Forecasting technological change in agriculture—An endogenous implementation in a global land use model

Dietrich, Jan Philipp; Schmitz, Christoph; Lotze‐Campen, Hermann; Popp, Alexander; Müller, Christoph

doi:10.1016/j.techfore.2013.02.003

Cited by 102 publications

(87 citation statements)

References 40 publications

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“…On the CGE side, land representation also varies strongly, from the simplified structure of substitution found in GTEM or ENVIS-AGE that does not consider land expansion into forest to MAGNET that relies on a land supply curve calibrated on a biophysical model (13). Finally, endogenous yield adjustments can differ widely between a CGE, which represents substitution with factors such as capital and labor; a bottom-up model like GLOBIOM, which explicitly represents switches between different management systems and the relocation of production between different grid-cell locations (14); and, for instance, MAgPIE, which features an endogenous mechanisms of public and private investments in agricultural productivity (15).…”

Section: Discussionmentioning

confidence: 99%

“…The SSP data are available at https://secure.iiasa.ac.at/web-apps/ene/SspDb. Exogenous agricultural productivity changes from research and extension efforts were also aligned across models using IMPACT modeling suite estimates (23), except for MAgPIE, which represents this effect through its own endogenous yield response (15). IMPACT values are based on expert opinion about potential biological yield gains for crops in individual countries based on historical yield gains and expectations about future private and public sector research and extension efforts.…”

Section: Methodsmentioning

confidence: 99%

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Climate change effects on agriculture: Economic responses to biophysical shocks

Nelson

Valin

Sands

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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655

446

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Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m 2 . The mean biophysical yield effect with no incremental CO 2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.climate change adaptation | model intercomparison | integrated assessment | agricultural productivity C limate change alters weather conditions and thus has direct, biophysical effects on agricultural production. Assessing the ultimate consequences of these effects after producers and consumers respond requires detailed assessments at every step in the impact chain from climate through to crop and economic modeling.Comparisons of results from global studies that have attempted such model integration in the past show substantial differences in effects on key economic variables. Studies in the early 1990s found that climate change would have limited agricultural impacts globally, but with varying effects across regions (1-3). Adaptation and carbon dioxide (CO 2 ) fertilization effects were the two largest sources of variation in the results. New simulation approaches emerged in the mid-2000s, with gridded representation of yield impacts and more comprehensive coverage of variability in climate model projections (4, 5). However, these studies still relied on a single crop model and a single economic model. The number of economic models used for these types of analysis has remained relatively limited, and there has been no attempt to compare their behavior systematically. The Fourth Assessment Report of the Inte...

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Climate change effects on agriculture: Economic responses to biophysical shocks

Nelson

Valin

Sands

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

655

446

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show abstract

“…Cropland expansion leads to additional costs and is limited by biophysical conditions as well as by competing land-use activities. In additon to land expansion, the model can also invest into yield-increasing research and technology 42 . Crop growth functions connect crop harvest to the production of above-and belowground residues 4 .…”

Section: Methodsmentioning

confidence: 99%

Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution

et al. 2014

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Reactive nitrogen (Nr) is an indispensable nutrient for agricultural production and human alimentation. Simultaneously, agriculture is the largest contributor to Nr pollution, causing severe damages to human health and ecosystem services. The trade-off between food availability and Nr pollution can be attenuated by several key mitigation options, including Nr efficiency improvements in crop and animal production systems, food waste reduction in households and lower consumption of Nr-intensive animal products. However, their quantitative mitigation potential remains unclear, especially under the added pressure of population growth and changes in food consumption. Here we show by model simulations, that under baseline conditions, Nr pollution in 2050 can be expected to rise to 102-156% of the 2010 value. Only under ambitious mitigation, does pollution possibly decrease to 36-76% of the 2010 value. Air, water and atmospheric Nr pollution go far beyond critical environmental thresholds without mitigation actions. Even under ambitious mitigation, the risk remains that thresholds are exceeded.

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“…The model predicts the impacts of agriculture on land use and GHG emissions. Applications of this model can be found in Dietrich et al [96], Schmitz et al [97] or Popp et al [98].…”

Section: Dynamic Modelsmentioning

confidence: 99%

Approaches for modelling the energy flow in food chains

Gowreesunker

Tassou

2015

Energ Sustain Soc

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Background: The heavy reliance of the global food chain on the use of fossil fuels and anticipated rise in global population threatens future global food security. Due to the complexity of the food and energy systems, the impact of adequate food, climate or energy policies should be carefully examined in a modelling framework which considers the interaction of the food and energy systems. However, due to the different modelling approaches available, it can be very difficult to identify which method best suits the required purpose.

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Forecasting technological change in agriculture—An endogenous implementation in a global land use model

Cited by 102 publications

References 40 publications

Climate change effects on agriculture: Economic responses to biophysical shocks

Climate change effects on agriculture: Economic responses to biophysical shocks

Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution

Approaches for modelling the energy flow in food chains

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