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62 SCF Folio<br />
Table 1. Summary of the simulated yields (kg/ha)<br />
<strong>for</strong> the most preferred planting schedules<br />
during the first season<br />
Schedule Rank Mean Standard Minimum<br />
Deviation<br />
La Niña<br />
June, week 3 1 2,591.29 133.27 2,419.00<br />
June, week 1 2 2,510.14 113.07 2,377.00<br />
April, week 4 3 2,476.67 78.56 2,371.00<br />
June, week 4 4 2,500.00 143.18 2,347.00<br />
June, week 2 5 2,528.57 135.76 2,286.00<br />
El Niño<br />
June, week 1 1 2,510.22 109.44 2,372.00<br />
June, week 2 2 2,490.78 151.16 2,052.00<br />
May, week 3 3 2,404.60 95.09 2,264.00<br />
June, week 4 4 2,411.00 113.70 2,221.00<br />
July, week 3 5 2,416.45 158.50 2,084.00<br />
Neutral<br />
July, week 1 1 2,418.73 145.71 2,141.00<br />
July, week 2 2 2,468.87 159.31 2,087.00<br />
July, week 3 3 2,417.80 152.50 2,081.00<br />
May, week 3 4 2,378.94 147.06 2,152.00<br />
June, week 3 5 2,397.11 184.20 2,093.00<br />
All Years<br />
May, week 3 1 2,412.88 135.53 2,152.00<br />
July, week 3 2 2,416.68 141.33 2,081.00<br />
June, week 3 3 2,455.71 191.45 2,066.00<br />
June, week 4 4 2,414.85 163.40 2,050.00<br />
May, week 4 5 2,403.53 158.53 2,066.00<br />
Table 2. Summary of the simulated yields (kg/ha)<br />
<strong>for</strong> the most preferred planting schedules<br />
during the second season<br />
Schedule Rank Mean Standard Minimum<br />
Deviation<br />
La Niña<br />
December, week 4 1 2,540.11 198.21 2,290.00<br />
December, week 1 2 2,527.78 173.09 2,204.00<br />
December, week 2 3 2,466.11 151.57 2,244.00<br />
December, week 3 4 2,443.33 167.73 2,246.00<br />
August, week 1 5 2,400.90 125.48 2,179.00<br />
El Niño<br />
December, week 1 1 2,602.82 178.54 2,351.00<br />
September, week 2 2 2,413.30 74.83 2,308.00<br />
September, week 3 3 2,452.80 125.61 2,229.00<br />
December, week 2 4 2,547.55 228.52 2,192.00<br />
December, week 4 5 2,451.82 169.58 2,171.00<br />
Neutral<br />
December, week 1 1 2,518.14 153.80 2,253.00<br />
September, week 1 2 2,370.71 90.14 2,237.00<br />
December, week 2 3 2,502.36 208.62 2,133.00<br />
August, week 4 4 2,374.23 134.37 2,211.00<br />
October, week 3 5 2,377.54 153.52 2,141.00<br />
All Years<br />
December, week 1 1 2,548.09 166.53 2,204.00<br />
December, week 2 2 2,507.38 198.87 2,133.00<br />
December, week 4 3 2,500.85 223.84 2,099.00<br />
December, week 3 4 2,449.21 185.02 2,056.00<br />
August, week 3 5 2,339.91 152.89 2,102.00<br />
weather station, soil characteristics of the site, cropspecific<br />
parameters, and common cultural practices in<br />
corn production. The method of stochastic dominance<br />
analysis was then applied on the probability<br />
distributions of the simulated yields using two criteria:<br />
first-degree stochastic dominance and stochastic<br />
dominance with respect to a function, with three levels<br />
of risk aversion.<br />
The process led to the identification of riskefficient<br />
strategies <strong>for</strong> each stochastic dominance<br />
criterion and the most preferred schedule within each<br />
season, given the ENSO episode during the cropping<br />
period. These schedules could be used as guide by<br />
farmers in the site if PAGASA could provide a <strong>for</strong>ecast<br />
about the ENSO episode in the next cropping period.<br />
Likewise, the study was able to identify the risk-efficient<br />
and most preferred schedules within every season<br />
without considering the ENSO episode during the<br />
cropping period. The schedules identified in this<br />
manner can be used by the farmers in the site if no<br />
<strong>for</strong>ecast is available (represented as All Years in Tables 1<br />
and 2).<br />
The study successfully demonstrated in principle<br />
that stochastic dominance analysis can be applied to<br />
identify risk-efficient schedules under the different<br />
ENSO episodes using probability distributions of<br />
simulated yields. It also showed that stochastic<br />
dominance analysis is sensitive in the sense that it can<br />
still provide a ranking of the strategies even with<br />
relatively small differences in the mean values. This<br />
implies that the method could be a good alternative<br />
when comparing outcomes of different strategies.<br />
The ultimate question would be on how to make<br />
the outputs of the study relevant to local farmers.<br />
Considering that the actual schedules followed by<br />
farmers in the site differed from the risk-efficient<br />
schedules identified in the study, the authors expressed<br />
the need <strong>for</strong> a more detailed enquiry. For one, the<br />
research did not incorporate all factors that may have<br />
some influence in a farmer’s choice of planting<br />
schedule. In the absence of relevant explanations <strong>for</strong><br />
farmers’ actual choices, dissemination of in<strong>for</strong>mation<br />
pertaining to risk-efficient planting schedules was<br />
thereupon advised. (SCF Project Updates, September 2008)