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History of Modifications

Acronyms

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1. Scope of the document 1. Scope of the document

Scope of the document

This document serves as Algorithm Theoretical Basis Document (ATBD) for Agroclimatic Indicators datasets, as part of the C3S Global Agriculture Sectoral Information Systems (SIS). More information about the project can be found at https://climate.copernicus.eu/global-agriculture-project.

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2. Executive summary 2. Executive summary

Executive summary

The C3S Global Agriculture (SIS) project aims to develop climate services in support of decision- making in agriculture sector. It does so in a process of co-creation with partners representing international crop research, international agricultural policy development and commercial agricultural consultancy services.

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3. Generic Agroclimatic Indicators 3. Generic Agroclimatic Indicators

Generic Agroclimatic Indicators

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3.1. Introduction 3.1. Introduction

Introduction

Agroclimatic indicators represent features of the climate that are used to characterise plant-climate interactions. They can be derived from daily or monthly meteorological variables (e.g. temperature and rainfall). Agroclimatic indicators are often used in species distribution modelling and related ecological modelling techniques, and also in studying phenological developments of plants under varying climate conditions.

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All of the generic agroclimatic indicators are pre-computed and the crop-specific indicators are computed on-demand, using standard CDS Toolbox workflows. The crop specific indicators cover four main crops of global interest: wheat, maize, rice and soybean.

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3.2. Input Data 3.2. Input Data

Input Data

0FTo generate the agroclimatic indicators for historical and future time periods, bias-corrected climate datasets provided through Inter-Sectoral Impact Model Intercomparison Project (ISIMIP ¹) have been used. ISIMIP is a community-driven climate impacts modelling initiative aimed at
contributing to a quantitative and cross-sectoral synthesis of the differential impacts of climate change. ISIMIP project was organised into 3 main simulation rounds:

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The ISIMIP Fast Track climate data is used for generating the current Agroclimatic indicators. Climate variables from ISIMIP Fast Track are bias-corrected using method described by Hempel et al. (2013). Table 1 shows the availability of ISIMIP Fast Track datasets for different GCM/emission scenarios, covering 1951 - 2099.

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In addition, as a proxy for historical observations, the "Watch Forcing Data methodology applied to ERA-Interim (WFDEI)" (Weedon et al. 2014) were used to generate observational historical Agroclimatic indicators. This datasets is available at the same spatial resolution of ISIMIP climate datasets and covers the time range of 1979 to 2013.

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3.3. Generic Agroclimatic Indicators 3.3. Generic Agroclimatic Indicators

Generic Agroclimatic Indicators

C3S Global Agriculture SIS delivers 26 generic agroclimatic indicators at the same spatial resolution of the input data (0.5° x 0.5° lat-lon). The geographic coverage is global land areas.
All indicators are computed from realizations of daily data, derived from two essential climate variables (ECV):

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A total of 26 indicators were adapted from the European Climate Assessment & Dataset project (ECA&D; Klein Tank, 2007) collection for their general relevance to agriculture, especially the priority crops, but not specific to any particular crop. Table 2 lists the agroclimatic indicators provided by

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*) these indicators have been pre-calculated for the range of threshold temperatures

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3.3.1. Temporal Resolution 3.3.1. Temporal Resolution

Temporal Resolution

The finest temporal resolution that is commonly used in climate science for generating climate indicators is 1 month. For agronomical practices an accurate indication of for example crop emergence, flowering occurrence etc., is useful. Therefore, to have a better indication when crop emergence, flowering, etc., takes places (given the provided weather data series) the temporal resolution should be finer than one month. Interpolation from two one month periods will provide a less accurate indication for example flowering indication than can be obtained when two 10 day periods are used. Hence the temporal resolution of agroclimatic indicators have been improved by a factor of 3, splitting the calendar year into chunks of nominally 10 day periods (also known as "dekads"). Thus the date scale within each year would be:
01-10 Jan (10 days)

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Note: The winter season (DJF) of a year is composed of December the current calendar year and January and February of the following calendar year (e.g. DJF 2000 is composed of Dec 2000, Jan 2001 and Feb 2001)

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3.3.2. Compute Algorithms 3.3.2. Compute Algorithms

Compute Algorithms

In this section the definitions and algorithms that has been used for generating agroclimatic indicators are outlined. The following information are provided for each indicator:

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Let TX_ij be the daily maximum temperature on day i of period j. Then the maximum daily maximum temperature for period j is:

TXx_j = max(TX_ij)

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3.4. Output Data 3.4. Output Data

Output Data

C3S global agriculture SIS gridded agroclimatic indicators are delivered in Network Common Data Form (NetCDF-4) format, each one covering 30 years climate periods. In the scope of this service, datasets for the climate periods in Table 3 are provided.

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Therefore for each agroclimatic indicator in Table 2, there are 71 netCDF files available as follows:

• 5 GCMs × 2 historical periods
• 5 GCMs × 4 RCPs x 3 future periods
• 1 historical from climate forcing data (WFDEI)

Figure 1 shows a graphical illustration of the time periods covered by these datasets, for each indicator:

Figure 1: Graphical illustration of the time periods covered for each indicator.

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4. Crop specific Agroclimatic Indicators 4. Crop specific Agroclimatic Indicators

Crop specific Agroclimatic Indicators

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4.1. Introduction 4.1. Introduction

Introduction

To facilitate the assessment of tailored crop-specific indicators we have chosen not to precalculate these as the number of options is near indefinite. Instead we provide the necessary information to allow the user to calculate these on demand, such as sowing date, harvest date, growing range of min and max temperatures, thermal requirements, geographical distribution, etc. This way the user can generate outputs specific to the crops of interest.
For spatially distributed crop characteristics NETCDF files have been created combining several pre- existing datasets in one common CF compliant format. They facilitate spatio- temporal masking of, and parameters for crop specific agroclimatic indicators. One file for each crop has been created, with global coverage at 0.5 degrees lat/lon resolution. A version at 5 minutes lat/lon resolution can be produced too. Initially these have been compiled for four crops: wheat (spring and winter variety), maize, rice and soybean.
The following data sets have been combined (details next sections below):

• crop maps, based on the SPAM-2005 dataset (You et al, 2017); they serve to facilitate spatial aggregation of historic and contemporary statistics to crop growing areas; it is not recommended to use these for future climate projections, as crop suitability may change
• crop calendars, based on the FAO-GAEZ data set (FAO/IIASA, 2010); the serve to derive temporal aggregation of historic and contemporary statistics to specific crop growing seasons; it is not recommended to use these for future climate projections, as crop calendars may change in response to climate change
• crop mega-environments, based on CGIAR definitions (for an overview see Fischer et al., 2014); they serve to derive crop specific parameters for phenological development (e.g. thermal requirements) of crop cultivars adapted to certain climates, and so to facilitate aggregation of historic and contemporary statistics to specific crop phenological phases; it is not recommended to use these maps for future climate projections, as crop suitability may change; the associated parameter tables can be used both contemporary and for the future
• crop thermal requirements, based on optimization of WOFOST simulated yield with respect to GAEZ calendar. they serve to determine crop development based on BEDD (biologically effective degree days, also known as growing degree days or temperature sums) Thus TSUMs have been calculated for emergence-anthesis (sowing-flowering), anthesis-maturity (flowering-harvest) and emergence-maturity (sowing-ripening; sum of previous two)

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4.2. Crop maps 4.2. Crop maps

Crop maps

Crop maps give for each pixel the number of hectares under that crop. The maps are representative for the situation around 2005. This leads to eight NETCDF variables, presented in the table below.
From the original SPAM database 2 'variables' and 4 'technologies' have been retained. SPAM data have a resolution of 5', so we summed the data over each set of 6x6 original grid boxes to come to the 0.5^o grid boxes in our set.

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Harvested area can be larger or smaller than physical area; larger implies that some form of double cropping is present; smaller implies that not all area suitable for the crop is actually planted / harvested. Please refer to http://mapspam.info/ for more information.

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4.3. Crop calendars 4.3. Crop calendars

Crop calendars

Crop calendars give for each pixel the sowing/planting and harvest dates for that crop. For each pixel and average sowing date, an early sowing date or a late sowing date is given (respectively defined as 1st, 2nd or 3rd dekad of month of sowing specified in FAO-GAEZ), and similar of harvest dates. For a number of crops two crop cycles are represented: in temperate climate wheat and similar cereals (e.g. barley, rapeseed, etc.) can be sown before winter (winter wheat) already or after winter in spring (spring wheat). For these winter crops the 'sowing date' given is not the actual
sowing date in autumn but rather the end of the winter dormancy period, i.e. when the already emerged crops restarts to grow. For tropical crops in some areas two crop cycles are represented, a main season and a secondary season. Dates are given as 'dekad', so 'dekad' = 6 represents February 21-End of February. This leads to 6 variables, presented in the table below.

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FAO-GAEZ data have a resolution of 5', so we aggregated the data over each set of 6x6 original grid boxes to come to the 0.5^o grid boxes in our set. For early sowing/harvest we took the minimum value found in the 6x6 boxes, for late sowing harvest the maximum value found, and for average sowing/harvest the rounded average of all 36 values.

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4.4. Crop mega-environments 4.4. Crop mega-environments

Crop mega-environments

Crop mega environments define similar environments on a global scale. The main classification ME1... MEn) reflects climatic constraints, e.g. average temperature and precipitation of the growing season, in corresponding altitude/latitude bands. Sub-classifications (e.g. ME2b) may reflect soil conditions. The concept is very useful for crop breeders, where for each mega environment a cultivar (or variety) can be developed that in principal should grow well everywhere in that ME. Often for each ME a benchmark cultivar and representative site can be identified.

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At a next level, the benchmark cultivar for each crop/ME combination should be specified by a set of generic crop model parameters. These include thermal requirements for each major phenological development stage, optimal climatic growing conditions, thresholds for hot/cold stress, etc. No publicly available data of any consistency have been found, so parameters for each of these ME are not provided in our NETCDF files. Nevertheless we have chosen to retain the maps, for those knowledgeable to use them wisely.

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4.5. Crop thermal-requirements 4.5. Crop thermal-requirements

Crop thermal-requirements

Crop crop thermal requirements are based on optimisation of WOFOST simulated yield with respect to the GAEZ calendar. Three TSUMs have been calculated for emergence-anthesis (sowing- flowering, TSUM1), anthesis-maturity (flowering-harvest, TSUM2) and emergence-maturity
(sowing-ripening; sum of previous two). Yield was optimised as max average yield over the period 2005-2015 using ERA-I global climate forcing. For the calculation of the thermal requirements the following base temperatures and TSUM ratios for determining anthesis/flowering have been used.

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4.6. Crop files 4.6. Crop files

Crop files

Thus for each crop a single NETCDF files has been compiled using a Matlab® script, containing all above mentioned variables:

• mainrice-char-05d_C3S-glob-agric_2005_v5.nc
• secondrice-char-05d_C3S-glob-agric_2005_v5.nc
• springwheat-char-05d_C3S-glob-agric_2005_v5.nc
• winterwheat-char-05d_C3S-glob-agric_2005_v5.nc
• maize-char-05d_C3S-glob-agric_2005_v5.nc
• soybean-char-05d_C3S-glob-agric_2005_v5.nc

These files are not meant to be downloadable from the CDS, but their contents can be accessed through a tool developed for the purpose that can be deployed in the C3S Toolbox:

getCropVariable(cropName, var)
Parameters:
cropName: string
Name of the crop. Currently ‘soybean’, ‘winterWheat’, ‘springWheat’, ‘mainRice’,
‘secondRice’ and ‘maize’ are available.
var: string
Name of the variables to be retrieved from the crop file. Currently these variables
can be selected: ‘area_rs_p’, ‘area_rs_h’, ‘area_rh_p’, ‘area_rh_h’, ‘area_rl_p’,
‘area_rl_h’, ‘area_ir_p’, ‘area_ir_h’, ‘sow_a1’, ‘sow_e1’, ‘sow_l1’, ‘mat_a1’,
‘mat_e1’, ‘mat_l1’, ‘ME1’, ‘ME2’, ‘ME3’, ‘ME4’, ‘ME5’, ‘ME6’, ‘ME7’, ‘ME8’, ‘ME9’,
‘tsumEM’, ‘tsumEA’, ‘tsumAM’; as defined above.
Returns:
out: xarray DataArray
A xarray dataarray containing the data for the crop variable.

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5. References 5. References

References

Bellon, M.R., Hodson, D.P., Bergvinson, D.J., Beck, D.L., Martinez-Romero, E., Montoya, Y. (2005). Targeting agricultural research to benefit poor farmers: Relating poverty mapping to maize environments in Mexico. Food Policy 30: 476-492. Data retrieved from http://hdl.handle.net/hdl/11529/10624

Braun, H.J., Atlin, G., Payne, T. (2010). Multi-location testing as a tool to identify plant response to global climate change. In: Climate Change and Crop Production. Reynolds MP (Ed). CABI,London, UK. p. 71-91. Data retrieved from http://hdl.handle.net/11529/10625

FAO/IIASA, 2010. Global Agro-ecological Zones 1960-1990 (GAEZ v3.0). FAO, Rome, Italy and IIASA, Laxenburg, Austria; Data retreived from https://gaez.fao.org/Main.html;

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You, L., U. Wood-Sichra, S. Fritz, Z. Guo, L. See, and J. Koo (2017). Spatial Production Allocation Model (SPAM) 2005 v3.2., Data retrieved from http://mapspam.info cop

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