A HINDCAST EXPERIMENT USING THE GCAM 3.0 AGRICULTURE AND LAND-USE MODULE

We report results of a “hindcast” experiment focusing on the agricultural and land-use component of the Global Change Assessment Model (GCAM). We initialize GCAM to reproduce observed agriculture and land use in 1990 and forecast agriculture and land use patterns on one-year time steps to 2010. We report overall model performance for nine crops in 14 regions. We report areas where the hindcast is in relatively good agreement with observations and areas where the correspondence is poorer. We find that when given observed crop yields as input data, producers in GCAM implicitly have perfect foresight for yields leading to over compensation for year-to-year yield variation. We explore a simple model in which planting decisions are based on expectations but production depends on actual yields and find that this addresses the implicit perfect foresight problem. Second, while existing policies are implicitly calibrated into IAMs, changes in those policies over the period of analysis can have a dramatic effect on the fidelity of model output. Third, we demonstrate that IAMs can employ techniques similar to those used by the climate modeling community to evaluate model skill. We find that hindcasting has the potential to yield substantial benefits to the IAM community.

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ECONOMIC AND PHYSICAL MODELING OF LAND USE IN GCAM 3.0 AND AN APPLICATION TO AGRICULTURAL PRODUCTIVITY, LAND, AND TERRESTRIAL CARBON

Climate Change Economics ◽

10.1142/s2010007814500031 ◽

2014 ◽

Vol 05 (02) ◽

pp. 1450003 ◽

Cited By ~ 50

Author(s):

MARSHALL WISE ◽

KATE CALVIN ◽

PAGE KYLE ◽

PATRICK LUCKOW ◽

JAE EDMONDS

Keyword(s):

Land Use ◽

Crop Yield ◽

Carbon Emissions ◽

Crop Yields ◽

Assessment Model ◽

Agricultural Crop ◽

Terrestrial Carbon ◽

Global Change Assessment Model ◽

The Impact

The release of the Global Change Assessment Model (GCAM) version 3.0 represents a major revision in the treatment of agriculture and land-use activities in a model of long-term, global human and physical Earth systems. GCAM 3.0 incorporates greater spatial and temporal resolution compared to GCAM 2.0. In this paper, we document the methods embodied in the new release, describe the motivation for the changes, compare GCAM 3.0 methods to those of other long-term, global agriculture-economy models and apply GCAM 3.0 to explore the impact of changes in agricultural crop yields on global land use and terrestrial carbon. In the absence of continued crop yield improvements throughout the century, not only are cumulative carbon emissions a major source of CO 2 emissions to the atmosphere, but bioenergy production remains trivial — land is needed for food. In contrast, the high crop yield improvement scenario cuts terrestrial carbon emissions dramatically and facilitates both food and energy production.

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Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6

Geoscientific Model Development ◽

10.5194/gmd-13-5425-2020 ◽

2020 ◽

Vol 13 (11) ◽

pp. 5425-5464 ◽

Cited By ~ 6

Author(s):

George C. Hurtt ◽

Louise Chini ◽

Ritvik Sahajpal ◽

Steve Frolking ◽

Benjamin L. Bodirsky ◽

...

Keyword(s):

Land Use ◽

Management Practices ◽

Initial Conditions ◽

Climate System ◽

Integrated Assessment Model ◽

Assessment Model ◽

Combined Effects ◽

Land Use Patterns ◽

Use Patterns ◽

The Future

Abstract. Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

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Distribution of agricultural lands and land ownership in Russia

Ekonomika poljoprivrede ◽

10.5937/ekopolj2104961r ◽

2021 ◽

Vol 68 (4) ◽

pp. 961-975

Author(s):

Ivan Ryazantsev ◽

Anna Ivolga

Keyword(s):

Land Use ◽

Agricultural Sector ◽

Agricultural Land ◽

Crop Yields ◽

Land Ownership ◽

Agricultural Lands ◽

Land Use Patterns ◽

Land Distribution ◽

Use Patterns ◽

Productive Land

Among the countries of the world, Russia is one of the richest in agricultural land. However, a quantitative advantage is poorly transformed into a qualitative one. As a result, there has been a gradual decrease in productive land, a decline in crop yields, the use of highly productive lands as less valuable land categories, and land degradation. These negative processes cause severe damage to both the agricultural sector and the country's economy as a whole. One of the reasons for such drawbacks is the underdevelopment of land use processes and forms of land ownership, which discourage land productivity growth and rational use of agricultural lands. In this paper, the authors analyze the most critical challenges in the sphere of agricultural land distribution in Russia and suggest ways to improve the efficiency of land ownership and land use patterns.

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Changing Traditional Land use Patterns in the Great Himalayas: A Case Study of Lahaul Valley

Journal of Environmental Systems ◽

10.2190/6451-t2jn-0n11-r0j1 ◽

1996 ◽

Vol 25 (2) ◽

pp. 195-211 ◽

Cited By ~ 9

Author(s):

G. S. Singh ◽

S. C. Ram ◽

J. C. Kuniyal

Keyword(s):

Land Use ◽

Land Use Patterns ◽

Use Patterns ◽

Traditional Land Use

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Conducting “Siteless Survey”: Results from Coastal New England

North American Archaeologist ◽

10.2190/rxce-py1q-pr1e-93m5 ◽

1993 ◽

Vol 14 (1) ◽

pp. 25-42 ◽

Cited By ~ 1

Author(s):

Jordan E. Kerber

Keyword(s):

Land Use ◽

New England ◽

Rhode Island ◽

Careful Consideration ◽

Archaeological Survey ◽

Land Use Patterns ◽

Use Patterns ◽

Statistical Validity ◽

Survey Results ◽

Survey Strategy

Selecting an effective archaeological survey takes careful consideration given the interaction of several variables, such as the survey's goals, nature of the data base, and budget constraints. This article provides justification for a “siteless survey” using evidence from a project on Potowomut Neck in Rhode Island whose objective was not to locate sites but to examine the distribution and density of prehistoric remains to test an hypothesis related to land use patterns. The survey strategy, random walk, was chosen because it possessed the advantages of probabilistic testing, as well as the ease of locating sample units. The results were within the limits of statistical validity and were found unable to reject the hypothesis. “Siteless survey” may be successfully applied in similar contexts where the distribution and density of materials, as opposed to ambiguously defined sites, are sought as evidence of land use patterns, in particular, and human adaptation, in general.

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The effects of different land use patterns on the microclimate and ecosystem services in the agro-pastoral ecotone of Northern China

Ecological Indicators ◽

10.1016/j.ecolind.2019.105522 ◽

2019 ◽

Vol 106 ◽

pp. 105522 ◽

Cited By ~ 3

Author(s):

Yuejuan Yang ◽

Kun Wang

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Northern China ◽

Land Use Patterns ◽

Use Patterns

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Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

Remote Sensing ◽

10.3390/rs13040631 ◽

2021 ◽

Vol 13 (4) ◽

pp. 631

Author(s):

Kyle D. Woodward ◽

Narcisa G. Pricope ◽

Forrest R. Stevens ◽

Andrea E. Gaughan ◽

Nicholas E. Kolarik ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Resource Use ◽

Training Data ◽

Systems Science ◽

Participatory Mapping ◽

Land Use Patterns ◽

Modeling Framework ◽

Use Patterns ◽

Human Habitation

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.

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