IMAGE - User contributions [en]

Land degradation/Policy issues

2022-11-21T13:00:16Z

Doelmanj:

{{ComponentPolicyIssueTemplate
}}
{{DisplayFigureTemplate|Flowchart Land degradation}}<div class="page_standard">
==Baseline developments==
The land degradation model in its current state is used to explore changes in degradation risk over time. Module A for Water Erosion Sensitivity (Flowchart Land degradation) is used to assess risks of soil erosion by water. Resulting maps are used to identify the most sensitive regions, and how areas under different risk categories change over time and space, subject to scenarios of future land use and climate change (the Baseline figure below).

Module B for Human-Induced Soil Changes (Flowchart Land degradation) is used to estimate how historical land degradation propagates through the IMAGE 3.0 framework via change in topsoil depth, soil organic matter content and hydrologic soil properties. As a result of changing soil properties, agricultural productivity calculated by the [[LPJmL model]] can change (Figure Policy interventions). This module is used for future projections to assess the effect of climate change , land-use change, land cover change (as vegetation cover), and restoration activities on soil properties, and to study the impact of these changes on crop production, hydrology, and land-use dynamics.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
The modules on soil degradation are used in the IMAGE framework to calculate the impacts of changes in factors driving risks of degradation, such as changes in land use or climate. This is illustrated with the [[Roads from Rio+20 (2012) project|Rio+20]] study by comparing the development of the Water Erosion Sensitivity Index under the baseline scenario with a sustainability scenario (Global technology). Areas characterised by high and very high risk increase strongly by 2050 with the development of land use and climate change under the baseline scenario by 33% and 69%, respectively, compared to 2010 levels (Baseline figure above). Under the Global Technology scenario, most of the increased risk is avoided because of less demand for agricultural land and reduction in climate change.

Both modules take into account climate change and land-use change and the effects on erosion risk and soil properties. The modules may be used to assess impact on the erosion risk of all policy interventions affecting climate and land use. However, the modules do not contain specific small-scale measures to reduce the degradation risks, such as reduced tillage and soil conservation practices. Future scenario studies could assess the aggregated effect of land-conservation-oriented policy interventions on the basis of more detailed relationships between agricultural practices and the land use intensity factor, f<sub>LUI</sub>, in module B (Flowchart Land degradation).

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>In the Global Land Outlook 2 report [REF GLO2] - prepared for the convention the combat desertification and land degradation (UNCCD) - the potential of land restoration was assessed using three scenarios: a Baseline scenario where land degradation continues, a Restoration scenario where degraded lands are restored leading to higher crop yields, and a Restoration&Protection scenario where both yield restoration and land protection is included. It is shown that restoration can have substantial benefit for yields, leading to less land use compared to the baseline and reduced food prices implying improved food security. However agricultural land still increases substantially from 2020 to 2050 leading to conversion of natural lands. When land protection is included in the Restoration&Protection scenario also this is prevented, however this does have a negative impact on food security most notably in the countries with high population growth such as in Sub-Saharan Africa.

[INCLUDE 4.13 from GLO2]

Land degradation/Policy issues

2022-11-21T12:53:23Z

Doelmanj:

Land degradation/Description

2022-11-21T11:15:20Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Oldeman et al., 1991; Batjes, 1997; Harris et al., 2013; Batjes, 2009; FAO et al., 2009;
}}<div class="page_standard">

Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:
==A. Risk of soil erosion caused by water==
Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:

* ''terrain erodibility index'': terrain erodibility represents the water erosion characteristics of the terrain in an index that combines surface relief and soil properties, expressed as index numbers. The relief index is a landform characteristic derived from a digital elevation model, calculated from the difference between minimum and maximum altitude in a 10 minute grid cell. The index is 1 for a difference of 300 m or more and zero for no altitude differences, with a linear relationship assumed between the two extremes. The soil erodibility index is derived from indices on soil texture, bulk density and soil depth. Soil characteristics were deduced from the 0.5x0.5 degree resolution in the [[WISE database]] ([[Batjes, 1997]]).

* ''rainfall erosivity index'': this index represents exposure to heavy rainfall, derived from the month of the year with the highest precipitation and number of wet (rainy) days in each month. Rainfall erosivity is largely determined by the intensity of rainfall events, because soil loss only occurs during periods of intense rainfall. Monthly rainfall intensities of between 0 and 2 mm per day are assigned an index value of zero, and days exceeding 20 mm receive a value of one, with a linear relationship assumed between these two end points. Climate data are used for the historical period ([[Harris et al., 2013]]). For future years, predictions are based on changes in precipitation according to scenarios generated by the climate model, see Component [[Atmospheric composition and climate]]. The number of wet days per month is assumed to be constant over time.

* ''land-use/land-cover index'': this index presents the level of protection against water erosion offered by various types of natural vegetation and crops. The basis for this index is the geographic distribution of land-cover types generated by the land-cover model. Most types of natural vegetation provide a high degree of protection against water erosion, while agriculture, and arable agriculture in particular, increases the vulnerability of the soil surface. A composite value is used for grid cells that contain agriculture, based on the distribution of agricultural crops in that world region.

All intermediate and resulting factors are expressed as dimensionless indices from zero to one, and so too is the end indicator, Water Erosion Sensitivity Index.

The susceptibility and sensitivity indices are calculated according to:

:<math>T = (Ia+ SE)/2 </math>
:<math>Ep = (T+R)/2</math>
:<math>WES = Ep*V</math>

with:
: <math>Ia</math> = relief index (-)
: <math>SE</math> = soil erodibility index (-)
: <math>T</math> = terrain erodibility index (-)
: <math>R</math> = rainfall erosivity index (-)
: <math>Ep</math> = water erosion susceptibility index (-)
: <math>V</math> = land-use/land-cover index (-)
: <math>WES</math> = Water Erosion Sensitivity Index (-)

Management systems are in use around the world to reduce the risk of erosion, such as building terraces, zero tillage, planting or conserving protective vegetation zones around fields, and high capacity drainage systems. The Water Erosion Sensitivity Index cannot capture all these and other interventions for the current situation, let alone into the future. The index only indicates areas potentially under threat. Impacts on crop production and soil quality cannot be derived directly from the indicator.

Comparison of the calculation above and the GLASOD degradation status maps by [[Oldeman et al., 1991|Oldeman et al. (1991)]] shows maximum correspondence with use of the classification in the table below. This classification can be used as a guide in analysing the water erosion sensitivity indicator.

<div class="thumbcaption dark">Classification of the Water Erosion Sensitivity Index</div>
<table class="pbltable">
<tr>
<th>Water Erosion Sensitivity Index</th>
<th>GLASOD soil degradation caused by water erosion</th>
</tr>
<tr>
<td>< 0.15</td>
<td>no/low</td>
</tr>
<tr>
<td>0.15 - 0.30</td>
<td>moderate</td>
</tr>
<tr>
<td>0.30 - 0.45</td>
<td>high</td>
</tr>
<tr>
<td>> 0.45</td>
<td>very high</td>
</tr>
</table>

==B. Human-induced soil changes==
Soil degradation is mostly reflected in changes in soil properties, such as soil depth, soil organic matter ({{abbrTemplate|SOM}}) content, and texture. Land cover and land use drive changes in soil properties. Land cover protects the soil against wind and water erosion, and provides organic matter to the soil. Land use tends to remove part of the biomass with harvested crops and residues and may increase mineralisation of SOM through tillage.

An empirical model denominated S-World has been developed that relates change in soil properties to topography, climate (average annual temperature and total annual precipitation), land management and land use, and land cover (as vegetation cover) ([[Stoorvogel, 2014]]; [[Stoorvogel et al., 2017]]). The following soil properties are considered:
* topsoil depth,
* soil depth,
* soil organic matter in the topsoil and subsoil , and
* soil texture (sand and clay content).

S-World is based on the global Harmonised World Soil Database ([[HWSD database|HWSD]]; ([[FAO et al., 2009]]) and the [[WISE database|WISE soil profile database]] ([[Batjes, 2009]]). The compound mapping units in HWSD were disaggregated using detailed terrain information, so that each grid cell could be linked to a unique soil type described in the WISE database. For each soil type, ranges for the main soil characteristics described above were assessed on the basis of the WISE soil profiles. The range of variable, i.e., soil property v for every soil type s is subsequently defined as [v<sub>ls</sub>..v<sub>hs</sub>] in which v<sub>ls</sub> corresponds to the 1<sup>st</sup> decile and v<sub>hs</sub> to the 9<sup>th</sup> decile. S-World downscales each soil property v based on 5 landscape properties or explanatory factors [''p<sub>1</sub>,p<sub>2</sub>… p<sub>5</sub>'']. These explanatory factors are: temperature, precipitation, slope, land management, and land cover. The land management is set to:
* 1.0 for cropland,
* 0.5 for mosaics of cropland and pasture or natural vegetation,
* 0.3 for pasture, and
* 0.0 for natural vegetation;
Land cover is characterised by a remotely sensed {{abbrTemplate|NDVI}} map.

The soil property v at location x with soil s is estimated as: {{FormulaAndTableTemplate|Formula1 Land degradation}}
with w<sub>x </sub>being a weight w∈ [0..1] that determines where v is in the range [v<sub>ls</sub>..v<sub>hs</sub> ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as:
{{FormulaAndTableTemplate|Formula4 Land degradation}}
The weight w<sub>px</sub> for landscape property p is calculated as:
{{FormulaAndTableTemplate|Formula2 Land degradation}}
In which c<sub>pv</sub> is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of c<sub>pv</sub> indicates whether there is a positive or negative relationship between the landscape property and the soil property.

When: {{FormulaAndTableTemplate|Formula3 Land degradation}}
and all the w∈ [0..1] then all values in the range [v<sub>ls</sub>..v<sub>hs</sub> ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.

The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.

With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions ([[Van Beek, 2012]]). These soil characteristics can be used in other models in the IMAGE framework, such as [[LPJmL model|LPJmL]] (Component [[Carbon cycle and natural vegetation]] ) and [[GLOFRIS model|GLOFRIS]] (Component [[Flood risks]]), as alternative input to assess the consequences of historical or future land degradation.
|=1〗, the w∈ [0..1] and all values in the range [v_ls..v_hs ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.
The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.
With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions (Van Beek, 2012). These soil characteristics can be used in other models in the IMAGE framework, such as LPJmL (Section 6.1) and GLOFRIS (Section 7.4), as alternative input to assess the consequences of historical or future land degradation.
<div>

== C. Land degradation effects in the agricultural economy ==
In IMAGE we want to take into account the effect of land degradation on crop and grassland yields and, through these, on the agricultural economy. To quantify this effect in relation to other drivers we use the satellite-observed NDVI trends that are shown to correlate with NPP and crop yields. it is assumed that in the 2000-2018 period crop yield reductions in line with NDVI trends take place. In a baseline approach, this trend is assumed to continue at a degressive rate. In a restoration scenario it is assumed that yields are gradually restored to the pre-2000 level by the year 2040 [REF TO GLO2]. This allows to compare degradation and restoration trends to for example climate change impacts or agronomic improvements [SEE FIG. xx].

[ADD FIGURE 3.22 from GLO2]

Land degradation

2022-11-21T10:57:07Z

Doelmanj:

{{ComponentTemplate2
|Application=Roads from Rio+20 (2012) project;Global Land Outlook 1;Global Land Outlook 2
|IMAGEComponent=Atmospheric composition and climate;Land cover and land use
|Model-Database=GlobCover database;WorldClim database;HWSD database;S-World database;WISE database
|KeyReference=Hootsmans et al., 2001;Stoorvogel et al., 2017;Stoorvogel, 2014;Van Beek, 2012
|Reference=FAO, 2011a;Wischmeier and Smith, 1978;Nkonya et al., 2011;Bindraban et al., 2012;UNCCD, 2012;Rozanov et al., 1990
|InputVar=Precipitation - grid;Number of wet days - grid;Land cover, land use - grid;Temperature - grid
|Parameter=Slope - grid;Land management;Initial land cover, land use;Initial temperature, precipitation;Soil types and profiles (S-World);Weighting factors for temperature, precipitation, land use and slope
|OutputVar=Erosion risk - grid;Change in soil properties - grid
|ComponentCode=LD
|AggregatedComponent=Impacts
|FrameworkElementType=impact component
}}
<div class="page_standard">

Land degradation is human-induced damage to ecosystems leading to a sustained loss of capacity. This is a serious and widespread problem leading ultimately to loss of arable land, and to demand for new arable land to compensate for decline in production on existing land. A key symptom of land degradation is loss of organic carbon from soils and vegetation, also contributing to global greenhouse gas emissions. The key mechanisms in land degradation are soil erosion (by water and wind), compaction, salinization, nutrient depletion, structural decay and contamination. The main causes are deforestation, land conversion, inadequate agricultural land use and management, and construction (urbanisation, road construction).

In 2012, the UN Convention to Combat Desertification ({{abbrTemplate|UNCCD}}) formulated the goal to achieve zero net land degradation as a Sustainable Development Goal for [[Roads from Rio+20 (2012) project|Rio+20]] ''‘to secure the contribution of our planet’s land and soil to sustainable development, including food security and poverty eradication’'' ([[UNCCD, 2012]]). Land degradation is also relevant to the other Rio Conventions, with one of the Aichi targets of the Convention on Biological Diversity ({{abbrTemplate|CBD}}) aiming to restore at least 15% of degraded ecosystems.

While recognized as a global threat, the impacts of land degradation are poorly understood, and studies report differing results. For instance, productive soil loss equals 15 million km<sup>2</sup> according to [[Rozanov et al., 1990]], while FAO reports about 43 million km<sup>2</sup> moderately to severely degraded land because of soil quality loss, water resource depletion and biodiversity loss ([[FAO, 2011a]]). As a result, the impacts on productivity and economic losses with consequences for food security are also very uncertain. In the same way, the costs and benefits of investments to prevent land degradation and to restore degraded areas are also largely unknown ([[Nkonya et al., 2011]]). Many reasons for these discrepancies and knowledge gaps are identified ([[Bindraban et al., 2012]]), including uncertainty about data, ambiguous definitions of land degradation, and methodology weaknesses in attributing changes in ecosystems to land degradation or to other causes.

Although a comprehensive model to capture the complex system interactions is not readily available, IMAGE 3.0 offers the following approaches to address soil degradation:

A. Water Erosion Risk: Risk assessment of soil erosion caused by water based on the Universal Soil Loss Equation ({{abbrTemplate|USLE}}; Wischmeier and Smith[[Wischmeier and Smith, 1978|(1978)]]).

B. Change in soil properties: Quantitative assessment of changes in soil properties, from a hypothetically undisturbed (pristine) situation to a new situation, accounting for changes in land cover and other changes caused by human activity. The effect of changes in soil properties on crop production, hydrology and water can be assessed in other components of IMAGE.

C. Including historically observed degradation trends derived from satellite-observed NDVI trends in land-use projections. Observed degradation is assumed to correlate with crop yield loss. By reducing or restoring yields the role of land degradation in the agricultural economy is assessed.

{{InputOutputParameterTemplate}}
</div>

Agricultural economy/Description

2022-11-21T10:51:57Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Hertel, 1997;Britz, 2003;Armington, 1969;Huang et al., 2004;Helming et al., 2010;Banse et al., 2008;Bruinsma, 2003;Woltjer et al., 2011;Van Meijl et al., 2006;Eickhout et al., 2009;Overmars et al., 2014;Alexandratos and Bruinsma, 2012;Gustavsson et al., 2011;Gustavsson et al., 2013
}}
<div class="page_standard">

The MAGNET model ([[Woltjer et al., 2014]]) is based on the standard GTAP model ([[Hertel, 1997]]), which is a multi-regional, static, applied computable general equilibrium ({{abbrTemplate|CGE}}) model based on neoclassical microeconomic theory. Although the model covers the entire economy, there is a special focus on agricultural sectors. It is a further development of GTAP regarding land use, household consumption, livestock, food, feed and energy crop production, and emission reduction from deforestation or afforestation.

===Demand and supply===
Household demand for agricultural products is calculated based on changes in income, income elasticities, preference shift, price elasticities, cross-price elasticities, and the commodity prices arising from changes in the supply side. Demand and supply are balanced via prices to reach equilibrium. Income elasticities for agricultural commodities are consistent with FAO estimates ([[Britz, 2003]]), and dynamically depend on purchasing power parity ({{abbrTemplate|PPP}}) corrected GDP per capita. The supply of all commodities is modelled by an input–output structure that explicitly links the production of goods and services for final consumption via different processing stages back to primary products (crops and livestock products) and resources. At each production level, input of labour, capital, and intermediate input or resources (e.g., land) can be substituted for one another. For example, labour, capital and land are input factors in crop production, and substitution of these production factors is driven by changes in their relative prices. If the price of one input factor increases, it is substituted by other factors, following the price elasticity of substitution.

===Regional aggregation and trade===
MAGNET is flexible in its regional aggregation (140 regions). In linking with IMAGE, MAGNET distinguishes 28 individual large world regions, closely matching the regions in IMAGE (Figure [[Region classification map|IMAGE regions]]). Slightly more detail is provided the European regions in order to properly model the EU single market. Similar to most other {{abbrTemplate|CGE}} models, MAGNET assumes that products traded internationally are differentiated according to country of origin. Thus, domestic and foreign products are not identical, but are imperfect substitutes (Armington assumption; [[Armington, 1969]]).

===Land use===
In addition to the standard [[GTAP database|GTAP model]], MAGNET includes a dynamic land-supply function ([[Van Meijl et al., 2006]]) that accounts for the availability and suitability of land for agricultural use, based on information from IMAGE (see below). A nested land-use structure accounts for the differences in substitutability of the various types of land use ([[Huang et al., 2004]]; [[Van Meijl et al., 2006]]). In addition, MAGNET includes international and EU agricultural policies, such as production quota and export/import tariffs ([[Helming et al., 2010]]).

===Biofuel crops===
MAGNET includes ethanol and biodiesel as first-generation biofuels made from wheat, sugar cane, maize, and oilseeds ([[Banse et al., 2008]]) and the use of by-products ({{abbrTemplate|DDGS}}, oilcakes) from biofuel production in the livestock sector. Second-generation biofuels are also included, with the potential amount of residues available from IMAGE/TIMER ([[Daioglou et al., 2016]]).

===Livestock===
MAGNET distinguishes the livestock commodities of beef cattle, dairy cattle, other cattle (sheep & goats), dairy cattle, poultry, and pig and other animal products. The first three are the ruminant sectors which are grass and crop fed, while the poultry and pigs sectors are crop fed. Modelling the livestock sector includes different feedstuffs, such as feed crops, co-products from biofuels (oil cakes from rapeseed-based biofuel, or distillers grain from wheat-based biofuels), and grass ([[Woltjer, 2011]]). Grass may be substituted by feed from crops for ruminants.

===Land supply===
In MAGNET, land supply is calculated using a land-supply curve that relates the area in use for agriculture to the land price. Total land supply includes all land that is potentially available for agriculture, where crop production is possible under soil and climatic conditions, and where no other restrictions apply such as urban or protected area designations (see also Component Land-use allocation). In the IMAGE model, total land supply for each region is obtained from potential crop productivity and land availability on a resolution of 5x5 arcminutes ([[Mandryk et al., 2015]]). The supply curve depends on total land supply, current agricultural area, current land price, and estimated price elasticity of land supply in the starting year. Regions differ with regard to the proportion of land in use, and with regard to change in land prices in relation to changes in agricultural land use.

===Reduced land availability===
By restricting land supply in IMAGE and MAGNET, the models can assess scenarios with additional protected areas, or reduced emissions from deforestation and forest degradation ({{abbrTemplate|REDD}}). These areas are excluded from the land supply curve in MAGNET, leading to lower elasticities, less land-use change and higher prices, and are also excluded from the allocation of agricultural land in IMAGE (e.g., [[Overmars et al., 2014]], [[Doelman et al., 2018]]).

===Intensification of crop and pasture production===
Crop and pasture yields in MAGNET may change as a result of the following four processes:
# autonomous technological change (external scenario assumption);
# intensification due to the substitution of production factors (endogenous);
# climate change (from IMAGE);
# change in agricultural area affecting crop yields (such as, decreasing average yields due to expansion into less suitable regions; from IMAGE).

Biophysical yield effects due to climate and area changes are calculated by the IMAGE crop model and communicated to MAGNET. Likewise, also the potential yields and thus the yield gap can be assessed with the crop model in IMAGE. External assumptions on autonomous technological changes are mostly based on FAO projections ([[Alexandratos and Bruinsma, 2012]]), which describe per region and commodity, the assumed future changes in yields for a wide range of crop types. In MAGNET, the biophysical yield changes are combined with the autonomous technological change to give the total exogenous yield change. In addition, during the simulation period, MAGNET calculates an endogenous intensification as a result of price-driven substitution between labour, land and capital. Projections of crop yield increase in IMAGE-MAGNET and other global agricultural models were evaluated recently ([[Van Zeist et al., 2020]]). In IMAGE, regional yield changes due to autonomous technological change and endogenous intensification according to MAGNET are used in the spatially explicit allocation of land use (Component [[Land-use allocation]]).

===Food Waste Reduction===
High- and medium-income regions can reduce food waste to achieve the lowest level among them (low-income regions are not required to reduce waste). This implementation is based on Gustavsson et al. ([[Gustavsson et al., 2011|2011]]; [[Gustavsson et al., 2013|2013]]), from where we define the levels of food waste for five commodity types (cereals, other_plant_based, meat, fish_seafood, melk_eggs) and three food supply chain steps (primary, processing, consumption). Then, we find the lowest food waste level among high- and medium-income regions for each commodity type and food supply chain step and define them as food waste targets. Then, we calculate the change in the production efficiency that resembles the food waste reduction necessary for the regions to achieve their food waste targets. Finally, we calculate the MAGNET shock required to meet the targets and run MAGNET with the new production efficiencies, which resemble the reduction in food waste. The reduction in food waste rduces the pressure on the food system resulting in less agricultural land use, lower GHG emissions and reduced food prices.

===Diet Changes===
Another way to reduce the impact of food consumption on the environment is by adopting healthier diets. These diets could be entirely plant-based or reduced in meat consumption, both cases leading to reduced meat demand and therefore reduced agricultural area demand. In the current implementation, there is a 50% reduction in meat consumption in High-income countries + substitution to artificial meat or plant-based diets in 2050.
</div>

Terrestrial biodiversity/Description

2022-11-17T15:26:15Z

Doelmanj: small fixes in the MSA/BII description of IMAGE-land

{{ComponentDescriptionTemplate
|Reference=Bartholome et al., 2004;UNEP-WCMC, 2005;Dixon et al., 2001;Visconti et al., 2011;Alkemade et al., 2012;Alkemade et al., 2011a;Bouwman et al., 2002b;Bobbink et al., 2010;DMA, 1992;Meijer and Klein Goldewijk, 2009;Benitez-Lopez et al., 2010;UNEP, 2001;Verboom et al., 2014;Faith et al., 2008;Musters et al., submitted
}}
<div class="page_standard">

The GLOBIO model calculates changes in biodiversity in terrestrial ecosystems, based on seven drivers of biodiversity change: land-use change, land-use intensity, climate change, atmospheric nitrogen deposition, infrastructural development, encroachment and fragmentation.
Four steps in the model are distinguished:
# Drivers of biodiversity change derived from IMAGE results are combined with additional data;
# Mean Species Abundance (MSA) is calculated for each driver and year, using empirical relationships between driver and change in MSA ([[Alkemade et al., 2009]]);
# MSA values for each driver are aggregated to obtain one MSA values;
# Two additional indicators are calculated: Wilderness area, and Species Richness Index (Figure Flowchart).

MSA expresses the relationship of mean species abundance between a disturbed or managed ecosystem and an undisturbed ecosystem, on a scale from 1 (undisturbed or pristine) to 0 (complete loss). This concept is applicable for most ecosystems and dynamics of biodiversity loss, and allows to compare and aggregate across ecosystems and drivers. However, it ignores possible increase in species abundance due to natural processes or in certain agricultural systems, such as European high nature value farmland.

===Land use and land-use intensity ===
Changes in land use and land cover are major drivers of biodiversity change. Land use includes all human activities with a spatial component, such as forestry, agriculture, infrastructure and urban development. The impact of land use on biodiversity ranges from small (where the habitat quality is too poor for a limited subset of species) to large (where complete conversion of ecosystems results in habitat loss for a large number of species).

GLOBIO calculates maps of land-use categories and intensities for the year 2000. The starting point is land-cover data from GLC2000 ([[Bartholome et al., 2004]]) on the major types of forests, rangelands and agricultural land areas, at around 30 arc seconds resolution (1x1km near the equator). These data are combined with the World Database on Protected Areas ([[WDPA database]]; [[UNEP-WCMC, 2005]]) that distinguishes protected and non-protected areas. The land-cover classes obtained are summarised as proportions of cropland, forest and pasture for IMAGE grid cells of 5x5 minutes resolution.

For the period after 2000, changes in land use and land-use intensity from IMAGE are used as regional totals and allocated to the starting map. Data on cropland areas derived from the [[land-use allocation]] model are used as a total claim for each region. Three intensity classes are distinguished on the basis of management intensity ([[land-use allocation]]) for each region, calibrated with areas of irrigated, extensive and intensive croplands from the farming system typology from the FAO ([[Dixon et al., 2001]]). Data on three forestry management types are derived from the [[forest management]] module, and data on two grazing intensities from the [[livestock systems]] module.

The pastoral grassland areas are allocated in natural rangelands. Grazing in mixed systems is assumed on managed pastures, where the natural vegetation would be densely forested biomes. The remaining grassland areas (e.g., semi-arid and arid grasslands, tundra) are considered natural areas. All regional cropland, forests and grazing areas are geographically distributed per land-use intensity class by adjusting the proportion per grid cell, avoiding protected areas ([[Visconti et al., 2011]]).

MSA values for all land-use types are derived from the literature (Alkemade et al., 2009; Alkemade et al., 2012) and applied to the land-use map, with proportions of each land-use intensity class to yield the MSA land-use map for the year considered.

===Climate ===
Climate is a key determinant of ecosystems and biodiversity. Climate change causes shifts in species occurrence and abundance, and ultimately may lead to local species extinction. Species distribution models ({{abbrTemplate|SDM}}) are used to describe relationships between climate variables and species distribution.

Regression equations are derived for each biome by applying a large number of SDMs to a series of climate scenarios, and calculating the proportion of remaining species per grid cell (0.5x0.5 degrees). The average proportion of remaining species per grid cell is related to the global mean temperature increase (GMTI) from IMAGE for the scenario considered ([[Alkemade et al., 2011a]]). The regression equation between GMTI and the proportion of remaining species is used to derive the map of MSA levels related to climate change for a given year.

===Nitrogen===
Nitrogen is a plant nutrient that stimulates growth, but some species benefit more than others and become more dominant with higher nitrogen availability. Thus, nitrogen deposition affects the species composition, mainly of plant and invertebrate species. Ecosystems can take up nitrogen without observable effects up to the level at which the assimilative capacity of the ecosystem is exceeded. This level of N input is defined as the critical load ({{abbrTemplate|CL}}).

Deposition rates of atmospheric nitrogen for current and future years are derived from IMAGE (Components [[Emissions]] and [[Nutrients]]), and the map of critical loads is based on Bouwman et al. ([[Bouwman et al., 2002b|2002b]]). The nitrogen exceedance is calculated by subtracting the critical load from the estimated deposition. For forested and grassland ecosystems, the {{abbrTemplate|MSA}} map for nitrogen is derived from the regression equation between nitrogen exceedance and the proportion of remaining species. Regression equations are derived from published impact studies on the effects of a nitrogen surplus on species composition ([[Bobbink et al., 2010]]).

===Infrastructure and Encroachment===
The construction and use of infrastructure, such as roads, railroads and built-up area, may have multiple impacts on biodiversity. Roads have a direct impact on species, for example as the result of traffic disturbance, road kills and habitat fragmentation (see below). There are also indirect impacts, such as increased human access to natural areas, increasing hunting, gathering and tourism. Traffic disturbance reduces the breeding success of bird and mammal species, reducing their abundance close to infrastructure. Hunting and gathering reduce populations when intensity exceeds threshold values.

Data on infrastructure are derived from globally available road maps, such as the Digital Chart of the World ([[DMA, 1992]]) and the GRIP, Global Roads Inventory Project ([[Meijer and Klein Goldewijk, 2009]]). Direct impacts occur in a 500 m zone on both sides of roads and an MSA value is derived from a meta-analysis on disturbance effects ([[Benitez-Lopez et al., 2010]]).

Human settlements are the major access points to natural areas, and are likely to correlate with agricultural areas. Thus, 20 km impact zones are calculated around cropland areas and assigned as encroachment areas. Based on literature review of hunting activities, an MSA value of 0.7 is attributed to such zones. The MSA map for infrastructure and encroachment is obtained by combining the MSA map for direct (infrastructure) and indirect (encroachment) effects. In projections, the impact zone of direct effects is broadened according to the GLOBIO2.0 procedure ([[UNEP, 2001]]). Future impact zones for indirect effects are determined by the projections for agricultural areas.

===Ecosystem fragmentation ===
Conversion of natural land to intensive cropping and road construction change vast areas of contiguous wilderness into a fragmented landscape with remnants of natural areas remaining as isolated islands. These relatively small patches are likely to house fewer species than could be expected from their habitat quality, because the individual patches may be too small to sustain viable populations of some species. Based on literature data on minimum area requirements of species, a relationship is constructed between patch size and relative number of species compared to a non-fragmented situation, known as the minimum area requirement (MAR) curve ([[Verboom et al., 2014]]). The relative number of species in a certain patch according to this MAR curve is used as a proxy for mean species abundance (MSA).

The area of natural vegetation patches is calculated by reclassifying the GLC2000 Global Land Cover data into two classes: human-dominated land (including croplands and urban areas) and natural land. Contiguous cells of natural land are grouped together and with an overlay of main roads (see above) are used to produce a map of natural land patches.

In scenario projections, patch sizes change as agricultural land use expands and as new roads emerge ([[Verboom et al., 2014]]). Changes in patch sizes also change the relative number of species and the MSA biodiversity indicator.

===Aggregation===
Total MSA values per area unit are calculated by multiplying the individual MSA values related to the separate drivers of biodiversity change (Figure Flowchart) to arrive at the total effect of all drivers. The contribution of individual drivers to biodiversity loss is also calculated.
Wilderness areas are defined as natural areas with high (>0.8) MSA values. The Species Richness Index (SRI) is calculated by applying species–area relationships according to Faith et al. ([[Faith et al., 2008|2008]]), and using MSA values as a proxy for their intactness parameter. Aggregation from regional to global species richness is based on species lists in the Wildfinder database to avoid double counting ([[Musters et al., submitted]]).

===IMAGE-Land Emulator of Terrestrial Biodiversity===
IMAGE-Land can provide two biodiversity indicators as outputs without running GLOBIO: the MSA and the Biodiversity Intactness Index (BII). This alternative is suitable for time-constraint evaluations (as there is no need for data exchange and GLOBIO runs). Still, it comes with the downside of being a less accurate measure yet reasonably similar to GLOBIO's.

The IMAGE-Land MSA is based on the GLOBIO implementation [REF Schipper 2020], but it only accounts for three pressures: Land use, Nitrogen Deposition and Climate Change. The validation of the results between IMAGE-LAnd and GLOBIO indicates a good agreement between the data regarding MSA-Plants (R2 = 0.92) and a lower agreement for MSA-Vertebrates (R2 = 0.72), suggesting that IMAGE-Land MSA Vertebrates should be used parsimoniously.

The BII evaluates terrestrial biodiversity based on land-use changes only. As this index is not part of the GLOBIO outputs, our approach derives from the BII calculations from the PREDICTS Biodiversity model [REF Palma et al., 2021], which is used in the Bending the Curve project [REF Leclère et al., 2020]. A procedure like the one used for the MSA is implemented to calculate BII as part of the IMAGE-land module directly. The BII is the average abundance across a set of species in each area relative to their reference population, which would be populations before any human impacts but are usually populations in the least impacted settings available. Unlike the MSA, the BII accounts only for land use as a driver of species populations. Even though it loses data on other pressures driving species losses, the level of detail in land-use classes for defining the index is larger than in the MSA, leading to a more sensitive land-use-specific index.
</div>

Livestock systems/Data uncertainties limitations

2021-11-26T15:27:44Z

Doelmanj:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=FAO, 2012a; Beusen et al., 2008; Bouwman et al., 2005; Seré and Steinfeld, 1996;
}}<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
Historical livestock numbers, milk production per animal, off-take rates and carcass weights were obtained from [[FAO]] ([[FAO, 2012a]]). For ruminants, the production systems have been aggregated to two systems: pastoral, and mixed & landless production systems ([[Bouwman et al., 2005]]). For pigs and poultry three systems are distinguished. For pigs these are backyard, intermediate and intensive systems, and for poultry these are backyard, broiler and laying hens systems ([[Lassaletta et al., 2019]]).

==Uncertainties==
There are several uncertainties in the calculation of livestock production in the different systems for historical years and scenarios. The first uncertainty is the aggregation level on the scale of country or world region, which does not take account of underlying heterogeneity. The second uncertainty concerns the use of average data for carcass weight, off-take rate, and milk production for total livestock populations. In reality, livestock populations cover different age classes, and not all animals in a population are productive. Calculations, such as energy requirement for maintenance, are a non-linear function of body weight, and thus use of average values, may lead to distortion. The third uncertainty is associated with livestock numbers. Methodology and frequency of data collection (for example, by census) vary between countries, and are probably less certain for some developing countries than for industrialised countries. This uncertainty on livestock numbers affects not only the livestock module, but also all impact IMAGE modules that depend on livestock numbers, such as ammonia emissions ([[Beusen et al., 2008]]).

The main uncertainties in construction scenarios concern agricultural demand ([[Agricultural economy]]), the distribution of production over the two systems, and production characteristics per system, including feed requirements and feed types.

==Limitations==
The key limitation in the current livestock module is that the ruminant livestock system have a soft linkage to the agricultural economy model MAGNET ([[Agricultural economy]]). Although [[MAGNET model|MAGNET]] has some representation of feed substitution and intensification as a result of land scarcity, and mimics the dynamics described here, there is no explicit representation of livestock systems and physically based feed compositions.
</div>

Afforestation policies

2021-11-26T15:09:42Z

Doelmanj:

{{PolicyInterventionTemplate
|Component=Agricultural economy
|Description=Increasing forest area to sequester CO<sub>2</sub> in biomass which helps to achieve stringent climate targets.
|Reference=Doelman et al., 2020
}}
{{PolicyInterventionEffectTemplate
|EffectOnComponent=Agricultural economy
|EffectDescription=Reduces agricultural land use in regions with cost-optimal afforestation leading to higher food prices, lower food availability and changes in trade.
}}
{{PolicyInterventionEffectTemplate
|EffectOnComponent=Carbon cycle and natural vegetation
|EffectDescription=Reduces agricultural land use in regions with cost-optimal afforestation leading to higher food prices, lower food availability and changes in trade.
}}

Carbon cycle and natural vegetation

2021-11-26T12:52:49Z

Doelmanj:

{{ComponentTemplate2
|IMAGEComponent=Carbon, vegetation, agriculture and water;Agriculture and land use;Atmospheric composition and climate;Ecosystem services;Land cover and land use
|Model-Database=HYDE database
|KeyReference=Sitch et al., 2003;Müller et al., 2016a
|Reference=Müller et al., 2007;Ballantyne et al., 2012;Gerten et al., 2004;Bondeau et al., 2007;Klein Goldewijk et al., 1994;Van Minnen et al., 2000;Doelman et al., 2019;Friedlingstein et al., 2019;Braakhekke et al., 2019;Von Bloh et al., 2018
|InputVar=Temperature - grid;Precipitation - grid;Number of wet days - grid;Cloudiness - grid;CO2 concentration;Timber use fraction;Land cover, land use - grid;Irrigation water supply - grid;Forest management type - grid
|Parameter=Soil properties - grid
|OutputVar=Potential natural vegetation - grid;NEP (net ecosystem production) - grid;Land-use CO2 emissions - grid;Carbon pools in vegetation - grid;NPP (net primary production) - grid;Soil respiration - grid;Carbon pools in soil and timber - grid
|ComponentCode=NVCC
|AggregatedComponent=Carbon, vegetation, agriculture and water
|FrameworkElementType=state component
}}
<div class="page_standard">

The terrestrial biosphere plays a key role in global and regional carbon cycles and thus in the climate system. Large amounts of carbon (between 2000 and 3000 PgC) are stored in the vegetation and soil components. Currently, the terrestrial biosphere absorbs about 30% of emitted CO<sub>2</sub> ([[Ballantyne et al., 2012]]), and this carbon sink can be maintained and even enhanced by, for instance, protecting established forests and by establishing new forests ([[Doelman et al., 2019]]). However, deforestation and other land use changes in the last few centuries have contributed considerably to the build-up of atmospheric carbon dioxide ([[Friedlingstein et al., 2019]]) and this trend is projected to continue [[Müller et al., 2007|(Müller et al., 2007]]).

Regardless of land cover and land use, the net carbon sink in the terrestrial biosphere is affected by a range of environmental conditions such as climate, atmospheric CO<sub>2</sub> concentration and moisture. These conditions influence processes that take up and release CO<sub>2</sub> from the terrestrial biosphere such as photosynthesis, plant and soil respiration, transpiration, carbon allocation and turnover, and disturbances such as fires.

In plant photosynthesis, CO<sub>2</sub> is taken from the atmosphere and converted to organic carbon compounds. This CO<sub>2</sub> conversion is referred to as gross primary production ({{abbrTemplate|GPP}}). The sequestered carbon is needed for plant maintenance and growth (autotrophic respiration), and for the development of new plant tissues, forming live biomass carbon pools. All plant parts (including leaf fall and mortality) are ultimately stored as carbon in carbon pools in the soil and atmosphere.

Terrestrial carbon cycle and vegetation models contribute to better understanding of the dynamics of the terrestrial biosphere in relation to these underlying processes and to the terrestrial water cycle (see Component [[Water]]) and land use (see Component [[Agriculture and land use]]).

The IMAGE-2 carbon cycle and biome model ([[Klein Goldewijk et al., 1994]]; [[Van Minnen et al., 2000]]) have been replaced by the Lund-Potsdam-Jena model with Managed Land ([[LPJmL model|LPJmL]]) model ([[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]])[TO ADD: Schaphoff 2018a/b]. An overview of the LPJmL model in the IMAGE context with regard to carbon and biome dynamics is presented here; the model and a sensitivity analysis is described in detail by Müller et al. ([[Müller et al., 2016a|2016]]).

{{InputOutputParameterTemplate}}
</div>

Water/Policy issues

2021-11-19T16:23:50Z

Doelmanj:

{{ComponentPolicyIssueTemplate
|Reference=Fischer et al., 2005;Molden, 2007;Alexandratos and Bruinsma, 2012;Gerten et al., 2013;Rost et al., 2009;OECD, 2012
}}
<div class="page_standard">
==Baseline developments==
In baseline scenarios, water use is typically projected to increase rapidly. This can be illustrated in the baseline scenario study for the [[OECD Environmental Outlook to 2050 (2012) project|OECD Environmental Outlook to 2050]] ([[OECD, 2012]]), in which water demand is projected to increase by 53% globally, mostly due to a high increase in non-agricultural water use (the figure below). However, this baseline scenario did not consider irrigated area expansion, which is expected to further increase demand for irrigation water (the figure below top left). As a result of the increase in total water demand, and a change in water availability due to climate change, the number of people living in medium to severely water stressed basins will increase by 80%, according to this baseline (the figure below top right).

Expansion of rain-fed and irrigated croplands together with increased crop yields are projected in studies on the future of the global food system ([[Fischer et al., 2005]]; [[Molden, 2007]]; [[Alexandratos and Bruinsma, 2012]]; [[Gerten et al., 2013]]). However, irrigation expansion and related increases in crop yields may not be feasible because of water scarcity (the figure below bottom).

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
Several water-related policy interventions can be assessed with IMAGE-LPJmL, including improved rainwater management, improved irrigation efficiency, increasing storage capacity and land-use related interventions. For example, Rost et al. ([[Rost et al., 2009|2009]]) evaluated the effect of improved rainwater management on crop production by decreasing soil evaporation and increasing rainwater harvesting. Biemans et al. ([[Biemans, 2012|2012]]) tested the effect of improved irrigation efficiency and expansion of storage capacity on irrigation water demand and available sources of supply for five river basins on the Indian subcontinent (the figure below). [[Jägermeyr et al., 2017]] concluded that 41% of current irrigation withdrawals depend on water at the expense of the environmental, but that improvements in irrigation efficiencies and water management can avoid these negative impacts.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

In combination with the crop model, the effect of land-use related policy interventions could be addressed, such as changes in crop types or improved land and water allocation (e.g. [[Bijl et al., 2018a]]). With the module for Environmental Flow Requirements (EFRs), impacts of prioritizing EFRs on other water users can be analyzed, and vice versa ([[De Vos et al., 2021]] and figure below).

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure Water/Policy issues}}

{{PIEffectOnComponentTemplate }}
</div>

Water/Description

2021-11-19T16:21:44Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Nilsson et al., 2005;Alcamo et al., 2003;Davies et al., 2013;Pastor et al., 2014;Bondeau et al., 2007;Sitch et al., 2003;Gerten et al., 2004;Rost et al., 2008;Biemans et al., 2013
}}
<div class="page_standard">

In IMAGE, the hydrological cycle is represented by LPJmL ([[Bondeau et al., 2007]]; [[Gerten et al., 2013]]; [[Schaphoff et al., 2018a]]), which simulates the global hydrological cycle as part of the dynamics of natural vegetation and agricultural production systems. Because LPJmL is linked to IMAGE, there is consistency in the way the [[Carbon cycle and natural vegetation|carbon cycle, natural vegetation]] dynamics, [[Crops and grass|crop growth and production]], [[land-use allocation]] and the water balance are modelled.

Data on annual [[land cover and land use]] are used as input to LPJmL, including information on the location of irrigated areas and crop types (Figure Flowchart and Input/Output Table at [[Water|Introduction part]]). This affects the amount of water that evaporates and runs off, as well as the amount of water needed for irrigated crops during the (simulated) growing season. Simultaneously, information on water availability and potential yields calculated by LPJmL is taken into account in the [[Land-use allocation]] model to identify suitable locations to expand irrigated areas. From the energy module, LPJmL receives water demands for industries, households and electricity. Together with water for irrigation these make up the total water demands in LPJmL.

Climate is used as input in LPJmL to determine potential evapotranspiration, and the precipitation input to the water balance ([[Gerten et al., 2004]]). The [[Crops and grass]] module, which is also part of LPJmL, calculates irrigation water demand based on crop characteristics, soil moisture and climate. If the amount of water available for irrigation is limited, water stress will occur which leads to reduction of crop yields calculated by the [[Crops and grass|crop and grassland]] model.

===The natural hydrological cycle===
The Hydrology module in LPJmL consists of a vertical water balance ([[Gerten et al., 2004]];[[Schaphoff et al., 2013]]) and a lateral flow component ([[Rost et al., 2008]]) that are simulated at 0.5 degree resolution in daily time steps (Figure Flowchart). The soil in each grid cell is represented by a five-layer soil column of 0.2, 0.3, 0.5, 1.0 and 1.0 m depth, partly covered with natural vegetation or crops.

The potential evapotranspiration rate in each grid cell depends primarily on net radiation and temperature, and is calculated using the Priestley-Taylor approach ([[Gerten et al., 2004]]). The actual evapotranspiration is calculated as the sum of three components: evaporation of water stored in the canopy (interception), bare soil evaporation and plant transpiration ([[Gerten et al., 2004]]). Water storage in the canopy is a function of vegetation type, leaf area index ({{abbrTemplate|LAI}}) and precipitation amount. Plant transpiration is modelled as the minimum of atmospheric demand and plant water supply. Plant water supply depends on the plant-dependent maximum transpiration rate and relative soil moisture. Soil evaporation occurs in the proportion of land in the grid cell that is not covered by vegetation. It equals potential evaporation when the soil moisture of the upper 20 cm is at field capacity, and declines linearly with relative soil moisture.

Precipitation reaching the soil (throughfall, precipitation minus interception) either accumulates as snow or infiltrates into the soil. Snowmelt is calculated using a simple degree-day method ([[Gerten et al., 2004]]). The soil is parameterised as a bucket model. The status of soil moisture of the soil layers is updated daily, accounting for throughfall, snowmelt, evapotranspiration, percolation and runoff. Percolation rates for the soil layers depend on soil type and decline exponentially with soil moisture. Total runoff is calculated as water in excess of field capacity from the soil layers and water percolating through the second soil layer. The current version of LPJmL has no explicit representation of groundwater recharge, but a groundwater scheme is under development. The daily (subsurface) runoff includes the renewable fraction of groundwater, but without any time delay.

All runoff is routed daily through a gridded river network, representing a system of rivers, natural lakes and reservoirs, using a simple routing algorithm ([[Rost et al., 2008]]). Local runoff is added to surface water storage in the cell, and subsequently flows downstream at a constant flow velocity of 1 m s-1 until reaching a lake or reservoir. Water accumulates in lakes and reservoirs, and outflow depends on actual storage relative to the maximum storage capacity (for lakes) and the operational purpose of the reservoir ([[Biemans et al., 2011]]). For man-made reservoirs, see further below ([[Biemans et al., 2011]])

===Supply and demand for irrigation water===
Water availability and demand in agriculture is simulated with LPJmL’s irrigation module and an algorithm to simulate the operation of large reservoirs to supply water to irrigated areas ([[Biemans et al., 2013]]).

The irrigation demand module (Figure Flowchart) is described in detail by Rost et al. ([[Rost et al., 2008|2008]]). Crop net irrigation demand is defined as the minimum atmospheric evaporative demand and the amount of water needed to fill the soil to field capacity. The irrigation withdrawal demand – the gross demand –is higher than the net water demand due to losses through evaporation, interception and conveyance. Thus, the quantity of water demanded by crops (water consumption) is always less than the quantity withdrawn (water use). This gross demand is calculated as the product of the crop irrigation demand and an irrigation efficiency that depends on the irrigation system (sprinker, surface or drip) that is allocated per country, as well as local soil characteristics ([[Jägermeyr et al., 2015]]).

Irrigation water is extracted from the rivers and lakes in the grid cell or a neighbouring grid cell. If these local surface water sources cannot meet the total demand, water is extracted from nearby reservoirs, if available. Finally, there is an option to supply from an unlimited source that can be interpreted as non-sustainable groundwater extraction or water imported from another basin. By excluding these water sources in a series of model runs, irrigation water supply and crop production can be attributed to different water sources.

===Large reservoirs===
Some 50% of global river systems are regulated by dams, most of which are in basins where there is irrigation and economic activity ([[Nilsson et al., 2005]]). The main purpose of approximately one-third of all large reservoirs is irrigation. Thus, in estimating agricultural water use, man-made reservoirs have to be taken into account.
The reservoir operation module in LPJmL ([[Biemans et al., 2011]]) distinguishes three types of reservoirs: reservoirs used primarily for irrigation; reservoirs used primarily for other purposes (e.g., hydropower and flood control) but also for irrigation; and reservoirs not used for irrigation. Each type of reservoir is managed differently. The outflow of irrigation reservoirs follows the temporal pattern of irrigation demand, whereas the other reservoirs are intended to release equal quantities of water throughout the year. Water from irrigation reservoirs is supplied to downstream irrigated areas.

===Water demand in other sectors===
Water demands for electricity, industries and households are calculated in <nowiki>[[TIMER model version overview| TIMER]]</nowiki>, following the method developed in [[Bijl et al., 2016]]. All three demands are calculated in separate sub-models, and are the product of an activity level, the water intensity of that activity, and efficiency factors. Future demands are based on historical water use obtained from AQUASTAT, and for the industry sector partly on the WATERGAP model ([[Florke et al., 2013]]). For electricity, the cooling water demands depend on the waste heat of power plants, and therefore on the fuel and power plant type determined in the [[Energy supply]] module. It also depends on the cooling type and efficiency.

Once-through cooling systems use large volumes of surface water that are returned almost entirely to the water body from which they were extracted, albeit at an elevated temperature. Wet cooling towers exploit the evaporation heat capacity of water and, thus require much lower water volumes. However, a significant part of the cooling water evaporates during the process and does not return to the original water body. In some regions, cooling ponds are used, where cooling water is pumped and recycled in a closed loop, with water demand somewhere between the once-through and wet tower cooling systems. Finally, dry cooling systems are deployed that use air as a coolant and thus do not require cooling water. The water demands are based on [[Macknick et al., 2011]], and transformed to water demand per unit of excess heat. Municipal and industrial water demands are functions of population size and Industry Value Added (IVA), respectively. They are influenced by GDP per capita and assumptions on efficiency improvements.

All non-agricultural water demands are calculated at the regional scale on a yearly timestep and subsequently downscaled to 0.5x0.5 degree grid cells using spatial explicit population data.

===Water extractions===
Water requirements in other sectors are extracted from local surface water, if available (rather than from reservoirs). Meeting the demand from these sectors receives priority over water withdrawal for irrigation.
</div>{{DisplayFigureTemplate|Baseline figure Water}}

=== Environmental flow requirements ===
Environmental Flow Requirements (EFRs) are defined as the quantity, timing and quality of water flows required to sustain freshwater and estuarine ecosystems ([[IRF, 2007]]). While these are difficult to quantify on a global scale, not including them at all can lead to an overestimation of available resources. Several global studies have therefore applied a proxy value, through means of a minimum discharge requirement ([[Gerten et al., 2013]]; [[Richter et al., 2011]]; [[Pastor et al., 2019]]; [[Jägermeyr et al., 2017]]). In IMAGE, EFRs are implemented following the methodology as developed by [[Pastor et al., 2014]]. This method is based on maintaining a percentage of the ‘pristine’ flow, which is the discharge without recent (i.e., less than 50 years) land-use developments and without withdrawals for irrigation and other sectors. <div class="page_standard">
===Impact indicators===

Water stress is often presented as the withdrawal-to-availability ratio at basin or country level. The population living with water stress is estimated by overlaying such a water-stress (or water availability) map with a population density map. These indicators are used to present IMAGE-LPJmL results (for instance, in the [[OECD Environmental Outlook to 2050 (2012) project|OECD Environmental Outlook]], see Figure) but they mask the potential occurrence of water shortages in the short-term or on sub-basin scale. Thus, water stress should also be calculated at higher spatial and temporal resolutions, as can principally be done with LPJmL (see [[Biemans, 2012]]).

Other water related impacts that can be addressed with IMAGE are impacts of water shortage on yields, and the impact of climate on aridity (which is the ratio between rainfall and potential evapotranspiration). Impacts of efficiency improvements in irrigation and rainwater management can be analyzed through their effects on these indicators, see for example also [[Jägermeyr et al., 2017]].
</div>

Water

2021-11-19T16:02:05Z

Doelmanj: minor textual corrections

{{ComponentTemplate2
|Application=OECD Environmental Outlook to 2050 (2012) project
|IMAGEComponent=Drivers;Agriculture and land use;Carbon, vegetation, agriculture and water;Carbon cycle and natural vegetation;Crops and grass;Human development;Energy demand;Land cover and land use;Land-use allocation;Livestock systems
|KeyReference=Gerten et al., 2004;Biemans et al., 2011;Biemans, 2012;Schaphoff et al., 2018a;Bijl et al., 2016;Bijl et al., 2018a;Jägermeyr et al., 2015;De Vos et al., 2021
|Reference=OECD, 2012;Portmann et al., 2010;Fischer et al., 2005;Molden, 2007;FAO, 2011a;OECD, 2012
|InputVar=Land cover, land use - grid;Temperature - grid;Precipitation - grid;Crop irrigation water demand - grid;Crop irrigation water demand - grid;Irrigation system
|Parameter=Soil properties - grid;Digital water network - grid;LOD (location of dams and reservoirs);Water demand other sectors - grid
|OutputVar=River discharge - grid;Water withdrawal other sectors - grid;Irrigation water withdrawal - grid;Irrigation water consumption - grid;Water stress - basin;Number of people at risk of severe water stress - grid;Water consumption other sectors - grid;Environmental flow requirements - grid;Transgression of environmental flows - grid
|ComponentCode=H
|AggregatedComponent=Carbon, vegetation, agriculture and water
|FrameworkElementType=state component
}}
<div class="page_standard">

Water availability is essential for natural vegetation and agricultural production, human settlements and industry. Around one third of the world’s population lives in countries suffering from medium to high water stress ([[OECD, 2012]]). This number is expected to increase as the water demand will increase due to the population growth, and as water availability may decrease due to global warming.

Today, agriculture accounts for 70% of the total global water withdrawals. Around one third of the total global crop production is irrigated, occupying 17% of croplands (e.g. [[Portmann et al., 2010]]). Irrigated agriculture is expected to increase further to meet the growing demand for food ([[Fischer et al., 2005]]; [[Molden, 2007]]; [[FAO, 2011a]]). Moreover, water demand in other sectors (domestic, electricity, manufacturing) is projected to increase substantially in the coming decades ([[OECD, 2012]]). As a result, competition between water users will increase and the resulting water shortages may affect future food production ([[Pastor et al., 2019]]; [[De Vos et al., 2021]]).

Although the global quantity of freshwater is more than sufficient to meet all human needs, uneven distribution makes water a scarce resource in some regions and watersheds. Furthermore, climate change will lead to changes in precipitation patterns, thus altering future water availability and adding to water stress in areas where precipitation levels are expected to decline.

To identify current and future areas of water stress, IMAGE includes a hydrology model that calculates water availability and demand. The hydrological module of LPJmL is fully integrated with the terrestrial carbon and land-use dynamics of LPJmL and the rest of IMAGE and dynamically calculates agricultural water demand as well as water availability and withdrawals. Availability of renewable water is the net result of precipitation, interception loss and evapotranspiration by plants and soils. In the model, the surplus in each grid cell flows to neighbouring grid cells in a watershed by means of a river routing scheme. River flows are modified by dams and reservoirs used for irrigation and hydropower production.

The effects of water stress on crop production can be quantified, and by including the feedback of water-limited crop production on land allocation, IMAGE can produce more realistic scenarios for cropland expansion and agricultural intensification. IMAGE and LPJmL are dynamically linked (see [[Carbon, vegetation, agriculture and water]]), and thus IMAGE scenarios include an integrated assessment of the water cycle and can be used to assess water availability and demand at high spatial (0.5x0.5 degree grid cells) and temporal (daily) resolutions.

{{InputOutputParameterTemplate}}
</div>

Carbon, vegetation, agriculture and water

2021-11-03T12:59:23Z

Doelmanj:

{{AggregatedComponentTemplate
|ComponentCode=VHA
|KeyReference=Sitch et al., 2003; Gerten et al., 2004; Bondeau et al., 2007;
|FrameworkElementType=state component
}}
<div class="page_standard">
==Description of {{ROOTPAGENAME}}==
[[LPJmL model|LPJmL]] is the carbon, vegetation, agricultural and hydrology model in IMAGE 3.0 and consists of the three components: [[Carbon cycle and natural vegetation]], [[Crops and grass]], [[Water]].

Within the Earth system, the terrestrial biosphere is the component that bears the most visible impact of human activity. Large proportions of the land surface and the terrestrial vegetation have been converted for human use, for instance, to cropland and urban areas.

Agriculture, terrestrial carbon, water and nutrient cycles were separate modules in previous versions of IMAGE and thus interactions were not adequately covered. IMAGE 3.2 covers natural and agricultural terrestrial ecosystems, and associated carbon and water dynamics via the link with the dynamic global vegetation, agriculture and water balance model [[LPJmL model|LPJmL]] (Lund-Potsdam-Jena model with managed Land; [[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]]; [[Schaphoff et al., 2018a]]; [[Schaphoff et al., 2018b]]). This enables more detailed and process-based representation of the interacting dynamics in vegetation, carbon and agricultural production, and extends the model scope to terrestrial freshwater dynamics.

LPJmL is one of the most extensively evaluated dynamic global vegetation models ({{abbrTemplate|DGVM}}) and is widely applied either separately or linked to other models. To show the complex dynamics in the terrestrial biosphere and to reflect the historical IMAGE modules, LPJmL is described in three components: [[Carbon cycle and natural vegetation|carbon cycle and vegetation]]; [[Crops and grass|agricultural land use]]; and [[Water|terrestrial freshwater flows]].

IMAGE 3.2 and LPJmL are linked through an interface that enables close and consistent interaction between the two models in annual time steps ([[Müller et al., 2016a]]). An even more direct link to simulate detailed land-atmosphere interaction would require higher temporal resolutions also in other IMAGE components (e.g., the climate model), which is not necessarily congruent with the philosophy of an integrated assessment model. Incorporating nutrient cycles and improving representations of grassland management in LPJmL will require further adjustments to other IMAGE 3.2 components, and will increase consistency.

The dynamic coupling between IMAGE and LPJmL makes it the standard approach to always take impacts of a changing climate into account: most importantly the effects on crop yields, natural vegetation and water dynamics from changes in temperature, precipitation and CO<sub>2</sub> concentrations. The CO<sub>2</sub> fertilisation effect on crop yields in LPJml is found to be relatively optimistic compared to other crop models, partly because other processes negatively affect yields such as nutrient limitations, and the effects of drought and extreme weather events are not accounted for (TO ADD: Toreti et al., 2020). Therefore, IMAGE assumes a 50% efficacy of the default CO<sub>2</sub> fertilisation in LPJmL.
</div>

Carbon, vegetation, agriculture and water

2021-11-03T12:59:02Z

Doelmanj:

{{AggregatedComponentTemplate
|ComponentCode=VHA
|KeyReference=Sitch et al., 2003; Gerten et al., 2004; Bondeau et al., 2007;
|FrameworkElementType=state component
}}
<div class="page_standard">
==Description of {{ROOTPAGENAME}}==
[[LPJmL model|LPJmL]] is the carbon, vegetation, agricultural and hydrology model in IMAGE 3.0 and consists of the three components: [[Carbon cycle and natural vegetation]], [[Crops and grass]], [[Water]].

Within the Earth system, the terrestrial biosphere is the component that bears the most visible impact of human activity. Large proportions of the land surface and the terrestrial vegetation have been converted for human use, for instance, to cropland and urban areas.

Agriculture, terrestrial carbon, water and nutrient cycles were separate modules in previous versions of IMAGE and thus interactions were not adequately covered. IMAGE 3.2 covers natural and agricultural terrestrial ecosystems, and associated carbon and water dynamics via the link with the dynamic global vegetation, agriculture and water balance model [[LPJmL model|LPJmL]] (Lund-Potsdam-Jena model with managed Land; [[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]]; [[Schaphoff et al., 2018a]]; [[Schaphoff et al., 2018b]]). This enables more detailed and process-based representation of the interacting dynamics in vegetation, carbon and agricultural production, and extends the model scope to terrestrial freshwater dynamics.

LPJmL is one of the most extensively evaluated dynamic global vegetation models ({{abbrTemplate|DGVM}}) and is widely applied either separately or linked to other models. To show the complex dynamics in the terrestrial biosphere and to reflect the historical IMAGE modules, LPJmL is described in three components: [[Carbon cycle and natural vegetation|carbon cycle and vegetation]]; [[Crops and grass|agricultural land use]]; and [[Water|terrestrial freshwater flows]].

IMAGE 3.2 and LPJmL are linked through an interface that enables close and consistent interaction between the two models in annual time steps ([[Müller et al., 2016a]]). An even more direct link to simulate detailed land-atmosphere interaction would require higher temporal resolutions also in other IMAGE components (e.g., the climate model), which is not necessarily congruent with the philosophy of an integrated assessment model. Incorporating nutrient cycles and improving representations of grassland management in LPJmL will require further adjustments to other IMAGE 3.2 components, and will increase consistency.
The dynamic coupling between IMAGE and LPJmL makes it the standard approach to always take impacts of a changing climate into account: most importantly the effects on crop yields, natural vegetation and water dynamics from changes in temperature, precipitation and CO<sub>2</sub> concentrations. The CO<sub>2</sub> fertilisation effect on crop yields in LPJml is found to be relatively optimistic compared to other crop models, partly because other processes negatively affect yields such as nutrient limitations, and the effects of drought and extreme weather events are not accounted for (Toreti et al., 2020). Therefore, IMAGE assumes a 50% efficacy of the default CO<sub>2</sub> fertilisation in LPJmL.
</div>

Carbon, vegetation, agriculture and water

2021-11-03T12:58:42Z

Doelmanj:

{{AggregatedComponentTemplate
|ComponentCode=VHA
|KeyReference=Sitch et al., 2003; Gerten et al., 2004; Bondeau et al., 2007;
|FrameworkElementType=state component
}}
<div class="page_standard">
==Description of {{ROOTPAGENAME}}==
[[LPJmL model|LPJmL]] is the carbon, vegetation, agricultural and hydrology model in IMAGE 3.0 and consists of the three components: [[Carbon cycle and natural vegetation]], [[Crops and grass]], [[Water]].

Within the Earth system, the terrestrial biosphere is the component that bears the most visible impact of human activity. Large proportions of the land surface and the terrestrial vegetation have been converted for human use, for instance, to cropland and urban areas.

Agriculture, terrestrial carbon, water and nutrient cycles were separate modules in previous versions of IMAGE and thus interactions were not adequately covered. IMAGE 3.2 covers natural and agricultural terrestrial ecosystems, and associated carbon and water dynamics via the link with the dynamic global vegetation, agriculture and water balance model [[LPJmL model|LPJmL]] (Lund-Potsdam-Jena model with managed Land; [[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]]; [[Schaphoff et al., 2018a]]; [[Schaphoff et al., 2018b]]). This enables more detailed and process-based representation of the interacting dynamics in vegetation, carbon and agricultural production, and extends the model scope to terrestrial freshwater dynamics.

LPJmL is one of the most extensively evaluated dynamic global vegetation models ({{abbrTemplate|DGVM}}) and is widely applied either separately or linked to other models. To show the complex dynamics in the terrestrial biosphere and to reflect the historical IMAGE modules, LPJmL is described in three components: [[Carbon cycle and natural vegetation|carbon cycle and vegetation]]; [[Crops and grass|agricultural land use]]; and [[Water|terrestrial freshwater flows]].

IMAGE 3.2 and LPJmL are linked through an interface that enables close and consistent interaction between the two models in annual time steps ([[Müller et al., 2016a]]). An even more direct link to simulate detailed land-atmosphere interaction would require higher temporal resolutions also in other IMAGE components (e.g., the climate model), which is not necessarily congruent with the philosophy of an integrated assessment model. Incorporating nutrient cycles and improving representations of grassland management in LPJmL will require further adjustments to other IMAGE 3.2 components, and will increase consistency.

The dynamic coupling between IMAGE and LPJmL makes it the standard approach to always take impacts of a changing climate into account: most importantly the effects on crop yields, natural vegetation and water dynamics from changes in temperature, precipitation and CO<sub>2</sub> concentrations. The CO<sub>2</sub> fertilisation effect on crop yields in LPJml is found to be relatively optimistic compared to other crop models, partly because other processes negatively affect yields such as nutrient limitations, and the effects of drought and extreme weather events are not accounted for (Toreti et al., 2020). Therefore, IMAGE assumes a 50% efficacy of the default CO<sub>2</sub> fertilisation in LPJmL.
</div>

Emissions/Description

2021-11-02T20:29:58Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=IPCC, 2006; Cofala et al., 2002; Stern, 2003; Smith et al., 2005; Van Ruijven et al., 2008; Carson, 2010; Smith et al., 2011; Bouwman et al., 1993; Velders et al., 2009; Kreileman and Bouwman, 1994; Bouwman et al., 1997; Bouwman et al., 2002a; Velders et al., 2009; Harnisch et al., 2009; Braspenning Radu et al., 2016; Velders et al., 2015; Lucas et al., 2007; Harmsen et al., 2019c; EC-JRC/PBL, 2016; Hoesly et al., 2018; Rao et al., 2016; Rao et al., 2017;
}}
<div class="page_standard">

===General approaches===
Air pollution emission sources included in IMAGE are listed in [[Emission table]], and emissions transported in water (nitrate, phosphorus) are discussed in Component [[Nutrients]]. In approach and spatial detail, gaseous emissions are represented in IMAGE in four ways:

1) ''World number (W)''

:The simplest way to estimate emissions in IMAGE is to use global estimates from the literature. This approach is used for natural sources that cannot be modelled explicitly ([[Emission table]]).

2) ''Emission factor (EF)''

:Past and future developments in anthropogenic emissions are estimated on the basis of projected changes in activity and emissions per unit of activity (Figure Flowchart).

:The equation for this emission factor approach is:
::<math>Emission_{r,i,t} = Activity_{r,i,t} * EFbase_{r,i,t} * AF_{r,i,t}</math> (Equation 1)
:where:
:* <math>Emission</math> is the emission of the specific gas or aerosol;[[Amann et al., 2011]]
:* <math>Activity</math> is the energy input or agricultural activity; r is the index for region;
:* <math>i</math> is the index for further specification (sector, energy carrier);
:* ''t'' is the index for time (years). All factors are time-dependent;
:* <math>EFbase</math> is the emission factor in the baseline;
:* <math>AF</math> is the abatement factor (reduction in the baseline emission factor as a result of climate policy).
:
:
:Following Equation 1, there is a direct relationship between level of economic activity and emission level. Shifts in economic activity (e.g., use of natural gas instead of coal) may influence total emissions. These activities are calculated in various sub-modules of the model (e.g. related to energy supply and demand, food production and industry). Finally, emissions can change as a result of changes in emission factors (EF) and climate policy (AF). Emission factors indicate the emission rate of an air pollutant per activity level and differences in time represent technological changes in air pollution control. This information is generally obtained from specialized research groups and databases, such as GAINS ([[Amann et al., 2011]]) , EDGAR ([[EC-JRC/PBL, 2016]]) and CEDS ([[Hoesly et al., 2018]]). Abatement factors are applied to non-CO<sub>2</sub> greenhouse gases (methane (CH<sub>4</sub>), nitrous oxide (N<sub>2</sub>O) and fluorinated gases (F-gases: HFCs, PFCs and SF<sub>6</sub>)) and are determined in the climate policy model FAIR (see Component [[Climate policy]]).
:The emission factor approach has some limitations, the most important of which is not capturing the consequences of specific emission control technologies (or management action) for multiple species, either synergies or trade-offs.
:
3) ''Gridded emission factor with spatial distribution (GEF)''

:GEF is a special case of the EF method, where a proxy distribution is used to present gridded emissions. This is done for a number of sources, such as emissions from livestock ([[Emission table]]).

4) ''Gridded process model (GPM)''
:Land-use related emissions of NH<sub>3</sub>, N<sub>2</sub>O and NO are calculated with grid-specific models (Figure Flowchart). The models included in IMAGE are simple regression models that generate an emission factor (Figure Flowchart). For comparison with other models, IMAGE also includes the N<sub>2</sub>O methodology generally proposed by {{abbrTemplate|IPCC}} ([[IPCC, 2006]]).

The approaches used to calculate emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below.

===Emissions from energy production and use===
Emission factors ([[#General approaches|Equation 1]]) are used for estimating emissions from the energy-related sources ([[Emission table]]). In general, the Tier 1 approach from IPCC guidelines ([[IPCC, 2006]]) is used. In the energy system, emissions are calculated by multiplying energy use fluxes by time-dependent emission factors. Changes in emission factors represent, for example, technology improvements and end-of-pipe control techniques, fuel emission standards for transport, and clean-coal technologies in industry.

The emission factors for the historical period for the energy system and industrial processes are calibrated with the EDGAR emission model described by Braspenning Radu et al. ([[Braspenning Radu et al., 2016]]). Calibration to the EDGAR database is not always straightforward because of differences in aggregation level. The general rule is to use weighted average emission factors for aggregation. However, where this results in incomprehensible emission factors (in particular, large differences between the emission factors for the underlying technologies), specific emission factors were chosen.

Future emission factors are based on the following rules:

* Emission factors can follow an exogenous scenario, which can be based on the storyline of the scenario. In some cases, exogenous emission factor scenarios are used, such as those developed by GAINS/IIASA ([[Amann et al., 2011]]). These represent the policies in different regions for the 2000–2030 and 2030-2100 periods. Future emission factors for air pollutants have been set in accordance with the SSP storylines ([[Rao et al., 2016]]).

* Alternatively, emission factors can be derived from generic rules, one of which in IMAGE is the {{abbrTemplate|EKC}}: Environmental Kuznets Curve ([[Stern, 2003]]; [[Smith et al., 2005]]; [[Van Ruijven et al., 2008]]; [[Carson, 2010]]; [[Smith et al., 2011]]). EKC suggests that starting from low-income levels, per-capita emissions will increase with increasing per-capita income and will peak at some point and then decline. The last is driven by increasingly stringent environmental policies, and by shifts within sectors to industries with lower emissions and improved technology. Although such shifts do not necessarily lead to lower absolute emissions, average emissions per unit of energy use decline. See below, for further discussion of EKC.

* Combinations of the methods described above for a specific period, followed by additional rules based on income levels.

In IMAGE, {{abbrTemplate|EKC}} is used as an empirically observed trend, as it offers a coherent framework to describe overall trends in emissions in an Integrated Assessment context. However , it is accepted that many driving forces other than income influence future emissions. For instance, more densely populated regions are likely to have more stringent air quality standards. Moreover, technologies developed in high-income regions often tend to spread within a few years to developing regions. The generic equations in IMAGE can capture this by decreasing the threshold values over time. For CO<sub>2</sub> and other greenhouse gases, such as halogenated gases for which there is no evidence of {{abbrTemplate|EKC}} behaviour, IMAGE uses an explicit description of fuel use and deforestation.

The methodology for EKC scenario development applied in the energy model is based on two types of variables: income thresholds (2–3 steps); and gas- and sector-dependent reduction targets for these income levels. The income thresholds are set to historical points: the average {{abbrTemplate|OECD}} income at which air pollution control policies were introduced in these countries; and current income level in OECD countries. The model assumes that emission factors will start to decline in developing countries, when they reach the first income threshold, reflecting more efficient and cleaner technology. It also assumes that when developing countries reach the second income threshold, the emission factors will be equal to the average level in OECD regions. Beyond this income level, the model assumes further reductions, slowly converging to the minimum emission factor in OECD regions by 2030, according to projections made by {{abbrTemplate|IIASA}} under current legislation (current abatement plans). The IMAGE rules act at the level of regions, this could be seen as a limitation, but as international agreements lead countries to act as a group, this may not be an important limitation.

===Emissions from industrial processes===
For the industry sector, the energy model includes three categories:

# Cement and steel production. IMAGE-TIMER includes detailed demand models for these commodities (Component [[Energy supply and demand]]). Similar to those from energy use, emissions are calculated by multiplying the activity levels to exogenously set emission factors.
# Other industrial activities. Activity levels are formulated as a regional function of industry value added, and include copper production and production of solvents. Emissions are also calculated by multiplying the activity levels by the emission factors.
# For halogenated gases, the approach used was developed by Harnisch et al. ([[Harnisch et al., 2009|2009]]), which derived relationships with income for the main uses of halogenated gases (HFCs, PFCs, SF<sub>6</sub>). In the actual use of the model, slightly updated parameters are used to better represent the projections as presented by Velders et al. ([[Velders et al., 2009|2009]], [[Velders et al., 2015|2015]]). The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. ([[Harnisch et al., 2009|2009]]).

===Land-use related emissions===
CO<sub>2</sub> exchanges between terrestrial ecosystems and the atmosphere computed by the LPJ model are described in [[Carbon cycle and natural vegetation]]. The land-use emissions model focuses on emissions of other compounds, including greenhouse gases (CH<sub>4</sub>, N<sub>2</sub>O), ozone precursors (NO<sub>x</sub>, CO, NMVOC), acidifying compounds (SO<sub>2</sub>, NH<sub>3</sub>) and aerosols (SO<sub>2</sub>, NO<sub>3</sub>, BC, OC).

For many sources, the emission factor ([[#General approaches|Equation 1]]) is used ([[Emission table]]). Most emission factors for anthropogenic sources are from the [[EDGAR database]], with time-dependent values for historical years. In the scenario period, most emission factors are constant, except for explicit climate abatement policies (see below).

There are some other exceptions: Various land-use related gaseous nitrogen emissions are modelled in grid-specific models (see further), and in several other cases, emission factors depend on the assumptions described in other parts of IMAGE. For example, enteric fermentation CH<sub>4</sub> emissions from non-dairy and dairy cattle are calculated on the basis of energy requirement and feed type (see Component [[Livestock systems]]). High-quality feed, such as concentrates from feed crops, have a lower CH<sub>4</sub> emission factor than feed with a lower protein level and a higher content of components of lower digestibility. This implies that when feed conversion ratios change, the level of CH<sub>4</sub> emissions will automatically change. Pigs, and sheep and goats have IPCC 2006 emission factors, which depend on the level of development of the countries. In IMAGE, agricultural productivity is used as a proxy for the development. For sheep and goats, the level of development is taken from EDGAR.

Constant emission factors may lead to decreasing emissions per unit of product, for example, when the emission factor is specified on a per-head basis. An increasing production per head may lead to a decrease in emissions per unit of product. For example, the CH<sub>4</sub> emission level for animal waste is a constant per animal, which leads to a decrease in emissions per unit of meat or milk when production per animal increases.

A special case is N<sub>2</sub>O emissions after forest clearing. After deforestation, litter remaining on the soil surface as well as root material and soil organic matter decompose in the first years after clearing, which may lead to pulses of N<sub>2</sub>O emissions. To mimic this effect, emissions in the first year after clearing are assumed to be five times the flux in the original ecosystem. Emissions decrease linearly to the level of the new ecosystem in the tenth year, usually below the flux in the original forest. For more details, see Kreileman and Bouwman ([[Kreileman and Bouwman, 1994|1994]]).

Land-use related emissions of NH<sub>3</sub>, N<sub>2</sub>O and NO are calculated withgrid-specific models.N<sub>2</sub>O from soils under natural vegetation is calculated with the model developed by Bouwman et al. (1993). This regression model is based on temperature, a proxy for soil carbon input, soil water and oxygen status, and for net primary production. Ammonia emissions from natural vegetation are calculated from net primary production, C:N ratio and an emission factor. The model accounts for in-canopy retention of the emitted NH<sub>3</sub> ([[Bouwman et al., 1997]]).

For N<sub>2</sub>O emissions from agriculture, the determining factors in IMAGE are N application rate, climate type, soil organic carbon content, soil texture, drainage, soil pH, crop type, and fertiliser type. The main factors used to calculate NO emissions include N application rate per fertiliser type, and soil organic carbon content and soil drainage (for detailed description, see Bouwman et al. ([[Bouwman et al., 2002a|2002a]]). For NH<sub>3</sub> emissions from fertilised cropland and grassland, the factors used in IMAGE are crop type, fertiliser application rate per type and application mode, temperature, soil pH, and CEC ([[Bouwman et al., 2002a]]).

For comparison with other models, IMAGE also includes the N<sub>2</sub>O methodology proposed by IPCC ([[IPCC, 2006|2006]]). This methodology represents only anthropogenic emissions. For emissions from fertilizer fields this is the emission from a fertilized plot minus that from a control plot with zero fertilizer application. For this reason, soil emissions calculated with this methodology cannot be compared with the above model approaches, which yields total N<sub>2</sub>O emissions.

'''Peatland emissions'''

In IMAGE 3.2 emissions from peatlands are added. To estimate these emissions, a map of peat soils from S-world (Stoorvogel et al., 2017) are combined with IMAGE agricultural land locations. IPCC emissions factors (TO ADD: IPCC 2013) for boreal, temperate and tropical degraded peatlands are used to estimate emissions from agriculture on peatlands. Different factors are included for rice paddies, annually harvested croplands, plantations and pastures.

===Emission abatement===
Emissions from energy, industry, agriculture, waste and land-use sources are also expected to vary in future years, as a result of climate policy. This is described using abatement coefficients, the values of which depend on the scenario assumptions and the stringency of climate policy described in the climate policy component. In scenarios with climate change or sustainability as the key feature in the storyline, abatement is more important than in business-as-usual scenarios. Abatement factors are used for CH<sub>4</sub> emissions from fossil fuel production and transport, N<sub>2</sub>O emissions from transport, CH<sub>4</sub> emissions from enteric fermentation and animal waste, and N<sub>2</sub>O emissions from animal waste according to the IPCC method. These abatement files are calculated in the IMAGE climate policy sub-model FAIR (Component [[Climate policy]]) by comparing the costs of non-CO<sub>2</sub> abatement in agriculture and other mitigation options.
</div>

Livestock systems/Description

2021-11-02T20:03:35Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Seré and Steinfeld, 1996;
}}<div class="page_standard">

===Livestock production===
IMAGE distinguishes two livestock production systems for ruminants (i.e. beef, dairy cattle, sheep and goats), namely pastoral systems, and mixed and industrial systems, based on FAO ([[Seré and Steinfeld, 1996]]). Pastoral systems are mostly dominated by extensive ruminant production, while mixed and industrial systems are more intensive. The distribution of livestock production in the two systems is constructed from historical data for the years up to the present, and for future years will depend on the scenario selected.

For the main monogastric sectors pigs and poultry IMAGE distinguishes three livestock systems: for pigs these are backyard, intermediate and intensive systems, and for poultry these are backyard, broiler and laying hens systems (TO ADD: Lassaletta et al., 2019).

===Livestock===
IMAGE distinguishes five types of livestock: beef, dairy cattle (large ruminants), the category sheep & goats (small ruminants), pigs, and poultry (monogastrics). The numbers of animals and the proportion per production system are calculated from data on domestic livestock production per region provided by the agro-economic model MAGNET ([[Agricultural economy]]). The number of animals in each of the five livestock types is calculated from the total production per region and the characteristics of the livestock systems in that region.
Stocks of dairy cows (POP) per country and world region are obtained from total milk production (PROD) and milk production per animal (MPH).
<math> POP = PROD / MPH </math>

Animal stocks per region of beef cattle, pigs, and sheep and goats are obtained from production and carcass weight (CW) and off-take rate (OR):
<math>POP = PROD/(OR*CW)</math>

Historical data on milk production per cow, off-take rate, and carcass weight are obtained from statistics, and values for future years will depend on the scenario selected.

===Energy requirements===
For dairy cattle, the energy requirements are calculated for maintenance (based on body weight), feeding (based on the proportion of grass in feed rations), lactation (based on milk production per cow) and pregnancy (based on the number of calves per year). The amount of feed dry matter is calculated on the basis of the proportion of digestible energy in the total energy intake, and the energy content of biomass.

Energy requirements for cattle, pigs and poultry are based on animal activity and production. For sheep and goats Feed Conversion Ratios (FCR) are used. This is the amount of feed (kg dry matter) required to produce one kilogram of milk or meat. The {{AbbrTemplate|FCR}} values are based on historical data and values for future years will depend on the scenario selected.

===Cropland and grassland required===
Areas for feed crop production and grass are calculated on the basis of feed crop and grass requirements ([[Land-use allocation]]), which are calculated from total feed requirement and diet composition (feed rations, see below).
Composition of animal feed
IMAGE distinguishes five feed categories:
#grass, including hay and grass silage;
#food crops and processing by-products;
#crop residues in the field after harvesting, and fodder crops;
#animal products;
#foraging including roadside grazing, scavenging household waste, and feedstuffs from backyard farming.

In pastoral ruminant production systems, the feed is almost entirely grass except in developing regions where foraging constitutes a larger but variable proportion of the total feed. Pigs and poultry are fed feed crops and by-products, crop residues and fodder. Since these animals are mainly farmed in mixed systems, the contribution of feed crops and residues to the total feed in these systems is much higher than in pastoral systems.

The required feed crop production per animal is calculated from feed rations, and this information is incorporated into the agro-economic model ([[Agricultural economy]]). The proportion of grass in feed rations determines total grass consumption. The amount of grassland area required, and the corresponding grazing intensity are based on the[[Agricultural economy]] module.

===Scenario definition===
A scenario includes assumptions on milk production per animal for dairy cattle, carcass weight and off-take rate for beef cattle, pigs, poultry, sheep and goats, and feed conversion rates ({{abbrTemplate|FCR}}) for pigs, poultry, sheep and goats. The changes in these parameters are generally based on the scenario, and on the economic growth scenario.
</div>

Livestock systems/Description

2021-11-02T19:56:32Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Seré and Steinfeld, 1996;
}}<div class="page_standard">

===Livestock production===
IMAGE distinguishes two livestock production systems for ruminants (i.e. beef, dairy cattle, sheep and goats), namely pastoral systems, and mixed and industrial systems, based on FAO ([[Seré and Steinfeld, 1996]]). Pastoral systems are mostly dominated by extensive ruminant production, while mixed and industrial systems are more intensive. The distribution of livestock production in the two systems is constructed from historical data for the years up to the present, and for future years will depend on the scenario selected.

For the main monogastric sectors pigs and poultry IMAGE distinguishes three livestock systems:

===Livestock===
IMAGE distinguishes five types of livestock: beef, dairy cattle (large ruminants), the category sheep & goats (small ruminants), pigs, and poultry (monogastrics). The numbers of animals and the proportion per production system are calculated from data on domestic livestock production per region provided by the agro-economic model MAGNET ([[Agricultural economy]]). The number of animals in each of the five livestock types is calculated from the total production per region and the characteristics of the livestock systems in that region.
Stocks of dairy cows (POP) per country and world region are obtained from total milk production (PROD) and milk production per animal (MPH).
<math> POP = PROD / MPH </math>

Animal stocks per region of beef cattle, pigs, and sheep and goats are obtained from production and carcass weight (CW) and off-take rate (OR):
<math>POP = PROD/(OR*CW)</math>

Historical data on milk production per cow, off-take rate, and carcass weight are obtained from statistics, and values for future years will depend on the scenario selected.

===Energy requirements===
For dairy cattle, the energy requirements are calculated for maintenance (based on body weight), feeding (based on the proportion of grass in feed rations), lactation (based on milk production per cow) and pregnancy (based on the number of calves per year). The amount of feed dry matter is calculated on the basis of the proportion of digestible energy in the total energy intake, and the energy content of biomass.

Energy requirements for cattle are based on animal activity and production, and for pigs, poultry, sheep and goats on Feed Conversion Ratios (FCR). This is the amount of feed (kg dry matter) required to produce one kilogram of milk or meat. The {{AbbrTemplate|FCR}} values are based on historical data and values for future years will depend on the scenario selected.

===Cropland and grassland required===
Areas for feed crop production and grass are calculated on the basis of feed crop and grass requirements ([[Land-use allocation]]), which are calculated from total feed requirement and diet composition (feed rations, see below).
Composition of animal feed
IMAGE distinguishes five feed categories:
#grass, including hay and grass silage;
#food crops and processing by-products;
#crop residues in the field after harvesting, and fodder crops;
#animal products;
#foraging including roadside grazing, scavenging household waste, and feedstuffs from backyard farming.

In pastoral ruminant production systems, the feed is almost entirely grass except in developing regions where foraging constitutes a larger but variable proportion of the total feed. Pigs and poultry are fed feed crops and by-products, crop residues and fodder. Since these animals are mainly farmed in mixed systems, the contribution of feed crops and residues to the total feed in these systems is much higher than in pastoral systems.

The required feed crop production per animal is calculated from feed rations, and this information is incorporated into the agro-economic model ([[Agricultural economy]]). The proportion of grass in feed rations determines total grass consumption. The amount of grassland area required, and the corresponding grazing intensity are based on the[[Agricultural economy]] module.

===Scenario definition===
A scenario includes assumptions on milk production per animal for dairy cattle, carcass weight and off-take rate for beef cattle, pigs, poultry, sheep and goats, and feed conversion rates ({{abbrTemplate|FCR}}) for pigs, poultry, sheep and goats. The changes in these parameters are generally based on the scenario, and on the economic growth scenario.
</div>

Livestock systems/Description

2021-11-02T19:55:50Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Seré and Steinfeld, 1996;
}}<div class="page_standard">

===Livestock production===
IMAGE distinguishes two livestock production systems for ruminants (i.e. beef, dairy cattle, sheep and goats), namely pastoral systems, and mixed and industrial systems, based on FAO ([[Seré and Steinfeld, 1996]]). Pastoral systems are mostly dominated by extensive ruminant production, while mixed and industrial systems are more intensive. The distribution of livestock production in the two systems is constructed from historical data for the years up to the present, and for future years will depend on the scenario selected.

For the main monogastric sectors pigs and poultry IMAGE distinguishes three livestock systems:

===Livestock===
IMAGE distinguishes five types of livestock: beef, dairy cattle (large ruminants), the category sheep & goats (small ruminants), pigs, and poultry (monogastrics). The numbers of animals and the proportion per production system are calculated from data on domestic livestock production per region provided by the agro-economic model MAGNET ([[Agricultural economy]]). The number of animals in each of the five livestock types is calculated from the total production per region and the characteristics of the livestock systems in that region.
Stocks of dairy cows (POP) per country and world region are obtained from total milk production (PROD) and milk production per animal (MPH).
<math> POP = PROD / MPH </math>

Animal stocks per region of beef cattle, pigs, and sheep and goats are obtained from production and carcass weight (CW) and off-take rate (OR):
<math>POP = PROD/(OR*CW)</math>

Historical data on milk production per cow, off-take rate, and carcass weight are obtained from statistics, and values for future years will depend on the scenario selected.

===Energy requirements===
For dairy cattle, the energy requirements are calculated for maintenance (based on body weight), feeding (based on the proportion of grass in feed rations), lactation (based on milk production per cow) and pregnancy (based on the number of calves per year). The amount of feed dry matter is calculated on the basis of the proportion of digestible energy in the total energy intake, and the energy content of biomass.

Energy requirements for cattle are based on animal activity and production, and for pigs, poultry, sheep and goats on Feed Conversion Ratios (FCR). This is the amount of feed (kg dry matter) required to produce one kilogram of milk or meat. The {{AbbrTemplate|FCR}} values are based on historical data and values for future years will depend on the scenario selected.

===Cropland and grassland required===
Areas for feed crop production and grass are calculated on the basis of feed crop and grass requirements ([[Land-use allocation]]), which are calculated from total feed requirement and diet composition (feed rations, see below).
Composition of animal feed
IMAGE distinguishes five feed categories:
#grass, including hay and grass silage;
#food crops and processing by-products;
#crop residues in the field after harvesting, and fodder crops;
#animal products;
#foraging including roadside grazing, scavenging household waste, and feedstuffs from backyard farming.

In pastoral ruminant production systems, the feed is almost entirely grass except in developing regions where foraging constitutes a larger but variable proportion of the total feed. Pigs and poultry are fed feed crops and by-products, crop residues and fodder. Since these animals are mainly farmed in mixed systems, the contribution of feed crops and residues to the total feed in these systems is much higher than in pastoral systems.

The required feed crop production per animal is calculated from feed rations, and this information is incorporated into the agro-economic model ([[Agricultural economy]]). The proportion of grass in feed rations determines total grass consumption. The amount of grassland area required, and the corresponding grazing intensity are based on the[[Agricultural economy]] module.

===Scenario definition===
A scenario includes assumptions on milk production per animal for dairy cattle, carcass weight and off-take rate for beef cattle, pigs, poultry, sheep and goats, and feed conversion rates ({{abbrTemplate|FCR}}) for pigs, poultry, sheep and goats. The changes in these parameters are generally based on the scenario, and on the economic growth scenario.
</div>

Production system mix

2021-11-02T19:40:19Z

Doelmanj:

{{VariableTemplate
|Label=Production system mix for livestock
|Description=Livestock production is distributed over two systems for dairy and beef production (intensive: mixed and industrial; extensive: pastoral grazing), and to three systems for pigs (backyard, intermediate, intensive) and poultry (backyard, boilers, laying hens) with specific intensities, rations and feed conversion ratios.
|Dimension=time, region
|VariableType=driver
|DriverGroup=Technological change in agriculture and forestry
|BasedOn=Own estimates
|Reference=Bouwman et al., 2005; Lassaletta et al., 2019
}}

Livestock rations

2021-11-02T19:37:11Z

Doelmanj:

{{VariableTemplate
|Label=Livestock ration
|Description=Determines the feed requirements per feed type (food crops; crop residues; grass and fodder; animal products; scavenging), specified per animal type and production system (extensive/intensive/backyard/intermediate/intensive/broiler/laying hens).
|Dimension=time, region
|VariableType=driver
|DriverGroup=Technological change in agriculture and forestry
|BasedOn=Own estimates
|Reference=Bouwman et al., 2005; Lassaletta et al., 2019
}}

Livestock systems

2021-11-02T19:35:25Z

Doelmanj:

{{ComponentTemplate2
|Application=Roads from Rio+20 (2012) project; Global Environmental Outlook - GEO4 (2007) project; Millennium Ecosystem Assessment - MA (2005) project; OECD Environmental Outlook to 2030 (2008) project; OECD Environmental Outlook to 2050 (2012) project; Global Environmental Outlook - GEO3 (2002) project; EU Resource efficiency (2011) project
|IMAGEComponent=Drivers; Agricultural economy; Land-use allocation; Agriculture and land use; Atmospheric composition and climate; Crops and grass;
|Model-Database=MAGNET model;
|KeyReference=Bouwman et al., 2005;
|Reference=Bruinsma, 2003; Bouwman et al., 2006; Bouwman et al., 2005; Delgado et al., 1999; Seré and Steinfeld, 1996; FAO, 2012a;
|InputVar=Production system mix; Feed conversion; Livestock rations; Livestock production; Management intensity livestock; Animal productivity;
|OutputVar=Animal stocks; Feed crop requirement; Grass requirement;
}}<div class="page_standard">

Food production will have to increase in order to feed the world’s growing population. However, with increasing prosperity and falling production costs, dietary patterns are shifting to include a higher proportion of meat and milk. In the last few decades, traditional mixed farming systems have not been able to raise production levels sufficiently to meet increasing demand. Consequently, modern livestock production systems are expanding rapidly particularly for poultry and pork, creating growing demand for feed crops. This trend started in high-income countries and is now observed in emerging and developing countries ([[Alexandratos and Bruinsma, 2012]]) (TO ADD: FAO 2018).

Interactions between crop and livestock production are described in the livestock systems module of IMAGE, and also the consequences of changing practices in livestock farming for production of food crops and grass. For this purpose, IMAGE distinguishes pastoral livestock systems, and mixed and landless (industrial) production systems. Pastoral systems are based on grazing ruminants, while mixed and landless systems integrate crop and livestock production in which livestock are fed a mix of crops, crop by-products, grass, fodder and crop residues ([[Bouwman et al., 2005]]; [[Bouwman et al., 2006]]).

In IMAGE 3.2 new modules were added describing the pigs and poultry sectors (TO ADD: Lassaletta et al., 2019). In both sectors, three systems are distinguished: for pigs these are backyard, intermediate and intensive systems, and for poultry these are backyard, broiler and laying hens systems.

Livestock production is related to a wide range of the environmental issues, and the consequences of changes in the livestock system can be studied in the IMAGE framework:
#Expansion of grazing land and particularly arable land for feed crop production, is required to support increasing livestock numbers. According to Bouwman et al. (2005) most arable land expansion is to increase feed production;
#Large amounts of methane (CH<sub>4</sub>) emitted by ruminants during enteric fermentation are the second major source of greenhouse gas emissions after CO2;
#Excreta from all livestock categories is a source of ammonia, methane, nitrous oxide and nitric oxide;
#Odour nuisance and nitrate leaching to groundwater are major local-scale problems;
#A significant amount of land used for ruminants grazing is marginal, low productive grassland with low carrying capacity and high risk of degradation due to overgrazing, especially in arid and semi-arid regions ([[Seré and Steinfeld, 1996]]; [[Delgado et al., 1999]]). To compensate for productivity losses in these areas, forests may be cleared to expand agricultural land areas.
|ComponentCode=LS
|AggregatedComponent=Agriculture and land use
|FrameworkElementType=pressure component
|LeadText=What are the impacts on land-use, greenhouse gases and other emissions to air, land and surface water of increasing livestock production? And how may use of marginal lands for grazing increase the risk of degradation and loss of productivity, inducing more forest clearing.

{{InputOutputParameterTemplate}}
</div>

Land-use allocation/Description

2021-11-01T20:11:55Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Alexandratos and Bruinsma, 2012;Klein Goldewijk et al., 2010;O'Neill, 2013;Lambin et al., 2000;IIASA and FAO, 2012;Klein Goldewijk et al., 2011;Letourneau et al., 2012;Doelman et al., 2018;Hurtt et al., 2020;Van Vuuren et al., 2021;IUCN, 2015
}}
<div class="page_standard">

IMAGE 3.2 uses a regression-based suitability assessment to determine future land-use patterns. Land-use allocation is driven by regional crop and grassland production and their respective intensity levels, as calculated by the agro-economic model MAGNET ([[Agricultural economy]]). As the agro-economic model uses a different crop aggregation than IMAGE a specific mapping is used to convert seven MAGNET crop types to 16 IMAGE crop types (TO ADD: TABLE; see table xx). Agricultural land use is allocated to grid cells in an iterative process until the required regional production of crops and grass is met. Land use in IMAGE is modelled using dominant land use per grid cell on a 5 x 5 minute resolution, distinguishing extensive grasslands, agricultural and non-agricultural grid cells, and within agricultural land areas fractions of grass, seven rain-fed and seven irrigated crop types, and bioenergy crops.

In each time step, maps of actual crop yields are computed by combining the potential crop and grassland yields calculated by the crop model ([[Crops and grass]]), and the regional management intensity from the agro-economic model ([[Agricultural economy]]). Starting with the land-cover and land-use map of the previous time step, actual yields are used to determine crop and grassland production on current agricultural land. This is compared to the required regional crop and grassland production. If the demand exceeds calculated production, the agricultural area needs to be expanded at the cost of natural vegetation. If the calculated production of current cropland exceeds the required production, agricultural land is abandoned to adjust to the production required.

Crop and grassland is either abandoned or expanded until the required production is met. Since actual yields are taken into account, changes in crop yields in time due to technological change, climate change and land heterogeneity are included. If yields in the new agricultural areas are lower than average in the current area, relatively more agricultural land is required compared to the production increase.

In determining the location of agricultural expansion or abandonment, all grid cells are assessed and ranked using an empirically based suitability map. This map is developed using artificial neural network models that relate locations of agricultural land conversions, as recorded from 2003 to 2013, to various explanatory variables reflecting topography, climate, soil and accessibility ([[Cengic et al., 2020]]). Agricultural land conversion data is derived from the satellite-based ESA-CCI land cover database which provides land use transition data for the 1992-2018 period at 300 m resolution ([[ESA, 2017]]).

Additionally, a few other rules are applied in determining the location of new agricultural land. For instance, agricultural expansion is not permitted in protected areas, and in areas otherwise protected, such as in assumed {{abbrTemplate|REDD}} (reducing emissions from deforestation and degradation) schemes. A grid cell is only regarded suitable for agriculture if the potential rain-fed production is at least 10% of the global maximum attainable crop yield. Grid cells with a production potential between 0.01 and 10% of the maximum attainable are still assumed suitable for extensive grassland. In addition, an additional anthropogenic other land use class is excluded from agricultural land use expansion as this is assumed to be used for other purposes such as landscape aspects (roads, hedges, gardens), recreation (e.g. golf courses) or other human purposes than agriculture or urban land. This class is defined as the differences between anthropogenic land from the ESA-CCI land cover database and agicultural land as provided by the HYDE database.

Irrigated areas are increased on a regional scale, prescribed by external scenario dependent assumptions, such as based on FAO ([[Alexandratos and Bruinsma, 2012]]). In each time-step, more irrigated areas are allocated in agricultural land based on the need for irrigation (the difference in rain-fed and irrigated yields), and water availability.

In agricultural areas, the fraction of specific crops is determined based on the initial fractions, and modified annually based on changes in regional demand and local crop yields. As a result, the land-use fraction of a certain crop increases when the demand for this crop increases faster than for other crops, or if the potential yield in this grid cell increases more than for other crops.

The land use allocation model enables new land-use and land cover maps to be created ([[Land cover and land use]]). These land-use maps specify agricultural land, extensive grassland, and, land for sustainable bio-energy production. Crop fractions are allocated for 16 food and other non-energy crop types in IMAGE (wheat, rice, maize, tropical cereals, other temperate cereals, pulses, soybeans, temperate oil crops, tropical oil crops, temperate roots & tubers, tropical roots & tubers, sugar crops, palm oil, vegetables & fruits, other non-food, plant-based fibres, both rain-fed and irrigated), for grass and for five dedicated bio-energy crop types (sugar cane, maize, oil crops, wood biomass and grass biomass). These data are calculated on a 5 minute resolution, and aggregated to proportional land use on 30 minute resolution of the carbon, crop and water model [[LPJmL model|LPJmL]]. As LPJmL uses a different set of crop types a specific mapping of IMAGE to LPJmL crop types is used (TO ADD: TABLE; see Table xx).

Land use in IMAGE is modelled using dominant land use types per grid cell on a 5 x 5 minute resolution. In reality, land use is more heterogeneous. For some applications, dominant land use on 5 x 5 minute resolution, or the derived proportional land use on a 30 x 30 minute resolution may be sufficient. However, many applications require higher resolution and additional data, such as studies on biodiversity and agricultural intensification ([[Verburg et al., 2013]]).
</div>

Crops and grass/Data uncertainties limitations

2021-11-01T20:03:20Z

Doelmanj:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Rosenzweig et al., 2013;
}}
<h2>Data, uncertainties and limitations</h2>
===Uncertainties===
Crop model simulations are subject to considerable uncertainties with respect to model implementations and process representation, and thus vary significantly at field and global scale. On a global scale, detailed data are often not available on basic management options, such as sowing dates and variety selection. Global simulations do not represent actual crop production systems, but at best represent plausible production systems.

Even though there may be significant differences in susceptibility to climate change, simulations of plausible cropping systems with global coverage are the best available indications of climate change impacts on actual cropping systems.

A major uncertainty in climate change projections is the effectiveness of CO<sub>2</sub> fertilisation on crop yields. Crop growth is stimulated under elevated atmospheric CO<sub>2</sub> concentrations for many crops (C3 photosynthesis, such as wheat and rice) and water-use efficiency improves for all crops. However, the translation of higher photosynthesis to higher yields is less clear and subject to interacting processes, such as photosynthetic downregulation, increased nutrient limitation, and increased susceptibility to insect damage.

LPJmL has been shown to be capable of reproducing agricultural water and carbon fluxes and pools for several sites (Bondeau et al., 2007). However, projections of global yield patterns are difficult to evaluate because of the strong management signal that is currently not represented at the process base in the model.

Initial results from comparison of the global gridded crop models (joint activity of the Agricultural Model Inter-comparison and Improvement Project and the Inter-Sectoral Impact Model Inter-comparison ([[AgMIP and ISI-MIP project]])) indicate that LPJmL results are within the range of other model projections, but are on the optimistic end for effectiveness of CO<sub>2</sub> fertilisation ([[Rosenzweig et al., 2013]]).

===Limitations===
A major limitation of LPJmL and most other global gridded crop models is poor representation of extreme weather events and the effects on crop productivity. The occurrence of such extreme events is uncertain in climate change projections and the effect on crop productivity is not well understood. An increase in precipitation intensity or hail during the cropping season could devastate crop yields. Extreme temperatures may have similar effects if they occur during sensitive phenological stages, such as flowering.

Similar to most other crop models, LPJmL does not address the impacts of an altered frequency in short-term extreme weather events, such as brief but heavy precipitation. Addressing these impacts is prohibited by the temporal resolution of the model (daily) and input data (monthly interpolated to daily). The effects of periods of heat and drought could be addressed because a daily time step is sufficient but the model’s performance has not been assessed in this respect and the climate model in IMAGE currently does not account for extreme weather events (Component [[Atmospheric composition and climate]]). From the perspective of weather extremes, all crop model projections must be considered to be on the optimistic side.

Land-use data from IMAGE available on a 5 minute spatial resolution are aggregated to the 30 minute resolution of LPJmL. Higher spatial resolution in the simulation of agricultural productivity would allow for more flexibility in land-use allocation, but is currently prohibited by computational requirements.

Crops and grass/Description

2021-11-01T19:57:10Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Lapola et al., 2009; Beringer et al., 2011; Fader et al., 2010;
}}
The LPJmL model is a global dynamic vegetation, agriculture and water balance model. The agriculture modules are intrinsically linked to natural vegetation via the carbon and water cycles and follow the same basic process-based modelling approaches, plus additional process representation (management) where needed.

Crop productivity is computed following the same representation of photosynthesis, maintenance and growth respiration as for natural vegetation (see Figure Flowchart [[Carbon cycle and natural vegetation]]), but with additional mechanisms for phenological development, allocation of photosynthesis to crop components (leaves, roots, storage organ, mobile pool/stem), and management (Figure Flowchart), which can greatly affect crop productivity and food supply.

In aggregating plant species to classes, the 12 crops currently implemented in LPJmL ([[Bondeau et al., 2007]]; [[Lapola et al., 2009]]) represent a broader group of crops, referred to as crop functional types (see [[Crop types in LPJmL]]). Grassland management can be represented in various ways including regular moving, grazing with different livestock intensities or rotation grazing (TO ADD: Rolinski et al., 2018). The standard setting used in IMAGE 3.2 for pasture is monthly mowing while extensive grasslands are modelled as natural grasslands.

For the cultivation of bioenergy plants, such as short-rotation tree plantations and switch grass, three additional functional types have been introduced: temperate short-rotation coppice trees (e.g., willow); tropical short-rotation coppice trees (e.g., eucalyptus); and Miscanthus ([[Beringer et al., 2011]]).

Climate-related management is included in the model endogenously to take account of smart farmer behaviour in long-term simulations. Sowing dates are calculated as a function of farmers’ climate experience ([[Waha et al., 2012]]), and also selection of crop varieties ([[Bondeau et al., 2007]]).

Individual crops and grass are assumed to be cultivated on separate fields, and thus simulated with separate water balances, but soil properties are averaged in fallow periods to account for crop rotations. All crops in one grid cell are simulated in parallel, both irrigated and non-irrigated crops.

Irrigation modules are constrained by available water from surface water bodies and reservoirs (see Component [[Water]]), or assume unconstrained availability of irrigation water (scenario setting) to account for prevalent use of (fossil) groundwater.

To compensate for no explicit representation of nutrient cycles and other management options that may affect productivity (e.g., pest control, soil preparation), LPJmL can account for management intensity levels, and can be calibrated to reproduce actual FAO yields ([[Fader et al., 2010]]). However, given the complex interaction with the [[Land-use allocation]] model, LPJmL simulates crop yields without nutrient constraints (potential water-limited yields) in the link with IMAGE. Actual yields are derived by IMAGE by combining potential yields from LPJmL with a management factor that can change over time (see Component [[Agricultural economy]]). As input for the IMAGE land-use components (Component [[Agriculture and land use]]), LPJmL calculates productivity of each crop in each grid cell under rain-fed and irrigated conditions.

The crop and grassland component is embedded in the dynamic global [[Carbon, vegetation, agriculture and water|vegetation, agriculture and water balance model]] LPJmL, and thus carbon and water dynamics (Components [[Carbon cycle and natural vegetation]] and [[Water]], respectively) consistently account for dynamics in agricultural productivity and land-use change.

Crops and grass

2021-11-01T19:46:17Z

Doelmanj:

{{ComponentTemplate2
|Application=AgMIP and ISI-MIP project;
|IMAGEComponent=Carbon, vegetation, agriculture and water; Land-use allocation; Agriculture and land use; Water; Carbon cycle and natural vegetation; Atmospheric composition and climate;
|KeyReference=Bondeau et al., 2007; Waha et al., 2012;
|Reference=IPCC, 2007a; Pitman et al., 2009; Strengers et al., 2010; Müller et al., 2009; Rosenzweig et al., 2013; Fader et al., 2010;
|InputVar=Temperature - grid; Precipitation - grid; Number of wet days - grid; Cloudiness - grid; CO2 concentration; Land cover, land use - grid; Change in soil properties - grid; Management intensity crops; Number of wet days - grid; Cloudiness - grid; Irrigation water supply - grid;
|Parameter=Residue management; Soil properties - grid;
|OutputVar=Potential crop and grass yield - grid; Crop irrigation water demand - grid; Potential bioenergy yield - grid; Actual crop and grass production - grid; Rainwater consumption - grid; Irrigation water consumption - grid;
|ComponentCode=CG
|AggregatedComponent=Carbon, vegetation, agriculture and water
|FrameworkElementType=state component
}}
<div class="page_standard">

World population and per capita consumption of agricultural products are projected to increase substantially, which will require a significant increase in agricultural production. Currently, over one third of the Earth’s land area is under agricultural production, which is already about half the area suitable for agriculture. Pasture covers 68% of the global agricultural area, and cropland covers 32%. Agricultural production can be increased by expanding the agricultural area (more hectares) and by intensification (higher output per hectare).

However, the extent and distribution of agricultural land affects the Earth system, because agricultural systems are closely linked with natural ecosystems, human societies and the climate system. Agricultural land differs significantly from natural ecosystems in biogeochemical (e.g., carbon, water, nutrients) and bio-geophysical (e.g., albedo, energy balance) properties. Current land-use patterns have a significant impact on climate ([[Pitman et al., 2009]]; [[Strengers et al., 2010]]), and climate directly affects agricultural productivity ([[Müller et al., 2009]]; [[Rosenzweig et al., 2013]]). A large proportion of anthropogenic greenhouse gas emissions is caused by agricultural production, mediated by management and associated land-use dynamics (TO ADD: IPCC 2019).

Crop growth models are used to assess future area requirements, spatial patterns of agricultural production, and available areas for biomass-based energy (bioenergy). IMAGE 3.2 uses the [[Carbon, vegetation, agriculture and water|LPJmL model]] on dynamic global vegetation, agriculture and hydrology ([[Bondeau et al., 2007]]; [[Fader et al., 2010]]; [[Waha et al., 2012]])(TO ADD: Schaphoff 2018). This model dynamically simulates plant growth, agricultural productivity, and the carbon and water dynamics of agricultural land with detailed processes of photosynthesis, respiration, growth and phenology. In the model’s current form, management intensity can be approximated per crop type on national scale ([[Fader et al., 2010]]). Irrigation patterns are obtained from the Land-use allocation model of IMAGE (Component [[Land-use allocation]]), and other management options are calculated internally, such as sowing dates, selection of crop varieties and the demand for irrigation water.

LPJmL simulates yields per crop under optimal management intensities for each grid cell and irrigation system as well as irrigation water requirements, which is input to the IMAGE Land-use allocation model (Component [[Land-use allocation]]) for simulations of land-use change dynamics. Climate change calculated by the IMAGE climate model (Component [[Atmospheric composition and climate]]) directly affects future agricultural productivity because these components are dynamically linked in annual time-steps.

''Remark on Input/Output Table below'': The LPJmL module on crop growth directly interacts with the modules on [[Carbon cycle and natural vegetation|terrestrial carbon]] and [[Water|water cycles]]; as they are all an integral part of the LPJmL model, sharing the same soil and water balance processes, the distinction in different modules is somewhat artificial.

{{InputOutputParameterTemplate}}
</div>

Carbon cycle and natural vegetation/Data uncertainties limitations

2021-11-01T19:38:12Z

Doelmanj:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=FAO et al., 2009; Heyder et al., 2011; Schaphoff et al., 2013; Vetter et al., 2008;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The LPJmL model uses the [[HWSD database|FAO harmonised world soil map]], to provide information on soil texture and hydraulic properties ([[FAO et al., 2009]]). Climate input data come from the IMAGE climate model. Comparison of carbon stocks and fluxes with IPCC estimates shows these estimates are well within the uncertainty range. The modelled distribution of plant functional types has been found to compare well to other data sources.

===Uncertainties===
Although the terrestrial biosphere plays a key role in the global carbon cycle, it is also subject to considerable uncertainty. Current carbon fluxes are highly uncertain because they cannot be observed directly on a large scale, and vary considerably in time and space. Thus, all available estimates of global carbon pools and fluxes are model-based.

For the future dynamics of the terrestrial carbon cycle, additional uncertainty arises from physiological and ecological processes and interactions, which change rapidly under changing environmental conditions. As a dynamic global vegetation model, LPJmL can simulate carbon dynamics under internally computed vegetation shifts that occur in response to climate change, the impacts of land-use change, water availability and CO<sub>2</sub> fertilisation ([[Heyder et al., 2011]]). The most uncertain parameters in future dynamics are the combined effect of temperature and precipitation change on soil respiration, and the effect of CO<sub>2</sub> fertilisation. An uncertainty range for how the terrestrial biosphere may react to climate change scenarios is presented above.

===Limitations===
Permafrost modules have recently been developed to improve assessment of future climate change impacts on the carbon balance ([[Schaphoff et al., 2013]]). Impacts of weather extremes can be assessed, provided they are represented in the climate input data (e.g., heat waves, dry spells). However, only few data are available on the effects of weather extremes on the carbon balance to enable evaluation of the model’s capability in this respect. Simulation results from LPJmL calculation are within current estimates ([[Vetter et al., 2008]]). A key limitation of LPJmL 4.0 which is part of IMAGE 3.2 is that it does not yet include nutrient flows, specifically nitrogen. A recent LPJmL version does include the nitrogen which substantially improves crop yield estimates especially in relation to CO<sub>2</sub> fertilisation (TO ADD: von Bloh et al., 2018).
</div>

Carbon cycle and natural vegetation/Policy issues

2021-11-01T19:27:22Z

Doelmanj:

{{ComponentPolicyIssueTemplate
|Reference=Van Minnen et al., 2008; Strengers et al., 2008; Overmars et al., 2014;
}}
<div class="page_standard">
==Baseline developments==
Several economic developments and policy interventions are related to the dynamics of the terrestrial carbon budget. The terrestrial and marine carbon budgets determine the overall reduction in greenhouse gas emissions and aerosols needed to limit CO<sub>2</sub> build-up in the atmosphere. If the current terrestrial sink diminishes or becomes a carbon source, additional emission reductions will be required. Furthermore, protecting natural ecosystems or using alternative forest management options may contribute to storing and retaining more carbon in the biosphere (TO ADD: Doelman et al., 2019; Braakhekke et al., 2019). These options can be evaluated with the linked IMAGE-LPJmL model for baseline scenarios and policy interventions.

For instance, the IMAGE-LPJmL model has been used to assess key uncertainties about the terrestrial carbon balance (the figure below). The study conducted by Müller et al. (2016) included multiple values for climate sensitivity, including multiple climate patterns, for two different socio-economic scenarios. These experiments showed a possible shift in the terrestrial biosphere from sink to source under a broad range of changes in mean global temperature (2.3-6.8 °C up until 2100), and atmospheric CO<sub>2</sub> concentrations (475–936 ppm). The rate of temperature increase was identified as the decisive threshold determining the shift, with values from 0.04 to 0.08 °C/y, depending on the {{abbrTemplate|GCM}} pattern. The LPJmL model calculations suggest that the likelihood of a carbon balance shift in the 21st century increases almost linearly from ~5% to ~90% when climate sensitivity is increased from 2.5°C to 5.0 °C.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
The model can also be used to study the impact of a range of policy measures, e.g. aimed at increasing the carbon storage of natural vegetation. Policy measures to increase carbon storage often generate co-benefits, such as restoration of watershed and wildlife habitats, and prevention of soil erosion. However, a critical issue is the permanency of additional carbon storage.

For instance, a policy intervention would be the use of forestry measures allowed under the Kyoto Protocol. The protocol provides opportunities for developed countries to partly achieve their emission reduction targets by planting new forests or by managing established forests and agricultural land to store more carbon in the soil ({{abbrTemplate|ARD}}), and to reduce emissions resulting from deforestation and degradation ({{abbrTemplate|REDD}}). IMAGE was used to estimate the emission reductions and costs related to REDD schemes (TO ADD: Overmars et al., 2014). Forest carbon stocks are protected expansion at increasingly high levels, and not available for agricultural use in [[MAGNET model|MAGNET]] and IMAGE. As a result, agriculture expands less, and CO<sub>2</sub> emissions are reduced by up to 100 Gt CO<sub>2</sub> compared to baseline levels, with most of the reduction potential in Latin America and Africa (the figure below).

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Carbon cycle and natural vegetation/Description

2021-11-01T19:22:12Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Prentice et al., 2007; Lauk et al., 2012; Klein Goldewijk et al., 2011;
}}
<div class="page_standard">

===Vegetation types===
LPJmL is a Dynamic Global Vegetation Model ({{abbrTemplate|DGVM}}) that was developed initially to assess the role of the terrestrial biosphere in the global carbon cycle ([[Prentice et al., 2007]]). DGVMs simulate vegetation distribution and dynamics, using the concept of multiple plant functional types ({{abbrTemplate|PFT}}s) differentiated according to their bioclimatic (e.g. temperature requirement), physiological, morphological, and phenological (e.g. growing season) attributes, and competition for resources (light and water).

To aggregate the vast diversity of plant species worldwide, with respect to major differences relevant to the carbon cycle, [[LPJmL model|LPJmL]] distinguishes eleven natural plant functional types. These include e.g. tropical evergreen trees, temperate deciduous broad-leaved trees and C3 herbaceous plants. Plant dynamics are computed for each PFT present in a grid cell. As IMAGE uses the concept of biomes (natural land cover types), combinations of PFTs in an area/grid cell are translated into a natural land cover (biome) type (see [[Plant functional types and natural land cover types]]).

===Carbon dynamics===
IMAGE-LPJmL covers the carbon cycle processes, and tracks all carbon fluxes between the atmosphere and the biosphere. Carbon cycle dynamics of the terrestrial biosphere are computed as carbon uptake and release in plants (photosynthesis, autotrophic respiration), transfer of plant carbon to the soil (shedding of leaves, turnover, mortality) and mineralisation of soil organic matter (heterotrophic respiration; see Figure Flowchart). Because these processes are closely related to weather conditions, they are computed in daily time steps.

The composition of natural vegetation depends on slower processes, such as the inter-annual and inter-seasonal variability in weather conditions and disturbances, such as natural fires. Thus, vegetation dynamics including competition between plant functional types, mortality, turnover, and fire disturbances are computed in annual time steps.

Allocation of newly established biomass is computed in annual time steps for perennial plants (natural grasses, trees) and in daily time steps for annual plants (crops). Allocation to plant organs (represented by a carbon pool for each) distinguishes up to four living plant carbon pools, depending on plant type. For grasses, the model distinguishes carbon pools of leaves and roots only, and for trees, there are two additional woody carbon pools (hardwood and sapwood). For agricultural crops, the pools are categorised as leaves, roots, storage organs, stems, and a mobile reserve pool.

To simulate mineralisation rates of soil organic carbon, the model distinguishes three soil carbon pools for litter, fast soil organic matter (10-year turnover rate) and slow soil organic matter (100-year turnover rate). All carbon from harvested products (crops, grass, biofuels) is assumed to be released to the atmosphere as CO<sub>2</sub> after consumption (food, feed, energy) in the same year. Residues are either left in the fields to enter the litter pool or are removed to subsequently decompose.

During wood harvesting, a proportion of the plant pools is cut down and harvested, as determined in the [[forest management]] model . The waste is left to enter the soil litter pool as dead biomass. Three classes of wood products are distinguished to account for differences in lifespan:
# Pulp and paper has fast turnover rates;
# Timber products, such as furniture, have longer turnover rates ([[Lauk et al., 2012]]);
# Traditional biomass used as an energy source and emitted within the same year.

The IMAGE land-use module (Component [[Agriculture and land use]]) determines annual land-use dynamics, including expansion or abandonment of pastures, cropland and bioenergy plantations, and wood harvested from natural vegetation.

===Model linkage and simulation procedure===
The [[LPJmL model]] has multiple links to other IMAGE components and uses IMAGE data on climate, atmospheric CO<sub>2</sub> concentration, land use (including wood demand), and timber use and deforestation (cutting and burning). LPJmL supplies other IMAGE components with information on annual carbon fluxes, net CO<sub>2</sub> exchange between biosphere and atmosphere, size of carbon pools, and natural land cover (biome) classes (see [[Carbon_cycle_and_natural_vegetation|Input/output Table]] at Introduction part ).

LPJmL and IMAGE are linked via an interface and starts in the simulation year of 1970. Before 1970, vegetation and soil carbon pools need to be initialised. This is done by using LPJmL first in a 5000-year spin up to initialise the natural ecosystems and their carbon pools and fluxes, followed by a 390-year spin up, in which agricultural land is gradually expanded based on historical [[HYDE database|HYDE]] land-use data ([[Klein Goldewijk et al., 2011]]). The pool sizes of timber products for 1970 are based on literature estimates ([[Lauk et al., 2012]]).

The linked IMAGE-LPJmL simulations start in 1970 with observed climate, followed by simulated climate from 2015 onwards (Component [[Atmospheric composition and climate]]). As the inter-annual variability in weather conditions is needed for the simulation of vegetation dynamics in IMAGE-LPJmL, smooth annual climate trends from IMAGE are superimposed with inter-annual variability fields, extracted from observed climate over the 1971–2000 period. To avoid repeating climate trends in these 30-year periods, annual anomalies are ordered at random before superimposition.
</div>

Agricultural economy/Policy issues

2021-11-01T19:13:42Z

Doelmanj:

{{ComponentPolicyIssueTemplate
|Reference=Banse et al., 2008; Verburg et al., 2009; PBL, 2010; PBL, 2011; Westhoek et al., in preparation; Overmars et al., 2014;
}}
<div class="page_standard">
==Baseline developments==
In the SSP scenarios, agricultural crop and livestock production increases rapidly driven by population increase and dietary changes ([[Doelman et al., 2018]]; [[Van Meijl et al., 2020b]]). As a consequence of production increases, the total area of cropland and pasture is projected to increase, although this is less certain. Depending on scenario and region, some scenarios may also show decreasing land areas, certainly after 2050 when the population starts to decline in several regions. Variations between SSP scenarios are substantial dependent on key drivers such as GDP, population, consumption, land use protection, trade and productivity assumptions. A decomposition of these effects was analyzed in a multi-model decomposition analysis as part of the AgMIP project ([[Stehfest et al., 2019]]).

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
Numerous policy interventions can be studied:
* Biofuel policies: Partly as an autonomous process under high oil prices but mainly driven by biofuel policies, the proportion of biofuels (so far, only first generation) in the transport sector is projected to increase ([[Banse et al., 2008]]). The model can be used to estimate direct and indirect land-use change and associated emissions.
* REDD policies: Forest protection leads to a reduction in CO<sub>2</sub> emissions from land-use change. The related opportunity costs can be used to estimate cost curves for the emission abatement that results from REDD policies ([[Overmars et al., 2014]]).
* Afforestation policies: Greenhouse gas pricing can make afforestation for carbon storage in biomass profitable. Cost-optimal levels of afforestation can be estimated in IMAGE and implemented as reductions in agricultural land in MAGNET to assess food system and security impacts ([[Doelman et al., 2020]]).
* Agricultural and trade policies can be assessed for their effects on land use, greenhouse gas emissions and biodiversity ([[Verburg et al., 2009]]).
* Measures to reduce biodiversity loss by increasing protected areas, increasing agricultural productivity, dietary changes, and reducing waste ([[PBL, 2010]]; [[Leclere et al., 2020]]). Several biodiversity options, in a stepwise introduction, affect land and commodity prices as well as land-use change (see figure below).
* Consumption changes, dietary preferences, and their effect on global land use, prices and emissions can be studied ([[PBL, 2011]]; [[Stehfest et al., 2013]]; [[Van Meijl et al., 2020b]]).
* Taxation of non-CO<sub>2</sub> greenhouse gas emissions in agriculture resulting in changes in production, consumption and trade patterns (TO ADD: Frank et al., 2019)

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Carbon cycle and natural vegetation

2021-10-29T13:40:12Z

Doelmanj:

{{ComponentTemplate2
|IMAGEComponent=Carbon, vegetation, agriculture and water; Agriculture and land use; Atmospheric composition and climate; Ecosystem services; Land cover and land use;
|Model-Database=HYDE database
|KeyReference=Sitch et al., 2003; Müller et al., 2016a;
|Reference=Van Minnen et al., 2008; Houghton, 2010; Müller et al., 2007; Ballantyne et al., 2012; Van Minnen et al., 2009; Gerten et al., 2004; Bondeau et al., 2007; Klein Goldewijk et al., 1994; Van Minnen et al., 2000; Van Minnen et al., 2009;
|InputVar=Temperature - grid; Precipitation - grid; Number of wet days - grid; Cloudiness - grid; CO2 concentration; Timber use fraction; Land cover, land use - grid; Irrigation water supply - grid; Forest management type - grid;
|Parameter=Soil properties - grid;
|OutputVar=Potential natural vegetation - grid; NEP (net ecosystem production) - grid; Land-use CO2 emissions - grid; Carbon pools in vegetation - grid; NPP (net primary production) - grid; Soil respiration - grid; Carbon pools in soil and timber - grid;
|ComponentCode=NVCC
|AggregatedComponent=Carbon, vegetation, agriculture and water
|FrameworkElementType=state component
}}
<div class="page_standard">

The terrestrial biosphere plays a key role in global and regional carbon cycles and thus in the climate system. Large amounts of carbon (between 2000 and 3000 PgC) are stored in the vegetation and soil components. Currently, the terrestrial biosphere absorbs about 30% of emitted CO<sub>2</sub> ([[Ballantyne et al., 2012]]), and this carbon sink can be maintained and even enhanced by, for instance, protecting established forests and by establishing new forests (TO ADD: Doelman 2019). However, deforestation and other land use changes in the last few centuries have contributed considerably to the build-up of atmospheric carbon dioxide (TO ADD: Friedlingstein 2020) and this trend is projected to continue [[Müller et al., 2007|(Müller et al., 2007]]).

Regardless of land cover and land use, the net carbon sink in the terrestrial biosphere is affected by a range of environmental conditions such as climate, atmospheric CO<sub>2</sub> concentration and moisture. These conditions influence processes that take up and release CO<sub>2</sub> from the terrestrial biosphere such as photosynthesis, plant and soil respiration, transpiration, carbon allocation and turnover, and disturbances such as fires.

In plant photosynthesis, CO<sub>2</sub> is taken from the atmosphere and converted to organic carbon compounds. This CO<sub>2</sub> conversion is referred to as gross primary production ({{abbrTemplate|GPP}}). The sequestered carbon is needed for plant maintenance and growth (autotrophic respiration), and for the development of new plant tissues, forming live biomass carbon pools. All plant parts (including leaf fall and mortality) are ultimately stored as carbon in carbon pools in the soil and atmosphere. CO<sub>2</sub> is also emitted from the soil pools to the atmosphere in the process of mineralisation.

Terrestrial carbon cycle and vegetation models contribute to better understanding of the dynamics of the terrestrial biosphere in relation to these underlying processes and to the terrestrial water cycle (see Component [[Water]]) and land use (see Component [[Agriculture and land use]]).

The IMAGE-2 carbon cycle and biome model ([[Klein Goldewijk et al., 1994]]; [[Van Minnen et al., 2000]]) have been replaced by the Lund-Potsdam-Jena model with Managed Land ([[LPJmL model|LPJmL]]) model ([[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]]). An overview of the LPJmL model in the IMAGE context with regard to carbon and biome dynamics is presented here; the model and a sensitivity analysis is described in detail by Muller et al. ([[Müller et al., 2016a|2016]]).

{{InputOutputParameterTemplate}}
</div>

Carbon, vegetation, agriculture and water

2021-10-29T10:33:55Z

Doelmanj:

{{AggregatedComponentTemplate
|ComponentCode=VHA
|KeyReference=Sitch et al., 2003; Gerten et al., 2004; Bondeau et al., 2007;
|FrameworkElementType=state component
}}
<div class="page_standard">
==Description of {{ROOTPAGENAME}}==
[[LPJmL model|LPJmL]] is the carbon, vegetation, agricultural and hydrology model in IMAGE 3.0 and consists of the three components: [[Carbon cycle and natural vegetation]], [[Crops and grass]], [[Water]].

Within the Earth system, the terrestrial biosphere is the component that bears the most visible impact of human activity. Large proportions of the land surface and the terrestrial vegetation have been converted for human use, for instance, to cropland and urban areas.

Agriculture, terrestrial carbon, water and nutrient cycles were separate modules in previous versions of IMAGE and thus interactions were not adequately covered. IMAGE 3.2 covers natural and agricultural terrestrial ecosystems, and associated carbon and water dynamics via the link with the dynamic global vegetation, agriculture and water balance model [[LPJmL model|LPJmL]] (Lund-Potsdam-Jena model with managed Land; [[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]])(TO ADD: Schaphoff 2018a; Schaphoff 2018b). This enables more detailed and process-based representation of the interacting dynamics in vegetation, carbon and agricultural production, and extends the model scope to terrestrial freshwater dynamics.

LPJmL is one of the most extensively evaluated dynamic global vegetation models ({{abbrTemplate|DGVM}}) and is widely applied either separately or linked to other models. To show the complex dynamics in the terrestrial biosphere and to reflect the historical IMAGE modules, LPJmL is described in three components: [[Carbon cycle and natural vegetation|carbon cycle and vegetation]]; [[Crops and grass|agricultural land use]]; and [[Water|terrestrial freshwater flows]].

IMAGE 3.2 and LPJmL are linked through an interface that enables close and consistent interaction between the two models in annual time steps (TO ADD: Müller 2016. An even more direct link to simulate detailed land-atmosphere interaction would require higher temporal resolutions also in other IMAGE components (e.g., the climate model), which is not necessarily congruent with the philosophy of an integrated assessment model. Incorporating nutrient cycles and improving representations of grassland management in LPJmL will require further adjustments to other IMAGE 3.2 components, and will increase consistency.
</div>

Land cover and land use

2021-10-28T08:49:07Z

Doelmanj:

{{ComponentTemplate2
|ComponentCode=LCU
|Application=Roads from Rio+20 (2012) project;
|KeyReference=Mandryk et al., 2015;
|InputVar=Crop fraction in agricultural area - grid; Potential natural vegetation - grid; Built-up area - grid; Bioenergy area; Extensive grassland area - grid; Potential natural vegetation - grid; Animal stock; Intensive grassland area; Management intensity crops; Management intensity livestock; Irrigation water withdrawal - grid; Water withdrawal other sectors - grid; Forest management type - grid; Change in soil properties - grid; Carbon pools in soil and timber - grid; Carbon pools in vegetation - grid; NPP (net primary production) - grid; MSA (mean species abundance) - grid; Regrowth forest area - grid; Agricultural area - grid; Protected area - grid; Degraded forest area; Harvested wood;
|OutputVar=Land cover, land use - grid; Land supply for bioenergy - grid; Land supply;
|FrameworkElementType=interaction component
}}<div class="page_standard">
In addition to emissions, land cover and land use are key linkages between the Human system and the Earth system. Land cover and use are changed by humans for a variety of purposes, such as to produce food, fibres, timber and energy, to raise animals, for shelter and housing, transport infrastructure, tourism, and recreation. These human activities have affected most areas in the world, transforming natural areas to human-dominated landscapes, changing ecosystem structure and species distribution, and water, nutrient and carbon cycles. Natural landscape characteristics and land cover also affect humans, determining suitable areas for settlement and agriculture, and delivering a wide range of ecosystem services. As such, land cover and land use can be understood as the complex description of the state and processes in a land system in a certain location. It results from the interplay of natural and human processes, such as crop cultivation, fertilizer input, livestock density, type of natural vegetation, forest management history, and built-up areas.

In IMAGE, elements of land cover and land use are calculated in several components, namely in land use allocation, forest management, livestock systems, carbon cycle and natural vegetation. The output from these components forms a description of gridded global land cover and land use that is used in these and other components of IMAGE. In addition, this description of gridded land cover and land use per time step can be provided as IMAGE scenario information to partners and other models for their specific assessments.

==Model description==
Land cover and land use described in an IMAGE scenario is a compilation of output from various IMAGE components. This compilation provides insight into key processes in land-use change described in the model and an overview of all gridded land cover and land use information available in IMAGE (Input/Output Table below).

Land cover and land use is also the basis for the land availability assessment, which provides information on regional land supply to the [[Agricultural economy|agro-economic model]] , based on potential crop yields, protected areas, and external datasets such as slope, soil properties, and wetlands ([[Mandryk et al., 2015]])(TO ADD: van der Esch 2017).

{{InputOutputParameterTemplate}}
</div>

Land-use allocation/Data uncertainties limitations

2021-10-28T08:43:05Z

Doelmanj:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Alexandratos and Bruinsma, 2012; Siebert et al., 2005; UNEP, 2011; Hurtt et al., 2011; Nelson, 2008; Hansen et al., 2013;
}}<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
As the starting point for the simulation in 1970, [[HYDE database|HYDE]] land use data are aggregated to dominant land use types on a 5 minute grid scale. For the period 1970–2015, the model can either allocate land use based the dynamic behaviour described above, or be constrained by the HYDE land use map in 2005. The latter option is used mainly when specific impact models require a close match between IMAGE land-use patterns and observations in 2015 (TO ADD: Hurtt et al., 2020). Other data sources include maps of protected areas (TO ADD: IUCN 2015) and irrigated areas ([[Siebert et al., 2005]]), all aggregated to the IMAGE 5 minute grid. The trend for future irrigated areas is based on FAO projections ([[Alexandratos and Bruinsma, 2012]]).

===Uncertainties===
The main uncertainty in land-use allocation obviously relates to the location of new agricultural land and land abandonment, and the effect on impacts and feedback. Global land-use change models are rarely validated, because adequate data for evaluation are not available. For instance, differences in satellite-based land-use maps for different time steps often relate to differences in methodologies, rather than to real transformation processes ([[Hansen et al., 2008]]). However, the need for evaluation is increasingly acknowledged, and with improved data availability, such assessments now become possible ([[Hansen et al., 2013]]).

Impacts and feedbacks of land-use change depend to differing degrees on the location. For carbon emissions, the vegetation type and carbon content at the location of agricultural expansion is decisive, while the exact location of the new land is less relevant. Likewise for feedback to agricultural production, the attainable crop yields are more relevant than the exact location. Some impacts, e.g. on biodiversity depend more on small-scale processes and landscape composition, which are currently not included in most integrated assessment models. To evaluate the IMAGE land-use allocation model, the simulated locations of new agricultural land need to be compared to empirical data on land cover transitions, or to maps of land-cover change (e.g. [[Hansen et al., 2013]]).

Another key uncertainty is the relation between agricultural intensification or expansion, when demand increases. So far, their relative contribution is calculated in [[MAGNET model|MAGNET]], but could be informed by the smaller scale land system models.

===Limitations===

A key limitation of the current land-use allocation model is the limited feedback to the agricultural economy. The suitability of land feeds back to agricultural production only for regional averages.
</div>

Land-use allocation/Policy issues

2021-10-28T07:35:02Z

Doelmanj:

{{ComponentPolicyIssueTemplate
}}<div class="page_standard">
==Baseline developments==
In a baseline scenario, agricultural land use may expand or decrease dependent on underlying scenario drivers and assumptions: in a SSP2 business-as-usual scenario substantial increases in agricultural land use take place leading to strong conversion of natural land. An SSP3 world shows even more conversion due to stronger population growth and very limited protection measures for nature and biodiversity. In the more sustainability oriented scenario SSP1 a major reduction takes place related to lower population, extensive protection of natural land and reduced consumption of animal products. The land-use allocation model is used to indicate where these changes may occur. Thus, it helps to assess the consequence of agricultural expansion and intensification on the environment, for example in relation to biodiversity, carbon storage, and water availability.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
The policy interventions that can be analysed are related to either the agricultural economy ([[Agricultural economy]]), or they are reflected in the allocation rules used in the land-use allocation module (e.g. more protected areas, {{AbbrTemplate|REDD+}} schemes). In a study for the Global Land Outlook (van Esch., 2021) the impacts of extensive protected area and the effect restoration (reverting agricultural land productivity loss) were compared. The results show that protection has especially strong effects on natural land, biodiversity and carbon storage. Crop yields also increase as a necessity to produce more on less land, however other studies show that this also negatively impacts food security (van Meijl., et al 2020; Hasegawa et al., 2019).

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Land-use allocation/Policy issues

2021-10-28T07:10:20Z

Doelmanj:

{{ComponentPolicyIssueTemplate
}}<div class="page_standard">
==Baseline developments==
In a baseline scenario, agricultural land use may expand or decrease dependent on underlying scenario drivers and assumptions: in a SSP2 business-as-usual scenario substantial increases in agricultural land use take place leading to strong conversion of natural land. An SSP3 world shows even more conversion due to stronger population growth and very limited protection measures for nature and biodiversity. In the more sustainability oriented scenario SSP1 a major reduction takes place related to lower population, extensive protection of natural land and reduced consumption of animal products. The land-use allocation model is used to indicate where these changes may occur. Thus, it helps to assess the consequence of agricultural expansion and intensification on the environment, for example in relation to biodiversity, carbon storage, and water availability.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
The policy interventions that can be analysed are related to either the agricultural economy ([[Agricultural economy]]), or they are reflected in the allocation rules used in the land-use allocation module (e.g. more protected areas, {{AbbrTemplate|REDD+}} schemes). In a study for the Global Land Outlook (van Esch., 2021) the impacts of extensive protection schemes and the effect restoration (reverting agricultural land productivity loss) were compared.

In a study using the OECD Environmental Outlook scenario, the model was used to evaluate impacts of protection levels of natural areas: on top of a baseline scenario with strong bioenergy mandates, it was assumed that 20% (Prot20) of 50% (Prot50) of the land area were protected as nature reserves, or that all forest and woodland was protected from agricultural expansion (see figure below). The relative reduction in land use and CO<sub>2</sub> emissions differ greatly depending on the type of areas protected. If forests are protected, almost the same amount of agricultural land is used by switching to non-forested land. Thus CO<sub>2</sub> emissions are reduced, but reduction in land use and related biodiversity loss is much less.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Land-use allocation/Policy issues

2021-10-28T07:07:03Z

Doelmanj:

{{ComponentPolicyIssueTemplate
}}<div class="page_standard">
==Baseline developments==
In a baseline scenario, agricultural land use may expand or decrease dependent on underlying scenario drivers and assumptions: in a SSP2 business-as-usual scenario substantial increases in agricultural land use take place leading to strong conversion of natural land. An SSP3 world shows even more conversion due to stronger population growth and very limited protection measures for nature and biodiversity. In the more sustainability oriented scenario SSP1 a major reduction takes place related to lower population, extensive protection of natural land and reduced consumption of animal products. The land-use allocation model is used to indicate where these changes may occur. Thus, it helps to assess the consequence of agricultural expansion and intensification on the environment, for example in relation to biodiversity, carbon storage, and water availability.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
The policy interventions that can be analysed are related to either the agricultural economy ([[Agricultural economy]]), or they are reflected in the allocation rules used in the land-use allocation module (e.g. more protected areas, {{AbbrTemplate|REDD+}} schemes). In a study using the OECD Environmental Outlook scenario, the model was used to evaluate impacts of protection levels of natural areas: on top of a baseline scenario with strong bioenergy mandates, it was assumed that 20% (Prot20) of 50% (Prot50) of the land area were protected as nature reserves, or that all forest and woodland was protected from agricultural expansion (see figure below). The relative reduction in land use and CO<sub>2</sub> emissions differ greatly depending on the type of areas protected. If forests are protected, almost the same amount of agricultural land is used by switching to non-forested land. Thus CO<sub>2</sub> emissions are reduced, but reduction in land use and related biodiversity loss is much less.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Land-use allocation/Description

2021-10-27T20:06:04Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Van Asselen and Verburg, 2013; Alexandratos and Bruinsma, 2012; Klein Goldewijk et al., 2010; O'Neill, 2013; Lambin et al., 2000; IIASA and FAO, 2012; Nelson, 2008; Klein Goldewijk et al., 2011; Letourneau et al., 2012; Doelman et al., 2018;
}}<div class="page_standard">

IMAGE 3.2 uses a regression-based suitability assessment to determine future land-use patterns. Land-use allocation is driven by regional crop and grassland production and their respective intensity levels, as calculated by the IMAGE agro-economic model ([[Agricultural economy]]). Agricultural land use is allocated to grid cells in an iterative process until the required regional production of crops and grass is met. Land use in IMAGE is modelled using dominant land use per grid cell on a 5 x 5 minute resolution, distinguishing extensive grasslands, agricultural and non-agricultural grid cells, and within agricultural land areas fractions of grass, seven rain-fed and seven irrigated crop types, and bioenergy crops.

In each time step, maps of actual crop yields are computed by combining the potential crop and grassland yields calculated by the crop model ([[Crops and grass]]), and the regional management intensity from the agro-economic model ([[Agricultural economy]]). Starting with the land-cover and land-use map of the previous time step, actual yields are used to determine crop and grassland production on current agricultural land. This is compared to the required regional crop and grassland production. If the demand exceeds calculated production, the agricultural area needs to be expanded at the cost of natural vegetation. If the calculated production of current cropland exceeds the required production, agricultural land is abandoned to adjust to the production required.

Crop and grassland is either abandoned or expanded until the required production is met. Since actual yields are taken into account, changes in crop yields in time due to technological change, climate change and land heterogeneity are included. If yields in the new agricultural areas are lower than average in the current area, relatively more agricultural land is required compared to the production increase.

In determining the location of agricultural expansion or abandonment, all grid cells are assessed and ranked using an empirically based suitability map. This map is developed using artificial neural network models that relate locations of agricultural land conversions, as recorded from 2003 to 2013, to various explanatory variables reflecting topography, climate, soil and accessibility (TO ADD: Cengic et al., 2020). Agricultural land conversion data is derived from the satellite-based ESA-CCI land cover database which provides land use transition data for the 1992-2018 period at 300 m resolution (TO ADD: ESA 2017).

Additionally, a few other rules are applied in determining the location of new agricultural land. For instance, agricultural expansion is not permitted in protected areas, and in areas otherwise protected, such as in assumed {{abbrTemplate|REDD}} (reducing emissions from deforestation and degradation) schemes. A grid cell is only regarded suitable for agriculture if the potential rain-fed production is at least 10% of the global maximum attainable crop yield. Grid cells with a production potential between 0.01 and 10% of the maximum attainable are still assumed suitable for extensive grassland. In addition, an additional anthropogenic other land use class is excluded from agricultural land use expansion as this is assumed to be used for other purposes such as landscape aspects (roads, hedges, gardens), recreation (e.g. golf courses) or other human purposes than agriculture or urban land. This class is defined as the differences between anthropogenic land from the ESA-CCI land cover database and agicrultural land as provided by the HYDE database.

Irrigated areas are increased on a regional scale, prescribed by external scenario dependent assumptions, such as based on FAO ([[Alexandratos and Bruinsma, 2012]]). In each time-step, more irrigated areas are allocated in agricultural land based on the need for irrigation (the difference in rain-fed and irrigated yields), and water availability.

In agricultural areas, the fraction of specific crops is determined based on the initial fractions, and modified annually based on changes in regional demand and local crop yields. As a result, the land-use fraction of a certain crop increases when the demand for this crop increases faster than for other crops, or if the potential yield in this grid cell increases more than for other crops.

The land use allocation model enables new land-use and land cover maps to be created ([[Land cover and land use]]). These land-use maps specify agricultural land, extensive grassland, and, land for sustainable bio-energy production. Crop fractions are allocated for 16 food and other non-energy crop types in IMAGE (wheat, rice, maize, tropical cereals, other temperate cereals, pulses, soybeans, temperate oil crops, tropical oil crops, temperate roots & tubers, tropical roots & tubers, sugar crops, palm oil, vegetables & fruits, other non-food, plant-based fibres, both rain-fed and irrigated), for grass and for five dedicated bio-energy crop types (sugar cane, maize, oil crops, wood biomass and grass biomass). These data are calculated on a 5 minute resolution, and aggregated to proportional land use on 30 minute resolution of the carbon, crop and water model [[LPJmL model|LPJmL]].

Land use in IMAGE is modelled using dominant land use types per grid cell on a 5 x 5 minute resolution. In reality, land use is more heterogeneous. For some applications, dominant land use on 5 x 5 minute resolution, or the derived proportional land use on a 30 x 30 minute resolution may be sufficient. However, many applications require higher resolution and additional data, such as studies on biodiversity and agricultural intensification ([[Verburg et al., 2013]]).
</div>

Land-use allocation

2021-10-27T19:42:13Z

Doelmanj:

{{ComponentTemplate2
|Application=Roads from Rio+20 (2012) project;
|IMAGEComponent=Drivers; Agricultural economy; Crops and grass
|KeyReference=Doelman et al., 2018; Van Asselen and Verburg, 2012;
|InputVar=Management intensity crops; Increase in irrigated area - grid; Grass requirement; Bioenergy production; River discharge - grid; Protected area - grid; Land cover, land use - grid; Population - grid; Potential crop and grass yield - grid; Crop production;
|Parameter=Accessibility - grid; Regression parameters; Slope - grid; Other crops; CLUmondo specific input - grid;
|OutputVar=Crop fraction in agricultural area - grid; Bioenergy area; Extensive grassland area - grid; Agricultural area - grid; Land suitability - grid; Intensive grassland area; Land systems - grid;
|ComponentCode=AS
|AggregatedComponent=Agriculture and land use
|FrameworkElementType=pressure component
}}<div class="page_standard">
About one third of the Earth’s land area is under cropland and pasture. The proportion of areas suitable for agriculture that is already in use is even larger. Humans strongly depend on agricultural production, as supported by soils and climatic circumstances, and thus need to rely on a continued functioning of these systems. On the other hand, major environmental problems arise from the size and intensity of agricultural land use, for example greenhouse gas emissions, distortions of the nutrient and water cycles, and biodiversity loss. Total agricultural area, globally or in a region, may be sufficient to assess the first order effects of production potential and environmental impacts. However, the location of agricultural land in a region or landscape is extremely important because yields of crops and grass depend on soil and climate, and also on spatially heterogeneous socio-economic factors, and because many impacts are location dependent.

The location of new agricultural area determines the vegetation type removed, and thus the amount of carbon emitted, and the biodiversity impacts related to a loss of the vegetation type. Extreme examples of location-specific impacts are conversion of carbon- and species-rich peatland and wetlands. Other factors include the impact of agriculture on nutrient and water cycles, and location characteristics such as soil properties and slope. As well as the location, the composition of landscapes is a determining factor because how land uses are connected determines to some extent the environmental impact and the production potential. For environmental impacts, the most prominent examples of landscape composition are biodiversity effects, wind and water erosion, hydrology, and ecosystem services. Some crops benefit from nearby forests for pollination and pest control, while others suffer additional pest pressure. Consequently, accurate and high resolution modelling of agricultural land use is essential in global integrated assessment.

In IMAGE, the spatial allocation of crops, pasture and bioenergy is driven by regional crop and grassland production and their respective intensity levels, as calculated by the the IMAGE agro-economic model ([[Agricultural economy]]), by the potential crop and grass yields ([[Crops and grass]]), and spatial allocation suitability factors.

{{InputOutputParameterTemplate}}
</div>

Forest management/Data uncertainties limitations

2021-10-21T20:38:53Z

Doelmanj:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=FAO, 2010; IEA, 2012; Brown, 2000; Carle and Holmgren, 2008; UNEP-INTERPOL, 2012; FAO, 2001a; FAO, 2008;
}}<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The main data source for the development and calibration of the forest management module is FAO Forest Resource Assessment ([[FAO, 2010]]), from which data on wood production and deforested areas are derived. In addition, statistics from the International Energy Agency ([[IEA, 2012]]]) are used to estimate the regional fuelwood production, based on household fuelwood and charcoal requirements in national energy statistics. Finally, national data were collected to parameterise the type and production parameters of forest management in world regions (see details in [[Arets et al., 2011]]) and establishment of new forest plantations was designed according to planting rates reported and projected by FAO ([[Brown, 2000]]; [[Carle and Holmgren, 2008]]).

===Uncertainties===
Several assumptions had to be made to project future production in forest management systems. These pinpoint the uncertainties in the forestry management model. Better data, monitoring and reporting would improve calibration of the IMAGE forest management module.

FAO Forest Resource Assessment reports are published regularly on quantities of industrially produced wood and the areas of primary and secondary forests. However, these reports do not include the area from which these wood quantities are harvested, and the forest management system of these areas. The amount of wood produced in deforestation processes is not reported, probably due to the illegal nature of many such operations.

Few data are available on the extent of illegal logging, they are not captured in the FAO statistics, but in satellite-based assessments, and only very rough estimates are available ([[UNEP-INTERPOL, 2012]]). In addition, few data are available on informal collection of fuelwood in forests in developing countries ([[FAO, 2001a]]; [[FAO, 2008]]). Estimates of total fuelwood demand are highly uncertain ([[IEA, 2012]]), and fuelwood demand is only partly met by the forestry operations in this IMAGE module.

Another uncertainty is the starting point, which is the state of forest use by age cohort in 1970. As forests take several decades to a century to regrow after felling, the effect of historic uncertainties in forest-use extends far into the future.

===Limitations===
Timber demand in IMAGE 3.2 is the total demand of sawlogs, pulpwood (both based on [[FAO]] for the historic period), and fuelwood (based on [[IEA]] and calculated by the [[TIMER model]]). The demand is fulfilled by harvesting wood from any type of forest without specifying the original source of demand. This can be relevant as different types of wood are used for timber, pulp and fuel. For fuelwood we assume that a certain fraction originate from informal sources, like gathering. For specific assessments it would be useful to include this detail in the allocation of forestry.

The timber demand in a region is the sum of local/regional demands and timber claims by other regions. The trade assumptions are adopted from external models, limiting the application of the model for cases with regional timber scarcity.

The only driver of deforestation modelled in IMAGE 3.2 is the net expansion of
agriculture per region. Many drivers of deforestation are not related to agricultural
expansion, but there is no global assessment of these other drivers. Therefore, total
deforestation rates in IMAGE are calibrated to FAO and satellite observed deforestation rates. Drivers and extent of deforestation are very uncertain and subject to debate, yet determine future deforestation and deforestation emissions in scenario simulations.
</div>

Forest management/Description

2021-10-21T20:36:02Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Kallio et al., 2004; FAO, 2001a; FAO, 2008; Brown, 2000; Carle and Holmgren, 2008; FAO, 2012b; FAO, 2010;
}}
<div class="page_standard">

The forest management module describes regional timber demand and the production of timber in the three different management systems clear felling, selective felling and forest plantations. Deforestation rates reported by {{abbrTemplate|FAO}} and double-checked by satellite-based estimates from ESA-CCI land cover data [TO ADD: ESA, 2017] are used to calibrate deforestation rates in IMAGE, using a so-called additional deforestation factor resulting in additional degraded forest area.

===Timber demand===
In IMAGE 3.2, the driver for forest harvest is timber demand per region. Timber demand is the sum of domestic and/or regional demand and timber claims by other regions (export/trade). Production and trade assumptions for saw logs and paper/pulp wood are based on simple historical relationships between population, GDP and timber use per capita [TO ADD: Doelman et al., 2018]. Domestic demand for fuelwood is based on the [[TIMER model]] (See Component [[Energy supply and demand]]) [TO ADD: Dagnachew et al., 2020].

Part of the global energy supply is met by fuelwood and charcoal, in particular in less developed world regions. Not all wood involved is produced from formal forestry activities, as it is also collected from non-forest areas, for example from thinning orchards and along roadsides ([[FAO, 2001a]]; [[FAO, 2008]]). As few reliable data are available on fuelwood production, own assumptions have been made in IMAGE. While fuelwood production in industrialized regions is dominated by large-scale, commercial operations, in transitional and developing regions smaller proportions of fuelwood volumes are assumed to come from forestry operations: 50% and 32% respectively.

===Timber supply & production in forests===
In IMAGE, felling in each region follows a stepwise procedure until timber demand is met, attributed to the three management systems. The proportion for each management system is derived from forest inventories for different world regions ([[Arets et al., 2011]]) and used as model input (Figure Flowchart). Firstly, timber is derived from forest land that has been converted to agriculture. Secondly, timber from forest plantations at the end of their rotation cycle are harvested. Finally, trees from natural forests are harvested, applying clear felling and/or selective felling. In all management systems, trees can only be harvested when the rotation cycle of forest regrowth has been completed.

===Selective logging===
Under selective felling, only a - regional and time specific- fraction of the trees is logged and the other trees remain in the forest. After logging, a fraction of the harvested wood is removed from the forest to fulfil the demand. Biomass left behind in the forest represents losses/residues during tree harvesting (from tree damage and unusable tree parts) or left in the forest because of environmental concerns (biodiversity and nutrient supply). This fraction take-away is derived from literature, defined for industrial roundwood (see [[Arets et al., 2011]]) It is further adjusted to account for the demand for wood fuel, for which it equals unity.

===Forest plantations===
Forest plantations are established for efficient, commercially viable wood production. Their regional establishment in IMAGE 3 is scenario driven (see also Input/Output Table at [[Forest management|Introduction part]]), based on FAO. The expectation is that increasingly more wood will be produced in plantations because sustainability criteria may limit harvest from natural forests ([[Brown, 2000]]; [[Carle and Holmgren, 2008]]; [[FAO, 2012b]]). Forest plantations are assumed to be established firstly on abandoned agricultural land. When sufficient abandoned land is not available, forest plantations are established on cleared forest areas. When a forest plantation has been established, the land cannot be used for other purposes or converted to natural vegetation until the tree rotation cycle has been completed. Forest growth rates are modelled in LPJmL and calibrated to empirical data [TO ADD: Braakhekke et al., 2019].

===Additional deforestation ===
Globally, conversion to agricultural land is the major driver of forest clearing; timber harvest does not result in deforestation, if natural vegetation is regrowing. But there are other causes of deforestation not related to food demand and timber production, such as urbanisation, mining and illegal logging. These activities contribute to loss of forest area, increased degradation risks and a decline in the supply of forest services. To account for this, deforestation rates are calibrated to FAO reported deforestation rates that are consistent with observed deforestation from satellite-based land cover time series from ESA-CCI [TO ADD: ESA, 2017]. The additional category used for this calibration is called ‘additional deforestation’. IMAGE assumes no recovery of natural vegetation in these areas and no agricultural activities.
</div>

Forest management/Policy issues

2021-10-21T20:32:11Z

Doelmanj:

{{ComponentPolicyIssueTemplate
|Reference=PBL, 2010; Brown, 2000;
}}
<div class="page_standard">
==Baseline developments==
In most baseline scenarios, areas of managed forests increase. The IMAGE forest management model was used in the scenario study ‘Rethinking global biodiversity strategies’ on future biodiversity developments ([[PBL, 2010]]). The study projects that, in the absence of additional forestry policy, the area of forest plantations will increase only slightly between 2000 and 2050 (from 1.1 to 1.2 million km<sup>2</sup>). The total forest area for wood production will increase from 9.5 to 14.5 million km<sup>2</sup> (the figure below, left panel). According to this projection, by 2050, just over a third of the global forest area will be used for wood production and consequently. In the same year, the area of primary forest, defined in IMAGE as established before 1970 and not exploited since, will decrease by more than 6 million km<sup>2</sup> from almost 30 million km<sup>2</sup> in 2000.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
Several policy interventions on forest management can be simulated in the IMAGE model 3.2:
* increase in production on highly productive forest plantations;
* increase in carbon storage to mitigate climate change;
* increasing harvest efficiencies, or using harvest residues for energy;
* more reduced impact logging (RIL) techniques, less conventional selective felling.

The scenario study [[Rethinking Biodiversity Strategies (2010) project|‘Rethinking global biodiversity strategies’]] implemented the following two ambition levels for improved forest management as alternatives for the baseline trend (the figures above and below):
# Moderate ambition level: partial substitution of conventional selective felling in tropical forests with RIL techniques, and forest plantations targeted at supplying 25% of the global wood demand;
# High ambition level: full substitution of conventional selective felling with RIL techniques as of 2010, and forest plantations targeted at supplying 40% of the global wood demand by 2050. This represents a plausible future development of plantation growth ([[Brown, 2000]]).

The ambitious improvements in forest management will result in considerably less land used for forestry by 2050 (about 10 million km<sup>2</sup>, or one third smaller area than under the baseline scenario) (see the figure above). With the reduced forest area, and the assumed positive effects of {{abbrTemplate|RIL}} techniques, biodiversity loss caused by forestry will be reduced. For the lower ambition level, gains will be smaller with forestry area expanding well over 3 million km<sup>2</sup>, and less biodiversity loss prevented.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Forest management/Description

2021-10-21T20:30:08Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Kallio et al., 2004; FAO, 2001a; FAO, 2008; Brown, 2000; Carle and Holmgren, 2008; FAO, 2012b; FAO, 2010;
}}
<div class="page_standard">

The forest management module describes regional timber demand and the production of timber in the three different management systems clear felling, selective felling and forest plantations. Deforestation rates reported by {{abbrTemplate|FAO}} and double-checked by satellite-based estimates from ESA-CCI land cover data are used to calibrate deforestation rates in IMAGE, using a so-called additional deforestation factor resulting in additional degraded forest area.

===Timber demand===
In IMAGE 3.2, the driver for forest harvest is timber demand per region. Timber demand is the sum of domestic and/or regional demand and timber claims by other regions (export/trade). Production and trade assumptions for saw logs and paper/pulp wood are based on simple historical relationships between population, GDP and timber use per capita [TO ADD: Doelman et al., 2018]. Domestic demand for fuelwood is based on the [[TIMER model]] (See Component [[Energy supply and demand]]) [TO ADD: Dagnachew et al., 2020].

Part of the global energy supply is met by fuelwood and charcoal, in particular in less developed world regions. Not all wood involved is produced from formal forestry activities, as it is also collected from non-forest areas, for example from thinning orchards and along roadsides ([[FAO, 2001a]]; [[FAO, 2008]]). As few reliable data are available on fuelwood production, own assumptions have been made in IMAGE. While fuelwood production in industrialized regions is dominated by large-scale, commercial operations, in transitional and developing regions smaller proportions of fuelwood volumes are assumed to come from forestry operations: 50% and 32% respectively.

===Timber supply & production in forests===
In IMAGE, felling in each region follows a stepwise procedure until timber demand is met, attributed to the three management systems. The proportion for each management system is derived from forest inventories for different world regions ([[Arets et al., 2011]]) and used as model input (Figure Flowchart). Firstly, timber is derived from forest land that has been converted to agriculture. Secondly, timber from forest plantations at the end of their rotation cycle are harvested. Finally, trees from natural forests are harvested, applying clear felling and/or selective felling. In all management systems, trees can only be harvested when the rotation cycle of forest regrowth has been completed.

===Selective logging===
Under selective felling, only a - regional and time specific- fraction of the trees is logged and the other trees remain in the forest. After logging, a fraction of the harvested wood is removed from the forest to fulfil the demand. Biomass left behind in the forest represents losses/residues during tree harvesting (from tree damage and unusable tree parts) or left in the forest because of environmental concerns (biodiversity and nutrient supply). This fraction take-away is derived from literature, defined for industrial roundwood (see [[Arets et al., 2011]]) It is further adjusted to account for the demand for wood fuel, for which it equals unity.

===Forest plantations===
Forest plantations are established for efficient, commercially viable wood production. Their regional establishment in IMAGE 3 is scenario driven (see also Input/Output Table at [[Forest management|Introduction part]]), based on FAO. The expectation is that increasingly more wood will be produced in plantations because sustainability criteria may limit harvest from natural forests ([[Brown, 2000]]; [[Carle and Holmgren, 2008]]; [[FAO, 2012b]]). Forest plantations are assumed to be established firstly on abandoned agricultural land. When sufficient abandoned land is not available, forest plantations are established on cleared forest areas. When a forest plantation has been established, the land cannot be used for other purposes or converted to natural vegetation until the tree rotation cycle has been completed. Forest growth rates are modelled in LPJmL and calibrated to empirical data [TO ADD: Braakhekke et al., 2019].

===Additional deforestation ===
Globally, conversion to agricultural land is the major driver of forest clearing; timber harvest does not result in deforestation, if natural vegetation is regrowing. But there are other causes of deforestation not related to food demand and timber production, such as urbanisation, mining and illegal logging. These activities contribute to loss of forest area, increased degradation risks and a decline in the supply of forest services. To account for this, deforestation rates are calibrated to FAO reported deforestation rates that are consistent with observed deforestation from satellite-based land cover time series from ESA-CCI. The additional category is called ‘additional deforestation’. IMAGE assumes no recovery of natural vegetation in these areas and no agricultural activities.
</div>

Forest management

2021-10-21T20:16:54Z

Doelmanj:

{{ComponentTemplate2
|Application=Rethinking Biodiversity Strategies (2010) project; Shared Socioeconomic Pathways - SSP (2014) project; EU Seventh Framework Programme - FP7;
|IMAGEComponent=Drivers; Land-use allocation; Carbon cycle and natural vegetation; Energy supply and demand;
|Model-Database=EFIGTM model;
|KeyReference=Arets et al., 2011;
|Reference=FAO, 2010; Carle and Holmgren, 2008; Putz et al., 2012; FAO, 2006b; Alkemade et al., 2009; Hartmann et al., 2010;
|InputVar=Demand traditional biomass; Land cover, land use - grid;
Forest plantation demand; Land suitability - grid; Harvest efficiency; Timber demand ; Carbon pools in vegetation - grid; Fraction of selective logging;
|Parameter=Traditional biomass from non-forest land; FAO deforestation rates;
|OutputVar=Timber use fraction; Forest residues; Forest management type - grid; Regrowth forest area - grid; Harvested wood; Degraded forest area;
|ComponentCode=FM
|AggregatedComponent=Agriculture and land use
|FrameworkElementType=pressure component
}}<div class="page_standard">
The global forest area and wooded land area has been estimated for 2010 at just over 40 and 11 million km<sup>2</sup>, respectively ([[FAO, 2010]]). Forest resources are used for multitude of purposes, including timber, fuel, food, water and other forest-related goods and services. In addition, (semi-) natural forests are home to many highly valued species of interest for nature conservation and biodiversity.

The total global forest area is continuing to decline at difference rates in different world regions. Although the rate of global deforestation has decreased in the last decade, deforestation is still occurring on a significant scale in large parts of Latin America, Africa and Southeastern Asia. At the same time, the net forest area is expanding in some regions, such as in Europe and China ([[FAO, 2010]]). Sustainable management of global forest resources may contribute to preserving forests, slowing down or reversing degradation processes, and conserving forest biodiversity and carbon stocks ([[FAO, 2010]]).

Several types of forest management systems are employed in meeting the worldwide demand for timber, paper, fibreboard, traditional or modern bioenergy and other products. Management practices depend on forest type, conservation policies and regulation, economics, and other, often local, factors. Practices differ with respect to timber volume harvested per area, rotation cycle, and carbon content and state of biodiversity of the forested areas.

Modelling of forests and forest management is an integral part of the IMAGE 3.2 framework, with a simulated forest area in 2010 at about 47 million km<sup>2</sup> , somewhat larger than observed by {{abbrTemplate|FAO}} as this area includes fractions of other wooded land (see Component [[Carbon cycle and natural vegetation]]). To manage these forests, three forest management systems are defined in IMAGE 3.2 in a simplification of the range of management systems implemented worldwide ([[Carle and Holmgren, 2008]]; [[Arets et al., 2011]]).
# The first forest management system is clear cutting or clear felling, in which all trees in an area are cut down followed by natural or ‘assisted’ regrowth, as widely applied in temperate regions.
# The second forest management system is selective logging of (semi)natural forests, in which only trees of the highest economic value are felled, commonly used in tropical forests with a high heterogeneity of tree species. An ecological variant of selective logging is reduced impact logging ({{abbrTemplate|RIL}}) directed to reducing harvest damage, stimulating regrowth and maintaining biodiversity levels ([[Putz et al., 2012]]).
# The third forest management system considered in IMAGE 3.2 is forest plantations, such as hardwood tree plantations in the tropics, and poplar plantations in temperate regions. Selected tree species, either endemic or exotic to the area, are planted and managed intensively, for example through pest control, irrigation and fertiliser use, to maximise production. Forest plantation growth is modelled in LPJmL and was recalibrated in IMAGE 3.2 to empirical data [TO ADD: Braakhekke et al., 2019] as forest plantations generally have a higher productivity level than natural forests ([[FAO, 2006b]]). By producing more wood products on less land, plantations may contribute to more sustainable forest management by reducing pressure on natural forests ([[Carle and Holmgren, 2008]]; [[Alkemade et al., 2009]]). However, the ecological value of biodiversity in many forest plantations is relatively low ([[Hartmann et al., 2010]]).

{{InputOutputParameterTemplate}}
</div>

Agricultural economy/Data uncertainties limitations

2021-10-21T20:09:33Z

Doelmanj:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Narayanan et al., 2012; Stehfest et al., 2013; Nelson et al., 2014; Bruinsma, 2003; Hertel, 2011; Woltjer et al., 2011; Alexandratos and Bruinsma, 2012; Woltjer, 2011;
}}
==Data, uncertainty and limitations==
===Data===
The MAGNET model uses the [ REF GTAP9 database ] for sectoral input–output tables and bilateral trade in the reference year 2011 ([[Narayanan et al., 2012]] [Update?]). The model applies all GTAP sectors for agriculture but industrial and service sectors are aggregated into a few groups of sectors. The regional representation of GTAP is aggregated to match the IMAGE regions. For the start year, agricultural land use for both arable land and permanent grassland is based on FAO statistics. In addition, the model also uses a large number of essential coefficients, such as Armington trade elasticities, consumption function parameters, substitution elasticities for all production nests, {{abbrTemplate|CET}} elasticities for land-use transformations, and elasticities in the land supply curve. Some parameters are based on econometric research or economic literature, while others are no more than ‘best guesses’ ([[Woltjer et al., 2011]]). The autonomous technological yield change in is based on FAO projections in both MAGNET and IMAGE ([[Alexandratos and Bruinsma, 2012]]).

===Uncertainties ===
Various model comparisons between different agro-economics have been carried out as part of the AgMIP project: for example, investigating the effects of variation in drivers between SSPs on key agro-economic variables such as crop area and food availability [TO ADD: Stehfest et al., 2020], the sensitivity of models for climate change impacts [TO ADD: van Meijl et al., 2018] and a comparison of the effects on food security of yield changes due to climate change and climate change mitigation policies [TO ADD: Hasegawa et al., 2019].

Another model comparison within [[AgMIP and ISI-MIP project|AgMIP]] included ten global agro-economic models using harmonised scenario drivers ([[Nelson et al., 2014]]; [[Von Lampe et al., 2014]]). Results indicate that MAGNET is in the upper range of other models, in terms of future land-use expansion. This is probably due to the relatively large land supply in MAGNET, which allows further expansion of agricultural land, particularly in North and South America, and Africa. In contrast, several other models do not explicitly consider agricultural land expansion, but only allow interchanges between, for example, arable land and grassland. In addition to land supply, the most relevant uncertainties in MAGNET are autonomous technological change, relative contribution of intensification or expansion to total production growth, retaining current trade patterns in long-term scenarios, and dynamics in the livestock sector, especially with respect to pasture area and grassland intensification ([[Stehfest et al., 2013]]), and long-term dietary preferences. The empirical basis for many of these parameters in MAGNET and all other agro-economic models needs to be improved ([[Hertel, 2011]]).

===Limitations ===
The MAGNET model provides a complete and internally consistent view of the world economy, covering all economic sectors, and a dynamic modelling of all primary and intermediate production and demand. However, a little known limitation is the uncertainties in constructing the GTAP/MAGNET database because many ad hoc assumptions need to be made to fill the database, for instance, allocating value added across inputs.

Furthermore, volumes in the model are not expressed in physical terms but in monetary values. Likewise, all substitutions in the model are based on monetary values. As a consequence, there is no guarantee that changes in composition are consistent with the physical requirements, such as in livestock feed. Thus, a closer link to physical units is needed ([[Woltjer, 2011]]).

Because of the highly aggregated and general character of MAGNET, most elasticities are kept constant over time. Some improvements have been introduced in the consumption function, by making the income elasticities dependent on income levels. Armington elasticities are also constant, and thus small trade flows in the starting year only increase very slowly in future years.

Although some limitations can be reduced by adding physical units and improving the empirical basis for the main elasticities, many simplifications in agro-economic models will remain. MAGNET provides a consistent system to assess economy-wide effects of policy measures on land use, income, welfare and production, and supports policymakers and scientists in gaining insights into the complex interlinkages in the agricultural system. Nevertheless, simplifications and uncertainties that result from such a broad coverage need to be kept in mind when interpreting results.

Agricultural economy/Policy issues

2021-10-21T19:55:17Z

Doelmanj:

{{ComponentPolicyIssueTemplate
|Reference=Banse et al., 2008; Verburg et al., 2009; PBL, 2010; PBL, 2011; Westhoek et al., in preparation; Overmars et al., 2014;
}}
<div class="page_standard">
==Baseline developments==
In the SSP scenarios, agricultural crop and livestock production increases rapidly driven by population increase and dietary changes ([[Doelman et al., 2018]])[TO ADD: van Meijl et al., 2020]. As a consequence of production increases, the total area of cropland and pasture is projected to increase, although this is less certain. Depending on scenario and region, some scenarios may also show decreasing land areas, certainly after 2050 when the population starts to decline in several regions. Variations between SSP scenarios are substantial dependent on key drivers such as GDP, population, consumption, land use protection, trade and productivity assumptions. A decomposition of these effects was analyzed in a multi-model decomposition analysis as part of the AgMIP project [TO ADD: Stehfest et al., 2019].

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
Numerous policy interventions can be studied:
* Biofuel policies: Partly as an autonomous process under high oil prices but mainly driven by biofuel policies, the proportion of biofuels (so far, only first generation) in the transport sector is projected to increase ([[Banse et al., 2008]]). The model can be used to estimate direct and indirect land-use change and associated emissions.
* REDD policies: Forest protection leads to a reduction in CO<sub>2</sub> emissions from land-use change. The related opportunity costs can be used to estimate cost curves for the emission abatement that results from REDD policies ([[Overmars et al., 2014]]).
* Afforestation policies: Greenhouse gas pricing can make afforestation for carbon storage in biomass profitable. Cost-optimal levels of afforestation can be estimated in IMAGE and implemented as reductions in agricultural land in MAGNET to assess food system and security impacts [TO ADD: Doelman et al., 2020]).
* Agricultural and trade policies can be assessed for their effects on land use, greenhouse gas emissions and biodiversity ([[Verburg et al., 2009]]).
* Measures to reduce biodiversity loss by increasing protected areas, increasing agricultural productivity, dietary changes, and reducing waste ([[PBL, 2010]])[TO ADD: Leclere et al., 2020]. Several biodiversity options, in a stepwise introduction, affect land and commodity prices as well as land-use change (the figure below).
* Consumption changes, dietary preferences, and their effect on global land use, prices and emissions can be studied ([[PBL, 2011]]; [[Stehfest et al., 2013]])[TO ADD: Van Meijl et al., 2020].

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Agricultural economy/Policy issues

2021-10-21T19:49:27Z

Doelmanj:

{{ComponentPolicyIssueTemplate
|Reference=Banse et al., 2008; Verburg et al., 2009; PBL, 2010; PBL, 2011; Westhoek et al., in preparation; Overmars et al., 2014;
}}
<div class="page_standard">
==Baseline developments==
In the SSP scenarios, agricultural crop and livestock production increases rapidly driven by population increase and dietary changes ([[Doelman et al., 2018]])[TO ADD: van Meijl et al., 2020]. As a consequence of production increases, the total area of cropland and pasture is projected to increase, although this is less certain. Depending on scenario and region, some scenarios may also show decreasing land areas, certainly after 2050 when the population starts to decline in several regions.

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==
Numerous policy interventions can be studied:
* Biofuel policies: Partly as an autonomous process under high oil prices but mainly driven by biofuel policies, the proportion of biofuels (so far, only first generation) in the transport sector is projected to increase ([[Banse et al., 2008]]). The model can be used to estimate direct and indirect land-use change and associated emissions.
* REDD policies: Forest protection leads to a reduction in CO<sub>2</sub> emissions from land-use change. The related opportunity costs can be used to estimate cost curves for the emission abatement that results from REDD policies ([[Overmars et al., 2014]]).
* Afforestation policies: Greenhouse gas pricing can make afforestation for carbon storage in biomass profitable. Cost-optimal levels of afforestation can be estimated in IMAGE and implemented as reductions in agricultural land in MAGNET to assess food system and security impacts [TO ADD: Doelman et al., 2020]).
* Agricultural and trade policies can be assessed for their effects on land use, greenhouse gas emissions and biodiversity ([[Verburg et al., 2009]]).
* Measures to reduce biodiversity loss by increasing protected areas, increasing agricultural productivity, dietary changes, and reducing waste ([[PBL, 2010]])[TO ADD: Leclere et al., 2020]. Several biodiversity options, in a stepwise introduction, affect land and commodity prices as well as land-use change (the figure below).
* Consumption changes, dietary preferences, and their effect on global land use, prices and emissions can be studied ([[PBL, 2011]]; [[Stehfest et al., 2013]])[TO ADD: Van Meijl et al., 2020].

{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}

{{PIEffectOnComponentTemplate }}
</div>

Agricultural economy/Description

2021-10-21T19:20:26Z

Doelmanj:

{{ComponentDescriptionTemplate
|Reference=Hertel, 1997; Britz, 2003; Armington, 1969; Huang et al., 2004; Helming et al., 2010; Banse et al., 2008; Bruinsma, 2003; Woltjer et al., 2011; Van Meijl et al., 2006; Eickhout et al., 2009; Overmars et al., 2014; Alexandratos and Bruinsma, 2012;
}}
<div class="page_standard">

The MAGNET model ([[Woltjer et al., 2014]]) is based on the standard GTAP model ([[Hertel, 1997]]), which is a multi-regional, static, applied computable general equilibrium ({{abbrTemplate|CGE}}) model based on neoclassical microeconomic theory. Although the model covers the entire economy, there is a special focus on agricultural sectors. It is a further development of GTAP regarding land use, household consumption, livestock, food, feed and energy crop production, and emission reduction from deforestation or afforestation.

===Demand and supply===
Household demand for agricultural products is calculated based on changes in income, income elasticities, preference shift, price elasticities, cross-price elasticities, and the commodity prices arising from changes in the supply side. Demand and supply are balanced via prices to reach equilibrium. Income elasticities for agricultural commodities are consistent with FAO estimates ([[Britz, 2003]]), and dynamically depend on purchasing power parity ({{abbrTemplate|PPP}}) corrected GDP per capita. The supply of all commodities is modelled by an input–output structure that explicitly links the production of goods and services for final consumption via different processing stages back to primary products (crops and livestock products) and resources. At each production level, input of labour, capital, and intermediate input or resources (e.g., land) can be substituted for one another. For example, labour, capital and land are input factors in crop production, and substitution of these production factors is driven by changes in their relative prices. If the price of one input factor increases, it is substituted by other factors, following the price elasticity of substitution.

===Regional aggregation and trade===
MAGNET is flexible in its regional aggregation (140 regions). In linking with IMAGE, MAGNET distinguishes 28 individual large world regions, closely matching the regions in IMAGE (Figure [[Region classification map|IMAGE regions]]). Slightly more detail is provided the European regions in order to properly model the EU single market. Similar to most other {{abbrTemplate|CGE}} models, MAGNET assumes that products traded internationally are differentiated according to country of origin. Thus, domestic and foreign products are not identical, but are imperfect substitutes (Armington assumption; [[Armington, 1969]]).

===Land use===
In addition to the standard [[GTAP database|GTAP model]], MAGNET includes a dynamic land-supply function ([[Van Meijl et al., 2006]]) that accounts for the availability and suitability of land for agricultural use, based on information from IMAGE (see below). A nested land-use structure accounts for the differences in substitutability of the various types of land use ([[Huang et al., 2004]]; [[Van Meijl et al., 2006]]). In addition, MAGNET includes international and EU agricultural policies, such as production quota and export/import tariffs ([[Helming et al., 2010]]).

===Biofuel crops===
MAGNET includes ethanol and biodiesel as first-generation biofuels made from wheat, sugar cane, maize, and oilseeds ([[Banse et al., 2008]]) and the use of by-products ({{abbrTemplate|DDGS}}, oilcakes) from biofuel production in the livestock sector. Second-generation biofuels are also included, with the potential amount of residues available from IMAGE/TIMER (TO ADD: Daioglou et al., 2016).

===Livestock===
MAGNET distinguishes the livestock commodities of beef cattle, dairy cattle, other cattle (sheep & goats), dairy cattle, poultry, and pig and other animal products. The first three are the ruminant sectors which are grass and crop fed, while the poultry and pigs sectors are crop fed. Modelling the livestock sector includes different feedstuffs, such as feed crops, co-products from biofuels (oil cakes from rapeseed-based biofuel, or distillers grain from wheat-based biofuels), and grass ([[Woltjer, 2011]]). Grass may be substituted by feed from crops for ruminants.

===Land supply===
In MAGNET, land supply is calculated using a land-supply curve that relates the area in use for agriculture to the land price. Total land supply includes all land that is potentially available for agriculture, where crop production is possible under soil and climatic conditions, and where no other restrictions apply such as urban or protected area designations (see also Component Land-use allocation). In the IMAGE model, total land supply for each region is obtained from potential crop productivity and land availability on a resolution of 5x5 arcminutes ([[Mandryk et al., 2015]]). The supply curve depends on total land supply, current agricultural area, current land price, and estimated price elasticity of land supply in the starting year. Regions differ with regard to the proportion of land in use, and with regard to change in land prices in relation to changes in agricultural land use.

===Reduced land availability===
By restricting land supply in IMAGE and MAGNET, the models can assess scenarios with additional protected areas, or reduced emissions from deforestation and forest degradation ({{abbrTemplate|REDD}}). These areas are excluded from the land supply curve in MAGNET, leading to lower elasticities, less land-use change and higher prices, and are also excluded from the allocation of agricultural land in IMAGE (e.g., [[Overmars et al., 2014]])(TO ADD: Doelman et al., 2018).

===Intensification of crop and pasture production===
Crop and pasture yields in MAGNET may change as a result of the following four processes:
# autonomous technological change (external scenario assumption);
# intensification due to the substitution of production factors (endogenous);
# climate change (from IMAGE);
# change in agricultural area affecting crop yields (such as, decreasing average yields due to expansion into less suitable regions; from IMAGE).

Biophysical yield effects due to climate and area changes are calculated by the IMAGE crop model and communicated to MAGNET. Likewise, also the potential yields and thus the yield gap can be assessed with the crop model in IMAGE. External assumptions on autonomous technological changes are mostly based on FAO projections ([[Alexandratos and Bruinsma, 2012]]), which describe per region and commodity, the assumed future changes in yields for a wide range of crop types. In MAGNET, the biophysical yield changes are combined with the autonomous technological change to give the total exogenous yield change. In addition, during the simulation period, MAGNET calculates an endogenous intensification as a result of price-driven substitution between labour, land and capital. In IMAGE, regional yield changes due to autonomous technological change and endogenous intensification according to MAGNET are used in the spatially explicit allocation of land use (Component [[Land-use allocation]]).
</div>

Agricultural economy

2021-10-21T19:06:07Z

Doelmanj:

{{ComponentTemplate2
|Application=Eururalis (2007) project; Millennium Ecosystem Assessment - MA (2005) project; AgMIP and ISI-MIP project; The Protein Puzzle (2011) project; Rethinking Biodiversity Strategies (2010) project; Roads from Rio+20 (2012) project;
|IMAGEComponent=Drivers; Land-use allocation;
|Model-Database=EFIGTM model;
|KeyReference=Stehfest et al., 2013; Woltjer et al., 2014; Von Lampe et al., 2014; Bijl et al., 2017;
|Reference=Woltjer et al., 2011; Kallio et al., 2004; Carpenter et al., 2006; Van Vuuren et al., 2018;
|InputVar=Population; GDP per capita; Capital supply; Labour supply; Trade policy; Biofuel policy; Land supply; Potential crop and grass yield - grid; Technological change (crops and livestocks);
|Parameter=Income and price elasticities;
|OutputVar=Management intensity crops; Management intensity livestock; Food availability per capita; Commodity price; Livestock production; Crop production; Demand (all commodities); Trade (all commodities);
|ComponentCode=AEF
|AggregatedComponent=Agriculture and land use
|FrameworkElementType=pressure component
}}
<div class="page_standard">
As a result of the growing world population and higher per capita consumption, production of food, feed, fibres and other products, such as bioenergy and timber, will need to increase rapidly in the coming decades. Even with the expected improvements in agricultural yields and efficiency, there will be increasing demand for more agricultural land. However, expansion of agricultural land will lead to deforestation and increases in greenhouse gas emissions, loss of biodiversity and ecosystem services, and nutrient imbalances. To reduce these environmental impacts, a further increase in agricultural yields is needed, together with other options such as reduced food losses, dietary changes, improved livestock systems, and better nutrient management.

In the IMAGE framework, future development of the agricultural economy can be calculated using the agro-economic model [[MAGNET model|MAGNET]] (Woltjer et al. ([[Woltjer et al., 2014|2014]])). MAGNET is a computable general equilibrium ({{abbrTemplate|CGE}}) model that is connected via a soft link to the core model of IMAGE. Demographic changes and rising incomes are the primary driving factors of the MAGNET model, and lead to increasing and changing demand for all commodities including agricultural commodities. In response to changing demand, agricultural production is increasing, and the model also takes into account changing prices of production factors, resource availability and technological progress. In MAGNET, agricultural production supplies domestic markets, and other countries and regions are supplied via international trade, depending on historical trade balances, competitiveness (relative price developments), transport costs and trade policies. MAGNET uses information from IMAGE on land availability and suitability, and on changes in crop yields due to climate change and agricultural expansion on inhomogeneous land areas. The results from MAGNET on agricultural production, grassland area, and endogenous yield efficiency (management factor) changes are used in IMAGE to calculate spatially explicit land-use change, and the environmental impacts on carbon, nutrient and water cycles, biodiversity, and climate.

Although MAGNET is the standard agro-economic model used with IMAGE, other models can be linked with IMAGE. For example, the [[IMPACT model]] was used with IMAGE in the [[Millennium Ecosystem Assessment - MA (2005) project|Millennium Ecosystem Assessment]] ([[Carpenter et al., 2006]]), and in a [[The Protein Puzzle (2011) project|PBL study on protein supply]], both the [[MAGNET model|MAGNET]] and the [[IMPACT model|IMPACT]] model were used to study the same set of scenarios. This allowed a systematic comparison between IMPACT and MAGNET ([[Stehfest et al., 2013]]). In a more recent study [[van Vuuren et al., 2018]] the Food Demand Model ([[Bijl et al., 2017]]) was used for projections of food demand with various diets. This is a physically oriented, statistical model using based on historical relations between income, food consumption and regional differences, which is also an integrated part of the IMAGE framework.

Other land-use changes, such as infrastructure expansion, which do not require interregional links, are described in the [[Land-use allocation|land-use allocation]] model). Demand for timber is described in the [[Forest management|forest management]] page.

{{InputOutputParameterTemplate}}
</div>

Frank et al., 2018

2021-06-16T14:39:27Z

Doelmanj: Created page with "{{ReferenceTemplate |Author=Stefan Frank, Petr Havlík, Elke Stehfest, Hans van Meijl, Peter Witzke, Ignacio Pérez-Domínguez, Michiel van Dijk, Jonathan C. Doelman, Thomas F..."

{{ReferenceTemplate
|Author=Stefan Frank, Petr Havlík, Elke Stehfest, Hans van Meijl, Peter Witzke, Ignacio Pérez-Domínguez, Michiel van Dijk, Jonathan C. Doelman, Thomas Fellmann, Jason F. L. Koopman, Andrzej Tabeau & Hugo Valin
|Year=2018
|Title=Agricultural non-CO2 emission reduction potential in the context of the 1.5 °C target.
|PBL-link=https://www.pbl.nl/en/publications/agricultural-non-co2-emission-reduction-potential-in-the-context-of-the-1-5-c-target
|DOI=https://doi.org/10.1038/s41558-018-0358-8
|PublicationType=Journal article
|Journal=Nature Climate Change
|Volume2=9
|Issue=1
|Pages2=66-72
}}