ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus PublicationsGöttingen, Germany10.5194/esd-9-563-2018Estimating sowing and harvest dates based on the Asian summer monsoonEstimating sowing and harvest dates based on the Asian summer monsoonMathisonCamillacamilla.mathison@metoffice.gov.ukDevaChetanFalloonPeteChallinorAndrew J.Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UKSchool of Earth and Environment, Institute for Climate and
Atmospheric Science, University of Leeds, Leeds, LS2 9AT, UKCamilla Mathison (camilla.mathison@metoffice.gov.uk)18May20189256359212October20171November201714March201825April2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://esd.copernicus.org/articles/9/563/2018/esd-9-563-2018.htmlThe full text article is available as a PDF file from https://esd.copernicus.org/articles/9/563/2018/esd-9-563-2018.pdf
Sowing and harvest dates are a significant source of uncertainty
within crop models, especially for regions where high-resolution data are
unavailable or, as is the case in future climate runs, where no data are
available at all. Global datasets are not always able to distinguish when
wheat is grown in tropical and subtropical regions, and they are also often
coarse in resolution. South Asia is one such region where large spatial
variation means higher-resolution datasets are needed, together with greater
clarity for the timing of the main wheat growing season. Agriculture in South
Asia is closely associated with the dominating climatological phenomenon, the
Asian summer monsoon (ASM). Rice and wheat are two highly important crops for
the region, with rice being mainly cultivated in the wet season during the summer
monsoon months and wheat during the dry winter. We present a method for
estimating the crop sowing and harvest dates for rice and wheat using the
ASM onset and retreat. The aim of this method is to provide a more accurate
alternative to the global datasets of cropping calendars than is currently
available and generate more representative inputs for climate impact
assessments.
We first demonstrate that there is skill in the model prediction of monsoon
onset and retreat for two downscaled general circulation models (GCMs) by
comparing modelled precipitation with observations. We then calculate and
apply sowing and harvest rules for rice and wheat for each simulation to
climatological estimates of the monsoon onset and retreat for a present day
period. We show that this method reproduces the present day sowing and
harvest dates for most parts of India. The application of the method to two
future simulations demonstrates that the estimated sowing and harvest dates
are successfully modified to ensure that the growing season remains
consistent with the internal model climate. The study therefore provides a
useful way of modelling potential growing season adaptations to changes in
future climate.
Introduction
Field studies dominate the modelling literature on crops and agriculture.
Many crop models are developed and applied at the field scale using site-specific observations to drive models and optimize outputs. The growing
awareness of climate change and the likely impact this will have on food
production has generated a demand for regional and global assessments of
climate impacts on food security through, for example, projects such as
the Agricultural Model Intercomparison and Improvement Project (AgMIP;
), the
Inter-Sectoral Impact Model Intercomparison Project (ISIMIP;
), and the Global Gridded Crop
Model Inter-comparison (GGCMI; ). Recent work in
such climate–crop impact studies has sought to quantify uncertainty from the
quality and scale of input data. A result from this work is that for global-scale simulations, planting dates are a significant source of uncertainty
.
Aside from their use in modelling studies, deciding when to plant crops is a
significant challenge, particularly in water-scarce regions such as parts of
sub-Saharan Africa (SSA; ) and South and South-east
Asia . These regions have crop sowing dates that are
closely associated with the onset of the rainy season. Any prolonged dry
spells of more than 2 weeks after sowing could have serious consequences
leading to crop failure or significant yield reduction because topsoil
layers dry out, preventing germination . For large parts
of SSA, deciding when to sow determines the length of the crop duration for
the agricultural season and is therefore an important tactical decision
.
Planting dates can be determined using a number of different methods; for
example, propose a cropping calendar model for rice
cultivation in the Vietnam Mekong Delta (VMD). The
model estimates the sowing date based on the suitability of the land for
crops given any flooding, saltwater intrusion, or erratic monsoon rains;
these are important factors for the water resources of the VMD region.
Alternatively, use a fuzzy-logic-based
algorithm developed to estimate the onset of the rainy season in order to
examine the impact of the planting date for the SSA. In the General Large
Area Model (GLAM; ), the sowing date can be
estimated by the model based on the soil moisture conditions, with the crop
sown when surface soil moisture exceeds a specified threshold during a given
time window and crop emergence occurring at a specified time after sowing.
base their estimates of sowing dates at the global scale
on climatic conditions and crop-specific temperature thresholds, therefore
providing a suitable method for taking climate change into account. However,
the method is not really intended for use in irrigated
multiple cropping regions. describe how sowing dates
are defined in the GGCMI project. The GGCMI protocols use a combination of
, , and model data to define
sowing dates, thus highlighting the challenges in defining a complete,
accurate dataset of sowing and harvest dates. This has influenced and driven
the development and application of crop models on broader scales. In this
study we are considering the whole South Asia region; this is a large-scale
problem with complicated cropping patterns, which means that assumptions and
generalizations need to be made across a region with a wide variety of
climatic conditions and cropping environments (soils etc).
highlight the fact that global crop calendars such as those used
in the GGCMI often only report individual crops, therefore limiting their
usefulness for regions with multiple cropping systems.
The growing interest in climate change and food security has influenced the
development of crop models for use in future climate impact assessments
; this represents a different challenge for crop
models in terms of the input data used. ISIMIP simulations use time-varying
crop management data until 2005, after which the data are held fixed at 2005
levels for the remainder of the simulations . Fixing
crop management to present day practices is not really suitable for
adaptation studies . The assumption that there will
be no large shifts in climate causing sowing and harvest dates to change
significantly from the present day could lead to the sowing and harvesting
of crops in the model in the future at unrealistic times of the year. Thus,
the appropriate sowing and harvest dates used in future simulations depends
on the intended application for the simulations. In many adaptation studies,
impacts without adaptation are assessed using present day estimates of sowing
dates, then the sowing dates are adjusted in response to climate change to
assess the benefits of adaptation .
suggest using autonomous adaptation in simulations
in order to avoid overestimating the effects of adaptation. On this basis
there is a requirement for estimates of sowing and harvest dates for climate
simulations that remain consistent with the future model climate. This makes
estimates of sowing and harvest dates important not only for understanding
the present day, but also for use in future simulations, especially when
considering potential adaptation to climate change.
Agriculture in South Asia is dominated by the Asian summer monsoon (ASM).
Kharif and Rabi are the two main seasons in South Asian agriculture and these
correspond to summer and winter–spring growing seasons respectively.
Rice–wheat systems are a major crop rotation across South Asia. Kharif crops
include rice, which is usually sown during the monsoon and harvested in the
autumn. Sowing and harvest dates for rice cultivated during the Kharif season
vary between states, with rice traditionally sown in some locations with the
first rains of the monsoon, while other regions such as eastern parts of the
Indo-Gangetic Plain (IGP) tend to plant rice late into June when the monsoon
is fully established . Rabi crops include wheat,
which is mainly cultivated during the dry season
. The close association of the sowing
dates of these crops and the ASM offers the potential for a new method of
defining the cropping calendar for this important rotation.
Rice–wheat systems, particularly those in Pakistan
and the Indo-Gangetic Plain (IGP), tend to plant varieties like Basmati that
take a long time to mature . Since this delays wheat
planting, this has a direct impact on wheat yield. In the eastern IGP this is
a particular problem as the season for which wheat is viable is relatively
short . Any delay between
the rice harvest and wheat planting can have a large impact on the success of
the wheat crop as this will reduce the time available before the temperatures
get too high for the successful cultivation of wheat .
The time between the rice harvest and wheat sowing also depends on the time
it takes to ensure the soil is in a suitable condition for wheat sowing after
the rice harvest. describe the zero-tillage
approach, which allows for a reduced turnaround time between the harvest of
rice and sowing of wheat. Potential avenues by which the uncertainty from
sowing and harvest dates can be reduced in inputs to crop simulations
include
the use of higher-resolution regional datasets of recorded
sowing and harvest dates for crop calendars rather than existing global
datasets; and
the use of new methods for estimating crop calendars in the
absence of higher-resolution regional datasets.
The 1.5-year precipitation climatology for
the 1990–2007 period averaged for South Asia for each simulation
(ERA-Interim (labelled “ERAint” in the figures throughout the paper) cyan line, ECHAM5 blue line, HadCM3 red line) and APHRODITE observations
(black line) using a 5-day smoothed rolling mean. Also shown are the
growing seasons averaged for 1990–2007 for South Asia for wheat
(orange) and rice (green) from two datasets:
(diagonal hatching-labelled sacks) and
(perpendicular hatching-labelled Minag) and the monsoon onset (blue
vertical lines) and retreat (pink vertical lines) from each of the
simulations (APHRODITE dotted, ERA-Interim dashed, HadCM3 solid, ECHAM5 dash dot).
Motivation
The correct representation of the crop duration within crop models is
crucial for the interpretation of the important outputs from the model. For
example, if the datasets used for sowing and harvest dates are inaccurate, the
simulations could grow crops during the wrong season, thereby affecting the
reliability of the simulated water use and crop yield. The main differences
between the regional dataset and the global
data are for spring wheat. Spring wheat grown in winter
is misclassified as winter wheat in the data. This is
discussed by as a potential limitation when using the
data for tropical and subtropical regions. Spring wheat is the more common
type of wheat grown in the South Asia region
because minimum temperatures there are not low enough to allow vernalization
to take place, which is needed for winter varieties of wheat
.
Figure shows the averaged rice (green rectangles) and
wheat (orange rectangles) growing season durations for
(diagonal hatching) and the dataset (perpendicular
hatching labelled MinAg) overlaid on the present day South Asia averaged
precipitation climatology and estimates of the monsoon onset and retreat.
This illustrates the differences between the and
datasets, showing that in the
main growing period for both rice and wheat appears to be during the monsoon.
While rice is usually grown during the monsoon it is not typical that wheat
should be grown during this period for this region. The growing season
durations for the dataset (see
Fig. , perpendicular hatching rectangles labelled
MinAg) are more typical of this region with rice (green) growing during the
monsoon and wheat (orange) growing during the dry season.
Figure highlights that where a global dataset is
unable to establish exactly when wheat is grown in tropical regions, an
alternative is needed.
Crop models such as those described by
and
require sowing information such as a sowing date or a sowing window, with the
crop model integrating an effective temperature over time as the crop
develops. The effective temperature is a function of air or leaf temperature
and differs between models. The integrated effective temperature in each
development stage is referred to as the thermal time of that development
stage ; there may also be an
additional photoperiod length dependence. The thermal time in each
development stage is typically set by the user and can be calibrated to
simulate different varietal properties. Where these varietal properties are
unavailable, e.g. for the global analysis in , in order
to mimic the spatial variation in the choice of crop variety, these thermal
times were determined from sowing and harvest dates and the temperature
climatology, which allowed them to vary spatially. This ensures that during
the simulation, the crop develops over the course of the crop season starting
at the sowing date and ending at approximately the harvest date (i.e. the
harvest date is the average over the course of the climatological period used).
The use of this predefined thermal time ancillary drives the requirement for
providing both a sowing and harvest date. Reliable high-resolution datasets
for sowing and harvest dates are often unavailable for either the region or
the time period that is needed. In addition, there is a demand for sowing and
harvest dates that maintain consistency with the model climate. Therefore, in
this paper we propose a new method, outlined in Fig. ,
for estimating sowing and harvest dates for use in the large-scale modelling
of the rice–wheat rotation in South Asia using estimates of monsoon onset and
retreat. This method does not require large amounts of data and the user can
elect to use either the sowing input data or, if needed, both sowing and
harvest data to run their chosen crop model. The main objectives of this
study are
to develop a method for determining sowing and harvest dates for
modelling the rice–wheat rotation in South Asia based on the ASM and
to test the method in current and future climates.
We therefore present the methodology in Sect. . We show that the
proposed method is viable and show that it works in Sect. .
A discussion of the results and conclusions are provided in
Sects. and respectively.
Methodology
The methodology is summarized in the flow chart in Fig. .
The model datasets, described in detail in Appendix , include general circulation models (GCMs) and a regional climate
model (RCM). GCMs provide spatially consistent boundary data to an RCM, which
generates 25 km regional fields (see Fig. blue boxes).
The two GCMs used in this analysis were specifically selected because they
were able to capture the main features of the ASM (see Appendix ). RCMs are based on the same physical equations as GCMs and
therefore represent the entire climate system, including the carbon and water
cycle. Their higher resolution allows for a better representation of the
regional-scale processes, adding detail to fields like precipitation
. The individual RCM simulations (also called HNRCMS;
see Appendix ) used in this analysis are referred to
using their global driving data abbreviations: HadCM3, ECHAM5, and ERA-Interim (labelled “ERAint” in the figures throughout the paper) as
described in Appendix . Precipitation fields are used
to generate a precipitation climatology, which is used to calculate the monsoon
statistics (see Sect. ) from which sowing and harvest
dates are estimated; shown by the pink rectangles (see
Sect. ). These estimated sowing and harvest
dates are referred to as relative monsoon sowing and harvest dates (see
Fig. ). Observations are used throughout the process to
ensure that the method is viable and produces sensible results; these are
described in Sect. and shown by the green boxes.
A flow chart summarizing the methodology. The blue rectangles
represent
datasets that are used within the methodology, green rectangles represent
observations, and pink rectangles represent any calculation parts of the methodology.
Observations
In order to demonstrate the viability of the methodology outlined in
Fig. , we compare the simulated precipitation with
observations from the Asian Precipitation-Highly Resolved Observational Data
Integration Towards the Evaluation of Water Resources (APHRODITE;
) dataset in Sect. .
APHRODITE is a daily, 0.25∘ resolution land-only gridded dataset that
is also used in to show that the RCMs in this
analysis capture the general hydrology of the region. The monsoon is a highly
variable and complex phenomenon that currently not all climate models are
able to represent; this may mean that some climate models would not yet be
suitable for use with this method, which relies on a good representation of
the monsoon. The method presented in Fig. will become
more robust with improving representations of the monsoon in climate models.
The datasets used for sowing and harvest dates include a global dataset,
, and a regional dataset, , from
the government of India, Ministry of Agriculture and Farmers Welfare. The
data are referred to from here on as MinAg data. The
MinAg observations of sowing and harvest dates for rice and wheat are given
as a range of days of year. The midpoints of these observed ranges are
calculated and compared against the midpoints of the model pentads for onset
and retreat in day of year. As a post-processing step the differences are
then masked using crop areas from the International Crops Research Institute
for the Semi-Arid Tropics so that only the areas
where rice or wheat is grown are considered.
Estimating monsoon onset and retreat
There are a wide variety of metrics for estimating the monsoon onset and
retreat. Some are specific to agriculture and include a representation of
breaks in the monsoon . More general metrics
include a combination of meteorological variables, such as 850 hPa wind and
precipitation as in , or only use precipitation, such
as in and the Normalized Pentad Precipitation Index
(NPPI) . The NPPI and methods
both use a long-term climatological average of precipitation because the
model data are too noisy to calculate the monsoon statistics per year.
Agricultural-specific definitions of monsoon onset and retreat represent
breaks in the monsoon which can adversely affect the germination of crops.
However, these metrics are not as effective when used in conjunction with long-term average precipitation fields such as those used here. This is probably
because the breaks that occur in the monsoon are quite variable from year to
year and are smoothed out within the climatology. The approach by
defines monsoon onset as the pentad in which the relative
rainfall exceeds 5 mm day-1 during the May–September period. However,
regrid to the GPCP rainfall dataset
, which is much coarser resolution than the APHRODITE
data used here. The NPPI metric uses Eq. () to estimate monsoon
onset, retreat, peak, and duration.
NPPI=P-PminPmax-Pmin,
where P is the unsmoothed pentad precipitation climatology and
Pmin and Pmax are the annual minimum and maximum at
each grid box respectively. The monsoon onset is then defined as the pentad
in
which the NPPI exceeds 0.618 for the first time and withdrawal as the last
time the NPPI drops below this threshold in the year. The NPPI only reaches a
value of 1.0 once in the annual cycle, which corresponds to the monsoon peak.
In the NPPI method the only regridding that takes place is to ensure that the
model and observations are on the same grid; as they are both 25 km
resolution there is no loss of resolution in doing this. The threshold for
NPPI is also independent of the resolution of the data, which is not the case
for the method. The NPPI metric has been successfully
applied previously by to analyse the monsoon in
models of a similar resolution to the simulations used here (see
Fig. ). Therefore in this analysis in the same way that
use the 1981–2000 climatology, we use a
1990–2017 climatology. The pentad provided by the NPPI is representative of
the climatological period and therefore cannot be compared to a particular
year; however, the pentad can be used to find the 5-day window for the
climatological period during which onset and retreat typically occur, which can then
be compared to APHRODITE observations also averaged for that period. We use
the NPPI metric to calculate the pentad of the monsoon onset, retreat, peak,
and duration for the APHRODITE observations and the three HNRCM simulations.
Plots of the 1990–2007 monsoon statistics; monsoon
onset (a, c, e, g) and retreat (b, d, f, h). The APHRODITE
precipitation observations (a and b) are shown and the three model
simulations, ERA-Interim (c and d), HadCM3 (e and f), and ECHAM5 (g and
h),
are calculated using the NPPI metric. White areas are the regions where the
model precipitation exceeds the threshold, indicating the start of the
monsoon at the initial pentad; this does not imply early monsoon but
more likely a model bias in the precipitation at this location.
Comparison of model monsoon onset and retreat with precipitation observations
Figure shows plots of the onset (left
column) and the retreat (right column) of the South Asian summer monsoon as
defined using the NPPI described in Sect. . The NPPIs
for the climatology of the APHRODITE precipitation observations
are shown in
Fig. a and b for comparison with the precipitation
climatology for each of the HNRCMs shown: ERA-Interim (c and d), HadCM3 (e and
f),
and ECHAM5 (g and h). The white regions are areas where the threshold was
exceeded at the first pentad; this implies that the monsoon had already started at
the first pentad, which suggests a model bias and therefore these regions were
masked out. Figure shows the
differences between the model onset (retreat) and APHRODITE onset (retreat)
for each model. On average the difference between the monsoon onset in
APHRODITE and the HNRCM simulations is between 1 and 7 days and the
difference between the retreat in APHRODITE and the HNRCM simulations is
between 4 and 10 days. However, there are regions where the differences
between the APHRODITE monsoon statistics are much larger than this; these are
highlighted by the darker red and blue regions in
Fig. . In general for most of
India the HNRCMS are within 25 days of the APHRODITE observations, with the
regions where the differences are larger explained by different monsoon
characteristics, for example the south of India and the Bangladesh region
(this is discussed further in Sect. ).
Plots of the 1990–2007 difference between model simulations
and APHRODITE observations for the monsoon statistics; monsoon
onset (a, c, e) and retreat (b, d, f); ERA-Interim (a and b), HadCM3
(c and d), and ECHAM5 (e and f) calculated using the NPPI metric.
Calculating sowing and harvest dates from monsoon characteristics
We use estimates of the monsoon onset and retreat together with present day
rules on sowing and harvest for rice and wheat, referred to as crop rules, to
calculate the sowing and harvest dates relative to the monsoon (see
Fig. ). This method allows any crop model that uses, for
example, a driving dataset similar to APHRODITE or the HNRCMs to derive
sowing and harvest dates that are consistent with the monsoon of the driving
data (see Fig. ). Thus, the crop is grown at the
appropriate time of the year; i.e rice is kept during the monsoon period and
wheat is sown and harvested during the dry season. The monsoon is a highly
variable phenomenon; however, the use of a long-term average (climatology) to
calculate the monsoon statistics smooths out their large inter-annual
variability. This highlights the consistency between the sowing and harvest
dates and the monsoon statistics. Therefore we do not expect the monsoon
statistics to be exactly the same as the observed sowing and harvest dates.
Rather, this method relies on consistency between the climatological estimate
of the monsoon statistics and the sowing and harvest dates across the region.
The introduction of a crop rule then moves the monsoon statistic to more
closely reflect the observed sowing and harvest dates. This means that even
if the difference between the most relevant monsoon statistic and the
observed sowing or harvest date is large then the difference is similar
across India. Although these sowing and harvest events may not always be
dictated entirely by the monsoon, the phenomenon provides the broader
seasonality associated with the crop seasons in this region. The consistency
between the crop practices and the monsoon statistics across the region
provides the empirical relationship exploited here to estimate the sowing and
harvest dates for use in both present day and future crop simulations. These
sowing and harvest dates are not really intended to offer advice to farmers
on when to sow or harvest on a year to year basis; rather, it provides a way
for sowing and harvest dates to remain relevant to this major climatological
feature. A key assumption is that the monsoon remains a defining feature of
the crop seasons for South Asia in the future.
Calculation of monsoon-derived estimates of sowing and harvest dates for rice and wheat
We use the precipitation climatologies from APHRODITE precipitation
observations and each of the HNRCM simulations (see
Fig. ) by calculating the difference between the monsoon
onset (or retreat) and the observed MinAg sowing (or harvest) dates for each
crop (see Fig. ). These differences are per grid box. We
then calculate a weighted area average (using the , package) to
produce a crop rule for the whole region for each crop and stage; these are
listed in Eq. (). Collectively, the crop rules given in
Eq. () are referred to as
RelMonsooncroprule. This provides a simple rule that can be
applied across the region, even where observations are not available.
Although calculating a rule per grid box would provide excellent results where
observations were available, it would limit the usefulness of the method
where observations were not available, which is one of the main aims of this
approach.
RiceSowingCroprule=AreaAverage(MonsoonOnset-RiceSowing)RiceHarvestCroprule=AreaAverage(MonsoonRetreat-RiceHarvest)WheatSowingCroprule=AreaAverage(MonsoonRetreat-WheatSowing)WheatHarvestCroprule=AreaAverage(MonsoonOnset-WheatHarvest)
The RelMonsooncroprule is then applied to the monsoon onset and
retreat field to provide an estimate of sowing and harvest dates for rice and
wheat based on the monsoon. We refer to these estimates of sowing and harvest
dates as “monsoon-derived crop dates” for brevity.
MonsoonDerivedCropDate=MonsoonStatistic-RelMonsooncroprule,
where the MonsoonStatistic can be monsoon onset or retreat and the
RelMonsooncroprule is one of the four crop rules given in
Eq. ().
The spatial variability of the monsoon-derived sowing and harvest dates is
accounted for by the monsoon onset and retreat in the climatology used to
calculate the RelMonsooncroprule. The monsoon-derived sowing and
harvest dates for both the APHRODITE and HNRCM simulations are provided and
compared against MinAg observed sowing and harvest dates in Sect. . The calculation of the
RelMonsooncroprule is based on observations for India (from MinAg
and ) and therefore the analysis for the present
day in Sect. focuses on these
areas. Because most of the South Asia region is dominated by the
ASM, the RelMonsooncroprule, though tuned using India
observations, can be applied to any region dominated by the ASM in order to
estimate sowing and harvest dates for larger areas with a rice–wheat rotation
(see Sect. ). The method does not currently
perform as well for parts of southern India where the climate is influenced
by the North-east Monsoon but could be modified to provide better results for
these areas. In Sect. , we
compare the monsoon-derived estimates of sowing and harvest dates for the
period 1990–2007 with the MinAg range of sowing and harvest dates to
establish if the method shown in Fig. gives good
results. There are four datasets used throughout this analysis: APHRODITE and
the three HNRCMS. Where three of the four datasets provide sowing or harvest
dates that are within the MinAg range, the method is said to give good
results; where two of the four datasets are within the MinAg range, the
results from the method are said to be fair. If no datasets are within the
MinAg range, the method is classed as poor. The sowing and harvest dates are
presented for each state in Sect. .
Demonstration using monsoon-derived estimates of sowing and harvest dates for two future periods
The method summarized in Fig. is applied to two future
periods using the ECHAM5 and HadCM3 RCM simulations (described in Appendix ). Global mean temperatures are used (within
the High-End cLimate Impacts and eXtremes project, HELIX) to define the
future climate in terms of specific warming levels (SWLs), i.e considering a
2, 4, and 6 ∘C world. The use of time periods is
much more common than SWLs; however, SWLs enable the analysis to focus less on
the climate scenarios and more on what the world will look like at
2, 4, and 6 ∘C . This will
differ depending on when the threshold is passed. The SWL approach is
therefore a benefit as it means that new scenarios that are developed as part
of new model intercomparison projects can be compared against older ones from
previous projects. Although the older scenarios may not contain the most
up-to-date socio-economic information, they are no less likely than the newer
scenarios. The simulations used here are for the period 1965 to 2100 and
therefore only the 2 ∘C threshold for global mean temperature is
actually passed during these simulations. For HadCM3 this occurs in 2047 and
for ECHAM5, 2055. Therefore the two future periods used in this analysis are
2040–2057 and 2080–2097. The 2040–2057 period is chosen because it
includes the year that the global mean temperature exceeds 2 ∘C in
the two simulations, and the 2080–2097 period is chosen because it is
furthest into the future in these simulations and therefore likely to show
the greatest warming. The length of the two future analysis periods has been
chosen for consistency with the ERA-Interim RCM simulation, which is only available
for the period 1990–2007. Although the threshold of 2 ∘C is exceeded
globally it is important to note that the relationship between the projected
global mean change in temperature and the regional climate change in
temperature for South Asia is complicated. Heat and moisture and how they
vary across the globe are not evenly distributed, with land warming faster
than the ocean ; therefore the actual temperature
change experienced in South Asia may be higher than the global mean change.
Results
We compare the model monsoon to the monsoon calculated from precipitation
observations to demonstrate that the model is able to reproduce the monsoon
(see Sect. ); therefore the methodology summarized
in Fig. and Sect. is viable. In
Sect. we compare the simulated monsoon with the observed
sowing and harvest dates in order to calculate the monsoon-derived sowing and
harvest dates and compare these new simulated sowing and harvest dates with
the observations. We then show results from applying the method in
Sect. . As a demonstration, we also
apply the method to two future periods in Sect. .
Comparing observed sowing and harvest dates with estimates of monsoon onset and retreat
The climatology in Fig. shows that on average
the observed rice and wheat sowing and harvest dates from MinAg align well
with the monsoon onset and retreat in the simulations. Observed rice sowing
dates generally compare well with the monsoon onset in the model as shown in
Figs. and .
Plots of the difference between the midpoint of the monsoon
onset in the model and the midpoint of the observed rice sowing period for 1990–2007.
Table of RelMonsooncroprule for each dataset, crop, and stage.
The RelMonsooncroprule is the value subtracted from the monsoon onset and retreat
in order to calculate a new sowing or harvest date based on the monsoon onset and retreat.
In each case the new estimate of the sowing and harvest dates is calculated by
subtracting the RelMonsooncroprule from the Monstat, where Monstat is
monsoon onset or monsoon retreat from HNRCM or APHRODITE precipitation observations.
Where the sowing or harvest is before the monsoon statistic, the crop rule is
in bold with normal type indicating that
sowing or harvest occurs after the monsoon statistic.
The monsoon onset and retreat estimates are provided in days of year
(pentads) and therefore with a range of plus or minus 2.5 days. The MinAg
observations are also provided in days of year with a range that varies from
plus or minus 15 days depending on the location.
Figure shows the range of the MinAg sowing and harvest
observations for each state; the full sowing or harvest window is shown by
the downward grey triangles, with the midpoints shown by black triangles
joined by a black line. Figure considers the
midpoints of these two ranges in order to summarize how well aligned the
monsoon onset range is to the observed range of rice sowing dates, i.e. how
the 5-day onset windows coincide with the observed sowing window. If the
monsoon onset range is completely within the range of sowing days provided by
the observations, this is classed as a “hit” (shown by the blue regions).
If the monsoon onset range is completely outside the range of observed sowing
days, this is classed as a “miss” (shown by the red regions). The yellow
regions in Fig. show the places where the
monsoon onset overlaps the range of observed sowing days but does not
completely fall within it; these regions are labelled “overlaps”.
Figure has only a small area of red indicating
that monsoon onset is, for large parts of India, within the range of days of
rice sowing. In each plot shown in Fig. the
region that is red or yellow is different, and this makes it difficult to say if
one dataset is better than another. ECHAM5 appears to have the smallest total
area in red or yellow, which is probably because ECHAM5 tends to have an earlier
onset than the other datasets and in general that makes it closer to the rice
sowing dates. Table lists the differences between the
monsoon statistics (onset and retreat) and the relevant sowing and harvest
dates for each crop calculated for each of the simulations and the APHRODITE
observations and averaged for India. Table shows
that on average across India rice sowing occurs between 10 and 20 days prior
to the averaged modelled monsoon onset (third block, Table ). We would not expect the different datasets to give
the same results; however, Table shows that they are
relatively consistent with each other and, importantly, with observations as
illustrated by the APHRODITE data. Table highlights
the fact that on average APHRODITE requires a larger crop rule than the simulations for
rice sowing; however, this is not always the case for sowing or harvest and
rice or wheat. The crop rules used here are based on the 1990–2007 period for
which ERA-Interim has the earliest onset (see Fig. ).
ECHAM5 has the smallest crop rule to
move it towards the rice sowing date but the highest variance in the mean
difference between the monsoon onset and the MinAg rice sowing date.
APHRODITE has the largest crop rule for rice sowing, indicating that the
weighted average of the APHRODITE monsoon onset is further from the rice
sowing date than for other datasets.
The comparison of the model monsoon onset in terms of the days of the year
(to within the pentad) and the range of days of
the year for the observed sowing date for rice. This is shown in
terms of hit (blue) and overlap (yellow); if there was no overlap
this is shown as a miss (red).
Analysis of the differences between the
midpoints of the MinAg data and monsoon onset and retreat
for rice and wheat sowing or harvest dates: the table shows
the minimum, maximum, mean, and standard deviation (SD)
averaged across South Asia where wheat or rice is planted.
In general the differences between rice harvest and monsoon retreat are
larger but still consistent across the region (see Fig. ), with rice harvest occurring on average
30–40 days after monsoon retreat (see fourth block, Table ). Wheat sowing tends to occur
approximately 60–70 days after monsoon retreat (see Fig. and first
block, Table ) and wheat harvest tends to occur
approximately 90–101 days before monsoon onset (see Fig. and second block Table ).
These values (given in Table ) provide the
RelMonsooncroprule values introduced in Sect. used to adjust the monsoon statistics and
calculate the new sowing and harvest dates based on the monsoon. There are
small regions with different monsoon characteristics and therefore much
earlier sowing days, for example for rice sowing in the southern and far
north of India. These regions have a direct impact on the values (minimum,
maximum, mean, and standard deviation) given in Table , which are averages for the whole of India
and are discussed in more detail in Sect. . Figure highlights the fact that the average sowing and harvest
dates for rice and wheat are closely aligned with the monsoon precipitation
from all three RCM simulations.
Monsoon-derived estimates of sowing and harvest dates for rice and wheat
The monsoon-derived sowing and harvest dates are calculated by applying the
RelMonsooncroprule for each model (see
Table ) to the simulated monsoon onset and retreat
fields (see Fig. ). Here we compare these with the
gridded observations to see how well the method performs for the present day.
The monsoon-derived sowing and harvest dates are compared with the MinAg
observations using regional maps and an analysis for each state area in order
to show the differences in the method across India.
Figure shows the monsoon-derived estimates of
rice sowing dates (left column) compared with MinAg observations (right
column). Figure shows the same plots for rice
harvest, with plots for wheat shown in Figs.
and for sowing and harvest respectively.
The RelMonsooncroprule values for wheat for both sowing and harvest are
much larger than those for rice, but there is still good agreement between the
monsoon-derived estimates and the MinAg observations across the region. On
average the monsoon-derived estimates of sowing and harvest dates are within
4 days of the midpoints for the sowing and harvest dates for rice and
within 7 days of the midpoints for sowing and harvest dates for wheat.
There is some variation across India with some regions showing larger
differences, but generally the monsoon-derived estimates for sowing and
harvest dates are within the range provided by the observations across much
of the region for both crops.
The monsoon-derived rice sowing dates (a, c, e, g) and the
difference between the MinAg observations and the monsoon-derived
rice sowing dates (b, d, f, h) for the period 1990–2007.
Figure shows the average crop duration for each state
where MinAg observations were available for the 1990 to 2007 period alongside
the crop duration for each of the four sets of monsoon-derived estimates
using the Fig. method. In the majority of states shown
in Fig. the sowing and harvest dates calculated using
the Fig. method were within the range of the MinAg
observations for rice and wheat sowing and harvest dates; however, the overall
performance was better for rice compared with wheat and sowing compared with
harvest in each crop. Figure also highlights the
difference in both the observed and simulated crop duration between the two
crops with rice having a shorter season than wheat. In general across most of
the states with available data the method provides a reasonable estimate of
the sowing date, harvest date, and crop duration. Even where the method does not
quite capture the observed sowing and harvest dates, the method is often just
outside the observed range.
The state averaged durations for rice (upper panel) and wheat (lower panel) for each dataset are shown
by the lines for each state together with the sowing and
harvest dates shown by the different shapes at the end of each line. The MinAg observations
are shown by the black line and downward triangles, with the paler triangles
representing the full range of sowing and harvest days for that state. The
APHRODITE observations are also shown by black lines and filled circles for
the sowing and harvest dates. ERA-Interim is shown by cyan lines and squares,
ECHAM5 by blue lines and asterisks, and HadCM3 by red lines and upward triangles.
In order to establish how well the method performs overall, we use Fig.
to assess if the results using the method are good,
poor, or fair compared to the MinAg data. Where the monsoon-derived sowing and
harvest dates from three of the four datasets using the method are within the
range of the MinAg data as shown in Fig. , the results
of the method are said to be “good” for a state. The results of the method
are said to be “fair” where two datasets are within the range of the MinAg
data and “poor” where the sowing and harvest dates fall outside the observed
range. In this analysis only the state of Assam did not have any “good”
scores for rice or wheat sowing or harvest. Most of the scores for most
states for sowing and harvest as well as wheat and rice had a score of
good or fair.
In general the regions where the monsoon-derived sowing and harvest dates are
not as close to the MinAg observations tend to be the states in the
south, such as Andhra Pradesh and Karnataka, or to the north of India, such as
Jammu and Himachal Pradesh. This is supported by the maps, particularly for
rice for these regions (in Figs. and ), which show that the method does not
perform as well for some of these states. These differences may be explained
by the differing monsoon characteristics in these regions compared to the
rest of India; these are highlighted in Fig. and discussed further in Sects.
and . Assam in the north-east
of India is also noticeable compared with the other states in Fig. , with the rice crop season in the MinAg data displaced
to an earlier part of the year. Assam tends to plant predominantly rice
with three distinct rice seasons (autumn, winter, and summer)
rather than a rice–wheat rotation . In this
analysis we use data for the Kharif paddy rice crop from the MinAg dataset,
which is planted and harvested earlier in Assam than in other states, with
sowing in February–March and harvest in June–July .
Analysis of future monsoon onset and retreat
As a demonstration of the method summarized in Fig. , the
HELIX SWLs (described in Sec.) are used to select
two future periods: 2040–2057 and 2080–2097. Considering only these future
periods, spatially HadCM3 and ECHAM5 show quite different future climates.
HadCM3 shows a similar onset to the present day for 2040–2057 (see Fig. a and c)
but a later onset compared with the
present day for 2080–2097 (see Fig. a and c).
ECHAM5 shows an earlier onset compared with the present day for the
2040–2057 period (see Fig. b and d) but
much later for the 2080–2097 period (see Fig. b and d).
This suggests high variability in monsoon onset in these
simulations. In fact, monsoon onset, peak, retreat, and duration all show a
large degree of variability as shown in Fig. in which each statistic has been averaged
for South Asia. Each point in Fig.
represents a 17-year time slice from between 1970 and 2097 for each of the
APHRODITE, ECHAM5, HadCM3, and ERA-Interim datasets.
Figure supports the points made
regarding the spatial plots and also shows how the four monsoon statistics
change between the 17-year time slices. The 2040–2057 period has a much
earlier onset for ECHAM5 than all the other periods except the 2000–2017
period, which is similar (see Fig. a).
For most of the periods ECHAM5 has an earlier onset than HadCM3; this
is also true of the retreat (see Fig. b),
but the duration is usually longer for ECHAM5 compared with HadCM3 (see
Fig. d).
The difference between the monsoon statistics for the 2040–2057
future period and the present day 1990–2007 for HadCM3 (a, c, e, g) and ECHAM5 (b, d, f, h).
Monsoon statistics: onset (a), retreat (b), peak (c), and
duration (d) averaged for South Asia for 12 total 17-year time slices between 1970 and 2097
to provide a time series of values for the region to assess the variability of the
monsoon.
In order to illustrate the method for deriving sowing and harvest dates,
Fig. shows the annual cycle of
precipitation averaged for South Asia for the two future periods (panel a
shows 2040–2057 and panel b shows 2080–2097) in the same way as the present
day is shown in Fig. . The crop sowing and harvest
dates used to provide the growing season durations in each of the plots shown
in Fig. for each of the
simulations are calculated using the method described in Fig. .
This shows that the proposed method provides an
estimate of sowing and harvest dates that ensures the crops can continue to
be grown in the simulation when the climate is most appropriate rather than
being fixed to the present day observed values.
The 1.5-year precipitation climatology for the
period 2040–2057 (a) and the 2080–2097 (b) averaged for the whole
of South Asia for each simulation (HadCM3-red line, ECHAM5-blue line)
using a 5-day smoothed rolling mean. Also shown are the monsoon-derived
growing seasons for wheat (orange) and rice (green) calculated using the
method described in Fig. for HadCM3 (upper rectangle with perpendicular hatching)
and ECHAM5 (lower rectangle with diagonal hatching). The monsoon onsets for each simulation are shown
using blue vertical lines and retreats using pink vertical lines (ECHAM5-dash dot lines,
HadCM3-solid).
The average irrigation fraction for rice (a) and
wheat (b) calculated from the ICRISAT observations of irrigation
area and area planted.
Discussion
Recent climate impact studies such as AgMIP
and ISIMIP
have highlighted the importance
of reliable input data for models. Section highlights
the scale of the uncertainties present when solely using a global sowing and
harvest dataset to simulate region-specific cropping patterns. We have
therefore proposed a new method for generating sowing and harvest dates for
South Asia based on the ASM. The method reproduces observed sowing and
harvest dates for much of India, and these results are discussed further in Sect. .
This method will also be useful in other monsoon
regions where data are scarce, unreliable, or unavailable such as in future
climate simulations. The future results are discussed further in Sect. .
Present day analysis
In general the method described by Fig. works well
across most of India for the present day, with the monsoon-derived estimates
of sowing and harvest dates falling within the range of days for sowing given
by the observations and therefore providing a good estimate of the crop
duration for most states (see Fig. ). However, there are
regions where the estimated sowing and harvest dates do not compare as well
against present day observations. Rice sowing is generally closely associated
with ASM onset across most of central India; however, in the south of India
there is a small region where the differences between the observations of
sowing dates and the monsoon are larger than everywhere else (see Fig. ). In Sect. this region is
shown to have different monsoon characteristics to the rest of India. This
part of India includes the state of Tamil Nadu, which is located on the
lee side of the Western Ghats and therefore does not receive the large
amount of ASM rainfall that is commonly associated with this part of
the world. Tamil Nadu receives up to 50 % of its annual rainfall during
October–December via the less stable North-east (NE) Monsoon. The NE
Monsoon is therefore more important for water resources for this part of
India than the ASM, which accounts for approximately 30 % of the annual
rainfall for this region . These differing monsoon
characteristics mean that different agricultural practices are required to
cultivate rice in this part of the country. This is illustrated by Fig. a, which shows that the southern region of
India with differing monsoon characteristics irrigates rice more intensively
than other parts of India. In the Tamil Nadu region, rivers are usually dry
except during the monsoon months and the flat gradients mean there are few
locations for building reservoirs; therefore approximately one-third of the
paddy rice crop is irrigated from a large network of water tanks
. The southern states of India have the highest
density of irrigation tanks with large numbers also found in Andhra Pradesh
and Karnataka; these are also regions shown to have a high irrigation
intensity in Fig. . Rice harvest is typically not as
closely associated with the monsoon onset as rice sowing, which usually
requires the monsoon to be fully established before planting.
The widespread irrigation of wheat shown in Fig. b has less of an impact on the estimates of wheat sowing and harvest
dates because this crop is less closely linked to the monsoon onset than
rice. Therefore the regional differences between the MinAg observations and
the monsoon-derived sowing and harvest dates for wheat are not as large as
some of those for rice (see Sect. ). Given that the method has
provided reasonable estimates of sowing and harvest dates for most of India,
it would be useful and interesting to extend this method to improve it for
the south of India.
Future analysis
The analysis of the future monsoon onset, retreat, peak, and duration shown in
Sect. shows how changeable the ASM is for these
simulations between time periods. show that there is
a high model agreement within the ensemble from the Coupled Model
Intercomparison Project Phase 5 (CMIP5) for an earlier onset and later withdrawal in
the future that therefore indicates a lengthening monsoon duration. However,
the simulations presented here do not show this with Fig. , instead highlighting the large amount
of variability in the ASM for this region. It is possible that an increase in
the monsoon duration does occur in these simulations for some parts of South
Asia, but this detail is lost through averaging over the region or as a result
of the time periods selected. also suggest that there
is medium confidence within the CMIP5 ensemble that the ASM rainfall will
increase to the end of the century. The simulations presented do indicate
this as shown by the time series in Fig. .
The annual time series of total monsoon
precipitation smoothed using 5-year averaging and averaged
for the whole of South Asia for all simulations; APHRODITE solid
black line, ERA-Interim solid cyan line, ECHAM5 blue dashed line, and HadCM3 red dotted line.
Assuming that crops continue to be grown in accordance with the monsoon,
Sect. shows that the method described in Sect.
provides a good estimate of sowing and harvest dates for
the two future periods shown. Spatial plots of the sowing and harvest dates
for the two future periods (not shown) are similar to those in Sect. for the present day with the
south of the Indian peninsula continuing to show different monsoon
characteristics (see Sect. ) to the rest of India in the
future, resulting in later estimated sowing and harvest dates for this
region.
The proposed method successfully adjusts the sowing and harvest dates when
the monsoon begins earlier in the future simulations and therefore provides a
good estimate of sowing and harvest dates for the two future periods
considered. This is a key benefit of using this method as it simulates the
decision a farmer might take to sow before the usual observed date if the
monsoon arrived early. This method therefore provides the capability for
climate simulations to replicate the type of adaptation response that would
happen in the real world. This method would also be useful for other regions
that have a crop calendar that is similarly defined such as the SSA; this is
a multiple cropping region with sowing and harvest dates closely associated
with the main rainy season .
Conclusions
Sowing and harvest dates are an important input within crop models but are a
source of considerable uncertainty. Global datasets, such as
, cannot always distinguish when wheat is grown in
tropical and subtropical regions, therefore driving a requirement for higher-resolution regional datasets. Crops across much of South Asia are heavily
dependent on the ASM and therefore sowing and harvest dates tend to be
closely linked to this climatological phenomenon. We have therefore presented
a new method for deriving sowing and harvest dates for rice and wheat for
South Asia from the ASM onset and retreat. For the present day, the method
generally shows good results for most areas of India with the derived sowing
and harvest dates within the range of the observations for most states. The
method does not work as well for the south of the Indian peninsula; this
region receives a lower proportion of annual rainfall from the ASM than much
of the rest of South Asia and irrigates intensively. Monsoon-derived
estimates of sowing and harvest dates for rice and wheat are useful for
regions where data are scarce and/or unreliable or in future climate impact
assessments. The method presented assumes that agricultural practices
will remain dependent on the monsoon in the future. Given this assumption, the
method presented successfully estimates the sowing and harvest dates for two
future periods by adjusting the sowing and harvest dates according to the
timing of the monsoon. Future work in this area could investigate refinements
to the method to take into account the different characteristics of the
monsoon in regions where the method does not work as well and the
differing agricultural practices there. It would also be interesting to
investigate how well the method works for different crop rotations in
different monsoon regions.
Observations:
APHRODITE data (Yatagai et al., 2012) are provided at this URL
with user registration:
http://dias-dmg.tkl.iis.u-tokyo.ac.jp/dmm/doc/APHRO_MA-DIAS-en.html.
The sowing and harvest dates used in the method are from Bodh et al. (2015) (http://eands.dacnet.nic.in).
The state-level data are provided by the Government of India, Ministry of Agriculture
and
Farmers Welfare, Directorate of Economics and Statistics at this URL:
http://eands.dacnet.nic.in/PDF/Agricultural_Statistics_At_Glance-2015.pdf (Government of India, 2015).
We also use Sacks et al. (2010) to motivate the method. This is available
via the Centre for Sustainability and the Global Environment,
Nelson Institute at the University of Wisconsin-Madison:
https://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset/index.php.
Irrigated area and crop area are from ICRISAT data, which are provided from this URL:
http://vdsa.icrisat.ac.in/ (ICRISAT, 2015). The details of the Meso dataset can be found here here:
http://vdsa.icrisat.ac.in/vdsa-mesodoc.aspx.
Access to these data requires registration
(http://vdsa.icrisat.ac.in/vdsa-requestData.aspx).
Details of the models used
This analysis uses two general circulation models (GCMs)
selected to capture a range of temperatures and variability in precipitation
similar to the AR4 ensemble for Asia and the main
features of the ASM
.
HadCM3, the third version of the Met Office Hadley Centre Climate Model
HadCM3;a version of the Met Office Unified Model, provides the positive variation in precipitation, and ECHAM5
third realization the negative variation in order
to estimate the uncertainty in the sign of the projected change in
precipitation over the coming century.
One RCM, the HadRM3 RCM , is used to downscale the GCM
data to provide more regional detail to the global datasets. HadRM3 has 19
atmospheric levels and the lateral atmospheric boundary conditions are
updated 3-hourly and interpolated to a 150 s time step. These simulations
include a detailed representation of the land surface in the form of version
2.2 of the Met Office Surface Exchange Scheme, which includes a full physical
energy balance snow model MOSESv2.2;. MOSESv2.2
treats subgrid land-cover heterogeneity explicitly with separate surface
temperatures, radiative fluxes (longwave and shortwave), heat fluxes
(sensible, latent, and ground), canopy moisture contents, snow masses, and
snowmelt rates computed for each surface type in a grid box
. However, the air temperature, humidity, and wind speed
above the surface are treated as homogenous across the grid box and
precipitation is applied uniformly over the different surface types of each
grid box . This RCM was included in an assessment of
four RCMs conducted by for the South Asia region,
which demonstrated that RCMs were able to capture the monsoon.
HadRM3 is driven by boundary data from the two GCMs (see
Fig. ) to provide 25 km resolution regional climate
modelling of the Indian subcontinent (25∘ N,
79∘ E–32∘ N, 88∘ E) for the period 1960–2100.
These RCM simulations are from the EU-HighNoon project (referred to hereafter
as HNRCMs), currently representing the finest-resolution climate modelling
available for this region .
The HNRCMs use the SRES A1B scenario, which represents a future world of very
rapid economic growth, global population that peaks in mid-century and
declines thereafter, and the rapid introduction of new and more efficient
technologies. The A1B scenario specifically represents this future world
where there is balance across energy sources, i.e. a mixture of fossil and
non-fossil fuels .
Comparing observed sowing and harvest dates with estimates of monsoon onset and retreat
The difference between the midpoint of the monsoon
retreat in the model and the midpoint of the observed rice harvest
period for 1990–2007.
The difference between the midpoint of the monsoon retreat
in the model and the midpoint of the observed wheat sowing period for 1990–2007.
The difference between the midpoint of the monsoon onset in
the model and the midpoint of the observed wheat harvest period for 1990–2007.
Monsoon-derived estimates of sowing and harvest dates for rice and wheat
The monsoon-derived rice harvest dates (a, c, e, g) and the
difference between the MinAg observations and the monsoon-derived
rice harvest dates (b, d, f, h) for the period 1990–2007.
The monsoon-derived wheat sowing dates (a, c, e, g) and the
difference between the MinAg observations and the monsoon-derived
wheat sowing dates (b, d, f, h) for the period 1990–2007.
The monsoon-derived wheat harvest dates (a, c, e, g) and the
difference between the MinAg observations and the monsoon-derived wheat harvest dates (b, d, f, h) for the period 1990–2007.
Analysis of future monsoon onset and retreat
The difference between the monsoon statistics for the
2080–2097 future period compared with the present day 1990–2007 for HadCM3 (a, c, e, g) and ECHAM5 (b, d, f, h).
The authors declare that they have no conflict of
interest.
Acknowledgements
The research leading to these results has received funding from the European
Union Seventh Framework Programme FP7/2007–2013 under grant agreement no.
603864. Camilla Mathison and Pete Falloon were supported by the Joint UK DECC/Defra Met Office
Hadley Centre Climate Programme (GA01101). Thanks to Andy Wiltshire for the
initial discussions that contributed to the original idea and Gill Martin for
reviewing code and helping with the development of the existing monsoon
statistics code into Python. Thanks also to Karina Williams for some valuable
discussions, help with Python code, and review comments.
Edited by: Daniel Kirk-Davidoff
Reviewed by: two anonymous referees
ReferencesAnbumozhi, V., Matsumoto, K., and Yamaji, E.: Sustaining Agriculture through
Modernization of Irrigation Tanks: An Opportunity and Challenge for
Tamilnadu, India, Agricultural Engineering International, vol. III, 2001, 1–11, available at:
http://www.cigrjournal.org/index.php/Ejounral/article/view/194 (last
access: 1 September 2017),
2001.Annamalai, H., Hamilton, K., and Sperber, K.: The South Asian summer
monsoon and its relationship with ENSO in the IPCC AR4 simulations,
J. Climate, 20, 1071–1092, 10.1175/JCLI4035.1,
2007.Bodh, S. P. C., Rai, S. J. P., Sharma, S. A., Gajria, S. P., Yadav, S. M.,
Virmani, S. S., and Pandey, S. R.: Agricultural Statistics at a Glance 2015,
Ministry of Agriculture & Farmers welfare, Directorate of Economics and
Statistics, available at: http://eands.dacnet.nic.in (last
access: 20 April 2018), 2015.Cannell, M. G. R. and Smith, R. I.: Thermal Time, Chill Days and Prediction
of
Budburst in Picea sitchensis, J. Appl. Ecol., 20, 951–963,
10.2307/2403139, 1983.Challinor, A., Wheeler, T., Craufurd, P., Slingo, J., and Grimes, D.: Design
and optimisation of a large-area process-based model for annual crops,
Agr. Forest Meteorol., 124, 99–120,
10.1016/j.agrformet.2004.01.002,
2004a.Challinor, A., Wheeler, T., Craufurd, P., Slingo, J., and Grimes, D.: Design
and optimisation of a large-area process-based model for annual crops,
Agr. Forest Meteorol., 124, 99–120,
10.1016/j.agrformet.2004.01.002,
2004b.Challinor, A. J., Slingo, J. M., Wheeler, T. R., Craufurd, P. Q., and Grimes,
D. I. F.: Toward a combined seasonal weather and crop productivity
forecasting system: Determination of the working spatial scale, J. Appl. Meteorol. Clim., 42, 175–192,
10.1175/1520-0450(2003)042<0175:TACSWA>2.0.CO;2,
2003.Challinor, A. J., Müller, C., Asseng, S., Deva, C., Nicklin, K. J.,
Wallach, D., Vanuytrecht, E., Whitfield, S., Ramirez-Villegas, J., and
Koehler, A.-K.: Improving the use of crop models for risk assessment and
climate change adaptation, Agr. Syst., 159, 296–306,
10.1016/j.agsy.2017.07.010,
2017.Christensen, J., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I.,
Jones,
R., Kolli, R., Kwon, W.-T., Laprise, R., na Rueda, V. M., Mearns, L.,
Meneńdez, C., Räisänen, J., Rinke, A., Sarr, A., and Whetton, P.:
Regional Climate Projections, in: Climate Change 2007: The Physical Science
Basis. Contribution of Working Group I, Fourth Assessment Report of the
Intergovernmental Panel on Climate Change,
available at:
https://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch11.html (last
access: 15 January 2018),
2007.Christensen, J., Krishna-Kumar, K., Aldrian, E., An, S.-I., Cavalcanti, I.,
de Castro, M., Dong, W., Goswami, P., Hall, A., Kanyanga, J., Kitoh, A.,
Kossin, J., Lau, N.-C., Renwick, J., Stephenson, D., Xie, S.-P., and Zhou,
T.: Climate Phenomena and their Relevance for Future Regional Climate Change,
book section 14, 1217–1308, Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 10.1017/CBO9781107415324.028,
2013.Dhar, O. N., Rakhecha, P. R., and Kulkarni, A. K.: Fluctuations in northeast
monsoon rainfall of Tamil Nadu, J. Climatol., 2, 339–345,
10.1002/joc.3370020404,
1982.Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J., Boote, K. J.,
Büchner, M., Foster, I., Glotter, M., Heinke, J., Iizumi, T., Izaurralde, R.
C., Mueller, N. D., Ray, D. K., Rosenzweig, C., Ruane, A. C., and Sheffield,
J.: The Global Gridded Crop Model Intercomparison: data and modeling
protocols for Phase 1 (v1.0), Geosci. Model Dev., 8, 261–277,
10.5194/gmd-8-261-2015, 2015.Erenstein, O. and Laxmi, V.: Zero tillage impacts in India's rice-wheat
systems: A review, Soil and Tillage Research, 100, 1–14,
10.1016/j.still.2008.05.001,
2008.Erenstein, O., Farooq, U., Malik, R., and Sharif, M.: On-farm impacts of zero
tillage wheat in South Asia's rice-wheat systems, Field Crop. Res., 105,
240–252, 10.1016/j.fcr.2007.10.010,
2008.Essery, R. L. H., Best, M. J., and Cox, P. M.: MOSES 2.2 technical
documentation, Hadley Centre Technical Note, 30,
available at:
http://jules.jchmr.org/sites/default/files/HCTN_30.pdf (last
access: 1 May 2018),
2001.Essery, R. L. H., Best, M. J., Betts, R. A., Cox, P. M., and Taylor, C.:
Explicit Representation of Subgrid Heterogeneity in a GCM Land Surface
Scheme, J. Hydrometeorol., 4, 530–543, 10.1175/1525-7541(2003)004<0530:EROSHI>2.0.CO;2, 2003.Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski,
L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K.,
Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R.,
Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K.,
Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F.,
Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V.,
Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor,
D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B.
L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H.,
Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts
of 1.5 ∘C global warming – simulation protocol of the
Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model
Dev., 10, 4321–4345, 10.5194/gmd-10-4321-2017,
2017.Gohar, L., Lowe, J., and Bernie, D.: The Impact of Bias Correction and Model
Selection on Passing Temperature Thresholds, J. Geophys. Res.-Atmos., 122, 12045–12061, 10.1002/2017JD026797,
2017.Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns,
T. C.,
Mitchell, J. F. B., and Wood, R. A.: The simulation of SST, sea ice extents
and ocean heat transports in a version of the Hadley Centre coupled model
without flux adjustments, Clim. Dynam., 16, 147–168,
10.1007/s003820050010, 2000.Government of India: Ministry of Agriculture &
Farmers welfare, Directorate of Economics and Statistics, state level data,
available at:
http://eands.dacnet.nic.in/PDF/Agricultural_Statistics_At_Glance-2015.pdf (last
access: 2 April 2018),
2015.Hodson, D. and White, J.: Paper Presented At International Workshop On
Increasing Wheat Yield Potential, Cimmyt, Obregon, Mexico, 20–24 March 2006
Use of spatial analyses for global characterization of wheat-based production
systems, J. Agr. Sci., 145, 115–125,
10.1017/S0021859607006855, 2007.Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S.,
Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree
Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2, 36–50,
10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2,
2001.ICRISAT: District Level Database Documentation, Tech. rep., International
Crops Research Institute for the Semi-Arid Tropics, Hyderabad, available
at: http://vdsa.icrisat.ac.in/ (last
access: 15 January 2018), 2015.Jat, R. K., Sapkota, T. B., Singh, R. G., Jat, M., Kumar, M., and Gupta,
R. K.:
Seven years of conservation agriculture in a rice–wheat rotation of Eastern
Gangetic Plains of South Asia: Yield trends and economic profitability, Field Crop. Res., 164, 199–210,
10.1016/j.fcr.2014.04.015,
2014.Jones, R. G., Noguer, M., Hassell, D. C., Hudson, D., Wilson, S. S., Jenkins,
G. J., and Mitchell, J. F.: Generating high resolution climate change
scenarios using PRECIS, Met Office Hadley Centre, Exeter, UK, 40 pp.,
available at:
http://precis.metoffice.com/docs/PRECIS_Handbook.pdf (last access: 29 April 2018),
2004.Joshi, A., Ortiz-Ferrara, G., Crossa, J., Singh, G., Sharma, R., Chand, R.,
and
Parsad, R.: Combining superior agronomic performance and terminal heat
tolerance with resistance to spot blotch (Bipolaris sorokiniana) of wheat in
the warm humid Gangetic Plains of South Asia, Field Crop. Res., 103, 53–61, 10.1016/j.fcr.2007.04.010,
2007.Kotera, A., Nguyen, K. D., Sakamoto, T., Iizumi, T., and Yokozawa, M.: A
modeling approach for assessing rice cropping cycle affected by flooding,
salinity intrusion, and monsoon rains in the Mekong Delta, Vietnam, Paddy Water Environ., 12, 343–354, 10.1007/s10333-013-0386-y,
2014.Kumar, P., Wiltshire, A., Mathison, C., Asharaf, S., Ahrens, B.,
Lucas-Picher,
P., Christensen, J. H., Gobiet, A., Saeed, F., Hagemann, S., and Jacob, D.:
Downscaled climate change projections with uncertainty assessment over
India using a high resolution multi-model approach, Sci. Total Environ., 468–469, Supplement, S18–S30,
10.1016/j.scitotenv.2013.01.051,
2013.Laik, R., Sharma, S., Idris, M., Singh, A., Singh, S., Bhatt, B., Saharawat,
Y., Humphreys, E., and Ladha, J.: Integration of conservation agriculture
with best management practices for improving system performance of the
rice–wheat rotation in the Eastern Indo-Gangetic Plains of India,
Agr. Ecosyst. Environ., 195, 68–82,
10.1016/j.agee.2014.06.001,
2014.Laux, P., Kunstmann, H., and Bárdossy, A.: Predicting the regional
onset
of the rainy season in West Africa, Int. J. Climatol., 28,
329–342, 10.1002/joc.1542,
2008.Laux, P., Jäckel, G., Tingem, R. M., and Kunstmann, H.: Impact of climate
change on agricultural productivity under rainfed conditions in Cameroon – A
method to improve attainable crop yields by planting date adaptations,
Agr. Forest Meteorol., 150, 1258–1271,
10.1016/j.agrformet.2010.05.008,
2010.Lobell, D. B.: Climate change adaptation in crop production: Beware of
illusions, Glob. Food Secur., 3, 72–76,
10.1016/j.gfs.2014.05.002,
2014.Lucas-Picher, P., Christensen, J. H., Saeed, F., Kumar, P., Asharaf, S.,
Ahrens, B., Wiltshire, A. J., Jacob, D., and Hagemann, S.: Can Regional
Climate Models Represent the Indian Monsoon?, J. Hydrometeorol., 12, 849–868, 10.1175/2011JHM1327.1, 2011.Martin, G., Arpe, K., Chauvin, F., Ferranti, L., Maynard, K., Polcher, J.,
Stephenson, D., and Tschuck, P.: Simulation of the Asian summer monsoon in
five European general circulation models, Atmos. Sci. Lett., 1,
37–55, 10.1006/asle.2000.0004,
2000.Mathison, C., Wiltshire, A., Dimri, A., Falloon, P., Jacob, D., Kumar, P.,
Moors, E., Ridley, J., Siderius, C., Stoffel, M., and Yasunari, T.: Regional
projections of North Indian climate for adaptation studies, Sci. Total Environ., 468–469, Supplement, S4–S17,
10.1016/j.scitotenv.2012.04.066,
2013.Mathison, C., Wiltshire, A. J., Falloon, P., and Challinor, A. J.: South Asia
river-flow projections and their implications for water resources, Hydrol.
Earth Syst. Sci., 19, 4783–4810, 10.5194/hess-19-4783-2015,
2015.McMaster, G. S. and Wilhelm, W.: Growing degree-days: one equation, two
interpretations, Agr. Forest Meteorol., 87, 291–300,
10.1016/S0168-1923(97)00027-0,
1997.Met Office: Iris: A Python library for analysing and visualising
meteorological and oceanographic data sets, Exeter, Devon, v1.13 edn.,
available at: http://scitools.org.uk/, last access: 2 April 2018.Moors, E. J., Groot, A., Biemans, H., van Scheltinga, C. T., Siderius, C.,
Stoffel, M., Huggel, C., Wiltshire, A., Mathison, C., Ridley, J., Jacob, D.,
Kumar, P., Bhadwal, S., Gosain, A., and Collins, D. N.: Adaptation to
changing water resources in the Ganges basin, northern India,
Environ. Sci. Policy, 14, 758–769,
10.1016/j.envsci.2011.03.005, 2011.Moron, V. and Robertson, A. W.: Interannual variability of Indian summer
monsoon rainfall onset date at local scale, Int. J. Climatol., 34, 1050–1061, 10.1002/joc.3745,
2014.Nakicenovic, N., Alcamo, J., Grubler, A., Riahi, K., Roehrl, R., Rogner,
H.-H.,
and Victor, N.: Special Report on Emissions Scenarios (SRES), A Special
Report of Working Group III of the Intergovernmental Panel on Climate Change,
available at:
http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=0 (last
access: 29 April 2017), 2000.Osborne, T., Gornall, J., Hooker, J., Williams, K., Wiltshire, A., Betts, R.,
and Wheeler, T.: JULES-crop: a parametrisation of crops in the Joint UK Land
Environment Simulator, Geosci. Model Dev., 8, 1139–1155,
10.5194/gmd-8-1139-2015, 2015.Pope, V., Gallani, M. L., Rowntree, P. R., and Stratton, R. A.: The impact of
new physical parametrizations in the Hadley Centre climate model: HadAM3,
Clim. Dynam., 16, 123–146, 10.1007/s003820050009,
2000.Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000-Global monthly
irrigated and rainfed crop areas around the year 2000: A new high-resolution
data set for agricultural and hydrological modeling, Global Biogeochem. Cy., 24, GB1011, 10.1029/2008GB003435,
2010.Rivington, M. and Koo, J.: Report on the Meta-Analysis of Crop Modelling for
Climate Change and Food Security Survey, Climate Change, Agriculture and Food
Security Challenge Program of the CGIAR,
available at: https://cgspace.cgiar.org/rest/bitstreams/9114/retrieve
(last access: 26 July 2017),
2010.Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M.,
Giorgetta,
M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A.,
Schlese, U., Schulzweida, U., and Tompkins, A.: The atmospheric general
circulation model ECHAM 5. PART I: Model description, Max Planck
Institute for Meteorology Rep. 349,
available at:
http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_349.pdf
(last access: 14 January 2017),
2003.Rosenzweig, C., Jones, J., Hatfield, J., Ruane, A., Boote, K., Thorburn, P.,
Antle, J., Nelson, G., Porter, C., Janssen, S., Asseng, S., Basso, B., Ewert,
F., Wallach, D., Baigorria, G., and Winter, J.: The Agricultural Model
Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies,
Agr. Forest Meteorol., 170, 166–182,
10.1016/j.agrformet.2012.09.011,
2013.Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C.,
Arneth,
A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K.,
Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., and Jones,
J. W.: Assessing agricultural risks of climate change in the 21st century in
a global gridded crop model intercomparison, P. Natl.
Acad. Sci. USA, 111, 3268–3273, 10.1073/pnas.1222463110,
2014.Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop planting
dates: an analysis of global patterns, Global Ecol. Biogeogr., 19,
607–620, 10.1111/j.1466-8238.2010.00551.x, 2010 (data available at:
https://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset/index.php, last access:
1 March 2018).Sharma, B. and Sharma, H.: Status of Rice Production in Assam, India,
Journal of Rice Research: Open Access, 3, e121, 10.4172/2375-4338.1000e121,
2015.Singh, D. K., Kumar, P., and Bhardwaj, A. K.: Evaluation of Agronomic
Management Practices on Farmers' Fields under Rice-Wheat Cropping System in
Northern India, International Journal of Agronomy, 2014, 740656,
10.1155/2014/740656,
2014.
Sperber, K. R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A.,
Wang, B., and Zhou, T.: The Asian summer monsoon: an intercomparison of CMIP5
vs. CMIP3 simulations of the late 20th century, Clim. Dynam., 41,
2711–2744, 10.1007/s00382-012-1607-6,
2013.van Bussel, L. G. J., Stehfest, E., Siebert, S., Müller, C., and Ewert,
F.:
Simulation of the phenological development of wheat and maize at the global
scale, Global Ecol. Biogeogr., 24, 1018–1029,
10.1111/geb.12351,
2015.Waha, K., van Bussel, L. G. J., Müller, C., and Bondeau, A.:
Climate-driven
simulation of global crop sowing dates, Global Ecol. Biogeogr., 21,
247–259, 10.1111/j.1466-8238.2011.00678.x,
2012.Waha, K., Müller, C., Bondeau, A., Dietrich, J., Kurukulasuriya, P.,
Heinke, J., and Lotze-Campen, H.: Adaptation to climate change through the
choice of cropping system and sowing date in sub-Saharan Africa, Global Environ. Chang., 23, 130–143,
10.1016/j.gloenvcha.2012.11.001,
2013.Waongo, M., Laux, P., Traoré, S. B., Sanon, M., and Kunstmann, H.: A
Crop
Model and Fuzzy Rule Based Approach for Optimizing Maize Planting Dates in
Burkina Faso, West Africa, J. Appl. Meteorol. Clim.,
53, 598–613, 10.1175/JAMC-D-13-0116.1,
2014.Warszawski, L., Friend, A., Ostberg, S., Frieler, K., Lucht, W., Schaphoff,
S.,
Beerling, D., Cadule, P., Ciais, P., Clark, D. B., Kahana, R., Ito, A.,
Keribin, R., Kleidon, A., Lomas, M., Nishina, K., Pavlick, R., Rademacher,
T. T., Buechner, M., Piontek, F., Schewe, J., Serdeczny, O., and
Schellnhuber, H. J.: A multi-model analysis of risk of ecosystem shifts under
climate change, Environ. Res. Lett., 8, 044018,
10.1088/1748-9326/8/4/044018, 2013.Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and
Schewe,
J.: The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP):
Project framework, P. Natl. Acad. Sci. USA, 111,
3228–3232, 10.1073/pnas.1312330110,
2014.Yan, L., Li, G., Yu, M., Fang, T., Cao, S., and Carver, B. F.: Genetic
Mechanisms of Vernalization Requirement Duration in Winter Wheat Cultivars,
117–125, Springer Japan, Tokyo, 10.1007/978-4-431-55675-6_13,
2015.Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh,
A.: Aphrodite: constructing a long-term daily gridded precipitation dataset
for asia based on a dense network of rain gauges, B. Am. Meteorol. Soc., 93, 1401–1415,
10.1175/BAMS-D-11-00122.1, 2012 (data available at:
http://dias-dmg.tkl.iis.u-tokyo.ac.jp/dmm/doc/APHRO_MA-DIAS-en.html, last access: 5 March 2018).