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This is an unpublished M.Sc.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
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
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
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
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
Winter (Dec-Jan-Feb) 21340 6 3 0 21
Spring (Mar-Apr-May) 21340 4 3 0 18
Summer (Jun-jul-Aug) 21340 7 3 0 18
Autumn (Sep-Oct-Nov) 21340 7 3 0 23
No. of warm days
Winter (Dec-Jan-Feb) 21340 15 8 0 34
Spring (Mar-Apr-May) 21340 19 6 5 35
Summer (Jun-jul-Aug) 21340 16 7 3 39
Autumn (Sep-Oct-Nov) 21340 14 9 0 34
Extreme temperature range
Winter (Dec-Jan-Feb) 21340 24 3 17 36
Spring (Mar-Apr-May) 21340 31 4 21 46
Summer (Jun-jul-Aug) 21340 27 3 17 35
Autumn (Sep-Oct-Nov) 21340 29 4 18 40
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
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
Dependent variable: Farm Family Income (Euro)
Milk Granivores Cattle Horticulture Mixed Cr+LS Mixed LS Other
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)
Milk Granivores Cattle Horticulture Mixed Cr+LS Mixed LS Other
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)
Total Livestock (LU) 30.6***
(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)
Area Under Agriculture (ha) -302.7***
(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)
UAA Owned (ha) 338.9***
(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
Residual Std. Error 12,814.4 (df = 4973) 22,143.2 (df = 1468) 54,912.3 (df = 3836) 34,974.9 (df = 2663) 15,637.5 (df = 3027) 13,326.0 (df = 1707) 21,374.5 (df = 1770)
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
Source: FADN data for Belgium (1990-2009)
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)
Milk Granivores Cattle Horticulture Mixed Cr+LS Mixed LS Other
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)
Electricity (€) -2.8***
(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)
Area Under Agriculture (ha) -976.2***
(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)
Mean temperature (°C)
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)
Mean precipitation (mm)
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
Dependent variable: Value of owned agricultural land (Euro)
Milk Granivores Cattle Horticulture Mixed Cr+LS Mixed LS Other
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)
No. of High precipitation days
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)
Extreme temperature range
(°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
Residual Std. Error 62,259.8 (df = 4952) 43,012.5 (df = 1447) 59,960.7 (df = 3815) 43,696.6 (df = 2642) 68,698.6 (df = 3006) 59,437.3 (df = 1686) 69,633.4 (df = 1749)
F Statistic 427.3***
(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
Source: FADN data for Belgium (1990-2009)
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)
Total Livestock (LU) -606.6***
(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)
UAA Owned (ha) 6,233.0***
(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
Residual Std. Error 63,739.4 (df = 4973) 43,924.1 (df = 1468) 60,874.4 (df = 3836) 43,807.5 (df = 2663) 69,306.6 (df = 3027) 61,101.9 (df = 1707) 69,517.1 (df = 1770)
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 €).
Dependent variable: Value of Owned Agricultural Land (Euro)
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)
Electricity (€) -2.8***
(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)
Total Assets (€) 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) -502.6***
(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)
Total Subsidies (€) -1.1***
(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)
Area Under Agriculture (ha) -249.6***
(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)
Mean temperature (°C)
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)
Mean precipitation (mm)
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
Dependent variable: Value of Owned Agricultural Land (Euro)
Milk Granivore Cattle# Horticulture Mixed Cr+LS Mixed LS Other
(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)
UAA Owned (ha) 7,589.6***
(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)
No. of High precipitation days
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)
Extreme temperature range
(°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
F Statistic 245.9***
(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
Source: FADN data for Belgium (1990-2009)
28
Table 9. Two way fixed effects panel 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.) 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)
Total Livestock (LU) -593.4***
(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)
Area Under Agriculture (ha) -312.3***
(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)
UAA Owned (ha) 7,544.0***
(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
Source: FADN data for Belgium (1990-2009)
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 €).
Dependent variable: Farm Family Income (Euro)
Milk Granivore Cattle# Horticulture Mixed Cr+LS Mixed LS Other
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)
Electricity (€) -1.1***
(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)
Total Assets (€) -0.03***
(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)
Total Livestock (LU) 45.4***
(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)
Crop Specific Inputs (€) -0.6***
(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)
Total Subsidies (€) 0.4***
(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)
Total Crop Output (€) 0.5***
(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)
Area Under Agriculture (ha) -230.9***
(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)
Mean temperature (°C)
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)
Mean precipitation (mm)
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
Dependent variable: Farm Family Income (Euro)
Milk Granivore Cattle# Horticulture Mixed Cr+LS Mixed LS Other
(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)
UAA Owned (ha) 170.9***
(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)
Extreme temperature range
(°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
F Statistic 108.8***
(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
Source: FADN data for Belgium (1990-2009)
32
Table 11. Two way fixed effects panel estimators - Farm family income (aggregated climate indicators)
Dependent variable: Farm family income (Euro)
Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other
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)
Total Livestock (LU) 51.2***
(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
F Statistic 232.6***
(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
Source: FADN data for Belgium (1990-2009)
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.
Dependent variable: Farm Family Income (Euro)
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)
Total Assets (€) -0.03***
(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)
Crop Specific Inputs (€) -0.6***
(0.04) -0.6***
(0.04) -0.5 -0.4***
(0.04) -0.7***
(0.04) -0.6***
(0.04) -0.7***
(0.04)
Total Subsidies (€) 0.4***
(0.1) 0.6***
(0.1) 0.8 0.7***
(0.1) 0.5***
(0.1) 0.5***
(0.1) 0.7***
(0.1)
Total Crop Output (€) 0.5***
(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)
Area Under Agriculture (ha) -230.9***
(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)
Mean temperature (°C)
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)
Mean precipitation (mm)
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
Dependent variable: Farm Family Income (Euro)
Milk Granivores Cattle (#) Horticulture Mixed Cr+LS Mixed LS Other
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)
No. of High precipitation days
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)
Extreme temperature range
(°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.
Dependent variable: Value of owned agricultural land (Euro)
Milk Granivores Cattle (#) Horticulture Mixed Cr+LS Mixed LS Other
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)
Total Assets (€) 0.2***
(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)
Total Livestock (LU) -502.6***
(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)
Total Subsidies (€) -1.1***
(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)
Mean temperature (°C)
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)
Mean precipitation (mm)
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
Dependent variable: Value of owned agricultural land (Euro)
Milk Granivores Cattle (#) Horticulture Mixed Cr+LS Mixed LS Other
(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)
UAA Owned (ha) 7,589.6***
(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)
No. of High precipitation
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)
Extreme temperature range
(°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
Source: FADN data for Belgium (1990-2009)
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
Area Under Agriculture
(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
Mean temperature (°C)
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
Mean precipitation (mm)
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
No. of High precipitation
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
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
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
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
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
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)
Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other
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)
Area Under Agriculture
(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)
Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other
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
Dependent variable: Value of Owned Agricultural Land (Euro)
Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other
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)
Dependent variable: Value of Owned Agricultural Land (Euro)
Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other
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
F Statistic 59.3*** (df = 36;
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)
Dependent variable: Farm family income (Euro)
Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other
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
F Statistic 220.4*** (df = 19;
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)
Dependent variable: Value of Owned Agricultural Land (Euro)
Milk Granivore Cattle Horticulture Mixed Cr+LS Mixed LS Other
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
F Statistic 478.9*** (df = 19;
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)
Scenario of 10 extra warm days
®
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically significant effect of warm days, p<5%
(Farm level)
Annexure (3)
All plots account for individual effects
Scenario of 10 extra warm days
®
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically significant effect of warm days, p<5%
(Farm level)
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)
Annexure (3)
All plots account for individual effects
Scenario of 10 extra warm days
®
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically significant effect of warm days, p<5%
(Farm level)
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)
Annexure (3)
All plots account for individual effects
Scenario of 10 extra warm days
®
Statistically significant effect of warm days
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Farm level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Farm level)
Annexure (3)
All plots account for individual effects
Scenario of 10 extra warm days
®
Statistically significant effect of warm days, p < 5%
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically significant effect of warm days, p<1%
(Farm level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Farm level)
Annexure (3)
All plots account for individual effects
Scenario of 10 extra warm days
®
Statistically significant effect of warm days, p<10%
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Farm level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically significant effect of warm days, p<5%
(Farm level)
Annexure (3)
All plots account for individual effects
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Farm level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Aggregation at NUTS3 level)
Scenario of 10 extra warm days
®
Statistically insignificant effect of warm days
(Farm level)
Annexure (3)
All plots account for individual effects
Top Related