Global Cultivated Land Spatial Transition, 1992–2015: Key Data and Characteristic Analysis

Global Cultivated Land Spatial Transition, 1992–2015: Key Data and Characteristic Analysis Wei Song1*, Huanhuan Li2 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 2. School of Geography and Tourism, Luoyang Normal University, Luoyang 471000, China Abstract: Built on the 1992–2015 global cultivated land spatial layer, this dataset integrates three core dimensions—cultivated land-area dynamics, landscape-pattern evolution and hotspot-region characteristics—to underpin worldwide cultivated land-transition research. Globally, total cultivated land experienced a “growth – rapid growth – decline” three-stage trajectory, with 1995 and 2005 as the pivotal turning points. Sweden and Brazil recorded the most pronounced net gains, whereas Ukraine faced continuous losses. Landscape indices such as Number of Patches (NP) and Largest Patch Index (LPI) displayed phased fluctuations. The dataset is applicable to sustainable cultivated land management, food-security assessment and land-use-transition studies. Keywords: global cultivated land; spatial transition; quantity change; landscape pattern; hotspot region 1 Data sources and processing 1.1 Basic data (1) Global cultivated land spatial data Derived from the ESA Climate Change Initiative (CCI) Land Cover (LC) product (1992–2015, 300 m, WGS84). Three cultivated land classes—“rain-fed cultivated land (value = 10)”, “irrigated cultivated land (20)” and “mosaic cultivated land / natural vegetation (30)”—were extracted. Overall accuracy is 84.5 % globally, ranging 78.8 %–93.9 % among continents. (2) Ancillary data Population data: FAOSTAT. Administrative boundaries: Resource and Environment Science and Data Center. 1.2 Processing methods (1) Quantity-change analysis Mann-Kendall non-parametric test detected abrupt cultivated land-area changes; outliers were calculated (Eq. 1) to delimit stages. (Eq. 1) Xi: area anomaly; x{i}: cultivated land area in year i. (2) Landscape-pattern metrics Fragstats computed NP, PD (Patch Density), LPI and LSI (Landscape Shape Index) to quantify fragmentation, connectivity and shape irregularity. (3) Hotspot identification Trend analysis plus t-test (P ≤ 0.1) screened significantly changing regions and typical hotspot countries. 2 Core results 2.1 Data Name Correspondence Explanation Table 1 Explanation of Data Naming Conventions in the Dataset Name symbols in the dataset Object Explanation gd Cultivated Land 92 1992 92...99 correspond to the years 1992 through 1999. 00 2000 00-09 corresponds to the years 2000 through 2009. 10 2010 10-15 corresponds to the years 2010 through 2015. af Africa The total amount of cultivated land in Africa an Antarctica The total amount of cultivated land in Antarctica as Asia The total amount of cultivated land in Asia au Australia The total amount of cultivated land in Australia eu Europe The total amount of cultivated land in Europe no North America The total amount of cultivated land in North America oc Oceania The total amount of cultivated land in Oceania so South America The total amount of cultivated land in South America 2.2 Global cultivated land-area dynamics (1992 - 2015) Table 2 Global cultivated land-area change characteristics, 1992–2015 Stage Period Net change (km²) Mean annual rate Key feature I 1992-1995 +96,712.32 0.19% Slight drop then growth, low fluctuation II 1995-2005 +509,735.18 0.30% Above-average after 2000, fastest growth III 2005-2015 -0.003% Overall decline, brief rebound 2010–2011 2.3 Hotspot-country cultivated land-area change (1992–2015) Table 3 Hotspot-country cultivated land-area change, 1992–2015 Country Rate of change Stage-wise features Main drivers Sweden +24.82% Growth in all three stages, fastest in II Agricultural technology; government carbon-sink policy (adding cultivated land & forest) Brazil +24.43% Sharp rise 1995–2005, drop 2005–2015 Early expansion; later conservation & land-use adjustment Kazakhstan +13.68% Steady rise across stages Large-scale mechanised farming; resource development Ukraine -3.49% Continuous decline in all stages Farm-management transition; geopolitical impacts Australia Growth before 2005, decline after Early development; post-2005 drought-induced abandonment 2.4 Global cultivated land landscape-pattern trends (1992–2015) Table 4 Global cultivated land landscape-pattern trends, 1992–2015 Metric Trend Turning years Ecological meaning Number of Patches (NP) Drop → fluctuating rise → drop 1996、2006 Fragmentation: first decrease, then increase, final decrease Patch Density (PD) Drop → stable No sharp turning point Lower density, higher connectivity Maximum Patch Index (LPI) Fluctuating drop → stable 1995、200 External disturbance peaked before 2005, then stabilised Landscape Shape Index (LSI) Fluctuating rise → stable 1997、2006 Shape irregularity rose then stabilised, strongly managed 3 Data notes and applications 3.1 Limitations (1) Cultivated land definitions / classifications differ regionally; figures may deviate from GlobeLand30 or MODIS C5. (2) Functional transitions (e.g., production vs. ecological functions) are not included; regional socio-economic data could be added later. 3.2 Application directions (1) Support global food-security assessment by linking hotspot cultivated land change to regional production volatility. (2) Inform land-use policy-making with continent-specific (e.g., Europe, South America) cultivated land protection & development references. (3) Assist eco-environmental research by analysing biodiversity & ecosystem-service impacts through landscape-pattern metrics.