Rainfall has been an unpredictable element in agricultural production for long, the farmer has to depend on the vagaries of nature to sustain his crops. The Indian subcontinent receives its rainfall from the south-west (summer) monsoon during June-September with very little rainfall in winter. June and July thus become crucial months for sowing the summer crop, which accounts for 50% of total agriculture input. Any deficit here will affect all the summer crops like groundnuts, cotton, sugarcane, kharif rice and soybeans. Experience and theory suggest that commodity prices and weather indices do not correlate well in a local area. This makes it virtually impossible to manage weather risk with a price hedge. There are no physical markets in weather. Moreover weather risk is localized and beyond human control Weather insurance often has failed because of inherent defects in its planning. We study the impact of weather i.e. rainfall on agricultural production and thus by extension the prices, and the GDP. Our results through ADF co-integration analysis indicate a short term relationship between the factors but there seems to be no convergence among these factors in the long term.

1. 1
International Conference on
Agribusiness and Food Industry in
Developing Countries:
Opportunities and Challenges
WEATHER RISK, AGRO COMMODITY PRICES
AND MACRO ECONOMIC LINKAGES:
EVIDENCE FROM INDIAN SCENARIO USING
CO-INTEGRATION MODEL
Presented By:
Ms.Preeti Laddha
Ms.Surabhi Agarwal

2. 2
AGRICULTURE IN INDIA
• India sustains 16% of the world’s population on 2.4%
of land resource
• Agriculture contributes 24% of the Indian GDP
• Employment to 57% of work force
• Single largest private sector occupation
• Raw material source to large number of industries
like (textiles, silk, sugar, rice, flour mills, milk
products)
Objectives of Study
• To measure and analyze the impact of
weather on commodity prices.
• Measure the degree of weather risk inherent
on commodity prices and consequent
linkages to inflation, exchange rates and
GDP
• Our recommendations

3. 3
Methodology
• Five major crops were selected namely
rice , wheat , cotton ,sugar and oilseeds.
• Index numbers of prices and production
has been taken for these commodities as
proxy for commodity prices and production
• Actual Rainfall as % of Normal Rainfall
has been taken
• Co-Integration Model has been used to
examine cause-effect relationship.

4. 4
Growth Rate in Agriculture GDP in various
Year
6.52001-02
6.871998-99
0.31999-2000
-0.12000-01
-2.821997-98
-5.22002-03
10.11996-97
-1.131995-96
5.081994-95
4.11993-94
6.221992-93
-1.851991-92
4.431990-91
GDP growth rate in agriculture (%)Year
Graph between Growth in Agricultural
GDP and Years
GDP Agriculture for various year
-6
-4
-2
0
2
4
6
8
10
12
1990-91
1992-92
1994-95
1996-97
1998-99
2000-01
2002-03
year
agricultureGDP
GDP Agriculture

5. 5
Risk in Agriculture
Dependence on Weather
• Up to 80% of variability in crop yields is attributed to
weather
• Less than 40% of net sown area is irrigated
• Most irrigation from non-perennial sources
• Extreme Weather Events in India (cold wave,
drought, fog, heat wave, tropical cyclones, floods)
• Dwindling ground water resources

6. 6
Actual Rainfall as % of Normal in various
year
922001-02
1061998-99
961999-2000
922000-01
1021997-98
812002-03
1031996-97
1001995-96
1101994-95
1001993-94
931992-93
911991-92
1191990-91
Actual Rainfall as % of Normal RainfallYear
Graph for Rainfall in various Year
rainfall in various year
0
20
40
60
80
100
120
140
1990-911992-921994-951996-971998-992000-012002-03
year
actualrainfallas%of
normal
actual rainfall as %of
normal rainfall

7. 7
Production of five selected Commodities
in various year
Cotton Oilseeds Rice Sugarcane Wheat
116.2 152.2 146.1 185.42 184.92002-03
132.9 194.8 187.5 190.2 206.72001-02
126.6 176.5 170.9 189.4 1982000-01
153.3 193.3 180.3 191.6 2171999-2000
163.4 224.9 173 184.8 202.51998-99
144.3 198.2 166 178.9 188.51997-98
189.2 231.3 164.4 177.6 1971996-97
171 212.1 154.8 179.9 176.41995-96
158.1 208.4 164.5 176.3 186.81994-95
142.8 203.4 161.5 147 1701993-94
151.6 193.6 146.5 145.9 162.51992-93
129.2 181.5 150.2 162.6 158.21991-92
130.9 179.5 149.4 154.3 156.61990-91
Production (Index number)Year
Graph of selected five commodity for
various year
Production of commodity for various year
0
50
100
150
200
250
1990-91
1991-92
1992-92
1993-94
1994-95
1995-96
1996-97
1997-98
1998-99
1999-2000
2000-01
2001-02
2002-03
year
production(indexnumber)
wheat
rice
cotton
oilseeds
sugarcane

8. 8
Prices of Five Selected commodities
445.28319.95441.56350.52349.312002-03
442.75346.02444.22287.02366.542001-02
447.81362.61446.88261.62386.222000-01
442.75369.72454.86309.88361.621999-2000
383.56364.98388.36353.06410.821998-99
349.14317.58356.44289.56381.31997-98
346.61282.03343.14292.1327.181996-97
283.36267.81311.22297.18391.141995-96
275.77282.03295.26281.94378.841994-95
2532372662542461993-94
2271802492652181992-93
2041602172662381991-92
1721521782231461990-91
WheatSugarcaneRiceOilseedsCotton
PricesYear
Graph of Prices of The Commodities in
various year
Price of commodity in various year
0
100
200
300
400
500
1990-91
1992-92
1994-95
1996-97
1998-99
2000-01
2002-03
Year
Prices
wheat
rice
cotton
oilseeds
sugar

9. 9
Analysis
OLS Model Result
0.3868530.621970.914760.956431Wheat
0.3117340.558330.7706840.87788Sugarcane
0.371370.60940.715330.845772Rice
0.522860.723090.2673770.51708Oilseeds
0.4482880.6695430.2085370.456658Cotton
R SquareMultiple RR SquareMultiple R
GDP, Price, production,
rainfall
Prices, Rainfall, productionCOMMODITY
OLS MODEL SIGNIFICANCE
RESULTS
SignificantSignificantSugarcane
Significance LevelSignificance Level
SignificantSignificantWheat
SignificantSignificantRice
SignificantSignificantOilseeds
SignificantSignificantCotton
GDP, Production, Prices,
Rainfall
Prices, Production,
Rainfall
COMMODITY

10. 10
CO-INTEGRATION RESULTS
Significant-2.261-3.009Not
significant
-2.261-1.1487Wheat
Significant-2.261-4.005Not
significant
-2.261-1.93812Sugarcane
Significant-2.261-3.989Not
significant
-2.261-1.26638Rice
Not
significant
-2.261-1.317Significant-2.261-2.56052Oilseeds
Not
significant
-2.261-1.987Not
significant
-2.261-1.92241Cotton
SignificantCritical
Value
T statSignificantCritical
Value
T stat
GDP, rainfall, production,
prices
Price, rainfall, productionCOMMODITY
OLS Model & Co-Integration Model
Results
OLS MODEL shows that there is significant
relationship in both cases for the sample of all
five commodity.
Co-integration Model show that there does not
exists a equilibrium between price, rainfall and
production.
Co-integration Model show that there exists a
equilibrium between GDP, price, rainfall and
production in case of Rice, sugarcane and
wheat.

11. 11
Inference
• High degree of co-relation between
commodity prices, rainfall and production
of commodity.
– In the case of cotton (45.66) and
oilseeds(51.7) it is less
• In the case of Price, Rainfall and
production only oilseeds is co-integrated.
This indicates a convergence in spite of a
low co-relation among them.
• The reduced dependence on the rainfall
aided by bumper production probably
results in the lag effects on the
macroeconomic factors.
• The development of instruments
(insurance, weather derivative) which
reduced the risk which will influence the
prices.
• Need to design weather risk insurance
models and strengthened the weather
derivative to trickle down the risk.

12. 12
• In the case of GDP, Price, Rainfall and
production Sugar and cotton is co-
integrated. This indicates a convergence
in spite of a low co-relation among them.
• Study shows that there is high impact of
rainfall over production and Prices.
• Lack/excess Rainfall can create a supply
shock.
THANKYOU !!!!