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\title{Technical Manual Global Soil Organic Carbon Sequestration Potential Map GSOCseq}
\author{Food and Agriculture Organization of the United Nations, Rome, 2020}
\date{25/11/2020}
\begin{document}
\maketitle
\frontmatter
\addtocontents{toc}{\protect\hypersetup{hidelinks}}
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\mainmatter
\hypertarget{list-of-contributors}{%
\chapter*{List of contributors}\label{list-of-contributors}}
\addcontentsline{toc}{chapter}{List of contributors}
\hypertarget{authors}{%
\section*{Authors}\label{authors}}
\addcontentsline{toc}{section}{Authors}
Guillermo Peralta (GSP Secretariat)\\
Luciano Di Paolo (GSP Secretariat)\\
Christian Omuto (GSP Secretariat)\\
Kostiantyn Viatkin (GSP Secretariat)\\
Isabel Luotto (GSP Secretariat)\\
Yusuf Yigini (GSP Secretariat)
\emph{Cover design:} Matteo Sala
\hypertarget{contributers-and-reviewers}{%
\section*{Contributers and Reviewers}\label{contributers-and-reviewers}}
\addcontentsline{toc}{section}{Contributers and Reviewers}
\begin{longtable}[]{@{}
>{\raggedright\arraybackslash}p{(\columnwidth - 2\tabcolsep) * \real{0.12}}
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\toprule
& \\
\midrule
\endhead
P4WG & (Pillar 4 Working Group Agency) \\
INSII & (International Network of Soil Information Institutions) \\
ITPS & (Intergovernmental Technical Panel on Soils) \\
4per1000 SCT & (4 per 1000 Scientific and Technical Committee) \\
CIRCASA & (Coordination of International Research Cooperation on Soil Carbon Sequestration in Agriculture) \\
UNCCD-SPI & (The UNCCD Science-Policy Interface) \\
\bottomrule
\end{longtable}
\hypertarget{special-advisor}{%
\section*{Special Advisor}\label{special-advisor}}
\addcontentsline{toc}{section}{Special Advisor}
Pete Smith (University of Aberdeen)
\hypertarget{github-page}{%
\section*{GitHub Page}\label{github-page}}
\addcontentsline{toc}{section}{GitHub Page}
Isabel Luotto (GSP Secretariat)
\vfill
\textbf{Recommended citation:}
\begin{quote}
FAO. 2020. GSOCseq Global Soil Organic Carbon Sequestration Potential Map
Technical Manual. G. Peralta,L. Di Paolo, C. Omuto, K. Viatkin, I. Luotto, Y. Yigini, 1st Edition, Rome.
\end{quote}
\hypertarget{presentation}{%
\chapter{\textbar{} Presentation}\label{presentation}}
Soils have become one of the world's most vulnerable resources in the face of climate change, land degradation, biodiversity loss and increased demand for food production. The role of soils and soil organic carbon (SOC) in the climate system and climate change adaptation and mitigation has been recognized widely and validated in various studies, both experimentally and through modelling. Maintaining and increasing SOC stocks is not only crucial for reducing greenhouse gas (GHG) emissions and removing CO\textsubscript{2} from the atmosphere but also for harnessing the benefits of increased SOC (and soil organic matter - SOM) for soil health and fertility by improving water storage and thereby increasing the access of plants to water, food production potential and resilience to drought (FAO, 2017). It may lead to changes in health threat to human beings, and poses a significant challenge to rural communities and to our ability to thrive on our planet. The widespread adoption of site-specific sustainable soil management (SSM) practices in agricultural lands can harness a large C sink capacity at the global scale and its role as an effective strategy to mitigate GHG has been widely documented.
The magnitude and rate of carbon sequestration in soils can vary greatly, depending on the different land uses and practices, soil characteristics, vegetation, topography and climate, among other soil forming factors and processes. It is thus relevant to identify which regions, environments and production systems have a greater potential to increase SOC stocks as well as establishing priorities for research and the implementation of public policies.
Responding to a request for support in addressing the Sustainable Development Goal Indicators, especially indicator 15.3 which includes the restoration of degraded soils, the Global Soil Partnership (GSP) Plenary Assembly in 2020 instructed the Intergovernmental Technical Panel on Soils (ITPS) and the GSP Secretariat to develop the Global Soil Organic Carbon Sequestration Potential map (GSOCseq map), following the same country-driven approach developed for the Global Soil Organic Carbon map (GSOCmap). This `bottom-up' approach is expected to generate a GSOCseq map from national SOCseq maps, developed and validated by local experts, based on the implementation of SOC models using standardized procedures and by leveraging the best available local data.
To this end, members under the International Network of Soil Information Institutions (INSII) umbrella developed general guidelines and technical specifications for the preparation of the GSOCseq map and countries were invited to prepare their national soil organic carbon sequestration potential maps according to these specifications. Given the scientific advances in the tools for modeling and mapping SOC, many countries requested the GSP Secretariat to support them in the process of preparing national SOCseq maps. Hence, an intensive capacity development program on SOC sequestration potential through the use of modelling and mapping techniques is being implemented. Regional and national training sessions are being organized using an on-the-job-training modality to ensure that national experts are trained on the state of the art modeling and mapping techniques using their own data sets to produce reliable SOCseq maps.
This Technical Manual was prepared as a comprehensible reference knowledge base to support the capacity development process. It provides the necessary background knowledge, methodologies and technical steps to produce national SOCseq maps. It includes step-by-step guidance for developing 1 km grids of SOC sequestration potential, as well as for the preparation of local data and the compilation and preprocessing of spatial data sets, using open source software environments and tools.
This Technical Manual supplements the Technical Specifications and Country Guidelines for Global Soil Organic Carbon Sequestration Potential Map (GSOCseq), (FAO and GSP, 2020), and part of its contents is included in the current Manual.
It is our hope that this Technical Manual will fulfill its mandate of easily enabling any user to produce digital SOC sequestration maps using soil legacy data, process oriented SOC models and modern techniques of digital soil mapping and realize the overall aim of improving decision making on soil management.
\hypertarget{how-to-use-this-book}{%
\section{How to use this book}\label{how-to-use-this-book}}
This Manual is organized in 13 Chapters, including this one. Chapter 2 provides scientific background on the importance of SOC sequestration as a GHG mitigation strategy, factors that regulate SOC sequestration and finally how it can be estimated. Chapter 3 provides scientific background on the use of different SOC models to estimate SOC changes and SOC sequestration potential. Chapter 4 describes one of the most used process-oriented SOC models, to be used as a standard to allow comparisons between countries: the RothC model. Chapter 5 describes the general modeling procedures to generate national SOCseq maps using this SOC model. Chapter 6 describes the required data sets, potential data sources and methodologies to estimate modeling inputs for the modeling approach described in Chapter 5. Chapter 7 gives an overview of the steps and software requirements to generate national SOCseq maps using a spatially explicit R-version of the RothC model. Chapters 8 to 11 constitute the core of this document, providing a step by step guide on how to run the R-scripts and generate the final product:
\begin{itemize}
\tightlist
\item
Chapter 8 summarizes the different steps and scripts to be used during the complete process;\\
\item
Chapter 9 summarizes the procedures and scripts harmonization and preparation of input data;\\
\item
Chapter 10 summarizes the procedures for running the model from the prepared data;\\
\item
Chapter 11 summarizes the procedures for the generation of the final maps from the modeling runs.
\end{itemize}
Chapter 12 provides an overview for the estimation of uncertainties. Lastly, Chapter 13 summarizes the procedure for the sharing of the national SOCseq maps.
\hypertarget{the-sample-data-set}{%
\section{The sample data set}\label{the-sample-data-set}}
The sample data set provided with this Technical Manual was compiled to model and map the potential soil organic carbon sequestration for the district of Pergamino, Buenos Aires. It covers an area of 2,950 km\textsuperscript{2} that is mainly dedicated to agriculture. Pergamino is located in the Corn Belt of Argentina and with its subtropical climate it is favorable for double cropping systems, allowing for two harvests a year. The lowlands of this district are used mainly as pastures for the production of calves.
This Technical Manual used in combination with the provided sample data set and scripts can be used as a step-by-step guide, which covers data preparation and harmonization, modeling potential soil organic carbon sequestration, rasterizing the results, estimating the uncertainty and finally data sharing.
The sample data set and scripts can be found in the following repository:
\url{https://drive.google.com/drive/folders/1Jonn17LAiCyaUSpUn6W63o16bjTg1P0E?usp=sharing}
More information on the necessary data and the provided data set can be found in Chapter 6, while an overview of the different steps and scripts needed is presented in Chapter 8.
\hypertarget{soil-organic-carbon-soc-sequestration}{%
\chapter{\textbar{} Soil Organic Carbon (SOC) sequestration}\label{soil-organic-carbon-soc-sequestration}}
\hypertarget{soil-carbon}{%
\section{Soil carbon}\label{soil-carbon}}
Soils constitute the largest terrestrial carbon (C) pool. Total soil carbon (C) stock comprises soil organic C (SOC) and soil inorganic C (SIC) components. SOC is the carbon component of soil organic matter (SOM), a heterogeneous pool of C comprised of diverse materials including fine fragments of litter, roots and soil fauna, microbial biomass C, products of microbial decay and other biotic processes (i.e.~such as particulate organic matter), and simple compounds such as sugar and polysaccharides (Jansson et al., 2010). The global SOC stock of ice-free land contains about 1500-2400 Pg C (1 Pg = 1 Gt) in the top 1 m, 2300 Pg C in the top 3 m, and 3000 Pg C in the soil profiles (Batjes et al., 1996; Scharlemann et al., 2014; Tifafi et al., 2018; Lorenz and Lal, 2018). This represents more than the sum of carbon contained in the atmosphere and vegetation (Smith et al., 2019). Soil inorganic C comprises pedogenic carbonates and bicarbonates, which are particularly abundant in arid regions and in alkaline soils. The SIC stock is estimated at 700-1700 Pg C in the top 1-m soil layer (Lorenz and Lal, 2018) and is believed to occur predominantly in the deeper layers of temperate soils
Although soils contribute to a major share of agricultural greenhouse gas emissions (GHGs), due to the size of the soil carbon pool, even small increments in the net soil C storage represent a substantial C sink potential (Paustian et al., 2016). Carbon sequestration implies transferring atmospheric CO\textsubscript{2} into long-lived pools and storing it securely so it is not immediately reemitted (Lal et al., 2018). Thus, soil C sequestration means increasing SOC and SIC stocks through judicious land use and sustainable soil management (SSM) practices. Due to the current knowledge on SOC dynamics, the global distribution, and the current knowledge on size of the SOC pool compared to the SIC pool, this technical manual will focus on SOC sequestration.
\hypertarget{soc-sequestration}{%
\section{SOC sequestration}\label{soc-sequestration}}
The basic process of SOC sequestration in the terrestrial biosphere involves transfer of atmospheric CO\textsubscript{2} into plant biomass and conversion of biomass into stable SOC through formation of organo-mineral complexes (Lal et al., 2018). Thus, soil carbon sequestration relies on plant photosynthesis to carry out the initial step of carbon ``removal'' from the atmosphere. However, rather than increasing the storage of carbon contained in plant biomass, SOC sequestration relies on management practices that increase the amount of carbon stored as soil organic matter, primarily in cropland and grazing lands. The main advantage of scaling up soil C sequestration as a biological negative emission strategy is that carbon stocks are most depleted on lands currently under agricultural management and thus this approach does not require land use conversions (e.g., to forests) nor it increases the competition for land resources. In addition, increases in SOC stocks are highly beneficial in maintaining and increasing soil health and soil fertility, which provides additional incentives for adopting SOC sequestering practices (Paustian et al., 2019).
\hypertarget{factors-affecting-soc-sequestration}{%
\section{Factors affecting SOC sequestration}\label{factors-affecting-soc-sequestration}}
SOC sequestration is governed by the balance between the rate of C added to the soil from plant residues (including roots) and organic amendments (e.g., manure, compost), and the rate of C lost from the soils, which is mainly as CO\textsubscript{2} from decomposition processes (i.e., heterotrophic soil respiration). Other forms of organic C can be lost as CH4 from anaerobic (e.g.~flooded) reactions and to a lesser extent through leaching of dissolved organic C. Soil erosion can greatly affect C stocks at a particular location, but at larger scales erosion may not represent a loss process per se but rather a redistribution of soil C (Paustian et al., 2019).
Decomposition rates are controlled by a variety of factors including soil temperature and moisture, drainage (impacting soil O2 status) and pH (Paustian et al., 2019). Soil physical characteristics such as texture and clay mineralogy also impact the longevity and persistence (i.e., mean residence time) of soil C, by affecting organic matter stabilization processes, through mineral-organic matter associations (Schmidt et al., 2011; Paustian et al., 2019). In native ecosystems the rate of C inputs is a function of the type (e.g., annual vs.~perennial, woody vs.~herbaceous) and productivity of the vegetation, largely governed by climate (mainly temperature and precipitation) but also nutrient availability and other growth determining factors. In managed ecosystems such as cropland and grazing land both the rate of C input as well as the rate of soil C loss via decomposition are impacted by the soil and crop management practices applied. There is no one universal management practice to increase SOC sequestration (Lal et al., 2018), but in general, soil C stocks can be increased by: (a) increasing the rate of C addition to the soil, which removes CO\textsubscript{2} from the atmosphere, and/or (b) reducing the relative rate of loss (as CO\textsubscript{2} ) via decomposition, which reduces emissions to the atmosphere that would otherwise occur (Paustian et al., 2019).
Three key aspects need to be considered regarding the pattern of gains or losses of soil C and hence SOC sequestration (Paustian et al., 2019). The first is that with increased C inputs and/or decreased decomposition rates, soil C stocks tend toward a new equilibrium state and thus after a few decades C gains attenuate, becoming increasingly small over time. Secondly, although sequestered SOC can be highly stable, changes in management that lead to C gains are potentially reversible, i.e., if management reverts back to its previous condition, much or all of the gained C can be lost. Thus, practices that led to increased soil C need to be maintained long term. Third, mineral soils (i.e., non-peat soils) have an upper limit or ``saturation level'' of soil C (Six et al., 2002). While this maximum soil C concentration is well above the observed C concentration of most managed soils, carbon rich mineral soils that already have very high SOC levels (e.g., \textgreater5\% C by mass) may have a propensity for further C gains.
\hypertarget{estimating-soc-sequestration-potential}{%
\section{Estimating SOC sequestration potential}\label{estimating-soc-sequestration-potential}}
Taking into account the above mentioned factors, SOC sequestration potential after the adoption of SSM practices under specific conditions can be expressed in different ways depending on the definition of SOC baseline stocks and time towards a new equilibrium state. This Technical Manual will refer to two types of SOC sequestration: an `absolute SOC sequestration' (SOCseq abs), expressed as the change in SOC stocks over time relative to a base period (or reference period, t0); and a `relative SOC sequestration' (SOCseq rel), expressed as the change in SOC stocks over time relative to business as usual practices (Fig. 2.1). Thus, the `absolute' attainable SOC sequestration can be determined for business as usual (BAU) and SSM practices (See Chapter 5), and can be either positive, neutral or negative:\\
\begin{equation}
\tag{2.1}
\Delta SOC_{ABS} t C ha^{-1} = SOC_{SSM/BAU \ t} - SOC_{t0}
\end{equation}
where SOC \(SSM/BAU\ t\) refers to the final SOC stocks once a new equilibrium is reached or after a defined period of time (e.g.~20 years), and \(SOC\ t0\) refers to the initial or base period SOC stocks (\(t\)=0). The `relative' attainable SOC sequestration is either neutral or positive, can be determined as:\\
\begin{equation}
\tag{2.2}
\Delta SOC t C ha^{-1} = SOC_{SSM \ t} - SOC_{BAU \ t }
\end{equation}
where \(SOC_{SSM \ t}\) refers to the final SOC stocks once a new equilibrium is reached or after a defined period of time (e.g.~20 years) after SSM practices are implemented, and \(SOC_{BAU \ t}\) refers to the final SOC stocks under business as usual (BAU) practices at the end of the same considered period. Mean annual SOC sequestration rates (t C ha\textsuperscript{-1} yr\textsuperscript{-1}; absolute or relative) can be determined by dividing SOC changes by the duration of the defined period. For more specifications on the approaches proposed in this manual, see Chapter 5.
\begin{figure}
\centering
\includegraphics{images/Figure_2.1.png}
\caption{\textbf{Figure 2.1.} \emph{Soil organic carbon theoretical evolutions under business-as-usual (BAU) practices and after the adoption of sustainable soil management (SSM) practices. This depicts: a) lands where SOC levels have reached equilibrium and it is possible to}}
\end{figure}
Thus, agricultural lands may show potential for improvement in their SOC stock after the adoption of SSM practices (compared to business as usual practices), by either gaining or maintaining SOC levels. Four situations are possible: a) lands where SOC levels have reached equilibrium and it is possible to increase levels through SSM; b) lands where the SOC is increasing but can be further increased through SSM; c) lands where SOC is declining and it is possible to stop or mitigate losses in SOC levels through SSM; and d) lands where SOC is declining and it is possible to reverse this fall through SSM. These situations are depicted in Fig. 2.1.
It has been estimated that the widespread adoption of site/biome-specific SSM practices can harness a large C sink capacity in agricultural systems at a global scale: 0.4-1.2 Pg C yr\textsuperscript{-1} (Lal, 2004); 1.0-1.32 Pg C yr\textsuperscript{-1} (Smith et al., 2008); 0.4-1.1Pg C yr\textsuperscript{-1} (De Vries, 2017); 0.32-1.01 Pg C yr\textsuperscript{-1} (Batjes et al., 2019). However, the extent and rates of SOC sequestration in agricultural lands may vary greatly depending on the different land uses and practices, soil characteristics, vegetation, topography and climate, among other soil forming factors and processes (Smith et al., 2008; Minasny et al., 2017; Lal et al., 2018; Batjes et al., 2019). Sequestration rates due to management practices in croplands and grasslands are usually in the range of 0.2 - 0.8 t C ha-1 year -1 (Poepleau and Don, 2015; Kampf et al., 2016; Minasny et al., 2017; Conant et al., 2017; Paustian et al., 2016; Paustian et al., 2019).
It is therefore relevant to identify which regions, environments and systems have a greater potential to increase SOC stocks and establish priorities for research and implementation of private and public policies. In this sense, coupling SOC models to GIS (Geographic Information Systems) platforms enables the transition t from site-specific SOC stocks estimations to spatial simulations and projections (e.g.~Smith et al.~2005; Milne et al., 2007; Kamoni et al., 2007; Falloon et al., 2007; Gottschalk et al., 2012; Lugato et al., 2014), allowing for the identification of conditions that increase the SOC sequestration potential.
\hypertarget{soc-modelling}{%
\chapter{\textbar{} SOC Modelling}\label{soc-modelling}}
Modelling is an approach used to infer SOC stocks and distributions in conditions where they have not been measured, such as: under future climatic conditions, at locations or regions where no measurement exists, for management scenarios that have not yet been implemented (FAO, 2019). In the last decades, a number of numerical models have been developed, including mathematical representations that quantitatively describe soil characteristics and processes. The breadth of these approaches can be illustrated by the recent compilation of 90 mathematical models describing SOC changes and biogeochemical related soil processes developed in the last 80 years (Falloon and Smith, 2009; Manzoni and Porporato, 2009; Campbell and Paustian, 2015). However, according to their structure, number of input variables required and temporal and spatial resolution, not all available C models are suitable for all studies (Manzoni and Porporato, 2009).
\hypertarget{process-oriented-models}{%
\section{Process-oriented models}\label{process-oriented-models}}
Among the different types of SOC models, process-oriented multicompartment models have been dominant in efforts to simulate changes in SOC in agricultural lands, grasslands and other production systems (Stockmann et al., 2013).
Process-oriented models are built considering the processes involved in the transfer of SOC across the soil profile and its transformations (Smith et al., 1998). They are generally used to predict SOC dynamics based on different conceptual C pools or compartments that alter in size via decomposition rates and stabilization mechanisms (each compartment or pool being a fraction of SOC with similar chemical and physical characteristics; Stockmann et al., 2013). Models belonging to this class can potentially have a variable degree of complexity, from one compartment to multiple compartments (Jenkinson et al., 1990). Early models simulated SOC as one homogeneous compartment (Jenny, 1949). Beek and Frisel (1973) and Jenkinson and Rayner (1977) proposed two-compartment models, and as computational tools became more accessible, multi-compartment models were developed (McGill, 1996).
According to Falloon and Smith (2009), decay rates \(k\) are usually expressed in this type of models by first-order kinetics with respect to the concentration \(C\) of the pool:\\
\begin{equation}
\tag{3.1}
\frac{dC}{dt} =- kC
\end{equation}
The flows of carbon within most models represent a sequence of carbon going from plant and animal debris to the microbial biomass and then, to soil organic pools of increasing stability. The output flow from an organic pool is usually split. It is directed to a microbial biomass pool, another organic pool and, under aerobic conditions, to CO\textsubscript{2} . This split simulates the simultaneous anabolic and catabolic activities and growth of a microbial population feeding on one substrate. Two parameters are generally required to quantify the split flow, often defined as a microbial (utilization) efficiency and a stabilization (humification) factor, which control the flow of decayed carbon to the biomass and humus pools, respectively.
\hypertarget{examples-of-process-oriented-models}{%
\section{Examples of process-oriented models}\label{examples-of-process-oriented-models}}
CENTURY ( Parton, 1996), RothC (Jenkinson et al 1990; Coleman and Jenkinson, 1996), SOCRATES (Grace et al., 2006), DNDC (Li, 1996), CANDY (Franko et al., 1997), DAISY (Hansen et al., 1991), NCSOIL (Hadas et al., 1998) and EPIC (Williams et al., 1983; 1984) are known examples of this kind of process-oriented multicompartment models. They have been developed and tested using long-run data sets, and in general they show a good ability to predict SOC dynamics over decades across a range of land uses, soil types and climatic regions (Smith et al., 1997). As mentioned before, process-oriented models can be combined with GIS software, giving a modelling platform well suited for global, national, and regional scale studies (e.g.~Smith et al.~2005; Milne et al., 2007; Kamoni et al., 2007; Falloon et al., 2007; Gottschalk et al., 2013; Lugato et al., 2014).
The review by Campbell and Paustian (2015) emphasizes the fact that among these known process-oriented models, no one clearly outperforms the others. The increase in multi-model comparison publications in the last decades shows the lack of consensus in SOC modelling approaches. It is also noteworthy that among these multi-model comparisons, there was no single model identified with conclusively higher performance capacity. For a detailed comparison between some of the most used SOC models refer to Campbell and Paustian (2015).
However, in order to obtain consistent results in SOC sequestration estimates at a global scale, and to allow comparisons between countries and regions, the use of a standard `process-oriented' SOC model, following standardized procedures is required in this first step.
The Rothamsted soil organic carbon model (RothC; Coleman and Jenkinson, 1996, Chapter 4) is proposed as the standard comparison model in this Technical Manual, for the following reasons:
\begin{itemize}
\tightlist
\item
It requires less and more easily obtainable data inputs when compared to other process-oriented models
\item
It has been applied using data from long-term experiments across several ecosystems, climate conditions, soils and land use classes;
\item
It has been successfully applied at national, regional and global scales; e.g.~Smith et al.~(2005), Smith et al.~(2007), Gottschalk et al.~(2012), Wiesmeier et al.~(2014), Farina et al.~(2017), Mondini et al.~(2018), Morais et al.(2019);
\item
It (or its modified/derived version) has been used to estimate carbon dioxide emissions and removals in different national GHG inventories as a Tier 3 approach; according to the latest review by Smith et al.~(2020): Australia (as part of the FullCam model, Japan (modified RothC), Switzerland, and UK (CARBINE, RothC).
\end{itemize}
The following Chapter describes the RothC model and its general requirements. Users are nevertheless encouraged to use modified versions of the RothC model (e.g.~Farina et al., 2013) if it has been demonstrated that these versions improve estimations under local conditions. Users are also encouraged to provide supplementary SOC sequestration maps developed using alternative preferred SOC models and procedures. The use of a multi-model ensemble approach (e.g.~Riggers et al, 2019; Lehtonen et al., 2020) with selected models is intended for future versions of the GSOCseq map.
\hypertarget{the-rothc-model}{%
\chapter{\textbar{} The RothC model}\label{the-rothc-model}}
\hypertarget{model-description}{%
\section{Model description}\label{model-description}}
RothC is a model for the turnover of organic carbon in non-waterlogged topsoil that allows for the inclusion of the effects of soil type, temperature, moisture content and plant cover on the turnover process, with a monthly time step (Coleman and Jenkinson, 1996). C sequestration in RothC is quantified solely based on soil processes, and as such it is not linked to a plant production model. The user defines carbon inputs to the soil. SOC is split into four active compartments and one inactive compartment which comprises the inert organic matter (IOM). The four active compartments differ in the mean residence time of organic carbon in the soil and are defined as:
\begin{itemize}
\tightlist
\item
Decomposable Plant Material (DPM);
\item
Resistant Plant Material (RPM);
\item
Microbial Biomass (BIO);
\item
Humified Organic Matter (HUM).
\end{itemize}
The structure of the model is shown in Figure 4.1.
The IOM compartment is resistant to decomposition and is calculated using the following equation (Falloon et al., 1998):
\begin{equation}
\tag{4.1}
IOM = 0.049 \times SOC^{1.139}
\end{equation}
Where \(SOC\) is soil organic carbon, t C ha\textsuperscript{-1}, and \(IOM\) is Inert organic matter, t C ha\textsuperscript{-1}.
Incoming carbon inputs are split between DPM and RPM, depending on the DPM/RPM ratio of the particular incoming material. For most agricultural crops and improved grassland, the default DPM/RPM ratio is 1.44, i.e.~59\% of the plant material is DPM and 41\% is RPM. For unimproved grassland and scrub (including Savanna) a default ratio of 0.67 is used. For a deciduous or tropical woodland a default DPM/RPM ratio of 0.25 is used, i.e.~20\% of the plant material is DPM and 80\% is RPM.
Both DPM and RPM decompose to form CO2, BIO and HUM. The proportion that goes to CO2 and to BIO + HUM is determined by the clay content of the soil. The BIO + HUM is then split into 46\% BIO and 54\% HUM. BIO and HUM both decompose to form more CO2, BIO and HUM. Each compartment decomposes by a first-order process with its own characteristic rate. If an active compartment contains Y t C/ha, this declines at the end of the month to:
\begin{equation}
\tag{4.2}
Y e^{-abckt} t C ha^-1
\end{equation}
where \(a\) is the rate-modifying factor for temperature; \(b\) is the rate-modifying factor for moisture; \(c\) is the soil cover rate-modifying factor; \(k\) is the decomposition rate constant for that compartment; and \(t\) is 1/12, since \(k\) is based on an annual decomposition rate. \(Y (1 - e^{-abckt})\) is the amount of the material in a compartment that decomposes in a particular month.
RothC has also been adapted to simulate N and S dynamics (Falloon and Smith, 2009), but nutrient and C dynamics are not interconnected in RothC. It was originally developed and parameterized to model the turnover of organic C in arable topsoil, and it was later extended to model turnover in grasslands, savannas and woodlands, and to operate in different soils and under different climates (Coleman and Jenkinson, 1996).
\begin{figure}
\centering
\includegraphics{images/Figure_4.1.png}
\caption{\textbf{Figure 4.1.} \emph{Structure of the pools, and flows of Carbon in the Roth-C model, including major factors controlling the fluxes (a = multiplier for effects of temperature, b = multiplier for effects of moisture, c = multiplier for effects of soil cover; DPM/RPM = Decomposable/resistant plant material ratio). Source: redrawn from Coleman and Jenkinson (1996) and Falloon and Smith (2009).}}
\end{figure}
\hypertarget{rothc-general-data-requirements}{%
\section{RothC general data requirements}\label{rothc-general-data-requirements}}
The model requires climatic, soil and management data that are relatively easy to obtain or estimate. Each modeling unit (e.g.~cell of a grid) requires the following minimum data (Table 4.1):
\textbf{Table 4.1} \emph{Roth-C model minimum data requirements}
\begin{table}
\centering
\begin{tabular}{l|l|l}
\hline
Climate Data & Soil Data & Land Use Management Data \\
\hline
1. Monthly rainfall(mm) & 1. Total initial 0-30cm SOC stocks (t C ha-1) & 1. Monthly Soil cover (binary: bare vs. vegetated)\\
\hline
2. Average monthly mean air temperature (ºC) & 2. Initial C stocks of the different pools (t C ha-1): DPM, RPM, BIO, HUM, IOM & 2. Irrigation (to be added to rainfall amounts)\\
\hline
3. Monthly open pan evaporation (mm)/evapotranspiration (mm) & 3. Clay content (\%) at simulation depth. & 3. Monthly Carbon inputs from plant residue (aboveground + roots + rhizodeposition), (t C ha-1)\\
\hline
& & 4. Monthly Carbon inputs from organic fertilizers and grazing animals’ excretion (t C ha-1)\\
\hline
& & 5. DPM/RPM ratio, an estimate of the decomposability of the incoming plant material\\
\hline
\end{tabular}
\end{table}
Careful harmonization of modeling procedures, datasets and input estimation methodologies is essential to obtain consistent SOC sequestration results across regions and countries. The general approach and modeling procedures to generate national SOCseq maps using the RothC model are described in Chapter 5. The land use datasets required for the proposed procedures are described in Chapter 6.
\hypertarget{modeling-approach-for-the-gsocseq}{%
\chapter{\textbar{} modeling approach for the GSOCseq}\label{modeling-approach-for-the-gsocseq}}
\hypertarget{general-framework}{%
\section{General framework}\label{general-framework}}
SOC sequestration estimates will focus on croplands and grazing lands for the current GSOCseq map version. As defined by IPCC (2006, 2019), croplands include: all annual and perennial crops (cereals, oils seeds, vegetables, root crops and forages); perennial crops (including trees and shrubs, orchards, vineyards and plantations such as cocoa, coffee, tea, oil palm, coconut, rubber trees, bananas, and others), and their combination with herbaceous crops (e.g., agroforestry); arable land which is normally used for cultivation of annual crops, but which is temporarily used for forage crops or grazing as part of an annual crop-pasture rotation (mixed system), is to be included under croplands. Grazing lands include different land uses permanently dedicated to livestock production with a predominant herbaceous cover, including intensively managed permanent pastures and hay land, extensively managed grasslands and rangelands, savannahs, and shrublands.
Since the proposed standardized methodology and the defined model are neither parameterized nor recommended for use on organic, sandy, saline, and waterlogged soils, soils with SOC stocks higher than 200 t C ha\textsuperscript{-1}, sand contents higher than 90\% and/or electrical conductivity higher than 4 dS m-1 at 0-30 cm depth, paddy rice lands, peatlands and wetlands will be masked out from the global results in this first version.
Excluded conditions and land uses can be included in future versions of the GSOCseq map, as harmonized procedures for specific conditions are developed. Countries are nevertheless encouraged to provide supplementary maps developed using preferred alternative SOC models and methodologies, especially for excluded conditions.
\hypertarget{potential-soc-sequestration-estimates-after-the-implementation-of-ssm-practices}{%
\section{Potential SOC sequestration estimates after the implementation of SSM practices}\label{potential-soc-sequestration-estimates-after-the-implementation-of-ssm-practices}}
In order to assess the SOC sequestration potential, SOC stocks in 0-30 cm of mineral soils shall be projected using the RothC model over a 20-year period, under business as usual (BAU) land use and management, and after adoption of SSM practices in croplands and grazing lands (See Chapter 2). A 20-year period is assumed to be the default period during which SOC stocks are approaching a new steady state, to be able to compare results among regions and countries, and with other estimation methods (e.g.~IPCC, 2006 Tier 1-2; IPCC, 2019). For some systems, it is acknowledged that the new steady state may take much longer, even more than 100 years, depending on soil and climate characteristics (e.g.~Poulton et al, 2018). Together with the 20-years projection, countries can project SOC stocks over 50 or 100 years or more, and determine the stocks and the period at which a new steady state is attained according to local conditions, and produce additional sequestration maps (See mandatory and optional products, Technical Specifications, sections 4.1 and 4.2).
As stated in Chapter 2, SOC sequestration potential after the adoption of SSM practices in current agricultural lands shall be estimated by: an `absolute SOC sequestration' (SOCseq abs), expressed as the change in SOC stocks over time relative to a base period (or reference period, t0); and a `relative SOC sequestration' (SOCseq rel), expressed as the change in SOC stocks over time relative to the business as usual scenario. Absolute and relative sequestration and sequestration rates for a 20-year period shall be estimated following the equations described in Chapter 2 (eq. 2.1 and 2.2)
\hypertarget{business-as-usual-and-sustainable-soil-management-scenarios}{%
\section{Business as usual and sustainable soil management scenarios}\label{business-as-usual-and-sustainable-soil-management-scenarios}}
SOC stocks in 0-30 cm of mineral soils in current agricultural lands shall be projected over a 20-year period, under a business as usual scenario (BAU) and under sustainable soil management (SSM) scenarios. The BAU scenario refers to the land use, land management, production practices or technologies that are currently being implemented (as in time = 0, or 2020) in croplands and grazing lands. BAU practices represent typical, prevailing practices in a specific agro-ecological zone and productive system. SSM practices refer to management practices that are expected to remove CO\textsubscript{2} from the atmosphere and retain it as SOC, to enhance SOC accumulation, or to mitigate or reverse SOC losses compared to the BAU (See Fig. 2.1). Although there is no universal soil management practice, basic principles are widely applicable, such as those identified in the Voluntary Guidelines for Sustainable Soil Management (VGSSM; FAO, 2017) for increasing soil carbon inputs to soil and enhancing soil organic matter content:
\begin{itemize}
\tightlist
\item
increasing biomass production and residue returns to the soil;
\item
using cover crops and/or vegetated fallows;
\item
implementing a balanced and integrated soil fertility management scheme;
\item
implementing crop rotations, combining legumes and pulses with high residue crops, or improving the crop-mix;
\item
effectively using organic amendments, manure, or other carbon-rich wastes (which are not currently applied to soils);
\item
promoting agro-forestry and alley cropping;
\item
managing crop residues and grazing to ensure optimum soil cover; among others.
\end{itemize}
A very wide range of management practices are currently being implemented and can potentially be introduced into the world's agricultural systems, depending on climatic, soil, socio-cultural and economic conditions. In turn, different SSM C-oriented practices are often combined, making it difficult to dissociate their effects on SOC dynamics. Thus, as a first step, and to harmonize the results on a global map, and because soil carbon turnover models are the most sensitive to carbon inputs (FAO, 2019), these guidelines propose to group SSM practices into three scenarios as a standard method, based on their expected relative effects on C inputs compared to BAU: Low, Medium and High increase in C inputs (referred as SSM1, SSM2, and SSM3 scenarios; for technical procedures, refer to section 5.4). National experts' opinion and local data are essential to accurately estimate or validate the target areas and carbon input levels for the different SSM scenarios in forthcoming versions.
\hypertarget{general-modeling-procedures}{%
\section{General modeling procedures}\label{general-modeling-procedures}}
The modeling approach proposed in the current Technical Manual is based on the studies by Smith et al.~(2005; 2006; 2007), Gottschalk et al.~(2012) and Dechow et al.~(2019). The following sections describe the different modeling phases of the approach.
\hypertarget{initialization-spin-up}{%
\subsection{Initialization: Spin up}\label{initialization-spin-up}}
Prior to the simulation of SOC stocks and sequestration under the different scenarios, model initialization is required to set the initial SOC condition (total SOC and partition of the different pools) at the start of the simulation period, and to adjust the C inputs estimates. This modeling phase is referred to as initialization or ´spin up´ through this document. This is a key step, as the outputs of this phase will be used as inputs for the next modeling phase (see sections 5.4.2 and 5.4.3).\\
Two methodologies to estimate initial carbon pools and initial C inputs are provided:\\
1. Initialization based on equilibrium runs\\
2. Initialization based on an analytical approach
\hypertarget{spin-up-phase-initialization-with-equilibrium-runs}{%
\subsubsection{Spin up phase: Initialization with equilibrium runs}\label{spin-up-phase-initialization-with-equilibrium-runs}}
This approach is based on studies by Smith et al.~(2005; 2006; 2007), Gottschalk et al.~(2012) in large scale studies. The standard procedure is to have a spin up period to initialize the model, so the soil carbon pools are in approximate equilibrium with the initial conditions regarding soil and climate variables, vegetation and land management. The length of the spin up simulation period needed to approach a steady state pool distribution can usually vary between 100s to 1000s years (FAO, 2019). The C input is adjusted so that the modelled final SOC of this period, and hence initial SOC of the following phases, matches a known SOC stock.
In a first initialization step, RothC shall be run iteratively to equilibrium to calculate the size of the SOC pools and the annual plant carbon inputs using constant environmental conditions (Phase 1, Figure 5.1), for each grid cell on the map. Ideally, a first equilibrium run for a standard 10,000-year period should be performed, considering constant climatic conditions as the average of historic climate data from 1980 to 2000 (see Chapter 6, Climate data sets), clay contents (see Chapter 6, soil data sets), and land use as in year 2000 (see Chapter 6, land use data sets). Due to the simulated time span and depending on the size of the target area, this modelling phase is the most time consuming and computationally demanding . The duration of the equilibrium run can be reduced if the data suggests that the equilibrium is reached with fewer iterations. A minimum of 500 years is suggested to approach equilibrium with reduced computational time to generate national maps. However, it must be noted that spin up runs for 500 years may not necessarily end up in equilibrium SOC stocks, depending on soil, climate and land use conditions. Increasing the duration (1000-2000 years) will reduce deviations with the cost of additional computation time.
The total annual plant C input can be initially assumed to be 1 t C ha\textsuperscript{-1} yr\textsuperscript{-1} and the proportions of plant material added to the soil for each month are set to describe the typical input pattern for each land use class (Smith et al., 2007; Mondini et al., 2017). After the first equilibrium run, the annual C inputs from plant residues need to be optimized so that the results of the spin up phase fit with the estimates of total SOC stocks of 0-30 cm provided in the FAO-ITPS GSOCmap. C equilibrium inputs can be adjusted using the following equation (Smith et al., 2005):
\begin{equation}
\tag{5.1}
C_{eq}=C_i \times[\frac{SOC_{GSOCmap}-IOM}{(SOC_{sim}-IOM}]
\end{equation}
where \(C_{eq}\) is the estimated annual C input at equilibrium, \(C_i\) is the initial annual C addition (the sum of the proportions of the C input in the first equilibrium is 1), \(SOC_{GSOCmap}\) is the estimated soil C given in FAO-ITPS GSOCmap, \(SOC_{sim}\) is the simulated soil C after the first equilibrium run, and \(IOM\) is the C content of the inert organic matter fraction in the soil (all in t C ha\textsuperscript{-1}). The size of the IOM fraction (t C ha\textsuperscript{-1}) can be set according to the equation given by Falloon et al.~(1998):
\begin{equation}
\tag{5.2}
IOM=0.049 \times SOC_{GSOCmap}^{1.139}
\end{equation}
A second long term (minimum 1,000 years) equilibrium run shall be performed using the estimated \(C_{eq}\), (under the same conditions as the first run), in order to obtain the size of the different SOC pools (t C ha\textsuperscript{-1}) at year 2000.
An alternative to further reduce computational time is to avoid this second run by estimating the size of the different SOC pools using pedotransfer functions (Weihermüller et al., 2013). The R implementation the spin up phase can be found in Chapter 10 (section 10.1).
The equilibrium run is a widely known approach to initialize RothC and other SOC models (FAO, 2019), and it has been implemented in other global and regional modeling-mapping studies to analyze SOC dynamics (e.g.~Smith et al., 2005; 2006; 2007; Gottschalk et al., 2012).\\
Compared to the analytical approach, which is presented in this Technical Manual as an alternative spin up approach, the equilibrium run allows for further user-defined modifications such as running the model under non-homogeneous conditions (e.g.~not constant climatic conditions, land use and management for a specified time period). The approach can be also used to estimate the required period to attain equilibrium SOC stocks under certain environmental conditions, among other relevant research questions. However, although users may be in general more familiar with this initialization approach, it can be considerably time consuming as well as computationally demanding, depending on the simulation area. If homogeneous environmental conditions are assumed during the spin up phase, other approaches (see following section) may be the preferred option.
\hypertarget{spin-up-phase-initialization-by-analytical-solution}{%
\subsubsection{Spin up phase: Initialization by analytical solution}\label{spin-up-phase-initialization-by-analytical-solution}}
Based on the Introductory Carbon Balance Model (ICBM B2) in Kätterer and Andren (2001) and pool-specific differential equations for the RothC model in Sierra and Müller (2015), Dechow et al.~(2019) developed an analytical solution of RothC which describes the topsoil SOC development assuming temporal homogeneous climatic and management conditions. This novel approach allows quantification of pool distribution and C input for RothC at equilibrium. The structure of the approach is based on the linear relationship between C input amounts and initial SOC that follow from the analytical solution of the RothC model.
Under homogeneous conditions, the SOC at time t is linearly correlated to the initial SOC (\(C_0\)) and the carbon input rate \(I\) (equation 5.3):
\begin{equation}
\tag{5.3}
C(t)=S_{impl}C_0 + \sum_{i=1}{N} I_i (u_{DPM} \gamma_{DPM_i} + u_{RPM}Y_{RPM_i}+u_{hum} \gamma_{hum_i}) \\
C_0 f_{IOM}[Mg \ C \ ha^{-1}]
\end{equation}
Where \(C_0\) is the initial SOC stock (which corresponds to \(SOC_{GSOCmap}\), the estimated soil C given in FAO GSOCmap). \(S_{impl}\),\(u_{DPM}\), \(u_{RPM}\) and \(u_{hum}\) are functions integrating model structure and parametrization of RothC. Parameters \(\gamma_{DPM}\), \(\gamma_{RPM}\), \(\gamma_{HUM}\) are the partition coefficients. These \(\gamma\) coefficients will depend on the decomposability of the incoming residues. For example, in conditions with carbon inputs with a DPM/RPM of 1.44, the \(\gamma\) DPM equals 0.59 and \(\gamma\) DPM equals 0.41 . N is the number of input substrates characterized by a specific set of partition coefficients and \(f_{IOM}\) is the fraction of inert SOC (IOM, equation 5.2). For stationary conditions time is assumed to be infinite and therefore the effect of initial active SOC (\(C_0\) - IOM) negligible (equarion 5.4):
\begin{equation}
\tag{5.4}
C_0 = \sum_{i=1}^{N} I_i (\gamma_{DPM_i}u_{DPM}+\gamma_{HUM_i}u_{HUM})+f_{IOM}C_0
\end{equation}
First, the fractions \(f_i\) of the DPM, RPM, BIO and HUM pools at equilibrium are estimated following the set of equations described in in the supplementary material of Dechow et al., 2019 (link to supplementary material). The estimated fractions of each SOC pool at equilibrium will depend on:
\begin{itemize}
\tightlist
\item
the decomposition rates constants (k) of the different carbon pools
\item
an average of the different modifying factors (temperature, soil moisture, vegetation factors)
\item
initial SOC stock (total SOC stock at equilibrium)
clay content
\item
the product ratio CO\textsubscript{2}/decomposed C remaining (depending on clay content)
\item
the ratio of C ???uxes to BIO and HUM
partition coefficients of the C input (DPM/RPM ratio)
\item
IOM fraction compared to total C
\end{itemize}
These equations simplify when assuming an infinite time t (equilibrium). Equations 5.5-5.8 quantify the fraction of each C pool related to the active C (\(C_0\) - IOM):
\begin{equation}
\tag{5.5}
f_{DPM}= \frac{u_{DPM}\gamma_{DPM}}{u_{DPM}\gamma_{DPM}+u_{RPM}\gamma_{RPM}+ u_{HUM}\gamma_{HUM}}
\end{equation}
\begin{equation}
\tag{5.6}
f_{RPM}= \frac{u_{RPM}\gamma_{RPM}}{u_{DPM}\gamma_{DPM}+u_{RPM}\gamma_{RPM}+ u_{HUM}\gamma_{HUM}}
\end{equation}
\begin{equation}
\tag{5.7}
f_{BIO}= \frac{u_{BIO\ DPM}\ \gamma_{DPM}+u_{BIO\ RPM}\gamma_{RPM}+ u_{BIO\ HUM}\ \gamma_{HUM}}{u_{DPM}\ \gamma_{DPM}+u_{RPM}\ \gamma_{RPM}+u_{HUM}\ \gamma_{HUM}}
\end{equation}
\begin{equation}
\tag{5.8}
f_{HUM}=\frac{u_{HUM\ DPM}\gamma_{DPM} + u_{HUM \ RPM} \gamma_{RPM}+ u_{HUM}\gamma_{HUM}}{u_{DPM}\gamma_{DPM}+u_{RPM}\gamma_{RPM}+u_{HUM}\gamma_{HUM}}
\end{equation}
\begin{equation}
\tag{5.9}
f_{IOM}= \frac{IOM}{C_0}
\end{equation}
Where \(\gamma\) are the partition coefficients of the incoming carbon inputs as explained in equation 5.3, and \(u\) coefficients are the result of functions integrating model structure and parametrization of RothC, following the equations in Annex 3.
Once the fractions of the different pools are estimated, the amount of Carbon (tC ha\textsuperscript{-1}) in each pool is estimated from the total and active (other than IOM) SOC stocks:
\begin{equation}
\tag{5.10}
SOC_{active}=SOC_{GSOCmap} -IOM
\end{equation}
\begin{equation}
\tag{5.11}
SOC_{pool_i}=f_{pool_i} \times SOC_{active}
\end{equation}
where \(SOC_{active}\) represents the SOC stocks of all the active pools of RothC model in t C ha\textsuperscript{-1} (DPM, RPM, BIO and HUM), IOM represents the Inert Organic Carbon estimated from equation 5.2, \(SOC_{GSOCmap}\) is the estimated soil C given in FAO-ITPS GSOCmap in t C ha\textsuperscript{-1} (representing the total SOC stocks at equilibrium), \(SOC_{pool_i}\) represents the SOC stock of each of the active pools in t C ha\textsuperscript{-1} , and \(f_{pool_i}\) represents the fraction of each active pool estimated by the analytical procedure and equations 5.5 to 5.8.
Finally, Carbon inputs (\(C_i\)) at equilibrium can be estimated as:
\begin{equation}
\tag{5.12}
C_i= \frac{SOC_{GSOCmap}-IOM}{\gamma_{DPM}u_{DPM} + \gamma_{RPM} u_{RPM} + \gamma_{HUM} u_{HUM}}
\end{equation}
Pool distributions and equilibrium C input quantification can be more accurate (closer to equilibrium) and is computationally faster with the analytical solution. The complete R implementation of this procedure can be found in Chapter 10 (section 10.1).
\hypertarget{warm-up}{%
\subsection{Warm up}\label{warm-up}}
Since FAO GSOCmap SOC was generated from individual SOC measurements taken over different decades (i.e.~1960s to 2000s), a temporal harmonization of SOC stocks can be performed as a second initialization step to minimize differences in current SOC stocks at year 0 (i.e.~initial SOC stocks at year 2020), and account for climatic variations in the 2000-2020 period:
\begin{itemize}
\tightlist
\item
SOC stocks from the GSOCmap shall be considered to be the stocks twenty years prior to the simulation (t = -20 yr; i.e.~year 2000).
\item
A 20-year `short spin up' run can be performed to adjust for major deviations among different measurement periods on the GSOCmap (figure 5, Phase 2), using year-to-year climatic conditions for the period 2001-2020 (See Chapter 6, Climate data sets), clay contents (See Chapter 6, soil data sets), the stocks in the different SOC pools from the results of the `long spin up' run, and land use as in year 2020 (land use representative of the period 2001-2020; or yearly land use data shall be used when available).
\item
Year-to-year C inputs over the period 2001-2020 should be adjusted considering year-to-year changes in estimated Net Primary Production (NPP), (details in Chapter 6, monthly carbon inputs). SOC stocks can either increase or decrease during this `short spin up' stage.
\end{itemize}
This `short spin up' period is intended to: reduce the effects of different time measurements in the GSOCmap (over- or underestimation of current initial SOC stocks); minimize initialization effects (e.g.~deviations in the estimation of initial pool sizes); and account for the effects of sub-regional, regional and global climatic and land use changes over the period 2001-2020 and their effects on NPP. If recent (2015-2020) national SOC monitoring campaigns have been undertaken to generate the latest version of the FAO-IPS GSOCmap, the SOC stocks from the GSOCmap can be considered as the current stocks (t = 0 y; i.e.~year 2020), and the `short spin up' phase is not required.
\hypertarget{forward-runs}{%
\subsection{Forward runs}\label{forward-runs}}
After the equilibrium and `short spin up' runs, SOC sequestration due to SSM practices can be estimated in a forward run (Figure 5.1, phase 3). SOC stocks can be simulated from 2020 (t=0) to 2040 (t = +20) for the BAU and the three SSM scenarios, using average mean monthly climate variables (2001-2020), C inputs adjusted as described in Chapter 6 and land use maps from 2020.
It should be noted that global climatic changes are to be expected over the next 20 years. However, climate change projections diverge significantly in the second half of the century, after the year 2050 (IPCC, 2014; 2018). As there is a lack of consensus over which climate projections to use for future scenarios as well as a significant divergence in terms of climatic trends after 2050, the use of monthly average climatic variables from 2001-2020 for the period 2020-2040 is set as the standard for the forward run. However, the proposed methodology allows for the integration of climate change scenarios, especially for longer-term projections (i.e.~+ 2050) in future versions.\\
The absolute SOC sequestration is estimated as the difference between the corresponding SOC stocks from the forward modeling at year +20 (2040) for the different scenarios and the estimated baseline SOC stocks for year 0 (year 2020; refer to equation 2.1). The relative SOC sequestration is to be determined as the difference between the corresponding SOC stocks from the forward at year +20 (2040) for the SSM scenarios and the simulated SOC stocks at year +20 (2020) for the BAU scenario (refer to equation 2.2).
\hypertarget{summary}{%
\section{Summary}\label{summary}}
The different modeling phases and their data requirements are summarized in in Figure 5.1 and Table 5.1.
\begin{figure}
\centering
\includegraphics{images/Figure_5.1.png}
\caption{\textbf{Figure 5.1.} \emph{SOC stocks simulated in the different phases according to the proposed general modeling procedure.}}
\end{figure}
\textbf{Table 5.1} \emph{Summary of the different modeling phases and data requirements.}
\begin{table}
\centering
\begin{tabular}{l|l|l|l}
\hline
Variables & Phase 1 Long spin up Equilibrium & Phase 2 Short spin up & Phase 3 Forward modeling\\
\hline
Time span & Minimum 500 years (using equilibrium runs procedure) & 20 years & 20 years\\
\hline
& Infinite (Analytical solution procedure) & & \\
\hline
Climatic inputs & 1980-2000 series monthly average: & 2001-2020 year to year monthly data: & 2001-2020 series monthly average:\\
\hline
& Rain, Temperature, Evaporation/ & Rain, Temperature, Evaporation/ & Rain, Temperature ,Evaporation/\\
\hline
& Evapotranspiration & Evapotranspiration & Evapotranspiration\\
\hline
& & & \\
\hline
Soil inputs & Topsoil clay content & Topsoil clay content & Topsoil clay content\\
\hline
& & & \\
\hline
Initial SOC stocks and pools & Inert organic matter (IOM) as determined by equation 5.2 & Inert organic matter (IOM as determined by equation 5.2 & Inert organic matter (IOM) as determined by equation 5.2\\
\hline
& “= 0” for all other fractions (when using equilibrium runs) & Other fractions equal to the final SOC pools modeled in phase 1 & Other fractions equal to the final SOC pools modeled in phase 2\\
\hline
& & & \\
\hline
Carbon inputs & First run : 1tC.ha-1 & NPP & NPP year-to year\\
\hline
& Adjusted C inputs from equation 5.1 (using equilibrium runs) & year-to year adjusted C inputs, from equation 7 & adjusted C inputs for the BAU, from equation 7\\
\hline
& From equation 5.12 (using analytical solution) & & \\
\hline
& & & Estimated from \% increase vs.\\
\hline
& & & BAU for SSM scenarios\\
\hline
Vegetation cover & Monthly cover determined: by expert opinion, NDVI 2000-2020 or preferred spectral index (see section 3.3.4) & Monthly cover determined: by expert opinion, NDVI 2000-2020 or preferred spectral index (see section 3.3.4) & Monthly cover determined: by expert opinion, NDVI 2000-2020 or preferred spectral index (see section 3.3.4)\\
\hline
Land Use & Representative land use of the 1980-2000 period (or layer for year 2000; or best available layer) & Year to year Land use 2000-2020 (or representative land use of the period; or best available layer) & Last available land use layer (e.g. 2015, 2018; 2020) (or best available layer)\\
\hline
Modeled Scenarios & BAU & BAU & BAU\\
\hline
& & & SSM Low\\
\hline
& & & SSM Medium\\
\hline
& & & SSM High\\
\hline
Expected Results & C inputs at equilibrium & Total SOC and SOC pools at year t=0 (2020) & Total SOC and SOC pools at year t=+20 (2040) for the BAU, and SSMs scenarios\\
\hline
& & & \\
\hline
& Total SOC and SOC pools at year t= -20 (2000) & & Absolute and relative Total Sequestration (3 SSMs)\\
\hline
& & & Absolute and relative Sequestration rates (3 SSMs)\\
\hline
& & & \\
\hline
& & & \\
\hline
\end{tabular}
\end{table}
The different data sets required to run the RothC model for the different modeling phases are described in Chapter 6.
\hypertarget{data-sets-and-inputs}{%
\chapter{\textbar{} Data sets and inputs}\label{data-sets-and-inputs}}
A careful harmonization of data sets and input estimation procedures is essential to obtain consistent results across regions and countries. This Chapter describes the required data sets, the potential data sources and the methodologies to estimate required inputs for the modeling approach described in Chapter 5. Procedures for the preparation and harmonization of input data are explained in Chapter 9.
\hypertarget{climate-data-sets}{%
\section{Climate data sets}\label{climate-data-sets}}
Gridded climate data shall be obtained from:
a.) National Sources or a preferred regional data source;\\
b.) Global data sets, when national or regional gridded historical climate data sets are not available.
The dataset provided by the Climate Research Unit (CRU), developed by the University of East Anglia, United Kingdom (Harris et al., 2014) at a resolution of 0.5\(^\circ\) (\textasciitilde50x50 km) was initially recommended to be used as the standard global data set if national or regional gridded data is not available, or if the available national data is at a coarser resolution.
To overcome limitations linked to the coarse resolution of the CRU products, the latest version of this Technical Manual has identified and recommends the TerraClimate dataset as an improved global alternative.
Since the map production phase for the implementation of the GSOCseq was initiated prior to the identification of the TerraClimate data set, this version of the Technical manual still presents both data sets (CRU and TerraClimate) as viable global options.
The CRU 2019 dataset (CRU TS v. 4.03) covers the period 1901-2018, including precipitation (pre), average/minimum and maximum air temperatures (tmp, tmn, tmx), cloud cover percentage (cld), diurnal temperature range (dtr), vapor pressure (vap), number of rainy days (wet), frost days (frs), and potential evapotranspiration (pet); (See Table 6.2, data sets and download sources).\\
TerraClimate is a data set of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. It has a monthly temporal resolution, a \textasciitilde4x4 km spatial resolution and was created by combining high-spatial resolution climatological normals from the WorldClim data set, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55) (Abatzoglou et al., 2018).
The following variables and data sets are required to run the model (See Chapter 5, General modeling procedures):
\begin{itemize}
\tightlist
\item
Monthly average air temperature (\(^\circ\)C),
\item
Monthly precipitation (mm),
\item
Monthly potential evapotranspiration (Penman-Monteith; mm)
\item
data sets: 1981-1990 (series average); 1991-2000 (series average); 2011-2010 (year to year); 2011-2018 (year to year).
\end{itemize}
The same data sources must be used in all modeling phases.
\hypertarget{soil-data-sets}{%
\section{Soil data sets}\label{soil-data-sets}}
\hypertarget{initial-total-soc-stocks}{%
\subsection{Initial total SOC stocks}\label{initial-total-soc-stocks}}
Initial total SOC stocks to 30cm depth (in t C ha\textsuperscript{-1}) are to be derived from the GSOCmap (30 arc seconds; \textasciitilde{} 1km x 1km resolution grid), latest revised version (FAO-ITPS, 2019). Countries wishing to include an updated or improved estimate of current SOC stocks, compared to the latest version of the GSOCmap, are encouraged to submit their updated national SOCmap to the GSP Secretariat and use it for modeling.
Since the GSOCmap was generated from national measurements taken between the 1960s and the 2000s, and no temporal corrections have been developed in many countries, GSOCmap values will represent SOC stocks for the year 2000. A `short spin-up' model run (20 years) with climate variables and management forcing for the period 2000-2020 shall be performed to reduce the effect of temporal deviations. Thus, the simulated SOC content at 2020 after the `short spin-up' run will represent the initial SOC stocks prior to implementation of SSM practices (See Chapter 6, General modeling procedures). If recent national SOC monitoring campaigns (2015-2020) have been undertaken to generate the latest version of the FAO-IPS GSOC map, the SOC stocks from the GSOCmap can be considered as the current stocks (t = 0 y; i.e.~year 2020), and the `short spin-up' phase is not required.
\hypertarget{initial-c-pools}{%
\subsection{Initial C pools}\label{initial-c-pools}}
The initial C stocks in the different pools (in t C ha\textsuperscript{-1}) considered in the RothC model (DPM, RPM, BIO, HUM and IOM, Fig. 4.1) shall be estimated following the `long spin-up' and `short spin-up' procedure described in Chapter 6.
\hypertarget{soil-texture-clay-content}{%
\subsection{Soil texture: clay content}\label{soil-texture-clay-content}}
The average clay contents over 0-30 cm depth is to be obtained from gridded data (raster format) from:
\begin{itemize}
\tightlist
\item
national Sources (1 km x 1 km resolution);
\item
global data sets, where national or regional data sets are not available.
\end{itemize}
The topsoil clay content (0-30 cm, \% mass fraction; 1 x 1 km resolution) from the Harmonized World Soil Database (HWSD) or SoilGrids developed by the- International Soil Reference and Information Centre (ISRIC) (see Table 6.2) shall be used as the standard global database if national or regional data is not available in the required format or resolution. Clay content can be averaged at finer resolutions to obtain 1 x 1 km grids. However, countries are encouraged to produce their own texture and clay content maps to be used as inputs for the SOCseq map, following the digital soil mapping approaches described in the GSOCmap Cookbook (FAO, 2018).
Average clay contents over a 0-30 cm depth interval can be derived by taking a weighted average of the predictions over the depth interval using numerical integration (Hengl et al., 2017).
\begin{equation}
\tag{6.1}
\frac{1}{b-a} \int_{a}^{b} f(x) dx \approx \frac{1}{(b-a)} \frac{1}{2} \sum_{k=1}^{N-1}(x_{k+1} - x_{k})(f(x_{k}) + f(x_{k +1}))
\end{equation}
where N is the number of depths; b is 30 cm, a is 0 cm, xk is the k-th depth and f(xk) is the value of the target variable (i.e., clay content) at depth xk. For example, for the 0-30 cm depth interval, with soil clay values at the first four standard depths (0, 5, 15 and 30 cm) equal to 14.5, 25.0, 25.3 and 25.0, clay content 0-30 cm equals:
\begin{equation}
\tag{6.2}
\frac{(5-0) \times(14.5+25.0)+(15-5) \times(25.0 + 25.3) + (30 - 15) \times (25.3 + 25.0)} {30\times 0.5} = 24.25
\end{equation}
\hypertarget{land-cover-data-sets}{%
\section{Land cover data sets}\label{land-cover-data-sets}}
The gridded land cover data layers shall be obtained from:
\begin{itemize}
\tightlist
\item
national or regional sources;
\item
global data sets, where national or regional land use or land cover data sets are not available.
\end{itemize}
Since land cover may vary substantially between data sources and estimates of past and current land cover may have important deviations from real land cover and land use, users should estimate land use from the source that best reflects national and subnational conditions. Land cover data sets should cover the 2000-2020 (or approximate) period.
The ESA (European Space Agency) land cover Global dataset (See Table 6.2), and its reclassification into FAO Global Land Cover - SHARE (GLC-SHARE; See Table 6.2) classes will be provided by the GSP Secretariat, if no national land use dataset is available. However, users should estimate land use from the source that best reflects national and subnational conditions. Other global and regional data sets are provided in Table 6.2 at the end of this Chapter.
The land cover classes will affect the decomposability of the incoming plant material (DPM/RPM ratio; See Section 6.6). A spatialized R-version of RothC is provided by the GSP Secretariat (See Chapter 7, software environment) and runs considering the 13 classes defined in the FAO Global Land Cover - SHARE (GLC-SHARE). A default DPM/RPM value is assigned to each class (Table 6.1). Thus, when using this spatialized R-version of RothC without modifying its scripts, the land use classes from the possible different data sets need to be re-classified into FAO Global Land Cover - SHARE (GLC-SHARE) land use classes. However, users can model alternative land use classes, and modify these default DPM/RPM values. If so, modifications in the R-version must be then introduced (See Chapter 7). Examples of land cover reclassification from the ESA land cover database into the RothC land use categories are presented (Table 6.4) at the end of this Chapter.
\textbf{Table 6.1} \emph{FAO Land cover classes, land cover number and default DPM/RPM ratios. An extra land use class (*Tree-crops) is shown as an example of the disaggregation of a land use class.}
\begin{table}
\centering
\begin{tabular}{l|r|l}
\hline
FAO Land aggregated cover class & Land Cover code number & Default DPM RPM ratio\\
\hline
Artificial surfaces & 1 & /\\
\hline
Cropland & 2 & 1.44\\
\hline
Grassland & 3 & 0.67\\
\hline
Tree covered & 4 & 0.25\\
\hline
Shrub covered & 5 & 0.67\\
\hline
Herbaceous vegetation & 6 & 0.67\\
\hline
Mangroves & 7 & /\\
\hline
Spare vegetation & 8 & 0.67\\
\hline
Bare soil & 9 & /\\
\hline
Snow and Glaciers & 10 & /\\
\hline
Water bodies & 11 & /\\
\hline
Cropland-Tree crops* & 12 & 1.44\\
\hline
Paddy fields & 13 & 1.44\\
\hline
\end{tabular}
\end{table}
As a minimum, land use for the year 2000 and land use for the year 2020 (or last available year) at 1x1 km resolution shall be defined. The predominant land use category in each cell of the 1x1 km grid shall be selected if finer resolutions are available.
\hypertarget{monthly-vegetation-cover}{%
\section{Monthly vegetation cover}\label{monthly-vegetation-cover}}
It is required to indicate the approximate annual distribution of monthly vegetation cover for the simulations in order to:
\begin{itemize}
\tightlist
\item
adjust the topsoil moisture deficit estimations (See Chapter 4, Fig. 4.1);
\item
consider the effects of soil cover on SOC decomposition rates (See Chapter 4, Fig.4.1).
\end{itemize}
The annual distribution of vegetation cover can be derived from:
\begin{itemize}
\tightlist
\item
public statistics of national and/or administrative units considering the predominant agricultural systems in a temporal series (2000-2020);
\item
derived from NDVI (normalized difference in vegetation index) values from historic satellite images (See data sets, Table 6.2).
\end{itemize}
The occurrence of plant cover can be assumed to be constant in grasslands, shrublands and savannas and during specific months (e.g.~1-6 months for croplands) (e.g.~Smith et al., 2005; Smith et al., 2007). The following coefficients can be set for based on the specific land cover and/or land use:
\begin{itemize}
\tightlist
\item
Perennial tree-crops, forests and grasslands (c=0.6);
\item
Months with predominantly bare soil and unvegetated fallows (c=1);
\item
Annual crops (c=0.6).
\end{itemize}
Considering a temporal series (2000-2020), the proportion of images with NDVI values greater than a specified threshold, indicating active vegetation growth, can be estimated (e.g.~NDVI \textgreater{} 0.6). The monthly probability of being vegetated (P veg) can be estimated for each cell grid and each month of the year (1-12), as:
\begin{equation}
\tag{6.3}
P_{veg} = \frac{Number\ of \ images \ NDVI > 0.6}{Total \ images}
\end{equation}
NDVI is proposed as an alternative for estimating vegetation cover when no vegetation cover data or local knowledge is available. The proposed threshold may vary according to local conditions. Global monthly vegetation cover data sets estimated by NDVI (2000-2020) will be provided by the GSP Secretariat. However, NDVI may be a biased indicator in areas with low vegetation cover (e.g.~drylands, shrublands), or high nubosity. In these cases, countries are encouraged to use other locally validated spectral indices to accurately estimate monthly vegetation cover (e.g.~Multi Sensor Vegetation Index; Moradizadeh and Saradjian, 2016).
\hypertarget{monthly-carbon-inputs}{%
\section{Monthly carbon inputs:}\label{monthly-carbon-inputs}}
\hypertarget{c-inputs-under-bau-practices}{%
\subsection{C inputs under BAU practices:}\label{c-inputs-under-bau-practices}}
Carbon inputs for the BAU scenarios shall be estimated using the approach proposed by Smith et al.~(2005; 2006; 2007) and Gottschalk et al.~(2012). Total plant C inputs to the soil, which include plant litter, root exudates and fine root turnover, are rarely known. To overcome this problem, RothC shall be run in `equilibrium mode' to calculate the initial plant carbon inputs to the soil (or `equilibrium Carbon inputs', Ceq), which led to the initial SOC stocks (GSOCmap), under historic forcing conditions. The Ceq thus represents the historical average of annual carbon input of the BAU scenario up to the year 2000. For further details on the equilibrium run and initialization to estimate Ceq, refer to section 3.2 (General modeling procedures).
Once these initial carbon inputs have been established (from the year 2000 onwards), year-to-year changes can be adjusted in accordance with changes in Net Primary Production (NPP), as changes in C inputs to the soil are assumed to be associated with changes in NPP (Smith et al., 2005). Thus, annual C inputs for the BAU scenario can be adjusted as:
\begin{equation}
\tag{6.4}
BAU_{Ct} = C_{t-1} \times (NPP_{t-1})-1 \times NPP_t
\end{equation}
where \(BAU_{Ct}\) is the annual carbon input of a specific year \(t\); \(C_{t-1}\) is the annual carbon input of the previous year; \(NPP_t\) is the net primary production of year \(t\), and \(NPP-t\) is the NPP of the previous year (in tC ha\textsuperscript{-1}). Thus, the average NPP over the initialization period shall be associated with Ceq and the annual C inputs for the BAU scenario can be adjusted as:
\begin{equation}
\tag{6.5}
BAU_{Ct \ 2001} = C_{eq} \times NPP_{1980-2000}^{-1} \times NPP_{2001}
\end{equation}
where \(BAU_{Ct \ 2001}\) is the annual carbon input for the first year of the `short spin-up' phase; \(C_{eq}\) is the estimated annual C input at equilibrium derived through the `long spin-up' process; \(NPP_{1980-2000}\) is the estimated average net primary production over the initialization period (1980-2000); and \(NPP_{2001}\) is the estimated annual net primary production for the first year of the `short spin-up' phase. The annual C inputs for the BAU scenario can be then adjusted following equation 7, according to changes in the NPP.
The estimation of NPP using the MIAMI model (Lieth, 1975) is defined as the standard method in this document. It requires little input and is easily applicable worldwide, can be used to estimate NPP under future climatic conditions, and can act as a baseline for different NPP data sets or projections (e.g.~Gottschalk et al., 2012). NPP estimated with the MIAMI model is computed with the following equations:
\begin{equation}
\tag{6.6}
NPP_{MIAMI}= min(NPP_T , NPP_P)
\end{equation}
\begin{equation}
\tag{6.7}
NPPT_{MIAMI}= \frac{3000}{1 + e^{1.315-0.119} \times T}
\end{equation}
\begin{equation}
\tag{6.8}
NPPP_{MIAMI} = 3000 \times 1 - e^{-0.000664P}
\end{equation}
where \(NPP\) is the climatic net primary production in dry matter (DM; g m\textsuperscript{-2} yr\textsuperscript{-1}), \(NPP_T\) is the temperature dependency term of NPP, where \(T\) is the annual mean temperature (\(^\circ\)C) and \(NPP_P\) is the moisture dependency term of NPP, where P is the mean annual sum of precipitation (mm). NPP is limited by either temperature or precipitation. MIAMI model NPP can be expressed in t C ha\textsuperscript{-1} yr\textsuperscript{-1} as:
\begin{equation}
\tag{6.9}
NPP_{MIAMI} \ tC ha^1 yr^{-1} = NPP_{MIAMI} (DM; g m^{-2} yr^{-1}) \times 0.01 \times 0.48
\end{equation}
The annual NPPMIAMI shall be estimated for each grid cell from the climatic data sets described in section 6.1 for the different simulation periods (1981-1990; 1991-2000; 2001-2010; 2011-2020; 2021-2040). The NPPMIAMI is used to estimate BAU carbon inputs under current and projected climatic conditions.
The change in NPP is used as a surrogate for estimating the change in C input and assumes that a similar proportion remains in the field (e.g.~Smith et al., 2005; Gottschalk et al., 2012). In a first instance, countries should focus on C inputs in agricultural lands in 2020, the use of which has not changed since the year 2000. Changes in land use and management over the period 2000-2020 and associated changes in C inputs can nevertheless be taken into account, if trends in biomass removal are known, in order to adjust C inputs (e.g.~Schulze et al., 2010; Plutzar et al., 2016; Neumann and Smith, 2018). Thus, the annual changes in C inputs by equations 7 and 8 can be adjusted using annual land cover data. For example, by assuming and approving an NPP of 12, 28 and 47\% for forests, grasslands and croplands (Schulze et al., 2010), the annual NPP of a specific year (NPPt) can be adjusted using these coefficients (equations 6.9 to 6.11), and the annual C inputs can then be estimated by equations 6.3 and 6.4:
\begin{equation}
\tag{6.10}
NPPt_{forests} = NPP_{MIAMI} \times 0.88
\end{equation}
\begin{equation}
\tag{6.11}
NPPt_{grasslands} = NPP_{MIAMI} \times 0.72
\end{equation}
\begin{equation}
\tag{6.12}
NPPt_{croplands} = NPP_{MIAMI} \times 0.53
\end{equation}
\hypertarget{c-inputs-under-ssm-practices}{%
\subsection{C inputs under SSM practices:}\label{c-inputs-under-ssm-practices}}
SSM practices shall be grouped into three scenarios as a standard method, based on their expected relative effects on C inputs compared to BAU: Low, Medium and High C inputs. The SSM practices considered in this approach are practices that affect C inputs to the soil, as changes in C inputs have been identified as one of the factors to which models are most sensitive when projecting changes in SOC stocks (FAO, 2019).
As with estimates of BAU C inputs, total plant C inputs to the soil, including plant litter, root exudates and fine root turnover, are rarely known. Thus, C inputs of SSM scenarios will represent a \% increase from BAU C inputs:
\begin{equation}
\tag{6.13}
\Delta \% CSSM-BAU = (Cinputs_{SSM} - Cinputs_{BAU}) \times Cinputs_{BAU}
\end{equation}
As a standard, the expected effects (\% increase in C inputs) of 3 scenarios have been conservatively set at:
\begin{itemize}
\tightlist
\item
Low: 5 \% increase in C inputs
\item
Medium: 10\% increase C inputs
\item
High: 20 \% increase in C inputs
\end{itemize}
These percentages (based on Smith, 2004; Wiesmeier et al., 2016) shall be used to produce the mandatory maps for the global product. An additional `High increase' scenario, considering a 30\% increase in C inputs, can be modeled, to compare results with recent `top-down' modeling approaches (e.g.~CIRCASA).
The use of default percentages in C input increase can be applied globally without complex configuration. However, countries should carefully check whether these scenarios are reasonable and under what type of management practices they are achievable. Countries are encouraged to produce and provide additional maps, taking into account their own estimates of the effects of different selected practices or land use changes, based on expert knowledge and local capacities. These effects can be determined on the basis of expert opinion and available information at the country level. A meta-analysis should be conducted based on the latest available local and regional studies to estimate how agricultural practices affect average annual C inputs (and the \% increase in C input compared to BAU practices). These practices may include, for example, the use of cover crops, rotation with high residue yielding crops or perennials, residue retention, grazing management, plant nutrition, species introduction, manure or organic amendment application, among others. If no data is directly provided in the compiled studies, carbon inputs and \% increase in C inputs relative to BAU practices shall be estimated considering the framework proposed by Bolinder et al.~(2007).
The annual C inputs required to model the effects of SSM practices under 3 scenarios (Low, Medium, High) for each modeling unit (i.e.~grid cells) shall be estimated from the annual BAU C inputs:
\begin{equation}
\tag{6.14}
SSM_{Ct} t C ha^-1 yr^-1 = BAU_{Ct} + \% \Delta CSSM_i - BAU \times BAU_{Ct}
\end{equation}
where \(SSM_{Ct}\) represents the estimated annual C inputs for a specific scenario (\(i\) =Low, Medium, High) for year \(t\); \(BAU_{Ct}\) represents the estimated annual C inputs for the BAU scenario for year t (determined from C inputs at equilibrium, as explained at the beginning of this section and in Chapter 5), and
\(\% \Delta CSSM_i - BAU\) is the representative \% increase in C inputs for a specific scenario (\(i\)=Low, Medium, High).
\hypertarget{residue-decomposability-decomposable-to-resistant-plant-material-ratio-dpmrpm}{%
\section{Residue decomposability: decomposable to resistant plant material ratio (DPM/RPM)}\label{residue-decomposability-decomposable-to-resistant-plant-material-ratio-dpmrpm}}
Default values for the DPM/RPM ratio (decomposability of incoming plant material) can be used (e.g.~1.44 for crops and improved grasslands; 0.67 for unimproved grasslands and shrublands, and 0.25 for forests, woodlands and tree crops; Falloon and Smith, 2009). Table 6.1 (Land cover data sets) show default DPM/RPM for FAO land use classes. These default values can be modified according to region-specific data and local knowledge.
\hypertarget{required-data-sets-and-global-data-sources.-summary}{%
\section{Required data sets and global data sources. Summary}\label{required-data-sets-and-global-data-sources.-summary}}
The required data sets described in this chapter are summarized in Table 6.2. The proposed regional and global data sources to obtain the required input data when no quality national or regional data is available are described in Table 6.3.
\textbf{Table 6.2} \emph{Summary of the input data requirements for the proposed modeling approach to generate national SOCseq maps}
\begin{table}
\centering
\begin{tabular}{l|l|l|l|l|l}
\hline
Data & Variables & Time series & Units & Type & Resolution\\
\hline
Climatic data & Monthly air temperature & 1980-2000; 2001-2020 & °C & Raster & 50 x 50 km or finer\\
\hline
& & (or until last year available) & & & \\
\hline
& Monthly evapotranspiration/pan evaporation & 1980-2000; 2001-2020 & mm & Raster & 50 x 50 km or finer\\
\hline
& & (or until last year available) & & & \\
\hline
& Monthly precipitation + irrigation & 1980-2000; 2001-2020 & mm & Raster & 50 x 50 km or finer\\
\hline
& & (or until last year available) & & & \\
\hline
Soil data & Topsoil clay content (0-30 cm) & - & \% & Raster & 1 x 1 km\\
\hline
& Current Soil organic carbon stocks (0-30 cm) & Latest version of national FAO-GSOC map & tC ha-1 & Raster & 1 x 1 km\\
\hline
Land use/cover & Predominant land use/cover, re-classified into: & Minimum: 2000 and 2020 (or last year available) & 01-mar & Raster & 1 x 1 km\\
\hline
& Minimum: 3 default classes required by model: agricultural crops, grassland/shrubland/savannas and forestry & Optimum: annual land use 2000 to 2020 & 01-nov & & \\
\hline
& Optimum: 13 classes defined in the FAO Global Land Cover - SHARE (GLC-SHARE) & & & & \\
\hline
& Monthly vegetation cover. & Minimum: average 2000- 2020 (or last year available) Optimum: annual land use 2000 to 2020 & 0-1 & Raster & 1 x 1 km\\
\hline
& Obtained from national statistics/local expert knowledge; or derived from NDVI or spectral indexes (see section 3.3.4) & & & & \\
\hline
& & & & & \\
\hline
\end{tabular}
\end{table}
\textbf{Table 6.3} \emph{Global and regional data sources to generate national SOCseq maps}
\begin{table}
\centering
\begin{tabular}{l|l|l|l}
\hline
Type & Source & Address & Resolution\\
\hline