Download Achieving Sustainable Food Security in the Face of Climate Change

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Achieving Sustainable Food Security in the Face of
Climate Change:
Adaptation Mechanisms and Policy Recommendations for Sound Economic
Livelihood of Northern Samar
By:
Ron Paolo Felipe
Marites Tiongco
Anelita Obrar
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Table of Contents
ABSTRACT.................................................................................................................................................. 3
INTRODUCTION ........................................................................................................................................ 4
OBJECTIVES ............................................................................................................................................... 8
HYPOTHESES ............................................................................................................................................. 9
LITERATURE REVIEW ........................................................................................................................... 10
Climate Change and Food Security ...................................................................................................... 10
Sustainable Development ....................................................................................................................... 17
Agricultural Management Response to Climate Change ..................................................................... 20
Government and Non-Government Policies and Action on Food Security ......................................... 22
RESEARCH QUESTIONS ........................................................................................................................ 33
CONCEPTUAL FRAMEWORK ............................................................................................................... 33
METHODOLOGY ..................................................................................................................................... 34
DATA ANALYSIS ..................................................................................................................................... 35
FURTHER STEPS FOR ANALYSIS ........................................................................................................ 54
REFERENCES ........................................................................................................................................... 61
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ABSTRACT
The effects of climate change are undeniable. In particular, it affects one of the important
factors in development, food security. Research shows that food production systems and
sustainable development efforts are susceptible to disasters, crises and conflicts associated with
climate change variability, especially for developing countries. The Philippine province of
Northern Samar is no stranger to these effects, dealing with issues such as poverty and
malnutrition. This paper aims to determine if there is indeed an issue of food security and
vulnerability to climate change in Northern Samar, if there are already policies in place for said
issues, and if improvements or additional policies and projects are needed to ensure food security
and climate change adaptability. Currently, Northern Samar has no specific policies for climate
change adaptation, but they have shown interest in adapting such. They are also covered under
the projects and policies implemented by the Department of Agriculture and the Climate Change
Commission.
Food security in the midst of climate change can be achieved through adaptation,
mitigation, and community involvement. Existing food and agricultural production systems must
become more adaptable to potential weather changes and disasters if sustainability and security
are to be achieved. Involving the community also greatly helps, allowing the adaptability to
reach grassroots levels. A geospatial approach is taken to try and increase food security in the
province through projects and policies that are based on sound literature and empirical analysis.
Data for the empirical analysis comes from a 2006 CBMS data set and is supplemented by a
follow-up survey in 2013 using a CBMS instrument.
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INTRODUCTION
As the world’s population increases, so does it’s usage of natural resources; this means
increases the demand for food and sustenance, increases in demand for land development and the
like. This ends up with a delicate balancing act that is often left in the hands of policymakers,
who have to balance efficiency, sustainability, social impacts, and other factors. This paper aims
to explore on that, with particular emphasis on the Philippine province of Northern Samar.
In order for people to properly function and for society to advance, the basic need for
food must be satisfied. This is where the question of food security comes in. Food security, as
defined by the Food and Agriculture Organization, is “when all people, at all times, have
physical, social and economic access to sufficient, safe and nutritious food that meets their
dietary needs and food preferences for an active and healthy life” (FAO 2006). This means that
the food consumed must be safe and nutritious, as well as easily accessible to the people. Before
dealing with other important issues, such as education and the like, a person has to satisfy his
hunger and feel secure about future meals (ADB 2012). While the task seems simple, looking at
it in a wider context reveals how complicated it can get. One has to factor in volatile food prices,
both expected and unexpected natural disasters, economic stability, sustainability, the potential
list of factors goes on and on.
In “The urgency to support resilient livelihoods: FAO Disaster Risk Reduction for Food
and Nutrition Security Framework Programme”, Amaral, Baas and Wabbes (2012) point out how
food production systems and sustainable development efforts are vulnerable to disasters, crises
and conflicts. They also mention that, albeit less visible internationally, small disasters
associated with climate variability that have damaged countries such as Benin, Brazil, Togo,
Colombia, The Philippines, and other countries. It should be noted, though, that disasters and the
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environment are closely interrelated. Damage to the environment reduces the capacity of nature
to defend itself against these disasters, and these disasters in turn further contribute to
environmental degradation. Further environmental degradation leads to a reduced number of
available goods and services for consumers, lessens economic and livelihood opportunities
available to communities, and contributes to greater food insecurity and hunger (Amaral, Baas &
Wabbes, 2012). To mitigate this and to help in climate change adaptation, they stress the
importance of the four pillars of the Disaster Risk Reduction for Food and Nutrition Security
Framework Program, to continue investments and improvements in already existing programs
and for further research and development in this regard.
Food security, especially in the long term, often requires a degree of sustainability. At
this point, food security and sustainable development overlap. Sustainable development can be
defined as "development that meets the needs of the present without compromising the ability of
future generations to meet their own needs" (WCED, 1987). This definition differentiates
sustainable development from simply economic or financial development. The sustainable and
long-term outlook of this type of development makes it very favourable for climate change
mitigation and adaptation. According to the United Nations, mitigation in the context of climate
change means “human intervention to reduce the sources or enhance sinks of greenhouse gases
(GHG)”. This can range from more efficient use of fossil fuels, using renewable sources of
energy for power generation, increasing forest cover and many more.
In order to concretize the concept of food security, it must work within realistic
parameters that are usually dictated by geospatial data concerning the location where it is to be
adopted. For this paper, that location is Northern Samar.
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The Philippine province of Northern Samar is just one of the three provinces on Samar
Island. It’s a 2nd Class province under Region VIII, also known as Eastern Visayas, and has
Catarman as its capital. It has a total land area of 3,498 square kilometers with 24 municipalities
and 569 barangays under its jurisdiction. The province has a total population of 549,759 with a
growth rate of 1.30%. There are also 94, 410 households in total, with an average household size
of 5.30. These figures are based on 2007 records of the National Statistics Office.
The province itself is divided into three municipal clusters: Western, Eastern, and
Central. It should be noted that these clusters specialize in different economic activities: the
Western Cluster pursues ecotourism through development and preservation of natural and
historical attractions; the Eastern Cluster is progressing towards being the province’s main
agricultural zone and food basket through the development and utilization of its substantial land
and fisheries resources; and the Central Cluster focuses on agri-industry and ecozone
development, serving as the agri-industrial processing center as well as the commercial and
educational hub for the province.
Northern Samar has a total land area of 349,800 hectares. Around 58% of this area is
used for agriculture, with lumber coming up second in terms for land use. The largest portion of
agricultural land is currently dedicated to coconut, followed by non-irrigated, abaca, root crops,
other commercial crops, irrigated rice, banana, corn, vegetables, and fruits. Swamps, mangroves,
fishponds, grasslands, as well as road networks, settlements and industrial areas make up the rest
of the province’s existing land use. There are seven rivers in the province as well with CatubigLas Navas, Catarman, Victoria, and Pambujas Rivers forming the major rivers. Two dams also
exist in the province, in Macagtas at Catarman and at Palapag.
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In 2007, Northern Samar had a population of 549, 759 inhabitants with a population
density of 157 persons per square kilometer. This population grew by 16.1% from 1995 to 2008.
Catarman has the largest share of the provincial population, with Silvino Lubos having the
highest population density numbering at 865.47 persons per square meter during 2007. In terms
of urban-rural population distribution, Catarman and Laoang have the highest urban population
distribution during 2000, while Laoang, Catarman and Las Navas have the highest rural
population. Based on the Provincial Physical Framework Plan, the number of urban households
is expected to increase from 31,751 to 44,880 in 2013. The largest increase in urban households
is projected to be in Catarman, followed by Laoang and Allen. Generally, there are 499,992
households in Northern Samar, with a household size of 5.3 persons.
The province of Northern Samar is, much like other provinces in the Philippines, still
dealing with problems such as poverty, crime, and poor services and utilities. According to the
National Statistical Coordination Board (NSCB), six municipalities showed a worsening poverty
situation in 2002; these were namely: Allen, Catubig, Lapinig, Rosario, San Isidro, and Victoria.
Of the six, San Isidro had the worst poverty situation in 2002. The municipalities of Lapinig,
Allen, San Isidro, Silvino Lobos, Catubig, and Las Navas all fared badly with the indexes for
health, nutririon, sanitation and access to clean water in the same year; with Allen and Lapinig
having the worst nutritional status in the province. Laoang and Silvino Lobos have the highest
percentage of households without access to safe water in 2002. Peace and order were not doing
too well in San Isidro, Rosario, San Jose, and Catarman in 2002 as these municipalities showed
high indices in security component. The region, as a whole, has a poverty incidence of 40.7%
and ranking 30th from the poorest across the 16 regions in the country in 2000.
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Despite the rather negative information above, Northern Samar has started to make
progress in curbing these problems. In terms of poverty, 14 out of 24 municipalities had poverty
indices below the provincial index of 100 in 2002. Only five municipalities improved in their
poverty indices based on the comparison of 2000 and 2002 indices however. For the enabling
index, which shows composite information for basic education and people’s participation, there
was a good performance in all municipalities with the exception of Silvino Lobos; Silvino Lobos
has shown the worst performance in terms of basic education between 2000 and 2002. For
nutrition: Victoria, San Antonio, Pambujan, Capul, Biri and Gamay all showed improvements in
their nutritional status.
Northern Samar has shown initiative in trying to develop their own policies and projects
regarding climate change. The Provincial Planning and Development Coordinator of Northern
Samar attended a study mission on climate change adaptation initiatives and policy in
Vancouver, British Columbia and Winnipeg, Manitoba (Cardenas, 2013), as well as participated
in a workshop on child-centered community-based climate change adaptation (CC-CBA) level
indicators held in Manila (Cardenas, 2013).
OBJECTIVES
This paper aims to determine and suggest appropriate projects and policies that will
improve food security, sustainability, and climate change adaptation in Northern Samar. This
will be done through the use of literature on agriculture, climate change, food security and
sustainable development to help in determining appropriate policies, projects and actions; as well
as making use of Community Based Monitoring System (CBMS) data and various statistical
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methods to determine the current situation of Northern Samar and thus make timely and
appropriate policies and projects.
These sustainable food security policies and projects are also aimed to uplift and provide
economic opportunities for the poor and marginalized.
HYPOTHESES
As previously defined, sustainable development is development in such a way that the
needs of the present are satisfied without compromising the needs of the future (WCED, 1987).
Purvis and Grainger (2004) explain this quite well; this can be seen as a neoclassical
development theory incorporated with environmental and multi-generational dimensions.
Productivity, capital accumulation, population growth and technology contribute to economic
growth, as per neoclassical economic growth, but the addition of environmental concerns and a
multi-generational facet to the growth model shakes things up a little. This means that simply
aiming for growth in food production to cover the demand for food is not enough, there must be
a degree of sustainability and long-term thinking in order to attain food security.
Northern Samar currently already has a degree of diversity in its economic activities
which shows potential in terms of flexibility and adaptation. Strengthening the agricultural base
further should increase food security as well as generate more employment opportunities
especially for the marginalized and create stronger community ties through community-directed
and oriented projects.
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LITERATURE REVIEW
Climate Change and Food Security
Agriculture is inexplicably intertwined with food security and plays a major role in
sustainable development, especially in rural development. Agriculture also is a potential source
of greenhouse gas emissions; The Intergovernmental Panel on Climate Change (IPCC) has
reported that agriculture is responsible for over a quarter of total global greenhouse gas
emissions. Given that agriculture’s share in global gross domestic product (GDP) is about 4%
and that emissions of GHG are generated throughout the entire food and agricultural supply and
distribution system, from the production of agricultural inputs through to the final consumption
of food products, these information suggest that agriculture is highly GHG intensive (Blandford
& Josling, 2009) (Lybbert & Summer, 2010). This allows agriculture to be a potential major
contributor to GHG emissions and at the same time a mitigating force of it, giving it an important
place in policy, institutional and technological innovation especially for developing countries.
While climate change affects everyone, developing countries in particular are susceptible
to its ill effects in many ways. Even if climate change forecasting is still imperfect, forecasters
agree that many developing countries will become less suitable for the agricultural practices they
currently practice due to temperature changes. To better understand the economic impacts this
reduction of agricultural productivity can have on developing countries, one must consider what
changes will happen in the future, something Lybbert and Summer (2010) explore in the
background of their paper entitled “Agricultural Technologies for Climate Change Mitigation
and Adaptation”. Such predictable future changes include population growth, income growth,
technological advancement, increase in food demand, and productivity gains in agriculture. By
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2080, we will be producing more food than we do currently, but it will be more expensive in real
terms (Lybbert & Summer 2010). The higher prices of food traditionally acts as a boon to
farmers because demand for food and other agricultural outputs tend to be inelastic, which means
that demand for food falls little even as prices rise. While these predictions seem rather calm,
they do not take into account regional disparities and differences. Poor farmers from developing
countries have limited resources and are net buyers of food, so high food prices are only going to
end up hurting them. These farmers, especially the ones in less favored regions, also have to
compete globally in terms of traditional crops and would lose out to those in the developed and
favored regions. While farms are already expected to decline in developing countries, climate
change and the aforementioned effects accelerate this decline even more such that marginal areas
in developing countries may be forced to abandon agriculture altogether (Lybbert & Summer,
2010). This produces an undesirable effect on the economies of developing countries, as they
tend to rely on agriculture but lack the infrastructure or the innovations in capital and technology
to respond to changing situations.
The core challenge of climate change mitigation and adaptation in agriculture is fourfold,
as Lybbert and Summer have put forward. First, more food must be produced. Second, this
increased food production must be efficient. Third, it must withstand or adapt to changing
conditions. Fourth, GHG emission on production and along the supply chain must be reduced.
Summarized neatly into one sentence, the core challenge is: “to produce more food, more
efficiently, under more volatile production conditions, and with net reductions in GHG emissions
from food production and marketing.” (Lybbert & Summer, 2010). Technology and innovation
will play a crucial role in this challenge because of how agriculture and climate are linked
together. Some of these technologies have a straightforward connection to climate change, but
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other connections are not as direct or visible, making it harder to determine which specific
technologies have the potential in climate change mitigation and adaptation. Efforts would best
be focused on highlighting relevant technologies, building and exploring policies and institutions
based on these to support distribution and diffusion of said technologies, and providing
incentives for technological breakthroughs and innovations.
The first challenge, increasing food production, requires technological advancements in
crop yields. This is especially true in developing countries, as Lybbert and Summer (2010) have
shown that developing countries lag behind their more developed counterparts in terms of yield
gains, and investment in agricultural technology and innovations. This highlights the role of the
technological frontier in developing countries, as implementing and adapting these innovations
and technologies can dramatically increase crop yields. This productivity can lead to less
intensive use of inputs such as land, fertilizers, pesticide, and equipment, as well as direct carbon
sequestration and biofuels potential thereby representing a possible mitigating mechanism for
climate change. These new technologies and innovations can also offer farmers greater flexibility
and adaptation to changing climates, such as crop strains resistant to drought, pests, extreme
moisture, salinity, and the like. Some well known examples of these are genetically modified
crops, like Bacillus Thuringiensis (Bt) crops, which offered the most direct reductions in GHG
emissions by simultaneously reducing demand for cultivated land and fossil fuel-based inputs. In
2007 alone, a year when GM crops were grown on only 7% of arable land in the world, the total
reduction due to both the direct and indirect emission effects of GM crops amounted to over
14,200 million kg of CO2 (Lybbert & Summer, 2010). Besides production technologies,
production techniques are also of importance in climate change mitigation in agriculture. In
particular, Lybbert and Summer (2010) mention conservation or reduced tillage agriculture,
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which aims to build up organic matter in soils and create a healthy ecosystem by not tilling the
soil before planting. This increase in organic matter in soils improves the moisture capacity, also
increasing water use efficiency. Reduced tilling also helps reduce GHG emissions, but then
requires more extensive pest and disease control.
Besides these mitigating methods in production, there is also a potential to minimize
GHG emissions after the products have left the farm as transportation tends to be a major GHG
contributor. Post-harvest GHG emissions per unit of consumption mainly depend on how
efficient transport is, like taking the rail versus roads, ocean shipping versus land shipping, and
large loads versus small loads, rather than distance traveled (Lybbert & Summer, 2010).
Improving transportation efficiency would be like killing two birds with one stone, it lessens
GHG emissions from agriculture as well as other sectors. Another point of improvement would
be post-harvest losses, as these represent one of the greatest sources of inefficiencies in food
production. These losses are often not given as much attention as the other factors, even if there
is the possibility of losing half of the harvest in this manner. Investments in improved harvesting,
processing, storage, distribution, logistics and necessary training investments can pay off as well
as improved crop yields in terms of gains to consumers and GHG emissions reduction. (Lybbert
& Summer, 2010). As the climate shifts and changes, the potential for larger post-harvest losses
looms, and the role of efficient transportation and storage becomes even more important.
Another study, done by Blandford and Josling (2009), also advocates adoption of
improved production technology and practices that lessen environmental impact but they focus
more on policies instead of technology. Specifically, they focus on six types: performance
standards, best-practice equipments, subsidies, carbon taxes, cap and trade schemes, and public
expenditure for research and extension. The effect of these policies on agriculture tends to be
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complex, especially when multiple policies are adopted or are present. The effects depend on
factors such as whether agriculture is the intended target or is just affected by policies applied to
other sectors; whether the policies generate private incentives, or generated solely by a publicly
funded incentive; and whether the net effect is to incentivize increases in agricultural output in
the aggregate or to change its composition. In general, policies that restrict current activities will
decrease production, while subsidies encourage more output.
Performance standards are a direct way to regulate GHG emissions from agricultural
activities (Blandford & Josling, 2009). GHG emission standards can be imposed on agricultural
production, similar to how it is usually imposed in other industries. The effectivity of this
approach is reliant on the ability to monitor emissions and the imposition of penalties, and as
such they can only be efficiently applied to concentrated production operations rather than the
more diversified ones. Blandford and Josling suggest another possible way to implement
regulatory standards, like limiting the size of operations or imposing production requirements
instead of directly regulating GHG emissions. This way is easier to enforce since it is simpler to
verify process standards applied than to monitor GHG emissions itself. Both approaches can only
be effective if there are disincentives, costs or sanctions to those who do not follow.
Due to difficulties in successfully and efficiently regulating agricultural practices, focus
has shifted to incentivizing changes in production practices that can result in better
environmental impacts. Blandford and Josling (2009) also mention that this is the principal
approach adopted in agri-environmental schemes in Europe and North America, especially when
it comes to reducing water pollution and maintaining biodiversity. Some practices suggested by
Blandford and Josling that can be incentivized are as follows: changing from conventional to
conservational tillage to reduce GHG emissions, tree planting to enhance carbon storage, and
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keeping land out of agricultural production to avoid carbon emissions. The act of incentivizing
ties in with the previously discussed paper by Lybbert and Summer, as this provides a way to
disseminate and distribute agricultural technologies and practices that they have elaborated on
their own study.
Another way to encourage change without resorting to regulatory measures is through
subsidies. Rather than providing incentives for changes in existing production practices,
payments may be made to increase the output of products that are viewed to contribute to lower
GHG emissions in the economy as a whole (Blandford & Josling, 2009). Instead of changing
production methods, subsidies encourage the expansion of specific outputs. Subsidies that
promote renewable energy, even if they aren’t specifically targeting agriculture, also affect the
land use in agriculture. This can potentially reduce agricultural production as farmland gets used
for power generation facilities, but it can also increase production on the remaining land with
realized additional income from renewable energy which they can invest in their operations.
Another way to mitigate GHG emissions via policy is to turn these emissions as costs,
like imposing a carbon tax. This can serve a double purpose, it dissuades activities which are
GHG intensive and it provides funding for the government. Agriculture uses a lot of fossil fuels
especially during the processes of production and transportation, which means a carbon tax
would significantly increase costs and thereby promote possible alternatives. The net effect of
carbon tax on GHG emissions of the economy as a whole, however, depends on where tax
revenues are allocated (Blandford & Josling, 2009). If they were used in ways that promoted
high GHG-emission production activities and products, then the mitigating effects of the tax
would be muted. On the other hand, if the tax revenues were used to promote low-carbon
production activities and products, the mitigating effects may be amplified even more. Cap and
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trade schemes for carbon emissions also yield similar results, but function slightly differently to
carbon taxes. Cap and trade schemes establish emission limits instead of influencing behavior
through taxes. With these limits established, firms can buy and sell permits to emit GHG. These
turn GHG emissions into costs, as well as potential windfalls. Farmers who exceed the stated
limit will incur more costs, and farmers who stay under the limit can then sell the unused permits
and gain additional income.
Agricultural research and extension can provide better technology that can reduce GHG
emissions and increase carbon capture. Even research not aimed specifically for these goals but
results in production gains can also lead to reduction in GHG emissions. Public support and
funding for agricultural research and training will definitely help in its cause, but it still rests on
the same premise that underlies using public funds to enhance investment in all agricultural
activities that are viewed to have a significant public good dimension (Blandford & Josling,
2009). In this case, the gains to society extend past those from increases in productivity and
impacts on food prices, to social benefits from GHG emission reduction.
Climate change policies will tend to affect agricultural production, even if their specific
target sectors do not include agriculture, and the effects these have are complex. Considering the
requirement to double food production by 2050 to meet the needs of an expanding global
population, a better understanding of the impacts of climate change and various climate change
policies on food production is critically important (Blandford & Josling, 2009). Food security is
also an integral part of poverty reduction, as without it the vicious cycle of poverty will continue
unabated (ADB, 2012). Because of the continually increasing demand for food, pursuing climate
change mitigation without considering food security will only serve to cause more problems.
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Sustainable Development
Agriculture, like any other sector in society, requires energy as an important factor of
production. This energy can be used directly, often in petroleum-based, natural gas, or electrical
forms for powering machines and equipment, controlling temperature in buildings and
greenhouses, lighting for farms, and indirectly as in the case of off-farm production of fertilizers
and chemicals to be used on the farm. According to Schnepf (2004), the U.S. agricultural sector
has used an estimated 1.7 quadrillion Btu of energy in both direct and indirect ways within the
past decade. Farm production in the U.S. has also become increasingly mechanized, highlighting
the importance of energy in particular stages of production to achieve optimal yields. Schnepf
(2004), in his report “Energy use in Agriculture: Background and Issues”, pointed out several
key points that emerged on this topic: Agriculture is reliant on the timely availability of energy;
Agriculture consumes energy both directly and indirectly as mentioned earlier; Energy share of
agricultural production varies by activity, production practice and locality; at farm level the
direct costs are significant, albeit a small part of total production costs; Energy costs play a much
larger role when both direct and indirect costs are combined; and energy price changes have
implications for crop and agricultural activity choices, including irrigation, cultivation, and postharvest activities and strategies.
Looking more closely at agriculture and its share of energy use in the US, we find that
direct energy use of agriculture comprises only around 1% (1.1 quadrillion Btu) of the total US
energy consumption of 9.8 quadrillion Btu in 2002. In contrast, the non-agricultural component
of the industrial sector consumed 31.4 quadrillion Btu, while the transportation sector consumed
26.5 quadrillion Btu in the same period (Schnepf, 2004). Given this small share of direct energy
consumption by agriculture, it is unlikely that it will affect overall supply and demand for
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energy. However, changes in the overall supply and demand for energy will affect agricultural
activity, especially in terms of productivity and profitability.
Indirect energy use of agriculture only comprises 35% of the total energy used by the
U.S. agricultural sector, with the remaining 65% going to direct energy use. In contrast to direct
energy use, the share of fertilizer and pesticide use of agriculture in the total nitrogen usage is
significantly higher, amounting to 56% or 12 million out of 21.4 million metric tons during 2002
(Schnepf, 2004). This is due to the indirect link between nitrogenous fertilizers used in the U.S.
and natural gas as an energy source. Natural gas is a common feedstock for nitrogenous
fertilizers, accounting for almost 90% of the cost of production of primary ingredients for these
fertilizers.
While energy is indeed one of the major inputs for agricultural production, agriculture
can also output energy in the form of biomasses, bio-oils and the like. While 80% of primary
power sources around the globe depend on fossil fuels, the poorest nations in Africa have to
depend on diesel and traditional biomasses as an alternative to fossil fuels due to their rising
costs. A United Nations Foundation report by Kimble, Pasdeloup and Spencer (2008) entitled
“Sustainable Bioenergy Development in UEMOA Member Countries” explores the potential of
the West African Economic and Monetary Union (UEMOA) in terms of bioenergy development
on the path to economic and social advancement. The eight nations that belong to UEMOA;
Benin, Burikina Faso, Cote d’Ivoire, Guinea Bissau, Mali, Niger, Senegal, and Togo, possess a
resource base that can be sustained by good policies and practices that expand both production
and access to food, fuel and fiber (Kimble, Pasdeloup & Spencer, 2008). These strategies revolve
around strengthening their climate change adaptability by increasing agricultural and forestry
productivity, protecting watersheds, and the production of biofuels.
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The UEMOA region has a significant amount of land, but not all of it is arable. Much of
it is infertile, eroded and plagued by degradation. This, plus the combination of poverty and
population growth, continues to eat away at the region’s forest cover as Kimble et al. (2008) has
found out. The average deforestation rate in the region is 1.28%, practically double than that of
the African deforestation rate of 0.62%. Traditional wood biomass also serves as the main energy
source for the UEMOA countries, comprising 73% of the primary energy in the region. This
current trend of usage is unsustainable for the region, as the data assembled for the report has
found out (Kimble, et al., 2008). If these countries are to move towards a modern bioenergy
economy, solving this issue is a main priority; this means further adaptation, development, and
use of wood biomasses to create cleaner and more efficient fuels. Deforestation must also be
addressed by reversing poor forestry practices, strengthening of sustainable forest management,
and reforestation programs. If left unchecked, deforestation could seriously curtail any efforts in
biomass utilization towards economic growth.
Another suggested path by the UN Foundation is to locally grow bioenergy crops, which
can be transformed into modern fuels, thereby expanding energy access, creating more
employment opportunities and generating higher income (Kimble et al., 2008). However, West
Africa’s agricultural sector held back by several issues such as lack of access to inputs (fertilizer,
irrigation, energy, agricultural equipment) and the constraint of existing arable land. Less than
2% of the arable land is irrigated, leaving the rest vulnerable to weather shocks. Improving and
expanding irrigation will help remedy this problem, but it must also be done with the limited
water resources of the region in mind.
As was said earlier, energy is necessary for development. The same can be said for
agriculture; energy is a vital input for increasing agricultural productivity. At the subsistence
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level, collection and use of firewood for heating and cooking consumes tremendous biomass
resources and labor. Fuel is needed to operate agricultural machinery, irrigation and water
pumps. Energy is also needed to process, transport, and store agricultural products. Indirect
energy use is necessary for the production and application of fertilizers and pesticides required to
boost crop yields. Yet, with few exceptions, current policies in the UEMOA region place limited
priority on widening energy access (Kimble et al., 2008). This mismatch in policy can cause
some issues when energy consumption rises as development plans for the agricultural sector are
implemented.
Agricultural Management Response to Climate Change
As proven above, there have been multiple ways in which science and policy-makers
have created to adapt and mitigate climate change. Farmers too have responded to climate
change, as they are the most vulnerable to the effects that climate change brings. Farm
management decisions have changed due to a broad array of factors, which is adequately
supported by literature and real-life examples. A study by the Organization for Economic
Cooperation and Development (OECD) done in 2012 determined the factors that can cause a
change in behaviour of farmers. These factors are then viewed under a behavioural economic
framework, providing important policy implications in the process for policy-makers.
Farmers’ goals are complex, and dividing them based simply on a profit maximizing
behaviour is difficult, as the OECD (2012) have found in previous literature. These goals are
important in analysis and prediction of farmer response to climate change, as they will most
likely react to the effects of climate change in such a way that still aligns with their goals. Some
of these goals that were found in the literature were: objectives and goals in farming, attitude
towards traditional/ethical farming, ability to cope with stress, satisfaction with farming, attitudes
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toward legislation, risk aversion, information access, the identity of the decision maker, problem
solving ability, personality, social norms, and financial incentives among others. Some literatures
agree and some disagree on these factors however; there are factors like household size and farm
size that were shown to have a positive relationship with conservation agriculture adoption in
some literatures, but show a negative relationship in others. A farmer’s education level has a
positive impact on conservation agriculture adoption for several studies, but some analyses have
shown that this factor has a negative correlation and may even be insignificant. Due to this lack
of consensus as to which factors can universally explain, it would be better to tailor the
promotion of conservation agriculture to the conditions of individual locations.
Besides conservation agriculture, farmers could also opt to mitigate or offset their
greenhouse gas emissions through biofuel production and carbon sequestration. Even something
as simple as changing the diet of livestock may contribute to the lessening of their contribution to
climate change. The effectiveness of these actions in mitigating or adapting to climate change
depend on the farmer’s response to benefits or penalties to be had, however.
It should also be noted that external factors outside the control of the farmers can also
affect their response to climate change. Stigter and Winarto (2012), in their paper entitled “What
Climate Change Means for Farmers in Asia”, pointed out that the slower rate of adaptation by
Indonesian farmers are partially due to the capricious weather patterns. Extreme climates such as
droughts and torrential rains can delay planting and destroy crops, and can even trigger a
climate-induced pest attack. Another cause was the lack of capability in most farming
communities, which was exacerbated by early agricultural programs that fostered a dependency
on external sources of expertise. Indonesia’s Integrated Pest Management (IPM) program that
was supposed to help eradicate Brown Plant Hopper epidemics ended up being sub-par in
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effectivity, and experts claim that the principles of IPM may have been too complicated for small
farmers to understand (Stigter & Winarto, 2012).
Government and Non-Government Policies and Action on Food Security
In Asia, the governments of Japan, China and Korea have prominently pursued strategies
and policies towards green and sustainable growth. Other countries such as Cambodia, Fiji,
Kazakhstan, and Mongolia have expressed support towards green and sustainable growth
policies. In country partnership strategies (CPS) with the ADB, environmental concerns and
support for environmentally sustainable growth are clearly reflected and presented. Climate
change is clearly an issue recognized by all CPS, and majority of the projects, strategies and
plans have an effect on climate change in one way or another. The environmental strategies
covered in these CPS range from protection of coastal ecosystems, water resource management,
clean energy, transportation efficiency, environmental resilience and institutional capacity
strengthening. Some concrete examples of these are: climate-proofing infrastructures in Kiribati,
Papua New Guinea and Vanatu; cleaner coal technology in China; integrated water resource
management in India; and adaptation and integrated watershed and coastal management in the
Solomon Islands (ADB, 2011). Other CPS with the ADB have broader scopes like support for
clean and sustainable energy operations, climate change consideration in project planning and
design, improved sustainability of natural resources and environmentally-friendly infrastructure.
Some CPSs involve loan projects for the use of renewable energy sources and energy efficient
technologies, like the ones in India, Pakistan, China and Sri Lanka. Technical assistance for
climate change adaptation and mitigation are also included.
The ADB (2011) have noticed that majority of these projects supporting environmental
sustainability were split into urban renewal projects (42%), clean energy projects (40%) and
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natural resource conservation projects (13%) with environmental policy (3%) and legislation
compromise (1%) trailing far behind. Based on loan amounts, the ADB have determined that the
energy sector has the highest proportion of investment, followed by water supply and services,
multisector investment, and finally agriculture and natural resources. The proportion of projects
by number also follows a similar trend, with the energy sector having the largest share of
projects.
The link between environmental protection and poverty reduction showed in CPS
projects that underscored environmental management of ecosystems and improvement of income
and welfare in rural areas. One project in China promotes environmental sustainability in
farming in an effort to reduce rural poverty and develop a sustainable agriculture sector (ADB,
2011). Water resource projects are also tied into similar projects like these, as they are often
tailored to boost agricultural productivity. Grants were also used to help achieve environmental
sustainability while combating poverty, encompassing a wide array of activities. These can range
from rural livelihood linkages to natural resource protection, establishing sustainable livelihoods
and farming methods for the poor, biodiversity conservation, and community-centered resource
management. These grants were used to supplement ADB loan projects which address growth
constraints and climate change issues like energy efficiency, improved urban infrastructure,
modernizing flood control and water supply systems, sustainable and conservative groundwater
use, sustainable transport systems, implementing innovative and clean technologies, and many
more.
Similar to the rest of Asia, the Philippines also shows interest and support in climate
change mitigating technologies and policies. The country makes monitors the climate change
situations and scenarios within itself, as well as forecasting and projecting climate change effects
P a g e | 24
through the Philippine Atmospheric, Geophysical and Astronomical Services Administation
(PAGASA). These climate change scenarios are developed using mathematical representations
of the climate system, simulating the various processes that determine global and regional
climate. PAGASA has determined that mean temperature anomalies have been increasing
steadily, estimating a 0.0108 °C per year increase in temperatures locally. Tropical cyclone
frequency remained relatively the same over the years despite the high variability, but a slight
increase in tropical cyclone passage over Visayas was noticeable. They also analyzed trends in
extreme daily rainfall and found an increase in frequency, but these results were deemed
insignificant due to changes in extreme rain events in certain areas in the country (PAGASA
2011).
In response to this, the Department of Agriculture (2011) has formulated and approved of
a climate change program involving multiple approaches and steps to mitigating and adapting to
climate change. The program is mostly centered on risk and how climate change increases risk
for agricultural activities, especially on the poor and marginalized. In this program, they defined
and briefly enumerated the various adaptation, mitigation, and policy implementations and
strategies that are to be used.
The Department of Agriculture defines adaptation strategies as tools, technologies and
practices that if widely adapted will help minimize the adverse effects of climate change to
agriculture and fisheries. These strategies include but are not limited to: disaster risk
management, water conservation, water use efficiency, management, and delivery systems,
precision agriculture, climate change adaptive crops, improved and more resilient aquaculture
species and livestock breeds, climate resilient agri-fishery infrastructures and urban agriculture
(DA, 2011). Mitigation strategies, on the other hand, are defined as tools, technologies and
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practices that if widely adapted will help reduce carbon emissions from food production or
provide carbon sinks to reduce the volume of greenhouse gasses that rise into the atmosphere
(DA, 2011). This can be achieved through: organic farming, novel feed formulations, waste
management, biotech crops, energy efficient and green agri-fishery machines and the like. Agroreforestation and multipurpose trees, as well as seaweed farming, contribute to the creation of
carbon sinks.
To ensure the rapid and wide adaptation of these measures, various policy instruments are
to be employed for maximum effectivity and efficiency of the projects. These range from:
climate information system for agriculture and fisheries, research and development for adaptive
tools, technologies and practices, fully engaged extension system, repair and improvement of
irrigation systems, climate resilient agriculture and fishery infrastructure, regulations to ensure
effectiveness and safety, and windows for financing and instruments for risk transfer (DA, 2011).
A climate information system for agriculture and fisheries allows for the generation of
timely and reliable information used in disaster risk reduction and management. This section of
the climate change program makes use of the expertise and talent of multiple agencies and
bureaus, such as the Bureau of Agricultural Statistics and the Bureau of Soils and Water
Management, who will work together to achieve this. One of the projects in this regard is the
improvement of agro-meteorological predictions via the expansion of the “agromet” stations in
partnership with PAGASA. This network expansion of agromet stations, when done at the
appropriate density, will provide accurate data and input for climate change models. Another
project would be pest control surveys, handled by multiple bureaus. The Bureau of Plant Industry
shall continue to conduct its population survey of pests in crop production; the Bureau of Animal
Industry will continue to do likewise with its epidemiological surveys of livestock pests, and the
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National Fisheries Research and Development Institute as well with aquaculture pests. A unit in
these agencies shall be established to develop predictive models to anticipate the resurgence of
pests (DA, 2011).
Research and development in adaptive tools, technologies and practices is also vital in
climate change mitigation; in the Department of Agriculture’s plan, this involves improving
infrastructure and construction design and protocols via new protocols for agri-fishery
infrastructure that can withstand strong winds, water intrusion and erosion, and other adverse
impacts of the weather. Water conservation and management also falls under this project
heading, where efficient and cost-effective rainwater collection systems for farms and homes as
well as community-based watershed management options are being developed. Due to the wide
scope of this section, it involves a lot more projects like: breeding and screening crops and
livestock for tolerance to pests, heat, humidity etc., reforestation, precision agriculture, urban
agriculture, organic farming, cultural management, farm waste management, novel feed
formulation, biotech crops, biopesticides, energy efficient and green agri-fisheries, risk
management especially for susceptible areas, and assessment of adoption rates and effectiveness
adaptation and mitigation measures (DA, 2011). Majority of the research and development will
be handled by the Bureau of Agricultural Research, to ensure that results will be obtained
through good science. Research results will also be required to be published in a peer-reviewed
journal before acceptance and implementation in the program.
Dissemination of vital information such as warnings for weather changes, evacuation
protocols for typhoons and natural disasters, alternative agricultural settlements, balanced
fertilization and the like, forms an integral part of a fully engaged extension system for climate
change adaptation (DA, 2011). These projects and actions are coordinated by the Agricultural
P a g e | 27
Training Institute, as well as NGOs and LGUs. The Department of Agriculture will also promote
new production methods and post-harvest facilities that are resistant to extreme weather
conditions and disturbances. These new production methods, infrastructure and facilities are to
be developed under the research and development institutions of the Department.
Repair and improvement of irrigation systems also done to increase the efficiency of the
system and to ensure that irrigation water will always be delivered at the right time and in the
required amounts to the farmers who need them. This will be handled by the National Irrigation
Administration with potential partnerships from the LGUs and irrigator associations.
Currently, there are already regulations imposed to protect food producers and the
environment through effectiveness and safety of fertilizers, seeds, and GM crops. Any climate
change program that involves these inputs would require proof of regulatory compliance from
the corresponding agencies (DA, 2011). In lieu with this, agri-fishery equipment will be
regulated and the Agriculture and Fisheries Mechanization Act will be passed by the Department
of Agriculture. It must be noted though, that no additional regulatory compliance is required
beyond those already set up by the respective agencies.
Besides technology and policy, capital access is also vital to the aims of climate change
mitigation. These adaptation and mitigation measures often entail capital which producers have
little to none of, hence the Department of Agriculture (2011) will provide access to this capital
via innovative financing windows like small grants, interest free loans, soft loans, and insurancerisk transfer mechanisms. Small grants would be given to the poorest of the poor farmers, while
majority of food producers can avail of interest free loans and soft loans. Loans for agri-fishery
machines, infrastructures, inputs, as well as housing will be made available.
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With all of these projects and measures to be undertaken in this climate change program,
a source of funding and a regulatory body needs to be secured. Funding for the program comes
from 50% of the commodity programs designed to support research and development excluding
vulnerability mapping, extension, regulations and small grants. Vulnerability mapping is to be
included under Strategic Agriculture and Fisheries Development Zones (SAFDZ) mapping,
while the Agricultural Competitiveness Enhancement Fund (ACEF) and other funds handled by
the Agricultural Credit Policy Council (ACPC) will provide financing for interest-free and soft
loans. The Department of Agriculture will also take a proactive stance to seek out grants or aid
from various funding sources that support climate change programs and projects. For a
regulatory and central body, the Climate Change Program Office (CCPO) will be created to
facilitate the creation and review of policies and regulations regarding the climate change
program, as well as ensuring consistency with national and international goals, agreements and
priorities.
Besides the actions of the Department of Agriculture, there is the Climate Change
Commission which is tasked to coordinate, monitor, and evaluate programs and action plans
regarding climate change (“National Climate Change Action Plan”, 2011). This commission
formulated the National Climate Change Action Plan (NCCAP), a series of realistic and
achievable programs of action for climate change adaptation and mitigation. The NCCAP has
seven priorities, namely: food security, water sufficiency, environmental and ecological stability,
human security, sustainable energy, climate-smart industries and services, and knowledge and
capacity development. To further development in these priorities, the NCCAP has strategic
actions laid out for 2011 to 2028.
P a g e | 29
For food security, the NCCAP incentivizes dissemination of knowledge concerning the
vulnerability of agriculture towards the impacts of climate change. This also includes conducting
research, monitoring and spreading new technologies and practices that can aid in climate change
adaptation. This includes integrating climate change adaptation to training programs in
agriculture and fishery as well as establishing risk transfer and social protection mechanisms for
agriculture and fisheries, such as insurance coverage, better access to credit, and safety nets for
severe weather shocks. These allow farmers and fishermen to be more flexible and efficient in
their production and distribution processes, while minimizing potential costs from adverse
climate change effects.
Closely tied with food security is water security, as some of the strategic actions laid out
for this priority can also have some impact on the former. This involves: profiling and
management of water sheds, rivers, and other important water resources, creating a responsive
and timely research and development program for water resources, full implementation of the
Clean Water Act and the National Septage and Sewerage Program, rehabilitation of ailing water
basins and other water resources, as well as increased coverage of safe water in waterless
municipalities. These strategic actions improve the efficiency of water use both in agricultural
and non-agricultural activities through research and proper management of water resources,
leading to the objective of having water management that is climate responsive, accessible and
sustainable water supply, and increased capacity of the water sector to cope with climate
changes.
Almost all of the ecosystems in the Philippines has been altered or damaged in some way
(“National Climate Change Action Plan”, 2011). Large scale conversion of forests and
grasslands into settlements and housing, cropland and mining areas, as well as diversion and
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storage of water behind dams, pollution from residential and industrial activities, and the loss of
coral reefs and mangroves have caused rapid and massive changes in these ecosystems. At
present, only around 8% of the country’s forest cover remains, 5% of total coral reefs have 75100% live coral, and 21% of Philippine vertebrates as well as half of known plant species are
considered threatened by the IUCN (“National Climate Change Action Plan”, 2011). In order to
have environmental and ecological stability, the NCCP focuses on achieving one immediate
outcome: the protection of critical ecosystems and the restoration of ecological services. This
involves a variety of actions, starting with nationwide risk and vulnerability assessments and
implementation of the National REDD (Reducing Emissions from Deforestation and Forest
Degredation) Plus Strategy. For the management and conservation of these ecosystems, the main
actions being done are: the expansion of protected areas and key biodiversity areas as well as the
establishment of ecosystem towns or ecotowns in these key biodiversity areas, implementing
gender-fair innovative financing mechanisms like loans, funding, and grants as well as other
climate change adaptation assistance for these ecotown communities, as well as increasing
knowledge and capacity-based integrated ecological management at the national, local, and
community levels. Implementing environmental laws are also vital to this goal, and as such a
moratorium on polluting and extractive industries in protected areas and environmentally critical
areas is implemented by the NCCAP. Another important thrust in this program is the
institutionalization of national resource accounting, which involves a review and possible
revision on the Philippine Economic-Environmental and National Resources Accounting
(ENRA) as well as training on wealth accounting and the ENRA for key government agencies.
Community involvement is vital for a sustainable and successful climate change
adaptation programs, and the NCCAP highlights this in its goal of human security. Through
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community involvement on various levels, the risks of men and women to climate change
disasters would be reduced. This is reflected in community-oriented actions in the previous goals
such as training and knowledge dissemination on climate change adaptation, establishing
financial assistance, and promoting community management of ecosystems. Inclusive in climate
change adaptation and disaster risk reduction are provisions for integrating climate change and
disaster risk reduction training for medical personnel, as well as improvement of existing
systems and infrastructures for health and disaster risk reduction.
Another long-term goal for the NCCAP is the transition to green growth by developing
climate-smart industries and services. Currently, this is being achieved through promotions and
partnerships between climate-smart industries and the private sector, creating green jobs and
sustainable livelihoods especially in the rural areas, and the promotion of climate-resilient and
sustainable cities (“National Climate Change Action Plan”, 2011). Promotions for climate-smart
industries vary, but generally involve implementing policies that enable and incentivize these
types of businesses and business practices, as well as developing knowledge and training on
climate-smart best practices, GHG emissions inventory, and carbon footprint management.
Climate-proofing of infrastructures, implementing green building principles and transport plans
help achieve the current goals of transitioning towards greener living in cities and municipalities.
In practically every important goal mentioned in the NCCAP, there is always some level
of risk and vulnerability assessment, information dissemination and training or research, and
development; the synthesis of these actions build the last goal of capacity development. An
increase in the capacity development of the nation in terms of climate change adaptation means
better knowledge on the science of climate change, the local and community level capacity for
climate change adaptation, mitigation and disaster risk reduction is enhanced, improved capacity
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for forecasting and modeling climate change scenarios, and climate change knowledge
management is easily accessible and available to all.
Another way to gauge how much a government prioritizes climate change strategies
would be to look at how much of the government budget is allocated to projects, actions,
policies, and commissions that focus on climate change mitigation and adaptation. Budget ng
Bayan, a Philippine website run by the Department of Budget and Management, allows the
general public to see how the Philippine National Budget is distributed and to which departments
and projects it is allocated to. Calamity funds have increased to 7.5 billion pesos in 2012, 2.5
billion more compared to the 5 billion pesos allocation for 2011. This 7.5 billion is then divided
further into projects; 2.65 billion pesos is to be used for aid, relief and rehabilitation for areas
affected by disasters, while 4.85 billion pesos is set aside for repair and reconstruction of
permanent structures. 2.1 billion pesos is also disbursed for Quick Response Funds, which is
used for emergencies in the wake of a national disaster.
Major programs and projects undertaken by the government intended to adapt to climate
change also are also included in the budget allocation of the Philippine government, according to
Budget ng Bayan. In 2012, 10.8 million pesos have been allocated to flood control projects,
which involved the construction of 8,000 km of flood barriers in major basins and principal
rivers to prevent the loss of lives and property. It is interesting to note that the budget for flood
control programs was bigger in the previous year, totalling 11.44 million pesos in 2011
compared to 10.8 million pesos in 2012. For timely weather forecasting and warning, 367
million pesos was allocated to the PAGASA Automation program. The Laguna Bay Institutional
Strengthening and Community Participation Project saw 408,000 pesos allocated for it to
improve environmental quality sustainably manage the resources of Laguna bay. 793,000 pesos
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is spent for solid waste disposal and management, providing disposal services and equipment for
17 local government units in Metro Manila as well as technical support for other local
government units regarding proper waste disposal. The National Greening Program also had a
similar amount allocated for it, 793,000 pesos for planting 200,000 hectares with forest and fruit
trees.
RESEARCH QUESTIONS
This paper aims to answer the following questions:

Does Northern Samar experience food insecurity?

Is Northern Samar vulnerable to climate change?

Are there currently policies and/or projects in place to address food insecurity and/or
climate change mitigation and adaptation?

If no, what would be appropriate policies and/or projects to address these issues?

If yes, are these policies and/or projects sufficient? If no, how could they be improved?
CONCEPTUAL FRAMEWORK
Fundamental to the effective execution of Regional policies aimed at optimizing the use
of land and land related information, is the knowledge of, and access to accurate, up-to-date,
timely, complete and relevant socio-economic, demographic, environmental and geospatial data
(Wall, 2009). Working under a geographic framework places emphasis on geographic data of the
target region and the utilization of these data to make sound decisions.
Geographic information, also referred to as geospatial information or data, has long been
recognized as a critical component of a nation’s infrastructure (Ryerson & Aronoff, 2010).
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Geospatial data provides important information about a location like political boundaries, natural
resources, land ownership, land use, transport, communication, demographics and social factors.
These types of information used to be stored in the form of maps, but nowadays this information
is also widely available digitally. The shift to digital means of storing and conveying this
information allows for greater accessibility, sophistication, and interactivity.
These information and data are indispensable for resource exploration and management,
economic planning, even disaster mitigation and recovery. Wall (2009) advocates for the
development and application of a Regional Spatial Data Infrastructure (RSDI) in the Caribbean
Community (CARICOM). This RSDI could serve as the framework to facilitate the coordinated
exchange of spatial information among spatial data stakeholders within the community, helping
the countries in the Caribbean to strategically plan, coordinate and adopt a framework for
disaster management in the region.
METHODOLOGY
The methodology for this paper requires analysis of both data on Northern Samar and
relevant literature to have a better understanding of the current situation in the province, existing
policies on food security and development, and the necessary information to come up with
policies and projects of these nature if none exist currently. These methods would be used to
answer the research questions mentioned earlier, with the end goal of improving welfare in
Northern Samar through improving food security and climate change adaptability.
The methodology will be mostly composed of descriptive analysis through focus group
discussions with sample farmers and other key stakeholders. Empirical analysis will be based on
a CBMS data set collected in 2006 as well as a follow-up survey in 2013 using the CBMS
P a g e | 35
instrument. The information gathered here would show how Northern Samar is currently
performing in terms of economic activity, as well as what the socio-political climate is like in the
province. Other important information collected involve trends in climate change from 1993 to
2013, vulnerability and impact of climate change on the livelihood of farmers and their coping
mechanisms to climate change risks.
The literature expounded in this study focuses mostly on food security and climate
change, particularly in terms of policies and projects improving the former and mitigating and
adapting to the latter. These literatures cover both international and local situations, and have
been published no later than 2004. Additional information is also obtained from a 2010
provincial profile of Northern Samar, covering socio-political, economic and some geospatial
aspects.
DATA ANALYSIS
The CBMS data used for this study has a total of 493,384 observations with 63 variables.
The observations represent individual respondents to the CBMS instrument and the variables are
different information about households and their members in Northern Samar. Respective tables
and other relevant data can be found at the end of this chapter for easy referencing.
Northern Samar is still not highly urbanized; 72.66% of the respondents live in rural
areas, with the remaining 27.34% living in urban areas. The most populous municipality is
Catarman, with 15.02% of the total respondents living there. The least populous municipality is
San Roque, with only 1.38% of the total respondents living there. 19.72% of the total
respondents are identified as the household heads, with 52.88% classified as the offspring of
these household heads and 16.57% classified as the spouse of these household heads. Household
P a g e | 36
heads are the primary decision makers in a household, which makes them an important factor to
consider in designing projects and policies that are aimed at the grassroots level. The data set
also shows that a sizeable part of the population is below 18 years old, with the largest
percentage of the respondents being aged 8 (3.05%), 10 (3%), and 12 (2.97) respectively. The
average age for the province is 24. 263 years old. The children in the province are well taken
care of, as 93.08% of children 0-5 years old have a normal nutritional status according to the
data.
The province is largely religious; the largest religious group is Iglesia ni Cristo with
52.88% of the respondents belonging to said religion. The second largest religious group is
Roman Catholic, with 19.72% of the respondents belonging to said religion. Majority of the
respondents have lived in the province since birth, comprising 71.65% of the total. These natives
of Northern Samar also move to other municipalities and barangays, with 49.42% of those who
transferred cited resettlement as their reason for residing in their current place. Majority of the
respondents were also registered voters, 87.32% of the total respondents to be specific.
One of the most interesting figures in the data set would be the education level of the
locals. 70.44% of the respondents have not attended school, meaning only 29.56% have had
formal schooling. Those who have attended school mostly only had elementary schooling as the
largest percentages fall under grades 1 to 3. Almost everyone who had formal schooling attended
a public school (96.80%) while only 3.20% had schooling in a private institution. Given these
information, it isn’t surprising that the highest educational achievement for 20.77% of the
respondents is none whatsoever. Reaffirming the statement earlier about most of the respondents
who had schooling only reaching until elementary, the highest percentage (9.58%) for those who
have had schooling fall under grade 6/7 as their highest educational achievement. Despite a large
P a g e | 37
percentage of the respondents having no formal schooling, the population in general is rather
literate; 92.85% of total respondents were literate.
Job indicators aren’t looking very good, as 72.42% of the total respondents reported to
have no job or work. This has the implication that this 72.42% live on a subsistence level,
growing food only for their consumption and survival. The reason for joblessness varies but for
majority of the unemployed, they believe it is due to their schooling (or lack thereof). A large
amount of people aren’t looking for jobs, as the data shows, 93.08% of total respondents are
either not currently looking for a job or have found no jobs available. For the 27.57% who have a
job, 52.22% of them are in agriculture, fisheries and forestry, highlighting the importance of the
agricultural sector in Northern Samar.
Utilities, properties and belongings were also covered by the data set, allowing some
insight into the welfare situation of Northern Samar. Majority of the respondents rely on a shared
community water system for their water, which implies a degree of sophistication in water
resources. However, while 42.51% of the respondents have access to a toilet facility that has a
water-sealed flush to a private sewerage/septic tank, 31.77% have no toilet facilities. For
electricity, half of the households have access to it while the other half do not. More than half
(67.80%) of the respondents do not have televisions, and only 17.52% of the respondents own a
refrigerator. 79.53% of the households do not have a Liquefied Petroleum Gas(LPG)/Gas stove
or range, and rely on coal or firewood for cooking purposes. Only 24.70% of the household
respondents have a telephone or cellular phone and only 3.03% own a computer. 89.40% of the
households do not own any vehicles, meaning only 10.60% have their own cars and/or vehicles.
More than half (57.21%) of the respondents live in their own house and lots, with the
next largest percentage (23.81%) owning their houses and living rent-free with consent of the
P a g e | 38
owner. For those who rent their house and/or lots, the average imputed rent per month is 488.203
pesos but actual rent per month averages at 24,710.15 pesos. The standard deviation for these
two figures are quite large, which means that the individual data points for rent may be far and
dispersed from these mean values. Light materials, such as bamboo, sawali, cogon and nipa,
have been used for the construction of the houses in which majority of these households reside.
49.03% of these houses use light materials for the construction of walls and 55.94% also use
light materials for the construction of roofs.
More than half (56.68%) of the household respondents are engaged in crop farming and
gardening, and the average income obtained from this activity is 7,651.571 pesos. The standard
deviation for this figure is also large however (280,289.2), so the figure should be taken with a
grain of salt. There aren’t a lot of households engaged in raising poultry and/or livestock, only
28.97% of the respondents do so. The average income for poultry/livestock raising is also
smaller than crop farming, only 1,807.476 pesos. The standard deviation for this figure
(79,725.18) is smaller compared to the standard deviation of crop farming, however. An even
smaller percentage of the responding households engage in fishing, only 15.70% have reported
to do so. The average income for those engaged in fishing is 2,211.097 pesos, with a standard
deviation of 10,590.21. Only 14.85% of the respondents were engaged in forestry practices,
netting them an average income of 554.981 pesos with a standard deviation of 15,327.66. For
wholesale/retail, 11.23% of the respondents have engaged in such activities. The average income
for wholesale/retail activities is 3,755.618 pesos, with a standard deviation of 30,301.85. Not a
lot of respondents were also engaged in manufacturing, as only 2.42% are engaged in such
activities. The average income for manufacturing is surprisingly only 331.956 pesos with a
standard deviation of 5520.372. Similarly, only 2.42% of the total respondents are in the service
P a g e | 39
industry, where they make an average income of 897.833 with a 35896.55 standard deviation. Of
the total households, only 6.15% are engaged in the transportation, storage and communication
which earns them an average income of 2,186.953 with a standard deviation of 27161.49. There
are almost no respondents who are engaged in mining and quarrying, only 0.75% are involved in
such activities. Those who are involved in mining and quarrying earn an average income of
134.009 pesos with a standard deviation of 3223.236. 5.63% of the responding households are
involved in construction, with an average income of 1865.858 pesos and a standard deviation of
55766.22. Majority of the respondents do not own any businesses (89.13%) but there are a few
that do. For the remaining respondents who own businesses, single proprietorships like sari-sari
stores are the most prevalent (10.16%). 79.09% of the households in the data set have no
member/s that earn a steady wage, while 15.67% only have one household member that has a
steady wage/salary. For households that have at least one household member with
wages/salaries, the average total income from salaries amounts to 32,849.97 pesos with a
standard deviation of 1404550. Overseas Filipino Workers (OFW) aren’t very common among
the households included, as only 2.08% of the households have a family member working as an
OFW. Some households also receive remittances from abroad or even domestically. Overseas
remittances per household averages at 2,130.338 pesos with a standard deviation of 34,411.56,
while domestic remittances per household average at 2,214.389 pesos with a standard deviation
of 41,536.89. The total income of the responding households, considering all possible sources of
income, averages to 69,166.11 pesos with a standard deviation of 1,457,701.
91.57% of the households in the data set have an average of 3 meals per day, which is a
good sign in terms of food security. The number of meals can change due to external factors, of
course, and the most common reason for the variation of meal number is food shortage. It should
P a g e | 40
be noted though, that only 83.82% of the respondents have reported to have experienced food
shortage. So while food shortages do occur in Northern Samar, a good amount of the population
still can have 3 full meals a day on average. For supplemental feeding of children 0-5 months
old, only 8.51% have received any form of supplement for the nutrition of their young children.
Majority (84.58%) of the household respondents have said that they have experienced
calamities. Almost everyone (98.83%) experienced typhoons, but only 39.61% have experienced
flooding. Other disasters were significantly less experienced; drought was experienced only by
4.14% of the respondents and fire was experienced only by 0.59%.
Households included in the data set show warming attitudes towards environmentallyfriendly and helpful practices. 58.33% of said households are willing to practice waste
management, even though currently only 36.41% of these households currently practice waste
management. Only 37.23% of the respondents do composting, but 65.10% of the total
respondents are willing to start practicing and learning about composting.
P a g e | 41
Figure 1: Map of Northern Samar
P a g e | 42
Figure 2: Map Indicating the Three Clusters
Table 1: Land Area and Barangays per Municipality
Municipality
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antonio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
No. of
Barangays
20
8
18
12
55
47
26
56
15
53
26
22
13
24
32
26
11
10
14
16
16
7
26
16
569
Source: National Statistics Office
Land Area
Hectares
4,750
2,800
13,000
3,500
28,540
27,630
11,510
21,470
5,700
21,100
11,950
28,000
12,170
28,900
17,960
15,500
3,160
2,750
25,600
2,820
18,310
1,590
22,420
18,670
349,800
%
1.4
0.8
3.7
1.0
8.2
7.9
3.3
6.1
1.6
6.0
3.4
8.0
3.5
8.3
5.1
4.4
0.9
0.8
7.3
0.8
5.2
0.5
6.4
5.3
100.0
P a g e | 43
Table 2: Population Distribution
Municipality
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antonio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Rural
11,794
7,503
11,828
5,360
38,217
17,566
18,063
43,273
6,921
24,356
23,315
9,839
5,706
21.575
16,230
6,640
7,235
21,172
13,051
13,807
4,917
10,352
9,696
10,053
Urban
6,357
2,906
5,334
4,602
35,895
0
2,536
11,407
3,416
6,601
4,485
2,166
3,042
5,172
10,274
2,541
763
3,095
3,457
10,312
1,893
3,078
2,753
2,827
Population
18,151
10,409
17,162
9,962
74,115
17,566
20,599
54,680
10,377
30,957
27,800
12,005
8,748
26,747
26,504
9,181
7,998
24,267
16,508
24,119
6,810
13,430
12,449
12,880
% to Total
3.68
2.11
3.48
2.02
15.02
3.56
4.18
11.08
2.10
6.27
5.63
2.43
1.77
5.42
5.37
1.86
1.62
4.92
3.35
4.89
1.38
2.72
2.52
2.61
Total
358,469
134,912
500,639
100.00
P a g e | 44
Table 3: Demographics
Average Age
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
26.44
23.91
23.94
26.67
23.98
23.99
24.97
24.64
24.86
22.96
25.01
22.55
24.04
24.43
23.14
23.26
27.56
24.92
24.61
23.1
26.49
22.12
25.56
23.03
Ave. length of Residency (yrs) Registered Voters Household Heads (HH) Spouse of HH Offspring of HH Religious (INC) Religious (RC)
794.14
94.14%
22.10%
18.18%
48.39%
48.49%
22.10%
841.81
92.56%
19.70%
16.82%
55.23%
55.23%
19.70%
827.36
93.33%
20.02%
17.01%
53.58%
53.58%
20.02%
793.23
92.54%
20.08%
15.91%
51.03%
51.03%
20.08%
698.54
84.22%
19.77%
16.61%
52.38%
52.38%
19.77%
828.73
81.86%
18.50%
16.20%
57.15%
57.17%
18.50%
875.17
95.05%
21.48%
17.77%
49.32%
49.32%
21.48%
849.59
83.50%
19.89%
16.31%
53.76%
53.76%
19.89%
903.23
91.76%
19.78%
16.89%
52.12%
52.12%
19.78%
804.3
86.46%
18.86%
16.48%
54.84%
54.84%
18.86%
21.88
87.93%
20.12%
16.94%
56.67%
52.67%
20.12%
852.9
93.17%
18.09%
15.65%
56.20%
56.20%
18.09%
492.93
81.48%
19.22%
16.49%
54.17%
54.17%
19.22%
838.71
83.45%
19.98%
16.36%
52.99%
52.99%
19.98%
891.89
89.56%
18.16%
15.68%
55.01%
55.01%
18.16%
853.75
91.10%
18.56%
15.98%
56.79%
56.79%
18.56%
766.98
86.56%
22.37%
17.25%
46.55%
46.55%
22.37%
749.9
71.69%
20.64%
16.81%
49.44%
49.44%
20.64%
18.34
85.57%
20.79%
17.20%
49.49%
49.49%
20.79%
895.06
91.44%
17.90%
15.35%
55.92%
55.92%
17.90%
874.93
96.51%
21.51%
17.70%
48.99%
48.99%
21.51%
912.13
95.61%
17.72%
16.36%
55.69%
55.69%
17.72%
770.42
96.21%
21.27%
17.34%
49.72%
49.72%
21.27%
189.97
94.54%
18.18%
15.91%
53.04%
53.04%
18.18%
87.32%
19.72%
16.57%
52.88%
52.88%
19.72%
Table 4: Construction Materials of House
CONSTRUCTION MATERIALS OF HOUSE Walls
Roof
Strong Materials
32.64%
28.53%
Light Materials
49.03%
55.94%
Salvaged/Makeshift Materials
1.81%
1.86%
Mixed but Predominantly Strong
9.66%
7.94%
Mixed but Predominantly Light
5.68%
4.67%
Mixed but Predominantly Salvaged
1.19%
1.07%
100%
100%
Table 5: Appliances and Amenities
HOME APPLIANCES AND AMENITIES
Electricity
Television
Refrigerator
Stove/Range
Telephone/Cellphone
Computer
Car/Automobile
Yes
No
49.28%
32.20%
17.52%
20.47%
24.70%
3.03%
10.60%
50.72%
67.80%
82.48%
79.53%
75.30%
96.97%
89.40%
P a g e | 45
Table 6: Reason for Residing in Barangay
Job transfer Entrepreneurship Jobseeking Education Resettlement
Allen
6.05%
5.68%
1.30%
5.78%
73.26%
Biri
4.22%
1.09%
1.45%
5.79%
70.81%
Bobon
7.57%
4.09%
3.24%
4.66%
65.91%
Capul
5.11%
1.54%
0.43%
10.75%
55.13%
Catarman
10.54%
7.86%
4.66%
15.09%
47.47%
Catubig
8.06%
3.13%
3.31%
20.88%
44.29%
Gamay
5.54%
2.29%
2.44%
7.17%
54.03%
Laoang
5.33%
3.37%
2.68%
8.85%
44.10%
Lapinig
7.32%
4.61%
5.02%
15.85%
44.43%
Las Navas
6.69%
2.46%
6.60%
11.54%
64.31%
Lavezares
10.04%
0.76%
1.01%
5.27%
53.21%
Lope de Vega
4.89%
0.73%
5.90%
3.60%
72.25%
Mapanas
6.77%
0.65%
1.26%
1.35%
87.58%
Mondragon
7.15%
2.67%
2.39%
4.92%
56.16%
Palapag
5.88%
4.48%
4.44%
12.25%
45.75%
Pambujan
6.08%
2.22%
3.26%
11.05%
76.80%
Rosario
8.72%
1.01%
5.00%
6.59%
48.86%
San Antionio
8.85%
2.97%
6.59%
4.85%
70.59%
San Isidro
3.50%
0.44%
0.82%
2.75%
16.43%
San Jose
8.13%
2.33%
3.90%
10.70%
45.31%
San Roque
5.38%
2.34%
4.44%
6.90%
51.35%
San Vicente
3.13%
1.27%
11.42%
5.16%
51.44%
Silvino Lobos
11.00%
2.44%
5.50%
4.70%
49.79%
Victoria
0.50%
0.16%
0.41%
1.03%
36.83%
Total
7.19%
2.72%
2.80%
7.35%
49.42%
P a g e | 46
Table 7: Tenure Status of House
Own House & Lot Own House, Rent-free Lot Own House, Rent Lot Rent-free House & Lot
Allen
63.15%
19.95%
1.80%
6.64%
Biri
58.98%
26.59%
3.27%
4.68%
Bobon
58.11%
29.67%
0.79%
4.72%
Capul
72.68%
18.65%
0.35%
3.96%
Catarman
41.81%
24.70%
16.84%
5.40%
Catubig
56.93%
12.04%
20.88%
3.59%
Gamay
69.37%
18.39%
1.40%
5.56%
Laoang
59.16%
26.30%
5.21%
4.55%
Lapinig
65.31%
21.13%
2.06%
5.50%
Las Navas
58.24%
25.21%
5.18%
6.16%
Lavezares
62.73%
19.23%
2.54%
7.21%
Lope de Vega
64.88%
23.13%
1.71%
3.73%
Mapanas
50.00%
21.22%
21.04%
2.40%
Mondragon
82.26%
8.17%
2.38%
3.80%
Palapag
53.00%
28.38%
6.07%
3.22%
Pambujan
64.02%
21.87%
4.76%
4.53%
Rosario
56.08%
26.55%
0.28%
8.96%
San Antionio
61.49%
20.66%
1.42%
7.68%
San Isidro
37.14%
49.28%
0.61%
4.82%
San Jose
40.03%
30.68%
2.56%
3.05%
San Roque
61.37%
25.27%
0.96%
7.74%
San Vicente
77.51%
6.89%
0.29%
1.30%
Silvino Lobos
60.98%
28.52%
0.81%
4.79%
Victoria
42.56%
38.80%
9.15%
2.78%
Total
57.21%
23.81%
5.95%
5.00%
Table 8: Rent per Month
Imputed
Actual
Average
Standard Deviation
488.203
3,938.695
24,710.150
2,324,034.000
P a g e | 47
Table 9: Economic Activity per Municipality
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
Engaged in Crops Ave. Income (Crops) Engaged in Livestock Ave. Income (Livestock) Engaged in Fishing Ave. Income (Fishing) Engaged in Forestry Ave. Income (Forestry) Engaged in Wholesale/Retail Ave. Income (Sales)
44.09%
27,279.92
29.89%
1,462.47
10.61%
1,614.77
9.66%
459.21
16.38%
5,321.08
50.34%
3,165.92
27.90%
1,313.41
64.29%
10,421.19
27.32%
413.52
12.54%
2,795.46
52.14%
4,746.12
41.56%
2,291.12
4.05%
376.89
7.77%
452.32
10.10%
2,104.30
73.98%
7,333.29
45.01%
2,469.56
33.58%
3,023.59
11.43%
321.09
14.74%
2,460.38
40.64%
6,064.98
23.82%
2,800.79
3.89%
480.31
8.37%
415.64
14.09%
6,783.65
78.16%
9,404.63
46.11%
2,595.11
2.04%
199.41
11.83%
719.12
5.78%
599.36
75.25%
9,893.89
32.12%
2,008.82
20.58%
2,983.79
17.37%
334.66
12.08%
4,355.84
47.92%
5,656.42
19.47%
1,131.23
21.76%
3,055.82
15.50%
839.39
9.96%
3,021.45
74.25%
7,581.26
10.12%
307.27
27.62%
1,576.15
21.33%
500.44
8.60%
2,070.61
75.34%
10,436.85
47.26%
2,186.63
1.59%
87.77
13.25%
250.61
8.20%
1,523.85
52.07%
6,497.49
19.76%
3,062.97
16.84%
2,493.69
7.31%
1,326.42
9.40%
2,523.80
51.06%
6,849.32
6.73%
210.67
10.65%
1,352.92
3.50%
126.77
9.95%
2,367.64
49.34%
5,429.53
41.50%
2,258.34
10.78%
3,256.01
7.60%
257.78
7.54%
1,793.35
67.38%
5,470.86
40.70%
1,979.39
19.90%
3,817.85
17.09%
516.87
11.56%
4,828.70
59.63%
5,901.24
21.83%
1,290.58
10.85%
2,135.16
7.39%
400.10
9.00%
3,543.19
55.50%
4,031.83
47.68%
2,110.33
27.19%
2,985.14
11.30%
795.18
9.42%
2,032.91
37.93%
1,970.37
18.26%
1,148.29
36.25%
2,897.95
9.41%
299.07
10.53%
1,445.61
53.79%
5,319.72
24.72%
1,070.23
29.01%
2,718.15
30.60%
998.15
15.25%
4,781.41
41.42%
7,922.56
21.28%
1,262.66
16.37%
4,740.41
11.68%
868.65
19.03%
7,393.85
62.65%
7,074.19
34.10%
1,750.43
8.84%
1,336.03
21.45%
420.29
8.31%
2,882.40
70.41%
6,012.15
33.22%
2,307.00
66.10%
12,402.47
30.41%
428.22
8.63%
2,263.49
80.24%
13,065.34
3.87%
162.65
0.17%
9.88
1.01%
123.71
2.52%
632.98
50.12%
5,931.01
22.43%
1,456.89
13.63%
1,772.92
8.96%
296.00
13.44%
4,310.56
88.29%
11,806.80
70.81%
718.50
15.56%
65.00
81.67%
580.39
11.03%
4,390.89
56.68%
7,651.57
28.97%
1,807.48
15.70%
2,211.09
14.85%
554.98
11.23%
3,755.62
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
Engaged in Manufacturing Ave. Income (Manufact) Engaged in Service Ave. Income (Service) Engaged in Transportation Ave. Income (Transport) Engaged in Mining Ave. Income (Mining) Engaged in Construction Ave. Income (Construct)
1.95%
348.18
3.25%
2,014.35
9.51%
3,298.34
1.07%
158.83
7.11%
2,430.18
2.39%
231.10
0.93%
106.66
3.76%
1,272.17
2.44%
111.78
14.05%
3,140.75
1.22%
413.45
1.92%
495.46
5.30%
1,917.35
0.90%
266.32
7.10%
1,942.56
2.56%
243.22
2.01%
543.18
5.06%
1,077.35
4.36%
179.68
8.87%
1,593.12
1.24%
287.41
3.15%
1,730.53
7.63%
3,474.46
0.78%
328.81
4.41%
4,221.23
1.24%
96.43
1.14%
126.95
2.19%
485.47
0.68%
41.15
3.49%
403.06
4.32%
704.04
4.16%
875.51
8.44%
2,565.85
0.50%
63.83
5.52%
1,247.47
2.17%
388.16
0.65%
159.77
4.19%
1,327.13
0.17%
90.62
0.62%
189.91
0.74%
190.52
0.64%
219.90
3.19%
1,409.88
0.20%
12.87
2.16%
610.37
2.18%
134.89
1.05%
228.90
3.26%
852.79
0.19%
34.89
3.05%
604.96
1.99%
295.28
1.93%
1,969.72
5.48%
1,965.30
1.11%
181.93
8.29%
1,911.99
1.38%
165.32
0.92%
487.19
2.40%
508.11
0.65%
48.66
1.84%
376.08
1.32%
203.65
0.84%
73.25
7.01%
1,413.28
0.30%
111.09
3.35%
569.56
3.00%
277.13
1.95%
496.37
6.48%
1,543.32
0.67%
74.89
4.97%
881.85
2.15%
269.87
3.15%
1,119.18
6.62%
2,371.61
0.54%
52.82
5.43%
1,254.39
2.41%
249.87
5.36%
741.79
2.88%
556.56
0.94%
31.90
10.77%
1,832.20
2.24%
92.17
2.46%
237.38
3.59%
484.48
1.18%
34.18
7.90%
1,279.17
1.67%
222.68
4.35%
890.47
6.68%
2,870.43
0.62%
174.27
5.78%
1,838.83
3.42%
1,351.73
6.19%
2,551.89
14.01%
6,652.76
0.53%
114.27
10.27%
6,779.22
9.75%
621.36
3.44%
711.73
9.91%
2,923.33
0.53%
67.07
7.06%
1,160.12
8.77%
360.92
3.84%
629.73
2.26%
770.55
0.68%
45.55
3.77%
771.24
0.13%
3.87
0.42%
38.88
0.80%
185.67
0.59%
38.55
0.80%
162.21
1.93%
219.27
2.28%
871.32
11.43%
4,269.52
1.81%
198.40
9.77%
2,538.46
1.45%
77.89
1.24%
664.92
4.96%
1,492.69
0.13%
55.98
21.24%
2,400.26
2.42%
331.96
2.42%
897.88
6.15%
2,186.95
0.75%
134.01
5.63%
1,865.86
P a g e | 48
Table 10: Income per Sector
Average
Standard Deviation
Farming
Php 7,651.57
280,289.200
Livestock
Php 1,807.48
79,725.180
Fishing
Php
221.11
10,590.210
Forestry
Php
554.98
15,327.660
Wholesale/Retail Php 3,755.62
30,301.850
Manufacturing
Php
331.96
5,520.372
Service
Php
897.88
35,896.550
Transportation
Php 2,186.95
27,161.900
Mining
Php
134.01
3,223.236
Construction
Php 1,865.86
55,766.220
Table 11: Work Statistics
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
Work Indicator Jobfinding Agriculture & Forestry Unskilled Labor Service
29.72%
8.98%
34.38%
23.34%
6.71%
27.85%
9.28%
55.37%
13.15%
8.84%
27.92%
6.35%
41.38%
26.10%
7.08%
30.23%
5.31%
58.33%
8.28%
4.19%
28.07%
6.59%
34.62%
16.51%
12.24%
26.05%
11.29%
67.17%
13.38%
5.69%
30.62%
3.88%
53.01%
18.24%
9.63%
26.49%
7.42%
55.70%
14.51%
4.63%
24.84%
6.95%
71.46%
6.28%
2.98%
29.19%
6.45%
73.29%
8.91%
3.58%
27.04%
17.09%
49.31%
13.67%
5.06%
28.13%
7.14%
57.32%
12.77%
11.56%
30.36%
8.66%
50.84%
18.22%
5.18%
26.99%
8.13%
57.26%
7.37%
10.75%
24.19%
7.47%
57.68%
11.45%
6.73%
25.17%
9.54%
48.96%
19.61%
12.34%
30.15%
9.85%
45.31%
16.15%
14.53%
30.98%
14.45%
51.83%
13.86%
10.16%
30.47%
5.42%
32.55%
29.05%
18.49%
25.56%
4.20%
53.22%
15.25%
6.35%
30.02%
7.00%
68.64%
6.01%
5.52%
18.68%
8.58%
88.09%
1.12%
0.68%
26.70%
6.29%
48.79%
11.70%
6.76%
26.99%
5.94%
71.33%
6.59%
2.82%
27.57%
8.00%
52.22%
14.46%
8.00%
P a g e | 49
Table 12: Sources of Household Income
Average
Standard Deviation
Salaries/Wages
32,849.970
1,404,550.000
Domestic Receipts
2,130.338
34,411.560
Foreign Receipts
2,214.389
41,536.890
Total Household Income
69,166.110
1,457,701.000
Table 13: Reasons for having no Job
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
Too Young/Old Schooling Housekeeping Believes None Available Bad Weather
41.16%
27.27%
19.98%
3.67%
0.09%
32.04%
38.30%
19.41%
3.24%
0.25%
34.74%
36.86%
20.02%
2.27%
0.20%
32.11%
41.66%
17.96%
2.94%
0.06%
31.65%
35.13%
17.27%
2.91%
0.33%
3.53%
33.56%
19.15%
6.02%
0.28%
36.29%
36.19%
22.33%
2.32%
0.20%
29.64%
38.57%
21.77%
2.41%
0.21%
30.24%
38.54%
21.70%
1.65%
0.01%
36.67%
35.29%
19.79%
2.69%
0.17%
25.37%
40.36%
20.12%
3.54%
0.08%
34.89%
38.29%
18.61%
3.02%
0.52%
25.49%
46.76%
18.13%
1.67%
0.35%
44.19%
26.08%
21.24%
3.66%
0.18%
35.70%
35.51%
19.42%
2.05%
0.92%
26.07%
44.38%
19.44%
3.39%
0.36%
33.92%
32.85%
20.18%
4.65%
0.77%
30.34%
40.36%
20.12%
1.84%
0.08%
28.45%
41.87%
21.16%
1.23%
0.14%
33.97%
37.26%
22.34%
2.00%
0.07%
21.88%
46.91%
22.54%
2.52%
0.00%
42.68%
25.31%
21.68%
1.56%
7.14%
29.93%
37.64%
23.22%
4.70%
0.04%
31.89%
37.89%
25.37%
1.02%
0.03%
32.94%
36.35%
20.27%
2.76%
0.45%
P a g e | 50
Table 14: Education
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
Literacy rate Attended Public School Attended Grade 6/7 Elementary Graduate Highschool Graduate College Graduate
95.61%
95.04%
9.40%
2.67%
3.59%
5.30%
96.72%
99.28%
6.49%
9.97%
8.52%
3.76%
93.26%
97.05%
9.45%
1.13%
2.40%
5.39%
98.95%
99.22%
4.45%
9.65%
9.78%
6.96%
92.55%
92.20%
9.05%
2.51%
2.74%
5.55%
91.76%
98.74%
15.36%
5.21%
2.69%
1.66%
92.00%
99.23%
9.65%
4.44%
4.23%
3.65%
94.12%
98.68%
14.51%
2.89%
2.24%
3.29%
95.53%
98.59%
10.61%
2.26%
4.88%
3.20%
83.02%
99.37%
8.12%
2.04%
1.63%
1.07%
97.85%
96.17%
7.73%
11.53%
8.19%
5.25%
95.25%
99.13%
3.88%
7.39%
5.64%
3.07%
91.17%
96.86%
11.00%
1.55%
3.52%
3.18%
93.41%
99.29%
10.99%
5.62%
3.23%
2.91%
93.17%
99.52%
10.19%
4.57%
4.30%
3.62%
95.97%
81.68%
5.45%
6.15%
11.91%
4.63%
94.83%
89.40%
9.74%
4.24%
6.25%
6.35%
90.00%
95.60%
6.56%
5.56%
7.31%
4.46%
92.98%
96.88%
9.70%
2.27%
2.63%
4.29%
95.80%
99.58%
9.88%
4.70%
4.64%
3.44%
97.37%
97.92%
7.94%
6.79%
8.82%
3.41%
80.50%
94.73%
7.66%
0.24%
0.89%
0.65%
96.18%
98.09%
10.45%
2.26%
4.45%
5.07%
88.87%
99.13%
7.66%
2.56%
3.82%
2.83%
92.85%
96.80%
9.58%
4.19%
4.16%
3.92%
P a g e | 51
Table 16: Calamities and Disasters
P a g e | 52
Table 17: Environmental Awareness
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
Willing to do Waste Management Doing Waste Management Willing to Compost Doing Compost
48.46%
46.00%
44.45%
35.96%
48.55%
25.54%
66.83%
61.02%
72.95%
59.03%
68.57%
58.50%
96.54%
10.38%
93.16%
6.17%
58.54%
41.53%
61.69%
40.49%
58.52%
21.21%
62.84%
28.20%
27.22%
25.11%
41.27%
48.55%
30.03%
30.10%
42.15%
44.59%
48.48%
24.11%
52.13%
27.05%
90.79%
8.08%
89.65%
7.94%
52.54%
38.42%
62.89%
41.25%
64.44%
45.48%
52.66%
24.80%
31.89%
21.93%
51.55%
38.23%
72.04%
48.85%
74.03%
29.69%
57.80%
37.45%
61.98%
41.95%
87.10%
51.50%
72.65%
27.31%
64.86%
24.76%
54.55%
20.11%
83.66%
69.80%
82.38%
38.20%
79.94%
44.73%
78.42%
32.75%
73.75%
25.00%
72.62%
26.81%
23.10%
78.90%
35.75%
86.85%
20.05%
21.61%
57.50%
49.33%
41.00%
46.37%
58.83%
58.72%
94.60%
20.13%
96.90%
18.68%
58.33%
36.41%
65.10%
37.23%
Table 18: Nutritional Data
Allen
Biri
Bobon
Capul
Catarman
Catubig
Gamay
Laoang
Lapinig
Las Navas
Lavezares
Lope de Vega
Mapanas
Mondragon
Palapag
Pambujan
Rosario
San Antionio
San Isidro
San Jose
San Roque
San Vicente
Silvino Lobos
Victoria
Total
Has 3 Meals a Day
Experienced Food Shortage Meal Number Variation (Shortage) Meal Number Variation (Fasting) Healthy Infant/Toddler Supp. feeding for child
97.85%
6.97%
19.51%
21.95%
94.39%
2.40%
89.27%
22.63%
83.04%
10.71%
98.76%
16.34%
97.73%
8.21%
75.00%
22.73%
93.22%
10.83%
96.69%
4.16%
30.00%
58.57%
97.70%
17.29%
92.38%
12.32%
43.55%
31.75%
94.47%
5.20%
88.74%
23.66%
65.77%
17.02%
89.02%
20.83%
93.10%
16.04%
80.92%
10.53%
82.68%
6.02%
95.27%
13.67%
81.96%
14.97%
96.34%
1.55%
97.25%
4.77%
70.59%
8.24%
88.20%
0.98%
84.93%
37.22%
83.22%
13.31%
92.80%
10.70%
96.54%
7.47%
47.83%
38.70%
98.03%
5.07%
84.47%
30.78%
88.01%
8.72%
85.35%
11.98%
93.93%
8.08%
37.50%
22.22%
96.04%
7.72%
85.83%
22.69%
87.64%
8.76%
95.89%
24.84%
71.01%
31.46%
87.26%
8.17%
95.81%
5.97%
97.41%
11.65%
43.66%
25.35%
98.95%
2.77%
97.14%
20.73%
74.47%
6.38%
88.89%
20.00%
94.48%
17.14%
79.05%
10.28%
87.32%
10.45%
96.44%
4.47%
56.25%
26.56%
92.68%
9.69%
84.91%
22.19%
70.12%
17.68%
95.20%
4.71%
93.15%
7.33%
86.67%
6.67%
89.23%
10.76%
85.37%
24.56%
90.82%
8.70%
81.43%
2.10%
97.88%
5.02%
34.38%
54.69%
97.20%
4.85%
96.88%
10.94%
79.00%
17.00%
88.88%
22.14%
91.57%
16.16%
71.18%
17.44%
93.08%
8.51%
P a g e | 53
Table 19: Water and Toilet Access
Shared Community Water System Toilet Access (Water flush) No Toilet Access
Allen
36.07%
52.01%
31.01%
Biri
20.78%
46.10%
29.07%
Bobon
6.55%
51.11%
20.12%
Capul
46.47%
51.53%
39.60%
Catarman
16.44%
43.95%
26.00%
Catubig
84.93%
48.56%
12.39%
Gamay
40.65%
39.81%
42.34%
Laoang
9.24%
35.06%
27.03%
Lapinig
37.59%
45.85%
43.34%
Las Navas
39.42%
40.23%
27.04%
Lavezares
51.49%
46.38%
33.32%
Lope de Vega
26.13%
38.53%
38.66%
Mapanas
3.72%
36.87%
37.59%
Mondragon
20.95%
55.71%
20.25%
Palapag
19.42%
40.11%
26.45%
Pambujan
40.75%
39.56%
36.45%
Rosario
3.36%
54.29%
36.75%
San Antionio
58.46%
47.35%
40.87%
San Isidro
33.60%
40.23%
36.99%
San Jose
16.02%
29.09%
35.95%
San Roque
56.44%
35.89%
50.62%
San Vicente
66.33%
13.87%
61.96%
Silvino Lobos
63.99%
51.91%
34.12%
Victoria
49.44%
34.87%
48.16%
Total
31.25%
42.51%
31.77%
P a g e | 54
ADDITIONAL STEPS AND DATA
In order to get a clearer picture, the information gathered from the analysis of the CBMS
data can be supplemented by a CBMS survey. This survey will bring more up-to-date
information both on the area of Northern Samar as well as the households that reside there. It
will also serve to add more data points for future analyses and maximizing the efficiency and
efficacy of potential projects and policies. The survey can be done in either English or Filipino,
depending on which language the respondents are more comfortable answering. The scope of the
survey will cover much of the same area that the current data set has, including some qualitative
data that has not been analysed completely in this paper.
Besides geospatial and sociological data on Northern Samar, meteorological data is also
another important factor to consider in making effective climate change mitigation and
adaptation projects and policies. PAGASA has meteorological data that outlines climate change
trends in the Philippines which can be used to supplement the current analysis done in this paper.
The Philippines, much like the rest of the world, has experienced steadily rising
temperatures. PAGASA (2011) has recorded an average increase in temperature of 0.648°C, or
0.0108°C on average annually, from 1951 to 2010. They’ve also seen an increase of 0.36°C and
1.0°C for maximum and minimum temperatures respectively. Trend analysis of tropical cyclone
occurrences show that an average of 20 cyclones enter the Philippine Area of Responsibility
(PAR) every year. While there is high variability in the number of cyclones that enter the PAR
yearly, there is no indication for increases in frequency (PAGASA, 2011). Cyclones with
maximum sustained winds of over 150 kph increased slightly during the El Niño event however.
PAGASA has also found that there a slight increase to cyclones passing over Visayas during
1971 to 2000 when they conducted a 30-year analysis on the passage of cyclones over the three
P a g e | 55
main islands of the Philippines. Indices have also been developed by PAGASA in order to detect
trends in extreme daily events. Their analysis of extreme daily maximum and minimum
temperatures reveal that there are a statistically significant increasing number of hot days and a
decreasing number of cool nights. Increases or decreases in extreme daily rainfall proved
statistically insignificant however, with intensity and frequency of extreme rainfall also being
insignificant.
PAGASA also has made climate projections, which offer an interesting view on what to
expect in the future. Mean temperatures in all areas of the Philippines are expected to increase by
0.9°C to 1.1°C in 2020, and 1.8°C to 2.2°C in 2050 (PAGASA, 2011). Hot temperatures have
also been projected to be more constant in the future. The number of days with maximum
temperatures exceeding 35°C have also been projected to increase in 2020 and 2050. To be
specific, Northern Samar is projected to experience 411 days with maximum temperatures
exceeding 35°C by 2020 and 1627 days by 2050 (PAGASA, 2011). Heavy rainfall is also
expected to continue increasing in frequency, albeit only in Luzon and Visayas. Northern Samar
is anticipated to have 86 days of heavy rainfall by 2020 and 94 days by 2050. Despite this, the
number of dry days is still expected to increase all over the country in 2020 and 2050. For
Northern Samar, this means an estimated 7288 dry days by 2020 and 6816 dry days by 2050
(PAGASA, 2011).
P a g e | 56
POLICY IMPLICATIONS
One of the most striking points in the data analysis are the figures on education; Around
92.85% of them are literate, but slightly over 70% of Northern Samar’s population have not had
any formal schooling at all. This crops up again in reasons why the unemployed do not have
jobs; 36.35% have (lack of) schooling as their reason for being unemployed. Those who have
had schooling have attended public school, with only a minority attending private schools. One
of the potential reasons for the lack of schooling in Northern Samar’s population would be
logistics, the route spanning the distance between the residence of students and the schools can
possibly be too far and difficult. Northern Samar is not that urbanized, which can mean that the
existing road system does not service certain barangays where students may be residing. Another
reason may be that, since a lot of the respondents work in the agricultural sector, they might be
too busy working to attend school. Though a large portion of the respondents were unemployed,
there still exists other potential reasons for the figure on the lack of schooling; a lack of income
to meet school-related expenses is one, classroom overcrowding and lack of educational facilities
is potentially another, so on and so forth.
Due to the pivotal role of education in development overall, this is indeed one noteworthy
focus for policies. They can target the problem directly, such as adding more classrooms or
distributing teaching staff better, or indirectly by improving road connections between schools
and more remote barangays for the benefit of both the students and the other residents. Investing
in education would increase the human capital of Northern Samar, as more value is added to
someone who has finished high school or even college. Education equips them with a broader
skill set for potentially higher-paying and more technically demanding work. For farmers and
those who are in the agricultural industry, education can provide opportunities for specialization
P a g e | 57
which lets them obtain particular skillsets and knowledge to improve on whichever part of the
agricultural industry they belong to. Integrating newer technologies into the learning
environment would help introduce and familiarize the students with such technologies, which
can potentially be used alongside the more established and traditional agricultural practices. An
example of this would be using the internet for weather forecasts, price checks, demand on crops
and other additional crop information and the like.
Agriculture plays a large role in the taken sample, due to having the most workers
compared to the other sectors. Policies on modernizing agricultural practices and making them
more efficient would greatly benefit smaller farmers which are in abundance in Northern Samar.
Modernizing and streamlining agricultural practices also would provide more food for the
consumption of both the farmers and the market in general, increasing food security as well.
Better dissemination of information such as prices, meteorological data, and the latest
developments in agriculture would improve both productivity and profitability for farmers.
Easier access to credit helps small-scale farmers modernize their practices and increase their
productivity, potentially increasing the scale of their farming activity as well. With Northern
Samar not being highly urbanized (72.66% of respondents live in rural areas), this can provide
more opportunities for the agricultural industry to expand further and generate more jobs as well
as output. The adaptation strategies by the Department of Agriculture, which were described
earlier, serve as a good starting point for policies and programs aimed at modernizing agriculture
and increasing overall sustainability, profitability, and productivity. In particular, easier credit
access for small famers would make it easier for them to improve and upgrade their farming
equipment and machinery thus improving their productivity.
P a g e | 58
Nutrition in general seems to a fairly good level in the given sample, with almost 92% of
the respondents being able to eat 3 times a day and only 8.51% of toddlers needing additional
nutritional supplements. 16.16% of the sample have experienced food shortages, which is also
the largest reason for variations in meal number for the given sample. Food shortage presents the
most direct and most pressing challenge to food security in the province, and thankfully the issue
isn’t that bad in Northern Samar. Even though only a small slice of the sample has experienced
food shortage, steps should still be undertaken to prevent this. A readily accessible stockpile of
staple food should provide a buffer in case shortages happen. Highly productive agricultural
activities, mentioned in the previous paragraph, also help in preventing the occurrences of food
shortages. Using different strains of crops that have been enriched with more nutrients could also
increase the nutrition status of the province as well as increase the value of the crops.
Without a doubt, disaster preparedness and adaptability should be a priority on
policymaking; the large amount of respondents who have experienced calamities (84.58%)
would benefit from these policies. While practically all of the respondents have experienced
typhoons, only 39.61% have experienced flooding; this means that more focus can temporarily
be given to other aspects for typhoon preparedness and adaptability over flood control. Flood
control is still an important step in disaster mitigation and adaptation and should not be ignored
completely however, especially since floods can ruin both crops and lives. Droughts were fairly
uncommon according to the respondents, which is a good sign for both food security and
agriculture. While efficient irrigation is a long-term goal for a productive and sustainable
agriculture industry, in the short run they can focus on other goals like improving crop yields
through better selections of seeds, improved farming practices, modernization and the like. The
National Climate Change Action Plan of the Climate Change Commission provides achievable
P a g e | 59
and realistic courses of actions that Northern Samar could undergo to increase their capability in
preventing, handling and adapting to potential disasters. Educating themselves and learning
about climate change and the various methods to potentially adapt to it is also a laudable effort
on the part of Northern Samar, and they should continue to further seek out and learn as much as
they can on the topic of climate change mitigation and adaptation.
The receptiveness of the respondents towards environmentally-friendly practices such as
composting and waste management could be reinforced further with properly designed policies
that incentivises such behaviour. This would increase the number of those who use
environmentally-friendly and environmentally-sound farming practices and lifestyle choices,
helping the people and potentially the province to be more adaptive and resilient towards climate
change and its adverse effects. This openness can also help policies and projects mentioned in
earlier paragraphs to be implemented and spread faster.
Inclusion is something policies should aspire for, and climate change adaptation and food
security are no exceptions. Building these policies and projects around the communities they are
implemented in lends sustainability and lets the community members feel and do their part in
disaster risk reduction, climate change adaptation and food security. This sense of inclusion and
participation allows them to feel that they are not only doing their part, but also to appreciate
further the boons that were brought about through their efforts. Working with this idea, smaller
and more spread-out projects are sustainable and cost-efficient compared to immediately going
for larger and grander projects. Most of these policy implications and recommendations have to
be tailored with the income bracket of the majority in mind in order to be most effective. As an
example: it would be of no use to small farmers if the fertilizer or equipment that they need is too
expensive for them to afford.
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CONCLUSION
Food security and climate change are two important issues that policymakers have to
wrestle with. Northern Samar is no exception. Diversifying the province’s sources of income
allowed them some degree of flexibility and sustainability, but there are still clearly areas in need
of improvement. According to the sample used in this study, a good number of those involved in
the agriculture industry are classified as small or even subsistence farmers. Seeing as Northern
Samar is mostly rural and has a significant portion of workers in the agricultural sector, this
allows for opportunities to expand and perhaps intensify farming and agricultural efforts. In
terms of potential policies and projects, there are already ones from the Department of
Agriculture and the National Climate Change Commission that are easily adaptable on a local
level by the province; examples of such are easier credit access, agricultural modernization
plans, infrastructure building, and information dissemination.
Achieving food security is by no means an easy feat, a lot of countries struggle in
providing a steady, sustainable and efficient source of food for their people. Climate change is no
easier, as even developed countries cannot fully control their contributions to climate change as
much as they would like to. It’s not going to be an easy road for Northern Samar, trying to adapt
and mitigate climate change and disaster risks while still having enough food to go around for
everybody. Showing initiative by trying to learn about climate change mitigation and adaptation
is a good starting point, but Northern Samar will need perseverance, determination, cooperation,
and properly informed decision-making to achieve goals on food security in the midst of climate
change.
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