Priority Analysis on the Production Layout of Potato in China

Priority Analysis on the Production Layout of Potato in China

Pengling LiuYun Zhou Kang Sun Zhen Fang 

College of Economics and Management, Anhui Agricultural University, Hefei 230036, China

Corresponding Author Email: 
liupengling@ahau.edu.cn
Page: 
1081-1087
|
DOI: 
https://doi.org/10.18280/ijsdp.150712
Received: 
6 May 2020
|
Revised: 
10 August 2020
|
Accepted: 
19 August 2020
|
Available online: 
13 November 2020
| Citation

© 2020 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

Based on the panel data 2009-2018 on 23 potato producing areas in China, this paper firstly analyzes the priority of each area in potato production layout, using the production concentration index (PCI). Then, the main factors affecting the PCI of potato were identified, and used to develop an evaluation index system (EIS) for production advantage. Through entropy method, the production advantage of each area in potato cultivation was evaluated, and ranked in descending order. Finally, the priority of each area in potato production layout was measured comprehensively, and a total of 11 areas were determined as priority areas. On this basis, several suggestions were put forward to optimize the production layout of potato in China: (1) The Chinese government should give priority to the following producing areas in the planning of potato production layout: Sichuan, Guizhou, Yunnan, and Chongqing in Northwest China; Gansu, Shaanxi, and Qinghai in Northwest China; Hebei, and Inner Mongolia in North China; Heilongjiang in Northeast China; Hubei in the winter cropping area in the south. (2) The 11 priority areas should arrange potato production as per the local situation, during the planning of crop production layout. (3) The relevant planning departments should grasp the change trend in the producing areas of potato and other water-saving crops, identify their main producing areas, and deploy water-saving crops in dry and water-deficient, which are not suitable for rice or wheat.

Keywords: 

production layout of potato, priority, production concentration index (PCI), production advantage, layout planning

1. Introduction

With the spread of Coronavirus Disease 2019 (COVID-19), some countries have reduced or banned the export of staple food crops. This raises concerns on food security [1, 2]. Being the most populous country in the world, China must give top priority to food security. It is important for the country to improve the overall food yield as per local conditions.

So far, China has basically used up the agricultural lands suitable for rice and wheat [3, 4]. Fortunately, potato, which serves as both staple food and vegetable, could be planted in some dry and water-deficient areas unfit for rice or wheat. Potato boasts advantages of short planting cycle, high yield, and wide adaptability [5], and plays an important role in improving the family income and nutrition of farmers [6]. In 2015, China positioned potato as a staple food. Currently, potation cultivation in China features great potential, wide range, and low yield [7]. Further research is needed to optimize the layout of potation production, and improve its production efficiency and yield.

At present, potato production has been widely studied at home and abroad. But few have attempted to optimize the layout of potato production in China. In terms of production efficiency, Osipov et al. [8] analyzed the efficiency of potato production in Russia, and proved the effectiveness of technical training. Through stochastic frontier analysis (SFA), Kamau et al. [9] examined the technical efficiency and influencing factors of Irish potato production in Monroe County, Kenya. Some scholars [10, 11] conducted data envelopment analysis (DEA) on the efficiency and scale efficiency of potato production.

In terms of storage technology, some scholars [12, 13] explored the effects of different conditions on the anti-browning of fresh-cut potato chips, and concluded that short-term high-oxygen pretreatment and Portulaca oleracea extract both have significant anti-browning effects. Lin et al. [14] investigated the cold reaction mechanism of potato tubers stored at low temperature, shedding light on the sugar accumulation and defense reaction of potato tubers under cold storage conditions.

In terms of production layout: Through linear programming optimization, Liu et al. [15] established a national grain production layout optimization model based on the comparative advantage index of agricultural food production (ACI), and achieved the synergy between land, water, and food. Qiao et al. [16] studied the impact of crop distribution and climate change on crop production, using the Environmental Policy Integrated Climate (EPIC) model. Davis et al. [17] derived the potential differences between 14 staple food crops in food production and water use from process-based crop moisture model and spatial interpolated yields, and reduced the water use by optimizing the crop layout. Based on the county statistics in 2000-2003, Yin et al. [18] captured the variation in China’s grain production layout in the 21st century. Lv and Sun [19] discovered that potato production in China is more and more accumulated geographically, and gradually shifts from east to west. Yang et al. [20] held that traditional potato production areas, such as the two-season cropping area in the Central Plains, and the one-season cropping area in the north, maintain a large comparative advantage, while the one- and two-season mixed cropping area in the southwest is gaining momentum. The above studies provide a useful reference for this research.

Based on the potato production data of China’s 23 provincial administrative regions (hereinafter referred to as provinces) in 2009-2018, this paper calculates the production concentration index (PCI) of potato in each province, and comprehensively evaluates the production advantage of each potato producing area. On this basis, the authors analyzed the priority in the production layout of potato in China, identified the producing area that should be prioritized in production layout. The research results provide insights to the optimization of production layout of potato in China.

2. PCI-Based Priority Analysis

2.1 Study areas

The one-season cropping area in the north mainly includes Heilongjiang, Jilin, and Liaoning in Northeast China; Shaanxi, Ningxia, Gansu, Qinghai, and Xinjiang in Northwest China; Hebei, Shanxi, and Inner Mongolia in North China. The two-season cropping area in the Central Plains mainly includes Zhejiang, Anhui, and Jiangxi. The one- and two-season mixed cropping area in the southwest mainly include Guizhou, Yunnan, Sichuan, Tibet, and Chongqing. The winter cropping area in the south mainly include Hubei, Hunan, Fujian, Guangdong, and Guangxi.

2.2 Methodology

The PCI, the main index of this research, was defined as the ratio of potato yield in a province to the nationwide yield. The PCI of a province PCIitcan be calculated by Eq. (1).

First, the regression equation for the correlation between PCIit and time t was established to analyze the change trend of PCIit in each province. Then, the potato producing areas in China were classified and sorted based on the significance of the correlation.

$P C I_{i t}=\frac{Q_{i t}}{\sum_{i=1}^{n} Q_{i t}} \times 100$      (1)

2.3 PCI change trend

Table 1 presents the regression results on the change trend of PCIit in each province. It can be seen that, in 2009-2018, the Chinese provinces differed significantly in the change trend of PCIit.

Based on the significance of the correlation between PCIit and time in 2008-2019, the Chinese provinces were divided into the following characteristic regions of potation production:

(1) Region with significant increase

Seven provinces, namely, Hebei, Shanxi, Guangdong, Sichuan, Guizhou, Tibet, and Shaanxi, saw significant increase in PCIit. The annual PCIit values of these provinces were added up into PCI1. It can be seen that: the PCI1 of the region with significance increase rose from 32.75% in 2009 to 43.56% in 2018 (as shown in Table 2); the change trend can be described as: PCI1=1.466*T+29.078 (R2=0.922).

(2) Region with significant decrease

Ten provinces, namely, Inner Mongolia, Heilongjiang, Zhejiang, Anhui, Fujian, Hunan, Yunnan, Gansu, Ningxia, and Xinjiang witnessed significant decrease in PCIit. The annual PCIit values of these provinces were added up into PCI2. The change trend of this region can be expressed as: PCI2=-1.477*T+52.458 (R2=0.906).

(3) Other region (region with insignificant change)

Six provinces, namely, Liaoning, Jilin, Hubei, Guangxi, Chongqing, and Qinghai, did not seen any obvious change in PCIit.

Table 2 lists the PCIs of the three characteristic regions in 2009-2018.

Table 1. The change trend of PCIit in Chinese provinces

Province

Regression equation

r

a

Province

Regression equation

r

a

Hebei

PCIit=0.431*T+1.28

0.89

***

Guangxi

PCIit=0.035*T+0.80

0.26

 

Shanxi

PCIit=0.135*T+0.98

0.92

***

Chongqing

PCIit=-0.033*T+6.85

-0.34

 

Inner Mongolia

PCIit=-0.388*T+11.40

-0.87

***

Sichuan

PCIit=0.300*T+13.38

0.73

**

Liaoning

PCIit=-0.016*T+2.00

-0.17

 

Guizhou

PCIit=0.475*T+8.84

0.90

***

Jilin

PCIit=-0.087*T+3.21

-0.33

 

Yunnan

PCIit=-0.193*T+10.20

-0.81

***

Heilongjiang

PCIit=-0.357*T+7.79

-0.85

***

Tibet

PCIit=0.002*T+0.02

0.79

***

Zhejiang

PCIit=-0.039*T+1.37

-0.63

**

Shaanxi

PCIit=0.103*T+3.41

0.85

***

Anhui

PCIit=-0.040*T+0.43

-0.85

***

Gansu

PCIit=-0.204*T+13.16

-0.69

**

Fujian

PCIit=-0.076*T+1.91

-0.78

***

Qinghai

PCIit=-0.045*T+2.28

-0.55

 

Hubei

PCIit=0.002*T+3.72

0.03

 

Ningxia

PCIit=-0.083*T+2.71

-0.94

***

Hunan

PCIit=-0.035*T+2.20

-0.66

**

Xinjiang

PCIit=-0.061*T+1.29

-0.71

**

Guangdong

PCIit=0.020*T+1.17

0.59

*

 

 

 

 

Note: r is the correlation coefficient; a is the degree of significance; ***, **, and * are the significance levels of 1%, 5%, and 10%, respectively.

Table 2. The PCIs of the three characteristic regions in 2009-2018

Year

Region with significant increase

Region with significant decrease

Other region

Year

Region with significant increase

Region with significant decrease

Other region

2009

32.75

49.94

17.32

2014

36.93

44.01

18.71

2010

32.27

47.68

20.05

2015

38.53

42.10

19.00

2011

31.99

49.74

18.59

2016

42.71

38.57

17.56

2012

34.21

47.62

17.73

2017

43.18

38.05

17.65

2013

35.28

46.91

17.47

2018

43.56

38.72

16.55

2.4 PCI-based priority

The above analysis shows that PCIit trend differed from province to province, suggesting that the layout of potato producing areas in China changed constantly in the sample period. Then, the 23 provinces were ranked by annual PCIit values in 2009-2018. The rankings of each province in the 10 years were added up into the total ranking score of that province (as shown in Table 3). Next, the provinces with relatively low total ranking scores were given relatively high priority.

As shown in Table 3, the 23 provinces can be ranked by the priority in potation production layout as: Sichuan> Gansu> Guizhou> Inner Mongolia> Yunnan> Chongqing> Heilongjiang> Shaanxi> Hubei> Hebei> Jilin> Ningxia> Qinghai> Hunan> Liaoning> Shanxi> Fujian> Guangdong> Zhejiang> Guangxi> Xinjiang> Anhui> Tibet. The top ranked areas are mostly concentrated in the southwest and northwest, which agrees with the results of Lv Chao et al. (2019).

3. Influencing Factors of PCI

3.1 Variable setting and data description

The PCIit of each province was explained by six variables: the PCI in the previous year, natural disaster, agricultural infrastructure, technological level, industrial structure, and non-agricultural employment. The panel data of the 23 potato producing areas were selected from statistical data like China Rural Statistical Yearbooks. The meanings and expected effects of model variables are given in Table 4.

3.2 Model construction

The theoretical model can be established as:

$\begin{align}  & PCIit=\alpha +\beta 1PCIit-1+\beta 2Disasterit \\ & +\beta 3Irrigationit+\beta 4Techno\log yit \\ & +\beta 5Structureit+\beta 6Nonfarmit+Zi\delta +Ui+vit \\\end{align}$       (2)

where, i is the serial number of province; t is year; the explained variable PCIit is the PCI of potato production; PCIit-1, Disasterit, Irrigationit, Technologyit, Structureit, and Nonfarmit are explanatory variables; α is a constant; β16 are the coefficients of the six explanatory variables, respectively; Zi is the time-invariant individual feature; Ui and Vit are intercept and disturbance, respectively.

3.3 Results analysis

The model estimation was carried out on StataSE14.0, using short panel data. Through Hausman test, the fixed-effects model was selected for the estimation. The estimation results are recorded in Table 5.

Table 3. The total ranking scores of Chinese provinces

Ranking

Province

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Total ranking score

1

Sichuan

1

1

2

1

1

1

1

1

1

1

11

2

Gansu

2

2

1

2

2

2

3

3

3

3

23

3

Guizhou

4

5

4

4

3

3

2

2

2

2

31

4

Inner Mongolia

3

3

3

3

4

5

5

5

5

4

40

5

Yunnan

5

4

5

5

5

4

4

4

4

5

45

6

Chongqing

6

7

7

7

6

6

6

6

6

6

63

7

Heilongjiang

7

6

6

6

7

7

7

9

8

9

72

8

Shaanxi

8

10

9

9

9

8

9

8

9

8

87

9

Hubei

9

9

8

8

8

9

8

10

10

10

89

10

Hebei

15

11

11

11

10

11

10

7

7

7

100

11

Jilin

16

8

10

10

11

10

11

11

11

15

113

12

Ningxia

10

12

12

12

12

12

13

14

14

12

123

13

Qinghai

11

13

14

14

14

15

14

13

15

13

136

14

Hunan

12

14

15

13

13

14

12

16

16

14

139

15

Liaoning

13

15

13

16

17

13

15

15

13

16

146

16

Shanxi

18

18

17

17

16

17

18

12

12

11

156

17

Fujian

14

16

16

15

15

16

16

18

18

18

162

18

Guangdong

20

17

20

18

19

20

20

17

17

17

185

19

Zhejiang

19

20

18

19

19

19

19

19

19

19

190

20

Guangxi

21

21

21

20

18

18

17

20

20

20

196

21

Xinjiang

17

19

19

21

21

21

21

21

21

21

202

22

Anhui

22

22

22

22

22

22

22

22

22

22

220

23

Tibet

23

23

23

23

23

23

23

23

23

23

230

Table 4. The meanings and expected effects of model variables

Variable

Code

Meaning

Expected effect

The PCI in the previous year

PCIit-1

The PCI in year t-1

Positive

Natural disaster

Disasterit

Disaster-affected area

Negative

Agricultural infrastructure

Irrigationit

Effectively irrigated area

Positive

Technological level

Technologyit

Potato yield per unit area

Positive

Industrial structure

Structureit

Sown area ratio of potato to all crops

Positive

Non-agricultural employment

Nonfarmit

Wage income as a proportion of net income of villagers

Negative

Table 5. The estimation results on panel data

Variable

Coefficient

Standard deviation

t-value

The PCI in the previous year

0.3818***

0.05025

7.60

Natural disaster

-0.0046**

0.00178

-2.59

Agricultural infrastructure

0.0103***

0.00323

3.18

Technological level

0.0003***

0.00004

6.71

Industrial structure

10.5795*

5.40432

1.96

Non-agricultural employment

-0.9809

0.9184

-1.07

Constant

0.5688

0.44448

1.28

R2=0.9296 F-statistic=43.27

Note: ***, **, and * are the significance levels of 1%, 5%, and 10%, respectively.

As shown in Table 5, our model achieved a good fitting effect. The PCI of potato was significantly promoted by the PCI in the previous year, agricultural infrastructure, technological level, and industrial structure, and significantly suppressed by natural disaster, and non-agricultural employment.

4. Production Advantage-Based Priority Analysis

4.1 Evaluation index system (EIS) for production advantage

The production advantage of each potato producing area in China was evaluated comprehensively through the entropy method. Drawing on the above results on the factors affecting the PCI, this paper sets up an EIS for production advantage of each potato producing area, which is systematic, effective, and comparable.There are three primary indices in the EIS: technology and facility, nature and economy, and production scale. Each primary index was supported by several secondary indices. Specifically, technology and facility was decomposed into effectively irrigated area (X1) and technical level (X2); nature and economy was decomposed into industrial structure (X3), disaster-affected area (X4), and non-agricultural employment (X5); production scale was decomposed into sown area (X6) and potato yield (X7).

4.2 Comprehensive evaluation of production advantage

Based on the statistics on each potato producing area in 2018, the production advantage of each area was comprehensively evaluated by entropy method. First, the weight of each index was calculated step by step (as shown in

Table 6). Then, the comprehensive production advantage scores of the 23 areas were obtained one by one (as shown in Table 7).

Table 6. The entropy, diversity factor, and weight of each index

Index

'

Entropy

Diversity factor

Weight

X1

0.8839

0.1161

0.1674

X2

0.9125

0.0875

0.1261

X3

0.8512

0.1488

0.2146

X4

0.9787

0.0213

0.0308

X5

0.9676

0.0324

0.0467

X6

0.8500

0.1500

0.2163

X7

0.8627

0.1373

0.1980

Table 7. The comprehensive production advantage scores in 2018

Region

Province

Technology and facility

Nature and economy

Production scale

Comprehensive score

Ranking

Score

Ranking

Score

Ranking

Score

Ranking

The one-season cropping area in the north

Northwest China

Shaanxi

0.0111

11

0.0175

6

0.0232

7

0.0519

7

Gansu

0.0250

2

0.0318

2

0.0478

3

0.1045

3

Qinghai

0.0086

19

0.0346

1

0.0084

13

0.0515

8

Ningxia

0.0080

20

0.0215

5

0.0094

12

0.0389

10

Xinjiang

0.0138

9

0.0052

19

0.0023

21

0.0213

18

North China

Hebei

0.0214

3

0.0062

15

0.0193

8

0.0468

9

Shanxi

0.0086

18

0.0108

10

0.0124

11

0.0319

13

Inner Mongolia

0.0190

4

0.0114

9

0.0323

5

0.0627

5

Northeast China

Liaoning

0.0099

15

0.0063

14

0.0064

15

0.0227

16

Jilin

0.0175

6

0.0060

17

0.0063

16

0.0298

14

Heilongjiang

0.0146

8

0.0067

13

0.0161

10

0.0375

11

The two-season cropping area in the Central Plains

Zhejiang

0.0069

21

0.0051

20

0.0035

20

0.0155

21

Anhui

0.0028

22

0.0040

23

0.0009

22

0.0077

23

The winter cropping area in the south

Fujian

0.0087

17

0.0087

12

0.0047

18

0.0222

17

Hubei

0.0103

14

0.0091

11

0.0172

9

0.0365

12

Hunan

0.0106

13

0.0051

21

0.0075

14

0.0232

15

Guangdong

0.0094

16

0.0050

22

0.0054

17

0.0199

19

Guangxi

0.0019

23

0.0060

16

0.0043

19

0.0122

22

The one- and two-season mixed cropping area in the southwest

Chongqing

0.0108

12

0.0229

4

0.0282

6

0.0620

6

Sichuan

0.0272

1

0.0172

8

0.0613

1

0.1057

2

Guizhou

0.0188

5

0.0283

3

0.0591

2

0.1062

1

Yunnan

0.0161

7

0.0172

7

0.0377

4

0.0711

4

Tibet

0.0125

10

0.0053

18

0.0006

23

0.0184

20

4.3 Production advantage-based priority

The above analysis shows that Guizhou had the highest comprehensive score of production advantage (0.1062), while Anhui had the lowest score (0.0077). Based on the comprehensive evaluation of production advantage, the 23 Chinese provinces can be ranked by the priority in production layout as: Guizhou> Sichuan> Gansu> Yunnan> Inner Mongolia> Chongqing> Shaanxi> Qinghai> Hebei> Ningxia> Heilongjiang> Hubei> Shanxi> Jilin> Hunan> Liaoning> Fujian> Xinjiang> Guangdong> Tibet> Zhejiang> Guangxi> Anhui.

The top-ranking provinces mainly concentrate in Southwest and Northwest China. In Southwest China, Sichuan, Guizhou, and Yunnan ranked high in technology and facility, nature and economy, as well as production scale, a sign of strong comprehensive advantages; Chongqing also had a clear edge in nature and economy, as well as production scale. In Northwest China, Gansu, Shaanxi, Qinghai, and Ningxia boasted strong comprehensive advantages; among them, Gansu ranked in the top 3 in terms of technology and facility, nature and economy, as well as production scale; Shaanxi, Qinghai, and Ningxia ranked among the top in terms of nature and economy.

In North China, Hebei finished the third in technology and facility, which reflects its development advantage; the advantages of Inner Mongolia lay in technology and facility, production scale, and the large area. In Northeast China, Heilongjiang occupied the eighth place in technology and facility, and thus had certain advantages.

5. Priority of Production Layout

5.1 Comprehensive measurement of priority

Drawing on the PCI-based priority and production advantage-based priority, this section comprehensively measures the priority of each potato producing area in production layout. Specifically, the rankings of each province in PCI-based priority and production advantage-based priority were added up, and the provinces with relatively low total ranking score were given relatively high priority. The results of comprehensive measurement are presented in Table 8.

Table 8. The results of comprehensive measurement

Region

Province

PCI-based ranking

Production advantage-based ranking

Total ranking score

Comprehensive ranking

The one-season cropping area in the north

Northwest China

Shaanxi

8

7

15

7

Gansu

2

3

5

3

Qinghai

13

8

21

10

Ningxia

12

10

22

12

Xinjiang

21

18

39

19

North China

Hebei

10

9

19

9

Shanxi

16

13

29

14

Inner Mongolia

4

5

9

4

Northeast China

Liaoning

15

16

31

16

Jilin

11

14

25

13

Heilongjiang

7

11

18

8

The two-season cropping area in the Central Plains

Zhejiang

19

21

40

20

Anhui

22

23

45

23

The winter cropping area in the south

Fujian

17

17

34

17

Hubei

9

12

21

10

Hunan

14

15

29

14

Guangdong

18

19

37

18

Guangxi

20

22

42

21

The one- and two-season mixed cropping area in the southwest

Chongqing

6

6

12

6

Sichuan

1

2

3

1

Guizhou

3

1

4

2

Yunnan

5

4

9

4

Tibet

23

20

43

22

5.2 Results analysis

As shown in Table 8, the 23 provinces can be ranked by priority of production layout as Sichuan> Guizhou> Gansu> Inner Mongolia = Yunnan> Chongqing> Shaanxi> Heilongjiang> Hebei> Hubei = Qinghai> Ningxia> Jilin> Hunan = Shanxi> Liaoning> Fujian> Guangdong> Xinjiang> Zhejiang> Guangxi> Tibet> Anhui.

Eleven provinces were among the top ten of the comprehensive ranking: Sichuan, Guizhou, Gansu, Inner Mongolia, Yunnan, Chongqing, Shaanxi, Heilongjiang, Hebei, Hubei, and Qinghai. These prioritized areas concentrate in Southwest and Northwest China.

In Southwest China, Sichuan, Guizhou, Yunnan, and Chongqing appeared in the top six whether in the PCI-based ranking or production advantage-based ranking. In Northwest China, Shaanxi, Gansu, and Qinghai were among the top 10 in the comprehensive ranking; Gansu even reached the third place. In addition, Hebei and Inner Mongolia in North China, Heilongjiang in Northeast China, and Hubei in the winter cropping area in the south, also ranked among the top 10 in the comprehensive ranking; these provinces have strong development potential, judging by PCI or production advantage.

6. Conclusions

Based on the 2009-2018 production data in 23 potato producing areas in China, this paper analyzes the priority of each area in the production layout of potato through PCI trend analysis. In addition, the main factors affecting the PCI of potato were identified, and used to set up an EIS for production advantage. On this basis, the production advantage of each area was evaluated by the entropy method, and ranked in descending order. Finally, the priorities of all the 23 areas in production layout were comprehensively measured, and a total of 11 areas were identified as the priority areas.

In view of the above results, this paper puts forward several suggestions to optimize the production layout of potato in China:

(1) The Chinese government should give priority to the following producing areas in the planning of potato production layout: Sichuan, Guizhou, Yunnan, and Chongqing in Northwest China; Gansu, Shaanxi, and Qinghai in Northwest China; Hebei, and Inner Mongolia in North China; Heilongjiang in Northeast China; Hubei in the winter cropping area in the south.

(2) The 11 priority areas should arrange potato production as per the local situation, during the planning of crop production layout.

(3) The relevant planning departments should grasp the change trend in the producing areas of potato and other water-saving crops, identify their main producing areas, and deploy water-saving crops in dry and water-deficient, which are not suitable for rice or wheat.

Acknowledgment

This work was supported by the Philosophy and Social Sciences Planning Fund, Anhui Province, China (Grant No.: AHSKZ2018D02).

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