DOI: 10.5061/dryad.51c59zwfs
title: A meta-analysis reveals increases in soil organic carbon following the restoration and recovery of croplands in Southwest China

description: In China, the Grain for Green Program (GGP) is an ambitious project to
 convert croplands into natural vegetation, but exactly how changes in
 vegetation translate into changes in soil organic carbon remains less
 clear. Here we conducted a meta-analysis using 734 observations to explore
 the effects of land recovery on the soil organic carbon and nutrients in 4
 provinces in Southwest China. Following GGP, the soil organic carbon
 content (SOCc) and soil organic carbon storage (SOCs) increased by 33.73%
 and 22.39%, respectively. Likewise, soil nitrogen increased, while
 phosphorus decreased. Outcomes were heterogeneous, however, depending on
 variation in soil and environmental characteristics. Both the regional
 land use and cover change indicated by landscape type transfer matrix and
 net primary production from 2000 to 2020 further confirmed that GGP
 promoted the forest area (2.95%) and regional mean net primary production
 (52.94%). Our findings suggest that GGP could enhance soil and vegetation
 carbon sequestration in Southwest China and help to develop carbon neutral
 strategy.
description: Title of Dataset: A meta-analysis reveals increases in soil organic carbon
 following the restoration and recovery of croplands in Southwest China
 Here, we collected data by
 searching published articles in the Web of Science from
 , and supplemented this with a more
 specific search on Chinese dissertations and other Chinese-language
 studies in the China National Knowledge Infrastructure Database from
 . Studies published up to 23 June 2022 that
 contained the following keywords phrases (themes), were kept: ‘Southwest
 China’, ‘landscape type transfer matrix’, ‘vegetation net primary
 production’, ‘Grain for Green Program’, ‘soil organic carbon’,
 ‘afforestation’, ‘plantation on cropland’, ‘total nitrogen’, ‘total
 phosphorus’, ‘available nitrogen’ and ‘available phosphorus’. In order to
 ensure the integrity and precision of the investigation, we also searched
 the references of the papers to obtain more comprehensive relevant data.
 The study area located in 4 provinces in SouthwestChina and a total of 57
 original research articles meeting the inclusion criteria were identified
 in this paper. Research data such as soil physicochemical properties were
 directly collected from the text and tables in the published studies and
 were extracted from the literature charts using GetData (V.2.25). In
 total, we extracted 734 observations from the studies to calculate the
 effect size of the GGP on SOCc, SOCs and other properties (please see
 Appendix S1, SOCc_data.csv and SOCs_data.csv). Description of the data and
 file structure Articles data selected for this meta-analysis had to meet
 the following criteria: (a) the main requirements for studies were that
 they must include field experiments in China, and the experiments had to
 include at least three replicates. When the same experiment was reported
 in multiple publications (e.g., a scientific article and dissertation from
 the same author), only one dataset was retained. Units of measurement were
 adjusted to be the same across experiments, only univariate analysis data
 were selected; (b) the study indicated the soil nutrient changes in
 ecosystem types, soil depth, experimental duration, soil texture, and
 slope direction; (c) studies up to 10 years in duration were selected; (d)
 SOCc or SOCs were reported since both are important indicators, and the
 changes in both SOCc and SOCs were from control (current adjacent
 croplands) and treatment and (e) standard deviation (SD), publication
 biases and sample size were reported or could be calculated from the data
 presented in the publications. We used the Landscape Type transfer Matrix
 for Southwest China from 2000-2020 (unit: km2) to describe the transform
 of landscape type in our paper (please see Appendix S2 Table S1) and
 raster numbers to represent changes in NPP in 2000 and 2020 (please see
 Appendix S2 Table S2). We also listed Effects of Grain for Green Program
 on pH (Appendix S2 Figure S1a), total nitrogen (TN, Appendix S2 Figure
 S1b), available nitrogen (AN, Appendix S2 Figure S1c), total phosphorus
 (TP, Appendix S2 Figure S1d), available phosphorus (AP, Appendix S2 Figure
 S1e), available potassium (AK, Appendix S2 Figure S1f), total potassium
 (TK, Appendix S2 Figure S1g) in Southwest China. Error bars are the 95%
 confidence intervals (CIs) of the weighted response ratios. The dashed
 line indicates points where the weighted response ratio equals 0. If any
 95% CIs did not overlap with zero, the effect was considered significant.
 The numbers in parentheses represent the sample sizes of each observation.
 *, , and *** indicate P &lt; 0.05, &lt; 0.01, and &lt; 0.001,
 respectively. Regression models of the response ratio of soil total
 nitrogen content (TN) and soil total nitrogen stock (TNs) to latitude (a,
 d), elevation (b), mean annual precipitation (MAP, c), and longitude (e)
 were listed in Appendix S2 Figure S2. The relative importance of each
 variable in the responses of total nitrogen (TN, a), total phosphorus (TP,
 b), total potassium (TK, c), available nitrogen (AN, d), available
 phosphorus (AP, e) and available potassium (AK, f) experimental duration,
 soil depth, ecosystem types, soil texture and slope direction of transform
 caused by Grain for Green Program (GGP) were listed in (Appendix S2 Figure
 S3), the importance values were calculated using a model selected with
 corrected Akaike’s information criteria, the cutoff was set to 0.8 to
 differentiate between important and non-significant variables. In Appendix
 S2 Figure S4, we described the net primary productivity (NPP) distribution
 of Yunnan, Guizhou, Sichuan, and Chongqing in 2000 (Figure S4a) and 2020
 (Figure S4b), different composition and flow direction represent different
 NPP value percentage from each city. The Technical flowchart of China Land
 Cover Data (CLCD) was also listed (please see Appendix S2 Figure S5), we
 used 30m annual land cover and its dynamics in China from 1990 to 2019,
 (Earth System Science Data, 2021) and the data set has the land use
 classification with 30m × 30m and has been updated to 1985 - 2020.
 Furthermore, to consider the publication biases, we used funnel plots of
 published offsets in Southwest China, showing the effect of main elements
 before and after GGP, including SOC content (SOCc, a), SOC stock (SOCs,
 b), total nitrogen (TN, c), total phosphorus (TP, d), total potassium (TK,
 e), available nitrogen (AN, f), available phosphorus (AP, g) and available
 potassium (AK, h). The Egger method was used to perform sensitivity
 analysis and test for publication bias on the funnel plot, and the
 stability of the results was evaluated. In order to reduce the existence
 of publication bias, the estimated value of combined effect is obtained by
 using the shearing method and compared with the original effect size
 (please see Appendix S3 Figure S1). Code/Software Research data such as
 soil physicochemical properties were directly collected from the text and
 tables in the published studies and were extracted from the literature
 charts using GetData (V.2.25). All the statistical analyses were performed
 with R from . The ‘Metafor’ package was used
 to conduct mixed-effects meta-analysis. The ‘glmulti’ package was used to
 calculate the relative importance of all predictors after GGP effects and
 a cutoff of 0.8 was set to differentiate between important and
 nonsignificant predictors. The ‘ggplot’ package was used to draw figures.
 The ‘stats’ package was used for linear regression analysis and set model
 significance at P &lt; 0.05. We obtained the NPP data from the annual
 China Land Cover Data set (CLCD) from Landsat. Land use dataset and
 classification method from 2000 to 2020 were obtained from Resources and
 Environment Sciences and Data Center, Chinese Academy of Sciences from
 , with the resolution was 30m × 30m. The
 annual land cover data set of China from Landsat was produced on the
 Google Earth Engine (GEE) platform in this paper. We used the ArcGIS 10.8
 software Geostatistical analyst method, input the corresponding longitude
 and latitude value, SOCc and SOCs values of the research sites and then
 performed Kriging interpolation to predict the overall content of SOCc and
 SOCs in the Southwest region in 2000 and 2020.