Page 1
Earth Syst. Sci. Data, 11, 823–843, 2019
https://doi.org/10.5194/essd-11-823-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Generating a rule-based global gridded tillage dataset
Vera Porwollik, Susanne Rolinski, Jens Heinke, and Christoph Müller
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam 14412, Germany
Correspondence:
Vera Porwollik (vera.porwollik@pik-potsdam.de)
Received: 4 December 2018 – Discussion started: 21 December 2018
Revised: 15 April 2019 – Accepted: 12 May 2019 – Published: 12 June 2019
Abstract.
Tillage is a central element in agricultural soil management and has direct and indirect effects on
processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem
models, but global assessments are hampered by lack of information on the type of tillage and their spatial
distribution. This study describes the generation of a classification of tillage practices and presents the spatially
explicit mapping of these crop-specific tillage systems for around the year 2005.
Tillage practices differ by the kind of equipment used, soil surface and depth affected, timing, and their pur-
pose within the cropping systems. We classified the broad variety of globally relevant tillage practices into
six categories: no-tillage in the context of Conservation Agriculture, traditional annual, traditional rotational,
rotational, reduced, and conventional annual tillage. The identified tillage systems were allocated to gridded
crop-specific cropland areas with a resolution of 5 arcmin. Allocation rules were based on literature findings and
combine area information on crop type, water management regime, field size, water erosion, income, and aridity.
We scaled reported national Conservation Agriculture areas down to grid cells via a probability-based approach
for 54 countries. We provide area estimates of the six tillage systems aggregated to global and country scale. We
found that 8.67 Mkm
2
of global cropland area was tilled intensively at least once a year, whereas the remaining
2.65 Mkm
2
was tilled less intensely. Further, we identified 4.67 Mkm
2
of cropland as an area where Conservation
Agriculture could be expanded to under current conditions.
The tillage classification enables the parameterization of different soil management practices in various kinds
of model simulations. The crop-specific tillage dataset indicates the spatial distribution of soil management
practices, which is a prerequisite to assess erosion, carbon sequestration potential, as well as water, and nutrient
dynamics of cropland soils. The dynamic definition of the allocation rules and accounting for national statistics,
such as the share of Conservation Agriculture per country, also allow for derivation of datasets for historical and
future global soil management scenarios. The resulting tillage system dataset and source code are accessible via
an open-data repository (DOIs: https://doi.org/10.5880/PIK.2019.009 and https://doi.org/10.5880/PIK.2019.010,
Porwollik et al., 2019a, b).
1
Introduction to tillage
Global cropland covers an area of about 15 Mkm
2
(Ra-
mankutty et al., 2008), which is approximately 13 % of
global ice-free land. Cropland and associated land manage-
ment contribute about 4.5 % of global anthropogenic GHG
emissions accounting for emissions from rice cultivation,
peatland drainage, and nitrogen fertilizer application in the
year 2000 (Carlson et al., 2016). Tillage and plowing (fur-
ther jointly referred to as tillage) are practiced on most of this
cropland (Erb et al., 2016; Pugh et al., 2015). Tillage com-
prises farm operations usually practiced for seedbed prepara-
tion, weed, and pest control, or incorporation of soil amend-
ments. According to Schmitz et al. (2015), conventional
tillage can be distinguished on the one hand into traditional
systems with manual labor and tools and on the other hand
into mechanized systems. Conventional tillage usually com-
prises inversion and mixing of the soil layers with the bio-
physical loosening of the soil, leading to altered tempera-
ture and soil moisture levels in the affected soil layer (S1
Published by Copernicus Publications.
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
for further terms and definitions used in this study). Current
global soil management practices trend towards a reduction
of tillage operations and intensity (Derpsch, 2008; Smith et
al., 2008). Reduced intensity of the tillage operation as ei-
ther in the case of strip-, mulch-, ridge-, and no-tillage is
also referred to as conservation tillage (CTIC, 2018). Re-
duced tillage practices are especially suitable for agricul-
tural production (a) of grain crops such as cereals, legumes,
and oilseed crops (Giller et al., 2015); (b) on large, mecha-
nized farms to save labor (Mitchell et al., 2012; Ngwira et
al., 2012), fuel (Young and Schillinger, 2012), and machine
wearing (Saharawat et al., 2010); (c) under arid climate con-
ditions, because of its soil moisture preserving effect (Kas-
sam et al., 2009; Pittelkow et al., 2015); and (d) on soils
with high erosion rates (Govaerts et al., 2009; Schmitz et al.,
2015).
Up to now there has been only little effort in the classifi-
cation and area assessment of tillage systems at the global
scale. Erb et al. (2016) reviewed data availability of land
management practices at the global scale and found that there
was no continental or global dataset on area, distribution,
and intensity of tillage practices. They report 7.43 Mkm
2
to
be under high-intensity tillage comprising the cropland area
of annual crops and 4.73 Mkm
2
of area under low-intensity
tillage, which comprises the cropland area of perennial crops,
zero-tillage as stated by Derpsch et al. (2010), and young
and temporal fallow cropland area as reported by Siebert et
al. (2010).
The only global statistical data on a kind of tillage system
area are provided by the FAO for the extent of Conserva-
tion Agriculture (CA) area (FAO, 2016) at the national scale.
CA is a soil management concept comprising minimum soil
disturbance (through direct seeding techniques), a permanent
organic soil cover as mulch or green manure, and a diversi-
fied crop rotation (Kassam et al., 2009). It is applied to about
10 % of the global cropland area (FAO, 2016). The widest
area spread of CA practice is reported for South America,
followed by North America (accounting for over 84.6 % of
the total global CA area), where it has been originally de-
veloped. Adoption of CA is much lower in Europe, Asia,
Australia, and New Zealand, and with the lowest adoption
rate in Africa (1.1 %, 2.3 %, 11.5 %, and 0.3 % of reported
total global CA area, respectively) (Derpsch et al., 2010).
The top three adopting countries of CA in terms of area are
Argentina, Paraguay, and Uruguay (73.51 %, 66.67 %, and
46.13 % of their arable land, respectively) (FAO, 2016).
Prestele et al. (2018) mapped reported national values of
CA area from Kassam et al. (2015) to cropland of the History
Database of the Global Environment database (HYDE; Klein
Goldewijk et al., 2017) for the year 2012. Based on literature
findings, Prestele et al. (2018) developed a CA adoption in-
dex per grid cell composed of a set of spatial predictors such
as aridity, field size, soil erosion, market access, and poverty
for downscaling reported national CA area values. Their re-
sulting global map of CA area at a spatial grid resolution
of 5 arcmin can be applied for impact assessments in global
model simulations.
Data
on
tillage
practices
are
available,
e.g.,
for
the
USA
through
the
reporting
of
the
National
Crop
Residue
Management
Survey
published
by
the
Conservation
Technology
Information
Center
(http://www.ctic.purdue.edu/CRM/crm_search/,
last
ac-
cess: 21 August 2018). The survey was pursued at national
level until 2004 and continued for a subset of counties
for subsequent years reporting on farming area managed
under conventional, reduced, and conservation tillage (with
further sub-categories of no-, ridge-, and mulch-tillage).
For Europe, tillage practices have most recently been
assessed by the Survey on agricultural production methods
(SAPM) in 2010 based on census and sample survey
data and published by EUROSTAT (https://ec.europa.
eu/eurostat/statistics-explained/index.php?title=Glossary:
Survey_on_agricultural_production_methods_(SAPM), last
access: 23 August 2018). In the EUROSTAT data portal farm
type and size, and their corresponding area managed under
the tillage categories, conventional, conservation tillage,
and zero-tillage (often used as a synonym for no-tillage
as referring to direct seeding techniques) are reported.
Analyzing tillage practices in the EU-27 for the year 2010, it
has been found that on average the share of conservation and
zero-tillage practices increases with the size of the arable
land area of a farm holding (EUROSTAT, 2018).
Soil, crop, vegetation, erosion, and Earth system mod-
els (ESMs) (in the following jointly referred to as ecosys-
tem models) can be applied to assess the effect of different
tillage practices on ecosystem elements, fluxes, and stocks.
Some global carbon studies assess the climate mitigation po-
tential of soils managed with no-tillage compared to con-
ventional tillage, which was simulated as a temporally lim-
ited enhancement of the decomposition factor on the soil
carbon pools under cultivated cropland (Levis et al., 2014;
Olin et al., 2015; Pugh et al., 2015; Smith et al., 2008).
More process-based representations of the tillage effect are
applied in models such as the decision support system for
agrotechnology transfer–cropping system model (DSSAT–
CSM, White et al., 2010) and the crop growth simulator
(CROPGRO-soybean, Andales et al., 2000) with direct and
indirect biophysical effects on soil, water, crop yield, and
emissions. Another field of global-scale studies assessing the
tillage effect refers to the analysis of albedo enhancement
perceived in cases of no-tillage in conjunction with associ-
ated increased residue levels left on the soil surface (Hirsch
et al., 2017; Lobell et al., 2006). Furthermore, tillage is im-
portant in soil erosion assessment studies, often represented
within the context of the land management factor amplifying
sub-factors such as surface cover and roughness (Nyakatawa
et al., 2007; Panagos et al., 2015).
McDermid et al. (2017) reviewed regional and ESMs’ ap-
proaches of representing agricultural management practices
and land use conversion with a focus on climate and land sur-
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
825
face interactions, including tillage modifying carbon stocks
in the soil as well as biogeophysical surface attributes. They
reveal sources of uncertainty due to missing land manage-
ment data and limited representation of processes in cur-
rent assessment models. In regard to the tillage effect, they
elaborate on the findings of Levis et al. (2014), who found
decreased soil carbon levels below cropped and cultivated
land compared to land without cultivation. McDermid et
al. (2017) mention a potential overestimation of the efficacy
of no-tillage practices’ contributions to mitigating anthro-
pogenic carbon by enhanced carbon stock based on findings
of Powlson et al. (2014).
Pongratz et al. (2018) also reviewed data availability and
process implementations within ESMs for 10 land manage-
ment practices and conclude tillage to be currently under-
represented. They recommend simple and complex methods
to model tillage effects on albedo, soil moisture, respiration,
and the resulting impact on soil carbon stocks and fluxes. In
the absence of detailed tillage area and type information, the
global ecosystem modeling community currently can assess
differences of contrasting tillage practice impacts just in the
form of stylized scenarios, simulating the effect on the en-
tire cropland area (Del Grosso et al., 2009; Olin et al., 2015;
Pugh et al., 2015). One recent exception is the assessment by
Hirsch et al. (2018), who estimate the effects of an altered
albedo from residues used for soil cover on CA areas, using
the spatial data of Prestele et al. (2018).
The objective of this study is to (a) increase the under-
standing of differences in tillage practices at the global scale,
(b) formulate rules to spatially map tillage systems to the
grid scale, and (c) develop an open source and open data
crop-specific tillage system dataset for the parameterization
of tillage events and area in global ecosystem models and
assessments. In order to do so we develop a global tillage
system classification. Further we analyze underlying causes
of the occurrence of different tillage systems and make use
of available data in order to map them to a global grid of
5 arcmin resolution.
2
Data and method
2.1
Tillage system classification
Globally tillage systems differ by the kind of implement
used, soil depth, and share of soil surface affected, mixing
efficiency, timing, frequency, and by their purpose within the
relevant cropping systems (Table 1).
Conventional tillage, often done with a moldboard plow,
refers to the inversion and mixing of soil layers for seedbed
preparation, incorporation of soil amendments and weed,
pest, and residue management. In traditional tillage systems
soils are usually managed with hand tools, e.g., hoe or cut-
lass (Schmitz et al., 2015), which is very labor and time in-
tensive. The application of animal-drawn plows or the use
of a moldboard plow attached to some motorized vehicle re-
sults in increased soil depth and mixing efficiencies of the
tillage operation compared to traditional tillage implements.
In the case of CA, there is only a minimal mechanical soil
disturbance by direct seeding equipment or none in the case
of broadcasting seeds.
The soil depth affected by the tillage operation is deter-
mined by the soil depth to bedrock, the implement used to
till the soil, and by the purpose of the tillage event. A mold-
board plow usually inverts and mixes the soil layers up to 20–
30 cm depth. Pimental and Sparks (2000) state the minimum
soil depth for agricultural production to be 15 cm, whereas
Kouwenhoven et al. (2002) find that, for burying green ma-
nure and annual weed, a minimum tillage depth of 12 cm
to be necessary and suggest 20 cm for the management of
perennial weeds. We decided for a minimum depth of mech-
anized tillage of 20 cm. For traditional tillage with manual la-
bor, tillage is assumed to reach only to a lesser depth, because
of limited capacity to penetrate the soil profile (Schmitz et
al., 2015). The affected depth by minimum soil disturbance
practices under CA is assumed to be as deep as the seed
placement requires, which is stated as approximately 5 cm
by White et al. (2010) for no-tillage systems.
In conventional tillage systems, the tillage implement is
usually applied to the entire soil surface to be effective. In
contrast to that, no-tillage under CA may affect at most 20 %
to 25 % of the soil surface during the direct seeding proce-
dure (Kassam et al., 2009; White et al., 2010). On the field,
reduced tillage as partial disturbance of the soil surface in
the case of strip-, mulch-, or ridge-tillage can be achieved by
applying either an inverting implement to a lesser soil depth
or a lower share of soil surface affected, by using harrows
or disks, or by fewer field passes. Reduced tillage practice
can be simulated in the form of lower soil disturbance, fre-
quency, depth, mixing efficiency, or higher residue share left
on the soil surface ranging between values of conventional
and no-tillage (15 % to 30 %).
Tillage mechanically loosens the soil by decreasing the
bulk density of the soil. Soil bulk density and pore space
determine the levels of surface contact between seeds and
soil particles, root growth, and water infiltration. The mix-
ing efficiency of tillage describes the degree of homogeneity
achieved, e.g., when burying crop residues and redistributing
soil particles in the affected soil horizon. The type of soil, its
moisture content, and the speed of the tillage practice are fur-
ther determining factors for the mixing efficiency of tillage
(White et al., 2010) under field conditions. Too intensively or
inappropriately tilled soils over a longer time period exhibit
the destruction of soil aggregates by increasing bulk density
leading to compaction or crusting (White et al., 2010). The
mixing efficiency can be modeled as a factor modifying the
homogeneity level of soil components and associated char-
acteristics.
Conventional tillage in both mechanized and traditional
farming systems leaves a low portion of residues covering
the soil surface after seeding – usually less than 15 % (CTIC,
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
T
ab
le
1.
Six
tillage
systems
and
suggested
parameteri
zation
for
model
applications
(note
that
(a)
se
v
eral
v
alues
per
tillage
system
refer
to
each
single
tillage
ev
ent
within
each
tillage
system
in
the
same
order
as
mentioned
under
the
frequenc
y
per
year
,
and
(b)
for
reduced
tillage
the
in
v
ersion
and
mixing
ef
ficienc
y
depends
on
the
specific
form
of
practi
ce
as
mentioned
abo
v
e).
T
illage
system
Con
v
entional
annual
tillage
Rotational
tillage
Conserv
ation
Agriculture
T
raditional
annual
tillage
T
raditional
rotational
tillage
Reduced
tillage
Soil
management
components
T
illage
for
seedbed
prepa-
ration,
culti
v
ation,
post-
harv
est
tillage
T
illage
for
seedbed
prepa-
ration,
culti
v
ation,
post-
harv
est
tillage
Minimum
mechanical
soil
disturbance
with
direct
seeding
Hoe
or
cutlass
for
seedbed
preparation,
culti
v
ation,
post-
harv
est
tillage
Hoe
or
cut
lass
for
seedbed
preparation,
culti
v
ation,
post-harv
est
tillage
T
illage
for
seedbed
prepa-
ration,
culti
v
ation,
post-
harv
est
tillage
Soil
layer
in
v
ersion
Y
es,
no,
yes
Y
es,
no,
yes
No
Y
es,
no,
yes
Y
es,
no,
yes
(Y
es),
no,
(yes)
Frequenc
y
and
timing
per
year
1
before
seeding,
1
to
2
cul-
ti
v
ation
(10
d
to
2
weeks
af-
ter
establishment),
1
after
harv
est
1
before
seeding,
1
to
2
cul-
ti
v
ation,
1
after
remo
v
al
1
at
seeding
1
before
seeding,
1
to
2
culti
v
ation
(10
d
to
2
w
eeks
after
establish-
ment),
1
after
harv
est
1
before
seeding,
1
to
2
cul-
ti
v
ation,
1
after
remo
v
al
1
before
seeding,
1
to
2
cul-
ti
v
ation
(10
d
to
2
weeks
af-
ter
establish-ment),
1
after
harv
est
Depth
(cm)
20,
5,
20
20,
5,
20
5
10,
5,
10
10,
5,
10
<
20
,
5
,
<
20
Mixing
ef
ficienc
y
(%)
90,
20,
90
90,
20,
90
5
50,
20,
50
50,
20,
50
90,
20,
90
Soil
surf
ace
af
fected
(%)
100,
33,
100
100,
33,
100
20
to
25
100,
33,
100
100,
33,
100
100,
33,
100
Soil
surf
ace
co
v
ered
by
residues
after
planting
(%)
<
15
<
15
>
30
<
15
<
15
15–30
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
827
2018; White et al., 2010). Reduced tillage may leave 15 %–
30 %, whereas in CA systems minimum soil surface covered
by organic mulch is defined as at least 30 % after the seed-
ing operation (CTIC, 2018). Timing and frequency of soil
disturbance by tillage depend on the type of cropping sys-
tem. For annual crops, tillage is performed annually at the
time of establishment, after harvest, or both. When model-
ing perennial crops, the interval of the main tillage events on
fields should reflect the length of the perceived entire plan-
tation cycle. During the growing period less intense tillage
may be necessary for weed management or intended inter-
cropping purposes several times. This soil management is
locally restricted to the space between the rows of the main
crop and can be replaced by herbicide applications. Within
CA-managed systems disturbance of the soil occurs only at
the time of seeding. Weed in CA systems is either managed
by sustaining a permanent soil cover of either mulch or cover
crops, by diversified rotations, or by application of herbicide
so that no further mechanical soil disturbance is necessary
during the growing season.
Based on the literature findings mentioned above, we con-
sider six different tillage systems, namely no-tillage in the
context of Conservation Agriculture, conventional annual,
rotational, traditional annual, traditional rotational, and re-
duced tillage (Table 1).
2.2
Datasets used for mapping tillage systems to the
grid
To map the six tillage systems, spatial indicators on the ba-
sis of several environmental and socio-economic datasets are
applied (Table 2). The basic data layer to this mapping study
is the cropland dataset by the spatial production allocation
model further referred to as SPAM2005 by the International
Food Policy Research Institute and International Institute
for Applied Systems Analysis (IFPRI/IIASA, 2017b). It re-
ports physical cropland area for 42 crop types (Table S2 in
the Supplement for a list of crop types) for the year 2005.
SPAM2005 is a result of a disaggregation of national and
sub-national data sources in a cross-entropy approach. The
SPAM2005 dataset comprises four technology levels of crop
production, distinguishing high input irrigated from purely
rainfed with further distinction of rainfed into high input,
low input, and subsistence production per crop type and grid
cell (You et al., 2014). In this study only the entire physi-
cal cropland and the separated irrigated and rainfed cropland
were used per grid cell. Adding up the reported cropland
area of SPAM2005 for 42 crop types results in a total sum
of 11.31 Mkm
2
.
The grid cell allocation key to countries accompanying
the SPAM2005 cropland dataset (IFPRI/IIASA, 2017a) was
applied in this study for any grid cell aggregation to coun-
try scale. Sub-national aggregations of grid cells to state or
province level were done with the Global Administrative Ar-
eas database (Global Administrative Areas, 2015).
The dataset on soil depth to bedrock (Hengl et al., 2014)
has been retrieved from SoilGrids, which is a soil infor-
mation system reporting spatial predictors of soil classes
and soil properties at several depths. It has been derived
on the basis of the United States Department of Agriculture
(USDA) soil taxonomy classes, World Reference Base soil
groups, regional and national compilations of soil profiles,
and several remote sensing and land cover products using
multiple linear regressions. The dataset reports on the abso-
lute depth to bedrock (centimeters) per grid cell.
The global gridded field size dataset by Fritz et al. (2015)
has been derived and validated on the basis of a crowd-
sourcing campaign. It reports four field size classes as “very
small” (smaller than 0.5 ha), “small” (0.5 to 2 ha), “medium”
(2 to 100 ha), and “large” (larger than 100 ha) (Herrero et al.,
2017) for the year 2005. The field size and the SPAM2006
datasets both use the cropland extent presented in Fritz et
al. (2015).
The Global Land Degradation Information System
(GLADIS) (Nachtergaele et al., 2011) reports land degrada-
tion types and their spatial extent around the year 2000. From
this database the global gridded water erosion data have been
selected. The water erosion data report the sediment erosion
load (t ha
−
1
yr
−
1
) per grid cell which the authors derived by
applying the Wischmeier equation (Wischmeier and Smith,
1978). Values of the data range from 0 to 12 110 t ha
−
1
yr
−
1
,
with the highest water erosion levels occurring in mountain-
ous areas.
The aridity index dataset was retrieved from the Food and
Agriculture Organization Statistics (FAO, 2015). The arid-
ity index was calculated as the average yearly precipitation
divided by the average yearly potential evapotranspiration
(PET), based on Climate Research Unit (CRU) CL 2.0 cli-
mate data averaged for the years from 1961 to 1990 apply-
ing the Penman–Monteith method. It reports values per grid
cell ranging from 0 to 10.48, where values smaller than 0.05
are regarded as “hyper arid”, 0.05–0.2 as “arid”, 0.2–0.5 as
“semi-arid”, 0.5–0.65 as “dry humid”, and values larger than
0.65 as “humid”.
The AQUASTAT online database reports annually the
spread of Conservation Agriculture (CA) practices at the na-
tional scale (FAO, 2016). From this data source, national CA
area values were retrieved for all 54 countries that reported
any CA with the total area sum of 1.1 Mkm
2
. Not all of these
countries reported values for the year 2005, so that values
closest to 2005 were selected from the available set, giving
preference to data availability over matching the year 2005.
The average farm size per country dataset (
n
=
133) (Low-
der et al., 2014) is based on FAO farm size time series data.
National average farm size was largest in land-rich countries,
with the top three countries being Australia (3243.2 ha), Ar-
gentina (582.4 ha), and Uruguay (287.4 ha) (Lowder et al.,
2014). The authors found average farm size to increase with
the elevated income level of a country.
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
Table 2.
Gridded and national-scale datasets used for mapping tillage.
Global gridded
Resolution
Temporal coverage
Source
dataset
(arcmin)
(year)
Crop-specific cropland
5
2005
SPAM2005: IFPRI/IIASA (2017b)
Soil depth to bedrock
6
1990–2014
SoilGrids: Hengl et al. (2014)
Field size
0.5
2005
Fritz et al. (2015)
Water erosion
5
1990–2011 (
∼
2000)
GLADIS: Nachtergaele et al. (2011)
Aridity
10
1961–1990
FAO (2015)
National data
Conservation Agriculture (CA) area
country
2002–2013
FAO (2016)
Income level
country
2005
World Bank (2017)
Furthermore, we retrieved the income level per country
by the World Bank (2017) for the year 2005. The data re-
fer to four categories of countries’ gross national income
(GNI capita
−
1
yr
−
1
), as “Low income” (less than USD 875),
“Lower middle income” (USD 876–3465), “Upper middle
income” (USD 3466–10 725), and “High income” (more than
USD 10 725).
2.3
Processing of input data and mapping rules
For calculation purposes, all gridded input datasets men-
tioned above were harmonized in terms of spatial extent, res-
olution, and origin. The spatial extent of the target dataset
comprises all cropland cells reported by SPAM2005 (IF-
PRI/IIASA, 2017b). The targeted resolution is 5 arcmin,
which partially required resampling and (dis)aggregation of
the applied datasets using R (R Development Core Team,
2013) version 3.3.2 loading packages “raster” (Hijmans and
van Etten, 2012), “fields” (Nychka et al., 2016), and “ncdf4”
(Pierce, 2015). More details on the input data harmonization
and processing can be found in the accompanying R code
(Porwollik et al., 2019a).
We developed several mapping rules to allocate the six
tillage systems to the grid-cell scale, employing a decision
tree as shown in Fig. 1. The decision tree approach has also
been applied in other spatial mapping exercises, e.g., in Ver-
burg et al. (2002) and Waha et al. (2012). Hierarchical clas-
sification procedures based on expert rules can be used to
distribute data of a larger spatial (e.g., administrative) unit to
the grid cell level (Dixon et al., 2001; Siebert et al., 2015;
van Asselen and Verburg, 2012; van de Steeg, 2010).
As a first step, the SPAM2005 cropland dataset is masked
for grid cells reporting cropland but soil depth to bedrock
of less than the required 15 cm for agricultural production
according to Pimental and Sparks (2000) (Fig. 1). This con-
textual mismatch between these two datasets may be caused
by different input data used by the producers or by their
method of averaging values within one grid cell, in which the
soil depth to bedrock is heterogeneous. The entire cropland
of these shallower grid cells is allocated directly to the re-
duced tillage system area, where ridging or raised beds may
be practiced by the farmer because of physical hindrance for
inverting tillage practices at increased depth.
The remaining cropland is treated separately for annual
and perennial crops following Erb et al. (2016)’s findings,
differing between plant-type associated tillage by intensity
in terms of frequency and timing of the tillage operation (Ta-
ble S2 for crop-type classification).
As a further step, we distinguished tillage practices per wa-
ter management regime. We assumed that soils of irrigated
crops are more regularly exposed to some level of mechan-
ical soil surface alteration, i.e., leveling off of the surface in
order to distribute irrigation water most efficiently and ho-
mogeneously over the field. We allocated all irrigated annual
cropland either to traditional or conventional annual tillage
area depending on field size and income level (Fig. 1).
Annual and perennial tillage systems are both further dis-
tinguished by the level of mechanization and commercial ori-
entation of the crop production. We follow the definition of
smallholder farming used in Lowder et al. (2016) if cultiva-
tion area is smaller than 2 ha. According to Fritz et al. (2015),
field size can be regarded as a proxy for agricultural mech-
anization and human development. Further, Levin (2006)
found that field size and farm size are positively related.
Based on these findings, we apply the field size dataset as a
proxy for farm size and mechanization. We categorize crop-
land per grid cell reporting field size equal to or larger than
2 ha as “large” scale assuming access of the farmer to mech-
anized farming equipment, and field size smaller than 2 ha
as “small” scale farming with rather manual labor. Field size
data are not available for all grid cells where SPAM2005 re-
ported cropland. Consequently we interpolated for missing
field size grid cell values using the mean of surrounding grid
cell values. The spatial distance to the Hawaiian islands was
too far for this operation, so there field size was set to a value
of 2 ha, assuming a land restriction to field size due to the
islands’ geographic pattern and in the absence of any alter-
native information.
We further assume that animal draught power and mech-
anized soil management practices on a farm also occur as a
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Figure 1.
Decision tree for allocating cropland to six derived tillage systems. The data processing and mapping were pursued as depicted
from top to bottom of the diagram. Each box represents a check on a grid cell of whether reporting values from the different data layers
meet the derived thresholds or specific cropland features. The arrows with solid lines indicate a “yes” and arrows with dotted lines a “no”
in the allocation procedure of crop-specific area to tillage systems. The box indicating the “Downscaling” represents our probability and
suitability indicators applied to downscale national CA area values to a spatially heterogeneous pattern at per grid cell. Boxes with darker
grey background shading and thicker frames show the derived types of tillage systems. (Abbreviation of gross national income as GNI).
function of income, indicating the financial capital a farmer
might have access to. Therefore, we additionally apply the
national average income level dataset to differentiate be-
tween small field sizes in higher-income countries, where
access to financial capital for investment in farm equipment
is perceived to be easier than for farmers with small field
sizes in lower-income countries. In order to do so, we re-
classified countries reported in the income dataset consid-
ered “low” and “lower-middle income” as “low income”,
and those countries formerly considered “upper-middle in-
come” and “high income” as “high income”, in this study.
In grid cells reporting newly derived small field size and
low income, we then allocated perennial cropland to tradi-
tional rotational tillage and annual cropland to traditional an-
nual tillage. In high-income countries or in a grid cell report-
ing field sizes larger than 2 ha situated in low-income coun-
tries, perennial cropland was assigned to rotational tillage
and annual cropland to conventional annual tillage assuming
a rather commercially oriented farming system with access
to market, financial capital, and therefore mechanized soil
management equipment (Fig. 1).
We applied a downscale algorithm of national reported CA
area values to a subset of rainfed annuals’ cropland area (see
Fig. 1, box “Downscaling”; see the following Sect. 2.4 for
more details). The remaining rainfed annuals’ cropland not
being assigned to CA area is checked again for soil depth to
bedrock. In case it was shallower than 20 cm, the cropland
was also assigned to reduced tillage, assuming less depth,
frequency, mixing efficiency, or alternative cultivation prac-
tices. In the case of a soil depth to bedrock of 20 cm or more,
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the remaining cropland depending on crop type was either
mapped to the conventional annual or to the rotational tillage
system.
2.4
Downscaling reported national CA area to the grid
cell
2.4.1
Mapping rules for downscaling CA
Generally it can be assumed that the entire cropland is suit-
able for some kind of sustainable farming technique, but in
the following we refer to “potential CA area” as the area
where we regard the adoption of CA as more likely than
for the remaining cropland where CA adoption would re-
quire additional assistance or support for the farmer. Poten-
tial CA area is derived from the cropland of 22 rainfed annual
crops in grid cells reporting dominant large field size in low-
income countries and all field sizes in high-income countries.
Cropland areas of annually planted rainfed crop types were
considered suitable for CA practice following the finding of
Kassam et al. (2009), who state that much of the CA de-
velopment to date has been associated with rainfed arable
crops. We selected the following annual crop types reported
by SPAM2005 as suitable for CA in this study: barley, beans,
chick peas, cotton, cowpea, groundnut, lentil, maize, other
cereals, other pulses (e.g., broad beans, vetches), pearl millet,
pigeon pea, rapeseed, rest (e.g., spices, other sugar crops),
sesame seed, small millet, sorghum, soybean, sunflower, to-
bacco, vegetables (e.g., cabbages and other brassicas), and
wheat (see Table S2), following Giller et al. (2015)’s find-
ings on CA suitability of (dryland) grain crop types. All an-
nual rainfed root, tuber, and rice cropland is excluded from
the potential CA area following Pittelkow et al. (2015), who
reported larger yield penalties for these crop types when ap-
plying no-tillage practices. Rice is often produced as paddy
rice, requiring puddling, which is a practice modifying the
soil aggregates a lot in order to facilitate the flooded condi-
tion, e.g., to suppress weed growth. A conversion from pud-
dled to dryland rice production as well as improved drainage
of tuber crop production area may require additional manage-
ment steps by the farmer in order to achieve comparable yield
levels with no-tillage to under conventional production meth-
ods. The resulting potential CA area amounts to 4.65 Mkm
2
.
As stated by Powlson et al. (2014) for the Americas and
Australia, by Rosegrant et al. (2014) in general on no-tillage,
by Scopel et al. (2013) for Brazil on CA, and by Ward et
al. (2018) on CA, the largest adoption rates of minimum
soil disturbance management principles can be found on
medium to large farms. There is low adoption of CA or no-
tillage among small-scale farms, with the exception of Brazil
(Rosegrant et al., 2014), where adoption of CA was sup-
ported through policies and technological investments.
We developed a linear regression with the “stats” pack-
age of R (R Development Core Team, 2013), applying the
linear correlation model (“lm function”) to assess the statis-
tical relation between national average farm size (Lowder et
al., 2014) and percentage share of CA area (FAO, 2016) on
arable land. The functional relation exhibits an increase in
the national share of CA on arable land with an increase in
average farm size over the country sample (Fig. S3).
Based on the literature findings and regression results, we
assumed that no-tillage in the context of CA was highly prob-
able for cropland in grid cells with large fields (here serving
as a spatial proxy for large farm size and mechanization).
Furthermore, we considered no-tillage to be suitable for
arable production under arid conditions (Kassam et al., 2009;
Pittelkow et al., 2015), because of less aeration, more stable
pores, and soil aggregates compared to soils managed with
conventional tillage. In CA systems, the evapotranspiration
is additionally reduced by a continuous biomass cover of at
least 30 % of the soil surface, which promotes yield stability
in drought-prone production environments.
As a last allocation criterion, CA was regarded as suit-
able for crop production in areas with elevated erosion lev-
els. Basso et al. (2006) find that farmers may make use of
green or residue cover to protect the soil surface during high-
intensity rainfall events. Our mapping rule too is in line with
the finding of Kassam et al. (2009) stating that wind and wa-
ter erosion were major drivers of CA adoption in Canada,
Brazil, and the USA. According to Schmitz et al. (2015) and
Govaerts et al. (2009), Asian and African agricultural pro-
ducers could also benefit from the positive effects of CA in
erosion-prone areas.
2.4.2
Logit model for downscaling national CA
Cropland, field size, water erosion, and aridity data per grid
cell are used as predictors determining the spatial distribution
of national reported CA area within a country (Fig. S4.1–4).
We developed a logit model to transform and combine these
four spatial predictors into probability values per grid cell,
indicating the likelihood of CA area occurrence. The logit
model was chosen because different ranges of the spatial pre-
dictor datasets are made comparable at equal weights without
losing much detail.
From the potential CA area data layer we computed the
input variable “crop mix” as the ratio of the area sum of 22
CA-suitable crop types over the sum of total cropland area
per grid cell. We assume an increasing probability for CA
area occurrence in grid cells with an increasing cultivated
area share of CA-suitable crop types. This was based on the
assumption that cropland within a grid cell belongs to one
management regime under which rotations with CA-suitable
crops are practiced and a similar set of soil working equip-
ment is employed. The assumptions also takes into account
peer group influence and knowledge spillover effects from
early adopters of a new technology (here CA practice) on
their neighbors (Case, 1992; Maertens and Barrett, 2013).
Regarding the statistical relation between farm size and
CA adoption, we assume that the larger the field size, the
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Table 3.
Correlation coefficients (
r
) according to Pearson between
spatial predictor variables (crop mix, field size, erosion, and aridity)
across all grid cells containing potential CA cropland globally.
(
r
)
Field size
Erosion
Aridity
Crop mix
0.322
−
0
.
104
−
0
.
241
Field size
−
0
.
356
−
0
.
141
Erosion
−
0
.
002
higher the CA probability, especially for field sizes equal to
or larger 2 ha depending on the income level of a country,
taking 2 ha as the midpoint of the transformed field size logit
curve.
We set missing water erosion values in grid cells report-
ing potential CA area to the neutral value of 12 t ha
−
1
yr
−
1
,
since it depends on very small-scale conditions, e.g., slope.
When transforming the water erosion values to logit, we set
12 t ha
−
1
yr
−
1
as the midpoint value of the function. Here the
corresponding mapping approach was to assume increased
probability of CA practices in cells which report water ero-
sion values exceeding 12 t ha
−
1
yr
−
1
as the upper bound of
the soil loss tolerance value (
T
-values) defined by the USDA
(Montgomery, 2007).
The midpoint of aridity’s logit regression curve is chosen
at 0.65, resulting in higher probabilities of CA area occur-
rence for grid cells reporting arid (values smaller than 0.65)
rather than humid (values larger than 0.65) growing condi-
tions. We interpolated missing aridity values in grid cells
where SPAM2005 reports cropland, except for one island
near Madagascar, which we set to the logit-neutral value of
0.65 because we assumed very special climatic conditions
there.
We tested for (Pearson) correlation among the four spatial
predictor variables with the R “base” package (R Develop-
ment Core Team, 2013), in order to prevent autocorrelation
effects (Table 3).
Generally correlation coefficients (
r
) among the datasets
are low and mostly negative, except for field size and crop
mix.
Those four cropping system indicators are used as ex-
planatory variables in the regression to get the probability
of cropland in a grid cell to be CA area as a value between 0
and 1. The probability of CA in a grid cell is derived via the
following Eq. (1):
CA
Grid cell
=
1
1
+
exp
−
4
P
i
=
1
k
i
(vx
i
−
xmid
i
)
!
,
(1)
where
i
represents the input datasets of water erosion, aridity,
crop mix, and field size (proxy for farm size),
k
i
refers to
the slope value, xmid
i
to the central points of each of the
logit curves, and vx
i
to grid cell values of the referring input
dataset.
A sensitivity analysis has been conducted to assess the ex-
planatory power of each of the four input variables and the
uncertainty of our parameter set and combination (Fig. S5).
The first step was to vary our chosen reference slope (
k
i
)
of each of the input dataset values by factors of 2 and 0.5
(
+
100 %,
−
50 %). As a next step each of the variables is
dropped, and finally each of the variables is used individu-
ally in the logit model. The sensitivity test was conducted
at the global scale and also for each of the 54 CA reporting
countries.
2.4.3
Mapping CA area per country
The downscaling of total national CA area values entailed
subsetting all grid cells with CA-suitable area per CA area
reporting country (FAO, 2016). Then these grid cells were
sorted in decreasing order according to their CA probability
values derived with the logit equation. As a next step, grid
cells with the highest logit model results were selected step-
wise while adding up the corresponding potential CA area
until the reported national CA area threshold was reached.
We received a heterogeneous pattern of allocated CA area at
5 arcmin resolution grid within a CA reporting country ac-
cording to the likelihood of CA area occurrence based on the
logit results, statistical data, and literature findings.
2.4.4
Scenario CA area
Similar to the “bottom-up scenario” of Prestele et al. (2018),
we deduced “scenario CA area” indicating the maximum
area extent of CA adoption, under assessed current socio-
economic and biophysical conditions. We add the subset of
22 annual rainfed crop-specific areas in grid cells with large
field sizes in low-income countries and all field sizes in high-
income countries from reduced tillage to the potential CA
area to calculate scenario CA area per grid cell.
3
Spatial pattern of six tillage systems
We allocated global cropland of SPAM2005 to the six tillage
systems at a spatial resolution of 5 arcmin according to a set
of rules (Table 4). In terms of area, conventional (Fig. 2)
and traditional (Fig. 3) annual tillages globally constitute the
most widespread tillage practices. Both systems are applied
for annual crops, which are globally growing on the largest
cropland fraction, are traded, and are consumed most. Large
parts of the cropland under traditional annual tillage for rain-
fed and irrigated annuals are located in South-East Asia, with
especially high cropland area shares in India followed by
sub-Saharan Africa and then South America (Table S9 for
aggregated tillage system areas to country scale). Conserva-
tion Agriculture globally constitutes the third largest tillage
system area (Table 4 and the following Sect. 3.1). Rotational
tillage (Fig. 4) is in fourth position in the ranking of tillage
system areas, followed by traditional rotational tillage area
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
(Fig. 5). Most traditional rotational tillage system area can
be found across the tropical region of South-East Asia and
western Africa. Reduced tillage has the smallest area extent
(Table 4), which we find mostly in a narrow band between
10 and 20
◦
northern latitude (Fig. 6). It occurs in Mexico and
south of the Sahel region, but mostly is found on cropland
in India (Table S8 for further metrics across tillage system
areas; Table S9).
3.1
Conservation Agriculture area
3.1.1
The results of the logit model
We deduced the likelihood of CA area in a grid cell via the
logit model approach according to the indicators crop mix,
field size, water erosion, and aridity (Fig. 7). The geographi-
cal pattern of the logit results (further referred to as ref-logit)
exhibits higher probabilities for cropland in grid cells out-
side the tropical climate zone and in rather continental re-
gions. Probability of CA is higher for cropland in grid cells
reporting large field sizes, which are mostly found in devel-
oped and land-rich countries, i.e., in the USA, Australia, and
large parts of Europe. Grid cells in the tropics receive rather
low logit results due to their humid conditions, smaller field
sizes, lower income levels, and crop types cultivated. In In-
dia, China, and Pakistan the majority of cropland showed
very low CA likelihood.
3.1.2
Results of the sensitivity analysis of the logit
model
The sensitivity analysis of the logit model shows mixed re-
sponses to the perturbations of slope or variable combina-
tion (Table 5, Fig. S5). Rank correlation (
r
) to the ref-logit
is much lower when taking one variable only compared to
each of the other drop-variable settings or slope modifica-
tions. Regarding modifications of the slope parameters of the
input variables, we calculated the lowest rank correlation co-
efficient for increasing the slope of aridity by
+
100 % and
for decreasing the slope of crop mix by
−
50 % compared to
changing the slopes of the other three variables, respectively.
Erosion has the lowest explanatory power, as can be in-
terpreted from the very high correlation coefficient with ref-
logit when dropping it – but even negative correlation when
taking it into the logit equation only. This finding is in line
with the findings of the sensitivity tests performed by Prestele
et al. (2018), who find erosion to be the variable with the
smallest explanatory power as well.
Crop mix has the largest explanatory power in the logit
equation, as shown by the lowest correlation coefficient value
when dropping it but highest when taking that variable only
(Table 5). We additionally report on the sensitivity results for
the 54 CA reporting countries, where the effects of slope and
variable perturbation show very different patterns per coun-
try (Table S6). However, as national CA areas are allocated
within individual countries, the sensitivity of ranking within
countries is of greater importance than the global rank corre-
lation.
3.1.3
Downscaled CA area
Total downscaled CA area (1 101 899 km
2
, Fig. 8) is slightly
lower than FAO reported total CA area for these countries
(1 102 900 km
2
). This difference occurs because of our algo-
rithm, which assigned the entire CA-suitable cropland area
per grid cell to CA, taking the cropland of the following grid
cell in or out of consideration striving for least deviation from
the threshold per country (Table S7 for comparison of re-
ported and downscaled country values). A further difference
is due to the insufficient potential CA area in North Korea
and New Zealand, resulting in the fact that only part of the
national reported CA area could be allocated to.
Aggregated crop-specific CA area values reveal that most
downscaled CA area was allocated to area cultivated with
soybean, followed by wheat and then maize (Table 6). These
three crops are among the most important produced, traded,
and consumed agricultural goods, making their production
highly competitive, and therefore the incentive to reduce op-
erational costs (e.g., regarding tillage) is high. Another rea-
son for soybean and maize being among the crops mostly
produced under CA may be the usage of high yielding or ge-
netically modified crops, coming along with improved pesti-
cide resistances, which make them more suitable for possible
herbicide applications (Giller et al., 2015) replacing tillage
operations on-field. In Argentina, soybeans are found to be
the most common plant cultivated under CA, with usually
lower residue coverage than required for being a CA system
(Pac, 2018). Subsistence farming crops, e.g., peas and millet,
contributed only a little cropland to the downscaled CA area
(Table 6), because they are more drought resistant (Jodha,
1977), and of rather regional importance in terms of food se-
curity, while being traded less on the international markets
(Andrews and Kumar, 1992).
3.1.4
Scenario CA area
We deduced the total global potential CA area of 4.65 Mkm
2
(see above). Additionally, we identified 0.02 Mkm
2
of
22 rainfed annual crop types’ areas on large fields in low-
income countries and all field sizes or in high-income coun-
tries from the reduced tillage system area, which potentially
could be converted to CA area as well. We calculated a to-
tal scenario CA area of 4.67 Mkm
2
, where perceived driv-
ing forces, e.g., CA adoption supporting agricultural policies,
targeted mechanization efforts, and knowledge dissemination
approaches could lead to an area expansion of CA practices.
4
Data availability
The presented tillage system dataset and source code
are available under the ODBL (data) and MIT (source
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833
Table 4.
Global aggregated tillage system areas and shares on total cropland (IFPRI/IIASA, 2017b).
Tillage
Tillage system
Share of tillage system
system
area sum (km
2
)
area on total cropland (%)
Conventional annual tillage
4 650 498
41.10
Traditional annual tillage
4 015 279
35.49
Conservation Agriculture
1 101 899
9.74
Rotational tillage
741 798
6.56
Traditional rotational tillage
650 509
5.75
Reduced tillage
154 403
1.36
World
11 314 386
100
Figure 2.
Conventional annual tillage area, which has been allocated to the majority of the global physical cropland area.
code) licenses. The tillage dataset can be downloaded
from
https://doi.org/10.5880/PIK.2019.009
(Porwol-
lik et al., 2019b) and the corresponding R-code from
https://doi.org/10.5880/PIK.2019.010
(Porwollik
et
al.,
2019a). The dataset is provided in netCDF format (version
4) and consists of 42 layers, each reporting crop-specific
tillage systems per grid cell. Additionally, we provide a layer
indicating area, where adoption of Conservation Agriculture
could be facilitated (scenario CA area). The dataset can be
used as a direct input or be applied as a mask or overlay
for identifying tillage area. The R-code is provided to
increase the transparency of our methods, but also to enable
other modeling groups to adjust our tillage area mapping
algorithm to their needs, e.g., for different input data or
scenarios.
5
Discussion
5.1
Comparison of results to other studies
In the absence of alternative tillage area datasets for valida-
tion at the global scale we here want to discuss the way our
tillage system area results relate to other studies’ findings.
We compare the spatial pattern of our added traditional
tillage system area to the one reported by the cropland sub-
sets of SPAM2005 for low-input and subsistence production.
According to You et al. (2014), both production levels are
characterized by a low level of mechanization, or rather man-
ual labor and low input usage. The sum of our traditional
tillage systems’ (rotational and annual) areas (4.63 Mkm
2
)
is slightly higher than the sum of SPAM2005 subsistence
and low-input technological-level cropland (4.55 Mkm
2
). We
deduced more traditional tillage system area in South-East
Asia, Sub-Saharan Africa, and Peru than SPAM2005 re-
ported under low and subsistence farming (see the differ-
ence map in Fig. S10). Further comparison reveals a mod-
erately lower amount of area under traditional tillage in our
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
Figure 3.
Traditional annual tillage area as sums over 29 annual crop types’ areas in grid cells reporting dominant field sizes smaller than
2 ha and in countries classified as low income in this study.
Figure 4.
Rotational tillage area on cropland area of 13 perennial crop types in grid cells with dominating field sizes of minimum 2 ha or
larger in low-income countries or all field sizes in high-income countries.
dataset for Europe, the Middle East, South America, and
Australia, i.e., in countries which are regarded as emerging
or developed economies. The spatial difference may be due
to the fact that SPAM2005 is a product of a sub-cell cross-
entropy optimization approach to distribute cropland of the
same crop species into several production levels per grid cell.
In contrast to this, we used the field size and gross national
income as spatial indicators of un-mechanized tillage sys-
tems by masking out cropland either per entire grid cell or
country-wise according to our derived thresholds. We cal-
culated the spatial correlation via a regression of the added
area values of our traditional tillage system and of the sum
of low-input and subsistence production level cropland re-
ported by SPAM2005. We found a regression factor (
r
2
) of
0.54 (
p <
0
.
001, slope of 1.139) among both datasets’ val-
ues.
Our estimate of a traditional tillage system area in turn
is lower than the finding by Lowder et al. (2016), stating
5.87 Mkm
2
to be under management of farms smaller than
2 ha in size (
∼
12 % of their arable cropland assumption).
In order to compare our results to the findings of Erb et
al. (2016) on tillage intensity areas, we added up our reduced,
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
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Figure 5.
Traditional rotational tillage area as cropland of 13 perennial crop types in grid cells characterized by field sizes smaller than 2 ha
in countries considered low income in this study.
Figure 6.
Reduced tillage area where soil depth to bedrock is limiting the depth of tillage.
both rotational tillage system, areas, and the downscaled CA
area to represent the “low intensity” tillage area, whereas
conventional and traditional annual tillage are summed up
to the “high intensity” tillage area. Since the description of
what is included in their “low intensity” area is inconsistent
within their main text, tables, and Supplement, we state two
different estimates of our results – both exhibiting different
absolute values and shares compared to the findings of Erb et
al. (2016) (Table 7).
We additionally pursued a provincial- and state-level com-
parison between our downscaled CA area to reported no-
tillage area values for Canada, Brazil, and Australia (Fig. S11
and Table S11), because these countries are among the top
four adopters of CA (see Table S7). CA area values from
AQUASTAT (FAO, 2016) for these three countries were
dated 2006, 2005, and 2006 and compared to reference re-
porting years of 2006, 2007–2008, and 2006, respectively.
Although this provides a comparison to independent data, it
cannot be considered a validation because of the temporal
mismatch among compared datasets and aggregation uncer-
tainty when using Global Administrative Areas (2015) for
aggregating tillage areas to sub-national scale. For each of
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
Figure 7.
Probabilities of Conservation Agriculture area per grid cell with high values as green to yellow and low ones in blue to purple
colors (white color indicates the absence of cropland, and grey the cropland (IFPRI/IIASA, 2017b) which is excluded from the potential CA
area due to soil depth, crop type, irrigation, field size, or income level).
Table 5.
Logit model input parameters, as midpoint (xmid) and slope (
k
) of the four logit model input datasets (columns 1 and 2), which
are altered per sensitivity setting. Correlation coefficients (
r
) for ranks according to “Spearman” between the reference case (Logit-ref) and
the perturbed slope and variable combinations of the logit model results are given, illustrating the sensitivity of the grid cell likelihood of
potential CA area (columns 3 to 6).
Variable
Logit-ref
Logit-ref
Logit-ref and
Logit-ref and
Logit-ref and drop
Logit-ref and
(xmid)
(
k
)
k
+
100 % (
r
)
k
−
50 % (
r
)
one variable (
r
)
one variable only (
r
)
Field size
20
0.25
0.975
0.988
0.944
0.555
Erosion
12
0.017
0.992
0.997
0.989
−
0
.
119
Aridity
0.65
−
5
0.966
0.982
0.901
0.607
Crop mix
0.50
10
0.981
0.971
0.773
0.826
the selected countries our downscale algorithm can quite well
reproduce the main no-tillage area, but tends to too strongly
concentrate CA area in some regions instead of a more ho-
mogenous spread, as observed in the associated reference
maps.
Prestele et al. (2018) analyzed CA area time series data
by FAOSTAT and found an increasing trend of CA adoption
within countries and to more countries since the 1970s. This
trend is likely going to continue as farm holdings increase in
size while decreasing in number in upper-middle- and high-
income countries (Lowder et al., 2016). At the same time,
the adoption rate of CA in smallholder farming systems in
low-income countries (e.g., in Sub-Saharan Africa) may con-
tinue to be low, where average farm size reveals a decreas-
ing trend (Jones, 2017). Adoption of CA practices by small-
holder farmers is hampered by competition for residue use
(Scopel et al., 2013), missing knowledge, as well as restricted
access to inputs and financial capital (Kassam et al., 2009),
making them more risk-averse towards adoption of new tech-
nology than large-scale farmers (Schmitz et al., 2015).
Prestele et al. (2018) state their potential CA area to be
11.3 Mkm
2
in their “Bottom-up” and 5.33 Mkm
2
in their
“Top-down” scenarios until the year 2050. Our estimate
of scenario CA area of 4.66 Mkm
2
is lower but on the
same magnitude as their “Top-down” scenario. Prestele et
al. (2018) used another cropland product, targeted another
time period, pursued a slightly different CA mapping ap-
proach, and had different assumptions about the scenario de-
sign, which might be causing the main area differences com-
pared to our derived scenario CA area. In order to take into
account that other modeling groups may apply other crop-
land inputs than SPAM2005 as presented here, we made the
tillage dataset and source code flexible in a way that each
modeling group may adjust it according to their individual
crop mix per grid cell.
Earth Syst. Sci. Data, 11, 823–843, 2019
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
837
Figure 8.
Downscaled Conservation Agriculture area (km
2
) (colored) on total cropland (grey) per grid cell for 54 reporting countries around
the year 2005.
Table 6.
Global sums over 22 CA suitable crop-type areas, sorted decreasing shares of downscaled CA area values on the identified potential
CA area, and crop-specific downscaled CA areas.
Crop
Potential CA
Share of downscaled on
Downscaled CA
type
area (km
2
)
potential CA (%)
area (km
2
)
Soybean
740 797
48
359 205
Wheat
1 341 590
24
321 305
Maize
762 415
19
143 432
Barley
485 428
12
57 959
Rapeseed
144 601
31
45 363
Sunflower
186 310
20
36 716
Sorghum
97 918
24
23 816
Bean
119 902
20
23 535
Other cereals
231 384
10
22 109
Cotton
84 069
25
21 121
Other pulses
76 869
21
15 932
Lentils
19 015
45
8565
Pearl millet
56 062
11
5938
Rest
82 063
5
4081
Groundnut
47 208
7
3308
Chicpea
28 489
11
3227
Small millet
13 419
21
2859
Vegetables
90 535
2
1834
Tobacco
13 678
7
916
Sesame seed
17 940
3
502
Pigeonpea
6411
2
129
Cowpea
6317
1
48
World
4 652 419
24
1 101 899
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
Table 7.
Tillage system area results compared to estimates of Erb et al. (2016) on tillage intensity areas. The first two columns show our
aggregated tillage system area values; columns 3 and 4 additionally include the young and temporal fallow cropland area by Siebert et
al. (2010), a cropland area not represented in SPAM2005 and therefore added to our total cropland as well as to the “low intensity” category
as described in Erb et al. (2016). Note that Siebert et al. (2010) state that about 4.4 Mkm
2
of cropland was young and temporal fallow
(
<
5 years) around the year 2000.
Tillage
Tillage area
Tillage area
Tillage area this
Tillage area this
Tillage area (km
2
)
Tillage share (%)
system
this study (km
2
)
this study (%)
study
+
fallow (km
2
)
study
+
fallow (%)
(Erb et al., 2016)
(Erb et al., 2016)
Low intensity
2 648 610
23.4
7 048 610
44.9
4 730 000
38.9
High intensity
8 665 776
76.6
8 665 776
55.1
7 430 000
61.1
World
11 314 386
100
15 714 386
100
12 160 000
100
Figure 9.
Scenario Conservation Agriculture area (km
2
) (colored) on total cropland (grey) per grid cell.
5.2
Potentials, limitations, and implications for
applications of the dataset
A limitation to our presented mapping approach is that the
input datasets applied cover different time periods, e.g.,
GLADIS reports water erosion values for approximately the
year 2000, SPAM2005 and the field size dataset for the year
2005; the aridity spans to the reference climate data of the
period from year 1961 to 1990, and for some countries we
extracted the only CA reporting year by FAO (2016) from
years 2002 up to 2013. By using SPAM2005, field size for
2005, and setting the objected year for the produced tillage
dataset to 2005 as well, we tried to minimize inconsistencies
in time coverage at least for the cropland extent. GLADIS
uses the Global Land Cover dataset (GLC2000, Bartholomé
and Belward, 2005) as land-use information, thus reporting
water erosion values as an average over the different ecosys-
tem and land-use types per grid cell. Land use as well as land
management are results of dynamic socio-economic and en-
vironmental processes. Local mismatches in the cropland ex-
tents between these datasets might be on the one hand due to
abandonment as a result of shifting cultivation or on the other
hand due to extension of cropland to converted other land-use
types between the years 2000 and 2005. Further mismatches
might exist due to different assumptions about crop types and
area between different data products. The choice of crop to
be cultivated is usually taken under consideration of rotations
for weed and pest management, household demand, and mar-
ket conditions, together leading to different cropping patterns
between the years 2000 and 2005. The aridity dataset does
not consider any land-use information, but relies on averages
of climatic data and parameters. Another source of uncer-
tainty is the used rule-based approach for mapping the tillage
system areas. We statistically proved the relation between na-
tional average farm size and CA adoption (S3). Whereas sta-
tistical relations between field and farm size can be found in
the literature, the mapping rules of distinguishing traditional
from mechanical tillage and the suitability of CA for erosion
and aridity-prone agricultural production environments are
based on qualitative literature findings and warrant further
research and scrutiny if new data become available.
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
839
The tillage dataset presented here can be employed in
various applications, depending on the type of model, con-
text, and objective of the user. Agricultural land management
practices are determined not only by environmental factors,
but are also embedded in local to regional systems of cul-
ture, traditions, and markets. This mosaic of farming con-
ditions can only be taken into account at high spatial reso-
lution. The developed tillage dataset is an effort to better ac-
count for heterogeneous patterns of agricultural soil manage-
ment across and within countries by using socio-economic
and biophysical data in conjunction. The resolution of the
generated dataset of 5 arcmin is quite high. Global ecosys-
tem models are currently mostly run at a coarser resolution
than our dataset’s resolution, and the tillage data may have
to be aggregated in such cases. This could introduce further
uncertainty to the area under a certain tillage system.
A challenge to the full usage of this dataset is the limited
implementation of the 42 crop types reported in SPAM2005
in global ecosystem models. Especially perennial crop types
are hardly ever parameterized in ecosystem models, or if
so rather address regional-scale applications (Fader et al.,
2015). One reason for the missing implementation may be
their relatively small cultivation areas globally (
∼
10 % of
global cropland, Erb et al., 2016). Woody and other peren-
nial plant species entail potential in the aspect of sustainable
agricultural practices because they keep the soil covered for
longer periods and thus better protect it from erosive and
radiative forces, promote soil organic carbon accumulation
(Smith et al., 2008), and stabilize soils more than annually
planted crop types.
Another challenge for the application of our tillage dataset
in model simulations is the differentiation of soil depth af-
fected by the tillage operation. Some models may be able
to differentiate between 20 or 30 cm depth affected by the
tillage operation mostly when having a site-based back-
ground and therefore a very detailed representation of agri-
cultural management practices (White et al., 2010). The LPJ-
GUESS global dynamic ecosystem model and the Commu-
nity Land Model (CLM) have implemented the tillage rou-
tines as a tillage factor accelerating the decomposition rate
of the different soil carbon pools (Levis et al., 2014; Olin et
al., 2015), so that implementations of spatial variability in
depth or mixing efficiency are not straightforward.
White et al. (2010) elaborate on the problem of gener-
ally implementing a three-dimensional aspect as “surface
affected” by the tillage practice, which would be the case
for simulating reduced tillage practices as strip-, mulch-, or
ridge-till, managing weeds during the growing period of the
main crop, or preparing the seedbed for inter-cropping cul-
tures. The reduction relates to depth, surface affected or both,
for which White et al. (2010) recommend an intermediate
model implementation mode which distinguishes two zones
as one share of the soil being affected and the other one not.
Some authors mention partial adoption of CA as referring
to the minimal soil disturbance practice only (Giller et al.,
2015; Scopel et al., 2013) where residues are not always re-
tained (Pittelkow et al., 2015). This no-tillage practice tries
to benefit from saving energy, work hours, machine wearing,
and field passes when skipping tillage. No-tillage without a
sufficient biological mulch is reliant on the application of in-
creased amounts of herbicides to comply with weeds (Mc-
Conkey et al., 2012; Mitchell et al., 2012) compared to con-
ventional tillage systems. Leaving the soil unprotected ex-
poses the soil surface to erosive forces and enhances nutrient
leakage, especially under high rainfall intensities. Crusting
and compaction of the soil can only be addressed by till-
ing these fields rotationally, as has been discussed in Erb et
al. (2016). This rotational tillage may lead to a decrease in
soil organic matter (SOM) due to increased mineralization
under aerated conditions, and the advantages of no-tilling
during the other years disappear (Powlson et al., 2014). The
effects of SOM increase under no-tillage, only in conjunction
with a certain level of residue inputs, may appear relevant af-
ter a transition time of about 10 to 20 years of continuous
practice until a new equilibrium state of SOM dynamics is
re-established (Sá et al., 2012). The other often missing as-
pect to the full implementation of the CA practice is the ro-
tation of diverse crop types, inter-cropping, or other green
manuring practices. It remains unclear to what extent coun-
tries reporting CA area to FAO may rather refer to partially
adopted practice of CA, i.e., no-tillage only.
Applying the presented tillage system dataset in global
assessment is a major step forward compared to globally
rather homogeneous assumptions on tillage systems (Hirsch
et al., 2017; Levis et al., 2014) or a total ignorance of soil
management practices (Folberth et al., 2016; Rosenzweig
et al., 2014). The rule-based approach and the publication
of the underlying data processing scripts allow for exten-
sions of this work if further relationships can be identified
or improved data become available. It also allows for con-
struction of future scenarios, consistent with other scenario
frameworks on climate, economic development, and land-use
change (e.g., Popp et al., 2017). Further research is needed
to generate global land management datasets with high res-
olution on crop rotations, residue management, and multiple
cropping, so that the full set of CA principles can be sim-
ulated and biophysically assessed in comparison to further
sustainable land practices.
Supplement.
The supplement related to this article is available
online at: https://doi.org/10.5194/essd-11-823-2019-supplement.
Author contributions.
VP, CM, SR, and JH developed the tillage
system dataset. VP collected the input data and wrote the scripts for
processing and analyzing the data. CM and JH suggested the CA
area downscaling procedure, whereas JH proposed the application
of the logit model. VP prepared the manuscript with contributions
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Earth Syst. Sci. Data, 11, 823–843, 2019
Page 18
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V. Porwollik et al.: Generating a rule-based global gridded tillage dataset
from all the co-authors with respect to interpretation of the results
and writing of the final paper.
Competing interests.
The authors declare that they have no con-
flict of interest.
Acknowledgements.
Susanne Rolinski and Vera Porwollik
acknowledge financial support from the MACMIT project
(01LN1317A) and Jens Heinke from the SUSTAg project
(031B0170A), both funded through the German Federal Ministry of
Education and Research (BMBF). We thank Jannes Breier for sup-
port in data processing as well as Steffen Fritz (IIASA) and Theodor
Friedrich (FAO) for personal communication. We thank all the ref-
erees for the productive reviews and comments.
Financial support.
This research has been supported by the Ger-
man Federal Ministry of Education and Research (grant nos.
01LN1317A and 031B0170A).
Review statement.
This paper was edited by Giulio G. R. Iovine
and reviewed by Theodor Friedrich, Rong Wang, and four anony-
mous referees.
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