DOI: 10.5281/zenodo.17257289
title: Global Cultivated Land Spatial Transition, 1992–2015: Key Data and Characteristic Analysis

description: 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.