Presented at International Seminar on “Approaches and Methodologies for Crop Monitoring and Production Forecasting” Under AMIS Global

inititaive, 25-26 May, 2016, Dhaka Bangladesh

FASAL Institute of Economic Growth (IEG)

University of Delhi Delhi 110007

EARLY SEASON CROP FORECASTING WITH FASAL ECONOMETRIC MODEL:

ITS USEFULNESS BEYOND FORECASTING

Nilabja Ghosh

OBJECTIVE

Consider Econometric modeling as a method to determine OUTLOOK of crop production before any crisis strikes- understand FASAL

Demonstration of estimates and forecasts

Select Cases to demonstrate usefulness: PULSES for price control

Policy options against crop promotion

Acknowledgement:

All Analysts who worked for FASAL at various times and contributed significantly to building up database, refining and applying model and method, setting up Software since 2005.

Contribution to this presentation by M. Rajeshwor, FASAL and Yogesh Bhatt for work on Biofuel acknowledged

ESTIMATION OF CROP OUTPUT IN INDIAN AGRICULTURE

A long time historical practice from colonial time

Associated with land revenue system, Crop cutting experiments

System improved over time- more scientific

Burden on administration

Errors, poor implementation of methods

Delays: Too late to be useful for Policy.

Gining significance for policy making in a world of volatile and integrated global markets, weather failures (climate change?),-

Need to avoid food insecurity, inflation, price crash, potential unrests and suffering Early information to plan stocking and procurement, timely

organization of logistics, credit, making trade strategy and market negotiations

EARLY ESTIMATES

Assessments of production and Acreages of crops early in the season- Hhistorical practice Generally always subjective- eye observation

State government responsibility – local officials in charge –maintained a register Excessive burden as they have multiple functions

Institution available only in some states, others have small sample surveys (EARAS, TRS)- time consuming

Early alerts and warnings useful even if imprecise

Need for rigorous -method based, transparent and data driven scientific forecasts with regular monitoring and updating in tune with changes in production climate and model performance for Timely Policy support

FASAL AN UMBRELLA PROGRAM -

Recommendation (1996-2000) for a ‘comprehensive project’ and ‘strong mechanism’ with ‘latest techniques’ to meet in-season forecast requirements

Led by ISRO-SAC, associated with India’s space program Visualized by SAC (Dr. Parihar): Since 2005 as a regular

Systematic mechanism Multi-discipline and innovative: Generating multiple in-season crop

forecasts at intervals in the year

Partnership of Institutions under coordination of MoA- IEG, SASA, State SAC, IMD ISRO NRSC etc Final most reliable estimate from FASAL -RS

Ministry puts on public domain 1AE-Sep 2AE-Jan 3AE-Apr 4AE-Jul and Final-Jan estimates of area and production of different crops. Inputs from State government, FASAL – validation, support and

comparison Under improvement, evolution

ECONOMETRIC MODEL BASED FORECAST

Earliest among all in-season forecasts (F0 F1) Least informed -Based on reasonable assumptions on driving

variables, known econ. Conditions, Prediction- Normal and alternate weather conditions scenarios. Policy assessment- potential of using estimated marginal

effects and simulations

Two stages: Acreage and Yield- Model estimation ,

Forecasts State level early estimates of area and yield (with ranges) for select crops in

major growing states

Forecast production- F-Area X F-Yield, Range based on SE of Area and yield predictions

Projected for All-India aggregate using state totals and proportion based on recent history.-or alternative methods. • Alternative scenarios of weather

Model in REDUCED FORM and ESTIMATION

Driving variables are predetermined or exogenous

Functional form-Linear allowing interactions and quadratic terms

Specification chosen on the basis of diagnostics- Sign of coefficients, t-stat>1

Robustness across specifications and sample sizes. Rbar Sq, DW, UR of error, AR corrected if indicated

Dynamic Area equation (Nerlovian partial adjustment, price expectations),

Yield allows for time trend, dummy variables for Policy (NFSM, BGREI, Bt. )

Estimation-Seemingly Unrelated Regression Equations (SURE) for competing crops in each state,

• Data: Official sources- MOA, IMD, M-Com&Ind Sample 1985-86 to 2013-14

Regular post sample validation, revision and up-dation of model

7

COVERAGE OF CROPS AND EXPLANATORY VARIABLES

Explaining dependent variable Crop area and Crop yield per hectare Kharif - Rice, Jowar, Bajra, Maize, Cotton, Jute, Groundnut, Soybean,

Sugarcane, Arhar, Moong and Urad. Rabi- Wheat, R&M, Groundnut, Jowar, Maize, Gram, - Major growing states, new states (JH, CHH, UKH, BH, MP, UP) with

limited data. Onion, Potato- experimentally

Explanatory variables • Economic: Expected prices of crops and substitute crops (using state

crop calendar), MSP (rice, wheat in procuring states), fertilizer price (cost)

• Irrigation: Source wise -Total available area as the variable (farmer allocates among crops)

• Rainfall and Temperature (States): monthly averages Sowing and growing seasons identified

• State level crop calendars, IMD monthly data • Rainfall effects: Distribution matters interactions with soil moisture,

reservoir, ground water and adverse effect of excess rainfall

• Temperature specification: Dummy for higher than average by 2%.

RAINFALL EFFECT IN MODEL

Distribution matters: allow pre-season-(also pre-sowing, pre-monsoon, last monsoon) RF for soil moisture effect-monthly data

Monsoon- June-Sept-Ocober,

Timely rainfall or adequate soil moisture can influence crop choice and allocation of non-water input among crops

Quadratic (squared) Rainfall: Excess rainfall (compared to optimum) may harm

Interactions: Interactions of irrigation (source-wise) with rainfall- temporal distribution

Complements: Pre-season (s) and current Rainfall can influence productivity of irrigation from specific sources-(enhancing reservoir level, ground water, tank water, help drainage of rainwater etc.)

Substitute: Current or recent rainfall can influence productivity of irrigation (good rainfall can reduce need for irrigation, create drainage and w-management problem etc.)

AREA EQUATION

Where = expected price (previous harvest month prices (Kharif/Rabi) and MSP Sub = Substitute crop in that season in the Region SRS = Source wise m = sowing/pre-sowing months T = Temperature

= Price of fertilizer =growing months/pre-sowing months Rainfall DumT = Dummy As necessary for Technology programme R F and Temp effect + or - Others as in Area slide

YIELD EQUATION

EXAMPLES

12

SEASONS CROP CALENDAR

West Bengal: Rice Kharif: Aus (Autumn, minor) : Feb-Apr (Sowing) – July-Aug (Harvesting) and

Aman (major): July-Aug (Sowing)- Nov-Dec (Harvesting)

Rabi (Boro): Nov-Dec (Sowing) – Mar – June (Harvesting)

Largest harvest is Aman, occurring in November and December second harvest is Aus, involving traditional strains but more often including high-yielding, dwarf varieties. Rice for the Aus harvest is sown in March or April, benefits from April and May rains, matures during in the summer rain, and is harvested during the summer. Another rice-growing season extending during the dry season from October to March. The production of this Boro rice

Madhya Pradesh: Wheat Rabi: Oct-Nov (Sowing) – Feb-Mar (Harvesting)

Karnataka-: Maize Kharif: May-June (Sowing) – Sep-Oct (Harvesting)

Uttar Pradesh: Potato Rabi: Oct- Nov (Sowing) – Feb-March (Harvesting)

13

RICE SEASONS WEST BENGAL

• Sowing Season: Rice is sown mainly thrice in a year:

• Aman – Rice sown in the rainy season (July-August) and harvested in winter. India produces Aman Rice mainly.

• Aus –Rice sown in summer along with the pre-monsoonal showers and harvested in autumn is called Aus Rice. The quality of this rice is rather rough.

• Boro – Rice sown in winter and harvested in summer is called Boro Rice or spring Rice.

14

AREA EQUATION

15

Back to back droughts in 2014-15 and 2015-16:

Challenges for FASAL

State-wise and Total (Select growing states) Rainfall Departure (%) in 2014

16

J U N - S E P T

O C T – D E C

State-wise and Total (Select growing states) Rainfall Departure (%) in 2015

17

And

hra

Prad

esh

Ass

am

Bih

ar

Guj

arat

Har

yana

Karn

atak

a

Mad

hya

Prad

esh

Mah

aras

htra

Ori

ssa

Punj

ab

Raj

asth

an

Tam

ilnad

u

Utt

ar P

rade

sh

Wes

t B

enga

l

All

Indi

a

-8.1

-4.9

-24.

7

-11.

1

-36.

6

-19.

9

-13.

9

-26.

5 -10.

0

-31.

6

9.7

-9.9

-43.

2

5.4

-17.

6

And

hra

Prad

esh

Ass

am

Bih

ar

Guj

arat

Har

yana

Karn

atak

a

Mad

hya

Prad

esh

Mah

aras

htra

Ori

ssa

Punj

ab

Raj

asth

an

Tam

ilnad

u

Utt

ar P

rade

sh

Wes

t B

enga

l

All

Indi

a

-4.8

-53.

1

-82.

6

-87.

6

-78.

9

-19.

8

-58.

1

-61.

8

-68.

1

-74.

6

-80.

3

51.8

-76.

0

-72.

7

-27.

0

J U N - S E P T

O C T – D E C

Validation of all India Production (recent 2 years)

IEG-2014-15 (MOA)

2014-15

Error

%

IEG-

2015-16

3AE-

2015-16

Error

%

Crops Million Tonnes Million Tonnes

Rice Kharif 84.8 91.4 -7.2 92.6 90.6 2.2

Rice Rabi 12.3 14.1 -12.8 12.3 12.8 -3.7

Rice Total 97.1 105.5 -8.0 104.8 103.4 1.4

Wheat 88.4 86.5 2.2 88.7 94.0 -5.7

Maize Kharif 17.5 17.1 2.3 16.0 15.5 3.2

Arhar 2.9 2.8 3.6 2.7 2.6 3.8

Moong Kharif 0.9 0.9 3.4 1.0 1.0 -2.0

Gram 7.3 7.3 0.2 8.5 7.5 13.6

Potato 44.6 48.0 -7.1 - 48.1 -

Usefulness -forecasting for controlling prices of Pulses

• Pulses - dominant items in Indian diet - sources of nutrition (protein)

• India is deficit in Pulses production

• Promotion by policy – TMOP-1990 , TMO-1980, ISOPOM-1995 etc.

• Need for timely imports • Pulses: 4.6 Ml. T in 2014-15 and 2.24 Ml. T imported in 2015 (April-Sept)

• Erratic Imports- options limited

• Managing scarcity of supply and price rise of Pulses a Challenge for the economy

• FASAL provided outlook in 2nd consecutive drought year 2015-16

• Presented at Krishi Bhavan, Ministry of Agriculture, New Delhi on 19th August, 2015, Forecast of Pulses (also Oilseeds) using actual rainfall data up to 18th August, 2015

8000

9000

10000

11000

12000

13000

14000

15000

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Total Pulses Area

Actual Fitted

5000

6000

7000

8000

9000

10000

11000

12000

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Total Pulses Production

Actual Fitted20

Policy of promoting Biofuel for Energy security and GHG emission control (taken from Yogesh Bhatt)

Agro-based Biofuels – considered possible solution to the depleting sources of fossil fuels and GHG emissions Limited land resources, Diversion of crop land to biofuel can

compromise food production, Yield improvement may offset decrease in food crop acreage

Possible feedstock in India- Maize, Sugarcane, Soybean

The National Biofuel Policy by the Ministry of New and Renewable Energy (MNRE) released in December, 2009 To accelerate promotion of use of biofuels to increasingly

substitute petrol and diesel for transport

India has a target of 5 % blending by 2012, 10 % by 2017 and 20 % after 2017 as per policy 2009.

FASAL Model- useful to identify crops whose acreage likely to be hit in different states, to Simulate yield improvement necessary to offset production loss due to land diversion

Yield improvement required at aggregate level (%): Simulation 2013-14

Crops Maize Sugarcane Soyabean

Fo

od

gra

ins

&

cere

als

Rice 0.7 2.1 0.03

wheat 0.5 0.8 -

Bajra 3.2 6.6 0.3

Jowar 5.0 0.7 0.8

Ragi 2.4 17.7 0.5

Pu

lses

Arhar 1.9 5.8 0.3

gram 1.0 1.7 -

Moong 2.2 4.8 0.7

Urad 1.8 4.7 0.5

Oil

seed

s Groundnut 1.1 7.7 0.4

rpmst 1.3 2.9 -

Fib

re

Cotton 1.1 2.9 0.1

Jute 3.9 8.2 -

Yield improvement (%) needed in Indian states for promoting biofuels (Rs. 1000/tones)

Food crop: Jowar, Substitute: Maize Food crop: Arhar, Substitute: Sugarcane

(7.75,8.96](4.4,7.75][2.85,4.4]No data

(3.88,19.73](1.06,3.88](.6,1.06][.2,.6]No data

TOWARDS A REFORMED POLICY PARADIGM

• Need for coordinated, well deliberated policy making on production, stocks, import, export, distribution, credit etc.

• Inter-Ministerial consultation required on outlook formed by multiple alternate agencies and rational methodologies

• Overcome errors and delays of forecasts/estimates by validation of field observation with FASAL

• Satellite RS information becoming more important • Early stages RS has limitations,

• Econometric model can generate method based prediction for early planning- also macro-planning by aiding GDP estimation-

• Can prevent serious hardship, crisis and policy emergency

• In the long run- can be extended over multiple-countries, linked by geography, trade and information flows for integrated good results of prevention of hunger and disaster

AREA EQUATION

Rice (K) West Bengal

Maize (K) Karnataka

Rice (R) West Bengal

Wheat (R) Madhya Pradesh

Potato (R) Uttar

Pradesh

Constant 6.2 0.03 -2.0 -0.2 -2.5

Price 3.1*** (Sub- Urad)

2.4** (Sub-Urad)

4.8*** (Sub- Urad)

3.8*** (Sub- Gram,

Moong)

1.5 (Sub-Wheat,

Gram, Moong)

Rainfall 2.72** (Pre-Monsoon+

Monsoon)

-2.3** (Monsoon)

2.6** (Aug) 5.1*** (Nov)

3.4*** (Monsoon) 2.7** (October)

Rainfall2 -2.8*** (Aug)

Irrigation -3.04*** (Well)

1.7 (Total)

3.2*** (Canal+Well)

-2.7** (Well)

Interaction 3.1*** Canal*Monsoon(-1)

1.9* All*Monsoon

-2.9*** Canal*Monsoon(-1)

4.2*** (Well*Monsoon)

2.3** (Tank*Aug)

3.2*** (Well*Feb)

Temperature -2.2** (April_Min)

1.9* (Sep_Max)

Area (-1) -1.6 1.8 11.4*** 4.4*** 7.3***

Adjusted R-squared

0.80 0.97 0.96 0.92 0.88

26

YIELD EQUATION Rice (K)

West Bengal Maize (K) Karnataka

Rice (R) West Bengal

Wheat (R) Madhya Pradesh

Potato (R) Uttar Pradesh

Constant 13.0 0.86 17.6 -0.10 9.5

Price 6.7*** (defl-Fert)

2.5** (defl-Fert)

2.0* (defl-Fert)

2.7** (defl-Fert)

3.9*** (defl -Wheat,

Moong)

Substitute Price

-3.0*** (Urad/

defl-Fert)

- - - -

Rainfall 2.0* (Jan) -2.5** (Feb) -4.1*** (Sep)

2.6** (Monsoon)

4.1*** Monsoon(-1)

2.4** (Apr) -3.3*** (June)

-1.6 (Oct) 8.1*** (Monsoon)

2.6** Jan(+1) -5.8*** Mar(+1)

-2.6** (Dec) 2.6**

Jan(+1)

Irrigation 2.7** (Canal+Well+Oth)

-1.1 (Well)

2.9*** (Well+Canal)

Interaction 1.9* (Well*Monsoo

n) -3.1***

Total*Dec(-1)

2.7** (Canal*Sep)

2.3** (Well*Dec(-1))

5.8*** (Canal+Well)*Jan

2.2** (Well*Oct)

Temperature Aug_Min Oct_Min

Adjusted R-squared

0.95 0.72 0.50 0.93 0.69

27