component 2b

2nd Mid-Term Progress Report for Component 2B of UPLBFI-SPICACC 3.1 Activity 3.3 i

As already mention, temperature is important for plant growth and development. However, for any crop, there is favourable temperature that could able them to grow and develop with good quality. With the results presented earlier, more crops and most of the farmers are said to be more vulnerable to extremely hot temperature than with extremely cold temperature. Higher temperatures eventually reduce yields of desirable crops while encouraging weed and pest proliferation. Changes in precipitation patterns increase the likelihood of short-run crop failures and long-run production declines. With these, most of the strategies for adaptation to extreme temperature are applied by a number of farmers to cope with extremely hot temperature.

Some of the main strategies followed by small percentage of farmers in Benguet are adjustment of planting dates, changes of crop or crop rotation, and the Kaingin system. Conversely, farmers from Ifugao exercise no-tillage, avoid burning plant residues, and applied Kaingin system. Some of them also find for other water source or set up irrigation.

Strengthening Philippine Institutional Capacity to Adapt to Climate Change Outcome 3.1 Activity 3.3

Component 2B: Validation, Verification

and Updating of the Community-based Crop Loss and Damage Assessment Procedures

WORKING DRAFT: CURRENTLY UNDERGOING REVIEW

BY THE DEPARTMENT OF AGRICULTURE

1656

UPLB Foundation Inc. Lanzones St., UPLB Campus,

College, Laguna, 4031 PHILIPPINES Tel: (049) 536 3688 Fax: (049) 536 6265

2nd Mid-Term Progress Report for Component 2B of UPLBFI-SPICACC 3.1 Activity 3.3 ii

DISCLAIMER

THIS REPORT IS A WORK IN PROGRESS AND THE COMMUNITY-BASED CROP LOSS AND DAMAGE ASSESSMENT PROCEDURES, INCLUDING ITS RESULTS, ARE CURRENTLY BEING REVIEWED

BY THE DEPARTMENT OF AGRICULTURE.

THE CONTENTS OF THIS DOCUMENT ARE NOT IN ANY WAY BEING ENDORSED OR RECOMMENDED BY THE DEPARTMENT OF AGRICULTURE AND FOOD AND AGRICULTURE ORGANIZATION

OF THE UNITED NATIONS.

2nd Mid-Term Progress Report for Component 2B of UPLBFI-SPICACC 3.1 Activity 3.3 iii

Prepared by

Senior Researcher: Dr. Consorcia E. Reaño

Research Assistant: Ms. Ivy M. Fernandez

2nd Mid-Term Progress Report for Component 2B of UPLBFI-SPICACC 3.1 Activity 3.3 iv

Table of Contents

Titles Page

Introduction

Objectives of the Study

1

2

Methodology

Crop Yield Data

Weather Variables

Statistical Analysis

Results and Discussion

Rice Yield Data

Weather Variables

Rainfall

Temperature

Development of the Crop Yield Model

Multiple Linear Regression Model for

Rice Yield

Dry Season

Wet Season

3

3

3

4

6

6

9

10

12

15

16

18

23

2nd Mid-Term Progress Report for Component 2B of UPLBFI-SPICACC 3.1 Activity 3.3 v

Rice Yield Model using Data for

Benguet

Rainfed Rice

Irrigated Rice

Percentage Reduction in Rice Yield

Vegetable Yield Data

Cabbage

Chinese Cabbage

Snap Bean

White Potato

Estimation of Crop Loss Coefficients

White Potato

Cabbage

Carrot

Snap Bean

Summary and Conclusion

References

31

31

31

36

38

45

46

47

47

48

49

53

55

57

60

62

2nd Mid-Term Progress Report for Component 2B of UPLBFI-SPICACC 3.1 Activity 3.3 1

Introduction

Crop growth models are functions that relate on the basis of physical and physiological processes weather variables to crop production. Crop growth models are constructed with basically two objectives: 1) to better understand the process involved in crop production; and 2) to use the derived model as a tool for managing agricultural systems. It is this second objective that this study wishes to focus on. Crop models are vital components of decision-support systems. Because agricultural policy makers and managers cannot control weather, risks in making agricultural decisions cannot be done without. It is therefore imperative that these policy makers be equipped with tools that will enable them to quantify weather related risks associated with crop production. More importantly, these tools will enable these agricultural policy makers to offer the best options in terms of crop and soil management practices which can reduce these risks to levels acceptable to the farmers. The Cordilleras has been identified as areas of high risks related to climate. Extreme weather conditions such as extremely low temperatures and extremely dry conditions have been experienced exacting their toll not only to humans but to the environment and crop production system as well.

Several procedures have been employed in crop-weather analysis. The Department of Agriculture (DA) for one has specifically developed a community-based Crop Damage Assessment procedure to estimate yield losses due to occurrence of biotic and abiotic stresses. The procedure uses a matrix of coefficients to determine yield of major crops such as rice, corn, coconut and vegetables. These crop yield reduction coefficients are determined based on historical data of crop yields and weather variables and climate data as well as based on experiences of technical experts. Magnitudes of yield losses due to these environmental stresses such as extreme climatic events (e.g. typhoons, floods, droughts, etc.) are crop-specific, and dependent on the stage of growth of the crop.

While these procedures are being used, they have not been subjected to field validation and verification. Moreover, with the changing climate, the extent and magnitude of effects and impacts of environmental stresses such as more frequent occurrence of more intense climatic extremes have to be re-evaluated, and the procedure updated. The validation and evaluation process must make use of the advances in science and technology including the use of process-based eco-physiological crop models, and other statistical models relating crop yields to environmental factors.

Crop-weather systems involves very complex systems such that even the most advanced crop models are limited and but boxed imitations of reality.


Thus, using crop models as a tool for agricultural management requires knowledge of systems research and of the problems related to the quality of the soil, crop and climatic parameters and their interrelationships. The traditional crop modelling procedures make use of correlation and regression analysis with weather variables as independent variables. Improvements have been made where functions and interactions of these weather variables are included in the model. New weather parameters which incorporate the interaction of several meteorological variables find their way into the model. Over the years, new variables such as potential evapotranspiration, actual evapotranspiration, their ratio and other modifications which are functions of simple weather variables have emerged in crop-weather modelling studies such as this one (Baier, 1973). Yoshida (1977) noted that the amount of rainfall needed during the vegetative and reproductive stage are 200 mm/month and 300 mm/month, respectively; and the growth and that yield of crops is affected by weather fluctuations that deviate from the optimum. He further cited that rice plants are most sensitive to water supply and temperature throughout the growing season (Uchijima, 1980). Moomaw and Vergara (1964) pointed out that 2,000 mm rainfall is adequate for one rice cropping season provided that distribution of rainfall is reasonably uniform. Although relative humidity at the reproductive stage led to increase in yield, increase in relative humidity at the ripening stage did not. Interactions of the variables had different effects on yield. These interactions may represent intermediate variables that may have direct influences on yield.

The customary unit of time over which these variables have been measured has also changed. While earlier models make use of monthly or yearly data, today’s crop models have focused on weekly and even daily intervals to study their effects on crop growth. Zahner and Stage (1966) described a procedure whereby daily values of computed moisture stress as a function of time to tree growth characteristics.

Objectives of the Study

The objectives of this study are: 1) to evaluate the reliability of the DA crop damage/ loss assessment procedure; 2) to develop robust statistical models and procedures for assessing crop yield losses due to occurrence of environmental stresses including weather and climate extreme events; and 3) to update the matrix of crop loss/ damage coefficients for selected crops in Benguet and Ifugao.

Several important acronyms will be repeatedly used throughout the paper and hence will be defined here. DA – Department of Agriculture NCT – National Cooperative Trials DAS – days after sowing PAGASA – Philippine Atmospheric and

Geophysical and Astronomical Services Administration

DS – dry season WS- wet season


METHODOLOGY Crop Yield Data

Data on rice yield were obtained from the National Rice Cooperative Testing Project: Selection for Adverse Environments(Cold Tolerance) conducted by the National Seed Industry Council from 1992 Dry Season to 2009 Dry Season. The trial locations included Tublay, Benguet to represent the low elevation, La Trinidad, Benguet and Banaue, Ifugao to represent the medium elevation and Tinglayan, Kalinga to represent the high elevation rice farms in the Cordilleras. Results from the NCT were used due to the absence of reliable long term seasonal data on the crop from the project sites.

Production data for four major vegetable crops grown specifically in Benguet municipalities from 1991 to 2009 were obtained from the Office of the Provincial Agriculturist. These crops are cabbage, Chinese cabbage, snap beans and white potato. Since major crops differed for the municipalities, the number of observations used in constructing the yield models differed for each crop.

Weather Variables

Daily data on amount of rainfall, maximum and minimum temperatures, mean temperature and relative humidity were used as independent variables to explain the variation in rice yields across seasons and years. The weather variables used were obtained from PAGASA Main and Region III Offices.

To evaluate the effect of varying climatic conditions, the growing period of

rice was divided into three stages: vegetative, reproductive and ripening. The vegetative stage starts from sowing to panicle initiation followed by the reproductive stage which starts from panicle initiation to grain-filling and lastly, the ripening stage which lasts up to maturity. Since the specific dates of these stages were not indicated in the data set, the onset of ripening and reproductive stages were obtained by counting backwards using the number of days to maturity. Hence ripening stage marked the last 30 days of the growing period while the reproductive stage took place 35 days before the ripening stage. The duration of the vegetative stage which starts from sowing was consequently made to vary from 60 to as high as 156 days for a local check. Traditional varieties and local checks generally were observed to have late maturity. The weather variables with their corresponding notation are given in Table 1.

To capture the effects of varying climatic conditions across months within a year, daily averages of weather variables per month such as rainfall, maximum temperature, minimum temperature and mean temperature were used as independent variables in the yield models. Hence the independent variables included RAIN1 to denote average rainfall in January for the year and TMAX2 to denote average maximum temperature for February for a particular year. Hence for any particular year, there were 12 variables on rain corresponding to each


month and denoted as RAIN1, RAIN2,and RAIN12 respectively. The same was done to generate monthly variables for maximum temperature (TMAX1, TMAX2, and TMAX12), minimum temperature (TMIN1, TMIN2, TMIN12) and mean temperature (TMEAN1, TMEAN2, TMEAN12). Due to the absence of data on weather variables for each municipality, data on weather variables were all derived from observations obtained at the single PAGASA station in Benguet State University.However, due to high variation in terrain and altitude which definitely differed for the municipalities

Table 1. Notations used to denote weather variables used for rice yield

Variable Name Definition RAINV Rainfall during the vegetative stage (from sowing

to panicle initiation stage) RAINRP Rainfall during the reproductive stage (65 days

from harvest) RAINP Rainfall during the ripening stage (30 days from

harvest) TMAXV Maximum temperature during the vegetative stage

(from sowing to panicle initiation stage) TMAXRP Maximum temperature during the reproductive

stage (65 days from harvest) TMAXP Maximum temperature during the ripening stage

(30 days from harvest) TMINV Minimum temperature during the vegetative stage

(from sowing to panicle initiation stage) TMINRP Minimum temperature during the reproductive

stage (65 days from harvest) TMINP Minimum temperature during the ripening stage

(30 days from harvest) RHUMV Relative humidity during the vegetative stage

(from sowing to panicle initiation stage) RHUMRP Relative humidity during the reproductive stage

(65 days from harvest) RHUMP Relative humidity during the ripening stage (30

days from harvest) Statistical Analysis

The sum for each of the weather variables were obtained for each of the three growth stages whenever the complete set of observations on daily weather variables are available. Otherwise, the cumulative sums were treated as missing data. Gaps occurred in the crop production data and in the weather data. Separate analyses were done for dry season and wet season.

Multiple linear regression analysis using stepwise method was employed to determine the functional relationship between yield and the weathervariablesthat significantly affected the yield performance of the varieties in the trial. Regression coefficients were used to determine the magnitude of


increase or decrease in yield that can be attributed to each factor. Whenever feasible, higher order regression equations using response surface regression were fitted as was suggested by earlier researches. Due to data gaps, some coefficients for the higher – order equations in response surface analysis may not be estimable. All computations were done using the Statistical Analysis System (SAS) Ver. 9.12.


RESULTS AND DISCUSSION Rice Yield Data

Data on rice yield for 12 Dry Season (December-April) and 12 Wet Season (May- October) plantings were used to model the variation in crop yield as a function of weather variables. The genotypes tested included elite breeding lines from IRRI and PhilRice, traditional varieties and some local checks.Figure 1 shows the distribution of rice yields through time from 1992 DS to 2009 DS. The wide fluctuations observed for each season can be attributed to the differing performance of the elite genotypes included in the tests.Notable events in the figure include the low yields obtained during the 1998 DS to 1999 DS when the country experienced ElNiño and the 2005 to 2006 seasons when the country experienced la Nina. The summary statistics of rice yield data are presented in Table 2. Yield ranged from 74 kg/ha to 7,192 kg/ha across seasons with a higher mean yield observed for the dry season at 2,727 kg/ha compared to that of the wet season at 1,907 kg/ha. Rice yields ranged from 80 kg/ha to 7,192 kg/ha during the dry season observed in 2001 and 1996, respectively. A slightly narrower range was observed for yieldduring the WS with the lowest yield of 74 kg/ha in 2000 and the highest at 6,622 kg/ha in 2006. The highest mean yield was obtained in 2006 WS with 4,588 kg/ha. The highest mean yield attained for the dry season was in 1996 at 4,580 kg/ha. The widest range of yield was observed in 2006 DS when yield ranged from 154 to 6,895 kg/ha for the different genotypes.

Analysis of variance reveals that rice yield differed significantly between

seasons with higher yields during the dry season. Significant year x season interaction was also observed. This is evidenced by the differing trends obtained in 1992 and 2006. In these years, the WS yields were unexpectedly higher than the DS yields.

Significant differences were also observed among locations with Kalinga having a mean of 5,139 kg/ha followed by Banaue and Lagawe which also gave high yields at 3,864 and 3,695 kg/ha, respectively. These two locations also exhibited the longest maturity at 162 and 165 DAS.


Figure 1. Distribution of rice yields(tons/ha) in Benguet (National Cooperative Trials), 1992 DS-2009 DS.


As expected with high elevation, maturity was delayed with the earliest flowering at 110 to as late as 278 days after sowing (DAS). Among the late maturing rice varieties in the trials were traditional paddy rice varieties Pinidua, Pinikitan, Pugot and some of the local checks.

Yield data from the trials were used in constructing the models since no available existing rice production data can be obtained from the project sites. However, data from other locations such as Tublay, Benguet, Banaue, Tinglayan and other sites cannot be used since only weather data from La Trinidad were available.Accordingly, weather stations from Banaue have stopped operating in the early 1990s. Hence, only yield data from the La Trinidad trials with complete weather data were used.

Table2. Summary statistics ofNCT crop yield datain LaTrinidad, Benguet.

YEAR SEASON YIELD (kg/ha) NUMBER OF DAYS TO MATURITY

(DAS) MINIMUM MAXIMUM MEAN MINIMUM MAXIMUM MEAN

1992 DS 2158 4966 3726 147 194 165 1994 DS 1267 4300 2305 122 160 132 1995 DS 1711 5084 3020 135 175 146 1996 DS 1520 7192 4580 163 193 173 1996 WS 1573 3051 2336 111 127 116 1997 WS 1032 4335 2131 154 178 162 1998 DS 1083 2868 1802 140 159 149 1998 WS 973 4571 1884 125 140 132 1999 WS 74 6274 1030 110 128 122 2000 DS 188 6324 2063 120 137 133 2001 DS 80 4097 2113 162 170 165 2001 WS 248 5456 1647 117 130 125 2002 DS 372 5357 3132 144 161 151 2002 WS 372 3769 1717 136 155 148 2003 DS 660 5159 2361 122 150 132 2004 DS 1389 3919 2640 156 189 168 2004 WS 174 2877 1034 113 173 134 2005 DS 1066 5332 2334 161 196 171 2005 WS 268 4018 1296 137 149 147 2006 DS 154 6895 2652 164 179 171 2006 WS 2803 6622 4588 148 170 153 2007 WS 868 3993 2347 128 139 137 2008 WS 918 3646 1697 147 156 151 2009 WS 273 3224 1180 141 156 149


Weather Variables

Weather variables such as amount of rainfall (in mm), maximum, minimum and mean temperatures (in oC)and relative humidity were obtained from the PAGASA Main Office and the PAGASA Weather Station in Benguet.The data set was characterized by some period gaps for which there were no data obtained on all of the weather variables like 2002 and 2004. In other cases, data gaps occurred as missing data on one or two of the weather variables. Only years with complete data on the weather variables were used in constructing the statistical models.

Table3. Cumulated values of weather variables for each growth stage.

YEAR SEASON VEGETATIVE REPRODUCTIVE RIPENING

RAIN TMAX TMIN RAIN TMAX TMIN RAIN TMAX TMIN

1992 DS 58.1 2361.7 1140.3 198.3 891.1 503.4 315.9 767.6 479.6

1994 DS 66.1 1589.4 807.1 180.3 869.1 509.5 311.6 770.3 477.3

1995 DS 19.1 1870.6 927.2 37.9 887.2 483.1 105.8 785.8 461.5

1996 DS 177.1 2563.7 1407.4 269 878.9 575 198.6 778.7 504.3

1998 DS 36.7 2074.4 942.1 39.4 906.1 493.9 400.3 791.4 493

2001 DS . . . 392.6 906 610.3 439.5 771.5 534

2002 DS 16.7 2008.1 1043.6 48.8 918.3 537.4 160.1 792 491.4

2003 DS 28.6 1631.6 830.1 163.5 896.1 533.9 244.1 817.3 525.4

2004 DS . . . 356.1 970.5 589.5 486.2 811.1 483.9

2005 DS 37 2585.3 1319.5 144.3 939.7 595 475.8 798.1 526.4

2006 DS 214.5 2706.6 1505.1 131.6 977.2 567.3 376.8 813.3 504.1

1996 WS 965.5 1232.6 833.6 596.8 857.3 560.1 330.7 741.6 475.1

1997 WS 1432.7 2360.7 1569.1 236.7 826.1 517.4 95.7 742 408.1

1998 WS 782 1714.2 1100.6 568.5 886.5 563.3 583.1 731.1 477

2001 WS 1041.2 1445.9 1001.8 603.4 852.6 600 377.7 739.4 490.9

2004 WS 1034.3 1786.9 1077.4 583.9 874 543.1 230.3 760.6 453.8

2005 WS 1382.7 2034.7 1441.8 526.4 865 602.1 67.1 785.8 503.6

2006 WS 1815.2 2223.2 1524.7 252.6 935.6 606.3 168.2 774.1 484.3

2007 WS 1310.8 1838.9 1247.2 499.6 . 599.9 419.4 766.9 474.3

2008 WS 1321.5 2164.3 1432.7 580.3 907.4 576.9 418.6 344.6 708

2009 WS 2577.6 2088.1 1425 2516.5 877.5 581.6 49.4 765.2 443.2


Rainfall

The cumulated values of the weather variables are given in Table 3. Weather variables are summed across the number of days for each stage for each genotype. Hence for the 1992 DS, the average total rainfall (RAIN) received by the crop at vegetative stage is 58.1 mm. It can be noted that the range of the amount of rainfall received is quite wide ranging from 19.1mm in 1995 DS to as high as 2,577mm in 2009 WS.A similar range was observed for the reproductive stage which ranged from 37 to 2516 mm whichwas observed in 1995 and 2009. According to Yoshida, the rice crop needs about 300 mm/month of rain during the reproductive stage. Shortage of water at this stage will lead to lower grain weight. Rainfall at the ripening stage ranged from 49.4 to 583mm. At this stage very minimal water is needed by the plant.

Figure 2 gives the rainfall distribution pattern in Benguet from 1991 to 2009. Mean rainfall per day ranged from 0 mm to 74 mm. This extremely high mean amount of rainfall was brought about by continuous rains due to typhoon Pepeng during the first week of October 2009. On the other hand, completely rainless days were observed seventeen times during the 20-year period. These rainless months occurred in December of ’91, ’92, ‘96,’97,’02 and ’03; January of ’95,’98,’03 and ’05; and February of ’92, ’98 ’99, ’02 ’05, and ’07. Since these months coincide with the vegetative stage of the dry season rice crop, adequate water should have been provided through irrigation since there was not enough rain. Yoshida (1977) noted that the amount of rainfall needed during the vegetative stage is 200 mm/month. With vegetative stage spanning an average of 65 days, the reported amounts clearly show inadequate water supply during the dry season. It was only in the years 2000, 2006 and 2008 when there was at most 155 mm rainfall around December to January. By Yoshida’s standards, this amount was still inadequate for rainfed fields to support the rice crop through its vegetative stage. On the other hand, more than adequate rainfall was observed for the wet season crop with the smallest amount of rainfall observed in June to August of 1993 at 975 mm.


Figure 2. Mean amount of rainfall per day (in mm) across cropping seasons.


Temperature

The same procedure was used in getting the cumulative minimum and maximum temperatures for each genotype. Temperature during the growing period is important as this determines the growing degree days of the crop. The concept of growing degree days is based on the idea that the development of a plant will occur only when the temperature exceeds a specific base temperature, which in this case is 10oC for a certain number of days. Growth, however, does not increase constantly with temperature. Just as there is a minimum or base temperature for growth, there is also a maximum temperature beyond which growth is retarded.

Maximum daily temperature ranged from 21.8 to 28.2oC. The highest daily maximum temperatures were observed in April 2008 while the lowest daily maximum temperature was observed in January of 2002. Previous research had found that higher maximum day time temperatures can be beneficial up to a point, beyond which they can be harmful.

The daily minimum temperatures ranged from 1oC recorded in February of 1993 to 22.8oC of May 2003.The critically low minimum temperatures were observed during the DS which is the period late December to about February. Frequency countof days with minimum temperatures lower than the base temperature of the rice of 10oC reveals that 1992 and 1993 DS had the most frequent occurrence of below-10oC days at 34 days each. This was observed to decrease consistently to about only four days in 2007 and 2008. Figure3 shows the average daily minimum temperature across seasons to be gradually increasing and exhibiting narrower ranges. The figure also shows that the same behaviour is exhibited by maximum temperature with gradual increase through time seasons. Both minimum and maximum temperatures were observed to have gradually narrowing ranges through time. The range of values for minimum temperature however is much wider than that observed for maximum temperature.


Fig. 3. Pattern of daily minimum and daily maximum temperatures across months from 1992 DS to 2009 WS.


Fig 4. Pattern of relative humidity across seasons from 1992 DS to 2009 WS


Development of the Crop Yield Models

Yield data used to construct the model were obtained from the National Rice Cooperative Trials for Cold Tolerance. Yield performance of promising rice genotypes were observed in designed experiments in various locations in the Cordilleras from 1992 to 2009 wet and dry seasons. The independent variables in the model consist of cumulated data on the amount of rainfall, maximum and minimum temperatures and relative humidity. The growing period for each season was divided into three stages: vegetative stage, reproductive stage and ripening stage.Based on the number of days to maturity and available records on dates of sowing and transplanting, the inclusive dates of each growth stage were established. Weather variables were summed for each period using daily data provided by PAGASA. Summing and averaging of the weather variables were done for each genotype as they differed for days to maturity. The variables taken at the vegetative stage are daily rainfall (RAINV), daily maximum temperature(TMAXV), daily minimum temperature (TMINV) and relative humidity (RHUMV). The same variables were obtained at the reproductive stage namely daily rainfall at the reproductive stage (RAINRP), daily maximum temperature (TMAXRP), daily minimum temperature (TMINRP) and daily relative humidity (RHUMRP). The variables obtained at ripening stage are denoted as RAINP, TMAXP, TMINP and RHUMP, accordingly. The ripening stage was identified to include the last 30 days, the reproductive stage as the period spanning 35 days before ripening starts while the vegetative stage included the remaining days of the growing period from sowing date up to the onset of the reproductive stage.

Since data on the weather variables were only available for La Trinidad, only the data coming from this site were used to construct the models. No separate weather data were obtained for Banaue (which represented the medium elevation conditions together with La Trinidad), Tinglayan (representing the high elevation) and Tublay (representing the low elevation). Missing data resulting from gaps in either the crop data set or the PAGASA records were not included in the analysis.

Preliminary analysis of yield data showed significant variation across years and seasons across years (Table4) with 85% R-square and with coefficient of variation of 40%. This was expected as the raw observations were mean yields of the genotypes which included promising breeding lines, local checks and traditional varieties, a fairly heterogeneous set. Since significant differences were obtained between dry and wet seasons, separate yield models were constructed.


Table 4. Analysis of variance of yield and some weather variables, 1992- 2009.

SOURCE OF VARIATION YIELD RAINV TMAXV TMINV RAINRP

Year 9234436.4 793.4 185.8 70.1 3450.0**

Season (Years) 12211143.1** 1055.2* 631.5** 97.0** 450.2**

Error 1901878.8 5.2 13.6 5.35 5.94

* - significant at 5% ** - significant at 1%

Table 4. Continued...

SOURCE OF VARIATION RHUMV TMINRP TMAXRP RAINP TMINP TMAXP

Year 714.4 13.7 11.4 240.8 38.9 106**

Season (Years) 589.7** 6.3** 16.5 403.5** 5.1** 10.3**

Error 45.2 0.2 0.6 9.6 0.8 3.1

* - significant at 5% ** - significant at 1%

Significant variation across seasons and across years was obtained for yield and all weather variables except relative humidity at ripening stage. With large variation due to seasons within years for yieldand all weather variables, data set promises to be a good set for modelling.

Multiple Linear Regression Model for Rice Yield

Correlation analysis showed that RAINV, RHUMV, RAINRP, TMAXRP were correlated with yield to a very weak degree. RAINRP showed weak negative correlations with yield. Although the variables were correlated among themselves, all were entered into the model using stepwise regression. A five-variable multiple regression model which included TMAXV, TMAXRP, TMINRP, RAINP and TMAXP was obtained.Results of the linear regression analysis are presented in Table 5.


Table 5. Estimates of multiple linear regression parameters for rice yield, DS.

PARAMETER ESTIMATES STANDARD ERROR T-VALUE P[|T| >tC] VIF*

INTERCEPT 17191 6925.99 2.48 0.0150 0

TMAXV -98.5 37.99 -2.59 0.0112 1.74

TMAXRP 518.0 223.75 2.32 0.0230 2.27

TMINRP 500.7 175.65 2.80 0.0063 1.75

RAINP -124.6 37.97 -3.28 0.0015 1.66

TMAXP -1197.3 316.0 -3.79 0.0003 1.68

*Variance Inflation Factor

Table 6. Estimates of multiple linear regression parameters for rice yield, DS.

PARAMETER ESTIMATES STANDARD ERROR T-VALUE P[|T| >tC] VIF*

INTERCEPT 1232.4 528.9 2.33 0.0218 0

RAINV 327.4 49.9 6.56 0.0001 7.4

TMAXV -133.2 35.2 -3.78 0.0003 2.8

RAINRP -98.0 12.6 -7.76 0.0001 3.9

RAINP 48.8 24.4 2.00 0.0481 1.5

*Variance Inflation Factor

The regression model for DS gave a 22.10% fit which means that it can only explain this much of the variation. The linear regression model for WS gave a slightly higher R-square of 38.67%. Both models included TMAXV and RAINP which are the average maximum temperature at the vegetative stage and mean rainfall at ripening, the way they affected the yield varied. The negative coefficient implies that TMAXV pulls down the yield, that is, holding all other variables constant, an increase in maximum temperature leads to a decrease in yield. On the other hand, rainfall at ripening decreases yield by 124.6 units for every 1mm increase in rainfall/day throughout the vegetative stage during the DS. This was the opposite however for the WS which gave a positive coefficient, but relatively small effect for RAINP.

Both maximum and minimum temperatures at the reproductive stage were significant and have large positive effects on the dry season yield. High maximum temperature at reproductive stage implies high yield holding all other factors constant. The same can be said for minimum temperature. High minimum temperatures lead to high yields. By La Trinidad’s standard, high minimum temperatures would be around greater than 10oC which isabout the


base temperature for rice while high maximum temperatures at ripening would be above 27oC.

The variance inflation factors were low indicating little problem with collinearity.

The R-square of the fitted models for both seasons are too low to be used in decision support systems. Several studies have pointed out the too simplistic approach of linear regression in dealing with complex crop-weather relationships. Jame and Cutforth (1996) noted that regression models, linear or nonlinear, are usually site–specific and require more than ten-year span of reliable data. Moreover, the adequacy of fit of these constructed models is not that high with about 40% of the total variation due to experimental error. Graphically, these result to a tremendous scatter of points about the regression line.

Recognizing the intricate crop-soil-weather relationships, a nonlinear model is fitted to the data to include interactions between variables.

Dry Season

Estimates of the parametersfor the rice yield model using 1992 to 2009 dry season data for La Trinidad, Benguet are presented in Table7. A second order model with R-square of 82.91% was obtained with significant interaction components. The linear component explained 23% of variation in yield while the quadratic and the interaction accounted for 18% and 41%, respectively. Among the components observed at the vegetative stage of the crop that were found to be significant at the 10% level of significance were the interactions of mean amount of rainfall with mean maximum temperature (TMAXV*RAINV) and with mean minimum temperature (TMINV*RAINV). Maximum temperature at vegetative stage had positive interaction effects with rainfall which implies that increase in maximum temperature coupled with increase in rainfall enhances yield. On the other hand, negative interactions obtained for minimum temperature and rainfall at vegetative stage may imply that the direction and/or magnitude of change in yield between two levels of minimum temperature differ with increase in rainfall. The quadratic component of maximum temperature at reproductive stage (TMAXRP2) implies that yield decreases with increase in temperature up to a certain point beyond which increases again. This is shown in Fig. 5 where high yields were observed at temperatures 25oC and 28oC with relatively lower yields in between.


Table7. Parameter estimates for rice yield model, Dry Season, (December – April), 1992-2009.

PARAMETER ESTIMATE STANDARD

ERROR t Pr > |t| INTERCEPT -4323235 4054061 -1.07 0.2933 TMAXV*RAINV 11029 5831.887 1.89 0.0667 TMINV*RAINV -22226 11730 -1.89 0.0662 TMAXRP*TMAXRP 8307.7 4916.483 1.69 0.0997 RAINP*TMAXV -1967.73 819.8539 -2.4 0.0217 RAINP*TMINV 3747.676 1475.998 2.54 0.0156 RAINP*RAINRP -1006.74 405.6902 -2.48 0.0179 RAINP*TMAXRP -2135.69 853.5885 -2.5 0.017 TMAXP*TMAXV -15317 4372.218 -3.5 0.0012 TMAXP*TMINV 29860 9728.417 3.07 0.0041 TMAXP*TMINRP -7696.64 5629.572 -1.37 0.18 TMINP*RAINRP 5270.686 2760.341 1.91 0.0642 TMINP*TMINRP 14807 7745.59 1.91 0.0639 TMINP*TMINP -15018 7711.223 -1.95 0.0593


Fig.5. Relationship between maximum temperature (oC) at reproductive stage and yield, Dry Season.


Significant interactions were also obtained between rain during the ripening stage and maximum temperature at vegetative stage (RAINP*TMAXV), minimum temperature at vegetative stage (RAINP*MTMINV), rainfall at reproductive stage (RAINP* RAINRP) and maximum temperature at reproductive stage (RAINP* TMAXRP). Maximum temperature at ripening stage also showed significant interactions with minimum (TMAXP*TMINV)and maximum temperatures (TMAXP*TMAXV) during the vegetative stage. Negative interactions between TMAXP and TMINV revealed that at low TMAXP, highvalues TMAXV are associated with high yield while at high values of TMAXP, the opposite occurs where lower values of TMAXV tend to be associated with high yields. Since the data used for modelling were obtained on experimental plots which were under irrigated conditions, rainfall during the vegetative stage which was very limiting during the dry season did not show significant effects. The quadratic component of minimum temperature during the ripening stage was significant and was negative. This implies that yield increases with increase in minimum temperature but decreases after reaching a certain temperature level. This may be due to the effect of prolonged ripening which occurs in cool areas leading to increased grain filling resulting to heavier grains. With ripening hastened at high temperature, yield declines. The minimum temperature at ripening stage during the dry season ranged from 20.5oC to 22.7oC. Fig 6 shows this trend where yield increases were observed at minimum temperatures 21oC to 22oC and generally decreases beyond 22oC.


Fig. 6. Relationship of yield (kg/ha) with minimum temperature (oC) at ripening stage.


Using stepwise regression analysis, a response surface equation was fitted to the data on dry season crop. Only 25.89% of the variation in the logarithm of the yield can be explained by the model. Model fitting using the untransformed yield observations gave an even smaller fit. The model shows the daily amount of rainfall, its quadratic component, and theinteractions between maximum and minimum temperatures at the reproductive and ripening stages as significant components. The model suggests that the dry season yield will increase by 1.096 6 kg/ha for every 1oC daily increase in temperature during the reproductive stage which is usually 65 to 100 DAS.

Table 8. Estimates of parameters for the rice yield model for Dry Season (December – April), Benguet.

PARAMETER ESTIMATES STANDARD ERROR

PARTIAL R-SQUARE

P-VALUE

INTERCEPT 5.30503 1.05076 - <.0001

RAINRP 0.04044 0.01555 0.0939 0.0112

RAINV2 -0.04887 0.01660 0.0665 0.0043

TMAXRP*TMINP 0.00407 0.00110 0.0325 0.0004

TMAXP*TMINP -0.00840 0.00269 0.0660 0.0026

Wet Season, May - October

To understand the effects of various weather variables on rice yield, response surface equation was fitted as suggested by earlier studies (Table 9). A second-order response surface regression equation was obtained with R-square of 72.49 with significant linear, quadratic and interaction components. Linear components of minimum temperatures during the vegetative (TMINV) and reproductive stages (TMINRP) were obtained to be significant at the 5% level of significance. The positive coefficient for TMINV suggests that as the average minimum daily temperature at the vegetative stage increases, the yield increases. The lowest minimum temperature at the vegetative stage for the wet seasons was recorded at 15.47oC while the highest minimum temperature was at 17.71oC. Since the base temperature for rice is 10oC, then temperatures greater than this will promote growth and development, hence the positive coefficient.On the other hand, the minimum temperatures at the reproductive stage showed a negative estimate implying a decrease in yield with increasing minimum temperature. This may be due to hastened ripening causing less than optimum grain filling leading to low grain weight. Rainfall per se did not have significant effects since the crop is irrigated. However, the interaction between the amount of rainfall and the maximum temperature during the vegetative stage was significant and was positive. The quadratic component of minimum temperature was also found to be significant with a negative effect. Combined with the positive linear


component at the vegetative stage, these suggest that yield increases linearly with minimum temperature at the vegetative stage up to a certain point beyond which starts to decrease. Increased temperature during the vegetative stage may result to high transpiration rates leading to decreases in yield under inadequate moisture regime. Except for the interaction with minimum temperature at the ripening stage, rainfall showed positive interactions with temperature variables. The quadratic component of minimum temperature at the reproductive stage was significant suggesting that minimum temperature at this stage decreases yield. As cited earlier, this may be attributed to hasten ripening leading to shortened grain filling period resulting in lighter grains. With both linear and quadratic components negative, this means that high temperatures at the reproductive stage will result to decreased yield.

Table9. Parameter estimates for rice yield model, Wet Season (May –October), 1996-2009.

PARAMETER ESTIMATE Pr > |t| TMINV 1032329 0.0131 TMINRP -637654 0.0390 TMAXV*RAINV 4923.02 0.0714 TMINV*TMINV -45586 0.0195 RAINRP*TMAXV 469.28 0.0957 RAINP*TMAXV 791.71 0.0082 RAINP*TMINV 4383.62 0.0283 RAINP*RAINRP 158.86 0.0125 RAINP* TMAXRP 2637.10 0.0072 TMINP*TMAXRP -21520 0.0692 TMAXP*TMINV -28446.00 0.0586 TMINP*RAINRP -2930.30 0.0018 TMINRP*TMINRP -20258.00 0.0228 TMINRP*TMINV 53556.00 0.0082 TMINRP*RAINRP 1529.66 0.0076

While prolonged ripening period occurs under cool elevated areas typical of the project site, there was no way to determine the onset of ripening with the data available. Significant and high but negative TMAXP*TMINV interaction effects were obtained indicating that the mean yields at high TMAXP-TMINV and low TMAXP-TMINV tend to be lower than the mean yields at low TMINV-high TMAXP and high TMINV-lowTMAXP conditions. It is shown that the effect of high temperatures at the reproductive stage is larger when combined with high minimum temperatures than when they are combined with low minimum temperatures at the vegetative stage.

Figure 8 indicates that as the minimum temperature increases during the reproductive stage, differential responses are observed favouring the highly adapted ones which are the local checks. Figures 9 shows that as the rainfall


increases during the ripening stage, there is a noticeable downward trend in the yield. Rain at this time, especially the heavy ones will cause damage to the grains especially those that are not fully ripened.


Fig.7. Relationship of yield (kg/ha) and rainfall(in mm) at vegetative stage, Wet Season.


Fig. 8. Relationship of yield (kg/ha) with minimum temperature(oC) at reproductive stage, WetSeason.


Fig. 9. Relationship of yield (kg/ha) with rainfall (in mm) at ripening stage, Wet Season.


Using stepwise regression, a response surface was fitted to the data with linear, quadratic and interaction effects in an attempt to construct a model that can be used for yield prediction and quantifying the effects of the weather variables on yield. The parameters of the model are shown in the table below (Table 10). The regression equation identified only five weather variables or functions of weather variables as significant at the 10% level of significance. These are the quadratic component of rain at the ripening stage , the interaction of rainfall and maximum temperature during the vegetative stage as well as during the ripening stages and the interaction of rainfall and minimum temperature at the vegetative stage. This last effect is predicted to decrease yield by about 77.03 kg per hectare. The quadratic component of rain during the reproductive stage was negative but small which translate to a decrease in yield of 7.374 kg/ha. Large effects were obtained for relative humidity at the vegetative level. Humidity during the vegetative stage proved to be the most crucial predicting a decrease in yield by 372 kg/ha for every unit increase in average relative humidity during the vegetative stage. With this magnitude of effect, it accounted for at least 20% of the 46.66% of the total variation explained by the model.

Table 10. Estimates of parameters for the rice yield model for Wet Season (May to October), Benguet.

PARAMETER ESTIMATES STANDARD ERROR

PARTIAL R-SQUARE

P-VALUE

INTERCEPT 31178 7224.739 0.2063 <.0001

RHUMV -372.295 81.933 0.1260 <.0001

RAINP2 -7.374 2.001 0.0278 0.0004

RAINV*TMAXV 61.131 18.640 0.0200 0.0015

RAINP*TMAXP 5.015 2.596 0.0387 0.0564

RAINV*TMINV -77.031 25.392 0.0517 0.0031

Estimated responses for various levels of crop-weather variables are presented in Table 11.


Table 11. Estimated maximum response for various levels of the weather variables.

ESTIMATED RESPONSE RAINV TMAXV TMINV RAINRP TMAXRP TMINRP RAINP TMAXP TMINP

1170.34 1.403 27.393 14.185 6.923 25.190 15.206 12.617 26.315 16.828 1281.16 1.402 27.399 14.182 6.924 25.190 15.206 12.614 26.315 16.828 1392.58 1.402 27.406 14.179 6.924 25.191 15.207 12.611 26.315 16.827 1504.61 1.402 27.413 14.176 6.925 25.191 15.207 12.608 26.315 16.826 1617.28 1.402 27.420 14.173 6.926 25.191 15.208 12.605 26.315 16.825 1730.59 1.402 27.427 14.170 6.926 25.192 15.208 12.602 26.315 16.825 1844.56 1.401 27.434 14.167 6.927 25.192 15.209 12.599 26.315 16.824 1959.21 1.401 27.441 14.164 6.928 25.192 15.209 12.596 26.315 16.823 2074.55 1.401 27.448 14.161 6.928 25.193 15.210 12.593 26.315 16.823 2190.60 1.401 27.455 14.158 6.929 25.193 15.210 12.590 26.315 16.822 2307.37 1.401 27.463 14.155 6.930 25.193 15.211 12.587 26.315 16.821 2424.88 1.401 27.470 14.152 6.930 25.193 15.211 12.584 26.315 16.821 2543.13 1.401 27.477 14.149 6.931 25.194 15.211 12.581 26.315 16.820 2662.16 1.400 27.484 14.146 6.931 25.194 15.212 12.578 26.315 16.820 2781.96 1.400 27.491 14.143 6.932 25.194 15.212 12.575 26.315 16.819 2902.55 1.400 27.499 14.140 6.933 25.195 15.213 12.571 26.314 16.818 3023.95 1.400 27.506 14.137 6.933 25.195 15.213 12.568 26.314 16.818 3146.16 1.400 27.513 14.134 6.934 25.195 15.213 12.565 26.314 16.817 3269.21 1.400 27.521 14.130 6.935 25.196 15.214 12.562 26.314 16.817 3393.09 1.400 27.528 14.127 6.935 25.196 15.214 12.558 26.314 16.816 3517.84 1.400 27.536 14.124 6.936 25.196 15.215 12.555 26.314 16.816 3643.45 1.400 27.543 14.121 6.937 25.197 15.215 12.552 26.314 16.816 3769.93 1.400 27.550 14.118 6.937 25.197 15.215 12.549 26.314 16.815 3897.31 1.400 27.558 14.114 6.938 25.197 15.216 12.545 26.314 16.815 4025.58 1.400 27.565 14.111 6.938 25.198 15.216 12.542 26.313 16.814 4154.77 1.400 27.573 14.108 6.939 25.198 15.216 12.539 26.313 16.814 4284.88 1.399 27.580 14.105 6.940 25.198 15.217 12.535 26.313 16.814 4415.91 1.399 27.588 14.102 6.940 25.199 15.217 12.532 26.313 16.813 4547.89 1.399 27.595 14.098 6.941 25.199 15.217 12.528 26.313 16.813 4680.81 1.399 27.603 14.095 6.941 25.199 15.217 12.525 26.313 16.813 4814.70 1.399 27.610 14.092 6.942 25.200 15.218 12.522 26.313 16.812 4949.55 1.399 27.618 14.088 6.943 25.200 15.218 12.518 26.312 16.812 5085.37 1.399 27.625 14.085 6.943 25.200 15.218 12.515 26.312 16.812 5222.18 1.399 27.633 14.082 6.944 25.201 15.219 12.512 26.312 16.811 5359.99 1.399 27.641 14.079 6.944 25.201 15.219 12.508 26.312 16.811 5498.79 1.399 27.648 14.075 6.945 25.201 15.219 12.505 26.312 16.811 5638.60 1.399 27.656 14.072 6.945 25.202 15.219 12.501 26.312 16.811 5779.43 1.399 27.663 14.069 6.946 25.202 15.219 12.498 26.311 16.811 5921.28 1.399 27.671 14.065 6.947 25.202 15.220 12.494 26.311 16.810 6064.15 1.399 27.678 14.062 6.947 25.203 15.220 12.491 26.311 16.810 6208.06 1.399 27.686 14.059 6.948 25.203 15.220 12.488 26.311 16.810 6353.02 1.400 27.694 14.055 6.948 25.203 15.220 12.484 26.311 16.810 6499.02 1.400 27.701 14.052 6.949 25.204 15.221 12.481 26.311 16.810 6646.08 1.400 27.709 14.049 6.950 25.204 15.221 12.477 26.310 16.809 6794.20 1.400 27.716 14.045 6.950 25.204 15.221 12.474 26.310 16.809 6943.39 1.400 27.724 14.042 6.951 25.205 15.221 12.470 26.310 16.809 7093.64 1.400 27.732 14.039 6.951 25.205 15.221 12.467 26.310 16.809


Rice Yield Model Using Data for Benguet (Bureau of Agricultural Statistics)

A second set of data on rice production in Benguet from the Bureau of Agricultural Statistics was used with the objective of validating the model since data used for modelling were obtained from controlled experiments. However, the number of points of this data set was quite small to validate a response surface model. There were not enough degrees of freedom to efficiently estimate error. Hence while high R-squares are being obtained, none of the coefficients turned out to be contributing significantly to the model.

A multiple linear regression was fitted to data on quarterly rice production in rainfed and irrigated palay separately from 1994 to 2009.

Rainfed Rice

The bulk of production came in at the last quarter for the period 1994 to

2009 for rainfed conditions because these areas solely depend on the abundant rains starting from late second quarter to the third quarter for irrigation. The highest production was observed in 2007 at 2.07 tons/ha while the lowest was observed in 1998 at 595 metric tons. Stepwise regression of production on weather variables showed that the minimum temperature during the immediate past quarter significantly affected production (R2=0.28) suggesting an increase of 2.31 kg/ha for every 1oC increase in the minimum temperature. The previous quarter, especially if the harvest comes in early October can be viewed as coinciding with reproductive and ripening stages of the crop. At reproductive stage, the rice plant is most sensitive to stresses such as low and high temperatures and drought.

Since the daily maximum temperatures in Benguet are not that high, only

the minimum temperature was found to be critical. Furthermore, water is not limiting at this stage with the cumulative amount of rainfall ranging from 1,260.8 in 2004 to over 2700 mm in 2002 and 2009. Low temperatures slow down rice growth at all stages but have the worst effect at the seedling and reproductive stages. Further some physiological processes such as mineral uptake are also slowed down with very low temperatures thus affecting spikelet fertility and grain filling.

Irrigated Rice

Rice yield in tons/ha for Benguet from 1994 to 2009 for irrigated rice are presented in Figure 11. Rice productivity during these years was characterized by alternating series of small and large fluctuations starting with small fluctuations in 1995 up to about the first quarter of 2001. This was followed by a five-year stretch with large fluctuations from quarter to quarter starting in 2001 to 2nd quarter of 2006. From thereon, small fluctuations were again observed up to the


last quarter of 2009. The highest productivity in yield per hectare was highest in the 2nd quarter followed by the yield productivity in the 4th quarter. The least productivity was observed during the 3rd quarter.

Unlike in the rainfed conditions, continuous production throughout the year was observed with production recorded in every quarter. A generally increasing trend in productivity was observed which may be attributed to improvement of technology or more favourable weather conditions. Unlike in the rainfed conditions, a slightly higher production was observed for the 2nd quarter compared to the 4th quarter when averaged across years.

Since the behaviour of production differed with quarter, regression analysis was done by quarter and showed the following results. The models show that production in each quarter was affected by prevailing temperatures in the previous quarter, that is, the quarter before harvest, which may be considered as coinciding with late vegetative, reproductive and ripening stages, depending on the harvest dates. Three variables showed significant effects on 1st quarter production explaining 95% of variation in production. These are the maximum temperature prevailing in the 4th quarter of the previous year, the maximum temperature and the mean temperature of the current quarter. The model shows that as the cumulative temperature during the late vegetative and reproductive stages increases the production increases by 5.89 kg/ha. In addition, the model also reveals that as the mean temperature increases the quarter production also increases by about 3.09 kg/ha for every degree Celsius increase.

On the other hand, the model shows that the minimum temperature and the amount of rainfall in the 1st quarter significantly affect production during the second quarter. Increase in rainfall shows a negative effect. These maybe rains during the reproductive and ripening stages likely to be interfering with flowering processes. Since the crop is irrigated, water is assumed to be not limiting. Rains therefore have adverse effects on flowering causing spikelet fertility and poor pollen dehiscence resulting to unfilled grains. During this stage, increase in the minimum temperature enhances yield as shown by the model where increase in temperature by 1oC in cumulative minimum temperature results to increase of 5.14 kg/ha. The production in the third quarter is similarly affected by the amount of rainfall in the second quarter but to a very minimal extent. The maximum temperature during the second quarter had a higher effect increasing production by about 4.5 kg/ha for every degree increase in cumulative maximum temperature.

The maximum temperature in the previous quarter was significant in 1st- and 3rd –quarter production while the minimum temperature was significant for 2nd and 4th quarter production.


Figure 10. Relationship of minimum temperature and rice production,Benguet, 1994-2000.


Figure 11. Productivity of irrigated rice in tons/ha by quarter from 1994 to 2009


Table 11. Estimates of regression coefficients using stepwise regression.

Quarter Mean

Production

Variables R-square Estimates P-values

1 2.225 lagtx1

tmax

tmean

0.9495 0.00589

-0.00376

0.00309

< 0.0001

0.00011

0.0028

2 2.487 lagrain1

lagtm1

0.6689 -0.00209

0.00514

0.0495

0.0008

3 2.191 Lagrain1

Lagtx1

0.6804 0.00085614

0.00450

0.0172

0.0089

4 2.394 Tmax

lagtm1

0.8321 0.00377

0.00117

.0325

.0199

When regression analysis is done across quarters, only the minimum

temperature was the critical. The fit however was very low, explaining only 8% of the total variation in production.


Percent Reduction in Rice Yield

The observed crop yields obtained may be categorized as potential, attainable and actual yield to allow the estimation of yield gaps. Potential yield is the yield obtained under optimal conditions defined by temperature, solar radiation and rainfall. Attainable yield refers to yield obtained under the best conditions in the farm including the best cultural management practices. Actual yield refers to yield obtained under farm conditions subjected to limitations in soil nutrients, irrigation, pests and diseases, etc. Percent yield reductions were estimated by obtaining the difference between NCT yields and those reported by the Bureau of Agricultural Statistics (BAS). Hence reductions in yield were estimated only for cropping seasons from 1994 to 2009, for which data in the two datasets exist. Percent reduction in yield was obtained as the ratio of the difference of the actual yield from the potential yield relative to the potential yield. The percent reduction in yield is presented in Table 12.

Table 12. Percent yield reduction in rice in Benguet, 1994-2009.

YEAR SEASON POTENTIAL YIELD(t/ha) ACTUAL PERCENT YIELD

REDUCTION

MAXIMUM MEAN YIELD Based on Maximum

Based on Mean

1994 DS 4.3 2.305 2.418 43.757 -4.921 1995 DS 5.084 3.02 1.792 64.743 40.646 1996 DS 7.192 4.58 1.967 72.645 57.045 1996 WS 3.051 2.336 1.894 37.912 18.908 1997 WS 4.335 2.131 1.926 55.570 9.618 1998 DS 2.868 1.802 1.824 36.396 -1.230 1998 WS 4.571 1.884 1.910 58.208 -1.396 1999 WS 6.274 1.03 1.946 68.980 -88.948 2000 DS 6.324 2.063 2.118 66.507 -2.670 2001 DS 4.097 2.113 2.144 47.670 -1.465 2001 WS 5.456 1.647 2.125 61.058 -29.003 2002 DS 5.357 3.132 2.493 53.461 20.400 2002 WS 3.769 1.717 2.458 34.796 -43.131 2003 DS 5.159 2.361 2.171 57.910 8.030 2004 DS 3.919 2.64 2.354 39.941 10.845 2004 WS 2.877 1.034 2.488 13.538 -140.571 2005 DS 5.332 2.334 2.574 51.728 -10.276 2005 WS 4.018 1.296 2.515 37.410 -94.049 2006 DS 6.895 2.652 2.612 62.122 1.520 2006 WS 6.622 4.588 2.774 58.109 39.537 2007 WS 3.993 2.347 2.803 29.791 -19.449 2008 WS 3.646 1.697 2.827 22.453 -66.609 2009 WS 3.224 1.18 2.636 18.238 -123.390

The BAS data set covers only 1994 to 2009 with yields reported by quarter

while NCT reports yields by cropping season from 1991 to 2009. BAS data were


equated to actual yield while those obtained from NCT were equated to the potential

yields or attainable yields. This was done on the assumption that the NCT, as per

protocol, was conducted under experimental conditions with all inputs – irrigation,

fertilizer and pesticides provided and therefore considered optimal. On the other

hand, BAS data were obtained from a sample of farmers’ fields. Furthermore, NCT

reports yield on a per cropping season basis while BAS reports it by quarters. Hence,

BAS data from quarters 1 and 2 were averaged to give BAS dry season yield while

quarters 3 and 4 were averaged to give the BAS wet season yield. There were

several cropping seasons for which no data exist, like DS and WS of 1993 and 1994

DS, which coincide with times that El Niño event occurred.

Two measures of yield loss were used namely; percent reduction using the

maximum yield as potential yield (or as attainable yield) and percent reduction using

theNCT mean (attainable yield level). Using the maximum yield as reference,

percent reduction in yield during the dry season ranged from 36.40 to 72.64, obtained

in 1998 and 1996, respectively. No data were obtained in 1997 which was an El Niño

year. Using the mean NCT yield as reference, percent yield reduction ranged from

0(-10.28) to 57.04 observed in 2005 and 1996, respectively.

For the wet season, percent yield reduction ranged from 13.54 to 61.06. The lowest

yield reduction was obtained in 2004 while the largest yield reduction, in 2001. Using

the mean NCT yield as reference, yield reduction went from 0 (-140.57) to 39.54%.

On the average, larger reductions were observed during the dry season than during

the wet season using both measures. The mean yield reduction during the dry

season was 50.75% while that for the wet season was 45.49%.

Estimates obtained by the Department of Agriculture ranged from 30 to 100%

depending on the stage when the risk factor occurred but irrespective of seasons.

Such information, however, cannot be estimated for this study since the specific

occurrences of risk factors, moreso the stage, at which they occurred, were not noted

in the dataset obtained. The yield reduction is expected to be large when the risk

factors appear early in the rice life cycle and specially so during reproductive stage

during panicle initiation. Reliable estimates of yield reduction can be obtained from

carefully designed controlled experiments. In their absence, careful monitoring of

events at each stage and diligent record keeping may provide information that will

allow good estimation of yield loss. Useful information includes data on occurrences


of biotic and abiotic stresses occurring at specific growth stages of the crop; and local

weather data which will allow simulation of crop yields using process-based,

parameterized and locally validated crop models. Presently, only limited data from

La Trinidad are available to represent Benguet. Crop production data which specify

important events such as date of planting, flowering time and information on soil

types and nutrient levels are desired.

Vegetable Yield Data The average annual productivity in terms of yield per hectare is

presented for cabbage, snap beans, Chinese cabbage and potato in Figure 13. A general increasing trend for cabbage production was observed through the years with minimum productivity observed in 1994. Productivity of Chinese cabbage and white potato generally exhibited an increasing trend from 1999 to 2009. Snap beans productivity, on the other hand, remained constant through the years.

Figure 12. Cabbage, Chinese cabbage, snapbeans and potato production

averaged across municipalities of Benguet, 1991-2009

Vegetable productions in some of the municipalities are presented in Figures 13.a to 13.l.


. Fig. 13a. Vegetable production of four major crops in Atok

Fig. 13b. Vegetable production of four major crops inBakun.


Fig. 13c. Vegetable production of four major crops in Buguias.

Fig. 13d. Vegetable production of four major crops inBokod.


Fig. 13e. Vegetable production of four major crops inKabakan.


Fig. 13f. Vegetable production of four major crops inItogon.

Fig. 13g. Vegetable production of four major crops inKibungan.

Fig. 13h. Vegetable production of four major crops in LaTrinidad.


Fig. 13i. Vegetable production of four major crops in Tublay.

Fig. 13j. Vegetable production of four major crops inTuba.


Fig. 13k. Vegetable production of four major crops inSablan

Fig. 13l. Vegetable production of four major crops inMankayan


Cabbage

Cabbage production data were obtained from all municipalities of Benguet except Tublay with missing values from 1991 to 1999 for Kabayan, Kibungan and Kapangan. Buguias proved to be the top producer of cabbage with highest production at 112,100 metric tons with 5,350 hectares planted in 2007 and 109,998 metric tons with 5,405 hectares planted in 2008. With the widest area planted, Buguias remained to be the major supplier of cabbage. The lowest production for Buguias was recorded in 1998 with only 810 hectares planted to the crop producing 12,104 metric tons. In terms of productivity expressed in yield per hectare, however, Bakun proved to be the most productive with 30.2 mt/ha in 2003. Yield per hectare ranged from 15.2 to 30.2 mt/ha which was characterized by an abrupt increase from 2000 to 2001. From 1991 to 2000, productivity ranged from 15.2 to 20 mt/ha which increased to about 30 mt/ha in 2001 and has remained at that level up to the present. The highest productivity was obtained for Kibungan at 39.9 in 2008. Although a general increasing trend in productivity was observed for cabbage production for the municipalities, drastic increases were only recorded for Bakun, Mankayan and Itogon. Productivity in the other municipalities either remained constant through the years or exhibited only small increases.

Stepwise regression revealed a yield model for cabbage productivity with five weather variables significant at the 5% level. The model gave an R-square of 97.35% implying that only about 3% of the variation in productivity cannot be explained by the model. The crop yield model constructed is as follows:

Where Y is yield in mt/ha; TMAX1 is average maximum temperature in January; TMAX4 is the average maximum temperature in April; TMIN4 is average minimum temperature in April; RAIN6 is the average daily rainfall in June and TMEAN7 is average mean temperature in July.

The model implies that the maximum temperature in January exerts a negative effect on yield decreasing it by about 0.55144 tons/ha for every oC degree increase in maximum temperature. All other weather variables tend to increase yield with the maximum temperature in April having the greatest effect of increasing the yield by 1.7351 tons/ha for every oC degree increase in maximum temperature. Mean temperature in July also gave positive effect on yield increasing yield by 1.2084 mt/ha for every oC degree increase. Rains in June also gave positive effects increasing yield by 0.5562 tons/ha assuming all other variables constant.

Y49.84140.55144*TMAX11.7351*TMAX40.5562* TMIN4 0.2176*RAIN61.2084*TMEAN7=−−++

++


Chinese cabbage

Chinese cabbage production data were obtained from all of the 13 municipalities with missing data from Tuba for the years 2000-2009. Chinese cabbage production ranged from 15.6 mt obtained in Kapangan in 2009 to 47,103 mt obtained in Buguias in 2008. Buguias proved to be the largest producer of Chinese cabbage with largest production volumes observed in 2007, 2008 and 2009 at more than 47,000 mt. The area planted to Chinese cabbage ranged from 750 hectares in 1997 to 2,215 hectares in 2007 and 2008. Mankayan followed with largest Chinese cabbage production observed in 1991 at 35,800 metric tons planted over 2,467 hectares. In terms of productivity expressed as yield per hectare, a trend similar to cabbage production was observed with Bakun giving the highest production per unit area. Productivity per unit area ranged from 19.8 to 25 mt/ha which has been observed since 2002 except only in 2006 when productivity dipped to 21.0 mt. Even then this is still higher than most productivity values in other areas. Mankayan showed a similar trend with the last two years having more than 20 mt/ha. Only slight increases in productivity were observed for Buguias and Atok even as productivity for Kibungan and Sablan dipped. Productivity in Itogon. La Trinidad, Bokod and Tuba remained fairly constant throughout the last 19 years.

Three variables were significantly affecting productivity of Chinese cabbage at the 5% level of significance and these are maximum temperature in April (TMAX4), mean temperature in July (TMEAN7) and maximum temperature in August (TMAX8). TMAX4 and TMEAN7 showed positive effects increasing yield by 1.5678 and 1.2980 mt/ha for every oC degree increase holding other independent variables constant. In contrast, maximum temperature in August showed negative effects pulling down yield by 1.0822 mt/ha for every oC degree increase in temperature. The yield model obtained for Chinese cabbage is obtained as:

Y24.31911.5678*TMAX41.2980*TMEAN71.08 22*TMAX8=−++−

Where TMAX4 = maximum temperature in April; TMEAN7= mean temperature in July; and TMAX8 = maximum temperature in August. The R-square obtained for the model is 85.88% indicating a fairly adequate model. This implies that 85.88% of the variation in yield productivity can be explained by the three variables given in the model above. The model reveals that as TMAX4 increases the yield increases by 1.5678 mt/ha which is similar to the


result obtained for cabbage. The same was also true for TMEAN7 which showed positive effects for yield in Chinese cabbage. It shows that as TMEAN7 increases, the yield productivity also increases by 1.298 mt/ha.

Snapbeans

Production data on snap beans were obtained from all 13 municipalities. Snap beans production ranged from 15 mt obtained in Atok in 2007 to 11,582 mt in Mankayan in 1994. Mankayan proved to be the top producer of snapbeans with smallest production of 961 mt in 2009. Production trend for snap beans was observed to be either constant for the 19 years considered or on the downtrend. This was observed even for the largest snapbean-producing municipality. A drastic decrease in snapbean production was observed for the period 1991-1999 and 2000-2009. This decrease may be attributed to decreasing area planted to the crop which was as wide as 1,454 hectares in 1992 to a measly 114 hectares in 2009. Since there was no significant variability in snapbean production, no model was constructed.

White Potato

Production date for white potato were obtained on 8 municipalities which included Atok, Buguias, Bakun, Bokod, La Trinidad, Mankayan, Tuba and Tublay. White potato production ranged from 0.1mt obtained in Atok in 2000 to 104,219 mt obtained in Buguias in 2008. The largest producer of white potato was Buguias, followed by Mankayan and Kibungan. For Buguias alone, the smallest volume of production was obtained in 1993 with 35,352 mt. In terms of production per hectare, Bakun gave the highest productivity with yield per hectare ranging from 10 to 25 mt/ha. From 1995 to 2009, yields per hectare of potato for Bakun did not fall below 20 mt/ha except in 2007 when white potato yield was only 17.9 mt/ha. Results showed that only Atok and Mankayan exhibited increases in productivity. The yield model obtained showed only maximum temperature in September as the critical weather variable accounting for 63.04% of the total variation in yield. The white potato yield model obtained is as follows:

Where Y= yield of white potato per hectare; and TMAX 9 is the temperature in September. The model shows that as the maximum temperature in September increases, the yield increases by 1.4455 mt/ha.

Y17.06131.4455*TMAX9=−+


There were some similarities observed for the models constructed like the significance of maximum temperatures in April (TMAX4) and mean temperature in July (TMEAN7) for cabbage and Chinese cabbage. Although high R-squares were obtained for these two models, there are several limitations of the study that hinder valid interpretation of the roles of these variables on yield. For one, there was no information as to the time of planting for the crops which could have been very informative in interpreting the effects of the weather variables considered. Seasonal data would have generated more information to allow valid interpretation. Secondly, the weather data used were common for all the municipalities which are clearly not the case in actual conditions. High variability in weather variables such as temperature exists among the municipalities, from Atok to La Trinidad to Sablan. To better quantify the effects of the weather variables, there is a need to generate them for the different municipalities. Likewise, careful monitoring to obtain high quality crop production data should be done. Participatory data collection where farmers observe their own crop, keep records of planting dates and other important dates related to crop growth and development including schedules of farm operations will help a lot to isolate the effects of weather variables. Since the models will be used as a component in decision support systems, high quality data should be used in constructing the models.

Estimation of Crop Loss Coefficients for Varying Climate-Related Hazards

Consultation with farmers through focus group discussions were conducted in Atok and Buguias in 11-12 July and 21 July 2011 in Tublay, Benguet to solicit farmers’ own estimates of crop loss resulting from various climate-related hazards. Farmers were encouraged to give their own assessments of the magnitude of crop loss resulting from different climate hazards based on their own farm experiences. Even with varying assessments, farmers agree that the magnitude of crop loss depends on the growth stage at which the hazards occur. The crop loss assessment activities were focused on four important crops of the province according to volume of production, namely potato, cabbage, carrot and snapbeans. Sixteen and fourteen growers of various vegetable crops from Atok and Buguias, respectively and nineteen snapbeans growers from Tublay were invited to separate focused group discussions to solicit the farmers’ estimates of crop loss caused by various climate-related hazards. Assessment was done by having farmers filled out individual forms followed by a group discussion soliciting their ideas to come up with a more or less average estimates.

Eleven participants from Atok planted potato once yearly with an average farm size of one-third hectare with the smallest at one-eighth and largest at one-half hectare with yields of 6,692 kg equivalent to 19,966 kg/ha. Farmers noted that the most common hazards during the growing season are frost, drought and insect pests and diseases.


White Potato

Potato has five growth stages, namely: a) sprout development, b) plant establishment, c) tuber initiation, d) tuber bulking, and e) tuber maturation. Among climate related hazards that were identified by the potato farmers were: a) frost or frostbites, b) drought, c) floods, d) strong winds, e) monsoon rains, and f) insect pests and diseases. Farmers’ estimates of yield loss in potato brought about by various climate-related hazards are presented in Table 14.

Table 14. Farmers’ estimates of yield loss due to various climate related hazards in potato.

MONTH AFTER

PLANTING

FROST HAILSTORM FLOOD DROUGHT STRONG WINDS MONSOON RAINS INSECT (leaf

miner, aphids, thrips,

cutworms; tuber - white grubs)

DISEASES (blight)

<60km/

hr 60-110 km/hr

>110 km/hr 1 wk 2 wks 3 wks

BUGUIAS

1ST 10-15% - 100% 20-30% 5-10% 10-20% 30-50% 3-5% 10-15% 20-50% cutworms, thrips - 2%

blight - 10-15%; bacterial wilt

- 20-30%

2ND 25-40% - 45-70% 40-70% 10-20% 30-50% 60-75% 10-30% 20-50% 50-60% all - 15-20%;

white grubs - 5%

blight - 15-20%; bacterial wilt

- 30-40%

3RD 20-30% - 10-20% 20-40% 0% 5% 10-20% 5-10% 5-15% 10-20%

all - 5-10%; white grubs -

10%

blight - 1-5%; bacterial wilt –

40-50%

4TH 0% - 0%

ATOK

1ST 0% 0%

0%

0% 0% 0% 50-

100% 25-30%

(cutworms) 0%

2ND 90% 50-90%

20-50%

50-90% 1-10% 20-50% 50-80% 0-5% 0-5% (Blight)

3RD 100% 50-90%

10-20%

30-50% 0-5% 20-50% 50-80% 0-5% 0-5% (Blight)

4TH 60-75% 20-50%

0%

0% 0% 10-20%

0-5% 0-5% (Blight)

Frost

Frost can cause partial or complete loss of leaf area of a potato crop, leading to reduction in photosynthesis and hence yield.

Yield loss in potato due to frost was placed by Buguias and Atok farmers at 0 to100% depending on the growth stage of occurrence. In Atok where potato crop duration is four months, farmers reported that 1 to 2 days of frost during the first month, which spans sprout development to early plant establishment, did not result to crop loss (0%). With low to very low temperatures right after planting, sprouting may be delayed. With continued cool temperatures, the seed piece continues its rest period until warmer temperatures conducive to growth occur. Rarely, however, has prolonged duration of low temperatures been observed in Atok, which between the two


high producers of potato gets to experience frost more often. Except maybe in 2006 when severe frost was observed as early as November 30 which extended to February of 2007. In 2008, experts assessed a 40% loss due to a three-day cold spell averaging 10.8oC for the newly planted crops. At this stage, initiation of stolons is observed including formation of roots and shoots. The mother tuber is very important at this stage in order to have a well established root system allowing for quicker regrowth after early season defoliation due to various hazards including frost. The quality of seed pieces also plays an importantrole at this stage as large or whole tubers are likely to have good root establishment than smaller eye pieces.

The occurrence of frost during the second and third months resulted to yield loss as high as 90 to 100%, respectively, in Atok. The second and third months span tuber initiation to early tuber bulking which are crucial stages in potato growth. It is at these stages where tubers form at the tips of the stolons and tubers start their linear growth phase. Although low temperatures are favourable to the plant at these stages, frost leads to leaf and canopy damage. This period is critical in determining the yield and quality of the tubers and any interruption of the ideal conditions can result to reduced yield. Tuber bulking is highly dependent on the amount of photosynthates which is in turn dependent on the ability of the canopy to sustain photosynthesis. Farmers in Atok observed that tubers tend to be smaller with frost occurring during the bulking stage. On the other hand, frost at the later stages sometimes results to the marble grades to become bigger if harvest is delayed to about 2 to 3 weeks. Buguias farmers, on the other hand experienced a lower reduction in yield at 25 to 40%. In both locations, frost during the maturation period had a smaller effect on yield reduction with Atok observing 60 to 75% while Buguias with 20 to 30% reduction.

Potato production of Atok and Buguias in 2006 when severe frosts episodes were experienced decreased by no less than 3 tons for Atok and 2 tons for Buguias. This loss may be attributed to frost during the dry season crop. The effects of frosts during the later years, however, were reduced by farmers’ practice of irrigating their fields early before sunrise. Other municipalities which experienced severe frosts in 2006, but with slightly higher temperatures than that of Atok saw less reduction.

Hailstorms were observed to cause a 50 to 90% reduction in yield if they occur during tuber initiation to tuber bulking stage. Less effect was observed as they occur in the later stages towards maturation stage at 20 to 50%. No damage due to hailstorms was observed when they occur early in sprout development.

Drought


The mossy forests of Mt. Pulag and other parts of the Cordillera mountain ranges have maintained the considerable water supply for the four mountain lakes in the province and adjoining second growth forests thereby cushioning the effects of the "El Nino" phenomenon in Benguet. However, in August 2007, a dry spell occurred after as series of typhoons in 2006.

Drought damage was only assessed for rainfed farms which showed most of Atok not affected as most of the farms are irrigated. Without irrigation, Atok farmers shift their cropping calendars and wait for the rains. They do not usually risk planting if the rains do not come to save on the cost of seed pieces. Furthermore, the construction of small water impounding facilities as one of the contingency measures to cushion the impact of the dry spell on the province’s vegetable farms has helped a lot. These measures have contributed to the reduced vulnerability of Atok farmers to drought.

However, Buguias farmers, a large proportion of who own rainfed farms experienced 100% yield reduction when drought occurred right after planting. Drought during the plant establishment stage result to short stunted plants, early tuber initiation and early flowering. Since canopy development will largely be affected, carbohydrate production will be reduced in turn leading to reduced growth rate for the tubers. Atok farmers place loss due to drought during plant establishment at 20 to 50% which decreases to 10 to 20% if drought comes later, with decreasing loss as drought comes later. Water stress primarily reduces potato canopy expansion and can delay tuber initiation and bulking (Walworth and Carling, 2002).

Buguias observed higher loss with drought at 45 to 70% and 20 to 40% during plant establishment to tuber initiation and tuber initiation to tuber bulking, respectively. Both municipalities observed greater effect of drought during plant establishment to tuber initiation stage compared to the later stages. Lahlou et al. (2003) assessed the effect of drought on tuber yields using four cultivars and obtained 11 to 53% loss. Although the effect of drought varied with early and late varieties, no clear advantage of one over the other was observed. They observed that in early varieties, tuber number is decreased while in late ones, leaf area index and leaf area are the parameters affected. The greater loss in Buguias observed maybe due to the fact that since duration in Buguias takes only three months and thus may be considered an early crop, tuber number among yield parameters may be more affected. On the other hand, Atok which may be considered a late maturing crop is affected through its leaf area duration and leaf area index. Atok showed no reduction in yield when drought occurred at maturation stage.

Strong Winds


Atok farmers did not observe significant crop loss when strong winds occurred during planting to sprout development regardless of velocity. Buguias farmers, however, noted 5 to 50% loss depending on wind velocity during sprout development to early plant establishment stage. The maximum loss was observed at high wind velocities (signal 3). The second month, which coincides with tuber initiation to tuber bulking, was observed to exhibit the greatest loss when strong winds occur. Atok farmers observed a 50 to 90% reduction while those from Buguias exhibited 60 to 75% with stronger winds. The large reduction may be attributed to the effect of the strong winds on the potato canopy which may slow down photosynthetic rate with reduced leaf area.

Monsoon Rains

The effect of monsoon rains was assessed independently proving to be a very important climate hazard for crop production in the two areas covered. Not only do they have direct physical effects on the crop but they also provide favourable environment for pest and diseases to set in. Monsoon rains extending to up to 3 continuous weeks did not have any effect in Atok crop but showed 20 to 50% damage in Buguias. Similarly, the greatest losses in crop yield are observed when the monsoon rains come during the tuber initiation to tuber bulking stages. Atok showed 50 to 100% damage which can be attributed not only to physical effects leading to defoliation but also to occurrence of diseases such as blight, blackened tubers and tuber rot. Buguias reported a more conservative 50 to 60%. A still high loss was observed at 50 to 80% even after the plant establishment to tuber initiation stage in Atok. This may be due to low level of solar radiation with cloudiness during monsoon rains leading to low photosynthetic rates resulting to smaller tubers, which is especially true for high elevation areas such as Atok.

Insects Pests and Diseases

Among the insect pests, cutworms were a consistent complaint of farmers from both locations while blight and bacterial wilt proved to be the more important diseases. Atok farmers estimated a 25 to 30% loss due to cutworms which attack the plant during the plant establishment to tuber initiation stage, while Buguias farmers placed damaged at not more than 2%. Other insects such as leafminers and aphids have also been observed at tuber initiation to tuber bulking but were observed to confer yield loss of at most 5%. Damage due to blight which sets in after plant establishment to tuber initiation was placed at no more than 5% in Atok but was assessed to confer a heavier damage in Buguias at 10 to 15%. Buguias showed a slight increase in loss from blight occurring during the tuber bulking towards


maturation stage which was placed at 10 to 15%. It is worth noting that the relatively small loss due to insects and pests is possible only because farmers practice chemical as well as mechanical (hand-picking) control of the insects. Disease infected plants are also uprooted to prevent the spread of diseases throughout the area. Without these measures, farmers assess damage due to insects and pests can go to as high as 100%.

Flood

While Atok farmers did not identify flood as a hazard posing risks to potato production, Buguias placed yield loss due to flood at 0 to 100%. Floods occurring during the first month when plants are establishing themselves can go to as high as 100% resulting from runoffs, uprooting and washing of the seed pieces away and hindering plant establishment. A slightly less reduction at 45 to 70% is observed during the second month after the plants have established their root system. At this time floods interfere with tuber initiation. Crop loss is reduced to 10 to 20% if floods occur during the later stage at tuber bulking and maturation with loss coming mainly from tuber rot and other diseases that set in as a result of the floods.

Cabbage

The three most important hazards plaguing cabbage production are drought, strong winds and insect pests and diseases according to Atok and Buguias growers. Farmers’ estimates of yield loss in cabbage are presented in Table 15. The effect of frost is very minimal that even Atok farmers placed loss at no more than 10% even if it occurs during plant establishment to tuber initiation. Atok farmers practice irrigation before sunrise to prevent “burns” thus minimizing deleterious effects of frost. Buguias, on the other hand, have had minimal experience with frost during cabbage crop.


Table 15. Farmers’ estimates of yield loss due to various climate related hazards in cabbage.

FROST FLOOD DROUGHT STRONG WINDS(65-110 KPH) MONSOON RAINS INSECT (diamondback moth, cutworms, armyworm)

<60km/hr 60-110 km/hr

>110 km/hr 1 wk 2 wks 3 wks

BUGUIAS 1ST MONTH - 5-10% 50-100% 1-6% 5-10% 15-20% 0% 0% 10%

100% (without spray); 1-5% (with spray)

2ND MONTH - 10-20% 30-50% 1-3% 5-10% 20-50% 0% 5-10% 5-10%

no cutworms - 5-10%; dbm only - 40-50%

3RD MONTH - 10-15% 5-10% 1-5% 0-30% 10-50% 0% 1-5% 5-10% armyworm - 5%

ATOK

1ST

0%

20-30%

30-90% (uprooted

plants)

0% 0% 5-10%

2ND

1-10%

10-20%

30-90% (uprooted

plants)

0% 0% 5-10%

3RD 0%

0-5%

10-40% 0% 0% 5-10%

4TH 0%

0%

5-10% 0% 0% 10-20%

Drought

The timing, intensity, and duration of drought spells determine the magnitude of the effect of drought. Farmers from both locations assessed loss from drought as greatest when it occurs during the first month, when most of the leaves are formed. Atok farmers placed loss at 20 to 30% while Buguias farmers placed it as 50 to 100%. Drought occurring later in the season resulted to lesser loss for locations, 30 to 50% for Buguias and 10 to 20% for Atok. Drought stress causes an increase of solute concentration in the environment (soil), leading to an osmotic flow of water out of plant cells. This leads to an increase of the solute concentration in plant cells, thereby lowering the water potential and disrupting membranes and cell processes such as photosynthesis (Dela Peña and Hughes, 2007). In a related study, drought treatments imposed at early vegetative, late vegetative and flowering stages in B. napus gave 59, 74, 88% lower seed yields (Hashem et al., 2008). In the same study, it was also shown that drought stress reduced leaf photosynthesis by 67 to 97%.

Strong Winds

Higher losses due to strong winds with velocity greater than 165 km/hr were experienced by Atok farmers compared to their Buguias counterparts, at 30 to 90% compared to 15 to 20% during the first month. Losses incurred are largely due to uprooted plants. With older plants, more leaves and more established root system. Areas that are open and with high elevation are


more prone to strong winds. The closer to harvest, the lesser is the damage due to strong winds in both locations. Larger losses were observed in Buguias during the later stages, where taller and plants with more leaves are more prone to damage.

Insect Pests and Diseases

Among the hazards, insect pests and especially diseases accounted for high losses in Buguias. The vegetable farmers placed losses due to clubroot at a high 70 to 100% during the first month. Although the losses due to the disease decrease as the crop grows older, farmers consider it still a major problem even at the latter stages placing their estimates at 50 to 70% and 30 to 50% during the last phase of the growing period. Clubroot of cabbage and related crucifers is caused by the soil-borne fungus Plasmodiophora brassicae which can cause drastic yield reduction and occasionally total losses in crucifers. At early stages of growth, infected plants are stunted and may die. Plants infected at the later stages of growth fail to make marketable heads hence the high estimates of loss. The disease is referred to as clubroot due to the malformation of the infected roots. When diseased plants are pulled out from the soil, the roots are usually swollen and distorted. Root malformation may vary in size from very small swellings on the tap and lateral roots to large club-shaped roots; depending on the stage the plants became infected. In addition to reducing the plant's ability to take up water, the clubbed tissue fails to develop a protective outer layer and, thus, is susceptible to invasion by soft rotting bacteria.

The most notorious insects are diamond backmoth (Plutella xylostella), cutworms, aphids and thrips. Farmers estimate a 100% yield loss if no chemical control is used. Buguias farmers assessed losses due to diamond backmoth at 40 to 50% which is comparable to that obtained in India at 52% (Krishnamoorthy, 2004). Macharia (2005) calculated smaller losses due to diamond backmoths at 31% from farmer-managed fields, and at 36% from farmer interviews. Damage due to cutworms was not as significant as diamond backmoth with losses amounting to at most 10%.

Carrot

The hazards faced by carrot farmers from both locations included strong winds, floods, monsoon rains, and insect pests and diseases. Atok farmers also identified drought as a problem which was not true for Buguias. Farmers’ estimates of yield loss in carrots are presented in Table 16.

http://www.sciencedirect.com/science/article/pii/S0261219405003327#bib7


Table 16. Farmers’ estimates of yield loss due to various climate related hazards in carrots.

MONTH AFTER

PLANTING FLOOD

DROUGHT

STRONG WINDS MONSOON RAINS INSECTS

DISEASES

<60km/hr 60-110 km/hr >110 km/hr 1 wk 2 wks 3 wks

BUGUIAS

1ST 100% 0% 0% 0% 0% 10% 15% 20% 5% 5%

2ND 5-10% 0% 20% 20% 30% 10% 15% 20% 0% 10%

3RD 30-50% 0% 10% 10% 10% 5% 10% 15% 0% 5%

4TH 5-10% 0% - - - 0% 0% 10%

0% (worm in the roots-

10%) 0%

ATOK

1ST -

50-90%

- -

30-50-% (washed-

out)

0-5% 0-5% 0-5% cutworm, slug (5-10%)

0%

2ND - 10-30%

- - 10-30% 0-5% 0-5% 0-5% cutworm (1-

5%) blight (0-

5%)

3RD - 5-10%

- - 5-10% 0-5% 5-10% 10-30% 0% 10-20%

4TH -

0-5%

- -

0-5% 0-5% 10-20% 20-30% rat (1-10%) blight, powdery

mildew (15-20%)

Drought

The effect of drought as in the previous discussions differed with the growth stage of occurrence. Atok farmers placed their yield loss from drought at a high 50 to 90% when it occurred during the first month. The effect of drought diminished to 10 to 30% when it occurred during the second month. By the third month, only about 5 to 10% of yield is lost. Buguias farmers, on the other hand, did not consider drought a problem. The estimates given by the farmers were much higher compared to those reported for carrots in the literature. Dragland (1978) reported that a 3-week period of drought at an early stage, from the 2-true leaf stage onwards, increased the yield, but that drought in the later stage or prior to harvest lowered the yield. Relying only on natural precipitation resulted in the poorest yield. Sørensen et al. (1997) found that drought stress during a 3-week period at any growth stage reduced the total yield, though always by less than 10%.

Strong Winds The farmers from the two areas covered differed in their assessments.

Buguias farmer’s pegged loss incurred from strong winds at 0 to 30%, with the second month as the most critical stage. This was quite low for the Atok farmers who assessed that losses from strong winds could go as high as 50% when experienced during the first month and coupled with heavy rains. Most of the losses in this case are due to washed outs. Since carrots are direct


seeded in this case, no pricking or hardening is done resulting to young plants with a shallow anchor which become very vulnerable to strong winds. Wind shelter may increase water use efficiency, since Taksdal (1992) reported yield increases and an improvement in quality due to the erection of artificial windbreaks especially in dry and sunny years.

Monsoon Rains

Losses incurred due to monsoon rains ranged from 0 to 30% for Atok and 0 to 20% for Buguias. The bulk of the losses come when monsoon rains last for about three weeks and during the third and fourth month of the growing period. While monsoon rains in Buguias would have their greatest effect in the first two months, Atok farmers experienced greater loss when the monsoon rains come towards the later stages resulting from root rot.

Pests and Diseases Farmers related that losses due to pests were attributed mainly to

cutworm and slugs and rodents which they estimate to account for at most 10% loss. The most important diseases are blight and powdery mildew accounting for 1 to 20%and 15 to 20% loss, respectively. Buguias farmers added that worms present in the roots at harvest time contributed to at most 10% loss in yield.

Snapbeans

The critical hazards to snapbeans growing identified by Tublay farmers included floods, monsoon rains, strong winds, drought, insect pests and diseases and to a certain extent , hailstorms. Farmers’ estimates of yield loss in snapbeans are presented in Table 17.

Table 17. Farmers’ estimates of yield loss due to various climate related hazards in snapbeans.

Growth Stage

FLOOD

MONSOON RAINS STRONG WINDS

DROUGHT

INSECT (leaf miner, aphids, thrips, cutworms, stem borer, pod borer, beetles, white flies, bean fly) 1 wk 2 wks 3 wks <60km/hr

60-110 km/hr

>110 km/hr

Dry Season

Vegetative 50-85% 20-30% 70-100% 100% 10-30% 30-90% 100% 20-40% 10-20%

Reproductive 10-100% 50-60% 70-80% 80-100% 50-80% 80-100% 100% 30-50% 10-20%

Maturation 30-40% 10-20% 20-30% 40-50% 50-80% 90-100% 100% 20-30% 20-50%

Wet Season

Vegetative 60-90% 20-30% 70-100% 100% 20-40% 40-90% 100% - 5-15%

Reproductive 30-100% 50-60% 70-80% 80-100% 60-90% 90-100% 100% - 5-15%

Maturation 30-40% 20-30% 30-60% 60-80% 60-90% 90-100% 100% - 20-30%


Flood For the two cropping seasons, October to December and June to

September, yield loss ranged from 10 to 100% with the reproductive and vegetative stages being the most vulnerable. Yield loss ranged from 50 to 85% when flood occurred during the vegetative staged. At maturation stage, since several priming’s are done in snapbeans (sometimes up to seven); large losses are still realized even as flood comes so late in the growing period. A slightly higher loss was observed during the June to September crop, maybe due to the frequency of floods brought about by the rainy season. Studies show that flooding or waterlogging has shown to decrease flower production and increase flower and young fruit abscission or abortion. Some studies point to ethylene build-up in flooded soil conditions can cause leaf drop, flower drop, fruit drop, or early plant decline in many vegetable crops (Gordon, 2011).

Monsoon Rains

Crop loss due to monsoon rains varied depending on the number of days and the stage at which the monsoon rains occur. A one-week duration of monsoon rains during the vegetative stage lead to 20 to 30% loss while a higher loss at 50 to 60% is observed when monsoon rains coincide with the reproductive stage. Increased flower fall due to the rains coupled with effects of saturated soils may have contributed to this. Furthermore, with monsoon rains, there is increased cloudiness and less solar radiation leading to low photosynthetic activity. Two weeks of continuous rains proved detrimental to snapbean as farmers observed 70 to 100% yield reduction with rains occurring during the vegetative stage. A slightly lower loss was observed when the rains come during the reproductive stage, 70 to 80%. With three weeks of continuous rains, total yield loss is observed when the rains come anytime during the vegetative and the reproductive stage. Comparative losses were observed during the rainy season but with higher loss incurred if the rains come at the maturation stage. Since there is reduced flowering, there will be less primings. Loss incurred during the maturation stage may be largely due to low quality of the beans in addition to increased flower fall during the subsequent flashes, notwithstanding the physical damage to the plant and the compounding effects of flooding. Continuous rains during the maturation stage will decrease the quality of the harvest in addition to physical damage to the plant.

Strong Winds

The effect of strong winds varies with intensity and the stage of the crop. The fruiting or maturation stage was observed to be most vulnerable to the damaging effects of strong winds. Farmers agreed that winds with velocities greater than 100 km/hr will not leave them any harvest regardless of the stage at which they occur. The reproductive and the maturation stages proved to be the most vulnerable stages to strong wind with farmers claiming at least 50% crop loss even with wind velocities less than 60 km/hr. Loss incurred because of strong winds may be aggravated by the falling or destruction of trellises bringing down with them the vines.


Drought

The effect of drought on crop loss varied with the stage of occurrence having the greatest effect during the reproductive stage. Loss incurred at this stage ranged from 30 to 50% slightly higher when drought occurs during the vegetative stage which was 2o to 40%. The least loss is obtained at maturation time which the farmers estimate as 20 to 30%.

Insect Pests and Diseases

The farmers practice chemical and mechanical measures to control insects such as leaf miners, aphids, thrips, white flies and beetles. Yield is reduced by as much 20% when these insects attack the plant at the vegetative to reproductive stage. The effect of these insects become severe and more pronounced at the maturation stage, and can reduce the yield by as much as 50%. Yield loss due to these insects is slightly lower during the rainy season which is at most 15%. The problem, however during the rainy season is diseases like bacterial wilt and damping-off which usually occurs during the vegetative stage. These two diseases account for at most 25% yield loss. Other diseases that farmers feel contribute to yield reduction were bean rust and powdery mildew whose effect can reduce the yield by 15 to 20%. Studies have shown bean rust as a very important disease in snap bean accounting for yield loss ranging from 13 to 100% when present at the vegetative and reproductive stages. Blight, also an important disease was reported to account for 10 to 45% yield loss (Hagedorn and Inglis).


SUMMARY AND CONCLUSIONS

Rice yield data from the National Cooperative trials were used to

construct a function that relates rice productivity to weather variables such as

amount of rainfall, maximum, minimum and mean temperatures and relative

humidity. Data on the weather variables were obtained from PAGASA Main

Office and Benguet PAGASA Station.

Two models were constructed one for the dry season and another for the

wet season. The models developed had very low R-squares with the wet season

model only explaining 46.66% of the variation in yield while that of the dry season

explaining 25.89% percent of the variation in log(yield). These translate to erratic

predictions in the yield when used. The model similarly constructed for the dry

season had an even lower R-square even with yield already transformed to its

logarithm.

Although the use of the models for prediction purposes may not be

feasible, several concepts such as the effects of weather variables on yield and

their interaction with several other variables were seen and demonstrated by the

model. The importance of minimum and maximum temperatures during the

vegetative, reproductive and ripening stages were demonstrated. The information

generated may shed light on how the factors affect yield for future activities of

similar nature.

These results can be attributed to the absence of reliable and complete

data. The data set used was heavy with data gaps. There was no available data

set on which to base the modelling activity. Even data on weather variables were

filled with data gaps, sometimes with one whole cropping season data missing.

Mismatches between crop and weather data were common. While data on crops

from other locations are available, data on the weather variables were not, as

seen in the case of Banaue. Rice data were available for Banaue for 1991 to

2009. However, weather data were available only for 1991 to 1993.

Furthermore, some data available were expressed in terms of annual production.

The use of response surface regression in place of multiple linear

regression may be a potent tool in modelling the problem at hand but requires a

lot of observations to come up with estimates of acceptable precision. High R-

squares are sometimes obtained without any predictor being declared significant

by the tests due to inadequate degrees of freedom for error. While literature


suggests nonlinear models which involve interactions between weather variables,

there was no adequate amount of data on which the response surface equation

can be validated.

Yield loss assessment using percent yield reduction was attempted using

the available data from the National Rice Cooperative Test for Adverse

Environments (Cold Tolerance). Percent reduction in yield was obtained as the

ratio of the difference of the actual yield from the potential yield to the potential

yield. Using the maximum yield as reference, percent reduction in yield ranged

from 13.54 to 72.64%. Using the mean NCT yield as reference, yield reduction

went from 0 (-140.57) to 57.04%. Estimates obtained by the Department of

Agriculture ranged from 30 to 100% depending on the stage when the risk factor

occurred. The use of NCT data to represent potential yield has its shortcomings

since the trials while provided inputs were also subject to biotic and abiotic risks.

Reliable estimates of yield reduction, especially for decision support systems,

need to be derived from carefully designed controlled experiments whereby

magnitudes of risks can also be controlled. In the absence of these experiments,

Meta analysis of results of physiological studies done under controlled conditions

can generate useful information.

The absence of good quality data did not allow estimation of yield

reduction for vegetables, an Focus Group Discussion was conducted in Atok,

Buguias and Tublay to get the crop loss experiences of vegetable farmers from

past hazards.


REFERENCES ARRAAUDEAU, M. A. and B.S. VERGARA. 1988. A Farmer’s Primer on Growing

Upland Rice. International Rice Research Institute. Los Baños, Laguna. 284 pp.

BAIER, W. 1973. Crop -Weather analysis model. Review and model development. J. Appl. Met. 12, 937-47. DRAGLAND, S. 1978. Nitrogen- og vassbehov hos gulrot. Forskning og forsøk i

landbruket 29: 139-159. GORDON JOHNSON. 2011. Extension. Flooding and Vegetables. August 26th, 2011.

Weekly Crop Updates.http://agdev.anr.udel.edu/weeklycropupdate/?p=3591

HAGEDORN,, DJ and DA INGLIS. _____. Handbook of Bean Diseases. http://learningstore.uwex.edu/assets/pdfs/A3374.PDF HASHEM, A., AMIN MAJUMDAR, M. N., HAMID, A. and HOSSAIN, M. M. 1998.

Drought Stress Effects on Seed Yield, Yield Attributes, Growth, Cell Membrane Stability and Gas Exchange of Synthesized Brassica napus L.Journal of Agronomy and Crop Science, 180: 129–136.

JAME, Y. W. and, H. W. CUTFORTH. 1996. Crop growth models for decision support

Systems. Can. J. Plant Sci. 76: 9–19.

LAHLOU, O, S OUATTTAR and J. LEDENT .2003. The effect of drought and cultivar on growth parameters, yield and yield components of potato. Agronomie 23 (2003) 257-268.

SØRENSEN, J.N., JØRGENSEN, U. & KUHN, B.F. 1997. Drought effects on the

marketable and nutritional quality of carrots. Journal of the Science of Food and Agriculture 74: 379-391.

TANGUILIG, HC. 2007. A methodology in predicting the influence of climatic variables

on crop yield. Paper presented at the 10th National Convention on Statistics (NCS). EDSA Shangri-La Hotel. October 1-2, 2007

WALWORTH, J.L., CARLING, D.E., 2002. Tuber initiation and development in irrigated

and non-irrigated potatoes. Am. J. Potato Res. 79, 387–395. http://vegetablemdonline.ppath.cornell.edu/factsheets/Crucifers_Clubroot.htm

http://agdev.anr.udel.edu/weeklycropupdate/?p=3591

http://vegetablemdonline.ppath.cornell.edu/factsheets/Crucifers_Clubroot.htm

http://vegetablemdonline.ppath.cornell.edu/factsheets/Crucifers_Clubroot.htm

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