EARLY SEASON CROP FORECASTING WITH FASAL ECONOMETRIC MODEL: ITS USEFULNESS BEYOND FORECASTING 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 Nilabja Ghosh
OBJECTIVE Consider Econometric modeling as a method to determine OUTLOOK of crop production before any crisis strikesunderstand 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, premonsoon, 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 rainfalltemporal 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
YIELD EQUATION = Price of fertilizer =growing months/pre-sowing months Rainfall Dum T = Dummy As necessary for Technology programme R F and Temp effect + or - Others as in Area slide
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 highyielding, 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 premonsoonal 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 Back to back droughts in 2014-15 and 2015-16: Challenges for FASAL 15
State-wise and Total (Select growing states) Rainfall Departure (%) in 2014 J U N - S E P T O C T D E C 16
-53.1-82.6-87.6-78.9-68.1-4.8 Andhra Pradesh -74.6-80.3-76.0-72.7 Assam Bihar Gujarat Haryana Karnataka -19.8 Madhya Pradesh -58.1-61.8 Maharashtra Orissa Punjab Rajasthan Tamilnadu Uttar Pradesh West Bengal -27.0 All India 51.8-43.2-36.6-8.1-31.6 Andhra Pradesh -24.7-11.1-26.5-4.9-19.9 Assam Bihar Gujarat Haryana Karnataka Madhya Pradesh Maharashtra -10.0-9.9 Orissa Punjab Rajasthan Tamilnadu Uttar Pradesh West Bengal -17.6 All India -13.9 5.4 9.7 State-wise and Total (Select growing states) Rainfall Departure (%) in 2015 J U N - S E P T O C T D E C 17
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 2 nd consecutive drought year 2015-16 Presented at Krishi Bhavan, Ministry of Agriculture, New Delhi on 19 th August, 2015, Forecast of Pulses (also Oilseeds) using actual rainfall data up to 18 th 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 Fitted 20
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
Fibre Oil seeds Pulses Foodgrains & cereals Yield improvement required at aggregate level (%): Simulation 2013-14 Crops Maize Sugarcane Soyabean 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 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 Groundnut 1.1 7.7 0.4 rpmst 1.3 2.9 - Cotton 1.1 2.9 0.1 Jute 3.9 8.2 -
Yield improvement (%) needed in Indian states for promoting biofuels (Rs. 1000/tones) (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 Food crop: Jowar, Substitute: Maize Food crop: Arhar, Substitute: Sugarcane
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) Rainfall 2.72** (Pre-Monsoon+ Monsoon) 2.4** (Sub-Urad) -2.3** (Monsoon) 4.8*** (Sub- Urad) 2.6** (Aug) 5.1*** (Nov) Rainfall 2-2.8*** (Aug) 3.8*** (Sub- Gram, Moong) 3.4*** (Monsoon) 2.7** (October) 1.5 (Sub-Wheat, Gram, Moong) 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
Rice (K) West Bengal YIELD EQUATION 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) Substitute Price Rainfall -3.0*** (Urad/ defl-fert) 2.0* (Jan) -2.5** (Feb) -4.1*** (Sep) Irrigation 2.7** (Canal+Well+Oth) Interaction 1.9* (Well*Monsoo n) -3.1*** Total*Dec(-1) 2.5** (defl-fert) 2.0* (defl-fert) 2.7** (defl-fert) 3.9*** (defl -Wheat, Moong) - - - - 2.6** (Monsoon) 4.1*** Monsoon(-1) -1.1 (Well) 2.7** (Canal*Sep) 2.4** (Apr) -3.3*** (June) 2.3** (Well*Dec(-1)) Temperature Aug_Min Oct_Min -1.6 (Oct) 8.1*** (Monsoon) 2.6** Jan(+1) -5.8*** Mar(+1) 2.9*** (Well+Canal) 5.8*** (Canal+Well)*Jan 2.2** (Well*Oct) -2.6** (Dec) 2.6** Jan(+1) Adjusted R- squared 0.95 0.72 0.50 0.93 0.69 27