Directory UMM :Data Elmu:jurnal:A:Agricultural & Forest Meterology:Vol102Issue1April2000:

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Evaluation of WGEN for generating long term

weather data for crop simulations

A. Soltani

a,∗

_{, N. Latifi}

_{, M. Nasiri}

a_{Department of Agronomy, Gorgan University of Agricultural Sciences, Gorgan, Iran} b_{Department of Agronomy, Ferdosi University of Mashhad, Mashhad, Iran}

Received 11 June 1999; received in revised form 20 December 1999; accepted 4 January 2000

Abstract

We evaluated the ability of the WGEN model to generate long term weather series in situations where the actual historic weather data record is only 3–10 years long. The series generated were used to simulate yield of irrigated and rainfed chickpea. To do this, four 100-year samples of weather data were generated for Tabriz, Iran. The WGEN parameters used to generate data were obtained from daily actual weather data of 3 (W3), 5 (W5), 7 (W7), and 10 (W10) recent years. The actual and generated weather series were each used as input to a chickpea crop model under irrigated and rainfed conditions at three planting dates. Results showed that the generated data are very similar to the actual data used for parameter estimation for all base periods tested. In comparison of the generated data and the historic data the means and the distributions of weather data variables differed significantly. However, with increasing the number of years used for parameter estimation of WGEN from 3 to 10, percent of significant differences were 38, 26, 17 and 13% for W3–W10, respectively. When generated weather data were evaluated as input to a chickpea crop model, simulated yields obtained using generated data were significantly different from that obtained using actual data in 50 and 8% of cases under irrigated and rainfed conditions, respectively. To generate data similar to long term historic data, a longer base period (>10 years) would be required for parameter estimation. However, when it is required that the generated data represent recent history rather than a long term period, the WGEN can be used as a reliable source of weather data even if it’s required parameters are obtained from only 3–10 years of actual historical weather data. © 2000 Elsevier Science B.V. All rights reserved.

Keywords:WGEN; Generating weather data; Crop simulation

1. Introduction

In agricultural science, crop simulation models are quantitative tools based on scientific knowledge that can evaluate the effects of climatic, edaphic, hy-drologic, agronomic, and genotypic factors on crop yield and stability (Boote et al., 1996). Such mod-els are used increasingly for genotype improvement

∗_{Corresponding} _author. _Fax: ₊_{98-171-2220981/}₊ 98-171-2220438.

E-mail address:irgouni@dci.iran.com (A. Soltani)

(Habekotte, 1997), environmental characterization and agro-ecological zonation (Aggarwal, 1993), es-timating production potential (Meinke and Hammer, 1995), quantifying climatic risk (Meinke et al., 1993), evaluating optimal management practices (Egli and Bruening, 1992; Muchow et al., 1994; O’Leary and Connor, 1998) and for predicting the effects of cli-matic change and clicli-matic variability on crop growth and yield (Matthews et al., 1997; Lal et al., 1998).

Long term weather data obtained at a specific site are usually required for simulation analyses. In regions with high climatic variability it is particularly

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tant in order to assess effects of climatic variability. For the most commonly used models, this requires long term daily values of rainfall, solar radiation, max-imum and minmax-imum temperatures. However, many lo-cations do not have sufficient historical weather data. Many workers have recognized this problem, and this has led to the development of a range of weather generators such as WGEN (Richarsdon and Wright, 1984), SIMMETEO (Geng et al., 1986, 1988), TAM-SIM (McCaskill, 1990) and others (e.g. Larsen and Pense, 1982; Bristow and Campbell, 1984; Guenni et al., 1991) which can be used to generate weather data sets.

The quality of model output is related to the quality of weather data used as input. It follows that the test-ing of the sensivity of model output to the quality of generated weather data is an essential prerequisite for simulation analyses. Weather data is used in crop mod-eling not only to predict crop growth and development in response to environmental variables but also to flag catastrophic events such as heat stress, water deficit, frost damage and etc. Often such effects can only be assessed by using the weather data as input into the simulation models in question because model act as a data filter and integrate the effect of deviations of generated from actual data (Richardson, 1984; Meinke et al., 1995). Meinke et al. (1995) demonstrated how crop simulation models can be used to assess the ad-equacy and quality of weather data generation. They showed that model complexity did not appear to influ-ence model sensivity to differinflu-ences in environmental input data.

WGEN is a well-known and widely used stochastic weather generator that requires a number of parame-ters to generate a weather series at a site. This genera-tor has been incorporated into the WeatherMan (short for weather data manager, Pickering et al., 1994), an application program of Decision Support System for Agrotechnology Transfer (DSSAT) software (Tsuji et al., 1994). DSSAT is a collection of crop models and computer programs integrated into a single software package in order to facilitate the application of crop simulation models in research and decision making. This software is a product of the International Bench-mark Sites Network for Agrotechnology Transfer (IB-SNAT) project and is distributed over the world.

Richardson (1984) and Meinke et al. (1995) tested the WGEN output applied to crop simulation models

and showed that yields obtained using generated data were not significantly different from that obtained us-ing actual data. In these tests, parameters required by the model were obtained from long term actual data (longer than 10 years). However, we have found no report(s) showing the capability of WGEN for gener-ating long term data when model parameters are de-rived from relatively short term weather data, say 3–10 years. Thus, the objective of this study was the evalua-tion of the WGEN model for generating long weather series parameterized using short (3–10 years) records of actual data to derive a specific crop model.

2. Materials and methods

Chickpea (Cicer arietinumL.) is the most

impor-tant legume crop of Iran, and is sown on more than 50% of the total legume areas. The crop is predom-inately rainfed and only 10% is grown under irriga-tion (Sadri and Banai, 1996). Most chickpea growing areas of Iran which have cool and cold semiarid cli-mates with terminal drought stress (similar to that of Tabriz, the location chosen for this study) are located in north west (NW) of the country. Nearly 50% of to-tal chickpea production in Iran is concentrated in NW. Recently, a project was initiated to analyse the bio-physical limitations in chickpea production in Iran us-ing the crop simulation approach. However, at many locations long term weather data (more than 10 years) are not available to be used in the analysis. Therefore, we are forced to use a weather data generator to pro-vide long term weather series for use with the crop model.

2.1. The weather generation model

The WGEN model provides daily values of precipi-tation, maximum and minimum temperatures and solar radiation for an-year period at a given location. The precipitation is generated independently for a given day. Maximum and minimum temperature and solar radiation are then generated depending on whether the generated day is wet or dry. The precipitation compo-nent of WGEN is a Markov chain-gamma distribution model. The occurrence of wet or dry days is gener-ated with a first-order Markov chain model in which the probability of rain on a given day is conditioned

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on whether the previous day was wet or dry. When a wet day is generated, the two-parameter gamma dis-tribution is used to generate the precipitation amount. For generating daily values of maximum and mini-mum temperature and solar radiation, the residual of the three variables are generated using a multivariate normal generation procedure that preserves the serial correlation and cross correlation of the variables. The final values of the three variables are determined by adding the seasonal means and standard deviations to the generated residual elements. For more details re-fer to Richardson (1981, 1984) and Richarsdon and Wright (1984).

2.2. The crop growth model

To evaluate the sensivity of crop models to observed versus generated data, we used the chickpea model of Soltani et al. (1999) with each of the observed and generated weather files. This model is similar to the models of Sinclair (1986), Amir and Sinclair (1991), and Hammer et al. (1995). The model is based on a daily time step and simulate crop growth and devel-opment as a function of temperature, solar radiation and water availability. Crop phenology is divided into two growth stages (before and after beginning seed growth), the duration of which are predicted based on daily temperature and water deficit. Leaf area devel-opment is calculated as functions of expansion and senescence of leaves. These functions are sensitive to temperature and water deficit. Daily biomass produc-tion is predicted from leaf area index, light extincproduc-tion coefficient and radiation use efficiency. Transpiration is calculated as a function of daily biomass produc-tion, transpiration efficiency coefficient and daily va-por pressure deficit. Daily biomass production and leaf growth decrease below potential values once the frac-tion of transpirable soil water (FTSW) declines below a threshold value (about 0.37). After the beginning of seed growth the accumulated biomass is partitioned into the grains, the rate of which depending on cli-matic conditions at and after beginning of seed growth. Soil evaporation, soil water drainage, and runoff are also calculated in the soil water balance sub-model. The model uses readily available weather and soil in-formation. The model was tested using independent data from a range of Iran’s environmental conditions.

In most cases, simulated grain yields were similar to that of observed yields. At this stage the model does not account for the effects of pests, diseases and soil fertility.

2.3. Procedure

Tabriz (38◦₈′_{N, 46}◦₁₇′_{E and 1361 m above sea}

level) was selected as the site for comparison of chick-pea model yield estimates made with actual weather data and data generated by WGEN as implemented in WeatherMan (Pickering et al., 1994). Precipitation and maximum and minimum temperature data were available for 30 years (1965–1995). Solar radiation data were calculated from sunshine hours and ex-traterrestrial radiation as outlined by Doorenbos and Pruitt (1977). With solar radiation data estimated, a complete 30-year daily data set of the required weather variables were available.

Four 100-year samples of weather data were gener-ated for Tabriz. The parameters used to generate data were obtained from daily weather data of 3, 5, 7, and 10 recent years as base periods (W3, W5, W7, and W10 thereafter). Monthly means of rainfall, solar ra-diation, maximum temperature and minimum temper-ature for 3, 5, 7 and 10 recent years are shown in Table 1. The actual and generated weather series were each used as input to the chickpea model. The same soil, cultivar and management inputs were used for all simulations. The soil that was chosen was sandy loam with a plant available water of 0.13 m3_m−3_{, a depth}

of 100 cm and a curve number of 80. Curve number of soil is required to estimate run off using the curve number technique (Knisel, 1980). Curve numbers are assigned to various soil categories depending on their texture and condition and then modified to take ac-count of standing crop cover. The chickpea cultivar Jam was chosen for simulations. Characteristics of this variety are given in Soltani et al. (1999). Simulations were run under irrigated and rainfed conditions with three sowing dates — 26 March, 10 and 25 April. The selected sowing dates do not necessarily reflect common practices, but were rather selected to trigger chickpea model responses to stressfull conditions to enable comparisons of model sensivity to weather data generation and to test the base periods used for param-eter estimation at various times of the year. Planting

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Table 1

Comparison of monthly rainfall amount, solar radiation, maximum and minimum temperatures averaged over recent 3, 5, 7 and 10 year periods

Month Rainfall Solar radiation

amount (mm) (MJ m−2_d−1₎

3 5 7 10 3 5 7 10

1 24.8 18.6 15.5 15.5 7.8 8.4 8.4 8.4

2 8.7 14.5 11.6 17.4 9.6 9.8 9.9 9.7

3 46.5 37.2 34.1 34.1 13.9 13.9 13.9 13.7

4 66.0 54.0 48.0 45.0 17.8 19.2 18.9 18.7

5 49.6 49.6 40.3 37.2 23.8 23.2 23.7 23.9

6 21.0 21.0 15.0 18.0 26.4 26.7 26.9 27.0

7 3.1 6.2 3.1 3.1 27.5 27.5 27.8 27.6

8 3.1 3.1 3.1 6.2 24.7 24.4 24.8 25.3

9 6.0 3.0 3.0 3.0 20.7 20.5 20.4 20.8

10 18.6 12.4 21.7 24.8 15.0 15.2 14.7 14.7

11 36.0 36.0 30.0 27.0 9.0 9.4 10.0 10.1

12 18.6 24.8 21.7 31.0 8.9 8.1 8.0 7.8

Annual 302.0 280.4 247.1 262.3 17.1 17.2 17.3 17.3

Maximum Minimum

temperature (◦_C) _{temperature (}◦_C)

1 2.6 2.4 1.6 2.3 −4.7 −5.6 −6.9 −6.4

2 5.6 5.0 3.9 4.6 −2.9 −3.8 −4.8 −3.9

3 11.0 9.8 10.1 10.0 0.7 0.3 0.4 0.4

4 17.3 17.2 17.6 17.3 6.6 6.5 6.6 6.5

5 22.4 21.4 22.2 22.5 10.9 10.3 10.5 10.6

6 27.8 27.8 28.6 28.6 15.3 15.4 15.8 15.7

7 32.1 32.5 33.0 33.0 19.2 19.4 19.9 19.9

8 32.8 32.3 32.4 32.2 19.6 19.3 19.3 19.1

9 28.0 27.9 28.2 28.3 14.7 14.6 14.5 14.5

10 20.5 20.8 20.8 20.2 8.3 8.5 8.6 8.5

11 10.6 11.3 12.1 11.8 1.9 2.3 2.7 2.4

12 3.6 3.6 4.5 4.7 −3.5 −3.1 −2.8 −2.8

Annual 17.9 17.7 18.0 18.0 7.2 7.1 7.1 7.1

densities were 25 and 50 plants per m2under rainfed and irrigated conditions, respectively. Under irrigated conditions, the water balance sub-model was turned off. The management inputs were the same for each year of crop growth model runs. Thirty year simula-tions were made with the actual data and 100 years with the four series of the generated weather data. The daily water balance (only for rainfed conditions) and crop growth and yield were simulated for each year.

In each case, generated data were compared to the related base period data and to the entire 30-year length of record. The former tests indicate whether WGEN assumptions are valid and the latter tests show whether the base periods (3–10) are of sufficient length to adequately represent the entire data set.t-Tests were

used to compare the means of precipitation, number of wet days, solar radiation, maximum temperature, min-imum temperature, number of days with a maxmin-imum

temperature greater than 35◦_{C and number of days}

with a minimum temperature less than 0◦_{C for each}

month. Means of simulated yields (and some other crop related variables) using actual and four series of generated data were also compared byt-tests.

Cumulative distribution functions (CDFs) of each weather variable and simulated grain yield were calculated. In the case of grain yield, data from different sowing dates were combined into one CDF. The CDFs were then compared with CDFs of actual weather data or simulated grain yield us-ing actual weather data and tested for significant

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differences using Kolmogorov–Smirnov test. The Kolmogorov–Smirnov statistic is simply the max-imum absolute difference between the cumulative probability distributions of the two samples in ques-tion. The smaller the calculated test statistic, the smaller are the differences between the distributions in question. Statistical comparisons were performed using SAS (SAS Institute, 1989).

3. Results and discussion

3.1. Comparison of recorded historic and generated weather data

The mean rainfall amounts and mean number of wet days for each month of the year and for the an-nual totals are shown in Table 2 for the observed and generated data. The mean precipitation amounts from generated data differed significantly from the values obtained from the observed data for 4, 5, 3 and 3 of the 12 months for W3–W10, respectively. Rainfall of October–December and January–March has an im-portant role for increasing stored soil water and the successful growth of the crop under rainfed condi-tions is related to this stored water. The greatest dif-ferences between generated (W3–W10) and historic rainfall amount occurred in these months, specially in January and February. The discrepancy may be

cru-Table 2

Comparison of average monthly historic and generated rainfall amount and number of wet days for the four base periods used to parameterise WGEN

Month Rainfall amount (mm) Number of wet days

Observed W3 W5 W7 W10 Observed W3 W5 W7 W10

1 23.0 23.1 16.7∗ _13.2∗ _15.0∗ _8.6 _11.0∗ _8.4 _7.6 _7.9

2 21.6 7.5∗ _10.7∗ _9.1∗ _14.4∗ _8.1 _7.3 _6.7 _6.7 _7.6

3 41.2 45.6 38.7 41.6 35.4 11.8 12.9 12.2 12.1 12.0

4 50.3 70.8∗ _50.2 _46.3 _47.3 _11.4 _11.6 _10.0 _10.1 _11.1

5 44.0 46.3 55.0 43.5 41.8 11.3 11.2 14.3∗ _11.8 _10.5

6 18.9 21.5 21.7 16.3 18.3 5.6 9.0∗ _8.5∗ _6.1 _6.9

7 3.7 1.7 5.5 3.5 4.0 1.4 0.6∗ _1.5 _1.6 _1.7

8 3.5 2.2 3.0 3.1 5.5 1.2 1.7 1.4 1.1 1.7

9 8.5 4.7 2.5∗ _2.2∗ _1.8∗ _2.0 _2.7 _1.5 _1.4 _1.0

10 23.6 17.7 11.0∗ _18.7 _28.4 _6.7 _3.2∗ _2.9∗ _3.7∗ _6.4

11 29.3 41.9∗ _41.8∗ _31.9 _26.6 _7.5 _15.7∗ _11.8∗ _9.3 _8.9

12 27.7 13.8∗ _25.7 _23.8 _35.2 _8.2 _3.8∗ _7.5 _6.8 _8.7

Annual 295.3 296.8 282.5 253.2 273.7 83.8 90.7 86.7 78.3 84.4

∗_{The observed mean and the generated mean are significantly different at the 1% level.}

cial when applied to the crop model and discussed in latter section. The average number of wet days gen-erated for each month did not compare well with the long term averages for 6, 4 and 1 of the 12 months for W3, W5 and W7, respectively. The proper description of the occurrence of wet days by season is important, because the generation of solar radiation and maxi-mum temperature are conditioned on the occurrence of wet or dry days. Most of the differences between the recorded and the generated data that are shown in Table 2 were due to deviations of means of base peri-ods from the means of the historic period. For exam-ple, February rainfall are 7.5, 10.7, 9.1 and 14.4 mm for W3–W10 in comparison to 21.6 mm for the ob-served historic mean. For this month averages of rain-fall amounts are 8.7, 14.5, 11.6 and 17.4 mm for base periods of 3, 5, 7 and 10 years, respectively. There was a trend of reducing significant differences with lengthening of the base period used for parameter es-timation of the WGEN.t-Tests showed no significant differences between the generated data and their cor-responding base periods used for parameter estimation for precipitation and number of wet days (data not shown), suggested that model assumptions are valid.

The solar radiation data generated with the model using various base periods for parameter estimation are compared with the observed data in Table 3. The means were significantly different from the historic in 58, 17, 0 and 17% of the time for W3–W10,

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Table 3

Comparison of average monthly historic and generated solar radi-ation for the four base periods used to parameterise of WGEN Month Solar radiation (MJ m−2_d−1₎

Observed W3 W5 W7 W10

1 8.0 7.7 8.4 8.3 8.3

2 9.9 9.2∗ _9.5 _9.5 _9.5

3 13.4 13.6 13.5 13.6 13.3

4 18.7 17.3∗ _19.1 _18.7 _18.4

5 23.5 24.1∗ _23.1 _23.4 _23.9

6 26.9 26.5 26.8 27.0 27.1

7 27.9 27.7 27.8 28.1 28.8

8 25.2 24.5∗ _24.5∗ _24.8 _25.4

9 20.5 21.0∗ _20.5 _20.4 _21.0∗

10 14.7 14.9 14.9 14.5 14.4

11 10.1 8.3∗ _8.7∗ _9.7 _9.8

12 7.8 9.0∗ _7.9 _7.8 _7.3∗

Annual 17.2 17.0 17.0 17.2 17.2

∗_{The observed mean and the generated mean are significantly} different at the 1% level.

respectively. The differences here were also due to deviations of means of base periods from the his-toric means. There were not significant differences between the generated data and their corresponding base periods for solar radiation (data not shown).

Table 4 shows a comparison between the historic and generated data (W3–W10) for maximum temper-ature, minimum tempertemper-ature, number of days with

maximum temperature greater than 35◦

C and num-ber of days with minimum temperature less than 0◦_C

by month and annually. For maximum temperature, 35% of the time significant differences were observed between the generated data of W3, W5 and W7 and the historic data. For W10 only 1 of the 12 months has a significant difference. For minimum tempera-ture significant differences were detected between the recorded and the generated data in 5, 3, 4 and 2 of the 12 months, respectively. There was a tendency for lower significant differences with increasing of year number used to parameter estimation of the WGEN. The greatest differences between generated and actual number of days per month with a maximum tempera-ture greater than 35◦

C and the number of days with a

minimum temperature less than 0◦

C were occurred in the warmest and coldest months of the year, respec-tively, specially when WGEN’s required parameters were calculated from 3 years of recent actual data. The total annual number of days with a maximum

temperature greater than 35◦_{C were significantly}

dif-ferent from the historic data for W7 and W10. The differences here between the generated data and their corresponding base periods data were not significant (data not shown).

The CDFs of the daily precipitation, solar radiation, maximum temperature and minimum temperature ob-tained from the generated and the recorded data were constructed and evaluated (data not shown). Statistical comparison using Kolmogorov–Smirnov test showed that all CDFs of generated maximum temperature and minimum temperature data and 50% of CDFs of gen-erated daily rainfall data differed significantly from the historic data (Table 5). There were not significant dif-ferences between simulated and actual solar radiation distributions in virtually all cases. The greater num-ber of years used to estimate the required parameters of WGEN did not affect on the number of significant differences.

Briefly, the limited number of significant differences between the means of generated data of W3–W10 and their corresponding means of base periods showed that the generated values for all climatic variables were very similar to that actual data used for parameter esti-mation for all base periods tested. Thereby, the model is capable of representing the characteristics that ex-isted in the observed data. However, the analysis also indicated that 3–10 years were not enough to ade-quately represent the 30 years of recorded data, be-cause of the deviations of means of base periods from the means of the historic. With increasing the num-ber of years used for parameter estimation of WGEN from 3 to 10, significant differences were 38, 26, 17 and 13% for W3–W10, respectively. Thus if adequate representation is required, a longer base period for parameter estimation would be required. Larsen and Pense (1982) showed that 17 years were not enough to adequately represent the 80 years of recorded data at Columbia. Therefore, a long base period, perhaps equal to that of historic period may be necessary. On the other hand, statistically significant differences presented in Tables 2–5, with exception of rainfall amount, are small. It is interesting to see whether these differences are important from the biological view of point to cause different responses when applied to crop models. Meinke et al. (1995) showed while the gener-ated and the recorded data had significant differences, crop models responses were the same using the

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gen-Table 4

Comparison of average monthly historic and generated maximum temperature, minimum temperature, number of days with maximum temperature greater than 35o_{C and number of days with minimum temperature less than 0}o_{C for the four base periods used to parameterise}

of WGEN

Month Maximum temperature (◦_C) _{Minimum temperature (}◦_C)

Observed W3 W5 W7 W10 Observed W3 W5 W7 W10

1 1.9 2.4 2.2 1.3 2.1 −6.6 −5.2∗ _−6.0 _−7.6 _−7.0

2 3.9 5.1 4.6 3.4 4.1 −4.5 −3.4 −4.2 −5.1 −4.4

3 9.9 10.6 9.3 9.8 9.1 0.3 0.3 −0.3 −0.1 −0.3

4 16.9 17.3 17.2 17.8∗ _17.5 _6.0 _6.8∗ _6.5 _6.6 _6.6

5 22.2 21.9 21.1∗ _22.2 _22.3 _10.6 _10.5 _10.0 _10.4 _10.3

6 28.4 27.5∗ _27.8 _28.5 _28.3 _15.2 _15.2 _15.4 _15.7 _15.4

7 32.7 32.2 32.8 33.3 33.4 19.5 19.5 19.8 20.3∗ _20.3∗

8 32.2 33.6∗ _33.1∗ _33.0∗ _32.7 _19.1 _20.5∗ _20.2∗ _20.0∗ _19.7

9 28.1 28.7 28.6 29.1∗ _29.0∗ _14.4 _15.2∗ _15.1∗ _15.0∗ _14.8

10 20.1 21.1∗ _21.5∗ _21.2∗ _20.7 _8.1 _8.6 _8.9∗ _8.7∗ _8.8∗

11 11.9 11.0 11.6 11.9 11.9 2.2 2.3 2.5 2.6 2.4

12 4.5 2.9∗ _3.0∗ _4.5 _4.5 ₋_3.0 ₋_4.2∗ ₋_3.6 ₋_2.6 ₋_2.8

Annual 17.7 17.9 17.7 18.0 17.9 6.8 7.2 7.0 7.0 7.0

Days >35◦_C _Days

<0◦C

1 0 0 0 0 0 28.5 26.9∗ _27.8 _28.8 _28.6

2 0 0 0 0 0 22.9 24.2 25.0 24.7 23.4

3 0 0 0 0 0 12.0 14.5 16.4∗ _15.4 _16.2∗

4 0 0 0 0 0 1.0 0.4∗ _0.9 _0.9 _1.0

5 0 0 0 0.1 0 0 0 0 0 0

6 0.6 0.9 0.6 1.0 1.2 0 0 0 0 0

7 7.0 4.2∗ _5.8 _8.5 _8.7 ₀ ₀ ₀ ₀ ₀

8 4.9 8.2∗ _8.5∗ _8.0∗ _7.5∗ ₀ ₀ ₀ ₀ ₀

9 0.1 2.0∗ _1.7∗ _2.1∗ _1.4∗ ₀ ₀ ₀ ₀ ₀

10 0 0 0 0 0 0.2 0 0 0.1 0.1

11 0 0 0 0 0 7.1 8.7 8.0 8.0 8.2

12 0 0 0 0 0 22.4 26.5∗ _25.5∗ _23.1 _22.7

Annual 12.6 15.3 16.6 19.7∗ _18.8∗ _94.1 _101.2 _103.6 _101.0 _100.2

∗_{The observed mean and the generated mean are significantly different at the 1% level.}

erated and recorded data and significant differences were rare. We examined this through analyses of out-puts of a chickpea crop model applied to generated and actual data.

3.2. Sensivity test using a chickpea crop model

Under rainfed conditions of NW Iran insufficient plant available soil water commonly overwrites any effects of variation in temperature and radiation. To eliminate this large influence of water availability on crop growth and hence on sensivity test, the chickpea crop model was run under irrigated (i.e. non-limiting water for plant growth) and rainfed conditions to eval-uate WGEN capability for generating temperature and radiation, and rainfall, respectively.

In the chickpea model the effects of non-optimal temperatures on plant growth is considered by multiplying radiation use efficiency (RUE) by a scalar

factor that has a value of 1 between 15–24◦_{C of}

Table 5

Kolmogorov–Smirnov test statistics for the comparison of the dis-tribution of actual vs generated rainfall, solar radiation, maximum temperature and minimum temperature for the four base periods used to parameterise WGEN

W3 W5 W7 W10

Rainfall 0.018∗ _0.008 _0.015∗ _0.007 Solar radiation 0.014 0.011 0.010 0.014 Maximum temperature 0.026∗ _0.032∗ _0.025∗ _0.022∗ Minimum temperature 0.021∗ _0.027∗ _0.024∗ _0.019∗ ∗_{Significantly differing distributions from those of actual data.}

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average daily temperature but declines linearly to 0 at 0 and 39◦_{C. The effect of higher temperature on}

hastening crop phenology and leaf senescence is also incorporated. As indicated in Section 3.1, there were statistically significant differences between the gen-erated maximum temperature, minimum temperature, number of days with maximum temperature greater

than 35◦_{C and number of days with minimum}

tem-perature less than 0◦_{C and the recorded ones during}

April–August months which coincides with growing season of chickpea. It should be answered to this question whether these differences have significant influences on plant growth. To do this, during model simulations the number of days to maturity, number of days with average temperature between 15 and

24◦_{C (optimal days), number of days with average}

temperature less than 15◦_{C (sub-optimal days) and}

number of days with average temperature greater than

24◦_{C (supra-optimal days) were saved and compared}

(Table 6). None of the base periods used had any influence on the simulated number of days to phys-iological maturity for all three sowing dates under

Table 6

Comparison of means of days to maturity (DTM), days with average temperature less than 15◦_{C (DLOPT), and days with average} temperature between 15 and 24◦_{C (DOPT) and days with average temperature greater than 24}◦_{C (DUOPT) simulated for three sowing} dates under irrigated and rainfed conditions using the chickpea model with actual weather data and weather data generated with WGEN

Irrigated Rainfed

DTM DLOPT DOPT DUOPT DTM DLOPT DOPT DUOPT

26 March

Actual 120.8 40.4 55.4 25.0 110.1 40.4 54.0 15.7

W3 119.8 40.6 53.4 25.8 109.6 40.6 52.0 17.0

W5 120.8 42.9 49.5∗ _28.4∗ _110.6 _42.9 _48.6∗ _19.1∗

W7 118.7 39.1 50.2∗ _29.5∗ _108.0 _39.1 _49.3∗ _19.6∗

W10 119.4 39.6 50.7 29.1∗ _108.6 _39.6 _49.6∗ _19.4∗

10 April

Actual 111.5 26.0 55.9 29.6 100.0 26.0 54.0 19.9

W3 110.9 26.1 53.5 31.3 99.9 26.1 52.2 21.5

W5 111.6 28.8 48.8∗ _33.9∗ _100.6 _28.8 _48.2∗ _23.6∗

W7 109.9 25.2 49.6∗ _35.1∗ _98.3 _25.2 _48.6∗ _24.5∗

W10 110.3 25.3 50.4∗ _34.6∗ _98.6 _25.3 _49.5 _23.8∗

25 April

Actual 104.1 13.9 55.0 35.2 91.6 13.9 52.1 25.6

W3 103.8 14.5 50.2∗ _39.1∗ _91.9 _14.5 _49.7 _27.6

W5 104.5 17.2∗ _46.0∗ _41.4∗ _92.8 _17.2 _45.3∗ _30.3∗

W7 103.0 14.4 46.1∗ _42.5∗ _90.5 _14.4 _45.1∗ _31.0∗

W10 103.0 13.9 47.8∗ _41.5∗ _90.6 _13.9 _46.4∗ _30.3∗

∗_{Statistically significant difference.}

irrigated and rainfed conditions. Significant differ-ences were observed between the generated data (W3–W10) and historic data for the number of opti-mal and supra-optiopti-mal days. In this respect, the lowest significant differences were observed for W3. Model output using recorded data showed that with delay in sowing date, percentage of sub-optimal and optimal days decreased but percentage of supra-optimal days increased. With the generated data of W3–W10 the same response was observed. Note that for 25 April planting date, each supra-optimal day has a more negative effect, because average daily temperatures are increased.

In NW Iran rainfed chickpea is sown early spring and grows mainly on stored moisture which is pro-gressively depleted with crop growth. The crop expe-riences drought stress from late vegetative stage until maturity. The intensity of drought stress varies from year to year, depending on the amount and distribution of rainfall and on spring and early summer tempera-tures. This phenomenon was evaluated by examining the days to beginning of terminal drought stress (from

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Table 7

Comparison of means of days to terminal drought stress (DTB), days with fraction of transpirable soil water less than 0.37(DL37), ratio of DL37 to days to maturity (FDL37) and crop evapotranspi-ration (CET, mm) simulated for three sowing dates under rainfed conditions using the chickpea model with actual weather data and weather data generated with WGEN

DTB DL37 FDL37 CET

26 March

Actual 10.4 33.6 0.31 235

W3 10.9 33.2 0.30 257∗

W5 10.7 33.9 0.31 258∗

W7 10.0 34.1 0.32 233

W10 9.4 34.5 0.32 236

10 April

Actual 7.7 34.9 0.35 220

W3 8.5 33.7 0.34 232

W5 8.5 34.3 0.34 237∗

W7 7.6 34.8 0.35 214

W10 7.2 35.2 0.36 216

25 April

Actual 5.7 35.3 0.39 200

W3 6.6 35.0 0.38 201

W5 6.7 34.2 0.37 213

W7 5.3 35.6 0.39 195

W10 5.3 36.0 0.40 196

∗_{Statistically significant difference.}

flowering as an origin) (DTB), days with FTSW less than 0.37 (DL37), ratio of DL37 to days to maturity (FDL37) and crop evapotranspiration (CET) using the generated and actual data inputted into the chickpea model and the results are presented in Table 7. Us-ing actual data, with delay in sowUs-ing date, terminal drought stress begins earlier and the crop experiences a greater number of days with FTSW less than 0.37 and FDL37 is increased. Simulations using generated data also showed the same response to sowing date, without any significant difference. With respect to considerable differences for winter rainfall between the generated and actual data (Table 2) and its influence on the stored soil water, the results of Table 6 seems questionable. However, because of the limited soil depth of the re-gion (100 cm, see Section 2.3) and its limited capacity for water storage, the differences were masked due to a higher water loss simulated using actual data. The differences could be of greater significance in other regions and climates or even in the same climate with deeper soils (although they are not common).

Table 8 shows simulated irrigated and rainfed yields at three sowing dates using actual and generated weather data. With actual weather data, irrigated yield

declined about 60 kg ha−1 (2% of 26 March yield)

for each 15-day delay in sowing date from 26 March. The same response was observed in simulated yields using W3–W10 weather data. At the two planting dates, i.e. 26 March and 10 April, there was no sig-nificant difference between irrigated yields obtained using actual weather data and those obtained using

W3–W10 weather data (p=0.01), with exception of

W3 at 26 March. However, at 25 April sowing date, simulated yields were significantly different at a prob-ability level of 0.05 from the simulated yields using actual weather data, because the growing season is moved into the summer months when the maximum differences between number of supra-optimal days using the generated and actual weather data occurred. Ranges of irrigated yields are generally similar and the F-test showed no significant difference between variances of simulated yields using actual weather data and W3–W10 weather data. The number of years used to estimate the required parameters of WGEN did not affect accuracy of the yield predictions.

Under rainfed conditions, simulated grain yield using actual weather data decreased by about 120 kg ha−1_{(13% of 26 March yield) per each 15-day}

delay in sowing date (Table 8). Simulated yields us-ing generated weather data also showed the same response to sowing date. Means of yields obtained using actual weather data and W3–W10 were similar (with exception of W3 at 25 April). As compared to irrigated conditions delayed sowing date did not in-crease differences in yield, because water availability is an overriding factor under rainfed conditions and the differences between the generated and the historic data were not significant in this respect (Table 7). Yield ranges obtained from generated data were sim-ilar to those obtained using actual data, and F-test showed no significant difference between variances, with exception of W7 sown on 26 March. Under rainfed conditions, the number of data years used to parameterize WGEN also did not affect the conver-gence between simulated yield obtained using actual weather data and generated data.

The CDFs of the yields under irrigated and rain-fed conditions obtained using the actual data and generated data indicate that using actual Tabriz

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Table 8

Comparison of means and ranges of chickpea grain yield (t ha−1_{) simulated for three sowing dates under irrigated and rainfed conditions}

using the chickpea model with actual weather data and weather data generated with WGEN

Irrigated Rainfed

Mean Pr>ta _Range _Pr>F _Mean _Pr>tb _Range _Pr>F

26 March

Actual 2.61 – 2.31–2.87 – 0.91 – 0.52–1.32 –

W3 2.71 0.0038 2.26–3.21 0.3745 0.97 0.1182 0.62–1.44 0.0442

W5 2.65 0.1609 2.26–3.10 0.9286 0.98 0.0900 0.48–1.35 0.0459

W7 2.61 0.9190 2.23–2.90 0.8562 0.90 0.9392 0.54–1.39 0.0150

W10 2.68 0.0413 2.28–3.16 0.4300 0.90 0.9316 0.43–1.37 0.0782

10 April

Actual 2.56 – 2.30–2.90 – 0.81 – 0.51–1.15 –

W3 2.59 0.1776 2.18–2.98 0.9543 0.84 0.2034 0.53–1.41 0.0783

W5 2.57 0.6587 2.23–2.96 0.8688 0.86 0.0720 0.48–1.27 0.2098

W7 2.53 0.4105 2.14–2.83 0.6115 0.79 0.6132 0.49–1.23 0.0865

W10 2.58 0.4494 2.12–3.00 0.3496 0.78 0.4373 0.39–1.15 0.0618

25 April

Actual 2.49 – 2.21–2.81 – 0.67 – 0.47–0.92 –

W3 2.42 0.0413 1.98–2.78 0.8635 0.71 0.0877 0.45–1.18 0.7960

W5 2.41 0.0082 2.00–2.71 0.5906 0.74 0.0094 0.48–1.15 0.8020

W7 2.38 0.0019 2.04–2.74 0.8473 0.66 0.7783 0.40–0.99 0.6040

W10 2.41 0.0194 2.06–2.84 0.8490 0.65 0.5625 0.40–0.95 0.7743

a_{Pr>t: Probability that a significant}_t_{value would occur by chance.} b_{Pr>F: Probability that a significant}_F_{value would occur by chance.}

weather data, irrigated yields are between 2.34 and

2.75 t ha−1 in 80% of years (data not shown). Same

quantities were 2.34–2.82, 2.32–2.74, 2.28–2.72, and

2.31–2.79 t ha−1 for W3–W10, respectively. Grain

yield obtained using W7 weather data was greater than yield obtained using actual data for most of the years. Under rainfed conditions grain yields were be-tween 0.55 and 1.11 t ha−1in 80% of the years. Same quantities were 0.63–1.09, 0.65–1.10, 0.58–1.01, and

0.56–1.02 t ha−1 for W3–W10, respectively. Median

of yields were 2.55, 2.57, 2.53, 2.52, and 2.57 t ha−1

for actual data and W3–W10 under irrigated condi-tions and 0.75, 0.82, 0.85, 0.78, and 0.77 for actual data and W3–W10 under rainfed conditions, respec-tively. Statistical comparison showed that with one exception (W5 under rainfed conditions) all CDFs of simulated yield using generated data did not differed significantly from the simulated yield using recorded data (Table 9).

Briefly, in comparison of grain yields simulated using the generated solar radiation, maximum tem-perature and minimum temtem-perature and actual ones (i.e. irrigated yields), significant differences were

found in 50% of cases. Under rainfed conditions and using generated rainfall in addition to generated so-lar radiation, maximum temperature and minimum temperature, only in one out of 12 cases was a signif-icant difference (W5 for the 25 April planting date) observed in the comparison of simulated grain yields between W3–W10 against actual data. The uniformity was not due to capability of WGEN to generating weather data with adequate representation of actual data. It was resulted because considerable differences in rainfall amounts were masked by the limited soil depth (typical of the region) chosen for simulations.

Table 9

Kolmogorov–Smirnov test statistics for the comparison of the distribution of grain yield simulated using actual weather data vs generated weather data by base period used to parameterization of WGEN

W3 W5 W7 W10

Irrigated yield 0.129 0.049 0.123 0.119 Rainfed yield 0.163 0.210∗ _0.091 _0.081 ∗_{Significantly differing distributions from those of actual data.}

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The longer base period used for parameter estimation of WGEN did not result in lower differences.

4. Concluding remarks

Our results show that

1. The WGEN’s generated data are very similar to the actual data used for parameter estimation for all base periods used (3, 5, 7 and 10 years). Therefore, WGEN assumptions are valid and the model is capable of representation of many of the characteristics that existed in the observed data. However, 3–10 years are not enough to adequately represent the historic data, because of the devia-tions of means of base periods from the means of the historic period.

2. In spite of similarity in trends and responses, sim-ulated yields obtained using generated data were significantly different from that obtained using ac-tual data in 50 and 8% of cases under irrigated and rainfed conditions, respectively. Under rainfed conditions differences were masked by the limited soil depth chosen for the simulations.

3. In many times it is better for the generated data to represent recent history rather than a long time pe-riod, particularly when the generated data are used to assess the risk of current decisions or represent future weather. In such cases, WGEN can be used as a reliable source of weather data for estimating crop yield.

Acknowledgements

The authors are thankful for the suggestions pro-vided by the referees and the regional editor which resulted in significant improvements.

References

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Matthews, R.B., Kropff, M.J., Horie, T., Bachelet, D., 1997. Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation. Agric. Syst. 54, 399–425.

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Table 4

of WGEN

Month Maximum temperature (◦_C) _{Minimum temperature (}◦_C)

Observed W3 W5 W7 W10 Observed W3 W5 W7 W10

1 1.9 2.4 2.2 1.3 2.1 −6.6 −5.2∗ _−6.0 _−7.6 _−7.0

2 3.9 5.1 4.6 3.4 4.1 −4.5 −3.4 −4.2 −5.1 −4.4

3 9.9 10.6 9.3 9.8 9.1 0.3 0.3 −0.3 −0.1 −0.3

4 16.9 17.3 17.2 17.8∗ _17.5 _6.0 _6.8∗ _6.5 _6.6 _6.6

5 22.2 21.9 21.1∗ _22.2 _22.3 _10.6 _10.5 _10.0 _10.4 _10.3

6 28.4 27.5∗ _27.8 _28.5 _28.3 _15.2 _15.2 _15.4 _15.7 _15.4

7 32.7 32.2 32.8 33.3 33.4 19.5 19.5 19.8 20.3∗ _20.3∗

8 32.2 33.6∗ _33.1∗ _33.0∗ _32.7 _19.1 _20.5∗ _20.2∗ _20.0∗ _19.7

9 28.1 28.7 28.6 29.1∗ _29.0∗ _14.4 _15.2∗ _15.1∗ _15.0∗ _14.8

10 20.1 21.1∗ _21.5∗ _21.2∗ _20.7 _8.1 _8.6 _8.9∗ _8.7∗ _8.8∗

11 11.9 11.0 11.6 11.9 11.9 2.2 2.3 2.5 2.6 2.4

12 4.5 2.9∗ _3.0∗ _4.5 _4.5 ₋_3.0 ₋_4.2∗ ₋_3.6 ₋_2.6 ₋_2.8

Annual 17.7 17.9 17.7 18.0 17.9 6.8 7.2 7.0 7.0 7.0

Days >35◦_C _Days_<0◦_C

1 0 0 0 0 0 28.5 26.9∗ _27.8 _28.8 _28.6

2 0 0 0 0 0 22.9 24.2 25.0 24.7 23.4

3 0 0 0 0 0 12.0 14.5 16.4∗ _15.4 _16.2∗

4 0 0 0 0 0 1.0 0.4∗ _0.9 _0.9 _1.0

5 0 0 0 0.1 0 0 0 0 0 0

6 0.6 0.9 0.6 1.0 1.2 0 0 0 0 0

7 7.0 4.2∗ _5.8 _8.5 _8.7 ₀ ₀ ₀ ₀ ₀

8 4.9 8.2∗ _8.5∗ _8.0∗ _7.5∗ ₀ ₀ ₀ ₀ ₀

9 0.1 2.0∗ _1.7∗ _2.1∗ _1.4∗ ₀ ₀ ₀ ₀ ₀

10 0 0 0 0 0 0.2 0 0 0.1 0.1

11 0 0 0 0 0 7.1 8.7 8.0 8.0 8.2

12 0 0 0 0 0 22.4 26.5∗ _25.5∗ _23.1 _22.7

Annual 12.6 15.3 16.6 19.7∗ _18.8∗ _94.1 _101.2 _103.6 _101.0 _100.2

∗_{The observed mean and the generated mean are significantly different at the 1% level.}

erated and recorded data and significant differences

were rare. We examined this through analyses of

out-puts of a chickpea crop model applied to generated

and actual data.

3.2. Sensivity test using a chickpea crop model

Under rainfed conditions of NW Iran insufficient

plant available soil water commonly overwrites any

effects of variation in temperature and radiation. To

eliminate this large influence of water availability on

crop growth and hence on sensivity test, the chickpea

crop model was run under irrigated (i.e. non-limiting

water for plant growth) and rainfed conditions to

eval-uate WGEN capability for generating temperature and

radiation, and rainfall, respectively.

In the chickpea model the effects of non-optimal

temperatures on plant growth is considered by

multiplying radiation use efficiency (RUE) by a scalar

factor that has a value of 1 between 15–24

◦

_{C of}

Table 5

W3 W5 W7 W10

Rainfall 0.018∗ _0.008 _0.015∗ _0.007

Solar radiation 0.014 0.011 0.010 0.014 Maximum temperature 0.026∗ _0.032∗ _0.025∗ _0.022∗

Minimum temperature 0.021∗ _0.027∗ _0.024∗ _0.019∗ ∗_{Significantly differing distributions from those of actual data.}

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average daily temperature but declines linearly to 0

at 0 and 39

◦

_{C. The effect of higher temperature on}

hastening crop phenology and leaf senescence is also

incorporated. As indicated in Section 3.1, there were

statistically significant differences between the

gen-erated maximum temperature, minimum temperature,

number of days with maximum temperature greater

than 35

◦

_{C and number of days with minimum}

tem-perature less than 0

◦

_{C and the recorded ones during}

April–August months which coincides with growing

season of chickpea. It should be answered to this

question whether these differences have significant

influences on plant growth. To do this, during model

simulations the number of days to maturity, number

of days with average temperature between 15 and

24

◦

_{C (optimal days), number of days with average}

temperature less than 15

◦

_{C (sub-optimal days) and}

number of days with average temperature greater than

24

◦

_{C (supra-optimal days) were saved and compared}

(Table 6). None of the base periods used had any

influence on the simulated number of days to

phys-iological maturity for all three sowing dates under

Table 6

Comparison of means of days to maturity (DTM), days with average temperature less than 15◦_{C (DLOPT), and days with average}

temperature between 15 and 24◦_{C (DOPT) and days with average temperature greater than 24}◦_{C (DUOPT) simulated for three sowing}

dates under irrigated and rainfed conditions using the chickpea model with actual weather data and weather data generated with WGEN

Irrigated Rainfed

DTM DLOPT DOPT DUOPT DTM DLOPT DOPT DUOPT

26 March

Actual 120.8 40.4 55.4 25.0 110.1 40.4 54.0 15.7

W3 119.8 40.6 53.4 25.8 109.6 40.6 52.0 17.0

W5 120.8 42.9 49.5∗ _28.4∗ _110.6 _42.9 _48.6∗ _19.1∗

W7 118.7 39.1 50.2∗ _29.5∗ _108.0 _39.1 _49.3∗ _19.6∗

W10 119.4 39.6 50.7 29.1∗ _108.6 _39.6 _49.6∗ _19.4∗

10 April

Actual 111.5 26.0 55.9 29.6 100.0 26.0 54.0 19.9

W3 110.9 26.1 53.5 31.3 99.9 26.1 52.2 21.5

W5 111.6 28.8 48.8∗ _33.9∗ _100.6 _28.8 _48.2∗ _23.6∗

W7 109.9 25.2 49.6∗ _35.1∗ _98.3 _25.2 _48.6∗ _24.5∗

W10 110.3 25.3 50.4∗ _34.6∗ _98.6 _25.3 _49.5 _23.8∗

25 April

Actual 104.1 13.9 55.0 35.2 91.6 13.9 52.1 25.6

W3 103.8 14.5 50.2∗ _39.1∗ _91.9 _14.5 _49.7 _27.6

W5 104.5 17.2∗ _46.0∗ _41.4∗ _92.8 _17.2 _45.3∗ _30.3∗

W7 103.0 14.4 46.1∗ _42.5∗ _90.5 _14.4 _45.1∗ _31.0∗

W10 103.0 13.9 47.8∗ _41.5∗ _90.6 _13.9 _46.4∗ _30.3∗

∗_{Statistically significant difference.}

irrigated and rainfed conditions. Significant

differ-ences were observed between the generated data

(W3–W10) and historic data for the number of

opti-mal and supra-optiopti-mal days. In this respect, the lowest

significant differences were observed for W3. Model

output using recorded data showed that with delay in

sowing date, percentage of sub-optimal and optimal

days decreased but percentage of supra-optimal days

increased. With the generated data of W3–W10 the

same response was observed. Note that for 25 April

planting date, each supra-optimal day has a more

negative effect, because average daily temperatures

are increased.

In NW Iran rainfed chickpea is sown early spring

and grows mainly on stored moisture which is

pro-gressively depleted with crop growth. The crop

expe-riences drought stress from late vegetative stage until

maturity. The intensity of drought stress varies from

year to year, depending on the amount and distribution

of rainfall and on spring and early summer

tempera-tures. This phenomenon was evaluated by examining

the days to beginning of terminal drought stress (from

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Table 7

DTB DL37 FDL37 CET

26 March

Actual 10.4 33.6 0.31 235

W3 10.9 33.2 0.30 257∗

W5 10.7 33.9 0.31 258∗

W7 10.0 34.1 0.32 233

W10 9.4 34.5 0.32 236

10 April

Actual 7.7 34.9 0.35 220

W3 8.5 33.7 0.34 232

W5 8.5 34.3 0.34 237∗

W7 7.6 34.8 0.35 214

W10 7.2 35.2 0.36 216

25 April

Actual 5.7 35.3 0.39 200

W3 6.6 35.0 0.38 201

W5 6.7 34.2 0.37 213

W7 5.3 35.6 0.39 195

W10 5.3 36.0 0.40 196

∗_{Statistically significant difference.}

flowering as an origin) (DTB), days with FTSW less

than 0.37 (DL37), ratio of DL37 to days to maturity

(FDL37) and crop evapotranspiration (CET) using the

generated and actual data inputted into the chickpea

model and the results are presented in Table 7.

Us-ing actual data, with delay in sowUs-ing date, terminal

drought stress begins earlier and the crop experiences a

greater number of days with FTSW less than 0.37 and

FDL37 is increased. Simulations using generated data

also showed the same response to sowing date, without

any significant difference. With respect to considerable

differences for winter rainfall between the generated

and actual data (Table 2) and its influence on the stored

soil water, the results of Table 6 seems questionable.

However, because of the limited soil depth of the

re-gion (100 cm, see Section 2.3) and its limited capacity

for water storage, the differences were masked due to

a higher water loss simulated using actual data. The

differences could be of greater significance in other

regions and climates or even in the same climate with

deeper soils (although they are not common).

Table 8 shows simulated irrigated and rainfed yields

at three sowing dates using actual and generated

weather data. With actual weather data, irrigated yield

declined about 60 kg ha

−1

(2% of 26 March yield)

for each 15-day delay in sowing date from 26 March.

The same response was observed in simulated yields

using W3–W10 weather data. At the two planting

dates, i.e. 26 March and 10 April, there was no

sig-nificant difference between irrigated yields obtained

using actual weather data and those obtained using

W3–W10 weather data (

p

=

0.01), with exception of

W3 at 26 March. However, at 25 April sowing date,

simulated yields were significantly different at a

prob-ability level of 0.05 from the simulated yields using

actual weather data, because the growing season is

moved into the summer months when the maximum

differences between number of supra-optimal days

using the generated and actual weather data occurred.

Ranges of irrigated yields are generally similar and

the

F

-test showed no significant difference between

variances of simulated yields using actual weather

data and W3–W10 weather data. The number of years

used to estimate the required parameters of WGEN

did not affect accuracy of the yield predictions.

Under rainfed conditions, simulated grain yield

using actual weather data decreased by about

120 kg ha

−1

_{(13% of 26 March yield) per each 15-day}

delay in sowing date (Table 8). Simulated yields

us-ing generated weather data also showed the same

response to sowing date. Means of yields obtained

using actual weather data and W3–W10 were similar

(with exception of W3 at 25 April). As compared to

irrigated conditions delayed sowing date did not

in-crease differences in yield, because water availability

is an overriding factor under rainfed conditions and

the differences between the generated and the historic

data were not significant in this respect (Table 7).

Yield ranges obtained from generated data were

sim-ilar to those obtained using actual data, and

F

-test

showed no significant difference between variances,

with exception of W7 sown on 26 March. Under

rainfed conditions, the number of data years used to

parameterize WGEN also did not affect the

conver-gence between simulated yield obtained using actual

weather data and generated data.

The CDFs of the yields under irrigated and

rain-fed conditions obtained using the actual data and

generated data indicate that using actual Tabriz

(4)

Table 8

Comparison of means and ranges of chickpea grain yield (t ha−1_{) simulated for three sowing dates under irrigated and rainfed conditions}

using the chickpea model with actual weather data and weather data generated with WGEN

Irrigated Rainfed

Mean Pr>ta Range Pr>F Mean Pr>tb Range Pr>F 26 March

Actual 2.61 – 2.31–2.87 – 0.91 – 0.52–1.32 –

W3 2.71 0.0038 2.26–3.21 0.3745 0.97 0.1182 0.62–1.44 0.0442

W5 2.65 0.1609 2.26–3.10 0.9286 0.98 0.0900 0.48–1.35 0.0459

W7 2.61 0.9190 2.23–2.90 0.8562 0.90 0.9392 0.54–1.39 0.0150

W10 2.68 0.0413 2.28–3.16 0.4300 0.90 0.9316 0.43–1.37 0.0782

10 April

Actual 2.56 – 2.30–2.90 – 0.81 – 0.51–1.15 –

W3 2.59 0.1776 2.18–2.98 0.9543 0.84 0.2034 0.53–1.41 0.0783

W5 2.57 0.6587 2.23–2.96 0.8688 0.86 0.0720 0.48–1.27 0.2098

W7 2.53 0.4105 2.14–2.83 0.6115 0.79 0.6132 0.49–1.23 0.0865

W10 2.58 0.4494 2.12–3.00 0.3496 0.78 0.4373 0.39–1.15 0.0618

25 April

Actual 2.49 – 2.21–2.81 – 0.67 – 0.47–0.92 –

W3 2.42 0.0413 1.98–2.78 0.8635 0.71 0.0877 0.45–1.18 0.7960

W5 2.41 0.0082 2.00–2.71 0.5906 0.74 0.0094 0.48–1.15 0.8020

W7 2.38 0.0019 2.04–2.74 0.8473 0.66 0.7783 0.40–0.99 0.6040

W10 2.41 0.0194 2.06–2.84 0.8490 0.65 0.5625 0.40–0.95 0.7743

a_Pr>_t_{: Probability that a significant}_t_{value would occur by chance.} b_Pr>_F_{: Probability that a significant}_F_{value would occur by chance.}

weather data, irrigated yields are between 2.34 and

2.75 t ha

−1

in 80% of years (data not shown). Same

quantities were 2.34–2.82, 2.32–2.74, 2.28–2.72, and

2.31–2.79 t ha

−1

for W3–W10, respectively. Grain

yield obtained using W7 weather data was greater

than yield obtained using actual data for most of the

years. Under rainfed conditions grain yields were

be-tween 0.55 and 1.11 t ha

−1

in 80% of the years. Same

quantities were 0.63–1.09, 0.65–1.10, 0.58–1.01, and

0.56–1.02 t ha

−1

for W3–W10, respectively. Median

of yields were 2.55, 2.57, 2.53, 2.52, and 2.57 t ha

−1

for actual data and W3–W10 under irrigated

condi-tions and 0.75, 0.82, 0.85, 0.78, and 0.77 for actual

data and W3–W10 under rainfed conditions,

respec-tively. Statistical comparison showed that with one

exception (W5 under rainfed conditions) all CDFs of

simulated yield using generated data did not differed

significantly from the simulated yield using recorded

data (Table 9).

Briefly, in comparison of grain yields simulated

using the generated solar radiation, maximum

tem-perature and minimum temtem-perature and actual ones

(i.e. irrigated yields), significant differences were

found in 50% of cases. Under rainfed conditions and

using generated rainfall in addition to generated

so-lar radiation, maximum temperature and minimum

temperature, only in one out of 12 cases was a

signif-icant difference (W5 for the 25 April planting date)

observed in the comparison of simulated grain yields

between W3–W10 against actual data. The uniformity

was not due to capability of WGEN to generating

weather data with adequate representation of actual

data. It was resulted because considerable differences

in rainfall amounts were masked by the limited soil

depth (typical of the region) chosen for simulations.

Table 9

W3 W5 W7 W10

Irrigated yield 0.129 0.049 0.123 0.119 Rainfed yield 0.163 0.210∗ _0.091 _0.081 ∗_{Significantly differing distributions from those of actual data.}

(5)

The longer base period used for parameter estimation

of WGEN did not result in lower differences.

4. Concluding remarks

Our results show that

1. The WGEN’s generated data are very similar

to the actual data used for parameter estimation

for all base periods used (3, 5, 7 and 10 years).

Therefore, WGEN assumptions are valid and the

model is capable of representation of many of the

characteristics that existed in the observed data.

However, 3–10 years are not enough to adequately

represent the historic data, because of the

devia-tions of means of base periods from the means of

the historic period.

2. In spite of similarity in trends and responses,

sim-ulated yields obtained using generated data were

significantly different from that obtained using

ac-tual data in 50 and 8% of cases under irrigated

and rainfed conditions, respectively. Under rainfed

conditions differences were masked by the limited

soil depth chosen for the simulations.

3. In many times it is better for the generated data to

represent recent history rather than a long time

pe-riod, particularly when the generated data are used

to assess the risk of current decisions or represent

future weather. In such cases, WGEN can be used

as a reliable source of weather data for estimating

crop yield.

Acknowledgements

The authors are thankful for the suggestions

pro-vided by the referees and the regional editor which

resulted in significant improvements.

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