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Copyright 2009 ACIAR-sponsored project on Bridging the Gap Between Seasonal Climate Forecast and Decisionmakers in Agriculture Printed in the Philippines. All rights reserved.

Design concept, layout and cover by Maria Gizelle G. Manuel

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Table of Contents

Preface

iii

1. What is seasonal climate forecast (SCF)?

1

2. Who may use SCF? What kind of decisions may benefit from SCF?

1

3. What is the use of SCF? In short, what ‘s in it for me?

2

4. What is the value of using SCF?

4

Decision Tree Analysis

4

Decision not to use SCF (without forecast)

4

Decision to use SCF (with forecast)

6

Value of climate forecast information (EV with forecast-EV without forecast)

8

5. What is the nature of SCF?

8

6. What should the farmers do when there is an El Niño or La Niña forecast?

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7. Where and how can SCF information be obtained?

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Glossary of Terms

12

About the Project

14

Project Team

16

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Preface

One of the major causes of climate variability in the Philippines is the El Niño Southern Oscillation (ENSO). In recent years, significant developments have been made to understand the atmospheric and oceanic processes causing ENSO. This knowledge is now being used to generate seasonal climate forecasts (SCF) on a regular basis. Climate anomalies associated with El Niño and La Niña are well recognized and are used to predict seasonal rainfall up to six months ahead. Advance information in the form of SCF has the potential to improve decisionmaking leading to increases in farm profits. Improved seasonal climate forecasts also offer society an opportunity to mitigate adverse consequences associated with ENSO events. Recognizing this, the Team Members of the Australian Centre for International Agricultural Research (ACIAR)-sponsored project on “Bridging the gap between seasonal climate forecasts and decisionmakers in agriculture in Australia and the Philippines” deemed it important to come up with a handbook addressed to agricultural extension workers in the Philippines that will help in their work with farmers so that the latter may be able to make more informed decisions on agricultural activities. Specifically, this Handbook aims to: (1) increase the understanding of the nature and implications of seasonal climate forecast; (2) determine the value of seasonal climate forecast in agriculture; and (iii) develop the skills of resource managers in the use of seasonal forecasts to manage climate variability. Again, it is intended for agricultural extension workers for them to help farmers make informed farm production decisions. Hopefully, the ultimate outcome will be to achieve improved productivity and higher agricultural sustainability. The Project Team

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A Handbook for Agricultural Extension Workers

1

What is seasonal climate forecast (SCF)?

Seasonal climate forecast (SCF) is a forecast for a ‘season’ that may range from one month to one year. SCF provides information on whether the coming season is likely to be wetter or drier and warmer or cooler than normal. In some cases, SCF can indicate whether there is an increased likelihood of extreme events such as El Niño and La Niña and can give early warning to farmers and other agricultural decisionmakers about future risks in order for them to make appropriate preparations for their farm and related activities. SCF provides a prediction of the likelihood of receiving above normal, near or below the normal rainfall in the coming three to six months for a particular area. It can be generated using different predictors like sea surface temperature (SST) and southern oscillation index (SOI), among others. In this Handbook, the SOI forecasting system is used to illustrate the value of SCF. 2

Who may use SCF? What kind of decisions may benefit from SCF?

Because SCF predicts the chance of rainfall for a season, this information can serve as a guide in deciding what actions to take in anticipation of the effects of the climate events. SCF is especially useful to farmers. The amount of rainfall forecast can help farmers decide what to do on their farms. For example, during a La Niña forecast, because there is an increased chance of wet conditions, many farmers may plant rice in their corn farms located in the lowland. On the other hand, when the forecast is El Niño, because there is an increased chance of dry conditions, many corn farmers cultivate only a portion – say one-half – of their farm. Other users of SCF may include decisionmakers who decide on the stockpiling and import of rice. The forecasts for either El Niño or La Niña can help them decide on the level and timing of importation to assess and manage the risks of over- and under-supply. Those who make decisions on water storage, allocation and distribution can also benefit from the information provided by SCFs, as can officials and members of disaster brigades and task forces. Climate forecasts can help in the design, preparation and implementation of their disaster mitigation programs.

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Understanding SCF and Its Applications

For the health sector, health officials and entire communities can also make appropriate plans in their health prevention programs for specific seasons and in response to expected healthaffecting factors brought about by certain climatic elements. For example, if the forecast indicates increased chances of high rainfall, communities could help protect themselves from dengue by taking extra precautions in observing cleanliness. Aedes aegypti, the mosquito thatis carries the of SCF? What is in it for me? What the use dengue virus, loves wet and dirty places.

Seasonal climate forecast (SCF) is an innovation for managing climate variability for the season ahead in order to minimize the risk of crop failure during bad outcomes and take advantage of 3 What is thegood use ofoutcomes. SCF? In short, ‘s in it for me? opportunities during Inwhat other words, SCF is useful in strategic cropping decision making for the next growing season. Some of the applications of SCF to address climate SCF is an innovation for managing climate variability for the season ahead inperiods order to minimize the risk of variability are as follows: (i) crop choice, (ii) timing of cropping or planting schedule, crop failure during bad outcomes and to take advantage of opportunities during good outcomes. In other (iii) levels of input use, etc. words, SCF is useful in planning cropping programs for the next growing season. Some of the applications of SCF to address climate variability are as follows: (i) crop choice, (ii) timing of cropping periods or To illustrate the usefulness of SCF and how it is valuable to farm decision makers in managing planting schedule, and (iii) levels of input use.

climate risk in rainfed corn farming systems, let us apply it in the context of cropping choice decision and levels of input (e.g., fertilizer) use. In the case of cropping choice strategy/decision To illustrate the usefulness of SCF and how it is valuable to farm decisionmakers in managing climate the farmers need to consider the climatically sensitive decision points for rainfed corn risk in rainfed corn farming systems, the following example is used in the context of a cropping choice production. The climatically sensitive decision points for rainfed corn production in the study decision. area of Mahaplag, Leyte are shown in Fig__. Given the forecast, farmers will decide whether to plantIncorn or fallow the choice farm. strategy/decision, The fallow is athe grazed is a low-risk, low-return the case of cropping farmersfallow need toand consider the climatically sensitive option. The decision to fallow will mean that there is more water stored in the profile for thepoints subsequent decision points for rainfed corn production. In the study area of Mahaplag, Leyte, these decision crop are butshown the main impact will be the mineralised soil nitrogen. The decision to plant corn in Figure 1. Given the forecast, farmers can decide whether to plant corn or fallow the field. in April or May needs be made In August, isThe a second choice thatmean willthat have been The fallow is to a grazed fallowin andMarch. is a low-risk, low-returnthere option. decision to fallow will influenced in part by the choice in April whether to plant a crop or not, but also there is more water stored for the subsequent crop but the main impact will be the mineralized soil it will be influenced the price to ofplant corncorn andinthe of be themade season. A few farmers will nitrogen.byThe decision Aprilexpectations or May needs to in March. In August, there is aconsider a third crop in the wet season in January, but most will have a fallow and plan for corn the following April. Figure 1. Climatically sensitive decision points for corn farmers in Leyte, Philippines April

May

June

July

Aug

Sep

Oct

Dec

2nd crop

1st crop

Corn Corn

Nov

Crop choice

Rice Crop choice Fallow

Crop choice Corn Fallow

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Fallow

Feb

3rd crop (few farmers only)

Fallow

Crop choice

Jan

Rice Crop choice Fallow

Figure 1. Climatically sensitive decision points for corn farmers in Leyte, Philippines

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A Handbook for Agricultural Extension Workers

second choice that will have been influenced in part by the choice in April on whether to plant a crop or not. It will also be influenced by the price of corn and the expectations of the season. A few farmers will consider a third crop such as rice in the wet season in January, but most will fallow and plan for corn the following April. Meanwhile, the climate-sensitive decision points for rainfed corn production for Cebu and Isabela are also shown in Figures 2 and 3. Figure 2. Climatically sensitive decision points for corn farmers in Cebu, Philippines

Figure 3. Climatically sensitive decision points for corn farmers in Isabela, Philippines

Apr

May

Jun

July

Aug

Sept

Oct

1st crop

Nov

Dec

2nd crop Traditional Corn

OPV* Hybrid

To Plant

Other crop

Fallow *Open Pollinated Variety

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Understanding SCF and Its Applications

4

What is the value of using SCF?

For a climate forecast to have economic value, it should stimulate actions/decisions that mitigate against adverse consequences or take advantage of potential gains from an extreme climate event. Specifically, a forecast has value if it leads to different and more profitable cropping decisions. Decision tree analysis Using a decision tree analysis, we can determine how, when and why SCFs are valuable to farmers and when they are best ignored. Decision tree analysis is a technique to aid decisionmakers in identifying the outcomes for each decision alternative. It involves assessing the probabilities associated with each outcome, assigning payoffs, and keeping the sequence of outcomes and decisions in chronological order. In the example of the decision tree for Mahaplag’s cropping choice options as seen in Figure 4, decision problems are shown with two different kinds of forks: decision forks and event forks or the sources of uncertainty. Decision forks – drawn as a small square at the node – have branches ‘sprouting’ from the decision node representing alternative choices while the Event forks – drawn as a small circle at the node – have branches sprouting from the event node representing alternative events or states. The decision fork to be selected is within the power of the decisionmaker but the branch of an event fork that applies is determined by chance or ‘luck’. Ideally, decision tree analysis can be easily implemented using any commercially available MS Excel add-in software package (e.g., Lumenaut Decision Tree Analysis Package, Precision Tree Decision Analysis, etc.). In the absence of these packages, though, we can just manually draw the decision tree and implement decision tree analysis using the MS Excel spreadsheet. For instance, the decision tree for Mahaplag was implemented in MS Excel spreadsheet without an add-in. The tree is drawn starting from left to right but solved from right to left. Decision not to use SCF (without forecast) At the start, the farmer (as decisionmaker) decides whether to make use of SCF or not in his cropping decision problem. Without the SCF, the farmer is faced with two alternatives: to plant corn or not to plant/ fallow his/her farm. If the farmer decides

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A Handbook for Agricultural Extension Workers Figure 4. Decision tree for cropping choice problem in Mahaplag, Leyte, Philippines

to plant corn, he will encounter three possible outcomes for the season: good, average, or poor. Only one of these outcomes will be realized in a given cropping season. Since there is uncertainty for each outcome or season, it is assigned with a probability of occurrence. Without the forecast, the farmer will have to rely on “all years of climate information,� otherwise known as climatology, that assume an equal probability for each season, that is, 0.33 (or 33%), 0.34 (or 34%), and 0.33 (or 33%) for good, average and poor seasons, respectively. Note that the sum of the probabilities for the three seasons must equal to one (or 100%). The return or gross margin (also known as conditional profit), which is derived from doing farmer surveys on the yield they obtained during good, average and poor seasons, is specified for each season. If long-term historical daily weather data (rainfall, maximum temperature, minimum temperature, solar radiation) are available, corn yield can be simulated using the CERES-Maize model in DSSAT. The gross margin here was computed as the difference between gross income (yield multiplied by selling price of shelled corn) and total variable cost of production. In this Mahaplag example, the gross margin was P3,184/ha for a good season, P1,750/ha for an average season, and P-958/ha for a poor season. If the farmer’s decision is to fallow his farm for livestock grazing, on the other hand, he will realize a gross margin of P1,000/ha.

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Understanding SCF and Its Applications

The expected value of the decision is computed by getting the sum of the products of the gross margin and the corresponding probability of occurrence of the forecast season. For example, the expected value of planting corn is computed as follows: EV = (3,184*0.33)+(1,750*0.34)+(-958*0.33) = P1,330. The expected value of fallow is P1,000 ( or 1,000x1). The decision or alternative with the highest expected value will be chosen as the optimal decision. Since planting corn gives the highest expected value of P1,330 compared with only P1,000 with fallow, the optimal decision is to plant corn. Consequently, the expected value for the farmer who does not use the forecast and plants corn every year is P1,330/ha. per season. This implies that in a certain forecast season, there is a 33 percent likelihood for a farmer to make an excellent return of more than P3,000, an equal 33 percent chance of experiencing a loss of P958 and another equal 33 percent chance of making P1,750. Thus, based on these figures, during the poor season when the farmer lost P958, he would have been better off if he had fallowed his land for livestock grazing. Decision to use SCF (with forecast) If the farmer decides to use the seasonal forecast, meanwhile, he will be confronted with one of three forecast types when using the SOI average forecast system. The forecast types are SOI positive (La Ni単a), SOI neutral (normal), and SOI negative (El Ni単o). For each forecast type, the farmer has to decide whether to plant corn or fallow his farm. The probability of outcomes or seasons is derived from SCFs for each type of forecast. This information will be provided by PAGASA or we can use the RAINMAN International software package for this purpose. For this Mahaplag example, the PAGASA station in Tacloban, Leyte was used. The summary for the probability of outcomes is presented in Table 1, which is obtained from the chance of rainfall charts (Figure 5) from RAINMAN for each forecast type. It should be noted that SCFs do not

Table 1. Summary of the probability of outcomes for each forecast type using SOI Average forecast system, Mahaplag, Leyte.

Forecast Type SOI above +5 (positive) SOI -5 to +5 (neutral) SOI below -5 (negative) All Years

Probability* 0.28

Low/Poor 0.17

Medium/Average 0.35

High/Good 0.48

0.44

0.28

0.36

0.36

0.28

0.57

0.30

0.13

0.33

0.34

0.33

*Data will be provided by PAGASA

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A Handbook for Agricultural Extension Workers

change the gross margin for each season; they only shift the odds (probability) of outcome occurrences as gleaned in the example in Figure 4. The expected value of planting corn for each forecast type is computed following the same procedures in the “without forecast� option. Likewise, the optimal decision is identified from the decision with the highest expected value. For this example, the expected values for SOI positive, SOI neutral, and SOI negative were computed at P1,978/ha, P1,508/ha, and P1,000/ha, respectively, as seen in Figure 4. Figure 5. Rainman rainfall charts for each forecast type

Chance of rainfall at TACLOBAN CITY 113250 550 Analysis of historical data (1904-2000) using Average SOI: Dec to Feb Leadtime of 1 month Rainfall period: Apr to Jul The average SOI relationship for this season is statistically significant bec KW test is above 0.9 and Skill Score (15.9) is above 7.6 (p=0.991) Source: RAINMAN International

Low rainfall (<548mm) 33%

High rainfall (>667mm) 33%

Low rainfall (<548 mm) 28%

High rainfall (>667 mm) 36%

Medium rainfall 36%

Medium rainfall 34%

SOI -5 to +5

All Years

High rainfall (>667 mm) 13%

Low rainfall (<548 mm) 17%

High rainfall (>667 mm) 48%

Low rainfall (<548 mm) 57%

Medium rainfall 30%

Medium rainfall 35%

SOI below -5

SOI above +5

To get the expected value of using the forecast, we will multiply the expected value for each forecast type by the probability associated with each forecast type (see Table 1) and then get the sum. That is, EV(using forecast) = (1,978*0.28)+(1,508*0.44)+(1,000*0.28) = P1,497. Notice that the forecast Forecast Type Low/Poor Medium/Average High/Good gets the farmer to select Probability* fallow when the forecast is for drier than normal conditions for SOI below -5 (negative). This SOI below -5 avoids the very poor outcomes of cropping in such seasons.

(negative) SOI -5 to +5 (neutral)

0.28

0.57

0.30

0.13

0.44

0.28

0.36

0.36

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Understanding SCF and Its Applications

Value of climate forecast information (EV with forecast-EV without forecast) The value of forecast information is derived by getting the difference between the expected value with forecast and expected value without forecast. Thus, the value of seasonal climate forecast information (VSCF) per ha per season is P1,497-P1,330 = P168/ha/season. 5

What is the nature of SCF?

Uncertainty is a fundamental characteristic of weather and seasonal climate. No forecast is complete without a description of its uncertainty which is in the form of probability. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Forecasts will only be beneficial when used very carefully. Having the knowledge of the normal climate pattern and cropping season of one’s area will allow SCF users to decide between options and make best choice decisions of forecasts. They will likewise make decisions more easily as the season progresses. The PAGASA’s SCF provides probabilities of above, near and below normal rainfall for the season ahead. To make best use of this information, users may want to consider how well an outlook performs for their particular area. An easy presentation of the forecast may be in the form of a “spinning wheel” used in this ACIAR project. The “spinning wheel”, which represents a circular pie chart, gives divisions that represent percentage probability of occurrence of the three (3) climatic conditions for the forecast period. The bigger the proportion of the pie means that the said condition is more likely to occur and the remaining lower proportions will likewise have the chances of occurring at lesser probability. In order for the SCF to provide farmers with options to make a good decision, farmers should use information available in their area like the average rainfall of their location during the selected season and the needed rainfall for their chosen crop for the season. This will allow them to decide on the farming activities to be followed for an optimum or high target yield. The pie charts in Figure 6 show the likelihood of low, normal (average) or high rainfall during different climatic episodes. During El Niño conditions, the “dry” category ( red) is largest while during the La Niña conditions, the “wet” category (blue) is largest. The proportions in each rainfall category will actually depend on the strength of the El Niño or La Niña event and will also differ from location to location and from season to season.

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A Handbook for Agricultural Extension Workers Figure 6. Likelihood of Rainfall Levels During Various Climate Episodes

EL Niño Year

Normal Year

La Niña Year

Look at the El Niño year pie chart. If you spin it, the pointer is most likely to land on ‘Dry’ because it makes up a larger part of the chart. It can thus be read as follows: during an El Niño event, there is a greater chance of receiving low rainfall (Dry) than normal or high rainfall (Wet). Many parts of the Philippines receive low rainfall (Dry) more often than high rainfall (Wet) during an El Niño episode. If there is a chance of wet conditions in an El Niño episode, it is just a reduced chance. The chances of landing on `wet’, meanwhile, are higher when you spin the La Niña year pie chart. However, for any given location, there have also been La Niña years which have been dry in the past and we should expect that there will likewise be some dry La Niña years in the future. The Normal Year pie chart, on the other hand, is divided into three equal parts. This means that each time the Normal pie chart spins and stops, there is an equal chance of landing on each of the three conditions. In the Philippines, the skill or robustness (level of accuracy) of the forecast is usually lower during the southwest monsoon season (June-August) compared with the northeast monsoon season (OctoberFebruary) because during the former, there are many other factors or elements that affect the climate. 6

What should farmers do when there is an El Niño or La Niña forecast?

There are obvious benefits in knowing in advance the chances of a cropping season being wetter or drier than normal, as this will allow farmers to take preventive action mainly in the areas of crop management and marketing (if commodity prices can be better predicted). Once the forecast is issued, farmers should decide on the appropriate combination of crops to sow, planting schedule to consider and levels of inputs to use in order to maximize the overall yield. Rice and corn are two of the primary crops grown in the Philippines and are highly sensitive to the quantities and

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Understanding SCF and Its Applications

timing of rainfall. Rice thrives on wet conditions during the growing season followed by drier conditions during the ripening phase. Corn can tolerate drier weather. Hence, a forecast of El Niño might induce farmers to sow more corn and less rice than in a year without El Niño. La Niña is typically thought of as the opposite of El Niño. Under La Niña conditions, the season is generally characterized by wetter than normal rainfall conditions. Below shows in tabular form the things to do to cope with an El Niño and/or La Niña forecast. Strategies on How to Cope With the Potential Impacts of an El Niño/La Niña Forecast El Niño Forecast La Niña Forecast 1) Seek the help of the municipal or provincial Same action as with El Niño forecast. agricultural extension workers in your area for any government policies/instructions to any increased risk of disaster, particularly instructions related to agricultural management.

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2) Secure the seasonal climate forecast (SCF) from the agricultural extension worker of your area for guidance on how to use the forecast, particularly in your location. The forecast may be for below normal rainfall in some areas while in your area, it may be normal or predicts no reduction in rainfall during the specified period.

2) Secure the seasonal climate forecast (SCF) from the agricultural extension worker of your area for guidance on how to use the forecast, particularly in your location. The forecast may be for above normal rainfall in some areas while in your area, it may be normal/below normal or predicts a reduction in rainfall during the specified period.

3) With a longer lead time of the SCF, farmers have time to reorganize their annual agricultural activities and can decide on what crops to plant that can withstand the increased chance of dry conditions during their growing period without much affecting yield.

3) With a longer lead time of the SCF, farmers have time to reorganize their annual agricultural activities and can decide on what crops to plant that can withstand the increased chance of wet conditions during their growing period without much affecting yield. Or utilize their accrued experiences that during a La Niña or normal year, good cropping season is possible; thus, having higher chances of getting more rains and higher yields can exist side by side.

4) When deciding to plant corn, the native variety may be chosen and since late onset of rain often occurs with an El Niño, delayed planting may likewise be considered.

4) When deciding on whether or not to plant corn, the high breed variety may be chosen. Since early onset of rain usually goes with a La Niña, early planting may likewise be considered. However, since too much water during the productive stage of the crop is not good , farmers should adjust the cropping season and hope for the sunshine to come during the late stages of the crop.


A Handbook for Agricultural Extension Workers

Strategies on How to Cope With the Potential Impacts of an El Ni単o/La Ni単a Forecast El Ni単o Forecast La Ni単a Forecast 5) For rainfed rice, if the forecast gives a 5) For rainfed rice, if the forecast gives a probability of continuous months of dry period in probability of continuous months of wet period your area, then decide to plant alternative crops in your area, then decide to take advantage of suitable for a dry season. But since SCF also has the abundant moisture to select crops that can a large margin of uncertainty, involving chances withstand plenty of water during their growing of occurrence, a daring farmer may gamble to period. Or go for the forecast and take the chances decide sowing rice on a delayed planting basis, of planting rice, take advantage of the early rainfall hoping that rain may come even up to the for the growing stages of the crop and hope for vegetative state of the crop, but at the same time the sunshine during the ripening phase to target a considering a lesser area to be planted. higher yield. This is in relation to the above normal forecast for the season, considering the area to be prone to flooding. 6) If the farmer has higher confidence in the forecast, then he could look for non-farm activities such as petty trade or construction to supplement the expected reduction in output during the longer dry period.

6) If the farmer has higher confidence in the forecast, then he may consider looking for nonfarm activities to supplement the expected reduction in output during the continuous wet period as predicted.

7) Continue monitoring the forecast because it is updated monthly so that decisions can be made in relation to the present event.

7) Continue monitoring the forecast because it is updated monthly so that decisions can be made in relation to the present event.

8) It is also beneficial to apply the various traditional and modern agronomic techniques of farming to reduce the impact of below-normal rainfall.

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Where and how can SCF information be obtained?

SCF information can be obtained from either the following: a)

PAGASA website http://www.pagasa.dost.gov.ph/

b)

Nearest PAGASA weather station, if any, or Municipal or Provincial agricultural office

c)

Request from the Climatology and Agrometeorology Division, PAGASA office Tel No. 434-09-55 / 929-19-53

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Understanding SCF and Its Applications

Glossary of Terms

Weather is the specific condition of the atmosphere at a particular place and time. It is measured in terms of such things as wind, temperature, humidity, atmospheric pressure, cloudiness and precipitation. In most places, weather can change from hour-to-hour and season-to-season. Climate is the “average weather” and its long term variability over a particular period or over a month, season, year or several years. The standard averaging period as defined by the World Meteorological Organization (WMO) is 30 years. Climate Variability refers to the fluctuations / variations of climate observed since the instrumental period (1860 to present). These fluctuations are due to natural causes and human activities. Climatology the scientific study of climate. ENSO

El Niño Southern Oscillation is large-scale oceanographic and meteorological phenomenon that develops over the central and eastern equatorial Pacific and is associated with extreme climatic variability such as devastating rains, winds, droughts and floods.

El Niño (La Niña) is a condition in the Pacific ocean and characterized by the cyclic warming (cooling) of the central and eastern equatorial Pacific (CEEP). SOI - Southern Oscillation Index is the atmospheric component of ENSO and measured in terms of the difference in standardized pressure anomalies over Tahiti and Darwin, and wind anomaly at low level winds (850 mb level). Sustained negative values of the SOI often indicate El Niño(<-5) episodes while sustained positive values often indicate La Niña episodes (>+5) Normal the average value of a meteorological element over any fixed period of years that is recognized as standard for the country and element concerned. In climatology, mean values are over a specified period, usually thirty years.

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A Handbook for Agricultural Extension Workers

Percent of Normal is the ratio between the actual rainfall and the normal rainfall of a particular area multiplied by 100 percent. Way below normal rainfall condition with values within the range of less than 60% of normal (<40%) Below Normal rainfall condition with values within the range of less than 40% to 80 % of normal (41-80) Near Normal rainfall condition with values within the range of 20% above and 20% below the normal (81-120) Above Normal rainfall condition with values within the range of greater than 20% above the normal (>120)) Probabilistic Forecast are forecasts that give the probability of an event of a certain range of magnitudes occurring in a specific region in a particular time period. An example would be: There is a 70% chance that rainfall will be above average in the coming season.

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Understanding SCF and Its Applications

About the project Background Agriculture in the Philippines and eastern Australia is greatly affected by the El Niño Southern Oscillation (ENSO). Climate in these two countries has higher season-to-season variability relative to other regions at the same latitude and level of annual rainfall. Such variability has significant effects on farm incomes. In Australia, it accounts for around 40 percent of the variation in its agricultural income. Similar consequences are also seen in the Philippines. Climate variability leaves rainfed agricultural producers exposed to high levels of risk when making decisions about the choice of outputs and inputs. It can also lead to conservative practices that, while reducing the negative effects of climatic extremes, may however come at the expense of reduced agricultural incomes and higher resource degradation. Because of all these, a strategic mitigation of climatic risk that is so endemic to rainfed agriculture would clearly be of significant value to farmers. Areas affected by ENSO suffer from increased variability, but one compensation is that improvements in the understanding of ENSO now provide a degree of predictability about climate fluctuations. Climate forecasts offer information on climatic conditions in the coming season and are sometimes presented in the form of a probability of receiving ‘above median’ or ‘below median’ rainfall. They offer skillful albeit uncertain information about climatic conditions in periods of 3–12 months ahead. In Australia, the Bureau of Meteorology provides three monthly seasonal climate outlooks based on the Southern Oscillation Index (SOI) and sea surface temperature (SST) anomalies. Although about 45 percent of Australian farmers claim to take seasonal climate forecasts into account when making decisions, focus groups show that many

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still have reservations on the accuracy, lead time and economic benefits of their application to a specific decision. The El Niño-related drought of 2002 that affected eastern Australia, however, has led to a heightened media and farmer interest in climate science. In the Philippines, PAGASA issues seasonal climate forecasts based on the state of the equatorial Pacific Ocean. The Philippines is a country greatly affected by ENSO. In this regard, PAGASA releases ENSO bulletins as part of the National ENSO Early Warning Monitoring System (NEEWMS). It is important to ensure the accuracy and timeliness of climate forecasts to reduce the difficulty of using probabilistic climate forecasts in decisionmaking. Forecasts that shift the odds but do not remove all the uncertainty are difficult for decisionmakers to use. Specifically, there is a widespread belief that the adoption of SCFs is hampered in both the Philippines and Australia by the lack of robust means of showing the economic value of SCF for specific decisions. Australia and the Philippines promote SCFs In an attempt to address the above shortcoming, a Memorandum of Subsidiary Arrangement was inked between the Philippine Council for Agriculture, Forestry and Natural Resources Research and Development (PCARRD) and the Australian Centre for International Agricultural Research (ACIAR) in October 2004 for the undertaking of a four-year project titled Bridging the gap between seasonal climate forecasts and decisionmakers in agriculture. Implementing institutions for the Philippines are the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA), the Philippine Institute for Development Studies (PIDS) and the Leyte State University (LSU) while for Australia, the key institutions involved are South


Australian Research and Development Institute (SARDI), New South Wales Department of Primary Industries (NSW-DPI), and University of Sydney. The SCF project between Australian and Philippine institutions will draw on economics and other disciplines to develop robust ways to use SCFs in risk management. This project will work with decisionmakers in the Philippines and Australia to see where, when, and why skillful but uncertain SCFs can be valuable, and the circumstances when they are best ignored. The end result will be increased incomes of rural communities in the Philippines and Australia. The project is expected to bring about improved economic, social, and environmental outcomes in the collaborating countries given that better management of climate variability has the potential to improve resource use efficiency by providing economic benefits through improved crop planting, management and grazing strategies. Case studies in the Philippines and Australia will be used to assess where economic, environmental and social benefits may arise. The Philippine studies will focus on poor Filipino farmers who are vulnerable to climate variability while Australian studies will consider the impact of droughts on farming families and rural communities.

decisionmakers use SCFs to make real decisions. An important component of the project is the development of extension strategies based on the case study experiences to promote the value of SCFs. To help implement this, the project will tap into extension networks in Australia and the Philippines and provide tools for agricultural advisers to confidently promote SCFs to decision problems with the greatest payoff. Objectives 􀁺

To improve the capacity of PAGASA to develop and deliver SCF for the case study regions of the Philippines;

􀁺 To distill key practical and methodological features of economic and psychological approaches to valuing SCF; 􀁺 To estimate the potential economic value of SCF for farm and policy or industry level case studies in the Philippines and Australia; 􀁺 To identify those factors leading to a gap between actual and potential values of SCF; and 􀁺 To develop and implement strategies to better match forecasts with decisionmaker’s needs.

Two key methods are to be employed in this project. The first is to value the potential contribution of SCF to decisionmaking under climate uncertainty based on insights from economics and psychology. The second method is the use of farm and policy-level case studies in the Philippines and Australia to gain a practical appreciation of how decisionmakers actually use SCF and how to bridge the gap between potential and actual use of SCF. Case studies will use representative farm models to estimate the potential value of SCFs and will provide information on how farmers and other

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Understanding SCF and Its Applications

PROJECT TEAM Australia Dr. Peter Hayman Principal Scientist, Climate Applications South Australian Research and Development Institute Prof. Kevin Parton Head and Professor, Orange Campus Charles Sturt University and Dean, School of Rural Management Charles Sturt University Dr. John Mullen Principal Research Scientist Research Leader, Economics Coordination and Evaluation New South Wales Department of Primary Industries Mr. Jason Crean Technical Specialist Economics Policy Research New South Wales Department of Primary Industries Ms. Bronya Alexander Research Scientist, Climate Applications South Australian Research and Development Institute

Philippines Philippine Institute for Development Studies Dr. Celia M. Reyes Senior Research Fellow Ms. Jennifer P. T. Liguton Director, Research Information Staff Mr. Sonny N. Domingo Supervising Research Specialist Mr. Christian D. Mina Research Specialist Ms. Kathrina G. Gonzales Senior Research Specialist Visayas State University Dr. Canesio D. Predo Assistant Professor National Abaca Research Center / Department of Economics

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Dr. Rotacio S. Gravoso Assistant Professor Department of Development Communication Dr. Remberto A. Patindol Dean and Professor College of Arts and Sciences Ms. Eva L. Monte Science Research Assistant National Abaca Research Center Philippine Atmospheric, Geophysical and Astronomical Services Administration Dr. Flaviana D. Hilario Weather Services Chief Climatology and Agrometeorology Division (CAD) Ms. Edna L. Juanillo Assistant Weather Services Chief Climatology and Agrometeorology Division (CAD) Ms. Rosalina G. De Guzman Assistant Weather Services Chief Climatology and Agrometeorology Division (CAD) Ms. Daisy F. Ortega Senior Weather Specialist Climate Information Monitoring and Prediction Services Center Philippine Rice Research Institute Dr. Eduardo Jimmy P. Quilang Supervising Science Research Specialist Agronomy and Soils Division Dr. Constancio Asis, Jr. Supervising Science Research Specialist Agronomy and Soils Division Ms. Rowena G. Manalili Senior Science Research Specialist Socioeconomic Division Mr. Jovino De Dios Supervising Science Research Specialist Agronomy and Soils Division Ms. Guadalupe Redondo Science Research Specialist II Socioeconomic Division Mr. Roy F. Tabalno Science Aide Socioeconomic Division



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