Economic Modeling for Agriculture, Food and the Environment Objectives, Approaches, Frontiers Alban THOMAS Head of the Social Science Division (SAE2), INRA AgreenSkills Seminar Toulouse, October 29, 2014 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
INTRODUCTION Economic modeling Objectives: Describe and represent behaviour of agents / actors in production, consumption activities Analyse and predict consequences of decisions for market outcomes (local markets, international trade) Methods: To describe: statistical analysis and econometrics ( Reduced form) To test and predict structural changes: system equation, structural econometrics ( Structural form) Data: Individual surveys, field surveys Experimental and behavioural economics Time series (country, region, etc.) .02 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014r
INTRODUCTION Predictions dealing with various dimensions: - time - space - population category In the following, three examples Food security and biofuel policy: modelling land use and energy demand for world regions Environmental impacts of agricultural production decisions Natural resources: management of irrigated cropping systems under water scarcity .03 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
Application 1. Food security and biofuel policy: modelling land use and energy demand for world regions An application of partial-equilibrium models Purpose: predict future food price changes given biofuel policy .04 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
Example: impact of biofuel policy Ujjayant Chakravorty, Marie-Hélène Hubert, Michel Moreaux and Linda Nøstbakken (2012), Do Biofuel Policies Really Increase Food Prices? Alberta University WP. US biofuel mandate Distribution of land quality Land class US EU Other HICs MICs LICs World Land already 1 100 100 25 300 150 675 under 2 48 32 20 250 250 590 Land under agriculture Agriculture 3 30 11 20 243 44 350 (million ha) and endowment of available Land available 1 0 0 0 0 0 0 land for farming (incl. 2 11 8 21 300 300 640 fallow lands) 3 11 8 21 500 500 1040 (million ha) Sources: Eswaran et al. (2003), FAO (2008a), Fischer and Shah (2010). Notes: Land available for US, EU and others HICs is not available per land class. We assume that half of the available land is class 2. .05 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
a) Define world regions and commodities b) Specify region-wise demand trends c) Specify production technology (production costs) d) Solve for equilibrium price on markets e) Derive regionwise optimal land use 𝐷 𝑛𝑛𝑛 𝑚 𝑑=1 ,…, 𝐷 � 𝑚 𝑑 𝑞 𝑑 𝑔 𝑚 𝑑 + 𝜐 𝑑 − 𝑠𝑚 𝑑 − 𝑋 𝑑 𝑑=1 𝐷 s.c. ∑ 𝑚 𝑑 ≤ 𝑀 𝑑=1 f) Simulation from scenarios on climate and technology .06 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
α β = l Demand for final product l l D A P w N l l l t − ∑ s s L ( 0 ) l i i = − φ θ = + φ 0 Land conversion cost (time t , use s ) C ( t ) ln s 1 2 s L ( 0 ) i η η 2 Total cost of product j in region j j j j = ∑ ∑ C ( k L ) k L j 1 i i i i i i ρ ρ − 1 ρ − 1 ρ − 1 ρ = λ µ + − µ + Production technology: energy from q q ( 1 )( q q ) ρ e g g g bf bs biofuel and gasoline ϕ 3 t θ ∑ x ( ) Cost of oil extraction θ = = ϕ + ϕ 0 C ( ( )) x t oil 1 2 X Unit costs of first generation biofuels US EU Other MICs LICs HICs Representative crop Corn Rapeseed Corn Sugar-cane Cassava (94%) (76%) (96%) (84%) (99%) Unit cost of production 1.01 0.55 1.10 0.94 1.30 ($/gallon) Sources: Production costs (FAO 2008a; Eisentraut 2010); Notes: The numbers in parentheses represent the percentage of first-generation biofuels produced from the representative crop (e.g., corn). .07 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
World food, biofuel and gasoline prices (in 2007 Dollars) BASE REG 2007 557 564 Weighted food price ($/ton) 2022 639(15%) 746(32%) Biofuel price 2007 2.14 2.18 ($/gallon) 2022 1.97 2.19 Crude oil price 2007 105 106 ($/barrel) 2022 121 119 Notes: Weighted food price is the average of cereal and meat prices weighted by the share of each commodity in total food consumption. The numbers in brackets represent the percentage change in prices between 2007- 22. Our predictions for crude oil prices are quite close to US Department of Energy (EIA 2010, p 28) projections of $115/barrel in 2022. World weighted food prices .08 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
Application 2. Environmental impacts of agricultural production decisions An application of farm-level production economics Purpose: predict environmental impact of changes in price and agricultural policy .09 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
Basic framework Lacroix, A. and A. Thomas, 2011. American Journal of Agricultural Economics . Trade and agricultural policy scenarios change in agricultural prices Simulation of environmental policy tax on nitrogen fertiliser How to evaluate changes in farmer decisions regarding crop output land use fertiliser application rate Dynamic model for simplified representation of crop rotations Model with multiple crops and multiple inputs Linking with environmental simulator for nitrogen leaching (groundwater nitrate contamination) .010 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
Previous crops Market prices Probability to select crops, c=1,2,…,C Ouput Pr 𝑚 𝑑𝑑 > 0| 𝑚 𝑑 , 𝑑−1 , 𝑄 , 𝜐 Input Crop yield decision 𝑟 𝑑𝑑 Policy instruments Constraint on total Land use decision 𝑚 𝑑𝑑 (CAP subsidy rates per ha) farm land Input decision 𝑋 𝑑 Expected crop yield, land-use and input, conditional on selected crops 𝐹 𝑟 𝑑𝑑 | 𝑚 𝑑𝑑 > 0, 𝑚 𝑑 ′ 𝑑 > 0, … 𝐹 𝑚 𝑑𝑑 | 𝑚 𝑑𝑑 > 0, 𝑚 𝑑 ′ 𝑑 > 0, … 𝐹 𝑋 𝑑 | 𝑚 𝑑𝑑 > 0, 𝑚 𝑑 ′ 𝑑 > 0, … Environmental impact simulator .011 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
The multi-crop model with multiple selection C crops, c=1,2,…,C K inputs , k=1,2,…,K l ct : land for crop c at time t p ct : output price for crop c at time t q ct : crop yield of crop c at time t τ ct : subsidy (per hectare) for crop c at time t W k : quantity of input k at time t r ct : unit price of input k at time t C K ∑ [ ] ∑ C = ∑ Π = + τ − Profit of farmer at time t l p q r W s.t. L l t ct ct ct ct kt kt t ct = = = c 1 k 1 c 1 ∂Π τ ∂Π τ ∂Π τ ( p , , , r L ) ( p , , , r L ) ( p , , , r L ) = = = − t t t t t t t t t t t t t t t l ; q ; W ∂ τ ∂ ∂ ct ct kt p r ct ct kt Optimal land use Optimal output level Optimal input demand .012 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
( ) > τ 1. Estimation of selection equations Prob l 0 p , , , r l / L ; − ct t t t t 1 t ( ) ( ) ( ) ε > ε > ε > ∀ ∀ ∀ q l W E l 0 , E l 0 , E l 0 , t , c , k ct ct ct ct kt ct 2. Parametric specification: quadratic profit function C C K C K C K ∑ ∑ ∑ ∑∑ ∑∑ Π = β + β + β τ τ + β + β + β τ τ * p r pr r p r p r r 0 c c c c k k ck c k ck c k = = = = = = = c 1 c 1 k 1 c 1 k 1 c 1 k 1 C C C C C C K K 1 1 1 ∑∑ ∑∑ ∑∑ ∑∑ + β + β τ τ ττ + β τ τ + β pp p rr p p p r r cc ' c c ' cc ' c c ' cc ' c c ' kk ' k k ' 2 2 2 = = = = = = = = c 1 c ' 1 c 1 c ' 1 c 1 c ' 1 k 1 k ' 1 C C K ∑ ∑ + ∑ + γ + γ τ τ γ Lp L Lr Lp L Lr c c c c k k = = = c 1 c 1 k 1 3. System estimation with correction of selection bias C ∑ = τ + λ q (output) q l F p ( , , , r L ) , ′ ct ct 1 t t t t cc t ′= c 1 C ∑ = τ + λ l (land) l F ( p , , , r L ) , ′ ct 2 t t t t cc t ′= c 1 C ∑ − = τ + λ w (input) W F p ( , , , r L ) . ′ kt 3 t t t t kc t ′= c 1 .013 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
Nitrogen balance From STICS crop simulator (Brisson et al., 1998) Predicted from model Nitrogen runoff WR: water repletion rate; IN: intercrop duration; f c : nitrogen application rate; a j : average nitrogen supplied by livestock j N j : livestock population of type j; b c : average nitrogen export of yield of crop c Application: 634 farmers, period 1995-2001 (2820 observations) Three French regions (Midi-Pyrénées, Pays-de-Loire, Rhône-Alpes) .014 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
Estimation of price elasticities under various model specifications Aggregate data Random effects Fixed effects Single crop selection Multiple crop selection .015 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
.016 Alban Thomas – AgreenSkills Seminar 29 / 10 / 2014
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