Overview

Dataset statistics

Number of variables9
Number of observations3048
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory229.3 KiB
Average record size in memory77.0 B

Variable types

Numeric5
Categorical3
Text1

Dataset

Description기상 여건 등에 따라 수급이 불안한 노지채소 품목에 대하여 농협과 농가가 계약재배를 통하여 수급과 가격 안정 유도제공 목록 : 연도, 시도, 시군구, 읍면동, 사업방식, 품목, 신청농가수, 신청물량, 신청면적, 신청금액, 계약농가수, 계약물량, 계약면적, 계약금액, 출하물량, 출하금액, 생성일시 , 갱신일시, 시도코드, 시군구코드, 읍면동코드
Author농림축산식품부
URLhttps://www.data.go.kr/data/3055089/fileData.do

Alerts

계약농가수 is highly overall correlated with 계약금액(천원) and 1 other fieldsHigh correlation
계약물량(킬로그램) is highly overall correlated with 계약금액(천원) and 1 other fieldsHigh correlation
계약금액(천원) is highly overall correlated with 계약농가수 and 2 other fieldsHigh correlation
계약면적(제곱미터) is highly overall correlated with 계약농가수 and 2 other fieldsHigh correlation
계약면적(제곱미터) is highly skewed (γ1 = 53.97937851)Skewed

Reproduction

Analysis started2023-12-12 06:04:19.461228
Analysis finished2023-12-12 06:04:24.132701
Duration4.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.8907
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T15:04:24.236258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12013
median2016
Q32019
95-th percentile2022
Maximum2022
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6366213
Coefficient of variation (CV)0.0018039774
Kurtosis-1.1704527
Mean2015.8907
Median Absolute Deviation (MAD)3
Skewness0.042625749
Sum6144435
Variance13.225014
MonotonicityIncreasing
2023-12-12T15:04:24.472516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2014 260
8.5%
2013 256
8.4%
2017 246
8.1%
2011 244
 
8.0%
2016 243
 
8.0%
2012 240
 
7.9%
2015 238
 
7.8%
2018 238
 
7.8%
2020 236
 
7.7%
2019 235
 
7.7%
Other values (3) 612
20.1%
ValueCountFrequency (%)
2010 205
6.7%
2011 244
8.0%
2012 240
7.9%
2013 256
8.4%
2014 260
8.5%
2015 238
7.8%
2016 243
8.0%
2017 246
8.1%
2018 238
7.8%
2019 235
7.7%
ValueCountFrequency (%)
2022 197
6.5%
2021 210
6.9%
2020 236
7.7%
2019 235
7.7%
2018 238
7.8%
2017 246
8.1%
2016 243
8.0%
2015 238
7.8%
2014 260
8.5%
2013 256
8.4%

시도
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
전남
629 
강원
445 
전북
416 
경북
361 
경남
285 
Other values (7)
912 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row강원
2nd row강원
3rd row전남
4th row전남
5th row전남

Common Values

ValueCountFrequency (%)
전남 629
20.6%
강원 445
14.6%
전북 416
13.6%
경북 361
11.8%
경남 285
9.4%
충남 284
9.3%
충북 274
9.0%
제주 153
 
5.0%
경기 133
 
4.4%
대구 43
 
1.4%
Other values (2) 25
 
0.8%

Length

2023-12-12T15:04:24.652055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전남 629
20.6%
강원 445
14.6%
전북 416
13.6%
경북 361
11.8%
경남 285
9.4%
충남 284
9.3%
충북 274
9.0%
제주 153
 
5.0%
경기 133
 
4.4%
대구 43
 
1.4%
Other values (2) 25
 
0.8%

시군
Text

Distinct97
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
2023-12-12T15:04:24.979920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0849738
Min length2

Characters and Unicode

Total characters9403
Distinct characters93
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.3%

Sample

1st row평창군
2nd row평창군
3rd row직할
4th row나주시
5th row목포신안시군
ValueCountFrequency (%)
직할 216
 
7.1%
부안군 109
 
3.6%
해남군 96
 
3.1%
정선군 95
 
3.1%
목포신안시군 91
 
3.0%
제주시 86
 
2.8%
고창군 82
 
2.7%
안동시 76
 
2.5%
거창군 74
 
2.4%
진도군 70
 
2.3%
Other values (87) 2053
67.4%
2023-12-12T15:04:25.553343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 1890
20.1%
시 1078
 
11.5%
안 422
 
4.5%
주 420
 
4.5%
천 317
 
3.4%
창 282
 
3.0%
할 216
 
2.3%
직 216
 
2.3%
포 206
 
2.2%
영 196
 
2.1%
Other values (83) 4160
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9403
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 1890
20.1%
시 1078
 
11.5%
안 422
 
4.5%
주 420
 
4.5%
천 317
 
3.4%
창 282
 
3.0%
할 216
 
2.3%
직 216
 
2.3%
포 206
 
2.2%
영 196
 
2.1%
Other values (83) 4160
44.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9403
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 1890
20.1%
시 1078
 
11.5%
안 422
 
4.5%
주 420
 
4.5%
천 317
 
3.4%
창 282
 
3.0%
할 216
 
2.3%
직 216
 
2.3%
포 206
 
2.2%
영 196
 
2.1%
Other values (83) 4160
44.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9403
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 1890
20.1%
시 1078
 
11.5%
안 422
 
4.5%
주 420
 
4.5%
천 317
 
3.4%
창 282
 
3.0%
할 216
 
2.3%
직 216
 
2.3%
포 206
 
2.2%
영 196
 
2.1%
Other values (83) 4160
44.2%

품목
Categorical

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
배추
737 
양파
577 
건고추
446 
마늘
403 
무
355 
Other values (5)
530 

Length

Max length3
Median length2
Mean length2.0734908
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row배추
2nd row감자
3rd row마늘
4th row양파
5th row대파

Common Values

ValueCountFrequency (%)
배추 737
24.2%
양파 577
18.9%
건고추 446
14.6%
마늘 403
13.2%
무 355
11.6%
감자 184
 
6.0%
대파 163
 
5.3%
홍고추 125
 
4.1%
당근 50
 
1.6%
양배추 8
 
0.3%

Length

2023-12-12T15:04:25.742993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:04:25.914410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
배추 737
24.2%
양파 577
18.9%
건고추 446
14.6%
마늘 403
13.2%
무 355
11.6%
감자 184
 
6.0%
대파 163
 
5.3%
홍고추 125
 
4.1%
당근 50
 
1.6%
양배추 8
 
0.3%

사업방식
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
매취
1917 
수탁
1131 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매취
2nd row매취
3rd row매취
4th row매취
5th row매취

Common Values

ValueCountFrequency (%)
매취 1917
62.9%
수탁 1131
37.1%

Length

2023-12-12T15:04:26.056303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:04:26.156057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매취 1917
62.9%
수탁 1131
37.1%

계약농가수
Real number (ℝ)

HIGH CORRELATION 

Distinct623
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.17651
Minimum1
Maximum3597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T15:04:26.280447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median33
Q3135.25
95-th percentile900.65
Maximum3597
Range3596
Interquartile range (IQR)126.25

Descriptive statistics

Standard deviation344.78553
Coefficient of variation (CV)2.1129606
Kurtosis20.507068
Mean163.17651
Median Absolute Deviation (MAD)30
Skewness3.9889098
Sum497362
Variance118877.06
MonotonicityNot monotonic
2023-12-12T15:04:26.447289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 147
 
4.8%
2 127
 
4.2%
3 109
 
3.6%
4 93
 
3.1%
5 79
 
2.6%
6 69
 
2.3%
8 65
 
2.1%
9 55
 
1.8%
11 55
 
1.8%
7 51
 
1.7%
Other values (613) 2198
72.1%
ValueCountFrequency (%)
1 147
4.8%
2 127
4.2%
3 109
3.6%
4 93
3.1%
5 79
2.6%
6 69
2.3%
7 51
 
1.7%
8 65
2.1%
9 55
 
1.8%
10 34
 
1.1%
ValueCountFrequency (%)
3597 1
< 0.1%
3092 1
< 0.1%
2932 1
< 0.1%
2856 1
< 0.1%
2777 1
< 0.1%
2746 1
< 0.1%
2678 1
< 0.1%
2638 1
< 0.1%
2615 1
< 0.1%
2503 1
< 0.1%

계약물량(킬로그램)
Real number (ℝ)

HIGH CORRELATION 

Distinct2739
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2626776.4
Minimum840
Maximum1.822 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T15:04:26.659757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum840
5-th percentile20780.5
Q1162161.25
median655076.5
Q32074361.2
95-th percentile11303818
Maximum1.822 × 108
Range1.8219916 × 108
Interquartile range (IQR)1912200

Descriptive statistics

Standard deviation7839932
Coefficient of variation (CV)2.984621
Kurtosis173.93776
Mean2626776.4
Median Absolute Deviation (MAD)568545.5
Skewness10.8802
Sum8.0064144 × 109
Variance6.1464533 × 1013
MonotonicityNot monotonic
2023-12-12T15:04:26.857805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000.0 22
 
0.7%
200000.0 16
 
0.5%
150000.0 12
 
0.4%
400000.0 11
 
0.4%
1000000.0 10
 
0.3%
500000.0 9
 
0.3%
300000.0 8
 
0.3%
250000.0 8
 
0.3%
120000.0 7
 
0.2%
10000.0 7
 
0.2%
Other values (2729) 2938
96.4%
ValueCountFrequency (%)
840.0 1
< 0.1%
1164.0 1
< 0.1%
1800.0 1
< 0.1%
2064.0 1
< 0.1%
2112.0 1
< 0.1%
2236.0 1
< 0.1%
2484.6 1
< 0.1%
3724.0 1
< 0.1%
4200.0 1
< 0.1%
4276.0 1
< 0.1%
ValueCountFrequency (%)
182200000.0 1
< 0.1%
126145672.0 1
< 0.1%
124431592.0 1
< 0.1%
118540030.0 1
< 0.1%
114500766.0 1
< 0.1%
93107490.0 1
< 0.1%
77771970.0 1
< 0.1%
74874872.0 1
< 0.1%
69799755.0 1
< 0.1%
56170379.0 1
< 0.1%

계약금액(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct2800
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1568565.1
Minimum3428.1
Maximum57635944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T15:04:27.015453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3428.1
5-th percentile37578.75
Q1132000
median374895
Q31228317
95-th percentile7031236
Maximum57635944
Range57632516
Interquartile range (IQR)1096317

Descriptive statistics

Standard deviation4094568.8
Coefficient of variation (CV)2.6103914
Kurtosis68.487289
Mean1568565.1
Median Absolute Deviation (MAD)296103.5
Skewness7.1154565
Sum4.7809864 × 109
Variance1.6765494 × 1013
MonotonicityNot monotonic
2023-12-12T15:04:27.154534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000.0 19
 
0.6%
200000.0 15
 
0.5%
120000.0 13
 
0.4%
90000.0 9
 
0.3%
80000.0 6
 
0.2%
30000.0 6
 
0.2%
180000.0 6
 
0.2%
40000.0 6
 
0.2%
45000.0 5
 
0.2%
20000.0 5
 
0.2%
Other values (2790) 2958
97.0%
ValueCountFrequency (%)
3428.1 1
< 0.1%
4330.0 1
< 0.1%
4892.25 1
< 0.1%
5000.0 1
< 0.1%
5085.99 1
< 0.1%
5311.14 1
< 0.1%
5646.16 1
< 0.1%
5886.9 1
< 0.1%
6069.93 1
< 0.1%
6300.0 1
< 0.1%
ValueCountFrequency (%)
57635944.0 1
< 0.1%
51953647.1 1
< 0.1%
50740095.9 1
< 0.1%
49761947.2 1
< 0.1%
49449853.9 1
< 0.1%
47831703.9 2
0.1%
47071762.0 1
< 0.1%
45402056.4 1
< 0.1%
41999720.0 1
< 0.1%
37843701.6 1
< 0.1%

계약면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2891
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1268861.9
Minimum925
Maximum1.3822654 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T15:04:27.311161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum925
5-th percentile18720.3
Q182394.25
median223795
Q3710490.5
95-th percentile3213441
Maximum1.3822654 × 109
Range1.3822645 × 109
Interquartile range (IQR)628096.25

Descriptive statistics

Standard deviation25218061
Coefficient of variation (CV)19.874552
Kurtosis2954.554
Mean1268861.9
Median Absolute Deviation (MAD)181684
Skewness53.979379
Sum3.867491 × 109
Variance6.359506 × 1014
MonotonicityNot monotonic
2023-12-12T15:04:27.519014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 7
 
0.2%
30000 5
 
0.2%
57750 4
 
0.1%
15000 4
 
0.1%
26446 4
 
0.1%
44750 4
 
0.1%
60000 3
 
0.1%
20000 3
 
0.1%
33058 3
 
0.1%
68752 3
 
0.1%
Other values (2881) 3008
98.7%
ValueCountFrequency (%)
925 1
< 0.1%
1103 1
< 0.1%
1491 1
< 0.1%
2500 1
< 0.1%
2778 1
< 0.1%
3088 1
< 0.1%
3334 1
< 0.1%
3488 1
< 0.1%
4000 1
< 0.1%
4500 1
< 0.1%
ValueCountFrequency (%)
1382265400 1
< 0.1%
105758023 1
< 0.1%
101929624 1
< 0.1%
38607612 1
< 0.1%
18550837 1
< 0.1%
18298799 1
< 0.1%
17431107 1
< 0.1%
16854055 1
< 0.1%
15074193 1
< 0.1%
14155426 1
< 0.1%

Interactions

2023-12-12T15:04:23.181202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:20.225716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:20.917912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.595953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:22.239534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:23.310577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:20.386092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.044405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.731794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:22.665617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:23.434083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:20.528640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.183088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.861776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:22.799719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:23.570281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:20.656268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.342829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.988818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:22.942425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:23.690138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:20.781846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:21.456661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:22.107073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:23.072882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:04:27.648410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도시군품목사업방식계약농가수계약물량(킬로그램)계약금액(천원)계약면적(제곱미터)
연도1.0000.0000.0000.1610.1470.0000.0000.0000.000
시도0.0001.0000.9920.6020.3370.2230.2020.2470.000
시군0.0000.9921.0000.8370.5960.6270.4630.5910.000
품목0.1610.6020.8371.0000.4240.4330.1180.2440.000
사업방식0.1470.3370.5960.4241.0000.2690.0890.1610.000
계약농가수0.0000.2230.6270.4330.2691.0000.3720.6530.000
계약물량(킬로그램)0.0000.2020.4630.1180.0890.3721.0000.7460.000
계약금액(천원)0.0000.2470.5910.2440.1610.6530.7461.0000.000
계약면적(제곱미터)0.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-12T15:04:27.797685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도품목사업방식
시도1.0000.3040.261
품목0.3041.0000.326
사업방식0.2610.3261.000
2023-12-12T15:04:27.907502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도계약농가수계약물량(킬로그램)계약금액(천원)계약면적(제곱미터)시도품목사업방식
연도1.0000.0360.0410.1170.0640.0000.0490.128
계약농가수0.0361.0000.3190.7390.7890.0950.1450.206
계약물량(킬로그램)0.0410.3191.0000.6880.6570.0870.0560.067
계약금액(천원)0.1170.7390.6881.0000.9290.1060.0770.123
계약면적(제곱미터)0.0640.7890.6570.9291.0000.0000.0000.000
시도0.0000.0950.0870.1060.0001.0000.3040.261
품목0.0490.1450.0560.0770.0000.3041.0000.326
사업방식0.1280.2060.0670.1230.0000.2610.3261.000

Missing values

2023-12-12T15:04:23.869812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:04:24.049372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연도시도시군품목사업방식계약농가수계약물량(킬로그램)계약금액(천원)계약면적(제곱미터)
02010강원평창군배추매취61648000.0798800.0272396
12010강원평창군감자매취431401037.0608907.39527886
22010전남직할마늘매취2959600.0101320.047285
32010전남나주시양파매취28424935.0169974.083677
42010전남목포신안시군대파매취3300000.090000.0230000
52010전남진도군대파수탁1192871938.03536178.1859565
62010전남해남군배추매취38221775527.03416260.952405273
72010전남고흥군마늘수탁1691300.0285800.098184
82010충남태안군마늘매취5941042760.01595277.0946065
92010경남창녕군양파매취1566999000.01749750.01141370
연도시도시군품목사업방식계약농가수계약물량(킬로그램)계약금액(천원)계약면적(제곱미터)
30382022충북제천시홍고추수탁410000.021500.056746
30392022충북증평군건고추매취2912580.0150960.069924
30402022충북진천군대파수탁8180000.0180000.045000
30412022충북청주시양파수탁19302948.0151474.075737
30422022충북청주시감자매취21100060.0110066.050030
30432022충북청주시대파수탁14349672.0249836.0134080
30442022충북청주시배추수탁443824742.0905718.08588187
30452022충북충주시감자수탁16172519.0120763.3173888
30462022충북충주시건고추매취8815180.0151800.088000
30472022충북충주시배추매취471988500.0564750.0376000