U
NIVERSITÀ DI
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ISA E
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CUOLA
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UPERIORE
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ANT
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AUREA
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AGISTRALE IN
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CONOMICS
Weather Variation Impact on Agricultural
Productivity: a Study on Italian Provinces
Candidate:
Matteo Grigoletto
Supervisor:
Prof Andrea Roventini
iii
UNIVERSITÀ DI PISA E SCUOLA SUPERIORE SANT’ANNA
Abstract
Laurea Magistrale in Economics
Weather Variation Impact on Agricultural Productivity: a Study on Italian Provinces
by Matteo Grigoletto
The aim of this study is to investigate the impact of weather on agriculture in Italian Provinces from 2007 to 2018. Four different types of cultivation are considered: maize, soft wheat, durum wheat and wine grape. The variable of interest is year to year growth rate of yields for each cultivation. Weather is represented by short-term variation of temperature and precipitation in deviation from long term trends during growing season, for crops, and winter, spring and summer, for wine grape. Individual and time fixed effect panel analysis shows negative impact of weather variation in almost all model specifications, with some exceptions; wheat proves to be the most resilient of the types of cultivation analysed whereas maize and wine grape show quadratic impact of temperature and precipitation on yields growth rate.
v
Acknowledgements
I would like to thank my supervisor Professor Andrea Roventini for his patience and for supporting me in the tight pace we kept in writing this work.
I would like to thank also Dr Francesco Lamperti for all the useful advice and help that he gave me in the development of this analysis. Along with Francesco, I also thank the rest of the commission. Thank you to all my mates that accompanied me during these two years, that listened to me that helped me every time I thought I could not make it. Thank you to all my colleagues that taught me so much and were so helpful for this thesis with their advice, A special thank to those colleagues who became real friends.
Thank you Didem, for being a friend besides a partner and for managing to support me, even from far away. Thank you Nicola, for being so strong, and Chiara, for all our laughs.
Least but not last, a big thank to my parents. Your humility, tenacity and dignity were the most meaningful and precious lessons for me.
vii
Contents
Abstract iii Acknowledgements v List of Figures ix List of Tables xi 1 Introduction 1 2 Literature Review 5 2.1 General Overview. . . 52.2 State of the Art: Climate Change and Agriculture . . . 7
2.2.1 Why Agriculture ? . . . 7
2.2.2 Impact of Climate Change . . . 8
2.2.3 Conclusion . . . 11
3 Data 13 3.1 Dependent Variable and Controls . . . 13
3.2 Independent Variable . . . 16
3.2.1 Geographical Information System (GIS): Geocomputation . . . 16
3.2.2 Computation of Meteorological Data . . . 17
3.2.3 Meteorological Variables: de-trending and Growing Season . . . 19
3.2.4 Independent Variable for Wine Grapes Analysis . . . 21
4 Methodology 23 4.1 Model and Identification Strategy. . . 24
5 Results and Discussion 27 5.1 Maize . . . 27
viii 5.2 Soft Wheat . . . 30 5.2.1 Discussion . . . 31 5.3 Durum Wheat . . . 31 5.3.1 Discussion . . . 31 5.4 Wine Grape . . . 32 5.4.1 Discussion . . . 34 6 Conclusions 37 A Appendix A 39 A.1 Data Manipulation: Independent Variable . . . 39
A.2 Growth Rate Outliers . . . 40
ix
List of Figures
3.1 Histogram of yields growth rate per cultivation. . . 15
3.2 Time series national mean growth rate per cultivation . . . 15
3.3 Rasters on 2m temperature (Kelvin) on 01.2006.. . . 19
3.4 Italian Provinces quantile map on 2m temperature (Kelvin) on 01.2006. . . 20
3.5 Independent variable density, maize and wheat . . . 21
3.6 Independent variable density during the three seasons, wine grape . . . 22
5.1 Scatter plot maize yields growth rate on temperature . . . 29
xi
List of Tables
5.1 Maize . . . 27 5.2 Soft Wheat . . . 30 5.3 Durum Wheat . . . 32 5.4 Wine Grape . . . 331
Chapter 1
Introduction
Last years have witnessed an increasing consensus among scientists in recognizing climate change at the global level. Many the consequences of this process: from rising of global average temperature to the increased likelihood of extreme events. Although the split between scientific community and the public has still relevance, with many among the latter claiming an exaggerated level of “alarmism”
(Whitmarsh,2011) coming from the world of information, it is widely accepted that climate change is
happening, now. Lately, social medias have payed great attention to movements and activism related to the problem with particular attention to the different level of sensibility shown by different gen-erations. Young people are embracing the cause, rising their voices, unfortunately, often with scarce consequences. In fact, representatives and policy makers have taken weak countermeasures, in most of the cases. Nevertheless, there are few virtuous exceptions that fortunately are gaining attention and favor: from, for instance, United Kingdom MP Caroline Lucas who co-founded a group with the intention of drafting a new economic plan based on Green Economy innovation and investment, to some European Governments proposing the so-called Green Bond to favour Green Investments and
Companies (D’Angerio,2019).
Despite the insensitivity proved by Governments, many scholars and researchers strongly com-mit not only in strengthening the empirical basis to prove climate change but also in finding a range of possible adaptation strategies, to build resilience (Leichenko (2011), Speranza (2013) ) to the prob-lem. The first step in order to outline effective coping strategies is understanding the nature and extent of the impact of climate change. The aim of this study, is to assess this impact in an econom-ical sector that, intuitively, more than any other is directly affected: agriculture. In the last decades agriculture has witnessed technological betterment able to partially free production from the tight dependence on weather evolution and, in an era of extreme technological development, many take for granted access to food. Nevertheless, understating impact of climate change on agriculture re-mains of focal importance.
2 Chapter 1. Introduction
Olken (2014)) weather is considered to be the random realization coming from a distribution, which
is climate; in other words weather could be considered as a short term manifestation. This study attempts to capture impact of weather variation, in the form of year to year evolution of temperature and precipitation means, on agricultural productivity at the Province level (European administrative framework NUTS 3 regions) in Italy. The choice of Italy has been led mostly by a void in the literature which concerns not only the country but Europe in general. This scarcity is mainly due to the lack of access to harmonised agricultural data on production at a certain disaggregate level.
The meteorological data set in this research is constructed with methods coming from the world of geocomputation, in the framework of Geographical Information Systems. Data on monthly means of temperature and precipitation from 1979 (first year available in the specific data set used) are used to construct long term monthly trends which are then subtracted to monthly values along the time span of this research, going from 2007 to 2018. The range of years is dictated by agricultural data on production, going from 2006 to 2018. As the variable of interest is growth rate of yearly yields, the first available year in the model is 2007. Four different types of cultivation are considered: maize, soft and durum wheat and wine grape. The analysis carried out follows a simple rationale: each Province carries its own climatic and technological specificity. That is why the panel analysis exploits (mainly) individual fixed effects in order to isolate the impact of weather variation.
When considering the results of this research one shall be aware of the two main limitations of the model: firstly, the limited time span, as the standard range of years considered in similar research in the field goes up to two or three decades. Secondly, bias emerging in the moment of creation of meteorological data set; data on temperature and precipitation are reported geographically divided in cells, covering roughly 25x25 km. In the process of aggregation to the province level, common practice is to consider only those cells covering agricultural soil, whereas in this analysis all but those covering Urban Centers are taken into account.
Despite the two main limitations three main results arose clearly from the research: first, when present, impact of weather variation assumes often a quadratic form, with an optimum threshold beyond which increase in average temperature and precipitation from long-term trends is harmful. Second, wheat appears to be the most resilient to weather variation among cultivation considered; third, wine grape presents strong seasonal patterns, with summer temperatures and precipitation presenting clear quadratic effect.
The implication of this research are practical and could represent a starting point in a long term strategy to develop resilience policies, which shall first be grounded on empirical research, clearly defining climate change impact in the regions of interest, and then proposing a strategy of adaptation.
Chapter 1. Introduction 3
The rest of this research is organized as follows. Chapter2is dedicated to a review of the
lit-erature focusing first on a general overview of the many fields of application and investigation on climate change impacts and subsequently on literature specifically dedicated to impact on
agricul-ture. Chapter3 provides information on data used in this study; a particular focus is given to the
manipulation done for meteorological data, with an application of Geographical Information System
techniques. Chapter4outlines the methodology applied in this study and specifies the identification
strategy in the analysis. Chapter5reports results and comments. Chapter6presents conclusions to
5
Chapter 2
Literature Review
The economic literature has given great attention to climate change and an increasing number of researchers explored many channels through which the phenomenon influenced and could influence (in future) economies around the world. In the attempt of creating an exhaustive reference in the
literature, the work by Dell, Jones, and Olken (2014) represents a milestone and the main guide to the
review hereby exposed. The authors not only provided a comprehensive list of the main publications but also created a methodological guide for the field shedding light on the process of refinement of econometric tools employed.
2.1
General Overview
Different branches of the literature originated following the exploration of impacts on different eco-nomic indicators. Not only there is heterogeneity among scholars in identifying the variable of in-terest but also in the different expedients used by economists to translate climate change into econo-metric covariates.
One of the first and main objectives of scholars was to investigate the relation between climate change and output measures. Higher mean temperatures, extreme rainfall, or deviation from long trends have been considered of relevant importance in shaping aggregate measures of interest. Al-though the relation could potentially interest also richer countries most of the literature focused on
the impact on Developing Countries. Barrios, Bertinelli, and Strobl (2010) focused on impact of
rain-fall on African countries aggregate production; the authors claim that African countries development witnessed a strong arrest during the ’70s that did not allow a subsequent recover. They argue, this general decline happened to go along with a decrease of rainfall started in the ’60s of the previous
century. Using a cross-country panel dataset Barrios, Bertinelli, and Strobl (2010) show a
signifi-cant decrease of the growth rate of Sub-Saharan African countries due to rainfall trends. Similar
6 Chapter 2. Literature Review
distinguishes this second research; Yang (2008) interprets climatic variable in a very different way,
focusing on extreme weather events, in particular, on hurricanes. Besides the major contribution constructing a storm index, the author finds that the response of foreign financial aid to countries hit by hurricane is large.
Although the link between climate change and aggregate variables as the ones exposed above is clear, when approaching the field, one of the most intuitive relations of interest arising among scholars concerns energy consumption. How would hotter/colder climates influence our need of
consumption in both the business and private life? Kitous and Després (2018) from European
Com-mission Joint Research Center have investigated the impact of climate change on future residential heating and cooling needs. Authors investigated five different scenarios of climate change and trans-late the phenomenon in so called Heating and Cooling Degree-Days namely the cumulative devia-tion of temperature respectively below and above a threshold of comfort temperature. Across all scenarios short-term need of cooling energy is high but the dominant driver forecast is the decreased level of heating energy required. Giving an answer to the question above then, impact could be positive on energy consumption, although there is room for increase in efficiency.
Climate change not only concerns economical factors but goes far beyond it, conditioning many spheres of our lives. Often, economists have investigated the impact of weather on variables cross-ing border with different disciplines such as psychology. “Who never felt happier in a sunny day?
How does it impact labor performance?” Connelly (2008) and Saunders (1993) have asked similar
questions. The latter, in an early study in 1993, investigated the impact of local weather in New York City on stock exchange prices. The author tried to assess possible effects of weather, represented by cloudy days, in the city on stock prices and the result indicates a mild but statistically significant
impact. Along the same path Connelly (2008) developed a model to estimate working and leisure
schedule decisions depending on weather. Although the relation varies by gender in principle the author found that only rain among weather indicators impacts significantly the decision on individ-uals.
Eventually, worth mentioning a growing field of investigation focuses on impacts on conflicts occurrence. Many authors argue that weather variation, longer-run change in temperature, precipi-tation or extreme events can severely impact the eventuality of conflicts happening. They claim the relation may go through different channels such as decrease of water availability, destruction of in-frastructures and worsening of fertility conditions. In this context an innovative work was done by
Fjelde and Uexkull (2012). The two authors moved away from using country level aggregate data
sets, as in most of the previous research, to a disaggregated data set of Sub-Saharan countries. The two researchers claimed that the reason why previous studies reported mild impacts of climate on
2.2. State of the Art: Climate Change and Agriculture 7
the likelihood of conflicts is due to the focus on civil war and rage against Government. On contrary, their research investigates the occurrence of communal conflicts between 1990 and 2008. Although, surprisingly, poverty does not appear to corroborate the impact estimated, they conclude negative deviation from normal levels of rainfall increases the likelihood of conflicts.
A comprehensive view on the many different fields of investigation of climate change effects is not the scope here and this brief overview is meant to give a glance of the complexity and extension of the literature which developed in many different directions.
2.2
State of the Art: Climate Change and Agriculture
As seen above economists have investigated manifold different channels of impact of weather and climate on societal and economical life. One of the first interests of researchers was understanding the possible impacts of climate change on agriculture. This section is meant to present an overview of past and current research in the field.
2.2.1
Why Agriculture ?
Agriculture has proved to be a relevant factor in determining many different interesting societal and economical dynamics.
Anríquez and López (2007) investigated the effect of growth in agriculture on poverty. The
inno-vative contribution stands on the attempt to isolate the various channels through which the relation works. The two authors proved that agriculture has a strong effect on poverty reduction. It is indeed through poverty and other channels that agriculture can influence intra-country migration. The topic has interested many different studies concerning Developing Countries in particular, and a clear
ex-ample comes from Goldsmith, Gunjal, and Ndarishikanye (2005). The authors developed a model
to test impact of agriculture on internal migration in Senegal using country level data from 1961 to 1996. The result of the model suggests that policymakers shall implement targeted investment in agricultural sector in order to reduce urban-rural migration via the reduction of income differen-tial. Nevertheless, when considering the issue of climate change impacting agriculture in a broader way, one can easily realize that the problem does not only influence poorer areas of the globe. One of the most interesting effects, not yet sufficiently investigated, is the impact of climate change on cross-country migration going through change in agricultural productivity. Feng, Krueger, and
Op-penheimer (2010) focus on Mexican migration from year 1995 to 2005. Even though considering only
changed agricultural productivity is probably underestimating the effect on agriculture, crop yields have apparently a significant and relevant influence on migration.
8 Chapter 2. Literature Review
For these and many other reasons the study of climate change impact on agriculture is relevant, not only purely to enhance the development of a field which has still room for improvement and novelty but also as a reference for policy makers and representative that have already started or will soon start to face economical and societal issues linked to the phenomenon.
2.2.2
Impact of Climate Change
Literature assessing impact of climate change on agriculture is well rooted in the past and interest
was growing already in the late ’80s. Already in year 1989, Adams (1989) was asking himself which
kind of role could economists play in the attempt of estimating this impact and in forecasting con-sequences in a reliable way. Much of the attention at the time was dedicated to the widely reported growing emission of greenhouse gases in the atmosphere. As reported by Dell, Jones, and Olken
(2014) much of the research relied on the attempt of finding the so called damage function through
complicated models. These models were calibrated on observed data and were taking into
consid-eration many different variables. Adams et al. (1990) use a crop-simulated model with two different
climate change scenarios focusing on wheat, maize and soybeans in United States. They find that climatic variables impact could be potentially off-set by the effect of CO2 in the atmosphere, enhanc-ing plant growth. The latter is not the only findenhanc-ing proposenhanc-ing a rather positive point of view in those
years. Tobey, Reilly, and Kane (1992) argue that negative effects of climate change on agriculture as
reported by part of the literature could be simply the effect of a myopic view on the problem. They claim that once accounted for global market interactions, inter-regional adjustments would mitigate or even off-set the impact of climate change.
USA agriculture has been monitored and described for very long time. The abundance of data al-lowed scholars to develop many different studies and the richness of information has been extremely lower in other parts of the globe. However, also the Mediterranean area was interested by this new wave of research. Agriculture is one of the most important sectors in the South-European area and interest grew among researchers with one of the first attempts of synthesis of the literature in the
middle of the decade (Rosenzweig and Tubiello,1997). Iglesias and Minguez (1997) focus on Spain,
arguing that the specific Spanish climatic conditions make the assessment on the country extremely relevant in the field. They use CERES model focusing on wheat and maize, with the second being an highly irrigated crop. Although maize does not appear to benefit, wheat productivity would in-crease strongly in some regions. The main contribution has been to highlight the spatial distribution of results, clustered in different regions. Spatial heterogeneity appears also in Kapetanaki and
Rosen-zweig (1997) who focus on maize in Greece. They find that Northern and Central areas maize
2.2. State of the Art: Climate Change and Agriculture 9
of these studies due to the high presence in the interested countries, however rice has been a pre-ponderant subject of research especially in Asian countries. Once again simulation model proposed
alternative results some suggesting declining trends due to climate change (Pathak et al.,2003), some
even potential increase (Saseendran et al.,2000).
One of the most relevant shortcomings of the so-called simulation models widely used in those years is the inability to account for possible adaptation mechanisms and the heavy relying on
calibra-tion. The study by Mendelsohn, Nordhaus, and Shaw (1994) represents a breakdown moment in the
literature. They propose an alternative method to overcome the problems pointed out about “pro-duction function approach”, namely the so called Ricardian approach. They propose a data-driven method based on an OLS cross section analysis that takes as dependent variable land value at the county level. Land value shall be proportional to all future potential land rent and they argue it shall account for all possible adaptation choices; in other words land values shall internalize the potential choice of adaptation of farmers. Estimates from OLS cross section model are used to forecast future impacts and they find that, depending on the weighting method, climate change could be beneficial to agricultural sector in USA. However, they consider irrigation as an endogenous variable affected by climate change excluding it from the model. The decision entailed many critiques specially by
Schlenker, Hanemann, and Fisher (2005) who argued that rainfall is not the only source of water
and that excluding irrigation from the regression equation corresponds to assume that regions with different climate have similar availability of water resources. They show that irrigate and dry-land
cannot be pooled together. In a subsequent research Schlenker, Hanemann, and Fisher (2006)
im-proved their study: they use only counties at East of the 100th meridian, not counting on heavy
irrigation, they allow for spatially correlated errors and they construct an innovative data set for cli-matic variables. The authors move closer to the agronomic literature using the so called Growing Degree Days, namely the cumulative sum of degrees falling in a certain range each day along the growing season. The method allows to account for non-linearity of effects. Indeed, they find that temperature above 34 are always harmful and that the positive impact of rainfall becomes negative after a certain threshold, using once again land value as independent variable.
Another moment of strong improvement in the literature is represented by the work by
De-schênes and Greenstone (2007). The authors propose a new approach focusing on short term
varia-tion of weather. They argue that cross secvaria-tion approach is mainly subject to endogeneity caused by omitted variable and to overcome the problem they exploit year-to-year variation with panel data set. The new approach moves the focus from long term equilibrium to short-term variation. The capability to account for adaptation is certainly decreased but the longitudinal set allows for county specific and year-state specific fixed effect, “cleaning” the estimation of climate change impact. They
10 Chapter 2. Literature Review
use four American census from 1987 and 2002 and a revised Ricardian approach taking as indepen-dent variable profits per acre and doing robustness checks for yields of corn and soybean. They find almost no effect of temperature (expressed in Growing Degree Days) and precipitation on dependent variables.
Although the indisputable contribution of their research, these findings have been largely ques-tioned and the choice of profit as independent value strongly criticized as a partial equilibrium
ap-proach, highly influenced by fluctuations of prices (Fisher et al.,2012). Studies carried using similar
approach but focusing on yields share a communal trend of outcomes. Temperature have strong non-linear impacts on agricultural yields and precipitation are positive until a certain threshold.
Schlenker and Roberts (2009) use a panel data set of yields for the two main crops in USA, wheat and
soybeans. They also investigate cotton, as a heat-resistant crop. Authors employ a more fine-scale weather dataset in a flexible regression model. They find harm threshold for the investigated crops: 29 Celsius degrees for corn, 20 for soybeans and 32 for cotton.
Although the new panel approach has improved research towards a more reliable estimation of causal effects, what many have argued is that short term estimates of weather variation cannot be
used in assessing long term effects of climate change. To this regard, Burke and Emerick (2016)
proposed a possible solution. What the authors do is a comparison between short-term year-to-year impacts and longer term impacts on yields of corn and soybeans. The latter are computed through “long differences”. The concept is simple: the piece-wise regression (using once again exposure to one degree ranges during the growing season) is computed on differences of two moment in time, 1980 and 2000. They find basically no sign of adaptation through time suggesting the fact that farmers do not change their farming decisions according to climate change and testing for many possible explanations of this.
The major part of the studies reported take place in USA. Literature on climate change impact on agriculture did not flourish in Europe mainly because researchers could not count on the same richness of time-series data, as pointed out above. Often, there is lack of harmonized data and sin-gle country use different weights or definitions for the variables of interest. Nevertheless, some attempted to mirror similar approaches and studies, in particular for Western Europe. Van Passel,
Massetti, and Mendelsohn (2017) for the first time use FADN (Farm Accountancy Data Network)
data set accounting for 41000 European farms in 2007. Authors use a revised Ricardian method ap-plying quantile regression (on median) to land value. Although findings are mixed and heteroge-neous across regions and seasons they find that increase in temperature and precipitation could have
positive impact on land value. Moore and Lobell (2014) attempt to estimate adaptation in the sector
2.2. State of the Art: Climate Change and Agriculture 11
They find that adaptation could off-set part of the negative impact on barley and wheat and it has
strong effect for maize. Ceglar et al. (2016) use a PLSR to estimate effects of temperature, cumulative
precipitation and radiation on winter wheat and grain maize, in French departments. They find grain maize is highly impacted by temperatures in July and August whereas winter wheat is more sensible to increase in autumnal temperatures. Once again geographical heterogeneity is proved with strong difference between western and eastern parts of the country.
For a more comprehensive review see Dell, Jones, and Olken (2014), Carleton and Hsiang (2016)
and for methodological review Kolstad and Moore (2019).
2.2.3
Conclusion
The prerogative of the present study is to fill a void in the literature. Lack of harmonized data led to a substantial lack of research on impact of climate change on agricultural productivity in Europe. Europe strongly counts on agricultural sector and the assessment of impact of a no longer questioned change in future climatic scenario shall be investigated. According to my knowledge of the relevant literature, Italian Provinces’ agricultural productivity has not yet been sufficiently investigated in the context of climate change. Although past studies among European countries have focused on the so-called hedonic approach (land values, profits) I deem of extreme importance to investigate a more direct sign of health condition of agriculture, namely productivity. The aim here is to exploit a similar
approach to the one developed by many authors (Deschênes and Greenstone (2007), Schlenker and
Roberts (2009) etc.) and assess impact of short-term variation of weather on crop yields.
Besides the most commonly investigated crops, I am interested also in the effects of climate change on vineyards. Despite the great importance of wine production in Southern-European coun-tries, research on impact of climate change on grape productivity is very limited. I will try then to approach the problem also for what concerns production of wine grapes (“uva da vino”).
13
Chapter 3
Data
This chapter is meant to present and describe data used in this work. In the present analysis I use two main sets of data: data on agricultural productivity and related variables and meteorological data on temperature and precipitation. In what follows the two sets are presented with particular attention to processes followed to create the final data sets used in the models.
3.1
Dependent Variable and Controls
Data on agriculture are provided by the Italian National Institute for Statistics and I accessed it via the
online service “I.Stat” (I.Stat). Data cover the time span going from 2006 to 2018. The geographical
unit of observation is the administrative level of Province and information is reported for each year; data on surface and production are a mixture of observed and estimated data. As mentioned above, four different type of cultivation are considered: maize (“mais”), soft wheat, durum wheat and wine grape. On the one hand, the first three have been subject of many studies as explained and reported
in Chapter2. On the other hand, grapes destined to wine has hardly ever interested research work;
the fact is surprising considering the great impact of wine production, specially in the Southern countries in Europe.
Before going to the explanation of computations it is important to enlighten an issue; data on crops and grapes report information for 111 Italian Provinces whereas meteorological data report information for 110 unit of observation. This is due to an administrative change in year 2016: “Sud Sardegna” province was founded encompassing the previous provinces of “Medio Cambidano” and “Carbonia-Iglesias”. To overcome the problem I avoid to consider the new-founded province, getting to 110 observations. The process to go from raw downloaded data to the dependent variable of model equations has followed different steps.
14 Chapter 3. Data
Firstly, yields per province per year are computed as follows:
yieldsi,t=
harvi,t
sur f acei,t
Namely, yields in province i in year t corresponds to the harvested production over surface for
the same unit of observation. The final unit of measurement is quintals per hectare1.
Secondly, I tried to overcome a major issue concerning yields. Numerous provinces present the same value for yields in three or more contiguous years. Similar problem interested agricultural data
on French departments in the study by Ceglar et al. (2016). I decided to adopt the same approach that
I deemed to be the most consistent and safe. I set to NA the whole time-series for those provinces
presenting a similar situation and I reported in appendixAthe list of all manipulations.
Thirdly, I computed yields growth rate from year t - 1 to year t. I computed the percentage change as the difference of the natural logarithm of yield in time t and the natural logarithm in time t - 1. Intuitively, the first disposable year is then 2007.
At last, I dealt with outliers. Growth rate for all cultivation are centered on 0 with a slight posi-tive twist for maize and wheat indicating that yields during the years of the analysis remained either steady or presented slightly increasing pattern (for wine grape the pattern seems the opposite). How-ever, all four types of cultivation present outliers with growth rate reaching even + or - 200% as one
can notice in figure 3.1; the figure presents frequency histogram (with 0.03 bin-width) for the four
types of cultivation: maize (1), soft wheat (2), durum wheat (3) and wine grapes (4). Although discre-tional, the decision concerning this issue is to eliminate those observations presenting a growth rate equal or above +100% and equal or below -100%. The decision consists in eliminating histogram’s
tails and a list of all provinces and years interested is reported in appendixA.
My final data set for what concerns agricultural productivity presents growth rate from 2007 to
2018 for each one of the four type of cultivation. Figure3.2presents pooled cross section time series,
that is the mean of growth rate among all provinces in the specific year, for the four cultivation con-sidered. As one can notice the two typologies of wheat considered have usually similar behaviour. Maize follows a similar path until 2011 and then presents an inverse pattern compared to wheat, at least until 2015. Growth rate for grapes yields present the higher variability giving the idea that each year carries its own specificity in terms of productivity.
Concluding, besides data on agricultural productivity I also consider two other variables, Gross Domestic Product and population, which have also important role in the relation investigated. Data on GDP and population per Province come from the same source of agricultural data. The first covering years 2006 to 2016 and the latter covering years from 2006 to 2018.
3.1. Dependent Variable and Controls 15 0 100 200 300 −2 −1 0 1 2
Corn Yields Growth Rate
(from t−1 to t)
Count
1
0 50 100 150 200 −2 −1 0 1 2Soft Wheat Yields Growth Rate
(from t−1 to t)
Count
2
0 100 200 −2 −1 0 1 2Hard Wheat Yields Growth Rate
(from t−1 to t)
Count
3
0 50 100 150 200 −1 0 1Wine Grape Yields Growth Rate
(from t−1 to t)
Count
4
FIGURE3.1: Histogram of yields growth rate per cultivation.
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.1 0.0 0.1 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 time P ooled Y ields Gro wth Rate
variable ● maize ● soft wheat ● durum wheat ● grapes
16 Chapter 3. Data
3.2
Independent Variable
Meteorological data are part of ERA5 dataset, a freely accessible climate reanalysis dataset developed
through the project Copernicus Climate Change Service (C3S) (Copernicus (C3S)). ERA5 covers (so
far) the time span going from 1979 to present, although when fully implemented it will allow access to data from 1950. C3S is one of the six thematic services developed under the Copernicus Earth Observation Programme. The latter has been developed and managed by the European Commission in collaboration with many other different bodies.
C3S is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) that
acts on behalf of the European Commission (ECMWF). The fifth (and last) global major reanalysis
produced by the centre is ERA5, which I accessed via the online platform. In particular I used sub-set
“ERA5 monthly averaged data on single levels from 1979 to present”, see ERA5 (CDS), among which
I selected the “monthly averaged reanalysis”2. Downloaded dataset encompasses two variables:
• “2m temperature” from 1979 to 2018 reports monthly average temperature at 2 meters from the surface. Temperature is reported as monthly means of daily means, the unit of measurement is Kelvin Degrees and a data point per each month throughout the time span is provided. • “total precipitation” from 1979 to 2018 reports monthly averages of daily means. The unit of
measurement is meters and a data point per each month throughout the time span is provided. Intuitively, data about temperature and precipitation are not purely observed data covering such an extensive geographical and time dimension. As the definition suggests, the information is the result of a re-analysis; ERA5 is elaborated using 4D-Var data assimilation in CY41R2 of ECMWF’s Integrated Forecast System. Data assimilation models try to combine theory, in this case coming from the world of physics, and real observations.
In the next sub-section I take an overview on the field of Geographical Information System and I give an explanation of the process of computation of meteorological data.
3.2.1
Geographical Information System (GIS): Geocomputation
One of the attempt of this research is to exploit the great richness of geographical data in order to implement econometric research.
Geography is an old discipline with centuries of history. Since the earliest day man kind has been curious about the surrounding world and Geography became the most objective way to inves-tigate it. The subject witnessed great changes during history and went from the mere observation to the attempt of representing and understanding scientifically and accurately natural and societal
3.2. Independent Variable 17
phenomena. The last decades represented an unprecedented phase of development of scientific tools aimed to increase the quantitative component of the discipline and make it replicable.
In this context I shall introduce Geographical Information System (GIS). In the years, many authors have tried to give a definition of the concept driven by focus on different aspects of GIS: its tools, its goals and so on. Some have even proposed a wider point of view supporting the idea that the
discipline could be defined as “Geographical Information Science”, see Goodchild (1992). For this
research I rely on definition proposed by Chrisman (1999) as I deem it clear and comprehensive: GIS
is the organized activity by which people measure geographic phenomena and processes, represent these measurements in order to operate on them integrating other sources and in the end transform
these measurements to relate to other frameworks3.
It is under the theoretical umbrella of GIS that one can define geocomputation. The latest is a branch of the discipline focused on the exploitation through computational power of the incredibly rich amount of data and information of the GIS world. As main reference for the computation of data and the access to GIS-related packages in R I referred to Lovelace, Nowosad, and Muenchow
(2018). The authors report a user friendly guide for R’s tools for Geocomputation. In what follows I
will give an explanation of the process followed in the elaboration of meteorological data.
3.2.2
Computation of Meteorological Data
Data are presented in raster form. The technical definition of raster element can be quite complicated but in simple terms it could be thought as a picture whose pixels report a piece of information each. The original resolution of each “pixel” is 31 km on a reduced Gaussian grid, however reanalysis
data has been re-gridded to a Latitude-Longitude grid of 0.25◦. Earth is not a perfect sphere so that
cells have a different extension in kilometers as, moving from the Equator to the poles, meridians intersect.
As already said above, data sets report information on monthly averages per each year of interest. The process in which monthly averages have been aggregated in order to get to an yearly data point will be exposed subsequently and it forms an integral part of the methodology. The aim in this section is to give a clear explanation of the process followed in order to extract an R-dataframe reporting a unique data point on temperature (and precipitation) per each Province per each month in the time span considered starting from a raster element reporting information on a global scale.
For sake of simplicity in what follows I will enumerate the various step taking as example the
variable “2m temperature” on January 20064:
3for a more exhaustive definition and explanation see the source
4A big thank to my friends and colleagues at the JRC Andrea Marcucci, Frank Neher (suggestion on Urban Centers) and
18 Chapter 3. Data
Step 1 Without any kind of process data downloaded represent the information to the global scale as
shown in figure3.3a. Pixels report information on monthly average temperature measured in
Kelvin Degrees on January 20065. The first step consists in isolating only those pixels covering
Italian surface. To do so, I use an R-shapefile reporting Italian Provinces and National bound-aries. The main issue at this stage is that raster and shapefile must share the same Coordinate Reference System (CRS), namely the same geometrical framework in which boundaries are drawn.
Step 2 Once I isolated only those pixels covering Italian surface a second important step is computed. As explained above grids reporting information are quite large covering areas of roughly 25x25 km of extension. Italian Provinces are mostly larger, however in the process of aggregation of information per Province it would have been likely to have raster cells overlapping adminis-trative boundaries. In order to minimize the problem I sub-divided each cell by a factor of 6,
therefore into 36 smaller cells6 of equal dimension all reporting the same values of the
orig-inal cell. The augmented level of resolution makes the overlapping less likely and increases the level of precision in the process of aggregation for each Province. Once the step is done
information on 01.2006 is presented as in figure3.3b.
Step 3 Once the raster representing Italian surface is disaggregate I try to overcome a major issue: urban centers could constitute so-called heating islands. The characteristics of the soil and the morphology of a city combined with the anthropomorphic activity can represent a source of
heat, see Kim (2007), that makes the city a warmer outlier in the area. The consequences of
this heating process can be positive or negative and the actual impacts depend on the
charac-teristics of local climates (Taha, 1997). However, as the aim is to find effects on agricultural
productivity, one should take into consideration a possible bias caused by urban centers in the process of aggregation and definition of temperature at the province level. The solution used is simple but hopefully effective. I used the rich dataset Global Human Settlement Layer Urban
Centres Database (GHS-UCDB), Florczyk et al. (GHS Urban Centre Database 2015). The database
gives a full comprehensive pictures on the status of Urban Centers in year 2015 at the global level focusing on a wide number of characteristics among which surface and extension of Ur-ban Centers. In particular the resource I used is an R-shapefile reporting (on the same CRS of previous elements) the profile of Urban Centers at the global level. The definition of Urban
5Once the dataset is uploaded in R it presents many different raster elements each named by letters and numbers as for
example “X2006.01.01.00.00.00”; I assumed the fifth and sixth digits to represent the month since the coupled digits range from 01 to 12 for each year
3.2. Independent Variable 19 0 50 100 150 200 250 300 350 −100 −50 0 50 100 240 260 280 300 (A) Global raster 260 265 270 275 280 285
(B) With urban centers
260 265 270 275 280 285
(C) Without urban centers
FIGURE3.3: Rasters on 2m temperature (Kelvin) on 01.2006.
Centers follows the Degree of Urbanization concept and states that an Urban Center is “high-density clusters of contiguous grid cells of 1 km2 with a “high-density of at least 1500 inhabitants per km2
and a minimum population of 50000” as defined by Dijkstra and Poelman (Urban Centers). The
R command previously used to extrapolate pixel within Italian boundaries has been used here inversely to extrapolate pixel outside Urban Centers shapes. Once the step is done the raster
for 2m temperature on 01.2006 is represented in figure3.3c.
Step 4 The last step in order to get a ready-to-use dataset is the extraction of the value for each unit of observation. In this case I computed the average of the cells laying within each Province administrative boundaries. The value of temperature for the province i is the mean of the value of those cells covered by the province i polygon. A cell is covered when its center is inside
the polygon. The final step is represented in figure3.4which is a quantile map of the Italian
Provinces’ temperature on 01.2006. Temperature will be expressed in Celsius Degrees in the analysis.
3.2.3
Meteorological Variables: de-trending and Growing Season
This subsection is meant to clarify the process of de-trending of meteorological variables and to clarify how growing seasons are taken into account.
The treatment of meteorological variables, namely the independent variable of my model, follows two common practices in the literature. Firstly, one major problem is usually taken into account by scholars in this branch of literature, namely the risk of spurious relations arising from common
20 Chapter 3. Data
FIGURE3.4: Italian Provinces quantile map on 2m temperature (Kelvin) on 01.2006.
increasing trends. That is why, I decided to de-trend meteorological variables adopting the following process.
I computed the long term trend per month for both temperature and precipitation, in other words, the mean of all monthly data from 1979 to 2005 per each month of the year. Subsequently, I subtracted to each month of each year of my panel data set, the computed trend. After this passage, a new data set for the time span of this research was available representing monthly means of temperature and precipitation in deviation from long term trends.
Secondly, I followed another common practice in the literature. Agricultural production is ex-tremely influenced by weather. However, as it is intuitive, what really matters is the condition of weather during the growing season of the plants. For what concerns cereals in this analysis, I fo-cused on those months of the year corresponding to the vegetative-reproductive stadium of the plant
as reported in crop calendars provided by Agri4Cast (crop calendar). That is, the independent variable
represents the mean and the standard deviation of temperature and precipitation of those months in the growing season. For what concerns maize I considered April, May, June, July and August. For what concerns wheat (both soft and durum) I considered December, January, February, March, April and May.
In figure3.5 the two graphs show the density distribution of independent variable across all
years of the analysis for maize (1) and wheat (2). In other words densities are obtained considering
all growing seasons of all years from 2007 to 20187.
The two graphs highlight also mean values; for maize they are 0.9293 for temperature and 0.1959 for precipitation. For wheat they are 0.93276 for temperature and 0.44026 for precipitation. The information carried by the graphs is clear; even in a tiny time span of 12 years it is temperature is growing compared to historical trends. On average along the years of this analysis growing seasons
3.2. Independent Variable 21 0.0 0.2 0.4 0.6 −2 −1 0 1 2
Deviation From Long Term Trend Growing Season Maize
Density
1
0.0 0.2 0.4 0.6 −2 0 2 4Deviation From Long Term Trend Growing Season Wheat
Density
2
FIGURE3.5: Kernel density distribution of independent variable for maize and wheat. Temperature in red, precipitation in blue. Mean value in dashed line.
for maize are 0.92 degrees hotter than the long term trend and growing seasons of wheat are 0.93 degrees hotter than long term trend. For what concerns precipitation there is not a big difference compared to trends in maize growing season and an increase for wheat growing season.
3.2.4
Independent Variable for Wine Grapes Analysis
For what concerns wine grapes the decision on which months to take into account was more cum-bersome. As said above, the literature does not present enough research done for this cultivation and there is not a common approach. Eventually, I tried to consider the characteristics of this plant. Grape is a fragile plant and all seasons can have an impact on its health conditions. In order to cap-ture in the best way this fragility I decided to use as independent variable averages across seasons. Once again temperature and precipitation are taken in deviation from long trends.
Figure 3.6 shows Kernel density distributions of temperature and precipitation in winter (1),
spring (2) and summer (3) during the 12 years of this analysis. Even to a first view it is clear that information carried by growing seasons for maize and wheat is in principle confirmed. Precipita-tion does not look extremely different from long term term trend with almost all data concentrated around 0. For what concerns temperature there is a positive deviation. On average winters are 0.7750 hotter, spring 1.0904 hotter and summer 0.9899 hotter than long time trends.
22 Chapter 3. Data
0.0
0.1
0.2
0.3
0.4
0.0
2.5
5.0
7.5
Deviation From Long Term Trend
Winter
Density
1
0.0
0.2
0.4
0.6
−2
0
2
4
Deviation From Long Term Trend
Spring
Density
2
0.0
0.2
0.4
0.6
−2
−1
0
1
2
3
Deviation From Long Term Trend
Summer
Density
3
FIGURE3.6: Kernel density distribution of independent variable for wine grape. Tem-perature in red, precipitation in blue. Mean in dashed line.
23
Chapter 4
Methodology
This chapter is meant to present the methodology followed in this analysis, partly already transpired
in Chapter3 when showing the characteristics of data sets involved in the analysis. The chapter
reports a quick overview of common methodologies in the field in order to contextualise the present study. Subsequently, a deeper view on the model is presented with a particular attention dedicated to the identification strategy.
One of the main concepts emerged in Chapter2is the importance of methodological choice in
the investigation of the relation between agriculture and climate change. Groundbreaking moments in the development of research have almost always coincided with improvement and big changes in
method. As already said above Dell, Jones, and Olken (2014) have done an exhaustive and extensive
work in producing a guide to the literature and in the meanwhile to the various methodologies
applied. It is indeed to those authors along with Kolstad and Moore (2019) that I referred to outline
the categorization exposed in the next paragraph and to the choice of the model employed in this study.
The attempt to identify effects of climate change on agriculture has witnessed different improve-ments. Sophisticated simulation models have held the scene for long time until the mid 1990s when data-driven approaches became protagonists in the field. Since then, studies could be grouped in three
major categories according to Moore and Lobell (2014):
1. Cross-Section Approach: these kind of models attempt to find the long term effects of climate on agricultural sector exploiting cross sectional variation. Agriculture is often represented by land values or profits and climate change by long term trends. Mendelsohn, Nordhaus, and Shaw
(1994) pioneered this approach with the so called Ricardian model, an hedonic approach claiming
that land value could internalize future adaptation choices of farmers. A major critique claims that this kind of model only represents a partial equilibrium model and it is subject to omitted variables problem.
24 Chapter 4. Methodology
2. Panel Analysis Approach: this kind of model exploit temporal variation besides cross sectional
one and were introduced by Deschênes and Greenstone (2007); location specific effects capture
time-invariant characteristics tackling effectively (although with some limitations) the problem of omitted variables. Agriculture is usually represented by yields or production and the present study inserts in this category. What is commonly claimed is that exploiting short term variation of weather does not allow to draw conclusions on long-term effects of climate change.
3. Hybrid Approaches: a category which collects different approaches emerging in the last years. These methods usually combine panel models with cross sectional variation in order to solve the main critiques addressed to the previous two categories of studies.
For a comprehensive review of all methods I suggest to refer two the articles mentioned above.
4.1
Model and Identification Strategy
Although the relevance of agriculture in European countries’ economies has decreased compared to the past, the importance of this sector is still focal in particular in Southern European regions. European Union has always paid great attention to agriculture and the Common Agriculture Policy (CAP) has represented large part of fund invested by European Commission for many years.
The economic impact of climate change spreads following different channels and agriculture is
a key sector through which this dynamic could develop. As already pointed out in Chapter2
agri-cultural trend can be the catalyst of many economical dynamics and changes. Countries whose economies still rely heavily on agricultural products, suffer the most by negative impact of weather. A reduced productivity could push farmers to migrate either to another sector or to other locations in the attempts of finding more favourable conditions.
Geographical characteristics of Italy, make the country an important subject of study. Northern Provinces have climates and land characteristics extremely different from Southern Provinces with the latter being drier and hotter on average. Besides the intuitive differences between Northern and Southern areas due to the long extension of the country each location has idiosyncratic characteristics influencing agriculture. This is the reason why the methodology followed in this analysis has a focused on location specificity. The main goal is the estimation of the variation of productivity in the agricultural sector as a consequence of variation of weather. The research question:
Q: how does year-to-year weather variation impact variation in agricultural productivity at the administrative level of Italian Provinces?
4.1. Model and Identification Strategy 25
The model employed to tackle the research question is an individual fixed effect panel model, that can be described by the following equation:
yi,t =βXi,t+γZi,t+αi+ei,t (4.1)
Where:
• yi,t represents Province i yield growth rate in year t computed as the percentage change of
yields from year t-1 to year t.
• Xi,t encompasses meteorological variables of temperature and precipitation in Province i at
time t. For what concerns crops, maize and wheat, variables are taken as averages of monthly data in the growing season of the plant and in deviation from long time trend. For what con-cerns grapes, variables are taken again in deviation from long time trends but during winter, spring and summer seasons.
• Zi,tencompasses a number of time-varying variables in Province i at time t deemed influential
in the relationship of interest.
• αi is the province specific fixed-effect capturing all time-invariant characteristics that could
affect and interact with estimates of weather variation on agricultural outcome. • ei,tis the Province i specific error in time t.
The identification strategies relies on the possibility of accounting for Province specific climate characteristics, as also for other time-invariant characteristics as for example morphology. Although the causal specification is extremely reliable the estimation does not allow to infer conclusions on long term effect of climate change but rather on short term weather variations from long time trends. Besides, it is still necessary to control for a number of variables which are correlated with weather and also influence agricultural productivity, such as the Gross Domestic Product and the population in each province.
There are two major assumptions: firstly, weather variation is considered exogenous, namely a random realizations from a distribution, commonly called climate, and secondly, I assume that irrigation technology did not witness any positive shock in the time span of my analysis and can be considered part of time invariant variables accounted by fixed effects.
The model is implemented with subsequent addition of regressors and in some model specifica-tion also time-fixed effects are introduced.
27
Chapter 5
Results and Discussion
This chapter is meant to report results of the analysis organized per each cultivation taken into
con-sideration. Models implemented are described in detail in Chapter4dedicated to the methodology.
Results are accompanied by a discussion and some of the comments are validated through a visual inspections of the relations investigated.
5.1
Maize
Table5.1reports results for maize.
TABLE5.1: Maize
Dependent variable: Yields Growth Rate
(1) (2) (3) (4) (5) (6) (7) mean_tp −0.032∗∗ 0.046∗ 0.040 0.050∗ 0.050 0.045 0.081 (0.013) (0.028) (0.028) (0.029) (0.033) (0.035) (0.050) mean_pre 0.080∗∗∗ 0.096∗∗∗ 0.087∗∗∗ 0.083∗∗∗ 0.073∗∗∗ 0.075∗∗∗ 0.076∗∗∗ (0.015) (0.017) (0.018) (0.018) (0.022) (0.022) (0.026) mean_tp2 −0.039∗∗∗ −0.035∗∗∗ −0.041∗∗∗ −0.060∗∗∗ −0.057∗∗∗ −0.097∗∗∗ (0.013) (0.013) (0.014) (0.017) (0.017) (0.021) mean_pre2 −0.028∗∗ −0.027∗∗ −0.026∗∗ −0.028∗ −0.029∗ −0.014 (0.013) (0.013) (0.013) (0.016) (0.016) (0.017) dev_tp −0.059∗∗ −0.060∗∗ 0.015 0.020 0.047 (0.025) (0.025) (0.031) (0.032) (0.041) dev_pre 0.015 0.015 0.003 0.001 −0.029 (0.017) (0.017) (0.018) (0.019) (0.021) log(pop) 0.634 0.799 0.999 0.401 (0.471) (0.571) (0.668) (0.683) log(gdp) −0.246 −0.226 −0.094 (0.243) (0.245) (0.269) trend −0.002 (0.004)
individual FE yes yes yes yes yes yes yes
time FE no no no no no no yes Observations 960 960 960 960 803 803 803 R2 0.101 0.118 0.123 0.125 0.117 0.118 0.080 Adjusted R2 0.010 0.026 0.030 0.031 −0.00001 −0.001 −0.055 F Statistic 48.721∗∗∗(df = 2; 871) 28.942∗∗∗(df = 4; 869) 20.311∗∗∗(df = 6; 867) 17.685∗∗∗(df = 7; 866) 11.749∗∗∗(df = 8; 708) 10.471∗∗∗(df = 9; 707) 7.648∗∗∗(df = 8; 699) Note: ∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
28 Chapter 5. Results and Discussion
The table reports model with increasing number of regressors involved; models from (1) to (6) complies with the methodology exposed in the previous Chapter whereas model (7) allows to ac-count for time fixed effects besides Province specific fixed effects.
Model (1) shows effects of temperature and precipitation when considered linearly. Temperature effect is negative and statistically significant whereas precipitation affects growth rate positively and the coefficient is also, statistically significant. Apparently, as the monthly temperatures averaged through the growing season deviate of one more degree from the long term trend the growth rate of yields decreases of roughly 3 percentage points. Once accounted for quadratic effects in model (2) the sign of the effect of temperature changes. The linear components show a positive effect on growth rate and the quadratic ones show a statistically significant negative impact suggesting that both temperature and precipitation have an optimum threshold, beyond which effects are negative. Model (3) accounts for the standard deviation of temperature and precipitation during the growing season; I shall remind that monthly data points are considered in deviation from trends so that the variability measure (standard deviation) accounts for standard deviation of temperature and precipi-tation when considered already in deviation from the trend. Although precipiprecipi-tation is not affected by the additional relationship involved, temperature shows that linear effects are no longer important and that both quadratic effects and standard deviation have negative impact. Despite being variation in a small range of time comprehensive of the four month of growing season, it is clear that as more the growing seasons presents “within” season variability, as less it is beneficial for the production of maize. Once accounted for the controlling variables, namely GDP, population and yearly national trend, results do not change in their sign but in their statistical significance. Model (6) presents the complete model; precipitation shows once again the same quadratic pattern whereas only quadratic term for temperature is statistically significant suggesting that in years considered, the relation of interest is only quadratic with a reversed-U centered in correspondence of the lower values of the independent variable. Standard deviation can no longer be considered significantly different from 0. To conclude, model (7) allows to control for year specific shocks; the linear component of precip-itation confirms its positive impact and standard deviation of temperature its negative impact, both statistically significant effects.
5.1.1
Discussion
Results on maize reported above are in line with the literature. The most interesting effect emerged in the analysis is the quadratic impact of temperature and precipitation on the productivity of maize. In other words, an increase in temperature and precipitation in the growing season of the plant is not harming per se, but harming once a particular threshold is reached. Not only, although the plant can
5.1. Maize 29 ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1.0 −0.5 0.0 0.5 1.0 0 1 2
Temeprature Deviation from Long Trend Growing Season Maiz e Y ields Gro wth Rate
FIGURE5.1: Maize yields growth rate on temperature, scatter plot.
apparently benefit from an increase in temperature, the variability of the latter during the growing season is affecting negatively the productivity of the plant suggesting a scarce resilience of the plant to intra-season variability.
Eventually, accounting for time fixed effects allows to take into consideration year specific shock and/or policies applied in a specific year. The quadratic effect is still clear as it is clear the positive impact of precipitation, an interesting result considering the level of complexity of the model.
Figure5.1represents a scatter plot of yields growth rate for maize on growing season mean
tem-perature (that is already considered in deviation from the long term trends), pooling data for all provinces and all years. For sake of clarity, when the coordinate on the x-axis equals 1, data point indicates that months of growing season of the specific year were on average one degree higher than their long term trends. A quadratic linear function has been fitted and it allows to visualize what is reported in the model. The line slope increases until a certain point and then decrease with a sharper steepness suggesting that the positive effect of increase in temperatures is completely off-set by the subsequent negative one.
The level of refinement of data would not allow to find with extreme reliability thresholds indi-cated as the turning point in which temperature (and precipitation) begins to be harmful to the plant; indeed most of the literature has proved the importance of so called Growing Degree Days (GDD) as
already mentioned in Chapter2in the attempt of spotting the mentioned threshold. Plants growth
and productivity is tightly linked to the cumulative quantity of heat (and precipitations) they receive during the growing season and monthly averages do not allow to have a clear idea of this measure. However, such a clear presence of quadratic effects emerging in a relatively short time span of 12 years can be considered a reliable signal that the relation is present and that should be investigated with more attention.
