D. Vignani (Istat), S. Auci (University of Palermo) - Climate Change Effects and Agriculture in...

SESSIONE VIII AGRICOLTURA E AMBIENTE

Climate Change Effects and Agriculture in Italy:A Stochastic Frontier Analysis At Regional Level

Relatore: Donatella Vignani

Donatella Vignani (Istat) e Sabrina Auci (University of Palermo)

GIORNATE DELLA RICERCA IN ISTAT | 10-11 NOVEMBRE 2014

Outline

1. Aim of the work

2. Effects of Climate Change

3. Evaluating CC effects on Italian agriculture

4. Economic literature about effects of CC on agriculture

5. Data description

6. SFA model

7. Empirical results

8. Conclusions

GIORNATE DELLA RICERCA IN ISTAT | 10-11 NOVEMBRE 2014Climate Change effects and Agriculture in Italy | Donatella Vignani and Sabrina Auci

Many agricultural products need suitable microclimatic conditions otherwise decreasing yields and quality due to changes in rainfalls and in minimum temperatures could occur

• analyzing the effects of CC on Italian agriculture in the period 2000-2010 at regional level

• using a Stochastic Frontier Approach (SFA)

• considering as agricultural production the sum of all harvested crops and as inefficiency factors deviation of rainfall and minimum temperature from the 1971-2000 mean value

Aim of the work

1GIORNATE DELLA RICERCA IN ISTAT | 10-11 NOVEMBRE 2014Climate Change effects and Agriculture in Italy | Donatella Vignani and Sabrina Auci

Further and more consistent CC in several areas of the world are predicted by IPCC 2013

• Europe recorded a warming of about 1°C during the last century and most of the heating occurred in the last fifty years

• many areas will cope with a rapid increasing of warming at the surface and an extremization of weather conditions

• impacts of CC are detected through many climatic variables: • rainfall• temperature• extreme weather events• river runoff• floods• heat waves

• almost half of the EU land is used for agricultural production

Effects of Climate Change (I)


Socio-economic development has created a growing concentration of GHG in the atmosphere:

• intensity of rainfalls and snowfalls has increased in Northern Europe while in Southern areas rains have decreased and drought periods are more frequent

• temperatures have become more extreme, economic losses linked to these weather events have risen

Strategies for facing Climate change effects:

• mitigation by reducing the “causes” of CC i.e. emissions of warming gases

• adaptation by decreasing the adverse effects

i.e. change in typology of cultivated crops

Effects of Climate Change (II)


Projected impacts of Climate Change in EU regions

4

Titolo Projected impacts from CC in different EU regions

Source: EEA Environmental European Agency


CC effects could lead to inefficiency in the agricultural production

• its geographical location in the Mediterranean basin

• a wide range of high quality products, typical mediterranean crops (IGP and DOP)

• sector structure including many small firms with a low capability to adapt themselves to a new situation

• irrigated agriculture is the major water user accounting for more

than 60% of total abstractions in Italy (IRSA 1999)

Evaluating CC effects on Italian agriculture (I)


CC effects could lead to inefficiency in the agricultural production

• changes in seasonal temperature mean (min and max) and in

annual rainfalls mean

• intensifying problems of water scarcity and irrigation requirements especially in the South of Italy

• the rise of “out of season” and extreme weather events

• heat stress in some areas of the country could threaten local cultivations

Evaluating CC effects on Italian agriculture (II)


• CEDEX 2000; Christensen and Christensen 2002; Giupponi and

Shechter 2003, Xiong et al. 2010: long-term analyses at the

aggregate level continental or national scales

• Dono and Mazzapicchio, 2010a and 2010b: short-term analyses at

a sub-regional level, using a Discrete Stochastic Programming

(DSP) model to forecast the effects on agriculture of changes in

water availability due to CC

• Dono et al. 2011: a three-stage discrete stochastic programming

model has been used to represent the farmer choice process based

on the expectation of possible scenarios of rainfall and maximum

temperatures for a specific irrigated area of Italy in the future

• our study contains a short-term CC effects analysis at sub-

regional level (NUTS 2)

Economic literature about effects of CC on agriculture


Panel data• 20 Italian Regions• years from 2000 to 2010

Istat Agricultural data source• Istat Survey of Estimate of Crop, Flower and Plot

Plant Production and Area • Istat Survey on Agricultural Holding Structure and

Output (SPA) • Istat V and VI Agricultural Census

Climate data source• Istat Meteo-Climatic and Hydrological Survey

Volumes of irrigation water used• official statistics have been issued for the first time

with VI Agricultural Census 2010. This force to use in the model the variable “irrigated areas” as proxy

Data Description


Volumes of irrigation water, irrigated areas, harvested production Italy, 2010 (thousands of m3, ha, hundreds of quintals)

9

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

5,000,000

0

100,000

200,000

300,000

400,000

500,000

600,000

tho

usa

nds

of

cubi

c m

eter

s

hu

nd

reds

of

qui

ntal

s an

d h

ecta

res

irrigated areas harvested production volumes of irrigation water

Source: our elaboration on Istat data from VI Agricultural Census - Annual Estimate of Crop, Flower and Plot Plant Production and Area


• Neoclassical paradigm: production is always efficient

• it is unrealistic that two regions – even if identical – can have a similar output with the same endowments

• the difference between two regions can be explained through the analysis of efficiency and some unforeseen exogenous shocks (Desli et al., 2002)

• to measure regional efficiency, we have estimated individual production functions using the stochastic frontier approach (SFA)

The SFA model: basic Hypotesis (I)


• this methodology allows to distinguish between production inputs and efficiency or inefficiency factors and to disentangle distances from the efficient frontier between those due to systematic components and those due to noise

• the main idea is that the maximum output frontier for a given input set, is assumed to be stochastic in order to capture exogenous shocks beyond the control of regions called technical inefficiency

The SFA model: basic Hypotesis (II)


The Battese and Coelli (1995) specification is a SF in which individual effects are assumed to be distributed as truncated normal random variables

where the unobserved random noise is divided into a first component vit which are random variables following the assumption of normally distributed error terms [iid N(0, V

2)], and ……

The SFA model: specification (I)

12

itititit uvxfy );(lnln (1)


…. a second independent component defined as uit which are non-negative random variables. These variables are assumed to capture the effects of technical inefficiency in production and are assumed to be independently distributed as truncations at zero of the N(mit, U

2) distribution with:

The SFA model: specification (II)

13

(2)ititit zm


…. assuming that the two components are uncorrelated, the parameters can be estimated using the maximum likelihood estimator.

The technical efficiency of the i-th individual is given by:

The SFA model: specification (III)

14

(3))()( ititit zuit eeTE


By assuming that the production function takes the log - linear Cobb-Douglas form, we estimated the following two specifications of the stochastic frontier production model:

where as variables we consider …

Our Specification (I)

15

(4) ititiit

ititit

uvLareaKirrig KfertKseedY

ln_lnlnln)ln(

43

210


The empirical model: variables definition

16

VARIABLES DEFINITION

Areas Cultivated areas (ha) - source ISTAT Annual Estimate of Crop, Flower and Plot Plant Production and Area

Production harvested

Crops harvested (100 kg) - source ISTAT Annual Estimate of Crop, Flower and Plot Plant Production and Area

Y Yields = harvested production/cultivated areas (100 kg/ha)

Kirrig_areaIrrigated areas (ha) - source ISTAT V and VI Agricultural Census, Survey on Agricultural Holding Structure and Output (SPA)

KfertFertilizers used (100 kg) - source ISTAT Annual Census Survey on Fertilizers Allocation

KseedSeeds used (100 kg) - source ISTAT Annual Census Survey on Seeds Allocation

L Days of work in the farms - source ISTAT V and VI Agricultural Census, Survey on Agricultural Holding Structure and Output (SPA)


and the following error function:

where as variabales we consider…

Our Specification (II)

17

(5)itititit

itititit

SouthCentreeastNoth

westNorthTempu

654

3210 min_Rainfall


The empirical model: variables definition

18

VARIABLES DEFINITION

RainfallDeviation of annual total rainfall average from 1971-2000 rainfall average value (mm) – source ISTAT Meteo-Climatic and Hydrological Survey

Temp_minDeviation of annual minimum temperature average from 1971-2000 minimum temperature average value (Celsius degree) – source ISTAT Meteo-Climatic and Hydrological Survey

Dummy macro-areas

Italian macro-areas: North-west, North-east, Centre, South (where islands is omitted for collinearity)


Stochastic frontier model

19

Dependent variable: Prod/L Model 1 Model 2 Model 3

Const -1.21*** -1.15*** -1.17***

t -2.87 -2.79 -2.80

Kseed -0.02 -0.02 -0.02

t -1.39 -1.08 -1.03

Kfert 0.27*** 0.26*** 0.26***

t 6.94 7.08 6.89

Kirrig_area 0.08* 0.09** 0.08*

t 1.94 2.21 1.83

L -0.95*** -0.95*** -0.95***

t -23.01 -22.79 -22.27


Inefficiency model

20

Dependent variable: Prod/L

Model 1 Model 2 Model 3

const 1.41*** 1.29*** 1.31***

t 8.44 7.27 7.02

Rainfall -0.01* -0.01*

t -1.74 -1.78

Temp_min 0.19 0.21

t 1.10 1.12

Dummy macro-areasYes

Neg. sign

Yes

Neg. sign

Yes

Neg. sign

Observations 200 200 200

sigma squared 0.44*** 0.41*** 0.41***

t 6.28 6.89 5.77

gamma 0.97*** 0.97*** 0.97***

t 160.87 139.77 119.73*, ** and *** indicate significance at 10%, 5% and 1% levels


• all specifications reject the null hypothesis of the insignificance of

the non random component of the production function residual

meaning that the SFA is a good model to analyze effects of CC on

regional agriculture performance

• in all columns, the results indicate that production functions

perform relatively well • not all the input factors coefficients show a positive sign

• Fertilizer and Irrigated areas have a positive and significant

coefficient as expected• while Seed and Labour Force show a negative coefficient but the

first variable is not significant

Empirical results (I)


• the Rainfall shows a negative and significance sign. Thus, the

more is the deviation of annual total rainfall average the more efficient

is the agricultural production on cultivated areas of Italian regions

• the Minimum Temperature shows a positive sign. Thus, the more

is the deviation of annual minimum temperature average the less

efficient is the agricultural production on cultivated areas

• the geographical location of regions is not relevant, because all

macro-areas considered have positive effects on efficiency

Empirical results (II)


Ranking of inefficiency effects among Regions (years 2000-2004-2009)

23

Titolo grafico

1° Trim.2° Trim.3° Trim.4° Trim.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

2000 2004 2009

Source: our elaboration on the SFA model results


1) CC effects (i.e. changes in rainfalls and min temperatures) seem to

contribute to not decrease efficiency in agricultural crops yields

2) our analysis represents a short run CC effects analysis as

bounded by the availability of long time series data and of irrigation

water data

3) this does not exclude that, the changes recorded by climatic

variables considered could lead to serious irreversible damages in

the agricultural sector in the long run

4) this analysis represents a very preliminary

study needing further investigations to evaluate

CC effects on agriculture at regional level, by using :

• harvested production disaggregated by typologies of crops

Conclusions (I)


• time series suitable for a medium-term CC effects analysis• data on mitigation policies undertaken by Italy, in accordance with EU

Policy related with Climate Change • data available on irrigation water by integrating different data sources

5) data on water consumption in agriculture are provided by:• Istat : a) VI Agricultural Census 2010 (by using Marsala methodology)

b) Survey on Agricultural Holding Structure and Output (SPA) 2013

c) Group of Work Environment and Agriculture (2014)

• INEA SIGRIA Sistema Informativo Gestione Risorse Idriche in

Agricoltura

• Italian Regions

• Consorzi di Bonifica

Conclusions (II)


D. Vignani (Istat), S. Auci (University of Palermo) - Climate Change Effects and Agriculture in...

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