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Article

Do Farmers Adapt to Climate Change? A Macro Perspective

1
School of Social Sciences and Humanities, National University of Sciences and Technology, Islamabad 44000, Pakistan
2
Department of Economics, Ca’ Foscari University of Venice, 30123 Venice, Italy
3
Department of Economics, Loyola Andalusia University, 41704 Seville, Spain
4
European Commission, Joint Research Centre, 41092 Seville, Spain
*
Author to whom correspondence should be addressed.
Agriculture 2020, 10(6), 212; https://doi.org/10.3390/agriculture10060212
Received: 7 March 2020 / Revised: 14 April 2020 / Accepted: 21 April 2020 / Published: 9 June 2020

Abstract

Greenhouse gas emissions cause climate change, and agriculture is the most vulnerable sector. Farmers do have some capability to adapt to changing weather and climate, but this capability is contingent on many factors, including geographical and socioeconomic conditions. Assessing the actual adaptation potential in the agricultural sector is therefore an empirical issue, to which this paper contributes by presenting a study examining the impacts of climate change on cereal yields in 55 developing and developed countries, using data from 1991 to 2015. The results indicate that cereal yields are affected in all regions by changes in temperature and precipitation, with significant differences in certain macro-regions in the world. In Southern Asia and Central Africa, farmers fail to adapt to climate change. The findings suggest that the world should focus more on enhancing adaptive capacity to moderate potential damage and on coping with the consequences of climate change.
Keywords: climate change; adaptations; cereal yields; emissions climate change; adaptations; cereal yields; emissions

1. Introduction

Climate change is considered to be an average change in weather patterns in the long-term sense, while climate variability refers to the fluctuations in weather patterns in the short term. Scientists and economists have come to a consensus opinion that agricultural production and crop yields are at risk due to variation and change in the climatic factors [1]. Crops are hit by droughts, floods, heavy or low levels of rainfall, humidity, decreasing water resources, and increasing windstorms. The climatic variability and change could create a shortage of food production in the future, especially in developing countries, which having fewer resources and are lagging far behind in terms of crop yields. It is expected that the world may face the severe problem of food scarcity in the coming years due to climate change. Climate change is becoming a threat to the Sustainable Development Goals (SDGs). The SDGs focus explicitly on food-related issues by seeking to end hunger, achieve food security by fighting against food scarcity and improving nutrition, and promote sustainable agriculture. SDGs also pay particular attention to poverty reduction, for which agriculture and food play a key role in developing countries [2].
A growing body of economic literature has focused on the impacts of climate change on the agriculture sector at a macro level [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. The common finding of these studies is that climatic changes adversely affect crop yields. However, these studies ignore the farmers’ long-term adapting behaviors, resulting in overestimations of the damages caused due to climate change. The adaptive capacity is the ability of a system to adjust to climate change, including climate variability and extremes, to dampen potential damage, take advantage of opportunities, and cope with undesired negative consequences [1]. The adoption of new technologies, such as drought-tolerant seeds, and changing farm practices, such as sowing dates, are moderating the impacts of climate variability and change on crop yields [19,20,21]. However, this capability is contingent on many factors: the farmer’s characteristics, the market structure, government policies, geographical and social conditions, education, etc. The farmers who have more access to credit facilities, for instance, can buy hybrid seeds and more advanced machinery [22]. A better level of education helps them to learn about the latest farming techniques; improved infrastructure gives them the advantage of easier access to markets; and an advanced technology and investment in research can mitigate the potential damages of climate change. The geographical conditions are also important because the cost of adaptation is too high for those countries that are already facing high temperatures or are more vulnerable to climate change.
There are numerous studies that have empirically investigated the impact of the farmers’ adaptation at the micro or farm level [23,24,25,26]. This indicates that some farmers are adapting, while other’s have failed to adapt to climate change, depending on the individual farmer’s characteristics. However, little attention has been paid to macroeconomic policies and the social indicators of a country that provide support for adaptation, such as the government spending in the agriculture sector, research and development, education and training schemes, policy relevance to agriculture, information availability, and climate risk identification. Such macro-level factors that enable farmers to adapt to climate change are missing in the existing literature. Developed countries are providing such services to their farmers, to facilitate them to make adaptations to climate change; however, the developing countries are lagging far behind and it can be difficult for them to adapt [27,28,29].
However, it is difficult to capture the concept of adaptations because it includes government policies and the behavior of farmers. Thus, assessing the actual adaptation potential at the macro level in the agricultural sector is an empirical issue. Thus, the objective of this study is to investigate whether farmers adapt to climate change or not. To achieve this, a panel data method with cross-country data is employed, with data ranging from 1991 to 2015, and including 55 developing and developed countries. Countries were grouped into eight regions; namely, Southern Europe, Central and West Europe, Northern Europe, Northern Africa, Central Africa, Southern Africa, Southern Asia, and Southeast Asia. The growing degree days and cumulative precipitation as climatic factors were used in the first difference estimation model to check the impact of climate change on cereal yields. Then, the lag effects of climatic variables were introduced to capture the adaptation to climatic change.
Several studies can be found in the literature which examine the impact of climate change on agriculture. All these studies use different models which have both advantages and disadvantages.
Most of the scholars analyzed, that the impact of climate change is solely on the production, and affects some major crops, i.e., cotton, maize, rice, and soybeans, which are termed “crop stimulation models.” The analysis of these models is mostly limited to the crop physiology and its productivity when exposed to different climate conditions [30,31]. Due to the emphasis on the change in the physiology and ecosystem of the crop and soil, they are considered agriculturally oriented. In the crop stimulated model, the management of crops are considered fixed, and no attention is paid towards the farmer’s behavior. These models are applicable for major crops at particular places, and their adjustment is limited to the grain crops [32].
Some scholars’ work has indicated how climate change impacts the yields, and how yields of the crops are sensitive to the changes of climatic patterns; for this purpose most studies have used the production function approach [33,34,35,36,37]. The approach of this model is based on the fact that climate and soil are explanatory variables in the model, and that their role is assessed in the production function.
In the formulation of the production function approach, the economic benefits gained from the crops are not considered a priority; instead, they are given secondary consideration and treated in a simple way [38]. The economic consideration is discussed later in some studies, which have touched on the economic aspect in the production function [39,40]. The economic factor of climate change has also been found in the analysis given by agroeconomic models by applying mathematical programming [41,42].
The major drawback found in the production function approach is that it focuses only on crops and its site-oriented model. It has analyzed and formed the hypothesis of the uneducated farmers; in this model it does not consider that farmers believe in the adoption of the strategies which help them to cope with change in climate; i.e., farmers replace those crops with others which are more sensitive to the climate [43,44].
When this limitation of the production function approach could not comply, another model was formulated named the Ricardian model in 1994 by Mendelsohn; in this model the climate was considered the black box in the agriculture sector. This model is used to assess the relationship that exists between outcomes of the agricultural land which a farmer utilizes and climate, which includes cross sectional data, analyzed by repressors and control variables. This model is implicitly considered as the farmer’s adaptation model [32].
The weakness of this model, as discussed above, is that the efforts of the farmer cannot be determined in this model, and to overcome this weakness the farmer’s efforts are assessed by the mathematical programming developed in the same model [41,45,46]. The main focus was the irrigation of the land [47]. This specification of the models also has limitations, such as the fact that it only considers hypothesized and simulated strategies, which cannot be correct. The most recent application of this model is the addition of the positive mathematical programming method commonly known as positive mathematical programming [48,49].
In recent times many studies have been conducted that show how the limitation of the Ricardian model can be overcome by considering the survey from a farmer’s point of view and by applying econometric models. This application of the model uses the adopted strategies of the farmer and its explanatory variables [20,24,50,51]. The adaptation to climate change was first considered in agriculture modeling in the pioneering works of [28,52,53,54,55,56] in terms of the decision to adapt and examine the impact of climate change in presence of adaptations.
The works of [57,58], empirical and theoretical, considered adaptation in the form of different types of technology adoptions and preventive measures. The technologies under consideration and preventive measures of adaptations are different across studies; therefore, the measures of the impact of adaptations are not comparable. A few comprehensive studies covering multiple regions show that the results of adaptation are considerably different in developed and developing countries, with little gain or net decline in the agricultural production in the developing countries, and net gain in the developed countries [27,28,55,58,59].
Climate changes are unevenly distributed throughout globally, penalizing some parts of the world more than others; some areas are getting benefits from changes in precipitation [3,56]. Undoubtedly, the adaptation practices are related to knowledge and perceptions about the climate change. There is a need for greater investment in research and implementing adaptation strategies to mitigate the risks of climate change [60,61].
Summing it up in a nutshell, just as the impacts of climate change vary from one region to another, the adaptation and its consequences also vary depending on the factors ranging from farmer’s characteristics to socioeconomic factors. These factors either limit or improve the farmer’s capability to move towards adaptations in order to avoid negative impacts or benefits from climate change. The farmer’s decision to move towards adaptations hinges on the perceptions of climate change and the benefits of using new technology, inputs, or techniques requiring investment. The developed countries generally have developed markets for agricultural inputs, including dissemination of information which helps farmers to move towards adaptions and invest in new technology. On the other hand, in developing countries, markets are not developed for the agricultural inputs and the farmers are relatively unaware of the available technologies to avoid the negative impacts of climate change.
The study proceeds as follows. Section 2 presents the material and methods. Section 3 is about results and discussion, and the final section concludes the study.

2. Materials and Methods

2.1. Climate Change

We used the production function approach to check the impacts of climate change and adaptations on cereal yields. The cereal yields (kg/ha) are related to climatic and non-climatic input variables. The vector of climatic variables is characterized here by temperature and precipitation (we also used the humidity as a climatic variable, but due to the problem of multicollinearity, we dropped it), while the non-climatic vector is related to capital stock and labor force. The use of capital stock is important in agricultural production, especially in case of the developing countries. The regression is as follows:
y i t = β 1 ( G D D i t ) + γ 1 ( P r i t ) + Π 1 ( K i t ) + Π 2 ( L F i t ) + β i + α t + ε i t ,
where y is cereal yields, G D D is growing degree days, P r is precipitation, K is capital stock, L F is the labor force in the agriculture sector, β i is the time-invariant and individual fixed effects, and α t is the time-fixed effect.
When investigating the impact of change, it makes sense to look at the first-order differences [62]. A panel first difference estimation model has been employed that incorporates a set of climatic and non-climatic inputs. This regression method has three advantages. First, it allows us to capture the oscillations in weather patterns; second, it easily addresses the problem of omitted variables in the panel data [63]; and third, it eliminates the time-invariant term and individual country fixed effects. The impact of climate change on cereal yield is therefore estimated as follows:
Δ y i t = β 1 ( Δ G D D i t ) + γ 1 ( Δ P r i t ) + Π 1 ( Δ K i t ) + Π 2 ( Δ L F i t ) + α t + ε i t ,
where Δ indicates the first difference, i index stands for country and t for year, α t is the time dummy variable, and ε is the residual term. (The country’s dummy variable is not included in the regression, as it is a first-difference estimator. Inter-country differences cancel out).

2.2. Adaptation to Climate Change

The concept of modelling adaptation is not new in the literature. Helson was among one of those who developed a quantitative model of adaptations [64]. More recently, Menz and Korhonen et al. [65,66] investigated the income adaptation by including lagged income into the life satisfaction equation. To check the impact of growing degree days and precipitation on cereal yield, it is important to understand that both the indicators of climate change are noticeable, gradually. Therefore, it is obvious to include the lags of climate change indicators; otherwise, their impacts will remain overestimated. Thus, the current study critically attempts to include lagged or past values of growing degree days and precipitation so that the truly representative impacts of climate change adaptation may be revealed. By doing so, the model of oscillations in weather patterns is granted. This modelling approach makes this study unique in the literature of climate change adaptation in the agriculture sector because no other method can appropriately estimate the impact.
To investigate whether farmers adapt to climate change or not, we include the lag of growing degree days and precipitation in the difference equation by following the Menz [65] and Korhonen et al. [66]. The model is as follows:
Δ y i t = l = 0 n β i , t l ( Δ G D D i , t l ) + k = 0 n γ i , t l ( Δ P r i , t l ) + Π 1 ( Δ K i t ) + Π 2 ( Δ L F i t ) + α t + ε i t
where l is the lag length of climatic variables, and the β i , t and   γ i , t coefficients represent the first-year effects of growing degree days and precipitation on cereal yields, respectively. The sum of the coefficients of growing degree days, β i , t + β i , t 1 + β i , t 2 + + β i , t n , gives the full effect of growing degree days. Similarly, γ i , t + γ i , t 1 + γ i , t 2 + + γ i , t n gives the full effect of precipitation. We set the null hypothesis of lag-independent climatic variables, β i , t 1 + β i , t 2 + + β i , t n = 0 and γ i , t 1 + γ i , t 2 + + γ i , t n = 0 . If the null hypotheses are accepted, then the cereal yield is affected by neither changing the growing degree days nor the precipitation, and resultantly, the farmers are adapting to climate change. This could be interpreted as the result of adaptive behavior, making the output level immune to meteorological contingencies.

2.3. Data

Data on the climatic variables (growing degree days (GDD) and cumulative precipitation (pr) were obtained from the World Bank Climate Knowledge Portal [67], for the period of 1991–2015, of 55 countries (The World Bank Climate Knowledge portal provide data at the country level with global coverage). GDD is the sum of heat that a crop receives over the growing period above the lower threshold. The crop-specific upper and lower thresholds are still in debate. Following [16,18,68], the study used 8 °C as the lower threshold. The growing degree days are calculated from the average monthly temperature as follows:
g ( T ) = { 0               i f   T 8 T 8       i f   T > 8  
Data on the cereal yields by country, on capital stock in agriculture and on labor force were retrieved from the Food and Agriculture Organization [69] database for the same time period. Several summary statistics for each country are reported in Appendix A Table A1.

3. Results and Discussion

3.1. Impacts of Climate Change on Cereal Yields

The results indicate that climate variability strongly affects the cereal yields. An increase in the growing degree days is negatively correlated with the cereal yields in all regions, except for Southeast Asia (Table 1). The largest impact is estimated for Southern Europe and Central Africa, at 0.74 and 0.73, respectively, followed by Northern Africa (0.55). As expected, an increase in precipitation has a positive effect on the cereal yields only in Southern Europe, Northern Africa, Central Africa, Southern Africa, and Southeast Asia. However, the impact of increasing precipitation is significantly negative in the Central, Western, and Northern European regions because the above-average rain causes an excess of moisture in the soil which decreases the cereal yield in these regions. The major cereal crop in these regions is wheat which requires less water. The impact of an increase in precipitation is also found to be negative in the Southern Asia region. The reason for the negative impact is a lack of water infrastructure, which results in the flooding of the river basins, especially when there is more rain, particularly in Bangladesh, India, and Pakistan, due to rivers flowing from the top of the Himalayas down to plain irrigated land. The cereal yields are also positively affected by changes in non-climatic explanatory variables that include labor force and capital stock, which are significant in some regions.

3.2. Adaptation to Climate Change

The lags of the first differences are introduced in the model to check whether farmers adapt to climate change (Equation (3)). The results indicate that the current impacts of changes in growing degree days are significantly negative in all regions except Northern Europe (Table 2). The lag effect of growing degree days is insignificant in Southern Europe, Central and Western Europe, Northern Africa, South Africa, and Southeast Asia.
Thus, the null hypothesis, γ i , t 1 + γ i , t 2 = 0 , cannot be rejected. This indicates that farmers are taking adaptation measures to change the number of growing degree days. However, Southern Asia and Central Africa are failing to adapt because of the lag effect on growing degree days is significantly negative in these regions. Thus, we reject the null hypothesis of adaptation. Notwithstanding, in Northern Europe, the absolute value of second and third-year lag coefficients of growing degree days (0.11) is statistically significant, positive, and different from zero. This indicates that the Northern European farmers are benefiting from an increase in the growing degree days. The reasons for improvements in the Northern Europe are mainly related to prolonged growing seasons, higher minimum winter temperatures, and an extension of the frost-free period [70].
The first-year impact of precipitation is positive in all the regions except for the Northern, Central, and Western Europe. However, the lag effect of precipitation is significant in the Central Africa, Southern Asia, and Southeast Asia regions, which indicates that these countries have failed to adapt to changes in the precipitation patterns. This is mainly due to their geographical location and dependency on precipitation. Furthermore, they belong to the developing world and it is difficult for them to cope with the changing precipitation patterns due to lack of famers’ training, social and human capital, and credit facility [29].
The developed countries are adapting to climate change because in these countries’ the government is paying attention to the agriculture sector, such as spending on research and development, education and training, policy relevance to agriculture, information availability, and climate risk identification. The adaptation and its consequences also depend on the factors ranging from farmer characteristics to socioeconomic factors. These factors improve the farmer’s capability to move towards adaptations in order to avoid negative impacts; meanwhile, developing countries are lagging far behind in providing such levels of services to their farmers, so it is difficult for them to adapt. South Asia and Southern Africa are two regions highly sensitive to climate changes, and hence demand more adaptation practices.

4. Conclusions

Given the importance of climate change and farmers’ adaptation, the present study examines the impact of climate change on the cereal yields for 55 developing and developed countries. The present study divides the selected countries into eight regions. The estimated results indicate that the cereal yields are affected in all regions by the change in the growing degree days and precipitation. The adaptation regression model has been used. This approach considers the lag effects of the climatic factors. It is found that the farmers of the South Asian and Central African countries are failing to adapt to the changes in growing degree days and precipitation. Moreover, the Southeast Asian countries are sensitive to the change in precipitation. The Central and Western European, Southern European, Northern European, North African, and South African countries are adapting to climate change. However, the Northern European countries are growing more crops due to the increase in growing degree days. Improvements in Northern Europe are mainly related to prolonged growing seasons, higher minimum winter temperatures, and an extension of the frost-free period. The results of this study also indicate that regions of high social and economic status, and the ones that are less vulnerable to climate change, are adapting to climate change. Countries that are developing and vulnerable to climate change are failing to adapt.
This study suggests that the world should focus on adaptive capacity to moderate potential damage and cope with the consequences of climate change and variability in the agriculture sector. The adoption of new technology and improved seeds, cultivating more land, relaxing trade barriers, and changing farms’ practices could be useful for mitigating the negative impacts of climate change and variability.
Notwithstanding, the developing countries need to take urgent adaptation measures to minimize the losses associated with climate change and to feed the growing population, especially in Central Africa and South Asia. In particular, there is a need to improve the water infrastructure and storage capacity in South Asia. Moreover, there is a global need to decrease the GHG emissions immediately.
Further, to achieve the SDGs, countries and communities need to develop adaptation solutions and implement actions to respond to the impacts of climate change that are already happening, as well as prepare for future impacts. Successful adaptation not only depends on the governments but also on the active and sustained engagement of stakeholders, including national, regional, multilateral, and international organizations; the public and private sectors; civil society; and other relevant stakeholders, and on effective management of knowledge.
One caveat of this study is that we have used only two climatic variables, precipitation and temperature, while ignoring the other variables due to non-availability of data at the country level, such as wind speed and humidity. Thus, the model may underpredict the situation. It is important to examine farmers’ adaptation strategies and their impacts on each crop’s yield at a country level. However, this topic is left for future research.

Author Contributions

Conceptualization, S.A. and R.R.; methodology, S.A.; software, S.A.; validation, S.A., R.R. and M.S.; formal analysis, S.A.; investigation, S.A.; resources, S.A.; data curation, S.A.; writing—original draft preparation, S.A.; writing—review and editing, S.A.; visualization, F.J.; supervision, R.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Summary statistics.
Table A1. Summary statistics.
CountryCereal Yields
(Kg per Hector)
Growing Degree Days
(Celsius)
Precipitation
(Millimeters)
MeanMaxMinMeanMaxMinMeanMaxMin
South Europe
Greece395645163553254029052203635869398
Italy506357094307200722931760635869397
Portugal3006460618052687290723288411374528
Spain304140471729230125471943594797437
Turkey248333071922208623691811570686437
Central‒West Europe
Austria588170565088989121475111721346927
France698575706125169019961363840982677
Germany657380505335136215851063722894561
The Netherlands7919907370631345163910958061058564
Switzerland6268704550869011331613152318811159
United Kingdom691879806215910111167412481422967
North Europe
Denmark60236884431411091414835740914501
Finland335137602402611820423560679473
Norway38304801281030546316410831233885
Sweden471860533359488686282670845572
North Africa
Algeria122318137415651590552338310655
Egypt691575565613551161424991304520
Libya666836621540058824964406029
Morocco11952140266367439853248304530195
Tunisia13941877893456047534072266415181
Central Africa
Angola5799812685056536949299811111837
Cameroon15211893959617763625936156117641338
Central African Rep.10431674869628865965584135814701232
Chad702934501705974836416339415259
Congo779811765601862185786147316391309
Cote d’Ivoire166522701050679569856658135017371113
Ghana14391830104271527322698211491318939
Guinea140115141151662468086409168819751474
Kenya1597191812426222642859956701018448
Mali11541800738763379407317319398256
Mauritania90716786377475779971829713669
Niger390551267725475696869176227134
Nigeria13111598109470137234675511391341919
Senegal9471376651753277347294716921502
Somalia6001190410695271406824276374224
Tanzania140120438585476563752719741173788
Uganda16642056120456856105528312481461951
South Africa
Madagascar250037721875570965495308142117091250
Malawi1493246748152465459506610071173733
Mozambique7211191177579958226087301630332853
Namibia381620159463048744473269420130
South Africa29634894944370238993407462605312
Zambia191130077635205558150139531107759
Zimbabwe8581502309506355074866633891421
South Asia
Bangladesh350946172475631166186101224029001787
India24332969192660416279583910181160835
Pakistan241730011805458248094228313427192
Sri Lanka341439742902700971996870171520971397
Southeast Asia
Cambodia229933771301712673206929188726711446
Indonesia434853063816662767936522286836052195
Malaysia327139482787648466586379310437952426
Myanmar32483798265855615819535915662288953
Philippines283936372042653067146429253532531894
Thailand281532592119681770706592154218591326
Vietnam442856013006605763335801186222431600

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Table 1. Impacts of climate change on cereal yields.
Table 1. Impacts of climate change on cereal yields.
VariableSouthern EuropeCentral &Western EuropeNorthern EuropeNorthern AfricaCentral AfricaSouthern AfricaSouthern AsiaSoutheast Asia
∆GDD−0.741 ***
(0.000)
−0.236 ***
(0.000)
0.145 ***
(0.001)
−0.553 **
(0.033)
−0.737 *
(0.089)
−0.463 **
(0.033)
−0.340 **
(0.011)
−0.226 **
(0.018)
∆Pr0.162 ***
(0.009)
−0.236 ***
(0.000)
−0.232 ***
(0.000)
0.155 ***
(0.009)
0.236 ***
(0.000)
0.677 ***
(0.000)
−0.050 ***
(0.001)
0.037 **
(0.017)
∆K0.233 **
(0.023)
−0.043
(0.383)
−0.007
(0.894)
0.706 ***
(0.000)
0.070 ***
(0.006)
0.088 **
(0.012)
0.097 ***
(0.003)
0.102 ***
(0.000)
∆LF0.023
(0.908)
0.053
(0.616)
0.212 *
(0.097)
0.309 ***
(0.009)
−0.103
(0.665)
0.024
(0.877)
0.119
(0.173)
−0.059
(0.203)
N1201449612040816896168
R-squared0.520.510.740.610.450.260.670.43
F-stats3.694.637.135.413.4014.645.109.52
Notes: ***, ** and * denote significance at the level of 1, 5 and 10% respectively. Source: Author’s own calculations.
Table 2. Climate change and adaptation.
Table 2. Climate change and adaptation.
VariableSouthern EuropeCentral & Western EuropeNorthern EuropeNorthern AfricaCentral AfricaSouthern AfricaSouthern AsiaSoutheast Asia
∆GDDt−0.762 ***
(0.0008)
−0.272 ***
(0.003)
0.157 ***
(0.001)
−0.114 *
(0.089)
−0.984 *
(0.069)
−0.013 *
(0.098)
−0.685 ***
(0.000)
−0.168
(0.442)
∆GDDt−10.273
(0.270)
−0.041
(0.693)
0.141 ***
(0.000)
0.113
(0.944)
−1.220 **
(0.050)
0.684
(0.278)
−0.367 ***
(0.050)
−0.019
(0.929)
∆GDDt−20.219
(0.303)
0.013
(0.884)
−0.026 **
(0.529)
0.875
(0.545)
−0.451
(0.389)
−0.357
(0.590)
−0.008
(0.730)
−0.005
(0.978)
∆Pr0.175 **
(0.022)
−0.264 ***
(0.000)
−0.223 ***
(0.000)
0.235 *
(0.099)
0.222 ***
(0.000)
0.278 *
(0.058)
0.044 **
(0.027)
0.046 **
(0.056)
∆Prt−10.085
(0.320)
−0.058
(0.466)
0.063
(0.1850)
0.174
(0.278)
−0.056 *
(0.418)
0.090
(0.557)
0.092 **
(0.012)
0.060 **
(0.017)
∆Prt−2−0.156 **
(0.049)
0.049
(0.520)
−0.109 ***
(0.009)
0.082
(0.553)
−0.145 **
(0.016)
−0.362 ***
(0.009)
0.009
(0.712)
0.028
(0.248)
∆K0.261 **
(0.014)
−0.034
(0.521)
0.027
(0.664)
0.690 ***
(0.000)
0.073 ***
(0.005)
−0.067
(0.151)
0.092 ***
(0.004)
0.140 ***
(0.000)
∆LF−0.215
(0.395)
0.084
(0.486)
0.053
(0.650)
0.394 *
(0.096)
0.009
(0.968)
−0.159 ***
(0.004)
0.247 ***
(0.008)
0.053
(0.446)
N1101328811037415492154
R-squared0.590.5380.900.450.510.4780.770.44
F-stats4.004.10219.062.3273.282.606.813.68
∑∆GDD−0.268−0.3010.2720.874-2.6520.313−1.060−0.193
F-stats
(p-value)
0.250
(0.618)
1.652
(0.201)
12.79 *
(0.000)
0.053
(0.817)
3.178 *
(0.075)
0.055
(0.814)
6.912 **
(0.011)
0.128
(0.720)
∑∆GDD (lags)0.493−0.0280.1150.988−1.6670.327−0.375−0.025
F-stats (lags)
(p-value)
1.518
(0.221)
0.027
(0.869)
7.561 ***
(0.008)
0.131
(0.716)
2.752 *
(0.098)
0.102
(0.784)
4.093 **
(0.048)
0.004
(0.946)
Adapt to GDDYesYes-YesNoYesNoYes
∑∆CPr0.104−0.273−0.2680.4930.0200.0060.1460.136
F-Stats
(p-value)
0.267
(0.606)
2.133
(0.147)
5.535 **
(0.022)
2.018
(0.159)
0.018
(0.89)
0.0003
(0.984)
4.068 **
(0.048)
6.652 ***
(0.010)
∑∆Pr (lags)−0.071−008−0.4530.2570.202−0.2720.1020.088
F-Stats (lags)
(p-value)
0.250
(0.618)
0.004
(0.947)
0.296
(0.588)
0.964
(0.329)
3.178 *
(0.075)
1.114
(0.293)
3.004 *
(0.089)
4.723 **
0.031
Adapt to PrYesYesYesYesNoYesNoNo
Notes: ***, ** and * denote significance at the level of 1, 5 and 10% respectively. Source: Author’s own calculation.
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