Modelling olive flowering date using chilling for dormancy release and thermal time

Transcription

1 Agricultural and Forest Meteorology 125 (2004) Modelling olive flowering date using chilling for dormancy release and thermal time J. Paulo De Melo-Abreu a,, Diego Barranco b, António M. Cordeiro c, Joan Tous d, Bento M. Rogado e, Francisco J. Villalobos b,f a Depto de Ciencias do Ambiente, ISA, UTL, Apartado 3381, Lisbon, Portugal b University Córdoba, Apartado 3048, Córdoba, Spain c ENMP, Apartado 6, Elvas, Portugal d IRTA, Centre Mas Bové, Apartat 415, Reus (Tarragona), Spain e DRARO-Div. Olivic., C. Exp. Soidos, Abitureiras STR, Portugal f Instituto de Agricultura Sostenible, CSIC, Apartado 4084, Córdoba, Spain Received 23 July 2003; accepted 24 February 2004 Abstract Phase development of the olive tree is important for many purposes (e.g. adaptability, management, crop modelling). Many studies on the prediction of flowering used only data from one location and/or use a simple thermal time approach, what impairs the model ability to be used under different conditions. In this study, three models were evaluated and compared. Model 1 is a chill heating model, that is a generalisation and simplification of the Utah model, with a thermal time approach in the forcing phase; Model 2 has a below 7 C chill-hours model, followed by the same approach in the forcing phase; and Model 3 has no chilling description, and relies on a thermal time approach after 1 February. All models were calibrated using a data set of dates of flowering of five olive varieties that were grown in at least three locations, and the total chilling units accumulated (TU) until bud dormancy release, in the first two models, and the thermal time (TT) from this phase until flowering occurrence were determined. Validation followed on pooled data from 10 varieties grown in Cordoba, using the parameters from the calibration process (i.e. species level parameters) and the variety-specific TUs and TTs. The modelling efficiency was 0.92, 0.90 and 0.85, and the root mean square error of the predictions was 2.2, 2.5 and 2.8 days for Models 1 3, respectively. Although all three models depicted a good performance, Model 1 is more appropriate because it is physiologically meaningful. It should be preferred in all cases that the satisfaction of the chilling requirements of the species or variety is in doubt, and under different climate conditions. Three global warming scenarios A C (daily maximum minimum temperature increases of 1 3 C) were analysed, using the three models. All models and scenarios predict that there is a substantial advancement of the date of flowering. Only Models 1 and 2 show that the warmer scenarios indicate no normal flowering in some varieties/years. Models 1 and 2 further show the possibility that some compensation occurs in the warmer scenarios. Scenario A predicts that flowering is 10.0 and 9.3 days earlier than normal using Models 1 and 2, respectively. Scenario C shows that the advance of flowering for 1 C average temperature increase, in relation to Scenario B, is 7.4 and 5.2 days for Models 1 and 2, respectively. Model 1 and the algorithm that accompanies it might be useful to model the flowering occurrence of other woody species Elsevier B.V. All rights reserved. Keywords: Olive trees; Olea europaea; Chilling; Thermal time; Dormancy; Global warming Corresponding author. Tel.: ; fax: address: jpabreu@isa.utl.pt (J.P. De Melo-Abreu) /$ see front matter 2004 Elsevier B.V. All rights reserved. doi: /j.agrformet

2 118 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) Introduction Olive orchards are a key component of agricultural systems of the mediterranean basin with more than 5 Mha in the European Union. The area occupied by this crop in the Iberian Peninsula is 2.61 Mha. Olive trees are grown in orchards with planting densities ranging from less than 100 trees/ha (traditional rain fed systems) to more than 300 trees/ha (intensive drip-irrigated systems). Predicting phase development of olive trees is important for different purposes: (a) assessing the thermal adaptation to new environments with no previous olive growing experience (Australia, Argentina, etc.); (b) pest management, as the cycle and impact of pests is associated with phenological development of the tree; (c) yield models, since yield is affected by environmental conditions during flowering, and the partitioning of carbohydrates in the tree is dependent on the phenological stage; (d) crop management: use of agrochemicals (e.g. NAA for fruit thinning), irrigation management, planning of pruning and harvesting; (e) human health hazards: prediction of pollen concentration in the air. Photoperiod has no significant effect on inflorescence production, but low temperatures during winter are required (Hackett and Hartmann, 1964). Flower initiation takes place at the end of winter on the previous year s internodes, and release of floral bud dormancy occurs whenever trees have been exposed to a long enough period of chilling temperatures (Pinney and Polito, 1990; Rallo and Martin, 1991; Fernandez- Escobar et al., 1992). Before release of bud dormancy, reversal of this chilling effect might happen due to relatively warm temperatures. Badr and Hartmann (1972) and Rallo and Martin (1991) showed that winter chilling is associated with a change in the ratio between endogenous gibberellins and some growth inhibitors, such as abscissic acid. The chilling requirement of the olive explains the absence of flowering in warm winter areas, making them unsuitable for olive production. Incomplete chilling delays the release of floral bud dormancy and thus first flowering, and expands the flowering period (Barranco et al., 1994). Morphological development of the floral buds follows the low-temperature treatment that enables them to grow at higher temperatures (Hackett and Hartmann, 1964). After bud break, the time to flowering decreases as temperature increases (Alcalá and Barranco, 1992). Furthermore, high temperatures shorten the flowering period (Barranco et al., 1994). In most olive growing areas this phase occurs during the spring. The fraction of flowers setting fruit is very low (less than 5%) and in some varieties may be improved by cross-pollination (e.g. Lavee and Datt, 1978; Suarez et al., 1984). Previous studies on modelling of olive phenology have focussed mainly on local prediction of bud break (Orlandi et al., 2002), olive flowering (e.g. Alcalá and Barranco, 1992) and fruit maturity (Barranco et al., 1998). In the case of flowering, direct observations at tree level (Barranco et al., 1994) and indirect measurements (pollen concentrations; e.g. Minero and Candau, 1997; Galán et al., 2001) have been used. In all these studies, empirical models based on air temperature were calibrated for a single location. Thus the models may not be suitable for predicting under different environmental conditions (e.g. other locations, climate change). The objectives of this work are to develop, calibrate and validate a model to predict the occurrence of flowering of olive trees, and to compare its performance with two commonly used models, calibrated and validated with the same data. Effects of global warming on the timing of olive flowering are briefly analysed, taking Cordoba as an example. 2. Materials and methods 2.1. Experimental sites and data Flowering dates of 15 olive varieties were observed in Cordoba (latitude: N, longitude: 4 51 W, 91 m a.m.s.l.), Mas Bové, Reus (Tarragona) (latitude: N, longitude: 4 51 W, 120 m a.m.s.l.), in Spain, Elvas (latitude: N, longitude: 7 09 W, 200 m a.m.s.l.) and Santarém (latitude: N, longitude: 8 41 W, 60 m a.m.s.l.), in Portugal. Maximum minimum daily temperatures were available at all sites. Except for Mas Bové, that had an automatic weather station in the orchard, measurements were done at classical climate stations not further than 2 km from the experimental orchards and at the same elevation. The average annual precipitation, average monthly minimum and maximum

3 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) Table 1 Average annual precipitation (P), and monthly average maximum minimum (in parentheses) temperatures at the experimental sites, from October till May Location P (mm) Temperature ( C) October November December January February March April May Cordoba (12.1) 18.9 (7.6) 15.3 (5.2) 14.7 (3.7) 16.9 (4.9) 20.5 (6.4) 22.1 (8.6) 26.2 (11.8) Mas Bové (12.9) 16.4 (5.8) 13.8 (3.3) 14.4 (4.0) 16.4 (4.2) 19.3 (7.6) 19.7 (8.4) 22.9 (12.4) Elvas (11.2) 17.0 (6.9) 13.5 (4.3) 13.2 (4.0) 14.5 (4.8) 16.9 (6.2) 19.9 (7.7) 24.4 (10.6) Santarém (11.8) 17.8 (8.0) 14.8 (5.7) 14.4 (5.5) 15.5 (6.1) 17.7 (7.4) 20.3 (8.6) 23.5 (10.8) temperatures for these locations, from October to May, are shown in Table 1. The observations were carried out every 3 days, between 1975 and 2002, using the procedure outlined by Fernandez-Escobar and Rallo (1981). The first letters of the alphabet are assigned to the phenological stages after the winter rest. On each recording date, three observations were made on each selected tree (less and more advanced, and most frequent stage). The varieties used in this study, in decreasing order of earliness, are: Borriol de Castellón, Cañivano Negro, Lechin de Sevilla, Alameño de Montilla, Manzanilla de Sevilla, Picudo, Gordal, Ascolana Tenera, Lechin de Granada, Verdial de Vélez, Hojiblanca, Verdial de Huevar, Picual, Arbequina and Moraiolo Tommaso Corsini. All the varieties are of Spanish origin with the exception of Ascolana Tenera and Moraiolo which come from Italy. The average flowering date of the earliest variety ( Borriol de C. ), using our data set from Cordoba was on 2 May (DOY 122), and the latest variety ( Moraiolo T.C. ) flowered, on average, on 14 May (DOY 134). An account of the relative flowering time of 137 varieties of the world collection of olive varieties grown in Cordoba is presented in Barranco et al. (1994). Average flowering dates for selected cultivars at the four locations are shown in Table 2. The date of full flowering is considered when 50% of the flowers are open, and is often referred in this paper as the date of flowering Models, calibration and validation procedures Three models were considered in this study. (a) Model 1 theoretically, the end of dormancy (i.e. endodormancy) occurs after a given period of low temperatures has been accumulated. Thereafter, in the forcing phase, flowering occurs after a given thermal time is accumulated (Richardson et al., 1974; Hänninen, 1990). A model to simulate the hourly increment of chilling is proposed. It generalises commonly used models (Richardson et al., 1974; Gilreath and Buchanan, 1981; Shaltout and Unrath, 1983), using a piecewise approximation. The accumulation of chilling (U) by the tree during 1 h is, therefore, given by 0, T h 0 T h, 0 <T h T o U = T o 1 (T h T o ) 1 a, T o <T h T x T x T o a, T h >T x (1) where T h is the hourly air temperature ( C), T o is the optimum temperature for chilling ( C) and T x ( C) is the breakpoint temperature above which a constant number of accumulated chilling units (a) are nullified. This negative parameter, a, represents the maximum number of chilling units that are lost, for each hour of high temperature that follows a chilling accumulation period. The assumption that the origin is at 0 C is based upon the work of Orlandi et al. (2002). In the forcing phase, we used the thermal time approach (Monteith, 1977), where the base temperature is a model parameter, and is computed by the algorithm. Using the calibration dataset, an optimisation algorithm estimated the parameters T o, T x, and

4 120 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) Table 2 Average and range of flowering dates for calibration dataset (first 5 varieties) and the validation dataset (last 10 varieties) Variety Location Flowering date (day in May) Flowering date (day of year) Arbequina C E M S Ascolana Tenera C E S Hojiblanca C E S Lechin de Sevilla C E S Picual C E M S Alameño de M. C Borriol de C. C Cañivano N. C Gordal C Lechin de G. C Manzanilla de S. C Moraiolo T.C. C Picudo C Verdial de H. C Verdial de V. C The varieties were grown in Cordoba (C), Elvas (E), Mas Bové (M), and Santarém (S). Range of flowering dates (days) a, ineq. (1), the total chilling units (TU in U) until the end of dormancy, the base temperature (T b in C) and thermal time (TT in C days) from then to full flowering. It assumes that there are two processes in the chain: a process leading to dormancy release which is dependent on chilling accumulation; and a forcing phase that depends upon the accumulation of thermal time above the base temperature. This algorithm uses the downhill simplex method (Nelder and Mead, 1965) to find the minimum standard error in predicted flowering dates. In this modelling approach, daily maximum and minimum temperatures are input to the model, while hourly temperatures are generated from maximum minimum temperatures using the model developed by De Wit et al. (1978), which has been shown to perform well under Mediterranean conditions (De Melo-Abreu and Campbell, 1996). The daily course of air temperature is described by a sine wave for the period from sunrise to 1400 h and another sine wave for the period from 1400 h to sunrise on the next day. Chilling accumulation starts on 1 October, and all negative values of this accumulation are discarded. After enough chilling has accumulated, the accumulation of daily temperature of the forcing phase starts. (b) Model 2 When this model is used, if the hourly temperature is below 7 C an hour of chilling is accumulated, otherwise, there is no accumulation or reduction of the accumulated total (Weinberger, 1950). This model lacks a

5 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) mechanism to reduce the number of accumulated chilling units when high temperatures occur. The forcing phase and the algorithm are dealt with in the same way as was explained with Model 1. (c) Model 3 This model is based only on thermal time from 1 February. It uses only the part of the algorithm relevant to the forcing phase, and computes the base temperature ( C) and thermal time ( C days) from this date until full flowering. In the calibration, the parameters of the models where fitted using only the data on the five varieties common to at least three locations ( Arbequina, Ascolana, Hojiblanca, Lechin de S., Picual ). A variant of the algorithm was constructed to let the parameters of the three models (i.e. T b, T o, T x, and a) to be fitted to all data (i.e. meant to be valid at species level), while a nested fit took place to find the variety-specific TUs and TTs. The objective of this approach was to get as much information as possible for the calibration of the models, across locations and varieties, allowing the TUs and TTs to reflect the different responses of the varieties. Similar studies done on this and other species (e.g. peaches, apple rootstocks, almonds) use model parameters for the species and the TUs and/or TTs that may be variety-specific (Richardson et al., 1974; Young and Werner, 1985; Rattigan and Hill, 1986; Atkins and Morgan, 1990; Barba and De Melo-Abreu, 2002). These models were later validated on the remaining 10 varieties grown in Cordoba. After adaptation, the algorithm used the parameters fitted in the calibration process (assumed valid for the species) and calculates the TUs and/or TTs that are variety specific. The algorithm estimated the day of flowering and produced the statistics of the comparison between the modelled and measured values. These results are presented for the validation and comparison of the models. The performance measures for comparing model predictions and observations used in this study were: (1) the root mean square error (RMSE) of the estimates of the days of flowering and (2) the modelling efficiency, ME (Loague and Green, 1991; Janssen and Heuberger, 1995; Vanclay and Skovsgaard, 1997). The ME is a statistic analogous to R 2 that provides an index of performance on a relative scale, where one indicates a perfect fit, 0 means that the model is no better than a simple average, and negative values indicate a really poor model. 3. Results and discussion 3.1. Model calibration Using the algorithm on the five varieties common to all locations, the parameters of the models, TUs and TTs, and statistics were obtained. These results are shown in Table 3. The estimates of parameters related to the chilling process in Model 1 were T o = 7.3 C, T x = 20.7 C and a = 0.56 U (see Fig. 1). These values are in good agreement with the literature on chilling. Rallo and Martin (1991) reported that the best temperature for chilling (i.e. T o )is7.2 C. Denney et al. (1985) consider days of expected vernalization are those on which the maximum temperature is between 12.5 and 21.1 C and the minimum temperature is in the C range. Therefore, the hourly temperatures of such days would almost coincide with the interval of positive contributions to chilling accumulation. The value of T b in Model 1 is 9.1 C. T b values determined by others are between 10 and 13 C (Hackett and Hartmann, 1964; Alcalá and Barranco, 1992). Torreño (1993) performed a series of experiments for cold and heat accumulation of olive cv. Manzanillo. After rest completion no bud break was observed for temperatures of 5 and 8 C, while bud development did occur for a temperature of 9.2 C. This implies Chilling units a To Tx Temperature (ºC) Fig. 1. Proportion of a chilling unit in response to an hour at different air temperatures ( C), according to Model 1. 30

6 122 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) Table 3 Model parameters for the species, TUs and TTs, for five varieties, obtained by calibration of the three models Model number Variety N T o ( C) T x ( C) a (U) TU (U) T b ( C) TT ( C days) 1 Arbequina 4 (C), 5 (E), 3 (M), 2 (S) Arbequina Arbequina Ascolana T. 9 (C), 5 (E), 3 (S) Ascolana T Ascolana T Hojiblanca 9 (C), 5 (E), 2 (S) Hojiblanca Hojiblanca Lechin de S. 9 (C), 5 (E), 2 (S) Lechin de S Lechin de S Picual 9 (C), 5 (E), 3 (M), 3 (S) Picual Picual RMSE (days) Data are from at least three locations (Cordoba, C; Elvas, E; Mas Bové, M; Santarém, S). Number of observations (N) and root mean square error (RMSE) of the estimates of the days of flowering are also shown. T o ( C), the optimum temperature for chilling accumulation; T x ( C), the breakpoint temperature above which a constant number of accumulated chilling units (a) are nullified; U, the chilling unit according to the current model; TU, the total chilling units until the end of endodormancy; T b ( C) the base temperature for the thermal time calculation; and TT ( C days), the thermal time from the end of endodormancy to full flowering (Models 1 and 2) and after 1 February (Model 3). that the actual base temperature for bud development should be between 8 and 9.2 C, which is in agreement with our result. Experiments conducted by Badr and Hartmann (1971) showed that significant production of flowers occurs when trees are exposed to a constant temperature of 12.5 C. Model 1 would predict such a result, since an hour at a temperature of 12.5 C contributes to about 0.4 U for TU and is above the estimated T b. On the other hand, after bud dormancy release, a day with mean temperature above the estimated T b value would have, in general, an appreciable amount of time with temperatures above 12.5 C and, hence, flowers would form. There are no published values for the olive of parameters that could be related to parameter a. However, it is in general well known that species that have chilling requirements loose part of the chilling that they accumulated, when temperatures above a given threshold occur (e.g. Richardson et al., 1974; Gilreath and Buchanan, 1981). Table 3 shows that TUs change more with variety than do the TTs. Model 2 has a T b (8.8 C) that is 0.3 C lower than that obtained with Model 1. Table 3 shows that TUs change more with variety than do the TTs, as with Model 1. The value of T b in Model 3 is 8.5 C, which is only slightly lower than the corresponding values obtained with the other models. All models had a low root mean square error (RMSE) of the estimates of the day of flowering within varieties ( days). When all varieties were pooled (83 observations), the ME was 0.74, 0.67 and 0.57, for Models 1 3, respectively. The RMSE of the estimates were 3.13, 3.98 and 3.65 days, for Models 1 3, respectively. The results above show that it was possible to calibrate all models so that the predictions closely approximated the data. Model 1 yielded the best estimates of the day of flowering, after calibration, but all the models exhibited rather small values of RMSE. This may seem as a logical outcome of the calibration process with a large number of parameters; but is nevertheless a strong indication that all these models are good estimators of the time of occurrence of flowering. Note that most of these parameters modulate the response to hourly and daily temperature in the chilling and

7 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) Table 4 TUs and TTs for 10 varieties grown in Cordoba, with model parameters for the species Model number Variety TU (U) TT ( C days) RMSE (days) Variety TU (U) TT ( C days) 1 Alameño de M Manzanilla de S Alameño de M Manzanilla de S Alameño de M Manzanilla de S Borriol de C Moraiolo T. C Borriol de C Moraiolo T. C Borriol de C Moraiolo T. C Cañivano N Picudo Cañivano N Picudo Cañivano N Picudo Gordal Verdial de H Gordal Verdial de H Gordal Verdial de H Lechin de G Verdial de V Lechin de G Verdial de V Lechin de G Verdial de V RMSE (days) The number of observations for each variety was nine (except for variety Alameño de M. that only had five) observations. The RMSE of the estimates of the days of flowering is also shown. U, the chilling unit according to the current model; TU, the total chilling units until the end of endodormancy; TT ( C days), the thermal time from the end of endodormancy to full flowering (Models 1 and 2) and after 1 February (Model 3). RMSE is the root mean square error. thermal time accumulation processes, and have very high sensitivity Model validation Data from Cordoba on the flowering dates of 10 varieties, that were not used in the calibration (see Section 2), were analysed using the algorithm. Notice that among these varieties there is the earliest ( Borriol de C. ) and one of the latest flowering ( Moraiolo T.C. ) varieties of those included in the world collection in Cordoba. The parameters for the species, obtained before (Table 3), were used. Only the TUs and TTs were considered variety-specific and fitted by the algorithm. The results of the analysis are shown in Table 4. Table 4 shows that both Models 1 and 2 led to TUs that varied more than the corresponding TTs, which was also observed in the calibration process. In general, the earlier a variety is the lower is its TT. Nevertheless, the closest match between earliness and TT occurs with Model 1. In contrast, the impact of TUs in the earliness of the variety is smaller, since when TUs get to be very low their effect on the date of flowering, in some cold winters or springs, may be insignificant. This may happen when the cold requirement of the variety is satisfied early, but mean daily air temperatures are still below T b for some days. Table 4 shows that all models have a good performance. The plots of predicted on observed days of full flowering for the threes models are shown in Fig. 2. The ME was 0.92, 0.90 and 0.85 for Models 1 3, respectively. The RMSE of the estimates of the DOY of flowering was 2.22, 2.51 and 2.78 days for Models 1 3, respectively. Statistically, Models 1 and 2 perform better and are not significantly different. However, as Table 3 shows Model 2 was the worst, on RMSE basis, when data from at least three locations was used. The comparison amongst the models cannot rely only on their predictive capability, but physiological meaning must be preserved as well. Olive trees do not flower if they are not subjected to some amount of chilling (e.g. Hackett and Hartmann, 1964). There are interesting examples that unequivocally demonstrate that neither Model 2 nor 3 are valid for general use. In the southwest of Angola, a few kilometres from the coastal city of Namibe, there have been productive olive orchards for decades (Portas et al., 1974). The average daily maximum minimum temperatures of the coldest month (July) are 19.8 and 12.8 C, but

8 124 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) minimum temperature only dropped below 7 C a few times in 30 years of records. Less than a couple of hundred kilometres northeast of these orchards, at an altitude of 1760 m, olive trees do not flower, although minimum temperatures of the coldest month are much lower: the average minimum temperature of June is 7.9 C. However, the average maximum temperature in the same month is 23.6 C. The explanation for the fact that enough chilling accumulates in the coastal orchards of Namibe is that hourly temperatures in winter are seldom above the threshold that causes reversal of the accumulated chilling, and there is a small but constant contribution to vernalization, because the hourly temperatures are usually in the range between T o and T x. In contrast, in the inland location, although there is some chilling accumulation during some hours of the night, the higher temperatures that occur around the time of maximum temperature may contribute to the reversal of accumulated chilling. Due to the fact that the 10 varieties grown in Cordoba include a few early varieties and some years had some warm winters, about the same parameters for Model 1 could be found using these varieties for calibration. The Model 1 parameters, using this alternative calibration procedure, are T o = 7.30 C, T x = C, a = 0.50 U, and T b = 8.75 C, with a RMSE of all estimates of 2.35 days. Both Models 2 and 3 may be applied in the location were they were developed, if chilling requirements are known to be satisfied. Some authors have used pure thermal time models for predicting peak olive pollen concentrations (Minero and Candau, 1997; Fornaciari et al., 1998). When this type of models is used for rising temperatures (expected global warming) they predict an earlier pollen release (as expected), leading to the conclusion that pollen emission could be a reliable indicator of global warming (Osborne et al., 2000). However, in trees with chilling requirements and warm locations this should not necessarily be the case. It depends on the temperature increase and its Fig. 2. Validation of the models on a data set containing 10 varieties grown in Cordoba (( ) Alameño de M. ; ( ) Borriol de C. ; ( ) Cañivano N. ; ( ) Gordal ; ( ) Lechin de G. ; ( ) Manzanilla de S. ; (+) Moraiolo T.C. ; ( ) Picudo ; ( ) Verdial de H. ; ( ) Verdial de V. ). Predicted vs. observed plots in relation to the 1:1 line: (a) Model 1; (b) Model 2; (c) Model 3. DOY is the day of the year (1 = 1 January) : (a) Observed flowering (DOY) 150 Predicted flowering (DOY). Predicted flowering (DOY) : (b) Observed flowering (DOY) 150 Predicted flowering (DOY) : (c) Observed flowering (DOY)

9 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) Table 5 Simulated average flowering date advance for the crops that flowered, and number of varieties and crops that fail to flower, in relation to model and scenario Model Scenario Average flowering date advance (days) S.D. for the date advance (days) Number varieties not flowering at least once 1 A B C A B C A B C Number crops without flowering distribution (warmer winter, warmer spring or both) whether the reduced chilling will imply a later end of the dormancy, and thus, a potential delay of flowering. Model 1 should be preferred to predict flowering under other environments, namely studies of cultural adaptation, response to climate change and variety selection. Another reason for choosing a chilling heating model is the contribution to improving our understanding of olive productivity Model predictions under climatic warming conditions To better understand the different consequences of the application of the three models in climatic warming scenarios, we analysed the effects of three simple scenarios on the dates of flowering in Cordoba, using the 10 varieties and the years of weather data used in the validation procedure. Scenarios A C were defined as an increase of both daily maximum minimum temperature in 1, 2 and 3 C, respectively. The model outputs for such scenarios are shown in Table 5. Scenario A yields an advance of flowering between 8.9 and 10.0 days, and no varieties stop flowering for any model. Model 3 predicts that for each C of temperature increase flowering advances about 8.5 days, and no varieties stop flowering. Models 1 and 2 show that, in some warmer years, some varieties that require more chilling stop flowering in Scenario B and, even more so, in Scenario C. In this last case, almost all varieties would have years without normal flowering. The advance of the date of flowering, in the varieties/years that flowering occurs, is not proportional to the temperature increase, with Models 1 and 2. Scenario A predicts that flowering is 10.0 and 9.3 days earlier than normal for Models 1 and 2, respectively. Scenario C shows that the advance of flowering for 1 C average temperature increase, in relation to Scenario B, is 7.4 and 5.2 days for Models 1 and 2, respectively. However, inspection of the individual data reveals that some varieties in some years have delayed flowering dates for a 1 C mean temperature rise, according to Models 1 and Conclusions Model 1 is more appropriate to predict the time of flowering in the olive. Although its statistical superiority is marginal, it is the only model considered in this study that is physiologically meaningful. The parameters given by the algorithm match closely almost all studies reported in the literature. It should be preferred whenever predictions are needed under environments that are different from the ones used in this study, namely for adaptation of the species to new areas, variety selection, and analysis of global change scenarios. The model of chill-hours below 7 C (Model 2) and the model of thermal time after 1 February (Model 3) are also good predictors of the time of flowering, but should not be used outside the areas where they were calibrated and/or in warm areas, when there is the possibility that some varieties do not reach the satisfaction of the chilling requirements, approximately, before the end of January.

10 126 J.P. De Melo-Abreu et al. / Agricultural and Forest Meteorology 125 (2004) Global warming may result in considerable losses of olive production in Cordoba and similar areas and/or force the replacement of some varieties that have high chilling requirements. For application to other species, the algorithm presented in this paper, using Model 1, can be calibrated on a relative small number of observations on some varieties and/or on a few locations, and validated using a comparable amount of data from other varieties and/or locations. It is, however, necessary that some of the varieties satisfy their chilling requirements, at least in some years or in some location, after the forcing temperatures start to occur. Otherwise, the algorithm is not able to find meaningful and robust parameters for the model. Acknowledgements The authors are grateful to our colleagues Paula Martins (ENMP Elvas) and Helena Matias (DRARO Santarém) for useful discussion and technical assistance, and Agustí Romero (IRTA-Mas Bové, Reus) for weather data collection and analysis. This work was partly funded by grant CAO-002 (Consejeria de Agricultura y Pesca, Junta de Andalucia, Spain). References Alcalá, A.R., Barranco, D., Prediction of flowering time in olive for the Cordoba Olive Collection. HortScience 27, Atkins, T.A., Morgan, E.R., Modelling the effects of possible climate change scenarios on the phenology of New Zealand fruit crops. Acta Hort. 276, Badr, S.A., Hartmann, H.T., Effect of diurnally fluctuating vs. constant temperatures on flower induction and sex expression in the olive (Olea europaea L.). Physiol. Plant. 24, Badr, S.A., Hartmann, H.T., Flowering response of the olive (Olea europaea L.) to certain growth regulators applied under inductive and non-inductive environments. Bot. Gaz. 133, Barba, N.W., De Melo-Abreu, J.P., Validation of rest completion models in peach trees in two regions of Portugal. Acta Hort. 592, Barranco, D., de Toro, C., Rallo, L., Epocas de maduracion de cultivares de olivo en Cordoba. Invest. Agraria Prod. Prot. Veg. 13, Barranco, D., Milona, G., Rallo, L., Epocas de floracion de cultivares de olivo en Cordoba. Invest. Agraria Prod. Prot. Veg. 9, De Melo-Abreu, J.P., Campbell, G.S., Simulation of weather variables. Inst. Sup. Agron. (Lisbon) 45, De Wit, C.T., Goudriaan, J., Van Laar, H.H., Simulation of Assimilation, Respiration and Transpiration of Crops. Pudoc, Wageningen, The Netherlands. Denney, J.O., McEachern, G.R., Griffiths, J.F., Modeling the thermal adaptability of the olive (Olea europaea L.) in Texas. Agric. Meteorol. 35, Fernandez-Escobar, R., Benlloch, M., Navarro, C., Martin, G.C., The time of floral induction in the olive. J. Am. Soc. Hort. Sci. 117, Fernandez-Escobar, R., Rallo, L., Influencia de la polinización cruzada en el cuajado de frutos de cultivares de olivo (Olea europaea L.). ITEA 45, Fornaciari, M., Pieroni, L., Ciuchi, P., Romano, B., A regression model for the start of the pollen season in Olea europaea. Grana 37, Galán, C., García-Mozo, H., Cariñanos, P., Alcázar, P., Domínguez- Vilches, E., The role of temperature in the onset of the Olea europaea L. Pollen season in southwestern Spain. Int. J. Biometeorol. 45, Gilreath, P.R., Buchanan, D.W., Rest prediction model for low-chilling sungold nectarine. J. Am. Soc. Hort. Sci. 106, Hackett, W.P., Hartmann, H.T., Inflorescence formation in olive as influenced by low temperature, photoperiod, and leaf area. Bot. Gaz. 125, Hänninen, H., Modelling bud dormancy release in trees from cool and temperate regions. Acta For. Fenn. 213, Janssen, P.H.M., Heuberger, P.S.C., Calibration of processoriented models. Ecol. Model. 83, Lavee, S., Datt, Z., The necessity of cross-pollination for fruit set of Manzanillo olives. J. Hort. Sci. 53, Loague, K., Green, R.E., Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol. 7, Minero, J.G., Candau, P., Olea europaea airborn pollen in southern Spain. Ann. Allergy Asthma Immunol. 78, Monteith, J.L., Climate and the efficiency of crop production in Britain. Phil. Trans. R. Soc. (London) 281, Nelder, J.A., Mead, R., A simplex method for function minimization. Comput. J. 7, Orlandi, F., Fornaciari, M., Romano, B., The use of phenological data to calculate chilling units in Olea europaea L. in relation to the onset of reproduction. Int. J. Biometeorol. 46, 2 8. Osborne, C.P., Chuine, I., Viner, D., Woodward, F.I., Olive phenology as a sensitive indicator of future climatic warming in the Mediterranean. Plant Cell Environ. 23, Pinney, K., Polito, V.S., Flower initiation in Manzanillo olive. Acta Hort. 286, Portas, C.A., Araújo, J.A., Fernandes, A.R., Notas sobre o olival de Moçamedes. IAA Nota Técnica 39, 1 23.

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