Journal of Geographical Sciences >
A 1000year history of cropland cover change along the middle and lower reaches of the Yellow River in China
Yang Fan (1991), PhD and Associate Professor, specialized in longterm land use/cover change and their environmental effects. Email: yangfan@henu.edu.cn 
Received date: 20230820
Accepted date: 20240306
Online published: 20240531
Supported by
National Natural Science Foundation of China(42201263)
National Key Research and Development Program of China on Global Change(2017YFA0603304)
Landscape in the middle and lower reaches of the Yellow River in China has undergone significant changes for thousands of years due to agricultural expansion. Lack of reliable longterm and highresolution historical cropland data has limited our ability in understanding and quantifying human impacts on regional climate change, carbon and water cycles. In this study, we used a datadriven modeling framework that combined multiple sources of data (historical provincial cropland area, historical coastlines, and satellite databased maximum cropland extent) with a new gridding allocation model for croplands distribution to reconstruct a historical cropland dataset for the middle and lower reaches of the Yellow River at a 10km resolution for 58 time points ranging from the period 1000 to 1999. The cropland area in the study area increased by 2.3 times from 21.87 million ha in 1000 to 50.64 million ha in 1999. Before 1393, the area of cropland increased slowly and was primarily concentrated in the Weihe and Fenhe plains. From 1393‒1820, the area of cropland increased rapidly, particularly on the North China Plain. Since 1820, cropland cover has tended to become saturated. Our newly reconstructed results agreed well with remotely sensed data as well as historical documentbased facts regarding cropland distribution.
YANG Fan , ZHANG Hang , HE Fanneng , WANG Yafei , ZHOU Shengnan , DONG Guanpeng . A 1000year history of cropland cover change along the middle and lower reaches of the Yellow River in China[J]. Journal of Geographical Sciences, 2024 , 34(5) : 921 941 . DOI: 10.1007/s114420242233z
Figure 1 Study area (a. the Yellow River Basin and the scope of historical influence in the lower reaches of the Yellow River; b. provincial units) 
Table 1 Data sources for historical cropland cover in the middle and lower reaches of the Yellow River (DEM: digital elevation model; CPP: climate potential productivity) 
Data variables  Temporal coverage  Spatial resolution  Data type  Data source/ Reference  

Historical cropland  Song, Liao, and Jin dynasties (1000, 1066, 1078, 1162, 1215)  Provincial level  Reconstructed values  Published literature  He et al. (2017); Li et al. (2018a) 
Yuan Dynasty (1290)  Li et al. (2018b)  
Ming Dynasty (1393, 1583, 1620)  Li et al. (2020)  
Qing Dynasty to the present (16611999) (49 time points)  Ge et al. (2004); Li et al. (2016)  
Historical coastlines  Song Dynasty (1142)  Reconstructed vector lines  Historical Atlas of China  Tan (1982)  
Yuan Dynasty (1280)  
Ming Dynasty (1433)  
Qing Dynasty (1820)  
Remotely sensed land use data  1980, 1990, 2000, 2010  1 km  Grid  Resources and Environmental Sciences Data Center of the Chinese Academy of Sciences, http://www.resdc.cn/ (last access: 10 February 2022)  
DEM  2000  90 m  Grid  Resources and Environmental Sciences Data Center of the Chinese Academy of Sciences, http://www.resdc.cn/ (last access: 10 August 2022)  
CPP  19511980  1 km  Grid  Data Sharing Infrastructure of Earth System Science, http://www.geodata.cn/ (last access: 10 August 2022) 
Figure 2 Historical coastline over the past millennium (a. Coastlines in the western coast of Bohai Bay; b. northern coast of Jiangsu, and the Yangtze Estuary) 
Figure 3 Remote sensing land use dataderived provincial maximum cropland allocation extent in the middle and lower reaches of the Yellow River 
Figure 4 Scheme for gridding reconstruction of historical cropland cover in the middle and lower reaches of the Yellow River 
Table 2 Flow of Shapley value calculation. 
Item  Definition  Formula 

Step 1: Input profiles (T)  T, the universe of input profiles, represents the set of all possible input profiles. The universe with N_{v} factors will have 2^{Nv} input profiles.  For example, for a threefactor model, the universe is: $\text{T}=\left\{ \left\{ 0,0,0 \right\};\left\{ 0,0,1 \right\};\left\{ 0,1,0 \right\};\left\{ 0,1,1 \right\} \right.;$ $\left. \left\{ 1,0,0 \right\};\left\{ 1,0,1 \right\};\left\{ 1,1,0 \right\};\left\{ 1,1,1 \right\} \right\}$ 
Step 2: Shapley set (Q)  Q is defined the set of all input profiles in the universe of the model for which a certain factors is marked as a nonparticipant.  For example, in a threefactor model, the Shapley set for the third factor would be: $\begin{aligned}&\mathrm{m}\Big(\mathrm{v}\big(\vec{\mathrm{x}}\big),\mathrm{v}_{\mathrm{p}}\Big)=\mathrm{R}^{2}\Big(\mathrm{v}\big(\vec{\mathrm{x}}\big)+\mathrm{v}_{\mathrm{p}}\Big)\mathrm{R}^{2}\Big(\mathrm{v}\big(\vec{\mathrm{x}}\big)\Big);\\&\mathrm{p\in1,2,...,N_{v}}\end{aligned}$ 
Step 3: Marginal contribution (m)  When v_{p} is added as a participant, the added value of Rsquared is defined as the marginal contribution.  $\mathrm{m}\left(\mathrm{v}(\overrightarrow{\mathrm{x}}), \mathrm{v}_{\mathrm{p}}\right)=\mathrm{R}^{2}\left(\mathrm{v}(\overrightarrow{\mathrm{x}})+\mathrm{v}_{\mathrm{p}}\right)\mathrm{R}^{2}(\mathrm{v}(\overrightarrow{\mathrm{x}})) ;$ $\text{p}\in 1,2,...,{{\text{N}}_{\text{v}}}$ $\mathrm{v}(\overrightarrow{\mathrm{x}})$denotes the variable set of the input profile. 
Step 4: Shapley value (S)  $\mathrm{S\big(v_p\big)=\sum_{\vec{x}\in Q(T,p)}\frac{\big(\big\vec{x}\big\big)!\big(N_v\big\vec{x}\big1\big)!}{N_v !}m\big(v\big(\vec{x}\big),v_p\big)}$  
Step 5: Relative important of factors (S_{%})  $S_{\%}\left(v_{p}\right)=\frac{S\left(v_{p}\right)}{\sum S(v)}$ 
Table 3 Contribution shares of factors on the distribution of cropland cover 
JingJinJi  Shaanxi  Henan  Shanxi  Shandong  Anhui  HuNing  GanNing  

Altitude  0.21  0.45  0.39  0.27  0.27  0.33  0.13  0.25 
CPP  0.11  0.17  0.08  0.05  0.06  0.08  0.15  0.59 
DR  0.41  0.24  0.33  0.42  0.39  0.30  0.51  0.11 
Slope  0.27  0.14  0.20  0.26  0.28  0.30  0.20  0.05 
CPP: Climatic potential productivity; DR: Degree of relief 
Table S1 Normalization formulas for altitude, CPP, DR, and slope 
Factor  Relationship with cropland distribution  Formula 

Altitude  Negative  ${{V}_{norm\_alti}}\left( i,j \right)=\frac{{{V}_{alti}}\left( i,max \right){{V}_{alti}}\left( i,j \right)}{{{V}_{alti}}\left( i,max \right)}$ 
CPP  Positive  ${{V}_{norm\_CPP}}\left( i,j \right)=\frac{{{V}_{CPP}}\left( i,j \right)}{{{V}_{CPP}}\left( i,max \right)}$ 
DR  Negative  ${{V}_{norm\_DR}}\left( i,j \right)=\frac{{{V}_{DR}}\left( i,max \right){{V}_{DR}}\left( i,j \right)}{{{V}_{DR}}\left( i,max \right)}$ 
Slope  Negative  ${{V}_{norm\_slop}}\left( i,j \right)=\frac{{{V}_{slop}}\left( i,max \right){{V}_{slop}}\left( i,j \right)}{{{V}_{slop}}\left( i,max \right)}$ 
Note: positive characterizes that the larger the factor value, the more cropland distribution; negative characterizes that the larger the factor value, the less cropland distribution. V_{norm}(i,j) represents the factor value of grid j in province i after the normalization; V(i,max) denotes the maximum factor value in province i; V(i,j) refers to the factor value of grid j in province i. 
Figure 5 Total cropland area in the middle and lower reaches of the Yellow River over the past millennium (Phase I: 10001393; Phase II: 13931724; Phase III: 17241999) 
Figure 6 Provincial cropland area in the middle and lower reaches of the Yellow River over the past millennium 
Figure 7 Cropland cover maps for the middle and lower reaches of the Yellow River over the past millennium (Panels ao denote 1000, 1066, 1078, 1162, 1215, 1290, 1393, 1583, 1620, 1661, 1724, 1820, 1910, 1949, and 1999, respectively.) 
Figure 8 Spatial patterns of the allocation result (a), remote sensingderived cropland cover in 1980 (b), and the differences between them (c) 
Table 4 Statistical value of differences in cropland cover in 1980 between allocation results and remote sensingderived cropland cover 
Difference (%)  Number of grids (%)  Difference (%)  Number of grids (%) 

<80  0.02  010  15.21 
80 to 70  0.07  1020  4.19 
70 to 60  0.46  2030  1.70 
60 to 50  0.90  3040  0.60 
50 to 40  1.88  4050  0.28 
40 to 30  4.88  5060  0.07 
30 to 20  11.72  6070  0.03 
20 to 10  18.71  7080  0.00 
10 to 0  39.30  >80  0.00 
Figure 9 Comparison of total and provincial cropland area from the HYDE, GCD, and our reconstruction (HYDE: global environment database; GCD: global cropland dataset. Total denotes the entire middle and lower reaches of the Yellow River.) 
Figure 10 Comparison of cropland maps among three datasets (ad. HYDE; eh. GCD; and il. our reconstruction. Panels (m) and (n) represent modern rivers and Yellow Rivers in different periods. HYDE: global environment database; GCD: global cropland dataset.) 
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