(formerly Center for Policy and Development Studies)

College of Public Affairs and Development

University of the Philippines Los Baños

College, Laguna 4031

Philippines

Telephone: (63-049) 536-3455

Fax: (63-049) 536-3637

Homepage: www.uplb.edu.ph

Center for Strategic Planning and Policy Studies

(formerly Center for Policy and Development Studies)

College of Public Affairs and Development

University of the Philippines Los Baños

College, Laguna 4031

Philippines

Telephone: (63-049) 536-3455

Fax: (63-049) 536-3637

Homepage: https://cpaf.uplb.edu.ph/

Working Paper No. 2017-04

STATE OF THE ART OF CLIMATE INFORMATION

SYSTEM (CIS) IN THE PHILIPPINES

Aileen Lapitan, Agnes C. Rola, Maria Cristina Alvarez, Catherine Cervantes,

Purisima Bayacag, Joy Lizada, Julius Manzano, Teresita Espinosa,

Fitz Jimenez, Daiseirie Pungtilan, and Ralf Tabanyag

ii

The CSPPS Working Paper series reports the results of studies by the Center

researchers and CPAf faculty, staff and students, which have not been reviewed.

These are circulated for the purpose of soliciting comments and suggestions.

The views expressed in the paper are those of the authors and do not

necessarily reflect those of CSPPS, the agency with which the authors are affiliated,

and the funding agencies, if applicable.

Please send your comments to:

The Director Center for Strategic Planning & Policy Studies (formerly CPDS)

College of Public Affairs and Development

University of the Philippines Los Baños

College, Laguna 4031

Philippines

Email: mmpaunlagui@up.edu.ph

iii

ABSTRACT

Climate information services (CIS) are key interventions to aid farmers in

their adaptation and mitigation decision making amidst increasing climate variability.

This paper describes the state of the art of climate data generation and climate

information dissemination for use by the agriculture sector in the Philippines. It

assesses current issues and concerns in the operationalization of the existing climate

information gathering instruments in selected study sites across the country and

analyzes the interaction of local climate data generators and users in the agricultural

sector. Findings indicate that due to unstable internet connection in many areas,

access of local extension agents to location specific climate data needs much

improvement. Despite availability of local climate information gathering instruments,

local extensionists continue to rely on generalized advisories reported through

PAGASA bulletins as well as televisions and radios. If local extensionists are to use

location specific climate data, they will need technical assistance in the processing of

climate data into information for farmers’ use in decision making. Meanwhile,

farmers await improved CIS to help them adapt to the vagaries of climate change.

These findings bolster the rationale for development of local seasonal climate forecast

and extension delivery that can be initiated by both the national and local

governments.

Keywords: climate change, climate information, extension, adaptation, mitigation,

agriculture

iv

Table of Contents

Page

I. Introduction 1

II. Methodology 2

2.1 Study Area 2

2.2 Data Collection 2

III. Climate Information System Data Generation 2

3.1 Weather data collection Instrument and facilities in the Philippines 2

3.1.1 Instrument Hardware 6

3.1.2 Functionality and Maintenance 7

3.1.3 Issues 9

3.2 How are data sent to the central server and to the end users 10

IV. Data Uses 12

4.1 Role of Agricultural Extension in Providing Climate Information

to Farmers

14

V. Knowledge of Farmers about CIS 16

VI. Summary and Conclusion 17

VII. References 17

List of Tables

Page

Table 1. PAGASA Weather Data Collection Instruments/Facilities 4

Table 2. Number of AWS distributed per region by the Agromet-cum

Climate Change Project

4

Table 3. Number and percentage of weather data collection instrument/

facilities visited by the CIS Project Team

5

Table 4. Maintenance of the climate data instrument 8

Table 5. Problems encountered and how these were resolved 9

Table 6. Knowledge of operators regarding data generated and storage 11

Table 7. Climate information received by the provincial and municipal

governments in the CIS municipal governments in the CIS study

sites

13

Table 8. Responses on the use of data generated from the climate data

instruments

13

Table 9. Formulation of seasonal forecast advisories and farmers’

responsiveness to these advisories 15

List of Figures

Page

Figure 1. Agromet Station and AWS in the Philippines and Agromet

Station and AWS covered by the CIS study

4

Figure 2. Parts of the Automatic weather station 4

Figure 3. Flow of weather data from AWS to the end users 5

Figure 4. PAGASA data/ information flow diagram 8

STATE OF THE ART OF CLIMATE INFORMATION SYSTEM (CIS)

IN THE PHILIPPINES

Aileen Lapitan, Agnes C. Rola, Maria Cristina Alvarez, Catherine Cervantes, Purisima

Bayacag, Joy Lizada, Julius Manzano, Teresita Espinosa, Fitz Jimenez,

Daiseirie Pungtilan, and Ralf Tabanyag

I. Introduction

Increasing climate variability, often characterized by changes in the occurrence of

extreme climate events like El Nino, La Nina, etc., affects almost all sectors in varying

degrees (Basco, n.d.). In the Philippines, agriculture is greatly affected by climate change.

Agricultural productivity is influenced by climate. According to Olesen & Bindi,

(2002), the possible increase in water shortage and extreme weather events may cause

lower harvestable yields, higher yield variability and a reduction in suitable areas for

traditional crops. Higher temperatures eventually reduce yields of desirable crops while

encouraging weed and pest proliferation. Change in precipitation pattern increase the

likelihood of short-run crop failure and long run production decline. Although there will

be gains in some crops in some regions of the world, the overall impacts of climate

change on agriculture are expected to be negative, threatening global food security

(Nelson et al. 2009).

Adaptation in agriculture to climate change is important for impact and

vulnerability assessment and for the development of climate change policy. A wide

variety of adaptation options has been proposed as having the potential to reduce

vulnerability of agricultural systems to risks related to climate change, often in an ad

hoc fashion (Smit & Skinner, 2002).

One way to adapt and mitigate the effects of climate change is through the use of

the climate information. Weather data generated can be used to create a weather forecast

for a short-period of time and a climate forecast for a longer period of time. Weather and

climate forecasts, for some, usually have convenience value and changes in weather

usually only mean adjustments in daily activities. For others, particularly farmers,

seasonal climate forecasts may have more profound impacts. Seasonal climate forecasts

are a potential tool that can assist individuals and organizations in coping and adapting to

variable climate conditions. Forecast information can only have value, moreover, if the

user can utilize the information, together with a range of other tools and methods to

enhance decision making and improve overall risk management (Klopper, et al.,

2006).Accurate forecasts of climate 3–6 months ahead of time can potentially allow

farmers and others in agriculture to make decisions to reduce unwanted impacts or take

advantage of expected favorable climate (Jones, 2000).

2

This paper describes the state of the art of climate data generation and climate

information dissemination for use by the agriculture sector in the Philippines. There are

several actors of the system: the data generators, data processors, information

communicators and information users. This paper focuses on the local extension and

farmers as climate information users. It assesses current issues and concerns in the

operationalization of the existing AWS in the study sites. Further, it analyzes the

interaction of local climate data generators andusers in the agricultural sector.

II. Methodology

2.1. Study Area

The study was conducted in the ten (10) provinces in Regions 1 (Ilocos Sur), 2

(Isabela), 3 (Tarlac), 4A (Quezon), 5 (Camarines Sur), 6 (Iloilo and Negros Occidental),

10 (Bukidnon), 11 (Davao del Norte), and 12 (North Cotabato). With the study conducted

under the Climate Information Services (CIS) Project of the AMIA 2 Program that was

funded by DA Bureau of Agricultural Research, these study sites were based on program-

level goals pertaining to potentials for coordinated adaptation and mitigation

interventions by various agencies.

2.2. Data Collection

Key Informant Interviews (KIIs) were conducted to gather information about the

availability and physical condition of the weather data collection instruments/facilities

(AWS, Agromet stations and rain gauges) in the study sites. Respondents included

twenty-seven (27) MAO/DA-RFOs, thirty-six (36)operators of climate data collection

equipment, thirty (30) Agricultural Extensionists/Technicians. The questionnaire also

included questions on technical specification that were answered by MAO/DA-RFO or

by the operator.

A related farmer survey was also conducted in the project sites to have baseline

information on CIS knowledge, attitudes and practices of farmers regarding CIS and

climate change adaptation.

III. Climate Information System Data Generation

3.1. Weather data collection Instruments and facilities in the Philippines

Weather data collection instruments/facilities that the Philippine Atmospheric,

Geophysical and Astronomical Service Administration (PAGASA) used to generate and

collect weather data are the following:

Synoptic station- A station where observations of almost all meteorological

elements are made at fixed observation times and are transmitted to the Central Office.

3

These stations are maintained and operated by PAGASA. It is responsible for the

dissemination of public weather forecasts, tropical cyclone bulletins, warnings and

advisories and other related information to protect the lives and property of the general

populace. They are mostly located in the airports (PAGASA website).

Agrometeorological Station (Agromet Station)-This station gathers and provides

on routine basis simultaneous meteorological and biological information. It is also

responsible for the dissemination of agricultural meteorological advice, warning, forecast,

bulletin and other important information needed by farmers (PAGASA website).

Automatic Weather Station (AWS)- a compact equipment powered by storage

battery and solar cells, which are more accurate and dependable instruments for

collecting climatic data. The AWS is capable of collecting nine (9) weather parameters.

This instrument is equipped with data storage system; thus, data downloading can be

done on a daily, weekly or monthly basis. The frequency of data collection can also be

programmed according to the needs and use of such information. Moreover, a telemetry

system could be connected to the AWS so that data can be remotely accessed through

mobile SMS or other form of messages in real time.

Rain Gauge - an instrument for measuring the precipitation that has fallen in a

given time interval. The rain gauge is part of every weather station. It is used in

meteorology as an additional tool of the weather forecast and the climatology for long-

term statistics of precipitation. There are also automatic rain gauges that are developed to

gather and record the amount of rainfall and automatically send data, via text message, to

a central database server on a predetermined interval basis.

The complete list of instruments/facilities that PAGASA uses is summarized in

Table 1. A total of five hundred three weather data collection instruments and facilities

managed by PAGASA are scattered all over the Philippines. Most of them are automatic

rain gauges (187 units) and Automatic weather station (155 stations) (See Figure 1).

In the Agromet – cum Climate Change Project of DA- Bureau of Soils and Water

Management (BSWM) and the Advanced Science and Technology Institute (ASTI) and

PAGASA of DOST with funding from the DA Philippine Council for Agriculture and

Fishery (PCAF formerly NAFC) that started December 2011 had established a total of

191 AWSs around the Philippines. Table 2 shows the distribution of AWS per region.

Most number of AWS were located in Region 6 with nineteen (19) AWSs followed by

Region 8 and 12 with sixteen (16) and fourteen (14) respectively.

The CIS Project team were able to visit and assess forty-four (44) weather data collection

instuments located in the 10 study provinces. The information regarding the location of

these instruments came fromBSWM and PAGASA, and KII with DA Regionlal offices

and Provincial and Municipal Agriculture Office (PAO/MAO). Some of the respondents

were not aware of the number or location of the weather data collection

instuments/facilitiesin their area. Some of them mentioned that those agencies

responsible for funding/installing the instrumentsometimes do not give a courtesy visit to

the respective provincial or municipal offices in the areas where instruments will be

installed, thus, the reason for inaccurate number of weather data collection

4

instuments/facilities known to these officials. The study team worked on the assumption

that there are about 28 AWS and 16 rain gauges/ agromet stations from all of the study

sites.

Table 1. PAGASA Weather Data Collection Instruments/Facilities

Type No

Synoptic Stations 57

Agromet Stations 23

Upper air stations 8

Automatic Weather Stations 155

Automatic Rain Gauge 187

Water level sensors 47

Wind profiler 1

Wind tunnel 1

Automatic Weather Observing System 2

High Frequency Doppler Radar 2

Marine buoys 2

Mobile Doppler radars 2

Doppler Weather radars 16

Source: PAG ASA Profile 2016 from PAG ASA Central Office

Table 2. Number of AWS distributed per region by the Agromet-cum Climate Change

Project

Region Number of AWS

Total Lufft Upgraded ASTI AWS Upgraded PAGASA AWS

NCR 7 0 0 7

CAR 5 2 1 8

ARMM 3 1 0 4

Region 1 8 0 2 10

Region 2 9 1 2 12

Region 3 11 1 1 13

Region 4a 8 1 1 10

Region 4b 7 4 1 12

Region 5 7 4 2 13

Region 6 13 4 2 19

Region 7 7 1 0 8

Region 8 10 4 2 16

Region 9 8 2 0 10

Region 10 8 2 1 11

5

Region Number of AWS

Total Lufft Upgraded ASTI AWS Upgraded PAGASA AWS

Region 11 9 1 2 12

Region 12 10 2 2 14

Region 13 7 4 1 12

TOTAL 137 34 20 191 Source: http://www.agromet.da.gov.ph/viewdata/ (Accessed date: Oct. 25, 2017)

The distribution per region of weather data collection instruments/facilities that

the CIS Project visited is summarized in Table 3. Region 6 (Iloilo and Negros

Occidental) has the most number of weather data collection instruments with a total of

15. As observed in the field, there are municipalities that has 2 instruments/facilities

located beside each other. Respondents said that those instruments/facilities were project

funded, there was no system in place regarding where these can be installed.

Table 3. Number and percentage of weather data collection instrument/facilities visited

by the CIS Project team.

Regions

Weather data collection instruments/facilities Total

AWS Agromet/rain gauge

n % N % n %

Region 1 1 3.6 1 6.3 2 4.5

Region 2 3 10.7 1 6.3 4 9.1

Region 3 1 3.6 2 12.5 3 6.8

Region 4a 2 7.1 1 6.3 3 6.8

Region 5 4 14.3 1 6.3 5 11.4

Region 6 10 35.7 5 31.3 15 34.1

Region 10 3 10.7 3 18.8 6 13.6

Region 11 1 3.6 1 6.3 2 4.5

Region 12 3 10.7 1 6.3 4 9.1

Total 28 100.0 16 100.0 44 100.0

Figure 1 shows the locations of the weather data collection instruments/facilities all

over the Philippines installed and upgraded through the Agromet – cum Climate Change

Project (left) and those visited by the CIS Project (right). According to Nota, et al.

(2012),site selection for the installation of AWS is very critical, hence, general site

criteria was developed. PAGASA provided the following criteria for AWS location:

Site is fairly level and free from obstruction

Site has a grass cover and no tall weeds

Site is not concrete, asphalt or crushed stone

Obstruction such as trees, buildings and nearby shrubs is not close to the instruments;

distance of AWS should be at least 4 times the height of obstruction

6

No obstruction can cast shadows during the greater part of the day, though brief

periods of shadow near sunrise and or sunset are sometimes unavoidable; hence the

east-west direction should be identified

Site is accessible from all means of transportation for operation and maintenance

Site should be located in a place truly representative of the natural conditions in the

agricultural region

Recipient (DA-RFU-ROS, LGUs, SUCs) is willing to provide observer for manual

reading of standard rain gauge and for overseeing the operation and maintenance of

installed AWS

With strong GSM signal (e.g. Globe, Smart, Sun)

Not visited by flood annually

No social/right of way problem; if the recipient is not the land owner, usufruct

agreement from the owner is mandatory

Security of the site is outmost concern

3.1.1. Instrument Hardware

The AWS consists of four sensors to measure the weather parameters (Figure 2). A

Multi-parameter sensor is used to measure the solar radiation, wind speed, wind

direction, air pressure, air temperature and humidity; Rain fall sensor; Sunshine duration

sensor; and Soil moisture and temperature sensor. It has a Main Controller that can

Legend: Agromet station BSWM AWS

Figure 1. Agromet Station and AWS in the Philippines (left) and Agromet Station and AWS covered

by the CIS Study (right).

Legend: Agromet station BSWM AWS

Rain gauges

7

perform the Communication (GSM Network), Data Logging (Flash memory,

downloadable via USB), Automatic data transmission thru SMS, AST is micro

controller-based intelligent system board, and it can also be configured through text

messaging. The instrument also has a battery and a solar panel. Those parts play a vital

role in the functionality of the whole AWS and the accuracy of the data that the

instrument will produce.

Figure 2. Parts of the Automatic weather station

Source: DOST-ASTI as cited by BSWM

3.1.2. Functionality and Maintenance

To be able to deliver more accurate climate information to farmers, weather data

collection instruments/ facilities should function smoothly. Instrument observer/operators

during the KII were asked regarding the functionality of the climate data instrument

(Table 4). More than half of the respondents (56%) said that the equipment is functioning

smoothly since the day they started operating it. Proper location of the equipment was the

response of 25% of the respondents. Other reasons for the functionality were the

equipment was newly installed, good quality, well monitored, and it doesn’t use SIM card

to send data were also mentioned.

To ensure that the instrument/facilities are transmitting accurate weather

measurements, they must be properly maintained. It was mentioned by respondents that

8

general maintenance of the equipment should be done by BSWM with the assistance of

PAGASA. Some maintenance worked were also done by the observer/agricultural

technician assigned to the instrument/facility. Table 4 summarizes the maintenance

information for the weather data collection instruments/facilities. Respondents were

asked if the equipment undergoes maintenance work. Majority (86%) said they perform

regular maintenance work for their instrument/facility and only 14% answered no. They

were also asked how frequent they perform regular maintenance work. Since the

instrument/facility is automated, some respondents (29%) perform regular maintenance

work only when necessary. Six respondents (19%) answered that they do daily and

monthly cleaning. Only four respondents answered they do it weekly and two

respondents do maintenance annually. Other responses were every 2 weeks, every

calibration, and quarterly.

Seventy seven (77%) of respondents said that the kind of maintenance work they

mostly perform is cleaning the instrument/facility. About 26% of the respondents perform

weeding as their maintenance work. Removing pests, replacing of batteries and repairing

the equipment were also considered as maintenance work. The regular maintenance work

is performed mostly by the operators or observers themselves. About 16% and 19% of

respondents said that the regular maintenance work was performed by the DA and

PAGASA personnel, respectively. Other responses were DOST, OPAG and the

engineering department of the municipality.

Table 4. Maintenance of the climate data instrument

N Percent

Is the equipment been functioning smoothly?

Yes 20 55.6

No 16 44.4

Total 36 100.0

If yes, what do you think would explain such performance?

Well-maintained 11 55.0

Proper location 5 25.0

Others 4 20.0

Total 20 100.0

Does the AWS undergo regular maintenance work?

Yes 31 86.1

No 5 13.9

Total 36 100.0

If yes, how often?

When necessary 9 29.0

Daily 6 19.4

Monthly 6 19.4

Weekly 4 12.9

Annually 2 6.5

Others 4 12.9

9

Total 31 100.0

Kind of maintenance done

Cleaning of equipment 24 77.4

Weeding 8 25.8

Repair for minor damage 2 6.5

Remove pests 2 6.5

Replace batteries 1 3.2

No response 2 6.5

Who provides the maintenance?

Operator 15 48.4

DOST-PAGASA personnel 6 19.4

DA personnel 5 16.1

Others 5 16.1

Total 31 100.0

3.1.3. Issues

Despite the maintenance done, there were still problems that arise due to normal

wear and tear of the instruments or human intervention. Table 5 shows the

observers/operators responses when asked what problems they have encountered that

influenced the non-functioning of the equipment. The main problem that the respondents

cited was stability of power source and hardware wear and tear due to pest (40%). Insects

like ants were found inside some parts of the instruments that cause the problem. One

respondent added that the reason why ants thrive inside the instruments is due to the cold

temperature inside it. Spiders and other insects can also interfere with the rotation of the

sensor that determines the wind speed and direction. Some of the AWSs are not solar-

powered and the batteries became defective so the stability of power source is considered

as another problem. Thirty-seven percent of the respondents answered that internet

connection is also one of their problems for they cannot transmit or download data

offline. Defective parts of the instruments were also mentioned.

Respondents were also asked how they addressed these local problems mentioned.

Most of them (44%) said that they contact a technical staff. Same percentage of

respondents answered that they clean the AWS. Three of the respondents answered that

they replace the batteries of the equipment.

Table 5. Problems encountered and how these were resolved.

Particulars N Percent

Problems

Stability of power source 7 43.8

Hardware wear & tear due to pests 7 43.8

Internet connection 6 37.5

Defective parts 3 18.8

No response 2 12.5

Others 4 25.0

10

How problems were resolved

Contact technical staff 7 43.8

Clean equipment 7 43.8

Battery replacement 3 18.8

No response 1 6.3

Others 11 68.8

3.2. How are data sent to the central server and to the end users

As mentioned earlier, AWS measures nine weather parameters. The data generated

are transmitted automatically though SMS. It has a Global System for Mobile

Communication that enables the AWS to send SMS to the DA- Information Technology

Center for Agriculture and Fisheries (ITCAF). ITCAF creates a back- up for the data and

then upload it in the internet that can be accessed by the end users of the weather data like

the National and Local Governments and also for the public (Figure 3). It can be accessed

through websites like BSWM Agro-meteorological Station (http://agromet.da.gov.ph/),

and DOST-ASTI website (http://fmon.asti.dost.gov.ph/).

In the case of the agromet stations, the weather data produced are sky cover, wind

speed and direction, minimum and maximum temperature, dry bulb, wet bulb, dew point,

vapor pressure, relative humidity, water temperature, rainfall, evaporation, sunshine, and

mean sea level (MSL). The observers collect the data every day. They send the collected

data through SMS, Single Sideband (SSB) radiophone or Transmission Control

Protocol/Internet Protocol (TCP/IP) on a daily basis to be able to produce a short-term

forecast. The observers also encode the data collected in their Monthly Weather Data

Form to be sent to PAGASA Central office monthly for climatology.

Figure 3. Flow of weather data from AWS to the end users Source: Nota, et al., 2016 (BSWM Online)

11

PAGASA Central then processes and analyzes the data. They store the processed

data for researchers and other users. They also disseminate the processed and analyzed

data in the form of weather forecasts, warnings, advisories and other information through

Office of the Civil Defense (OCD), Maritime, Public, Media, Aviation, Land transport

and their field stations. They also make maps, plots, images and animations out of the

processed data for display in the PAGASA website (https://www1.pagasa.dost.gov.ph/).

The observer/operator respondents of the KII were asked of their knowledge

about weather data measurements generated by the instruments and the location of data

storage. Results in Table 6 show that, in general, they have knowledge of the data being

generated and the storage locations of these data.

Table 6. Knowledge of operators regarding data generated and storage

Particulars AWS Agromet

n Percent n Percent

Climate data generated*

Rainfall Amount, Duration, and

Intensity

28 100.0 16 100.0

Air Humidity 27 96.4 6 37.5

Figure 4. PAGASA data/ information flow diagram Source: Engineering Technical Services Division, PAGASA

12

Air Temperature 23 82.1 10 62.5

Solar Radiation 22 78.6 7 43.8

Soil Moisture and Temperature 22 78.6 3 18.8

Wind Speed and Direction 22 78.6 10 62.5

Air Pressure 20 71.4 4 25.0

Sunshine Duration 18 64.3 4 25.0

Evaporation 2 7.1 5 31.3

Where the data was saved

ASTI website 7 25.0

BSWM Central Office 6 21.4 2 12.5

Computer/ Logbook 3 10.7 3 18.8

Weather Philippines Website 2 7.1

Sim card /memory card 2 7.1

PAGASA server and MAO 2 7.1 4 25.0

ARQ 2 7.1

DA-RFO 3 18.8

Others 4 14.3 4 25.0

IV. Data Uses

Weather data collected were used to formulate weather and climate forecasts,

warnings, and advisories by PAGASA. More and more, the agriculture sector has to tap

on these data for extension advisory, as modern-day agricultural enterprises are becoming

increasingly dependent upon detailed and accurate predictions of agrometeorological

elements. Many benefits could result from the use of agrometeorological advisory

services to agriculture (Basco, n.d).

The respondents from MDRRM and Provincial and Municipal Agriculture Office

(PAO/MAO)were asked whether they were recipients of the 10-day weather forecast

from PAGASA. As summarized in Table 7, 37% of themanswered in the affirmative.

Fifty nine percent didn’t receive any forecast information.Respondents claim that forecast

data were shared with DRRM and Provincial and Municipal Agriculture Offices

(PAO/MAO). The data were also sent to all the departments in the municipality and to all

barangay captains. Other municipalities do not receive 10-day weather forecast but they

said that PAGASA sends text messages that contains weather forecast for the day; but

only those who registered their cellphone number to PAGASA can receive the said text

message. Others also mentioned that PAGASA only sends weather forecast if a typhoon

is coming to their municipality. Respondents were likewise aware that forecast can be

sent by PAGASA upon request of the municipality.

According to about half of the respondents, the 10-day weather forecast received

were used by their respective MLGUs to give advisory to farmers and residents of the

municipality. Forty percent answered that MLGU uses it for the advisory of MDRRMO.

Ten percent answered that they use the data for their rice projects.

13

The climate data gathered from the AWS/Agromet/rain gauges according to the

respondents were mostly used by farmers and fisherfolks, and the MAO.Users also

include MDRRMO, the academe. students, private agencies, OPAG, PCIC, and BSWM.

One respondent said that there are no users of the data generated by the equipment. Two

respondents didn’t give any response and same number said that they have no idea who

uses the data.

Table 7. Climate information received by the provincial and municipal governments in

the CIS study sites.

Particulars N Percent

Municipality as recipient of the 10 day weather forecast from PAGASA?

Yes 10 37.0

No 16 59.3

Don't know 1 3.7

Total 27 100.0

Use of the 10 day weather forecast by the MLGU?

Advisory of MDRRMO 4 40.0

Advisory for farmers and residents of the municipality 5 50.0

Used in rice projects 1 10.0

Total 10 100.0

Users of Climate data according to respondents

Farmers and fisherfolks 11 40.7

MDRRMO 7 25.9

MAO 11 40.7

Academe 3 11.1

Others 7 25.9

None 1 3.7

No response 2 7.4

Don’t know 2 7.4 *Multiple response

Most of the users of the data from the data collection instrument according to the

observers/operators were the LGUs (33.3%), the academe (27.8%), BSWM, DRRMO,

farmers, PAGASA, private companies, researchers, Weather Philippines and agriculture

offices (Table 8). Majority of the respondents (53%) said that the data are used for

research purposes. About 17 % of the respondents use the data for disaster advisories and

14% of the respondents use the data for forecasting. Other responses were the data was

used for planning and for teaching in the Farmer Field School (FFS). There was no

mention that the data are used by extension persons as advisories to farmers.

Table 8.Responses on the use of data generated from the climate data instruments

Particulars N Percent

Who uses the data?*

LGU 12 33.3

Academe 10 27.8

BSWM 7 19.4

14

Farmers 7 19.4

Private companies 7 19.4

DRRMO 4 11.1

PAGASA 4 11.1

Others 9 25.0

How is data used?

Research purposes 19 52.8

Disaster advisory 6 16.7

Forecasting 5 13.9

No idea 1 2.8

No response 2 5.6

Don't know 1 2.8

Others 2 5.6

Total 36 100.0 *Multiple response

4.1 Role of Agricultural Extension in Providing Climate Information to

Farmers

This lack of use of climate forecast in agriculture could be due to the lack of

knowledge by the municipal agricultural office on how to do so. Access to seasonal

climate forecasts can benefit farmers by allowing them to make more informed decisions

about their farming practices (Gunda, et al., 2017). With this in mind, the CIS study team

also asked agricultural technicians/extensionists on how extension advisories for the

farmers are formulated. Results in Table 9 show that some 23% formulate advisories

based on climate information through PAGASA that are disseminated through radio/ TV.

Some depend on the DA for information. Some based their advisories on climate data

from AWS (10%), local knowledge (13.3%) and trainings (10%). Other answers were

based on data provided by CDRRMO monitoring stations and through information from

the MAO.

Table 9 also shows the percentage of respondents who uses the locally installed

climate data collector as a source of information in making extension advisories. The

majority (63%) of the respondents do not make use of information from the equipment..

About 32% derive climate information from PAGASA but only as provided by the mass

media (TV/radio). Twenty-one percent of the respondents generated climate information

from their respective Disaster Risk Reduction Management Office (DRRMOs). About

10.5% use the data from PAGASA stations. Other sources are simplified rain gauge,

agromet station, and private agencies.

Respondents were asked why they are not using the data from the locally installed

climate data collector. Most of them (42%) replied that the climate information/data from

the equipment were not given to them (Table 9). Climate data from the locally installed

climate data collector were automatically sent to a central server, with the expectation

that anybody can access this. However, users need to have a good internet connection to

be able to access the data. Five percent answered they only have raw data but they don’t

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know how to process it. This also reveals that LGU needs assistance in the processing of

climate data into information for farmers’ decisions.

Responsiveness of farmer-clients to climate information-based extension

advisories was also asked (see Table 9). Majority of the respondents (57%) said that

farmers are very responsive to the climate information-based extension advisories they

deliver. About 27% said that farmers are responsive because they have experienced

problems and that they are now aware of climate information. Respondents were asked

about the farm household decisions influenced by the climate advisories. Majority of the

respondents (59%) said that the information they delivered affected the farmer’s planting

decisions. According to 27% of the respondents, crop insurance decisions of farmers

were also affected. Other decisions that are affected were pest management, fertilizer

application, preparation for rain, and use of organic technology.

Table 9. Formulation of seasonal forecast advisories and farmers’ responsiveness to

these advisories

Particulars N %

How do you formulate climate forecast advisories? *

Based on climate data from AWS 3 10.0

Based on climate information from PAGASA through

radio/TV

7 23.3

Based on info from DA 7 23.3

Based on local knowledge 4 13.3

Based on the data provided by CDRRMO monitoring stations 2 6.7

Through the information from the MAO 2 6.7

Through trainings 3 10.0

No response 6 20.0

Others 6 20.0

Do you make use of information from locally installed AWS/rain gauge for forecast

advisory?

Yes 11 36.7

No 19 63.3

Total 30 100.0

What are the reasons for not using data from local AWS/rain gauge?

Lack of knowledge of processing data 1 5.3

Data are not given to them 8 42.1

Incomplete data 2 10.5

No response 8 42.1

Total 19 100.0

How responsive are farmer-clients to climate information-based extension advisories?

Very responsive 17 56.7

Responsive due to experience and awareness 8 26.7

No response 5 16.7

Total 30 100.0

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What kinds of farm household decisions have you observed to be influenced by such

advisories? *

Crop insurance decisions 10 33.3

Planting decisions 21 70.0

No response 7 23.3

Others 4 13.3 *Multiple response

V. Knowledge of farmers about CIS

Climate information is very important to the farmers. Agricultural productivity can

be increased, costs reduced and impending crop shortfalls can be mitigated or avoided

through the judicious use of information and knowledge about climate and weather,

including early warning and agrometeorological advisories (Basco, n.d.).

Four hundred five (405) farmer- respondents from the CIS project area were

interviewed to know their knowledge about CIS. In general, farmers reported not being

able to predict the occurrence of floods or typhoons. The thirty-six percent who said that

they were able to predict the occurrence of the extreme events had observed a 40%

chance of correct prediction. For both flood and drought events, the farmers’ basis for

prediction was prevalent/observed weather in their locality (34%) followed by their past

experiences (21%). Other responses were months of the year, information from TV/radio,

weather forecast, population and behavior of birds etc.

The reason why some farmers don’t use their predictions as basis for farm decision

making is that they rely on their usual practice for drought occurrence and they consider

crop suitability for flood/typhoon occurrence.

Majority of the farmers get their climate information from the media such as radio/

TV/ newspaper, followed by PAGASA and MAO/AT/Barangay officials. Farmers also

obtain climate information from other farmers and from their own experience. Majority

(82%) get their climate forecast from the media, then from PAGASA. Climate forecast

can also be obtained from MAO/AT/Barangay officials and from farmer’s own

experience.

The results from the farmer survey also confirm the results from the MAO/Ag

extension survey. In general, extension also just gets information from the media which is

not very location specific. The local data being generated lack analysis, due to the lack of

capacities by the extension to do so. Farmers still depend on their experiences and

mentioned that their predictions of extreme events are not any more accurate. Despite

this, farmers still depend on any forecast to plant and to decide on crop insurance. These

findings bolster the rationale for development of local seasonal climate forecast and

extension delivery that can be initiated by both the national and local governments so

farmers can adapt to the vagaries of climate change.

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VI. Summary and Conclusion

Climate information service is very important to the agriculture sector especially in

adapting to climate change. Having climate information knowledge can help farmers in

their decision making during planting season. Losses can be mitigated or avoided if

farmers have this knowledge.

Weather data collection instruments/facilities are vital in generating climate

information such as the 10-day weather forecast, climate forecast and climate extension

advisories. There are a lot of instruments/ facilities all over the Philippines that can be

used in producing climate information to the farmers. Accuracy of the data generated by

these instruments/facilities is very important and this can be achieved through proper

maintenance and constant monitoring of the data that are produced.

Data generated by the instruments/facilities are available online thru BSWM

website (http://www.agromet.da.gov.ph/viewdata/) and PAGASA website

(https://www1.pagasa.dost.gov.ph/). In the case of those BSWM instruments/facilities,

provincial and municipal governments need to access the data from the web. This can be

improved as this arrangement constrains the LGUs from use of the location specific data.

LGUs said that they are having hard time accessing the data in the internet because the

internet connection in their area is not stable. Some just use information from PAGASA

and advisories in the television and radio, which may not be location specific. Analysis

above also reveals that LGU needs assistance in the processing of climate data into

information for farmers’ decisions.

Farmers still depend on their experiences and mentioned that their predictions of

extreme events are not any more accurate. Despite this, farmers still depend on any

forecast to plant and to decide on crop insurance. These findings bolster the rationale for

development of local seasonal climate forecast and extension delivery that can be

initiated by both the national and local governments so farmers can adapt to the vagaries

of climate change.

VII. References

Basco, D.T. No date. Farm Weather Services. Accessed date: Oct. 30, 2017.

http://www.agrometeorology.org/files-folder/repository/delia_basco.pdf

BSWM Agro-meteorological Station website, Accessed date: Oct. 25,

2017.http://www.agromet.da.gov.ph/viewdata/

Elazegui, D.D., Luyun, R.A. Jr., and Anastacio, N.J.C. 2017. Climate Information

Services for Agriculture: Analysis of Institutional Landscape. UPLB.

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Gunda, T., Bazuin, J. T., Nay, J., & Yeung, K. L. (2017). Impact of seasonal forecast use

on agricultural income in a system with varying crop costs and returns: an

empirically-grounded simulation. Environmental Research Letters, 12(3), 034001.

Jones, J. W., Hansen, J. W., Royce, F. S., & Messina, C. D. (2000).Potential benefits of

climate forecasting to agriculture. Agriculture, ecosystems & environment, 82(1),

169-184.

Klopper, E., Vogel, C. H., & Landman, W. A. (2006). Seasonal climate forecasts–

potential agricultural-risk management tools?. Climatic Change, 76(1), 73-90.

Nelson, G. C., Rosegrant, M. W., Koo, J., Robertson, R., Sulser, T., Zhu, T.,

...&Magalhaes, M. (2009). Climate change: Impact on agriculture and costs of

adaptation (Vol. 21). Intl Food Policy Res Inst.

Nota, M., and Dela Cruz, M.A. 2012. Frequently asked questions (FAQs) on the

Automatic Weather Stations (PL480 Project). Retrieved from the BSWM Online

Website.Accessed date: Oct. 27,

2017.http://bswm.da.gov.ph/article/0021/frequently-asked-questions-faq-s-on-the-

automated-weather-stations-pl480-project

Olesen, J. E., &Bindi, M. (2002). Consequences of climate change for European

agricultural productivity, land use and policy. European journal of

agronomy, 16(4), 239-262.

PAGASA website, Accessed date: Oct.

25,2017.https://www1.pagasa.dost.gov.ph/index.php/45-regional-services

Smit, B., & Skinner, M. W. (2002). Adaptation options in agriculture to climate change: a

typology. Mitigation and adaptation strategies for global change, 7(1), 85-114.