Total-factor cultivated land efficiency in China

LEAH WU

Faculty of Economics Management Sichuan Agricultural University

Ya’an, China leahlikefood@hotmail.com

Wen Xiu ZHANG Faculty of Economics Management

Sichuan Agricultural University Ya’an, China

cndzwx@163.com

Abstract---The paper utilizes data envelopment analysis (DEA) and a newly proposed total-factor cultivated land efficiency (TFCLE) index to analyze cultivated land use efficiencies of 31 cantons in China from 1999 through 2008. The results indicate that the national TFCLE is 0.649, which shows 35.1 percent of improvement. In terms of geographical distribution, the uppermost category including Beijing, Hainan, Xinjiang, and Qinghai is the lowest one. The TFCLE of coastland is higher than Hinterland’s. Then, after advanced analysis of our results, the paper concludes that enhancing the capacity of preventing natural disaster in agriculture, increasing invest in agricultural machinery and R & D, and raising the multiple crop index are advised to improve the cultivated land use efficiencies. Keywords- data envelopment analysis (DEA); total-factor cultivated land efficiency (TFCLE); cultivated land use efficiency;

I. INTRODUCTION As non-reproducible and irreplaceable resource, cultivated

land is the most basic material for subsistence of the mankind. Compared with the fast development of market economy and industrialization, the expedition of cultivated lands is relatively running low. It is an important avenue for sustainable development of the socialist economy and mitigating the contradiction of supply and demand, to strictly protect, save and efficiently use arable lands. Therefore, lots of researchers have studied on arable lands from different aspects: Based on the analysis on the sown area of grain, multiple crop index and per unit area yield, Zhu Huiyi [1] analyzed deeply the changes of efficiency and intensive degree of arable land use in setting of domestic marketing economy via national and regional dimensions respectively, and revealed the characteristics of changing in the intensive degree of cultivated land use at present in china; Li Chunhua [2] evaluated the points of pressure on arable land use in 31 provinces of china, autonomous regions and municipalities in 2004; Pang Ying [3] analyzed overall efficiency of arable land use in different regions by the method of factor analysis; Yu Yongjun [4] studied the driving factors causing the reduction of arable land in JiangYin of JiangSu province, and they used the methods of principal component analysis to estimate the cultivated land use efficiency.

The analysis and evaluation measures used in above researches are restricted by metric units; especially the data necessary to be non-dimensional quantities before applying. The results of evaluation vary according to different methods of non-dimensional quantities. In order to make the evaluated results more exact, some researchers start to evaluate arable

land use efficiency by DEA recently. DEA has three good advantages as follow:

1) study on the efficiency of each decision package, the ratio of input and output of decision package, calculate and evaluate the efficiency of multiple input and output of decision package at same time;

2) more precise without the influence of metric unit and dimension;

3) provide the information about how to perfect decision package.

Liu Xinping [5] analyzed the validity of the arable land use efficiency in thirteen regions of XinJiang by DEA; Liang Liutao and Qu Futian [6] analyzed the efficiency of arable land use from 1997 to 2004 in china. However, these studies only calculated factors efficiency from the aspect of overall inputs, without analyzing single input factor efficiency which against intensive studies on the status of cultivated land use .In order to analyze the single factor use efficiency, we should introduce the index of Total Factor Input Efficiency.

Total Factor Input Efficiency computes the single production factor efficiency under the impacts of other factors. HU [7] firstly introduced this concept on the energy use efficiency and established energy index systems of total factor efficiency, the ratio of target energy and actual energy consumed. The present paper extends the concept and computational methods, and establishes Total-Factor Cultivated Land Efficiency (economics meaning is arable land efficiency under consideration of other input factors) to calculate and analyze the use efficiency of arable land from 1997 to 2006 in China.

II. RESEARCH METHOD, INDEX SELECTION AND DATA SOURCES

A. Research method Methodology of DEA

Data envelopment analysis (DEA), occasionally called frontier analysis, was first put forward by Charnes, Cooper and Rhodes in 1978. It is a performance measurement technique which can be used for evaluating the relative efficiency of decision-making units (DMU's) in organizations with multi-input and multi-output via mathematic programming model. Those most efficient DMUs, which are located at the efficiency frontier, have the maximum outputs generated among all DMUs by taking the minimum level of

978-1-4244-5326-9/10/$26.00 ©2010 IEEE

inputs, and own the best efficiency among all DMUs. DEA produces detailed information on the efficiency of the unit, not only relative to the efficiency frontier, but also relative to specific efficient units which can be identified as role models or comparators [8]. The general model of DEA is Constant Return to Scale (CRS). This model finds the overall technical efficiency (OTE) of each DMU. This model further decomposes the OTE into the product of scale efficiency (SE) and pure technical efficiency (PTE) (Charnee, 1984), that is OTE = PTE×SE. In order to pursue OTE with arable land inputs, our study adopts the input-oriented CRS DEA model which can exactly generate the efficiency scores and CRS model:

0

. . 0

Ti

T Ti i

M axu Y

s t X u Yω − ≥

10 0

TiXu

ωω

=≥ ≥

1, 2,...,i n= (1)

Antithetic planning of the judgment on validity express as:

0. i i

Mins t X S X

θλ θ−− =∑

0 ( 1, 2,..., )0

0 0

i i

i

Y S Y i n

S S

λλ

+

− +

− = =

≥

≥ ≥

∑ (2)

Total-factor cultivated land efficiency We employ DEA to estimate the agricultural production

efficiency frontier, which not only calculates the technical efficiency in each region, but also the each factor target input in every DMU. Then, the DMU may adjust inputs because the inefficiency production undoubtedly indicates over-estimated inputs. The more the amount of total adjustments, the less efficient the cultivated land used in the region. Thus, there is no need to have any adjustment if the region utilizes cultivated land at the “target cultivated land input” level that is optimal efficiency of cultivated land use. Therefore, cultivated land efficiency in a region is defined in Equation (3) as below, which is named total-factor cultivated land efficiency (TFCLE) for region i at time t since the index is established based on the viewpoint of total factor productivity:

),(Input Land Cultivated Actual),(Input Land CluivatedTarget ),(

tititiTFCLE =

(3)

This implies in the i th region and in the t th year.

As Equation (3) shows, the index TFCLE represents the efficiency level of cultivated land use in a region. As the target input is the best practical minimum level of input in a region, the actual input is therefore always larger than or equal to this target input. This makes the index TFCLE score among the value from zero to unity. When the actual cultivated land input level of a DMU is equivalent or near to the target input, it indicates that the region achieved high efficiency. Oppositely, if actual input is far away from target input, it suggests a low efficiency.

B. Index Agricultural production is considered of as conjunction with

conventional inputs cultivated land, labor and agricultural machinery, which are normally used in an agricultural productivity analysis as the total inputs to produce agricultural output –agricultural added value. All the indexes are shown in Table 1.

[Insert Table 1 here]

C．Data resources Data of our study are collected from China Statistics Yearbook, China Rural Statistics Yearbook, the official statistics form national bureau of statistics of china, and the website of agricultural information of china complement, which are not available in any statistical yearbooks.

III. ANALYSIS OF TOTAL-FACTOR CULTIVATED LAND USE EFFICIENCY

We use the software Deap2.1, which are kindly provided by Coelli [9], to solve the linear programming problems. The total-factor cultivated land use efficiency of 31 cantons on Chinese Mainland (1999-2008) worked out by formula TFCLE. The paper further analyzes the result and figures out the fluctuating movement. The detailed data are shown in Table 2.

[Insert Table 2 here]

A. Analysis of the fluctuating total–factor cultivated land use efficiency in China

From the results shown, the average of the total-factor cultivated land efficiency of the nation (1999~2008) is 0.649, and it is the same as that of 2008, which surpasses the results obtained from several years ago. Therefore, it has 35.1 percent of improvement.

[Insert Figure 1 here]

As is illustrated in Fig. 1, the cultivated land use efficiency of our nation has fluctuated like an L-shape since 1999. That is, it went down from 0.712 in 1999 to 0.601 in 2002, and it fluctuated around 0.635 from 2001 to 2005 and went up to 0.649 in 2006. Our country experienced industrialization and urbanization from 1999 to 2002. At that time, some cantons occupied vast cultivated land to establish various developing districts, and industrial areas, and resulted in the shortage of the amount of cultivated land. In addition, our country suffered from great disasters in that period, like flood in 1998 and drought in 1999 and 2000. Then, the dry land crop production in 2000 went down to a great extent, and agricultural value-added output went down at a percentage of 8.76, from 1374.22 million dollars in 1999 to 1263.58 million dollars in 2001. However, starting from 2001, China adopted the tightest policy to protect of cultivated land, which controlled the reducing speed and degree of the land. And on the other hand, with the progressing technology, it increased the input of capital, labor force, etc. to improve the output. Agricultural value-added output at length reached 1666.72 million dollars in 2008, which increased by 31.9 percent compared to that of 2002. Actually,

the practical cultivated land was reducing, but the output was increasing. It means that the land use of efficiency is rising.

B. Analysis of the cultivated land use efficiency in each canton With the help of formula TFCLE, we firstly calculated the use efficiency of cultivated land in each canton, and then eventually figured out that of the whole nation. The detailed statistics are shown in table 3.

[Insert Table 3 here]

From Table 3, the majority of cantons enjoy a relatively stable using efficiency of cultivated land from 1999 through 2008, while some of them underwent a fairly big fluctuation, including Jinlin, Liaolin, Tianjing, Tibet, Shanghai and Jiangsu. However, in average, the land use efficiency takes on an absolute increasing trend. In a landscape orientation angle, Beijing, Hainan and Xinjiang stand out the most with a efficient value of 1.0. Meanwhile, Shanghai, Zhejiang, Guangdong and Fujian’s reach 0.958. At the same time, the values among Tianjing, Liaolin, Jilin, and Jiangsu are among 0.7 and 0.8. Shandong, Hubei, Chongqing, Sichuan and Yunnan are among 0.6 and 0.7. Hunan, Henan, Inner Mongolia, Hebei and Guangxi are amnog 0.5 and 0.6. In addition, all of Heilongjiang , Shaanxi , Anhui , Jiangxi , Yunnan , Guizhou, Shanxi, Ningxia ,Gansu and Qinghai’s are below 0.5. Finally, Qinghai has the lowest value of 0.294. The results are shown in Fig. 2.

[Insert Figure 2 here]

C. Comparison of efficiency to use arable lands between coastal regions and inland 31 National Administrative regions in China can be further

divided into coastal regions and inland. Coastal regions contain a total of 12 administrative districts including Beijing, Tianjin, Hebei, Shandong, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan. Then, the remaining 19 provinces or autonomous areas belong to the inland. After calculating data from 1999 to 2008, we find that the average efficiency of coastal regions is 0.831 while that of inland is 0.572. Specifically speaking, the value of island looked like a concave since it dropped from 1999, and was in a stable state, and then became upward in 2002. Meanwhile, in coastal region, the average efficiency was in a upward trend during 1999, but it began to decline since 2000, and then it had a smooth rise since 2002 (Fig.3).

[Insert Figure 3 here]

Figure 3 shows the fact that the efficiency of using arable lands between inland and coastal districts is significantly different. Especially, the difference was as high as 0.37 in 2008. Such difference is mainly due to the following factors.

1) Condition of natural resources. Costal areas have open flat terrain with rich soil, warm and humid climate, and adequate resources such as light, heat and water. Therefore, their productivity is quite high. Conversely, inland provinces located in arid, semiarid, alpine, and plateau areas, and that

resulted in poor quality of arable land. Thir productivity is low with the effect of precipitation, heat and topography.

2) Labor input. When compared to coastal districts, the ecoomic development and comparative effectiveness of agriculture in islands are both lower bacause more comparative effectiveness and labor input from inlands always transfer to industries of high benefits or coastal areas. “Part-time agricultrue” is very prevalent in inland, and that results in low efficiency of the usage of arable lands.

3) Multiple Crop Index of Arable Land (MCIAL). MCIAL is a quantitative indicator showing the degree of exploring arable land. Influenced by natural and economic condition, MCIAL of coastal areas was high, which especially came to 128.6% while that of inland was just 51% during 2006.

III. CONCLUSION AND SUGGESTION From 1999 to 2008, the average efficiency of using arable

land in China comes to 0.649 (the efficiency value is the same as the average value in 2008), with 35.1% to be improved. Concerning geographical distribution, Beijing, Hannan and Xinjiang have the highest efficiency, while Qinghai has the minimum efficiency. In addition, the efficiency of using arable land in coastal areas is much higher than that of inland. Generally speaking, there is more room for inland to improve. According to the research results, we have four suggestions for the purpose of improving the cultivated land used efficiency ：

1) First, government must strictly control the amount of reduction in cultivated land. The output of cultivated land is impossible to be increased by inputing more labors and material resources without a certain amount of arable land. On the one hand, government tries to reduce land non-agricultural conversion, scientificly constitute overall planning of urban development, improve the land market, strongly restrict the abuse of land according to laws ; on the other hand, it is necessary to enhance land development and make efforts to increase the cultivated areas which help to achieve dynamic balance of the total cultivated land, coordinated development of the population, food and the total cultivated land.

2) The high-quality farmland put into the same circumstances often has a greater output that lead to a higher efficiency. Therefore, the local argiculture department should optimize the land use structure, strengthen the agricultural infrastructure construction,such as water, farmland, roads and other infrastructure construction, accelerate the low-yielding land reform and improve quality of cultivated land.

3) scientific and reasonable adjustment of the inputs will result in the better efficiency. For improving the efficiency of China's cultivated land, the relevant department should intensify the use of the material and technical inputs in agriculture, and improve the science and technology contribution to agriculture, such as the increase of green fertilizer, pollution-free pesticides, good seed technology research and development efforts in the implementation of "fertile land project" to restore and improve land productivity, improve crops, multiple crop index. In addition, enhancing

transfer of agricultural land, promoting large-scale operation to increase agricultural labor input, and preventing the use of low efficiency in arable land and industry are all feasible.

4) It is necssary to strengthen ecological and environmental protection of arable land. It may contribute to increase agricultural disaster prevention and resilience. The frist step is to control the pollution of farmland ecological environment, such as industrial waste residue, waste, waste water. Then, the subsequent step is to rectify these contaminated areas through the construction of water conservancy projects related to resist natural disasters.

REFERENCES

[1] ZHU Hui-yi, LI Xui-bin “Intensity Change in Cultivated Land Use in China and Its Policy Implications,” Journal of Natural Resources, 2007,vol 22(6), pp.907-91.

[2] LI Chun-hua and LI Ning, “Analysis on Regional Differentiation of Cultivated Land Use Pressure in China by Radial Basis Function Networks,” China Population Resources and Environment, vol.16(5), 2006, pp. 67–71

[3] PANG Ying, WANG Bao-hai “Comprehensive Efficiency of the Cultivated Land Utilization: A Case study in Shandong Province” , Resources Science, vol 29(2),2006, pp.131-136

[4] YU Yong-jun and LU Yu-qi, “Quantitative Stusies on The Using Efficiency and Driving Factiors of Chanig of Cultivated Land in JIANGYIN City,” Economic Geography,2002.vol22(4), pp.440-443

[5] LIU Xin-ping, MENG Mei and LOU Qiao-shun, “Evaluation of Agricultural Land Use Efficiency in Xinjiang Based on Data Envelopment Analysis,” Journal of Arid Land Resources and Environment., 2008, vol. 22(1), pp. 41–43.

[6] LIANG Liu-tao, QU Fu-tian and WANG Chun-hua “Analysis on Cultivated Land Use Efficiemcy Based on DEA”, Resources and Environment in the Yangtze Basin, 2008,vol.17(2), pp.242-246

[7] Jin-Li Hu Shih-Chuan Wang, “Total-factor energy efficiency of regions in China”.Energy Policy,2006,vol34(17), pp.3206-3217

[8] Charnes A ,Cooper W W,Rhodes E. “Measuring the efficiency of decision making units”, European Journal of Operational Research, vol.2(6), pp. 429-444

[9] Baker R D,“Estimating most productive scale size using data envelopment analysis”, European Journal of Operational Research, 1984,vol.17 (1), pp.429-444

Tab. 3 Total-Factor Cultivated Land Efficiency of Each Province (1999-2008)

Table 1 Input -Output Index of DEA Model

Table 2 The National Average TCLE and TE (1999～2008)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

TCLE 0.71 0.71 0.66 0.60 0.64 0.63 0.63 0.64 0.63 0.65

OTE 0.75 0.74 0.79 0.64 0.69 0.67 0.65 0.66 0.64 0.66

Fig.1 Total-Factor Cultivated Land Efficiency(1999~2008)

Fig. 2 Each Province’s Total-Factor Cultivated Land Efficiency (1999～2008)

Region 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Beijing 1.00 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Tianjing 0.898 0.890 0.857 0.711 0.630 0.607 0.691 0.686 0.663 0.735 Hebei 0.546 0.549 0.549 0.494 0.491 0.507 0.568 0.592 0.619 0.723 Shanxi 0.371 0.400 0.287 0.313 0.287 0.320 0.334 0.344 0.288 0.311

InnerMongolia0.465 0.512 0.548 0.502 0.584 0.608 0.435 0.509 0.503 0.523 Liaoning 0.806 1.000 0.906 0.707 0.808 0.811 0.698 0.740 0.717 0.769 Jilin 0.711 0.893 0.920 0.623 0.813 0.879 0.663 0.677 0.659 0.731

Helongjiang 0.606 0.513 0.504 0.406 0.459 0.496 0.378 0.428 0.461 0.466 Shanghai 0.727 0.958 0.848 0.820 0.967 0.987 1.000 1.000 1.000 1.000 Jiangsu 0.842 0.805 0.780 0.715 0.804 0.817 0.819 0.957 0.961 0.996 Zhejiang 0.875 0.907 0.889 0.962 1.000 1.000 1.000 1.000 1.000 1.000 Anhui 0.560 0.484 0.523 0.451 0.466 0.421 0.363 0.424 0.389 0.404 Fujian 0.918 0.943 0.937 0.942 0.995 0.939 1.000 0.975 0.955 0.939 Jiangxi 0.635 0.541 0.456 0.428 0.471 0.466 0.479 0.496 0.482 0.481

Shandong 0.593 0.642 0.641 0.631 0.629 0.591 0.714 0.680 0.692 0.767 Henan 0.534 0.547 0.553 0.536 0.551 0.498 0.401 0.485 0.504 0.513 Hubei 0.781 0.682 0.570 0.500 0.566 0.572 0.594 0.632 0.619 0.640 Hunan 0.711 0.629 0.568 0.520 0.598 0.550 0.546 0.592 0.588 0.546

Guangdong 1.000 1.000 1.000 0.950 0.968 0.939 1.000 1.000 1.000 1.000 Guangxi 0.767 0.658 0.550 0.453 0.480 0.460 0.511 0.529 0.551 0.575 Hainan 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Chongqing 1.000 0.822 0.559 0.487 0.517 0.522 0.589 0.620 0.661 0.563 Sichuan 1.000 1.000 0.652 0.575 0.579 0.550 0.585 0.622 0.643 0.616 Guizhou 0.761 0.648 0.438 0.381 0.394 0.352 0.364 0.356 0.365 0.361 Yunnan 0.630 0.549 0.504 0.455 0.473 0.449 0.452 0.461 0.450 0.459 Tibet 0.641 0.651 0.739 0.745 0.735 0.669 0.738 0.405 0.359 0.476

Shaanxi 0.565 0.531 0.456 0.419 0.461 0.448 0.446 0.469 0.488 0.475 Gansu 0.400 0.451 0.400 0.358 0.406 0.366 0.405 0.400 0.402 0.397 Qinghai 0.337 0.344 0.304 0.225 0.283 0.274 0.299 0.300 0.295 0.279 Ningxia 0.415 0.431 0.406 0.325 0.357 0.365 0.304 0.348 0.343 0.375 Xinjiang 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

index input index output index

type I1 I2 I3 O1

index cultivated agricultural labor agricultural land area machinery added value

unit 1000 10000kw 10000 100 million

hectares persons yuan