outline...data emilia romagna aieaa conference, parma 6-7 june 2013 19 y341 municipalities yestimate...
TRANSCRIPT
AIEAA Conference, Parma 6-7 june 2013
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How do Agri-Environmental Schemes contribute to High Nature Value farmland:
a case study in Emilia-Romagna.
Claudio Signorotti, Valentina Marconi, Meri Raggi, Davide Viaggi
University of Bologna
AIEAA Conference, Parma 6-7 june 2013
AIEAA Conference, Parma 6-7 june 2013
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• Background
• Objectives
• Methods - HNV impact indicator
• Results
• Discussion
Outline
Background/1Definition of HNV
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The core characteristics of HNV farming were developed through projects undertaken for the European Environmental Agency (Andersen et al., 2003) and for the European Commission. These are:
AIEAA Conference, Parma 6-7 june 2013
Variety in land use
Low intensity agriculture
Semi-natural vegetation
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Background/2Characteristics of HNV
Objectives
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First objective is to develop a statistical indicatorthat allows a description of the High Nature Value (HNV) character at the municipality level.
Main objective is to analyse whether and how the rural development plan has been effective in shaping the distribution of HNV for the Emilia-Romagna Region.
Methods/1Impact indicator of HNV
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1° component: for the agricultural area other than meadows and pasture
(“non grassland”)
2° component: for the agricultural area with meadows and pasture (“grassland”)
Methods/2 Calculation of HNV indicator
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Variety in land use
Degree of intensity in agricultural management activities: nitrogen surplus
Degree of intensity in zoo-technical management activities: stocking density
Methods/3 Variety in land use
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Modified Shannon index
Variety is greater as more are the shares of land uses.
Index values between 0 and 1
Index = 0 : no variety
Index = 1 : maximum variety
Methods/4 Modified Shannon index
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The Shannon index is described by the formula:
where p is the fraction of a crop’s area over the total utilised area and log is the logarithm with base number N (number of crops).
We have considered ten crops, and the shares lower than 0.1 were ignored in the computation.
∑=
−=N
iii ppH
1)log(
Methods/5 Management intensity in agriculture
AIEAA Conference, Parma 6-7 june 2013
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Used indicator: nitrogen surplus
Other than:
- per hectare cost of productive factors,
- yield difference with national average,
- nitrogen application rate…
Methods/6 Transformation of management intensity
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With a logaritmic transformation and a regression line, the index is bounded between zero (maximum intensity) and one (minimum intensity)
Each management intensity value is replaced with the logarithm Values above the 95° percentile are set to zeroValues below the 5° percentiale are set to oneThe values in between are taken from the least square regression among the fifth percentile, the median and the 95° percentile
Methods/7 First component of HNV indicator
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Referred to land uses other than pasture and meadows (non grassland)
HNV1= Shannon index* MGT intensity index
HNV1 is higher when Shannon index is higher (crops variety) or MGT intensity index is higher (low intensity)
Methods/8 Management intensity in grassland
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The management intensity in areas where the land use is meadows and pasture is identified with the stocking density.
Methods/9 Stocking density index
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The stocking density index is equal to the ratio between the number of livestock units and the grassland area.
Methods/10 Transformation of stocking density
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With a linear transformation and the logarithm the index is bounded between zero (“highest intensity”) and one (“lowest intensity”).
The logarithmic transformation is takenValues above the 95° percentile are set to zeroValues below the 5° percentile are set to oneThe values in between are taken from the least square regression among the fifth percentile, the median and the 95° percentile
Methods/11 Second component of HNV indicator
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Referred to grassland land uses
HNV2=stocking density index
The component HNV2 is higher when the stocking density index is higher (low stocking density)
Methods/12 HNV indicator
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HNV= weighted average (1° component, 2° component)
HNV=SI*MI*NG/SUP+(1/SD)*G/SUPSI Shannon index
MI Management intensity index
NG Non grassland area
SUP Total area
SD Stocking density index
G Grassland area
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Near to one: high variety, extensive farming, extensive breeding
Near to zero: low variety, intensive management
Methods/13 HNV values
Methods/14 Data Emilia Romagna
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341 municipalities
Estimate HNV1
Estimate HNV2
Compute HNV
Rural development plan 2007-2013
National census of agriculture year 2000
National census of agriculture year 2010
Methods/15 Participation
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Rural development plan of Emilia-Romagna, years 2007-2013
Participation referring to years 2007-2008-2009-2010
Participation measured as number of participating farms divided by total number of farms
Methods/16 Three models for change in HNV
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Ordinary least squares
Spatial lag model
Spatial error model
Diagnostics indicate that spatial error model performs better than spatial lag model since spatial association is not left in the residuals and homoskedasticity is not rejected.
R-squared is 0.3
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OLS SPATIAL LAG MODEL SPATIAL ERROR MODEL
DELTA HNV DELTA HNV DELTA HNV
RHO 0.4006***(6.2665)
CONSTANT 0.1215*(1.6988)
0.06981(1.0621)
0.0990(1.383)
UAA_530 0.0000 (0.0663)
0.0004(0.3742)
0.0004(0.3330)
UAA_M30 ‐0.0007 (‐0.3866)
‐0.0001(‐0.061)
‐0.0007(‐0.3767)
AGE_L40 ‐0.0063*(‐1.6577)
‐0.0048(‐1.3693)
‐0.0057(‐1.5094)
AGE40_54 ‐0.0024 (‐1.0113)
‐0.0020(‐0.9317)
‐0.0024(‐1.0314)
H.S. DIPLOMA ‐0.0007 (‐1.0826)
‐0.0007**(‐1.9809)
‐0.0007(‐0.9997)
UNIVERSITY DEGREE 0.0051 (1.1974)
0.0041(1.0507)
0.0044(1.0314)
HILL 0.0210 (0.6533)
0.0164(0.5572)
0.0149(0.4750)
MOUNTAIN 0.1013***(3.1706)
0.07755***(2.6178)
0.0086***(2.6751)
M214/1 ‐0.0008 (‐0.1486)
‐0.0000(‐0.0200)
‐0.0002(‐0.0432)
M214/2 0.0071***(3.7431)
0.0048***(2.7183)
0.0067***(3.5849)
M214/9 ‐0.1121***(‐3.1584)
‐0.0626*(‐1.9086)
‐0.1008***(‐2.8468)
DENS_AB ‐0.0001***(‐3.4071)*
‐0.0001**(‐2.5202)
‐0.0001***(‐2.4851)
LAMBDA 0.3018**(2.1561)
R‐SQUARED 0.239 0.3341 0.300
Results/1Insights from model for HNV change
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DeltaHNV=HNV2010-HNV2000
Positive relationship between DeltaHNV and participation to organic farming measureBeta coefficient=0.0067 A 1% increase in participation to organic farming determine an increase in HNV of 0.0067
Negative relationship between DeltaHNV and participation to measure for less developed areas
Results/2 More insights
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Mountain proxy is positively linked to change in HNV
Density of inhabitants is negatively related to change in HNV
Discussion
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Organic farming appear to be connected to the HNVChange in HNV between the two censuses is related to participation to organic farmingWhy is that? Limiting the use of chemicals seems to be favouring the increase in HNV, which is an extensive type of agricultureParticipation to measure for less developed areas is negatively related to HNV. Why is that? Because in the regression the mountain variable takes all the effectPossible extension: measure participation as the participating UAA divided by total UAA
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Thank you !