A 1 km global cropland dataset from 10 000 BCE to 2100 CE

Cropland greatly impacts food security, energy supply, biodiversity, biogeochemical cycling, and climate change. Accurately and systematically understanding the effects of agricultural activities requires cropland spatial information with high resolution and a long time span. In this study, the first 1 km resolution global cropland proportion dataset for 10 000 BCE–2100 CE was produced. With the cropland map initialized in 2010 CE, we first harmonized the cropland demands extracted from the History Database of the Global Environment 3.2 (HYDE 3.2) and the Land-Use Harmonization 2 (LUH2) datasets and then spatially allocated the demands based on the combination of cropland suitability, kernel density, and other constraints. According to our maps, cropland originated from several independent centers and gradually spread to other regions, influenced by some important historical events. The spatial patterns of future cropland change differ in various scenarios due to the different socioeconomic pathways and mitigation levels. The global cropland area generally shows an increasing trend over the past years, from 0×106 km2 in 10 000 BCE to 2.8×106 km2 in 1500 CE, 6.2×106 km2 in 1850 CE, and 16.4×106 km2 in 2010 CE. It then follows diverse trajectories under future scenarios, with the growth rate ranging from 16.4 % to 82.4 % between 2010 CE and 2100 CE. There are large area disparities among different geographical regions. The mapping result coincides well with widely used datasets at present in both distribution pattern and total amount. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The spatiotemporally continuous and conceptually consistent global cropland dataset serves as a more comprehensive alternative for long-term earth system simulations and other precise analyses. The flexible and efficient harmonization and downscaling framework can be applied to specific regions or extended to other land use and cover types through the adjustable parameters and open model structure. The 1 km global cropland maps are available at https://doi.org/10.5281/zenodo.5105689 (Cao et al., 2021a).

Behavioral Study of Bajulmati Dam Deformation Through Dam History Database-based Assessment

Dam will experience pressure from its own load up to the effect of loading reservoir water. As a result of this pressure force, the dam body will be deformed. The behavior of dam deformation needs to be monitored to know the vertical and horizontal deformation that occurs. This paper discusses the deformation behavior of bajulmati dam during the construction stage, first filling, and post-construction. The purpose of this analysis is to determine "normal" deformation behavior so that case studies showing "abnormal" deformation can be identified early and can then be further analyzed. Evaluation results of Bajulmati Dam deformation behavior based on the acceptance criteria from a similar dam history database showed that most instruments meet the criteria of deformation acceptance so that it is considered normal. Only a small percentage do not meet the acceptance criteria because the value is outside of the requirements. These results may be a concern and initial recommendation for further analysis of abnormal deformation behaviors occurring.

A 1 km global cropland dataset from 10000 BCE to 2100 CE

Cropland greatly impacts food security, energy supply, biodiversity, biogeochemical cycling, and climate change. Accurately and systematically understanding the effects of agricultural activities requires cropland spatial information with high resolution and a long time span. In this study, the first 1 km resolution global cropland proportion dataset for 10000 BCE–2100 CE was produced. With the cropland map initialized in 2010 CE, we first harmonized the cropland demands extracted from the History Database of the Global Environment 3.2 (HYDE 3.2) and the Land-Use Harmonization 2 (LUH2) datasets, and then spatially allocated the demands based on the combination of cropland suitability, kernel density, and other constraints. According to our maps, cropland originated from several independent centers and gradually spread to other regions, influenced by some important historical events. The spatial patterns of future cropland change differ in various scenarios due to the different socioeconomic pathways and mitigation levels. The global cropland area generally shows an increasing trend over the past years, from 0 million km2 in 10000 BCE grows to 2.8 million km2 in 1500 CE, 6.2 million km2 in 1850 CE, and 16.4 million km2 in 2010 CE. It then follows diverse trajectories under future scenarios, with the growth rate ranging from 18.6 % to 82.4 % between 2010 CE and 2100 CE. There are large area disparities among different geographical regions. The mapping result coincides well with widely-used datasets at present in both distribution pattern and total amount. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The spatiotemporally continuous and conceptually consistent global cropland dataset serves as a more comprehensive alternative for long-term earth system simulations and other precise analyses. The flexible and efficient harmonization and downscaling framework can be applied to specific regions or extended to other land use/cover types through the adjustable parameters and open model structure. The 1 km global cropland maps are available at https://doi.org/10.5281/zenodo.5105689 (Cao et al., 2021a).

A historical reconstruction of cropland in China from 1900 to 2016

A spatially explicit cropland distribution time-series dataset is the basis for the accurate assessment of biogeochemical processes in terrestrial ecosystems and their feedback to the climate system; however, this type of dataset is lacking in China. Existing cropland maps have a coarse resolution, are intermittently covered, or the data are inconsistent. We reconstructed a continuously covered cropland distribution dataset in China spanning from 1900 to 2016 by assimilating multiple data sources. In total, national cropland acreage expanded from 77.72 Mha in 1900 to a peak of 151.00 Mha in 1979, but it consistently decreased thereafter to 134.92 Mha in 2016. The cropland was primarily distributed in three historically cultivated plains in China: the Sichuan Plain, the Northern China Plain, and the Northeast China Plain. Cropland abandonment was approximately 43.12 Mha: it was mainly concentrated in the Northern China Plain and the Sichuan Plain and occurred during the 1990–2010 period. Cropland expansion was over 74.37 Mha: it was primarily found in the southeast, northern central, and northeast regions of China and occurred before 1950. In comparison, the national total and spatial distribution of cropland in the Food and Agriculture Organization (FAO) of the United Nations and the History Database of the Global Environment (HYDE) were distorted during the period from 1960 to 1980 due to the biased signal from the Chinese Agricultural Yearbook. We advocate that newly reconstructed cropland data, in which the bias has been corrected, should be used as the updated data for regional and global assessments, such as greenhouse gas emission accounting studies and food production simulations. The cropland dataset is available via an open-data repository (https://doi.org/10.6084/m9.figshare.13356680; Yu et al., 2020).

Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia

Spatially explicit historical land cover datasets are essential not only for simulations of climate and environmental dynamics but also for projections of future land use, food security, climate, and biodiversity. However, widely used global datasets are developed for continental- to global-scale analysis and simulations. Their accuracy depends on the verification of more regional reconstruction results. This study collects cropland area data of each administrative unit (parish/municipality/county) in Scandinavia from multiple sources. The cropland area data are validated, calibrated, interpolated, and allocated into 1 km×1 km grid cells. Then, we develop a dataset with spatially explicit cropland area from 1690 to 1999. Results indicate that the cropland area increased from 1.82×106 ha to 6.71×106 ha from 1690 to 1950 and then decreased to 5.90×106 ha in 1999. Before 1810, cropland cover expanded in southern Scandinavia and remained stable in northern Scandinavia. From 1810 to 1910, northern Scandinavia experienced slight cropland expansion. The cropland area increased rapidly in the southern part of the study area before changing slightly. After 1950, the cropland areas began to decrease in most regions, especially in eastern Scandinavia. When comparing global datasets with this study, although the total Scandinavia cropland area is in agreement among SAGE (Center for Sustainability and the Global Environment), HYDE (History Database of the Global Environment ) 3.2, PJ (Pongratz Julia), and this study, the spatial patterns show considerable differences, except for in Denmark between HYDE 3.2 and this study. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.926591 (Wei et al., 2021).

A historical reconstruction of cropland in China from 1900 to 2016

A spatially-explicit cropland distribution time-series dataset is the basis for the accurate assessment of biogeochemical processes in terrestrial ecosystems and their feedback to the climate system; however, this type of dataset is lacking in China. Existing cropland maps have a coarse resolution, are intermittently covered, or the data are inconsistent. We reconstructed a continuously covered cropland distribution dataset in China spanning from 1900 to 2016 by assimilating multiple data sources. In total, national cropland acreage expanded from 77.72 Mha in 1900 to the peak of 151.00 Mha in 1979, but it consistently decreased thereafter to 134.92 Mha in 2016. The cropland was primarily distributed in three historically cultivated plains in China: the Sichuan Plain, the Northern China Plain, and the Northeast China Plain. Cropland abandonment was approximately 29.90 Mha; it was mainly concentrated in the Northern China Plain and the Sichuan Plain and occurred during the 1990–2010 period. Cropland expansion was over 74.30 Mha; it was primarily found in the southeast, northern central, and northeast regions of China and occurred before 1950. In comparison, the national total and spatial-distribution of cropland in the Food and Agriculture Organization (FAO) of the United Nations and the History Database of the Global Environment (HYDE) were distorted during the period of 1960–1980 due to the biased signal from the Chinese Agricultural Yearbook. We advocate that newly reconstructed cropland data, in which the bias has been corrected, should be used as the updated data for regional and global assessments, such as greenhouse gas emission accountings and food production simulations. The cropland dataset is available via an open-data repository (https://doi.org/10.6084/m9.figshare.13356680) (Yu et al., 2020).

Combining historical and archaeological data with crop models to estimate agricultural productivity in past societies

Carrying capacity, population pressure, and agricultural productivity are of central importance to understanding key innovations in human social and cultural evolution. In this paper we outline how crop yield models can be combined with the historical and archaeological information about past societies compiled by Seshat: Global History Database to infer how agricultural productivity and potential have changed over time in different parts of the world. To aid comparative research we focus on developing a method for calculating the carrying capacity of a particular region based on a number of simplifying assumptions. Here we present two case studies demonstrating the calculation of ancient crop yields and carrying capacity for the regions of Latium (Italy) and Oaxaca (Mexico); regions selected to illustrate a number of different features of past agricultural systems, as well as different staple crops. We outline the strengths and weaknesses of this approach and discuss ways in which it could be adapted to address a range of research questions, e.g. relating to archaeological demography and anthropogenic change. Comparison of our reconstructed carrying capacity series with independent estimates of ancient population from these regions demonstrate broadly good agreement with some notable mismatches as well, highlighting a fruitful area of focus for future studies exploring the gap between achieved population and potential carrying capacity.