PIAHSProceedings of the International Association of Hydrological SciencesPIAHSProc. IAHS2199-899XCopernicus PublicationsGöttingen, Germany10.5194/piahs-373-115-2016A socio-hydrological comparative assessment explaining regional variances in
suicide rate amongst farmers in Maharashtra, Indiaden BestenNadja I.n.i.den.besten@student.tudelft.nlPandeSaketSavenijeHubert H. G.https://orcid.org/0000-0002-2234-7203Department of Water Management, Delft University of Technolgy, Delft, the NetherlandsNadja I. den Besten (n.i.den.besten@student.tudelft.nl)12May2016373115118This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://piahs.copernicus.org/articles/373/115/2016/piahs-373-115-2016.htmlThe full text article is available as a PDF file from https://piahs.copernicus.org/articles/373/115/2016/piahs-373-115-2016.pdf
Maharashtra is one of the states in India that has witnessed one of the
highest rates of farmer suicides as proportion of total number of suicides.
Most of the farmer suicides in Maharashtra are from semi-arid divisions such
as Marathwada where cotton has been historically grown. Other dominant crops
produced include cereals, pulses, oilseeds and sugarcane. Cotton (fibers),
oilseeds and sugarcane providing highest value addition per unit cultivated
area and cereals and pulses the least. Hence it is not surprising that
smallholders take risks growing high value crops without “visualising” the
risks it entails such as those corresponding to price and weather shocks.
We deploy recently developed smallholder socio-hydrology modelling framework
to understand the underlying dynamics of the crisis. It couples the dynamics
of six main variables that are most relevant at the scale of a smallholder:
water storage capacity (root zone storage and other ways of water storage),
capital, livestock, soil fertility and fodder biomass. The hydroclimatic
variability is accounted for at sub-annual scale and influences the
socio-hydrology at annual scale. The model incorporates rule-based adaptation
mechanisms (e.g., adjusting expenditures on food and fertilizers, selling
livestocks) of smallholders when they face adverse conditions, such as high
variability in rainfall or in agricultural prices.
The model is applied to two adjoining divisions of Maharashtra: Marathwada
and Desh. The former is the division with relatively higher farmer suicide
rates than the latter. Diverse spatial data sets of precipitation, potential
evaporation, soil, agricultural census based farm inputs, cropping pattern
and prices are used to understand the dynamics of small farmers in these
divisions, and to attribute farmer distress rates to soil types,
hydroclimatic variability and crops grown.
Comparative socio-hydrologic assessment across the two regions confirms
existing narratives: low (soil) water storage capacities, no irrigation and
poor access to alternative sources of incomes are to blame for the crisis,
suggesting that smart indigenous solutions such as rain-water harvesting and
better integration of smallholder systems to efficient agricultural supply
chains are needed to tackle this development challenge.
Location of simulated districts. Model simulations for the following
eleven districts were conducted in Maharashtra, the districts in Marathwada:
Aurangabad, Beed, Jalna, Latur, Osmanabad, Parbhani, and the districts in
Desh: Pune, Sangli, Satara, Solapur and Kholapur. Spatial variable data sets
on potential evaporation, soil properties and rainfall were weighted on
district level.
Introduction
Smallholders contribute significantly to the total value of
agricultural output in India, though their resilience to climate change and
price volality make them susceptible to distress
. The fragility of smallholders is very
noticeable in farmer suicides rates in India. Between 1995 and 2012,
approximately 284 673 farmers committed suicide in several regions in India,
with a peak in 2006 and 2010 .
Understanding the challenges encountered by (small) farm holders requires a
multi-disciplinary approach. The following research builds upon
to explain smallholder dynamics between its natural resources and
socio-economic situation within a water-centric approach. We zoom into the
state of Maharashtra where one of the highest rates of suicide amongst
farmers have been recorded (NCRB, 2014). The socio-hydrological modelling
framework is applied to two regions of Maharashtra: Marathwada and Desh (see
Fig. ). We examine small farmers with 1–4 ha of
ground. The agricultural income mainly depends on cotton and sugarcane
production. Besides these crops, jowar (Sorghum), rice and soybean
cultivation are also abundant. Near to sixty percent of the smallholders in
the regions under study produce these five crops .
Marathwada division has experienced relatively higher farmer suicide rates
than Desh . The aim of this paper is to
explain this difference between the two sub-divisions through comparative
assessment of smallholder socio-hydrological model simulations in terms of
differences in hydro-climatic variability, soil heterogeneity and types of
crops grown.
Methodology
The smallholder socio-hydrologic modelling framework proposed by
is a dynamic model that can be made location specific. It
consists of six socio-hydrological state variables: soil moisture, soil
fertility, capital, livestock, fodder and labor availability. All these state
variables are modelled by simple differential equations. Seven
socio-hydrologic flux variables determine the interrelationships between
these state variables, these are: crop production, livestock sales,
expenditure, livestock production costs, crop production costs, labor factor
and fertilizer factor. Climatic forcing and wage rate are external to the
smallholder model system and influence the system as such.
To explain the modelling framework in a nutshell, consider a small farmer
experiencing a year with disappointing crop yields due to poor rainfall, e.g.
his/her capital encounters deficits as a result and therefore the farmer cuts
down on his/her expenditure by selling livestocks, cutting down on
investments, school fees and so on. The farmer stops cutting down on his/her
expenditure when capital becomes positive again. These adjustments
consequently affect the socio-hydrological state variables for the next year.
The evolution of farmers capital thus depends on how much he or she is
exposed to hydro-climatic variability (that affects crop yields) and price
volatility (that affects crop income) and whether the farmer has access to
hydrologic or financial instruments that can buffer these variabilities.
Transpiration demand not met, gives the percentage of mean annual
transpiration demand that is not met when rainfall is not enough to sustain
crop water demand during growing season. The values in the brackets display
the average yearly values of these deficits in [mmyr-1] over the
simulated time (1983–2009). % rainwater buffer: gives the percentage of
years when the amount of annual rainfall was not enough to sustain
transpiration demand, and consequently it gives the percentage of years when
harvesting rainwater was not a sufficient strategy.
The dominant soil types in the study regions were obtained through the
Harmonized World Soil Database v1.2 . All dominant soil textures
throughout Marathwada and Desh are classified as loamy to clayey textures
(USDA Texture classification) in the subsoil . We assume that
Available Water Storage Capacity (AWSC) is 175 mmm-1 for loam
and 200 mmm-1 for clay. The soil water storages for every
district are then calculated by weighted mean soil depth of the
districts multiplied by the previously mentioned AWSC.
Precipitation and potential evaporation data-series are created based on
satellite products provided by . Freely
available Global Potential Evapo-Transpiration (Global-PET) with a spatial
resolution of 30 arcsec (∼ 1 km at equator) is used to
compute average potential evaporation on district level. The product used
provides monthly averages over the years 1950–2000. These monthly averages
are assumed to represent the potential evaporation over the simulated years.
For precipitation a coarser product from the CRU-TS 3.0 Climate Database,
with a 0.5∘ (30 arcmin) spatial resolution on land areas, is
used.
Fertilizer and crop prices are obtained from The Worldbank (2010) with an
exchange rate of Rs 45 = USD 1 to convert The Worldbank (2010) prices to Rs
per kilogram. Crop specific yield coefficients are obtained as described by
. Assumed application of fertilizers is as follows:
27 kgNha-1 for Jowar, 57 kgNha-1 for Paddy and
10 kgNha-1 applied for soybean production . The
three crops are modelled as Kharif crops, growing from June until maximum
October. All the crop factors have been obtained from FAO .
Results
Marathwada is climatologically different from Desh: 860 mmyr-1
of rainfall falls on average in Marathwada, while Desh approximately receives
1000 mmyr-1. The Solapur district within Desh that neighbours
Marathwada is drier compared to other districts in Desh. Rain-fed agriculture
is dominant in the districts under study (except under sugarcane production),
therefore the water demands (crop transpiration) and supplies (rainfall) are
analysed among all crops in the study area (Table ).
The soil depths are highly variable within the study area. Shallow soils
(0–25 mm) are primarily located in various districts of Marathwada,
resulting in a disadvantage in average soil water storages for Marathwada
compared to the region of Desh. The shallow soils limit the rooting depths of
the crops grown in the area , hence decreasing
estimated soil storage capacities.
The estimated soil water storages and district specific hydroclimatic forcing
were used to simulate the effect on smallholder's capital and shown in
Fig. .
Discussion
Marathwada is faced with higher suicide rate than
the region of Desh. With the help of the water-centric approach within the
socio-hydrological modelling framework, we tested a hypothesis that inclusion
of spatial heterogeneity within the model can explain the differences in
well-being of smallholders between the two regions. This is in addition to
observed differences in hydro-climatic variability between the two regions.
These outcomes suggest that high hydro-climatic variability, shallow soils
and poor (risky) crop choices made by smallholders, can explain higher
suicide rates in Marathwada. From Fig. one can see that crop
income falls in Marathwada when suicide rates start to rise. This indicates
that Marathwada might contribute to the distress that farmers are facing in
Maharashtra.
Comparing hydro-climatological, soil characteristics and crops grown between
the divisions one can see that the characteristics are unfavourable for
Marathwada. Table displays that the need to overcome crop
transpiration demand deficits is higher in Marathwada than in the division of
Desh. Further, even rainwater harvesting in Marathwada is not as successful
as in Desh.
The capital (model) outcomes for all crops in all districts within a
given sub-division (i.e. Desh and Marathwada) under study were aggregated
based on agricultural census data on smallholders. Crop area weighted mean
capital time series were estimated for the two divisions. Suicide rates at
state level (Maharashtra) are plotted to indicate distress amongst farmers
within the state. All 3 time series were normalized by substracting the mean
and dividing by the standard deviation
High water demanding crops such as cotton are generally favoured in
Marathwada (24 % of the smallholders), while in Desh only 0.6 % of
the smallholders choose to grow cotton primarily under rain-fed condition.
Taking into account the observations in Table , creating local
storages in Marathwada will not be enough to bridge monthly deficits during a
growing season. A combination of solutions is indeed needed to outweigh
unfavourable farming conditions in Marathwada. The policy and interventions
in such regions could be steered in the direction of (1) preventing farmers
from betting on a good monsoon and deciding to high risk crops in inadequate
locations, and (2) to invest in local storages where biophysical
characteristics (as soil depth e.g.) are adequate.
Data availability
The spatial distribution of soils was analyzed with a data set available at
the repository of World Data Centre for Soils,
re3data.org.
ReferencesAgriculture Census Division: Agricultural Census Data Base, available at:
http://agcensus.dacnet.nic.in/, last access: 20 December 2015, 2011.
FAO: Fertilizer use by crop in India, Tech. rep., Food and Agriculture
Organization of the United Nations (FAO), Viale delle Terme di Caracalla,
Rome, Italy, 2005.FAO: Crop Water Information, available at:
http://www.fao.org/nr/water/c, last access: 11 January 2016, 2015.
Fan, S., Brzeska, J., Keyzer, M., and Halsema, A.: From subsistence to
profit: Transforming smallholder farms, International Food Policy Research
Institute (IFPRI), Tech. rep., 30 pp., 2013.
Fischer, G., Nachtergaele, F. O., Prieler, S., Teixeira, E., Tóth, G.,
van Velthuizen, H., Verelst, L., and Wiberg, D.: Global Agro-ecological Zones
Assessment for Agriculture (GAEZ 2008), IIASA, Laxenburg, Austria and FAO,
Rome, Italy, 2008.India-WRIS WebGIS: Soil depth, available at:
http://india-wris.nrsc.gov.in/soilapp.html, last access: 10 January
2016.
Jones, P. D. and Harris, I.: Climatic Research Unit (CRU) time-series
datasets of variations in climate with variations in other phenomena, NCAS
British Atmospheric Data Centre, Harwell Oxford, 2013
Khairnar, D. R., Bhosale, M. J., and Jadhav, M. A.: Lack of irrigation
facilities, drought conditions and farmers suicides in Marathwada region,
India, American Journal of Rural Development, 3, 74–78, 2015.Letey, J.: Relationship between soil physical properties and crop production,
in: Advances in soil science, Springer, 1, 277–294,
10.1007/978-1-4612-5046-3_8, 1985.
Mishra, S.: Farmers' suicides in India, 1995–2012: measurement and
interpretation, Asia Research centre, London, 2014.
Nagaraj, K.: Farmers' suicides in India: magnitudes, trends and spatial
patterns, 4th Edn., Bharathi puthakalayam, 421, Anna Salai, Teynampet,
Chennai, 2008.Pande, S. and Savenije, H. H.: A sociohydrological model for smallholder
farmers in Maharashtra, India, Water Resour. Res., 52,
10.1002/2015WR017841, online first, 2016.Ramanna, A.: Farmers rights in India: a case study, Tech. Rep. 4, The
Fridtjof Nansen Institute, available at:
http://www.mtnforum.org/sites/default/files/publication/files/4046.pdf,
last access: 15 January 2016, 2006.re3data.org: Registry of Research Data
Repositories, re3data.org – 10.17616/R3X01J, last access: 26 April
2016.
Zomer, R. J., Trabucco, A., Bossio, D. A., and Verchot, L. V.: Climate Change
Mitigation: A Spatial Analysis of Global Land Suitability for Clean
Development Mechanism Afforestation and Reforestation, Agr. Ecosyst.
Environ., 126, 67–80, 2008.