Advertisement

International Journal of Plant Production

, Volume 12, Issue 1, pp 53–60 | Cite as

Development and Validation of a Predictive Model for Seedling Emergence of Volunteer Canola (Brassica napus) Under Semi-Arid Climate

  • Elias Soltani
  • Jose L. Gonzalez-Andujar
  • Mostafa Oveisi
  • Nader Salehi
Original Paper
  • 101 Downloads

Abstract

Volunteer canola (Brassica napus L.) can damage the production of subsequent canola crops and other crops. Timely and more accurate control could be developed if there is a better understanding of its temporal emergence patterns. The objectives of this study were to develop and validate a predictive model of emergence for B. napus under semi-arid conditions based on thermal time (TT). Experiments were conducted during 3 years to obtain cumulative seedling emergence data and used to develop and validate the model. A Weibull function was fitted to cumulative seedling emergence and TT. The model closely fitted the observed emergence patterns, accounting for 99% of the variation observed. According to this model, seedling emergence of B. napus started at 56.1 TT and increased to 50 and 95% of maximum seedling emergence at 86.3 and 105.4 TT, respectively. Validation was performed with the Weibull model and two logistics models (taken from the literature) developed under different climate conditions. The validation indicated that the Weibull model performed better than the logistic models. The Weibull model proposed is robust enough and could be useful as a predictive tool for effective control of B. napus under semi-arid climate.

Keywords

Weibull model Logistic model Base temperature Soil depth Thermal time Degree days Weed emergence 

Introduction

Volunteer canola (Brassica napus L.) infestations is consequence of significant seed losses that occur in canola crops as results of seed shedding before and during harvest. Seed losses can refill the seed bank with new seeds. Gulden et al. (2003) have reported harvest losses of 3600 seed m−2. Furthermore, seeds of volunteer canola can persist for years (Legere et al. 2001). There are reports on the seed persistence of canola in the soil up 10 years (Sauermann 1993; Simard et al. 2002). Volunteer canola can also damage the production of subsequent canola crops (Gulden et al. 2003) and other crops, such as winter wheat (Lawson et al. 2006; Gruber et al. 2010). This creates difficulties for management decision-makers in the future (Gulden et al. 2003). Generally, to minimise volunteer B. napus two strategies are used. Herbicide applications not usually used to control volunteer oilseed in Iran but when applied, it is habitual to use Trifluralin (2.5 l ha−1) as pre-plant application (Nowroozian 2000). Post-harvest tillage is frequently done because it is assumed to stimulate summer germination of new seeds and the emerged seedling can be destroyed (Gruber et al. 2005).

Seedling emergence is one of the most important stage of plant growth as the time it occurs largely determines its survival and success (Forcella et al. 2000). Important advances have been made over the last years in the development of predictive field emergence models (Gonzalez-Andujar et al. 2016). Models can estimate the timing of weed seedling emergence and be valuable management decision tools for optimizing weed control schedules (Forcella 1998). Thermal and hydrothermal time models have been used extensively to describe the effect of temperature and water potential on seedling emergence (e.g., Leguizamon et al. 2005; Schwinghamet and Van Acker 2008; Izquierdo et al. 2013; Yousefi et al. 2014). Thermal time models are simpler as data are easily available from weather stations or directly from the soil and they seem to be accurate enough for prediction purposes (Leguizamon et al. 2005, 2009; Izquierdo et al. 2009; Zambrano-Navea et al. 2013).

Soil temperature is the most important factor that influences the dormancy releasing and time to seedling emergence of weeds. Most rainfalls occur during autumn and winter in semi-arid climates and soil water condition is not restricting during seedling emergence of B. napus. Much is known about the dormancy releasing and germination requirements of rape seeds, especially with regard to responses to temperature (Schlink 1994; Gulden et al. 2004; Gruber et al. 2010; Soltani et al. 2013; Farzaneh et al. 2014). However, there are only a couple of reports using thermal time models to predict seedling emergence in B. napus. Bullied et al. (2003) and Lawson et al. (2006) developed Logistic models to predict the emergence of B. napus in Canada.

Although it is theoretically thought that thermal or hydrothermal models can be generalized to any place (Grundy 2003), reality dictates that such adaptation fails in many locations and it is then necessary to develop new predictive models (Gonzalez-Andujar et al. 2016; Soltani and Sinclair 2012). Therefore, the objective of this study was to develop and validate a predictive model of B. napus under semi-arid conditions that could help growers in the decision making process to determine when to implement weed control practices and maximize the control of this weed species and compare its performance with previously developed models under different climatic conditions.

Materials and Methods

Seedling emergence of volunteer canola was studied in a roofless greenhouse at Gorgan University of Agricultural Science and Natural Resources (36°51′N, 54°16′E and 13 m.s.l., cold semi-arid climate, Iran) so that the buried seeds could be exposed to outdoor weather conditions. Seeds were harvested from canola (Hayola401, with a spring type of growing) fields around Gorgan, Iran during May 2009. A germination test of fresh seeds was conducted in the dark at 20 °C and 100% of the seeds were viable and 94% were non-dormant. Seeds were stored in the laboratory (20 ± 5 °C, 40 ± 10% relative humidity, in the dark) until the beginning of the experiment on 1 Jan 2010. Fifty seeds were selected and sown in plastic pots (15 cm diameter and 40 cm deep) at different depths (1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 20 and 30 cm). Plastic pots were filled with silty clay loam soil (28% clay, 62% silt, 10% sand). Different depths were considered to mimic the real situation under field conditions, where seeds emerge from different depths. The soil was dug from a depth > 0.5 m from farmland that had never been ploughed to avoid the presence of volunteer canola seeds, which would invalidate the experimental data on seedling emergence.

The experiment was conducted based on a completely randomised design with four replicates for each seeding depth. Maximum and minimum temperatures were recorded by a min/max thermometer, located between the pots (Fig. 1). Each pot was irrigated at 2- or 3-day intervals with 100 ml of water. The content of soil water was determined based on soil moisture release curve (Saxton et al. 1986). Before the experiment, three samples of wet soil (wet soil = dry soil + soil moisture content) were dried and the soil moisture content was determined at the beginning of the experiment. Then, the pots were equally filled with the wet soil. A pot was considered as a reference and it was weighted each day. Any increase or decrease in the soil moisture content could be obtainable after weighing. Soil water potential was around − 0.05 MPa during the experiment. Pots were observed daily for seedling emergence during 45 days and emerged seedlings were counted and removed with minimum soil disturbance.
Fig. 1

Minimum (Tmin) and maximum (Tmax) temperature after planting in greenhouse

Estimation of Thermal Time

Daily soil temperature was used to calculate accumulated thermal time (TT) with the following equation:
$$TT = \mathop \sum \limits_{i = 1}^{n} \left[ {\left( {\left( {T_{max} + T_{min} } \right)/2} \right) - T_{b} } \right]$$
(1)
where T max and T min are the daily maximum and minimum temperatures, T b is the base temperature at which germination occurs and n is the number of days after sowing. When the minimum temperature was < T b no thermal time was assumed to accumulate. No upper threshold for emergence was considered because maximum temperatures for the duration of the emergence period were generally below the optimal temperatures. The value for the base temperature was set to 3.79 °C according to the literature (Soltani et al. 2013).

Modelling Seedling Emergence

The seedlings emerging observed at each soil depth were pooled. Data (each depth and pooled) were regressed against TT using the following Weibull model (Leguizamon et al. 2005):
$$CE = 1 - \exp \left[ { - \ln \left( 2 \right)\left( {\frac{TT}{a}} \right)^{b} } \right]$$
(2)
where CE is the cumulative proportion of emergence and a and b are shape parameters. The parameters were estimated using the non-linear (NLIN) regression procedure in SAS (SAS Institute INC 2011). Goodness of fit of the models was assessed by estimating root-mean-square error (RMSE) and the pseudo-R2 based on the deviance residual (Cameron and Windmeijer 1996).

Model Validation

We compared the models’ estimates against data sets independent from those used to generate the models. Validation was carried out during Oct to Dec 2015 and 2016 in the research farm of Abourahian Campus, University of Tehran, Pakdasht, Tehran, Iran (35°28′N; 51°40′E, 1003 m.s.l., cold semi-arid climate). This farm had never been cultivated with canola and hence there was no infestation. Soil was a clay loam (37.1% sand, 26.2% silt, and 36.7% clay, 0.75 OC, 7.7 pH). Seeds were collected from three different places in Iran (Karaj, Moghan and Gorgan) and they included four different cultivars (Hayola401, RGS, Opera, and Okapi); Opera and Okapi were produced in Karaj, Hayola401s were produced in Karaj and Moghan, and RGSs were produced in Karaj and Gorgan during May 2015 (total of six seed lots). The growing types were spring for Hayola401 and RGS and it was facultative for Opera and Okapi (Farzaneh et al. 2014; Soltani et al. 2017). Seeds were kept in the laboratory (20 ± 5 °C, 40 ± 10% relative humidity, in the dark) until used.

The experiments consisted of 24 (4 replicates by 6 seed lots) microplots (0.3 × 0.5 m) for each year that were established at random. In each microplot, 100 seeds were sown with mixing at the upper 5 cm soil layer. Seedling emergence was monitored daily and removed after each counting. Maximum and minimum soil temperatures were recorded daily during the experiment using a min/max thermometer buried at the soil depth of 5 cm (Fig. 2). Soil water content was kept at the field capacity by regular irrigation as a winter crop (e.g. wheat or canola) required in this area. As soil moisture content was adequate, the first day after sowing was considered as start point of the accumulation of the thermal time. We selected portions of field that they were not cultivated under canola crops for more than 10 years to be sure that there was no B. napus seed into the soil seed bank.
Fig. 2

Minimum (Tmin) and maximum (Tmax) soil temperature a in 2015 and b 2016, during 50 days after planting in validation experiment. Base temperature (Tb) was 3.79 °C

Data from these experiments were applied to validate three models for seedling emergence of volunteer canola: (1) The two Logistic models developed by Lawson et al. (2006): Logistic 2003’s model (CE = −2.7 + 101.7/[1 + (TT/86)**− 1.91] and Logistic 2004’s model (CE = − 2.7 + 101.7/[1 + (TT/130)**− 4.30], both calculated with Tb = 5 °C and (2) the Weibull model developed at the current study (Tb = 3.79 °C). The models were validated at both temperature bases. Prediction accuracies of the three models at the different base temperatures were evaluated by comparing predicted versus observed values by calculating the regression coefficient r2 and evaluating whether the slope and the y-intercept of the regression differed statistically from 1 and 0, respectively. All the statistical analyses were performed with SAS. Additionally, the Theil’s coefficients (U) were calculated. These coefficients distinguish between different sources of predictive error (Smith and Rose 1995). The squared sum of the predictive error (SSPE = Σ n (obsi − prei)2) provides a measure of the goodness-of-fit. It is possible decompose the SSPE by calculating Theil’s partial inequality coefficients (U).
  1. 1.
    A proportion associated with mean differences between observed and predicted values,
    $${\text{U}}_{{{\text{bias}} = }} \left[ {n\left( {{\text{OBS }} - {\text{PRE}}} \right)^{ 2} } \right]/{\text{SSPE}} .$$
     
  2. 2.
    A proportion associated with the slope (b) of the fitted model and the 1:1 line,
    $$U_{b = 1} = \left[ {\left( {{\text{b}} - 1} \right)^{ 2} \varSigma_{n} \left( {{\text{pre}}_{\text{i}} - {\text{PRE}}} \right)^{ 2} } \right]/{\text{SSPE}} .$$
     
  3. 3.
    A proportion associated with the unexplained variance,
    $$U_{e} = \, \varSigma_{n} \left( {{\text{pre}}_{\text{i}} - {\text{obs}}_{\text{i}} } \right)^{ 2} /{\text{SSPE}} .$$
     

where obs and pre are the observed and predicted values, respectively; OBS and PRE are the means of the observed and predicted values, respectively; b slope and n is the number of sites.

Results

Seedling emergence of volunteer canola was severely affected by burial depth. Emergence ranged between 100% (1 cm depth) and 15% (8 cm depth). No seedlings emerging from seeds buried at a depth of > 10 cm was found. Decreasing seedling emergence was mainly caused by fatal germination and secondary dormancy (data not showed). Dormancy was not observed at burial depth of < 8 cm, but almost 40% of seeds were induced to secondary dormancy at burial depths of 20 and 30 cm.

Seedling emergence percentages were almost the same between seed lots, but it was lower in 2016 than in 2015 for all seed lots (Fig. 3). Seedling emergence was started earlier in 2016 than 2015. All seed lots started seedling emergence after sowing at 8 or 9 days in 2016, and after 13 days in 2015.
Fig. 3

Seedling emergence of volunteer canola for six different seed lot in two validation experiments in 2015 and 2016

Seedling emergence data from each burial depth separately and all of them together were regressed against thermal time satisfactory. The cumulative proportion of emergence and fitted model for each depth are shown in Fig. 4. The predicted emergence time courses at the various depths generally fitted well with the observed emergence data, with pseudo-R2 values between 0.91 and 0.99 and RMSE values between 0.03 and 0.13 (Table 1). The model parameters differed for each burial depth (Tables 1, 2). Results indicated that the a parameter had no change from a burial depth of 1 to 1.9 cm exhibiting a value of 77.2 for thermal time. Burial in deeper layers of soil from 1.9 to 8 cm led to an increase in the a values (Fig. 5a). The b parameter decreased as burial depth increased from 1 to 6 cm without considering 8 cm (Fig. 5b). The Weibull thermal time model produced a good fit to the pooled data (Table 1). According to the model, seedling emergence of B. napus started at 56.1 TT and increased to 50 and 95% of maximum seedling emergence at 86.3 and 105.4 TT, respectively.
Fig. 4

Cumulative proportion of seedling emergence (CE) of volunteer canola as a function of cumulative thermal time (TT)

Table 1

Parameter values for Eq. (2) and goodness of fit using thermal time (TT) as an independent variable for 8 soil depths and pooled soil depths (All)

Soil depth (cm)

a ± SE

b ± SE

RMSE

Pseudo-R2

1

77.17 ± 0.187

19.01 ± 1.007

0.026

0.99

2

75.94 ± 0.468

17.71 ± 2.184

0.065

0.98

3

83.58 ± 0.758

15.27 ± 2.625

0.087

0.97

4

97.67 ± 0.682

13.47 ± 1.500

0.059

0.98

5

100.00 ± 1.173

14.51 ± 3.218

0.101

0.95

6

104.80 ± 1.476

10.60 ± 1.740

0.135

0.91

8

122.00 ± 0.572

41.21 ± 9.690

0.105

0.94

Pooled

86.28 ± 0.416

8.63 ± 0.443

0.048

0.99

Fig. 5

Changes for the a parameter as affected by soil depth, when TT or HTT accumulations were used for prediction. Changes for the b parameter as affected by soil depth, when TT or HTT accumulations were used for prediction

Weibull (pooled data) and Logistic (Lawson et al. 2006) thermal time models were validated against data set collected from two different experiments. With a base temperature of 5 °C, the performance of the Weibull model was slightly lower than the Logistic 2003 model and clearly superior to the Logistic 2004 model (Fig. 6; Table 2). The slope and y-intercept of the logistic models (Fig. 6) were significantly from 1 and 0, respectively (Table 2), suggesting an overestimation of these models. The slope of the Weibull model did not differ significantly from 1 (Fig. 6; Table 2) and most of the observed lack of fit was associated with the unexplained variance (Ue; Table 3).
Fig. 6

Seedling emergence model validation for Brassica napus as predicted by: Weibull model, Logistic 2003 model and Logistic 2004 model in Pakdasht, Tehran in 2015 and 2016. Two base temperatures (3.79 and 5 °C) were tested to calculate thermal time. Markers are showing observed fraction of emergence data for six different seed lots. Goodness of fitness are indicated in Tables 2 and 3

Table 2

Regression coefficients (a and b) of the regression model fitted to the predicted and observed values, coefficient of determination (r2)

Model

a

b

r2

Tb = 5 °C

 Weibull

− 8.36 (1.46)

0.96 (0.02)

0.82

 Logistic 2003

13.65 (0.55)

0.47 (0.01)

0.88

 Logistic 2004

− 5.30 (0.80)

0.36 (0.01)

0.67

Tb = 3.79 °C

 Weibull

1.67 (1.22)

1.02 (0.01)

0.88

 Logistic 2003

20.35 (0.61)

0.53 (0.01)

0.88

 Logistic 2004

− 5.12 (0.84)

0.63 (0.01)

0.86

The values in the parenthesis are standard errors. All the regression models were significant (P < 0.001; n = 522). Values of a and b in italics indicate significant differences from 0 and 1, respectively (P < 0.01)

Tb base temperature

Table 3

Theil’s coefficients. These coefficients indicate different sources of predictive error: a proportion associated with mean differences between observed and predicted values, Ubias; a proportion associated with the slope the fitted model and the 1:1 line, Ub=1; a proportion associated with the unexplained variance, Ue

 

Ubias

Ub=1

Ue

Tb = 5 °C

 Weibull

0.245

0.006

0.748

 Logistic 2003

0.391

0.136

0.472

 Logistic 2004

0.706

0.047

0.246

Tb = 3.79 °C

 Weibull

0.032

0.003

0.964

 Logistic 2003

0.131

0.233

0.634

 Logistic 2004

0.682

0.146

0.170

When considering a base temperature of 3.79 °C, the performance of the Weibull model was higher than Logistic 2003 and 2004 models (Fig. 6; Table 2). As in the previous case, the two Logistic models showed significantly different slopes and y-intercept of 1 and 0, respectively (Table 2), suggesting a lower estimation of these models. Slope and y-intersect of the Weibull model did not differ significantly from 1 and 0, respectively (Fig. 6; Table 2) and the term associated with the unexplained variance was the most important in determining goodness-of-fit (Table 3).

Discussion

Results indicated that the seedling emergence percentage decreased by increasing soil depth and ceased at a depth of > 10 cm. Gruber et al. (2010) showed that seedling emergence of B. napus did not change with increasing soil depth from 1 to 5 cm, but it was significantly reduced at a depth of 7 cm, and no emergence occurred at 12 cm. Thomas et al. (1994) also observed that soil depths deeper than 3 cm lead to reduced seedling emergence of B. napus. Failure of seedlings emergence from soil surface could result from (i) inhibition of germination, (ii) induction of dormancy, or (iii) pre-emergence mortality (Soltani et al. 2016). Recovery of ungerminated seeds (data not shown), showed that failure to emerge was almost exclusively the result of fatal germination rather than induction of dormancy. A deep depth impairs the ability of seeds to reach the soil surface due to an increase for hypocotyl or epicotyl length and reduces their ability to overcome the physical constriction of soil and this can result in pre-emergence mortality (Benvenuti et al. 2001). A deep burial depth decreases soil aeration and exposes seeds to dark, thereby reducing conditions for germination (Cardina et al. 1998). Changes in temperature, water availability, and gas (CO2/O2) exchange can induce secondary dormancy in deeper soil depths (Baskin and Baskin 2014).

One of the most critical aspects of weed management is the timing of weed control practices in relation to weed emergence dynamics (Forcella et al. 2000). Although this type of information is available for many weeds (Gonzalez-Andujar et al. 2016), no information is available for B. napus under semi-arid conditions. In our study, the thermal time Weibull model developed offers a good description of the field emergence of B. napus. The model predicts that 50% emergence is reached with 86.2 TT, similar to Lawson et al. (2006) who reported values of 86 and 130 TT under three different tillage system but differed from those of Bullied et al. (2003), who found values of 511 and 417 TT in conventional and conservation soil tillage, respectively. The tillage system does not appear to have a significant influence on the 50% emergence (Lawson et al. 2006), then that difference could be explained by the use of different base temperatures. Bullied et al. (2003) used 0 °C as base temperature while Lawson et al. (2006) used 5 °C, closer to our value (3.79 °C).

Empirical models may not be accurate if environmental conditions vary significantly from the conditions in which the experiment was carried out. By model validation using data set of different varieties and localities, we aimed to incorporate as much variability as possible. We used independent data to validate the model with seed lots from three sites (Karaj, Moghan and Gorgan) and four different cultivars (Hayola401, RGS, Opera, and Okapi). Our Weibull model was confronted with the Logistic models previously developed by Lawson et al. (2006) in Canada. The Weibull model showed a higher performance than the logistic ones. In general, the logistic models presented an overestimation of the observed values. It is possible that differences in climatic conditions between the climates of Canada and Iran determine the emergence pattern. These differences may affect local emergence conditions through differential dormancy loss associated with different environmental conditions. Secondary dormancy can be induced by drought stress (Momoh et al. 2002; Gulden et al. 2004). Dry summers are common under semi-arid conditions and induce dormancy. Induced dormant seeds of volunteer B. napus can be entered into a dormancy/non-dormancy cycle (Schlink 1994) and they were non-dormant from mid-summer to spring in the semi-arid condition (Soltani et al. 2013). Colbach et al. (2008) also showed that there was a cycle of dormancy in buried seeds of volunteer B. napus and the fraction of non-dormant seeds was at the highest and lowest levels on December and June, respectively. Volunteer seeds remain dormant until mid-summer or autumn depending on the climate condition and can emerge after first irrigation or rainfall.

The Weibull model developed from this study was fairly representative of the observed field data and seems to be robust enough to predict B. napus emergence. Results of this study indicated that 105 TT are required for complete seedling emergence of B. napus. This information might help farmers to determine the best time to control the volunteers. However, some important considerations should be addressed regarding this emergence model. First, this study was conducted in the absence of any crop. Interspecific competition with other weeds and crop will affect emergence patterns and second, crop management systems can influence seedling emergence. Further research is required to establish more generally the usefulness of the Weibull model for predictive purposes under semi-arid climate.

Notes

Acknowledgements

JLG-A was partially supported by FEDER (European Regional Development Fund) and the Spanish Ministry of Economy and Competitiveness Grant (AGL2015-64130-R).

References

  1. Baskin, C. C., & Baskin, J. M. (2014). Seeds: ecology, biogeography, and evolution of dormancy and germination (2nd ed.). San Diego: Elsevier/Academic Press.Google Scholar
  2. Benvenuti, S., Macchia, M., & Miele, S. (2001). Quantitative analysis of emergence of seedlings from buried weed seeds with increasing soil depth. Weed Science, 49, 528–535.CrossRefGoogle Scholar
  3. Bullied, W. J., Marginet, A. M., & Van Acker, R. C. (2003). Conventional and conservation-tillage systems influence emergence periodicity of annual weed species in canola. Weed Science, 51, 886–897.CrossRefGoogle Scholar
  4. Cameron, A. C., & Windmeijer, F. A. G. (1996). R -squared measures for count data regression models with applications to health-care utilization. Journal of Business and Economic Statistics, 14, 209–220.Google Scholar
  5. Cardina, J., Webster, T. M., & Herms, C. P. (1998). Long-term tillage and rotation effects on soil seedbank characteristics. Aspects of Applied Biology, 51, 213–220.Google Scholar
  6. Colbach, N., Dürr, C., Gruber, S., & Pekrun, C. (2008). Modelling the seed bank evolution and emergence of oilseed rapevolunteers for managing co-existence of GM and non-GM varieties. European Journal of Agronomy, 28, 19–32.CrossRefGoogle Scholar
  7. Farzaneh, S., Soltani, E., Zeinali, E., & Ghaderi-far, F. (2014). Screening oilseed rape germination for thermotolerance using a laboratory-based method. Seed Technology, 36, 15–27.Google Scholar
  8. Forcella, F. (1998). Real-time assessment of seed dormancy and seedling growth for weed management. Seed Science Research, 8, 201–209.CrossRefGoogle Scholar
  9. Forcella, F., Benech-Arnold, R. L., Sanchez, R., & Ghersa, C. M. (2000). Modeling weed emergence. Field Crops Research, 67, 123–139.CrossRefGoogle Scholar
  10. Gonzalez-Andujar, J. L., Chantre, G. R., Morvillo, C., Blanco, A., & Forcella, F. (2016). Predicting field weed emergence with empirical models and soft computing techniques. Weed Research, 56, 415–423.CrossRefGoogle Scholar
  11. Gruber, S., Bühler, A., Möhring, J., & Claupein, W. (2010). Sleepers in the soil—Vertical distribution by tillage and long-term survival of oilseed rape seeds compared with plastic pellets. European Journal of Agronomy, 33, 81–88.CrossRefGoogle Scholar
  12. Gruber, S., Pekrun, C., & Claupein, W. (2005). Life cycle and potential gene flow of volunteer oilseed rape in differenttillage systems. Weed Research, 45, 83–93.CrossRefGoogle Scholar
  13. Grundy, A. C. (2003). Predicting weed emergence: A review of approaches and future challenges. Weed Research, 43, 1–11.CrossRefGoogle Scholar
  14. Gulden, R. H., Shirtliffe, S. J., & Thomas, A. G. (2003). Harvest losses of canola (Brassica napus) cause large seedbank inputs. Weed Science, 51, 83–86.CrossRefGoogle Scholar
  15. Gulden, R. H., Thomas, A. G., & Shirtliffe, S. J. (2004). Secondary dormancy, temperature, and burial depth regulate seedbank dynamics in canola. Weed Science, 52, 382–388.CrossRefGoogle Scholar
  16. Izquierdo, J., Bastida, F., Lezaun, J. M., Sanchez Del Arco, M. J., & Gonzalez-Andujar, J. L. (2013). Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area. Weed Research, 53, 269–278.CrossRefGoogle Scholar
  17. Izquierdo, J., Gonzalez-Andujar, J. L., Bastida, F., Lezaun, J. A., & Sanchez Del Arco, M. J. (2009). A thermal time model to predict corn poppy (Papaver rhoeas) emergence in cereal fields. Weed Science, 57, 660–664.CrossRefGoogle Scholar
  18. Lawson, A. N., Van Acker, R. C., & Friesen, L. F. (2006). Emergence timing of volunteer canola in spring wheat fields in Manitoba. Weed Science, 54, 873–882.CrossRefGoogle Scholar
  19. Legere, A., Simard, M.J., Thomas, A.G., Pageau, D., Lajeunesse, J., Warwick, S.I., Derksen, D.A., 2001. Presence and persistence of volunteer canola in Canadian cropping systems. In Pages 143–148 in British Crop Protection Council, ed. Proceedings of the Brighton Crop Protection Conference—Weeds 2001. Surrey, Great Britain: British Crop Protection Council.Google Scholar
  20. Leguizamon, E. S., Fernandez-Quintanilla, C., Barroso, J., & Gonzalez-Andujar, J. L. (2005). Using thermal and hydrothermal time to model seedling emergence of Avenasterilis ssp. ludoviciana in Spain. Weed Research, 45, 149–156.CrossRefGoogle Scholar
  21. Leguizamon, E. S., Rodriguez, N., Rainero, H., Perez, M., Perez, L., Zorza, E., et al. (2009). Modelling the emergence pattern of six summer annual weed grasses under no tillage systems in Argentina. Weed Research, 49, 98–106.CrossRefGoogle Scholar
  22. Momoh, E. J. J., Zhou, W. J., Kristiansson, B. (2002). Variation in the development of secondary dormancy in oilseed rape genotypes under conditions of stress. Weed Research, 42, 446–455.CrossRefGoogle Scholar
  23. Nowroozian, M. (2000). The list of permissive toxins in Iran. Tehran, Iran: Plant Protection Organization Press (in Persian).Google Scholar
  24. SAS Institute INC (2011) SAS/STAT 9.3 user’s guide, the PLSprocedure, SAS Campus Drive, Cary, North Carolina 27513.Google Scholar
  25. Sauermann, W. (1993). Einflüsse auf den Glucosinolatgehalt—Ergebnisse 2-jähriger Untersuchungenaus den Land-essortenversuchen. Raps, 11, 82–86.Google Scholar
  26. Saxton, K. E., Rawls, W. J., Romberger, J. S., & Papendick, R. I. (1986). Estimation generalized soil water characteristics from texture. Soil Science Society of America Journal, 50, 1031–1036.CrossRefGoogle Scholar
  27. Schlink, S., 1994. Ecology of germination and dormancy in oilseed rape (Brassica napus L.) and their importance forthe survival of the seeds in soil. 193 f. Dissertation (Master in Botany)—Faculty of Biology, University of Berlin, Berlin, 1994.Google Scholar
  28. Schwinghamet, T. D., & Van Acker, R. C. (2008). Emergence Timing and Persistence of Kochia (Kochia scoparia). Weed Science, 56, 37–41.CrossRefGoogle Scholar
  29. Simard, M. J., Légère, A., Pageau, D., Lajeunesse, J., & Warwick, S. (2002). The frequency and persistence of volunteer Canola (Brassica napus) in Québec cropping systems. Weed Technology, 16, 433–439.CrossRefGoogle Scholar
  30. Smith, E. P., & Rose, K. A. (1995). Model goodness-of-fit analysis using regression and related techniques. Ecological Modeling, 77, 49–64.CrossRefGoogle Scholar
  31. Soltani, E., Baskin, C. C., Baskin, J. M., Soltani, A., Galeshi, S., Ghaderi-far, F., et al. (2016). A quantitative analysis of seed dormancy and germination in the winter annual weed Sinapis arvensis (Brassicaceae). Botany, 94, 289–300.CrossRefGoogle Scholar
  32. Soltani, E., Gruber, S., Oveisi, M., Salehi, N., Alahdadi, I., Ghorbani-Javid, M. (2017). Water stress, temperature regimes, and light control secondary dormancy induction and loss in Brassica napus L. seeds. Seed Science Research, 27, 217–230.  https://doi.org/10.1017/s0960258517000186.CrossRefGoogle Scholar
  33. Soltani, A., & Sinclair, T. (2012). Modeling physiology of crop development, growth and yield. Wallingford: CABI Publication.CrossRefGoogle Scholar
  34. Soltani, E., Soltani, A., Galeshi, S., Ghaderi-far, F., & Zeinali, E. (2013). Seed bank modelling of volunteer oil seed rape: from seeds fate in the soil to seedling emergence. Planta Daninha, 31, 267–279.CrossRefGoogle Scholar
  35. Thomas, D. L., Raymer, P. L., & Breve, M. A. (1994). Seeding depth and packing wheel pressure effects on oilseed rape emergence. Journal of Production Agriculture, 7, 94–97.CrossRefGoogle Scholar
  36. Yousefi, A. R., Oveisi, M., & Gonzalez-Andujar, J. L. (2014). Prediction of annual weed seed emergence in garlic (Allium sativum L.) using soil thermal time. Scientia Horticulturae, 168, 189–192.CrossRefGoogle Scholar
  37. Zambrano-Navea, C., Bastida, F., & Gonzalez-Andujar, J. L. (2013). A hydrothermal seedling emergence model for Conyza bonariensis. Weed Research, 53, 213–220.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  • Elias Soltani
    • 1
  • Jose L. Gonzalez-Andujar
    • 2
  • Mostafa Oveisi
    • 3
  • Nader Salehi
    • 1
  1. 1.Department of Agronomy and Plant Breeding Sciences, College of AburaihanUniversity of TehranPakdashtIran
  2. 2.Instituto de Agricultura Sostenible (CSIC)CórdobaSpain
  3. 3.Department of Agronomy and Plant Breeding, College of Agriculture and Natural ResourcesUniversity of TehranKarajIran

Personalised recommendations