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**Abstract**

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Biogeochemical simulation models are important tools for describing and quantifying the
contribution of agricultural systems to C sequestration and GHG source/sink status. The
abundance of simulation tools developed over recent decades, however, creates a difficulty
because predictions from different models show large variability. Discrepancies between the
conclusions of different modelling studies are often ascribed to differences in the physical and
biogeochemical processes incorporated in equations of C and N cycles and their interactions.

Here we review the literature to determine the state-of-the-art in modelling agricultural (crop
and grassland) systems. In order to carry out this study, we selected the range of
biogeochemical models used by the CN-MIP consortium of FACCE-JPI
(http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim,
RothC and STICS. In our analysis, these models were assessed for the quality and
comprehensiveness of underlying processes related to pedo-climatic conditions and
management practices, but also with respect to time and space of application, and for their
accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined
pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed
by limitations in the algorithms simulating the effects of management practices (33.1%). The
multiplicity of scales in both time and space is a fundamental feature, which explains the
remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future
development of C and N models. They include the explicit representation of soil microbial
biomass to drive soil organic matter turnover, the effect of N shortage on SOM
decomposition, the improvements related to the production and consumption of gases and an
adequate simulations of gas transport in soil. On these bases, the assessment of trends and
gaps in the modelling approaches currently employed to represent biogeochemical cycles in
crop and grassland systems appears an essential step for future research.

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Keywords: Biogeochemical models, C cycle, N cycle, management, pedo-climate

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# 1. Introduction

The sensitivity of soil carbon (C) stocks and greenhouse gas (GHG) emissions to climate and management practices demands a comprehensive methodology for effective policy analyses (Li et al., 1994). Enhancing soil C sequestration and reducing GHG emissions from agricultural soils are key objectives for reducing the climate impact of food production and they strongly depend on agricultural practices such as crop residue return, soil tillage modalities, and enhanced nitrogen (N) fertilization management. Whether C return to soils appear as a main controlling factor, in some cases (e.g. dry climates) reduced tillage may also be an effective measure for enhancing C sequestration (e.g. Chatskikh et al., 2008; Powlson et al., 2012). To avoid pollution swapping, assessments of the potential to reduce climate impact should also include other impacts such as nitrate ($NO_3^-$) leaching into groundwater, ammonia volatilization and soil erosion, which can also be reduced, for example, by increasing the use of grazed pastures in dairy farms (Rotz et al., 2009, Peyraud, 2011). In addition, it is important to consider the interactions on the hundred-year timescale of soil C equilibration (Lardy et al., 2011) and the relatively more rapid changes induced by agricultural practices (Angers et al., 1995). It is likely that most agricultural soils are not in equilibrium with respect to C storage and have the greatest potential for short-term C losses or gains, while they may also be sensitive to the effects of long-term, climate-driven processes (Wutzler and Reichstein, 2007). It is also important to recall that C and nitrogen (N) cycling strongly depends on interactions among plant growth processes, soil water dynamics and soil N dynamics that are highly non-linear and thus difficult to predict with simple approaches.

Process-based ecosystem models take the approach of simulating underlying biogeochemical processes, such as plant photosynthesis and respiration, using mathematical equations that determine the allocation of C from atmospheric $CO_2$ into biomass down to the soil organic matter (SOM). A relatively complete suite of biogeochemical processes (e.g. plant growth, organic matter decomposition, fermentation, ammonia volatilisation, nitrification and denitrification) is generally embedded in these models, enabling computation of transport and transformations in plant-soil ecosystems. Sub-models are designed to interact with each other to describe cycles of water, C and N for target ecosystems, thus any change in the environmental factors collectively affect a group of biogeochemical reactions. Extensively tested biogeochemical models (with the coupled C-N cycling) are effective tools for examining the magnitude and spatial-temporal patterns of C and N fluxes, and play an important role in designing specific policies appropriate to the soils, climate, and agricultural conditions of a location or region. However, results of state-of the-art terrestrial
biogeochemical models, describing the contribution of agricultural systems to C sequestration and GHG source/sink status, may diverge significantly. Such differences between model results are often attributed to physical and biogeochemical processes being inadequately resolved and, for these models, the improvement of algorithms and structure is recommended beyond parameter optimization (Tian et al., 2011, Lu and Tian, 2013).

It is the goal of this paper to examine the strengths and weaknesses of nine crop and grassland models that incorporate C and N fluxes into biogeochemical frameworks and fully assess C and GHG dynamics in agricultural soils. These models are commonly applied worldwide and are used to simulate biogeochemical and related outputs by the project “C and N models intercomparison and improvement to assess management options for GHG mitigation in agro-systems worldwide” (CN-MIP, 2014-2017), established within the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI, http://www.faccejpi.com), which brings together 10 organizations from six countries. With this analysis we are not arguing against the quality of models. While highlighting weaknesses and limits of current modelling approaches as documented in several published studies, we intend to offer a general overview as a basis for new ways of improvement current modelling approaches.

The following rationale has been used in the organization of this article. We first present the conceptual basis and the equations of the modelling approaches examined (Section 2). Section 3 reports on the documented performance of biogeochemical models against data, and discuss their relative strengths and weaknesses. Section 4 presents an outlook on recent research developments and future approaches. In Section 5, remarks are made concerning the bearing of the findings on a wider interpretation of biogeochemical modelling.

## 2. Modelling approaches

### 2.1. *Basic model assumptions*

Biophysical and biogeochemical models are widely applied for studying crop and grassland productivity and GHG emissions in agricultural systems worldwide. In recent decades, these tools have also been used for assessing the expected impacts of future climate, as represented by several climate change scenarios (Graux et al., 2013). According to several studies, however (Palosuo et al., 2011, Rotter et al., 2012, Asseng et al., 2013, Sándor et al., 2016), key model limitations have been identified, and different models have been found to provide different results when run in the same conditions of climate, soil and management.
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More specifically, a typical process can be described by using different approaches, thus
resulting in different final outputs.

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All the models selected within CN-MIP are process-based models. They attempt to reproduce the most relevant ecological and physiological process through a theoretical understanding grounded in state-of-the art knowledge. In this way, they reproduce specific agro-ecological dynamics under prescribed conditions of climate, soil and management, thanks to the concepts and relationships that interlink entities of the real world. Most models represent plant phenology and yield-formation processes, together with functional processes at the basis of SOM (Soil Organic Matter) turnover, gas exchange at the soil-plant-atmosphere interface and soil water dynamics.

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## 2.2. The CN-MIP models

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The nine models considered for the CN-MIP exercise were mainly developed for crop
or grassland ecosystems. These models were chosen since they are able to simulate GHG
emissions under several management options. We were able to assess their ability to represent
the GHG emission mitigation by modelling a variety of land management practices. The nine
models analysed for this intercomparison are: APSIM, CERES-EGC, DayCent, DNDC,
DSSAT, EPIC, PaSim, RothC and STICS (Tab. 1). Below, a brief description of each model is
provided.

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i) APSIM (The Agricultural Production Systems sIMulator) (Keating et al., 2003) simulates several systems through the interaction among plants, animals, soil, climate and management. The model allows the analysis of the whole-farm system, including crop and pasture sequences and rotations, and livestock.

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ii) CERES-EGC (Crop Environment REsource Synthesis - Environnement et Grandes Cultures) (Gabrielle et al., 1995) simulates the biogeochemical cycles of water, C and N in agro-ecosystems. The model predicts crop production and the environmental impacts related to the agriculture activity (e.g. N₂O, NO, NH₃, CO₂, NO₃) based on management for a wide range of arable crops (e.g. wheat, barley, maize, sorghum, sunflower, pea, sugar-beet, oilseed rape and miscantus). Crop-specific modules include approaches for plant growth and development, coupled to a generic soil sub-model.

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iii) *DayCent* (Parton et al., 1994) is a biogeochemical model able to simulate crop growth, soil C dynamics, N leaching, gaseous emissions (e.g. N₂O, NO, N₂, NH₃, CH₄ and CO₂) and C fluxes (e.g. NPP, NEE) in crop fields, grasslands, forests, and savanna ecosystems. The
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model allows to simulate also several management practices (i.e. fertilization, tillage, pruning, cutting, grazing, etc.) as well as specific external disturbances (i.e. fires).

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iv) *DNDC* (DeNitrification-DeComposition) (Li et al., 1992a) simulates C and N biogeochemistry in agro-ecosystems. The model predicts crop growth, soil regimes (i.e. temperature and moisture), soil C dynamics, N leaching, and trace gases emissions (e.g. N₂O, NO, N₂, NH₃, CH₄ and CO₂). The model was expanded in 2012 to include biophysical processes in whole-farm systems (Li et al., 2012).

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v) *DSSAT* (Decision Support System For Agrotechnology Transfer) (IBSNAT, 1993, Tsuji, 1998, Uehara, 1998 and Jones et al., 1998), was originally developed to facilitate the application of crop models in a systems approach to agronomic research. DSSAT ver. 4.6 (i.e. cropping system model, CSM) and its crop simulation models integrates the effects of soil, crop phenotype, weather and management options. DSSAT includes improved application programs for seasonal, spatial, sequence and crop rotation analyses that assess the economic risks and environmental impacts associated with irrigation, fertilizer and nutrient management, climate variability, climate change, soil carbon sequestration, and precision management. The model can predict crop yield, resource dynamics such as for water, N and C, environmental impact (i.e. N leaching), evapotranspiration and SOM accumulation.

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vi) *EPIC* (Environmental Policy Integrated Climate) (Williams, 1995) can simulate about 80 crops through its crop growth model which uses unique parameter values for each crop. It can predict changes in soil, water, nutrient, pesticide movements, and crop yields due to effects of management decisions. Moreover, it can also assess water quality, N and C cycling, climate change impacts, and the effects of atmospheric CO₂.

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vii) *PaSim* (Pasture Simulation model) (Riedo et al., 1998) is a process-based, grassland-specific ecosystem model that simulates grassland and pasture productivity and GHG emissions to the atmosphere. The model consists of sub-models for grass, animals, microclimate, soil biology, soil physics and management.

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viii) *RothC* (Rothamsted Carbon model) (Coleman and Jenkinson, 1999) is a specific tool for the assessment of organic C turnover in non-waterlogged topsoil. The model allows for the effects of soil type, temperature, moisture content and plant cover on the turnover process.

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ix) *STICS* (Simulateur multi-disciplinaire pour les Cultures Standard) (Brisson et al., 1998) is a soil-crop model which is built on a generic framework for plant description. Within this framework, the selection of adequate options and parameters values allows to simulate a wide range of plants, from annual crops to perennial grasses or trees. The model simulates plant

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growth as well as water, C and N fluxes. It allows to consider the effect of a large range of management options on agronomic (biomass or grain productivity and quality) and environmental (C and N storage, nitrate leaching, N₂O emissions) outputs.

# 3. Results

## 3.1. *Model analysis*

For reducing the uncertainty in estimating the magnitude and spatial-temporal patterns of C and N fluxes from several agro-systems (i.e. crops, grassland and livestock), and for improving the understanding of how these tools work, we analysed the most important processes and approaches implemented into the models. This analysis was based on a top-down approach focused at gaining insight into compositional sub-systems. On this basis, we indicated three levels of information containing specific processes/approaches that were subdivided according to different levels of detail.

The starting point (level 1) was the detection of discrete units considered in agricultural modelling, which are essential to characterize agricultural systems. In this level, characterized by the lowest level of detail required for the analysis, we differentiated five general classes that should be implemented within all biophysical/biogeochemical process-based models for crops and grasslands. These classes concern ecological and physiological processes, management options, soil structure, and weather inputs (Tab. 2).

Then, in the level 2 (intermediate level of detail) specific processes were identified within each general class (level 2). In this level 20 "main processes" were identified, which we retained as basic to describe the most important biophysical/biogeochemical dynamics (Tab. 3) of each general class indicated in the previous level.

Finally, in the level 3 (highest level of detail) almost 200 modelling approaches (i.e. methods, options or components), identifying specific dynamics or mechanisms contained within the previous main processes (supplementary material) were reported (level 3). These approaches were extrapolated taking into account the current existing knowledge on the different methods, options and components able to describe the most important biophysical/biogeochemical dynamics (Tab. 3a-e in supplementary material).

There are a number of advantages to such a “top-down” approach. An advantage is the insight that can be gained from examining the level of detail that each model provides. This in turn helps in identifying areas in the model structures to establish their reliability and relevance for intended purposes. Such an approach also helps in tracing possible links with the basic processes of each model (identification of the strengths and weaknesses) either in
the case of mismatch between model outputs and measurements, or in the case of disagreement among model results in similar conditions.

Results reported below were based on the highest level of detail (level 3 – see supplementary material).

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237 **3.1.1. Meteorological variables**

Meteorological inputs strongly influence model outputs since they affect plant growth, plant development stages, and soil turnover/balances, including flux exchanges at the soil-plant-atmosphere interface. The number and type of climatic variables required by each model informs us about the relationship between model outputs and climate drivers. In principle, for the modelling of surface reactions and diffusion of volatile products (e.g. N₂O emissions, soil water content dynamics), the higher the resolution in the climate information (e.g. hourly to sub-hourly weather inputs), the more accurate the model response is for short-term processes but the higher the probability that missing data may be present in the weather series used. For longer term processes such as soil organic carbon (SOC) decomposition, higher temporal resolution data may not improve the accuracy of the model response.

From our analysis (Tab. 3a, see supplementary material) we observed that the nine models involved in CN-MIP mostly use climate inputs at daily resolution (89%), whereas PaSim uses the hourly time scale (but with an option also available for daily inputs), and RothC uses a monthly time-step.

The most commonly used meteorological variables are precipitation, air temperature and wind speed. Concerning air temperature, the daily maximum and minimum air temperatures are used by almost all models (89%).

Relative humidity (daily mean) and global solar radiation are also used by 67% and 56% of the models, respectively. The atmospheric concentration of CO₂ is an optional input for many models (78%), with the exception of CERES-EGC and RothC.

Finally, only a few models use specific meteorological variables such as cloudiness, sunshine duration, dew-point temperature and actual vapour pressure.

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261 **3.1.2. Soil**

Similarly to climate inputs, soil characteristics also have a great influence on model outputs. These characteristics strongly influence crop growth and fluxes related to the gaseous biogeochemical cycles as water, C and N. Some soil inputs are assumed as constant values (i.e. parameters), not changing during the simulation. Different soil properties (e.g. texture,
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pH, bulk density, etc.) can affect plant growth and the environmental conditions for the
microbial activity driving the formation and decomposition of SOM and mediating
biochemical processes.

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From our analysis (Tab. 3b, see supplementary material), it emerged that soil processes
are mostly calculated based on the differentiation of the soil profile into a sequence of distinct
layers, with generation of outputs for each of these subdivisions. In PaSim model, the whole
soil profile is the basis for the modelling of C dynamics. The soil temperature is calculated
from energy balance (44%) or based on a response function of air temperature (56%).

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The water transport calculation scheme in soil is mainly described by the capacity (or
tipping bucket) approach (78%).

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For the transport and transformation of N in soil profile, most models estimate pools
and fluxes of NO3-N (78%) or/and NH4-N (89%).

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### 3.1.3. Plant ecophysiology and partitioning

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Crop and grassland models differ in the algorithms reflecting plant ecophysiology (growth and development) and partitioning (above and below-ground biomass and yield), which can lead to differences in simulated yield and total biomass, in turn affecting estimated C and N fluxes.

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In our analysis (Tab. 3c, see supplementary material), almost half of the models
consider the mechanism of C allocation as a function of development stage (56%), whilst
almost all the models take into account C assimilation (89%). The latter is mainly driven by
RUE-type processes (Radiation Use Efficiency) and/or P-R = gross photosynthesis –
respiration-type processes (56%).

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Phenology is simulated by almost all models (89%) through the use of growing degree
days (GDD) (89%), whilst photoperiod and vernalization are represented by 56% of the
models.

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Leaf area is accounted for by considering the leaf area index (LAI) (89%), whilst the
simulation of the number of leaves and evolution of the specific leaf area are almost ignored.

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Reference evapotranspiration is accounted by Penman-Monteith (56%), Penman and
Priestley-Taylor (44%).

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Root distribution is simulated by 78% of the models, mainly through a linear approach (56%).

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For the most part, models consider a dynamic partitioning of assimilates among plant
organs (78%), based on the age of organs (78%). Within-plant partitioning occurs across

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<figure><img src="image_3.png" /></figure>

roots, grains, stems and sheaths, and leaf blades, for 89, 78, 78 and 67% of the models, respectively.

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

Yield formation is mainly based on partitioning during reproductive stages (67%) and harvest index-type (44%). The yields mostly simulated are forage (89%), roots and grains (78%), tubers (67%) and fibre (56%).

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

The factors limiting plant growth most strongly among the nine models were water deficit and nitrogen deficiency (88%).

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

### 3.1.4. GHG emissions and other fluxes

<figure><img src="image_12.png" /></figure>

For better assessing how C and N cycles were involved in terms of GHG emissions and processes within several models, three main processes were detected (Tab. 3d, see supplementary material).

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

In general from our analysis emerged that the three main processes belonging to the general class of GHG emissions and other fluxes are almost fully simulated by the considered models.

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

In the main process called CO₂-GHG the most important C-fluxes from the ecosystems were considered. More specifically, they include the Gross Primary Production (GPP), the Net Primary Production (NPP), the Net Ecosystem Exchange (NEE), the Net Biome Production (NBP) and several types of respiration processes (i.e. Ecosystem respiration or RECO), heterotrophic respiration from both soil and grazing animals, and autotrophic respiration.

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

NPP and NEE are the most commonly simulated C-fluxes (67%), followed by GPP (56%) and RECO (44%), whilst just a few models simulate the NBP. Despite only 44% of the models taking into account RECO, most of them only consider soil respiration (89%). Plant respiration is considered by 56% of the models, whilst only 33% of the models take into account respiration from grazing animals.

<figure><img src="image_24.png" /></figure>

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<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

Among all of the models analysed only DNDC is able to simulate all the CO₂-GHG fluxes considered. More than 70% of CO₂-GHG can be simulated also by APSIM, DayCent and PaSim. The CO₂-GHG simulated by the highest number of models (i.e. six models) are NPP, NEE and soil respiration.

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

The main non-CO₂ fluxes (for simplicity called Non CO₂-GHG) include CH₄, N₂O, several N emissions (i.e. NH₃, NOₓ, N₂) and O₃.

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

N₂O emissions are most commonly simulated (78%), followed by NH₃ (56%). By contrast, only a few models generate CH₄ and N₂ emission outputs (44%) and NOₓ (33%). None of the models provide O₃ emissions output.

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>
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N₂O emissions provided by the models are mostly generated by denitrification and nitrification (78%), mainly based (i.e. >70% of the models) on a soil N pools (e.g. nitrate pool, NH₄ pool) with soil water and temperature acting as main drivers of change on mineral N pools.

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Among all models analysed DayCent and DNDC were able to simulate all non CO₂-GHG considered in our analysis. However, more than 70% of non CO₂-GHG can be simulated also by APSIM, PaSim and CERES-EGC. The non CO₂-GHG simulated by the highest number of models (i.e. seven models) was N₂O. The models able to simulate the highest number of variables (i.e. CH₄, N₂O and N₂) were APSIM, DayCent, DNDC and PaSim.

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Ten specific N processes were considered in the models: nitrification, denitrification, volatilization, leaching, symbiotic fixation, assimilation, mineralization, immobilization, plant uptake, and clay fixation. All these processes were widely simulated (i.e. >70 %) by the models considered in our analyses, with the only exception of clay fixation, that is considered only by DNDC model.

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Among the models analysed, only RothC does not take into account any N process. All the remaining models are able to simulate each of the N processes considered in our analysis, with the only exceptions being APSIM, which does not consider NH₃ volatilization, and PaSim and STICS, which only take account of assimilation indirectly (C:N-driven).

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### 3.1.5. Management

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All models are able to simulate the impact of the most common farming practices (i.e. harvesting, mowing, fertilization, tillage, irrigation, etc.) on the processes described so far. By contrast, specific options for grasslands, such as plant use and nutrient returns from grazing animals (as well as animal performances such as weight growth and milk production) are simulated by a lower number of models (Tab. 3e, see supplementary material).

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Harvesting, cutting, tillage, irrigation and crop rotation are widely simulated (>70% of models). Moreover, all models simulate fertilization and residue management. Concerning fertilization, however, only application of mineral N and organic amendments are widely simulated, while only a few models simulate other types of fertilizer such as phosphorus, potassium, sulphur and calcium. Similarly, the management of crop residues is based mainly on their burning or leaving on the ground surface, whilst only 33% of the models also consider burial (e.g. STICS accounts burial through tillage). Among other agricultural

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practices, about half of the models consider pruning and water management (i.e. rice), but
only a few consider pesticide application.

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The practices considered in the analysis are generally set by users. Some models also
offer options to trigger management events (i.e. fertilization and irrigation) based on changing
conditions during the simulation.

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Simulation of grazing, animal performances and nutrient returns were taken into
account as specific options for grasslands.

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Concerning grazing, models are for the most part based on user-determined settings (start and end dates, animal density); some of them also include options related to evolving conditions (APSIM, EPIC and PaSim), selective grazing (APSIM and PaSim) and trampling effect (APSIM).

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Animal performance simulation is considered by 55% of the models through
simple/static methods (APSIM and EPIC) or detailed/dynamic methods (PaSim), and based
on feeding standards or fill units (APSIM, DNDC and RothC).

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Finally, nutrient return was considered by 66% of the models, based on uniform
distribution of returns across the whole field.

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CERES-EGC, DSSAT and STICS do not include very specific agricultural options for grasslands. APSIM is the most detailed model for grasslands.

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4. C and N cycles: performance, strengths and weaknesses

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In this section, we provide an overview of the C and N approaches used by the CN-MIP
models (see Tab. 4 and supplementary), and their performance as documented for a broad
gradient of geographic and climatic conditions, as well as a variety of soil types and
management practices, to gain insight into their main strengths and weaknesses. To do that,
we have summarised the results of 130 published modelling studies (Tab. 5).

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In the analysis of the effects on C and N cycles of pedo-climatic conditions, we
considered variations of soil features such as temperature and moisture, texture, bulk density,
pH, SOC, C and N dynamics and water-filled pore space, and climate conditions such as
patterns of air temperature, precipitation, solar radiation, also including frequency and
intensity of extreme events such as floods and drought. Management practices include
changes in agricultural practices such as tillage, fertilization, irrigation, crop variety on soil
and vegetation and, in turn, on C and N cycles.

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Several types of weaknesses emerged in 94 modelling studies (Tab. 6), where
criticalities in assessing the impact of pedo-climatic conditions (46.2%) and management

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practices (33.1%) on environmental variables are reflected in unsatisfactory model performances. These latter were mostly related to limitations of model structure with respect to difficulties of the algorithms in simulating the effects of different management practices on C and N cycling. By contrast, only a few weaknesses were due to the scale of application, strictly related to the high variability in time and space of C and N cycles (16.5% and 4.2% for pedo-climatic conditions and management practices, respectively). For the C cycle, major limitations of model structure were related to management practices (43.4%), whilst for the scale of application, the major weaknesses were due to different pedo-climatic conditions (11.8%). For the N cycle, however, limitations inherent in model structure were predominant under different pedo-climatic conditions (51.7%), whilst for the scale of application, major weaknesses were due to different pedo-climatic conditions (20.4%).

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## 4.1. *Model structures and pedo-climatic conditions*

Soil properties and climate conditions emerged as important factors for ensuring the effective representation of outputs (Tab. 7). While climate issues were mainly related to precipitation only, pedological factors concerned both the effect of changes of physical (texture, bulk density and soil hydrologic properties) and chemical (C and N processes) soil features on C and N cycles.

Concerning soil physical characteristics, a primary role in modelling issues was played by the soil water properties. Errors in the simulation of soil water content (SWC) were the main cause of general discrepancies concerning C and N emissions in many studies (Tab. 7). Discrepancies in C and N outputs were also observed under specific soil water conditions such as the impact of soil freezing and thawing (Li et al., 2010) or soil shrinking and swelling (Babu et al., 2006). Again, an inappropriate setting of initial state variables determined discrepancies in N emissions (i.e. under- overestimation of N₂O emissions peaks, Gabrielle et al., 2006). Considerable overestimations of N₂O emissions were found to be closely related to overestimation of water-filled pore space (WFPS). WFPS is indeed one of the most important soil variables influencing C and N cycles. For instance, microbially-mediated soil respiration and N cycling processes tend to be higher or lower with increasing soil water content (e.g. increased nitrification under aerobic conditions, increased denitrification under anaerobic conditions, e.g. Bollmann, 1988). As WFPS reaches high values, soil respiration tends to decline and denitrification occurs, resulting in N losses via N₂O and N₂ emissions. This condition was observed especially for DayCent (Stehfest and Muller, 2004, Abdalla et al., 2010, Xing et al., 2011, Ryals et al., 2014, 2015) and DNDC (Saggar et al., 2004, Abdalla et al., 2007).

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al., 2010). Fast drainage is a particular issue for both the DayCent and DNDC models which drain water in excess of field capacity immediately. This condition makes these models unable to accurately predict N emissions at sites that consistently show soil moisture above FC (e.g. Uzoma et al., 2015).

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Soil bulk density (BD) was also a source of modelling error in simulating C and N cycles. For CERES-EGC, Gabrielle et al. (2006) found a discrepancy in N₂O emission peaks due to inappropriate parametrization of soil water retention properties and bulk density from test site to regional scales. Drouet et al. (2011) confirmed that BD was one of the most influential factors for N₂O emissions in CERES-EGC. The effect of BD increase was also reported for DayCent by De Gryze et al. (2010) and Abdalla et al. (2009), respectively, which observed an underestimation of N₂O emissions in a conservation tillage treatment due to the increase in BD, and an associated decrease in pore space over time as DayCent maintains a steady BD and simulation compaction, while the conservation tilled field site resulted in increased BD and reduced N₂O emissions (Pisante et al., 2015). In fact, most of the selected models, with the exception of EPIC, DNDC and STICS, do not simulate soil compaction or loosening, as BD remains constant over time.

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Texture was also found to be an influential soil physical characteristic. Congreves et al. (2016) found an underestimation in NH₃ emissions with the DNDC model, which is unable to simulate a heterogeneous soil profile. Similarly, Gagnon et al. (2016) confirmed that DNDC does not effectively discriminate across soil textures to simulate soil CO₂ respiration. Clay concentration affects SOC accumulation in different ways. According to some studies (Nichols, 1984, Burke et al., 1989), SOC increases with increasing clay content due to the bonds between the surface of clay particles and organic matter that retard the decomposition process. Also, the increase of clay content affects soil aggregation, indirectly affecting SOC through the creation of macro-aggregates that can physically protect organic matter molecules from further microbial mineralization (Rice, 2002, Plante et al., 2006). However, a recent study (Gregorich et al., 2016) indicated that only temperature (not soil texture or other soil properties) was a driver of decomposition for 10 sites in Canada (¹³C-labelled study), thus suggesting as the effect of texture on SOC decomposition is controversial. Furthermore, texture parametrization is another possible source of error. For instance, Gijsman et al. (2002) indicated that inaccuracies in soil texture data used as inputs may have affected soil retention characteristics, thus resulting in discrepancies in SOC and soil mineral N dynamics.

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Soil chemical processes are generally similar between the models and all models considered showed difficulty in reproducing the observed C and N cycles. The processes

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influencing soil organic matter (SOM) in the models include nitrification, denitrification, immobilization and mineralization.

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Discrepancies between modelled and observed data were often related to an inappropriate SOC content parametrization (Pathak et al., 2005, Calanca et al., 2007, Causarano et al., 2007, Smith et al., 2012, Gagnon et al., 2016). However, a considerable source of error was also due to overestimation of SOC content (Abdalla et al., 2010, Gijsman et al., 2002) or to the rate of soil C decomposition (Snow et al., 1999, De Gryze et al., 2010, Li et al., 2015).

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Nitrification is a two-stage process, performed by different groups of Archaea, consisting in the oxidation of ammonia or ammonium to nitrite (step 1) followed by the oxidation of the nitrite to nitrate (step 2). For DayCent, Li et al. (2005) and Del Grosso et al. (2008) found that overestimation in the nitrification rate was one of the main sources of error for N emissions estimation. This was also found by Drouet et al. (2011), showing that discrepancies in N₂O emissions simulated by CERES-EGC were due to the high sensitivity of the model to the maximum rate of nitrification. The nitrification rate, however, is usually associated with a number of environmental factors including the substrate and oxygen concentration, temperature and pH. For instance, this was observed by Li et al. (2005), who pointed out that poor simulation of NH₄⁺ was caused by the inaccurate regulation of the effect of temperature on nitrification in DayCent.

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Denitrification is a process where the reduction of soil nitrate to N-containing gases takes place. The major discrepancies between modelled and observed N emissions were due to an underestimation of the denitrification rate (Thorburn et al., 2010, Xing et al. 2011, Fitton et al., 2014a, b). The underestimation of the denitrification rate can be due to different type of errors. For instance, for APSIM, Thorburn et al. (2010) found the source of error in the model parametrization, with the default value of denitrification coefficient much lower than the optimized value. By contrast, Xing et al. (2011) indicated the response of denitrification rate to soil temperature and moisture (or WFPS) as the main source leading to the underestimation of denitrification. Generally, denitrification rates have been reported to be directly proportional to temperature (Seitzinger, 1988), whilst soils with high organic matter content (high dissolved organic C) and anaerobic conditions (i.e. waterlogged or poorly-drained soils) can more easily favour high denitrification rates.

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Another important source of modelling error resulted from the inaccurate estimation of the immobilization-mineralization processes. In the EPIC model, He et al. (2006) observed that general discrepancies in C and N dynamics (i.e. lower net N mineralization rate,

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humification, etc.) were likely due to N mineralization algorithms which may have
underpredicted net N mineralization (NMN) observable under field conditions. Smith et al.
(2008) and Fitton et al. (2014a, 2014b) found that the underestimation in mineralization rate
led to underestimation of N₂O emissions. In the same way, Del Grosso et al. (2010) indicated
that overestimation of N₂O emissions was due to N mineralization rates that were too high
and too responsive to climate drivers. Nitrogen immobilization or mineralization depends on
the C/N ratio of the plant residues. The C/N ratio generally tends to decrease as the organic
matter becomes more decomposed. Erroneous C/N parametrization can easily lead to errors in
C and N cycle related outputs. For instance, Li et al. (2015) observed for the DSSAT model
that differences between the modelled and measured soil C/N ratio led to SOC
overestimation.

Finally, climate conditions influence the C and N outputs in several studies analysed.
Some issues were related to how the climate data have been used. For instance, in APSIM,
Thorburn et al. (2010) found discrepancies in N emissions (i.e. underestimation of
denitrification and N₂O peaks) due to the application of spatially averaged rainfall data
instead of the use of specific test-site rainfall data. In other cases, the main issues were due to
the sensitivity of the models subroutines. For instance, Wattenbach et al. (2010) observed
overestimation in NEE peaks in southern European regions due to issues in coupling water
and C-fluxes. These issues were probably caused by the fact the model was developed for
Northern regions. Again, Lawton et al. (2006) reported overestimation of NEE because of the
oversensitivity of PaSim to initial conditions/winter conditions. Most of the issues related to
general discrepancies in simulated C and N cycles, however, were related to precipitation only
(Stehfest and Muller, 2004, Jarecki et al., 2008, De Gyrze et al., 2010, Ludwig et al., 2011,
Lehuger et al., 2014). Precipitation and the resulting soil water dynamics strongly influence N
cycling in terrestrial ecosystems since it affects both physical transport and N biological
transformations by soil microorganisms (Brooks et al., 1999, Corre et al., 2002, Aranibar et
al., 2004).

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4.2. *Model structure and management*

Management has a great impact on C and N cycles. In biophysical and biogeochemical
models, the correct representation of practices such as fertilization, irrigation and tillage in
crop systems, and cutting and grazing in grassland systems, is needed to ensure the greatest
suitability of outputs.

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In the models, fertilization, which influences soil C and N transformations (e.g. acidification following fertilization) and trace gas emissions, was often not well represented (Tab. 7). For DayCent, Fitton et al. (2014a, b) indicated an underestimation of N₂O emissions due to the low sensitivity of the model at low N application rates. In DNDC, Congreves et al. (2016) found that NH₃ emissions were underestimated due to a simple modelled cascade water flow, which may have limited the ability of the model to simulate slurry infiltration rates. Also, Causarano et al. (2007) observed general discrepancies in C-dynamics (i.e. overestimation of microbial biomass C and total organic C, underestimation of particulate organic C), due to inadequate representation of the effects of tillage and manure in the EPIC model. Another issue related to fertilization was the inability of many models to replicate the effect of specific types of fertilizer. For instance, using DayCent Stehfest and Muller (2004) found overestimation of N₂O emissions under urine application, where N was concentrated in small hotspots. For the same model, Ryals et al. (2014 and 2015) underestimated CO₂ emissions since no soil water benefits were provided by adding compost. This condition was likely due to the lack of increased modelled decomposition because the model was not able to increase soil water contents when compost was added. Gu et al. (2014) overestimated N₂O emissions, soil nitrate and ammonia concentrations due to the inability of DNDC to include canopy interception and foliar N uptake when spraying liquid fertilizer.

Finally, residue management was one of the main weaknesses related to N management (Cavero et al., 1996, Sleutel et al., 2006, Rampazzo Todorovic et al., 2010, Wang et al., 2013). The amount of N applied with residues depends on the quantity of residues and their N concentration. These two factors affect the mineralization-immobilization turnover, whilst their net balance varies with environmental conditions (mainly soil moisture and temperature) and the characteristics of the OM (i.e. C:N and the decomposition rate). Since residues directly influence soil C and N processes, residue management in the models resulted in consistent modelling weaknesses. For instance, Justes et al. (2009) underestimated the N mineralization in STICS due to inappropriate parametrization of the model (i.e. default values of the decomposition module were used). In a similar way, Liu et al. (2009) overestimated the SOC content when stubble (wheat and lupine) was applied due to the use of the conventional setting of the stubble retention factor in RothC. Using DayCent and DNDC, Smith et al. (2012) underestimated SOC due to a slight overestimation of residue removal impact. However, the authors indicated that this could have been partly due to the inherent variability in SOC measurements. Smith et al. (2012) also found that DNDC tended to underestimate the rate of SOC change as affected by residue removal at some sites. Using DSSAT, Hartkamp et
al. (2004) overestimated SOC in the crop rotations with N fertilization. This overestimation was due to inaccurate initial SOC (i.e. overestimated SOC values) which was related to an overestimation of the biomass incorporated into the soil. Similarly, Wang et al. (2005) underestimated the SOC content using the EPIC model due to a structural error in underestimating the return of corn residues. He et al. (2006) found general discrepancies in C and N dynamics due to underestimation of the soil capacity to transform crop residue in SOC.

Tillage is one of the agricultural practices most commonly simulated by the models and an issue in most modelling applications. The use of tillage or reduced tillage can greatly affect soil properties, and since the models don't adjust some soil properties over time (such as bulk density) which results in inaccuracies in simulations. Also, the use of tillage or no/reduced tillage may lead to increasing rather than decreasing emissions (e.g. due to higher density and WFPS, more SOM near the soil surface thus higher denitrification potential, tendency to acidification and thus lower reduction of N₂O to N₂, etc.). Identifying mechanisms which help understand simulate emissions with no tillage is thus a key issue. In our analysis management effects (i.e. tillage) which influences topsoil erosion emerged as a point of weakness. This is because many models do not take into account adequately C-losses due to erosion. For instance, Nieto et al. (2010, 2013) overestimated SOC content using RothC, whilst Billen et al. (2009) observed general discrepancies in SOC content with EPIC.

Another point of weakness in simulated tillage was the inadequate representation of changes in soil properties over time. For instance, Luo et al. (2011), using APSIM, underestimated SOC decomposition. In this case, whilst tillage may have led to acceleration in soil C oxidation due to changes in soil environmental parameters (i.e. water retention, porosity, aeration, etc.), APSIM failed to simulate changes in these soil properties over time, which is a common issue amongst most models. Similarly, Causarano et al. (2007) found general discrepancies in C dynamics (i.e. overestimation of microbial biomass C and total organic C, underestimation particulate organic C) due to an inadequate reproduction of the effects of tillage and manure on soil properties.

In addition to fertilization and tillage, probably the most common simulated agronomic practices, model weaknesses were found in relation to other practices. For instance irrigation, especially accompanied by fertilization, was observed to affect simulated C and N cycles. Jackson et al. (1994) and Cavero et al. (1999) underestimated N fluxes under irrigated experiments using EPIC. The main source of error was related to an overestimation of the soil N losses *via* leaching or denitrification during the irrigated crop period. Grassland management was also seen to be a possible point of weakness for the models. For instance,
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Lawton et al. (2006), Vuichard et al. (2007a) and Ma et al. (2015) observed general discrepancies in C-fluxes (i.e. net ecosystem exchange and ecosystem respiration) under different grazing intensities using a grassland-specific model (PaSim). As suggested by Vuichard et al. (2007a), a continuous defoliation by grazing is indeed difficult to account for as a permanent disturbance in the model. The grazing effect, however, links with many other parameters related to the ecosystem and climate which makes it difficult to define the parameter which most strongly influences the uncertainty of the model output (Gottschalk et al., 2007).

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Finally, model weaknesses also result from management options that are not included. This type of weakness has emerged in several studies carried out using the RothC model. For instance Skjemstad et al. (2004) found general discrepancies in C dynamics due to ecosystem disturbances which were not included in RothC (i.e. clearing and burning of pulled vegetation). Shirato and Yokazawa (2005) underestimated SOC content due to the decomposition rate of SOM under rice cultivation (i.e. effect of waterlogged soil not included in RothC) being too low, and Farina et al. (2013) reported some discrepancies in C-fluxes when the model simulated rotations that included a fallow period.

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### 4.3. *Time-scale*

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Biophysical and biogeochemical models enable the estimation of C and N emissions at various temporal and spatial scales. Compared to the emission factor approaches often used by organizations and individuals to calculate greenhouse gas (GHG) emissions for a range of activities, these tools include the influences of agricultural practices, land-use change, soil properties and estimate the influences of weather on emissions over time.

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The ability of these models to accurately reproduce detailed dynamics of C and N emissions depends on the degrees of complexity of the model itself. Current process models, with high complexity, are able to calculate in detail both C and N emissions due to their consideration of all soil-plant-atmosphere interactions. These tools are able to provide reasonable estimates of trace gas emissions from soils, usually for a specific site and at seasonal or annual time scales. By contrast, however, they are less successful at finer time resolution (e.g. daily) and on different sites from the ones where have been previously calibrated. In our analysis several studies showed weaknesses due to the time-spatial scale associated with both pedo-climatic conditions and management.

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Concerning time-scale weaknesses, Xing et al. (2011) underestimated N₂O emissions at the daily time step using APSIM, while the use of the hourly time step may have likely

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improved the estimate of predicted total daily emissions. This is because, in the APSIM model, as in most models, N₂O emissions were released immediately to the atmosphere without delay upon change in environmental conditions whereas the observations indicated that there was a 1-10 hour lag between peaks of soil moisture and gaseous emissions. Similarly, Lehuger et al. (2011) using CERES-EGC indicated an overestimation in N₂O emissions, probably due to a possible time lag between the production of gaseous N₂O in the soil and its emission to the atmosphere. Also, several studies carried out using DayCent (Parton et al., 2001, Del Grosso et al., 2005, 2010) observed some discrepancies in simulated N emissions due to time-lag. This was found to agree with Li et al. (2005), which indicated that DayCent often has a 1 day lag before emissions occur. In all these cases, the use of hourly time step may result in better predictions especially in conjunction with the addition of a description of gas diffusion into soil which could result in a delay between N₂O production and emission.

Concerning spatial-scale weaknesses, Gabrielle et al. (2006) found discrepancies in N₂O emission peaks using CERES-EGC. This was probably due to soil property parametrization (i.e. soil water retention properties and bulk density) which may have led to differences in N outputs from test sites to the regional scale. Using EPIC, general discrepancies in C-fluxes (i.e. overestimation of microbial biomass C and total organic C, underestimation of particulate organic C) were likely caused by spatial differences in C fraction due to differing soil landscapes (Calanca et al., 2007). Schnebelen et al. (2004) overestimated soil N absorption with the STICS model. This was probably due to propagation of errors for continuous simulations compared to single-year simulations. More specifically, the underestimation of some parameters in the previous year may have led to errors in the following years.

## 5. New developments/future perspectives

In the above analysis, an indication was given of models' predictive strength, while also hinting at possible limitations in the underlying hypotheses from the literature in the cases where discrepancies between model and observation occurred. Despite this extensive analysis, knowledge basic mechanisms driving C and N cycles in agricultural systems is still far from complete and key questions remain, including: what exactly triggers the cascade of events that finally lead to biological responses? How to differentiate between causes and consequences? How does the knowledge derived from system observations relate to mechanistic events? How does the current knowledge on C and N cycling in agriculture fits with available mechanistic representations? Discrepancies between model outputs and observations can be
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ascribed to a wide diversity of causes, without any real tendency to associate them with one or
another cause. The analysis reported in this work suggested however three (quite large) areas
of interest for possible improvements of C and N models: i) soil biology, comprising SOM
heterogeneity, decomposition kinetics, and N immobilization; ii) soil physics, including the
representation of soil physical properties and the simulation of its effects on reaction rates;
and iii) soil management, which indirectly affect soil processes by modifying soil physical,
chemical and biological properties.

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Based on the main issues found in our analysis, despite recognizing the importance of
soil management, here we focus on some innovative aspects related to soil biology and soil
physics, and interface that requires attention (Blagodatsky and Smith, 2012). This choice is
justified in that development of robust predictive frameworks is critical to managing soil
biology and its essential functions and services (Thrall et al., 2011). They can help
disentangling the causal links between soil biology and structure, physical-chemical factors
and ecological processes (e.g. nutrient cycling, soil C sequestration) that contribute to plant
community development and function. In addition, how soil communities respond to and
impact on plant succession (e.g. via regulatory networks that respond to the availability of
fixed N) may be important for predicting the role of plant-soil feedbacks in determining the
dynamics of soil microbial communities and the impact of anthropogenic disturbance on soil
diversity and function.

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Soil microbial biomass (SMB) is generally only implicitly modelled by representing it
as a C pool not affecting substrate decomposition directly (Manzoni and Porporato, 2009).
Approaches of this type mostly implement solutions that are biologically meaningful (e.g.
representing realistically SOM turnover) and computationally tractable within a simulation
(i.e. with reduced overall complexity of the full model and a limited number of free
parameters to be tuned), which make them suitable for analyses in long-term studies
(Manzoni and Porporato, 2009, Sierra et al., 2015a). In recent years, researchers have
advocated a representation of SOM turnover driven by SMB to gain insight into decomposing
SOM-SMB interactions (Schimel and Weintraub, 2003, Lawrence et al., 2009, Blagodatsky et
al., 2010, Schmidt et al., 2011). For C and N substrates, concentration constraints driven by
microbial allocation patterns could thus be represented in novel biogeochemical models based
on microbial physiology (Allison et al., 2014). In this way, models based on microbial
biomass-driven SOM decomposition are promising to provide a realistic simulation of SOM
turnover in relation to changes in environmental conditions compared to existing models that
do not explicitly simulate SMB (Lawrence et al., 2009, Allison et al., 2010, Conant et al.,

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2011, Sierra et al., 2015b). It is quite common to use classical enzymatic kinetics like Michaelis-Menten or Monod-type kinetics to implement substrate-SMB co-limitation (Blagodatsky and Richter, 1998, Hadas et al., 1998, Wutzler and Reichstein, 2013, Cavalli et al., 2016), even if simpler decomposition kinetics have also been proposed (Manzoni and Porporato, 2007, Withmore, 2007, Wutzler and Reichstein, 2008). Conversely, more general model formulations are described in Neill and Gignoux (2006) and Neill and Guenet (2010) to simulate microbial growth in soil accounting for both positive and negative priming effects. The priming effect is defined as any change (positive or negative) of native SOM decomposition rate following the addition of exogenous organic matter or nutrients, compared to no addition (Fontaine et al., 2007, Kuzyakov et al., 2000, Kuzyakov, 2010, Chen et al., 2014, Perveen et al., 2014).

Another important aspect regarding SOM turnover is the effect of N shortage on SOM decomposition. Soil microorganisms are characterised by a narrow range of variation in their C to N ratio (Cleveland and Liptzin, 2007, Xu et al., 2013); thus, they can be approximately considered homeostatic (i.e. they do not change markedly their C to N ratio according to substrate C to N ratio). Mechanisms of adaptation to stoichiometric imbalances between substrates and SMB were reviewed in detail by Mooshammer et al. (2014a). One postulated mechanism of adaptation regards the variation of microbial C use efficiency (CUE, defined as the ratio between newly-formed biomass C and decomposed C) and of N use efficiency (NUE, defined similarly to CUE) to accommodate for excess or deficit of C or N (Manzoni et al., 2012, Sinsabaugh et al., 2013, Mooshammer et al., 2014b). According to this hypothesis, when decomposition is N-limited, excess C is lost through overflow metabolism (Russel and Cook, 1995), either with the synthesis of extracellular C compounds (as polysaccharides) (Hadas et al., 1998, Cavalli et al., 2016), or as CO₂ (Schimel and Weintraub, 2003, Neill and Gignoux, 2006). Conversely, when N is in excess (decomposition is limited by C availability), net N mineralisation occurs. Models usually implement N deficit effects on SOM decomposition with the N inhibition hypothesis (Manzoni and Porporato, 2009), that is, SOM turnover is reduced according to N availability, and thus CUE does not change. Alternatively, other models (Izaurralde et al., 2006, Withmore, 2007) allow SMB to vary its C to N ratio according to stoichiometric imbalances, and thus they consider SMB as non-homeostatic.

Decomposition of SOM in soil occurs at microsites showing varying N availability (Schimel and Bennett, 2004). This is caused by heterogeneity of both SOM and of soil physical properties (Schmidt et al., 2011). Thus, N is supposed to flow from micro-sites
showing net N mineralisation to others showing net N immobilisation (Schimel and Bennett, 2004). Mathematically, the heterogeneity of SOM decomposition in a first approximation can be simulated considering that not all organic N in substrates is available to SMB, according to the parallel hypothesis (Manzoni and Porporato, 2007). The use of a simple lumped SOM model, based on the parallel approach, was shown to provide almost similar results to the same model structure that explicitly took into account the heterogeneity of soil decomposition, and in which all organic N in substrates was available to decomposers, according to a direct assimilation pathway (Manzoni et al., 2008).

The heterogeneity of SOM is simulated with models that comprise several pools of different decomposability (Nicolardot et al., 2001, Manzoni and Porporato, 2009, Sierra et al., 2011, Sierra and Müller, 2015). In many models, decomposition constants of model pools incorporate intrinsic chemical recalcitrance of SOM, and availability of SOM to decomposers (Nicolardot et al., 2001, Sierra and Müller, 2015). However, it was recently emphasised that chemically-labile (or high-quality, and thus potentially easily-degradable) molecules can persist in soil for a long time due to constraints on their microbial decomposition not related to intrinsic chemical characteristics (Kleber, 2010, Marschner et al., 2008): biology of decomposers, abiotic reactions and desorption, environmental variables and physicochemical stabilisation processes (Ekschmitt et al., 2005, Kemmit et al., 2008, Kleber et al., 2011, Schmidt et al., 2011, Dungait et al., 2012). Regarding SOM physical and chemical stabilisation, models that explicitly represent protected and unprotected SOM pools of similar chemical characteristics (Kuka et al., 2007) allow separating intrinsic recalcitrance (substrate quality) from availability, and thus enable simulating long-term stabilisation of chemically easily-decomposable high-quality SOM in soil (Dungait et al., 2012). In addition, more sophisticated and realistic approaches to simulate soil physicochemical heterogeneity, and thus variability of SOM decomposition, were implemented in SOM models that represent soil as 3D structure in which decomposition takes place (Garnier et al., 2008, Masse et al., 2007, Monga et al., 2009, 2014).

Improving soil biology aspects related to the production and consumption of gases ($O_2$, $CO_2$, $CH_4$, $N_2O$, and $N_2$) will improve the simulation of soil gas concentrations. However, this is not sufficient to achieve proper simulations of GHG emissions, as accounting for gas transport through the soil profile is also important. As pointed out by Blagodatsky and Smith (2012), it is necessary to find the right balance in complexity between biological and soil physical simulations. For example, the higher soil tortuosity the higher the $N_2/N_2O$ ratio, because $N_2O$ has more possibilities to be reduced when the escape pathway from the $N_2O$
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production sites to the atmosphere (and thus its diffusion time) is longer. Adequate simulation
of gas transport in soil can be achieved using mechanistic models based on water, heat, and
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gas transport equations, and gas-liquid phase exchange. A further connection among soil
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biology and soil physics research will be to simulate SOM turnover and gas production,
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consumption, and transport in a 3D soil structure using the concepts presented above, so as to
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achieve a more realistic representation of environmental effects (soil temperature and
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moisture), especially in the context of climate change.
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One final observation is that all of the model improvements presented above require
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adequate simulation of initial conditions of inorganic N availability. Thus, it is mandatory that
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all processes affecting soil ammonium concentration are accurately simulated. Among these,
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ammonium fixation in non-exchangeable form by clay minerals in fine-textured soils is not
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frequently considered in modelling practice (Nieder et al., 2011). After its application to soil
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with fertilisers, a relevant fraction of ammonium can be very rapidly (hours or days) fixed by
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clay minerals (Nõmmik, 1957) in a form that is very slowly released during the following
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weeks or months (Steffens and Sparks, 1997). This fraction of applied N is thus not
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immediately available for nitrification, microbial immobilisation, and plant uptake. Despite its
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importance, ammonium fixation / release is not commonly simulated by crop/grassland
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system and SOM models. The rapid fixation can be simulated with well-known isotherms,
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which represent the static adsorption of an ion onto a surface (Cameron and Kowalenko,
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1976, Cavalli et al., 2015) as a function of ion concentration. Research is needed to estimate
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isotherm parameters depending on soil characteristics (such as type of clay, potassium
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concentration, and soil water content) and to simulate ammonium release over time.
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# 6. Summary and concluding remarks

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At present, process-based biogeochemical models represent a valuable tool for
examining the magnitude and spatial-temporal patterns of C and N fluxes in terrestrial
biosphere dynamics. Our analysis shows that there is still great divergence between models in
the simulation of C sequestration and GHG source/sink status, in relation to a different
interpretation of physical and biogeochemical processes.
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Representative works have been summarized to provide a general overview of the state-
of the-art of models, and to allow process-based models (the nine identified in this study) to
be compared and selected for the simulation of C and N cycles in crop and grassland systems.
We classified models into categories according to three levels of knowledge: five general
classes (level 1), 20 main processes (level 2), and 196 methods/options/components (level 3),
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and then we assessed the tools in terms of the comprehensiveness of processes related to
pedo-climatic and management options, and their accuracy in a variety of contexts.

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This review highlighted strengths and weaknesses of the models analysed. Essentially,
they involve limitations in simulating the effects of pedo-climatic conditions (46.2%) and
different management practices (33.1%). Other weaknesses (i.e. 20.7%) were due to the scale
of application in time and space.

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The major limitations of model structure related to C-cycles were observed under
management practices (43.4%), whilst for the scale of application the major weaknesses were
due to different pedo-climatic conditions (11.8%). For the N cycle, the main limitations
inherent in model structure were found under different pedo-climatic conditions (51.7%),
whilst for the scale of application the major weaknesses were due to different pedo-climatic
conditions (20.4%).

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All the models considered here showed positive and negative features and none may
necessarily be ideal in any particular circumstance. If the model chosen is not able to
reproduce the output required, two or more of these models may be combined to derive upper
and lower values for all simulated outputs. Moreover, a decision about which model or
models to use should be seen as dynamic, not static. As conditions change, or if one model
proves unsuccessful, they can be adapted or replaced with other, more suitable, models.

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Although the above reported weaknesses were already known due to a wide number of
published studies, in the present analysis we have tried to relate them to their causes in the
view of using them as an effective basis for improving current modelling approaches.
Although different avenues could be considered to improve models (e.g. Coucheney et al.,
2015), mainly depending on the purpose of modelling, to overcome the reported limitations
and account for the effect of multiple disturbances (i.e. pedo-climatic conditions, management
practices, scale of analysis) affecting basic processes, as well as to simplify the decision of
which model to choose to understand mechanistically specific study-context and to make
detailed predictions in a large diversity of situations, some innovative aspects should be
considered in the modelling work. Among these, we target the representation of SOM
turnover driven by SMB, the effect of N shortage on SOM decomposition, improvement
related to the production and consumption of gases (O₂, CO₂, CH₄, N₂O, and N₂), adequate
simulations of gas transport in soil, the use of a 3D soil structure in order to achieve a more
realistic representation of environmental effects (soil temperature and moisture), especially in
the context of climate change.
Model improvement thus implies extending the existing body of knowledge on
ecological and biogeochemical concepts, to allow them to be incorporated using novel
approaches, thus improving the representation of the dynamics of the ecosystems, and the
related advantages for stakeholders.

845
846 Acknowledgements

847 This work was developed by the CN-MIP project of the Joint Programming Initiative

848 ‘FACCE’ (https://www.faccejpi.com) under the auspices of the Global Research Alliance for

849 Agricultural Greenhouse Gases – Integrative Research Group (http://globalresearchalliance.org/research/integrative). The project, coordinated by the French

850 National Institute for Agricultural Research (INRA), received funding by the ‘FACCE’ Multi-partner Call on Agricultural Greenhouse Gas Research through its national financing bodies.
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1603
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<table><caption>Tables</caption><thead><tr><td>Model Name</td><td>Version</td><td>URL or contact for documentation/description</td><td>References</td></tr></thead><tbody><tr><td>APSIM</td><td>7.6</td><td>http://www.apim.info</td><td>Keating et al., 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267-288. Holzworth DP, Huth NI, et al. (2014) APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327-350. http://www.apsim.info/Documentation.aspx</td></tr><tr><td>CERES-EGC</td><td></td><td>https://ecosys.versailles-grignon.inra.fr/ceres_mais/ceres.html</td><td>Gabrielle et al., 1995. Analysis and field-evaluation of the CERES models' water balance component. Soil Science Society Of America Journal 59, 1402-1411; Gabrielle et al., 1996. Analysis and field evaluation of the CERES models' soil components: Nitrogen transfer and transformations. Soil Science Society of America Journal 60, 142-149; Gabrielle et al., 1998. Development and evaluation of a CERES-type model for winter oilseed rape. Field Crops Research 57, 95-111; Gabrielle et al., 1998. A model of leaf area development and senescence for winter oilseed rape. Field Crops Research 57, 209-222; Hénault et al., 2005. Predicting in situ soil N<sub>2</sub>O emissions using NOE algorithm and soil data base. Global Change Biology 11, 115-127, 2005</td></tr><tr><td>DayCent</td><td>DayCent 4.5</td><td>http://www.nrel.colostate.edu/projects/daycent-home.html</td><td>Parton et al., 1994. A general model for soil organic matter dynamics: Sensitivity to litter chemistry, texture and management. p. 147-167. In: Quantitative modeling of soil forming processes, SSSA Spec. Public. No. 39. Madison, WI, USA; Parton et al., 2001. Generalized model for N<sub>2</sub>O and N<sub>2</sub> emissions from soils. Journal of Geophysical Research 106(D15), 17403-17419. doi:10.1029/2001JD900101. Del Grosso et al., 2000. General model for N<sub>2</sub>O and N<sub>2</sub> gas emissions from soils due to denitrification. Global Biogeochemical Cycles 14, 1045-1060. doi:10.1029/1999GB001225. Del Grosso et al., 2002. Simulated effects of dryland cropping intensification on soil organic matter and greenhouse gas exchanges using the DAYCENT ecosystem model. Environmental Pollution 116, S75-S83. doi:10.1016/S0269-7491(01)00260-3</td></tr><tr><td>DNDC</td><td>DNDC 95 and Manure DNDC</td><td>http://www.dndc.sr.unh.edu/</td><td>Li, 2000. Modeling trace gas emissions from agricultural ecosystems. Nutrient Cycling in Agroecosystems 58, 259-276. doi:10.1023/A:1009859006242; Li et al., 2012. Manure-DNDC: A biogeochemical process model for quantifying greenhouse gas and ammonia emissions from livestock manure systems. Nutrient Cycling in Agroecosystems 93, 163-200. doi:10.1007/s10705-012-9507-z</td></tr><tr><td>DSSAT</td><td>4.6</td><td>http://dssat.net</td><td>Jones, et al., 2003. The DSSAT cropping system model. European Journal of Agronomy 18, 235-265. doi:10.1016/S1161-0301(02)00107-7; Thorp et al., 2012. Methodology to evaluate the performance of simulation models for alternative compiler and operating system configurations. Computers and Electronics In Agriculture 81, 62-71. doi:10.1016/j.compag.2011.11.008; White et al., 2011. Methodologies for simulating impacts of climate change on crop production. Field Crops Research 124, 357-368. doi:10.1016/j.fcr.2011.07.001.</td></tr><tr><td>EPIC</td><td>V.0810</td><td>http://epicapex.tamu.edu/</td><td>Williams, J.R. 1995. The EPIC model, 1995 V.P. Singh (Ed.), Computer Models of Watershed Hydrology, Water Resources Publications, Highlands Ranch, CO (1995), pp. 909-1000.</td></tr><tr><td>PaSim</td><td>5.3</td><td>https://www1.clermont.inra.fr/urep/modeles/pasim.htm</td><td>Ma et al., 2015. Regional-scale analysis of carbon and water cycles on managed grassland systems. Environmental Modelling & Software 72, 356-371. doi:dx.doi.org/10.1016/j.envsoft.2015.03.007</td></tr><tr><td>RothC</td><td>RothC10N</td><td>http://www.rothamsted.ac.uk/sustainable-soils-and-grassland-systems/rothamsted-carbon-model-rothc</td><td>Coleman, K., Jenkinson, D.S., 1996. RothC-26.3. A model for the turnover of carbon in soil. In: Powlson, D.S, Smith, P., Smith, J.U. (eds) Evaluation of soil organic matter models using existing, long-term datasets. NATO ASI series no. 1, vol 38. Springer, Berlin Heidelberg New York, pp. 237-246; Coleman, K., Jenkinson, D.S., 1996. A Model for the Turnover of Carbon in Soil: Model description and user's guide. Lawes Agricultural Trust, Harpenden, UK; Farina, et al., 2013. Modification of the RothC model for simulations of soil organic C dynamics in dryland regions. Geoderma 200, 18-30. doi:10.1016/j.geoderma.2013.01.021</td></tr><tr><td>STICS</td><td>8.3.1</td><td>http://www6.paca.inra.fr/stics/</td><td>Brisson et al., 1998. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18, 311-346; Brisson et al., 2003. An overview of the crop model STICS. European Journal of Agronomy 18, 309-332; Brisson et al., 2009. Conceptual basis, formalizations and parameterization of the STICS crop model, ed. Quae, 297 pp.; Coucheney et al., 2015. Accuracy, robustness and behavior of the STICS soil-crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Environmental Modelling & Software 64, 177-190. doi:10.1016/j.envsoft.2014.11.024</td></tr></tbody></table>

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4

5 **Table 1** - The nine biogeochemical models used for the intercomparison.

6
Table 2 - Level 1 of compositional sub-systems: general classes as usually considered in agricultural, the main processes identified within each general class and the percentage of models able to simulate at least 1 main process contained within each general class. * No information is available.

13

<table><thead><tr><th colspan="2">General Classes (Lev.1)</th><th colspan="3">% of models able to simulate at least 1 Main process contained within each General Class</th></tr><tr><th>Name of Class</th><th>N° of Main Processes contained within each General Class</th><th>Able (%)</th><th>Not able (%)</th><th>N.A *</th></tr></thead><tbody><tr><td>Plant ecophysiology and partitioning</td><td>10</td><td>100</td><td>0</td><td>---</td></tr><tr><td>Soil</td><td>4</td><td>100</td><td>0</td><td>---</td></tr><tr><td>Climate</td><td>1</td><td>100</td><td>0</td><td>---</td></tr><tr><td>Management</td><td>2</td><td>100</td><td>0</td><td>---</td></tr><tr><td>GHG emissions and other fluxes</td><td>3</td><td>100</td><td>0</td><td>---</td></tr></tbody></table>
<table><thead><tr><th colspan="2">Main Processes (Lev.2)</th><th colspan="3">% of models able to simulate at least 1 method, options or components contained within each Main Processes</th></tr><tr><th>Name of the Main Processes</th><th>N° of methods, options or components contained within each Main Processes</th><th>Able (%)</th><th>Not able (%)</th><th>N.A *</th></tr></thead><tbody><tr><td>Carbon allocation mechanism</td><td>1</td><td>55.6</td><td>44.4</td><td>---</td></tr><tr><td>Carbon assimilation</td><td>4</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>Stomata</td><td>3</td><td>33.3</td><td>66.7</td><td>---</td></tr><tr><td>Phenology</td><td>4</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>Leaf area</td><td>3</td><td>77.8</td><td>22.2</td><td>---</td></tr><tr><td>Reference evapotranspiration</td><td>10</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>Root distribution</td><td>3</td><td>77.8</td><td>22.2</td><td>---</td></tr><tr><td>Plant partitioning</td><td>9 (2)</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>Yield formation</td><td>8</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>Limiting factors</td><td>9</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>Soil carbon</td><td>8</td><td>100</td><td>0.0</td><td>---</td></tr><tr><td>Soil temperature</td><td>4</td><td>100</td><td>0.0</td><td>---</td></tr><tr><td>Soil water transport</td><td>4</td><td>100</td><td>0.0</td><td>---</td></tr><tr><td>Soil N transport and transformation</td><td>5</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>Data input</td><td>14 (19)</td><td>100</td><td>0.0</td><td>---</td></tr><tr><td>General options</td><td>20 (8)</td><td>100</td><td>0.0</td><td>---</td></tr><tr><td>Pastures options</td><td>3 (12)</td><td>66.7</td><td>33.3</td><td>---</td></tr><tr><td>CO<sub>2</sub></td><td>8</td><td>100</td><td>0.0</td><td>---</td></tr><tr><td>Non CO<sub>2</sub>-gas</td><td>6 (19)</td><td>88.9</td><td>11.1</td><td>---</td></tr><tr><td>N processes</td><td>10</td><td>88.9</td><td>11.1</td><td>---</td></tr></tbody></table>

Table 3 – Level 2 of compositional sub-systems: the main processes identified within each general class, the number of methods, options or components contained within each main processes and the percentage of models able to simulate at least 1 methods, options or components contained within each main processes. * No information is available. Numbers in brackets represents specific information related to the modelling approaches (see Tables S1-5).
<table><thead><tr><th></th><th>APSIM</th><th>CERES-EGC</th><th>DayCent</th><th>DNDC</th><th>DSSAT</th><th>EPIC</th><th>PaSim</th><th>RothC</th><th>STICS</th></tr></thead><tbody><tr><td>N° of organic pools</td><td>7</td><td>4</td><td>3</td><td>6</td><td>5</td><td>5</td><td>5</td><td>5</td><td>6</td></tr><tr><td>Microbial biomass</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Humus</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Added organic matter labile</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Added organic matter recalcitrante</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>DOC</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>DON</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Kinetic of conversion of organic pools</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Kinetic of nitrification</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Kinetic of nitrification - environmental factors involved</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>N<sub>2</sub>O losses from nitrification</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes *</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Kinetic of denitrification</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr></tbody></table>
<table><tr><td>Kinetic of denitrification - environmental factors involved</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Denitrification: N<sub>2</sub>/N<sub>2</sub>O ratio</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes *</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Soil physical properties variation (impact on fluxes)</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr></table>

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Table 4 – Overview of the C and N approaches used by the CN-MIP models. * Only in the latest version (EPIC V.1102)
<table><thead><tr><th>Model</th><th>Location</th><th>Type of environment</th><th>Biogeochemical cycles involved</th><th>Type of version *</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="7">APSIM</td><td>Australia</td><td>Plantation forestry</td><td>N</td><td>O</td><td>Snow et al. (1999)</td></tr><tr><td>Australia</td><td>Arable</td><td>C-N</td><td>M</td><td>Thorburn et al. (2010)</td></tr><tr><td>Australia</td><td>Arable</td><td>C-N</td><td>O</td><td>Huth et al. (2010)</td></tr><tr><td>New Zealand</td><td>Arable</td><td>N</td><td>O</td><td>Sharp et al. (2011)</td></tr><tr><td>Australia</td><td>Arable</td><td>C</td><td>O</td><td>Luo et al. (2011)</td></tr><tr><td>Australia</td><td>Grassland</td><td>C</td><td>M</td><td>Xing et al. (2011)</td></tr><tr><td>New Zealand</td><td>Grassland</td><td>N</td><td>O</td><td>Giltrap et al. (2015)</td></tr><tr><td rowspan="15">CERES-EGC</td><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Gabrielle et al. (2006)</td></tr><tr><td>France</td><td>Arable</td><td>C-N</td><td>O</td><td>Lehuger et al. (2007)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O/M</td><td>Rolland et al. (2008)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Lamboni et al. (2009)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Rolland et al. (2010)</td></tr><tr><td>France, Germany, Switzerland</td><td>Arable</td><td>C</td><td>O</td><td>Wattenbach et al. (2010)</td></tr><tr><td>France</td><td>Arable/Grassland</td><td>N</td><td>O</td><td>Drouet et al. (2011)</td></tr><tr><td>France, Germany</td><td>Arable</td><td>C-N</td><td>O</td><td>Lehuger (2011)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Dufossè et al. (2013)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Goglio et al. (2013)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Lehuger et al. (2014)</td></tr><tr><td>France</td><td>Arable</td><td>C-N</td><td>M</td><td>Noirot-Cosson (2016)</td></tr><tr><td rowspan="7">DayCent</td><td>Germany, USA, Scotland</td><td>Arable/Grassland</td><td>N</td><td>O</td><td>Parton et al. (1998)</td></tr><tr><td>USA</td><td>Arable/Grassland</td><td>C</td><td>O</td><td>Del Grosso et al. (2002)</td></tr><tr><td>New Zealand</td><td>Grassland</td><td>N</td><td>O</td><td>Stehfest and Muller (2004)</td></tr><tr><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Del Grosso et al. (2005)</td></tr><tr><td>China</td><td>Arable</td><td>N</td><td>O</td><td>Li et al. (2005)</td></tr><tr><td>Global</td><td>Arable</td><td>C-N</td><td>O</td><td>Stehfest et al. (2007)</td></tr><tr><td>Canada</td><td>Arable</td><td>N</td><td>O</td><td>Smith et al. (2008)</td></tr></tbody></table>
<table><tbody><tr><td></td><td>USA</td><td>Arable</td><td>C-N</td><td>O</td><td>Del Grosso et al. (2008)</td></tr><tr><td></td><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Jarecki et al. (2008)</td></tr><tr><td></td><td>USA</td><td>Arable/tile drained</td><td>N</td><td>O</td><td>David et al. (2009)</td></tr><tr><td></td><td>Ireland</td><td>Grassland</td><td>N</td><td>O</td><td>Abdalla et al. (2010)</td></tr><tr><td></td><td>USA</td><td>Arable</td><td>C-N</td><td>O</td><td>De Gyrze et al. (2010)</td></tr><tr><td></td><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Del Grosso et al. (2010)</td></tr><tr><td></td><td>USA (Incubation exp.)</td><td>-----</td><td>C-N</td><td>O/M</td><td>Li et al. (2010)</td></tr><tr><td></td><td>Australia</td><td>Grassland</td><td>C-N</td><td>O</td><td>Xing et al. (2011)</td></tr><tr><td></td><td>USA</td><td>Switchgrass</td><td>C</td><td>O</td><td>Chamberlain et al. (2011)</td></tr><tr><td></td><td>USA</td><td>Grassland</td><td>C-N</td><td>O</td><td>Hartman et al. (2011)</td></tr><tr><td></td><td>Canada/USA</td><td>Arable</td><td>C</td><td>O</td><td>Smith et al. (2012)</td></tr><tr><td></td><td>Canada</td><td>Arable</td><td>C-N</td><td>O</td><td>Chang et al. (2013)</td></tr><tr><td></td><td>Canada</td><td>Arable</td><td>N</td><td>O</td><td>Sansoulet et al. (2014)</td></tr><tr><td></td><td>Australia</td><td>Arable</td><td>N</td><td>?</td><td>Scheer et al. (2014)</td></tr><tr><td></td><td>UK</td><td>Arable</td><td>C-N</td><td>O</td><td>Fitton et al. (2014a,b)</td></tr><tr><td></td><td>USA</td><td>Grassland</td><td>C-N</td><td>O</td><td>Ryals et al. (2015)</td></tr><tr><td></td><td>USA</td><td>Switchgrass</td><td>C-N</td><td>O</td><td>Field et al. (2016)</td></tr><tr><td rowspan="10">DNDC</td><td>Costa Rica</td><td>Bare soil</td><td>C</td><td>O</td><td>Li et al. (1994)</td></tr><tr><td>Europe/Australia</td><td>Arable/Grassland</td><td>C</td><td>O</td><td>Li et al. (1997)</td></tr><tr><td>UK</td><td>Grassland</td><td>N</td><td>M</td><td>Brown et al. (2002)</td></tr><tr><td>Canada</td><td>Arable</td><td>N</td><td>O</td><td>Smith et al. (2002)</td></tr><tr><td>India</td><td>Paddy soil</td><td>C-N</td><td>O</td><td>Pathak et al. (2003)</td></tr><tr><td>China, Japan, Thailand</td><td>Paddy soil</td><td>C-N</td><td>O</td><td>Cai et al. (2003)</td></tr><tr><td>New Zealand</td><td>Grassland</td><td>N</td><td>M</td><td>Saggar et al. (2004)</td></tr><tr><td>USA, China, Germany</td><td>Arable</td><td>C-N</td><td>O</td><td>Li et al. (2005)</td></tr><tr><td>India</td><td>Paddy soil</td><td>C-N</td><td>M</td><td>Pathak et al. (2005)</td></tr><tr><td>India</td><td>Paddy soil</td><td>C-N</td><td>O</td><td>Babu et al. (2006)</td></tr><tr><td></td><td>Belgium</td><td>Arable</td><td>C</td><td>O</td><td>Sleutel et al. (2006)</td></tr><tr><td></td><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Tomitto et al. (2007)</td></tr><tr><td></td><td>Canada</td><td>Arable</td><td>N</td><td>O</td><td>Smith et al. (2008)</td></tr></tbody></table>
<table><tbody><tr><td></td><td>China, Japan</td><td>Rice</td><td>C</td><td>M</td><td>Fumoto et al. (2008)</td></tr><tr><td></td><td>Ireland</td><td>Grassland</td><td>N</td><td>O</td><td>Abdalla et al. (2010)</td></tr><tr><td></td><td>China</td><td>Arable</td><td>C-N</td><td>O</td><td>Li et al. (2010)</td></tr><tr><td></td><td>France, Germany, Switzerland</td><td>Arable</td><td>C</td><td>O</td><td>Wattenbach et al. (2010)</td></tr><tr><td></td><td>Germany</td><td>Arable</td><td>N</td><td>O</td><td>Ludwig et al. (2011)</td></tr><tr><td></td><td>Canada/USA</td><td>Arable</td><td>C</td><td>M</td><td>Smith et al. (2012)</td></tr><tr><td></td><td>France, Germany, Belgium, UK, Netherlands, EU-15</td><td>Arable/Grassland</td><td>N</td><td>M</td><td>Leip et al. (2011)</td></tr><tr><td></td><td>China</td><td>Arable</td><td>N</td><td>M</td><td>Wu and Zhang (2014)</td></tr><tr><td></td><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Gu et al. (2014)</td></tr><tr><td></td><td>Canada</td><td>Arable/Grassland</td><td>N</td><td>M</td><td>Uzoma et al. (2015)</td></tr><tr><td></td><td>Australia</td><td>Arable</td><td>N</td><td>O</td><td>Chen et al. (2015)</td></tr><tr><td></td><td>USA (Alaska)</td><td>Peatland</td><td>C</td><td>O</td><td>Deng et al. (2015)</td></tr><tr><td></td><td>China</td><td>Arable</td><td>C</td><td>M</td><td>Yu et al. (2015)</td></tr><tr><td></td><td>New Zealand</td><td>Grassland</td><td>N</td><td>M</td><td>Giltrap et al. (2015)</td></tr><tr><td></td><td>China</td><td>Arable</td><td>N</td><td>M</td><td>Zhang et al. (2015)</td></tr><tr><td></td><td>Canada</td><td>Arable</td><td>N</td><td>M</td><td>Congreves et al. (2016)</td></tr><tr><td></td><td>Canada</td><td>Arable</td><td>C</td><td>M</td><td>Gagnon et al. (2016)</td></tr><tr><td rowspan="10">DSSAT</td><td>UK, Brasil</td><td>Arable/Bare soil</td><td>C/N</td><td>O</td><td>Gijsman et al. (2002)</td></tr><tr><td>Mexico</td><td>Arable</td><td>C</td><td>M</td><td>Hartkamp et al. (2004)</td></tr><tr><td>Canada</td><td>Arable</td><td>N</td><td>O</td><td>Liu et al. (2011)</td></tr><tr><td>Burkina Faso</td><td>Arable</td><td>C</td><td>M</td><td>Soler et al. (2011)</td></tr><tr><td>Italy</td><td>Arable</td><td>C</td><td>O</td><td>De Sanctis et al. (2012)</td></tr><tr><td>Spain</td><td>Arable</td><td>C/N</td><td>O</td><td>Soldevilla-Martinez et al. (2013)</td></tr><tr><td>China</td><td>Arable</td><td>C/N</td><td>O</td><td>Yang et al. (2013)</td></tr><tr><td>China</td><td>Arable</td><td>C/N</td><td>O</td><td>Li et al. (2015)</td></tr><tr><td>Canada</td><td>Arable</td><td>C/N</td><td>O</td><td>Li et al. (2015)</td></tr></tbody></table>
<table><tbody><tr><td></td><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Prasad et al. (2015)</td></tr><tr><td rowspan="12">EPIC</td><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Jackson et al. (1994)</td></tr><tr><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Cavero et al. (1996)</td></tr><tr><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Ramanarayanan et al. (1998)</td></tr><tr><td>Canada</td><td>Arab</td><td>C-N</td><td>O</td><td>Roloff et al. (1998)</td></tr><tr><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Cavero et al. (1999)</td></tr><tr><td>Argentina</td><td>Arable/Grassland</td><td>N</td><td>O</td><td>Bernardos et al. (2001)</td></tr><tr><td>USA</td><td>Arable</td><td>N</td><td>O</td><td>Chung et al. (2002)</td></tr><tr><td>USA</td><td>Arable/Grassland</td><td>C</td><td>O</td><td>Potter et al. (2004)</td></tr><tr><td>USA</td><td>Arab</td><td>C-N</td><td>O</td><td>Wang et al. (2005)</td></tr><tr><td>Lab. Experiment</td><td>Lab. Experiment</td><td>C-N</td><td>O</td><td>He et al. (2006)</td></tr><tr><td>USA, Canada</td><td>Arable</td><td>C-N</td><td>O</td><td>Izaurralde et al. (2006)</td></tr><tr><td>USA</td><td>Arable</td><td>C</td><td>O</td><td>Causarano et al. (2007)</td></tr><tr><td>USA</td><td>Arable</td><td>C</td><td>O</td><td>Abrahamson et al. (2009)</td></tr><tr><td>Argentina</td><td>Arable</td><td>C</td><td>O</td><td>Apezteguia et al. (2009)</td></tr><tr><td>Germany</td><td>Arable</td><td>C</td><td>O</td><td>Billen et al. (2009)</td></tr><tr><td>Italy</td><td>Arable</td><td>C</td><td>O</td><td>Farina et al. (2011)</td></tr><tr><td></td><td>USA</td><td>Arable</td><td>C</td><td>O</td><td>Zhang et al. (2015)</td></tr><tr><td rowspan="7">PaSim</td><td>Switzerland</td><td>Grassland</td><td>N</td><td>O</td><td>Schmid et al. (2001)</td></tr><tr><td>Scotland</td><td>Grassland</td><td>N</td><td>O</td><td>Riedo et al. (2002)</td></tr><tr><td>Ireland</td><td>Grassland</td><td>C</td><td>O</td><td>Lawton et al. (2006)</td></tr><tr><td>Hungary, Scotland, Ireland, France, Switzerland</td><td>Grassland</td><td>C-N</td><td>O</td><td>Calanca et al. (2007)</td></tr><tr><td>Switzerland, Ireland, France, Scotland</td><td>Grassland</td><td>C</td><td>O</td><td>Gottschalk et al. (2007)</td></tr><tr><td>France, Switzerland, Ireland</td><td>Grassland</td><td>C</td><td>O</td><td>Vuichard et al. (2007a)</td></tr><tr><td>France</td><td>Grassland</td><td>C</td><td>O</td><td>Aulagnier et al. (2013)</td></tr></tbody></table>
<table><tbody><tr><td></td><td>France, Germany, Hungary, Ireland, Italy, Portugal, Spain, Switzerland, The Netherlands, UK</td><td>Grassland</td><td>C</td><td>O</td><td>Ma et al. (2015)</td></tr><tr><td></td><td>France, Germany, Italy, Switzerland</td><td>Grassland</td><td>C</td><td>O</td><td>Sandor et al. (2016)</td></tr><tr><td rowspan="15">RothC</td><td>Czech republic</td><td>Arable</td><td>C</td><td>O</td><td>Coleman et al. (1997)</td></tr><tr><td>Hungary, Sweden, UK</td><td>Arable/Grassland</td><td>C</td><td>O</td><td>Falloon and Smith (2002)</td></tr><tr><td>Australia</td><td>Arable</td><td>C</td><td>O</td><td>Skjemstad et al. (2004)</td></tr><tr><td>Japan</td><td>Paddy soil</td><td>C</td><td>O/M</td><td>Shirato and Yokazawa (2005)</td></tr><tr><td>Syria</td><td>Arable</td><td>C</td><td>O/M</td><td>Jenkinson et al., 2005</td></tr><tr><td>China</td><td>Arable</td><td>C</td><td>O</td><td>Guo et al. (2007)</td></tr><tr><td>Switzerland</td><td>Arable/Grassland</td><td>C</td><td>O</td><td>Zimmermann et al. (2007)</td></tr><tr><td>Kenya</td><td>Arable</td><td>C</td><td>O</td><td>Kamoni et al (2007b)</td></tr><tr><td>Zambia</td><td>Arable</td><td>C</td><td>O</td><td>Kaonga et al (2008)</td></tr><tr><td>Australia</td><td>Arable</td><td>C</td><td>O</td><td>Liu et al. (2009)</td></tr><tr><td>Ireland</td><td>Arable</td><td>C</td><td>O</td><td>Dondini et al. (2009)</td></tr><tr><td>Slovakia</td><td>Arable/Grassland</td><td>C</td><td>O</td><td>Barancikova et al. (2010)</td></tr><tr><td>Spain</td><td>Orchard</td><td>C</td><td>O</td><td>Nieto et al. (2010)</td></tr><tr><td>Austria</td><td>Arable</td><td>C</td><td>O</td><td>Rampazzo Todorovic et al. (2010)</td></tr><tr><td>Australia</td><td>Grasslands</td><td>C</td><td>O</td><td>Liu et al. (2011)</td></tr><tr><td>Ireland</td><td>Grassland</td><td>C</td><td>O</td><td>Xu et al. (2011)</td></tr><tr><td>Mexico</td><td>Arable/Grassland/Forest/Rangeland</td><td>C</td><td>O</td><td>Gonzalez-Molina et al. (2011)</td></tr><tr><td>China</td><td>Arable</td><td>C</td><td>O</td><td>Wang et al. (2013)</td></tr><tr><td>Italy, Spain, Australia, Syria, UK</td><td>Arable</td><td>C</td><td>O/M</td><td>Farina et al. (2013)</td></tr><tr><td rowspan="2">STICS</td><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Schnebelen et al. (2004)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Corre-Hellou et al. (2009)</td></tr></tbody></table>
<table><tr><td>France</td><td>Arable</td><td>C-N</td><td>O</td><td>Justes et al. (2009)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Jego et al. (2012)</td></tr><tr><td>France</td><td>Arable</td><td>N</td><td>O</td><td>Constantin et al. (2012)</td></tr></table>

24

25

26

27

Table 5 – Overview of the studies carried out using the CN-MIP models for a broad gradient of geographic and climatic conditions, as well as a variety of soil types and management practices. * O = original version; M = modified version.
<table><thead><tr><th rowspan="3">Model</th><th rowspan="3">N° of Ref.</th><th rowspan="3">Factor of weaknesses</th><th colspan="4">N° of weaknesses per each model</th><th colspan="4">% of weaknesses per each model</th></tr><tr><th colspan="2">C-Cycle</th><th colspan="2">N-Cycle</th><th colspan="2">C-Cycle</th><th colspan="2">N-Cycle</th></tr><tr><th>Modelling</th><th>Scale of analysis</th><th>Modelling</th><th>Scale of analysis</th><th>Modelling</th><th>Scale of analysis</th><th>Modelling</th><th>Scale of analysis</th></tr></thead><tbody><tr><td rowspan="2">APSIM</td><td rowspan="2">6</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>5</td><td>1</td><td>0.0</td><td>0.0</td><td>62.5</td><td>12.5</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>1</td><td>0</td><td>12.5</td><td>0.0</td><td>12.5</td><td>0.0</td></tr><tr><td rowspan="2">CERES-EGC</td><td rowspan="2">8</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>6</td><td>2</td><td>7.7</td><td>0.0</td><td>46.2</td><td>15.4</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>3</td><td>0</td><td>7.7</td><td>0.0</td><td>23.1</td><td>0.0</td></tr><tr><td rowspan="2">DayCent</td><td rowspan="2">17</td><td>Pedo-climatic</td><td>4</td><td>0</td><td>14</td><td>8</td><td>11.8</td><td>0.0</td><td>41.2</td><td>23.5</td></tr><tr><td>Management</td><td>3</td><td>1</td><td>4</td><td>0</td><td>8.8</td><td>2.9</td><td>11.8</td><td>0.0</td></tr><tr><td rowspan="2">DNDC</td><td rowspan="2">23</td><td>Pedo-climatic</td><td>10</td><td>6</td><td>13</td><td>6</td><td>18.5</td><td>11.1</td><td>24.1</td><td>11.1</td></tr><tr><td>Management</td><td>9</td><td>3</td><td>6</td><td>2</td><td>16.7</td><td>5.6</td><td>9.3</td><td>3.7</td></tr><tr><td rowspan="2">DSSAT</td><td rowspan="2">7</td><td>Pedo-climatic</td><td>4</td><td>0</td><td>2</td><td>1</td><td>40.0</td><td>0.0</td><td>20.0</td><td>10.0</td></tr><tr><td>Management</td><td>2</td><td>0</td><td>1</td><td>0</td><td>20.0</td><td>0.0</td><td>10.0</td><td>0.0</td></tr><tr><td rowspan="2">EPIC</td><td rowspan="2">13</td><td>Pedo-climatic</td><td>3</td><td>1</td><td>5</td><td>1</td><td>14.3</td><td>4.8</td><td>23.8</td><td>4.8</td></tr><tr><td>Management</td><td>7</td><td>0</td><td>4</td><td>0</td><td>33.3</td><td>0.0</td><td>19</td><td>0.0</td></tr><tr><td rowspan="2">PaSim</td><td rowspan="2">7</td><td>Pedo-climatic</td><td>4</td><td>2</td><td>2</td><td>0</td><td>33.3</td><td>16.7</td><td>16.7</td><td>0.0</td></tr><tr><td>Management</td><td>2</td><td>0</td><td>2</td><td>0</td><td>16.7</td><td>0.0</td><td>16.7</td><td>0.0</td></tr><tr><td rowspan="2">RothC</td><td rowspan="2">11</td><td>Pedo-climatic</td><td>5</td><td>0</td><td>0</td><td>0</td><td>35.7</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Management</td><td>9</td><td>0</td><td>0</td><td>0</td><td>64.3</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td rowspan="2">STICS</td><td rowspan="2">3</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0.0</td><td>0.0</td><td>20.0</td><td>0.0</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>3</td><td>1</td><td>0.0</td><td>0.0</td><td>60.0</td><td>20.0</td></tr><tr><td rowspan="2">Total</td><td rowspan="2">95</td><td>Pedo-climatic</td><td>31</td><td>9</td><td>48</td><td>19</td><td>18</td><td>5.2</td><td>27.9</td><td>11</td></tr><tr><td>Management</td><td>34</td><td>4</td><td>24</td><td>3</td><td>19.8</td><td>2.3</td><td>14</td><td>1.7</td></tr></tbody></table>

Table 6 – Number of weaknesses and the relative percentage emerged in 95 modelling studies. Model performances were mainly unsatisfactory due to erroneous accounting of pedo-climatic conditions (45.9 %) and management practices (33.8 %).
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="10">APSIM</td><td>Snow et al. (1999)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of soil N supply</td><td>Soil properties (C)</td><td>Modelling HUM decomposition too slow</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Thorburn et al. (2010)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (Underestimation of Denitrification, Unpredicted N<sub>2</sub>O emissions peaks)</td><td>Soil properties (N) - Climate</td><td>Model parametrization: Default value of denitrification coefficient within the model was much lower than the optimized - Errors in rainfall data used (i.e. spatially averaged rainfall data were used vs specific test site rainfall data)</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Sharp et al. (2011)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Overestimation of annual leaching</td><td>Soil properties (N)</td><td>Overestimation of soil solution nitrate concentration</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Xing et al. (2011)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td>Underestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N)</td><td>Underestimation of denitrification (response of denitrification rate to soil temperature and moisture (or WFPS) were the two primary factors leading to the underestimation of denitrification)</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Luo et al. (2011)</td><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Underestimation of SOC decomposition</td><td>Management (Tillage - Crop type)</td><td>Tillage: effect on soil features can lead to possible acceleration in soil C decomposition due to changes in soil environment. Tillage effects in APSIM is very simple and could not take in account the real effect on soil - Crop type: crop varietal changes could have significant effect on crop production and, in turn, on C input.</td></tr><tr><td rowspan="2">Giltrap et al. (2015)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Under/Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (general) - Climate</td><td>Average field soil properties used for running the model rather than specific values</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>---</td><td>Soil properties (Fertilization)</td><td>Fertilization type: urine patches is much more extreme than typical fertilization as NH<sub>4</sub><sup>+</sup></td></tr><tr><td rowspan="2">CERES-EGC</td><td rowspan="2">Gabrielle et al. (2006)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>1</td><td>Under/Overestimation of N<sub>2</sub>O emission peaks</td><td>Soil properties (SWD - BD)</td><td>Soil water retention properties and bulk density. Different parametrization lead to differences in N outputs from test site to regional scale</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="10"></td><td>Lamboni et al. (2009)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (N<sub>2</sub>O emissions)</td><td>Soil properties (N)</td><td>Sensitivity of N<sub>2</sub>O emissions: Denitrification, Potential denitrification rate, Fraction of denitrified N.</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Wattenbach et al. (2010)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td rowspan="2">Overestimation NEE peaks</td><td>Climate</td><td>Climate Model was developed for Northern regions. Main issues in Southern regions of Europe. Issues in coupling water and carbon fluxes</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Phenology</td><td>Possible mismatch due to ecophysiology: overestimation NEE peaks and fluxes during senescence and mismatch in the cumulative NEE for the year. Poor performance in reproducing LE flux</td></tr><tr><td rowspan="2">Drouet et al. (2011)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td rowspan="2">General discrepancies (N<sub>2</sub>O emissions)</td><td>Soil properties (N - BD)</td><td>Sensitivity of N<sub>2</sub>O emissions: N<sub>2</sub>O emissions from denitrification; Max. rate of nitrification, Soil Bulk Density</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Management (Crop type)</td><td>Sensitivity of N<sub>2</sub>O emissions: Cropland area (crop variety and management)</td></tr><tr><td rowspan="2">Lehuger et al. (2011)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td rowspan="2">Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N)</td><td>N<sub>2</sub>O emission peak was produced in response of the high ammonium content in topsoil - Possible time lag in N<sub>2</sub>O emissions</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Goglio et al. (2013)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td rowspan="2">Underestimation of N<sub>2</sub>O emissions peaks</td><td>Climate</td><td>Inter-annual variability</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Management (Fertilization - water conservation)</td><td>Legumes incorporation and mulching</td></tr><tr><td rowspan="2">Lehuger et al. (2014)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td rowspan="2">General discrepancies (N<sub>2</sub>O emissions)</td><td>Climate (Rainfall)</td><td>Rainfall</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Noirot-Cosson (2016)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td rowspan="2">Overestimation of mineral N</td><td>Soil properties (T - SWD)</td><td>Temperature and water on mineralization dynamics</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Management (Fertilization)</td><td>Effect of N fertilizer</td></tr><tr><td rowspan="2">DayCent</td><td rowspan="2">Parton et al. (2001)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td rowspan="2">General discrepancies (N<sub>2</sub>O and NO<sub>x</sub> emissions)</td><td>Soil properties (Texture)</td><td>Texture</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="2">Stehfest and Muller (2004)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td></td><td>Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N - WFPS) - Climate</td><td>WFPS overestimation and issues in the ratio Denitrification/Nitrification - Rainfall</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td></td><td></td><td>Management (Fertilization)</td><td>Urine application</td></tr><tr><td rowspan="2">Del Grosso et al. (2005)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td></td><td>General discrepancies (N<sub>2</sub>O emissions)</td><td>Soil properties (general) - Climate</td><td>Soil properties</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td></td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Li et al. (2005)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td></td><td>Underestimation of NH<sub>4</sub></td><td>Soil properties (N)</td><td>Overestimation nitrification rate and underestimation Nitrification (mainly due to temperature effect)</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td></td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Smith et al. (2008)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td></td><td>Underestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N - SWD)</td><td>Under prediction of Mineralization and soil properties (SWC, soil N and soil ammonium)</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td></td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Jarecki et al. (2008)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td></td><td>Underestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N - SWD) - Climate</td><td>Soil properties (inorganic N, soil moisture) - Rainfall</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td></td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Del Grosso et al. (2008)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td></td><td>Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N)</td><td>Nitrification rates too high, NO<sub>3</sub> too low, N<sub>2</sub>O too high.</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td></td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">David et al. (2009)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td></td><td>Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (general)</td><td>Crop evapotranspiration and the impact of tile drainage</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td></td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Li et al. (2010)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>1</td><td>0</td><td></td><td>General discrepancies (CO<sub>2</sub>, N mineralization and Nitrification)</td><td>Soil properties (SWD)</td><td>Soil moisture</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td></td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Abdalla et al. (2010)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td></td><td>General discrepancies (Overestimation N<sub>2</sub>O emissions, underestimation N<sub>2</sub>O emissions)</td><td>Soil water flows (WFPS)</td><td>Overestimation WFPS</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td></td><td></td><td>Management (Fertilization - Crop type)</td><td>Fertilization maintained high mineral N along with secondary peaks compared to field data, underestimated biomass.</td></tr><tr><td>Del Grosso et al. (2010)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td></td><td>Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N)</td><td>N mineralization rates too high and too responsive to climate drivers</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="10"></td><td rowspan="3">De Gyrze et al. (2010)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (SOC; underestimation N<sub>2</sub>O emissions)</td><td>Soil properties (C - BD - texture) - Climate Management (Tillage - Crop type)</td><td>Texture, decomposition rate, Bulk Density - Rainfall</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (CO<sub>2</sub> emissions)</td><td>Soil properties (SWD - WFPS)</td><td>Tillage and cover crop</td></tr><tr><td rowspan="2">Xing et al. (2011)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>Soil moisture variations, high sensitivity to WFPS</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Smith et al. (2012)</td><td>Management</td><td>1</td><td>1</td><td>0</td><td>0</td><td>Underestimation of SOC</td><td>Management (Fertilization)</td><td>Slight overestimation of residue removal impact on SOC partly because of the inherent variability in SOC measurements and also partly due to imperfections in the models themselves</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td>General discrepancies (Overestimation N<sub>2</sub>O emissions and soil NH<sub>4</sub>; Underestimation NO<sub>3</sub> emissions)</td><td>Climate</td><td>Drying out periods</td></tr><tr><td rowspan="4">Scheer et al. (2014)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of N<sub>2</sub>O emissions</td><td>Soil properties (N) - Climate</td><td>N subroutine heavily affected by soil parameters. Mineralization and denitrification rates may be too low, freeze-thaw fluxes need work - N subroutine heavily affected by climate</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of CO<sub>2</sub> emissions</td><td>Management (Fertilization) Soil properties (N - WFPS)</td><td>Model sensitive at low N application rates</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Underestimation of CO<sub>2</sub> emissions</td><td>Management (Fertilization)</td><td>N mineralization rates may be off, underestimated WFPS</td></tr><tr><td rowspan="4">Ryals et al. (2014, 2015)</td><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>No soil water benefits provided by adding of compost</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td>Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (general)</td><td>Overestimation N<sub>2</sub>O emissions from shoulder position (upper landscape). Inability to characterize differences in soil properties and water/nutrient flow for 3-D landscape</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (CH<sub>4</sub>, N<sub>2</sub>O and NO emissions)</td><td>Soil properties (general)</td><td>Soil properties</td></tr><tr><td rowspan="2">DNDC</td><td>Smith et al. (2002)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Cai et al. (2003)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>1</td><td>0</td><td>---</td><td>---</td><td>---</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="10"></td><td rowspan="3">Pathak et al. (2003)</td><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td rowspan="3">Overestimations of CH<sub>4</sub> emissions</td><td>Management (Crop type)</td><td>Type of cultivar (crop parameters) - daily timescale</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Soil properties (N)</td><td>Leaking rate</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="3">Saggar et al. (2004)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td rowspan="3">Underestimation of N<sub>2</sub>O emissions</td><td>Soil properties (T - SWD)</td><td>Under prediction of Temperature effect and moisture after rainfall (size and timing)</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Overestimations of CH<sub>4</sub> emissions</td><td>Soil properties (C - pH)</td><td>Initial SOC content and soil redox potential</td></tr><tr><td rowspan="3">Pathak et al. (2005)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>1</td><td rowspan="2">General discrepancies (CH<sub>4</sub> and N<sub>2</sub>O peaks)</td><td>Soil properties (SWD)</td><td>Soil shrinking and swelling - daily timescale</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>1</td><td>Management (Crop type)</td><td>CH<sub>4</sub> and N<sub>2</sub>O peaks not well captured at the beginning and end of growing season. Type of cultivar (crop parameters)</td></tr><tr><td rowspan="2">Babu et al. (2006)</td><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>0</td><td rowspan="2">Under/Overestimation of SOC</td><td>Soil properties (general)</td><td>Soil type</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Management (Fertilization)</td><td>Residues incorporation</td></tr><tr><td rowspan="2">Sleutel et al. (2006)</td><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>0</td><td rowspan="2">Overestimation of SOC</td><td>Climate</td><td>Spatial heterogeneity in environmental parameters</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Management (general)</td><td>Spatial heterogeneity in farm management practices</td></tr><tr><td rowspan="2">Liu et al. (2006)</td><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>0</td><td rowspan="2">General discrepancies (SOC)</td><td>Soil properties (N)</td><td>The base DNDC model greatly over predicted N leaching: calibration of 4 leaching parameters (within code) was required to improve model performance.</td></tr><tr><td>Management</td><td>1</td><td>1</td><td>0</td><td>0</td><td>Soil properties (N)</td><td></td></tr><tr><td rowspan="5">Tonitto et al. (2007)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td rowspan="5">Overestimation of N leaching</td><td>Soil properties (N - SWD)</td><td>Soil water content and soil N underestimated. Too few chamber measurements of N<sub>2</sub>O emissions available for detailed temporal testing.</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>1</td><td>Soil properties (Fertilization)</td><td>Error in plant N uptake equation. Highest slurry rate</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Soil properties (general)</td><td>Soil properties and effect of temperature on Leaf Area development</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Management (Crop type) - Phenology</td><td>Type and stage of cultivar</td></tr><tr><td>Fumoto et al. (2008)</td><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of CH<sub>4</sub> emissions</td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="2">Abdalla et al. (2010)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (C - WFPS)</td><td>Overestimation WFPS and SOC content</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Li et al. (2010)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of N<sub>2</sub>O emissions</td><td>Soil properties (SWD)</td><td>Impact of soil freezing and thawing</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Wattenbach et al. (2010)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (cumulative NEE and Reco, overestimation NEE peaks).</td><td>Climate Management (Fertilization - Tillage - Crop type)</td><td>Issues in coupling water and carbon fluxes, air temperature (i.e. mild winter)</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (general) - Climate</td><td>Overestimation NEE peaks and general discrepancies at senescence and post-harvesting, (cumulative NEE and Reco). Crop rotation, fertilization and tillage</td></tr><tr><td>Ludwig et al. (2011)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>---</td><td>---</td><td>Soil properties - Rainfall</td></tr><tr><td rowspan="2">Leip et al. (2011)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td>Under/Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (general) - Climate</td><td>Soil properties - Rainfall</td></tr><tr><td rowspan="2">Smith et al. (2012)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>0</td><td>Overestimation of SOC following residue removal</td><td>Soil properties (C)</td><td>High spatial heterogeneity in SOC measurements. The conceptual SOC "passive fraction" may have been set too high in DNDC from some locations/soil types.</td></tr><tr><td rowspan="2">Yu et al. (2015)</td><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Under/Overestimation of N<sub>2</sub>O emissions</td><td>Soil properties (SWD) - Climate</td><td>Inability of cascade flow hydrology model to effectively simulate water content above field capacity - N<sub>2</sub>O emissions overestimated during alfalfa production and underestimated during long periods of episodic rainfall.</td></tr><tr><td>Uzoma et al. (2015)</td><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>---</td><td>---</td><td>Interception and uptake of water by alfalfa was likely underestimated.</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="10"></td><td rowspan="5">Zhang et al. (2015)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (timing of daily N<sub>2</sub>O and NO emissions)</td><td>Soil properties (SWD)</td><td>Soil water content was not well simulated</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Overestimation of N<sub>2</sub>O and NO emissions</td><td>Management (Fertilization)</td><td>High fertilizer treatments: Need to better simulate the limitation of dissolved organic carbon on denitrification</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Overestimation of N<sub>2</sub>O emissions, soil nitrate and ammonia concentrations.</td><td>Soil properties (N)</td><td>The model had incorrect nitrogen partitioning for urea ammonium nitrate applications.</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of NH<sub>3</sub> emissions</td><td>Management (Fertilization)</td><td>Model doesn't include canopy interception and foliar N uptake when spraying liquid fertilizer.</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of NH<sub>3</sub> emissions</td><td>Soil properties (Texture - SWD)</td><td>NH<sub>3</sub> emissions were greatly improved for a newly developed NH<sub>3</sub> sub-model but emissions were still largely underestimated for one treatment. The model could not simulate a heterogeneous soil profile.</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (soil CO<sub>2</sub> respiration).</td><td>Management (Fertilization)</td><td>Due to simple cascade water flow DNDC had limited ability to simulate slurry infiltration rates.</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>0</td><td>General discrepancies (soil CO<sub>2</sub> respiration).</td><td>Soil properties (C - texture)</td><td>Inputs data describing long-term field history was not available. Thus the initial assumed fractions of litter, humads and humus may have been wrong.</td></tr><tr><td>Management</td><td>1</td><td>1</td><td>0</td><td>0</td><td>General discrepancies (soil CO<sub>2</sub> respiration).</td><td>Management (Fertilization)</td><td>DNDC could not simulate the differences in soil CO<sub>2</sub> respiration between soil textures and produced opposite values than was observed when N fertilizer was added (respiration was increased rather than decreased). DNDC does not include soil processes which reduce soil CO<sub>2</sub> respiration after fertilizer addition (these processes are not well understood).</td></tr><tr><td rowspan="3">DSSAT</td><td rowspan="2">Gijsman et al. (2002)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (SOC dynamics and soil mineral N)</td><td>Soil properties (C - texture)</td><td>Relative proportion of SOM pools and the rate of some pools were not likely to be well simulated. Other factor can be that the soil texture data used as inputs for the simulation were expressed in ISSS textural units, in which silt is the 2- to 20-µm class, while DSSAT use the American unit system with silt equals to 2 to 50 µm. This has likely affected also soil retention characteristics.</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Liu et al. (2011)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td>Overestimation of N losses</td><td>Soil properties (N - texture)</td><td>Consistently overestimation of nitrate loss from no fertilization treatment in long term experimental sites. This overestimation may reflect inadequate model representation of degraded soil profile for long-term simulations</td></tr><tr><td></td><td></td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="12"></td><td rowspan="3">Hartkamp et al. (2004)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Overestimation of SOC</td><td>Management (Fertilization)</td><td>Fertilization: Overestimation SOC (SOC was overestimated in the crop rotations with N fertilization) Initial values for SOC not accurately defined. SOC overestimation associated to overestimation of the biomass incorporated into the soil. In fact, SOC in the fallow quite well simulated.</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Underestimation of SOC</td><td>Soil properties (SWD)</td><td>The long-term increase of SOC in the top soil layers can have a relevant influence on soil hydraulic properties but this is not automatically simulated by DSSAT</td></tr><tr><td rowspan="3">De Sanctis et al. (2012)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (SOC)</td><td>Soil properties (C) - Climate</td><td>Overestimation of the rate of soil C decomposition and the underestimation of the efficiency of conversion of crop residue C to soil C set in the model - Soil C decomposition rates set in the CENTURY model may be too high for semi-arid Canadian soils.</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (SOC)</td><td>Management (Crop type)</td><td>Improvements in the cultivar coefficients are required, in fact deviations in straw and root yields were highlighted</td></tr><tr><td rowspan="3">Li et al. (2015)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Overestimation of SOC</td><td>Soil properties (C - N)</td><td>Differences between the model soil C/N ratio and the measured C/N ratio parameter</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="3">Prasad et al. (2015)</td><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (leaching loss and gaseous loss of N via volatilization and denitrification).</td><td>Management (Fertilization) - Phenology</td><td>High soil mineral N concentrations that might have resulted from late application of large amounts of N that were not utilized by potato plants - During the tuber bulking phase, the potato plant slows down N uptake and starts N translocation from leaves to tubers. The presence of large amount of mineral N might have created hot spot area in the potato beds where soil sampling was carried out.</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of soil NO<sub>3</sub> content</td><td>Management (Irrigation)</td><td>Leached or denitrified during the irrigated crop period.</td></tr><tr><td rowspan="4">EPIC</td><td rowspan="2">Jackson et al. (1994)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Cavero et al. (1996)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Overestimation of inorganic N concentration</td><td>Management (Fertilization)</td><td>Crop residues incorporation (i.e. overestimation N uptake at harvest)</td></tr><tr><td rowspan="2">Ramanarayanan et al. (1998)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Overestimation of soil NO<sub>3</sub> content</td><td>Climate</td><td>Weather condition</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="2">Roloff et al. (1998)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of soil N content</td><td>Soil properties (N - SWD)</td><td>N transformation, water dynamics and soil water balance routine (PET and water distribution within profile) are probably the main issues</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Cavero et al. (1999)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of inorganic N concentration, N losses</td><td>Soil properties (N) Management (Fertilization - Crop type - Irrigation)</td><td>N distribution in the bed</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Overestimation NO<sub>3</sub>-N losses</td><td>Soil properties (SWD) - Climate</td><td>Crop (Access of roots to inorganic N), irrigation</td></tr><tr><td rowspan="2">Chung et al. (2002)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>1</td><td>---</td><td>---</td><td>Simplistic tile drainage routine/lack of a flow component - Storm events</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Potter et al. (2004)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Underestimation of soil C</td><td>Soil properties (general) Management (Tillage)</td><td>Soil properties Rate of C losses in tilled management too high or C accumulation in grassed area reaching a plateau after "quick" increase (possible cause: lack of available N)</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Wang et al. (2005)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Underestimation (SOC content)</td><td>Management (Fertilization)</td><td>Issues in observations and model structural error in underestimating the return of corn residues. Disturbance of the soil sample (consequent increase of mineralization) - N mineralization algorithms may underpredict Net Nitrogen Mineralization (NMN) observable under field conditions - Problem in reproducing the lab. Experiment.</td></tr><tr><td rowspan="2">He et al. (2006)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (soil C, Overestimation of N mineralization)</td><td>Soil properties (C) Management (Fertilization)</td><td>Disturbance of the soil sample (consequent increase of mineralization) - N mineralization algorithms may underpredict Net Nitrogen Mineralization (NMN) observable under field conditions - Problem in reproducing the lab. Experiment.</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>1</td><td>0</td><td>General discrepancies (Overestimation of microbial biomass C and total organic C, Underestimation particulate organic C)</td><td>Soil properties (C) Management (Fertilization - Tillage)</td><td>Underestimation of the soil capacity to transform crop residue in SOC Spatial differences in C fraction due to differing soil landscapes</td></tr><tr><td rowspan="2">Causarano et al. (2007)</td><td>Pedo-climatic</td><td>0</td><td>1</td><td>0</td><td>0</td><td>---</td><td>---</td><td>Tillage and manure effects not adequately simulated by the model</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="2">Apezteguia et al. (2009)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (SOC content)</td><td>Phenology</td><td>Inability of the model to capture the yield trends as well to the overestimation of the contribution of monoculture to TOC.</td></tr><tr><td>Billen et al. (2009)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr></tbody></table>
<table><thead><tr><th>Model Name</th><th>References</th><th>Factor of weaknesses</th><th>C-Cycle Model structure</th><th>C-Cycle Scale of analysis</th><th>N-Cycle Model structure</th><th>N-Cycle Scale of analysis</th><th>Type of weaknesses</th><th>Cause of weaknesses</th><th>Possible explanation</th></tr></thead><tbody><tr><td rowspan="3"></td><td rowspan="3">Zhang et al. (2015)</td><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (SOC content)</td><td>Management (Tillage)</td><td>Tillage effect</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (differences in the magnitude of NPP and NEE, and in the spatial pattern of SOC change).</td><td>Soil properties- Climate</td><td>Errors in climate records, inaccurate soil parameters, incomplete management information, and interactions among these factors, inaccurate representation of crop rotations</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td></td><td>Management</td><td></td></tr><tr><td rowspan="3"></td><td rowspan="3">Schmid et al. (2001)</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>1</td><td>0</td><td rowspan="3">Underestimation of N<sub>2</sub>O emission peaks</td><td>Climate</td><td>Wet conditions</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Phenology</td><td>Overestimation transpiration and N uptake by plants;</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="3"></td><td rowspan="3">Riedo et al. (2002)</td><td>Management</td><td>0</td><td>0</td><td>1</td><td>0</td><td>Underestimation of NH<sub>3</sub> emission peaks</td><td>Management (Fertilization - Cutting)</td><td>Soil ammoniacal nitrogen pool is partitioned between soil surface and soil layers with the NH<sub>3</sub> emissions being driven by NH<sub>4</sub> in the 0-3 mm soil layer. The drawback here is that models does not consider the form of N taken up by roots, Accordingly high NH<sub>4</sub> absorption by plants leads to high NH<sub>3</sub> emissions and vice versa - explaining some discrepancies between simulations and measurements during the period after fertilisation</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Overestimation of NEE</td><td>Climate</td><td>Over prediction of the uptake due to the oversensitivity of PaSim to initial conditions/winter conditions.</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (Under/Overestimation of GPP, Reco and N<sub>2</sub>O emissions; daily CO<sub>2</sub> emissions)</td><td>Management</td><td>Type of management (intensive vs extensive)</td></tr><tr><td rowspan="3"></td><td rowspan="3">Lawton et al. (2006)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td rowspan="3">---</td><td>Soil properties (C - SWD)</td><td>Inappropriate setting of initial parameters, SOM stock initialization, over emphatization of water stress effect on assimilation</td></tr><tr><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>1</td><td>1</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td rowspan="3"></td><td rowspan="3">PaSim</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of NEE</td><td>Management</td><td>Grazing effect</td></tr><tr><td rowspan="3"></td><td rowspan="3">Calanca et al. (2007)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of GPP and Reco</td><td>Soil properties (T - SWD)</td><td>SWC and soil temperature</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (NEE)</td><td>Management</td><td>Type of management (intensive vs extensive)</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>0</td><td>---</td><td>Soil properties (T - SWD)</td><td>Improper representation of soil water content and soil temperature</td></tr><tr><td rowspan="3"></td><td rowspan="3">Vuichard et al. (2007a)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of NEE</td><td>Management</td><td>Grazing effect</td></tr><tr><td rowspan="3"></td><td rowspan="3">Ma et al. (2015)</td><td>Pedo-climatic</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of GPP and Reco</td><td>Soil properties (T - SWD)</td><td>SWC and soil temperature</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>General discrepancies (NEE)</td><td>Management</td><td>Type of management (intensive vs extensive)</td></tr><tr><td>Pedo-climatic</td><td>1</td><td>1</td><td>0</td><td>0</td><td>---</td><td>Soil properties (T - SWD)</td><td>Improper representation of soil water content and soil temperature</td></tr><tr><td rowspan="3"></td><td rowspan="3">Sandor et al. (2016)</td><td>Management</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of NEE</td><td>Management</td><td>Grazing effect</td></tr><tr><td rowspan="3"></td><td rowspan="3">RothC</td><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Pedo-climatic</td><td>0</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td><td>---</td></tr><tr><td>Management</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Under/Overestimation of NEE</td><td>Management</td><td>Grazing effect</td></tr></tbody></table>