CLIMATE CHANGE AND ADAPTATION ON SELECTED
CROPS IN SOUTHERN PHILIPPINES
Norma U. Gomez, PhD.
ABSTRACT
This study assessed the vulnerability of the farmer-respondents in Southern Philippines,
specifically Region XI and XII to climate change. This study conducted an empirical analysis of
the impact of climate change on maize (Zea mays), banana (Musa sapientum), and durian (Durio
zibethinus) production. Furthermore, it estimated the determinants of adaptation to climate
change and its corresponding effect on farm productivity. The analysis used primary data from
541 farmer-respondents producing maize, banana, and durian in the six provinces, eighteen
municipalities of the sample areas.
Based on the probit estimate results, farmers adaptation decisions were influenced by information
about future climate change conditions, social capital, access to formal extension and farmer-to-farmer
extension. We also found from the Stochastic Frontier Estimation in the production function that climate
change adaptations exerted a significant impact on farm productivity. It helped in coping with the
adverse effects and risk of climate change while increasing agricultural productivities of the farmerrespondents. Strengthening of the Department of Agriculture extension program to the farmers in the
study sites and strengthening the social network or capital will be very helpful in equipping the farmers in
the study sites against the adverse effects of climate change.
Key words: Climate change, adaptation, probit estimate, production function, stochastic frontier
estimation, Southern Philippines
1
1. INTRODUCTION
The Philippine agriculture sector contributes 12.31% of 2010 Gross Domestic Product
according to a World Bank report. The biggest contributors to growth were palay, maize, poultry
and livestock (NSCB, 2010).
The agriculture sector employs 36.1% of the population
(Anonymous, 2009. UN data/country profile/Philippines). The contribution of the agricultural
sector to the total economy, however, is challenged by its vulnerability to climate change. It is
because agriculture is arguably the most important sector of the economy that is highly
dependent on climate. Agriculture systems are vulnerable to variability in climate, whether
naturally-forced, or due to human activities.
Climate change extremes have been manifested in the Philippines such as floods and
droughts. The disasters these have brought have caused losses amounting to billions of pesos.
The decline in production and productivity will possibly threaten the country’s food security.
Climate change is a serious risk to poverty reduction and threatens to undo decades of
development effort. Climate change will generally reduce production potential and increase risk
of hunger and starvation.
Vulnerability can be viewed as a function of the sensitivity of agriculture to changes in
climate, the adaptive capacity of the system and the degree of the exposure to climate hazards
(IPCC 2001b, p.89). Luers et al. (2003) proposed a method for quantifying vulnerability (given
the system, outcome variable, and stressor of concern) based on exposure, sensitivity, and
adaptive capacity. In the IPCC report, exposure is defined as “the nature and the degree to which
a system is exposed to significant climatic variations,” sensitivity is defined as “the degree to
which a system is affected, either adversely or beneficially; by climate-related stimuli,” and
2
adaptive capacity is defined as “the ability of a system to adjust to climate change (including
climate variability and extremes), to moderate the potential damage from it, to take advantage of
its opportunities, or to cope with its consequences.”
Turner et al (2003) recognized that
vulnerability is determined not by exposure to hazards (perturbations and stresses) alone, but also
depends on the sensitivity and resilience of the system that is experiencing such hazards. These
authors developed an integrated conceptual framework of vulnerability built on these three major
dimensions of vulnerability, namely, exposure, sensitivity and adaptation/resilience.
The level of vulnerability of different social groups to climate change is determined by
both socio-economic and environmental factors. The selection of indicators was done through an
extensive review of previous reports; in particular, the socio-economic factors most cited in the
literature and are used as indicators in this study included income and education (Cutter et al.
2003; Adger 1996; Haan et al 2001), social networks (Vincent et al. 2010; Gbetibouo et al. 2009;
Deressa et al., 2008; Ford, Barry and Wandel, 2005), access to formal and non-formal credit,
farm income and farm size (Gbetobouo et al., 2009), perception about preparedness, and amount
of fertilizer used (www.icrisat.org). The environmental attributes used as indicators in this study
included change in temperature and precipitation (Gbetibouo et al. 2009 and Deressa et al. 2008).
There are many conceptual methodological approaches to vulnerability analysis. The
major ones include the socioeconomic, biophysical, and integrated approaches. The biophysical
approach assesses the level of damage that a given environmental stress causes on both social
and biological systems. The biophysical, or impact assessment, approach is mainly concerned
with the physical impact of climate change on different attributes, such as yield and income
(Fussel and Klein, 2006).
Although very informative, the biophysical approach has its
limitations. The major limitation it focuses mainly on the physical damages, such as yield,
3
income, and so on. For example, a study on the impact of climate change on yield can show the
reduction in yield due to simulated climatic variables such as increased temperature or reduced
precipitation. In other words, these simulations can provide the quantities of yield reduced due
to climate change but they do not show what that particular reduction means for different people.
There had been extensive research on the impacts of the climate change, but little
attention was devoted to the socio-economic aspects of the vulnerability and risks of climate
change in major food crops such as corn, durian, and banana in Region XI and XII, Philippines.
To fill this empirical gap, this study carries out a socio-economic analysis of the vulnerability
and risk of climate change in Regions XI and XII agricultural sectors at the farm level. To bring
more insights into adaptation strategies that are crucial to coping with climatic variability and
change, this study also investigates key factors that govern farmers’ decisions to use climate
change adaptations and the impact of this action on food production. The objectives of this
research were to measure and analyze the vulnerability of the farmers to climate change, to
capture farmers’ perceptions and understanding of climate change as well as their approaches
and adaptations, to investigate key factors that govern farmers’ decisions to use climate change
adaptations and the impact that conveys action on food production, and to provide estimates of
the determinants of adaptation to climate change and the implications of these strategies on farm
productivity.
4
2. DESCRIPTION OF THE STUDY AREA
The study was undertaken in the major areas of maize, durian, and banana in Southern
Philippines, specifically these are in Region XI and XII. The grouping of sample areas are
shown below:
Region XI Provinces: 1) Davao del Sur, 2) Davao del Norte, and 3) Compostela Valley.
The total land area is 11,098.43 sq. km. Region XII Provinces: 1) North Cotabato, 2) South
Cotabato, and 3) Sultan Kudarat. The total land area is 14, 819 sq. km. The study areas are
depicted in Figure 1.
3. THEORETICAL FRAMEWORK
3.1. The Econometrics Approach
The theoretical model presented here was adopted from the work of Yesuf et al (2008).
We framed our analysis using the standard theory of technology adoption, wherein the problem
facing a representative risk-averse farm
household was to choose a mix of climate change
adaptation strategies that will maximize the expected utility from final wealth at the end of the
production period. Assuming that the utility function is state independent, solving this problem
would give an optimal mix of adaptation measures undertaken by the representative farm
household, as given by


Aht  A x hth , x htl , x htc ;   E ht
(1)
Where A is household h adaptation strategy at time t; x hth , x htl , x htc are household characteristics,
land and other characteristics, and climate variables, respectively; β is the vector of parameters;
5
and  ht is the household-specific random error term. Households will choose adaptation strategy
1 over adaptation strategy 2 if and only if the expected utility from adaptation strategy 1 is
greater than from adaptation strategy 2 – that is, EU  A1   EU  A2  .
A probit regression fit our data to estimate determinants of adaptation as specified in
equation ( 1 ). This study’s central focus was to investigate whether climate change and
adaptation have any impact on food production, with adaptation measured by a dummy variable
entered into a standard household production function, y ht ;


y ht  f x hts , x htc , Aht , y   ht
(2)
Where x hts , x htc , rht are conventional inputs, climatic factors, and climate change adaptation
measure, respectively; y is a vector of parameters; and  ht is a household-specific random error
term.
4. METHODOLOGY
Sampling Procedure. Maize, durian, and banana farmers were the target respondents
for the study. A total of 541 farmers-respondents were picked from the study areas. The
sampling technique employed was multistage stratified random sampling technique. As the
study area was stratified first by province then by municipality and lastly by barangay. The first
stage involved purposive selection of the vulnerable or barangays prone to climate change. The
second stage was random sampling through selection of 541 farmers in the study areas.
Source of Data. The project made use of both primary and secondary data. The
secondary data used were from the socio-economic profile report of the six provinces and the
base maps were from the different Provincial Agriculture Offices and Provincial Planning
6
Offices. Monthly precipitation and temperature data for the past 15 years were taken from the
eight meteorological stations distributed across the regions which served as basis for the
interpolation procedure (precipitation and temperature). The eight meteorological stations were
PAGASA Synoptic Station in Gensan Airport, General Santos City; Hinatuan, Surigao del Sur,
Butuan City Airport, Cagayan de Oro, Davao City, Davao Airport, Sasa, Davao City; PAGASA
Agromet Station, USeP, Apokon, Tagum City; PAGASA Bukidnon, Malaybalay City; and DACEMIARC for Upland Plain Agromet Station in Balindog, Kidapawan City. The interpolated
precipitation and temperature were used as variables in the probit regression model.
Primary data were taken from the personal interview with the farmers making use of pretested questionnaires and interview schedule designed specifically for maize, durian, and banana
farmer respondents.
Data Analysis. The data obtained from the field survey were subjected to analysis using
inferential statistics.
A probit regression was used to estimate the determinants of adaptation. The Probit
regression was utilized to determine the factors affecting adoption of adaptation measures against
climate change. The decision to employ adaptation measures was assumed to a function of
household characteristics (i.e. age, gender, marital status, literacy, farming experience, and
household size), formal and informal institutional support (formal extension, farmer-to-farmer
extension, access to credit, and social capital), climatic factors (i.e. precipitation level,
temperature level, information about future climatic conditions) and the farm household’s agroecological setting. For this analysis, the data was run using data analysis and statistical software
called (STATA).
The estimated production function made use of the Stochastic Frontier
7
Estimation method.
For this analysis, the data was run using data analysis and statistical
software (FRONTIER version 4.1c).
5. RESULTS AND DISCUSSION
5.1. Climate Change and Adaptation in the Study Sites
In response to long-term actual experience changes in climate, the farmer-respondents in
the study areas have undertaken a number of adaptation measures which were yield related. The
adaptation measures adopted by the farmer-respondents in Region XI included tree planting
(40%), provision of irrigation (18%), drainage or construction of canal (19%), and changing crop
varieties (15%). On the other hand, the adaptation measures adopted by the farmer-respondents
in Region XII included changing crop varieties (55%), tree planting (33%), and intercropping
(30%).
The non-yield related adaptation measures included sought off-farm activities e.g.
driving and carpentry (Table 1).
About 82% of the farmer-respondents in Region XI who did not adopt adaptation
measures in response to climate change indicated lack of money as major reason for not doing
so. This was followed by lack of information as reported by 59% of the farmer-respondents. On
the other hand, 45% of the farmer-respondents in Region XII indicated they did not see the need
to adopt adaptation or counter measures to climate change. Reasons for not adopting included
lack of information (40%) and lack of money (29%). Overall, the predominant reasons of the
farmer-respondents in Regions XI and XII not adopting adaptation measures were lack of
money (59%) and lack of information (51%) as shown in Table 2.
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Results of the study revealed that the farmer-respondents in both Regions XI and XII who
have adopted climate change adaptation measures enjoyed higher average yield per hectare per
year and average gross income per hectare per year than those who did not adopt adaptation
measures. Specifically, the average yield per hectare per year of farmer-respondents in Region
XI was 9,167 kg while those in Region XII was 8,111 kg. In contrast, the average yield per
hectare per year of farmer-respondents who did not adopt adaptation measures in Regions XI and
XII were 5,135 kg and 5,850 kg, respectively. Based on the marginal effect estimates of our
results, farmer-respondents with climate change adaptation measures tended to produce about
4,032 kgs and 2,261 kg (in Regions XI and XII, respectively) or a greater average yield per
hectare than those who did not adopt adaptation measures. On the other hand, the average gross
income per hectare per year of adopters in Regions XI and XII were Php144,914 and
Php120,354, respectively. This was higher compared to non-adopters whose average gross
income per hectare per year were Php80,852 and Php90,027 in Regions XI and XII, respectively
(Table 3). Overall, the pooled data revealed that both the average yield per hectare per year and
average gross income per hectare per year of adopters was higher than the non-adopters.
5.2. Determinants of Climate Change Adaptation
5.2.1. Basic Descriptive Statistics of Sampled Farmer-Respondents
Table 4 shows the basic descriptive statistics of the farmer-respondents in Region XI. The
table reveals that the mean age of farmer-respondents was 48.67 years and had spent schooling
for 9.5 years on the average. The mean number of years in farming was 18.8 years and the mean
household size was 5. On the other hand, in terms of climatic factors, the results revealed that
the average annual rainfall for the past 15 years was 191.44 mm and the average temperature,
9
27.410C. The farmer-respondents in Region XI had an average yield of 8,488.33 kgs/ha/yr. The
amount of fertilizer used averaged at 744 kg/ha/yr while the amount of chemicals used averaged
at 6.4 liters/ha/yr. The farmer-respondents in Region XI used labor at an average of 63 mandays/ha/yr.
Table 5 shows the basic descriptive statistics of the sampled farmer-respondents in Region
XII. The table reveals that the mean age of farmer-respondents was 48.81 years and spent
schooling for 9.91 years on the average. The mean number of years in farming was 22.5 years
and the mean household size, 5. On the other hand, in terms of climatic factors, the results
revealed that the average annual rainfall for the past 15 years was 175.51 mm and the average
temperature, 27.50C. The farmer-respondents in Region XII averaged of 7,769 kg/ha/yr yield.
The amount of fertilizer used is averaged at 930.31 kg/ha/yr while the amount of chemicals used
averaged at 6.78 liters/ha/yr. The farmer-respondents in Region XII used labor at an average of
63 man-days/ha/yr.
The basic descriptive statistics of the sampled farmer-respondents in both regions XI and XII
(pooled data) is shown in Table 6. It revealed that the mean age of farmer-respondents was
48.75 years and spent schooling for 9.75 years on the average. The mean number of years in
farming was 20.71 years and the mean household size, 5. In terms of climatic factors, the pooled
data revealed that the average annual rainfall for the past 15 years was 183.25 mm and the
average temperature 27.460C. The pooled data revealed that the average yield of the farmerrespondent was 8,118.75 kg/ha/yr. The amount of fertilizer used averaged at 839.77 kg/ha/yr
while the amount of chemicals applied averaged at 6.6 liters/ha/yr. The farmer-respondents in
both regions used labor at an average of 63 man-days/ha/yr.
10
5.2.2. Probit Estimate of the Adaptation Regression
Tables 7, 8, and 9 report the estimates of the empirical analysis. Table 7 presents the probit
results of the adaptation regression. The results suggested that information about future climate
change condition, number of relatives in a barangay, and access to formal credit tend to strongly
governed each household’s adaptation decisions in Region XI. The afore said variables were
statistically significant as indicated in the p-value (0.001, 0.008, and 0.04), significant at 0.01,
0.01 and 0.05 alpha level, respectively.
These results are consistent with a similar study by Deressa et al. (2008), which used a
multinomial logit model in the Nile Basin and the study by Yesuf et al (2008) which also used a
probit regression model. Likewise, age, number of years in schooling, household size, and
access to farmer-to-farmer extension were also significant variables that could affect the decision
of the farmers to adopt the adaptation measure against climate change. There was a significant
difference across the size of households. Larger households were more likely to adopt than were
smaller households, highlighting the role of household labor on the adoption decision. Although
age had a negative sign implying that as farmer’s age increased the chance of supporting and
adopting the adaptation measures against climate change decreased. The full model registered an
LR chi2 which was significant at 0.01 alpha level indicating that the full model fitted reasonably
well (As seen in Table 7).
On the other hand, Table 8 shows that access to future climatic condition, access to
formal credit, access to formal extension and marital status of household head were
significant variables that could affect the decision
the
of the farmers to adopt the adaptation
11
measures against climate change in Region XII. The full model registered an LR chi 2 which was
significant at 0.10 alpha level.
The probit estimates of the pooled data revealed that access to future climatic condition,
average annual rainfall for 15 years, access to formal extension, household size, number of years
in schooling, gently slope and age of household head tend to strongly govern each household
adaptation decisions. Age of household head had a negative sign implying that as farmer’s age
increased the chance of supporting and adopting the adaptation measures against climate change
decreased. Furthermore, the average annual rainfall for 15 years also had a negative sign
implying that as the average annual rainfall increased the chance of supporting and adopting the
adaptation measures against climate change decreases (As seen in Table 9). The full model
registered an LR chi2 which was significant at 0.05 alpha level indicating that the full model
fitted reasonably well.
5.2.3. Estimated Production Function
Maize in Regions XI and XII. Table 10 shows the estimated parameters of the
production function of maize in Region XI using the Stochastic Frontier Estimation method. As
shown in the table, all climatic factors and topography of the farm (agro-ecology) and the
production inputs were highly significant, all estimates obtained p-value less than the 0.01 alpha
level.
The results showed that the estimated coefficient for adaptation was positive and
statistically significant. Farmers who adopted climate change adaptation strategies had higher
maize production than those who did not. Based on marginal effect estimates of our results,
households with climate change adaptation measures tended to produce about 4,032 kgs more
crops per hectare than did those who did not take such measures. In other words, the effect of
climate change are expected to
be reduced by such a magnitude if households followed
12
adaptation measures. All the conventional inputs exhibited signs consistent with predictions of
economic theory, and all are statistically significant. As expected, more use of fertilizers,
chemicals and labor tended to increase maize production. Finally, the Gamma coefficient was
also statistically significant at 0.01 alpha level indicating that the stochastic frontier model
reasonably fitted the data.
Table 11 shows the estimated parameters of the production function of maize in Region
XII using Stochastic Frontier Estimation method.
The results showed that the estimated
coefficient for average annual rainfall was negative and statistically significant at 0.05 alpha
level. This implied that too much precipitation affected maize production negatively in Region
XII. In terms of topography ( highland, midland, rolling, and gently sloped) were negative and
statistically significant at 0.05 and 0.10 alpha level.
Higher topography affected maize
production negatively. Inputs like chemicals and labor were statistically significant at 0.01 and
0.05 alpha level, respectively. This implied that more labor and chemicals tended to increase
maize production. Finally, the Gamma coefficient was also statistically significant at 0.01 alpha
level indicating that the stochastic frontier model reasonably fitted the data.
Banana in Regions XI and XII. The estimated parameter of the production function of
banana in Region XI is shown in Table 12. As shown in the table, the estimated coefficient for
adaptation is positive and statistically significant at 0.10 alpha level. This implied that farmers
who adopted climate change adaptation strategies had higher production than those who did not.
All the conventional inputs exhibited signs consistent with predictions of economic theory, and
all are statistically significant at 0.01 alpha level.
As expected, more use of fertilizers,
chemicals, and labor tended to increase banana production. Finally, the Gamma coefficient was
also statistically significant at 0.01 alpha level indicating that the stochastic frontier model
13
reasonably fitted the data.
Table 13 shows the estimated parameters of the production
function of banana in Region XII. The results showed that the estimated coefficient for average
annual rainfall was positively and statistically significant at 0.05 alpha level. This implied that
more precipitation will affect banana production positively in Region XII.
The Gamma
coefficient was also statistically significant at 0.01 alpha level indicating that the stochastic
frontier model reasonably fitted the data.
Durian in Regions XI and XII. The estimated parameter of the production function of
durian in Region XI is shown in Table 14. As shown in the table, the estimated coefficient for
temperature was negative and statistically significant at 0.01 alpha level. This implied that
higher temperature tended to decrease durian production. In terms of topography, highland was
negatively and statistically significant at 0.01 alpha level. This implied that higher topography
affected durian production negatively. On the other hand, labor input was statistically significant
at 0.01 alpha level. This implied that more labor tended to increase durian production. Finally,
the Gamma coefficient was also statistically significant at 0.01 alpha level indicating that the
stochastic frontier model reasonably fitted the data.
Table 15 shows the estimated parameters of the production function of durian in Region
XII.
The results showed that the estimated coefficient for adaptation was
positive and
statistically significant at 0.01 alpha level. This implied that farmers who adopted climate
change adaptation strategies had higher production than those who did not. Furthermore, the
results showed that the estimated coefficient for average temperature was positive and
statistically significant at 0.10 alpha level. This implied that higher temperature tended to
increase durian production. In terms of inputs, amount of chemicals used was negative and
statistically significant at 0.10 alpha level. This implied that more amount of chemicals used
14
tended to decrease durian production.
Finally, the Gamma coefficient is also statistically
significant at 0.01 alpha level indicating that the stochastic frontier model reasonably fitted the
data.
Pooled Data in Regions XI and XII. Table 16 shows the estimated parameters of the
production function of the pooled data in Region XI using Stochastic Frontier Estimation
method.
As shown in the table, the estimated coefficient for adaptation is positive and
statistically significant at 0.05 alpha level. This implied that farmers who adopted climate
change adaptation strategies had higher production than those who did not. Based on marginal
effect estimates of our results, households with climate change adaptation measures tended to
produce about 4,032 kg more crops per hectare than did those who did not take such measures.
In other words, the effect of climate change is expected to reduce by such a magnitude if
households took adaptation measures. The results showed that the estimated coefficient for
average annual rainfall was positively and statistically significant at 0.05 alpha level. This
implied that more precipitation will affect crop production positively in Region XI. In like
manner, the estimated coefficient for labor input was positive and highly significant at 0.01 alpha
level. This implied that more labor tended to increase crop production in Region XI.
Table 17 shows the estimated parameters of the production function of the pooled data in
Region XII. As shown in the table, the estimated coefficient for adaptation is positive and
statistically significant at 0.05 alpha level. This implied that farmers who adopted climate
change adaptation strategies had higher production than those who did not. Based on marginal
effect estimates of our results, households with climate change adaptation measures tended to
produce about 2,261 kg more crops per hectare than those who did not take such measures. In
other words, the negative effect of climate change will be reduced by such a magnitude if
15
households took adaptation measures. In terms of labor input, the estimated coefficient for labor
input was positive and statistically significant at 0.05 alpha level. This implied that more labor
tended to increase crop production in Region XII.
For Overall Pooled Data of Both Regions. Table 18 shows the estimated parameters of
the production function for the overall pooled data of both regions using Stochastic Frontier
Estimation method. As shown in the table, the estimated coefficient for adaptation was positive
and statistically significant at 0.05 alpha level. This implied that farmers who adopted climate
change adaptation strategies had higher crop production than those who did not. Based on
marginal effects estimates of our results, households in both Regions XI and XII with climate
change adaptation measures tended to produce about 3,135 kgs more crops per hectare than did
those who did not take such measures. In other words, the effect of climate change will be
reduced by such a magnitude if households took adaptation measures. The estimated coefficients
for average annual rainfall and average temperature were negative and statistically significant at
0.05 and 0.10 alpha level, respectively. This implied that too much precipitation and high
average temperature tended to decrease crop production in the study sites.
Similarly, the
estimated coefficient for topography, specifically highland was negative and highly significant.
This implied that higher topography tended to decrease crop production.
The estimated
coefficient for labor input was positive and highly significant. This implied that more labor
affected crop production positively in both Regions XI and XII. Finally, the Gamma coefficient
was also statistically significant at 0.01 alpha level indicating that the stochastic frontier model
reasonably fitted the data.
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6. CONCLUSIONS AND IMPLICATIONS
The following points can be inferred based on the results and analysis made in the study:
1) Based on the probit results of the adaptation regression, information about future
climate change condition, number of relatives in a barangay, access to farmer-tofarmer extension, number of years in schooling, household size, and access to formal
credit tended to strongly govern each household’s adaptation decisions in Region XI.
2) For Region XII, access to future climatic condition, and access to formal extension
tended to strongly govern each household’s adaptation decisions. For the pooled
data, the factors which strongly govern each household’s adaptation decisions were
access to future climatic condition, access to formal extension, household size, and
number of years in schooling. This result underlines the need to provide appropriate
and timely information on future climate changes to farmers in order to increase their
level of awareness and preparedness and to alert them to take appropriate averting
actions.
3) The fact that access to credit markets, social ties and networks, government or formal
extension, and farmer-to-farmer extension were significant in the probit model
indicated the role of both formal and informal institutions in addressing the issues of
climate change adaptations in the study sites.
Strengthening of the Department of Agriculture extension program to the farmers in the
study sites and strengthening the social network or capital will be very helpful in equipping the
farmers in the study sites against the adverse effects of climate change.
17
ACKNOWLEDGMENT:
acknowledged.
The helpful comments of the anonymous reviewers are fully
Funding for this research was provided by the Philippine Department of
Agriculture – Bureau of Agricultural Research (DA-BAR).
Figure I. The study sites
18
Table 1. Adaptation measures against climate change by the farmer- respondents in
Regions XI and XII, Philippines, 2011- 2012
Adaptation Measures
Yield Related
Changing crop varieties
Cover cropping
Adapting water and soil
conservation
Mulching
Water harvesting
Intercropping
Tree planting
Drainage or construction of
canal
Changing
planting
and
harvesting schedule
Provide irrigation
Condition the life of plant or
nutritional management
Others
Non- Yield Related
Smoking
Sought off- farm activities
Carboning
Migrated to urban area
Proper segregation of waste
Composting
Others
Region XI
Frequency
(n=214)
%
Region XII
Frequency
(n=240 )
%
Pooled Data
Frequency
(n=547)
%
32
4
15
2
133*
15
55
6
165*
19
33
4
17
18
3
67*
85*
8
8
1
31
40
19
27
11
73*
79*
8
11
4.6
30
33
36
45
14
140*
164*
7
9
3
28
32
38
18
18
8
56
11
10
38
5
18
22
16
9
7
32
54
6
11
9
23
4.2
11
22
9
9
4
31
32
6
6
11
1
1
1
17
5
1
5
.5
.5
.5
8
2
.5
19
17
3
4
24
17
11
8
7
1
2
10
7
5
30
18
4
5
41
22
12
6
4
.8
1
8
4
2
* multiple responses
19
Table 2. Reasons for not adopting adaptation measures as reported by the farmer respondents in Regions XI and XII, Philippines, 2011- 2012
Reasons
1.Lack of Information
2.Lack of Money/ Finances
3.Labor shortage
4.Land shortage
5.Water Shortage
6.Lack of Credit
7.Do not see the need
8.Others
Region XI
Frequency
(n=49 )
%
29*
59
40*
82
2
4
6
12
4
8
6
12
13
27
6
12
Region XII
Frequency
(n=38 )
%
15*
40
11
29
0
0
0
0
1
3
0
0
17*
45
3
8
Pooled Data
Frequency
(n=87)
%
44*
51
51*
59
2
2
6
7
5
6
6
7
30*
34
9
10
* multiple responses
20
Table 4. Basic descriptive statistics of sampled farm households in Region XI,
2011-2012
Philippines,
Mean
Std.
Dev.
Minimu
m
Maximu
m
48.6768
0.6236
13.0871
0.4854
17
0
79
1
0.8783
9.5323
18.8023
4.9354
0.3275
3.8241
14.2355
2.0968
0
0
1
1
1
18
62
17
0.7034
0.5894
0.2243
0.2586
72.27
0.4576
0.4929
0.4179
0.4387
106.5893
0
0
0
0
0
1
1
1
1
700
191.4419
27.4197
0.9392
0.8327
25.2419
0.2088
0.2395
0.3740
155.02
27.21
0
0
216.97
27.7
1
1
0.6160
0.0304
0.2814
0.0570
0.0076
0.4873
0.1721
0.4505
0.2324
0.0870
0
0
0
0
0
1
1
1
1
1
Yield ( kg/ha.year)
8,488.334
6
126
537,600
Amount of Fertilizer used ( kg/ha.year)
744.0760
0
14,400
Labor used ( man.days/ha.year)
Amount of Chemicals used used (l/ha.year)
63.0152
6.3908
33,367.3
068
1,350.91
53
83.8425
10.1825
2
0
844
80
Variables
Household/head characteristics
Age of household head (years)
Gender of the household head (1 = male)
Marital status of household head (1 =
married)
Number of years in schooling (years)
Number of years in farming (years)
Household size
Access to formal and informal institutional
support
Access to formal extension (1 = yes)
Farmer-to-farmer extension (1 = yes)
Access to formal credit (1 = yes)
Access to non-formal credit (1 = yes)
Number of relatives in a Barangay
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Access to future climatic condition (1 = yes)
Adopt adaptation measure against climate
change (1 = yes)
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
21
Table 5. Basic descriptive statistics of sampled farm households in Region XII, Philippines,
2011-2012.
Mean
Std.
Dev.
Minimu
m
Maximu
m
48.8165
0.6007
11.6844
0.4906
22
0
76
1
0.8957
0.3062
0
1
9.9119
22.5270
4.8885
3.1134
13.9801
1.7819
0
1
1
16
60
12
0.8309
0.6871
0.2698
0.3669
80.6583
0.3755
0.4645
0.4446
0.4828
92.3808
0
0
0
0
0
1
1
1
1
500
175.5076
27.5010
0.9568
0.8489
32.5661
0.1315
0.2036
0.3588
119.82
27.39
0
0
200.12
27.71
1
1
0.6619
0.1223
0.2122
0.0108
0.0036
0.4739
0.3282
0.4096
0.1035
0.0600
0
0
0
0
0
1
1
1
1
1
Yield ( kg/ha.year)
7,769.118
7
150
38,400
Amount of Fertilizer used ( kg/ha.year)
930.3129
0
9,600
Labor used ( man.days/ha.year)
Amount of Chemicals used used (l/ha.year)
63.4433
6.7815
6,559.86
97
1,004.61
72
47.2874
7.0049
0
0
277
56
Variables
Household/head characteristics
Age of household head (years)
Gender of the household head (1 = male)
Marital status of household head (1 =
married)
Number of years in schooling (years)
Number of years in farming (years)
Household size
Access to formal and informal institutional
support
Access to formal extension (1 = yes)
Farmer-to-farmer extension (1 = yes)
Access to formal credit (1 = yes)
Access to non-formal credit (1 = yes)
Number of relatives in a Barangay
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Access to future climatic condition (1 = yes)
Adopt adaptation measure against climate
change (1 = yes)
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
22
Table 6. Basic descriptive statistics of sampled farm households (pooled data)
Mean
Std.
Dev.
Minimu
m
Maximu
m
48.7486
0.61183
12.3748
0.487785
17
0
79
1
0.887246
0.316585
0
1
9.7491
20.7052
4.911275
3.4754
14.2196
1.939712
0
1
1
18
62
17
0.7689
0.6396
0.24768
0.3142
76.1183
0.4219
0.4806
0.432072
0.4646
95.60544
0
0
0
0
0
1
1
1
1
500
183.2539
27.4615
0.9482
0.8410
30.2771
0.1780
0.2217
0.3660
119.82
27.21
0
0
216.97
27.71
1
1
0.63956
0.0776
0.2458
0.0333
0.0055
0.4806
0.2678
0.4310
0.1795
0.0743
0
0
0
0
0
1
1
1
1
1
Yield ( kg/ha.year)
8,118.756
0
126
537,600
Amount of Fertilizer used ( kg/ha.year)
839.7763
0
14,400
Labor used ( man.days/ha.year)
Amount of Chemicals used used (l/ha.year)
63.2352
6.5916
23,714.9
178
1,188.20
90
67.5109
8.6899
0
0
844
80
Variables
Household/head characteristics
Age of household head (years)
Gender of the household head (1 = male)
Marital status of household head (1 =
married)
Number of years in schooling (years)
Number of years in farming (years)
Household size
Access to formal and informal institutional
support
Access to formal extension (1 = yes)
Farmer-to-farmer extension (1 = yes)
Access to formal credit (1 = yes)
Access to non-formal credit (1 = yes)
Number of relatives in a Barangay
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Access to future climatic condition (1 = yes)
Adopt adaptation measure against climate
change (1 = yes)
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
23
Table 7. The determinants of on climate adaptation: Probit estimates (Region XI), Philippines,
2011-2012
Variables
Estimated
Coefficient
9.1655
Constant
Household/head characteristics
Age of household head (years)
-0.0180**
Gender of the household head (1 = male)
-0.0809
Marital status of household head (1 =
0.3825
married)
Number of years in schooling (years)
0.0413*
Number of years in farming (years)
0.0004
Household size
0.1069**
Access to formal and informal
institutional support
Access to formal extension (1 = yes)
0.16582
Farmer-to-farmer extension (1 = yes)
0.36162*
Access to formal credit (1 = yes)
0.62826***
Access to non-formal credit (1 = yes)
0.00066
Number of relatives in a Barangay
-0.00240****
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
-0.00313
Average temperature (°C)
-0.32274
Access to future climatic condition (1 =
1.20817****
yes)
Topography of the farm
Flat (1 = yes)
0.34301
Gently Slope (1 = yes)
0.03483
Rolling (1 = yes)
0.81919
Midland (1 = yes)
0.38092
Highland (1 = yes)
2.7500
2
2
LR chi (18) = 45.48; Prob> chi = 0.0004; Pseudo R2 = 0.192
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20% probability level
Standard
Error
23.4975
p-value
0.696
0.01026
0.23924
0.080
0.735
0.30400
0.208
0.03219
0.00873
0.05890
0.199
0.963
0.069
0.23694
0.24286
0.30644
0.26893
0.00091
0.484
0.136
0.040
0.998
0.008
0.00683
0.81550
0.646
0.692
0.36446
0.001
1.03065
1.19441
1.04939
1.12639
12.777
0.739
0.977
0.435
0.735
0.716
24
Table 8. The determinants of on climate adaptation: Probit estimates (Region XII), Philippines,
2011-2012
Variables
Estimated
Coefficient
Constant
-61.1591
Household/head characteristics
Age of household head (years)
-0.0066
Gender of the household head (1 = male)
0.0882
Marital status of household head (1 = married)
-0.9196**
Number of years in schooling (years)
-0.0414
Number of years in farming (years)
0.0056
Household size
-0.0215
Access to formal and informal institutional
support
Access to formal extension (1 = yes)
0.5071**
Farmer-to-farmer extension (1 = yes)
-0.2816
Access to formal credit (1 = yes)
-0.5373**
Access to non-formal credit (1 = yes)
0.3296
Number of relatives in a Barangay
0.0010
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
0.00457
Average temperature (°C)
2.07947
Access to future climatic condition (1 = yes)
0.90956***
Topography of the farm
Flat (1 = yes)
4.6631
Gently Slope (1 = yes)
5.2419
Rolling (1 = yes)
4.2987
Midland (1 = yes)
3.3690
Highland (1 = yes)
3.6685
2
2
2
LR chi (17) = 24.22; Prob> chi = 0.1135; Pseudo R = 0.1032
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20% probability level
Standard
Error
p-value
138.45
0.896
0.0124
0.2202
0.5516
0.0384
0.0596
0.0596
0.593
0.689
0.095
0.281
0.576
0.719
0.2962
0.2763
0.2844
0.2684
0.0012
0.087
0.308
0.059
0.219
0.393
0.0197
4.9015
0.4330
0.817
0.671
0.036
138.2474
138.242
138.2453
5.7961
8.2895
0.973
0.970
0.975
0.712
0.689
25
Table 9. The determinants of on climate adaptation: Probit estimates (Pooled Data)
Variables
Estimated
Coefficient
Constant
17.9844
Household/head characteristics
Age of household head (years)
-0.0131**
Gender of the household head (1 = male)
0.1177
Marital status of household head (1 = married)
-0.1378
Number of years in schooling (years)
0.0341*
Number of years in farming (years)
0.0021
Household size
0.0514*
Access to formal and informal institutional
support
Access to formal extension (1 = yes)
0.2511*
Farmer-to-farmer extension (1 = yes)
0.1048
Access to formal credit (1 = yes)
0.0718
Access to non-formal credit (1 = yes)
0.0035
Number of relatives in a Barangay
-0.0008
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
-0.0059*
Average temperature (°C)
-0.6240
Access to future climatic condition (1 = yes)
0.8781****
Topography of the farm
Flat (1 = yes)
0.9369
Gently Slope (1 = yes)
1.1558*
Rolling (1 = yes)
0.9538
Midland (1 = yes)
1.0976
Highland (1 = yes)
2.9494
LR chi2 (18) = 32.72; Prob> chi2 = 0.018; Pseudo R2 = 0.0692
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20% probability level
Standard
Error
p-value
20.0527
0.370
0.0072
0.1459
0.2316
0.0220
0.0060
0.0386
0.070
0.420
0.552
0.120
0.728
0.183
0.1707
0.1614
0.1803
0.1687
0.0007
0.141
0.516
0.690
0.983
0.215
0.0041
0.7029
0.2570
0.151
0.375
0.001
0.8236
0.8610
0.8329
0.9214
4.0817
0.255
0.179
0.252
0.234
0.7226
26
Table 10. Production model of maize (Region XI): Stochastic Frontier Estimation
(MLE)
Variables
Coefficient
Standard
Errors
p-value
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
-440.2375****
1.0270
0.0000
7.7213****
17.7284****
0.5643
0.9079
0.0000
0.0000
2.4660****
0.2355
0.0000
349.1170****
374.1311****
348.2366****
353.4819****
347.7393****
0.8939
1.8349
0.8047
0.5021
0.7604
0.0000
0.0000
0.0000
0.0000
0.0000
-0.5266****
0.2237****
1.8081****
118.8936****
1.0000****
0.0368
0.0440
0.1878
18.9313
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
27
Table 11. Production model of maize (Region XII): Stochastic Frontier Estimation
(MLE)
Variables
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
Coefficient
Standard
Errors
p-value
57.2700
139.8809
0.6829
-1.5742***
-11.9538
0.7628
41.1554
0.0409
0.7719
-0.0891
0.1217
0.4654
-0.3129
-0.6781*
-0.6830*
-0.7987*
-1.5750***
0.4541
0.4572
0.4706
0.5472
0.6578
0.4919
0.1403
0.1490
0.1467
0.0180
-0.1160*
0.0394****
0.2409***
0.4191****
0.7719****
0.0878
0.0137
0.1074
0.0921
0.1203
0.1889
0.0048
0.0264
0.0000
0.0000
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
28
Table 12. Production model of banana (Region XI): Stochastic Frontier Estimation (MLE)
Variables
Coefficient
Standard
Errors
p-value
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
-225.1066****
75.1080
0.0035
-0.2943
-38.0061*
1.4619
26.3969
0.8409
0.1535
0.7212**
0.3737
0.0568
360.6477****
360.6222****
361.0124****
360.3807****
-
18.7686
18.7803
18.8320
18.7784
-
0.0000
0.0000
0.0000
0.0000
-
-0.0090
0.0205
0.2004
1.8994****
0.6243****
0.0250
0.0200
0.1598
0.5165
0.1959
0.7210
0.3090
0.2132
0.0004
0.0020
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
29
Table 13. Production model of banana (Region XII): Stochastic Frontier Estimation (MLE)
Variables
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
Coefficient
Standard
Errors
p-value
7.3927****
0.9918
0.0000
1.9489***
-2.4499***
0.7384
0.9063
0.0129
0.0110
0.0662
0.9925
0.9472
-0.8668
-0.9184
-0.5392
0.9927
0.9996
0.9983
0.3893
0.3653
0.5930
-
-
-
0.0606
-0.0211
0.1697
0.9915
0.5922****
0.0739
0.0582
0.8239
0.8184
0.1416
0.4186
0.7191
0.8382
0.2349
0.0002
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
30
Table 14. Production model of durian (Region XI): Stochastic Frontier Estimation (MLE)
Variables
Coefficient
Standard
Errors
p-value
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
32.7767****
0.9736
0.0000
-0.2197
-6.9497****
0.3751
0.6060
0.5601
0.0000
-0.2678*
0.2017
0.1891
0.4652
0.7449*
0.5257
0.6554
-1.7294****
0.4272
0.5234
0.4429
0.5322
0.5826
0.2804
0.1597
0.2398
0.2228
0.0043
-0.0071
0.0151
0.2489****
2.7151****
1.0000****
0.0121
0.0161
0.0811
0.4118
0.0000
0.5585
0.3509
0.0032
0.0000
0.0000
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
31
Table 15. Production model of durian (Region XII): Stochastic Frontier Estimation (MLE)
Variables
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
Coefficient
Standard
Errors
p-value
7.6759****
0.9846
0.0000
-0.6115
1.4236**
0.4729
0.8099
0.1997
0.0826
0.5035****
0.2411
0.0400
-0.8077
-0.3874
-0.9425
0.9792
0.8725
1.0098
0.4119
0.6582
0.3535
-
-
-
-0.0057
-0.0378**
-0.0687
1.4384****
0.7061****
0.0165
0.0191
0.0557
0.4435
0.2001
0.7290
0.0515
0.2206
0.0017
0.0007
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
32
Table 16. Production model of pooled data (Region XI): Stochastic Frontier
Estimation (MLE)
Variables
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
Coefficient
Standard
Errors
p-value
-13.5365
46.4656
0.7710
1.5391***
4.0412
0.7251
13.0860
0.0348
0.7577
0.3329***
0.1595
0.0379
0.0724
0.3321
0.0735
-0.0882
-0.9736
0.6712
0.7524
0.6774
0.7131
0.9453
0.9142
0.6593
0.9137
0.9017
0.3040
0.0079
0.0053
0.2707****
1.7119****
0.7124****
0.0102
0.0108
0.0593
0.2630
0.0921
0.4423
0.6243
0.0000
0.0000
0.0000
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
33
Table 17. Production model of pooled data (Region XII): Stochastic Frontier
Estimation (MLE)
Variables
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
Coefficient
Standard
Errors
p-value
43.5605
-159.2161
0.7846
-0.5986
16.9924
1.0280
46.4691
0.5609
0.7149
0.2676***
0.1293
0.0396
-0.7033*
-0.8040**
-0.8955**
-1.1742**
-1.8983***
0.4569
0.4496
0.4743
0.6562
0.8862
0.1250
0.0749
0.0601
0.0747
0.0331
0.0026
0.0049
0.0809***
1.3580****
0.8080****
0.0112
0.0123
0.0363
0.2090
0.0764
0.8157
0.6896
0.0266
0.0000
0.0000
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
34
Table 18. Production model of overall pooled data for both Regions XI and XII:
Stochastic Frontier Estimation (MLE)
Variables
Constant
Climatic factors and adaptation
Average annual rainfall for 15 years (mm)
Average temperature (°C)
Adopt adaptation measure against climate
change
Topography of the farm
Flat (1 = yes)
Gently Slope (1 = yes)
Rolling (1 = yes)
Midland (1 = yes)
Highland (1 = yes)
Production variables
Amount of Fertilizer used ( kg/ha.year)
Amount of Chemicals used used (l/ha.year)
Labor used ( man.days/ha.year)
Sigm2
Gamma
Coefficient
Standard
Errors
p-value
73.6698***
36.7763
0.0457
-0.8316***
-18.2543**
0.3653
10.6184
0.0232
0.0862
0.2917***
0.1053
0.0058
-0.3572
-0.4935
-0.5187
-0.7584
-1.5179****
0.4395
0.4358
0.4472
0.4880
0.6767
0.4168
0.2580
0.2466
0.1208
0.0253
0.0087
0.0090
0.1603****
1.4953****
0.6990****
0.0073
0.0080
0.0336
0.1686
0.0716
0.2309
0.2598
0.0000
0.0000
0.0000
****Significant @ 0.01 probability level
*** Significant @ 0.05 probability level
** Significant @ 0.10 probability level
* Significant @ 0.20 probability level
35
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