Datasets for the development of hemp (Cannabis sativa L.) as a crop for the future in tropical environments (Malaysia)

Abstract

An evidence base was developed to facilitate adoption of hemp (Cannabis sativa L.) in tropical environments [1]. Agro-ecological requirements data of hemp were acquired from international databases and was contrasted against local climate and soil conditions using an augmented species ecological niche modelling. The outputs were then used to map the suitability for all locations for 12 possible calendar-year seasons within peninsular Malaysia. The most probable seasonal map was then used to generate a land suitability map for agricultural areas across 5 standard land suitability categories. Having developed the general suitability maps of hemp in Malaysia, detailed crop growth data were collected from literature and was then used to simulate an ideotype crop model (for both seed and fiber) for selected locations across Malaysia, where detailed daily climate data and soil information were available. Following the development of a downscaled future climate dataset, a simulated dataset of yield for the future conditions were also developed. Next, the simulated seed and fiber yield data were used to create yield maps for hemp across peninsular Malaysia. An economic value and cost-benefit analyses were also carried out using data that were collected from literature and local sources to simulate the true cost and benefit of growing hemp both for now and future conditions. This data provides the first ever evidence base for an underutilised crop in Southeast Asia. All data that was generated using the proposed published framework for the adoption of hemp in the future are stored in their original format in an online repository and is described in this article. The data can be used to map the suitability at finer scales, analyse and re-calibrate a yield model using any climate scenario and evaluate the economics of production using the standard methodology described in the above-mentioned publication.

Full text

Data in Brief 40 (2022) 107807
Contents lists available at ScienceDirect
Data in Brief
journal homepage: www.elsevier.com/locate/dib
Data Article
Datasets for the development of hemp
( Cannabis sativa L.) as a crop for the future in
tropical environments (Malaysia)
Eranga M. Wimalasiri
a , b
, Ebrahim Jahanshiri
a , c , ∗,
Tengku Adhwa Syaherah
a , d
, Niluka Kuruppuarachchi
e
,
Vimbayi G.P. Chimonyo
f
, Sayed N. Azam-Ali
a
, Peter J. Gregory
a , g
a
Crops Fo r the Future UK, NIAB, 93 Lawrence Weave r Road, Cambridge CB3 0LE, UK.
b
Department of Export Agriculture, Faculty of Agricultural Sciences, Sabaragamuwa University of Sri Lanka,
Belihuloya, Sri Lanka
c
Department of Computer Science, University of Gothenburge, Gothenburg 40530, Sweden
d
Faculty of Engineering, University Putra Malaysia, Serdang, Selangor Darul Ehsan 43400, Malaysia
e
Postgraduate Institute of Agriculture, University of Peradeniya, Sri Lanka
f
CIMMYT-Zimbabwe, 12 .5 KM Peg , Mazowe Road, Mount Pleasant, Harare, Zimbabwe
g
School of Agriculture, Pol icy & Development, University of Reading, Earley Gate, Reading, UK
a r t i c l e i n f o
Article history:
Received 17 September 2021
Revised 2 January 2022
Accepted 5 January 2022
Available online 11 January 2022
Keywo rds:
Agri-environmental data
AquaCrop
Hemp economics
Industrial hemp
Land suitability assessment
NPVB
Yield potential
a b s t r a c t
An evidence base was developed to facilitate adoption
of hemp ( Cannabis sativa L.) in tropical environments
(Wimalasiri et al. (2021)). Agro-ecological requirements data
of hemp were acquired from international databases and was
contrasted against local climate and soil conditions using an
augmented species ecological niche modeling. The outputs
were then used to map the suitability for all locations for 12
possible calendar-year seasons within peninsular Malaysia.
The most probable seasonal map was then used to gener-
ate a land suitability map for agricultural areas across 5 stan-
dard land suitability categories. Having developed the general
suitability maps of hemp in Malaysia, detailed crop growth
data were collected from literature and was then used to
simulate an ideotype crop model (for both seed and fiber)
∗Corresponding author at: NIAB, Crops for the Future UK, 93 Lawrence Weave r Road, Cambridge, England, UK.
E-mail address: e.jahan@cropsforthefutureuk.org (E. Jahanshiri).
Social media: (E. Jahanshiri)
https://doi.org/10.1016/j.dib.2022.107807
2352-3409/© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND
license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

2 E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807
for selected locations across Malaysia, where detailed daily
climate data and soil information were available. Following
the development of a downscaled future climate dataset, a
simulated dataset of yield for the future conditions were
also developed. Next, the simulated seed and fiber yield data
were used to create yield maps for hemp across peninsular
Malaysia. An economic value and cost-benefit analyses were
also carried out using data that were collected from litera-
ture and local sources to simulate the true cost and benefit
of growing hemp both for now and future conditions. This
data provides the first ever evidence base for an underuti-
lized crop in Southeast Asia. All data that was generated us-
ing the proposed published framework for the adoption of
hemp in the future are stored in their original format in an
online repository and is described in this article. The data
can be used to map the suitability at finer scales, analyze
and re-calibrate a yield model using any climate scenario
and evaluate the economics of production using the standard
methodology described in the above-mentioned publication.
© 2022 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND
license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Specifications Tabl e
Subject Agricultural Sciences, Agronomy and Crop Science
Specific subject area Leveraging on open data to develop evidence basis for agricultural
diversification as a pathway to ensure food and nutrition security for now and
in the future in tropical countries.
Type of data Tabl e
Image
Chart
Graph
Figure
How the data were acquired The primary and secondary data sources are mentioned in the data description
section. Deposited data is a compilation of data files, developed by applying
specific data science algorithms to the primary and secondary or raw data files.
The output data that were mostly in geospatia l format were used to
develop
visualisations and aggregations. Raw geospatial data that were collected from
various primary sources underwent a harmonization process to make them
adaptable for the type of analysis that was performed in the main article.
Please see Section 1.3 for detail description of map files and their metadata.
Tota l climate and soil suitability and overall suitability data were generated
using the Land evaluation framework for agricultural diversification using R
statistical software [2–5] . Hemp grain and fiber yields were simulated using
the AquaCrop model (Version 6.1).
Interpolated data were mapped using ArcGIS software ve rsion 10.6 (ESRI,
Munich, Germany).
Computers:
1- Desktop computer
with Dual-Core Intel Core i7 CPU@3.5 GHz 16 GB RAM.
2- Desktop computer with Intel
®Core (TM)i7–4600 U
CPU@2.10 GHz with 16G B RAM
3- Laptop computer with Intel(R) Core (TM) i5 with 8 GB RAM
Data format Raw: GEOTIFF, SHP
Analysed: GEOTIFF, SHP, MXD
Filtered (resampled): GEOTIFF
( continued on next page )

E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807 3
Description of data collection Processed data were collected through analysis on various raw data files. Total
climate and soil suitability and overall suitability data were generated using
the Land evaluation framework for agricultural diversification using R
statistical software [2–5] . Processed data were acquired following four main
published methodologies to generate the final data points:
1- Agro-ecological crop suitability assessment [2]
2- land suitability analysis [6]
3- Crop model ideotyping [7]
4- Economic analysis [1]
Data source location Institution: Crops for the Future UK (CIC)
City/Town/Region: Cambridge
Country: England, UK
The primary data sources are mainly databases, research articles, web articles
etc. However, large portion of the data were collected from:
1- International databases; Global Knowledge Base for underutilized crops
[8 , 9] , SoilGrids [10] , WorldCli m [11] , Global Biodiversity Information Faci lity
(GBIF) [12] , Globecover [13] , Global Administra tive Boundary database
( https://gadm.org ).
2- Local providers; Observed daily rainfall, minimum and maximum
temperatures of six meteorological stations of the Meteorological Department
of Malaysia were purchased for the period 2010–2019.
3- Literature sources: all respected data points that were collected and cited in
the main article [1]
Data accessibility Repository name: Mendeley Data
Data identification number: 10.17632/g9dnfxbvgt.2
Direct URL
to data: https://data.mendeley.com/datasets/g9dnfxbvgt/2
Instructions for accessing these data:
The data can be downloaded free of charge into any local commuter.
Geospatial data can be used for visualization using any GIS software online
and offline.
Related research article Wimalasiri E.M., Jahanshiri, E., Chimonyo, V., Kuruppuarachchi, N., Suhairi,
T.A .S .T. M. , Azam-Ali, S.N. & Gregory PJ. (2021) A Framework for the
Development of Hemp ( Cannabis sativa L.) as a Crop for the Future in Trop ical
Environments. Industrial Crops and Products . 172 : 113999
https://doi.org/10.1016/j.indcrop.2021.113999
Value of the Data
• Hemp, Cannabis sativa L., is one of the most controversial crops of human history, which is
still illegal/ neglected in tropical countries. However, it is a billion-dollar business in some of
the temperate countries. There is a growing interest in Malaysia to cultivate hemp. Present
data provides all the datasets that were used to provide an evidence base for the adoption
of hemp as crop for the future in Malaysia.
• Raw Suitability data (map files) can be readily used for analysing suitability for a particular
area in Malaysia. Subsets can be overlaid in any geographic information system (GIS) for
further analysis or combined with other information such as socio-economics data to develop
further insights.
• Releasing the data ensures reproducibility, hence transparency of all analysis that was per-
formed by Wimalasiri et al. [1] . Scientists, planners, and government bodies can delineate na-
tional and regional development plans for the development of this valuable crop in Malaysia.
• Yield simulation data, together with the calibration data [7] can be used to estimate yield for
new locations and develop ‘what if’ scenarios regarding future climate conditions.
• Economics data together with the published data [1] to re-evaluate the cost and benefits
using more accurate data from local sources.

4 E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807
1. Data Description
Five types of primary and secondary data are described in the database as; climate data,
soil data, suitability assessment, crop-related data and economic data. The following different
sections describe relevant data types and their composition. It should be noted that the figures
shown in this paper ( Figs. 1–8 ) are for illustration purposes only. Individual data are available at
the open repository (see data accessibility section above).
1.1. Climate data
The study was carried out in six locations in Malaysia; Alor Setar (AS), Cameron Highlands
(CH), Kuala Terengganu (KT), Petaling Jaya (PJ), Senai (SN) and Temerloh (TM). The weather data
collection sites, which were used as different locations in yield simulations are shown in Fig. 1 .
All the data were generated for the locations shown in Fig. 1 .
The total rainfall and reference evapotranspiration (simulated) of 6 locations are shown in
Fig. 2 . It should be noted that the period between 1st August and 18t h December was considered
as the most suitable hemp cultivation period in Malaysia [1] .
Other than the 6 locations, simulations were carried out across Peninsular Malaysia for the
locations shown in Fig. 1 . Fig. 3 shows the interpolated maps of rainfall and reference evapotran-
spiration of Malaysia. The raw files of the maps are all available in an open repository mentioned
in the data accessibility section.
Since the yield simulation and economic assessments were performed in the future climates
(2040–2065), the future rainfall and reference evapotranspiration data of the study locations are
also available in the data repository. The files are available in Excel format.
1.2 . Soil data
Infiltration (infiltrated water in soil profile), runoff (water lost by surface runoff) and drainage
(water drained out of the soil profile) are three important soil data types that are important in
agricultural water management. These parameters can be generated in AquaCrop simulations.
The infiltration, runoff and drainage data of 6 study locations are available in Excel format. The
summary statistics of the data are shown in Table 1 .
1.3 . Agroecological suitability data
To perform suitability analysis, variety of geospatial data was required. The following are de-
scription of codes for suitability files/data that was used to create suitability analysis:
Raw data that was used to create suitability maps:
10,0 01,0 02,50 0 CropID for hemp [8]
SRTM SRTM data acquired from (Jarvis et al.) [14] .
MYS three-letter country abbreviation for Malaysia ISO-3166 Alpha-3
https://laendercode.net/en/3-letter-code/mys ).
WC Worl dClim data version 2 [11]
SG Soilgrids data [10]

E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807 5
Fig. 1. Map of Peninsular Malaysia with locations used in yield simulations (adapted from Wimalasiri et al. [7 , 1] ).

6 E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807
Fig. 2. Variation of growing seasonal (a) ra infall and (b) refe rence evapotranspirat ion of 6 locations studied. The loca-
tions marked as Alor Setar = AS, Cameron Highlands = CH, Kuala Terengganu = KT, Petaling Jaya = PJ, Senai = SN and
Tem erl oh = TM.
Description of codes for processed files following the method by (Jahanshiri et al.) [2] :
TSM: Seasonal Temperature Suitability 12 files
RSM: Seasonal Rainfall Suitability 12 files
TCSM: Product of Seasonal Climate and soil Suitability 12 files
ACSM: Average of Seasonal Climate and soil suitability 12 files
MTCS: Mean of total climate suitability for 12 months 1 file
MTS: Maximum Temperature Suitability 1 file
MATSS: Mean of soil suitability and Maximum Temperature suitability 1 file
pHS: pH suitability 1 file
DTBS: Depth suitability 1 file
TXTS: Text ur e suitability 1 file
MTSS: Weig hte d mean of soil layers (60% pH, 20% Depth, 20%
texture)
1 file
Elev: Elevation Suitability 1 file

E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807 7
Fig. 3. Interpolated (A) growing seasonal rainfall and (B) reference evapotranspiration map of Malaysia (data available
in the repository).
Tabl e 1
Summary statistics of infiltration, runoff and drain data of the study locations in Malaysia.
Location Parameter Average Standard deviation Range
Alor Setar Infiltration (mm) 905 13 4 723–1134
Runoff (mm) 244 101 96–415
Drain (mm) 19 8 115 61–420
Cameron Highlands Infiltration (mm) 996 15 0 813–1236
Runoff (mm) 223 64 147–344
Drain (mm) 37 0 145 225–598
Kuala Terengannu Infiltration (mm) 986 158 806–1184
Runoff (mm) 495 215 223–936
Drain (mm) 302 13 2 104–475
Petaling Jaya Infiltration (mm) 10 41 142 827–1341
Runoff (mm) 331 109 197–560
Drain (mm) 280 12 5 58–524
Senai Infiltration (mm) 899 12 2 710– 1073
Runoff (mm) 212 80 129–345
Drain (mm) 18 0 93 32–315
Tem erl oh Infiltration (mm) 736 174 526–1074
Runoff (mm) 125 72 63–238
Drain (mm) 23 68 0–215
Standard metadata that was used to harmonize the primary data and generate output data:
Dimensions: 655, 589, 385,795, 6 (nrow, ncol, ncell, nlayers)
Resolution: 0.008333333, 0.008333333 (x, y) or approximately 1 km
Extent: 99.64167, 104.55, 1.26 6 6 67, 6.725 (xmin, xmax, ymin, ymax)
Coordinate reference system: + proj = longlat + datum = WGS84 + no_defs + ellps = WGS84
+ towgs84 = 0,0,0

8 E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807
Fig. 4. Overall suitability map for hemp in Peninsular Malaysia (adapted from Wimalasiri et al. [7] ) [1] (data available
in the repository).

E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807 9
Fig. 5. Variation of simulated hemp seed and fiber yield of 6 locations in Malaysia.
1.4 . Land suitability data
The overall suitability map of hemp for Malaysia is shown in Fig. 4 after contrasting with
land-use classes using data from GlobeCover [13] , a land suitability map was developed to aid
with delineating suitable areas for planting hemp for both seed and fiber. This suitability map
has been provided as GEOTIFF raster format to allow further analysis to be done at all levels.
1.5 . Crop data
The simulated hemp seed and fiber yields of 6 locations under current climate (2010–2019
period) is available as Excel file. The hemp yield variation of 6 locations and summary statistics
are shown in Fig. 5 and Table 2 , respectively. Tools and procedures to develop the simulated
seed and fiber yield were described in Section 2.3 .
Table 2 shows the summary of hemp yield. The simulated range can be used to develop a
confidence analysis for the performance of hemp in Malaysia or any other type of analysis that

10 E.M. Wimalasiri, E. Jahanshiri and T.A. Syaherah et al. / Data in Brief 40 (2022) 107807
Fig. 6. Interpolated hemp (A) seed and (B) yield map of Malaysia (adapted from Wimalasiri et al. [7] ) [1] (data available
in the repository).
Fig. 7. Change of future (2040–2065) hemp (a) seed and (b) fiber yields compared to 2010–2019 period in Malaysia. The
locations marked as Alor Setar = AS, Cameron Highlands = CH, Kuala Terengganu = KT, Petaling Jaya = PJ, Senai = SN
and Tem erl oh = TM.
Tabl e 2
Summary statistics of hemp seed and fiber yield during 2010–2019 period.
Seed Fiber
Location Mean and SD Range Mean and SD Range
Alor setar 1.8 1 ±0.11 1.53–1.91 3.10 ±0.17 2.68–3.25
Cameron highlands 1.8 4 ±0.05 1.74–1.90 3.13 ±0.10 2.95–3.24
Kuala terengannu 1. 40 ±0.43 0.39–1.82 2.49 ±0.59 1.10–3.09
Petaling jaya 1.7 6 ±0.19 1.24–1.88 3.00 ±0.28 2.24–3.19
Senai 1.6 5 ±0.58 0.00–1.90 2.81 ±0.99 0.0 01–3.23
Tem erl oh 1.21 ±0.74 0.001–1.90 2.14 ±1.16 0.005–3.24

E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807 11
Fig. 8. The NPVB valu es for hemp seed (a and b) and fiber (c and d) during 2010–2019 (a and c) and 2040–2065 (b and
d) period. The locations marked as Alor Setar = AS, Cameron Highlands = CH, Kuala Terengganu = KT, Petaling Jaya = PJ,
Senai = SN and Tem er lo h = TM.
require quantitative values of hemp yield in Malaysia and other possible areas with similar agro-
ecological characteristics.
Potential yield maps for seed and fiber for the 1990–2019 period for Malaysia were created
( Fig. 6 ). As crop physiological data, temperature stress affecting crop transpiration (TempStr), leaf
expansion stress (ExpStr), stomatal stress (StoStr) and evapotranspiration water productivity for
yield part (kg yield produced per m
3 water evapotranspired (WPet) are provided in the data
repository as Excel files. This data can be readily used for any other type of analysis involving
hydrological processes across peninsular Malaysia.
Simulated hemp seed and fiber yield under future climate (2040–2065) are available as Excel
files. As a use case for the data, Fig. 7 shows the percentage yield change under future climate,
compared to the 2010–2019 period.
1.6 . Economic data
The cost benefit analysis data of hemp seed and fiber under both current (2010–2019) and
future (2040–2069) climates are included in the dataset as Excel files. The Summary of the eco-
nomic analysis data for hemp seed and fiber production is shown in Fig. 8 .
2. Experimental Design, Materials and Methods
Detailed methodology of the generation of the database was previously described by
Wimalasiri et al. [7] . Therefore, only a summary is presented here. The process flow chart of
the generation of data is shown in Fig. 9 .

12 E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807
Fig. 9. Flow chart showing input and output data, their usage and possible users for such data.
2.1. Data collection
2.1.1. Climate data
Observed daily climate data for 2010–2019 period were collected for 6 meteorological lo-
cations ( Fig. 1 ) from the Meteorological Department of Malaysia. This included daily rainfall
and minimum and maximum temperatures. Reanalysis daily climate data (rainfall, tempera-
ture and solar radiation) for 1990–2019 period were collected from NASA POWER database, de-
scribed by Zhang et al. [15] . The data are available at 0.5-degree resolution which created 46
different climate files. The WorldClim dataset [11] was used in the climate suitability assess-
ment. Bias-corrected daily climate data for 2040–2065 period were obtained from the (CCAFS)
database ( http://ccafs-climate.org/ ) for future simulations. The data were downscaled for 5
GCMs; BNU_ESM of College of Global Change and Earth System Science, Beijing Normal Univer-
sity, China, CNRM_CM5 of center National de Recherches Meteorologiques/center Europeen de
Recherche et Formation Avancees en Calcul Scientifique, Italy, MIROC_ESM from Japan Agency
for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute, and Na-
tional Institute for Environmental Studies, Japan, MOHC_HadGEM2_CC from Met-Office Hadley
center, United Kingdom and NCC_NorESM1_M of Norwegian Climate center, Norway.
2.1.2. Soil data
Soil data were collected from the Soilgrids 2.0 database ( www.soil grids.org ). The database
was previously used in crop modeling studies [16] .
2.2. Crop suitability assessment
The climate and soil suitability of hemp in Malaysia was performed using the land evaluation
framework for agricultural diversification which previously developed by Jahanshiri et al. [2] .
Climate data (temperature and rainfall) and soil data (pH and texture) were masked and then
harmonised for peninsular Malaysia. The following steps were used to create the final suitability
analysis:
1- Estimate a typical season length in months for hemp based on data from [8]

E.M. Wimalasiri, E. Jahanshiri and T.A . Syaherah et al. / Data in Brief 40 (2022) 107807 13
2- For each pixel on the map estimate 12 seasonal temperature suitability (12 starting months)
by calculating suitability for each month within the season (step 1) against the temperature
data [11] . Choose the minimum temperature suitability among all months as the representa-
tive temperature suitability for that season.
3- For each pixel in the map, estimate 12 seasonal rainfall suitability by accumulating monthly
rainfall [11] for each season (step 1) and contrast with the total seasonal water requirement.
4- Identify the climate suitability as the highest suitable season for hemp.
5- Estimate soil pH and texture soil suitability by contrasting the soil data [10] at each pixel
with the pH and texture requirement for hemp [1 , 2] .
6- The total hemp suitability is the average of climate suitability and soil suitability for each
pixel. This will create a map of suitability for hemp as shown in Fig. 4 .
The final suitability layers (see Section 1.2 for the description of GeoTIFF files format) were
average climate suitability, weighted average of soil suitability, average of climate and soil suit-
ability and average of climate product of climate and soil suitability. Raw files for crop suitability
were developed using R statistical software [3–5] .
2.3. Yield simulation
The AquaCrop model [17] was used for hemp yield simulations. The calibration and validation
of the model was described in a separated method paper [7] . The input parameters for the hemp
grain and fiber crop in AquaCrop model was described in Wimalasiri et al. [1] , therefore, the
parameters were not included into the dataset described in this paper. Fiber yield was calculated
manually [1] . Reference evapotranspiration data (Excel files) and their maps (GeoTIFF) and yield
maps (GeoTIFF) were generated using the data derived from the yield simulation.
2.4. Mapping
The maps were generated using ArcGIS software version 10.6 (ESRI, Munich, Germany) using
the 46 locations. The ordinary kriging was used as the interpolation method ( Figs. 3 and 6 ).
2.5. Economic analysis
In the detailed economic analysis, Future Values (FV), Present Values (PV) and Net Present
Value Benefit (NPVB) in relation to the Cost-Benefit (CB) approach were calculated and the data
are available as Excel files ( Section 1.6 ). The FV is corresponded to the total amount of money
which will ensue over the period of investment that is calculated separately for all the years
concerned. The PV is the current value of money resulted from investment of future over a
period of time. The equations are as follows [18] .
F V = 
(
Quant it y of the item x Market v alue of the item
)
P V =
F V
(
1 + r
)
n
where r is the discount rate or lending interest rate (4.9% in 2019 is used in the analysis for
period of 2019–2065) and n is the year. For the period of 2010–2019, past values which is similar
to the FVs in CB approach were converted to PVs by Malaysia Consumer Prices Index inflation
calculator since the base year is 2019. NPVB was used to describe the benefits for each year
which is similar to the net cash flow. The NPVB was calculated as follows.
NPVB = Present Value Benefit of the t th year −Present Value Cost of the t th year
where t is any year in the period of consideration.

14 E.M. Wimalasiri, E. Jahanshiri and T.A. Syaherah et al. / Data in Brief 40 (2022) 107807
3. Data and Stakeholders
As one of the important sectors in Malaysia, agriculture needs viable future-proof options to
ensure its sustainability. Crop diversification can be a major source of innovation in Malaysia
and elsewhere [19 , 20] . In particular, Malaysia should invest in new industrial crops apart from
oil palm and rubber that can ensure income sustainability in the future, particularly for marginal
areas and indigenous people [20] . This need has been reflected in the national agro-food policy
in Malaysia which is also one of the pillars of United Nations Sustainability Goals. In this regard,
presently published data can play an important part in the development of hemp as a poten-
tial industrial crop in Malaysia. Fig. 9 lists primary, secondary as well as published data, their
application and possible stakeholders.
Ethics Statement
There is no conflict of interest. The data is available in public domain.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal rela-
tionships that could have appeared to influence the work reported in this paper.
CRediT Author Statement
Eranga M. Wimalasiri: Conceptualization, Methodology, Software, Formal analysis, Writing
– original draft, Visualization; Ebrahim Jahanshiri: Conceptualization, Methodology, Software,
Formal analysis, Writing – original draft; Tengku Adhwa Syaherah: Methodology, Software, Vi-
sualization; Niluka Kuruppuarachchi: Methodology, Formal analysis; Vimbayi G.P. Chimonyo:
Methodology, Software, Validation; Sayed N. Azam-Ali: Writing –review & editing; Peter J. Gre-
gory: Writing –review & editing.
Acknowledgments
The authors gratefully acknowledge Dr Francesco Danuso from University of Udine, Italy and
Dr Kailei Tang from Wageningen University and Research, Netherlands, who shared their ob-
served weather data with us to parameterise the crop model. We also acknowledge Anil Shekar
Tharmandram, S.S. Mohd Sinin, N. M. Mohd Nizar and Nurul Jannah Abdullah of Crops For the
Future Research center, Malaysia for their support.
Funding
This research was funded by European Union’s Horizon 2020 research and innovation pro-
gram, grant agreement No. 774234.
References
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framework for the development of hemp ( Cannabis sativa L.) as a crop for the future in tropical environments, Ind.
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