Spatial patterns and effects of soil organic carbon on grain productivity assessment in China

Spatial patterns and effects of soil organic carbon ongrain productivity assessment in China

L. Ye1 , H. Tang

2 , J . Zhu2 , A. Verdoodt

1 & E. Van Ranst1

1Department of Geology and Soil Science, Ghent University, Krijgslaan 281 (S8), 9000 Gent, Belgium, and 2Institute of

Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, 12 Zhonguancun Nan Dajie,

100081 Beijing, China

Abstract

In this paper, we present an assessment of the content and effects of cropland soil organic carbon

(SOC) on grain productivity at the national scale in China using a Web-based Land Evaluation Sys-

tem. Homogeneous 5 km · 5 km grid data sets of climate, crop, soil and management parameters

were created and grain production in 2005 was simulated. Attempts were made to incorporate SOC

into the land evaluation procedure and to quantify the potential effects of SOC deficiency on grain

productivity. Results were statistically analysed and the modelling approach was validated. National

cropland SOC maps were generated. At the national scale, the cropland SOC content averaged 1.20,

0.58, 0.41, 0.31 and 0.26% for the five 20-cm sections consecutively from the surface downwards. At

the regional scale it tended to decline slightly from northeast (1.63%) to southwest (1.11%). On aver-

age, 64% of grain yield was lost due to SOC deficiency for the humid provinces and 7% for the arid

and sub-arid ones. Soil management options are suggested based on the simulation results.

Keywords: Soil organic carbon, grain productivity, land evaluation, China

Introduction

In 2005, in China grain crops of rice, wheat, maize, millet,

sorghum, etc. occupied 78% of the sown area of all food

crops (i.e. grain plus pulse, root and tuber crops) and pro-

duced 88% of the total food production. The average unit

yield of grain crops was 5187 kg ha)1 which was 566 kg ha)1

higher than that of food crops. The grains are the primary

source of food and play the most important role in food

security in China.

Climate, soil, crop performance and management practices

are the four most important factors that influence grain pro-

ductivity. Soil organic carbon (SOC) is among a range of soil

characteristics essential for crop growth. Many aspects, espe-

cially geographical patterns and storage of SOC in China, as

well as correlation to grain productivity, have been studied

at the national (Wu et al., 2003b; c; Zhou et al., 2003; Tang

et al., 2006) or regional scale (Liu et al., 2006). Although the

SOC–grain productivity relationship was reported in many

cases as positive (Fan et al., 2005), negative observations

(Cai & Qin, 2006) were not negligible. We attempt to reveal

the rationale by which the effect of SOC on grain productiv-

ity should be assessed in a comprehensive approach. In par-

ticular, this paper focuses on (i) the content of the cropland

SOC in China and its spatial patterns, (ii) the role of SOC in

grain productivity assessments and the way to integrate it

into a broader process of land evaluation based on limita-

tions and (iii) the effects of SOC on the productivity of grain

crops, individually or collectively.

Materials and methods

Inventory of croplands

The ‘farmland’ and ‘mosaic cropping’ classes were extracted

from the GLC2000 land cover map (Wu et al., 2003a) and

put into the target map of cropland (Figure 1a). The latter

was then projected from WGS84 to Lambert Azimuthal

Equal Area coordinate system, and its spatial resolution was

downscaled to homogeneous 5 km · 5 km. The spatial distri-

bution of popular cultivars of rice (Oryza sativa), wheat

(Triticum aestivum) and maize (Zea mays) is shown in

Figure 1b.

Data manipulation

The study area was divided into 808 rows by 963 columns of

grid cells, each of 25 km2. A unique serial number, runningCorrespondence: E. Van Ranst. E-mail: [email protected]

Received June 2007; accepted after revision September 2007

Soil Use and Management, March 2008, 24, 80–91 doi: 10.1111/j.1475-2743.2007.00136.x

80 ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science

R U S S I A

MONGOLIA

KAZAKSTAN

MYANMAR

LAOSTHAILAND

VIETNA

M

PHILIPPINES

N. KOREA

S. KOREA

SouthChinaSea

EastChinaSea

YellowSea

0 500 1000250

Kilometers

Legend

River, lake

Urban area

Mosaic cropping (3%)

Farmland (97%)

80°E 90°E 100°E 110°E 120°E

40°N

30°N

20°N

70°E

(a)

(b)

70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E50°N

40°N

30°N

20°N

50°N

Rice Wheat

Maize

0 30001500Kilometers

Northern rice

Early and late rice

Early, interm. & late rice

Spring wheat

Winter wheat

Spring & winter wheat

Spring maize

Summer maize

Figure 1 Map of cropland in China in 2000 (a) and spatial distribution of grain crops and cultivars (b).

Soil OC effects on grain productivity assessment 81

ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 80–91

from 1 at the upper left to 778 104 at the lower right corner of

the coverage, was assigned to every cell. This cell number was

used as an index to store the climatic, soil, crop and manage-

ment data in a SQL Server 2000 database for easy and fast

access. The dataset collected and required by the Web-based

Land Evaluation System (WLES) for model run is summa-

rized in Table 1. The crop, soil and management parameters

were first geo-referenced, projected and then rasterized to

Arc ⁄ Info GRID format with 5 km · 5 km as the resolution.

The climatic data set was derived from 374 stations (IAP &

CDIAC, 1991; ISSAS & ISRIC, 1994; WMO, 1996) to provide

a representative cover for the study area (Figure 2a). Due to

the multi-source, multi-density nature of the point datasets,

climatic parameters were interpolated to generate continuous

surfaces of 5 km · 5 km cells using ordinary kriging (ESRI,

2001). Figure 2b shows the annual reference evapotranspira-

tion (ETo), summed from monthly subdivisions that were

calculated from these generated climatic surfaces. Spatial

patterns of high-low ETo values were found to match

climatic, ecological and topographical conditions.

The soil dataset was extracted and converted from the

ISRIC-WISE database of 5¢ · 5¢ (Batjes, 1997, 2002a) basedon an inventory of the FAO soil units in China (FAO, 1999)

correlated to the revised Legend (FAO-UNESCO-ISRIC,

1990). WISE is intrinsically profile-based. A locally represen-

tative soil profile was linked at the very beginning by native

experts, with each FAO soil unit within the original

0.5� · 0.5� grid cell of the Digital Soil Map of the World, on

which the unique soil mapping unit number (SNUM) was

based. The type and relative area of the component soil

units, including the dominant, associated and included units,

occurring at the centre of each 5 km · 5 km grid cell were

identified and characterized using FAO composing rules

FAO (1999). Priority had been given to local experts on esti-

mating missing values. The median of all soil units within

the same grouping was used as the last resort in filling

parameter gaps where local knowledge was unavailable. The

WISE data set was considered appropriate for SOC inven-

tories at the national (Batjes, 2004, 2005, 2006), continental

(Batjes, 2002b) and global (Batjes, 1996) scales. A grid cell

was viewed as a virtual soil profile of five equal sections of

20 cm down to 1 m. Crop physiological and phenological

data were arranged according to cropping system and

agro-ecological zones. The crop ID was used to link crop

parameters to crop rotations and the zone ID to attribute

geographical coordinates to cropping systems.

Assessment of grain productivity

The quantitative assessment of grain productivity was con-

ducted using WLES (http://weble.ugent.be) (Ye & Van

Ranst, 2004; Ye et al., 2004) as the evaluation engine which

uses a three-step, hierarchical, deterministic land evaluation

model (Ye & Van Ranst, 2002), based for specific crops on

the radiation regime (FAO, 1984), and the water-limited and

land production potentials (Sys et al., 1991; Tang et al.,

1992). The evaluation process was looped to iterate all the

778 104 grid cells with data retrieved from and results saved

in the database. The potential productivities were then analy-

sed and mapped.

The average productivities of rainfed (Yrain) and irrigated

(Yirri) grains were assessed using the following equations,

respectively:

Yrain ¼Xcropi¼ 0

Bn �HI � fW � Sy �My

� �i

ð1Þ

Yirri ¼Xcropi¼ 0

Bn �HI � Sy �My

� �i

ð2Þ

where Bn is the net biomass, HI harvest index, fW yield

reduction coefficient due to water stress, Sy soil index and

My management index. To calculate Sy, crop-specific soil

requirement tables (Sys et al., 1993) were used to obtain rat-

ing values of (i) CEC-soil, SOC and the more limiting one

between pH-H2O and exchangeable S(Ca2++Mg2++K+)

for regions of LGP ‡ 120 days (FAO, 1996), or (ii) CaCO3,

gypsum and the more limiting one between EC and ESP for

regions of LGP < 120 days where LGP is length of growing

period. The soil requirements for summer maize, for exam-

ple, are given in Table 2. The Sy was achieved as the product

of the rating values:

Sy ¼Yi

Ri

100

� �ð3Þ

where R is the rating of a soil parameter with a value 0–100.

The My was assigned to a crop in relation to a particular

level of factor inputs (Table 3) which was defined on the

basis of the overall scores from correlation with factor inputs

(Table 4). The actual grain productivity (Y) was therefore

Table 1 Parameters required by the WLES

Category Parameters

Climatic Tmax, Tmin, RH, P and frequency, daily sunshine

duration, wind speed

Crop Name and cultivar, leguminity, photosynthetic

group, leaf area index at maximum growth rate,

harvest index, optimal rooting depth, sowing date,

lengths of the crop cycle and phenological

subdivisions

Soil Parent material and type, soil structure, sand class,

root-limiting layer, % clay-silt-sand, % coarse

fragments, bulk density, pF-curve, % CaCO3,

gypsum, CEC-soil or -clay, concentrations Ca2+,

Mg2+, K+ and Na+, pH-H2O and pH-KCl,

% SOC, EC and ESP

Factor inputs Fertilizers and chemicals, agro-electrical power

consumption, effective irrigation rate

82 L. Ye et al.


R U S S I A

MONGOLIA

KAZAKSTAN

LAOS

THAILAND

VIE

TNA

M

PHILIPPINES

N. KOREA

S. KOREA

SouthChinaSea

EastChinaSea

YellowSea

0 500 1000250

Kilometers

MYANMAR

Data source

IAP & CDIAC (1991):266 stationsISSAS & ISRIC (1994):24 stations

WMO (1996): 84 stations

80°E 90°E 100°E 110°E 120°E

40°N

30°N

20°N

70°E

70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E50°N(a)

40°N

30°N

20°N

50°N

Bay of Bangal

I N D I A

R U S S I A

MONGOLIA

KAZAKSTAN

MYANMAR

LAOSTHAILAND

VIE

TNA

M

PHILIPPINES

N. KOREA

S. KOREA

SouthChinaSea

EastChinaSea

YellowSea

0 500 1000250

Kilometers

ETo (mm)

High : 1705

: 303

70°E

80°E

80°E

90°E

90°E

100°E

100°E 110°E

110°E

120°E

120°E

130°E 140°E

40°N40°N

30°N30°N

20°N20°N

70°E

50°N(b)

50°N

Figure 2 Distribution of weather stations (a) and grid surface of ETo (b).



simulated by combining equations (1) and (2) using the effec-

tive irrigation rate, r:

Y ¼ r � Yirri þ ð100� rÞ � Yrain

100ð4Þ

where r is expressed as a percentage (%) of the area of crop-

land with permanent irrigating infrastructures over the total

area of cropland.

Results and discussion

Model validation: simulated versus observed yields

The province-specific averages of yields of grain crops were

calculated after the grain yields had been simulated on a per

cell basis. The weighted mean of grain productivity (Table 5)

was then estimated by applying the actual sown structure of

grain crops and cultivars in 2005.

Yield difference analysis. A visual check of the mean

observed and simulated yields, shown as box-and-whisker

plots (a, c, e, g) in Figure 3, and the goodness of fit between

them, represented by scattered plots against the 1:1 line (b,

d, f, h), reveals that the productivities of rice (a, b) and

wheat (c, d) are closely simulated whereas the productivity of

maize (e, f) is overestimated. The fitted line (f) suggests that

the overestimation probably occurred in the croplands with a

yield of 5 t ha)1 or higher. Nevertheless, the observed-simu-

lated differences in productivity of all grain crops (g) are still

acceptable as the underestimation counterbalances the over-

estimation (h) to a great deal.

Statistical analyses. The equality of the means of the simu-

lated and observed yields of grain was statistically tested

using the paired t-test (R Development Core Team, 2006)

with the yield data grouped by province. Results (Table 6)

confirm that the simulated yields have the same means as the

observed ones, either individually (as rice, wheat, maize) or

collectively (as grain). In other words, results from the simu-

lation proved to be statistically representative of reality and

vice versa.

Table 2 Soil requirements of summer maize100 95 85 60 40 25 0

CaCO3 (%) 0 6 15 25 35 – >55

Gypsum (%) 0 2 4 10 20 – >20

CEC-claya >24 24 16 <16(–) <16(+) – –

BSb (%) >80 80 50 35 20 <20 –

(Ca + Mg + K)c >8 8 5 3.5 2 <2 –

pH-H2O 6.6 6.2 5.8 5.5 5.2 <5.2 –

6.6 7.0 7.8 8.2 8.5 – >8.5

SOC (%)

(1) >2.0 2.0 1.2 0.8 <0.8 – –

(2) <1.2 1.2 0.8 0.5 <0.5 – –

(3) >0.8 0.8 0.4 <0.4 – – –

EC (dS m)1) 0 2 4 6 8 12 >12

ESP (%) 0 8 15 20 25 – >25

Parent materials: (1) kaolinitic; (2) non-kaolinitic, non-calcareous; (3) calcareous. acmol(+)

kg)1 clay. bBase saturation. ccmol(+) kg)1 soil.

Table 4 Definition of input levels based on factor input scores of economic-development belts

Belt ProvincesaFertilizers and

chemicals

Machinery and

electricity

Irrigation and

infrastructure

Overall

score

Input

level

East 1–3, 6, 9–11, 13, 15, 19–21, 31–33 1.41 5.44 1.64 8.00 H

Central 4–5, 7–8, 12, 14, 16–18 2.20 1.36 1.32 5.00 I

West 22–30 1.00 1.00 1.00 3.00 L

aRefer to Table 5 for province names.

Table 3 Management index in relation to input levels

Crop group

My

Example cropHa-input Ia-input La-input

I 1.00 0.65 0.45 Maize

II 1.00 0.55 0.30 Cotton

III 1.00 0.70 0.50 Wheat

IV 1.00 0.75 0.60 Rice

aInput levels: H = high; I = intermediate; L = low.

84 L. Ye et al.


Spatial patterns of cropland SOC

Cropland SOC was mapped for the topsoil (0–20 cm) and

the four subsoil sections (20–100 cm) to demonstrate its ver-

tical and lateral distributions (Figure 4). At the national

scale, cropland SOC content averaged 1.20, 0.58, 0.41, 0.31

and 0.26% for the 0–20, 20–40, 40–60, 60–80 and 80–100 cm

sections, respectively. The top-section contained twice more

SOC than the first sub-section and 3 times more than the

average of all sub-sections. The decreasing rate of SOC val-

ues over depth was 3.1, 0.9, 0.5 and 0.3% per meter between

two adjacent depth ranges, counted consecutively from sur-

face downwards. On average, SOC decreased at a rate of

1.2% per meter over 1 m depth.

At the provincial scale, the SOC of the topsoil varied

greatly from one province to another, with values ranging

from 0.7% in the loess dominant province of Gansu in the

northwest to 1.8% in the ‘black soil’ province of Hei Longji-

ang in the northeast. In general, high SOC values of the

uppermost depth range were found in north-eastern prov-

inces and lower ones in south-western ones. This is similar to

patterns previously reported (Wu et al., 2003b; Tang et al.,

2006). Although north-western provinces had the lowest

values (0.8%), they hardly influenced the national pattern

due to the scarcity of cropland in the region. The cropland

SOC content tended to decline slightly from the northeast

(1.63%) to the southwest (1.11%). The subsoil followed the

same patterns as the topsoil (Figure 4). Such spatial patterns

correlated with climatic patterns (Wu et al., 2003c). In the

southwest soil carbon decomposed at a much higher rate

than it accumulated.

Potential effects of SOC on grain productivity

Relationships between a range of selected soil characteristics

(CaCO3, gypsum, CEC, BS, pH, EC and ESP) and crop

Table 5 Province-specific observed and simulated yields of grain, individually and collectively (kg ha)1)

ID Province Rice(Oa) Rice(Sb) Wheat(O) Wheat(S) Maize(O) Maize(S) Grain(O) Grain(S)

1 Beijing 6250 NaNc 5179 2119 4652 6592 4779 5264

2 Tianjin 8102 NaN 4785 3211 5059 6183 5058 5093

3 Hebei 5665 12 626 4873 4561 4401 8707 4357 6916

4 Shanxi 4231 NaN 3654 1208 5614 3931 4222 2969

5 Nei Monggol 6737 5955 2639 1207 5658 2998 4569 2684

6 Liaoning 7378 8207 4320 2750 6753 7171 6653 7105

7 Jilin 7292 6718 2983 2223 6238 5694 6359 5807

8 Heilongjiang 7117 6326 3255 1826 4311 4314 5247 4860

9 Shanghai 8005 7644 3653 6161 6191 NaN 7090 7349

10 Jiangsu 7919 9426 4295 6805 5567 10 540 6185 8485

11 Zhejiang 6681 5723 3193 3713 4128 NaN 6314 5586

12 Anhui 6067 8779 3836 5726 4844 8975 4957 7480

13 Fujian 5539 6118 3065 3379 3360 7204 5429 6131

14 Jiangxi 5213 6871 1518 5159 3333 NaN 5177 6858

15 Shandong 7283 NaN 5338 5512 6107 9397 5704 7270

16 Henan 7044 12 312 5109 7065 4339 10 245 4981 8385

17 Hubei 7548 7462 2924 4358 5010 6757 6235 6714

18 Hunan 6149 7803 1916 5913 4579 9958 5923 7899

19 Guangdong 5251 5795 2833 2445 4068 6174 5169 5801

20 Guangxi 4768 5345 1624 2346 3002 4101 4391 5087

21 Hainan 4398 3102 NaN 1572 3431 3408 4356 3114

22 Sichuan 7213 7156 3216 2389 4808 5260 5387 5308

23 Guizhou 6657 3802 1789 893 4726 2761 4673 2695

24 Yunnan 5887 4738 2240 848 3831 1553 4133 2558

25 Xizang 5455 NaN 6429 NaN 4849 NaN 5299 NaN

26 Shannxi 5967 6853 3560 1248 3886 2551 3727 2170

27 Gansu 7959 5011 2917 557 5024 1564 3410 998

28 Qinghai NaN NaN 3630 748 7500 1388 3560 867

29 Ningxia 8152 NaN 2882 1215 6264 1418 4237 1323

30 Xinjiang 5883 NaN 5138 3134 6979 4208 5864 3569

31 Taiwan 4974 7832 NaN 3994 NaN 7709 4974 7832

aO = Observed. bS = Simulated. cNaN = Not a number.



productivity were cross-analysed in an attempt to iden-

tify the potential effects of cropland SOC on grain pro-

ductivity.

Soil characteristics–grain productivity regression analyses.

Single factor linear regression models were applied to reveal

the relationships between a soil property, for example SOC,

as the factor and the grain productivity as the dependent.

Results show that variations in yields are barely explained

by SOC contents alone. In the best case, only 37% of the

variation in grain yield is explained by variation in subsoil

SOC values. The goodness of fit is not better for any other

soil characteristic. It is clear that soil interacts with crops as

a whole and no single soil characteristic alone correlates well

with crop yields.

Soil characteristics–grain productivity ANOVA. The vari-

ances of the productivities of grain crops were analysed

against the variances of the soil characteristics in trying to

relate the variations in yield to soil values. Analysis of

variance (ANOVA) was used for this. Results show that

the variation in cropland SOC values indeed explains the

variation in the overall yield of grain. However, it

does not explain yield variations for any individual crop.

Observed Simulated Observed Simulated Observed Simulated Observed Simulated

Rice(a) (c) (e) (g)

(b) (d) (f) (h)

Yie

ld (

kg h

a–1 )

Yie

ld (

kg h

a–1 )

Yie

ld (

kg h

a–1 )

Yie

ld (

kg h

a–1 )

Wheat Maize Grain

Rice Wheat Maize Grain

5447 5019

5477 4826 3546 3454

6718 6657

5000 7000

3000

50

00

7000

90

00

3000

50

00

7000

90

00

Observed v. Simulated Observed v. Simulated Observed v. Simulated Observed v. Simulated

Observed (kg ha–1) Observed (kg ha–1) Observed (kg ha–1) Observed (kg ha–1)

Sim

ulat

ed (

kg h

a–1 )

Sim

ulat

ed (

kg h

a–1 )

Sim

ulat

ed (

kg h

a–1 )

Sim

ulat

ed (

kg h

a–1 )

2000 4000

2000

40

00

6000

3000 5000 7000

2000

60

00

10 0

00

4000 6000

2000

40

00

6000

80

00

2000

40

00

6000

2000

60

00

10 0

00

2000

40

00

6000

80

00

Figure 3 Comparability of the means and goodness of fit between simulated and observed yields.

Table 6 Results of Shapiro–Wilk, F- and paired t-tests on simulated and observed yields

Crop

Shapiro–Wilk test

(H0 = normal distribution)

F-test

(H0 = same variance)

Paired t-test

(H0 = same mean)Observed Simulated

W (p) H0 W (p) H0 F (p) H0 | t | (p) H0

Rice 0.95 (.) T 0.99 (.) T 0.49 (.) T 0.27 (.) T

Wheat 0.93 (.) T 0.92 (.) T 0.50 (.) T 0.90 (.) T

Maize 0.98 (.) T 0.94 (.) T 0.15 (***) F 0.79 (.) T

Grain 0.98 (.) T 0.94 (.) T 0.16 (***) F 0.11 (.) T

Significance levels: (.) <0.1; (*) £0.05; (**) £0.01; (***) £0.001; T, true; F, false.

86 L. Ye et al.


This applied to all soil properties. But all soil characteris-

tics taken together are capable of explaining variations in

yields of any crop, individually or collectively. This sug-

gests that SOC should not be separated from other soil

characteristics in the soil limitations evaluation (equation

3). Instead, the limiting soil characteristics should be trea-

ted as a whole.

SOC suitability to grain production

Figure 5a shows the weighted average of the soil indices of

grain crops (Figure 1b) in China in 2005 and Figure 5b

shows the suitability classes of cropland SOC to grain pro-

duction in 2005. The maps can be compared to show, for

example, that soils with high indices, as indicated by dark

shading in Figure 5a, are more productive than soils with

lower indices. It is thus possible to ascribe agricultural

importance at the national scale to regions such as the north-

east, north and Sichuan Basin in terms of grain production.

Similar patterns were also observed in Figure 5b. SOC

appeared to be suitable (S1) to moderately suitable (S2) in

most of the croplands in China. The dominance of the mar-

ginally suitable (S3) and the actually unsuitable but poten-

tially suitable (N1) classes in the southeast reflect the

decreasing agricultural importance of the region although it

was one of the originating localities for China’s traditional

agriculture (Chang et al., 2002). Anthropogenic influences

on soil quality, especially the impact of short-term human

activities in this economically booming region, may account

for these unfavourable SOC classes (Chen, 2003).

Relative yield loss due to SOC deficiency

In general SOC was found more limiting in provinces with a

humid climate (northeast and southeast) and less limiting in

arid and sub-arid climates (northwest); on average 64% of

grain yield was lost due to SOC deficiency for the humid

provinces and 7% for the arid and semi-arid ones. The loss

applies to 30% of the whole country (Table 7).

Uncertainties

Scalability and compatibility of data sets are the main

sources of uncertainty in this study. Significant improve-

ments have been made on the quality of the WISE data-

base by adopting additional locally representative soil

profiles (Batjes, 2002a); for example, the number of profiles

increased by 14% between WISE-1 and -2 and the inclu-

sion of more recently created reference profiles is crucial in

coping with uncertainty in derived soil parameters. Efforts

are needed in applying procedures similar to SOTER

(FAO, 1995) for creating parameter and horizon homoge-

neous datasets out of the results of the second national

soil survey in China (NSSO, 1998). However, the reliabil-

ity, consistency and spatial configuration (Figure 2a) of the

source points contribute more to the quality of the

0 2000 40001000

km

40–60 cm

60–80 cm 80–100 cm

0–20 cm 20–40 cm

> 2.0

1.5 – 2.0

0.5 – 1.0

1.0 – 1.5

< 0.5

Legend

Figure 4 Spatial distribution of cropland SOC (%) in China.



Bay of Bangal

R U S S I A

MONGOLIA

KAZAKSTAN

LAOSTHAILAND

VIE

TNA

M

PHILIPPINES

N. KOREA

S. KOREA

SouthChinaSea

EastChinaSea

YellowSea

0 500 1000250

Kilometers

Legend

N1

N2

S1S2

S3

R U S S I A

MONGOLIA

KAZAKSTAN

LAOSTHAILAND

VIE

TNA

M

PHILIPPINES

N. KOREA

S. KOREA

SouthChinaSea

EastChinaSea

YellowSea

0 500 1000250

Kilometers

MYANMAR

Legend

< 0.50

0.50 – 0.60

0.75 – 0.80

0.80 – 0.85

0.85 – 0.90

0.90 – 0.95

0.60 – 0.65

0.65 – 0.70

0.70 – 0.75 > 0.95

80°E 90°E 100°E 110°E 120°E

40°N

30°N

20°N

50°N

70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E50°N(a)

40°N

30°N

20°N

70°E

80°E 90°E 100°E 110°E 120°E

40°N

30°N

20°N

50°N70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E50°N(b)

40°N

30°N

20°N

70°E

Figure 5 Cropland SOC suitability classes for grain production (a) and average soil index (b).

88 L. Ye et al.


resulting datasets—as confirmed by the generated climatic

surfaces (Figure 2b)—than the quality of the source points

above a certain threshold.

Results of SOC inventories are mostly static in space and

time. Monitoring changing trends is the key to controlling

uncertainties as well as stimulating appropriate management

practices. The relatively high SOC in the northeast, for

instance, may hide the fact that SOC there is being lost from

soils at a very high rate (Wu et al., 2003b; Tang et al.,

2006), probably due to anthropogenic factors (Chen, 2003),

especially land use (Wu et al., 2003c). Uncertainty also

comes from downscaling of cropping system and manage-

ment parameters from regional to field scales (Anderson

et al., 2003). With downscaling it is common and pragmatic

to assume parametric homogeneity and to aggregate simu-

lated yields back to regional scale for validation (Hatfield,

2001). In practice this is due at the regional scale to the lack

of homogeneous cover of field-scale parameters, such as

effective irrigation rate. Confidence in the overall reliability

of the modelling approach was largely based on statistical

tests on the final results. Confidence in intermediate parame-

ters such as the potential effects of SOC on productivity was

assumed to fluctuate but this needs data support. Further

research is needed to verify this under typical agro-ecological

conditions as present in the Chinese croplands; this could

possibly lead to the removal of the word ‘potential’ in this

context.

Soil management options

The following management options are suggested based on

the SOC inventory made in this study:

1. Balanced fertilizer application. China is currently the

biggest producer, importer and consumer of manufac-

tured fertilizers in the world. Fifty per cent of increased

grain productivity was attributed to fertilizers (National

Soil Survey Office (NSSO), 1998). Balanced application

of organic and manufactured fertilizers is currently the

most feasible means of addressing food security and soil

quality issues (Fan et al., 2005; Cai & Qin, 2006).

2. Promotion of cover crops and use of crop residues.

Removal of crop residues was found to deplete SOC over

the long-term (Chen, 2003), particularly in the arid and

semi-arid north and northwest where the accumulation

rate is relatively low (Wu et al., 2003c). This is the first but

most important step to return the organic matter to soil.

3. Adoption of reduced tillage and systematic control of soil

and water erosion, especially in the northwest.

Conclusions

The methodologies applied in this large-scale quantitative

assessment of grain productivities in China proved to be suc-

cessful and efficient. Statistical validation procedures sug-

gested a close match between simulated and observed yields of

grain crops. Regression and variance analysis of grain yields

against soil characteristics revealed that yields of grain crops

were not significantly correlated with cropland SOC alone.

Data on limiting soil characteristics should be collectively eval-

uated in order to derive a single soil index. The uncertainty

analysis showed that the quality of large climatic and soil data

sets is largely dependent on the reliability, consistency and spa-

tial configuration of the source points once a threshold num-

ber of input source points is available. The croplands of the

north-eastern provinces have higher SOC values than those in

the southwest. The relative loss in grain yield due to SOC defi-

ciency varied greatly from 7% for the arid and semi-arid

regions to 64% for the humid regions, with an average of 30%

for the country. Proper soil management practices with special

emphasis on cropland SOC are of great importance with

respect to conservation and use of the soil resources in China,

especially in the SOC-depleted southeast and in the erosion-

prone northwest.

Table 7 Relative yield loss (%) due to SOC deficiency

Provincea Yield loss (%)

1 0.52

2 5.20

3 1.39

4 0.09

5 1.59

6 3.31

7 1.07

8 0.01

9 67.14

10 46.39

11 62.70

12 42.65

13 64.00

14 60.25

15 2.01

16 12.60

17 55.50

18 64.48

19 69.83

20 68.17

21 68.50

22 58.47

23 62.96

24 48.04

25 –

26 4.40

27 3.21

28 44.09

29 8.13

30 4.72

31 74.63

aRefer to Table 5 for province names.



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Spatial patterns and effects of soil organic carbon on grain productivity assessment in China

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