Zip Codes with the Highest Percentage of Population employed in Agriculture in the United States

United States Report:

Percentage of Population employed in Agriculture

#	Zip Code	Location	City	Population	% In Agricultural	National Rank
1.	21627	38.175538, -76.047801	Crocheron, Maryland	34	100.00 %	#1
2.	25855	37.942806, -81.151143	Hilltop, West Virginia	38	100.00 %	#2
3.	41729	37.265606, -83.212055	Combs, Kentucky	170	100.00 %	#3
4.	36910	32.226242, -88.173882	Jachin, Alabama	117	100.00 %	#4
5.	69219	42.687956, -101.037101	Nenzel, Nebraska	9	100.00 %	#5
6.	99732	63.903609, -142.055746	Chicken, Alaska	27	100.00 %	#6
7.	72383	34.487922, -91.043086	Turner, Arkansas	74	100.00 %	#7
8.	79259	34.369432, -100.415313	Tell, Texas	60	100.00 %	#8
9.	79330	33.039383, -101.173427	Justiceburg, Texas	60	100.00 %	#9
10.	81653	40.894275, -107.241644	Slater, Colorado	42	100.00 %	#10
11.	82058	42.278751, -105.618185	Garrett, Wyoming	20	100.00 %	#11
12.	72133	34.174795, -91.567555	Reydell, Arkansas	23	100.00 %	#12
13.	95481	39.131189, -123.163371	Talmage, California	98	92.72 %	#13
14.	59332	45.346722, -104.828559	Hammond, Montana	186	90.29 %	#14
15.	59545	48.950383, -111.111186	Whitlash, Montana	39	89.47 %	#15
16.	89834	41.408342, -116.228124	Tuscarora, Nevada	266	89.26 %	#16
17.	97722	42.887808, -118.621786	Diamond, Oregon	67	86.95 %	#18
18.	88136	34.353608, -104.764019	Yeso, New Mexico	39	86.20 %	#19
19.	93627	36.520537, -120.118055	Helm, California	148	86.15 %	#20
20.	97908	44.317558, -117.885909	Ironside, Oregon	74	85.29 %	#21
21.	59076	46.278042, -107.075551	Sanders, Montana	47	84.21 %	#22
22.	88424	36.438012, -103.627063	Grenville, New Mexico	142	82.35 %	#23
23.	59077	47.133524, -107.581976	Sand Springs, Montana	83	81.57 %	#24
24.	59039	46.686216, -107.447771	Ingomar, Montana	121	81.39 %	#25
25.	69354	42.490539, -103.218623	Marsland, Nebraska	65	80.55 %	#27
26.	57650	45.820761, -103.003917	Ralph, South Dakota	68	80.00 %	#28
27.	59456	48.270599, -111.298285	Ledger, Montana	119	79.06 %	#30
28.	82224	42.886336, -104.923722	Lost Springs, Wyoming	84	78.94 %	#31
29.	79243	33.774479, -100.990114	Mcadoo, Texas	102	78.72 %	#32
30.	59529	48.802903, -108.690287	Hogeland, Montana	93	78.72 %	#33
31.	59535	47.973035, -109.260996	Lloyd, Montana	97	78.46 %	#34
32.	99321	46.860418, -119.857169	Beverly, Washington	620	77.85 %	#36
33.	59058	47.025164, -107.805201	Mosby, Montana	40	77.77 %	#37
34.	57651	45.475472, -103.148843	Reva, South Dakota	150	76.71 %	#38
35.	84313	41.149551, -113.661460	Grouse Creek, Utah	121	76.66 %	#39
36.	79025	34.925917, -102.217648	Dawn, Texas	138	75.92 %	#40
37.	78338	26.876569, -97.777837	Armstrong, Texas	86	75.51 %	#41
38.	96863	21.436909, -157.745489	M C B H Kaneohe Bay, Hawaii	2,505	75.00 %	#42
39.	58430	47.529396, -100.269507	Denhoff, North Dakota	119	75.00 %	#43
40.	70787	30.981734, -91.467693	Weyanoke, Louisiana	181	75.00 %	#44
41.	97736	42.722903, -119.053079	Frenchglen, Oregon	78	75.00 %	#45
42.	93608	36.488056, -120.407691	Cantua Creek, California	1,746	74.49 %	#46
43.	22722	38.452834, -78.236239	Haywood, Virginia	59	74.07 %	#47
44.	59642	46.246842, -110.794358	Ringling, Montana	55	73.68 %	#49
45.	62962	37.055863, -89.329533	Miller City, Illinois	93	73.68 %	#50
46.	93249	35.637715, -119.893068	Lost Hills, California	2,501	73.54 %	#51
47.	57644	45.430009, -102.120481	Meadow, South Dakota	308	73.38 %	#52
48.	81522	38.739630, -108.983003	Gateway, Colorado	61	73.07 %	#54
49.	87750	35.805440, -104.852008	Valmora, New Mexico	79	72.72 %	#55
50.	79759	31.841338, -102.749297	Notrees, Texas	20	72.72 %	#56
51.	57537	44.544939, -101.076340	Hayes, South Dakota	134	72.00 %	#57
52.	84329	41.646430, -113.431312	Park Valley, Utah	270	71.62 %	#58
53.	88213	33.457940, -103.638498	Caprock, New Mexico	20	71.42 %	#59
54.	57649	45.526333, -102.900156	Prairie City, South Dakota	232	71.42 %	#60
55.	57792	44.580625, -102.375981	White Owl, South Dakota	91	71.11 %	#61
56.	59214	47.228159, -105.944204	Brockway, Montana	131	71.05 %	#62
57.	59085	46.431084, -110.219629	Two Dot, Montana	76	70.58 %	#63
58.	79755	31.557774, -101.919865	Midkiff, Texas	245	70.40 %	#64
59.	59736	45.302723, -113.352674	Jackson, Montana	134	69.86 %	#65
60.	59025	45.118822, -106.673672	Decker, Montana	96	69.62 %	#66
61.	57755	45.846200, -103.331974	Ludlow, South Dakota	153	69.51 %	#67
62.	69135	42.227830, -100.206025	Elsmere, Nebraska	58	69.04 %	#68
63.	80824	39.650352, -102.507129	Kirk, Colorado	291	68.93 %	#69
64.	59311	45.203101, -104.417307	Alzada, Montana	202	68.86 %	#70
65.	59244	48.578706, -106.336522	Larslan, Montana	112	68.75 %	#71
66.	59322	47.036069, -106.563274	Cohagen, Montana	203	68.62 %	#72
67.	97721	43.122165, -118.389265	Princeton, Oregon	218	68.33 %	#73
68.	59055	46.131567, -109.899138	Melville, Montana	142	68.00 %	#74
69.	65484	37.375913, -92.234415	Huggins, Missouri	182	67.64 %	#75
70.	80735	39.724316, -102.245353	Idalia, Colorado	380	67.56 %	#76
71.	82081	41.506324, -104.474808	Meriden, Wyoming	73	67.44 %	#78
72.	77973	28.532467, -96.964200	Mcfaddin, Texas	50	67.44 %	#79
73.	59314	45.089348, -105.433413	Biddle, Montana	127	67.08 %	#80
74.	58713	48.552655, -100.614455	Bantry, North Dakota	34	66.66 %	#81
75.	59351	45.912524, -105.692352	Volborg, Montana	77	66.66 %	#82
76.	69132	40.751236, -100.942719	Dickens, Nebraska	49	66.66 %	#83
77.	58236	48.715802, -97.459784	Glasston, North Dakota	17	66.66 %	#84
78.	59059	46.424302, -108.076360	Musselshell, Montana	164	66.12 %	#85
79.	99345	45.941592, -119.689159	Paterson, Washington	222	66.07 %	#86
80.	85333	32.932623, -113.455721	Dateland, Arizona	852	65.78 %	#87
81.	97819	44.490173, -117.731756	Bridgeport, Oregon	44	65.62 %	#88
82.	69340	42.221532, -102.198221	Ellsworth, Nebraska	147	65.43 %	#89
83.	59466	48.818911, -111.527164	Oilmont, Montana	143	65.27 %	#90
84.	97710	42.133464, -118.579797	Fields, Oregon	86	65.21 %	#91
85.	79056	36.217812, -100.298186	Lipscomb, Texas	46	65.21 %	#92
86.	59062	45.170138, -106.088702	Otter, Montana	79	65.00 %	#93
87.	59444	48.691083, -111.312762	Galata, Montana	210	64.93 %	#94
88.	83348	43.280340, -114.127093	Picabo, Idaho	128	64.76 %	#95
89.	97909	44.243161, -117.428615	Jamieson, Oregon	49	64.70 %	#96
90.	82837	44.790905, -106.198347	Leiter, Wyoming	28	64.70 %	#97
91.	97904	43.795559, -118.438625	Drewsey, Oregon	145	64.61 %	#98
92.	57758	44.980444, -102.784344	Mud Butte, South Dakota	146	64.58 %	#99
93.	83312	42.026182, -113.591375	Almo, Idaho	140	64.28 %	#100
94.	83285	42.912211, -111.286619	Wayan, Idaho	191	64.15 %	#101
95.	57574	43.106893, -101.364053	Tuthill, South Dakota	178	64.00 %	#102
96.	88055	32.834693, -108.870557	Redrock, New Mexico	53	63.63 %	#103
97.	59424	47.394232, -110.005500	Coffee Creek, Montana	22	63.63 %	#104
98.	59343	45.688683, -105.464483	Olive, Montana	135	63.33 %	#105
99.	93624	36.397745, -120.119911	Five Points, California	1,847	63.10 %	#106
100.	73855	36.874731, -99.853036	Rosston, Oklahoma	186	63.01 %	#107

1-100 out of 31,697 total

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% Apart./Condos
% Arabs
% Asians
% Blacks
% Bottled Gas
% Carpool to Work
% Children Under 10
% Chinese
% Coal / Coke
% College Grad.
% Construction Jobs
% Cubans
% Czechs
% Danish
% Detached
% Drive to Work
% Dutch
% Electrical
% English
% Farming Jobs
% Filipinos
% French
% French Canadians
% Fuel/Oil
% Germans
% Gov. Employees

% Greeks
% H/Income < $10k
% H/Income > $100k
% H/holds 1+ Car
% H/holds 2+ Cars
% High School Grad.
% Hispanics
% Hungarians
% In Agricultural
% In Arts
% In College
% In Construction
% In Finance
% In High School
% In IT
% In Labor Force
% In Labor Force (f)
% In Labor Force (m)
% In Manufacturing
% In Military
% In Retail Trade
% In Science
% In Social Work
% In Transportation
% Income Over $200k
% Indians (Asian)

% Irish
% Italians
% Japanese
% Korens
% Lithuanians
% Management Jobs
% Mexicans
% Natives
% No-Mortgage Prop.
% Norwegians
% Not in Labor Force
% Owner-Occupied
% Poles
% Portugese
% Poverty Level
% Poverty Level
% Production Jobs
% Properties < $200k
% Properties < $500k
% Properties > $1M
% Properties > $500k
% Pub/Transit
% Russians
% Sales Jobs
% Scotts
% Self-Employed

% Seniors Over 65
% Service Jobs
% Slovaks
% Solar Heated
% Swedes
% Swiss
% Tenant-Occupied
% Ukranians
% Unemployment Rate
% Utility Gas
% Vacant Homes
% Walk to Work
% Welsh
% West Indians
% Whites
% Wood Heated
% Work from Home
Avg. Income/H/hold
Avg. Income/Person
Avg. Population Age
Commute Time
Females / Male
Males / Female
People / Sq. Mile
Rooms / Home