0.00437^abc
SW_RS	16.72^abc	0.0402^abcde	0.7396^abc	25.17^bcd	0.0644^bcde	1.1461^bcd	7.658^bcd	0.0201^bcd	0.3265^bcd	0.1734^a	0.000427^ab	0.00684^a
S_SC	10.30^bcd	0.0178^de	0.2265^de	18.35^cd	0.0327^cde	0.4386^cd	9.713^bcd	0.0156^cd	0.2138^cd	0.1318^ab	0.000174^ab	0.00226^bc
Itajaí Valley_SC	10.43^bcd	0.0201^cde	0.2983^cde	18.06^cd	0.0343^cde	0.5073^cd	11.395^bc	0.0167^cd	0.2210^cd	0.1523^ab	0.00023^ab	0.003187^abc

Variables		Cluster
Variables	1	2	3	4	5
MC	1326.33^e	10283.76^b	15424.57^a	6321.68^c	3760.11^d
AM	2155.61^e	30348.56^b	50198.14^a	17292.68^c	8613.95^d
Produc	1713.66^c	3201.21^ab	3492.14^a	3050.18^ab	2570.86^b
MCEH	24.20^a	25.24^a	23.86^a	20.28^a	24.33^a
MC_area	0.11^d	0.36^b	0.47^a	0.31^bc	0.25^c
AM_area	0.20^d	1.17^b	1.59^a	0.92^b	0.63^c
Produc_area	0.25^a	0.13^a	0.11^a	0.18^a	0.21^a
MCEH_area	0.003^a	0.001^a	0.001^a	0.001^a	0.002^a
MC_GDP	0.02^b	0.09^a	0.08^a	0.07^a	0.06^a
AM_GDP	0.03^d	0.23^b	0.35^a	0.22^bc	0.14^c
Produc_GDP	0.03^a	0.02^a	0.03^a	0.04^a	0.04^a
MCEH_GDP	0.0004^a	0.0002^a	0.0002^a	0.0002^a	0.0004^a
MC_pop	0.25^b	1.29^a	1.12^a	1.07^a	0.86^a
AM_pop	0.43^d	3.60^ab	4.49^a	3.20^b	2.02^c
Produc_pop	0.35^a	0.38^a	0.36^a	0.61^a	0.62^a
MCEH_pop	0.005^a	0.003^a	0.002^a	0.004^a	0.01^a
Number of observations	441	34	7	78	229

Variable	PartialR-Square	F Value	Pr> F
AM	0.9301	2591.71	<.0001
Produc	0.1211	26.8	<.0001
MC	0.2072	50.77	<.0001
AM_pop	0.0634	13.13	<.0001
Produc_pop	0.0452	9.16	<.0001
MC_area	0.0492	9.99	<.0001
Produc_area	0.0607	12.48	<.0001
MC_pop	0.0277	5.5	0.0002
AM_GDP	0.0175	3.43	0.0086
MC_GDP	0.0378	7.56	<.0001

Variables	Partial R-Square	F Value	Pr > F
Farmers with off-farm jobs	0.0147	2.36	0.0518
Fanners with other agricultural activities	0.0284	4.61	0.001
Forests planted with forest trees per area	0.0118	1.89	0.111
Water resources in establishments with springs and protected forest	0.0251	4.07	0.0029
Water resources in establishments with springs without protected forest	0.0162	2.61	0.0347
Water resources in establishments with rivers or streams and protected forest	0.0196	3.15	0.0139
Water resources in establishments with rivers or streams without protected forest	0.0338	5.53	0.0002
Water resources in establishments with natural lakes and/or ponds acid protected forest	0.0196	3.15	0.0139
Water resources in establishments with natural lakes and/or ponds without: protected forest	0.0488	8.11	<.0001
Technical guidance received by the government (federal, state, or municipal)	0.0121	1.94	0.1021
Technical guidance sought by the producer him herself	0.0242	3.92	0.0038
Technical guidance received by private planning	0.0319	5.2	0.0004
Men responsible for the farm for 5 to JO years	0.0254	4.12	0.0027
Management of agricultural pasture rotation	0.4288	118.6	<.0001
Municipal human development index	0.0238	3.86	0.0042
Altitude (meters above sea level)	0.0405	6.67	<.0001
Mean temperature	0.0497	8.26	<.0001
Solar radiation	0.0111	1.78	0.1321

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[1] *Corresponding author - vanessa.peripolli@hotmail.com