ARTICLE
Auteur(s) : Francis
Flénet1, Philippe Debaeke2,
Pierre
Casadebaig2
1CETIOM, Avenue Lucien Brétignières, 78850
Thiverval-Grignon, France
2INRA, UMR 1248 AGIR, BP 52627, 31326 Castanet-Tolosan
cedex, France
In France, there has been a stagnation in sunflower seed yield
during the past 20 years. The annual mean seed yield has ranged
from 2.1 to 2.7 t ha–1. The highest value was observed
in 2007, but it was mainly the result of the high rainfall during
summer. During that period of time, the area cultivated in
sunflower has decreased by almost 50%. Overall, there has been a
decrease in seed production, although there is a need for greater
quantities because of the increased demand for biodiesel. Hence,
both increases in seed yield and in the area cultivated in
sunflower are necessary. Improvements in crop management would
probably contribute to these objectives. However, during the past
few years, sustainable agriculture has become a priority in the
European Union. In France, the French government decided in 2007
that the applications of pesticides should decrease by 50% within
10 years. This has to be taken into account when improving crop
management. The objective of this article is to review the main
characteristics of sunflower crop management in France and in other
countries, in order to emphasize the need for improvement and to
evaluate if the recent advances in crop modelling could help to
find solutions.
Discussion
The main problems in sunflower crop management
Sunflower crop management in France is characterized by few
applications of pesticides. Most of the time, insects are not a
major problem. Hence, only 40% of the sunflower area received one
insecticide in 2006. The reduction of plant population due to
damage by slugs, birds or game animals has been observed more
often. However, only 54% of the sunflower area was sprayed against
slugs in 2006, while no treatments are allowed against birds or
game animals. Moreover, the application of fungicides is rare (only
7% in 2006). Fungal diseases are mainly controlled by seed
treatments, long crop rotation, destruction of volunteers and of
some weeds, and by the use of resistant or tolerant varieties.
However, pathological premature ripening due to phoma or
macrophomina is often observed, especially in dry areas. Herbicides
are applied in almost 100% of the area, but between-row cultivation
also contributes to weed management. This cultural operation was
observed in 41% of the area in 2006. Hence, sunflower may
contribute to the decrease in the application of pesticides in
France, through an increase in the area of this crop to the
detriment of other crops. This would be effective as long as
improvements in crop protection were focused on other ways than
increasing the application of pesticides. For instance, date of
sowing, seeding rate and the amount of N fertiliser have an effect
on several diseases, such as Phoma black stem [1, 2]. This
indicates that there are possibilities for decreasing disease
incidence without applying fungicides.
In France, sunflower is mainly cultivated on clay soils. Most of
the time, seeds are sown after a deep tillage (72% of the area in
2006), while there is very little direct sowing (2% in 2006). There
is almost no irrigation (only 4% of the area in 2006). The range of
soil depth is wide, resulting in a large range of seed yields. For
instance, in south-west of France in 2006, mean yields were 2.28,
2.34 and 2.73 t ha–1, respectively, on shallow (13% of
the area), medium (75%) and deep soils (12%). However, there is
little adaptation of crop management to the expected water
availability (soil field capacity, expected rainfall and
irrigation). The main adaptation to reduce the effect of water
shortage is earlier sowing in south of France, because severe
deficits in summer are expected. In the Aude region however, where
mid-summer storms are predictable, the date of sowing is delayed.
The objective is to postpone the seed filling period, so that it
occurs during mid-summer. The amount of N fertiliser applied is
also adapted to the target yield, which results from the expected
water availability. However, there are no further adaptations of
the crop management to the expected water availability. For
instance, the drought-tolerance of commercial cultivars, if it
exists, is unknown and thus not available for farmer’s
decision.
The first objective of any improvement in crop management is a
better adaptation to the expected water availability. It would
result in more accurate date of sowing, planting density, amount of
N fertiliser (more accurate target yield) and variety maturity
type. The choice of the variety should also account for the
differences in leaf area and in stomatal closure, which play an
important role in drought tolerance [3]. The second objective is to
control diseases more efficiently without foliar-applied
fungicides, especially those responsible for premature ripening in
dry areas.
In other countries, there are some differences in crop
management, compared to France. The following section is not an
exhaustive list of the main differences which have been noticed,
but it is the description of three of them which could also be
improved by crop modelling. Firstly, in countries other than
France, crop management is sometimes more adapted to the expected
water availability, especially through the target plant population.
The objective of plant number per hectare in Australia is 20-25,000
in marginal dryland, 25-35,000 in favourable dryland, 35-50,000 in
limited irrigation and 50-75,000 in full irrigation [4]. In the USA
High Plains, plant population for irrigated sunflower should be
between 42,000 and 54,000 final plant per hectare, while it should
be lower for lower yield potentials [5].
Secondly, the occurrence of insect problems is more acute
outside Western Europe, where insect damage on sunflower is less.
In North America, there is a wide pest complex because sunflower is
native to this region of the world [6]. On other continents,
numerous insects also attack sunflower. For example, in Africa,
sunflower has been grown for a long time as an ornamental plant.
Insects attacking ornamental crops later moved to commercial ones
[6]. In the countries where insects cause significant yield
reductions, the planting date can play a role in controlling them.
For instance, in Canada, delaying planting until late May or early
June has been effective in reducing densities of stem weevil larvae
[7]. It also helps to prevent the first major emergence of the
overwintering sunflower midge population. On the contrary, early
planting reduces seed damage of sunflower seed weevils because
early planted sunflowers complete anthesis and are no longer
susceptible to egg laying at the time of peak populations [7].
Thirdly, in some countries, crop management contributes to seed
quality. In France, sunflower oil is mainly used for biodiesel or
for food. Oil with a high oleic content is required for biodiesel.
The quality required is obtained through the cultivation of high
oleic varieties, while the rest of the crop management is similar
to that for other varieties. However, planting date can affect the
oil quality, because warm temperatures during anthesis and the
seed-filling period increase the seed content in oleic acid [8].
Hence, in Australia, for example, planting dates are grouped into
an early and a late sowing window [4]. For spring sowing, high
oleic acid varieties are preferred. Hence, the high temperatures
occurring during seed filling for this sowing time is not a
problem. In order to produce high linoleic varieties, sowing in the
late plant window (December-January) is recommended so that crops
fill seeds in the cooler autumn months.
In order to adapt crop management accurately to each situation,
many data are needed because environmental conditions are highly
variable, between years and between locations. Hence, the optimum
of one cultural operation is also highly variable from one
experiment to another. For instance, Robinson [9] stated that
“disagreement on the optimum plant population is common”. Moreover,
there are many cultural operations which interact with each other.
For example, the optimum plant population density tends to be
greater with irrigation [8, 10]. It is not possible to conduct the
huge number of factorial experiments needed for an accurate
adaptation of crop management. In France, the recommendation of an
early sowing date is mainly based on field surveys. For instance,
in the south west of France, the results of 300 fields per year
from 1996 to 2006 show a decrease in yield when the sowing date was
delayed after 10 April [11]. The difference in yield was 0.27 t
ha–1, between sowings before 10 April and after 10 May.
However, these results are rough estimates, because they compare
different fields with possible differences in other cultural
operations and in soils. In order to take into account possible
interactions with other factors (variety, soil depth…), and/or to
give more site-specific recommendations, many more data would be
necessary. There is also a need to keep adapting crop management to
keep up with technical progress (new varieties…), and with changes
in objectives (quality) and in environmental conditions (the
possibility to irrigate or climate change). The number of years
necessary to give recommendations taking into account these changes
would be too great, if results from either experiments or surveys
were used. All these difficulties can be overcome by using crop
models, because thousands of situations can be simulated in a few
hours, once these tools are validated.
Sunflower crop models
Villalobos [12] reviewed sunflower crop models at the
15th International Sunflower Conference in Toulouse. At
that time, several specific models of sunflower had been developed,
and a few others were applicable to several crops including
sunflower (generic models). These models are mathematical
representations of crops and soils which take into account
dynamically and on a daily basis the effects of weather and crop
management on seed yield. The QSUN model was developed in the early
nineties [13]. It takes into account sowing date, irrigation and
variety. The OILCROP-SUN model [14] also considers these factors,
along with fertiliser management.
Two models which have been developed since 2000 provide further
possibilities. A simple model based on published relationships
calculates oil quality along with seed yield [15]. The cultural
operations taken into account are the effect of sowing date, plant
density and variety.
Another sunflower crop model was developed by Casadebaig [3] to
gain new insight into the way to discriminate yield build-up
between varieties. Generally, in sunflower models, varieties only
differ in yield components and maturity types. In this new model,
varietal parameters are required for crop development, leaf area
and its ability to intercept light, response of leaf expansion and
stomatal closure to soil water deficit, harvest index and the
maximum percentage of kernel in achenes. These parameters are
easily measurable, in order to be able to account for the dozens of
new varieties appearing each year on the market [16]. Sowing date,
plant density, irrigation and N fertiliser are also considered.
However, the sunflower crop models presented above do not
include diseases, insects or weeds. There has been one attempt to
connect the EPIC crop model adapted to sunflower to Phomopsis stem
canker [17]. The climatic risk of contamination by ascospores was
predicted from spring and summer rainfall. Then, the disease
symptoms were simulated using the relationship between infected
stems and the fraction of intercepted photosynthetically active
radiation (IPAR), which was simulated by the EPIC crop model. Yield
loss was then correlated with the symptoms, bearing in mind the
period of contamination. The relationship between symptoms of
Phomopsis stem canker and the IPAR or Leaf area index (LAI) was
also reported by Debaeke and Estragnat [18]. Debaeke and Pérès [1]
were also able to correlate Phoma black stem damage with IPAR or
LAI at anthesis.
The use of sunflower crop models to adapt crop management
Sunflower crop models could be used to optimize crop management, by
considering crop response to long-term historical weather records.
For example, simulated seed yields were compared for a range of
sowing dates, in order to select the best one [3, 19-21].
In Casadebaig [3], seed yield was simulated for 5 sowing dates,
7 locations, 3 available soil water content and 25 years (figure 1). Sowing
dates were 1 March (D1), 25 March (D2), 15 April (D3), 10 May (D4)
or 25 May (D5). Locations were representative of South of France,
from the Western side (left on the figure 1) to the eastern
(right on the figure
1): Agen (AGE), Auch (AUC), Blagnac (BLA), Villefranche de
Lauragais (VFL), Castelnaudary (CAS), Carcassonne (CAR) and
Montpellier (MON). Soil water capacities were 80 mm(S1), 150 mm
(S2) or 250 mm (S3). In most combinations of location × soil water
capacity, seed yield decreased with delaying the sowing date until
the third or the fourth date, because of a greater water deficit.
Then, an increase in seed yield was observed between the third or
fourth date and the fifth date, due to the delaying of seed filling
until the period of mid-summer storms. This pattern was less marked
on deep soils with high water capacities. Hence, depending on
location and on soil water capacity, the greatest yield could be
obtained at the early sowing date, at both the early and the late
sowing dates because of mid-summer storms, or at all sowing dates
because of a little water deficit due to both deep soils and humid
climates.
The best maturity type was similarly studied by Meinke et al.
[19]. Debaeke et al. [22] and Rinaldi et al. [20] also compared the
effect of irrigation strategies on simulated yields. Oil seed
quality can also be taken into account, along with seed yield, when
using a crop model to optimize crop management [15].
These simulations could assist farmers in making management
decisions. It provides information on the effect of one or several
cultural operations, in each specific soil x climate situation,
which takes into account the variability between years. Experiments
or surveys fail to give such precise information. However, users of
crop models should be aware of 2 limits: (1) accuracy and
robustness, and (2) relevance (factors not taken into account).
Crop models will be powerful tools to assist farmers as long as
these limits are properly managed.
Model accuracy is the ability to give simulations close to the
measurements. Robustness is the capability to be accurate in other
environmental conditions than those prevailing for the data set
used for calibration. Both are crucial for helping farmers to make
good decisions. Model accuracy is evaluated by comparing
simulations and measurements not used for calibration. However, the
minimum of accuracy necessary to help farmers to make the best
decision is usually not discussed. This would need specific works
that have never been done when using sunflower crop models. For
instance, Rinaldi et al. [20] observed a good correlation between
simulated yields and independent measurements (almost perfect
regression slope (0.95) and intercept (–0.07), and a fairly good
R2 value of 0.74). Observed values were obtained for
several years, in locations, irrigation regimes and sowing dates
similar to those prevailing for the use of the model. This
evaluation of the model was encouraging. However, it was not a
proof that simulations were accurate enough to make the good
decision, which was to use a threshold value of 40% of total soil
water to trigger irrigation. Moreover, robustness is not usually
discussed, even though the ability to give results in other
situations than those prevailing in experiments is exactly the
expected benefit of a crop model.
Many factors affecting seed yield or quality are not taken into
account by crop models. For example, sowing date does not only have
an effect on climate conditions during crop growth. Diseases and
insects are also affected [7, 23, 24]. Models considering these
factors would be very helpful. However, this does not seem as if it
will become a reality in the near future. There are numerous
diseases and insects which depend on many other factors than those
in the sunflower field (cropping history, spatial cropping
pattern…). Moreover, their effects depend on plant tolerance or
resistance, and on the application of pesticides.
However, crop models could be useful for contributing to define
management strategies. Debaeke and Nolot [9] illustrated the
definition of management strategies based on a target yield (which
depended on water availability), and also based on the combination
of avoidance and/or tolerance of limiting factors and vegetative
rationing. The limiting factors involved were both nutritional and
disease ones. For each combination of soil and climate, a crop
model would be useful for establishing the potential yields allowed
by the water availability, solar radiation and temperature. Results
of potential yields would depend on sowing date, plant number and
on variety. These results could be associated with the knowledge of
diseases and insects in order to define management strategies. One
strategy could aim at the maximum yield. According to the
hypothesis that several combinations of sowing date x plant number
x variety exist, the one recommended would be that minimizing the
risks of major diseases and insects. In order to minimize them
further, other management strategies could aim at lower target
yields. Crop models could also help to estimate the risks of
diseases and insects by simulating variables correlated with them.
Examples of such correlations are given in section 2 of this
article (IPAR or LAI correlated with Phoma black stem or Phomopsis
stem canker). Models could also be used for insect damage. For
instance, they could simulate the stages of development when
sunflower is more susceptible to damage from a particular
insect.
Conclusion
The issue investigated in this article is the possibility of using
crop models for improving sunflower crop management. In France, a
better adaptation of crop management to water availability is
needed, as well as a more efficient control of diseases without
applying more fungicides. A huge number of experiments would be
needed to reach these objectives, while surveys give only rough
estimates on the effect of cultural operations. Moreover, both
experiments and surveys need too many years to keep up with the
continuous technical progress (new varieties…), and for the quick
changes in objectives (quality) and in environmental conditions
(the possibility to irrigate or climate change) that are expected
in the future. Crop modelling is the only way to obtain data
quickly enough. Similarly, crop models could be helpful in
countries other than France, although their needs for improving
crop management may be different. There have been recent advances
in sunflower crop models, in simulating oil quality and in defining
differences between varieties. Although diseases and insects are
still not taken into account, crop models could be used to trigger
management strategies. These strategies would be based on simulated
potential yields and on knowledge of diseases and insects. However,
the condition for using simulated results to improve crop
management is the confidence in the model. Until now, models have
been mainly evaluated by comparing the simulations and the
measurements made in a few independent experiments, which is not
enough. There is a need to evaluate the ability of models to help
to make the best decision in a large range of environmental
conditions.
Acknowledgements
The authors wish to thank the organizers of the 17th
International Sunflower Conference for their invitation to write
this article.
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