Faculty of Bioscience Engineering Academic Year 2015...

Faculty of Bioscience Engineering

Academic Year 2015 – 2016

Modeling the Impact of Climate Change at Farm Level: The Case of Belgium

Mishra, Abhijeet

Promotor : Prof. dr. ir. Jeroen Buysse

Tutor : Bérénice Dupeux

Master thesis submitted in partial fulfillment of the requirements for the degree of

Master of Science in Nutrition and Rural Development

Main subject: Rural Economics and Management

Copyright

All rights reserved with the author.

This is an unpublished M.Sc. thesis and is not prepared for further distribution. The author

and the promoter(s) give the permission to use this thesis for consultation and to copy parts of

it for personal use. Every other use is subject to the copyright laws, particularly the stringent

obligation to explicitly mention the source when citing parts out of this Master’s thesis.

Universiteit Gent, Belgium, August/September 2016

Promoter Tutor

Prof. dr. ir. Jeroen Buysse Bérénice Dupeux

[email protected] [email protected]

Author

Abhijeet Mishra

[email protected]

mailto:[email protected]



Dedication

To my father, Retd. Hon. Flight Lieutenant Braj Bhushan Mishra and the most beautiful woman

on this entire planet, my mother, Mrs. Kiran Mishra. I hope that this step of my academic career

will complete the dream that you had for me all those many years ago when you chose to give

me the best education you could even when it meant that you had to forego your comfort. Above

all, thank you, Ashish, for telling me that eventually, all things fall into place.

कर्मण्येवाधिकारस्ते र्ा फलेषु कदाचन । र्ा कर्मफलहेतुर्भमर्ाम ते सङ्गोऽस््वकर्मणि ॥

Karmanye Vaadhika-raste, Maa Phaleshu Kadachana;

Maa karma-phala-hetur-bhoorma, MaTe sangostwakarmini

Bhagavad Gita, Chapter 2, Verse: 47

Translation: You have the right to perform your actions, but you are not entitled to the fruits of

the actions. Be sincere in doing your work and don’t worry about what you’ll get for it.

Abhijeet Mishra

August/September 2016

Acknowledgement

First and foremost, I would like to thank European Union’s Erasmus Mundus program and the

whole team of Project NAMASTE (Networking and Mobility Actions for Sustainable

Technology and Environment in India) at University of Göttingen, Germany, for providing me

an opportunity to come to Europe and study in Belgium on a scholarship. This has been a life

changing experience so far. Special thanks to Dr. C.P.Gracy without whose support I wouldn’t

have been where I am today.

I would then like to express gratitude to my tutor Ms. Bérénice Dupeux. Her constant advice

helped me to improve this thesis at every step. When I look back to the timeline of past one

year, I realize how patient she has been with me (especially when I see my old drafts and wonder

how she did not look at me with disappointment), motivating me at every step and giving

precious details about the process of research. I thank her for making herself available for

feedbacks at regular intervals.

The door to my promoter Prof. dr. ir. Jeroen Buysse’s office was always open for me whenever

I ran into trouble or had a question about my research or writing. He consistently allowed this

thesis to be my own work and steered me in the right direction whenever he thought I needed

it. I also thank the University of Gent for making available the computational resources without

which this thesis wouldn’t have been possible.

Heartfelt thanks to my girlfriend María del Refugio Boa Alvarado for her patience while I was

working on this thesis and for proofreading all of my drafts followed by warm hugs. Special

thanks to my colleagues (and three musketeers of my life) - Ali Sayyed, Davide Guariento and

Erand Llanaj for making this journey worthwhile. I shall also thank my dear friend Vipul and

soon to be Dr. Goldi Tewari for all the motivation and making these two years feel like home.

Abhijeet Mishra

August/September 2016

“Extraordinary claims require

extraordinary evidence”

-

Dr. Carl Sagan

Astrophysicist

(1934-1996)

Abstract

This thesis analyzes the potential impacts of climate change on agriculture at farm level in

Belgium using a Ricardian model with panel data specification. The analysis uses farm level

data of Belgium provided by the Farm Accountancy Data Network for the period of 1990 to

2009. The study incorporates different types of farm groupings and capital indicators along with

climate related variables like mean temperature and precipitation (along with other indicators

of extreme weather events) to explain the value of owned agricultural land and farm family

incomes in Belgium and how unmitigated climate change would have an impact on them.

Results from two-way fixed effects panel model indicate the relevance and importance of farm

level models against aggregate modeling practices. It is also seen that different farm types have

different response towards climate change and there cannot be “one size fits all” policy

approach towards taking mitigation and adaptation actions against climate change. This

research envisages potential shift in agricultural practices towards mixed farming in Belgium.

Keywords: Climate change, Agriculture, Belgium, Ricardian model, Panel data.

Contents

1. Introduction ..................................................................................................... 1

1.1. Background information ............................................................................. 2

1.2. Problem statement ....................................................................................... 3

1.3. Research objectives and research questions ............................................... 4

1.3.1. Research objectives ............................................................................... 4

1.3.2. Research questions ................................................................................ 5

1.4. Academic relevance and motivation ........................................................... 5

2. Literature review ............................................................................................. 6

2.1. Crop simulation models .............................................................................. 7

2.2. Empirical yield models ............................................................................... 7

2.3. Farm simulation or economic management models ................................... 8

2.4. Inter-temporal (panel) net revenue approach .............................................. 9

2.5. Ricardian (cross-sectional) analysis ............................................................ 9

2.6. CGE Models .............................................................................................. 10

3. Materials and Methods ................................................................................. 11

3.1. Data sources, methods and geographical scope ....................................... 11

3.2. Econometric Modeling .............................................................................. 15

4. Results ............................................................................................................. 17

4.1. Ordinary least squares ............................................................................... 17

4.2. Panel model ............................................................................................... 25

4.3. Diagnostics ................................................................................................ 34

4.3.1. Serial correlation ................................................................................. 34

4.3.2. Heteroscedasticity ............................................................................... 34

4.3.3. Normality ............................................................................................ 40

4.3.4. Multicollinearity.................................................................................. 41

5. Discussion ....................................................................................................... 44

5.1. Comparison with available literature ........................................................ 46

5.2. Statistical relationship vs. “causality” ....................................................... 46

5.3. Shortcomings ............................................................................................. 47

6. General conclusions ....................................................................................... 49

6.1. Policy implications: Adaptation and mitigation actions ........................... 49

6.2. Recommendation for further research....................................................... 50

7. References ...................................................................................................... 52

8. Annexure (only in electronic format) ................................................................... 59

Tables

Table 1. Approach categories at a glance. .................................................................................. 6

Table 2. Weather station locations in Belgium. ....................................................................... 11

Table 3. Descriptive statistics for Belgian farms (1990-2009) ................................................ 14

Table 4. OLS estimators - Farm Family Income ...................................................................... 18

Table 5. OLS estimators - Farm Family Income (aggregated climate indicators) ................... 20

Table 6. OLS estimators - Value of owned agricultural land ................................................... 22

Table 7. OLS estimators - Value of owned agricultural land (aggregated climate indicators) 24

Table 8. Two-way fixed effects panel model estimates - Value of owned agricultural land (in

€). .............................................................................................................................................. 27

Table 9. Two-way fixed effects panel estimators - Value of owned agricultural land

(aggregated climate indicators) ................................................................................................ 29

Table 10. Two-way fixed effects panel model estimates - Farm family income (in €). .......... 31

Table 11. Two-way fixed effects panel estimators - Farm family income (aggregated climate

indicators) ................................................................................................................................. 33

Table 12. Results of Breush-Pagan test for heteroscedasticity. ............................................... 35

Table 13. Panel model estimates (accounted for heteroscedasticity) - Farm family income. .. 36

Table 14. Panel model estimates (accounted for heteroscedasticity) - Value of owned

agricultural land. ....................................................................................................................... 38

Table 15. Variation inflation factor values ............................................................................... 42

Figures

Figure 1. Number of holdings by main farming types in Belgium. ........................................... 4

Figure 2. Location of weather stations across Belgium.. ......................................................... 11

Figure 3. Flow of Information in preliminary data processing stage.. ..................................... 13

Figure 4. Distribution of Studentized Residuals -Value of owned farm land.. ........................ 40

Figure 5. Distribution of Studentized Residuals - Farm Family Income. ................................ 40

Figure 6. Correlation matrix ..................................................................................................... 41

Acronyms

C3 : Type of carbon fixation in (all) plants as the first step of Calvin-Benson cycle.

C4 : Elaborate form of C3 type carbon fixation in plants.

CO2 : Carbon Dioxide

CAP : Common Agricultural Policy (of European Union)

CGE : Computable General Equilibrium

ECA&D : European Climate Assessment & Dataset

EPA : Environmental Protection Agency

EU : European Union

FADN : Farm Accountancy Data Network

IPCC : Intergovernmental Panel on Climate Change

LM : Breusch Pagan Lagrange Multiplier Test

NASA : National Aeronautics and Space Administration

NUTS : Nomenclature of Territorial Units for Statistics

NUTS3 : Lowest NUTS level (specification for small regions)

OLS : Ordinary Least Squares

RM : Ricardian Model

UAA : Utilized agricultural area

USA : United States of America

vif : Variance Inflation Factor

FADN dummies and ECA&D variables

MILK : Dairy farms (milk, milk and cattle rearing)

CATTLE : Cattle farms (cattle rearing and fattening)

GRANI : Granivores farms (pigs, poultry, other granivores)

HORTI : Horticulture farms (vegetables, ornamentals, mushrooms, others)

MIXEDCrLS : Mixed crop and livestock farms

MIXEDLS : Mixed livestock farms

OtFC : Other field crops farms (roots/cereals/tobacco/cotton/others)

MT (or “tg”) : Mean temperature (in degree Celsius (°C))

MP (or “pp”) : Mean precipitation (in millimeters (mm))

HP10 : Number of High precipitation days

WD : Number of warm days

ETR : Extreme Temperature Range

WIN_DJF : Value recorded in winter (December-January-February)

SPR_MAM : Value recorded in spring (March-April-May)

SUM_JJA : Value recorded in summer (June-July-August)

AUT_SON : Value recorded in autumn (September-October-November)

Explanation of FADN codes

FADN code Variable name Units Description

SE510 Average farm capital Euro Average value ( = [opening + closing] / 2 ) of Working capital = Livestock + Permanent crops +

Land improvements + Buildings + Machinery and equipment + Circulating capital.

SE005 Economic Size ESU Economic size of holding expressed in European size units (based on community typology).

Total standard gross margin in euro / 1200.

F79 Electricity Euro The cost of Electricity used.

SE436 Total Assets Euro

Only assets in ownership are taken into account. Capital indicators are based on the value of the

various assets at closing valuation.

= Fixed assets + current assets.

DTOTLU Total Livestock Livestock Units

(LU) Based on an average number of livestock. Coefficients used from RICC 882.

SE281 Crop specific costs Euro

= Crop-specific inputs (seeds and seedlings, fertilizers, crop protection products, other specific

crop costs), livestock specific inputs (feed for grazing stock and granivores, other specific

livestock costs) and specific forestry costs.

SE605 Total Subsidies Euro Subsidies on current operations linked to production (not investments). Payments for cessation of

farming activities are therefore not included.

SE131 Total Output Euro Total of output of crops and crop products, livestock and livestock products and of other output.

F81 Water Euro The cost of water used.

AA Area under agriculture Ha Area in ha under agricultural practices.

G95CV Value of owned agricultural

land Euro Closing value of owned agricultural land

SE430 Farm Family Income Euro Farm net income expressed per family labor unit. Accounts for differences in the family labor

force. Calculated only for farms with family labor

B48 Owned farm land Ha Utilized agricultural area (UAA) in owner’s occupation.

Source: Definitions of variables used in FADN standard results. European Commission. Directorate-General for Agriculture and Rural Development

1. Introduction

NASA (2011) defines climate change as a transformation in the average weather of a region or

city. This could be a change in a region's average annual rainfall, or it could be a change in a

city's average temperature for a given month or season. Climate change is also a simultaneous

modification in Earth's overall climate. This could be a variation in global average temperature,

or it could be a variation in global typical precipitation patterns.

Due to excessive dependence on climate, agriculture remains one of the most vulnerable sectors

(EPA, 2010). Global analyses of the total and partial impacts of climate change have

consistently raised concerns about impact on agriculture (Dinar and Mendelsohn, 2011;

Morton, 2007; Nelson, Mensbrugghe, et al., 2014; Rosenzweig et al., 2014; Wheeler and von

Braun, 2013). Practically, all developed countries are concerned if climate change will damage

their agricultural sectors (Iglesias et al., 2012). Some authors are, however, concerned that

agricultural losses due to climate change will be harmful to developing countries (Mendelsohn

et al., 2006).

In the absence of mitigation plans, global temperatures are expected to rise between 2°C and

4°C depending on the emissions scenario over the next century (Intergovernmental Panel on

Climate Change, 2012). However, even these estimates are uncertain, so the range of actual

warming by next century may be even broader. Dinar and Mendelsohn (2011) explain three

reasons for this uncertainty. First, it is unpredictable how much greenhouse gas the future

economy will emit. Secondly, it is uncertain how much CO2 will be absorbed by the biosphere

and the ocean. Third, it is not clear whether other forces such as sea ice and clouds in the earth-

climate system will dampen or enhance the greenhouse effect.

To conclude, there is a great deal of uncertainty about future climate change scenarios. The

question is not whether climate change would have an impact on agriculture as we are certain

it will. The main concern is how much will these changes impact the agricultural sector and

how will these changes be distributed across the globe? Climatologists predict that there may

be other changes in climate conditions as well rather than just gradual variation in yearly

temperature and precipitation (Kharin et al., 2013).

1

1.1. Background information

In the past twenty years, many studies emphasized the vulnerability of agriculture towards both

climate change and climate variability (Burton, 1997; Challinor et al., 2007; Schilling et al.,

2012; Smit et al., 1988). Common results from these studies and available literature points out

that variations in temperature and precipitation will cause changes in use of crucial resources

(like land, water, etc.) and will subsequently affect agricultural productivity thus affecting

agricultural revenues (Kurukulasuriya and Rosenthal, 2013).

Integral and effective policies are needed in place to address climate change affecting

agriculture. Even though conventional policy dialogue has focused on mitigating emissions

(Shindell et al., 2012) that induce climate change; there has been comparatively limited

discussion of policies that reflect on climate change impacts and adaptation actions.

It is only intuitive to suggest that climate change impact on agriculture would surely reflect on

food security around the globe. Climate change can possibly hamper progress toward a world

without hunger (Challinor et al., 2014; Wheeler and von Braun, 2013; Zacharias et al., 2015)

because of its impacts on crop productivity and food availability which are well evident from

past studies (Bassu et al., 2014; Kumar et al., 2011; Lobell and Gourdji, 2012).

The impact of climate change is now part of the political discourse as well. Mr. Barack Obama

(President of The United States of America, 2009-2016 and Nobel Prize laureate, 2009) in his

speech at the 2015 Global Leadership in the Arctic: Cooperation, Innovation, Engagement and

Resilience (GLACIER) summit stated that “(…) climate change is no longer some far-off

problem. It is happening here. It is happening now. Climate change is already disrupting our

agriculture and ecosystems (…) few things can have as negative an impact on our economy as

climate change”.

On the other side of the Atlantic, the European Union’s Common Agricultural Policy (EU-

CAP) reform for 2014-2020 also specifically stresses on climate change and agriculture

(European Comission, 2013). It states that “Farmers have to adapt to challenges stemming from

changes to the climate by pursuing climate change mitigation and adaption actions (e.g. by

developing greater resilience to disasters such as flooding, drought, and fire)” (p.4). It is only

logical to infer that there is a need of considerable investment in adaptation and mitigation

actions towards a “climate smart agriculture” (Wheeler and von Braun, 2013) which is also

evident from the abovementioned view of global political powers .

2

Climate change is generally used as an alternative discourse (EPA, 2010) to talk about elevated

temperatures and variable precipitation levels. A recent study (Dell et al., 2012), focusing on

the effect of temperature on economic growth and agricultural productivity on a global scale

came up with three primary outcomes from historical fluctuations in temperatures. First, higher

temperatures reduce economic growth in poor countries on a considerable scale. Second, higher

temperatures result in lower agricultural growth rate and revenue. Third, higher temperatures

not only result in lower agricultural output but also reduced industrial output and political

stability.

Although forecasts and estimates suggest (Godfray et al., 2010) that global food production is

likely to be robust due to technological advancements in agricultural sector, recent studies

(Dhanush et al., 2015; Nelson, Valin, et al., 2014) have predicted an overall reduction in

agricultural productivity, also, agricultural inputs (e.g., land and water) would become

increasingly susceptible to expected climate pressures (Fader et al., 2015) like temperature rise.

Improved understanding of the influence of climate on agricultural production is needed to deal

with expected changes in temperature and precipitation. Climate variability is expected to

increase in some regions (Rowhani et al., 2011) and have significant consequences on food

production. Climate change adaptation is not new in production activities but if future

adaptation replicates past adaptation, unmitigated warming can restructure the global economy

by reducing average global incomes up to 23% by 2100 (Burke et al., 2015).

1.2. Problem statement

The mean temperature in Europe has increased by 0.8 °C over the past century and is expected

to increase by 1 to 5.5°C by 2080 (Solomon, 2007). Agricultural activities are responsive to

climate conditions (Wiebe et al., 2015). Policy makers require comprehensive impact

assessments on agriculture in order to formulate adequate response strategies. This is also

relevant in Belgian context considering the current conditions of agricultural activities, such as

intensive agriculture and livestock farming (Vanuytrecht et al., 2015).

In Belgium, agricultural sector contributes about 0.7% to the Gross Domestic Production (GDP)

(World Bank, 2016) and employs about 81,000 people in farm activities (Eurostat, 2012).

Figure 1 shows the number of holdings by main type of farming in Belgium in 2010. As visible,

cattle farms, dairy farms, pig farms and mixed cropping farms (livestock and crops) are few of

the major farming types by land holdings.

3

Figure 1. Number of holdings by main farming types in Belgium, 2010. Source: Agricultural census in Belgium, 2010

No study has looked into the impact of climate change on agricultural land values and farm

family incomes in Belgium till date. Hanewinkel et al. (2013) claim that climate change may

cause severe loss in the economic value of European forest land. Climate and land use are

undergoing rapid changes at present (Pielke, 2005) with initial range shifts already visible

(Heikkinen et al., 2006). Hanewinkel et al. (2013) also demonstrate that forecasted changes in

temperature and precipitation may have severe economic consequences in Europe. In terms of

farm income, overall, EU agricultural income is expected to drop by 3.4% as a result of crop

price decrease leading to moderate income decreases in most EU regions (Blanco et al., 2014).

1.3. Research objectives and research questions

1.3.1. Research objectives

The objective of this study is to explore potential climate change impacts on farm family

incomes and agricultural land values over a relatively homogeneous space which exhibits

sufficient climatic variation (like in Belgium) over years using a panel data model. The idea is

to explore aforementioned climate change impacts using “Ricardian approach”. This research

would rely on a method which is a modification of the “law of rent” by Ricardo et al. (1819),

which implies that land rents reflect the net productivity of farmland (Mendelsohn and

Reinsborough, 2007).

8,210

6,300

5,520

3,420

3,210

2,620

2,230

2,030

1,760

1,110

1,090

5,360

Specialist cattle-rearing and fattening

General field cropping

Specialist dairying

Field crops-grazing livestock combined

Specialist pigs

Cattle-dairying, rearing and fattening combined

Sheep, goats and other grazing livestock

Specialist cereals, oilseed and protein crops

Specialist horticulture indoor

Specialist fruit and citrus fruit

Other horticulture

Others

4

http://ec.europa.eu/eurostat/statistics-explained/images/4/4c/BE_agricultural_census_2010.xls

Economic theory suggests that land value equals the discounted sum of future profits; in theory,

it reflects the expectation of farmers on how well they can cope with a change in the climatic

conditions. This Ricardian model (RM) would consider the consequences of farming costs,

farming types, area under agricultural activities and climate related variables like temperature

and precipitation. As all farming activities intrinsically differ from each other, all the analyses

and econometric models would be farm specific and at farm level.

1.3.2. Research questions

1. What is the relationship between climate change indicators (temperature, precipitation

etc.) and value of owned farmland and farm family income in Belgium?

2. How unmitigated climate change affects farmland values in Belgium?

3. How unmitigated climate change affects farm family incomes in Belgium?

1.4. Academic relevance and motivation

Till date, no study focuses specifically on Belgium and climate change impact on its agriculture

from a Ricardian point of view. Academic relevance of this thesis is based on the platform

already set by numerous RM researches (De Salvo et al., 2013; Dinar and Mendelsohn, 2011;

Eakin, 2015; Galindo et al., 2015; Kurukulasuriya and Rosenthal, 2013) concerning climate

change impact on agriculture. This thesis provides a first illustration of the disaggregate effect

of climate change and its potential impact on agriculture given farm specialization using

Belgium as a case study.

Given that climate change influences agriculture to a significant extent, studying its impact at

farm level would provide an insight into how to mitigate negative impacts. Following this,

policy change suggestions can be made on a provincial level which can provide better support

to the farming community in battling climate change. This may result in sound adaptation

strategies and overall gain in welfare of the Belgian farmers.

5

2. Literature review

There are various approaches to understand the impact of climate change on agriculture but this

thesis makes a distinction amongst six main types of approaches, viz. (1) Crop simulation

models; (2) Cross-sectional analyses of yields; (3) Agro-economic simulation models of farms;

(4) Panel (inter-temporal) analysis of net agricultural revenues; (5) Cross-sectional analyses of

net revenues or land values per hectare (Ricardian Models); and (6) Computable General

Equilibrium (CGE) models. This research uses a combination of inter-temporal analyses as well

as the Ricardian method of climate impact assessment on agriculture.

This subsection does not go into technical details, and instead takes a broad and critical view

of the most significant methodological innovations focusing mainly on the past decade or so.

Summarized approach categories and their geographical scopes are provided in Table 1:

Table 1. Approach categories at a glance.

Category Geographical scope Aggregation level Example(s)

Crop simulation

models

Location specific,

Regional Farm

M. Parry et al. (2013)

Yu et al. (2014)

Challinor et al. (2007)

Robertson et al. (2013)

Empirical yield models Regional Region, Country

Dinar and Mendelsohn

(2011)

Basso et al. (2007)

Farm simulation

models Farm, Region

Farm, Sector,

Country

Deressa and Hassan

(2009)

Deressa et al. (2008)

Inter-temporal

approach Farm, Region

Farm, Sector,

Country

Deschênes and

Greenstone (2007)

Kunimitsu and Kudo

(2015)

Cross-sectional

approach Farm, Region

Farm, Sector,

Country Galindo et al. (2015)

CGE models Macro or Global level Sector van Ruijven et al.

(2015)

Source: Author’s elaboration.

6

2.1. Crop simulation models

One of the most popular methods for estimating the impacts of climate change on agriculture

relies on crop simulation models. Crop simulation models employ use of functions that

demonstrate the interaction between crop growth and climate as well as soils and management

practices (Challinor et al., 2014). Calibration of crop simulation models is done with selected

locations (Angulo et al., 2013). Different climate change scenarios are simulated for each

location (for selected crops) provided a particular management practice. The yield changes are

then extrapolated to an aggregate effect (Parry et al., 2004).

Strength

Developed upon on a deep comprehension of agronomic sciences and can be meticulously

linked with hydrologic conditions (Rosenzweig et al., 2013).

Can incorporate the effects of CO2 fertilization (Yu et al., 2014).

Calibrated to local conditions (González-Zeas et al., 2014).

Weakness

Crop simulation models are based purely on agronomic association (Rötter et al., 2015) and

are unable to capture the behavior of farmers.

Management practices adopted by the farmer are assumed to be fixed (Robertson et al.,

2013).

Crop simulation models do not predict how farmers are likely to change their behavior in

response to climate change (Rötter et al., 2015).

These models are neither targeted at climate change nor are they motivated by profit

maximization (M. L. Parry et al., 2013).

Only one crop is modeled at a time (Mbungu et al., 2015).

Crop switching is not predicted by crop simulation models, despite having clear importance

in climate change (Robertson et al., 2013).

2.2. Empirical yield models

Another method to measure the elasticity of yields to climate is to measure how yields vary

under different climatic conditions. Cross-sectional studies of yields across different climate

zones can be conducted. Using an empirical production function model, effects of climate can

be isolated from other factors influencing yields (Dinar and Mendelsohn, 2011).

7

Strength

The production function approach links water, soil, climate and economic inputs, to crop

yields for specific crops (Basso et al., 2007).

Econometric methods are used to predict the climate sensitivity of agricultural yields

(Chavas, 2001; Sands and Edmonds, 2005).

Weakness

Farmers are assumed to continue growing the same crop, with the same technology

regardless of the change in climate (Dinar and Mendelsohn, 2011).

The analyses often focus only on a limited set of crops and the full set of adaptations

available to farmers is underestimated (Dinar and Mendelsohn, 2011).

2.3. Farm simulation or economic management models

To capture farm (and farmer) behavior, it is important to model the farming sector and the farm

as well (not only crops/plants). Farm simulation models assume that farmers desire to maximize

their profits (Rotz et al., 2015). It can also figure out which farm adaptations would maximize

profit in response to climate change (Dinar and Mendelsohn, 2011).

Strength

Farm simulation model can capture the behavior of a single farm (Deressa et al., 2008) or

all farms in a country (Deressa and Hassan, 2009).

The single farm model can describe in detail the alternative choices a farmer might make to

maximize profits at a specific location. A national farm sector model can describe how these

farm choices vary spatially.

Weakness

Almost all major farm simulation models are focused on USA.

Farm simulation models rely heavily on crop simulation models to establish a link between

changes in climate coupled with crop yields. This link may also be a function of other

management choices by farmers (e.g. when to plant).

8

2.4. Inter-temporal (panel) net revenue approach

Instead of empirically determining how agricultural yields change over time, focus is laid on

determining the changes in net revenue in this approach. Using panel data, the effect(s) of

climate change on farm net revenue are estimated (Deschênes and Greenstone, 2007).

Strength

Using fixed effects to control for the differences between one county and another, this

approach controls for all the permanent differences between counties, including climate, as

well as other differences that are hard to measure (Kunimitsu and Kudo, 2015).

Fixed effects also control for adaptations that farmers have made till date to adjust to climate.

This approach is an ideal method to measure short-term responses to sudden changes in

weather (Deschênes and Greenstone, 2007).

In terms of weaknesses, this approach suffers from all the limitations of the empirical yield

models in that it does not reflect adaptation.

2.5. Ricardian (cross-sectional) analysis

The Ricardian method of climate change impact assessment on agriculture estimates the net

productivity of farmland as a function of climate, soils and other climate related control

variables (Mendelsohn et al., 1994). The technique uses a cross-sectional sample of farms that

span on a range of climate, to measure the sensitivity of land value or farm net revenue to

climate. A Ricardian method is a cross-sectional approach that allows for an examination the

impacts of climate change on agricultural production and implicitly takes into account farmers’

adaptation strategies.

Economic theory suggests that land value equals the discounted sum of future profits; it should

reflect the expectation of farmers on how well they can cope with a change in the climatic

conditions. Accordingly, if farmers allocate land among different agricultural activities (e.g.

crop choice, livestock) in order to maximize revenues, the farmland value will be equal to the

discounted sum of future expected cash flows when land is at its most productive use. When

markets expect productivity to be persistently reduced by higher temperatures in the future, in

spite of any adaptation efforts, then land values should decline in regions that have warmed.

Ricardian models are well established and widely applied in agricultural economics research.

While there are numerous studies on the US, India and Africa (Massetti et al., 2013; Massetti

9

and Mendelsohn, 2011; Mendelsohn and Reinsborough, 2007; Schlenker et al., 2006), the

literature for Europe has not been elaborate until recently (De Salvo et al., 2013). The most

recent and comprehensive analysis of climate change impact on European Agriculture comes

from Trapp et al., (2014). They argue that the early crop production models (Lobell and Gourdji,

2012; Lobell et al., 2011) of the impact of climate on agriculture were inappropriate for

estimating economic impacts because they did not incorporate farmer adaptation strategies such

as crop switching and, thus, likely overstated the economic impacts of climate (Wood and

Mendelsohn, 2015). As farmers adapt to climate changes in their own way, understanding the

exact effect of climate change on agriculture hence requires studying the farmer adaptation.

Weakness

Does not take into account the effect of CO2 fertilization (Dinar and Mendelsohn, 2011).

Based on current farming practices and includes potential adaptation processes (Mendelsohn

et al., 1994) but implicitly, it neglects other possible adaptations (Reidsma et al., 2010).

Uses mean of ‘normal’ climate indicators and hence does not explicitly takes account of

‘extreme’ climate events (Schlenker et al., 2006; Schlenker and Roberts, 2009).

The coefficients obtained for climate variables are not stable over time and space. This is

due to the fact that development reduces sensitivity towards climate change (Mendelsohn et

al., 2001).

2.6. CGE Models

If the changes to climate are on large enough scale, they may affect the entire economy and

change both input and output prices. Aforementioned partial equilibrium methods can not deal

with such situation. Hence an economy-wide approach is needed to capture these broader

changes. Computable General Equilibrium (CGE) models are used to capture these economy-

wide and global changes (van Ruijven et al., 2015).

Strength

CGE models can predict how large shifts in supply and demand can alter prices and thus

capture changes that the partial equilibrium models miss (Hosoe et al., 2010).

Weakness

CGE models invariably miss changes in interest rates or labour prices.

The partial equilibrium models also tend to be country-specific so that they miss trade effects

as well.

10

3. Materials and Methods

3.1. Data sources, methods and geographical scope

This research uses information from the Farm Accountancy Data Network (FADN) of the

European Union. Data pans over all the national territories of Belgium considering individual

farms at third level of Nomenclature of Territorial Units for Statistics (NUTS) levels as the

main unit of analysis. Data for climate related variables was available from 16 different weather

stations located across Belgium (Figure 2 and Table 2).

Figure 2. Location of weather stations across Belgium. Source: ECA&D.

Table 2. Weather station locations in Belgium.

Location NUTS3 Level Station ID

Uccle BE241 17

Deurne BE211 935

Eeklo BE233 937

Chievres BE321 938

Kleine-Brogel BE221 940

Beauvechain BE310 943

Bierset BE331 944

Dourbes BE351 945

Mont-Rigi BE335 946

Virton BE341 949

Beitem BE251 950

Ostend B BE255 934

Florennes BE351 952

Elsenborn BE336 954

Koksijde BE252 2178

Saint-Hubert BE345 2179

Source: Eurostat methodologies and working papers

11

http://ec.europa.eu/eurostat/documents/3859598/5916917/KS-RA-11-011-EN.PDF

The database used in this study consists of an unbalanced panel from 1990 to 2009 with

observations from 1,200 farmers on an average (24,047 observations in total) spread across

Belgium over 43 NUTS-3 levels and 11 provinces. The data used in this study comes from the

FADN. The farm level data belongs to twelve different farming types. This data set is then

segregated into twelve different data sets using dummies based on their TF14 (farm

classification) code.

Economic variables are converted to constant 2009 Euros using an appropriate deflator

(discounted at 1%) before analysis. The data1 for climate variables is obtained from European

Climate Assessment & Dataset (ECA&D) libraries. This data in annual in nature and is

collected from 16 weather stations spread across Belgium. This includes mean annual

temperature and precipitation values, segregated in quarterly basis (winter: Dec/Jan/Feb,

spring: Mar/Apr/May, summer: Jun/Jul/Aug and autumn: Sep/Oct/Nov).

The data made available was processed primarily using R-Studio 0.99.891 (RStudio Team,

2015). From the FADN data available following variables are used: Economic size of the farm,

electricity used by the farm (in €), average farm capital available to farm (in €), total assets of

farm (in €), livestock units at the farm, crop specific costs borne by the farm (in €), total

subsidies received by the farm (in €), total output (in €), water used (in €), area under agriculture

(ha), area under irrigation (ha) and the amount of owned farm land.

Hired labor used in agricultural practices was not included in the analysis because it is

considered to be exogenous and not permitted to be used in Ricardian models. Other indicators

regarding purchase and sale of livestock, debts, value added taxes, quotas etc. were not included

in the analysis because there is no economic link of these indicators with climate change in

Ricardian model or inter-temporal analysis of climate change impact on agriculture.

Figure 3 illustrates the data cleaning process and flow of information. After extracting variables

of interest from the FADN data available, data for identities of weather stations is read followed

by data from weather stations pertaining to mean temperature, mean precipitation, heavy

precipitation days (defined as the number of days per annum with precipitation more than or

equal to 10mm rainfall), warm days (defined as the number of days per annum with temperature

more than 90th percentile of daily mean temperature) and extreme temperature range (defined

as the difference between maximum and minimum temperature in one year).

1 Data available in public domain at: http://www.ecad.eu/download/ensembles/ensembles.php

12

http://www.ecad.eu/download/ensembles/ensembles.php

NUTS3 levels are used as primary key and respective weather station identities (IDs) are

assigned to each year of farm data. To merge the weather related variables into data tables,

weather station identities are then used as primary key and the data-tables now consist of farm

related information as well as weather related information. As the final data set to work with

contains null values as well, missing values are substituted with mean values.

Figure 3. Flow of information in preliminary data processing stage. Source: Author's exposition.

Descriptive statistics for the whole data set are summarized in Table 3.

13

Table 3. Descriptive statistics for Belgian farms (1990-2009). Source: FADN data

N Mean SD Min Max Average farm capital (in €) 21340 301816 234422 5120 3572130

Economic size (Martínez et al.) 21340 100 81 12 1737

Electricity (in €) 21340 2869 4968 0 205274

Farm family income (in €) 21340 34356 41131 -1727666 2865227

Value of owned farm land (in €) 21340 143884 172411 0 2913598

Total assets (in €) 21340 477979 357140 4505 4813192

Livestock (LU) 21340 110 134 0 2462

Specific costs (in €) 21340 76074 90431 0 1916336

Total subsidies (in €) 21340 15797 19312 -32250 498677

Total output (in €) 21340 195175 183816 -75976 2473330

Water (in €) 21340 493 831 0 21371

Owned farm land (ha) 21340 12 14 0 122

Area under agriculture (ha) 21340 48 41 0 424

Area under irrigation (ha) 21340 0 2 0 80

Mean temperature (in °C)

Winter 21340 4 2 -1 7

Spring 21340 10 1 6 13

Summer 21340 17 1 14 20

Autumn 21340 11 2 6 14

Mean precipitation (in mm)

Winter (Dec-Jan-Feb) 21340 226 82 61 654

Spring (Mar-Apr-May) 21340 175 57 61 385

Summer (Jun-jul-Aug) 21340 236 70 68 473

Autumn (Sep-Oct-Nov) 21340 229 76 93 542

No. of high precipitation days





No. of warm days





Extreme temperature range





14

3.2. Econometric Modeling

With the Ricardian technique, land value or net revenue is regressed on a set of climate variables

and other control variables. This exercise is done separately for each farm time. RM proposes

that the value of land reflects the present value of future net revenues and therefore it is closely

related to land productivity (Mendelsohn and Dinar, 2009). Farmers try to maximize their

profits by selecting among alternative economic options, including livestock, crops and other

production factors, provided the weather conditions. The econometric specification of the RM

includes owned farmland values or farm family income as endogenous variable and farm

characteristics and climate related indiactors as control variables.

𝑉𝑖 = 𝛽0 + 𝛽1𝐶𝑖 + 𝛽2𝐶𝑖2 + 𝛽3𝑍𝑖 + 𝛽4𝑋𝑖 + 𝑢𝑖 (1)

Where 𝑉𝑖 represents the owned farmland value or farm family income for farm i. 𝐶𝑖 are the

climate variables (temperature and precipitation). 𝑍𝑖 are extreme weather events and 𝑋𝑖 are

other control variables included in the analysis. 𝛽𝑖 are the estimated coefficients and 𝑢𝑖 is the

error term. The estimated coefficients of the above mentioned econometric specification of the

RM in equation (1) can vary over time (Massetti and Mendelsohn, 2011). Further derivations

from Mendelsohn and Dinar (2009) show that the climate change impact on welfare can be

estimated as:

∆𝑊 = 𝑉(𝐶𝑓𝑢𝑡𝑢𝑟𝑒) − 𝑉(𝐶𝑝𝑎𝑠𝑡) (2)

During the modeling exercise in this research, differences in individual farm(ers) are taken

account of. Individual farms differ (i.e., are heterogeneous) from each other and years between

1990-2009 differ from each other as well, hence a simple way to take account of this

heterogeneity across individuals and/or through time is to use the variable intercept models

(panel model). Assumption2 of such panel models is that, conditional on the observed

explanatory variables (independent variables), the effects of all omitted (or excluded) variables

are driven by three types of variables: individual time-invariant, period individual-invariant,

and individual time-varying.

Individual time-invariant variables are same for a given cross-sectional unit (in one time period)

but vary across cross-sectional units (between time periods). The period individual-invariant

variables are same for all cross-sectional units but vary through time. The individual time-

2 Analysis of Panel Data, Cheng Hsiao, 2014.

15

varying variables vary across cross-sectional units and also display changes in time. A variable-

intercept (i.e., panel) model can provide a useful specification for fitting regression models

using panel data. For example:

In this regression equation (with i = 1, ...., N and t = 1,....,T), y is the dependent variable and

X1, . . . , XK are the independent variables. However, this equation ignores variables reflecting

managerial and other technical differences between farmers which affect productivity of

farmers and factors fluctuating over time (such as weather). Such farm- and time-effects

variables, say Mi and Pt , should ideally be present in this equation. With Uit representing the

effects of all remaining omitted variables Thus, Vit can be written as:

By introducing the farm- and/or time-specific variables into the specification for panel data, it

is possible to reduce (or avoid) the omitted-variable bias. Generalization of the constant-

intercept-and-slope model for panel data is to introduce dummy variables to allow for the effects

of those omitted variables that are specific to individual cross-sectional units but stay constant

over time, and the effects that are specific to each time period but are the same for all cross-

sectional units. This is also known as least square dummy variable or LSDV method.

In this thesis, dependent variables i.e., farm family incomes and value of owned agricultural

land are modeled based on explanatory variables from both FADN and ECA&D. This includes

climate related variables (temperature, precipitation, extreme temperature range, number of

warm days and number of high precipitation days) as well as farm related variables such as

economic size units, electricity use (in Euro), total assets (in Euro), total livestock units, crop

specific inputs (in Euro), total subsidies (in Euro), total crop output (in Euro), water use (in

Euro), area under agriculture (ha), area under irrigation (ha) and area of owned agricultural land

(ha).

16

4. Results

Ordinary least squares (OLS) method of regression is tested against panel regression in this

research. Before presenting the results, three extreme climate indicators need to be defined as

below:

1. Heavy precipitation days (HP10): Number of days with precipitation >= 10 mm.

2. Warm days: Number of days where mean temperature > 90th percentile of daily mean

temperature.

3. Extreme temperature range: Intra-period extreme temperature range (difference between

maximum and minimum tempearture in a period)

However, it is important to take note that the panels used here are unbalanced panel data sets.

This results in an extra error term in the models. The additional disturbance is attributed to the

unbalanced panels. Inclusion of this disturbance could have potentially caused inflation in the

error terms of models.

One easy solution to deal with unbalanced panels is to remove the data points in order to balance

the panel. In this research, however, it was decided not to delete observations and let the

software3 take account of relevance of estimators in the unbalanced panel data sets. Other

limitations of this research are further elaborated in section ‘5.3 Shortcomings’. Keeping this

in mind, OLS and panel estimates are discussed in the subsections below.

4.1. Ordinary least squares

Starting with the most basic econometric approach, OLS method of regression was applied on

datasets belonging to various farming types. The results can be found in Table 4 and Table 5

regarding the farm family incomes.

3 Package plm in R-Studio has in-built capacity to deal with unbalanced panels. More info: plm

package release (p.47).

17

https://cran.r-project.org/web/packages/plm/vignettes/plm.pdf

https://cran.r-project.org/web/packages/plm/vignettes/plm.pdf

Table 4. OLS estimators - Farm Family Income

Dependent variable: Farm Family Income (Euro)

Milk Granivores Cattle Horticulture Mixed Cr+LS Mixed LS Other

Economic size (€) 26.7* (13.9) -163.1

*** (43.7) -32.7 (50.8) -96.0

*** (10.5) -100.2

*** (15.4) -118.8

*** (18.9) -299.7

*** (34.5)

Electricity (€) -2.2***

(0.2) -1.7***

(0.2) -3.1***

(0.9) -1.0***

(0.1) -4.8***

(0.2) -1.4***

(0.2) -7.6***

(0.6)

Total Assets (€) -0.03***

(0.001) -0.1***

(0.004) -0.04***

(0.01) -0.1***

(0.004) -0.04***

(0.002) -0.1***

(0.003) -0.04***

(0.004)

Total Livestock (LU) 32.7***

(8.4) 31.9***

(12.0) 18.7 (26.9) 42.3 (40.4) 28.4***

(7.3) 38.7***

(6.0) -106.1***

(18.4)

Crop Specific Inputs (€) -0.5***

(0.01) -0.7***

(0.01) -0.5***

(0.1) -0.3***

(0.01) -0.5***

(0.01) -0.5***

(0.01) -0.5***

(0.03)

Total Subsidies (€) 0.4***

(0.02) 0.6***

(0.1) 0.7***

(0.1) -0.1 (0.1) 0.5***

(0.02) 0.5***

(0.04) 0.7***

(0.04)

Total Crop Output (€) 0.4***

(0.01) 0.6***

(0.01) 0.5***

(0.03) 0.3***

(0.01) 0.5***

(0.01) 0.5***

(0.01) 0.5***

(0.01)

Water (€) -1.1***

(0.2) -2.4***

(0.9) -1.6 (1.1) -3.3***

(0.7) -1.8***

(0.4) -0.9**

(0.4) 0.6 (0.9)

Area Under Agriculture (ha) -294.5***

(15.1) -250.2***

(64.9) -319.1***

(54.7) 507.1***

(98.8) -127.9***

(15.5) -242.8***

(29.6) -46.1 (34.5)

Area Under Irrigation (ha) 91.2 (93.0) 348.1 (492.4) -18.3 (1,559.6) 164.9 (404.8) -343.0***

(124.3) 165.9 (186.4) -520.8**

(210.0)

Mean temperature (°C)

Winter -673.1 (694.9) -6,444.6* (3,614.7) -938.1 (2,799.5) -7,087.8 (5,891.1) -1,732.7 (1,217.2) -3,383.9

* (1,893.3) -894.6 (2,285.8)

Spring -6,224.2**

(2,531.0) -4,695.0 (13,374.0) -9,108.4 (11,257.6) 36,809.2* (21,904.2) 1,225.6 (3,384.4) 2,925.5 (7,003.6) 3,448.7 (7,063.1)

Summer 6,504.3 (4,179.7) -22,971.0 (24,894.8) 5,164.3 (21,362.3) -47,050.4 (43,187.8) 890.8 (6,076.8) -9,894.7 (13,362.4) -3,119.0 (11,691.2)

Autumn 3,226.4* (1,947.6) 23,753.8

** (9,240.7) -7,952.6 (8,218.9) -2,511.1 (12,131.3) 3,669.6 (3,420.4) 5,074.6 (4,477.4) -13,128.1

** (6,471.4)

(Sq.)Winter 257.3***

(95.4) 1,253.8***

(453.1) -346.7 (392.4) 975.4 (743.7) 29.9 (162.7) 533.7**

(239.3) 149.7 (303.5)

(Sq.)Spring 470.2***

(129.4) 118.6 (652.0) 747.9 (574.2) -1,862.9* (1,060.4) -35.4 (178.6) -137.3 (343.8) -265.4 (363.1)

(Sq.)Summer -208.1* (122.9) 745.9 (711.2) -233.7 (628.5) 1,544.7 (1,220.5) 27.4 (182.9) 294.3 (384.7) 221.7 (346.8)

(Sq.)Autumn -108.5 (93.1) -1,091.1***

(420.9) 423.6 (394.5) 131.7 (550.7) -137.9 (162.1) -152.7 (210.3) 559.8* (305.2)

Mean precipitation (mm)

Winter -28.3**

(11.7) -80.2 (62.8) -30.4 (50.2) -31.4 (99.2) 6.1 (24.2) 24.9 (29.2) 74.3 (55.9)

Spring -27.4 (19.5) -27.9 (88.5) 76.6 (81.0) -11.0 (126.0) -84.1***

(32.5) -120.6**

(49.7) 24.6 (67.4)

Summer 53.0***

(17.5) -18.8 (62.1) -63.4 (88.4) 103.7 (87.7) -75.4**

(31.7) 16.3 (33.0) 23.3 (53.7)

Autumn 14.4 (16.3) -1.2 (53.1) -37.9 (75.2) -42.4 (66.3) -43.0* (23.8) 32.7 (26.7) 6.3 (43.6)

(Sq.)Winter 0.02 (0.02) 0.1 (0.1) 0.04 (0.1) 0.01 (0.2) -0.01 (0.05) -0.1 (0.1) -0.1 (0.1)

(Sq.)Spring 0.2***

(0.05) 0.002 (0.2) -0.2 (0.2) -0.02 (0.3) 0.3***

(0.1) 0.4***

(0.1) -0.1 (0.2)

(Sq.)Summer -0.1***

(0.03) 0.04 (0.1) 0.04 (0.2) -0.2 (0.1) 0.1**

(0.1) -0.04 (0.1) -0.01 (0.1)

(Sq.)Autumn -0.1***

(0.03) -0.1 (0.1) 0.1 (0.1) 0.1 (0.1) 0.04 (0.04) -0.1***

(0.04) -0.004 (0.1)

UAA Owned (ha) 354.8***

(21.9) 655.0***

(128.2) 300.6***

(79.2) 1,591.5***

(329.4) 414.0***

(29.1) 566.8***

(51.4) 783.9***

(61.1)

18



No. of High precipitation days

Winter -108.3 (147.3) -183.3 (480.3) -36.3 (684.0) 688.8 (675.5) -314.8 (256.8) 16.1 (265.5) -127.0 (439.4)

Spring -164.2 (161.3) 21.0 (541.8) -585.1 (757.7) 428.8 (685.4) -276.6 (269.3) -345.3 (321.0) -203.4 (460.6)

Summer 248.0**

(123.4) -287.3 (434.1) 562.1 (642.2) -1,382.4***

(503.2) 319.5* (193.5) 270.6 (247.6) -327.2 (346.9)

Autumn 430.1***

(123.1) 1,149.2***

(392.9) -292.3 (622.8) 110.7 (541.4) 240.2 (196.0) 482.0**

(229.9) -29.0 (361.8)

No. of Warm days

Winter -44.7 (50.7) -271.5 (202.7) 216.3 (268.5) 204.1 (295.9) 246.6**

(101.5) -46.2 (111.8) 104.2 (171.1)

Spring -118.1**

(58.2) -331.6* (192.4) -90.3 (292.1) 191.1 (266.2) 144.6 (95.5) -92.1 (107.3) -17.8 (176.6)

Summer 102.8 (67.6) -673.0***

(225.1) 117.7 (332.7) -956.3***

(319.0) -180.1 (121.3) -142.2 (116.2) -328.8 (220.6)

Autumn -159.7**

(66.1) 203.3 (236.9) 157.5 (328.7) -29.7 (324.8) -28.1 (114.3) -89.9 (131.7) -69.6 (212.5)

Extreme temperature range (°C)

Winter -192.2**

(89.6) 22.9 (297.0) 82.2 (465.8) 315.2 (385.5) -129.4 (145.8) -118.6 (155.1) 617.5**

(260.2)

Spring 285.7***

(74.1) -301.7 (264.3) -288.6 (395.8) -205.3 (335.1) 321.8***

(113.8) 215.7 (140.9) 151.1 (199.6)

Summer 568.7***

(94.5) -138.5 (324.0) 759.7 (491.4) 530.8 (434.2) 502.2***

(162.5) -80.9 (183.8) -22.3 (268.2)

Autumn 130.3* (71.1) 387.0 (261.7) -103.4 (347.1) -452.0 (344.0) -219.8

* (113.8) 285.2

** (138.9) -2.9 (213.2)

Constant -67,414.6**

(30,628.2) 136,944.3 (192,821.5) 55,211.5 (155,574.5) 215,830.6 (336,362.9) -34,935.3 (45,143.8) 41,709.0 (106,005.6) 40,432.4 (85,592.9)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

Adjusted R2 0.6 0.8 0.1 0.4 0.7 0.7 0.7

Residual Std. Error 12,538.1 (df = 4952) 21,987.3 (df = 1447) 54,934.9 (df = 3815) 34,846.1 (df = 2642) 15,536.3 (df = 3006) 13,253.4 (df = 1686) 21,290.4 (df = 1749)

F Statistic 185.4***

(df = 39;

4952)

140.9***

(df = 39;

1447) 9.0

*** (df = 39; 3815)

56.4***

(df = 39;

2642)

152.4***

(df = 39;

3006)

111.2***

(df = 39;

1686)

111.1***

(df = 39;

1749)

Note: *p<0.1;

**p<0.05;

***p<0.01

Source: FADN data for Belgium (1990-2009)

19

Table 5. OLS estimators - Farm Family Income (aggregated climate indicators)

Dependent variable: Farm family income (Euro)


Economic size (Martínez et

al.) 17.6 (13.7) -197.8

*** (40.0) -36.7 (49.6) -102.5

*** (10.3) -112.2

*** (15.0) -113.7

*** (18.5) -312.2

*** (33.7)

Electricity -2.1***

(0.2) -1.7***

(0.2) -3.0***

(0.9) -1.0***

(0.1) -4.9***

(0.2) -1.3***

(0.2) -7.9***

(0.6)

Total Assets (Euro) -0.03***

(0.001) -0.1***

(0.004) -0.04***

(0.01) -0.1***

(0.004) -0.04***

(0.002) -0.1***

(0.003) -0.04***

(0.004)


(8.3) 36.4***

(11.3) 17.8 (26.3) 26.1 (40.3) 33.2***

(7.3) 38.1***

(5.9) -99.1***

(18.0)

Crop Specific Inputs -0.5***

(0.01) -0.7***

(0.01) -0.5***

(0.1) -0.3***

(0.01) -0.5***

(0.01) -0.5***

(0.01) -0.5***

(0.03)

Total Subsidies 0.5***

(0.02) 0.6***

(0.1) 0.7***

(0.1) 0.004 (0.1) 0.6***

(0.02) 0.5***

(0.04) 0.7***

(0.04)

Total Crop Output 0.4***

(0.01) 0.6***

(0.01) 0.5***

(0.03) 0.3***

(0.01) 0.5***

(0.01) 0.5***

(0.01) 0.5***

(0.01)

Water -0.9***

(0.2) -2.6***

(0.9) -1.7 (1.1) -3.3***

(0.6) -1.8***

(0.4) -0.8**

(0.4) 0.5 (0.9)


(15.2) -270.6***

(62.9) -311.0***

(53.4) 579.0***

(96.5) -116.1***

(15.2) -257.5***

(28.7) -39.7 (33.4)

Area Under Irrigation (ha) 125.6 (94.4) 288.9 (492.8) 136.6 (1,550.1) -16.6 (400.2) -314.3**

(124.8) 188.9 (185.4) -487.3**

(209.0)

Mean Temperature (°C) -11,190.0***

(3,077.2) -19,598.3 (17,388.4) -7,377.1 (11,600.0) -14,793.8 (23,749.0) 3,026.8 (4,549.0) -15,694.5 (9,673.2) -6,075.4 (9,208.0)

Mean Precipitation (mm) -3.6 (8.8) -8.2 (38.7) -75.6**

(33.9) -26.0 (48.1) -83.0***

(19.0) -2.9 (18.8) 1.3 (34.4)

Mean Temperature Squared 804.1***

(158.9) 1,122.7 (842.5) 442.1 (610.4) 903.9 (1,130.7) -2.2 (234.7) 908.2* (472.3) 286.1 (470.6)

Mean Precipitation Squared -0.001 (0.005) -0.01 (0.02) 0.04**

(0.02) 0.01 (0.03) 0.04***

(0.01) -0.001 (0.01) -0.002 (0.02)


(22.1) 657.8***

(126.9) 282.4***

(78.0) 1,403.6***

(326.1) 416.4***

(28.8) 597.2***

(50.1) 823.6***

(60.1)

No. of high precipitation

(days) 133.7**

(54.3) 49.5 (165.2) -182.8 (277.6) -5.8 (182.3) 149.3 (91.0) 51.0 (95.9) 142.0 (163.5)

No. of warm (days) -103.1***

(18.1) -258.3***

(63.7) -26.1 (76.7) -155.6* (81.3) -15.3 (30.6) -89.6

** (35.4) 37.9 (53.6)

ETR (°C) 461.3***

(93.1) -343.1 (276.3) 325.0 (416.9) 210.9 (393.3) 321.6**

(145.6) 338.7**

(164.1) 355.8 (248.5)

Constant 36,109.4** (15,964.2) 133,903.1 (90,921.8) 78,509.1 (58,638.5) 87,944.9 (124,635.5) 7,019.1 (24,439.5) 73,112.2 (50,616.6) 30,859.2 (48,513.7)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.6 0.8 0.1 0.4 0.7 0.7 0.7

Adjusted R2 0.6 0.8 0.1 0.4 0.7 0.7 0.7


F Statistic 371.7*** (df = 18; 4973) 298.6*** (df = 18;

1468)

18.6*** (df = 18;

3836)

119.1*** (df = 18;

2663)

322.6*** (df = 18;

3027)

236.0*** (df = 18;

1707)

236.9*** (df = 18;

1770)

Note: *p**

p***

p<0.01


20

In terms of farm family incomes, mean annual temperature only display a significant negative

impact in dairy farms (β= -11,190.3, p<0.01). No statistically significant linear dependence of

the mean of farm family incomes on annual mean temperature was detected in rest of the farms.

This aggregate level of temperature dependence does not provide a clear picture of impact of

high mean temperatures on farm family incomes because the OLS estimates from Table 4 with

disaggregated climate indicators shows that annual mean temperatures have statistically

significant impact on farm family incomes for other farms as well albeit in different season (or

squared specifications).

From point of view of mean annual precipitation, only cattle (β= -75.6, p<0.01) and mixed (crop

and livestock) farms exhibit statistically significant negative impact (β= -83.0, p<0.01). No

statistically significant linear dependence of the mean of farm family incomes on annual mean

precipitation was detected in rest of the farms. Number of warm days on the other hand

exhibited statistically significant negative impact on dairy farms (β= -103.1, p<0.01),

granivores farms (β= -258.3, p<0.01), horticulture farms (β= -155.6, p<0.10) and mixed

(livestock) farms (β= -89.6, p<0.10). No statistically significant linear dependence of the mean

of owned farm land values on number of warm days was detected in rest of the farms.

Results regarding value of owned agricultural land can be found in Table 6 and Table 7. On an

aggregate level, annual mean temperatures show a significant negative impact on value of

owned farm land for milk farms (β= -89,047.3, p<0.01), cattle farms (β= -32,812.1, p<0.05),

mixed (crop and livestock) farms (β= -49,179.4, p<0.01), mixed (livestock) farms (β= -

83,629.1, p<0.10) and other field crop farms (β= -64,218.2, p<0.05). No statistically significant

linear dependence of the mean of owned farm land values on annual mean temperature was

detected in Granivores and horticultural farms.

Annual mean precipitation shows a significant negative impact on value of owned farm land

value for dairy farms (β= -183.2, p<0.01), cattle farms (β= -161.3, p<0.01) and mixed (crop and

livestock) farms (β= -303.7, p<0.01). No statistically significant linear dependence of the mean

of owned farm land values on annual mean precipitation is detected in Granivores, horticultural

and mixed livestock farms. Number of warm days also show a significant negative impact on

the value of owned farm land in case of milk farms (β= -544.4, p<0.01) and mixed livestock

farms (β= -441.2, p<0.01). No statistically significant linear dependence of the mean of owned

farmland values on number of warm days was detected in Granivores, horticultural, cattle, other

field crop and mixed (crop and livestock) farms.

21

Table 6. OLS estimators - Value of owned agricultural land

Dependent variable: Value of owned agricultural land (Euro)


Economic size (€) 803.6***

(69.2) -176.0**

(85.5) 440.6***

(55.5) 41.3***

(13.1) -112.3* (67.9) -510.5

*** (84.8) -105.5 (113.0)


(0.8) -0.2 (0.4) 5.4***

(1.0) -0.5***

(0.1) 1.6 (1.1) -1.5**

(0.8) -11.1***

(1.9)

Total Assets (€) 0.3***

(0.01) 0.1***

(0.01) 0.4***

(0.01) 0.1***

(0.01) 0.5***

(0.01) 0.3***

(0.01) 0.6***

(0.01)

Total Livestock (LU) -542.1***

(41.6) 18.3 (23.6) -568.6***

(29.3) 137.5***

(50.7) -98.6***

(32.3) -85.5***

(26.9) -719.7***

(60.2)

Crop Specific Inputs (€) 0.6***

(0.1) -0.1* (0.03) 0.1 (0.1) -0.1

*** (0.02) 0.2

*** (0.1) 0.4

*** (0.1) 0.3

*** (0.1)

Total Subsidies (€) -2.4***

(0.1) -0.7***

(0.2) -1.1***

(0.1) -1.9***

(0.2) -0.8***

(0.1) -1.7***

(0.2) -0.1 (0.1)

Total Crop Output (€) -0.3***

(0.03) 0.02 (0.02) -0.3***

(0.04) -0.01 (0.01) -0.3***

(0.04) -0.2***

(0.04) -0.2***

(0.04)

Water (€) -5.3***

(1.2) -2.8 (1.7) -3.0**

(1.2) 0.4 (0.8) -0.6 (1.6) -2.6 (1.8) 7.6***

(2.9)


(74.9) -260.9**

(127.0) -569.1***

(59.7) -1,342.8***

(123.9) -894.2***

(68.6) -39.6 (132.8) -774.9***

(112.7)

Area Under Irrigation (ha) 2,412.0***

(461.9) 54.3 (963.3) 4,659.1***

(1,702.3) -247.7 (507.6) 4,356.6***

(549.5) 2,023.1**

(835.9) -759.3 (687.0)


Winter -685.7 (3,450.5) 6,924.6 (7,071.2) -7,261.8**

(3,055.6) -3,888.1 (7,387.3) -5,077.8 (5,382.2) -28,140.6***

(8,490.9) -8,326.5 (7,476.1)

Spring 6,795.6 (12,568.2) 383.8 (26,162.8) -8,643.1 (12,287.5) 1,389.4 (27,467.6) 10,879.5 (14,965.3) 88,850.6***

(31,408.8) 7,727.9 (23,100.9)

Summer -57,732.9***

(20,754.9) 121,831.6

** (48,700.3) -19,575.0 (23,316.6) -21,936.5 (54,157.1) -42,883.7 (26,870.5) -82,520.4 (59,926.4) -3,581.7 (38,237.7)

Autumn 7,603.9 (9,671.3) -24,417.8 (18,077.0) 26,052.6***

(8,970.8) -18,462.3 (15,212.5) -4,533.6 (15,124.4) -12,430.3 (20,079.9) -21,880.1 (21,165.6)

(Sq.)Winter 1,515.7***

(473.9) -879.9 (886.3) 781.7* (428.2) 212.9 (932.6) 817.2 (719.6) 3,786.9

*** (1,073.2) 971.7 (992.8)

(Sq.)Spring -70.2 (642.6) 104.6 (1,275.4) 878.0 (626.8) -253.2 (1,329.7) -287.8 (789.7) -3,976.5***

(1,541.7) -513.1 (1,187.5)

(Sq.)Summer 1,912.9***

(610.2) -3,451.2**

(1,391.3) 486.7 (686.0) 712.6 (1,530.5) 1,236.6 (808.7) 2,540.2 (1,725.2) 246.3 (1,134.2)

(Sq.)Autumn -291.5 (462.5) 641.6 (823.4) -787.1* (430.6) 708.4 (690.5) 409.4 (716.8) 554.6 (943.3) 1,033.9 (998.1)


Winter -94.4 (58.2) -59.5 (122.8) -0.8 (54.8) -79.8 (124.4) 63.3 (106.9) 329.9**

(130.9) 67.6 (182.9)

Spring -279.0***

(96.7) 57.9 (173.1) -188.5**

(88.4) 65.2 (158.0) -119.6 (143.7) 340.6 (223.1) 36.7 (220.6)

Summer 123.6 (87.0) -51.1 (121.6) 76.5 (96.5) 49.2 (110.0) -272.0* (140.0) 341.9

** (148.2) 148.9 (175.8)

Autumn -25.3 (81.1) -561.6***

(103.9) -212.4***

(82.1) -53.0 (83.1) -365.5***

(105.4) -299.4**

(119.7) 73.6 (142.6)

(Sq.)Winter 0.2**

(0.1) 0.1 (0.2) 0.02 (0.1) 0.1 (0.2) -0.3 (0.2) -0.7***

(0.3) 0.1 (0.4)

(Sq.)Spring -0.4 (0.2) -0.5 (0.5) 0.5***

(0.2) -0.3 (0.4) -0.2 (0.3) -1.1* (0.6) -0.1 (0.6)

(Sq.)Summer -0.4***

(0.1) -0.3 (0.2) -0.3* (0.2) -0.2 (0.2) 0.3 (0.2) -0.6

** (0.3) -0.2 (0.3)

(Sq.)Autumn 0.2 (0.1) 0.8***

(0.2) 0.6***

(0.1) 0.1 (0.1) 0.6***

(0.2) 0.4**

(0.2) -0.002 (0.2)

22



UAA Owned (ha) 6,282.4***

(108.6) 15,719.0***

(250.8) 3,919.6***

(86.5) 19,173.6***

(413.1) 5,861.3***

(128.8) 10,303.8***

(230.7) 6,125.3***

(200.0)


Winter -1,084.2 (731.6) -2,157.4**

(939.5) -2,295.8***

(746.6) 80.1 (847.0) -954.5 (1,135.5) -268.2 (1,190.9) -1,800.6 (1,437.2)

Spring 3,339.7***

(801.0) 1,910.8* (1,059.9) -1,583.1

* (827.0) 1,182.8 (859.5) 2,719.4

** (1,190.7) -2,070.8 (1,439.5) -600.1 (1,506.6)

Summer 2,792.7***

(612.8) 3,793.9***

(849.2) 1,043.3 (700.9) 1,584.1**

(631.0) 3,252.7***

(855.8) -94.0 (1,110.5) 200.0 (1,134.5)

Autumn 1,940.4***

(611.2) 1,147.6 (768.7) -1,074.7 (679.8) -425.6 (678.9) 2,139.5**

(866.7) 805.1 (1,030.9) -609.4 (1,183.4)

No. of Warm days

Winter -1,483.5***

(251.8) 258.2 (396.5) -277.2 (293.0) 523.9 (371.1) -231.4 (448.7) -641.9 (501.3) -225.4 (559.6)

Spring -1,140.5***

(288.8) 335.7 (376.3) 43.9 (318.8) 573.0* (333.8) -66.3 (422.2) -351.4 (481.0) 776.3 (577.7)

Summer 613.6* (335.7) 9.1 (440.4) 1,202.3

*** (363.1) -404.4 (400.0) 878.7 (536.2) -848.6 (521.3) 314.0 (721.5)

Autumn 651.0**

(328.4) 1,456.5***

(463.5) -688.1* (358.8) 870.9

** (407.2) -19.7 (505.6) 1,620.1

*** (590.8) -66.7 (695.1)


(°C)

Winter 101.6 (444.8) -791.1 (581.0) -2,021.3***

(508.4) -54.0 (483.4) -1,735.4***

(644.7) -1,524.9**

(695.5) -119.8 (851.1)

Spring 595.0 (367.8) -593.7 (517.0) 638.9 (432.0) -374.3 (420.2) -762.1 (503.0) 145.3 (632.1) 636.7 (652.8)

Summer -1,510.8***

(469.3) 2,207.5***

(633.7) -1,714.9***

(536.3) -261.3 (544.4) 81.3 (718.4) 288.0 (824.3) 47.0 (877.2)

Autumn 971.8***

(353.0) -1,299.7**

(511.9) -159.3 (378.8) -657.0 (431.3) -375.3 (503.0) -266.4 (622.9) -116.2 (697.3)

Constant 348,695.6**

(152,088.9) -826,553.5

** (377,206.3) 159,906.2 (169,807.3) 322,581.2 (421,795.6) 448,461.7

**

(199,618.0) 294,010.7 (475,401.8) 218.4 (279,943.8)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.8 0.9 0.8 0.7 0.9 0.9 0.9

Adjusted R2 0.8 0.9 0.8 0.7 0.9 0.9 0.9



(df = 39;

4952) 245.7

*** (df = 39; 1447)

456.0***

(df = 39;

3815)

158.9***

(df = 39;

2642)

767.0***

(df = 39;

3006)

264.3***

(df = 39;

1686)

720.9***

(df = 39;

1749)

Note: *p<0.1;

**p<0.05;

***p<0.01


23

Table 7. OLS estimators - Value of owned agricultural land (aggregated climate indicators)

Dependent variable: Value of Owned Agricultural Land (Euro)

Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other

Economic size (Martínez et al.) 971.5***

(68.1) -102.7 (79.3) 508.7***

(54.9) 45.2***

(12.9) -76.1 (66.4) -416.0***

(84.7) -124.9 (109.6)

Electricity -2.2***

(0.8) 0.1 (0.4) 6.4***

(1.0) -0.5***

(0.1) 2.1**

(1.0) -0.9 (0.8) -10.9***

(1.9)

Total Assets (Euro) 0.3***

(0.01) 0.1***

(0.01) 0.4***

(0.01) 0.1***

(0.005) 0.5***

(0.01) 0.2***

(0.01) 0.6***

(0.01)


(41.4) -1.3 (22.4) -593.9***

(29.1) 140.6***

(50.5) -98.7***

(32.2) -86.3***

(27.3) -709.8***

(58.7)

Crop Specific Inputs 0.6***

(0.1) -0.03 (0.03) 0.1 (0.1) -0.1***

(0.02) 0.2***

(0.1) 0.4***

(0.1) 0.2***

(0.1)

Total Subsidies -2.7***

(0.1) -0.6***

(0.2) -0.9***

(0.1) -1.9***

(0.2) -0.8***

(0.1) -1.5***

(0.2) -0.2 (0.1)

Total Crop Output -0.3***

(0.03) 0.01 (0.02) -0.3***

(0.04) -0.01 (0.01) -0.3***

(0.04) -0.2***

(0.04) -0.2***

(0.04)

Water -6.5***

(1.2) -3.2* (1.7) -3.9

*** (1.2) 0.5 (0.8) -0.8 (1.6) -2.5 (1.9) 7.4

*** (2.9)

Area Under Agriculture (ha) -1,050.5***

(75.4) -336.4***

(124.7) -648.3***

(59.2) -1,392.1***

(120.9) -950.2***

(67.2) -113.1 (131.6) -737.8***

(108.5)

Area Under Irrigation (ha) 2,352.4***

(469.6) 326.6 (977.5) 5,123.1***

(1,718.4) -334.9 (501.3) 4,386.1***

(552.9) 2,128.6**

(850.1) -627.6 (679.8)

Mean Temperature (°C) -89,047.3***

(15,306.0) 22,414.7 (34,492.2) -32,812.1**

(12,859.5) 2,190.9 (29,746.7) -49,179.4**

(20,161.3) -83,629.1* (44,353.5) -64,218.2

** (29,947.5)

Mean Precipitation (mm) -183.2***

(43.7) -114.5 (76.8) -161.3***

(37.5) -40.3 (60.3) -303.7***

(84.4) 79.4 (86.2) -132.6 (111.9)

Mean Temperature Squared 5,661.6***

(790.3) -749.8 (1,671.2) 2,274.0***

(676.7) -111.9 (1,416.3) 2,991.0***

(1,040.2) 5,291.9**

(2,165.7) 3,227.0**

(1,530.4)

Mean Precipitation Squared 0.1**

(0.02) 0.01 (0.04) 0.1***

(0.02) 0.01 (0.03) 0.1**

(0.05) -0.1 (0.05) 0.1 (0.1)


(109.9) 15,865.7***

(251.8) 3,903.8***

(86.5) 19,374.3***

(408.5) 5,785.0***

(127.8) 10,547.1***

(229.9) 6,141.3***

(195.5)

No. of high precipitation (days) 1,017.0***

(270.2) 1,270.3***

(327.6) -699.6**

(307.7) 456.0**

(228.4) 1,302.7***

(403.4) -506.8 (439.6) -277.8 (531.6)

No. of warm (days) -544.4***

(90.2) 132.2 (126.4) -132.1 (85.1) 137.7 (101.8) -44.5 (135.7) -441.2***

(162.5) 149.0 (174.3)

ETR (°C) 2,888.0***

(463.1) 422.5 (548.2) -1,888.3***

(462.2) -129.4 (492.6) -1,037.0 (645.4) 1,974.5***

(752.6) 1,002.5 (808.2)

Constant 373,276.0*** (79,406.2) -112,611.2 (180,355.6) 265,910.7*** (65,005.2) 3,188.0 (156,111.3) 358,199.5*** (108,317.4) 259,696.0 (232,086.0) 314,247.0** (157,782.7)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.8 0.9 0.8 0.7 0.9 0.8 0.9

Adjusted R2 0.8 0.9 0.8 0.7 0.9 0.8 0.9


F Statistic 869.6*** (df = 18; 4973) 506.1*** (df = 18;

1468) 951.2*** (df = 18; 3836)

340.6*** (df = 18;

2663)

1,628.8*** (df = 18;

3027)

535.7*** (df = 18;

1707)

1,566.4*** (df = 18;

1770)

Note: *p

**p

***p<0.01

Source: FADN data for Belgium (1990-2009) 24

OLS estimators do not account for individual and time specific heterogeneities and it makes

intuitive sense to use panel model estimates in this research. Panel model is also more realistic

as it allows correlation between the explanatory variables and the unobserved components.

Taking a take a look through recent research (Galindo et al., 2015) on panel data application in

understanding climate change and agriculture, there seem to be more instances of panel models

as compared against OLS method of regression.

4.2. Panel model

To account for individual and temporal heterogeneity, a panel model approach is adopted.

FADN data structure also helped in this regard. A standard panel specification can be written

as:

Yit = α + βiXit + Uit (i = 1, . . . , N; t = 1, ..., T)

Uit = Ui + λt + Vit (i = 1, . . . , N; t = 1, . . . , T)

with i denoting households, individuals, firms, countries, etc. and t denoting time. The i

subscript, denotes the cross-section dimension whereas t denotes the time-series dimension. α

is a constant and Xit is the ith observation on explanatory variables. Two-way error component

Ui denotes the unobservable individual effect, λt denotes the unobservable time effect and Vit

is the remainder stochastic disturbance term. λt is individual-invariant and it accounts for any

time-specific effect that is not included in the regression. For example, it could account for year

effects where production was disrupted; policy effects that disrupt the supply of raw materials,

output prices etc.

The farm family income (in €) and value of owned land (in €) are regressed on climate and

other control variables for the whole sample. Two-way fixed effects model appeared to be a

better fit for the data in hand when compared to OLS models (based on results of F test for two-

ways effects) and random effects models (based on results of Hausman test). This is in line with

the literature available on this matter which commonly points to the existence of two-way fixed

effects rather than random effects. The adjusted-R² values for explaining agricultural land

values are 0.6, 0.6, 0.8, 0.3, 0.7, 0.6 and 0.7 for milk, granivores, cattle, horticultural, mixed

(crop and livestock), mixed (livestock) and other field crop farms respectively (Table 8). R²

values when modeling farm family incomes using the same set of predictors are 0.4, 0.6, 0.03,

0.5, 0.5, 0.5 and 0.5 for milk, granivores, cattle, horticultural, mixed (crop and livestock),

mixed (livestock) and other field crop farms respectively (Table 10).

25

Detailed results regarding value of owned agricultural land can be seen in Table 8 (with

disaggregated climate indicators) and Table 9 (with aggregated climate indicators). It is to be

noticed that these values are not adjusted for heteroscedasticity and the adjusted values can be

comprehended from section ‘4.3.2. Heteroscedasticity’.

On aggregated climate level, It can be seen that an increase in mean temperature has a negative

significant effect on mean owned farm land values in cattle farms (β= -37,154.5, p<0.01) and a

positive significant effect on mean owned farm land values in horticultural farms (β= -71,842.9,

p<0.10). No statistically significant linear dependence of the mean of owned farmland values

on mean temperature was detected in rest of the farms.

Precipitation is also seen to have no statistically significant impact on mean owned farm land

values of any type of farm when looked from aggregated climate point of view. It is obviously

not true when compared against results from Table 8. This further emphasizes the fact that

aggregate level analysis of a research question like this reduces the resolution of results.

Number of warms days seem to have a significant negative impact on value of owned

agricultural land in cattle farms (β= -405.8, p<0.10) and horticultural farms (β= -319.1, p<0.01).

A positive significant impact of number of warm days in same regard is seen in dairy farms (β=

393.5, p<0.10) and mixed livestock farms (β= 671.0 p<0.01).

26

Table 8. Two-way fixed effects panel model estimates - Value of owned agricultural land (in €).


Milk Granivore Cattle# Horticulture Mixed Cr+LS Mixed LS Other

Economic size (€) 303.7***

(80.6) -229.9* (129.2) 275.0

*** (66.9) 42.7

** (18.1) -627.5

*** (96.1) -115.1 (95.1) -505.5

*** (133.0)


(0.6) -0.3 (0.4) 3.5***

(1.0) -0.02 (0.1) -2.1**

(1.0) -1.3***

(0.5) -2.3 (2.4)


(0.01) 0.1***

(0.01) 0.4***

(0.01) 0.1***

(0.005) 0.5***

(0.01) 0.1***

(0.01) 0.7***

(0.01)


(55.7) 62.0* (33.6) -461.8

*** (38.5) 157.3 (319.2) -66.4 (59.5) -125.5

*** (29.6) -505.3

*** (134.3)

Crop Specific Inputs (€) 0.2***

(0.1) -0.1***

(0.03) 0.1**

(0.1) -0.01 (0.02) 0.2***

(0.1) 0.2***

(0.1) -0.6***

(0.1)


(0.1) -0.2 (0.2) -0.6***

(0.1) -0.7***

(0.1) -0.2***

(0.1) -0.2* (0.1) 0.03 (0.1)

Total Crop Output (€) -0.2***

(0.04) 0.01 (0.02) -0.3***

(0.03) -0.01 (0.01) -0.3***

(0.04) -0.2***

(0.04) -0.2***

(0.04)

Water (€) -5.1***

(1.1) -1.9 (1.9) -3.4***

(1.1) 0.5 (0.9) -7.3***

(1.8) 3.5**

(1.7) 1.2 (3.1)


(92.4) 6.5 (181.1) -418.7***

(69.6) -1,060.1***

(305.7) -8.6 (107.1) 566.3***

(145.7) 70.6 (134.4)

Area Under Irrigation (ha) 999.4**

(454.0) 400.3 (1,512.6) 456.8 (1,530.8) 229.3 (711.3) -141.8 (517.8) -1,152.4* (598.3) 736.5 (899.6)


Winter -12,735.3***

(2,955.7) -4,815.8 (9,419.8)

-5,926.8***

(2,146.0) -2,659.7 (8,011.1) 7,639.1 (6,361.4) -2,603.5 (8,687.4) -1,686.3 (9,607.8)

Spring -8,434.6 (10,710.4) -17,201.1 (33,248.4) -6,402.5 (8,156.2) -9,672.2 (37,466.5) -3,379.5 (15,342.5) -10,542.2 (33,367.7) 16,971.2 (25,144.9)

Summer 8,175.2 (14,506.2) -15,566.9 (42,820.9) 17,565.9 (16,020.8) -21,765.1 (46,229.0) -4,705.5 (23,910.0) -16,330.7 (44,269.5) 14,366.1 (37,083.8)

Autumn -4,325.9 (10,344.9) -10,411.5 (32,470.0) -2,278.2 (6,418.8) 38,732.3 (32,808.7) -25,934.1 (24,817.6) -18,058.5 (30,017.7) -22,577.6 (39,168.7)

(Sq.)Winter -311.1 (350.0) -876.5 (1,087.8) 54.8 (286.3) -266.8 (961.0) -70.8 (749.1) -788.1 (1,000.0) 136.5 (1,141.8)

(Sq.)Spring 309.6 (589.2) 841.6 (1,708.4) 708.9* (418.7) 457.9 (1,906.7) 24.1 (890.0) 455.9 (1,747.8) -1,087.7 (1,427.5)

(Sq.)Summer -196.3 (440.1) 610.3 (1,229.7) -570.1 (469.3) 421.7 (1,349.9) -160.3 (727.9) 652.8 (1,284.7) -179.3 (1,117.5)

(Sq.)Autumn 648.8 (499.7) 121.2 (1,455.2) 465.4 (304.5) -1,605.1 (1,529.0) 1,135.4 (1,144.7) 328.9 (1,346.3) 521.3 (1,805.9)


Winter -57.5 (50.7) 85.0 (120.3) 17.9 (37.5) -75.0 (117.4) -4.7 (99.9) -188.1* (106.8) -80.9 (185.7)

Spring 109.8 (81.0) 140.4 (188.5) 43.3 (61.5) 71.6 (158.3) -414.0**

(168.1) -20.2 (175.9) -43.2 (248.8)

Summer 78.0 (68.3) -55.7 (114.7) 122.2* (66.2) -182.3

* (98.0) 40.0 (121.7) 125.4 (117.8) 106.4 (164.5)

Autumn 0.9 (53.7) 47.6 (94.2) -108.4* (57.0) 103.1 (71.4) -108.8 (86.9) 78.8 (92.0) 155.6 (125.0)

(Sq.)Winter 0.2***

(0.1) 0.01 (0.2) -0.01 (0.04) 0.3 (0.2) -0.1 (0.2) 0.4**

(0.2) 0.3 (0.4)

(Sq.)Spring -0.7***

(0.2) -0.1 (0.5) 0.04 (0.1) -0.4 (0.4) 0.8**

(0.3) 0.2 (0.4) 0.3 (0.6)

27



(Sq.)Summer -0.1 (0.1) -0.1 (0.2) -0.3***

(0.1) 0.3* (0.2) -0.2 (0.2) -0.1 (0.2) -0.1 (0.3)

(Sq.)Autumn 0.1 (0.1) -0.1 (0.2) 0.3***

(0.1) -0.2* (0.1) 0.2 (0.2) -0.1 (0.2) -0.4

* (0.2)


(120.4) 15,212.2***

(342.7) 4,152.4***

(101.3) 14,698.9***

(596.4) 6,110.3***

(152.7) 12,784.8***

(300.6) 4,594.4***

(237.2)


Winter -146.9 (487.5) -1,291.5 (802.8) -657.5 (498.4) -1,001.0 (679.0) 1,675.6**

(787.7) 443.8 (843.3) -1,713.3 (1,128.5)

Spring 897.2* (535.1) -1,921.2

** (951.3) -1,238.9

** (568.1) 972.7 (705.3) 1,676.1

* (956.2) -580.7 (1,037.7) -807.2 (1,305.4)

Summer 363.4 (404.2) 2,206.5***

(743.0) 605.7 (471.4) 1,006.1* (548.7) 1,137.3

* (658.9) -1,136.6 (743.8) -194.7 (953.6)

Autumn -612.7 (426.6) 0.6 (698.8) -1,141.0**

(453.1) -529.2 (610.0) -847.3 (728.8) -1,647.6**

(748.9) -500.4 (1,069.9)

No. of Warm days

Winter 2,225.0***

(337.2) 593.9 (778.3) 162.2 (216.1) -282.0 (648.1) -484.0 (619.7) 488.5 (818.3) -654.4 (896.3)

Spring 358.0 (347.4) 420.7 (589.8) -86.4 (225.4) 1,007.3* (572.8) 1,081.3 (671.4) -251.7 (644.7) 143.0 (951.7)

Summer -435.2 (401.7) -1,992.3***

(599.5) 733.1***

(246.0) -318.0 (601.5) -1,064.5 (706.6) 415.2 (601.6) -1,010.8 (913.9)

Autumn -1,228.3***

(386.7) 937.7 (678.3) -706.2***

(250.2) -1,044.9* (622.8) 706.1 (745.3) 1,560.5

** (697.7) 1,886.0

* (995.3)


(°C)

Winter 387.8 (430.6) -233.0 (806.2) -1,091.7***

(353.1) 893.9 (631.6) -642.3 (684.3) -178.1 (749.5) 2,959.4***

(999.7)

Spring 141.8 (552.5) -653.6 (861.9) 730.9**

(306.4) -2,247.7***

(842.1) 162.1 (807.5) -145.3 (819.4) 51.0 (1,239.0)

Summer -1,698.0***

(539.0) 1,149.7 (963.3) -978.5**

(391.9) -191.3 (854.6) 588.8 (903.4) 203.5 (868.5) 1,465.3 (1,220.8)

Autumn 2,283.1***

(562.7) -480.3 (939.9) 83.0 (252.6) 1,491.5 (910.4) -855.4 (973.5) 391.9 (842.6) -343.2 (1,409.1)

Constant -123,316.5 (118,697.5)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.7 0.7 0.8 0.4 0.8 0.8 0.8

Adjusted R2 0.6 0.6 0.8 0.3 0.7 0.6 0.7


(df = 39;

4269)

86.7***

(df = 39; 1216) 335.0***

(df = 39;

3815)

39.6***

(df = 39; 2198) 313.5***

(df = 39;

2470)

127.0***

(df = 39;

1363)

197.9***

(df = 39; 1473)

Note: *p

**p

***p<0.01


28

Table 9. Two way fixed effects panel estimators - Value of owned agricultural land (aggregated climate indicators)



Economic size (Martínez et al.) 438.6***

(79.0) -228.4* (127.9) 126.3 (80.2) 44.8

** (18.1) -631.3

*** (96.0) -105.5 (94.4) -532.5

*** (132.0)

Electricity -3.0***

(0.6) -0.2 (0.4) -0.5 (1.1) -0.01 (0.1) -2.3**

(1.0) -1.3***

(0.5) -2.5 (2.4)

Total Assets (Euro) 0.2***

(0.01) 0.1***

(0.01) 0.4***

(0.01) 0.1***

(0.005) 0.5***

(0.01) 0.1***

(0.01) 0.7***

(0.01)


(55.5) 58.3* (33.3) -383.1

*** (49.4) 137.4 (315.4) -59.5 (59.5) -123.4

*** (29.3) -481.6

*** (133.3)

Crop Specific Inputs 0.2***

(0.1) -0.1***

(0.03) 0.03 (0.1) -0.01 (0.02) 0.2***

(0.1) 0.2***

(0.1) -0.6***

(0.1)

Total Subsidies -1.2***

(0.1) -0.2 (0.2) -0.7***

(0.1) -0.7***

(0.1) -0.2***

(0.1) -0.2* (0.1) 0.03 (0.1)

Total Crop Output -0.2***

(0.04) 0.01 (0.02) -0.3***

(0.03) -0.01* (0.01) -0.3

*** (0.04) -0.2

*** (0.04) -0.2

*** (0.04)

Water -5.6***

(1.2) -1.9 (1.9) -2.7**

(1.1) 0.3 (0.9) -6.7***

(1.8) 3.8**

(1.6) 1.0 (3.1)


(93.1) 18.5 (180.5) -184.8**

(76.4) -1,019.1***

(302.3) -38.8 (107.2) 531.0***

(143.9) 86.3 (133.7)

Area Under Irrigation (ha) 1,005.5**

(459.1) 275.8 (1,510.5) -1,066.4 (1,628.9) 340.7 (706.9) -141.0 (519.5) -1,389.3**

(595.6) 821.7 (890.9)

Mean Temperature (°C) 19,077.8 (12,860.9) -16,231.4 (39,048.6) -37,154.5***

(11,538.8) 71,842.9* (42,716.6) -26,936.0 (19,592.0) 31,715.5 (38,030.1) 11,358.4 (33,082.0)

Mean Precipitation (mm) -3.8 (32.2) 110.0 (68.7) 13.1 (27.3) 28.9 (54.2) -86.9 (72.4) 32.4 (60.1) 4.7 (102.5)

Mean Temperature Squared -1,307.1* (676.6) 507.8 (1,939.9) 1,743.1

*** (612.5) -3,663.0

* (2,049.4) 696.1 (1,051.4) -2,608.4 (1,892.1) -977.0 (1,751.8)

Mean Precipitation Squared -0.01 (0.02) -0.1* (0.04) 0.01 (0.01) -0.03 (0.03) 0.02 (0.04) -0.001 (0.03) 0.01 (0.1)


(121.3) 15,161.6***

(340.4) 4,010.3***

(102.7) 14,409.5***

(592.1) 6,093.2***

(153.1) 12,777.7***

(296.3) 4,566.1***

(235.9)

No. of high precipitation (days) 143.4 (192.3) 96.4 (281.5) -621.5***

(221.9) 123.3 (191.9) 692.6**

(324.5) -514.5* (304.6) -839.9

* (477.2)

No. of warm (days) 393.5***

(113.8) -287.3 (192.8) -405.8***

(125.0) -319.1* (166.2) 290.4 (192.2) 671.0

*** (204.9) -64.9 (290.7)

ETR (°C) 2,178.1***

(814.6) 431.5 (1,187.4) 1,629.9**

(811.7) -935.5 (999.3) -2,656.8**

(1,256.0) 330.2 (1,125.1) 3,255.7* (1,891.6)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.7 0.7 0.7 0.4 0.8 0.8 0.8

Adjusted R2 0.6 0.6 0.6 0.3 0.7 0.6 0.7

F Statistic 505.6*** (df = 18; 4290) 184.9*** (df = 18; 1237) 459.9*** (df = 18; 3244) 83.6*** (df = 18; 2219) 669.1*** (df = 18; 2491) 272.8*** (df = 18; 1384) 428.6*** (df = 18; 1494)

Note: *p

**p

***p<0.01


29

Panel modeling results in terms of farm family income are summarized in Table 10 (with

disaggregated climate indicators) and Table 11 (with aggregated climate indicators). Mean

temperature does not seem to have a statistically significant impact on farm family incomes.

This is in direct contrast with studies discussed in the literature review of this thesis. This

insignificant result can be attributed to the climate indicators being aggregate in nature (from

Table 11). Results from Table 10 however point towards statistically significant impact of

mean temperature level indicators on farm family incomes (in different seasons).

Significant negative impact of mean precipitation is seen in cattle (β= -91.9, p<0.05) and mixed

(crop and livestock) farms (β= -31.1, p<0.10). A positive significant impact is seen in other

field crop farms (β= 57.6, p<0.10). No statistically significant linear dependence of the mean

of farm family incomes on mean precipitation was detected in rest of the farms when

approached from aggregated climate indicators’ point of view.

Number of warm days displayed significant negative impact on granivores farm (β= -420.1,

p<0.01) and mixed (crop and livestock) farms (β= -106.7, p<0.01). No statistically significant

linear dependence of the mean of farm family incomes on number of warm days was detected

in rest of the farms.

It is interesting to see how panel estimates differ from each other when looked from an

aggregated point of view. This aggregation clearly changes the way some predictors behave

(and show statistical significance/insignificance).

30

Table 10. Two-way fixed effects panel model estimates - Farm family income (in €).



Economic size (€) -109.1***

(19.3) -303.5***

(71.9) -80.3 (54.2) -159.5***

(17.7) -177.5***

(24.8) -112.3***

(27.2) -261.4***

(40.2)


(0.1) -0.7***

(0.2) -3.6***

(1.2) -0.3***

(0.1) -2.4***

(0.3) -1.0***

(0.1) -4.6***

(0.7)


(0.002) -0.05***

(0.004) -0.1***

(0.01) -0.1***

(0.004) -0.02***

(0.003) -0.1***

(0.003) -0.02***

(0.004)


(13.3) 72.0***

(18.7) 57.0**

(29.1) -22.4 (311.6) 42.8***

(15.3) 24.0***

(8.5) -20.1 (40.6)


(0.02) -0.6***

(0.02) -0.5***

(0.1) -0.4***

(0.02) -0.7***

(0.02) -0.6***

(0.01) -0.7***

(0.04)


(0.02) 0.6***

(0.1) 0.8***

(0.1) 0.7***

(0.1) 0.5***

(0.02) 0.5***

(0.04) 0.7***

(0.04)


(0.01) 0.6***

(0.01) 0.5***

(0.05) 0.4***

(0.01) 0.5***

(0.01) 0.5***

(0.01) 0.6***

(0.01)

Water (€) -0.3 (0.3) 1.3 (1.1) -2.3 (1.5) -3.1***

(0.8) -0.9* (0.5) -0.9

* (0.5) -0.3 (0.9)


(22.1) 0.4 (100.8) -253.0***

(70.3) 696.9**

(298.3) -106.7***

(27.6) -182.2***

(41.6) -169.2***

(40.7)

Area Under Irrigation (ha) 334.7***

(108.5) 439.0 (841.8) 139.0 (1,904.6) -708.8 (694.2) 47.7 (133.6) 33.8 (170.9) -606.9**

(272.1)


Winter 558.4 (706.4) 4,325.9 (5,242.3) -4,838.4 (4,863.5) 2,507.7 (7,818.3) 2,977.1* (1,640.9) 652.7 (2,481.0) -1,583.9 (2,906.0)

Spring -2,295.7 (2,559.6) -27,671.2 (18,503.5) -6,219.9 (19,614.3) 28,767.3 (36,564.7) 2,918.4 (3,957.4) -737.1 (9,529.2) 21,867.7***

(7,605.4)

Summer 451.4 (3,466.7) 9,606.6 (23,830.8) -13,568.8

(33,441.1) -52,132.5 (45,116.3) 12,986.7

** (6,167.3) -6,429.6 (12,642.6) -4,664.4 (11,216.4)

Autumn 6,042.5**

(2,472.2) -6,185.7 (18,070.3) 2,961.6 (13,479.2) 32,870.6 (32,019.0) 1,318.0 (6,401.4) -8,381.8 (8,572.6) -16,521.1 (11,847.0)

(Sq.)Winter -35.3 (83.6) -256.5 (605.4) -158.0 (673.3) 700.9 (937.9) -32.6 (193.2) -97.5 (285.6) 267.1 (345.3)

(Sq.)Spring 124.0 (140.8) 1,548.2 (950.8) 646.2 (984.0) -1,195.6 (1,860.8) -70.8 (229.6) 157.2 (499.1) -1,271.3***

(431.8)

(Sq.)Summer -39.1 (105.2) -59.9 (684.4) 275.6 (983.4) 1,401.2 (1,317.4) -411.4**

(187.8) 151.5 (366.9) 253.2 (338.0)

(Sq.)Autumn -299.0**

(119.4) 150.9 (809.8) -83.3 (642.9) -1,729.8 (1,492.2) -28.8 (295.3) 284.6 (384.5) 749.6 (546.2)


Winter 11.9 (12.1) -86.3 (67.0) -58.4 (83.2) 18.0 (114.6) -21.5 (25.8) 23.9 (30.5) -42.2 (56.2)

Spring 40.3**

(19.4) 167.4 (104.9) 174.5 (132.5) -36.6 (154.5) -106.5**

(43.4) 1.4 (50.2) -74.6 (75.2)

Summer 14.8 (16.3) -9.3 (63.8) -337.4**

(147.8) -32.4 (95.7) -23.8 (31.4) -2.2 (33.6) 64.7 (49.7)

Autumn -0.5 (12.8) -52.2 (52.4) -114.9 (119.3) 49.8 (69.6) -7.3 (22.4) 18.0 (26.3) 83.9**

(37.8)

(Sq.)Winter -0.02* (0.01) 0.2 (0.1) 0.1 (0.1) -0.1 (0.2) 0.003 (0.04) -0.1 (0.1) 0.1 (0.1)

31



(Sq.)Spring -0.02 (0.04) -0.4 (0.3) -0.3 (0.3) 0.1 (0.4) 0.2**

(0.1) 0.1 (0.1) 0.1 (0.2)

(Sq.)Summer -0.03 (0.03) 0.02 (0.1) 0.5* (0.3) 0.1 (0.2) 0.05 (0.1) -0.02 (0.1) -0.1 (0.1)

(Sq.)Autumn 0.000 (0.02) 0.01 (0.1) 0.2 (0.2) -0.1 (0.1) 0.000 (0.04) -0.05 (0.05) -0.1**

(0.1)


(28.8) 502.2***

(190.7) 371.5***

(100.5) -27.8 (582.0) 261.2***

(39.4) 560.6***

(85.9) 293.6***

(71.7)

No. of High precipitation

days

Winter -100.1 (116.5) -382.4 (446.8) -533.2 (1,122.6) 383.5 (662.7) -114.2 (203.2) 281.4 (240.8) 190.0 (341.3)

Spring -266.4**

(127.9) -583.1 (529.4) -1,831.1 (1,237.0) -95.0 (688.4) 272.6 (246.6) -312.3 (296.3) 472.2 (394.8)

Summer 71.3 (96.6) -393.7 (413.5) 1,742.1* (997.1) -803.8 (535.5) -195.4 (170.0) -100.5 (212.4) -495.0

* (288.4)

Autumn -4.4 (102.0) 1,202.9***

(388.9) -228.3 (1,030.0) 435.5 (595.3) 123.2 (188.0) -22.3 (213.9) -16.8 (323.6)

No. of Warm days

Winter -67.1 (80.6) -553.0 (433.1) 779.7**

(388.7) -125.4 (632.5) -262.8 (159.9) -176.4 (233.7) -163.5 (271.1)

Spring -70.8 (83.0) -710.2**

(328.2) -129.6 (479.5) -627.6 (559.0) -219.9 (173.2) -209.2 (184.1) 92.4 (287.9)

Summer -98.5 (96.0) -537.2 (333.7) -155.8 (536.4) 809.6 (587.0) 102.9 (182.2) 8.1 (171.8) -105.6 (276.4)

Autumn 207.6**

(92.4) -201.7 (377.5) 388.8 (540.3) 195.7 (607.8) -203.5 (192.2) 449.7**

(199.3) 7.7 (301.0)


(°C)

Winter 1.4 (102.9) 108.0 (448.6) 215.3 (740.1) -96.1 (616.4) -4.6 (176.5) -342.4 (214.1) 568.4* (302.4)

Spring -97.8 (132.0) -625.3 (479.6) -182.2 (617.1) -1,052.3 (821.9) 138.6 (208.3) -212.1 (234.0) 43.7 (374.8)

Summer 268.7**

(128.8) 182.0 (536.1) 1,530.8**

(773.2) 1,541.5* (834.0) -44.9 (233.0) -230.1 (248.0) -350.2 (369.3)

Autumn -226.3* (134.5) -77.2 (523.1) -553.9 (581.1) -849.4 (888.5) -351.2 (251.1) 139.9 (240.6) -173.9 (426.2)

Constant 175,895.3

(238,249.7)

Observations 4,992 1,487 2,173 2,682 3,046 1,726 1,789

R2 0.5 0.7 NA 0.6 0.6 0.7 0.7

Adjusted R2 0.4 0.6 NA 0.5 0.5 0.5 0.5


(df = 39;

4269)

73.2***

(df = 39;

1216) NA 76.5

*** (df = 39;

2198)

108.9***

(df = 39;

2470)

72.0***

(df = 39;

1363)

73.6***

(df = 39;

1473)

Note: *p

**p

***p<0.01


32

Table 11. Two way fixed effects panel estimators - Farm family income (aggregated climate indicators)



Economic size (Martínez et

al.) -124.2***

(18.6) -304.5***

(71.1) -18.7 (136.1) -160.7***

(17.6) -183.0***

(24.6) -108.6***

(26.9) -267.1***

(40.0)

Electricity -1.1***

(0.1) -0.7***

(0.2) -1.1 (1.9) -0.3***

(0.1) -2.5***

(0.3) -1.0***

(0.1) -4.5***

(0.7)

Total Assets (Euro) -0.03***

(0.002) -0.04***

(0.004) -0.04***

(0.01) -0.1***

(0.004) -0.02***

(0.003) -0.1***

(0.003) -0.02***

(0.004)


(13.1) 71.4***

(18.5) -45.0 (83.8) 91.8 (307.1) 45.3***

(15.3) 23.2***

(8.4) -11.8 (40.4)

Crop Specific Inputs -0.6***

(0.02) -0.6***

(0.02) -0.6***

(0.1) -0.4***

(0.02) -0.7***

(0.02) -0.6***

(0.01) -0.7***

(0.04)

Total Subsidies 0.4***

(0.02) 0.6***

(0.1) 0.7***

(0.1) 0.6***

(0.1) 0.5***

(0.02) 0.5***

(0.04) 0.7***

(0.04)

Total Crop Output 0.5***

(0.01) 0.6***

(0.01) 0.6***

(0.1) 0.4***

(0.01) 0.5***

(0.01) 0.5***

(0.01) 0.6***

(0.01)

Water -0.2 (0.3) 1.1 (1.1) -0.5 (1.8) -3.1***

(0.8) -0.8* (0.5) -0.9

* (0.5) -0.1 (0.9)

Area Under Agriculture

(ha) -223.3***

(21.9) 5.8 (100.3) -443.7***

(129.6) 738.4**

(294.3) -108.8***

(27.5) -182.1***

(41.1) -156.9***

(40.6)

Area Under Irrigation (ha) 311.4***

(108.1) 298.2 (839.3) 771.8 (2,764.3) -797.3 (688.3) 31.3 (133.2) -4.4 (170.1) -564.1**

(270.2)

Mean Temperature -282.6 (3,027.9) 19,208.1 (21,697.1) -2,711.9 (19,581.9) -8,396.5 (41,590.2) 6,448.7 (5,023.2) -1,224.4 (10,859.5) 612.8 (10,032.6)

Mean Precipitation 3.9 (7.6) -35.9 (38.2) -91.9**

(46.3) 16.4 (52.8) -31.1* (18.6) -0.7 (17.2) 57.6

* (31.1)

Mean Temperature Sq 8.2 (159.3) -587.9 (1,077.9) 214.2 (1,039.4) 603.7 (1,995.4) -159.4 (269.6) -46.0 (540.3) 27.6 (531.3)

Mean Precipitation Sq 0.001 (0.004) 0.01 (0.02) 0.04* (0.02) -0.01 (0.03) 0.01 (0.01) -0.001 (0.01) -0.04

** (0.02)

UAA Owned 163.1***

(28.5) 436.8**

(189.2) 254.1 (174.3) 51.2 (576.5) 257.4***

(39.3) 544.8***

(84.6) 297.7***

(71.5)

Precipitation Anomaly -47.2 (45.3) 44.6 (156.4) -92.8 (376.6) -90.9 (186.9) -40.7 (83.2) 41.7 (87.0) -40.6 (144.7)

Temperature Anomaly -18.0 (26.8) -420.1***

(107.1) -51.8 (212.1) 62.8 (161.8) -106.7**

(49.3) 47.2 (58.5) -100.3 (88.2)

ETR -156.6 (191.8) 106.5 (659.8) 407.6 (1,377.5) 533.1 (972.9) -369.8 (322.0) -475.5 (321.3) 590.7 (573.6)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.5 0.7 0.04 0.6 0.6 0.7 0.7

Adjusted R2 0.4 0.6 0.03 0.5 0.5 0.5 0.5


(df = 18;

4290)

156.7***

(df = 18;

1237)

7.0***

(df = 18;

3244)

164.5***

(df = 18;

2219)

235.1***

(df = 18;

2491)

154.1***

(df = 18;

1384)

157.5***

(df = 18;

1494)

Note: *p

**p

***p<0.01


33

4.3. Diagnostics

To test if panel estimates are better off than OLS estimates, Breusch-Pagan Lagrange multiplier

(LM) test is employed. The null hypothesis of Breusch-Pagan Lagrange multiplier test assumes

that pooled-OLS regression estimates are better than panel estimates. All the tests4 showed that

panel effect exists and pooled-OLS estimators are not better than panel estimators.

4.3.1. Serial correlation

To test for serial correlation, Breusch-Godfrey Test for Panel Models is used. Null hypothesis

of this test assumes no serial correlation. Breusch-Godfrey test performed on two-way fixed

effect models point towards the fact that serial correlation exists. Results from this test are

available in annexure B. This means that the error terms from different (neighboring) time

periods (or cross-section observations) are correlated, and hence, the error term is serially

correlated. Serial correlation normally occurs in time-series studies when the errors related with

a given time period pass on to future time periods which might as well be the case in this

research.

As far as consequences of serial correlation are concerned, it does not affect the unbiasedness

(or consistency) of OLS estimators. Serial correlation, however, does affect the efficiency of

OLS estimators. Existence of serial correlation causes OLS estimates of standard errors to be

smaller than the true standard errors. In this research, however, only results from panel model

are of concern because it has already been established that the panel model fits research

problems of this thesis better.

4.3.2. Heteroscedasticity

Heteroscedasticity refers to the settings where the variability of a variable is unequal for a range

of values of an independent variable that predicts it. To test for heteroscedasticity in this

research, Breusch-Pagan test is used where the null hypothesis states that homoscedasticity

exists (i.e., there is no heteroscedasticity).

4 All results from LM test yielded p value smaller than 0.05 resulting in rejection of H0 of Breusch-

Pagan Lagrange multiplier test. Results available in annexure A.

34

When modeling value of owned agricultural land and farm family incomes, all tests on two-

way fixed effects models show that the heteroscedasticity exists. Summarized results can be

found in Table 12.

Table 12. Results of Breush-Pagan test for heteroscedasticity.

Farm type df Value of owned farm land

(p-value)

Farm family income

(p-value)

Dairy farms 39 < 2.2e-16 < 2.2e-16

Cattle farms 39 < 2.2e-16 < 2.2e-16

Granivores farms 39 < 2.2e-16 < 2.2e-16

Horticultural farms 39 < 2.2e-16 < 2.2e-16

Mixed (crop and livestock)

farms 39 < 2.2e-16 < 2.2e-16

Mixed (livestock) farms 39 < 2.2e-16 < 2.2e-16

Other field crop farms 39 < 2.2e-16 < 2.2e-16

Existence of heteroscedasticity does not result in biased parameter estimates. However, the

standard errors are biased. This in turn leads to bias in test statistics and confidence intervals.

To account for heteroscedasticity, robust covariance matrix estimator5 is employed in this

research. Robust covariance matrix estimator accounts for both heteroscedasticity as well as

serial correlation and is recommended for fixed effects. Heteroscedasticity corrected results can

be found in Table 13 and 14.

5 Going into details of how robust covariance matrix estimator accounts for heteroscedasticity in panel

models is beyond the scope of this thesis.

Source: BP test on FADN data for Belgium (1990-2009)

35

Table 13. Panel model estimates (accounted for heteroscedasticity) - Farm family income.


Milk Granivores Cattle (#) Horticulture Mixed Cr+LS Mixed LS Other

Economic size (€) -109.1**

(46.8) -303.5***

(46.8) -80.3 -159.5***

(46.8) -177.5***

(46.8) -112.3**

(46.8) -261.4***

(46.8)

Electricity (€) -1.1**

(0.4) -0.7* (0.4) -3.6 -0.3 (0.4) -2.4

*** (0.4) -1.0

** (0.4) -4.6

*** (0.4)


(0.003) -0.05***

(0.003) -0.1 -0.1***

(0.003) -0.02***

(0.003) -0.1***

(0.003) -0.02***

(0.003)

Total Livestock (LU) 45.4 (31.0) 72.0**

(31.0) 57.0 -22.4 (31.0) 42.8 (31.0) 24.0 (31.0) -20.1 (31.0)


(0.04) -0.6***

(0.04) -0.5 -0.4***

(0.04) -0.7***

(0.04) -0.6***

(0.04) -0.7***

(0.04)


(0.1) 0.6***

(0.1) 0.8 0.7***

(0.1) 0.5***

(0.1) 0.5***

(0.1) 0.7***

(0.1)


(0.02) 0.6***

(0.02) 0.5 0.4***

(0.02) 0.5***

(0.02) 0.5***

(0.02) 0.6***

(0.02)

Water (€) -0.3 (0.5) 1.3**

(0.5) -2.3 -3.1***

(0.5) -0.9* (0.5) -0.9 (0.5) -0.3 (0.5)


(40.1) 0.4 (40.1) -253.0 696.9***

(40.1) -106.7***

(40.1) -182.2***

(40.1) -169.2***

(40.1)

Area Under Irrigation (ha) 334.7 (236.3) 439.0* (236.3) 139.0 -708.8

*** (236.3) 47.7 (236.3) 33.8 (236.3) -606.9

** (236.3)


Winter 558.4 (815.8) 4,325.9***

(815.8) -4,838.4 2,507.7***

(815.8) 2,977.1***

(815.8) 652.7 (815.8) -1,583.9* (815.8)

Spring -2,295.7 (2,197.3) -27,671.2***

(2,197.3) -6,219.9 28,767.3

***

(2,197.3) 2,918.4 (2,197.3) -737.1 (2,197.3) 21,867.7

***

(2,197.3)

Summer 451.4 (3,804.0) 9,606.6**

(3,804.0) -13,568.8 -52,132.5***

(3,804.0)

12,986.7***

(3,804.0) -6,429.6

* (3,804.0) -4,664.4 (3,804.0)

Autumn 6,042.5***

(2,061.0)

-6,185.7***

(2,061.0) 2,961.6 32,870.6

***

(2,061.0) 1,318.0 (2,061.0) -8,381.8

***

(2,061.0)

-16,521.1***

(2,061.0)

(Sq.)Winter -35.3 (65.1) -256.5***

(65.1) -158.0 700.9***

(65.1) -32.6 (65.1) -97.5 (65.1) 267.1***

(65.1)

(Sq.)Spring 124.0 (125.1) 1,548.2***

(125.1) 646.2 -1,195.6***

(125.1) -70.8 (125.1) 157.2 (125.1) -1,271.3***

(125.1)

(Sq.)Summer -39.1 (118.9) -59.9 (118.9) 275.6 1,401.2***

(118.9) -411.4***

(118.9) 151.5 (118.9) 253.2**

(118.9)

(Sq.)Autumn -299.0***

(102.8) 150.9 (102.8) -83.3 -1,729.8***

(102.8) -28.8 (102.8) 284.6***

(102.8) 749.6***

(102.8)


Winter 11.9 (12.6) -86.3***

(12.6) -58.4 18.0 (12.6) -21.5* (12.6) 23.9

* (12.6) -42.2

*** (12.6)

Spring 40.3**

(17.9) 167.4***

(17.9) 174.5 -36.6**

(17.9) -106.5***

(17.9) 1.4 (17.9) -74.6***

(17.9)

36



Summer 14.8 (17.1) -9.3 (17.1) -337.4 -32.4* (17.1) -23.8 (17.1) -2.2 (17.1) 64.7

*** (17.1)

Autumn -0.5 (11.1) -52.2***

(11.1) -114.9 49.8***

(11.1) -7.3 (11.1) 18.0 (11.1) 83.9***

(11.1)

(Sq.)Winter -0.02* (0.01) 0.2

*** (0.01) 0.1 -0.1

*** (0.01) 0.003 (0.01) -0.1

*** (0.01) 0.1

*** (0.01)

(Sq.)Spring -0.02 (0.04) -0.4***

(0.04) -0.3 0.1***

(0.04) 0.2***

(0.04) 0.1***

(0.04) 0.1**

(0.04)

(Sq.)Summer -0.03 (0.03) 0.02 (0.03) 0.5 0.1**

(0.03) 0.05* (0.03) -0.02 (0.03) -0.1

*** (0.03)

(Sq.)Autumn 0.000 (0.02) 0.01 (0.02) 0.2 -0.1***

(0.02) 0.000 (0.02) -0.05**

(0.02) -0.1***

(0.02)

UAA Owned (ha) 170.9* (92.9) 502.2

*** (92.9) 371.5 -27.8 (92.9) 261.2

*** (92.9) 560.6

*** (92.9) 293.6

*** (92.9)


Winter -100.1 (124.8) -382.4***

(124.8) -533.2 383.5***

(124.8) -114.2 (124.8) 281.4**

(124.8) 190.0 (124.8)

Spring -266.4**

(112.3) -583.1***

(112.3) -1,831.1 -95.0 (112.3) 272.6**

(112.3) -312.3***

(112.3) 472.2***

(112.3)

Summer 71.3 (123.7) -393.7***

(123.7) 1,742.1 -803.8***

(123.7) -195.4 (123.7) -100.5 (123.7) -495.0***

(123.7)

Autumn -4.4 (103.0) 1,202.9***

(103.0) -228.3 435.5***

(103.0) 123.2 (103.0) -22.3 (103.0) -16.8 (103.0)

No. of Warm days

Winter -67.1 (98.0) -553.0***

(98.0) 779.7 -125.4 (98.0) -262.8***

(98.0) -176.4* (98.0) -163.5

* (98.0)

Spring -70.8 (95.3) -710.2***

(95.3) -129.6 -627.6***

(95.3) -219.9**

(95.3) -209.2**

(95.3) 92.4 (95.3)

Summer -98.5 (94.5) -537.2***

(94.5) -155.8 809.6***

(94.5) 102.9 (94.5) 8.1 (94.5) -105.6 (94.5)

Autumn 207.6**

(91.1) -201.7**

(91.1) 388.8 195.7**

(91.1) -203.5**

(91.1) 449.7***

(91.1) 7.7 (91.1)


(°C)

Winter 1.4 (103.8) 108.0 (103.8) 215.3 -96.1 (103.8) -4.6 (103.8) -342.4***

(103.8) 568.4***

(103.8)

Spring -97.8 (137.8) -625.3***

(137.8) -182.2 -1,052.3***

(137.8) 138.6 (137.8) -212.1 (137.8) 43.7 (137.8)

Summer 268.7* (140.9) 182.0 (140.9) 1,530.8 1,541.5

*** (140.9) -44.9 (140.9) -230.1 (140.9) -350.2

** (140.9)

Autumn -226.3* (137.6) -77.2 (137.6) -553.9 -849.4

*** (137.6) -351.2

** (137.6) 139.9 (137.6) -173.9 (137.6)

Constant 175,895.3

Note: *p

**p

***p<0.01, # = Random Effect

Source: FADN data for Belgium (1990-2009) 37

Table 14. Panel model estimates (accounted for heteroscedasticity) - Value of owned agricultural land.



Economic size (€) 303.7 (202.0) -229.9 (202.0) 275.0 (226.1) 42.7 (202.0) -627.5***

(202.0) -115.1 (202.0) -505.5**

(202.0)

Electricity (€) -2.8**

(1.4) -0.3 (1.4) 3.5 (3.3) -0.02 (1.4) -2.1 (1.4) -1.3 (1.4) -2.3* (1.4)


(0.03) 0.1***

(0.03) 0.4***

(0.04) 0.1**

(0.03) 0.5***

(0.03) 0.1***

(0.03) 0.7***

(0.03)


(115.1) 62.0 (115.1) -461.8***

(146.2) 157.3 (115.1) -66.4 (115.1) -125.5 (115.1) -505.3***

(115.1)

Crop Specific Inputs (€) 0.2 (0.2) -0.1 (0.2) 0.1 (0.2) -0.01 (0.2) 0.2 (0.2) 0.2 (0.2) -0.6***

(0.2)


(0.3) -0.2 (0.3) -0.6***

(0.2) -0.7**

(0.3) -0.2 (0.3) -0.2 (0.3) 0.03 (0.3)

Total Crop Output (€) -0.2**

(0.1) 0.01 (0.1) -0.3***

(0.1) -0.01 (0.1) -0.3***

(0.1) -0.2***

(0.1) -0.2***

(0.1)

Water (€) -5.1**

(2.1) -1.9 (2.1) -3.4**

(1.7) 0.5 (2.1) -7.3***

(2.1) 3.5* (2.1) 1.2 (2.1)

Area Under Agriculture (ha) -249.6 (186.5) 6.5 (186.5) -418.7**

(204.9) -1,060.1***

(186.5) -8.6 (186.5) 566.3***

(186.5) 70.6 (186.5)

Area Under Irrigation (ha) 999.4 (1,307.2) 400.3 (1,307.2) 456.8 (1,212.7) 229.3 (1,307.2) -141.8 (1,307.2) -1,152.4 (1,307.2) 736.5 (1,307.2)


Winter -12,735.3***

(4,535.3) -4,815.8 (4,535.3) -5,926.8***

(1,999.2) -2,659.7 (4,535.3) 7,639.1

* (4,535.3) -2,603.5 (4,535.3) -1,686.3 (4,535.3)

Spring -8,434.6 (14,569.7) -17,201.1 (14,569.7) -6,402.5 (6,776.1) -9,672.2 (14,569.7) -3,379.5 (14,569.7) -10,542.2 (14,569.7) 16,971.2 (14,569.7)

Summer 8,175.2 (15,451.0) -15,566.9 (15,451.0) 17,565.9 (12,054.4) -21,765.1 (15,451.0) -4,705.5 (15,451.0) -16,330.7 (15,451.0) 14,366.1 (15,451.0)

Autumn -4,325.9 (9,923.4) -10,411.5 (9,923.4) -2,278.2 (5,588.0) 38,732.3***

(9,923.4)

-25,934.1***

(9,923.4) -18,058.5

* (9,923.4)

-22,577.6**

(9,923.4)

(Sq.)Winter -311.1 (415.4) -876.5**

(415.4) 54.8 (236.4) -266.8 (415.4) -70.8 (415.4) -788.1* (415.4) 136.5 (415.4)

(Sq.)Spring 309.6 (835.1) 841.6 (835.1) 708.9**

(354.8) 457.9 (835.1) 24.1 (835.1) 455.9 (835.1) -1,087.7 (835.1)

(Sq.)Summer -196.3 (475.0) 610.3 (475.0) -570.1* (343.6) 421.7 (475.0) -160.3 (475.0) 652.8 (475.0) -179.3 (475.0)

(Sq.)Autumn 648.8 (509.4) 121.2 (509.4) 465.4* (280.6) -1,605.1

*** (509.4) 1,135.4

** (509.4) 328.9 (509.4) 521.3 (509.4)


Winter -57.5 (55.5) 85.0 (55.5) 17.9 (44.5) -75.0 (55.5) -4.7 (55.5) -188.1***

(55.5) -80.9 (55.5)

Spring 109.8 (82.5) 140.4* (82.5) 43.3 (65.0) 71.6 (82.5) -414.0

*** (82.5) -20.2 (82.5) -43.2 (82.5)

Summer 78.0 (71.6) -55.7 (71.6) 122.2**

(59.9) -182.3**

(71.6) 40.0 (71.6) 125.4* (71.6) 106.4 (71.6)

Autumn 0.9 (53.9) 47.6 (53.9) -108.4* (63.1) 103.1

* (53.9) -108.8

** (53.9) 78.8 (53.9) 155.6

*** (53.9)

38



(Sq.)Winter 0.2***

(0.1) 0.01 (0.1) -0.01 (0.05) 0.3***

(0.1) -0.1**

(0.1) 0.4***

(0.1) 0.3***

(0.1)

(Sq.)Spring -0.7***

(0.2) -0.1 (0.2) 0.04 (0.1) -0.4**

(0.2) 0.8***

(0.2) 0.2 (0.2) 0.3 (0.2)

(Sq.)Summer -0.1 (0.1) -0.1 (0.1) -0.3***

(0.1) 0.3**

(0.1) -0.2* (0.1) -0.1 (0.1) -0.1 (0.1)

(Sq.)Autumn 0.1 (0.1) -0.1 (0.1) 0.3***

(0.1) -0.2**

(0.1) 0.2**

(0.1) -0.1 (0.1) -0.4***

(0.1)


(473.0) 15,212.2***

(473.0) 4,152.4***

(349.4) 14,698.9***

(473.0) 6,110.3***

(473.0) 12,784.8***

(473.0) 4,594.4***

(473.0)


days

Winter -146.9 (478.1) -1,291.5***

(478.1) -657.5 (440.5) -1,001.0**

(478.1) 1,675.6***

(478.1) 443.8 (478.1) -1,713.3***

(478.1)

Spring 897.2* (485.6) -1,921.2

*** (485.6) -1,238.9

* (645.5) 972.7

** (485.6) 1,676.1

*** (485.6) -580.7 (485.6) -807.2

* (485.6)

Summer 363.4 (505.3) 2,206.5***

(505.3) 605.7 (506.1) 1,006.1**

(505.3) 1,137.3**

(505.3) -1,136.6**

(505.3) -194.7 (505.3)

Autumn -612.7 (377.2) 0.6 (377.2) -1,141.0***

(439.8) -529.2 (377.2) -847.3**

(377.2) -1,647.6***

(377.2) -500.4 (377.2)

No. of Warm days

Winter 2,225.0***

(433.0) 593.9 (433.0) 162.2 (238.7) -282.0 (433.0) -484.0 (433.0) 488.5 (433.0) -654.4 (433.0)

Spring 358.0 (361.2) 420.7 (361.2) -86.4 (260.1) 1,007.3***

(361.2) 1,081.3***

(361.2) -251.7 (361.2) 143.0 (361.2)

Summer -435.2 (424.3) -1,992.3***

(424.3) 733.1***

(245.7) -318.0 (424.3) -1,064.5**

(424.3) 415.2 (424.3) -1,010.8**

(424.3)

Autumn -1,228.3**

(507.7) 937.7* (507.7) -706.2

*** (244.9) -1,044.9

** (507.7) 706.1 (507.7) 1,560.5

*** (507.7) 1,886.0

*** (507.7)


(°C)

Winter 387.8 (581.4) -233.0 (581.4) -1,091.7***

(367.1) 893.9 (581.4) -642.3 (581.4) -178.1 (581.4) 2,959.4***

(581.4)

Spring 141.8 (531.2) -653.6 (531.2) 730.9**

(322.6) -2,247.7***

(531.2) 162.1 (531.2) -145.3 (531.2) 51.0 (531.2)

Summer -1,698.0***

(628.6) 1,149.7* (628.6) -978.5

** (397.4) -191.3 (628.6) 588.8 (628.6) 203.5 (628.6) 1,465.3

** (628.6)

Autumn 2,283.1***

(564.4) -480.3 (564.4) 83.0 (206.9) 1,491.5***

(564.4) -855.4 (564.4) 391.9 (564.4) -343.2 (564.4)

Constant -123,316.5 (97,702.4)

Note: *p

**p

***p<0.01, # = Random Effect


39

4.3.3. Normality

Normality of distribution of residuals is visually inspected. Figure 4 represents distribution of

standardized residuals for OLS models concerning farm family income and Figure 5 represents

distribution of standardized residuals concerning value of owned agricultural land for various

farm types. Even though in few cases, the residuals do not seem to be normally distributed but

due to sheer large size of sample, this normality assumption (for both, distribution of sample

and residuals) can be assumed considering the central limit theorem.

Figure 4. Distribution of Studentized Residuals -Value of owned farm land. Source: FADN data for Belgium (1990-2009)

Figure 5. Distribution of Studentized Residuals - Farm Family Income. Source: FADN data for Belgium (1990-2009)

Milk farms

sresid

Density

-6 -2 2 6

0.0

0.2

0.4

CATTLE farms

sresid

Density

-5 5

0.0

00.1

5

GRANI farms

sresid

Density

-5 50.0

00.1

5

HORTI farms

sresid

Density

-5 0 5 10

0.0

0.3

MIXEDCrLS farms

sresid

Density

-4 0 4 8

0.0

0.2

0.4

MIXEDLS farms

sresid

Density

-8 -4 0 4

0.0

0.2

0.4

OtFC farms

sresid

Density

-6 -2 2 6

0.0

0.2

0.4

Milk farms

sresid

Density

-6 -2 2 6

0.0

0.2

0.4

CATTLE farms

sresid

Density

-40 0 40 100

0.0

00.0

3

GRANI farms

sresid

Density

-6 -2 2

0.0

0.2

0.4

HORTI farms

sresid

Density

-15 -5 5

0.0

00.1

5

MIXEDCrLS farms

sresid

Density

-5 0 5 10

0.0

0.2

0.4

MIXEDLS farms

sresid

Density

-8 -4 0 4

0.0

0.2

0.4

OtFC farms

sresid

Density

-5 0 5 10

0.0

0.2

0.4

40

4.3.4. Multicollinearity

Variance inflation factors6 (Vif) are used to explain the extent of multicollinearity (correlation

between predictors) existing in a regression analysis. Multicollinearity is of concern because it

increases the variance of regression coefficients, making them difficult and unstable to interpret.

Vif values greater than 10 show existence of multicollinearity.

Looking at the values from Table 15, it is clear that mean temperature and precipitation values

show very high values of correlation but that’s understandable. Due to feature engineering,

squared values of these temperature and precipitation exist in the database which are then highly

correlated to un-squared values. In order to do a visual inspection, a regression coefficient

matrix was also generated to figure out potential multicollinearity. The results can be seen in

Figure 6 below.

Figure 6. Correlation matrix. Source: FADN data for Belgium (1990-2009)

6 This indicates how inflated are the variance of the estimated regression coefficients as compared to

when the predictors are not linearly related (Wall, 2004).

41

Table 15. Variation inflation factor values (F = Fixed effects model, R = Random effects model). Source: FADN data for Belgium (1990-2009)

Variables

Value of owned agricultural land Farm family income

Milk

(F)

Granivores

(F)

Cattle

(R)

Horticulture

(F)

Mixed

Cr+LS

(F)

Mixed

LS (F)

Other

(F)

Milk

(F)

Granivores

(F)

Cattle

(R)

Horticulture

(F)

Mixed

Cr+LS

(F)

Mixed

LS (F)

Other

(F)

Economic size (€) 8.43 26.79 9.82 3.92 10.00 13.10 18.83 8.43 26.79 9.82 3.92 10.00 13.10 18.83

Electricity (€) 2.09 2.48 2.17 2.05 1.89 2.17 2.37 2.09 2.48 2.17 2.05 1.89 2.17 2.37

Total Assets (€) 4.56 5.70 7.48 6.05 7.29 8.27 7.88 4.56 5.70 7.48 6.05 7.29 8.27 7.88

Total Livestock (LU) 6.37 24.10 5.83 1.12 4.63 7.86 2.91 6.37 24.10 5.83 1.12 4.63 7.86 2.91

Crop Specific Inputs (€) 7.25 16.97 8.94 4.09 10.18 17.01 9.19 7.25 16.97 8.94 4.09 10.18 17.01 9.19

Total Subsidies (€) 3.03 2.39 4.47 2.03 2.91 2.62 2.51 3.03 2.39 4.47 2.03 2.91 2.62 2.51

Total Crop Output (€) 9.44 21.47 14.63 9.64 15.62 23.84 13.08 9.44 21.47 14.63 9.64 15.62 23.84 13.08

Water (€) 1.08 1.17 1.14 1.14 1.17 1.07 1.41 1.08 1.17 1.14 1.14 1.17 1.07 1.41


(ha) 5.30 3.08 6.17 2.69 6.25 5.71 13.62 5.30 3.08 6.17 2.69 6.25 5.71 13.62

Area Under Irrigation (ha) 1.09 1.06 1.04 1.15 1.07 1.07 1.37 1.09 1.06 1.04 1.15 1.07 1.07 1.37


Winter 37.07 82.33 29.85 145.00 47.15 66.49 48.94 37.07 82.33 29.85 145.00 47.15 66.49 48.94

Spring 273.62 467.49 292.52 921.06 240.03 396.91 268.37 273.62 467.49 292.52 921.06 240.03 396.91 268.37

Summer 748.12 1619.29 887.70 3500.25 800.17 1570.80 770.36 748.12 1619.29 887.70 3500.25 800.17 1570.80 770.36

Autumn 265.65 415.30 260.46 517.21 370.59 305.85 345.90 265.65 415.30 260.46 517.21 370.59 305.85 345.90

(Sq.)Winter 35.29 77.55 25.65 151.74 42.51 65.88 45.33 35.29 77.55 25.65 151.74 42.51 65.88 45.33

(Sq.)Spring 271.32 465.62 276.97 952.89 248.05 394.94 274.54 271.32 465.62 276.97 952.89 248.05 394.94 274.54

(Sq.)Summer 750.34 1649.25 869.17 3592.50 807.34 1596.80 788.70 750.34 1649.25 869.17 3592.50 807.34 1596.80 788.70

(Sq.)Autumn 284.47 435.46 267.73 564.43 386.45 333.78 366.79 284.47 435.46 267.73 564.43 386.45 333.78 366.79


Winter 29.63 69.60 33.56 105.72 37.07 51.62 58.00 29.63 69.60 33.56 105.72 37.07 51.62 58.00

Spring 36.73 66.06 37.45 76.43 37.81 68.30 46.88 36.73 66.06 37.45 76.43 37.81 68.30 46.88

Summer 53.24 59.75 43.66 84.49 51.25 54.87 53.64 53.24 59.75 43.66 84.49 51.25 54.87 53.64

Autumn 50.57 52.72 45.01 54.61 37.89 40.78 39.32 50.57 52.72 45.01 54.61 37.89 40.78 39.32

(Sq.)Winter 17.40 57.40 20.31 91.46 30.84 41.75 53.28 17.40 57.40 20.31 91.46 30.84 41.75 53.28

(Sq.)Spring 34.08 55.69 35.51 67.97 30.29 60.54 43.66 34.08 55.69 35.51 67.97 30.29 60.54 43.66

(Sq.)Summer 43.75 43.53 37.41 64.20 41.70 39.49 43.61 43.75 43.53 37.41 64.20 41.70 39.49 43.61

(Sq.)Autumn 42.90 43.64 36.49 44.22 31.83 36.90 33.05 42.90 43.64 36.49 44.22 31.83 36.90 33.05

UAA Owned (ha) 1.89 1.90 2.30 2.02 3.00 2.52 4.34 1.89 1.90 2.30 2.02 3.00 2.52 4.34


days

Winter 7.75 6.81 9.82 7.64 6.16 6.92 5.59 7.75 6.81 9.82 7.64 6.16 6.92 5.59

Spring 5.02 4.44 7.38 3.80 4.27 4.61 4.13 5.02 4.44 7.38 3.80 4.27 4.61 4.13

Summer 4.93 5.56 4.89 4.69 3.59 5.33 3.98 4.93 5.56 4.89 4.69 3.59 5.33 3.98

42

Variables

Value of owned agricultural land Farm family income

Milk

(F)

Granivores

(F)

Cattle

(R)

Horticulture

(F)

Mixed

Cr+LS

(F)

Mixed

LS (F)

Other

(F)

Milk

(F)

Granivores

(F)

Cattle

(R)

Horticulture

(F)

Mixed

Cr+LS

(F)

Mixed

LS (F)

Other

(F)

Autumn 4.82 5.17 5.49 7.14 4.71 5.86 5.44 4.82 5.17 5.49 7.14 4.71 5.86 5.44

No. of Warm days

Winter 5.52 8.44 6.10 13.19 9.10 7.53 8.33 5.52 8.44 6.10 13.19 9.10 7.53 8.33

Spring 3.42 3.79 3.69 5.47 3.74 3.69 4.12 3.42 3.79 3.69 5.47 3.74 3.69 4.12

Summer 7.64 10.53 7.14 12.27 9.17 9.22 10.45 7.64 10.53 7.14 12.27 9.17 9.22 10.45

Autumn 11.44 14.00 12.10 22.07 13.96 12.72 14.52 11.44 14.00 12.10 22.07 13.96 12.72 14.52

Extreme temperature

range (°C)

Winter 2.83 3.09 2.93 3.24 2.77 2.47 3.11 2.83 3.09 2.93 3.24 2.77 2.47 3.11

Spring 2.54 3.38 3.04 4.11 2.81 2.87 2.66 2.54 3.38 3.04 4.11 2.81 2.87 2.66

Summer 2.18 3.25 2.54 3.46 2.38 2.99 2.36 2.18 3.25 2.54 3.46 2.38 2.99 2.36

Autumn 2.48 3.39 2.27 3.44 2.43 2.85 2.77 2.48 3.39 2.27 3.44 2.43 2.85 2.77

43

5. Discussion

Different farms clearly have different response to climate change in Belgium. This can be

attributed to the fact that different farms have different infrastructure available to them. Also,

the degree of exposure to surrounding environment is different for different farms. For example:

Farming activity in a greenhouse or controlled environment would be less affected by climate

change as compared to a farming activity taking place in an open field.

For the ease of interpretation and visualization of results, a scenario of “10 extra warm days per

year” was simulated. This helps in avoiding the uncertainty of the exact amount of climate

change (in absolute °C figures) in one hand and on the other hand, gives a clearer picture in a

sense of “more number of warm days”.

In terms of value of owned agricultural land, the median change in comparative7 farmland value

seems to go down by €24,686 in cattle farms. Granivores, horticulture and mixed (crop and

livestock) farms seem to suffer through similar outcome with comparative median land values

expected to fall down by €1,545, €5,121 and €5,384 respectively. On the contrary, comparative

median value of owned farm land in dairy farms seems to go up by ≈ €350.

Comparative median value of owned farm land also looks to go up by €15,100 in mixed

livestock farms. No statistically significant8 linear dependence of mean value of owned farm

land on number of warm days was detected in other field crop farms hence no conclusion can

be drawn at the moment about other field crop farms. Summarized results of this simulation

can be found in Annexure 2.

Ricardian model establishes that the current farmland values are a reflection of present value of

future returns expected from farm. Looking at these numbers, this research draws a conclusion

that in future, farmers may want to shift towards dairy and mixed livestock farming (provided

land availability and tradability).

In regards to farm family income, negative impact of 10 extra warm days on farm family income

is evident in granivores and mixed (crop and livestock) farms. Comparative median farm family

7 Difference from mean value and accounted for individual differences.

8 With aggregated data for number of warm days.

44

incomes seem to drop by €2,600 in granivores farms and €1,400 in mixed (crop and livestock)

farms. On the other hand, comparative median farm family income seems to improve by €2,000

in cattle farms, €800 in other field crop farms and €1,500 in mixed livestock farms.

No statistically significant linear dependence of mean value of farm family income on number

of warm days was detected in rest of the farms hence no conclusion can be drawn at the moment

about those farms. Summarized results of this simulation can be found in Annexure 3.

Not much can be read into the results pertaining to dairy farms due to the fact that production

(and indirectly income) has been regulated by CAP (according to dairy policy) hence this

research can not pinpoint the farm level adaptation by farmers from obtained results (especially

regarding farm family incomes).

To answer the research questions, there appears to be a clear negative relationship between

increased temperature and decreased precipitation with agricultural land values and farm family

incomes. Relatively extreme weather events like an increase in number of warm days seems to

impact negatively, both, farmland values as well as farm family income in granivores farms and

mixed (crop and livestock) farms. Horticultural farms and mixed (livestock farms) also seem to

be negatively impacted in terms of value of owned agricultural land.

It is also important to point out that looking at the research questions from an aggregate

(averaged) point of view would be unjustified. One disadvantage of averaging is smoothing out

variation at the individual level which leads to artificially high values of correlation coefficients.

Another serious issue with aggregate data is the “ecological fallacy”. Ecological fallacy is the

inappropriate assumption that relationships at the individual level would also hold at aggregate

level. Aggregation not only compromises the resolution with which effects can be seen, it also

results in change of statistical significance of aggregated predictors.

For instance, when seen from a NUTS3 level aggregation, number of warm days displays no

statistically significant impact on farmland values in any of the farms except for other field crop

farms. On the contrary, when approached from farm level, all farms (except other field crop

farms) display a statistically significant impact of number of warm days on value of owned

farm lands. The same logic is true for a few farms with respect to farm family incomes as seen

here. Extensive graphs for the same can be found in annexure 3#.

______________________________________________________

#Annexure 3 is made available in the print version of this thesis as well. 45

5.1. Comparison with available literature

Agriculture accounts for only a small part of gross domestic production (GDP) in Belgium, and

it is considered that the overall vulnerability of the European (and Belgian) economy to climatic

changes that affect agriculture is low (Bindi and Olesen, 2011). However, agriculture is much

more important in terms of area occupied (farmland and forest land cover approximately 90 %

of the EU's land surface), and rural population and income (Olesen and Bindi, 2002).

Regarding vulnerabilities in Belgium, It is established that if local temperatures do not rise by

more than three degrees, climate change will have little impact on agriculture in Belgium,

according to all scenarios for the 21st century (Marbaix and van Ypersele, 2004). However,

results from this thesis are in slight contrast with these claims as some of the farm types seem

to be potentially vulnerable to climate change, provided it goes unmitigated but this can be

attributed to the fact that the study done in 2004 took account of CO2 fertilization, something

which Ricardian model clearly ignores. However, external extreme events such as heavy rains

and warm days may also have a yet unknown significant (negative) effect which is also

established by this study.

5.2. Statistical relationship vs. “causality”

There are two main uses of multiple regression: prediction and causal analysis. This research

tries to build evidence for a good predictive model instead of proving causation. In a prediction

study (like this research), the goal is to develop a formula (or model) for making predictions

about the dependent variable (farm family incomes and agricultural land values), based on the

observed values of the independent variables. In a causal analysis, the independent variables

are regarded as causes of the dependent variable (Allison, 1999).

Despite the fact that regression can be used for both causal inference and prediction, there are

some important differences9 and the same applies to this research as well:

1. Omitted variables: For causal inference, the goal is to get unbiased estimates of the regression

coefficients. There may exist variables that affect dependent variables and are correlated with

the variables that are currently in the model. Omission of such variables may invalidate

statistical conclusions. In this research, clearly farm family incomes and agricultural land values

can be predicted more efficiently by using variables such as quantity of agricultural produce

9 Prediction vs. Causation in Regression Analysis. Elaboration of Allison (1999).

46

http://statisticalhorizons.com/prediction-vs-causation-in-regression-analysis

sold, agricultural prices, soil characteristics of land etc. hence the results from this research

have intrinsic omitted variable bias.

2. R2 values: For parameter estimation, a low R2 can be counterbalanced by a large sample size.

But for predictive modeling maximization of R2 is crucial because large sample sizes cannot

compensate for models that are lacking in predictive power. In this research, the R2 values for

models pertaining to different models vary between 0.3-0.7 which indicates that effects of

independent variables on dependent variable can be estimated with a decent level of certainty

(due to large sample sizes) but on the other hand the predictive power of respective models is

average at best.

3. Multicollinearity: Multicollinearity is often a major concern in causal inference. When two

or more variables are highly correlated, it is difficult to get reliable estimates of the coefficients

for each one of them (meanwhile controlling for the others). As the goal is accurate coefficient

estimates, this can be problematic. In the econometric models specified in this research, even if

two variables are highly correlated (cf. Table 15), it is worth including both of them as each

one contributes significantly to the predictive power of the model.

Regression deals with dependence amongst variables within a model but it does not always

imply causation. For example, rainfall affects crop yields and there is statistical-empirical

evidence that supports this. However, this is a one-way relationship. It means there is no cause

and effect reaction on regression if there is no causation. Ergo, a statistical relationship does

not necessarily imply causation.

5.3. Shortcomings

There are a number of caveats from this research, which should be kept in mind when

interpreting these results. As current research relies heavily on the Ricardian model of climate

change impact analysis it inherently suffers from all the shortcomings of Ricardian models. The

same have been discussed in section ‘2.5. Ricardian (cross-sectional) analysis’. Most important

of which is taking account of CO2 fertilization which this research does not account for.

According to National Climate Commission Belgium (2010), up to around 2-3°C, agricultural

yield reduction tends to be compensated for by the fertilizing effect of increased CO2

concentration for most crops. Carbon dioxide also improves the efficiency of water use in

plants, and increased temperatures are favorable for some crops such as maize.

47

As discussed earlier, aggregation of descriptors takes away the variability which is naturally

present in the data. Temperature, precipitation and extreme weather events data have been

accessed in their aggregate forms. Another way in which aggregation is used while doing

analyses is in the form of data availability at NUTS3 levels. As the weather stations are not

present in each NUTS 3 region, a lot of regions contain weather related data which corresponds

to the nearest weather station available.

Another drawback of this research is the fact that soil related data was not used when modeling

farmland prices. Also, price related data as well as data about quantity traded was not used

when modeling farm incomes. Using this information may have improved the resolution at

which models explain the dependent variable in respective models.

In a nutshell, four major caveats are associated with this research. First, the regression analysis

is vulnerable to omitted variables; second, CO2 fertilization effect is not taken into account;

third, the analysis does not include changes in agricultural prices; and fourth, the analysis does

not take into account future technological change.

Another important caveat to mention here is about the relevance and representativeness of

analysis about value of owned farm lands. The FADN data (for Belgium) in this regard seems

to be inconsistent and not fully reliable. Reliability of this data is also inconsistent because the

land values in Belgium are driven to a strong extent by urbanization.

48

6. General conclusions

Increase in temperature and more number of warm days are likely to cause considerable damage

to the healthiness and productivity of livestock (dairy farms, granivores farms, cattle farms,

mixed farms). Unanticipated changes in existing situation of precipitation and the increase of

dry spells (i.e., lower precipitations) will have additional effects on the supply of locally

accessible feed for livestock in Belgium. Augmented struggle in livestock production in

Belgium will then relate to constrained supply of livestock and animal products. Response of

livestock systems in Belgium to these requirements will be diverse (based on how these farms

react to a warmer climate).

Cattle, mixed (livestock) and mixed (crop and livestock) systems, which depend primarily on

the availability of feed from grasslands and fodder crops, will be the considerably impacted by

climate change. The positive association between livestock units and farm family incomes seen

in this research could stagnate, deteriorate or even become negative if farmers do not adapt to

imminent warmer climate in Belgium. For livestock related farms, scientific research can be

useful in the battle against climate change.

Up until now, livestock selection criteria had been oriented toward productive traits in Belgium

as well as in Europe. To encounter climate change and diminish its negative impacts, focus

must now be moved towards vigor of livestock and adaptability to heat stress. Animal scientists

and livestock breeders must pool resources closely in this regard. Conclusions from this

research also call for dependable weather forecast reports to inform the farmers in advance.

More focus on water conservation shall also be made.

6.1. Policy implications: Adaptation and mitigation actions

The results of studies done in recent years over Europe have pointed towards consistent

increases in temperature, more number of warm days and altered patterns of precipitation in

northern Europe. These changes in climate configurations are expected to affect all components

of the European agricultural ecosystems (e.g. crop suitability, farm income, production and

livestock). The same has been established by this research where it is visible that every type of

farm is affected by climate change in one way or another.

49

Thus, adaptation strategies should be introduced to reduce negative effects and exploit

(potential) positive effects of climate change. Amongst these, two kinds of approaches can be

considered:

1. Implicit farmer level adaptations:

Planting different crop species and using different livestock species.

Using resistant varieties or cultivars of crop or livestock.

2. Planned adaptations:

Changing land allocation

Modification of farming system

Results from this research also suggest the relevance of distinguishing between the farm types

when making policy decisions. All farm types are affected by climate change albeit differently.

For example, granivores farms are more likely to suffer from increased number of warm days

as compared to cattle or mixed farms. Also, these results suggest the relevance of developing a

policy which provides incentives to not abandon the vulnerable farming types (potentially in

favor of potentially robust10 farming types).

The same goes for mitigating impact of climate change on farm family incomes in different

farm types. There should be a regional policy in place, which should make sure that farmers

with different farming practices are informed and trained against climate change and its

potential impacts. The magnitude of the impact of climate change on agricultural land value

and farm incomes is rather important considering the agricultural production in Belgium is

concentrated around dairy, livestock production and food processing and farming is the sole

employment and income provider for most of these farms.

6.2. Recommendation for further research

A variety of emerging tools for further research are available – from macro-level to micro-level

approaches. The research that has been undertaken for this thesis has highlighted a number of

topics on which further research would be beneficial. Several areas where information or

analysis is lacking were highlighted in the shortcomings and literature review of this thesis.

Whilst some of these were addressed by the research in this thesis, others remain unaddressed.

10 Robust against warmer climate.

50

In particular, there is a lack of observational studies of changes in the farm family income of

European (and Belgian) farmers due to climate change.

The uncertainty levels associated with the estimation methods used in this study might be

further investigated using additional data from other regions in Belgium using a gridded dataset

available in netCDF format (from NASA and Climate Research Unit, United Kingdom). Also,

this may address the additional uncertainty that arises when estimates are based on a small

number of stations (which are in turn aggregated in nature). This would benefit additional

investigation in order to determine how much these uncertainty bounds might vary for different

regions, seasons and climatic regimes.

The panel data approach used here to estimate the projected land values and farm family

incomes might be usefully applied to other regions as well for use in other studies in EU region.

A particularly promising area can be to do simulations with agent-based modeling especially in

relation to mitigation policy regarding climate change and its impact on agricultural farm

incomes.

As this thesis reflects on potential decision making by farmers in lieu of climate change, a

Monte Carlo simulation approach can also be used to account for risk in decision making by

farmers. A range of possible outcomes and probabilities could be furnished and appropriate

inferences can be made about “best choice” for farmers.

Keeping the limitations of current work in mind, an indicative negative impact of climate

change on Belgian agriculture is envisaged from this research. It is the first attempt to

understand the influence of future warmer climate on farm family income and owned

agricultural land values in Belgium. Studies related to this research are relevant in

demonstrating impact of climate change on agriculture and may have policy implications as

well. It is important to remark that actions need to be taken now in order to combat the negative

consequences of imminent climate change for a better future of current and upcoming

generations.

51

7. References

Allison, P. D. (1999). Multiple regression: A primer: Pine Forge Press.

Angulo, C., Rötter, R., Lock, R., Enders, A., Fronzek, S., & Ewert, F. (2013). Implication of

crop model calibration strategies for assessing regional impacts of climate change in

Europe. Agricultural and Forest Meteorology, 170, 32-46.

Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., & García-Herrera, R. (2011).

The hot summer of 2010: redrawing the temperature record map of Europe. Science,

332(6026), 220-224.

Basso, B., Cammarano, D., & Carfagna, E. (2007). Review of crop yield forecasting methods

and early warning systems.

Bassu, S., Brisson, N., Durand, J. L., Boote, K., Lizaso, J., Jones, J. W., . . . Baron, C. (2014).

How do various maize crop models vary in their responses to climate change factors?

Global change biology, 20(7), 2301-2320.

Bindi, M., & Olesen, J. E. (2011). The responses of agriculture in Europe to climate change.

Regional Environmental Change, 11(1), 151-158.

Blanco, M., Ramos, F., & Van Doorslaer, B. (2014). Economic impacts of climate change on

agrifood markets: A bio-economic approach with a focus on the EU. Paper presented at

the XIVth EAAE Congress" Agri-Food and Rural Innovations for Healthier Societies".

Ljubbljana, Slovenia.

Büntgen, U., Tegel, W., Nicolussi, K., McCormick, M., Frank, D., Trouet, V., . . . Wanner, H.

(2011). 2500 years of European climate variability and human susceptibility. Science,

331(6017), 578-582.

Burke, M., Hsiang, S. M., & Miguel, E. (2015). Global non-linear effect of temperature on

economic production. Nature.

Burton, I. (1997). Vulnerability and adaptive response in the context of climate and climate

change. Climatic change, 36(1-2), 185-196.

Challinor, A., Watson, J., Lobell, D., Howden, S., Smith, D., & Chhetri, N. (2014). A meta-

analysis of crop yield under climate change and adaptation. Nature Climate Change,

4(4), 287-291.

Challinor, A., Wheeler, T., Garforth, C., Craufurd, P., & Kassam, A. (2007). Assessing the

vulnerability of food crop systems in Africa to climate change. Climatic change, 83(3),

381-399.

Chavas, J.-P. (2001). Structural change in agricultural production: Economics, technology and

policy. Handbook of agricultural economics, 1, 263-285.

De Salvo, M., Raffaelli, R., & Moser, R. (2013). The impact of climate change on permanent

crops in an Alpine region: A Ricardian analysis. Agricultural systems, 118, 23-32.

52

Dell, M., Jones, B. F., & Olken, B. A. (2012). Temperature shocks and economic growth:

Evidence from the last half century. American Economic Journal: Macroeconomics,

66-95.

Deressa, T., Hassan, R. M., & Ringler, C. (2008). Measuring Ethiopian farmers' vulnerability

to climate change across regional states: Intl Food Policy Res Inst.

Deressa, T. T., & Hassan, R. M. (2009). Economic impact of climate change on crop production

in Ethiopia: evidence from cross-section measures. Journal of African Economies,

ejp002.

Deschênes, O., & Greenstone, M. (2007). Climate change, mortality, and adaptation: evidence

from annual fluctuations in weather in the US: National Bureau of Economic Research

Cambridge, Mass., USA.

Dhanush, D., Bett, B., Boone, R., Grace, D., Kinyangi, J., Lindahl, J., . . . Rosenstock, T. (2015).

Impact of climate change on African agriculture: focus on pests and diseases.

Dinar, A., & Mendelsohn, R. O. (2011). Handbook on climate change and agriculture: Edward

Elgar Publishing.

Eakin, H. C. (2015). Handbook on Climate Change and Agriculture, edited by Ariel Dinar and

Robert Mendelsohn. 2011. Cheltenham, UK and Northampton, Massachusetts: Edward

Elgar. 515+ xvi. ISBN: 978‐1‐84980‐116 4, $220.50 (cloth); ISBN: 978‐1‐78100‐194‐3, $48 (paper). Journal of Regional Science, 55(2), 334-336.

Environment Protection Agency. (2010). Climate Impacts on Agriculture and Food Supply. .

Agriculture and Food Supply. Retrieved 14th June 2016, from

https://www.epa.gov/climatechange/impacts/agriculture.html

European Comission. (2013). Overview of CAP Reform 2014-2020. Agricultural Policy

Perspectives Brief, . Retrieved 19 November, 2015, from

http://ec.europa.eu/agriculture/policy-perspectives/policy-briefs/05_en.pdf

Eurostat. (2012). Agricultural census in Belgium. Retrieved 21st March, 2016, from

http://ec.europa.eu/eurostat/statistics-

explained/index.php/Agricultural_census_in_Belgium#Labour_force

Fader, M., Shi, S., von Bloh, W., Bondeau, A., & Cramer, W. (2015). Mediterranean irrigation

under climate change: more efficient irrigation needed to compensate increases in

irrigation water requirements. Hydrol. Earth Syst. Sci. Discuss, 12, 8459-8504.

Galindo, L. M., Reyes, O., & Alatorre, J. E. (2015). Climate change, irrigation and agricultural

activities in Mexico: A Ricardian analysis with panel data. Journal of Development and

Agricultural Economics, 7(7), 262-273.

Gjerris, M., Huber, R., Lassen, J., Olsson, I. A. S., & Sandøe, P. (2013). Transgenic Livestock

transgenic crop livestock, Ethical Concerns transgenic crop livestock ethical concerns

and Debate Sustainable Food Production (pp. 1767-1788): Springer.

53

http://www.epa.gov/climatechange/impacts/agriculture.html

http://ec.europa.eu/agriculture/policy-perspectives/policy-briefs/05_en.pdf

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_census_in_Belgium#Labour_force

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_census_in_Belgium#Labour_force

Godfray, H. C. J., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Nisbett, N., . . . Whiteley,

R. (2010). The future of the global food system. Philosophical Transactions of the Royal

Society B: Biological Sciences, 365(1554), 2769-2777.

González-Zeas, D., Quiroga, S., Iglesias, A., & Garrote, L. (2014). Looking beyond the average

agricultural impacts in defining adaptation needs in Europe. Regional Environmental

Change, 14(5), 1983-1993.

Hanewinkel, M., Cullmann, D. A., Schelhaas, M.-J., Nabuurs, G.-J., & Zimmermann, N. E.

(2013). Climate change may cause severe loss in the economic value of European forest

land. Nature Climate Change, 3(3), 203-207.

Heikkinen, R. K., Luoto, M., Araújo, M. B., Virkkala, R., Thuiller, W., & Sykes, M. T. (2006).

Methods and uncertainties in bioclimatic envelope modelling under climate change.

Progress in Physical Geography, 30(6), 751-777.

Hosoe, N., Gasawa, K., & Hashimoto, H. (2010). Textbook of Computable General Equilibrium

Modeling: Programming and Simulations: Palgrave Macmillan.

Iglesias, A., Garrote, L., Quiroga, S., & Moneo, M. (2012). A regional comparison of the effects

of climate change on agricultural crops in Europe. Climatic change, 112(1), 29-46.

Intergovernmental Panel on Climate Change. (2012). Managing the risks of extreme events and

disasters to advance climate change adaptation.

Intergovernmental Panel on Climate Change. (2015). Climate Change 2014: Mitigation of

Climate Change (Vol. 3): Cambridge University Press.

Karkatsoulis, P., Capros, P., Fragkos, P., Paroussos, L., & Tsani, S. (2016). First‐mover

advantages of the European Union's climate change mitigation strategy. International

Journal of Energy Research.

Kharin, V. V., Zwiers, F., Zhang, X., & Wehner, M. (2013). Changes in temperature and

precipitation extremes in the CMIP5 ensemble. Climatic change, 119(2), 345-357.

Kumar, S. N., Aggarwal, P., Rani, S., Jain, S., Saxena, R., & Chauhan, N. (2011). Impact of

climate change on crop productivity in Western Chats, coastal and northeastern regions

of India. Current Science(Bangalore), 101(3), 332-341.

Kunimitsu, Y., & Kudo, R. (2015). Fluctuations in Rice Productivity Caused by Long and

Heavy Rain Under Climate Change in Japan: Evidence from Panel Data Regression

Analysis. Japan Agricultural Research Quarterly: JARQ, 49(2), 159-172.

Kurukulasuriya, P., & Rosenthal, S. (2013). Climate change and agriculture: A review of

impacts and adaptations.

Lobell, D. B., & Gourdji, S. M. (2012). The influence of climate change on global crop

productivity. Plant Physiology, 160(4), 1686-1697.

Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop

production since 1980. Science, 333(6042), 616-620.

54

Maraun, D. (2012). Nonstationarities of regional climate model biases in European seasonal

mean temperature and precipitation sums. Geophysical Research Letters, 39(6).

Marbaix, P., & van Ypersele, J. (2004). Impacts of climate change in Belgium. Brussels,

Greenpeace.

Martínez, A., Pérez, J., Molinero, J., Sagarduy, M., & Pozo, J. (2015). Effects of flow scarcity

on leaf-litter processing under oceanic climate conditions in calcareous streams. Science

of the Total Environment, 503, 251-257.

Massetti, E., Carmo, R. d., Guiducci, N., de Oliveira, A., & Mendelsohn, R. (2013). The impact

of climate change on the Brazilian agriculture: a Ricardian study at microregion level.

CMCC Research Paper(RP0200).

Massetti, E., & Mendelsohn, R. (2011). Estimating Ricardian models with panel data. Climate

Change Economics, 2(04), 301-319.

Mbungu, W. B., Mahoo, H. F., Tumbo, S. D., Kahimba, F. C., Rwehumbiza, F. B., & Mbilinyi,

B. P. (2015). Using Climate and Crop Simulation Models for Assessing Climate Change

Impacts on Agronomic Practices and Productivity Sustainable Intensification to

Advance Food Security and Enhance Climate Resilience in Africa (pp. 201-219):

Springer.

Mendelsohn, R., Dinar, A., & Sanghi, A. (2001). The effect of development on the climate

sensitivity of agriculture. Environment and Development Economics, 6(01), 85-101.

Mendelsohn, R., Dinar, A., & Williams, L. (2006). The distributional impact of climate change

on rich and poor countries. Environment and Development Economics, 11(02), 159-178.

Mendelsohn, R., Nordhaus, W. D., & Shaw, D. (1994). The impact of global warming on

agriculture: a Ricardian analysis. The American economic review, 753-771.

Mendelsohn, R., & Reinsborough, M. (2007). A Ricardian analysis of US and Canadian

farmland. Climatic change, 81(1), 9-17.

Mendelsohn, R. O., & Dinar, A. (2009). Climate change and agriculture: an economic analysis

of global impacts, adaptation and distributional effects: Edward Elgar Publishing.

Morton, J. F. (2007). The impact of climate change on smallholder and subsistence agriculture.

Proceedings of the National Academy of Sciences, 104(50), 19680-19685.

NASA. (2011). What Is Climate and Climate Change? NASA Knows! . Retrieved 14th June

2016, from http://www.nasa.gov/audience/forstudents/5-8/features/nasa-knows/what-

is-climate-change-58.html

Nelson, G. C., Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., . . . Lotze‐Campen, H. (2014). Agriculture and climate change in global scenarios: why don't the

models agree. Agricultural Economics, 45(1), 85-101.

Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D., . . . Heyhoe, E.

(2014). Climate change effects on agriculture: Economic responses to biophysical

shocks. Proceedings of the National Academy of Sciences, 111(9), 3274-3279.

55

http://www.nasa.gov/audience/forstudents/5-8/features/nasa-knows/what-is-climate-change-58.html

http://www.nasa.gov/audience/forstudents/5-8/features/nasa-knows/what-is-climate-change-58.html

Nikulin, G., Kjellström, E., Hansson, U., Strandberg, G., & Ullerstig, A. (2011). Evaluation and

future projections of temperature, precipitation and wind extremes over Europe in an

ensemble of regional climate simulations. Tellus A, 63(1), 41-55.

Olesen, J. E., & Bindi, M. (2002). Consequences of climate change for European agricultural

productivity, land use and policy. European journal of agronomy, 16(4), 239-262.

Olesen, J. E., Trnka, M., Kersebaum, K., Skjelvåg, A., Seguin, B., Peltonen-Sainio, P., . . .

Micale, F. (2011). Impacts and adaptation of European crop production systems to

climate change. European journal of agronomy, 34(2), 96-112.

Parry, M., Jackson, M., & Ford-Lloyd, B. (2013). Effects of climate change on potential food

production and risk of hunger. Plant Genetic Resources and Climate Change, 4, 61.

Parry, M. L., Carter, T. R., & Konijn, N. T. (2013). The Impact of Climatic Variations on

Agriculture: Volume 1: Assessment in Cool Temperate and Cold Regions: Springer

Science & Business Media.

Parry, M. L., Rosenzweig, C., Iglesias, A., Livermore, M., & Fischer, G. (2004). Effects of

climate change on global food production under SRES emissions and socio-economic

scenarios. Global Environmental Change, 14(1), 53-67.

Pielke, R. A. (2005). Land use and climate change. Science, 310(5754), 1625-1626.

Reidsma, P., Ewert, F., Lansink, A. O., & Leemans, R. (2010). Adaptation to climate change

and climate variability in European agriculture: the importance of farm level responses.

European journal of agronomy, 32(1), 91-102.

Ricardo, D., Gonner, E. C. K., & Li, Q. (1819). The principles of political economy and

taxation: World Scientific.

Robertson, R., Nelson, G., Thomas, T., & Rosegrant, M. (2013). Incorporating process-based

crop simulation models into global economic analyses. American Journal of

Agricultural Economics, 95(2), 228-235.

Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., . . . Khabarov, N.

(2014). Assessing agricultural risks of climate change in the 21st century in a global

gridded crop model intercomparison. Proceedings of the National Academy of Sciences,

111(9), 3268-3273.

Rosenzweig, C., Jones, J., Hatfield, J., Ruane, A., Boote, K., Thorburn, P., . . . Janssen, S.

(2013). The agricultural model intercomparison and improvement project (AgMIP):

protocols and pilot studies. Agricultural and Forest Meteorology, 170, 166-182.

Rötter, R., Tao, F., Höhn, J., & Palosuo, T. (2015). Use of crop simulation modelling to aid

ideotype design of future cereal cultivars. Journal of experimental botany, erv098.

Rotz, C. A., Skinner, R. H., Stoner, A. M., & Hayhoe, K. (2015). Farm simulation: a tool for

evaluating the mitigation of greenhouse gas emissions and the adaptation of dairy

production to climate change. Paper presented at the ASABE 1st Climate Change

Symposium: Adaptation and Mitigation Conference Proceedings.

56

Rowhani, P., Lobell, D. B., Linderman, M., & Ramankutty, N. (2011). Climate variability and

crop production in Tanzania. Agricultural and Forest Meteorology, 151(4), 449-460.

RStudio Team. (2015). RStudio: Integrated Development Environment for R (Version

0.99.891). Boston, MA: RStudio, Inc. Retrieved from http://www.rstudio.com/

Sands, R. D., & Edmonds, J. A. (2005). Climate change impacts for the conterminous USA: An

integrated assessment. Climatic change, 69(1), 127-150.

Schilling, J., Freier, K. P., Hertig, E., & Scheffran, J. (2012). Climate change, vulnerability and

adaptation in North Africa with focus on Morocco. Agriculture, ecosystems &

environment, 156, 12-26.

Schlenker, W., Hanemann, W. M., & Fisher, A. C. (2006). The impact of global warming on

US agriculture: an econometric analysis of optimal growing conditions. Review of

Economics and Statistics, 88(1), 113-125.

Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages

to US crop yields under climate change. Proceedings of the National Academy of

Sciences, 106(37), 15594-15598.

Shindell, D., Kuylenstierna, J. C., Vignati, E., van Dingenen, R., Amann, M., Klimont, Z., . . .

Raes, F. (2012). Simultaneously mitigating near-term climate change and improving

human health and food security. Science, 335(6065), 183-189.

Smit, B., Ludlow, L., & Brklacich, M. (1988). Implications of a global climatic warming for

agriculture: a review and appraisal. Journal of Environmental Quality, 17(4), 519-527.

Solomon, S. (2007). Climate change 2007-the physical science basis: Working group I

contribution to the fourth assessment report of the IPCC (Vol. 4): Cambridge University

Press.

Trapp, N., Lange, A., & Held, H. (2014). The economic impacts of climate change and options

for adaptation: A study of the farming sector in the European Union. Universität

Hamburg Hamburg.

van Ruijven, B. J., O’Neill, B. C., & Chateau, J. (2015). Methods for including income

distribution in global CGE models for long-term climate change research. Energy

Economics, 51, 530-543.

Vanuytrecht, E., Raes, D., & Willems, P. (2015). Regional and global climate projections

increase mid-century yield variability and crop productivity in Belgium. Regional

Environmental Change, 1-14.

Vermeulen, S. J., Aggarwal, P. K., Ainslie, A., Angelone, C., Campbell, B. M., Challinor, A., .

. . Kristjanson, P. (2012). Options for support to agriculture and food security under

climate change. Environmental Science & Policy, 15(1), 136-144.

Wheeler, T., & von Braun, J. (2013). Climate change impacts on global food security. Science,

341(6145), 508-513.

57

http://www.rstudio.com/

Wiebe, K., Lotze-Campen, H., Sands, R., Tabeau, A., van der Mensbrugghe, D., Biewald, A., .

. . Mason-D’Croz, D. (2015). Climate change impacts on agriculture in 2050 under a

range of plausible socioeconomic and emissions scenarios. Environmental Research

Letters, 10(8), 085010.

Wood, S. A., & Mendelsohn, R. O. (2015). The impact of climate change on agricultural net

revenue: a case study in the Fouta Djallon, West Africa. Environment and Development

Economics, 20(01), 20-36.

World Bank. (2016). Agriculture, value added (% of GDP) Retrieved 24th March, 2016, from

http://data.worldbank.org/indicator/NV.AGR.TOTL.ZS?page=1

Yu, Y., Zhang, W., & Huang, Y. (2014). Impact assessment of climate change, carbon dioxide

fertilization and constant growing season on rice yields in China. Climatic change,

124(4), 763-775.

Zacharias, M., Kumar, S. N., Singh, S., Rani, D. S., & Aggarwal, P. (2015). Evaluation of a

regional climate model for impact assessment of climate change on crop productivity in

the tropics. CURRENT SCIENCE, 108(6), 1119.

58

http://data.worldbank.org/indicator/NV.AGR.TOTL.ZS?page=1

8. Annexure

59

2500

030

000

3500

040

000

4500

0Heterogeineity across Years (Farm family income in 2009 Euros)

YEAR

Val

ue in

Eur

o

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Annexure (i)

1000

0015

0000

2000

00Heterogeineity across Years (Value of Owned Land in 2009 Euros)

YEAR

Val

ue in

Eur

o

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Annexure (ii)

Dimensions of unbalanced panel models

Farm type n

(number of groups/panels)

T

(number of years)

N

(number of observations)

Cattle 574 1-20 (1990 to 2009) 3855

Granivores 213 1-20 (1990 to 2009) 1487

Horticulture 426 1-20 (1990 to 2009) 2682

Milk (dairy) 655 1-20 (1990 to 2009) 4992

Mixed (Crop and livestock) 518 1-20 (1990 to 2009) 3046

Mixed (livestock) 305 1-20 (1990 to 2009) 1726

Other field crops 258 1-20 (1990 to 2009) 1789

Annexure (iii)

Results from Lagrange Multiplier Test - (Breusch-Pagan)

Dairy farms

chisq = 695710, df = 1, p-value < 2.2e-16

Cattle farms

chisq = 2142200, df = 1, p-value < 2.2e-16

Granivores farms

chisq = 145570, df = 1, p-value < 2.2e-16

Horticultural farms

chisq = 387170, df = 1, p-value < 2.2e-16

Mixed (crop and livestock) farms

chisq = 818610, df = 1, p-value < 2.2e-16

Mixed (livestock) farms

chisq = 370000, df = 1, p-value < 2.2e-16

Other field crop farms

chisq = 339710, df = 1, p-value < 2.2e-16

Annexure (A)

Results from Breusch-Godfrey/Wooldridge test for serial correlation in panel models

Dairy farms

chisq = 1084.7, df = 1, p-value < 2.2e-16

Cattle farms

chisq = 964.28, df = 1, p-value < 2.2e-16

Granivores farms

chisq = 98.877, df = 1, p-value < 2.2e-16

Horticultural farms:

chisq = 364.24, df = 1, p-value < 2.2e-16

Mixed (crop and livestock) farms

chisq = 472.62, df = 1, p-value < 2.2e-16

Mixed (livestock) farms

chisq = 186.39, df = 1, p-value < 2.2e-16

Other field crop farms

chisq = 566.1, df = 1, p-value < 2.2e-16

Annexure (B)

NUTS Level aggregation. Panel estimates: Farm family income

Dependent variable: Value of Farm Family Income (Euro)


Economic size (ESU) -35.1 (36.9) -120.7 (89.1) -99.8 (78.2) -28.7 (17.6) -190.0*** (39.1) -16.6 (42.9) -50.3 (65.1)

Electricity (in €) -1.7*** (0.4) -1.2** (0.6) -1.3 (1.2) -0.7*** (0.2) -4.5*** (0.6) -1.7*** (0.5) -7.8*** (0.9)

Total Assets (in €) -0.03*** (0.004) -0.04*** (0.01) -0.03** (0.01) -0.1*** (0.01) -0.03*** (0.01) -0.05*** (0.01) -0.1*** (0.01)

Total Livestock (LU) 36.8 (24.5) 10.6 (25.3) 20.2 (39.5) 58.6 (70.6) 22.2 (18.4) -7.6 (10.4) 13.8 (40.0)

Crop Specific Inputs (in €) -0.6*** (0.04) -0.6*** (0.03) -0.5*** (0.1) -0.4*** (0.03) -0.6*** (0.03) -0.5*** (0.03) -0.7*** (0.1)

Total Subsidies (in €) 0.6*** (0.1) 0.6*** (0.2) 0.8*** (0.2) 0.6** (0.3) 0.6*** (0.1) 0.5*** (0.1) 0.8*** (0.1)

Total Crop Output (in €) 0.5*** (0.02) 0.5*** (0.02) 0.5*** (0.05) 0.3*** (0.01) 0.6*** (0.02) 0.5*** (0.02) 0.6*** (0.02)

Water (in €) 0.3 (0.6) -3.8* (2.1) -1.5 (2.3) -1.4 (1.1) -2.7*** (0.8) -0.2 (0.7) 1.7 (1.4)


(ha) -186.1*** (51.5) -172.8 (153.7) -535.2*** (100.5) 133.8 (164.7) -241.5*** (43.6) -185.1*** (70.9) -272.7*** (68.5)

Area Under Irrigation (ha) -1,308.2**

(563.0)

-2,427.8

(3,124.0)

1,009.6

(2,414.0) 1,115.1 (741.5) -311.1*** (116.2) -239.5 (635.6)

-1,549.0***

(444.0)

TG Winter -1,203.2

(1,023.3) 4,065.4 (6,249.5)

2,024.0

(3,367.1) -164.9 (4,598.0) -676.5 (2,607.2)

-1,587.1

(3,297.8)

-4,254.0

(3,859.8)

TG Spring -6,751.4

(4,415.9)

-12,220.1

(20,781.9)

-5,802.6

(13,831.7) 6,247.8 (21,428.4) 4,281.1 (7,854.5)

16,792.0

(11,589.5)

15,384.2

(10,925.6)

TG Summer 382.9 (5,845.0) 14,472.7

(28,559.0)

-7,977.1

(19,248.6) 2,920.8 (23,893.7)

19,098.3*

(10,787.8)

-17,973.7

(15,445.6)

-28,588.2*

(15,442.3)

TG Autumn 2,258.2

(3,998.5)

-4,451.5

(25,617.4)

-2,370.5

(12,790.6) -426.7 (20,831.2)

3,067.5

(10,856.2)

-5,288.2

(13,738.2)

-18,646.3

(16,593.9)

PP Winter 8.7 (19.3) 9.2 (99.0) -20.6 (63.8) 78.2 (82.6) 1.4 (35.4) 45.2 (49.7) -93.6 (75.5)

PP Spring 80.2** (31.1) 152.6 (134.8) 47.5 (102.7) -5.7 (115.3) -34.0 (67.6) -30.8 (77.5) -88.2 (100.7)

PP Summer -26.1 (26.3) -46.1 (85.1) -6.7 (90.8) -148.0** (71.4) -46.3 (47.4) 44.0 (49.1) 12.1 (65.8)

PP Autumn 23.0 (22.9) -116.1 (73.3) -19.1 (75.0) 59.4 (57.2) -23.2 (37.0) -41.7 (41.0) 132.6** (55.9)

TG² Winter -55.0 (136.4) -39.3 (784.6) 15.2 (448.1) 635.8 (635.6) -16.2 (330.8) 45.1 (427.7) 586.6 (493.3)

TG² Spring 338.4 (234.6) 731.5 (1,073.3) 380.0 (731.9) -91.4 (1,106.9) -235.0 (419.6) -957.9 (611.6) -977.1* (587.4)

TG² Summer 28.5 (175.1) -339.8 (820.7) 244.1 (575.6) -118.3 (691.5) -472.2 (319.9) 574.2 (448.5) 904.7** (455.2)

TG² Spring -118.2 (190.2) 31.2 (1,162.3) 103.1 (612.3) 62.7 (958.1) -238.5 (502.0) 265.9 (628.5) 973.5 (766.6)

PP² Winter -0.02 (0.02) -0.1 (0.2) 0.1 (0.1) -0.2 (0.2) -0.02 (0.1) 0.01 (0.1) 0.2 (0.2)

PP² Spring -0.05 (0.1) -0.4 (0.3) -0.2 (0.2) 0.1 (0.3) 0.1 (0.1) 0.1 (0.2) 0.2 (0.2)

Annexure (1)

Dependent variable: Value of Farm Family Income (Euro)


PP² Summer 0.04 (0.04) 0.1 (0.1) -0.01 (0.1) 0.2* (0.1) 0.1 (0.1) -0.1 (0.1) 0.000 (0.1)

PP² Autumn -0.05 (0.04) 0.2 (0.1) 0.04 (0.1) -0.1 (0.1) 0.004 (0.1) 0.05 (0.1) -0.2** (0.1)

UAA Owned 242.3*** (67.7) -172.2 (295.6) 236.2 (197.9) 1,421.8*** (541.3) 286.4*** (78.7) 559.5*** (112.2) 576.8*** (90.5)

Hp10 Winter -400.5** (192.4) -13.7 (667.5) -677.9 (637.3) 828.1* (499.5) -250.1 (347.6) -486.0 (390.9) 644.2 (498.8)

Hp10 Spring -667.2*** (228.2) -332.6 (761.9) -266.7 (743.8) -667.6 (611.1) -158.0 (411.2) -92.8 (462.8) -224.0 (580.9)

Hp10 Summer -4.0 (167.6) -249.3 (565.1) -44.0 (554.4) 71.2 (459.8) -152.2 (292.8) -112.3 (330.2) -257.3 (436.7)

Hp10 Autumn -23.3 (191.3) 890.3 (607.1) 358.4 (624.2) -72.8 (515.7) 291.3 (328.1) 271.3 (379.3) -61.7 (469.8)

ETR Winter 148.5 (177.4) -150.4 (595.7) 245.4 (581.0) 705.7 (481.0) 125.5 (294.6) -984.9*** (310.8) 457.1 (435.8)

ETR Spring -214.6 (200.1) -610.6 (633.7) -480.5 (664.3) -834.3 (551.7) 31.2 (337.6) -209.5 (356.1) -27.0 (504.3)

ETR Summer 30.4 (199.0) 158.3 (671.8) 1,008.6 (693.4) 509.7 (560.1) -376.9 (349.1) -318.0 (379.6) -627.2 (495.9)

ETR Autumn -278.5 (213.5) -393.8 (662.3) -503.8 (724.4) -29.3 (613.2) -776.3** (359.8) 198.9 (376.0) 562.3 (553.2)

waem days 86.0* (48.8) -361.1** (169.7) -171.1 (156.7) -212.6 (154.1) 77.2 (87.7) 47.0 (104.3) -71.9 (135.2)

Observations 769 454 675 395 631 513 496

R2 0.6 0.7 0.2 0.7 0.7 0.7 0.7

Adjusted R2 0.5 0.6 0.2 0.6 0.6 0.5 0.6

F Statistic 26.5*** (df = 36;

673)

22.3*** (df = 36;

366)

5.0*** (df = 36;

579)

22.1*** (df = 36;

310)

37.0*** (df = 36;

539)

22.6*** (df = 36;

421)

27.5*** (df = 36;

406)

Note: *p**p***p<0.01

Annexure (1)

NUTS Level aggregation. Panel estimates: value of owned farm land



Economic size (ESU) 409.5*** (142.1) -85.0 (183.4) -275.8** (126.6) -7.1 (34.0) -308.2** (148.4) -105.4 (145.9) -882.5*** (229.4)

Electricity (in €) 4.7*** (1.4) -0.7 (1.3) 9.3*** (1.9) -2.1*** (0.5) -2.5 (2.4) -2.6* (1.6) -9.0*** (3.1)

Total Assets (in €) 0.2*** (0.02) 0.1*** (0.02) 0.4*** (0.02) 0.2*** (0.02) 0.5*** (0.02) 0.2*** (0.02) 0.6*** (0.03)

Total Livestock (LU) -648.8*** (94.2) -73.7 (52.0) -68.7 (64.0) 117.2 (136.0) 128.7* (69.9) -168.5*** (35.4) -260.8* (140.8)

Crop Specific Inputs (in €) 0.5*** (0.2) -0.1** (0.1) -0.3** (0.1) -0.01 (0.1) 0.1 (0.1) 0.6*** (0.1) -0.3* (0.2)

Total Subsidies (in €) -1.6*** (0.3) -0.8** (0.4) -1.3*** (0.3) -1.6*** (0.5) -0.5** (0.2) -1.0*** (0.3) -0.6** (0.3)

Total Crop Output (in €) -0.2*** (0.1) 0.1*** (0.05) -0.2** (0.1) 0.004 (0.03) -0.2** (0.1) -0.3*** (0.1) -0.2* (0.1)

Water (in €) -3.3 (2.4) 13.5*** (4.2) -18.2*** (3.7) 24.0*** (2.1) -0.8 (3.2) 3.7 (2.3) 6.3 (4.9)

Area Under Agriculture (ha) -407.8** (198.3) -484.9 (316.3) -234.7 (162.6) -680.4** (317.2) -634.4*** (165.5) 194.9 (241.4) 278.9 (241.3)

Area Under Irrigation (ha) -7,376.8***

(2,166.7)

-1,491.5

(6,427.8)

-4,702.9

(3,906.7)

-5,141.5***

(1,427.8) 1,767.4*** (440.8)

-1,657.5

(2,163.2)

-1,077.4

(1,563.3)

TG Winter -2,579.9

(3,938.6)

3,098.0

(12,858.8) 2,456.3 (5,449.2) -5,623.5 (8,853.7) 6,814.0 (9,890.9)

-8,952.8

(11,224.4)

-13,701.4

(13,592.0)

TG Spring 2,215.0

(16,995.9)

28,092.6

(42,760.1)

-3,040.8

(22,385.0)

39,836.0

(41,261.8)

6,781.6

(29,797.8)

-8,286.8

(39,445.7)

-2,319.3

(38,473.3)

TG Summer 7,809.2

(22,496.3)

58,643.6

(58,762.2)

33,722.9

(31,151.6)

-35,226.1

(46,008.7)

12,762.3

(40,925.8)

-11,542.4

(52,570.4)

8,713.7

(54,378.1)

TG Autumn -11,501.6

(15,389.3)

-5,816.2

(52,709.4)

-18,261.8

(20,700.1)

44,760.4

(40,111.8)

-61,088.4

(41,185.4) 782.9 (46,758.9)

9,042.1

(58,433.2)

PP Winter -155.3** (74.4) -16.7 (203.7) 11.2 (103.3) 166.9 (159.0) -197.8 (134.4) -150.4 (169.1) -103.7 (266.0)

PP Spring 27.2 (119.6) 36.5 (277.3) -56.4 (166.2) 42.2 (222.1) -197.4 (256.4) -306.0 (263.8) 353.1 (354.5)

PP Summer 2.7 (101.3) 61.4 (175.0) 166.3 (147.0) 49.4 (137.4) 85.7 (179.9) 127.3 (167.0) 78.4 (231.7)

PP Autumn 43.9 (88.0) 78.2 (150.8) -91.4 (121.4) 236.2** (110.1) -33.2 (140.4) 1.0 (139.4) 91.8 (196.8)

TG² Winter 84.5 (525.1) -1,211.2

(1,614.4) -218.0 (725.2) 1,824.1 (1,223.8) 355.6 (1,254.9)

-1,227.7

(1,455.6) 1,304.1 (1,737.1)

TG² Spring -80.2 (902.9) -1,802.5

(2,208.4) 346.7 (1,184.5) -1,781.1 (2,131.3) 164.5 (1,592.0) -14.3 (2,081.6) -197.4 (2,068.3)

TG² Summer -198.4 (673.9) -1,418.6

(1,688.6) -1,157.1 (931.5) 517.9 (1,331.5) -969.8 (1,213.7) 994.8 (1,526.5) -583.2 (1,602.8)

TG² Spring 646.1 (732.1) -76.7 (2,391.4) 629.9 (990.9) -2,145.6 (1,844.8) 2,833.5 (1,904.5) -202.4 (2,139.2) -821.6 (2,699.3)

Annexure (1)



PP² Winter 0.1 (0.1) 0.1 (0.4) -0.02 (0.1) -0.3 (0.3) 0.1 (0.2) 0.5 (0.3) 0.2 (0.6)

PP² Spring -0.3 (0.3) 0.2 (0.7) 0.2 (0.3) 0.03 (0.5) -0.2 (0.6) 0.8 (0.7) 0.1 (0.8)

PP² Summer -0.1 (0.2) -0.3 (0.3) -0.3 (0.2) -0.1 (0.2) -0.2 (0.3) -0.3 (0.3) -0.2 (0.4)

PP² Autumn -0.03 (0.2) -0.3 (0.3) 0.2 (0.2) -0.4** (0.2) -0.02 (0.2) 0.1 (0.3) -0.4 (0.4)

UAA Owned 6,983.4***

(260.7)

13,919.0***

(608.3)

6,301.2***

(320.3)

14,855.4***

(1,042.2) 5,432.9*** (298.5)

9,125.8***

(381.9)

4,708.7***

(318.7)

Hp10 Winter 1,503.1** (740.7) -969.2 (1,373.5) 765.6 (1,031.4) 1,641.0* (961.9) 1,699.0 (1,318.9) 287.9 (1,330.5) -1,410.7

(1,756.5)

Hp10 Spring 331.5 (878.4) -1,072.4

(1,567.6)

-1,465.8

(1,203.8) 722.1 (1,176.8)

3,674.7**

(1,560.0) 598.2 (1,575.0)

-2,143.8

(2,045.4)

Hp10 Summer 433.5 (645.0) 2,244.6*

(1,162.7) -461.1 (897.3) 664.8 (885.4) 1,019.6 (1,110.6)

-1,064.8

(1,123.8) 1,296.2 (1,537.6)

Hp10 Autumn -393.1 (736.2) -25.9 (1,249.1) -1,387.9

(1,010.2) -1,602.9 (993.1) 30.3 (1,244.7)

-2,555.5**

(1,290.8)

-2,477.6

(1,654.2)

ETR Winter 1,784.5***

(682.7) -283.3 (1,225.7) 277.2 (940.2) -954.3 (926.1) 1,803.2 (1,117.7) -722.6 (1,058.0)

4,490.6***

(1,534.5)

ETR Spring -325.5 (770.1) -158.6 (1,304.0) 2,760.6**

(1,075.2) -1,653.9 (1,062.4) -825.9 (1,280.8) 520.1 (1,212.0) 649.7 (1,775.8)

ETR Summer -826.4 (765.8) 1,103.3 (1,382.3) 425.0 (1,122.2) 352.0 (1,078.5) 546.5 (1,324.3) 555.4 (1,292.1) 1,346.6 (1,746.1)

ETR Autumn 2,696.7***

(821.6) -537.9 (1,362.8) 773.6 (1,172.3) 971.6 (1,180.8) -585.4 (1,365.1) 1,337.7 (1,279.7) 534.7 (1,948.1)

waem days 70.3 (187.8) -327.9 (349.2) -143.5 (253.6) 456.5 (296.8) 117.5 (332.6) 190.5 (355.1) 940.0** (476.2)

Observations 769 454 675 395 631 513 496

R2 0.8 0.8 0.8 0.8 0.9 0.8 0.9

Adjusted R2 0.7 0.6 0.7 0.6 0.8 0.7 0.7


673)

40.5*** (df = 36;

366)

80.2*** (df = 36;

579)

39.3*** (df = 36;

310)

169.3*** (df = 36;

539)

57.0*** (df = 36;

421)

97.9*** (df = 36;

406)

Note: *p**p***p<0.01

Annexure (1)

Farm level panel estimates: Farm family income (using aggregated number of warm days)



Economic size (ESU) -125.9*** (18.6) -304.3*** (71.2) -17.0 (136.2) -161.1*** (17.6) -182.7*** (24.6) -109.0*** (26.9) -266.9*** (40.0)

Electricity -1.1*** (0.1) -0.7*** (0.2) -1.0 (1.9) -0.3*** (0.1) -2.5*** (0.3) -1.0*** (0.1) -4.4*** (0.7)

Total Assets (Euro) -0.03*** (0.002) -0.04*** (0.004) -0.04*** (0.01) -0.1*** (0.004) -0.02*** (0.003) -0.1*** (0.003) -0.02*** (0.004)

Total Livestock (LU) 51.4*** (13.1) 71.3*** (18.5) -45.3 (83.8) 94.9 (307.1) 44.7*** (15.3) 23.5*** (8.4) -12.3 (40.4)

Crop Specific Inputs -0.6*** (0.02) -0.6*** (0.02) -0.6*** (0.1) -0.4*** (0.02) -0.7*** (0.02) -0.6*** (0.01) -0.7*** (0.04)

Total Subsidies 0.4*** (0.02) 0.6*** (0.1) 0.7*** (0.1) 0.6*** (0.1) 0.5*** (0.02) 0.5*** (0.04) 0.7*** (0.04)

Total Crop Output 0.5*** (0.01) 0.6*** (0.01) 0.6*** (0.1) 0.4*** (0.01) 0.5*** (0.01) 0.5*** (0.01) 0.6*** (0.01)

Water -0.2 (0.3) 1.1 (1.1) -0.5 (1.8) -3.1*** (0.8) -0.8* (0.5) -0.9* (0.5) -0.04 (0.9)

Area Under Agriculture (ha) -221.4*** (22.0) 5.8 (100.3) -447.8*** (130.1) 730.6** (294.4) -107.4*** (27.5) -182.4*** (41.1) -157.9*** (40.6)

Area Under Irrigation (ha) 309.8*** (108.1) 297.8 (839.6) 796.6 (2,765.6) -814.4 (688.6) 33.1 (133.2) -8.7 (170.1) -566.4** (270.3)

Mean Temperature 264.6 (3,060.7) 19,262.4

(21,721.1)

-5,542.6

(21,182.0)

-16,030.3

(42,419.2) 5,722.3 (5,044.1)

-1,125.8

(10,858.6) 46.8 (10,072.7)

Mean Precipitation 17.7 (13.6) -31.6 (74.9) -123.6 (101.4) 111.2 (116.3) -60.6** (26.7) 36.2 (36.5) 35.8 (45.9)

Mean Temperature Sq 40.8 (161.5) -566.7 (1,124.4) 231.6 (1,040.8) 1,442.4 (2,195.7) -247.5 (275.5) 151.4 (567.0) -42.0 (542.2)

Mean Precipitation Sq 0.000 (0.004) 0.02 (0.03) 0.04* (0.02) 0.01 (0.04) 0.01 (0.01) 0.01 (0.01) -0.04** (0.02)

UAA Owned 164.1*** (28.6) 436.5** (189.3) 252.7 (174.4) 54.6 (576.5) 258.4*** (39.3) 545.9*** (84.6) 296.8*** (71.6)

Precipitation Anomaly -47.7 (45.3) 44.4 (156.5) -98.6 (377.0) -120.5 (189.7) -44.9 (83.2) 38.3 (87.0) -41.4 (144.8)

ETR -159.2 (191.8) 107.4 (660.2) 386.9 (1,378.9) 618.3 (977.4) -342.8 (322.4) -454.9 (321.7) 604.3 (574.1)

Warm Days -21.0 (26.9) -421.5*** (109.0) -41.8 (214.0) 47.8 (162.6) -116.7** (49.7) 34.7 (59.5) -103.1 (88.3)

TG:PP -1.2 (1.0) -0.5 (8.2) 2.2 (6.3) -11.7 (12.7) 2.8 (1.8) -4.7 (4.1) 2.2 (3.4)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.5 0.7 0.04 0.6 0.6 0.7 0.7

Adjusted R2 0.4 0.6 0.03 0.5 0.5 0.5 0.5


4289)

148.4*** (df = 19;

1236)

6.7*** (df = 19;

3243)

155.9*** (df = 19;

2218)

223.0*** (df = 19;

2490)

146.1*** (df = 19;

1383)

149.1*** (df = 19;

1493)

Note: *p**p***p<0.01

Annexure (2)

Farm level panel estimates: value of owned farm land (using aggregated number of warm days)



Economic size (ESU) 434.8*** (79.2) -221.5* (128.0) 120.6 (80.2) 44.8** (18.1) -632.9*** (96.0) -104.7 (94.4) -531.0*** (131.9)

Electricity -3.0*** (0.6) -0.2 (0.4) -0.5 (1.1) -0.01 (0.1) -2.3** (1.0) -1.3*** (0.5) -2.5 (2.4)

Total Assets (Euro) 0.2*** (0.01) 0.1*** (0.01) 0.4*** (0.01) 0.1*** (0.005) 0.5*** (0.01) 0.1*** (0.01) 0.7*** (0.01)

Total Livestock (LU) -593.0*** (55.5) 56.8* (33.3) -381.8*** (49.4) 137.8 (315.5) -56.8 (59.5) -124.0*** (29.3) -485.5*** (133.3)

Crop Specific Inputs 0.2*** (0.1) -0.1*** (0.03) 0.04 (0.1) -0.01 (0.02) 0.2*** (0.1) 0.2*** (0.1) -0.6*** (0.1)

Total Subsidies -1.2*** (0.1) -0.2 (0.2) -0.7*** (0.1) -0.7*** (0.1) -0.2*** (0.1) -0.2* (0.1) 0.04 (0.1)

Total Crop Output -0.2*** (0.04) 0.01 (0.02) -0.3*** (0.03) -0.01* (0.01) -0.3*** (0.04) -0.2*** (0.04) -0.2*** (0.04)

Water -5.6*** (1.2) -1.9 (1.9) -2.6** (1.1) 0.3 (0.9) -6.6*** (1.8) 3.8** (1.6) 1.2 (3.1)

Area Under Agriculture (ha) -307.9*** (93.4) 18.1 (180.4) -170.9** (76.6) -1,020.0*** (302.4) -45.5 (107.2) 531.6*** (143.9) 78.8 (133.8)

Area Under Irrigation (ha) 1,001.9** (459.2) 264.7 (1,510.5) -1,150.7 (1,628.6) 338.6 (707.3) -149.4 (519.3) -1,381.4** (595.9) 804.7 (890.6)

Mean Temperature 20,356.0

(13,002.0)

-14,701.8

(39,074.8)

-27,536.5**

(12,474.0)

70,906.4

(43,576.1)

-23,497.9

(19,669.2)

31,532.8

(38,039.9)

7,112.9

(33,194.8)

Mean Precipitation 28.2 (57.6) 230.7* (134.7) 120.6** (59.7) 40.5 (119.4) 52.9 (104.3) -35.9 (127.7) -159.0 (151.3)

Mean Temperature Sq -1,230.8* (686.1) 1,104.9 (2,022.7) 1,683.8*** (612.9) -3,560.1 (2,255.6) 1,113.1 (1,074.4) -2,973.6 (1,986.2) -1,499.6 (1,786.8)

Mean Precipitation Sq -0.01 (0.02) -0.05 (0.05) -0.003 (0.01) -0.02 (0.04) 0.02 (0.04) -0.02 (0.04) 0.001 (0.1)

UAA Owned 7,546.4*** (121.3) 15,153.9***

(340.5) 4,014.9*** (102.7)

14,409.9***

(592.2) 6,088.4*** (153.1)

12,775.6***

(296.4) 4,559.5*** (235.9)

Precipitation Anomaly 142.3 (192.3) 91.4 (281.5) -602.0*** (222.0) 119.7 (194.8) 712.5** (324.6) -508.4* (304.8) -846.2* (477.1)

ETR 2,172.0*** (814.7) 456.8 (1,187.7) 1,700.2** (812.0) -925.0 (1,004.0) -2,784.5**

(1,257.3) 292.0 (1,127.1) 3,357.3* (1,892.1)

Warm Days 386.7*** (114.2) -324.4* (196.0) -439.9*** (126.0) -320.9* (167.1) 337.6* (193.7) 694.1*** (208.5) -86.6 (290.9)

TG:PP -2.8 (4.2) -15.4 (14.7) -7.5** (3.7) -1.4 (13.1) -13.2* (7.1) 8.7 (14.4) 16.5 (11.2)

Observations 4,992 1,487 3,855 2,682 3,046 1,726 1,789

R2 0.7 0.7 0.7 0.4 0.8 0.8 0.8

Adjusted R2 0.6 0.6 0.6 0.3 0.7 0.6 0.7


4289)

175.3*** (df = 19;

1236)

436.3*** (df = 19;

3243)

79.1*** (df = 19;

2218)

634.7*** (df = 19;

2490)

258.3*** (df = 19;

1383)

406.5*** (df = 19;

1493)

Note: *p**p***p<0.01

Annexure (2)

CATTLE GRANI HORTI MILK MIXEDCrLS MIXEDLS OtFC

−15

0000

−10

0000

−50

000

050

000

Effect by farm size: Owned agricultural land (in Euro)Annexure (2)

CATTLE GRANI HORTI MILK MIXEDCrLS MIXEDLS OtFC

−50

000

−40

000

−30

000

−20

000

−10

000

010

000

2000

0Effect by farm size: Farm family incomes (in Euro)

Annexure (2)

Scenario of 10 extra warm days.

®

(Aggregation at NUTS3 level)

Scenario of 10 extra warm days

®

Statistically significant effect of warm days, p<1%

(Farm level)


®



®


(Farm level)

Annexure (3)

All plots account for individual effects


®



®


(Farm level)


®



®


(Farm level)

Annexure (3)



®

Statistically significant effect of warm days



®

Statistically insignificant effect of warm days

(Farm level)


®




®


(Farm level)

Annexure (3)



®

Statistically significant effect of warm days, p < 5%



®


(Farm level)


®




®


(Farm level)

Annexure (3)



®




®


(Farm level)


®




®


(Farm level)

Annexure (3)


Faculty of Bioscience Engineering Academic Year 2015...

Documents

Transcript of Faculty of Bioscience Engineering Academic Year 2015...