Last modified on Feb 27, 2024 14:56

Contributors: Mohamad Nobakht (TELESPAZIO), Phil Beavis (TELESPAZIO), Siân O'Hara (TELESPAZIO), Ronald Hutjes (WAGENINGEN ENVIRONMENTAL RESEARCH), Iwan Supit (WAGENINGEN ENVIRONMENTAL RESEARCH)

History of Modifications

Acronyms

1. 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.

2. 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.

As part of the C3S Global Agriculture SIS, the agroclimatic indicators are generated to represent features of the climate that are used to characterise plant-climate interactions. Agroclimatic indicators are useful in conveying climate variability and change in the terms that are meaningful to agriculture. The objective of this service is to provide these indicators at global scale in an easily accessible and usable format for further downstream analysis and forcing of agricultural impact models, both gridded and location specific.

A total of 26 indicators are provided, covering the global land area at the spatial resolution of 0.5°x0.5° lat-lon grid. A brief review of the agroclimatic indicators provided by C3S global agrilculture SIS is given in tables below:

3. Generic Agroclimatic Indicators

3.1. 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.

Agroclimatic indicators are based on formulas that measure climatic factors and conditions that may positively/negatively affect vegetation and may correlate to the main type of vegetation of an area. They are commonly used in agriculture to reconstruct climate and environmental changes such as climatically induced phases of plant growth, moisture and heat supply, drought spells, etc.

Agroclimatic indicators provided by this service are calculated directly from climate variables, and consist of two main categories of indicator:

• Generic Agroclimatic Indicators: generally these are aggregation, accumulation or occurrence indicators calculated as a function of particular atmospheric variable (temperature and precipitation).

• Tailored crop-specific indicators: These indicators require information such as sowing date, harvest date, growing range of min and max temperatures, thermal requirements, geographical distribution, etc. in order to provide outputs specific to the crops of interest.

C3S Global Agriculture SIS provides both pre-computed indicator datasets and workflows for on- demand generation of crop-specific indicators. The former ensures that indicators of common interest, that do not require tailoring, are readily available to users via the C3S CDS Catalogue and CDS Toolbox. The latter case provides the opportunity for users to generate data that is specific to their purpose, with appropriate guidance and support provided.

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.

3.2. 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:

• ISIMIP Fast Track
• ISIMIP2 Phase a (ISIMIP2a)
• ISIMIP2 Phase b (ISIMIP2b)

For each simulation round a set of gridded bias-corrected climate variables have been produced to be used as input data for running impact models. These climate datasets contain daily-resolution, bias-corrected climate data from 5 CMIP5 GCMs covering the period 1950-2099 (historical run up to 2005), downscaled to a 0.5°x0.5° lat-lon grid. They cover the global land area.

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.

Table 1: Availability of ISIMIP Fast Track climate datasets

Agricultural indicators have been pre-calculated for this complete matrix of GCM/RCP combinations.

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.

3.3. 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):

1. Surface air temperature
   • Daily 2m surface air temperature minimum (TN)
   • Daily 2m surface air temperature maximum (TX)
   • Daily 2m surface mean air temperature (TG)
2. Precipitation
   • Daily total precipitation (RR)

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

C3S global agriculture SIS along with information on their application in agriscience. Algorithm specification details of the agroclimatic indicators are provided in the next section of this document.

Table 2: List of agroclimatic indicators, their description and general application in agriscience

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

3.3.1. 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)

11-20 Jan (10 days)

21-31 Jan (11 days)

01-10 Feb (10 days)

11-20 Feb (10 days)

21-28/29 Feb (8/9 days)

…

From the dekadal resolution, it will be possible to aggregate the data up to calendar months, quarterly seasons, or indeed to approximate any arbitrary growing season. The aggregation method will depend on the indicator, e.g. min, max, sum, mean. To compensate for slightly varying number of days, the mean should be weighted accordingly.

It should be noted that dekadal resolution is not appropriate for indicators representing a continuous spell of weather (e.g. warm, cold, wet, dry, etc.), since the 10 day boundary will interfere with the number of consecutive days attaining a threshold. Therefore these indicators are computed for 3 month periods representing the standard meteorological seasons:

• Dec, Jan, Feb (DJF)
• Mar, Apr, May (MAM)
• Jun, Jul, Aug (JJA)
• Sep, Oct, Nov (SON)

This applies to the following indicators: CDD, CFD, CSDI , WSDI, CSU, CWD.

One indicator, the growing season length GSL, can be computed only on an annual basis, i.e. for the Jan – Dec period in the Northern Hemisphere and for the Juli to June period in the Southern Hemisphere.

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)

3.3.2. 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:

• Description: definition of the indicator
• Units: SI units of the variable
• Temporal resolution: time frequency of the provided indicator
• Definition: algorithm for computing the indicator using daily climate data

CDD

Description:                                     Maximum number of consecutive dry days (drought spell)

Units:                                                days

Temporal resolution:                       seasonal Definition:

Let RR_ij be the daily precipitation amount for day i of period j. Then counted is the largest number of consecutive days where:

RR_ij < 1 mm

CFD

Description:                                     Maximum number of consecutive frost days (cold spell)

Units:                                                days

Temporal resolution:                       seasonal Definition:

Let TN_ij be the daily minimum temperature at day i of period j. Then counted is the largest number of consecutive days where:

TN_ij < 0 °C

CDSI

Description:                                     Cold-spell duration index

Units:                                                days

Temporal resolution:                       seasonal Definition:

Let TN_ij be the daily minimum temperature at day i of period j and let TN_in10 be the calendar day 10^th percentile calculated for a 5-day window centered on each calendar day in the 1981–2010 period. Then counted is the number of days per period where, in intervals of at least 6 consecutive days:

TNij < TNin10

WSDI

Description:                                     Warm-spell duration index

Units:                                                days

Temporal resolution:                       seasonal Definition:

Let TX_ij be the daily maximum temperature at day i of period j and let TX_in90 be the calendar day 90^th percentile calculated for a 5-day window centered on each calendar day in the 1981–2010 period.

Then counted is the number of days per period where, in intervals of at least 6 consecutive days:

TXij > TXin90

CSU

Description:                                     Maximum number of consecutive summer days (hot spell)

Units:                                                days

Temporal resolution:                       seasonal Definition:

Let TX_ij be the daily maximum temperature for day i of period j. Then counted is the largest number of consecutive days where:

TX_ij > 25°C

CWD

Description:                                      Maximum number of consecutive wet days (wet spell)

Units:                                                days

Temporal resolution:                       seasonal Definition:

Let RR_ij be the daily precipitation amount for day i of period j. Then counted is the largest number of consecutive days where:

RR_ij ≥ 1 mm

WW

Description:                                     Warm/Wet days

Units:                                                days

Temporal resolution:                       seasonal Definition:

Let TG_ij be the daily mean temperature at day i of period j and let TG_in75 be the calendar day 75^th percentile calculated for a 5-day window centered on each calendar day in the 1981–2010 period. Let RR_wj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and let RR_wn75 be the 75^th percentile of precipitation at wet days in the 1981–2010 period. Then counted is the number of days where:

TG_ij > TG_in75 AND RR_wj > RR_wn75

DRT

Description:                                     Mean of diurnal temperature range

Units:                                                Degrees Celsius

Temporal resolution:                       dekadal Definition:

Let TX_ij and TN_ij be the daily maximum and minimum temperature at day i of period j, then the mean diurnal temperature range in period j is:

BEDD

Description: Biologically Effective Degree Days
Units: Degrees Celsius
Temporal resolution: dekadal
Definition:
Let TGii be the daily mean temperature at day i of period j. BEDD is calculated by:

where 𝑇_{ℎ𝑖𝑔ℎ} and 𝑇_𝑙𝑜𝑤 are effective temperature upper and lower thresholds respectively.
Note:
BEDD indicator (v1.0) is calculated using the following parameters:
𝑇𝑙𝑜𝑤 = 10 °C
𝑇_{ℎ𝑖𝑔ℎ} = 30 °C
BEDD indicator (v1.1) is calculated using the following parameters:
𝑇_𝑙𝑜𝑤 = 0 °C , 2 °C , 4 °C , 6 °C , 8 °C , 10 °C
𝑇_{ℎ𝑖𝑔ℎ} = 30 °C

GSL

Description: Growing Season Length
Units: days
Temporal resolution: yearly
Definition:
Let TG_ij be the mean temperature at day i of period j. Then counted is the number of days between the first occurrence after 1st January (1st July in southern hemisphere) of at least 6 consecutive days with:
TG_ij > 5 °C
and the first occurrence after 1st July (1st January in southern hemisphere) of at least 6 consecutive days with:
TG_ij < 5 °C

FD

Description:                                     Frost days

Units:                                                days

Temporal resolution:                       dekadal Definition:

Let TN_ij be the daily minimum temperature at day i of period j. Then counted is the number of days where:

TN_ij < 0 °C

 ID

Description:                                      Ice days

Units:                                                days

Temporal resolution:                       dekadal Definition:

Let TX_ij be the daily maximum temperature at day i of period j. Then counted is the number of days where:

TX_ij < 0 °C

RR

Description:                                     Precipitation sum

Units:                                                mm

Temporal resolution:                       dekadal Definition:

Let RR_ij be the daily precipitation amount for day i of period j. Then sum values are given by:

R10mm

Description:                                     Heavy precipitation days

Units:                                                days

Temporal resolution:                       dekadal Definition:

Let RR_ij be the daily precipitation amount for day i of period j. Then counted is the number of days where:

RR_ij ≥ 10 mm

R20mm

Description:                                     Very heavy precipitation days

Units:                                                days

Temporal resolution:                       dekadal

Definition:

Let RR_ij be the daily precipitation amount for day i of period j. Then counted is the number of days where:

RR_ij ≥ 20 mm

RR1

Description:                                     Wet days

Units:                                                days

Temporal resolution:                       dekadal Definition:

Let RR_ij be the daily precipitation amount for day i of period j. Then counted is the number of days where:

RR_ij ≥ 1 mm

SDII

Description:                                     Simple daily intensity index

Units:                                                mm

Temporal resolution:                       dekadal Definition:

Let RR_wj be the daily precipitation amount for wet day w (RR ≥ 1 mm) of period j. Then the mean precipitation amount of wet days is given by:

SU

Description:                                     Summer days

Units:                                                days

Temporal resolution:                       dekadal

Definition:

Let TX_ij be the daily maximum temperature at day i of period j. Then counted is the number of days where:

TX_ij > 25 °C

Note:

SU indicator (v1.1) is calculated using the following thresholds for TX_ij :

20 °C, 25 °C, 30 °C, 35 °C

TG

Description:                                     Mean of daily mean temperature

Units:                                                Kelvins

Temporal resolution:                       dekadal Definition:

Let TG_ij be the mean temperature at day i of period j. Then mean values in period j are given by:

TN

Description:                                     Mean of daily minimum temperature

Units:                                                Kelvins

Temporal resolution:                       dekadal Definition:

Let TN_ij be the minimum temperature at day i of period j. Then mean values in period j are given by:

TNn

Description:                                     Minimum value of daily minimum temperature

Units:                                                Kelvins

Temporal resolution:                       dekadal Definition:

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

TNn_j = min(TN_ij)

TNx

Description:                                     Maximum value of daily minimum temperature

Units:                                                Kelvins

Temporal resolution:                       dekadal Definition:

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

TNx_j = max(TN_ij)

TR

Description:                                     Tropical nights

Units:                                                days

Temporal resolution:                       dekadal Definition:

Let TN_ij be the daily minimum temperature at day i of period j. Then counted is the number of days where:

TN_ij > 20 °C

TX

Description:                                     Mean of daily maximum temperature

Units:                                                Kelvins

Temporal resolution:                       dekadal Definition:

Let TX_ij be the maximum temperature at day i of period j. Then mean values in period j are given by:

TXn

Description:                                     Minimum value of daily maximum temperature

Units:                                                Kelvins

Temporal resolution:                       dekadal Definition:

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

TXn_j = min(TX_ij)

 TXx

Description:                                     Maximum value of daily maximum temperature

Units:                                                Kelvins

Temporal resolution:                       dekadal Definition:

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)

3.4. 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.

Table 3: Climate periods covered by gridded datasets for each indicator

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.

4. Crop specific Agroclimatic Indicators

4.1. 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)

4.2. 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.

Table 4: Map variables available for each crop

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.

4.3. 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.

Table 5: Cropping calendars variables avialable for each crop

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.

4.4. 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.

The number of MEs defined for each crop varies. In this collection the following have been retained, and only so for wheat and maize these have been included in the respective NETCDF files., as for the others no publicly available data have been found.
ME maps have been compiled from high resolution (ca. 3' equivalent) shape files, tagging a 0.5o grid box as belonging to a certain ME if its polygon occupied any fraction of the grid box. This means there is overlap in the ME's, i.e. one grid box can be classified to more than one ME. This is not unrealistic as cultivars optimized for a certain ME will thrive best (i.e. have the highest yields) in the interior of their domain, whereas at its fringes, also other cultivars may become suitable.

Table 6: MegaEnvironment numbers available for each crop

Each crop/ME combination is specified by clearly defined set of climate requirements or climate suitability criteria. These are given in the following table.

Table 7: MegaEnvironment criteria for each crop

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.

4.5. 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.

Table 8: Parameters used for thermal requirement calculation for each crop

Thus for each crop the following thermal requirements have been defined:

Table 9: Thermal requirement variables available for each crop

4.6. 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.

5. 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;

Fischer R.A., Byerlee D. and Edmeades G.O.* (2014). Crop yields and global food security: will yield increase continue to feed the world? ACIAR Monograph No. 158. Australian Centre for International Agricultural Research: Canberra. xxii + 634 pp. Retrieved from http://aciar.gov.au/files/mn- 158/pdf/aciar-MN158_web-5.pdf

Hempel, S., Frieler, K., Warszawski, L., Schewe, J. and Piontek, F., 2013. A trend-preserving bias correction–the ISI-MIP approach. Earth System Dynamics, 4(2), pp.219-236.

Klein Tank, A., 2007. EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4. Report EPJ029135.

Weedon, G.P., Balsamo, G., Bellouin, N., Gomes, S., Best, M.J. and Viterbo, P., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data. Water Resources Research, 50(9), pp.7505-7514.

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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