Philippine Journal of Development Number 66, First Semester 2009 Volume XXXVI, No. 1

Risk-efficient Planting Schedules for Corn in Matalom, ,

Remberto A. Patindol, Canesio D. Predo, and Rosalina G. De Guzman1

ABSTRACT The study was conducted to identify risk-efficient cropping schedules for corn farmers in Matalom, Leyte, Philippines using stochastic dominance analysis of simulated yields, given the El Niño Southern Oscillation (ENSO) forecasts during the cropping period. Actual weather data, with missing observations estimated using a weather generating software, were used in constructing weather data sets. These data, together with crop parameters and soil characteristics in the study site, were used as inputs to generate probability distributions of yields during different planting schedules. The simulated yield distributions were classified according to the ENSO phases prevailing during the cropping period. Stochastic dominance analysis was applied on the yield distributions to determine the first-degree stochastic dominance (FSD) set and the second-degree stochastic dominance (SSD) set. Finally, stochastic dominance with respect to a function (SDWRF) was applied on the SSD set to identify risk-efficient schedules at different levels of risk aversion.

1 Remberto A. Patindol is from the Visayas State University, City, Leyte, Philippines. Email for correspondence: [email protected]. Canesio D. Predo is from the College of Forestry and Natural Resources, University of the Philippines Los Baños but previously with the Visayas State University during the implementation of the ACIAR funded project “Bridging the gap between seasonal climate forecast and decisionmakers in agriculture.” Rosalina de Guzman is with the Philippine Atmospheric, Geophysical and Astronomical Services Administration of the Department of Science and Technology, Quezon City, Philippines. 66 Philippine Journal of Development 2009 Risk-efficient schedules were identified for each cropping season and under each ENSO phase. It was found out that some June-July schedules (during the first season) and some December schedules (during the second season) are more risk-efficient than traditional schedules.

INTRODUCTION

Importance of the study One of the most important decisions affecting crop production in rainfed areas is the timing of planting. In most cases, farmers follow traditional planting schedules under the assumption that the conditions 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. Risk can be defined in several ways. It may be defined as an expected loss in decision theory, or the probability of loss in some economic applications (Roumasset 1979). Risk can also be viewed in terms of the probability distributions of the outcomes of the action choices or strategies. A strategy may be considered less risky than another if it would be preferred by all risk-averse decisionmakers. Thus, a risk-efficient strategy can be viewed as one that will be preferred by all risk-averse decisionmakers of a particular class of risk aversion (Rothchild and Stiglitz 1970). A risk-efficient strategy can lead to an increase in the mean and/or a reduction in the dispersion about the mean (Pandey 2000). Based on the above definitions and descriptions of risk and risk efficiency, two basic requirements are necessary for risk-efficiency analysis, namely, the probability distribution of the outcomes of the strategies and the risk attitude of the decisionmakers. Recent developments in technology have led to availability of forecasts about El Niño Southern Oscillation (ENSO) phases or episodes. The Oceanic Niño Index (ONI) from the US National Oceanic and Atmospheric Administration (US- NOAA) is a principal measure for monitoring, assessing, and predicting ENSO (NOAA 2007). The ONI is based on sea surface temperature (SST) departures from the average in the Niño 3.4 region, and is defined as the three-month running-mean SST departures in the Niño 3.4 region. Departures are based on a set of improved homogeneous historical SST analyses (Extended Reconstructed SST—ERSST.v2). The NOAA operational definitions for El Niño and La Niña are as follows: El Niño: characterized by a positive ONI greater than or equal to +0.5°C. La Niña: characterized by a negative ONI less than or equal to -0.5°C. Patindol, Predo and De Guzman 67 To be classified as a full-fledged El Niño or La Niña episode, these thresholds must be exceeded for a period of at least five consecutive overlapping 3-month seasons. A lot of people, including farmers, are now paying attention to seasonal forecasts. A farmer can use such forecasts to avert possible damage or, at least, caution the impacts these ENSO episodes could bring on his crops. Eventually, he may select a planting schedule that would be risk-efficient or less risky, given the forecast. One approach that can be used in identifying risk-efficient planting schedules is stochastic dominance analysis. However, this method requires the probability distributions of the outcomes (such as yield) of the different strategies (planting schedules, in this paper). This would imply getting data for each planting schedule over several years. In the absence of actual yield data to be used in the probability distributions, one can generate the probability distributions using simulated yields for each strategy to be evaluated. The simulations can use actual or synthetic weather data that reflect the variability associated with the different ENSO episodes.

Objectives of the study This study is an attempt to make use of historical weather data and information about past occurrences of the different ENSO episodes to see if these can be used in selecting cropping schedules that may be less affected by the occurrence of these episodes. Moreover, the study explores the application of the method of stochastic dominance on probability distributions of simulated yields in the choice of risk-efficient planting schedules. The study aims to identify risk-efficient cropping schedules for corn farmers in Matalom, Leyte, Philippines using stochastic dominance analysis of simulated yields given ENSO forecasts during the cropping period.

Attitude towards risk In terms of attitude towards risk, a decisionmaker can be classified as risk-averse, risk-preferring, or risk-neutral. According to Fleisher (1990), a risk-averse decisionmaker will forego some possible gains to reduce the probability of losses, while a risk-preferring individual will select a strategy that offers some chance of high returns even if it faces the possibility of incurring low returns or losses. A risk-neutral decisionmaker will select a strategy that will maximize the expected value of the outcomes. 68 Philippine Journal of Development 2009 Stochastic dominance analysis Several methods are available for risk analysis; among them are the expected utility model and the mean-variance analysis. The expected utility model selects the strategy that maximizes the expected utility of the random variable representing the outcome of the risky strategy or action. However, it requires complete specification of the decisionmaker’s utility function, which is a difficult task. The mean-variance analysis rests on the assumption of normality of the distribution of the outcomes (which few outcomes could satisfy) or a quadratic utility function, which is criticized for implying increasing absolute risk aversion beyond a certain level of wealth (Hanson and Ladd 1991). Stochastic dominance analysis is a nonparametric approach that makes comparisons among the cumulative probability distributions of the outcomes of the different strategies, under limited assumptions on the decisionmaker’s utility function. First-degree stochastic dominance assumes that the decisionmaker has a nondecreasing utility function (Hadar and Russel 1969) while second-degree stochastic dominance assumes a concave utility function (Hanoch and Levy 1969). The assumption of a nondecreasing utility function under FSD implies that the decisionmaker prefers “more” to “less,” while the assumption of a concave utility function implies that the decisionmaker is risk-averse. Thus, risk-efficient strategies in FSD sense will be preferred by those who prefer “more” to “less.” Risk-efficient strategies in SSD sense imply those that will be preferred by risk- averse decisionmakers. Another method, which has a more discriminating power than SSD is the stochastic dominance with respect to a function, proposed by Meyer (1977). The method introduces bounds on the absolute risk-aversion coefficient within an SSD analysis. In this sense, FSD can be considered a special case of SDWRF with bounds of –∞ and +∞. Similarly, SSD is a special case, with bounds, 0 and +∞. Values of the risk aversion coefficients (RAC) are typically categorized into low (0

Simulation of yield distributions One basic requirement in stochastic dominance analysis is the set of probability distributions of the outcomes of the different strategies. However, in general, no records of actual yields covering a reasonable length of time are available. Thus, the use of stochastic dominance analysis would not be possible. As a solution to this problem, simulation of the yield distributions covering the desired period becomes necessary. Stochastic dominance analysis could then be applied on the simulated probability distributions. This approach was used by Lansigan et al. (1997) in the evaluation of alternative management options in the light of farmers’ Patindol, Predo and De Guzman 69 attitude toward risk and by Patindol (2001) in the selection of risk-efficient cropping strategies.

METHODOLOGY

Generation of weather data The study site (Matalom, Leyte, Philippines) does not have a weather station, so the data from the nearest weather station (, ), which is about 25 kilometers away, were used in this study. Daily weather data covering 34 years (1973 to 2006) were obtained from the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). The data set included rainfall, maximum temperature, minimum temperature, relative humidity, and solar radiation. There were several missing data in the file, and considering that the data would be used in crop yield simulation, it was necessary to generate synthetic data to fill in the missing values. The weather generation component (WeatherMan) of the software, Decision Support System for Agrotechnology Transfer Version 4.0 (Hoogenboom et al. 2004) was used for this purpose.

Cropping calendar A survey among farmers in the site revealed that the schedule of planting during the first cropping season was distributed from March to July, with over 80 percent within April and May. The second season usually starts in August but there were farmers who started as early as July and some who planted as late as December, with over 90 percent within August to September. For the purpose of this study, the first season was from March to July and the second season was from August to December. This allows exploring the possibility of other nonpopular schedules.

Identification of the ENSO phase In order to differentiate the different cropping seasons (with respect to the prevailing ENSO conditions) to be used in the crop yield simulation, values of the ONI from the US-NOAA were used. Every cropping season planned for inclusion in the simulation was categorized accordingly, with the ENSO phase prevailing over more than half (usually during two of the first three months) of the simulated cropping period considered as the category of the cropping period. As an example, if the cropping period fell within May–June–July and the prevailing ENSO episodes of two of these three months were classified as El Niño, then the cropping period would be classified as El Niño. 70 Philippine Journal of Development 2009 Inputs for crop yield simulation The DSSAT Version 4.0 software (Hoogenboom et al. 2004) was used in crop yield simulation. This software combines different modules to generate weather data and simulate soil water and nitrogen dynamics, crop growth, development, and yield. It can be used in simulating yields of several important crops under different scenarios based on field conditions, weather variability, and cultural practices. Input weather data for simulation included the corrected daily rainfall, maximum temperature, minimum temperature, and solar radiation. Soil information such as the soil type, texture, and drainage characteristics were based on most commonly mentioned responses from the survey among farmers in the specified site. For the crop parameters, the variety used in the simulation was IPB 911 since this variety has a complete data on different crop coefficients (Table 1) obtained from experiments in several locations by Lansigan et al. (2002). Planting method, spacing and depth, and other cultural management practices were chosen based on recommended practices. All the crop parameters, soil characteristics, and cultural management practices were kept constant in the simulation.

Simulation of yield distributions Crop yields were simulated for different weekly planting schedules. During the simulation process, the selected planting date was the middle of the week. For instance, for the first week of April (April 1–7), planting date was April 4 (Julian Day 94) and for the second week (April 8–14), April 11 (Julian Day 101) was chosen as input planting date. Yields for each weekly schedule were

Table 1. Crop coefficients for corn variety IPB 911

Genetic Coefficient

Coefficient Name Description Value

P1 Thermal time from seedling emergence to the end of juvenile 316 phase (expressed in degree days above a base temperature of 8oC)

P5 Thermal time from silking to physiological maturity (expressed in 977 degree days above a base temperature of 8ºC)

G2 Maximum possible number of kernels per plant 396

G5 Kernel filling rate during the linear grain filling stage and under 691 optimum conditions

PHINT Phylochron interval; the interval in thermal time (degree days) 42 between successive leaf tip appearances

Source: Lansigan et al. (2002). Patindol, Predo and De Guzman 71 simulated for all years (1973 to 2006) using DSSAT. From these yields, four probability distributions were created, one distribution without considering the ENSO condition of the cropping period and one distribution for each ENSO episode.

Stochastic dominance analysis Two stochastic dominance programs were used in stochastic dominance analysis. The first program was the SDRF (Goh et al. 1987), a DOS-based program which was used to perform quasi-FSD, quasi-SSD, and stochastic dominance with respect to a function (SDWRF) at different levels of risk aversion. The other software used was SIMETAR, an acronym for SIMulation for Excel To Analyze Risk (Richardson et al. 2004), which is a Microsoft Excel add-in. In this study, SIMETAR was also used to perform SDWRF, especially on the probability distributions that have unequal number of observations and in cases where the SDRF program produced SDWRF sets consisting of two or more planting schedules. SIMETAR can handle unequal number of observations in the yield probability distributions and has the capability to rank the distributions within the same SDWRF set. Planting schedules within the same season and under the same ENSO episode were subjected to quasi-FSD analysis to identify the preferred strategies under first-degree stochastic dominance. Those in the quasi-FSD set are those schedules that will be preferred by farmers who would simply prefer high yield without considering variability and its associated risk. The schedules in the SSD set are those which will be preferred by risk-averse farmers. Further analysis using SDWRF was used to identify the preferred schedules under different levels of risk aversion. Three levels of risk-aversion coefficients were used under SDWRF, namely: low (0

Distribution of rainfall, temperature, and solar radiation in the site The distribution of rainfall in the study site is presented in Figure 1. Generally, the trend is the same for all ENSO types. Rainfall is typically low from March to May and high from August to January. However, it should be noted that during the months of April to August, the means of the monthly rainfall do not vary very much among the different ENSO types. The distributions of the maximum and minimum temperatures in the site are shown in Figures 2 and 3, respectively. Generally, both temperatures are higher during April to June and low from November to January. Solar radiation also behaved similarly (Figure 4). Only small differences were observed among

Figure 1. Distribution of average monthly rainfall in Maasin, Southern Leyte, Philippines, by ENSO type

Figure 2. Distribution of mean daily maximum temperature in Maasin, Southern Leyte, Philippines, by ENSO type Patindol, Predo and De Guzman 73 Figure 3. Distribution of mean daily minimum temperature in Maasin, Southern Leyte, Philippines, by ENSO type

Figure 4. Distribution of mean daily solar radiation in Maasin, Southern Leyte, Philippines, by ENSO type

the different ENSO phases with respect to the weather variables, indicating that the site was not much affected by the extreme conditions associated with the ENSO phases.

Simulated yields The simulated yields from the different schedules are summarized in Figure 5 (first season ) and Figure 6 (second season). As shown, the distribution of the simulated yields is relatively flat throughout each season, with little dispersion within each month. This is explained by the type of climate in the site. As described by PAGASA, the site does not have a pronounced dry season and rainfall is relatively evenly distributed throughout the year. 74 Philippine Journal of Development 2009 Figure 5. Summary of the distributions of the simulated yields during the first season in Matalom, Leyte, Philippines

Figure 6. Summary of the distributions of the simulated yields during the second season in Matalom, Leyte, Philippines

Risk-efficient schedules for the first season The risk-efficient planting schedules for the first cropping season are summarized in Tables 2 to 5. These tables contain the risk-efficient schedules according to FSD and SDWRF with three levels of risk aversion. Application of the FSD criterion to the schedules during the El Niño episodes resulted in nine risk-efficient schedules in FSD sense (Table 2). All planting schedules in July, three in June and two in May comprised the FSD set. The five top-ranked schedules that will be preferred by risk-averse farmers (SSD set) included the third week of July and the first, second, and fourth weeks of June, regardless of the level of risk aversion. The third week of May is among the top choices for farmers with high (0.001

Most risk-efficient* schedules under SDWRF

Planting Risk-efficient 0

May week 3, week 4 week 3

June week 1-week 3 week 1 to week 4 week 1 to week 4 week 1,

week 2

week 4

July week 1- week 4 week 3 week 3 week 3 * Top 5 schedules based on SDWRF ranking.

Table 3. Risk-efficient schedules for the first cropping season, under La Niña conditions

Most risk-efficient* schedules under SDWRF

Planting Risk-efficient 0

April week 3, week 4 week 4

May week 3, week 4 week 3 week 3

June week 3, week 4 week 1 to week 4 week 1 to week 4 week 1 to week 4

July week 1 * Top 5 schedules based on SDWRF ranking

Table 4. Risk-efficient schedules for the first cropping season, under neutral conditions

Most risk-efficient* schedules under SDWRF

Planting Risk-efficient 0

April week 3

May week 2, week 4 week 2 week 2 week 3

June week 1 to week 3 week 3 week 3 week 4

July week 1, week 2 week 1 to week 3 week 1 to week 3 week 1 to week 3

* Top 5 schedules based on SDWRF ranking.

During the La Niña years, the third and fourth weeks of April, May, and June and the first week of July are the risk-efficient schedules in FSD sense (Table 3). These are the schedules that will be preferred by farmers 76 Philippine Journal of Development 2009 Table 5. Risk-efficient schedules for the first cropping season, without considering ENSO type Most risk-efficient* schedules under SDWRF Planting Risk-efficient 0

Risk-efficient schedules for the second season The risk-efficient sets under FSD and SDWRF for the second season are summarized in Tables 6 to 9. In general, most of the schedules in December are among the risk-efficient choices based on the different criteria. Patindol, Predo and De Guzman 77 Table 6 shows that under El Niño conditions, the FSD set consists of all schedules in November and December and the third week of September. Regardless of the level of risk aversion, the first, second, and fourth weeks of December and the third week of September are among the top five schedules in terms of risk efficiency. The fourth week of November is included among the best schedules under low to moderate levels of risk aversion, while the second week of September is included under high risk-aversion level. The risk-efficient schedules under La Niña conditions in the second season are shown in Table 7. The FSD set is a large data set consisting of more than half of the schedules in the second season, and is not discriminating enough to be of practical use. The first, second, and fourth weeks of December are consistently in the top schedules under all levels of risk aversion. The second week of November is among the top five schedules for farmers under low to moderate levels of risk aversion, while the fourth week is included at the low level. The best risk-efficient schedules under high level of risk aversion included the first week of August. The FSD set under neutral conditions is composed of the December schedules (Table 8). At the low to moderate risk-aversion levels, all December schedules are in the top five. The fourth week of November is among the best schedules for farmers with low risk-aversion level while the first week of September is included under moderate to high levels. The fourth week of August and the third week of October are among the best schedules under high level of risk aversion. Table 9 summarizes the risk-efficient schedules considering all years regardless of the ENSO episode of the cropping season. The first, second, and fourth week of December comprise the risk-efficient schedules under FSD. In addition, all schedules in December are in the top five schedules in all levels of

Table 6. Risk-efficient schedules for the second cropping season, under ElNiño conditions

Most risk-efficient* schedules under SDWRF Planting Risk-efficient 0

Table 8. Risk-efficient schedules for the second cropping season, under neutral conditions Most risk-efficient* schedules under SDWRF Planting Risk-efficient 0

Table 9. Risk-efficient schedules for the second cropping season, without considering ENSO type

Most risk-efficient* schedules underSDWRF Planting Risk-efficient 0

* Top 5 schedules based on SDWRF ranking. risk aversion. The fourth week of November is included in the best schedules at the low to moderate levels of risk aversion while the third week of August is included under high risk-aversion level. Patindol, Predo and De Guzman 79 Most preferred schedules The means, standard deviations, and minimum values of the probability distributions of the yields of the most-preferred strategies (schedules) are summarized in Table 10 (Season 1) and Table 11 (Season 2). The most-preferred schedules, as used here, are the top five schedules at the high level of risk aversion. These schedules will be preferred by the class of farmers with risk-aversion coefficients (RAC) within the interval 0.001

SUMMARY AND CONCLUDING REMARKS This study was conducted to identify risk-efficient planting schedules for corn farmers in Matalom, Leyte, Philippines, considering the variations of the weather associated with the occurrences of different ENSO episodes. Weekly planting schedules were selected based on the actual planting dates obtained from a survey in the given site. Other planting schedules were included to explore possible risk- efficient schedules that are not commonly followed in the area. Long-term yield data for the different planting schedules were not available, so the required probability distributions of yield were obtained by simulation of 80 Philippine Journal of Development 2009 Table 10. Summary of the simulated yields (kg/ha) for the most preferred planting schedules during the first season Schedule Rank Mean Standard Deviation Minimum 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 2,411.00 113.7 2,221.00 July, week 3 5 2,416.45 158.5 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 2,417.80 152.5 2,081.00 May, week 3 4 2,378.94 147.06 2,152.00 June, week 3 5 2,397.11 184.2 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 2,414.85 163.4 2,050.00 May, week 4 5 2,403.53 158.53 2,066.00 the yields using a crop simulation program. Inputs in the simulation included weather data from the nearest weather station, corrected for missing observations using a weather generation program, soil characteristics of the site, crop-specific parameters, and common cultural practices in corn production. Stochastic dominance analysis was then applied on the probability distributions of the simulated yields. Two criteria for stochastic dominance were used, namely, first-degree stochastic dominance and stochastic dominance with respect to a function, with three levels of risk aversion. Application of stochastic dominance analyses on the probability distributions led to identification of risk-efficient strategies for each stochastic Patindol, Predo and De Guzman 81 Figure 7. Cumulative distributions of the yields for the most-preferred schedules during the first season, under all ENSO types

Figure 8. Cumulative distributions of the yields for the most-preferred schedules during the second season, under all ENSO types

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 the PAGASA could provide a forecast about the ENSO episode in the next cropping period. Likewise, the procedure 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. The study has demonstrated that in principle, stochastic dominance analysis can be applied to identify risk-efficient schedules under the different ENSO episodes, using probability distributions of the simulated yields. The study 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 82 Philippine Journal of Development 2009

Table 11. Summary of the simulated yields (kg/ha) for the most preferred planting schedules during the second season

Schedule Rank Mean Standard Deviation Minimum

La Niña 1 December, week 4 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, 2 2,413.30 74.83 2,308.00 week 2

September, 3 2,452.80 125.61 2,229.00 week 3

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 2,518.14 153.8 2,253.00

September, 2 2,370.71 90.14 2,237.00 week 1

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 1 December, week 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 Patindol, Predo and De Guzman 83 differences in the mean values. This is an implication that the method could be a good alternative when comparing outcomes of different strategies. Considering that the actual schedules followed by farmers in the site differ from the risk-efficient schedules identified in this study, there is a need to look deeper into the possible reasons. The study has not really incorporated all factors that may have some influence in the farmers’ choice of planting schedule. In the absence of relevant explanations for their actual choices, dissemination of information pertaining to risk-efficient planting schedules may be necessary.

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