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Institute of Farm Management
Production Theory and Resource Economics
Prof. Dr. Stephan Dabbert
Organic controls in Germany – is there a need to harmonize?
Alexander Zorn, Christian Lippert, Stephan Dabbert
Contact: alexander.zorn@agroscope.admin.ch
1 Introduction
In Germany, organic farming is monitored by 20 approved private control bodies (CBs). Pooling data from five important German
CBs, we complement earlier studies by Gambelli et al. (2014), Zorn et al. (2013) and Lippert et al. (2014). These studies have
shown for single CBs in different European countries that non-compliance can partly be explained by farm characteristics. Here,
we extend the analysis to the influence of CBs and national or regional competent authorities on control results.
2 Hypotheses
We assume that opportunistic farmers, making a decision on whether to comply with an organic standard or not, implicitly balance
the expected net benefits B of non-compliance. B depends on (i) compliance costs, (ii) probabilities of detection and (iii) farmers’
future sanction related income losses in case of detected non-compliance. Factors (i) through (iii) are likely to be influenced by
farm characteristics; factors (ii) and (iii) are also affected by the behaviour of the control body and the regional authority.
Table. Results of selected logit models explaining the occurrence of at
least one severe sanction.
Basic farm model
(2009-2010)
Basic farm model
(year 2010) with lagged
severe sanctions
Basic farm model &
dummy variables for
CB B & federal state BW
Basic farm model &
dummy variables for
CB C & federal state NI
Observations
29,157
13,821
29,157
13,821
29,157
29,157
Bayesian Information Criterion (BIC)
-587.6
-327.4
-676.1
-418.3
-702.5
-757.2
McFadden's R² (Pseudo-R²)
0.061
0.081
0.061
0.080
0.064
0.060
0.001 *
0.001 **
Model specified
y* = β0 + β1 x1 +… + βn xn + ε.
The larger y*, the higher is the probability P(y = 1) of noncompliance. The occurrence of a severe sanction issued by
the CB is our proxy variable for y. Binary logit models are
used to test the effects of xi on the probability P(y = 1):
1
P( y =
1 | x1 , ..., xn ) =
− ( β 0 + β1 x1 +...+ β n xn )
1+ e
.
The data originates from five important German organic CBs
that provided their complete 2009 and 2010 control data base
on organic farms. The sample represents more than twothirds of the German organic farms. On average, a farm in
the dataset is controlled 1.22 times a year. The mean control
frequency however differs between the CBs in a range from
1.14 up to 1.29. These differences could result from different
control implementations but also from different risk
classifications of the farmers by the CB.
For a pooled dataset covering both years and for each year
separately we estimate models, firstly, as unrestricted models
with all potential explanatory variables and, secondly, as
restricted models by stepwise excluding non-significant
variables.
Starting point of the analysis is a model based on farm
characteristics only. This model then is extended by adding
dummy variables for former sanctions, for the CB, and, for
large federal states. Finally, models for combinations of these
extensions are estimated.
Restricted models
Unrestricted models
Basic farm model
(year 2010) with lagged
severe sanctions
Model type
Basic farm model
(2009-2010)
3 Method and data
A latent, unobservable variable y* (e.g., the expected net
benefit of not complying with the organic standard) is
considered to depend on some observed variables xi like
farm size or control body (CB) (see first column of the Table):
Agricultural area (ha)
0.001 ***
Organic control experience (years)
0.003
Processor (yes=1) a
Contract processor (yes=1)
Farm is controlled for private organic
standards (yes=1)
Farm is controlled for international
organic standards (yes=1)
Conversion area (yes=1)
-0.017
-0.115 *
0.001 *
0.001 ***
-0.019 **
0.001 ***
-0.019 **
0.019
-0.029
-0.112 *
-0.127 *
-0.091 *
0.495 ***
0.364 ***
0.503 ***
0.368 ***
0.526 ***
0.298 ***
0.449 *
0.543 *
0.448 *
0.536 *
0.428 *
0.378 *
0.337 ***
0.223 **
0.330 ***
0.230 **
0.328 ***
0.305 ***
Conventional area (yes=1)
0.328 ***
0.439 ***
0.323 ***
0.439 ***
0.263 ***
0.166 **
Cereals (yes=1)
0.004
0.102
Root crops (yes=1)
0.028
-0.036
-0.064
-0.021
Industrial crops (yes=1)
Fresh vegetables (yes=1)
0.178 **
0.167 *
0.187 **
0.180 *
0.157 *
0.141 *
Fodder crop production (yes=1)
0.160 *
0.109
0.171 **
0.161 *
0.178 **
0.202 ***
Other arable crops (yes=1)
0.203
0.315
-0.323 ***
-0.403 **
-0.308 ***
Permanent grassland (yes=1)
Fruits and berries (yes=1)
Grapes (yes=1)
-0.332 ***
0.218 *
-0.407 **
0.107
0.222 *
0.226 *
-0.289 **
0.224 *
-0.723 ***
-0.751 **
-0.727 ***
-0.744 **
-0.749 ***
Other permanent crops (yes=1)
0.327 ***
0.262 *
0.342 ***
0.280 *
0.309 ***
0.376 ***
Bovine animals (yes=1)
0.579 ***
0.519 ***
0.576 ***
0.500 ***
0.553 ***
0.607 ***
Pigs (yes=1)
0.241 ***
0.250 *
0.244 ***
0.261 **
0.237 ***
0.248 ***
Sheep (yes=1)
0.062
0.024
Goats (yes=1)
-0.073
0.051
0.453 ***
0.400 ***
0.410 **
0.404 **
0.142 **
0.146 **
-0.186 **
-0.280 **
Poultry (yes=1)
Bees (yes=1)
Dummy var. for the year 2010 (yes=1)
Mixed orchards (“Streuobst ”, yes=1)
Dummy variable for severe sanction in
previous year (yes=1)
Dummy variable for control body (CB) B
0.451 ***
-0.160
0.143 **
-0.290 ***
0.393 ***
-0.253
X
-0.393 ***
0.143 **
-0.285 ***
-0.395 ***
X
1.172 ***
X
1.175 ***
X
X
X
X
Dummy variable for CB C
X
X
X
X
Dummy v. for Baden-Württemberg (BW)
X
X
X
X
Dummy v. for Niedersachsen (NI)
X
X
X
X
Constant
-3.546 ***
-0.541 **
-3.0652 ***
-3.537 ***
-3.0515 ***
X
-0.398 ***
X
-0.320 ***
X
-3.401 ***
X
X
0.530 ***
X
0.293 ***
-3.742 ***
Own calculations based on CB data provided by Bundesanstalt für Landwirtschaft und Ernährung (2011).
X – variable was not used. Variables with empty cells, were excluded from the restricted models.
Significance levels: * < 0.1, ** < 0.01, *** < 0.001.
4 Results and discussion
Among the significant effects the sanction probability reduction due to contract processing and grape production and the
increasing effect of an adherence to a stricter private standard are surprising; the positive effect of the agricultural area may be
explained by higher farm complexity. The low Pseudo-R² values suggest to consider further variables that better represent
farmers’ personal characteristics, all the more as we partly attribute the high relevance of former sanctions to such characteristics.
As we tried to control for regionally different natural conditions by including farm characteristics into our analysis, the significant
federal state effects (see dummies BW and NI) and the CB effects hint at differences in the control implementation. Hence, our
results support the need for a more harmonized implementation of organic control systems.
References:
Bundesanstalt für Landwirtschaft und Ernährung (2011). Strukturdaten zum Ökologischen Landbau für das Jahr 2010. Bonn: BLE. – Gambelli, D., Solfanelli, F. and Zanoli, R. (2014). Feasibility of risk-based
inspections in organic farming: results from a probabilistic model. Agricultural Economics 45: 267-277. – Lippert, C., Zorn, A. and Dabbert, S. (2014). Econometric Analysis of Non-compliance with Organic Farming
Standards in Switzerland. Agricultural Economics 45: 313-325. – Zorn, A., Lippert, C. and Dabbert, S. (2013). An analysis of the risks of non-compliance with the European organic
standard: a categorical analysis of farm data from a German control body. Food Control 30: 692-699.
This research project was funded by the Federal Office for Agriculture and Food (BLE) in the Federal Program on Organic Farming (BÖLN, FKZ 10OE019).
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