# Spatial diffusion of Organic Farming

Ghislain Geniaux

► To cite this version:

Ghislain Geniaux. Spatial diffusion of Organic Farming. Impact of the location of agricultural production on ecosystem services, Nov 2015, Dijon, France. 16 p. hal-01606145

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Submitted on 5 Jun 2020

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# Spatial diffusion of OF

Ghislain Geniaux

INRA Ecodéveloppement

UR 767.

geniaux@avignon.inra.fr
Statistical model of OF
switching : motives and
determinants

* Determinant of OF choice at farm level are extensively studied, qualitatively and quantitatively.

* I propose to start the discussion at the conclusion of a recent litterature survey on this issue : Geniaux et al. 2010 « Les déterminants de la conversion à l'agriculture biologique : une revue de la littérature économique », livrable AgriBio3
# Motives and Determinants

* Motives (Padel 2001)

* Agronomic, agro-ecologic challenge,

* Ethical, religious and societal choices,

* Pollution and environmental issues,

* Health issues,

* Profit/rentability (Geniaux, Mzoughi, Napoleone, 2014)

* Classification of observable determinants of choices

* Farmer characteristics

* Farm characteristics

* External (neighbouring, market, policy)
# Modelisation of OF switching

* Qualitative analysis of small sample
  see Bellon et Lamine 2010 for a review

* Econometric models at farm scale :

* Dependant variable : Lohr and Salomonson 2000, Pietola and Oude Lansink 2001, Genius et al., 2006, Sauer and Park 2009
    * binary (OF/CF) → logit/probit

* multinomial (several levels of engagement in OF, alternatives label, AC) → logit/probit Anderson et al 2005, Burton et al 2003, Koesling et al 2008
    * Share of OF → Tobit Khaledi et al. 2010

* Number of Year in CF before switching → duration model Lapple and Donnellan 2009, Kallas et al. 2010
# Explanatory Variables

* Farmer characteristics

* Age -

* Education +

* Farming experience +/-

* Risk aversion --/+

* Environmental/ethical motives* +

* Other sources of earning/work +/-
# Explanatory Variables

* Farms characteristics

* Size +/-

* Production type (wine +, green garden +, cereal -, ...)

* Specialisation level – except wine sector

* Juridic statute* (collective forms +)

* technical efficiency +/-

* short supply* chains +
# Explanatory Variables

* Factors external to the farm

* Neighbouring context (population, agronomic, soil, climat) +/-

* Transfomers and supply chains +

* Policy (local aids,...) :

* National policy

* Local policy (promote for ?)
Some remarks about theses
models

* Poor explanatory power without « motives » variables

* Endogenety problem with ex post « motives » variables, and with some ex post farm characteristics.

* Discrete choice model with spatial depandance are complicated to implement particularly on large sample.

* For internal characteristics, only age and education have universal effect.
# Spatial dependence and OF diffusion

* What for :

* Accurate estimation of determinants effects β

* Spatial diffusion process

* More developed at aggregated scale (municipality or higher, Bichler et al 2005a, Lewis et al. 2009, Geniaux et al. 2009, **Allaire et al. 2015**)

* How to interpret spatial dependence in SAR model :

$$Y = \lambda WY + X\beta + err$$
Precaution

* Share of OF are highly heterogeneous between type of production, and share of type of production are highly spatially correlated

* Global OF analysis can be problematic

* A ratio between CF and OF has to be prefered → OLS/SAR innapropriate → logit/probit

* logit/probit with spatial dependance are undeveloped, particularly for large sample.

* Discrete choice model at farm scale would be prefered to take into account soil, farm and farmer characteristics, and to analyse close neighboring effects.
Precaution

* Where Spatial Autocorrelation come from in SAR model ?

* Missing covariates spatially autocorrelated

* Non linearity of spatially autocorrelated covariates.

* Spatially varying coefficients (spatial heterogeneity)

* Spatial process of diffusion

* A model with low adjustment fit R2<0.4 imply a correction trough WY that constitutes a proxy of unobserved spatiilay covariates X.

* A high spatial parameter is a diagnostic of model miss-specification when adjustment fit is low and perhaps a spatial process when adjustment fit is high
Spatial dependence and OF
diffusion

* Why observed Spatial Dependence is high ?

* Poor explanatory power of empirical models.

* Bad specification of model.

* Missing explanatory variables that are spatially correlated

* Non linearity

-> Overestimate spatial parameters

-> Biased estimation of spatial effect (I-lambda W)^(k-1)
# A working program for analysing
Spatial Diffusion of OF

* Exhaustive database with farm and farmer characteristics for whole France (French Census 2010)

* Take benefit of MSA database in which annual Farm earning are available for 2002-2015 and Agence Bio Data that allow to distinguish between OF installation and CF to OF switch.

* Privilegiate analysis at farm scale

* To analyse small neighbouring effects

* To take into account farm and farmer characteristics

* Sector based analysis

* Is spatial dependence related to the total number of OF neighbors or to the number of OF neighbors in same agricultural sector

* Analysis of OF/AC and OF intention to switch.
A working program for analysing
Spatial Diffusion of OF

* PROBIT MODEL with spatial dependence using
  « Approximate likelihood estimation of spatial auto-
  regressive binary choice models » D. Martinetti and G.
  Geniaux 2015

* Available for SAR, SEM, SARAR and SDM models,

* Highly accurate estimation using maximum likelihood technics,

* Usable for large sample size : Up to 1 million observations with sufficiently spare spatial weight matrices,

* Soon available as R cran package
# First results

* Municipal scale results (36 000 obs)

* Share of UAA of OF at municipal

* SAR model → High spatial parameter (~0.6) with low adjustment R2 around 0.2

* Wine and Fruit +, crop -

* Population municipal + (Wpop -)

* Short supply chaines + (Wssc not significant)

* Share of UAA of intention to OF

* SAR model → High spatial parameter (~0.5) with low adjustment R2 less 0.2
# First results

* Sector by sector

* No spatial autocorrelation for crop/cereal and maraichage (vegetables)

* High spatial autocorellation for WINE and for FRUIT, more related to the total share of OF in the neighbouring than the share of OF in the same production sector.