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60 SCF Folio ...The notion that probabilistic forecasts cannot be used in decisionmaking is not true. Notwithstanding difficulties in communication, a forecast should be presented as a probability because it is the honest way of doing it...The challenge is how to communicate and use in decisionmaking skillful but uncertain forecasts that are best represented as shifts in climatological probability distributions. excessive rains rather than to a more qualified statement of increased chances of the events occurring. Implied in these inclinations is the notion that probabilistic forecasts cannot be used in decisionmaking. This notion, however, needs to be corrected because it is not true. Notwithstanding difficulties in communication, a forecast should be presented as a probability because it is the honest way of doing it. As an expert puts it, the atmosphere is a complex chaotic fluid, and although patterns of ocean temperatures ‘nudge’ this chaos in certain directions, there will always be a significant proportion of unexplained variation. Hence, the challenge is how to communicate and use in decisionmaking skillful but uncertain forecasts that are best represented as shifts in climatological probability distributions. Probabilistic forecasts also ensure that risk management is not hindered. A farmer who misunderstands SCF as a categorical forecast may be led to devise poorer risk management strategies compared to a situation where he did not hear of the Figure 1. Decision tree analysis showing gross margins for different fertilizer rates and season types, and probability weighted value for each of the three fertilizer rates forecast at all. A crop grower may plan for a wide range of outcomes in the absence of a forecast. But if only one outcome is in his mind, then the planning exercise will definitely be narrower. An imposing challenge therefore is how to use uncertain information for decisionmaking. The use of decision analysis was mentioned as an approach that provides a logical framework for a decisionmaker to formulate preferences, assess uncertainty, and make judgments. There has been a tradition in agricultural science to talk the language of choice-consequence. For example, if you put on x units of nitrogen, you will get a yield of y. A more forward-looking language is that of choice-chance-consequences. This means that if you put on x units of nitrogen, depending on the season type, you will get a yield of either y 1 , y 2 , or y 3 . A good example (Figure 1) is the use of decision tree analysis in determining the level of fertilizer inputs given uncertain forecasts. 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 the proper chronological order. Because the decision tree reflects choices, probabilities, and consequences, it thereupon effectively illustrates how uncertain forecasts might be used to change fertilizer decisions. The figure shows how forecasts can influence the decision of N fertilizer rates application. For instance, during the season with a poor, average, and good outcome, about 20, 60, and 100 units, respectively, of N fertilizer will be applied. Given this information and knowing the expected season from the seasonal climate forecast, the farmer can therefore decide on the level of fertilizer application. Results from the figure show that if the forecast is neutral, the farmer is better off when he will apply 100 units than 20 units and even 60 units of N fertilizer. However, if he will apply 100
61 units of N fertilizer based on good outcome season but the actual season turns out to be poor, the farmer will incur a loss or negative gross margin. Similarly, if he is expecting a poor season outcome by applying 20 units of N fertilizer but a good season has actually occurred, then he has missed the opportunity for a bigger gross margin. In the paper, the authors also present a fresher and more fun way of looking at decision analysis through ‘Wonder Bean,’ an innovative game about choosing the right crop to plant given SCF and seasonal climate variability. The game features spinning probability disks in a simple Excel®-based spreadsheet where participants decide on the area of a farm to plant to a higher-return but higher-risk crop vis-à-vis the area to leave to a lowerreturn but lower-risk crop. Although the enumerated applications with spinning probability disks, decision trees and crop choice games are not intended for regular decision support systems, they are nonetheless useful in organizing ideas and engaging decisionmakers. A step toward bridging the gap between climate science and decisionmaking, no matter how small, is after all a step toward better managing the risks from seasonal climate variability. (SCF Project Updates, September 2008) Choosing risk-efficient planting schedules for corn: the Matalom, Leyte case One of the most important decisions affecting crop production in rainfed areas is the timing of planting. A farmer may select a planting schedule in such a way that the cropping period would be less risky, avoiding or minimizing the impact of projected destructive seasonal climatic events within the growing season. This is now made more possible with recent developments in atmospheric science, particularly on seasonal climate forecasting (SCF). Remberto Patindol, Canesio Predo, and Rosalina de Guzman 1 explored this possibility of shifting cropping schedules from traditional dates to fit forecast seasonal climatic events in a rainfed area in Matalom, Leyte, Philippines. In a study titled “Risk-efficient planting schedules for corn in Matalom, Leyte,” they looked into historical weather data and information about past occurrences of the different El Niño Southern Oscillation (ENSO) phases to see if these can be used in selecting the best cropping schedules. Local farmers usually follow traditional planting schedules under the assumption that the conditions ____________ 1 Associate Professor and Assistant Professor at the Visayas State University, and Assistant Head, Climate Information, Monitoring, and Prediction Services Center of the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), respectively. during a particular planting period are repeated over the years. Thus, it would not be uncommon to observe farmers in a given locality, for example, to plant corn in the first week of May and repeat this schedule over the years. This practice, however, makes local farming prone to damages because farmers usually do not use SCF and account for seasonal climate variability especially during El Niño and La Niña events. The authors thus identified risk-efficient planting schedules for corn using stochastic dominance analysis of simulated yields given ENSO forecasts for different cropping periods. The method requires the use of probability distributions of corn yields for different planting schedules. Given the absence of historical data and lack of time for conducting multiyear experiments, corn yields for the different planting scenarios were generated through the use of a simulation modelling software. The model utilized actual and synthetic data to reflect the variability associated with the different ENSO phases. Inputs in the yield simulation modelling included actual and generated weather data from the nearest
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SCF Folio A compilation of informat
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3 Administration (PAGASA), the Phil
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5 Learning the basics Dr. John Mull
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7 The dry season is also subdivided
Page 9 and 10: 9 Tropical cyclone average tracks J
Page 11 and 12: 11 inevitably have impacts on the v
Page 13 and 14: 13 Reaching out and increasing awar
Page 15 and 16: 15 To ensure that the SCFs are rend
Page 17 and 18: Communicating through climate indic
Page 19 and 20: 19 Table 1. Awareness, usefulness,
Page 21 and 22: 21 Very often, this leads to extrem
Page 23 and 24: 23 agency, PAGASA, and sent out to
Page 25 and 26: 25 municipality. LGUs may send in t
Page 27 and 28: 27 SCF is popular in Bukidnon but..
Page 29 and 30: 29 the Provincial Agriculture Offic
Page 31 and 32: 31 Analysis and research/survey res
Page 33 and 34: 33 As the results in this initial s
Page 35 and 36: 35 Table 2. El Niño and La Niña e
Page 37 and 38: 37 average, and wet years were calc
Page 39 and 40: 39 local corn growers. In guiding f
Page 41 and 42: 41 dominated regions of northern Ne
Page 43 and 44: 43 The influence of ENSO on frost r
Page 45 and 46: 45 It is the timing of the latest f
Page 47 and 48: A model for valuing seasonal climat
Page 49 and 50: 49 And on rice crop... Nueva Ecija
Page 51 and 52: 51 Security against climate variabi
Page 53 and 54: 53 been dominating the Table 1. Bor
Page 55 and 56: 55 The need to help rice and corn f
Page 57 and 58: 57 Figure 1. Rainfall outlook, July
Page 59: 59 In Cebu: use of SCF gives higher
Page 63 and 64: 63 Determining corn farmers’ deci
Page 65 and 66: 65 Table 2. Farmers’ responses to
Page 67 and 68: 67 Project Team Australia Dr. Peter
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