outline...data emilia romagna aieaa conference, parma 6-7 june 2013 19 y341 municipalities yestimate...

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


[email protected]


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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:


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


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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

AIEAA Conference, Parma 6-7 june 2013 22

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 !

outline...data emilia romagna aieaa conference, parma 6-7 june 2013 19 y341 municipalities yestimate...

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