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Impact of climate change on agriculture in Karnataka

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Karnataka state is having the second largest rainfed agricultural area in the Country and food production is mainly depending on the south-west monsoon. The State's mean annual rainfall is found to be in decreasing trend along with its sixteen years cyclic periodicity. The State first half century's (1901-1950) normal of 1204 mm has been reduced to 1140 mm during second half of the century (1951-2000). Nevertheless, few districts like Bengaluru, Kolar and Tumkur are gaining in their mean annual rainfall and some traditionally heavy rainfall receiving districts like Kodagu, Chikmagalur and South Canara are loosing in their mean annual rainfall. The eastern districts of the state are tending to be more dependent on North East monsoon than terminal rains of the South West monsoon. Consequently individual crop growing area, growing period are changing. The normal sowing season rains are being delayed due to the shift of July rains to the August month and September peak rainfall is being shifted to October month. The maximum water available period for the grand growth period is shifting towards the end of September and beginning of October in many districts. Finger millet crop area (main food crop of southern Karnataka) in Chikmagalur district, Groundnut area in Chitradurga and Tumkur districts, Red gram in Bidar and Gulbarga districts is increasing. Where as, Groundnut area in Belgaum and Gulbarga districts and Red gram area in Belgam and Tumkur is decreasing.
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Impact of climate change on agriculture in Karnataka
M. B. RAJEGOWDA, B.T. RAVINDRA BABU, N.A. JANARDHANAGOWDA and K.S. MURALIDHARA
University of Agricultural Sciences, GKVK, Bengaluru-560 065.
ABSTARCT
Karnataka state is having the second largest rainfed agricultural area in the Country and food production is mainly
depending on the south-west monsoon. The State’s mean annual rainfall is found to be in decreasing trend along with its
sixteen years cyclic periodicity. The State first half century’s (1901-1950) normal of 1204 mm has been reduced to 1140
mm during second half of the century (1951-2000). Nevertheless, few districts like Bengaluru, Kolar and Tumkur are
gaining in their mean annual rainfall and some traditionally heavy rainfall receiving districts like Kodagu, Chikmagalur and
South Canara are loosing in their mean annual rainfall. The eastern districts of the state are tending to be more dependent
on North East monsoon than terminal rains of the South West monsoon. Consequently individual crop growing area,
growing period are changing. The normal sowing season rains are being delayed due to the shift of July rains to the
August month and September peak rainfall is being shifted to October month. The maximum water available period for the
grand growth period is shifting towards the end of September and beginning of October in many districts. Finger millet
crop area (main food crop of southern Karnataka) in Chikmagalur district, Groundnut area in Chitradurga and Tumkur
districts, Red gram in Bidar and Gulbarga districts is increasing. Where as, Groundnut area in Belgaum and Gulbarga
districts and Red gram area in Belgam and Tumkur is decreasing.
Key words: Climate change, rainfall, productivity, global warming
Journal of Agrometeorology 11 (2): 125-131 (Dec. 2009)
The Karnataka State is located between 11.5º N and
18.5º N latitude and between 74º E and 78.5º E longitude.
The mean elevation varies between 600 m to 900 m above
mean sea level (Rajegowda 1990). The State comprises of
ten Agro climatic zones. Global Climate change and its
impact on agriculture is becoming an important issue even
at the micro level. A slight change in the climate may lead to
major changes in plant and animal life. Rainfall is one of the
most important parameters that influence the agriculture of
the region and food production. Therefore, a case study was
taken up to analyze the rainfall pattern for few districts in
Karnataka state where rainfall is showing definite trend to
examine the cropped area variability for major crops. Sastri
and Urkurkar (1996) observed a decrease in pre-monsoon
rainfall in some parts of Chhattisgarh region in the months
of May and June which has detrimental effect on the pre-
sowing operations of rice crop. Saseendran et.al. (2000)
showed that the plausible climate change scenario for the
Indian subcontinent as expected by the middle of the present
century. Kumar et.al. (2001) estimated the relationship
between farm level net-revenue and climate variables in India
using cross-sectional evidence. Sinha et. al. (1988) indicated
that food supplies in smaller nations would be affected more
by climate change than those of larger nations.
MATERIALS AND METHODS
A case study has been taken up to analyze the rainfall
pattern of few districts in Karnataka State where rainfall is
showing definite trend and the cropped area variability trend
for major crops. Published data of rainfall and cropped area
of different districts were collected from Drought Monitoring
Cell- Bangalore and Directorate of Economics and Statistics-
Bangalore. Annual total rainfall for the years 1950 to 2006
is respect of several districts, zones and the state was analyzed
and plotted on time series. Similarly the districtwise cropped
area for the period from 1955-2006 was analyzed and with
time series. The trend in rainfall and the area under different
crops in several districts were examined and discussed.
RESULTS AND DISCUSSION
The time series of the mean annual rainfall of the State
indicates a definite cycle of sixteen years starting 1950 to
1964 and so on. The first half of the cycle received less than
the normal rainfall for the period from 1950 to 1958 and the
second half of the cycle received more than the normal for
the period from 1959 to 1964. During this half of the cycle,
two or three of eight years have received the rainfall opposite
to their trends. This cycle is repeated up to 2004 and the
state is in the positive half of the cycle from 2004 and likely
to continue till 2012 (Fig 1).
The State’s mean annual rainfall for the period from 1901
to 1950 was 1204 mm (Rajegowda et. al. 1990) and it during
the period 1951 to 2000 is reduced to 1140 mm (Annual Report
2003. Drought -2002). The mean annual rainfall of the State
for the period from 1901 to 2000 was reported to be in declining
(Panduranga et. al. 2006). There is s definite declining trend in
rainfall in Kodagu (Guruprasanna et. al. 2006), Chikmagalur
and South Canara districts. In Kodagu district, the mean annual
rainfall for the period 1901-1950 was reduced from 2725 mm
to 2625 mm during the period 1951-2006. Chikmagalur district’s
mean annual rainfall of 1927 mm declined to 1872 mm and
Dec 2009] 126Impact of climate change on agriculture in Karnataka
700
1100
1500
1900
50 55 60 65 70 75 80 85 90 95 2000 2005
Mean rainfall=1140mm
Mean rainfall=1140mm
Low High
Cyclic trend
Actual rainfall
Low Low LowHighHigh
Years
Fig 1: Cyclical trend of Karnataka State mean annual rainfall for the period from 1950 to 2006
800
1800
2800
3800
4800
5800
50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 0 2 4 6
Year
Rainfall (mm
)
Kodag u Chikmagalur
D.Kannada Linear (D.Kannada)
Line ar (Kodag u) Line ar (Chikmag alur)
Fig 2: Declining trend of rainfall in Kodagu, Chikmagalur and South Canara districts.
300
500
700
900
1100
1300
50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 0 2 4 6
Year
Rainfall (mm
)
Bengaluru Urban Kolar
Tumkur Linear (Tumkur)
Linear (Bengaluru Urban ) Linear (Kolar )
Fig 3: Increasing trend of rainfall in Bengaluru, Kolar and Tumkur districts.
127 [Vol. 11, No. 2RAJEGOWDA et al
1.4 9.1 14.7
27.9
100.4
70.8
114.3 109.4
216.7
135.0
55.6
13.8
1.8 5.0 11.3
45.1
96.3
113.6
73.0
158.7
191.5
237.9
65.7
16.0
0.0
50.0
100.0
150.0
200.0
250.0
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec .
Month
Rainfall (mm)
Before 1990 After 1990
Fig 4: Rainfall shift in the Eastern Dry zone of Karnataka
0
50
100
150
200
250
300
350
400
72 75 78 81 84 87 90 93 96 99
Yea r
Rainfall (m
m
JUL. Aug. J.avg. A.avg
Fig 5: Declining in July rains and increasing in August rains in the Eastern Dry zone of Karnataka
Rice area
30000
50000
70000
90000
110000
130000
150000
170000
190000
1955 1960 1965 1970 1975 1980 1985 1990 1995
Year
Area (ha)
Mandya Mysore Linear (Mysore) Linear (
Fig 6: Increase in rice area in Mandya and Mysore districts and their trends
Dec 2009] 128Impact of climate change on agriculture in Karnataka
South Canara district’s mean annual rainfall of 3976 mm has
been reduced to 3960 mm for the corresponding periods which
clearly indicates declining trend and the same is shown in
figure.2. Further few districts of the state have shown increasing
trend in the annual rainfall. Bengaluru, Kolar and Tumkur
districts have shown the considerable increasing trend in the
annual rainfall. Their mean annual for the period from 1901 to
1950 are 867 mm, 745 mm and 688 mm respectively and
compared to are 883 mm, 767 mm and 730 mm respectively
for the period from 1951 to 2006. (Fig 2).
The Eastern Dry zone of Karnataka consists of Bengaluru
and Kolar districts and parts of Tumkur district. This Zone is
also called as the Tank fed region and constitutes 9.42 per cent
of the State’s geographical area. Eighty per cent of the area is at
an altitude of 800 -900 m above mean sea level (Rajegowda
1990). 47 per cent of its area is under agriculture/horticulture
crops. Rajegowda et. al. (2000) have shown that there is a
predominant shift in the initiation and termination of rainfall to
supply adequate moisture for crop growing period. This shift
was observed after 1990 and their mean monthly values also
have changed. Before 1990, the annual rainfall ranged from
619 to 1119 mm with a mean of 869 mm. After 1990, the annual
rainfall ranged between 611 and 1311 mm with a mean of 1011
mm. During the first period, on the average the peaks were
observed during the months May, July and September (Fig 4).
During the second period, the peaks were observed during the
months May, August and October months.
Consequently growing area under different is varying.
Crop growing period is changing and crop productivity is also
varying. The normal sowing season gets delayed due to the
shift of July rains to the month of August (Fig.5).The peak
normally occurring during September shifted to October The
maximum water available period for the grand growth period is
shifting towards the end of September and beginning of October
in many eastern districts.
The changes in cropped area under different crops in
various districts was examined for the period from 1950 to 2006.
It is observed that area under some crops is increasing while it
is decreasing in some districts over years. A definite was
observed for the following crops. The area under rice in Mysore
and Mandya districts, finger millet (Ragi) in Chikmagalur
district, Red gram in Bidar district and Groundnut in Chitradurga
and Tumkur districts is increasing (Figs 6 to 9). The decline of
area of in red gram area in Belgaum and Tumkur districts,
Groundnut in Belgaum and Gulbarga districts are shown in
figures 10 and 11 respectively. Such change in the cropped area
is found to be influenced mainly due to the availability of the
rain water during the cropping season.
The declining trend of annual rainfall in Kodagu,
Chikmagalur and South Canara districts in is The increasing
trend of annual rainfall in Bengaluru, Kolar and Tumkur districts
indicates better water availability to the for getting higher yield.
The distribution of rainfall during the cropping season
has high influence on the cropping area and crop selection. In
both the periods considered for this study, the quantum of May
rains received during both the periods more or less remain same.
The rainfall received during the south-west monsoon, i.e.,
starting from June to October which is the crucial period for the
growth of the crop apart from the hydrological utility is much
more important. The quantum of rain received during June is
low and it remains unchanged more or less in both the periods.
The average rainfall during July, which was 114.3 mm during
1972-90, decreased to 73.0 mm during 1991-99. This reduction
in July rains seems to be compensated by an increase in August
rains (158.7 mm) during 1991-99 compared to the period 1972-
90 (109.4 mm). This clearly shows that there is a perceptible
shift in rainfall pattern from July to August and also from
September to October in this Agroclimatic zone. A distinguished
peak was observed in the month of September (216.7 mm)
during 1972-90 and October was the next highest rainfall-
receiving month. The analysis of monthly rainfall beyond 1991
showed that the highest rains are now received during October
i.e. (237.9 mm) and the next highest rainfall is received during
September (191.5 mm). This implies that the peak, which was
being observed during 1972-90, has shifted to October during
1991-99. There is a marginal increase even in the rainfall of
November month after 1990.
The crop sown during July rains would reach the grand
growth period i.e., flowering to grain formation stage (long
duration crops of about 115 days) during September month
which was receiving the highest rainfall till 1990, so that there
was no moisture stress during the grand growth period. After
1990, as a result of reduction in July and September rains, the
crops can not be sown during July, though the land preparation
could be done using June rains. Even with scanty rains, if the
sowing is done during July, the crop would suffer from moisture
stress due to the reduction in rainfall during September and
also the crop grown would be caught in the October rains causing
considerable loss in the grain yield at the harvest. The change
in the mean monthly rainfall pattern beyond 1990 does not
favour the sowing of crops during July month. This analysis
reveals that the sowing of the crops (medium duration variety
crops of about 115 days) could be done during August preparing
the land using June and July rains. In the years of early onset of
south-west monsoon, sowing can be recommended during last
week of July also. The crop sown during August would reach
the grand growth period during October. As the October month
receives higher rainfall the crop in its grand growth period would
not suffer for want of moisture. The crop sown beyond August
129 [Vol. 11, No. 2RAJEGOWDA et al
Chickman
g
alur
0
20000
40000
60000
80000
100000
120000
140000
1955 1960 1965 1970 1975 1980 1985 1990 1995 200 0
Year
Area (ha)
Finger millet area
Fig 7: Increase in finger millet area in Chikmagalur district and its trend
Redgram area
0
50000
100000
150000
200000
250000
300000
350000
400000
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Area ( ha)
Bidar Gulburga Linear (Gulburga) Linear (Bidar)
Fig. 8: Increase in red gram area in Bidar and Gulbarga districts and their trends
Groundnut area
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Area ( ha )
Chitradurga Tumkur
Linear (Tumkur) Linear (Chitradur
g
a)
Fig 9: Increase in groundnut area in Chitradurga and Tumkur districts and their trends
Dec 2009] 130Impact of climate change on agriculture in Karnataka
R
0
5000
10000
15000
20000
25000
30000
1955 1960 1965 1970 1975 1980 1985 1990 199
Year
Are a ( ha )
Belgaum Tumkur Linear (Tumkur) Line
Fig. 10: Decrease in red gram area in Belgaum and Tumkur districts and their trends
Groundnut area
50000
70000
90000
110000
130000
150000
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Area (ha)
Belgaum Gulburga Linear (Belgaum) Linear (Gulburga)
Fig. 11: Decrease in groundnut area in Belgaum and Gulbarga districts and their trends
may not be able to complete its life cycle as a result of inadequate
moisture availability beyond 2nd fortnight of November (in the
event of the intensity of north-east monsoon being low) as crop
maturity coincides during this period. Under such
circumstances, the short duration variety crops have to be
preferred. Further micro level studies in quantum of rainfall
shift are needed.
The increasing in rice area in Mysore and Mandya districts
is due to increase in irrigation area and also reduction in rainfed
agriculture. In case of Chikmagalur district, the fingermillet area
s increasing due to the declining trend in rainfall. The farmers
are changing their crops to raise better crops based on the
available water through rain water. Hence, the area under finger
millet is increasing. Red gram in Bidar and Gulbarga districts
and Groundnut in Tumkur and Chitradurga districts is increasing
due to the decline in the terminal southwest monsoon rainfall
and increase in the northeast monsoon rainfall. In the same
districts the groundnut area is decreasing as the water
requirement is not met during the pod filling and pod maturity
of the crops as the September month rainfall is decreasing.
During this period, the redgram will not be affected as the crop
is in vegetative stage and does not demand more water and
hence the red gram area is increasing in these districts and
groundnut area is decreasing.
CONCLUSION
The state’s mean annual rainfall is found to be in
decreasing trend along with its sixteen years cyclic periodicity.
131 [Vol. 11, No. 2RAJEGOWDA et al
The State’s normal of 1204 mm is reduced to 1140 mm.
Bengaluru, Kolar and Tumkur are gaining in their mean annual
rainfall and some traditionally heavy rainfall receiving districts
like Kodagu, Chikmagalur and South Canara are loosing in their
mean annual rainfall. The eastern districts of the State are tending
to be more dependent on North East monsoon than terminal
rains of the South West monsoon. The predominant shift in
initiation and termination of rainfall to supply the adequate
moisture for crop growth has been observed in many agro
climatic zones of the state retaining the same length of rainy
period. Consequently to this, individual crop growing area is
varying, crop growing period is changing and crop productivity
is also varying. The normal sowing season rains are being
delayed due to the shift of July rains to the August month and
September peak rainfall is being shifted to October month. The
maximum water available period for the grand growth period is
shifting towards the end of September and beginning of October
in many districts.
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Rajegowda, M.B.(2006) Rainfall and runoff pattern of
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Kumar, K. S. Kavi, and Jyoti Parikh. (2001). “Indian
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Received: December 2008; Accepted: July 2009
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The monsoon rainfall (MN) is the main source of water for the agriculture, domestic and industrial related activities in the Indian subcontinent. Therefore, under the rapidly growing population, information about variability of MN is vital for planning and management of available water resource and agriculture related operations (Bal et al., 2004). Past studies showed that the Indian Ocean dipole mode (IOD) and El Niño Southern Oscillation (ENSO) had significant role in inter annual variability of monsoon rainfall and also on behavior of extreme events of monsoon rainfall in the Indian subcontinent (Sikka, 1980; Singh et al., 2005; Rajegowda et al., 2009; Krishnaswamy et al., 2015;Bothale and Katpatal, 2015). Most of the studies used either correlation techniques, traditional regression and/or composite techniques to analyze the ENSO-MN-IOD teleconnection in the Indian subcontinent. These techniques followed the assumption of the linearity in modelling of the ENSO-MN-IOD teleconnection. While, due to constant dynamism over time and space domain, climate phenomena are highly nonlinear ABSTRACT The present study examines the teleconnection of ENSO andIOD with monsoon rainfall (MN) and low, moderate and heavy rainfall events (LREs, MREs and HREs) over the Krishna river basin, using generalized additive models (GAMs) with suitable distribution. The outputs of GAMs indicate that, Poisson distribution is superior to the other distributions in assessing the teleconnection of ENSO-MN-IOD in the study area. Further, study resultsshowed that ENSO and IOD has significant (p < 0.001) non-linear responses to theLREs, MREs,HREs and MN. The influence of IOD on MN, LREs, MREs and HREs found positive on some parts, while negative on the other parts of the study area (i.e. heterogeneous in nature). W hile, ENSO has consistent negative influence on MN, LREs, MREs and HREsin the study area. Furthermore, La Niña and El Niño had positive and negative influenceon the MN, LREs, MREs and HREs respectively. The study outcomes will help the hydro-meteorologist and water related policy makers in modeling the impact of monsoon rainfall system on water, agriculture and allied sectors. Keywords: Monsoon rainfall, generalized additive models (GAMs), ENSO, IOD. in nature (Box et al., 2015). Therefore, analyses of the ENSO-MN-IOD relationship with use of traditional regression methods and composite technique needs to be very cautious or else it misleads the results. Hence it is required to model potential non-linearity under constant dynamism climate to understand the phenomena precisely. Generalized Additive Models (GAMs) is one of the advanced tools that have capability to detect nonlinear relationship between dependent variable and explanatory variables. GAMs are popularizing in recent decades in the field of statistics due to its wide variety of application (Hastie and Tibshirani, 1990). Therefore, present study is attempted to use the GAMs in assessing the non-linear influence of ENSO and IOD on the MN and rain events in the Krishna river basin. Krishnaswamy et al. (2015) have used GAM to detect non-linear influence of IOD and ENSO on Indian summer monsoon rainfall (ISMR) / extreme rainfall events (EREs). In their study, Gaussian distribution has been used for the ISMR and Poisson distribution was used for the EREs.
Conference Paper
Farming activities and crop productivity has been greatly affected by climate change, soil fertility, and the availability of planting areas in recent decades. Early estimation of yields and their quality are the prime requirements in the global food market which depend on several input parameters. Predicting crop yields gives the farmers an insight to decide on the crop production rate and cultivate suitable crops for the given climatic conditions. The present work aims at estimating yields of major and cash crops of Karnataka using climate and crop-related data through four ensemble regression approaches. The feature importance concept is incorporated that describes which features contribute most to the prediction results in all the models that help in better data interpretation. The performance metrics such as MSE, MAE, and R2 were adapted to measure the accuracy. The extreme gradient boosting regressor was found to deliver the best performance (R2 of 0.999) while incurring the lowest computational cost among the four ensemble regressor models. The results showed that ensemble regression methods can be effectively used for yield predictions when there is a medium-sized dataset.KeywordsAdaptive boostingCrop yieldEnsemble methodsExtreme gradientFeature importanceGradient boostingRandom forestPrediction
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Karnataka, a state in south India with nearly 80% of the cultivated land under rainfed farming, is very much dependent on rainfall for agricultural productivity. The spatio-temporal variability in observed rainfall over Karnataka is investigated using various data analytical techniques such as parametric and non-parametric methods, rotated empirical orthogonal function (REOF), clustering and spectral analysis. The observed data used for studying rainfall variability is the daily taluk-wise telemetric rain gauge data for a period of 1960–2016. A similar pattern in trend is observed in annual and south-west monsoon (SWM) rainfall over Karnataka such that taluks in the western and northern parts showed a decreasing trend, whereas the south interior part showed an increasing trend. A significant increasing trend in rainfall was found during pre-monsoon seasons whereas the northeast monsoon (NEM) rainfall showed a decreasing trend. The REOF analysis also indicated an upward (downward) trend in SWM and annual over the northern (southern) Karnataka and a weakening trend in the NEM rainfall. Using the hierarchical clustering method, six homogeneous rainfall clusters were identified over Karnataka based on distribution and variability of rainfall. The spectral analysis over different clusters showed significant oscillations in the annual and SWM rainfall in the 1970s and recent decades except the Western Ghat region where oscillations were much weaker during recent decades. The pre-monsoon and NEM rainfall also showed strong variability with a periodicity of 2–4 years in recent decades. The findings of this study can have implications while designing water resource management strategies across various sectors in Karnataka.
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India ranks second in population after China and is projected that it will be first by 2050. With increasing population, food demand is also increasing, and to support this, agricultural production and imports should be improved. The Karnataka state in India ranks ninth for agricultural production. Many of the government programs are implemented in the state of Karnataka to improve agriculture. The soil type in the state also varies from majorly black soil in northern region and red soil in southern regions. The annual average rainfall in the northern and southern parts of Karnataka ranges between 800 and 1300 mm/year and coastal areas receiving highest on an average of 3000–4000 mm. The state is drained by five river basins, namely, Godavari, Krishna, Cauvery, Pennar, and West Flow rivers. The state is dependent on groundwater for its all-round development. There is a spurt in groundwater development since the 1990s. The current chapter focuses on the fact that in spite of the state being blessed by natural resources, government aids, and advisories, how challenging the improvement of agricultural efficiency can be? As an example to highlight the importance of sustainable agriculture, a case study from southern part of Karnataka in Berambadi village for 2014, 2015, and 2016 is shown. The farmer in this project applies irrigation in 2 years (2015 and 2016), but the lack of knowledge about crop water use and irrigation scheduling results in reduced water use efficiency. This study highlights that irrigation surely improves the agricultural productivity, but it also leads to loss of water if sustainable strategies are lacking.
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Research background: Cocoa remains the Nigeria’s highest foreign exchange earner among all agricultural commodities, Contributed 12.5-14% of the national GDP. Currently, Nigeria is the fourth largest cocoa producing country in the world, produced approximately 328,652 tons annually. Occupational risk is a major factor reducing productivity of farm workers as it impairs physical capacity and increase vulnerability to ill health, diseases and injuries. Risk of agrochemical exposure has been attributed to work demand and unhealthy work environment. Purpose of the article: This study aimed to estimate life quality for agrochemical exposure risks of cocoa farm workers in Ondo state Nigeria. The study specifically estimates the amount an individual willingness to pay by respondents for occupational risk reduction. Methods: Multistage sampling technique that guaranteed cocoa farmers who could provide desired information on the basis of the objectives of the study was adopted for the study. Random selection of 180 cocoa farm workers from the study area. Descriptive statistics (frequency, mean and percentage) and Discrete Choice Experiment (DCE) approach that dovetailed into choice modelling and conditional logistic regression were the analytical tools used. Findings & Value added: the result revealed that 74% of the cocoa farm workers are on active age and mainly male with the mean age of 46 years. Most of the workers are illiterate that cannot read instructions on the agrochemical container. Average workers are willing to pay 830 Nigerian naira for personal protective equipment, 92 Nigerian naira for 15% wage discount as financial benefit of workplace injuries and 1024 Nigerian naira for training of workers in pesticide usage. The study concluded that better health conditions and appropriate use of personal protective equipment minimize the occupational risk. It was therefore recommended that educational programmes that will enhance farmer’s knowledge, skills and attitude to use safe methods (appropriate use of protective equipment) in pesticide usage should be adequately planned. Appropriate use of personal protective equipment to reduce exposure to agrochemicals and the risks involved in the misuse and abuse of agrochemicals should be adopted.
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This study estimates the relationship between farm level net-revenue and climate variables in India using cross-sectional evidence. Using the observed reactions of farmers, the study seeks to understand how they have adapted to different climatic conditions across India. District level data is used for the analysis. The study also explores the influence of annual weather and crop prices on the climate response function. The estimated climate response function is used to assess the possible impacts of a ‘best-guess’ climate change scenario on Indian agriculture.
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The CERES-Rice v3. crop simulation model, calibrated and validated for its suitability to simulate rice production in the tropical humid climate Kerala State of India, is used for analysing the effect of climate change on rice productivity in the state. The plausible climate change scenario for the Indian subcontinent as expected by the middle of the next century, taking into account the projected emissions of greenhouse gases and sulphate aerosols, in a coupled atmosphere-ocean model experiment performed at Deutsches Klimarechenzentrum, Germany, is adopted for the study. The adopted scenario represented an increase in monsoon seasonal mean surface temperature of the order of about 1.5C, and an increase in rainfall of the order of 2 mm per day, over the state of Kerala in the decade 2040–2049 with respect to the 1980s. The IPCC Business-as-usual scenario projection of plant usable concentration of CO2 about 460 PPM by the middle of the next century are also used in the crop model simulation. On an average over the state with the climate change scenario studied, the rice maturity period is projected to shorten by 8% and yield increase by 12%. When temperature elevations only are taken into consideration, the crop simulations show a decrease of 8% in crop maturity period and 6% in yield. This shows that the increase in yield due to fertilisation effect of elevated CO2 and increased rainfall over the state as projected in the climate change scenario nearly makes up for the negative impact on rice yield due to temperature rise. The sensitivity experiments of the rice model to CO2 concentration changes indicated that over the state, an increase in CO2 concentration leads to yield increase due to its fertilisation effect and also enhance the water use efficiency of the paddy. The temperature sensitivity experiments have shown that for a positive change in temperature up to 5C, there is a continuous decline in the yield. For every one degree increment the decline in yield is about 6%. Also, in another experiment it is observed that the physiological effect of ambient CO2 at 425 ppm concentration compensated for the yield losses due to increase in temperature up to 2C. Rainfall sensitivity experiments have shown that increase in rice yield due to increase in rainfall above the observed values is near exponential. But decrease in rainfall results in yield loss at a constant rate of about 8% per 2 mm/day, up to about 16 mm/day.
Drought-2002 in Karnataka State. Impact and Response. Drought Monitoring Cell
Annual Report (2003). Drought-2002 in Karnataka State. Impact and Response. Drought Monitoring Cell, Govt. of Karnataka, Bangalore, pp.1.
Climatic conditions in different Agroclimatic zones of Karnataka
  • M B Rajegowda
Rajegowda, M.B. (1990). "Climatic conditions in different Agroclimatic zones of Karnataka". Tech. Bulletin, UAS, Bangalore. pp.9
Climatic variability and crop productivity: A Case study for Chhattisgarh region of Central India
  • A S R A Sastri
  • J S Urkurkar
Sastri, A.S.R.A.S and Urkurkar, J.S., (1996). Climatic variability and crop productivity: A Case study for Chhattisgarh region of Central India. In: Climatic Variability and Agriculture (Eds. Y.P. Abroal, Sulochina Gadgil and G.B. Panth), Narosa Publishing House, New Delhi, pp.394-410.