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62 SCF Folio Table 1. Summary of the simulated yields (kg/ha) for the most preferred planting schedules during the first season Schedule Rank Mean Standard Minimum Deviation La Niña June, week 3 1 2,591.29 133.27 2,419.00 June, week 1 2 2,510.14 113.07 2,377.00 April, week 4 3 2,476.67 78.56 2,371.00 June, week 4 4 2,500.00 143.18 2,347.00 June, week 2 5 2,528.57 135.76 2,286.00 El Niño June, week 1 1 2,510.22 109.44 2,372.00 June, week 2 2 2,490.78 151.16 2,052.00 May, week 3 3 2,404.60 95.09 2,264.00 June, week 4 4 2,411.00 113.70 2,221.00 July, week 3 5 2,416.45 158.50 2,084.00 Neutral July, week 1 1 2,418.73 145.71 2,141.00 July, week 2 2 2,468.87 159.31 2,087.00 July, week 3 3 2,417.80 152.50 2,081.00 May, week 3 4 2,378.94 147.06 2,152.00 June, week 3 5 2,397.11 184.20 2,093.00 All Years May, week 3 1 2,412.88 135.53 2,152.00 July, week 3 2 2,416.68 141.33 2,081.00 June, week 3 3 2,455.71 191.45 2,066.00 June, week 4 4 2,414.85 163.40 2,050.00 May, week 4 5 2,403.53 158.53 2,066.00 Table 2. Summary of the simulated yields (kg/ha) for the most preferred planting schedules during the second season Schedule Rank Mean Standard Minimum Deviation La Niña December, week 4 1 2,540.11 198.21 2,290.00 December, week 1 2 2,527.78 173.09 2,204.00 December, week 2 3 2,466.11 151.57 2,244.00 December, week 3 4 2,443.33 167.73 2,246.00 August, week 1 5 2,400.90 125.48 2,179.00 El Niño December, week 1 1 2,602.82 178.54 2,351.00 September, week 2 2 2,413.30 74.83 2,308.00 September, week 3 3 2,452.80 125.61 2,229.00 December, week 2 4 2,547.55 228.52 2,192.00 December, week 4 5 2,451.82 169.58 2,171.00 Neutral December, week 1 1 2,518.14 153.80 2,253.00 September, week 1 2 2,370.71 90.14 2,237.00 December, week 2 3 2,502.36 208.62 2,133.00 August, week 4 4 2,374.23 134.37 2,211.00 October, week 3 5 2,377.54 153.52 2,141.00 All Years December, week 1 1 2,548.09 166.53 2,204.00 December, week 2 2 2,507.38 198.87 2,133.00 December, week 4 3 2,500.85 223.84 2,099.00 December, week 3 4 2,449.21 185.02 2,056.00 August, week 3 5 2,339.91 152.89 2,102.00 weather station, soil characteristics of the site, cropspecific parameters, and common cultural practices in corn production. The method of stochastic dominance analysis was then applied on the probability distributions of the simulated yields using two criteria: first-degree stochastic dominance and stochastic dominance with respect to a function, with three levels of risk aversion. The process led to the identification of riskefficient strategies for each stochastic dominance criterion and the most preferred schedule within each season, given the ENSO episode during the cropping period. These schedules could be used as guide by farmers in the site if PAGASA could provide a forecast about the ENSO episode in the next cropping period. Likewise, the study was able to identify the risk-efficient and most preferred schedules within every season without considering the ENSO episode during the cropping period. The schedules identified in this manner can be used by the farmers in the site if no forecast is available (represented as All Years in Tables 1 and 2). The study successfully demonstrated in principle that stochastic dominance analysis can be applied to identify risk-efficient schedules under the different ENSO episodes using probability distributions of simulated yields. It also showed that stochastic dominance analysis is sensitive in the sense that it can still provide a ranking of the strategies even with relatively small differences in the mean values. This implies that the method could be a good alternative when comparing outcomes of different strategies. The ultimate question would be on how to make the outputs of the study relevant to local farmers. Considering that the actual schedules followed by farmers in the site differed from the risk-efficient schedules identified in the study, the authors expressed the need for a more detailed enquiry. For one, the research did not incorporate all factors that may have some influence in a farmer’s choice of planting schedule. In the absence of relevant explanations for farmers’ actual choices, dissemination of information pertaining to risk-efficient planting schedules was thereupon advised. (SCF Project Updates, September 2008)
63 Determining corn farmers’ decisions based on SCFs Gian Carlo M. Borines, Rotacio S. Gravoso, Canesio D. Predo * The advent of the anomalous weather and climate conditions aggravated by global warming has underscored the need to disseminate climate information to guide farmers in their farm decisions. Advance climate information, like the seasonal climate forecast (SCF), helps farmers decide which land to use for a particular crop, chart out production schedules, and devise commercialization strategies—decisions that are normally made by farmers long before the sowing season starts. Experiences from other countries show that the risk of production losses due to anomalous climatic conditions can be mitigated if farmers are aware of and use SCF. In the Philippines, the project, “Bridging the gap between seasonal climate forecasts and decisionmakers in agriculture,” funded by the Australian Centre for International Agricultural Research (ACIAR), has shown the possibility for farmers to improve their income if they use SCF. Thus, the project intends to actively disseminate and encourage farmers to incorporate SCF into their farming practices. Central to the use and application of climate information is the decisionmaking by farmers. Based on existing literature, in dealing with uncertain climate information, farmers engage themselves in descriptionand experience-based modes of decisionmaking, thereupon increasing the risks of possible losses. This study was therefore conducted to find out how farmers will decide if they are presented with probabilistic climate forecasts. Methods This study was conducted in Brgy. Miglamin, Brgy. Laguitas, and Brgy. Magsaysay in Malaybalay City, Bukidnon Province in consultation with the Department of Agriculture in Malaybalay City. ____________ * Staff and faculty, Visayas State University. A focus group discussion (FGD) was conducted to gather the background on the farmers’ exposure, access and use of SCF. To find out about the farmers’ decisionmaking based on uncertain information, two decisionmaking workshops were conducted. In each workshop, farmer participants were made to assume five varying hypothetic assumptions wherein they have experienced unfavorable cropping seasons in the past three consecutive years (March–June, 2005–2007). The participants were then presented with a climate forecast (in video) developed specifically for this study (Table 1). Subsequently, farmers were asked to make decisions or courses of action for the next cropping season based on the forecast (Table 2). A total of 30 farmers participated in the two workshops. Highlights of results Exposure and access to SCF information Data showed that farmers in this study were aware of SCFs and climate information. Farmers, especially those who are planting in big areas of land or are producing crops on a large scale, pay visits to the PAGASA station in Malaybalay or the Department of Agriculture (DA) office to consult on what the climate would be like before they begin to plant and what crops would be best to plant. Information obtained from these consultations is used to schedule the time of planting and to decide on which crop to plant. Not all farmers in Malaybalay, however, are able to go to the city proper to inquire about the climatic conditions. Thus, PAGASA and the DA hold seminars and fora about the climate in areas surrounding the province. Likewise, staff from the PAGASA station are invited occasionally to air climate forecasts and issues over the local radio station. Evaluation of SCF information Farmers reported that they get climate forecasts from the radio or television through the national stations. In general, they felt that climate forecasts are helpful. However, to be more useful, they suggested some changes. These suggestions include: 1) avoid the use of
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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
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9 Tropical cyclone average tracks J
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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 and 60: 59 In Cebu: use of SCF gives higher
Page 61: 61 units of N fertilizer based on g
Page 65 and 66: 65 Table 2. Farmers’ responses to
Page 67 and 68: 67 Project Team Australia Dr. Peter
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