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Encuesta Nacional Agropecuaria 2017

Peru, 2017
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Reference ID
PER_2017_ENA_v01_M
Producer(s)
Instituto Nacional de Estadística e Informática, Dirección Nacional de Censos y Encuestas
Metadata
DDI/XML JSON
Created on
Jan 16, 2021
Last modified
Jan 16, 2021
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Nombre de la provincia (NOMBREPV)

Data file: 09_Cap400a_2

Overview

Valid: 52474
Invalid: 0
Type: Discrete
Start: 22
End: 46
Width: 25
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
ABANCAY 462
0.9%
ACOBAMBA 395
0.8%
ACOMAYO 71
0.1%
AIJA 34
0.1%
ALTO AMAZONAS 200
0.4%
AMBO 221
0.4%
ANDAHUAYLAS 1338
2.5%
ANGARAES 393
0.7%
ANTA 296
0.6%
ANTABAMBA 103
0.2%
ANTONIO RAYMONDI 48
0.1%
AREQUIPA 345
0.7%
ASCOPE 53
0.1%
ASUNCION 51
0.1%
ATALAYA 207
0.4%
AYABACA 631
1.2%
AYMARAES 302
0.6%
AZANGARO 558
1.1%
BAGUA 208
0.4%
BARRANCA 107
0.2%
BELLAVISTA 168
0.3%
BOLIVAR 101
0.2%
BOLOGNESI 18
0%
BONGARA 191
0.4%
CAJABAMBA 222
0.4%
CAJAMARCA 464
0.9%
CAJATAMBO 59
0.1%
CALCA 213
0.4%
CALLAO 18
0%
CAMANA 53
0.1%
CANAS 148
0.3%
CANCHIS 287
0.5%
CANDARAVE 160
0.3%
CANGALLO 270
0.5%
CANTA 43
0.1%
CARABAYA 129
0.2%
CARAVELI 104
0.2%
CARHUAZ 460
0.9%
CARLOS FERMIN FITZCARRALD 168
0.3%
CASMA 82
0.2%
CASTILLA 273
0.5%
CASTROVIRREYNA 125
0.2%
CAYLLOMA 653
1.2%
CAÑETE 230
0.4%
CELENDIN 178
0.3%
CHACHAPOYAS 500
1%
CHANCHAMAYO 110
0.2%
CHEPEN 34
0.1%
CHICLAYO 351
0.7%
CHINCHA 260
0.5%
CHINCHEROS 626
1.2%
CHOTA 519
1%
CHUCUITO 368
0.7%
CHUMBIVILCAS 326
0.6%
CHUPACA 281
0.5%
CHURCAMPA 411
0.8%
CONCEPCION 356
0.7%
CONDESUYOS 193
0.4%
CONDORCANQUI 81
0.2%
CONTRALMIRANTE VILLAR 214
0.4%
CONTUMAZA 67
0.1%
CORONEL PORTILLO 450
0.9%
CORONGO 49
0.1%
COTABAMBAS 453
0.9%
CUSCO 48
0.1%
CUTERVO 397
0.8%
DANIEL ALCIDES CARRION 633
1.2%
DATEM DEL MARAÑÓN 170
0.3%
DOS DE MAYO 342
0.7%
EL COLLAO 213
0.4%
EL DORADO 113
0.2%
ESPINAR 89
0.2%
FERREÑAFE 894
1.7%
GENERAL SANCHEZ CERRO 862
1.6%
GRAN CHIMU 224
0.4%
GRAU 279
0.5%
HUACAYBAMBA 205
0.4%
HUALGAYOC 170
0.3%
HUALLAGA 97
0.2%
HUAMALIES 434
0.8%
HUAMANGA 453
0.9%
HUANCA SANCOS 70
0.1%
HUANCABAMBA 1048
2%
HUANCANE 295
0.6%
HUANCAVELICA 784
1.5%
HUANCAYO 447
0.9%
HUANTA 454
0.9%
HUANUCO 721
1.4%
HUARAL 217
0.4%
HUARAZ 409
0.8%
HUARI 431
0.8%
HUARMEY 18
0%
HUAROCHIRI 186
0.4%
HUAURA 151
0.3%
HUAYLAS 260
0.5%
HUAYTARA 228
0.4%
ICA 254
0.5%
ILO 5
0%
ISLAY 51
0.1%
JAEN 93
0.2%
JAUJA 558
1.1%
JORGE BASADRE 121
0.2%
JULCAN 245
0.5%
JUNIN 90
0.2%
LA CONVENCIÓN 272
0.5%
LA MAR 336
0.6%
LA UNION 266
0.5%
LAMAS 224
0.4%
LAMBAYEQUE 1297
2.5%
LAMPA 59
0.1%
LAURICOCHA 230
0.4%
LEONCIO PRADO 66
0.1%
LIMA 144
0.3%
LORETO 112
0.2%
LUCANAS 180
0.3%
LUYA 781
1.5%
MANU 115
0.2%
MARAÑON 241
0.5%
MARISCAL CACERES 104
0.2%
MARISCAL LUZURIAGA 108
0.2%
MARISCAL NIETO 677
1.3%
MARISCAL RAMÓN CASTILLA 114
0.2%
MAYNAS 317
0.6%
MELGAR 126
0.2%
MOHO 71
0.1%
MORROPON 255
0.5%
MOYOBAMBA 352
0.7%
NAZCA 66
0.1%
OTUZCO 805
1.5%
OXAPAMPA 569
1.1%
OYON 19
0%
PACASMAYO 68
0.1%
PACHITEA 368
0.7%
PADRE ABAD 409
0.8%
PAITA 29
0.1%
PALLASCA 111
0.2%
PALPA 30
0.1%
PARINACOCHAS 169
0.3%
PARURO 305
0.6%
PASCO 1287
2.5%
PATAZ 328
0.6%
PAUCAR DEL SARA SARA 49
0.1%
PAUCARTAMBO 283
0.5%
PICOTA 167
0.3%
PISCO 176
0.3%
PIURA 442
0.8%
POMABAMBA 149
0.3%
PUERTO INCA 363
0.7%
PUNO 523
1%
PURÚS 10
0%
QUISPICANCHI 336
0.6%
RECUAY 128
0.2%
REQUENA 111
0.2%
RIOJA 177
0.3%
RODRIGUEZ DE MENDOZA 149
0.3%
SAN ANTONIO DE PUTINA 80
0.2%
SAN IGNACIO 188
0.4%
SAN MARCOS 165
0.3%
SAN MARTIN 141
0.3%
SAN MIGUEL 222
0.4%
SAN PABLO 79
0.2%
SAN ROMAN 141
0.3%
SANCHEZ CARRION 1070
2%
SANDIA 103
0.2%
SANTA 326
0.6%
SANTA CRUZ 171
0.3%
SANTIAGO DE CHUCO 420
0.8%
SATIPO 133
0.3%
SECHURA 103
0.2%
SIHUAS 139
0.3%
SUCRE 26
0%
SULLANA 143
0.3%
TACNA 753
1.4%
TAHUAMANU 239
0.5%
TALARA 30
0.1%
TAMBOPATA 534
1%
TARATA 151
0.3%
TARMA 245
0.5%
TAYACAJA 641
1.2%
TOCACHE 142
0.3%
TRUJILLO 116
0.2%
TUMBES 470
0.9%
UCAYALI 164
0.3%
URUBAMBA 126
0.2%
UTCUBAMBA 452
0.9%
VICTOR FAJARDO 208
0.4%
VILCAS HUAMAN 59
0.1%
VIRU 147
0.3%
YAROWILCA 313
0.6%
YAULI 41
0.1%
YAUYOS 194
0.4%
YUNGAY 291
0.6%
YUNGUYO 81
0.2%
ZARUMILLA 106
0.2%
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.

Description

Definition
La variable permite concocer el nombre de la provincia donde se ubica la unidad agropecauria.
Universe
La encuesta cubre a todas las unidades agropecuarias del país con menos de 50 ha y las unidades agropecuarias que son empresas agrícolas o agropecuarios.
Source of information
Encuestador.
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