Improved Prediction of Leaf Emergence for Efficacious Crop Protection: Assessing Field Variability in Phyllotherms for Upper Leaves in Winter Wheat and Winter Barley

The choice of the phyllotherm value for predicting leaf emergence under field conditions is pivotal to the success of fungicide-based disease risk management in temperate cereals. In this study, we investigated phyllotherm variability for predicting the emergence of the three uppermost leaves (i.e., three last leaves to emerge) in winter wheat and winter barley fields. Data from four sites representative of wheat and barley growing regions in Luxembourg were used within the PROCULTURE model to predict the emergence of F-2, F-1 and F (F being the flag leaf) during the 2014–2019 cropping seasons. The phyllotherms tested ranged between 100 ◦Cd and 160 ◦Cd, in 15 ◦Cd steps, including the current default value of 130 ◦Cd. The comparisons between the observed and predicted emerged leaf area were qualitatively evaluated using the mean absolute error (MAE), the root mean square error (RMSE) and Willmott’s index (WI). A phyllotherm of 100 ◦Cd accurately and reliably predicted the emergence of all three upper leaves under the various environmental conditions and crop cultivars of winter wheat and winter barley over the study period. MAE and RMSE were generally <5% and the WI values were most often ≥0.90 for F-1 and F. For phyllotherm values ≥115 ◦Cd, the prediction errors generally increased for F-1 and F, with MAE and RMSE exceeding 20% in most cases. F-2 agreement between observed and predicted values was generally similar when using 100 ◦Cd or 115 ◦Cd. These results tie in valuable, complementary information regarding the variability of phyllotherms within leaf layers in winter wheat and winter barley in Luxembourg. Accurate and reliable leaf emergence prediction from F-2 to F allows for timely fungicide application, which ensures lasting protection against infections by foliar fungal disease pathogens. Hence, understanding phyllotherms can help ensure timely, environmentally sound, and efficacious fungicide application while increasing the likelihood of improved yields of winter wheat and winter barley.


Introduction
The upper three leaves (the flag leaf 'F', and the two leaves below F, F-1 and F-2) of wheat (Triticum aestivum L.), and the F-1 and F-2 leaves and the ear of barley (Hordeum vulgare L.) contribute leaves F-2 to F in winter wheat and winter barley will help ensure timely, environmentally friendly and efficacious fungicide applications to help maximize yields.
The main objective of this study was to investigate the variability in phyllotherm for predicting the emergence of the three upper leaves, F-2 to F, in winter wheat and winter barley in the field. The overarching objective was to improve the performance of the DSS used for managing foliar fungal disease risk in the Grand-Duchy of Luxembourg (GDL). Specifically, five phyllotherms (i.e., 100 • Cd to 160 • Cd, in 15 • Cd steps) were assessed for predicting the emergence of leaves F-2 to F for both winter wheat and winter barley using the PROCULTURE model [29]. The predicted values were compared to field data collected during the 2014-2019 cropping seasons at four sites representative of the Luxembourgish wheat-and barley-growing regions. The emergence of F-2 to F, which correspond generally to the period spanning GS31 (first node detectable) to GS37 in winter wheat and winter barley in the GDL, is a critical period for efficacious fungicide-based fungal disease management [6]. Thus, accurate and reliable leaf emergence prediction for F-2 to F will allow for timely fungicide application to ensure lasting protection against infections by foliar fungal disease pathogens and to maximize disease control.

Study Areas
Data from fields of winter wheat and winter barley located at Bettendorf (6.19 E, 49.87 N), Burmerange (6.28 E, 49.52 N), Everlange (5.95 E, 49.78 N), and Reuler (6.04 E, 50.06 N) in the GDL were used in the study. The experimental sites were selected across commercial winter wheat and winter barley fields during the 2014-2019 cropping seasons; they included early and medium-maturity cultivars (Table 1). Experiments were designed in a randomized block with four replicates, with one replicate plot size = 8.0 m × 1.5 m. Sowing and harvest methods, as well as crop practices, were typical of wheat and barley production in the GDL. Winter barley and wheat are generally sown between the end of September and the end of October. Plant densities ranged from 200 to 250 plants m −2 and 170 to 200 plants m −2 for winter wheat and winter barley, respectively, with sowing depths varying between 1 and 2 cm for both crops. Nitrogen fertilizers were applied three times: the first applications were generally made between the end of February and early March; the second nitrogen fertilizer was applied during the first fortnight of April (which corresponds to the first node stage); and the third and last application of nitrogen fertilizer was made during the second half of May (which corresponds to the period when the flag leaf emerges). Growth regulators are often applied from the end of April to early May, in conjunction with herbicides or fungicides. Herbicides were typically applied from the end of March to early April. The application and frequency of fungicide application depend on the prevalence and severity of foliar disease at earlier growth stages, and the prevailing weather conditions.

Data
Daily weather data (mean air temperature, relative humidity, and precipitation) were recorded at an automatic weather station located within 1 to 2 km of each experimental field. Mean air temperature and relative humidity were measured at 2 m above the soil surface. Total precipitation was measured at 1 m above the soil surface. The raw weather variables, recorded at 10 min intervals, were automatically retrieved from the web-based database system (www.agrimeteo.lu) and processed using an automatic data processing chain within which data were quality checked [33]. The mean daily weather variables by month during the 2014-2019 period for each of the study sites are presented in Figure 1. automatic weather station located within 1 to 2 km of each experimental field. Mean air temperature and relative humidity were measured at 2 m above the soil surface. Total precipitation was measured at 1 m above the soil surface. The raw weather variables, recorded at 10 min intervals, were automatically retrieved from the web-based database system (www.agrimeteo.lu) and processed using an automatic data processing chain within which data were quality checked [33]. The mean daily weather variables by month during the 2014-2019 period for each of the study sites are presented in Figure 1. Data for leaf appearance used in the study originated from plots which received no foliar fungicide throughout the cropping seasons. Ten plants per plot (40 plants total) were randomly selected and marked when delineating the experimental plots at each site. To closely monitor the emergence of the three upper leaves, a reference marking was made on each of the 10 plants at the time of selecting the plants. The mark consisted of manually cutting a small section of the leaf at the extremity of the third leaf layer. The correct number of the leaf layers was determined upon the emergence of the flag leaf (F). The percentage of emerged leaf area for each leaf layer was estimated by comparison to the total area of the preceding fully formed leaf Data for leaf appearance used in the study originated from plots which received no foliar fungicide throughout the cropping seasons. Ten plants per plot (40 plants total) were randomly selected and marked when delineating the experimental plots at each site. To closely monitor the emergence of the three upper leaves, a reference marking was made on each of the 10 plants at the time of selecting the plants. The mark consisted of manually cutting a small section of the leaf at the extremity of the third leaf layer. The correct number of the leaf layers was determined upon the emergence of the flag leaf (F). The percentage of emerged leaf area for each leaf layer was estimated by comparison to the total area of the preceding fully formed leaf (assumed to be 100%), and by checking the ligule of the emerging leaf (a fully visible leaf ligule corresponds to a fully emerged leaf). Thus, the percentage of emerged area of each of the three leaf layers was estimated relative to the preceding leaf layer for each of the 40 plants. Observations were carried out weekly between March and July each year by experienced agronomists and plant pathologists. Care was also taken to ensure the same rater assessed the same replicate during each of the monitoring weeks.

Simulations of Leaf Emergence
The PROCULTURE model [29,31] was used to simulate the emergence of the three upper leaves. PROCULTURE is a mechanistic model used for simulating the risk of infection and progress of STB, as well as the emergence of the five upper leaves, within the DSS for managing fungal disease risks in cereals in Luxembourg [6,30]. For each cropping season and for each of the study sites, the default phyllotherm value (130 • Cd) in PROCULTURE was used to predict the emergence of F-4, F-3 and F-2 based on the sowing dates. Given that PROCULTURE allows for correction when predicting F-2 before the prediction of subsequent leaves (F-1 and F), adjustments were applied based on the date of the first report of the emergence of F-2 on the plants in the fields, and the percentage of that leaf position that had emerged, where necessary [30].
Five phyllotherm values, 100 • Cd to 160 • Cd (in 15 • Cd steps), including the default value (130 • Cd), were used for predicting the emergence of leaves F-2 to F for both winter wheat and winter barley during the 2014-2019 cropping seasons. The range was chosen based on previously reported phyllotherm values [20,22,24]. A base temperature of 0 • C was used for both crops [26,34]. As noted, the simulation of F-2 emergence was empirically corrected based on field observations ( Table 2). a : The percentage of emerged leaf area for each leaf layer was estimated by comparison to the total area of the preceding fully formed leaf (assumed as 100%), and by checking the ligule of the emerging leaf (a fully visible leaf ligule corresponds to a fully emerged leaf). Thus, the percentage of the emerged area of each of the three leaf layers was estimated relative to the preceding leaf layer for each of the 40 plants.

Statistical Analyses
The predicted percentage leaf emergence (expressed as the percentage of emerged leaf area) for each of the three upper leaves during each phyllotherm was compared to field assessments (the estimated area emerged for that leaf layer relative to the previous, fully emerged layer). The accuracy of predictions was evaluated using the mean absolute error (MAE), the root mean square error (RMSE) and the Willmott's index of agreement (WI) [35]. The three statistics were calculated as follows: where n is the number of field observations; O i is the ith observed value; O is the mean observed value; and P i is the ith predicted value.
The smaller the value of the MAE or RMSE, the more accurate are the predictions. Values of WI closer to 1 are indicative of good agreements between predicted and observed values, and are thereby indicative of good model performance.
Considering that data were available for two winter wheat cultivars during each of the 2017 and 2019 cropping seasons at Bettendorf (Table 1), a comparison of leaf emergence using the default phyllotherm in PROCULTURE and those obtained using the best-performing phyllotherm (found after comparisons of all five phyllotherms) was made to check whether the performance of the latter phyllotherm was cultivar-sensitive.
Additionally, the ability of the best-performing pyhllotherm -adjusted PROCULTURE model (i.e., the model using the best-performing phyllotherm) to predict the point of first emergences of each of the three upper leaves F-2 to F for both winter wheat and winter barley was also investigated. Statistical scores derived from a contingency table analysis were used to evaluate the ability of the best-performing phyllotherm to predict the point of the first observed emergence of leaves F-2 to F during the 6-year period at each of the study sites. The statistical scores used were the probability of detection (POD), the false alarm ratio (FAR), and the critical success index (CSI). They were calculated as follows: POD = a/(a + c), FAR = b/(a + b), and CSI = a/(a + b + c), where a, b, and c refer to leaf emergence either observed or predicted, leaf emergence predicted but not observed, and leaf emergence observed but not predicted, respectively.

Prediction of Emergence of Leaves F-2, F-1 and F in Winter Wheat
The performance of various models differed when simulating the emergence of F-2, F-1 and F using the five different phyllotherms. Overall, accurate predictions of leaf emergence were found when using the 100 • Cd phyllotherm in all years and at all study sites, irrespective of the leaf. For F-1 and F, the MAE and RMSE were ≤5% (Figure 2). Exceptions included the sites at Burmerange in 2017 for F-1 (MAE = 11%; RMSE = 12%), and Everlange in 2015 for F (MAE = 12%; RMSE = 15%), which indicate only modest deviations in accuracy in these cases ( Figure 2). For the F-1 and F leaves, using a phyllotherm greater than or equal to 115 • Cd yielded generally greater prediction errors when compared to those found using a phyllotherm of 100 • Cd, indicating less accuracy. For example, for phyllotherms 130 • Cd, 145 • Cd and 160 • Cd, the MAE and RMSE values were most often ≥20% for all sites and in all years, indicating less accuracy in prediction ( Figure 2). With regard to F-2, the 100 • Cd and 115 • Cd phyllotherms, and to a lesser extent the 130 • Cd phyllotherm, resulted in a similar model performance ( Figure 2). However, prediction errors were more frequent (≥10% on   Figures  S1 and S2; results for F were presented in the text to reduce redundancy). Similarities between the curve shape based on the observed values and the curve shape based on the predicted values showed that, in most of the site-year cases, the 100 °Cd phyllotherm outperformed the other phyllotherms tested. Thus, the 100 °Cd phyllotherm appears appropriate for simulating leaf emergence in winter wheat fields under Luxembourgish conditions. The agreements between the observed and predicted leaf emergence values for F-1 and F based on the 100 °Cd phyllotherm were confirmed by the WI values; the WI values were consistently ≥0.90, indicating close agreement (Figure 4). For phyllotherms >130 °Cd, the WIs were generally ≤0.50 for F in all site-year cases (Figure 4). A similar pattern was observed for F-1 for phyllotherms 145 °Cd and 160 °Cd. With F-2, the WI values were all greater than 0.70 for the majority of site-year cases (Figure 4), irrespective of the phyllotherm value.  Figures S1 and S2; results for F were presented in the text to reduce redundancy). Similarities between the curve shape based on the observed values and the curve shape based on the predicted values showed that, in most of the site-year cases, the 100 • Cd phyllotherm outperformed the other phyllotherms tested. Thus, the 100 • Cd phyllotherm appears appropriate for simulating leaf emergence in winter wheat fields under Luxembourgish conditions. The agreements between the observed and predicted leaf emergence values for F-1 and F based on the 100 • Cd phyllotherm were confirmed by the WI values; the WI values were consistently ≥0.90, indicating close agreement (Figure 4). For phyllotherms >130 • Cd, the WIs were generally ≤0.50 for F in all site-year cases (Figure 4). A similar pattern was observed for F-1 for phyllotherms 145 • Cd and 160 • Cd. With F-2, the WI values were all greater than 0.70 for the majority of site-year cases (Figure 4), irrespective of the phyllotherm value.    Table S1) showed that using a phyllotherm of 100 °Cd for simulating the emergence of the upper leaves gave accurate results, regardless of the cultivar. A similar range of prediction errors were found when comparing the cultivar Kerubino to cultivars Achat (in 2017) or Desamo (in 2018 and 2019). For Kerubino, RMSE and MAE ranged from 1 to 6%, and from 1 to 3%, respectively, over the three cropping seasons (all leaves considered), indicating accurate predictions. The respective range of errors for the other cultivars was from 2 to 65%, and from 1 to 4% (Table S1). Although larger prediction errors were found with the emergence of the F-2 leaves of the cultivar Desamo in 2018 (RMSE = 11% and MAE = 10%; Table S1), the lowest prediction errors occurred when using the phyllotherm of 100 °Cd, indicating that this phyllotherm can be applied to accurately and reliably predict the emergence of the three uppermost leaves in different winter wheat cultivars in the GDL.  Table S1) showed that using a phyllotherm of 100 • Cd for simulating the emergence of the upper leaves gave accurate results, regardless of the cultivar. A similar range of prediction errors were found when comparing the cultivar Kerubino to cultivars Achat (in 2017) or Desamo (in 2018 and 2019). For Kerubino, RMSE and MAE ranged from 1 to 6%, and from 1 to 3%, respectively, over the three cropping seasons (all leaves considered), indicating accurate predictions. The respective range of errors for the other cultivars was from 2 to 65%, and from 1 to 4% (Table S1). Although larger prediction errors were found with the emergence of the F-2 leaves of the cultivar Desamo in 2018 (RMSE = 11% and MAE = 10%; Table S1), the lowest prediction errors occurred when using the phyllotherm of 100 • Cd, indicating that this phyllotherm can be applied to accurately and reliably predict the emergence of the three uppermost leaves in different winter wheat cultivars in the GDL.

Prediction of Emergence of Leaves F-2, F-1 and F in Winter Barley
The patterns of leaf emergence and prediction errors in winter barley were similar to those found for winter wheat. Good predictions were observed when using a phyllotherm of 100 °Cd for F and F-1 for the majority of the site-year cases; the MAE and RMSE were generally <5% ( Figure 6). Exceptions occurred in 2014 and 2019 for F at Reuler where the MAE and RMSE were 9%. For phyllotherm ≥115 °Cd, the prediction errors most often increased for both F and F-1 leaves, with values exceeding 20% in most cases ( Figure 6). For the prediction of F-2, the errors were most often lower compared to those for F and F-1 leaves. Leaf emergence predictions using a phyllotherm of 100 °Cd outperformed those based on phyllotherms from 115 °Cd to 160 °Cd. The differences in prediction errors were generally ≤5% for phyllotherms of 115 °Cd and 130 °Cd ( Figure  6), suggesting similar accuracies.

Prediction of Emergence of Leaves F-2, F-1 and F in Winter Barley
The patterns of leaf emergence and prediction errors in winter barley were similar to those found for winter wheat. Good predictions were observed when using a phyllotherm of 100 • Cd for F and F-1 for the majority of the site-year cases; the MAE and RMSE were generally <5% ( Figure 6). Exceptions occurred in 2014 and 2019 for F at Reuler where the MAE and RMSE were 9%. For phyllotherm ≥115 • Cd, the prediction errors most often increased for both F and F-1 leaves, with values exceeding 20% in most cases ( Figure 6). For the prediction of F-2, the errors were most often lower compared to those for F and F-1 leaves. Leaf emergence predictions using a phyllotherm of 100 • Cd outperformed those based on phyllotherms from 115 • Cd to 160 • Cd. The differences in prediction errors were generally ≤5% for phyllotherms of 115 • Cd and 130 • Cd (Figure 6), suggesting similar accuracies. The analyses of the WI values corroborate these results. There was clear and strong agreement between the observed and predicted leaf emergence values for F-1 and F in all years for all the study sites ( Figure 7). Similar to winter wheat, in most cases, the WI values for F-2 were greater than or equal to 0.70. Moreover, a visual inspection of the relationships between the observed or predicted emerged leaf area based on the 100 °Cd phyllotherm showed, in most cases, that the curves had very similar shapes ( Figure 8, and Figures S3 and  S4). The analyses of the WI values corroborate these results. There was clear and strong agreement between the observed and predicted leaf emergence values for F-1 and F in all years for all the study sites (Figure 7). Similar to winter wheat, in most cases, the WI values for F-2 were greater than or equal to 0.70. Moreover, a visual inspection of the relationships between the observed or predicted emerged leaf area based on the 100 • Cd phyllotherm showed, in most cases, that the curves had very similar shapes ( Figure 8, and Figures S3 and S4).

Improvement of Leaf Emergence Prediction within the DSS
We have shown that the 100 • Cd phyllotherm is an accurate and reliable basis for predicting leaf emergence for F, F-1 and F-2 in both winter wheat and winter barley. Using the 100 • Cd phyllotherm, the predicted visible emergence of the three uppermost leaves was compared to the first observed emergence. The analysis of the statistical scores indicated satisfactory levels of visible leaf emergence prediction for all the study sites during the 2014-2019 cropping seasons for both winter wheat and winter barley. POD and CSI were greater than or equal to 0.70 (a perfect score for either statistic is 1.0), with the lower values associated with the prediction of the first emergence of F ( Table 3). The maximum FAR values were 0.16 for F-2 in winter wheat at Burmerange, and F-2 and F-1 in winter barley at Reuler. For the remainder of the site-crop cases, a perfect FAR score of 0.00 was obtained (Table 3).  a Forecasted and emerged. b Forecasted but not emerged. c Emerged but not forecasted. d POD, probability of detection, it is the probability of correctly forecasting the observed event; it ranges between zero and one (perfect score = 1). e FAR, false alarm ratio, is the number of times an event is forecast but is not observed, divided by the total number of forecasts of that event. Perfect value = 0. f CSI, critical success index, considers both false alarms and missed events; it ranges between zero and one (perfect score = 1).
The performance of the prediction of first emergence based on different phyllotherms is presented in Figures 3 and 8, as well as Figures S1-S4. The predicted first emergence dates of F and F-1 based on the 100 • Cd phyllotherm generally coincided with the observed values, whereas the predicted first emergence was delayed by up to 10 days in some cases when using the default phyllotherm value (130 • Cd). This was especially so when predicting the first emergence of F in winter wheat (Figures 3  and 8). In winter barley, there was little or no delay in the prediction of the first emergence of F-1 when using either the default phyllotherm values of 130 • Cd or 100 • Cd ( Figure S3).

Discussion
Despite its relatively small size (approximately 2586 km 2 ), the Grand-Duchy of Luxembourg is characterized by noticeable climatic contrasts between and within its agricultural regions, which affect crop growth and the development and severity of foliar fungal diseases in winter wheat and winter barley throughout the cropping season [37,38]. The variable within-and between-season disease risks imply tailored fungicide-based crop protection to meet growers' needs to maintain yield while minimizing the cost of inputs and any adverse environmental effects of the crop protection measures. Extending the duration of the green leaf area of the upper leaves in both winter wheat and winter barley through the application of certain foliar fungicides benefits the grain filling period, that is, the fungicide effect leads to extended leaf area greenness, allowing grain filling over a longer period [7,39,40]. Such a long grain filling period could ultimately result in an increased final grain yield. In the GDL, preventive fungicide applications following a phenology-based calendar are integral to wheat and barley production [6,30]: the first treatment is applied during stem elongation to control early season fungal diseases (i.e., wheat powdery mildew and eyespot); the second treatment is typically applied at flag leaf emergence to protect against STB; and the third is occasionally applied at early flowering to protect against Fusarium head blight [6,30]. In this study, the prediction of leaf emergence of the upper leaves F-2 to F within the DSS, used for managing foliar fungal disease risks in the GDL, was assessed based on five phyllotherm values over six cropping seasons at different sites. The results indicate that the most accurate leaf emergence predictions based on the various environmental conditions and crop cultivars of winter wheat and winter barley used during the study period were based on a 100 • Cd phyllotherm for both crops, irrespective of the leaf. These findings are in agreement with previous reports (e.g., [24,41,42]), which concluded the average phyllotherms for the upper leaves were in the range 100 to 115 • Cd. The slight differences observed in the current study could be explained by environmental conditions, including variable sowing dates and varieties, variable nutrient application rates and availabilities, and differences in the effect of temperatures on leaf emergence.
Various factors, considered alone or in interaction with one another, regulate the rate of development and leaf emergence in wheat and barley, with the major variables being temperature, photoperiod and vernalization [16,26,[43][44][45][46][47][48]. Nutrition (including availability of nitrogen, phosphorus and sulfur, aluminum toxicity) also affects the duration of the ontogenic phases from seedling emergence to flowering, though to a lesser extent [49][50][51][52][53][54]. Our experiments were conducted in commercial winter wheat and winter barley fields, and the responses of leaf emergence to varying sowing dates or nutrient rates in each of the cropping seasons were beyond the scope of the study's objectives, and thus were not investigated. Nevertheless, the results we present are of value and can guide future research in relation to the effects of other factors.
Although the contribution of F (the flag leaf) to grain filling and final yield in barley is almost insignificant due to its small size (hence the focus on keeping F-1 free of disease; [2]), we extended our analysis to F and F-2 because the potential severity of some diseases on F-1 might depend upon their severity in F or F-2. Thus, ensuring accurate and reliable prediction for all leaf layers (F-2 to F) could help minimize yield losses from diseases. Our analyses demonstrated that, for winter barley, as well as for winter wheat, accurate predictions of leaf emergence (i.e., the time of first appearance of the leaf) ( Table 3) and subsequent leaf area development (Figures 3 and 8; Figures S1-S4), were obtained using a phyllotherm of 100 • Cd. Compared to the prediction errors when using the default phyllotherm value (130 • Cd), using a 100 • Cd phyllotherm improved the performance of PROCULTURE under the environmental conditions prevailing in Luxembourg, and thereby the overall performance of the DSS. A 100 • Cd phyllotherm allows more timely fungicide application for lasting protection [6]. Under changing climate conditions [55], and considering the continued improvements in crop breeding, it is worth re-evaluating phyllotherm variability for the uppermost leaf layers of major winter wheat and winter barley cultivars across major cropping regions beyond Luxembourg. To this end, our findings may provide valuable insights for broader applications regarding the variability of phyllotherm within the leaf layers of these two economically important food crops.
In conclusion, our research confirms that a phyllotherm of 100 • Cd can be considered accurate and reliable for predicting the leaf emergence of the three uppermost leaf layers in winter wheat and winter barley under variable environmental and crop conditions in the GDL. The simulations of leaf emergence of F-2, F-1 and F in both crops at all the study sites were improved using the 100 • Cd phyllotherm when compared to phyllotherm values ≥ 115 • Cd, including the current phyllotherm standard of 130 • Cd used in the DSS. Moreover, based on the prediction errors when comparing the emergence of the three uppermost leaves for different winter wheat cultivars, our results indicate that the 100 • Cd phyllotherm is reliable for predicting the emergence of F-2, F-1 and F. Within the DSS, for managing disease risks based on foliar fungicide applications, leaf emergence simulations are important for efficacious crop protection. Thus, the results we present can help ensure timely fungicide applications to maximize disease control, to reduce the risk of disease and increase the likelihood of an improved yield for both winter wheat and winter barley, while minimizing the impact on the environment. Further research involving multiyear, multilocation experiments with major wheat and barley cultivars is warranted to extend the results across a broader range of conditions and cropping practices in Europe and elsewhere.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4395/10/11/1825/s1, Table S1: Comparisons of leaf emergence predictions for different winter wheat cultivars at Bettendorf using five different phyllotherm values; Figure S1: Observed (dashed lines) and predicted (solid lines) percentage emergence of the leaf below the flag leaf (F-1) in winter wheat during the 2017-2019 growing seasons at the different study sites in Luxembourg; Figure