Discrepancies between observed and predicted growth stages in wheat

Transcription

1 Journal of Agricultural Science, Cambridge (1997), 129, Cambridge University Press Printed in the United Kingdom 379 Discrepancies between observed and predicted growth stages in wheat E. J. M. KIRBY AND R. M. WEIGHTMAN * The Studio, Blythburgh Road, Westleton, Saxmundham IP17 3AS, UK ADAS Arable Research Centre, Anstey Hall, Maris Lane, Cambridge CB2 2LF, UK (Revised MS received 2 April 1997) SUMMARY A model to predict wheat growth stage is briefly described. It is based on prediction of the number of emerged leaves and the final number of leaves on the main shoot, and the co-ordination between leaf emergence and apex development, including stem elongation. The input variables are daily maximum and minimum temperatures, date of sowing and site latitude, from which thermal time, vernalization and daylength are calculated. Selected growth stages were predicted for six sites in each of three growing seasons. The differences between observations made by independent observers and predictions were mostly 7 days or less but in three site season combinations the average difference was 1 days. Observer errors were implicated and examined, but it is concluded that the prediction scheme must also have been partly responsible for the discrepancies. INTRODUCTION Modern management techniques for wheat crops depend on the application of agrochemicals and nutrients at particular stages of plant development. Development stages involve changes in, for example, the form of the apex or the stem. They are usually recognised in terms of characters such as number of emerged leaves or detectable nodes which can be seen easily; generally they do not involve the intricate dissections necessary for the inspection of shoot apices. These external stages are referred to as growth stages and have been summarized in codes such as that described by Zadoks et al. (1974). Recognition of growth stages by untrained field personnel is subject to inaccuracy and uncertainty, notwithstanding attempts to improve their definition (Tottman et al. 1985; Tottman 1987). Thus there may be advantages in developing a more systematic approach to the determination of growth stages on farms. Rate of development and the time at which growth stages occur depend on environmental factors, particularly temperature and daylength. Therefore management factors such as date of sowing, site factors such as latitude and elevation and seasonal differences in weather will modify the timing of growth stages. Management techniques based on crop growth stages * Present address: Dalgety Food Technology Centre, Station Road, Cambridge CB1 2JN, UK. need to allow for these effects, either by inspection or by predicting the specified stages. Development of the main shoot apex and leaf emergence are closely co-ordinated. For example, the double ridge stage occurs when a predetermined number of leaves have emerged, depending on the total number of leaves on the shoot (Kirby 199). Elongation of internodes on the culm is synchronized with leaf elongation (emergence) and can be defined in a simple function with parameters for the number of emerged leaves and number of internodes destined to elongate (Kirby et al. 1994). The functions which measure development in terms of leaf number and emergence, combined with functions which describe the response of leaves to the environment, have been used to model wheat development and predict the growth stages directly applicable to crop management (Kirby 1994). These functions offer a basis for predicting the occurrence of growth stages field by field, and for obviating the need for field inspections. However, before predictions can replace inspections, it is necessary to consider the accuracy, precision and practicalities associated with the two different approaches. In this paper we take data from an experiment where four or more personnel worked independently (but to a common protocol) at six sites dispersed through the UK over three successive seasons, observing leaf emergence and growth stages; we compare their observations with model predictions based on weather data taken from adjacent meteoro-

2 38 E. J. M. KIRBY AND R. M. WEIGHTMAN logical stations. In a subsequent paper we compare predicted and observed estimates of leaf emergence and other characters on which the model is based (Weightman et al. 1997). MATERIALS AND METHODS Sites and agronomy Winter wheat (cv. Mercia) was sown at six sites in England and Scotland (Table 1) in 1992, 1993 and 1994, giving a total of 18 site season combinations. The objective was to monitor plant development and yield in crops managed to achieve full expression of yield potential by removing all limiting factors except water stress (no crop was irrigated). Sowing was planned to take place in the last week of September or the first 2 weeks of October, but was delayed in 1993 by the wet conditions, particularly at SB and ED (2 November) (see Table 1 for site codes). The experimental plots were sited within a commercial crop. Most crops were grown after a non-cereal crop; the exceptions were SB in , HA in and ED in which followed winter oats, winter oats and winter barley, respectively. This reduced the risk of take-all (Gaeumannomyces graminis) although HA, suffered an infestation after a previous crop of winter beans. There were either five ( ) or three ( and ) replicates. All seed was dressed with the same chemical (guazatine), which was purchased locally and thus might be a source of site-to-site variation. Seed rate was determined at each site to aim for a target plant population of 275 plants m in the spring. Plot size was 24 m long and between 3 and 4 m wide, depending on site. Nitrogen fertilizer was applied such that the sum of nitrate and ammonium N from fertilizer and from soil to 9 cm depth, measured in spring, was c. 3 kg ha, sufficient not to restrict grain yield but adequate to avoid lodging. A prophylactic fungicide programme was used with applications at stem extension, flag leaf emergence and ear emergence. All crops were treated with plant growth regulators and no lodging occurred. Measurements Measurements of growth stage began as near to 15 February as possible, with recordings at 4, 6 and 8 weeks after the first measurement and weekly thereafter. A common set of guidelines, specifying sampling details and growth stage definitions were followed at all sites, but different people made the observations at each site. In and more frequent measurements were made of ear half emerged (growth stage (GS) 55) and beginning of anthesis (GS61). Prior to GS55, 1 shoots were marked in each replicate and Table 1. Details of sites Site Code Latitude ( N) ADAS Boxworth, Cambridgeshire BW 52 2 University of Edinburgh, Bush Estate ED 55 8 ADAS Gleadthorpe, Nottinghamshire GL 53 2 Harper Adams Agricultural College, HA 52 8 Shropshire ADAS Rosemaund, Hereford RM 52 1 University of Nottingham, Sutton SB 52 8 Bonington, Leicestershire Sowing dates are given in Table 1, Weightman et al. (1997). plots were visited every 2 or 3 days. As each shoot reached the relevant growth stage it was tagged with wire and the date when 5% of shoots had achieved the growth stage was noted. Daily maximum and minimum temperatures were recorded at a meteorological station within 1 km of the sites. DESCRIPTION OF THE GROWTH STAGE MODEL The model to predict plant development examined in this paper was described in detail by Kirby (1994). Predictions are based on functions involving number of emerged and final number of leaves. In this paper, some growth stages important for management techniques were selected, but other growth stages and shoot apex stage can also be predicted. The rate of leaf emergence (L) was calculated by the function of Baker et al. (198; corrected version): L a br (1) where the coefficients a and b depend on variety. For Mercia, a was set to 19 and b to 26 (Kirby 1994). The rate of change of daylength at seedling emergence (R) was calculated using the photoperiod function described by Keisling (1962). Final number of leaves ( f ) was calculated from Kirby (1992) : f α βv γd (2) where α, β and γ are variety coefficients, set to 9 3, 64 and 88, respectively, for Mercia. V is accumulated thermal time from sowing to full vernalization and D is the daylength at the time of full vernalization (Kirby 1992). Full vernalization was defined as the time when plants had experienced 5 vernalization days. Vernalization was estimated on a daily basis using a function which assumed that the temperatures between 5 and 8 C were fully effective and that temperatures 5 and 12 C were ineffective. The contribution of temperatures between 5 and 5 and between 8 and 12 C were estimated by linear interpolation (Kirby 1992).

3 Observed and predicted growth stages in wheat 381 Growth stages 3 and 32 were estimated by the function (Kirby et al. 1994): GS c (3) where c is number of emerged leaves on the culm (elongated stem). It is derived from: c e ( f n) (4) where e is number of emerged leaves, f is the final number of leaves and n is the number of culm internodes (internodes 1 mm). This was set to 5. The number of emerged leaves (e) was estimated at any thermal time (T) from: e (T s) L (5) where the symbols L and s have the same meanings as in Eqn (6) below, and T is the accumulated thermal time from sowing, base temperature Cd. The thermal time of GS39 was calculated by: GS39 f L s (6) where f is final number of leaves, L is the rate of leaf emergence (leaves ( Cd) ) and s is the thermal time from sowing at the start of leaf emergence. The value of s was set to 18 Cd (Kirby 1994). Thermal time (T) of ear emergence (GS55) and anthesis (GS61) were estimated by: T (f p) L s (7) where f and s have the same meanings as above and p is the interval from flag leaf fully emerged to ear emergence or anthesis, expressed in phyllochrons (phyllochron 1 L). The interval, p, was set at 2 and 3 for ear emergence and anthesis, respectively (Kirby 1988). For this paper, the above functions were programmed in VISUAL BASIC. Inputs were sowing date and latitude, together with a file of daily maximum and minimum temperatures. Thermal time was calculated using the technique of Meteorological Office Form 33 (Kirby 1994) as follows: Tt (T max ) 2 T b T max & T min Tt (T max ) 2 (T b ) 4 T max, T min, (T max ) 2 Tt (T max T b ) 4 T max, T min, (T max ) 2 where Tt is thermal time, T max and T min are maximum and minimum temperature respectively and T b is base temperature ( C). Using a standard 486 personal computer, the program completed predictions for a site season instance in 5 sec, thus making it suitable for on-farm use, where rapid prediction for several different fields is essential. Predicted growth stages Five growth stages (Tottman 1987)) were selected for prediction, as follows: Growth stage Description 3 Ear at 1 cm 32 Second node detectable 39 Flag leaf ligule visible 55 Half of ear emerged 61 Beginning of anthesis RESULTS Growth stage prediction and observation The dates on which the selected growth stages were observed are shown in Table 2. The average root mean square of the differences (R.M.S.D.; Wallach & Goffinet 1991) was 8 and that for each of the seasons was similar (range ; Table 2. Dates of observation of growth stages. Entries in italic are interpolation where the stage in question occurred between two consecutive samples. Bold entries are where a weekly sample was missed and the stage probably occurred at that sample BW* ED GL HA RM SB GS3 16 Apr 2 Apr 24 Apr 7 Apr 6 Apr N.R. GS32 27 Apr 18 May 5 May 26 Apr 26 Apr 26 Apr GS39 13 May 3 Jun 25 May 17 May 17 May 24 May GS55 2 Jun 22 Jun 2 Jun 5 Jun 4 Jun 4 Jun GS61 4 Jun 2 Jul 16 Jun N.R. N.R. 9 Jun GS3 26 Apr 17 May 18 Apr 26 Apr 25 Apr 18 Apr GS32 3 May 3 May 3 May 1 May 9 May 3 May GS39 23 May 13 Jun 23 May 26 May 27 May 23 May GS55 7 Jun 27 Jun 11 Jun 14 Jun 9 Jun 1 Jun GS61 16 Jun 4 Jul 2 Jun 23 Jun 16 Jun 19 Jun GS3 27 Mar 24 Apr 27 Mar 1 Apr 13 Mar 29 Mar GS32 24 Apr 8 May 1 May 1 May 24 Apr 1 May GS39 15 May 29 May 22 May 15 May 15 May 19 May GS55 27 May 19 Jun 3 Jun 6 Jun 2 Jun 5 Jun GS61 7 Jun 22 Jun 19 Jun N.R. 8 Jun 14 Jun N.R. indicates that there was no record for a stage. * For site codes, see Table 1.

4 382 E. J. M. KIRBY AND R. M. WEIGHTMAN Table 3. Difference between prediction and observation of growth stages (observed predicted). The mean difference (Mean diff.), the mean absolute difference (Mean abs. diff.) and the root mean square of the difference (R.M.S.D.) are shown Mean Mean BW* ED GL HA RM SB diff. abs. diff. R.M.S.D GS N.R. GS GS GS GS N.R. N.R GS GS GS GS GS GS GS GS GS GS N.R Mean diff Mean abs. diff R.M.S.D N.R. indicates that there was no record for a stage. * For site codes, see Table 1. Table 3). The size of the R.M.S.D. for sites varied from 5 3 for ED to 1 for HA and was 7 days for BW, ED and GL. Particularly large differences were found for SB in and , HA and RM The mean difference was positive for all sites except ED and generally not much different from the R.M.S.D., indicating that stages were generally predicted earlier than they were observed. In some cases there was a large difference interposed among smaller ones (e.g. ED, , where the differences were 3, 13, 2, 1, and 6 for stages 3, 32, 39, 55 and 61, respectively; Table 3). To try and determine the reasons for the variation in differences between observation and prediction, comparisons between sites within one season were made. In the plots at BW and SB were sown on the same day. While the predictions for the former were mostly within 7 days of observations, the differences between observation and prediction for the latter were mostly 1 days (Table 3). When the accumulated thermal times for these sites were compared, it was found that the maximum difference between sites on any one day during the period from sowing to 3 June was 21 Cd (Fig. 1). The average difference in the daily mean temperature was 3 C. The difference in latitude was c. 5 of arc so that, if the crops emerged at the same time, the rate of change of daylength would be similar. Therefore the maximum difference in the date on which stages would be expected to occur would probably be 2 3 days depending on the temperature prevailing at the date when a stage occurred. The differences, however, varied from 2 to 9 days (2, 8, 4, 9, 7 days for growth stages 3, 32, 39, 55 and 61, respectively). In BW was sown 6 days earlier than SB and the former had accumulated 7 Cd during that time. Until 24 January the daily mean temperatures were similar (Fig. 2a), maintaining the thermal time differential. Thereafter the daily mean temperature at SB was c. 1 C higher than at BW so that the difference in accumulated thermal time at the end of March had declined to c. 3 Cd (Fig. 2). Depending on daily temperature, this might be expected to give a difference in the date of a stage of 3 4 days, rather than the differences of up to 11 days which were recorded. For example, on 13 May (GS39 at BW) the accumulated thermal times were 159 and 1481 Cd at BW and SB, respectively. The mean temperature

5 Observed and predicted growth stages in wheat 383 Accumulated thermal time (ºCd) Difference (a) 15 (b) 25 1 Oct Dec 94 1 Apr 95 1 Jul 95 Fig. 1. (a) Accumulated thermal time for sowing for Boxworth (-----) and Sutton Bonington ( ), and (b) Thermal time difference between the two sites (BW SB). Accumulated thermal time (ºCd) Difference (a) (b) 1 Oct Dec 92 1 Apr 93 1 Jul 93 Fig. 2. (a) Accumulated thermal time for sowing for Boxworth (-----) and Sutton Bonington ( ), and (b) Thermal time difference between the two sites (BW SB). was c. 1 C, indicative of a 3-day difference, compared with the observed difference of 11 days. DISCUSSION Overall, of the 86 separate growth stage predictions (measured as days from sowing) reported here, 53 were within 4% and 3 within 2% of the observed number of days from sowing. The R.M.S.D. of all differences between observed and predicted growth stage was 8 and the average difference was 4 8 days (Table 2). This difference was similar among seasons but there was considerable variation amongst sites. ED, with a R.M.S.D.of5 27 and a maximum difference of 13 days, showed the best agreement between model prediction and observation. HA had the greatest R.M.S.D. and considerable variation from stage to stage with a maximum difference of 3 weeks between observation and prediction. There may have been local physical factors such as field exposure or soil characteristics which affected development and which were not reflected in the meteorological data, but these were not obvious, and in general such factors have not been found to have such large influences on development. It must be concluded that operator error probably accounted for some of the differences; field personnel were largely the same across seasons but differed across sites. This conclusion is supported by two (SB, and SB, ) of the four cases (including HA, and RM, ) where it was possible to examine the large differences between observation and prediction further. In and in the sowing dates at SB and BW were the same or nearly so; mean daily temperature differed only by a fraction of a degree Celsius and, because the site latitudes were about one half of a degree of arc different, the daylength at any time was almost the same. Leaf emergence was also similar at both sites (Weightman et al. 1997). In these circumstances, the difference between observation and prediction at the two sites would be expected to be similar. The differences in the date at which growth stages were observed, up to 9 days in and 11 days in , were not explicable in terms of differences in the major environmental factors. It is important to identify the aspects of operator performance that are responsible for these differences as closely as possible. Growth stage was sampled weekly and therefore differences of up to 7 days could arise even if the dates of stages were predicted exactly and recorded accurately. Also the plant sampling strategy will not have fully accommodated plant-toplant variation in the field. This might imply that differences of up to 14 days (two sampling intervals) were not detected and that to achieve an estimate of the time of a stage change to the nearest week more

6 384 E. J. M. KIRBY AND R. M. WEIGHTMAN frequent sampling would be necessary. However, differences were often 7 days and they tended to be consistent in sign for any particular site season combination, so it seems unlikely that either frequency or precision of sampling was the only factor involved. There are further possible sources of error in the identification of the predicted stages. For example, the peculiarities in the way that basal internodes extend, which were addressed by Tottman (1987), may not be correctly recognised when defining growth stages 3 and 32, and the definition of stage 39 should apply to main shoots only, whereas it may have been recorded later by being based upon all shoots. Given that the difficulties in comprehensively, accurately and precisely monitoring growth stages of several crops on a commercial farm are considerably greater than were encountered using local trained staff in this exercise, the prospect of adopting a predictive scheme, given that it can be proven reliable, appears relatively attractive. However, it seems unlikely that observer errors would have fully accounted for predictions being generally earlier than observations, and the differences being consistent within site season cases. There must therefore be a consideration of inaccuracies in the model itself. This is described in a subsequent paper with particular reference to leaf emergence, final number of leaves and detectable nodes (Weightman et al. 1997). The model described in this paper generally predicted some significant growth stages to within about a week of when they were observed. Allowing for such considerations as variation within the crop, this is probably adequate for stage-based management operations. However, differences between observations and predictions varied amongst sites. The source of such differences can partly be attributed to observer error, but possible inaccuracies in the prediction scheme require further study. We are grateful to R. Sylvester-Bradley, R. W. Clare and R. K. Scott who conceived and designed the experiment, for the help of A. M. Blair (BW), G. Russell (ED), A. R. Mills (GL), P. S. Kettlewell (HA), J. H. Spink (RM) and D. T. Stokes (SB), who managed the plots and organized data collection. P. S. Kettlewell also reviewed the manuscript and provided helpful comments. The work was funded by the Home-Grown Cereals Authority under grants and BAKER, C. K., GALLAGHER, J. N.& MONTEITH, J. L. (198). Daylength change and leaf appearance in winter wheat. Plant, Cell and Environment 3, KEISLING, T. C. (1962). Calculation of the length of day. Agronomy Journal 74, KIRBY, E. J. M. (1988). Analysis of leaf, stem, and ear growth in wheat from terminal spikelet stage to anthesis. Field Crops Research 18, KIRBY, E. J. M. (199). Co-ordination of leaf emergence and leaf and spikelet primordium initiation in wheat. Field Crops Research 25, KIRBY, E. J. M. (1992). A field study of the number of main shoot leaves in wheat in relation to vernalization and photoperiod. Journal of Agricultural Science, Cambridge 118, KIRBY, E. J. M. (1994). Identification and prediction of stages of wheat development for management decisions. Project Report No. 9, Home-Grown Cereals Authority, Hamlyn House, Highgate Hill, London N19 5PR. KIRBY, E. J. M., APPLEYARD, M.& SIMPSON, N. A. (1994). REFERENCES Co-ordination of stem elongation and Zadoks growth stages with leaf emergence in wheat and barley. Journal of Agricultural Science, Cambridge 122, TOTTMAN, D. R. (1987). The decimal code for the growth stages of cereals, with illustrations. Annals of Applied Biology 11, TOTTMAN, D. R., APPLEYARD, M.& SYLVESTER-BRADLEY, R. (1985). Cereal growth stages stem extension and apical development. Aspects of Applied Biology 1, Field Trials Methods and Data Handling, WALLACH, D.& GOFFINET, B. (1991). Mean squared error of prediction as a criterion for evaluating and comparing systems models. Ecological Modelling 44, WEIGHTMAN, R. M., KIRBY, E. J. M., SYLVESTER-BRADLEY, R., SCOTT, R. K., CLARE, R. W.& GILLETT, A. (1997). Prediction of leaf and internode development in wheat. Journal of Agricultural Science, Cambridge 129, ZADOKS, J. C., CHANG, T. T.& KONZAK, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14,